diff --git a/.clang-format b/.clang-format
new file mode 100644
index 000000000..5f6222b32
--- /dev/null
+++ b/.clang-format
@@ -0,0 +1,86 @@
+AccessModifierOffset: -4
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlinesLeft: false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortFunctionsOnASingleLine: false
+AllowShortIfStatementsOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine : false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterDefinitionReturnType: false
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: true
+BinPackParameters: false
+BreakBeforeBinaryOperators: false
+BreakBeforeBraces: Custom
+BraceWrapping:
+  AfterClass: true
+  AfterControlStatement: true
+  AfterEnum: true
+  AfterFunction: true
+  AfterNamespace: true
+  AfterObjCDeclaration: true
+  AfterStruct: true
+  AfterUnion: true
+  AfterExternBlock: true
+  BeforeCatch: true
+  BeforeElse: true
+  IndentBraces: false
+  SplitEmptyFunction: true
+  SplitEmptyRecord: true
+  SplitEmptyNamespace : true
+BreakBeforeTernaryOperators: false
+BreakConstructorInitializersBeforeComma: false
+BreakStringLiterals: false
+ColumnLimit: 120
+CommentPragmas: ''
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: false
+DerivePointerBinding: false
+FixNamespaceComments: true
+IndentCaseLabels: false
+IndentPPDirectives: AfterHash
+IndentFunctionDeclarationAfterType: false
+IndentWidth: 4
+SortIncludes: false
+IncludeCategories:
+  - Regex:           '[<"](.*\/)?defines.h[>"]'
+    Priority:        1
+#  - Regex:           '<cuslide\/.+>'
+#    Priority:        3
+  - Regex:           '<[[:alnum:]_.]+>'
+    Priority:        5
+  - Regex:           '<[[:alnum:]_.\/]+>'
+    Priority:        4
+  - Regex:           '".*"'
+    Priority:        2
+IncludeBlocks: Regroup
+Language: Cpp
+MaxEmptyLinesToKeep: 2
+NamespaceIndentation: None
+ObjCSpaceAfterProperty: true
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakBeforeFirstCallParameter: 0
+PenaltyBreakComment: 1
+PenaltyBreakFirstLessLess: 0
+PenaltyBreakString: 1
+PenaltyExcessCharacter: 10
+PenaltyReturnTypeOnItsOwnLine: 1000
+PointerAlignment: Left
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles: false
+SpacesInCStyleCastParentheses: false
+SpacesInContainerLiterals: false
+SpacesInParentheses: false
+Standard: Cpp11
+ReflowComments: true
+TabWidth: 4
+UseTab: Never
diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 000000000..26120d7d9
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,7 @@
+*
+!docker
+#cmake-build-*
+#build
+#install
+!temp/gds
+
diff --git a/.editorconfig b/.editorconfig
new file mode 100644
index 000000000..c69a96fa2
--- /dev/null
+++ b/.editorconfig
@@ -0,0 +1,7 @@
+[*]
+indent_style = space
+indent_size = 4
+charset = utf-8
+trim_trailing_whitespace = true
+max_line_length = 120
+insert_final_newline = true
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 000000000..722e34904
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,18 @@
+#cpp code owners
+cpp/               @rapidsai/<repo>-cpp-codeowners
+
+#python code owners
+python/            @rapidsai/<repo>-python-codeowners
+python/dask_cudf/  @rapidsai/<repo>-dask-codeowners
+
+#cmake code owners
+**/CMakeLists.txt  @rapidsai/<repo>-cmake-codeowners
+**/cmake/          @rapidsai/<repo>-cmake-codeowners
+
+#build/ops code owners
+.github/           @rapidsai/ops-codeowners 
+ci/                @rapidsai/ops-codeowners
+conda/             @rapidsai/ops-codeowners
+**/Dockerfile      @rapidsai/ops-codeowners
+**/.dockerignore   @rapidsai/ops-codeowners
+docker/            @rapidsai/ops-codeowners
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 000000000..0045d3142
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,26 @@
+---
+name: Bug report
+about: Create a bug report to help us improve ___PROJECT___
+title: "[BUG]"
+labels: "? - Needs Triage, bug"
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Steps/Code to reproduce bug**
+Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Environment details (please complete the following information):**
+ - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)]
+ - Method of ___PROJECT___ install: [conda, Docker, or from source]
+   - If method of install is [Docker], provide `docker pull` & `docker run` commands used
+ 
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/documentation-request.md b/.github/ISSUE_TEMPLATE/documentation-request.md
new file mode 100644
index 000000000..89a026f34
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/documentation-request.md
@@ -0,0 +1,35 @@
+---
+name: Documentation request
+about: Report incorrect or needed documentation
+title: "[DOC]"
+labels: "? - Needs Triage, doc"
+assignees: ''
+
+---
+
+## Report incorrect documentation
+
+**Location of incorrect documentation**
+Provide links and line numbers if applicable.
+
+**Describe the problems or issues found in the documentation**
+A clear and concise description of what you found to be incorrect.
+
+**Steps taken to verify documentation is incorrect**
+List any steps you have taken:
+
+**Suggested fix for documentation**
+Detail proposed changes to fix the documentation if you have any.
+
+---
+
+## Report needed documentation
+
+**Report needed documentation**
+A clear and concise description of what documentation you believe it is needed and why.
+
+**Describe the documentation you'd like**
+A clear and concise description of what you want to happen.
+
+**Steps taken to search for needed documentation**
+List any steps you have taken:
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 000000000..12a85e0dd
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for ___PROJECT___
+title: "[FEA]"
+labels: "? - Needs Triage, feature request"
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I wish I could use ___PROJECT___ to do [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context, code examples, or references to existing implementations about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/submit-question.md b/.github/ISSUE_TEMPLATE/submit-question.md
new file mode 100644
index 000000000..3714e22fe
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/submit-question.md
@@ -0,0 +1,10 @@
+---
+name: Submit question
+about: Ask a general question about ___PROJECT___
+title: "[QST]"
+labels: "? - Needs Triage, question"
+assignees: ''
+
+---
+
+**What is your question?**
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 000000000..402e8bb6c
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,44 @@
+<!--
+
+Thank you for contributing to ___PROJECT___ :)
+
+Here are some guidelines to help the review process go smoothly.
+
+1. Please write a description in this text box of the changes that are being
+   made.
+
+2. Please ensure that you have written units tests for the changes made/features
+   added.
+
+3. If you are closing an issue please use one of the automatic closing words as
+   noted here: https://help.github.com/articles/closing-issues-using-keywords/
+
+4. If your pull request is not ready for review but you want to make use of the
+   continuous integration testing facilities please label it with `[WIP]`.
+
+5. If your pull request is ready to be reviewed without requiring additional
+   work on top of it, then remove the `[WIP]` label (if present) and replace
+   it with `[REVIEW]`. If assistance is required to complete the functionality,
+   for example when the C/C++ code of a feature is complete but Python bindings
+   are still required, then add the label `[HELP-REQ]` so that others can triage
+   and assist. The additional changes then can be implemented on top of the
+   same PR. If the assistance is done by members of the rapidsAI team, then no
+   additional actions are required by the creator of the original PR for this,
+   otherwise the original author of the PR needs to give permission to the
+   person(s) assisting to commit to their personal fork of the project. If that
+   doesn't happen then a new PR based on the code of the original PR can be
+   opened by the person assisting, which then will be the PR that will be
+   merged.
+
+6. Once all work has been done and review has taken place please do not add
+   features or make changes out of the scope of those requested by the reviewer
+   (doing this just add delays as already reviewed code ends up having to be
+   re-reviewed/it is hard to tell what is new etc!). Further, please do not
+   rebase your branch on main/force push/rewrite history, doing any of these
+   causes the context of any comments made by reviewers to be lost. If
+   conflicts occur against main they should be resolved by merging main
+   into the branch used for making the pull request.
+
+Many thanks in advance for your cooperation!
+
+-->
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 000000000..0fe9721d0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,154 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+*.so.*
+
+# Distribution / packaging
+.Python
+build/
+.cmake
+cmake-build*/
+build-*/
+install/
+temp/
+releases/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+# lib/
+# lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+## VSCode IDE
+.vscode
+
+## internal doc's notebook
+python/cucim/docs/notebooks
+
+# Clion
+# (Reference: https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore)
+## User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+## Generated files
+.idea/**/contentModel.xml
+
+## Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+## Remote development-related files
+/**/deployment.xml
+
+## Module files
+.idea/**/modules.xml
+
+# CCache folder
+.ccache
+
+# Conda folders
+.conda
+
+# Large Images
+*.svs
+
diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 000000000..73f69e095
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/
diff --git a/.idea/codeStyles/Project.xml b/.idea/codeStyles/Project.xml
new file mode 100644
index 000000000..f60388162
--- /dev/null
+++ b/.idea/codeStyles/Project.xml
@@ -0,0 +1,7 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <clangFormatSettings>
+      <option name="ENABLED" value="true" />
+    </clangFormatSettings>
+  </code_scheme>
+</component>
\ No newline at end of file
diff --git a/.idea/codeStyles/codeStyleConfig.xml b/.idea/codeStyles/codeStyleConfig.xml
new file mode 100644
index 000000000..79ee123c2
--- /dev/null
+++ b/.idea/codeStyles/codeStyleConfig.xml
@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
+  </state>
+</component>
\ No newline at end of file
diff --git a/.idea/cucim.iml b/.idea/cucim.iml
new file mode 100644
index 000000000..24b221981
--- /dev/null
+++ b/.idea/cucim.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module classpath="CMake" type="CPP_MODULE" version="4">
+  <component name="FacetManager">
+    <facet type="Python" name="Python facet">
+      <configuration sdkName="" />
+    </facet>
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake b/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
new file mode 100644
index 000000000..7272e0dec
--- /dev/null
+++ b/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
@@ -0,0 +1,14 @@
+#
+# Copyright (c) $YEAR, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
diff --git a/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h b/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
new file mode 100644
index 000000000..b0e223c0a
--- /dev/null
+++ b/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
@@ -0,0 +1,15 @@
+/*
+ * Copyright (c) $YEAR, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
\ No newline at end of file
diff --git a/.idea/fileTemplates/internal/C Header File.h b/.idea/fileTemplates/internal/C Header File.h
new file mode 100644
index 000000000..9cb1d09e2
--- /dev/null
+++ b/.idea/fileTemplates/internal/C Header File.h	
@@ -0,0 +1,5 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/.idea/fileTemplates/internal/C Source File.c b/.idea/fileTemplates/internal/C Source File.c
new file mode 100644
index 000000000..b04dd6c62
--- /dev/null
+++ b/.idea/fileTemplates/internal/C Source File.c	
@@ -0,0 +1,4 @@
+#parse("NVIDIA_C_HEADER.h")
+#if (${HEADER_FILENAME})
+#[[#include]]# "${HEADER_FILENAME}"
+#end
diff --git a/.idea/fileTemplates/internal/C++ Class Header.h b/.idea/fileTemplates/internal/C++ Class Header.h
new file mode 100644
index 000000000..f521fa555
--- /dev/null
+++ b/.idea/fileTemplates/internal/C++ Class Header.h	
@@ -0,0 +1,13 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+${NAMESPACES_OPEN}
+
+class ${NAME} {
+
+};
+
+${NAMESPACES_CLOSE}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/.idea/fileTemplates/internal/C++ Class.cc b/.idea/fileTemplates/internal/C++ Class.cc
new file mode 100644
index 000000000..42f43ccf4
--- /dev/null
+++ b/.idea/fileTemplates/internal/C++ Class.cc	
@@ -0,0 +1,2 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#include]]# "${HEADER_FILENAME}"
diff --git a/.idea/fileTemplates/internal/CMakeLists.txt.cmake b/.idea/fileTemplates/internal/CMakeLists.txt.cmake
new file mode 100644
index 000000000..d71d94dba
--- /dev/null
+++ b/.idea/fileTemplates/internal/CMakeLists.txt.cmake
@@ -0,0 +1 @@
+#parse("NVIDIA_CMAKE_HEADER.cmake")
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 000000000..8822db8f1
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 000000000..94a25f7f4
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/3rdparty/LICENSE.CLI11 b/3rdparty/LICENSE.CLI11
new file mode 100644
index 000000000..b2e9e76bc
--- /dev/null
+++ b/3rdparty/LICENSE.CLI11
@@ -0,0 +1,25 @@
+CLI11 1.8 Copyright (c) 2017-2019 University of Cincinnati, developed by Henry
+Schreiner under NSF AWARD 1414736. All rights reserved.
+
+Redistribution and use in source and binary forms of CLI11, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.Catch2 b/3rdparty/LICENSE.Catch2
new file mode 100644
index 000000000..127a5bc39
--- /dev/null
+++ b/3rdparty/LICENSE.Catch2
@@ -0,0 +1,23 @@
+Boost Software License - Version 1.0 - August 17th, 2003
+
+Permission is hereby granted, free of charge, to any person or organization
+obtaining a copy of the software and accompanying documentation covered by
+this license (the "Software") to use, reproduce, display, distribute,
+execute, and transmit the Software, and to prepare derivative works of the
+Software, and to permit third-parties to whom the Software is furnished to
+do so, all subject to the following:
+
+The copyright notices in the Software and this entire statement, including
+the above license grant, this restriction and the following disclaimer,
+must be included in all copies of the Software, in whole or in part, and
+all derivative works of the Software, unless such copies or derivative
+works are solely in the form of machine-executable object code generated by
+a source language processor.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
+SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
+FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.abseil-cpp b/3rdparty/LICENSE.abseil-cpp
new file mode 100644
index 000000000..62589edd1
--- /dev/null
+++ b/3rdparty/LICENSE.abseil-cpp
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        https://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       https://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/3rdparty/LICENSE.aicsimageio b/3rdparty/LICENSE.aicsimageio
new file mode 100644
index 000000000..9d2714de3
--- /dev/null
+++ b/3rdparty/LICENSE.aicsimageio
@@ -0,0 +1,11 @@
+Copyright 2020 Allen Institute for Cell Science
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.benchmark b/3rdparty/LICENSE.benchmark
new file mode 100644
index 000000000..7a4a3ea24
--- /dev/null
+++ b/3rdparty/LICENSE.benchmark
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.boost b/3rdparty/LICENSE.boost
new file mode 100644
index 000000000..127a5bc39
--- /dev/null
+++ b/3rdparty/LICENSE.boost
@@ -0,0 +1,23 @@
+Boost Software License - Version 1.0 - August 17th, 2003
+
+Permission is hereby granted, free of charge, to any person or organization
+obtaining a copy of the software and accompanying documentation covered by
+this license (the "Software") to use, reproduce, display, distribute,
+execute, and transmit the Software, and to prepare derivative works of the
+Software, and to permit third-parties to whom the Software is furnished to
+do so, all subject to the following:
+
+The copyright notices in the Software and this entire statement, including
+the above license grant, this restriction and the following disclaimer,
+must be included in all copies of the Software, in whole or in part, and
+all derivative works of the Software, unless such copies or derivative
+works are solely in the form of machine-executable object code generated by
+a source language processor.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
+SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
+FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.click b/3rdparty/LICENSE.click
new file mode 100644
index 000000000..e63d78d30
--- /dev/null
+++ b/3rdparty/LICENSE.click
@@ -0,0 +1,28 @@
+Copyright 2014 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.cuda b/3rdparty/LICENSE.cuda
new file mode 100644
index 000000000..57888aca3
--- /dev/null
+++ b/3rdparty/LICENSE.cuda
@@ -0,0 +1,1590 @@
+End User License Agreement
+--------------------------
+
+
+Preface
+-------
+
+The Software License Agreement in Chapter 1 and the Supplement
+in Chapter 2 contain license terms and conditions that govern
+the use of NVIDIA software. By accepting this agreement, you
+agree to comply with all the terms and conditions applicable
+to the product(s) included herein.
+
+
+NVIDIA Driver
+
+
+Description
+
+This package contains the operating system driver and
+fundamental system software components for NVIDIA GPUs.
+
+
+NVIDIA CUDA Toolkit
+
+
+Description
+
+The NVIDIA CUDA Toolkit provides command-line and graphical
+tools for building, debugging and optimizing the performance
+of applications accelerated by NVIDIA GPUs, runtime and math
+libraries, and documentation including programming guides,
+user manuals, and API references.
+
+
+Default Install Location of CUDA Toolkit
+
+Windows platform:
+
+%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#
+
+
+NVIDIA CUDA Samples
+
+
+Description
+
+This package includes over 100+ CUDA examples that demonstrate
+various CUDA programming principles, and efficient CUDA
+implementation of algorithms in specific application domains.
+
+
+Default Install Location of CUDA Samples
+
+Windows platform:
+
+%ProgramData%\NVIDIA Corporation\CUDA Samples\v#.#
+
+Linux platform:
+
+/usr/local/cuda-#.#/samples
+
+and
+
+$HOME/NVIDIA_CUDA-#.#_Samples
+
+Mac platform:
+
+/Developer/NVIDIA/CUDA-#.#/samples
+
+
+NVIDIA Nsight Visual Studio Edition (Windows only)
+
+
+Description
+
+NVIDIA Nsight Development Platform, Visual Studio Edition is a
+development environment integrated into Microsoft Visual
+Studio that provides tools for debugging, profiling, analyzing
+and optimizing your GPU computing and graphics applications.
+
+
+Default Install Location of Nsight Visual Studio Edition
+
+Windows platform:
+
+%ProgramFiles(x86)%\NVIDIA Corporation\Nsight Visual Studio Edition #.#
+
+
+1. License Agreement for NVIDIA Software Development Kits
+---------------------------------------------------------
+
+
+Release Date: May 21, 2019
+--------------------------
+
+
+Important Notice—Read before downloading, installing,
+copying or using the licensed software:
+-------------------------------------------------------
+
+This license agreement, including exhibits attached
+("Agreement”) is a legal agreement between you and NVIDIA
+Corporation ("NVIDIA") and governs your use of a NVIDIA
+software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here
+is a description of the types of items that may be included in
+a SDK: source code, header files, APIs, data sets and assets
+(examples include images, textures, models, scenes, videos,
+native API input/output files), binary software, sample code,
+libraries, utility programs, programming code and
+documentation.
+
+This Agreement can be accepted only by an adult of legal age
+of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company
+or other legal entity, you represent that you have the legal
+authority to bind the entity to this Agreement, in which case
+“you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept
+this Agreement, or if you don’t accept all the terms and
+conditions of this Agreement, do not download, install or use
+the SDK.
+
+You agree to use the SDK only for purposes that are permitted
+by (a) this Agreement, and (b) any applicable law, regulation
+or generally accepted practices or guidelines in the relevant
+jurisdictions.
+
+
+1.1. License
+
+
+1.1.1. License Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants
+you a non-exclusive, non-transferable license, without the
+right to sublicense (except as expressly provided in this
+Agreement) to:
+
+  1. Install and use the SDK,
+
+  2. Modify and create derivative works of sample source code
+    delivered in the SDK, and
+
+  3. Distribute those portions of the SDK that are identified
+    in this Agreement as distributable, as incorporated in
+    object code format into a software application that meets
+    the distribution requirements indicated in this Agreement.
+
+
+1.1.2. Distribution Requirements
+
+These are the distribution requirements for you to exercise
+the distribution grant:
+
+  1. Your application must have material additional
+    functionality, beyond the included portions of the SDK.
+
+  2. The distributable portions of the SDK shall only be
+    accessed by your application.
+
+  3. The following notice shall be included in modifications
+    and derivative works of sample source code distributed:
+    “This software contains source code provided by NVIDIA
+    Corporation.”
+
+  4. Unless a developer tool is identified in this Agreement
+    as distributable, it is delivered for your internal use
+    only.
+
+  5. The terms under which you distribute your application
+    must be consistent with the terms of this Agreement,
+    including (without limitation) terms relating to the
+    license grant and license restrictions and protection of
+    NVIDIA’s intellectual property rights. Additionally, you
+    agree that you will protect the privacy, security and
+    legal rights of your application users.
+
+  6. You agree to notify NVIDIA in writing of any known or
+    suspected distribution or use of the SDK not in compliance
+    with the requirements of this Agreement, and to enforce
+    the terms of your agreements with respect to distributed
+    SDK.
+
+
+1.1.3. Authorized Users
+
+You may allow employees and contractors of your entity or of
+your subsidiary(ies) to access and use the SDK from your
+secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users
+enrolled or employed by the academic institution to access and
+use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this
+Agreement by your authorized users. If you become aware that
+your authorized users didn’t follow the terms of this
+Agreement, you agree to take reasonable steps to resolve the
+non-compliance and prevent new occurrences.
+
+
+1.1.4. Pre-Release SDK
+
+The SDK versions identified as alpha, beta, preview or
+otherwise as pre-release, may not be fully functional, may
+contain errors or design flaws, and may have reduced or
+different security, privacy, accessibility, availability, and
+reliability standards relative to commercial versions of
+NVIDIA software and materials. Use of a pre-release SDK may
+result in unexpected results, loss of data, project delays or
+other unpredictable damage or loss.
+
+You may use a pre-release SDK at your own risk, understanding
+that pre-release SDKs are not intended for use in production
+or business-critical systems.
+
+NVIDIA may choose not to make available a commercial version
+of any pre-release SDK. NVIDIA may also choose to abandon
+development and terminate the availability of a pre-release
+SDK at any time without liability.
+
+
+1.1.5. Updates
+
+NVIDIA may, at its option, make available patches, workarounds
+or other updates to this SDK. Unless the updates are provided
+with their separate governing terms, they are deemed part of
+the SDK licensed to you as provided in this Agreement. You
+agree that the form and content of the SDK that NVIDIA
+provides may change without prior notice to you. While NVIDIA
+generally maintains compatibility between versions, NVIDIA may
+in some cases make changes that introduce incompatibilities in
+future versions of the SDK.
+
+
+1.1.6. Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be
+distributed with, third party software licensed by a NVIDIA
+supplier and/or open source software provided under an open
+source license. Use of third party software is subject to the
+third-party license terms, or in the absence of third party
+terms, the terms of this Agreement. Copyright to third party
+software is held by the copyright holders indicated in the
+third-party software or license.
+
+
+1.1.7. Reservation of Rights
+
+NVIDIA reserves all rights, title, and interest in and to the
+SDK, not expressly granted to you under this Agreement.
+
+
+1.2. Limitations
+
+The following license limitations apply to your use of the
+SDK:
+
+  1. You may not reverse engineer, decompile or disassemble,
+    or remove copyright or other proprietary notices from any
+    portion of the SDK or copies of the SDK.
+
+  2. Except as expressly provided in this Agreement, you may
+    not copy, sell, rent, sublicense, transfer, distribute,
+    modify, or create derivative works of any portion of the
+    SDK. For clarity, you may not distribute or sublicense the
+    SDK as a stand-alone product.
+
+  3. Unless you have an agreement with NVIDIA for this
+    purpose, you may not indicate that an application created
+    with the SDK is sponsored or endorsed by NVIDIA.
+
+  4. You may not bypass, disable, or circumvent any
+    encryption, security, digital rights management or
+    authentication mechanism in the SDK.
+
+  5. You may not use the SDK in any manner that would cause it
+    to become subject to an open source software license. As
+    examples, licenses that require as a condition of use,
+    modification, and/or distribution that the SDK be:
+
+      a. Disclosed or distributed in source code form;
+
+      b. Licensed for the purpose of making derivative works;
+        or
+
+      c. Redistributable at no charge.
+
+  6. Unless you have an agreement with NVIDIA for this
+    purpose, you may not use the SDK with any system or
+    application where the use or failure of the system or
+    application can reasonably be expected to threaten or
+    result in personal injury, death, or catastrophic loss.
+    Examples include use in nuclear, avionics, navigation,
+    military, medical, life support or other life critical
+    applications. NVIDIA does not design, test or manufacture
+    the SDK for these critical uses and NVIDIA shall not be
+    liable to you or any third party, in whole or in part, for
+    any claims or damages arising from such uses.
+
+  7. You agree to defend, indemnify and hold harmless NVIDIA
+    and its affiliates, and their respective employees,
+    contractors, agents, officers and directors, from and
+    against any and all claims, damages, obligations, losses,
+    liabilities, costs or debt, fines, restitutions and
+    expenses (including but not limited to attorney’s fees
+    and costs incident to establishing the right of
+    indemnification) arising out of or related to your use of
+    the SDK outside of the scope of this Agreement, or not in
+    compliance with its terms.
+
+
+1.3. Ownership
+
+  1.  NVIDIA or its licensors hold all rights, title and
+    interest in and to the SDK and its modifications and
+    derivative works, including their respective intellectual
+    property rights, subject to your rights described here .
+    This SDK may include software and materials from
+    NVIDIA’s licensors, and these licensors are intended
+    third party beneficiaries that may enforce this Agreement
+    with respect to their intellectual property rights.
+
+  2.  You hold all rights, title and interest in and to your
+    applications and your derivative works of the sample
+    source code delivered in the SDK, including their
+    respective intellectual property rights, subject to
+    NVIDIA’s rights described here .
+
+  3. You may, but don’t have to, provide to NVIDIA
+    suggestions, feature requests or other feedback regarding
+    the SDK, including possible enhancements or modifications
+    to the SDK. For any feedback that you voluntarily provide,
+    you hereby grant NVIDIA and its affiliates a perpetual,
+    non-exclusive, worldwide, irrevocable license to use,
+    reproduce, modify, license, sublicense (through multiple
+    tiers of sublicensees), and distribute (through multiple
+    tiers of distributors) it without the payment of any
+    royalties or fees to you. NVIDIA will use feedback at its
+    choice. NVIDIA is constantly looking for ways to improve
+    its products, so you may send feedback to NVIDIA through
+    the developer portal at https://developer.nvidia.com.
+
+
+1.4. No Warranties
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL
+FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND
+ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND
+OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING,
+BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE
+ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO
+WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF
+DEALING OR COURSE OF TRADE.
+
+
+1.5. Limitation of Liability
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS
+AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL,
+PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS
+OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF
+PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION
+WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK,
+WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH
+OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE),
+PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF
+LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES
+TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS
+AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE
+NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS
+LIMIT.
+
+These exclusions and limitations of liability shall apply
+regardless if NVIDIA or its affiliates have been advised of
+the possibility of such damages, and regardless of whether a
+remedy fails its essential purpose. These exclusions and
+limitations of liability form an essential basis of the
+bargain between the parties, and, absent any of these
+exclusions or limitations of liability, the provisions of this
+Agreement, including, without limitation, the economic terms,
+would be substantially different.
+
+
+1.6. Termination
+
+  1. This Agreement will continue to apply until terminated by
+    either you or NVIDIA as described below.
+
+  2. If you want to terminate this Agreement, you may do so by
+    stopping to use the SDK.
+
+  3. NVIDIA may, at any time, terminate this Agreement if:
+
+      a. (i) you fail to comply with any term of this
+        Agreement and the non-compliance is not fixed within
+        thirty (30) days following notice from NVIDIA (or
+        immediately if you violate NVIDIA’s intellectual
+        property rights);
+
+      b. (ii) you commence or participate in any legal
+        proceeding against NVIDIA with respect to the SDK; or
+
+      c. (iii) NVIDIA decides to no longer provide the SDK in
+        a country or, in NVIDIA’s sole discretion, the
+        continued use of it is no longer commercially viable.
+
+  4. Upon any termination of this Agreement, you agree to
+    promptly discontinue use of the SDK and destroy all copies
+    in your possession or control. Your prior distributions in
+    accordance with this Agreement are not affected by the
+    termination of this Agreement. Upon written request, you
+    will certify in writing that you have complied with your
+    commitments under this section. Upon any termination of
+    this Agreement all provisions survive except for the
+    license grant provisions.
+
+
+1.7. General
+
+If you wish to assign this Agreement or your rights and
+obligations, including by merger, consolidation, dissolution
+or operation of law, contact NVIDIA to ask for permission. Any
+attempted assignment not approved by NVIDIA in writing shall
+be void and of no effect. NVIDIA may assign, delegate or
+transfer this Agreement and its rights and obligations, and if
+to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably
+requested information to verify your compliance with this
+Agreement.
+
+This Agreement will be governed in all respects by the laws of
+the United States and of the State of Delaware as those laws
+are applied to contracts entered into and performed entirely
+within Delaware by Delaware residents, without regard to the
+conflicts of laws principles. The United Nations Convention on
+Contracts for the International Sale of Goods is specifically
+disclaimed. You agree to all terms of this Agreement in the
+English language.
+
+The state or federal courts residing in Santa Clara County,
+California shall have exclusive jurisdiction over any dispute
+or claim arising out of this Agreement. Notwithstanding this,
+you agree that NVIDIA shall still be allowed to apply for
+injunctive remedies or an equivalent type of urgent legal
+relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any
+provision of this Agreement is illegal, invalid or
+unenforceable, such provision will be construed as limited to
+the extent necessary to be consistent with and fully
+enforceable under the law and the remaining provisions will
+remain in full force and effect. Unless otherwise specified,
+remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an
+independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is
+“commercial items” consisting of “commercial computer
+software” and “commercial computer software
+documentation” provided with RESTRICTED RIGHTS. Use,
+duplication or disclosure by the U.S. Government or a U.S.
+Government subcontractor is subject to the restrictions in
+this Agreement pursuant to DFARS 227.7202-3(a) or as set forth
+in subparagraphs (c)(1) and (2) of the Commercial Computer
+Software - Restricted Rights clause at FAR 52.227-19, as
+applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas
+Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and
+regulations. You agree that you will not ship, transfer or
+export the SDK into any country, or use the SDK in any manner,
+prohibited by the United States Bureau of Industry and
+Security or economic sanctions regulations administered by the
+U.S. Department of Treasury’s Office of Foreign Assets
+Control (OFAC), or any applicable export laws, restrictions or
+regulations. These laws include restrictions on destinations,
+end users and end use. By accepting this Agreement, you
+confirm that you are not a resident or citizen of any country
+currently embargoed by the U.S. and that you are not otherwise
+prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement
+will be delivered via mail, email or fax. You agree that any
+notices that NVIDIA sends you electronically will satisfy any
+legal communication requirements. Please direct your legal
+notices or other correspondence to NVIDIA Corporation, 2788
+San Tomas Expressway, Santa Clara, California 95051, United
+States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this
+Agreement constitute the entire agreement of the parties with
+respect to the subject matter of this Agreement and supersede
+all prior negotiations or documentation exchanged between the
+parties relating to this SDK license. Any additional and/or
+conflicting terms on documents issued by you are null, void,
+and invalid. Any amendment or waiver under this Agreement
+shall be in writing and signed by representatives of both
+parties.
+
+
+2. CUDA Toolkit Supplement to Software License Agreement for
+NVIDIA Software Development Kits
+------------------------------------------------------------
+
+
+Release date: August 16, 2018
+-----------------------------
+
+The terms in this supplement govern your use of the NVIDIA
+CUDA Toolkit SDK under the terms of your license agreement
+(“Agreement”) as modified by this supplement. Capitalized
+terms used but not defined below have the meaning assigned to
+them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is
+incorporated as an integral part of the Agreement. In the
+event of conflict between the terms in this supplement and the
+terms in the Agreement, the terms in this supplement govern.
+
+
+2.1. License Scope
+
+The SDK is licensed for you to develop applications only for
+use in systems with NVIDIA GPUs.
+
+
+2.2. Distribution
+
+The portions of the SDK that are distributable under the
+Agreement are listed in Attachment A.
+
+
+2.3. Operating Systems
+
+Those portions of the SDK designed exclusively for use on the
+Linux or FreeBSD operating systems, or other operating systems
+derived from the source code to these operating systems, may
+be copied and redistributed for use in accordance with this
+Agreement, provided that the object code files are not
+modified in any way (except for unzipping of compressed
+files).
+
+
+2.4. Audio and Video Encoders and Decoders
+
+You acknowledge and agree that it is your sole responsibility
+to obtain any additional third-party licenses required to
+make, have made, use, have used, sell, import, and offer for
+sale your products or services that include or incorporate any
+third-party software and content relating to audio and/or
+video encoders and decoders from, including but not limited
+to, Microsoft, Thomson, Fraunhofer IIS, Sisvel S.p.A.,
+MPEG-LA, and Coding Technologies. NVIDIA does not grant to you
+under this Agreement any necessary patent or other rights with
+respect to any audio and/or video encoders and decoders.
+
+
+2.5. Licensing
+
+If the distribution terms in this Agreement are not suitable
+for your organization, or for any questions regarding this
+Agreement, please contact NVIDIA at
+nvidia-compute-license-questions@nvidia.com.
+
+
+2.6. Attachment A
+
+The following portions of the SDK are distributable under the
+Agreement:
+
+Component
+
+CUDA Runtime
+
+Windows
+
+cudart.dll, cudart_static.lib, cudadevrt.lib
+
+Mac OSX
+
+libcudart.dylib, libcudart_static.a, libcudadevrt.a
+
+Linux
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Android
+
+libcudart.so, libcudart_static.a, libcudadevrt.a
+
+Component
+
+CUDA FFT Library
+
+Windows
+
+cufft.dll, cufftw.dll, cufft.lib, cufftw.lib
+
+Mac OSX
+
+libcufft.dylib, libcufft_static.a, libcufftw.dylib,
+libcufftw_static.a
+
+Linux
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Android
+
+libcufft.so, libcufft_static.a, libcufftw.so,
+libcufftw_static.a
+
+Component
+
+CUDA BLAS Library
+
+Windows
+
+cublas.dll, cublasLt.dll
+
+Mac OSX
+
+libcublas.dylib, libcublasLt.dylib, libcublas_static.a,
+libcublasLt_static.a
+
+Linux
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Android
+
+libcublas.so, libcublasLt.so, libcublas_static.a,
+libcublasLt_static.a
+
+Component
+
+NVIDIA "Drop-in" BLAS Library
+
+Windows
+
+nvblas.dll
+
+Mac OSX
+
+libnvblas.dylib
+
+Linux
+
+libnvblas.so
+
+Component
+
+CUDA Sparse Matrix Library
+
+Windows
+
+cusparse.dll, cusparse.lib
+
+Mac OSX
+
+libcusparse.dylib, libcusparse_static.a
+
+Linux
+
+libcusparse.so, libcusparse_static.a
+
+Android
+
+libcusparse.so, libcusparse_static.a
+
+Component
+
+CUDA Linear Solver Library
+
+Windows
+
+cusolver.dll, cusolver.lib
+
+Mac OSX
+
+libcusolver.dylib, libcusolver_static.a
+
+Linux
+
+libcusolver.so, libcusolver_static.a
+
+Android
+
+libcusolver.so, libcusolver_static.a
+
+Component
+
+CUDA Random Number Generation Library
+
+Windows
+
+curand.dll, curand.lib
+
+Mac OSX
+
+libcurand.dylib, libcurand_static.a
+
+Linux
+
+libcurand.so, libcurand_static.a
+
+Android
+
+libcurand.so, libcurand_static.a
+
+Component
+
+CUDA Accelerated Graph Library
+
+Windows
+
+nvgraph.dll, nvgraph.lib
+
+Mac OSX
+
+libnvgraph.dylib, libnvgraph_static.a
+
+Linux
+
+libnvgraph.so, libnvgraph_static.a
+
+Android
+
+libnvgraph.so, libnvgraph_static.a
+
+Component
+
+NVIDIA Performance Primitives Library
+
+Windows
+
+nppc.dll, nppc.lib, nppial.dll, nppial.lib, nppicc.dll,
+nppicc.lib, nppicom.dll, nppicom.lib, nppidei.dll,
+nppidei.lib, nppif.dll, nppif.lib, nppig.dll, nppig.lib,
+nppim.dll, nppim.lib, nppist.dll, nppist.lib, nppisu.dll,
+nppisu.lib, nppitc.dll, nppitc.lib, npps.dll, npps.lib
+
+Mac OSX
+
+libnppc.dylib, libnppc_static.a, libnppial.dylib,
+libnppial_static.a, libnppicc.dylib, libnppicc_static.a,
+libnppicom.dylib, libnppicom_static.a, libnppidei.dylib,
+libnppidei_static.a, libnppif.dylib, libnppif_static.a,
+libnppig.dylib, libnppig_static.a, libnppim.dylib,
+libnppisu_static.a, libnppitc.dylib, libnppitc_static.a,
+libnpps.dylib, libnpps_static.a
+
+Linux
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Android
+
+libnppc.so, libnppc_static.a, libnppial.so,
+libnppial_static.a, libnppicc.so, libnppicc_static.a,
+libnppicom.so, libnppicom_static.a, libnppidei.so,
+libnppidei_static.a, libnppif.so, libnppif_static.a
+libnppig.so, libnppig_static.a, libnppim.so,
+libnppim_static.a, libnppist.so, libnppist_static.a,
+libnppisu.so, libnppisu_static.a, libnppitc.so
+libnppitc_static.a, libnpps.so, libnpps_static.a
+
+Component
+
+NVIDIA JPEG Library
+
+Linux
+
+libnvjpeg.so, libnvjpeg_static.a
+
+Component
+
+Internal common library required for statically linking to
+cuBLAS, cuSPARSE, cuFFT, cuRAND, nvJPEG and NPP
+
+Mac OSX
+
+libculibos.a
+
+Linux
+
+libculibos.a
+
+Component
+
+NVIDIA Runtime Compilation Library
+
+Windows
+
+nvrtc.dll, nvrtc-builtins.dll
+
+Mac OSX
+
+libnvrtc.dylib, libnvrtc-builtins.dylib
+
+Linux
+
+libnvrtc.so, libnvrtc-builtins.so
+
+Component
+
+NVIDIA Optimizing Compiler Library
+
+Windows
+
+nvvm.dll
+
+Mac OSX
+
+libnvvm.dylib
+
+Linux
+
+libnvvm.so
+
+Component
+
+NVIDIA Common Device Math Functions Library
+
+Windows
+
+libdevice.10.bc
+
+Mac OSX
+
+libdevice.10.bc
+
+Linux
+
+libdevice.10.bc
+
+Component
+
+CUDA Occupancy Calculation Header Library
+
+All
+
+cuda_occupancy.h
+
+Component
+
+CUDA Half Precision Headers
+
+All
+
+cuda_fp16.h, cuda_fp16.hpp
+
+Component
+
+CUDA Profiling Tools Interface (CUPTI) Library
+
+Windows
+
+cupti.dll
+
+Mac OSX
+
+libcupti.dylib
+
+Linux
+
+libcupti.so
+
+Component
+
+NVIDIA Tools Extension Library
+
+Windows
+
+nvToolsExt.dll, nvToolsExt.lib
+
+Mac OSX
+
+libnvToolsExt.dylib
+
+Linux
+
+libnvToolsExt.so
+
+Component
+
+NVIDIA CUDA Driver Libraries
+
+Linux
+
+libcuda.so, libnvidia-fatbinaryloader.so,
+libnvidia-ptxjitcompiler.so
+
+The NVIDIA CUDA Driver Libraries are only distributable in
+applications that meet this criteria:
+
+  1. The application was developed starting from a NVIDIA CUDA
+    container obtained from Docker Hub or the NVIDIA GPU
+    Cloud, and
+
+  2. The resulting application is packaged as a Docker
+    container and distributed to users on Docker Hub or the
+    NVIDIA GPU Cloud only.
+
+In addition to the rights above, for parties that are
+developing software intended solely for use on Jetson
+development kits or Jetson modules, and running Linux for
+Tegra software, the following shall apply:
+
+  * The SDK may be distributed in its entirety, as provided by
+    NVIDIA, and without separation of its components, for you
+    and/or your licensees to create software development kits
+    for use only on the Jetson platform and running Linux for
+    Tegra software.
+
+
+2.7. Attachment B
+
+
+Additional Licensing Obligations
+
+The following third party components included in the SOFTWARE
+are licensed to Licensee pursuant to the following terms and
+conditions:
+
+  1. Licensee's use of the GDB third party component is
+    subject to the terms and conditions of GNU GPL v3:
+
+    This product includes copyrighted third-party software licensed
+    under the terms of the GNU General Public License v3 ("GPL v3").
+    All third-party software packages are copyright by their respective
+    authors. GPL v3 terms and conditions are hereby incorporated into
+    the Agreement by this reference:     http://www.gnu.org/licenses/gpl.txt
+
+    Consistent with these licensing requirements, the software
+    listed below is provided under the terms of the specified
+    open source software licenses. To obtain source code for
+    software provided under licenses that require
+    redistribution of source code, including the GNU General
+    Public License (GPL) and GNU Lesser General Public License
+    (LGPL), contact oss-requests@nvidia.com. This offer is
+    valid for a period of three (3) years from the date of the
+    distribution of this product by NVIDIA CORPORATION.
+
+    Component          License
+    CUDA-GDB           GPL v3
+
+  2. Licensee represents and warrants that any and all third
+    party licensing and/or royalty payment obligations in
+    connection with Licensee's use of the H.264 video codecs
+    are solely the responsibility of Licensee.
+
+  3. Licensee's use of the Thrust library is subject to the
+    terms and conditions of the Apache License Version 2.0.
+    All third-party software packages are copyright by their
+    respective authors. Apache License Version 2.0 terms and
+    conditions are hereby incorporated into the Agreement by
+    this reference.
+    http://www.apache.org/licenses/LICENSE-2.0.html
+
+    In addition, Licensee acknowledges the following notice:
+    Thrust includes source code from the Boost Iterator,
+    Tuple, System, and Random Number libraries.
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  4. Licensee's use of the LLVM third party component is
+    subject to the following terms and conditions:
+
+    ======================================================
+    LLVM Release License
+    ======================================================
+    University of Illinois/NCSA
+    Open Source License
+
+    Copyright (c) 2003-2010 University of Illinois at Urbana-Champaign.
+    All rights reserved.
+
+    Developed by:
+
+        LLVM Team
+
+        University of Illinois at Urbana-Champaign
+
+        http://llvm.org
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to
+    deal with the Software without restriction, including without limitation the
+    rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+    sell copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    *  Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimers.
+
+    *  Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimers in the
+       documentation and/or other materials provided with the distribution.
+
+    *  Neither the names of the LLVM Team, University of Illinois at Urbana-
+       Champaign, nor the names of its contributors may be used to endorse or
+       promote products derived from this Software without specific prior
+       written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL
+    THE CONTRIBUTORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+    OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+    ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+    DEALINGS WITH THE SOFTWARE.
+
+  5. Licensee's use of the PCRE third party component is
+    subject to the following terms and conditions:
+
+    ------------
+    PCRE LICENCE
+    ------------
+    PCRE is a library of functions to support regular expressions whose syntax
+    and semantics are as close as possible to those of the Perl 5 language.
+    Release 8 of PCRE is distributed under the terms of the "BSD" licence, as
+    specified below. The documentation for PCRE, supplied in the "doc"
+    directory, is distributed under the same terms as the software itself. The
+    basic library functions are written in C and are freestanding. Also
+    included in the distribution is a set of C++ wrapper functions, and a just-
+    in-time compiler that can be used to optimize pattern matching. These are
+    both optional features that can be omitted when the library is built.
+
+    THE BASIC LIBRARY FUNCTIONS
+    ---------------------------
+    Written by:       Philip Hazel
+    Email local part: ph10
+    Email domain:     cam.ac.uk
+    University of Cambridge Computing Service,
+    Cambridge, England.
+    Copyright (c) 1997-2012 University of Cambridge
+    All rights reserved.
+
+    PCRE JUST-IN-TIME COMPILATION SUPPORT
+    -------------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2010-2012 Zoltan Herczeg
+    All rights reserved.
+
+    STACK-LESS JUST-IN-TIME COMPILER
+    --------------------------------
+    Written by:       Zoltan Herczeg
+    Email local part: hzmester
+    Emain domain:     freemail.hu
+    Copyright(c) 2009-2012 Zoltan Herczeg
+    All rights reserved.
+
+    THE C++ WRAPPER FUNCTIONS
+    -------------------------
+    Contributed by:   Google Inc.
+    Copyright (c) 2007-2012, Google Inc.
+    All rights reserved.
+
+    THE "BSD" LICENCE
+    -----------------
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+      * Redistributions of source code must retain the above copyright notice,
+        this list of conditions and the following disclaimer.
+
+      * Redistributions in binary form must reproduce the above copyright
+        notice, this list of conditions and the following disclaimer in the
+        documentation and/or other materials provided with the distribution.
+
+      * Neither the name of the University of Cambridge nor the name of Google
+        Inc. nor the names of their contributors may be used to endorse or
+        promote products derived from this software without specific prior
+        written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+    ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+    INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+    CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+    ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  6. Some of the cuBLAS library routines were written by or
+    derived from code written by Vasily Volkov and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2007-2009, Regents of the University of California
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the University of California, Berkeley nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  7. Some of the cuBLAS library routines were written by or
+    derived from code written by Davide Barbieri and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2008-2009 Davide Barbieri @ University of Rome Tor Vergata.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * The name of the author may not be used to endorse or promote
+          products derived from this software without specific prior
+          written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE AUTHOR "AS IS" AND ANY EXPRESS OR
+    IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+    WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+    INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+    (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+    HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+    STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+    IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+    POSSIBILITY OF SUCH DAMAGE.
+
+  8. Some of the cuBLAS library routines were derived from
+    code developed by the University of Tennessee and are
+    subject to the Modified Berkeley Software Distribution
+    License as follows:
+
+    Copyright (c) 2010 The University of Tennessee.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer listed in this license in the documentation and/or
+          other materials provided with the distribution.
+        * Neither the name of the copyright holders nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  9. Some of the cuBLAS library routines were written by or
+    derived from code written by Jonathan Hogg and are subject
+    to the Modified Berkeley Software Distribution License as
+    follows:
+
+    Copyright (c) 2012, The Science and Technology Facilities Council (STFC).
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the STFC nor the names of its contributors
+          may be used to endorse or promote products derived from this
+          software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE STFC BE
+    LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+    CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+    SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+    BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+    WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+    OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN
+    IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  10. Some of the cuBLAS library routines were written by or
+    derived from code written by Ahmad M. Abdelfattah, David
+    Keyes, and Hatem Ltaief, and are subject to the Apache
+    License, Version 2.0, as follows:
+
+     -- (C) Copyright 2013 King Abdullah University of Science and Technology
+      Authors:
+      Ahmad Abdelfattah (ahmad.ahmad@kaust.edu.sa)
+      David Keyes (david.keyes@kaust.edu.sa)
+      Hatem Ltaief (hatem.ltaief@kaust.edu.sa)
+
+      Redistribution  and  use  in  source and binary forms, with or without
+      modification,  are  permitted  provided  that the following conditions
+      are met:
+
+      * Redistributions  of  source  code  must  retain  the above copyright
+        notice,  this  list  of  conditions  and  the  following  disclaimer.
+      * Redistributions  in  binary  form must reproduce the above copyright
+        notice,  this list of conditions and the following disclaimer in the
+        documentation  and/or other materials provided with the distribution.
+      * Neither  the  name of the King Abdullah University of Science and
+        Technology nor the names of its contributors may be used to endorse
+        or promote products derived from this software without specific prior
+        written permission.
+
+      THIS  SOFTWARE  IS  PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+      ``AS IS''  AND  ANY  EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+      LIMITED  TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+      A  PARTICULAR  PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+      HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+      SPECIAL,  EXEMPLARY,  OR  CONSEQUENTIAL  DAMAGES  (INCLUDING,  BUT NOT
+      LIMITED  TO,  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+      DATA,  OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+      THEORY  OF  LIABILITY,  WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+      (INCLUDING  NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+      OF  THIS  SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE
+
+  11. Some of the cuSPARSE library routines were written by or
+    derived from code written by Li-Wen Chang and are subject
+    to the NCSA Open Source License as follows:
+
+    Copyright (c) 2012, University of Illinois.
+
+    All rights reserved.
+
+    Developed by: IMPACT Group, University of Illinois, http://impact.crhc.illinois.edu
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal with the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimers in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the names of IMPACT Group, University of Illinois, nor
+          the names of its contributors may be used to endorse or promote
+          products derived from this Software without specific prior
+          written permission.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE CONTRIBUTORS OR COPYRIGHT
+    HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+    IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS WITH THE
+    SOFTWARE.
+
+  12. Some of the cuRAND library routines were written by or
+    derived from code written by Mutsuo Saito and Makoto
+    Matsumoto and are subject to the following license:
+
+    Copyright (c) 2009, 2010 Mutsuo Saito, Makoto Matsumoto and Hiroshima
+    University. All rights reserved.
+
+    Copyright (c) 2011 Mutsuo Saito, Makoto Matsumoto, Hiroshima
+    University and University of Tokyo.  All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of the Hiroshima University nor the names of
+          its contributors may be used to endorse or promote products
+          derived from this software without specific prior written
+          permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  13. Some of the cuRAND library routines were derived from
+    code developed by D. E. Shaw Research and are subject to
+    the following license:
+
+    Copyright 2010-2011, D. E. Shaw Research.
+
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+        * Redistributions of source code must retain the above copyright
+          notice, this list of conditions, and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+          copyright notice, this list of conditions, and the following
+          disclaimer in the documentation and/or other materials provided
+          with the distribution.
+        * Neither the name of D. E. Shaw Research nor the names of its
+          contributors may be used to endorse or promote products derived
+          from this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  14. Some of the Math library routines were written by or
+    derived from code developed by Norbert Juffa and are
+    subject to the following license:
+
+    Copyright (c) 2015-2017, Norbert Juffa
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions
+    are met:
+
+    1. Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  15. Licensee's use of the lz4 third party component is
+    subject to the following terms and conditions:
+
+    Copyright (C) 2011-2013, Yann Collet.
+    BSD 2-Clause License (http://www.opensource.org/licenses/bsd-license.php)
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are
+    met:
+
+        * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+        * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following disclaimer
+    in the documentation and/or other materials provided with the
+    distribution.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+    A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+    OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+    SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+    LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+    DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+    THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+    (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+  16. The NPP library uses code from the Boost Math Toolkit,
+    and is subject to the following license:
+
+    Boost Software License - Version 1.0 - August 17th, 2003
+    . . . .
+
+    Permission is hereby granted, free of charge, to any person or
+    organization obtaining a copy of the software and accompanying
+    documentation covered by this license (the "Software") to use,
+    reproduce, display, distribute, execute, and transmit the Software,
+    and to prepare derivative works of the Software, and to permit
+    third-parties to whom the Software is furnished to do so, all
+    subject to the following:
+
+    The copyright notices in the Software and this entire statement,
+    including the above license grant, this restriction and the following
+    disclaimer, must be included in all copies of the Software, in whole
+    or in part, and all derivative works of the Software, unless such
+    copies or derivative works are solely in the form of machine-executable
+    object code generated by a source language processor.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
+    NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR
+    ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR
+    OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+    OTHER DEALINGS IN THE SOFTWARE.
+
+  17. Portions of the Nsight Eclipse Edition is subject to the
+    following license:
+
+    The Eclipse Foundation makes available all content in this plug-in
+    ("Content"). Unless otherwise indicated below, the Content is provided
+    to you under the terms and conditions of the Eclipse Public License
+    Version 1.0 ("EPL"). A copy of the EPL is available at http://
+    www.eclipse.org/legal/epl-v10.html. For purposes of the EPL, "Program"
+    will mean the Content.
+
+    If you did not receive this Content directly from the Eclipse
+    Foundation, the Content is being redistributed by another party
+    ("Redistributor") and different terms and conditions may apply to your
+    use of any object code in the Content. Check the Redistributor's
+    license that was provided with the Content. If no such license exists,
+    contact the Redistributor. Unless otherwise indicated below, the terms
+    and conditions of the EPL still apply to any source code in the
+    Content and such source code may be obtained at http://www.eclipse.org.
+
+  18. Some of the cuBLAS library routines uses code from
+    OpenAI, which is subject to the following license:
+
+    License URL
+    https://github.com/openai/openai-gemm/blob/master/LICENSE
+
+    License Text
+    The MIT License
+
+    Copyright (c) 2016 OpenAI (http://openai.com), 2016 Google Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
+
+  19. Licensee's use of the Visual Studio Setup Configuration
+    Samples is subject to the following license:
+
+    The MIT License (MIT)
+    Copyright (C) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without restriction,
+    including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software,
+    and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+  20. Licensee's use of linmath.h header for CPU functions for
+    GL vector/matrix operations from lunarG is subject to the
+    Apache License Version 2.0.
+
+  21. The DX12-CUDA sample uses the d3dx12.h header, which is
+    subject to the MIT license .
+
+-----------------
diff --git a/3rdparty/LICENSE.cupy b/3rdparty/LICENSE.cupy
new file mode 100644
index 000000000..db8ef9d96
--- /dev/null
+++ b/3rdparty/LICENSE.cupy
@@ -0,0 +1,20 @@
+Copyright (c) 2015 Preferred Infrastructure, Inc.
+Copyright (c) 2015 Preferred Networks, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.dask b/3rdparty/LICENSE.dask
new file mode 100644
index 000000000..720334e99
--- /dev/null
+++ b/3rdparty/LICENSE.dask
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2014-2018, Anaconda, Inc. and contributors
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.dask-cuda b/3rdparty/LICENSE.dask-cuda
new file mode 100644
index 000000000..97b4c9dd9
--- /dev/null
+++ b/3rdparty/LICENSE.dask-cuda
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2019 NVIDIA Corporation
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.dlpack b/3rdparty/LICENSE.dlpack
new file mode 100644
index 000000000..330d6516b
--- /dev/null
+++ b/3rdparty/LICENSE.dlpack
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "{}"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2017 by Contributors
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.fmt b/3rdparty/LICENSE.fmt
new file mode 100644
index 000000000..8f9216805
--- /dev/null
+++ b/3rdparty/LICENSE.fmt
@@ -0,0 +1,27 @@
+Copyright (c) 2012 - present, Victor Zverovich
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+--- Optional exception to the license ---
+
+As an exception, if, as a result of your compiling your source code, portions
+of this Software are embedded into a machine-executable object form of such
+source code, you may redistribute such embedded portions in such object form
+without including the above copyright and permission notices.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.folly b/3rdparty/LICENSE.folly
new file mode 100644
index 000000000..347b899b2
--- /dev/null
+++ b/3rdparty/LICENSE.folly
@@ -0,0 +1,200 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+
+Files in folly/external/farmhash licensed as follows
+
+    Copyright (c) 2014 Google, Inc.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in
+    all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.googletest b/3rdparty/LICENSE.googletest
new file mode 100644
index 000000000..65c76c50c
--- /dev/null
+++ b/3rdparty/LICENSE.googletest
@@ -0,0 +1,28 @@
+Copyright 2008, Google Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+notice, this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following disclaimer
+in the documentation and/or other materials provided with the
+distribution.
+    * Neither the name of Google Inc. nor the names of its
+contributors may be used to endorse or promote products derived from
+this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.json b/3rdparty/LICENSE.json
new file mode 100644
index 000000000..548b989de
--- /dev/null
+++ b/3rdparty/LICENSE.json
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2013-2020 Niels Lohmann
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.libdeflate b/3rdparty/LICENSE.libdeflate
new file mode 100644
index 000000000..784bb2131
--- /dev/null
+++ b/3rdparty/LICENSE.libdeflate
@@ -0,0 +1,21 @@
+Copyright 2016 Eric Biggers
+
+Permission is hereby granted, free of charge, to any person
+obtaining a copy of this software and associated documentation files
+(the "Software"), to deal in the Software without restriction,
+including without limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of the Software,
+and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.libjpeg-turbo b/3rdparty/LICENSE.libjpeg-turbo
new file mode 100644
index 000000000..2ebba184e
--- /dev/null
+++ b/3rdparty/LICENSE.libjpeg-turbo
@@ -0,0 +1,132 @@
+libjpeg-turbo Licenses
+======================
+
+libjpeg-turbo is covered by three compatible BSD-style open source licenses:
+
+- The IJG (Independent JPEG Group) License, which is listed in
+  [README.ijg](README.ijg)
+
+  This license applies to the libjpeg API library and associated programs
+  (any code inherited from libjpeg, and any modifications to that code.)
+
+- The Modified (3-clause) BSD License, which is listed below
+
+  This license covers the TurboJPEG API library and associated programs, as
+  well as the build system.
+
+- The [zlib License](https://opensource.org/licenses/Zlib)
+
+  This license is a subset of the other two, and it covers the libjpeg-turbo
+  SIMD extensions.
+
+
+Complying with the libjpeg-turbo Licenses
+=========================================
+
+This section provides a roll-up of the libjpeg-turbo licensing terms, to the
+best of our understanding.
+
+1.  If you are distributing a modified version of the libjpeg-turbo source,
+    then:
+
+    1.  You cannot alter or remove any existing copyright or license notices
+        from the source.
+
+        **Origin**
+        - Clause 1 of the IJG License
+        - Clause 1 of the Modified BSD License
+        - Clauses 1 and 3 of the zlib License
+
+    2.  You must add your own copyright notice to the header of each source
+        file you modified, so others can tell that you modified that file (if
+        there is not an existing copyright header in that file, then you can
+        simply add a notice stating that you modified the file.)
+
+        **Origin**
+        - Clause 1 of the IJG License
+        - Clause 2 of the zlib License
+
+    3.  You must include the IJG README file, and you must not alter any of the
+        copyright or license text in that file.
+
+        **Origin**
+        - Clause 1 of the IJG License
+
+2.  If you are distributing only libjpeg-turbo binaries without the source, or
+    if you are distributing an application that statically links with
+    libjpeg-turbo, then:
+
+    1.  Your product documentation must include a message stating:
+
+        This software is based in part on the work of the Independent JPEG
+        Group.
+
+        **Origin**
+        - Clause 2 of the IJG license
+
+    2.  If your binary distribution includes or uses the TurboJPEG API, then
+        your product documentation must include the text of the Modified BSD
+        License (see below.)
+
+        **Origin**
+        - Clause 2 of the Modified BSD License
+
+3.  You cannot use the name of the IJG or The libjpeg-turbo Project or the
+    contributors thereof in advertising, publicity, etc.
+
+    **Origin**
+    - IJG License
+    - Clause 3 of the Modified BSD License
+
+4.  The IJG and The libjpeg-turbo Project do not warrant libjpeg-turbo to be
+    free of defects, nor do we accept any liability for undesirable
+    consequences resulting from your use of the software.
+
+    **Origin**
+    - IJG License
+    - Modified BSD License
+    - zlib License
+
+
+The Modified (3-clause) BSD License
+===================================
+
+Copyright (C)2009-2020 D. R. Commander.  All Rights Reserved.
+Copyright (C)2015 Viktor Szathmáry.  All Rights Reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+- Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+- Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+- Neither the name of the libjpeg-turbo Project nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS",
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+
+Why Three Licenses?
+===================
+
+The zlib License could have been used instead of the Modified (3-clause) BSD
+License, and since the IJG License effectively subsumes the distribution
+conditions of the zlib License, this would have effectively placed
+libjpeg-turbo binary distributions under the IJG License.  However, the IJG
+License specifically refers to the Independent JPEG Group and does not extend
+attribution and endorsement protections to other entities.  Thus, it was
+desirable to choose a license that granted us the same protections for new code
+that were granted to the IJG for code derived from their software.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.libspng b/3rdparty/LICENSE.libspng
new file mode 100644
index 000000000..95edda67c
--- /dev/null
+++ b/3rdparty/LICENSE.libspng
@@ -0,0 +1,25 @@
+BSD 2-Clause License
+
+Copyright (c) 2018-2020, Randy
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVERs
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.libtiff b/3rdparty/LICENSE.libtiff
new file mode 100644
index 000000000..0c45c085e
--- /dev/null
+++ b/3rdparty/LICENSE.libtiff
@@ -0,0 +1,21 @@
+Copyright (c) 1988-1997 Sam Leffler
+Copyright (c) 1991-1997 Silicon Graphics, Inc.
+
+Permission to use, copy, modify, distribute, and sell this software and
+its documentation for any purpose is hereby granted without fee, provided
+that (i) the above copyright notices and this permission notice appear in
+all copies of the software and related documentation, and (ii) the names of
+Sam Leffler and Silicon Graphics may not be used in any advertising or
+publicity relating to the software without the specific, prior written
+permission of Sam Leffler and Silicon Graphics.
+
+THE SOFTWARE IS PROVIDED "AS-IS" AND WITHOUT WARRANTY OF ANY KIND,
+EXPRESS, IMPLIED OR OTHERWISE, INCLUDING WITHOUT LIMITATION, ANY
+WARRANTY OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE.
+
+IN NO EVENT SHALL SAM LEFFLER OR SILICON GRAPHICS BE LIABLE FOR
+ANY SPECIAL, INCIDENTAL, INDIRECT OR CONSEQUENTIAL DAMAGES OF ANY KIND,
+OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+WHETHER OR NOT ADVISED OF THE POSSIBILITY OF DAMAGE, AND ON ANY THEORY OF
+LIABILITY, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+OF THIS SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.numpy b/3rdparty/LICENSE.numpy
new file mode 100644
index 000000000..6eddd9a2c
--- /dev/null
+++ b/3rdparty/LICENSE.numpy
@@ -0,0 +1,30 @@
+Copyright (c) 2005-2020, NumPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+       notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+       copyright notice, this list of conditions and the following
+       disclaimer in the documentation and/or other materials provided
+       with the distribution.
+
+    * Neither the name of the NumPy Developers nor the names of any
+       contributors may be used to endorse or promote products derived
+       from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.nvjpeg b/3rdparty/LICENSE.nvjpeg
new file mode 100644
index 000000000..11bc4ea75
--- /dev/null
+++ b/3rdparty/LICENSE.nvjpeg
@@ -0,0 +1,108 @@
+SOFTWARE LICENSE AGREEMENT
+(April 19, 2018 version)
+
+This Software License Agreement, including exhibits attached ("Agreement”) is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of a NVIDIA software and materials provided hereunder (“SOFTWARE”).
+
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the SOFTWARE is used.
+
+If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case “you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not download, install, copy or use the SOFTWARE.
+
+You agree to use the SOFTWARE only for purposes that are permitted by (a) this Agreement, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1.	License.
+
+1.1	Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants you a non-exclusive, non-transferable license, without the right to sublicense to install, use and make copies of the SOFTWARE for your use.
+
+1.2 Authorized Users
+
+You may allow employees and contractors of your entity or of your subsidiary(ies) to access and use the SOFTWARE from your secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users enrolled or employed by the academic institution to access and use the SOFTWARE from your secure network.
+
+You are responsible for the compliance with the terms of this Agreement by your authorized users. If you become aware that your authorized users didn’t follow the terms of this Agreement, you agree to take reasonable steps to resolve the non-compliance and prevent new occurrences.
+
+1.3 Pre-Release SOFTWARE
+The SOFTWARE versions identified as alpha, beta, preview or otherwise as pre-release, may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, accessibility, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. Use of a pre-release SOFTWARE may result in unexpected results, loss of data, project delays or other unpredictable damage or loss.
+You may use a pre-release SOFTWARE at your own risk, understanding that pre-release SOFTWARE is not intended for use in production or business-critical systems.
+NVIDIA may choose not to make available a commercial version of any pre-release SOFTWARE. NVIDIA may also choose to abandon development and terminate the availability of a pre-release SOFTWARE at any time without liability.
+1.4	 Updates
+
+NVIDIA may, at its option, make available patches, workarounds or other updates to this SOFTWARE. Unless the updates are provided with their separate governing terms, they are deemed part of the SOFTWARE licensed to you as provided in this Agreement.
+
+1.5	 Third Party Licenses
+
+The SOFTWARE may come bundled with, or otherwise include or be distributed with, third party software licensed by a NVIDIA supplier and/or open source software provided under an open source license. Use of third party software is subject to the third party license terms, or in the absence of third party terms, the terms of this Agreement. Copyright to third party software is held by the copyright holders indicated in the third-party software or license.
+
+1.6 Reservation of Rights
+
+NVIDIA reserves all rights, title and interest in and to the SOFTWARE not expressly granted to you under this Agreement.
+
+2.	Limitations.
+
+The following license limitations apply to your use of the SOFTWARE:
+
+2.1	You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SOFTWARE or copies of the SOFTWARE.
+
+2.2	Except as expressly provided in this Agreement, you may not copy, sell, rent, sublicense, transfer, distribute, modify, or create derivative works of any portion of the SOFTWARE.
+
+2.3	You may not bypass, disable, or circumvent any encryption, security, digital rights management or authentication mechanism in the SOFTWARE.
+
+2.4	You may not use the SOFTWARE in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SOFTWARE be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+
+2.5	 Unless you have an agreement with NVIDIA for this purpose, you may not use the SOFTWARE with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in nuclear, avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SOFTWARE for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses.
+
+2.6	You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SOFTWARE outside of the scope of this Agreement, or not in compliance with its terms.
+
+3.	Ownership.
+
+3.1	The SOFTWARE, modifications thereto, and the respective intellectual property rights therein are owned by NVIDIA or its licensors and are licensed to you as described in this Agreement. NVIDIA’s licensors are intended third party beneficiaries with the rights to enforce this Agreement with respect to their intellectual property rights.
+
+3.2	 You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SOFTWARE, including possible enhancements or modifications to the SOFTWARE. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will decide if and how to respond to feedback and if to incorporate feedback into the SOFTWARE.
+
+4.	 No Warranties.
+
+THE SOFTWARE IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE.
+
+5.	Limitations of Liability.
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SOFTWARE, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT.
+
+These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different.
+
+6.   Termination.
+
+6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below.
+
+6.2 If you want to terminate this Agreement, you may do so by stopping to use the SOFTWARE.
+
+6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SOFTWARE; or (iii) NVIDIA decides to no longer provide the SOFTWARE or certain parts of the SOFTWARE to users in a country, or the provision of the SOFTWARE or to you by NVIDIA is, in NVIDIA’s sole discretion, no longer commercially viable.
+
+6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SOFTWARE and destroy all copies in your possession or control. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the license grant provisions.
+
+7.  General.
+
+If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement.
+
+This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language.
+
+The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement.
+
+Neither party will be responsible for any failure or delay in its performance under this Agreement to the extent due to causes beyond its reasonable control for so long as the cause or event continues in effect.
+
+The SOFTWARE has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (c)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+The SOFTWARE is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SOFTWARE into any country, or use the SOFTWARE in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SOFTWARE.
+
+Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated to this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations, conversations, or discussions between the parties relating to this subject matter. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties.
diff --git a/3rdparty/LICENSE.nvjpeg2000 b/3rdparty/LICENSE.nvjpeg2000
new file mode 100644
index 000000000..098bda266
--- /dev/null
+++ b/3rdparty/LICENSE.nvjpeg2000
@@ -0,0 +1,171 @@
+Software License Agreement
+--------------------------
+
+
+LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+This license agreement, including exhibits attached ("Agreement”) is a legal agreement between you and NVIDIA Corporation ("NVIDIA") and governs your use of a NVIDIA software development kit (“SDK”).
+
+Each SDK has its own set of software and materials, but here is a description of the types of items that may be included in a SDK: source code, header files, APIs, data sets and assets (examples include images, textures, models, scenes, videos, native API input/output files), binary software, sample code, libraries, utility programs, programming code and documentation.
+
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the SDK is used.
+
+If you are entering into this Agreement on behalf of a company or other legal entity, you represent that you have the legal authority to bind the entity to this Agreement, in which case “you” will mean the entity you represent.
+
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not download, install or use the SDK.
+
+You agree to use the SDK only for purposes that are permitted by (a) this Agreement, and (b) any applicable law, regulation or generally accepted practices or guidelines in the relevant jurisdictions.
+
+1.	License.
+
+1.1	Grant
+
+Subject to the terms of this Agreement, NVIDIA hereby grants you a non-exclusive, non-transferable license, without the right to sublicense (except as expressly provided in this Agreement) to:
+
+(i)	Install and use the SDK,
+
+(ii)	Modify and create derivative works of sample source code delivered in the SDK, and
+
+(iii) Distribute those portions of the SDK that are identified in this Agreement as distributable, as incorporated in object code format into a software application that meets the distribution requirements indicated in this Agreement.
+
+1.2 Distribution Requirements
+
+These are the distribution requirements for you to exercise the distribution grant:
+
+(i)	Your application must have material additional functionality, beyond the included portions of the SDK.
+
+(ii)	The distributable portions of the SDK shall only be accessed by your application.
+
+(iii)	 The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+
+(iv)	 Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+
+(v)	The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights. Additionally, you agree that you will protect the privacy, security and legal rights of your application users.
+
+(vi) You agree to notify NVIDIA in writing of any known or suspected distribution or use of the SDK not in compliance with the requirements of this Agreement, and to enforce the terms of your agreements with respect to distributed SDK.
+
+1.3 Authorized Users
+
+You may allow employees and contractors of your entity or of your subsidiary(ies) to access and use the SDK from your secure network to perform work on your behalf.
+
+If you are an academic institution you may allow users enrolled or employed by the academic institution to access and use the SDK from your secure network.
+
+You are responsible for the compliance with the terms of this Agreement by your authorized users. If you become aware that your authorized users didn’t follow the terms of this Agreement, you agree to take reasonable steps to resolve the non-compliance and prevent new occurrences.
+
+1.4 Pre-Release SDK
+The SDK versions identified as alpha, beta, preview or otherwise as pre-release, may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, accessibility, availability, and reliability standards relative to commercial versions of NVIDIA software and materials. Use of a pre-release SDK may result in unexpected results, loss of data, project delays or other unpredictable damage or loss.
+You may use a pre-release SDK at your own risk, understanding that pre-release SDKs are not intended for use in production or business-critical systems.
+NVIDIA may choose not to make available a commercial version of any pre-release SDK. NVIDIA may also choose to abandon development and terminate the availability of a pre-release SDK at any time without liability.
+1.5	 Updates
+
+NVIDIA may, at its option, make available patches, workarounds or other updates to this SDK. Unless the updates are provided with their separate governing terms, they are deemed part of the SDK licensed to you as provided in this Agreement.
+
+You agree that the form and content of the SDK that NVIDIA provides may change without prior notice to you. While NVIDIA generally maintains compatibility between versions, NVIDIA may in some cases make changes that introduce incompatibilities in future versions of the SDK.
+
+1.6	 Third Party Licenses
+
+The SDK may come bundled with, or otherwise include or be distributed with, third party software licensed by a NVIDIA supplier and/or open source software provided under an open source license. Use of third party software is subject to the third-party license terms, or in the absence of third party terms, the terms of this Agreement. Copyright to third party software is held by the copyright holders indicated in the third-party software or license.
+
+1.7 Reservation of Rights
+
+NVIDIA reserves all rights, title and interest in and to the SDK not expressly granted to you under this Agreement.
+
+2.	Limitations.
+
+The following license limitations apply to your use of the SDK:
+
+2.1	You may not reverse engineer, decompile or disassemble, or remove copyright or other proprietary notices from any portion of the SDK or copies of the SDK.
+
+2.2	Except as expressly provided in this Agreement, you may not copy, sell, rent, sublicense, transfer, distribute, modify, or create derivative works of any portion of the SDK.
+
+2.3	Unless you have an agreement with NVIDIA for this purpose, you may not indicate that an application created with the SDK is sponsored or endorsed by NVIDIA.
+
+2.4	You may not bypass, disable, or circumvent any encryption, security, digital rights management or authentication mechanism in the SDK.
+
+2.5	You may not use the SDK in any manner that would cause it to become subject to an open source software license. As examples, licenses that require as a condition of use, modification, and/or distribution that the SDK be (i) disclosed or distributed in source code form; (ii) licensed for the purpose of making derivative works; or (iii) redistributable at no charge.
+
+2.6	 Unless you have an agreement with NVIDIA for this purpose, you may not use the SDK with any system or application where the use or failure of the system or application can reasonably be expected to threaten or result in personal injury, death, or catastrophic loss. Examples include use in avionics, navigation, military, medical, life support or other life critical applications. NVIDIA does not design, test or manufacture the SDK for these critical uses and NVIDIA shall not be liable to you or any third party, in whole or in part, for any claims or damages arising from such uses.
+
+2.7	You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney’s fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the SDK outside of the scope of this Agreement, or not in compliance with its terms.
+
+3.	Ownership.
+
+3.1	NVIDIA or its licensors hold all rights, title and interest in and to the SDK and its modifications and derivative works, including their respective intellectual property rights, subject to your rights under Section 3.2. This SDK may include software and materials from NVIDIA’s licensors, and these licensors are intended third party beneficiaries that may enforce this Agreement with respect to their intellectual property rights.
+
+3.2 You hold all rights, title and interest in and to your applications and your derivative works of the sample source code delivered in the SDK, including their respective intellectual property rights, subject to NVIDIA’s rights under section 3.1.
+
+3.3	You may, but don’t have to, provide to NVIDIA suggestions, feature requests or other feedback regarding the SDK, including possible enhancements or modifications to the SDK. For any feedback that you voluntarily provide, you hereby grant NVIDIA and its affiliates a perpetual, non-exclusive, worldwide, irrevocable license to use, reproduce, modify, license, sublicense (through multiple tiers of sublicensees), and distribute (through multiple tiers of distributors) it without the payment of any royalties or fees to you. NVIDIA will use feedback at its choice. NVIDIA is constantly looking for ways to improve its products, so you may send feedback to NVIDIA through the developer portal at https://developer.nvidia.com.
+
+4.	 No Warranties.
+
+THE SDK IS PROVIDED BY NVIDIA “AS IS” AND “WITH ALL FAULTS.” TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES EXPRESSLY DISCLAIM ALL WARRANTIES OF ANY KIND OR NATURE, WHETHER EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, NON-INFRINGEMENT, OR THE ABSENCE OF ANY DEFECTS THEREIN, WHETHER LATENT OR PATENT. NO WARRANTY IS MADE ON THE BASIS OF TRADE USAGE, COURSE OF DEALING OR COURSE OF TRADE.
+
+5.	Limitations of Liability.
+
+TO THE MAXIMUM EXTENT PERMITTED BY LAW, NVIDIA AND ITS AFFILIATES SHALL NOT BE LIABLE FOR ANY SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES, OR ANY LOST PROFITS, LOSS OF USE, LOSS OF DATA OR LOSS OF GOODWILL, OR THE COSTS OF PROCURING SUBSTITUTE PRODUCTS, ARISING OUT OF OR IN CONNECTION WITH THIS AGREEMENT OR THE USE OR PERFORMANCE OF THE SDK, WHETHER SUCH LIABILITY ARISES FROM ANY CLAIM BASED UPON BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY OR ANY OTHER CAUSE OF ACTION OR THEORY OF LIABILITY. IN NO EVENT WILL NVIDIA’S AND ITS AFFILIATES TOTAL CUMULATIVE LIABILITY UNDER OR ARISING OUT OF THIS AGREEMENT EXCEED US$10.00. THE NATURE OF THE LIABILITY OR THE NUMBER OF CLAIMS OR SUITS SHALL NOT ENLARGE OR EXTEND THIS LIMIT.
+
+These exclusions and limitations of liability shall apply regardless if NVIDIA or its affiliates have been advised of the possibility of such damages, and regardless of whether a remedy fails its essential purpose. These exclusions and limitations of liability form an essential basis of the bargain between the parties, and, absent any of these exclusions or limitations of liability, the provisions of this Agreement, including, without limitation, the economic terms, would be substantially different.
+
+6.   Termination.
+
+6.1 This Agreement will continue to apply until terminated by either you or NVIDIA as described below.
+
+6.2 If you want to terminate this Agreement, you may do so by stopping to use the SDK.
+
+6.3 NVIDIA may, at any time, terminate this Agreement if: (i) you fail to comply with any term of this Agreement and the non-compliance is not fixed within thirty (30) days following notice from NVIDIA (or immediately if you violate NVIDIA’s intellectual property rights); (ii) you commence or participate in any legal proceeding against NVIDIA with respect to the SDK; or (iii) NVIDIA decides to no longer provide the SDK in a country or, in NVIDIA’s sole discretion, the continued use of it is no longer commercially viable.
+
+6.4 Upon any termination of this Agreement, you agree to promptly discontinue use of the SDK and destroy all copies in your possession or control. Your prior distributions in accordance with this Agreement are not affected by the termination of this Agreement. Upon written request, you will certify in writing that you have complied with your commitments under this section. Upon any termination of this Agreement all provisions survive except for the licenses granted to you.
+
+7.  General.
+
+If you wish to assign this Agreement or your rights and obligations, including by merger, consolidation, dissolution or operation of law, contact NVIDIA to ask for permission. Any attempted assignment not approved by NVIDIA in writing shall be void and of no effect. NVIDIA may assign, delegate or transfer this Agreement and its rights and obligations, and if to a non-affiliate you will be notified.
+
+You agree to cooperate with NVIDIA and provide reasonably requested information to verify your compliance with this Agreement.
+
+This Agreement will be governed in all respects by the laws of the United States and of the State of Delaware as those laws are applied to contracts entered into and performed entirely within Delaware by Delaware residents, without regard to the conflicts of laws principles. The United Nations Convention on Contracts for the International Sale of Goods is specifically disclaimed. You agree to all terms of this Agreement in the English language.
+
+The state or federal courts residing in Santa Clara County, California shall have exclusive jurisdiction over any dispute or claim arising out of this Agreement. Notwithstanding this, you agree that NVIDIA shall still be allowed to apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.
+
+If any court of competent jurisdiction determines that any provision of this Agreement is illegal, invalid or unenforceable, such provision will be construed as limited to the extent necessary to be consistent with and fully enforceable under the law and the remaining provisions will remain in full force and effect. Unless otherwise specified, remedies are cumulative.
+
+Each party acknowledges and agrees that the other is an independent contractor in the performance of this Agreement.
+
+The SDK has been developed entirely at private expense and is “commercial items” consisting of “commercial computer software” and “commercial computer software documentation” provided with RESTRICTED RIGHTS. Use, duplication or disclosure by the U.S. Government or a U.S. Government subcontractor is subject to the restrictions in this Agreement pursuant to DFARS 227.7202-3(a) or as set forth in subparagraphs (b)(1) and (2) of the Commercial Computer Software - Restricted Rights clause at FAR 52.227-19, as applicable. Contractor/manufacturer is NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051.
+
+The SDK is subject to United States export laws and regulations. You agree that you will not ship, transfer or export the SDK into any country, or use the SDK in any manner, prohibited by the United States Bureau of Industry and Security or economic sanctions regulations administered by the U.S. Department of Treasury’s Office of Foreign Assets Control (OFAC), or any applicable export laws, restrictions or regulations. These laws include restrictions on destinations, end users and end use. By accepting this Agreement, you confirm that you are not a resident or citizen of any country currently embargoed by the U.S. and that you are not otherwise prohibited from receiving the SDK.
+
+Any notice delivered by NVIDIA to you under this Agreement will be delivered via mail, email or fax. You agree that any notices that NVIDIA sends you electronically will satisfy any legal communication requirements. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+
+This Agreement and any exhibits incorporated into this Agreement constitute the entire agreement of the parties with respect to the subject matter of this Agreement and supersede all prior negotiations or documentation exchanged between the parties relating to this SDK license. Any additional and/or conflicting terms on documents issued by you are null, void, and invalid. Any amendment or waiver under this Agreement shall be in writing and signed by representatives of both parties.
+
+(v. January 28, 2020)
+
+
+
+
+
+
+
+
+
+
+
+
+
+nvJPEG2K SUPPLEMENT TO SOFTWARE LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS
+
+The terms in this supplement govern your use of the NVIDIA nvJPEG2K SDK under the terms of your license agreement (“Agreement”) as modified by this supplement. Capitalized terms used but not defined below have the meaning assigned to them in the Agreement.
+
+This supplement is an exhibit to the Agreement and is incorporated as an integral part of the Agreement. In the event of conflict between the terms in this supplement and the terms in the Agreement, the terms in this supplement govern.
+
+4.1	License Scope. The SDK is licensed for you to develop applications only for use in systems with NVIDIA GPUs.
+
+2. Distribution. The following portions of the SDK are distributable under the Agreement: the runtime files .so and .h, and libnvjpeg2k_static.a.
+
+3. Licensing. If the distribution terms in this Agreement are not suitable for your organization, or for any questions regarding this Agreement, please contact NVIDIA at nvidia-compute-license-questions@nvidia.com.
+ (v. August 27, 2020)
+
+
+
+
+
diff --git a/3rdparty/LICENSE.opencv-contrib-python b/3rdparty/LICENSE.opencv-contrib-python
new file mode 100644
index 000000000..7a4a3ea24
--- /dev/null
+++ b/3rdparty/LICENSE.opencv-contrib-python
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.openjpeg b/3rdparty/LICENSE.openjpeg
new file mode 100644
index 000000000..120e57eb9
--- /dev/null
+++ b/3rdparty/LICENSE.openjpeg
@@ -0,0 +1,39 @@
+/*
+ * The copyright in this software is being made available under the 2-clauses 
+ * BSD License, included below. This software may be subject to other third 
+ * party and contributor rights, including patent rights, and no such rights
+ * are granted under this license.
+ *
+ * Copyright (c) 2002-2014, Universite catholique de Louvain (UCL), Belgium
+ * Copyright (c) 2002-2014, Professor Benoit Macq
+ * Copyright (c) 2003-2014, Antonin Descampe
+ * Copyright (c) 2003-2009, Francois-Olivier Devaux
+ * Copyright (c) 2005, Herve Drolon, FreeImage Team
+ * Copyright (c) 2002-2003, Yannick Verschueren
+ * Copyright (c) 2001-2003, David Janssens
+ * Copyright (c) 2011-2012, Centre National d'Etudes Spatiales (CNES), France 
+ * Copyright (c) 2012, CS Systemes d'Information, France
+ *
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * 1. Redistributions of source code must retain the above copyright
+ *    notice, this list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright
+ *    notice, this list of conditions and the following disclaimer in the
+ *    documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS `AS IS'
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ * ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+ * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+ * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+ * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+ * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+ * POSSIBILITY OF SUCH DAMAGE.
+ */
\ No newline at end of file
diff --git a/3rdparty/LICENSE.openslide b/3rdparty/LICENSE.openslide
new file mode 100644
index 000000000..40c1a45fb
--- /dev/null
+++ b/3rdparty/LICENSE.openslide
@@ -0,0 +1,18 @@
+OpenSlide
+
+Carnegie Mellon University and others
+
+https://openslide.org/
+
+====================
+
+Unless otherwise specified, this code is copyright Carnegie Mellon
+University.
+
+This code is licensed under the GNU LGPL version 2.1, not any later
+version.  See the file lgpl-2.1.txt for the text of the license.
+
+OpenSlide is distributed in the hope that it will be useful, but
+WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+Lesser General Public License for more details.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.pugixml b/3rdparty/LICENSE.pugixml
new file mode 100644
index 000000000..8df4e0df7
--- /dev/null
+++ b/3rdparty/LICENSE.pugixml
@@ -0,0 +1,24 @@
+MIT License
+
+Copyright (c) 2006-2020 Arseny Kapoulkine
+
+Permission is hereby granted, free of charge, to any person
+obtaining a copy of this software and associated documentation
+files (the "Software"), to deal in the Software without
+restriction, including without limitation the rights to use,
+copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following
+conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.pybind11 b/3rdparty/LICENSE.pybind11
new file mode 100644
index 000000000..596c20cde
--- /dev/null
+++ b/3rdparty/LICENSE.pybind11
@@ -0,0 +1,29 @@
+Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>, All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Please also refer to the file .github/CONTRIBUTING.md, which clarifies licensing of
+external contributions to this project including patches, pull requests, etc.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.pybind11_json b/3rdparty/LICENSE.pybind11_json
new file mode 100644
index 000000000..645936957
--- /dev/null
+++ b/3rdparty/LICENSE.pybind11_json
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2019,
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.pytest b/3rdparty/LICENSE.pytest
new file mode 100644
index 000000000..ca658c45a
--- /dev/null
+++ b/3rdparty/LICENSE.pytest
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2004-2020 Holger Krekel and others
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.pytorch b/3rdparty/LICENSE.pytorch
new file mode 100644
index 000000000..244b249f2
--- /dev/null
+++ b/3rdparty/LICENSE.pytorch
@@ -0,0 +1,73 @@
+From PyTorch:
+
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+
+From Caffe2:
+
+Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+
+All contributions by Facebook:
+Copyright (c) 2016 Facebook Inc.
+
+All contributions by Google:
+Copyright (c) 2015 Google Inc.
+All rights reserved.
+
+All contributions by Yangqing Jia:
+Copyright (c) 2015 Yangqing Jia
+All rights reserved.
+
+All contributions by Kakao Brain:
+Copyright 2019-2020 Kakao Brain
+
+All contributions from Caffe:
+Copyright(c) 2013, 2014, 2015, the respective contributors
+All rights reserved.
+
+All other contributions:
+Copyright(c) 2015, 2016 the respective contributors
+All rights reserved.
+
+Caffe2 uses a copyright model similar to Caffe: each contributor holds
+copyright over their contributions to Caffe2. The project versioning records
+all such contribution and copyright details. If a contributor wants to further
+mark their specific copyright on a particular contribution, they should
+indicate their copyright solely in the commit message of the change when it is
+committed.
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+   and IDIAP Research Institute nor the names of its contributors may be
+   used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.rmm b/3rdparty/LICENSE.rmm
new file mode 100644
index 000000000..f49a4e16e
--- /dev/null
+++ b/3rdparty/LICENSE.rmm
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.scifio b/3rdparty/LICENSE.scifio
new file mode 100644
index 000000000..38b089cc9
--- /dev/null
+++ b/3rdparty/LICENSE.scifio
@@ -0,0 +1,24 @@
+Copyright (c) 2011 - 2020, SCIFIO developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.scikit-image b/3rdparty/LICENSE.scikit-image
new file mode 100644
index 000000000..956f518e9
--- /dev/null
+++ b/3rdparty/LICENSE.scikit-image
@@ -0,0 +1,81 @@
+Copyright (C) 2019, the scikit-image team
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+ 1. Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+ 2. Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in
+    the documentation and/or other materials provided with the
+    distribution.
+ 3. Neither the name of skimage nor the names of its contributors may be
+    used to endorse or promote products derived from this software without
+    specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
+IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
+IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
+
+skimage/_shared/version_requirements.py:_check_version
+
+    Copyright (c) 2013 The IPython Development Team
+    All rights reserved.
+
+    Redistribution and use in source and binary forms, with or without
+    modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright notice, this
+      list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above copyright notice,
+      this list of conditions and the following disclaimer in the documentation
+      and/or other materials provided with the distribution.
+
+    * Neither the name of the copyright holder nor the names of its
+      contributors may be used to endorse or promote products derived from
+      this software without specific prior written permission.
+
+    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+    AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+    IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+    DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+    FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+    DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+    SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+    CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+    OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+    OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+skimage/_shared/version_requirements.py:is_installed:
+
+    Original Copyright (C) 2009-2011 Pierre Raybaut
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal in the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+
+    The above copyright notice and this permission notice shall be
+    included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+    LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+    OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+    WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.spdlog b/3rdparty/LICENSE.spdlog
new file mode 100644
index 000000000..4abea1358
--- /dev/null
+++ b/3rdparty/LICENSE.spdlog
@@ -0,0 +1,25 @@
+The MIT License (MIT)
+
+Copyright (c) 2016 Gabi Melman.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+
+-- NOTE: Third party dependency used by this software --
+This software depends on the fmt lib (MIT License),
+and users must comply to its license: https://github.com/fmtlib/fmt/blob/master/LICENSE.rst
diff --git a/3rdparty/LICENSE.tifffile b/3rdparty/LICENSE.tifffile
new file mode 100644
index 000000000..baada32fb
--- /dev/null
+++ b/3rdparty/LICENSE.tifffile
@@ -0,0 +1,30 @@
+BSD 3-Clause License
+
+Copyright (c) 2008-2020, Christoph Gohlke
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice,
+   this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/3rdparty/LICENSE.zarr-python b/3rdparty/LICENSE.zarr-python
new file mode 100644
index 000000000..22c4904c4
--- /dev/null
+++ b/3rdparty/LICENSE.zarr-python
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2015-2018 Zarr Developers <https://github.com/zarr-developers>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/CHANGELOG.md b/CHANGELOG.md
new file mode 100644
index 000000000..9f1ebb62b
--- /dev/null
+++ b/CHANGELOG.md
@@ -0,0 +1,13 @@
+# ___PROJECT___ 0.0.0 (DD Mon YYYY)
+
+## New Features
+
+- ...
+
+## Improvements
+
+- ...
+
+## Bug Fixes
+
+- ...
diff --git a/CMakeLists.txt b/CMakeLists.txt
new file mode 100644
index 000000000..478024852
--- /dev/null
+++ b/CMakeLists.txt
@@ -0,0 +1,232 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# CUDA_STANDARD 17 is supported from CMAKE 3.18
+# : https://cmake.org/cmake/help/v3.18/prop_tgt/CUDA_STANDARD.html
+cmake_minimum_required(VERSION 3.18)
+
+################################################################################
+# Prerequisite statements
+################################################################################
+
+# Set VERSION and BUILD
+unset(VERSION CACHE)
+file(STRINGS ${CMAKE_CURRENT_LIST_DIR}/VERSION VERSION)
+set(PROJECT_VERSION_BUILD dev)
+
+# Append local cmake module path
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cpp/cmake/modules")
+project(libcucim VERSION ${VERSION} DESCRIPTION "libcucim" LANGUAGES CXX CUDA)
+
+################################################################################
+# Include utilities
+################################################################################
+include(SuperBuildUtils)
+include(CuCIMUtils)
+
+################################################################################
+# Basic setup
+################################################################################
+
+# Set default build type
+set(DEFAULT_BUILD_TYPE "Release")
+if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    message(STATUS "Setting build type to '${DEFAULT_BUILD_TYPE}' as none was specified.")
+    set(CMAKE_BUILD_TYPE "${DEFAULT_BUILD_TYPE}" CACHE STRING "Choose the type of build." FORCE)
+    # Set the possible values of build type for cmake-gui
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif ()
+
+# Set default output directories
+if (NOT CMAKE_ARCHIVE_OUTPUT_DIRECTORY)
+    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif ()
+if (NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif ()
+if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/bin")
+endif ()
+
+find_package(CUDAToolkit REQUIRED)
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CUDA_STANDARD 17) # Clion issue: https://youtrack.jetbrains.com/issue/CPP-19165 (fixed)
+set(CMAKE_CUDA_STANDARD_REQUIRED YES)
+set(CMAKE_CXX_STANDARD_REQUIRED YES)
+cucim_define_cuda_architectures(60;70;75;80;86)
+# https://github.com/Kitware/CMake/blob/master/Modules/Compiler/NVIDIA-CUDA.cmake#L11
+# https://gitlab.kitware.com/cmake/cmake/-/issues/19017
+# For CUDA >= 10.2, we cannot use --compiler-options as '-forward-unknown-to-host-compiler' wouldbe added by default to nvcc options.
+# For the reason, we add "${CMAKE_CXX_FLAGS}" instead of "--compiler-options ${CMAKE_CXX_FLAGS}" here.
+# ==> We changed to use "${CMAKE_CUDA_FLAGS}" instead of "${CMAKE_CXX_FLAGS}" ${CMAKE_CXX_FLAGS} can have wrong options such as '-march=nocona'
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -use_fast_math -Xptxas=-v")
+set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -G")
+set(CMAKE_CUDA_FLAGS_RELEASE "${CMAKE_CUDA_FLAGS_RELEASE} -lineinfo")
+set(CMAKE_CUDA_FLAGS_RELWITHDEBINFO "${CMAKE_CUDA_FLAGS_RELWITHDEBINFO} -lineinfo")
+
+# Include CUDA headers explicitly for VSCode intelli-sense
+include_directories(AFTER SYSTEM ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+
+# Disable visibility to not expose unnecessary symbols
+set(CMAKE_CXX_VISIBILITY_PRESET hidden)
+set(CMAKE_VISIBILITY_INLINES_HIDDEN YES)
+
+# Set RPATH
+if (NOT APPLE)
+    set(CMAKE_INSTALL_RPATH $ORIGIN)
+endif()
+
+# Set Installation setup
+if (NOT CMAKE_INSTALL_PREFIX)
+    set(CMAKE_INSTALL_PREFIX ${CMAKE_CURRENT_LIST_DIR}/install) # CACHE PATH "install here" FORCE)
+endif ()
+include(GNUInstallDirs)
+# Force to set CMAKE_INSTALL_LIBDIR to lib as the library can be built with Cent OS ('lib64' is set) and
+# /usr/local/lib64 or /usr/local/lib is not part of ld.so.conf* (`cat /etc/ld.so.conf.d/* | grep lib64`)
+# https://gitlab.kitware.com/cmake/cmake/-/issues/20565
+set(CMAKE_INSTALL_LIBDIR lib)
+
+include(ExternalProject)
+
+################################################################################
+# Options
+################################################################################
+option(CUCIM_SUPPORT_GDS "Support cufile library" OFF)
+option(CUCIM_STATIC_GDS "Use static cufile library" OFF)
+
+# Setup CXX11 ABI
+# : Adds CXX11 ABI definition to the compiler command line for targets in the current directory,
+#   whether added before or after this command is invoked, and for the ones in sub-directories added after.
+add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) # TODO: create two library, one with CXX11 ABI and one without it.
+
+################################################################################
+# Define dependencies
+################################################################################
+superbuild_depend(fmt)
+#superbuild_depend(boost)
+superbuild_depend(abseil)
+superbuild_depend(rmm) # this imports googletest internally
+#superbuild_depend(googletest)
+superbuild_depend(googlebenchmark)
+#superbuild_depend(gds)
+superbuild_depend(openslide)
+superbuild_depend(catch2)
+superbuild_depend(cli11)
+superbuild_depend(pybind11)
+superbuild_depend(json)
+
+################################################################################
+# Define some names
+################################################################################
+set(CUCIM_PACKAGE_NAME cucim) # cucim
+
+################################################################################
+# Add subdirectories
+################################################################################
+add_subdirectory(cpp)
+add_subdirectory(gds)
+add_subdirectory(benchmarks)
+add_subdirectory(examples/cpp)
+
+################################################################################
+# Write CMakeLists.txt for C++ examples
+################################################################################
+
+configure_file(${CMAKE_CURRENT_LIST_DIR}/examples/cpp/CMakeLists.txt.examples.release.in
+    ${CMAKE_CURRENT_BINARY_DIR}/CMakeLists.txt.examples.release
+    @ONLY)
+
+################################################################################
+# Install
+################################################################################
+set(INSTALL_TARGETS
+        ${CUCIM_PACKAGE_NAME}
+#        ${CUCIM_PACKAGE_NAME}-header-only
+#        rmm
+        fmt-header-only
+#        spdlog_header_only # required by rmm
+        cucim_benchmarks
+#        cufile_stub
+#        cucim_tests
+        )
+
+install(TARGETS ${INSTALL_TARGETS}
+        EXPORT ${CUCIM_PACKAGE_NAME}-targets
+        RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+                COMPONENT ${CUCIM_PACKAGE_NAME}_Runtime
+        LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
+                COMPONENT ${CUCIM_PACKAGE_NAME}_Runtime
+                NAMELINK_COMPONENT ${CUCIM_PACKAGE_NAME}_Development
+        ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
+                COMPONENT ${CUCIM_PACKAGE_NAME}_Development
+        )
+install(EXPORT ${CUCIM_PACKAGE_NAME}-targets
+        FILE
+            ${CUCIM_PACKAGE_NAME}-targets.cmake
+        NAMESPACE
+            ${CUCIM_PACKAGE_NAME}::
+        DESTINATION
+            ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PACKAGE_NAME})
+
+# Write package configs
+include(CMakePackageConfigHelpers)
+configure_package_config_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cpp/cmake/${CUCIM_PACKAGE_NAME}-config.cmake.in
+    ${CMAKE_CURRENT_BINARY_DIR}/cpp/cmake/${CUCIM_PACKAGE_NAME}-config.cmake
+    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PACKAGE_NAME}
+)
+write_basic_package_version_file(
+    ${CMAKE_CURRENT_BINARY_DIR}/cpp/cmake/${CUCIM_PACKAGE_NAME}-config-version.cmake
+    VERSION ${PROJECT_VERSION}
+    COMPATIBILITY AnyNewerVersion
+)
+install(
+    FILES
+        ${CMAKE_CURRENT_BINARY_DIR}/cpp/cmake/${CUCIM_PACKAGE_NAME}-config.cmake
+        ${CMAKE_CURRENT_BINARY_DIR}/cpp/cmake/${CUCIM_PACKAGE_NAME}-config-version.cmake
+    DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PACKAGE_NAME}
+)
+
+install(DIRECTORY
+            ${CMAKE_CURRENT_SOURCE_DIR}/cpp/include/ # Suffix '/' is necessary to not copy to install/include/include folder.
+#            ${deps-rmm_SOURCE_DIR}/include/
+#            ${deps-rmm_SOURCE_DIR}/../thrust-src/thrust
+#            ${deps-rmm_SOURCE_DIR}/../spdlog-src/spdlog
+#            ${THRUST_INCLUDE_DIR}/thrust # thrust needs to be installed because rmm depends on thrust
+#            ${SPDLOG_INCLUDE_DIR}/spdlog # spdlog needs to be installed because rmm depends on spdlog
+        DESTINATION
+            ${CMAKE_INSTALL_INCLUDEDIR})
+
+# Copy 3rdparty headers
+install(DIRECTORY
+            ${deps-fmt_SOURCE_DIR}/include/
+        DESTINATION
+            ${CMAKE_INSTALL_INCLUDEDIR}/${CUCIM_PACKAGE_NAME}/3rdparty)
+
+set(CMAKE_EXPORT_PACKAGE_REGISTRY ON)
+export(PACKAGE ${CUCIM_PACKAGE_NAME})
+
+#set(CPACK_PACKAGE_NAME "${CUCIM_PACKAGE_NAME}")
+#set(CPACK_PACKAGE_VENDOR "nvidia.com")
+#set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "cuCIM - GPU-accelerated image processing toolkit")
+#set(CPACK_PACKAGE_VERSION "${PROJECT_VERSION}")
+#set(CPACK_PACKAGE_VERSION_MAJOR "${PROJECT_VERSION_MAJOR}")
+#set(CPACK_PACKAGE_VERSION_MINOR "${PROJECT_VERSION_MINOR}")
+#set(CPACK_PACKAGE_VERSION_PATCH "${PROJECT_VERSION_PATCH}")
+#set(CPACK_PACKAGE_INSTALL_DIRECTORY "cucim_cpack")  # TODO: update this
+#include(CPack)
+
+# Unset cached options needed
+unset(CUCIM_STATIC_GDS CACHE)
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 000000000..867d38920
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,147 @@
+# Contribute to cuCIM
+
+If you are interested in contributing to cuCIM, your contributions will fall
+into three categories:
+1. You want to report a bug, feature request, or documentation issue
+    - File an [issue](https://github.com/rapidsai/cucim/issues/new/choose)
+    describing what you encountered or what you want to see changed.
+    - The RAPIDS team will evaluate the issues and triage them, scheduling
+    them for a release. If you believe the issue needs priority attention
+    comment on the issue to notify the team.
+2. You want to propose a new Feature and implement it
+    - Post about your intended feature, and we shall discuss the design and
+    implementation.
+    - Once we agree that the plan looks good, go ahead and implement it, using
+    the [code contributions](#code-contributions) guide below.
+3. You want to implement a feature or bug-fix for an outstanding issue
+    - Follow the [code contributions](#code-contributions) guide below.
+    - If you need more context on a particular issue, please ask and we shall
+    provide.
+
+## Code contributions
+
+### Your first issue
+
+1. Read the project's [README.md](https://github.com/rapidsai/cucim/blob/main/README.md)
+    to learn how to setup the development environment
+2. Find an issue to work on. The best way is to look for the [good first issue](https://github.com/rapidsai/cucim/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
+    or [help wanted](https://github.com/rapidsai/cucim/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) labels
+3. Comment on the issue saying you are going to work on it
+4. Code! Make sure to update unit tests!
+5. When done, [create your pull request](https://github.com/rapidsai/cucim/compare)
+6. Verify that CI passes all [status checks](https://help.github.com/articles/about-status-checks/). Fix if needed
+7. Wait for other developers to review your code and update code as needed
+8. Once reviewed and approved, a RAPIDS developer will merge your pull request
+
+Remember, if you are unsure about anything, don't hesitate to comment on issues
+and ask for clarifications!
+
+### Seasoned developers
+
+Once you have gotten your feet wet and are more comfortable with the code, you
+can look at the prioritized issues of our next release in our [project boards](https://github.com/rapidsai/cucim/projects).
+
+> **Pro Tip:** Always look at the release board with the highest number for
+issues to work on. This is where RAPIDS developers also focus their efforts.
+
+Look at the unassigned issues, and find an issue you are comfortable with
+contributing to. Start with _Step 3_ from above, commenting on the issue to let
+others know you are working on it. If you have any questions related to the
+implementation of the issue, ask them in the issue instead of the PR.
+
+
+## Setting Up Your Build Environment
+
+The following instructions are for developers and contributors to cuCIM OSS development. These instructions are tested on Linux Ubuntu 16.04 & 18.04. Use these instructions to build cuCIM from source and contribute to its development.  Other operating systems may be compatible, but are not currently tested.
+
+### Code Formatting
+
+#### Python
+
+cuCIM uses [Black](https://black.readthedocs.io/en/stable/),
+[isort](https://readthedocs.org/projects/isort/), and
+[flake8](http://flake8.pycqa.org/en/latest/) to ensure a consistent code format
+throughout the project. `Black`, `isort`, and `flake8` can be installed with
+`conda` or `pip`:
+
+```bash
+conda install black isort flake8
+```
+
+```bash
+pip install black isort flake8
+```
+
+These tools are used to auto-format the Python code in the repository. Additionally, there is a CI check in place to enforce
+that committed code follows our standards. You can use the tools to
+automatically format your python code by running:
+
+```bash
+isort --atomic python/**/*.py
+black python
+```
+
+### Get libcucim Dependencies
+
+Compiler requirements:
+
+* `gcc`     version 9.0+
+* `nvcc`    version 11.0+
+* `cmake`   version 3.18.0+
+
+CUDA/GPU requirements:
+
+* CUDA 11.0+
+* NVIDIA driver 450.36+
+* Pascal architecture or better
+
+You can obtain CUDA from [https://developer.nvidia.com/cuda-downloads](https://developer.nvidia.com/cuda-downloads).
+
+
+# Script to build cuCIM from source
+
+### Build from Source
+
+- Clone the repository
+```bash
+CUCIM_HOME=$(pwd)/cucim
+git clone https://github.com/rapidsai/cucim.git $CUCIM_HOME
+cd $CUCIM_HOME
+```
+
+- Create the conda development environment `cucim`:
+```bash
+conda env create -f ./conda/environments/env.yml
+# activate the environment
+conda activate cucim
+```
+
+- Build and install `libcucim` and `cucim` (python bindings):
+```bash
+export CC=$CONDA_PREFIX/bin/x86_64-conda_cos6-linux-gnu-gcc
+export CXX=$CONDA_PREFIX/bin/x86_64-conda_cos6-linux-gnu-g++
+./run build_local all release $CONDA_PREFIX
+```
+
+- Build command will create the following files:
+  - ./install/lib/libcucim*
+  - ./python/install/lib/_cucim.cpython-38-x86_64-linux-gnu.so
+  - ./cpp/plugins/cucim.kit.cuslide/install/lib/cucim.kit.cuslide@*.so
+
+- Install libcucim/cuslide/cucim(python):
+```bash
+# libcucim
+cp -P -r install/bin/* $CONDA_PREFIX/bin/
+cp -P -r install/lib/* $CONDA_PREFIX/lib/
+cp -P -r install/lib/* $CONDA_PREFIX/lib/
+cp -P -r install/include/* $CONDA_PREFIX/include/
+
+# cuslide plugin
+cp -P -r cpp/plugins/cucim.kit.cuslide/install/bin/* $CONDA_PREFIX/bin
+cp -P -r cpp/plugins/cucim.kit.cuslide/install/lib/* $CONDA_PREFIX/lib/
+
+# cucim (python)
+cp -P python/install/lib/* python/cucim/src/cucim/clara/
+cd python/cucim/
+python -m pip install .
+```
diff --git a/Dockerfile-cuda110 b/Dockerfile-cuda110
new file mode 100644
index 000000000..fbf169cc2
--- /dev/null
+++ b/Dockerfile-cuda110
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+FROM gigony/manylinux2014-x64:20201013-46b1bff
+
+ENV DEFAULT_DOCKCROSS_IMAGE gigony/manylinux2014-x64:cuda110
+ENV PATH=/usr/local/cuda/bin/:$PATH
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/local/cuda/nvvm/lib64:${LD_LIBRARY_PATH}
+
+RUN curl -LO https://developer.download.nvidia.com/compute/cuda/11.0.3/local_installers/cuda_11.0.3_450.51.06_linux.run && \
+    chmod +x cuda_*.run && \
+    ./cuda_*.run --silent --no-opengl-libs --toolkit && \
+    rm -f cuda_*.run;
+
+RUN curl -LO https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/libnvjpeg2k0-0.0.1.17-1.x86_64.rpm && \
+    curl -LO https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/libnvjpeg2k-devel-0.0.1.17-1.x86_64.rpm && \
+    rpm -i libnvjpeg2k*.rpm
+
+# TODO: Currently we don't install dependencies from libtiff here.
+RUN yum install -y openslide-python openslide-devel python-devel python3-devel
+
+# Copy stub libcuda file
+RUN cp /usr/local/cuda-11.0/targets/x86_64-linux/lib/stubs/libcuda.so /usr/lib64/libcuda.so.1
+
+RUN cp -P /usr/include/nvjpeg2k* /usr/local/cuda/include/ && \
+    cp -P /usr/lib64/libnvjpeg2k* /usr/local/cuda/lib64/
diff --git a/Dockerfile-cuda111 b/Dockerfile-cuda111
new file mode 100644
index 000000000..ff8a31881
--- /dev/null
+++ b/Dockerfile-cuda111
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+FROM gigony/manylinux2014-x64:20201013-46b1bff
+
+ENV DEFAULT_DOCKCROSS_IMAGE gigony/manylinux2014-x64:cuda111
+ENV PATH=/usr/local/cuda/bin/:$PATH
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/local/cuda/nvvm/lib64:${LD_LIBRARY_PATH}
+
+RUN curl -LO https://developer.download.nvidia.com/compute/cuda/11.1.0/local_installers/cuda_11.1.0_455.23.05_linux.run && \
+    chmod +x cuda_*.run && \
+    ./cuda_*.run --silent --no-opengl-libs --toolkit && \
+    rm -f cuda_*.run;
+
+RUN curl -LO https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/libnvjpeg2k0-0.0.1.17-1.x86_64.rpm && \
+    curl -LO https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/libnvjpeg2k-devel-0.0.1.17-1.x86_64.rpm && \
+    rpm -i libnvjpeg2k*.rpm
+
+# TODO: Currently we don't install dependencies from libtiff here.
+RUN yum install -y openslide-python openslide-devel python-devel python3-devel
+
+# Copy stub libcuda file
+RUN cp /usr/local/cuda-11.1/targets/x86_64-linux/lib/stubs/libcuda.so /usr/lib64/libcuda.so.1
+
+RUN cp -P /usr/include/nvjpeg2k* /usr/local/cuda/include/ && \
+    cp -P /usr/lib64/libnvjpeg2k* /usr/local/cuda/lib64/
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 000000000..aa19d8080
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,203 @@
+Copyright (c) 2020-2021, NVIDIA CORPORATION.  All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
\ No newline at end of file
diff --git a/LICENSE-3rdparty.md b/LICENSE-3rdparty.md
new file mode 100644
index 000000000..49c07d323
--- /dev/null
+++ b/LICENSE-3rdparty.md
@@ -0,0 +1,238 @@
+cuCIM is licensed under the terms of the Apache-2.0 License.
+
+However, cuCIM utilizes third-party software from various sources.
+Portions of this software are copyrighted by their respective owners as indicated in the copyright
+notices below.
+
+The following acknowledgments pertain to this software license.
+
+The full license text of the third-party software is available in `3rdparty` folder
+in the repository/distribution.
+
+---
+
+libjpeg-turbo
+- This software is based in part on the work of the Independent JPEG Group.
+- License: libjpeg-turbo is covered by three compatible BSD-style open source licenses
+  - The IJG (Independent JPEG Group) License
+  - The Modified (3-clause) BSD License
+  - The zlib License
+  - https://github.com/libjpeg-turbo/libjpeg-turbo/blob/master/LICENSE.md
+- Copyright:
+  - D. R. Commander
+  - Viktor Szathmáry
+- Files:
+  - cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.cpp : Implementation of jpeg decoder.
+
+libtiff
+- License: BSD-like License
+  - https://gitlab.com/libtiff/libtiff/-/blob/master/COPYRIGHT
+- Copyright:
+  - Sam Leffler
+  - Silicon Graphics, Inc.
+
+fmt
+- License: MIT License
+  - https://github.com/fmtlib/fmt/blob/master/LICENSE.rst
+- Copyright: Victor Zverovich
+
+spdlog
+- License: MIT License
+  - https://github.com/gabime/spdlog/blob/v1.x/LICENSE
+- Copyright: Gabi Melman
+
+Google Benchmark
+- License: Apache-2.0 License
+  - https://github.com/google/benchmark/blob/master/LICENSE
+- Copyright: Google Inc.
+
+Google Test
+- License: BSD-3-Clause License
+  - https://github.com/google/googletest/blob/master/LICENSE
+- Copyright: Google Inc.
+
+Catch2
+- License: BSL-1.0 License
+  - https://github.com/catchorg/Catch2/blob/devel/LICENSE.txt
+- Copyright: Catch2 Authors
+
+CLI11
+- License: BSD-3-Clause License
+  - https://github.com/CLIUtils/CLI11/blob/master/LICENSE
+- Copyright: University of Cincinnati
+
+pybind11
+- License: BSD-3-Clause License
+  - https://github.com/pybind/pybind11/blob/master/LICENSE
+- Copyright: Wenzel Jakob
+- Files:
+  - python/pybind11/cucim_py.cpp : Implementation of `vector2pytuple()` method.
+
+JSON for Modern C++
+- License: MIT License
+  - https://github.com/nlohmann/json/blob/develop/LICENSE.MIT
+- Copyright: Niels Lohmann
+
+pybind11_json
+- License: BSD-3-Clause License
+  - https://github.com/pybind/pybind11_json/blob/master/LICENSE
+- Copyright: Martin Renou
+
+DLPack
+- License: Apache-2.0 License
+  - https://github.com/dmlc/dlpack/blob/main/LICENSE
+- Copyright: DLPack Contributors
+
+NVIDIA CUDA TOOLKIT
+- License: NVIDIA License
+  - https://docs.nvidia.com/cuda/pdf/EULA.pdf
+- Copyright: NVIDIA Corporation
+
+NVIDIA cufile (GDS)
+- License: NVIDIA License
+  - TBD
+- Copyright: NVIDIA Corporation
+
+RAPIDS RMM
+- License: Apache-2.0 License
+  - https://github.com/rapidsai/rmm/blob/branch-0.17/LICENSE
+- Copyright: NVIDIA Corporation
+
+OpenJPEG
+- License: BSD-2-Clause License
+  - https://github.com/uclouvain/openjpeg/blob/master/LICENSE
+- Copyright:
+  - Universite catholique de Louvain (UCL), Belgium
+  - Professor Benoit Macq
+  - Antonin Descampe
+  - Francois-Olivier Devaux
+  - Herve Drolon, FreeImage Team
+  - Yannick Verschueren
+  - David Janssens
+  - Centre National d'Etudes Spatiales (CNES), France
+  - CS Systemes d'Information, France
+
+NVIDIA nvJPEG
+- License: NVIDIA License
+  - https://developer.download.nvidia.com/compute/redist/libnvjpeg/EULA-nvjpeg.txt
+- Copyright: NVIDIA Corporation
+
+NVIDIA nvJPEG2000
+- License: NVIDIA License
+  - https://docs.nvidia.com/cuda/nvjpeg2000/license.html
+- Copyright: NVIDIA Corporation
+
+libspng
+- License: BSD-2-Clause License
+  - https://github.com/randy408/libspng/blob/master/LICENSE
+- Copyright: Randy
+
+PyTorch
+- License: BSD-3-Clause License
+  - https://github.com/pytorch/pytorch/blob/master/LICENSE
+- Copyright: PyTorch Contributors (See above link for the detail)
+
+Abseil
+- License: Apache-2.0 License
+  - https://github.com/abseil/abseil-cpp/blob/master/LICENSE
+- Copyright: The Abseil Authors
+
+Boost C++ Libraries
+- License: BSL-1.0 License
+  - https://github.com/boostorg/boost/blob/master/LICENSE_1_0.txt
+- Copyright: The Boost Authors
+
+Folly
+- License: Apache-2.0 License
+  - https://github.com/facebook/folly/blob/master/LICENSE
+- Copyright: Facebook, Inc. and its affiliates.
+
+NumPy
+- License: BSD-3-Clause License
+  - https://github.com/numpy/numpy/blob/master/LICENSE.txt
+- Copyright: NumPy Developers.
+
+pytest
+- License: MIT License
+  - https://github.com/pytest-dev/pytest/blob/master/LICENSE
+- Copyright: Holger Krekel and others
+
+CuPy
+- License: MIT License
+  - https://github.com/cupy/cupy/blob/master/LICENSE
+- Copyright:
+  - Preferred Infrastructure, Inc.
+  - Preferred Networks, Inc.
+
+OpenSlide
+- License: GNU Lesser General Public License v2.1
+  - https://github.com/openslide/openslide/blob/master/LICENSE.txt
+- Copyright: Carnegie Mellon University and others
+- Usage: For comparing performance in benchmark binaries
+
+Click
+- License: BSD-3-Clause License
+  - https://github.com/pallets/click/blob/master/LICENSE.rst
+- Copyright: Pallets
+
+tifffile
+- License: BSD-3-Clause License
+  - https://github.com/cgohlke/tifffile/blob/master/LICENSE
+- Copyright: Christoph Gohlke
+
+Dask
+- License: BSD-3-Clause License
+  - https://github.com/dask/dask/blob/master/LICENSE.txt
+- Copyright: Anaconda, Inc. and contributors
+
+Dask CUDA
+- License: Apache-2.0 License
+  - https://github.com/rapidsai/dask-cuda/blob/branch-0.17/LICENSE
+- Copyright: Dask CUDA Authors
+
+Zarr
+- License: MIT License
+  - https://github.com/zarr-developers/zarr-python/blob/master/LICENSE
+- Copyright: Zarr Developers
+
+scikit-image
+- License: BSD-3-Clause License
+  - https://github.com/scikit-image/scikit-image/blob/master/LICENSE.txt
+- Copyright: the scikit-image team
+
+OpenCV (extra modules, opencv-contrib-python)
+- License: Apache-2.0 License
+  - https://github.com/opencv/opencv_contrib/blob/master/LICENSE
+- Copyright:
+  - Intel Corporation
+  - Willow Garage Inc.
+  - NVIDIA Corporation
+  - Advanced Micro Devices, Inc.
+  - OpenCV Foundation
+  - Itseez Inc.
+  - Xperience AI
+  - Shenzhen Institute of Artificial Intelligence and Robotics for Society
+
+SCIFIO
+- License: BSD-2-Clause License
+  - https://github.com/scifio/scifio/blob/master/LICENSE.txt
+- Copyright: SCIFIO developers
+- Usage: Image format interface is inspired by this library.
+
+AICSImageIO
+- License: BSD-3-Clause License
+  - https://github.com/AllenCellModeling/aicsimageio/blob/master/LICENSE
+- Copyright: Allen Institute for Cell Science
+- Usage: Some Python API design is inspired by this library.
+
+pugixml
+- License: MIT License
+  - https://github.com/zeux/pugixml/blob/master/LICENSE.md
+- Copyright: Arseny Kapoulkine
+- Usage: Parsing XML metadata for Philips TIFF file (@cuslide plugin)
+
+libdeflate
+- License: MIT License
+  - https://github.com/ebiggers/libdeflate/blob/master/COPYING
+- Copyright: Eric Biggers
+- Usage: Extracting tile image (zlib/deflate compressed)for TIFF file (@cuslide plugin)
diff --git a/README.md b/README.md
new file mode 100644
index 000000000..1ace94143
--- /dev/null
+++ b/README.md
@@ -0,0 +1,39 @@
+# <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;cuCIM</div>
+
+
+[RAPIDS](https://rapids.ai) cuCIM is an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.
+
+**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cucim/blob/main/README.md) ensure you are on the `main` branch.
+
+## Install cuCIM
+
+### Conda
+
+#### Conda (stable)
+
+> conda create -n cucim -c rapidsai -c conda-forge/label/cupy_rc cucim
+
+#### Conda (nightlies)
+
+> conda create -n cucim -c rapidsai-nightly -c conda-forge/label/cupy_rc cucim
+
+## Build/Install from Source
+See build [instructions](CONTRIBUTING.md#setting-up-your-build-environment).
+
+## Contributing Guide
+
+Contributions to cuCIM are more than welcome!
+Please review the [CONTRIBUTING.md](https://github.com/rapidsai/cucim/blob/main/CONTRIBUTING.md) file for information on how to contribute code and issues to the project.
+
+## Acknowledgments
+
+Without awesome third-party open source software, this project wouldn't exist.
+
+Please find [LICENSE-3rdparty.md](LICENSE-3rdparty.md) to see which third-party open source software
+is used in this project.
+
+## License
+
+Apache-2.0 License (see [LICENSE](LICENSE) file).
+
+Copyright (c) 2020-2021, NVIDIA CORPORATION.
diff --git a/VERSION b/VERSION
new file mode 100644
index 000000000..1cf0537c3
--- /dev/null
+++ b/VERSION
@@ -0,0 +1 @@
+0.19.0
diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt
new file mode 100644
index 000000000..d7f9edcf1
--- /dev/null
+++ b/benchmarks/CMakeLists.txt
@@ -0,0 +1,79 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+################################################################################
+# Add executable: cucim_benchmarks
+################################################################################
+
+add_executable(cucim_benchmarks main.cpp config.h)
+#set_source_files_properties(main.cpp PROPERTIES LANGUAGE CUDA) # failed with CLI11 library
+
+set_target_properties(cucim_benchmarks
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(cucim_benchmarks PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cucim_benchmarks PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_compile_definitions(cucim_benchmarks
+    PUBLIC
+        CUCIM_VERSION=${PROJECT_VERSION}
+        CUCIM_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUCIM_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUCIM_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUCIM_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+)
+target_link_libraries(cucim_benchmarks
+        PRIVATE
+            ${CUCIM_PACKAGE_NAME}
+            deps::googlebenchmark
+            deps::openslide
+            deps::cli11
+        )
+
+
+################################################################################
+# Add executable: cucim_primitives_benchmarks
+################################################################################
+
+add_executable(cucim_primitives_benchmarks primitives.cpp)
+#set_source_files_properties(main.cpp PROPERTIES LANGUAGE CUDA) # failed with CLI11 library
+
+set_target_properties(cucim_primitives_benchmarks
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(cucim_primitives_benchmarks PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cucim_primitives_benchmarks PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_link_libraries(cucim_primitives_benchmarks
+        PRIVATE
+            ${CUCIM_PACKAGE_NAME}
+            deps::googlebenchmark
+        )
diff --git a/benchmarks/config.h b/benchmarks/config.h
new file mode 100644
index 000000000..9a01df5c0
--- /dev/null
+++ b/benchmarks/config.h
@@ -0,0 +1,47 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2020 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_CONFIG_H
+#define CUCIM_CONFIG_H
+
+#include <string>
+
+struct AppConfig
+{
+    std::string input_file = "test_data/private/generic_tiff_000.tif";
+    bool discard_cache = false;
+    int random_seed = 0;
+    bool random_start_location = false;
+
+    int64_t image_width = 0;
+    int64_t image_height = 0;
+
+    // Pseudo configurations for google benchmark
+    bool benchmark_list_tests = false;
+    std::string benchmark_filter; // <regex>
+    int benchmark_min_time = 0; // <min_time>
+    int benchmark_repetitions = 0; // <num_repetitions>
+    bool benchmark_report_aggregates_only = false;
+    bool benchmark_display_aggregates_only = false;
+    std::string benchmark_format; // <console|json|csv>
+    std::string benchmark_out; // <filename>
+    std::string benchmark_out_format; // <json|console|csv>
+    std::string benchmark_color; //  {auto|true|false}
+    std::string benchmark_counters_tabular;
+    std::string v; // <verbosity>
+};
+
+#endif // CUCIM_CONFIG_H
diff --git a/benchmarks/main.cpp b/benchmarks/main.cpp
new file mode 100644
index 000000000..c280a3f44
--- /dev/null
+++ b/benchmarks/main.cpp
@@ -0,0 +1,196 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "config.h"
+
+#include <fcntl.h>
+#include <unistd.h>
+#include <cstdlib>
+#include <cstring>
+#include <fmt/format.h>
+
+#include <benchmark/benchmark.h>
+#include <openslide/openslide.h>
+#include <CLI/CLI.hpp>
+
+#include "cucim/cuimage.h"
+
+static AppConfig g_config;
+
+static void test_cucim(benchmark::State& state)
+{
+    int arg = -1;
+    for (auto state_item : state)
+    {
+        state.PauseTiming();
+        {
+            // Use a different start random seed for the different argument
+            if (arg != state.range())
+            {
+                arg = state.range();
+                srand(g_config.random_seed + arg);
+            }
+
+            if (g_config.discard_cache)
+            {
+                int fd = open(g_config.input_file.c_str(), O_RDONLY);
+                fdatasync(fd);
+                posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
+                close(fd);
+            }
+        }
+        state.ResumeTiming();
+
+        int64_t request_location[2] = { 0, 0 };
+        if (g_config.random_start_location)
+        {
+            request_location[0] = rand() % (g_config.image_width - state.range(0));
+            request_location[1] = rand() % (g_config.image_height - state.range(0));
+        }
+
+        cucim::CuImage image = cucim::CuImage(g_config.input_file.c_str());
+        cucim::CuImage region =
+            image.read_region({ request_location[0], request_location[1] }, { state.range(0), state.range(0) }, 0,
+                              cucim::DimIndices{}, "cpu", nullptr, "");
+    }
+}
+
+static void test_openslide(benchmark::State& state)
+{
+    int arg = -1;
+    for (auto _ : state)
+    {
+        state.PauseTiming();
+        {
+            // Use a different start random seed for the different argument
+            if (arg != state.range())
+            {
+                arg = state.range();
+                srand(g_config.random_seed + arg);
+            }
+
+            if (g_config.discard_cache)
+            {
+                int fd = open(g_config.input_file.c_str(), O_RDONLY);
+                fdatasync(fd);
+                posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
+                close(fd);
+            }
+        }
+        state.ResumeTiming();
+
+        openslide_t* slide = openslide_open(g_config.input_file.c_str());
+        uint32_t* buf = (uint32_t*)cucim_malloc(state.range(0) * state.range(0) * 4);
+        int64_t request_location[2] = { 0, 0 };
+        if (g_config.random_start_location)
+        {
+            request_location[0] = rand() % (g_config.image_width - state.range(0));
+            request_location[1] = rand() % (g_config.image_height - state.range(0));
+        }
+
+        openslide_read_region(slide, buf, request_location[0], request_location[1], 0, state.range(0), state.range(0));
+        cucim_free(buf);
+        openslide_close(slide);
+    }
+}
+
+
+BENCHMARK(test_cucim)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096);
+BENCHMARK(test_openslide)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096);
+
+static bool remove_help_option(int* argc, char** argv)
+{
+    for (int i = 1; argc && i < *argc; ++i)
+    {
+        if (strncmp(argv[i], "-h", 3) == 0 || strncmp(argv[i], "--help", 7) == 0)
+        {
+            for (int j = i + 1; argc && j < *argc; ++j)
+            {
+                argv[j - 1] = argv[j];
+            }
+            --(*argc);
+            argv[*argc] = nullptr;
+            return true;
+        }
+    }
+    return false;
+}
+
+static bool setup_configuration()
+{
+    openslide_t* slide = openslide_open(g_config.input_file.c_str());
+    if (slide == nullptr)
+    {
+        fmt::print("[Error] Cannot load {}!\n", g_config.input_file);
+        return false;
+    }
+    int64_t w, h;
+    openslide_get_level0_dimensions(slide, &w, &h);
+
+    g_config.image_width = w;
+    g_config.image_height = h;
+
+    openslide_close(slide);
+    return true;
+}
+
+// BENCHMARK_MAIN();
+int main(int argc, char** argv)
+{
+
+    // Skip processing help option
+    bool has_help_option = remove_help_option(&argc, argv);
+
+    ::benchmark::Initialize(&argc, argv);
+    //    if (::benchmark::ReportUnrecognizedArguments(argc, argv))
+    //        return 1;
+
+    CLI::App app{ "cuCIM Benchmark" };
+    app.add_option("--test_file", g_config.input_file, "An input .tif/.svs file path");
+    app.add_option("--discard_cache", g_config.discard_cache, "Discard page cache for the input file for each iteration");
+    app.add_option("--random_seed", g_config.random_seed, "A random seed number");
+    app.add_option(
+        "--random_start_location", g_config.random_start_location, "Randomize start location of read_region()");
+
+    // Pseudo benchmark options
+    app.add_option("--benchmark_list_tests", g_config.benchmark_list_tests, "{true|false}");
+    app.add_option("--benchmark_filter", g_config.benchmark_filter, "<regex>");
+    app.add_option("--benchmark_min_time", g_config.benchmark_min_time, "<min_time>");
+    app.add_option("--benchmark_repetitions", g_config.benchmark_repetitions, "<num_repetitions>");
+    app.add_option("--benchmark_report_aggregates_only", g_config.benchmark_report_aggregates_only, "{true|false}");
+    app.add_option("--benchmark_display_aggregates_only", g_config.benchmark_display_aggregates_only, "{true|false}");
+    app.add_option("--benchmark_format", g_config.benchmark_format, "<console|json|csv>");
+    app.add_option("--benchmark_out", g_config.benchmark_out, "<filename>");
+    app.add_option("--benchmark_out_format", g_config.benchmark_out_format, "<json|console|csv>");
+    app.add_option("--benchmark_color", g_config.benchmark_color, "{auto|true|false}");
+    app.add_option("--benchmark_counters_tabular", g_config.benchmark_counters_tabular, "{true|false}");
+    app.add_option("--v", g_config.v, "<verbosity>");
+
+    // Append help option if exists
+    if (has_help_option)
+    {
+        argv[argc] = const_cast<char*>("--help");
+        ++argc;
+    }
+    CLI11_PARSE(app, argc, argv);
+
+    if (!setup_configuration())
+    {
+        return 1;
+    }
+
+    ::benchmark::RunSpecifiedBenchmarks();
+}
diff --git a/benchmarks/primitives.cpp b/benchmarks/primitives.cpp
new file mode 100644
index 000000000..49d8d8960
--- /dev/null
+++ b/benchmarks/primitives.cpp
@@ -0,0 +1,204 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/memory/memory_manager.h"
+
+#include <vector>
+#include <string>
+#include <cstring>
+#include <memory_resource>
+#include <benchmark/benchmark.h>
+
+
+static void vector_copy_push_back(benchmark::State& state)
+{
+    const int data_count = 50000;
+    uint64_t data[data_count];
+
+    // Code inside this loop is measured repeatedly
+    for (auto _ : state)
+    {
+        std::vector<uint64_t> data_vec;
+        for (int i = 0; i < data_count; ++i)
+        {
+            data_vec.push_back(data[i]);
+        }
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(data_vec);
+    }
+}
+// Register the function as a benchmark
+BENCHMARK(vector_copy_push_back);
+
+static void vector_copy_insert(benchmark::State& state)
+{
+    const int data_count = 50000;
+    uint64_t data[data_count];
+
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        std::vector<uint64_t> data_vec;
+        data_vec.insert(data_vec.end(), &data[0], &data[data_count]);
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(data_vec);
+    }
+}
+BENCHMARK(vector_copy_insert);
+
+static void vector_copy_vector_vector(benchmark::State& state)
+{
+    const int data_count = 50000;
+    uint64_t data[data_count];
+
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        std::vector<uint64_t> data_vec;
+        data_vec.insert(data_vec.end(), &data[0], &data[data_count]);
+        std::vector<uint64_t> data_vec2(&data[0], &data[data_count]);
+
+        data_vec.insert(data_vec.end(), data_vec2.begin(), data_vec2.end());
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(data_vec);
+    }
+}
+BENCHMARK(vector_copy_vector_vector);
+
+static void string_memcpy(benchmark::State& state)
+{
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        std::string data = "#########################################################################################################################################################################################";
+        const int size = data.size();
+
+        char * c_str = (char*) malloc(size + 1);
+        memcpy(c_str, data.data(), size);
+        c_str[size] = '\0';
+        free(c_str);
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(c_str);
+        benchmark::DoNotOptimize(size);
+    }
+}
+BENCHMARK(string_memcpy);
+
+static void string_strcpy(benchmark::State& state)
+{
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        std::string data = "#########################################################################################################################################################################################";
+        char * c_str = (char*) malloc(data.size() + 1);
+        strcpy(c_str, data.data());
+        free(c_str);
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(c_str);
+    }
+}
+BENCHMARK(string_strcpy);
+
+static void string_strdup(benchmark::State& state)
+{
+
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        std::string data = "#########################################################################################################################################################################################";
+        char * c_str = strdup(data.data());
+        free(c_str);
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(c_str);
+    }
+}
+BENCHMARK(string_strdup);
+
+
+static void alloc_malloc(benchmark::State& state)
+{
+
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        char* arr[30000];
+        for (int i = 0; i < 30000; i++)
+        {
+            arr[i] = (char*)malloc(10);
+            arr[i][0] = i;
+        }
+        for (int i = 0; i < 30000; i++)
+        {
+            free(arr[i]);
+        }
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(arr);
+    }
+}
+BENCHMARK(alloc_malloc);//->Iterations(100);
+
+
+static void alloc_pmr(benchmark::State& state)
+{
+
+    // Code before the loop is not measured
+    for (auto _ : state)
+    {
+        char* arr[30000];
+        for (int i = 0; i < 30000; i++)
+        {
+            arr[i] = (char*)cucim_malloc(10);
+            arr[i][0] = i;
+        }
+        for (int i = 0; i < 30000; i++)
+        {
+            cucim_free(arr[i]);
+        }
+        // Make sure the variable is not optimized away by compiler
+        benchmark::DoNotOptimize(arr);
+    }
+}
+BENCHMARK(alloc_pmr);//->Iterations(100);
+
+BENCHMARK_MAIN();
+
+// Debug
+
+// ```
+// --------------------------------------------------------------------
+// Benchmark                          Time             CPU   Iterations
+// --------------------------------------------------------------------
+// vector_copy_push_back         591517 ns       591510 ns         1267
+// vector_copy_insert              8488 ns         8488 ns        85160
+// vector_copy_vector_vector     225441 ns       225439 ns         3069
+// string_memcpy                    169 ns          169 ns      3854598
+// string_strcpy                    202 ns          202 ns      4114834
+// string_strdup                    184 ns          184 ns      3666944
+// ```
+
+// Release
+
+// ```
+// --------------------------------------------------------------------
+// Benchmark                          Time             CPU   Iterations
+// --------------------------------------------------------------------
+// vector_copy_push_back         118518 ns       118518 ns         5745
+// vector_copy_insert              7779 ns         7779 ns        92190
+// vector_copy_vector_vector     198800 ns       198793 ns         3347
+// string_memcpy                   20.3 ns         20.3 ns     32102053
+// string_strcpy                   24.8 ns         24.8 ns     27352024
+// string_strdup                   32.4 ns         32.4 ns     21458177
+// ```
\ No newline at end of file
diff --git a/benchmarks/skimage/_image_bench.py b/benchmarks/skimage/_image_bench.py
new file mode 100644
index 000000000..c7d5abfd8
--- /dev/null
+++ b/benchmarks/skimage/_image_bench.py
@@ -0,0 +1,207 @@
+import itertools
+import math
+import time
+import types
+from collections import abc
+import re
+import subprocess
+
+import cupy as cp
+import cupyx.scipy.ndimage
+import numpy as np
+import pandas as pd
+import scipy.ndimage
+import skimage.data
+from cucim.time import repeat
+
+
+def product_dict(**kwargs):
+    # https://stackoverflow.com/questions/5228158/cartesian-product-of-a-dictionary-of-lists
+    keys = kwargs.keys()
+    vals = kwargs.values()
+    for instance in itertools.product(*vals):
+        yield dict(zip(keys, instance))
+
+
+class ImageBench(object):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,  # extra string to append to dataframe index
+        # set_args_kwargs={},  # for passing additional arguments to custom set_args method
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+        function_is_generator=False,
+    ):
+
+        self.shape = shape
+        self.function_name = function_name
+        self.fixed_kwargs_cpu = self._update_kwargs_arrays(fixed_kwargs, "cpu")
+        self.fixed_kwargs_gpu = self._update_kwargs_arrays(fixed_kwargs, "gpu")
+        self.var_kwargs = var_kwargs
+        self.index_str = index_str
+        # self.set_args_kwargs = set_args_kwargs
+        if not isinstance(dtypes, abc.Sequence):
+            dtypes = [dtypes]
+        self.dtypes = [np.dtype(d) for d in dtypes]
+        if not function_is_generator:
+            self.func_cpu = getattr(module_cpu, function_name)
+            self.func_gpu = getattr(module_gpu, function_name)
+        else:
+            # benchmark by generating all values
+            def gen_cpu(*args, **kwargs):
+                generator = getattr(module_cpu, function_name)(*args, **kwargs)
+                return list(generator)
+
+            def gen_gpu(*args, **kwargs):
+                generator = getattr(module_gpu, function_name)(*args, **kwargs)
+                return list(generator)
+
+            self.func_cpu = gen_cpu
+            self.func_gpu = gen_gpu
+
+        self.module_name_cpu = module_cpu.__name__
+        self.module_name_gpu = module_gpu.__name__
+
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        image = np.tile(im1, n_tile)[slices]
+        imaged = cp.asarray(image)
+        assert imaged.dtype == dtype
+        assert imaged.shape == self.shape
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+    def _update_array(self, array, target="cpu"):
+        if target == "gpu" and isinstance(array, np.ndarray):
+            array = cp.asarray(array)
+        elif target == "cpu" and isinstance(array, cp.ndarray):
+            array = cp.asnumpy(array)
+        return array
+
+    def _update_kwargs_arrays(self, kwargs, target="cpu"):
+        new_dict = {}
+        for k, v in kwargs.items():
+            new_dict[k] = self._update_array(v, target=target)
+        return new_dict
+
+    def _index(self, name, var_kwargs, dtype=None, shape=None):
+        index = name
+        if var_kwargs:
+            index += " ("
+        params = []
+        for k, v in var_kwargs.items():
+            if isinstance(v, types.FunctionType):
+                params.append(f"{k}={v.__name__}")
+            elif isinstance(v, (np.ndarray, cp.ndarray)):
+                params.append(f"{k}=array,shape={v.shape},dtype={v.dtype.name}")
+            else:
+                params.append(f"{k}={v}")
+        if dtype is not None:
+            params.append(f", {np.dtype(dtype).name}")
+        if shape is not None:
+            params.append(f"s={shape}")
+        index += ", ".join(params)
+        index.replace(",,", ",")
+        if var_kwargs:
+            index += ") "
+        if self.index_str is not None:
+            index += ", " + self.index_str
+        return index
+
+    def get_reps(self, func, args, kwargs, target_duration=5, cpu=True):
+        if not cpu:
+            # dry run
+            func(*args, **kwargs)
+        # time 1 repetition
+        d = cp.cuda.Device()
+        tstart = time.time()
+        func(*args, **kwargs)
+        d.synchronize()
+        dur = time.time() - tstart
+        n_repeat = max(1, math.ceil(target_duration / dur))
+        if cpu:
+            n_warmup = 0
+        else:
+            n_warmup = max(1, math.ceil(n_repeat / 5))
+        reps = dict(n_warmup=n_warmup, n_repeat=n_repeat)
+        return reps
+
+    def run_benchmark(self, duration=3, verbose=True):
+        df = pd.DataFrame()
+        self.df = df
+        kw_lists = self.var_kwargs
+        pdict = list(product_dict(**kw_lists))
+        for dtype in self.dtypes:
+            self.set_args(dtype)
+            for i, var_kwargs1 in enumerate(pdict):
+                # arr_index = indices[i]
+                index = self._index(self.function_name, var_kwargs1)
+
+                # transfer any arrays in kwargs to the appropriate device
+                var_kwargs_cpu = self._update_kwargs_arrays(var_kwargs1, "cpu")
+                var_kwargs_gpu = self._update_kwargs_arrays(var_kwargs1, "gpu")
+
+                # Note: brute_force=True on 'gpu' because False is not implemented
+                if "brute_force" in var_kwargs_gpu:
+                    var_kwargs_gpu["brute_force"] = True
+
+                kw_cpu = {**self.fixed_kwargs_cpu, **var_kwargs_cpu}
+                kw_gpu = {**self.fixed_kwargs_gpu, **var_kwargs_gpu}
+                rep_kwargs_cpu = self.get_reps(
+                    self.func_cpu, self.args_cpu, kw_cpu, duration, cpu=True
+                )
+                rep_kwargs_gpu = self.get_reps(
+                    self.func_gpu, self.args_gpu, kw_gpu, duration, cpu=False
+                )
+                perf = repeat(self.func_cpu, self.args_cpu, kw_cpu, **rep_kwargs_cpu)
+                perf_gpu = repeat(self.func_gpu, self.args_gpu, kw_gpu, **rep_kwargs_gpu)
+                df.at[index, "GPU accel"] = perf.cpu_times.mean() / perf_gpu.gpu_times.mean()
+                df.at[index, "shape"] = f"{self.shape}"
+                # df.at[index,  "description"] = index
+                df.at[index, "function_name"] = self.function_name
+                df.at[index, "dtype"] = np.dtype(dtype).name
+                df.at[index, "ndim"] = len(self.shape)
+
+                df.at[index, "CPU: host (mean)"] = perf.cpu_times.mean()
+                df.at[index, "CPU: host (std)"] = perf.cpu_times.std()
+
+                df.at[index, "GPU: host (mean)"] = perf_gpu.cpu_times.mean()
+                df.at[index, "GPU: host (std)"] = perf_gpu.cpu_times.std()
+                df.at[index, "GPU: device (mean)"] = perf_gpu.gpu_times.mean()
+                df.at[index, "GPU: device (std)"] = perf_gpu.gpu_times.std()
+                with cp.cuda.Device() as device:
+                    props = cp.cuda.runtime.getDeviceProperties(device.id)
+                    gpu_name = props['name'].decode()
+
+                df.at[index, "GPU: DEV Name"] = [gpu_name for i in range(len(df))]
+                cmd = "cat /proc/cpuinfo"
+                cpuinfo = subprocess.check_output(cmd, shell=True).strip()
+                cpu_name = re.search("\nmodel name.*\n", cpuinfo.decode()).group(0).strip('\n')
+                cpu_name = cpu_name.replace('model name\t: ', '')
+                df.at[index, "CPU: DEV Name"] = [cpu_name for i in range(len(df))]
+
+                # accelerations[arr_index] = df.at[index,  "GPU accel"]
+                if verbose:
+                    print(df.loc[index])
+
+        results = {}
+        results["full"] = df
+        results["var_kwargs_names"] = list(self.var_kwargs.keys())
+        results["var_kwargs_values"] = list(self.var_kwargs.values())
+        results["function_name"] = self.function_name
+        results["module_name_cpu"] = self.module_name_cpu
+        results["module_name_gpu"] = self.module_name_gpu
+        return results
diff --git a/benchmarks/skimage/cucim_color_bench.py b/benchmarks/skimage/cucim_color_bench.py
new file mode 100644
index 000000000..66ce6b74c
--- /dev/null
+++ b/benchmarks/skimage/cucim_color_bench.py
@@ -0,0 +1,185 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.color
+import cupy
+import cupy as cp
+import cupyx.scipy.ndimage
+import numpy as np
+import pandas as pd
+import scipy
+import skimage
+import skimage.color
+
+from _image_bench import ImageBench
+
+
+class ColorBench(ImageBench):
+    def set_args(self, dtype):
+        if self.shape[-1] != 3:
+            raise ValueError("shape must be 3 on the last axis")
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype, scale=1.0)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class RGBABench(ImageBench):
+    def set_args(self, dtype):
+        if self.shape[-1] != 4:
+            raise ValueError("shape must be 4 on the last axis")
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype, scale=1.0)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class LabelBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        contiguous_labels=True,
+        dtypes=np.float32,
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+        self.contiguous_labels = contiguous_labels
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        a = np.array(
+            [
+                [0, 0, 1, 1, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 4, 0],
+                [2, 2, 0, 0, 3, 0, 4, 4],
+                [0, 0, 0, 0, 0, 5, 0, 0],
+            ],
+            dtype=int,
+        )
+        tiling = tuple(s // a_s for s, a_s in zip(shape, a.shape))
+        if self.contiguous_labels:
+            label = np.kron(a, np.ones(tiling, dtype=a.dtype))
+        else:
+            label = np.tile(a, tiling)
+        labeld = cp.asarray(label)
+        if self.shape[-1] != 3:
+            raise ValueError("shape must be 3 on the last axis")
+        imaged = cupy.testing.shaped_random(labeld.shape, xp=cp, dtype=dtype, scale=1.0)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (
+            label,
+            image,
+        )
+        self.args_gpu = (
+            labeld,
+            imaged,
+        )
+
+
+pfile = "cucim_color_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+all_colorspaces = True
+
+for shape in [(256, 256, 3), (3840, 2160, 3), (192, 192, 192, 3)]:
+    ndim = len(shape)
+
+    if all_colorspaces:
+        color_spaces = ["RGB", "HSV", "RGB CIE", "XYZ", "YUV", "YIQ", "YPbPr", "YCbCr", "YDbDr"]
+    else:
+        color_spaces = ["RGB", "HSV", "YUV", "XYZ"]
+    for fromspace in color_spaces:
+        for tospace in color_spaces:
+            if fromspace == tospace:
+                continue
+
+            B = ColorBench(
+                function_name="convert_colorspace",
+                shape=shape,
+                dtypes=dtypes,
+                fixed_kwargs=dict(fromspace=fromspace, tospace=tospace),
+                var_kwargs={},
+                index_str=f"{fromspace.lower()}2{tospace.lower()}",
+                module_cpu=skimage.color,
+                module_gpu=cucim.skimage.color,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+    # rgb2hed and hed2rgb test combine_stains and separate_stains and all other
+    # stains should have equivalent performance.
+    #
+    # Probably not necessary to benchmark combine_stains and separate_stains
+    # e.g.
+    #    ihc_hdx = separate_stains(ihc, hdx_from_rgb)
+    #    ihc = combine_stains(ihc_hdx, rgb_from_hdx)
+    #
+
+    for fname in ["rgb2hed", "hed2rgb", "lab2lch", "lch2lab", "xyz2lab",
+                  "lab2xyz"]:
+        B = ColorBench(
+            function_name=fname,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs={},
+            var_kwargs={},
+            module_cpu=skimage.color,
+            module_gpu=cucim.skimage.color,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+    B = RGBABench(
+        function_name="rgba2rgb",
+        shape=shape[:-1] + (4,),
+        dtypes=dtypes,
+        fixed_kwargs={},
+        var_kwargs={},
+        module_cpu=skimage.color,
+        module_gpu=cucim.skimage.color,
+    )
+    results = B.run_benchmark(duration=1)
+    all_results = all_results.append(results["full"])
+
+    for contiguous_labels in [True, False]:
+        if contiguous_labels:
+            index_str = "contiguous"
+        else:
+            index_str = None
+        B = LabelBench(
+            function_name="label2rgb",
+            shape=shape,
+            dtypes=dtypes,
+            contiguous_labels=contiguous_labels,
+            index_str=index_str,
+            fixed_kwargs=dict(bg_label=0),
+            var_kwargs=dict(kind=["avg", "overlay"]),
+            module_cpu=skimage.color,
+            module_gpu=cucim.skimage.color,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_exposure_bench.py b/benchmarks/skimage/cucim_exposure_bench.py
new file mode 100644
index 000000000..c721969d1
--- /dev/null
+++ b/benchmarks/skimage/cucim_exposure_bench.py
@@ -0,0 +1,133 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.exposure
+import cupy
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.exposure
+
+from _image_bench import ImageBench
+
+
+class ExposureBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            scale = 256
+        else:
+            scale = 1.0
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype, scale=scale)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class MatchHistogramBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            scale = 256
+        else:
+            scale = 1.0
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype, scale=scale)
+        imaged2 = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype, scale=scale)
+        image = cp.asnumpy(imaged)
+        image2 = cp.asnumpy(imaged2)
+        self.args_cpu = (image, image2)
+        self.args_gpu = (imaged, imaged2)
+
+
+pfile = "cucim_exposure_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.uint8, np.float32]
+
+exposure_config = {
+    "equalize_adapthist": dict(
+        fixed_kwargs=dict(clip_limit=0.01, nbins=256),
+        variable_kwargs=dict(),
+        color_required=False,
+        grayscale_only=False,
+        dtypes=None,
+        shapes=None,
+    ),
+    "histogram": dict(
+        fixed_kwargs=dict(source_range="image"),
+        variable_kwargs=dict(nbins=[16, 256], normalize=[True, False]),
+        color_required=False,
+        grayscale_only=True,
+        dtypes=None,
+        shapes=None,
+    ),
+}
+
+for function_name, fixed_kwargs, var_kwargs, allow_color in [
+    ("equalize_adapthist", dict(clip_limit=0.01, nbins=256), dict(), True),
+    (
+        "histogram",
+        dict(source_range="image"),
+        dict(nbins=[16, 256], normalize=[True, False]),
+        False,
+    ),
+    ("cumulative_distribution", dict(), dict(nbins=[16, 256]), False),
+    ("equalize_hist", dict(mask=None), dict(nbins=[16, 256]), False),
+    ("rescale_intensity", dict(in_range="image", out_range="dtype"), dict(), False),
+    ("adjust_gamma", dict(), dict(), False),
+    ("adjust_log", dict(), dict(), False),
+    ("adjust_sigmoid", dict(), dict(inv=[False, True]), False),
+    ("is_low_contrast", dict(), dict(), False),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+        ndim = len(shape)
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "equalize_adapthist":
+            #  TODO: fix equalize_adapthist for size (3840, 2160) and kernel_size = [16, 16]
+            size_factors = [4, 8, 16]
+            kernel_sizes = []
+            for size_factor in size_factors:
+                kernel_sizes.append([max(s // size_factor, 1) for s in shape if s != 3])
+            var_kwargs.update(dict(kernel_size=kernel_sizes))
+
+        B = ExposureBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.exposure,
+            module_gpu=cucim.skimage.exposure,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+    ndim = len(shape)
+
+    multichannel = shape[-1] in [3, 4]
+
+    B = MatchHistogramBench(
+        function_name="match_histograms",
+        shape=shape,
+        dtypes=dtypes,
+        fixed_kwargs=dict(multichannel=multichannel),
+        var_kwargs=dict(),
+        module_cpu=skimage.exposure,
+        module_gpu=cucim.skimage.exposure,
+    )
+    results = B.run_benchmark(duration=1)
+    all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_feature_bench.py b/benchmarks/skimage/cucim_feature_bench.py
new file mode 100644
index 000000000..e78ee7773
--- /dev/null
+++ b/benchmarks/skimage/cucim_feature_bench.py
@@ -0,0 +1,127 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.feature
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.feature
+
+from _image_bench import ImageBench
+
+
+class MatchTemplateBench(ImageBench):
+    def set_args(self, dtype):
+        rstate = cp.random.RandomState(5)
+        imaged = rstate.standard_normal(self.shape) > 2
+        imaged = imaged.astype(dtype)
+        templated = cp.zeros((3,) * imaged.ndim, dtype=dtype)
+        templated[(1,) * imaged.ndim] = 1
+        image = cp.asnumpy(imaged)
+        template = cp.asnumpy(templated)
+        assert imaged.dtype == dtype
+        assert imaged.shape == self.shape
+        self.args_cpu = (image, template)
+        self.args_gpu = (imaged, templated)
+
+
+pfile = "cucim_feature_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    ("multiscale_basic_features", dict(edges=True), dict(texture=[True, False]), True, True),
+    ("canny", dict(sigma=1.8), dict(), False, False),
+    # reduced default rings, histograms, orientations to fit daisy at (3840, 2160) into GPU memory
+    (
+        "daisy",
+        dict(step=4, radius=15, rings=2, histograms=5, orientations=4),
+        dict(normalization=["l1", "l2", "daisy"]),
+        False,
+        False,
+    ),
+    ("structure_tensor", dict(sigma=1, mode="reflect", order="rc"), dict(), False, True),
+    ("hessian_matrix", dict(sigma=1, mode="reflect", order="rc"), dict(), False, True),
+    ("hessian_matrix_det", dict(sigma=1, approximate=False), dict(), False, True),
+    ("shape_index", dict(sigma=1, mode="reflect"), dict(), False, False),
+    ("corner_kitchen_rosenfeld", dict(mode="reflect"), dict(), False, False),
+    ("corner_harris", dict(k=0.05, eps=1e-6, sigma=1), dict(method=["k", "eps"]), False, False),
+    ("corner_shi_tomasi", dict(sigma=1), dict(), False, False),
+    ("corner_foerstner", dict(sigma=1), dict(), False, False),
+    ("corner_peaks", dict(), dict(min_distance=(2, 3, 5)), False, True),
+]:
+
+    for shape in [(128, 128, 128), (512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        if function_name in ["corner_peaks", "peak_local_max"] and np.prod(shape) > 1000000:
+            # skip any large sizes that take too long
+            continue
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "multiscale_basic_features":
+            fixed_kwargs["multichannel"] = shape[-1] == 3
+            if ndim == 3 and shape[-1] != 3:
+                # Omit texture=True case to avoid excessive GPU memory usage
+                var_kwargs["texture"] = [False]
+
+        B = ImageBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.feature,
+            module_gpu=cucim.skimage.feature,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    ("match_template", dict(), dict(pad_input=[False], mode=["reflect"]), False, True),
+]:
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        B = MatchTemplateBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.feature,
+            module_gpu=cucim.skimage.feature,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_filters_bench.py b/benchmarks/skimage/cucim_filters_bench.py
new file mode 100644
index 000000000..486e73ad6
--- /dev/null
+++ b/benchmarks/skimage/cucim_filters_bench.py
@@ -0,0 +1,157 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.filters
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.filters
+
+from _image_bench import ImageBench
+
+pfile = "cucim_filters_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _gabor.py
+    (
+        "gabor",
+        dict(n_stds=3),
+        dict(frequency=[0.075, 0.1, 0.2, 0.3]),
+        False,
+        False,
+    ),
+    # _gaussian.py
+    (
+        "gaussian",
+        dict(truncate=4.0, preserve_range=True),
+        dict(sigma=[0.25, 1, 4]),
+        True,
+        True,
+    ),
+    # _median.py
+    ("median", dict(mode="nearest"), dict(), False, True),
+    # _rank_order.py
+    ("rank_order", dict(), dict(), False, True),
+    # _unsharp_mask.py
+    (
+        "unsharp_mask",
+        dict(),
+        dict(radius=[0.5, 1.0, 2.0, 3.0]),
+        True,
+        True,
+    ),
+    # edges.py
+    ("sobel", dict(), dict(axis=[None, 0, -1]), False, True),
+    ("prewitt", dict(), dict(axis=[None, 0, -1]), False, True),
+    ("scharr", dict(), dict(axis=[None, 0, -1]), False, True),
+    ("roberts", dict(), dict(), False, False),
+    ("roberts_pos_diag", dict(), dict(), False, False),
+    ("roberts_neg_diag", dict(), dict(), False, False),
+    ("farid", dict(), dict(), False, False),
+    ("laplace", dict(ksize=3), dict(), False, True),
+    # lpi_filter.py
+    # TODO: benchmark wiener
+    # ridges.py
+    # TODO: had to set meijering, etc allow_nd to False just due to insufficient GPU memory
+    (
+        "meijering",
+        dict(sigmas=range(1, 10, 2), alpha=None),
+        dict(black_ridges=[True, False], mode=["reflect"]),
+        False,
+        False,
+    ),
+    (
+        "sato",
+        dict(sigmas=range(1, 10, 2)),
+        dict(black_ridges=[True, False], mode=["reflect"]),
+        False,
+        False,
+    ),
+    (
+        "frangi",
+        dict(sigmas=range(1, 10, 2)),
+        dict(black_ridges=[True, False], mode=["reflect"]),
+        False,
+        False,
+    ),
+    (
+        "hessian",
+        dict(sigmas=range(1, 10, 2)),
+        dict(black_ridges=[True, False], mode=["reflect"]),
+        False,
+        False,
+    ),
+    # thresholding.py
+    ("threshold_isodata", dict(), dict(nbins=[64, 256]), False, True),
+    ("threshold_otsu", dict(), dict(nbins=[64, 256]), False, True),
+    ("threshold_yen", dict(), dict(nbins=[64, 256]), False, True),
+    # TODO: threshold_local should support n-dimensional data
+    (
+        "threshold_local",
+        dict(),
+        dict(block_size=[5, 15], method=["gaussian", "mean", "median"]),
+        False,
+        False,
+    ),
+    ("threshold_li", dict(), dict(), False, True),
+    ("threshold_minimum", dict(), dict(nbins=[64, 256]), False, True),
+    ("threshold_mean", dict(), dict(), False, True),
+    ("threshold_triangle", dict(), dict(nbins=[64, 256]), False, True),
+    ("threshold_niblack", dict(), dict(window_size=[7, 15, 65]), False, True),
+    ("threshold_sauvola", dict(), dict(window_size=[7, 15, 65]), False, True),
+    ("apply_hysteresis_threshold", dict(low=0.15, high=0.6), dict(), False, True),
+    ("threshold_multiotsu", dict(), dict(nbins=[64, 256], classes=[3]), False, True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "gabor" and np.prod(shape) > 1000000:
+            # avoid cases that are too slow on the CPU
+            var_kwargs["frequency"] = [f for f in var_kwargs["frequency"] if f >= 0.1]
+
+        if function_name == "median":
+            selems = []
+            ndim = len(shape)
+            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for selem_size in [3, 5, 7, 9]:
+                selems.append(np.ones((selem_size,) * ndim, dtype=bool))
+            var_kwargs["selem"] = selems
+
+        if function_name in ["gaussian", "unsharp_mask"]:
+            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+
+        B = ImageBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.filters,
+            module_gpu=cucim.skimage.filters,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_measure_bench.py b/benchmarks/skimage/cucim_measure_bench.py
new file mode 100644
index 000000000..c0da669ed
--- /dev/null
+++ b/benchmarks/skimage/cucim_measure_bench.py
@@ -0,0 +1,276 @@
+import math
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.measure
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.measure
+
+from _image_bench import ImageBench
+
+
+class LabelBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        contiguous_labels=True,
+        dtypes=np.float32,
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=skimage.measure,
+        module_gpu=cucim.skimage.measure,
+    ):
+
+        self.contiguous_labels = contiguous_labels
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        a = np.array(
+            [
+                [0, 0, 1, 1, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 4, 0],
+                [2, 2, 0, 0, 3, 0, 4, 4],
+                [0, 0, 0, 0, 0, 5, 0, 0],
+            ]
+        )
+        tiling = tuple(s // a_s for s, a_s in zip(shape, a.shape))
+        if self.contiguous_labels:
+            image = np.kron(a, np.ones(tiling, dtype=a.dtype))
+        else:
+            image = np.tile(a, tiling)
+        imaged = cp.asarray(image)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class RegionpropsBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        contiguous_labels=True,
+        dtypes=np.float32,
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=skimage.measure,
+        module_gpu=cucim.skimage.measure,
+    ):
+
+        self.contiguous_labels = contiguous_labels
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        a = np.array(
+            [
+                [0, 0, 1, 1, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 4, 0],
+                [2, 2, 0, 0, 3, 0, 4, 4],
+                [0, 0, 0, 0, 0, 5, 0, 0],
+            ]
+        )
+        tiling = tuple(s // a_s for s, a_s in zip(shape, a.shape))
+        if self.contiguous_labels:
+            image = np.kron(a, np.ones(tiling, dtype=a.dtype))
+        else:
+            image = np.tile(a, tiling)
+        imaged = cp.asarray(image)
+        label_dev = cucim.skimage.measure.label(imaged).astype(int)
+        label = cp.asnumpy(label_dev)
+
+        self.args_cpu = (label, image)
+        self.args_gpu = (label_dev, imaged)
+
+
+class FiltersBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        image = np.tile(im1, n_tile)[slices]
+        imaged = cp.asarray(image)
+        assert imaged.dtype == dtype
+        assert imaged.shape == self.shape
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+pfile = "cucim_measure_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _gaussian.py
+    (
+        "label",
+        dict(return_num=False, background=0),
+        dict(connectivity=[1, 2]),
+        False,
+        True,
+    ),
+    # regionprops.py
+    ("regionprops", dict(), dict(), False, True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        Tester = LabelBench if function_name == "label" else RegionpropsBench
+
+        for contiguous_labels in [True, False]:
+            if contiguous_labels:
+                index_str = f"contiguous"
+            else:
+                index_str = None
+            B = Tester(
+                function_name=function_name,
+                shape=shape,
+                dtypes=dtypes,
+                contiguous_labels=contiguous_labels,
+                index_str=index_str,
+                fixed_kwargs=fixed_kwargs,
+                var_kwargs=var_kwargs,
+                module_cpu=skimage.measure,
+                module_gpu=cucim.skimage.measure,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _moments.py
+    ("moments", dict(), dict(order=[1, 2, 3, 4]), False, False),
+    ("moments_central", dict(), dict(order=[1, 2, 3]), False, True),
+    # omited from benchmarks (only tiny arrays): moments_normalized, moments_hu
+    ("centroid", dict(), dict(), False, True),
+    ("inertia_tensor", dict(), dict(), False, True),
+    ("inertia_tensor_eigvals", dict(), dict(), False, True),
+    # _polygon.py
+    # TODO: approximate_polygon, subdivide_polygon
+    # block.py
+    (
+        "block_reduce",
+        dict(),
+        dict(
+            func=[
+                cp.sum,
+            ]
+        ),
+        True,
+        True,
+    ),  # variable block_size configured below
+    # entropy.py
+    ("shannon_entropy", dict(base=2), dict(), True, True),
+    # profile.py
+    (
+        "profile_line",
+        dict(src=(5, 7)),
+        dict(reduce_func=[cp.mean], linewidth=[1, 2, 4], order=[1, 3]),
+        True,
+        False,
+    ),  # variable block_size configured below
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "gabor" and np.prod(shape) > 1000000:
+            # avoid cases that are too slow on the CPU
+            var_kwargs["frequency"] = [f for f in var_kwargs["frequency"] if f >= 0.1]
+
+        if function_name == "block_reduce":
+            ndim = len(shape)
+            if shape[-1] == 3:
+                block_sizes = [(b,) * (ndim - 1) + (3,) for b in (16, 32, 64)]
+            else:
+                block_sizes = [(b,) * ndim for b in (16, 32, 64)]
+            var_kwargs["block_size"] = block_sizes
+
+        if function_name == "profile_line":
+            fixed_kwargs["dst"] = (shape[0] - 32, shape[1] + 9)
+
+        if function_name == "median":
+            selems = []
+            ndim = len(shape)
+            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for selem_size in [3, 5, 7, 9]:
+                selems.append(np.ones((selem_sizes,) * ndim, dtype=bool))
+            var_kwargs["selem"] = selems
+
+        if function_name in ["gaussian", "unsharp_mask"]:
+            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+
+        B = FiltersBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.measure,
+            module_gpu=cucim.skimage.measure,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_metrics_bench.py b/benchmarks/skimage/cucim_metrics_bench.py
new file mode 100644
index 000000000..ddb7a9064
--- /dev/null
+++ b/benchmarks/skimage/cucim_metrics_bench.py
@@ -0,0 +1,90 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.metrics
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.metrics
+
+from _image_bench import ImageBench
+
+
+class MetricsBench(ImageBench):
+    def set_args(self, dtype):
+        imaged = cp.testing.shaped_arange(self.shape, dtype=dtype)
+        imaged2 = cp.testing.shaped_arange(self.shape, dtype=dtype)
+        imaged2 = imaged2 + 0.05 * cp.random.standard_normal(self.shape)
+        imaged /= imaged.max()
+        imaged2 /= imaged2.max()
+        imaged2 = imaged2.clip(0, 1.0)
+        self.args_cpu = (cp.asnumpy(imaged), cp.asnumpy(imaged2))
+        self.args_gpu = (imaged, imaged2)
+
+
+pfile = "cucim_metrics_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _structural_similarity.py
+    (
+        "structural_similarity",
+        dict(data_range=1.0),
+        dict(gradient=[False, True], gaussian_weights=[False, True]),
+        True,
+        True,
+    ),
+    # simple_metrics.py
+    ("mean_squared_error", dict(), dict(), True, True),
+    (
+        "normalized_root_mse",
+        dict(),
+        dict(normalization=["euclidean", "min-max", "mean"]),
+        True,
+        True,
+    ),
+    ("peak_signal_noise_ratio", dict(data_range=1.0), dict(), True, True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name in ["structural_similarity"]:
+            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+
+        B = MetricsBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.metrics,
+            module_gpu=cucim.skimage.metrics,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_morphology_bench.py b/benchmarks/skimage/cucim_morphology_bench.py
new file mode 100644
index 000000000..d748e8306
--- /dev/null
+++ b/benchmarks/skimage/cucim_morphology_bench.py
@@ -0,0 +1,231 @@
+import copy
+import functools
+import math
+import operator
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.morphology
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.morphology
+import scipy.ndimage as ndi
+
+from _image_bench import ImageBench
+
+
+class BinaryMorphologyBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        selem=None,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        index_str="",
+        var_kwargs={},
+        module_cpu=skimage.morphology,
+        module_gpu=cucim.skimage.morphology,
+    ):
+
+        array_kwargs = dict(selem=selem)
+        if "selem" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'selem'")
+        fixed_kwargs = copy.deepcopy(fixed_kwargs)
+        fixed_kwargs.update(array_kwargs)
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        imaged = cp.random.standard_normal(self.shape).astype(dtype) > 0
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class ReconstructionBench(ImageBench):
+    def set_args(self, dtype):
+        coords = cp.meshgrid(*[cp.linspace(0, 6 * cp.pi, s) for s in self.shape], sparse=True)
+        bumps = functools.reduce(operator.add, [cp.sin(c) for c in coords])
+        h = 0.6
+        seed = bumps - h
+        self.args_cpu = (cp.asnumpy(seed), cp.asnumpy(bumps))
+        self.args_gpu = (seed, bumps)
+
+
+class RemoveSmallObjectsBench(ImageBench):
+    def _init_test_data(self, dtype):
+        ndim = len(self.shape)
+        if ndim < 2 or ndim > 3:
+            raise ValueError("only 2d and 3d test cases are available")
+        a = cp.array([[0, 0, 0, 1, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 1]], dtype)
+        if ndim == 3:
+            a = a[..., cp.newaxis]
+            a = cp.tile(a, (1, 1, 2))
+            a = cp.pad(a, ((0, 0), (0, 0), (1, 1)), mode="constant")
+        ntile = [math.ceil(self.shape[i] / a.shape[i]) for i in range(ndim)]
+        a = cp.tile(a, tuple(ntile))
+        return a[tuple([slice(s) for s in self.shape])]
+
+    def set_args(self, dtype):
+        a = self._init_test_data(dtype)
+        self.args_cpu = (cp.asnumpy(a), 6)
+        self.args_gpu = (a, 6)
+
+
+class RemoveSmallHolesBench(RemoveSmallObjectsBench):
+    def set_args(self, dtype):
+        a = ~self._init_test_data(dtype)
+        self.args_cpu = (cp.asnumpy(a), 5)
+        self.args_gpu = (a, 5)
+
+
+pfile = "cucim_morphology_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes_grey = [np.float32]
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_nd in [
+    ("binary_erosion", dict(), dict(), True),
+    ("binary_dilation", dict(), dict(), True),
+    ("binary_opening", dict(), dict(), True),
+    ("binary_closing", dict(), dict(), True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd and ndim > 2:
+            continue
+
+        for connectivity in range(1, ndim + 1):
+            index_str = f"conn={connectivity}"
+            selem = ndi.generate_binary_structure(ndim, connectivity)
+
+            B = BinaryMorphologyBench(
+                function_name=function_name,
+                shape=shape,
+                dtypes=[bool],
+                selem=selem,
+                fixed_kwargs={},
+                var_kwargs=var_kwargs,
+                index_str=index_str,
+                module_cpu=skimage.morphology,
+                module_gpu=cucim.skimage.morphology,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_nd in [
+    # misc.py
+    ("remove_small_objects", dict(), dict(), True),
+    ("remove_small_holes", dict(), dict(), True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd and ndim > 2:
+            continue
+
+        if function_name == "remove_small_objects":
+            TestClass = RemoveSmallObjectsBench
+        elif function_name == "remove_small_holes":
+            TestClass = RemoveSmallHolesBench
+        else:
+            raise ValueError(f"unknown function: {function_name}")
+        B = TestClass(
+            function_name=function_name,
+            shape=shape,
+            dtypes=[bool],
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.morphology,
+            module_gpu=cucim.skimage.morphology,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # grey.py
+    ("erosion", dict(), dict(), False, True),
+    ("dilation", dict(), dict(), False, True),
+    ("opening", dict(), dict(), False, True),
+    ("closing", dict(), dict(), False, True),
+    ("white_tophat", dict(), dict(), False, True),
+    ("black_tophat", dict(), dict(), False, True),
+    # greyreconstruct.py
+    ("reconstruction", dict(), dict(), False, True),
+    # selem.py
+    # OMIT the functions from this file (each creates a structuring element)
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "gabor" and np.prod(shape) > 1000000:
+            # avoid cases that are too slow on the CPU
+            var_kwargs["frequency"] = [f for f in var_kwargs["frequency"] if f >= 0.1]
+
+        if function_name == "median":
+            selems = []
+            ndim = len(shape)
+            selem_sizes = [3, 5, 7, 9] if ndim == 2 else [3, 5, 7]
+            for selem_size in [3, 5, 7, 9]:
+                selems.append(np.ones((selem_sizes,) * ndim, dtype=bool))
+            var_kwargs["selem"] = selems
+
+        if function_name in ["gaussian", "unsharp_mask"]:
+            fixed_kwargs["multichannel"] = True if shape[-1] == 3 else False
+
+        if function_name == "reconstruction":
+            TestClass = ReconstructionBench
+        else:
+            TestClass = ImageBench
+
+        B = TestClass(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes_grey,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.morphology,
+            module_gpu=cucim.skimage.morphology,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_registration_bench.py b/benchmarks/skimage/cucim_registration_bench.py
new file mode 100644
index 000000000..d90bd16a4
--- /dev/null
+++ b/benchmarks/skimage/cucim_registration_bench.py
@@ -0,0 +1,135 @@
+import math
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.registration
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.registration
+
+from _image_bench import ImageBench
+
+
+class RegistrationBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        image = np.tile(im1, n_tile)[slices]
+        image2 = np.roll(image, (10, 20))
+        imaged = cp.asarray(image)
+        imaged2 = cp.asarray(image2)
+
+        self.args_cpu = (image, image2)
+        self.args_gpu = (imaged, imaged2)
+
+
+pfile = "cucim_registration_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _phase_cross_correlation.py
+    ("phase_cross_correlation", dict(), dict(), False, True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        for masked in [True, False]:
+
+            index_str = f"masked={masked}"
+            if masked:
+                moving_mask = cp.ones(shape, dtype=bool)
+                moving_mask[20:-20, :] = 0
+                moving_mask[:, 20:-20] = 0
+                reference_mask = cp.ones(shape, dtype=bool)
+                reference_mask[80:-80, :] = 0
+                reference_mask[:, 80:-80] = 0
+                fixed_kwargs["moving_mask"] = moving_mask
+                fixed_kwargs["reference_mask"] = reference_mask
+            else:
+                fixed_kwargs["moving_mask"] = None
+                fixed_kwargs["reference_mask"] = None
+
+            B = RegistrationBench(
+                function_name=function_name,
+                shape=shape,
+                dtypes=dtypes,
+                fixed_kwargs=fixed_kwargs,
+                var_kwargs=var_kwargs,
+                index_str=index_str,
+                module_cpu=skimage.registration,
+                module_gpu=cucim.skimage.registration,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _phase_cross_correlation.py
+    ("optical_flow_tvl1", dict(), dict(num_iter=[10], num_warp=[5]), False, True),
+    (
+        "optical_flow_ilk",
+        dict(),
+        dict(radius=[3, 7], num_warp=[10], gaussian=[False, True], prefilter=[False, True]),
+        False,
+        True,
+    ),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        B = RegistrationBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.registration,
+            module_gpu=cucim.skimage.registration,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_restoration_bench.py b/benchmarks/skimage/cucim_restoration_bench.py
new file mode 100644
index 000000000..90c86cb7e
--- /dev/null
+++ b/benchmarks/skimage/cucim_restoration_bench.py
@@ -0,0 +1,188 @@
+import math
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.restoration
+import cupy as cp
+import cupyx.scipy.ndimage as ndi
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.restoration
+from cucim.skimage.restoration import denoise_tv_chambolle as tv_gpu
+from skimage.restoration import denoise_tv_chambolle as tv_cpu
+
+from _image_bench import ImageBench
+
+
+class DenoiseBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+
+        # add noise
+        if np.dtype(dtype).kind in "iu":
+            sigma = 0.05 * 255
+            im1 = im1 + sigma * np.random.randn(*im1.shape)
+            im1 = np.clip(im1, 0, 255).astype(dtype)
+        else:
+            sigma = 0.05
+            im1 = im1 + sigma * np.random.randn(*im1.shape)
+
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        image = np.tile(im1, n_tile)[slices]
+        imaged = cp.asarray(image)
+
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class CalibratedDenoiseBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+
+        # add noise
+        if np.dtype(dtype).kind in "iu":
+            sigma = 0.05 * 255
+            im1 = im1 + sigma * np.random.randn(*im1.shape)
+            im1 = np.clip(im1, 0, 255).astype(dtype)
+        else:
+            sigma = 0.05
+            im1 = im1 + sigma * np.random.randn(*im1.shape)
+
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        image = np.tile(im1, n_tile)[slices]
+        imaged = cp.asarray(image)
+
+        denoise_parameters = {"weight": np.linspace(0.01, 0.4, 10)}
+        self.args_cpu = (image, tv_cpu, denoise_parameters)
+        self.args_gpu = (imaged, tv_gpu, denoise_parameters)
+
+
+class DeconvolutionBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+        im1 = cp.array(im1)
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        imaged = cp.tile(im1, n_tile)[slices]
+
+        psfd = cp.ones((5,) * imaged.ndim) / 25
+        imaged = ndi.convolve(imaged, psfd)
+
+        image = cp.asnumpy(imaged)
+        psf = cp.asnumpy(psfd)
+
+        self.args_cpu = (image, psf)
+        self.args_gpu = (imaged, psfd)
+
+
+pfile = "cucim_restoration_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _denoise.py
+    ("denoise_tv_chambolle", dict(), dict(weight=[0.02]), True, True),
+    # j_invariant.py
+    ("calibrate_denoiser", dict(), dict(), False, True),
+]:
+
+    for shape in [(512, 512), (1980, 1080), (1980, 1080, 3), (128, 128, 128)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "denoise_tv_chambolle":
+            fixed_kwargs["multichannel"] = shape[-1] == 3
+
+        if function_name == "calibrate_denoiser":
+            denoise_class = CalibratedDenoiseBench
+        else:
+            denoise_class = DenoiseBench
+
+        B = denoise_class(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.restoration,
+            module_gpu=cucim.skimage.restoration,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+# function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd = ('unsupervised_wiener', dict(), dict(), False, True)
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # deconvolution.py
+    ("wiener", dict(balance=100.0), dict(), False, False),
+    ("unsupervised_wiener", dict(), dict(), False, False),
+    ("richardson_lucy", dict(), dict(iterations=[5]), False, True),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        B = DeconvolutionBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.restoration,
+            module_gpu=cucim.skimage.restoration,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_segmentation_bench.py b/benchmarks/skimage/cucim_segmentation_bench.py
new file mode 100644
index 000000000..c504edafa
--- /dev/null
+++ b/benchmarks/skimage/cucim_segmentation_bench.py
@@ -0,0 +1,191 @@
+import math
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.segmentation
+import cupy as cp
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.segmentation
+
+from _image_bench import ImageBench
+
+
+class LabelBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        contiguous_labels=True,
+        dtypes=np.float32,
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=skimage.measure,
+        module_gpu=cucim.skimage.measure,
+    ):
+
+        self.contiguous_labels = contiguous_labels
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        a = np.array(
+            [
+                [0, 0, 1, 1, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 4, 0],
+                [2, 2, 0, 0, 3, 0, 4, 4],
+                [0, 0, 0, 0, 0, 5, 0, 0],
+            ],
+            dtype=dtype,
+        )
+        tiling = tuple(s // a_s for s, a_s in zip(shape, a.shape))
+        if self.contiguous_labels:
+            image = np.kron(a, np.ones(tiling, dtype=a.dtype))
+        else:
+            image = np.tile(a, tiling)
+        imaged = cp.asarray(image)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class MorphGeodesicBench(ImageBench):
+    def set_args(self, dtype):
+        if np.dtype(dtype).kind in "iu":
+            im1 = skimage.data.camera()
+        else:
+            im1 = skimage.data.camera() / 255.0
+            im1 = im1.astype(dtype)
+        if len(self.shape) == 3:
+            im1 = im1[..., np.newaxis]
+        im1 = cp.array(im1)
+        n_tile = [math.ceil(s / im_s) for s, im_s in zip(self.shape, im1.shape)]
+        slices = tuple([slice(s) for s in self.shape])
+        imaged = cp.tile(im1, n_tile)[slices]
+
+        # need this preprocessing for morphological_geodesic_active_contour
+        imaged = skimage.segmentation.inverse_gaussian_gradient(imaged)
+
+        image = cp.asnumpy(imaged)
+        assert imaged.dtype == dtype
+        assert imaged.shape == self.shape
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+pfile = "cucim_segmentation_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.int32]
+
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _denoise.py
+    (
+        "find_boundaries",
+        dict(),
+        dict(connectivity=[1], mode=["thick", "inner", "outer", "subpixel"]),
+        False,
+        True,
+    ),
+]:
+
+    for shape in [
+        (64, 64),
+    ]:  # (512, 512), (1980, 1080), (1980, 1080, 3), (128, 128, 128)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        B = LabelBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.segmentation,
+            module_gpu=cucim.skimage.segmentation,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+dtypes = [np.float32]
+# function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd = ('unsupervised_wiener', dict(), dict(), False, True)
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # morphsnakes.py
+    ("inverse_gaussian_gradient", dict(), dict(), False, True),
+    (
+        "morphological_geodesic_active_contour",
+        dict(),
+        dict(iterations=[16], init_level_set=["checkerboard", "disk"]),
+        False,
+        False,
+    ),
+    (
+        "morphological_chan_vese",
+        dict(),
+        dict(iterations=[16], init_level_set=["checkerboard", "disk"]),
+        False,
+        False,
+    ),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        if function_name == "morphological_geodesic_active_contour":
+            bench_class = MorphGeodesicBench
+        else:
+            bench_class = ImageBench
+
+        B = ImageBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.segmentation,
+            module_gpu=cucim.skimage.segmentation,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cucim_transform_bench.py b/benchmarks/skimage/cucim_transform_bench.py
new file mode 100644
index 000000000..b713140d2
--- /dev/null
+++ b/benchmarks/skimage/cucim_transform_bench.py
@@ -0,0 +1,135 @@
+import os
+import pickle
+
+import cucim.skimage
+import cucim.skimage.transform
+import numpy as np
+import pandas as pd
+import skimage
+import skimage.transform
+
+from _image_bench import ImageBench
+
+pfile = "cucim_transform_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+dtypes = [np.float32]
+
+for function_name, fixed_kwargs, var_kwargs, allow_color, allow_nd in [
+    # _warps.py
+    (
+        "resize",
+        dict(preserve_range=True),
+        dict(order=[0, 1, 3], mode=["reflect"], anti_aliasing=[True]),
+        True,
+        True,
+    ),  # scale handled in loop below
+    (
+        "rescale",
+        dict(preserve_range=True),
+        dict(order=[0, 1, 3], mode=["reflect"], anti_aliasing=[True]),
+        True,
+        True,
+    ),  # output_shape handled in loop below
+    (
+        "rotate",
+        dict(angle=15, preserve_range=True),
+        dict(order=[0, 1, 3], mode=["reflect"], resize=[False, True]),
+        False,
+        False,
+    ),
+    ("downscale_local_mean", dict(), dict(), True, True),  # factors handled in loop below
+    (
+        "swirl",
+        dict(strength=1, preserve_range=True),
+        dict(order=[0, 1, 3], mode=["reflect"]),
+        False,
+        False,
+    ),
+    # TODO : warp? already indirectly benchmarked via swirl, etc
+    ("warp_polar", dict(), dict(scaling=["linear", "log"]), True, False),
+    # integral.py
+    ("integral_image", dict(), dict(), False, True),
+    # TODO: integrate
+    # pyramids.py
+    (
+        "pyramid_gaussian",
+        dict(max_layer=6, downscale=2, preserve_range=True),
+        dict(order=[0, 1, 3]),
+        True,
+        True,
+    ),
+    (
+        "pyramid_laplacian",
+        dict(max_layer=6, downscale=2, preserve_range=True),
+        dict(order=[0, 1, 3]),
+        True,
+        True,
+    ),
+]:
+
+    for shape in [(512, 512), (3840, 2160), (3840, 2160, 3), (192, 192, 192)]:
+
+        ndim = len(shape)
+        if not allow_nd:
+            if not allow_color:
+                if ndim > 2:
+                    continue
+            else:
+                if ndim > 3 or (ndim == 3 and shape[-1] not in [3, 4]):
+                    continue
+        if shape[-1] == 3 and not allow_color:
+            continue
+
+        ndim_spatial = ndim - 1 if shape[-1] == 3 else ndim
+
+        if function_name in ["rescale", "warp_polar", "pyramid_gaussian", "pyramid_laplacian"]:
+            fixed_kwargs["multichannel"] = ndim_spatial < ndim
+
+        function_is_generator = function_name in ["pyramid_gaussian", "pyramid_laplacian"]
+
+        if function_name in ["rescale", "resize"]:
+            scales = [0.75, 1.25]
+            if function_name == "rescale":
+                var_kwargs["scale"] = [(s,) * ndim_spatial for s in scales]
+            elif function_name == "resize":
+                out_shapes = [[int(s_ * s) for s_ in shape] for s in scales]
+                if ndim_spatial < ndim:
+                    # don't resize along channels dimension
+                    out_shapes = [
+                        tuple([int(s_ * s) for s_ in shape[:-1]]) + (shape[-1],) for s in scales
+                    ]
+                else:
+                    out_shapes = [tuple([int(s_ * s) for s_ in shape]) for s in scales]
+                var_kwargs["output_shape"] = out_shapes
+
+        elif function_name == "downscale_local_mean":
+            if ndim_spatial < ndim:
+                # no downscaling along channels axis
+                factors = [(2,) * (ndim - 1) + (1,)]
+            else:
+                factors = [(2,) * (ndim - 1) + (4,)]
+            var_kwargs["factors"] = factors
+
+        B = ImageBench(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=skimage.transform,
+            module_gpu=cucim.skimage.transform,
+            function_is_generator=function_is_generator,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cupyx_scipy_ndimage_filter_bench.py b/benchmarks/skimage/cupyx_scipy_ndimage_filter_bench.py
new file mode 100644
index 000000000..4c5143246
--- /dev/null
+++ b/benchmarks/skimage/cupyx_scipy_ndimage_filter_bench.py
@@ -0,0 +1,128 @@
+import os
+import pickle
+
+import cupy
+import cupy as cp
+import cupyx.scipy.ndimage
+import numpy as np
+import pandas as pd
+import scipy
+
+from _image_bench import ImageBench
+
+
+class ConvolveBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        weights_shape,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        var_kwargs={},
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+
+        self.weights_shape = weights_shape
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
+        image = cp.asnumpy(imaged)
+
+        wd = cupy.testing.shaped_random(self.weights_shape, xp=cp, dtype=dtype)
+        w = cp.asnumpy(wd)
+
+        self.args_cpu = (image, w)
+        self.args_gpu = (imaged, wd)
+
+
+class FilterBench(ImageBench):
+    def set_args(self, dtype):
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+pfile = "filter_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+
+modes = ["constant", "mirror"]
+prefilter = True
+dtypes = [np.float32]
+for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+    ndim = len(shape)
+    weights_shape = (3,) * ndim
+    weights_shape1d = weights_shape[:1]
+
+    # TODO: add cases for generic_filter and generic_filter1d?
+
+    for fname, var_kwargs in [
+        ("uniform_filter", dict(mode=["nearest"], size=[3, 5, 7, 9])),
+        ("uniform_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
+        ("gaussian_filter", dict(mode=["nearest"], sigma=[0.33, 1, 3, 4, 9])),
+        (
+            "gaussian_filter1d",
+            dict(mode=["nearest"], sigma=[0.33, 3, 9], axis=[0, -1], order=[0, 1]),
+        ),
+        ("maximum_filter", dict(mode=["nearest"], size=[3, 5, 7])),
+        ("maximum_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
+        ("minimum_filter", dict(mode=["nearest"], size=[3, 5, 7])),
+        ("minimum_filter1d", dict(mode=["nearest"], size=[3, 7], axis=[0, -1])),
+        ("median_filter", dict(mode=["nearest"], size=[3, 5, 7])),
+        ("percentile_filter", dict(mode=["nearest"], size=[3, 5, 7], percentile=[30])),
+        ("rank_filter", dict(mode=["nearest"], size=[3, 5, 7], rank=[-2])),
+        ("prewitt", dict(mode=["nearest"], axis=[0, -1])),
+        ("sobel", dict(mode=["nearest"], axis=[0, -1])),
+        ("laplace", dict(mode=["nearest"])),
+        ("gaussian_laplace", dict(mode=["nearest"], sigma=[0.33, 3, 9])),
+        ("gaussian_gradient_magnitude", dict(mode=["nearest"], sigma=[0.33, 3, 9])),
+    ]:
+
+        B = FilterBench(
+            function_name=fname,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=dict(output=None),
+            var_kwargs=var_kwargs,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+    for fname, wshape, var_kwargs in [
+        ("convolve", weights_shape, dict(mode=modes)),
+        ("correlate", weights_shape, dict(mode=modes)),
+        ("convolve1d", weights_shape1d, dict(mode=modes, axis=[0, -1])),
+        ("correlate1d", weights_shape1d, dict(mode=modes, axis=[0, -1])),
+    ]:
+        B = ConvolveBench(
+            function_name=fname,
+            shape=shape,
+            weights_shape=wshape,
+            dtypes=dtypes,
+            fixed_kwargs=dict(output=None, origin=0),
+            var_kwargs=var_kwargs,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cupyx_scipy_ndimage_fourier_bench.py b/benchmarks/skimage/cupyx_scipy_ndimage_fourier_bench.py
new file mode 100644
index 000000000..bfc6b448d
--- /dev/null
+++ b/benchmarks/skimage/cupyx_scipy_ndimage_fourier_bench.py
@@ -0,0 +1,53 @@
+import os
+import pickle
+
+import cupy
+import cupy as cp
+import numpy as np
+import pandas as pd
+
+from _image_bench import ImageBench
+
+pfile = "fourier_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+
+dtypes = [np.float32]
+
+for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+    # shape = (200, 200, 200)
+    ndim = len(shape)
+
+    class FourierBench(ImageBench):
+        def set_args(self, dtype):
+            cplx_dt = np.promote_types(dtype, np.complex64)
+            imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=cplx_dt)
+            image = cp.asnumpy(imaged)
+            self.args_cpu = (image,)
+            self.args_gpu = (imaged,)
+
+    for fname, fixed_kwargs, var_kwargs in [
+        ("fourier_gaussian", dict(sigma=5), {}),
+        ("fourier_uniform", dict(size=16), {}),
+        ("fourier_shift", dict(shift=5), {}),
+        ("fourier_ellipsoid", dict(size=15.0), {}),
+    ]:
+        B = FourierBench(
+            function_name=fname,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cupyx_scipy_ndimage_interp_bench.py b/benchmarks/skimage/cupyx_scipy_ndimage_interp_bench.py
new file mode 100644
index 000000000..294c33d5e
--- /dev/null
+++ b/benchmarks/skimage/cupyx_scipy_ndimage_interp_bench.py
@@ -0,0 +1,148 @@
+import math
+import os
+import pickle
+
+import cupy
+import cupy as cp
+import numpy as np
+import pandas as pd
+
+from _image_bench import ImageBench
+
+
+class InterpolationBench(ImageBench):
+    def set_args(self, dtype):
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class MapCoordinatesBench(ImageBench):
+    def set_args(self, dtype):
+
+        imaged = cupy.testing.shaped_random(self.shape, xp=cp, dtype=dtype)
+        image = cp.asnumpy(imaged)
+
+        rstate = cp.random.RandomState(5)
+        ndim = len(self.shape)
+        coordsd = cp.indices(self.shape) + 0.1 * rstate.standard_normal((ndim,) + self.shape)
+        coords = cupy.asnumpy(coordsd)
+
+        self.args_cpu = (image, coords)
+        self.args_gpu = (imaged, coordsd)
+
+
+pfile = "interp_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+
+orders = [0, 1, 3, 5]  # 2, 3, 4, 5]
+modes = ["constant", "reflect"]
+prefilter = True
+dtypes = [np.float32]
+# (4608, 3456) = 16MP as in IPOL paper
+for shape in [(512, 512), (3840, 2160), (4608, 3456), (192, 192, 192)]:
+    ndim = len(shape)
+
+    B = MapCoordinatesBench(
+        function_name="map_coordinates",
+        shape=shape,
+        dtypes=dtypes,
+        fixed_kwargs=dict(output=None, prefilter=prefilter),
+        var_kwargs=dict(mode=modes, order=orders),
+    )
+    results = B.run_benchmark(duration=1)
+    all_results = all_results.append(results["full"])
+
+    for fname, fixed_kwargs, var_kwargs in [
+        (
+            "affine_transform1",  # see special case below
+            dict(output=None, output_shape=None, prefilter=prefilter),
+            dict(mode=modes, order=orders),
+        ),
+        (
+            "affine_transform2",  # see special case below
+            dict(output=None, output_shape=None, prefilter=prefilter),
+            dict(mode=modes, order=orders),
+        ),
+        ("zoom", dict(output=None, zoom=1.1, prefilter=prefilter), dict(mode=modes, order=orders)),
+        (
+            "shift",
+            dict(output=None, shift=1.5, prefilter=prefilter),
+            dict(mode=modes, order=orders),
+        ),
+        (
+            "rotate",
+            dict(output=None, reshape=True, axes=(0, 1), angle=30, prefilter=prefilter),
+            dict(mode=modes, order=orders),
+        ),
+        (
+            "spline_filter",
+            dict(output=np.float32),
+            dict(
+                mode=[
+                    "mirror",
+                ],
+                order=[2, 3, 4, 5],
+            ),
+        ),
+        (
+            "spline_filter1d",
+            dict(output=np.float32),
+            dict(
+                mode=[
+                    "mirror",
+                ],
+                order=[2, 3, 4, 5],
+                axis=[0, -1],
+            ),
+        ),
+    ]:
+
+        if fname == "affine_transform1":
+            # affine_transform case 1: the general affine matrix code path
+            fname = fname[:-1]
+            ndim = len(shape)
+            angle = np.deg2rad(30)
+            cos = math.cos(angle)
+            sin = math.cos(angle)
+            matrix = np.identity(ndim)
+            axes = (0, 1)
+            matrix[axes[0], axes[0]] = cos
+            matrix[axes[0], axes[1]] = sin
+            matrix[axes[1], axes[0]] = -sin
+            matrix[axes[1], axes[1]] = cos
+            offset = np.full((ndim,), 1.5, dtype=float)
+            fixed_kwargs["matrix"] = matrix
+            fixed_kwargs["offset"] = offset
+        elif fname == "affine_transform2":
+            # affine_transform case 2: exercises the zoom + shift code path
+            fname = fname[:-1]
+            if len(shape) == 2:
+                matrix = np.asarray([0.5, 2.0])
+                offset = np.asarray([20.0, -25.0])
+            elif len(shape) == 3:
+                matrix = np.asarray([0.5, 2.0, 0.6])
+                offset = np.asarray([0.0, -5.0, 15])
+            fixed_kwargs["matrix"] = matrix
+            fixed_kwargs["offset"] = offset
+
+        B = InterpolationBench(
+            function_name=fname,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cupyx_scipy_ndimage_measurements_bench.py b/benchmarks/skimage/cupyx_scipy_ndimage_measurements_bench.py
new file mode 100644
index 000000000..9f6e531af
--- /dev/null
+++ b/benchmarks/skimage/cupyx_scipy_ndimage_measurements_bench.py
@@ -0,0 +1,189 @@
+import math
+import os
+import pickle
+
+import cupy
+import cupy as cp
+import cupyx.scipy.ndimage
+import numpy as np
+import pandas as pd
+import scipy
+import scipy.ndimage as ndi
+
+from _image_bench import ImageBench
+
+
+class LabelBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        structure=None,
+        contiguous_labels=True,
+        dtypes=np.float32,
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+
+        self.contiguous_labels = contiguous_labels
+        array_kwargs = dict(structure=structure)
+        if "structure" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'structure'")
+        fixed_kwargs.update(array_kwargs)
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        a = np.array(
+            [
+                [0, 0, 1, 1, 0, 0, 0, 0],
+                [0, 0, 0, 1, 0, 0, 4, 0],
+                [2, 2, 0, 0, 3, 0, 4, 4],
+                [0, 0, 0, 0, 0, 5, 0, 0],
+            ]
+        )
+        tiling = tuple(s // a_s for s, a_s in zip(shape, a.shape))
+        if self.contiguous_labels:
+            image = np.kron(a, np.ones(tiling, dtype=a.dtype))
+        else:
+            image = np.tile(a, tiling)
+        imaged = cp.asarray(image)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class MeasurementsBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        use_index=False,
+        nlabels=16,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str=None,
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+
+        self.nlabels = nlabels
+        self.use_index = use_index
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            index_str=index_str,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        size = math.prod(self.shape)
+        imaged = cupy.arange(size, dtype=dtype).reshape(self.shape)
+        labelsd = cupy.random.choice(self.nlabels, size)
+        labelsd = labelsd.reshape(self.shape) + 1
+
+        image = cp.asnumpy(imaged)
+        labels = cp.asnumpy(labelsd)
+        if self.use_index:
+            indexd = cupy.arange(1, self.nlabels + 1, dtype=cupy.intp)
+            index = cp.asnumpy(indexd)
+        else:
+            indexd = None
+            index = None
+
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+        # store labels and index as fixed_kwargs since histogram does not use
+        # them in the same position
+        self.fixed_kwargs_gpu.update(dict(labels=labelsd, index=indexd))
+        self.fixed_kwargs_cpu.update(dict(labels=labels, index=index))
+
+
+pfile = "measurements_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+
+dtypes = [np.float32]
+for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+    ndim = len(shape)
+
+    for fname, var_kwargs in [
+        ("label", {}),  # dict(greyscale_mode=[False, True]) not available in cupyx
+    ]:
+        for contiguous_labels in [True, False]:
+            if contiguous_labels:
+                index_str = "contiguous"
+            else:
+                index_str = None
+            B = LabelBench(
+                function_name=fname,
+                shape=shape,
+                dtypes=dtypes,
+                structure=ndi.generate_binary_structure(ndim, ndim),
+                contiguous_labels=contiguous_labels,
+                index_str=index_str,
+                fixed_kwargs=dict(output=None),
+                var_kwargs=var_kwargs,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+    for fname in [
+        "sum",
+        "mean",
+        "variance",
+        "standard_deviation",
+        "minimum",
+        "minimum_position",
+        "maximum",
+        "maximum_position",
+        "median",
+        "extrema",
+        "center_of_mass",
+    ]:
+        for use_index in [True, False]:
+            if use_index:
+                nlabels_cases = [4, 16, 64, 256]
+            else:
+                nlabels_cases = [16]
+            for nlabels in nlabels_cases:
+                if use_index:
+                    index_str = f"{nlabels} labels, no index"
+                else:
+                    index_str = f"{nlabels} labels, with index"
+                B = MeasurementsBench(
+                    function_name=fname,
+                    shape=shape,
+                    dtypes=dtypes,
+                    use_index=use_index,
+                    nlabels=nlabels,
+                    index_str=index_str,
+                    var_kwargs=var_kwargs,
+                )
+                results = B.run_benchmark(duration=1)
+                all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/cupyx_scipy_ndimage_morphology_bench.py b/benchmarks/skimage/cupyx_scipy_ndimage_morphology_bench.py
new file mode 100644
index 000000000..54d58d667
--- /dev/null
+++ b/benchmarks/skimage/cupyx_scipy_ndimage_morphology_bench.py
@@ -0,0 +1,161 @@
+import math
+import os
+import pickle
+
+import cupy
+import cupy as cp
+import cupyx.scipy.ndimage
+import numpy as np
+import pandas as pd
+import scipy
+import scipy.ndimage as ndi
+
+from _image_bench import ImageBench
+
+
+class BinaryMorphologyBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        structure=None,
+        mask=None,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        var_kwargs={},
+        index_str="",
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+
+        array_kwargs = dict(structure=structure, mask=mask)
+        if "structure" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'structure'")
+        if "mask" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'mask'")
+        fixed_kwargs.update(array_kwargs)
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            index_str=index_str,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        imaged = cp.random.standard_normal(self.shape).astype(dtype) > 0
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+class MorphologyBench(ImageBench):
+    def __init__(
+        self,
+        function_name,
+        shape,
+        structure=None,
+        footprint=None,
+        dtypes=[np.float32],
+        fixed_kwargs={},
+        var_kwargs={},
+        module_cpu=scipy.ndimage,
+        module_gpu=cupyx.scipy.ndimage,
+    ):
+
+        array_kwargs = dict(structure=structure, footprint=footprint)
+        if "structure" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'structure'")
+        if "footprint" in fixed_kwargs:
+            raise ValueError("fixed_kwargs cannot contain 'footprint'")
+        fixed_kwargs.update(array_kwargs)
+
+        super().__init__(
+            function_name=function_name,
+            shape=shape,
+            dtypes=dtypes,
+            fixed_kwargs=fixed_kwargs,
+            var_kwargs=var_kwargs,
+            module_cpu=module_cpu,
+            module_gpu=module_gpu,
+        )
+
+    def set_args(self, dtype):
+        imaged = cp.random.standard_normal(self.shape).astype(dtype)
+        image = cp.asnumpy(imaged)
+        self.args_cpu = (image,)
+        self.args_gpu = (imaged,)
+
+
+pfile = "morphology_results.pickle"
+if os.path.exists(pfile):
+    with open(pfile, "rb") as f:
+        all_results = pickle.load(f)
+else:
+    all_results = pd.DataFrame()
+
+modes = ["reflect"]
+sizes = [3, 5, 7, 9]
+
+dtypes = [np.float32]
+for shape in [(512, 512), (3840, 2160), (192, 192, 192)]:
+    ndim = len(shape)
+
+    for fname, var_kwargs in [
+        ("grey_erosion", dict(mode=modes, size=sizes)),
+        ("grey_dilation", dict(mode=modes, size=sizes)),
+        ("grey_opening", dict(mode=modes, size=sizes)),
+        ("grey_closing", dict(mode=modes, size=sizes)),
+        ("morphological_gradient", dict(mode=modes, size=sizes)),
+        ("morphological_laplace", dict(mode=modes, size=sizes)),
+        ("white_tophat", dict(mode=modes, size=sizes)),
+        ("black_tophat", dict(mode=modes, size=sizes)),
+    ]:
+        B = MorphologyBench(
+            function_name=fname,
+            shape=shape,
+            dtypes=dtypes,
+            structure=None,
+            footprint=None,
+            # Note: Benchmark runner will change brute_force to True for the GPU
+            fixed_kwargs=dict(output=None),
+            var_kwargs=var_kwargs,
+        )
+        results = B.run_benchmark(duration=1)
+        all_results = all_results.append(results["full"])
+
+    iterations = [1, 10, 30]
+    for fname, var_kwargs in [
+        ("binary_erosion", dict(iterations=iterations, brute_force=[False])),
+        ("binary_dilation", dict(iterations=iterations, brute_force=[False])),
+        ("binary_opening", dict(iterations=iterations, brute_force=[False])),
+        ("binary_closing", dict(iterations=iterations, brute_force=[False])),
+        ("binary_propagation", dict()),
+    ]:
+        for connectivity in range(1, ndim + 1):
+            index_str = f"conn={connectivity}"
+            structure = ndi.generate_binary_structure(ndim, connectivity)
+
+            B = BinaryMorphologyBench(
+                function_name=fname,
+                shape=shape,
+                dtypes=dtypes,
+                structure=structure,
+                mask=None,
+                index_str=index_str,
+                # Note: Benchmark runner will change brute_force to True for the GPU
+                fixed_kwargs=dict(output=None),
+                var_kwargs=var_kwargs,
+            )
+            results = B.run_benchmark(duration=1)
+            all_results = all_results.append(results["full"])
+
+fbase = os.path.splitext(pfile)[0]
+all_results.to_csv(fbase + ".csv")
+all_results.to_pickle(pfile)
+with open(fbase + ".md", "wt") as f:
+    f.write(all_results.to_markdown())
diff --git a/benchmarks/skimage/run-all.sh b/benchmarks/skimage/run-all.sh
new file mode 100644
index 000000000..ab763b0d1
--- /dev/null
+++ b/benchmarks/skimage/run-all.sh
@@ -0,0 +1,5 @@
+for file in ./cu*py
+do
+  echo $file
+  time python "$file" 
+done
diff --git a/build.sh b/build.sh
new file mode 100755
index 000000000..4cd9df76a
--- /dev/null
+++ b/build.sh
@@ -0,0 +1,245 @@
+#!/bin/bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if [ "$0" != "/bin/bash" ]; then
+    SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+fi
+
+################################################################################
+# Utility functions
+################################################################################
+
+c_str() {
+    local old_color=39
+    local old_attr=0
+    local color=39
+    local attr=0
+    local text=""
+    local no_change=0
+    for i in "$@"; do
+        case "$i" in
+            r|R)
+                color=31
+                ;;
+            g|G)
+                color=32
+                ;;
+            y|Y)
+                color=33
+                ;;
+            b|B)
+                color=34
+                ;;
+            p|P)
+                color=35
+                ;;
+            c|C)
+                color=36
+                ;;
+            w|W)
+                color=37
+                ;;
+
+            z|Z)
+                color=0
+                ;;
+        esac
+        case "$i" in
+            l|L|R|G|Y|B|P|C|W)
+                attr=1
+                ;;
+            n|N|r|g|y|b|p|c|w)
+                attr=0
+                ;;
+            z|Z)
+                attr=0
+                ;;
+            *)
+                text="${text}$i"
+        esac
+        if [ ${old_color} -ne ${color} ] || [ ${old_attr} -ne ${attr} ]; then
+            text="${text}\033[${attr};${color}m"
+            old_color=$color
+            old_attr=$attr
+        fi
+    done
+    /bin/echo -en "$text"
+}
+
+c_echo() {
+    local old_opt="$(shopt -op xtrace)" # save old xtrace option
+    set +x # unset xtrace
+    local text="$(c_str "$@")"
+    /bin/echo -e "$text\033[0m"
+    eval "${old_opt}" # restore old xtrace option
+}
+
+
+echo_err() {
+    >&2 echo "$@"
+}
+
+c_echo_err() {
+    >&2 c_echo "$@"
+}
+
+printf_err() {
+    >&2 printf "$@"
+}
+
+get_item_ranges() {
+  local indexes="$1"
+  local list="$2"
+  echo -n "$(echo "${list}" | xargs | cut -d " " -f "${indexes}")"
+  return $?
+}
+
+get_unused_ports() {
+    local num_of_ports=${1:-1}
+    comm -23 \
+    <(seq 49152 61000 | sort) \
+    <(ss -tan | awk '{print $4}' | while read line; do echo ${line##*\:}; done | grep '[0-9]\{1,5\}' | sort -u) \
+    | shuf | tail -n ${num_of_ports} # use tail instead head to avoid broken pipe in VSCode terminal
+}
+
+newline() {
+    echo
+}
+
+info() {
+    c_echo W "$(date -u '+%Y-%m-%d %H:%M:%S') [INFO] " Z "$@"
+}
+
+error() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [ERROR] " Z "$@"
+}
+
+fatal() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [FATAL] " Z "$@"
+    echo
+    if [ -n "${SCRIPT_DIR}" ]; then
+        exit 1
+    fi
+}
+
+run_command() {
+  local status=0
+  local cmd="$@"
+
+  c_echo B "$(date -u '+%Y-%m-%d %H:%M:%S') \$ " G "${cmd}"
+
+  [ "$(echo -n "$@")" = "" ] && return 1  # return 1 if there is no command available
+
+  eval "$@"
+  status=$?
+
+  if [ -n "${cmd_result}" ]; then
+    echo "${cmd_result}"
+  fi
+  unset IFS
+
+  return $status
+}
+
+retry() {
+  local retries=$1
+  shift
+
+  local count=0
+  until run_command "$@"; do
+    exit=$?
+    wait=$((2 ** $count))
+    count=$(($count + 1))
+    if [ $count -lt $retries ]; then
+      info "Retry $count/$retries. Exit code=$exit, Retrying in $wait seconds..."
+      sleep $wait
+    else
+      fatal "Retry $count/$retries. Exit code=$exit, no more retries left."
+      return 1
+    fi
+  done
+  return 0
+}
+
+parse_args() {
+    local OPTIND
+    while getopts 'yh' option;
+    do
+        case "${option}" in
+            # a)
+            #     VALUE=${OPTARG}
+            #     ;;
+            y)
+                ALWAYS_YES=true;
+                ;;
+            h)
+                print_usage
+                exit 1
+                ;;
+        esac
+    done
+    shift $((OPTIND-1))
+
+    CMD="$1"
+    shift
+
+    ARGS=("$@")
+}
+
+print_usage() {
+    set +x
+    echo_err
+    echo_err "USAGE: $0 [command] [arguments]..."
+    echo_err ""
+    echo_err "Global Arguments"
+    echo_err
+    echo_err "Command List"
+
+    echo_err
+    echo_err "Examples"
+}
+
+init_script() {
+    TOP=$(git rev-parse --show-toplevel || pwd)
+}
+
+main() {
+    parse_args "$@"
+    local file_type
+    case "$CMD" in
+        list|ls)
+            # list_testdata "${ARGS[@]}"
+            ;;
+        ''|main)
+            print_usage
+            ;;
+        *)
+            if type ${CMD} > /dev/null 2>&1; then
+                init_script
+                run_command "$CMD" "${ARGS[@]}"
+            else
+                print_usage
+                exit 1
+            fi
+            ;;
+    esac
+}
+
+if [ -n "${SCRIPT_DIR}" ]; then
+    main "$@"
+fi
+
+# CLARA_VERSION=0.7.1-2008.4 ./serverctl get_latest_version_of recipes clara_bootstrap 2> /dev/null
\ No newline at end of file
diff --git a/build_docker.sh b/build_docker.sh
new file mode 100755
index 000000000..f7845c594
--- /dev/null
+++ b/build_docker.sh
@@ -0,0 +1,98 @@
+#!/bin/bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+set -eu
+
+CMAKE_CMD=cmake
+CMAKE_BUILD_TYPE=Release
+NUM_THREADS=$(nproc)
+
+SRC_ROOT=/work
+BUILD_ROOT=/work/temp
+
+CUCIM_SDK_PATH=${BUILD_ROOT}/libcucim
+
+# Build libcucim
+${CMAKE_CMD} -S ${SRC_ROOT} -B ${BUILD_ROOT}/libcucim \
+    -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+    -DCMAKE_INSTALL_PREFIX=${CUCIM_SDK_PATH}
+${CMAKE_CMD} --build ${BUILD_ROOT}/libcucim --target cucim -- -j ${NUM_THREADS}
+${CMAKE_CMD} --build ${BUILD_ROOT}/libcucim --target install -- -j ${NUM_THREADS}
+
+# Build cuslide plugin
+${CMAKE_CMD} -S ${SRC_ROOT}/cpp/plugins/cucim.kit.cuslide -B ${BUILD_ROOT}/cuslide \
+    -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+    -DCMAKE_INSTALL_PREFIX=${BUILD_ROOT}/cuslide/install \
+    -DCUCIM_SDK_PATH=${CUCIM_SDK_PATH}
+${CMAKE_CMD} --build ${BUILD_ROOT}/cuslide --target cucim.kit.cuslide -- -j ${NUM_THREADS}
+${CMAKE_CMD} --build ${BUILD_ROOT}/cuslide --target install -- -j ${NUM_THREADS}
+
+# Build Python bind
+
+for PYBIN in /opt/python/*/bin; do
+    ${CMAKE_CMD} -S ${SRC_ROOT}/python -B ${BUILD_ROOT}/cucim \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+        -DCMAKE_INSTALL_PREFIX=${BUILD_ROOT}/cucim/install \
+        -DCUCIM_SDK_PATH=${CUCIM_SDK_PATH} \
+        -DPYTHON_EXECUTABLE=${PYBIN}/python
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/cucim --target cucim -- -j ${NUM_THREADS}
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/cucim --target install -- -j ${NUM_THREADS}
+done
+
+# Copy .so files to pybind's build folder
+# (it uses -P to copy symbolic links as they are)
+cp -P ${BUILD_ROOT}/libcucim/install/lib/lib* ${BUILD_ROOT}/cucim/lib/cucim/
+cp -P ${BUILD_ROOT}/cuslide/install/lib/cucim* ${BUILD_ROOT}/cucim/lib/cucim/
+
+# Copy .so files from pybind's build folder to cucim Python source folder
+cp -P ${BUILD_ROOT}/cucim/lib/cucim/* ${SRC_ROOT}/python/cucim/src/cucim/clara/
+
+
+
+
+set -e -u -x
+
+function repair_wheel {
+    wheel="$1"
+    if ! auditwheel show "$wheel"; then
+        echo "Skipping non-platform wheel $wheel"
+    else
+        auditwheel repair --plat "$PLAT" -w wheelhouse/ "$wheel"
+    fi
+}
+
+PLAT=manylinux2014_x86_64
+
+cd /work/python/cucim
+# Compile wheels (one python binary is enough)
+for PYBIN in /opt/python/cp36-cp36m/bin; do # /opt/python/*/bin
+    "${PYBIN}/python" setup.py bdist_wheel -p $PLAT
+done
+
+mkdir -p /work/python/cucim/wheelhouse
+
+# Bundle external shared libraries into the wheels
+for whl in dist/*.whl; do
+    repair_wheel "$whl"
+done
+
+# # Install packages and test
+# for PYBIN in /opt/python/*/bin/; do
+#     "${PYBIN}/pip" install python-manylinux-demo --no-index -f /io/wheelhouse
+#     (cd "$HOME"; "${PYBIN}/nosetests" pymanylinuxdemo)
+# done
+
+# python setup.py bdist_wheel -p manylinux2014-x86_64
diff --git a/ci/checks/style.sh b/ci/checks/style.sh
new file mode 100644
index 000000000..21eb536e2
--- /dev/null
+++ b/ci/checks/style.sh
@@ -0,0 +1,75 @@
+#!/bin/bash
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+######################
+# cuCIM Style Tester #
+######################
+
+# Ignore errors and set path
+set +e
+PATH=/opt/conda/bin:$PATH
+LC_ALL=C.UTF-8
+LANG=C.UTF-8
+
+# Activate common conda env
+. /opt/conda/etc/profile.d/conda.sh
+conda activate rapids
+
+# Switch to `python/`
+cd python/cucim
+
+# Run isort and get results/return code
+ISORT=`isort --recursive --check-only python`
+ISORT_RETVAL=$?
+
+# Run black and get results/return code
+#BLACK=`black --check .`
+#BLACK_RETVAL=$?
+
+# Run flake8 and get results/return code
+FLAKE=`flake8 .`
+FLAKE_RETVAL=$?
+
+# Run flake8-cython and get results/return code
+#FLAKE_CYTHON=`flake8 --config=.flake8.cython .`
+#FLAKE_CYTHON_RETVAL=$?
+
+# Output results if failure otherwise show pass
+if [ "$ISORT_RETVAL" != "0" ]; then
+  echo -e "\n\n>>>> FAILED: isort style check; begin output\n\n"
+  echo -e "$ISORT"
+  echo -e "\n\n>>>> FAILED: isort style check; end output\n\n"
+else
+  echo -e "\n\n>>>> PASSED: isort style check\n\n"
+fi
+
+#if [ "$BLACK_RETVAL" != "0" ]; then
+#  echo -e "\n\n>>>> FAILED: black style check; begin output\n\n"
+#  echo -e "$BLACK"
+#  echo -e "\n\n>>>> FAILED: black style check; end output\n\n"
+#else
+#  echo -e "\n\n>>>> PASSED: black style check\n\n"
+#fi
+
+if [ "$FLAKE_RETVAL" != "0" ]; then
+  echo -e "\n\n>>>> FAILED: flake8 style check; begin output\n\n"
+  echo -e "$FLAKE"
+  echo -e "\n\n>>>> FAILED: flake8 style check; end output\n\n"
+else
+  echo -e "\n\n>>>> PASSED: flake8 style check\n\n"
+fi
+
+if [ "$FLAKE_CYTHON_RETVAL" != "0" ]; then
+  echo -e "\n\n>>>> FAILED: flake8-cython style check; begin output\n\n"
+  echo -e "$FLAKE_CYTHON"
+  echo -e "\n\n>>>> FAILED: flake8-cython style check; end output\n\n"
+else
+  echo -e "\n\n>>>> PASSED: flake8-cython style check\n\n"
+fi
+
+#RETVALS=($ISORT_RETVAL $BLACK_RETVAL $FLAKE_RETVAL $FLAKE_CYTHON_RETVAL)
+RETVALS=($FLAKE_RETVAL $FLAKE_CYTHON_RETVAL)
+IFS=$'\n'
+RETVAL=`echo "${RETVALS[*]}" | sort -nr | head -n1`
+
+exit $RETVAL
+# exit 0 # don't force style checks yet
diff --git a/ci/cpu/build.sh b/ci/cpu/build.sh
new file mode 100755
index 000000000..30981de15
--- /dev/null
+++ b/ci/cpu/build.sh
@@ -0,0 +1,109 @@
+#!/bin/bash
+# Copyright (c) 2020, NVIDIA CORPORATION.
+######################################
+# ucx-py CPU conda build script for CI #
+######################################
+set -e
+
+# Set path and build parallel level
+export PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
+export PARALLEL_LEVEL=${PARALLEL_LEVEL:-4}
+
+# Set home to the job's workspace
+export HOME=$WORKSPACE
+
+# Switch to project root; also root of repo checkout
+cd $WORKSPACE
+
+# Get latest tag and number of commits since tag
+export GIT_DESCRIBE_TAG=`git describe --abbrev=0 --tags`
+export GIT_DESCRIBE_NUMBER=`git rev-list ${GIT_DESCRIBE_TAG}..HEAD --count`
+
+# If nightly build, append current YYMMDD to version
+if [[ "$BUILD_MODE" = "branch" && "$SOURCE_BRANCH" = branch-* ]] ; then
+  export VERSION_SUFFIX=`date +%y%m%d`
+fi
+
+# Get CUDA and Python version
+export CUDA_VERSION=${CUDA_VERSION:-$(cat /usr/local/cuda/version.txt | egrep -o "[[:digit:]]+.[[:digit:]]+.[[:digit:]]+")}
+export CUDA_VER=${CUDA_VERSION%.*}
+export PYTHON_VER=${PYTHON:-$(python -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")}
+echo "CUDA_VERSION: ${CUDA_VERSION}"
+echo "CUDA_VER    : ${CUDA_VER}"
+echo "PYTHON_VER  : ${PYTHON_VER}"
+
+# Setup 'gpuci_conda_retry' for build retries (results in 2 total attempts)
+export GPUCI_CONDA_RETRY_MAX=1
+export GPUCI_CONDA_RETRY_SLEEP=30
+
+export CONDA_BLD_DIR="${WORKSPACE}/.conda-bld"
+
+################################################################################
+# SETUP - Check environment
+################################################################################
+
+gpuci_logger "Get env"
+env
+
+gpuci_logger "Activate conda env"
+. /opt/conda/etc/profile.d/conda.sh
+
+
+conda install -c conda-forge conda-build
+
+gpuci_logger "Check versions"
+python --version
+$CC --version
+$CXX --version
+conda info
+conda config --show-sources
+conda list --show-channel-urls
+
+# FIX Added to deal with Anancoda SSL verification issues during conda builds
+conda config --set ssl_verify False
+
+################################################################################
+# BUILD - Conda package builds (conda deps: libcucim)
+################################################################################
+
+# We get some error message like below if we use 'rapids' conda environment:
+#    CMake Warning at build-release/_deps/deps-rmm-src/tests/CMakeLists.txt:52 (add_executable):
+#      Cannot generate a safe runtime search path for target LOGGER_PTDS_TEST
+#      because files in some directories may conflict with libraries in implicit
+#      directories:
+#        runtime library [libcudart.so.11.0] in /opt/conda/envs/rapids/conda-bld/libcucim_1616020264601/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl/lib may be hidden by files in:
+#          /opt/conda/envs/rapids/lib
+
+if [ "$BUILD_LIBCUCIM" == 1 ]; then
+  gpuci_conda_retry build -c conda-forge/label/cupy_rc -c conda-forge -c rapidsai-nightly \
+    --python=${PYTHON_VER} \
+    --dirty \
+    --no-remove-work-dir \
+    --no-build-id \
+    --croot ${CONDA_BLD_DIR} \
+    --use-local \
+    conda/recipes/libcucim
+    # Copy the conda working directory for Project Flash
+    mkdir -p ${CONDA_BLD_DIR}/libcucim/work
+    cp -r ${CONDA_BLD_DIR}/work/* ${CONDA_BLD_DIR}/libcucim/work
+fi
+
+if [ "$BUILD_CUCIM" == 1 ]; then
+  gpuci_conda_retry build -c conda-forge/label/cupy_rc -c conda-forge -c rapidsai-nightly \
+    --python=${PYTHON_VER} \
+    --dirty \
+    --no-remove-work-dir \
+    --croot ${CONDA_BLD_DIR} \
+    --use-local \
+    conda/recipes/cucim
+fi
+
+################################################################################
+# UPLOAD - Conda packages
+################################################################################
+
+logger "Upload conda pkgs"
+source ci/cpu/upload_anaconda.sh
+
+# gpuci_logger "Upload pypi pkg..."
+# source ci/cpu/upload-pypi.sh
diff --git a/ci/cpu/upload_anaconda.sh b/ci/cpu/upload_anaconda.sh
new file mode 100755
index 000000000..d18eb4c4b
--- /dev/null
+++ b/ci/cpu/upload_anaconda.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+#
+# Adopted from https://github.com/tmcdonell/travis-scripts/blob/dfaac280ac2082cd6bcaba3217428347899f2975/update-accelerate-buildbot.sh
+
+set -e
+
+# Setup 'gpuci_retry' for upload retries (results in 4 total attempts)
+export GPUCI_RETRY_MAX=3
+export GPUCI_RETRY_SLEEP=30
+
+# Set default label options if they are not defined elsewhere
+export LABEL_OPTION=${LABEL_OPTION:-"--label main"}
+
+# Skip uploads unless BUILD_MODE == "branch"
+if [ ${BUILD_MODE} != "branch" ]; then
+  echo "Skipping upload"
+  return 0
+fi
+
+# Skip uploads if there is no upload key
+if [ -z "$MY_UPLOAD_KEY" ]; then
+  echo "No upload key"
+  return 0
+fi
+
+################################################################################
+# SETUP - Get conda file output locations
+################################################################################
+
+gpuci_logger "Get conda file output locations"
+export LIBCUCIM_FILE=`conda build --no-build-id --croot ${CONDA_BLD_DIR} conda/recipes/libcucim --output`
+export CUCIM_FILE=`conda build --croot ${CONDA_BLD_DIR} conda/recipes/cucim --python=$PYTHON --output`
+
+################################################################################
+# UPLOAD - Conda packages
+################################################################################
+
+gpuci_logger "Starting conda uploads"
+
+if [[ "$BUILD_LIBCUCIM" == "1" ]]; then
+    gpuci_logger "Upload libcuCIM"
+    gpuci_retry anaconda -t ${MY_UPLOAD_KEY} upload -u ${CONDA_USERNAME:-rapidsai} ${LABEL_OPTION} --skip-existing ${LIBCUCIM_FILE}
+fi
+
+if [[ "$BUILD_CUCIM" == "1" ]]; then
+    gpuci_logger "Upload cuCIM"
+    gpuci_retry anaconda -t ${MY_UPLOAD_KEY} upload -u ${CONDA_USERNAME:-rapidsai} ${LABEL_OPTION} --skip-existing ${CUCIM_FILE}
+fi
diff --git a/ci/docs/build.sh b/ci/docs/build.sh
new file mode 100644
index 000000000..853285310
--- /dev/null
+++ b/ci/docs/build.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+# Copyright (c) 2020, NVIDIA CORPORATION.
+#################################
+# cuCIM Docs build script for CI #
+#################################
+
+if [ -z "$PROJECT_WORKSPACE" ]; then
+    echo ">>>> ERROR: Could not detect PROJECT_WORKSPACE in environment"
+    echo ">>>> WARNING: This script contains git commands meant for automated building, do not run locally"
+    exit 1
+fi
+
+export DOCS_WORKSPACE=$WORKSPACE/docs
+export PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
+export HOME=$WORKSPACE
+export PROJECT_WORKSPACE=/rapids/cucim
+export NIGHTLY_VERSION=$(echo $BRANCH_VERSION | awk -F. '{print $2}')
+export PROJECTS=(cucim)
+
+gpuci_logger "Check environment"
+env
+
+gpuci_logger "Check GPU usage"
+nvidia-smi
+
+gpuci_logger "Activate conda env"
+. /opt/conda/etc/profile.d/conda.sh
+conda activate rapids
+# TODO: Move installs to docs-build-env meta package
+gpuci_conda_retry install -c anaconda markdown beautifulsoup4 jq
+pip install sphinx-markdown-tables
+
+gpuci_logger "Check versions"
+python --version
+$CC --version
+$CXX --version
+
+gpuci_logger "Check conda environment"
+conda info
+conda config --show-sources
+conda list --show-channel-urls
+
+# Build Python docs
+gpuci_logger "Build Sphinx docs"
+cd $PROJECT_WORKSPACE/docs
+make html
+
+#Commit to Website
+cd $DOCS_WORKSPACE
+
+for PROJECT in ${PROJECTS[@]}; do
+    if [ ! -d "api/$PROJECT/$BRANCH_VERSION" ]; then
+        mkdir -p api/$PROJECT/$BRANCH_VERSION
+    fi
+    rm -rf $DOCS_WORKSPACE/api/$PROJECT/$BRANCH_VERSION/*	
+done
+
+mv $PROJECT_WORKSPACE/docs/build/html/* $DOCS_WORKSPACE/api/cucim/$BRANCH_VERSION
diff --git a/ci/gpu/build.sh b/ci/gpu/build.sh
new file mode 100755
index 000000000..c561f9e2c
--- /dev/null
+++ b/ci/gpu/build.sh
@@ -0,0 +1,126 @@
+#!/bin/bash
+# Copyright (c) 2020, NVIDIA CORPORATION.
+#########################################
+# ucx-py GPU build and test script for CI #
+#########################################
+set -e
+NUMARGS=$#
+ARGS=$*
+
+# apt-get install libnuma libnuma-dev
+
+# Arg parsing function
+function hasArg {
+    (( ${NUMARGS} != 0 )) && (echo " ${ARGS} " | grep -q " $1 ")
+}
+
+# Set path and build parallel level
+export PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
+export PARALLEL_LEVEL=${PARALLEL_LEVEL:-4}
+
+# Set home to the job's workspace
+export HOME=$WORKSPACE
+
+# Parse git describe
+cd $WORKSPACE
+export GIT_DESCRIBE_TAG=`git describe --abbrev=0 --tags`
+export MINOR_VERSION=`echo $GIT_DESCRIBE_TAG | grep -o -E '([0-9]+\.[0-9]+)'`
+
+# Get CUDA and Python version
+export CUDA_VERSION=${CUDA_VERSION:-$(cat /usr/local/cuda/version.txt | egrep -o "[[:digit:]]+.[[:digit:]]+.[[:digit:]]+")}
+export CUDA_VER=${CUDA_VERSION%.*}
+export PYTHON_VER=${PYTHON:-$(python -c "import sys; print('.'.join(map(str, sys.version_info[:2])))")}
+echo "CUDA_VERSION: ${CUDA_VERSION}"
+echo "CUDA_VER    : ${CUDA_VER}"
+echo "PYTHON_VER  : ${PYTHON_VER}"
+
+################################################################################
+# SETUP - Check environment
+################################################################################
+
+gpuci_logger "Check environment"
+env
+
+gpuci_logger "Check GPU usage"
+nvidia-smi
+
+gpuci_logger "Activate conda env"
+. /opt/conda/etc/profile.d/conda.sh
+
+conda install -c conda-forge conda-build -y
+
+################################################################################
+# BUILD - Build cuCIM
+################################################################################
+
+# We don't use 'rapids' conda environment here.
+# To use 'conda-forge'-based package installation and to use 'Project Flash' feature,
+# we fake conda build folder for libcucim to '$WORKSPACE/ci/artifacts/cucim/cpu/conda-bld/' which is
+# conda build folder for CPU build.
+# For GPU build, we fake conda build folder for cucim to '/opt/conda/envs/rapids/conda-bld'.
+LIBCUCIM_BLD_PATH=$WORKSPACE/ci/artifacts/cucim/cpu/.conda-bld
+CUCIM_BLD_PATH=/opt/conda/envs/rapids/conda-bld
+mkdir -p ${CUCIM_BLD_PATH}
+
+
+gpuci_conda_retry build -c ${LIBCUCIM_BLD_PATH} -c conda-forge/label/cupy_rc -c conda-forge -c rapidsai-nightly \
+    --python=${PYTHON_VER} \
+    --dirty \
+    --no-remove-work-dir \
+    --croot ${CUCIM_BLD_PATH} \
+    conda/recipes/cucim
+
+
+################################################################################
+# TEST - Run py.tests for cuCIM
+################################################################################
+
+# Install cuCIM and its dependencies
+gpuci_logger "Install cuCIM and its dependencies"
+
+gpuci_logger "Install dependencies"
+gpuci_conda_retry create -n cucim -y -c conda-forge -c conda-forge/label/cupy_rc -c rapidsai-nightly \
+    rapids-doc-env \
+    flake8 \
+    pytest \
+    pytest-cov \
+    python=${PYTHON_VER} \
+    conda-forge/label/cupy_rc::cupy=9 \
+    cudatoolkit=${CUDA_VER} \
+    numpy \
+    scipy \
+    scikit-image=0.18.1 \
+    openslide
+
+conda activate cucim
+
+gpuci_logger "Installing cuCIM"
+gpuci_conda_retry install -y -c ${LIBCUCIM_BLD_PATH} -c ${CUCIM_BLD_PATH} \
+    libcucim \
+    cucim
+
+gpuci_logger "Testing cuCIM import"
+python -c 'import cucim' 
+
+gpuci_logger "Check versions"
+python --version
+$CC --version
+$CXX --version
+conda info
+conda config --show-sources
+conda list --show-channel-urls
+
+if hasArg --skip-tests; then
+    gpuci_logger "Skipping Tests"
+else
+    gpuci_logger "Check GPU usage"
+    nvidia-smi
+
+    gpuci_logger "Check NICs"
+    awk 'END{print $1}' /etc/hosts
+    cat /etc/hosts
+
+    gpuci_logger "Python py.test for cuCIM"
+    cd $WORKSPACE/python/cucim
+    py.test --cache-clear -v --ignore-glob . --rootdir=src
+fi
diff --git a/ci/local/README.md b/ci/local/README.md
new file mode 100644
index 000000000..26b1960ab
--- /dev/null
+++ b/ci/local/README.md
@@ -0,0 +1,58 @@
+## Purpose
+
+This script is designed for developer and contributor use. This tool mimics the actions of gpuCI on your local machine. This allows you to test and even debug your code inside a gpuCI base container before pushing your code as a GitHub commit.
+The script can be helpful in locally triaging and debugging RAPIDS continuous integration failures.
+
+## Requirements
+
+```
+nvidia-docker
+```
+
+## Usage
+
+```
+bash build.sh [-h] [-H] [-s] [-r <repo_dir>] [-i <image_name>]
+Build and test your local repository using a base gpuCI Docker image
+
+where:
+    -H   Show this help text
+    -r   Path to repository (defaults to working directory)
+    -i   Use Docker image (default is gpuci/rapidsai-base:cuda10.0-ubuntu16.04-gcc5-py3.6)
+    -s   Skip building and testing and start an interactive shell in a container of the Docker image
+```
+
+Example Usage:
+`bash build.sh -r ~/rapids/ucx-py -i gpuci/rapidsai-base:cuda10.1-ubuntu16.04-gcc5-py3.6`
+
+For a full list of available gpuCI docker images, visit our [DockerHub](https://hub.docker.com/r/gpuci/rapidsai-base/tags) page.
+
+Style Check:
+```bash
+$ bash ci/local/build.sh -r ~/rapids/ucx-py -s
+$ . /opt/conda/etc/profile.d/conda.sh
+$ conda activate rapids   #Activate gpuCI conda environment
+$ cd rapids
+$ flake8 python
+```
+
+## Information
+
+There are some caveats to be aware of when using this script, especially if you plan on developing from within the container itself.
+
+
+### Docker Image Build Repository
+
+The docker image will generate build artifacts in a folder on your machine located in the `root` directory of the repository you passed to the script. For the above example, the directory is named `~/rapids/ucx-py/build_rapidsai-base_cuda10.1-ubuntu16.04-gcc5-py3.6/`. Feel free to remove this directory after the script is finished.
+
+*Note*: The script *will not* override your local build repository. Your local environment stays in tact.
+
+
+### Where The User is Dumped
+
+The script will build your repository and run all tests. If any tests fail, it dumps the user into the docker container itself to allow you to debug from within the container. If all the tests pass as expected the container exits and is automatically removed. Remember to exit the container if tests fail and you do not wish to debug within the container itself.
+
+
+### Container File Structure
+
+Your repository will be located in the `/rapids/` folder of the container. This folder is volume mounted from the local machine. Any changes to the code in this repository are replicated onto the local machine. The `cpp/build` and `python/build` directories within your repository is on a separate mount to avoid conflicting with your local build artifacts.
diff --git a/ci/local/build.sh b/ci/local/build.sh
new file mode 100755
index 000000000..598969bcd
--- /dev/null
+++ b/ci/local/build.sh
@@ -0,0 +1,146 @@
+#!/bin/bash
+
+
+GIT_DESCRIBE_TAG=`git describe --tags`
+MINOR_VERSION=`echo $GIT_DESCRIBE_TAG | grep -o -E '([0-9]+\.[0-9]+)'`
+
+DOCKER_IMAGE="gpuci/rapidsai:${MINOR_VERSION}-cuda10.1-devel-ubuntu16.04-py3.7"
+REPO_PATH=${PWD}
+RAPIDS_DIR_IN_CONTAINER="/rapids"
+CPP_BUILD_DIR="cpp/build"
+PYTHON_BUILD_DIR="python/build"
+CONTAINER_SHELL_ONLY=0
+
+SHORTHELP="$(basename "$0") [-h] [-H] [-s] [-r <repo_dir>] [-i <image_name>]"
+LONGHELP="${SHORTHELP}
+Build and test your local repository using a base gpuCI Docker image
+
+where:
+    -H   Show this help text
+    -r   Path to repository (defaults to working directory)
+    -i   Use Docker image (default is ${DOCKER_IMAGE})
+    -s   Skip building and testing and start an interactive shell in a container of the Docker image
+"
+
+# Limit GPUs available to container based on CUDA_VISIBLE_DEVICES
+if [[ -z "${CUDA_VISIBLE_DEVICES}" ]]; then
+    NVIDIA_VISIBLE_DEVICES="all"
+else
+    NVIDIA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}
+fi
+
+while getopts ":hHr:i:s" option; do
+    case ${option} in
+        r)
+            REPO_PATH=${OPTARG}
+            ;;
+        i)
+            DOCKER_IMAGE=${OPTARG}
+            ;;
+        s)
+            CONTAINER_SHELL_ONLY=1
+            ;;
+        h)
+            echo "${SHORTHELP}"
+            exit 0
+            ;;
+        H)
+            echo "${LONGHELP}"
+            exit 0
+            ;;
+        *)
+            echo "ERROR: Invalid flag"
+            echo "${SHORTHELP}"
+            exit 1
+            ;;
+    esac
+done
+
+REPO_PATH_IN_CONTAINER="${RAPIDS_DIR_IN_CONTAINER}/$(basename "${REPO_PATH}")"
+CPP_BUILD_DIR_IN_CONTAINER="${RAPIDS_DIR_IN_CONTAINER}/$(basename "${REPO_PATH}")/${CPP_BUILD_DIR}"
+PYTHON_BUILD_DIR_IN_CONTAINER="${RAPIDS_DIR_IN_CONTAINER}/$(basename "${REPO_PATH}")/${PYTHON_BUILD_DIR}"
+
+
+# BASE_CONTAINER_BUILD_DIR is named after the image name, allowing for
+# multiple image builds to coexist on the local filesystem. This will
+# be mapped to the typical BUILD_DIR inside of the container. Builds
+# running in the container generate build artifacts just as they would
+# in a bare-metal environment, and the host filesystem is able to
+# maintain the host build in BUILD_DIR as well.
+# FIXME: Fix the shellcheck complaints
+# shellcheck disable=SC2001,SC2005,SC2046
+BASE_CONTAINER_BUILD_DIR=${REPO_PATH}/build_$(echo $(basename "${DOCKER_IMAGE}")|sed -e 's/:/_/g')
+CPP_CONTAINER_BUILD_DIR=${BASE_CONTAINER_BUILD_DIR}/cpp
+PYTHON_CONTAINER_BUILD_DIR=${BASE_CONTAINER_BUILD_DIR}/python
+# Create build directories. This is to ensure correct owner for directories. If
+# directories don't exist there is side effect from docker volume mounting creating build
+# directories owned by root(volume mount point(s))
+mkdir -p "${REPO_PATH}/${CPP_BUILD_DIR}"
+mkdir -p "${REPO_PATH}/${PYTHON_BUILD_DIR}"
+
+BUILD_SCRIPT="#!/bin/bash
+set -e
+WORKSPACE=${REPO_PATH_IN_CONTAINER}
+PREBUILD_SCRIPT=${REPO_PATH_IN_CONTAINER}/ci/gpu/prebuild.sh
+BUILD_SCRIPT=${REPO_PATH_IN_CONTAINER}/ci/gpu/build.sh
+cd \${WORKSPACE}
+if [ -f \${PREBUILD_SCRIPT} ]; then
+    source \${PREBUILD_SCRIPT}
+fi
+yes | source \${BUILD_SCRIPT}
+"
+
+if (( CONTAINER_SHELL_ONLY == 0 )); then
+    COMMAND="${CPP_BUILD_DIR_IN_CONTAINER}/build.sh || bash"
+else
+    COMMAND="bash"
+fi
+
+# Create the build dir for the container to mount, generate the build script inside of it
+mkdir -p "${BASE_CONTAINER_BUILD_DIR}"
+mkdir -p "${CPP_CONTAINER_BUILD_DIR}"
+mkdir -p "${PYTHON_CONTAINER_BUILD_DIR}"
+echo "${BUILD_SCRIPT}" > "${CPP_CONTAINER_BUILD_DIR}/build.sh"
+chmod ugo+x "${CPP_CONTAINER_BUILD_DIR}/build.sh"
+
+# Mount passwd and group files to docker. This allows docker to resolve username and group
+# avoiding these nags:
+#   * groups: cannot find name for group ID ID
+#   * I have no name!@id:/$
+# For ldap user user information is not present in system /etc/passwd and /etc/group files.
+# Hence we generate dummy files for ldap users which docker uses to resolve username and group
+
+PASSWD_FILE="/etc/passwd"
+GROUP_FILE="/etc/group"
+
+USER_FOUND=$(grep -wc "$(whoami)" < "$PASSWD_FILE")
+if [ "$USER_FOUND" == 0 ]; then
+  echo "Local User not found, LDAP WAR for docker mounts activated. Creating dummy passwd and group"
+  echo "files to allow docker resolve username and group"
+  cp "$PASSWD_FILE" /tmp/passwd
+  PASSWD_FILE="/tmp/passwd"
+  cp "$GROUP_FILE" /tmp/group
+  GROUP_FILE="/tmp/group"
+  echo "$(whoami):x:$(id -u):$(id -g):$(whoami),,,:$HOME:$SHELL" >> "$PASSWD_FILE"
+  echo "$(whoami):x:$(id -g):" >> "$GROUP_FILE"
+fi
+
+# Run the generated build script in a container
+docker pull "${DOCKER_IMAGE}"
+
+DOCKER_MAJOR=$(docker -v|sed 's/[^[0-9]*\([0-9]*\).*/\1/')
+GPU_OPTS="--gpus device=${NVIDIA_VISIBLE_DEVICES}"
+if [ "$DOCKER_MAJOR" -lt 19 ]
+then
+    GPU_OPTS="--runtime=nvidia -e NVIDIA_VISIBLE_DEVICES='${NVIDIA_VISIBLE_DEVICES}'"
+fi
+
+docker run --rm -it ${GPU_OPTS} \
+       -u "$(id -u)":"$(id -g)" \
+       -v "${REPO_PATH}":"${REPO_PATH_IN_CONTAINER}" \
+       -v "${CPP_CONTAINER_BUILD_DIR}":"${CPP_BUILD_DIR_IN_CONTAINER}" \
+       -v "${PYTHON_CONTAINER_BUILD_DIR}":"${PYTHON_BUILD_DIR_IN_CONTAINER}" \
+       -v "$PASSWD_FILE":/etc/passwd:ro \
+       -v "$GROUP_FILE":/etc/group:ro \
+       --cap-add=SYS_PTRACE \
+       "${DOCKER_IMAGE}" bash -c "${COMMAND}"
diff --git a/ci/release/update-version.sh b/ci/release/update-version.sh
new file mode 100755
index 000000000..f0bd8c7e9
--- /dev/null
+++ b/ci/release/update-version.sh
@@ -0,0 +1,58 @@
+#!/bin/bash
+#########################
+# cuCIM Version Updater #
+#########################
+
+## Usage
+# bash update-version.sh <type>
+#     where <type> is either `major`, `minor`, `patch`
+
+set -e
+
+# Grab argument for release type
+RELEASE_TYPE=$1
+
+# Get current version and calculate next versions
+CURRENT_TAG=`git tag | grep -xE 'v[0-9\.]+' | sort --version-sort | tail -n 1 | tr -d 'v'`
+CURRENT_MAJOR=`echo $CURRENT_TAG | awk '{split($0, a, "."); print a[1]}'`
+CURRENT_MINOR=`echo $CURRENT_TAG | awk '{split($0, a, "."); print a[2]}'`
+CURRENT_PATCH=`echo $CURRENT_TAG | awk '{split($0, a, "."); print a[3]}'`
+NEXT_MAJOR=$((CURRENT_MAJOR + 1))
+NEXT_MINOR=$((CURRENT_MINOR + 1))
+NEXT_PATCH=$((CURRENT_PATCH + 1))
+CURRENT_SHORT_TAG=${CURRENT_MAJOR}.${CURRENT_MINOR}
+NEXT_FULL_TAG=""
+NEXT_SHORT_TAG=""
+
+# Determine release type
+if [ "$RELEASE_TYPE" == "major" ]; then
+  NEXT_FULL_TAG="${NEXT_MAJOR}.0.0"
+  NEXT_SHORT_TAG="${NEXT_MAJOR}.0"
+elif [ "$RELEASE_TYPE" == "minor" ]; then
+  NEXT_FULL_TAG="${CURRENT_MAJOR}.${NEXT_MINOR}.0"
+  NEXT_SHORT_TAG="${CURRENT_MAJOR}.${NEXT_MINOR}"
+elif [ "$RELEASE_TYPE" == "patch" ]; then
+  NEXT_FULL_TAG="${CURRENT_MAJOR}.${CURRENT_MINOR}.${NEXT_PATCH}"
+  NEXT_SHORT_TAG="${CURRENT_MAJOR}.${CURRENT_MINOR}"
+else
+  echo "Incorrect release type; use 'major', 'minor', or 'patch' as an argument"
+  exit 1
+fi
+
+echo "Preparing '$RELEASE_TYPE' release [$CURRENT_TAG -> $NEXT_FULL_TAG]"
+
+# Inplace sed replace; workaround for Linux and Mac
+function sed_runner() {
+    sed -i.bak ''"$1"'' $2 && rm -f ${2}.bak
+}
+
+
+# Update version-related files using bump2version
+# (Need to execute this before other version updates because this command
+#  would checks if the git repository is dirty or not)
+./run bump_version ${RELEASE_TYPE}
+
+# RTD update
+sed_runner 's/version = .*/version = '"'${NEXT_SHORT_TAG}'"'/g' docs/source/conf.py
+sed_runner 's/release = .*/release = '"'${NEXT_FULL_TAG}'"'/g' docs/source/conf.py
+
diff --git a/conda/environments/env.yml b/conda/environments/env.yml
new file mode 100644
index 000000000..6dbe3a0c1
--- /dev/null
+++ b/conda/environments/env.yml
@@ -0,0 +1,25 @@
+name: cucim
+channels:
+  - conda-forge/label/cupy_rc
+  - conda-forge
+dependencies:
+  - conda-forge/label/cupy_rc::cupy=9.0.0b3
+  - scikit-image=0.18.1
+  - openslide
+  - zlib
+  - jpeg
+  - jbig
+  - xz
+  - zstd
+  - libwebp-base  # [linux or osx]
+  - numpy
+  - xorg-libxcb
+  - scipy
+  - python=3.8
+  - cudatoolkit=11.0
+  - cmake
+  - automake
+  - make
+  - gcc_linux-64=9
+  - compilers
+  - click
diff --git a/conda/recipes/cucim/build.sh b/conda/recipes/cucim/build.sh
new file mode 100644
index 000000000..b9fabc4ca
--- /dev/null
+++ b/conda/recipes/cucim/build.sh
@@ -0,0 +1,31 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+CUCIM_BUILD_TYPE=${CUCIM_BUILD_TYPE:-release}
+
+echo "Current Folder: ${SRC_DIR}"
+echo "CUDA_VERSION: ${CUDA_VERSION}"
+echo "PYTHON: ${PYTHON}"
+
+# For now CUDAHOSTCXX is set to `/usr/bin/g++` by
+# https://github.com/rapidsai/docker/blob/161b200157206660d88fb02cf69fe58d363ac95e/generated-dockerfiles/rapidsai-core_ubuntu18.04-devel.Dockerfile
+# To use GCC-9 in conda build environment, need to set it to $CXX (=$BUILD_PREFIX/bin/x86_64-conda-linux-gnu-c++)
+# This can be removed once we switch to use gcc-9
+# : https://docs.rapids.ai/notices/rdn0002/
+export CUDAHOSTCXX=${CXX}
+
+# CUDA needs to include $PREFIX/include as system include path
+export CUDAFLAGS="-isystem $BUILD_PREFIX/include -isystem $PREFIX/include "
+export LD_LIBRARY_PATH="$BUILD_PREFIX/lib:$PREFIX/lib:$LD_LIBRARY_PATH"
+
+# It is assumed that this script is executed from the root of the repo directory by conda-build
+# (https://conda-forge.org/docs/maintainer/knowledge_base.html#using-cmake)
+./run build_local cucim ${CUCIM_BUILD_TYPE}
+
+cp -P python/install/lib/* python/cucim/src/cucim/clara/
+
+pushd python/cucim
+
+echo "PYTHON: ${PYTHON}"
+$PYTHON setup.py install
+
+popd
diff --git a/conda/recipes/cucim/meta.yaml b/conda/recipes/cucim/meta.yaml
new file mode 100644
index 000000000..cb41249db
--- /dev/null
+++ b/conda/recipes/cucim/meta.yaml
@@ -0,0 +1,53 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
+{% set minor_version =  version.split('.')[0] + '.' + version.split('.')[2] %}
+{% set py_version=environ.get('CONDA_PY', 37) %}
+{% set python_version=environ.get('PYTHON_VER', '3.7') %}
+{% set cuda_version='.'.join(environ.get('CUDA_VERSION', '11.0').split('.')[:2]) %}
+
+package:
+  name: cucim
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda_{{ cuda_version }}_py{{ py_version }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+
+requirements:
+  build:
+    - cmake >=3.18.0
+    - {{ compiler("cxx") }}
+  host:
+    - cudatoolkit {{ cuda_version }}.*
+    - python {{ python_version }}.*
+    - libcucim {{ version }}.*
+    - click
+    - conda-forge/label/cupy_rc::cupy=9
+    - numpy
+    - scipy
+    - scikit-image 0.18.1
+  run:
+    - cudatoolkit {{ cuda_version }}.*
+    - python {{ python_version }}.*
+    - libcucim {{ version }}.*
+    - click
+    - conda-forge/label/cupy_rc::cupy=9
+    - numpy
+    - scipy
+    - scikit-image 0.18.1
+    # - openslide # skipping here but benchmark binary would needs openslide library
+  # test:
+  #   - openslide
+  #   - pytest
+  #   - pytest-cov
+
+about:
+  home: http://rapids.ai/
+  license: Apache-2.0
+  license_family: Apache
+  license_file: LICENSE
+  summary: cucim Python package
diff --git a/conda/recipes/libcucim/build.sh b/conda/recipes/libcucim/build.sh
new file mode 100644
index 000000000..2616b55b4
--- /dev/null
+++ b/conda/recipes/libcucim/build.sh
@@ -0,0 +1,36 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+CUCIM_BUILD_TYPE=${CUCIM_BUILD_TYPE:-release}
+
+echo "Current Folder: ${SRC_DIR}"
+echo "CUDA_VERSION: ${CUDA_VERSION}"
+
+# For now CUDAHOSTCXX is set to `/usr/bin/g++` by
+# https://github.com/rapidsai/docker/blob/161b200157206660d88fb02cf69fe58d363ac95e/generated-dockerfiles/rapidsai-core_ubuntu18.04-devel.Dockerfile
+# To use GCC-9 in conda build environment, need to set it to $CXX (=$BUILD_PREFIX/bin/x86_64-conda-linux-gnu-c++)
+# This can be removed once we switch to use gcc-9
+# : https://docs.rapids.ai/notices/rdn0002/
+export CUDAHOSTCXX=${CXX}
+
+# CUDA needs to include $PREFIX/include as system include path
+export CUDAFLAGS="-isystem $BUILD_PREFIX/include -isystem $PREFIX/include "
+export LD_LIBRARY_PATH="$BUILD_PREFIX/lib:$PREFIX/lib:$LD_LIBRARY_PATH"
+
+# It is assumed that this script is executed from the root of the repo directory by conda-build
+# (https://conda-forge.org/docs/maintainer/knowledge_base.html#using-cmake)
+
+# Build libcucim core
+./run build_local libcucim ${CUCIM_BUILD_TYPE} ${PREFIX}
+
+mkdir -p $PREFIX/bin $PREFIX/lib $PREFIX/include
+cp -P -r install/bin/* $PREFIX/bin/ || true
+cp -P -r install/lib/* $PREFIX/lib/ || true
+cp -P -r install/include/* $PREFIX/include/ || true
+
+# Build libcucim.kit.cuslide plugin
+./run build_local cuslide ${CUCIM_BUILD_TYPE} ${PREFIX}
+
+mkdir -p $PREFIX/bin $PREFIX/lib $PREFIX/include
+cp -P -r cpp/plugins/cucim.kit.cuslide/install/bin/* $PREFIX/bin/ || true
+cp -P -r cpp/plugins/cucim.kit.cuslide/install/lib/* $PREFIX/lib/ || true
+cp -P -r cpp/plugins/cucim.kit.cuslide/install/include/* $PREFIX/include/ || true
diff --git a/conda/recipes/libcucim/meta.yaml b/conda/recipes/libcucim/meta.yaml
new file mode 100644
index 000000000..0ec27f133
--- /dev/null
+++ b/conda/recipes/libcucim/meta.yaml
@@ -0,0 +1,47 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.
+
+{% set version = environ.get('GIT_DESCRIBE_TAG', '0.0.0.dev').lstrip('v') + environ.get('VERSION_SUFFIX', '') %}
+{% set minor_version =  version.split('.')[0] + '.' + version.split('.')[1] %}
+{% set python_version=environ.get('PYTHON_VER', '3.7') %}
+{% set cuda_version='.'.join(environ.get('CUDA_VERSION', '11.0').split('.')[:2]) %}
+
+package:
+  name: libcucim
+  version: {{ version }}
+
+source:
+  git_url: ../../..
+
+build:
+  number: {{ GIT_DESCRIBE_NUMBER }}
+  string: cuda{{ cuda_version }}_{{ GIT_DESCRIBE_HASH }}_{{ GIT_DESCRIBE_NUMBER }}
+
+requirements:
+  build:
+    - cmake >=3.18.0
+    - {{ compiler("cxx") }}
+  host:
+    - cudatoolkit {{ cuda_version }}.*
+    - openslide
+    - zlib
+    - jpeg
+    - jbig
+    - xz
+    - zstd
+    - libwebp-base  # [linux or osx]
+  run:
+    - cudatoolkit {{ cuda_version }}.*
+    # - openslide # skipping here but benchmark binary would needs openslide library
+    - zlib
+    - jpeg
+    - jbig
+    - xz
+    - zstd
+    - libwebp-base  # [linux or osx]
+
+about:
+  home: http://rapids.ai/
+  license: Apache-2.0
+  license_family: Apache
+  license_file: LICENSE
+  summary: libcucim C++ library
\ No newline at end of file
diff --git a/cpp/CMakeLists.txt b/cpp/CMakeLists.txt
new file mode 100644
index 000000000..c24172f69
--- /dev/null
+++ b/cpp/CMakeLists.txt
@@ -0,0 +1,188 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+################################################################################
+# Set cmake policy
+################################################################################
+if(${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.19")
+    cmake_policy(SET CMP0110 NEW) # For add_test() to support arbitrary characters in test name
+endif()
+
+################################################################################
+# Define compile options
+################################################################################
+
+if(NOT BUILD_SHARED_LIBS)
+    set(BUILD_SHARED_LIBS ON)
+endif()
+
+set(CMAKE_C_FLAGS "-O2")
+
+################################################################################
+# Set definitions
+################################################################################
+
+################################################################################
+# Add library: cucim
+################################################################################
+add_library(${CUCIM_PACKAGE_NAME}
+        src/core/framework.cpp
+        include/cucim/cuimage.h
+        include/cucim/codec/base64.h
+        include/cucim/codec/methods.h
+        include/cucim/core/framework.h
+        include/cucim/core/plugin.h
+        include/cucim/core/plugin_util.h
+        include/cucim/core/interface.h
+        include/cucim/core/version.h
+        include/cucim/dynlib/helper.h
+        include/cucim/filesystem/cufile_driver.h
+        include/cucim/filesystem/file_handle.h
+        include/cucim/filesystem/file_path.h
+        include/cucim/io/device.h
+        include/cucim/io/format/image_format.h
+        include/cucim/logger/logger.h
+        include/cucim/logger/timer.h
+        include/cucim/macros/defines.h
+        include/cucim/memory/dlpack.h
+        include/cucim/memory/memory_manager.h
+        include/cucim/3rdparty/dlpack/dlpack.h
+        include/cucim/3rdparty/dlpack/dlpackcpp.h
+        src/cuimage.cpp
+        src/codec/base64.cpp
+        src/core/cucim_framework.h
+        src/core/cucim_framework.cpp
+        src/core/cucim_plugin.h
+        src/core/cucim_plugin.cpp
+        src/core/plugin_manager.h
+        src/core/plugin_manager.cpp
+        src/core/version.inl
+        src/filesystem/cufile_driver.cpp
+        src/io/device.cpp
+        src/io/format/image_format.cpp
+        src/logger/logger.cpp
+        src/logger/timer.cpp
+        src/memory/memory_manager.cu)
+
+# Compile options
+set_target_properties(${CUCIM_PACKAGE_NAME}
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+        SOVERSION ${PROJECT_VERSION_MAJOR}
+        VERSION ${PROJECT_VERSION}
+)
+# At least one file needs to be compiled with nvcc.
+# Otherwise, it will cause `/usr/bin/ld: cannot find -lcudart` error message.
+set_source_files_properties(src/cucim.cpp src/filesystem/cufile_driver.cpp PROPERTIES LANGUAGE CUDA)
+
+# Note: Looks like the following line causes error on CMake 3.18.4 (it is working on 3.18.2). Keeping it for now.
+set(CUCIM_REQUIRED_FEATURES cxx_std_17 cuda_std_17)
+target_compile_features(${CUCIM_PACKAGE_NAME} PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(${CUCIM_PACKAGE_NAME}
+    PRIVATE
+        $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>
+        )
+target_compile_definitions(${CUCIM_PACKAGE_NAME}
+    PUBLIC
+        CUCIM_VERSION=${PROJECT_VERSION}
+        CUCIM_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUCIM_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUCIM_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUCIM_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+        CUCIM_SUPPORT_GDS=$<BOOL:${CUCIM_SUPPORT_GDS}>
+        _GLIBCXX_USE_CXX11_ABI=0  # TODO: create two library, one with CXX11 ABI and one without it.
+)
+
+# Link libraries
+target_link_libraries(${CUCIM_PACKAGE_NAME}
+        PUBLIC
+            ${CMAKE_DL_LIBS}
+            $<BUILD_INTERFACE:deps::fmt>
+            $<BUILD_INTERFACE:deps::gds>
+#            $<BUILD_INTERFACE:deps::boost>
+#            $<BUILD_INTERFACE:deps::rmm>
+#            $<BUILD_INTERFACE:deps::gds>
+            $<INSTALL_INTERFACE:cucim::fmt-header-only>
+##            $<INSTALL_INTERFACE:cucim::boost>
+            # $<INSTALL_INTERFACE:cucim::rmm>
+##            $<INSTALL_INTERFACE:cucim::gds>
+        PRIVATE
+            deps::abseil
+        )
+
+if (CUCIM_STATIC_GDS)
+    target_link_libraries(${CUCIM_PACKAGE_NAME}
+        PUBLIC
+            CUDA::cuda_driver # this may not be needed
+            $<BUILD_INTERFACE:deps::gds_static>
+        )
+endif ()
+
+target_include_directories(${CUCIM_PACKAGE_NAME}
+        PUBLIC
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include/${CUCIM_PACKAGE_NAME}/3rdparty>
+#            $<BUILD_INTERFACE:${CMAKE_SOURCE_DIR}/gds/lib> # for GDS header
+#            $<BUILD_INTERFACE:${THRUST_INCLUDE_DIR}>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/${CUCIM_PACKAGE_NAME}/3rdparty> # for 3rdparty libraries such as dlpack and fmt
+        PRIVATE
+            ${CMAKE_CURRENT_LIST_DIR}/src
+        )
+
+add_library(${CUCIM_PACKAGE_NAME}::${CUCIM_PACKAGE_NAME} ALIAS ${CUCIM_PACKAGE_NAME})
+
+################################################################################
+# Add library: cucim-header-only
+################################################################################
+add_library(${CUCIM_PACKAGE_NAME}-header-only INTERFACE)
+target_compile_definitions(${CUCIM_PACKAGE_NAME}-header-only
+    INTERFACE
+        CUCIM_VERSION=${PROJECT_VERSION}
+        CUCIM_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUCIM_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUCIM_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUCIM_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+        CUCIM_SUPPORT_GDS=$<BOOL:${CUCIM_SUPPORT_GDS}>
+        CUCIM_HEADER_ONLY=1
+        )
+target_compile_features(${CUCIM_PACKAGE_NAME}-header-only INTERFACE ${CUCIM_REQUIRED_FEATURES})
+target_include_directories(${CUCIM_PACKAGE_NAME}-header-only
+        INTERFACE
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/${CUCIM_PACKAGE_NAME}/3rdparty>
+        )
+add_library(${CUCIM_PACKAGE_NAME}::${CUCIM_PACKAGE_NAME}-header-only ALIAS ${CUCIM_PACKAGE_NAME}-header-only)
+
+################################################################################
+# Add tests
+################################################################################
+add_subdirectory(tests)
+
+#################################################################################
+## Add bindings
+#################################################################################
+#add_subdirectory(bindings/python)
+
+unset(BUILD_SHARED_LIBS CACHE)
diff --git a/cpp/cmake/cucim-config.cmake.in b/cpp/cmake/cucim-config.cmake.in
new file mode 100644
index 000000000..42face690
--- /dev/null
+++ b/cpp/cmake/cucim-config.cmake.in
@@ -0,0 +1,26 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+@PACKAGE_INIT@
+
+# Find dependent libraries
+# ...
+include(CMakeFindDependencyMacro)
+#find_dependency(Boost x.x.x REQUIRED)
+find_dependency(CUDAToolkit)
+
+if(NOT TARGET cucim::cucim)
+    include(${CMAKE_CURRENT_LIST_DIR}/cucim-targets.cmake)
+endif()
\ No newline at end of file
diff --git a/cpp/cmake/deps/abseil.cmake b/cpp/cmake/deps/abseil.cmake
new file mode 100644
index 000000000..d82f50334
--- /dev/null
+++ b/cpp/cmake/deps/abseil.cmake
@@ -0,0 +1,44 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::abseil)
+    FetchContent_Declare(
+            deps-abseil
+            GIT_REPOSITORY https://github.com/abseil/abseil-cpp.git
+            GIT_TAG 20200225.2
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-abseil)
+    if (NOT deps-abseil_POPULATED)
+        message(STATUS "Fetching abseil sources")
+        FetchContent_Populate(deps-abseil)
+        message(STATUS "Fetching abseil sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    set(BUILD_TESTING FALSE) # Disable BUILD_TESTING (cmake-build-debug/_deps/deps-abseil-src/CMakeLists.txt:97)
+    add_subdirectory(${deps-abseil_SOURCE_DIR} ${deps-abseil_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: ../lib/libabsl_strings.a(escaping.cc.o): relocation R_X86_64_PC32 against symbol `_ZN4absl14lts_2020_02_2516numbers_internal8kHexCharE' can not be used when making a shared object; recompile with -fPIC
+    set_target_properties(absl_strings absl_strings_internal absl_int128 absl_raw_logging_internal PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::abseil INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::abseil INTERFACE absl::strings)
+    set(deps-abseil_SOURCE_DIR ${deps-abseil_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-abseil_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/cmake/deps/boost.cmake b/cpp/cmake/deps/boost.cmake
new file mode 100644
index 000000000..721ebcd0b
--- /dev/null
+++ b/cpp/cmake/deps/boost.cmake
@@ -0,0 +1,75 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::boost)
+    set(Boost_VERSION 1.73.0)
+    set(Boost_BUILD_COMPONENTS container)
+    set(Boost_BUILD_OPTIONS "threading=multi cxxflags=-fPIC runtime-link=static variant=release link=static address-model=64 --layout=system")
+    set(Boost_COMPILE_DEFINITIONS
+        BOOST_COROUTINES_NO_DEPRECATION_WARNING=1
+        BOOST_ALL_NO_LIB=1
+        BOOST_UUID_RANDOM_PROVIDER_FORCE_WINCRYPT=1
+        CACHE INTERNAL "Boost compile definitions")
+
+    set(Boost_USE_STATIC_LIBS ON)
+    set(Boost_USE_MULTITHREADED ON)
+    set(Boost_USE_STATIC_RUNTIME ON)
+
+    foreach(component_name ${Boost_BUILD_COMPONENTS})
+	    list(APPEND Boost_BUILD_VARIANTS --with-${component_name})
+    endforeach()
+
+    FetchContent_Declare(
+            deps-boost
+            GIT_REPOSITORY https://github.com/boostorg/boost.git
+            GIT_TAG boost-${Boost_VERSION}
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-boost)
+    if (NOT deps-boost_POPULATED)
+        message(STATUS "Fetching boost sources")
+        FetchContent_Populate(deps-boost)
+        message(STATUS "Fetching boost sources - done")
+    endif ()
+
+    if (deps-boost_POPULATED AND NOT EXISTS "${deps-boost_BINARY_DIR}/install")
+        include(ProcessorCount)
+        ProcessorCount(PROCESSOR_COUNT)
+
+        execute_process(COMMAND /bin/bash -c "./bootstrap.sh --prefix=${deps-boost_BINARY_DIR}/install && ./b2 install --build-dir=${deps-boost_BINARY_DIR}/build --stagedir=${deps-boost_BINARY_DIR}/stage -j${PROCESSOR_COUNT} ${Boost_BUILD_VARIANTS} ${Boost_BUILD_OPTIONS}"
+                        WORKING_DIRECTORY ${deps-boost_SOURCE_DIR}
+                        COMMAND_ECHO STDOUT
+                        RESULT_VARIABLE Boost_BUILD_RESULT)
+        if(NOT Boost_BUILD_RESULT EQUAL "0")
+            message(FATAL_ERROR "boost library build failed with ${Boost_BUILD_RESULT}, please checkout the boost module configurations")
+        endif()
+    endif()
+
+    find_package(Boost 1.73 CONFIG REQUIRED COMPONENTS ${Boost_BUILD_COMPONENTS}
+        HINTS ${deps-boost_BINARY_DIR}/install) # /lib/cmake/Boost-${Boost_VERSION}
+
+    message(STATUS "Boost version: ${Boost_VERSION}")
+
+    add_library(deps::boost INTERFACE IMPORTED GLOBAL)
+
+    set_target_properties(deps::boost PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${Boost_INCLUDE_DIRS}"
+        INTERFACE_COMPILE_DEFINITIONS "${Boost_COMPILE_DEFINITIONS}"
+        INTERFACE_LINK_LIBRARIES "${Boost_LIBRARIES}"
+    )
+
+    set(deps-boost_SOURCE_DIR ${deps-boost_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-boost_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/cmake/deps/catch2.cmake b/cpp/cmake/deps/catch2.cmake
new file mode 100644
index 000000000..56d22a3c3
--- /dev/null
+++ b/cpp/cmake/deps/catch2.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::catch2)
+    FetchContent_Declare(
+            deps-catch2
+            GIT_REPOSITORY https://github.com/catchorg/Catch2.git
+            GIT_TAG v2.13.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-catch2)
+    if (NOT deps-catch2_POPULATED)
+        message(STATUS "Fetching catch2 sources")
+        FetchContent_Populate(deps-catch2)
+        message(STATUS "Fetching catch2 sources - done")
+    endif ()
+
+    add_subdirectory(${deps-catch2_SOURCE_DIR} ${deps-catch2_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Include Append catch2's cmake module path so that we can use `include(ParseAndAddCatchTests)`.
+    list(APPEND CMAKE_MODULE_PATH "${deps-catch2_SOURCE_DIR}/contrib")
+    set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} PARENT_SCOPE)
+
+    add_library(deps::catch2 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::catch2 INTERFACE Catch2::Catch2)
+    set(deps-catch2_SOURCE_DIR ${deps-catch2_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-catch2_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/cmake/deps/cli11.cmake b/cpp/cmake/deps/cli11.cmake
new file mode 100644
index 000000000..343e69e18
--- /dev/null
+++ b/cpp/cmake/deps/cli11.cmake
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::cli11)
+    FetchContent_Declare(
+            deps-cli11
+            GIT_REPOSITORY https://github.com/CLIUtils/CLI11.git
+            GIT_TAG v1.9.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-cli11)
+    if (NOT deps-cli11_POPULATED)
+        message(STATUS "Fetching cli11 sources")
+        FetchContent_Populate(deps-cli11)
+        message(STATUS "Fetching cli11 sources - done")
+    endif ()
+
+    add_subdirectory(${deps-cli11_SOURCE_DIR} ${deps-cli11_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::cli11 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::cli11 INTERFACE CLI11::CLI11)
+    set(deps-cli11_SOURCE_DIR ${deps-cli11_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-cli11_SOURCE_DIR)
+endif ()
+
+# Note that library had a failure with nvcc compiler and gcc 9.x headers
+#  ...c++/9/tuple(553): error: pack "_UElements" does not have the same number of elements as "_Elements"
+#            __and_<is_nothrow_assignable<_Elements&, _UElements>...>::value;
+# Not using nvcc for main code that uses cli11 solved the issue.
\ No newline at end of file
diff --git a/cpp/cmake/deps/fmt.cmake b/cpp/cmake/deps/fmt.cmake
new file mode 100644
index 000000000..59e9c1fce
--- /dev/null
+++ b/cpp/cmake/deps/fmt.cmake
@@ -0,0 +1,43 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::fmt)
+    FetchContent_Declare(
+            deps-fmt
+            GIT_REPOSITORY https://github.com/fmtlib/fmt.git
+            GIT_TAG 7.0.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-fmt)
+    if (NOT deps-fmt_POPULATED)
+        message(STATUS "Fetching fmt sources")
+        FetchContent_Populate(deps-fmt)
+        message(STATUS "Fetching fmt sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    add_subdirectory(${deps-fmt_SOURCE_DIR} ${deps-fmt_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: ../lib/libfmtd.a(format.cc.o): relocation R_X86_64_PC32 against symbol `stderr@@GLIBC_2.2.5' can not be used when making a shared object; recompile with -fPIC
+    set_target_properties(fmt PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::fmt INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::fmt INTERFACE fmt::fmt-header-only)
+    set(deps-fmt_SOURCE_DIR ${deps-fmt_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-fmt_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/cmake/deps/gds.cmake b/cpp/cmake/deps/gds.cmake
new file mode 100644
index 000000000..57685840a
--- /dev/null
+++ b/cpp/cmake/deps/gds.cmake
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if(NOT TARGET deps::gds)
+    if(NOT GDS_SDK_PATH)
+        get_filename_component(GDS_SDK_PATH "${CMAKE_SOURCE_DIR}/gds" ABSOLUTE)
+        message("GDS_SDK_PATH is not set. Using '${GDS_SDK_PATH}'")
+    else()
+        message("GDS_SDK_PATH is set to ${GDS_SDK_PATH}")
+    endif()
+
+    if(EXISTS "${GDS_SDK_PATH}/lib64/libcufile.so")
+        add_library(deps::gds SHARED IMPORTED GLOBAL)
+        set_target_properties(deps::gds PROPERTIES
+            IMPORTED_LOCATION "${GDS_SDK_PATH}/lib64/libcufile.so"
+            INTERFACE_INCLUDE_DIRECTORIES "${GDS_SDK_PATH}/lib64/"
+        )
+    else()
+        message("'${GDS_SDK_PATH}/lib64/libcufile.so' is not available. Set CUCIM_SUPPORT_GDS to OFF and import cufile.h only.")
+        # Do not support GDS
+        set(CUCIM_SUPPORT_GDS OFF PARENT_SCOPE)
+        add_library(deps::gds INTERFACE IMPORTED GLOBAL)
+        set_target_properties(deps::gds PROPERTIES
+            INTERFACE_INCLUDE_DIRECTORIES "${GDS_SDK_PATH}/lib64/"
+        )
+    endif()
+endif()
\ No newline at end of file
diff --git a/cpp/cmake/deps/googlebenchmark.cmake b/cpp/cmake/deps/googlebenchmark.cmake
new file mode 100644
index 000000000..c5f46ba3e
--- /dev/null
+++ b/cpp/cmake/deps/googlebenchmark.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::googlebenchmark)
+    FetchContent_Declare(
+            deps-googlebenchmark
+            GIT_REPOSITORY https://github.com/google/benchmark.git
+            GIT_TAG v1.5.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-googlebenchmark)
+    if (NOT deps-googlebenchmark_POPULATED)
+        message(STATUS "Fetching googlebenchmark sources")
+        FetchContent_Populate(deps-googlebenchmark)
+        message(STATUS "Fetching googlebenchmark sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    set(BENCHMARK_ENABLE_GTEST_TESTS OFF)
+    add_subdirectory(${deps-googlebenchmark_SOURCE_DIR} ${deps-googlebenchmark_BINARY_DIR} EXCLUDE_FROM_ALL)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::googlebenchmark INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::googlebenchmark INTERFACE benchmark::benchmark)
+    set(deps-googlebenchmark_SOURCE_DIR ${deps-googlebenchmark_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-googlebenchmark_SOURCE_DIR)
+endif ()
diff --git a/cpp/cmake/deps/googletest.cmake b/cpp/cmake/deps/googletest.cmake
new file mode 100644
index 000000000..5d93a95f3
--- /dev/null
+++ b/cpp/cmake/deps/googletest.cmake
@@ -0,0 +1,45 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::googletest)
+    FetchContent_Declare(
+            deps-googletest
+            GIT_REPOSITORY https://github.com/google/googletest.git
+            GIT_TAG release-1.10.0
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-googletest)
+    if (NOT deps-googletest_POPULATED)
+        message(STATUS "Fetching googletest sources")
+        FetchContent_Populate(deps-googletest)
+        message(STATUS "Fetching googletest sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    add_subdirectory(${deps-googletest_SOURCE_DIR} ${deps-googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::googletest INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::googletest INTERFACE googletest)
+    set(deps-googletest_SOURCE_DIR ${deps-googletest_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-googletest_SOURCE_DIR)
+endif ()
+
+#CMake Warning (dev) in cmake-build-debug/_deps/deps-googletest-src/googlemock/CMakeLists.txt:
+#  Policy CMP0082 is not set: Install rules from add_subdirectory() are
+#  interleaved with those in caller.  Run "cmake --help-policy CMP0082" for
+#  policy details.  Use the cmake_policy command to set the policy and
+#  suppress this warning.
\ No newline at end of file
diff --git a/cpp/cmake/deps/json.cmake b/cpp/cmake/deps/json.cmake
new file mode 100644
index 000000000..4f716f120
--- /dev/null
+++ b/cpp/cmake/deps/json.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::json)
+    FetchContent_Declare(
+            deps-json
+            GIT_REPOSITORY https://github.com/nlohmann/json.git
+            GIT_TAG v3.9.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-json)
+    if (NOT deps-json_POPULATED)
+        message(STATUS "Fetching json sources")
+        FetchContent_Populate(deps-json)
+        message(STATUS "Fetching json sources - done")
+    endif ()
+
+    # Typically you don't care so much for a third party library's tests to be
+    # run from your own project's code.
+    set(JSON_BuildTests OFF CACHE INTERNAL "")
+
+    add_subdirectory(${deps-json_SOURCE_DIR} ${deps-json_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::json INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::json INTERFACE nlohmann_json::nlohmann_json)
+    set(deps-json_SOURCE_DIR ${deps-json_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-json_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/cmake/deps/openslide.cmake b/cpp/cmake/deps/openslide.cmake
new file mode 100644
index 000000000..651acd5ff
--- /dev/null
+++ b/cpp/cmake/deps/openslide.cmake
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::openslide)
+    add_library(deps::openslide SHARED IMPORTED GLOBAL)
+
+    if (DEFINED ENV{CONDA_BUILD})
+        set(OPENSLIDE_LIB_PATH "$ENV{PREFIX}/lib/libopenslide.so")
+    elseif (DEFINED ENV{CONDA_PREFIX})
+        set(OPENSLIDE_LIB_PATH "$ENV{CONDA_PREFIX}/lib/libopenslide.so")
+    elseif (EXISTS /usr/lib/x86_64-linux-gnu/libopenslide.so)
+        set(OPENSLIDE_LIB_PATH /usr/lib/x86_64-linux-gnu/libopenslide.so)
+    else () # CentOS 6
+        set(OPENSLIDE_LIB_PATH /usr/lib64/libopenslide.so)
+    endif ()
+
+    if (DEFINED ENV{CONDA_BUILD})
+        set(OPENSLIDE_INCLUDE_PATH "$ENV{PREFIX}/include/")
+    elseif (DEFINED ENV{CONDA_PREFIX})
+        set(OPENSLIDE_INCLUDE_PATH "$ENV{CONDA_PREFIX}/include/")
+    else ()
+        set(OPENSLIDE_INCLUDE_PATH "/usr/include/")
+    endif ()
+
+    set_target_properties(deps::openslide PROPERTIES
+        IMPORTED_LOCATION "${OPENSLIDE_LIB_PATH}"
+        INTERFACE_INCLUDE_DIRECTORIES "${OPENSLIDE_INCLUDE_PATH}"
+    )
+endif ()
diff --git a/cpp/cmake/deps/pybind11.cmake b/cpp/cmake/deps/pybind11.cmake
new file mode 100644
index 000000000..b7dca11f7
--- /dev/null
+++ b/cpp/cmake/deps/pybind11.cmake
@@ -0,0 +1,38 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::pybind11)
+    FetchContent_Declare(
+            deps-pybind11
+            GIT_REPOSITORY https://github.com/pybind/pybind11.git
+            GIT_TAG v2.6.2
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-pybind11)
+    if (NOT deps-pybind11_POPULATED)
+        message(STATUS "Fetching pybind11 sources")
+        FetchContent_Populate(deps-pybind11)
+        message(STATUS "Fetching pybind11 sources - done")
+    endif ()
+
+    # https://pybind11.readthedocs.io/en/stable/compiling.html#configuration-variables
+
+    add_subdirectory(${deps-pybind11_SOURCE_DIR} ${deps-pybind11_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::pybind11 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::pybind11 INTERFACE pybind11::module)
+    set(deps-pybind11_SOURCE_DIR ${deps-pybind11_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-pybind11_SOURCE_DIR)
+endif ()
diff --git a/cpp/cmake/deps/rmm.cmake b/cpp/cmake/deps/rmm.cmake
new file mode 100644
index 000000000..9363ce188
--- /dev/null
+++ b/cpp/cmake/deps/rmm.cmake
@@ -0,0 +1,45 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Note: importing rmm is tricky as it depends on googletest/thrust/spdlog and can conflicts with target names in
+#       the original project.
+#       There is a suggestion in CMake but it seems that it takes time to resolve the issue.
+#       - Namespace support for target names in nested projects: https://gitlab.kitware.com/cmake/cmake/-/issues/16414
+
+if (NOT TARGET deps::rmm)
+    FetchContent_Declare(
+            deps-rmm
+            GIT_REPOSITORY https://github.com/rapidsai/rmm.git
+            GIT_TAG branch-0.17
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-rmm)
+    if (NOT deps-rmm_POPULATED)
+        message(STATUS "Fetching rmm sources")
+        FetchContent_Populate(deps-rmm)
+        message(STATUS "Fetching rmm sources - done")
+    endif ()
+
+    # Create shared library
+    cucim_set_build_shared_libs(ON) # Since rmm doesn't use BUILD_SHARED_LIBS, it always build shared library
+
+    add_subdirectory(${deps-rmm_SOURCE_DIR} ${deps-rmm_BINARY_DIR} EXCLUDE_FROM_ALL)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::rmm INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::rmm INTERFACE rmm)
+    set(deps-rmm_SOURCE_DIR ${deps-rmm_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-rmm_SOURCE_DIR)
+endif ()
diff --git a/cpp/cmake/modules/CuCIMUtils.cmake b/cpp/cmake/modules/CuCIMUtils.cmake
new file mode 100644
index 000000000..cfe0b1495
--- /dev/null
+++ b/cpp/cmake/modules/CuCIMUtils.cmake
@@ -0,0 +1,60 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Store current BUILD_SHARED_LIBS setting in CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_set_build_shared_libs)
+    macro(cucim_set_build_shared_libs new_value)
+        set(CUCIM_OLD_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}})
+        if (DEFINED CACHE{BUILD_SHARED_LIBS})
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED TRUE)
+        else()
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED FALSE)
+        endif()
+        set(BUILD_SHARED_LIBS ${new_value} CACHE BOOL "" FORCE)
+    endmacro()
+endif()
+
+# Restore BUILD_SHARED_LIBS setting from CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_restore_build_shared_libs)
+    macro(cucim_restore_build_shared_libs)
+        if (CUCIM_OLD_BUILD_SHARED_LIBS_CACHED)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
+        else()
+            unset(BUILD_SHARED_LIBS CACHE)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS})
+        endif()
+    endmacro()
+endif()
+
+# Define CMAKE_CUDA_ARCHITECTURES for the given architecture values
+#
+# Params:
+#   arch_list - architecture value list (e.g., '60;70;75;80;86')
+if(NOT COMMAND cucim_define_cuda_architectures)
+    function(cucim_define_cuda_architectures arch_list)
+        set(arch_string "")
+        # Create SASS for all architectures in the list
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${arch}-real")
+        endforeach(arch)
+
+        # Create PTX for the latest architecture for forward-compatibility.
+        list(GET arch_list -1 latest_arch)
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${latest_arch}-virtual")
+        endforeach(arch)
+        set(CMAKE_CUDA_ARCHITECTURES ${arch_string} PARENT_SCOPE)
+    endfunction()
+endif()
diff --git a/cpp/cmake/modules/SuperBuildUtils.cmake b/cpp/cmake/modules/SuperBuildUtils.cmake
new file mode 100644
index 000000000..faec66ee1
--- /dev/null
+++ b/cpp/cmake/modules/SuperBuildUtils.cmake
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+include(FetchContent)
+
+set(CMAKE_SUPERBUILD_DEPS_ROOT_DIR "${CMAKE_CURRENT_LIST_DIR}/..")
+
+if(NOT COMMAND superbuild_depend)
+    function(superbuild_depend module_name)
+        include("${CMAKE_SUPERBUILD_DEPS_ROOT_DIR}/deps/${module_name}.cmake")
+    endfunction()
+endif()
diff --git a/cpp/include/cucim/3rdparty/dlpack/dlpack.h b/cpp/include/cucim/3rdparty/dlpack/dlpack.h
new file mode 100644
index 000000000..0ebd1f806
--- /dev/null
+++ b/cpp/include/cucim/3rdparty/dlpack/dlpack.h
@@ -0,0 +1,175 @@
+// From https://github.com/dmlc/dlpack/blob/v0.3/include/dlpack/dlpack.h
+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file dlpack.h
+ * \brief The common header of DLPack.
+ */
+#ifndef DLPACK_DLPACK_H_
+#define DLPACK_DLPACK_H_
+
+#ifdef __cplusplus
+#define DLPACK_EXTERN_C extern "C"
+#else
+#define DLPACK_EXTERN_C
+#endif
+
+/*! \brief The current version of dlpack */
+#define DLPACK_VERSION 020
+
+/*! \brief DLPACK_DLL prefix for windows */
+#ifdef _WIN32
+#ifdef DLPACK_EXPORTS
+#define DLPACK_DLL __declspec(dllexport)
+#else
+#define DLPACK_DLL __declspec(dllimport)
+#endif
+#else
+#define DLPACK_DLL
+#endif
+
+#include <stdint.h>
+#include <stddef.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+/*!
+ * \brief The device type in DLContext.
+ */
+typedef enum {
+  /*! \brief CPU device */
+  kDLCPU = 1,
+  /*! \brief CUDA GPU device */
+  kDLGPU = 2,
+  /*!
+   * \brief Pinned CUDA GPU device by cudaMallocHost
+   * \note kDLCPUPinned = kDLCPU | kDLGPU
+   */
+  kDLCPUPinned = 3,
+  /*! \brief OpenCL devices. */
+  kDLOpenCL = 4,
+  /*! \brief Vulkan buffer for next generation graphics. */
+  kDLVulkan = 7,
+  /*! \brief Metal for Apple GPU. */
+  kDLMetal = 8,
+  /*! \brief Verilog simulator buffer */
+  kDLVPI = 9,
+  /*! \brief ROCm GPUs for AMD GPUs */
+  kDLROCM = 10,
+  /*!
+   * \brief Reserved extension device type,
+   * used for quickly test extension device
+   * The semantics can differ depending on the implementation.
+   */
+  kDLExtDev = 12,
+} DLDeviceType;
+
+/*!
+ * \brief A Device context for Tensor and operator.
+ */
+typedef struct {
+  /*! \brief The device type used in the device. */
+  DLDeviceType device_type;
+  /*! \brief The device index */
+  int device_id;
+} DLContext;
+
+/*!
+ * \brief The type code options DLDataType.
+ */
+typedef enum {
+  kDLInt = 0U,
+  kDLUInt = 1U,
+  kDLFloat = 2U,
+  kDLBfloat = 4U,
+} DLDataTypeCode;
+
+/*!
+ * \brief The data type the tensor can hold.
+ *
+ *  Examples
+ *   - float: type_code = 2, bits = 32, lanes=1
+ *   - float4(vectorized 4 float): type_code = 2, bits = 32, lanes=4
+ *   - int8: type_code = 0, bits = 8, lanes=1
+ */
+typedef struct {
+  /*!
+   * \brief Type code of base types.
+   * We keep it uint8_t instead of DLDataTypeCode for minimal memory
+   * footprint, but the value should be one of DLDataTypeCode enum values.
+   * */
+  uint8_t code;
+  /*!
+   * \brief Number of bits, common choices are 8, 16, 32.
+   */
+  uint8_t bits;
+  /*! \brief Number of lanes in the type, used for vector types. */
+  uint16_t lanes;
+} DLDataType;
+
+/*!
+ * \brief Plain C Tensor object, does not manage memory.
+ */
+typedef struct {
+  /*!
+   * \brief The opaque data pointer points to the allocated data. This will be
+   * CUDA device pointer or cl_mem handle in OpenCL. This pointer is always
+   * aligned to 256 bytes as in CUDA.
+   *
+   * For given DLTensor, the size of memory required to store the contents of
+   * data is calculated as follows:
+   *
+   * \code{.c}
+   * static inline size_t GetDataSize(const DLTensor* t) {
+   *   size_t size = 1;
+   *   for (tvm_index_t i = 0; i < t->ndim; ++i) {
+   *     size *= t->shape[i];
+   *   }
+   *   size *= (t->dtype.bits * t->dtype.lanes + 7) / 8;
+   *   return size;
+   * }
+   * \endcode
+   */
+  void* data;
+  /*! \brief The device context of the tensor */
+  DLContext ctx;
+  /*! \brief Number of dimensions */
+  int ndim;
+  /*! \brief The data type of the pointer*/
+  DLDataType dtype;
+  /*! \brief The shape of the tensor */
+  int64_t* shape;
+  /*!
+   * \brief strides of the tensor (in number of elements, not bytes)
+   *  can be NULL, indicating tensor is compact and row-majored.
+   */
+  int64_t* strides;
+  /*! \brief The offset in bytes to the beginning pointer to data */
+  uint64_t byte_offset;
+} DLTensor;
+
+/*!
+ * \brief C Tensor object, manage memory of DLTensor. This data structure is
+ *  intended to facilitate the borrowing of DLTensor by another framework. It is
+ *  not meant to transfer the tensor. When the borrowing framework doesn't need
+ *  the tensor, it should call the deleter to notify the host that the resource
+ *  is no longer needed.
+ */
+typedef struct DLManagedTensor {
+  /*! \brief DLTensor which is being memory managed */
+  DLTensor dl_tensor;
+  /*! \brief the context of the original host framework of DLManagedTensor in
+   *   which DLManagedTensor is used in the framework. It can also be NULL.
+   */
+  void * manager_ctx;
+  /*! \brief Destructor signature void (*)(void*) - this should be called
+   *   to destruct manager_ctx which holds the DLManagedTensor. It can be NULL
+   *   if there is no way for the caller to provide a reasonable destructor.
+   *   The destructors deletes the argument self as well.
+   */
+  void (*deleter)(struct DLManagedTensor * self);
+} DLManagedTensor;
+#ifdef __cplusplus
+}  // DLPACK_EXTERN_C
+#endif
+#endif  // DLPACK_DLPACK_H_
\ No newline at end of file
diff --git a/cpp/include/cucim/3rdparty/dlpack/dlpackcpp.h b/cpp/include/cucim/3rdparty/dlpack/dlpackcpp.h
new file mode 100644
index 000000000..0cba3e943
--- /dev/null
+++ b/cpp/include/cucim/3rdparty/dlpack/dlpackcpp.h
@@ -0,0 +1,66 @@
+// From https://github.com/dmlc/dlpack/blob/v0.3/contrib/dlpack/dlpackcpp.h
+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file dlpackcpp.h
+ * \brief Example C++ wrapper of DLPack
+ */
+#ifndef DLPACK_DLPACKCPP_H_
+#define DLPACK_DLPACKCPP_H_
+
+#include <dlpack/dlpack.h>
+
+#include <cstdint>  // for int64_t etc
+#include <cstdlib>  // for free()
+#include <functional>  // for std::multiplies
+#include <memory>
+#include <numeric>
+#include <vector>
+
+namespace dlpack {
+
+// Example container wrapping of DLTensor.
+class DLTContainer {
+ public:
+  DLTContainer() {
+    // default to float32
+    handle_.data = nullptr;
+    handle_.dtype.code = kDLFloat;
+    handle_.dtype.bits = 32U;
+    handle_.dtype.lanes = 1U;
+    handle_.ctx.device_type = kDLCPU;
+    handle_.ctx.device_id = 0;
+    handle_.shape = nullptr;
+    handle_.strides = nullptr;
+    handle_.byte_offset = 0;
+  }
+  ~DLTContainer() {
+    if (origin_ == nullptr) {
+      free(handle_.data);
+    }
+  }
+  operator DLTensor() {
+    return handle_;
+  }
+  operator DLTensor*() {
+    return &(handle_);
+  }
+  void Reshape(const std::vector<int64_t>& shape) {
+    shape_ = shape;
+    int64_t sz = std::accumulate(std::begin(shape), std::end(shape),
+                                 int64_t(1), std::multiplies<int64_t>());
+    int ret = posix_memalign(&handle_.data, 256, sz);
+    if (ret != 0) throw std::bad_alloc();
+    handle_.shape = &shape_[0];
+    handle_.ndim = static_cast<uint32_t>(shape.size());
+  }
+
+ private:
+  DLTensor handle_;
+  std::vector<int64_t> shape_;
+  std::vector<int64_t> strides_;
+  // original space container, if
+  std::shared_ptr<DLTContainer> origin_;
+};
+
+}  // namespace dlpack
+#endif  // DLPACK_DLPACKCPP_H_
\ No newline at end of file
diff --git a/cpp/include/cucim/codec/base64.h b/cpp/include/cucim/codec/base64.h
new file mode 100644
index 000000000..95636cf5c
--- /dev/null
+++ b/cpp/include/cucim/codec/base64.h
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_BASE64_H
+#define CUCIM_BASE64_H
+
+#include "cucim/macros/defines.h"
+
+namespace cucim::codec::base64
+{
+EXPORT_VISIBLE bool encode(const char* src, int src_count, char** out_dst, int* out_count);
+EXPORT_VISIBLE bool decode(const char* src, int src_count, char** out_dst, int* out_count);
+}
+#endif // CUCIM_BASE64_H
diff --git a/cpp/include/cucim/codec/methods.h b/cpp/include/cucim/codec/methods.h
new file mode 100644
index 000000000..6cc373b41
--- /dev/null
+++ b/cpp/include/cucim/codec/methods.h
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_METHODS_H
+#define CUCIM_METHODS_H
+
+#include "cucim/macros/defines.h"
+
+namespace cucim::codec
+{
+
+/// Compression method (Followed https://www.awaresystems.be/imaging/tiff/tifftags/compression.html)
+enum class CompressionMethod : uint16_t
+{
+    NONE = 1,
+    JPEG = 7,
+};
+
+} // namespace cucim::codec
+
+#endif // CUCIM_METHODS_H
diff --git a/cpp/include/cucim/core/framework.h b/cpp/include/cucim/core/framework.h
new file mode 100644
index 000000000..dfdb614d7
--- /dev/null
+++ b/cpp/include/cucim/core/framework.h
@@ -0,0 +1,100 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_FRAMEWORK_H
+#define CUCIM_FRAMEWORK_H
+
+#include "cucim/core/plugin.h"
+#include "cucim/memory/memory_manager.h"
+
+#define CUCIM_FRAMEWORK_GLOBALS(client_name)                                                                           \
+    CUCIM_NO_EXPORT const char* g_cucim_client_name = client_name;                                                     \
+    CUCIM_NO_EXPORT cucim::Framework* g_cucim_framework = nullptr;
+
+
+namespace cucim
+{
+
+const struct Version kFrameworkVersion = { CUCIM_VERSION_MAJOR, CUCIM_VERSION_MINOR, CUCIM_VERSION_PATCH };
+
+struct PluginRegistrationDesc
+{
+    OnPluginRegisterFn on_register; ///< Required
+    //    OnPluginStartupFn on_startup_fn; ///! Can be nullptr
+    //    OnPluginShutdownFn on_shutdown_fn; ///! Can be nullptr
+    OnGetPluginDepsFn on_get_deps; ///! Can be nullptr
+    //    OnReloadDependencyFn on_reload_dependency_fn; ///! Can be nullptr
+    //    OnPluginPreStartupFn on_pre_startup_fn; ///! Can be nullptr
+    //    OnPluginPostShutdownFn on_post_shutdown_fn;  ///! Can be nullptr
+};
+
+struct PluginLoadingDesc
+{
+    const char* plugin_path;
+
+    static PluginLoadingDesc get_default()
+    {
+        static constexpr const char* default_plugin_path = "cucim@0.0.1.so";
+        return { default_plugin_path };
+    }
+};
+
+
+struct Framework
+{
+    void load_plugins(const PluginLoadingDesc& desc = PluginLoadingDesc::get_default());
+    bool(CUCIM_ABI* register_plugin)(const char* client_name, const PluginRegistrationDesc& desc);
+    void*(CUCIM_ABI* acquire_interface_from_library_with_client)(const char* client_name,
+                                                                 InterfaceDesc desc,
+                                                                 const char* library_path);
+    void(CUCIM_ABI* unload_all_plugins)();
+
+    template <typename T>
+    T* acquire_interface_from_library(const char* library_path);
+
+    // cuCIM-specific methods
+    const char*(CUCIM_ABI* get_plugin_root)();
+    void(CUCIM_ABI* set_plugin_root)(const char* path);
+};
+
+CUCIM_API cucim::Framework* acquire_framework(const char* app_name, Version framework_version = kFrameworkVersion);
+
+CUCIM_API void release_framework();
+
+} // namespace cucim
+
+extern const char* g_cucim_client_name;
+extern cucim::Framework* g_cucim_framework;
+
+namespace cucim
+{
+
+inline Framework* get_framework()
+{
+    return g_cucim_framework;
+}
+
+
+template <typename T>
+T* Framework::acquire_interface_from_library(const char* library_path)
+{
+    const auto desc = T::get_interface_desc();
+    return static_cast<T*>(this->acquire_interface_from_library_with_client(g_cucim_client_name, desc, library_path));
+}
+} // namespace cucim
+
+
+#endif // CUCIM_FRAMEWORK_H
diff --git a/cpp/include/cucim/core/interface.h b/cpp/include/cucim/core/interface.h
new file mode 100644
index 000000000..801796861
--- /dev/null
+++ b/cpp/include/cucim/core/interface.h
@@ -0,0 +1,45 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_INTERFACE_H
+#define CUCIM_INTERFACE_H
+
+
+#include "cucim/core/version.h"
+
+namespace cucim
+{
+
+struct InterfaceDesc
+{
+    const char* name = nullptr;
+    InterfaceVersion version = { 0, 1 };
+};
+
+/**
+ * Macro to declare a plugin interface.
+ */
+#define CUCIM_PLUGIN_INTERFACE(name, major, minor)                                                                   \
+    static cucim::InterfaceDesc get_interface_desc()                                                                     \
+    {                                                                                                                  \
+        return cucim::InterfaceDesc{ name, { major, minor } };                                                           \
+    }
+
+} // namespace cucim
+
+#include "../macros/defines.h"
+
+#endif // CUCIM_INTERFACE_H
diff --git a/cpp/include/cucim/core/plugin.h b/cpp/include/cucim/core/plugin.h
new file mode 100644
index 000000000..d0391aded
--- /dev/null
+++ b/cpp/include/cucim/core/plugin.h
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_PLUGIN_H
+#define CUCIM_PLUGIN_H
+
+
+#include "cucim/macros/api_header.h"
+#include "cucim/core/interface.h"
+
+#include <cstddef>
+#include <cstdint>
+
+namespace cucim
+{
+
+enum class PluginHotReload : std::uint8_t
+{
+    kDisabled,
+    kEnabled,
+};
+
+struct PluginImplDesc
+{
+    const char* name;
+    Version version;
+    const char* build;
+    const char* author;
+    const char* description;
+    const char* long_description;
+    const char* license;
+    const char* url;
+    const char* platforms;
+    PluginHotReload hot_reload;
+};
+
+struct PluginEntry
+{
+    PluginImplDesc desc;
+
+    struct Interface
+    {
+        InterfaceDesc desc;
+        const void* ptr;
+        size_t size;
+    };
+
+    Interface* interfaces;
+    size_t interface_count;
+};
+
+struct PluginDesc
+{
+    PluginImplDesc desc;
+    const char* lib_path;
+    const InterfaceDesc* interfaces;
+    size_t interface_count;
+    const InterfaceDesc* dependencies;
+    size_t dependency_count;
+};
+
+typedef Version(CUCIM_ABI* OnGetFrameworkVersionFn)();
+typedef void(CUCIM_ABI* OnPluginRegisterFn)(struct Framework* framework, PluginEntry* out_entry);
+typedef void(CUCIM_ABI* OnGetPluginDepsFn)(InterfaceDesc** interface_desc, size_t* count);
+
+
+} // namespace cucim
+#endif // CUCIM_PLUGIN_H
diff --git a/cpp/include/cucim/core/plugin_util.h b/cpp/include/cucim/core/plugin_util.h
new file mode 100644
index 000000000..39dab96a9
--- /dev/null
+++ b/cpp/include/cucim/core/plugin_util.h
@@ -0,0 +1,199 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_PLUGIN_UTIL_H
+#define CUCIM_PLUGIN_UTIL_H
+
+#include "plugin.h"
+
+constexpr const char* const kCuCIMOnGetFrameworkVersionFnName =
+    "cucim_on_get_framework_version"; // type: OnGetFrameworkVersionFn
+
+constexpr const char* const kCuCIMOnPluginRegisterFnName = "cucim_on_plugin_register"; // type: OnPluginRegisterFn
+
+/*
+ * Optional functions:
+ */
+constexpr const char* const kCuCIMOnGetPluginDepsFnName = "cucim_on_get_plugin_deps"; // type: OnGetPluginDepsFn
+
+
+// const char* const kCarbOnPluginPreStartupFnName = "carbOnPluginPreStartup"; // type: OnPluginPreStartupFn
+// const char* const kCarbOnPluginStartupFnName = "carbOnPluginStartup"; // type: OnPluginStartupFn
+//
+// const char* const kCarbOnPluginShutdownFnName = "carbOnPluginShutdown"; // type: OnPluginShutdownFn
+// const char* const kCarbOnPluginPostShutdownFnName = "carbOnPluginPostShutdown"; // type: OnPluginPostShutdownFn
+
+//
+// const char* const kCarbOnReloadDependencyFnName = "carbOnReloadDependency"; // type: OnReloadDependencyFn
+
+/**
+ * FOR_EACH macro implementation, use as FOR_EACH(OTHER_MACRO, p0, p1, p2,)
+ */
+#define EXPAND(x) x
+#define FE_1(WHAT, X) EXPAND(WHAT(X))
+#define FE_2(WHAT, X, ...) EXPAND(WHAT(X) FE_1(WHAT, __VA_ARGS__))
+#define FE_3(WHAT, X, ...) EXPAND(WHAT(X) FE_2(WHAT, __VA_ARGS__))
+#define FE_4(WHAT, X, ...) EXPAND(WHAT(X) FE_3(WHAT, __VA_ARGS__))
+#define FE_5(WHAT, X, ...) EXPAND(WHAT(X) FE_4(WHAT, __VA_ARGS__))
+#define FE_6(WHAT, X, ...) EXPAND(WHAT(X) FE_5(WHAT, __VA_ARGS__))
+#define FE_7(WHAT, X, ...) EXPAND(WHAT(X) FE_6(WHAT, __VA_ARGS__))
+#define FE_8(WHAT, X, ...) EXPAND(WHAT(X) FE_7(WHAT, __VA_ARGS__))
+#define FE_9(WHAT, X, ...) EXPAND(WHAT(X) FE_8(WHAT, __VA_ARGS__))
+#define FE_10(WHAT, X, ...) EXPAND(WHAT(X) FE_9(WHAT, __VA_ARGS__))
+#define FE_11(WHAT, X, ...) EXPAND(WHAT(X) FE_10(WHAT, __VA_ARGS__))
+#define FE_12(WHAT, X, ...) EXPAND(WHAT(X) FE_11(WHAT, __VA_ARGS__))
+#define FE_13(WHAT, X, ...) EXPAND(WHAT(X) FE_12(WHAT, __VA_ARGS__))
+#define FE_14(WHAT, X, ...) EXPAND(WHAT(X) FE_13(WHAT, __VA_ARGS__))
+#define FE_15(WHAT, X, ...) EXPAND(WHAT(X) FE_14(WHAT, __VA_ARGS__))
+#define FE_16(WHAT, X, ...) EXPAND(WHAT(X) FE_15(WHAT, __VA_ARGS__))
+#define FE_17(WHAT, X, ...) EXPAND(WHAT(X) FE_16(WHAT, __VA_ARGS__))
+#define FE_18(WHAT, X, ...) EXPAND(WHAT(X) FE_17(WHAT, __VA_ARGS__))
+#define FE_19(WHAT, X, ...) EXPAND(WHAT(X) FE_18(WHAT, __VA_ARGS__))
+#define FE_20(WHAT, X, ...) EXPAND(WHAT(X) FE_19(WHAT, __VA_ARGS__))
+#define FE_21(WHAT, X, ...) EXPAND(WHAT(X) FE_20(WHAT, __VA_ARGS__))
+#define FE_22(WHAT, X, ...) EXPAND(WHAT(X) FE_21(WHAT, __VA_ARGS__))
+#define FE_23(WHAT, X, ...) EXPAND(WHAT(X) FE_22(WHAT, __VA_ARGS__))
+#define FE_24(WHAT, X, ...) EXPAND(WHAT(X) FE_23(WHAT, __VA_ARGS__))
+#define FE_25(WHAT, X, ...) EXPAND(WHAT(X) FE_24(WHAT, __VA_ARGS__))
+#define FE_26(WHAT, X, ...) EXPAND(WHAT(X) FE_25(WHAT, __VA_ARGS__))
+#define FE_27(WHAT, X, ...) EXPAND(WHAT(X) FE_26(WHAT, __VA_ARGS__))
+#define FE_28(WHAT, X, ...) EXPAND(WHAT(X) FE_27(WHAT, __VA_ARGS__))
+#define FE_29(WHAT, X, ...) EXPAND(WHAT(X) FE_28(WHAT, __VA_ARGS__))
+#define FE_30(WHAT, X, ...) EXPAND(WHAT(X) FE_29(WHAT, __VA_ARGS__))
+#define FE_31(WHAT, X, ...) EXPAND(WHAT(X) FE_30(WHAT, __VA_ARGS__))
+#define FE_32(WHAT, X, ...) EXPAND(WHAT(X) FE_31(WHAT, __VA_ARGS__))
+
+
+//... repeat as needed
+#define GET_MACRO(_1, _2, _3, _4, _5, _6, _7, _8, _9, _10, _11, _12, _13, _14, _15, _16, _17, _18, _19, _20, _21, _22, \
+                  _23, _24, _25, _26, _27, _28, _29, _30, _31, _32, NAME, ...)                                         \
+    NAME
+#define FOR_EACH(action, ...)                                                                                          \
+    EXPAND(GET_MACRO(__VA_ARGS__, FE_32, FE_31, FE_30, FE_29, FE_28, FE_27, FE_26, FE_25, FE_24, FE_23, FE_22, FE_21,  \
+                     FE_20, FE_19, FE_18, FE_17, FE_16, FE_15, FE_14, FE_13, FE_12, FE_11, FE_10, FE_9, FE_8, FE_7,    \
+                     FE_6, FE_5, FE_4, FE_3, FE_2, FE_1)(action, __VA_ARGS__))
+
+
+#define DECLARE_FILL_FUNCTION(X) void fill_interface(X& iface);
+
+/**
+ * Macros to declare a plugin implementation with custom static initializer.
+ *
+ * It does the following:
+ *
+ *     1. Defines cucim_on_get_framework_version and cucim_on_plugin_register functions.
+ *     2. Defines global framework variable for cucim::getFramework() to work.
+ *     3. Defines global client variable (which is set to a plugin name). It is used for acquiring interfaces,
+ * such that framework knows who calls it.
+ *     4. Forward declares void fill_interface(InterfaceType& iface) functions for every interface to be
+ * used to provide interfaces to the framework.
+ *
+ * This macro must be defined in a global namespace.
+ *
+ * @param impl The PluginImplDesc constant to be used as plugin description.
+ * @param ... One or more interface types to be implemented by the plugin. Interface is a struct with
+ * CUCIM_PLUGIN_INTERFACE() macro inside.
+ */
+#define CUCIM_PLUGIN_IMPL_WITH_INIT(impl, ...)                                                                       \
+                                                                                                                       \
+    /* Forward declare fill functions for every interface */                                                           \
+    FOR_EACH(DECLARE_FILL_FUNCTION, __VA_ARGS__)                                                                       \
+                                                                                                                       \
+    template <typename T1>                                                                                             \
+    void fill_interface(cucim::PluginEntry::Interface* interfaces)                                                                   \
+    {                                                                                                                  \
+        interfaces[0].desc = T1::get_interface_desc();                                                                 \
+        static T1 s_plugin_interface;                                                                                  \
+        fill_interface(s_plugin_interface);                                                                            \
+        interfaces[0].ptr = &s_plugin_interface;                                                                       \
+        interfaces[0].size = sizeof(T1);                                                                               \
+    }                                                                                                                  \
+                                                                                                                       \
+    template <typename T1, typename T2, typename... Types>                                                             \
+    void fill_interface(cucim::PluginEntry::Interface* interfaces)                                                                   \
+    {                                                                                                                  \
+        fill_interface<T1>(interfaces);                                                                                \
+        fill_interface<T2, Types...>(interfaces + 1);                                                                  \
+    }                                                                                                                  \
+                                                                                                                       \
+    template <typename... Types>                                                                                       \
+    static void on_plugin_register(cucim::Framework* framework, cucim::PluginEntry* out_entry)                                                     \
+    {                                                                                                                  \
+        static cucim::PluginEntry::Interface s_interfaces[sizeof...(Types)];                                                         \
+        fill_interface<Types...>(s_interfaces);                                                                        \
+        out_entry->interfaces = s_interfaces;                                                                          \
+        out_entry->interface_count = sizeof(s_interfaces) / sizeof(s_interfaces[0]);                                   \
+        out_entry->desc = impl;                                                                                        \
+                                                                                                                       \
+        g_cucim_framework = framework;                                                                                               \
+        g_cucim_client_name = impl.name;                                                                                             \
+    }                                                                                                                  \
+                                                                                                                       \
+    CUCIM_API void cucim_on_plugin_register(cucim::Framework* framework, cucim::PluginEntry* out_entry)                                                        \
+    {                                                                                                                  \
+        on_plugin_register<__VA_ARGS__>(framework, out_entry);                                                         \
+    }                                                                                                                  \
+                                                                                                                       \
+    CUCIM_API cucim::Version cucim_on_get_framework_version()                                                                                    \
+    {                                                                                                                  \
+        return cucim::kFrameworkVersion;                                                                                             \
+    }
+
+
+/**
+ * Macros to declare a plugin implementation dependencies.
+ *
+ * If a plugin lists an interface "A" as dependency it is guaranteed that Framework::acquireInterface<A>() call
+ * will return it, otherwise it can return nullptr. Framework checks and resolves all dependencies before loading the
+ * plugin.
+ *
+ * @param ... One or more interface types to list as dependencies for this plugin.
+ */
+#define CUCIM_PLUGIN_IMPL_DEPS(...)                                                                                  \
+    template <typename... Types>                                                                                       \
+    static void get_plugin_deps_typed(struct cucim::InterfaceDesc** deps, size_t* count)                                   \
+    {                                                                                                                  \
+        static cucim::InterfaceDesc depends[] = { Types::get_interface_desc()... };                                        \
+        *deps = depends;                                                                                               \
+        *count = sizeof(depends) / sizeof(depends[0]);                                                                 \
+    }                                                                                                                  \
+                                                                                                                       \
+    CUCIM_API void cucim_on_get_plugin_deps(struct cucim::InterfaceDesc** deps, size_t* count)                               \
+    {                                                                                                                  \
+        get_plugin_deps_typed<__VA_ARGS__>(deps, count);                                                               \
+    }
+
+/**
+ * Macro to declare no plugin implementation dependencies.
+ */
+#define CUCIM_PLUGIN_IMPL_NO_DEPS()                                                                                  \
+    CUCIM_API void cucim_on_get_plugin_deps(struct cucim::InterfaceDesc** deps, size_t* count)                       \
+    {                                                                                                                  \
+        *deps = nullptr;                                                                                               \
+        *count = 0;                                                                                                    \
+    }
+
+/**
+ * Macro to declare a plugin implementation with an empty scoped initializer.
+ * Useful for those who wants to use bare Carbonite Framework without the pre-registered plugins,
+ * contrary to what CUCIM_PLUGIN_IMPL suggests.
+ */
+#define CUCIM_PLUGIN_IMPL_MINIMAL(impl, ...)                                                                         \
+    CUCIM_FRAMEWORK_GLOBALS(kPluginImpl.name)                                                                        \
+    CUCIM_PLUGIN_IMPL_WITH_INIT(impl, __VA_ARGS__)
+
+
+#endif // CUCIM_PLUGIN_UTIL_H
diff --git a/cpp/include/cucim/core/version.h b/cpp/include/cucim/core/version.h
new file mode 100644
index 000000000..9622dc243
--- /dev/null
+++ b/cpp/include/cucim/core/version.h
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_VERSION_H
+#define CUCIM_VERSION_H
+
+#include <cstdint>
+
+#ifndef CUCIM_VERSION_MAJOR
+#    error "CUCIM_VERSION_MAJOR is not defined"
+#endif
+
+#ifndef CUCIM_VERSION_MINOR
+#    error "CUCIM_VERSION_MINOR is not defined"
+#endif
+
+#ifndef CUCIM_VERSION_PATCH
+#    error "CUCIM_VERSION_PATCH is not defined"
+#endif
+
+#ifndef CUCIM_VERSION_BUILD
+#    error "CUCIM_VERSION_BUILD is not defined"
+#endif
+
+namespace cucim
+{
+
+struct InterfaceVersion
+{
+    uint32_t major;
+    uint32_t minor;
+};
+
+struct Version
+{
+    uint32_t major;
+    uint32_t minor;
+    uint32_t patch;
+};
+
+} // namespace cucim
+#endif // CUCIM_VERSION_H
diff --git a/cpp/include/cucim/cuimage.h b/cpp/include/cucim/cuimage.h
new file mode 100644
index 000000000..b608c4a0c
--- /dev/null
+++ b/cpp/include/cucim/cuimage.h
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_CUIMAGE_H
+#define CUCIM_CUIMAGE_H
+
+#include <array>
+#include <memory>
+#include <vector>
+#include <string>
+#include <mutex>
+#include <set>
+
+#include "cucim/core/framework.h"
+#include "cucim/filesystem/file_path.h"
+#include "cucim/io/device.h"
+#include "cucim/io/format/image_format.h"
+#include "cucim/memory/dlpack.h"
+
+namespace cucim
+{
+
+using DetectedFormat = std::pair<std::string, std::vector<std::string>>;
+using Metadata = std::string;
+using Shape = std::vector<int64_t>;
+
+constexpr int64_t kWholeRange = -1;
+
+/**
+ *
+ * This class is used in both cases:
+ *   1. Specifying index for dimension string (e.g., "YXC" => Y:0, X:1, C:2)
+ *   2. Specifying index for read_region() (e.g., {{'C', -1}, {'T', 0}} => C:(whole range), T:0)
+ */
+class EXPORT_VISIBLE DimIndices
+{
+public:
+    DimIndices(const char* dims = nullptr);
+    DimIndices(std::vector<std::pair<char, int64_t>> init_list);
+    int64_t index(char dim_char) const;
+
+private:
+    io::format::DimIndicesDesc dim_indices_{ { -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
+                                               -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 } };
+};
+
+class EXPORT_VISIBLE ResolutionInfo
+{
+public:
+    ResolutionInfo(io::format::ResolutionInfoDesc desc);
+
+    uint16_t level_count() const;
+    const std::vector<int64_t>& level_dimensions() const;
+    std::vector<int64_t> level_dimension(uint16_t level) const;
+    const std::vector<float>& level_downsamples() const;
+    float level_downsample(uint16_t level) const;
+
+private:
+    uint16_t level_count_;
+    uint16_t level_ndim_;
+    std::vector<int64_t> level_dimensions_;
+    std::vector<float> level_downsamples_;
+};
+
+/**
+ * Detect available formats (plugins) from the input path.
+ *
+ * The plugin name can be used later to specify the plugin to load the image file explicitly.
+ *
+ * @param path An input path to detect available formats
+ * @return A tuple that describes the format (file format or format vendor) and the list of plugin names that
+ * supports the file
+ */
+DetectedFormat detect_format(filesystem::Path path);
+
+class EXPORT_VISIBLE CuImage : public std::enable_shared_from_this<CuImage>
+{
+public:
+    CuImage(const filesystem::Path& path);
+    CuImage(const filesystem::Path& path, const std::string& plugin_name);
+    CuImage(const CuImage& cuimg) = delete;
+    CuImage(CuImage&& cuimg);
+    CuImage(const CuImage* cuimg,
+            io::format::ImageMetadataDesc* image_metadata,
+            cucim::io::format::ImageDataDesc* image_data);
+
+    ~CuImage();
+
+    operator bool() const
+    {
+        return !!image_formats_ && !is_loaded_;
+    }
+
+    static Framework* get_framework();
+
+    filesystem::Path path() const;
+
+    bool is_loaded() const;
+
+    io::Device device() const;
+
+    Metadata raw_metadata() const;
+
+    Metadata metadata() const;
+
+    uint16_t ndim() const;
+
+    std::string dims() const;
+
+    Shape shape() const;
+
+    std::vector<int64_t> size(std::string dim_order = std::string{}) const;
+
+    DLDataType dtype() const;
+
+    std::vector<std::string> channel_names() const;
+
+    std::vector<float> spacing(std::string dim_order = std::string{}) const;
+
+    std::vector<std::string> spacing_units(std::string dim_order = std::string{}) const;
+
+    std::array<float, 3> origin() const;
+
+    std::array<std::array<float, 3>, 3> direction() const;
+
+    std::string coord_sys() const;
+
+    ResolutionInfo resolutions() const;
+
+    memory::DLTContainer container() const;
+
+    CuImage read_region(std::vector<int64_t> location,
+                        std::vector<int64_t> size,
+                        uint16_t level = 0,
+                        DimIndices region_dim_indices = {},
+                        io::Device device = "cpu",
+                        DLTensor* buf = nullptr,
+                        std::string shm_name = std::string{});
+
+    std::set<std::string> associated_images() const;
+    CuImage associated_image(const std::string& name) const;
+
+    void save(std::string file_path) const;
+
+private:
+    using Mutex = std::mutex;
+    using ScopedLock = std::scoped_lock<Mutex>;
+
+    explicit CuImage();
+
+    void ensure_init();
+    bool crop_image(io::format::ImageMetadataDesc* metadata,
+                    io::format::ImageReaderRegionRequestDesc* request,
+                    io::format::ImageDataDesc* out_image_data) const;
+
+
+    static Framework* framework_;
+
+    mutable Mutex mutex_;
+    cucim::io::format::IImageFormat* image_formats_ = nullptr;
+    CuCIMFileHandle file_handle_{};
+    io::format::ImageMetadataDesc* image_metadata_ = nullptr;
+    io::format::ImageDataDesc* image_data_ = nullptr;
+    bool is_loaded_ = false;
+    DimIndices dim_indices_{};
+    std::set<std::string> associated_images_;
+};
+
+
+} // namespace cucim
+
+
+#endif // CUCIM_CUIMAGE_H
diff --git a/cpp/include/cucim/dynlib/helper.h b/cpp/include/cucim/dynlib/helper.h
new file mode 100644
index 000000000..072b5067b
--- /dev/null
+++ b/cpp/include/cucim/dynlib/helper.h
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_DYNAMIC_LIBRARY_H
+#define CUCIM_DYNAMIC_LIBRARY_H
+
+// Copyright (c) 2019-2020, NVIDIA CORPORATION. All rights reserved.
+//
+// NVIDIA CORPORATION and its licensors retain all intellectual property
+// and proprietary rights in and to this software, related documentation
+// and any modifications thereto.  Any use, reproduction, disclosure or
+// distribution of this software and related documentation without an express
+// license agreement from NVIDIA CORPORATION is strictly prohibited.
+//
+#pragma once
+
+#include "../macros/defines.h"
+
+#include <string>
+
+#if CUCIM_PLATFORM_LINUX
+#    include <dlfcn.h>
+#else
+#    error "This platform is not supported!"
+#endif
+
+namespace cucim
+{
+namespace dynlib
+{
+
+#if CUCIM_PLATFORM_LINUX
+using LibraryHandle = void*;
+#else
+#    error "This platform is not supported!"
+#endif
+
+template <typename T>
+T get_library_symbol(LibraryHandle libHandle, const char* name)
+{
+#if CUCIM_PLATFORM_LINUX
+    return reinterpret_cast<T>(::dlsym(libHandle, name));
+#else
+#    error "This platform is not supported!"
+#endif
+}
+
+inline LibraryHandle load_library(const char* library_name)
+{
+#if CUCIM_PLATFORM_LINUX
+    LibraryHandle handle = dlopen(library_name, RTLD_LAZY);
+#else
+#    error "This platform is not supported!"
+#endif
+    return handle;
+}
+
+inline std::string get_last_load_library_error()
+{
+#if CUCIM_PLATFORM_LINUX
+    return dlerror();
+#else
+#    error "This platform is not supported!"
+#endif
+}
+
+inline void unload_library(LibraryHandle library_handle)
+{
+    if (library_handle)
+    {
+#if CUCIM_PLATFORM_LINUX
+        ::dlclose(library_handle);
+#else
+#    error "This platform is not supported!"
+#endif
+    }
+}
+
+} // namespace dynlib
+} // namespace cucim
+
+#endif // CUCIM_DYNAMIC_LIBRARY_H
diff --git a/cpp/include/cucim/filesystem/cufile_driver.h b/cpp/include/cucim/filesystem/cufile_driver.h
new file mode 100644
index 000000000..c5d9f5d9a
--- /dev/null
+++ b/cpp/include/cucim/filesystem/cufile_driver.h
@@ -0,0 +1,190 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_CUFILE_DRIVER_H
+#define CUCIM_CUFILE_DRIVER_H
+
+#include "file_handle.h"
+#include "file_path.h"
+#include <memory>
+#include <mutex>
+
+namespace cucim::filesystem
+{
+
+using Mutex = std::mutex;
+using ScopedLock = std::scoped_lock<Mutex>;
+
+
+// Forward declaration.
+class CuFileDriver;
+
+/**
+ * Open file with specific flags and mode.
+ *
+ * 'flags' can be one of the following flag string:
+ * - "r": O_RDONLY
+ * - "r+": O_RDWR
+ * - "w": O_RDWR | O_CREAT | O_TRUNC
+ * - "a": O_RDWR | O_CREAT
+ * In addition to above flags, the method append O_CLOEXEC and O_DIRECT by default.
+ *
+ * The following is optional flags that can be added to above string:
+ * - 'p': Use POSIX APIs only (first try to open with O_DIRECT). It does not use GDS.
+ * - 'n': Do not add O_DIRECT flag.
+ * - 'm': Use memory-mapped file. This flag is supported only for the read-only file descriptor.
+ *
+ * When 'm' is used, `PROT_READ` and `MAP_SHARED` are used for the parameter of mmap() function.
+ *
+ * @param file_path A file path to open.
+ * @param flags File flags in string. Default value is "r".
+ * @param mode A file mode. Default value is '0644'.
+ * @return a std::shared_ptr object of CuFileDriver.
+ */
+std::shared_ptr<CuFileDriver> EXPORT_VISIBLE open(const char* file_path, const char* flags = "r", mode_t mode = 0644);
+
+/**
+ * Open file with existing file descriptor.
+ *
+ * @param fd A file descriptor. To use GDS, fd needs to be opened with O_DIRECT flag.
+ * @param no_gds true if you do not want to use GDS. Default value is `false`.
+ * @param use_mmap true if you want to use memory-mapped IO. This flag is supported only for the read-only file descriptor. Default value is `false`.
+ * @return A std::shared_ptr object of CuFileDriver.
+ */
+std::shared_ptr<CuFileDriver> EXPORT_VISIBLE open(int fd, bool no_gds = false, bool use_mmap = false);
+
+/**
+ * Close the given file driver.
+ *
+ * @param fd A std::shared_ptr object of CuFileDriver.
+ * @return true if succeed, false otherwise.
+ */
+bool EXPORT_VISIBLE close(const std::shared_ptr<CuFileDriver>& fd);
+
+/**
+ * Read up to `count` bytes from file driver `fd` at offset `file_offset` (from the start of the file) into the buffer
+ * `buf` at offset `buf_offset`. The file offset is not changed.
+ *
+ * @param fd A std::shared_ptr object of CuFileDriver.
+ * @param buf A buffer where read bytes are stored. Buffer can be either in CPU memory or (CUDA) GPU memory.
+ * @param count The number of bytes to read.
+ * @param file_offset An offset from the start of the file.
+ * @param buf_offset An offset from the start of the buffer. Default value is 0.
+ * @return The number of bytes read if succeed, -1 otherwise.
+ */
+ssize_t EXPORT_VISIBLE pread(const std::shared_ptr<CuFileDriver>& fd, void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+
+/**
+ * Write up to `count` bytes from the buffer `buf` at offset `buf_offset` to the file driver `fd` at offset
+ * `file_offset` (from the start of the file). The file offset is not changed.
+ *
+ *
+ * @param fd A std::shared_ptr object of CuFileDriver.
+ * @param buf A buffer where write bytes come from. Buffer can be either in CPU memory or (CUDA) GPU memory.
+ * @param count The number of bytes to write.
+ * @param file_offset An offset from the start of the file.
+ * @param buf_offset An offset from the start of the buffer. Default value is 0.
+ * @return The number of bytes written if succeed, -1 otherwise.
+ */
+ssize_t EXPORT_VISIBLE pwrite(const std::shared_ptr<CuFileDriver>& fd, const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+
+/**
+ * Discard a system (page) cache for the given file path.
+ * @param file_path A file path to drop system cache.
+ * @return true if succeed, false otherwise.
+ */
+bool EXPORT_VISIBLE discard_page_cache(const char* file_path);
+
+class CuFileDriverInitializer
+{
+public:
+    CuFileDriverInitializer();
+
+    inline operator bool() const
+    {
+        return is_available_;
+    }
+    inline uint64_t max_device_cache_size()
+    {
+        return max_device_cache_size_;
+    }
+    inline uint64_t max_host_cache_size()
+    {
+        return max_host_cache_size_;
+    }
+
+    ~CuFileDriverInitializer();
+
+private:
+    bool is_available_ = false;
+    uint64_t max_device_cache_size_ = 0;
+    uint64_t max_host_cache_size_ = 0;
+};
+
+class CuFileDriverCache
+{
+public:
+    CuFileDriverCache();
+
+    void* device_cache();
+    void* host_cache();
+
+    inline bool is_device_cache_available()
+    {
+        return !!device_cache_;
+    }
+    inline bool is_host_cache_available()
+    {
+        return !!host_cache_;
+    }
+
+    ~CuFileDriverCache();
+
+private:
+    void* device_cache_ = nullptr;
+    void* device_cache_aligned_ = nullptr;
+    void* host_cache_ = nullptr;
+    void* host_cache_aligned_ = nullptr;
+};
+
+class EXPORT_VISIBLE CuFileDriver : public std::enable_shared_from_this<CuFileDriver>
+{
+public:
+    CuFileDriver() = delete;
+
+    CuFileDriver(int fd, bool no_gds = false, bool use_mmap = false, const char* file_path = nullptr);
+
+    ssize_t pread(void* buf, size_t count, off_t file_offset, off_t buf_offset = 0) const;
+    ssize_t pwrite(const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+
+    bool close();
+
+    filesystem::Path path() const;
+
+    ~CuFileDriver();
+
+private:
+    static Mutex driver_mutex_; // TODO: not used yet.
+
+    std::string file_path_;
+    size_t file_size_ = 0;
+    int file_flags_ = -1;
+    void* mmap_ptr_ = nullptr;
+    ::CuCIMFileHandle handle_;
+};
+
+} // namespace cucim::filesystem
+
+#endif // CUCIM_CUFILE_DRIVER_H
diff --git a/cpp/include/cucim/filesystem/file_handle.h b/cpp/include/cucim/filesystem/file_handle.h
new file mode 100644
index 000000000..c735fbc71
--- /dev/null
+++ b/cpp/include/cucim/filesystem/file_handle.h
@@ -0,0 +1,50 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_FILE_HANDLE_H
+#define CUCIM_FILE_HANDLE_H
+
+
+#include "../macros/defines.h"
+#include <cstdio>
+#include <cstdint>
+
+typedef void* CUfileHandle_t;
+
+enum class FileHandleType: uint16_t
+{
+    kUnknown = 0,
+    kPosix = 1,
+    kPosixODirect = 1 << 1,
+    kMemoryMapped = 1 << 2,
+    kGPUDirect = 1 << 3,
+};
+
+
+#if CUCIM_PLATFORM_LINUX
+struct CuCIMFileHandle
+{
+    int fd;
+    CUfileHandle_t cufile;
+    FileHandleType type; /// 1: POSIX, 2: POSIX+ODIRECT, 4: MemoryMapped, 8: GPUDirect
+    char* path;
+    void* client_data;
+};
+#else
+#    error "This platform is not supported!"
+#endif
+
+#endif // CUCIM_FILE_HANDLE_H
diff --git a/cpp/include/cucim/filesystem/file_path.h b/cpp/include/cucim/filesystem/file_path.h
new file mode 100644
index 000000000..6dc58ac29
--- /dev/null
+++ b/cpp/include/cucim/filesystem/file_path.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_FILE_PATH_H
+#define CUCIM_FILE_PATH_H
+
+#include <string>
+namespace cucim::filesystem
+{
+
+using Path = std::string;
+
+} // namespace cucim::filesystem
+
+#endif // CUCIM_FILE_PATH_H
diff --git a/cpp/include/cucim/io/device.h b/cpp/include/cucim/io/device.h
new file mode 100644
index 000000000..94e70a565
--- /dev/null
+++ b/cpp/include/cucim/io/device.h
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_DEVICE_H
+#define CUCIM_DEVICE_H
+
+#include <cucim/macros/api_header.h>
+#include <cstdint>
+#include <string>
+
+namespace cucim::io
+{
+
+using DeviceIndex = int16_t;
+
+/**
+ * Value for each device type follows https://github.com/dmlc/dlpack/blob/v0.3/include/dlpack/dlpack.h
+ * Naming convention follows PyTorch (torch/include/c10/core/DeviceType.h)
+ */
+enum class DeviceType : int16_t
+{
+    kCPU = 1,
+    kCUDA = 2,
+    kPinned = 3,
+
+    kCPUShared = 101, /// custom type for CPU-shared memory
+    kCUDAShared = 102, /// custom type for GPU-shared memory
+};
+
+// Make the following public libraries visible (default visibility) as this header's implementation is in device.cpp
+// and provided by cucim library.
+// Without that, a plugin module cannot find the definition of those methods when Device class is used in the plugin
+// module.
+class EXPORT_VISIBLE Device
+{
+public:
+    explicit Device();
+    Device(const Device& device);
+    explicit Device(const std::string& device_name);
+    Device(const char* device_name);
+    Device(DeviceType type, DeviceIndex index);
+    Device(DeviceType type, DeviceIndex index, const std::string& param);
+
+    static DeviceType parse_type(const std::string& device_name);
+    explicit operator std::string() const;
+
+    DeviceType type() const;
+    DeviceIndex index() const;
+    const std::string& shm_name() const;
+
+    void set_values(DeviceType type, DeviceIndex index = -1, const std::string& param = "");
+
+private:
+    DeviceType type_ = DeviceType::kCPU;
+    DeviceIndex index_ = -1;
+    std::string shm_name_; /// used for shared memory name
+
+    bool validate_device();
+};
+
+} // namespace cucim::io
+
+
+#endif // CUCIM_DEVICE_H
diff --git a/cpp/include/cucim/io/format/image_format.h b/cpp/include/cucim/io/format/image_format.h
new file mode 100644
index 000000000..2644a5562
--- /dev/null
+++ b/cpp/include/cucim/io/format/image_format.h
@@ -0,0 +1,261 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_IMAGE_FORMAT_H
+#define CUCIM_IMAGE_FORMAT_H
+
+#include "cucim/core/interface.h"
+#include "cucim/filesystem/file_handle.h"
+#include "dlpack/dlpack.h"
+
+#include <memory_resource>
+#include <string>
+
+
+namespace cucim::io::format
+{
+
+struct DimIndicesDesc
+{
+    int64_t indices[26]; /// Indices for each alphabet ('A'= 0, 'Z'= 25)
+};
+
+struct ResolutionInfoDesc
+{
+    uint16_t level_count;
+    uint16_t level_ndim;
+    int64_t* level_dimensions;
+    float* level_downsamples;
+};
+
+struct AssociatedImageInfoDesc
+{
+    uint16_t image_count;
+    char** image_names;
+};
+
+struct ImageMetadataDesc
+{
+    void* handle; /// Handle for ImageMetadata object
+    uint16_t ndim; /// Number of dimensions
+    const char* dims; /// Dimension characters (E.g., "STCZYX")
+    int64_t* shape; /// Size of each dimension
+    DLDataType dtype; /// Data type of the array
+    char** channel_names; /// Channel name list   TODO: 'S', 'T', and other dimension can have names so need to be
+                                /// generalized.
+    float* spacing; /// Physical size
+    char** spacing_units; /// Units for each spacing element (size is same with `ndim`)
+    float* origin; /// Physical location of (0, 0, 0) (size is always 3)
+    float* direction; /// Direction cosines (size is always 3x3)
+    const char* coord_sys; /// The coordinate frame in which the direction cosines are measured (either
+                           /// 'LPS'(ITK/DICOM) or 'RAS'(NIfTI/3D Slicer))
+    ResolutionInfoDesc resolution_info; /// Resolution information
+    AssociatedImageInfoDesc associated_image_info; /// Associated image information
+    const char* raw_data; /// Metadata in text format from the original image
+    char* json_data; /// cucim & vendor's metadata in JSON format. Will be merged with above standard metadata. Memory
+                     /// for this needs to be released manually.
+};
+
+// Without raw_data and json_data, metadata size is approximately 1104 bytes.
+// It might be good to allocate 4k for that.
+constexpr size_t IMAGE_METADATA_BUFFER_SIZE = 4096;
+class EXPORT_VISIBLE ImageMetadata
+{
+public:
+    ImageMetadata();
+    ~ImageMetadata();
+    void* allocate(size_t size);
+    std::pmr::monotonic_buffer_resource& get_resource();
+    constexpr uint8_t* get_buffer()
+    {
+        return buffer_.data();
+    }
+
+    ImageMetadataDesc& desc();
+
+    ImageMetadata& ndim(uint16_t ndim);
+    ImageMetadata& dims(const std::string_view& dims);
+    ImageMetadata& shape(const std::pmr::vector<int64_t>& shape);
+    ImageMetadata& dtype(const DLDataType& dtype);
+    ImageMetadata& channel_names(const std::pmr::vector<std::string_view>& channel_names);
+
+    ImageMetadata& spacing(const std::pmr::vector<float>& spacing);
+    ImageMetadata& spacing_units(const std::pmr::vector<std::string_view>& spacing_units);
+
+    ImageMetadata& origin(const std::pmr::vector<float>& origin);
+    ImageMetadata& direction(const std::pmr::vector<float>& direction);
+    ImageMetadata& coord_sys(const std::string_view& coord_sys);
+
+    // ResolutionInfoDesc
+    ImageMetadata& level_count(uint16_t level_count);
+    ImageMetadata& level_ndim(uint16_t level_ndim);
+    ImageMetadata& level_dimensions(const std::pmr::vector<int64_t>& level_dimensions);
+    ImageMetadata& level_downsamples(const std::pmr::vector<float>& level_downsamples);
+
+    // AssociatedImageInfoDesc
+    ImageMetadata& image_count(uint16_t image_count);
+    ImageMetadata& image_names(const std::pmr::vector<std::string_view>& image_names);
+
+    ImageMetadata& raw_data(const std::string_view& raw_data);
+    ImageMetadata& json_data(const std::string_view& json_data);
+
+private:
+    ImageMetadataDesc desc_{};
+    std::array<uint8_t, IMAGE_METADATA_BUFFER_SIZE> buffer_{};
+    std::pmr::monotonic_buffer_resource res_{ buffer_.data(), sizeof(buffer_) };
+
+// manylinux2014 requires gcc4-compatible libstdcxx-abi(gcc is configured with
+// '--with-default-libstdcxx-abi=gcc4-compatible', https://gcc.gnu.org/onlinedocs/libstdc++/manual/configure.html) which
+// forces to set _GLIBCXX_USE_CXX11_ABI=0 so std::pmr::string wouldn't be available on CentOS 7.
+#if _GLIBCXX_USE_CXX11_ABI
+    std::pmr::string dims_{ &res_ };
+    std::pmr::vector<int64_t> shape_{ &res_ };
+    std::pmr::vector<std::pmr::string> channel_names_{ &res_ };
+    std::pmr::vector<float> spacing_{ &res_ };
+    std::pmr::vector<std::pmr::string> spacing_units_{ &res_ };
+    std::pmr::vector<float> origin_{ &res_ };
+    std::pmr::vector<float> direction_{ &res_ };
+    std::pmr::string coord_sys_{ &res_ };
+
+    std::pmr::vector<int64_t> level_dimensions_{ &res_ };
+    std::pmr::vector<float> level_downsamples_{ &res_ };
+
+    std::pmr::vector<std::pmr::string> image_names_{ &res_ };
+#else
+    std::string dims_;
+    std::pmr::vector<int64_t> shape_{ &res_ };
+    std::pmr::vector<std::string> channel_names_{ &res_ };
+    std::pmr::vector<float> spacing_{ &res_ };
+    std::pmr::vector<std::string> spacing_units_{ &res_ };
+    std::pmr::vector<float> origin_{ &res_ };
+    std::pmr::vector<float> direction_{ &res_ };
+    std::string coord_sys_;
+
+    std::pmr::vector<int64_t> level_dimensions_{ &res_ };
+    std::pmr::vector<float> level_downsamples_{ &res_ };
+
+    std::pmr::vector<std::string> image_names_{ &res_ };
+#endif
+    // Memory for raw_data and json_data needs to be created with cucim_malloc();
+};
+
+struct ImageDataDesc
+{
+    DLTensor container;
+};
+
+struct ImageCheckerDesc
+{
+    size_t header_start_offset; /// Start offset to look at the image header
+    size_t header_read_size; /// Number of bytes from the start offset, needed to check image format
+    /**
+     * Returns true if the given file is valid for the format
+     * @param file_name
+     * @param buf
+     * @return
+     */
+    bool(CUCIM_ABI* is_valid)(const char* file_name, const char* buf);
+};
+
+struct ImageParserDesc
+{
+    /**
+     *
+     * @param file_path
+     * @return
+     */
+    CuCIMFileHandle(CUCIM_ABI* open)(const char* file_path);
+
+    /**
+     *
+     * @param handle
+     * @param out_metadata
+     * @return
+     */
+    bool(CUCIM_ABI* parse)(CuCIMFileHandle* handle, ImageMetadataDesc* out_metadata);
+
+    /**
+     *
+     * @param handle
+     * @return
+     */
+    bool(CUCIM_ABI* close)(CuCIMFileHandle* handle);
+};
+
+struct ImageReaderRegionRequestDesc
+{
+    int64_t* location;
+    int64_t* size;
+    uint16_t level;
+    DimIndicesDesc region_dim_indices;
+    char* associated_image_name;
+    char* device;
+    DLTensor* buf;
+    char* shm_name;
+};
+
+struct ImageReaderDesc
+{
+    /**
+     *
+     * @param handle
+     * @param metadata
+     * @param out_image_data
+     * @param out_image_metadata needed for associated_image
+     * @return
+     */
+    bool(CUCIM_ABI* read)(const CuCIMFileHandle* handle,
+                          const ImageMetadataDesc* metadata,
+                          const ImageReaderRegionRequestDesc* request,
+                          ImageDataDesc* out_image_data,
+                          ImageMetadataDesc* out_metadata);
+};
+
+struct ImageWriterDesc
+{
+    /**
+     *
+     * @param handle
+     * @param metadata
+     * @param image_data
+     * @return
+     */
+    bool(CUCIM_ABI* write)(const CuCIMFileHandle* handle,
+                           const ImageMetadataDesc* metadata,
+                           const ImageDataDesc* image_data);
+};
+
+struct ImageFormatDesc
+{
+    void(CUCIM_ABI* set_enabled)(bool val); /// Sets if this format will be used in cucim (default: true).
+    bool(CUCIM_ABI* is_enabled)(); /// true if this format is used when checking image compatibility.
+    const char*(CUCIM_ABI* get_format_name)(); /// Returns the name of this format.
+    ImageCheckerDesc image_checker;
+    ImageParserDesc image_parser;
+    ImageReaderDesc image_reader;
+    ImageWriterDesc image_writer;
+};
+
+struct IImageFormat
+{
+    CUCIM_PLUGIN_INTERFACE("cucim::io::IImageFormat", 0, 1)
+    ImageFormatDesc* formats;
+    size_t format_count;
+};
+
+} // namespace cucim::io::format
+
+#endif // CUCIM_IMAGE_FORMAT_H
diff --git a/cpp/include/cucim/logger/logger.h b/cpp/include/cucim/logger/logger.h
new file mode 100644
index 000000000..8235f6f9a
--- /dev/null
+++ b/cpp/include/cucim/logger/logger.h
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_LOGGER_H
+#define CUCIM_LOGGER_H
+
+#endif // CUCIM_LOGGER_H
diff --git a/cpp/include/cucim/logger/timer.h b/cpp/include/cucim/logger/timer.h
new file mode 100644
index 000000000..179f28732
--- /dev/null
+++ b/cpp/include/cucim/logger/timer.h
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_TIMER_H
+#define CUCIM_TIMER_H
+
+#include "cucim/macros/defines.h"
+
+#include <chrono>
+
+namespace cucim::logger
+{
+class EXPORT_VISIBLE Timer
+{
+public:
+    Timer(const char* message, bool auto_start = true, bool auto_output = true);
+    void start();
+    double stop();
+    double elapsed_time();
+    void print(const char* message = nullptr);
+    ~Timer();
+
+private:
+    const char* message_ = nullptr;
+    bool is_auto_output_ = false;
+    double elapsed_seconds_ = -1;
+    std::chrono::time_point<std::chrono::system_clock> start_{};
+    std::chrono::time_point<std::chrono::system_clock> end_{};
+};
+
+} // namespace cucim::logger
+
+
+#endif // CUCIM_TIMER_H
diff --git a/cpp/include/cucim/macros/api_header.h b/cpp/include/cucim/macros/api_header.h
new file mode 100644
index 000000000..2784cd1f8
--- /dev/null
+++ b/cpp/include/cucim/macros/api_header.h
@@ -0,0 +1,96 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_API_H
+#define CUCIM_API_H
+
+#if defined(__linux__)
+#    define CUCIM_PLATFORM_LINUX 1
+#    define CUCIM_PLATFORM_WINDOWS 0
+#elif _WIN32
+#    define CUCIM_PLATFORM_LINUX 0
+#    define CUCIM_PLATFORM_WINDOWS 1
+#else
+#    error "This platform is not supported!"
+#endif
+
+#if CUCIM_PLATFORM_WINDOWS
+#    define CUCIM_ABI __cdecl
+#else
+#    define CUCIM_ABI
+#endif
+
+//#ifdef CARB_EXPORTS
+//#    ifdef __cplusplus
+//#        define CARB_EXPORT_C extern "C"
+//#    else
+//#        define CARB_EXPORT_C
+//#    endif
+//
+//#    undef CARB_EXPORT
+//#    define CARB_EXPORT CARB_EXPORT_C CARB_DECLSPEC(dllexport) CARB_ATTRIBUTE(visibility("default"))
+//#else
+//#    undef CARB_EXPORT
+//#    define CARB_EXPORT extern "C"
+//#endif
+
+#ifndef EXPORT_VISIBLE
+#   define EXPORT_VISIBLE __attribute__((visibility("default")))
+#endif
+#ifndef EXPORT_HIDDEN
+#   define EXPORT_HIDDEN __attribute__((visibility("hidden")))
+#endif
+
+#ifdef CUCIM_STATIC_DEFINE
+#    define CUCIM_API
+#    define CUCIM_NO_EXPORT
+#else
+#    ifdef __cplusplus
+#        define CUCIM_EXPORT_C extern "C"
+#    else
+#        define CUCIM_EXPORT_C
+#    endif
+#    ifdef CUCIM_EXPORTS
+#        undef CUCIM_API
+#        define CUCIM_API CUCIM_EXPORT_C EXPORT_VISIBLE
+#    else
+#        undef CUCIM_API
+#        define CUCIM_API CUCIM_EXPORT_C
+#    endif
+#    ifndef CUCIM_NO_EXPORT
+#        define CUCIM_NO_EXPORT EXPORT_HIDDEN
+#    endif
+#endif
+
+#ifndef CUCIM_DEPRECATED
+#    define CUCIM_DEPRECATED __attribute__((__deprecated__))
+#endif
+
+#ifndef CUCIM_DEPRECATED_EXPORT
+#    define CUCIM_DEPRECATED_EXPORT CUCIM_API CUCIM_DEPRECATED
+#endif
+
+#ifndef CUCIM_DEPRECATED_NO_EXPORT
+#    define CUCIM_DEPRECATED_NO_EXPORT CUCIM_NO_EXPORT CUCIM_DEPRECATED
+#endif
+
+#if 0 /* DEFINE_NO_DEPRECATED */
+#    ifndef CUCIM_NO_DEPRECATED
+#        define CUCIM_NO_DEPRECATED
+#    endif
+#endif
+
+#endif /* CUCIM_API_H */
diff --git a/cpp/include/cucim/macros/defines.h b/cpp/include/cucim/macros/defines.h
new file mode 100644
index 000000000..53f09ee39
--- /dev/null
+++ b/cpp/include/cucim/macros/defines.h
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_DEFINES_H
+#define CUCIM_DEFINES_H
+
+#include "cucim/macros/api_header.h"
+
+
+/*******************************************************************************
+ Platform-related definitions
+*******************************************************************************/
+
+
+/*******************************************************************************
+ Memory-related definitions
+*******************************************************************************/
+
+#define CUCIM_ALIGN_AS(T) alignas(T)
+
+
+/*******************************************************************************
+ Debug-related definitions
+*******************************************************************************/
+
+#if CUCIM_PLATFORM_LINUX
+#    include <signal.h>
+#    define CUCIM_BREAK_POINT() ::raise(SIGTRAP)
+#elif CUCIM_PLATFORM_WINDOWS
+#    define CUCIM_BREAK_POINT() ::__debugbreak()
+#else
+#    error "This platform is not supported!"
+#endif
+
+#define CUCIM_CHECK_ENABLED 1
+#define CUCIM_CHECK(cond, ...) ((void)0)
+
+
+#if CUCIM_DEBUG
+#    define CUCIM_ASSERT_ENABLED 1
+#    define CUCIM_ASSERT(cond, ...) CUCIM_CHECK(cond, ##__VA_ARGS__)
+#else
+#    define CUCIM_ASSERT_ENABLED 0
+#    define CUCIM_ASSERT(cond, ...) (void)0;
+#endif
+
+
+#include <cstdio>
+#include <cinttypes>
+#define CUCIM_LOG_VERBOSE(fmt, ...) ::fprintf(stderr, fmt "\n", ##__VA_ARGS__)
+#define CUCIM_LOG_INFO(fmt, ...) ::fprintf(stderr, fmt "\n", ##__VA_ARGS__)
+#define CUCIM_LOG_WARN(fmt, ...) ::fprintf(stderr, fmt "\n", ##__VA_ARGS__)
+#define CUCIM_LOG_ERROR(fmt, ...) ::fprintf(stderr, fmt "\n", ##__VA_ARGS__)
+#define CUCIM_LOG_FATAL(fmt, ...) ::fprintf(stderr, fmt "\n", ##__VA_ARGS__)
+
+#include <exception>
+#define CUCIM_ERROR(fmt, ...)                                                                                        \
+    do                                                                                                                 \
+    {                                                                                                                  \
+        ::fprintf(stderr, fmt "\n", ##__VA_ARGS__);                                                                    \
+        throw std::runtime_error("Error!");                                                                            \
+    } while (0)
+
+// Check float type size
+#include <climits>
+static_assert(sizeof(float) * CHAR_BIT == 32, "float data type is not 32 bits!");
+
+#endif // CUCIM_DEFINES_H
diff --git a/cpp/include/cucim/memory/dlpack.h b/cpp/include/cucim/memory/dlpack.h
new file mode 100644
index 000000000..bacc513d5
--- /dev/null
+++ b/cpp/include/cucim/memory/dlpack.h
@@ -0,0 +1,116 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_DLPACK_H
+#define CUCIM_DLPACK_H
+
+#include "dlpack/dlpack.h"
+#include <fmt/format.h>
+
+namespace cucim::memory
+{
+
+class DLTContainer
+{
+public:
+    DLTContainer() = delete;
+    DLTContainer(DLTensor* handle) : tensor_(handle)
+    {
+    }
+
+    /**
+     * Returns a string providing the basic type of the homogenous array in NumPy.
+     *
+     * Note: This method assumes little-endian for now.
+     *
+     * @return A const character pointer that represents a string
+     */
+    const char* numpy_dtype() {
+        // TODO: consider bfloat16: https://github.com/dmlc/dlpack/issues/45
+        // TODO: consider other byte-order
+        if (!tensor_) {
+            return "";
+        }
+
+        const DLDataType& dtype = tensor_->dtype;
+        uint8_t code = dtype.code;
+        uint8_t bits = dtype.bits;
+        switch(code) {
+
+        case kDLInt:
+            switch(bits) {
+            case 8:
+                return "|i1";
+            case 16:
+                return "<i2";
+            case 32:
+                return "<i4";
+            case 64:
+                return "<i8";
+            }
+            throw std::logic_error(fmt::format("DLDataType(code: kDLInt, bits: {}) is not supported!", bits));
+        case kDLUInt:
+            switch(bits) {
+            case 8:
+                return "|u1";
+            case 16:
+                return "<u2";
+            case 32:
+                return "<u4";
+            case 64:
+                return "<u8";
+            }
+            throw std::logic_error(fmt::format("DLDataType(code: kDLUInt, bits: {}) is not supported!", bits));
+        case kDLFloat:
+            switch(bits) {
+            case 16:
+                return "<f2";
+            case 32:
+                return "<f4";
+            case 64:
+                return "<f8";
+            }
+            break;
+        case kDLBfloat:
+            throw std::logic_error(fmt::format("DLDataType(code: kDLBfloat, bits: {}) is not supported!", bits));
+        }
+        throw std::logic_error(fmt::format("DLDataType(code: {}, bits: {}) is not supported!", code, bits));
+    }
+
+    operator DLTensor() const
+    {
+        if (tensor_)
+        {
+            return *tensor_;
+        }
+        else
+        {
+            return DLTensor{};
+        }
+    }
+
+    operator DLTensor*() const
+    {
+        return tensor_;
+    }
+
+private:
+    DLTensor* tensor_;
+};
+
+} // namespace cucim::memory
+
+#endif // CUCIM_DLPACK_H
diff --git a/cpp/include/cucim/memory/memory_manager.h b/cpp/include/cucim/memory/memory_manager.h
new file mode 100644
index 000000000..739735f9b
--- /dev/null
+++ b/cpp/include/cucim/memory/memory_manager.h
@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+//
+#ifndef CUCIM_MEMORY_MANAGER_H
+#define CUCIM_MEMORY_MANAGER_H
+
+#include "cucim/io/device.h"
+
+#include <cucim/macros/api_header.h>
+#include <cstddef>
+
+/**
+ * Host memory allocator for exchanged data
+ * @param size Number of bytes to allocate
+ * @return Pointer to the allocated memory
+ */
+CUCIM_API void* cucim_malloc(size_t size);
+
+/**
+ * Free allocated memory by cucim_malloc
+ * @param Pointer to the allocated memory
+ */
+CUCIM_API void cucim_free(void* ptr);
+
+namespace cucim
+{
+namespace memory
+{
+
+/**
+ * Pointer attributes
+ */
+struct PointerAttributes
+{
+    /**
+     * The type of device
+     */
+    cucim::io::Device device{};
+
+    /**
+     * The address which may be dereferenced on the current device to access
+     * the memory or nullptr if no such address exists.
+     */
+    void* ptr = nullptr;
+};
+
+/**
+ * A wrapper for cudaPointerGetAttributes() in CUDA.
+ *
+ * Instead of cudaPointerAttributes
+ *
+ * @param ptr Pointer to the allocated memory
+ * @return Pointer attribute information in 'PointerAttributes' struct
+ */
+CUCIM_API void get_pointer_attributes(PointerAttributes& attr, const void* ptr);
+
+
+} // namespace memory
+} // namespace cucim
+#endif // CUCIM_MEMORY_MANAGER_H
diff --git a/cpp/plugins/cucim.kit.cuslide/.clang-format b/cpp/plugins/cucim.kit.cuslide/.clang-format
new file mode 100644
index 000000000..bcadc9d0b
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.clang-format
@@ -0,0 +1,86 @@
+AccessModifierOffset: -4
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlinesLeft: false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortFunctionsOnASingleLine: false
+AllowShortIfStatementsOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine : false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterDefinitionReturnType: false
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: true
+BinPackParameters: false
+BreakBeforeBinaryOperators: false
+BreakBeforeBraces: Custom
+BraceWrapping:
+  AfterClass: true
+  AfterControlStatement: true
+  AfterEnum: true
+  AfterFunction: true
+  AfterNamespace: true
+  AfterObjCDeclaration: true
+  AfterStruct: true
+  AfterUnion: true
+  AfterExternBlock: true
+  BeforeCatch: true
+  BeforeElse: true
+  IndentBraces: false
+  SplitEmptyFunction: true
+  SplitEmptyRecord: true
+  SplitEmptyNamespace : true
+BreakBeforeTernaryOperators: false
+BreakConstructorInitializersBeforeComma: false
+BreakStringLiterals: false
+ColumnLimit: 120
+CommentPragmas: ''
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: false
+DerivePointerBinding: false
+FixNamespaceComments: true
+IndentCaseLabels: false
+IndentPPDirectives: AfterHash
+IndentFunctionDeclarationAfterType: false
+IndentWidth: 4
+SortIncludes: false
+IncludeCategories:
+  - Regex:           '[<"](.*\/)?Defines.h[>"]'
+    Priority:        1
+#  - Regex:           '<cuslide\/.+>'
+#    Priority:        3
+  - Regex:           '<[[:alnum:]_.]+>'
+    Priority:        5
+  - Regex:           '<[[:alnum:]_.\/]+>'
+    Priority:        4
+  - Regex:           '".*"'
+    Priority:        2
+IncludeBlocks: Regroup
+Language: Cpp
+MaxEmptyLinesToKeep: 2
+NamespaceIndentation: None
+ObjCSpaceAfterProperty: true
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakBeforeFirstCallParameter: 0
+PenaltyBreakComment: 1
+PenaltyBreakFirstLessLess: 0
+PenaltyBreakString: 1
+PenaltyExcessCharacter: 10
+PenaltyReturnTypeOnItsOwnLine: 1000
+PointerAlignment: Left
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles: false
+SpacesInCStyleCastParentheses: false
+SpacesInContainerLiterals: false
+SpacesInParentheses: false
+Standard: Cpp11
+ReflowComments: true
+TabWidth: 4
+UseTab: Never
diff --git a/cpp/plugins/cucim.kit.cuslide/.editorconfig b/cpp/plugins/cucim.kit.cuslide/.editorconfig
new file mode 100644
index 000000000..c69a96fa2
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.editorconfig
@@ -0,0 +1,7 @@
+[*]
+indent_style = space
+indent_size = 4
+charset = utf-8
+trim_trailing_whitespace = true
+max_line_length = 120
+insert_final_newline = true
diff --git a/cpp/plugins/cucim.kit.cuslide/.gitignore b/cpp/plugins/cucim.kit.cuslide/.gitignore
new file mode 100644
index 000000000..f593ea4f4
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.gitignore
@@ -0,0 +1,3 @@
+cmake-build*
+install
+
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/.gitignore b/cpp/plugins/cucim.kit.cuslide/.idea/.gitignore
new file mode 100644
index 000000000..73f69e095
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/.name b/cpp/plugins/cucim.kit.cuslide/.idea/.name
new file mode 100644
index 000000000..2d6ff4e37
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/.name
@@ -0,0 +1 @@
+cuslide
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/Project.xml b/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/Project.xml
new file mode 100644
index 000000000..f60388162
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/Project.xml
@@ -0,0 +1,7 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <clangFormatSettings>
+      <option name="ENABLED" value="true" />
+    </clangFormatSettings>
+  </code_scheme>
+</component>
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/codeStyleConfig.xml b/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/codeStyleConfig.xml
new file mode 100644
index 000000000..79ee123c2
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/codeStyles/codeStyleConfig.xml
@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
+  </state>
+</component>
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/cucim.kit.cuslide.iml b/cpp/plugins/cucim.kit.cuslide/.idea/cucim.kit.cuslide.iml
new file mode 100644
index 000000000..f08604bb6
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/cucim.kit.cuslide.iml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module classpath="CMake" type="CPP_MODULE" version="4" />
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
new file mode 100644
index 000000000..7272e0dec
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
@@ -0,0 +1,14 @@
+#
+# Copyright (c) $YEAR, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
new file mode 100644
index 000000000..b0e223c0a
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
@@ -0,0 +1,15 @@
+/*
+ * Copyright (c) $YEAR, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Header File.h b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Header File.h
new file mode 100644
index 000000000..9cb1d09e2
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Header File.h	
@@ -0,0 +1,5 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Source File.c b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Source File.c
new file mode 100644
index 000000000..b04dd6c62
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C Source File.c	
@@ -0,0 +1,4 @@
+#parse("NVIDIA_C_HEADER.h")
+#if (${HEADER_FILENAME})
+#[[#include]]# "${HEADER_FILENAME}"
+#end
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class Header.h b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class Header.h
new file mode 100644
index 000000000..f521fa555
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class Header.h	
@@ -0,0 +1,13 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+${NAMESPACES_OPEN}
+
+class ${NAME} {
+
+};
+
+${NAMESPACES_CLOSE}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class.cc b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class.cc
new file mode 100644
index 000000000..42f43ccf4
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/C++ Class.cc	
@@ -0,0 +1,2 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#include]]# "${HEADER_FILENAME}"
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/CMakeLists.txt.cmake b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/CMakeLists.txt.cmake
new file mode 100644
index 000000000..d71d94dba
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/fileTemplates/internal/CMakeLists.txt.cmake
@@ -0,0 +1 @@
+#parse("NVIDIA_CMAKE_HEADER.cmake")
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/misc.xml b/cpp/plugins/cucim.kit.cuslide/.idea/misc.xml
new file mode 100644
index 000000000..8822db8f1
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/misc.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
+  <component name="JavaScriptSettings">
+    <option name="languageLevel" value="ES6" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/.idea/vcs.xml b/cpp/plugins/cucim.kit.cuslide/.idea/vcs.xml
new file mode 100644
index 000000000..c2365ab11
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../../.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/CMakeLists.txt b/cpp/plugins/cucim.kit.cuslide/CMakeLists.txt
new file mode 100644
index 000000000..a642e177d
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/CMakeLists.txt
@@ -0,0 +1,325 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# CUDA_STANDARD 17 is supported from CMAKE 3.18
+# : https://cmake.org/cmake/help/v3.18/prop_tgt/CUDA_STANDARD.html
+cmake_minimum_required(VERSION 3.18)
+
+################################################################################
+# Prerequisite statements
+################################################################################
+
+# Set VERSION
+unset(VERSION CACHE)
+file(STRINGS ${CMAKE_CURRENT_LIST_DIR}/VERSION VERSION)
+
+# Append local cmake module path
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/modules")
+
+project(cuslide VERSION ${VERSION} DESCRIPTION "cuslide" LANGUAGES CXX CUDA)
+set(CUCIM_PLUGIN_NAME "cucim.kit.cuslide")
+
+################################################################################
+# Include utilities
+################################################################################
+include(SuperBuildUtils)
+include(CuCIMUtils)
+
+################################################################################
+# Set cmake policy
+################################################################################
+if(${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.19")
+    cmake_policy(SET CMP0110 NEW) # For add_test() to support arbitrary characters in test name
+endif()
+
+################################################################################
+# Basic setup
+################################################################################
+
+# Set default build type
+set(DEFAULT_BUILD_TYPE "Release")
+if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    message(STATUS "Setting build type to '${DEFAULT_BUILD_TYPE}' as none was specified.")
+    set(CMAKE_BUILD_TYPE "${DEFAULT_BUILD_TYPE}" CACHE STRING "Choose the type of build." FORCE)
+    # Set the possible values of build type for cmake-gui
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif ()
+
+# Set default output directories
+if (NOT CMAKE_ARCHIVE_OUTPUT_DIRECTORY)
+    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif()
+if (NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif()
+if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/bin")
+endif()
+
+# Find CUDAToolkit as rmm depends on it
+find_package(CUDAToolkit REQUIRED)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CUDA_STANDARD 17) # Clion issue: https://youtrack.jetbrains.com/issue/CPP-19165 (fixed)
+set(CMAKE_CUDA_STANDARD_REQUIRED YES)
+set(CMAKE_CXX_STANDARD_REQUIRED YES)
+cucim_define_cuda_architectures(60;70;75;80;86)
+# https://github.com/Kitware/CMake/blob/master/Modules/Compiler/NVIDIA-CUDA.cmake#L11
+# https://gitlab.kitware.com/cmake/cmake/-/issues/19017
+# For CUDA >= 10.2, we cannot use --compiler-options as '-forward-unknown-to-host-compiler' would be added by default to nvcc options.
+# For the reason, we add "${CMAKE_CXX_FLAGS}" instead of "--compiler-options ${CMAKE_CXX_FLAGS}" here.
+# ==> We changed to use "${CMAKE_CUDA_FLAGS}" instead of "${CMAKE_CXX_FLAGS}" ${CMAKE_CXX_FLAGS} can have wrong options such as '-march=nocona'
+set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -use_fast_math -Xptxas=-v")
+set(CMAKE_CUDA_FLAGS_DEBUG "${CMAKE_CUDA_FLAGS_DEBUG} -G")
+set(CMAKE_CUDA_FLAGS_RELEASE "${CMAKE_CUDA_FLAGS_RELEASE} -lineinfo")
+set(CMAKE_CUDA_FLAGS_RELWITHDEBINFO "${CMAKE_CUDA_FLAGS_RELWITHDEBINFO} -lineinfo")
+
+# Include CUDA headers explicitly for VSCode intelli-sense
+include_directories(AFTER SYSTEM ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
+
+# Disable visibility to not expose unnecessary symbols
+set(CMAKE_CXX_VISIBILITY_PRESET hidden)
+set(CMAKE_VISIBILITY_INLINES_HIDDEN YES)
+
+# Set RPATH
+if (NOT APPLE)
+    set(CMAKE_INSTALL_RPATH $ORIGIN)
+endif()
+
+# Set Installation setup
+if (NOT CMAKE_INSTALL_PREFIX)
+    set(CMAKE_INSTALL_PREFIX ${CMAKE_CURRENT_LIST_DIR}/install) # CACHE PATH "install here" FORCE)
+endif ()
+
+include(GNUInstallDirs)
+# Force to set CMAKE_INSTALL_LIBDIR to lib as the library can be built with Cent OS ('lib64' is set) and
+# /usr/local/lib64 or /usr/local/lib is not part of ld.so.conf* (`cat /etc/ld.so.conf.d/* | grep lib64`)
+# https://gitlab.kitware.com/cmake/cmake/-/issues/20565
+set(CMAKE_INSTALL_LIBDIR lib)
+
+include(ExternalProject)
+
+################################################################################
+# Options
+################################################################################
+
+# Setup CXX11 ABI
+# : Adds CXX11 ABI definition to the compiler command line for targets in the current directory,
+#   whether added before or after this command is invoked, and for the ones in sub-directories added after.
+add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) # TODO: create two library, one with CXX11 ABI and one without it.
+
+################################################################################
+# Define dependencies
+################################################################################
+#superbuild_depend(cucim)
+superbuild_depend(fmt)
+superbuild_depend(libjpeg-turbo) # libjpeg-turbo should be located before libtiff as libtiff depends on libjpeg-turbo
+superbuild_depend(libtiff)
+superbuild_depend(gds)
+superbuild_depend(catch2)
+superbuild_depend(openslide)
+superbuild_depend(googletest)
+superbuild_depend(googlebenchmark)
+superbuild_depend(cli11)
+superbuild_depend(pugixml)
+superbuild_depend(json)
+superbuild_depend(libdeflate)
+
+################################################################################
+# Find cucim package
+################################################################################
+if (NOT CUCIM_SDK_PATH)
+    get_filename_component(CUCIM_SDK_PATH "${CMAKE_SOURCE_DIR}/../../.." ABSOLUTE)
+    message("CUCIM_SDK_PATH is not set. Using '${CUCIM_SDK_PATH}'")
+else()
+    message("CUCIM_SDK_PATH is set to ${CUCIM_SDK_PATH}")
+endif()
+
+find_package(cucim CONFIG REQUIRED
+    HINTS ${CUCIM_SDK_PATH}/install/${CMAKE_INSTALL_LIBDIR}/cmake/cucim
+          $ENV{PREFIX}/include/cmake/cucim # In case conda build is used
+    )
+
+
+################################################################################
+# Define compile options
+################################################################################
+
+if(NOT BUILD_SHARED_LIBS)
+    set(BUILD_SHARED_LIBS ON)
+endif()
+
+################################################################################
+# Add library: cucim
+################################################################################
+
+# Add library
+add_library(${CUCIM_PLUGIN_NAME}
+    src/cuslide/cuslide.cpp
+    src/cuslide/cuslide.h
+    src/cuslide/deflate/deflate.cpp
+    src/cuslide/deflate/deflate.h
+    src/cuslide/jpeg/libjpeg_turbo.cpp
+    src/cuslide/jpeg/libjpeg_turbo.h
+    src/cuslide/tiff/ifd.cpp
+    src/cuslide/tiff/ifd.h
+    src/cuslide/tiff/tiff.cpp
+    src/cuslide/tiff/tiff.h
+    src/cuslide/tiff/types.h)
+
+# At least one file needs to be compiled with nvcc.
+# Otherwise, it will cause `/usr/bin/ld: cannot find -lcudart` error message.
+# Note: Since cuslide.cpp is using nlohmann/json.hpp which nvcc cannot parse properly, set other files for cudart.
+set_source_files_properties(src/cuslide/jpeg/libjpeg_turbo.cpp PROPERTIES LANGUAGE CUDA)
+
+# Compile options
+set_target_properties(${CUCIM_PLUGIN_NAME}
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+        SOVERSION ${PROJECT_VERSION_MAJOR}
+        VERSION ${PROJECT_VERSION}
+)
+target_compile_features(${CUCIM_PLUGIN_NAME} PRIVATE cxx_std_17)
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(${CUCIM_PLUGIN_NAME} PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+
+# Link libraries
+target_link_libraries(${CUCIM_PLUGIN_NAME}
+        PRIVATE
+            deps::fmt
+            #cucim::rmm
+            cucim::cucim
+            deps::libtiff
+            deps::libjpeg-turbo
+            deps::pugixml
+            deps::json
+            deps::libdeflate
+        )
+
+target_include_directories(${CUCIM_PLUGIN_NAME}
+        PUBLIC
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
+            $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
+        PRIVATE
+            ${CMAKE_CURRENT_LIST_DIR}/src
+            # turbojpeg.h is not included in 'turbojpeg-static' so manually include
+            ${deps-libjpeg-turbo_SOURCE_DIR}
+        )
+
+# Do not generate SONAME as this would be used as plugin
+# Need to use IMPORTED_NO_SONAME when using this .so file.
+set_target_properties(${CUCIM_PLUGIN_NAME} PROPERTIES NO_SONAME 1)
+# Prevent relative path problem of .so with no DT_SONAME.
+# : https://stackoverflow.com/questions/27261288/cmake-linking-shared-c-object-from-externalproject-produces-binaries-with-rel
+target_link_options(${CUCIM_PLUGIN_NAME} PRIVATE "LINKER:-soname=${CUCIM_PLUGIN_NAME}@${PROJECT_VERSION}.so")
+
+# Do not add 'lib' prefix for the library
+set_target_properties(${CUCIM_PLUGIN_NAME} PROPERTIES PREFIX "")
+# Postfix version
+set_target_properties(${CUCIM_PLUGIN_NAME} PROPERTIES OUTPUT_NAME "${CUCIM_PLUGIN_NAME}@${PROJECT_VERSION}")
+
+#set_target_properties(${CUCIM_PLUGIN_NAME} PROPERTIES LINK_FLAGS
+#                        "-Wl,--version-script=${CMAKE_CURRENT_SOURCE_DIR}/cuslide.map")
+
+################################################################################
+# Add tests
+#########################################################std#######################
+add_subdirectory(tests)
+add_subdirectory(benchmarks)
+
+################################################################################
+# Install
+################################################################################
+set(INSTALL_TARGETS
+        ${CUCIM_PLUGIN_NAME}
+        cuslide_tests
+        cuslide_benchmarks
+        )
+
+install(TARGETS ${INSTALL_TARGETS}
+        EXPORT ${CUCIM_PLUGIN_NAME}-targets
+        RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+        COMPONENT ${CUCIM_PLUGIN_NAME}_Runtime
+        LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
+        COMPONENT ${CUCIM_PLUGIN_NAME}_Runtime
+        NAMELINK_COMPONENT ${CUCIM_PLUGIN_NAME}_Development
+        ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
+        COMPONENT ${CUCIM_PLUGIN_NAME}_Development
+        )
+
+# Currently cuslide plugin doesn't have include path so comment out
+# install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include/ DESTINATION ${CMAKE_INSTALL_INCLUDEDIR})
+install(EXPORT ${CUCIM_PLUGIN_NAME}-targets
+        FILE
+        ${CUCIM_PLUGIN_NAME}-targets.cmake
+        NAMESPACE
+        ${PROJECT_NAME}::
+        DESTINATION
+        ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PLUGIN_NAME})
+
+# Write package configs
+include(CMakePackageConfigHelpers)
+configure_package_config_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake.in
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake
+    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PLUGIN_NAME}
+)
+write_basic_package_version_file(
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config-version.cmake
+    VERSION ${PROJECT_VERSION}
+    COMPATIBILITY AnyNewerVersion
+)
+install(
+    FILES
+        ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake
+        ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config-version.cmake
+    DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PLUGIN_NAME}
+)
+
+
+set(CMAKE_EXPORT_PACKAGE_REGISTRY ON)
+export(PACKAGE ${CUCIM_PLUGIN_NAME})
+
+
+# Write package configs
+include(CMakePackageConfigHelpers)
+configure_package_config_file(
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake.in
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake
+    INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PLUGIN_NAME}
+)
+write_basic_package_version_file(
+    ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config-version.cmake
+    VERSION ${PROJECT_VERSION}
+    COMPATIBILITY AnyNewerVersion
+)
+install(
+    FILES
+        ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config.cmake
+        ${CMAKE_CURRENT_BINARY_DIR}/cmake/${CUCIM_PLUGIN_NAME}-config-version.cmake
+    DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/${CUCIM_PLUGIN_NAME}
+)
+
+set(CMAKE_EXPORT_PACKAGE_REGISTRY ON) # TODO: duplicate?
+export(PACKAGE ${CUCIM_PLUGIN_NAME})
+
+unset(BUILD_SHARED_LIBS CACHE)
diff --git a/cpp/plugins/cucim.kit.cuslide/VERSION b/cpp/plugins/cucim.kit.cuslide/VERSION
new file mode 100644
index 000000000..1cf0537c3
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/VERSION
@@ -0,0 +1 @@
+0.19.0
diff --git a/cpp/plugins/cucim.kit.cuslide/benchmarks/CMakeLists.txt b/cpp/plugins/cucim.kit.cuslide/benchmarks/CMakeLists.txt
new file mode 100644
index 000000000..d0a405c37
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/benchmarks/CMakeLists.txt
@@ -0,0 +1,50 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+################################################################################
+# Add executable: cuslide_benchmarks
+################################################################################
+add_executable(cuslide_benchmarks main.cpp config.h)
+#set_source_files_properties(main.cpp PROPERTIES LANGUAGE CUDA) # failed with CLI11 library
+
+set_target_properties(cuslide_benchmarks
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(cuslide_benchmarks PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cuslide_benchmarks PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_compile_definitions(cuslide_benchmarks
+    PUBLIC
+        CUSLIDE_VERSION=${PROJECT_VERSION}
+        CUSLIDE_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUSLIDE_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUSLIDE_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUSLIDE_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+)
+target_link_libraries(cuslide_benchmarks
+        PRIVATE
+            cucim::cucim
+            deps::googlebenchmark
+            deps::openslide
+            deps::cli11
+        )
diff --git a/cpp/plugins/cucim.kit.cuslide/benchmarks/config.h b/cpp/plugins/cucim.kit.cuslide/benchmarks/config.h
new file mode 100644
index 000000000..e82cb685e
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/benchmarks/config.h
@@ -0,0 +1,47 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2020 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUSLIDE_CONFIG_H
+#define CUSLIDE_CONFIG_H
+
+#include <string>
+
+struct AppConfig
+{
+    std::string input_file = "test_data/private/generic_tiff_000.tif";
+    bool discard_cache = false;
+    int random_seed = 0;
+    bool random_start_location = false;
+
+    int64_t image_width = 0;
+    int64_t image_height = 0;
+
+    // Pseudo configurations for google benchmark
+    bool benchmark_list_tests = false;
+    std::string benchmark_filter; // <regex>
+    int benchmark_min_time = 0; // <min_time>
+    int benchmark_repetitions = 0; // <num_repetitions>
+    bool benchmark_report_aggregates_only = false;
+    bool benchmark_display_aggregates_only = false;
+    std::string benchmark_format; // <console|json|csv>
+    std::string benchmark_out; // <filename>
+    std::string benchmark_out_format; // <json|console|csv>
+    std::string benchmark_color; //  {auto|true|false}
+    std::string benchmark_counters_tabular;
+    std::string v; // <verbosity>
+};
+
+#endif // CUSLIDE_CONFIG_H
diff --git a/cpp/plugins/cucim.kit.cuslide/benchmarks/main.cpp b/cpp/plugins/cucim.kit.cuslide/benchmarks/main.cpp
new file mode 100644
index 000000000..b1f3c9651
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/benchmarks/main.cpp
@@ -0,0 +1,245 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "config.h"
+
+
+#include <benchmark/benchmark.h>
+#include <openslide/openslide.h>
+#include <CLI/CLI.hpp>
+#include <fmt/format.h>
+
+#include <cstring>
+#include <fcntl.h>
+#include <unistd.h>
+#include <cstdlib>
+
+#include "cucim/core/framework.h"
+#include "cucim/io/format/image_format.h"
+#include "cucim/memory/memory_manager.h"
+
+#define XSTR(x) STR(x)
+#define STR(x) #x
+
+//#include <chrono>
+
+CUCIM_FRAMEWORK_GLOBALS("cuslide.app")
+
+static AppConfig g_config;
+
+
+static void test_basic(benchmark::State& state)
+{
+    int arg = -1;
+    for (auto state_item : state)
+    {
+        state.PauseTiming();
+        {
+            // Use a different start random seed for the different argument
+            if (arg != state.range())
+            {
+                arg = state.range();
+                srand(g_config.random_seed + arg);
+            }
+
+            if (g_config.discard_cache)
+            {
+                int fd = open(g_config.input_file.c_str(), O_RDONLY);
+                fdatasync(fd);
+                posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
+                close(fd);
+            }
+        }
+        state.ResumeTiming();
+
+        //        auto start = std::chrono::high_resolution_clock::now();
+        cucim::Framework* framework = cucim::acquire_framework("cuslide.app");
+        if (!framework)
+        {
+            fmt::print("framework is not available!\n");
+            return;
+        }
+
+        cucim::io::format::IImageFormat* image_format =
+            framework->acquire_interface_from_library<cucim::io::format::IImageFormat>(
+                "cucim.kit.cuslide@" XSTR(CUSLIDE_VERSION) ".so");
+        //        std::cout << image_format->formats[0].get_format_name() << std::endl;
+        if (image_format == nullptr)
+        {
+            fmt::print("plugin library is not available!\n");
+            return;
+        }
+
+        auto handle = image_format->formats[0].image_parser.open(g_config.input_file.c_str());
+
+        cucim::io::format::ImageMetadata metadata{};
+        metadata.level_count(1).level_downsamples({ 1.0 }).level_ndim(3);
+        image_format->formats[0].image_parser.parse(&handle, &metadata.desc());
+
+        cucim::io::format::ImageReaderRegionRequestDesc request{};
+        int64_t request_location[2] = { 0, 0 };
+        if (g_config.random_start_location)
+        {
+            request_location[0] = rand() % (g_config.image_width - state.range(0));
+            request_location[1] = rand() % (g_config.image_height - state.range(0));
+        }
+
+        request.location = request_location;
+        request.level = 0;
+        int64_t request_size[2] = { state.range(0), state.range(0) };
+        request.size = request_size;
+        request.device = const_cast<char*>("cpu");
+
+        cucim::io::format::ImageDataDesc image_data;
+
+        image_format->formats[0].image_reader.read(
+            &handle, &metadata.desc(), &request, &image_data, nullptr /*out_metadata*/);
+        cucim_free(image_data.container.data);
+
+        image_format->formats[0].image_parser.close(&handle);
+
+        //        auto end = std::chrono::high_resolution_clock::now();
+        //        auto elapsed_seconds = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+        //        state.SetIterationTime(elapsed_seconds.count());
+    }
+}
+
+static void test_openslide(benchmark::State& state)
+{
+    int arg = -1;
+    for (auto _ : state)
+    {
+        state.PauseTiming();
+        {
+            // Use a different start random seed for the different argument
+            if (arg != state.range())
+            {
+                arg = state.range();
+                srand(g_config.random_seed + arg);
+            }
+
+            if (g_config.discard_cache)
+            {
+                int fd = open(g_config.input_file.c_str(), O_RDONLY);
+                fdatasync(fd);
+                posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED);
+                close(fd);
+            }
+        }
+        state.ResumeTiming();
+
+        openslide_t* slide = openslide_open(g_config.input_file.c_str());
+        uint32_t* buf = (uint32_t*)cucim_malloc(state.range(0) * state.range(0) * 4);
+        int64_t request_location[2] = { 0, 0 };
+        if (g_config.random_start_location)
+        {
+            request_location[0] = rand() % (g_config.image_width - state.range(0));
+            request_location[1] = rand() % (g_config.image_height - state.range(0));
+        }
+        openslide_read_region(slide, buf, request_location[0], request_location[1], 0, state.range(0), state.range(0));
+        cucim_free(buf);
+        openslide_close(slide);
+    }
+}
+
+BENCHMARK(test_basic)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096); //->UseManualTime();
+BENCHMARK(test_openslide)->Unit(benchmark::kMicrosecond)->RangeMultiplier(2)->Range(1, 4096);
+
+static bool remove_help_option(int* argc, char** argv)
+{
+    for (int i = 1; argc && i < *argc; ++i)
+    {
+        if (strncmp(argv[i], "-h", 3) == 0 || strncmp(argv[i], "--help", 7) == 0)
+        {
+            for (int j = i + 1; argc && j < *argc; ++j)
+            {
+                argv[j - 1] = argv[j];
+            }
+            --(*argc);
+            argv[*argc] = nullptr;
+            return true;
+        }
+    }
+    return false;
+}
+
+static bool setup_configuration()
+{
+    openslide_t* slide = openslide_open(g_config.input_file.c_str());
+    if (slide == nullptr)
+    {
+        fmt::print("[Error] Cannot load {}!\n", g_config.input_file);
+        return false;
+    }
+
+    int64_t w, h;
+    openslide_get_level0_dimensions(slide, &w, &h);
+
+    g_config.image_width = w;
+    g_config.image_height = h;
+
+    openslide_close(slide);
+
+    return true;
+}
+
+// BENCHMARK_MAIN();
+int main(int argc, char** argv)
+{
+    // Skip processing help option
+    bool has_help_option = remove_help_option(&argc, argv);
+
+    ::benchmark::Initialize(&argc, argv);
+    //    if (::benchmark::ReportUnrecognizedArguments(argc, argv))
+    //        return 1;
+    CLI::App app{ "benchmark: cuSlide" };
+    app.add_option("--test_file", g_config.input_file, "An input .tif/.svs file path");
+    app.add_option("--discard_cache", g_config.discard_cache, "Discard page cache for the input file for each iteration");
+    app.add_option("--random_seed", g_config.random_seed, "A random seed number");
+    app.add_option(
+        "--random_start_location", g_config.random_start_location, "Randomize start location of read_region()");
+
+    // Pseudo benchmark options
+    app.add_option("--benchmark_list_tests", g_config.benchmark_list_tests, "{true|false}");
+    app.add_option("--benchmark_filter", g_config.benchmark_filter, "<regex>");
+    app.add_option("--benchmark_min_time", g_config.benchmark_min_time, "<min_time>");
+    app.add_option("--benchmark_repetitions", g_config.benchmark_repetitions, "<num_repetitions>");
+    app.add_option("--benchmark_report_aggregates_only", g_config.benchmark_report_aggregates_only, "{true|false}");
+    app.add_option("--benchmark_display_aggregates_only", g_config.benchmark_display_aggregates_only, "{true|false}");
+    app.add_option("--benchmark_format", g_config.benchmark_format, "<console|json|csv>");
+    app.add_option("--benchmark_out", g_config.benchmark_out, "<filename>");
+    app.add_option("--benchmark_out_format", g_config.benchmark_out_format, "<json|console|csv>");
+    app.add_option("--benchmark_color", g_config.benchmark_color, "{auto|true|false}");
+    app.add_option("--benchmark_counters_tabular", g_config.benchmark_counters_tabular, "{true|false}");
+    app.add_option("--v", g_config.v, "<verbosity>");
+
+    // Append help option if exists
+    if (has_help_option)
+    {
+        argv[argc] = const_cast<char*>("--help");
+        ++argc;
+        // https://github.com/matepek/vscode-catch2-test-adapter detects google benchmark binaries by the following
+        // text:
+        printf("benchmark [--benchmark_list_tests={true|false}]\n");
+    }
+    CLI11_PARSE(app, argc, argv);
+
+    if (!setup_configuration())
+    {
+        return 1;
+    }
+    ::benchmark::RunSpecifiedBenchmarks();
+}
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/cucim.kit.cuslide-config.cmake.in b/cpp/plugins/cucim.kit.cuslide/cmake/cucim.kit.cuslide-config.cmake.in
new file mode 100644
index 000000000..e62e35716
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/cucim.kit.cuslide-config.cmake.in
@@ -0,0 +1,25 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+@PACKAGE_INIT@
+
+# Find dependent libraries
+# ...
+include(CMakeFindDependencyMacro)
+#find_dependency(Boost x.x.x REQUIRED)
+
+if(NOT TARGET cuslide::cuslide)
+    include(${CMAKE_CURRENT_LIST_DIR}/cucim.kit.cuslide-targets.cmake)
+endif()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/catch2.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/catch2.cmake
new file mode 100644
index 000000000..56d22a3c3
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/catch2.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::catch2)
+    FetchContent_Declare(
+            deps-catch2
+            GIT_REPOSITORY https://github.com/catchorg/Catch2.git
+            GIT_TAG v2.13.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-catch2)
+    if (NOT deps-catch2_POPULATED)
+        message(STATUS "Fetching catch2 sources")
+        FetchContent_Populate(deps-catch2)
+        message(STATUS "Fetching catch2 sources - done")
+    endif ()
+
+    add_subdirectory(${deps-catch2_SOURCE_DIR} ${deps-catch2_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Include Append catch2's cmake module path so that we can use `include(ParseAndAddCatchTests)`.
+    list(APPEND CMAKE_MODULE_PATH "${deps-catch2_SOURCE_DIR}/contrib")
+    set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} PARENT_SCOPE)
+
+    add_library(deps::catch2 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::catch2 INTERFACE Catch2::Catch2)
+    set(deps-catch2_SOURCE_DIR ${deps-catch2_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-catch2_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/cli11.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/cli11.cmake
new file mode 100644
index 000000000..343e69e18
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/cli11.cmake
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::cli11)
+    FetchContent_Declare(
+            deps-cli11
+            GIT_REPOSITORY https://github.com/CLIUtils/CLI11.git
+            GIT_TAG v1.9.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-cli11)
+    if (NOT deps-cli11_POPULATED)
+        message(STATUS "Fetching cli11 sources")
+        FetchContent_Populate(deps-cli11)
+        message(STATUS "Fetching cli11 sources - done")
+    endif ()
+
+    add_subdirectory(${deps-cli11_SOURCE_DIR} ${deps-cli11_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::cli11 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::cli11 INTERFACE CLI11::CLI11)
+    set(deps-cli11_SOURCE_DIR ${deps-cli11_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-cli11_SOURCE_DIR)
+endif ()
+
+# Note that library had a failure with nvcc compiler and gcc 9.x headers
+#  ...c++/9/tuple(553): error: pack "_UElements" does not have the same number of elements as "_Elements"
+#            __and_<is_nothrow_assignable<_Elements&, _UElements>...>::value;
+# Not using nvcc for main code that uses cli11 solved the issue.
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/fmt.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/fmt.cmake
new file mode 100644
index 000000000..be0fa2ec1
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/fmt.cmake
@@ -0,0 +1,42 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+if (NOT TARGET deps::fmt)
+    FetchContent_Declare(
+            deps-fmt
+            GIT_REPOSITORY https://github.com/fmtlib/fmt.git
+            GIT_TAG 7.0.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-fmt)
+    if (NOT deps-fmt_POPULATED)
+        message(STATUS "Fetching fmt sources")
+        FetchContent_Populate(deps-fmt)
+        message(STATUS "Fetching fmt sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    add_subdirectory(${deps-fmt_SOURCE_DIR} ${deps-fmt_BINARY_DIR} EXCLUDE_FROM_ALL)
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: ../lib/libfmtd.a(format.cc.o): relocation R_X86_64_PC32 against symbol `stderr@@GLIBC_2.2.5' can not be used when making a shared object; recompile with -fPIC
+    set_target_properties(fmt PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::fmt INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::fmt INTERFACE fmt::fmt-header-only)
+    set(deps-fmt_SOURCE_DIR ${deps-fmt_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-fmt_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/gds.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/gds.cmake
new file mode 100644
index 000000000..57685840a
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/gds.cmake
@@ -0,0 +1,39 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if(NOT TARGET deps::gds)
+    if(NOT GDS_SDK_PATH)
+        get_filename_component(GDS_SDK_PATH "${CMAKE_SOURCE_DIR}/gds" ABSOLUTE)
+        message("GDS_SDK_PATH is not set. Using '${GDS_SDK_PATH}'")
+    else()
+        message("GDS_SDK_PATH is set to ${GDS_SDK_PATH}")
+    endif()
+
+    if(EXISTS "${GDS_SDK_PATH}/lib64/libcufile.so")
+        add_library(deps::gds SHARED IMPORTED GLOBAL)
+        set_target_properties(deps::gds PROPERTIES
+            IMPORTED_LOCATION "${GDS_SDK_PATH}/lib64/libcufile.so"
+            INTERFACE_INCLUDE_DIRECTORIES "${GDS_SDK_PATH}/lib64/"
+        )
+    else()
+        message("'${GDS_SDK_PATH}/lib64/libcufile.so' is not available. Set CUCIM_SUPPORT_GDS to OFF and import cufile.h only.")
+        # Do not support GDS
+        set(CUCIM_SUPPORT_GDS OFF PARENT_SCOPE)
+        add_library(deps::gds INTERFACE IMPORTED GLOBAL)
+        set_target_properties(deps::gds PROPERTIES
+            INTERFACE_INCLUDE_DIRECTORIES "${GDS_SDK_PATH}/lib64/"
+        )
+    endif()
+endif()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/googlebenchmark.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/googlebenchmark.cmake
new file mode 100644
index 000000000..c5f46ba3e
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/googlebenchmark.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::googlebenchmark)
+    FetchContent_Declare(
+            deps-googlebenchmark
+            GIT_REPOSITORY https://github.com/google/benchmark.git
+            GIT_TAG v1.5.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-googlebenchmark)
+    if (NOT deps-googlebenchmark_POPULATED)
+        message(STATUS "Fetching googlebenchmark sources")
+        FetchContent_Populate(deps-googlebenchmark)
+        message(STATUS "Fetching googlebenchmark sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    set(BENCHMARK_ENABLE_GTEST_TESTS OFF)
+    add_subdirectory(${deps-googlebenchmark_SOURCE_DIR} ${deps-googlebenchmark_BINARY_DIR} EXCLUDE_FROM_ALL)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::googlebenchmark INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::googlebenchmark INTERFACE benchmark::benchmark)
+    set(deps-googlebenchmark_SOURCE_DIR ${deps-googlebenchmark_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-googlebenchmark_SOURCE_DIR)
+endif ()
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/googletest.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/googletest.cmake
new file mode 100644
index 000000000..5d93a95f3
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/googletest.cmake
@@ -0,0 +1,45 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::googletest)
+    FetchContent_Declare(
+            deps-googletest
+            GIT_REPOSITORY https://github.com/google/googletest.git
+            GIT_TAG release-1.10.0
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-googletest)
+    if (NOT deps-googletest_POPULATED)
+        message(STATUS "Fetching googletest sources")
+        FetchContent_Populate(deps-googletest)
+        message(STATUS "Fetching googletest sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    add_subdirectory(${deps-googletest_SOURCE_DIR} ${deps-googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::googletest INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::googletest INTERFACE googletest)
+    set(deps-googletest_SOURCE_DIR ${deps-googletest_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-googletest_SOURCE_DIR)
+endif ()
+
+#CMake Warning (dev) in cmake-build-debug/_deps/deps-googletest-src/googlemock/CMakeLists.txt:
+#  Policy CMP0082 is not set: Install rules from add_subdirectory() are
+#  interleaved with those in caller.  Run "cmake --help-policy CMP0082" for
+#  policy details.  Use the cmake_policy command to set the policy and
+#  suppress this warning.
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/json.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/json.cmake
new file mode 100644
index 000000000..4f716f120
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/json.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::json)
+    FetchContent_Declare(
+            deps-json
+            GIT_REPOSITORY https://github.com/nlohmann/json.git
+            GIT_TAG v3.9.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-json)
+    if (NOT deps-json_POPULATED)
+        message(STATUS "Fetching json sources")
+        FetchContent_Populate(deps-json)
+        message(STATUS "Fetching json sources - done")
+    endif ()
+
+    # Typically you don't care so much for a third party library's tests to be
+    # run from your own project's code.
+    set(JSON_BuildTests OFF CACHE INTERNAL "")
+
+    add_subdirectory(${deps-json_SOURCE_DIR} ${deps-json_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::json INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::json INTERFACE nlohmann_json::nlohmann_json)
+    set(deps-json_SOURCE_DIR ${deps-json_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-json_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/libdeflate.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libdeflate.cmake
new file mode 100644
index 000000000..ee04efb00
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libdeflate.cmake
@@ -0,0 +1,59 @@
+#
+# Copyright (c) 2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::libdeflate)
+    FetchContent_Declare(
+            deps-libdeflate
+            GIT_REPOSITORY https://github.com/ebiggers/libdeflate.git
+            GIT_TAG v1.7
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-libdeflate)
+    if (NOT deps-libdeflate_POPULATED)
+        message(STATUS "Fetching libdeflate sources")
+        FetchContent_Populate(deps-libdeflate)
+        message(STATUS "Fetching libdeflate sources - done")
+    endif ()
+
+    if (deps-libdeflate_POPULATED AND NOT EXISTS "${deps-libdeflate_BINARY_DIR}/install")
+        include(ProcessorCount)
+        ProcessorCount(PROCESSOR_COUNT)
+
+        # /opt/rh/devtoolset-9/root/usr/libexec/gcc/x86_64-redhat-linux/9/ld: _deps/deps-libdeflate-build/install/lib/libdeflate.a(deflate_decompress.o): relocation R_X86_64_32 against `.rodata' can not be used when making a shared object; recompile with -fPIC
+        if (CMAKE_BUILD_TYPE STREQUAL "Debug")
+            set(LIBDEFLATE_CMAKE_ARGS "-e CFLAGS='-O0 -g3 -fPIC'")
+        else()
+            set(LIBDEFLATE_CMAKE_ARGS "-e CFLAGS='-fPIC'")
+        endif()
+
+        execute_process(COMMAND /bin/bash -c "make -e PREFIX=${deps-libdeflate_BINARY_DIR}/install ${LIBDEFLATE_CMAKE_ARGS} install -j${PROCESSOR_COUNT}"
+                        WORKING_DIRECTORY ${deps-libdeflate_SOURCE_DIR}
+                        COMMAND_ECHO STDOUT
+                        RESULT_VARIABLE libdeflate_BUILD_RESULT)
+        if(NOT libdeflate_BUILD_RESULT EQUAL "0")
+            message(FATAL_ERROR "libdeflate library build failed with ${libdeflate_BUILD_RESULT}, please checkout the configurations")
+        endif()
+    endif()
+
+    add_library(deps::libdeflate INTERFACE IMPORTED GLOBAL)
+
+    set_target_properties(deps::libdeflate PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${deps-libdeflate_BINARY_DIR}/install/include"
+        INTERFACE_LINK_LIBRARIES "${deps-libdeflate_BINARY_DIR}/install/lib/libdeflate.a"
+    )
+
+    set(deps-libdeflate_SOURCE_DIR ${deps-libdeflate_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-libdeflate_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo-policies-fix.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo-policies-fix.cmake
new file mode 100644
index 000000000..a95016b9e
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo-policies-fix.cmake
@@ -0,0 +1,22 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The following cmake policies are set by `CMAKE_PROJECT_INCLUDE_BEFORE` variables
+# when `FetchContent` command is used (see https://gitlab.kitware.com/cmake/cmake/-/issues/19854).
+cmake_policy(SET CMP0048 NEW)  # project() command manages VERSION variables. for libjpeg-turbo
+cmake_policy(SET CMP0054 NEW)  # cmake-build-debug/_deps/deps-libjpeg-turbo-src/cmakescripts/GNUInstallDirs.cmake:174 (elseif):
+cmake_policy(SET CMP0063 NEW)  # Honor the visibility properties for all target types including static library.
+# https://cmake.org/cmake/help/v3.18/policy/CMP0065.html : Do not add flags to export symbols from executables without the ENABLE_EXPORTS target property.
+#   : this policy is not handled yet so always enable exports.
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo.cmake
new file mode 100644
index 000000000..faf4d0ea7
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libjpeg-turbo.cmake
@@ -0,0 +1,71 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+if (NOT TARGET deps::libjpeg-turbo)
+#    add_library(deps::libjpeg-turbo SHARED IMPORTED GLOBAL)
+#
+#    set_target_properties(deps::libjpeg-turbo PROPERTIES
+#        IMPORTED_LOCATION "/usr/lib/x86_64-linux-gnu/libjpeg-turbo.so"
+#        INTERFACE_INCLUDE_DIRECTORIES "/usr/include/x86_64-linux-gnu"
+#    )
+
+    FetchContent_Declare(
+            deps-libjpeg-turbo
+            GIT_REPOSITORY https://github.com/libjpeg-turbo/libjpeg-turbo.git
+            GIT_TAG 2.0.6
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-libjpeg-turbo)
+    if (NOT deps-libjpeg-turbo_POPULATED)
+        message(STATUS "Fetching libjpeg-turbo sources")
+        FetchContent_Populate(deps-libjpeg-turbo)
+        message(STATUS "Fetching libjpeg-turbo sources - done")
+    endif ()
+
+    # Set policies for libjpeg-turbo
+    set(CMAKE_PROJECT_INCLUDE_BEFORE "${CMAKE_CURRENT_LIST_DIR}/libjpeg-turbo-policies-fix.cmake")
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+
+    # Tell CMake where to find the compiler by setting either the environment
+    # variable "ASM_NASM" or the CMake cache entry CMAKE_ASM_NASM_COMPILER to the
+    # full path to the compiler, or to the compiler name if it is in the PATH.
+    # nasm is available through `sudo apt-get install nasm` on Debian Linux.
+    set(REQUIRE_SIMD 1)
+
+    add_subdirectory(${deps-libjpeg-turbo_SOURCE_DIR} ${deps-libjpeg-turbo_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Disable visibility to not expose unnecessary symbols
+    set_target_properties(turbojpeg-static
+    PROPERTIES
+        C_VISIBILITY_PRESET hidden
+        CXX_VISIBILITY_PRESET hidden
+        VISIBILITY_INLINES_HIDDEN YES)
+
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: lib/libturbojpeg.a(turbojpeg.c.o): relocation R_X86_64_TPOFF32 against `errStr' can not be used when making a shared object; recompile with -fPIC
+    #   /usr/bin/ld: final link failed: Nonrepresentable section on output
+    set_target_properties(turbojpeg-static PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::libjpeg-turbo INTERFACE IMPORTED GLOBAL)
+
+    target_link_libraries(deps::libjpeg-turbo INTERFACE turbojpeg-static)
+    set(deps-libjpeg-turbo_SOURCE_DIR ${deps-libjpeg-turbo_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-libjpeg-turbo_SOURCE_DIR)
+    set(deps-libjpeg-turbo_BINARY_DIR ${deps-libjpeg-turbo_BINARY_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-libjpeg-turbo_BINARY_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff-policies-fix.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff-policies-fix.cmake
new file mode 100644
index 000000000..91572ecda
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff-policies-fix.cmake
@@ -0,0 +1,21 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# The following cmake policies are set by `CMAKE_PROJECT_INCLUDE_BEFORE` variables
+# when `FetchContent` command is used (see https://gitlab.kitware.com/cmake/cmake/-/issues/19854).
+cmake_policy(SET CMP0072 NEW)  # FindOpenGL prefers GLVND by default when available. for libtiff
+cmake_policy(SET CMP0048 NEW)  # project() command manages VERSION variables. for libtiff
+cmake_policy(SET CMP0063 NEW)  # Honor the visibility properties for all target types including static library.
+cmake_policy(SET CMP0077 NEW)  # Honor normal variables. Without this, `set(jpeg OFF)` trick to force using static libjpeg-turbo doesn't work.
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff.cmake
new file mode 100644
index 000000000..6ef9e6995
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/libtiff.cmake
@@ -0,0 +1,74 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+if (NOT TARGET deps::libtiff)
+#    add_library(deps::libtiff SHARED IMPORTED GLOBAL)
+#
+#    set_target_properties(deps::libtiff PROPERTIES
+#        IMPORTED_LOCATION "/usr/lib/x86_64-linux-gnu/libtiff.so"
+#        INTERFACE_INCLUDE_DIRECTORIES "/usr/include/x86_64-linux-gnu"
+#    )
+
+    FetchContent_Declare(
+            deps-libtiff
+            GIT_REPOSITORY https://gitlab.com/libtiff/libtiff.git
+            GIT_TAG v4.1.0
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-libtiff)
+    if (NOT deps-libtiff_POPULATED)
+        message(STATUS "Fetching libtiff sources")
+        FetchContent_Populate(deps-libtiff)
+        message(STATUS "Fetching libtiff sources - done")
+    endif ()
+
+    # Set policies for libtiff
+    set(CMAKE_PROJECT_INCLUDE_BEFORE "${CMAKE_CURRENT_LIST_DIR}/libtiff-policies-fix.cmake")
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+
+    # The following does some tricks so that libtiff uses libjpeg-turbo instead of system's libjpeg.
+    # - set jpeg to OFF so that we can manually specify LIBRARIES and INCLUDES
+    #   (status message in cmake shows jpeg is OFF but it actually use libjpeg)
+    # - set TIFF_INCLUDES instead of JPEG_INCLUDE_DIR to set libjpeg-turbo's include folder with higher priority
+    #   (otherwise, jpeg's include dir wouldn't be the first of TIFF_INCLUDES)
+    # Otherwise, libtiff would use system's shared libjpeg(8.0) whereas libjpeg turbo uses static libjpeg(6.2)
+    # so symbol conflict(such as jpeg_CreateDecompress) happens.
+    # See 'cmake-build-debug/_deps/deps-libtiff-src/CMakeLists.txt' for existing libtiff's logic.
+    set(jpeg OFF)
+    set(JPEG_FOUND TRUE)
+    set(JPEG_LIBRARIES deps::libjpeg-turbo)
+    # for jpeglib.h and jconfig.h/jconfigint.h
+    set(TIFF_INCLUDES ${deps-libjpeg-turbo_SOURCE_DIR} ${deps-libjpeg-turbo_BINARY_DIR} )
+    add_subdirectory(${deps-libtiff_SOURCE_DIR} ${deps-libtiff_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Disable visibility to not expose unnecessary symbols
+    set_target_properties(tiff tiffxx
+    PROPERTIES
+        C_VISIBILITY_PRESET hidden
+        CXX_VISIBILITY_PRESET hidden
+        VISIBILITY_INLINES_HIDDEN YES)
+
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: lib/libtiff.a(tif_close.c.o): relocation R_X86_64_PC32 against symbol `TIFFCleanup' can not be used when making a shared object; recompile with -fPIC
+    set_target_properties(tiff PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::libtiff INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::libtiff INTERFACE tiffxx)
+    set(deps-libtiff_SOURCE_DIR ${deps-libtiff_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-libtiff_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/openslide.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/openslide.cmake
new file mode 100644
index 000000000..651acd5ff
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/openslide.cmake
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::openslide)
+    add_library(deps::openslide SHARED IMPORTED GLOBAL)
+
+    if (DEFINED ENV{CONDA_BUILD})
+        set(OPENSLIDE_LIB_PATH "$ENV{PREFIX}/lib/libopenslide.so")
+    elseif (DEFINED ENV{CONDA_PREFIX})
+        set(OPENSLIDE_LIB_PATH "$ENV{CONDA_PREFIX}/lib/libopenslide.so")
+    elseif (EXISTS /usr/lib/x86_64-linux-gnu/libopenslide.so)
+        set(OPENSLIDE_LIB_PATH /usr/lib/x86_64-linux-gnu/libopenslide.so)
+    else () # CentOS 6
+        set(OPENSLIDE_LIB_PATH /usr/lib64/libopenslide.so)
+    endif ()
+
+    if (DEFINED ENV{CONDA_BUILD})
+        set(OPENSLIDE_INCLUDE_PATH "$ENV{PREFIX}/include/")
+    elseif (DEFINED ENV{CONDA_PREFIX})
+        set(OPENSLIDE_INCLUDE_PATH "$ENV{CONDA_PREFIX}/include/")
+    else ()
+        set(OPENSLIDE_INCLUDE_PATH "/usr/include/")
+    endif ()
+
+    set_target_properties(deps::openslide PROPERTIES
+        IMPORTED_LOCATION "${OPENSLIDE_LIB_PATH}"
+        INTERFACE_INCLUDE_DIRECTORIES "${OPENSLIDE_INCLUDE_PATH}"
+    )
+endif ()
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/deps/pugixml.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/deps/pugixml.cmake
new file mode 100644
index 000000000..06850918e
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/deps/pugixml.cmake
@@ -0,0 +1,48 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+if (NOT TARGET deps::pugixml)
+    FetchContent_Declare(
+            deps-pugixml
+            GIT_REPOSITORY https://github.com/zeux/pugixml.git
+            GIT_TAG v1.11.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-pugixml)
+    if (NOT deps-pugixml_POPULATED)
+        message(STATUS "Fetching pugixml sources")
+        FetchContent_Populate(deps-pugixml)
+        message(STATUS "Fetching pugixml sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+
+    add_subdirectory(${deps-pugixml_SOURCE_DIR} ${deps-pugixml_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Disable visibility to not expose unnecessary symbols
+    set_target_properties(pugixml-static
+    PROPERTIES
+        C_VISIBILITY_PRESET hidden
+        CXX_VISIBILITY_PRESET hidden
+        VISIBILITY_INLINES_HIDDEN YES)
+
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::pugixml INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::pugixml INTERFACE pugixml-static)
+    set(deps-pugixml_SOURCE_DIR ${deps-pugixml_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-pugixml_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/modules/CuCIMUtils.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/modules/CuCIMUtils.cmake
new file mode 100644
index 000000000..cfe0b1495
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/modules/CuCIMUtils.cmake
@@ -0,0 +1,60 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Store current BUILD_SHARED_LIBS setting in CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_set_build_shared_libs)
+    macro(cucim_set_build_shared_libs new_value)
+        set(CUCIM_OLD_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}})
+        if (DEFINED CACHE{BUILD_SHARED_LIBS})
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED TRUE)
+        else()
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED FALSE)
+        endif()
+        set(BUILD_SHARED_LIBS ${new_value} CACHE BOOL "" FORCE)
+    endmacro()
+endif()
+
+# Restore BUILD_SHARED_LIBS setting from CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_restore_build_shared_libs)
+    macro(cucim_restore_build_shared_libs)
+        if (CUCIM_OLD_BUILD_SHARED_LIBS_CACHED)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
+        else()
+            unset(BUILD_SHARED_LIBS CACHE)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS})
+        endif()
+    endmacro()
+endif()
+
+# Define CMAKE_CUDA_ARCHITECTURES for the given architecture values
+#
+# Params:
+#   arch_list - architecture value list (e.g., '60;70;75;80;86')
+if(NOT COMMAND cucim_define_cuda_architectures)
+    function(cucim_define_cuda_architectures arch_list)
+        set(arch_string "")
+        # Create SASS for all architectures in the list
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${arch}-real")
+        endforeach(arch)
+
+        # Create PTX for the latest architecture for forward-compatibility.
+        list(GET arch_list -1 latest_arch)
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${latest_arch}-virtual")
+        endforeach(arch)
+        set(CMAKE_CUDA_ARCHITECTURES ${arch_string} PARENT_SCOPE)
+    endfunction()
+endif()
diff --git a/cpp/plugins/cucim.kit.cuslide/cmake/modules/SuperBuildUtils.cmake b/cpp/plugins/cucim.kit.cuslide/cmake/modules/SuperBuildUtils.cmake
new file mode 100644
index 000000000..e0bae3cdc
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cmake/modules/SuperBuildUtils.cmake
@@ -0,0 +1,24 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+include(FetchContent)
+
+set(CMAKE_SUPERBUILD_DEPS_ROOT_DIR "${CMAKE_CURRENT_LIST_DIR}/..")
+
+if(NOT COMMAND superbuild_depend)
+    function(superbuild_depend module_name)
+        include("${CMAKE_SUPERBUILD_DEPS_ROOT_DIR}/deps/${module_name}.cmake")
+    endfunction()
+endif()
diff --git a/cpp/plugins/cucim.kit.cuslide/cuslide.map b/cpp/plugins/cucim.kit.cuslide/cuslide.map
new file mode 100644
index 000000000..6ebbbdfbb
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/cuslide.map
@@ -0,0 +1,4 @@
+CUSLIDE_0.1 {
+  local:
+    *;
+};
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.cpp b/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.cpp
new file mode 100644
index 000000000..8be129254
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.cpp
@@ -0,0 +1,300 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#define CUCIM_EXPORTS
+
+#include "cuslide.h"
+
+#include "cucim/io/format/image_format.h"
+
+#include "cucim/core/framework.h"
+#include "cucim/core/plugin_util.h"
+
+#include <memory>
+//#include "tiffio.h"
+//#include "tif_dir.h"
+#include "tiff/tiff.h"
+#include <fcntl.h>
+#include <cassert>
+#include <cstring>
+#include <array>
+#include <nlohmann/json.hpp>
+// #include <rmm/mr/host/new_delete_resource.hpp>
+
+using json = nlohmann::json;
+
+const struct cucim::PluginImplDesc kPluginImpl = {
+    "cucim.kit.cuslide", // name
+    { 0, 1, 0 }, // version
+    "dev", // build
+    "clara team", // author
+    "cuslide", // description
+    "cuslide plugin", // long_description
+    "Apache-2.0", // license
+    "https://github.com/rapidsai/cucim", // url
+    "linux", // platforms,
+    cucim::PluginHotReload::kDisabled, // hot_reload
+};
+
+// Using CARB_PLUGIN_IMPL_MINIMAL instead of CARB_PLUGIN_IMPL
+// This minimal macro doesn't define global variables for logging, profiler, crash reporting,
+// and also doesn't call for the client registration for those systems
+CUCIM_PLUGIN_IMPL_MINIMAL(kPluginImpl, cucim::io::format::IImageFormat)
+CUCIM_PLUGIN_IMPL_NO_DEPS()
+
+
+static void set_enabled(bool val)
+{
+    (void)val;
+}
+
+static bool is_enabled()
+{
+    return true;
+}
+
+static const char* get_format_name()
+{
+    return "Generic TIFF";
+}
+
+static bool CUCIM_ABI checker_is_valid(const char* file_name, const char* buf) // TODO: need buffer size parameter
+{
+    // TODO implement this
+    (void)file_name;
+    (void)buf;
+    return true;
+}
+
+static CuCIMFileHandle CUCIM_ABI parser_open(const char* file_path)
+{
+    auto tif = new cuslide::tiff::TIFF(file_path, O_RDONLY);
+    tif->construct_ifds();
+    return tif->file_handle();
+}
+
+static bool CUCIM_ABI parser_parse(CuCIMFileHandle* handle, cucim::io::format::ImageMetadataDesc* out_metadata_desc)
+{
+    if (!out_metadata_desc || !out_metadata_desc->handle)
+    {
+        throw std::runtime_error("out_metadata_desc shouldn't be nullptr!");
+    }
+    cucim::io::format::ImageMetadata& out_metadata =
+        *reinterpret_cast<cucim::io::format::ImageMetadata*>(out_metadata_desc->handle);
+
+    auto tif = static_cast<cuslide::tiff::TIFF*>(handle->client_data);
+
+    std::vector<size_t> main_ifd_list;
+
+    size_t ifd_count = tif->ifd_count();
+    size_t level_count = tif->level_count();
+    for (size_t i = 0; i < ifd_count; i++)
+    {
+        const std::shared_ptr<cuslide::tiff::IFD>& ifd = tif->ifd(i);
+
+        //        const char* char_ptr = ifd->model().c_str();
+        //        uint32_t width = ifd->width();
+        //        uint32_t height = ifd->height();
+        //        uint32_t bits_per_sample = ifd->bits_per_sample();
+        //        uint32_t samples_per_pixel = ifd->samples_per_pixel();
+        uint64_t subfile_type = ifd->subfile_type();
+        //        printf("image_description:\n%s\n", ifd->image_description().c_str());
+        //        printf("model=%s, width=%u, height=%u, model=%p bits_per_sample:%u, samples_per_pixel=%u, %lu \n",
+        //        char_ptr,
+        //               width, height, char_ptr, bits_per_sample, samples_per_pixel, subfile_type);
+        if (subfile_type == 0)
+        {
+            main_ifd_list.push_back(i);
+        }
+    }
+
+    // Assume that the image has only one main (high resolution) image.
+    assert(main_ifd_list.size() == 1);
+
+    //
+    // Metadata Setup
+    //
+
+    // Note: int-> uint16_t due to type differences between ImageMetadataDesc.ndim and DLTensor.ndim
+    const uint16_t ndim = 3;
+    auto& resource = out_metadata.get_resource();
+
+    std::string_view dims{ "YXC" };
+
+    const auto& level0_ifd = tif->level_ifd(0);
+    std::pmr::vector<int64_t> shape(
+        { level0_ifd->height(), level0_ifd->width(), level0_ifd->samples_per_pixel() }, &resource);
+
+    DLDataType dtype{ kDLUInt, 8, 1 };
+
+    // TODO: Fill correct values for cucim::io::format::ImageMetadataDesc
+    // TODO: Do not assume channel names as 'RGB'
+    std::pmr::vector<std::string_view> channel_names(
+        { std::string_view{ "R" }, std::string_view{ "G" }, std::string_view{ "B" } }, &resource);
+
+    // TODO: Set correct spacing value
+    std::pmr::vector<float> spacing(&resource);
+    spacing.reserve(ndim);
+    spacing.insert(spacing.end(), ndim, 1.0);
+
+    // TODO: Set correct spacing units
+    std::pmr::vector<std::string_view> spacing_units(&resource);
+    spacing_units.reserve(ndim);
+    spacing_units.emplace_back(std::string_view{ "micrometer" });
+    spacing_units.emplace_back(std::string_view{ "micrometer" });
+    spacing_units.emplace_back(std::string_view{ "color" });
+
+    std::pmr::vector<float> origin({ 0.0, 0.0, 0.0 }, &resource);
+    // Direction cosines (size is always 3x3)
+    // clang-format off
+    std::pmr::vector<float> direction({ 1.0, 0.0, 0.0,
+                                        0.0, 1.0, 0.0,
+                                        0.0, 0.0, 1.0}, &resource);
+    // clang-format on
+
+    // The coordinate frame in which the direction cosines are measured (either 'LPS'(ITK/DICOM) or 'RAS'(NIfTI/3D
+    // Slicer))
+    std::string_view coord_sys{ "LPS" };
+
+    const uint16_t level_ndim = 2;
+    std::pmr::vector<int64_t> level_dimensions(&resource);
+    level_dimensions.reserve(level_count * 2);
+    for (size_t i = 0; i < level_count; ++i)
+    {
+        const auto& level_ifd = tif->level_ifd(i);
+        level_dimensions.emplace_back(level_ifd->width());
+        level_dimensions.emplace_back(level_ifd->height());
+    }
+
+    std::pmr::vector<float> level_downsamples(&resource);
+    float orig_width = static_cast<float>(shape[1]);
+    float orig_height = static_cast<float>(shape[0]);
+    for (size_t i = 0; i < level_count; ++i)
+    {
+        const auto& level_ifd = tif->level_ifd(i);
+        level_downsamples.emplace_back(((orig_width / level_ifd->width()) + (orig_height / level_ifd->height())) / 2);
+    }
+
+    const size_t associated_image_count = tif->associated_image_count();
+    std::pmr::vector<std::string_view> associated_image_names(&resource);
+    for (const auto& associated_image : tif->associated_images())
+    {
+        associated_image_names.emplace_back(std::string_view{ associated_image.first.c_str() });
+    }
+
+    auto& image_description = level0_ifd->image_description();
+    std::string_view raw_data{ image_description.empty() ? "" : image_description.c_str() };
+
+    // Dynamically allocate memory for json_data (need to be freed manually);
+    const std::string& json_str = tif->metadata();
+    char* json_data_ptr = static_cast<char*>(cucim_malloc(json_str.size() + 1));
+    memcpy(json_data_ptr, json_str.data(), json_str.size() + 1);
+    std::string_view json_data{ json_data_ptr, json_str.size() };
+
+    out_metadata.ndim(ndim);
+    out_metadata.dims(dims);
+    out_metadata.shape(shape);
+    out_metadata.dtype(dtype);
+    out_metadata.channel_names(channel_names);
+    out_metadata.spacing(spacing);
+    out_metadata.spacing_units(spacing_units);
+    out_metadata.origin(origin);
+    out_metadata.direction(direction);
+    out_metadata.coord_sys(coord_sys);
+    out_metadata.level_count(level_count);
+    out_metadata.level_ndim(level_ndim);
+    out_metadata.level_dimensions(level_dimensions);
+    out_metadata.level_downsamples(level_downsamples);
+    out_metadata.image_count(associated_image_count);
+    out_metadata.image_names(associated_image_names);
+    out_metadata.raw_data(raw_data);
+    out_metadata.json_data(json_data);
+
+    return true;
+}
+
+static bool CUCIM_ABI parser_close(CuCIMFileHandle* handle)
+{
+    auto tif = static_cast<cuslide::tiff::TIFF*>(handle->client_data);
+    delete tif;
+    handle->client_data = nullptr;
+    return true;
+}
+
+static bool CUCIM_ABI reader_read(const CuCIMFileHandle* handle,
+                                  const cucim::io::format::ImageMetadataDesc* metadata,
+                                  const cucim::io::format::ImageReaderRegionRequestDesc* request,
+                                  cucim::io::format::ImageDataDesc* out_image_data,
+                                  cucim::io::format::ImageMetadataDesc* out_metadata = nullptr)
+{
+    auto tif = static_cast<cuslide::tiff::TIFF*>(handle->client_data);
+    bool result = tif->read(metadata, request, out_image_data, out_metadata);
+
+    return result;
+}
+
+static bool CUCIM_ABI writer_write(const CuCIMFileHandle* handle,
+                                   const cucim::io::format::ImageMetadataDesc* metadata,
+                                   const cucim::io::format::ImageDataDesc* image_data)
+{
+    (void)handle;
+    (void)metadata;
+    (void)image_data;
+
+    return true;
+}
+
+void fill_interface(cucim::io::format::IImageFormat& iface)
+{
+    static cucim::io::format::ImageCheckerDesc image_checker = { 0, 80, checker_is_valid };
+    static cucim::io::format::ImageParserDesc image_parser = { parser_open, parser_parse, parser_close };
+
+    static cucim::io::format::ImageReaderDesc image_reader = { reader_read };
+    static cucim::io::format::ImageWriterDesc image_writer = { writer_write };
+
+    // clang-format off
+    static cucim::io::format::ImageFormatDesc image_format_desc = {
+        set_enabled,
+        is_enabled,
+        get_format_name,
+        image_checker,
+        image_parser,
+        image_reader,
+        image_writer
+    };
+    // clang-format on
+
+    // clang-format off
+    iface =
+    {
+        &image_format_desc,
+        1
+    };
+    // clang-format on
+}
+
+//
+//
+//#include <iostream>
+//#include "fmt/format.h"
+//
+//
+//
+// CUCIM_API int foo()
+//{
+//    std::cout << "Foo!" << std::endl;
+////    std::string a = fmt::format(b.getName());
+//    return 0;
+//}
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.h
new file mode 100644
index 000000000..63e2d9c31
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.h
@@ -0,0 +1,19 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUSLIDE_CUSLIDE_H
+#define CUSLIDE_CUSLIDE_H
+#endif // CUSLIDE_CUSLIDE_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.cpp b/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.cpp
new file mode 100644
index 000000000..d58159233
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.cpp
@@ -0,0 +1,75 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2021 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Code below is using libdeflate library which is under MIT license
+ * Please see LICENSE-3rdparty.md for the detail.
+ */
+
+#include "deflate.h"
+
+#include <stdexcept>
+#include <unistd.h>
+#include "libdeflate.h"
+
+namespace cuslide::deflate
+{
+
+bool decode_deflate(int fd,
+                    unsigned char* deflate_buf,
+                    uint64_t offset,
+                    uint64_t size,
+                    uint8_t** dest,
+                    uint64_t dest_nbytes,
+                    const cucim::io::Device& out_device)
+{
+    (void)out_device;
+    struct libdeflate_decompressor* d;
+
+    d = libdeflate_alloc_decompressor();
+
+    if (d == nullptr)
+    {
+        throw std::runtime_error("Unable to allocate decompressor for libdeflate!");
+    }
+
+    if (deflate_buf == nullptr)
+    {
+        if ((deflate_buf = (unsigned char*)malloc(size)) == nullptr)
+        {
+            throw std::runtime_error("Unable to allocate buffer for libdeflate!");
+        }
+
+        if (pread(fd, deflate_buf, size, offset) < 1)
+        {
+            throw std::runtime_error("Unable to read file for libdeflate!");
+        }
+    }
+    else
+    {
+        fd = -1;
+        deflate_buf += offset;
+    }
+
+    size_t out_size;
+    libdeflate_zlib_decompress(d, deflate_buf, size /*in_nbytes*/, *dest, dest_nbytes /*out_nbytes_avail*/, &out_size);
+
+    libdeflate_free_decompressor(d);
+    return true;
+}
+
+} // namespace cuslide::deflate
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.h
new file mode 100644
index 000000000..3ad5cf069
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/deflate/deflate.h
@@ -0,0 +1,33 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2021 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUSLIDE_DEFLATE_H
+#define CUSLIDE_DEFLATE_H
+
+#include <cucim/io/device.h>
+
+namespace cuslide::deflate
+{
+
+bool decode_deflate(int fd,
+                    unsigned char* deflate_buf,
+                    uint64_t offset,
+                    uint64_t size,
+                    uint8_t** dest,
+                    uint64_t dest_nbytes,
+                    const cucim::io::Device& out_device);
+}
+#endif // CUSLIDE_DEFLATE_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.cpp b/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.cpp
new file mode 100644
index 000000000..887cdd7d0
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.cpp
@@ -0,0 +1,221 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2020 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/**
+ * Code below is derived from the libjpeg-turbo's example code (tjexample.c) which is under three compatible
+ * BSD-style open source licenses
+ * - The IJG (Independent JPEG Group) License
+ * - The Modified (3-clause) BSD License
+ * - The zlib License
+ * Please see LICENSE-3rdparty.md for the detail.
+ * (https://github.com/libjpeg-turbo/libjpeg-turbo/blob/00607ec260efa4cfe10f9b36d6e3d3590ae92d79/tjexample.c)
+ */
+
+#include "libjpeg_turbo.h"
+
+#include <cstring>
+#include <jpeglib.h>
+#include <setjmp.h>
+#include <turbojpeg.h>
+#include <unistd.h>
+
+struct my_error_mgr
+{
+    struct jpeg_error_mgr pub;
+    jmp_buf setjmp_buffer;
+    void (*emit_message)(j_common_ptr, int);
+    boolean warning, stopOnWarning;
+};
+
+typedef struct _tjinstance
+{
+    struct jpeg_compress_struct cinfo;
+    struct jpeg_decompress_struct dinfo;
+    struct my_error_mgr jerr;
+    int init, headerRead;
+    char errStr[JMSG_LENGTH_MAX];
+    boolean isInstanceError;
+} tjinstance;
+
+extern "C" void jpeg_mem_src_tj(j_decompress_ptr, const unsigned char*, unsigned long);
+
+namespace cuslide::jpeg
+{
+
+
+#define THROW(action, message)                                                                                         \
+    {                                                                                                                  \
+        printf("ERROR in line %d while %s:\n%s\n", __LINE__, action, message);                                         \
+        retval = -1;                                                                                                   \
+        goto bailout;                                                                                                  \
+    }
+
+#define THROW_TJ(action) THROW(action, tjGetErrorStr2(tjInstance))
+
+#define THROW_UNIX(action) THROW(action, strerror(errno))
+
+#define DEFAULT_SUBSAMP TJSAMP_444
+#define DEFAULT_QUALITY 95
+
+// static const char* subsampName[TJ_NUMSAMP] = { "4:4:4", "4:2:2", "4:2:0", "Grayscale", "4:4:0", "4:1:1" };
+
+// static const char* colorspaceName[TJ_NUMCS] = { "RGB", "YCbCr", "GRAY", "CMYK", "YCCK" };
+
+bool decode_libjpeg(int fd,
+                    unsigned char* jpeg_buf,
+                    uint64_t offset,
+                    uint64_t size,
+                    const void* jpegtable_data,
+                    uint32_t jpegtable_count,
+                    uint8_t** dest,
+                    const cucim::io::Device& out_device)
+{
+    (void)out_device;
+
+    //    tjscalingfactor scalingFactor = { 1, 1 };
+    tjtransform xform;
+    int flags = 0;
+    //    flags |= TJFLAG_FASTUPSAMPLE;
+    //    flags |= TJFLAG_FASTDCT;
+    //    flags |= TJFLAG_ACCURATEDCT;
+    int width, height;
+    int retval = 0, pixelFormat = TJPF_RGB;
+    (void)retval; // retval is used by macro THROW
+    tjhandle tjInstance = nullptr;
+
+    memset(&xform, 0, sizeof(tjtransform));
+
+    /* Input image is a JPEG image.  Decompress and/or transform it. */
+
+    int inSubsamp, inColorspace;
+    //    int doTransform = (xform.op != TJXOP_NONE || xform.options != 0 || xform.customFilter != NULL);
+
+    /* Read the JPEG file/buffer into memory. */
+    if (size == 0)
+        THROW("determining input file size", "Input file contains no data");
+
+    if (jpeg_buf == nullptr)
+    {
+        if ((jpeg_buf = (unsigned char*)tjAlloc(size)) == nullptr)
+            THROW_UNIX("allocating JPEG buffer");
+
+        if (pread(fd, jpeg_buf, size, offset) < 1)
+            THROW_UNIX("reading input file");
+    }
+    else
+    {
+        fd = -1;
+        jpeg_buf += offset;
+    }
+
+    if ((tjInstance = tjInitDecompress()) == nullptr)
+        THROW_TJ("initializing decompressor");
+
+    // Read jpeg tables if exists
+    if (jpegtable_count)
+    {
+        if (!read_jpeg_header_tables(tjInstance, jpegtable_data, jpegtable_count))
+        {
+            THROW_TJ("reading JPEG header tables");
+        }
+    }
+
+    if (tjDecompressHeader3(tjInstance, jpeg_buf, size, &width, &height, &inSubsamp, &inColorspace) < 0)
+        THROW_TJ("reading JPEG header");
+
+    //    printf("%s Image:  %d x %d pixels, %s subsampling, %s colorspace\n", (doTransform ? "Transformed" : "Input"),
+    //    width,
+    //           height, subsampName[inSubsamp], colorspaceName[inColorspace]);
+
+    // Allocate memory only when dest is not null
+    if (*dest == nullptr)
+    {
+        if ((*dest = (unsigned char*)tjAlloc(width * height * tjPixelSize[pixelFormat])) == nullptr)
+            THROW_UNIX("allocating uncompressed image buffer");
+    }
+
+    if (tjDecompress2(tjInstance, jpeg_buf, size, (unsigned char*)*dest, width, 0, height, pixelFormat, flags) < 0)
+        THROW_TJ("decompressing JPEG image");
+
+    if (fd != -1)
+    {
+        tjFree(jpeg_buf);
+    }
+    tjDestroy(tjInstance);
+    return true;
+
+bailout:
+    if (tjInstance)
+        tjDestroy(tjInstance);
+    if (fd != -1)
+    {
+        tjFree(jpeg_buf);
+    }
+    return false;
+}
+
+bool read_jpeg_header_tables(const void* handle, const void* jpeg_buf, unsigned long jpeg_size)
+{
+    tjinstance* instance = (tjinstance*)handle;
+    j_decompress_ptr dinfo = NULL;
+    dinfo = &instance->dinfo;
+    instance->jerr.warning = FALSE;
+    instance->isInstanceError = FALSE;
+
+    if (setjmp(instance->jerr.setjmp_buffer))
+    {
+        /* If we get here, the JPEG code has signaled an error. */
+        return false;
+    }
+
+    jpeg_mem_src_tj(dinfo, static_cast<const unsigned char*>(jpeg_buf), jpeg_size);
+    if (jpeg_read_header(dinfo, FALSE) != JPEG_HEADER_TABLES_ONLY)
+    {
+        return false;
+    }
+
+    return true;
+}
+
+bool get_dimension(const void* image_buf, uint64_t offset, uint64_t size, int* out_width, int* out_height)
+{
+    int retval = 0;
+    (void)retval; // retval is used by macro THROW
+    tjhandle tjInstance = nullptr;
+
+    int inSubsamp, inColorspace;
+
+    if (image_buf == nullptr || size == 0)
+        THROW("determining input buffer size", "Input buffer contains no data");
+
+    if ((tjInstance = tjInitDecompress()) == nullptr)
+        THROW_TJ("initializing decompressor");
+
+    if (tjDecompressHeader3(tjInstance, static_cast<const unsigned char*>(image_buf) + offset, size, out_width,
+                            out_height, &inSubsamp, &inColorspace) < 0)
+        THROW_TJ("reading JPEG header");
+
+    tjDestroy(tjInstance);
+    return true;
+
+bailout:
+    if (tjInstance)
+        tjDestroy(tjInstance);
+    return false;
+}
+
+} // namespace cuslide::jpeg
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.h
new file mode 100644
index 000000000..a1e6a6d2b
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/jpeg/libjpeg_turbo.h
@@ -0,0 +1,55 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2020 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUSLIDE_LIBJPEG_TURBO_H
+#define CUSLIDE_LIBJPEG_TURBO_H
+
+#include <cucim/io/device.h>
+
+namespace cuslide::jpeg
+{
+
+bool decode_libjpeg(int fd,
+                    unsigned char* jpeg_buf,
+                    uint64_t offset,
+                    uint64_t size,
+                    const void* jpegtable_data,
+                    uint32_t jpegtable_count,
+                    uint8_t** dest,
+                    const cucim::io::Device& out_device);
+
+/**
+ * Reads jpeg header tables.
+ *
+ * TIFF file's TIFFTAG_JPEGTABLES tag has the information about JPEG Quantization table.
+ * This method is for reading the information.
+ * If Quantization table information is not interpreted, the following error message can occurs:
+ *
+ *     Quantization table 0x00 was not defined
+ *
+ * @param handle A pointer to tjinstance
+ * @param jpeg_buf jpeg buffer data
+ * @param jpeg_size jpeg buffer size
+ * @return true if it succeeds
+ */
+bool read_jpeg_header_tables(const void* handle, const void* jpeg_buf, unsigned long jpeg_size);
+
+bool get_dimension(const void* image_buf, uint64_t offset, uint64_t size, int* out_width, int* out_height);
+
+} // namespace cuslide::jpeg
+
+
+#endif // CUSLIDE_LIBJPEG_TURBO_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/srctest.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/srctest.h
new file mode 100644
index 000000000..673c0ed66
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/srctest.h
@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUSLIDE_SRCTEST_H
+#define CUSLIDE_SRCTEST_H
+
+#endif // CUSLIDE_SRCTEST_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp
new file mode 100644
index 000000000..7955116e7
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp
@@ -0,0 +1,734 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ifd.h"
+
+#include "tiff.h"
+#include "cuslide/jpeg/libjpeg_turbo.h"
+#include "cuslide/deflate/deflate.h"
+
+#include <tiffio.h>
+#include <tiffiop.h> // this is not included in the released library
+#include <turbojpeg.h>
+#include <fmt/format.h>
+
+
+namespace cuslide::tiff
+{
+
+IFD::IFD(TIFF* tiff, uint16_t index, ifd_offset_t offset) : tiff_(tiff), ifd_index_(index), ifd_offset_(offset)
+{
+    auto tif = tiff->client();
+    int ret;
+
+    char* software_char_ptr = nullptr;
+    char* model_char_ptr = nullptr;
+    // TODO: error handling
+
+    ret = TIFFGetField(tif, TIFFTAG_SOFTWARE, &software_char_ptr);
+    software_ = std::string(software_char_ptr ? software_char_ptr : "");
+    ret = TIFFGetField(tif, TIFFTAG_MODEL, &model_char_ptr);
+    model_ = std::string(model_char_ptr ? model_char_ptr : "");
+    ret = TIFFGetField(tif, TIFFTAG_IMAGEDESCRIPTION, &model_char_ptr);
+    image_description_ = std::string(model_char_ptr ? model_char_ptr : "");
+
+    TIFFDirectory& tif_dir = tif->tif_dir;
+    flags_ = tif->tif_flags;
+
+    width_ = tif_dir.td_imagewidth;
+    height_ = tif_dir.td_imagelength;
+    if ((flags_ & TIFF_ISTILED) != 0)
+    {
+        tile_width_ = tif_dir.td_tilewidth;
+        tile_height_ = tif_dir.td_tilelength;
+    }
+    bits_per_sample_ = tif_dir.td_bitspersample;
+    samples_per_pixel_ = tif_dir.td_samplesperpixel;
+    subfile_type_ = tif_dir.td_subfiletype;
+    planar_config_ = tif_dir.td_planarconfig;
+    photometric_ = tif_dir.td_photometric;
+    compression_ = tif_dir.td_compression;
+
+    //    ret = TIFFGetField(tif, TIFFTAG_IMAGEWIDTH, &width_);
+    //    ret = TIFFGetField(tif, TIFFTAG_IMAGELENGTH, &height_);
+    //    ret = TIFFGetField(tif, TIFFTAG_TILEWIDTH, &tile_width_);
+    //    ret = TIFFGetField(tif, TIFFTAG_TILELENGTH, &tile_height_);
+    //    ret = TIFFGetField(tif, TIFFTAG_BITSPERSAMPLE, &bits_per_sample_);
+    //    ret = TIFFGetField(tif, TIFFTAG_SAMPLESPERPIXEL, &samples_per_pixel_);
+    //    ret = TIFFGetField(tif, TIFFTAG_SUBFILETYPE, &subfile_type_); // for checking if FILETYPE_REDUCEDIMAGE
+    //    ret = TIFFGetField(tif, TIFFTAG_PLANARCONFIG, &planar_config_);
+    //    ret = TIFFGetField(tif, TIFFTAG_PHOTOMETRIC, &photometric_);
+    //    ret = TIFFGetField(tif, TIFFTAG_COMPRESSION, &compression_);
+    //    printf("[GB] offset_entry:%lu %p\n", tif->tif_dir.td_stripoffset_entry.tdir_count,
+    //    tif->tif_dir.td_stripoffset_p); printf("[GB] width: %d %d\n", tif->tif_dir.td_imagewidth, width_);
+    //    printf("[GB] bytecount entry2:%lu %p\n", tif->tif_dir.td_stripbytecount_entry.tdir_count,
+    //    tif->tif_dir.td_stripbytecount_p);
+    (void)ret;
+
+    subifd_count_ = tif_dir.td_nsubifd;
+    uint64_t* subifd_offsets = tif_dir.td_subifd;
+    //    ret = TIFFGetField(tif, TIFFTAG_SUBIFD, &subifd_count, &subifd_offsets);
+    if (subifd_count_)
+    {
+        subifd_offsets_.resize(subifd_count_);
+        subifd_offsets_.insert(subifd_offsets_.end(), &subifd_offsets[0], &subifd_offsets[subifd_count_]);
+    }
+
+    // Read jpeg tables if we can read the jpeg directly
+    if (is_read_optimizable())
+    {
+        if (compression_ == COMPRESSION_JPEG)
+        {
+            uint8_t* jpegtable_data = nullptr;
+            uint32_t jpegtable_count = 0;
+
+            ret = TIFFGetField(tif, TIFFTAG_JPEGTABLES, &jpegtable_count, &jpegtable_data);
+            jpegtable_.reserve(jpegtable_count);
+            jpegtable_.insert(jpegtable_.end(), jpegtable_data, jpegtable_data + jpegtable_count);
+        }
+
+        image_piece_count_ = tif_dir.td_stripoffset_entry.tdir_count;
+
+        image_piece_offsets_.reserve(image_piece_count_);
+        uint64* td_stripoffset_p = tif_dir.td_stripoffset_p;
+        uint64* td_stripbytecount_p = tif_dir.td_stripbytecount_p;
+
+        // Copy data to vector
+        image_piece_offsets_.insert(
+            image_piece_offsets_.end(), &td_stripoffset_p[0], &td_stripoffset_p[image_piece_count_]);
+        image_piece_bytecounts_.insert(
+            image_piece_bytecounts_.end(), &td_stripbytecount_p[0], &td_stripbytecount_p[image_piece_count_]);
+    }
+
+    //    TIFFPrintDirectory(tif, stdout, TIFFPRINT_STRIPS);
+}
+
+bool IFD::read(const TIFF* tiff,
+               const cucim::io::format::ImageMetadataDesc* metadata,
+               const cucim::io::format::ImageReaderRegionRequestDesc* request,
+               cucim::io::format::ImageDataDesc* out_image_data)
+{
+    ::TIFF* tif = tiff->tiff_client_;
+
+    uint16_t ifd_index = ifd_index_;
+
+    std::string device_name(request->device);
+
+    if (request->shm_name)
+    {
+        device_name = device_name + "[" + request->shm_name + "]"; // TODO: check performance
+    }
+    cucim::io::Device out_device(device_name);
+
+    int64_t sx = request->location[0];
+    int64_t sy = request->location[1];
+    int64_t w = request->size[0];
+    int64_t h = request->size[1];
+    int32_t n_ch = 3; // number of channels
+
+    void* raster = nullptr;
+
+    DLTensor* out_buf = request->buf;
+    if (out_buf && out_buf->data)
+    {
+        // TODO: memory size check if out_buf->data has high-enough memory (>= tjBufSize())
+        raster = out_buf->data;
+    }
+
+    if (is_read_optimizable())
+    {
+        if (!raster)
+        {
+            raster = cucim_malloc(w * h * samples_per_pixel_); // RGB image
+            memset(raster, 0, w * h * 3);
+        }
+
+
+        if (!read_region_tiles(tiff, this, sx, sy, w, h, raster, out_device))
+        {
+            fmt::print(stderr, "[Error] Failed to read region with libjpeg!\n");
+        }
+    }
+    else
+    {
+        // Handle out-of-boundary case
+        int64_t ex = sx + w - 1;
+        int64_t ey = sy + h - 1;
+        if (sx < 0 || sy < 0 || sx >= width_ || sy >= height_ || ex < 0 || ey < 0 || ex >= width_ || ey >= height_)
+        {
+            throw std::invalid_argument(fmt::format(
+                "Cannot handle the out-of-boundary cases for a non-RGB image or a non-Jpeg/Deflate-compressed image."));
+        }
+
+        if (tif->tif_curdir != ifd_index)
+        {
+            TIFFSetDirectory(tif, ifd_index);
+        }
+        // RGBA -> 4 channels
+        n_ch = 4;
+
+        char emsg[1024];
+        if (TIFFRGBAImageOK(tif, emsg))
+        {
+            TIFFRGBAImage img;
+            if (TIFFRGBAImageBegin(&img, tif, -1, emsg))
+            {
+                size_t npixels;
+                npixels = w * h;
+                if (!raster)
+                {
+                    raster = cucim_malloc(npixels * sizeof(uint32_t));
+                }
+                img.col_offset = sx;
+                img.row_offset = sy;
+                img.req_orientation = ORIENTATION_TOPLEFT;
+
+                if (raster != nullptr)
+                {
+                    if (!TIFFRGBAImageGet(&img, (uint32_t*)raster, w, h))
+                    {
+                        memset(raster, 0, w * h * sizeof(uint32_t));
+                    }
+                }
+            }
+            TIFFRGBAImageEnd(&img);
+        }
+    }
+
+    int ndim = 3;
+    int64_t* shape = (int64_t*)cucim_malloc(sizeof(int64_t) * ndim);
+    shape[0] = h;
+    shape[1] = w;
+    shape[2] = n_ch;
+
+    out_image_data->container.data = raster;
+    out_image_data->container.ctx = DLContext{ static_cast<DLDeviceType>(cucim::io::DeviceType::kCPU), 0 };
+    out_image_data->container.ndim = ndim;
+    out_image_data->container.dtype = metadata->dtype;
+    out_image_data->container.shape = shape;
+    out_image_data->container.strides = nullptr; // Tensor is compact and row-majored
+    out_image_data->container.byte_offset = 0;
+
+    return true;
+}
+
+uint32_t IFD::index() const
+{
+    return ifd_index_;
+}
+ifd_offset_t IFD::offset() const
+{
+    return ifd_offset_;
+}
+
+std::string& IFD::software()
+{
+    return software_;
+}
+std::string& IFD::model()
+{
+    return model_;
+}
+std::string& IFD::image_description()
+{
+    return image_description_;
+}
+uint32_t IFD::width() const
+{
+    return width_;
+}
+uint32_t IFD::height() const
+{
+    return height_;
+}
+uint32_t IFD::tile_width() const
+{
+    return tile_width_;
+}
+uint32_t IFD::tile_height() const
+{
+    return tile_height_;
+}
+uint32_t IFD::bits_per_sample() const
+{
+    return bits_per_sample_;
+}
+uint32_t IFD::samples_per_pixel() const
+{
+    return samples_per_pixel_;
+}
+uint64_t IFD::subfile_type() const
+{
+    return subfile_type_;
+}
+uint16_t IFD::planar_config() const
+{
+    return planar_config_;
+}
+uint16_t IFD::photometric() const
+{
+    return photometric_;
+}
+
+uint16_t IFD::compression() const
+{
+    return compression_;
+}
+uint16_t IFD::subifd_count() const
+{
+    return subifd_count_;
+}
+std::vector<uint64_t>& IFD::subifd_offsets()
+{
+    return subifd_offsets_;
+}
+uint32_t IFD::image_piece_count() const
+{
+    return image_piece_count_;
+}
+const std::vector<uint64_t>& IFD::image_piece_offsets() const
+{
+    return image_piece_offsets_;
+}
+const std::vector<uint64_t>& IFD::image_piece_bytecounts() const
+{
+    return image_piece_bytecounts_;
+}
+
+bool IFD::is_read_optimizable() const
+{
+    return (compression_ == COMPRESSION_ADOBE_DEFLATE || compression_ == COMPRESSION_JPEG ||
+            compression_ == COMPRESSION_DEFLATE) &&
+           bits_per_sample_ == 8 && samples_per_pixel_ == 3 && planar_config_ == PLANARCONFIG_CONTIG &&
+           (photometric_ == PHOTOMETRIC_RGB || photometric_ == PHOTOMETRIC_YCBCR) &&
+           !tiff_->is_in_read_config(TIFF::kUseLibTiff);
+}
+
+bool IFD::read_region_tiles(const TIFF* tiff,
+                            const IFD* ifd,
+                            const int64_t sx,
+                            const int64_t sy,
+                            const int64_t w,
+                            const int64_t h,
+                            void* raster,
+                            const cucim::io::Device& out_device)
+{
+    // Reference code: https://github.com/libjpeg-turbo/libjpeg-turbo/blob/master/tjexample.c
+
+    int64_t ex = sx + w - 1;
+    int64_t ey = sy + h - 1;
+
+    uint32_t width = ifd->width_;
+    uint32_t height = ifd->height_;
+
+    // Handle out-of-boundary case
+    if (sx < 0 || sy < 0 || sx >= width || sy >= height || ex < 0 || ey < 0 || ex >= width || ey >= height)
+    {
+        return read_region_tiles_boundary(tiff, ifd, sx, sy, w, h, raster, out_device);
+    }
+
+    uint8_t background_value = tiff->background_value_;
+    uint16_t compression_method = ifd->compression_;
+
+    // TODO: revert this once we can get RGB data instead of RGBA
+    uint32_t samples_per_pixel = 3; // ifd->samples_per_pixel();
+
+    const void* jpegtable_data = ifd->jpegtable_.data();
+    uint32_t jpegtable_count = ifd->jpegtable_.size();
+
+    uint32_t tw = ifd->tile_width_;
+    uint32_t th = ifd->tile_height_;
+
+    uint32_t offset_sx = static_cast<uint32_t>(sx / tw); // x-axis start offset for the requested region in the ifd tile
+                                                         // array as grid
+    uint32_t offset_ex = static_cast<uint32_t>(ex / tw); // x-axis end  offset for the requested region in the ifd tile
+                                                         // array as grid
+    uint32_t offset_sy = static_cast<uint32_t>(sy / th); // y-axis start offset for the requested region in the ifd tile
+                                                         // array as grid
+    uint32_t offset_ey = static_cast<uint32_t>(ey / th); // y-axis end offset for the requested region in the ifd tile
+                                                         // array as grid
+
+    uint32_t pixel_offset_sx = static_cast<uint32_t>(sx % tw);
+    uint32_t pixel_offset_ex = static_cast<uint32_t>(ex % tw);
+    uint32_t pixel_offset_sy = static_cast<uint32_t>(sy % th);
+    uint32_t pixel_offset_ey = static_cast<uint32_t>(ey % th);
+
+    uint32_t stride_y = width / tw + !!(width % tw); // # of tiles in a row(y) in the ifd tile array as grid
+
+    uint32_t start_index_y = offset_sy * stride_y;
+    uint32_t end_index_y = offset_ey * stride_y;
+
+    // Memory for tile_raster would be manually allocated here, instead of using decode_libjpeg().
+    // Need to free the manually. Usually it is set to nullptr and memory is created by decode_libjpeg() by using
+    // tjAlloc() (Also need to free with tjFree() after use. See the documentation of tjAlloc() for the detail.)
+    const int pixel_format = TJPF_RGB; // TODO: support other pixel format
+    const int pixel_size_nbytes = tjPixelSize[pixel_format];
+    const size_t tile_raster_nbytes = tw * th * pixel_size_nbytes;
+    uint8_t* tile_raster = static_cast<uint8_t*>(cucim_malloc(tile_raster_nbytes));
+
+    int tiff_file = tiff->file_handle_.fd;
+
+
+    //    uint32_t nbytes_offset_sx = offset_sx * samples_per_pixel;
+    //    uint32_t nbytes_offset_ex = offset_ex * samples_per_pixel;
+    uint32_t dest_pixel_step_y = w * samples_per_pixel;
+    //    uint32_t dest_pixel_tile_step_y = dest_pixel_step_y * th;
+
+    uint32_t nbytes_tw = tw * samples_per_pixel;
+    //    uint32_t nbytes_th = th * samples_per_pixel;
+    //    uint32_t nbytes_offset_sy = offset_sy * nbytes_tw;
+    //    uint32_t nbytes_offset_ey = offset_ey * nbytes_tw;
+
+    auto dest_start_ptr = static_cast<uint8_t*>(raster);
+
+    // TODO: Current implementation doesn't consider endianness so need to consider later
+    // TODO: Consider tile's depth tag.
+    for (uint32_t index_y = start_index_y; index_y <= end_index_y; index_y += stride_y)
+    {
+        uint32_t tile_pixel_offset_sy = (index_y == start_index_y) ? pixel_offset_sy : 0;
+        uint32_t tile_pixel_offset_ey = (index_y == end_index_y) ? pixel_offset_ey : (th - 1);
+        uint32_t dest_pixel_offset_len_y = tile_pixel_offset_ey - tile_pixel_offset_sy + 1;
+
+        uint32_t dest_pixel_index_x = 0;
+
+        uint32_t index = static_cast<uint32_t>(index_y) + offset_sx;
+        for (uint32_t offset_x = offset_sx; offset_x <= offset_ex; ++offset_x, ++index)
+        {
+            auto tiledata_offset = static_cast<uint64_t>(ifd->image_piece_offsets_[index]);
+            auto tiledata_size = static_cast<uint64_t>(ifd->image_piece_bytecounts_[index]);
+
+            uint32_t tile_pixel_offset_x = (offset_x == offset_sx) ? pixel_offset_sx : 0;
+            uint32_t nbytes_tile_pixel_size_x = (offset_x == offset_ex) ?
+                                                    (pixel_offset_ex - tile_pixel_offset_x + 1) * samples_per_pixel :
+                                                    (tw - tile_pixel_offset_x) * samples_per_pixel;
+
+            uint32_t nbytes_tile_index = (tile_pixel_offset_sy * tw + tile_pixel_offset_x) * samples_per_pixel;
+            uint32_t dest_pixel_index = dest_pixel_index_x;
+            if (tiledata_size > 0)
+            {
+                if (compression_method == COMPRESSION_JPEG)
+                {
+                    cuslide::jpeg::decode_libjpeg(tiff_file, nullptr, tiledata_offset, tiledata_size, jpegtable_data,
+                                                  jpegtable_count, &tile_raster, out_device);
+                }
+                else
+                {
+                    cuslide::deflate::decode_deflate(tiff_file, nullptr, tiledata_offset, tiledata_size, &tile_raster,
+                                                     tile_raster_nbytes, out_device);
+                }
+
+                for (uint32_t ty = tile_pixel_offset_sy; ty <= tile_pixel_offset_ey;
+                     ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                {
+                    //                printf("[GB] index_y: %d, offset_x: %d   y:%d, %d, %d %d\n", index_y, offset_x,
+                    //                ty, dest_pixel_index, nbytes_tile_index, nbytes_tile_pixel_size_x);
+                    memcpy(dest_start_ptr + dest_pixel_index, tile_raster + nbytes_tile_index, nbytes_tile_pixel_size_x);
+                }
+            }
+            else
+            {
+                for (uint32_t ty = tile_pixel_offset_sy; ty <= tile_pixel_offset_ey;
+                     ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                {
+                    // Set (255,255,255)
+                    memset(dest_start_ptr + dest_pixel_index, background_value, nbytes_tile_pixel_size_x);
+                }
+            }
+            //            printf("\n");
+            dest_pixel_index_x += nbytes_tile_pixel_size_x;
+        }
+        dest_start_ptr += dest_pixel_step_y * dest_pixel_offset_len_y;
+    }
+
+    cucim_free(tile_raster);
+
+    return true;
+}
+
+bool IFD::read_region_tiles_boundary(const TIFF* tiff,
+                                     const IFD* ifd,
+                                     const int64_t sx,
+                                     const int64_t sy,
+                                     const int64_t w,
+                                     const int64_t h,
+                                     void* raster,
+                                     const cucim::io::Device& out_device)
+{
+    (void)out_device;
+    // Reference code: https://github.com/libjpeg-turbo/libjpeg-turbo/blob/master/tjexample.c
+
+    uint8_t background_value = tiff->background_value_;
+    uint16_t compression_method = ifd->compression_;
+    int64_t ex = sx + w - 1;
+    int64_t ey = sy + h - 1;
+
+    uint32_t width = ifd->width_;
+    uint32_t height = ifd->height_;
+
+    uint32_t tw = ifd->tile_width_;
+    uint32_t th = ifd->tile_height_;
+
+    // Memory for tile_raster would be manually allocated here, instead of using decode_libjpeg().
+    // Need to free the manually. Usually it is set to nullptr and memory is created by decode_libjpeg() by using
+    // tjAlloc() (Also need to free with tjFree() after use. See the documentation of tjAlloc() for the detail.)
+    const int pixel_format = TJPF_RGB; // TODO: support other pixel format
+    const int pixel_size_nbytes = tjPixelSize[pixel_format];
+    const size_t tile_raster_nbytes = tw * th * pixel_size_nbytes;
+    uint8_t* tile_raster = static_cast<uint8_t*>(cucim_malloc(tile_raster_nbytes));
+    auto dest_start_ptr = static_cast<uint8_t*>(raster);
+
+    bool is_out_of_image = (ex < 0 || width <= sx || ey < 0 || height <= sy);
+    if (is_out_of_image)
+    {
+        // Fill (255,255,255) and return
+        memset(dest_start_ptr, background_value, w * h * pixel_size_nbytes);
+        return true;
+    }
+
+    // TODO: revert this once we can get RGB data instead of RGBA
+    uint32_t samples_per_pixel = 3; // ifd->samples_per_pixel();
+
+    const void* jpegtable_data = ifd->jpegtable_.data();
+    uint32_t jpegtable_count = ifd->jpegtable_.size();
+
+    bool sx_in_range = (sx >= 0 && sx < width);
+    bool ex_in_range = (ex >= 0 && ex < width);
+    bool sy_in_range = (sy >= 0 && sy < height);
+    bool ey_in_range = (ey >= 0 && ey < height);
+
+    int64_t offset_boundary_x = (static_cast<int64_t>(width) - 1) / tw;
+    int64_t offset_boundary_y = (static_cast<int64_t>(height) - 1) / th;
+
+    int64_t offset_sx = sx / tw; // x-axis start offset for the requested region in the
+                                 // ifd tile array as grid
+
+    int64_t offset_ex = ex / tw; // x-axis end  offset for the requested region in the
+                                 // ifd tile array as grid
+
+    int64_t offset_sy = sy / th; // y-axis start offset for the requested region in the
+                                 // ifd tile array as grid
+    int64_t offset_ey = ey / th; // y-axis end offset for the requested region in the
+                                 // ifd tile array as grid
+    int64_t pixel_offset_sx = (sx % tw);
+    int64_t pixel_offset_ex = (ex % tw);
+    int64_t pixel_offset_sy = (sy % th);
+    int64_t pixel_offset_ey = (ey % th);
+    int64_t pixel_offset_boundary_x = ((width - 1) % tw);
+    int64_t pixel_offset_boundary_y = ((height - 1) % th);
+
+    // Make sure that division and modulo has same value with Python's one (e.g., making -1 / 3 == -1 instead of 0)
+    if (pixel_offset_sx < 0)
+    {
+        pixel_offset_sx += tw;
+        --offset_sx;
+    }
+    if (pixel_offset_ex < 0)
+    {
+        pixel_offset_ex += tw;
+        --offset_ex;
+    }
+    if (pixel_offset_sy < 0)
+    {
+        pixel_offset_sy += th;
+        --offset_sy;
+    }
+    if (pixel_offset_ey < 0)
+    {
+        pixel_offset_ey += th;
+        --offset_ey;
+    }
+    int64_t offset_min_x = sx_in_range ? offset_sx : 0;
+    int64_t offset_max_x = ex_in_range ? offset_ex : offset_boundary_x;
+    int64_t offset_min_y = sy_in_range ? offset_sy : 0;
+    int64_t offset_max_y = ey_in_range ? offset_ey : offset_boundary_y;
+
+    uint32_t stride_y = width / tw + !!(width % tw); // # of tiles in a row(y) in the ifd tile array as grid
+
+    int64_t start_index_y = offset_sy * stride_y;
+    int64_t start_index_min_y = offset_min_y * stride_y;
+    int64_t end_index_y = offset_ey * stride_y;
+    int64_t end_index_max_y = offset_max_y * stride_y;
+    int64_t boundary_index_y = offset_boundary_y * stride_y;
+
+
+    int tiff_file = tiff->file_handle_.fd;
+
+    uint32_t dest_pixel_step_y = w * samples_per_pixel;
+    uint32_t nbytes_tw = tw * samples_per_pixel;
+
+
+    // TODO: Current implementation doesn't consider endianness so need to consider later
+    // TODO: Consider tile's depth tag.
+    for (int64_t index_y = start_index_y; index_y <= end_index_y; index_y += stride_y)
+    {
+        uint32_t tile_pixel_offset_sy = (index_y == start_index_y) ? pixel_offset_sy : 0;
+        uint32_t tile_pixel_offset_ey = (index_y == end_index_y) ? pixel_offset_ey : (th - 1);
+        uint32_t dest_pixel_offset_len_y = tile_pixel_offset_ey - tile_pixel_offset_sy + 1;
+
+        uint32_t dest_pixel_index_x = 0;
+
+        int64_t index = index_y + offset_sx;
+        for (int64_t offset_x = offset_sx; offset_x <= offset_ex; ++offset_x, ++index)
+        {
+            uint64_t tiledata_offset = 0;
+            uint64_t tiledata_size = 0;
+            if (offset_x >= offset_min_x && offset_x <= offset_max_x && index_y >= start_index_min_y &&
+                index_y <= end_index_max_y)
+            {
+                tiledata_offset = static_cast<uint64_t>(ifd->image_piece_offsets_[index]);
+                tiledata_size = static_cast<uint64_t>(ifd->image_piece_bytecounts_[index]);
+            }
+
+            uint32_t tile_pixel_offset_x = (offset_x == offset_sx) ? pixel_offset_sx : 0;
+            uint32_t nbytes_tile_pixel_size_x = (offset_x == offset_ex) ?
+                                                    (pixel_offset_ex - tile_pixel_offset_x + 1) * samples_per_pixel :
+                                                    (tw - tile_pixel_offset_x) * samples_per_pixel;
+
+            uint32_t nbytes_tile_index = (tile_pixel_offset_sy * tw + tile_pixel_offset_x) * samples_per_pixel;
+            uint32_t dest_pixel_index = dest_pixel_index_x;
+            if (tiledata_size > 0)
+            {
+                bool copy_partial = false;
+                uint32_t fixed_nbytes_tile_pixel_size_x = nbytes_tile_pixel_size_x;
+                uint32_t fixed_tile_pixel_offset_ey = tile_pixel_offset_ey;
+
+                if (offset_x == offset_boundary_x)
+                {
+                    copy_partial = true;
+                    if (offset_x != offset_ex)
+                    {
+                        fixed_nbytes_tile_pixel_size_x =
+                            (pixel_offset_boundary_x - tile_pixel_offset_x + 1) * samples_per_pixel;
+                    }
+                    else
+                    {
+                        fixed_nbytes_tile_pixel_size_x =
+                            (std::min(pixel_offset_boundary_x, pixel_offset_ex) - tile_pixel_offset_x + 1) *
+                            samples_per_pixel;
+                    }
+                }
+                if (index_y == boundary_index_y)
+                {
+                    copy_partial = true;
+                    if (index_y != end_index_y)
+                    {
+                        fixed_tile_pixel_offset_ey = pixel_offset_boundary_y;
+                    }
+                    else
+                    {
+                        fixed_tile_pixel_offset_ey = std::min(pixel_offset_boundary_y, pixel_offset_ey);
+                    }
+                }
+
+                if (compression_method == COMPRESSION_JPEG)
+                {
+                    cuslide::jpeg::decode_libjpeg(tiff_file, nullptr, tiledata_offset, tiledata_size, jpegtable_data,
+                                                  jpegtable_count, &tile_raster, out_device);
+                }
+                else
+                {
+                    cuslide::deflate::decode_deflate(tiff_file, nullptr, tiledata_offset, tiledata_size, &tile_raster,
+                                                     tile_raster_nbytes, out_device);
+                }
+
+                if (copy_partial)
+                {
+                    uint32_t fill_gap_x = nbytes_tile_pixel_size_x - fixed_nbytes_tile_pixel_size_x;
+                    // Fill original, then fill white for remaining
+                    if (fill_gap_x > 0)
+                    {
+                        for (uint32_t ty = tile_pixel_offset_sy; ty <= fixed_tile_pixel_offset_ey;
+                             ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                        {
+                            memcpy(dest_start_ptr + dest_pixel_index, tile_raster + nbytes_tile_index,
+                                   fixed_nbytes_tile_pixel_size_x);
+                            memset(dest_start_ptr + dest_pixel_index + fixed_nbytes_tile_pixel_size_x, background_value,
+                                   fill_gap_x);
+                        }
+                    }
+                    else
+                    {
+                        for (uint32_t ty = tile_pixel_offset_sy; ty <= fixed_tile_pixel_offset_ey;
+                             ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                        {
+                            memcpy(dest_start_ptr + dest_pixel_index, tile_raster + nbytes_tile_index,
+                                   fixed_nbytes_tile_pixel_size_x);
+                        }
+                    }
+
+                    for (uint32_t ty = fixed_tile_pixel_offset_ey + 1; ty <= tile_pixel_offset_ey;
+                         ++ty, dest_pixel_index += dest_pixel_step_y)
+                    {
+                        memset(dest_start_ptr + dest_pixel_index, background_value, nbytes_tile_pixel_size_x);
+                    }
+                }
+                else
+                {
+                    for (uint32_t ty = tile_pixel_offset_sy; ty <= tile_pixel_offset_ey;
+                         ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                    {
+                        memcpy(dest_start_ptr + dest_pixel_index, tile_raster + nbytes_tile_index,
+                               nbytes_tile_pixel_size_x);
+                    }
+                }
+            }
+            else
+            {
+                for (uint32_t ty = tile_pixel_offset_sy; ty <= tile_pixel_offset_ey;
+                     ++ty, dest_pixel_index += dest_pixel_step_y, nbytes_tile_index += nbytes_tw)
+                {
+                    // Set (255,255,255)
+                    memset(dest_start_ptr + dest_pixel_index, background_value, nbytes_tile_pixel_size_x);
+                }
+            }
+            dest_pixel_index_x += nbytes_tile_pixel_size_x;
+        }
+        dest_start_ptr += dest_pixel_step_y * dest_pixel_offset_len_y;
+    }
+
+    cucim_free(tile_raster);
+    return true;
+}
+
+} // namespace cuslide::tiff
+
+
+// Hidden methods for benchmarking.
+
+#include <fmt/format.h>
+#include <langinfo.h>
+#include <iostream>
+#include <fstream>
+
+namespace cuslide::tiff
+{
+void IFD::write_offsets_(const char* file_path)
+{
+    std::ofstream offsets(fmt::format("{}.offsets", file_path), std::ios::out | std::ios::binary | std::ios::trunc);
+    std::ofstream bytecounts(fmt::format("{}.bytecounts", file_path), std::ios::out | std::ios::binary | std::ios::trunc);
+
+    offsets.write(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));
+    bytecounts.write(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));
+    for (uint32_t i = 0; i < image_piece_count_; i++)
+    {
+        offsets.write(reinterpret_cast<char*>(&image_piece_offsets_[i]), sizeof(image_piece_offsets_[i]));
+        bytecounts.write(reinterpret_cast<char*>(&image_piece_bytecounts_[i]), sizeof(image_piece_bytecounts_[i]));
+    }
+    bytecounts.close();
+    offsets.close();
+}
+
+} // namespace cuslide::tiff
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.h
new file mode 100644
index 000000000..a4a0c7328
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.h
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUSLIDE_IFD_H
+#define CUSLIDE_IFD_H
+
+#include "types.h"
+
+#include <cucim/io/format/image_format.h>
+#include <cucim/io/device.h>
+//#include <tiffio.h>
+
+#include <memory>
+#include <vector>
+
+namespace cuslide::tiff
+{
+
+// Forward declaration.
+class TIFF;
+
+class EXPORT_VISIBLE IFD : public std::enable_shared_from_this<IFD>
+{
+public:
+    IFD(TIFF* tiff, uint16_t index, ifd_offset_t offset);
+    ~IFD() = default;
+
+    static bool read_region_tiles(const TIFF* tiff,
+                                  const IFD* ifd,
+                                  const int64_t sx,
+                                  const int64_t sy,
+                                  const int64_t w,
+                                  const int64_t h,
+                                  void* raster,
+                                  const cucim::io::Device& out_device);
+
+    static bool read_region_tiles_boundary(const TIFF* tiff,
+                                           const IFD* ifd,
+                                           const int64_t sx,
+                                           const int64_t sy,
+                                           const int64_t w,
+                                           const int64_t h,
+                                           void* raster,
+                                           const cucim::io::Device& out_device);
+
+    bool read(const TIFF* tiff,
+              const cucim::io::format::ImageMetadataDesc* metadata,
+              const cucim::io::format::ImageReaderRegionRequestDesc* request,
+              cucim::io::format::ImageDataDesc* out_image_data);
+
+
+    uint32_t index() const;
+    ifd_offset_t offset() const;
+
+    std::string& software();
+    std::string& model();
+    std::string& image_description();
+    uint32_t width() const;
+    uint32_t height() const;
+    uint32_t tile_width() const;
+    uint32_t tile_height() const;
+    uint32_t bits_per_sample() const;
+    uint32_t samples_per_pixel() const;
+    uint64_t subfile_type() const;
+    uint16_t planar_config() const;
+    uint16_t photometric() const;
+    uint16_t compression() const;
+
+    uint16_t subifd_count() const;
+    std::vector<uint64_t>& subifd_offsets();
+
+    uint32_t image_piece_count() const;
+    const std::vector<uint64_t>& image_piece_offsets() const;
+    const std::vector<uint64_t>& image_piece_bytecounts() const;
+
+    // Hidden methods for benchmarking
+    void write_offsets_(const char* file_path);
+
+    // Make TIFF available to access private members of IFD
+    friend class TIFF;
+
+private:
+    TIFF* tiff_; // cannot use shared_ptr as IFD is created during the construction of TIFF using 'new'
+    uint32_t ifd_index_ = 0;
+    ifd_offset_t ifd_offset_ = 0;
+
+    std::string software_;
+    std::string model_;
+    std::string image_description_;
+    uint32_t flags_ = 0;
+    uint32_t width_ = 0;
+    uint32_t height_ = 0;
+    uint32_t tile_width_ = 0;
+    uint32_t tile_height_ = 0;
+    uint32_t bits_per_sample_ = 0;
+    uint32_t samples_per_pixel_ = 0;
+    uint64_t subfile_type_ = 0;
+    uint16_t planar_config_ = 0;
+    uint16_t photometric_ = 0;
+    uint16_t compression_ = 0;
+
+    uint16_t subifd_count_ = 0;
+    std::vector<uint64_t> subifd_offsets_;
+
+    std::vector<uint8_t> jpegtable_;
+
+    uint32_t image_piece_count_ = 0;
+    std::vector<uint64_t> image_piece_offsets_;
+    std::vector<uint64_t> image_piece_bytecounts_;
+
+    /**
+     *
+     * Note: This method is called by the constructor of IFD and read() method so it is possible that the output of
+     *       'is_read_optimizable()' could be changed during read() method if user set read configuration
+     *       after opening TIFF file.
+     * @return
+     */
+    bool is_read_optimizable() const;
+};
+} // namespace cuslide::tiff
+
+#endif // CUSLIDE_IFD_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp
new file mode 100644
index 000000000..ec57f9b92
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp
@@ -0,0 +1,894 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "tiff.h"
+#include "ifd.h"
+#include "cuslide/jpeg/libjpeg_turbo.h"
+
+#include <algorithm>
+#include <fcntl.h>
+#include <tiffiop.h>
+
+#include <cucim/codec/base64.h>
+#include <cucim/memory/memory_manager.h>
+#include <cucim/logger/timer.h>
+
+#include <fmt/format.h>
+#include <string_view>
+#include <pugixml.hpp>
+#include <nlohmann/json.hpp>
+
+
+static constexpr int DEFAULT_IFD_SIZE = 32;
+
+using json = nlohmann::json;
+
+namespace cuslide::tiff
+{
+
+// djb2 algorithm from http://www.cse.yorku.ca/~oz/hash.html
+constexpr uint32_t hash_str(const char* str)
+{
+    uint32_t hash = 5381;
+    uint32_t c = 0;
+    while ((c = *str++))
+        hash = ((hash << 5) + hash) + c; // hash * 33 + c
+    return hash;
+}
+
+enum class PhilipsMetadataStage : uint8_t
+{
+    ROOT = 0,
+    SCANNED_IMAGE,
+    PIXEL_DATA_PRESENTATION,
+    ELEMENT,
+    ARRAY_ELEMENT
+};
+enum class PhilipsMetadataType : uint8_t
+{
+    IString = 0,
+    IDouble,
+    IUInt16,
+    IUInt32,
+    IUInt64
+};
+static void parse_string_array(const char* values, json& arr, PhilipsMetadataType type)
+{
+    std::string_view text(values);
+    std::string_view::size_type pos = 0;
+    while ((pos = text.find('"', pos)) != std::string_view::npos)
+    {
+        auto next_pos = text.find('"', pos + 1);
+        if (next_pos != std::string_view::npos)
+        {
+            if (text[next_pos - 1] != '\\')
+            {
+                switch (type)
+                {
+                case PhilipsMetadataType::IString:
+                    arr.emplace_back(std::string(text.substr(pos + 1, next_pos - pos - 1)));
+                    break;
+                case PhilipsMetadataType::IDouble:
+                    arr.emplace_back(std::stod(std::string(text.substr(pos + 1, next_pos - pos - 1))));
+                    break;
+                case PhilipsMetadataType::IUInt16:
+                case PhilipsMetadataType::IUInt32:
+                case PhilipsMetadataType::IUInt64:
+                    arr.emplace_back(std::stoul(std::string(text.substr(pos + 1, next_pos - pos - 1))));
+                    break;
+                }
+                pos = next_pos + 1;
+            }
+        }
+    }
+}
+static void parse_philips_tiff_metadata(const pugi::xml_node& node,
+                                        json& metadata,
+                                        const char* name,
+                                        PhilipsMetadataStage stage)
+{
+    switch (stage)
+    {
+    case PhilipsMetadataStage::ROOT:
+    case PhilipsMetadataStage::SCANNED_IMAGE:
+    case PhilipsMetadataStage::PIXEL_DATA_PRESENTATION:
+        for (pugi::xml_node attr = node.child("Attribute"); attr; attr = attr.next_sibling("Attribute"))
+        {
+            const pugi::xml_attribute& attr_attribute = attr.attribute("Name");
+            if (attr_attribute)
+            {
+                parse_philips_tiff_metadata(attr, metadata, attr_attribute.value(), PhilipsMetadataStage::ELEMENT);
+            }
+        }
+        break;
+    case PhilipsMetadataStage::ARRAY_ELEMENT:
+        break;
+    case PhilipsMetadataStage::ELEMENT:
+        const pugi::xml_attribute& attr_attribute = node.attribute("PMSVR");
+        auto p_attr_name = attr_attribute.as_string();
+        if (p_attr_name != nullptr && *p_attr_name != '\0')
+        {
+            if (name)
+            {
+                switch (hash_str(p_attr_name))
+                {
+                case hash_str("IString"):
+                    metadata.emplace(name, node.text().as_string());
+                    break;
+                case hash_str("IDouble"):
+                    metadata.emplace(name, node.text().as_double());
+                    break;
+                case hash_str("IUInt16"):
+                    metadata.emplace(name, node.text().as_uint());
+                    break;
+                case hash_str("IUInt32"):
+                    metadata.emplace(name, node.text().as_uint());
+                    break;
+                case hash_str("IUint64"):
+                    metadata.emplace(name, node.text().as_ullong());
+                    break;
+                case hash_str("IStringArray"): { // Process text such as `"a" "b" "c"`
+                    auto item_iter = metadata.emplace(name, json::array());
+                    parse_string_array(node.child_value(), *(item_iter.first), PhilipsMetadataType::IString);
+                    break;
+                }
+                case hash_str("IDoubleArray"): { // Process text such as `"0.0" "0.1" "0.2"`
+                    auto item_iter = metadata.emplace(name, json::array());
+                    parse_string_array(node.child_value(), *(item_iter.first), PhilipsMetadataType::IDouble);
+                    break;
+                }
+                case hash_str("IUInt16Array"): { // Process text such as `"1" "2" "3"`
+                    auto item_iter = metadata.emplace(name, json::array());
+                    parse_string_array(node.child_value(), *(item_iter.first), PhilipsMetadataType::IUInt16);
+                    break;
+                }
+                case hash_str("IUInt32Array"): { // Process text such as `"1" "2" "3"`
+                    auto item_iter = metadata.emplace(name, json::array());
+                    parse_string_array(node.child_value(), *(item_iter.first), PhilipsMetadataType::IUInt32);
+                    break;
+                }
+                case hash_str("IUInt64Array"): { // Process text such as `"1" "2" "3"`
+                    auto item_iter = metadata.emplace(name, json::array());
+                    parse_string_array(node.child_value(), *(item_iter.first), PhilipsMetadataType::IUInt64);
+                    break;
+                }
+                case hash_str("IDataObjectArray"):
+                    if (strcmp(name, "PIIM_PIXEL_DATA_REPRESENTATION_SEQUENCE") == 0)
+                    {
+                        const auto& item_array_iter =
+                            metadata.emplace(std::string("PIIM_PIXEL_DATA_REPRESENTATION_SEQUENCE"), json::array());
+                        for (pugi::xml_node data_node = node.child("Array").child("DataObject"); data_node;
+                             data_node = data_node.next_sibling("DataObject"))
+                        {
+                            auto& item_iter = item_array_iter.first->emplace_back(json{});
+                            parse_philips_tiff_metadata(
+                                data_node, item_iter, nullptr, PhilipsMetadataStage::PIXEL_DATA_PRESENTATION);
+                        }
+                    }
+                    break;
+                }
+            }
+        }
+        break;
+    }
+}
+
+TIFF::~TIFF()
+{
+    close();
+}
+
+TIFF::TIFF(const cucim::filesystem::Path& file_path, int mode) : file_path_(file_path)
+{
+    // Copy file path (Allocated memory would be freed at close() method.)
+    char* file_path_cstr = static_cast<char*>(cucim_malloc(file_path.size() + 1));
+    memcpy(file_path_cstr, file_path.c_str(), file_path.size());
+    file_path_cstr[file_path.size()] = '\0';
+
+    int fd = ::open(file_path_cstr, mode, 0666);
+    if (fd == -1)
+    {
+        cucim_free(file_path_cstr);
+        throw std::invalid_argument(fmt::format("Cannot open {}!", file_path));
+    }
+    tiff_client_ = ::TIFFFdOpen(fd, file_path_cstr, "rm"); // Add 'm' to disable memory-mapped file
+    // TODO: make file_handle_ object to pointer
+    file_handle_ = CuCIMFileHandle{ fd, nullptr, FileHandleType::kPosix, file_path_cstr, this };
+
+    // TODO: warning if the file is big endian
+    is_big_endian_ = ::TIFFIsBigEndian(tiff_client_);
+
+    metadata_ = new json{};
+}
+TIFF::TIFF(const cucim::filesystem::Path& file_path, int mode, uint64_t read_config) : TIFF(file_path, mode)
+{
+    read_config_ = read_config;
+}
+
+std::shared_ptr<TIFF> TIFF::open(const cucim::filesystem::Path& file_path, int mode)
+{
+    auto tif = std::make_shared<TIFF>(file_path, mode);
+    tif->construct_ifds();
+
+    return tif;
+}
+
+std::shared_ptr<TIFF> TIFF::open(const cucim::filesystem::Path& file_path, int mode, uint64_t config)
+{
+    auto tif = std::make_shared<TIFF>(file_path, mode, config);
+    tif->construct_ifds();
+
+    return tif;
+}
+
+void TIFF::close()
+{
+    if (tiff_client_)
+    {
+        TIFFClose(tiff_client_);
+        tiff_client_ = nullptr;
+    }
+    if (file_handle_.path)
+    {
+        cucim_free(file_handle_.path);
+        file_handle_.path = nullptr;
+    }
+    if (file_handle_.client_data)
+    {
+        // Deleting file_handle_.client_data is parser_close()'s responsibility
+        // Do not execute this: `delete static_cast<cuslide::tiff::TIFF*>(file_handle_.client_data);`
+        file_handle_.client_data = nullptr;
+    }
+    if (metadata_)
+    {
+        delete reinterpret_cast<json*>(metadata_);
+        metadata_ = nullptr;
+    }
+}
+
+void TIFF::construct_ifds()
+{
+    ifd_offsets_.clear();
+    ifd_offsets_.reserve(DEFAULT_IFD_SIZE);
+    ifds_.clear();
+    ifds_.reserve(DEFAULT_IFD_SIZE);
+
+    uint16_t ifd_index = 0;
+    do
+    {
+        uint64_t offset = TIFFCurrentDirOffset(tiff_client_);
+        ifd_offsets_.push_back(offset);
+
+        auto ifd = std::make_shared<cuslide::tiff::IFD>(this, ifd_index, offset);
+        ifds_.emplace_back(std::move(ifd));
+        ++ifd_index;
+    } while (TIFFReadDirectory(tiff_client_));
+
+    // Set index for each level
+    level_to_ifd_idx_.reserve(ifd_index);
+    for (size_t index = 0; index < ifd_index; ++index)
+    {
+        level_to_ifd_idx_.emplace_back(index);
+    }
+
+    // Resolve format and fix `level_to_ifds_idx_`
+    resolve_vendor_format();
+
+    // Sort index by resolution (the largest resolution is index 0)
+    std::sort(level_to_ifd_idx_.begin(), level_to_ifd_idx_.end(), [this](const size_t& a, const size_t& b) {
+        uint32_t width_a = this->ifds_[a]->width();
+        uint32_t width_b = this->ifds_[b]->width();
+        if (width_a > width_b)
+        {
+            return true;
+        }
+        else if (width_a < width_b)
+        {
+            return false;
+        }
+        else
+        {
+            uint32_t height_a = this->ifds_[a]->height();
+            uint32_t height_b = this->ifds_[b]->height();
+            return height_a > height_b;
+        }
+    });
+}
+void TIFF::resolve_vendor_format()
+{
+    uint16_t ifd_count = ifds_.size();
+    if (ifd_count == 0)
+    {
+        return;
+    }
+    json* json_metadata = reinterpret_cast<json*>(metadata_);
+
+    // Detect Philips TIFF
+    auto& first_ifd = ifds_[0];
+    std::string& software = first_ifd->software();
+    std::string_view prefix("Philips");
+    std::string_view macro_prefix("Macro");
+    std::string_view label_prefix("Label");
+
+    auto res = std::mismatch(prefix.begin(), prefix.end(), software.begin());
+    if (res.first == prefix.end())
+    {
+        pugi::xml_document doc;
+        const char* image_desc_cstr = first_ifd->image_description().c_str();
+        pugi::xml_parse_result result = doc.load_string(image_desc_cstr);
+        if (result)
+        {
+            const auto& data_object = doc.child("DataObject");
+            if (std::string_view(data_object.attribute("ObjectType").as_string("")) != "DPUfsImport")
+            {
+                fmt::print(
+                    stderr,
+                    "[Warning] Failed to read as Philips TIFF. It looks like Philips TIFF but the image description of the first IFD doesn't have '<DataObject ObjectType=\"DPUfsImport\">' node!\n");
+                return;
+            }
+
+            pugi::xpath_query PIM_DP_IMAGE_TYPE(
+                "Attribute[@Name='PIM_DP_SCANNED_IMAGES']/Array/DataObject[Attribute/@Name='PIM_DP_IMAGE_TYPE' and Attribute/text()='WSI']");
+            pugi::xpath_node_set wsi_nodes = PIM_DP_IMAGE_TYPE.evaluate_node_set(data_object);
+            if (wsi_nodes.size() != 1)
+            {
+                fmt::print(
+                    stderr,
+                    "[Warning] Failed to read as Philips TIFF. Expected only one 'DPScannedImage' node with PIM_DP_IMAGE_TYPE='WSI'.\n");
+                return;
+            }
+
+            pugi::xpath_query DICOM_PIXEL_SPACING(
+                "Attribute[@Name='PIIM_PIXEL_DATA_REPRESENTATION_SEQUENCE']/Array/DataObject/Attribute[@Name='DICOM_PIXEL_SPACING']");
+            pugi::xpath_node_set pixel_spacing_nodes = DICOM_PIXEL_SPACING.evaluate_node_set(wsi_nodes[0]);
+
+            std::vector<std::pair<double, double>> pixel_spacings;
+            pixel_spacings.reserve(pixel_spacings.size());
+
+            for (const pugi::xpath_node& pixel_spacing : pixel_spacing_nodes)
+            {
+                std::string values = pixel_spacing.node().text().as_string();
+
+                // Assume that 'values' has a '"<height spacing in mm>" "<width spacing in mm>"' form.
+                double spacing_x = 0.0;
+                double spacing_y = 0.0;
+
+                std::string::size_type offset = values.find("\"");
+                if (offset != std::string::npos)
+                {
+                    spacing_y = std::atof(&values.c_str()[offset + 1]);
+                    offset = values.find(" \"", offset);
+                    if (offset != std::string::npos)
+                    {
+                        spacing_x = std::atof(&values.c_str()[offset + 2]);
+                    }
+                }
+                if (spacing_x == 0.0 || spacing_y == 0.0)
+                {
+                    fmt::print(stderr, "[Warning] Failed to read DICOM_PIXEL_SPACING: {}\n", values);
+                    return;
+                }
+                pixel_spacings.emplace_back(std::pair{ spacing_x, spacing_y });
+            }
+
+            double spacing_x_l0 = pixel_spacings[0].first;
+            double spacing_y_l0 = pixel_spacings[0].second;
+
+            uint32_t width_l0 = first_ifd->width();
+            uint32_t height_l0 = first_ifd->height();
+
+            uint16_t spacing_index = 1;
+            for (int index = 1, level_index = 1; index < ifd_count; ++index, ++level_index)
+            {
+                auto& ifd = ifds_[index];
+                if (ifd->tile_width() == 0)
+                {
+                    // TODO: check macro and label
+                    AssociatedImageBufferDesc buf_desc{};
+                    buf_desc.type = AssociatedImageBufferType::IFD;
+                    buf_desc.compression = static_cast<cucim::codec::CompressionMethod>(ifd->compression());
+                    buf_desc.ifd_index = index;
+
+                    auto& image_desc = ifd->image_description();
+                    if (std::mismatch(macro_prefix.begin(), macro_prefix.end(), image_desc.begin()).first ==
+                        macro_prefix.end())
+                    {
+                        associated_images_.emplace("macro", buf_desc);
+                    }
+                    else if (std::mismatch(label_prefix.begin(), label_prefix.end(), image_desc.begin()).first ==
+                             label_prefix.end())
+                    {
+                        associated_images_.emplace("label", buf_desc);
+                    }
+
+                    // Remove item at index `ifd_index` from `level_to_ifd_idx_`
+                    level_to_ifd_idx_.erase(level_to_ifd_idx_.begin() + level_index);
+                    --level_index;
+                    continue;
+                }
+                double downsample = std::round((pixel_spacings[spacing_index].first / spacing_x_l0 +
+                                                pixel_spacings[spacing_index].second / spacing_y_l0) /
+                                               2);
+                // Fix width and height of IFD
+                ifd->width_ = width_l0 / downsample;
+                ifd->height_ = height_l0 / downsample;
+                ++spacing_index;
+            }
+
+            constexpr int associated_image_type_count = 2;
+            pugi::xpath_query ASSOCIATED_IMAGES[associated_image_type_count] = {
+                pugi::xpath_query(
+                    "Attribute[@Name='PIM_DP_SCANNED_IMAGES']/Array/DataObject[Attribute/@Name='PIM_DP_IMAGE_TYPE' and Attribute/text()='MACROIMAGE'][1]/Attribute[@Name='PIM_DP_IMAGE_DATA']"),
+                pugi::xpath_query(
+                    "Attribute[@Name='PIM_DP_SCANNED_IMAGES']/Array/DataObject[Attribute/@Name='PIM_DP_IMAGE_TYPE' and Attribute/text()='LABELIMAGE'][1]/Attribute[@Name='PIM_DP_IMAGE_DATA']")
+            };
+            constexpr const char* associated_image_names[associated_image_type_count] = { "macro", "label" };
+
+            // Add associated image from XML if available (macro and label images)
+            // : Refer to PIM_DP_IMAGE_TYPE in
+            // https://www.openpathology.philips.com/wp-content/uploads/isyntax/4522%20207%2043941_2020_04_24%20Pathology%20iSyntax%20image%20format.pdf
+
+            for (int associated_image_type_idx = 0; associated_image_type_idx < associated_image_type_count;
+                 ++associated_image_type_idx)
+            {
+                pugi::xpath_node associated_node =
+                    ASSOCIATED_IMAGES[associated_image_type_idx].evaluate_node(data_object);
+                const char* associated_image_name = associated_image_names[associated_image_type_idx];
+
+                // If the associated image doesn't exist
+                if (associated_images_.find(associated_image_name) == associated_images_.end())
+                {
+                    if (associated_node)
+                    {
+                        auto node_offset = associated_node.node().offset_debug();
+
+                        if (node_offset >= 0)
+                        {
+                            // `image_desc_cstr[node_offset]` would point to the following text:
+                            //   Attribute Element="0x1004" Group="0x301D" Name="PIM_DP_IMAGE_DATA" PMSVR="IString">
+                            //     (base64-encoded JPEG image)
+                            //   </Attribute>
+                            //
+
+                            // 34 is from `Attribute Name="PIM_DP_IMAGE_DATA"`
+                            char* data_ptr = const_cast<char*>(image_desc_cstr) + node_offset + 34;
+                            uint32_t data_len = 0;
+                            while (*data_ptr != '>' && *data_ptr != '\0')
+                            {
+                                ++data_ptr;
+                            }
+                            if (*data_ptr != '\0')
+                            {
+                                ++data_ptr; // start of base64-encoded data
+                                char* data_end_ptr = data_ptr;
+                                // Seek until it finds '<' for '</Attribute>'
+                                while (*data_end_ptr != '<' && *data_end_ptr != '\0')
+                                {
+                                    ++data_end_ptr;
+                                }
+                                data_len = data_end_ptr - data_ptr;
+                            }
+
+                            if (data_len > 0)
+                            {
+                                AssociatedImageBufferDesc buf_desc{};
+                                buf_desc.type = AssociatedImageBufferType::IFD_IMAGE_DESC;
+                                buf_desc.compression = cucim::codec::CompressionMethod::JPEG;
+                                buf_desc.desc_ifd_index = 0;
+                                buf_desc.desc_offset = data_ptr - image_desc_cstr;
+                                buf_desc.desc_size = data_len;
+
+                                associated_images_.emplace(associated_image_name, buf_desc);
+                            }
+                        }
+                    }
+                }
+            }
+
+            // Set TIFF type
+            tiff_type_ = TiffType::Philips;
+
+            // Set background color
+            background_value_ = 0xFF;
+
+            // Get metadata
+            if (json_metadata)
+            {
+                json philips_metadata;
+                parse_philips_tiff_metadata(data_object, philips_metadata, nullptr, PhilipsMetadataStage::ROOT);
+                parse_philips_tiff_metadata(
+                    wsi_nodes[0].node(), philips_metadata, nullptr, PhilipsMetadataStage::SCANNED_IMAGE);
+                (*json_metadata).emplace("philips", std::move(philips_metadata));
+            }
+        }
+    }
+
+
+    // Append TIFF metadata
+
+    if (json_metadata)
+    {
+        json tiff_metadata;
+
+        tiff_metadata.emplace("model", first_ifd->model());
+        tiff_metadata.emplace("software", first_ifd->software());
+        tiff_metadata.emplace("model", first_ifd->model());
+
+        (*json_metadata).emplace("tiff", std::move(tiff_metadata));
+    }
+}
+
+bool TIFF::read(const cucim::io::format::ImageMetadataDesc* metadata,
+                const cucim::io::format::ImageReaderRegionRequestDesc* request,
+                cucim::io::format::ImageDataDesc* out_image_data,
+                cucim::io::format::ImageMetadataDesc* out_metadata)
+{
+    if (request->associated_image_name)
+    {
+        // 'out_metadata' is only needed for reading associated image
+        return read_associated_image(metadata, request, out_image_data, out_metadata);
+    }
+
+    // TODO: assume length of location/size to 2.
+    constexpr int32_t ndims = 2;
+
+    if (request->level >= level_to_ifd_idx_.size())
+    {
+        throw std::invalid_argument(fmt::format(
+            "Invalid level ({}) in the request! (Should be < {})", request->level, level_to_ifd_idx_.size()));
+    }
+    auto main_ifd = ifds_[level_to_ifd_idx_[0]];
+    auto ifd = ifds_[level_to_ifd_idx_[request->level]];
+    auto original_img_width = main_ifd->width();
+    auto original_img_height = main_ifd->height();
+
+    for (int32_t i = 0; i < ndims; ++i)
+    {
+        if (request->size[i] <= 0)
+        {
+            throw std::invalid_argument(
+                fmt::format("Invalid size ({}) in the request! (Should be > 0)", request->size[i]));
+        }
+    }
+    if (request->size[0] > original_img_width)
+    {
+        throw std::invalid_argument(
+            fmt::format("Invalid size (it exceeds the original image width {})", original_img_width));
+    }
+    if (request->size[1] > original_img_height)
+    {
+        throw std::invalid_argument(
+            fmt::format("Invalid size (it exceeds the original image height {})", original_img_height));
+    }
+
+    float downsample_factor = metadata->resolution_info.level_downsamples[request->level];
+
+    // Change request based on downsample factor. (normalized value at level-0 -> real location at the requested level)
+    for (int32_t i = 0; i < ndims; ++i)
+    {
+        request->location[i] /= downsample_factor;
+    }
+    return ifd->read(this, metadata, request, out_image_data);
+}
+
+bool TIFF::read_associated_image(const cucim::io::format::ImageMetadataDesc* metadata,
+                                 const cucim::io::format::ImageReaderRegionRequestDesc* request,
+                                 cucim::io::format::ImageDataDesc* out_image_data,
+                                 cucim::io::format::ImageMetadataDesc* out_metadata_desc)
+{
+    // TODO: implement
+    (void)metadata;
+
+    std::string device_name(request->device);
+    if (request->shm_name)
+    {
+        device_name = device_name + "[" + request->shm_name + "]"; // TODO: check performance
+    }
+    cucim::io::Device out_device(device_name);
+
+    uint8_t* raster = nullptr;
+    uint32_t width = 0;
+    uint32_t height = 0;
+    uint32_t samples_per_pixel = 0;
+
+    // Raw metadata for the associated image
+    const char* raw_data_ptr = nullptr;
+    size_t raw_data_len = 0;
+    // Json metadata for the associated image
+    char* json_data_ptr = nullptr;
+
+    auto associated_image = associated_images_.find(request->associated_image_name);
+    if (associated_image != associated_images_.end())
+    {
+        auto& buf_desc = associated_image->second;
+
+        switch (buf_desc.type)
+        {
+        case AssociatedImageBufferType::IFD: {
+            const auto& image_ifd = ifd(buf_desc.ifd_index);
+
+            auto& image_description = image_ifd->image_description();
+            auto image_description_size = image_description.size();
+
+            // Assign image description into raw_data_ptr
+            raw_data_ptr = image_description.c_str();
+            raw_data_len = image_description_size;
+
+            width = image_ifd->width();
+            height = image_ifd->height();
+            samples_per_pixel = image_ifd->samples_per_pixel();
+            uint64_t image_size_in_bytes = width * height * samples_per_pixel;
+
+            raster = static_cast<uint8_t*>(cucim_malloc(image_size_in_bytes)); // RGB image
+
+            // TODO: here we assume that the image has a single strip.
+            uint64_t offset = image_ifd->image_piece_offsets_[0];
+            uint64_t size = image_ifd->image_piece_bytecounts_[0];
+            const void* jpegtable_data = image_ifd->jpegtable_.data();
+            uint32_t jpegtable_count = image_ifd->jpegtable_.size();
+
+            if (!cuslide::jpeg::decode_libjpeg(file_handle_.fd, nullptr /*jpeg_buf*/, offset, size, jpegtable_data,
+                                               jpegtable_count, &raster, out_device))
+            {
+                cucim_free(raster);
+                fmt::print(stderr, "[Error] Failed to read region with libjpeg!\n");
+                return false;
+            }
+            break;
+        }
+        case AssociatedImageBufferType::IFD_IMAGE_DESC: {
+            const auto& image_ifd = ifd(buf_desc.desc_ifd_index);
+            const char* image_desc_buf = image_ifd->image_description().data();
+            char* decoded_buf = nullptr;
+            int decoded_size = 0;
+
+            if (!cucim::codec::base64::decode(
+                    image_desc_buf, image_ifd->image_description().size(), &decoded_buf, &decoded_size))
+            {
+                fmt::print(stderr, "[Error] Failed to decode base64-encoded string from the metadata!\n");
+                return false;
+            }
+
+            int image_width = 0;
+            int image_height = 0;
+
+            if (!cuslide::jpeg::get_dimension(decoded_buf, 0, decoded_size, &image_width, &image_height))
+            {
+                fmt::print(stderr, "[Error] Failed to read jpeg header for image dimension!\n");
+                return false;
+            }
+
+            width = image_width;
+            height = image_height;
+            samples_per_pixel = 3; // NOTE: assumes RGB image
+            uint64_t image_size_in_bytes = image_width * image_height * samples_per_pixel;
+
+            raster = static_cast<uint8_t*>(cucim_malloc(image_size_in_bytes)); // RGB image
+
+            if (!cuslide::jpeg::decode_libjpeg(-1, reinterpret_cast<unsigned char*>(decoded_buf), 0 /*offset*/,
+                                               decoded_size, nullptr /*jpegtable_data*/, 0 /*jpegtable_count*/, &raster,
+                                               out_device))
+            {
+                cucim_free(raster);
+                fmt::print(stderr, "[Error] Failed to read image from metadata with libjpeg!\n");
+                return false;
+            }
+            break;
+        }
+        case AssociatedImageBufferType::FILE_OFFSET:
+            // TODO: implement
+            break;
+        case AssociatedImageBufferType::BUFFER_POINTER:
+            // TODO: implement
+            break;
+        case AssociatedImageBufferType::OWNED_BUFFER_POINTER:
+            // TODO: implement
+            break;
+        }
+    }
+
+    // Populate image data
+    const uint16_t ndim = 3;
+
+    int64_t* container_shape = (int64_t*)cucim_malloc(sizeof(int64_t) * ndim);
+    container_shape[0] = height;
+    container_shape[1] = width;
+    container_shape[2] = 3; // TODO: hard-coded for 'C'
+
+    out_image_data->container.data = raster;
+    out_image_data->container.ctx = DLContext{ static_cast<DLDeviceType>(cucim::io::DeviceType::kCPU), 0 };
+    out_image_data->container.ndim = ndim;
+    out_image_data->container.dtype = { kDLUInt, 8, 1 };
+    out_image_data->container.shape = container_shape;
+    out_image_data->container.strides = nullptr; // Tensor is compact and row-majored
+    out_image_data->container.byte_offset = 0;
+
+    // Populate metadata
+    if (out_metadata_desc && out_metadata_desc->handle)
+    {
+        cucim::io::format::ImageMetadata& out_metadata =
+            *reinterpret_cast<cucim::io::format::ImageMetadata*>(out_metadata_desc->handle);
+        auto& resource = out_metadata.get_resource();
+
+        std::string_view dims{ "YXC" };
+
+        std::pmr::vector<int64_t> shape(&resource);
+        shape.reserve(ndim);
+        shape.insert(shape.end(), &container_shape[0], &container_shape[ndim]);
+
+        DLDataType dtype{ kDLUInt, 8, 1 };
+
+
+        // TODO: Do not assume channel names as 'RGB'
+        std::pmr::vector<std::string_view> channel_names(
+            { std::string_view{ "R" }, std::string_view{ "G" }, std::string_view{ "B" } }, &resource);
+
+
+        // We don't know physical pixel size for associated image so fill it with default value 1
+        std::pmr::vector<float> spacing(&resource);
+        spacing.reserve(ndim);
+        spacing.insert(spacing.end(), ndim, 1.0);
+
+        std::pmr::vector<std::string_view> spacing_units(&resource);
+        spacing_units.reserve(ndim);
+        spacing_units.emplace_back(std::string_view{ "micrometer" });
+        spacing_units.emplace_back(std::string_view{ "micrometer" });
+        spacing_units.emplace_back(std::string_view{ "color" });
+
+        std::pmr::vector<float> origin({ 0.0, 0.0, 0.0 }, &resource);
+
+        // Direction cosines (size is always 3x3)
+        // clang-format off
+        std::pmr::vector<float> direction({ 1.0, 0.0, 0.0,
+                                            0.0, 1.0, 0.0,
+                                            0.0, 0.0, 1.0}, &resource);
+        // clang-format on
+
+        // The coordinate frame in which the direction cosines are measured (either 'LPS'(ITK/DICOM) or 'RAS'(NIfTI/3D
+        // Slicer))
+        std::string_view coord_sys{ "LPS" };
+
+        // Manually set resolution dimensions to 2
+        const uint16_t level_ndim = 2;
+        std::pmr::vector<int64_t> level_dimensions(&resource);
+        level_dimensions.reserve(level_ndim * 1); // it has only one size
+        level_dimensions.emplace_back(shape[1]); // width
+        level_dimensions.emplace_back(shape[0]); // height
+
+        std::pmr::vector<float> level_downsamples(&resource);
+        level_downsamples.reserve(1);
+        level_downsamples.emplace_back(1.0);
+
+        // Empty associated images
+        const size_t associated_image_count = 0;
+        std::pmr::vector<std::string_view> associated_image_names(&resource);
+
+        std::string_view raw_data{ raw_data_ptr ? raw_data_ptr : "", raw_data_len };
+        std::string_view json_data{ json_data_ptr ? json_data_ptr : "" };
+
+        out_metadata.ndim(ndim);
+        out_metadata.dims(dims);
+        out_metadata.shape(shape);
+        out_metadata.dtype(dtype);
+        out_metadata.channel_names(channel_names);
+        out_metadata.spacing(spacing);
+        out_metadata.spacing_units(spacing_units);
+        out_metadata.origin(origin);
+        out_metadata.direction(direction);
+        out_metadata.coord_sys(coord_sys);
+        out_metadata.level_count(1);
+        out_metadata.level_ndim(2);
+        out_metadata.level_dimensions(level_dimensions);
+        out_metadata.level_downsamples(level_downsamples);
+        out_metadata.image_count(associated_image_count);
+        out_metadata.image_names(associated_image_names);
+        out_metadata.raw_data(raw_data);
+        out_metadata.json_data(json_data);
+    }
+
+    return true;
+}
+
+cucim::filesystem::Path TIFF::file_path() const
+{
+    return file_path_;
+}
+
+CuCIMFileHandle TIFF::file_handle() const
+{
+    return file_handle_;
+}
+::TIFF* TIFF::client() const
+{
+    return tiff_client_;
+}
+const std::vector<ifd_offset_t>& TIFF::ifd_offsets() const
+{
+    return ifd_offsets_;
+}
+std::shared_ptr<IFD> TIFF::ifd(size_t index) const
+{
+    return ifds_.at(index);
+}
+std::shared_ptr<IFD> TIFF::level_ifd(size_t level_index) const
+{
+    return ifds_.at(level_to_ifd_idx_.at(level_index));
+}
+size_t TIFF::ifd_count() const
+{
+    return ifd_offsets_.size();
+}
+size_t TIFF::level_count() const
+{
+    return level_to_ifd_idx_.size();
+}
+const std::map<std::string, AssociatedImageBufferDesc>& TIFF::associated_images() const
+{
+    return associated_images_;
+}
+size_t TIFF::associated_image_count() const
+{
+    return associated_images_.size();
+}
+bool TIFF::is_big_endian() const
+{
+    return is_big_endian_;
+}
+
+uint64_t TIFF::read_config() const
+{
+    return read_config_;
+}
+
+bool TIFF::is_in_read_config(uint64_t configs) const
+{
+    return (read_config_ & configs) == configs;
+}
+
+void TIFF::add_read_config(uint64_t configs)
+{
+    read_config_ |= configs;
+}
+
+TiffType TIFF::tiff_type()
+{
+    return tiff_type_;
+}
+
+std::string TIFF::metadata()
+{
+    json* metadata = reinterpret_cast<json*>(metadata_);
+
+    if (metadata)
+    {
+        return metadata->dump();
+    }
+    else
+    {
+        return std::string{};
+    }
+}
+
+void* TIFF::operator new(std::size_t sz)
+{
+    return cucim_malloc(sz);
+}
+
+void TIFF::operator delete(void* ptr)
+{
+    cucim_free(ptr);
+}
+} // namespace cuslide::tiff
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.h
new file mode 100644
index 000000000..8d520d42f
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.h
@@ -0,0 +1,120 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUSLIDE_TIFF_H
+#define CUSLIDE_TIFF_H
+
+#include "types.h"
+#include "ifd.h"
+//#include <tiffio.h>
+
+#include <cucim/macros/api_header.h>
+#include <cucim/filesystem/file_path.h>
+#include <cucim/filesystem/file_handle.h>
+#include <cucim/memory/memory_manager.h>
+#include <cucim/io/format/image_format.h>
+
+#include <cstdint>
+#include <fcntl.h>
+#include <memory>
+#include <vector>
+#include <map>
+
+typedef struct tiff TIFF;
+
+namespace cuslide::tiff
+{
+
+/**
+ * TIFF file handler class.
+ *
+ * This class doesn't use PImpl idiom for performance reasons and is not
+ * intended to be used for subclassing.
+ */
+class EXPORT_VISIBLE TIFF : public std::enable_shared_from_this<TIFF>
+{
+public:
+    TIFF(const cucim::filesystem::Path& file_path, int mode);
+    TIFF(const cucim::filesystem::Path& file_path, int mode, uint64_t config);
+    static std::shared_ptr<TIFF> open(const cucim::filesystem::Path& file_path, int mode);
+    static std::shared_ptr<TIFF> open(const cucim::filesystem::Path& file_path, int mode, uint64_t config);
+    void close();
+    void construct_ifds();
+
+    /**
+     * Resolve vendor format and fix values for `associated_image_descs_` and `level_to_ifd_idx_.
+     */
+    void resolve_vendor_format();
+    bool read(const cucim::io::format::ImageMetadataDesc* metadata,
+              const cucim::io::format::ImageReaderRegionRequestDesc* request,
+              cucim::io::format::ImageDataDesc* out_image_data,
+              cucim::io::format::ImageMetadataDesc* out_metadata = nullptr);
+
+    bool read_associated_image(const cucim::io::format::ImageMetadataDesc* metadata,
+                               const cucim::io::format::ImageReaderRegionRequestDesc* request,
+                               cucim::io::format::ImageDataDesc* out_image_data,
+                               cucim::io::format::ImageMetadataDesc* out_metadata);
+
+    cucim::filesystem::Path file_path() const;
+    CuCIMFileHandle file_handle() const;
+    ::TIFF* client() const;
+    const std::vector<ifd_offset_t>& ifd_offsets() const;
+    std::shared_ptr<IFD> ifd(size_t index) const;
+    std::shared_ptr<IFD> level_ifd(size_t level_index) const;
+    size_t ifd_count() const;
+    size_t level_count() const;
+    const std::map<std::string, AssociatedImageBufferDesc>& associated_images() const;
+    size_t associated_image_count() const;
+    bool is_big_endian() const;
+    uint64_t read_config() const;
+    bool is_in_read_config(uint64_t configs) const;
+    void add_read_config(uint64_t configs);
+    TiffType tiff_type();
+    std::string metadata();
+
+    ~TIFF();
+
+    static void* operator new(std::size_t sz);
+    static void operator delete(void* ptr);
+    //    static void* operator new[](std::size_t sz);
+    //    static void operator delete(void* ptr, std::size_t sz);
+    //    static void operator delete[](void* ptr, std::size_t sz);
+
+    // const values for read_configs_
+    static constexpr uint64_t kUseDirectJpegTurbo = 1;
+    static constexpr uint64_t kUseLibTiff = 1 << 1;
+
+    // Make IFD available to access private members of TIFF
+    friend class IFD;
+
+private:
+    cucim::filesystem::Path file_path_;
+    CuCIMFileHandle file_handle_{};
+    ::TIFF* tiff_client_ = nullptr;
+    std::vector<ifd_offset_t> ifd_offsets_; /// IFD offset for an index (IFD index)
+    std::vector<std::shared_ptr<IFD>> ifds_; /// IFD object for an index (IFD index)
+    std::vector<size_t> level_to_ifd_idx_;
+    // note: we use std::map instead of std::unordered_map as # of associated_image would be usually less than 10.
+    std::map<std::string, AssociatedImageBufferDesc> associated_images_;
+    bool is_big_endian_ = false; /// if big endian
+    uint8_t background_value_ = 0x00; /// background_value
+    uint64_t read_config_ = 0;
+    TiffType tiff_type_ = TiffType::Generic;
+    void* metadata_ = nullptr;
+};
+} // namespace cuslide::tiff
+
+#endif // CUSLIDE_TIFF_H
diff --git a/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/types.h b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/types.h
new file mode 100644
index 000000000..299ee510c
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/types.h
@@ -0,0 +1,80 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUSLIDE_TYPES_H
+#define CUSLIDE_TYPES_H
+
+#include <cucim/codec/methods.h>
+
+#include <cstdint>
+
+namespace cuslide::tiff
+{
+
+using ifd_offset_t = uint64_t;
+
+enum class TiffType : uint32_t
+{
+    Generic = 0,
+    Philips = 1,
+};
+
+enum class AssociatedImageBufferType : uint8_t
+{
+    IFD = 0,
+    IFD_IMAGE_DESC = 1,
+    FILE_OFFSET = 2,
+    BUFFER_POINTER = 3,
+    OWNED_BUFFER_POINTER = 4,
+};
+
+struct AssociatedImageBufferDesc
+{
+    AssociatedImageBufferType type; /// 0: IFD index, 1: IFD index + image description offset&size (base64-encoded text)
+                                    /// 2: file offset + size, 3: buffer pointer (owned by others) + size
+                                    /// 4: allocated (owned) buffer pointer (so need to free after use) + size
+    cucim::codec::CompressionMethod compression;
+    union
+    {
+        ifd_offset_t ifd_index;
+        struct
+        {
+            ifd_offset_t desc_ifd_index;
+            uint64_t desc_offset;
+            uint64_t desc_size;
+        };
+        struct
+        {
+            uint64_t file_offset;
+            uint64_t file_size;
+        };
+        struct
+        {
+            void* buf_ptr;
+            uint64_t buf_size;
+        };
+        struct
+        {
+            void* owned_ptr;
+            uint64_t owned_size;
+        };
+    };
+};
+
+
+} // namespace cuslide::tiff
+
+#endif // CUSLIDE_TYPES_H
diff --git a/cpp/plugins/cucim.kit.cuslide/test_data b/cpp/plugins/cucim.kit.cuslide/test_data
new file mode 120000
index 000000000..6c6c77553
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/test_data
@@ -0,0 +1 @@
+../../../test_data
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/CMakeLists.txt b/cpp/plugins/cucim.kit.cuslide/tests/CMakeLists.txt
new file mode 100644
index 000000000..e998219b2
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/CMakeLists.txt
@@ -0,0 +1,71 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+include(CTest)
+enable_testing()
+
+################################################################################
+# Add executable: cuslide_tests
+################################################################################
+add_executable(cuslide_tests
+        config.h
+        main.cpp
+        test_read_region.cpp
+        test_read_rawtiff.cpp
+        test_philips_tiff.cpp
+        )
+set_source_files_properties(test_read_rawtiff.cpp PROPERTIES LANGUAGE CUDA) # failed with CLI11 library
+
+set_target_properties(cuslide_tests
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(cuslide_tests PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cuslide_tests PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_compile_definitions(cuslide_tests
+    PUBLIC
+        CUSLIDE_VERSION=${PROJECT_VERSION}
+        CUSLIDE_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUSLIDE_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUSLIDE_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUSLIDE_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+)
+target_link_libraries(cuslide_tests
+        PRIVATE
+            cucim::cucim
+            ${CUCIM_PLUGIN_NAME}
+            deps::catch2
+            deps::openslide
+            deps::cli11
+            deps::fmt
+        )
+
+# Add headers in src
+target_include_directories(cuslide_tests
+        PUBLIC
+            $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../src>
+        )
+
+include(ParseAndAddCatchTests)
+# See https://github.com/catchorg/Catch2/blob/master/docs/cmake-integration.md#parseandaddcatchtestscmake for other options
+ParseAndAddCatchTests(cuslide_tests)
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/config.h b/cpp/plugins/cucim.kit.cuslide/tests/config.h
new file mode 100644
index 000000000..e44a5df73
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/config.h
@@ -0,0 +1,63 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2020 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUSLIDE_TESTS_CONFIG_H
+#define CUSLIDE_TESTS_CONFIG_H
+
+#include <string>
+#include <cstdlib>
+
+struct AppConfig
+{
+    std::string test_folder;
+    std::string test_file;
+    std::string temp_folder = "/tmp";
+    std::string get_input_path(const char* default_value = "private/generic_tiff_000.tif") const
+    {
+        // If `test_file` is absolute path
+        if (!test_folder.empty() && test_file.substr(0, 1) == "/")
+        {
+            return test_file;
+        }
+        else
+        {
+            std::string test_data_folder = test_folder;
+            if (test_data_folder.empty())
+            {
+                if (const char* env_p = std::getenv("CUCIM_TESTDATA_FOLDER"))
+                {
+                    test_data_folder = env_p;
+                }
+                else
+                {
+                    test_data_folder = "test_data";
+                }
+            }
+            if (test_file.empty())
+            {
+                return test_data_folder + "/" + default_value;
+            }
+            else
+            {
+                return test_data_folder + "/" + test_file;
+            }
+        }
+    }
+};
+
+extern AppConfig g_config;
+
+#endif // CUSLIDE_TESTS_CONFIG_H
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/main.cpp b/cpp/plugins/cucim.kit.cuslide/tests/main.cpp
new file mode 100644
index 000000000..0f4e3f544
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/main.cpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//#define CATCH_CONFIG_MAIN
+//#include <catch2/catch.hpp>
+
+// Implement main explicitly to handle additional parameters.
+#define CATCH_CONFIG_RUNNER
+#include "config.h"
+#include "cucim/core/framework.h"
+
+#include <catch2/catch.hpp>
+#include <string>
+#include <fmt/format.h>
+
+CUCIM_FRAMEWORK_GLOBALS("sample.app")
+
+// Global config object
+AppConfig g_config;
+
+/**
+ * Extract `--[option]` or `--[option]=` string from command and set the value to g_config object.
+ *
+ * @param argc number of arguments used for command
+ * @param argv arguments for command
+ * @param obj object reference to modify
+ * @param argument name of argument(option)
+ * @return true if it extracted the value for the option
+ */
+static bool extract_test_file_option(int* argc, char** argv, std::string& obj, const char* argument)
+{
+    std::string arg_str = fmt::format("--{}=", argument); // test_file => --test_file=
+    std::string arg_str2 = fmt::format("--{}", argument); // test_file => --test_file
+
+    char* value_ptr = nullptr;
+    for (int i = 1; argc && i < *argc; ++i)
+    {
+        if (strncmp(argv[i], arg_str.c_str(), arg_str.size()) == 0)
+        {
+            value_ptr = &argv[i][arg_str.size()];
+            for (int j = i + 1; argc && j < *argc; ++j)
+            {
+                argv[j - 1] = argv[j];
+            }
+            --(*argc);
+            argv[*argc] = nullptr;
+            break;
+        }
+        if (strncmp(argv[i], arg_str2.c_str(), arg_str2.size()) == 0 && i + 1 < *argc)
+        {
+            value_ptr = argv[i + 1];
+            for (int j = i + 2; argc && j < *argc; ++j)
+            {
+                argv[j - 2] = argv[j];
+            }
+            *argc -= 2;
+            argv[*argc] = nullptr;
+            argv[*argc + 1] = nullptr;
+            break;
+        }
+    }
+
+    if (value_ptr) {
+        obj = value_ptr;
+        return true;
+    }
+    else {
+        return false;
+    }
+}
+
+int main (int argc, char** argv) {
+    extract_test_file_option(&argc, argv, g_config.test_folder, "test_folder");
+    extract_test_file_option(&argc, argv, g_config.test_file, "test_file");
+    extract_test_file_option(&argc, argv, g_config.temp_folder, "temp_folder");
+    printf("Target test folder: %s (use --test_folder option to change this)\n", g_config.test_folder.c_str());
+    printf("Target test file  : %s (use --test_file option to change this)\n", g_config.test_file.c_str());
+    printf("Temp folder       : %s (use --temp_folder option to change this)\n", g_config.temp_folder.c_str());
+    int result = Catch::Session().run(argc, argv);
+    return result;
+}
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/test_philips_tiff.cpp b/cpp/plugins/cucim.kit.cuslide/tests/test_philips_tiff.cpp
new file mode 100644
index 000000000..4f48646fd
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/test_philips_tiff.cpp
@@ -0,0 +1,59 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cucim/memory/memory_manager.h>
+#include <openslide/openslide.h>
+#include "cuslide/tiff/tiff.h"
+#include "config.h"
+
+#include <catch2/catch.hpp>
+#include <chrono>
+
+TEST_CASE("Verify philips tiff file", "[test_philips_tiff.cpp]")
+{
+
+    auto tif = std::make_shared<cuslide::tiff::TIFF>(g_config.get_input_path("private/philips_tiff_000.tif").c_str(),
+                                                     O_RDONLY); // , cuslide::tiff::TIFF::kUseLibTiff
+    tif->construct_ifds();
+
+    int64_t test_sx = 0;
+    int64_t test_sy = 0;
+
+    int64_t test_width = 500;
+    int64_t test_height = 500;
+
+    cucim::io::format::ImageMetadata metadata{};
+    cucim::io::format::ImageReaderRegionRequestDesc request{};
+    cucim::io::format::ImageDataDesc image_data{};
+
+    metadata.level_count(1).level_downsamples({ 1.0 }).level_ndim(3);
+
+    int64_t request_location[2] = { test_sx, test_sy };
+    request.location = request_location;
+    request.level = 0;
+    int64_t request_size[2] = { test_width, test_height };
+    request.size = request_size;
+    request.device = const_cast<char*>("cpu");
+
+    tif->read(&metadata.desc(), &request, &image_data);
+
+    request.associated_image_name = const_cast<char*>("label");
+    tif->read(&metadata.desc(), &request, &image_data, nullptr /*out_metadata*/);
+
+    tif->close();
+
+    REQUIRE(1 == 1);
+}
\ No newline at end of file
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/test_read_rawtiff.cpp b/cpp/plugins/cucim.kit.cuslide/tests/test_read_rawtiff.cpp
new file mode 100644
index 000000000..f76b83db6
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/test_read_rawtiff.cpp
@@ -0,0 +1,385 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <openslide/openslide.h>
+#include "cuslide/tiff/tiff.h"
+#include "config.h"
+
+#include <cuda_runtime.h>
+#include <catch2/catch.hpp>
+#include <fmt/format.h>
+#include <cucim/filesystem/cufile_driver.h>
+#include <cstdlib>
+#include <ctime>
+#include <fcntl.h>
+#include <unistd.h>
+#include <string_view>
+#include <cucim/logger/timer.h>
+#include <iostream>
+#include <fstream>
+#include <sys/stat.h>
+#include <sys/mman.h>
+
+#define ALIGN_UP(x, align_to) (((uint64_t)(x) + ((uint64_t)(align_to)-1)) & ~((uint64_t)(align_to)-1))
+#define ALIGN_DOWN(x, align_to) ((uint64_t)(x) & ~((uint64_t)(align_to)-1))
+
+#define CUDA_ERROR(stmt)                                                                                               \
+    {                                                                                                                  \
+        cuda_status = stmt;                                                                                            \
+        if (cudaSuccess != cuda_status)                                                                                \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", cudaGetErrorString(cuda_status)));                                   \
+            REQUIRE(cudaSuccess == cuda_status);                                                                       \
+        }                                                                                                              \
+    }
+
+#define POSIX_ERROR(stmt)                                                                                              \
+    {                                                                                                                  \
+        err = stmt;                                                                                                    \
+        if (err < 0)                                                                                                   \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", std::strerror(errno)));                                              \
+            REQUIRE(err >= 0);                                                                                         \
+        }                                                                                                              \
+    }
+
+static void shuffle_offsets(uint32_t count, uint64_t* offsets, uint64_t* bytecounts)
+{
+    // Fisher-Yates shuffle
+    for (int i = 0; i < count; ++i)
+    {
+        int j = (std::rand() % (count - i)) + i;
+        std::swap(offsets[i], offsets[j]);
+        std::swap(bytecounts[i], bytecounts[j]);
+    }
+}
+
+TEST_CASE("Verify raw tiff read", "[test_read_rawtiff.cpp]")
+{
+//    cudaError_t cuda_status;
+//    int err;
+    constexpr int BLOCK_SECTOR_SIZE = 4096;
+    constexpr bool SHUFFLE_LIST = true;
+    //    constexpr int iter_max = 32;
+    //    constexpr int skip_count = 2;
+    constexpr int iter_max = 1;
+    constexpr int skip_count = 0;
+
+    std::srand(std::time(nullptr));
+
+    auto input_file = g_config.get_input_path();
+
+    struct stat sb;
+    auto fd_temp = ::open(input_file.c_str(), O_RDONLY);
+    fstat(fd_temp, &sb);
+    uint64_t test_file_size = sb.st_size;
+    ::close(fd_temp);
+
+    auto tif = std::make_shared<cuslide::tiff::TIFF>(input_file,
+                                                     O_RDONLY); // , cuslide::tiff::TIFF::kUseLibTiff
+    tif->construct_ifds();
+    tif->ifd(0)->write_offsets_(input_file.c_str());
+
+
+    std::ifstream offsets(fmt::format("{}.offsets", input_file), std::ios::in | std::ios::binary);
+    std::ifstream bytecounts(fmt::format("{}.bytecounts", input_file), std::ios::in | std::ios::binary);
+
+    // Read image piece count
+    uint32_t image_piece_count_ = 0;
+    offsets.read(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));
+    bytecounts.read(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));
+
+    uint64_t image_piece_offsets_[image_piece_count_];
+    uint64_t image_piece_bytecounts_[image_piece_count_];
+    uint64_t min_bytecount = 9999999999;
+    uint64_t max_bytecount = 0;
+    uint64_t sum_bytecount = 0;
+
+    uint64_t min_offset = 9999999999;
+    uint64_t max_offset = 0;
+    for (uint32_t i = 0; i < image_piece_count_; i++)
+    {
+        offsets.read((char*)&image_piece_offsets_[i], sizeof(image_piece_offsets_[i]));
+        bytecounts.read((char*)&image_piece_bytecounts_[i], sizeof(image_piece_bytecounts_[i]));
+
+        min_bytecount = std::min(min_bytecount, image_piece_bytecounts_[i]);
+        max_bytecount = std::max(max_bytecount, image_piece_bytecounts_[i]);
+        sum_bytecount += image_piece_bytecounts_[i];
+
+        min_offset = std::min(min_offset, image_piece_offsets_[i]);
+        max_offset = std::max(max_offset, image_piece_offsets_[i] + image_piece_bytecounts_[i]);
+    }
+    bytecounts.close();
+    offsets.close();
+
+    fmt::print("file_size    : {}\n", test_file_size);
+    fmt::print("min_bytecount: {}\n", min_bytecount);
+    fmt::print("max_bytecount: {}\n", max_bytecount);
+    fmt::print("avg_bytecount: {}\n", static_cast<double>(sum_bytecount) / image_piece_count_);
+    fmt::print("min_offset   : {}\n", min_offset);
+    fmt::print("max_offset   : {}\n", max_offset);
+
+    // Shuffle offsets
+    if (SHUFFLE_LIST)
+    {
+        shuffle_offsets(image_piece_count_, image_piece_offsets_, image_piece_bytecounts_);
+    }
+
+    // Allocate memory
+    uint8_t* unaligned_host = static_cast<uint8_t*>(malloc(test_file_size + BLOCK_SECTOR_SIZE * 2));
+    uint8_t* buffer_host = static_cast<uint8_t*>(malloc(test_file_size + BLOCK_SECTOR_SIZE * 2));
+    uint8_t* aligned_host = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_host, BLOCK_SECTOR_SIZE));
+
+    //    uint8_t* unaligned_device;
+    //    CUDA_ERROR(cudaMalloc(&unaligned_device, test_file_size + BLOCK_SECTOR_SIZE));
+    //    uint8_t* aligned_device = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device, BLOCK_SECTOR_SIZE));
+    //
+    //    uint8_t* unaligned_device_host;
+    //    CUDA_ERROR(cudaMallocHost(&unaligned_device_host, test_file_size + BLOCK_SECTOR_SIZE));
+    //    uint8_t* aligned_device_host = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device_host, BLOCK_SECTOR_SIZE));
+    //
+    //    uint8_t* unaligned_device_managed;
+    //    CUDA_ERROR(cudaMallocManaged(&unaligned_device_managed, test_file_size + BLOCK_SECTOR_SIZE));
+    //    uint8_t* aligned_device_managed = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device_managed,
+    //    BLOCK_SECTOR_SIZE));
+
+    cucim::filesystem::discard_page_cache(input_file.c_str());
+
+    fmt::print("count:{} \n", image_piece_count_);
+
+    SECTION("Regular POSIX")
+    {
+        fmt::print("Regular POSIX\n");
+
+        double total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rpn");
+            {
+                cucim::logger::Timer timer("- read whole : {:.7f}\n", true, false);
+
+                ssize_t read_cnt = fd->pread(aligned_host, test_file_size, 0);
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Read whole average: {}\n", total_elapsed_time / (iter_max - skip_count));
+
+        total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rpn");
+            {
+                cucim::logger::Timer timer("- read tiles : {:.7f}\n", true, false);
+
+                for (uint32_t i = 0; i < image_piece_count_; ++i)
+                {
+                    ssize_t read_cnt = fd->pread(aligned_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);
+                }
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Read tiles average: {}\n", total_elapsed_time / (iter_max - skip_count));
+    }
+
+    SECTION("O_DIRECT")
+    {
+        fmt::print("O_DIRECT\n");
+
+        double total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rp");
+            {
+                cucim::logger::Timer timer("- read whole : {:.7f}\n", true, false);
+
+                ssize_t read_cnt = fd->pread(aligned_host, test_file_size, 0);
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Read whole average: {}\n", total_elapsed_time / (iter_max - skip_count));
+
+        total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rp");
+            {
+                cucim::logger::Timer timer("- read tiles : {:.7f}\n", true, false);
+
+                for (uint32_t i = 0; i < image_piece_count_; ++i)
+                {
+                    ssize_t read_cnt = fd->pread(buffer_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);
+                }
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Read tiles average: {}\n", total_elapsed_time / (iter_max - skip_count));
+    }
+
+    SECTION("O_DIRECT pre-load")
+    {
+        fmt::print("O_DIRECT pre-load\n");
+
+        size_t file_start_offset = ALIGN_DOWN(min_offset, BLOCK_SECTOR_SIZE);
+        size_t end_boundary_offset = ALIGN_UP(max_offset + max_bytecount, BLOCK_SECTOR_SIZE);
+        size_t large_block_size = end_boundary_offset - file_start_offset;
+
+        fmt::print("- size:{}\n", end_boundary_offset - file_start_offset);
+
+        double total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rp");
+            {
+                cucim::logger::Timer timer("- preload : {:.7f}\n", true, false);
+
+                ssize_t read_cnt = fd->pread(aligned_host, large_block_size, file_start_offset);
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Preload average: {}\n", total_elapsed_time / (iter_max - skip_count));
+
+        total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rp");
+            {
+                cucim::logger::Timer timer("- read tiles : {:.7f}\n", true, false);
+
+                for (uint32_t i = 0; i < image_piece_count_; ++i)
+                {
+                    memcpy(buffer_host, aligned_host + image_piece_offsets_[i] - file_start_offset,
+                           image_piece_bytecounts_[i]);
+                }
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- Read tiles average: {}\n", total_elapsed_time / (iter_max - skip_count));
+    }
+
+    SECTION("mmap")
+    {
+        fmt::print("mmap\n");
+
+        double total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            auto fd_mmap = open(input_file.c_str(), O_RDONLY);
+            {
+                cucim::logger::Timer timer("- open/close : {:.7f}\n", true, false);
+
+                void* mmap_host = mmap((void*)0, test_file_size, PROT_READ, MAP_SHARED, fd_mmap, 0);
+
+                REQUIRE(mmap_host != MAP_FAILED);
+
+                if (mmap_host != MAP_FAILED)
+                {
+                    REQUIRE(munmap(mmap_host, test_file_size) != -1);
+                    close(fd_mmap);
+                }
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+        }
+        fmt::print("- mmap/munmap average: {}\n", total_elapsed_time / (iter_max - skip_count));
+
+
+        total_elapsed_time = 0;
+        for (int iter = 0; iter < iter_max; ++iter)
+        {
+            cucim::filesystem::discard_page_cache(input_file.c_str());
+            //            auto fd_mmap = open(input_file, O_RDONLY);
+            //            void* mmap_host = mmap((void*)0, test_file_size, PROT_READ, MAP_SHARED, fd_mmap, 0);
+            //            REQUIRE(mmap_host != MAP_FAILED);
+            auto fd = cucim::filesystem::open(input_file.c_str(), "rm");
+            {
+                cucim::logger::Timer timer("- read tiles : {:.7f}\n", true, false);
+
+                for (uint32_t i = 0; i < image_piece_count_; ++i)
+                {
+                    // 3.441 => 3.489
+                    ssize_t read_cnt = fd->pread(buffer_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);
+                    //                                        memcpy(buffer_host, static_cast<char*>(mmap_host) +
+                    //                                        image_piece_offsets_[i], image_piece_bytecounts_[i]);
+                }
+
+                double elapsed_time = timer.stop();
+                if (iter >= skip_count)
+                {
+                    total_elapsed_time += elapsed_time;
+                }
+                timer.print();
+            }
+
+            //            if (mmap_host != MAP_FAILED)
+            //            {
+            //                REQUIRE(munmap(mmap_host, test_file_size) != -1);
+            //            }
+            //            close(fd_mmap);
+        }
+        fmt::print("- Read tiles average: {}\n", total_elapsed_time / (iter_max - skip_count));
+    }
+
+    free(unaligned_host);
+    free(buffer_host);
+}
diff --git a/cpp/plugins/cucim.kit.cuslide/tests/test_read_region.cpp b/cpp/plugins/cucim.kit.cuslide/tests/test_read_region.cpp
new file mode 100644
index 000000000..92590d83f
--- /dev/null
+++ b/cpp/plugins/cucim.kit.cuslide/tests/test_read_region.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cucim/memory/memory_manager.h>
+#include <openslide/openslide.h>
+#include "cuslide/tiff/tiff.h"
+#include "config.h"
+
+#include <catch2/catch.hpp>
+#include <chrono>
+
+TEST_CASE("Verify read_region()", "[test_read_region.cpp]")
+{
+    SECTION("Test with different parameters")
+    {
+        auto test_sx = GENERATE(as<int64_t>{}, 1, 255, 256, 511, 512);
+        auto test_sy = GENERATE(as<int64_t>{}, 1, 255, 256, 511, 512);
+        auto test_width = GENERATE(as<int64_t>{}, 1, 255, 256, 511, 512);
+        auto test_height = GENERATE(as<int64_t>{}, 1, 255, 256, 511, 512);
+
+        INFO("Execute with [sx:" << test_sx << ", sy:" << test_sy << ", width:" << test_width
+                                 << ", height:" << test_height << "]");
+
+        int openslide_count = 0;
+        int cucim_count = 0;
+
+        printf("[sx:%ld, sy:%ld, width:%ld, height:%ld]\n", test_sx, test_sy, test_width, test_height);
+        {
+            auto start = std::chrono::high_resolution_clock::now();
+
+            openslide_t* slide = openslide_open(g_config.get_input_path().c_str());
+            REQUIRE(slide != nullptr);
+
+            auto buf = (uint32_t*)cucim_malloc(test_width * test_height * 4);
+            openslide_read_region(slide, buf, test_sx, test_sy, 0, test_width, test_height);
+
+            openslide_close(slide);
+
+            auto end = std::chrono::high_resolution_clock::now();
+            auto elapsed_seconds = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+            printf("openslide: %f\n", elapsed_seconds.count());
+
+            auto out_image = reinterpret_cast<uint8_t*>(buf);
+            for (int i = 0; i < test_width * test_height * 4; i += 4)
+            {
+                openslide_count += out_image[i] + out_image[i + 1] + out_image[i + 2];
+            }
+            INFO("openslide value count: " << openslide_count);
+
+            cucim_free(buf);
+        }
+
+        {
+            auto start = std::chrono::high_resolution_clock::now();
+
+            auto tif = std::make_shared<cuslide::tiff::TIFF>(g_config.get_input_path().c_str(),
+                                                             O_RDONLY); // , cuslide::tiff::TIFF::kUseLibTiff
+            tif->construct_ifds();
+
+            cucim::io::format::ImageMetadata metadata{};
+            cucim::io::format::ImageReaderRegionRequestDesc request{};
+            cucim::io::format::ImageDataDesc image_data{};
+
+            metadata.level_count(1).level_downsamples({ 1.0 }).level_ndim(3);
+
+            int64_t request_location[2] = { test_sx, test_sy };
+            request.location = request_location;
+            request.level = 0;
+            int64_t request_size[2] = { test_width, test_height };
+            request.size = request_size;
+            request.device = const_cast<char*>("cpu");
+
+            tif->read(&metadata.desc(), &request, &image_data);
+
+            tif->close();
+
+            auto end = std::chrono::high_resolution_clock::now();
+            auto elapsed_seconds = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+
+            printf("cucim: %f\n", elapsed_seconds.count());
+            auto out_image = reinterpret_cast<uint8_t*>(image_data.container.data);
+            for (int i = 0; i < test_width * test_height * 3; i += 3)
+            {
+                cucim_count += out_image[i] + out_image[i + 1] + out_image[i + 2];
+            }
+            INFO("cucim value count: " << cucim_count);
+
+            cucim_free(image_data.container.data);
+            printf("\n");
+        }
+
+        REQUIRE(openslide_count == cucim_count);
+
+        /**
+         * Note: Experiment with OpenSlide with various level values (2020-09-28)
+         *
+         * When other level (1~) is used (for example, sx=4, sy=4, level=2, assuming that down factor is 4 for
+         * level 2), openslide's output is same with the values of cuCIM on the start position (sx/4, sy/4). If sx and
+         * sy is not multiple of 4, openslide's output was not trivial and performance was low.
+         */
+    }
+}
\ No newline at end of file
diff --git a/cpp/src/codec/base64.cpp b/cpp/src/codec/base64.cpp
new file mode 100644
index 000000000..d28a7f727
--- /dev/null
+++ b/cpp/src/codec/base64.cpp
@@ -0,0 +1,81 @@
+/*
+ * Copyright (c) 2021, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/codec/base64.h"
+
+#include "cucim/memory/memory_manager.h"
+
+#include <absl/strings/escaping.h>
+
+namespace cucim::codec::base64
+{
+
+bool encode(const char* src, int src_count, char** out_dst, int* out_count)
+{
+    if (src == nullptr)
+    {
+        return 1;
+    }
+
+    absl::string_view sv(src, src_count);
+    std::string output;
+    absl::Base64Escape(sv, &output);
+
+    int count = output.size();
+
+    if (out_dst == nullptr)
+    {
+        *out_dst = static_cast<char*>(cucim_malloc(count + 1));
+    }
+    memcpy(*out_dst, output.c_str(), count);
+    *out_dst[count] = '\0';
+
+    if (out_count != nullptr)
+    {
+        *out_count = count;
+    }
+
+    return 0;
+}
+
+bool decode(const char* src, int src_count, char** out_dst, int* out_count)
+{
+    if (src == nullptr)
+    {
+        return 1;
+    }
+
+    absl::string_view sv(src, src_count);
+    std::string output;
+    if (absl::Base64Unescape(sv, &output))
+    {
+        int count = output.size();
+
+        if (out_dst == nullptr)
+        {
+            *out_dst = static_cast<char*>(cucim_malloc(count + 1));
+        }
+        memcpy(*out_dst, output.c_str(), count);
+        *out_dst[count] = '\0';
+
+        if (out_count != nullptr)
+        {
+            *out_count = count;
+        }
+    }
+    return 0;
+}
+} // namespace cucim::codec::base64
\ No newline at end of file
diff --git a/cpp/src/core/cucim_framework.cpp b/cpp/src/core/cucim_framework.cpp
new file mode 100644
index 000000000..66be59081
--- /dev/null
+++ b/cpp/src/core/cucim_framework.cpp
@@ -0,0 +1,616 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim_framework.h"
+#include "cucim_plugin.h"
+
+#include <memory>
+#include <algorithm>
+
+namespace cucim
+{
+
+CuCIMFramework::CuCIMFramework()
+{
+}
+
+CuCIMFramework::~CuCIMFramework()
+{
+    g_cucim_framework = nullptr;
+    //    assert::deregisterAssertForClient();
+    //    logging::deregisterLoggingForClient();
+}
+
+
+bool CuCIMFramework::register_plugin(const char* client_name, const PluginRegistrationDesc& desc)
+{
+    (void)client_name;
+    (void)desc;
+
+    return false;
+}
+
+bool CuCIMFramework::unregister_plugin(const char* plugin_name)
+{
+    ScopedLock g(mutex_);
+
+    Plugin* plugin = get_plugin(plugin_name);
+    if (!plugin)
+    {
+        CUCIM_LOG_WARN("unregisterPlugin: Failed to find a plugin with a name: %s.", plugin_name ? plugin_name : "");
+        return false;
+    }
+
+    std::vector<Plugin*> plugins_to_unload;
+    if (try_terminate_plugin(plugin, &plugins_to_unload))
+    {
+        for (size_t idx = 0, plugin_count = plugins_to_unload.size(); idx < plugin_count; ++idx)
+        {
+            plugins_to_unload[idx]->unload();
+        }
+        unregister_plugin(plugin);
+        return true;
+    }
+
+    return false;
+}
+
+void CuCIMFramework::unregister_plugin(Plugin* plugin)
+{
+    // Remove plugin from all storages
+
+    name_to_plugin_index_.erase(plugin->name_cstr());
+
+    const std::string& file_path = plugin->library_path();
+    if (!file_path.empty())
+        library_path_to_plugin_index_.erase(file_path);
+
+    // Remove all its interfaces from candidates and reset selected if it not valid anymore
+    const auto& interfaces = plugin->get_interfaces();
+    for (size_t i = 0; i < interfaces.size(); i++)
+    {
+        CandidatesEntry& entry = interface_candidates_[interfaces[i].name];
+        for (size_t k = 0; k < entry.candidates.size(); k++)
+        {
+            if (entry.candidates[k].plugin_index == plugin->index_)
+            {
+                // Replace with last element (unordered fast remove)
+                if (entry.candidates.size() > 1)
+                {
+                    entry.candidates[k] = entry.candidates.back();
+                }
+                entry.candidates.resize(entry.candidates.size() - 1);
+            }
+        }
+
+        if (!entry.selected.get_plugin(plugin_manager_))
+            entry.selected = {};
+    }
+
+    plugin_manager_.remove_plugin(plugin->index_);
+    delete plugin;
+}
+
+void CuCIMFramework::load_plugins(const PluginLoadingDesc& desc)
+{
+    (void)desc;
+}
+
+
+bool CuCIMFramework::register_plugin(const std::shared_ptr<Plugin>& plugin)
+{
+    ScopedLock g(mutex_);
+
+    // TODO: duplicate check
+
+    // Success storing plugin in all registries
+    size_t plugin_index = plugin_manager_.add_plugin(plugin);
+    plugin->index_ = plugin_index;
+
+    const auto& interfaces = plugin->get_interfaces();
+    for (size_t i = 0; i < interfaces.size(); i++)
+    {
+        interface_candidates_[interfaces[i].name].candidates.push_back({ plugin_index, i });
+    }
+
+    // TODO: reloadable check
+    name_to_plugin_index_[plugin->name_cstr()] = plugin_index;
+    return true;
+}
+
+
+size_t CuCIMFramework::get_plugin_index(const char* name) const
+{
+    auto it = name_to_plugin_index_.find(name);
+    if (it != name_to_plugin_index_.end())
+    {
+        return it->second;
+    }
+    return kInvalidPluginIndex;
+}
+Plugin* CuCIMFramework::get_plugin(size_t index) const
+{
+    return index != kInvalidPluginIndex ? plugin_manager_.get_plugin(index) : nullptr;
+}
+
+Plugin* CuCIMFramework::get_plugin(const char* name) const
+{
+    return get_plugin(get_plugin_index(name));
+}
+
+
+Plugin* CuCIMFramework::get_plugin_by_library_path(const std::string& library_path)
+{
+
+    auto it = library_path_to_plugin_index_.find(library_path);
+    if (it != library_path_to_plugin_index_.end())
+    {
+        return get_plugin(it->second);
+    }
+    return nullptr;
+}
+
+bool CuCIMFramework::resolve_plugin_dependencies(Plugin* plugin)
+{
+    if (plugin->resolve_state_ == Plugin::ResolveState::kResolved)
+        return true;
+
+    //    const bool failed_before = (plugin->resolve_state_ == Plugin::ResolveState::kFailed);
+
+    plugin->resolve_state_ = Plugin::ResolveState::kInprocess;
+    //    bool resolveFailed = false;
+    //    for (auto& dep : plugin->getDeps())
+    //    {
+    //        if (!resolveInterfaceDependencyWithLogging(dep))
+    //        {
+    //            CUCIM_LOG_ERROR("[Plugin: %s] Dependency: %s failed to be resolved.", plugin->getName(), CSTR(dep));
+    //            resolveFailed = true;
+    //        }
+    //    }
+    //
+    //    if (resolveFailed)
+    //    {
+    //        plugin->resolveState = Plugin::ResolveState::eFailed;
+    //        return false;
+    //    }
+    //
+    //    if (failed_before)
+    //    {
+    //        CUCIM_LOG_INFO("[Plugin: %s] Dependencies were resolved now (failed before).", plugin->getName());
+    //    }
+
+    plugin->resolve_state_ = Plugin::ResolveState::kResolved;
+    return true;
+}
+
+bool CuCIMFramework::resolve_interface_dependency(const Plugin::InterfaceData& desc, bool log_errors)
+{
+    (void)log_errors;
+
+    const auto it = interface_candidates_.find(desc.name);
+    if (it != interface_candidates_.cend())
+    {
+        // Check for selected (default) plugins first
+        CandidatesEntry& entry = (*it).second;
+        Plugin* plugin = entry.selected.get_plugin(plugin_manager_);
+        if (plugin)
+        {
+            if (plugin->resolve_state_ == Plugin::ResolveState::kInprocess)
+            {
+                //                // todo: Give more insight on how it happened
+                //                if (log_errors)
+                //                {
+                //                    CUCIM_LOG_ERROR(
+                //                        "Circular dependency detected! Interface: %s requested. But plugin with an
+                //                        interface: %s is already in resolving state.", CSTR(desc),
+                //                        CSTR(entry.selected.get_interface_desc(m_registry)));
+                //                }
+                return false;
+            }
+            if (!is_version_semantically_compatible(
+                    desc.version, entry.selected.get_interface_desc(plugin_manager_).version))
+            {
+                //                if (log_errors)
+                //                {
+                //                    CUCIM_LOG_ERROR(
+                //                        "Interface: %s requested. But there is already a plugin with an interface: %s
+                //                        loaded. Versions are incompatible. Only one version of the same
+                //                        interface/plugin can exist at a time.", CSTR(desc),
+                //                        CSTR(entry.selected.get_interface_desc(m_registry)));
+                //                }
+                return false;
+            }
+            return true;
+        }
+
+        // Search for all plugins with that interface for matching version. If any of them marked as default - pick it
+        // and early out. If there is no defaults - the first one to match is selected, which should the highest
+        // compatible version.
+        Plugin::Interface candidate = {};
+        for (Plugin::Interface& c : entry.candidates)
+        {
+            // Check that candidate is still valid (could have been unregistered)
+            Plugin* candidatePlugin = c.get_plugin(plugin_manager_);
+            if (candidatePlugin)
+            {
+                if (candidate.plugin_index == kInvalidPluginIndex)
+                    candidate = c;
+                if (c.get_plugin(plugin_manager_)->name_str() == entry.specifiedDefaultPlugin)
+                {
+                    candidate = c;
+                    break;
+                }
+            }
+        }
+
+        // Resolve all dependencies recursively for the candidate if it has changed
+        Plugin* candidate_plugin = candidate.get_plugin(plugin_manager_);
+        if (candidate_plugin && entry.selected.plugin_index != candidate_plugin->index_)
+        {
+            // set candidate as selected to catch circular dependencies
+            entry.selected = candidate;
+
+            if (resolve_plugin_dependencies(candidate_plugin))
+            {
+                //                // the default plugin was just set for this interface: notify subscribers
+                //                CUCIM_LOG_INFO(
+                //                    "FrameworkImpl::resolveInterfaceDependency(): default plugin: %s was set for an
+                //                    interface: %s", candidate_plugin->getName(), CSTR(desc));
+                //                checkIfBasicPluginsAcquired(entry);
+                return is_version_semantically_compatible(
+                    desc.version, candidate.get_interface_desc(plugin_manager_).version);
+            }
+            else
+            {
+                entry.selected = {};
+                return false;
+            }
+        }
+    }
+    return false;
+}
+
+bool CuCIMFramework::resolve_interface_dependency_with_logging(const Plugin::InterfaceData& desc)
+{
+    return resolve_interface_dependency(desc, true);
+}
+
+bool CuCIMFramework::resolve_interface_dependency_no_logging(const Plugin::InterfaceData& desc)
+{
+    return resolve_interface_dependency(desc, false);
+}
+
+
+bool CuCIMFramework::try_terminate_plugin(Plugin* plugin, std::vector<Plugin*>* plugins_to_unload)
+{
+    //    // Terminate plugin if all clients released it
+    //    if (!plugin->hasAnyParents())
+    {
+        // Shut down the plugin first
+        plugin->terminate();
+
+        //        // Release parent <-> child dependency recursively
+        //        const Plugin::InterfaceSet& children = plugin->getChildren();
+        //        for (const Plugin::Interface& child : children)
+        //        {
+        //            releasePluginDependency(plugin->getName(), child, pluginsToUnload);
+        //        }
+        //        plugin->clearChildren();
+
+        if (plugins_to_unload)
+        {
+            plugins_to_unload->push_back(plugin);
+        }
+        else
+        {
+            CUCIM_LOG_WARN("%s: out-of-order unloading plugin %s", __func__, plugin->name_cstr());
+            plugin->unload();
+        }
+
+        return true;
+    }
+
+    return false;
+}
+
+
+Plugin::Interface CuCIMFramework::get_default_plugin(const InterfaceDesc& desc, bool optional)
+{
+    const auto it = interface_candidates_.find(desc.name);
+    if (it != interface_candidates_.cend())
+    {
+        CandidatesEntry& entry = (*it).second;
+
+        // If there is already selected plugin for this interface name, take it. Otherwise run
+        // resolve process with will select plugins for all dependent interfaces recursively
+        if (!entry.selected.get_plugin(plugin_manager_))
+        {
+            resolve_interface_dependency_no_logging(Plugin::InterfaceData{ desc.name, desc.version });
+        }
+
+        // In case of successful resolve there should be a valid candidate in this registry entry
+        const Plugin::Interface& candidate = entry.selected;
+        if (candidate.get_plugin(plugin_manager_))
+        {
+            // The version still could mismatch in case the candidate is the result of previous getInterface
+            // calls
+            if (!is_version_semantically_compatible(desc.version, candidate.get_interface_desc(plugin_manager_).version))
+            {
+                if (!optional)
+                {
+                    //                    CUCIM_LOG_ERROR(
+                    //                        "Interface: %s requested. But there is already a plugin with an interface:
+                    //                        %s loaded. Versions are incompatible. Only one version of the same
+                    //                        interface/plugin can exist at a time.", CSTR(desc),
+                    //                        CSTR(candidate.get_interface_desc(plugin_manager_)));
+                }
+                return {};
+            }
+            return candidate;
+        }
+    }
+    return {};
+}
+
+
+Plugin::Interface CuCIMFramework::get_specific_plugin(const InterfaceDesc& desc, const char* plugin_name, bool optional)
+{
+    // Search plugin by name
+    Plugin* plugin = get_plugin(plugin_name);
+    if (!plugin)
+    {
+        if (!optional)
+        {
+            //            CUCIM_LOG_ERROR("Failed to find a plugin with a name: %s", plugin_name);
+        }
+        return {};
+    }
+
+    // The interface version or name could mismatch, need to check
+    const auto& interfaces = plugin->get_interfaces();
+    Plugin::Interface candidate = {};
+    for (size_t i = 0; i < interfaces.size(); i++)
+    {
+        if (interfaces[i].name == desc.name && is_version_semantically_compatible(desc.version, interfaces[i].version))
+        {
+            candidate = { plugin->index_, i };
+            break;
+        }
+    }
+
+    Plugin* candidatePlugin = candidate.get_plugin(plugin_manager_);
+    if (!candidatePlugin)
+    {
+        if (!optional)
+        {
+            //            CUCIM_LOG_ERROR("Interface: %s with a plugin name: %s requested. Interface mismatched, it
+            //            has interfaces: %s",
+            //                           CSTR(desc), plugin->name_cstr(), CSTR(plugin->get_interfaces()));
+        }
+        return {};
+    }
+
+    // Check deps resolve, the actual resolve process could be triggered here if that's the first time plugin is
+    // requested
+    if (!resolve_plugin_dependencies(candidatePlugin))
+    {
+        if (!optional)
+        {
+            //            CUCIM_LOG_ERROR(
+            //                "Interface: %s with a plugin name: %s requested. One of the plugin's dependencies failed
+            //                to resolve.", CSTR(desc), candidatePlugin->name_cstr());
+        }
+        return {};
+    }
+
+    return candidate;
+}
+
+void CuCIMFramework::unload_all_plugins()
+{
+    ScopedLock g(mutex_);
+
+    CUCIM_LOG_VERBOSE("Unload all plugins.");
+
+    // Get all plugins from the registry and copy the set (because we are updating registry it inside of loops below)
+    std::unordered_set<size_t> plugins = plugin_manager_.get_plugin_indices();
+
+    // Unregister all plugins which aren't initialized (not used atm).
+    for (size_t plugin_index : plugins)
+    {
+        Plugin* plugin = plugin_manager_.get_plugin(plugin_index);
+        if (plugin && !plugin->is_initialized())
+            unregister_plugin(plugin);
+    }
+
+    // Terminate and unload all plugins in reverse order compared to initialization
+    for (auto it = plugin_load_order_.rbegin(); it != plugin_load_order_.rend(); ++it)
+    {
+        Plugin* plugin = get_plugin(*it);
+        if (plugin)
+            plugin->terminate();
+    }
+    for (auto it = plugin_load_order_.rbegin(); it != plugin_load_order_.rend(); ++it)
+    {
+        Plugin* plugin = get_plugin(*it);
+        if (plugin)
+            plugin->unload();
+    }
+    plugin_load_order_.clear();
+
+    // Destroy all plugins in registry
+    for (size_t plugin_index : plugins)
+    {
+        Plugin* plugin = plugin_manager_.get_plugin(plugin_index);
+        if (plugin)
+            unregister_plugin(plugin);
+    }
+
+    //    m_reloadablePlugins.clear();
+    interface_candidates_.clear();
+
+    // Verify that now everything is back to initial state
+    CUCIM_ASSERT(plugin_manager_.get_plugin_indices().empty() == true);
+    CUCIM_ASSERT(name_to_plugin_index_.empty() == true);
+    CUCIM_ASSERT(library_path_to_plugin_index_.empty() == true);
+}
+
+void* CuCIMFramework::acquire_interface(const char* client, const InterfaceDesc& desc, const char* plugin_name, bool optional)
+{
+    if (!client)
+        return nullptr;
+
+    ScopedLock g(mutex_);
+
+    const bool acquire_as_default = plugin_name ? false : true;
+    Plugin::Interface candidate =
+        acquire_as_default ? get_default_plugin(desc, optional) : get_specific_plugin(desc, plugin_name, optional);
+    Plugin* plugin = get_plugin(candidate.plugin_index);
+    if (!plugin)
+    {
+        if (!optional)
+        {
+            //            CUCIM_LOG_ERROR(
+            //                "Failed to acquire interface: %s, by client: %s (plugin name: %s)", CSTR(desc), client,
+            //                pluginName);
+        }
+        return nullptr;
+    }
+
+    if (!plugin->is_initialized())
+    {
+        // Don't hold the mutex during initialization
+        g.unlock();
+
+        // Lazily initialize plugins only when requested (on demand)
+        Plugin::InitResult result = plugin->ensure_initialized();
+
+        g.lock();
+
+        if (result != Plugin::InitResult::kAlreadyInitialized)
+        {
+            if (result == Plugin::InitResult::kFailedInitialize)
+            {
+                if (!optional)
+                {
+                    if (plugin->is_in_initialization())
+                    {
+                        //                        CUCIM_LOG_ERROR(
+                        //                            "Trying to acquire plugin during it's initialization: %s
+                        //                            (interfaces: %s) (impl: %s). Circular acquire calls.",
+                        //                            plugin->name_cstr(), CSTR(plugin->get_interfaces()),
+                        //                            CSTR(plugin->get_impl_desc()));
+                    }
+                    else
+                    {
+                        //                        CUCIM_LOG_ERROR("Plugin load failed: %s (interfaces: %s) (impl:
+                        //                        %s).", plugin->name_cstr(),
+                        //                                       CSTR(plugin->get_interfaces()),
+                        //                                       CSTR(plugin->get_impl_desc()));
+                    }
+                }
+                return nullptr;
+            }
+
+            // Add to the load order since loading was successful
+            // TODO: Replace load order with dependency graph
+            if (std::find(plugin_load_order_.begin(), plugin_load_order_.end(), plugin->index_) ==
+                plugin_load_order_.end())
+            {
+                plugin_load_order_.push_back(plugin->index_);
+            }
+        }
+    }
+
+    // Finish up now that the plugin is initialized
+    CUCIM_ASSERT(g.owns_lock());
+
+    void* iface = candidate.get_interface_desc(plugin_manager_).ptr;
+    CUCIM_ASSERT(iface);
+
+    // Store plugin in the interface->plugin map
+    ptr_to_interface_[iface] = candidate;
+
+    //    // Saving callers/clients of a plugin.
+    //    plugin->addParent(candidate.interfaceIndex, client, acquireAsDefault);
+    //
+    //    // Saving child for a parent
+    //    if (parent)
+    //        parent->addChild(candidate);
+
+    return iface;
+}
+
+
+void* CuCIMFramework::acquire_interface_from_library(const char* client,
+                                                     const InterfaceDesc& desc,
+                                                     const char* library_path,
+                                                     bool optional)
+{
+    ScopedLock g(mutex_);
+    // Check if plugin with this library path was already loaded
+
+    const std::string canonical_library_path(library_path);
+
+    Plugin* plugin = get_plugin_by_library_path(canonical_library_path);
+    if (!plugin)
+    {
+        // It was not loaded, try to register such plugin and get it again
+        if (register_plugin(canonical_library_path))
+        {
+            plugin = get_plugin_by_library_path(canonical_library_path);
+        }
+    }
+
+    if (plugin)
+    {
+        // Library path leads to valid plugin which now was loaded, try acquire requested interface on it:
+        return acquire_interface(client, desc, plugin->name_cstr(), optional);
+    }
+
+    return nullptr;
+}
+bool CuCIMFramework::register_plugin(const std::string& file_path, bool reloadable, bool unload)
+{
+    std::shared_ptr<Plugin> plugin = std::make_shared<Plugin>(file_path);
+
+    // Try preload
+    if (!plugin->preload(reloadable, unload))
+    {
+        //        CUCIM_LOG_WARN("Potential plugin preload failed: %s", plugin->library_path());
+        return false;
+    }
+
+    if (register_plugin(plugin))
+    {
+        library_path_to_plugin_index_[file_path] = plugin->index_;
+        return true;
+    }
+    return false;
+}
+
+// cuCIM-specific methods
+std::string& CuCIMFramework::get_plugin_root()
+{
+    return plugin_root_path_;
+}
+
+void CuCIMFramework::set_plugin_root(const char* path)
+{
+    plugin_root_path_ = std::string(path);
+}
+
+} // namespace cucim
\ No newline at end of file
diff --git a/cpp/src/core/cucim_framework.h b/cpp/src/core/cucim_framework.h
new file mode 100644
index 000000000..966c33ba8
--- /dev/null
+++ b/cpp/src/core/cucim_framework.h
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_CUCIM_FRAMEWORK_H
+#define CUCIM_CUCIM_FRAMEWORK_H
+
+#include "cucim/core/framework.h"
+#include "plugin_manager.h"
+#include "cucim_plugin.h"
+
+#include <mutex>
+#include <memory>
+#include <string>
+#include <unordered_map>
+
+namespace cucim
+{
+
+class CuCIMFramework
+{
+public:
+    CuCIMFramework();
+    ~CuCIMFramework();
+
+    bool register_plugin(const char* client_name, const PluginRegistrationDesc& desc);
+    bool register_plugin(const std::string& file_path, bool reloadable = false, bool unload = false);
+    bool register_plugin(const std::shared_ptr<Plugin>& plugin);
+    bool unregister_plugin(const char* name);
+    void unregister_plugin(Plugin* plugin);
+    bool try_terminate_plugin(Plugin* plugin, std::vector<Plugin*>* plugins_to_load);
+    void load_plugins(const PluginLoadingDesc& desc);
+    void unload_all_plugins();
+
+
+    bool resolve_plugin_dependencies(Plugin* plugin);
+    bool resolve_interface_dependency(const Plugin::InterfaceData& info, bool log_errors);
+    bool resolve_interface_dependency_with_logging(const Plugin::InterfaceData& desc);
+    bool resolve_interface_dependency_no_logging(const Plugin::InterfaceData& desc);
+    Plugin::Interface get_default_plugin(const InterfaceDesc& desc, bool optional);
+    Plugin::Interface get_specific_plugin(const InterfaceDesc& desc, const char* plugin_name, bool optional);
+
+
+    void* acquire_interface(const char* client, const InterfaceDesc& desc, const char* plugin_name, bool optional = false);
+    void* acquire_interface_from_library(const char* client,
+                                         const InterfaceDesc& desc,
+                                         const char* library_path,
+                                         bool optional = false);
+    size_t get_plugin_index(const char* name) const;
+    Plugin* get_plugin(size_t index) const;
+    Plugin* get_plugin(const char* name) const;
+    Plugin* get_plugin_by_library_path(const std::string& library_path);
+
+    // cuCIM-specific methods;
+    std::string& get_plugin_root();
+    void set_plugin_root(const char* path);
+
+private:
+    struct CandidatesEntry
+    {
+        std::vector<Plugin::Interface> candidates;
+        Plugin::Interface selected = {};
+        std::string specifiedDefaultPlugin;
+    };
+
+    using Mutex = std::recursive_mutex;
+    using ScopedLock = std::unique_lock<Mutex>;
+    mutable Mutex mutex_;
+
+    std::vector<size_t> plugin_load_order_;
+    PluginManager plugin_manager_;
+    std::unordered_map<std::string, size_t> library_path_to_plugin_index_;
+    std::unordered_map<std::string, size_t> name_to_plugin_index_;
+    std::unordered_map<std::string, CandidatesEntry> interface_candidates_;
+    std::unordered_map<const void*, Plugin::Interface> ptr_to_interface_;
+
+    // cuCIM-specific fields;
+    std::string plugin_root_path_;
+};
+} // namespace cucim
+
+#endif // CUCIM_CUCIM_FRAMEWORK_H
diff --git a/cpp/src/core/cucim_plugin.cpp b/cpp/src/core/cucim_plugin.cpp
new file mode 100644
index 000000000..8e6aa11bc
--- /dev/null
+++ b/cpp/src/core/cucim_plugin.cpp
@@ -0,0 +1,458 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+
+#include "cucim_plugin.h"
+#include "cucim/core/framework.h"
+#include "cucim/core/plugin_util.h"
+
+#include <algorithm>
+#include <cstring>
+
+namespace cucim
+{
+
+Plugin::Plugin()
+    : index_(0),
+      resolve_state_(ResolveState::kUnused),
+      plugin_desc_(),
+      library_handle_(nullptr),
+      on_get_framework_version_(nullptr),
+      on_register_(nullptr),
+      on_get_deps_(nullptr),
+      //      m_carbOnPluginPreStartupFn(nullptr),
+      //      m_carbOnPluginStartupFn(nullptr),
+      //      m_carbOnPluginShutdownFn(nullptr),
+      //      m_carbOnPluginPostShutdownFn(nullptr),
+      //      m_carbOnReloadDependencyFn(nullptr),
+      is_loaded_(false),
+      is_initialized_(false),
+      is_in_initialization_(false),
+      is_reloadable_(false),
+      next_version_(0)
+//      m_fileSystem(fs)
+{
+}
+
+Plugin::Plugin(const std::string& file_path) : Plugin()
+{
+    library_path_ = file_path;
+    name_ = "cucim.kit.cuslide"; // TODO: Get plgin name from file_path
+}
+
+Plugin::~Plugin()
+{
+    unload();
+}
+
+template <typename T>
+bool Plugin::init_plugin_fn(T& handle, const char* name, bool optional) const
+{
+    handle = dynlib::get_library_symbol<T>(library_handle_, name);
+    if (!handle && !optional)
+    {
+        CUCIM_LOG_WARN("[Plugin: %s] Could not locate the function: %s", name_cstr(), name);
+        return false;
+    }
+    return true;
+}
+
+
+bool Plugin::prepare_file_to_load(std::string& out_lib_file_path, int version)
+{
+    (void) version;
+
+    //    if (!is_reloadable_)
+    //    {
+    out_lib_file_path = library_path_;
+    return true;
+    //    }
+
+    //    if (m_tempFolder.empty())
+    //    {
+    //        CUCIM_LOG_ERROR("Can't load plugin %s as reloadable, temp folder doesn't exist.", getName());
+    //        m_reloadable = false;
+    //        return true;
+    //    }
+    //
+    //    m_lastWriteTime = m_fileSystem->getModTime(m_libFilePath.c_str());
+    //
+    //    extras::Path path(m_libFilePath.c_str());
+    //    std::string newLibFilename = path.getStem() + std::to_string(version) + path.getExtension();
+    //    auto newLibPath = m_tempFolder + "/" + newLibFilename;
+    //
+    //    if (!m_fileSystem->exists(newLibPath.c_str()))
+    //    {
+    //        if (!m_fileSystem->copy(m_libFilePath.c_str(), newLibPath.c_str()))
+    //        {
+    //            return false;
+    //        }
+    //
+    //#if CARB_COMPILER_MSC
+    //        extras::Path newPdbFile(newLibPath.c_str());
+    //        newPdbFile.replaceExtension(".pdb");
+    //
+    //        if (!relinkAndCopyPdbFile(newLibPath.c_str(), newPdbFile))
+    //        {
+    //            CUCIM_LOG_WARN(
+    //                "[Plugin: %s] Couldn't process PDB, debugging may be affected and/or reload may fail.",
+    //                getName());
+    //        }
+    //#endif
+    //    }
+    //    out_lib_file_path = newLibPath;
+    //    return true;
+}
+
+bool Plugin::fill_registration_data(int version, bool full, const std::string& lib_file)
+{
+    (void)lib_file;
+
+    // Retrieve registration information
+    PluginEntry entry;
+    on_register_(get_framework(), &entry);
+
+    // Versioned data to fill:
+    VersionedData& d = data_[kVersionStateCurrent];
+
+    // Sort interfaces by name to keep order always the same
+    std::sort(entry.interfaces, entry.interfaces + entry.interface_count,
+              [](const PluginEntry::Interface& a, const PluginEntry::Interface& b) -> bool {
+                  return std::strcmp(a.desc.name, b.desc.name) < 0;
+              });
+
+    d.plugin_interfaces.resize(entry.interface_count);
+    d.interfaces.resize(entry.interface_count);
+    for (size_t i = 0; i < entry.interface_count; i++)
+    {
+        d.interfaces[i].store(entry.interfaces[i].desc);
+        d.plugin_interfaces[i] = d.interfaces[i].to_interface_desc();
+    }
+    d.desc.store(entry.desc);
+    name_ = d.desc.name;
+
+    if (full)
+    {
+        //        // Load the plugin interfaces
+        //        {
+        //            // Prepare interface buffers count
+        //            if (version == 0)
+        //            {
+        //                m_interfaceBufs.resize(entry.interfaceCount);
+        //                m_interfaceParents.resize(entry.interfaceCount);
+        //            }
+        //            else
+        //            {
+        //                if (m_interfaceBufs.size() != entry.interfaceCount)
+        //                {
+        //                    CUCIM_LOG_ERROR(
+        //                        "[Plugin: %s] New version is incompatible for reload: interfaces count changed.",
+        //                        getName());
+        //                    return false;
+        //                }
+        //            }
+        //
+        for (size_t i = 0; i < entry.interface_count; i++)
+        {
+            const void* iface_ptr = entry.interfaces[i].ptr;
+            uint64_t iface_size = entry.interfaces[i].size;
+            //                if (ifaceSize == 0 || ifacePtr == nullptr)
+            //                {
+            //                    CUCIM_LOG_ERROR("[Plugin: %s] Interface is empty.", name_cstr());
+            //                    return false;
+            //                }
+            //                if (version == 0)
+            //                {
+            //                    // First time allocating place for an interface buffer of a particular interface
+            //                    // let's for now reserve twice as much space in case the plugin will be reloaded (or
+            //                    implementation
+            //                    // changes to other version) in runtime. That would allow it to grow.
+            //                    m_interfaceBufs[i].resize(ifaceSize * 2);
+            //                }
+            //                if (m_interfaceBufs[i].size() < ifaceSize)
+            //                {
+            //                    CUCIM_LOG_ERROR("[Plugin: %s] New version is incompatible for reload: interface size
+            //                    grown too much.",
+            //                                   getName());
+            //                    return false;
+            //                }
+            //                    // Copy an interface in a buffer, that allows us to reuse the same pointer if a plugin
+            //                    is reloaded. std::memcpy(m_interfaceBufs[i].data(), ifacePtr, ifaceSize);
+            d.interfaces[i].ptr = const_cast<void*>(iface_ptr); // m_interfaceBufs[i].data();
+            d.interfaces[i].size = iface_size;
+        }
+        //        }
+    }
+    //
+    //    // Data sections:
+    //    if (m_reloadable && full && !lib_file.empty())
+    //    {
+    //        // Failed to load sections
+    //        if (!loadSections(m_fileSystem, m_libraryHandle, lib_file, d.bssSection, d.stateSection))
+    //            m_reloadable = false;
+    //    }
+    //
+    //    // Get dependencies
+    //    d.dependencies.clear();
+    //    d.pluginDependencies.clear();
+    //    if (m_carbGetPluginDepsFn)
+    //    {
+    //        InterfaceDesc* depDescs;
+    //        size_t depDescCount;
+    //        m_carbGetPluginDepsFn(&depDescs, &depDescCount);
+    //        d.dependencies.reserve(depDescCount);
+    //        d.pluginDependencies.resize(depDescCount);
+    //        for (size_t i = 0; i < depDescCount; i++)
+    //        {
+    //            d.dependencies.push_back({ depDescs[i].name, depDescs[i].version });
+    //            d.pluginDependencies[i] = d.dependencies[i].to_interface_desc();
+    //        }
+    //    }
+
+    // Fill PluginDesc
+    plugin_desc_ = { get_impl_desc().to_plugin_impl(), library_path_.c_str(),        d.plugin_interfaces.data(),
+                     d.plugin_interfaces.size(),       d.plugin_dependencies.data(), d.plugin_dependencies.size() };
+
+    // Save version
+    d.version = version;
+
+    return true;
+}
+
+bool Plugin::check_framework_version()
+{
+    const Version version = on_get_framework_version_();
+    if (kFrameworkVersion.major != version.major)
+    {
+        CUCIM_LOG_ERROR(
+            "[Plugin: %s] Incompatible Framework API major version: %" PRIu32 "", name_cstr(), kFrameworkVersion.major);
+        return false;
+    }
+    if (kFrameworkVersion.minor < version.minor)
+    {
+        CUCIM_LOG_ERROR(
+            "[Plugin: %s] Incompatible Framework API minor version: %" PRIu32 "", name_cstr(), kFrameworkVersion.major);
+        return false;
+    }
+    return true;
+}
+
+
+bool Plugin::try_load(int version, bool full)
+{
+    if (is_loaded_)
+    {
+        return is_loaded_;
+    }
+    CUCIM_LOG_VERBOSE("[Plugin: %s] %s", name_cstr(), full ? "Loading..." : "Preloading...");
+
+    std::string lib_file;
+    if (!prepare_file_to_load(lib_file, version))
+    {
+        return false;
+    }
+    // Load library
+    CUCIM_LOG_VERBOSE("[Plugin: %s] Loading the dynamic library from: %s", name_cstr(), lib_file.c_str());
+    library_handle_ = dynlib::load_library(lib_file.c_str());
+
+    if (!library_handle_)
+    {
+        CUCIM_LOG_ERROR("[Plugin: %s] Could not load the dynamic library from %s. Error: %s", name_cstr(),
+                        lib_file.c_str(), dynlib::get_last_load_library_error().c_str());
+        return false;
+    }
+
+    // Load all the plugin function handles
+    if (!init_plugin_fn(on_get_framework_version_, kCuCIMOnGetFrameworkVersionFnName))
+        return false;
+    if (!check_framework_version())
+        return false;
+    if (!init_plugin_fn(on_register_, kCuCIMOnPluginRegisterFnName))
+        return false;
+    if (!init_plugin_fn(on_get_deps_, kCuCIMOnGetPluginDepsFnName, true))
+        return false;
+
+    //    if (full)
+    //    {
+    //        init_plugin_fn(m_carbOnPluginPreStartupFn, kCarbOnPluginPreStartupFnName, true);
+    //        init_plugin_fn(m_carbOnPluginStartupFn, kCarbOnPluginStartupFnName, true);
+    //        init_plugin_fn(m_carbOnPluginShutdownFn, kCarbOnPluginShutdownFnName, true);
+    //        init_plugin_fn(m_carbOnPluginPostShutdownFn, kCarbOnPluginPostShutdownFnName, true);
+    //        init_plugin_fn(m_carbOnReloadDependencyFn, kCarbOnReloadDependencyFnName, true);
+    //    }
+
+    // Register
+    if (!fill_registration_data(version, full, lib_file))
+    {
+        return false;
+    }
+
+    // Load was successful
+    CUCIM_LOG_VERBOSE("[Plugin: %s] %s successfully. Version: %d", name_cstr(), full ? "loaded" : "preloaded", version);
+    is_loaded_ = true;
+    return is_loaded_;
+}
+
+
+bool Plugin::load(int version, bool full)
+{
+    if (!try_load(version, full))
+    {
+        unload();
+        return false;
+    }
+    return true;
+}
+
+void Plugin::unload()
+{
+    if (library_handle_)
+    {
+        dynlib::unload_library(library_handle_);
+        library_handle_ = nullptr;
+        is_loaded_ = false;
+        CUCIM_LOG_VERBOSE("[Plugin: %s] Unloaded.", name_cstr());
+    }
+}
+
+
+bool Plugin::preload(bool reloadable, bool unload)
+{
+    is_reloadable_ = reloadable;
+
+    bool full_load = !unload;
+    if (load(0, full_load))
+    {
+        if (unload)
+            this->unload();
+        return true;
+    }
+    return false;
+}
+
+Plugin::InitResult Plugin::ensure_initialized()
+{
+    // Fast path: already initialized
+    if (is_initialized_)
+    {
+        return InitResult::kAlreadyInitialized;
+    }
+
+    // Check again after locking mutex
+    std::lock_guard<std::recursive_mutex> lock(init_lock_);
+    if (is_initialized_)
+    {
+        return InitResult::kAlreadyInitialized;
+    }
+
+    return initialize() ? InitResult::kDidInitialize : InitResult::kFailedInitialize;
+}
+
+bool Plugin::initialize()
+{
+    std::lock_guard<std::recursive_mutex> lock(init_lock_);
+
+    // another thread could have beaten us into the locked region between when the 'initialized'
+    // flag was originally checked (before this call) and when the lock was actually acquired.
+    // If this flag is set, that means the other thread won and the plugin has already been
+    // fully initialized.  In this case there is nothing left for us to do here but succeed.
+    if (is_initialized_)
+    {
+        return true;
+    }
+
+    if (is_in_initialization_)
+    {
+        // Don't recursively initialize
+        return false;
+    }
+
+    CUCIM_LOG_INFO("Initializing plugin: %s (interfaces: %s) (impl: %s)", name_cstr(), CSTR(get_interfaces()),
+                   CSTR(get_impl_desc()));
+
+    is_in_initialization_ = true;
+
+    // failed to load the plugin library iself => fail and allow the caller to try again later.
+    if (load(next_version_++))
+    {
+        //        // run the pre-startup function for the plugin.
+        //        if (m_carbOnPluginPreStartupFn)
+        //        {
+        //            m_carbOnPluginPreStartupFn();
+        //        }
+        //
+        //        // run the startup function for the plugin.
+        //        if (m_carbOnPluginStartupFn)
+        //        {
+        //            m_carbOnPluginStartupFn();
+        //        }
+
+        is_initialized_ = true;
+    }
+
+    is_in_initialization_ = false;
+
+    return is_initialized_;
+}
+
+void Plugin::terminate()
+{
+    std::lock_guard<std::recursive_mutex> lock(init_lock_);
+
+    if (!is_initialized_ || !is_loaded_)
+        return;
+
+    //    if (m_carbOnPluginShutdownFn)
+    //    {
+    //        m_carbOnPluginShutdownFn();
+    //    }
+    //
+    //    if (m_carbOnPluginPostShutdownFn)
+    //    {
+    //        m_carbOnPluginPostShutdownFn();
+    //    }
+
+    is_initialized_ = false;
+}
+
+
+static void update_if_changed(std::string& str, const char* value)
+{
+    if (str != value)
+        str = value;
+}
+
+void Plugin::InterfaceData::store(const InterfaceDesc& desc)
+{
+    update_if_changed(name, desc.name);
+    version = desc.version;
+}
+
+void Plugin::ImplementationDesc::store(const PluginImplDesc& desc)
+{
+    update_if_changed(name, desc.name);
+    version = desc.version;
+    update_if_changed(build, desc.build);
+    update_if_changed(author, desc.author);
+    update_if_changed(description, desc.description);
+    update_if_changed(long_description, desc.long_description);
+    update_if_changed(license, desc.license);
+    update_if_changed(url, desc.url);
+    update_if_changed(platforms, desc.platforms);
+    hot_reload = desc.hot_reload;
+}
+
+} // namespace cucim
\ No newline at end of file
diff --git a/cpp/src/core/cucim_plugin.h b/cpp/src/core/cucim_plugin.h
new file mode 100644
index 000000000..3e2ec6fd0
--- /dev/null
+++ b/cpp/src/core/cucim_plugin.h
@@ -0,0 +1,269 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_CUCIM_PLUGIN_H
+#define CUCIM_CUCIM_PLUGIN_H
+
+#include <string>
+#include <vector>
+#include <mutex>
+#include <sstream>
+#include "cucim/core/plugin.h"
+#include "cucim/core/interface.h"
+#include "cucim/dynlib/helper.h"
+#include "plugin_manager.h"
+#include "version.inl"
+
+namespace cucim
+{
+
+class Plugin
+{
+public:
+    enum class ResolveState
+    {
+        kUnused,
+        kInprocess,
+        kResolved,
+        kFailed
+    };
+
+    // Returns whether the initialization has happened. didNow is set true only if the initialize happened during this
+    // call This ensures atomicity between isInitialized/initialize()
+    enum class InitResult
+    {
+        kFailedInitialize = 0,
+        kAlreadyInitialized,
+        kDidInitialize
+    };
+
+    struct InterfaceData
+    {
+        std::string name;
+        InterfaceVersion version = { 0, 0 };
+        void* ptr = nullptr;
+        uint64_t size = 0;
+
+        InterfaceDesc to_interface_desc() const
+        {
+            return InterfaceDesc{ name.c_str(), { version.major, version.minor } };
+        }
+
+        void store(const InterfaceDesc& desc);
+    };
+
+    struct ImplementationDesc
+    {
+        std::string name;
+        Version version;
+        std::string build;
+        std::string author;
+        std::string description;
+        std::string long_description;
+        std::string license;
+        std::string url;
+        std::string platforms;
+        PluginHotReload hot_reload = PluginHotReload::kDisabled;
+
+        PluginImplDesc to_plugin_impl() const
+        {
+            return PluginImplDesc{ name.c_str(),        version,
+                                   build.c_str(),       author.c_str(),
+                                   description.c_str(), long_description.c_str(),
+                                   license.c_str(),     url.c_str(),
+                                   platforms.c_str(), PluginHotReload::kDisabled};
+        }
+
+        void store(const PluginImplDesc& desc);
+    };
+
+    struct Interface
+    {
+        Interface() : plugin_index(kInvalidPluginIndex), interface_index(0)
+        {
+        }
+
+        Interface(size_t plugin_idx, size_t interface_idx) : plugin_index(plugin_idx), interface_index(interface_idx)
+        {
+        }
+
+        size_t plugin_index;
+        size_t interface_index;
+
+        Plugin* get_plugin(const PluginManager& registry) const
+        {
+            return plugin_index != kInvalidPluginIndex ? registry.get_plugin(plugin_index) : nullptr;
+        }
+
+        const Plugin::InterfaceData& get_interface_desc(const PluginManager& registry) const
+        {
+            return registry.get_plugin(plugin_index)->get_interfaces()[interface_index];
+        }
+
+        bool operator==(const Interface& other) const
+        {
+            return ((plugin_index == other.plugin_index) && (interface_index == other.interface_index));
+        }
+    };
+
+    Plugin();
+    explicit Plugin(const std::string& file_path);
+    ~Plugin();
+
+    const char* name_cstr() const
+    {
+        return name_.c_str();
+    }
+
+    std::string name_str() const
+    {
+        return name_;
+    }
+    const char* library_path() const
+    {
+        return library_path_.c_str();
+    }
+
+    bool is_initialized() const
+    {
+        return is_initialized_;
+    }
+    bool is_in_initialization() const
+    {
+        return is_in_initialization_;
+    }
+
+
+    bool preload(bool reloadable, bool unload);
+    InitResult ensure_initialized();
+    bool initialize();
+    void terminate();
+    void unload();
+
+
+    size_t index_;
+    ResolveState resolve_state_;
+
+
+    const std::vector<Plugin::InterfaceData>& get_interfaces() const
+    {
+        return data_[kVersionStateCurrent].interfaces;
+    }
+    const ImplementationDesc& get_impl_desc() const
+    {
+        return data_[kVersionStateCurrent].desc;
+    }
+    const PluginDesc& get_plugin_desc() const
+    {
+        return plugin_desc_;
+    }
+
+private:
+    static constexpr uint32_t kVersionStateBackup = 0;
+    static constexpr uint32_t kVersionStateCurrent = 1;
+    static constexpr uint32_t kVersionStateCount = 2;
+
+    struct VersionedData
+    {
+        VersionedData() = default;
+        int version = 0;
+        ImplementationDesc desc;
+        uint64_t interface_size = 0;
+        std::vector<InterfaceData> interfaces;
+        std::vector<InterfaceDesc> plugin_interfaces;
+        std::vector<InterfaceData> dependencies;
+        std::vector<InterfaceDesc> plugin_dependencies;
+    };
+
+    template <typename T>
+    bool init_plugin_fn(T& handle, const char* name, bool optional = false) const;
+    bool prepare_file_to_load(std::string& out_lib_file_path, int version);
+    bool fill_registration_data(int version, bool full, const std::string& lib_file);
+    bool check_framework_version();
+
+    bool try_load(int version, bool full);
+    bool load(int version = 0, bool full = true);
+
+
+    VersionedData data_[kVersionStateCount];
+
+    std::string library_path_;
+    std::string name_;
+    PluginDesc plugin_desc_;
+    dynlib::LibraryHandle library_handle_;
+
+
+    OnGetFrameworkVersionFn on_get_framework_version_;
+    OnPluginRegisterFn on_register_;
+    OnGetPluginDepsFn on_get_deps_;
+
+    bool is_loaded_;
+    bool is_initialized_;
+    bool is_in_initialization_;
+    bool is_reloadable_;
+    int next_version_;
+
+    std::recursive_mutex init_lock_;
+};
+
+
+inline bool operator==(const Plugin::InterfaceData& lhs, const Plugin::InterfaceData& rhs)
+{
+    return lhs.name == rhs.name && lhs.version == rhs.version;
+}
+
+inline std::ostream& operator<<(std::ostream& o, const Plugin::InterfaceData& info)
+{
+    o << "[" << info.name << " v" << info.version.major << "." << info.version.minor << "]";
+    return o;
+}
+
+inline std::ostream& operator<<(std::ostream& o, const std::vector<Plugin::InterfaceData>& interfaces)
+{
+    for (size_t i = 0; i < interfaces.size(); i++)
+    {
+        o << (i > 0 ? "," : "") << interfaces[i];
+    }
+    return o;
+}
+
+inline std::ostream& operator<<(std::ostream& o, const Plugin::ImplementationDesc& info)
+{
+    o << info.name;
+    return o;
+}
+
+inline std::ostream& operator<<(std::ostream& o, const InterfaceDesc& info)
+{
+    o << Plugin::InterfaceData{ info.name, info.version };
+    return o;
+}
+
+
+template <class T>
+std::string toString(const T& x)
+{
+    std::ostringstream ss;
+    ss << x;
+    return ss.str();
+}
+
+#define CSTR(x) toString(x).c_str()
+
+
+} // namespace cucim
+
+#endif // CUCIM_CUCIM_PLUGIN_H
diff --git a/cpp/src/core/framework.cpp b/cpp/src/core/framework.cpp
new file mode 100644
index 000000000..15765db68
--- /dev/null
+++ b/cpp/src/core/framework.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#define CUCIM_EXPORTS
+
+#include "cucim/core/framework.h"
+#include "cucim_framework.h"
+#include "cucim/macros/defines.h"
+#include <memory>
+#include <mutex>
+
+
+CUCIM_FRAMEWORK_GLOBALS("cucim")
+
+
+namespace cucim
+{
+
+static std::unique_ptr<CuCIMFramework> g_framework;
+
+
+static bool register_plugin(const char* client_name, const PluginRegistrationDesc& desc)
+{
+    CUCIM_ASSERT(g_framework);
+    return g_framework->register_plugin(client_name, desc);
+}
+
+// static void load_plugins(const PluginLoadingDesc& desc)
+//{
+//    CUCIM_ASSERT(g_framework);
+//    return g_framework->load_plugins(desc);
+//}
+
+static void* acquire_interface_from_library_with_client(const char* client_name,
+                                                        InterfaceDesc desc,
+                                                        const char* library_path)
+{
+    CUCIM_ASSERT(g_framework);
+    return g_framework->acquire_interface_from_library(client_name, desc, library_path, false);
+}
+
+static void unload_all_plugins()
+{
+    CUCIM_ASSERT(g_framework);
+    g_framework->unload_all_plugins();
+}
+
+static const char* get_plugin_root()
+{
+    CUCIM_ASSERT(g_framework);
+    return g_framework->get_plugin_root().c_str();
+}
+
+static void set_plugin_root(const char* path)
+{
+    CUCIM_ASSERT(g_framework);
+    g_framework->set_plugin_root(path);
+}
+
+static Framework get_framework_impl()
+{
+    // clang-format off
+    return
+    {
+        register_plugin,
+        acquire_interface_from_library_with_client,
+        unload_all_plugins,
+        get_plugin_root,
+        set_plugin_root,
+    };
+    // clang-format on
+}
+
+
+namespace
+{
+std::mutex& acquire_framework_mutex()
+{
+    static std::mutex mutex;
+    return mutex;
+}
+} // namespace
+
+
+CUCIM_API Framework* acquire_framework(const char* app_name, Version framework_version)
+{
+    (void) app_name;
+    (void) framework_version;
+    //    if (!is_version_semantically_compatible(kFrameworkVersion, frameworkVersion))
+    //    {
+    //        // Using CARB_LOG here is pointless because logging hasn't been set up yet.
+    //        fprintf(stderr,
+    //                "[App: %s] Incompatible Framework API version. Framework version: %" PRIu32 ".%" PRIu32
+    //                ". Application requested version: %" PRIu32 ".%" PRIu32 ".\n",
+    //                appName, kFrameworkVersion.major, kFrameworkVersion.minor, frameworkVersion.major,
+    //                frameworkVersion.minor);
+    //        return nullptr;
+    //    }
+
+    static Framework framework = get_framework_impl();
+    if (!g_framework)
+    {
+        std::lock_guard<std::mutex> g(acquire_framework_mutex());
+        if (!g_framework) // Try again after locking mutex
+        {
+            g_framework = std::make_unique<CuCIMFramework>();
+            g_cucim_framework = &framework;
+            g_cucim_client_name = "cucim";
+        }
+    }
+    return &framework;
+}
+
+CUCIM_API void release_framework()
+{
+    std::lock_guard<std::mutex> g(acquire_framework_mutex());
+    if (g_framework)
+    {
+        g_framework->unload_all_plugins();
+        g_cucim_framework = nullptr;
+        g_framework.reset(nullptr);
+    }
+}
+
+
+} // namespace cucim
diff --git a/cpp/src/core/plugin_manager.cpp b/cpp/src/core/plugin_manager.cpp
new file mode 100644
index 000000000..64b199304
--- /dev/null
+++ b/cpp/src/core/plugin_manager.cpp
@@ -0,0 +1,17 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "plugin_manager.h"
diff --git a/cpp/src/core/plugin_manager.h b/cpp/src/core/plugin_manager.h
new file mode 100644
index 000000000..e3e377686
--- /dev/null
+++ b/cpp/src/core/plugin_manager.h
@@ -0,0 +1,69 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_PLUGIN_MANAGER_H
+#define CUCIM_PLUGIN_MANAGER_H
+
+#include <cstddef>
+#include <limits>
+#include <vector>
+#include <memory>
+#include <unordered_set>
+
+#include "cucim/macros/defines.h"
+namespace cucim
+{
+
+class Plugin;
+
+const size_t kInvalidPluginIndex = std::numeric_limits<size_t>::max();
+
+class PluginManager
+{
+public:
+    size_t add_plugin(std::shared_ptr<Plugin> plugin)
+    {
+        size_t index = plugin_list_.size();
+        plugin_list_.push_back(std::move(plugin));
+        plugin_indices_.insert(index);
+        return index;
+    }
+
+    void remove_plugin(size_t index)
+    {
+        CUCIM_ASSERT(plugin_indices_.find(index) != plugin_indices_.end());
+        CUCIM_ASSERT(index < plugin_list_.size());
+        plugin_indices_.erase(index);
+        plugin_list_[index] = nullptr;
+    }
+
+    Plugin* get_plugin(size_t index) const
+    {
+        CUCIM_ASSERT(index < plugin_list_.size());
+        return plugin_list_[index].get();
+    }
+
+    const std::unordered_set<size_t>& get_plugin_indices() const
+    {
+        return plugin_indices_;
+    }
+
+private:
+    std::vector<std::shared_ptr<Plugin>> plugin_list_;
+    std::unordered_set<size_t> plugin_indices_;
+};
+} // namespace cucim
+#endif // CUCIM_PLUGIN_MANAGER_H
diff --git a/cpp/src/core/version.inl b/cpp/src/core/version.inl
new file mode 100644
index 000000000..6f0fa6b79
--- /dev/null
+++ b/cpp/src/core/version.inl
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef CUCIM_VERSION_INL
+#define CUCIM_VERSION_INL
+
+#include "cucim/core/version.h"
+
+namespace cucim
+{
+
+constexpr bool operator<(const InterfaceVersion& lhs, const InterfaceVersion& rhs)
+{
+    if (lhs.major == rhs.major)
+    {
+        return lhs.minor < rhs.minor;
+    }
+    return lhs.major < rhs.major;
+}
+
+constexpr bool operator==(const InterfaceVersion& lhs, const InterfaceVersion& rhs)
+{
+    return lhs.major == rhs.major && lhs.minor == rhs.minor;
+}
+
+constexpr bool is_version_semantically_compatible(const InterfaceVersion& minimum, const InterfaceVersion& candidate)
+{
+    if (minimum.major != candidate.major)
+    {
+        return false;
+    }
+    else
+    {
+        // Need to special case when major is equal but zero, then any difference in minor makes them
+        // incompatible. See http://semver.org for details.
+        if (minimum.major == 0 && minimum.minor != candidate.minor)
+        {
+            return false;
+        }
+    }
+
+    if (minimum.minor > candidate.minor)
+    {
+        return false;
+    }
+    return true;
+}
+} // namespace cucim
+
+
+#endif // CUCIM_VERSION_INL
diff --git a/cpp/src/cuimage.cpp b/cpp/src/cuimage.cpp
new file mode 100644
index 000000000..537009990
--- /dev/null
+++ b/cpp/src/cuimage.cpp
@@ -0,0 +1,867 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/cuimage.h"
+
+#include <iostream>
+#include <fstream>
+#include <cstring>
+#include <sys/stat.h>
+
+#include "cucim/core/framework.h"
+#include <fmt/format.h>
+
+namespace cucim
+{
+
+DimIndices::DimIndices(const char* dims)
+{
+    if (!dims)
+    {
+        return;
+    }
+    // TODO: check illegal characters
+
+    int index = 0;
+    for (const char* ptr = dims; *ptr != 0; ++ptr, ++index)
+    {
+        char dim_char = toupper(*ptr);
+        dim_indices_.indices[dim_char - 'A'] = index;
+    }
+}
+DimIndices::DimIndices(std::vector<std::pair<char, int64_t>> init_list)
+{
+    // TODO: check illegal characters
+    for (auto& object : init_list)
+    {
+        char dim_char = toupper(object.first);
+        dim_indices_.indices[dim_char - 'A'] = object.second;
+    }
+}
+int64_t DimIndices::index(char dim_char) const
+{
+    dim_char = toupper(dim_char);
+    return dim_indices_.indices[dim_char - 'A'];
+}
+
+ResolutionInfo::ResolutionInfo(io::format::ResolutionInfoDesc desc)
+{
+    level_count_ = desc.level_count;
+    level_ndim_ = desc.level_ndim;
+
+    level_dimensions_.insert(
+        level_dimensions_.end(), &desc.level_dimensions[0], &desc.level_dimensions[level_count_ * level_ndim_]);
+    level_downsamples_.insert(
+        level_downsamples_.end(), &desc.level_downsamples[0], &desc.level_downsamples[level_count_]);
+}
+uint16_t ResolutionInfo::level_count() const
+{
+    return level_count_;
+}
+const std::vector<int64_t>& ResolutionInfo::level_dimensions() const
+{
+    return level_dimensions_;
+}
+std::vector<int64_t> ResolutionInfo::level_dimension(uint16_t level) const
+{
+    if (level >= level_count_)
+    {
+        throw std::invalid_argument(fmt::format("'level' should be less than {}", level_count_));
+    }
+    std::vector<int64_t> result;
+    auto start_index = level_dimensions_.begin() + (level * level_ndim_);
+    result.insert(result.end(), start_index, start_index + level_ndim_);
+    return result;
+}
+const std::vector<float>& ResolutionInfo::level_downsamples() const
+{
+    return level_downsamples_;
+}
+float ResolutionInfo::level_downsample(uint16_t level) const
+{
+    if (level >= level_count_)
+    {
+        throw std::invalid_argument(fmt::format("'level' should be less than {}", level_count_));
+    }
+    return level_downsamples_.at(level);
+}
+
+DetectedFormat detect_format(filesystem::Path path)
+{
+    // TODO: implement this
+    (void)path;
+    return { "Generic TIFF", { "cucim.kit.cuslide" } };
+}
+
+
+Framework* CuImage::framework_ = cucim::acquire_framework("cucim");
+
+CuImage::CuImage(const filesystem::Path& path)
+{
+    //    printf("[cuCIM] CuImage::CuImage(filesystem::Path path)\n");
+    ensure_init();
+    (void)path;
+
+    // TODO: need to detect available format for the file path
+    file_handle_ = image_formats_->formats[0].image_parser.open(path.c_str());
+    //    printf("[GB] file_handle: %s\n", file_handle_.path);
+    //    fmt::print("[GB] CuImage path char: '{}'\n", file_handle_.path[0]);
+
+
+    io::format::ImageMetadata& image_metadata = *(new io::format::ImageMetadata{});
+    image_metadata_ = &image_metadata.desc();
+    is_loaded_ = image_formats_->formats[0].image_parser.parse(&file_handle_, image_metadata_);
+    dim_indices_ = DimIndices(image_metadata_->dims);
+
+    auto& associated_image_info = image_metadata_->associated_image_info;
+    uint16_t image_count = associated_image_info.image_count;
+    if (image_count != associated_images_.size())
+    {
+        for (int i = 0; i < image_count; ++i)
+        {
+            associated_images_.emplace(associated_image_info.image_names[i]);
+        }
+    }
+}
+CuImage::CuImage(const filesystem::Path& path, const std::string& plugin_name)
+{
+    // TODO: implement this
+    (void)path;
+    (void)plugin_name;
+}
+
+// CuImage::CuImage(const CuImage& cuimg) : std::enable_shared_from_this<CuImage>()
+//{
+//    printf("[cuCIM] CuImage::CuImage(const CuImage& cuimg)\n");
+//    (void)cuimg;
+//
+//}
+
+CuImage::CuImage(CuImage&& cuimg) : std::enable_shared_from_this<CuImage>()
+{
+    // printf("[cuCIM] CuImage::CuImage(CuImage&& cuimg) %s\n", cuimg.file_handle_.path);
+    (void)cuimg;
+    std::swap(file_handle_, cuimg.file_handle_);
+    std::swap(image_formats_, cuimg.image_formats_);
+    std::swap(image_metadata_, cuimg.image_metadata_);
+    std::swap(image_data_, cuimg.image_data_);
+    std::swap(is_loaded_, cuimg.is_loaded_);
+    std::swap(dim_indices_, cuimg.dim_indices_);
+    cuimg.associated_images_.swap(associated_images_);
+}
+
+CuImage::CuImage(const CuImage* cuimg,
+                 io::format::ImageMetadataDesc* image_metadata,
+                 cucim::io::format::ImageDataDesc* image_data)
+    : std::enable_shared_from_this<CuImage>()
+{
+    //    printf(
+    //        "[cuCIM] CuImage::CuImage(CuImage* cuimg, io::format::ImageMetadataDesc* image_metadata,
+    //        cucim::io::format::ImageDataDesc* image_data)\n");
+
+    //    file_handle_ = cuimg->file_handle_; ==> Don't do this. it will cause a double free.
+    image_formats_ = cuimg->image_formats_;
+    image_metadata_ = image_metadata;
+    image_data_ = image_data;
+    is_loaded_ = true;
+    if (image_metadata)
+    {
+        dim_indices_ = DimIndices(image_metadata->dims);
+    }
+
+    auto& associated_image_info = image_metadata_->associated_image_info;
+    uint16_t image_count = associated_image_info.image_count;
+    if (image_count != associated_images_.size())
+    {
+        for (int i = 0; i < image_count; ++i)
+        {
+            associated_images_.emplace(associated_image_info.image_names[i]);
+        }
+    }
+}
+
+CuImage::CuImage() : std::enable_shared_from_this<CuImage>()
+{
+    file_handle_.path = const_cast<char*>("<null>");
+}
+
+CuImage::~CuImage()
+{
+    //    printf("[cuCIM] CuImage::~CuImage()\n");
+    if (file_handle_.client_data)
+    {
+        image_formats_->formats[0].image_parser.close(&file_handle_);
+    }
+    image_formats_ = nullptr; // memory release is handled by the framework
+    if (image_metadata_)
+    {
+        // Memory for json_data needs to be manually released if image_metadata_->json_data is not ""
+        if (image_metadata_->json_data && *image_metadata_->json_data != '\0')
+        {
+            cucim_free(image_metadata_->json_data);
+            image_metadata_->json_data = nullptr;
+        }
+        // Delete object (cucim::io::format::ImageMetadata) that embeds image_metadata_
+        if (image_metadata_->handle)
+        {
+            // Keep original handle pointer before clearing it and delete the class object.
+            void* handle_ptr = image_metadata_->handle;
+            image_metadata_->handle = nullptr;
+            delete static_cast<cucim::io::format::ImageMetadata*>(handle_ptr);
+        }
+        image_metadata_ = nullptr;
+    }
+    if (image_data_)
+    {
+        if (image_data_->container.data)
+        {
+            cucim_free(image_data_->container.data);
+            image_data_->container.data = nullptr;
+        }
+        if (image_data_->container.shape)
+        {
+            cucim_free(image_data_->container.shape);
+            image_data_->container.shape = nullptr;
+        }
+        if (image_data_->container.strides)
+        {
+            cucim_free(image_data_->container.strides);
+            image_data_->container.strides = nullptr;
+        }
+        cucim_free(image_data_);
+        image_data_ = nullptr;
+    }
+}
+
+Framework* CuImage::get_framework()
+{
+    return framework_;
+}
+
+filesystem::Path CuImage::path() const
+{
+    return file_handle_.path == nullptr ? "" : file_handle_.path;
+}
+bool CuImage::is_loaded() const
+{
+    return is_loaded_;
+}
+io::Device CuImage::device() const
+{
+    return io::Device("cpu");
+}
+Metadata CuImage::raw_metadata() const
+{
+    if (image_metadata_ && image_metadata_->raw_data)
+    {
+        return Metadata(image_metadata_->raw_data);
+    }
+    return Metadata{};
+}
+Metadata CuImage::metadata() const
+{
+    if (image_metadata_)
+    {
+        return Metadata(image_metadata_->json_data);
+    }
+    return Metadata{};
+}
+uint16_t CuImage::ndim() const
+{
+    return image_metadata_->ndim;
+}
+std::string CuImage::dims() const
+{
+    if (image_metadata_)
+    {
+        return image_metadata_->dims;
+    }
+    return std::string{};
+}
+Shape CuImage::shape() const
+{
+    std::vector<int64_t> result_shape;
+    if (image_metadata_)
+    {
+        uint16_t ndim = image_metadata_->ndim;
+        result_shape.reserve(ndim);
+        for (int i = 0; i < ndim; ++i)
+        {
+            result_shape.push_back(image_metadata_->shape[i]);
+        }
+    }
+
+    return result_shape;
+}
+std::vector<int64_t> CuImage::size(std::string dim_order) const
+{
+    std::vector<int64_t> result_size;
+    if (image_metadata_)
+    {
+        if (dim_order.empty())
+        {
+            dim_order = std::move(std::string(image_metadata_->dims));
+        }
+
+        result_size.reserve(dim_order.size());
+        for (const char& c : dim_order)
+        {
+            auto index = dim_indices_.index(c);
+            if (index != -1)
+            {
+                result_size.push_back(image_metadata_->shape[index]);
+            }
+        }
+    }
+    return result_size;
+}
+DLDataType CuImage::dtype() const
+{
+    // TODO: support string conversion like Device class
+    return DLDataType({ DLDataTypeCode::kDLUInt, 8, 1 });
+}
+std::vector<std::string> CuImage::channel_names() const
+{
+    std::vector<std::string> channel_names;
+    if (image_metadata_)
+    {
+        auto channel_index = dim_indices_.index('C');
+        if (channel_index != -1)
+        {
+            int channel_size = image_metadata_->shape[channel_index];
+            channel_names.reserve(channel_size);
+            for (int i = 0; i < channel_size; ++i)
+            {
+                channel_names.emplace_back(std::string(image_metadata_->channel_names[i]));
+            }
+        }
+    }
+    return channel_names;
+}
+std::vector<float> CuImage::spacing(std::string dim_order) const
+{
+    std::vector<float> result_spacing;
+    result_spacing.reserve(dim_order.size());
+    if (image_metadata_)
+    {
+        if (dim_order.empty())
+        {
+            dim_order = std::move(std::string(image_metadata_->dims));
+            result_spacing.reserve(dim_order.size());
+        }
+
+        for (const char& c : dim_order)
+        {
+            auto index = dim_indices_.index(c);
+            if (index != -1)
+            {
+                result_spacing.push_back(image_metadata_->spacing[index]);
+            }
+            else
+            {
+                result_spacing.push_back(1.0);
+            }
+        }
+    }
+    else
+    {
+        for (const char& c : dim_order)
+        {
+            (void)c;
+            result_spacing.push_back(1.0);
+        }
+    }
+    return result_spacing;
+}
+
+std::vector<std::string> CuImage::spacing_units(std::string dim_order) const
+{
+    std::vector<std::string> result_spacing_units;
+    result_spacing_units.reserve(dim_order.size());
+    if (image_metadata_)
+    {
+        if (dim_order.empty())
+        {
+            dim_order = std::move(std::string(image_metadata_->dims));
+            result_spacing_units.reserve(dim_order.size());
+        }
+
+        for (const char& c : dim_order)
+        {
+            auto index = dim_indices_.index(c);
+            if (index != -1)
+            {
+                result_spacing_units.emplace_back(std::string(image_metadata_->spacing_units[index]));
+            }
+            else
+            {
+                result_spacing_units.emplace_back(std::string(""));
+            }
+        }
+    }
+    else
+    {
+        for (const char& c : dim_order)
+        {
+            (void)c;
+            result_spacing_units.emplace_back(std::string(""));
+        }
+    }
+
+    return result_spacing_units;
+}
+
+std::array<float, 3> CuImage::origin() const
+{
+    std::array<float, 3> result_origin;
+    if (image_metadata_->origin)
+    {
+        std::memcpy(result_origin.data(), image_metadata_->origin, sizeof(float) * 3);
+    }
+    return std::array<float, 3>{ 0., 0., 0. };
+}
+
+std::array<std::array<float, 3>, 3> CuImage::direction() const
+{
+    std::array<std::array<float, 3>, 3> result_direction;
+    if (image_metadata_->direction)
+    {
+        std::memcpy(result_direction.data(), image_metadata_->direction, sizeof(float) * 9);
+        return result_direction;
+    }
+    else
+    {
+        result_direction = { { { 1, 0, 0 }, { 0, 1, 0 }, { 0, 0, 1 } } };
+    }
+    return result_direction;
+}
+
+std::string CuImage::coord_sys() const
+{
+    if (image_metadata_->coord_sys)
+    {
+        return std::string(image_metadata_->coord_sys);
+    }
+    return std::string("LPS");
+}
+
+ResolutionInfo CuImage::resolutions() const
+{
+    if (image_metadata_)
+    {
+        return ResolutionInfo(image_metadata_->resolution_info);
+    }
+    return ResolutionInfo(io::format::ResolutionInfoDesc{});
+}
+
+memory::DLTContainer CuImage::container() const
+{
+    if (image_data_)
+    {
+        return memory::DLTContainer(&image_data_->container);
+    }
+    else
+    {
+        return memory::DLTContainer(nullptr);
+    }
+}
+
+CuImage CuImage::read_region(std::vector<int64_t> location,
+                             std::vector<int64_t> size,
+                             uint16_t level,
+                             DimIndices region_dim_indices,
+                             io::Device device,
+                             DLTensor* buf,
+                             std::string shm_name)
+{
+    (void)location;
+    (void)size;
+    (void)level;
+    (void)region_dim_indices;
+    (void)device;
+    (void)buf;
+    (void)shm_name;
+
+    // If location is not specified, location would be (0, 0) if Z=0. Otherwise, location would be (0, 0, 0)
+    if (location.empty())
+    {
+        location.emplace_back(0);
+        location.emplace_back(0);
+    }
+    // If `size` is not specified, size would be (width, height) of the image at the specified `level`.
+    if (size.empty())
+    {
+        const ResolutionInfo& res_info = resolutions();
+        const auto level_count = res_info.level_count();
+        if (level_count == 0)
+        {
+            throw std::runtime_error("[Error] No available resolutions in the image!");
+        }
+        const auto& level_dimension = res_info.level_dimension(level);
+        size.insert(size.end(), level_dimension.begin(), level_dimension.end());
+    }
+
+    cucim::io::format::ImageReaderRegionRequestDesc request{};
+    int64_t request_location[2] = { location[0], location[1] };
+    request.location = request_location;
+    request.level = level;
+    int64_t request_size[2] = { size[0], size[1] };
+    request.size = request_size;
+    request.device = const_cast<char*>("cpu");
+
+    //    cucim::io::format::ImageDataDesc image_data{};
+
+    cucim::io::format::ImageDataDesc* image_data =
+        static_cast<cucim::io::format::ImageDataDesc*>(cucim_malloc(sizeof(cucim::io::format::ImageDataDesc)));
+    memset(image_data, 0, sizeof(cucim::io::format::ImageDataDesc));
+    try
+    {
+        // Read region from internal file if image_data_ is nullptr
+        if (image_data_ == nullptr)
+        {
+            if (!image_formats_->formats[0].image_reader.read(
+                    &file_handle_, image_metadata_, &request, image_data, nullptr /*out_metadata*/))
+            {
+                cucim_free(image_data);
+                throw std::runtime_error("[Error] Failed to read image!");
+            }
+        }
+        else // Read region by cropping image
+        {
+            crop_image(image_metadata_, &request, image_data);
+        }
+    }
+    catch (std::invalid_argument& e)
+    {
+        cucim_free(image_data);
+        throw e;
+    }
+
+    //
+    // Metadata Setup
+    //
+
+    // TODO: fill correct metadata information
+
+    io::format::ImageMetadata& out_metadata = *(new io::format::ImageMetadata{});
+    DLTensor& image_container = image_data->container;
+
+    // Note: int-> uint16_t due to type differences between ImageMetadataDesc.ndim and DLTensor.ndim
+    const uint16_t ndim = image_container.ndim;
+    auto& resource = out_metadata.get_resource();
+
+    std::string_view dims{ "YXC" };
+
+    // Information from image_data
+    std::pmr::vector<int64_t> shape(&resource);
+    shape.reserve(ndim);
+    shape.insert(shape.end(), &image_container.shape[0], &image_container.shape[ndim]);
+
+    DLDataType& dtype = image_container.dtype;
+
+    // TODO: Do not assume channel names as 'RGB' or 'RGBA'
+    uint8_t n_ch = image_container.shape[2];
+    std::pmr::vector<std::string_view> channel_names(&resource);
+    channel_names.reserve(n_ch);
+    if (n_ch == 3)
+    {
+        // std::pmr::vector<std::string_view> channel_names(
+        //     { std::string_view{ "R" }, std::string_view{ "G" }, std::string_view{ "B" } }, &resource);
+        channel_names.emplace_back(std::string_view{ "R" });
+        channel_names.emplace_back(std::string_view{ "G" });
+        channel_names.emplace_back(std::string_view{ "B" });
+    }
+    else
+    {
+        channel_names.emplace_back(std::string_view{ "R" });
+        channel_names.emplace_back(std::string_view{ "G" });
+        channel_names.emplace_back(std::string_view{ "B" });
+        channel_names.emplace_back(std::string_view{ "A" });
+    }
+
+
+    std::pmr::vector<float> spacing(&resource);
+    spacing.reserve(ndim);
+    float* image_spacing = image_metadata_->spacing;
+    spacing.insert(spacing.end(), &image_spacing[0], &image_spacing[ndim]);
+
+    std::pmr::vector<std::string_view> spacing_units(&resource);
+    spacing_units.reserve(ndim);
+    for (int i = 0; i < ndim; i++)
+    {
+        int64_t dim_char = dim_indices_.index(dims[i]);
+
+        const char* str_ptr = image_metadata_->spacing_units[dim_char];
+        size_t str_len = strlen(image_metadata_->spacing_units[dim_char]);
+
+        char* spacing_unit = static_cast<char*>(resource.allocate(str_len + 1));
+        memcpy(spacing_unit, str_ptr, str_len);
+        spacing_unit[str_len] = '\0';
+        // std::pmr::string spacing_unit{ image_metadata_->spacing_units[dim_char], &resource };
+
+        spacing_units.emplace_back(std::string_view{ spacing_unit });
+    }
+
+    std::pmr::vector<float> origin(&resource);
+    origin.reserve(3);
+    float* image_origin = image_metadata_->origin;
+    origin.insert(origin.end(), &image_origin[0], &image_origin[3]);
+
+    // Direction cosines (size is always 3x3)
+    std::pmr::vector<float> direction(&resource);
+    direction.reserve(3);
+    float* image_direction = image_metadata_->direction;
+    direction.insert(direction.end(), &image_direction[0], &image_direction[3 * 3]);
+
+    // The coordinate frame in which the direction cosines are measured (either 'LPS'(ITK/DICOM) or 'RAS'(NIfTI/3D
+    // Slicer))
+
+    std::string_view coord_sys{ "" };
+    const char* coord_sys_ptr = image_metadata_->coord_sys;
+    if (coord_sys_ptr)
+    {
+        size_t coord_sys_len = strlen(coord_sys_ptr);
+        char* coord_sys_str = static_cast<char*>(resource.allocate(coord_sys_len + 1));
+        memcpy(coord_sys_str, coord_sys_ptr, coord_sys_len);
+        coord_sys_str[coord_sys_len] = '\0';
+        coord_sys = std::string_view{ coord_sys_str };
+    }
+    // std::pmr::string coord_sys_str{ image_metadata_->coord_sys ? image_metadata_->coord_sys : "", &resource };
+    // std::string_view coord_sys{ coord_sys_str };
+
+    // Manually set resolution dimensions to 2
+    const uint16_t level_ndim = 2;
+    std::pmr::vector<int64_t> level_dimensions(&resource);
+    level_dimensions.reserve(level_ndim * 1); // it has only one size
+    level_dimensions.insert(level_dimensions.end(), &size[0], &size[level_ndim]);
+
+    std::pmr::vector<float> level_downsamples(&resource);
+    level_downsamples.reserve(1);
+    level_downsamples.emplace_back(1.0);
+
+    // Empty associated images
+    const size_t associated_image_count = 0;
+    std::pmr::vector<std::string_view> associated_image_names(&resource);
+
+    // Partial image doesn't include raw metadata
+    std::string_view raw_data{ "" };
+    // Partial image doesn't include json metadata
+    std::string_view json_data{ "" };
+
+    out_metadata.ndim(ndim);
+    out_metadata.dims(dims);
+    out_metadata.shape(shape);
+    out_metadata.dtype(dtype);
+    out_metadata.channel_names(channel_names);
+    out_metadata.spacing(spacing);
+    out_metadata.spacing_units(spacing_units);
+    out_metadata.origin(origin);
+    out_metadata.direction(direction);
+    out_metadata.coord_sys(coord_sys);
+    out_metadata.level_count(1);
+    out_metadata.level_ndim(2);
+    out_metadata.level_dimensions(level_dimensions);
+    out_metadata.level_downsamples(level_downsamples);
+    out_metadata.image_count(associated_image_count);
+    out_metadata.image_names(associated_image_names);
+    out_metadata.raw_data(raw_data);
+    out_metadata.json_data(json_data);
+
+    return CuImage(this, &out_metadata.desc(), image_data);
+}
+
+std::set<std::string> CuImage::associated_images() const
+{
+    return associated_images_;
+}
+
+CuImage CuImage::associated_image(const std::string& name) const
+{
+    auto it = associated_images_.find(name);
+    if (it != associated_images_.end())
+    {
+        io::format::ImageReaderRegionRequestDesc request{};
+        request.associated_image_name = const_cast<char*>(name.c_str());
+        request.device = const_cast<char*>("cpu");
+
+        io::format::ImageDataDesc* out_image_data =
+            static_cast<cucim::io::format::ImageDataDesc*>(cucim_malloc(sizeof(cucim::io::format::ImageDataDesc)));
+
+        io::format::ImageMetadata& out_metadata = *(new io::format::ImageMetadata{});
+
+        if (!image_formats_->formats[0].image_reader.read(
+                &file_handle_, image_metadata_, &request, out_image_data, &out_metadata.desc()))
+        {
+            cucim_free(out_image_data);
+            delete &out_metadata;
+            throw std::runtime_error("[Error] Failed to read image!");
+        }
+
+        return CuImage(this, &out_metadata.desc(), out_image_data);
+    }
+    return CuImage{};
+}
+
+void CuImage::save(std::string file_path) const
+{
+    // Save ppm file for now.
+    if (image_data_)
+    {
+        std::fstream fs(file_path, std::fstream::out | std::fstream::binary);
+
+        if (fs.bad())
+        {
+            CUCIM_ERROR("Opening file failed!");
+        }
+        fs << "P6\n";
+        auto image_size = size("XY");
+        auto width = image_size[0];
+        auto height = image_size[1];
+        fs << width << "\n" << height << "\n" << 0xff << "\n";
+
+        uint8_t* data = static_cast<uint8_t*>(image_data_->container.data);
+        size_t data_size = width * height * 3;
+        for (unsigned int i = 0; (i < data_size) && fs.good(); ++i)
+        {
+            fs << data[i];
+        }
+        fs.flush();
+        if (fs.bad())
+        {
+            CUCIM_ERROR("Writing data failed!");
+        }
+        fs.close();
+    }
+}
+void CuImage::ensure_init()
+{
+    ScopedLock g(mutex_);
+
+    if (!framework_)
+    {
+        CUCIM_ERROR("Framework is not initialized!");
+    }
+    if (!image_formats_)
+    {
+        auto plugin_root = framework_->get_plugin_root();
+        // TODO: Here 'LINUX' path separator is used. Need to make it generalize once filesystem library is
+        // available.
+        std::string plugin_file_path = (plugin_root && *plugin_root != 0) ?
+                                           fmt::format("{}/cucim.kit.cuslide@{}.{}.{}.so", plugin_root,
+                                                       CUCIM_VERSION_MAJOR, CUCIM_VERSION_MINOR, CUCIM_VERSION_PATCH) :
+                                           fmt::format("cucim.kit.cuslide@{}.{}.{}.so", CUCIM_VERSION_MAJOR,
+                                                       CUCIM_VERSION_MINOR, CUCIM_VERSION_PATCH);
+        struct stat st_buff;
+        if (stat(plugin_file_path.c_str(), &st_buff) != 0)
+        {
+            plugin_file_path = fmt::format(
+                "cucim.kit.cuslide@{}.{}.{}.so", CUCIM_VERSION_MAJOR, CUCIM_VERSION_MINOR, CUCIM_VERSION_PATCH);
+        }
+        image_formats_ =
+            framework_->acquire_interface_from_library<cucim::io::format::IImageFormat>(plugin_file_path.c_str());
+        if (image_formats_ == nullptr)
+        {
+            throw std::runtime_error(fmt::format("Dependent library 'cucim.kit.cuslide@{}.{}.{}.so' cannot be loaded!",
+                                                 CUCIM_VERSION_MAJOR, CUCIM_VERSION_MINOR, CUCIM_VERSION_PATCH));
+        }
+    }
+}
+
+bool CuImage::crop_image(io::format::ImageMetadataDesc* metadata,
+                         io::format::ImageReaderRegionRequestDesc* request,
+                         io::format::ImageDataDesc* out_image_data) const
+{
+    // TODO: assume length of location/size to 2.
+    constexpr int32_t ndims = 2;
+
+    if (request->level >= metadata->resolution_info.level_count)
+    {
+        throw std::invalid_argument(fmt::format("Invalid level ({}) in the request! (Should be < {})", request->level,
+                                                metadata->resolution_info.level_count));
+    }
+
+    auto original_img_width = image_metadata_->shape[dim_indices_.index('X')];
+    auto original_img_height = image_metadata_->shape[dim_indices_.index('Y')];
+    // TODO: consider other cases where samples_per_pixel is not same with # of channels
+    //       (we cannot use `ifd->samples_per_pixel()` here)
+    uint32_t samples_per_pixel = static_cast<uint32_t>(image_metadata_->shape[dim_indices_.index('C')]);
+
+    for (int32_t i = 0; i < ndims; ++i)
+    {
+        if (request->location[i] < 0)
+        {
+            throw std::invalid_argument(
+                fmt::format("Invalid location ({}) in the request! (Should be >= 0)", request->location[i]));
+        }
+        if (request->size[i] <= 0)
+        {
+            throw std::invalid_argument(
+                fmt::format("Invalid size ({}) in the request! (Should be > 0)", request->size[i]));
+        }
+    }
+    if (request->location[0] + request->size[0] > original_img_width)
+    {
+        throw std::invalid_argument(
+            fmt::format("Invalid location/size (it exceeds the image width {})", original_img_width));
+    }
+    if (request->location[1] + request->size[1] > original_img_height)
+    {
+        throw std::invalid_argument(
+            fmt::format("Invalid location/size (it exceeds the image height {})", original_img_height));
+    }
+
+    int64_t sx = request->location[0];
+    int64_t sy = request->location[1];
+    int64_t w = request->size[0];
+    int64_t h = request->size[1];
+
+    uint64_t ex = sx + w - 1;
+    uint64_t ey = sy + h - 1;
+
+    uint8_t* src_ptr = static_cast<uint8_t*>(image_data_->container.data);
+
+    void* raster = cucim_malloc(w * h * samples_per_pixel); // RGB image
+    auto dest_ptr = static_cast<uint8_t*>(raster);
+    int64_t dest_stride_x_bytes = w * samples_per_pixel;
+
+    int64_t src_stride_x = original_img_width;
+    int64_t src_stride_x_bytes = original_img_width * samples_per_pixel;
+
+    int64_t start_offset = (sx + (sy * src_stride_x)) * samples_per_pixel;
+    int64_t end_offset = (ex + (ey * src_stride_x)) * samples_per_pixel;
+
+    for (int64_t src_offset = start_offset; src_offset <= end_offset; src_offset += src_stride_x_bytes)
+    {
+        memcpy(dest_ptr, src_ptr + src_offset, dest_stride_x_bytes);
+        dest_ptr += dest_stride_x_bytes;
+    }
+
+    out_image_data->container.data = raster;
+    out_image_data->container.ctx = DLContext{ static_cast<DLDeviceType>(cucim::io::DeviceType::kCPU), 0 };
+    out_image_data->container.ndim = metadata->ndim;
+    out_image_data->container.dtype = metadata->dtype;
+    out_image_data->container.strides = nullptr; // Tensor is compact and row-majored
+    out_image_data->container.byte_offset = 0;
+    // Set correct shape
+    out_image_data->container.shape = static_cast<int64_t*>(cucim_malloc(sizeof(int64_t) * metadata->ndim));
+    memcpy(out_image_data->container.shape, metadata->shape, sizeof(int64_t) * metadata->ndim);
+    out_image_data->container.shape[0] = h;
+    out_image_data->container.shape[1] = w;
+
+    return true;
+}
+
+} // namespace cucim
\ No newline at end of file
diff --git a/cpp/src/filesystem/cufile_driver.cpp b/cpp/src/filesystem/cufile_driver.cpp
new file mode 100644
index 000000000..da3a95367
--- /dev/null
+++ b/cpp/src/filesystem/cufile_driver.cpp
@@ -0,0 +1,1163 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/filesystem/cufile_driver.h"
+
+#include "fmt/format.h"
+#include "cufile_stub.h"
+
+#include <fcntl.h>
+#include <unistd.h>
+#include <cuda_runtime.h>
+#include <linux/fs.h>
+#include <sys/ioctl.h>
+#include <sys/mman.h>
+#include <sys/statvfs.h>
+#include <sys/stat.h>
+#include <chrono>
+
+
+#define ALIGN_UP(x, align_to) (((uint64_t)(x) + ((uint64_t)(align_to)-1)) & ~((uint64_t)(align_to)-1))
+#define ALIGN_DOWN(x, align_to) ((uint64_t)(x) & ~((uint64_t)(align_to)-1))
+
+#define CUDA_TRY(stmt)                                                                                                 \
+    {                                                                                                                  \
+        cuda_status = stmt;                                                                                            \
+        if (cudaSuccess != cuda_status)                                                                                \
+        {                                                                                                              \
+            fmt::print(stderr, "[Error] CUDA Runtime call {} in line {} of file {} failed with '{}' ({}).\n", #stmt,   \
+                       __LINE__, __FILE__, cudaGetErrorString(cuda_status), cuda_status);                              \
+        }                                                                                                              \
+    }
+
+namespace cucim::filesystem
+{
+static constexpr unsigned int PAGE_SIZE = 4096;
+static constexpr uint64_t DEFAULT_MAX_CACHE_SIZE = 128 << 20; // 128MiB
+static CuFileDriverInitializer s_cufile_initializer;
+thread_local static CuFileDriverCache s_cufile_cache;
+Mutex CuFileDriver::driver_mutex_;
+
+
+static std::string get_fd_path(int fd)
+{
+    pid_t pid = getpid();
+    ssize_t file_path_len = 0;
+
+    char real_path[PATH_MAX];
+
+    std::string src_path = fmt::format("/proc/{}/fd/{}", pid, fd);
+
+    if ((file_path_len = readlink(src_path.c_str(), real_path, PATH_MAX - 1)) > 0)
+    {
+        real_path[file_path_len] = '\0';
+    }
+    else
+    {
+        throw std::runtime_error(fmt::format("Cannot get the real path from process entries ({})", strerror(errno)));
+    }
+
+    return std::string(real_path);
+}
+
+static int get_file_flags(const char* flags)
+{
+    int file_flags = -1;
+    if (flags == nullptr || flags[0] == '\0')
+    {
+        return -1;
+    }
+    switch (flags[0])
+    {
+    case 'r':
+        file_flags = O_RDONLY;
+        if (flags[1] == '+')
+        {
+            file_flags = O_RDWR;
+        }
+        break;
+    case 'w':
+        file_flags = O_RDWR | O_CREAT | O_TRUNC;
+        break;
+    case 'a':
+        file_flags = O_RDWR | O_CREAT;
+        break;
+    default:
+        return -1;
+    }
+
+
+    file_flags |= O_CLOEXEC;
+
+    return file_flags;
+}
+
+std::shared_ptr<CuFileDriver> open(const char* file_path, const char* flags, mode_t mode)
+{
+    bool use_o_direct = true;
+    bool no_gds = false;
+    bool use_mmap = false;
+    int file_flags = get_file_flags(flags);
+
+    for (const char* ch = (flags[1] == '+' ? &flags[2] : &flags[1]); *ch; ch++)
+        switch (*ch)
+        {
+        case 'n':
+            use_o_direct = false;
+            break;
+        case 'p':
+            no_gds = true;
+            break;
+        case 'm':
+            use_mmap = true;
+            break;
+        }
+    if (use_o_direct)
+    {
+        file_flags |= O_DIRECT;
+    }
+
+    if (file_flags < 0)
+    {
+        return std::shared_ptr<CuFileDriver>();
+    }
+
+    FileHandleType file_type = (file_flags & O_DIRECT ? FileHandleType::kPosixODirect : FileHandleType::kPosix);
+
+    int fd = ::open(file_path, file_flags, mode);
+    if (fd < 0)
+    {
+        if (errno == ENOENT)
+        {
+            throw std::invalid_argument(fmt::format("File '{}' doesn't exist!", file_path));
+        }
+        if (file_type == FileHandleType::kPosix)
+        {
+            throw std::invalid_argument(fmt::format("File '{}' cannot be open!", file_path));
+        }
+        else // if kFileHandlePosixODirect
+        {
+            file_flags &= ~O_DIRECT;
+            fd = ::open(file_path, file_flags, mode);
+            fmt::print(
+                stderr, "The file {} doesn't support O_DIRECT. Trying to open the file without O_DIRECT\n", file_path);
+            if (fd < 0)
+            {
+                throw std::invalid_argument(fmt::format("File '{}' cannot be open!", file_path));
+            }
+            file_type = FileHandleType::kPosix; // POSIX
+        }
+    }
+
+    return std::make_shared<CuFileDriver>(fd, no_gds, use_mmap, file_path);
+}
+
+std::shared_ptr<CuFileDriver> open(int fd, bool no_gds, bool use_mmap)
+{
+    return std::make_shared<CuFileDriver>(fd, no_gds, use_mmap, nullptr);
+}
+
+CuFileDriver::CuFileDriver(int fd, bool no_gds, bool use_mmap, const char* file_path)
+{
+    if (file_path == nullptr || *file_path == '\0')
+    {
+        file_path_ = get_fd_path(fd);
+    }
+    else
+    {
+        file_path_ = file_path;
+    }
+
+    struct stat st;
+    fstat(fd, &st);
+    file_size_ = st.st_size;
+
+    int flags;
+    // Note: the following method cannot detect flags such as O_EXCL and O_TRUNC.
+    flags = fcntl(fd, F_GETFL);
+    if (flags < 0)
+    {
+        throw std::runtime_error(fmt::format("[Error] fcntl failed for fd {} ({})", fd, std::strerror(errno)));
+    }
+    file_flags_ = flags;
+
+    FileHandleType file_type = (flags & O_DIRECT) ? FileHandleType::kPosixODirect : FileHandleType::kPosix;
+    handle_ = CuCIMFileHandle{ fd, nullptr, file_type, const_cast<char*>(file_path_.c_str()), this };
+
+    CUfileError_t status;
+    CUfileDescr_t cf_descr{}; // It is important to set zero!
+
+    if ((file_type == FileHandleType::kPosixODirect || file_type == FileHandleType::kGPUDirect) && !no_gds &&
+        !use_mmap && s_cufile_initializer)
+    {
+        cf_descr.handle.fd = fd;
+        cf_descr.type = CU_FILE_HANDLE_TYPE_OPAQUE_FD;
+        status = cuFileHandleRegister(&handle_.cufile, &cf_descr);
+        if (status.err == CU_FILE_SUCCESS)
+        {
+            handle_.type = FileHandleType::kGPUDirect;
+        }
+        else
+        {
+            fmt::print(
+                stderr,
+                "[Error] cuFileHandleRegister fd: {} ({}), status: {}. Would work with cuCIM's compatibility mode.\n",
+                fd, file_path_, cufileop_status_error(status.err));
+        }
+    }
+    else if (use_mmap)
+    {
+        if (flags & (O_RDWR || O_WRONLY))
+        {
+            throw std::runtime_error(
+                fmt::format("[Error] Memory-mapped IO for writable file descriptor is not supported!"));
+        }
+
+        mmap_ptr_ = mmap((void*)0, file_size_, PROT_READ, MAP_SHARED, fd, 0);
+        if (mmap_ptr_ != MAP_FAILED)
+        {
+            handle_.type = FileHandleType::kMemoryMapped;
+        }
+        else
+        {
+            mmap_ptr_ = nullptr;
+            throw std::runtime_error(fmt::format("[Error] failed to call mmap ({})", std::strerror(errno)));
+        }
+    }
+}
+
+bool close(const std::shared_ptr<CuFileDriver>& fd)
+{
+    return fd->close();
+}
+ssize_t pread(const std::shared_ptr<CuFileDriver>& fd, void* buf, size_t count, off_t file_offset, off_t buf_offset)
+{
+    if (fd != nullptr)
+    {
+        return fd->pread(buf, count, file_offset, buf_offset);
+    }
+    else
+    {
+        fmt::print(stderr, "fd (CuFileDriver) is null!");
+        return -1;
+    }
+}
+ssize_t pwrite(const std::shared_ptr<CuFileDriver>& fd, const void* buf, size_t count, off_t file_offset, off_t buf_offset)
+{
+    if (fd != nullptr)
+    {
+        return fd->pwrite(buf, count, file_offset, buf_offset);
+    }
+    else
+    {
+        fmt::print(stderr, "fd (CuFileDriver) is null!");
+        return -1;
+    }
+}
+
+bool discard_page_cache(const char* file_path)
+{
+    int fd = ::open(file_path, O_RDONLY);
+    if (fd < 0)
+    {
+        return false;
+    }
+    if (::fdatasync(fd) < 0)
+    {
+        return false;
+    }
+    if (::posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED) < 0)
+    {
+        return false;
+    }
+    if (::close(fd) < 0)
+    {
+        return false;
+    }
+    return true;
+}
+
+
+CuFileDriverInitializer::CuFileDriverInitializer()
+{
+    // Initialize libcufile library
+    open_cufile_stub();
+
+    CUfileError_t status = cuFileDriverOpen();
+    if (status.err == CU_FILE_SUCCESS)
+    {
+        is_available_ = true;
+        CUfileDrvProps_t props;
+
+        status = cuFileDriverGetProperties(&props);
+        if (status.err == CU_FILE_SUCCESS)
+        {
+            // kb -> bytes
+            max_device_cache_size_ = static_cast<uint64_t>(props.max_device_cache_size) << 10;
+            max_host_cache_size_ = static_cast<uint64_t>(props.max_device_cache_size) << 10;
+        }
+        else
+        {
+            fmt::print(stderr, "cuFileDriverGetProperties() failed!\n");
+        }
+        // fmt::print(stderr, "CuFileDriver opened!\n");
+    }
+    else
+    {
+        is_available_ = false;
+        max_device_cache_size_ = DEFAULT_MAX_CACHE_SIZE;
+        max_host_cache_size_ = DEFAULT_MAX_CACHE_SIZE;
+
+        // fmt::print(stderr, "[warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.\n");
+    }
+}
+CuFileDriverInitializer::~CuFileDriverInitializer()
+{
+    if (is_available_)
+    {
+        CUfileError_t status = cuFileDriverClose();
+        if (status.err != CU_FILE_SUCCESS)
+        {
+            fmt::print(stderr, "Unable to close cuFileDriver ({})\n", cufileop_status_error(status.err));
+        }
+        else
+        {
+            // fmt::print(stderr, "CuFileDriver closed!\n");
+        }
+        is_available_ = false;
+    }
+
+    // Close cufile stub
+    close_cufile_stub();
+}
+
+CuFileDriverCache::CuFileDriverCache()
+{
+}
+void* CuFileDriverCache::device_cache()
+{
+    if (device_cache_)
+    {
+        return device_cache_aligned_;
+    }
+    else
+    {
+        cudaError_t cuda_status;
+        unsigned int cache_size = s_cufile_initializer.max_device_cache_size();
+        CUDA_TRY(cudaMalloc(&device_cache_, PAGE_SIZE + cache_size));
+        if (cuda_status)
+        {
+            throw std::bad_alloc();
+        }
+        device_cache_aligned_ = reinterpret_cast<void*>(ALIGN_UP(device_cache_, PAGE_SIZE));
+        CUfileError_t status = cuFileBufRegister(device_cache_aligned_, cache_size, 0);
+        if (status.err != CU_FILE_SUCCESS)
+        {
+            CUDA_TRY(cudaFree(device_cache_));
+            device_cache_ = nullptr;
+            device_cache_aligned_ = nullptr;
+            if (cuda_status)
+            {
+                throw std::bad_alloc();
+            }
+            throw std::runtime_error("Failed to call cuFileBufRegister()!");
+        }
+
+        return device_cache_aligned_;
+    }
+}
+void* CuFileDriverCache::host_cache()
+{
+    if (host_cache_)
+    {
+        return host_cache_aligned_;
+    }
+    else
+    {
+        if (posix_memalign(&host_cache_, PAGE_SIZE, s_cufile_initializer.max_host_cache_size()))
+        {
+            throw std::bad_alloc();
+        }
+        host_cache_aligned_ = host_cache_;
+
+        return host_cache_aligned_;
+    }
+}
+CuFileDriverCache::~CuFileDriverCache()
+{
+
+    if (device_cache_)
+    {
+        cudaError_t cuda_status;
+        CUfileError_t status = cuFileBufDeregister(device_cache_aligned_);
+        if (status.err != CU_FILE_SUCCESS)
+        {
+            fmt::print(stderr, "Failed on cuFileBufDeregister()! (status: {})\n", cufileop_status_error(status.err));
+        }
+        CUDA_TRY(cudaFree(device_cache_));
+        if (cuda_status)
+        {
+            fmt::print(stderr, "Failed on cudaFree()!\n");
+        }
+        device_cache_ = nullptr;
+        device_cache_aligned_ = nullptr;
+    }
+    if (host_cache_)
+    {
+        free(host_cache_);
+        host_cache_ = nullptr;
+        host_cache_aligned_ = nullptr;
+    }
+}
+ssize_t CuFileDriver::pread(void* buf, size_t count, off_t file_offset, off_t buf_offset) const
+{
+    if (file_flags_ == -1)
+    {
+        fmt::print(stderr, "File is not open yet.\n");
+        return -1;
+    }
+    if ((file_flags_ & O_ACCMODE) == O_WRONLY)
+    {
+        fmt::print(stderr, "The file is open with write-only mode!\n");
+        return -1;
+    }
+
+    cudaError_t cuda_status;
+    ssize_t total_read_cnt = 0;
+
+    cudaPointerAttributes attributes;
+    cudaMemoryType memory_type;
+
+    FileHandleType file_type = handle_.type;
+
+    CUDA_TRY(cudaPointerGetAttributes(&attributes, buf));
+    if (cuda_status)
+    {
+        //        if (cuda_status == cudaErrorInvalidValue)
+        //        {
+        //            attributes.type = cudaMemoryTypeDevice;
+        //        }
+        //        else
+        //        {
+        return -1;
+        //        }
+    }
+    memory_type = attributes.type;
+
+    if (file_type == FileHandleType::kPosix)
+    {
+        if (memory_type != cudaMemoryTypeUnregistered)
+        {
+            uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+            uint64_t remaining_size = count;
+            ssize_t read_cnt;
+            uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+            uint8_t* output_buf = static_cast<uint8_t*>(buf) + buf_offset;
+            off_t read_offset = file_offset;
+            while (true)
+            {
+                size_t bytes_to_copy = std::min(cache_size, remaining_size);
+
+                if (bytes_to_copy == 0)
+                {
+                    break;
+                }
+                read_cnt = ::pread(handle_.fd, cache_buf, bytes_to_copy, read_offset);
+                CUDA_TRY(cudaMemcpy(output_buf, cache_buf, bytes_to_copy, cudaMemcpyHostToDevice));
+                if (cuda_status)
+                {
+                    return -1;
+                }
+                read_offset += read_cnt;
+                output_buf += read_cnt;
+                remaining_size -= read_cnt;
+
+                total_read_cnt += bytes_to_copy;
+            }
+        }
+        else
+        {
+            total_read_cnt = ::pread(handle_.fd, reinterpret_cast<char*>(buf) + buf_offset, count, file_offset);
+        }
+    }
+    else if (file_type == FileHandleType::kMemoryMapped)
+    {
+        if (memory_type != cudaMemoryTypeUnregistered)
+        {
+            CUDA_TRY(cudaMemcpy(reinterpret_cast<char*>(buf) + buf_offset,
+                                reinterpret_cast<char*>(mmap_ptr_) + file_offset, count, cudaMemcpyHostToDevice));
+            if (cuda_status)
+            {
+                return -1;
+            }
+        }
+        else
+        {
+            memcpy(reinterpret_cast<char*>(buf) + buf_offset, reinterpret_cast<char*>(mmap_ptr_) + file_offset, count);
+        }
+        total_read_cnt = count;
+    }
+    else if (memory_type == cudaMemoryTypeUnregistered || handle_.type == FileHandleType::kPosixODirect)
+    {
+        uint64_t buf_align = (reinterpret_cast<uint64_t>(buf) + buf_offset) % PAGE_SIZE;
+        bool is_aligned = (buf_align == 0) && ((file_offset % PAGE_SIZE) == 0);
+
+        if (is_aligned)
+        {
+            ssize_t read_cnt;
+            size_t block_read_size = ALIGN_DOWN(count, PAGE_SIZE);
+            auto start = std::chrono::high_resolution_clock::now();
+            if (block_read_size > 0)
+            {
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    read_cnt = ::pread(handle_.fd, reinterpret_cast<char*>(buf) + buf_offset, block_read_size, file_offset);
+                    total_read_cnt += read_cnt;
+                }
+                else
+                {
+                    uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+                    uint64_t remaining_size = block_read_size;
+                    ssize_t read_cnt;
+                    uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+                    uint8_t* input_buf = static_cast<uint8_t*>(buf) + buf_offset;
+                    off_t read_offset = file_offset;
+                    while (true)
+                    {
+                        size_t bytes_to_copy = std::min(cache_size, remaining_size);
+
+                        if (bytes_to_copy == 0)
+                        {
+                            break;
+                        }
+
+                        read_cnt = ::pread(handle_.fd, cache_buf, bytes_to_copy, read_offset);
+                        CUDA_TRY(cudaMemcpy(input_buf, cache_buf, bytes_to_copy, cudaMemcpyHostToDevice));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                        read_offset += read_cnt;
+                        input_buf += read_cnt;
+                        remaining_size -= read_cnt;
+
+                        total_read_cnt += bytes_to_copy;
+                    }
+                }
+            }
+
+            size_t remaining = count - block_read_size;
+            if (remaining)
+            {
+                uint8_t internal_buf[PAGE_SIZE * 2]; // no need to initialize for pread()
+                uint8_t* buf_pos = reinterpret_cast<uint8_t*>(ALIGN_UP(static_cast<uint8_t*>(internal_buf), PAGE_SIZE));
+
+                // Read the remaining block (size of PAGE_SIZE)
+                ssize_t read_cnt;
+                read_cnt = ::pread(handle_.fd, buf_pos, PAGE_SIZE, block_read_size);
+                if (read_cnt < 0)
+                {
+                    fmt::print(stderr, "Cannot read the remaining file content block! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+                // Copy a buffer to read, from the intermediate remaining block (buf_pos)
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    memcpy(reinterpret_cast<uint8_t*>(buf) + buf_offset + block_read_size, buf_pos, remaining);
+                }
+                else
+                {
+                    CUDA_TRY(cudaMemcpy(reinterpret_cast<uint8_t*>(buf) + buf_offset + block_read_size, buf_pos,
+                                        remaining, cudaMemcpyHostToDevice));
+                    if (cuda_status)
+                    {
+                        return -1;
+                    }
+                }
+
+                total_read_cnt += remaining;
+            }
+        }
+        else
+        {
+            uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+            uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+
+            off_t file_start_offset = ALIGN_DOWN(file_offset, PAGE_SIZE);
+            off_t end_offset = count + file_offset;
+            off_t end_boundary_offset = ALIGN_UP(end_offset, PAGE_SIZE);
+            size_t large_block_size = end_boundary_offset - file_start_offset;
+            off_t page_offset = file_offset - file_start_offset;
+            uint8_t* output_buf = static_cast<uint8_t*>(buf) + buf_offset;
+
+            if (large_block_size <= cache_size) // Optimize if bytes to load is less than cache_size
+            {
+                ssize_t read_cnt = ::pread(handle_.fd, cache_buf, large_block_size, file_start_offset);
+                if (read_cnt < 0)
+                {
+                    fmt::print(stderr, "Cannot read the file content block! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    memcpy(output_buf, cache_buf + page_offset, count);
+                }
+                else
+                {
+                    CUDA_TRY(cudaMemcpy(output_buf, cache_buf + page_offset, count, cudaMemcpyHostToDevice));
+                    if (cuda_status)
+                    {
+                        return -1;
+                    }
+                }
+                total_read_cnt += std::min(static_cast<size_t>(read_cnt - page_offset), count);
+            }
+            else
+            {
+                off_t overflow_offset = page_offset + count;
+                size_t header_size = (overflow_offset > PAGE_SIZE) ? PAGE_SIZE - page_offset : count;
+                size_t tail_size = (overflow_offset > PAGE_SIZE) ? end_offset - ALIGN_DOWN(end_offset, PAGE_SIZE) : 0;
+                uint64_t body_remaining_size = count - header_size - tail_size;
+                off_t read_offset = file_start_offset;
+
+                size_t bytes_to_copy;
+                ssize_t read_cnt;
+
+                uint8_t internal_buf[PAGE_SIZE * 2]; // no need to initialize for pread()
+                uint8_t* internal_buf_pos =
+                    reinterpret_cast<uint8_t*>(ALIGN_UP(static_cast<uint8_t*>(internal_buf), PAGE_SIZE));
+
+
+                // Handle the head part of the file content
+                if (header_size)
+                {
+                    read_cnt = ::pread(handle_.fd, internal_buf_pos, PAGE_SIZE, read_offset);
+                    if (read_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot read the head part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+
+                    bytes_to_copy = header_size;
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(output_buf, internal_buf_pos + page_offset, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(
+                            output_buf, internal_buf_pos + page_offset, bytes_to_copy, cudaMemcpyHostToDevice));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+
+                    output_buf += bytes_to_copy;
+                    read_offset += read_cnt;
+
+                    total_read_cnt += bytes_to_copy;
+                }
+
+                // Copy n * PAGE_SIZE bytes
+                while (true)
+                {
+                    size_t bytes_to_copy = std::min(cache_size, body_remaining_size);
+
+                    if (bytes_to_copy == 0)
+                    {
+                        break;
+                    }
+
+                    read_cnt = ::pread(handle_.fd, cache_buf, bytes_to_copy, read_offset);
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(output_buf, cache_buf, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(output_buf, cache_buf, bytes_to_copy, cudaMemcpyHostToDevice));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+                    read_offset += read_cnt;
+                    output_buf += read_cnt;
+                    body_remaining_size -= read_cnt;
+
+                    total_read_cnt += bytes_to_copy;
+                }
+
+                // Handle the tail part of the file content
+                if (tail_size)
+                {
+                    //                memset(internal_buf_pos, 0, PAGE_SIZE); // no need to initialize for pread()
+                    read_cnt = ::pread(handle_.fd, internal_buf_pos, PAGE_SIZE, read_offset);
+                    if (read_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot read the tail part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                    // Copy the region
+                    bytes_to_copy = tail_size;
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(output_buf, internal_buf_pos, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(output_buf, internal_buf_pos, bytes_to_copy, cudaMemcpyHostToDevice));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+                    total_read_cnt += tail_size;
+                }
+            }
+        }
+    }
+    else if (file_type == FileHandleType::kGPUDirect)
+    {
+        (void*)s_cufile_cache.device_cache(); // Lazy initialization
+
+        ssize_t read_cnt = cuFileRead(handle_.cufile, reinterpret_cast<char*>(buf) + buf_offset, count, file_offset, 0);
+        total_read_cnt += read_cnt;
+        if (read_cnt < 0)
+        {
+            fmt::print(stderr, "Failed to read file with cuFileRead().\n");
+            return -1;
+        }
+    }
+
+    return total_read_cnt;
+}
+ssize_t CuFileDriver::pwrite(const void* buf, size_t count, off_t file_offset, off_t buf_offset)
+{
+    if (file_flags_ == -1)
+    {
+        fmt::print(stderr, "File is not open yet.\n");
+        return -1;
+    }
+    if ((file_flags_ & O_ACCMODE) == O_RDONLY)
+    {
+        fmt::print(stderr, "The file is open with read-only mode!\n");
+        return -1;
+    }
+
+    cudaError_t cuda_status;
+    ssize_t total_write_cnt = 0;
+
+    cudaPointerAttributes attributes;
+    cudaMemoryType memory_type;
+
+    FileHandleType file_type = handle_.type;
+
+    CUDA_TRY(cudaPointerGetAttributes(&attributes, buf));
+    if (cuda_status)
+    {
+        return -1;
+    }
+    memory_type = attributes.type;
+
+    if (file_type == FileHandleType::kPosix)
+    {
+        if (memory_type != cudaMemoryTypeUnregistered)
+        {
+            uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+            uint64_t remaining_size = count;
+            ssize_t write_cnt;
+            uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+            const uint8_t* input_buf = static_cast<const uint8_t*>(buf) + buf_offset;
+            off_t write_offset = file_offset;
+            while (true)
+            {
+                size_t bytes_to_copy = std::min(cache_size, remaining_size);
+
+                if (bytes_to_copy == 0)
+                {
+                    break;
+                }
+
+                CUDA_TRY(cudaMemcpy(cache_buf, input_buf, bytes_to_copy, cudaMemcpyDeviceToHost));
+                if (cuda_status)
+                {
+                    return -1;
+                }
+                write_cnt = ::pwrite(handle_.fd, cache_buf, bytes_to_copy, write_offset);
+                write_offset += write_cnt;
+                input_buf += write_cnt;
+                remaining_size -= write_cnt;
+
+                total_write_cnt += bytes_to_copy;
+            }
+        }
+        else
+        {
+            total_write_cnt = ::pwrite(handle_.fd, reinterpret_cast<const char*>(buf) + buf_offset, count, file_offset);
+        }
+    }
+    else if (file_type == FileHandleType::kMemoryMapped)
+    {
+        fmt::print(stderr, "[Error] pwrite() is not supported for Memory-mapped IO file type!\n");
+        return -1;
+    }
+    else if (memory_type == cudaMemoryTypeUnregistered || handle_.type == FileHandleType::kPosixODirect)
+    {
+        uint64_t buf_align = (reinterpret_cast<uint64_t>(buf) + buf_offset) % PAGE_SIZE;
+        bool is_aligned = (buf_align == 0) && ((file_offset % PAGE_SIZE) == 0);
+
+        if (is_aligned)
+        {
+            ssize_t write_cnt;
+            size_t block_write_size = ALIGN_DOWN(count, PAGE_SIZE);
+
+            if (block_write_size > 0)
+            {
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    write_cnt = ::pwrite(handle_.fd, reinterpret_cast<const char*>(buf) + buf_offset, block_write_size, file_offset);
+                    total_write_cnt += write_cnt;
+                }
+                else
+                {
+                    uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+                    uint64_t remaining_size = block_write_size;
+                    ssize_t write_cnt;
+                    uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+                    const uint8_t* input_buf = static_cast<const uint8_t*>(buf) + buf_offset;
+                    off_t write_offset = file_offset;
+                    while (true)
+                    {
+                        size_t bytes_to_copy = std::min(cache_size, remaining_size);
+
+                        if (bytes_to_copy == 0)
+                        {
+                            break;
+                        }
+
+                        CUDA_TRY(cudaMemcpy(cache_buf, input_buf, bytes_to_copy, cudaMemcpyDeviceToHost));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                        write_cnt = ::pwrite(handle_.fd, cache_buf, bytes_to_copy, write_offset);
+                        write_offset += write_cnt;
+                        input_buf += write_cnt;
+                        remaining_size -= write_cnt;
+
+                        total_write_cnt += bytes_to_copy;
+                    }
+                }
+            }
+
+            size_t remaining = count - block_write_size;
+            if (remaining)
+            {
+                uint8_t internal_buf[PAGE_SIZE * 2]{};
+                uint8_t* internal_buf_pos =
+                    reinterpret_cast<uint8_t*>(ALIGN_UP(static_cast<uint8_t*>(internal_buf), PAGE_SIZE));
+
+                // Read the remaining block (size of PAGE_SIZE)
+                ssize_t read_cnt;
+                read_cnt = ::pread(handle_.fd, internal_buf_pos, PAGE_SIZE, block_write_size);
+                if (read_cnt < 0)
+                {
+                    fmt::print(stderr, "Cannot read the remaining file content block! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+                // Overwrite a buffer to write, to the intermediate remaining block (internal_buf_pos)
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    memcpy(internal_buf_pos, reinterpret_cast<const uint8_t*>(buf) + buf_offset + block_write_size,
+                           remaining);
+                }
+                else
+                {
+                    CUDA_TRY(cudaMemcpy(internal_buf_pos,
+                                        reinterpret_cast<const uint8_t*>(buf) + buf_offset + block_write_size,
+                                        remaining, cudaMemcpyDeviceToHost));
+                    if (cuda_status)
+                    {
+                        return -1;
+                    }
+                }
+                // Write the constructed block
+                write_cnt = ::pwrite(handle_.fd, internal_buf_pos, PAGE_SIZE, block_write_size);
+                if (write_cnt < 0)
+                {
+                    fmt::print(stderr, "Cannot write the remaining file content! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+
+                total_write_cnt += remaining;
+            }
+        }
+        else
+        {
+            uint64_t cache_size = s_cufile_initializer.max_host_cache_size();
+            uint8_t* cache_buf = static_cast<uint8_t*>(s_cufile_cache.host_cache());
+
+            off_t file_start_offset = ALIGN_DOWN(file_offset, PAGE_SIZE);
+            off_t end_offset = count + file_offset;
+            off_t end_boundary_offset = ALIGN_UP(end_offset, PAGE_SIZE);
+            size_t large_block_size = end_boundary_offset - file_start_offset;
+            off_t page_offset = file_offset - file_start_offset;
+            const uint8_t* input_buf = static_cast<const uint8_t*>(buf) + buf_offset;
+
+            if (large_block_size <= cache_size) // Optimize if bytes to write is less than cache_size
+            {
+                memset(cache_buf, 0, PAGE_SIZE);
+                ssize_t read_cnt = ::pread(handle_.fd, cache_buf, PAGE_SIZE, file_start_offset);
+                if (read_cnt < 0)
+                {
+                    fmt::print(
+                        stderr, "Cannot read the head part of the file content block! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+                if (large_block_size > PAGE_SIZE)
+                {
+                    read_cnt = ::pread(handle_.fd, cache_buf + large_block_size - PAGE_SIZE, PAGE_SIZE,
+                                       end_boundary_offset - PAGE_SIZE);
+                    if (read_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot read the tail part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                }
+
+                if (memory_type == cudaMemoryTypeUnregistered)
+                {
+                    memcpy(cache_buf + page_offset, input_buf, count);
+                }
+                else
+                {
+                    CUDA_TRY(cudaMemcpy(cache_buf + page_offset, input_buf, count, cudaMemcpyDeviceToHost));
+                    if (cuda_status)
+                    {
+                        return -1;
+                    }
+                }
+
+                // Write the constructed block
+                ssize_t write_cnt = ::pwrite(handle_.fd, cache_buf, large_block_size, file_start_offset);
+                if (write_cnt < 0)
+                {
+                    fmt::print(stderr, "Cannot write the file content block! ({})\n", std::strerror(errno));
+                    return -1;
+                }
+
+                total_write_cnt += std::min(static_cast<size_t>(write_cnt - page_offset), count);
+            }
+            else
+            {
+                off_t overflow_offset = page_offset + count;
+                size_t header_size = (overflow_offset > PAGE_SIZE) ? PAGE_SIZE - page_offset : count;
+                size_t tail_size = (overflow_offset > PAGE_SIZE) ? end_offset - ALIGN_DOWN(end_offset, PAGE_SIZE) : 0;
+                uint64_t body_remaining_size = count - header_size - tail_size;
+                off_t write_offset = file_start_offset;
+
+                size_t bytes_to_copy;
+                ssize_t read_cnt;
+                ssize_t write_cnt;
+
+                uint8_t internal_buf[PAGE_SIZE * 2]{};
+                uint8_t* internal_buf_pos =
+                    reinterpret_cast<uint8_t*>(ALIGN_UP(static_cast<uint8_t*>(internal_buf), PAGE_SIZE));
+                // Handle the head part of the file content
+                if (header_size)
+                {
+                    read_cnt = ::pread(handle_.fd, internal_buf_pos, PAGE_SIZE, write_offset);
+                    if (read_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot read the head part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                    // Overwrite the region to write
+                    bytes_to_copy = header_size;
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(internal_buf_pos + page_offset, input_buf, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(
+                            internal_buf_pos + page_offset, input_buf, bytes_to_copy, cudaMemcpyDeviceToHost));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+
+                    // Write the constructed block
+                    write_cnt = ::pwrite(handle_.fd, internal_buf_pos, PAGE_SIZE, write_offset);
+                    if (write_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot write the head part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                    input_buf += bytes_to_copy;
+                    write_offset += write_cnt;
+
+                    total_write_cnt += bytes_to_copy;
+                }
+
+                // Copy n * PAGE_SIZE bytes
+                while (true)
+                {
+                    size_t bytes_to_copy = std::min(cache_size, body_remaining_size);
+
+                    if (bytes_to_copy == 0)
+                    {
+                        break;
+                    }
+
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(cache_buf, input_buf, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(cache_buf, input_buf, bytes_to_copy, cudaMemcpyDeviceToHost));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+                    write_cnt = ::pwrite(handle_.fd, cache_buf, bytes_to_copy, write_offset);
+                    write_offset += write_cnt;
+                    input_buf += write_cnt;
+                    body_remaining_size -= write_cnt;
+
+                    total_write_cnt += bytes_to_copy;
+                }
+
+                // Handle the tail part of the file content
+                if (tail_size)
+                {
+                    memset(internal_buf_pos, 0, PAGE_SIZE);
+                    read_cnt = ::pread(handle_.fd, internal_buf_pos, PAGE_SIZE, write_offset);
+                    if (read_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot read the tail part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                    // Overwrite the region to write
+                    bytes_to_copy = tail_size;
+                    if (memory_type == cudaMemoryTypeUnregistered)
+                    {
+                        memcpy(internal_buf_pos, input_buf, bytes_to_copy);
+                    }
+                    else
+                    {
+                        CUDA_TRY(cudaMemcpy(internal_buf_pos, input_buf, bytes_to_copy, cudaMemcpyDeviceToHost));
+                        if (cuda_status)
+                        {
+                            return -1;
+                        }
+                    }
+
+                    // Write the constructed block
+                    write_cnt = ::pwrite(handle_.fd, internal_buf_pos, PAGE_SIZE, write_offset);
+                    if (write_cnt < 0)
+                    {
+                        fmt::print(stderr, "Cannot write the tail part of the file content block! ({})\n",
+                                   std::strerror(errno));
+                        return -1;
+                    }
+                    total_write_cnt += tail_size;
+                }
+            }
+        }
+    }
+    else if (file_type == FileHandleType::kGPUDirect)
+    {
+        (void*)s_cufile_cache.device_cache(); // Lazy initialization
+
+        ssize_t write_cnt = cuFileWrite(handle_.cufile, reinterpret_cast<const char*>(buf) + buf_offset, count, file_offset, 0);
+        if (write_cnt < 0)
+        {
+            fmt::print(stderr, "[cuFile Error] {}\n", CUFILE_ERRSTR(write_cnt));
+            return -1;
+        }
+        total_write_cnt += write_cnt;
+    }
+    // Update file size
+    if (total_write_cnt > 0)
+    {
+        file_size_ = std::max(file_size_, file_offset + static_cast<size_t>(total_write_cnt));
+    }
+
+    return total_write_cnt;
+}
+bool CuFileDriver::close()
+{
+    if (handle_.cufile)
+    {
+        cuFileHandleDeregister(handle_.cufile);
+        handle_.cufile = nullptr;
+    }
+    if (mmap_ptr_)
+    {
+        int err = munmap(mmap_ptr_, file_size_);
+        if (err < 0)
+        {
+            fmt::print(stderr, "[Error] Cannot call munmap() ({})\n", std::strerror(errno));
+        }
+        mmap_ptr_ = nullptr;
+    }
+    if (handle_.fd != -1)
+    {
+        // If block write was used
+        if ((file_flags_ & O_RDWR) &&
+            (handle_.type == FileHandleType::kGPUDirect || handle_.type == FileHandleType::kPosixODirect))
+        {
+            // Truncate file assuming that `file_size_` is up to date during pwrite() calls
+            int err = ::ftruncate(handle_.fd, file_size_);
+            if (err < 0)
+            {
+                fmt::print(stderr, "[Error] Cannot resize the file {} to {} ({})\n", handle_.path, file_size_,
+                           std::strerror(errno));
+            }
+        }
+        ::close(handle_.fd);
+        handle_.fd = -1;
+    }
+    file_path_.clear();
+    file_size_ = 0;
+    file_flags_ = -1;
+    return true;
+}
+
+filesystem::Path CuFileDriver::path() const
+{
+    return file_path_;
+}
+
+CuFileDriver::~CuFileDriver()
+{
+    close();
+}
+
+} // namespace cucim::filesystem
diff --git a/cpp/src/io/device.cpp b/cpp/src/io/device.cpp
new file mode 100644
index 000000000..fe40c3dff
--- /dev/null
+++ b/cpp/src/io/device.cpp
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/macros/defines.h"
+#include "cucim/io/device.h"
+#include <regex>
+#include <string>
+#include <string_view>
+#include <fmt/format.h>
+
+
+namespace cucim::io
+{
+
+
+Device::Device()
+{
+    // TODO: consider default case (how to handle -1 index?)
+}
+
+Device::Device(const Device& device) : type_(device.type_), index_(device.index_), shm_name_(device.shm_name_)
+{
+}
+
+Device::Device(const std::string& device_name)
+{
+    // 'cuda', 'cuda:0', 'cpu[shm0]', 'cuda:0[cuda_shm0]'
+    static const std::regex name_regex("([a-z]+)(?::(0|[1-9]\\d*))?(?:\\[([a-zA-Z0-9_\\-][a-zA-Z0-9_\\-\\.]*)\\])?");
+
+    std::smatch match;
+    if (std::regex_match(device_name, match, name_regex))
+    {
+        type_ = parse_type(match[1].str());
+        if (match[2].matched)
+        {
+            index_ = std::stoi(match[2].str());
+        }
+        if (match[3].matched)
+        {
+            shm_name_ = match[3].str();
+        }
+    }
+    else
+    {
+        CUCIM_ERROR("Device name doesn't match!");
+    }
+
+    validate_device();
+}
+Device::Device(const char* device_name) : Device::Device(std::string(device_name))
+{
+}
+
+Device::Device(DeviceType type, DeviceIndex index)
+{
+    type_ = type;
+    index_ = index;
+    validate_device();
+}
+
+Device::Device(DeviceType type, DeviceIndex index, const std::string& param)
+{
+    type_ = type;
+    index_ = index;
+    shm_name_ = param;
+    validate_device();
+}
+
+DeviceType Device::parse_type(const std::string& device_name)
+{
+    (void)device_name;
+
+    // TODO: implement this
+    return DeviceType::kCPU;
+}
+Device::operator std::string() const
+{
+    static const std::unordered_map<DeviceType, std::string> device_type_map{
+        { DeviceType::kCPU, "cpu" },   { DeviceType::kPinned, "pinned" },   { DeviceType::kCPUShared, "cpu" },
+        { DeviceType::kCUDA, "cuda" }, { DeviceType::kCUDAShared, "cuda" },
+    };
+    if (index_ == -1 && shm_name_.empty())
+    {
+        return fmt::format("{}", device_type_map.at(static_cast<DeviceType>(type_)));
+    }
+    else if (index_ != -1 && shm_name_.empty())
+    {
+        return fmt::format("{}:{}", device_type_map.at(static_cast<DeviceType>(type_)), index_);
+    }
+    else
+    {
+        return fmt::format("{}:{}[{}]", device_type_map.at(static_cast<DeviceType>(type_)), index_, shm_name_);
+    }
+}
+
+DeviceType Device::type() const
+{
+    return type_;
+};
+DeviceIndex Device::index() const
+{
+    return index_;
+}
+const std::string& Device::shm_name() const
+{
+    return shm_name_;
+}
+
+void Device::set_values(DeviceType type, DeviceIndex index, const std::string& param)
+{
+    type_ = type;
+    index_ = index;
+    shm_name_ = param;
+}
+
+bool Device::validate_device()
+{
+    // TODO: implement this
+    return true;
+}
+
+} // namespace cucim::io
\ No newline at end of file
diff --git a/cpp/src/io/format/image_format.cpp b/cpp/src/io/format/image_format.cpp
new file mode 100644
index 000000000..01c4382fc
--- /dev/null
+++ b/cpp/src/io/format/image_format.cpp
@@ -0,0 +1,214 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/macros/defines.h"
+#include "cucim/io/format/image_format.h"
+#include "cucim/memory/memory_manager.h"
+
+#include <fmt/format.h>
+
+
+namespace cucim::io::format
+{
+
+ImageMetadata::ImageMetadata()
+{
+    desc_.handle = this;
+}
+
+void* ImageMetadata::allocate(size_t size)
+{
+    return res_.allocate(size);
+}
+
+std::pmr::monotonic_buffer_resource& ImageMetadata::get_resource()
+{
+    return res_;
+}
+
+ImageMetadataDesc& ImageMetadata::desc()
+{
+    return desc_;
+}
+
+ImageMetadata& ImageMetadata::ndim(uint16_t ndim)
+{
+    desc_.ndim = ndim;
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::dims(const std::string_view& dims)
+{
+    dims_ = std::move(dims);
+    desc_.dims = dims_.data();
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::shape(const std::pmr::vector<int64_t>& shape)
+{
+    shape_ = std::move(shape);
+    desc_.shape = const_cast<int64_t*>(shape_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::dtype(const DLDataType& dtype)
+{
+    desc_.dtype = dtype;
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::channel_names(const std::pmr::vector<std::string_view>& channel_names)
+{
+    const int channel_len = channel_names.size();
+    channel_names_.clear();
+    channel_names_.reserve(channel_len);
+
+    for (int i = 0; i < channel_len; ++i)
+    {
+        channel_names_.emplace_back(channel_names[i]);
+    }
+
+    desc_.channel_names = static_cast<char**>(allocate(channel_len * sizeof(char*)));
+    for (int i = 0; i < channel_len; ++i)
+    {
+        desc_.channel_names[i] = const_cast<char*>(channel_names_[i].data());
+    }
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::spacing(const std::pmr::vector<float>& spacing)
+{
+    spacing_ = std::move(spacing);
+    desc_.spacing = const_cast<float*>(spacing_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::spacing_units(const std::pmr::vector<std::string_view>& spacing_units)
+{
+    const int ndim = spacing_units.size();
+    spacing_units_.clear();
+    spacing_units_.reserve(ndim);
+
+    for (int i = 0; i < ndim; ++i)
+    {
+        spacing_units_.emplace_back(spacing_units[i]);
+    }
+
+    desc_.spacing_units = static_cast<char**>(allocate(ndim * sizeof(char*)));
+    for (int i = 0; i < ndim; ++i)
+    {
+        desc_.spacing_units[i] = const_cast<char*>(spacing_units_[i].data());
+    }
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::origin(const std::pmr::vector<float>& origin)
+{
+    origin_ = std::move(origin);
+    desc_.origin = const_cast<float*>(origin_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::direction(const std::pmr::vector<float>& direction)
+{
+    direction_ = std::move(direction);
+    desc_.direction = const_cast<float*>(direction_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::coord_sys(const std::string_view& coord_sys)
+{
+    coord_sys_ = std::move(coord_sys);
+    desc_.coord_sys = coord_sys_.data();
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::level_count(uint16_t level_count)
+{
+    desc_.resolution_info.level_count = level_count;
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::level_ndim(uint16_t level_ndim)
+{
+    desc_.resolution_info.level_ndim = level_ndim;
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::level_dimensions(const std::pmr::vector<int64_t>& level_dimensions)
+{
+    level_dimensions_ = std::move(level_dimensions);
+    desc_.resolution_info.level_dimensions = const_cast<int64_t*>(level_dimensions_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::level_downsamples(const std::pmr::vector<float>& level_downsamples)
+{
+    level_downsamples_ = std::move(level_downsamples);
+    desc_.resolution_info.level_downsamples = const_cast<float*>(level_downsamples_.data());
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::image_count(uint16_t image_count)
+{
+    desc_.associated_image_info.image_count = image_count;
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::image_names(const std::pmr::vector<std::string_view>& image_names)
+{
+    const int image_size = image_names.size();
+    image_names_.clear();
+    image_names_.reserve(image_size);
+
+    for (int i = 0; i < image_size; ++i)
+    {
+        image_names_.emplace_back(image_names[i]);
+    }
+
+    desc_.associated_image_info.image_names = static_cast<char**>(allocate(image_size * sizeof(char*)));
+    for (int i = 0; i < image_size; ++i)
+    {
+        desc_.associated_image_info.image_names[i] = const_cast<char*>(image_names_[i].data());
+    }
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::raw_data(const std::string_view& raw_data)
+{
+    desc_.raw_data = raw_data.data();
+    return *this;
+}
+
+ImageMetadata& ImageMetadata::json_data(const std::string_view& json_data)
+{
+    desc_.json_data = const_cast<char*>(json_data.data());
+    return *this;
+}
+
+ImageMetadata::~ImageMetadata()
+{
+    // Memory for json_data needs to be manually released if image_metadata_->json_data is not ""
+    // This logic may be already executed(@CuImage::~CuImage()) if this object is part of CuImage object.
+    if (desc_.json_data && *desc_.json_data != '\0')
+    {
+        cucim_free(desc_.json_data);
+        desc_.json_data = nullptr;
+    }
+    desc_.handle = nullptr;
+}
+
+} // namespace cucim::io::format
diff --git a/cpp/src/logger/logger.cpp b/cpp/src/logger/logger.cpp
new file mode 100644
index 000000000..bf8acefe3
--- /dev/null
+++ b/cpp/src/logger/logger.cpp
@@ -0,0 +1,15 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
diff --git a/cpp/src/logger/timer.cpp b/cpp/src/logger/timer.cpp
new file mode 100644
index 000000000..c230f0903
--- /dev/null
+++ b/cpp/src/logger/timer.cpp
@@ -0,0 +1,78 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/logger/timer.h"
+
+#include <fmt/format.h>
+
+namespace cucim::logger
+{
+
+Timer::Timer(const char* message, bool auto_start, bool auto_output)
+{
+    message_ = message;
+    is_auto_output_ = auto_output;
+    if (auto_start)
+    {
+        elapsed_seconds_ = 0.0;
+        start_ = std::chrono::high_resolution_clock::now();
+    }
+}
+
+void Timer::start()
+{
+    elapsed_seconds_ = 0.0;
+    start_ = std::chrono::high_resolution_clock::now();
+}
+
+double Timer::stop()
+{
+    end_ = std::chrono::high_resolution_clock::now();
+    elapsed_seconds_ = std::chrono::duration_cast<std::chrono::duration<double>>(end_ - start_).count();
+    return elapsed_seconds_;
+}
+
+double Timer::elapsed_time()
+{
+    return elapsed_seconds_;
+}
+
+void Timer::print(const char* message)
+{
+    if (message)
+    {
+        fmt::print(message, elapsed_seconds_);
+    }
+    else
+    {
+        fmt::print(message_, elapsed_seconds_);
+    }
+}
+
+Timer::~Timer()
+{
+    if (elapsed_seconds_ <= 0.0)
+    {
+        end_ = std::chrono::high_resolution_clock::now();
+        elapsed_seconds_ = std::chrono::duration_cast<std::chrono::duration<double>>(end_ - start_).count();
+    }
+    if (is_auto_output_)
+    {
+        print();
+    }
+}
+
+} // namespace cucim::logger
diff --git a/cpp/src/memory/memory_manager.cu b/cpp/src/memory/memory_manager.cu
new file mode 100644
index 000000000..7b418ef61
--- /dev/null
+++ b/cpp/src/memory/memory_manager.cu
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/memory/memory_manager.h"
+
+#include <fmt/format.h>
+#include <cuda_runtime.h>
+
+#include <memory_resource>
+
+#define CUDA_TRY(stmt)                                                                                                 \
+    {                                                                                                                  \
+        cuda_status = stmt;                                                                                            \
+        if (cudaSuccess != cuda_status)                                                                                \
+        {                                                                                                              \
+            fmt::print(stderr, "[Error] CUDA Runtime call {} in line {} of file {} failed with '{}' ({}).\n", #stmt,   \
+                       __LINE__, __FILE__, cudaGetErrorString(cuda_status), cuda_status);                              \
+        }                                                                                                              \
+    }
+
+CUCIM_API void* cucim_malloc(size_t size)
+{
+    return malloc(size);
+}
+
+CUCIM_API void cucim_free(void* ptr)
+{
+    free(ptr);
+}
+
+namespace cucim::memory
+{
+
+void get_pointer_attributes(PointerAttributes& attr, const void* ptr)
+{
+    cudaError_t cuda_status;
+
+    cudaPointerAttributes attributes;
+    CUDA_TRY(cudaPointerGetAttributes(&attributes, ptr));
+    if (cuda_status)
+    {
+        return;
+    }
+
+    cudaMemoryType& memory_type = attributes.type;
+    switch (memory_type)
+    {
+    case cudaMemoryTypeUnregistered:
+        attr.device = cucim::io::Device(cucim::io::DeviceType::kCPU, -1);
+        attr.ptr = const_cast<void*>(ptr);
+        break;
+    case cudaMemoryTypeHost:
+        attr.device = cucim::io::Device(cucim::io::DeviceType::kPinned, attributes.device);
+        attr.ptr = attributes.hostPointer;
+        break;
+    case cudaMemoryTypeDevice:
+    case cudaMemoryTypeManaged:
+        attr.device = cucim::io::Device(cucim::io::DeviceType::kCUDA, attributes.device);
+        attr.ptr = attributes.devicePointer;
+        break;
+    }
+}
+
+} // namespace cucim::memory
\ No newline at end of file
diff --git a/cpp/tests/CMakeLists.txt b/cpp/tests/CMakeLists.txt
new file mode 100644
index 000000000..32b082bdd
--- /dev/null
+++ b/cpp/tests/CMakeLists.txt
@@ -0,0 +1,61 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+include(CTest)
+enable_testing()
+
+################################################################################
+# Add executable: cucim_tests
+################################################################################
+add_executable(cucim_tests
+        main.cpp
+        test_read_region.cpp
+        test_cufile.cpp
+        test_metadata.cpp
+        )
+set_source_files_properties(main.cpp test_read_region.cpp test_cufile.cpp test_metadata.cpp PROPERTIES LANGUAGE CUDA)
+
+set_target_properties(cucim_tests
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(cucim_tests PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cucim_tests PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_compile_definitions(cucim_tests
+    PUBLIC
+        CUCIM_VERSION=${PROJECT_VERSION}
+        CUCIM_VERSION_MAJOR=${PROJECT_VERSION_MAJOR}
+        CUCIM_VERSION_MINOR=${PROJECT_VERSION_MINOR}
+        CUCIM_VERSION_PATCH=${PROJECT_VERSION_PATCH}
+        CUCIM_VERSION_BUILD=${PROJECT_VERSION_BUILD}
+)
+target_link_libraries(cucim_tests
+        PRIVATE
+            ${CUCIM_PACKAGE_NAME}
+            deps::catch2
+            deps::openslide
+        )
+
+include(ParseAndAddCatchTests)
+# See https://github.com/catchorg/Catch2/blob/master/docs/cmake-integration.md#parseandaddcatchtestscmake for other options
+ParseAndAddCatchTests(cucim_tests)
diff --git a/cpp/tests/config.h b/cpp/tests/config.h
new file mode 100644
index 000000000..1baba463f
--- /dev/null
+++ b/cpp/tests/config.h
@@ -0,0 +1,72 @@
+/*
+ * Apache License, Version 2.0
+ * Copyright 2021 NVIDIA Corporation
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_TESTS_CONFIG_H
+#define CUCIM_TESTS_CONFIG_H
+
+#include <string>
+#include <cstdlib>
+
+struct AppConfig
+{
+    std::string test_folder;
+    std::string test_file;
+    std::string temp_folder = "/tmp";
+    std::string get_input_path(const char* default_value = "private/generic_tiff_000.tif") const
+    {
+        // If `test_file` is absolute path
+        if (!test_folder.empty() && test_file.substr(0, 1) == "/")
+        {
+            return test_file;
+        }
+        else
+        {
+            std::string test_data_folder = test_folder;
+            if (test_data_folder.empty())
+            {
+                if (const char* env_p = std::getenv("CUCIM_TESTDATA_FOLDER"))
+                {
+                    test_data_folder = env_p;
+                }
+                else
+                {
+                    test_data_folder = "test_data";
+                }
+            }
+            if (test_file.empty())
+            {
+                return test_data_folder + "/" + default_value;
+            }
+            else
+            {
+                return test_data_folder + "/" + test_file;
+            }
+        }
+    }
+    std::string get_plugin_path(const char* default_value = "cucim.kit.cuslide@0.0.0.so")
+    {
+        std::string plugin_path = default_value;
+        if (const char* env_p = std::getenv("CUCIM_TEST_PLUGIN_PATH"))
+        {
+            plugin_path = env_p;
+        }
+        return plugin_path;
+    }
+};
+
+extern AppConfig g_config;
+
+#endif // CUCIM_TESTS_CONFIG_H
diff --git a/cpp/tests/main.cpp b/cpp/tests/main.cpp
new file mode 100644
index 000000000..bdcdf1f43
--- /dev/null
+++ b/cpp/tests/main.cpp
@@ -0,0 +1,94 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+//#define CATCH_CONFIG_MAIN
+//#include <catch2/catch.hpp>
+
+// Implement main explicitly to handle additional parameters.
+#define CATCH_CONFIG_RUNNER
+#include "config.h"
+#include "cucim/core/framework.h"
+
+#include <catch2/catch.hpp>
+#include <string>
+#include <fmt/format.h>
+
+CUCIM_FRAMEWORK_GLOBALS("sample.app")
+
+// Global config object
+AppConfig g_config;
+
+/**
+ * Extract `--[option]` or `--[option]=` string from command and set the value to g_config object.
+ *
+ * @param argc number of arguments used for command
+ * @param argv arguments for command
+ * @param obj object reference to modify
+ * @param argument name of argument(option)
+ * @return true if it extracted the value for the option
+ */
+static bool extract_test_file_option(int* argc, char** argv, std::string& obj, const char* argument)
+{
+    std::string arg_str = fmt::format("--{}=", argument); // test_file => --test_file=
+    std::string arg_str2 = fmt::format("--{}", argument); // test_file => --test_file
+
+    char* value_ptr = nullptr;
+    for (int i = 1; argc && i < *argc; ++i)
+    {
+        if (strncmp(argv[i], arg_str.c_str(), arg_str.size()) == 0)
+        {
+            value_ptr = &argv[i][12];
+            for (int j = i + 1; argc && j < *argc; ++j)
+            {
+                argv[j - 1] = argv[j];
+            }
+            --(*argc);
+            argv[*argc] = nullptr;
+            break;
+        }
+        if (strncmp(argv[i], arg_str2.c_str(), arg_str2.size()) == 0 && i + 1 < *argc)
+        {
+            value_ptr = argv[i + 1];
+            for (int j = i + 2; argc && j < *argc; ++j)
+            {
+                argv[j - 2] = argv[j];
+            }
+            *argc -= 2;
+            argv[*argc] = nullptr;
+            argv[*argc + 1] = nullptr;
+            break;
+        }
+    }
+
+    if (value_ptr) {
+        obj = value_ptr;
+        return true;
+    }
+    else {
+        return false;
+    }
+}
+
+int main (int argc, char** argv) {
+    extract_test_file_option(&argc, argv, g_config.test_folder, "test_folder");
+    extract_test_file_option(&argc, argv, g_config.test_file, "test_file");
+    extract_test_file_option(&argc, argv, g_config.temp_folder, "temp_folder");
+    printf("Target test folder: %s (use --test_folder option to change this)\n", g_config.test_folder.c_str());
+    printf("Target test file  : %s (use --test_file option to change this)\n", g_config.test_file.c_str());
+    printf("Temp folder       : %s (use --temp_folder option to change this)\n", g_config.temp_folder.c_str());
+    int result = Catch::Session().run(argc, argv);
+    return result;
+}
diff --git a/cpp/tests/test_cufile.cpp b/cpp/tests/test_cufile.cpp
new file mode 100644
index 000000000..be037fcff
--- /dev/null
+++ b/cpp/tests/test_cufile.cpp
@@ -0,0 +1,631 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim/logger/timer.h"
+#include "config.h"
+
+#include <catch2/catch.hpp>
+#include <fmt/format.h>
+#include <cucim/filesystem/cufile_driver.h>
+#include <chrono>
+#include <cstdlib>
+#include <fcntl.h>
+#include <unistd.h>
+#include <string_view>
+// Test
+#include <iostream>
+#include <fstream>
+#include <sys/stat.h>
+#include <sys/mman.h>
+#include <cuda_runtime.h>
+
+#define ALIGN_UP(x, align_to) (((uint64_t)(x) + ((uint64_t)(align_to)-1)) & ~((uint64_t)(align_to)-1))
+
+#define CUDA_ERROR(stmt)                                                                                               \
+    {                                                                                                                  \
+        cuda_status = stmt;                                                                                            \
+        if (cudaSuccess != cuda_status)                                                                                \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", cudaGetErrorString(cuda_status)));                                   \
+            REQUIRE(cudaSuccess == cuda_status);                                                                       \
+        }                                                                                                              \
+    }
+
+#define POSIX_ERROR(stmt)                                                                                              \
+    {                                                                                                                  \
+        err = stmt;                                                                                                    \
+        if (err < 0)                                                                                                   \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", std::strerror(errno)));                                              \
+            REQUIRE(err >= 0);                                                                                         \
+        }                                                                                                              \
+    }
+
+static void create_test_file(const char* file_name, int size)
+{
+    int fd = open(file_name, O_RDWR | O_CREAT | O_TRUNC, 0666);
+    char test_data[size];
+    ::srand(0);
+    for (int i = 0; i < size; i++)
+    {
+        test_data[i] = ::rand() % 256; // or i % 256;
+    }
+    ssize_t write_cnt = write(fd, test_data, size);
+    assert(write_cnt == size);
+    close(fd);
+}
+
+TEST_CASE("Verify libcufile usage", "[test_cufile.cpp]")
+{
+    cudaError_t cuda_status;
+    int err;
+    constexpr int BLOCK_SECTOR_SIZE = 4096;
+    constexpr char const* test_w_flags[] = { "wpn", "wp", "wn", "w" };
+    constexpr char const* test_flags_desc[] = { "regular file", "o_direct", "gds with no O_DIRECT", "gds with O_DIRECT" };
+    constexpr int W_FLAG_LEN = sizeof(test_w_flags) / sizeof(test_w_flags[0]);
+    constexpr char const* test_r_flags[] = { "rpn", "rp", "rn", "r" };
+    // clang-format off
+    constexpr int test_buf_offsets[] =  { 0,   0,   0,    0,  0,     0,    0,    0,    0,            0 };
+    constexpr int test_file_offsets[] = { 0, 500,   0,    0, 400,  400, 4000, 4500, 4500, 4096 * 2 - 1 };
+    constexpr int test_counts[] =       { 0,   0, 500, 4097, 500, 4097,  500,  500, 4097,          500 };
+    // clang-format on
+    constexpr int TEST_PARAM_LEN = sizeof(test_counts) / sizeof(test_counts[0]);
+    uint8_t test_data[BLOCK_SECTOR_SIZE * 3];
+
+    std::string output_file = fmt::format("{}/test_cufile.raw", g_config.temp_folder);
+
+    ::srand(777);
+    for (int i = 0; i < BLOCK_SECTOR_SIZE * 3; i++)
+    {
+        test_data[i] = ::rand() % 256; // or (BLOCK_SECTOR_SIZE * 3 - i) % 256;
+    }
+
+    std::hash<std::string_view> str_hash;
+
+    for (int test_param_index = 0; test_param_index < TEST_PARAM_LEN; ++test_param_index)
+    {
+        int test_buf_offset = test_buf_offsets[test_param_index];
+        int test_file_offset = test_file_offsets[test_param_index];
+        int test_count = test_counts[test_param_index];
+
+        // Allocate memory
+        uint8_t* unaligned_host = static_cast<uint8_t*>(malloc(test_count + test_buf_offset));
+        uint8_t* aligned_host;
+        POSIX_ERROR(posix_memalign(reinterpret_cast<void**>(&aligned_host), 512, test_count + test_buf_offset));
+
+        uint8_t* unaligned_device;
+        CUDA_ERROR(cudaMalloc(&unaligned_device, test_count + test_buf_offset + BLOCK_SECTOR_SIZE));
+        uint8_t* aligned_device = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device, BLOCK_SECTOR_SIZE));
+
+        uint8_t* unaligned_device_host;
+        CUDA_ERROR(cudaMallocHost(&unaligned_device_host, test_count + test_buf_offset + BLOCK_SECTOR_SIZE));
+        uint8_t* aligned_device_host = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device_host, BLOCK_SECTOR_SIZE));
+
+        uint8_t* unaligned_device_managed;
+        CUDA_ERROR(cudaMallocManaged(&unaligned_device_managed, test_count + test_buf_offset + BLOCK_SECTOR_SIZE));
+        uint8_t* aligned_device_managed =
+            reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_device_managed, BLOCK_SECTOR_SIZE));
+
+        SECTION(fmt::format("Write Test with different parameters (offset:{}, count:{})", test_file_offset, test_count))
+        {
+            {
+                INFO("# unaligned_host");
+                size_t reference_whole_hash = 0;
+                size_t reference_hash = 0;
+                size_t reference_write_cnt = 0;
+                size_t reference_read_cnt = 0;
+                for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                {
+                    INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                     test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                    {
+                        INFO(fmt::format("memory: unaligned_host \n"));
+                        create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                        auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                        memcpy(unaligned_host + test_buf_offset, test_data, test_count);
+
+                        ssize_t write_cnt = fd->pwrite(unaligned_host, test_count, test_file_offset, test_buf_offset);
+                        if (flag_idx == 0)
+                        {
+                            reference_write_cnt = write_cnt;
+                        }
+                        else
+                        {
+                            REQUIRE(write_cnt == reference_write_cnt);
+                        }
+
+                        fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                        memset(unaligned_host, 0, test_count + test_buf_offset);
+
+                        ssize_t read_cnt = fd->pread(unaligned_host, test_count, test_file_offset, test_buf_offset);
+                        if (flag_idx == 0)
+                        {
+                            reference_read_cnt = read_cnt;
+                        }
+                        else
+                        {
+                            REQUIRE(read_cnt == reference_read_cnt);
+                        }
+
+                        size_t posix_hash = str_hash(std::string_view((char*)unaligned_host + test_buf_offset, read_cnt));
+
+                        char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                        int fd2 = open(output_file.c_str(), O_RDONLY);
+                        read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                        size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                        if (flag_idx == 0)
+                        {
+                            reference_hash = posix_hash;
+                            reference_whole_hash = file_hash;
+                        }
+                        else
+                        {
+                            REQUIRE(reference_hash == posix_hash);
+                            REQUIRE(reference_whole_hash == file_hash);
+                        }
+                    }
+                }
+            }
+            {
+                INFO("# aligned_host");
+                size_t reference_whole_hash = 0;
+                size_t reference_hash = 0;
+                size_t reference_write_cnt = 0;
+                size_t reference_read_cnt = 0;
+                for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                {
+                    INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                     test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                    {
+                        INFO(fmt::format("memory: aligned_host \n"));
+                        create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                        auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                        memcpy(aligned_host + test_buf_offset, test_data, test_count + test_buf_offset);
+                        ssize_t write_cnt = fd->pwrite(aligned_host, test_count, test_file_offset, test_buf_offset);
+                        if (flag_idx == 0)
+                        {
+                            reference_write_cnt = write_cnt;
+                        }
+                        else
+                        {
+                            REQUIRE(write_cnt == reference_write_cnt);
+                        }
+
+                        fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                        memset(aligned_host, 0, test_count + test_buf_offset);
+                        ssize_t read_cnt = fd->pread(aligned_host, test_count, test_file_offset, test_buf_offset);
+                        if (flag_idx == 0)
+                        {
+                            reference_read_cnt = read_cnt;
+                        }
+                        else
+                        {
+                            REQUIRE(read_cnt == reference_read_cnt);
+                        }
+
+                        size_t posix_hash = str_hash(std::string_view((char*)aligned_host + test_buf_offset, read_cnt));
+
+                        char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                        int fd2 = open(output_file.c_str(), O_RDONLY);
+                        read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                        size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                        if (flag_idx == 0)
+                        {
+                            reference_hash = posix_hash;
+                            reference_whole_hash = file_hash;
+                        }
+                        else
+                        {
+                            REQUIRE(reference_hash == posix_hash);
+                            REQUIRE(reference_whole_hash == file_hash);
+                        }
+                    }
+                }
+            }
+            {
+                // Device Memory
+                {
+                    INFO("# unaligned_device");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: unaligned_device \n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(unaligned_device + test_buf_offset, test_data, test_count, cudaMemcpyHostToDevice);
+                            ssize_t write_cnt =
+                                fd->pwrite(unaligned_device, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(unaligned_device, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt = fd->pread(unaligned_device, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(unaligned_host, unaligned_device, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)unaligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+                {
+                    INFO("# aligned_device");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: aligned_device \n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(aligned_device + test_buf_offset, test_data, test_count, cudaMemcpyHostToDevice);
+                            ssize_t write_cnt = fd->pwrite(aligned_device, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(aligned_device, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt = fd->pread(aligned_device, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(aligned_host, aligned_device, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)aligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+
+                // Pinned Host Memory
+                {
+                    INFO("# unaligned_device (pinned host)");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: unaligned_device (pinned host)\n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(
+                                unaligned_device_host + test_buf_offset, test_data, test_count, cudaMemcpyHostToDevice);
+                            ssize_t write_cnt =
+                                fd->pwrite(unaligned_device_host, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(unaligned_device_host, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt =
+                                fd->pread(unaligned_device_host, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(unaligned_host, unaligned_device_host, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)unaligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+                {
+                    INFO("# aligned_device (pinned host)");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: aligned_device (pinned host)\n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(
+                                aligned_device_host + test_buf_offset, test_data, test_count, cudaMemcpyHostToDevice);
+                            ssize_t write_cnt =
+                                fd->pwrite(aligned_device_host, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(aligned_device_host, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt =
+                                fd->pread(aligned_device_host, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(aligned_host, aligned_device_host, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)aligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+
+                // ManageDevice Memory
+                {
+                    INFO("# unaligned_device (managed)");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: unaligned_device (managed)\n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(unaligned_device_managed + test_buf_offset, test_data, test_count,
+                                       cudaMemcpyHostToDevice);
+                            ssize_t write_cnt =
+                                fd->pwrite(unaligned_device_managed, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(unaligned_device_managed, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt =
+                                fd->pread(unaligned_device_managed, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(unaligned_host, unaligned_device_managed, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)unaligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+
+                {
+                    INFO("# aligned_device (managed)");
+                    size_t reference_whole_hash = 0;
+                    size_t reference_hash = 0;
+                    size_t reference_write_cnt = 0;
+                    size_t reference_read_cnt = 0;
+                    for (int flag_idx = 0; flag_idx < W_FLAG_LEN; ++flag_idx)
+                    {
+                        INFO(fmt::format("flag_index: {} ({})\n  count: {}\n  file_offset: {}  buf_offset: {}\n", flag_idx,
+                                         test_flags_desc[flag_idx], test_count, test_file_offset, test_buf_offset));
+                        {
+                            INFO(fmt::format("memory: aligned_device (managed)\n"));
+                            create_test_file(output_file.c_str(), BLOCK_SECTOR_SIZE * 3);
+
+                            auto fd = cucim::filesystem::open(output_file.c_str(), test_w_flags[flag_idx]);
+                            cudaMemcpy(aligned_device_managed + test_buf_offset, test_data, test_count,
+                                       cudaMemcpyHostToDevice);
+                            ssize_t write_cnt =
+                                fd->pwrite(aligned_device_managed, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_write_cnt = write_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(write_cnt == reference_write_cnt);
+                            }
+
+                            fd = cucim::filesystem::open(output_file.c_str(), test_r_flags[flag_idx]);
+                            cudaMemset(aligned_device_managed, 0, test_count + test_buf_offset);
+                            ssize_t read_cnt =
+                                fd->pread(aligned_device_managed, test_count, test_file_offset, test_buf_offset);
+                            if (flag_idx == 0)
+                            {
+                                reference_read_cnt = read_cnt;
+                            }
+                            else
+                            {
+                                REQUIRE(read_cnt == reference_read_cnt);
+                            }
+
+                            cudaMemcpy(aligned_host, aligned_device_managed, test_count, cudaMemcpyDeviceToHost);
+                            size_t posix_hash =
+                                str_hash(std::string_view((char*)aligned_host + test_buf_offset, read_cnt));
+
+                            char file_data[BLOCK_SECTOR_SIZE * 4]{};
+                            int fd2 = open(output_file.c_str(), O_RDONLY);
+                            read_cnt = read(fd2, file_data, BLOCK_SECTOR_SIZE * 4);
+
+                            size_t file_hash = str_hash(std::string_view(file_data, read_cnt));
+                            if (flag_idx == 0)
+                            {
+                                reference_hash = posix_hash;
+                                reference_whole_hash = file_hash;
+                            }
+                            else
+                            {
+                                REQUIRE(reference_hash == posix_hash);
+                                REQUIRE(reference_whole_hash == file_hash);
+                            }
+                        }
+                    }
+                }
+            }
+        }
+
+        CUDA_ERROR(cudaFree(unaligned_device));
+        CUDA_ERROR(cudaFreeHost(unaligned_device_host));
+        CUDA_ERROR(cudaFree(unaligned_device_managed));
+        free(aligned_host);
+        free(unaligned_host);
+    }
+}
diff --git a/cpp/tests/test_metadata.cpp b/cpp/tests/test_metadata.cpp
new file mode 100644
index 000000000..2c2d6ddd3
--- /dev/null
+++ b/cpp/tests/test_metadata.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "config.h"
+#include "cucim/cuimage.h"
+#include "cucim/logger/timer.h"
+#include "cucim/core/framework.h"
+#include "cucim/io/format/image_format.h"
+
+#include <catch2/catch.hpp>
+#include <fmt/format.h>
+#include <chrono>
+#include <cstdlib>
+#include <memory>
+#include <fcntl.h>
+#include <unistd.h>
+#include <string_view>
+// Test
+#include <iostream>
+#include <fstream>
+#include <sys/stat.h>
+#include <sys/mman.h>
+#include <cuda_runtime.h>
+
+
+#define ALIGN_UP(x, align_to) (((uint64_t)(x) + ((uint64_t)(align_to)-1)) & ~((uint64_t)(align_to)-1))
+
+#define CUDA_ERROR(stmt)                                                                                               \
+    {                                                                                                                  \
+        cuda_status = stmt;                                                                                            \
+        if (cudaSuccess != cuda_status)                                                                                \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", cudaGetErrorString(cuda_status)));                                   \
+            REQUIRE(cudaSuccess == cuda_status);                                                                       \
+        }                                                                                                              \
+    }
+
+#define POSIX_ERROR(stmt)                                                                                              \
+    {                                                                                                                  \
+        err = stmt;                                                                                                    \
+        if (err < 0)                                                                                                   \
+        {                                                                                                              \
+            INFO(fmt::format("Error message: {}", std::strerror(errno)));                                              \
+            REQUIRE(err >= 0);                                                                                         \
+        }                                                                                                              \
+    }
+
+class Document
+{
+    bool is_cached_{};
+    double rank_{};
+    int id_{};
+};
+#include <string>
+void test(float* haha)
+{
+    fmt::print("T {} {} {}\n", haha[0], haha[1], haha[2]);
+}
+
+TEST_CASE("Verify metadata", "[test_metadata.cpp]")
+{
+    cucim::Framework* framework = cucim::acquire_framework("sample.app");
+    REQUIRE(framework != nullptr);
+
+    cucim::io::format::IImageFormat* image_format =
+        framework->acquire_interface_from_library<cucim::io::format::IImageFormat>(g_config.get_plugin_path().c_str());
+    // fmt::print("{}\n", image_format->formats[0].get_format_name());
+    REQUIRE(image_format != nullptr);
+
+    auto handle =
+        image_format->formats[0].image_parser.open(g_config.get_input_path("private/philips_tiff_000.tif").c_str());
+
+    cucim::io::format::ImageMetadata metadata{};
+    image_format->formats[0].image_parser.parse(&handle, &metadata.desc());
+
+    // Using fmt::print() has a problem with TestMate VSCode plugin (output is not caught by the plugin)
+    std::cout << fmt::format("metadata: {}\n", metadata.desc().raw_data);
+    const uint8_t* buf = metadata.get_buffer();
+    const uint8_t* buf2 = static_cast<uint8_t*>(metadata.allocate(1));
+    std::cout << fmt::format("test: {}\n", buf2 - buf);
+
+    image_format->formats[0].image_parser.close(&handle);
+
+    // cucim::CuImage img{ g_config.get_input_path("private/philips_tiff_000.tif") };
+    // const auto& img_metadata = img.metadata();
+    // std::cout << fmt::format("metadata: {}\n", img_metadata);
+    // auto v = img.spacing();
+    // std::cout << fmt::format("spacing: {}\n", v.size());
+    // delete ((cuslide::tiff::TIFF*)handle.client_data);
+    // cucim_free(handle.client_data);
+    // cucim_free(handle.client_data);
+
+    // fmt::print("alignment: {}\n", alignof(int));
+    // fmt::print("Document: {}\n", sizeof(Document));
+    // fmt::print("max align: {}\n", alignof(size_t));
+    // auto a = std::string{ "" };
+    // fmt::print("size of ImageMetadataDesc :{}\n", sizeof(cucim::io::format::ImageMetadataDesc));
+    // fmt::print("size of ImageMetadata :{}\n", sizeof(cucim::io::format::ImageMetadata));
+
+    // cucim::io::format::ImageMetadata metadata;
+
+
+    // test(std::array<float, 3>{ 1.0, 2.0, 3.0 }.data());
+
+
+    // std::vector<float> d(3);
+
+
+    // fmt::print("metadata: {} \n", (size_t)std::addressof(metadata));
+    // fmt::print("handle: {} \n", (size_t)std::addressof(metadata.desc()));
+    // fmt::print("ndim: {} \n", ((cucim::io::format::ImageMetadataDesc*)&metadata)->ndim);
+
+    // // cucim::io::format::ImageMetadata a;
+
+    // REQUIRE(1 == 1);
+}
+
+TEST_CASE("Load test", "[test_metadata.cpp]")
+{
+    cucim::CuImage img{ g_config.get_input_path("private/philips_tiff_000.tif") };
+
+    auto test = img.read_region({ -10, -10 }, { 100, 100 });
+
+    fmt::print("{}", img.metadata());
+}
diff --git a/cpp/tests/test_read_region.cpp b/cpp/tests/test_read_region.cpp
new file mode 100644
index 000000000..5e9117ac5
--- /dev/null
+++ b/cpp/tests/test_read_region.cpp
@@ -0,0 +1,126 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <catch2/catch.hpp>
+#include <iostream>
+
+#include "config.h"
+#include "cucim/io/format/image_format.h"
+#include "cucim/core/framework.h"
+//#include "rmm/mr/host/new_delete_resource.hpp"
+//#include <iostream>
+#include "cucim/memory/memory_manager.h"
+
+#include <cuda_runtime.h>
+#include "cucim/io/device.h"
+
+#include <chrono>
+#include <openslide/openslide.h>
+
+SCENARIO("Verify read_region()", "[test_read_region.cpp]")
+{
+    constexpr int test_sx = 200;
+    constexpr int test_sy = 300;
+    constexpr int test_width = 3;
+    constexpr int test_height = 2;
+
+    for (int iter=0; iter< 100; iter++)
+    {
+        auto start = std::chrono::high_resolution_clock::now();
+        openslide_t* slide = openslide_open(g_config.get_input_path().c_str());
+        REQUIRE(slide != nullptr);
+
+        auto buf = (uint32_t*)cucim_malloc(test_width * test_height * 4);
+        int64_t w, h;
+        openslide_get_level0_dimensions(slide, &w, &h);
+        printf("w = %ld h=%ld\n", w, h);
+        openslide_read_region(slide, buf, test_sx, test_sy, 0, test_width, test_height);
+        auto out_image = reinterpret_cast<uint8_t*>(buf);
+        int hash = 0;
+        for(int i = 0 ;i < test_width * test_height * 4; i+= 4) {
+            hash += out_image[i] + out_image[i+1] + out_image[i+2];
+            printf("%d %d %d ", out_image[i + 2], out_image[i+1], out_image[i]);
+        }
+        printf("\nopenslide count: %d\n", hash);
+        cucim_free(buf);
+        openslide_close(slide);
+        auto end = std::chrono::high_resolution_clock::now();
+        auto elapsed_seconds = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+        printf("time:%f\n", elapsed_seconds.count());
+    }
+    printf("\n\n");
+
+
+    cucim::Framework* framework = cucim::acquire_framework("sample.app");
+
+    //TODO: Parameterize input library/image
+    cucim::io::format::IImageFormat* image_format =
+        framework->acquire_interface_from_library<cucim::io::format::IImageFormat>(g_config.get_plugin_path().c_str());
+    if (!image_format)
+    {
+        throw std::runtime_error("Cannot load plugin!");
+    }
+    std::cout << image_format->formats[0].get_format_name() << std::endl;
+
+    for (int iter=0; iter< 100; iter++)
+    {
+
+        auto start = std::chrono::high_resolution_clock::now();
+        auto handle = image_format->formats[0].image_parser.open(g_config.get_input_path().c_str());
+
+        cucim::io::format::ImageMetadata metadata{};
+        image_format->formats[0].image_parser.parse(&handle, &metadata.desc());
+
+        cucim::io::format::ImageReaderRegionRequestDesc request{};
+        int64_t request_location[2] = { test_sx, test_sy };
+        request.location = request_location;
+        request.level = 0;
+        int64_t request_size[2] = { test_width, test_height };
+        request.size = request_size;
+        request.device = const_cast<char*>("cpu");
+
+        cucim::io::format::ImageDataDesc image_data{};
+
+        image_format->formats[0].image_reader.read(
+            &handle, &metadata.desc(), &request, &image_data, nullptr /*out_metadata*/);
+        auto out_image = reinterpret_cast<uint8_t*>(image_data.container.data);
+
+        int hash = 0;
+        for (int i = 0; i < test_width * test_height * 3; i += 3)
+        {
+            hash += out_image[i] + out_image[i + 1] + out_image[i + 2];
+            printf("%d %d %d ", out_image[i], out_image[i + 1], out_image[i + 2]);
+        }
+        printf("\ncucim count: %d\n", hash);
+        //        for (int i = 0; i < test_width * test_height * 4; i += 4)
+        //        {
+        //            hash += out_image[i] + out_image[i + 1] + out_image[i + 2];
+        //            printf("%d %d %d ", out_image[i], out_image[i + 1], out_image[i + 2]);
+        //        }
+        printf("\ncucim count: %d\n", hash);
+        cucim_free(image_data.container.data);
+        image_format->formats[0].image_parser.close(&handle);
+
+        auto end = std::chrono::high_resolution_clock::now();
+        auto elapsed_seconds = std::chrono::duration_cast<std::chrono::duration<double>>(end - start);
+
+        printf("time2:%f\n", elapsed_seconds.count());
+    }
+
+
+
+    REQUIRE(3 == 3);
+}
diff --git a/cucim.code-workspace b/cucim.code-workspace
new file mode 100644
index 000000000..016c2bda4
--- /dev/null
+++ b/cucim.code-workspace
@@ -0,0 +1,293 @@
+{
+	"folders": [
+		{
+			"path": "."
+		},
+		{
+			"path": "cpp/plugins/cucim.kit.cuslide"
+		},
+		{
+			"path": "python"
+		}
+	],
+	"remoteAuthority": "wsl+Ubuntu-20.04",
+	"extensions": {
+		"recommendations": [
+			"ms-vscode.cpptools-extension-pack",
+			"matepek.vscode-catch2-test-adapter",
+			"ms-python.python",
+			"ms-python.vscode-pylance",
+			"shardulm94.trailing-spaces"
+		]
+	},
+	"settings": {
+		"editor.formatOnSave": true,
+		"editor.formatOnSaveMode": "modifications",
+		"cmake.sourceDirectory": "${fileWorkspaceFolder}",
+		"testMate.cpp.test.advancedExecutables": [
+			{
+				"pattern": "build-debug/**/*{test,Test,TEST,_tests,_benchmarks}*",
+				"env": {
+					"CUCIM_TESTDATA_FOLDER": "${workspaceDirectory}/test_data",
+					// Add cuslide plugin's library path to LD_LIBRARY_PATH
+					"LD_LIBRARY_PATH": "${workspaceDirectory}/build-debug/lib:${workspaceDirectory}/cpp/plugins/cucim.kit.cuslide/build-debug/lib:${os_env:LD_LIBRARY_PATH}",
+					"CUCIM_TEST_PLUGIN_PATH": "cucim.kit.cuslide@0.19.0.so"
+				},
+				"cwd": "${workspaceDirectory}",
+				"catch2": {
+					"ignoreTestEnumerationStdErr": true
+				},
+				"gbenchmark": {
+					"ignoreTestEnumerationStdErr": true
+				}
+			}
+		],
+		"files.associations": {
+			"cstdlib": "cpp",
+			"iostream": "cpp",
+			"chrono": "cpp",
+			"memory_resource": "cpp",
+			"string": "cpp",
+			"type_traits": "cpp",
+			"any": "cpp",
+			"future": "cpp",
+			"memory": "cpp",
+			"new": "cpp",
+			"format": "cpp",
+			"array": "cpp",
+			"atomic": "cpp",
+			"bit": "cpp",
+			"*.tcc": "cpp",
+			"bitset": "cpp",
+			"cctype": "cpp",
+			"cfenv": "cpp",
+			"cinttypes": "cpp",
+			"clocale": "cpp",
+			"cmath": "cpp",
+			"codecvt": "cpp",
+			"complex": "cpp",
+			"condition_variable": "cpp",
+			"csignal": "cpp",
+			"cstdarg": "cpp",
+			"cstddef": "cpp",
+			"cstdint": "cpp",
+			"cstdio": "cpp",
+			"cstring": "cpp",
+			"ctime": "cpp",
+			"cwchar": "cpp",
+			"cwctype": "cpp",
+			"deque": "cpp",
+			"forward_list": "cpp",
+			"list": "cpp",
+			"map": "cpp",
+			"set": "cpp",
+			"unordered_map": "cpp",
+			"unordered_set": "cpp",
+			"vector": "cpp",
+			"exception": "cpp",
+			"algorithm": "cpp",
+			"functional": "cpp",
+			"iterator": "cpp",
+			"numeric": "cpp",
+			"optional": "cpp",
+			"random": "cpp",
+			"ratio": "cpp",
+			"regex": "cpp",
+			"string_view": "cpp",
+			"system_error": "cpp",
+			"tuple": "cpp",
+			"utility": "cpp",
+			"fstream": "cpp",
+			"initializer_list": "cpp",
+			"iomanip": "cpp",
+			"iosfwd": "cpp",
+			"istream": "cpp",
+			"limits": "cpp",
+			"mutex": "cpp",
+			"ostream": "cpp",
+			"scoped_allocator": "cpp",
+			"shared_mutex": "cpp",
+			"sstream": "cpp",
+			"stdexcept": "cpp",
+			"streambuf": "cpp",
+			"thread": "cpp",
+			"typeindex": "cpp",
+			"typeinfo": "cpp",
+			"valarray": "cpp",
+			"variant": "cpp",
+			"__nullptr": "cpp",
+			"locale": "cpp",
+			"*.cu": "cpp",
+			"*.inc": "cpp",
+			"__config": "cpp",
+			"__functional_03": "cpp",
+			"__hash_table": "cpp",
+			"__split_buffer": "cpp",
+			"__tree": "cpp",
+			"queue": "cpp",
+			"stack": "cpp"
+		}
+	},
+	"tasks": {
+		"version": "2.0.0",
+		"tasks": [
+			{
+				"label": "Build All (debug)",
+				"type": "shell",
+				"command": "./run build_local all debug",
+				"options": {
+					"cwd": "${workspaceFolder}"
+				},
+				"presentation": {
+					"reveal": "always",
+					"focus": true
+				},
+				"problemMatcher": [],
+				"group": {
+					"kind": "build",
+					"isDefault": true
+				}
+			},
+			{
+				"label": "Build All (release)",
+				"type": "shell",
+				"command": "./run build_local all release",
+				"options": {
+					"cwd": "${workspaceFolder}"
+				},
+				"presentation": {
+					"reveal": "always",
+					"focus": true
+				},
+				"problemMatcher": [],
+				"group": "build",
+			}
+		]
+	},
+	"launch": {
+		"version": "0.2.0",
+		// https://code.visualstudio.com/docs/editor/variables-reference
+		"configurations": [
+			{
+				"name": "(gdb) cucim_tests",
+				"type": "cppdbg",
+				"request": "launch",
+				"program": "${workspaceFolder:cucim}/build-debug/bin/cucim_tests",
+				// https://github.com/catchorg/Catch2/blob/devel/docs/command-line.md#specifying-which-tests-to-run
+				"args": [
+					"-d",
+					"yes",
+					"Load test"
+				],
+				"stopAtEntry": false,
+				"cwd": "${workspaceFolder:cucim}",
+				"environment": [
+					{
+						"name": "LD_LIBRARY_PATH",
+						"value": "${workspaceFolder:cucim}/build-debug/lib:${workspaceFolder:cucim.kit.cuslide}/build-debug/lib:${env:LD_LIBRARY_PATH}"
+					},
+					{
+						"name": "CUCIM_TEST_PLUGIN_PATH",
+						"value": "cucim.kit.cuslide@0.19.0.so"
+					}
+				],
+				"console": "externalTerminal",
+				"MIMode": "gdb",
+				"setupCommands": [
+					{
+						"description": "Enable pretty-printing for gdb",
+						"text": "-enable-pretty-printing",
+						"ignoreFailures": true
+					}
+				]
+			},
+			{
+				"name": "(gdb) cuslide_tests",
+				"type": "cppdbg",
+				"request": "launch",
+				"program": "${workspaceFolder:cucim}/cpp/plugins/cucim.kit.cuslide/build-debug/bin/cuslide_tests",
+				"args": [],
+				"stopAtEntry": false,
+				"cwd": "${workspaceFolder:cucim}",
+				"environment": [
+					{
+						"name": "LD_LIBRARY_PATH",
+						"value": "${workspaceFolder:cucim}/build-debug/lib:${workspaceFolder:cucim.kit.cuslide}/build-debug/lib:${env:LD_LIBRARY_PATH}"
+					},
+					{
+						"name": "CUCIM_TEST_PLUGIN_PATH",
+						"value": "cucim.kit.cuslide@0.19.0.so"
+					}
+				],
+				"console": "externalTerminal",
+				"MIMode": "gdb",
+				"setupCommands": [
+					{
+						"description": "Enable pretty-printing for gdb",
+						"text": "-enable-pretty-printing",
+						"ignoreFailures": true
+					}
+				]
+			},
+			{
+				"name": "(gdb) cucim_py",
+				"type": "cppdbg",
+				"request": "launch",
+				"program": "/usr/bin/python3",
+				// https://github.com/catchorg/Catch2/blob/devel/docs/command-line.md#specifying-which-tests-to-run
+				"args": [
+					"${workspaceFolder:python}/cucim/src/localtest.py",
+				],
+				"stopAtEntry": false,
+				"cwd": "${workspaceFolder:cucim}",
+				"environment": [
+					{
+						"name": "LD_LIBRARY_PATH",
+						"value": "${workspaceFolder:cucim}/build-debug/lib:${workspaceFolder:cucim.kit.cuslide}/build-debug/lib:${env:LD_LIBRARY_PATH}"
+					},
+					{
+						"name": "CUCIM_TEST_PLUGIN_PATH",
+						"value": "cucim.kit.cuslide@0.19.0.so"
+					}
+				],
+				"console": "externalTerminal",
+				"MIMode": "gdb",
+				"setupCommands": [
+					{
+						"description": "Enable pretty-printing for gdb",
+						"text": "-enable-pretty-printing",
+						"ignoreFailures": true
+					}
+				]
+			},
+			{
+				"name": "(gdb) tiff_image (C++)",
+				"type": "cppdbg",
+				"request": "launch",
+				"program": "${workspaceFolder:cucim}/build-debug/bin/tiff_image",
+				// https://github.com/catchorg/Catch2/blob/devel/docs/command-line.md#specifying-which-tests-to-run
+				"args": [
+					"${workspaceFolder:cucim}/notebooks/input/image.tif",
+					"${workspaceFolder:cucim}/notebooks",
+				],
+				"stopAtEntry": false,
+				"cwd": "${workspaceFolder:cucim}",
+				"environment": [
+					{
+						"name": "LD_LIBRARY_PATH",
+						"value": "${workspaceFolder:cucim}/build-debug/lib:${workspaceFolder:cucim.kit.cuslide}/build-debug/lib:${env:LD_LIBRARY_PATH}"
+					}
+				],
+				"console": "externalTerminal",
+				"MIMode": "gdb",
+				"setupCommands": [
+					{
+						"description": "Enable pretty-printing for gdb",
+						"text": "-enable-pretty-printing",
+						"ignoreFailures": true
+					}
+				]
+			},
+		]
+	}
+}
\ No newline at end of file
diff --git a/dockcross-manylinux2014-x64 b/dockcross-manylinux2014-x64
new file mode 100755
index 000000000..18e7f50a9
--- /dev/null
+++ b/dockcross-manylinux2014-x64
@@ -0,0 +1,270 @@
+#!/usr/bin/env bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+DEFAULT_DOCKCROSS_IMAGE=gigony/manylinux2014-x64:cuda110
+
+#------------------------------------------------------------------------------
+# Helpers
+#
+err() {
+    echo -e >&2 ERROR: $@\\n
+}
+
+die() {
+    err $@
+    exit 1
+}
+
+has() {
+    # eg. has command update
+    local kind=$1
+    local name=$2
+
+    type -t $kind:$name | grep -q function
+}
+
+#------------------------------------------------------------------------------
+# Command handlers
+#
+command:update-image() {
+    docker pull $FINAL_IMAGE
+}
+
+help:update-image() {
+    echo Pull the latest $FINAL_IMAGE .
+}
+
+command:update-script() {
+    if cmp -s <( docker run --rm $FINAL_IMAGE ) $0; then
+        echo $0 is up to date
+    else
+        echo -n Updating $0 '... '
+        docker run --rm $FINAL_IMAGE > $0 && echo ok
+    fi
+}
+
+help:update-image() {
+    echo Update $0 from $FINAL_IMAGE .
+}
+
+command:update() {
+    command:update-image
+    command:update-script
+}
+
+help:update() {
+    echo Pull the latest $FINAL_IMAGE, and then update $0 from that.
+}
+
+command:help() {
+    if [[ $# != 0 ]]; then
+        if ! has command $1; then
+            err \"$1\" is not an dockcross command
+            command:help
+        elif ! has help $1; then
+            err No help found for \"$1\"
+        else
+            help:$1
+        fi
+    else
+        cat >&2 <<ENDHELP
+Usage: dockcross [options] [--] command [args]
+
+By default, run the given *command* in an dockcross Docker container.
+
+The *options* can be one of:
+
+    --args|-a           Extra args to the *docker run* command
+    --image|-i          Docker cross-compiler image to use
+    --config|-c         Bash script to source before running this script
+
+
+Additionally, there are special update commands:
+
+    update-image
+    update-script
+    update
+
+For update command help use: $0 help <command>
+ENDHELP
+        exit 1
+    fi
+}
+
+#------------------------------------------------------------------------------
+# Option processing
+#
+special_update_command=''
+while [[ $# != 0 ]]; do
+    case $1 in
+
+        --)
+            shift
+            break
+            ;;
+
+        --args|-a)
+            ARG_ARGS="$2"
+            shift 2
+            ;;
+
+        --config|-c)
+            ARG_CONFIG="$2"
+            shift 2
+            ;;
+
+        --image|-i)
+            ARG_IMAGE="$2"
+            shift 2
+            ;;
+        update|update-image|update-script)
+            special_update_command=$1
+            break
+            ;;
+        -*)
+            err Unknown option \"$1\"
+            command:help
+            exit
+            ;;
+
+        *)
+            break
+            ;;
+
+    esac
+done
+
+# The precedence for options is:
+# 1. command-line arguments
+# 2. environment variables
+# 3. defaults
+
+# Source the config file if it exists
+DEFAULT_DOCKCROSS_CONFIG=~/.dockcross
+FINAL_CONFIG=${ARG_CONFIG-${DOCKCROSS_CONFIG-$DEFAULT_DOCKCROSS_CONFIG}}
+
+[[ -f "$FINAL_CONFIG" ]] && source "$FINAL_CONFIG"
+
+# Set the docker image
+FINAL_IMAGE=${ARG_IMAGE-${DOCKCROSS_IMAGE-$DEFAULT_DOCKCROSS_IMAGE}}
+
+# Handle special update command
+if [ "$special_update_command" != "" ]; then
+    case $special_update_command in
+
+        update)
+            command:update
+            exit $?
+            ;;
+
+        update-image)
+            command:update-image
+            exit $?
+            ;;
+
+        update-script)
+            command:update-script
+            exit $?
+            ;;
+
+    esac
+fi
+
+# Set the docker run extra args (if any)
+FINAL_ARGS=${ARG_ARGS-${DOCKCROSS_ARGS}}
+
+# Bash on Ubuntu on Windows
+UBUNTU_ON_WINDOWS=$([ -e /proc/version ] && grep -l Microsoft /proc/version || echo "")
+# MSYS, Git Bash, etc.
+MSYS=$([ -e /proc/version ] && grep -l MINGW /proc/version || echo "")
+
+if [ -z "$UBUNTU_ON_WINDOWS" -a -z "$MSYS" ]; then
+    USER_IDS=(-e BUILDER_UID="$( id -u )" -e BUILDER_GID="$( id -g )" -e BUILDER_USER="$( id -un )" -e BUILDER_GROUP="$( id -gn )")
+fi
+
+# Change the PWD when working in Docker on Windows
+if [ -n "$UBUNTU_ON_WINDOWS" ]; then
+    WSL_ROOT="/mnt/"
+    CFG_FILE=/etc/wsl.conf
+	if [ -f "$CFG_FILE" ]; then
+		CFG_CONTENT=$(cat $CFG_FILE | sed -r '/[^=]+=[^=]+/!d' | sed -r 's/\s+=\s/=/g')
+		eval "$CFG_CONTENT"
+		if [ -n "$root" ]; then
+			WSL_ROOT=$root
+		fi
+	fi
+    HOST_PWD=`pwd -P`
+    HOST_PWD=${HOST_PWD/$WSL_ROOT//}
+elif [ -n "$MSYS" ]; then
+    HOST_PWD=$PWD
+    HOST_PWD=${HOST_PWD/\//}
+    HOST_PWD=${HOST_PWD/\//:\/}
+else
+    HOST_PWD=$PWD
+    [ -L $HOST_PWD ] && HOST_PWD=$(readlink $HOST_PWD)
+fi
+
+# Mount Additional Volumes
+if [ -z "$SSH_DIR" ]; then
+    SSH_DIR="$HOME/.ssh"
+fi
+
+HOST_VOLUMES=
+if [ -e "$SSH_DIR" -a -z "$MSYS" ]; then
+    HOST_VOLUMES+="-v $SSH_DIR:/home/$(id -un)/.ssh"
+fi
+
+#------------------------------------------------------------------------------
+# Now, finally, run the command in a container
+#
+TTY_ARGS=
+tty -s && [ -z "$MSYS" ] && TTY_ARGS=-ti
+CONTAINER_NAME=dockcross_$RANDOM
+nvidia-docker run $TTY_ARGS --name $CONTAINER_NAME \
+    -v "$HOST_PWD":/work \
+    $HOST_VOLUMES \
+    "${USER_IDS[@]}" \
+    $FINAL_ARGS \
+    $FINAL_IMAGE "$@"
+run_exit_code=$?
+
+# Attempt to delete container
+rm_output=$(docker rm -f $CONTAINER_NAME 2>&1)
+rm_exit_code=$?
+if [[ $rm_exit_code != 0 ]]; then
+  if [[ "$CIRCLECI" == "true" ]] && [[ $rm_output == *"Driver btrfs failed to remove"* ]]; then
+    : # Ignore error because of https://circleci.com/docs/docker-btrfs-error/
+  else
+    echo "$rm_output"
+    exit $rm_exit_code
+  fi
+fi
+
+exit $run_exit_code
+
+################################################################################
+#
+# This image is not intended to be run manually.
+#
+# To create a dockcross helper script for the
+# gigony/manylinux2014-x64:cuda110 image, run:
+#
+# docker run --rm gigony/manylinux2014-x64:cuda110 > gigony-manylinux2014-x64-cuda111
+# chmod +x gigony-manylinux2014-x64-cuda111
+#
+# You may then wish to move the dockcross script to your PATH.
+#
+################################################################################
diff --git a/docker/Dockerfile-claratrain b/docker/Dockerfile-claratrain
new file mode 100644
index 000000000..4526ebbdd
--- /dev/null
+++ b/docker/Dockerfile-claratrain
@@ -0,0 +1,28 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+FROM nvcr.io/nvidian/dlmed/clara-train-sdk:v3.1-ga-qa-5
+
+RUN apt-get update \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        libopenslide0 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY ./docker/requirements-claratrain.txt ./
+COPY ./*.whl ./
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install --no-cache-dir -r requirements-claratrain.txt \
+    && python -m pip install cu*.whl
diff --git a/docker/Dockerfile-cmake b/docker/Dockerfile-cmake
new file mode 100644
index 000000000..edf32abca
--- /dev/null
+++ b/docker/Dockerfile-cmake
@@ -0,0 +1,76 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+FROM nvidia/cuda:11.0-devel-ubuntu18.04
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Download and install Python3 PIP.
+RUN apt-get update --yes \
+    && apt-get upgrade --yes \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        software-properties-common \
+        ca-certificates \
+        python3-minimal \
+        python3-pip \
+    && add-apt-repository ppa:ubuntu-toolchain-r/test \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 --version
+
+# Set additional environment values that make usage more pleasant.
+ENV TERM=xterm-256color
+
+# Make /usr/bin/python point to the ${VERSION_PYTHON3} version of python
+RUN VERSION_PYTHON3=$(python3 --version | cut -c8-) && VERSION_PYTHON3=${VERSION_PYTHON3%.*} \
+    && rm -f /usr/bin/python \
+    && rm -f /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1` \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1`
+
+# Make /usr/bin/pip point to the ${VERSION_PIP3} version of python
+RUN rm -f /usr/bin/pip \
+    && ln -s /usr/bin/pip3 /usr/bin/pip
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        python3-dev \
+        gcc-9 \
+        g++-9 \
+        libopenslide-dev \
+        wget \
+        git \
+        curl \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+WORKDIR /workspace
+ENV HOME=/workspace
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install cmake
+
+# Setup gcc-9
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 10 \
+    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 20 \
+    && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-7 10 \
+    && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-9 20
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/nvvm/lib64
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/docker/Dockerfile-jupyter b/docker/Dockerfile-jupyter
new file mode 100644
index 000000000..cc6a2403b
--- /dev/null
+++ b/docker/Dockerfile-jupyter
@@ -0,0 +1,91 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+FROM nvidia/cuda:11.0-devel-ubuntu18.04
+
+ARG NODE_VERSION=v14.13.1
+ARG NODE_DISTRO=linux-x64
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Download and install Python3 PIP.
+RUN apt-get update --yes \
+    && apt-get upgrade --yes \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        ca-certificates \
+        python3-minimal \
+        python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 --version
+
+# Set additional environment values that make usage more pleasant.
+ENV TERM=xterm-256color
+
+# Make /usr/bin/python point to the ${VERSION_PYTHON3} version of python
+RUN VERSION_PYTHON3=$(python3 --version | cut -c8-) && VERSION_PYTHON3=${VERSION_PYTHON3%.*} \
+    && rm -f /usr/bin/python \
+    && rm -f /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1` \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1`
+
+# Make /usr/bin/pip point to the ${VERSION_PIP3} version of python
+RUN rm -f /usr/bin/pip \
+    && ln -s /usr/bin/pip3 /usr/bin/pip
+
+# libgl1 is needed for opencv at `cucim convert` CLI command.
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        python3-dev \
+        gcc \
+        g++ \
+        libopenslide-dev \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        libglib2.0-0 \
+        libgl1 \
+        wget \
+        git \
+        xz-utils \
+        curl \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+WORKDIR /workspace
+ENV HOME=/workspace
+
+# Install nodejs
+RUN mkdir -p /usr/local/lib/nodejs \
+    && wget https://nodejs.org/dist/$NODE_VERSION/node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz \
+    && tar -xJvf node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz -C /usr/local/lib/nodejs \
+    && rm node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz
+ENV PATH=/usr/local/lib/nodejs/node-$NODE_VERSION-$NODE_DISTRO/bin:$PATH
+
+COPY ./docker/requirements-jupyter.txt ./
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install --no-cache-dir -r requirements-jupyter.txt
+
+# Install Jupyter Extensions
+RUN jupyter labextension install dask-labextension \
+    && jupyter serverextension enable dask_labextension
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/nvvm/lib64
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/docker/Dockerfile-jupyter-dev b/docker/Dockerfile-jupyter-dev
new file mode 100644
index 000000000..5c7e7c2b1
--- /dev/null
+++ b/docker/Dockerfile-jupyter-dev
@@ -0,0 +1,112 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+FROM nvidia/cuda:11.0-devel-ubuntu18.04
+
+ARG NODE_VERSION=v14.13.1
+ARG NODE_DISTRO=linux-x64
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+# Download and install Python3 PIP.
+RUN apt-get update \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        python3-minimal \
+        python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 --version
+
+# Set additional environment values that make usage more pleasant.
+ENV TERM=xterm-256color
+
+# Make /usr/bin/python point to the ${VERSION_PYTHON3} version of python
+RUN VERSION_PYTHON3=$(python3 --version | cut -c8-) && VERSION_PYTHON3=${VERSION_PYTHON3%.*} \
+    && rm -f /usr/bin/python \
+    && rm -f /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1` \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1`
+
+# Make /usr/bin/pip point to the ${VERSION_PIP3} version of python
+RUN rm -f /usr/bin/pip \
+    && ln -s /usr/bin/pip3 /usr/bin/pip
+
+# libgl1 is needed for opencv at `cucim convert` CLI command.
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        python3-dev \
+        gcc \
+        g++ \
+        libopenslide-dev \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        libglib2.0-0 \
+        libgl1 \
+        wget \
+        git \
+        xz-utils \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+WORKDIR /workspace
+ENV HOME=/workspace
+
+# Install nodejs
+RUN mkdir -p /usr/local/lib/nodejs \
+    && wget https://nodejs.org/dist/$NODE_VERSION/node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz \
+    && tar -xJvf node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz -C /usr/local/lib/nodejs \
+    && rm node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz
+ENV PATH=/usr/local/lib/nodejs/node-$NODE_VERSION-$NODE_DISTRO/bin:$PATH
+
+COPY ./docker/requirements-jupyter-dev.txt ./
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install --no-cache-dir -r requirements-jupyter-dev.txt
+
+# Install Jupyter Extensions
+RUN jupyter labextension install dask-labextension \
+    && jupyter serverextension enable dask_labextension
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        curl \
+#         build-essential \
+#         cmake \
+#         git \
+#         zlib1g-dev \
+#         libssl-dev \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/*
+
+# Download TRITON Client
+ARG TRITON_CLIENTS_URL=https://github.com/triton-inference-server/server/releases/download/v2.5.0/v2.5.0_ubuntu1804.clients.tar.gz
+RUN mkdir -p /opt/nvidia/triton-clients \
+    && curl -L ${TRITON_CLIENTS_URL} | tar xvz -C /opt/nvidia/triton-clients
+
+RUN pip install --no-cache-dir \
+        /opt/nvidia/triton-clients/python/*manylinux1_x86_64.whl
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/nvvm/lib64
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/docker/Dockerfile-jupyter-gds b/docker/Dockerfile-jupyter-gds
new file mode 100644
index 000000000..1238c7265
--- /dev/null
+++ b/docker/Dockerfile-jupyter-gds
@@ -0,0 +1,142 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+ARG UBUNTU_VER=18.04
+FROM nvidia/cuda:11.0-devel-ubuntu${UBUNTU_VER}
+
+ARG UBUNTU_VER=18.04
+ARG NODE_VERSION=v14.13.1
+ARG NODE_DISTRO=linux-x64
+ENV UBUNTU_VER=${UBUNTU_VER}
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Download and install Python3 PIP.
+RUN apt-get update --yes \
+    && apt-get upgrade --yes \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        ca-certificates \
+        python3-minimal \
+        python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 --version
+
+# Set additional environment values that make usage more pleasant.
+ENV TERM=xterm-256color
+
+# Make /usr/bin/python point to the ${VERSION_PYTHON3} version of python
+RUN VERSION_PYTHON3=$(python3 --version | cut -c8-) && VERSION_PYTHON3=${VERSION_PYTHON3%.*} \
+    && rm -f /usr/bin/python \
+    && rm -f /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1` \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1`
+
+# Make /usr/bin/pip point to the ${VERSION_PIP3} version of python
+RUN rm -f /usr/bin/pip \
+    && ln -s /usr/bin/pip3 /usr/bin/pip
+
+# libgl1 is needed for opencv at `cucim convert` CLI command.
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        python3-dev \
+        gcc \
+        g++ \
+        libopenslide-dev \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        libglib2.0-0 \
+        libgl1 \
+        wget \
+        git \
+        xz-utils \
+        curl \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+WORKDIR /workspace
+ENV HOME=/workspace
+
+# Install nodejs
+RUN mkdir -p /usr/local/lib/nodejs \
+    && wget https://nodejs.org/dist/$NODE_VERSION/node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz \
+    && tar -xJvf node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz -C /usr/local/lib/nodejs \
+    && rm node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz
+ENV PATH=/usr/local/lib/nodejs/node-$NODE_VERSION-$NODE_DISTRO/bin:$PATH
+
+COPY ./docker/requirements-jupyter.txt ./
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install --no-cache-dir -r requirements-jupyter.txt
+
+# Install Jupyter Extensions
+RUN jupyter labextension install dask-labextension \
+    && jupyter serverextension enable dask_labextension
+
+# Supporting GDS
+
+ARG GDS_VER=0.9.0
+ARG MLNX_OFED_VER=5.1-2.5.8.0
+
+COPY ./temp/gds/tools/README /usr/local/cuda/gds/
+COPY ./temp/gds/samples/ /usr/local/cuda/gds/samples/
+COPY ./temp/gds/tools/ /usr/local/cuda/gds/tools/
+COPY ./temp/gds/lib64/cufile.h /usr/local/cuda/lib64/cufile.h
+COPY ./temp/gds/lib64/libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so.${GDS_VER}
+COPY ./temp/gds/lib64/libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so.${GDS_VER}
+
+# Somehow libcufile.so.0 and libcufile_rdma.so.0 are auto-generated during the copy
+    #&& ln -s libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so.0 \
+    #&& ln -s libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so.0
+RUN ln -sfn /usr/local/cuda/gds /usr/local/gds \
+    && ln -s libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so \
+    && ln -s libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so
+
+# dpkg: dependency problems prevent configuration of mlnx-iproute2:
+#  mlnx-iproute2 depends on libcap2 (>= 1:2.10); however:
+#   Package libcap2 is not installed.
+#
+#   liburcu-bp.so.6 => not found
+#   liburcu-cds.so.6 => not found
+#   libjsoncpp.so.1 => not found
+RUN apt-get update \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        libcap2 \
+        liburcu-dev \
+        libjsoncpp-dev \
+    && wget http://content.mellanox.com/ofed/MLNX_OFED-${MLNX_OFED_VER}/MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64.tgz \
+    && tar -xzvf MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64.tgz \
+    && MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64/mlnxofedinstall --user-space-only --without-fw-update --all -q --force \
+    && rm -rf MLNX_OFED_LINUX* \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+# Installation of MLNX_OFED would install python2, overwriting /usr/bin/python
+RUN ln -sf python3 /usr/bin/python \
+    && ln -sf pip3 /usr/bin/pip
+
+COPY ./docker/cufile.json /etc/cufile.json
+RUN sed -i 's/"allow_compat_mode": false,/"allow_compat_mode": true,/' /etc/cufile.json \
+    && echo "/usr/local/gds/lib/" > /etc/ld.so.conf.d/cufile.conf \
+    && ldconfig
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/nvvm/lib64
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/docker/Dockerfile-jupyter-gds-dev b/docker/Dockerfile-jupyter-gds-dev
new file mode 100644
index 000000000..cc7e2ef2c
--- /dev/null
+++ b/docker/Dockerfile-jupyter-gds-dev
@@ -0,0 +1,160 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+ARG UBUNTU_VER=18.04
+FROM nvidia/cuda:11.0-devel-ubuntu${UBUNTU_VER}
+
+ARG UBUNTU_VER=18.04
+ARG NODE_VERSION=v14.13.1
+ARG NODE_DISTRO=linux-x64
+ENV UBUNTU_VER=${UBUNTU_VER}
+
+ENV LC_ALL=C.UTF-8
+ENV LANG=C.UTF-8
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Download and install Python3 PIP.
+RUN apt-get update --yes \
+    && apt-get upgrade --yes \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        ca-certificates \
+        python3-minimal \
+        python3-pip \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN python3 --version
+
+# Set additional environment values that make usage more pleasant.
+ENV TERM=xterm-256color
+
+# Make /usr/bin/python point to the ${VERSION_PYTHON3} version of python
+RUN VERSION_PYTHON3=$(python3 --version | cut -c8-) && VERSION_PYTHON3=${VERSION_PYTHON3%.*} \
+    && rm -f /usr/bin/python \
+    && rm -f /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1` \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python \
+    && ln -s /usr/bin/python${VERSION_PYTHON3} /usr/bin/python`echo ${VERSION_PYTHON3} | cut -c1-1`
+
+# Make /usr/bin/pip point to the ${VERSION_PIP3} version of python
+RUN rm -f /usr/bin/pip \
+    && ln -s /usr/bin/pip3 /usr/bin/pip
+
+# libgl1 is needed for opencv at `cucim convert` CLI command.
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        python3-dev \
+        gcc \
+        g++ \
+        libopenslide-dev \
+        libsm6 \
+        libxext6 \
+        libxrender-dev \
+        libglib2.0-0 \
+        libgl1 \
+        wget \
+        git \
+        xz-utils \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+WORKDIR /workspace
+ENV HOME=/workspace
+
+# Install nodejs
+RUN mkdir -p /usr/local/lib/nodejs \
+    && wget https://nodejs.org/dist/$NODE_VERSION/node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz \
+    && tar -xJvf node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz -C /usr/local/lib/nodejs \
+    && rm node-${NODE_VERSION}-${NODE_DISTRO}.tar.xz
+ENV PATH=/usr/local/lib/nodejs/node-$NODE_VERSION-$NODE_DISTRO/bin:$PATH
+
+COPY docker/requirements-jupyter-dev.txt ./
+
+# Use `python -m pip` to avoid using an old script wrapper.
+RUN python -m pip install --no-cache-dir --upgrade pip setuptools wheel \
+    && python -m pip install --no-cache-dir -r requirements-jupyter-dev.txt
+
+# Install Jupyter Extensions
+RUN jupyter labextension install dask-labextension \
+    && jupyter serverextension enable dask_labextension
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        curl \
+#         build-essential \
+#         cmake \
+#         git \
+#         zlib1g-dev \
+#         libssl-dev \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/*
+
+# Download TRITON Client
+ARG TRITON_CLIENTS_URL=https://github.com/triton-inference-server/server/releases/download/v2.5.0/v2.5.0_ubuntu1804.clients.tar.gz
+RUN mkdir -p /opt/nvidia/triton-clients \
+    && curl -L ${TRITON_CLIENTS_URL} | tar xvz -C /opt/nvidia/triton-clients
+
+RUN pip install --no-cache-dir \
+        /opt/nvidia/triton-clients/python/*manylinux1_x86_64.whl
+
+# Supporting GDS
+
+ARG GDS_VER=0.9.0
+ARG MLNX_OFED_VER=5.1-2.5.8.0
+
+COPY ./temp/gds/tools/README /usr/local/cuda/gds/
+COPY ./temp/gds/samples/ /usr/local/cuda/gds/samples/
+COPY ./temp/gds/tools/ /usr/local/cuda/gds/tools/
+COPY ./temp/gds/lib64/cufile.h /usr/local/cuda/lib64/cufile.h
+COPY ./temp/gds/lib64/libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so.${GDS_VER}
+COPY ./temp/gds/lib64/libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so.${GDS_VER}
+
+# Somehow libcufile.so.0 and libcufile_rdma.so.0 are auto-generated during the copy
+    #&& ln -s libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so.0 \
+    #&& ln -s libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so.0
+RUN ln -sfn /usr/local/cuda/gds /usr/local/gds \
+    && ln -s libcufile.so.${GDS_VER} /usr/local/cuda/lib64/libcufile.so \
+    && ln -s libcufile_rdma.so.${GDS_VER} /usr/local/cuda/lib64/libcufile_rdma.so
+
+# dpkg: dependency problems prevent configuration of mlnx-iproute2:
+#  mlnx-iproute2 depends on libcap2 (>= 1:2.10); however:
+#   Package libcap2 is not installed.
+#
+#   liburcu-bp.so.6 => not found
+#   liburcu-cds.so.6 => not found
+#   libjsoncpp.so.1 => not found
+RUN apt-get update \
+    && apt-get install --yes --fix-missing --no-install-recommends \
+        libcap2 \
+        liburcu-dev \
+        libjsoncpp-dev \
+    && wget http://content.mellanox.com/ofed/MLNX_OFED-${MLNX_OFED_VER}/MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64.tgz \
+    && tar -xzvf MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64.tgz \
+    && MLNX_OFED_LINUX-${MLNX_OFED_VER}-ubuntu${UBUNTU_VER}-x86_64/mlnxofedinstall --user-space-only --without-fw-update --all -q --force \
+    && rm -rf MLNX_OFED_LINUX* \
+    && apt-get autoremove -y \
+    && rm -rf /var/lib/apt/lists/* \
+    && rm -rf /var/cache/apt/archives/partial/*
+
+# Installation of MLNX_OFED would install python2, overwriting /usr/bin/python
+RUN ln -sf python3 /usr/bin/python \
+    && ln -sf pip3 /usr/bin/pip
+
+COPY ./docker/cufile.json /etc/cufile.json
+RUN sed -i 's/"allow_compat_mode": false,/"allow_compat_mode": true,/' /etc/cufile.json \
+    && echo "/usr/local/gds/lib/" > /etc/ld.so.conf.d/cufile.conf \
+    && ldconfig
+
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/nvvm/lib64
+
+ENTRYPOINT ["/bin/bash"]
diff --git a/docker/cufile.json b/docker/cufile.json
new file mode 100644
index 000000000..da9f7cee0
--- /dev/null
+++ b/docker/cufile.json
@@ -0,0 +1,82 @@
+{
+    // NOTE : Application can override custom configuration via export CUFILE_ENV_PATH_JSON=<filepath>
+    // e.g : export CUFILE_ENV_PATH_JSON="/home/<xxx>/cufile.json"
+
+            "logging": {
+                            // log directory, if not enabled will create log file under current working directory
+                            //"dir": "/home/<xxxx>",
+
+                            // ERROR|WARN|INFO|DEBUG|TRACE (in decreasing order of priority)
+                            "level": "ERROR"
+            },
+
+            "profile": {
+                            // nvtx profiling on/off
+                            "nvtx": false,
+                            // cufile stats level(0-3)
+                            "cufile_stats": 0
+            },
+
+            "properties": {
+                            // max IO chunk size (parameter should be 4K aligned) used by cuFileRead/Write internally per IO request
+                            "max_direct_io_size_kb" : 16384,
+                            // device memory size (parameter should be 4K aligned) for reserving bounce buffers for the entire GPU
+                            "max_device_cache_size_kb" : 131072,
+                            // limit on maximum device memory size (parameter should be 4K aligned) that can be pinned for a given process
+                            "max_device_pinned_mem_size_kb" : 33554432,
+                            // true or false (true will enable asynchronous io submission to nvidia-fs driver)
+                            // Note : currently the overall IO will still be synchronous
+                            "use_poll_mode" : false,
+                            // maximum IO request size (parameter should be 4K aligned) within or equal to which library will use polling for IO completion
+                            "poll_mode_max_size_kb": 4,
+                            // allow compat mode, this will enable use of cuFile posix read/writes
+                            "allow_compat_mode": false,
+                            // client-side rdma addr list for user-space file-systems(e.g ["10.0.1.0", "10.0.2.0"])
+                            "rdma_dev_addr_list": [ ],
+                            // load balancing policy for RDMA memory registration(MR), (RoundRobin, RoundRobinMaxMin)
+                            // In RoundRobin, MRs will be distributed uniformly across NICS closest to a GPU
+                            // In RoundRobinMaxMin, MRs will be distributed across NICS closest to a GPU
+                            // with minimal sharing of NICS acros GPUS
+                            "rdma_load_balancing_policy": "RoundRobin"
+            },
+
+            "fs": {
+                    "generic": {
+
+                            // for unaligned writes, setting it to true will, cuFileWrite use posix write internally instead of regular GDS write
+                            "posix_unaligned_writes" : false
+                    },
+
+                    "lustre": {
+
+                            // IO threshold for read/write (param should be 4K aligned)) equal to or below which cuFile will use posix read/write
+                            "posix_gds_min_kb" : 0
+                    },
+
+                    "weka": {
+
+                            // enable/disable RDMA write
+                            "rdma_write_support" : false
+                    }
+            },
+
+            "denylist": {
+                            // specify list of vendor driver modules to deny for nvidia-fs (e.g. ["nvme" , "nvme_rdma"])
+                            "drivers":  [ ],
+
+                            // specify list of block devices to prevent IO using cuFile (e.g. [ "/dev/nvme0n1" ])
+                            "devices": [ ],
+
+                            // specify list of mount points to prevent IO using cuFile (e.g. ["/mnt/test"])
+                            "mounts": [ ],
+
+                            // specify list of file-systems to prevent IO using cuFile (e.g ["lustre", "wekafs"])
+                            "filesystems": [ ]
+            },
+        
+            "miscellaneous": {
+                            // enable only for enforcing strict checks at API level for debugging
+                            "api_check_aggressive": false
+	    }
+}
+
diff --git a/docker/requirements-claratrain.txt b/docker/requirements-claratrain.txt
new file mode 100644
index 000000000..722129399
--- /dev/null
+++ b/docker/requirements-claratrain.txt
@@ -0,0 +1,10 @@
+openslide-python==1.1.2
+tifffile==2020.9.3
+itk==5.1.2
+dask[array,delayed,distributed]==2021.2.0
+dask-cuda==0.17.0
+zarr==2.6.1
+fsspec==0.8.5
+numpy # 1.17.3 already exists in the image
+opencv-contrib-python==4.5.1.48
+imagecodecs==2020.5.30
diff --git a/docker/requirements-jupyter-dev.txt b/docker/requirements-jupyter-dev.txt
new file mode 100644
index 000000000..2463d177d
--- /dev/null
+++ b/docker/requirements-jupyter-dev.txt
@@ -0,0 +1,20 @@
+openslide-python==1.1.2
+tifffile==2020.9.3
+itk==5.1.2
+dask[array,delayed,distributed]==2021.2.0
+dask-cuda==0.17.0
+zarr==2.6.1
+fsspec==0.8.5
+numpy==1.19.5
+opencv-contrib-python==4.5.1.48
+imagecodecs==2020.5.30
+cupy-cuda110==8.4.0
+jupyterlab==3.0.7
+dask_labextension==5.0.0
+cmake>=3.18
+--extra-index-url https://developer.download.nvidia.com/compute/redist
+nvidia-dali-cuda110
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==1.7.1+cu110
+torchvision==0.8.2+cu110
+torchaudio===0.7.2
diff --git a/docker/requirements-jupyter.txt b/docker/requirements-jupyter.txt
new file mode 100644
index 000000000..2463d177d
--- /dev/null
+++ b/docker/requirements-jupyter.txt
@@ -0,0 +1,20 @@
+openslide-python==1.1.2
+tifffile==2020.9.3
+itk==5.1.2
+dask[array,delayed,distributed]==2021.2.0
+dask-cuda==0.17.0
+zarr==2.6.1
+fsspec==0.8.5
+numpy==1.19.5
+opencv-contrib-python==4.5.1.48
+imagecodecs==2020.5.30
+cupy-cuda110==8.4.0
+jupyterlab==3.0.7
+dask_labextension==5.0.0
+cmake>=3.18
+--extra-index-url https://developer.download.nvidia.com/compute/redist
+nvidia-dali-cuda110
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==1.7.1+cu110
+torchvision==0.8.2+cu110
+torchaudio===0.7.2
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 000000000..aeb3540aa
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SPHINXPROJ    = cuImage
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/README.md b/docs/README.md
new file mode 100644
index 000000000..e2654d443
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1,19 @@
+# Building Documentation
+
+A basic python environment with packages listed in `./requirement.txt` is
+enough to build the docs.
+
+## Get additional dependency
+
+```bash
+pip install -r requirement.txt
+```
+
+## Run makefile:
+
+```bash
+make html
+```
+
+Outputs to `build/html/index.html`
+
diff --git a/docs/adr/README.md b/docs/adr/README.md
new file mode 100644
index 000000000..b6c0a1321
--- /dev/null
+++ b/docs/adr/README.md
@@ -0,0 +1,20 @@
+# Architecture Decision Record
+
+Documents in this folder followes the concept from [this blog - Why Write ADRs?](https://github.blog/2020-08-13-why-write-adrs/).
+
+Please refer to to https://github.com/joelparkerhenderson/architecture_decision_record for examples.
+
+
+## ADR file name conventions
+
+- The name starts with three-digit number to specify the ID of the ADR. e.g., `001_choose_database.md`.
+- The name has a present tense imperative verb phrase. This helps readability and matches our commit message format.
+- The name uses lowercase and underscores (same as this repo). This is a balance of readability and system usability.
+- The extension is markdown. This can be useful for easy formatting.
+
+Example
+
+- 001_choose_database.md
+- 002_format_timestamps.md
+- 003_manage_passwords.md
+- 004_handle_exceptions.md
\ No newline at end of file
diff --git a/docs/adr/example.md b/docs/adr/example.md
new file mode 100644
index 000000000..36a3522c8
--- /dev/null
+++ b/docs/adr/example.md
@@ -0,0 +1,217 @@
+# 001 - Choose Programming languages
+
+Note: this example is from https://github.com/joelparkerhenderson/architecture_decision_record/blob/master/examples/programming-languages.md#related-decisions
+
+Contents:
+
+* [Summary](#summary)
+  * [Issue](#issue)
+  * [Decision](#decision)
+  * [Status](#status)
+* [Details](#details)
+  * [Assumptions](#assumptions)
+  * [Constraints](#constraints)
+  * [Positions](#positions)
+  * [Argument](#argument)
+  * [Implications](#implications)
+* [Related](#related)
+  * [Related decisions](#related-decisions)
+  * [Related requirements](#related-requirements)
+  * [Related artifacts](#related-artifacts)
+  * [Related principles](#related-principles)
+* [Notes](#notes)
+
+
+## Summary
+
+
+### Issue
+
+We need to choose programming languages for our software. We have two major needs: a front-end programming language suitable for web applications, and a back-end programming language suitable for server applications.
+
+
+### Decision
+
+We are choosing TypeScript for the front-end.
+
+We are choosing Rust for the back-end.
+
+
+### Status
+
+Decided. We are open to new alternatives as they arise.
+
+
+## Details
+
+
+### Assumptions
+
+The front-end applications are typical:
+
+  * Typical users and interactions
+
+  * Typical browsers and systems
+
+  * Typical developments and deployments
+
+The front-end applications is likely to evolve quickly:
+
+  * We want to ensure fast easy developments, deployments, iterations, etc.
+
+  * We value provability, such as type safety, and we are fine doing a bit more work to achieve it.
+
+  * We do not need legacy compatibility.
+
+The back-end applications are higher-than-typical:
+
+  * Higher-than-typical goals for quality, especially provability, reliability, security, etc.
+
+  * Higher-than-typical goals for near-real-time, i.e. we do not want pauses due to virtual machine garbage collection.
+
+  * Higher-than-typical goals for functional programming, especially for parallelization, multi-core processing, and memory safety.
+
+We accept lower compile-time speeds in favor of compile-time safety and runtime speeds.
+
+
+### Constraints
+
+We have a strong constraint on languages that are usuable with major cloud provider services for functions, such as Amazon Lambda.
+
+
+### Positions
+
+We considered these langauges:
+
+  * C
+
+  * C++
+
+  * Clojure
+
+  * Elixir
+
+  * Erlang
+
+  * Elm
+
+  * Flow
+
+  * Go
+
+  * Haskell
+
+  * Java
+
+  * JavaScript
+
+  * Kotlin
+
+  * Python
+
+  * Ruby
+
+  * Rust
+
+  * TypeScript
+
+
+
+### Argument
+
+Summary per language:
+
+  * C: rejected because of low safety; Rust can do nearly everything better.
+
+  * C++: rejected because it's a mess; Rush can do nearly everything better.
+
+  * Clojure: excellent modeling; best Lisp approximation; great runtime on the JVM.
+
+  * Elixir: excellent runtime including deployability and concurrency; excellent developer experience; relatively small ecosystem.
+
+  * Erlang: excellent runtime including deployability and concurrency; challenging developer experience; relatively small ecosystem.
+
+  * Elm: looks very promising; IBM is publishing major case studies with good resutls; smaller ecosystem.
+
+  * Flow: interesting improvement over JavaScript; however; developers are moving away from it.
+
+  * Go: excellent developer experience; excellent concurrency; but a track record of bad decisions that cripple the language.
+
+  * Haskell: best functional language; smaller developer community; hasn't achieved enough published production successes.
+
+  * Java: excellent runtime; excellent ecosystem; sub-par developer experience.
+
+  * JavaScript: most popular language ever; most widespread ecosystem.
+
+  * Kotlin: fixes so much of Java; excelent backing by JetBrains; good published cases of porting from Java to Kotlin.
+
+  * Python: most popular language for systems administration; great analytics tooling; good web frameworks; but abandonded by Google in favor of Go.
+
+  * Ruby: best developer experience ever; best web frameworks; nicest community; but very slow; somewhat hard to package.
+
+  * Rust: best new language; zero-abstraction emphasis; concurrency emphasis; however relatively small ecosystem; and has deliberate limits on some kinds of compiler accelerations e.g. direct memory access needs to be explicitly unsafe.
+
+  * TypeScript: adds types to JavaScript; great transpiler; growing developer emphasis on porting from JavaScript to TypeScript; strong backing from Microsoft.
+
+We decided that VMs have a set of tradeoffs that we do not need right now, such as additional complexity that provides runtime capabilities.
+
+We believe that our core decision is driven by two cross-cutting concerns:
+
+  * For fastest runtime speed and tightest system access, we would choose JavaScript and C.
+
+  * For close-to-fastest runtime speed and close-to-tightest system access, we choose TypeScript and Rust.
+
+Honorable mentions go to the VM languages and web frameworks that we would choose if we wanted a VM lanauge:
+
+  * Closure and Luminous
+
+  * Java and Spring
+
+  * Elixir and Phoenix
+
+
+### Implications
+
+Front-end developers will need to learn TypeScript. This is likely an easy learning curve if the developer's primary experience is using JavaScript.
+
+Back-end developers will need to learn Rust. This is likely a moderate learning curve if the developer's primary experience is using  C/C++, and a hard learning curve if the developer's primary experience is using Java, Python, Ruby, or similar memory-managed languages.
+
+TypeScript and Rust are both relatively new. This means that many tools do not yet have documentation for these languages. For example, the devops pipeline will need to be set up for these languages, and so far, none of the devops tools that we are evaluating have default examples for these langauges.
+
+Compile times for TypeScript and Rust are quite slow. Some of this may be due to the newness of the languages. We may want to look at how to mitigate slow compile times, such as by compile-on-demand, compile-concurrency, etc.
+
+IDE support for these languages is not yet ubiquitous and not yet first-class. For example, JetBrains sells the PyCharm IDE for first-class support for Python, but does not sell and IDE with first-class support for Rust; instead, JetBrains can use a Rust plug-in that provides perhaps 80% of Rust language support vis a vis Python language support.
+
+
+## Related
+
+
+### Related decisions
+
+We will aim toward ecosystem choices that align with these langauges.
+
+For example, we want to choose an IDE that has good capabilties for these languages.
+
+For example, for our front-end web framework, we are more-likley to decide on a framework that tends to aim toward TypeScript (e.g. Vue) than a framework that tends to aim toward plain JavaScript (e.g. React).
+
+
+### Related requirements
+
+Our entire toolchain must support these languages.
+
+
+### Related artifacts
+
+We expect we may export some secrets to environment variables.
+
+
+### Related principles
+
+Measure twice, build once. We are prioritizing some safety over some speed.
+
+Runtime is more valuable than compile time. We are prioritizing customer usage over developer usage.
+
+
+## Notes
+
+Any notes here.
\ No newline at end of file
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 000000000..efadb1dca
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,36 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+set SPHINXPROJ=cuImage
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%
+
+:end
+popd
diff --git a/docs/requirement.txt b/docs/requirement.txt
new file mode 100644
index 000000000..f636fd879
--- /dev/null
+++ b/docs/requirement.txt
@@ -0,0 +1,4 @@
+sphinx
+sphinx_rtd_theme
+numpydoc
+ipython
\ No newline at end of file
diff --git a/docs/source/_static/EMPTY b/docs/source/_static/EMPTY
new file mode 100644
index 000000000..e69de29bb
diff --git a/docs/source/_static/params.css b/docs/source/_static/params.css
new file mode 100644
index 000000000..dc5cb9640
--- /dev/null
+++ b/docs/source/_static/params.css
@@ -0,0 +1,9 @@
+/* Mirrors the change in:
+ *   https://github.com/sphinx-doc/sphinx/pull/5976
+ * which is not showing up in our theme.
+ */
+.classifier:before {
+    font-style: normal;
+    margin: 0.5em;
+    content: ":";
+}
diff --git a/docs/source/api.rst b/docs/source/api.rst
new file mode 100644
index 000000000..ead2b26c5
--- /dev/null
+++ b/docs/source/api.rst
@@ -0,0 +1,131 @@
+~~~~~~~~~~~~~~~~~~~~~~
+cuCIM API Reference
+~~~~~~~~~~~~~~~~~~~~~~
+
+CuImage Submodules
+==================
+
+CuImage
+-------
+
+.. automodule:: cucim.CuImage
+    :members:
+    :undoc-members:
+
+
+
+skimage Submodules
+==================
+
+color
+-----
+
+.. automodule:: cucim.skimage.color
+    :members:
+    :undoc-members:
+
+
+data
+----
+
+.. automodule:: cucim.skimage.data
+    :members:
+    :undoc-members:
+
+exposure
+--------
+
+.. automodule:: cucim.skimage.exposure
+    :members:
+    :undoc-members:
+
+
+feature
+-------
+
+.. automodule:: cucim.skimage.feature
+    :members:
+    :undoc-members:
+
+
+filters
+-------
+
+.. automodule:: cucim.skimage.filters
+    :members:
+    :undoc-members:
+
+
+measure
+-------
+
+.. automodule:: cucim.skimage.measure
+    :members:
+    :undoc-members:
+
+
+metrics
+-------
+
+.. automodule:: cucim.skimage.metrics
+    :members:
+    :undoc-members:
+
+
+morphology
+----------
+
+.. automodule:: cucim.skimage.morphology
+    :members:
+    :undoc-members:
+
+
+registration
+------------
+
+.. automodule:: cucim.skimage.registration
+    :members:
+    :undoc-members:
+
+
+restoration
+-----------
+
+.. automodule:: cucim.skimage.restoration
+    :members:
+    :undoc-members:
+
+
+segmentation
+------------
+
+.. automodule:: cucim.skimage.segmentation
+    :members:
+    :undoc-members:
+
+
+transform
+---------
+
+.. automodule:: cucim.skimage.transform
+    :members:
+    :undoc-members:
+
+
+util
+----
+
+.. automodule:: cucim.skimage.util
+    :members:
+    :undoc-members:
+
+
+Submodule Contents
+==================
+
+skimage
+-------
+
+.. automodule:: cucim.skimage
+    :members:
+    :undoc-members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 000000000..f5093a9e2
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2018, NVIDIA CORPORATION.
+#
+# pygdf documentation build configuration file, created by
+# sphinx-quickstart on Wed May  3 10:59:22 2017.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+
+sys.path.insert(0, os.path.abspath('../..'))
+curpath = os.path.dirname(__file__)
+sys.path.append(os.path.join(curpath, 'ext'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'numpydoc',
+    'doi_role',
+    'IPython.sphinxext.ipython_console_highlighting',
+    'IPython.sphinxext.ipython_directive',
+    'nbsphinx',
+    'recommonmark',
+]
+
+ipython_mplbackend = 'str'
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = 'cuCIM'
+copyright = '2020-2021, NVIDIA'
+author = 'NVIDIA'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+version = '0.19'
+# The full version, including alpha/beta/rc tags.
+release = '0.19.0'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = None
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = []
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+
+html_theme = 'sphinx_rtd_theme'
+
+# on_rtd is whether we are on readthedocs.org
+on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
+
+if not on_rtd:
+    # only import and set the theme if we're building docs locally
+    # otherwise, readthedocs.org uses their theme by default,
+    # so no need to specify it
+    import sphinx_rtd_theme
+    html_theme = 'sphinx_rtd_theme'
+    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#
+# html_theme_options = {}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+
+# -- Options for HTMLHelp output ------------------------------------------
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'cucimdoc'
+
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+    # The paper size ('letterpaper' or 'a4paper').
+    #
+    # 'papersize': 'letterpaper',
+
+    # The font size ('10pt', '11pt' or '12pt').
+    #
+    # 'pointsize': '10pt',
+
+    # Additional stuff for the LaTeX preamble.
+    #
+    # 'preamble': '',
+
+    # Latex figure (float) alignment
+    #
+    # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+#  author, documentclass [howto, manual, or own class]).
+latex_documents = [
+    (master_doc, 'cucim.tex', 'cucim Documentation',
+     'Continuum Analytics', 'manual'),
+]
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    (master_doc, 'cucim', 'cucim Documentation',
+     [author], 1)
+]
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+#  dir menu entry, description, category)
+texinfo_documents = [
+    (master_doc, 'cucim', 'cucim Documentation',
+     author, 'cucim', 'One line description of project.',
+     'Miscellaneous'),
+]
+
+
+# Example configuration for intersphinx: refer to the Python standard library.
+intersphinx_mapping = {'https://docs.python.org/': None}
+
+
+# Config numpydoc
+numpydoc_show_inherited_class_members = False
+numpydoc_class_members_toctree = False
+
+
+def setup(app):
+    app.add_css_file('params.css')
+    app.add_css_file("https://docs.rapids.ai/assets/css/custom.css")
diff --git a/docs/source/ext/doi_role.py b/docs/source/ext/doi_role.py
new file mode 100644
index 000000000..86aebbda4
--- /dev/null
+++ b/docs/source/ext/doi_role.py
@@ -0,0 +1,53 @@
+# From scikit-image:
+# https://github.com/scikit-image/scikit-image/blob/16e0b87b8cb1abc4c78ebf6cd013dadc90810f39/doc/ext/doi_role.py
+# -*- coding: utf-8 -*-
+"""
+    doilinks
+    ~~~~~~~~~~~~~~~~~~~
+    Extension to add links to DOIs. With this extension you can use e.g.
+    :doi:`10.1016/S0022-2836(05)80360-2` in your documents. This will
+    create a link to a DOI resolver
+    (``https://doi.org/10.1016/S0022-2836(05)80360-2``).
+    The link caption will be the raw DOI.
+    You can also give an explicit caption, e.g.
+    :doi:`Basic local alignment search tool <10.1016/S0022-2836(05)80360-2>`.
+
+    :copyright: Copyright 2015  Jon Lund Steffensen. Based on extlinks by
+        the Sphinx team.
+    :license: BSD.
+"""
+
+from docutils import nodes, utils
+from sphinx.util.nodes import split_explicit_title
+
+
+def doi_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
+    text = utils.unescape(text)
+    has_explicit_title, title, part = split_explicit_title(text)
+    full_url = 'https://doi.org/' + part
+    if not has_explicit_title:
+        title = 'DOI:' + part
+    pnode = nodes.reference(title, title, internal=False, refuri=full_url)
+    return [pnode], []
+
+
+def arxiv_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):
+    text = utils.unescape(text)
+    has_explicit_title, title, part = split_explicit_title(text)
+    full_url = 'https://arxiv.org/abs/' + part
+    if not has_explicit_title:
+        title = 'arXiv:' + part
+    pnode = nodes.reference(title, title, internal=False, refuri=full_url)
+    return [pnode], []
+
+
+def setup_link_role(app):
+    app.add_role('doi', doi_role)
+    app.add_role('DOI', doi_role)
+    app.add_role('arXiv', arxiv_role)
+    app.add_role('arxiv', arxiv_role)
+
+
+def setup(app):
+    app.connect('builder-inited', setup_link_role)
+    return {'version': '0.1', 'parallel_read_safe': True}
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 000000000..cc1b890e0
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,24 @@
+Welcome to cuCIM's documentation!
+====================================
+
+cuCIM is a an extensible toolkit designed to provide GPU-accelearted I/O,
+computer vision and image processing primitives for N-Dimensional images
+with a focus on biomedical imaging.  Our API mirrors `scikit-image
+<https://scikit-image.org/>`_ for image manipulation and `OpenSlide
+<https://openslide.org/>`_ for image loading.
+
+cuCIM is fully open sourced under the Apache-2.0 license, and the Clara and RAPIDS teams welcomes new and seasoned
+contributors, users and hobbyists! Thank you for your wonderful support!
+
+.. toctree::
+   :maxdepth: 4
+   :caption: Contents:
+
+   api.rst
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt
new file mode 100644
index 000000000..827921447
--- /dev/null
+++ b/examples/cpp/CMakeLists.txt
@@ -0,0 +1,41 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+################################################################################
+# Add executable: tiff_image
+################################################################################
+
+add_executable(tiff_image tiff_image/main.cpp)
+set_source_files_properties(tiff_image/main.cpp PROPERTIES LANGUAGE CUDA)
+
+set_target_properties(tiff_image
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(tiff_image PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(tiff_image PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_link_libraries(tiff_image
+        PRIVATE
+            ${CUCIM_PACKAGE_NAME}
+            deps::fmt
+        )
diff --git a/examples/cpp/CMakeLists.txt.examples.release.in b/examples/cpp/CMakeLists.txt.examples.release.in
new file mode 100644
index 000000000..ddb8570de
--- /dev/null
+++ b/examples/cpp/CMakeLists.txt.examples.release.in
@@ -0,0 +1,79 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# CUDA_STANDARD 17 is supported from CMAKE 3.18
+# : https://cmake.org/cmake/help/v3.18/prop_tgt/CUDA_STANDARD.html
+cmake_minimum_required(VERSION 3.18)
+
+project(cucim-cpp-examples VERSION @VERSION@ DESCRIPTION "cuCIM CPP examples" LANGUAGES CUDA CXX)
+
+# Set default build type
+set(DEFAULT_BUILD_TYPE "Release")
+if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    message(STATUS "Setting build type to '${DEFAULT_BUILD_TYPE}' as none was specified.")
+    set(CMAKE_BUILD_TYPE "${DEFAULT_BUILD_TYPE}" CACHE STRING "Choose the type of build." FORCE)
+    # Set the possible values of build type for cmake-gui
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif ()
+
+# Set default output directories
+if (NOT CMAKE_ARCHIVE_OUTPUT_DIRECTORY)
+    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif()
+if (NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif()
+if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/bin")
+endif()
+
+################################################################################
+# Find cucim package
+################################################################################
+
+if (NOT CUCIM_SDK_PATH)
+    get_filename_component(CUCIM_SDK_PATH "${CMAKE_SOURCE_DIR}/../.." ABSOLUTE)
+    message("CUCIM_SDK_PATH is not set. Using '${CUCIM_SDK_PATH}'")
+else()
+    message("CUCIM_SDK_PATH is set to ${CUCIM_SDK_PATH}")
+endif()
+
+find_package(cucim CONFIG REQUIRED
+    HINTS ${CUCIM_SDK_PATH}/install/lib/cmake/cucim)
+
+################################################################################
+# Add executable: tiff_image
+################################################################################
+
+add_executable(tiff_image tiff_image/main.cpp)
+
+set_target_properties(tiff_image
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+)
+target_compile_features(tiff_image PRIVATE cxx_std_17 cuda_std_17)
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(tiff_image PRIVATE $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>)
+target_link_libraries(tiff_image
+        PRIVATE
+            cucim::cucim
+        )
diff --git a/examples/cpp/tiff_image/main.cpp b/examples/cpp/tiff_image/main.cpp
new file mode 100644
index 000000000..bd9586da5
--- /dev/null
+++ b/examples/cpp/tiff_image/main.cpp
@@ -0,0 +1,89 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <cucim/cuimage.h>
+#include <fmt/format.h>
+#include <fmt/ranges.h>
+
+int main(int argc, char* argv[])
+{
+    // Check the number of parameters
+    if (argc < 3) {
+        fmt::print(stderr, "Usage: {} INPUT_FILE_PATH OUTPUT_FOLDER\n", argv[0]);
+        return 1;
+    }
+    const char* input_file_path = argv[1];
+    const char* output_folder_path = argv[2];
+
+    cucim::CuImage image = cucim::CuImage(input_file_path);
+
+    fmt::print("is_loaded: {}\n", image.is_loaded());
+    fmt::print("device: {}\n", std::string(image.device()));
+    fmt::print("metadata: {}\n", image.metadata());
+    fmt::print("dims: {}\n", image.dims());
+    fmt::print("shape: ({})\n", fmt::join(image.shape(), ", "));
+    fmt::print("size('XYC'): ({})\n", fmt::join(image.size("XYC"), ", "));
+    fmt::print("channel_names: ({})\n", fmt::join(image.channel_names(), ", "));
+
+    auto resolutions = image.resolutions();
+    fmt::print("level_count: {}\n", resolutions.level_count());
+    fmt::print("level_dimensions: ({})\n", fmt::join(resolutions.level_dimensions(), ", "));
+    fmt::print("level_dimension (level 0): ({})\n", fmt::join(resolutions.level_dimension(0), ", "));
+    fmt::print("level_downsamples: ({})\n", fmt::join(resolutions.level_downsamples(), ", "));
+
+    auto associated_images = image.associated_images();
+    fmt::print("associated_images: ({})\n", fmt::join(associated_images, ", "));
+
+    fmt::print("#macro\n");
+    auto associated_image = image.associated_image("macro");
+    fmt::print("is_loaded: {}\n", associated_image.is_loaded());
+    fmt::print("device: {}\n", std::string(associated_image.device()));
+    fmt::print("metadata: {}\n", associated_image.metadata());
+    fmt::print("dims: {}\n", associated_image.dims());
+    fmt::print("shape: ({})\n", fmt::join(associated_image.shape(), ", "));
+    fmt::print("size('XYC'): ({})\n", fmt::join(associated_image.size("XYC"), ", "));
+    fmt::print("channel_names: ({})\n", fmt::join(associated_image.channel_names(), ", "));
+    fmt::print("\n");
+
+    cucim::CuImage region =
+        image.read_region({ 10000, 10000 }, { 1024, 1024 }, 0, cucim::DimIndices{}, "cpu", nullptr, "");
+
+    fmt::print("is_loaded: {}\n", region.is_loaded());
+    fmt::print("device: {}\n", std::string(region.device()));
+    fmt::print("metadata: {}\n", region.metadata());
+    fmt::print("dims: {}\n", region.dims());
+    fmt::print("shape: ({})\n", fmt::join(region.shape(), ", "));
+    fmt::print("size('XY'): ({})\n", fmt::join(region.size("XY"), ", "));
+    fmt::print("channel_names: ({})\n", fmt::join(region.channel_names(), ", "));
+
+    resolutions = region.resolutions();
+    fmt::print("level_count: {}\n", resolutions.level_count());
+    fmt::print("level_dimensions: ({})\n", fmt::join(resolutions.level_dimensions(), ", "));
+    fmt::print("level_dimension (level 0): ({})\n", fmt::join(resolutions.level_dimension(0), ", "));
+    fmt::print("level_downsamples: ({})\n", fmt::join(resolutions.level_downsamples(), ", "));
+
+    associated_images = region.associated_images();
+    fmt::print("associated_images: ({})\n", fmt::join(associated_images, ", "));
+    fmt::print("\n");
+
+    region.save(fmt::format("{}/output.ppm", output_folder_path));
+
+    cucim::CuImage region2 =
+        image.read_region({ 5000, 5000 }, { 1024, 1024 }, 1, cucim::DimIndices{}, "cpu", nullptr, "");
+    region2.save(fmt::format("{}/output2.ppm", output_folder_path));
+
+    return 0;
+}
diff --git a/examples/python/tiff_image/main.py b/examples/python/tiff_image/main.py
new file mode 100644
index 000000000..4147bfc4f
--- /dev/null
+++ b/examples/python/tiff_image/main.py
@@ -0,0 +1,57 @@
+#
+# Copyright (c) 2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import json
+
+import numpy as np
+from PIL import Image
+
+from cucim import CuImage
+
+img = CuImage('image.tif')
+
+print(img.is_loaded)        # True if image data is loaded & available.
+print(img.device)           # A device type.
+print(img.ndim)             # The number of dimensions.
+print(img.dims)             # A string containing a list of dimensions being requested.
+print(img.shape)            # A tuple of dimension sizes (in the order of `dims`).
+print(img.size('XYC'))      # Returns size as a tuple for the given dimension order.
+print(img.dtype)            # The data type of the image.
+print(img.channel_names)    # A channel name list.
+print(img.spacing())        # Returns physical size in tuple.
+print(img.spacing_units())  # Units for each spacing element (size is same with `ndim`).
+print(img.origin)           # Physical location of (0, 0, 0) (size is always 3).
+print(img.direction)        # Direction cosines (size is always 3x3).
+print(img.coord_sys)        # Coordinate frame in which the direction cosines are
+                            # measured. Available Coordinate frame is not finalized yet.
+
+# Returns a set of associated image names.
+print(img.associated_images)
+# Returns a dict that includes resolution information.
+print(json.dumps(img.resolutions, indent=2))
+# A metadata object as `dict`
+print(json.dumps(img.metadata, indent=2))
+# A raw metadata string.
+print(img.raw_metadata)
+
+# Read whole slide at the lowest resolution
+resolutions = img.resolutions
+level_count = resolutions["level_count"]
+# Note: 'level' is at 3rd parameter (OpenSlide has it at 2nd parameter)
+region = img.read_region(location=(10000, 10000), size=(512, 512), level=level_count-1)
+
+region.save("test.ppm")
+
+Image.fromarray(np.asarray(region))
diff --git a/gds/CMakeLists.txt b/gds/CMakeLists.txt
new file mode 100644
index 000000000..6c6d907ea
--- /dev/null
+++ b/gds/CMakeLists.txt
@@ -0,0 +1,122 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+cucim_set_build_shared_libs(OFF)
+
+# Disable visibility to not expose unnecessary symbols
+set(CMAKE_CXX_VISIBILITY_PRESET hidden)
+set(CMAKE_VISIBILITY_INLINES_HIDDEN YES)
+
+# Set RPATH
+if (NOT APPLE)
+    set(CMAKE_INSTALL_RPATH $ORIGIN)
+endif ()
+
+################################################################################
+# Add library: cufile_stub
+################################################################################
+add_library(cufile_stub
+        include/cufile_stub.h
+        src/cufile_stub.cpp
+        )
+
+set_source_files_properties(src/cufile_stub.cpp PROPERTIES LANGUAGE CUDA)
+
+# Compile options
+set_target_properties(cufile_stub
+    PROPERTIES
+        CXX_STANDARD 17
+        CXX_STANDARD_REQUIRED YES
+        CXX_EXTENSIONS NO
+        CUDA_STANDARD 17
+        CUDA_STANDARD_REQUIRED YES
+        CUDA_EXTENSIONS NO
+        CUDA_SEPARABLE_COMPILATION ON
+        CUDA_RUNTIME_LIBRARY Shared
+        # To prevent the error message: /usr/bin/ld: ../lib/libcufile_stub.a(cufile_stub.cpp.o): relocation R_X86_64_PC32 against symbol `__fatbinwrap_46_tmpxft_00005869_00000000_6_cufile_stub_cpp1_ii_1e2ddd49' can not be used when making a shared object; recompile with -fPIC
+        POSITION_INDEPENDENT_CODE ON
+#        SOVERSION ${PROJECT_VERSION_MAJOR}
+#        VERSION ${PROJECT_VERSION}
+)
+
+# Note: Looks like the following line causes error on CMake 3.18.4 (it is working on 3.18.2). Keeping it for now.
+set(CUCIM_REQUIRED_FEATURES cxx_std_17 cuda_std_17)
+target_compile_features(cufile_stub PRIVATE ${CUCIM_REQUIRED_FEATURES})
+# Use generator expression to avoid `nvcc fatal   : Value '-std=c++17' is not defined for option 'Werror'`
+target_compile_options(cufile_stub
+    PRIVATE
+        $<$<COMPILE_LANGUAGE:CXX>:-Werror -Wall -Wextra>
+        )
+
+## Link libraries
+    target_link_libraries(cufile_stub
+            PUBLIC
+                ${CMAKE_DL_LIBS}
+            )
+# Enabling CUCIM_STATIC_GDS assumes that lib/libcufile_static.a and include/cufile.h is available
+# under the current folder.
+if (CUCIM_STATIC_GDS)
+    add_library(deps::gds_static STATIC IMPORTED GLOBAL)
+    set(GDS_STATIC_LIB_PATH ${CMAKE_CURRENT_LIST_DIR}/lib/libcufile_static.a)
+    set_target_properties(deps::gds_static PROPERTIES
+        IMPORTED_LOCATION "${GDS_STATIC_LIB_PATH}"
+        INTERFACE_INCLUDE_DIRECTORIES "${CMAKE_CURRENT_LIST_DIR}/include"
+    )
+    target_link_libraries(cufile_stub
+            PUBLIC
+                ${CMAKE_DL_LIBS}
+            INTERFACE
+                $<BUILD_INTERFACE:deps::gds_static>
+            )
+
+    target_compile_definitions(cufile_stub
+        PUBLIC
+            CUCIM_STATIC_GDS=1
+    )
+    target_include_directories(cufile_stub
+            PUBLIC
+                $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>
+            PRIVATE
+                ${CMAKE_CURRENT_SOURCE_DIR}/../cpp/include # for including helper.h in cucim/dynlib
+            )
+else()
+    target_include_directories(cufile_stub
+            PUBLIC
+                # add path to cufile.h explicitly. ${TOP}/temp/gds would be available by `./run copy_gds_files_`
+                $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../temp/gds/lib64>
+                $<BUILD_INTERFACE:${CMAKE_CURRENT_LIST_DIR}/include>
+            PRIVATE
+                ${CMAKE_CURRENT_SOURCE_DIR}/../cpp/include # for including helper.h in cucim/dynlib
+            )
+endif()
+
+add_library(deps::gds ALIAS cufile_stub)
+
+# Do not generate SONAME as this would be used as a stub library for building on CentOS until cufile has a static library.
+# Need to use IMPORTED_NO_SONAME when using this .so file.
+# : https://stackoverflow.com/questions/27261288/cmake-linking-shared-c-object-from-externalproject-produces-binaries-with-rel
+#set_target_properties(cufile_stub PROPERTIES NO_SONAME 1)
+#target_link_options(cufile_stub PRIVATE "LINKER:-soname=cufile.so")
+## Build a fake libcufile.so
+#set_target_properties(cufile_stub PROPERTIES OUTPUT_NAME "cufile")
+
+
+#
+#################################################################################
+## Add tests
+#################################################################################
+#add_subdirectory(tests)
+
+cucim_restore_build_shared_libs()
diff --git a/gds/include/cufile_stub.h b/gds/include/cufile_stub.h
new file mode 100644
index 000000000..3ce68cfe1
--- /dev/null
+++ b/gds/include/cufile_stub.h
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef CUCIM_CUFILE_STUB_H
+#define CUCIM_CUFILE_STUB_H
+
+#include "cufile.h"
+
+extern "C" void open_cufile_stub();
+extern "C" void close_cufile_stub();
+
+#endif // CUCIM_CUFILE_STUB_H
diff --git a/gds/src/cufile_stub.cpp b/gds/src/cufile_stub.cpp
new file mode 100644
index 000000000..1cc4020a1
--- /dev/null
+++ b/gds/src/cufile_stub.cpp
@@ -0,0 +1,263 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cufile_stub.h"
+#include "cucim/dynlib/helper.h"
+
+#define IMPORT_FUNCTION(handle, name) impl_##name = cucim::dynlib::get_library_symbol<t_##name>(handle, #name);
+
+typedef CUfileError_t (*t_cuFileHandleRegister)(CUfileHandle_t* fh, CUfileDescr_t* descr);
+typedef void (*t_cuFileHandleDeregister)(CUfileHandle_t fh);
+typedef CUfileError_t (*t_cuFileBufRegister)(const void* devPtr_base, size_t length, int flags);
+typedef CUfileError_t (*t_cuFileBufDeregister)(const void* devPtr_base);
+typedef ssize_t (*t_cuFileRead)(CUfileHandle_t fh, void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset);
+typedef ssize_t (*t_cuFileWrite)(
+    CUfileHandle_t fh, const void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset);
+typedef CUfileError_t (*t_cuFileDriverOpen)(void);
+typedef CUfileError_t (*t_cuFileDriverClose)(void);
+typedef CUfileError_t (*t_cuFileDriverGetProperties)(CUfileDrvProps_t* props);
+typedef CUfileError_t (*t_cuFileDriverSetPollMode)(bool poll, size_t poll_threshold_size);
+typedef CUfileError_t (*t_cuFileDriverSetMaxDirectIOSize)(size_t max_direct_io_size);
+typedef CUfileError_t (*t_cuFileDriverSetMaxCacheSize)(size_t max_cache_size);
+typedef CUfileError_t (*t_cuFileDriverSetMaxPinnedMemSize)(size_t max_pinned_size);
+
+static t_cuFileHandleRegister impl_cuFileHandleRegister = nullptr;
+static t_cuFileHandleDeregister impl_cuFileHandleDeregister = nullptr;
+static t_cuFileBufRegister impl_cuFileBufRegister = nullptr;
+static t_cuFileBufDeregister impl_cuFileBufDeregister = nullptr;
+static t_cuFileRead impl_cuFileRead = nullptr;
+static t_cuFileWrite impl_cuFileWrite = nullptr;
+static t_cuFileDriverOpen impl_cuFileDriverOpen = nullptr;
+static t_cuFileDriverClose impl_cuFileDriverClose = nullptr;
+static t_cuFileDriverGetProperties impl_cuFileDriverGetProperties = nullptr;
+static t_cuFileDriverSetPollMode impl_cuFileDriverSetPollMode = nullptr;
+static t_cuFileDriverSetMaxDirectIOSize impl_cuFileDriverSetMaxDirectIOSize = nullptr;
+static t_cuFileDriverSetMaxCacheSize impl_cuFileDriverSetMaxCacheSize = nullptr;
+static t_cuFileDriverSetMaxPinnedMemSize impl_cuFileDriverSetMaxPinnedMemSize = nullptr;
+
+
+class CuFileStub
+{
+public:
+    void load()
+    {
+#if !CUCIM_SUPPORT_GDS
+        return;
+#endif
+
+#if !CUCIM_STATIC_GDS
+        if (handle_ == nullptr)
+        {
+            handle_ = cucim::dynlib::load_library("libcufile.so");
+            if (handle_ == nullptr)
+            {
+                return;
+            }
+            IMPORT_FUNCTION(handle_, cuFileDriverOpen);
+            IMPORT_FUNCTION(handle_, cuFileHandleRegister);
+            IMPORT_FUNCTION(handle_, cuFileHandleDeregister);
+            IMPORT_FUNCTION(handle_, cuFileBufRegister);
+            IMPORT_FUNCTION(handle_, cuFileBufDeregister);
+            IMPORT_FUNCTION(handle_, cuFileRead);
+            IMPORT_FUNCTION(handle_, cuFileWrite);
+            IMPORT_FUNCTION(handle_, cuFileDriverOpen);
+            IMPORT_FUNCTION(handle_, cuFileDriverClose);
+            IMPORT_FUNCTION(handle_, cuFileDriverGetProperties);
+            IMPORT_FUNCTION(handle_, cuFileDriverSetPollMode);
+            IMPORT_FUNCTION(handle_, cuFileDriverSetMaxDirectIOSize);
+            IMPORT_FUNCTION(handle_, cuFileDriverSetMaxCacheSize);
+            IMPORT_FUNCTION(handle_, cuFileDriverSetMaxPinnedMemSize);
+        }
+#endif
+    }
+    void unload()
+    {
+#if !CUCIM_SUPPORT_GDS
+        return;
+#endif
+
+#if !CUCIM_STATIC_GDS
+        if (handle_)
+        {
+            cucim::dynlib::unload_library(handle_);
+            handle_ = nullptr;
+
+            impl_cuFileDriverOpen = nullptr;
+            impl_cuFileHandleRegister = nullptr;
+            impl_cuFileHandleDeregister = nullptr;
+            impl_cuFileBufRegister = nullptr;
+            impl_cuFileBufDeregister = nullptr;
+            impl_cuFileRead = nullptr;
+            impl_cuFileWrite = nullptr;
+            impl_cuFileDriverOpen = nullptr;
+            impl_cuFileDriverClose = nullptr;
+            impl_cuFileDriverGetProperties = nullptr;
+            impl_cuFileDriverSetPollMode = nullptr;
+            impl_cuFileDriverSetMaxDirectIOSize = nullptr;
+            impl_cuFileDriverSetMaxCacheSize = nullptr;
+            impl_cuFileDriverSetMaxPinnedMemSize = nullptr;
+        }
+#endif
+    }
+    ~CuFileStub()
+    {
+        // Note: unload() would be called explicitly by CuFileDriverInitializer to unload the shared library after calling
+        // cuFileDriverClose() in CuFileDriverInitializer::~CuFileDriverInitializer()
+//        unload();
+    }
+
+private:
+    cucim::dynlib::LibraryHandle handle_ = nullptr;
+};
+
+static CuFileStub g_cufile_stub;
+
+#if __cplusplus
+extern "C"
+{
+#endif
+
+    void open_cufile_stub()
+    {
+        g_cufile_stub.load();
+    }
+    void close_cufile_stub()
+    {
+        g_cufile_stub.unload();
+    }
+
+#if !CUCIM_STATIC_GDS
+    CUfileError_t cuFileHandleRegister(CUfileHandle_t* fh, CUfileDescr_t* descr)
+    {
+        if (impl_cuFileHandleRegister)
+        {
+            return impl_cuFileHandleRegister(fh, descr);
+        }
+
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    void cuFileHandleDeregister(CUfileHandle_t fh)
+    {
+        if (impl_cuFileHandleDeregister)
+        {
+            impl_cuFileHandleDeregister(fh);
+        }
+    }
+
+    CUfileError_t cuFileBufRegister(const void* devPtr_base, size_t length, int flags)
+    {
+        if (impl_cuFileBufRegister)
+        {
+            return impl_cuFileBufRegister(devPtr_base, length, flags);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileBufDeregister(const void* devPtr_base)
+    {
+        if (impl_cuFileBufDeregister)
+        {
+            return impl_cuFileBufDeregister(devPtr_base);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    ssize_t cuFileRead(CUfileHandle_t fh, void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset)
+    {
+        if (impl_cuFileRead)
+        {
+            return impl_cuFileRead(fh, devPtr_base, size, file_offset, devPtr_offset);
+        }
+        return -1;
+    }
+
+    ssize_t cuFileWrite(CUfileHandle_t fh, const void* devPtr_base, size_t size, off_t file_offset, off_t devPtr_offset)
+    {
+        if (impl_cuFileWrite)
+        {
+            return impl_cuFileWrite(fh, devPtr_base, size, file_offset, devPtr_offset);
+        }
+        return -1;
+    }
+
+    CUfileError_t cuFileDriverOpen(void)
+    {
+        if (impl_cuFileDriverOpen)
+        {
+            return impl_cuFileDriverOpen();
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverClose(void)
+    {
+        if (impl_cuFileDriverClose)
+        {
+            return impl_cuFileDriverClose();
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverGetProperties(CUfileDrvProps_t* props)
+    {
+        if (impl_cuFileDriverGetProperties)
+        {
+            return impl_cuFileDriverGetProperties(props);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverSetPollMode(bool poll, size_t poll_threshold_size)
+    {
+        if (impl_cuFileDriverSetPollMode)
+        {
+            return impl_cuFileDriverSetPollMode(poll, poll_threshold_size);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverSetMaxDirectIOSize(size_t max_direct_io_size)
+    {
+        if (impl_cuFileDriverSetMaxDirectIOSize)
+        {
+            return impl_cuFileDriverSetMaxDirectIOSize(max_direct_io_size);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverSetMaxCacheSize(size_t max_cache_size)
+    {
+        if (impl_cuFileDriverSetMaxCacheSize)
+        {
+            return impl_cuFileDriverSetMaxCacheSize(max_cache_size);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+
+    CUfileError_t cuFileDriverSetMaxPinnedMemSize(size_t max_pinned_size)
+    {
+        if (impl_cuFileDriverSetMaxPinnedMemSize)
+        {
+            return impl_cuFileDriverSetMaxPinnedMemSize(max_pinned_size);
+        }
+        return CUfileError_t{ CU_FILE_DRIVER_NOT_INITIALIZED, CUDA_SUCCESS };
+    }
+#endif
+
+#if __cplusplus
+}
+#endif
diff --git a/notebooks/Accessing_File_with_GDS.ipynb b/notebooks/Accessing_File_with_GDS.ipynb
new file mode 100644
index 000000000..6b1acb4e2
--- /dev/null
+++ b/notebooks/Accessing_File_with_GDS.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Accessing File with GDS (since `v0.2.0`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisite\n",
+    "\n",
+    "[NVIDIA® GPUDirect® Storage (GDS)](https://docs.nvidia.com/gpudirect-storage/) needs to be installed to use GDS feature. File access APIs would still work without GDS but you won't see the speed up. \\\n",
+    "Please follow the [release note](https://docs.nvidia.com/gpudirect-storage/release-notes/index.html) or the [installation guide](https://docs.nvidia.com/gpudirect-storage/troubleshooting-guide/index.html#abstract) to install GDS in your host system.\n",
+    "- Note:: During the GDS installation, you would need MOFED (Mellanox OpenFabrics Enterprise Distribution) installed. MOFED is available at https://www.mellanox.com/products/infiniband-drivers/linux/mlnx_ofed .\n",
+    "\n",
+    "To use GDS feature in the provided Jupyter Notebook container, you need to launch the Jupyter Notebook with `-g <Folder path in NVMe storage>` option\n",
+    "\n",
+    "**Example**\n",
+    "```bash\n",
+    "./run launch_notebooks -g /nvme\n",
+    "```\n",
+    "Then, the folder in NVMe storage would be mounted on `nvme` folder under Jupyter Notebook root folder (`/notebooks`) in the docker container."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ./cucim-0.19.0-py3-none-manylinux2014_x86_64.whl\n",
+      "Collecting click\n",
+      "  Using cached click-7.1.2-py2.py3-none-any.whl (82 kB)\n",
+      "Installing collected packages: click, cucim\n",
+      "  Attempting uninstall: click\n",
+      "    Found existing installation: click 7.1.2\n",
+      "    Uninstalling click-7.1.2:\n",
+      "      Successfully uninstalled click-7.1.2\n",
+      "  Attempting uninstall: cucim\n",
+      "    Found existing installation: cucim 0.19.0\n",
+      "    Uninstalling cucim-0.19.0:\n",
+      "      Successfully uninstalled cucim-0.19.0\n",
+      "Successfully installed click-7.1.2 cucim-0.19.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --force-reinstall *.whl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Open File\n",
+    "\n",
+    "You can use either `CuFileDriver` class or `open` method in `cucim.clara.filesystem` package."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Opening/Closing file with CuFileDriver\n",
+    "\n",
+    "A file descriptor would be needed to create a CuFileDriver instance.\n",
+    "\n",
+    "To use GDS, the file needs to be opened with `os.O_DIRECT`. See [NVIDIA GPUDirect Storage O_DIRECT Requirements Guide](https://docs.nvidia.com/gpudirect-storage/o-direct-guide/index.html).\n",
+    "\n",
+    "Please also see [os.open()](https://docs.python.org/3/library/os.html#os.open) for the detailed options available.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on instancemethod in module cucim.clara._cucim.filesystem:\n",
+      "\n",
+      "__init__(...)\n",
+      "    __init__(self: cucim.clara._cucim.filesystem.CuFileDriver, fd: int, no_gds: bool = False, use_mmap: bool = False, file_path: str = '') -> None\n",
+      "    \n",
+      "    Constructor of CuFileDriver.\n",
+      "    \n",
+      "    Args:\n",
+      "        fd: A file descriptor (in `int` type) which is available through `os.open()` method.\n",
+      "        no_gds: If True, use POSIX APIs only even when GDS can be supported for the file.\n",
+      "        use_mmap: If True, use memory-mapped IO. This flag is supported only for the read-only file descriptor. Default value is `False`.\n",
+      "        file_path: A file path for the file descriptor. It would retrieve the absolute file path of the file descriptor if not specified.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from cucim.clara.filesystem import CuFileDriver\n",
+    "\n",
+    "fno = os.open( \"nvme/image.tif\", os.O_RDONLY | os.O_DIRECT)\n",
+    "fno2 = os.dup(fno) \n",
+    "\n",
+    "fd = CuFileDriver(fno, False)\n",
+    "fd.close()\n",
+    "\n",
+    "# Do not use GDS even when GDS can be supported for the file.\n",
+    "fd2 = CuFileDriver(fno2, True)\n",
+    "fd2.close()\n",
+    "\n",
+    "help(CuFileDriver.__init__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Opening file with `open()` method in cucim.clara.filesystem package\n",
+    "\n",
+    "`cucim.clara.filesystem.open()` method accepts the three parameters (`file_path`, `flags`, `mode`).\n",
+    "\n",
+    "\n",
+    "#### file_path\n",
+    "\n",
+    "A string for the file path.\n",
+    "\n",
+    "#### flags\n",
+    "\n",
+    "`flags` can be one of the following flag string:\n",
+    "\n",
+    "- **\"r\"**  : `os.O_RDONLY`\n",
+    "- **\"r+\"** : `os.O_RDWR`\n",
+    "- **\"w\"**  : `os.O_RDWR`   | `os.O_CREAT` | `os.O_TRUNC`\n",
+    "- **\"a\"**  : `os.O_RDWR`   | `os.O_CREAT`\n",
+    "\n",
+    "In addition to above flags, the method append `os.O_CLOEXEC` and `os.O_DIRECT` by default.\n",
+    "\n",
+    "The following is optional flags that can be added to above string:\n",
+    "- **'p'**: Use POSIX APIs only (first try to open with O_DIRECT). It does not use GDS.\n",
+    "- **'n'**: Do not add O_DIRECT flag.\n",
+    "- **'m'**: Use memory-mapped file. This flag is supported only for the read-only file descriptor.\n",
+    "\n",
+    "When **'m'** is used, `PROT_READ` and `MAP_SHARED` are used for the parameter of [mmap()](https://man7.org/linux/man-pages/man2/mmap.2.html) function.\n",
+    "\n",
+    "#### mode\n",
+    "\n",
+    "A file mode. Default value is `0o644`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "fd = fs.open(\"nvme/image.tif\", \"r\")\n",
+    "fs.close(fd)\n",
+    "\n",
+    "# Open file without using GDS\n",
+    "fd2 = fs.open(\"nvme/image.tif\", \"rp\")\n",
+    "fs.close(fd2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read/Write File\n",
+    "\n",
+    "You can use `pread()`/`pwrite()` method in either `CuFileDriver` class or `cucim.clara.filesystem` package.\n",
+    "\n",
+    "Those methods are similar to POSIX [pread()](https://man7.org/linux/man-pages/man2/pread.2.html)&[pwrite()](https://man7.org/linux/man-pages/man2/pwrite.2.html) methods which requires `buf`, `count`, and `offset`(`file_offset`) parameters.\n",
+    "\n",
+    "However, for user's convenient, an optional `buf_offset` parameter (default value: `0`) is also added to specify an offset of the input/output buffer and it would have `0` if not specified."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using CPU memory\n",
+    "\n",
+    "Any Python object supporting [\\_\\_array_interface__](https://numpy.org/doc/stable/reference/arrays.interface.html) (such as numpy.array or numpy.ndarray) can be used for `buf` parameter.\n",
+    "Or, any pointer address (`int` type) can be used for `buf` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "np_arr     cnt: 8  content: [  1   2 101 102 103 104 105 106 107 108]\n",
+      "np_arr     cnt: 10  content: [101 102 103 104 105 106 107 108 109 110]\n",
+      "torch_arr  cnt: 7  content: tensor([104, 105, 106, 107, 108, 109, 110, 108, 109, 110], dtype=torch.uint8)\n",
+      "output.raw cnt: 10  content: [0, 0, 0, 0, 0, 104, 105, 106, 107, 108, 109, 110, 108, 109, 110]\n",
+      "\n",
+      "np_arr     cnt: 10  content: [  0   0   0   0   0 104 105 106 107 108]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from cucim.clara.filesystem import CuFileDriver\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "import os, numpy as np, torch\n",
+    "\n",
+    "# Write a file with size 10 (in bytes)\n",
+    "with open(\"input.raw\", \"wb\") as input_file:\n",
+    "    input_file.write(bytearray([101, 102, 103, 104, 105, 106, 107, 108, 109, 110]))\n",
+    "\n",
+    "# Create an array with size 10 (in bytes)\n",
+    "np_arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=np.uint8)\n",
+    "torch_arr = torch.from_numpy(np_arr) # Note: np_arr shares internal data with torch_arr\n",
+    "\n",
+    "# Using CuFileDriver\n",
+    "fno = os.open( \"input.raw\", os.O_RDONLY)\n",
+    "fd = CuFileDriver(fno)\n",
+    "read_count = fd.pread(np_arr, 8, 0, 2)      # read 8 bytes starting from file offset 0 into buffer offset 2\n",
+    "print(\"{:10} cnt: {}  content: {}\".format(\"np_arr\", read_count, np_arr))\n",
+    "read_count = fd.pread(np_arr, 10, 0)      # read 10 bytes starting from file offset 0\n",
+    "print(\"{:10} cnt: {}  content: {}\".format(\"np_arr\", read_count, np_arr))\n",
+    "read_count = fd.pread(torch_arr.data_ptr(), 10, 3)      # read 10 bytes starting from file offset 3\n",
+    "print(\"{:10} cnt: {}  content: {}\".format(\"torch_arr\", read_count, torch_arr))\n",
+    "fd.close()\n",
+    "\n",
+    "fno = os.open(\"output.raw\", os.O_RDWR | os.O_CREAT | os.O_TRUNC)\n",
+    "fd = CuFileDriver(fno)\n",
+    "write_count = fd.pwrite(np_arr, 10, 5)      # write 10 bytes from np_array to file starting from offset 5\n",
+    "fd.close()\n",
+    "print(\"{:10} cnt: {}  content: {}\".format(\"output.raw\", write_count, list(open(\"output.raw\", \"rb\").read())))\n",
+    "\n",
+    "\n",
+    "print()\n",
+    "# Using filesystem package\n",
+    "fd = fs.open(\"output.raw\", \"r\")\n",
+    "read_count = fs.pread(fd, np_arr, 10, 0)  # read 10 bytes starting from offset 0\n",
+    "print(\"{:10} cnt: {}  content: {}\".format(\"np_arr\", read_count, np_arr))\n",
+    "fs.close(fd)                              # same with fd.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using GPU memory\n",
+    "\n",
+    "Any Python object supporting [\\_\\_cuda_array_interface__](http://numba.pydata.org/numba-doc/latest/cuda/cuda_array_interface.html) (such as cupy.array, cupy.ndarray, or Pytorch Cuda Tensor) can be used for `buf` parameter.\n",
+    "Or, any pointer address (`int` type) can be used for `buf` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "np_arr               cnt: 8  content: [  1   2 101 102 103 104 105 106 107 108]\n",
+      "cp_arr               cnt: 10  content: [  0   0   0   0   0 104 105 106 107 108]\n",
+      "torch_arr            cnt: 7  content: tensor([104, 105, 106, 107, 108, 109, 110,   0,   0,   0], device='cuda:0',\n",
+      "       dtype=torch.uint8)\n",
+      "nvme/output.raw      cnt: 10  content: [0, 0, 0, 0, 0, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110]\n",
+      "\n",
+      "cp_arr               cnt: 10  content: [  0   0   0   0   0 104 105 106 107 108]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from cucim.clara.filesystem import CuFileDriver\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "import os\n",
+    "import cupy as cp\n",
+    "import torch\n",
+    "\n",
+    "# Write a file with size 10 (in bytes)\n",
+    "with open(\"nvme/input.raw\", \"wb\") as input_file:\n",
+    "    input_file.write(bytearray([101, 102, 103, 104, 105, 106, 107, 108, 109, 110]))\n",
+    "\n",
+    "# Create an array with size 10 (in bytes)\n",
+    "cp_arr = cp.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=cp.uint8)\n",
+    "\n",
+    "cuda0 = torch.device('cuda:0')\n",
+    "torch_arr = torch.zeros(10, dtype=torch.uint8, device=cuda0)\n",
+    "\n",
+    "# Using CuFileDriver\n",
+    "fno = os.open( \"nvme/input.raw\", os.O_RDONLY | os.O_DIRECT)\n",
+    "fd = CuFileDriver(fno)\n",
+    "\n",
+    "read_count = fd.pread(cp_arr, 8, 0, 2)      # read 8 bytes starting from file offset 0 into buffer offset 2\n",
+    "print(\"{:20} cnt: {}  content: {}\".format(\"np_arr\", read_count, cp_arr))\n",
+    "read_count = fd.pread(cp_arr, 10, 0)      # read 10 bytes starting from offset 0\n",
+    "print(\"{:20} cnt: {}  content: {}\".format(\"cp_arr\", read_count, np_arr))\n",
+    "read_count = fd.pread(torch_arr, 10, 3)      # read 10 bytes starting from offset 3\n",
+    "print(\"{:20} cnt: {}  content: {}\".format(\"torch_arr\", read_count, torch_arr))\n",
+    "fd.close()\n",
+    "\n",
+    "fno = os.open(\"nvme/output.raw\", os.O_RDWR | os.O_CREAT | os.O_TRUNC)\n",
+    "fd = CuFileDriver(fno)\n",
+    "write_count = fd.pwrite(cp_arr, 10, 5)      # write 10 bytes from np_array to file starting from offset 5\n",
+    "fd.close()\n",
+    "print(\"{:20} cnt: {}  content: {}\".format(\"nvme/output.raw\", write_count, list(open(\"nvme/output.raw\", \"rb\").read())))\n",
+    "\n",
+    "print()\n",
+    "# Using filesystem package\n",
+    "fd = fs.open(\"nvme/output.raw\", \"r\")\n",
+    "read_count = fs.pread(fd, cp_arr, 10, 0)  # read 10 bytes starting from offset 0\n",
+    "print(\"{:20} cnt: {}  content: {}\".format(\"cp_arr\", read_count, np_arr))\n",
+    "fs.close(fd)                              # same with fd.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shape': (10,),\n",
+       " 'typestr': '|u1',\n",
+       " 'descr': [('', '|u1')],\n",
+       " 'version': 2,\n",
+       " 'strides': None,\n",
+       " 'data': (140054638886912, False)}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cp_arr.__cuda_array_interface__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'typestr': '|u1',\n",
+       " 'shape': (10,),\n",
+       " 'strides': None,\n",
+       " 'data': (140054332702720, False),\n",
+       " 'version': 2}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch_arr.__cuda_array_interface__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discarding system (page) cache for a file\n",
+    "\n",
+    "You can use `discard_page_cache()` method for discarding system (page) cache for the given file, before any performance measurement on a file.\n",
+    "\n",
+    "```python\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "fs.discard_page_cache(\"input/image.tif\")\n",
+    "# ... file APIs on `input/image.tif`\n",
+    "```\n",
+    "\n",
+    "Its implementation looks like below\n",
+    "```C++\n",
+    "bool discard_page_cache(const char* file_path)\n",
+    "{\n",
+    "    int fd = ::open(file_path, O_RDONLY);\n",
+    "    if (fd < 0)\n",
+    "    {\n",
+    "        return false;\n",
+    "    }\n",
+    "    if (::fdatasync(fd) < 0)\n",
+    "    {\n",
+    "        return false;\n",
+    "    }\n",
+    "    if (::posix_fadvise(fd, 0, 0, POSIX_FADV_DONTNEED) < 0)\n",
+    "    {\n",
+    "        return false;\n",
+    "    }\n",
+    "    if (::close(fd) < 0)\n",
+    "    {\n",
+    "        return false;\n",
+    "    }\n",
+    "    return true;\n",
+    "}\n",
+    "```\n",
+    "\n",
+    "It helps measure accurate file access performance without the effect of the page cache."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File read with GDS (for a big file such as .mhd)\n",
+    "\n",
+    "This is for reading 10GB of data\n",
+    "```\n",
+    "  second method(posix + cudamemcpy)         : 5.031040154863149\n",
+    "  second method(posix+odirect + cudamemcpy) : 4.7419630330987275\n",
+    "  second method(gds)                        : 4.235773948952556\n",
+    "```\n",
+    "15.8% improvements.\n",
+    "\n",
+    "\n",
+    "This is for reading 2GB of data\n",
+    "\n",
+    "```bash\n",
+    "  second method(posix)         : 1.0681836600415409\n",
+    "  second method(posix+odirect) : 0.9496012150775641\n",
+    "  second method(gds)           : 0.8406150250229985\n",
+    "```\n",
+    "21.3% improvements.\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Basic_Usage.ipynb b/notebooks/Basic_Usage.ipynb
new file mode 100644
index 000000000..86f1fc38e
--- /dev/null
+++ b/notebooks/Basic_Usage.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ./cucim-0.19.0-py3-none-manylinux2014_x86_64.whl\n",
+      "Collecting click\n",
+      "  Using cached click-7.1.2-py2.py3-none-any.whl (82 kB)\n",
+      "Installing collected packages: click, cucim\n",
+      "  Attempting uninstall: click\n",
+      "    Found existing installation: click 7.1.2\n",
+      "    Uninstalling click-7.1.2:\n",
+      "      Successfully uninstalled click-7.1.2\n",
+      "  Attempting uninstall: cucim\n",
+      "    Found existing installation: cucim 0.19.0\n",
+      "    Uninstalling cucim-0.19.0:\n",
+      "      Successfully uninstalled cucim-0.19.0\n",
+      "Successfully installed click-7.1.2 cucim-0.19.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --force-reinstall *.whl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert .svs file to .tif file\n",
+    "\n",
+    "This package has the CLI tool to convert image in other formats (such as .svs) to TIFF (JPEG-compressed) image by using [tifffile](https://github.com/cgohlke/tifffile) library. You can exploit the tool until cuCIM natively supports Aperio SVS format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.\n",
+      "Usage: cucim convert [OPTIONS] SRC_FILE [DEST_FOLDER]\n",
+      "\n",
+      "  Convert file format\n",
+      "\n",
+      "Options:\n",
+      "  --tile-size INTEGER\n",
+      "  --overlap INTEGER\n",
+      "  --num-workers INTEGER\n",
+      "  --output-filename TEXT\n",
+      "  --help                  Show this message and exit.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cucim convert --help"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.\n",
+      "INFO:cucim.clara.converter.tiff:Parameters\n",
+      "INFO:cucim.clara.converter.tiff:       input file: input/TUPAC-TR-467.svs\n",
+      "INFO:cucim.clara.converter.tiff:    output folder: .\n",
+      "INFO:cucim.clara.converter.tiff:        tile size: 512\n",
+      "INFO:cucim.clara.converter.tiff:          overlap: 0\n",
+      "INFO:cucim.clara.converter.tiff:      num_workers: 12\n",
+      "INFO:cucim.clara.converter.tiff:  output filename: TUPAC-TR-467.tif\n",
+      "INFO:cucim.clara.converter.tiff:  Created level0.mmap (26420, 19920, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Created level1.mmap (13210, 9960, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Created level2.mmap (6605, 4980, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Created level3.mmap (3302, 2490, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Created level4.mmap (1651, 1245, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Created level5.mmap (826, 622, 3)\n",
+      "INFO:cucim.clara.converter.tiff:Processing tiles...\n",
+      "INFO:cucim.clara.converter.tiff:Storing low resolution images...\n",
+      "INFO:cucim.clara.converter.tiff:  Level 1: (13210, 9960, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Level 2: (6605, 4980, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Level 3: (3302, 2490, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Level 4: (1651, 1245, 3)\n",
+      "INFO:cucim.clara.converter.tiff:  Level 5: (826, 622, 3)\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 0 image (19920 x 26420)...\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 1 image (9960 x 13210)...\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 2 image (4980 x 6605)...\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 3 image (2490 x 3302)...\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 4 image (1245 x 1651)...\n",
+      "INFO:cucim.clara.converter.tiff:Saving Level 5 image (622 x 826)...\n",
+      "INFO:cucim.clara.converter.tiff:Done.\n",
+      "INFO:cucim.clara.converter.tiff:Removing memmapped files...\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cucim convert input/TUPAC-TR-467.svs --output-filename TUPAC-TR-467.tif --tile-size 512"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cucim import CuImage\n",
+    "\n",
+    "img = CuImage(\"input/image.tif\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### See metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "cpu\n",
+      "3\n",
+      "YXC\n",
+      "[26420, 19920, 3]\n",
+      "[19920, 26420, 3]\n",
+      "<cucim.DLDataType code:DLUInt(1) bits:8 lanes:1>\n",
+      "['R', 'G', 'B']\n",
+      "[1.0, 1.0, 1.0]\n",
+      "['micrometer', 'micrometer', 'color']\n",
+      "[0.0, 0.0, 0.0]\n",
+      "[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]\n",
+      "LPS\n",
+      "set()\n",
+      "{\n",
+      "  \"level_count\": 7,\n",
+      "  \"level_dimensions\": [\n",
+      "    [\n",
+      "      19920,\n",
+      "      26420\n",
+      "    ],\n",
+      "    [\n",
+      "      9960,\n",
+      "      13210\n",
+      "    ],\n",
+      "    [\n",
+      "      4980,\n",
+      "      6605\n",
+      "    ],\n",
+      "    [\n",
+      "      2490,\n",
+      "      3302\n",
+      "    ],\n",
+      "    [\n",
+      "      1245,\n",
+      "      1651\n",
+      "    ],\n",
+      "    [\n",
+      "      622,\n",
+      "      826\n",
+      "    ],\n",
+      "    [\n",
+      "      311,\n",
+      "      413\n",
+      "    ]\n",
+      "  ],\n",
+      "  \"level_downsamples\": [\n",
+      "    1.0,\n",
+      "    2.0,\n",
+      "    4.0,\n",
+      "    8.000605583190918,\n",
+      "    16.001211166381836,\n",
+      "    32.00559616088867,\n",
+      "    64.01119232177734\n",
+      "  ]\n",
+      "}\n",
+      "{\n",
+      "  \"cucim\": {\n",
+      "    \"associated_images\": [],\n",
+      "    \"channel_names\": [\n",
+      "      \"R\",\n",
+      "      \"G\",\n",
+      "      \"B\"\n",
+      "    ],\n",
+      "    \"coord_sys\": \"LPS\",\n",
+      "    \"dims\": \"YXC\",\n",
+      "    \"direction\": [\n",
+      "      [\n",
+      "        1,\n",
+      "        0,\n",
+      "        0\n",
+      "      ],\n",
+      "      [\n",
+      "        0,\n",
+      "        1,\n",
+      "        0\n",
+      "      ],\n",
+      "      [\n",
+      "        0,\n",
+      "        0,\n",
+      "        1\n",
+      "      ]\n",
+      "    ],\n",
+      "    \"dtype\": {\n",
+      "      \"bits\": 8,\n",
+      "      \"code\": 1,\n",
+      "      \"lanes\": 1\n",
+      "    },\n",
+      "    \"ndim\": 3,\n",
+      "    \"origin\": [\n",
+      "      0,\n",
+      "      0,\n",
+      "      0\n",
+      "    ],\n",
+      "    \"path\": \"input/image.tif\",\n",
+      "    \"resolutions\": {\n",
+      "      \"level_count\": 7,\n",
+      "      \"level_dimensions\": [\n",
+      "        [\n",
+      "          19920,\n",
+      "          26420\n",
+      "        ],\n",
+      "        [\n",
+      "          9960,\n",
+      "          13210\n",
+      "        ],\n",
+      "        [\n",
+      "          4980,\n",
+      "          6605\n",
+      "        ],\n",
+      "        [\n",
+      "          2490,\n",
+      "          3302\n",
+      "        ],\n",
+      "        [\n",
+      "          1245,\n",
+      "          1651\n",
+      "        ],\n",
+      "        [\n",
+      "          622,\n",
+      "          826\n",
+      "        ],\n",
+      "        [\n",
+      "          311,\n",
+      "          413\n",
+      "        ]\n",
+      "      ],\n",
+      "      \"level_downsamples\": [\n",
+      "        1,\n",
+      "        2,\n",
+      "        4,\n",
+      "        8.000605583190918,\n",
+      "        16.001211166381836,\n",
+      "        32.00559616088867,\n",
+      "        64.01119232177734\n",
+      "      ]\n",
+      "    },\n",
+      "    \"shape\": [\n",
+      "      26420,\n",
+      "      19920,\n",
+      "      3\n",
+      "    ],\n",
+      "    \"spacing\": [\n",
+      "      1,\n",
+      "      1,\n",
+      "      1\n",
+      "    ],\n",
+      "    \"spacing_units\": [\n",
+      "      \"micrometer\",\n",
+      "      \"micrometer\",\n",
+      "      \"color\"\n",
+      "    ]\n",
+      "  },\n",
+      "  \"tiff\": {\n",
+      "    \"model\": \"\",\n",
+      "    \"software\": \"Glencoe/Faas pyramid\"\n",
+      "  }\n",
+      "}\n",
+      "{\"axes\": \"YXC\", \"shape\": [26420, 19920, 3]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "print(img.is_loaded)                             # True if image data is loaded & available.\n",
+    "print(img.device)                                # A device type.\n",
+    "print(img.ndim)                                  # The number of dimensions.\n",
+    "print(img.dims)                                  # A string containing a list of dimensions being requested.\n",
+    "print(img.shape)                                 # A tuple of dimension sizes (in the order of `dims`).\n",
+    "print(img.size('XYC'))                           # Returns size as a tuple for the given dimension order.\n",
+    "print(img.dtype)                                 # The data type of the image.\n",
+    "print(img.channel_names)                         # A channel name list.\n",
+    "print(img.spacing())                             # Returns physical size in tuple.\n",
+    "print(img.spacing_units())                       # Units for each spacing element (size is same with `ndim`).\n",
+    "print(img.origin)                                # Physical location of (0, 0, 0) (size is always 3).\n",
+    "print(img.direction)                             # Direction cosines (size is always 3x3).\n",
+    "print(img.coord_sys)                             # Coordinate frame in which the direction cosines are measured. Available Coordinate frame is not finalized yet.\n",
+    "print(img.associated_images)                     # Returns a set of associated image names.\n",
+    "print(json.dumps(img.resolutions, indent=2))     # Returns a dict that includes resolution information.\n",
+    "print(json.dumps(img.metadata, indent=2))        # A metadata object as `dict`\n",
+    "print(img.raw_metadata)                          # A raw metadata string."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read region\n",
+    "\n",
+    "Please keep in mind that values for `location` is based on level-0 resolution (the largest resolution)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read whole slide at the lowest resolution\n",
+    "resolutions = img.resolutions\n",
+    "level_count = resolutions[\"level_count\"]\n",
+    "region = img.read_region(location=[0,0], size=resolutions[\"level_dimensions\"][level_count - 1], level=level_count - 1)\n",
+    "region.save(\"thumbnail.ppm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.PpmImagePlugin.PpmImageFile image mode=RGB size=311x413 at 0x7F3D386315C0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "Image.open(\"thumbnail.ppm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "region = img.read_region([10000,10000], [512,512], 0)\n",
+    "region.save(\"test.ppm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.PpmImagePlugin.PpmImageFile image mode=RGB size=512x512 at 0x7F3D30140C18>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "Image.open(\"test.ppm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `__array_interface__` support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': (30467904, False), 'strides': None, 'descr': [('', '<i8')], 'typestr': '<i8', 'shape': (3,), 'version': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np_arr = np.array([1,2,3])\n",
+    "print(np_arr.__array_interface__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see the above result, a numpy array has `__array_interface__` property.\n",
+    "\n",
+    "A Python object that has `__array_interface__` property is considered as a numpy array and can be converted to a numpy array through `np.asarray(obj)` method.\n",
+    "\n",
+    "[\\_\\_array_interface__](https://numpy.org/doc/stable/reference/arrays.interface.html) is supported in CuImage object so the following code works to show the image in the jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=512x512 at 0x7F3D1C067C88>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np_img_arr = np.asarray(region)\n",
+    "Image.fromarray(np_img_arr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Associated images\n",
+    "\n",
+    "Some image formats such as Philips TIFF and Aperio SVS have associated images (Macro or Label images) in addition to the multi-resolution images.\n",
+    "\n",
+    "Let's assume that the image has only `macro` image.\n",
+    "\n",
+    "```python\n",
+    ">>> img.associated_images\n",
+    "{'macro'}\n",
+    "\n",
+    ">>> 'macro' in img.associated_images\n",
+    "True\n",
+    "\n",
+    ">>> macro_image = img.associated_image('macro')\n",
+    "<cucim.CuImage path:>\n",
+    "```\n",
+    "You can see the macro image by using the following statements:\n",
+    "\n",
+    "```python\n",
+    ">>> import numpy as np\n",
+    ">>> from PIL import Image\n",
+    ">>> np_img_arr = np.asarray(macro_image)\n",
+    ">>> Image.fromarray(np_img_arr)   \n",
+    "```\n",
+    "\n",
+    "You can check if an associated image with a specific name (e.g., `label`) exists or not, like below:\n",
+    "```python\n",
+    ">>> img.associated_image('label')\n",
+    "<cucim.CuImage path:<null>>\n",
+    "\n",
+    ">>> 'label' in img.associated_images\n",
+    "False\n",
+    "\n",
+    ">>> if not img.associated_image('label'):\n",
+    ">>>     print(\"There is no associated image named 'label'!\")\n",
+    "There is no associated image named 'label'!\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/File-access_Experiments_on_TIFF.ipynb b/notebooks/File-access_Experiments_on_TIFF.ipynb
new file mode 100644
index 000000000..b875591f6
--- /dev/null
+++ b/notebooks/File-access_Experiments_on_TIFF.ipynb
@@ -0,0 +1,543 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# File-access Experiments on TIFF File (since `v0.2.0`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TIFF File Structure\n",
+    "\n",
+    "The following is the structure of the TIFF file.\n",
+    "\n",
+    "Each Image File Directory (IFD) has the information of a sub-resolution (including the main/highest resolution) image as TAGs.\n",
+    "\n",
+    "![](static_images/File-access_Experiments_on_TIFF_FileFormat.png)\n",
+    "\n",
+    "(Above image is from http://paulbourke.net/dataformats/tiff/tiff_summary.pdf [accessed Dec 9th, 2020])\n",
+    "\n",
+    "\n",
+    "For a tiled-multi-resolution TIFF image, `TileWidth` and `TileLength` TAGs of an IFD have tile size information, and `TileOffsets` and `TileByteCounts` TAGs include the information on each tile's the byte offset and the number of (compressed) bytes in the tile.\n",
+    "\n",
+    "([This link](https://libtiff.gitlab.io/libtiff/man/TIFFGetField.3tiff.html) shows all the TAGs available through the `libtiff` library.)\n",
+    "\n",
+    "\n",
+    "![](static_images/File-access_Experiments_on_TIFF_FileFormat2.png)\n",
+    "\n",
+    "(Above image is from https://www.blackice.com/images/Cisco.GIF and https://docs.nframes.com/input-%2526-output/output-formats/  [accessed July 30th, 2020]])\n",
+    "\n",
+    "\n",
+    "Since `TileOffsets` and `TileByteCounts` are an array of numbers to access each tile's raw(compressed) data, it is important to fast-read relevant tiles' compressed RAW image data from the file in any access patterns.\n",
+    "\n",
+    "### Access patterns\n",
+    "\n",
+    "#### 1. Accessing tiles sequentially (left to right, top to bottom) from one TIFF file\n",
+    "\n",
+    "This can happen when a TIFF file is read from a single thread/process to convert/inference without any optimization.\n",
+    "\n",
+    "#### 2. Accessing tiles randomly from one TIFF file\n",
+    "\n",
+    "This access pattern can happen usually on DeepLearning model **inference** use cases.\n",
+    "For inference, only part of images are used, and accessing each tile is not done sequentially.\n",
+    "\n",
+    "For example, a list of regions to be loaded/processed can be split into multiple threads/processes so accessing tiles can be out of order.\n",
+    "Forthermore, (internal) tiles to be read for a specific region (patch) are not necessarily contiguous (e.g., tile index for position[x, y] (0, 0) and (0, 1) wouldn't be contiguous).\n",
+    "\n",
+    "#### 3. Accessing partial tiles randomly from multiple TIFF files\n",
+    "\n",
+    "This access pattern usually happens on DeepLearning model **training** use cases.\n",
+    "\n",
+    "To get unbiased weights of the neural network, it is necessary to provide *randomized* augmented training data during the model training, which means a random partial image region(patch) with the label needs to be picked from possible patch positions and file paths.\n",
+    "\n",
+    "\n",
+    "In the following experiment, we are exploring the implication of the various file access methods on reading partial images with different access patterns.\n",
+    "We didn't experiment with access pattern #3 yet but experiment results for #1 and #2 would give us some insight about the possible improvements.\n",
+    "\n",
+    "## Experiment Setup\n",
+    "\n",
+    "### TIFF File Information\n",
+    "\n",
+    "Information on the TIFF file under experiment:\n",
+    "```bash\n",
+    "# 92344 x 81017 pixels (@highest resolution) JPEG-compressed RGB image. Tile size: 256x256\n",
+    "# (input/image2.tif)\n",
+    "\n",
+    "- file_size         : 3,253,334,884\n",
+    "- tile_count        :       114,437      (at the highest resolution)\n",
+    "- min_tile_bytecount:         1,677\n",
+    "- max_tile_bytecount:        31,361\n",
+    "- avg_tile_bytecount:        17,406.599404038905\n",
+    "- min_tile_offset   :     1,373,824\n",
+    "- max_tile_offset   : 1,993,332,840\n",
+    "```\n",
+    "\n",
+    "### System Information\n",
+    "\n",
+    "- OS: Ubuntu 18.04\n",
+    "- CPU: [Intel(R) Core(TM) i7-7800X CPU @ 3.50GHz](https://www.cpubenchmark.net/cpu.php?cpu=Intel+Core+i7-7800X+%40+3.50GHz&id=3037)\n",
+    "- Memory: 64GB (G-Skill DDR4 2133 16GB X 4)\n",
+    "- Storage\n",
+    "  - NVMe SSD: [Samsung SSD 970 PRO 1TB](https://www.samsung.com/us/computing/memory-storage/solid-state-drives/ssd-970-pro-nvme-m2-1tb-mz-v7p1t0bw/)\n",
+    "  - SATA SSD: [Samsung SSD 850 EVO 1TB](https://www.samsung.com/us/computing/memory-storage/solid-state-drives/ssd-850-evo-2-5-sata-iii-1tb-mz-75e1t0b-am/)\n",
+    "  - HDD: [WDC WD40EZRX-00SPEB0 4TB](http://products.wdc.com/library/SpecSheet/ENG/2879-771438.pdf)\n",
+    "  \n",
+    "### Procedure\n",
+    "\n",
+    "We tried to load all tiles' raw data in the 3GB TIFF image 1) sequentially and 2) randomly with the following methods:\n",
+    "\n",
+    "#### 1) Regular POSIX\n",
+    "\n",
+    "Using [pread()](https://man7.org/linux/man-pages/man2/pread.2.html) with a regular file descriptor, read each tile's raw (compressed) data into CPU memory.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "fd = fs.open(\"image2.tif\", \"rnp\")\n",
+    "...\n",
+    "fd.close()\n",
+    "```\n",
+    "\n",
+    "#### 2) O_DIRECT\n",
+    "\n",
+    "Using [pread()](https://man7.org/linux/man-pages/man2/pread.2.html) with a file descriptor having `O_DIRECT` flag, read each tile's raw (compressed) data into CPU memory.\n",
+    "\n",
+    "cuCIM's filesystem API handles unaligned memory/file offset for direct access (O_DIRECT).\n",
+    "\n",
+    "```python\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "fd = fs.open(\"image2.tif\", \"rp\")\n",
+    "...\n",
+    "fd.close()\n",
+    "```\n",
+    "\n",
+    "#### 3) O_DIRECT pre-load\n",
+    "\n",
+    "Load necessary whole data block at once (with O_DIRECT flag) that is necessary to access all tiles at the highest-resolution, into the temporary CPU memory.\n",
+    "Then, copy the necessary data for each tile into the target buffer.\n",
+    "\n",
+    "#### 4) mmap\n",
+    "\n",
+    "Use [mmap()](https://man7.org/linux/man-pages/man2/mmap.2.html) methods internally.\n",
+    "\n",
+    "```python\n",
+    "import cucim.clara.filesystem as fs\n",
+    "\n",
+    "fd = fs.open(\"image2.tif\", \"rm\")\n",
+    "...\n",
+    "fd.close()\n",
+    "```\n",
+    "\n",
+    "Note:: Actual experiment was done with C++ implementation/APIs.\n",
+    "\n",
+    "## Results\n",
+    "\n",
+    "Link to the spreadsheet: https://docs.google.com/spreadsheets/d/1DbPe0m2KRqlEFbZZTmP9rhDLZdG6mn_Uv8_DrZy97Uc/edit#gid=1257255419\n",
+    "\n",
+    "### NVMe\n",
+    "\n",
+    "![](static_images/File-access_Experiments_on_TIFF_NVMe.png)\n",
+    "\n",
+    "### SSD\n",
+    "\n",
+    "![](static_images/File-access_Experiments_on_TIFF_SSD.png)\n",
+    "\n",
+    "### HDD\n",
+    "\n",
+    "![](static_images/File-access_Experiments_on_TIFF_HDD.png)\n",
+    "\n",
+    "\n",
+    "## Analysis & Implication\n",
+    "\n",
+    "- Reading tile data sequentially doesn't show much difference across configurations (except `O_DIRECT`)\n",
+    "  - Using `O_DIRECT` doesn't perform well due to its unaligned memory access\n",
+    "- Using `O_DIRECT pre-load` approach performs best, and using `mmap` performs better than `Regular POSIX` or `O_DIRECT` methods\n",
+    "  - but `O_DIRECT pre-load` approach requires more CPU memory for pre-loading data so it may not good for the use case where only very small numbers of patches are needed from the file or the list of the patches to load from the file is not available in advance.\n",
+    "  - Using `mmap` for accessing TIFF tiles is a viable solution (current OpenSlide and cuCIM is using Regular POSIX APIs to access tile data) for improving cuCIM's performance and we can leverage `O_DIRECT pre-load` approach depending on the workflow.\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix\n",
+    "\n",
+    "### Code used to measure performance\n",
+    "\n",
+    "The following variables were changed according to the configuration.\n",
+    "\n",
+    "```C++\n",
+    "    constexpr bool SHUFFLE_LIST = true;\n",
+    "    constexpr int iter_max = 32;\n",
+    "    constexpr int skip_count = 2;\n",
+    "```\n",
+    "\n",
+    "```C++\n",
+    "/*\n",
+    " * Copyright (c) 2020, NVIDIA CORPORATION.\n",
+    " *\n",
+    " * Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+    " * you may not use this file except in compliance with the License.\n",
+    " * You may obtain a copy of the License at\n",
+    " *\n",
+    " *     http://www.apache.org/licenses/LICENSE-2.0\n",
+    " *\n",
+    " * Unless required by applicable law or agreed to in writing, software\n",
+    " * distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+    " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+    " * See the License for the specific language governing permissions and\n",
+    " * limitations under the License.\n",
+    " */\n",
+    "\n",
+    "#include \"cuslide/tiff/tiff.h\"\n",
+    "#include \"config.h\"\n",
+    "\n",
+    "#include <catch2/catch.hpp>\n",
+    "#include <fmt/format.h>\n",
+    "#include <cucim/filesystem/cufile_driver.h>\n",
+    "#include <cstdlib>\n",
+    "#include <ctime>\n",
+    "#include <fcntl.h>\n",
+    "#include <unistd.h>\n",
+    "#include <cucim/logger/timer.h>\n",
+    "#include <iostream>\n",
+    "#include <fstream>\n",
+    "#include <sys/stat.h>\n",
+    "#include <sys/mman.h>\n",
+    "\n",
+    "#define ALIGN_UP(x, align_to) (((uint64_t)(x) + ((uint64_t)(align_to)-1)) & ~((uint64_t)(align_to)-1))\n",
+    "#define ALIGN_DOWN(x, align_to) ((uint64_t)(x) & ~((uint64_t)(align_to)-1))\n",
+    "\n",
+    "static void shuffle_offsets(uint32_t count, uint64_t* offsets, uint64_t* bytecounts)\n",
+    "{\n",
+    "    // Fisher-Yates shuffle\n",
+    "    for (int i = 0; i < count; ++i)\n",
+    "    {\n",
+    "        int j = (std::rand() % (count - i)) + i;\n",
+    "        std::swap(offsets[i], offsets[j]);\n",
+    "        std::swap(bytecounts[i], bytecounts[j]);\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "TEST_CASE(\"Verify raw tiff read\", \"[test_read_rawtiff.cpp]\")\n",
+    "{\n",
+    "    cudaError_t cuda_status;\n",
+    "    int err;\n",
+    "    constexpr int BLOCK_SECTOR_SIZE = 4096;\n",
+    "    constexpr bool SHUFFLE_LIST = true;\n",
+    "    constexpr int iter_max = 32;\n",
+    "    constexpr int skip_count = 2;\n",
+    "\n",
+    "    std::srand(std::time(nullptr));\n",
+    "\n",
+    "    auto input_file = g_config.input_file.c_str(); // \"/nvme/image2.tif\"\n",
+    "\n",
+    "    struct stat sb;\n",
+    "    auto fd_temp = ::open(input_file, O_RDONLY);\n",
+    "    fstat(fd_temp, &sb);\n",
+    "    uint64_t test_file_size = sb.st_size;\n",
+    "    ::close(fd_temp);\n",
+    "\n",
+    "    auto tif = std::make_shared<cuslide::tiff::TIFF>(input_file, O_RDONLY);\n",
+    "    tif->construct_ifds();\n",
+    "    tif->ifd(0)->write_offsets_(input_file);\n",
+    "\n",
+    "\n",
+    "    std::ifstream offsets(fmt::format(\"{}.offsets\", input_file), std::ios::in | std::ios::binary);\n",
+    "    std::ifstream bytecounts(fmt::format(\"{}.bytecounts\", input_file), std::ios::in | std::ios::binary);\n",
+    "\n",
+    "    // Read image piece count\n",
+    "    uint32_t image_piece_count_ = 0;\n",
+    "    offsets.read(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));\n",
+    "    bytecounts.read(reinterpret_cast<char*>(&image_piece_count_), sizeof(image_piece_count_));\n",
+    "\n",
+    "    uint64_t image_piece_offsets_[image_piece_count_];\n",
+    "    uint64_t image_piece_bytecounts_[image_piece_count_];\n",
+    "    uint64_t min_bytecount = 9999999999;\n",
+    "    uint64_t max_bytecount = 0;\n",
+    "    uint64_t sum_bytecount = 0;\n",
+    "\n",
+    "    uint64_t min_offset = 9999999999;\n",
+    "    uint64_t max_offset = 0;\n",
+    "    for (uint32_t i = 0; i < image_piece_count_; i++)\n",
+    "    {\n",
+    "        offsets.read((char*)&image_piece_offsets_[i], sizeof(image_piece_offsets_[i]));\n",
+    "        bytecounts.read((char*)&image_piece_bytecounts_[i], sizeof(image_piece_bytecounts_[i]));\n",
+    "\n",
+    "        min_bytecount = std::min(min_bytecount, image_piece_bytecounts_[i]);\n",
+    "        max_bytecount = std::max(max_bytecount, image_piece_bytecounts_[i]);\n",
+    "        sum_bytecount += image_piece_bytecounts_[i];\n",
+    "\n",
+    "        min_offset = std::min(min_offset, image_piece_offsets_[i]);\n",
+    "        max_offset = std::max(max_offset, image_piece_offsets_[i] + image_piece_bytecounts_[i]);\n",
+    "    }\n",
+    "    bytecounts.close();\n",
+    "    offsets.close();\n",
+    "\n",
+    "    fmt::print(\"file_size    : {}\\n\", test_file_size);\n",
+    "    fmt::print(\"min_bytecount: {}\\n\", min_bytecount);\n",
+    "    fmt::print(\"max_bytecount: {}\\n\", max_bytecount);\n",
+    "    fmt::print(\"avg_bytecount: {}\\n\", static_cast<double>(sum_bytecount) / image_piece_count_);\n",
+    "    fmt::print(\"min_offset   : {}\\n\", min_offset);\n",
+    "    fmt::print(\"max_offset   : {}\\n\", max_offset);\n",
+    "\n",
+    "    uint64_t test_size = max_offset + max_bytecount;\n",
+    "\n",
+    "    // Shuffle offsets\n",
+    "    if (SHUFFLE_LIST)\n",
+    "    {\n",
+    "        shuffle_offsets(image_piece_count_, image_piece_offsets_, image_piece_bytecounts_);\n",
+    "    }\n",
+    "\n",
+    "    // Allocate memory\n",
+    "    uint8_t* unaligned_host = static_cast<uint8_t*>(malloc(test_file_size + BLOCK_SECTOR_SIZE * 2));\n",
+    "    uint8_t* buffer_host = static_cast<uint8_t*>(malloc(test_file_size + BLOCK_SECTOR_SIZE * 2));\n",
+    "    uint8_t* aligned_host = reinterpret_cast<uint8_t*>(ALIGN_UP(unaligned_host, BLOCK_SECTOR_SIZE));\n",
+    "\n",
+    "    cucim::filesystem::discard_page_cache(input_file);\n",
+    "\n",
+    "    fmt::print(\"count:{} \\n\", image_piece_count_);\n",
+    "\n",
+    "    SECTION(\"Regular POSIX\")\n",
+    "    {\n",
+    "        fmt::print(\"Regular POSIX\\n\");\n",
+    "\n",
+    "        double total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rpn\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read whole : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                ssize_t read_cnt = fd->pread(aligned_host, test_file_size, 0);\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read whole average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "\n",
+    "        total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rpn\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read tiles : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                for (uint32_t i = 0; i < image_piece_count_; ++i)\n",
+    "                {\n",
+    "                    ssize_t read_cnt = fd->pread(aligned_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);\n",
+    "                }\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read tiles average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "    }\n",
+    "\n",
+    "    SECTION(\"O_DIRECT\")\n",
+    "    {\n",
+    "        fmt::print(\"O_DIRECT\\n\");\n",
+    "\n",
+    "        double total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rp\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read whole : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                ssize_t read_cnt = fd->pread(aligned_host, test_file_size, 0);\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read whole average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "\n",
+    "        total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rp\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read tiles : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                for (uint32_t i = 0; i < image_piece_count_; ++i)\n",
+    "                {\n",
+    "                    ssize_t read_cnt = fd->pread(buffer_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);\n",
+    "                }\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read tiles average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "    }\n",
+    "\n",
+    "    SECTION(\"O_DIRECT pre-load\")\n",
+    "    {\n",
+    "        fmt::print(\"O_DIRECT pre-load\\n\");\n",
+    "\n",
+    "        size_t file_start_offset = ALIGN_DOWN(min_offset, BLOCK_SECTOR_SIZE);\n",
+    "        size_t end_boundary_offset = ALIGN_UP(max_offset + max_bytecount, BLOCK_SECTOR_SIZE);\n",
+    "        size_t large_block_size = end_boundary_offset - file_start_offset;\n",
+    "\n",
+    "        fmt::print(\"- size:{}\\n\", end_boundary_offset - file_start_offset);\n",
+    "\n",
+    "        double total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rp\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- preload : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                ssize_t read_cnt = fd->pread(aligned_host, large_block_size, file_start_offset);\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Preload average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "\n",
+    "        total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rp\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read tiles : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                for (uint32_t i = 0; i < image_piece_count_; ++i)\n",
+    "                {\n",
+    "                    memcpy(buffer_host, aligned_host + image_piece_offsets_[i] - file_start_offset,\n",
+    "                           image_piece_bytecounts_[i]);\n",
+    "                }\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read tiles average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "    }\n",
+    "\n",
+    "    SECTION(\"mmap\")\n",
+    "    {\n",
+    "        fmt::print(\"mmap\\n\");\n",
+    "\n",
+    "        double total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd_mmap = open(input_file, O_RDONLY);\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- open/close : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                void* mmap_host = mmap((void*)0, test_file_size, PROT_READ, MAP_SHARED, fd_mmap, 0);\n",
+    "\n",
+    "                REQUIRE(mmap_host != MAP_FAILED);\n",
+    "\n",
+    "                if (mmap_host != MAP_FAILED)\n",
+    "                {\n",
+    "                    REQUIRE(munmap(mmap_host, test_file_size) != -1);\n",
+    "                    close(fd_mmap);\n",
+    "                }\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- mmap/munmap average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "\n",
+    "        total_elapsed_time = 0;\n",
+    "        for (int iter = 0; iter < iter_max; ++iter)\n",
+    "        {\n",
+    "            cucim::filesystem::discard_page_cache(input_file);\n",
+    "            auto fd = cucim::filesystem::open(input_file, \"rm\");\n",
+    "            {\n",
+    "                cucim::logger::Timer timer(\"- read tiles : {:.7f}\\n\", true, false);\n",
+    "\n",
+    "                for (uint32_t i = 0; i < image_piece_count_; ++i)\n",
+    "                {\n",
+    "                    ssize_t read_cnt = fd->pread(buffer_host, image_piece_bytecounts_[i], image_piece_offsets_[i]);\n",
+    "                }\n",
+    "\n",
+    "                double elapsed_time = timer.stop();\n",
+    "                if (iter >= skip_count)\n",
+    "                {\n",
+    "                    total_elapsed_time += elapsed_time;\n",
+    "                }\n",
+    "                timer.print();\n",
+    "            }\n",
+    "        }\n",
+    "        fmt::print(\"- Read tiles average: {}\\n\", total_elapsed_time / (iter_max - skip_count));\n",
+    "    }\n",
+    "\n",
+    "    free(unaligned_host);\n",
+    "    free(buffer_host);\n",
+    "}\n",
+    "```\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Multi-thread_and_Multi-process_Tests.ipynb b/notebooks/Multi-thread_and_Multi-process_Tests.ipynb
new file mode 100644
index 000000000..067d27ebb
--- /dev/null
+++ b/notebooks/Multi-thread_and_Multi-process_Tests.ipynb
@@ -0,0 +1,721 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi-thread and Multi-process Tests\n",
+    "\n",
+    "In this notebook, we compare cuCIM with OpenSlide in a multi-thread/multi-process environment.\n",
+    "\n",
+    "`input/image2.tif` file (whose size is 92344x81017 and tile size is 256x256) is used.\n",
+    "\n",
+    "Since cuCIM doesn't implement internal cache yet, according to `start_location` variable in the experiment code, cuCIM would have a different performance.\n",
+    "\n",
+    "![](static_images/Multi-thread_and_Multi-process_Tests_Alignment.png)\n",
+    "\n",
+    "For the first case (`start_location = 0`), when we try to read the whole image starting from (0,0) with 256x256 patch size, both OpenSlide and cuCIM would read each time only once.\n",
+    "However, in the second case (`start_location = 1`) that starts reading patch from (1,1), cuCIM would have a disadvantage -- for the second patch (second red box), cuCIM should need four tiles whereas OpenSlide would use only two tiles (two tiles in the middle would be cached when OpenSlide read the first patch)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --force-reinstall *.whl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from contextlib import ContextDecorator\n",
+    "from time import perf_counter\n",
+    "\n",
+    "class Timer(ContextDecorator):\n",
+    "    def __init__(self, message):\n",
+    "        self.message = message\n",
+    "        self.end = None\n",
+    "    def elapsed_time(self):\n",
+    "        self.end = perf_counter()\n",
+    "        return self.end - self.start\n",
+    "    def __enter__(self):\n",
+    "        self.start = perf_counter()\n",
+    "        return self\n",
+    "    def __exit__(self, exc_type, exc, exc_tb):\n",
+    "        if not self.end:\n",
+    "            self.elapsed_time()\n",
+    "        print(\"{} : {}\".format(self.message, self.end - self.start))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multithreading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from openslide import OpenSlide\n",
+    "import concurrent.futures\n",
+    "from cucim import CuImage\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "num_threads = os.cpu_count()\n",
+    "\n",
+    "input_file = \"input/image2.tif\"\n",
+    "start_location = 0\n",
+    "patch_size = 256\n",
+    "\n",
+    "\n",
+    "def load_tile_openslide(slide, start_loc, patch_size):\n",
+    "#     print(start_loc)\n",
+    "    region = slide.read_region(start_loc, 0, [patch_size, patch_size])\n",
+    "#     print(region)\n",
+    "#     print(start_loc)\n",
+    "\n",
+    "def load_tile_cucim(slide, start_loc, patch_size):\n",
+    "    region = slide.read_region(start_loc, [patch_size, patch_size], 0)\n",
+    "openslide_tot_time = 0\n",
+    "cucim_tot_time = 0\n",
+    "for num_workers in range(1, num_threads + 1):\n",
+    "\n",
+    "    print(\"# of thread : {}\".format(num_workers))\n",
+    "    openslide_time = 0\n",
+    "    # (92344 x 81017)\n",
+    "    with OpenSlide(input_file) as slide:\n",
+    "        width, height = slide.dimensions\n",
+    "\n",
+    "        count = 0\n",
+    "        for h in range(start_location, height, patch_size):\n",
+    "            for w in range(start_location, width, patch_size):\n",
+    "                count += 1\n",
+    "        start_loc_iter = ((sx, sy)\n",
+    "                          for sy in range(start_location, height, patch_size)\n",
+    "                              for sx in range(start_location, width, patch_size))\n",
+    "        with Timer(\"  Thread elapsed time (OpenSlide)\") as timer:\n",
+    "            with concurrent.futures.ThreadPoolExecutor(\n",
+    "                max_workers=num_workers\n",
+    "            ) as executor:\n",
+    "                executor.map(\n",
+    "                    lambda start_loc: load_tile_openslide(slide, start_loc, patch_size),\n",
+    "                    start_loc_iter,\n",
+    "                )\n",
+    "            openslide_time = timer.elapsed_time()\n",
+    "            openslide_tot_time += openslide_time\n",
+    "\n",
+    "    cucim_time = 0\n",
+    "    slide = CuImage(input_file)\n",
+    "    start_loc_iter = ((sx, sy)\n",
+    "                      for sy in range(start_location, height, patch_size)\n",
+    "                          for sx in range(start_location, width, patch_size))\n",
+    "    with Timer(\"  Thread elapsed time (cuCIM)\") as timer:\n",
+    "        with concurrent.futures.ThreadPoolExecutor(\n",
+    "            max_workers=num_workers\n",
+    "        ) as executor:\n",
+    "            executor.map(\n",
+    "                lambda start_loc: load_tile_cucim(slide, start_loc, patch_size),\n",
+    "                start_loc_iter,\n",
+    "            )\n",
+    "        cucim_time = timer.elapsed_time()\n",
+    "        cucim_tot_time += cucim_time\n",
+    "    print(\"  Performance gain (OpenSlide/cuCIM): {}\".format(openslide_time / cucim_time))\n",
+    "\n",
+    "print(\"Total time (OpenSlide):\", openslide_tot_time)\n",
+    "print(\"Total time (cuCIM):\", cucim_tot_time)\n",
+    "print(\"Average performance gain (OpenSlide/cuCIM): {}\".format(openslide_tot_time / cucim_tot_time))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### start_location = 0\n",
+    "```\n",
+    "# of thread : 1\n",
+    "  Thread elapsed time (OpenSlide): 203.12034743092954\n",
+    "  Thread elapsed time (cuCIM): 35.13261566311121\n",
+    "# of thread : 2\n",
+    "  Thread elapsed time (OpenSlide): 102.09872915921733\n",
+    "  Thread elapsed time (cuCIM): 19.746847699861974\n",
+    "# of thread : 3\n",
+    "  Thread elapsed time (OpenSlide): 69.23487223219126\n",
+    "  Thread elapsed time (cuCIM): 14.231686793733388\n",
+    "# of thread : 4\n",
+    "  Thread elapsed time (OpenSlide): 53.13889923784882\n",
+    "  Thread elapsed time (cuCIM): 11.085060752928257\n",
+    "# of thread : 5\n",
+    "  Thread elapsed time (OpenSlide): 44.01897697104141\n",
+    "  Thread elapsed time (cuCIM): 9.731189775746316\n",
+    "# of thread : 6\n",
+    "  Thread elapsed time (OpenSlide): 39.78462764201686\n",
+    "  Thread elapsed time (cuCIM): 9.279538444709033\n",
+    "# of thread : 7\n",
+    "  Thread elapsed time (OpenSlide): 39.40460350224748\n",
+    "  Thread elapsed time (cuCIM): 8.312216511927545\n",
+    "# of thread : 8\n",
+    "  Thread elapsed time (OpenSlide): 38.2298303861171\n",
+    "  Thread elapsed time (cuCIM): 8.083018650766462\n",
+    "# of thread : 9\n",
+    "  Thread elapsed time (OpenSlide): 36.2004044582136\n",
+    "  Thread elapsed time (cuCIM): 7.664179248735309\n",
+    "# of thread : 10\n",
+    "  Thread elapsed time (OpenSlide): 35.32523050904274\n",
+    "  Thread elapsed time (cuCIM): 8.259015129879117\n",
+    "# of thread : 11\n",
+    "  Thread elapsed time (OpenSlide): 34.73069435125217\n",
+    "  Thread elapsed time (cuCIM): 7.8271108330227435\n",
+    "# of thread : 12\n",
+    "  Thread elapsed time (OpenSlide): 35.79060472594574\n",
+    "  Thread elapsed time (cuCIM): 8.684423762373626\n",
+    "```\n",
+    "    \n",
+    "### start_location = 1\n",
+    "```\n",
+    "\n",
+    "# of thread : 1\n",
+    "  Thread elapsed time (OpenSlide): 246.3082786342129\n",
+    "  Thread elapsed time (cuCIM): 125.12755820900202\n",
+    "# of thread : 2\n",
+    "  Thread elapsed time (OpenSlide): 123.19027538970113\n",
+    "  Thread elapsed time (cuCIM): 68.67328959237784\n",
+    "# of thread : 3\n",
+    "  Thread elapsed time (OpenSlide): 83.65639087790623\n",
+    "  Thread elapsed time (cuCIM): 46.031415150966495\n",
+    "# of thread : 4\n",
+    "  Thread elapsed time (OpenSlide): 63.73335528932512\n",
+    "  Thread elapsed time (cuCIM): 35.13549166591838\n",
+    "# of thread : 5\n",
+    "  Thread elapsed time (OpenSlide): 52.45986012322828\n",
+    "  Thread elapsed time (cuCIM): 28.303977627772838\n",
+    "# of thread : 6\n",
+    "  Thread elapsed time (OpenSlide): 46.916810180060565\n",
+    "  Thread elapsed time (cuCIM): 25.7577864988707\n",
+    "# of thread : 7\n",
+    "  Thread elapsed time (OpenSlide): 45.930785153992474\n",
+    "  Thread elapsed time (cuCIM): 24.895688469987363\n",
+    "# of thread : 8\n",
+    "  Thread elapsed time (OpenSlide): 45.12975976616144\n",
+    "  Thread elapsed time (cuCIM): 22.422960069030523\n",
+    "# of thread : 9\n",
+    "  Thread elapsed time (OpenSlide): 43.284258441999555\n",
+    "  Thread elapsed time (cuCIM): 22.672365427017212\n",
+    "# of thread : 10\n",
+    "  Thread elapsed time (OpenSlide): 41.37739813886583\n",
+    "  Thread elapsed time (cuCIM): 20.014441611245275\n",
+    "# of thread : 11\n",
+    "  Thread elapsed time (OpenSlide): 40.737238076049834\n",
+    "  Thread elapsed time (cuCIM): 19.632989757228643\n",
+    "# of thread : 12\n",
+    "  Thread elapsed time (OpenSlide): 40.8493790011853\n",
+    "  Thread elapsed time (cuCIM): 19.66802476812154\n",
+    "  ```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiprocessing (method1: Slow)\n",
+    "\n",
+    "For each patch, it open the image file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import concurrent.futures\n",
+    "from itertools import repeat\n",
+    "\n",
+    "import numpy as np\n",
+    "from openslide import OpenSlide\n",
+    "from cucim import CuImage\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "num_processes = os.cpu_count()\n",
+    "\n",
+    "input_file = \"input/image2.tif\"\n",
+    "start_location = 0\n",
+    "patch_size = 256\n",
+    "\n",
+    "\n",
+    "def load_tile_openslide_mp(inp_file, start_loc, patch_size):\n",
+    "    with OpenSlide(inp_file) as slide:\n",
+    "        region = slide.read_region(start_loc, 0, [patch_size, patch_size])\n",
+    "\n",
+    "def load_tile_cucim_mp(inp_file, start_loc, patch_size):\n",
+    "    slide = CuImage(inp_file)\n",
+    "    region = slide.read_region(start_loc, [patch_size, patch_size], 0)\n",
+    "\n",
+    "openslide_tot_time = 0\n",
+    "cucim_tot_time = 0\n",
+    "for num_workers in range(1, num_processes + 1):\n",
+    "\n",
+    "    print(\"# of processes : {}\".format(num_workers))\n",
+    "    openslide_time = 0\n",
+    "    # (92344 x 81017)\n",
+    "    with OpenSlide(input_file) as slide:\n",
+    "        width, height = slide.dimensions\n",
+    "\n",
+    "        start_loc_iter = ((sy, sx)\n",
+    "                          for sy in range(start_location, height, patch_size)\n",
+    "                              for sx in range(start_location, width, patch_size))\n",
+    "\n",
+    "        with Timer(\"  Process elapsed time (OpenSlide)\") as timer:\n",
+    "            with concurrent.futures.ProcessPoolExecutor(\n",
+    "                max_workers=num_workers\n",
+    "            ) as executor:\n",
+    "                executor.map(\n",
+    "                    load_tile_openslide_mp,\n",
+    "                    repeat(input_file),\n",
+    "                    start_loc_iter,\n",
+    "                    repeat(patch_size)\n",
+    "                )\n",
+    "            openslide_time = timer.elapsed_time()\n",
+    "            openslide_tot_time += openslide_time\n",
+    "\n",
+    "    cucim_time = 0\n",
+    "    slide = CuImage(input_file)\n",
+    "    start_loc_iter = ((sy, sx)\n",
+    "                      for sy in range(start_location, height, patch_size)\n",
+    "                          for sx in range(start_location, width, patch_size))\n",
+    "    with Timer(\"  Process elapsed time (cuCIM)\") as timer:\n",
+    "        with concurrent.futures.ProcessPoolExecutor(\n",
+    "            max_workers=num_workers\n",
+    "        ) as executor:\n",
+    "            executor.map(\n",
+    "                load_tile_cucim_mp,\n",
+    "                repeat(input_file),\n",
+    "                start_loc_iter,\n",
+    "                repeat(patch_size)\n",
+    "            )\n",
+    "        cucim_time = timer.elapsed_time()\n",
+    "        cucim_tot_time += cucim_time\n",
+    "    print(\"  Performance gain (OpenSlide/cuCIM): {}\".format(openslide_time / cucim_time))\n",
+    "\n",
+    "print(\"Total time (OpenSlide):\", openslide_tot_time)\n",
+    "print(\"Total time (cuCIM):\", cucim_tot_time)\n",
+    "print(\"Average performance gain (OpenSlide/cuCIM): {}\".format(openslide_tot_time / cucim_tot_time))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### start_location = 0\n",
+    "\n",
+    "```\n",
+    "# of processes : 1\n",
+    "  Process elapsed time (OpenSlide): 441.52679147804156\n",
+    "  Process elapsed time (cuCIM): 208.89401917299256\n",
+    "# of processes : 2\n",
+    "  Process elapsed time (OpenSlide): 235.22407114738598\n",
+    "  Process elapsed time (cuCIM): 115.76672784099355\n",
+    "# of processes : 3\n",
+    "  Process elapsed time (OpenSlide): 169.28083365410566\n",
+    "  Process elapsed time (cuCIM): 91.57951975474134\n",
+    "# of processes : 4\n",
+    "  Process elapsed time (OpenSlide): 138.3362634689547\n",
+    "  Process elapsed time (cuCIM): 78.2894302061759\n",
+    "# of processes : 5\n",
+    "  Process elapsed time (OpenSlide): 121.89170560985804\n",
+    "  Process elapsed time (cuCIM): 74.86900206608698\n",
+    "# of processes : 6\n",
+    "  Process elapsed time (OpenSlide): 110.64038014411926\n",
+    "  Process elapsed time (cuCIM): 71.43692379305139\n",
+    "# of processes : 7\n",
+    "  Process elapsed time (OpenSlide): 101.48756717005745\n",
+    "  Process elapsed time (cuCIM): 74.7042864956893\n",
+    "# of processes : 8\n",
+    "  Process elapsed time (OpenSlide): 96.16556345298886\n",
+    "  Process elapsed time (cuCIM): 71.8208787702024\n",
+    "# of processes : 9\n",
+    "  Process elapsed time (OpenSlide): 92.71181897399947\n",
+    "  Process elapsed time (cuCIM): 72.84391884505749\n",
+    "# of processes : 10\n",
+    "  Process elapsed time (OpenSlide): 91.19949483824894\n",
+    "  Process elapsed time (cuCIM): 78.10580187477171\n",
+    "# of processes : 11\n",
+    "  Process elapsed time (OpenSlide): 91.57920746784657\n",
+    "  Process elapsed time (cuCIM): 78.9079754636623\n",
+    "# of processes : 12\n",
+    "  Process elapsed time (OpenSlide): 90.7518733246252\n",
+    "  Process elapsed time (cuCIM): 76.84036188805476\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiprocessing (method2: Faster)\n",
+    "\n",
+    "For each process, reuse the opened file but submit a job for each patch request."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import concurrent.futures\n",
+    "from itertools import repeat\n",
+    "from functools import partial\n",
+    "\n",
+    "import numpy as np\n",
+    "from openslide import OpenSlide\n",
+    "from cucim import CuImage\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "num_processes = os.cpu_count()\n",
+    "\n",
+    "input_file = \"input/image2.tif\"\n",
+    "start_location = 0\n",
+    "patch_size = 256\n",
+    "\n",
+    "is_process_initialized = False\n",
+    "openslide_obj = None\n",
+    "cucim_obj = None\n",
+    "\n",
+    "\n",
+    "def load_tile_openslide_mp(slide, start_loc, patch_size):\n",
+    "    region = slide.read_region(start_loc, 0, [patch_size, patch_size])\n",
+    "\n",
+    "def proc_init_openslide(inp_file, f, *iters):\n",
+    "    global is_process_initialized, openslide_obj\n",
+    "    if not is_process_initialized:\n",
+    "        is_process_initialized = True\n",
+    "        openslide_obj = OpenSlide(inp_file)\n",
+    "    return f(openslide_obj, *iters)\n",
+    "\n",
+    "def load_tile_cucim_mp(slide, start_loc, patch_size):\n",
+    "    region = slide.read_region(start_loc, [patch_size, patch_size], 0)\n",
+    "\n",
+    "def proc_init_cucim(inp_file, f, *iters):\n",
+    "    global is_process_initialized, cucim_obj\n",
+    "    if not is_process_initialized:\n",
+    "        is_process_initialized = True\n",
+    "        cucim_obj = CuImage(inp_file)\n",
+    "    return f(cucim_obj, *iters)\n",
+    "\n",
+    "openslide_tot_time = 0\n",
+    "cucim_tot_time = 0\n",
+    "for num_workers in range(1, num_processes + 1):\n",
+    "\n",
+    "    print(\"# of processes : {}\".format(num_workers))\n",
+    "    openslide_time = 0\n",
+    "    # (92344 x 81017)\n",
+    "    with OpenSlide(input_file) as slide:\n",
+    "        width, height = slide.dimensions\n",
+    "\n",
+    "        start_loc_iter = ((sx, sy)\n",
+    "                          for sy in range(start_location, height, patch_size)\n",
+    "                              for sx in range(start_location, width, patch_size))\n",
+    "\n",
+    "        with Timer(\"  Process elapsed time (OpenSlide)\") as timer:\n",
+    "            with concurrent.futures.ProcessPoolExecutor(\n",
+    "                max_workers=num_workers\n",
+    "            ) as executor:\n",
+    "                executor.map(\n",
+    "                    partial(proc_init_openslide, input_file, load_tile_openslide_mp),\n",
+    "                    start_loc_iter,\n",
+    "                    repeat(patch_size)\n",
+    "                )\n",
+    "            openslide_time = timer.elapsed_time()\n",
+    "            openslide_tot_time += openslide_time\n",
+    "\n",
+    "    cucim_time = 0\n",
+    "    slide = CuImage(input_file)\n",
+    "    start_loc_iter = ((sx, sy)\n",
+    "                      for sy in range(start_location, height, patch_size)\n",
+    "                          for sx in range(start_location, width, patch_size))\n",
+    "    with Timer(\"  Process elapsed time (cuCIM)\") as timer:\n",
+    "        with concurrent.futures.ProcessPoolExecutor(\n",
+    "            max_workers=num_workers\n",
+    "        ) as executor:\n",
+    "            executor.map(\n",
+    "                partial(proc_init_cucim, input_file, load_tile_cucim_mp),\n",
+    "                start_loc_iter,\n",
+    "                repeat(patch_size)\n",
+    "            )\n",
+    "        cucim_time = timer.elapsed_time()\n",
+    "        cucim_tot_time += cucim_time\n",
+    "    print(\"  Performance gain (OpenSlide/cuCIM): {}\".format(openslide_time / cucim_time))\n",
+    "\n",
+    "print(\"Total time (OpenSlide):\", openslide_tot_time)\n",
+    "print(\"Total time (cuCIM):\", cucim_tot_time)\n",
+    "print(\"Average performance gain (OpenSlide/cuCIM): {}\".format(openslide_tot_time / cucim_tot_time))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### start_location = 0\n",
+    "```\n",
+    "# of processes : 1\n",
+    "  Process elapsed time (OpenSlide): 208.6686624987051\n",
+    "  Process elapsed time (cuCIM): 48.001787026878446\n",
+    "# of processes : 2\n",
+    "  Process elapsed time (OpenSlide): 108.32851185882464\n",
+    "  Process elapsed time (cuCIM): 27.654730859212577\n",
+    "# of processes : 3\n",
+    "  Process elapsed time (OpenSlide): 75.08803005004302\n",
+    "  Process elapsed time (cuCIM): 21.817759499885142\n",
+    "# of processes : 4\n",
+    "  Process elapsed time (OpenSlide): 59.7227668906562\n",
+    "  Process elapsed time (cuCIM): 20.43205594085157\n",
+    "# of processes : 5\n",
+    "  Process elapsed time (OpenSlide): 51.258338663727045\n",
+    "  Process elapsed time (cuCIM): 20.458562731277198\n",
+    "# of processes : 6\n",
+    "  Process elapsed time (OpenSlide): 46.47623342694715\n",
+    "  Process elapsed time (cuCIM): 20.85869163228199\n",
+    "# of processes : 7\n",
+    "  Process elapsed time (OpenSlide): 46.49370166473091\n",
+    "  Process elapsed time (cuCIM): 21.7327726688236\n",
+    "# of processes : 8\n",
+    "  Process elapsed time (OpenSlide): 45.238605635240674\n",
+    "  Process elapsed time (cuCIM): 22.58527811197564\n",
+    "# of processes : 9\n",
+    "  Process elapsed time (OpenSlide): 44.749732580035925\n",
+    "  Process elapsed time (cuCIM): 23.556206807959825\n",
+    "# of processes : 10\n",
+    "  Process elapsed time (OpenSlide): 44.475309615023434\n",
+    "  Process elapsed time (cuCIM): 24.051936954259872\n",
+    "# of processes : 11\n",
+    "  Process elapsed time (OpenSlide): 44.4071687720716\n",
+    "  Process elapsed time (cuCIM): 25.294292493723333\n",
+    "# of processes : 12\n",
+    "  Process elapsed time (OpenSlide): 44.7593243108131\n",
+    "  Process elapsed time (cuCIM): 25.84700824506581\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multiprocessing (method3: Fastest)\n",
+    "\n",
+    "Patch requests are divided into multiple processes and, for each process, request only one job with the list of patch requests."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import concurrent.futures\n",
+    "from itertools import repeat\n",
+    "\n",
+    "import numpy as np\n",
+    "from openslide import OpenSlide\n",
+    "from cucim import CuImage\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "num_processes = os.cpu_count()\n",
+    "\n",
+    "input_file = \"input/image2.tif\"\n",
+    "start_location = 0\n",
+    "patch_size = 256\n",
+    "\n",
+    "\n",
+    "def load_tile_openslide_chunk_mp(inp_file, start_loc_list, patch_size):\n",
+    "    with OpenSlide(inp_file) as slide:\n",
+    "        for start_loc in start_loc_list:\n",
+    "            region = slide.read_region(start_loc, 0, [patch_size, patch_size])\n",
+    "\n",
+    "def load_tile_cucim_chunk_mp(inp_file, start_loc_list, patch_size):\n",
+    "    slide = CuImage(inp_file)\n",
+    "    for start_loc in start_loc_list:\n",
+    "        region = slide.read_region(start_loc, [patch_size, patch_size], 0)\n",
+    "\n",
+    "openslide_tot_time = 0\n",
+    "cucim_tot_time = 0\n",
+    "print(\"Total # of processes : {}\".format(num_processes))\n",
+    "for num_workers in range(1, num_processes + 1):\n",
+    "\n",
+    "    print(\"# of processes : {}\".format(num_workers))\n",
+    "    openslide_time = 0\n",
+    "    # (92344 x 81017)\n",
+    "    with OpenSlide(input_file) as slide:\n",
+    "        width, height = slide.dimensions\n",
+    "\n",
+    "        start_loc_data = [(sx, sy)\n",
+    "                          for sy in range(start_location, height, patch_size)\n",
+    "                              for sx in range(start_location, width, patch_size)]\n",
+    "        chunk_size = len(start_loc_data) // num_workers\n",
+    "        start_loc_list_iter = [start_loc_data[i:i+chunk_size] for i in range(0, len(start_loc_data), chunk_size)]\n",
+    "\n",
+    "\n",
+    "        with Timer(\"  Process elapsed time (OpenSlide)\") as timer:\n",
+    "            with concurrent.futures.ProcessPoolExecutor(\n",
+    "                max_workers=num_workers\n",
+    "            ) as executor:\n",
+    "                executor.map(\n",
+    "                    load_tile_openslide_chunk_mp,\n",
+    "                    repeat(input_file),\n",
+    "                    start_loc_list_iter,\n",
+    "                    repeat(patch_size)\n",
+    "                )\n",
+    "            openslide_time = timer.elapsed_time()\n",
+    "            openslide_tot_time += openslide_time\n",
+    "\n",
+    "    cucim_time = 0\n",
+    "    slide = CuImage(input_file)\n",
+    "    start_loc_data = [(sx, sy)\n",
+    "                      for sy in range(start_location, height, patch_size)\n",
+    "                          for sx in range(start_location, width, patch_size)]\n",
+    "    chunk_size = len(start_loc_data) // num_workers\n",
+    "    start_loc_list_iter = [start_loc_data[i:i+chunk_size] for i in range(0, len(start_loc_data), chunk_size)]\n",
+    "\n",
+    "    with Timer(\"  Process elapsed time (cuCIM)\") as timer:\n",
+    "        with concurrent.futures.ProcessPoolExecutor(\n",
+    "            max_workers=num_workers\n",
+    "        ) as executor:\n",
+    "            executor.map(\n",
+    "                load_tile_cucim_chunk_mp,\n",
+    "                repeat(input_file),\n",
+    "                start_loc_list_iter,\n",
+    "                repeat(patch_size)\n",
+    "            )\n",
+    "        cucim_time = timer.elapsed_time()\n",
+    "        cucim_tot_time += cucim_time\n",
+    "    print(\"  Performance gain (OpenSlide/cuCIM): {}\".format(openslide_time / cucim_time))\n",
+    "\n",
+    "print(\"Total time (OpenSlide):\", openslide_tot_time)\n",
+    "print(\"Total time (cuCIM):\", cucim_tot_time)\n",
+    "print(\"Average performance gain (OpenSlide/cuCIM): {}\".format(openslide_tot_time / cucim_tot_time))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### start_location = 0\n",
+    "```\n",
+    "# of processes : 1\n",
+    "  Process elapsed time (OpenSlide): 198.9614152610302\n",
+    "  Process elapsed time (cuCIM): 34.512199216056615\n",
+    "# of processes : 2\n",
+    "  Process elapsed time (OpenSlide): 101.16406151233241\n",
+    "  Process elapsed time (cuCIM): 18.7446903497912\n",
+    "# of processes : 3\n",
+    "  Process elapsed time (OpenSlide): 68.10482547199354\n",
+    "  Process elapsed time (cuCIM): 12.816827611997724\n",
+    "# of processes : 4\n",
+    "  Process elapsed time (OpenSlide): 51.85946137504652\n",
+    "  Process elapsed time (cuCIM): 9.313994630239904\n",
+    "# of processes : 5\n",
+    "  Process elapsed time (OpenSlide): 41.984213249292225\n",
+    "  Process elapsed time (cuCIM): 7.512824849225581\n",
+    "# of processes : 6\n",
+    "  Process elapsed time (OpenSlide): 37.449110239744186\n",
+    "  Process elapsed time (cuCIM): 6.9438614239916205\n",
+    "# of processes : 7\n",
+    "  Process elapsed time (OpenSlide): 37.975524694658816\n",
+    "  Process elapsed time (cuCIM): 6.320528977084905\n",
+    "# of processes : 8\n",
+    "  Process elapsed time (OpenSlide): 36.37545741070062\n",
+    "  Process elapsed time (cuCIM): 6.549180408939719\n",
+    "# of processes : 9\n",
+    "  Process elapsed time (OpenSlide): 36.17362955166027\n",
+    "  Process elapsed time (cuCIM): 5.6686060433276\n",
+    "# of processes : 10\n",
+    "  Process elapsed time (OpenSlide): 34.56402690522373\n",
+    "  Process elapsed time (cuCIM): 5.5428653210401535\n",
+    "# of processes : 11\n",
+    "  Process elapsed time (OpenSlide): 33.02037419890985\n",
+    "  Process elapsed time (cuCIM): 5.224415393080562\n",
+    "# of processes : 12\n",
+    "  Process elapsed time (OpenSlide): 32.9791039316915\n",
+    "  Process elapsed time (cuCIM): 5.0348134520463645\n",
+    "```\n",
+    "\n",
+    "### start_location = 1\n",
+    "\n",
+    "```\n",
+    "# of processes : 1\n",
+    "  Process elapsed time (OpenSlide): 240.61588192591444\n",
+    "  Process elapsed time (cuCIM): 131.02941245539114\n",
+    "# of processes : 2\n",
+    "  Process elapsed time (OpenSlide): 123.80615371605381\n",
+    "  Process elapsed time (cuCIM): 71.65121614700183\n",
+    "# of processes : 3\n",
+    "  Process elapsed time (OpenSlide): 83.54661530908197\n",
+    "  Process elapsed time (cuCIM): 47.34036159096286\n",
+    "# of processes : 4\n",
+    "  Process elapsed time (OpenSlide): 63.7056167148985\n",
+    "  Process elapsed time (cuCIM): 37.40374026214704\n",
+    "# of processes : 5\n",
+    "  Process elapsed time (OpenSlide): 51.50155539019033\n",
+    "  Process elapsed time (cuCIM): 27.897105684969574\n",
+    "# of processes : 6\n",
+    "  Process elapsed time (OpenSlide): 44.712373277172446\n",
+    "  Process elapsed time (cuCIM): 25.32637894200161\n",
+    "# of processes : 7\n",
+    "  Process elapsed time (OpenSlide): 44.199173680040985\n",
+    "  Process elapsed time (cuCIM): 19.60028947889805\n",
+    "# of processes : 8\n",
+    "  Process elapsed time (OpenSlide): 44.04563747579232\n",
+    "  Process elapsed time (cuCIM): 20.579743378795683\n",
+    "# of processes : 9\n",
+    "  Process elapsed time (OpenSlide): 41.323462426662445\n",
+    "  Process elapsed time (cuCIM): 17.126207023859024\n",
+    "# of processes : 10\n",
+    "  Process elapsed time (OpenSlide): 40.54832462500781\n",
+    "  Process elapsed time (cuCIM): 12.304737649857998\n",
+    "# of processes : 11\n",
+    "  Process elapsed time (OpenSlide): 39.315781021956354\n",
+    "  Process elapsed time (cuCIM): 16.732423092238605\n",
+    "# of processes : 12\n",
+    "  Process elapsed time (OpenSlide): 38.80393008608371\n",
+    "  Process elapsed time (cuCIM): 14.9841771251522\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Single-process_Tests.ipynb b/notebooks/Single-process_Tests.ipynb
new file mode 100644
index 000000000..7303dbcca
--- /dev/null
+++ b/notebooks/Single-process_Tests.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Single-process Tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ./cucim-0.19.0-py3-none-manylinux2014_x86_64.whl\n",
+      "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from cucim==0.19.0) (7.1.2)\n",
+      "cuclara-image is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install *.whl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.1.3)\n",
+      "Requirement already satisfied: altair in /usr/local/lib/python3.6/dist-packages (4.1.0)\n",
+      "Requirement already satisfied: altair_viewer in /usr/local/lib/python3.6/dist-packages (0.3.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2020.1)\n",
+      "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
+      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from altair) (2.11.2)\n",
+      "Requirement already satisfied: entrypoints in /usr/local/lib/python3.6/dist-packages (from altair) (0.3)\n",
+      "Requirement already satisfied: jsonschema in /usr/local/lib/python3.6/dist-packages (from altair) (3.2.0)\n",
+      "Requirement already satisfied: toolz in /usr/local/lib/python3.6/dist-packages (from altair) (0.11.1)\n",
+      "Requirement already satisfied: altair-data-server>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from altair_viewer) (0.4.1)\n",
+      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
+      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->altair) (1.1.1)\n",
+      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (45.2.0)\n",
+      "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (0.17.3)\n",
+      "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (20.2.0)\n",
+      "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from jsonschema->altair) (2.0.0)\n",
+      "Requirement already satisfied: portpicker in /usr/local/lib/python3.6/dist-packages (from altair-data-server>=0.4.0->altair_viewer) (1.3.1)\n",
+      "Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from altair-data-server>=0.4.0->altair_viewer) (6.0.4)\n",
+      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->jsonschema->altair) (3.3.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 20.2.4 is available.\n",
+      "You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install pandas altair altair_viewer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from contextlib import ContextDecorator\n",
+    "from time import perf_counter\n",
+    "\n",
+    "class Timer(ContextDecorator):\n",
+    "    def __init__(self, data, lib, start_location, patch_size):\n",
+    "        self.data = data\n",
+    "        self.lib = lib\n",
+    "        self.start_location = start_location\n",
+    "        self.patch_size = patch_size\n",
+    "    def __enter__(self):\n",
+    "        self.start = perf_counter()\n",
+    "    def __exit__(self, exc_type, exc, exc_tb):\n",
+    "        self.end = perf_counter()\n",
+    "        data.append([self.lib, self.start_location, self.patch_size, self.end - self.start])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cucim import CuImage\n",
+    "from openslide import OpenSlide\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "patch_sizes = (256, 512, 1024) #(32, 64, 128, 256, 512, 1024, 2048, 4096)\n",
+    "\n",
+    "\n",
+    "start_locs = (0, 1, 256, 512, 1024, 4096) #(4096 + 256, 4096 + 256 * 2, 4096 + 256 * 3, 4096 + 256 * 4, 8192, 8192 + 1024, 8192 + 1024 + 1)\n",
+    "repeat = 30\n",
+    "\n",
+    "for start_loc in start_locs:\n",
+    "    data = []\n",
+    "    for patch_size in patch_sizes:\n",
+    "\n",
+    "        # cuCIM\n",
+    "        for i in range(repeat):\n",
+    "            with Timer(data, 'cuCIM(reload)', f'({start_loc},{start_loc})', patch_size):\n",
+    "                img = CuImage(\"input/image.tif\")\n",
+    "                region = img.read_region([start_loc, start_loc], [patch_size, patch_size], 0)\n",
+    "\n",
+    "        img = CuImage(\"input/image.tif\")\n",
+    "        for i in range(repeat):\n",
+    "            with Timer(data, 'cuCIM(reuse)', f'({start_loc},{start_loc})', patch_size):\n",
+    "                region = img.read_region([start_loc, start_loc], [patch_size, patch_size],0)\n",
+    "\n",
+    "        # OpenSlide\n",
+    "        for i in range(repeat):\n",
+    "            with Timer(data, 'openslide(reload)', f'({start_loc},{start_loc})', patch_size):\n",
+    "                img2 = OpenSlide(\"input/image.tif\")\n",
+    "                region2 = img2.read_region([start_loc, start_loc], 0, [patch_size, patch_size])\n",
+    "\n",
+    "        img2 = OpenSlide(\"input/image.tif\")\n",
+    "        for i in range(repeat):\n",
+    "            with Timer(data, 'openslide(reuse)', f'({start_loc},{start_loc})', patch_size):\n",
+    "                region2 = img2.read_region([start_loc, start_loc], 0, [patch_size, patch_size])\n",
+    "\n",
+    "    df = pd.DataFrame(data=data, columns=['Library', 'Start position', 'Patch Size', 'Time'] )\n",
+    "#     print(start_loc)\n",
+    "#     print(\"    cuCIM: {:.5f}\".format(df[repeat*0:][:repeat]['Time'].mean()))\n",
+    "#     print(\"    OpenSlide   : {:.5f}\".format(df[repeat*2:][:repeat]['Time'].mean()))\n",
+    "#     print()\n",
+    "#     print(\"    Gain        : {:.2f}\".format(df[repeat*2:][:repeat]['Time'].mean() / df[repeat*0:][:repeat]['Time'].mean()))\n",
+    "\n",
+    "#     print(\"{},{:.5f},{:.5f},{:.2f}\".format(start_loc, df[repeat*1:][:repeat]['Time'].mean(), df[repeat*3:][:repeat]['Time'].mean(), df[repeat*3:][:repeat]['Time'].mean() / df[repeat*1:][:repeat]['Time'].mean()))\n",
+    "\n",
+    "#     print(\"    OpenSlide   : {:.5f}\".format())\n",
+    "#     print()\n",
+    "#     print(\"    Gain        : {:.2f}\".format(df[repeat*2:][:repeat]['Time'].mean() / df[repeat*0:][:repeat]['Time'].mean()))\n",
+    "            \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Library</th>\n",
+       "      <th>Start position</th>\n",
+       "      <th>Patch Size</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cuCIM(reload)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.000573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cuCIM(reload)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.000489</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cuCIM(reload)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.000434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cuCIM(reload)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.000432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>cuCIM(reload)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>256</td>\n",
+       "      <td>0.000429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>355</th>\n",
+       "      <td>openslide(reuse)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>0.025841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>openslide(reuse)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>0.022839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>357</th>\n",
+       "      <td>openslide(reuse)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>0.025384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>openslide(reuse)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>0.022736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>openslide(reuse)</td>\n",
+       "      <td>(4096,4096)</td>\n",
+       "      <td>1024</td>\n",
+       "      <td>0.025405</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>360 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Library Start position  Patch Size      Time\n",
+       "0     cuCIM(reload)    (4096,4096)         256  0.000573\n",
+       "1     cuCIM(reload)    (4096,4096)         256  0.000489\n",
+       "2     cuCIM(reload)    (4096,4096)         256  0.000434\n",
+       "3     cuCIM(reload)    (4096,4096)         256  0.000432\n",
+       "4     cuCIM(reload)    (4096,4096)         256  0.000429\n",
+       "..                ...            ...         ...       ...\n",
+       "355  openslide(reuse)    (4096,4096)        1024  0.025841\n",
+       "356  openslide(reuse)    (4096,4096)        1024  0.022839\n",
+       "357  openslide(reuse)    (4096,4096)        1024  0.025384\n",
+       "358  openslide(reuse)    (4096,4096)        1024  0.022736\n",
+       "359  openslide(reuse)    (4096,4096)        1024  0.025405\n",
+       "\n",
+       "[360 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.DataFrame(data=data, columns=['Library', 'Start position', 'Patch Size', 'Time'] )\n",
+    "#df.to_csv(f'data_{start_loc}.csv')\n",
+    "#df.groupby(['Patch Size', 'Library']).mean()['Time'].to_csv(f\"time_{start_loc}.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-b384af6a9c894420972599d5b19f2d64\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-b384af6a9c894420972599d5b19f2d64\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-b384af6a9c894420972599d5b19f2d64\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function loadScript(lib) {\n",
+       "      return new Promise(function(resolve, reject) {\n",
+       "        var s = document.createElement('script');\n",
+       "        s.src = paths[lib];\n",
+       "        s.async = true;\n",
+       "        s.onload = () => resolve(paths[lib]);\n",
+       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "      });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else if (typeof vegaEmbed === \"function\") {\n",
+       "      displayChart(vegaEmbed);\n",
+       "    } else {\n",
+       "      loadScript(\"vega\")\n",
+       "        .then(() => loadScript(\"vega-lite\"))\n",
+       "        .then(() => loadScript(\"vega-embed\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-b2ef6b1abafdecae81ac50d67e6bcefd\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"Library\"}, \"row\": {\"type\": \"nominal\", \"field\": \"Patch Size\"}, \"text\": {\"type\": \"quantitative\", \"aggregate\": \"average\", \"field\": \"Time\", \"format\": \".3f\"}, \"x\": {\"type\": \"quantitative\", \"aggregate\": \"average\", \"field\": \"Time\"}, \"y\": {\"type\": \"ordinal\", \"field\": \"Library\"}}, \"height\": 100, \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-b2ef6b1abafdecae81ac50d67e6bcefd\": [{\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0005726590752601624}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004886696115136147}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00043403636664152145}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00043159909546375275}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004289969801902771}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042689405381679535}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004441775381565094}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00045500509440898895}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.000428943894803524}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004233652725815773}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004289420321583748}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042734295129776}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004264404997229576}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004297057166695595}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042553991079330444}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004359520971775055}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004279492422938347}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004255976527929306}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042562466114759445}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004256870597600937}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042326655238866806}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004507843405008316}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004281057044863701}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004215706139802933}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00043417979031801224}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00042715761810541153}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004274342209100723}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004254896193742752}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004271389916539192}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0004245070740580559}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00021638069301843643}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00021066702902317047}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00020264927297830582}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00020075403153896332}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019967462867498398}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019964668899774551}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019963085651397705}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00020536594092845917}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00020072981715202332}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019944552332162857}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019921176135540009}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019909348338842392}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019890908151865005}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.000199158675968647}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019849464297294617}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0001985868439078331}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019847135990858078}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019824691116809845}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019837822765111923}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019830092787742615}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0001988215371966362}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019815005362033844}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0001983409747481346}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019847415387630463}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019841734319925308}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019801780581474304}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019857753068208694}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0002041216939687729}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019982457160949707}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00019855331629514694}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0035852696746587753}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002795286476612091}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0027126027271151543}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002698039636015892}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026813726872205734}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026963306590914726}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026953257620334625}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026816977187991142}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026884758844971657}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026930179446935654}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026825666427612305}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002691091038286686}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002749873325228691}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002712743356823921}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002706412225961685}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002832920290529728}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002993971109390259}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026944326236844063}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002743874676525593}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002700505778193474}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026949215680360794}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002700713463127613}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002685701474547386}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026789605617523193}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.00268398504704237}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026850122958421707}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026786187663674355}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0027419067919254303}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0026948731392621994}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.002683534286916256}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0016877641901373863}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013517113402485847}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013986192643642426}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013443948701024055}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013396777212619781}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013462677597999573}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013417620211839676}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013447040691971779}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.001345483586192131}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013414574787020683}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013405410572886467}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013470035046339035}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.001340688206255436}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013415990397334099}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013958383351564407}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013466542586684227}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013417378067970276}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.001348627731204033}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013412227854132652}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.001340745948255062}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013721277937293053}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013489648699760437}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0015949849039316177}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013639992102980614}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013434924185276031}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013401499018073082}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013459548354148865}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.001358266919851303}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013429271057248116}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 256, \"Time\": 0.0013526305556297302}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0012945439666509628}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0012289918959140778}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00109119713306427}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010607978329062462}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001182056963443756}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001074422150850296}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010715881362557411}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010604513809084892}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010668542236089706}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010679569095373154}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010748347267508507}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010629212483763695}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010678358376026154}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001064489595592022}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010663950815796852}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010588252916932106}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010720184072852135}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010582348331809044}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001065853051841259}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010611489415168762}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010840771719813347}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001065845601260662}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00114351324737072}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0011081015691161156}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0011158473789691925}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010763704776763916}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010742200538516045}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010698670521378517}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0010706819593906403}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.001059134490787983}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000941217876970768}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009446647018194199}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009291833266615868}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009266054257750511}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009409170597791672}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009409477934241295}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000928354449570179}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009271614253520966}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009261053055524826}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009269732981920242}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009315116330981255}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009753666818141937}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000933593139052391}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009239120408892632}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009479383006691933}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009340038523077965}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009314315393567085}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009289011359214783}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000932612456381321}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009277332574129105}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009597130119800568}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009457971900701523}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009384555742144585}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009287791326642036}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009316662326455116}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000933443196117878}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009301826357841492}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0009455177932977676}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000933302566409111}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.000927414745092392}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0078341756016016}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007730958051979542}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076835621148347855}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007666644640266895}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007621467113494873}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007685963995754719}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007687609642744064}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076690129935741425}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076486459001898766}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076629528775811195}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.008100160397589207}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007717229425907135}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007734010927379131}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076710814610123634}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007685880176723003}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007647497579455376}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007667556405067444}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007666990160942078}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007966531440615654}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.008203040808439255}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007788512855768204}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007851390168070793}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007903913967311382}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0077154552564024925}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0077440571039915085}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00770717766135931}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007638309150934219}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.007716347463428974}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0076029179617762566}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.008061446249485016}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.006650196388363838}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005506567656993866}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005431908182799816}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005370596423745155}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0054356688633561134}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00542115792632103}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005460583604872227}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0054773129522800446}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005417836830019951}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005411158315837383}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005382722243666649}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00545916985720396}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0053766220808029175}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005378774367272854}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005424166098237038}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005374333821237087}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005368777550756931}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00544483307749033}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005370002239942551}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005406707525253296}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005449310876429081}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005391483195126057}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005388322286307812}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005434904247522354}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.00542353093624115}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005407250486314297}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005865289829671383}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005467797629535198}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.0054590944200754166}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 512, \"Time\": 0.005521446466445923}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.005305503495037556}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.005452345125377178}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004211354069411755}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004284392111003399}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004131048917770386}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0042612794786691666}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004226210527122021}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004289520904421806}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004136545583605766}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004302581772208214}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0042119696736335754}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004298818297684193}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004190950654447079}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004334080033004284}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004171627573668957}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004319513216614723}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004156599752604961}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004299666732549667}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0041367970407009125}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004231137223541737}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004093722440302372}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.00420913752168417}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0040923673659563065}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004225430078804493}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004060529172420502}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004182884469628334}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004099343903362751}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.00423547625541687}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004044480621814728}, {\"Library\": \"cucim(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004211535677313805}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.004272510297596455}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003829110413789749}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038400767371058464}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038131652399897575}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038711288943886757}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037962133064866066}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038039078935980797}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037678051739931107}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038213124498724937}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038188453763723373}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038240039721131325}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037927450612187386}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037956079468131065}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038328934460878372}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038037383928894997}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038203690201044083}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003797447308897972}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038442760705947876}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003817269578576088}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038321390748023987}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037972936406731606}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003797651268541813}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038301898166537285}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037799160927534103}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0037881694734096527}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003817332908511162}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.00383762177079916}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0038036461919546127}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003793390467762947}, {\"Library\": \"cucim(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.003786042332649231}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02996162697672844}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.030469423159956932}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028383676894009113}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03112758230417967}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028527123853564262}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03119850717484951}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028882659040391445}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03117899876087904}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028393651358783245}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0312110660597682}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028423664160072803}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03151513636112213}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028427683748304844}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03109177015721798}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028519006446003914}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.031136661767959595}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.029203017242252827}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03133974876254797}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028507824055850506}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.031232526525855064}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028520844876766205}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03166852239519358}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028456464409828186}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.031191530637443066}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028555335476994514}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.031219620257616043}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028916853480041027}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.031336864456534386}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.028457168489694595}, {\"Library\": \"openslide(reload)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.03131472133100033}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.027511442080140114}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025807098485529423}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02280346490442753}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02552660834044218}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022794300690293312}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025495524518191814}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022864741273224354}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025818374007940292}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022779468446969986}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02536892145872116}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02274985332041979}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02545754238963127}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022804412990808487}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025896580889821053}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02275237627327442}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02574137505143881}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022734355181455612}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025816483423113823}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022824987769126892}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02541393879801035}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022715328261256218}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0253874771296978}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02275893185287714}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.02569426130503416}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.023655869998037815}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0258414875715971}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022839482873678207}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.025383652187883854}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.022736444137990475}, {\"Library\": \"openslide(reuse)\", \"Start position\": \"(4096,4096)\", \"Patch Size\": 1024, \"Time\": 0.0254053957760334}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import altair as alt\n",
+    "\n",
+    "bars = alt.Chart(df).mark_bar().encode(\n",
+    "    x='average(Time):Q',\n",
+    "    y='Library:O',\n",
+    "    color='Library:N',\n",
+    "    row='Patch Size:N',\n",
+    "    text=alt.Text('average(Time):Q', format='.3f')\n",
+    ").properties(\n",
+    "    width=600,\n",
+    "    height=100\n",
+    ")\n",
+    "bars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cuCIM\n",
+      "End 0.003916740417480469\n",
+      "End 0.0035552978515625\n",
+      "End 0.003542661666870117\n",
+      "End 0.0035364627838134766\n",
+      "End 0.0035610198974609375\n",
+      "End 0.003505706787109375\n",
+      "End 0.0036716461181640625\n",
+      "End 0.003631114959716797\n",
+      "End 0.0035119056701660156\n",
+      "End 0.003532886505126953\n",
+      "\n",
+      "cuCIM (without reopening)\n",
+      "End 0.0032813549041748047\n",
+      "End 0.0032656192779541016\n",
+      "End 0.003276824951171875\n",
+      "End 0.003265380859375\n",
+      "End 0.0032677650451660156\n",
+      "End 0.0032622814178466797\n",
+      "End 0.0032775402069091797\n",
+      "End 0.0032546520233154297\n",
+      "End 0.0032753944396972656\n",
+      "End 0.003261566162109375\n",
+      "\n",
+      "OpenSlide\n",
+      "End 0.011467456817626953\n",
+      "End 0.010767698287963867\n",
+      "End 0.010530948638916016\n",
+      "End 0.010488033294677734\n",
+      "End 0.01053619384765625\n",
+      "End 0.010490179061889648\n",
+      "End 0.010544538497924805\n",
+      "End 0.010465621948242188\n",
+      "End 0.010503292083740234\n",
+      "End 0.010487079620361328\n",
+      "\n",
+      "OpenSlide (without reopening)\n",
+      "End 0.009581804275512695\n",
+      "End 0.005589723587036133\n",
+      "End 0.005550384521484375\n",
+      "End 0.0054779052734375\n",
+      "End 0.0055086612701416016\n",
+      "End 0.005495309829711914\n",
+      "End 0.00547480583190918\n",
+      "End 0.005516529083251953\n",
+      "End 0.005514383316040039\n",
+      "End 0.005527973175048828\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "print(\"cuCIM\")\n",
+    "for i in range(10):\n",
+    "    s = time.time()\n",
+    "    img = CuImage(\"input/image.tif\")\n",
+    "    region = img.read_region([10000,10000], [512,512], 0)\n",
+    "    e = time.time()\n",
+    "    print(\"End\", e - s)\n",
+    "print()\n",
+    "\n",
+    "print(\"cuCIM (without reopening)\")\n",
+    "for i in range(10):\n",
+    "    s = time.time()\n",
+    "    region = img.read_region([10000,10000], [512,512], 0)\n",
+    "    e = time.time()\n",
+    "    print(\"End\", e - s)\n",
+    "print()\n",
+    "\n",
+    "from openslide import OpenSlide\n",
+    "\n",
+    "print(\"OpenSlide\")\n",
+    "for i in range(10):\n",
+    "    s = time.time()\n",
+    "    img2 = OpenSlide(\"input/image.tif\")\n",
+    "    region2 = img2.read_region([10000,10000], 0, [512,512])\n",
+    "    e = time.time()\n",
+    "    print(\"End\", e-s)\n",
+    "\n",
+    "    img2 = OpenSlide(\"input/image.tif\")\n",
+    "print()\n",
+    "\n",
+    "print(\"OpenSlide (without reopening)\")\n",
+    "for i in range(10):\n",
+    "    s = time.time()\n",
+    "    region2 = img2.read_region([10000,10000], 0, [512,512])\n",
+    "    e = time.time()\n",
+    "    print(\"End\", e-s)\n",
+    "print()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Welcome.ipynb b/notebooks/Welcome.ipynb
new file mode 100644
index 000000000..9b19736d1
--- /dev/null
+++ b/notebooks/Welcome.ipynb
@@ -0,0 +1,41 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "willing-protest",
+   "metadata": {},
+   "source": [
+    "# Welcome to cuCIM\n",
+    "\n",
+    "- [Basic Usage](Basic_Usage.ipynb)\n",
+    "- [Accessing File with GDS](Accessing_File_with_GDS.ipynb)\n",
+    "- [File-access Experiments on TIFF File](File-access_Experiments_on_TIFF.ipynb)\n",
+    "- [Working with DALI](Working_with_DALI.ipynb)\n",
+    "- [Single-process Tests](Single-process_Tests.ipynb)\n",
+    "- [Multi-thread and Multi-process Tests](Multi-thread_and_Multi-process_Tests.ipynb)\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Working_with_Albumentation.ipynb b/notebooks/Working_with_Albumentation.ipynb
new file mode 100644
index 000000000..25e336dbe
--- /dev/null
+++ b/notebooks/Working_with_Albumentation.ipynb
@@ -0,0 +1,395 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with Albumentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "INPUT_PATH = '0486052bb.tiff'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cucim import CuImage\n",
+    "\n",
+    "img = CuImage(INPUT_PATH)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "def visualize(image):\n",
+    "    dpi = 80.0\n",
+    "    height, width, _ = image.shape\n",
+    "    plt.figure(figsize=(width / dpi, height / dpi))\n",
+    "    plt.axis('off')\n",
+    "    plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[34937, 25784]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x460.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "import numpy as np\n",
+    "# Read whole slide at the lowest redsolution\n",
+    "resolutions = img.resolutions\n",
+    "level_count = resolutions[\"level_count\"]\n",
+    "print(img.size('XY'))\n",
+    "region = img.read_region(location=[10000, 10000], size=(512, 512), level=0)\n",
+    "\n",
+    "#Image.fromarray(np.asarray(region))\n",
+    "visualize(region)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Cupy\n",
+    "\n",
+    "CuImage object can be converted into Cupy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x460.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cupy as cp\n",
+    "\n",
+    "img = CuImage(INPUT_PATH)\n",
+    "region = np.asarray(img.read_region((10000, 10000), (512, 512)))\n",
+    "visualize(cp.asarray(region).get())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stain separation with cuCIM's `scikit-image`-compatible filter API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1008x1008 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The following code is modified from https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_ihc_color_separation.html#sphx-glr-auto-examples-color-exposure-plot-ihc-color-separation-py\n",
+    "#\n",
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
+    "\n",
+    "from cucim.skimage import color\n",
+    "\n",
+    "#transfer our array to the device\n",
+    "ihc_rgb = cp.asarray(region)\n",
+    "\n",
+    "# transform to colorspace where the stains are separated\n",
+    "ihc_hed = color.rgb2hed(ihc_rgb)\n",
+    "\n",
+    "# Create an RGB image for visualizing each of the stains\n",
+    "null = cp.zeros_like(ihc_hed[:, :, 0])\n",
+    "ihc_h = color.hed2rgb(cp.stack((ihc_hed[:, :, 0], null, null), axis=-1))\n",
+    "ihc_e = color.hed2rgb(cp.stack((null, ihc_hed[:, :, 1], null), axis=-1))\n",
+    "ihc_d = color.hed2rgb(cp.stack((null, null, ihc_hed[:, :, 2]), axis=-1))\n",
+    "\n",
+    "# Transfer each color image back to the CPU prior to visualization\n",
+    "ihc_h, ihc_e, ihc_d = map(cp.asnumpy, [ihc_h, ihc_e, ihc_d])\n",
+    "\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 14), sharex=True, sharey=True)\n",
+    "fontdict = dict(fontsize=18, fontweight='bold')\n",
+    "\n",
+    "ax = axes.ravel()\n",
+    "ax[0].imshow(cp.asnumpy(ihc_rgb))\n",
+    "ax[0].set_title(\"Original image\", fontdict=fontdict)\n",
+    "ax[1].imshow(ihc_h)\n",
+    "ax[1].set_title(\"Hematoxylin\", fontdict=fontdict)\n",
+    "ax[2].imshow(ihc_e)\n",
+    "ax[2].set_title(\"Eosin\", fontdict=fontdict)\n",
+    "ax[3].imshow(ihc_d)\n",
+    "ax[3].set_title(\"DAB\", fontdict=fontdict)\n",
+    "for a in ax.ravel():\n",
+    "    a.axis('off')\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Applying Albumentation\n",
+    "\n",
+    "[Albumentation](https://albumentations.ai/) is a popular library for image augmentation in DeepLearning. It has [transforms interface](https://albumentations.ai/docs/api_reference/core/transforms_interface/) that we can add additional operations.\n",
+    "\n",
+    "Many operations in Albumentation inherits [DualTransform](https://albumentations.ai/docs/api_reference/core/transforms_interface/#albumentations.core.transforms_interface.DualTransform) interface, but here we provide an example of using [ImageOnlyTransform](https://albumentations.ai/docs/api_reference/core/transforms_interface/#albumentations.core.transforms_interface.ImageOnlyTransform) interface with [Compose](https://albumentations.ai/docs/api_reference/core/composition/#albumentations.core.composition.Compose)\n",
+    "\n",
+    "The example uses scikit-image's [resize](https://scikit-image.org/docs/dev/api/skimage.transform.html?highlight=resize#skimage.transform.resize) API.\n",
+    "\n",
+    "Since its output data type is float64, we need to use [img_as_ubyte](https://scikit-image.org/docs/dev/api/skimage.util.html#skimage.util.img_as_ubyte) utility method to convert back to 8-bit RGB image.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install albumentations\n",
+    "try:\n",
+    "    import albumentations\n",
+    "except ImportError:\n",
+    "    raise ImportError(\"This example requires albumentations.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "float64\n",
+      "uint8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 115.2x115.2 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import matplotlib.pyplot as plt\n",
+    "from cucim.skimage.transform import resize\n",
+    "from cucim.skimage.util import img_as_ubyte\n",
+    "import cupy as cp\n",
+    "\n",
+    "resized_image = resize(cp.asarray(region),(128, 128))\n",
+    "# PIL.Image accepts only 8-bit image and resized_image has float64 data\n",
+    "# See https://scikit-image.org/docs/dev/user_guide/data_types.html#input-types\n",
+    "print(resized_image.dtype)\n",
+    "# Convert to 8-bit image\n",
+    "resized_image = img_as_ubyte(resized_image)\n",
+    "print(resized_image.dtype)\n",
+    "\n",
+    "#Image.fromarray(resized_image.get()) \n",
+    "visualize(resized_image.get())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading and Resize Image with Albumentation's Compose method\n",
+    "\n",
+    "We can define `Resize` operation that inherits `ImageOnlyTransform` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 460.8x460.8 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 230.4x230.4 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "import cv2\n",
+    "from matplotlib import pyplot as plt\n",
+    "import albumentations as A\n",
+    "\n",
+    "from albumentations import Compose\n",
+    "from albumentations.core.transforms_interface import ImageOnlyTransform\n",
+    "from cucim import CuImage\n",
+    "from cucim.skimage.transform import resize\n",
+    "from cucim.skimage.util import img_as_ubyte\n",
+    "import numpy as np\n",
+    "import cupy as cp\n",
+    "\n",
+    "def visualize(image):\n",
+    "    dpi = 80.0\n",
+    "    height, width, _ = image.shape\n",
+    "    plt.figure(figsize=(width / dpi, height / dpi))\n",
+    "    plt.axis('off')\n",
+    "    plt.imshow(image)\n",
+    "\n",
+    "class Resize(ImageOnlyTransform):\n",
+    "    def __init__(self, height, width, order=None, mode='reflect', cval=0, clip=True, preserve_range=False, anti_aliasing=None,\n",
+    "                 anti_aliasing_sigma=None, **params):\n",
+    "        super().__init__(self)\n",
+    "        self.height = height\n",
+    "        self.width = width\n",
+    "#         self.order = order\n",
+    "#         self.mode = mode\n",
+    "#         self.cval = cval\n",
+    "#         self.clip = clip\n",
+    "#         self.preserve_range = preserve_range\n",
+    "#         self.anti_aliasing = anti_aliasing\n",
+    "\n",
+    "    def apply(self, img, **params):\n",
+    "        # Note: output_shape argument is (height, width)\n",
+    "        resized_image = img_as_ubyte(resize(cp.asarray(region), (256, 256))).get() \n",
+    "        return resized_image\n",
+    "\n",
+    "img = CuImage(INPUT_PATH)\n",
+    "image = np.asarray(img.read_region((10000, 10000), (512, 512)))\n",
+    "\n",
+    "visualize(image)\n",
+    "\n",
+    "transform = Compose([\n",
+    "    # Note: input argument is (height, width), not (width, height)\n",
+    "    Resize(256, 256), \n",
+    "])\n",
+    "\n",
+    "augmented_image = transform(image=image)['image']\n",
+    "\n",
+    "visualize(augmented_image)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, to adapt Albumentation, we convert numpy array to Cupy array and copy back to numpy array after processing.\n",
+    "\n",
+    "In practice, it is better to avoid copying back to CPU memory so better to combining GPU operations in a single transform operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/Working_with_DALI.ipynb b/notebooks/Working_with_DALI.ipynb
new file mode 100644
index 000000000..7152109cc
--- /dev/null
+++ b/notebooks/Working_with_DALI.ipynb
@@ -0,0 +1,289 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working with DALI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing ./cuclara_image-0.2.0-py3-none-manylinux2014_x86_64.whl\n",
+      "Collecting click\n",
+      "  Using cached click-7.1.2-py2.py3-none-any.whl (82 kB)\n",
+      "Installing collected packages: click, cuclara-image\n",
+      "  Attempting uninstall: click\n",
+      "    Found existing installation: click 7.1.2\n",
+      "    Uninstalling click-7.1.2:\n",
+      "      Successfully uninstalled click-7.1.2\n",
+      "  Attempting uninstall: cuclara-image\n",
+      "    Found existing installation: cuclara-image 0.2.0\n",
+      "    Uninstalling cuclara-image-0.2.0:\n",
+      "      Successfully uninstalled cuclara-image-0.2.0\n",
+      "Successfully installed click-7.1.2 cuclara-image-0.2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --force-reinstall *.whl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.3.4)\n",
+      "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.19.5)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (8.1.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.3.1)\n",
+      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib) (1.15.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interoperability with DALI through Python Custom Operator\n",
+    "\n",
+    "<https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/custom_operations/python_operator.html#>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cucim import CuImage\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from nvidia.dali.pipeline import Pipeline\n",
+    "import nvidia.dali.ops as ops\n",
+    "import numpy as np\n",
+    "\n",
+    "def filter_images(image):\n",
+    "    # Filter with CuImage's filter methods (future work)\n",
+    "    #   example)\n",
+    "    #     from cucim import filters\n",
+    "    #     filtered_image = filters.sobel(image)\n",
+    "    filtered_image = image\n",
+    "    return filtered_image\n",
+    "\n",
+    "def gen_image(file_name, location_list, size_list, level_list, batch_size):\n",
+    "    image = CuImage(file_name)\n",
+    "    batch = []\n",
+    "    for location, size, level in zip(location_list, size_list, level_list):\n",
+    "        region = image.read_region(location=location, size=size, level=level)\n",
+    "        batch.append(np.asarray(region))\n",
+    "        if (len(batch) == batch_size):\n",
+    "            yield (batch,)\n",
+    "            batch = []\n",
+    "\n",
+    "class DPLoaderPipeline(Pipeline):\n",
+    "    def __init__(self, file_name, location_list, size_list, level_list, batch_size, num_threads, device_id):\n",
+    "        super(DPLoaderPipeline, self).__init__(batch_size, num_threads, device_id, exec_async=False,\n",
+    "                                             exec_pipelined=False, seed=99)\n",
+    "        self.input = ops.ExternalSource(\n",
+    "            source=gen_image(file_name, location_list, size_list, level_list, batch_size), num_outputs=1\n",
+    "        )\n",
+    "        self.filter = ops.PythonFunction(function=filter_images, num_outputs=1)\n",
+    "        self.resize = ops.Resize(resize_x=500, resize_y=500)\n",
+    "\n",
+    "    def define_graph(self):\n",
+    "        image = self.input()\n",
+    "        filtered_image = self.filter(image[0])\n",
+    "        return self.resize(filtered_image)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 2304x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 2304x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.gridspec as gridspec\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "batch_size = 4\n",
+    "\n",
+    "def show_images(image_batch):\n",
+    "    columns = 4\n",
+    "    rows = (batch_size + 1) // (columns)\n",
+    "    fig = plt.figure(figsize = (32,(32 // columns) * rows))\n",
+    "    gs = gridspec.GridSpec(rows, columns)\n",
+    "    for j in range(rows*columns):\n",
+    "        plt.subplot(gs[j])\n",
+    "        plt.axis(\"off\")\n",
+    "        plt.imshow(image_batch.at(j))\n",
+    "\n",
+    "file_name = \"input/image.tif\"\n",
+    "location_list = [    (5000, 5000), (5000, 5000), (5000, 5000), (5000, 5000), (5000, 5000), (5000, 5000), (5000, 5000), (5000, 5000)]\n",
+    "size_list     = [        (64, 64),   (128, 128),   (512, 512),   (800, 800),   (800, 800),   (800, 800),   (800, 800),   (800, 800)]\n",
+    "level_list    = [               0,            0,            0,            0,           1,             2,            3,            4]\n",
+    "\n",
+    "num_threads = 1\n",
+    "pipe = DPLoaderPipeline(file_name, location_list, size_list, level_list, batch_size, num_threads, 0)\n",
+    "pipe.build()\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        output = pipe.run()        \n",
+    "        show_images(output[0])\n",
+    "    except StopIteration:\n",
+    "        break\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Limitations of Python operators\n",
+    "\n",
+    "<https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/custom_operations/python_operator.html#Limitations-of-Python-Operators>\n",
+    "\n",
+    "> As we could see, pipelines that incorporate Python operators has to be constructed with `exec_async=False` and `exec_pipelined=False` specified. It was necesary to make it possible to call Python code from inside of DALI but it hits the performance of data pipelines. In addition to that, Python operators cannot utilize more than one CPU core due to Python threading model. Taking all that into account, Python operators can be very useful for testing, debugging or prototyping, but are not considered as a production level solution for extending DALI."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interoperability with DALI through C++ Plugin (future work)\n",
+    "\n",
+    "<https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/custom_operations/custom_operator/create_a_custom_operator.html#Operator-Definition>\n",
+    "\n",
+    "By linking cuCIM's C++ library, we can make universal cuCIM adaptors for `cucim::io::format::IImageFormat` (image file loading/saving) and `cucim::filter::IImageFilter` (image filtering) interfaces.\n",
+    "\n",
+    "**Example**\n",
+    "\n",
+    "```python\n",
+    "import nvidia.dali.plugin_manager as plugin_manager\n",
+    "plugin_manager.load_library('./cucim-adaptor/build/libcucim_format.so') # CuImageReader/CuImageWriter\n",
+    "plugin_manager.load_library('./cucim-adaptor/build/libcucim_filter.so') # CuImageFilter\n",
+    "\n",
+    "import nvidia.dali.ops as ops\n",
+    "\n",
+    "image_dir = \"data/images\"\n",
+    "batch_size = 2\n",
+    "\n",
+    "class CuImagePipeline(Pipeline):\n",
+    "    def __init__(self, file_list, params_list, label_list, batch_size, num_threads, device_id):\n",
+    "        super(CuImagePipeline, self).__init__(batch_size, num_threads, device_id, seed = 12)\n",
+    "        self.image = ops.CuImageReader(file_list, params_list, labels=label_list, device = 'cpu')\n",
+    "        self.filter = ops.CuImageFilter(\"sobel\", axis=1, mode='reflect')\n",
+    "\n",
+    "    def define_graph(self):\n",
+    "        images, labels = self.image()\n",
+    "        filtered_images = self.filter(images)\n",
+    "        return (filtered_images, labels)\n",
+    "\n",
+    "# Input list (can use generator/iterator)\n",
+    "file_list   = [\"input/image.tif\", \"input/image2.tif\"]\n",
+    "params_list = [{'location': (100, 100), 'size': (256, 256), 'level': 0}, {'location': (500, 500), 'size': (256, 256), 'level': 1}]\n",
+    "label_list  = ['tumor', 'tissue']\n",
+    "\n",
+    "num_threads = 1\n",
+    "pipe = CuImagePipeline(file_list, params_list, label_list, batch_size, num_threads, 0)\n",
+    "pipe.build()\n",
+    "\n",
+    "output = pipe.run()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TODO\n",
+    "\n",
+    "- Support DALI's CPU/GPU Tensor: <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/data_types.html#tensor>\n",
+    "- Provide universal cucim adaptors for DALI (for `cucim::io::format::IImageFormat` and `cucim::filter::IImageFilter` interfaces)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/gabor_example.ipynb b/notebooks/gabor_example.ipynb
new file mode 100644
index 000000000..90cca95e7
--- /dev/null
+++ b/notebooks/gabor_example.ipynb
@@ -0,0 +1,235 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "accessible-promise",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rotated images matched against references using Gabor filter banks:\n",
+      "original: brick, rotated: 30deg, match result: brick\n",
+      "original: brick, rotated: 70deg, match result: brick\n",
+      "original: grass, rotated: 145deg, match result: brick\n",
+      "Duration cpu = 6.379706621170044 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x1008 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rotated images matched against references using Gabor filter banks:\n",
+      "original: brick, rotated: 30deg, match result: brick\n",
+      "original: brick, rotated: 70deg, match result: brick\n",
+      "original: grass, rotated: 145deg, match result: brick\n",
+      "Duration gpu = 0.14396929740905762 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x1008 with 20 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU Acceleration = 44.3130\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "import cupy as cp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from skimage import data\n",
+    "\n",
+    "durations = {}\n",
+    "for use_gpu in (False, True):\n",
+    "\n",
+    "    if use_gpu:\n",
+    "        from cupyx.scipy import ndimage as ndi\n",
+    "        from cucim.skimage.util import img_as_float32\n",
+    "        from cucim.skimage.filters import gabor_kernel\n",
+    "        xp = cp\n",
+    "        asnumpy = cp.asnumpy\n",
+    "        device_name = \"gpu\"\n",
+    "    else:\n",
+    "        from scipy import ndimage as ndi\n",
+    "        from skimage.util import img_as_float32\n",
+    "        from skimage.filters import gabor_kernel\n",
+    "        xp = np\n",
+    "        asnumpy = np.asarray\n",
+    "        device_name = \"cpu\"\n",
+    "\n",
+    "        \n",
+    "    def compute_feats(image, kernels):\n",
+    "        feats = xp.zeros((len(kernels), 2), dtype=np.double)\n",
+    "        for k, kernel in enumerate(kernels):\n",
+    "            filtered = ndi.convolve(image, kernel, mode='wrap')\n",
+    "            feats[k, 0] = filtered.mean()\n",
+    "            feats[k, 1] = filtered.var()\n",
+    "        return feats\n",
+    "\n",
+    "\n",
+    "    def match(feats, ref_feats):\n",
+    "        min_error = np.inf\n",
+    "        min_i = None\n",
+    "        for i in range(ref_feats.shape[0]):\n",
+    "            error = xp.sum((feats - ref_feats[i, :])**2)\n",
+    "            if error < min_error:\n",
+    "                min_error = error\n",
+    "                min_i = i\n",
+    "        return min_i\n",
+    "\n",
+    "    tstart = time.time()\n",
+    "\n",
+    "    # prepare filter bank kernels\n",
+    "    kernels = []\n",
+    "    for theta in range(4):\n",
+    "        theta = theta / 4. * np.pi\n",
+    "        for sigma in (1, 3):\n",
+    "            for frequency in (0.05, 0.25):\n",
+    "                kernel = gabor_kernel(frequency, theta=theta,\n",
+    "                                      sigma_x=sigma, sigma_y=sigma)\n",
+    "                kernels.append(kernel.real)\n",
+    "\n",
+    "\n",
+    "    #shrink = (slice(0, None, 3), slice(0, None, 3))\n",
+    "    brick = img_as_float32(xp.asarray(data.brick()))  # [shrink]\n",
+    "    grass = img_as_float32(xp.asarray(data.grass()))  # [shrink]\n",
+    "    gravel = img_as_float32(xp.asarray(data.gravel()))  # [shrink]\n",
+    "    image_names = ('brick', 'grass', 'gravel')\n",
+    "    images = (brick, grass, gravel)\n",
+    "\n",
+    "    # prepare reference features\n",
+    "    ref_feats = xp.zeros((3, len(kernels), 2), dtype=np.double)\n",
+    "    ref_feats[0, :, :] = compute_feats(brick, kernels)\n",
+    "    ref_feats[1, :, :] = compute_feats(grass, kernels)\n",
+    "    ref_feats[2, :, :] = compute_feats(gravel, kernels)\n",
+    "\n",
+    "    print('Rotated images matched against references using Gabor filter banks:')\n",
+    "\n",
+    "    print('original: brick, rotated: 30deg, match result: ', end='')\n",
+    "    feats = compute_feats(ndi.rotate(brick, angle=190, reshape=False), kernels)\n",
+    "    print(image_names[match(feats, ref_feats)])\n",
+    "\n",
+    "    print('original: brick, rotated: 70deg, match result: ', end='')\n",
+    "    feats = compute_feats(ndi.rotate(brick, angle=70, reshape=False), kernels)\n",
+    "    print(image_names[match(feats, ref_feats)])\n",
+    "\n",
+    "    print('original: grass, rotated: 145deg, match result: ', end='')\n",
+    "    feats = compute_feats(ndi.rotate(grass, angle=145, reshape=False), kernels)\n",
+    "    print(image_names[match(feats, ref_feats)])\n",
+    "\n",
+    "\n",
+    "    def power(image, kernel):\n",
+    "        # Normalize images for better comparison.\n",
+    "        image = (image - image.mean()) / image.std()\n",
+    "        return xp.sqrt(ndi.convolve(image, kernel.real, mode='wrap')**2 +\n",
+    "                       ndi.convolve(image, kernel.imag, mode='wrap')**2)\n",
+    "\n",
+    "    # Plot a selection of the filter bank kernels and their responses.\n",
+    "    results = []\n",
+    "    kernel_params = []\n",
+    "    for theta in (0, 1):\n",
+    "        theta = theta / 4. * np.pi\n",
+    "        for frequency in (0.1, 0.4):\n",
+    "            kernel = gabor_kernel(frequency, theta=theta)\n",
+    "            params = 'theta=%d,\\nfrequency=%.2f' % (theta * 180 / np.pi, frequency)\n",
+    "            kernel_params.append(params)\n",
+    "            # Save kernel and the power image for each image\n",
+    "            results.append((kernel, xp.stack([power(img, kernel) for img in images])))\n",
+    "            \n",
+    "    dur = time.time() - tstart\n",
+    "    print(f\"Duration {device_name} = {dur} s\")\n",
+    "    durations[device_name] = dur\n",
+    "\n",
+    "    fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(12, 14))\n",
+    "    plt.gray()\n",
+    "\n",
+    "    fig.suptitle('Image responses for Gabor filter kernels', fontsize=12)\n",
+    "\n",
+    "    axes[0][0].axis('off')\n",
+    "\n",
+    "    # Plot original images\n",
+    "    for label, img, ax in zip(image_names, images, axes[0][1:]):\n",
+    "        ax.imshow(asnumpy(img))\n",
+    "        ax.set_title(label, fontsize=9)\n",
+    "        ax.axis('off')\n",
+    "\n",
+    "    for label, (kernel, powers), ax_row in zip(kernel_params, results, axes[1:]):\n",
+    "        # Plot Gabor kernel\n",
+    "        ax = ax_row[0]\n",
+    "        ax.imshow(asnumpy(kernel.real))\n",
+    "        ax.set_ylabel(label, fontsize=7)\n",
+    "        ax.set_xticks([])\n",
+    "        ax.set_yticks([])\n",
+    "\n",
+    "        # Plot Gabor responses with the contrast normalized for each filter\n",
+    "        vmin = float(powers.min())\n",
+    "        vmax = float(powers.max())\n",
+    "        for patch, ax in zip(powers, ax_row[1:]):\n",
+    "            ax.imshow(asnumpy(patch), vmin=vmin, vmax=vmax)\n",
+    "            ax.axis('off')\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "print(f\"GPU Acceleration = {durations['cpu']/durations['gpu']:0.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "durable-johnson",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/input/README.md b/notebooks/input/README.md
new file mode 100644
index 000000000..eb5aefaa6
--- /dev/null
+++ b/notebooks/input/README.md
@@ -0,0 +1,13 @@
+
+# Test Dataset
+
+TUPAC-TR-488.svs and TUPAC-TR-467.svs are from the dataset
+of Tumor Proliferation Assessment Challenge 2016 (TUPAC16 | MICCAI Grand Challenge).
+
+- Website: http://tupac.tue-image.nl/node/3
+- Data link: https://drive.google.com/drive/u/0/folders/0B--ztKW0d17XYlBqOXppQmw0M2M
+
+## Converted files
+
+- image.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-467.svs
+- image2.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-488.svs
diff --git a/notebooks/random_walker_example.ipynb b/notebooks/random_walker_example.ipynb
new file mode 100644
index 000000000..5b94d3091
--- /dev/null
+++ b/notebooks/random_walker_example.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "dense-standard",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data.shape = (1500, 1500)\n",
+      "data.dtype=float32\n",
+      "Duration cpu = 4.279931545257568 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x345.6 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data.shape = (1500, 1500)\n",
+      "data.dtype=float32\n",
+      "Duration gpu = 0.2667715549468994 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x345.6 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU Acceleration = 16.0434\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "import cupy as cp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "durations = {}\n",
+    "for use_gpu in (False, True):\n",
+    "\n",
+    "    length = 1500\n",
+    "    blob_size_fraction = 0.025\n",
+    "    if use_gpu:\n",
+    "        from cucim import skimage\n",
+    "        from cucim.skimage.exposure import rescale_intensity\n",
+    "        from cucim.skimage.segmentation import random_walker\n",
+    "        try:\n",
+    "            from cucim.skimage.data import binary_blobs\n",
+    "            blobs = binary_blobs(length=length, seed=1, blob_size_fraction=blob_size_fraction)\n",
+    "        except ImportError:\n",
+    "            from skimage.data import binary_blobs\n",
+    "            blobs = cp.asarray(binary_blobs(length=length, seed=1, blob_size_fraction=blob_size_fraction))\n",
+    "        asnumpy = cp.asnumpy\n",
+    "        xp = cp\n",
+    "        device_name = 'gpu'\n",
+    "    else:\n",
+    "        import skimage\n",
+    "        from skimage.data import binary_blobs\n",
+    "        from skimage.exposure import rescale_intensity\n",
+    "        from skimage.segmentation import random_walker\n",
+    "\n",
+    "        blobs = binary_blobs(length=length, seed=1, blob_size_fraction=blob_size_fraction)\n",
+    "        asnumpy = np.asarray\n",
+    "        xp = np\n",
+    "        device_name = 'cpu'\n",
+    "  \n",
+    "\n",
+    "    # Generate noisy synthetic data\n",
+    "    data = skimage.img_as_float(blobs)\n",
+    "    print(f\"data.shape = {data.shape}\")\n",
+    "    sigma = .3\n",
+    "    data += xp.random.normal(loc=0, scale=sigma, size=data.shape)\n",
+    "    data = rescale_intensity(data, in_range=(-sigma, 1 + sigma),\n",
+    "                             out_range=(-1, 1))\n",
+    "    data = data.astype(np.float32, copy=False)\n",
+    "\n",
+    "    print(f\"data.dtype={data.dtype}\")\n",
+    "    # The range of the binary image spans over (-1, 1).\n",
+    "    # We choose the hottest and the coldest pixels as markers.\n",
+    "    markers = xp.zeros(data.shape, dtype=np.uint)\n",
+    "    markers[data < -0.95] = 1\n",
+    "    markers[data > 0.95] = 2\n",
+    "\n",
+    "    tstart = time.time()\n",
+    "    # Run random walker algorithm\n",
+    "    labels = random_walker(data, markers, beta=5, mode='cg', tol=1e-5)\n",
+    "\n",
+    "    dur = time.time() - tstart\n",
+    "    durations[device_name] = dur\n",
+    "    print(f\"Duration {device_name} = {dur} s\")\n",
+    "\n",
+    "    # Plot results\n",
+    "    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4.8),\n",
+    "                                        sharex=True, sharey=True)\n",
+    "    ax1.imshow(asnumpy(data[:200, :200]), vmin=-.5, vmax=1.5, cmap='gray')\n",
+    "    ax1.axis('off')\n",
+    "    ax1.set_title('Noisy data')\n",
+    "    ax2.imshow(asnumpy(markers[:200, :200]), cmap='magma')\n",
+    "    ax2.axis('off')\n",
+    "    ax2.set_title('Markers')\n",
+    "    ax3.imshow(asnumpy(labels[:200, :200]), cmap='gray')\n",
+    "    ax3.axis('off')\n",
+    "    ax3.set_title('Segmentation')\n",
+    "\n",
+    "    fig.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "print(f\"GPU Acceleration = {durations['cpu']/durations['gpu']:0.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "educational-paraguay",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat.png b/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat.png
new file mode 100644
index 000000000..608666836
Binary files /dev/null and b/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat.png differ
diff --git a/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat2.png b/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat2.png
new file mode 100644
index 000000000..5bfcf3e98
Binary files /dev/null and b/notebooks/static_images/File-access_Experiments_on_TIFF_FileFormat2.png differ
diff --git a/notebooks/static_images/File-access_Experiments_on_TIFF_HDD.png b/notebooks/static_images/File-access_Experiments_on_TIFF_HDD.png
new file mode 100644
index 000000000..981e2f85f
Binary files /dev/null and b/notebooks/static_images/File-access_Experiments_on_TIFF_HDD.png differ
diff --git a/notebooks/static_images/File-access_Experiments_on_TIFF_NVMe.png b/notebooks/static_images/File-access_Experiments_on_TIFF_NVMe.png
new file mode 100644
index 000000000..fe0bd398a
Binary files /dev/null and b/notebooks/static_images/File-access_Experiments_on_TIFF_NVMe.png differ
diff --git a/notebooks/static_images/File-access_Experiments_on_TIFF_SSD.png b/notebooks/static_images/File-access_Experiments_on_TIFF_SSD.png
new file mode 100644
index 000000000..5c032b165
Binary files /dev/null and b/notebooks/static_images/File-access_Experiments_on_TIFF_SSD.png differ
diff --git a/notebooks/static_images/Multi-thread_and_Multi-process_Tests_Alignment.png b/notebooks/static_images/Multi-thread_and_Multi-process_Tests_Alignment.png
new file mode 100644
index 000000000..ba953d59b
Binary files /dev/null and b/notebooks/static_images/Multi-thread_and_Multi-process_Tests_Alignment.png differ
diff --git a/notebooks/vesselness_example.ipynb b/notebooks/vesselness_example.ipynb
new file mode 100644
index 000000000..65558ae7a
--- /dev/null
+++ b/notebooks/vesselness_example.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "hearing-gazette",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image.shape = (1011, 1011)\n",
+      "duration = 0.6739721298217773 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "from skimage import data\n",
+    "from skimage import color\n",
+    "from skimage.filters import meijering, sato, frangi, hessian\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "def identity(image, **kwargs):\n",
+    "    \"\"\"Return the original image, ignoring any kwargs.\"\"\"\n",
+    "    return image\n",
+    "\n",
+    "retina = data.retina()[200:-200, 200:-200]\n",
+    "\n",
+    "image = color.rgb2gray(retina)\n",
+    "image = image.astype(np.float32)\n",
+    "# image = np.tile(image, (4, 4))  # tile to increase size to roughly (4000, 4000)\n",
+    "print(f\"image.shape = {image.shape}\")\n",
+    "\n",
+    "kwargs = {'sigmas': [2], 'mode': 'reflect'}\n",
+    "fig, axes = plt.subplots(2, 5, figsize=[16, 8])\n",
+    "cmap = plt.cm.gray\n",
+    "tstart = time()\n",
+    "for i, black_ridges in enumerate([1, 0]):\n",
+    "    for j, func in enumerate([identity, meijering, sato, frangi, hessian]):\n",
+    "        kwargs['black_ridges'] = black_ridges\n",
+    "        \n",
+    "        result = func(image, **kwargs)\n",
+    "        vmin, vmax = np.percentile(result, q=[1, 99.5])\n",
+    "        axes[i, j].imshow(result, cmap=cmap, vmin=vmin, vmax=vmax, aspect='auto')\n",
+    "        if i == 0:\n",
+    "            axes[i, j].set_title(['Original\\nimage', 'Meijering\\nneuriteness',\n",
+    "                                  'Sato\\ntubeness', 'Frangi\\nvesselness',\n",
+    "                                  'Hessian\\nvesselness'][j])\n",
+    "        if j == 0:\n",
+    "            axes[i, j].set_ylabel('black_ridges = ' + str(bool(black_ridges)))\n",
+    "        axes[i, j].set_xticks([])\n",
+    "        axes[i, j].set_yticks([])\n",
+    "print(f\"duration = {time() - tstart} s\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "electric-macintosh",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image.shape = (1011, 1011)\n",
+      "duration = 0.6661205291748047 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "image.shape = (1011, 1011)\n",
+      "duration = 0.1250770092010498 s\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1152x576 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GPU Acceleration = 5.3257\n"
+     ]
+    }
+   ],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "import cupy as cp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from skimage import data\n",
+    "\n",
+    "durations = {}\n",
+    "for use_gpu in (False, True):\n",
+    "    \n",
+    "    if use_gpu:\n",
+    "        from cucim.skimage import color\n",
+    "        from cucim.skimage.filters import meijering, sato, frangi, hessian\n",
+    "        xp = cp\n",
+    "        asnumpy = cp.asnumpy\n",
+    "        device_name = \"gpu\"\n",
+    "    else:\n",
+    "        from skimage import color\n",
+    "        from skimage.filters import meijering, sato, frangi, hessian\n",
+    "        xp = np\n",
+    "        asnumpy = np.asarray\n",
+    "        device_name = \"cpu\"\n",
+    "\n",
+    "    def identity(image, **kwargs):\n",
+    "        \"\"\"Return the original image, ignoring any kwargs.\"\"\"\n",
+    "        return image\n",
+    "\n",
+    "    retina = data.retina()[200:-200, 200:-200]\n",
+    "\n",
+    "    # transfer image to the GPU\n",
+    "    retina = xp.asarray(retina)\n",
+    "\n",
+    "    image = color.rgb2gray(retina)\n",
+    "    image = image.astype(np.float32)\n",
+    "    # image = cp.tile(image, (4, 4))  # tile to increase size to roughly (4000, 4000)\n",
+    "    print(f\"image.shape = {image.shape}\")\n",
+    "\n",
+    "    cmap = plt.cm.gray\n",
+    "\n",
+    "    kwargs = {'sigmas': [2], 'mode': 'reflect'}\n",
+    "    fig, axes = plt.subplots(2, 5, figsize=[16, 8])\n",
+    "\n",
+    "    tstart = time()\n",
+    "    for i, black_ridges in enumerate([1, 0]):\n",
+    "        for j, func in enumerate([identity, meijering, sato, frangi, hessian]):\n",
+    "            kwargs['black_ridges'] = black_ridges\n",
+    "\n",
+    "            result = func(image, **kwargs)\n",
+    "\n",
+    "            # transfer back to host for visualization with Matplotlib\n",
+    "            result_cpu = asnumpy(result)\n",
+    "            vmin, vmax = map(float, xp.percentile(result, q=[1, 99.5]))\n",
+    "            axes[i, j].imshow(result_cpu, cmap=cmap, vmin=vmin, vmax=vmax, aspect='auto')\n",
+    "            if i == 0:\n",
+    "                axes[i, j].set_title(['Original\\nimage', 'Meijering\\nneuriteness',\n",
+    "                                      'Sato\\ntubeness', 'Frangi\\nvesselness',\n",
+    "                                      'Hessian\\nvesselness'][j])\n",
+    "            if j == 0:\n",
+    "                axes[i, j].set_ylabel('black_ridges = ' + str(bool(black_ridges)))\n",
+    "            axes[i, j].set_xticks([])\n",
+    "            axes[i, j].set_yticks([])\n",
+    "    dur = time() - tstart\n",
+    "    print(f\"duration = {dur} s\")\n",
+    "    durations[device_name] = dur\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "print(f\"GPU Acceleration = {durations['cpu']/durations['gpu']:0.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "african-infrared",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/.clang-format b/python/.clang-format
new file mode 100644
index 000000000..5f6222b32
--- /dev/null
+++ b/python/.clang-format
@@ -0,0 +1,86 @@
+AccessModifierOffset: -4
+AlignAfterOpenBracket: Align
+AlignConsecutiveAssignments: false
+AlignConsecutiveDeclarations: false
+AlignEscapedNewlinesLeft: false
+AlignTrailingComments: false
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowShortFunctionsOnASingleLine: false
+AllowShortIfStatementsOnASingleLine: false
+AllowShortCaseLabelsOnASingleLine : false
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterDefinitionReturnType: false
+AlwaysBreakBeforeMultilineStrings: true
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: true
+BinPackParameters: false
+BreakBeforeBinaryOperators: false
+BreakBeforeBraces: Custom
+BraceWrapping:
+  AfterClass: true
+  AfterControlStatement: true
+  AfterEnum: true
+  AfterFunction: true
+  AfterNamespace: true
+  AfterObjCDeclaration: true
+  AfterStruct: true
+  AfterUnion: true
+  AfterExternBlock: true
+  BeforeCatch: true
+  BeforeElse: true
+  IndentBraces: false
+  SplitEmptyFunction: true
+  SplitEmptyRecord: true
+  SplitEmptyNamespace : true
+BreakBeforeTernaryOperators: false
+BreakConstructorInitializersBeforeComma: false
+BreakStringLiterals: false
+ColumnLimit: 120
+CommentPragmas: ''
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: false
+DerivePointerBinding: false
+FixNamespaceComments: true
+IndentCaseLabels: false
+IndentPPDirectives: AfterHash
+IndentFunctionDeclarationAfterType: false
+IndentWidth: 4
+SortIncludes: false
+IncludeCategories:
+  - Regex:           '[<"](.*\/)?defines.h[>"]'
+    Priority:        1
+#  - Regex:           '<cuslide\/.+>'
+#    Priority:        3
+  - Regex:           '<[[:alnum:]_.]+>'
+    Priority:        5
+  - Regex:           '<[[:alnum:]_.\/]+>'
+    Priority:        4
+  - Regex:           '".*"'
+    Priority:        2
+IncludeBlocks: Regroup
+Language: Cpp
+MaxEmptyLinesToKeep: 2
+NamespaceIndentation: None
+ObjCSpaceAfterProperty: true
+ObjCSpaceBeforeProtocolList: true
+PenaltyBreakBeforeFirstCallParameter: 0
+PenaltyBreakComment: 1
+PenaltyBreakFirstLessLess: 0
+PenaltyBreakString: 1
+PenaltyExcessCharacter: 10
+PenaltyReturnTypeOnItsOwnLine: 1000
+PointerAlignment: Left
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 1
+SpacesInAngles: false
+SpacesInCStyleCastParentheses: false
+SpacesInContainerLiterals: false
+SpacesInParentheses: false
+Standard: Cpp11
+ReflowComments: true
+TabWidth: 4
+UseTab: Never
diff --git a/python/.editorconfig b/python/.editorconfig
new file mode 100644
index 000000000..c69a96fa2
--- /dev/null
+++ b/python/.editorconfig
@@ -0,0 +1,7 @@
+[*]
+indent_style = space
+indent_size = 4
+charset = utf-8
+trim_trailing_whitespace = true
+max_line_length = 120
+insert_final_newline = true
diff --git a/python/.gitignore b/python/.gitignore
new file mode 100644
index 000000000..51dc371a1
--- /dev/null
+++ b/python/.gitignore
@@ -0,0 +1,6 @@
+cmake-build*
+
+# C++ libraries
+lib*.so*
+# cucim plugins
+cucim.*.so
diff --git a/python/.idea/.gitignore b/python/.idea/.gitignore
new file mode 100644
index 000000000..73f69e095
--- /dev/null
+++ b/python/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/
diff --git a/python/.idea/.name b/python/.idea/.name
new file mode 100644
index 000000000..1fec8eaec
--- /dev/null
+++ b/python/.idea/.name
@@ -0,0 +1 @@
+pycucim
\ No newline at end of file
diff --git a/python/.idea/codeStyles/Project.xml b/python/.idea/codeStyles/Project.xml
new file mode 100644
index 000000000..f60388162
--- /dev/null
+++ b/python/.idea/codeStyles/Project.xml
@@ -0,0 +1,7 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <clangFormatSettings>
+      <option name="ENABLED" value="true" />
+    </clangFormatSettings>
+  </code_scheme>
+</component>
\ No newline at end of file
diff --git a/python/.idea/codeStyles/codeStyleConfig.xml b/python/.idea/codeStyles/codeStyleConfig.xml
new file mode 100644
index 000000000..79ee123c2
--- /dev/null
+++ b/python/.idea/codeStyles/codeStyleConfig.xml
@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="USE_PER_PROJECT_SETTINGS" value="true" />
+  </state>
+</component>
\ No newline at end of file
diff --git a/python/.idea/dataSources.xml b/python/.idea/dataSources.xml
new file mode 100644
index 000000000..e5ea029d1
--- /dev/null
+++ b/python/.idea/dataSources.xml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="" uuid="ea10d78f-beb8-495c-ae91-89f4c7ad72b9">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/cucim/.coverage</jdbc-url>
+    </data-source>
+  </component>
+</project>
\ No newline at end of file
diff --git a/python/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake b/python/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
new file mode 100644
index 000000000..7272e0dec
--- /dev/null
+++ b/python/.idea/fileTemplates/includes/NVIDIA_CMAKE_HEADER.cmake
@@ -0,0 +1,14 @@
+#
+# Copyright (c) $YEAR, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
diff --git a/python/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h b/python/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
new file mode 100644
index 000000000..b0e223c0a
--- /dev/null
+++ b/python/.idea/fileTemplates/includes/NVIDIA_C_HEADER.h
@@ -0,0 +1,15 @@
+/*
+ * Copyright (c) $YEAR, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
\ No newline at end of file
diff --git a/python/.idea/fileTemplates/internal/C Header File.h b/python/.idea/fileTemplates/internal/C Header File.h
new file mode 100644
index 000000000..9cb1d09e2
--- /dev/null
+++ b/python/.idea/fileTemplates/internal/C Header File.h	
@@ -0,0 +1,5 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/python/.idea/fileTemplates/internal/C Source File.c b/python/.idea/fileTemplates/internal/C Source File.c
new file mode 100644
index 000000000..b04dd6c62
--- /dev/null
+++ b/python/.idea/fileTemplates/internal/C Source File.c	
@@ -0,0 +1,4 @@
+#parse("NVIDIA_C_HEADER.h")
+#if (${HEADER_FILENAME})
+#[[#include]]# "${HEADER_FILENAME}"
+#end
diff --git a/python/.idea/fileTemplates/internal/C++ Class Header.h b/python/.idea/fileTemplates/internal/C++ Class Header.h
new file mode 100644
index 000000000..f521fa555
--- /dev/null
+++ b/python/.idea/fileTemplates/internal/C++ Class Header.h	
@@ -0,0 +1,13 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#ifndef]]# ${INCLUDE_GUARD}
+#[[#define]]# ${INCLUDE_GUARD}
+
+${NAMESPACES_OPEN}
+
+class ${NAME} {
+
+};
+
+${NAMESPACES_CLOSE}
+
+#[[#endif]]# //${INCLUDE_GUARD}
diff --git a/python/.idea/fileTemplates/internal/C++ Class.cc b/python/.idea/fileTemplates/internal/C++ Class.cc
new file mode 100644
index 000000000..42f43ccf4
--- /dev/null
+++ b/python/.idea/fileTemplates/internal/C++ Class.cc	
@@ -0,0 +1,2 @@
+#parse("NVIDIA_C_HEADER.h")
+#[[#include]]# "${HEADER_FILENAME}"
diff --git a/python/.idea/fileTemplates/internal/CMakeLists.txt.cmake b/python/.idea/fileTemplates/internal/CMakeLists.txt.cmake
new file mode 100644
index 000000000..d71d94dba
--- /dev/null
+++ b/python/.idea/fileTemplates/internal/CMakeLists.txt.cmake
@@ -0,0 +1 @@
+#parse("NVIDIA_CMAKE_HEADER.cmake")
\ No newline at end of file
diff --git a/python/.idea/misc.xml b/python/.idea/misc.xml
new file mode 100644
index 000000000..79b3c9483
--- /dev/null
+++ b/python/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
+</project>
\ No newline at end of file
diff --git a/python/.idea/pycucim.iml b/python/.idea/pycucim.iml
new file mode 100644
index 000000000..f08604bb6
--- /dev/null
+++ b/python/.idea/pycucim.iml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module classpath="CMake" type="CPP_MODULE" version="4" />
\ No newline at end of file
diff --git a/python/.idea/python.iml b/python/.idea/python.iml
new file mode 100644
index 000000000..f08604bb6
--- /dev/null
+++ b/python/.idea/python.iml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module classpath="CMake" type="CPP_MODULE" version="4" />
\ No newline at end of file
diff --git a/python/.idea/vcs.xml b/python/.idea/vcs.xml
new file mode 100644
index 000000000..6c0b86358
--- /dev/null
+++ b/python/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/.." vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
new file mode 100644
index 000000000..63ad1d358
--- /dev/null
+++ b/python/CMakeLists.txt
@@ -0,0 +1,207 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# CUDA_STANDARD 17 is supported from CMAKE 3.18
+# : https://cmake.org/cmake/help/v3.18/prop_tgt/CUDA_STANDARD.html
+cmake_minimum_required(VERSION 3.18)
+
+################################################################################
+# Prerequisite statements
+################################################################################
+
+# Set VERSION
+unset(VERSION CACHE)
+file(STRINGS ${CMAKE_CURRENT_LIST_DIR}/../VERSION VERSION)
+
+# Append local cmake module path
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/modules")
+project(cucim VERSION ${VERSION} DESCRIPTION "cucim" LANGUAGES CXX)
+
+################################################################################
+# Include utilities
+################################################################################
+include(SuperBuildUtils)
+include(CuCIMUtils)
+
+################################################################################
+# Basic setup
+################################################################################
+
+# Set default build type
+set(DEFAULT_BUILD_TYPE "Release")
+if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
+    message(STATUS "Setting build type to '${DEFAULT_BUILD_TYPE}' as none was specified.")
+    set(CMAKE_BUILD_TYPE "${DEFAULT_BUILD_TYPE}" CACHE STRING "Choose the type of build." FORCE)
+    # Set the possible values of build type for cmake-gui
+    set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
+endif ()
+
+# Set default output directories
+if (NOT CMAKE_ARCHIVE_OUTPUT_DIRECTORY)
+    set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif ()
+if (NOT CMAKE_LIBRARY_OUTPUT_DIRECTORY)
+    set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/lib")
+endif ()
+if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+    set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/bin")
+endif ()
+
+find_package(CUDAToolkit) # cucim library depends on CUDA::cudart
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_STANDARD_REQUIRED YES)
+
+# Disable visibility to not expose unnecessary symbols
+set(CMAKE_CXX_VISIBILITY_PRESET hidden)
+set(CMAKE_VISIBILITY_INLINES_HIDDEN YES)
+
+# Set RPATH
+if (NOT APPLE)
+    set(CMAKE_INSTALL_RPATH $ORIGIN)
+endif()
+
+# Set Installation setup
+if (NOT CMAKE_INSTALL_PREFIX)
+    set(CMAKE_INSTALL_PREFIX ${CMAKE_CURRENT_LIST_DIR}/install) # CACHE PATH "install here" FORCE)
+endif ()
+
+include(GNUInstallDirs)
+# Force to set CMAKE_INSTALL_LIBDIR to lib as the library can be built with Cent OS ('lib64' is set) and
+# /usr/local/lib64 or /usr/local/lib is not part of ld.so.conf* (`cat /etc/ld.so.conf.d/* | grep lib64`)
+# https://gitlab.kitware.com/cmake/cmake/-/issues/20565
+set(CMAKE_INSTALL_LIBDIR lib)
+
+include(ExternalProject)
+
+################################################################################
+# Options
+################################################################################
+
+# Setup CXX11 ABI
+# : Adds CXX11 ABI definition to the compiler command line for targets in the current directory,
+#   whether added before or after this command is invoked, and for the ones in sub-directories added after.
+add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0) # TODO: create two library, one with CXX11 ABI and one without it.
+
+################################################################################
+# Define dependencies
+################################################################################
+superbuild_depend(pybind11)
+superbuild_depend(fmt)
+superbuild_depend(json)
+superbuild_depend(pybind11_json)
+
+################################################################################
+# Find cucim package
+################################################################################
+if (NOT CUCIM_SDK_PATH)
+    get_filename_component(CUCIM_SDK_PATH "${CMAKE_SOURCE_DIR}/.." ABSOLUTE)
+    message("CUCIM_SDK_PATH is not set. Using '${CUCIM_SDK_PATH}'")
+else()
+    message("CUCIM_SDK_PATH is set to ${CUCIM_SDK_PATH}")
+endif()
+
+find_package(cucim CONFIG REQUIRED
+    HINTS ${CUCIM_SDK_PATH}/install/${CMAKE_INSTALL_LIBDIR}/cmake/cucim
+          $ENV{PREFIX}/include/cmake/cucim # In case conda build is used
+    )
+
+################################################################################
+# Define compile options
+################################################################################
+
+if(NOT BUILD_SHARED_LIBS)
+    set(BUILD_SHARED_LIBS ON)
+endif()
+
+# Note: On CentOS, if we do not set MAKE_BUILD_RPATH to $ORIGIN, installed library also do not have $ORIGIN in RPATH.
+#       The following is same with `set(CMAKE_BUILD_RPATH_USE_ORIGIN TRUE)`.
+set(CMAKE_BUILD_RPATH $ORIGIN)
+
+
+################################################################################
+# Add library: cucim
+################################################################################
+
+#get_target_property(TT cucim::cucim INTERFACE_INCLUDE_DIRECTORIES)
+#set(PYBIND11_INCLUDE_DIR ${PYBIND11_INCLUDE_DIR} /ssd/repo/cucim/install/include)
+
+pybind11_add_module(cucim
+    MODULE
+        pybind11/macros.h
+        pybind11/cucim_py.h
+        pybind11/cucim_pydoc.h
+        pybind11/cucim_py.cpp
+        pybind11/io/init.h
+        pybind11/io/io_pydoc.h
+        pybind11/io/io_py.cpp
+        pybind11/io/device_pydoc.h
+        pybind11/io/device_py.cpp
+        pybind11/filesystem/init.h
+        pybind11/filesystem/filesystem_pydoc.h
+        pybind11/filesystem/filesystem_py.cpp
+        pybind11/filesystem/cufile_pydoc.h
+        pybind11/filesystem/cufile_py.cpp
+        pybind11/memory/init.h
+        pybind11/memory/memory_pydoc.h
+        pybind11/memory/memory_py.cpp
+        )
+target_link_libraries(cucim
+    PRIVATE
+        cucim::cucim
+        deps::fmt
+        deps::json
+        deps::pybind11_json
+        )
+
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_INSTALL_LIBDIR}/cucim)
+set_target_properties(cucim PROPERTIES
+        LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_INSTALL_LIBDIR}
+        OUTPUT_NAME cucim/_cucim
+        )
+
+#target_compile_options(cucim PRIVATE -g -O0)
+
+#pybind11_add_module(cucim_io
+#    MODULE
+#        pybind11/io/device.cpp
+#        )
+#target_link_libraries(cucim_io
+#    PRIVATE
+#        cucim::cucim)
+#file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/lib/cucim/io)
+#set_target_properties(cucim_io PROPERTIES OUTPUT_NAME cucim/io/_io)
+
+
+################################################################################
+# Install
+################################################################################
+set(INSTALL_TARGETS
+        cucim
+        )
+
+install(TARGETS cucim
+        EXPORT cucim-targets
+        RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
+                COMPONENT cucim_Runtime
+        LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
+                COMPONENT cucim_Runtime
+                NAMELINK_COMPONENT cucim_Development
+        ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
+                COMPONENT cucim_Development
+        )
+
+export(PACKAGE cucim)
+
+unset(BUILD_SHARED_LIBS CACHE)
diff --git a/python/cmake/deps/fmt.cmake b/python/cmake/deps/fmt.cmake
new file mode 100644
index 000000000..59e9c1fce
--- /dev/null
+++ b/python/cmake/deps/fmt.cmake
@@ -0,0 +1,43 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::fmt)
+    FetchContent_Declare(
+            deps-fmt
+            GIT_REPOSITORY https://github.com/fmtlib/fmt.git
+            GIT_TAG 7.0.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-fmt)
+    if (NOT deps-fmt_POPULATED)
+        message(STATUS "Fetching fmt sources")
+        FetchContent_Populate(deps-fmt)
+        message(STATUS "Fetching fmt sources - done")
+    endif ()
+
+    # Create static library
+    cucim_set_build_shared_libs(OFF)
+    add_subdirectory(${deps-fmt_SOURCE_DIR} ${deps-fmt_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    # Set PIC to prevent the following error message
+    # : /usr/bin/ld: ../lib/libfmtd.a(format.cc.o): relocation R_X86_64_PC32 against symbol `stderr@@GLIBC_2.2.5' can not be used when making a shared object; recompile with -fPIC
+    set_target_properties(fmt PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    cucim_restore_build_shared_libs()
+
+    add_library(deps::fmt INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::fmt INTERFACE fmt::fmt-header-only)
+    set(deps-fmt_SOURCE_DIR ${deps-fmt_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-fmt_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/python/cmake/deps/json.cmake b/python/cmake/deps/json.cmake
new file mode 100644
index 000000000..4f716f120
--- /dev/null
+++ b/python/cmake/deps/json.cmake
@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::json)
+    FetchContent_Declare(
+            deps-json
+            GIT_REPOSITORY https://github.com/nlohmann/json.git
+            GIT_TAG v3.9.1
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-json)
+    if (NOT deps-json_POPULATED)
+        message(STATUS "Fetching json sources")
+        FetchContent_Populate(deps-json)
+        message(STATUS "Fetching json sources - done")
+    endif ()
+
+    # Typically you don't care so much for a third party library's tests to be
+    # run from your own project's code.
+    set(JSON_BuildTests OFF CACHE INTERNAL "")
+
+    add_subdirectory(${deps-json_SOURCE_DIR} ${deps-json_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::json INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::json INTERFACE nlohmann_json::nlohmann_json)
+    set(deps-json_SOURCE_DIR ${deps-json_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-json_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/python/cmake/deps/pybind11.cmake b/python/cmake/deps/pybind11.cmake
new file mode 100644
index 000000000..6af6bfbd9
--- /dev/null
+++ b/python/cmake/deps/pybind11.cmake
@@ -0,0 +1,42 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::pybind11)
+    FetchContent_Declare(
+            deps-pybind11
+            GIT_REPOSITORY https://github.com/pybind/pybind11.git
+            GIT_TAG v2.6.2
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-pybind11)
+    if (NOT deps-pybind11_POPULATED)
+        message(STATUS "Fetching pybind11 sources")
+        FetchContent_Populate(deps-pybind11)
+        message(STATUS "Fetching pybind11 sources - done")
+    endif ()
+
+    # https://pybind11.readthedocs.io/en/stable/compiling.html#configuration-variables
+    #    set(PYBIND11_PYTHON_VERSION 3.6) # It doesn't find python in manylinux2014 image
+    if (NOT PYTHON_EXECUTABLE)
+        set(PYTHON_EXECUTABLE /usr/bin/python3)
+    endif ()
+
+    add_subdirectory(${deps-pybind11_SOURCE_DIR} ${deps-pybind11_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::pybind11 INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::pybind11 INTERFACE pybind11::module)
+    set(deps-pybind11_SOURCE_DIR ${deps-pybind11_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-pybind11_SOURCE_DIR)
+endif ()
diff --git a/python/cmake/deps/pybind11_json.cmake b/python/cmake/deps/pybind11_json.cmake
new file mode 100644
index 000000000..fb422be39
--- /dev/null
+++ b/python/cmake/deps/pybind11_json.cmake
@@ -0,0 +1,36 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+if (NOT TARGET deps::pybind11_json)
+    FetchContent_Declare(
+            deps-pybind11_json
+            GIT_REPOSITORY https://github.com/pybind/pybind11_json.git
+            GIT_TAG 0.2.9
+            GIT_SHALLOW TRUE
+    )
+    FetchContent_GetProperties(deps-pybind11_json)
+    if (NOT deps-pybind11_json_POPULATED)
+        message(STATUS "Fetching pybind11_json sources")
+        FetchContent_Populate(deps-pybind11_json)
+        message(STATUS "Fetching pybind11_json sources - done")
+    endif ()
+
+    add_subdirectory(${deps-pybind11_json_SOURCE_DIR} ${deps-pybind11_json_BINARY_DIR} EXCLUDE_FROM_ALL)
+
+    add_library(deps::pybind11_json INTERFACE IMPORTED GLOBAL)
+    target_link_libraries(deps::pybind11_json INTERFACE pybind11_json)
+    set(deps-pybind11_json_SOURCE_DIR ${deps-pybind11_json_SOURCE_DIR} CACHE INTERNAL "" FORCE)
+    mark_as_advanced(deps-pybind11_json_SOURCE_DIR)
+endif ()
\ No newline at end of file
diff --git a/python/cmake/modules/CuCIMUtils.cmake b/python/cmake/modules/CuCIMUtils.cmake
new file mode 100644
index 000000000..cfe0b1495
--- /dev/null
+++ b/python/cmake/modules/CuCIMUtils.cmake
@@ -0,0 +1,60 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# Store current BUILD_SHARED_LIBS setting in CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_set_build_shared_libs)
+    macro(cucim_set_build_shared_libs new_value)
+        set(CUCIM_OLD_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS}})
+        if (DEFINED CACHE{BUILD_SHARED_LIBS})
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED TRUE)
+        else()
+            set(CUCIM_OLD_BUILD_SHARED_LIBS_CACHED FALSE)
+        endif()
+        set(BUILD_SHARED_LIBS ${new_value} CACHE BOOL "" FORCE)
+    endmacro()
+endif()
+
+# Restore BUILD_SHARED_LIBS setting from CUCIM_OLD_BUILD_SHARED_LIBS
+if(NOT COMMAND cucim_restore_build_shared_libs)
+    macro(cucim_restore_build_shared_libs)
+        if (CUCIM_OLD_BUILD_SHARED_LIBS_CACHED)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
+        else()
+            unset(BUILD_SHARED_LIBS CACHE)
+            set(BUILD_SHARED_LIBS ${CUCIM_OLD_BUILD_SHARED_LIBS})
+        endif()
+    endmacro()
+endif()
+
+# Define CMAKE_CUDA_ARCHITECTURES for the given architecture values
+#
+# Params:
+#   arch_list - architecture value list (e.g., '60;70;75;80;86')
+if(NOT COMMAND cucim_define_cuda_architectures)
+    function(cucim_define_cuda_architectures arch_list)
+        set(arch_string "")
+        # Create SASS for all architectures in the list
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${arch}-real")
+        endforeach(arch)
+
+        # Create PTX for the latest architecture for forward-compatibility.
+        list(GET arch_list -1 latest_arch)
+        foreach(arch IN LISTS arch_list)
+            set(arch_string "${arch_string}" "${latest_arch}-virtual")
+        endforeach(arch)
+        set(CMAKE_CUDA_ARCHITECTURES ${arch_string} PARENT_SCOPE)
+    endfunction()
+endif()
diff --git a/python/cmake/modules/SuperBuildUtils.cmake b/python/cmake/modules/SuperBuildUtils.cmake
new file mode 100644
index 000000000..faec66ee1
--- /dev/null
+++ b/python/cmake/modules/SuperBuildUtils.cmake
@@ -0,0 +1,24 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+include(FetchContent)
+
+set(CMAKE_SUPERBUILD_DEPS_ROOT_DIR "${CMAKE_CURRENT_LIST_DIR}/..")
+
+if(NOT COMMAND superbuild_depend)
+    function(superbuild_depend module_name)
+        include("${CMAKE_SUPERBUILD_DEPS_ROOT_DIR}/deps/${module_name}.cmake")
+    endfunction()
+endif()
diff --git a/python/cucim/.appveyor.yml b/python/cucim/.appveyor.yml
new file mode 100644
index 000000000..71d045008
--- /dev/null
+++ b/python/cucim/.appveyor.yml
@@ -0,0 +1,78 @@
+version: '{branch}-{build}'
+build: off
+environment:
+  matrix:
+    - TOXENV: check
+      TOXPYTHON: C:\Python36\python.exe
+      PYTHON_HOME: C:\Python36
+      PYTHON_VERSION: '3.6'
+      PYTHON_ARCH: '32'
+    - TOXENV: py27,codecov
+      TOXPYTHON: C:\Python27\python.exe
+      PYTHON_HOME: C:\Python27
+      PYTHON_VERSION: '2.7'
+      PYTHON_ARCH: '32'
+    - TOXENV: py27,codecov
+      TOXPYTHON: C:\Python27-x64\python.exe
+      PYTHON_HOME: C:\Python27-x64
+      PYTHON_VERSION: '2.7'
+      PYTHON_ARCH: '64'
+      WINDOWS_SDK_VERSION: v7.0
+    - TOXENV: py35,codecov
+      TOXPYTHON: C:\Python35\python.exe
+      PYTHON_HOME: C:\Python35
+      PYTHON_VERSION: '3.5'
+      PYTHON_ARCH: '32'
+    - TOXENV: py35,codecov
+      TOXPYTHON: C:\Python35-x64\python.exe
+      PYTHON_HOME: C:\Python35-x64
+      PYTHON_VERSION: '3.5'
+      PYTHON_ARCH: '64'
+    - TOXENV: py36,codecov
+      TOXPYTHON: C:\Python36\python.exe
+      PYTHON_HOME: C:\Python36
+      PYTHON_VERSION: '3.6'
+      PYTHON_ARCH: '32'
+    - TOXENV: py36,codecov
+      TOXPYTHON: C:\Python36-x64\python.exe
+      PYTHON_HOME: C:\Python36-x64
+      PYTHON_VERSION: '3.6'
+      PYTHON_ARCH: '64'
+    - TOXENV: py37,codecov
+      TOXPYTHON: C:\Python37\python.exe
+      PYTHON_HOME: C:\Python37
+      PYTHON_VERSION: '3.7'
+      PYTHON_ARCH: '32'
+    - TOXENV: py37,codecov
+      TOXPYTHON: C:\Python37-x64\python.exe
+      PYTHON_HOME: C:\Python37-x64
+      PYTHON_VERSION: '3.7'
+      PYTHON_ARCH: '64'
+    - TOXENV: py38,codecov
+      TOXPYTHON: C:\Python38\python.exe
+      PYTHON_HOME: C:\Python38
+      PYTHON_VERSION: '3.8'
+      PYTHON_ARCH: '32'
+    - TOXENV: py38,codecov
+      TOXPYTHON: C:\Python38-x64\python.exe
+      PYTHON_HOME: C:\Python38-x64
+      PYTHON_VERSION: '3.8'
+      PYTHON_ARCH: '64'
+init:
+  - ps: echo $env:TOXENV
+  - ps: ls C:\Python*
+install:
+  - '%PYTHON_HOME%\python -mpip install --progress-bar=off tox -rci/requirements.txt'
+  - '%PYTHON_HOME%\Scripts\virtualenv --version'
+  - '%PYTHON_HOME%\Scripts\easy_install --version'
+  - '%PYTHON_HOME%\Scripts\pip --version'
+  - '%PYTHON_HOME%\Scripts\tox --version'
+test_script:
+  - cmd /E:ON /V:ON /C .\ci\appveyor-with-compiler.cmd %PYTHON_HOME%\Scripts\tox
+on_failure:
+  - ps: dir "env:"
+  - ps: get-content .tox\*\log\*
+
+### To enable remote debugging uncomment this (also, see: http://www.appveyor.com/docs/how-to/rdp-to-build-worker):
+# on_finish:
+#   - ps: $blockRdp = $true; iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))
diff --git a/python/cucim/.bumpversion.cfg b/python/cucim/.bumpversion.cfg
new file mode 100644
index 000000000..020baf3a0
--- /dev/null
+++ b/python/cucim/.bumpversion.cfg
@@ -0,0 +1,32 @@
+[bumpversion]
+current_version = 0.19.0
+commit = False
+tag = False
+
+[bumpversion:file:../../VERSION]
+search = {current_version}
+replace = {new_version}
+
+[bumpversion:file:VERSION]
+search = {current_version}
+replace = {new_version}
+
+[bumpversion:file:../../cpp/plugins/cucim.kit.cuslide/VERSION]
+search = {current_version}
+replace = {new_version}
+
+[bumpversion:file:docs/index.md]
+search = [Version {current_version}](release_notes/v{current_version}.md)
+replace = [Version {new_version}](release_notes/v{new_version}.md)
+
+[bumpversion:file:docs/getting_started/index.md]
+search = v{current_version}
+replace = v{new_version}
+
+[bumpversion:file:docs/getting_started/./index.md]
+search = cucim.kit.cuslide@{current_version}.so
+replace = cucim.kit.cuslide@{new_version}.so
+
+[bumpversion:file:../../cucim.code-workspace]
+search = cucim.kit.cuslide@{current_version}.so
+replace = cucim.kit.cuslide@{new_version}.so
diff --git a/python/cucim/.cookiecutterrc b/python/cucim/.cookiecutterrc
new file mode 100644
index 000000000..437fca81c
--- /dev/null
+++ b/python/cucim/.cookiecutterrc
@@ -0,0 +1,70 @@
+# This file exists so you can easily regenerate your project.
+#
+# `cookiepatcher` is a convenient shim around `cookiecutter`
+# for regenerating projects (it will generate a .cookiecutterrc
+# automatically for any template). To use it:
+#
+#    pip install cookiepatcher
+#    cookiepatcher gh:ionelmc/cookiecutter-pylibrary cucim
+#
+# See:
+#    https://pypi.org/project/cookiepatcher
+#
+# Alternatively, you can run:
+#
+#    cookiecutter --overwrite-if-exists --config-file=cucim/.cookiecutterrc gh:ionelmc/cookiecutter-pylibrary
+
+default_context:
+
+    _extensions:               ['jinja2_time.TimeExtension']
+    _template:                 'https://github.com/ionelmc/cookiecutter-pylibrary'
+    allow_tests_inside_package: 'no'
+    appveyor:                  'yes'
+    c_extension_function:      'longest'
+    c_extension_module:        '_cucim'
+    c_extension_optional:      'no'
+    c_extension_support:       'no'
+    c_extension_test_pypi:     'no'
+    c_extension_test_pypi_username: ''
+    codacy:                    'no'
+    codacy_projectid:          '[Get ID from https://app.codacy.com/app/rapidsai/cucim/settings]'
+    codeclimate:               'no'
+    codecov:                   'yes'
+    command_line_interface:    'click'
+    command_line_interface_bin_name: 'cucim'
+    coveralls:                 'no'
+    coveralls_token:           '[Required for Appveyor, take it from https://coveralls.io/github/rapidsai/cucim]'
+    distribution_name:         'cucim'
+    email:                     ''
+    full_name:                 'cuCIM Developers'
+    landscape:                 'no'
+    license:                   'Apache Software License 2.0'
+    linter:                    'flake8'
+    package_name:              'cucim'
+    pre_commit:                'yes'
+    project_name:              'cucim'
+    project_short_description: 'A CUDA-accerlated image processing library'
+    pypi_badge:                'yes'
+    pypi_disable_upload:       'no'
+    release_date:              'today'
+    repo_hosting:              'github.com'
+    repo_hosting_domain:       'github.com'
+    repo_name:                 'cucim'
+    repo_username:             ''
+    requiresio:                'yes'
+    scrutinizer:               'no'
+    setup_py_uses_setuptools_scm: 'no'
+    setup_py_uses_test_runner: 'no'
+    sphinx_docs:               'yes'
+    sphinx_docs_hosting:       'https://cucim.readthedocs.io/'
+    sphinx_doctest:            'yes'
+    sphinx_theme:              'sphinx-rtd-theme'
+    test_matrix_configurator:  'no'
+    test_matrix_separate_coverage: 'no'
+    test_runner:               'pytest'
+    travis:                    'yes'
+    travis_osx:                'no'
+    version:                   '0.0.0'
+    website:                   'https://rapids.ai/'
+    year_from:                 '2021'
+    year_to:                   '2021'
diff --git a/python/cucim/.coveragerc b/python/cucim/.coveragerc
new file mode 100644
index 000000000..11903948f
--- /dev/null
+++ b/python/cucim/.coveragerc
@@ -0,0 +1,29 @@
+# .coveragerc to control coverage.py
+[run]
+omit =
+    # omit vendored files
+    */_vendored/*
+    src/cucim/time.py
+    # omit versioneer file
+    src/cucim/_version.py
+
+
+[html]
+directory = coverage_html_report
+
+# [paths]
+# source =
+#    src
+#    */site-packages
+#
+# [run]
+# branch = true
+# source =
+#     src/cucim
+#     tests
+# parallel = true
+#
+# [report]
+# show_missing = true
+# precision = 2
+# omit = *migrations*
diff --git a/python/cucim/.editorconfig b/python/cucim/.editorconfig
new file mode 100644
index 000000000..a9c7977a2
--- /dev/null
+++ b/python/cucim/.editorconfig
@@ -0,0 +1,16 @@
+# see https://editorconfig.org/
+root = true
+
+[*]
+end_of_line = lf
+trim_trailing_whitespace = true
+insert_final_newline = true
+indent_style = space
+indent_size = 4
+charset = utf-8
+
+[*.{bat,cmd,ps1}]
+end_of_line = crlf
+
+[*.{yml,yaml}]
+indent_size = 2
diff --git a/python/cucim/.gitattributes b/python/cucim/.gitattributes
new file mode 100644
index 000000000..3708d19e1
--- /dev/null
+++ b/python/cucim/.gitattributes
@@ -0,0 +1 @@
+src/cucim/_version.py export-subst
diff --git a/python/cucim/.gitignore b/python/cucim/.gitignore
new file mode 100644
index 000000000..dfe58380d
--- /dev/null
+++ b/python/cucim/.gitignore
@@ -0,0 +1,71 @@
+*.py[cod]
+__pycache__
+
+# C extensions
+*.so
+
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+.eggs
+parts
+bin
+var
+sdist
+wheelhouse
+develop-eggs
+.installed.cfg
+lib
+lib64
+venv*/
+pyvenv*/
+pip-wheel-metadata/
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+.coverage.*
+.pytest_cache/
+nosetests.xml
+coverage.xml
+htmlcov
+
+# Translations
+*.mo
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+.idea
+*.iml
+*.komodoproject
+
+# Complexity
+output/*.html
+output/*/index.html
+
+# Sphinx
+docs/_build
+
+.DS_Store
+*~
+.*.sw[po]
+.build
+.ve
+.env
+.cache
+.pytest
+.benchmarks
+.bootstrap
+.appveyor.token
+*.bak
+
+# Mypy Cache
+.mypy_cache/
diff --git a/python/cucim/.pre-commit-config.yaml b/python/cucim/.pre-commit-config.yaml
new file mode 100644
index 000000000..6e974cba9
--- /dev/null
+++ b/python/cucim/.pre-commit-config.yaml
@@ -0,0 +1,20 @@
+# To install the git pre-commit hook run:
+#   pre-commit install
+# To update the pre-commit hooks run:
+#   pre-commit install-hooks
+exclude: '^(\.tox|ci/templates|\.bumpversion\.cfg)(/|$)'
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: master
+    hooks:
+      - id: trailing-whitespace
+      - id: end-of-file-fixer
+      - id: debug-statements
+  - repo: https://github.com/timothycrosley/isort
+    rev: master
+    hooks:
+      - id: isort
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: master
+    hooks:
+      - id: flake8
diff --git a/python/cucim/.readthedocs.yml b/python/cucim/.readthedocs.yml
new file mode 100644
index 000000000..59ff5c04f
--- /dev/null
+++ b/python/cucim/.readthedocs.yml
@@ -0,0 +1,10 @@
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+version: 2
+sphinx:
+  configuration: docs/conf.py
+formats: all
+python:
+  install:
+    - requirements: docs/requirements.txt
+    - method: pip
+      path: .
diff --git a/python/cucim/.travis.yml b/python/cucim/.travis.yml
new file mode 100644
index 000000000..09858acea
--- /dev/null
+++ b/python/cucim/.travis.yml
@@ -0,0 +1,57 @@
+language: python
+dist: xenial
+cache: false
+env:
+  global:
+    - LD_PRELOAD=/lib/x86_64-linux-gnu/libSegFault.so
+    - SEGFAULT_SIGNALS=all
+    - LANG=en_US.UTF-8
+matrix:
+  include:
+    - python: '3.6'
+      env:
+        - TOXENV=check
+    - python: '3.6'
+      env:
+        - TOXENV=docs
+    - env:
+        - TOXENV=py27,codecov
+      python: '2.7'
+    - env:
+        - TOXENV=py35,codecov
+      python: '3.5'
+    - env:
+        - TOXENV=py36,codecov
+      python: '3.6'
+    - env:
+        - TOXENV=py37,codecov
+      python: '3.7'
+    - env:
+        - TOXENV=py38,codecov
+      python: '3.8'
+    - env:
+        - TOXENV=pypy,codecov
+      python: 'pypy'
+    - env:
+        - TOXENV=pypy3,codecov
+        - TOXPYTHON=pypy3
+      python: 'pypy3'
+before_install:
+  - python --version
+  - uname -a
+  - lsb_release -a || true
+install:
+  - python -mpip install --progress-bar=off tox -rci/requirements.txt
+  - virtualenv --version
+  - easy_install --version
+  - pip --version
+  - tox --version
+script:
+  - tox -v
+after_failure:
+  - cat .tox/log/*
+  - cat .tox/*/log/*
+notifications:
+  email:
+    on_success: never
+    on_failure: always
diff --git a/python/cucim/AUTHORS.md b/python/cucim/AUTHORS.md
new file mode 100644
index 000000000..c16398d00
--- /dev/null
+++ b/python/cucim/AUTHORS.md
@@ -0,0 +1,7 @@
+# Authors
+
+* Gigon Bae (gigony)
+* Gregory R. Lee (grlee77)
+* Benjamin Zaitlen (quasiben)
+* John Kirkham (jakirkham)
+* Ray Douglass (raydouglass)
diff --git a/python/cucim/CHANGELOG.md b/python/cucim/CHANGELOG.md
new file mode 100644
index 000000000..0b0ec408e
--- /dev/null
+++ b/python/cucim/CHANGELOG.md
@@ -0,0 +1,24 @@
+
+# Changelog
+
+## 0.18.2 (2021-03-29)
+
+- Use the white background only for Philips TIFF file.
+  - Generic TIFF file would have the black background by default.
+- Fix upside-downed image for TIFF file if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
+  - Use slow path if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
+    - Show an error message if the out-of-boundary cases are requested with the slow path.
+    - `ValueError: Cannot handle the out-of-boundary cases for a non-RGB image or a non-Jpeg/Deflate-compressed image.`
+
+## 0.18.1 (2021-03-17)
+
+- Disable using cuFile
+  - Remove warning messages when libcufile.so is not available.
+    - `[warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.`
+
+## 0.18.0 (2021-03-16)
+
+- First release on PyPI with only cuClaraImage features.
+- The namespace of the project is changed from `cuimage` to `cucim` and project name is now `cuCIM`
+- Support Deflate(zlib) compression in Generic TIFF Format.
+  - [libdeflate](https://github.com/ebiggers/libdeflate) library is used to decode the deflate-compressed data.
diff --git a/python/cucim/CONTRIBUTING.md b/python/cucim/CONTRIBUTING.md
new file mode 100644
index 000000000..77f80ae8a
--- /dev/null
+++ b/python/cucim/CONTRIBUTING.md
@@ -0,0 +1,96 @@
+# Contributing
+
+Contributions are welcome, and they are greatly appreciated! Every
+little bit helps, and credit will always be given.
+
+# Bug reports
+
+When [reporting a bug](https://github.com/rapidsai/cucim/issues) please include:
+
+    * Your operating system name and version.
+    * Any details about your local setup that might be helpful in troubleshooting.
+    * Detailed steps to reproduce the bug.
+
+# Documentation improvements
+
+cucim could always use more documentation, whether as part of the
+official cucim docs, in docstrings, or even on the web in blog posts,
+articles, and such.
+
+# Feature requests and feedback
+
+The best way to send feedback is to file an issue at https://github.com/rapidsai/cucim/issues.
+
+If you are proposing a feature:
+
+* Explain in detail how it would work.
+* Keep the scope as narrow as possible, to make it easier to implement.
+* Remember that this is a volunteer-driven project, and that code contributions are welcome :)
+
+# Development
+
+To set up `cucim` for local development:
+
+1. Fork [cucim](https://github.com/rapidsai/cucim)
+    (look for the "Fork" button).
+
+2. Clone your fork locally:
+
+    ```bash
+    git clone git@github.com:YOURGITHUBNAME/cucim.git
+    ```
+
+3. Create a branch for local development::
+
+    ```bash
+    git checkout -b name-of-your-bugfix-or-feature
+    ```
+
+    Now you can make your changes locally.
+
+4. When you're done making changes run all the checks and docs builder with [tox](https://tox.readthedocs.io/en/latest/install.html) one command::
+
+    ```bash
+    tox
+    ```
+
+5. Commit your changes and push your branch to GitHub:
+
+    ```bash
+    git add .
+    git commit -m "Your detailed description of your changes."
+    git push origin name-of-your-bugfix-or-feature
+    ```
+
+6. Submit a pull request through the GitHub website.
+
+## Pull Request Guidelines
+
+If you need some code review or feedback while you're developing the code just make the pull request.
+
+For merging, you should:
+
+1. Include passing tests (run `tox`).
+
+    If you don't have all the necessary python versions available locally you can rely on Travis - it will [run the tests](https://travis-ci.org/rapidsai/cucim/pull_requests) for each change you add in the pull request.
+
+    It will be slower though ...
+2. Update documentation when there's new API, functionality etc.
+3. Add a note to `CHANGELOG.md`` about the changes.
+4. Add yourself to `AUTHORS.md`.
+
+
+## Tips
+
+
+To run a subset of tests::
+
+```bash
+tox -e envname -- pytest -k test_myfeature
+```
+
+To run all the test environments in *parallel*::
+
+```bash
+tox -p auto
+```
diff --git a/python/cucim/MANIFEST.in b/python/cucim/MANIFEST.in
new file mode 100644
index 000000000..462e3037f
--- /dev/null
+++ b/python/cucim/MANIFEST.in
@@ -0,0 +1,25 @@
+# https://packaging.python.org/guides/using-manifest-in/
+graft docs
+graft src
+graft ci
+graft tests
+
+include .bumpversion.cfg
+include .coveragerc
+include .cookiecutterrc
+include .editorconfig
+
+include VERSION
+include AUTHORS.md
+include CHANGELOG.md
+include CONTRIBUTING.md
+include ../../LICENSE
+include ../../LICENSE-3rdparty.md
+include README.md
+
+include tox.ini .travis.yml .appveyor.yml .readthedocs.yml .pre-commit-config.yaml
+
+include versioneer.py
+include src/cucim/clara/*.so*
+
+global-exclude *.py[cod] __pycache__/*
diff --git a/python/cucim/README.md b/python/cucim/README.md
new file mode 100644
index 000000000..7cc9632ca
--- /dev/null
+++ b/python/cucim/README.md
@@ -0,0 +1,94 @@
+# [cuCIM](https://github.com/rapidsai/cucim)
+
+<!-- start-include-here -->
+
+The [RAPIDS](https://rapids.ai) [cuCIM](https://github.com/rapidsai/cucim) is an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.
+
+**NOTE:** For the latest stable [README.md](https://github.com/rapidsai/cucim/blob/main/README.md) ensure you are on the `main` branch.
+
+## Quick Start
+
+### Install cuCIM
+
+```
+pip install cucim
+```
+
+### Open Image
+
+```python
+from cucim import CuImage
+img = CuImage('image.tif')
+```
+
+### See Metadata
+
+```python
+import json
+print(img.is_loaded)        # True if image data is loaded & available.
+print(img.device)           # A device type.
+print(img.ndim)             # The number of dimensions.
+print(img.dims)             # A string containing a list of dimensions being requested.
+print(img.shape)            # A tuple of dimension sizes (in the order of `dims`).
+print(img.size('XYC'))      # Returns size as a tuple for the given dimension order.
+print(img.dtype)            # The data type of the image.
+print(img.channel_names)    # A channel name list.
+print(img.spacing())        # Returns physical size in tuple.
+print(img.spacing_units())  # Units for each spacing element (size is same with `ndim`).
+print(img.origin)           # Physical location of (0, 0, 0) (size is always 3).
+print(img.direction)        # Direction cosines (size is always 3x3).
+print(img.coord_sys)        # Coordinate frame in which the direction cosines are 
+                            # measured. Available Coordinate frame is not finalized yet.
+
+# Returns a set of associated image names.
+print(img.associated_images)
+# Returns a dict that includes resolution information.
+print(json.dumps(img.resolutions, indent=2))
+# A metadata object as `dict`
+print(json.dumps(img.metadata, indent=2))
+# A raw metadata string.
+print(img.raw_metadata) 
+```
+
+### Read Region
+
+```python
+from matplotlib import pyplot as plt
+def visualize(image):
+    dpi = 80.0
+    height, width, _ = image.shape
+    plt.figure(figsize=(width / dpi, height / dpi))
+    plt.axis('off')
+    plt.imshow(image)
+
+```
+
+```python
+import numpy as np
+
+# Read whole slide at the lowest resolution
+resolutions = img.resolutions
+level_count = resolutions["level_count"]
+
+# Note: ‘level’ is at 3rd parameter (OpenSlide has it at 2nd parameter)
+#   `location` is level-0 based coordinates (using the level-0 reference frame)
+#   If `size` is not specified, size would be (width, height) of the image at the specified `level`.
+region = img.read_region(location=(10000, 10000), size=(512, 512), level=level_count-1)
+
+visualize(region)
+#from PIL import Image
+#Image.fromarray(np.asarray(region))
+```
+
+## Acknowledgments
+
+Without awesome third-party open source software, this project wouldn't exist.
+
+Please find `LICENSE-3rdparty.md` to see which third-party open source software
+is used in this project.
+
+## License
+
+Apache-2.0 License (see `LICENSE` file).
+
+Copyright (c) 2020-2021, NVIDIA CORPORATION.
diff --git a/python/cucim/VERSION b/python/cucim/VERSION
new file mode 100644
index 000000000..1cf0537c3
--- /dev/null
+++ b/python/cucim/VERSION
@@ -0,0 +1 @@
+0.19.0
diff --git a/python/cucim/ci/appveyor-with-compiler.cmd b/python/cucim/ci/appveyor-with-compiler.cmd
new file mode 100644
index 000000000..289585fc1
--- /dev/null
+++ b/python/cucim/ci/appveyor-with-compiler.cmd
@@ -0,0 +1,23 @@
+:: Very simple setup:
+:: - if WINDOWS_SDK_VERSION is set then activate the SDK.
+:: - disable the WDK if it's around.
+
+SET COMMAND_TO_RUN=%*
+SET WIN_SDK_ROOT=C:\Program Files\Microsoft SDKs\Windows
+SET WIN_WDK="c:\Program Files (x86)\Windows Kits\10\Include\wdf"
+ECHO SDK: %WINDOWS_SDK_VERSION% ARCH: %PYTHON_ARCH%
+
+IF EXIST %WIN_WDK% (
+    REM See: https://connect.microsoft.com/VisualStudio/feedback/details/1610302/
+    REN %WIN_WDK% 0wdf
+)
+IF "%WINDOWS_SDK_VERSION%"=="" GOTO main
+
+SET DISTUTILS_USE_SDK=1
+SET MSSdk=1
+"%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Setup\WindowsSdkVer.exe" -q -version:%WINDOWS_SDK_VERSION%
+CALL "%WIN_SDK_ROOT%\%WINDOWS_SDK_VERSION%\Bin\SetEnv.cmd" /x64 /release
+
+:main
+ECHO Executing: %COMMAND_TO_RUN%
+CALL %COMMAND_TO_RUN% || EXIT 1
diff --git a/python/cucim/ci/bootstrap.py b/python/cucim/ci/bootstrap.py
new file mode 100755
index 000000000..bec7bb204
--- /dev/null
+++ b/python/cucim/ci/bootstrap.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import unicode_literals
+
+import os
+import subprocess
+import sys
+from os.path import abspath
+from os.path import dirname
+from os.path import exists
+from os.path import join
+
+base_path = dirname(dirname(abspath(__file__)))
+
+
+def check_call(args):
+    print("+", *args)
+    subprocess.check_call(args)
+
+
+def exec_in_env():
+    env_path = join(base_path, ".tox", "bootstrap")
+    if sys.platform == "win32":
+        bin_path = join(env_path, "Scripts")
+    else:
+        bin_path = join(env_path, "bin")
+    if not exists(env_path):
+        import subprocess
+
+        print("Making bootstrap env in: {0} ...".format(env_path))
+        try:
+            check_call([sys.executable, "-m", "venv", env_path])
+        except subprocess.CalledProcessError:
+            try:
+                check_call([sys.executable, "-m", "virtualenv", env_path])
+            except subprocess.CalledProcessError:
+                check_call(["virtualenv", env_path])
+        print("Installing `jinja2` into bootstrap environment...")
+        check_call([join(bin_path, "pip"), "install", "jinja2", "tox"])
+    python_executable = join(bin_path, "python")
+    if not os.path.exists(python_executable):
+        python_executable += '.exe'
+
+    print("Re-executing with: {0}".format(python_executable))
+    print("+ exec", python_executable, __file__, "--no-env")
+    os.execv(python_executable, [python_executable, __file__, "--no-env"])
+
+
+def main():
+    import jinja2
+
+    print("Project path: {0}".format(base_path))
+
+    jinja = jinja2.Environment(
+        loader=jinja2.FileSystemLoader(join(base_path, "ci", "templates")),
+        trim_blocks=True,
+        lstrip_blocks=True,
+        keep_trailing_newline=True
+    )
+
+    tox_environments = [
+        line.strip()
+        # 'tox' need not be installed globally, but must be importable
+        # by the Python that is running this script.
+        # This uses sys.executable the same way that the call in
+        # cookiecutter-pylibrary/hooks/post_gen_project.py
+        # invokes this bootstrap.py itself.
+        for line in subprocess.check_output(
+            [sys.executable, '-m', 'tox', '--listenvs'],
+            universal_newlines=True).splitlines()
+    ]
+    tox_environments = [line for line in tox_environments
+                        if line.startswith('py')]
+
+    for name in os.listdir(join("ci", "templates")):
+        with open(join(base_path, name), "w") as fh:
+            fh.write(
+                jinja.get_template(name).render(
+                    tox_environments=tox_environments))
+        print("Wrote {}".format(name))
+    print("DONE.")
+
+
+if __name__ == "__main__":
+    args = sys.argv[1:]
+    if args == ["--no-env"]:
+        main()
+    elif not args:
+        exec_in_env()
+    else:
+        print("Unexpected arguments {0}".format(args), file=sys.stderr)
+        sys.exit(1)
diff --git a/python/cucim/ci/requirements.txt b/python/cucim/ci/requirements.txt
new file mode 100644
index 000000000..d7f5177e6
--- /dev/null
+++ b/python/cucim/ci/requirements.txt
@@ -0,0 +1,4 @@
+virtualenv>=16.6.0
+pip>=19.1.1
+setuptools>=18.0.1
+six>=1.14.0
diff --git a/python/cucim/ci/templates/.appveyor.yml b/python/cucim/ci/templates/.appveyor.yml
new file mode 100644
index 000000000..bb4a05599
--- /dev/null
+++ b/python/cucim/ci/templates/.appveyor.yml
@@ -0,0 +1,49 @@
+version: '{branch}-{build}'
+build: off
+environment:
+  matrix:
+    - TOXENV: check
+      TOXPYTHON: C:\Python36\python.exe
+      PYTHON_HOME: C:\Python36
+      PYTHON_VERSION: '3.6'
+      PYTHON_ARCH: '32'
+{% for env in tox_environments %}
+{% if env.startswith(('py2', 'py3')) %}
+    - TOXENV: {{ env }},codecov{{ "" }}
+      TOXPYTHON: C:\Python{{ env[2:4] }}\python.exe
+      PYTHON_HOME: C:\Python{{ env[2:4] }}
+      PYTHON_VERSION: '{{ env[2] }}.{{ env[3] }}'
+      PYTHON_ARCH: '32'
+{% if 'nocov' in env %}
+      WHEEL_PATH: .tox/dist
+{% endif %}
+    - TOXENV: {{ env }},codecov{{ "" }}
+      TOXPYTHON: C:\Python{{ env[2:4] }}-x64\python.exe
+      PYTHON_HOME: C:\Python{{ env[2:4] }}-x64
+      PYTHON_VERSION: '{{ env[2] }}.{{ env[3] }}'
+      PYTHON_ARCH: '64'
+{% if 'nocov' in env %}
+      WHEEL_PATH: .tox/dist
+{% endif %}
+{% if env.startswith('py2') %}
+      WINDOWS_SDK_VERSION: v7.0
+{% endif %}
+{% endif %}{% endfor %}
+init:
+  - ps: echo $env:TOXENV
+  - ps: ls C:\Python*
+install:
+  - '%PYTHON_HOME%\python -mpip install --progress-bar=off tox -rci/requirements.txt'
+  - '%PYTHON_HOME%\Scripts\virtualenv --version'
+  - '%PYTHON_HOME%\Scripts\easy_install --version'
+  - '%PYTHON_HOME%\Scripts\pip --version'
+  - '%PYTHON_HOME%\Scripts\tox --version'
+test_script:
+  - cmd /E:ON /V:ON /C .\ci\appveyor-with-compiler.cmd %PYTHON_HOME%\Scripts\tox
+on_failure:
+  - ps: dir "env:"
+  - ps: get-content .tox\*\log\*
+
+### To enable remote debugging uncomment this (also, see: http://www.appveyor.com/docs/how-to/rdp-to-build-worker):
+# on_finish:
+#   - ps: $blockRdp = $true; iex ((new-object net.webclient).DownloadString('https://raw.githubusercontent.com/appveyor/ci/master/scripts/enable-rdp.ps1'))
diff --git a/python/cucim/ci/templates/.travis.yml b/python/cucim/ci/templates/.travis.yml
new file mode 100644
index 000000000..cb15bd09c
--- /dev/null
+++ b/python/cucim/ci/templates/.travis.yml
@@ -0,0 +1,47 @@
+language: python
+dist: xenial
+cache: false
+env:
+  global:
+    - LD_PRELOAD=/lib/x86_64-linux-gnu/libSegFault.so
+    - SEGFAULT_SIGNALS=all
+    - LANG=en_US.UTF-8
+matrix:
+  include:
+    - python: '3.6'
+      env:
+        - TOXENV=check
+    - python: '3.6'
+      env:
+        - TOXENV=docs
+{%- for env in tox_environments %}{{ '' }}
+    - env:
+        - TOXENV={{ env }},codecov
+{%- if env.startswith('pypy3') %}{{ '' }}
+        - TOXPYTHON=pypy3
+      python: 'pypy3'
+{%- elif env.startswith('pypy') %}{{ '' }}
+      python: 'pypy'
+{%- else %}{{ '' }}
+      python: '{{ '{0[2]}.{0[3]}'.format(env) }}'
+{%- endif %}{{ '' }}
+{%- endfor %}{{ '' }}
+before_install:
+  - python --version
+  - uname -a
+  - lsb_release -a || true
+install:
+  - python -mpip install --progress-bar=off tox -rci/requirements.txt
+  - virtualenv --version
+  - easy_install --version
+  - pip --version
+  - tox --version
+script:
+  - tox -v
+after_failure:
+  - cat .tox/log/*
+  - cat .tox/*/log/*
+notifications:
+  email:
+    on_success: never
+    on_failure: always
diff --git a/python/cucim/docs/_static/css/custom.css b/python/cucim/docs/_static/css/custom.css
new file mode 100644
index 000000000..87f612eaf
--- /dev/null
+++ b/python/cucim/docs/_static/css/custom.css
@@ -0,0 +1,20 @@
+/*
+https: //github.com/pandas-dev/pydata-sphinx-theme/blob/master/pydata_sphinx_theme/static/css/theme.css
+https: //pydata-sphinx-theme.readthedocs.io/en/latest/user_guide/customizing.html#customizing-the-css
+*/
+
+/* To fix a bug: `.col-xl-2` and `.bd-toc` conflicts in the following HTML of right navbar in the theme.
+  - `.col-xl-2` has `position: relative`
+  - `.bd-toc` has `position: sticky`
+
+    <div class="d-none d-xl-block col-xl-2 bd-toc">
+
+*/
+div.bd-toc {
+    position: sticky;
+}
+
+/* Set max width of container */
+div.container-xl {
+    max-width: 1400px;
+}
diff --git a/python/cucim/docs/_static/images/RAPIDS_cuCIM.png b/python/cucim/docs/_static/images/RAPIDS_cuCIM.png
new file mode 100644
index 000000000..aa7ed8cdc
Binary files /dev/null and b/python/cucim/docs/_static/images/RAPIDS_cuCIM.png differ
diff --git a/python/cucim/docs/api_reference/cucim.CuImage.rst b/python/cucim/docs/api_reference/cucim.CuImage.rst
new file mode 100644
index 000000000..e66690f20
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.CuImage.rst
@@ -0,0 +1,5 @@
+cucim.CuImage
+-------------
+
+.. autoclass:: cucim.CuImage
+    :members:
diff --git a/python/cucim/docs/api_reference/cucim.clara.filesystem.CuFileDriver.rst b/python/cucim/docs/api_reference/cucim.clara.filesystem.CuFileDriver.rst
new file mode 100644
index 000000000..9c7e0fa84
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.clara.filesystem.CuFileDriver.rst
@@ -0,0 +1,5 @@
+cucim.clara.filesystem.CuFileDriver
+-----------------------------------
+
+.. autoclass:: cucim.clara.filesystem.CuFileDriver
+    :members:
diff --git a/python/cucim/docs/api_reference/cucim.clara.filesystem.rst b/python/cucim/docs/api_reference/cucim.clara.filesystem.rst
new file mode 100644
index 000000000..995fc0860
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.clara.filesystem.rst
@@ -0,0 +1,5 @@
+cucim.clara.filesystem
+----------------------
+
+.. automodule:: cucim.clara.filesystem
+    :members:
diff --git a/python/cucim/docs/api_reference/cucim.clara.io.Device.rst b/python/cucim/docs/api_reference/cucim.clara.io.Device.rst
new file mode 100644
index 000000000..5e5cd0b3c
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.clara.io.Device.rst
@@ -0,0 +1,5 @@
+cucim.clara.io.Device
+---------------------
+
+.. autoclass:: cucim.clara.io.Device
+    :members:
diff --git a/python/cucim/docs/api_reference/cucim.clara.io.rst b/python/cucim/docs/api_reference/cucim.clara.io.rst
new file mode 100644
index 000000000..230d12541
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.clara.io.rst
@@ -0,0 +1,4 @@
+cucim.clara.io
+--------------
+
+.. autoclass:: cucim.clara.io.DeviceType
diff --git a/python/cucim/docs/api_reference/cucim.rst b/python/cucim/docs/api_reference/cucim.rst
new file mode 100644
index 000000000..f67c1fc3a
--- /dev/null
+++ b/python/cucim/docs/api_reference/cucim.rst
@@ -0,0 +1,9 @@
+cucim
+-----
+
+.. testsetup::
+
+    from cucim import *
+
+.. automodule:: cucim
+    :members:
diff --git a/python/cucim/docs/api_reference/index.md b/python/cucim/docs/api_reference/index.md
new file mode 100644
index 000000000..951c37970
--- /dev/null
+++ b/python/cucim/docs/api_reference/index.md
@@ -0,0 +1,28 @@
+# API Reference
+
+
+```{toctree}
+:glob:
+:hidden:
+
+cucim
+cucim.CuImage
+cucim.clara.io
+cucim.clara.io.Device
+cucim.clara.filesystem
+cucim.clara.filesystem.CuFileDriver
+
+```
+
+## Python API
+
+```{toctree}
+:maxdepth: 2
+
+cucim
+cucim.CuImage
+cucim.clara.io
+cucim.clara.io.Device
+cucim.clara.filesystem
+cucim.clara.filesystem.CuFileDriver
+```
diff --git a/python/cucim/docs/conf.py b/python/cucim/docs/conf.py
new file mode 100644
index 000000000..06e804d83
--- /dev/null
+++ b/python/cucim/docs/conf.py
@@ -0,0 +1,133 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+import os
+
+# Versioning
+with open("../../../VERSION") as f:
+    version_long = f.readline().strip()
+
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.coverage',
+    'sphinx.ext.doctest',
+    'sphinx.ext.extlinks',
+    'sphinx.ext.ifconfig',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.todo',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.intersphinx',
+    'sphinxcontrib.bibtex',
+    'myst_nb',
+    'sphinx_copybutton',
+    'sphinx_togglebutton',
+    'sphinx_panels',
+    'ablog',
+    'sphinxemoji.sphinxemoji',
+]
+# source_suffix = {
+#     '.rst': 'restructuredtext',
+#     '.ipynb': 'myst-nb',
+#     '.myst': 'myst-nb',
+# }
+master_doc = 'index'
+project = 'cuCIM'
+year = '2020-2021'
+author = 'NVIDIA'
+copyright = '{0}, {1}'.format(year, author)
+version = release = version_long
+
+pygments_style = 'trac'
+templates_path = ['.']
+extlinks = {
+    'issue': ('https://github.com/rapidsai/cucim/issues/%s', '#'),
+    'pr': ('https://github.com/rapidsai/cucim/pull/%s', 'PR #'),
+}
+# on_rtd is whether we are on readthedocs.org
+on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
+
+if not on_rtd:  # only set the theme if we're building docs locally
+    html_theme = 'pydata_sphinx_theme'  # 'sphinx_book_theme'
+    # https://github.com/pandas-dev/pydata-sphinx-theme
+    # https://pydata-sphinx-theme.readthedocs.io/en/latest/user_guide/index.html
+
+html_use_smartypants = True
+html_last_updated_fmt = '%b %d, %Y'
+html_split_index = False
+# html_sidebars = {
+#     '**': ['searchbox.html', 'globaltoc.html', 'sourcelink.html'],
+# }
+html_short_title = '%s-%s' % (project, version)
+
+napoleon_use_ivar = True
+napoleon_use_rtype = False
+napoleon_use_param = False
+
+html_show_sourcelink = True
+
+# Options for linkcheck builder
+#
+# Reference
+# : https://www.sphinx-doc.org/en/master/usage/configuration.html?highlight=linkcheck#options-for-the-linkcheck-builder)  # noqa
+linkcheck_ignore = [r'^\/', r'^\.\.']
+
+# Options for sphinx.ext.todo
+# (reference: https://www.sphinx-doc.org/en/master/usage/extensions/todo.html)
+
+todo_include_todos = True
+
+# Options for sphinxemoji.sphinxemoji
+# (reference: https://sphinxemojicodes.readthedocs.io/en/stable/#supported-codes)  # noqa
+
+
+# Options for myst
+# (reference: https://myst-parser.readthedocs.io/en/latest/index.html)
+
+# https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html#markdown-figures  # noqa
+myst_enable_extensions = ["colon_fence"]
+
+# Options for pydata-sphinx-theme
+# (reference: https://pydata-sphinx-theme.readthedocs.io/en/latest/user_guide/configuring.html)  # noqa
+
+html_static_path = ['_static']
+html_css_files = [
+    'css/custom.css',
+]
+
+html_theme_options = {
+    "external_links": [
+        {
+            "name": "Submit Issue",
+            "url": "https://github.com/rapidsai/cucim/issues/new/choose",  # noqa
+        }
+    ]
+}
+
+# Options for Sphinx Book Theme
+# (reference: https://github.com/executablebooks/sphinx-book-theme/blob/master/setup.py)  # noqa
+
+# html_theme_options = {
+#     "repository_url": "https://github.com/rapidsai/cucim",
+#     "use_repository_button": True,
+#     "use_issues_button": True,
+#     #"use_edit_page_button": True,
+#     "repository_branch": "dev",
+#     #"path_to_docs": "python/cucim/docs",
+#     "home_page_in_toc": True,
+# }
+
+# Options for myst-nb
+# (reference: https://myst-nb.readthedocs.io/en/latest/)
+
+# Prevent the following error
+#     MyST NB Configuration Error:
+#    `nb_render_priority` not set for builder: doctest
+nb_render_priority = {
+    "doctest": ()
+}
+
+# Prevent creating jupyter_execute folder in dist
+#  https://myst-nb.readthedocs.io/en/latest/use/execute.html#executing-in-temporary-folders  # noqa
+execution_in_temp = True
+jupyter_execute_notebooks = "off"
diff --git a/python/cucim/docs/development/index.md b/python/cucim/docs/development/index.md
new file mode 100644
index 000000000..459110d34
--- /dev/null
+++ b/python/cucim/docs/development/index.md
@@ -0,0 +1 @@
+# Development
diff --git a/python/cucim/docs/getting_started/index.md b/python/cucim/docs/getting_started/index.md
new file mode 100644
index 000000000..59201766b
--- /dev/null
+++ b/python/cucim/docs/getting_started/index.md
@@ -0,0 +1,173 @@
+# Getting Started
+
+```{toctree}
+:glob:
+:hidden:
+
+../notebooks/Basic_Usage.ipynb
+../notebooks/Accessing_File_with_GDS.ipynb
+../notebooks/File-access_Experiments_on_TIFF.ipynb
+../notebooks/Working_with_DALI.ipynb
+../notebooks/Working_with_Albumentation.ipynb
+../notebooks/Single-process_Tests.ipynb
+../notebooks/Multi-thread_and_Multi-process_Tests.ipynb
+```
+
+## Installation
+
+Please download the latest SDK package (`cuCIM-v0.19.0-linux.tar.gz`).
+
+Untar the downloaded file.
+
+```bash
+mkdir -p cuCIM-v0.19.0
+tar -xzvf cuCIM-v0.19.0-linux.tar.gz -C cuCIM-v0.19.0
+
+cd cuCIM-v0.19.0
+```
+
+## Run command
+
+Executing `./run` command would show you available commands:
+
+```bash
+./run
+```
+```
+USAGE: ./run [command] [arguments]...
+
+Global Arguments
+
+Command List
+    help  ----------------------------  Print detailed description for a given argument (command name)
+  Example
+    download_testdata  ---------------  Download test data from Docker Hub
+    launch_notebooks  ----------------  Launch jupyter notebooks
+  Build
+    build_train  ---------------------  Build Clara Train Docker image with cuCIM (& OpenSlide)
+    build_examples  ------------------  Build cuCIM C++ examples
+```
+
+`./run help <command>` would show you detailed information about the command.
+
+```bash
+./run help build_train
+```
+```
+Build Clara Train Docker image with cuCIM (& OpenSlide)
+
+Build image from docker/Dockerfile-claratrain
+
+Arguments:
+  $1 - docker image name (default:cucim-train)
+```
+
+### download_testdata
+
+It downloads test data from DockerHub (`gigony/svs-testdata:little-big`) and make it available at `notebooks/input` folder.
+
+The folder has the following four files.
+
+- `TUPAC-TR-488.svs`
+- `TUPAC-TR-467.svs`
+- `image.tif`
+- `image2.tif`
+
+#### Test Dataset
+
+`TUPAC-TR-488.svs` and `TUPAC-TR-467.svs` are from the dataset
+of Tumor Proliferation Assessment Challenge 2016 (TUPAC16 | MICCAI Grand Challenge).
+
+- Website: <http://tupac.tue-image.nl/node/3>
+- Data link: <https://drive.google.com/drive/u/0/folders/0B--ztKW0d17XYlBqOXppQmw0M2M>
+
+#### Converted files
+
+- `image.tif` : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-467.svs
+- `image2.tif` : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-488.svs
+
+
+### launch_notebooks
+
+It launches a **Jupyter Lab** instance so that the user can experiment with cuCIM.
+
+After executing the command, type a password for the instance and open a web browser to access the instance.
+
+```bash
+./run launch_notebooks
+```
+
+```bash
+...
+Port 10001 would be used...(http://172.26.120.129:10001)
+2021-02-13 01:12:44 $ nvidia-docker run --gpus all -it --rm -v /home/repo/cucim/notebooks:/notebooks -p 10001:10001 cucim-jupyter -c echo -n 'Enter New Password: '; jupyter lab --ServerApp.password="$(python3 -u -c "from jupyter_server.auth import passwd;pw=input();print(passwd(pw));" | egrep 'sha|argon')" --ServerApp.root_dir=/notebooks --allow-root --port=10001 --ip=0.0.0.0 --no-browser
+Enter New Password: <password>
+[I 2021-02-13 01:12:47.981 ServerApp] dask_labextension | extension was successfully linked.
+[I 2021-02-13 01:12:47.981 ServerApp] jupyter_server_proxy | extension was successfully linked.
+...
+```
+
+### build_train
+
+It builds an image from the Clara Deploy SDK image. The image would install other useful python package as well as cu
+CIM wheel file.
+
+`nvcr.io/nvidian/dlmed/clara-train-sdk:v3.1-ga-qa-5` is used and `docker/Dockerfile-claratrain` has the recipe of the image.
+
+You will need to have a permission to access `nvidian/dlmed` group in NGC.
+
+```bash
+./run build_train
+
+docker run -it --rm cucim-train /bin/bash
+```
+
+### build_examples
+
+It builds C++ examples at `examples/cpp` folder by using `cmake` in `cucim-cmake` image that is built in runtime.
+
+After the execution, it would copy built file into `bin` folder and show how to execute it.
+
+```bash
+./run build_examples
+```
+
+```bash
+...
+
+Execute the binary with the following commands:
+  # Set library path
+  export LD_LIBRARY_PATH=/ssd/repo/cucim/dist/install/lib:$LD_LIBRARY_PATH
+  # Execute
+  ./bin/tiff_image notebooks/input/image.tif .
+```
+
+Its execution would show some metadata information and create two files -- `output.ppm` and `output2.ppm`.
+
+`.ppm` file can be viewed by `eog` in Ubuntu.
+```
+$ ./bin/tiff_image notebooks/input/image.tif .
+[Plugin: cucim.kit.cuslide] Loading...
+[Plugin: cucim.kit.cuslide] Loading the dynamic library from: cucim.kit.cuslide@0.19.0.so
+[Plugin: cucim.kit.cuslide] loaded successfully. Version: 0
+Initializing plugin: cucim.kit.cuslide (interfaces: [cucim::io::IImageFormat v0.1]) (impl: cucim.kit.cuslide)
+is_loaded: true
+device: cpu
+metadata: {"key": "value"}
+dims: YXC
+shape: (26420, 19920, 3)
+size('XY'): (19920, 26420)
+channel_names: (R, G, B)
+
+is_loaded: true
+device: cpu
+metadata: {"key": "value"}
+dims: YXC
+shape: (1024, 1024, 3)
+size('XY'): (1024, 1024)
+channel_names: (R, G, B)
+[Plugin: cucim.kit.cuslide] Unloaded.
+
+$ eog output.ppm
+$ eog output2.ppm
+```
diff --git a/python/cucim/docs/index.md b/python/cucim/docs/index.md
new file mode 100644
index 000000000..8e0e67d08
--- /dev/null
+++ b/python/cucim/docs/index.md
@@ -0,0 +1,86 @@
+<!-- ```{eval-rst}
+:notoc:
+
+``` -->
+
+```{toctree}
+:maxdepth: 3
+:hidden:
+
+getting_started/index
+api_reference/index
+release_notes/index
+roadmap/index
+```
+<!-- user_guide/index
+development/index -->
+
+
+# cuCIM Documentation
+
+Current latest version is [Version 0.19.0](release_notes/v0.19.0.md).
+
+**cuCIM** a toolkit to provide GPU accelerated I/O, image  processing & computer vision primitives for N-Dimensional images with a focus on biomedical imaging.
+
+:::{figure-md} fig-cucim-architecture
+:class: myclass
+
+<img src="_static/images/RAPIDS_cuCIM.png" alt="cuCIM Architecture" class="bg-primary mb-1" width="600px">
+
+RAPIDS cuCIM Architecture
+:::
+
+<!-- .. raw:: html
+   <div> </div> -->
+
+<!-- ```{toctree}
+:maxdepth: 3
+:hidden:
+
+
+reference/index.md
+
+``` -->
+
+<!--
+```{include} readme.md
+```
+
+## Site contents
+
+```{toctree}
+:maxdepth: 2
+:caption: Main docs
+
+jupyter.md
+installation.md
+usage.md
+changelog.md
+Github repository <https://github.com/rapidsai/cucim>
+```
+
+## Reference pages
+
+```{toctree}
+:caption: Reference items
+:maxdepth: 2
+
+reference/index.md
+```
+
+## Development
+
+```{toctree}
+:caption: Development
+:maxdepth: 1
+
+contributing.md
+authors.md
+```
+
+## Indices and tables
+
+* {ref}`genindex`
+* {ref}`modindex`
+* {ref}`search`
+ -->
diff --git a/python/cucim/docs/release_notes/index.md b/python/cucim/docs/release_notes/index.md
new file mode 100644
index 000000000..3f17b1917
--- /dev/null
+++ b/python/cucim/docs/release_notes/index.md
@@ -0,0 +1,49 @@
+# Release Notes
+
+```{toctree}
+:glob:
+:hidden:
+:maxdepth: 2
+
+v0.18.2
+v0.18.1
+v0.18.0
+v0.3.0
+v0.2.0
+v0.1.1
+v0.1.0
+```
+
+## Version 0.18
+
+```{toctree}
+:maxdepth: 2
+
+v0.18.2
+v0.18.1
+v0.18.0
+```
+
+## Version 0.3
+
+```{toctree}
+:maxdepth: 2
+
+v0.3.0
+```
+
+## Version 0.2
+
+```{toctree}
+:maxdepth: 2
+
+v0.2.0
+```
+## Version 0.1
+
+```{toctree}
+:maxdepth: 2
+
+v0.1.1
+v0.1.0
+```
diff --git a/python/cucim/docs/release_notes/v0.1.0.md b/python/cucim/docs/release_notes/v0.1.0.md
new file mode 100644
index 000000000..0d2f40150
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.1.0.md
@@ -0,0 +1,24 @@
+# Version 0.1.0 (October 28, 2020)
+
+## What are provided in the package?
+
+- API Documents
+- C++ & Python library packages
+- Example project (using CMake. For C++)
+- Example code in a Jupyter notebook (with a docker image)
+
+## Features
+
+For v1, we have a limited feature focusing on generic tiled/multi-resolution TIFF file format (Jpeg-compressed RGB image).
+
+- Loading part of the image using read_region() API
+- Saving the loaded image in .ppm format (loadable by 'eog' viewer in Ubuntu or PIL library in Python)
+
+## Limitations
+
+- The following feature is not implemented yet
+  - Accessing image data through container() API (in C++) or as a numpy array (using `__array_interface__` in Python)
+- Errors are not handled properly yet (e.g., loading non-existing file would cause a crash)
+- Some metadata (e.g., physical size) is hard-coded for now
+- C++ library is forced to set `_GLIBCXX_USE_CXX11_ABI` to 0 due to [Dual ABI](https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html) problem
+  - Will package CXX11 ABI library separately later
diff --git a/python/cucim/docs/release_notes/v0.1.1.md b/python/cucim/docs/release_notes/v0.1.1.md
new file mode 100644
index 000000000..7ed9b5832
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.1.1.md
@@ -0,0 +1,18 @@
+# Version 0.1.1 (November 3, 2020)
+
+## What's new?
+
+The following features are implemented.
+- Access image data through `container()` API (in C++) or as a numpy array (using `__array_interface__` in Python)
+  - [Example](../notebooks/Basic_Usage.html#array-interface-support)
+- Remove hard-coded metadata for `resolutions`
+  - [Example](../notebooks/Basic_Usage.html#see-metadata)
+- Sort resolution levels (level 0: the largest resolution) for `CuImage::read_region()` method
+  - Add `TIFF::level_ifd(size_t level_index)` method
+- Pass SWIPAT
+
+## Fixes
+
+- Fix a crash that occurs when opening a non-existing file
+- Fix an error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag
+  - `Quantization table 0x00 was not defined` message can be shown
diff --git a/python/cucim/docs/release_notes/v0.18.0.md b/python/cucim/docs/release_notes/v0.18.0.md
new file mode 100644
index 000000000..f4d16f682
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.18.0.md
@@ -0,0 +1,7 @@
+# Version 0.18.0 (March 16, 2021)
+
+## What's new?
+
+- The namespace of the project is changed from `cuimage` to `cucim` and project name is now `cuCIM`
+- Support Deflate(zlib) compression in Generic TIFF Format.
+  - [libdeflate](https://github.com/ebiggers/libdeflate) library is used to decode the deflate-compressed data.
diff --git a/python/cucim/docs/release_notes/v0.18.1.md b/python/cucim/docs/release_notes/v0.18.1.md
new file mode 100644
index 000000000..0a1d15001
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.18.1.md
@@ -0,0 +1,7 @@
+# Version 0.18.1 (March 17, 2021)
+
+## What's new?
+
+- Disable using cuFile
+  - Remove warning messages when libcufile.so is not available.
+    - `[warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.`
diff --git a/python/cucim/docs/release_notes/v0.18.2.md b/python/cucim/docs/release_notes/v0.18.2.md
new file mode 100644
index 000000000..44b81fba3
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.18.2.md
@@ -0,0 +1,13 @@
+# Version 0.18.2 (March 29, 2021)
+
+## What's new?
+
+- Use the white background only for Philips TIFF file.
+  - Generic TIFF file would have the black background by default.
+
+## Fixes
+
+- Fix upside-downed image for TIFF file if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
+  - Use slow path if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
+    - Show an error message if the out-of-boundary cases are requested with the slow path.
+    - `ValueError: Cannot handle the out-of-boundary cases for a non-RGB image or a non-Jpeg/Deflate-compressed image.`
diff --git a/python/cucim/docs/release_notes/v0.2.0.md b/python/cucim/docs/release_notes/v0.2.0.md
new file mode 100644
index 000000000..d6d16b368
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.2.0.md
@@ -0,0 +1,33 @@
+# Version 0.2.0 (December 18, 2020)
+
+## What's new?
+
+The following features are implemented.
+- Make it work without CUDA runtime installed
+  - CUDA 11.0 runtime is embedded in the .whl file
+- Develop a wrapper for cufile API
+  - Refer to `Accessing File with GDS` (/notebooks/Accessing_File_with_GDS.html) notebook
+  - Did some experiments on accessing TIFF files (see `File-access Experiments on TIFF File` (/notebooks/File-access_Experiments_on_TIFF.html) notebook)
+- Support loading [Philips TIFF](https://openslide.org/formats/philips/) files
+  - Loading multi-resolution images and associated images (such as 'macro' and 'label') from TIFF Image File Directory (IFD) are available
+    - Please see `Basic Usage` (/notebooks/Basic_Usage.html#associated-images) notebook to know how to access the associated images.
+
+    ```{admonition} Characteristic of Philips TIFF format
+    As specified in [Philips format](https://openslide.org/formats/philips/),
+
+    "slides may omit pixel data for TIFF tiles not in an ROI; this is represented as a TileOffset of 0 and a TileByteCount of 0. When such tiles are downsampled into a tile that does contain pixel data, their contents are rendered as white pixels."
+
+    For the above reason, some Philips TIFF images can actually hold important information (‘tiles that are not ROIs or tissues’) which can expedite pre-processing by discarding unnecessarily tiles. Due to feature parity with Openslide, cuClaraImage also renders such tiles as white pixels. Please let us know and suggest APIs for getting the information if such non-ROI region information is useful to you.
+    ```
+  - The following tasks remain for feature-parity with OpenSlide
+    - Support Philips TIFF associated image from metadata
+    - Expose XML metadata of the Philips TIFF file as JSON
+- Provide an example/plan for the interoperability with DALI
+  - Created a notebook for the feasibility and plan (see `Working with DALI` (/notebooks/Working_with_DALI.html) notebook)
+
+## Fixes/Improvements
+
+- Fix again for the error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag
+  - `ERROR in line 126 while reading JPEG header tables: Not a JPEG file: starts with 0x01 0x00` message can be shown
+- Force-reinstall cucim Python package in the Tox environment whenever `gen_docs` or `gen_docs_dev` command is executed
+
diff --git a/python/cucim/docs/release_notes/v0.3.0.md b/python/cucim/docs/release_notes/v0.3.0.md
new file mode 100644
index 000000000..bac97083b
--- /dev/null
+++ b/python/cucim/docs/release_notes/v0.3.0.md
@@ -0,0 +1,53 @@
+# Version 0.3.0 (February 16, 2021)
+
+## What's new?
+
+- A new name and namespace (currently `cuClaraImage` and `cucim`) will be picked in `v0.4.0` once it's finalized
+- Add metadata and associated images for Philips TIFF Format
+  - Support Philips TIFF associated image from XML metadata
+- Expose metadata of the image as JSON
+  - `raw_metadata` property returns the image description of the first IFD in the TIFF image
+  - `resolution_dim_start` property of `CuImage` is removed
+  - `physical_pixel_size` property is renamed to `spacing`
+  - `ndim`/`origin`/`direction`/`coord_sys`/`spacing_units` properties are added
+  - Please see `Basic Usage` (/notebooks/Basic_Usage.html#see-metadata) notebook to know how to access metadata.
+- Support reading out of boundary region
+  - `read_region()` method now accepts a region that is out of the image boundary
+  - `size` parameter accepts values that are up to the size of the highest-resolution image
+  - The out of the boundary area would be filled with the white color
+- Showcase the interoperability with DALI
+  - Please see `Working with DALI` (/notebooks/Working_with_DALI.html) notebook
+
+## Fixes/Improvements
+
+- Fix wrong parameter interpretation (`size` in `read_region()` method). Now only `location` is level-0 based coordinates (using the level-0 reference frame). `size` is output image size. (Thanks `@Behrooz Hashemian`!)
+- Static link with cufile when [libcufile.a is available](https://docs.google.com/document/d/1DQ_T805dlTcDU9bGW32E2ak5InX8iUcNI7Tq_lXAtLc/edit?ts=5f90bc5f) -- Implemented but disabled for now
+- Fix a memory leak for cuslide::tiff::TIFF object (248 bytes) in CuImage class.
+- Fix incorrect method visibility in a plugin file (.so)
+- Replace malloc with better allocator for small-sized memory
+  - Use a custom allocator(pmr) for metadata data
+- Copy data using `std::vector::insert()` instead of `std::vector::push_back()`
+  - Small improvement (when opening TIFF file), but benchmark result showed that time for assigning 50000 tile offset/size (uint64_t) is reduced from 118 us to 8 us
+- Parameterize input library/image for testing
+- Update test input path
+  - Add test data under `test_data/private` : See `test_data/README.md` file.
+- Setup development environment with VSCode (in addition to CLion)
+- Use a VSCode plugin for local test execution
+  - Now it uses `matepek.vscode-catch2-test-adapter` extension
+    - <https://github.com/matepek/vscode-catch2-test-adapter/blob/master/documents/configuration/test.advancedExecutables.md>
+- Prevent relative path problem of .so with no DT_SONAME
+- Refactoring
+  - Add Development environment for VSCode
+    - Update run script
+    - Add settings for VSCode
+  - Refactor CMakeLists.txt
+    - Add definition `_GLIBCXX_USE_CXX11_ABI=0` to all sub directories
+    - Compile multiple architectures for CUDA Kernels
+  - Parameterize input files for tests
+    - Add `test_data` folder for test data
+    - plugin folder is from `CUCIM_TEST_PLUGIN_PATH` environment variable now (with static plugin name (cucim.kit.cuslide@0.3.0.so))
+  - Move cucim_malloc() to memory_manager.cu
+
+## Limitations
+
+- Some metadata (`origin`/`direction`/`coord_sys`/`spacing`/`spacing_units`) doesn't have correct values for now.
diff --git a/python/cucim/docs/requirements.txt b/python/cucim/docs/requirements.txt
new file mode 100644
index 000000000..5fa61c4ee
--- /dev/null
+++ b/python/cucim/docs/requirements.txt
@@ -0,0 +1,23 @@
+Sphinx==3.5.2
+sphinx-autobuild==2020.9.1
+myst-parser==0.13.5
+sphinx-book-theme==0.0.40
+numpy==1.19.5
+matplotlib==3.3.4
+ipywidgets==7.6.3
+pandas==1.1.5
+nbclient==0.5.3
+myst-nb==0.12.0
+sphinx-togglebutton==0.2.3
+sphinx-copybutton==0.3.1
+plotly==4.14.3
+sphinxcontrib-bibtex<2.0.0 # https://github.com/executablebooks/jupyter-book/issues/1137
+sphinx-thebe==0.0.8
+sphinx-panels==0.5.2
+ablog==0.10.13
+docutils==0.16 # 0.17 causes error. https://github.com/executablebooks/MyST-Parser/issues/343
+pydata_sphinx_theme==0.4.3
+sphinxemoji==0.1.8
+cupy-cuda110==9.0.0b3
+scipy
+scikit-image
\ No newline at end of file
diff --git a/python/cucim/docs/roadmap/index.md b/python/cucim/docs/roadmap/index.md
new file mode 100644
index 000000000..3e7f9380a
--- /dev/null
+++ b/python/cucim/docs/roadmap/index.md
@@ -0,0 +1,372 @@
+# Roadmap
+
+<!-- https://fontawesome.com/icons?d=listing&m=free -->
+<!-- https://getbootstrap.com/docs/4.0/utilities/colors/ -->
+<!-- https://myst-parser.readthedocs.io/en/latest/using/syntax.html -->
+<!-- {fa}`cart-plus,text-info mr-1` -->
+
+```{eval-rst}
+The following list is on the road |:smile:|
+```
+
+## cuCIM
+
+### {fa}`calendar-alt,text-info mr-1` `v0.1.0`
+
+- {fa}`check,text-success mr-1` Abstract C++ API -- [v0.1.0](../release_notes/v0.1.0.md)
+- {fa}`check,text-success mr-1` Benchmark with openslide (for generic tiff file) : link -- [v0.1.0](../release_notes/v0.1.0.md)
+- {fa}`check,text-success mr-1` Usage with C++ API -- [v0.1.0](../release_notes/v0.1.0.md)
+- {fa}`check,text-success mr-1` Implement Python API -- [v0.1.0](../release_notes/v0.1.0.md)
+- {fa}`check,text-success mr-1` Usage with Python API -- [v0.1.0](../release_notes/v0.1.0.md)
+  1. Setup document/build system
+  1. Package it
+- {fa}`check,text-success mr-1` Sort resolution levels (level 0: the largest resolution) for `CuImage::read_region()` method -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Fix a crash that occurs when opening a non-existing file -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Fix an error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag -- [v0.1.1](../release_notes/v0.1.1.md)
+  - `Quantization table 0x00 was not defined` message can be shown
+- {fa}`check,text-success mr-1` Sort resolution levels (level 0: the largest resolution) for `CuImage::read_region()` method -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Pass SWIPAT -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Ignore link check for relative link with header that starts with `/` or `..` -- [v0.1.1](../release_notes/v0.1.1.md)
+
+### {fa}`calendar-alt,text-info mr-1` `v0.2.0`
+
+- {fa}`check,text-success mr-1` Make it work with various CUDA versions -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Develop a wrapper for cufile API -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Support loading [Philips TIFF](https://openslide.org/formats/philips/) files
+  - {fa}`check,text-success mr-1` Support Philips TIFF multi-resolution images -- [v0.2.0](../release_notes/v0.2.0.md)
+  - {fa}`check,text-success mr-1` Support Philips TIFF associated image from IFD -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Provide an example/plan for the interoperability with DALI -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Fix again for the error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Force-reinstall cucim Python package in the Tox environment whenever `gen_docs` or `gen_docs_dev` command is executed -- [v0.2.0](../release_notes/v0.2.0.md)
+
+### {fa}`calendar-alt,text-info mr-1` `v0.3.0`
+
+- {fa}`check,text-success mr-1` Add metadata and associated images for Philips TIFF Format
+  - {fa}`check,text-success mr-1` Support Philips TIFF associated image from XML metadata -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Expose metadata of the image as JSON -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Support reading out of boundary region -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Showcase the interoperability with DALI -- [v0.3.0](../release_notes/v0.3.0.md)
+
+
+### {fa}`calendar-alt,text-info mr-1` `v0.18.0`
+
+- {fa}`check,text-success mr-1` Support Deflate(zlib)-compressed RGB Tiff Image -- [v0.18.0](../release_notes/v0.18.0.md)
+- {fa}`check,text-success mr-1` Change the namespaces (`cuimage` to `cucim`) -- [v0.18.0](../release_notes/v0.18.0.md)
+
+### {fa}`calendar-alt,text-info mr-1` `v0.19.0`
+
+- Refactor the cupyimg package to incorporate it in the adaption layer of cuCIM. Change the namespaces
+- Support `__cuda_array_interface__` and DLPack object in Python API
+- Support loading data to CUDA memory
+- Implement cache mechanism for tile-based image formats
+
+### {fa}`calendar-alt,text-info mr-1` `v0.20.0`
+
+- Make use of nvJPEG to decode TIFF Files
+- Support .svs format with nvJPEG2000
+- Design a plug-in mechanism for developing CUDA based 2D/3D imaging filters
+- Implement a filter (example: Otsu Thresholding)
+- Support loading MHD files
+
+### {fa}`calendar-alt,text-info mr-1` `v0.21.0`
+
+- Support JPEG, Jpeg 2000, PNG, BMP formats
+- Support MIRAX/3DHISTECH (.mrxs) format
+- Support LEICA (.scn) format
+
+### {fa}`calendar-alt,text-info mr-1` `v0.22.0`
+
+- Design a CT bone segmentation filter
+- Provide a robust CI/CD system
+- Define KPIs and publish report
+- Update project to use the latest [Carbonite SDK](https://docs.omniverse.nvidia.com/prod_kit/prod_kit/developer_api.html#carbonite-sdk) for supporting plug-in architecture
+
+## TODOs
+
+### Image Format
+
+#### Generic TIFF(.tif)
+
+- {fa}`check,text-success mr-1` Access image data through container() API (in C++) or as a numpy array (using `__array_interface__` in Python) -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Fix a crash that occurs when opening a non-existing file -- [v0.1.1](../release_notes/v0.1.1.md)
+- {fa}`check,text-success mr-1` Fix an error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag -- [v0.1.1](../release_notes/v0.1.1.md)
+  - `Quantization table 0x00 was not defined` message can be shown
+- {fa}`check,text-success mr-1` Fix again for the error that occurs when loading a TIFF image that has `TIFFTAG_JPEGTABLES` tag -- [v0.2.0](../release_notes/v0.2.0.md)
+  - `ERROR in line 126 while reading JPEG header tables: Not a JPEG file: starts with 0x01 0x00` message can be shown
+- {fa}`check,text-success mr-1` Expose metadata of the TIFF file as JSON -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Support reading out of boundary region -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Support Deflate(zlib)-compressed RGB Tiff Image -- [v0.18.0](../release_notes/v0.18.0.md)
+- Implement cache mechanism for tile-based image formats -- [v0.19.0](../release_notes/v0.19.0.md)
+- Use CuFileDriver class for reading files
+- Make use of nvJPEG to decode TIFF Files -- [v0.20.0](../release_notes/v0.20.0.md)
+
+- Remove hard-coded metadata (Fill correct values for `cucim::io::format::ImageMetadataDesc`)
+  - {fa}`check,text-success mr-1` `resolutions` -- [v0.1.1](../release_notes/v0.1.1.md)
+  - `metadata`
+- Check if the `tile_rester` memory is freed by jpeg-turbo or not
+  - {fa}`check,text-success mr-1` `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:365` in `IFD::read_region_tiles_libjpeg()` -- [v0.3.0](../release_notes/v0.3.0.md)
+    - `cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.cpp:123` in `parser_parse` -- [v0.19.0](../release_notes/v0.19.0.md)
+- Fill correct metadata information for `CuImage::read_region()`
+  - `cpp/src/cucim.cpp:417` -- [v0.19.0](../release_notes/v0.19.0.md)
+- Check and use `ifd->samples_per_pixel()` once we can get RGB data instead of RGBA
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:280` in `IFD::read_region_tiles_libjpeg()` -- [v0.19.0](../release_notes/v0.19.0.md)
+- Consider endianness of the .tif file
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:329` in `IFD::read_region_tiles_libjpeg()`
+- Consider tile's depth tag
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:329` in `IFD::read_region_tiles_libjpeg()`
+- Make `file_handle_` object to pointer
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp:50` in `TIFF::TIFF()`
+- Remove assumption of sub-resolution dims to 2
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp:140` in `TIFF::read()`
+
+#### [Philips TIFF](https://openslide.org/formats/philips/) (.tif)
+
+- {fa}`check,text-success mr-1` Support Philips TIFF multi-resolution images -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Support Philips TIFF associated image from IFD -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Support Philips TIFF associated image from XML metadata -- [v0.3.0](../release_notes/v0.3.0.md)
+- {fa}`check,text-success mr-1` Expose XML metadata of the Philips TIFF file as JSON -- [v0.3.0](../release_notes/v0.3.0.md)
+
+#### .mhd
+
+- Support loading MHD files -- [v0.20.0](../release_notes/v0.20.0.md)
+
+#### .svs
+
+- Support .svs format with nvJPEG2000 -- [v0.20.0](../release_notes/v0.20.0.md)
+
+#### .png
+
+- Support .png with [libspng](https://github.com/randy408/libspng/) -- [v0.21.0](../release_notes/v0.21.0.md)
+  - **libspng** is faster than **libpng** (but doesn't support encoding)
+
+#### .jpg
+
+- Support .jpg with libjpeg-turbo and nvJPEG -- [v0.21.0](../release_notes/v0.21.0.md)
+
+#### .jp2/.j2k
+
+- Support .jp2/.j2k files with OpenJpeg and nvJPEG2000 -- [v0.21.0](../release_notes/v0.21.0.md)
+
+#### .bmp
+
+- Support .bmp file natively -- [v0.21.0](../release_notes/v0.21.0.md)
+
+#### .mrxs
+
+- Support MIRAX/3DHISTECH (.mrxs) format -- [v0.21.0](../release_notes/v0.21.0.md)
+
+#### .scn
+
+- Support LEICA (.scn) format -- [v0.21.0](../release_notes/v0.21.0.md)
+
+#### .dcm
+
+- Support DICOM format
+- Support reading segmentation image instead of main pixel array
+  - `examples/cpp/dicom_image/main.cpp:37`
+
+#### .iSyntax
+
+- Support Philips iSyntax format
+  - <https://thepathologist.com/fileadmin/issues/App_Notes/0016-024-app-note-Philips__iSyntax_for_Digital_Pathology.pdf>
+  - <https://www.openpathology.philips.com/wp-content/uploads/isyntax/4522%20207%2043941_2020_04_24%20Pathology%20iSyntax%20image%20format.pdf>
+
+### Image Filter
+
+#### Basic Filter
+
+- Design a plug-in mechanism for developing CUDA based 2D/3D imaging filters -- [v0.20.0](../release_notes/v0.20.0.md)
+- Implement a filter (example: Otsu Thresholding) -- [v0.20.0](../release_notes/v0.20.0.md)
+
+#### Medical-specific Filter
+
+- Design a CT bone segmentation filter -- [v0.22.0](../release_notes/v0.22.0.md)
+
+### Performance Improvements
+
+- {fa}`check,text-success mr-1` Copy data using `std::vector::insert()` instead of `std::vector::push_back()` -- [v0.3.0](../release_notes/v0.3.0.md)
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:78` in `IFD::IFD()`
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:98` in `IFD::IFD()`
+  - Benchmark result showed that time for assigning 50000 tile offset/size (uint64_t) is reduced from 118 us to 8 us.
+- {fa}`check,text-success mr-1` Replace malloc with better allocator for small-sized memory -- [v0.3.0](../release_notes/v0.3.0.md)
+  - Use a custom allocator(pmr) for metadata data.
+- Try to use `__array_struct__`. Access to array interface could be faster
+  - <https://numpy.org/doc/stable/reference/arrays.interface.html#c-struct-access>
+  - Check the performance difference between python int vs python long later
+  - `python/pybind11/cucim_py.cpp:234` in `get_array_interface()`
+- Check performance
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp:122` in `IFD::read()` : string concatenation
+
+### GPUDirect-Storage (GDS) Support
+
+- {fa}`check,text-success mr-1` Develop a wrapper for cufile API -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Static link with cufile when [libcufile.a is available](https://docs.google.com/document/d/1DQ_T805dlTcDU9bGW32E2ak5InX8iUcNI7Tq_lXAtLc/edit?ts=5f90bc5f) -- [v0.3.0](../release_notes/v0.3.0.md)
+
+### Interoperability
+
+- {fa}`check,text-success mr-1` Provide an example/plan for the interoperability with DALI -- [v0.2.0](../release_notes/v0.2.0.md)
+- {fa}`check,text-success mr-1` Showcase the interoperability with DALI -- [v0.3.0](../release_notes/v0.3.0.md)
+- Support `__cuda_array_interface__` and DLPack object in Python API -- [v0.19.0](../release_notes/v0.19.0.md)
+  - https://docs.cupy.dev/en/stable/reference/interoperability.html#dlpack
+  - https://github.com/pytorch/pytorch/pull/11984
+- Refactor the cupyimg package to incorporate it in the adaption layer of cuCIM. Change the namespaces -- [v0.19.0](../release_notes/v0.19.0.md)
+  - Implement/expose `scikit-image`-like image loading APIs (such as `imread`) and filtering APIs for cuCIM library by using cuCIM's APIs
+- Support DALI's CPU/GPU Tensor: <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/data_types.html#tensor>
+- Support loading data to CUDA memory -- [v0.19.0](../release_notes/v0.19.0.md)
+- Consider adding `to_xxx()` methods in Python API
+  - `examples/python/tiff_image/main.py:125`
+- Support byte-like object for CuImage object so that the following method works -- [v0.19.0](../release_notes/v0.19.0.md)
+    ```python
+    from PIL import Image
+    ...
+    #np_img_arr = np.asarray(region)
+    #Image.fromarray(np_img_arr)
+
+    Image.fromarray(region)
+    # /usr/local/lib/python3.6/dist-packages/PIL/Image.py in frombytes(self, data, decoder_name, *args)
+    #     792         d = _getdecoder(self.mode, decoder_name, args)
+    #     793         d.setimage(self.im)
+    # --> 794         s = d.decode(data)
+    #     795
+    #     796         if s[0] >= 0:
+    # TypeError: a bytes-like object is required, not 'cucim._cucim.CuImage'
+    ```
+- Provide universal cucim adaptors for DALI (for cucim::io::format::IImageFormat and cucim::filter::IImageFilter interfaces)
+- Support pretty display for IPython(Jupyter Notebook)
+  - https://ipython.readthedocs.io/en/stable/config/integrating.html#integrating-your-objects-with-ipython
+
+### Pipeline
+
+- Use app_dp_sample pipeline to convert input image(.svs) of Nuclei segmentation pipeline(app_dp_nuclei) to .tif image
+  - Load .tif file using cuCIM for Nuclei segmentation pipeline
+
+### Python API
+
+- Feature parity with OpenSlide
+- Add context manager for CuImage class (for `close()` method) -- [v0.19.0](../release_notes/v0.19.0.md)
+
+### C++ API
+
+- Design filtering API (which can embrace CuPy/CVCore/CuPyImg/OpenCV/scikit-image/dask-image)
+- Feature parity with OpenSlide
+
+- {fa}`check,text-success mr-1` Sort resolution levels (level 0: the largest resolution) for `CuImage::read_region()` method -- [v0.1.1](../release_notes/v0.1.1.md)
+  - Add `TIFF::level_ifd(size_t level_index)` method
+- {fa}`check,text-success mr-1` Support `metadata` and `raw_metadata` properties/methods -- [v0.3.0](../release_notes/v0.3.0.md)
+  - Implement `CuImage::metadata()` with JSON library (Folly or Modern JSON)
+    - `cpp/src/cucim.cpp:238`
+- `ImageMetadataDesc` struct
+  - {fa}`check,text-success mr-1` `resolution_dim_start` field: Reconsider its use (may not be needed) -- [v0.3.0](../release_notes/v0.3.0.md)
+    - `cpp/include/cucim/io/format/image_format.h:53`
+  - `channel_names` field : `S`, `T`, and other dimension can have names so need to be generalized
+    - `cpp/include/cucim/io/format/image_format.h:51`
+- `numpy_dtype()` method
+  - Consider bfloat16: <https://github.com/dmlc/dlpack/issues/45>
+  - Consider other byte-order (currently, we assume `little-endian`)
+    - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/tiff.cpp:53)
+  - `cpp/include/cucim/memory/dlpack.h:41`
+- `checker_is_valid()` method
+  - Add `buf_size` parameter and implement the method
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/cuslide.cpp:68`
+
+- Consider default case (how to handle -1 index?)
+  - `cpp/src/io/device.cpp` in `Device::Device()`
+- Implement `Device::parse_type()`
+  - `cpp/src/io/device.cpp:81`
+- Implement `Device::validate_device()`
+  - `cpp/src/io/device.cpp:116`
+
+- Check illegal characters for `DimIndices::DimIndices()`
+  - `cpp/src/cucim.cpp:35`
+  - `cpp/src/cucim.cpp:46`
+
+- Implement `detect_format()` method
+  - `cpp/src/cucim.cpp:103`
+- Detect available format for the file path
+  - Also consider if the given file path is folder path (DICOM case)
+  - `cpp/src/cucim.cpp:117` in `CuImage::CuImage()`
+- Implement `CuImage::CuImage(const filesystem::Path& path, const std::string& plugin_name)`
+  - `cpp/src/cucim.cpp:128`
+- Implement `CuImage::dtype()`
+  - Support string conversion like Device class
+  - `cpp/src/cucim.cpp:283`
+
+### Build
+
+- Check if `CMAKE_EXPORT_PACKAGE_REGISTRY` is duplicate and remove it
+  - `cucim/cpp/plugins/cucim.kit.cuslide/CMakeLists.txt:255`
+- Install other dependencies for libtiff so that other compression methods are available
+  - `cucim/Dockerfile:32`
+- {fa}`check,text-success mr-1` Setup development environment with VSCode (in addition to CLion) -- [v0.3.0](../release_notes/v0.3.0.md)
+- Use prebuilt libraries for dependencies
+
+### Test
+
+- {fa}`check,text-success mr-1` Parameterize input library/image -- [v0.3.0](../release_notes/v0.3.0.md)
+  - `/ssd/repo/cucim/cpp/tests/test_read_region.cpp:69` in `Verify read_region`
+  - `/ssd/repo/cucim/cpp/tests/test_cufile.cpp:79` in `Verify libcufile usage`
+- {fa}`check,text-success mr-1` Use a VSCode plugin for local test execution -- [v0.3.0](../release_notes/v0.3.0.md)
+  - `matepek.vscode-catch2-test-adapter` extension
+    - <https://github.com/matepek/vscode-catch2-test-adapter/blob/master/documents/configuration/test.advancedExecutables.md>
+
+### Platform
+
+- Support Windows (currently only Linux package is available)
+
+### Package & CI/CD
+
+- {fa}`check,text-success mr-1` Make it work with various CUDA versions -- [v0.2.0](../release_notes/v0.2.0.md)
+  - Currently, it is linked to CUDA 11.0 library
+  - Refer to PyTorch's PyPi package
+    - The PyPi package embeds CUDA runtime library.
+    - https://github.com/pytorch/pytorch/issues/47268#issuecomment-721996861
+- Move to Github Project
+- Add `bump2version` command in `run` (version management)
+- Move `tox` setup from python folder to the project root folder
+- Setup Conda recipe
+- Setup automated test cases
+- Provide a robust CI/CD system -- [v0.22.0](../release_notes/v0.22.0.md)
+- Define KPIs and publish report -- [v0.22.0](../release_notes/v0.22.0.md)
+
+- Add license files to the package
+- Package a separate CXX11 ABI library
+  - Currently, C++ library is forced to set `_GLIBCXX_USE_CXX11_ABI` to 0 due to [Dual ABI](https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html) problem
+  - `cpp/CMakeLists.txt:98`
+- Support CPack
+  - `CMakeLists.txt:177`
+
+### Documentation
+
+- {fa}`check,text-success mr-1` Pass SWIPAT -- [v0.1.1](../release_notes/v0.1.1.md)
+- Refine README.md and relevant documents for the project
+- Move Sphinx docs to the project root folder
+- Add C++ API document
+- Add C++ examples to Jupyter Notebook
+  - Can install C++ Kernel: <https://xeus-cling.readthedocs.io/en/latest/installation.html#from-source-with-cmake>
+- {fa}`check,text-success mr-1` Ignore link check for relative link with header that starts with `/` or `..` -- [v0.1.1](../release_notes/v0.1.1.md)
+  - `python/cucim/docs/conf.py:71`
+  - <https://www.sphinx-doc.org/en/master/usage/configuration.html?highlight=linkcheck#options-for-the-linkcheck-builder>
+- {fa}`check,text-success mr-1` Force-reinstall cucim Python package in the Tox environment whenever `gen_docs` or `gen_docs_dev` command is executed -- [v0.2.0](../release_notes/v0.2.0.md)
+  - <https://tox.readthedocs.io/en/latest/config.html#conf-usedevelop>
+- Simplify method signatures in Python API Docs
+  - `cucim._cucim.CuImage` -> `cucim.CuImage`
+- Use new feature to reference a cross-link with header (from v0.13.0 of [myst-parser](https://pypi.org/project/myst-parser/))
+  - <https://github.com/executablebooks/MyST-Parser/issues/149>
+  - <https://myst-parser.readthedocs.io/en/latest/using/howto.html#automatically-create-targets-for-section-headers>
+  - <https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html#auto-generated-header-anchors>
+
+### Plugin-system (Carbonite)
+
+- Update project to use the latest [Carbonite SDK](https://docs.omniverse.nvidia.com/prod_kit/prod_kit/developer_api.html#carbonite-sdk) for supporting plug-in architecture -- [v0.22.0](../release_notes/v0.22.0.md)
+  - Migrate to use Carbonite SDK as it is
+  - Update to use Minimal Carbonite SDK
+
+- Handle errors and log error message once switched to use Carbonite SDK's built-in error routine
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp` : when reading field info
+  - `cpp/plugins/cucim.kit.cuslide/src/cuslide/tiff/ifd.cpp` in `IFD::read()` : memory size check if `out_buf->data` has high-enough memory
+- Get plugin name from file_path
+  - `cpp/src/core/cucim_plugin.cpp:53` in `Plugin::Plugin()`
+- Generalize `CuImage::ensure_init()`
+  - 'LINUX' path separator is used. Need to make it generalize once filesystem library is available
+  - `cucim/cpp/src/cucim.cpp:520`
+
diff --git a/python/cucim/docs/spelling_wordlist.txt b/python/cucim/docs/spelling_wordlist.txt
new file mode 100644
index 000000000..f95eb78d8
--- /dev/null
+++ b/python/cucim/docs/spelling_wordlist.txt
@@ -0,0 +1,11 @@
+builtin
+builtins
+classmethod
+staticmethod
+classmethods
+staticmethods
+args
+kwargs
+callstack
+Changelog
+Indices
diff --git a/python/cucim/docs/user_guide/index.md b/python/cucim/docs/user_guide/index.md
new file mode 100644
index 000000000..cd3d45227
--- /dev/null
+++ b/python/cucim/docs/user_guide/index.md
@@ -0,0 +1 @@
+# User Guide
diff --git a/python/cucim/setup.cfg b/python/cucim/setup.cfg
new file mode 100644
index 000000000..da00f63dd
--- /dev/null
+++ b/python/cucim/setup.cfg
@@ -0,0 +1,62 @@
+[versioneer]
+VCS = git
+style = pep440
+versionfile_source = src/cucim/_version.py
+versionfile_build = src/cucim/_version.py
+tag_prefix = v
+parentdir_prefix = cucim-
+
+[bdist_wheel]
+universal = 0
+
+[flake8]
+max-line-length = 80
+ignore =
+    # line break before binary operator
+    W503
+    # line break after binary operator
+    W504
+    # whitespace before :
+    E203
+exclude = .tox,.eggs,ci/templates,build,dist,.git,__pycache__,doc/conf.py,doc/sphinxext,build,dist,__init__.py
+per-file-ignores =
+    setup.py:F821
+    versioneer.py:W605
+    src/localtest.py:E127
+    src/cucim/skimage/__init__.py:F401
+    src/cucim/skimage/util/tests/test_shape.py:E201,E202,E241
+    src/cucim/skimage/measure/tests/test_block.py:E201,E202,E241
+    src/cucim/skimage/transform/tests/test_warps.py:E201,E202,E241,W605
+    src/cucim/skimage/transform/_geometric.py:E201,E202,E241
+
+[tool:pytest]
+# If a pytest section is found in one of the possible config files
+# (pytest.ini, tox.ini or setup.cfg), then pytest will not look for any others,
+# so if you add a pytest config section elsewhere,
+# you will need to delete this section from setup.cfg.
+norecursedirs =
+    migrations
+
+python_files =
+    test_*.py
+    *_test.py
+    tests.py
+# PytestDeprecationWarning: The --strict option is deprecated, use --strict-markers instead.
+addopts =
+    -ra
+    --strict-markers
+    # --doctest-modules
+    # --doctest-glob=\*.rst
+    --tb=short
+testpaths =
+    tests
+
+[tool:isort]
+force_single_line = True
+line_length = 80
+known_first_party = cucim
+default_section = THIRDPARTY
+forced_separate = test_cucim
+skip = .tox,.eggs,ci/templates,build,dist,versioneer.py
+sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER
+multi_line_output = 0
diff --git a/python/cucim/setup.py b/python/cucim/setup.py
new file mode 100755
index 000000000..47a4260e5
--- /dev/null
+++ b/python/cucim/setup.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+from __future__ import absolute_import
+from __future__ import print_function
+
+import io
+import sys
+from glob import glob
+from os.path import basename
+from os.path import dirname
+from os.path import join
+from os.path import splitext
+
+from setuptools import find_packages
+from setuptools import setup
+
+# import versioneer
+
+# Give setuptools a hint to complain if it's too old a version
+# 24.2.0 added the python_requires option
+# Should match pyproject.toml
+SETUP_REQUIRES = ['setuptools >= 24.2.0']
+# This enables setuptools to install wheel on-the-fly
+SETUP_REQUIRES += ['wheel'] if 'bdist_wheel' in sys.argv else []
+
+
+def read(*names, **kwargs):
+    with io.open(
+        join(dirname(__file__), *names),
+        encoding=kwargs.get('encoding', 'utf8')
+    ) as fh:
+        return fh.read()
+
+# lib_paths = list(map(lambda x: basename(x), glob("src/cucim/clara/*.so*")))
+
+
+opts = dict(
+    name='cucim',
+    version=read('VERSION').strip(),  # versioneer.get_version(),
+    # cmdclass=versioneer.get_cmdclass()
+    license='Apache-2.0',
+    description='cuCIM - an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.',  # noqa
+    long_description='%s\n%s' % (
+        read('README.md'),
+        read('CHANGELOG.md')
+    ),
+    long_description_content_type='text/markdown',
+    author='NVIDIA Corporation',
+    url='https://github.com/rapidsai/cucim',
+    packages=find_packages('src'),
+    package_dir={'cucim': 'src/cucim'},
+    py_modules=[splitext(basename(path))[0] for path in glob('src/*.py')],
+    # If True, the data files [of include_package_data] must be under version
+    # control or specified via the distutils' MANIFEST.in file
+    # (https://setuptools.readthedocs.io/en/latest/userguide/datafiles.html)
+    include_package_data=True,
+    # package_data={
+    #     '': lib_paths,
+    # },
+    zip_safe=False,
+    classifiers=[
+        # complete classifier list:
+        #   http://pypi.python.org/pypi?%3Aaction=list_classifiers
+        'Development Status :: 4 - Beta',
+        'Intended Audience :: Developers',
+        'Intended Audience :: Education',
+        'Intended Audience :: Science/Research',
+        'Intended Audience :: Healthcare Industry',
+        'Operating System :: POSIX :: Linux',
+        'Environment :: Console',
+        'Environment :: GPU :: NVIDIA CUDA :: 11.0',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: C++',
+        'Programming Language :: Python',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        # 'Operating System :: OS Independent',
+        # 'Operating System :: Unix',
+        # 'Operating System :: POSIX',
+        # 'Operating System :: Microsoft :: Windows',
+        # 'Programming Language :: Python :: Implementation :: CPython',
+        # uncomment if you test on these interpreters:
+        # 'Programming Language :: Python :: Implementation :: PyPy',
+        # 'Programming Language :: Python :: Implementation :: IronPython',
+        # 'Programming Language :: Python :: Implementation :: Jython',
+        # 'Programming Language :: Python :: Implementation :: Stackless',
+        'Topic :: Scientific/Engineering :: Image Processing',
+    ],
+    project_urls={
+        'Documentation': 'https://cucim.readthedocs.io/',
+        'Changelog': 'https://cucim.readthedocs.io/en/latest/changelog.html',
+        'Issue Tracker': 'https://github.com/rapidsai/cucim/issues',
+    },
+    keywords=[
+        # eg: 'keyword1', 'keyword2', 'keyword3',
+    ],
+    python_requires='>= 3.6',
+    platforms=['manylinux2014_x86_64'],
+    setup_requires=SETUP_REQUIRES,
+    install_requires=[
+        # TODO: Check cupy dependency based on cuda version
+        'click', 'numpy',  # 'scipy', 'scikit-image'
+        # eg: 'aspectlib==1.1.1', 'six>=1.7',
+    ],
+    extras_require={
+        # eg:
+        #   'rst': ['docutils>=0.11'],
+        #   ':python_version=="2.6"': ['argparse'],
+    },
+    entry_points={
+        'console_scripts': [
+            'cucim = cucim.clara.cli:main',
+        ]
+    },
+)
+
+if __name__ == '__main__':
+    setup(**opts)
diff --git a/python/cucim/src/cucim/__init__.py b/python/cucim/src/cucim/__init__.py
new file mode 100644
index 000000000..7190f2b8f
--- /dev/null
+++ b/python/cucim/src/cucim/__init__.py
@@ -0,0 +1,58 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""CuPy Extensions
+
+This project contains CuPy-based implementations of functions from NumPy,
+SciPy and scikit-image that are not currently available in CuPy itself.
+
+Most functions are not provided via the top level-import. Instead, individual
+subpackages should be imported instead.
+
+Subpackages
+-----------
+numpy
+    Functions from NumPy which are not available via CuPy.
+scipy
+    Functions from SciPy which are not available via CuPy.
+skimage
+    Functions from scikit-image.
+
+Additional documentation and usage examples for the functions can be found
+at the main documentation pages of the various packges:
+
+"""
+
+try:
+    import cupy
+
+    try:
+        memoize = cupy.util.memoize
+    except AttributeError:
+        memoize = cupy.memoize
+
+    del cupy
+except ImportError:
+    import sys
+    print('[warning] CuPy is not available. cucim.skimage package may not work correctly.', file=sys.stderr)
+
+# from ._version import get_versions
+
+# __version__ = get_versions()['version']
+# del get_versions
+
+from .clara import CuImage
+from .clara import __version__
+from .clara import cli
diff --git a/python/cucim/src/cucim/__main__.py b/python/cucim/src/cucim/__main__.py
new file mode 100644
index 000000000..1ba9772d9
--- /dev/null
+++ b/python/cucim/src/cucim/__main__.py
@@ -0,0 +1,29 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Entrypoint module, in case you use `python -mcucim`.
+
+
+Why does this file exist, and why __main__? For more info, read:
+
+- https://www.python.org/dev/peps/pep-0338/
+- https://docs.python.org/2/using/cmdline.html#cmdoption-m
+- https://docs.python.org/3/using/cmdline.html#cmdoption-m
+"""
+from cucim.clara.cli import main
+
+if __name__ == "__main__":
+    main()
diff --git a/python/cucim/src/cucim/_misc.py b/python/cucim/src/cucim/_misc.py
new file mode 100644
index 000000000..78eaf2de7
--- /dev/null
+++ b/python/cucim/src/cucim/_misc.py
@@ -0,0 +1,49 @@
+"""Misc utility functions that are not from SciPy, NumPy or scikit-image.
+
+"""
+import math
+
+import numpy
+
+if hasattr(math, 'prod'):
+    prod = math.prod  # available in Python 3.8+ only
+else:
+    prod = numpy.prod
+
+
+def ndim(a):
+    """
+    Return the number of dimensions of an array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.  If it is not already an ndarray, a conversion is
+        attempted.
+
+    Returns
+    -------
+    number_of_dimensions : int
+        The number of dimensions in `a`.  Scalars are zero-dimensional.
+
+    See Also
+    --------
+    ndarray.ndim : equivalent method
+    shape : dimensions of array
+    ndarray.shape : dimensions of array
+
+    Examples
+    --------
+    >>> from cucim.numpy import ndim
+    >>> ndim([[1,2,3],[4,5,6]])
+    2
+    >>> ndim(cupy.asarray([[1,2,3],[4,5,6]]))
+    2
+    >>> ndim(1)
+    0
+
+    """
+    try:
+        return a.ndim
+    except AttributeError:
+        return numpy.asarray(a).ndim
diff --git a/python/cucim/src/cucim/_version.py b/python/cucim/src/cucim/_version.py
new file mode 100644
index 000000000..8eac307ec
--- /dev/null
+++ b/python/cucim/src/cucim/_version.py
@@ -0,0 +1,520 @@
+
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.18 (https://github.com/warner/python-versioneer)
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "$Format:%d$"
+    git_full = "$Format:%H$"
+    git_date = "$Format:%ci$"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = "v"
+    cfg.parentdir_prefix = "cucim-"
+    cfg.versionfile_source = "cucim/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Decorator to mark a method as the handler for a particular VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            dispcmd = str([c] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
+                                 stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, p.returncode
+    return stdout, p.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for i in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        else:
+            rootdirs.append(root)
+            root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+            if line.strip().startswith("git_date ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["date"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if not keywords:
+        raise NotThisMethod("no keywords at all, weird")
+    date = keywords.get("date")
+    if date is not None:
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                          hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
+                                          "--always", "--long",
+                                          "--match", "%s*" % tag_prefix],
+                                   cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                    cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
+                       cwd=root)[0].strip()
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post.dev%d" % pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Eexceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for i in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
diff --git a/python/cucim/src/cucim/clara/__init__.py b/python/cucim/src/cucim/clara/__init__.py
new file mode 100644
index 000000000..542f37118
--- /dev/null
+++ b/python/cucim/src/cucim/clara/__init__.py
@@ -0,0 +1,32 @@
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+
+from . import cli
+from . import converter
+# import hidden methods
+from ._cucim import CuImage
+from ._cucim import __version__
+from ._cucim import filesystem
+from ._cucim import io
+
+__all__ = ['cli', 'CuImage', 'filesystem', 'io', 'converter', '__version__']
+
+
+from ._cucim import _get_plugin_root  # isort:skip
+from ._cucim import _set_plugin_root  # isort:skip
+# Set plugin root path
+_set_plugin_root(os.path.dirname(os.path.realpath(__file__)))
diff --git a/python/cucim/src/cucim/clara/cli.py b/python/cucim/src/cucim/clara/cli.py
new file mode 100644
index 000000000..c4faceebc
--- /dev/null
+++ b/python/cucim/src/cucim/clara/cli.py
@@ -0,0 +1,60 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Module that contains the command line app.
+
+Why does this file exist, and why not put this in __main__?
+
+  You might be tempted to import things from __main__ later, but that will cause
+  problems: the code will get executed twice:
+
+  - When you run `python -mcucim` python will execute
+    ``__main__.py`` as a script. That means there won't be any
+    ``cucim.__main__`` in ``sys.modules``.
+  - When you import __main__ it will get executed again (as a module) because
+    there's no ``cucim.__main__`` in ``sys.modules``.
+
+  Also see (1) from http://click.pocoo.org/5/setuptools/#setuptools-integration
+"""
+import logging
+import os
+from pathlib import Path
+
+import click
+
+
+@click.group()
+def main():
+    """nothing for now"""
+    pass
+
+
+@main.command()
+@click.argument('src_file', type=click.Path(exists=True))
+@click.argument('dest_folder', type=click.Path(
+    exists=True, dir_okay=True, file_okay=False), default=Path('.'))
+@click.option('--tile-size', type=int, default=256)
+@click.option('--overlap', type=int, default=0)
+@click.option('--num-workers', type=int, default=os.cpu_count())
+@click.option('--output-filename', type=str, default='image.tif')
+def convert(src_file, dest_folder, tile_size, overlap, num_workers,
+            output_filename):
+    """Convert file format"""
+    from .converter import tiff
+    logging.basicConfig(level=logging.INFO)
+
+    tiff.svs2tif(src_file, Path(dest_folder), tile_size, overlap, num_workers,
+                 output_filename)
diff --git a/python/cucim/src/cucim/clara/converter/tiff.py b/python/cucim/src/cucim/clara/converter/tiff.py
new file mode 100644
index 000000000..d3cfcddbb
--- /dev/null
+++ b/python/cucim/src/cucim/clara/converter/tiff.py
@@ -0,0 +1,191 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import concurrent.futures
+import gc
+import logging
+import os
+from itertools import repeat
+from pathlib import Path
+
+import cv2
+import numpy as np
+from openslide import OpenSlide
+from openslide.deepzoom import DeepZoomGenerator
+from tifffile import TiffWriter
+
+SUBFILETYPE_NONE = 0
+SUBFILETYPE_REDUCEDIMAGE = 1
+
+logger = logging.getLogger(__name__)
+
+
+def filter_tile(
+    tiles, dim_index, index, tile_size, output_array
+):
+    try:
+        x, y = index
+        tile = tiles.get_tile(dim_index, index)
+
+        tile_width, tile_height = tile.size
+
+        # Make image the same size for inference
+        if tile.size != (tile_size, tile_size):
+            tile = tile.crop((0, 0, tile_size, tile_size))
+        ax = x * tile_size
+        ay = y * tile_size
+
+        tile_arr = np.array(tile)  # H x W x C
+
+        output_array[ay: ay + tile_height, ax: ax + tile_width, :] = tile_arr[
+            :tile_height, :tile_width]
+    except Exception as e:
+        logger.exception(e)
+
+
+def svs2tif(input_file, output_folder, tile_size, overlap,
+            num_workers=os.cpu_count(), output_filename="image.tif"):
+    output_folder = str(output_folder)
+
+    logger.info("Parameters")
+    logger.info("       input file: %s", input_file)
+    logger.info("    output folder: %s", output_folder)
+    logger.info("        tile size: %d", tile_size)
+    logger.info("          overlap: %d", overlap)
+    logger.info("      num_workers: %d", num_workers)
+    logger.info("  output filename: %s", output_filename)
+
+    with OpenSlide(input_file) as slide:
+        properties = slide.properties
+        slide_dimensions = slide.dimensions
+
+        tiles = DeepZoomGenerator(
+            slide, tile_size=tile_size, overlap=overlap, limit_bounds=False
+        )
+
+        output_file = Path(output_folder) / output_filename
+
+        np_memmap = []
+        width, height = slide_dimensions
+        img_w, img_h = width, height
+        for level in range(tiles.level_count):
+            memmap_filename = Path(output_folder, "level{}.mmap".format(level))
+            memmap_shape = (img_h, img_w, 3)
+            np_memmap_arr = np.memmap(
+                memmap_filename, dtype=np.uint8, mode="w+", shape=memmap_shape
+            )
+            np_memmap.append(np_memmap_arr)
+            logger.info("  Created %s %s", memmap_filename, repr(memmap_shape))
+
+            img_w = round(img_w / 2)
+            img_h = round(img_h / 2)
+            if max(img_w, img_h) < tile_size:
+                break
+        try:
+
+            # Multithread processing for each tile in the largest
+            # image (index 0)
+            logger.info("Processing tiles...")
+            dim_index = tiles.level_count - 1
+            tile_pos_x, tile_pos_y = tiles.level_tiles[dim_index]
+            index_iter = np.ndindex(tile_pos_x, tile_pos_y)
+            with concurrent.futures.ThreadPoolExecutor(
+                    max_workers=num_workers) as executor:
+                executor.map(
+                    filter_tile,
+                    repeat(tiles),
+                    repeat(dim_index),
+                    index_iter,
+                    repeat(tile_size),
+                    repeat(np_memmap[0]),
+                )
+
+            logger.info("Storing low resolution images...")
+            for index in range(1, len(np_memmap)):
+                src_arr = np_memmap[index - 1]
+                target_arr = np_memmap[index]
+                target_arr[:] = cv2.resize(
+                    src_arr, (0, 0), fx=0.5, fy=0.5,
+                    interpolation=cv2.INTER_AREA
+                )
+                # th, tw = target_arr.shape[:2]
+                # target_arr[:] = src_arr[
+                #     : th * 2 : 2, : tw * 2 : 2, :
+                # ]  # Fast resizing. No anti-aliasing.
+                logger.info("  Level %d: %s", index, repr(target_arr.shape))
+
+            # Calculate resolution
+            if (
+                properties.get("tiff.ResolutionUnit")
+                and properties.get("tiff.XResolution")
+                and properties.get("tiff.YResolution")
+            ):
+                resolution_unit = properties.get("tiff.ResolutionUnit")
+                x_resolution = float(properties.get("tiff.XResolution"))
+                y_resolution = float(properties.get("tiff.YResolution"))
+            else:
+                resolution_unit = properties.get("tiff.ResolutionUnit", "inch")
+                if properties.get("tiff.ResolutionUnit",
+                                  "inch").lower() == "inch":
+                    numerator = 25400  # Microns in Inch
+                else:
+                    numerator = 10000  # Microns in CM
+                x_resolution = int(numerator
+                                   // float(properties.get('openslide.mpp-x',
+                                                           1)))
+                y_resolution = int(numerator
+                                   // float(properties.get('openslide.mpp-y',
+                                                           1)))
+
+            # Write TIFF file
+            with TiffWriter(output_file, bigtiff=True) as tif:
+                # Save from the largest image (openslide requires that)
+                for level in range(len(np_memmap)):
+                    src_arr = np_memmap[level]
+                    height, width = src_arr.shape[:2]
+                    logger.info("Saving Level %d image (%d x %d)...",
+                                level, width, height)
+                    if level:
+                        subfiletype = SUBFILETYPE_REDUCEDIMAGE
+                    else:
+                        subfiletype = SUBFILETYPE_NONE
+
+                    tif.save(
+                        src_arr,
+                        software="Glencoe/Faas pyramid",
+                        metadata={"axes": "YXC"},
+                        tile=(tile_size, tile_size),
+                        photometric="RGB",
+                        planarconfig="CONTIG",
+                        resolution=(
+                            x_resolution // 2 ** level,
+                            y_resolution // 2 ** level,
+                            resolution_unit,
+                        ),
+                        compress=("jpeg", 95),  # requires imagecodecs
+                        subfiletype=subfiletype,
+                    )
+                logger.info("Done.")
+        finally:
+            # Remove memory-mapped file
+            logger.info("Removing memmapped files...")
+            src_arr = None
+            target_arr = None
+            np_memmap_arr = None
+            del np_memmap
+            gc.collect()
+            mmap_file_iter = Path(output_folder).glob("*.mmap")
+            for fp in mmap_file_iter:
+                fp.unlink()
diff --git a/python/cucim/src/cucim/clara/filesystem/__init__.py b/python/cucim/src/cucim/clara/filesystem/__init__.py
new file mode 100644
index 000000000..5e82149fa
--- /dev/null
+++ b/python/cucim/src/cucim/clara/filesystem/__init__.py
@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from cucim.clara._cucim.filesystem import *
+
+__all__ = ['open', 'pread', 'pwrite', 'close', 'discard_page_cache', 'CuFileDriver']
\ No newline at end of file
diff --git a/python/cucim/src/cucim/clara/io/__init__.py b/python/cucim/src/cucim/clara/io/__init__.py
new file mode 100644
index 000000000..33693fa78
--- /dev/null
+++ b/python/cucim/src/cucim/clara/io/__init__.py
@@ -0,0 +1,18 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from cucim.clara._cucim.io import *
+
+__all__ = ['DeviceType', 'Device']
diff --git a/python/cucim/src/cucim/skimage/__init__.py b/python/cucim/src/cucim/skimage/__init__.py
new file mode 100644
index 000000000..85d39b836
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/__init__.py
@@ -0,0 +1,64 @@
+"""GPU Image Processing for Python
+
+This module is a CuPy based implementation of a subset of scikit-image.
+
+It is a collection of algorithms for image processing and computer vision.
+
+The main package only provides a few utilities for converting between image
+data types; for most features, you need to import one of the following
+subpackages:
+
+Subpackages
+-----------
+color
+    Color space conversion.
+exposure
+    Image intensity adjustment, e.g., histogram equalization, etc.
+feature
+    Feature detection and extraction, e.g., texture analysis corners, etc.
+filters
+    Sharpening, edge finding, rank filters, thresholding, etc.
+measure
+    Measurement of image properties, e.g., region properties and contours.
+metrics
+    Metrics corresponding to images, e.g. distance metrics, similarity, etc.
+morphology
+    Morphological operations, e.g., opening or skeletonization.
+restoration
+    Restoration algorithms, e.g., deconvolution algorithms, denoising, etc.
+segmentation
+    Partitioning an image into multiple regions.
+transform
+    Geometric and other transforms, e.g., rotation or the Radon transform.
+util
+    Generic utilities.
+
+Utility Functions
+-----------------
+img_as_float
+    Convert an image to floating point format, with values in [0, 1].
+    Is similar to `img_as_float64`, but will not convert lower-precision
+    floating point arrays to `float64`.
+img_as_float32
+    Convert an image to single-precision (32-bit) floating point format,
+    with values in [0, 1].
+img_as_float64
+    Convert an image to double-precision (64-bit) floating point format,
+    with values in [0, 1].
+img_as_uint
+    Convert an image to unsigned integer format, with values in [0, 65535].
+img_as_int
+    Convert an image to signed integer format, with values in [-32768, 32767].
+img_as_ubyte
+    Convert an image to unsigned byte format, with values in [0, 255].
+img_as_bool
+    Convert an image to boolean format, with values either True or False.
+dtype_limits
+    Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
+
+"""
+
+# All skimage root imports go here
+from .util.dtype import (dtype_limits, img_as_bool, img_as_float,
+                         img_as_float32, img_as_float64, img_as_int,
+                         img_as_ubyte, img_as_uint)
diff --git a/python/cucim/src/cucim/skimage/_shared/__init__.py b/python/cucim/src/cucim/skimage/_shared/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/python/cucim/src/cucim/skimage/_shared/_warnings.py b/python/cucim/src/cucim/skimage/_shared/_warnings.py
new file mode 100644
index 000000000..2dab87df0
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/_warnings.py
@@ -0,0 +1,146 @@
+import functools
+import os
+import re
+import sys
+import warnings
+from contextlib import contextmanager
+
+__all__ = ['all_warnings', 'expected_warnings', 'warn']
+
+
+# A version of `warnings.warn` with a default stacklevel of 2.
+# functool is used so as not to increase the call stack accidentally
+warn = functools.partial(warnings.warn, stacklevel=2)
+
+
+@contextmanager
+def all_warnings():
+    """
+    Context for use in testing to ensure that all warnings are raised.
+
+    Examples
+    --------
+    >>> import warnings
+    >>> def foo():
+    ...     warnings.warn(RuntimeWarning("bar"), stacklevel=2)
+
+    We raise the warning once, while the warning filter is set to "once".
+    Hereafter, the warning is invisible, even with custom filters:
+
+    >>> with warnings.catch_warnings():
+    ...     warnings.simplefilter('once')
+    ...     foo()                         # doctest: +SKIP
+
+    We can now run ``foo()`` without a warning being raised:
+
+    >>> from numpy.testing import assert_warns
+    >>> foo()                             # doctest: +SKIP
+
+    To catch the warning, we call in the help of ``all_warnings``:
+
+    >>> with all_warnings():
+    ...     assert_warns(RuntimeWarning, foo)
+    """
+    # _warnings.py is on the critical import path.
+    # Since this is a testing only function, we lazy import inspect.
+    import inspect
+
+    # Whenever a warning is triggered, Python adds a __warningregistry__
+    # member to the *calling* module.  The exercize here is to find
+    # and eradicate all those breadcrumbs that were left lying around.
+    #
+    # We proceed by first searching all parent calling frames and explicitly
+    # clearing their warning registries (necessary for the doctests above to
+    # pass).  Then, we search for all submodules of skimage and clear theirs
+    # as well (necessary for the skimage test suite to pass).
+
+    frame = inspect.currentframe()
+    if frame:
+        for f in inspect.getouterframes(frame):
+            f[0].f_locals['__warningregistry__'] = {}
+    del frame
+
+    for mod_name, mod in list(sys.modules.items()):
+        try:
+            mod.__warningregistry__.clear()
+        except AttributeError:
+            pass
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+        yield w
+
+
+@contextmanager
+def expected_warnings(matching):
+    r"""Context for use in testing to catch known warnings matching regexes
+
+    Parameters
+    ----------
+    matching : None or a list of strings or compiled regexes
+        Regexes for the desired warning to catch
+        If matching is None, this behaves as a no-op.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> image = np.random.randint(0, 2**16, size=(100, 100), dtype=np.uint16)
+    >>> # rank filters are slow when bit-depth exceeds 10 bits
+    >>> from skimage import filters
+    >>> with expected_warnings(['Bad rank filter performance']):
+    ...     median_filtered = filters.rank.median(image)
+
+    Notes
+    -----
+    Uses `all_warnings` to ensure all warnings are raised.
+    Upon exiting, it checks the recorded warnings for the desired matching
+    pattern(s).
+    Raises a ValueError if any match was not found or an unexpected
+    warning was raised.
+    Allows for three types of behaviors: `and`, `or`, and `optional` matches.
+    This is done to accommodate different build environments or loop conditions
+    that may produce different warnings.  The behaviors can be combined.
+    If you pass multiple patterns, you get an orderless `and`, where all of the
+    warnings must be raised.
+    If you use the `|` operator in a pattern, you can catch one of several
+    warnings.
+    Finally, you can use `|\A\Z` in a pattern to signify it as optional.
+
+    """
+    if isinstance(matching, str):
+        raise ValueError('``matching`` should be a list of strings and not '
+                         'a string itself.')
+
+    # Special case for disabling the context manager
+    if matching is None:
+        yield None
+        return
+
+    strict_warnings = os.environ.get('SKIMAGE_TEST_STRICT_WARNINGS', '1')
+    if strict_warnings.lower() == 'true':
+        strict_warnings = True
+    elif strict_warnings.lower() == 'false':
+        strict_warnings = False
+    else:
+        strict_warnings = bool(int(strict_warnings))
+
+    with all_warnings() as w:
+        # enter context
+        yield w
+        # exited user context, check the recorded warnings
+        # Allow users to provide None
+        while None in matching:
+            matching.remove(None)
+        remaining = [m for m in matching if r'\A\Z' not in m.split('|')]
+        for warn in w:
+            found = False
+            for match in matching:
+                if re.search(match, str(warn.message)) is not None:
+                    found = True
+                    if match in remaining:
+                        remaining.remove(match)
+            if strict_warnings and not found:
+                raise ValueError('Unexpected warning: %s' % str(warn.message))
+        if strict_warnings and (len(remaining) > 0):
+            msg = 'No warning raised matching:\n%s' % '\n'.join(remaining)
+            raise ValueError(msg)
diff --git a/python/cucim/src/cucim/skimage/_shared/coord.py b/python/cucim/src/cucim/skimage/_shared/coord.py
new file mode 100644
index 000000000..9b6774193
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/coord.py
@@ -0,0 +1,55 @@
+import cupy as cp
+import numpy as np
+from scipy.spatial import cKDTree, distance
+
+
+# TODO: avoid host/device transfers (currently needed for cKDTree)
+def ensure_spacing(coord, spacing=1, p_norm=np.inf):
+    """Returns a subset of coord where a minimum spacing is guaranteed.
+
+    Parameters
+    ----------
+    coord : ndarray
+        The coordinates of the considered points.
+    spacing : float
+        the maximum allowed spacing between the points.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+
+    Returns
+    -------
+    output : ndarray
+        A subset of coord where a minimum spacing is guaranteed.
+
+    """
+
+    output = coord
+    if len(coord):
+        coord = cp.asnumpy(coord)
+        # Use KDtree to find the peaks that are too close to each other
+        tree = cKDTree(coord)
+
+        indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
+        rejected_peaks_indices = set()
+        for idx, candidates in enumerate(indices):
+            if idx not in rejected_peaks_indices:
+                # keep current point and the points at exactly spacing from it
+                candidates.remove(idx)
+                dist = distance.cdist([coord[idx]],
+                                      coord[candidates],
+                                      distance.minkowski,
+                                      p=p_norm).reshape(-1)
+                candidates = [c for c, d in zip(candidates, dist)
+                              if d < spacing]
+
+                # candidates.remove(keep)
+                rejected_peaks_indices.update(candidates)
+
+        # Remove the peaks that are too close to each other
+        output = np.delete(coord, tuple(rejected_peaks_indices), axis=0)
+        output = cp.asarray(output)
+
+    return output
diff --git a/python/cucim/src/cucim/skimage/_shared/fft.py b/python/cucim/src/cucim/skimage/_shared/fft.py
new file mode 100644
index 000000000..3c9fe7b63
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/fft.py
@@ -0,0 +1,13 @@
+"""Prefer FFTs via the new scipy.fft module when available (SciPy 1.4+)
+
+Otherwise fall back to numpy.fft.
+
+Like numpy 1.15+ scipy 1.3+ is also using pocketfft, but a newer
+C++/pybind11 version called pypocketfft
+"""
+import cupyx.scipy.fft
+from cupyx.scipy.fft import next_fast_len
+
+fftmodule = cupyx.scipy.fft
+
+__all__ = ['fftmodule', 'next_fast_len']
diff --git a/python/cucim/src/cucim/skimage/_shared/testing.py b/python/cucim/src/cucim/skimage/_shared/testing.py
new file mode 100644
index 000000000..9914a9994
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/testing.py
@@ -0,0 +1,24 @@
+import pytest
+from numpy.testing import (TestCase, assert_, assert_allclose,  # noqa
+                           assert_almost_equal, assert_array_almost_equal,
+                           assert_array_almost_equal_nulp, assert_array_equal,
+                           assert_array_less, assert_equal, assert_no_warnings,
+                           assert_warns)
+from skimage import data
+
+from ._warnings import expected_warnings  # noqa
+
+skipif = pytest.mark.skipif
+xfail = pytest.mark.xfail
+parametrize = pytest.mark.parametrize
+raises = pytest.raises
+fixture = pytest.fixture
+
+
+def fetch(data_filename):
+    """Attempt to fetch data, but if unavailable, skip the tests."""
+    try:
+        # CuPy Backend: TODO: avoid call to non-public _fetch method
+        return data._fetch(data_filename)
+    except (ConnectionError, ModuleNotFoundError):
+        pytest.skip(f'Unable to download {data_filename}')
diff --git a/python/cucim/src/cucim/skimage/_shared/tests/test_utils.py b/python/cucim/src/cucim/skimage/_shared/tests/test_utils.py
new file mode 100644
index 000000000..95bc90ef0
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/tests/test_utils.py
@@ -0,0 +1,124 @@
+import sys
+
+import numpy as np
+import pytest
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.utils import (_validate_interpolation_order,
+                                         change_default_value, check_nD,
+                                         deprecate_kwarg)
+
+
+def test_change_default_value():
+    @change_default_value("arg1", new_value=-1, changed_version="0.12")
+    def foo(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    @change_default_value('arg1', new_value=-1, changed_version='0.12',
+                          warning_msg="Custom warning message")
+    def bar(arg0, arg1=0, arg2=1):
+        """Expected docstring"""
+        return arg0, arg1, arg2
+
+    # Assert warning messages
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0) == (0, 0, 1)
+        assert bar(0) == (0, 0, 1)
+
+    expected_msg = ("The new recommended value for arg1 is -1. Until "
+                    "version 0.12, the default arg1 value is 0. From "
+                    "version 0.12, the arg1 default value will be -1. "
+                    "To avoid this warning, please explicitly set arg1 value.")
+
+    assert str(record[0].message) == expected_msg
+    assert str(record[1].message) == "Custom warning message"
+
+    # Assert that nothing happens if arg1 is set
+    with pytest.warns(None) as record:
+        # No kwargs
+        assert foo(0, 2) == (0, 2, 1)
+        assert foo(0, arg1=0) == (0, 0, 1)
+
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert foo.__doc__ == 'Expected docstring'
+
+    # Assert no warning was raised
+    assert not record.list
+
+
+def test_deprecated_kwarg():
+
+    @deprecate_kwarg({'old_arg1': 'new_arg1'})
+    def foo(arg0, new_arg1=1, arg2=None):
+        """Expected docstring"""
+        return arg0, new_arg1, arg2
+
+    @deprecate_kwarg({'old_arg1': 'new_arg1'},
+                     warning_msg="Custom warning message")
+    def bar(arg0, new_arg1=1, arg2=None):
+        """Expected docstring"""
+        return arg0, new_arg1, arg2
+
+    # Assert that the DeprecationWarning is raised when the deprecated
+    # argument name is used and that the reasult is valid
+    with pytest.warns(FutureWarning) as record:
+        assert foo(0, old_arg1=1) == (0, 1, None)
+        assert bar(0, old_arg1=1) == (0, 1, None)
+
+    msg = ("'old_arg1' is a deprecated argument name "
+           "for `foo`. Please use 'new_arg1' instead.")
+    assert str(record[0].message) == msg
+    assert str(record[1].message) == "Custom warning message"
+
+    # Assert that nothing happens when the function is called with the
+    # new API
+    with pytest.warns(None) as record:
+        # No kwargs
+        assert foo(0) == (0, 1, None)
+        assert foo(0, 2) == (0, 2, None)
+        assert foo(0, 1, 2) == (0, 1, 2)
+        # Kwargs without deprecated argument
+        assert foo(0, new_arg1=1, arg2=2) == (0, 1, 2)
+        assert foo(0, new_arg1=2) == (0, 2, None)
+        assert foo(0, arg2=2) == (0, 1, 2)
+        assert foo(0, 1, arg2=2) == (0, 1, 2)
+        # Function name and doc is preserved
+        assert foo.__name__ == 'foo'
+        if sys.flags.optimize < 2:
+            # if PYTHONOPTIMIZE is set to 2, docstrings are stripped
+            assert foo.__doc__ == 'Expected docstring'
+
+    # Assert no warning was raised
+    assert not record.list
+
+
+def test_check_nD():
+    z = np.random.random(200 ** 2).reshape((200, 200))
+    x = z[10:30, 30:10]
+    with pytest.raises(ValueError):
+        check_nD(x, 2)
+
+
+@pytest.mark.parametrize('dtype', [bool, int, np.uint8, np.uint16,
+                                   float, np.float32, np.float64])
+@pytest.mark.parametrize('order', [None, -1, 0, 1, 2, 3, 4, 5, 6])
+def test_validate_interpolation_order(dtype, order):
+    if order is None:
+        # Default order
+        assert (_validate_interpolation_order(dtype, None) == 0
+                if dtype == bool else 1)
+    elif order < 0 or order > 5:
+        # Order not in valid range
+        with pytest.raises(ValueError):
+            _validate_interpolation_order(dtype, order)
+    elif dtype == bool and order != 0:
+        # Deprecated order for bool array
+        with expected_warnings(["Input image dtype is bool"]):
+            assert _validate_interpolation_order(bool, order) == order
+    else:
+        # Valid use case
+        assert _validate_interpolation_order(dtype, order) == order
diff --git a/python/cucim/src/cucim/skimage/_shared/tests/test_warnings.py b/python/cucim/src/cucim/skimage/_shared/tests/test_warnings.py
new file mode 100644
index 000000000..30d697580
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/tests/test_warnings.py
@@ -0,0 +1,39 @@
+import os
+
+import pytest
+
+from cucim.skimage._shared._warnings import expected_warnings
+
+
+@pytest.fixture(scope='function')
+def setup():
+    # Remove any environment variable if it exists
+    old_strictness = os.environ.pop('SKIMAGE_TEST_STRICT_WARNINGS', None)
+    yield
+    # Add the user's desired strictness
+    if old_strictness is not None:
+        os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = old_strictness
+
+
+def test_strict_warnigns_default(setup):
+    # By default we should fail on missing expected warnings
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['1', 'true', 'True', 'TRUE'])
+def test_strict_warning_true(setup, strictness):
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with pytest.raises(ValueError):
+        with expected_warnings(['some warnings']):
+            pass
+
+
+@pytest.mark.parametrize('strictness', ['0', 'false', 'False', 'FALSE'])
+def test_strict_warning_false(setup, strictness):
+    # If the user doesnn't wish to be strict about warnigns
+    # the following shouldn't raise any error
+    os.environ['SKIMAGE_TEST_STRICT_WARNINGS'] = strictness
+    with expected_warnings(['some warnings']):
+        pass
diff --git a/python/cucim/src/cucim/skimage/_shared/utils.py b/python/cucim/src/cucim/skimage/_shared/utils.py
new file mode 100644
index 000000000..57c04b598
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_shared/utils.py
@@ -0,0 +1,422 @@
+import functools
+import inspect
+import numbers
+import warnings
+
+import cupy as cp
+import numpy as np
+
+from ..util import img_as_float
+from ._warnings import all_warnings, warn
+
+__all__ = ['deprecated', 'get_bound_method_class', 'all_warnings',
+           'safe_as_int', 'check_nD', 'check_shape_equality', 'warn']
+
+
+class skimage_deprecation(Warning):
+    """Create our own deprecation class, since Python >= 2.7
+    silences deprecations by default.
+
+    """
+
+    pass
+
+
+class change_default_value:
+    """Decorator for changing the default value of an argument.
+
+    Parameters
+    ----------
+    arg_name: str
+        The name of the argument to be updated.
+    new_value: any
+        The argument new value.
+    changed_version : str
+        The package version in which the change will be introduced.
+    warning_msg: str
+        Optional warning message. If None, a generic warning message
+        is used.
+
+    """
+
+    def __init__(self, arg_name, *, new_value, changed_version,
+                 warning_msg=None):
+        self.arg_name = arg_name
+        self.new_value = new_value
+        self.warning_msg = warning_msg
+        self.changed_version = changed_version
+
+    def __call__(self, func):
+        parameters = inspect.signature(func).parameters
+        arg_idx = list(parameters.keys()).index(self.arg_name)
+        old_value = parameters[self.arg_name].default
+
+        if self.warning_msg is None:
+            self.warning_msg = (
+                f"The new recommended value for {self.arg_name} is "
+                f"{self.new_value}. Until version {self.changed_version}, "
+                f"the default {self.arg_name} value is {old_value}. "
+                f"From version {self.changed_version}, the {self.arg_name} "
+                f"default value will be {self.new_value}. To avoid "
+                f"this warning, please explicitly set {self.arg_name} value.")
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            if len(args) < arg_idx + 1 and self.arg_name not in kwargs.keys():
+                # warn that arg_name default value changed:
+                warnings.warn(self.warning_msg, FutureWarning, stacklevel=2)
+            return func(*args, **kwargs)
+
+        return fixed_func
+
+
+class remove_arg:
+    """Decorator to remove an argument from function's signature.
+
+    Parameters
+    ----------
+    arg_name: str
+        The name of the argument to be removed.
+    changed_version : str
+        The package version in which the warning will be replaced by
+        an error.
+    help_msg: str
+        Optional message appended to the generic warning message.
+
+    """
+
+    def __init__(self, arg_name, *, changed_version, help_msg=None):
+        self.arg_name = arg_name
+        self.help_msg = help_msg
+        self.changed_version = changed_version
+
+    def __call__(self, func):
+        parameters = inspect.signature(func).parameters
+        arg_idx = list(parameters.keys()).index(self.arg_name)
+        warning_msg = (
+            f"{self.arg_name} argument is deprecated and will be removed "
+            f"in version {self.changed_version}. To avoid this warning, "
+            f"please do not use the {self.arg_name} argument. Please "
+            f"see {func.__name__} documentation for more details.")
+
+        if self.help_msg is not None:
+            warning_msg += f" {self.help_msg}"
+
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            if len(args) > arg_idx or self.arg_name in kwargs.keys():
+                # warn that arg_name is deprecated
+                warnings.warn(warning_msg, FutureWarning, stacklevel=2)
+            return func(*args, **kwargs)
+
+        return fixed_func
+
+
+class deprecate_kwarg:
+    """Decorator ensuring backward compatibility when argument names are
+    modified in a function definition.
+
+    Parameters
+    ----------
+    arg_mapping: dict
+        Mapping between the function's old argument names and the new
+        ones.
+    warning_msg: str
+        Optional warning message. If None, a generic warning message
+        is used.
+    removed_version : str
+        The package version in which the deprecated argument will be
+        removed.
+
+    """
+
+    def __init__(self, kwarg_mapping, warning_msg=None, removed_version=None):
+        self.kwarg_mapping = kwarg_mapping
+        if warning_msg is None:
+            self.warning_msg = ("'{old_arg}' is a deprecated argument name "
+                                "for `{func_name}`. ")
+            if removed_version is not None:
+                self.warning_msg += ("It will be removed in version {}. "
+                                     .format(removed_version))
+            self.warning_msg += "Please use '{new_arg}' instead."
+        else:
+            self.warning_msg = warning_msg
+
+    def __call__(self, func):
+        @functools.wraps(func)
+        def fixed_func(*args, **kwargs):
+            for old_arg, new_arg in self.kwarg_mapping.items():
+                if old_arg in kwargs:
+                    #  warn that the function interface has changed:
+                    warnings.warn(self.warning_msg.format(
+                        old_arg=old_arg, func_name=func.__name__,
+                        new_arg=new_arg), FutureWarning, stacklevel=2)
+                    # Substitute new_arg to old_arg
+                    kwargs[new_arg] = kwargs.pop(old_arg)
+
+            # Call the function with the fixed arguments
+            return func(*args, **kwargs)
+        return fixed_func
+
+
+class deprecated(object):
+    """Decorator to mark deprecated functions with warning.
+
+    Adapted from <http://wiki.python.org/moin/PythonDecoratorLibrary>.
+
+    Parameters
+    ----------
+    alt_func : str
+        If given, tell user what function to use instead.
+    behavior : {'warn', 'raise'}
+        Behavior during call to deprecated function: 'warn' = warn user that
+        function is deprecated; 'raise' = raise error.
+    removed_version : str
+        The package version in which the deprecated function will be removed.
+    """
+
+    def __init__(self, alt_func=None, behavior='warn', removed_version=None):
+        self.alt_func = alt_func
+        self.behavior = behavior
+        self.removed_version = removed_version
+
+    def __call__(self, func):
+
+        alt_msg = ''
+        if self.alt_func is not None:
+            alt_msg = ' Use ``%s`` instead.' % self.alt_func
+        rmv_msg = ''
+        if self.removed_version is not None:
+            rmv_msg = (' and will be removed in version %s' %
+                       self.removed_version)
+
+        msg = ('Function ``%s`` is deprecated' % func.__name__
+               + rmv_msg + '.' + alt_msg)
+
+        @functools.wraps(func)
+        def wrapped(*args, **kwargs):
+            if self.behavior == 'warn':
+                func_code = func.__code__
+                warnings.simplefilter('always', skimage_deprecation)
+                warnings.warn_explicit(msg,
+                                       category=skimage_deprecation,
+                                       filename=func_code.co_filename,
+                                       lineno=func_code.co_firstlineno + 1)
+            elif self.behavior == 'raise':
+                raise skimage_deprecation(msg)
+            return func(*args, **kwargs)
+
+        # modify doc string to display deprecation warning
+        doc = '**Deprecated function**.' + alt_msg
+        if wrapped.__doc__ is None:
+            wrapped.__doc__ = doc
+        else:
+            wrapped.__doc__ = doc + '\n\n    ' + wrapped.__doc__
+
+        return wrapped
+
+
+def get_bound_method_class(m):
+    """Return the class for a bound method.
+
+    """
+    return m.__self__.__class__
+
+
+def safe_as_int(val, atol=1e-3):
+    """
+    Attempt to safely cast values to integer format.
+
+    Parameters
+    ----------
+    val : scalar or iterable of scalars
+        Number or container of numbers which are intended to be interpreted as
+        integers, e.g., for indexing purposes, but which may not carry integer
+        type.
+    atol : float
+        Absolute tolerance away from nearest integer to consider values in
+        ``val`` functionally integers.
+
+    Returns
+    -------
+    val_int : NumPy scalar or ndarray of dtype `cupy.int64`
+        Returns the input value(s) coerced to dtype `cupy.int64` assuming all
+        were within ``atol`` of the nearest integer.
+
+    Notes
+    -----
+    This operation calculates ``val`` modulo 1, which returns the mantissa of
+    all values. Then all mantissas greater than 0.5 are subtracted from one.
+    Finally, the absolute tolerance from zero is calculated. If it is less
+    than ``atol`` for all value(s) in ``val``, they are rounded and returned
+    in an integer array. Or, if ``val`` was a scalar, a NumPy scalar type is
+    returned.
+
+    If any value(s) are outside the specified tolerance, an informative error
+    is raised.
+
+    Examples
+    --------
+    >>> safe_as_int(7.0)
+    7
+
+    >>> safe_as_int([9, 4, 2.9999999999])
+    array([9, 4, 3])
+
+    >>> safe_as_int(53.1)
+    Traceback (most recent call last):
+        ...
+    ValueError: Integer argument required but received 53.1, check inputs.
+
+    >>> safe_as_int(53.01, atol=0.01)
+    53
+
+    """
+    mod = np.asarray(val) % 1  # Extract mantissa
+
+    # Check for and subtract any mod values > 0.5 from 1
+    if mod.ndim == 0:                        # Scalar input, cannot be indexed
+        if mod > 0.5:
+            mod = 1 - mod
+    else:                                    # Iterable input, now ndarray
+        mod[mod > 0.5] = 1 - mod[mod > 0.5]  # Test on each side of nearest int
+
+    try:
+        np.testing.assert_allclose(mod, 0, atol=atol)
+    except AssertionError:
+        raise ValueError(
+            "Integer argument required but received "
+            "{0}, check inputs.".format(val)
+        )
+
+    return np.around(val).astype(np.int64)
+
+
+def check_shape_equality(im1, im2):
+    """Raise an error if the shape do not match."""
+    if not im1.shape == im2.shape:
+        raise ValueError("Input images must have the same dimensions.")
+    return
+
+
+def check_nD(array, ndim, arg_name="image"):
+    """
+    Verify an array meets the desired ndims and array isn't empty.
+
+    Parameters
+    ----------
+    array : array-like
+        Input array to be validated
+    ndim : int or iterable of ints
+        Allowable ndim or ndims for the array.
+    arg_name : str, optional
+        The name of the array in the original function.
+
+    """
+    msg_incorrect_dim = "The parameter `%s` must be a %s-dimensional array"
+    msg_empty_array = "The parameter `%s` cannot be an empty array"
+    if isinstance(ndim, int):
+        ndim = [ndim]
+    if array.size == 0:
+        raise ValueError(msg_empty_array % (arg_name))
+    if array.ndim not in ndim:
+        raise ValueError(
+            msg_incorrect_dim % (arg_name, '-or-'.join([str(n) for n in ndim]))
+        )
+
+
+def check_random_state(seed):
+    """Turn seed into a `cupy.random.RandomState` instance.
+
+    Parameters
+    ----------
+    seed : None, int or cupy.random.RandomState
+           If `seed` is None, return the RandomState singleton used by`cupy.random`.
+           If `seed` is an int, return a new RandomState instance seeded with `seed`.
+           If `seed` is already a RandomState instance, return it.
+
+    Raises
+    ------
+    ValueError
+        If `seed` is of the wrong type.
+
+    """  # noqa
+    # Function originally from scikit-learn's module sklearn.utils.validation
+    if seed is None or seed is cp.random:
+        return cp.random.mtrand._rand
+    if isinstance(seed, (numbers.Integral, cp.integer)):
+        return cp.random.RandomState(seed)
+    if isinstance(seed, cp.random.RandomState):
+        return seed
+    raise ValueError('%r cannot be used to seed a numpy.random.RandomState'
+                     ' instance' % seed)
+
+
+def convert_to_float(image, preserve_range):
+    """Convert input image to float image with the appropriate range.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    preserve_range : bool
+        Determines if the range of the image should be kept or transformed
+        using img_as_float. Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Notes:
+    ------
+    * Input images with `float32` data type are not upcast.
+
+    Returns
+    -------
+    image : ndarray
+        Transformed version of the input.
+
+    """
+    if preserve_range:
+        # Convert image to double only if it is not single or double
+        # precision float
+        if image.dtype.char not in 'df':
+            image = image.astype(float)
+    else:
+        image = img_as_float(image)
+    return image
+
+
+def _validate_interpolation_order(image_dtype, order):
+    """Validate and return spline interpolation's order.
+
+    Parameters
+    ----------
+    image_dtype : dtype
+        Image dtype.
+    order : int, optional
+        The order of the spline interpolation. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+
+    Returns
+    -------
+    order : int
+        if input order is None, returns 0 if image_dtype is bool and 1
+        otherwise. Otherwise, image_dtype is checked and input order
+        is validated accordingly (order > 0 is not supported for bool
+        image dtype)
+
+    """
+
+    if order is None:
+        return 0 if image_dtype == bool else 1
+
+    if order < 0 or order > 5:
+        raise ValueError("Spline interpolation order has to be in the "
+                         "range 0-5.")
+
+    if image_dtype == bool and order != 0:
+        warn("Input image dtype is bool. Interpolation is not defined "
+             "with bool data type. Please set order to 0 or explicitely "
+             "cast input image to another data type. Starting from version "
+             "0.19 a ValueError will be raised instead of this warning.",
+             FutureWarning, stacklevel=2)
+
+    return order
diff --git a/python/cucim/src/cucim/skimage/_vendored/__init__.py b/python/cucim/src/cucim/skimage/_vendored/__init__.py
new file mode 100644
index 000000000..6c7d90791
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/__init__.py
@@ -0,0 +1,6 @@
+"""
+This module will hold copies of any upstream CuPy code that is needed, but has
+not yet been merged to CuPy master.
+"""
+
+from cucim.skimage._vendored.signaltools import *  # noqa
diff --git a/python/cucim/src/cucim/skimage/_vendored/_internal.py b/python/cucim/src/cucim/skimage/_vendored/_internal.py
new file mode 100644
index 000000000..663631a4d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/_internal.py
@@ -0,0 +1,68 @@
+import cupy
+import numpy
+
+
+try:
+    # try importing Cython-based private axis handling functions from CuPy
+    if hasattr(cupy, '_core'):
+        # CuPy 10 renames core->_core
+        from cupy._core.internal import (
+            _normalize_axis_index,
+            _normalize_axis_indices,
+        )  # NOQA
+    else:
+        from cupy.core.internal import (
+            _normalize_axis_index,
+            _normalize_axis_indices,
+        )  # NOQA
+
+except ImportError:
+    # Fallback to local Python implementations
+
+    def _normalize_axis_index(axis, ndim):  # NOQA
+        """
+        Normalizes an axis index, ``axis``, such that is a valid positive
+        index into the shape of array with ``ndim`` dimensions. Raises a
+        ValueError with an appropriate message if this is not possible.
+        Args:
+            axis (int):
+                The un-normalized index of the axis. Can be negative
+            ndim (int):
+                The number of dimensions of the array that ``axis`` should
+                be normalized against
+        Returns:
+            int:
+                The normalized axis index, such that
+                `0 <= normalized_axis < ndim`
+        """
+        if axis < 0:
+            axis += ndim
+        if not (0 <= axis < ndim):
+            raise numpy.AxisError("axis out of bounds")
+        return axis
+
+    def _normalize_axis_indices(axes, ndim):  # NOQA
+        """Normalize axis indices.
+        Args:
+            axis (int, tuple of int or None):
+                The un-normalized indices of the axis. Can be negative.
+            ndim (int):
+                The number of dimensions of the array that ``axis`` should
+                be normalized against
+        Returns:
+            tuple of int:
+                The tuple of normalized axis indices.
+        """
+        if axes is None:
+            axes = tuple(range(ndim))
+        elif not isinstance(axes, tuple):
+            axes = (axes,)
+
+        res = []
+        for axis in axes:
+            axis = _normalize_axis_index(axis, ndim)
+            if axis in res:
+                raise ValueError("Duplicate value in 'axis'")
+            res.append(axis)
+
+        return tuple(sorted(res))
diff --git a/python/cucim/src/cucim/skimage/_vendored/_ndimage_util.py b/python/cucim/src/cucim/skimage/_vendored/_ndimage_util.py
new file mode 100644
index 000000000..f3a899b54
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/_ndimage_util.py
@@ -0,0 +1,11 @@
+"""Copy of private functions from CuPy's cupyx.scipy.ndimage._util"""
+
+
+def _get_inttype(input):
+    # The integer type to use for indices in the input array
+    # The indices actually use byte positions and we can't just use
+    # input.nbytes since that won't tell us the number of bytes between the
+    # first and last elements when the array is non-contiguous
+    nbytes = sum((x - 1) * abs(stride) for x, stride in
+                 zip(input.shape, input.strides)) + input.dtype.itemsize
+    return 'int' if nbytes < (1 << 31) else 'ptrdiff_t'
diff --git a/python/cucim/src/cucim/skimage/_vendored/_signaltools_core.py b/python/cucim/src/cucim/skimage/_vendored/_signaltools_core.py
new file mode 100644
index 000000000..5c29c2f83
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/_signaltools_core.py
@@ -0,0 +1,165 @@
+import cupy
+from cupyx.scipy import fft
+from cupyx.scipy.ndimage import filters
+
+from . import _internal as internal
+from . import _ndimage_util as _util
+
+
+def _check_conv_inputs(in1, in2, mode, convolution=True):
+    if in1.ndim == in2.ndim == 0:
+        return in1 * (in2 if convolution else in2.conj())
+    if in1.ndim != in2.ndim:
+        raise ValueError('in1 and in2 should have the same dimensionality')
+    if in1.size == 0 or in2.size == 0:
+        return cupy.array([], dtype=in1.dtype)
+    if mode not in ('full', 'same', 'valid'):
+        raise ValueError('acceptable modes are "valid", "same", or "full"')
+    return None
+
+
+def _direct_correlate(in1, in2, mode='full', output=float, convolution=False,
+                      boundary='constant', fillvalue=0.0, shift=False):
+    if in1.ndim != 1 and (in1.dtype.kind == 'b' or
+                          (in1.dtype.kind == 'f' and in1.dtype.itemsize < 4)):
+        raise ValueError('unsupported type in SciPy')
+
+    # Swaps inputs so smaller one is in2:
+    # NOTE: when mode != 'valid' we can only swap with a constant-0 boundary
+    swapped_inputs = False
+    orig_in1_shape = in1.shape
+    if _inputs_swap_needed(mode, in1.shape, in2.shape) or (
+            in2.size > in1.size and boundary == 'constant' and fillvalue == 0):
+        in1, in2 = in2, in1
+        swapped_inputs = not convolution
+
+    # Due to several optimizations, the second array can only be 2 GiB
+    if in2.nbytes >= (1 << 31):
+        raise RuntimeError('smaller array must be 2 GiB or less, '
+                           'use method="fft" instead')
+
+    # At this point, in1.size > in2.size
+    # (except some cases when boundary != 'constant' or fillvalue != 0)
+    # Figure out the output shape and the origin of the kernel
+    if mode == 'full':
+        out_shape = tuple(x1 + x2 - 1 for x1, x2 in zip(in1.shape, in2.shape))
+        offsets = tuple(x - 1 for x in in2.shape)
+    elif mode == 'valid':
+        out_shape = tuple(x1 - x2 + 1 for x1, x2 in zip(in1.shape, in2.shape))
+        offsets = (0,) * in1.ndim
+    else:  # mode == 'same':
+        # In correlate2d: When using "same" mode with even-length inputs, the
+        # outputs of correlate and correlate2d differ: There is a 1-index
+        # offset between them.
+        # This is dealt with by using "shift" parameter.
+        out_shape = orig_in1_shape
+        if orig_in1_shape == in1.shape:
+            offsets = tuple((x - shift) // 2 for x in in2.shape)
+        else:
+            offsets = tuple((2 * x2 - x1 - (not convolution) + shift) // 2
+                            for x1, x2 in zip(in1.shape, in2.shape))
+
+    # Check the output
+    if not isinstance(output, cupy.ndarray):
+        output = cupy.empty(out_shape, output)
+    elif output.shape != out_shape:
+        raise ValueError('out has wrong shape')
+
+    # Get and run the CuPy kernel
+    int_type = _util._get_inttype(in1)
+    kernel = filters._get_correlate_kernel(
+        boundary, in2.shape, int_type, offsets, fillvalue)
+    in2 = _reverse_and_conj(in2) if convolution else in2
+    if not swapped_inputs:
+        kernel(in1, in2, output)
+    elif output.dtype.kind != 'c':
+        # Avoids one array copy
+        kernel(in1, in2, _reverse_and_conj(output))
+    else:
+        kernel(in1, in2, output)
+        output = cupy.ascontiguousarray(_reverse_and_conj(output))
+    return output
+
+
+def _reverse_and_conj(x):
+    # Reverse array `x` in all dimensions and perform the complex conjugate
+    return x[(slice(None, None, -1),) * x.ndim].conj()
+
+
+def _inputs_swap_needed(mode, shape1, shape2, axes=None):
+    # See scipy's documentation in scipy.signal.signaltools
+    if mode != 'valid' or not shape1:
+        return False
+    if axes is None:
+        axes = range(len(shape1))
+    not_ok1 = any(shape1[i] < shape2[i] for i in axes)
+    not_ok2 = any(shape1[i] > shape2[i] for i in axes)
+    if not_ok1 and not_ok2:
+        raise ValueError('For "valid" mode, one must be at least '
+                         'as large as the other in every dimension')
+    return not_ok1
+
+
+def _init_freq_conv_axes(in1, in2, mode, axes, sorted_axes=False):
+    # See scipy's documentation in scipy.signal.signaltools
+    s1, s2 = in1.shape, in2.shape
+    axes = _init_nd_and_axes(in1, axes)
+    # Length-1 axes can rely on broadcasting rules, no fft needed
+    axes = [ax for ax in axes if s1[ax] != 1 and s2[ax] != 1]
+    if sorted_axes:
+        axes.sort()
+
+    # Check that unused axes are either 1 (broadcast) or the same length
+    for ax, (dim1, dim2) in enumerate(zip(s1, s2)):
+        if ax not in axes and dim1 != dim2 and dim1 != 1 and dim2 != 1:
+            raise ValueError('incompatible shapes for in1 and in2:'
+                             ' {} and {}'.format(s1, s2))
+
+    # Check that input sizes are compatible with 'valid' mode.
+    if _inputs_swap_needed(mode, s1, s2, axes=axes):
+        # Convolution is commutative
+        in1, in2 = in2, in1
+
+    return in1, in2, axes
+
+
+def _init_nd_and_axes(x, axes):
+    # See documentation in scipy.fft._helper._init_nd_shape_and_axes
+    # except shape argument is always None and doesn't return new shape
+    try:
+        axes = internal._normalize_axis_indices(axes, x.ndim, sort_axes=False)
+    except TypeError:
+        axes = internal._normalize_axis_indices(axes, x.ndim)
+    if not len(axes):
+        raise ValueError('when provided, axes cannot be empty')
+    if any(x.shape[ax] < 1 for ax in axes):
+        raise ValueError('invalid number of data points specified')
+    return axes
+
+
+def _freq_domain_conv(in1, in2, axes, shape, calc_fast_len=False):
+    # See scipy's documentation in scipy.signal.signaltools
+    real = (in1.dtype.kind != 'c' and in2.dtype.kind != 'c')
+    fshape = ([fft.next_fast_len(shape[a], real) for a in axes]
+              if calc_fast_len else shape)
+    fftn, ifftn = (fft.rfftn, fft.irfftn) if real else (fft.fftn, fft.ifftn)
+
+    # Perform the convolution
+    sp1 = fftn(in1, fshape, axes=axes)
+    sp2 = fftn(in2, fshape, axes=axes)
+    out = ifftn(sp1 * sp2, fshape, axes=axes)
+
+    return out[tuple(slice(x) for x in shape)] if calc_fast_len else out
+
+
+def _apply_conv_mode(full, s1, s2, mode, axes):
+    # See scipy's documentation in scipy.signal.signaltools
+    if mode == 'full':
+        return cupy.ascontiguousarray(full)
+    if mode == 'valid':
+        s1 = [full.shape[a] if a not in axes else s1[a] - s2[a] + 1
+              for a in range(full.ndim)]
+    starts = [(cur - new) // 2 for cur, new in zip(full.shape, s1)]
+    slices = tuple(slice(start, start + length)
+                   for start, length in zip(starts, s1))
+    return cupy.ascontiguousarray(full[slices])
diff --git a/python/cucim/src/cucim/skimage/_vendored/signaltools.py b/python/cucim/src/cucim/skimage/_vendored/signaltools.py
new file mode 100644
index 000000000..ec35f7377
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/signaltools.py
@@ -0,0 +1,738 @@
+import timeit
+import warnings
+
+import cupy
+import numpy as np
+from cupyx.scipy.ndimage import _util, filters
+
+from cucim import _misc
+from cucim.skimage._vendored import _signaltools_core as _st_core
+
+_prod = _misc.prod
+
+
+def convolve(in1, in2, mode='full', method='auto'):
+    """Convolve two N-dimensional arrays.
+
+    Convolve ``in1`` and ``in2``, with the output size determined by the
+    ``mode`` argument.
+
+    Args:
+        in1 (cupy.ndarray): First input.
+        in2 (cupy.ndarray): Second input. Should have the same number of
+            dimensions as `in1`.
+        mode (str): Indicates the size of the output:
+
+            - ``'full'``: output is the full discrete linear convolution \
+                (default)
+            - ``'valid'``: output consists only of those elements that do \
+                not rely on the zero-padding. Either ``in1`` or ``in2`` must \
+                be at least as large as the other in every dimension.
+            - ``'same'``: - output is the same size as ``in1``, centered with \
+                respect to the ``'full'`` output
+
+        method (str): Indicates which method to use for the computations:
+
+            - ``'direct'``: The convolution is determined directly from sums, \
+                the definition of convolution
+            - ``'fft'``: The Fourier Transform is used to perform the \
+                convolution by calling ``fftconvolve``.
+            - ``'auto'``: Automatically choose direct of FFT based on an \
+                estimate of which is faster for the arguments (default).
+
+    Returns:
+        cupy.ndarray: the result of convolution.
+
+    .. seealso:: :func:`cupyx.scipy.signal.choose_conv_method`
+    .. seealso:: :func:`cupyx.scipy.signal.correlation`
+    .. seealso:: :func:`cupyx.scipy.signal.fftconvolve`
+    .. seealso:: :func:`cupyx.scipy.signal.oaconvolve`
+    .. seealso:: :func:`cupyx.scipy.ndimage.convolve`
+    .. seealso:: :func:`scipy.signal.convolve`
+    .. note::
+        By default, ``convolve`` and ``correlate`` use ``method='auto'``, which
+        calls ``choose_conv_method`` to choose the fastest method using
+        pre-computed values. CuPy may not choose the same method to compute
+        the convolution as SciPy does given the same inputs.
+    """
+    return _correlate(in1, in2, mode, method, True)
+
+
+def correlate(in1, in2, mode='full', method='auto'):
+    """Cross-correlate two N-dimensional arrays.
+
+    Cross-correlate ``in1`` and ``in2``, with the output size determined by the
+    ``mode`` argument.
+
+    Args:
+        in1 (cupy.ndarray): First input.
+        in2 (cupy.ndarray): Second input. Should have the same number of
+            dimensions as ``in1``.
+        mode (str): Indicates the size of the output:
+
+            - ``'full'``: output is the full discrete linear convolution \
+                (default)
+            - ``'valid'``: output consists only of those elements that do \
+                not rely on the zero-padding. Either ``in1`` or ``in2`` must \
+                be at least as large as the other in every dimension.
+            - ``'same'``: - output is the same size as ``in1``, centered with \
+                respect to the ``'full'`` output
+
+        method (str): Indicates which method to use for the computations:
+
+            - ``'direct'``: The convolution is determined directly from sums, \
+                the definition of convolution
+            - ``'fft'``: The Fourier Transform is used to perform the \
+                convolution by calling ``fftconvolve``.
+            - ``'auto'``: Automatically choose direct of FFT based on an \
+                estimate of which is faster for the arguments (default).
+
+    Returns:
+        cupy.ndarray: the result of correlation.
+
+    .. seealso:: :func:`cupyx.scipy.signal.choose_conv_method`
+    .. seealso:: :func:`cupyx.scipy.signal.convolve`
+    .. seealso:: :func:`cupyx.scipy.signal.fftconvolve`
+    .. seealso:: :func:`cupyx.scipy.signal.oaconvolve`
+    .. seealso:: :func:`cupyx.scipy.ndimage.correlation`
+    .. seealso:: :func:`scipy.signal.correlation`
+    .. note::
+        By default, ``convolve`` and ``correlate`` use ``method='auto'``, which
+        calls ``choose_conv_method`` to choose the fastest method using
+        pre-computed values. CuPy may not choose the same method to compute
+        the convolution as SciPy does given the same inputs.
+    """
+    return _correlate(in1, in2, mode, method, False)
+
+
+def _correlate(in1, in2, mode='full', method='auto', convolution=False):
+    quick_out = _st_core._check_conv_inputs(in1, in2, mode, convolution)
+    if quick_out is not None:
+        return quick_out
+    if method not in ('auto', 'direct', 'fft'):
+        raise ValueError('acceptable methods are "auto", "direct", or "fft"')
+
+    if method == 'auto':
+        method = choose_conv_method(in1, in2, mode=mode)
+
+    if method == 'direct':
+        return _st_core._direct_correlate(in1, in2, mode, in1.dtype,
+                                          convolution)
+
+    # if method == 'fft':
+    inputs_swapped = _st_core._inputs_swap_needed(mode, in1.shape, in2.shape)
+    if inputs_swapped:
+        in1, in2 = in2, in1
+    if not convolution:
+        in2 = _st_core._reverse_and_conj(in2)
+    out = fftconvolve(in1, in2, mode)
+    result_type = cupy.result_type(in1, in2)
+    if result_type.kind in 'ui':
+        out = out.round()
+    out = out.astype(result_type, copy=False)
+    if not convolution and inputs_swapped:
+        out = cupy.ascontiguousarray(_st_core._reverse_and_conj(out))
+    return out
+
+
+def fftconvolve(in1, in2, mode='full', axes=None):
+    """Convolve two N-dimensional arrays using FFT.
+
+    Convolve ``in1`` and ``in2`` using the fast Fourier transform method, with
+    the output size determined by the ``mode`` argument.
+
+    This is generally much faster than the ``'direct'`` method of ``convolve``
+    for large arrays, but can be slower when only a few output values are
+    needed, and can only output float arrays (int or object array inputs will
+    be cast to float).
+
+    Args:
+        in1 (cupy.ndarray): First input.
+        in2 (cupy.ndarray): Second input. Should have the same number of
+            dimensions as ``in1``.
+        mode (str): Indicates the size of the output:
+            ``'full'``: output is the full discrete linear cross-correlation
+                        (default)
+            ``'valid'``: output consists only of those elements that do not
+                         rely on the zero-padding. Either ``in1`` or ``in2``
+                         must be at least as large as the other in every
+                         dimension.
+            ``'same'``: output is the same size as ``in1``, centered
+                        with respect to the 'full' output
+        axes (scalar or tuple of scalar or None): Axes over which to compute
+            the convolution. The default is over all axes.
+
+    Returns:
+        cupy.ndarray: the result of convolution
+
+    .. seealso:: :func:`cupyx.scipy.signal.choose_conv_method`
+    .. seealso:: :func:`cupyx.scipy.signal.correlation`
+    .. seealso:: :func:`cupyx.scipy.signal.convolve`
+    .. seealso:: :func:`cupyx.scipy.signal.oaconvolve`
+    .. seealso:: :func:`cupyx.scipy.ndimage.convolve`
+    .. seealso:: :func:`scipy.signal.correlation`
+    """
+    out = _st_core._check_conv_inputs(in1, in2, mode)
+    if out is not None:
+        return out
+    in1, in2, axes = _st_core._init_freq_conv_axes(in1, in2, mode, axes, False)
+    shape = [max(x1, x2) if a not in axes else x1 + x2 - 1
+             for a, (x1, x2) in enumerate(zip(in1.shape, in2.shape))]
+    out = _st_core._freq_domain_conv(in1, in2, axes, shape, calc_fast_len=True)
+    return _st_core._apply_conv_mode(out, in1.shape, in2.shape, mode, axes)
+
+
+def _conv_ops(x_shape, h_shape, mode):
+    """
+    Find the number of operations required for direct/fft methods of
+    convolution. The direct operations were recorded by making a dummy class to
+    record the number of operations by overriding ``__mul__`` and ``__add__``.
+    The FFT operations rely on the (well-known) computational complexity of the
+    FFT (and the implementation of ``_freq_domain_conv``).
+
+    """
+    if mode == "full":
+        out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
+    elif mode == "valid":
+        out_shape = [abs(n - k) + 1 for n, k in zip(x_shape, h_shape)]
+    elif mode == "same":
+        out_shape = x_shape
+    else:
+        raise ValueError(
+            "Acceptable mode flags are 'valid',"
+            " 'same', or 'full', not mode={}".format(mode)
+        )
+
+    s1, s2 = x_shape, h_shape
+    if len(x_shape) == 1:
+        s1, s2 = s1[0], s2[0]
+        if mode == "full":
+            direct_ops = s1 * s2
+        elif mode == "valid":
+            direct_ops = (s2 - s1 + 1) * s1 if s2 >= s1 else (s1 - s2 + 1) * s2
+        elif mode == "same":
+            direct_ops = (
+                s1 * s2 if s1 < s2 else s1 * s2 - (s2 // 2) * ((s2 + 1) // 2)
+            )
+    else:
+        if mode == "full":
+            direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
+        elif mode == "valid":
+            direct_ops = min(_prod(s1), _prod(s2)) * _prod(out_shape)
+        elif mode == "same":
+            direct_ops = _prod(s1) * _prod(s2)
+
+    full_out_shape = [n + k - 1 for n, k in zip(x_shape, h_shape)]
+    N = _prod(full_out_shape)
+    fft_ops = 3 * N * np.log(N)  # 3 separate FFTs of size full_out_shape
+    return fft_ops, direct_ops
+
+
+def _fftconv_faster(x, h, mode):
+    """
+    See if using fftconvolve or convolve is faster.
+
+    Parameters
+    ----------
+    x : cupy.ndarray
+        Signal
+    h : cupy.ndarray
+        Kernel
+    mode : str
+        Mode passed to convolve
+
+    Returns
+    -------
+    fft_faster : bool
+
+    Notes
+    -----
+    See docstring of `choose_conv_method` for details on tuning hardware.
+
+    See pull request 11031 for more detail:
+    https://github.com/scipy/scipy/pull/11031.
+
+    """
+    fft_ops, direct_ops = _conv_ops(x.shape, h.shape, mode)
+    offset = -1e-3 if x.ndim == 1 else -1e-4
+    constants = (
+        {
+            "valid": (1.89095737e-9, 2.1364985e-10, offset),
+            "full": (1.7649070e-9, 2.1414831e-10, offset),
+            "same": (3.2646654e-9, 2.8478277e-10, offset)
+            if h.size <= x.size
+            else (3.21635404e-9, 1.1773253e-8, -1e-5),
+        }
+        if x.ndim == 1
+        else {
+            "valid": (1.85927e-9, 2.11242e-8, offset),
+            "full": (1.99817e-9, 1.66174e-8, offset),
+            "same": (2.04735e-9, 1.55367e-8, offset),
+        }
+    )
+    O_fft, O_direct, O_offset = constants[mode]
+    return O_fft * fft_ops < O_direct * direct_ops + O_offset
+
+
+def _numeric_arrays(arrays, kinds="buifc"):
+    """
+    See if a list of arrays are all numeric.
+
+    Parameters
+    ----------
+    ndarrays : array or list of arrays
+        arrays to check if numeric.
+    numeric_kinds : string-like
+        The dtypes of the arrays to be checked. If the dtype.kind of
+        the ndarrays are not in this string the function returns False and
+        otherwise returns True.
+    """
+    if type(arrays) == cupy.ndarray:
+        return arrays.dtype.kind in kinds
+    for array_ in arrays:
+        if array_.dtype.kind not in kinds:
+            return False
+    return True
+
+
+def _timeit_fast(stmt="pass", setup="pass", repeat=3):
+    """
+    Returns the time the statement/function took, in seconds.
+
+    Faster, less precise version of IPython's timeit. `stmt` can be a statement
+    written as a string or a callable.
+
+    Will do only 1 loop (like IPython's timeit) with no repetitions
+    (unlike IPython) for very slow functions.  For fast functions, only does
+    enough loops to take 5 ms, which seems to produce similar results (on
+    Windows at least), and avoids doing an extraneous cycle that isn't
+    measured.
+
+    """
+    timer = timeit.Timer(stmt, setup)
+
+    # determine number of calls per rep so total time for 1 rep >= 5 ms
+    x = 0
+    for p in range(0, 10):
+        number = 10 ** p
+        x = timer.timeit(number)  # seconds
+        if x >= 5e-3 / 10:  # 5 ms for final test, 1/10th that for this one
+            break
+    if x > 1:  # second
+        # If it's macroscopic, don't bother with repetitions
+        best = x
+    else:
+        number *= 10
+        r = timer.repeat(repeat, number)
+        best = min(r)
+
+    sec = best / number
+    return sec
+
+
+# TODO: grlee77: tune this for CUDA when measure=False rather than falling
+#                back to the choices made by SciPy
+
+def choose_conv_method(in1, in2, mode="full", measure=False):
+    """
+    Find the fastest convolution/correlation method.
+
+    This primarily exists to be called during the ``method='auto'`` option in
+    `convolve` and `correlate`. It can also be used to determine the value of
+    ``method`` for many different convolutions of the same dtype/shape.
+    In addition, it supports timing the convolution to adapt the value of
+    ``method`` to a particular set of inputs and/or hardware.
+
+    Parameters
+    ----------
+    in1 : array_like
+        The first argument passed into the convolution function.
+    in2 : array_like
+        The second argument passed into the convolution function.
+    mode : str {'full', 'valid', 'same'}, optional
+        A string indicating the size of the output:
+
+        ``full``
+           The output is the full discrete linear convolution
+           of the inputs. (Default)
+        ``valid``
+           The output consists only of those elements that do not
+           rely on the zero-padding.
+        ``same``
+           The output is the same size as `in1`, centered
+           with respect to the 'full' output.
+    measure : bool, optional
+        If True, run and time the convolution of `in1` and `in2` with both
+        methods and return the fastest. If False (default), predict the fastest
+        method using precomputed values.
+
+    Returns
+    -------
+    method : str
+        A string indicating which convolution method is fastest, either
+        'direct' or 'fft'
+    times : dict, optional
+        A dictionary containing the times (in seconds) needed for each method.
+        This value is only returned if ``measure=True``.
+
+    See Also
+    --------
+    convolve
+    correlate
+
+    Notes
+    -----
+    Generally, this method is 99% accurate for 2D signals and 85% accurate
+    for 1D signals for randomly chosen input sizes. For precision, use
+    ``measure=True`` to find the fastest method by timing the convolution.
+    This can be used to avoid the minimal overhead of finding the fastest
+    ``method`` later, or to adapt the value of ``method`` to a particular set
+    of inputs.
+
+    Experiments were run on an Amazon EC2 r5a.2xlarge machine to test this
+    function. These experiments measured the ratio between the time required
+    when using ``method='auto'`` and the time required for the fastest method
+    (i.e., ``ratio = time_auto / min(time_fft, time_direct)``). In these
+    experiments, we found:
+
+    * There is a 95% chance of this ratio being less than 1.5 for 1D signals
+      and a 99% chance of being less than 2.5 for 2D signals.
+    * The ratio was always less than 2.5/5 for 1D/2D signals respectively.
+    * This function is most inaccurate for 1D convolutions that take between 1
+      and 10 milliseconds with ``method='direct'``. A good proxy for this
+      (at least in our experiments) is ``1e6 <= in1.size * in2.size <= 1e7``.
+
+    The 2D results almost certainly generalize to 3D/4D/etc because the
+    implementation is the same (the 1D implementation is different).
+
+    All the numbers above are specific to the EC2 machine. However, we did find
+    that this function generalizes fairly decently across hardware. The speed
+    tests were of similar quality (and even slightly better) than the same
+    tests performed on the machine to tune this function's numbers (a mid-2014
+    15-inch MacBook Pro with 16GB RAM and a 2.5GHz Intel i7 processor).
+
+    There are cases when `fftconvolve` supports the inputs but this function
+    returns `direct` (e.g., to protect against floating point integer
+    precision).
+
+    .. versionadded:: 0.19
+
+    Examples
+    --------
+    Estimate the fastest method for a given input:
+
+    >>> from scipy import signal
+    >>> img = cupy.random.rand(32, 32)
+    >>> filter = cupy.random.rand(8, 8)
+    >>> method = signal.choose_conv_method(img, filter, mode='same')
+    >>> method
+    'fft'
+
+    This can then be applied to other arrays of the same dtype and shape:
+
+    >>> img2 = cupy.random.rand(32, 32)
+    >>> filter2 = cupy.random.rand(8, 8)
+    >>> corr2 = signal.correlate(img2, filter2, mode='same', method=method)
+    >>> conv2 = signal.convolve(img2, filter2, mode='same', method=method)
+
+    The output of this function (``method``) works with `correlate` and
+    `convolve`.
+
+    """
+    volume = cupy.asarray(in1)
+    kernel = cupy.asarray(in2)
+
+    if measure:
+        times = {}
+        for method in ["fft", "direct"]:
+            times[method] = _timeit_fast(
+                lambda: convolve(volume, kernel, mode=mode, method=method)
+            )
+
+        chosen_method = "fft" if times["fft"] < times["direct"] else "direct"
+        return chosen_method, times
+
+    # for integer input,
+    # catch when more precision required than float provides (representing an
+    # integer as float can lose precision in fftconvolve if larger than 2**52)
+    if any([_numeric_arrays([x], kinds="ui") for x in [volume, kernel]]):
+        max_value = int(cupy.abs(volume).max()) * int(cupy.abs(kernel).max())
+        max_value *= int(min(volume.size, kernel.size))
+        if max_value > 2 ** np.finfo("float").nmant - 1:
+            return "direct"
+
+    if _numeric_arrays([volume, kernel], kinds="b"):
+        return "direct"
+
+    if _numeric_arrays([volume, kernel]):
+        if _fftconv_faster(volume, kernel, mode):
+            return "fft"
+
+    return "direct"
+
+
+def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
+    """Convolve two 2-dimensional arrays.
+
+    Convolve ``in1`` and ``in2`` with output size determined by ``mode``, and
+    boundary conditions determined by ``boundary`` and ``fillvalue``.
+
+    Args:
+        in1 (cupy.ndarray): First input.
+        in2 (cupy.ndarray): Second input. Should have the same number of
+            dimensions as ``in1``.
+        mode (str): Indicates the size of the output:
+
+            - ``'full'``: output is the full discrete linear convolution \
+                (default)
+            - ``'valid'``: output consists only of those elements that do \
+                not rely on the zero-padding. Either ``in1`` or ``in2`` must \
+                be at least as large as the other in every dimension.
+            - ``'same'``: - output is the same size as ``in1``, centered with \
+                respect to the ``'full'`` output
+
+        boundary (str): Indicates how to handle boundaries:
+
+            - ``fill``: pad input arrays with fillvalue (default)
+            - ``wrap``: circular boundary conditions
+            - ``symm``: symmetrical boundary conditions
+
+        fillvalue (scalar): Value to fill pad input arrays with. Default is 0.
+
+    Returns:
+        cupy.ndarray: A 2-dimensional array containing a subset of the discrete
+            linear convolution of ``in1`` with ``in2``.
+
+    .. seealso:: :func:`cupyx.scipy.signal.convolve`
+    .. seealso:: :func:`cupyx.scipy.signal.fftconvolve`
+    .. seealso:: :func:`cupyx.scipy.signal.oaconvolve`
+    .. seealso:: :func:`cupyx.scipy.signal.correlate2d`
+    .. seealso:: :func:`cupyx.scipy.ndimage.convolve`
+    .. seealso:: :func:`scipy.signal.convolve2d`
+    """
+    return _correlate2d(in1, in2, mode, boundary, fillvalue, True)
+
+
+def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0):
+    """Cross-correlate two 2-dimensional arrays.
+
+    Cross correlate ``in1`` and ``in2`` with output size determined by
+    ``mode``, and boundary conditions determined by ``boundary`` and
+    ``fillvalue``.
+
+    Args:
+        in1 (cupy.ndarray): First input.
+        in2 (cupy.ndarray): Second input. Should have the same number of
+            dimensions as ``in1``.
+        mode (str): Indicates the size of the output:
+
+            - ``'full'``: output is the full discrete linear convolution \
+                (default)
+            - ``'valid'``: output consists only of those elements that do \
+                not rely on the zero-padding. Either ``in1`` or ``in2`` must \
+                be at least as large as the other in every dimension.
+            - ``'same'``: - output is the same size as ``in1``, centered with \
+                respect to the ``'full'`` output
+
+        boundary (str): Indicates how to handle boundaries:
+
+            - ``fill``: pad input arrays with fillvalue (default)
+            - ``wrap``: circular boundary conditions
+            - ``symm``: symmetrical boundary conditions
+
+        fillvalue (scalar): Value to fill pad input arrays with. Default is 0.
+
+    Returns:
+        cupy.ndarray: A 2-dimensional array containing a subset of the discrete
+            linear cross-correlation of ``in1`` with ``in2``.
+
+    Note:
+        When using ``"same"`` mode with even-length inputs, the outputs of
+        ``correlate`` and ``correlate2d`` differ: There is a 1-index offset
+        between them.
+
+    .. seealso:: :func:`cupyx.scipy.signal.correlate`
+    .. seealso:: :func:`cupyx.scipy.signal.convolve2d`
+    .. seealso:: :func:`cupyx.scipy.ndimage.correlate`
+    .. seealso:: :func:`scipy.signal.correlate2d`
+    """
+    return _correlate2d(in1, in2, mode, boundary, fillvalue, False)
+
+
+def _correlate2d(in1, in2, mode, boundary, fillvalue, convolution=False):
+    if not (in1.ndim == in2.ndim == 2):
+        raise ValueError('{} inputs must both be 2-D arrays'.format(
+            'convolve2d' if convolution else 'correlate2d'))
+    _boundaries = {
+        'fill': 'constant', 'pad': 'constant',
+        'wrap': 'wrap', 'circular': 'wrap',
+        'symm': 'reflect', 'symmetric': 'reflect',
+    }
+    boundary = _boundaries.get(boundary)
+    if boundary is None:
+        raise ValueError('Acceptable boundary flags are "fill" (or "pad"), '
+                         '"circular" (or "wrap"), and '
+                         '"symmetric" (or "symm").')
+    quick_out = _st_core._check_conv_inputs(in1, in2, mode, convolution)
+    if quick_out is not None:
+        return quick_out
+    return _st_core._direct_correlate(in1, in2, mode, in1.dtype, convolution,
+                                      boundary, fillvalue, not convolution)
+
+
+def wiener(im, mysize=None, noise=None):
+    """Perform a Wiener filter on an N-dimensional array.
+
+    Apply a Wiener filter to the N-dimensional array `im`.
+
+    Args:
+        im (cupy.ndarray): An N-dimensional array.
+        mysize (int or cupy.ndarray, optional): A scalar or an N-length list
+            giving the size of the Wiener filter window in each dimension.
+            Elements of mysize should be odd. If mysize is a scalar, then this
+            scalar is used as the size in each dimension.
+        noise (float, optional): The noise-power to use. If None, then noise is
+            estimated as the average of the local variance of the input.
+
+    Returns:
+        cupy.ndarray: Wiener filtered result with the same shape as `im`.
+
+    .. seealso:: :func:`scipy.signal.wiener`
+    """
+    if im.dtype.kind == 'c':
+        # TODO: adding support for complex types requires ndimage filters
+        # to support complex types (which they could easily if not for the
+        # scipy compatibility requirement of forbidding complex and using
+        # float64 intermediates)
+        raise TypeError("complex types not currently supported")
+    if mysize is None:
+        mysize = 3
+    mysize = _util._fix_sequence_arg(mysize, im.ndim, 'mysize', int)
+    im = im.astype(float, copy=False)
+
+    # Estimate the local mean
+    local_mean = filters.uniform_filter(im, mysize, mode='constant')
+
+    # Estimate the local variance
+    local_var = filters.uniform_filter(im * im, mysize, mode='constant')
+    local_var -= local_mean * local_mean
+
+    # Estimate the noise power if needed.
+    if noise is None:
+        noise = local_var.mean()
+
+    # Perform the filtering
+    res = im - local_mean
+    res *= 1 - noise / local_var
+    res += local_mean
+    return cupy.where(local_var < noise, local_mean, res)
+
+
+def order_filter(a, domain, rank):
+    """Perform an order filter on an N-D array.
+
+    Perform an order filter on the array in. The domain argument acts as a mask
+    centered over each pixel. The non-zero elements of domain are used to
+    select elements surrounding each input pixel which are placed in a list.
+    The list is sorted, and the output for that pixel is the element
+    corresponding to rank in the sorted list.
+
+    Args:
+        a (cupy.ndarray): The N-dimensional input array.
+        domain (cupy.ndarray): A mask array with the same number of dimensions
+            as `a`. Each dimension should have an odd number of elements.
+        rank (int): A non-negative integer which selects the element from the
+            sorted list (0 corresponds to the smallest element).
+
+    Returns:
+        cupy.ndarray: The results of the order filter in an array with the same
+            shape as `a`.
+
+    .. seealso:: :func:`cupyx.scipy.ndimage.rank_filter`
+    .. seealso:: :func:`scipy.signal.order_filter`
+    """
+    if a.dtype.kind in 'bc' or a.dtype == cupy.float16:
+        # scipy doesn't support these types
+        raise ValueError("data type not supported")
+    if any(x % 2 != 1 for x in domain.shape):
+        raise ValueError("Each dimension of domain argument "
+                         " should have an odd number of elements.")
+    return filters.rank_filter(a, rank, footprint=domain, mode='constant')
+
+
+def medfilt(volume, kernel_size=None):
+    """Perform a median filter on an N-dimensional array.
+
+    Apply a median filter to the input array using a local window-size
+    given by `kernel_size`. The array will automatically be zero-padded.
+
+    Args:
+        volume (cupy.ndarray): An N-dimensional input array.
+        kernel_size (int or list of ints): Gives the size of the median filter
+            window in each dimension. Elements of `kernel_size` should be odd.
+            If `kernel_size` is a scalar, then this scalar is used as the size
+            in each dimension. Default size is 3 for each dimension.
+
+    Returns:
+        cupy.ndarray: An array the same size as input containing the median
+        filtered result.
+
+    .. seealso:: :func:`cupyx.scipy.ndimage.median_filter`
+    .. seealso:: :func:`scipy.signal.medfilt`
+    """
+    if volume.dtype.kind == 'c':
+        # scipy doesn't support complex
+        # (and filters.rank_filter raise TypeError)
+        raise ValueError("complex types not supported")
+    # output is forced to float64 to match scipy
+    kernel_size = _get_kernel_size(kernel_size, volume.ndim)
+    if any(k > s for k, s in zip(kernel_size, volume.shape)):
+        warnings.warn('kernel_size exceeds volume extent: '
+                      'volume will be zero-padded')
+
+    size = np.prod(kernel_size)
+    return filters.rank_filter(volume, size // 2, size=kernel_size,
+                               output=float, mode='constant')
+
+
+def medfilt2d(input, kernel_size=3):
+    """Median filter a 2-dimensional array.
+
+    Apply a median filter to the `input` array using a local window-size given
+    by `kernel_size` (must be odd). The array is zero-padded automatically.
+
+    Args:
+        input (cupy.ndarray): A 2-dimensional input array.
+        kernel_size (int of list of ints of length 2): Gives the size of the
+            median filter window in each dimension. Elements of `kernel_size`
+            should be odd. If `kernel_size` is a scalar, then this scalar is
+            used as the size in each dimension. Default is a kernel of size
+            (3, 3).
+
+    Returns:
+        cupy.ndarray: An array the same size as input containing the median
+            filtered result.
+    See also
+    --------
+    .. seealso:: :func:`cupyx.scipy.ndimage.median_filter`
+    .. seealso:: :func:`cupyx.scipy.signal.medfilt`
+    .. seealso:: :func:`scipy.signal.medfilt2d`
+    """
+    if input.dtype not in (cupy.uint8, cupy.float32, cupy.float64):
+        # Scipy's version only supports uint8, float32, and float64
+        raise ValueError("only supports uint8, float32, and float64")
+    if input.ndim != 2:
+        raise ValueError('input must be 2d')
+    kernel_size = _get_kernel_size(kernel_size, input.ndim)
+    order = kernel_size[0] * kernel_size[1] // 2
+    return filters.rank_filter(input, order, size=kernel_size, mode='constant')
+
+
+def _get_kernel_size(kernel_size, ndim):
+    if kernel_size is None:
+        kernel_size = (3,) * ndim
+    kernel_size = _util._fix_sequence_arg(kernel_size, ndim,
+                                          'kernel_size', int)
+    if any((k % 2) != 1 for k in kernel_size):
+        raise ValueError("Each element of kernel_size should be odd")
+    return kernel_size
diff --git a/python/cucim/src/cucim/skimage/_vendored/time.py b/python/cucim/src/cucim/skimage/_vendored/time.py
new file mode 100644
index 000000000..15d7f74b5
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/_vendored/time.py
@@ -0,0 +1,151 @@
+"""Timing utility copied from cupyx.time
+
+added kwargs support to repeat
+removed experimental warning
+"""
+
+import math
+import time
+
+import cupy
+import numpy
+
+
+class _PerfCaseResult(object):
+    def __init__(self, name, ts, devices):
+        assert ts.ndim == 2
+        assert ts.shape[0] == len(devices) + 1
+        assert ts.shape[1] > 0
+        self.name = name
+        self._ts = ts
+        self._devices = devices
+
+    @property
+    def cpu_times(self):
+        return self._ts[0]
+
+    @property
+    def gpu_times(self):
+        return self._ts[1:]
+
+    @staticmethod
+    def _to_str_per_item(device_name, t):
+        assert t.ndim == 1
+        assert t.size > 0
+        t_us = t * 1e6
+
+        s = "    {}:{:9.03f} us".format(device_name, t_us.mean())
+        if t.size > 1:
+            s += "   +/-{:6.03f} (min:{:9.03f} / max:{:9.03f}) us".format(
+                t_us.std(), t_us.min(), t_us.max()
+            )
+        return s
+
+    def to_str(self, show_gpu=False):
+        results = [self._to_str_per_item("CPU", self._ts[0])]
+        if show_gpu:
+            for i, d in enumerate(self._devices):
+                results.append(
+                    self._to_str_per_item("GPU-{}".format(d), self._ts[1 + i])
+                )
+        return "{:<20s}:{}".format(self.name, " ".join(results))
+
+    def __str__(self):
+        return self.to_str(show_gpu=True)
+
+
+def repeat(
+    func,
+    args=(),
+    kwargs={},
+    n_repeat=10000,
+    *,
+    name=None,
+    n_warmup=10,
+    max_duration=math.inf,
+    devices=None,
+):
+    if name is None:
+        try:
+            name = func.__name__
+        except AttributeError:
+            name = "unknown"
+
+    if devices is None:
+        devices = (cupy.cuda.get_device_id(),)
+
+    if not callable(func):
+        raise ValueError("`func` should be a callable object.")
+    if not isinstance(args, tuple):
+        raise ValueError("`args` should be of tuple type.")
+    if not isinstance(kwargs, dict):
+        raise ValueError("`kwargs` should be of dict type.")
+    if not isinstance(n_repeat, int):
+        raise ValueError("`n_repeat` should be an integer.")
+    if not isinstance(name, str):
+        raise ValueError("`str` should be a string.")
+    if not isinstance(n_warmup, int):
+        raise ValueError("`n_warmup` should be an integer.")
+    if not isinstance(devices, tuple):
+        raise ValueError("`devices` should be of tuple type")
+
+    return _repeat(
+        func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices
+    )
+
+
+def _repeat(
+    func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices
+):
+
+    events_1 = []
+    events_2 = []
+
+    for i in devices:
+        with cupy.cuda.Device(i):
+            events_1.append(cupy.cuda.stream.Event())
+            events_2.append(cupy.cuda.stream.Event())
+
+    ev1 = cupy.cuda.stream.Event()
+    ev2 = cupy.cuda.stream.Event()
+
+    for i in range(n_warmup):
+        func(*args, **kwargs)
+
+    for event, device in zip(events_1, devices):
+        with cupy.cuda.Device(device):
+            event.record()
+        event.synchronize()
+
+    cpu_times = []
+    gpu_times = [[] for i in events_1]
+    duration = 0
+    for i in range(n_repeat):
+        for event, device in zip(events_1, devices):
+            with cupy.cuda.Device(device):
+                event.record()
+
+        t1 = time.perf_counter()
+
+        func(*args, **kwargs)
+
+        t2 = time.perf_counter()
+        cpu_time = t2 - t1
+        cpu_times.append(cpu_time)
+
+        for event, device in zip(events_2, devices):
+            with cupy.cuda.Device(device):
+                event.record()
+        for event, device in zip(events_2, devices):
+            with cupy.cuda.Device(device):
+                event.synchronize()
+        for i, (ev1, ev2) in enumerate(zip(events_1, events_2)):
+            gpu_time = cupy.cuda.get_elapsed_time(ev1, ev2) * 1e-3
+            gpu_times[i].append(gpu_time)
+
+        duration += time.perf_counter() - t1
+        if duration > max_duration:
+            break
+
+    ts = numpy.asarray([cpu_times] + gpu_times, dtype=numpy.float64)
+    return _PerfCaseResult(name, ts, devices=devices)
diff --git a/python/cucim/src/cucim/skimage/color/__init__.py b/python/cucim/src/cucim/skimage/color/__init__.py
new file mode 100644
index 000000000..dcf06875b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/__init__.py
@@ -0,0 +1,83 @@
+from .colorconv import (ahx_from_rgb, bex_from_rgb, bpx_from_rgb, bro_from_rgb,
+                        combine_stains, convert_colorspace, fgx_from_rgb,
+                        gdx_from_rgb, gray2rgb, gray2rgba, grey2rgb,
+                        hax_from_rgb, hdx_from_rgb, hed2rgb, hed_from_rgb,
+                        hpx_from_rgb, hsv2rgb, lab2lch, lab2rgb, lab2xyz,
+                        lch2lab, luv2rgb, luv2xyz, rbd_from_rgb, rgb2gray,
+                        rgb2grey, rgb2hed, rgb2hsv, rgb2lab, rgb2luv,
+                        rgb2rgbcie, rgb2xyz, rgb2ycbcr, rgb2ydbdr, rgb2yiq,
+                        rgb2ypbpr, rgb2yuv, rgb_from_ahx, rgb_from_bex,
+                        rgb_from_bpx, rgb_from_bro, rgb_from_fgx, rgb_from_gdx,
+                        rgb_from_hax, rgb_from_hdx, rgb_from_hed, rgb_from_hpx,
+                        rgb_from_rbd, rgba2rgb, rgbcie2rgb, separate_stains,
+                        xyz2lab, xyz2luv, xyz2rgb, ycbcr2rgb, ydbdr2rgb,
+                        yiq2rgb, ypbpr2rgb, yuv2rgb)
+from .colorlabel import color_dict, label2rgb
+from .delta_e import deltaE_cie76, deltaE_ciede94, deltaE_ciede2000, deltaE_cmc
+
+__all__ = ['convert_colorspace',
+           'rgba2rgb',
+           'rgb2hsv',
+           'hsv2rgb',
+           'rgb2xyz',
+           'xyz2rgb',
+           'rgb2rgbcie',
+           'rgbcie2rgb',
+           'rgb2grey',
+           'rgb2gray',
+           'gray2rgb',
+           'gray2rgba',
+           'grey2rgb',
+           'xyz2lab',
+           'lab2xyz',
+           'lab2rgb',
+           'rgb2lab',
+           'xyz2luv',
+           'luv2xyz',
+           'luv2rgb',
+           'rgb2luv',
+           'rgb2hed',
+           'hed2rgb',
+           'lab2lch',
+           'lch2lab',
+           'rgb2yuv',
+           'yuv2rgb',
+           'rgb2yiq',
+           'yiq2rgb',
+           'rgb2ypbpr',
+           'ypbpr2rgb',
+           'rgb2ycbcr',
+           'ycbcr2rgb',
+           'rgb2ydbdr',
+           'ydbdr2rgb',
+           'separate_stains',
+           'combine_stains',
+           'rgb_from_hed',
+           'hed_from_rgb',
+           'rgb_from_hdx',
+           'hdx_from_rgb',
+           'rgb_from_fgx',
+           'fgx_from_rgb',
+           'rgb_from_bex',
+           'bex_from_rgb',
+           'rgb_from_rbd',
+           'rbd_from_rgb',
+           'rgb_from_gdx',
+           'gdx_from_rgb',
+           'rgb_from_hax',
+           'hax_from_rgb',
+           'rgb_from_bro',
+           'bro_from_rgb',
+           'rgb_from_bpx',
+           'bpx_from_rgb',
+           'rgb_from_ahx',
+           'ahx_from_rgb',
+           'rgb_from_hpx',
+           'hpx_from_rgb',
+           'color_dict',
+           'label2rgb',
+           'deltaE_cie76',
+           'deltaE_ciede94',
+           'deltaE_ciede2000',  # TODO: fix accuracy
+           'deltaE_cmc',
+           ]
diff --git a/python/cucim/src/cucim/skimage/color/adapt_rgb.py b/python/cucim/src/cucim/skimage/color/adapt_rgb.py
new file mode 100644
index 000000000..5a1cc9631
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/adapt_rgb.py
@@ -0,0 +1,78 @@
+import functools
+
+import cupy as cp
+
+from .. import color
+from ..util.dtype import _convert
+
+__all__ = ['adapt_rgb', 'hsv_value', 'each_channel']
+
+
+def is_rgb_like(image):
+    """Return True if the image *looks* like it's RGB.
+
+    This function should not be public because it is only intended to be used
+    for functions that don't accept volumes as input, since checking an image's
+    shape is fragile.
+    """
+    return (image.ndim == 3) and (image.shape[2] in (3, 4))
+
+
+def adapt_rgb(apply_to_rgb):
+    """Return decorator that adapts to RGB images to a gray-scale filter.
+
+    This function is only intended to be used for functions that don't accept
+    volumes as input, since checking an image's shape is fragile.
+
+    Parameters
+    ----------
+    apply_to_rgb : function
+        Function that returns a filtered image from an image-filter and RGB
+        image. This will only be called if the image is RGB-like.
+    """
+    def decorator(image_filter):
+        @functools.wraps(image_filter)
+        def image_filter_adapted(image, *args, **kwargs):
+            if is_rgb_like(image):
+                return apply_to_rgb(image_filter, image, *args, **kwargs)
+            else:
+                return image_filter(image, *args, **kwargs)
+        return image_filter_adapted
+    return decorator
+
+
+def hsv_value(image_filter, image, *args, **kwargs):
+    """Return color image by applying `image_filter` on HSV-value of `image`.
+
+    Note that this function is intended for use with `adapt_rgb`.
+
+    Parameters
+    ----------
+    image_filter : function
+        Function that filters a gray-scale image.
+    image : array
+        Input image. Note that RGBA images are treated as RGB.
+    """
+    # Slice the first three channels so that we remove any alpha channels.
+    hsv = color.rgb2hsv(image[:, :, :3])
+    value = hsv[:, :, 2].copy()
+    value = image_filter(value, *args, **kwargs)
+    hsv[:, :, 2] = _convert(value, hsv.dtype)
+    return color.hsv2rgb(hsv)
+
+
+def each_channel(image_filter, image, *args, **kwargs):
+    """Return color image by applying `image_filter` on channels of `image`.
+
+    Note that this function is intended for use with `adapt_rgb`.
+
+    Parameters
+    ----------
+    image_filter : function
+        Function that filters a gray-scale image.
+    image : array
+        Input image.
+    """
+    c_new = [image_filter(c, *args, **kwargs)
+             for c in cp.moveaxis(image, -1, 0)]
+    return cp.stack(c_new, axis=-1)
diff --git a/python/cucim/src/cucim/skimage/color/colorconv.py b/python/cucim/src/cucim/skimage/color/colorconv.py
new file mode 100644
index 000000000..f1cf5f052
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/colorconv.py
@@ -0,0 +1,2247 @@
+"""Functions for converting between color spaces.
+
+The "central" color space in this module is RGB, more specifically the linear
+sRGB color space using D65 as a white-point [1]_.  This represents a
+standard monitor (w/o gamma correction). For a good FAQ on color spaces see
+[2]_.
+
+The API consists of functions to convert to and from RGB as defined above, as
+well as a generic function to convert to and from any supported color space
+(which is done through RGB in most cases).
+
+
+Supported color spaces
+----------------------
+* RGB : Red Green Blue.
+        Here the sRGB standard [1]_.
+* HSV : Hue, Saturation, Value.
+        Uniquely defined when related to sRGB [3]_.
+* RGB CIE : Red Green Blue.
+        The original RGB CIE standard from 1931 [4]_. Primary colors are 700 nm
+        (red), 546.1 nm (blue) and 435.8 nm (green).
+* XYZ CIE : XYZ
+        Derived from the RGB CIE color space. Chosen such that
+        ``x == y == z == 1/3`` at the whitepoint, and all color matching
+        functions are greater than zero everywhere.
+* LAB CIE : Lightness, a, b
+        Colorspace derived from XYZ CIE that is intended to be more
+        perceptually uniform
+* LUV CIE : Lightness, u, v
+        Colorspace derived from XYZ CIE that is intended to be more
+        perceptually uniform
+* LCH CIE : Lightness, Chroma, Hue
+        Defined in terms of LAB CIE.  C and H are the polar representation of
+        a and b.  The polar angle C is defined to be on ``(0, 2*pi)``
+
+:author: Nicolas Pinto (rgb2hsv)
+:author: Ralf Gommers (hsv2rgb)
+:author: Travis Oliphant (XYZ and RGB CIE functions)
+:author: Matt Terry (lab2lch)
+:author: Alex Izvorski (yuv2rgb, rgb2yuv and related)
+
+:license: modified BSD
+
+References
+----------
+.. [1] Official specification of sRGB, IEC 61966-2-1:1999.
+.. [2] http://www.poynton.com/ColorFAQ.html
+.. [3] https://en.wikipedia.org/wiki/HSL_and_HSV
+.. [4] https://en.wikipedia.org/wiki/CIE_1931_color_space
+"""
+
+import functools
+from warnings import warn
+
+import cupy as cp
+import numpy as np
+from scipy import linalg
+
+from ..util import dtype, dtype_limits
+
+
+def convert_colorspace(arr, fromspace, tospace):
+    """Convert an image array to a new color space.
+
+    Valid color spaces are:
+        'RGB', 'HSV', 'RGB CIE', 'XYZ', 'YUV', 'YIQ', 'YPbPr', 'YCbCr', 'YDbDr'
+
+    Parameters
+    ----------
+    arr : (..., 3) array_like
+        The image to convert. Final dimension denotes channels.
+    fromspace : str
+        The color space to convert from. Can be specified in lower case.
+    tospace : str
+        The color space to convert to. Can be specified in lower case.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The converted image. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If fromspace is not a valid color space
+    ValueError
+        If tospace is not a valid color space
+
+    Notes
+    -----
+    Conversion is performed through the "central" RGB color space,
+    i.e. conversion from XYZ to HSV is implemented as ``XYZ -> RGB -> HSV``
+    instead of directly.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import data
+    >>> img = cp.array(data.astronaut())
+    >>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
+    """
+    fromdict = {'rgb': lambda im: im, 'hsv': hsv2rgb, 'rgb cie': rgbcie2rgb,
+                'xyz': xyz2rgb, 'yuv': yuv2rgb, 'yiq': yiq2rgb,
+                'ypbpr': ypbpr2rgb, 'ycbcr': ycbcr2rgb, 'ydbdr': ydbdr2rgb}
+    todict = {'rgb': lambda im: im, 'hsv': rgb2hsv, 'rgb cie': rgb2rgbcie,
+              'xyz': rgb2xyz, 'yuv': rgb2yuv, 'yiq': rgb2yiq,
+              'ypbpr': rgb2ypbpr, 'ycbcr': rgb2ycbcr, 'ydbdr': rgb2ydbdr}
+
+    fromspace = fromspace.lower()
+    tospace = tospace.lower()
+    if fromspace not in fromdict:
+        msg = '`fromspace` has to be one of {}'.format(fromdict.keys())
+        raise ValueError(msg)
+    if tospace not in todict:
+        msg = '`tospace` has to be one of {}'.format(todict.keys())
+        raise ValueError(msg)
+
+    return todict[tospace](fromdict[fromspace](arr))
+
+
+def _prepare_colorarray(arr, force_copy=False, force_c_contiguous=True):
+    """Check the shape of the array and convert it to floating point
+    representation.
+    """
+    if arr.shape[-1] != 3:
+        raise ValueError("Input array must have a shape == (..., 3)), "
+                         f"got {arr.shape}")
+    out = dtype.img_as_float(arr, force_copy=force_copy)
+    if force_c_contiguous and not out.flags.c_contiguous:
+        out = cp.ascontiguousarray(out)
+    return out
+
+
+@cp.memoize(for_each_device=True)
+def _rgba2rgb_kernel(background, name='rgba2rgb'):
+    code = """
+    X alpha = rgba[4*i + 3];
+    X val;
+    """
+    for ch in range(3):
+        code += f"""
+        val = (1 - alpha) * {background[ch]} + alpha * rgba[4*i + {ch}];
+        rgb[3*i + {ch}] = min(max(val, (X)0.0), (X)1.0);
+        """
+    return cp.ElementwiseKernel(
+        'raw X rgba',
+        'raw X rgb',
+        code,
+        name=name)
+
+
+def rgba2rgb(rgba, background=(1, 1, 1)):
+    """RGBA to RGB conversion using alpha blending [1]_.
+
+    Parameters
+    ----------
+    rgba : (..., 4) array_like
+        The image in RGBA format. Final dimension denotes channels.
+    background : array_like
+        The color of the background to blend the image with (3 floats
+        between 0 to 1 - the RGB value of the background).
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgba` is not at least 2-D with shape (..., 4).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import color
+    >>> from skimage import data
+    >>> img_rgba = cp.array(data.logo())
+    >>> img_rgb = color.rgba2rgb(img_rgba)
+    """
+    if rgba.shape[-1] != 4:
+        msg = ("the input array must have shape == (..., 4)), "
+               f"got {rgba.shape}")
+        raise ValueError(msg)
+
+    rgba = dtype.img_as_float(rgba)
+    if not rgba.flags.c_contiguous:
+        rgba = cp.ascontiguousarray(rgba)
+
+    if isinstance(background, cp.ndarray):
+        background = cp.asnumpy(background)  # synchronize
+    background = tuple(float(b) for b in background)
+    if len(background) != 3:
+        raise ValueError('background must be an array-like containing 3 RGB '
+                         f'values. Got {len(background)} items')
+    if any((b < 0 or b > 1) for b in background):
+        raise ValueError('background RGB values must be floats between '
+                         '0 and 1.')
+
+    name = f'rgba2rgb_{rgba.dtype.char}'
+    kern = _rgba2rgb_kernel(background, name)
+    rgb = cp.empty(rgba.shape[:-1] + (3,), dtype=rgba.dtype)
+    kern(rgba, rgb, size=rgb.size // 3)
+    return rgb
+
+
+@cp.memoize(for_each_device=True)
+def _rgb_to_hsv_kernel(name='rgb2hsv'):
+    code = """
+    X minv = rgb[3*i];
+    X maxv = rgb[3*i];
+    X tmp;
+    for (int ch=1; ch < 3; ch++)
+    {
+        tmp = rgb[3*i + ch];
+        if (tmp > maxv)
+        {
+            maxv = tmp;
+        } else if (tmp < minv)
+        {
+            minv = tmp;
+        }
+    }
+    X delta = maxv - minv;
+    if (delta == 0.0)
+    {
+        hsv[3*i] = 0.0;
+        hsv[3*i + 1] = 0.0;
+    } else {
+        hsv[3*i + 1] = delta / maxv;
+        if (rgb[3*i] == maxv)
+        {
+           hsv[3*i]  = (rgb[3*i + 1] - rgb[3*i + 2]) / delta;
+        } else if (rgb[3*i + 1] == maxv)
+        {
+           hsv[3*i]  = 2.0 + (rgb[3*i + 2] - rgb[3*i]) / delta;
+        } else if (rgb[3*i + 2] == maxv)
+        {
+           hsv[3*i]  = 4.0 + (rgb[3*i] - rgb[3*i + 1]) / delta;
+        }
+        hsv[3*i] /= 6.0;
+        hsv[3*i] = hsv[3*i] - floor(hsv[3*i] / (X)1.0);
+    }
+    hsv[3*i + 2] = maxv;
+    """
+    return cp.ElementwiseKernel(
+        'raw X rgb',
+        'raw X hsv',
+        code,
+        name=name)
+
+
+def rgb2hsv(rgb):
+    """RGB to HSV color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in HSV format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Conversion between RGB and HSV color spaces results in some loss of
+    precision, due to integer arithmetic and rounding [1]_.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/HSL_and_HSV
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import color
+    >>> from skimage import data
+    >>> img = cp.array(data.astronaut())
+    >>> img_hsv = color.rgb2hsv(img)
+    """
+    input_is_one_pixel = rgb.ndim == 1
+    if input_is_one_pixel:
+        rgb = rgb[np.newaxis, ...]
+
+    rgb = _prepare_colorarray(rgb, force_c_contiguous=True)
+    hsv = cp.empty_like(rgb)
+
+    name = f'rgb2hsv_{rgb.dtype.char}'
+    kern = _rgb_to_hsv_kernel(name=name)
+    kern(rgb, hsv, size=rgb.size // 3)
+
+    if input_is_one_pixel:
+        hsv = cp.squeeze(hsv, axis=0)
+
+    return hsv
+
+
+@cp.memoize(for_each_device=True)
+def _hsv_to_rgb_kernel(name='hsv2rgb'):
+    code = """
+    int hi = (int)floor(hsv[3*i] * 6.0);
+
+    X f = hsv[3*i] * 6 - hi;
+    X v = hsv[3*i + 2];
+    X p = v * (1 - hsv[3*i + 1]);
+
+    int rem = (int)hi % 6;
+    switch(rem)
+    {
+        case 0:
+            rgb[3*i] = v;
+            rgb[3*i + 1] = v * (1 - (1 - f) * hsv[3*i + 1]);
+            rgb[3*i + 2] = p;
+            break;
+        case 1:
+            rgb[3*i] = v * (1 - f * hsv[3*i + 1]);
+            rgb[3*i + 1] = v;
+            rgb[3*i + 2] = p;
+            break;
+        case 2:
+            rgb[3*i] = p;
+            rgb[3*i + 1] = v;
+            rgb[3*i + 2] = v * (1 - (1 - f) * hsv[3*i + 1]);
+            break;
+        case 3:
+            rgb[3*i] = p;
+            rgb[3*i + 1] = v * (1 - f * hsv[3*i + 1]);
+            rgb[3*i + 2] = v;
+            break;
+        case 4:
+            rgb[3*i] = v * (1 - (1 - f) * hsv[3*i + 1]);
+            rgb[3*i + 1] = p;
+            rgb[3*i + 2] = v;
+            break;
+        case 5:
+            rgb[3*i] = v;
+            rgb[3*i + 1] = p;
+            rgb[3*i + 2] = v * (1 - f * hsv[3*i + 1]);
+            break;
+    }
+    """
+    return cp.ElementwiseKernel(
+        'raw X hsv',
+        'raw X rgb',
+        code,
+        name=name)
+
+
+def hsv2rgb(hsv):
+    """HSV to RGB color space conversion.
+
+    Parameters
+    ----------
+    hsv : (..., 3) array_like
+        The image in HSV format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `hsv` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Conversion between RGB and HSV color spaces results in some loss of
+    precision, due to integer arithmetic and rounding [1]_.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/HSL_and_HSV
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> img = cp.array(data.astronaut())
+    >>> img_hsv = rgb2hsv(img)
+    >>> img_rgb = hsv2rgb(img_hsv)
+    """
+    hsv = _prepare_colorarray(hsv, force_c_contiguous=True)
+
+    rgb = cp.empty_like(hsv)
+
+    name = f'hsv2rgb_{hsv.dtype.char}'
+    kern = _hsv_to_rgb_kernel(name=name)
+    kern(hsv, rgb, size=hsv.size // 3)
+    return rgb
+
+
+# ---------------------------------------------------------------
+# Primaries for the coordinate systems
+# ---------------------------------------------------------------
+cie_primaries = np.array([700, 546.1, 435.8])
+sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5
+
+# ---------------------------------------------------------------
+# Matrices that define conversion between different color spaces
+# ---------------------------------------------------------------
+
+# From sRGB specification
+# fmt: off
+xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423],
+                         [0.212671, 0.715160, 0.072169],
+                         [0.019334, 0.119193, 0.950227]])
+
+rgb_from_xyz = linalg.inv(xyz_from_rgb)
+
+# From https://en.wikipedia.org/wiki/CIE_1931_color_space
+# Note: Travis's code did not have the divide by 0.17697
+xyz_from_rgbcie = np.array([[0.49, 0.31, 0.20],
+                            [0.17697, 0.81240, 0.01063],
+                            [0.00, 0.01, 0.99]]) / 0.17697
+
+rgbcie_from_xyz = linalg.inv(xyz_from_rgbcie)
+
+# construct matrices to and from rgb:
+rgbcie_from_rgb = rgbcie_from_xyz @ xyz_from_rgb
+rgb_from_rgbcie = rgb_from_xyz @ xyz_from_rgbcie
+
+
+gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721],
+                          [0, 0, 0],
+                          [0, 0, 0]])
+
+yuv_from_rgb = np.array([[ 0.299     ,  0.587     ,  0.114     ],   # noqa
+                         [-0.14714119, -0.28886916,  0.43601035],   # noqa
+                         [ 0.61497538, -0.51496512, -0.10001026]])  # noqa
+
+rgb_from_yuv = linalg.inv(yuv_from_rgb)
+
+yiq_from_rgb = np.array([[0.299     ,  0.587     ,  0.114     ],   # noqa
+                         [0.59590059, -0.27455667, -0.32134392],   # noqa
+                         [0.21153661, -0.52273617,  0.31119955]])  # noqa
+
+rgb_from_yiq = linalg.inv(yiq_from_rgb)
+
+
+ypbpr_from_rgb = np.array([[ 0.299   ,  0.587   ,  0.114   ],   # noqa
+                           [-0.168736, -0.331264,  0.5     ],   # noqa
+                           [ 0.5     , -0.418688, -0.081312]])  # noqa
+# fmt: on
+
+rgb_from_ypbpr = linalg.inv(ypbpr_from_rgb)
+
+ycbcr_from_rgb = np.array([[ 65.481,   128.553,    24.966],   # noqa
+                           [-37.797,   -74.203,   112.0  ],   # noqa
+                           [ 112.0 ,   -93.786,   -18.214]])  # noqa
+
+rgb_from_ycbcr = linalg.inv(ycbcr_from_rgb)
+
+ydbdr_from_rgb = np.array([[ 0.299,   0.587,    0.114],   # noqa
+                           [-0.45 ,  -0.883,    1.333],   # noqa
+                           [-1.333,   1.116,    0.217]])  # noqa
+
+rgb_from_ydbdr = linalg.inv(ydbdr_from_rgb)
+
+
+# CIE LAB constants for Observer=2A, Illuminant=D65
+# NOTE: this is actually the XYZ values for the illuminant above.
+lab_ref_white = np.array([0.95047, 1., 1.08883])
+
+# XYZ coordinates of the illuminants, scaled to [0, 1]. For each illuminant I
+# we have:
+#
+#   illuminant[I][0] corresponds to the XYZ coordinates for the 2 degree
+#   field of view.
+#
+#   illuminant[I][1] corresponds to the XYZ coordinates for the 10 degree
+#   field of view.
+#
+# The XYZ coordinates are calculated from [1], using the formula:
+#
+#   X = x * ( Y / y )
+#   Y = Y
+#   Z = ( 1 - x - y ) * ( Y / y )
+#
+# where Y = 1. The only exception is the illuminant "D65" with aperture angle
+# 2, whose coordinates are copied from 'lab_ref_white' for
+# backward-compatibility reasons.
+#
+#     References
+#    ----------
+#    .. [1] https://en.wikipedia.org/wiki/Standard_illuminant
+
+illuminants = \
+    {"A": {'2': (1.098466069456375, 1, 0.3558228003436005),
+           '10': (1.111420406956693, 1, 0.3519978321919493)},
+     "D50": {'2': (0.9642119944211994, 1, 0.8251882845188288),
+             '10': (0.9672062750333777, 1, 0.8142801513128616)},
+     "D55": {'2': (0.956797052643698, 1, 0.9214805860173273),
+             '10': (0.9579665682254781, 1, 0.9092525159847462)},
+     "D65": {'2': (0.95047, 1., 1.08883),   # This was: `lab_ref_white`
+             '10': (0.94809667673716, 1, 1.0730513595166162)},
+     "D75": {'2': (0.9497220898840717, 1, 1.226393520724154),
+             '10': (0.9441713925645873, 1, 1.2064272211720228)},
+     "E": {'2': (1.0, 1.0, 1.0),
+           '10': (1.0, 1.0, 1.0)}}
+
+
+def get_xyz_coords(illuminant, observer):
+    """Get the XYZ coordinates of the given illuminant and observer [1]_.
+
+    Parameters
+    ----------
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+    dtype: dtype, optional
+        Output data type.
+
+    Returns
+    -------
+    out : array
+        Array with 3 elements containing the XYZ coordinates of the given
+        illuminant.
+
+    Raises
+    ------
+    ValueError
+        If either the illuminant or the observer angle are not supported or
+        unknown.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Standard_illuminant
+    """
+    illuminant = illuminant.upper()
+    try:
+        return illuminants[illuminant][observer]
+    except KeyError:
+        raise ValueError("Unknown illuminant/observer combination\
+        (\'{0}\', \'{1}\')".format(illuminant, observer))
+
+# Haematoxylin-Eosin-DAB colorspace
+# From original Ruifrok's paper: A. C. Ruifrok and D. A. Johnston,
+# "Quantification of histochemical staining by color deconvolution.,"
+# Analytical and quantitative cytology and histology / the International
+# Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4,
+# pp. 291-9, Aug. 2001.
+# fmt: off
+
+
+rgb_from_hed = np.array([[0.65, 0.70, 0.29],
+                         [0.07, 0.99, 0.11],
+                         [0.27, 0.57, 0.78]])
+hed_from_rgb = linalg.inv(rgb_from_hed)
+
+
+# Following matrices are adapted form the Java code written by G.Landini.
+# The original code is available at:
+# https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html
+
+# Hematoxylin + DAB
+rgb_from_hdx = np.array([[0.650, 0.704, 0.286],
+                         [0.268, 0.570, 0.776],
+                         [0.0, 0.0, 0.0]])
+rgb_from_hdx[2, :] = np.cross(rgb_from_hdx[0, :], rgb_from_hdx[1, :])
+hdx_from_rgb = linalg.inv(rgb_from_hdx)
+
+# Feulgen + Light Green
+rgb_from_fgx = np.array([[0.46420921, 0.83008335, 0.30827187],
+                         [0.94705542, 0.25373821, 0.19650764],
+                         [0.0, 0.0, 0.0]])
+rgb_from_fgx[2, :] = np.cross(rgb_from_fgx[0, :], rgb_from_fgx[1, :])
+fgx_from_rgb = linalg.inv(rgb_from_fgx)
+
+# Giemsa: Methyl Blue + Eosin
+rgb_from_bex = np.array([[0.834750233, 0.513556283, 0.196330403],
+                         [0.092789, 0.954111, 0.283111],
+                         [0.0, 0.0, 0.0]])
+rgb_from_bex[2, :] = np.cross(rgb_from_bex[0, :], rgb_from_bex[1, :])
+bex_from_rgb = linalg.inv(rgb_from_bex)
+
+# FastRed + FastBlue +  DAB
+rgb_from_rbd = np.array([[0.21393921, 0.85112669, 0.47794022],
+                         [0.74890292, 0.60624161, 0.26731082],
+                         [0.268, 0.570, 0.776]])
+rbd_from_rgb = linalg.inv(rgb_from_rbd)
+
+# Methyl Green + DAB
+rgb_from_gdx = np.array([[0.98003, 0.144316, 0.133146],
+                         [0.268, 0.570, 0.776],
+                         [0.0, 0.0, 0.0]])
+rgb_from_gdx[2, :] = np.cross(rgb_from_gdx[0, :], rgb_from_gdx[1, :])
+gdx_from_rgb = linalg.inv(rgb_from_gdx)
+
+# Hematoxylin + AEC
+rgb_from_hax = np.array([[0.650, 0.704, 0.286],
+                         [0.2743, 0.6796, 0.6803],
+                         [0.0, 0.0, 0.0]])
+rgb_from_hax[2, :] = np.cross(rgb_from_hax[0, :], rgb_from_hax[1, :])
+hax_from_rgb = linalg.inv(rgb_from_hax)
+
+# Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G
+rgb_from_bro = np.array([[0.853033, 0.508733, 0.112656],
+                         [0.09289875, 0.8662008, 0.49098468],
+                         [0.10732849, 0.36765403, 0.9237484]])
+bro_from_rgb = linalg.inv(rgb_from_bro)
+
+# Methyl Blue + Ponceau Fuchsin
+rgb_from_bpx = np.array([[0.7995107, 0.5913521, 0.10528667],
+                         [0.09997159, 0.73738605, 0.6680326],
+                         [0.0, 0.0, 0.0]])
+rgb_from_bpx[2, :] = np.cross(rgb_from_bpx[0, :], rgb_from_bpx[1, :])
+bpx_from_rgb = linalg.inv(rgb_from_bpx)
+
+# Alcian Blue + Hematoxylin
+rgb_from_ahx = np.array([[0.874622, 0.457711, 0.158256],
+                         [0.552556, 0.7544, 0.353744],
+                         [0.0, 0.0, 0.0]])
+rgb_from_ahx[2, :] = np.cross(rgb_from_ahx[0, :], rgb_from_ahx[1, :])
+ahx_from_rgb = linalg.inv(rgb_from_ahx)
+
+# Hematoxylin + PAS
+rgb_from_hpx = np.array([[0.644211, 0.716556, 0.266844],
+                         [0.175411, 0.972178, 0.154589],
+                         [0.0, 0.0, 0.0]])
+rgb_from_hpx[2, :] = np.cross(rgb_from_hpx[0, :], rgb_from_hpx[1, :])
+hpx_from_rgb = linalg.inv(rgb_from_hpx)
+# fmt on
+
+# -------------------------------------------------------------
+# The conversion functions that make use of the matrices above
+# -------------------------------------------------------------
+
+
+@cp.memoize(for_each_device=True)
+def _get_convert_kernel(matrix_tuple, pre, post, name):
+    # pre code may modify x so set both x and y as outputs
+    return cp.ElementwiseKernel(
+        '',
+        'raw X x, raw X y',
+        pre + _get_core_colorconv_operation(matrix_tuple) + post,
+        name=name)
+
+
+def _convert(matrix, arr, pre='', post='', name='_convert'):
+    """Do the color space conversion.
+
+    Parameters
+    ----------
+    matrix : array_like
+        The 3x3 matrix to use.
+    arr : (..., 3) array_like
+        The input array. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The converted array. Same dimensions as input.
+    """
+    arr = _prepare_colorarray(arr)
+    name = name + f'_{arr.dtype.char}'
+    kern = _get_convert_kernel(tuple(matrix.ravel()), pre, post, name)
+    out = cp.empty_like(arr)
+    kern(arr, out, size=arr.size // 3)
+    return out
+
+
+def _get_core_colorconv_operation(m):
+    """Generate inline CUDA kernel code for color conversions.
+
+    x is the input image with 3 channels on the last axis
+    y is the output image with 3 channels on the last axis
+    m is a 3x3 color conversion matrix
+    """
+    return f"""
+        y[3*i] = x[3*i] * {m[0]} + x[3*i + 1] * {m[1]} + x[3*i + 2] * {m[2]};
+        y[3*i + 1] = x[3*i] * {m[3]} + x[3*i + 1] * {m[4]} + x[3*i + 2] * {m[5]};
+        y[3*i + 2] = x[3*i] * {m[6]} + x[3*i + 1] * {m[7]} + x[3*i + 2] * {m[8]};
+    """  # noqa
+
+
+def xyz2rgb(xyz):
+    """XYZ to RGB color space conversion.
+
+    Parameters
+    ----------
+    xyz : (..., 3) array_like
+        The image in XYZ format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `xyz` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    The CIE XYZ color space is derived from the CIE RGB color space. Note
+    however that this function converts to sRGB.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2xyz, xyz2rgb
+    >>> img = cp.array(data.astronaut())
+    >>> img_xyz = rgb2xyz(img)
+    >>> img_rgb = xyz2rgb(img_xyz)
+    """
+    # Follow the algorithm from http://www.easyrgb.com/index.php
+    # except we don't multiply/divide by 100 in the conversion
+    arr = _prepare_colorarray(xyz, force_c_contiguous=True)
+
+    # scaling applied after the 3x3 conversion matrix multiplication
+    # (c indexes over color channels here)
+    _post_colorconv = """
+        for (int c=0; c < 3; c++) {
+            if (y[3*i + c] > 0.0031308) {
+                y[3*i + c] = 1.055 * pow(y[3*i + c], (X)(1 / 2.4)) - 0.055;
+            } else {
+                y[3*i + c] *= 12.92;
+            }
+            y[3*i + c] = min(max(y[3*i + c], (X)0.0), (X)1.0);
+        }
+    """
+    return _convert(rgb_from_xyz, arr, post=_post_colorconv, name='xyz2rgb')
+
+
+def rgb2xyz(rgb):
+    """RGB to XYZ color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in XYZ format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    The CIE XYZ color space is derived from the CIE RGB color space. Note
+    however that this function converts from sRGB.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> img = cp.array(data.astronaut())
+    >>> img_xyz = rgb2xyz(img)
+    """
+    # Follow the algorithm from http://www.easyrgb.com/index.php
+    # except we don't multiply/divide by 100 in the conversion
+    rgb = _prepare_colorarray(rgb, force_copy=True, force_c_contiguous=True)
+
+    # scaling applied to the input before 3x3 conversion matrix multiplication
+    # (c indexes over color channels here)
+    _pre_colorconv = """
+        for (int c=0; c < 3; c++) {
+            if (x[3*i + c] > 0.04045) {
+                x[3*i + c] = pow((x[3*i + c] + (X)0.055) / (X)1.055, (X)2.4);
+            } else {
+                x[3*i + c] /= 12.92;
+            }
+        }
+    """
+    return _convert(xyz_from_rgb, rgb, pre=_pre_colorconv, name='rgb2xyz')
+
+
+def rgb2rgbcie(rgb):
+    """RGB to RGB CIE color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB CIE format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2rgbcie
+    >>> img = cp.array(data.astronaut())
+    >>> img_rgbcie = rgb2rgbcie(img)
+    """
+    return _convert(rgbcie_from_rgb, rgb, name='rgb2rgbcie')
+
+
+def rgbcie2rgb(rgbcie):
+    """RGB CIE to RGB color space conversion.
+
+    Parameters
+    ----------
+    rgbcie : (..., 3) array_like
+        The image in RGB CIE format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgbcie` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2rgbcie, rgbcie2rgb
+    >>> img = cp.array(data.astronaut())
+    >>> img_rgbcie = rgb2rgbcie(img)
+    >>> img_rgb = rgbcie2rgb(img_rgbcie)
+    """
+    return _convert(rgb_from_rgbcie, rgbcie, name='rgbcie2rgb')
+
+
+@cp.memoize(for_each_device=True)
+def _rgb_to_gray_kernel(dtype):
+    return cp.ElementwiseKernel(
+        'raw X rgb',
+        'raw X gray',
+        """
+        gray[i] = 0.2125 * rgb[3*i] + 0.7154 * rgb[3*i + 1] + 0.0721 * rgb[3*i + 2];
+        """,  # noqa
+        name=f'rgb2gray_{np.dtype(dtype).char}')
+
+
+def rgb2gray(rgb):
+    """Compute luminance of an RGB image.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : ndarray
+        The luminance image - an array which is the same size as the input
+        array, but with the channel dimension removed.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    The weights used in this conversion are calibrated for contemporary
+    CRT phosphors::
+
+        Y = 0.2125 R + 0.7154 G + 0.0721 B
+
+    If there is an alpha channel present, it is ignored.
+
+    References
+    ----------
+    .. [1] http://poynton.ca/PDFs/ColorFAQ.pdf
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.color import rgb2gray
+    >>> from skimage import data
+    >>> img = cp.array(data.astronaut())
+    >>> img_gray = rgb2gray(img)
+    """
+
+    if rgb.ndim == 2:
+        warn('The behavior of rgb2gray will change in scikit-image 0.19. '
+             'Currently, rgb2gray allows 2D grayscale image to be passed '
+             'as inputs and leaves them unmodified as outputs. '
+             'Starting from version 0.19, 2D arrays will '
+             'be treated as 1D images with 3 channels.',
+             FutureWarning, stacklevel=2)
+        return cp.ascontiguousarray(rgb)
+
+    if rgb.shape[-1] > 3:
+        warn('Non RGB image conversion is now deprecated. For RGBA images, '
+             'please use rgb2gray(rgba2rgb(rgb)) instead. In version 0.19, '
+             'a ValueError will be raised if input image last dimension '
+             'length is not 3.', FutureWarning, stacklevel=2)
+        rgb = rgb[..., :3]
+
+    rgb = _prepare_colorarray(rgb, force_c_contiguous=True)
+    kern = _rgb_to_gray_kernel(rgb.dtype)
+    gray = cp.empty(rgb.shape[:-1], dtype=rgb.dtype)
+    kern(rgb, gray, size=gray.size)
+    return gray
+
+
+@functools.wraps(rgb2gray)
+def rgb2grey(rgb):
+    warn('rgb2grey is deprecated. It will be removed in version 0.19.'
+         'Please use rgb2gray instead.', FutureWarning, stacklevel=2)
+    return rgb2gray(rgb)
+
+
+def gray2rgba(image, alpha=None):
+    """Create a RGBA representation of a gray-level image.
+
+    Parameters
+    ----------
+    image : array_like
+        Input image.
+    alpha : array_like, optional
+        Alpha channel of the output image. It may be a scalar or an
+        array that can be broadcast to ``image``. If not specified it is
+        set to the maximum limit corresponding to the ``image`` dtype.
+
+    Returns
+    -------
+    rgba : ndarray
+        RGBA image. A new dimension of length 4 is added to input
+        image shape.
+    """
+
+    alpha_min, alpha_max = dtype_limits(image, clip_negative=False)
+
+    if alpha is None:
+        alpha = alpha_max
+
+    if not cp.can_cast(alpha, image.dtype):
+        warn("alpha can't be safely cast to image dtype {}"
+             .format(image.dtype.name), stacklevel=2)
+
+    rgba = cp.empty(image.shape + (4,), dtype=image.dtype)
+    rgba[..., :3] = image[..., np.newaxis]
+    rgba[..., 3] = alpha
+
+    return rgba
+
+
+def gray2rgb(image, alpha=None):
+    """Create an RGB representation of a gray-level image.
+
+    Parameters
+    ----------
+    image : array_like
+        Input image.
+    alpha : bool, optional
+        Ensure that the output image has an alpha layer. If None,
+        alpha layers are passed through but not created.
+
+    Returns
+    -------
+    rgb : (..., 3) ndarray
+        RGB image. A new dimension of length 3 is added to input image.
+
+    Notes
+    -----
+    If the input is a 1-dimensional image of shape ``(M, )``, the output
+    will be shape ``(M, 3)``.
+    """
+
+    if alpha is not None:
+        warn("alpha argument is deprecated and will be removed in "
+             "version 0.19. Please use the gray2rgba function instead"
+             "to obtain an RGBA image.", FutureWarning, stacklevel=2)
+    is_rgb = False
+    is_alpha = False
+    dims = cp.squeeze(image).ndim
+
+    if dims == 3:
+        if image.shape[2] == 3:
+            is_rgb = True
+        elif image.shape[2] == 4:
+            is_alpha = True
+            is_rgb = True
+
+    if is_rgb:
+        warn('Pass-through of possibly RGB images in gray2rgb is deprecated. '
+             'In version 0.19, input arrays will always be considered '
+             'grayscale, even if the last dimension has length 3 or 4. '
+             'To prevent this warning and ensure compatibility with future '
+             'versions, detect RGB images outside of this function.',
+             FutureWarning, stacklevel=2)
+        if alpha is False:
+            image = image[..., :3]
+
+        elif alpha is True and is_alpha is False:
+            alpha_layer = (cp.ones_like(image[..., 0, cp.newaxis]) *
+                           dtype_limits(image, clip_negative=False)[1])
+            image = cp.concatenate((image, alpha_layer), axis=2)
+
+        return image
+
+    else:
+        image = image[..., np.newaxis]
+
+        if alpha:
+            alpha_layer = (cp.ones_like(image)
+                           * dtype_limits(image, clip_negative=False)[1])
+            return cp.concatenate(3 * (image,) + (alpha_layer,), axis=-1)
+        else:
+            return cp.concatenate(3 * (image,), axis=-1)
+
+
+@functools.wraps(gray2rgb)
+def grey2rgb(image):
+    warn('grey2rgb is deprecated. It will be removed in version 0.19.'
+         'Please use gray2rgb instead.', FutureWarning, stacklevel=2)
+    return gray2rgb(image)
+
+
+@cp.memoize(for_each_device=True)
+def _get_xyz_to_lab_kernel(xyz_ref_white, name='xyz2lab'):
+    _xyz_to_lab = f"""
+        arr[3*i] /= {xyz_ref_white[0]};
+        arr[3*i + 1] /= {xyz_ref_white[1]};
+        arr[3*i + 2] /= {xyz_ref_white[2]};
+        for (int ch=0; ch < 3; ch++)
+        {{
+            if (arr[3*i + ch] > 0.008856)
+            {{
+                arr[3*i + ch] = cbrt(arr[3*i + ch]);
+            }} else {{
+                arr[3*i + ch] = 7.787 * arr[3*i + ch] + 16.0 / 116.0;
+            }}
+        }}
+        lab[3*i] = (116. * arr[3*i + 1]) - 16.0;
+        lab[3*i + 1] = 500.0 * (arr[3*i] - arr[3*i + 1]);
+        lab[3*i + 2] = 200.0 * (arr[3*i + 1] - arr[3*i + 2]);
+    """
+
+    # array will be modified in-place
+    return cp.ElementwiseKernel(
+        '',
+        'raw X arr, raw X lab',
+        _xyz_to_lab,
+        name=name)
+
+
+def xyz2lab(xyz, illuminant="D65", observer="2"):
+    """XYZ to CIE-LAB color space conversion.
+
+    Parameters
+    ----------
+    xyz : (..., 3) array_like
+        The image in XYZ format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in CIE-LAB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `xyz` is not at least 2-D with shape (..., 3).
+    ValueError
+        If either the illuminant or the observer angle is unsupported or
+        unknown.
+
+    Notes
+    -----
+    By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values
+    x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for
+    a list of supported illuminants.
+
+    References
+    ----------
+    .. [1] http://www.easyrgb.com/index.php?X=MATH&H=07
+    .. [2] https://en.wikipedia.org/wiki/Lab_color_space
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2xyz, xyz2lab
+    >>> img = cp.array(data.astronaut())
+    >>> img_xyz = rgb2xyz(img)
+    >>> img_lab = xyz2lab(img_xyz)
+    """
+    xyz = _prepare_colorarray(xyz, force_copy=True, force_c_contiguous=True)
+
+    xyz_ref_white = get_xyz_coords(illuminant, observer)
+
+    name = f'xyz2lab_{xyz.dtype.char}'
+    kern = _get_xyz_to_lab_kernel(xyz_ref_white, name=name)
+    lab = cp.empty_like(xyz)
+    kern(xyz, lab, size=lab.size // 3)
+    return lab
+
+
+@cp.memoize(for_each_device=True)
+def _get_lab_to_xyz_kernel(xyz_ref_white, name='lab2xyz'):
+    _lab_to_xyz = f"""
+
+        xyz[3*i + 1] = (lab[3*i] + 16.) / 116.;
+        xyz[3*i] = (lab[3*i + 1] / 500.0) + xyz[3*i + 1];
+        xyz[3*i + 2] = xyz[3*i + 1] - (lab[3*i + 2] /200.0);
+        if (xyz[3*i + 2] < 0.0)
+        {{
+            xyz[3*i + 2] = 0.0;
+            warn[i] = 1;
+        }}
+
+        for (int ch=0; ch < 3; ch++)
+        {{
+            if (xyz[3*i + ch] > 0.2068966)
+            {{
+                xyz[3*i + ch] *= xyz[3*i + ch] * xyz[3*i + ch];
+            }} else {{
+                xyz[3*i + ch] = (xyz[3*i + ch] - 16.0 / 116.0) / 7.787;
+            }}
+        }}
+
+        xyz[3*i] *= {xyz_ref_white[0]};
+        xyz[3*i + 1] *= {xyz_ref_white[1]};
+        xyz[3*i + 2] *= {xyz_ref_white[2]};
+
+        // xyz[3*i] = min(max(xyz[3*i], 0.0), 1.0);
+        // xyz[3*i + 1] = min(max(xyz[3*i + 1], 0.0), 1.0);
+        // xyz[3*i + 2] = min(max(xyz[3*i + 2], 0.0), 1.0);
+    """
+
+    # array will be modified in-place
+    return cp.ElementwiseKernel(
+        '',
+        'raw X lab, raw X xyz, raw int32 warn',
+        _lab_to_xyz,
+        name=name)
+
+
+def lab2xyz(lab, illuminant="D65", observer="2"):
+    """CIE-LAB to XYZcolor space conversion.
+
+    Parameters
+    ----------
+    lab : (..., 3) array_like
+        The image in Lab format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in XYZ format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `lab` is not at least 2-D with shape (..., 3).
+    ValueError
+        If either the illuminant or the observer angle are not supported or
+        unknown.
+    UserWarning
+        If any of the pixels are invalid (Z < 0).
+
+    Notes
+    -----
+    By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values x_ref
+    = 95.047, y_ref = 100., z_ref = 108.883. See function 'get_xyz_coords' for
+    a list of supported illuminants.
+
+    References
+    ----------
+    .. [1] http://www.easyrgb.com/index.php?X=MATH&H=07
+    .. [2] https://en.wikipedia.org/wiki/Lab_color_space
+    """
+    lab = _prepare_colorarray(lab, force_c_contiguous=True)
+
+    xyz_ref_white = get_xyz_coords(illuminant, observer)
+
+    name = f'lab2xyz_{lab.dtype.char}'
+    kern = _get_lab_to_xyz_kernel(xyz_ref_white, name=name)
+    xyz = cp.empty_like(lab)
+
+    # TODO: better to use array for warn or a single element with atomic
+    #       operations?
+    warnings = cp.zeros(lab.shape[:-1], dtype=np.int32)
+    kern(lab, xyz, warnings, size=lab.size // 3)
+
+    nwarn = int(cp.count_nonzero(warnings))
+    if nwarn > 0:  # synchronize!
+        warn('Color data out of range: Z < 0 in %s pixels' % nwarn,
+             stacklevel=2)
+    return xyz
+
+
+def rgb2lab(rgb, illuminant="D65", observer="2"):
+    """Conversion from the sRGB color space (IEC 61966-2-1:1999)
+    to the CIE Lab colorspace under the given illuminant and observer.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in Lab format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    RGB is a device-dependent color space so, if you use this function, be
+    sure that the image you are analyzing has been mapped to the sRGB color
+    space.
+
+    This function uses rgb2xyz and xyz2lab.
+    By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values
+    x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for
+    a list of supported illuminants.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Standard_illuminant
+    """
+    return xyz2lab(rgb2xyz(rgb), illuminant, observer)
+
+
+def lab2rgb(lab, illuminant="D65", observer="2"):
+    """Lab to RGB color space conversion.
+
+    Parameters
+    ----------
+    lab : (..., 3) array_like
+        The image in Lab format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `lab` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    This function uses lab2xyz and xyz2rgb.
+    By default Observer= 2A, Illuminant= D65. CIE XYZ tristimulus values
+    x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for
+    a list of supported illuminants.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Standard_illuminant
+    """
+    return xyz2rgb(lab2xyz(lab, illuminant, observer))
+
+
+@cp.memoize(for_each_device=True)
+def _get_xyz_to_luv_kernel(xyz_ref_white, dtype):
+
+    eps = np.finfo(dtype).eps
+
+    preamble = f"""
+    // u' and v' helper functions
+
+    static __device__ __inline__ X fu(X v0, X v1, X v2)
+    {{
+        return (4.0 * v0) / (v0 + 15.0 * v1 + 3.0 * v2 + {eps});
+    }}
+
+    static __device__ __inline__ X fv(X v0, X v1, X v2)
+    {{
+        return (9.0 * v1) / (v0 + 15.0 * v1 + 3.0 * v2 + {eps});
+    }}
+    """
+
+    denom = np.asarray([1, 15, 3]) @ np.asarray(xyz_ref_white, dtype=float)
+    denom = float(denom)
+    u0 = 4 * xyz_ref_white[0] / denom
+    v0 = 9 * xyz_ref_white[1] / denom
+
+    _xyz_to_luv = f"""
+        luv[3*i] = xyz[3*i + 1] / {xyz_ref_white[1]};
+        if (luv[3*i] > 0.008856)
+        {{
+            luv[3*i] = 116.0 * cbrt(luv[3*i]) - 16.0;
+        }} else {{
+            luv[3*i] *= 903.3;
+        }}
+
+        luv[3*i + 1] = (
+            13.0 * luv[3*i] * (fu(xyz[3*i], xyz[3*i + 1], xyz[3*i + 2]) - {u0})
+        );
+        luv[3*i + 2] = (
+            13.0 * luv[3*i] * (fv(xyz[3*i], xyz[3*i + 1], xyz[3*i + 2]) - {v0})
+        );
+
+    """
+
+    # array will be modified in-place
+    return cp.ElementwiseKernel(
+        '',
+        'raw X xyz, raw X luv',
+        _xyz_to_luv,
+        preamble=preamble,
+        name=f'xyz2luv_{np.dtype(dtype).char}')
+
+
+def xyz2luv(xyz, illuminant="D65", observer="2"):
+    """XYZ to CIE-Luv color space conversion.
+
+    Parameters
+    ----------
+    xyz : (..., 3) array_like
+        The image in XYZ format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in CIE-Luv format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `xyz` is not at least 2-D with shape (..., 3).
+    ValueError
+        If either the illuminant or the observer angle are not supported or
+        unknown.
+
+    Notes
+    -----
+    By default XYZ conversion weights use observer=2A. Reference whitepoint
+    for D65 Illuminant, with XYZ tristimulus values of ``(95.047, 100.,
+    108.883)``. See function 'get_xyz_coords' for a list of supported
+    illuminants.
+
+    References
+    ----------
+    .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
+    .. [2] https://en.wikipedia.org/wiki/CIELUV
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2xyz, xyz2luv
+    >>> img = cp.array(data.astronaut())
+    >>> img_xyz = rgb2xyz(img)
+    >>> img_luv = xyz2luv(img_xyz)
+    """
+    input_is_one_pixel = xyz.ndim == 1
+    if input_is_one_pixel:
+        xyz = xyz[np.newaxis, ...]
+
+    xyz = _prepare_colorarray(xyz, force_c_contiguous=True)
+
+    xyz_ref_white = get_xyz_coords(illuminant, observer)
+    kern = _get_xyz_to_luv_kernel(xyz_ref_white, xyz.dtype)
+    luv = cp.empty_like(xyz)
+    kern(xyz, luv, size=xyz.size // 3)
+
+    if input_is_one_pixel:
+        luv = cp.squeeze(luv, axis=0)
+
+    return luv
+
+
+@cp.memoize(for_each_device=True)
+def _get_luv_to_xyz_kernel(xyz_ref_white, dtype):
+
+    eps = np.finfo(dtype).eps
+
+    denom = np.asarray([1, 15, 3]) @ np.asarray(xyz_ref_white, dtype=float)
+    denom = float(denom)
+    u0 = 4 * xyz_ref_white[0] / denom
+    v0 = 9 * xyz_ref_white[1] / denom
+
+    _luv_to_xyz = f"""
+        if (luv[3*i] > 7.999625)
+        {{
+            xyz[3*i + 1] = (luv[3 * i] + 16.0) / 116.0;
+            xyz[3*i + 1] *= xyz[3*i + 1] * xyz[3*i + 1];
+        }} else {{
+            xyz[3*i + 1] = luv[3*i] / 903.3;
+        }}
+        xyz[3*i + 1] *= {xyz_ref_white[1]};
+
+        X a = {u0} + luv[3*i + 1] / (13.0 * luv[3*i] + {eps});
+        X b = {v0} + luv[3*i + 2] / (13.0 * luv[3*i] + {eps});
+        X c = 3.0 * xyz[3*i + 1] * (5.0 * b - 3.0);
+
+        xyz[3*i + 2] = ((a - 4.0) * c - 15.0 * a * b * xyz[3*i + 1]) / (12.0 * b);
+        xyz[3*i] = -(c / b + 3.0 * xyz[3*i + 2]);
+
+    """  # noqa
+    return cp.ElementwiseKernel(
+        '',
+        'raw X luv, raw X xyz',
+        _luv_to_xyz,
+        name=f'luv2xyz_{np.dtype(dtype).char}')
+
+
+def luv2xyz(luv, illuminant="D65", observer="2"):
+    """CIE-Luv to XYZ color space conversion.
+
+    Parameters
+    ----------
+    luv : (..., 3) array_like
+        The image in CIE-Luv format. Final dimension denotes channels.
+    illuminant : {"A", "D50", "D55", "D65", "D75", "E"}, optional
+        The name of the illuminant (the function is NOT case sensitive).
+    observer : {"2", "10"}, optional
+        The aperture angle of the observer.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in XYZ format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `luv` is not at least 2-D with shape (..., 3).
+    ValueError
+        If either the illuminant or the observer angle are not supported or
+        unknown.
+
+    Notes
+    -----
+    XYZ conversion weights use observer=2A. Reference whitepoint for D65
+    Illuminant, with XYZ tristimulus values of ``(95.047, 100., 108.883)``. See
+    function 'get_xyz_coords' for a list of supported illuminants.
+
+    References
+    ----------
+    .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
+    .. [2] https://en.wikipedia.org/wiki/CIELUV
+    """
+    luv = _prepare_colorarray(luv, force_c_contiguous=True)
+    xyz_ref_white = get_xyz_coords(illuminant, observer)
+    kern = _get_luv_to_xyz_kernel(xyz_ref_white, luv.dtype)
+    xyz = cp.empty_like(luv)
+    kern(luv, xyz, size=luv.size // 3)
+    return xyz
+
+
+def rgb2luv(rgb):
+    """RGB to CIE-Luv color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in CIE Luv format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    This function uses rgb2xyz and xyz2luv.
+
+    References
+    ----------
+    .. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
+    .. [2] http://www.easyrgb.com/index.php?X=MATH&H=02#text2
+    .. [3] https://en.wikipedia.org/wiki/CIELUV
+    """
+    return xyz2luv(rgb2xyz(rgb))
+
+
+def luv2rgb(luv):
+    """Luv to RGB color space conversion.
+
+    Parameters
+    ----------
+    luv : (..., 3) array_like
+        The image in CIE Luv format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `luv` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    This function uses luv2xyz and xyz2rgb.
+    """
+    return xyz2rgb(luv2xyz(luv))
+
+
+def rgb2hed(rgb):
+    """RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in HED format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical
+           staining by color deconvolution.," Analytical and quantitative
+           cytology and histology / the International Academy of Cytology [and]
+           American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2hed
+    >>> ihc = cp.array(data.immunohistochemistry())
+    >>> ihc_hed = rgb2hed(ihc)
+    """
+    return separate_stains(rgb, hed_from_rgb)
+
+
+def hed2rgb(hed):
+    """Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
+
+    Parameters
+    ----------
+    hed : (..., 3) array_like
+        The image in the HED color space. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `hed` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] A. C. Ruifrok and D. A. Johnston, "Quantification of histochemical
+           staining by color deconvolution.," Analytical and quantitative
+           cytology and histology / the International Academy of Cytology [and]
+           American Society of Cytology, vol. 23, no. 4, pp. 291-9, Aug. 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2hed, hed2rgb
+    >>> ihc = cp.array(data.immunohistochemistry())
+    >>> ihc_hed = rgb2hed(ihc)
+    >>> ihc_rgb = hed2rgb(ihc_hed)
+    """
+    return combine_stains(hed, rgb_from_hed)
+
+
+@cp.memoize(for_each_device=True)
+def _separate_stains_kernel(m):
+    log_adjust = 1 / np.log(1e-6)
+    code = f"""
+    X tmp[3];
+    for (int ch=0; ch<3; ch++)
+    {{
+        tmp[ch] = log(max(rgb[3*i + ch], 1e-6)) * {log_adjust};
+    }}
+    stains[3*i] = tmp[0] * {m[0]} + tmp[1] * {m[3]} + tmp[2] * {m[6]};
+    stains[3*i + 1] = tmp[0] * {m[1]} + tmp[1] * {m[4]} + tmp[2] * {m[7]};
+    stains[3*i + 2] = tmp[0] * {m[2]} + tmp[1] * {m[5]} + tmp[2] * {m[8]};
+    """  # noqa
+    return cp.ElementwiseKernel(
+        'raw X rgb',
+        'raw X stains',
+        code,
+        name='seperate_stains')
+
+
+def separate_stains(rgb, conv_matrix):
+    """RGB to stain color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+    conv_matrix: ndarray
+        The stain separation matrix as described by G. Landini [1]_.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in stain color space. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Stain separation matrices available in the ``color`` module and their
+    respective colorspace:
+
+    * ``hed_from_rgb``: Hematoxylin + Eosin + DAB
+    * ``hdx_from_rgb``: Hematoxylin + DAB
+    * ``fgx_from_rgb``: Feulgen + Light Green
+    * ``bex_from_rgb``: Giemsa stain : Methyl Blue + Eosin
+    * ``rbd_from_rgb``: FastRed + FastBlue +  DAB
+    * ``gdx_from_rgb``: Methyl Green + DAB
+    * ``hax_from_rgb``: Hematoxylin + AEC
+    * ``bro_from_rgb``: Blue matrix Anilline Blue + Red matrix Azocarmine\
+                        + Orange matrix Orange-G
+    * ``bpx_from_rgb``: Methyl Blue + Ponceau Fuchsin
+    * ``ahx_from_rgb``: Alcian Blue + Hematoxylin
+    * ``hpx_from_rgb``: Hematoxylin + PAS
+
+    This implementation borrows some ideas from DIPlib [2]_, e.g. the
+    compensation using a small value to avoid log artifacts when
+    calculating the Beer-Lambert law.
+
+    References
+    ----------
+    .. [1] https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html
+    .. [2] https://github.com/DIPlib/diplib/
+    .. [3] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical
+           staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol.
+           23, no. 4, pp. 291–299, Aug. 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import separate_stains, hdx_from_rgb
+    >>> ihc = cp.array(data.immunohistochemistry())
+    >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
+    """  # noqa
+    rgb = _prepare_colorarray(rgb, force_c_contiguous=True)
+
+    if conv_matrix.shape != (3, 3):
+        raise ValueError("conv_matrix must have shape (3, 3)")
+    conv_matrix = tuple(cp.asnumpy(conv_matrix).ravel())
+
+    # #cp.maximum(rgb, 1e-6, out=rgb)  # avoiding log artifacts
+    # log_adjust = np.log(1e-6)  # used to compensate the sum above
+
+    # conv_matrix = cp.asarray(conv_matrix, dtype=rgb.dtype)
+    # stains = (cp.log(rgb) / log_adjust) @ conv_matrix
+
+    kern = _separate_stains_kernel(conv_matrix)
+    stains = cp.empty_like(rgb)
+    kern(rgb, stains, size=rgb.size // 3)
+    return stains
+
+
+@cp.memoize(for_each_device=True)
+def _combine_stains_kernel(m):
+    # log_adjust here is used to compensate the sum within separate_stains()
+    log_adjust = np.log(1e-6)
+    code = f"""
+    X tmp[3];
+    for (int ch=0; ch<3; ch++)
+    {{
+        tmp[ch] = stains[3*i + ch] * {log_adjust};
+    }}
+
+    rgb[3*i] = tmp[0] * {m[0]} + tmp[1] * {m[3]} + tmp[2] * {m[6]};
+    rgb[3*i + 1] = tmp[0] * {m[1]} + tmp[1] * {m[4]} + tmp[2] * {m[7]};
+    rgb[3*i + 2] = tmp[0] * {m[2]} + tmp[1] * {m[5]} + tmp[2] * {m[8]};
+
+    for (int ch=0; ch<3; ch++)
+    {{
+        rgb[3*i + ch] = min(max(exp(rgb[3*i + ch]), (X)0.0), (X)1.0);
+    }}
+    """  # noqa
+    return cp.ElementwiseKernel(
+        'raw X stains',
+        'raw X rgb',
+        code,
+        name='combine_stains')
+
+
+def combine_stains(stains, conv_matrix):
+    """Stain to RGB color space conversion.
+
+    Parameters
+    ----------
+    stains : (..., 3) array_like
+        The image in stain color space. Final dimension denotes channels.
+    conv_matrix: ndarray
+        The stain separation matrix as described by G. Landini [1]_.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `stains` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Stain combination matrices available in the ``color`` module and their
+    respective colorspace:
+
+    * ``rgb_from_hed``: Hematoxylin + Eosin + DAB
+    * ``rgb_from_hdx``: Hematoxylin + DAB
+    * ``rgb_from_fgx``: Feulgen + Light Green
+    * ``rgb_from_bex``: Giemsa stain : Methyl Blue + Eosin
+    * ``rgb_from_rbd``: FastRed + FastBlue +  DAB
+    * ``rgb_from_gdx``: Methyl Green + DAB
+    * ``rgb_from_hax``: Hematoxylin + AEC
+    * ``rgb_from_bro``: Blue matrix Anilline Blue + Red matrix Azocarmine\
+                        + Orange matrix Orange-G
+    * ``rgb_from_bpx``: Methyl Blue + Ponceau Fuchsin
+    * ``rgb_from_ahx``: Alcian Blue + Hematoxylin
+    * ``rgb_from_hpx``: Hematoxylin + PAS
+
+    References
+    ----------
+    .. [1] https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html
+    .. [2] A. C. Ruifrok and D. A. Johnston, “Quantification of histochemical
+           staining by color deconvolution,” Anal. Quant. Cytol. Histol., vol.
+           23, no. 4, pp. 291–299, Aug. 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.color import (separate_stains, combine_stains,
+    ...                                    hdx_from_rgb, rgb_from_hdx)
+    >>> ihc = cp.array(data.immunohistochemistry())
+    >>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
+    >>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
+    """  # noqa
+    stains = _prepare_colorarray(stains, force_c_contiguous=True)
+
+    if conv_matrix.shape != (3, 3):
+        raise ValueError("conv_matrix must have shape (3, 3)")
+    conv_matrix = tuple(cp.asnumpy(conv_matrix).ravel())
+
+    kern = _combine_stains_kernel(conv_matrix)
+    rgb = cp.empty_like(stains)
+    kern(stains, rgb, size=stains.size // 3)
+
+    return rgb
+
+
+@cp.memoize(for_each_device=True)
+def _lab2lch_kernel(nchannels=3, name='lab2lch'):
+    code = f"""
+    X a = lab[{nchannels}*i + 1];
+    X b = lab[{nchannels}*i + 2];
+
+    // update lab array in-place with the lch values
+    lab[{nchannels}*i + 1] = hypot(a, b);
+    lab[{nchannels}*i + 2] = atan2(b, a);
+
+    // NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than ``(-pi, +pi)``
+    if (lab[{nchannels}*i + 2] < 0)
+    {{
+       lab[{nchannels}*i + 2] += 2 * M_PI;
+    }}
+    """  # noqa
+    return cp.ElementwiseKernel(
+        '',
+        'raw X lab',
+        code,
+        name=name)
+
+
+def lab2lch(lab):
+    """CIE-LAB to CIE-LCH color space conversion.
+
+    LCH is the cylindrical representation of the LAB (Cartesian) colorspace
+
+    Parameters
+    ----------
+    lab : (..., 3) array_like
+        The N-D image in CIE-LAB format. The last (``N+1``-th) dimension must
+        have at least 3 elements, corresponding to the ``L``, ``a``, and ``b``
+        color channels. Subsequent elements are copied.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in LCH format, in a N-D array with same shape as input `lab`.
+
+    Raises
+    ------
+    ValueError
+        If `lch` does not have at least 3 color channels (i.e. l, a, b).
+
+    Notes
+    -----
+    The Hue is expressed as an angle between ``(0, 2*pi)``
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2lab, lab2lch
+    >>> img = cp.array(data.astronaut())
+    >>> img_lab = rgb2lab(img)
+    >>> img_lch = lab2lch(img_lab)
+    """
+    lab = _prepare_lab_array(lab, force_copy=True)
+    nchannels = lab.shape[-1]
+
+    name = f'lab2lch_{nchannels}channel_{lab.dtype}'
+    kern = _lab2lch_kernel(nchannels, name=name)
+    kern(lab, size=lab.size // nchannels)
+    return lab
+
+
+def _cart2polar_2pi(x, y):
+    """convert cartesian coordinates to polar (uses non-standard theta range!)
+
+    NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than usual ``(-pi, +pi)``
+    """
+    r, t = cp.hypot(x, y), cp.arctan2(y, x)
+    t += cp.where(t < 0., 2 * np.pi, 0)
+    return r, t
+
+
+@cp.memoize(for_each_device=True)
+def _lch2lab_kernel(nchannels=3, name='lch2lab'):
+    code = f"""
+    X sin_h = sin(lch[{nchannels}*i + 2]);
+    X cos_h = cos(lch[{nchannels}*i + 2]);
+
+    // update lch array in-place with the lab values
+    lch[{nchannels}*i + 2] = lch[{nchannels}*i + 1] * sin_h;
+    lch[{nchannels}*i + 1] = lch[{nchannels}*i + 1] * cos_h;
+
+    """  # noqa
+    return cp.ElementwiseKernel(
+        '',
+        'raw X lch',
+        code,
+        name=name)
+
+
+def lch2lab(lch):
+    """CIE-LCH to CIE-LAB color space conversion.
+
+    LCH is the cylindrical representation of the LAB (Cartesian) colorspace
+
+    Parameters
+    ----------
+    lch : (..., 3) array_like
+        The N-D image in CIE-LCH format. The last (``N+1``-th) dimension must
+        have at least 3 elements, corresponding to the ``L``, ``a``, and ``b``
+        color channels.  Subsequent elements are copied.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in LAB format, with same shape as input `lch`.
+
+    Raises
+    ------
+    ValueError
+        If `lch` does not have at least 3 color channels (i.e. l, c, h).
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.color import rgb2lab, lch2lab
+    >>> img = cp.array(data.astronaut())
+    >>> img_lab = rgb2lab(img)
+    >>> img_lch = lab2lch(img_lab)
+    >>> img_lab2 = lch2lab(img_lch)
+    """
+
+    # make a copy because lch will be modified in-place by the kernel below
+    lch = _prepare_lab_array(lch, force_copy=True)
+    nchannels = lch.shape[-1]
+
+    name = f'lch2lab_{nchannels}channel_{lch.dtype}'
+    kern = _lch2lab_kernel(nchannels, name=name)
+    kern(lch, size=lch.size // nchannels)
+    return lch
+
+
+def _prepare_lab_array(arr, force_copy=True):
+    """Ensure input for lab2lch, lch2lab are well-posed.
+
+    Arrays must be in floating point and have at least 3 elements in
+    last dimension.  Return a new array.
+    """
+    shape = arr.shape
+    if shape[-1] < 3:
+        raise ValueError('Input array has less than 3 color channels')
+    return dtype.img_as_float(arr, force_copy=force_copy)
+
+
+def rgb2yuv(rgb):
+    """RGB to YUV color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in YUV format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Y is between 0 and 1.  Use YCbCr instead of YUV for the color space
+    commonly used by video codecs, where Y ranges from 16 to 235.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YUV
+    """
+    return _convert(yuv_from_rgb, rgb, name='rgb2yuv')
+
+
+def rgb2yiq(rgb):
+    """RGB to YIQ color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in YIQ format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+    """
+    return _convert(yiq_from_rgb, rgb, name='rgb2yiq')
+
+
+def rgb2ypbpr(rgb):
+    """RGB to YPbPr color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in YPbPr format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YPbPr
+    """
+    return _convert(ypbpr_from_rgb, rgb, name='rgb2ypbpr')
+
+
+def rgb2ycbcr(rgb):
+    """RGB to YCbCr color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in YCbCr format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Y is between 16 and 235. This is the color space commonly used by video
+    codecs; it is sometimes incorrectly called "YUV".
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YCbCr
+    """
+    _post_colorconv = """
+        y[3*i] += 16;
+        y[3*i + 1] += 128;
+        y[3*i + 2] += 128;
+    """
+    arr = _convert(ycbcr_from_rgb, rgb, post=_post_colorconv, name='rgb2ycbcr')
+    return arr
+
+
+def rgb2ydbdr(rgb):
+    """RGB to YDbDr color space conversion.
+
+    Parameters
+    ----------
+    rgb : (..., 3) array_like
+        The image in RGB format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in YDbDr format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `rgb` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    This is the color space commonly used by video codecs. It is also the
+    reversible color transform in JPEG2000.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YDbDr
+    """
+    return _convert(ydbdr_from_rgb, rgb, name='rgb2ydbdr')
+
+
+def yuv2rgb(yuv):
+    """YUV to RGB color space conversion.
+
+    Parameters
+    ----------
+    yuv : (..., 3) array_like
+        The image in YUV format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `yuv` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YUV
+    """
+    return _convert(rgb_from_yuv, yuv, name='yuv2rgb')
+
+
+def yiq2rgb(yiq):
+    """YIQ to RGB color space conversion.
+
+    Parameters
+    ----------
+    yiq : (..., 3) array_like
+        The image in YIQ format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `yiq` is not at least 2-D with shape (..., 3).
+    """
+    return _convert(rgb_from_yiq, yiq, name='yiq2rgb')
+
+
+def ypbpr2rgb(ypbpr):
+    """YPbPr to RGB color space conversion.
+
+    Parameters
+    ----------
+    ypbpr : (..., 3) array_like
+        The image in YPbPr format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `ypbpr` is not at least 2-D with shape (..., 3).
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YPbPr
+    """
+    return _convert(rgb_from_ypbpr, ypbpr, name='ypbpr2rgb')
+
+
+def ycbcr2rgb(ycbcr):
+    """YCbCr to RGB color space conversion.
+
+    Parameters
+    ----------
+    ycbcr : (..., 3) array_like
+        The image in YCbCr format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `ycbcr` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    Y is between 16 and 235. This is the color space commonly used by video
+    codecs; it is sometimes incorrectly called "YUV".
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YCbCr
+    """
+    arr = ycbcr.copy()
+    _pre_colorconv = """
+        x[3*i] -= 16;
+        x[3*i + 1] -= 128;
+        x[3*i + 2] -= 128;
+    """
+    return _convert(rgb_from_ycbcr, arr, pre=_pre_colorconv,
+                    name='ycbcr2rgb')
+
+
+def ydbdr2rgb(ydbdr):
+    """YDbDr to RGB color space conversion.
+
+    Parameters
+    ----------
+    ydbdr : (..., 3) array_like
+        The image in YDbDr format. Final dimension denotes channels.
+
+    Returns
+    -------
+    out : (..., 3) ndarray
+        The image in RGB format. Same dimensions as input.
+
+    Raises
+    ------
+    ValueError
+        If `ydbdr` is not at least 2-D with shape (..., 3).
+
+    Notes
+    -----
+    This is the color space commonly used by video codecs, also called the
+    reversible color transform in JPEG2000.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/YDbDr
+    """
+    return _convert(rgb_from_ydbdr, ydbdr, name='ydbdr2rgb')
diff --git a/python/cucim/src/cucim/skimage/color/colorlabel.py b/python/cucim/src/cucim/skimage/color/colorlabel.py
new file mode 100644
index 000000000..ab7b3d6d1
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/colorlabel.py
@@ -0,0 +1,239 @@
+import itertools
+
+import cupy as cp
+import numpy as np
+
+from .._shared.utils import change_default_value, warn
+from ..util import img_as_float
+from . import rgb_colors
+from .colorconv import gray2rgb, rgb2gray
+
+__all__ = ['color_dict', 'label2rgb', 'DEFAULT_COLORS']
+
+
+DEFAULT_COLORS = ('red', 'blue', 'yellow', 'magenta', 'green',
+                  'indigo', 'darkorange', 'cyan', 'pink', 'yellowgreen')
+
+
+color_dict = {k: v for k, v in rgb_colors.__dict__.items()
+              if isinstance(v, tuple)}
+
+
+def _rgb_vector(color):
+    """Return RGB color as (1, 3) array.
+
+    This RGB array gets multiplied by masked regions of an RGB image, which are
+    partially flattened by masking (i.e. dimensions 2D + RGB -> 1D + RGB).
+
+    Parameters
+    ----------
+    color : str or array
+        Color name in `color_dict` or RGB float values between [0, 1].
+    """
+    if isinstance(color, str):
+        color = color_dict[color]
+    # Slice to handle RGBA colors.
+    return np.asarray(color[:3])  # CuPy Backend: leave this array on the host
+
+
+def _match_label_with_color(label, colors, bg_label, bg_color):
+    """Return `unique_labels` and `color_cycle` for label array and color list.
+
+    Colors are cycled for normal labels, but the background color should only
+    be used for the background.
+    """
+    # Temporarily set background color; it will be removed later.
+    if bg_color is None:
+        bg_color = (0, 0, 0)
+    bg_color = _rgb_vector(bg_color)
+
+    # map labels to their ranks among all labels from small to large
+    unique_labels, mapped_labels = cp.unique(label, return_inverse=True)
+
+    # get rank of bg_label
+    bg_label_rank_list = mapped_labels[label.ravel() == bg_label]
+
+    # The rank of each label is the index of the color it is matched to in
+    # color cycle. bg_label should always be mapped to the first color, so
+    # its rank must be 0. Other labels should be ranked from small to large
+    # from 1.
+    if len(bg_label_rank_list) > 0:
+        bg_label_rank = bg_label_rank_list[0]
+        mapped_labels[mapped_labels < bg_label_rank] += 1
+        mapped_labels[label.ravel() == bg_label] = 0
+    else:
+        mapped_labels += 1
+
+    # Modify labels and color cycle so background color is used only once.
+    color_cycle = itertools.cycle(colors)
+    color_cycle = itertools.chain([bg_color], color_cycle)
+    return mapped_labels, color_cycle
+
+
+@change_default_value("bg_label", new_value=0, changed_version="0.19")
+def label2rgb(label, image=None, colors=None, alpha=0.3,
+              bg_label=-1, bg_color=(0, 0, 0), image_alpha=1, kind='overlay'):
+    """Return an RGB image where color-coded labels are painted over the image.
+
+    Parameters
+    ----------
+    label : array, shape (M, N)
+        Integer array of labels with the same shape as `image`.
+    image : array, shape (M, N, 3), optional
+        Image used as underlay for labels. If the input is an RGB image, it's
+        converted to grayscale before coloring.
+    colors : list, optional
+        List of colors. If the number of labels exceeds the number of colors,
+        then the colors are cycled.
+    alpha : float [0, 1], optional
+        Opacity of colorized labels. Ignored if image is `None`.
+    bg_label : int, optional
+        Label that's treated as the background. If `bg_label` is specified,
+        `bg_color` is `None`, and `kind` is `overlay`,
+        background is not painted by any colors.
+    bg_color : str or array, optional
+        Background color. Must be a name in `color_dict` or RGB float values
+        between [0, 1].
+    image_alpha : float [0, 1], optional
+        Opacity of the image.
+    kind : string, one of {'overlay', 'avg'}
+        The kind of color image desired. 'overlay' cycles over defined colors
+        and overlays the colored labels over the original image. 'avg' replaces
+        each labeled segment with its average color, for a stained-class or
+        pastel painting appearance.
+
+    Returns
+    -------
+    result : array of float, shape (M, N, 3)
+        The result of blending a cycling colormap (`colors`) for each distinct
+        value in `label` with the image, at a certain alpha value.
+    """
+    if kind == 'overlay':
+        return _label2rgb_overlay(label, image, colors, alpha, bg_label,
+                                  bg_color, image_alpha)
+    elif kind == 'avg':
+        return _label2rgb_avg(label, image, bg_label, bg_color)
+    else:
+        raise ValueError("`kind` must be either 'overlay' or 'avg'.")
+
+
+def _label2rgb_overlay(label, image=None, colors=None, alpha=0.3,
+                       bg_label=-1, bg_color=None, image_alpha=1):
+    """Return an RGB image where color-coded labels are painted over the image.
+
+    Parameters
+    ----------
+    label : array, shape (M, N)
+        Integer array of labels with the same shape as `image`.
+    image : array, shape (M, N, 3), optional
+        Image used as underlay for labels. If the input is an RGB image, it's
+        converted to grayscale before coloring.
+    colors : list, optional
+        List of colors. If the number of labels exceeds the number of colors,
+        then the colors are cycled.
+    alpha : float [0, 1], optional
+        Opacity of colorized labels. Ignored if image is `None`.
+    bg_label : int, optional
+        Label that's treated as the background. If `bg_label` is specified and
+        `bg_color` is `None`, background is not painted by any colors.
+    bg_color : str or array, optional
+        Background color. Must be a name in `color_dict` or RGB float values
+        between [0, 1].
+    image_alpha : float [0, 1], optional
+        Opacity of the image.
+
+    Returns
+    -------
+    result : array of float, shape (M, N, 3)
+        The result of blending a cycling colormap (`colors`) for each distinct
+        value in `label` with the image, at a certain alpha value.
+    """
+    if colors is None:
+        colors = DEFAULT_COLORS
+    colors = [_rgb_vector(c) for c in colors]
+
+    if image is None:
+        image = cp.zeros(label.shape + (3,), dtype=np.float64)
+        # Opacity doesn't make sense if no image exists.
+        alpha = 1
+    else:
+        if not image.shape[:2] == label.shape:
+            raise ValueError("`image` and `label` must be the same shape")
+
+        if image.min() < 0:
+            warn("Negative intensities in `image` are not supported")
+
+        if image.ndim > label.ndim:
+            image = img_as_float(rgb2gray(image))
+        else:
+            image = img_as_float(image)
+        image = gray2rgb(image) * image_alpha + (1 - image_alpha)
+
+    # Ensure that all labels are non-negative so we can index into
+    # `label_to_color` correctly.
+    offset = min(int(label.min()), bg_label)
+    if offset != 0:
+        label = label - offset  # Make sure you don't modify the input array.
+        bg_label -= offset
+
+    new_type = np.min_scalar_type(int(label.max()))
+    if new_type == bool:
+        new_type = np.uint8
+    label = label.astype(new_type)
+
+    mapped_labels_flat, color_cycle = _match_label_with_color(
+        label, colors, bg_label, bg_color)
+
+    if len(mapped_labels_flat) == 0:
+        return image
+
+    dense_labels = range(int(mapped_labels_flat.max()) + 1)
+
+    # CuPy Backend: small color_cycle arrays are left on the CPU
+    label_to_color = np.stack([c for i, c in zip(dense_labels, color_cycle)])
+    # CuPy Backend: transfer to GPU after concatenation of small host arrays
+    label_to_color = cp.asarray(label_to_color)
+
+    mapped_labels = mapped_labels_flat.reshape(label.shape)
+    label = mapped_labels
+    result = label_to_color[mapped_labels] * alpha + image * (1 - alpha)
+
+    # Remove background label if its color was not specified.
+    remove_background = 0 in mapped_labels_flat and bg_color is None
+    if remove_background:
+        result[label == bg_label] = image[label == bg_label]
+
+    return result
+
+
+def _label2rgb_avg(label_field, image, bg_label=0, bg_color=(0, 0, 0)):
+    """Visualise each segment in `label_field` with its mean color in `image`.
+
+    Parameters
+    ----------
+    label_field : array of int
+        A segmentation of an image.
+    image : array, shape ``label_field.shape + (3,)``
+        A color image of the same spatial shape as `label_field`.
+    bg_label : int, optional
+        A value in `label_field` to be treated as background.
+    bg_color : 3-tuple of int, optional
+        The color for the background label
+
+    Returns
+    -------
+    out : array, same shape and type as `image`
+        The output visualization.
+    """
+    out = cp.zeros(label_field.shape + (3,))
+    labels = cp.unique(label_field)
+    bg = (labels == bg_label)
+    if bg.any():
+        labels = labels[labels != bg_label]
+        mask = (label_field == bg_label).nonzero()
+        out[mask] = bg_color
+    for label in labels:
+        mask = (label_field == label).nonzero()
+        color = image[mask].mean(axis=0)
+        out[mask] = color
+    return out
diff --git a/python/cucim/src/cucim/skimage/color/delta_e.py b/python/cucim/src/cucim/skimage/color/delta_e.py
new file mode 100644
index 000000000..531dd9096
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/delta_e.py
@@ -0,0 +1,350 @@
+"""
+Functions for calculating the "distance" between colors.
+
+Implicit in these definitions of "distance" is the notion of "Just Noticeable
+Distance" (JND).  This represents the distance between colors where a human can
+perceive different colors.  Humans are more sensitive to certain colors than
+others, which different deltaE metrics correct for with varying degrees of
+sophistication.
+
+The literature often mentions 1 as the minimum distance for visual
+differentiation, but more recent studies (Mahy 1994) peg JND at 2.3
+
+The delta-E notation comes from the German word for "Sensation" (Empfindung).
+
+Reference
+---------
+https://en.wikipedia.org/wiki/Color_difference
+
+"""
+
+import warnings
+
+import cupy as cp
+import numpy as np
+
+from .colorconv import _cart2polar_2pi, lab2lch
+
+
+def deltaE_cie76(lab1, lab2):
+    """Euclidean distance between two points in Lab color space
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+
+    Returns
+    -------
+    dE : array_like
+        distance between colors `lab1` and `lab2`
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] A. R. Robertson, "The CIE 1976 color-difference formulae,"
+           Color Res. Appl. 2, 7-11 (1977).
+    """
+    L1, a1, b1 = cp.rollaxis(lab1, -1)[:3]
+    L2, a2, b2 = cp.rollaxis(lab2, -1)[:3]
+    out = (L2 - L1) * (L2 - L1)
+    out += (a2 - a1) * (a2 - a1)
+    out += (b2 - b1) * (b2 - b1)
+    return cp.sqrt(out, out=out)
+
+
+def deltaE_ciede94(lab1, lab2, kH=1, kC=1, kL=1, k1=0.045, k2=0.015):
+    """Color difference according to CIEDE 94 standard
+
+    Accommodates perceptual non-uniformities through the use of application
+    specific scale factors (`kH`, `kC`, `kL`, `k1`, and `k2`).
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    kH : float, optional
+        Hue scale
+    kC : float, optional
+        Chroma scale
+    kL : float, optional
+        Lightness scale
+    k1 : float, optional
+        first scale parameter
+    k2 : float, optional
+        second scale parameter
+
+    Returns
+    -------
+    dE : array_like
+        color difference between `lab1` and `lab2`
+
+    Notes
+    -----
+    deltaE_ciede94 is not symmetric with respect to lab1 and lab2.  CIEDE94
+    defines the scales for the lightness, hue, and chroma in terms of the first
+    color.  Consequently, the first color should be regarded as the "reference"
+    color.
+
+    `kL`, `k1`, `k2` depend on the application and default to the values
+    suggested for graphic arts
+
+    ==========  ==============  ==========
+    Parameter    Graphic Arts    Textiles
+    ==========  ==============  ==========
+    `kL`         1.000           2.000
+    `k1`         0.045           0.048
+    `k2`         0.015           0.014
+    ==========  ==============  ==========
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
+    """
+    L1, C1 = cp.rollaxis(lab2lch(lab1), -1)[:2]
+    L2, C2 = cp.rollaxis(lab2lch(lab2), -1)[:2]
+
+    dL = L1 - L2
+    dC = C1 - C2
+    dH2 = get_dH2(lab1, lab2)
+
+    SL = 1
+    SC = 1 + k1 * C1
+    SH = 1 + k2 * C1
+
+    dE2 = dL / (kL * SL)
+    dE2 *= dE2
+    tmp = dC / (kC * SC)
+    tmp *= tmp
+    dE2 += tmp
+    tmp = kH * SH
+    tmp *= tmp
+    dE2 += dH2 / tmp
+    return cp.sqrt(cp.maximum(dE2, 0, out=dE2), out=dE2)
+
+
+def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1):
+    """Color difference as given by the CIEDE 2000 standard.
+
+    CIEDE 2000 is a major revision of CIDE94.  The perceptual calibration is
+    largely based on experience with automotive paint on smooth surfaces.
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+    kL : float (range), optional
+        lightness scale factor, 1 for "acceptably close"; 2 for "imperceptible"
+        see deltaE_cmc
+    kC : float (range), optional
+        chroma scale factor, usually 1
+    kH : float (range), optional
+        hue scale factor, usually 1
+
+    Returns
+    -------
+    deltaE : array_like
+        The distance between `lab1` and `lab2`
+
+    Notes
+    -----
+    CIEDE 2000 assumes parametric weighting factors for the lightness, chroma,
+    and hue (`kL`, `kC`, `kH` respectively).  These default to 1.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
+           :DOI:`10.1364/AO.33.008069`
+    .. [3] M. Melgosa, J. Quesada, and E. Hita, "Uniformity of some recent
+           color metrics tested with an accurate color-difference tolerance
+           dataset," Appl. Opt. 33, 8069-8077 (1994).
+    """
+    warnings.warn(
+        "The numerical accuracy of this function on the GPU is reduced "
+        "relative to the CPU version"
+    )
+    unroll = False
+    if lab1.ndim == 1 and lab2.ndim == 1:
+        unroll = True
+        if lab1.ndim == 1:
+            lab1 = lab1[None, :]
+        if lab2.ndim == 1:
+            lab2 = lab2[None, :]
+    L1, a1, b1 = cp.rollaxis(lab1, -1)[:3]
+    L2, a2, b2 = cp.rollaxis(lab2, -1)[:3]
+
+    # distort `a` based on average chroma
+    # then convert to lch coordines from distorted `a`
+    # all subsequence calculations are in the new coordiantes
+    # (often denoted "prime" in the literature)
+    Cbar = 0.5 * (cp.hypot(a1, b1) + cp.hypot(a2, b2))
+    c7 = Cbar ** 7
+    G = 0.5 * (1 - cp.sqrt(c7 / (c7 + 25 ** 7)))
+    scale = 1 + G
+    C1, h1 = _cart2polar_2pi(a1 * scale, b1)
+    C2, h2 = _cart2polar_2pi(a2 * scale, b2)
+    # recall that c, h are polar coordiantes.  c==r, h==theta
+
+    # cide2000 has four terms to delta_e:
+    # 1) Luminance term
+    # 2) Hue term
+    # 3) Chroma term
+    # 4) hue Rotation term
+
+    # lightness term
+    Lbar = 0.5 * (L1 + L2)
+    tmp = Lbar - 50
+    tmp *= tmp
+    SL = 1 + 0.015 * tmp / cp.sqrt(20 + tmp)
+    L_term = (L2 - L1) / (kL * SL)
+
+    # chroma term
+    Cbar = 0.5 * (C1 + C2)  # new coordiantes
+    SC = 1 + 0.045 * Cbar
+    C_term = (C2 - C1) / (kC * SC)
+
+    # hue term
+    h_diff = h2 - h1
+    h_sum = h1 + h2
+    CC = C1 * C2
+
+    dH = h_diff.copy()
+    dH[h_diff > np.pi] -= 2 * np.pi
+    dH[h_diff < -np.pi] += 2 * np.pi
+    dH[CC == 0.] = 0.  # if r == 0, dtheta == 0
+    dH_term = 2 * cp.sqrt(CC) * cp.sin(dH / 2)
+
+    Hbar = h_sum.copy()
+    mask = cp.logical_and(CC != 0., cp.abs(h_diff) > np.pi)
+    Hbar[mask * (h_sum < 2 * np.pi)] += 2 * np.pi
+    Hbar[mask * (h_sum >= 2 * np.pi)] -= 2 * np.pi
+    Hbar[CC == 0.] *= 2
+    Hbar *= 0.5
+
+    T = (1 -
+         0.17 * cp.cos(Hbar - np.deg2rad(30)) +
+         0.24 * cp.cos(2 * Hbar) +
+         0.32 * cp.cos(3 * Hbar + np.deg2rad(6)) -
+         0.20 * cp.cos(4 * Hbar - np.deg2rad(63))
+         )
+    SH = 1 + 0.015 * Cbar * T
+
+    H_term = dH_term / (kH * SH)
+
+    # hue rotation
+    c7 = Cbar ** 7
+    Rc = 2 * cp.sqrt(c7 / (c7 + 25 ** 7))
+    tmp = (cp.rad2deg(Hbar) - 275) / 25
+    tmp *= tmp
+    dtheta = np.deg2rad(30) * cp.exp(-tmp)
+    R_term = -cp.sin(2 * dtheta) * Rc * C_term * H_term
+
+    # put it all together
+    dE2 = L_term * L_term
+    dE2 += C_term * C_term
+    dE2 += H_term * H_term
+    dE2 += R_term
+    cp.sqrt(cp.maximum(dE2, 0, out=dE2), out=dE2)
+    if unroll:
+        dE2 = dE2[0]
+    return dE2
+
+
+def deltaE_cmc(lab1, lab2, kL=1, kC=1):
+    """Color difference from the  CMC l:c standard.
+
+    This color difference was developed by the Colour Measurement Committee
+    (CMC) of the Society of Dyers and Colourists (United Kingdom). It is
+    intended for use in the textile industry.
+
+    The scale factors `kL`, `kC` set the weight given to differences in
+    lightness and chroma relative to differences in hue.  The usual values are
+    ``kL=2``, ``kC=1`` for "acceptability" and ``kL=1``, ``kC=1`` for
+    "imperceptibility".  Colors with ``dE > 1`` are "different" for the given
+    scale factors.
+
+    Parameters
+    ----------
+    lab1 : array_like
+        reference color (Lab colorspace)
+    lab2 : array_like
+        comparison color (Lab colorspace)
+
+    Returns
+    -------
+    dE : array_like
+        distance between colors `lab1` and `lab2`
+
+    Notes
+    -----
+    deltaE_cmc the defines the scales for the lightness, hue, and chroma
+    in terms of the first color.  Consequently
+    ``deltaE_cmc(lab1, lab2) != deltaE_cmc(lab2, lab1)``
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Color_difference
+    .. [2] http://www.brucelindbloom.com/index.html?Eqn_DeltaE_CIE94.html
+    .. [3] F. J. J. Clarke, R. McDonald, and B. Rigg, "Modification to the
+           JPC79 colour-difference formula," J. Soc. Dyers Colour. 100, 128-132
+           (1984).
+    """
+    L1, C1, h1 = cp.rollaxis(lab2lch(lab1), -1)[:3]
+    L2, C2, h2 = cp.rollaxis(lab2lch(lab2), -1)[:3]
+
+    dC = C1 - C2
+    dL = L1 - L2
+    dH2 = get_dH2(lab1, lab2)
+
+    T = cp.where(cp.logical_and(cp.rad2deg(h1) >= 164, cp.rad2deg(h1) <= 345),
+                 0.56 + 0.2 * cp.abs(np.cos(h1 + cp.deg2rad(168))),
+                 0.36 + 0.4 * cp.abs(np.cos(h1 + cp.deg2rad(35)))
+                 )
+    c1_4 = C1 ** 4
+    F = cp.sqrt(c1_4 / (c1_4 + 1900))
+
+    SL = cp.where(L1 < 16, 0.511, 0.040975 * L1 / (1. + 0.01765 * L1))
+    SC = 0.638 + 0.0638 * C1 / (1. + 0.0131 * C1)
+    SH = SC * (F * T + 1 - F)
+
+    dE2 = (dL / (kL * SL)) ** 2
+    dE2 += (dC / (kC * SC)) ** 2
+    dE2 += dH2 / (SH ** 2)
+    return cp.sqrt(cp.maximum(dE2, 0, out=dE2), out=dE2)
+
+
+def get_dH2(lab1, lab2):
+    """squared hue difference term occurring in deltaE_cmc and deltaE_ciede94
+
+    Despite its name, "dH" is not a simple difference of hue values.  We avoid
+    working directly with the hue value, since differencing angles is
+    troublesome.  The hue term is usually written as:
+        c1 = sqrt(a1**2 + b1**2)
+        c2 = sqrt(a2**2 + b2**2)
+        term = (a1-a2)**2 + (b1-b2)**2 - (c1-c2)**2
+        dH = sqrt(term)
+
+    However, this has poor roundoff properties when a or b is dominant.
+    Instead, ab is a vector with elements a and b.  The same dH term can be
+    re-written as:
+        |ab1-ab2|**2 - (|ab1| - |ab2|)**2
+    and then simplified to:
+        2*|ab1|*|ab2| - 2*dot(ab1, ab2)
+    """
+    a1, b1 = cp.rollaxis(lab1, -1)[1:3]
+    a2, b2 = cp.rollaxis(lab2, -1)[1:3]
+
+    # magnitude of (a, b) is the chroma
+    C1 = cp.hypot(a1, b1)
+    C2 = cp.hypot(a2, b2)
+
+    term = (C1 * C2) - (a1 * a2 + b1 * b2)
+    return 2 * term
diff --git a/python/cucim/src/cucim/skimage/color/rgb_colors.py b/python/cucim/src/cucim/skimage/color/rgb_colors.py
new file mode 100644
index 000000000..230461050
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/rgb_colors.py
@@ -0,0 +1,146 @@
+aliceblue = (0.941, 0.973, 1)
+antiquewhite = (0.98, 0.922, 0.843)
+aqua = (0, 1, 1)
+aquamarine = (0.498, 1, 0.831)
+azure = (0.941, 1, 1)
+beige = (0.961, 0.961, 0.863)
+bisque = (1, 0.894, 0.769)
+black = (0, 0, 0)
+blanchedalmond = (1, 0.922, 0.804)
+blue = (0, 0, 1)
+blueviolet = (0.541, 0.169, 0.886)
+brown = (0.647, 0.165, 0.165)
+burlywood = (0.871, 0.722, 0.529)
+cadetblue = (0.373, 0.62, 0.627)
+chartreuse = (0.498, 1, 0)
+chocolate = (0.824, 0.412, 0.118)
+coral = (1, 0.498, 0.314)
+cornflowerblue = (0.392, 0.584, 0.929)
+cornsilk = (1, 0.973, 0.863)
+crimson = (0.863, 0.0784, 0.235)
+cyan = (0, 1, 1)
+darkblue = (0, 0, 0.545)
+darkcyan = (0, 0.545, 0.545)
+darkgoldenrod = (0.722, 0.525, 0.0431)
+darkgray = (0.663, 0.663, 0.663)
+darkgreen = (0, 0.392, 0)
+darkgrey = (0.663, 0.663, 0.663)
+darkkhaki = (0.741, 0.718, 0.42)
+darkmagenta = (0.545, 0, 0.545)
+darkolivegreen = (0.333, 0.42, 0.184)
+darkorange = (1, 0.549, 0)
+darkorchid = (0.6, 0.196, 0.8)
+darkred = (0.545, 0, 0)
+darksalmon = (0.914, 0.588, 0.478)
+darkseagreen = (0.561, 0.737, 0.561)
+darkslateblue = (0.282, 0.239, 0.545)
+darkslategray = (0.184, 0.31, 0.31)
+darkslategrey = (0.184, 0.31, 0.31)
+darkturquoise = (0, 0.808, 0.82)
+darkviolet = (0.58, 0, 0.827)
+deeppink = (1, 0.0784, 0.576)
+deepskyblue = (0, 0.749, 1)
+dimgray = (0.412, 0.412, 0.412)
+dimgrey = (0.412, 0.412, 0.412)
+dodgerblue = (0.118, 0.565, 1)
+firebrick = (0.698, 0.133, 0.133)
+floralwhite = (1, 0.98, 0.941)
+forestgreen = (0.133, 0.545, 0.133)
+fuchsia = (1, 0, 1)
+gainsboro = (0.863, 0.863, 0.863)
+ghostwhite = (0.973, 0.973, 1)
+gold = (1, 0.843, 0)
+goldenrod = (0.855, 0.647, 0.125)
+gray = (0.502, 0.502, 0.502)
+green = (0, 0.502, 0)
+greenyellow = (0.678, 1, 0.184)
+grey = (0.502, 0.502, 0.502)
+honeydew = (0.941, 1, 0.941)
+hotpink = (1, 0.412, 0.706)
+indianred = (0.804, 0.361, 0.361)
+indigo = (0.294, 0, 0.51)
+ivory = (1, 1, 0.941)
+khaki = (0.941, 0.902, 0.549)
+lavender = (0.902, 0.902, 0.98)
+lavenderblush = (1, 0.941, 0.961)
+lawngreen = (0.486, 0.988, 0)
+lemonchiffon = (1, 0.98, 0.804)
+lightblue = (0.678, 0.847, 0.902)
+lightcoral = (0.941, 0.502, 0.502)
+lightcyan = (0.878, 1, 1)
+lightgoldenrodyellow = (0.98, 0.98, 0.824)
+lightgray = (0.827, 0.827, 0.827)
+lightgreen = (0.565, 0.933, 0.565)
+lightgrey = (0.827, 0.827, 0.827)
+lightpink = (1, 0.714, 0.757)
+lightsalmon = (1, 0.627, 0.478)
+lightseagreen = (0.125, 0.698, 0.667)
+lightskyblue = (0.529, 0.808, 0.98)
+lightslategray = (0.467, 0.533, 0.6)
+lightslategrey = (0.467, 0.533, 0.6)
+lightsteelblue = (0.69, 0.769, 0.871)
+lightyellow = (1, 1, 0.878)
+lime = (0, 1, 0)
+limegreen = (0.196, 0.804, 0.196)
+linen = (0.98, 0.941, 0.902)
+magenta = (1, 0, 1)
+maroon = (0.502, 0, 0)
+mediumaquamarine = (0.4, 0.804, 0.667)
+mediumblue = (0, 0, 0.804)
+mediumorchid = (0.729, 0.333, 0.827)
+mediumpurple = (0.576, 0.439, 0.859)
+mediumseagreen = (0.235, 0.702, 0.443)
+mediumslateblue = (0.482, 0.408, 0.933)
+mediumspringgreen = (0, 0.98, 0.604)
+mediumturquoise = (0.282, 0.82, 0.8)
+mediumvioletred = (0.78, 0.0824, 0.522)
+midnightblue = (0.098, 0.098, 0.439)
+mintcream = (0.961, 1, 0.98)
+mistyrose = (1, 0.894, 0.882)
+moccasin = (1, 0.894, 0.71)
+navajowhite = (1, 0.871, 0.678)
+navy = (0, 0, 0.502)
+oldlace = (0.992, 0.961, 0.902)
+olive = (0.502, 0.502, 0)
+olivedrab = (0.42, 0.557, 0.137)
+orange = (1, 0.647, 0)
+orangered = (1, 0.271, 0)
+orchid = (0.855, 0.439, 0.839)
+palegoldenrod = (0.933, 0.91, 0.667)
+palegreen = (0.596, 0.984, 0.596)
+palevioletred = (0.686, 0.933, 0.933)
+papayawhip = (1, 0.937, 0.835)
+peachpuff = (1, 0.855, 0.725)
+peru = (0.804, 0.522, 0.247)
+pink = (1, 0.753, 0.796)
+plum = (0.867, 0.627, 0.867)
+powderblue = (0.69, 0.878, 0.902)
+purple = (0.502, 0, 0.502)
+red = (1, 0, 0)
+rosybrown = (0.737, 0.561, 0.561)
+royalblue = (0.255, 0.412, 0.882)
+saddlebrown = (0.545, 0.271, 0.0745)
+salmon = (0.98, 0.502, 0.447)
+sandybrown = (0.98, 0.643, 0.376)
+seagreen = (0.18, 0.545, 0.341)
+seashell = (1, 0.961, 0.933)
+sienna = (0.627, 0.322, 0.176)
+silver = (0.753, 0.753, 0.753)
+skyblue = (0.529, 0.808, 0.922)
+slateblue = (0.416, 0.353, 0.804)
+slategray = (0.439, 0.502, 0.565)
+slategrey = (0.439, 0.502, 0.565)
+snow = (1, 0.98, 0.98)
+springgreen = (0, 1, 0.498)
+steelblue = (0.275, 0.51, 0.706)
+tan = (0.824, 0.706, 0.549)
+teal = (0, 0.502, 0.502)
+thistle = (0.847, 0.749, 0.847)
+tomato = (1, 0.388, 0.278)
+turquoise = (0.251, 0.878, 0.816)
+violet = (0.933, 0.51, 0.933)
+wheat = (0.961, 0.871, 0.702)
+white = (1, 1, 1)
+whitesmoke = (0.961, 0.961, 0.961)
+yellow = (1, 1, 0)
+yellowgreen = (0.604, 0.804, 0.196)
diff --git a/python/cucim/src/cucim/skimage/color/tests/ciede2000_test_data.txt b/python/cucim/src/cucim/skimage/color/tests/ciede2000_test_data.txt
new file mode 100755
index 000000000..b7e3fd57c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/tests/ciede2000_test_data.txt
@@ -0,0 +1,38 @@
+# input, intermediate, and output values for CIEDE2000 dE function
+# data taken from "The CIEDE2000 Color-Difference Formula: Implementation Notes, ..." http://www.ece.rochester.edu/~gsharma/ciede2000/ciede2000noteCRNA.pdf
+# tab delimited data
+# pair	1	L1	a1	b1	ap1	cp1	hp1	hbar1	G	T	SL	SC	SH	RT	dE 2	L2	a2	b2	ap2	cp2	hp2
+1	1	50.0000	2.6772	-79.7751	2.6774	79.8200	271.9222	270.9611	0.0001	0.6907	1.0000	4.6578	1.8421	-1.7042	2.0425 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+2	1	50.0000	3.1571	-77.2803	3.1573	77.3448	272.3395	271.1698	0.0001	0.6843	1.0000	4.6021	1.8216	-1.7070	2.8615 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+3	1	50.0000	2.8361	-74.0200	2.8363	74.0743	272.1944	271.0972	0.0001	0.6865	1.0000	4.5285	1.8074	-1.7060	3.4412 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+4	1	50.0000	-1.3802	-84.2814	-1.3803	84.2927	269.0618	269.5309	0.0001	0.7357	1.0000	4.7584	1.9217	-1.6809	1.0000 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+5	1	50.0000	-1.1848	-84.8006	-1.1849	84.8089	269.1995	269.5997	0.0001	0.7335	1.0000	4.7700	1.9218	-1.6822	1.0000 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+6	1	50.0000	-0.9009	-85.5211	-0.9009	85.5258	269.3964	269.6982	0.0001	0.7303	1.0000	4.7862	1.9217	-1.6840	1.0000 2	50.0000	0.0000	-82.7485	0.0000	82.7485	270.0000
+7	1	50.0000	0.0000	0.0000	0.0000	0.0000	0.0000	126.8697	0.5000	1.2200	1.0000	1.0562	1.0229	0.0000	2.3669 2	50.0000	-1.0000	2.0000	-1.5000	2.5000	126.8697
+8	1	50.0000	-1.0000	2.0000	-1.5000	2.5000	126.8697	126.8697	0.5000	1.2200	1.0000	1.0562	1.0229	0.0000	2.3669 2	50.0000	0.0000	0.0000	0.0000	0.0000	0.0000
+9	1	50.0000	2.4900	-0.0010	3.7346	3.7346	359.9847	269.9854	0.4998	0.7212	1.0000	1.1681	1.0404	-0.0022	7.1792 2	50.0000	-2.4900	0.0009	-3.7346	3.7346	179.9862
+10	1	50.0000	2.4900	-0.0010	3.7346	3.7346	359.9847	269.9847	0.4998	0.7212	1.0000	1.1681	1.0404	-0.0022	7.1792 2	50.0000	-2.4900	0.0010	-3.7346	3.7346	179.9847
+11	1	50.0000	2.4900	-0.0010	3.7346	3.7346	359.9847	89.9839	0.4998	0.6175	1.0000	1.1681	1.0346	0.0000	7.2195 2	50.0000	-2.4900	0.0011	-3.7346	3.7346	179.9831
+12	1	50.0000	2.4900	-0.0010	3.7346	3.7346	359.9847	89.9831	0.4998	0.6175	1.0000	1.1681	1.0346	0.0000	7.2195 2	50.0000	-2.4900	0.0012	-3.7346	3.7346	179.9816
+13	1	50.0000	-0.0010	2.4900	-0.0015	2.4900	90.0345	180.0328	0.4998	0.9779	1.0000	1.1121	1.0365	0.0000	4.8045 2	50.0000	0.0009	-2.4900	0.0013	2.4900	270.0311
+14	1	50.0000	-0.0010	2.4900	-0.0015	2.4900	90.0345	180.0345	0.4998	0.9779	1.0000	1.1121	1.0365	0.0000	4.8045 2	50.0000	0.0010	-2.4900	0.0015	2.4900	270.0345
+15	1	50.0000	-0.0010	2.4900	-0.0015	2.4900	90.0345	0.0362	0.4998	1.3197	1.0000	1.1121	1.0493	0.0000	4.7461 2	50.0000	0.0011	-2.4900	0.0016	2.4900	270.0380
+16	1	50.0000	2.5000	0.0000	3.7496	3.7496	0.0000	315.0000	0.4998	0.8454	1.0000	1.1406	1.0396	-0.0001	4.3065 2	50.0000	0.0000	-2.5000	0.0000	2.5000	270.0000
+17	1	50.0000	2.5000	0.0000	3.4569	3.4569	0.0000	346.2470	0.3827	1.4453	1.1608	1.9547	1.4599	-0.0003	27.1492 2	73.0000	25.0000	-18.0000	34.5687	38.9743	332.4939
+18	1	50.0000	2.5000	0.0000	3.4954	3.4954	0.0000	51.7766	0.3981	0.6447	1.0640	1.7498	1.1612	0.0000	22.8977 2	61.0000	-5.0000	29.0000	-6.9907	29.8307	103.5532
+19	1	50.0000	2.5000	0.0000	3.5514	3.5514	0.0000	272.2362	0.4206	0.6521	1.0251	1.9455	1.2055	-0.8219	31.9030 2	56.0000	-27.0000	-3.0000	-38.3556	38.4728	184.4723
+20	1	50.0000	2.5000	0.0000	3.5244	3.5244	0.0000	11.9548	0.4098	1.1031	1.0400	1.9120	1.3353	0.0000	19.4535 2	58.0000	24.0000	15.0000	33.8342	37.0102	23.9095
+21	1	50.0000	2.5000	0.0000	3.7494	3.7494	0.0000	3.5056	0.4997	1.2616	1.0000	1.1923	1.0808	0.0000	1.0000 2	50.0000	3.1736	0.5854	4.7596	4.7954	7.0113
+22	1	50.0000	2.5000	0.0000	3.7493	3.7493	0.0000	0.0000	0.4997	1.3202	1.0000	1.1956	1.0861	0.0000	1.0000 2	50.0000	3.2972	0.0000	4.9450	4.9450	0.0000
+23	1	50.0000	2.5000	0.0000	3.7497	3.7497	0.0000	5.8190	0.4999	1.2197	1.0000	1.1486	1.0604	0.0000	1.0000 2	50.0000	1.8634	0.5757	2.7949	2.8536	11.6380
+24	1	50.0000	2.5000	0.0000	3.7493	3.7493	0.0000	1.9603	0.4997	1.2883	1.0000	1.1946	1.0836	0.0000	1.0000 2	50.0000	3.2592	0.3350	4.8879	4.8994	3.9206
+25	1	60.2574	-34.0099	36.2677	-34.0678	49.7590	133.2085	132.0835	0.0017	1.3010	1.1427	3.2946	1.9951	0.0000	1.2644 2	60.4626	-34.1751	39.4387	-34.2333	52.2238	130.9584
+26	1	63.0109	-31.0961	-5.8663	-32.6194	33.1427	190.1951	188.8221	0.0490	0.9402	1.1831	2.4549	1.4560	0.0000	1.2630 2	62.8187	-29.7946	-4.0864	-31.2542	31.5202	187.4490
+27	1	61.2901	3.7196	-5.3901	5.5668	7.7487	315.9240	310.0313	0.4966	0.6952	1.1586	1.3092	1.0717	-0.0032	1.8731 2	61.4292	2.2480	-4.9620	3.3644	5.9950	304.1385
+28	1	35.0831	-44.1164	3.7933	-44.3939	44.5557	175.1161	176.4290	0.0063	1.0168	1.2148	2.9105	1.6476	0.0000	1.8645 2	35.0232	-40.0716	1.5901	-40.3237	40.3550	177.7418
+29	1	22.7233	20.0904	-46.6940	20.1424	50.8532	293.3339	291.3809	0.0026	0.3636	1.4014	3.1597	1.2617	-1.2537	2.0373 2	23.0331	14.9730	-42.5619	15.0118	45.1317	289.4279
+30	1	36.4612	47.8580	18.3852	47.9197	51.3256	20.9901	21.8781	0.0013	0.9239	1.1943	3.3888	1.7357	0.0000	1.4146 2	36.2715	50.5065	21.2231	50.5716	54.8444	22.7660
+31	1	90.8027	-2.0831	1.4410	-3.1245	3.4408	155.2410	167.1011	0.4999	1.1546	1.6110	1.1329	1.0511	0.0000	1.4441 2	91.1528	-1.6435	0.0447	-2.4651	2.4655	178.9612
+32	1	90.9257	-0.5406	-0.9208	-0.8109	1.2270	228.6315	218.4363	0.5000	1.3916	1.5930	1.0620	1.0288	0.0000	1.5381 2	88.6381	-0.8985	-0.7239	-1.3477	1.5298	208.2412
+33	1	6.7747	-0.2908	-2.4247	-0.4362	2.4636	259.8025	263.0049	0.4999	0.9556	1.6517	1.1057	1.0337	-0.0004	0.6377 2	5.8714	-0.0985	-2.2286	-0.1477	2.2335	266.2073
+34	1	2.0776	0.0795	-1.1350	0.1192	1.1412	275.9978	268.0910	0.5000	0.7826	1.7246	1.0383	1.0100	0.0000	0.9082 2	0.9033	-0.0636	-0.5514	-0.0954	0.5596	260.18421
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_a_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_a_2.npy
new file mode 100755
index 000000000..cd1b81d15
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_a_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_10.npy
new file mode 100755
index 000000000..7eea857aa
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_2.npy
new file mode 100755
index 000000000..61270a05b
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d50_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_10.npy
new file mode 100755
index 000000000..566bc1105
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_2.npy
new file mode 100755
index 000000000..87a309b99
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d55_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_10.npy
new file mode 100755
index 000000000..4ac9c0ae7
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_2.npy
new file mode 100755
index 000000000..71e233d1b
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d65_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_10.npy
new file mode 100755
index 000000000..642ccaf68
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_2.npy
new file mode 100755
index 000000000..1ef368552
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_d75_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/lab_array_e_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_e_2.npy
new file mode 100755
index 000000000..f795d3be3
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/lab_array_e_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_a_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_a_2.npy
new file mode 100755
index 000000000..ae25c1194
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_a_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_10.npy
new file mode 100755
index 000000000..cf05a2adb
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_2.npy
new file mode 100755
index 000000000..8f3711f4e
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d50_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_10.npy
new file mode 100755
index 000000000..b2ce26acb
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_2.npy
new file mode 100755
index 000000000..88daa5db3
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d55_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_10.npy
new file mode 100755
index 000000000..43ce45abd
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_2.npy
new file mode 100755
index 000000000..75ed4792d
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d65_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_10.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_10.npy
new file mode 100755
index 000000000..a04d69659
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_10.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_2.npy
new file mode 100755
index 000000000..a2ef298cc
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_d75_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/data/luv_array_e_2.npy b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_e_2.npy
new file mode 100755
index 000000000..f2f4c2804
Binary files /dev/null and b/python/cucim/src/cucim/skimage/color/tests/data/luv_array_e_2.npy differ
diff --git a/python/cucim/src/cucim/skimage/color/tests/test_adapt_rgb.py b/python/cucim/src/cucim/skimage/color/tests/test_adapt_rgb.py
new file mode 100644
index 000000000..9096db9d0
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/tests/test_adapt_rgb.py
@@ -0,0 +1,95 @@
+from functools import partial
+
+import cupy as cp
+import numpy as np
+from skimage import data
+
+from cucim.skimage import color, filters, img_as_float, img_as_uint
+from cucim.skimage.color.adapt_rgb import adapt_rgb, each_channel, hsv_value
+
+# Down-sample image for quicker testing.
+COLOR_IMAGE = cp.asarray(data.astronaut()[::5, ::6])
+GRAY_IMAGE = cp.asarray(data.camera()[::5, ::5])
+
+SIGMA = 3
+smooth = partial(filters.gaussian, sigma=SIGMA)
+assert_allclose = partial(cp.testing.assert_allclose, atol=1e-8)
+
+
+@adapt_rgb(each_channel)
+def edges_each(image):
+    return filters.sobel(image)
+
+
+@adapt_rgb(each_channel)
+def smooth_each(image, sigma):
+    return filters.gaussian(image, sigma)
+
+
+@adapt_rgb(each_channel)
+def mask_each(image, mask):
+    result = image.copy()
+    result[mask] = 0
+    return result
+
+
+@adapt_rgb(hsv_value)
+def edges_hsv(image):
+    return filters.sobel(image)
+
+
+@adapt_rgb(hsv_value)
+def smooth_hsv(image, sigma):
+    return filters.gaussian(image, sigma)
+
+
+@adapt_rgb(hsv_value)
+def edges_hsv_uint(image):
+    return img_as_uint(filters.sobel(image))
+
+
+def test_gray_scale_image():
+    # We don't need to test both `hsv_value` and `each_channel` since
+    # `adapt_rgb` is handling gray-scale inputs.
+    assert_allclose(edges_each(GRAY_IMAGE), filters.sobel(GRAY_IMAGE))
+
+
+def test_each_channel():
+    filtered = edges_each(COLOR_IMAGE)
+    for i, channel in enumerate(cp.rollaxis(filtered, axis=-1)):
+        expected = img_as_float(filters.sobel(COLOR_IMAGE[:, :, i]))
+        assert_allclose(channel, expected)
+
+
+def test_each_channel_with_filter_argument():
+    filtered = smooth_each(COLOR_IMAGE, SIGMA)
+    for i, channel in enumerate(cp.rollaxis(filtered, axis=-1)):
+        assert_allclose(channel, smooth(COLOR_IMAGE[:, :, i]))
+
+
+def test_each_channel_with_asymmetric_kernel():
+    mask = cp.triu(cp.ones(COLOR_IMAGE.shape[:2], dtype=np.bool_))
+    mask_each(COLOR_IMAGE, mask)
+
+
+def test_hsv_value():
+    filtered = edges_hsv(COLOR_IMAGE)
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    assert_allclose(color.rgb2hsv(filtered)[:, :, 2], filters.sobel(value))
+
+
+def test_hsv_value_with_filter_argument():
+    filtered = smooth_hsv(COLOR_IMAGE, SIGMA)
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    assert_allclose(color.rgb2hsv(filtered)[:, :, 2], smooth(value))
+
+
+def test_hsv_value_with_non_float_output():
+    # Since `rgb2hsv` returns a float image and the result of the filtered
+    # result is inserted into the HSV image, we want to make sure there isn't
+    # a dtype mismatch.
+    filtered = edges_hsv_uint(COLOR_IMAGE)
+    filtered_value = color.rgb2hsv(filtered)[:, :, 2]
+    value = color.rgb2hsv(COLOR_IMAGE)[:, :, 2]
+    # Reduce tolerance because dtype conversion.
+    assert_allclose(filtered_value, filters.sobel(value), rtol=1e-5, atol=1e-5)
diff --git a/python/cucim/src/cucim/skimage/color/tests/test_colorconv.py b/python/cucim/src/cucim/skimage/color/tests/test_colorconv.py
new file mode 100644
index 000000000..7eb8cce32
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/tests/test_colorconv.py
@@ -0,0 +1,814 @@
+"""Tests for color conversion functions.
+
+Authors
+-------
+- the rgb2hsv test was written by Nicolas Pinto, 2009
+- other tests written by Ralf Gommers, 2009
+
+:license: modified BSD
+"""
+
+import colorsys
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_equal
+from skimage import data
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.testing import TestCase, fetch
+from cucim.skimage.color import (combine_stains, convert_colorspace, gray2rgb,
+                                 gray2rgba, hed2rgb, hsv2rgb, lab2lch, lab2rgb,
+                                 lab2xyz, lch2lab, luv2rgb, luv2xyz, rgb2gray,
+                                 rgb2hed, rgb2hsv, rgb2lab, rgb2luv,
+                                 rgb2rgbcie, rgb2xyz, rgb2ycbcr, rgb2ydbdr,
+                                 rgb2yiq, rgb2ypbpr, rgb2yuv, rgba2rgb,
+                                 rgbcie2rgb, separate_stains, xyz2lab, xyz2luv,
+                                 xyz2rgb, ycbcr2rgb, ydbdr2rgb, yiq2rgb,
+                                 ypbpr2rgb, yuv2rgb)
+from cucim.skimage.util import img_as_float, img_as_float32, img_as_ubyte
+
+
+class TestColorconv(TestCase):
+
+    img_rgb = cp.asarray(data.colorwheel())
+    img_grayscale = cp.asarray(data.camera())
+    # ftm: off
+    img_rgba = cp.array([[[0, 0.5, 1, 0],
+                          [0, 0.5, 1, 1],
+                          [0, 0.5, 1, 0.5]]]).astype(float)
+
+    colbars = cp.array([[1, 1, 0, 0, 1, 1, 0, 0],
+                        [1, 1, 1, 1, 0, 0, 0, 0],
+                        [1, 0, 1, 0, 1, 0, 1, 0]]).astype(float)
+
+    colbars_array = cp.swapaxes(colbars.reshape(3, 4, 2), 0, 2)
+    colbars_point75 = colbars * 0.75
+    colbars_point75_array = cp.swapaxes(colbars_point75.reshape(3, 4, 2), 0, 2)
+
+    xyz_array = cp.asarray([[[0.4124, 0.21260, 0.01930]],    # red
+                            [[0, 0, 0]],    # black
+                            [[.9505, 1., 1.089]],    # white
+                            [[.1805, .0722, .9505]],    # blue
+                            [[.07719, .15438, .02573]],    # green
+                            ])
+    lab_array = cp.asarray([[[53.233, 80.109, 67.220]],    # red
+                            [[0., 0., 0.]],    # black
+                            [[100.0, 0.005, -0.010]],    # white
+                            [[32.303, 79.197, -107.864]],    # blue
+                            [[46.229, -51.7, 49.898]],    # green
+                            ])
+
+    luv_array = cp.asarray([[[53.233, 175.053, 37.751]],   # red
+                            [[0., 0., 0.]],   # black
+                            [[100., 0.001, -0.017]],   # white
+                            [[32.303, -9.400, -130.358]],   # blue
+                            [[46.228, -43.774, 56.589]],   # green
+                            ])
+    # ftm: on
+
+    # RGBA to RGB
+    def test_rgba2rgb_conversion(self):
+        rgba = self.img_rgba
+        rgb = rgba2rgb(rgba)
+        # ftm: off
+        expected = cp.asarray([[[1, 1, 1],
+                                [0, 0.5, 1],
+                                [0.5, 0.75, 1]]]).astype(float)
+
+        # ftm: on
+        self.assertEqual(rgb.shape, expected.shape)
+        assert_array_almost_equal(rgb, expected)
+
+    def test_rgba2rgb_error_grayscale(self):
+        self.assertRaises(ValueError, rgba2rgb, self.img_grayscale)
+
+    def test_rgba2rgb_error_rgb(self):
+        self.assertRaises(ValueError, rgba2rgb, self.img_rgb)
+
+    def test_rgba2rgb_dtype(self):
+        rgba = self.img_rgba.astype("float64")
+        rgba32 = img_as_float32(rgba)
+
+        assert rgba2rgb(rgba).dtype == rgba.dtype
+        assert rgba2rgb(rgba32).dtype == rgba32.dtype
+
+    # RGB to HSV
+    def test_rgb2hsv_conversion(self):
+        rgb = img_as_float(self.img_rgb)[::16, ::16]
+        hsv = rgb2hsv(rgb).reshape(-1, 3)
+        # ground truth from colorsys
+        gt = np.asarray([colorsys.rgb_to_hsv(pt[0], pt[1], pt[2])
+                         for pt in cp.asnumpy(rgb).reshape(-1, 3)])
+        assert_array_almost_equal(hsv, gt)
+
+    def test_rgb2hsv_error_grayscale(self):
+        self.assertRaises(ValueError, rgb2hsv, self.img_grayscale)
+
+    def test_rgb2hsv_dtype(self):
+        rgb = img_as_float(self.img_rgb)
+        rgb32 = img_as_float32(self.img_rgb)
+
+        assert rgb2hsv(rgb).dtype == rgb.dtype
+        assert rgb2hsv(rgb32).dtype == rgb32.dtype
+
+    # HSV to RGB
+    def test_hsv2rgb_conversion(self):
+        rgb = self.img_rgb.astype("float32")[::16, ::16]
+        # create HSV image with colorsys
+        hsv = cp.asarray(
+            [
+                colorsys.rgb_to_hsv(pt[0], pt[1], pt[2])
+                for pt in rgb.reshape(-1, 3).get()
+            ]
+        ).reshape(rgb.shape)
+        # convert back to RGB and compare with original.
+        # relative precision for RGB -> HSV roundtrip is about 1e-6
+        assert_array_almost_equal(rgb, hsv2rgb(hsv), decimal=4)
+
+    def test_hsv2rgb_error_grayscale(self):
+        self.assertRaises(ValueError, hsv2rgb, self.img_grayscale)
+
+    def test_hsv2rgb_dtype(self):
+        rgb = self.img_rgb.astype("float32")[::16, ::16]
+        # create HSV image with colorsys
+        hsv = cp.asarray(
+            [
+                colorsys.rgb_to_hsv(pt[0], pt[1], pt[2])
+                for pt in rgb.reshape(-1, 3).get()
+            ],
+            dtype="float64",
+        ).reshape(rgb.shape)
+        hsv32 = hsv.astype("float32")
+
+        assert hsv2rgb(hsv).dtype == hsv.dtype
+        assert hsv2rgb(hsv32).dtype == hsv32.dtype
+
+    # RGB to XYZ
+    def test_rgb2xyz_conversion(self):
+        # ftm: off
+        gt = cp.asarray([[[0.950456, 1.      , 1.088754],    # noqa
+                          [0.538003, 0.787329, 1.06942 ],    # noqa
+                          [0.592876, 0.28484 , 0.969561],    # noqa
+                          [0.180423, 0.072169, 0.950227]],   # noqa
+                         [[0.770033, 0.927831, 0.138527],    # noqa
+                          [0.35758 , 0.71516 , 0.119193],    # noqa
+                          [0.412453, 0.212671, 0.019334],    # noqa
+                          [0.      , 0.      , 0.      ]]])  # noqa
+        # ftm: on
+        assert_array_almost_equal(rgb2xyz(self.colbars_array), gt)
+
+    # stop repeating the "raises" checks for all other functions that are
+    # implemented with color._convert()
+    def test_rgb2xyz_error_grayscale(self):
+        self.assertRaises(ValueError, rgb2xyz, self.img_grayscale)
+
+    def test_rgb2xyz_dtype(self):
+        img = self.colbars_array
+        img32 = img.astype('float32')
+
+        assert rgb2xyz(img).dtype == img.dtype
+        assert rgb2xyz(img32).dtype == img32.dtype
+
+    # XYZ to RGB
+    def test_xyz2rgb_conversion(self):
+        assert_array_almost_equal(xyz2rgb(rgb2xyz(self.colbars_array)),
+                                  self.colbars_array)
+
+    def test_xyz2rgb_dtype(self):
+        img = rgb2xyz(self.colbars_array)
+        img32 = img.astype('float32')
+
+        assert xyz2rgb(img).dtype == img.dtype
+        assert xyz2rgb(img32).dtype == img32.dtype
+
+    # RGB<->XYZ roundtrip on another image
+    def test_xyz_rgb_roundtrip(self):
+        img_rgb = img_as_float(self.img_rgb)
+        assert_array_almost_equal(xyz2rgb(rgb2xyz(img_rgb)), img_rgb)
+
+    # RGB<->HED roundtrip with ubyte image
+    def test_hed_rgb_roundtrip(self):
+        img_rgb = img_as_ubyte(self.img_rgb)
+        new = img_as_ubyte(hed2rgb(rgb2hed(img_rgb)))
+        assert_array_equal(new, img_rgb)
+
+    # RGB<->HED roundtrip with float image
+    def test_hed_rgb_float_roundtrip(self):
+        img_rgb = img_as_float(self.img_rgb)
+        assert_array_almost_equal(hed2rgb(rgb2hed(img_rgb)), img_rgb)
+
+    # RGB<->HDX roundtrip with ubyte image
+    def test_hdx_rgb_roundtrip(self):
+        from cucim.skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
+        img_rgb = self.img_rgb
+        conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
+                              rgb_from_hdx)
+        assert_array_equal(img_as_ubyte(conv), img_rgb)
+
+    # RGB<->HDX roundtrip with float image
+    def test_hdx_rgb_roundtrip_float(self):
+        from cucim.skimage.color.colorconv import hdx_from_rgb, rgb_from_hdx
+
+        img_rgb = img_as_float(self.img_rgb)
+        conv = combine_stains(separate_stains(img_rgb, hdx_from_rgb),
+                              rgb_from_hdx)
+        assert_array_almost_equal(conv, img_rgb)
+
+    # RGB to RGB CIE
+    def test_rgb2rgbcie_conversion(self):
+        # ftm: off
+        gt = cp.asarray([[[ 0.1488856 ,  0.18288098,  0.19277574],    # noqa
+                          [ 0.01163224,  0.16649536,  0.18948516],    # noqa
+                          [ 0.12259182,  0.03308008,  0.17298223],    # noqa
+                          [-0.01466154,  0.01669446,  0.16969164]],   # noqa
+                         [[ 0.16354714,  0.16618652,  0.0230841 ],    # noqa
+                          [ 0.02629378,  0.1498009 ,  0.01979351],    # noqa
+                          [ 0.13725336,  0.01638562,  0.00329059],    # noqa
+                          [ 0.        ,  0.        ,  0.        ]]])  # noqa
+        # ftm: on
+        assert_array_almost_equal(rgb2rgbcie(self.colbars_array), gt)
+
+    def test_rgb2rgbcie_dtype(self):
+        img = self.colbars_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgb2rgbcie(img).dtype == img.dtype
+        assert rgb2rgbcie(img32).dtype == img32.dtype
+
+    # RGB CIE to RGB
+    def test_rgbcie2rgb_conversion(self):
+        # only roundtrip test, we checked rgb2rgbcie above already
+        assert_array_almost_equal(rgbcie2rgb(rgb2rgbcie(self.colbars_array)),
+                                  self.colbars_array)
+
+    def test_rgbcie2rgb_dtype(self):
+        img = rgb2rgbcie(self.colbars_array).astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgbcie2rgb(img).dtype == img.dtype
+        assert rgbcie2rgb(img32).dtype == img32.dtype
+
+    def test_convert_colorspace(self):
+        colspaces = ['HSV', 'RGB CIE', 'XYZ', 'YCbCr', 'YPbPr', 'YDbDr']
+        colfuncs_from = [
+            hsv2rgb, rgbcie2rgb, xyz2rgb,
+            ycbcr2rgb, ypbpr2rgb, ydbdr2rgb
+        ]
+        colfuncs_to = [
+            rgb2hsv, rgb2rgbcie, rgb2xyz,
+            rgb2ycbcr, rgb2ypbpr, rgb2ydbdr
+        ]
+
+        assert_array_almost_equal(
+            convert_colorspace(self.colbars_array, 'RGB', 'RGB'),
+            self.colbars_array)
+
+        for i, space in enumerate(colspaces):
+            # print(f"space={space}")
+            gt = colfuncs_from[i](self.colbars_array)
+            assert_array_almost_equal(
+                convert_colorspace(self.colbars_array, space, 'RGB'), gt)
+            gt = colfuncs_to[i](self.colbars_array)
+            assert_array_almost_equal(
+                convert_colorspace(self.colbars_array, 'RGB', space), gt)
+
+        self.assertRaises(ValueError, convert_colorspace,
+                          self.colbars_array, 'nokey', 'XYZ')
+        self.assertRaises(ValueError, convert_colorspace,
+                          self.colbars_array, 'RGB', 'nokey')
+
+    def test_rgb2gray(self):
+        x = cp.asarray([1, 1, 1]).reshape((1, 1, 3)).astype(float)
+        g = rgb2gray(x)
+        assert_array_almost_equal(g, 1)
+
+        assert_array_equal(g.shape, (1, 1))
+
+    def test_rgb2gray_contiguous(self):
+        x = cp.random.rand(10, 10, 3)
+        assert rgb2gray(x).flags["C_CONTIGUOUS"]
+        assert rgb2gray(x[:5, :5]).flags["C_CONTIGUOUS"]
+
+    def test_rgb2gray_alpha(self):
+        x = cp.random.rand(10, 10, 4)
+        with expected_warnings(['Non RGB image conversion']):
+            assert rgb2gray(x).ndim == 2
+
+    def test_rgb2gray_on_gray(self):
+        with expected_warnings(['The behavior of rgb2gray will change']):
+            rgb2gray(cp.random.rand(5, 5))
+
+    def test_rgb2gray_dtype(self):
+        img = cp.random.rand(10, 10, 3).astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgb2gray(img).dtype == img.dtype
+        assert rgb2gray(img32).dtype == img32.dtype
+
+    # test matrices for xyz2lab and lab2xyz generated using
+    # http://www.easyrgb.com/index.php?X=CALC
+    # Note: easyrgb website displays xyz*100
+    def test_xyz2lab(self):
+        assert_array_almost_equal(xyz2lab(self.xyz_array),
+                                  self.lab_array, decimal=3)
+
+        # Test the conversion with the rest of the illuminants.
+        for i in ["d50", "d55", "d65", "d75"]:
+            for obs in ["2", "10"]:
+                fname = "color/tests/data/lab_array_{0}_{1}.npy".format(i, obs)
+                lab_array_i_obs = np.load(fetch(fname))
+                assert_array_almost_equal(lab_array_i_obs,
+                                          xyz2lab(self.xyz_array, i, obs),
+                                          decimal=2)
+        for i in ["a", "e"]:
+            fname = "color/tests/data/lab_array_{0}_2.npy".format(i)
+            lab_array_i_obs = np.load(fetch(fname))
+            assert_array_almost_equal(lab_array_i_obs,
+                                      xyz2lab(self.xyz_array, i, "2"),
+                                      decimal=2)
+
+    def test_xyz2lab_dtype(self):
+        img = self.xyz_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert xyz2lab(img).dtype == img.dtype
+        assert xyz2lab(img32).dtype == img32.dtype
+
+    def test_lab2xyz(self):
+        assert_array_almost_equal(lab2xyz(self.lab_array),
+                                  self.xyz_array, decimal=3)
+
+        # Test the conversion with the rest of the illuminants.
+        for i in ["d50", "d55", "d65", "d75"]:
+            for obs in ["2", "10"]:
+                fname = "color/tests/data/lab_array_{0}_{1}.npy".format(i, obs)
+                lab_array_i_obs = cp.array(np.load(fetch(fname)))
+                assert_array_almost_equal(lab2xyz(lab_array_i_obs, i, obs),
+                                          self.xyz_array, decimal=3)
+        for i in ["a", "e"]:
+            fname = "lab_array_{0}_2.npy".format(i)
+            lab_array_i_obs = cp.array(
+                np.load(fetch('color/tests/data/' + fname)))
+            assert_array_almost_equal(lab2xyz(lab_array_i_obs, i, "2"),
+                                      self.xyz_array, decimal=3)
+
+        # And we include a call to test the exception handling in the code.
+        try:
+            lab2xyz(lab_array_i_obs, "Nai", "2")  # Not an illuminant
+        except ValueError:
+            pass
+
+        try:
+            lab2xyz(lab_array_i_obs, "d50", "42")  # Not a degree
+        except ValueError:
+            pass
+
+    def test_lab2xyz_dtype(self):
+        img = self.lab_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert lab2xyz(img).dtype == img.dtype
+        assert lab2xyz(img32).dtype == img32.dtype
+
+    def test_rgb2lab_brucelindbloom(self):
+        """
+        Test the RGB->Lab conversion by comparing to the calculator on the
+        authoritative Bruce Lindbloom
+        [website](http://brucelindbloom.com/index.html?ColorCalculator.html).
+        """
+        # Obtained with D65 white point, sRGB model and gamma
+        # ftm: off
+        gt_for_colbars = cp.asarray([
+            [100, 0, 0],
+            [97.1393, -21.5537, 94.4780],
+            [91.1132, -48.0875, -14.1312],
+            [87.7347, -86.1827, 83.1793],
+            [60.3242, 98.2343, -60.8249],
+            [53.2408, 80.0925, 67.2032],
+            [32.2970, 79.1875, -107.8602],
+            [0, 0, 0]]).T
+
+        # ftm: on
+        gt_array = cp.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2)
+        assert_array_almost_equal(rgb2lab(self.colbars_array), gt_array,
+                                  decimal=2)
+
+    def test_lab_rgb_roundtrip(self):
+        img_rgb = img_as_float(self.img_rgb)
+        assert_array_almost_equal(lab2rgb(rgb2lab(img_rgb)), img_rgb)
+
+    def test_rgb2lab_dtype(self):
+        img = self.colbars_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgb2lab(img).dtype == img.dtype
+        assert rgb2lab(img32).dtype == img32.dtype
+
+    def test_lab2rgb_dtype(self):
+        img = self.lab_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert lab2rgb(img).dtype == img.dtype
+        assert lab2rgb(img32).dtype == img32.dtype
+
+    # test matrices for xyz2luv and luv2xyz generated using
+    # http://www.easyrgb.com/index.php?X=CALC
+    # Note: easyrgb website displays xyz*100
+    def test_xyz2luv(self):
+        assert_array_almost_equal(xyz2luv(self.xyz_array),
+                                  self.luv_array, decimal=3)
+
+        # Test the conversion with the rest of the illuminants.
+        for i in ["d50", "d55", "d65", "d75"]:
+            for obs in ["2", "10"]:
+                fname = "color/tests/data/luv_array_{0}_{1}.npy".format(i, obs)
+                luv_array_i_obs = cp.array(np.load(fetch(fname)))
+                assert_array_almost_equal(luv_array_i_obs,
+                                          xyz2luv(self.xyz_array, i, obs),
+                                          decimal=2)
+        for i in ["a", "e"]:
+            fname = "color/tests/data/luv_array_{0}_2.npy".format(i)
+            luv_array_i_obs = cp.array(np.load(fetch(fname)))
+            assert_array_almost_equal(luv_array_i_obs,
+                                      xyz2luv(self.xyz_array, i, "2"),
+                                      decimal=2)
+
+    def test_xyz2luv_dtype(self):
+        img = self.xyz_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert xyz2luv(img).dtype == img.dtype
+        assert xyz2luv(img32).dtype == img32.dtype
+
+    def test_luv2xyz(self):
+        assert_array_almost_equal(luv2xyz(self.luv_array),
+                                  self.xyz_array, decimal=3)
+
+        # Test the conversion with the rest of the illuminants.
+        for i in ["d50", "d55", "d65", "d75"]:
+            for obs in ["2", "10"]:
+                fname = "color/tests/data/luv_array_{0}_{1}.npy".format(i, obs)
+                luv_array_i_obs = cp.array(np.load(fetch(fname)))
+                assert_array_almost_equal(luv2xyz(luv_array_i_obs, i, obs),
+                                          self.xyz_array, decimal=3)
+        for i in ["a", "e"]:
+            fname = "color/tests/data/luv_array_{0}_2.npy".format(i)
+            luv_array_i_obs = cp.array(np.load(fetch(fname)))
+            assert_array_almost_equal(luv2xyz(luv_array_i_obs, i, "2"),
+                                      self.xyz_array, decimal=3)
+
+    def test_luv2xyz_dtype(self):
+        img = self.luv_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert luv2xyz(img).dtype == img.dtype
+        assert luv2xyz(img32).dtype == img32.dtype
+
+    def test_rgb2luv_brucelindbloom(self):
+        """
+        Test the RGB->Lab conversion by comparing to the calculator on the
+        authoritative Bruce Lindbloom
+        [website](http://brucelindbloom.com/index.html?ColorCalculator.html).
+        """
+        # Obtained with D65 white point, sRGB model and gamma
+        # ftm: off
+        gt_for_colbars = cp.asarray([
+            [100, 0, 0],
+            [97.1393, 7.7056, 106.7866],
+            [91.1132, -70.4773, -15.2042],
+            [87.7347, -83.0776, 107.3985],
+            [60.3242, 84.0714, -108.6834],
+            [53.2408, 175.0151, 37.7564],
+            [32.2970, -9.4054, -130.3423],
+            [0, 0, 0]]).T
+
+        # ftm: on
+        gt_array = cp.swapaxes(gt_for_colbars.reshape(3, 4, 2), 0, 2)
+        assert_array_almost_equal(rgb2luv(self.colbars_array),
+                                  gt_array, decimal=2)
+
+    def test_rgb2luv_dtype(self):
+        img = self.colbars_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgb2luv(img).dtype == img.dtype
+        assert rgb2luv(img32).dtype == img32.dtype
+
+    def test_luv2rgb_dtype(self):
+        img = self.luv_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert luv2rgb(img).dtype == img.dtype
+        assert luv2rgb(img32).dtype == img32.dtype
+
+    def test_luv_rgb_roundtrip(self):
+        img_rgb = img_as_float(self.img_rgb)
+        assert_array_almost_equal(luv2rgb(rgb2luv(img_rgb)), img_rgb)
+
+    def test_lab_rgb_outlier(self):
+        lab_array = np.ones((3, 1, 3))
+        lab_array[0] = [50, -12, 85]
+        lab_array[1] = [50, 12, -85]
+        lab_array[2] = [90, -4, -47]
+        lab_array = cp.asarray(lab_array)
+        # ftm: off
+        rgb_array = cp.asarray([[[0.501, 0.481, 0]],
+                                [[0, 0.482, 1.]],
+                                [[0.578, 0.914, 1.]],
+                                ])
+
+        # ftm: on
+        assert_array_almost_equal(lab2rgb(lab_array), rgb_array, decimal=3)
+
+    def test_lab_full_gamut(self):
+        a, b = cp.meshgrid(cp.arange(-100, 100), cp.arange(-100, 100))
+        L = cp.ones(a.shape)
+        lab = cp.dstack((L, a, b))
+        for value in [0, 10, 20]:
+            lab[:, :, 0] = value
+            with expected_warnings(['Color data out of range']):
+                lab2xyz(lab)
+
+    def test_lab_lch_roundtrip(self):
+        rgb = img_as_float(self.img_rgb)
+        lab = rgb2lab(rgb)
+        lab2 = lch2lab(lab2lch(lab))
+        assert_array_almost_equal(lab2, lab)
+
+    def test_rgb_lch_roundtrip(self):
+        rgb = img_as_float(self.img_rgb)
+        lab = rgb2lab(rgb)
+        lch = lab2lch(lab)
+        lab2 = lch2lab(lch)
+        rgb2 = lab2rgb(lab2)
+        assert_array_almost_equal(rgb, rgb2)
+
+    def test_lab_lch_0d(self):
+        lab0 = self._get_lab0()
+        lch0 = lab2lch(lab0)
+        lch2 = lab2lch(lab0[None, None, :])
+        assert_array_almost_equal(lch0, lch2[0, 0, :])
+
+    def test_lab_lch_1d(self):
+        lab0 = self._get_lab0()
+        lch0 = lab2lch(lab0)
+        lch1 = lab2lch(lab0[None, :])
+        assert_array_almost_equal(lch0, lch1[0, :])
+
+    def test_lab_lch_3d(self):
+        lab0 = self._get_lab0()
+        lch0 = lab2lch(lab0)
+        lch3 = lab2lch(lab0[None, None, None, :])
+        assert_array_almost_equal(lch0, lch3[0, 0, 0, :])
+
+    def _get_lab0(self):
+        rgb = img_as_float(self.img_rgb[:1, :1, :])
+        return rgb2lab(rgb)[0, 0, :]
+
+    def test_yuv(self):
+        rgb = cp.asarray([[[1.0, 1.0, 1.0]]])
+        assert_array_almost_equal(rgb2yuv(rgb), cp.asarray([[[1, 0, 0]]]))
+        assert_array_almost_equal(rgb2yiq(rgb), cp.asarray([[[1, 0, 0]]]))
+        assert_array_almost_equal(rgb2ypbpr(rgb), cp.asarray([[[1, 0, 0]]]))
+        assert_array_almost_equal(
+            rgb2ycbcr(rgb), cp.asarray([[[235, 128, 128]]])
+        )
+        assert_array_almost_equal(rgb2ydbdr(rgb), cp.asarray([[[1, 0, 0]]]))
+        rgb = cp.asarray([[[0.0, 1.0, 0.0]]])
+        assert_array_almost_equal(
+            rgb2yuv(rgb), cp.asarray([[[0.587, -0.28886916, -0.51496512]]])
+        )
+        assert_array_almost_equal(
+            rgb2yiq(rgb), cp.asarray([[[0.587, -0.27455667, -0.52273617]]])
+        )
+        assert_array_almost_equal(
+            rgb2ypbpr(rgb), cp.asarray([[[0.587, -0.331264, -0.418688]]])
+        )
+        assert_array_almost_equal(
+            rgb2ycbcr(rgb), cp.asarray([[[144.553, 53.797, 34.214]]])
+        )
+        assert_array_almost_equal(
+            rgb2ydbdr(rgb), cp.asarray([[[0.587, -0.883, 1.116]]])
+        )
+
+    def test_yuv_roundtrip(self):
+        img_rgb = img_as_float(self.img_rgb)[::16, ::16]
+        assert_array_almost_equal(yuv2rgb(rgb2yuv(img_rgb)), img_rgb)
+        assert_array_almost_equal(yiq2rgb(rgb2yiq(img_rgb)), img_rgb)
+        assert_array_almost_equal(ypbpr2rgb(rgb2ypbpr(img_rgb)), img_rgb)
+        assert_array_almost_equal(ycbcr2rgb(rgb2ycbcr(img_rgb)), img_rgb)
+        assert_array_almost_equal(ydbdr2rgb(rgb2ydbdr(img_rgb)), img_rgb)
+
+    def test_rgb2yuv_dtype(self):
+        img = self.colbars_array.astype('float64')
+        img32 = img.astype('float32')
+
+        assert rgb2yuv(img).dtype == img.dtype
+        assert rgb2yuv(img32).dtype == img32.dtype
+
+    def test_yuv2rgb_dtype(self):
+        img = rgb2yuv(self.colbars_array).astype('float64')
+        img32 = img.astype('float32')
+
+        assert yuv2rgb(img).dtype == img.dtype
+        assert yuv2rgb(img32).dtype == img32.dtype
+
+    def test_rgb2yiq_conversion(self):
+        rgb = img_as_float(self.img_rgb)[::16, ::16]
+        yiq = rgb2yiq(rgb).reshape(-1, 3)
+        gt = np.asarray([colorsys.rgb_to_yiq(pt[0], pt[1], pt[2])
+                         for pt in cp.asnumpy(rgb).reshape(-1, 3)]
+                        )
+        assert_array_almost_equal(yiq, gt, decimal=2)
+
+
+def test_gray2rgb():
+    x = cp.asarray([0, 0.5, 1])
+    w = gray2rgb(x)
+    # fmt off
+    expected_output = cp.asarray([[0, 0, 0],
+                                  [0.5, 0.5, 0.5],
+                                  [1, 1, 1]])
+    # fmt on
+    assert_array_equal(w, expected_output)
+
+    x = x.reshape((3, 1))
+    y = gray2rgb(x)
+
+    assert_array_equal(y.shape, (3, 1, 3))
+    assert_array_equal(y.dtype, x.dtype)
+    assert_array_equal(y[..., 0], x)
+    assert_array_equal(y[0, 0, :], [0, 0, 0])
+
+    x = cp.asarray([[0, 128, 255]], dtype=np.uint8)
+    z = gray2rgb(x)
+
+    assert_array_equal(z.shape, (1, 3, 3))
+    assert_array_equal(z[..., 0], x)
+    assert_array_equal(z[0, 1, :], [128, 128, 128])
+
+
+def test_gray2rgb_rgb():
+    x = cp.random.rand(5, 5, 4)
+    with expected_warnings(['Pass-through of possibly RGB images']):
+        y = gray2rgb(x)
+    assert_array_equal(x, y)
+
+
+def test_gray2rgb_alpha():
+    x = cp.random.random((5, 5, 4))
+    with expected_warnings(['Pass-through of possibly RGB images']):
+        assert_equal(gray2rgb(x, alpha=None).shape, (5, 5, 4))
+    with expected_warnings(['Pass-through of possibly RGB images',
+                            'alpha argument is deprecated']):
+        assert_equal(gray2rgb(x, alpha=False).shape, (5, 5, 3))
+    with expected_warnings(['Pass-through of possibly RGB images',
+                            'alpha argument is deprecated']):
+        assert_equal(gray2rgb(x, alpha=True).shape, (5, 5, 4))
+
+    x = cp.random.random((5, 5, 3))
+    with expected_warnings(['Pass-through of possibly RGB images']):
+        assert_equal(gray2rgb(x, alpha=None).shape, (5, 5, 3))
+    with expected_warnings(['Pass-through of possibly RGB images',
+                            'alpha argument is deprecated']):
+        assert_equal(gray2rgb(x, alpha=False).shape, (5, 5, 3))
+    with expected_warnings(['Pass-through of possibly RGB images',
+                            'alpha argument is deprecated']):
+        assert_equal(gray2rgb(x, alpha=True).shape, (5, 5, 4))
+
+    with expected_warnings(['alpha argument is deprecated']):
+        assert_array_equal(gray2rgb(cp.asarray([[1, 2], [3, 4.]]),
+                           alpha=True)[0, 0, 3], 1)
+    with expected_warnings(['alpha argument is deprecated']):
+        assert_array_equal(
+            gray2rgb(cp.asarray([[1, 2], [3, 4]], dtype=np.uint8),
+                     alpha=True)[0, 0, 3],
+            255)
+
+
+@pytest.mark.parametrize("shape", [(5, 5), (5, 5, 4), (5, 4, 5, 4)])
+def test_gray2rgba(shape):
+    # nD case
+    img = cp.random.random(shape)
+    rgba = gray2rgba(img)
+
+    # Shape check
+    assert_equal(rgba.shape, shape + (4,))
+
+    # dtype check
+    assert rgba.dtype == img.dtype
+
+    # RGB channels check
+    for channel in range(3):
+        assert_array_equal(rgba[..., channel], img)
+
+    # Alpha channel check
+    assert_array_equal(rgba[..., 3], 1.0)
+
+
+def test_gray2rgba_dtype():
+    img_f64 = cp.random.random((5, 5))
+    img_f32 = img_f64.astype('float32')
+    img_u8 = img_as_ubyte(img_f64)
+    img_int = img_u8.astype(int)
+
+    for img in [img_f64, img_f32, img_u8, img_int]:
+        assert gray2rgba(img).dtype == img.dtype
+
+
+def test_gray2rgba_alpha():
+    img = cp.random.random((5, 5))
+    img_u8 = img_as_ubyte(img)
+
+    # Default
+    alpha = None
+    rgba = gray2rgba(img, alpha)
+
+    assert_array_equal(rgba[..., :3], gray2rgb(img))
+    assert_array_equal(rgba[..., 3], 1.0)
+
+    # Scalar
+    alpha = 0.5
+    rgba = gray2rgba(img, alpha)
+
+    assert_array_equal(rgba[..., :3], gray2rgb(img))
+    assert_array_equal(rgba[..., 3], alpha)
+
+    # Array
+    alpha = cp.random.random((5, 5))
+    rgba = gray2rgba(img, alpha)
+
+    assert_array_equal(rgba[..., :3], gray2rgb(img))
+    assert_array_equal(rgba[..., 3], alpha)
+
+    # Warning about alpha cast
+    alpha = 0.5
+    with expected_warnings(["alpha can't be safely cast to image dtype"]):
+        rgba = gray2rgba(img_u8, alpha)
+        assert_array_equal(rgba[..., :3], gray2rgb(img_u8))
+
+    # Invalid shape
+    alpha = cp.random.random((5, 5, 1))
+    # expected_err_msg = ("could not broadcast input array from shape (5,5,1) "
+    #                     "into shape (5,5)")
+
+    with pytest.raises(ValueError):  # as err:
+        rgba = gray2rgba(img, alpha)
+    # assert expected_err_msg == str(err.value)
+
+
+@pytest.mark.parametrize("func", [rgb2gray, gray2rgb, gray2rgba])
+@pytest.mark.parametrize("shape", ([(3, ), (2, 3), (4, 5, 3), (5, 4, 5, 3),
+                                    (4, 5, 4, 5, 3)]))
+def test_nD_gray_conversion(func, shape):
+    img = cp.random.rand(*shape)
+
+    msg_list = []
+    if img.ndim == 3 and func == gray2rgb:
+        msg_list.append('Pass-through of possibly RGB images in gray2rgb')
+    elif img.ndim == 2 and func == rgb2gray:
+        msg_list.append('The behavior of rgb2gray will change')
+
+    with expected_warnings(msg_list):
+        out = func(img)
+
+    common_ndim = min(out.ndim, len(shape))
+
+    assert out.shape[:common_ndim] == shape[:common_ndim]
+
+
+@pytest.mark.parametrize("func", [rgb2hsv, hsv2rgb,
+                                  rgb2xyz, xyz2rgb,
+                                  rgb2hed, hed2rgb,
+                                  rgb2rgbcie, rgbcie2rgb,
+                                  xyz2lab, lab2xyz,
+                                  lab2rgb, rgb2lab,
+                                  xyz2luv, luv2xyz,
+                                  luv2rgb, rgb2luv,
+                                  lab2lch, lch2lab,
+                                  rgb2yuv, yuv2rgb,
+                                  rgb2yiq, yiq2rgb,
+                                  rgb2ypbpr, ypbpr2rgb,
+                                  rgb2ycbcr, ycbcr2rgb,
+                                  rgb2ydbdr, ydbdr2rgb])
+@pytest.mark.parametrize("shape", ([(3, ), (2, 3), (4, 5, 3), (5, 4, 5, 3),
+                                    (4, 5, 4, 5, 3)]))
+def test_nD_color_conversion(func, shape):
+    img = cp.random.rand(*shape)
+    out = func(img)
+
+    assert out.shape == img.shape
+
+
+@pytest.mark.parametrize("shape", ([(4, ), (2, 4), (4, 5, 4), (5, 4, 5, 4),
+                                    (4, 5, 4, 5, 4)]))
+def test_rgba2rgb_nD(shape):
+    img = cp.random.rand(*shape)
+    out = rgba2rgb(img)
+
+    expected_shape = shape[:-1] + (3,)
+
+    assert out.shape == expected_shape
diff --git a/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py b/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py
new file mode 100644
index 000000000..acfcb60f9
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/tests/test_colorlabel.py
@@ -0,0 +1,224 @@
+import itertools
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+
+from cucim.skimage._shared.testing import assert_no_warnings
+from cucim.skimage.color.colorlabel import label2rgb
+
+
+def test_deprecation_warning():
+
+    image = cp.ones((3, 3))
+    label = cp.ones((3, 3))
+
+    with pytest.warns(FutureWarning) as record:
+        label2rgb(image, label)
+
+    expected_msg = "The new recommended value"
+
+    assert str(record[0].message).startswith(expected_msg)
+
+
+def test_shape_mismatch():
+    image = cp.ones((3, 3))
+    label = cp.ones((2, 2))
+    with pytest.raises(ValueError):
+        label2rgb(image, label, bg_label=-1)
+
+
+def test_wrong_kind():
+    label = cp.ones((3, 3))
+    # Must not raise an error.
+    label2rgb(label, bg_label=-1)
+    # kind='foo' is wrong.
+    with pytest.raises(ValueError):
+        label2rgb(label, kind="foo", bg_label=-1)
+
+
+def test_uint_image():
+    img = cp.random.randint(0, 255, (10, 10), dtype=cp.uint8)
+    labels = cp.zeros((10, 10), dtype=cp.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    output = label2rgb(labels, image=img, bg_label=0)
+    # Make sure that the output is made of floats and in the correct range
+    assert cp.issubdtype(output.dtype, cp.floating)
+    assert output.max() <= 1
+
+
+def test_rgb():
+    image = cp.ones((1, 3))
+    label = cp.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb = label2rgb(label, image=image, colors=colors, alpha=1,
+                    image_alpha=1, bg_label=-1)
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_alpha():
+    image = cp.random.uniform(size=(3, 3))
+    label = cp.random.randint(0, 9, size=(3, 3))
+    # If we set `alpha = 0`, then rgb should match image exactly.
+    rgb = label2rgb(label, image=image, alpha=0, image_alpha=1,
+                    bg_label=-1)
+    assert_array_almost_equal(rgb[..., 0], image)
+    assert_array_almost_equal(rgb[..., 1], image)
+    assert_array_almost_equal(rgb[..., 2], image)
+
+
+def test_no_input_image():
+    label = cp.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    rgb = label2rgb(label, colors=colors, bg_label=-1)
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_image_alpha():
+    image = cp.random.uniform(size=(1, 3))
+    label = cp.arange(3).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # If we set `image_alpha = 0`, then rgb should match label colors exactly.
+    rgb = label2rgb(label, image=image, colors=colors, alpha=1,
+                    image_alpha=0, bg_label=-1)
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_color_names():
+    image = cp.ones((1, 3))
+    label = cp.arange(3).reshape(1, -1)
+    cnames = ['red', 'lime', 'blue']
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb = label2rgb(label, image=image, colors=cnames, alpha=1,
+                    image_alpha=1, bg_label=-1)
+    assert_array_almost_equal(rgb, [colors])
+
+
+def test_bg_and_color_cycle():
+    image = cp.zeros((1, 10))  # dummy image
+    label = cp.arange(10).reshape(1, -1)
+    colors = [(1, 0, 0), (0, 0, 1)]
+    bg_color = (0, 0, 0)
+    rgb = label2rgb(label, image=image, bg_label=0, bg_color=bg_color,
+                    colors=colors, alpha=1)
+    assert_array_almost_equal(rgb[0, 0], bg_color)
+    for pixel, color in zip(rgb[0, 1:], itertools.cycle(colors)):
+        assert_array_almost_equal(pixel, color)
+
+
+def test_negative_labels():
+    labels = cp.array([0, -1, -2, 0])
+    rout = cp.array([(0., 0., 0.), (0., 0., 1.), (1., 0., 0.), (0., 0., 0.)])
+    assert_array_almost_equal(
+        rout, label2rgb(labels, bg_label=0, alpha=1, image_alpha=1))
+
+
+def test_nonconsecutive():
+    labels = cp.array([0, 2, 4, 0])
+    colors = [(1, 0, 0), (0, 0, 1)]
+    rout = cp.array([(1., 0., 0.), (0., 0., 1.), (1., 0., 0.), (1., 0., 0.)])
+    assert_array_almost_equal(
+        rout, label2rgb(labels, colors=colors, alpha=1,
+                        image_alpha=1, bg_label=-1))
+
+
+def test_label_consistency():
+    """Assert that the same labels map to the same colors."""
+    label_1 = cp.arange(5).reshape(1, -1)
+    label_2 = cp.array([0, 1])
+    colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1)]
+    # Set alphas just in case the defaults change
+    rgb_1 = label2rgb(label_1, colors=colors, bg_label=-1)
+    rgb_2 = label2rgb(label_2, colors=colors, bg_label=-1)
+    for label_id in label_2.ravel():
+        assert_array_almost_equal(rgb_1[label_1 == label_id],
+                                  rgb_2[label_2 == label_id])
+
+
+def test_leave_labels_alone():
+    labels = cp.array([-1, 0, 1])
+    labels_saved = labels.copy()
+
+    label2rgb(labels, bg_label=-1)
+    label2rgb(labels, bg_label=1)
+    assert_array_equal(labels, labels_saved)
+
+
+# TODO: diagnose test error that occurs only with CUB enabled: CuPy bug?
+def test_avg():
+    # label image
+    # fmt: off
+    label_field = cp.asarray([[1, 1, 1, 2],
+                              [1, 2, 2, 2],
+                              [3, 3, 4, 4]], dtype=np.uint8)
+
+    # color image
+    r = cp.asarray([[1., 1., 0., 0.],
+                    [0., 0., 1., 1.],
+                    [0., 0., 0., 0.]])
+    g = cp.asarray([[0., 0., 0., 1.],
+                    [1., 1., 1., 0.],
+                    [0., 0., 0., 0.]])
+    b = cp.asarray([[0., 0., 0., 1.],
+                    [0., 1., 1., 1.],
+                    [0., 0., 1., 1.]])
+    image = cp.dstack((r, g, b))
+
+    # reference label-colored image
+    rout = cp.asarray([[0.5, 0.5, 0.5, 0.5],
+                       [0.5, 0.5, 0.5, 0.5],
+                       [0. , 0. , 0. , 0. ]])   # noqa
+    gout = cp.asarray([[0.25, 0.25, 0.25, 0.75],
+                       [0.25, 0.75, 0.75, 0.75],
+                       [0. , 0. , 0. , 0.  ]])  # noqa
+    bout = cp.asarray([[0. , 0. , 0. , 1. ],    # noqa
+                       [0. , 1. , 1. , 1. ],    # noqa
+                       [0.0, 0.0, 1.0, 1.0]])   # noqa
+    expected_out = cp.dstack((rout, gout, bout))
+
+    # test standard averaging
+    out = label2rgb(label_field, image, kind='avg', bg_label=-1)
+    assert_array_equal(out, expected_out)
+
+    # test averaging with custom background value
+    out_bg = label2rgb(label_field, image, bg_label=2, bg_color=(0, 0, 0),
+                       kind='avg')
+    expected_out_bg = expected_out.copy()
+    expected_out_bg[label_field == 2] = 0
+    assert_array_equal(out_bg, expected_out_bg)
+
+    # test default background color
+    out_bg = label2rgb(label_field, image, bg_label=2, kind='avg')
+    assert_array_equal(out_bg, expected_out_bg)
+
+
+def test_negative_intensity():
+    labels = cp.arange(100).reshape(10, 10)
+    image = cp.full((10, 10), -1, dtype="float64")
+    with pytest.warns(UserWarning):
+        label2rgb(labels, image, bg_label=-1)
+
+
+def test_bg_color_rgb_string():
+    img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    img = cp.asarray(img)
+    labels = cp.asarray(labels)
+    output = label2rgb(labels, image=img, alpha=0.9, bg_label=0, bg_color='red')
+    assert output[0, 0, 0] > 0.9  # red channel
+
+
+def test_avg_with_2d_image():
+    img = np.random.randint(0, 255, (10, 10), dtype=np.uint8)
+    labels = np.zeros((10, 10), dtype=np.int64)
+    labels[1:3, 1:3] = 1
+    labels[6:9, 6:9] = 2
+    img = cp.asarray(img)
+    labels = cp.asarray(labels)
+    assert_no_warnings(label2rgb, labels, image=img, bg_label=0, kind='avg')
diff --git a/python/cucim/src/cucim/skimage/color/tests/test_delta_e.py b/python/cucim/src/cucim/skimage/color/tests/test_delta_e.py
new file mode 100644
index 000000000..59e55f4f7
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/color/tests/test_delta_e.py
@@ -0,0 +1,202 @@
+"""Test for correctness of color distance functions"""
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import (assert_allclose, assert_array_almost_equal,
+                          assert_array_equal)
+
+from cucim.skimage._shared.testing import fetch, expected_warnings
+from cucim.skimage.color.delta_e import (deltaE_cie76, deltaE_ciede94,
+                                         deltaE_ciede2000, deltaE_cmc)
+
+
+def test_ciede2000_dE():
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3))
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3))
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = cp.asarray(lab1)
+    lab2 = cp.asarray(lab2)
+
+    with pytest.warns(UserWarning):
+        dE2 = deltaE_ciede2000(lab1, lab2)
+
+    # TODO: grlee77 had to raise rtol from 1e-4 to 1e-2 for this to pass.
+    #       look into why this was necessary
+    assert_allclose(dE2, cp.asarray(data['dE']), rtol=1.0e-2)
+
+
+def load_ciede2000_data():
+    dtype = [('pair', int),
+             ('1', int),
+             ('L1', float),
+             ('a1', float),
+             ('b1', float),
+             ('a1_prime', float),
+             ('C1_prime', float),
+             ('h1_prime', float),
+             ('hbar_prime', float),
+             ('G', float),
+             ('T', float),
+             ('SL', float),
+             ('SC', float),
+             ('SH', float),
+             ('RT', float),
+             ('dE', float),
+             ('2', int),
+             ('L2', float),
+             ('a2', float),
+             ('b2', float),
+             ('a2_prime', float),
+             ('C2_prime', float),
+             ('h2_prime', float),
+             ]
+
+    # note: ciede_test_data.txt contains several intermediate quantities
+    path = fetch('color/tests/ciede2000_test_data.txt')
+    return np.loadtxt(path, dtype=dtype)
+
+
+def test_cie76():
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3))
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3))
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = cp.asarray(lab1)
+    lab2 = cp.asarray(lab2)
+
+    dE2 = deltaE_cie76(lab1, lab2)
+    # fmt: off
+    oracle = cp.asarray([
+        4.00106328, 6.31415011, 9.1776999, 2.06270077, 2.36957073,
+        2.91529271, 2.23606798, 2.23606798, 4.98000036, 4.9800004,
+        4.98000044, 4.98000049, 4.98000036, 4.9800004, 4.98000044,
+        3.53553391, 36.86800781, 31.91002977, 30.25309901, 27.40894015,
+        0.89242934, 0.7972, 0.8583065, 0.82982507, 3.1819238,
+        2.21334297, 1.53890382, 4.60630929, 6.58467989, 3.88641412,
+        1.50514845, 2.3237848, 0.94413208, 1.31910843
+    ])
+    # fmt: on
+    assert_allclose(dE2, oracle, rtol=1.0e-8)
+
+
+def test_ciede94():
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3))
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3))
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = cp.asarray(lab1)
+    lab2 = cp.asarray(lab2)
+
+    dE2 = deltaE_ciede94(lab1, lab2)
+    # fmt: off
+    oracle = cp.asarray([
+        1.39503887, 1.93410055, 2.45433566, 0.68449187, 0.6695627,
+        0.69194527, 2.23606798, 2.03163832, 4.80069441, 4.80069445,
+        4.80069449, 4.80069453, 4.80069441, 4.80069445, 4.80069449,
+        3.40774352, 34.6891632, 29.44137328, 27.91408781, 24.93766082,
+        0.82213163, 0.71658427, 0.8048753, 0.75284394, 1.39099471,
+        1.24808929, 1.29795787, 1.82045088, 2.55613309, 1.42491303,
+        1.41945261, 2.3225685, 0.93853308, 1.30654464
+    ])
+    # fmt: on
+    assert_allclose(dE2, oracle, rtol=1.0e-8)
+
+
+def test_cmc():
+    data = load_ciede2000_data()
+    N = len(data)
+    lab1 = np.zeros((N, 3))
+    lab1[:, 0] = data['L1']
+    lab1[:, 1] = data['a1']
+    lab1[:, 2] = data['b1']
+
+    lab2 = np.zeros((N, 3))
+    lab2[:, 0] = data['L2']
+    lab2[:, 1] = data['a2']
+    lab2[:, 2] = data['b2']
+
+    lab1 = cp.asarray(lab1)
+    lab2 = cp.asarray(lab2)
+
+    dE2 = deltaE_cmc(lab1, lab2)
+    # fmt: off
+    oracle = cp.asarray([
+        1.73873611, 2.49660844, 3.30494501, 0.85735576, 0.88332927,
+        0.97822692, 3.50480874, 2.87930032, 6.5783807, 6.57838075,
+        6.5783808, 6.57838086, 6.67492321, 6.67492326, 6.67492331,
+        4.66852997, 42.10875485, 39.45889064, 38.36005919, 33.93663807,
+        1.14400168, 1.00600419, 1.11302547, 1.05335328, 1.42822951,
+        1.2548143, 1.76838061, 2.02583367, 3.08695508, 1.74893533,
+        1.90095165, 1.70258148, 1.80317207, 2.44934417
+    ])
+    # fmt: on
+
+    assert_allclose(dE2, oracle, rtol=1.0e-8)
+
+    # Equal or close colors make `delta_e.get_dH2` function to return
+    # negative values resulting in NaNs when passed to sqrt (see #1908
+    # issue on Github):
+    lab1 = lab2
+    expected = cp.zeros_like(oracle)
+    assert_array_almost_equal(deltaE_cmc(lab1, lab2), expected, decimal=6)
+
+    lab2[0, 0] += cp.finfo(float).eps
+    assert_array_almost_equal(deltaE_cmc(lab1, lab2), expected, decimal=6)
+
+    # Single item case:
+    lab1 = lab2 = cp.array([0., 1.59607713, 0.87755709])
+    assert_array_equal(deltaE_cmc(lab1, lab2), 0)
+
+    lab2[0] += cp.finfo(float).eps
+    assert_array_equal(deltaE_cmc(lab1, lab2), 0)
+
+
+def test_single_color_cie76():
+    lab1 = cp.array((0.5, 0.5, 0.5))
+    lab2 = cp.array((0.4, 0.4, 0.4))
+    deltaE_cie76(lab1, lab2)
+
+
+def test_single_color_ciede94():
+    lab1 = cp.array((0.5, 0.5, 0.5))
+    lab2 = cp.array((0.4, 0.4, 0.4))
+    deltaE_ciede94(lab1, lab2)
+
+
+def test_single_color_ciede2000():
+    lab1 = cp.array((0.5, 0.5, 0.5))
+    lab2 = cp.array((0.4, 0.4, 0.4))
+    with expected_warnings(["The numerical accuracy of this function"]):
+        deltaE_ciede2000(lab1, lab2)
+
+
+def test_single_color_cmc():
+    lab1 = cp.array((0.5, 0.5, 0.5))
+    lab2 = cp.array((0.4, 0.4, 0.4))
+    deltaE_cmc(lab1, lab2)
diff --git a/python/cucim/src/cucim/skimage/data/__init__.py b/python/cucim/src/cucim/skimage/data/__init__.py
new file mode 100644
index 000000000..54b68103c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/data/__init__.py
@@ -0,0 +1,3 @@
+from ._binary_blobs import binary_blobs
+
+__all__ = ['binary_blobs']
diff --git a/python/cucim/src/cucim/skimage/data/_binary_blobs.py b/python/cucim/src/cucim/skimage/data/_binary_blobs.py
new file mode 100644
index 000000000..a1788b67c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/data/_binary_blobs.py
@@ -0,0 +1,64 @@
+import cupy as cp
+
+
+def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
+                 volume_fraction=0.5, seed=None):
+    """
+    Generate synthetic binary image with several rounded blob-like objects.
+
+    Parameters
+    ----------
+    length : int, optional
+        Linear size of output image.
+    blob_size_fraction : float, optional
+        Typical linear size of blob, as a fraction of ``length``, should be
+        smaller than 1.
+    n_dim : int, optional
+        Number of dimensions of output image.
+    volume_fraction : float, default 0.5
+        Fraction of image pixels covered by the blobs (where the output is 1).
+        Should be in [0, 1].
+    seed : int, optional
+        Seed to initialize the random number generator.
+        If `None`, a random seed from the operating system is used.
+
+    Returns
+    -------
+    blobs : ndarray of bools
+        Output binary image
+
+    Notes
+    -----
+    Warning: CuPy does not give identical randomly generated numbers as NumPy,
+    so using a specific seed here will not give an identical pattern to the
+    scikit-image implementation.
+
+    The behavior for a given random seed may also change across CuPy major
+    versions.
+    See: https://docs.cupy.dev/en/stable/reference/random.html
+
+    Examples
+    --------
+    >>> from cucim.skimage import data
+    >>> # tiny size (5, 5)
+    >>> blobs = data.binary_blobs(length=5, blob_size_fraction=0.2, seed=1)
+    >>> # larger size
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
+    >>> # Finer structures
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
+    >>> # Blobs cover a smaller volume fraction of the image
+    >>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
+    """
+    # filters is quite an expensive import since it imports all of scipy.signal
+    # We lazy import here
+    from ..filters import gaussian
+
+    rs = cp.random.RandomState(seed)
+    shape = tuple([length] * n_dim)
+    mask = cp.zeros(shape)
+    n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
+    points = (length * rs.rand(n_dim, n_pts)).astype(int)
+    mask[tuple(indices for indices in points)] = 1
+    mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction)
+    threshold = cp.percentile(mask, 100 * (1 - volume_fraction))
+    return cp.logical_not(mask < threshold)
diff --git a/python/cucim/src/cucim/skimage/data/tests/test_data.py b/python/cucim/src/cucim/skimage/data/tests/test_data.py
new file mode 100644
index 000000000..c56133cc1
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/data/tests/test_data.py
@@ -0,0 +1,16 @@
+import cupy as cp
+from numpy.testing import assert_almost_equal
+
+from cucim.skimage import data
+
+
+def test_binary_blobs():
+    blobs = data.binary_blobs(length=128)
+    assert_almost_equal(blobs.mean(), 0.5, decimal=1)
+    blobs = data.binary_blobs(length=128, volume_fraction=0.25)
+    assert_almost_equal(blobs.mean(), 0.25, decimal=1)
+    blobs = data.binary_blobs(length=32, volume_fraction=0.25, n_dim=3)
+    assert_almost_equal(blobs.mean(), 0.25, decimal=1)
+    other_realization = data.binary_blobs(length=32, volume_fraction=0.25,
+                                          n_dim=3)
+    assert not cp.all(blobs == other_realization)
diff --git a/python/cucim/src/cucim/skimage/exposure/__init__.py b/python/cucim/src/cucim/skimage/exposure/__init__.py
new file mode 100644
index 000000000..6d74f539a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/__init__.py
@@ -0,0 +1,16 @@
+from ._adapthist import equalize_adapthist
+from .exposure import (adjust_gamma, adjust_log, adjust_sigmoid,
+                       cumulative_distribution, equalize_hist, histogram,
+                       is_low_contrast, rescale_intensity)
+from .histogram_matching import match_histograms
+
+__all__ = ['histogram',
+           'equalize_hist',
+           'equalize_adapthist',
+           'rescale_intensity',
+           'cumulative_distribution',
+           'adjust_gamma',
+           'adjust_sigmoid',
+           'adjust_log',
+           'is_low_contrast',
+           'match_histograms']
diff --git a/python/cucim/src/cucim/skimage/exposure/_adapthist.py b/python/cucim/src/cucim/skimage/exposure/_adapthist.py
new file mode 100644
index 000000000..8f0db862e
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/_adapthist.py
@@ -0,0 +1,346 @@
+"""
+Adapted code from "Contrast Limited Adaptive Histogram Equalization" by Karel
+Zuiderveld <karel@cv.ruu.nl>, Graphics Gems IV, Academic Press, 1994.
+
+http://tog.acm.org/resources/GraphicsGems/
+
+The Graphics Gems code is copyright-protected.  In other words, you cannot
+claim the text of the code as your own and resell it. Using the code is
+permitted in any program, product, or library, non-commercial or commercial.
+Giving credit is not required, though is a nice gesture.  The code comes as-is,
+and if there are any flaws or problems with any Gems code, nobody involved with
+Gems - authors, editors, publishers, or webmasters - are to be held
+responsible.  Basically, don't be a jerk, and remember that anything free
+comes with no guarantee.
+"""
+import functools
+import itertools
+import math
+import numbers
+import operator
+
+import cupy as cp
+import numpy as np
+
+from cucim import _misc
+from cucim.skimage.exposure.exposure import rescale_intensity
+
+from ..color.adapt_rgb import adapt_rgb, hsv_value
+from ..util import img_as_float, img_as_uint
+
+NR_OF_GRAY = 2 ** 14  # number of grayscale levels to use in CLAHE algorithm
+
+
+@adapt_rgb(hsv_value)
+def equalize_adapthist(image, kernel_size=None,
+                       clip_limit=0.01, nbins=256):
+    """Contrast Limited Adaptive Histogram Equalization (CLAHE).
+
+    An algorithm for local contrast enhancement, that uses histograms computed
+    over different tile regions of the image. Local details can therefore be
+    enhanced even in regions that are darker or lighter than most of the image.
+
+    Parameters
+    ----------
+    image : (N1, ...,NN[, C]) ndarray
+        Input image.
+    kernel_size: int or array_like, optional
+        Defines the shape of contextual regions used in the algorithm. If
+        iterable is passed, it must have the same number of elements as
+        ``image.ndim`` (without color channel). If integer, it is broadcasted
+        to each `image` dimension. By default, ``kernel_size`` is 1/8 of
+        ``image`` height by 1/8 of its width.
+    clip_limit : float, optional
+        Clipping limit, normalized between 0 and 1 (higher values give more
+        contrast).
+    nbins : int, optional
+        Number of gray bins for histogram ("data range").
+
+    Returns
+    -------
+    out : (N1, ...,NN[, C]) ndarray
+        Equalized image with float64 dtype.
+
+    See Also
+    --------
+    equalize_hist, rescale_intensity
+
+    Notes
+    -----
+    * For color images, the following steps are performed:
+       - The image is converted to HSV color space
+       - The CLAHE algorithm is run on the V (Value) channel
+       - The image is converted back to RGB space and returned
+    * For RGBA images, the original alpha channel is removed.
+
+    .. versionchanged:: 0.17
+        The values returned by this function are slightly shifted upwards
+        because of an internal change in rounding behavior.
+
+    References
+    ----------
+    .. [1] http://tog.acm.org/resources/GraphicsGems/
+    .. [2] https://en.wikipedia.org/wiki/CLAHE#CLAHE
+    """
+
+    image = img_as_uint(image)
+    image = cp.around(
+        rescale_intensity(image, out_range=(0, NR_OF_GRAY - 1))
+    ).astype(cp.uint16)
+
+    if kernel_size is None:
+        kernel_size = tuple(image.shape[dim] // 8
+                            for dim in range(image.ndim))
+    elif isinstance(kernel_size, numbers.Number):
+        kernel_size = (kernel_size,) * image.ndim
+    elif len(kernel_size) != image.ndim:
+        ValueError('Incorrect value of `kernel_size`: {}'.format(kernel_size))
+
+    kernel_size = [int(k) for k in kernel_size]
+
+    image = _clahe(image, kernel_size, clip_limit, nbins)
+    image = img_as_float(image)
+    return rescale_intensity(image)
+
+
+def _clahe(image, kernel_size, clip_limit, nbins):
+    """Contrast Limited Adaptive Histogram Equalization.
+
+    Parameters
+    ----------
+    image : (N1,...,NN) ndarray
+        Input image.
+    kernel_size: int or N-tuple of int
+        Defines the shape of contextual regions used in the algorithm.
+    clip_limit : float
+        Normalized clipping limit between 0 and 1 (higher values give more
+        contrast).
+    nbins : int
+        Number of gray bins for histogram ("data range").
+
+    Returns
+    -------
+    out : (N1,...,NN) ndarray
+        Equalized image.
+
+    The number of "effective" graylevels in the output image is set by `nbins`;
+    selecting a small value (e.g. 128) speeds up processing and still produces
+    an output image of good quality. A clip limit of 0 or larger than or equal
+    to 1 results in standard (non-contrast limited) AHE.
+    """
+    ndim = image.ndim
+    dtype = image.dtype
+
+    # pad the image such that the shape in each dimension
+    # - is a multiple of the kernel_size and
+    # - is preceded by half a kernel size
+    pad_start_per_dim = [k // 2 for k in kernel_size]
+
+    pad_end_per_dim = [(k - s % k) % k + math.ceil(k / 2.)
+                       for k, s in zip(kernel_size, image.shape)]
+
+    image = cp.pad(image, [[p_i, p_f] for p_i, p_f in
+                           zip(pad_start_per_dim, pad_end_per_dim)],
+                   mode='reflect')
+
+    # determine gray value bins
+    bin_size = 1 + NR_OF_GRAY // nbins
+    lut = cp.arange(NR_OF_GRAY)
+    lut //= bin_size
+
+    image = lut[image]
+
+    # calculate graylevel mappings for each contextual region
+    # rearrange image into flattened contextual regions
+    ns_hist = [int(s / k) - 1 for s, k in zip(image.shape, kernel_size)]
+    hist_blocks_shape = functools.reduce(
+        operator.add, [(s, k) for s, k in zip(ns_hist, kernel_size)]
+    )
+    hist_blocks_axis_order = (tuple(range(0, ndim * 2, 2)) +
+                              tuple(range(1, ndim * 2, 2)))
+    hist_slices = [
+        slice(k // 2, k // 2 + n * k) for k, n in zip(kernel_size, ns_hist)
+    ]
+    hist_blocks = image[tuple(hist_slices)].reshape(hist_blocks_shape)
+    hist_blocks = hist_blocks.transpose(hist_blocks_axis_order)
+    hist_block_assembled_shape = hist_blocks.shape
+    hist_blocks = hist_blocks.reshape((_misc.prod(ns_hist), -1))
+
+    # Calculate actual clip limit
+    if clip_limit > 0.0:
+        clim = int(max(clip_limit * _misc.prod(kernel_size), 1))
+    else:
+        # largest possible value, i.e., do not clip (AHE)
+        clim = np.product(kernel_size)
+
+    # Note: for 4096, 4096 input and default args, shapes are:
+    #    hist_blocks.shape = (64, 262144)
+    #    hist.shape = (64, 256)
+    hist = cp.apply_along_axis(cp.bincount, -1, hist_blocks, minlength=nbins)
+    if isinstance(hist_blocks, cp.ndarray):
+        # CuPy Backend:
+        #    faster to loop over the arrays on the host
+        #    (hist is small and clip_histogram has too much overhead)
+        # TODO: implement clip_histogram kernel to avoid synchronization?
+        hist = cp.asarray(np.apply_along_axis(  # synchronize!
+            clip_histogram, -1, cp.asnumpy(hist), clip_limit=clim
+        ))
+    else:
+        hist = cp.apply_along_axis(clip_histogram, -1, hist, clip_limit=clim)
+    hist = map_histogram(hist, 0, NR_OF_GRAY - 1, _misc.prod(kernel_size))
+    hist = hist.reshape(hist_block_assembled_shape[:ndim] + (-1,))
+
+    # duplicate leading mappings in each dim
+    map_array = cp.pad(hist,
+                       [(1, 1) for _ in range(ndim)] + [(0, 0)],
+                       mode='edge')
+
+    # Perform multilinear interpolation of graylevel mappings
+    # using the convention described here:
+    # https://en.wikipedia.org/w/index.php?title=Adaptive_histogram_
+    # equalization&oldid=936814673#Efficient_computation_by_interpolation
+
+    # rearrange image into blocks for vectorized processing
+    ns_proc = [int(s / k) for s, k in zip(image.shape, kernel_size)]
+    blocks_shape = functools.reduce(
+        operator.add, [(s, k) for s, k in zip(ns_proc, kernel_size)]
+    )
+    blocks_axis_order = hist_blocks_axis_order
+
+    blocks = image.reshape(blocks_shape)
+    blocks = blocks.transpose(blocks_axis_order)
+    blocks_flattened_shape = blocks.shape
+    blocks = blocks.reshape((_misc.prod(ns_proc),
+                             _misc.prod(blocks.shape[ndim:])))
+
+    # calculate interpolation coefficients
+    coeffs = cp.meshgrid(*tuple([cp.arange(k) / k
+                                 for k in kernel_size[::-1]]), indexing='ij')
+    coeffs = [cp.transpose(c).flatten() for c in coeffs]
+    inv_coeffs = [1 - c for c in coeffs]
+
+    # sum over contributions of neighboring contextual
+    # regions in each direction
+    result = cp.zeros(blocks.shape, dtype=cp.float32)
+    for iedge, edge in enumerate(itertools.product(*((range(2),) * ndim))):
+
+        edge_maps = map_array[tuple(slice(e, e + n)
+                                    for e, n in zip(edge, ns_proc))]
+        edge_maps = edge_maps.reshape((_misc.prod(ns_proc), -1))
+
+        # apply map
+        edge_mapped = cp.take_along_axis(edge_maps, blocks, axis=-1)
+
+        # interpolate
+        edge_coeffs = functools.reduce(
+            operator.mul,
+            [[inv_coeffs, coeffs][e][d] for d, e in enumerate(edge[::-1])],
+        )
+
+        result += (edge_mapped * edge_coeffs).astype(result.dtype)
+
+    result = result.astype(dtype)
+
+    # rebuild result image from blocks
+    result = result.reshape(blocks_flattened_shape)
+    blocks_axis_rebuild_order = functools.reduce(
+        operator.add,
+        [(s, k) for s, k in zip(range(0, ndim), range(ndim, ndim * 2))],
+    )
+    result = result.transpose(blocks_axis_rebuild_order)
+    result = result.reshape(image.shape)
+
+    # undo padding
+    unpad_slices = tuple([slice(p_i, s - p_f) for p_i, p_f, s in
+                          zip(pad_start_per_dim, pad_end_per_dim,
+                              image.shape)])
+    result = result[unpad_slices]
+
+    return result
+
+
+# TODO: refactor this clip_histogram bottleneck.
+def clip_histogram(hist, clip_limit):
+    """Perform clipping of the histogram and redistribution of bins.
+
+    The histogram is clipped and the number of excess pixels is counted.
+    Afterwards the excess pixels are equally redistributed across the
+    whole histogram (providing the bin count is smaller than the cliplimit).
+
+    Parameters
+    ----------
+    hist : ndarray
+        Histogram array.
+    clip_limit : int
+        Maximum allowed bin count.
+
+    Returns
+    -------
+    hist : ndarray
+        Clipped histogram.
+    """
+    # calculate total number of excess pixels
+    excess_mask = hist > clip_limit
+    excess = hist[excess_mask]
+    n_excess = excess.sum() - excess.size * clip_limit
+    hist[excess_mask] = clip_limit
+
+    # Second part: clip histogram and redistribute excess pixels in each bin
+    bin_incr = n_excess // hist.size  # average binincrement
+    xp = cp.get_array_module(hist)
+    upper = clip_limit - bin_incr  # Bins larger than upper set to cliplimit
+
+    low_mask = hist < upper
+    n_excess -= hist[low_mask].size * bin_incr
+    hist[low_mask] += bin_incr
+
+    mid_mask = xp.logical_and(hist >= upper, hist < clip_limit)
+    mid = hist[mid_mask]
+    n_excess += mid.sum() - mid.size * clip_limit
+    hist[mid_mask] = clip_limit
+
+    while n_excess > 0:  # Redistribute remaining excess
+        prev_n_excess = n_excess
+        for index in range(hist.size):
+            under_mask = hist < clip_limit
+            step_size = max(1, xp.count_nonzero(under_mask) // n_excess)
+            under_mask = under_mask[index::step_size]
+            hist[index::step_size][under_mask] += 1
+            n_excess -= xp.count_nonzero(under_mask)
+            if n_excess <= 0:
+                break
+        if prev_n_excess == n_excess:
+            break
+
+    return hist
+
+
+def map_histogram(hist, min_val, max_val, n_pixels):
+    """Calculate the equalized lookup table (mapping).
+
+    It does so by cumulating the input histogram.
+    Histogram bins are assumed to be represented by the last array dimension.
+
+    Parameters
+    ----------
+    hist : ndarray
+        Clipped histogram.
+    min_val : int
+        Minimum value for mapping.
+    max_val : int
+        Maximum value for mapping.
+    n_pixels : int
+        Number of pixels in the region.
+
+    Returns
+    -------
+    out : ndarray
+       Mapped intensity LUT.
+    """
+    xp = cp.get_array_module(hist)
+    out = xp.cumsum(hist, axis=-1).astype(float)
+    out *= (max_val - min_val) / n_pixels
+    out += min_val
+    cp.clip(out, a_min=None, a_max=max_val, out=out)
+
+    return out.astype(int)
diff --git a/python/cucim/src/cucim/skimage/exposure/exposure.py b/python/cucim/src/cucim/skimage/exposure/exposure.py
new file mode 100644
index 000000000..5f88da027
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/exposure.py
@@ -0,0 +1,668 @@
+import cupy as cp
+import numpy as np
+
+from .._shared.utils import warn
+from ..color import rgb2gray, rgba2rgb
+from ..util.dtype import dtype_limits, dtype_range
+
+__all__ = ['histogram', 'cumulative_distribution', 'equalize_hist',
+           'rescale_intensity', 'adjust_gamma', 'adjust_log', 'adjust_sigmoid']
+
+
+DTYPE_RANGE = dtype_range.copy()
+DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
+DTYPE_RANGE.update({'uint10': (0, 2 ** 10 - 1),
+                    'uint12': (0, 2 ** 12 - 1),
+                    'uint14': (0, 2 ** 14 - 1),
+                    'bool': dtype_range[bool],
+                    'float': dtype_range[np.float64]})
+
+
+def _offset_array(arr, low_boundary, high_boundary):
+    """Offset the array to get the lowest value at 0 if negative."""
+    if low_boundary < 0:
+        offset = low_boundary
+        dyn_range = high_boundary - low_boundary
+        # get smallest dtype that can hold both minimum and offset maximum
+        offset_dtype = np.promote_types(np.min_scalar_type(dyn_range),
+                                        np.min_scalar_type(low_boundary))
+        if arr.dtype != offset_dtype:
+            # prevent overflow errors when offsetting
+            arr = arr.astype(offset_dtype)
+        arr = arr - offset
+    else:
+        offset = 0
+    return arr, offset
+
+
+def _bincount_histogram(image, source_range):
+    """
+    Efficient histogram calculation for an image of integers.
+
+    This function is significantly more efficient than cupy.histogram but
+    works only on images of integers. It is based on cupy.bincount.
+
+    Parameters
+    ----------
+    image : array
+        Input image.
+    source_range : string
+        'image' determines the range from the input image.
+        'dtype' determines the range from the expected range of the images
+        of that data type.
+
+    Returns
+    -------
+    hist : array
+        The values of the histogram.
+    bin_centers : array
+        The values at the center of the bins.
+    """
+    if source_range not in ['image', 'dtype']:
+        raise ValueError('Incorrect value for `source_range` argument: '
+                         f'{source_range}')
+    if source_range == 'image':
+        image_min = int(image.min().astype(np.int64))
+        image_max = int(image.max().astype(np.int64))
+    elif source_range == 'dtype':
+        image_min, image_max = dtype_limits(image, clip_negative=False)
+    image, offset = _offset_array(image, image_min, image_max)
+    hist = cp.bincount(image.ravel(), minlength=image_max - image_min + 1)
+    bin_centers = cp.arange(image_min, image_max + 1)
+    if source_range == 'image':
+        idx = max(image_min, 0)
+        hist = hist[idx:]
+    return hist, bin_centers
+
+
+def histogram(image, nbins=256, source_range='image', normalize=False):
+    """Return histogram of image.
+
+    Unlike `numpy.histogram`, this function returns the centers of bins and
+    does not rebin integer arrays. For integer arrays, each integer value has
+    its own bin, which improves speed and intensity-resolution.
+
+    The histogram is computed on the flattened image: for color images, the
+    function should be used separately on each channel to obtain a histogram
+    for each color channel.
+
+    Parameters
+    ----------
+    image : array
+        Input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+    source_range : string, optional
+        'image' (default) determines the range from the input image.
+        'dtype' determines the range from the expected range of the images
+        of that data type.
+    normalize : bool, optional
+        If True, normalize the histogram by the sum of its values.
+
+    Returns
+    -------
+    hist : array
+        The values of the histogram.
+    bin_centers : array
+        The values at the center of the bins.
+
+    See Also
+    --------
+    cumulative_distribution
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage import exposure, img_as_float
+    >>> image = img_as_float(cp.array(data.camera()))
+    >>> cp.histogram(image, bins=2)
+    (array([ 93585, 168559]), array([0. , 0.5, 1. ]))
+    >>> exposure.histogram(image, nbins=2)
+    (array([ 93585, 168559]), array([0.25, 0.75]))
+    """
+    sh = image.shape
+    if len(sh) == 3 and sh[-1] < 4:
+        warn("This might be a color image. The histogram will be "
+             "computed on the flattened image. You can instead "
+             "apply this function to each color channel.")
+
+    image = image.flatten()
+    # For integer types, histogramming with bincount is more efficient.
+    if np.issubdtype(image.dtype, np.integer):
+        hist, bin_centers = _bincount_histogram(image, source_range)
+    else:
+        if source_range == 'image':
+            hist_range = None
+        elif source_range == 'dtype':
+            hist_range = dtype_limits(image, clip_negative=False)
+        else:
+            ValueError('Wrong value for the `source_range` argument')
+        hist, bin_edges = cp.histogram(image, bins=nbins, range=hist_range)
+        bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.
+
+    if normalize:
+        hist = hist / cp.sum(hist)
+    return hist, bin_centers
+
+
+def cumulative_distribution(image, nbins=256):
+    """Return cumulative distribution function (cdf) for the given image.
+
+    Parameters
+    ----------
+    image : array
+        Image array.
+    nbins : int, optional
+        Number of bins for image histogram.
+
+    Returns
+    -------
+    img_cdf : array
+        Values of cumulative distribution function.
+    bin_centers : array
+        Centers of bins.
+
+    See Also
+    --------
+    histogram
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cumulative_distribution_function
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage import exposure, img_as_float
+    >>> image = img_as_float(cp.array(data.camera()))
+    >>> hi = exposure.histogram(image)
+    >>> cdf = exposure.cumulative_distribution(image)
+    >>> cp.alltrue(cdf[0] == cp.cumsum(hi[0])/float(image.size))
+    True
+    """
+    hist, bin_centers = histogram(image, nbins)
+    img_cdf = hist.cumsum()
+    img_cdf = img_cdf / float(img_cdf[-1])
+    return img_cdf, bin_centers
+
+
+def equalize_hist(image, nbins=256, mask=None):
+    """Return image after histogram equalization.
+
+    Parameters
+    ----------
+    image : array
+        Image array.
+    nbins : int, optional
+        Number of bins for image histogram. Note: this argument is
+        ignored for integer images, for which each integer is its own
+        bin.
+    mask: ndarray of bools or 0s and 1s, optional
+        Array of same shape as `image`. Only points at which mask == True
+        are used for the equalization, which is applied to the whole image.
+
+    Returns
+    -------
+    out : float array
+        Image array after histogram equalization.
+
+    Notes
+    -----
+    This function is adapted from [1]_ with the author's permission.
+
+    References
+    ----------
+    .. [1] http://www.janeriksolem.net/histogram-equalization-with-python-and.html
+    .. [2] https://en.wikipedia.org/wiki/Histogram_equalization
+
+    """  # noqa
+    if mask is not None:
+        mask = mask.astype(bool, copy=False)
+        cdf, bin_centers = cumulative_distribution(image[mask], nbins)
+    else:
+        cdf, bin_centers = cumulative_distribution(image, nbins)
+    out = cp.interp(image.ravel(), bin_centers, cdf)
+    return out.reshape(image.shape)
+
+
+def intensity_range(image, range_values="image", clip_negative=False):
+    """Return image intensity range (min, max) based on desired value type.
+
+    Parameters
+    ----------
+    image : array
+        Input image.
+    range_values : str or 2-tuple, optional
+        The image intensity range is configured by this parameter.
+        The possible values for this parameter are enumerated below.
+
+        'image'
+            Return image min/max as the range.
+        'dtype'
+            Return min/max of the image's dtype as the range.
+        dtype-name
+            Return intensity range based on desired `dtype`. Must be valid key
+            in `DTYPE_RANGE`. Note: `image` is ignored for this range type.
+        2-tuple
+            Return `range_values` as min/max intensities. Note that there's no
+            reason to use this function if you just want to specify the
+            intensity range explicitly. This option is included for functions
+            that use `intensity_range` to support all desired range types.
+
+    clip_negative : bool, optional
+        If True, clip the negative range (i.e. return 0 for min intensity)
+        even if the image dtype allows negative values.
+
+    Returns
+    -------
+    i_range : tuple
+        A 2-tuple where the first element is the minimum and the second is the
+        maximum.
+    """
+    if range_values == 'dtype':
+        range_values = image.dtype.type
+
+    if range_values == 'image':
+        i_min = image.min().item()
+        i_max = image.max().item()
+    elif range_values in DTYPE_RANGE:
+        i_min, i_max = DTYPE_RANGE[range_values]
+        if clip_negative:
+            i_min = 0
+    else:
+        i_min, i_max = range_values
+    return i_min, i_max
+
+
+def _output_dtype(dtype_or_range):
+    """Determine the output dtype for rescale_intensity.
+
+    The dtype is determined according to the following rules:
+    - if ``dtype_or_range`` is a dtype, that is the output dtype.
+    - if ``dtype_or_range`` is a dtype string, that is the dtype used, unless
+      it is not a NumPy data type (e.g. 'uint12' for 12-bit unsigned integers),
+      in which case the data type that can contain it will be used
+      (e.g. uint16 in this case).
+    - if ``dtype_or_range`` is a pair of values, the output data type will be
+      float.
+
+    Parameters
+    ----------
+    dtype_or_range : type, string, or 2-tuple of int/float
+        The desired range for the output, expressed as either a NumPy dtype or
+        as a (min, max) pair of numbers.
+
+    Returns
+    -------
+    out_dtype : type
+        The data type appropriate for the desired output.
+    """
+    if type(dtype_or_range) in [list, tuple, np.ndarray]:
+        # pair of values: always return float.
+        return float
+    if type(dtype_or_range) == type:
+        # already a type: return it
+        return dtype_or_range
+    if dtype_or_range in DTYPE_RANGE:
+        # string key in DTYPE_RANGE dictionary
+        try:
+            # if it's a canonical numpy dtype, convert
+            return np.dtype(dtype_or_range).type
+        except TypeError:  # uint10, uint12, uint14
+            # otherwise, return uint16
+            return np.uint16
+    else:
+        raise ValueError(
+            'Incorrect value for out_range, should be a valid image data '
+            f'type or a pair of values, got {dtype_or_range}.'
+        )
+
+
+def rescale_intensity(image, in_range="image", out_range="dtype"):
+    """Return image after stretching or shrinking its intensity levels.
+
+    The desired intensity range of the input and output, `in_range` and
+    `out_range` respectively, are used to stretch or shrink the intensity range
+    of the input image. See examples below.
+
+    Parameters
+    ----------
+    image : array
+        Image array.
+    in_range, out_range : str or 2-tuple, optional
+        Min and max intensity values of input and output image.
+        The possible values for this parameter are enumerated below.
+
+        'image'
+            Use image min/max as the intensity range.
+        'dtype'
+            Use min/max of the image's dtype as the intensity range.
+        dtype-name
+            Use intensity range based on desired `dtype`. Must be valid key
+            in `DTYPE_RANGE`.
+        2-tuple
+            Use `range_values` as explicit min/max intensities.
+
+    Returns
+    -------
+    out : array
+        Image array after rescaling its intensity. This image is the same dtype
+        as the input image.
+
+    Notes
+    -----
+    .. versionchanged:: 0.17
+        The dtype of the output array has changed to match the output dtype, or
+        float if the output range is specified by a pair of floats.
+
+    See Also
+    --------
+    equalize_hist
+
+    Examples
+    --------
+    By default, the min/max intensities of the input image are stretched to
+    the limits allowed by the image's dtype, since `in_range` defaults to
+    'image' and `out_range` defaults to 'dtype':
+
+    >>> image = cp.array([51, 102, 153], dtype=np.uint8)
+    >>> rescale_intensity(image)
+    array([  0, 127, 255], dtype=uint8)
+
+    It's easy to accidentally convert an image dtype from uint8 to float:
+
+    >>> 1.0 * image
+    array([ 51., 102., 153.])
+
+    Use `rescale_intensity` to rescale to the proper range for float dtypes:
+
+    >>> image_float = 1.0 * image
+    >>> rescale_intensity(image_float)
+    array([0. , 0.5, 1. ])
+
+    To maintain the low contrast of the original, use the `in_range` parameter:
+
+    >>> rescale_intensity(image_float, in_range=(0, 255))
+    array([0.2, 0.4, 0.6])
+
+    If the min/max value of `in_range` is more/less than the min/max image
+    intensity, then the intensity levels are clipped:
+
+    >>> rescale_intensity(image_float, in_range=(0, 102))
+    array([0.5, 1. , 1. ])
+
+    If you have an image with signed integers but want to rescale the image to
+    just the positive range, use the `out_range` parameter. In that case, the
+    output dtype will be float:
+
+    >>> image = cp.asarray([-10, 0, 10], dtype=np.int8)
+    >>> rescale_intensity(image, out_range=(0, 127))
+    array([  0. ,  63.5, 127. ])
+
+    To get the desired range with a specific dtype, use ``.astype()``:
+
+    >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int8)
+    array([  0,  63, 127], dtype=int8)
+
+    If the input image is constant, the output will be clipped directly to the
+    output range:
+    >>> image = cp.asarray([130, 130, 130], dtype=np.int32)
+    >>> rescale_intensity(image, out_range=(0, 127)).astype(np.int32)
+    array([127, 127, 127], dtype=int32)
+    """
+    if out_range in ['dtype', 'image']:
+        out_dtype = _output_dtype(image.dtype.type)
+    else:
+        out_dtype = _output_dtype(out_range)
+
+    imin, imax = map(float, intensity_range(image, in_range))
+    omin, omax = map(float, intensity_range(image, out_range,
+                                            clip_negative=(imin >= 0)))
+
+    if np.any(np.isnan([imin, imax, omin, omax])):
+        warn(
+            "One or more intensity levels are NaN. Rescaling will broadcast "
+            "NaN to the full image. Provide intensity levels yourself to "
+            "avoid this. E.g. with np.nanmin(image), np.nanmax(image).",
+            stacklevel=2
+        )
+
+    image = cp.clip(image, imin, imax)
+
+    if imin != imax:
+        image = (image - imin) / (imax - imin)
+        return cp.asarray(image * (omax - omin) + omin, dtype=out_dtype)
+    else:
+        return cp.clip(image, omin, omax).astype(out_dtype, copy=False)
+
+
+def _assert_non_negative(image):
+
+    if cp.any(image < 0):  # synchronize!
+        raise ValueError('Image Correction methods work correctly only on '
+                         'images with non-negative values. Use '
+                         'skimage.exposure.rescale_intensity.')
+
+
+def _adjust_gamma_u8(image, gamma, gain):
+    """LUT based implmentation of gamma adjustement."""
+    lut = (255 * gain * (np.linspace(0, 1, 256) ** gamma)).astype("uint8")
+    lut = cp.asarray(lut)
+    return lut[image]
+
+
+def adjust_gamma(image, gamma=1, gain=1):
+    """Performs Gamma Correction on the input image.
+
+    Also known as Power Law Transform.
+    This function transforms the input image pixelwise according to the
+    equation ``O = I**gamma`` after scaling each pixel to the range 0 to 1.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    gamma : float, optional
+        Non negative real number. Default value is 1.
+    gain : float, optional
+        The constant multiplier. Default value is 1.
+
+    Returns
+    -------
+    out : ndarray
+        Gamma corrected output image.
+
+    See Also
+    --------
+    adjust_log
+
+    Notes
+    -----
+    For gamma greater than 1, the histogram will shift towards left and
+    the output image will be darker than the input image.
+
+    For gamma less than 1, the histogram will shift towards right and
+    the output image will be brighter than the input image.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Gamma_correction
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage import exposure, img_as_float
+    >>> image = img_as_float(cp.array(data.moon()))
+    >>> gamma_corrected = exposure.adjust_gamma(image, 2)
+    >>> # Output is darker for gamma > 1
+    >>> image.mean() > gamma_corrected.mean()
+    True
+    """
+    if gamma < 0:
+        raise ValueError("Gamma should be a non-negative real number.")
+
+    dtype = image.dtype.type
+
+    if dtype is cp.uint8:
+        out = _adjust_gamma_u8(image, gamma, gain)
+    else:
+        _assert_non_negative(image)
+
+        scale = float(dtype_limits(image, True)[1]
+                      - dtype_limits(image, True)[0])
+
+        out = (((image / scale) ** gamma) * scale * gain).astype(dtype)
+
+    return out
+
+
+def adjust_log(image, gain=1, inv=False):
+    """Performs Logarithmic correction on the input image.
+
+    This function transforms the input image pixelwise according to the
+    equation ``O = gain*log(1 + I)`` after scaling each pixel to the range
+    0 to 1.
+
+    For inverse logarithmic correction, the equation is
+    ``O = gain*(2**I - 1)``.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    gain : float, optional
+        The constant multiplier. Default value is 1.
+    inv : float, optional
+        If True, it performs inverse logarithmic correction,
+        else correction will be logarithmic. Defaults to False.
+
+    Returns
+    -------
+    out : ndarray
+        Logarithm corrected output image.
+
+    See Also
+    --------
+    adjust_gamma
+
+    References
+    ----------
+    .. [1] http://www.ece.ucsb.edu/Faculty/Manjunath/courses/ece178W03/EnhancePart1.pdf
+
+    """  # noqa
+    _assert_non_negative(image)
+    dtype = image.dtype.type
+    scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
+
+    if inv:
+        out = (2 ** (image / scale) - 1) * scale * gain
+        return out.astype(dtype, copy=False)
+
+    out = cp.log2(1 + image / scale) * scale * gain
+    return out.astype(dtype, copy=False)
+
+
+def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False):
+    """Performs Sigmoid Correction on the input image.
+
+    Also known as Contrast Adjustment.
+    This function transforms the input image pixelwise according to the
+    equation ``O = 1/(1 + exp*(gain*(cutoff - I)))`` after scaling each pixel
+    to the range 0 to 1.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    cutoff : float, optional
+        Cutoff of the sigmoid function that shifts the characteristic curve
+        in horizontal direction. Default value is 0.5.
+    gain : float, optional
+        The constant multiplier in exponential's power of sigmoid function.
+        Default value is 10.
+    inv : bool, optional
+        If True, returns the negative sigmoid correction. Defaults to False.
+
+    Returns
+    -------
+    out : ndarray
+        Sigmoid corrected output image.
+
+    See Also
+    --------
+    adjust_gamma
+
+    References
+    ----------
+    .. [1] Gustav J. Braun, "Image Lightness Rescaling Using Sigmoidal Contrast
+           Enhancement Functions",
+           http://www.cis.rit.edu/fairchild/PDFs/PAP07.pdf
+
+    """
+    _assert_non_negative(image)
+    dtype = image.dtype.type
+    scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
+
+    if inv:
+        out = (1 - 1 / (1 + cp.exp(gain * (cutoff - image / scale)))) * scale
+        return out.astype(dtype, copy=False)
+
+    out = (1 / (1 + cp.exp(gain * (cutoff - image / scale)))) * scale
+    return out.astype(dtype, copy=False)
+
+
+def is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1,
+                    upper_percentile=99, method='linear'):
+    """Determine if an image is low contrast.
+
+    Parameters
+    ----------
+    image : array-like
+        The image under test.
+    fraction_threshold : float, optional
+        The low contrast fraction threshold. An image is considered low-
+        contrast when its range of brightness spans less than this
+        fraction of its data type's full range. [1]_
+    lower_percentile : float, optional
+        Disregard values below this percentile when computing image contrast.
+    upper_percentile : float, optional
+        Disregard values above this percentile when computing image contrast.
+    method : str, optional
+        The contrast determination method.  Right now the only available
+        option is "linear".
+
+    Returns
+    -------
+    out : bool
+        True when the image is determined to be low contrast.
+
+    References
+    ----------
+    .. [1] https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> image = cp.linspace(0, 0.04, 100)
+    >>> is_low_contrast(image)
+    True
+    >>> image[-1] = 1
+    >>> is_low_contrast(image)
+    True
+    >>> is_low_contrast(image, upper_percentile=100)
+    False
+    """
+    if image.ndim == 3:
+        if image.shape[2] == 4:
+            image = rgba2rgb(image)
+        if image.shape[2] == 3:
+            image = rgb2gray(image)
+
+    dlimits = dtype_limits(image, clip_negative=False)
+    limits = cp.percentile(image, [lower_percentile, upper_percentile])
+    ratio = (limits[1] - limits[0]) / (dlimits[1] - dlimits[0])
+
+    return ratio < fraction_threshold
diff --git a/python/cucim/src/cucim/skimage/exposure/histogram_matching.py b/python/cucim/src/cucim/skimage/exposure/histogram_matching.py
new file mode 100644
index 000000000..0eeb33744
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/histogram_matching.py
@@ -0,0 +1,70 @@
+import cupy as cp
+
+
+def _match_cumulative_cdf(source, template):
+    """
+    Return modified source array so that the cumulative density function of
+    its values matches the cumulative density function of the template.
+    """
+    src_values, src_unique_indices, src_counts = cp.unique(source.ravel(),
+                                                           return_inverse=True,
+                                                           return_counts=True)
+    tmpl_values, tmpl_counts = cp.unique(template.ravel(), return_counts=True)
+
+    # calculate normalized quantiles for each array
+    src_quantiles = cp.cumsum(src_counts) / source.size
+    tmpl_quantiles = cp.cumsum(tmpl_counts) / template.size
+
+    interp_a_values = cp.interp(src_quantiles, tmpl_quantiles, tmpl_values)
+    return interp_a_values[src_unique_indices].reshape(source.shape)
+
+
+def match_histograms(image, reference, *, multichannel=False):
+    """Adjust an image so that its cumulative histogram matches that of another.
+
+    The adjustment is applied separately for each channel.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image. Can be gray-scale or in color.
+    reference : ndarray
+        Image to match histogram of. Must have the same number of channels as
+        image.
+    multichannel : bool, optional
+        Apply the matching separately for each channel.
+
+    Returns
+    -------
+    matched : ndarray
+        Transformed input image.
+
+    Raises
+    ------
+    ValueError
+        Thrown when the number of channels in the input image and the reference
+        differ.
+
+    References
+    ----------
+    .. [1] http://paulbourke.net/miscellaneous/equalisation/
+
+    """
+    if image.ndim != reference.ndim:
+        raise ValueError('Image and reference must have the same number '
+                         'of channels.')
+
+    if multichannel:
+        if image.shape[-1] != reference.shape[-1]:
+            raise ValueError('Number of channels in the input image and '
+                             'reference image must match!')
+
+        matched = cp.empty(image.shape, dtype=image.dtype)
+        for channel in range(image.shape[-1]):
+            matched_channel = _match_cumulative_cdf(image[..., channel],
+                                                    reference[..., channel])
+            matched[..., channel] = matched_channel
+    else:
+        matched = _match_cumulative_cdf(image, reference)
+
+    return matched
diff --git a/python/cucim/src/cucim/skimage/exposure/tests/test_exposure.py b/python/cucim/src/cucim/skimage/exposure/tests/test_exposure.py
new file mode 100644
index 000000000..44bdddeef
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/tests/test_exposure.py
@@ -0,0 +1,778 @@
+import warnings
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_almost_equal
+from skimage import data
+
+from cucim.skimage import exposure, util
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.color import rgb2gray
+from cucim.skimage.exposure.exposure import intensity_range
+from cucim.skimage.util.dtype import dtype_range
+
+# Test integer histograms
+# =======================
+
+
+def test_wrong_source_range():
+    im = cp.array([-1, 100], dtype=np.int8)
+    with pytest.raises(ValueError):
+        frequencies, bin_centers = exposure.histogram(im, source_range="foobar")
+
+
+def test_negative_overflow():
+    im = cp.array([-1, 100], dtype=np.int8)
+    frequencies, bin_centers = exposure.histogram(im)
+    assert_array_equal(bin_centers, cp.arange(-1, 101))
+    assert frequencies[0] == 1
+    assert frequencies[-1] == 1
+    assert_array_equal(frequencies[1:-1], 0)
+
+
+def test_all_negative_image():
+    im = cp.array([-100, -1], dtype=np.int8)
+    frequencies, bin_centers = exposure.histogram(im)
+    assert_array_equal(bin_centers, cp.arange(-100, 0))
+    assert frequencies[0] == 1
+    assert frequencies[-1] == 1
+    assert_array_equal(frequencies[1:-1], 0)
+
+
+def test_int_range_image():
+    im = cp.array([10, 100], dtype=np.int8)
+    frequencies, bin_centers = exposure.histogram(im)
+    assert len(bin_centers) == len(frequencies)
+    assert bin_centers[0] == 10
+    assert bin_centers[-1] == 100
+
+
+def test_peak_uint_range_dtype():
+    im = cp.array([10, 100], dtype=np.uint8)
+    frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
+    assert_array_equal(bin_centers, cp.arange(0, 256))
+    assert frequencies[10] == 1
+    assert frequencies[100] == 1
+    assert frequencies[101] == 0
+    assert frequencies.shape == (256,)
+
+
+def test_peak_int_range_dtype():
+    im = cp.array([10, 100], dtype=np.int8)
+    frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
+    assert_array_equal(bin_centers, cp.arange(-128, 128))
+    assert frequencies[128 + 10] == 1
+    assert frequencies[128 + 100] == 1
+    assert frequencies[128 + 101] == 0
+    assert frequencies.shape == (256,)
+
+
+def test_flat_uint_range_dtype():
+    im = cp.linspace(0, 255, 256, dtype=np.uint8)
+    frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
+    assert_array_equal(bin_centers, cp.arange(0, 256))
+    assert frequencies.shape == (256,)
+
+
+def test_flat_int_range_dtype():
+    im = cp.linspace(-128, 128, 256, dtype=np.int8)
+    frequencies, bin_centers = exposure.histogram(im, source_range="dtype")
+    assert_array_equal(bin_centers, cp.arange(-128, 128))
+    assert frequencies.shape == (256,)
+
+
+def test_peak_float_out_of_range_image():
+    im = cp.array([10, 100], dtype=np.float16)
+    frequencies, bin_centers = exposure.histogram(im, nbins=90)
+    # offset values by 0.5 for float...
+    assert_array_equal(bin_centers, cp.arange(10, 100) + 0.5)
+
+
+def test_peak_float_out_of_range_dtype():
+    im = cp.array([10, 100], dtype=np.float16)
+    nbins = 10
+    frequencies, bin_centers = exposure.histogram(
+        im, nbins=nbins, source_range='dtype'
+    )
+    assert_almost_equal(cp.min(bin_centers).get(), -0.9, 3)
+    assert_almost_equal(cp.max(bin_centers).get(), 0.9, 3)
+    assert len(bin_centers) == 10
+
+
+def test_normalize():
+    im = cp.array([0, 255, 255], dtype=np.uint8)
+    frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
+                                                  normalize=False)
+
+    expected = cp.zeros(256)
+    expected[0] = 1
+    expected[-1] = 2
+    assert_array_equal(frequencies, expected)
+    frequencies, bin_centers = exposure.histogram(im, source_range='dtype',
+                                                  normalize=True)
+
+    expected /= 3.0
+    assert_array_equal(frequencies, expected)
+
+
+# Test histogram equalization
+# ===========================
+
+np.random.seed(0)
+
+test_img_int = cp.array(data.camera())
+# squeeze image intensities to lower image contrast
+test_img = util.img_as_float(test_img_int)
+test_img = exposure.rescale_intensity(test_img / 5.0 + 100)
+test_img = cp.array(test_img)
+
+
+def test_equalize_uint8_approx():
+    """Check integer bins used for uint8 images."""
+    img_eq0 = exposure.equalize_hist(test_img_int)
+    img_eq1 = exposure.equalize_hist(test_img_int, nbins=3)
+    cp.testing.assert_allclose(img_eq0, img_eq1)
+
+
+def test_equalize_ubyte():
+    img = util.img_as_ubyte(test_img)
+    img_eq = exposure.equalize_hist(img)
+
+    cdf, bin_edges = exposure.cumulative_distribution(img_eq)
+    check_cdf_slope(cdf)
+
+
+def test_equalize_float():
+    img = util.img_as_float(test_img)
+    img_eq = exposure.equalize_hist(img)
+
+    cdf, bin_edges = exposure.cumulative_distribution(img_eq)
+    check_cdf_slope(cdf)
+
+
+def test_equalize_masked():
+    img = util.img_as_float(test_img)
+    mask = cp.zeros(test_img.shape)
+    mask[100:400, 100:400] = 1
+    img_mask_eq = exposure.equalize_hist(img, mask=mask)
+    img_eq = exposure.equalize_hist(img)
+
+    cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq)
+    check_cdf_slope(cdf)
+
+    assert not (img_eq == img_mask_eq).all()
+
+
+def check_cdf_slope(cdf):
+    """Slope of cdf which should equal 1 for an equalized histogram."""
+    norm_intensity = np.linspace(0, 1, len(cdf))
+    slope, intercept = np.polyfit(norm_intensity, cp.asnumpy(cdf), 1)
+    assert 0.9 < slope < 1.1
+
+
+# Test intensity range
+# ====================
+
+
+@pytest.mark.parametrize("test_input,expected", [
+    ('image', [0, 1]),
+    ('dtype', [0, 255]),
+    ((10, 20), [10, 20])
+])
+def test_intensity_range_uint8(test_input, expected):
+    image = cp.array([0, 1], dtype=np.uint8)
+    out = intensity_range(image, range_values=test_input)
+    assert_array_equal(out, cp.array(expected))
+
+
+@pytest.mark.parametrize("test_input,expected", [
+    ('image', [0.1, 0.2]),
+    ('dtype', [-1, 1]),
+    ((0.3, 0.4), [0.3, 0.4])
+])
+def test_intensity_range_float(test_input, expected):
+    image = cp.array([0.1, 0.2], dtype=np.float64)
+    out = intensity_range(image, range_values=test_input)
+    assert_array_equal(out, expected)
+
+
+def test_intensity_range_clipped_float():
+    image = cp.array([0.1, 0.2], dtype=np.float64)
+    out = intensity_range(image, range_values="dtype", clip_negative=True)
+    assert_array_equal(out, (0, 1))
+
+
+# Test rescale intensity
+# ======================
+
+uint10_max = 2**10 - 1
+uint12_max = 2**12 - 1
+uint14_max = 2**14 - 1
+uint16_max = 2**16 - 1
+
+
+def test_rescale_stretch():
+    image = cp.array([51, 102, 153], dtype=np.uint8)
+    out = exposure.rescale_intensity(image)
+    assert out.dtype == np.uint8
+    assert_array_almost_equal(out, [0, 127, 255])
+
+
+def test_rescale_shrink():
+    image = cp.array([51.0, 102.0, 153.0])
+    out = exposure.rescale_intensity(image)
+    assert_array_almost_equal(out, [0, 0.5, 1])
+
+
+def test_rescale_in_range():
+    image = cp.array([51.0, 102.0, 153.0])
+    out = exposure.rescale_intensity(image, in_range=(0, 255))
+    assert_array_almost_equal(out, [0.2, 0.4, 0.6])
+
+
+def test_rescale_in_range_clip():
+    image = cp.array([51.0, 102.0, 153.0])
+    out = exposure.rescale_intensity(image, in_range=(0, 102))
+    assert_array_almost_equal(out, [0.5, 1, 1])
+
+
+def test_rescale_out_range():
+    """Check that output range is correct.
+
+    .. versionchanged:: 0.17
+        This function used to return dtype matching the input dtype. It now
+        matches the output.
+    """
+    image = cp.array([-10, 0, 10], dtype=np.int8)
+    out = exposure.rescale_intensity(image, out_range=(0, 127))
+    assert out.dtype == float
+    assert_array_almost_equal(out, [0, 63.5, 127])
+
+
+def test_rescale_named_in_range():
+    image = cp.array([0, uint10_max, uint10_max + 100], dtype=np.uint16)
+    out = exposure.rescale_intensity(image, in_range='uint10')
+    assert_array_almost_equal(out, [0, uint16_max, uint16_max])
+
+
+def test_rescale_named_out_range():
+    image = cp.array([0, uint16_max], dtype=np.uint16)
+    out = exposure.rescale_intensity(image, out_range='uint10')
+    assert_array_almost_equal(out, [0, uint10_max])
+
+
+def test_rescale_uint12_limits():
+    image = cp.array([0, uint16_max], dtype=np.uint16)
+    out = exposure.rescale_intensity(image, out_range='uint12')
+    assert_array_almost_equal(out, [0, uint12_max])
+
+
+def test_rescale_uint14_limits():
+    image = cp.array([0, uint16_max], dtype=np.uint16)
+    out = exposure.rescale_intensity(image, out_range='uint14')
+    assert_array_almost_equal(out, [0, uint14_max])
+
+
+def test_rescale_all_zeros():
+    image = cp.zeros((2, 2), dtype=np.uint8)
+    out = exposure.rescale_intensity(image)
+    assert ~cp.isnan(out).all()
+    assert_array_almost_equal(out, image)
+
+
+def test_rescale_constant():
+    image = cp.array([130, 130], dtype=np.uint16)
+    out = exposure.rescale_intensity(image, out_range=(0, 127))
+    assert_array_almost_equal(out, [127, 127])
+
+
+def test_rescale_same_values():
+    image = cp.ones((2, 2))
+    out = exposure.rescale_intensity(image)
+    assert ~cp.isnan(out).all()
+    assert_array_almost_equal(out, image)
+
+
+@pytest.mark.parametrize(
+    "in_range,out_range", [("image", "dtype"),
+                           ("dtype", "image")]
+)
+def test_rescale_nan_warning(in_range, out_range):
+    image = cp.arange(12, dtype=float).reshape(3, 4)
+    image[1, 1] = np.nan
+
+    msg = (
+        r"One or more intensity levels are NaN\."
+        r" Rescaling will broadcast NaN to the full image\."
+    )
+
+    with expected_warnings([msg]):
+        exposure.rescale_intensity(image, in_range, out_range)
+
+
+@pytest.mark.parametrize(
+    "out_range, out_dtype", [
+        ('uint8', np.uint8),
+        ('uint10', np.uint16),
+        ('uint12', np.uint16),
+        ('uint16', np.uint16),
+        ('float', float),
+    ]
+)
+def test_rescale_output_dtype(out_range, out_dtype):
+    image = cp.array([-128, 0, 127], dtype=np.int8)
+    output_image = exposure.rescale_intensity(image, out_range=out_range)
+    assert output_image.dtype == out_dtype
+
+
+def test_rescale_no_overflow():
+    image = cp.array([-128, 0, 127], dtype=np.int8)
+    output_image = exposure.rescale_intensity(image, out_range=np.uint8)
+    cp.testing.assert_array_equal(output_image, [0, 128, 255])
+    assert output_image.dtype == np.uint8
+
+
+def test_rescale_float_output():
+    image = cp.array([-128, 0, 127], dtype=np.int8)
+    output_image = exposure.rescale_intensity(image, out_range=(0, 255))
+    cp.testing.assert_array_equal(output_image, [0, 128, 255])
+    assert output_image.dtype == float
+
+
+def test_rescale_raises_on_incorrect_out_range():
+    image = cp.array([-128, 0, 127], dtype=np.int8)
+    with pytest.raises(ValueError):
+        _ = exposure.rescale_intensity(image, out_range="flat")
+
+
+# Test adaptive histogram equalization
+# ====================================
+
+
+def test_adapthist_grayscale():
+    """Test a grayscale float image"""
+    img = util.img_as_float(cp.array(data.astronaut()))
+    img = rgb2gray(img)
+    img = cp.dstack((img, img, img))
+    adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51),
+                                          clip_limit=0.01, nbins=128)
+    assert img.shape == adapted.shape
+    assert_almost_equal(float(peak_snr(img, adapted)), 100.140, 3)
+    assert_almost_equal(float(norm_brightness_err(img, adapted)), 0.0529, 3)
+
+
+def test_adapthist_color():
+    """Test an RGB color uint16 image
+    """
+    img = util.img_as_uint(cp.array(data.astronaut()))
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter('always')
+        hist, bin_centers = exposure.histogram(img)
+        assert len(w) > 0
+    adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
+
+    assert adapted.min() == 0
+    assert adapted.max() == 1.0
+    assert img.shape == adapted.shape
+    full_scale = exposure.rescale_intensity(img)
+    assert_almost_equal(float(peak_snr(full_scale, adapted)), 109.393, 1)
+    assert_almost_equal(
+        float(norm_brightness_err(full_scale, adapted)), 0.02, 2)
+    return data, adapted
+
+
+def test_adapthist_alpha():
+    """Test an RGBA color image"""
+    img = util.img_as_float(cp.array(data.astronaut()))
+    alpha = cp.ones((img.shape[0], img.shape[1]), dtype=float)
+    img = cp.dstack((img, alpha))
+    adapted = exposure.equalize_adapthist(img)
+    assert adapted.shape != img.shape
+    img = img[:, :, :3]
+    full_scale = exposure.rescale_intensity(img)
+    assert img.shape == adapted.shape
+    assert_almost_equal(float(peak_snr(full_scale, adapted)), 109.393, 2)
+    assert_almost_equal(
+        float(norm_brightness_err(full_scale, adapted)), 0.0248, 3
+    )
+
+
+def test_adapthist_grayscale_Nd():
+    """
+    Test for n-dimensional consistency with float images
+    Note: Currently if img.ndim == 3, img.shape[2] > 4 must hold for the image
+    not to be interpreted as a color image by @adapt_rgb
+    """
+    # take 2d image, subsample and stack it
+    img = util.img_as_float(cp.array(data.astronaut()))
+    img = rgb2gray(img)
+    a = 15
+    img2d = util.img_as_float(img[0:-1:a, 0:-1:a])
+    img3d = cp.stack([img2d] * (img.shape[0] // a), axis=0)
+
+    # apply CLAHE
+    adapted2d = exposure.equalize_adapthist(img2d,
+                                            kernel_size=5,
+                                            clip_limit=0.05)
+    adapted3d = exposure.equalize_adapthist(img3d,
+                                            kernel_size=5,
+                                            clip_limit=0.05)
+
+    # check that dimensions of input and output match
+    assert img2d.shape == adapted2d.shape
+    assert img3d.shape == adapted3d.shape
+
+    # check that the result from the stack of 2d images is similar
+    # to the underlying 2d image
+    assert cp.mean(cp.abs(adapted2d
+                          - adapted3d[adapted3d.shape[0] // 2])) < 0.02
+
+
+def test_adapthist_constant():
+    """Test constant image, float and uint
+    """
+    img = cp.zeros((8, 8))
+    img += 2
+    img = img.astype(np.uint16)
+    adapted = exposure.equalize_adapthist(img, 3)
+    assert cp.min(adapted) == cp.max(adapted)
+
+    img = cp.zeros((8, 8))
+    img += 0.1
+    img = img.astype(np.float64)
+    adapted = exposure.equalize_adapthist(img, 3)
+    assert cp.min(adapted) == cp.max(adapted)
+
+
+def test_adapthist_borders():
+    """Test border processing
+    """
+    img = rgb2gray(util.img_as_float(cp.array(data.astronaut())))
+
+    # maximize difference between orig and processed img
+    img /= 100.0
+    img[img.shape[0] // 2, img.shape[1] // 2] = 1.0
+
+    # check borders are processed for different kernel sizes
+    border_index = -1
+    for kernel_size in range(51, 71, 2):
+        adapted = exposure.equalize_adapthist(img, kernel_size, clip_limit=0.5)
+        # Check last columns are processed
+        assert norm_brightness_err(adapted[:, border_index],
+                                   img[:, border_index]) > 0.1
+        # Check last rows are processed
+        assert norm_brightness_err(adapted[border_index, :],
+                                   img[border_index, :]) > 0.1
+
+
+def test_adapthist_clip_limit():
+    img_u = cp.array(data.moon())
+    img_f = util.img_as_float(img_u)
+
+    # uint8 input
+    img_clahe0 = exposure.equalize_adapthist(img_u, clip_limit=0)
+    img_clahe1 = exposure.equalize_adapthist(img_u, clip_limit=1)
+    assert_array_equal(img_clahe0, img_clahe1)
+
+    # float64 input
+    img_clahe0 = exposure.equalize_adapthist(img_f, clip_limit=0)
+    img_clahe1 = exposure.equalize_adapthist(img_f, clip_limit=1)
+    assert_array_equal(img_clahe0, img_clahe1)
+
+
+def peak_snr(img1, img2):
+    """Peak signal to noise ratio of two images
+
+    Parameters
+    ----------
+    img1 : array-like
+    img2 : array-like
+
+    Returns
+    -------
+    peak_snr : float
+        Peak signal to noise ratio
+    """
+    if img1.ndim == 3:
+        img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy())
+    img1 = util.img_as_float(img1)
+    img2 = util.img_as_float(img2)
+    mse = 1.0 / img1.size * cp.square(img1 - img2).sum()
+    _, max_ = dtype_range[img1.dtype.type]
+    return 20 * cp.log(max_ / mse)
+
+
+def norm_brightness_err(img1, img2):
+    """Normalized Absolute Mean Brightness Error between two images
+
+    Parameters
+    ----------
+    img1 : array-like
+    img2 : array-like
+
+    Returns
+    -------
+    norm_brightness_error : float
+        Normalized absolute mean brightness error
+    """
+    if img1.ndim == 3:
+        img1, img2 = rgb2gray(img1), rgb2gray(img2)
+    ambe = cp.abs(img1.mean() - img2.mean())
+    nbe = ambe / dtype_range[img1.dtype.type][1]
+    return nbe
+
+
+# Test Gamma Correction
+# =====================
+
+
+def test_adjust_gamma_1x1_shape():
+    """Check that the shape is maintained"""
+    img = cp.ones([1, 1])
+    result = exposure.adjust_gamma(img, 1.5)
+    assert img.shape == result.shape
+
+
+def test_adjust_gamma_one():
+    """Same image should be returned for gamma equal to one"""
+    image = cp.random.uniform(0, 255, (8, 8))
+    result = exposure.adjust_gamma(image, 1)
+    assert_array_almost_equal(result, image)
+
+
+def test_adjust_gamma_zero():
+    """White image should be returned for gamma equal to zero"""
+    image = cp.random.uniform(0, 255, (8, 8))
+    result = exposure.adjust_gamma(image, 0)
+    dtype = image.dtype.type
+    assert_array_almost_equal(result, dtype_range[dtype][1])
+
+
+def test_adjust_gamma_less_one():
+    """Verifying the output with expected results for gamma
+    correction with gamma equal to half"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  0,  31,  45,  55,  63,  71,  78,  84],  # noqa
+        [ 90,  95, 100, 105, 110, 115, 119, 123],  # noqa
+        [127, 131, 135, 139, 142, 146, 149, 153],
+        [156, 159, 162, 165, 168, 171, 174, 177],
+        [180, 183, 186, 188, 191, 194, 196, 199],
+        [201, 204, 206, 209, 211, 214, 216, 218],
+        [221, 223, 225, 228, 230, 232, 234, 236],
+        [238, 241, 243, 245, 247, 249, 251, 253]], dtype=np.uint8)
+    # fmt: on
+
+    result = exposure.adjust_gamma(image, 0.5)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_gamma_greater_one():
+    """Verifying the output with expected results for gamma
+    correction with gamma equal to two"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  0,   0,   0,   0,   1,   1,   2,   3],  # noqa
+        [  4,   5,   6,   7,   9,  10,  12,  14],  # noqa
+        [ 16,  18,  20,  22,  25,  27,  30,  33],  # noqa
+        [ 36,  39,  42,  45,  49,  52,  56,  60],  # noqa
+        [ 64,  68,  72,  76,  81,  85,  90,  95],  # noqa
+        [100, 105, 110, 116, 121, 127, 132, 138],
+        [144, 150, 156, 163, 169, 176, 182, 189],
+        [196, 203, 211, 218, 225, 233, 241, 249]], dtype=np.uint8)
+    # fmt: on
+
+    result = exposure.adjust_gamma(image, 2)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_gamma_neggative():
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    with pytest.raises(ValueError):
+        exposure.adjust_gamma(image, -1)
+
+
+# Test Logarithmic Correction
+# ===========================
+
+
+def test_adjust_log_1x1_shape():
+    """Check that the shape is maintained"""
+    img = cp.ones([1, 1])
+    result = exposure.adjust_log(img, 1)
+    assert img.shape == result.shape
+
+
+def test_adjust_log():
+    """Verifying the output with expected results for logarithmic
+    correction with multiplier constant multiplier equal to unity"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  0,   5,  11,  16,  22,  27,  33,  38],  # noqa
+        [ 43,  48,  53,  58,  63,  68,  73,  77],  # noqa
+        [ 82,  86,  91,  95, 100, 104, 109, 113],  # noqa
+        [117, 121, 125, 129, 133, 137, 141, 145],
+        [149, 153, 157, 160, 164, 168, 172, 175],
+        [179, 182, 186, 189, 193, 196, 199, 203],
+        [206, 209, 213, 216, 219, 222, 225, 228],
+        [231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8)
+    # fmt: on
+
+    result = exposure.adjust_log(image, 1)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_inv_log():
+    """Verifying the output with expected results for inverse logarithmic
+    correction with multiplier constant multiplier equal to unity"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  0,   2,   5,   8,  11,  14,  17,  20],  # noqa
+        [ 23,  26,  29,  32,  35,  38,  41,  45],  # noqa
+        [ 48,  51,  55,  58,  61,  65,  68,  72],  # noqa
+        [ 76,  79,  83,  87,  90,  94,  98, 102],  # noqa
+        [106, 110, 114, 118, 122, 126, 130, 134],
+        [138, 143, 147, 151, 156, 160, 165, 170],
+        [174, 179, 184, 188, 193, 198, 203, 208],
+        [213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8)
+    # fmt: on
+
+    result = exposure.adjust_log(image, 1, True)
+    assert_array_equal(result, expected)
+
+
+# Test Sigmoid Correction
+# =======================
+
+
+def test_adjust_sigmoid_1x1_shape():
+    """Check that the shape is maintained"""
+    img = cp.ones([1, 1])
+    result = exposure.adjust_sigmoid(img, 1, 5)
+    assert img.shape == result.shape
+
+
+def test_adjust_sigmoid_cutoff_one():
+    """Verifying the output with expected results for sigmoid correction
+    with cutoff equal to one and gain of 5"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  1,   1,   1,   2,   2,   2,   2,   2],  # noqa
+        [  3,   3,   3,   4,   4,   4,   5,   5],  # noqa
+        [  5,   6,   6,   7,   7,   8,   9,  10],  # noqa
+        [ 10,  11,  12,  13,  14,  15,  16,  18],  # noqa
+        [ 19,  20,  22,  24,  25,  27,  29,  32],  # noqa
+        [ 34,  36,  39,  41,  44,  47,  50,  54],  # noqa
+        [ 57,  61,  64,  68,  72,  76,  80,  85],  # noqa
+        [ 89,  94,  99, 104, 108, 113, 118, 123]], dtype=np.uint8)  # noqa
+    # fmt: on
+
+    result = exposure.adjust_sigmoid(image, 1, 5)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_sigmoid_cutoff_zero():
+    """Verifying the output with expected results for sigmoid correction
+    with cutoff equal to zero and gain of 10"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [127, 137, 147, 156, 166, 175, 183, 191],
+        [198, 205, 211, 216, 221, 225, 229, 232],
+        [235, 238, 240, 242, 244, 245, 247, 248],
+        [249, 250, 250, 251, 251, 252, 252, 253],
+        [253, 253, 253, 253, 254, 254, 254, 254],
+        [254, 254, 254, 254, 254, 254, 254, 254],
+        [254, 254, 254, 254, 254, 254, 254, 254],
+        [254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8)
+    # fmt: on
+
+    result = exposure.adjust_sigmoid(image, 0, 10)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_sigmoid_cutoff_half():
+    """Verifying the output with expected results for sigmoid correction
+    with cutoff equal to half and gain of 10"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [  1,   1,   2,   2,   3,   3,   4,   5],  # noqa
+        [  5,   6,   7,   9,  10,  12,  14,  16],  # noqa
+        [ 19,  22,  25,  29,  34,  39,  44,  50],  # noqa
+        [ 57,  64,  72,  80,  89,  99, 108, 118],  # noqa
+        [128, 138, 148, 158, 167, 176, 184, 192],
+        [199, 205, 211, 217, 221, 226, 229, 233],
+        [236, 238, 240, 242, 244, 246, 247, 248],
+        [249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8)
+    # fmt: on
+    result = exposure.adjust_sigmoid(image, 0.5, 10)
+    assert_array_equal(result, expected)
+
+
+def test_adjust_inv_sigmoid_cutoff_half():
+    """Verifying the output with expected results for inverse sigmoid
+    correction with cutoff equal to half and gain of 10"""
+    image = cp.arange(0, 255, 4, np.uint8).reshape((8, 8))
+    # fmt: off
+    expected = cp.array([
+        [253, 253, 252, 252, 251, 251, 250, 249],
+        [249, 248, 247, 245, 244, 242, 240, 238],
+        [235, 232, 229, 225, 220, 215, 210, 204],
+        [197, 190, 182, 174, 165, 155, 146, 136],
+        [126, 116, 106,  96,  87,  78,  70,  62],  # noqa
+        [ 55,  49,  43,  37,  33,  28,  25,  21],  # noqa
+        [ 18,  16,  14,  12,  10,   8,   7,   6],  # noqa
+        [  5,   4,   4,   3,   3,   2,   2,   1]], dtype=np.uint8)  # noqa
+    # fmt: on
+
+    result = exposure.adjust_sigmoid(image, 0.5, 10, True)
+    assert_array_equal(result, expected)
+
+
+def test_negative():
+    image = cp.arange(-10, 245, 4).reshape((8, 8)).astype(np.double)
+    with pytest.raises(ValueError):
+        exposure.adjust_gamma(image)
+
+
+def test_is_low_contrast():
+    image = cp.linspace(0, 0.04, 100)
+    assert exposure.is_low_contrast(image)
+    image[-1] = 1
+    assert exposure.is_low_contrast(image)
+    assert not exposure.is_low_contrast(image, upper_percentile=100)
+
+    image = (image * 255).astype(np.uint8)
+    assert exposure.is_low_contrast(image)
+    assert not exposure.is_low_contrast(image, upper_percentile=100)
+
+    image = (image.astype(np.uint16)) * 2 ** 8
+    assert exposure.is_low_contrast(image)
+    assert not exposure.is_low_contrast(image, upper_percentile=100)
+
+
+# Test Dask Compatibility
+# =======================
+
+
+# TODO: this Dask-based test case does not work (segfault!)
+# @pytest.mark.xfail(True, reason="dask case not currently supported")
+@pytest.mark.skip("dask case not currently supported")
+def test_dask_histogram():
+    pytest.importorskip('dask', reason="dask python library is not installed")
+    import dask.array as da
+
+    dask_array = da.from_array(cp.array([[0, 1], [1, 2]]), chunks=(1, 2))
+    output_hist, output_bins = exposure.histogram(dask_array)
+    expected_bins = [0, 1, 2]
+    expected_hist = [1, 2, 1]
+    assert cp.allclose(expected_bins, output_bins)
+    assert cp.allclose(expected_hist, output_hist)
diff --git a/python/cucim/src/cucim/skimage/exposure/tests/test_histogram_matching.py b/python/cucim/src/cucim/skimage/exposure/tests/test_histogram_matching.py
new file mode 100644
index 000000000..717c370a4
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/exposure/tests/test_histogram_matching.py
@@ -0,0 +1,84 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal
+from numpy.testing import assert_almost_equal
+from skimage import data
+
+from cucim.skimage import exposure
+from cucim.skimage.exposure import histogram_matching
+
+
+@pytest.mark.parametrize('array, template, expected_array', [
+    (cp.arange(10), cp.arange(100), cp.arange(9, 100, 10)),
+    (cp.random.rand(4), cp.ones(3), cp.ones(4))
+])
+def test_match_array_values(array, template, expected_array):
+    # when
+    matched = histogram_matching._match_cumulative_cdf(array, template)
+
+    # then
+    assert_array_almost_equal(matched, expected_array)
+
+
+class TestMatchHistogram:
+
+    image_rgb = cp.asarray(data.chelsea())
+    template_rgb = cp.asarray(data.astronaut())
+
+    @pytest.mark.parametrize('image, reference, multichannel', [
+        (image_rgb, template_rgb, True),
+        (image_rgb[:, :, 0], template_rgb[:, :, 0], False)
+    ])
+    def test_match_histograms(self, image, reference, multichannel):
+        """Assert that pdf of matched image is close to the reference's pdf for
+        all channels and all values of matched"""
+
+        # when
+        matched = exposure.match_histograms(image, reference,
+                                            multichannel=multichannel)
+
+        matched = cp.asnumpy(matched)
+        matched_pdf = self._calculate_image_empirical_pdf(matched)
+        reference_pdf = self._calculate_image_empirical_pdf(
+            cp.asnumpy(reference))
+
+        # then
+        for channel in range(len(matched_pdf)):
+            reference_values, reference_quantiles = reference_pdf[channel]
+            matched_values, matched_quantiles = matched_pdf[channel]
+
+            for i, matched_value in enumerate(matched_values):
+                closest_id = (
+                    np.abs(reference_values - matched_value)
+                ).argmin()
+                assert_almost_equal(matched_quantiles[i],
+                                    reference_quantiles[closest_id],
+                                    decimal=1)
+
+    @pytest.mark.parametrize('image, reference', [
+        (image_rgb, template_rgb[:, :, 0]),
+        (image_rgb[:, :, 0], template_rgb)
+    ])
+    def test_raises_value_error_on_channels_mismatch(self, image, reference):
+        with pytest.raises(ValueError):
+            exposure.match_histograms(image, reference)
+
+    @classmethod
+    def _calculate_image_empirical_pdf(cls, image):
+        """Helper function for calculating empirical probability density
+        function of a given image for all channels"""
+
+        if image.ndim > 2:
+            image = image.transpose(2, 0, 1)
+        channels = np.array(image, copy=False, ndmin=3)
+
+        channels_pdf = []
+        for channel in channels:
+            channel_values, counts = np.unique(channel, return_counts=True)
+            channel_quantiles = np.cumsum(counts).astype(np.float64)
+            channel_quantiles /= channel_quantiles[-1]
+
+            channels_pdf.append((channel_values, channel_quantiles))
+
+        return np.asarray(channels_pdf, dtype=object)
diff --git a/python/cucim/src/cucim/skimage/feature/__init__.py b/python/cucim/src/cucim/skimage/feature/__init__.py
new file mode 100644
index 000000000..57d100893
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/__init__.py
@@ -0,0 +1,66 @@
+from .._shared.utils import deprecated
+from ._basic_features import multiscale_basic_features
+from ._canny import canny
+from ._daisy import daisy
+from .corner import (corner_foerstner, corner_harris, corner_kitchen_rosenfeld,
+                     corner_peaks, corner_shi_tomasi, hessian_matrix,
+                     hessian_matrix_det, hessian_matrix_eigvals, shape_index,
+                     structure_tensor, structure_tensor_eigenvalues,
+                     structure_tensor_eigvals)
+from .peak import peak_local_max
+from .template import match_template
+
+
+@deprecated(
+    alt_func="cucim.skimage.registration.phase_cross_correlation",
+    removed_version="0.19",
+)
+def masked_register_translation(
+    src_image, target_image, src_mask, target_mask=None, overlap_ratio=0.3
+):
+    from ..registration import phase_cross_correlation
+
+    return phase_cross_correlation(
+        src_image,
+        target_image,
+        reference_mask=src_mask,
+        moving_mask=target_mask,
+        overlap_ratio=overlap_ratio,
+    )
+
+
+@deprecated(
+    alt_func="cucim.skimage.registration.phase_cross_correlation",
+    removed_version="0.19",
+)
+def register_translation(
+    src_image, target_image, upsample_factor=1, space="real", return_error=True
+):
+    from ..registration._phase_cross_correlation import \
+        phase_cross_correlation as func
+    return func(src_image, target_image, upsample_factor, space, return_error)
+
+
+__all__ = ['canny',
+           'daisy',
+           'multiscale_basic_features',
+           'peak_local_max',
+           'structure_tensor',
+           'structure_tensor_eigenvalues',
+           'structure_tensor_eigvals',
+           'hessian_matrix',
+           'hessian_matrix_det',
+           'hessian_matrix_eigvals',
+           'shape_index',
+           'corner_kitchen_rosenfeld',
+           'corner_harris',
+           'corner_shi_tomasi',
+           'corner_foerstner',
+           # 'corner_subpix',
+           'corner_peaks',
+           # 'corner_moravec',
+           # 'corner_fast',
+           # 'corner_orientations',
+           'match_template',
+           'register_translation',
+           'masked_register_translation']
diff --git a/python/cucim/src/cucim/skimage/feature/_basic_features.py b/python/cucim/src/cucim/skimage/feature/_basic_features.py
new file mode 100644
index 000000000..7709b591b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/_basic_features.py
@@ -0,0 +1,172 @@
+import functools
+import itertools
+import math
+from itertools import combinations_with_replacement
+
+import cupy as cp
+import numpy as np
+
+from cucim.skimage import feature, filters, img_as_float32
+
+
+def _texture_filter(gaussian_filtered):
+    combos = combinations_with_replacement
+    H_elems = [
+        cp.gradient(cp.gradient(gaussian_filtered)[ax0], axis=ax1)
+        for ax0, ax1 in combos(range(gaussian_filtered.ndim), 2)
+    ]
+    eigvals = feature.hessian_matrix_eigvals(H_elems)
+    return eigvals
+
+
+def _singlescale_basic_features_singlechannel(
+    img, sigma, intensity=True, edges=True, texture=True
+):
+    results = ()
+    gaussian_filtered = filters.gaussian(img, sigma)
+    if intensity:
+        results += (gaussian_filtered,)
+    if edges:
+        results += (filters.sobel(gaussian_filtered),)
+    if texture:
+        results += (*_texture_filter(gaussian_filtered),)
+    return results
+
+
+def _mutiscale_basic_features_singlechannel(
+    img,
+    intensity=True,
+    edges=True,
+    texture=True,
+    sigma_min=0.5,
+    sigma_max=16,
+    num_sigma=None,
+    num_workers=None,
+):
+    """Features for a single channel nd image.
+
+    Parameters
+    ----------
+    img : ndarray
+        Input image, which can be grayscale or multichannel.
+    intensity : bool, default True
+        If True, pixel intensities averaged over the different scales
+        are added to the feature set.
+    edges : bool, default True
+        If True, intensities of local gradients averaged over the different
+        scales are added to the feature set.
+    texture : bool, default True
+        If True, eigenvalues of the Hessian matrix after Gaussian blurring
+        at different scales are added to the feature set.
+    sigma_min : float, optional
+        Smallest value of the Gaussian kernel used to average local
+        neighbourhoods before extracting features.
+    sigma_max : float, optional
+        Largest value of the Gaussian kernel used to average local
+        neighbourhoods before extracting features.
+    num_sigma : int, optional
+        Number of values of the Gaussian kernel between sigma_min and sigma_max.
+        If None, sigma_min multiplied by powers of 2 are used.
+    num_workers : int or None, optional
+        The number of parallel threads to use. If set to ``None``, the full
+        set of available cores are used.
+
+    Returns
+    -------
+    features : list
+        List of features, each element of the list is an array of shape as img.
+    """
+    # computations are faster as float32
+    img = cp.ascontiguousarray(img_as_float32(img))
+    if num_sigma is None:
+        num_sigma = int(math.log2(sigma_max) - math.log2(sigma_min) + 1)
+    sigmas = np.logspace(
+        math.log2(sigma_min),
+        math.log2(sigma_max),
+        num=num_sigma,
+        base=2,
+        endpoint=True,
+    )
+    singlescale_func = functools.partial(
+        _singlescale_basic_features_singlechannel,
+        intensity=intensity, edges=edges, texture=texture
+    )
+    out_sigmas = [singlescale_func(img, s) for s in sigmas]
+    features = itertools.chain.from_iterable(out_sigmas)
+    return features
+
+
+def multiscale_basic_features(
+    image,
+    multichannel=False,
+    intensity=True,
+    edges=True,
+    texture=True,
+    sigma_min=0.5,
+    sigma_max=16,
+    num_sigma=None,
+    num_workers=None,
+):
+    """Local features for a single- or multi-channel nd image.
+
+    Intensity, gradient intensity and local structure are computed at
+    different scales thanks to Gaussian blurring.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image, which can be grayscale or multichannel.
+    multichannel : bool, default False
+        True if the last dimension corresponds to color channels.
+    intensity : bool, default True
+        If True, pixel intensities averaged over the different scales
+        are added to the feature set.
+    edges : bool, default True
+        If True, intensities of local gradients averaged over the different
+        scales are added to the feature set.
+    texture : bool, default True
+        If True, eigenvalues of the Hessian matrix after Gaussian blurring
+        at different scales are added to the feature set.
+    sigma_min : float, optional
+        Smallest value of the Gaussian kernel used to average local
+        neighbourhoods before extracting features.
+    sigma_max : float, optional
+        Largest value of the Gaussian kernel used to average local
+        neighbourhoods before extracting features.
+    num_sigma : int, optional
+        Number of values of the Gaussian kernel between sigma_min and sigma_max.
+        If None, sigma_min multiplied by powers of 2 are used.
+    num_workers : int or None, optional
+        The number of parallel threads to use. If set to ``None``, the full
+        set of available cores are used.
+
+
+    Returns
+    -------
+    features : cp.ndarray
+        Array of shape ``image.shape + (n_features,)``
+    """
+    if not any([intensity, edges, texture]):
+        raise ValueError(
+            "At least one of ``intensity``, ``edges`` or ``textures``"
+            "must be True for features to be computed."
+        )
+    if image.ndim < 3:
+        multichannel = False
+    if not multichannel:
+        image = image[..., cp.newaxis]
+    all_results = (
+        _mutiscale_basic_features_singlechannel(
+            image[..., dim],
+            intensity=intensity,
+            edges=edges,
+            texture=texture,
+            sigma_min=sigma_min,
+            sigma_max=sigma_max,
+            num_sigma=num_sigma,
+            num_workers=num_workers,
+        )
+        for dim in range(image.shape[-1])
+    )
+    features = list(itertools.chain.from_iterable(all_results))
+    return cp.stack(features, axis=-1)
diff --git a/python/cucim/src/cucim/skimage/feature/_canny.py b/python/cucim/src/cucim/skimage/feature/_canny.py
new file mode 100644
index 000000000..11e4b08eb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/_canny.py
@@ -0,0 +1,316 @@
+"""
+canny.py - Canny Edge detector
+
+Reference: Canny, J., A Computational Approach To Edge Detection, IEEE Trans.
+    Pattern Analysis and Machine Intelligence, 8:679-714, 1986
+
+Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
+Website: http://www.cellprofiler.org
+Copyright (c) 2003-2009 Massachusetts Institute of Technology
+Copyright (c) 2009-2011 Broad Institute
+All rights reserved.
+Original author: Lee Kamentsky
+"""
+import functools
+
+import cupy as cp
+import cupyx.scipy.ndimage as ndi
+from cupyx.scipy.ndimage import binary_erosion, generate_binary_structure
+
+from .. import dtype_limits, img_as_float
+from .._shared.utils import check_nD
+from ..filters import gaussian
+
+
+# fuse several commonly paired ufunc operations into a single kernel call
+@cp.fuse()
+def _fused_comparison(w, c1, c2, m):
+    return c2 * w + c1 * (1.0 - w) <= m
+
+
+def smooth_with_function_and_mask(image, function, mask):
+    """Smooth an image with a linear function, ignoring masked pixels.
+
+    Parameters
+    ----------
+    image : array
+        Image you want to smooth.
+    function : callable
+        A function that does image smoothing.
+    mask : array
+        Mask with 1's for significant pixels, 0's for masked pixels.
+
+    Notes
+    ------
+    This function calculates the fractional contribution of masked pixels
+    by applying the function to the mask (which gets you the fraction of
+    the pixel data that's due to significant points). We then mask the image
+    and apply the function. The resulting values will be lower by the
+    bleed-over fraction, so you can recalibrate by dividing by the function
+    on the mask to recover the effect of smoothing from just the significant
+    pixels.
+    """
+    bleed_over = function(mask.astype(cp.float32))
+    masked_image = cp.zeros(image.shape, image.dtype)
+    masked_image[mask] = image[mask]
+    smoothed_image = function(masked_image)
+    output_image = smoothed_image / (bleed_over + cp.finfo(cp.float32).eps)
+    return output_image
+
+
+def canny(image, sigma=1., low_threshold=None, high_threshold=None, mask=None,
+          use_quantiles=False):
+    """Edge filter an image using the Canny algorithm.
+
+    Parameters
+    -----------
+    image : 2D array
+        Grayscale input image to detect edges on; can be of any dtype.
+    sigma : float, optional
+        Standard deviation of the Gaussian filter.
+    low_threshold : float, optional
+        Lower bound for hysteresis thresholding (linking edges).
+        If None, low_threshold is set to 10% of dtype's max.
+    high_threshold : float, optional
+        Upper bound for hysteresis thresholding (linking edges).
+        If None, high_threshold is set to 20% of dtype's max.
+    mask : array, dtype=bool, optional
+        Mask to limit the application of Canny to a certain area.
+    use_quantiles : bool, optional
+        If True then treat low_threshold and high_threshold as quantiles of the
+        edge magnitude image, rather than absolute edge magnitude values. If
+        True, then the thresholds must be in the range [0, 1].
+
+    Returns
+    -------
+    output : 2D array (image)
+        The binary edge map.
+
+    See also
+    --------
+    skimage.sobel
+
+    Notes
+    -----
+    The steps of the algorithm are as follows:
+
+    * Smooth the image using a Gaussian with ``sigma`` width.
+
+    * Apply the horizontal and vertical Sobel operators to get the gradients
+      within the image. The edge strength is the norm of the gradient.
+
+    * Thin potential edges to 1-pixel wide curves. First, find the normal
+      to the edge at each point. This is done by looking at the
+      signs and the relative magnitude of the X-Sobel and Y-Sobel
+      to sort the points into 4 categories: horizontal, vertical,
+      diagonal and antidiagonal. Then look in the normal and reverse
+      directions to see if the values in either of those directions are
+      greater than the point in question. Use interpolation to get a mix of
+      points instead of picking the one that's the closest to the normal.
+
+    * Perform a hysteresis thresholding: first label all points above the
+      high threshold as edges. Then recursively label any point above the
+      low threshold that is 8-connected to a labeled point as an edge.
+
+    References
+    -----------
+    .. [1] Canny, J., A Computational Approach To Edge Detection, IEEE Trans.
+           Pattern Analysis and Machine Intelligence, 8:679-714, 1986
+           :DOI:`10.1109/TPAMI.1986.4767851`
+    .. [2] William Green's Canny tutorial
+           https://en.wikipedia.org/wiki/Canny_edge_detector
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import feature
+    >>> # Generate noisy image of a square
+    >>> im = cp.zeros((256, 256))
+    >>> im[64:-64, 64:-64] = 1
+    >>> im += 0.2 * cp.random.rand(*im.shape)
+    >>> # First trial with the Canny filter, with the default smoothing
+    >>> edges1 = feature.canny(im)
+    >>> # Increase the smoothing for better results
+    >>> edges2 = feature.canny(im, sigma=3)
+    """
+
+    #
+    # The steps involved:
+    #
+    # * Smooth using the Gaussian with sigma above.
+    #
+    # * Apply the horizontal and vertical Sobel operators to get the gradients
+    #   within the image. The edge strength is the sum of the magnitudes
+    #   of the gradients in each direction.
+    #
+    # * Find the normal to the edge at each point using the arctangent of the
+    #   ratio of the Y sobel over the X sobel - pragmatically, we can
+    #   look at the signs of X and Y and the relative magnitude of X vs Y
+    #   to sort the points into 4 categories: horizontal, vertical,
+    #   diagonal and antidiagonal.
+    #
+    # * Look in the normal and reverse directions to see if the values
+    #   in either of those directions are greater than the point in question.
+    #   Use interpolation to get a mix of points instead of picking the one
+    #   that's the closest to the normal.
+    #
+    # * Label all points above the high threshold as edges.
+    # * Recursively label any point above the low threshold that is 8-connected
+    #   to a labeled point as an edge.
+    #
+    # Regarding masks, any point touching a masked point will have a gradient
+    # that is "infected" by the masked point, so it's enough to erode the
+    # mask by one and then mask the output. We also mask out the border points
+    # because who knows what lies beyond the edge of the image?
+    #
+    check_nD(image, 2)
+    dtype_max = dtype_limits(image, clip_negative=False)[1]
+
+    if low_threshold is None:
+        low_threshold = 0.1
+    elif use_quantiles:
+        if not (0.0 <= low_threshold <= 1.0):
+            raise ValueError("Quantile thresholds must be between 0 and 1.")
+    else:
+        low_threshold = low_threshold / dtype_max
+
+    if high_threshold is None:
+        high_threshold = 0.2
+    elif use_quantiles:
+        if not (0.0 <= high_threshold <= 1.0):
+            raise ValueError("Quantile thresholds must be between 0 and 1.")
+    else:
+        high_threshold = high_threshold / dtype_max
+
+    _gaussian = functools.partial(gaussian, sigma=sigma)
+
+    def fsmooth(x, mode='constant'):
+        return img_as_float(_gaussian(x, mode=mode))
+
+    if mask is None:
+        smoothed = fsmooth(image, mode='reflect')
+        # mask that is ones everywhere except the borders
+        eroded_mask = cp.ones(image.shape, dtype=bool)
+        eroded_mask[:1, :] = 0
+        eroded_mask[-1:, :] = 0
+        eroded_mask[:, :1] = 0
+        eroded_mask[:, -1:] = 0
+    else:
+        smoothed = smooth_with_function_and_mask(image, fsmooth, mask)
+        #
+        # Make the eroded mask. Setting the border value to zero will wipe
+        # out the image edges for us.
+        #
+        s = generate_binary_structure(2, 2)
+        eroded_mask = binary_erosion(mask, s, border_value=0)
+
+    jsobel = ndi.sobel(smoothed, axis=1)
+    isobel = ndi.sobel(smoothed, axis=0)
+    abs_isobel = cp.abs(isobel)
+    abs_jsobel = cp.abs(jsobel)
+    magnitude = cp.hypot(isobel, jsobel)
+    eroded_mask = eroded_mask & (magnitude > 0)
+    # TODO: implement custom kernel to compute local maxima
+
+    #
+    # --------- Find local maxima --------------
+    #
+    # Assign each point to have a normal of 0-45 degrees, 45-90 degrees,
+    # 90-135 degrees and 135-180 degrees.
+    #
+    local_maxima = cp.zeros(image.shape, bool)
+
+    isobel_gt_0 = isobel >= 0
+    jsobel_gt_0 = jsobel >= 0
+    isobel_lt_0 = isobel <= 0
+    jsobel_lt_0 = jsobel <= 0
+    abs_isobel_lt_jsobel = abs_isobel <= abs_jsobel
+    abs_isobel_gt_jsobel = abs_isobel >= abs_jsobel
+
+    # ----- 0 to 45 degrees ------
+    pts_plus = isobel_gt_0 & jsobel_gt_0
+    pts_minus = isobel_lt_0 & jsobel_lt_0
+    pts_tmp = (pts_plus | pts_minus) & eroded_mask
+    pts = pts_tmp & abs_isobel_gt_jsobel
+    # Get the magnitudes shifted left to make a matrix of the points to the
+    # right of pts. Similarly, shift left and down to get the points to the
+    # top right of pts.
+
+    c1 = magnitude[1:, :][pts[:-1, :]]
+    c2 = magnitude[1:, 1:][pts[:-1, :-1]]
+    m = magnitude[pts]
+    w = abs_jsobel[pts] / abs_isobel[pts]
+    c_plus = _fused_comparison(w, c1, c2, m)
+    c1 = magnitude[:-1, :][pts[1:, :]]
+    c2 = magnitude[:-1, :-1][pts[1:, 1:]]
+    c_minus = _fused_comparison(w, c1, c2, m)
+    local_maxima[pts] = c_plus & c_minus
+    # ----- 45 to 90 degrees ------
+    # Mix diagonal and vertical
+    #
+    pts = pts_tmp & abs_isobel_lt_jsobel
+    c1 = magnitude[:, 1:][pts[:, :-1]]
+    c2 = magnitude[1:, 1:][pts[:-1, :-1]]
+    m = magnitude[pts]
+    w = abs_isobel[pts] / abs_jsobel[pts]
+    c_plus = _fused_comparison(w, c1, c2, m)
+    c1 = magnitude[:, :-1][pts[:, 1:]]
+    c2 = magnitude[:-1, :-1][pts[1:, 1:]]
+    c_minus = _fused_comparison(w, c1, c2, m)
+    local_maxima[pts] = c_plus & c_minus
+    # ----- 90 to 135 degrees ------
+    # Mix anti-diagonal and vertical
+    #
+    pts_plus = isobel_lt_0 & jsobel_gt_0
+    pts_minus = isobel_gt_0 & jsobel_lt_0
+    pts_tmp = (pts_plus | pts_minus) & eroded_mask
+    pts = pts_tmp & abs_isobel_lt_jsobel
+    c1a = magnitude[:, 1:][pts[:, :-1]]
+    c2a = magnitude[:-1, 1:][pts[1:, :-1]]
+    m = magnitude[pts]
+    w = abs_isobel[pts] / abs_jsobel[pts]
+    c_plus = _fused_comparison(w, c1a, c2a, m)
+    c1 = magnitude[:, :-1][pts[:, 1:]]
+    c2 = magnitude[1:, :-1][pts[:-1, 1:]]
+    c_minus = _fused_comparison(w, c1, c2, m)
+    local_maxima[pts] = c_plus & c_minus
+    # ----- 135 to 180 degrees ------
+    # Mix anti-diagonal and anti-horizontal
+    #
+    pts = pts_tmp & abs_isobel_gt_jsobel
+    c1 = magnitude[:-1, :][pts[1:, :]]
+    c2 = magnitude[:-1, 1:][pts[1:, :-1]]
+    m = magnitude[pts]
+    w = abs_jsobel[pts] / abs_isobel[pts]
+    c_plus = _fused_comparison(w, c1, c2, m)
+    c1 = magnitude[1:, :][pts[:-1, :]]
+    c2 = magnitude[1:, :-1][pts[:-1, 1:]]
+    c_minus = _fused_comparison(w, c1, c2, m)
+    local_maxima[pts] = c_plus & c_minus
+
+    #
+    # ---- If use_quantiles is set then calculate the thresholds to use
+    #
+    if use_quantiles:
+        high_threshold = cp.percentile(magnitude, 100.0 * high_threshold)
+        low_threshold = cp.percentile(magnitude, 100.0 * low_threshold)
+
+    #
+    # ---- Create two masks at the two thresholds.
+    #
+    high_mask = local_maxima & (magnitude >= high_threshold)
+    low_mask = local_maxima & (magnitude >= low_threshold)
+
+    #
+    # Segment the low-mask, then only keep low-segments that have
+    # some high_mask component in them
+    #
+    labels, count = ndi.label(low_mask, structure=cp.ones((3, 3), bool))
+    if count == 0:
+        return low_mask
+
+    nonzero_sums = cp.unique(labels[high_mask])
+    good_label = cp.zeros((count + 1,), bool)
+    good_label[nonzero_sums] = True
+    output_mask = good_label[labels]
+    return output_mask
diff --git a/python/cucim/src/cucim/skimage/feature/_daisy.py b/python/cucim/src/cucim/skimage/feature/_daisy.py
new file mode 100644
index 000000000..f23dc0932
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/_daisy.py
@@ -0,0 +1,235 @@
+import math
+
+import cupy as cp
+from cupyx.scipy.ndimage import gaussian_filter
+
+from .. import img_as_float
+from .._shared.utils import check_nD
+
+
+def daisy(image, step=4, radius=15, rings=3, histograms=8, orientations=8,
+          normalization='l1', sigmas=None, ring_radii=None, visualize=False):
+    """Extract DAISY feature descriptors densely for the given image.
+
+    DAISY is a feature descriptor similar to SIFT formulated in a way that
+    allows for fast dense extraction. Typically, this is practical for
+    bag-of-features image representations.
+
+    The implementation follows Tola et al. [1]_ but deviate on the following
+    points:
+
+      * Histogram bin contribution are smoothed with a circular Gaussian
+        window over the tonal range (the angular range).
+      * The sigma values of the spatial Gaussian smoothing in this code do not
+        match the sigma values in the original code by Tola et al. [2]_. In
+        their code, spatial smoothing is applied to both the input image and
+        the center histogram. However, this smoothing is not documented in [1]_
+        and, therefore, it is omitted.
+
+    Parameters
+    ----------
+    image : (M, N) array
+        Input image (grayscale).
+    step : int, optional
+        Distance between descriptor sampling points.
+    radius : int, optional
+        Radius (in pixels) of the outermost ring.
+    rings : int, optional
+        Number of rings.
+    histograms  : int, optional
+        Number of histograms sampled per ring.
+    orientations : int, optional
+        Number of orientations (bins) per histogram.
+    normalization : [ 'l1' | 'l2' | 'daisy' | 'off' ], optional
+        How to normalize the descriptors
+
+          * 'l1': L1-normalization of each descriptor.
+          * 'l2': L2-normalization of each descriptor.
+          * 'daisy': L2-normalization of individual histograms.
+          * 'off': Disable normalization.
+
+    sigmas : 1D array of float, optional
+        Standard deviation of spatial Gaussian smoothing for the center
+        histogram and for each ring of histograms. The array of sigmas should
+        be sorted from the center and out. I.e. the first sigma value defines
+        the spatial smoothing of the center histogram and the last sigma value
+        defines the spatial smoothing of the outermost ring. Specifying sigmas
+        overrides the following parameter.
+
+            ``rings = len(sigmas) - 1``
+
+    ring_radii : 1D array of int, optional
+        Radius (in pixels) for each ring. Specifying ring_radii overrides the
+        following two parameters.
+
+            ``rings = len(ring_radii)``
+            ``radius = ring_radii[-1]``
+
+        If both sigmas and ring_radii are given, they must satisfy the
+        following predicate since no radius is needed for the center
+        histogram.
+
+            ``len(ring_radii) == len(sigmas) + 1``
+
+    visualize : bool, optional
+        Generate a visualization of the DAISY descriptors
+
+    Returns
+    -------
+    descs : array
+        Grid of DAISY descriptors for the given image as an array
+        dimensionality  (P, Q, R) where
+
+            ``P = ceil((M - radius*2) / step)``
+            ``Q = ceil((N - radius*2) / step)``
+            ``R = (rings * histograms + 1) * orientations``
+
+    descs_img : (M, N, 3) array (only if visualize==True)
+        Visualization of the DAISY descriptors.
+
+    References
+    ----------
+    .. [1] Tola et al. "Daisy: An efficient dense descriptor applied to wide-
+           baseline stereo." Pattern Analysis and Machine Intelligence, IEEE
+           Transactions on 32.5 (2010): 815-830.
+    .. [2] http://cvlab.epfl.ch/software/daisy
+    """
+
+    check_nD(image, 2, "img")
+
+    image = img_as_float(image)
+
+    # Validate parameters.
+    if sigmas is not None and ring_radii is not None \
+            and len(sigmas) - 1 != len(ring_radii):
+        raise ValueError('`len(sigmas)-1 != len(ring_radii)`')
+    if ring_radii is not None:
+        rings = len(ring_radii)
+        radius = ring_radii[-1]
+    if sigmas is not None:
+        rings = len(sigmas) - 1
+    if sigmas is None:
+        sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)]
+    if ring_radii is None:
+        ring_radii = [radius * (i + 1) / float(rings) for i in range(rings)]
+    if normalization not in ['l1', 'l2', 'daisy', 'off']:
+        raise ValueError('Invalid normalization method.')
+
+    # Compute image derivatives.
+    dx = cp.zeros(image.shape)
+    dy = cp.zeros(image.shape)
+    dx[:, :-1] = cp.diff(image, n=1, axis=1)
+    dy[:-1, :] = cp.diff(image, n=1, axis=0)
+
+    # Compute gradient orientation and magnitude and their contribution
+    # to the histograms
+    grad_mag = dx * dx
+    grad_mag += dy * dy
+    cp.sqrt(grad_mag, out=grad_mag)
+    grad_ori = cp.arctan2(dy, dx)
+    pi = cp.pi
+    orientation_kappa = orientations / pi
+    orientation_angles = [2 * o * pi / orientations - pi
+                          for o in range(orientations)]
+    hist = cp.empty((orientations,) + image.shape, dtype=float)
+    for i, o in enumerate(orientation_angles):
+        # Weigh bin contribution by the circular normal distribution
+        hist[i, :, :] = cp.exp(orientation_kappa * cp.cos(grad_ori - o))
+        # Weigh bin contribution by the gradient magnitude
+        hist[i, :, :] = cp.multiply(hist[i, :, :], grad_mag)
+
+    # Smooth orientation histograms for the center and all rings.
+    sigmas = [sigmas[0]] + sigmas
+    hist_smooth = cp.empty((rings + 1,) + hist.shape, dtype=float)
+    for i in range(rings + 1):
+        for j in range(orientations):
+            hist_smooth[i, j, :, :] = gaussian_filter(hist[j, :, :],
+                                                      sigma=sigmas[i])
+
+    # Assemble descriptor grid.
+    theta = [2 * pi * j / histograms for j in range(histograms)]
+    desc_dims = (rings * histograms + 1) * orientations
+    descs = cp.empty((desc_dims, image.shape[0] - 2 * radius,
+                      image.shape[1] - 2 * radius))
+    descs[:orientations, :, :] = hist_smooth[0, :, radius:-radius,
+                                             radius:-radius]
+    idx = orientations
+    for i in range(rings):
+        for j in range(histograms):
+            y_min = radius + int(round(ring_radii[i] * math.sin(theta[j])))
+            y_max = descs.shape[1] + y_min
+            x_min = radius + int(round(ring_radii[i] * math.cos(theta[j])))
+            x_max = descs.shape[2] + x_min
+            descs[idx:idx + orientations, :, :] = hist_smooth[i + 1, :,
+                                                              y_min:y_max,
+                                                              x_min:x_max]
+            idx += orientations
+    descs = descs[:, ::step, ::step]
+    descs = descs.swapaxes(0, 1).swapaxes(1, 2)
+
+    # Normalize descriptors.
+    if normalization != 'off':
+        descs += 1e-10
+        if normalization == 'l1':
+            descs /= cp.sum(descs, axis=2)[:, :, cp.newaxis]
+        elif normalization == 'l2':
+            descs /= cp.sqrt(cp.sum(descs * descs, axis=2))[:, :, cp.newaxis]
+        elif normalization == 'daisy':
+            for i in range(0, desc_dims, orientations):
+                norms = descs[:, :, i:i + orientations]
+                norms = norms * norms
+                norms = norms.sum(axis=2)
+                cp.sqrt(norms, out=norms)
+                descs[:, :, i:i + orientations] /= norms[:, :, cp.newaxis]
+
+    if visualize:
+        from skimage import draw
+        from skimage.color import gray2rgb
+
+        image = cp.asnumpy(image)
+        descs_img = gray2rgb(image)
+        for i in range(descs.shape[0]):
+            for j in range(descs.shape[1]):
+                # Draw center histogram sigma
+                color = [1, 0, 0]
+                desc_y = i * step + radius
+                desc_x = j * step + radius
+                rows, cols, val = draw.circle_perimeter_aa(
+                    desc_y, desc_x, int(sigmas[0]))
+                draw.set_color(descs_img, (rows, cols), color, alpha=val)
+                max_bin = float(cp.max(descs[i, j, :]))
+                for o_num, o in enumerate(orientation_angles):
+                    # Draw center histogram bins
+                    bin_size = descs[i, j, o_num] / max_bin
+                    dy = sigmas[0] * bin_size * math.sin(o)
+                    dx = sigmas[0] * bin_size * math.cos(o)
+                    rows, cols, val = draw.line_aa(
+                        desc_y, desc_x, int(desc_y + dy), int(desc_x + dx))
+                    draw.set_color(descs_img, (rows, cols), color, alpha=val)
+                for r_num, r in enumerate(ring_radii):
+                    color_offset = float(1 + r_num) / rings
+                    color = (1 - color_offset, 1, color_offset)
+                    for t_num, t in enumerate(theta):
+                        # Draw ring histogram sigmas
+                        hist_y = desc_y + int(round(r * math.sin(t)))
+                        hist_x = desc_x + int(round(r * math.cos(t)))
+                        rows, cols, val = draw.circle_perimeter_aa(
+                            hist_y, hist_x, int(sigmas[r_num + 1]))
+                        draw.set_color(
+                            descs_img, (rows, cols), color, alpha=val)
+                        for o_num, o in enumerate(orientation_angles):
+                            # Draw histogram bins
+                            bin_size = descs[i, j, orientations + r_num *
+                                             histograms * orientations +
+                                             t_num * orientations + o_num]
+                            bin_size /= max_bin
+                            dy = sigmas[r_num + 1] * bin_size * math.sin(o)
+                            dx = sigmas[r_num + 1] * bin_size * math.cos(o)
+                            rows, cols, val = draw.line_aa(hist_y, hist_x,
+                                                           int(hist_y + dy),
+                                                           int(hist_x + dx))
+                            draw.set_color(
+                                descs_img, (rows, cols), color, alpha=val)
+        return descs, descs_img
+    else:
+        return descs
diff --git a/python/cucim/src/cucim/skimage/feature/corner.py b/python/cucim/src/cucim/skimage/feature/corner.py
new file mode 100644
index 000000000..98a99af52
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/corner.py
@@ -0,0 +1,955 @@
+from itertools import combinations_with_replacement
+from warnings import warn
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+from scipy import spatial  # TODO: use RAPIDS cuSpatial?
+
+# from ..transform import integral_image
+from .. import img_as_float
+from .peak import peak_local_max
+from .util import _prepare_grayscale_input_nD
+
+
+def _compute_derivatives(image, mode="constant", cval=0):
+    """Compute derivatives in axis directions using the Sobel operator.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    derivatives : list of ndarray
+        Derivatives in each axis direction.
+
+    """
+
+    derivatives = [
+        ndi.sobel(image, axis=i, mode=mode, cval=cval)
+        for i in range(image.ndim)
+    ]
+
+    return derivatives
+
+
+def structure_tensor(image, sigma=1, mode="constant", cval=0, order=None):
+    """Compute structure tensor using sum of squared differences.
+
+    The (2-dimensional) structure tensor A is defined as::
+
+        A = [Arr Arc]
+            [Arc Acc]
+
+    which is approximated by the weighted sum of squared differences in a local
+    window around each pixel in the image. This formula can be extended to a
+    larger number of dimensions (see [1]_).
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, which is used as a
+        weighting function for the local summation of squared differences.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    order : {'rc', 'xy'}, optional
+        NOTE: Only applies in 2D. Higher dimensions must always use 'rc' order.
+        This parameter allows for the use of reverse or forward order of
+        the image axes in gradient computation. 'rc' indicates the use of
+        the first axis initially (Arr, Arc, Acc), whilst 'xy' indicates the
+        usage of the last axis initially (Axx, Axy, Ayy).
+
+    Returns
+    -------
+    A_elems : list of ndarray
+        Upper-diagonal elements of the structure tensor for each pixel in the
+        input image.
+
+    See also
+    --------
+    structure_tensor_eigenvalues
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Structure_tensor\
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.feature import structure_tensor
+    >>> square = np.zeros((5, 5))
+    >>> square[2, 2] = 1
+    >>> Arr, Arc, Acc = structure_tensor(square, sigma=0.1, order="rc")
+    >>> Acc
+    array([[0., 0., 0., 0., 0.],
+           [0., 1., 0., 1., 0.],
+           [0., 4., 0., 4., 0.],
+           [0., 1., 0., 1., 0.],
+           [0., 0., 0., 0., 0.]])
+
+    """
+    if order == "xy" and image.ndim > 2:
+        raise ValueError('Only "rc" order is supported for dim > 2.')
+
+    if order is None:
+        if image.ndim == 2:
+            # The legacy 2D code followed (x, y) convention, so we swap the
+            # axis order to maintain compatibility with old code
+            warn(
+                "deprecation warning: the default order of the structure "
+                'tensor values will be "row-column" instead of "xy" starting '
+                'in skimage version 0.20. Use order="rc" or order="xy" to '
+                'set this explicitly.  (Specify order="xy" to maintain the '
+                "old behavior.)",
+                category=FutureWarning,
+                stacklevel=2,
+            )
+            order = "xy"
+        else:
+            order = "rc"
+
+    image = _prepare_grayscale_input_nD(image)
+
+    derivatives = _compute_derivatives(image, mode=mode, cval=cval)
+
+    if order == "xy":
+        derivatives = reversed(derivatives)
+
+    # structure tensor
+    A_elems = [
+        ndi.gaussian_filter(der0 * der1, sigma, mode=mode, cval=cval)
+        for der0, der1 in combinations_with_replacement(derivatives, 2)
+    ]
+
+    return A_elems
+
+
+def hessian_matrix(image, sigma=1, mode="constant", cval=0, order="rc"):
+    """Compute Hessian matrix.
+
+    The Hessian matrix is defined as::
+
+        H = [Hrr Hrc]
+            [Hrc Hcc]
+
+    which is computed by convolving the image with the second derivatives
+    of the Gaussian kernel in the respective r- and c-directions.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    sigma : float
+        Standard deviation used for the Gaussian kernel, which is used as
+        weighting function for the auto-correlation matrix.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    order : {'rc', 'xy'}, optional
+        This parameter allows for the use of reverse or forward order of
+        the image axes in gradient computation. 'rc' indicates the use of
+        the first axis initially (Hrr, Hrc, Hcc), whilst 'xy' indicates the
+        usage of the last axis initially (Hxx, Hxy, Hyy)
+
+    Returns
+    -------
+    Hrr : ndarray
+        Element of the Hessian matrix for each pixel in the input image.
+    Hrc : ndarray
+        Element of the Hessian matrix for each pixel in the input image.
+    Hcc : ndarray
+        Element of the Hessian matrix for each pixel in the input image.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.feature import hessian_matrix
+    >>> square = cp.zeros((5, 5))
+    >>> square[2, 2] = 4
+    >>> Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc')
+    >>> Hrc
+    array([[ 0.,  0.,  0.,  0.,  0.],
+           [ 0.,  1.,  0., -1.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.],
+           [ 0., -1.,  0.,  1.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.]])
+    """
+
+    image = img_as_float(image)
+
+    gaussian_filtered = ndi.gaussian_filter(
+        image, sigma=sigma, mode=mode, cval=cval
+    )
+
+    gradients = cp.gradient(gaussian_filtered)
+    axes = range(image.ndim)
+
+    if order == "rc":
+        axes = reversed(axes)
+
+    H_elems = [
+        cp.gradient(gradients[ax0], axis=ax1)
+        for ax0, ax1 in combinations_with_replacement(axes, 2)
+    ]
+
+    return H_elems
+
+
+def hessian_matrix_det(image, sigma=1, approximate=True):
+    """Compute the approximate Hessian Determinant over an image.
+
+    The 2D approximate method uses box filters over integral images to
+    compute the approximate Hessian Determinant, as described in [1]_.
+
+    Parameters
+    ----------
+    image : array
+        The image over which to compute Hessian Determinant.
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, used for the Hessian
+        matrix.
+    approximate : bool, optional
+        If ``True`` and the image is 2D, use a much faster approximate
+        computation. This argument has no effect on 3D and higher images.
+
+    Returns
+    -------
+    out : array
+        The array of the Determinant of Hessians.
+
+    References
+    ----------
+    .. [1] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool,
+           "SURF: Speeded Up Robust Features"
+           ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
+
+    Notes
+    -----
+    For 2D images when ``approximate=True``, the running time of this method
+    only depends on size of the image. It is independent of `sigma` as one
+    would expect. The downside is that the result for `sigma` less than `3`
+    is not accurate, i.e., not similar to the result obtained if someone
+    computed the Hessian and took its determinant.
+    """
+    image = img_as_float(image)
+    if image.ndim == 2 and approximate:
+        raise NotImplementedError("approximate=True case not implemented")
+        # integral = integral_image(image)
+        # return cp.asarray(_hessian_matrix_det(integral, sigma))
+    else:  # slower brute-force implementation for nD images
+        hessian_mat_array = _symmetric_image(hessian_matrix(image, sigma))
+        return cp.linalg.det(hessian_mat_array)
+
+
+def _image_orthogonal_matrix22_eigvals(M00, M01, M11):
+    """
+    analytical formula below optimized for in-place computations.
+    It corresponds to::
+
+    l1 = (M00 + M11) / 2 + cp.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2
+    l2 = (M00 + M11) / 2 - cp.sqrt(4 * M01 ** 2 + (M00 - M11) ** 2) / 2
+    """
+    tmp1 = M01 * M01
+    tmp1 *= 4
+
+    tmp2 = M00 - M11
+    tmp2 *= tmp2
+    tmp2 += tmp1
+    cp.sqrt(tmp2, out=tmp2)
+    tmp2 /= 2
+
+    tmp1 = M00 + M11
+    tmp1 /= 2
+    l1 = tmp1 + tmp2
+    l2 = tmp1 - tmp2
+    return l1, l2
+
+
+def _symmetric_compute_eigenvalues(S_elems):
+    """Compute eigenvalues from the upperdiagonal entries of a symmetric matrix
+
+    Parameters
+    ----------
+    S_elems : list of ndarray
+        The upper-diagonal elements of the matrix, as returned by
+        `hessian_matrix` or `structure_tensor`.
+
+    Returns
+    -------
+    eigs : ndarray
+        The eigenvalues of the matrix, in decreasing order. The eigenvalues are
+        the leading dimension. That is, ``eigs[i, j, k]`` contains the
+        ith-largest eigenvalue at position (j, k).
+    """
+
+    if len(S_elems) == 3:  # Use fast Cython code for 2D
+        eigs = cp.stack(_image_orthogonal_matrix22_eigvals(*S_elems))
+    else:
+        matrices = _symmetric_image(S_elems)
+        # eigvalsh returns eigenvalues in increasing order. We want decreasing
+        eigs = cp.linalg.eigvalsh(matrices)[..., ::-1]
+        leading_axes = tuple(range(eigs.ndim - 1))
+        eigs = cp.transpose(eigs, (eigs.ndim - 1,) + leading_axes)
+    return eigs
+
+
+def _symmetric_image(S_elems):
+    """Convert the upper-diagonal elements of a matrix to the full
+    symmetric matrix.
+
+    Parameters
+    ----------
+    S_elems : list of array
+        The upper-diagonal elements of the matrix, as returned by
+        `hessian_matrix` or `structure_tensor`.
+
+    Returns
+    -------
+    image : array
+        An array of shape ``(M, N[, ...], image.ndim, image.ndim)``,
+        containing the matrix corresponding to each coordinate.
+    """
+    image = S_elems[0]
+    symmetric_image = cp.zeros(image.shape + (image.ndim, image.ndim))
+    for idx, (row, col) in enumerate(
+        combinations_with_replacement(range(image.ndim), 2)
+    ):
+        symmetric_image[..., row, col] = S_elems[idx]
+        symmetric_image[..., col, row] = S_elems[idx]
+    return symmetric_image
+
+
+def structure_tensor_eigenvalues(A_elems):
+    """Compute eigenvalues of structure tensor.
+
+    Parameters
+    ----------
+    A_elems : list of ndarray
+        The upper-diagonal elements of the structure tensor, as returned
+        by `structure_tensor`.
+
+    Returns
+    -------
+    ndarray
+        The eigenvalues of the structure tensor, in decreasing order. The
+        eigenvalues are the leading dimension. That is, the coordinate
+        [i, j, k] corresponds to the ith-largest eigenvalue at position (j, k).
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.feature import structure_tensor
+    >>> from cucim.skimage.feature import structure_tensor_eigenvalues
+    >>> square = cp.zeros((5, 5))
+    >>> square[2, 2] = 1
+    >>> A_elems = structure_tensor(square, sigma=0.1, order='rc')
+    >>> structure_tensor_eigenvalues(A_elems)[0]
+    array([[0., 0., 0., 0., 0.],
+           [0., 2., 4., 2., 0.],
+           [0., 4., 0., 4., 0.],
+           [0., 2., 4., 2., 0.],
+           [0., 0., 0., 0., 0.]])
+
+    """
+    return _symmetric_compute_eigenvalues(A_elems)
+
+
+"""
+TODO: add an _image_symmetric_real33_eigvals() based on:
+Oliver K. Smith. 1961.
+Eigenvalues of a symmetric 3 × 3 matrix.
+Commun. ACM 4, 4 (April 1961), 168.
+DOI:https://doi.org/10.1145/355578.366316
+
+def _image_symmetric_real33_eigvals(M00, M01, M02, M11, M12, M22):
+
+"""
+
+
+def structure_tensor_eigvals(Axx, Axy, Ayy):
+    """Compute eigenvalues of structure tensor.
+
+    Parameters
+    ----------
+    Axx : ndarray
+        Element of the structure tensor for each pixel in the input image.
+    Axy : ndarray
+        Element of the structure tensor for each pixel in the input image.
+    Ayy : ndarray
+        Element of the structure tensor for each pixel in the input image.
+
+    Returns
+    -------
+    l1 : ndarray
+        Larger eigen value for each input matrix.
+    l2 : ndarray
+        Smaller eigen value for each input matrix.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.feature import (structure_tensor,
+    ...                                      structure_tensor_eigvals)
+    >>> square = np.zeros((5, 5))
+    >>> square[2, 2] = 1
+    >>> Arr, Arc, Acc = structure_tensor(square, sigma=0.1, order="rc")
+    >>> structure_tensor_eigvals(Acc, Arc, Arr)[0]
+    array([[0., 0., 0., 0., 0.],
+           [0., 2., 4., 2., 0.],
+           [0., 4., 0., 4., 0.],
+           [0., 2., 4., 2., 0.],
+           [0., 0., 0., 0., 0.]])
+
+    """
+    warn(
+        "deprecation warning: the function structure_tensor_eigvals is "
+        "deprecated and will be removed in version 0.20. Please use "
+        "structure_tensor_eigenvalues instead.",
+        category=FutureWarning,
+        stacklevel=2,
+    )
+
+    return _image_orthogonal_matrix22_eigvals(Axx, Axy, Ayy)
+
+
+def hessian_matrix_eigvals(H_elems):
+    """Compute eigenvalues of Hessian matrix.
+
+    Parameters
+    ----------
+    H_elems : list of ndarray
+        The upper-diagonal elements of the Hessian matrix, as returned
+        by `hessian_matrix`.
+
+    Returns
+    -------
+    eigs : ndarray
+        The eigenvalues of the Hessian matrix, in decreasing order. The
+        eigenvalues are the leading dimension. That is, ``eigs[i, j, k]``
+        contains the ith-largest eigenvalue at position (j, k).
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.feature import (hessian_matrix,
+    ...                                      hessian_matrix_eigvals)
+    >>> square = cp.zeros((5, 5))
+    >>> square[2, 2] = 4
+    >>> H_elems = hessian_matrix(square, sigma=0.1, order='rc')
+    >>> hessian_matrix_eigvals(H_elems)[0]
+    array([[ 0.,  0.,  2.,  0.,  0.],
+           [ 0.,  1.,  0.,  1.,  0.],
+           [ 2.,  0., -2.,  0.,  2.],
+           [ 0.,  1.,  0.,  1.,  0.],
+           [ 0.,  0.,  2.,  0.,  0.]])
+    """
+    return _symmetric_compute_eigenvalues(H_elems)
+
+
+def shape_index(image, sigma=1, mode="constant", cval=0):
+    """Compute the shape index.
+
+    The shape index, as defined by Koenderink & van Doorn [1]_, is a
+    single valued measure of local curvature, assuming the image as a 3D plane
+    with intensities representing heights.
+
+    It is derived from the eigen values of the Hessian, and its
+    value ranges from -1 to 1 (and is undefined (=NaN) in *flat* regions),
+    with following ranges representing following shapes:
+
+    .. table:: Ranges of the shape index and corresponding shapes.
+
+      ===================  =============
+      Interval (s in ...)  Shape
+      ===================  =============
+      [  -1, -7/8)         Spherical cup
+      [-7/8, -5/8)         Through
+      [-5/8, -3/8)         Rut
+      [-3/8, -1/8)         Saddle rut
+      [-1/8, +1/8)         Saddle
+      [+1/8, +3/8)         Saddle ridge
+      [+3/8, +5/8)         Ridge
+      [+5/8, +7/8)         Dome
+      [+7/8,   +1]         Spherical cap
+      ===================  =============
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, which is used for
+        smoothing the input data before Hessian eigen value calculation.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    s : ndarray
+        Shape index
+
+    References
+    ----------
+    .. [1] Koenderink, J. J. & van Doorn, A. J.,
+           "Surface shape and curvature scales",
+           Image and Vision Computing, 1992, 10, 557-564.
+           :DOI:`10.1016/0262-8856(92)90076-F`
+
+    Examples
+    --------
+    >>> from cucim.skimage.feature import shape_index
+    >>> square = cp.zeros((5, 5))
+    >>> square[2, 2] = 4
+    >>> s = shape_index(square, sigma=0.1)
+    >>> s
+    array([[ nan,  nan, -0.5,  nan,  nan],
+           [ nan, -0. ,  nan, -0. ,  nan],
+           [-0.5,  nan, -1. ,  nan, -0.5],
+           [ nan, -0. ,  nan, -0. ,  nan],
+           [ nan,  nan, -0.5,  nan,  nan]])
+    """
+
+    H = hessian_matrix(image, sigma=sigma, mode=mode, cval=cval, order="rc")
+    l1, l2 = hessian_matrix_eigvals(H)
+
+    return (2.0 / np.pi) * np.arctan((l2 + l1) / (l2 - l1))
+
+
+def corner_kitchen_rosenfeld(image, mode="constant", cval=0):
+    """Compute Kitchen and Rosenfeld corner measure response image.
+
+    The corner measure is calculated as follows::
+
+        (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy)
+            / (imx**2 + imy**2)
+
+    Where imx and imy are the first and imxx, imxy, imyy the second
+    derivatives.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    response : ndarray
+        Kitchen and Rosenfeld response image.
+
+    References
+    ----------
+    .. [1] Kitchen, L., & Rosenfeld, A. (1982). Gray-level corner detection.
+           Pattern recognition letters, 1(2), 95-102.
+           :DOI:`10.1016/0167-8655(82)90020-4`
+    """
+
+    imx, imy = _compute_derivatives(image, mode=mode, cval=cval)
+    imxx, imxy = _compute_derivatives(imx, mode=mode, cval=cval)
+    imyx, imyy = _compute_derivatives(imy, mode=mode, cval=cval)
+
+    # numerator = imxx * imy ** 2 + imyy * imx ** 2 - 2 * imxy * imx * imy
+    numerator = imxx * imy
+    numerator *= imy
+    tmp = imyy * imx
+    tmp *= imx
+    numerator += tmp
+    tmp = 2 * imxy
+    tmp *= imx
+    tmp *= imy
+    numerator -= tmp
+
+    # denominator = imx ** 2 + imy ** 2
+    denominator = imx * imx
+    denominator += imy * imy
+
+    response = cp.zeros_like(image, dtype=np.double)
+
+    mask = denominator != 0
+    response[mask] = numerator[mask] / denominator[mask]
+
+    return response
+
+
+def corner_harris(image, method="k", k=0.05, eps=1e-6, sigma=1):
+    """Compute Harris corner measure response image.
+
+    This corner detector uses information from the auto-correlation matrix A::
+
+        A = [(imx**2)   (imx*imy)] = [Axx Axy]
+            [(imx*imy)   (imy**2)]   [Axy Ayy]
+
+    Where imx and imy are first derivatives, averaged with a gaussian filter.
+    The corner measure is then defined as::
+
+        det(A) - k * trace(A)**2
+
+    or::
+
+        2 * det(A) / (trace(A) + eps)
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    method : {'k', 'eps'}, optional
+        Method to compute the response image from the auto-correlation matrix.
+    k : float, optional
+        Sensitivity factor to separate corners from edges, typically in range
+        `[0, 0.2]`. Small values of k result in detection of sharp corners.
+    eps : float, optional
+        Normalisation factor (Noble's corner measure).
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, which is used as
+        weighting function for the auto-correlation matrix.
+
+    Returns
+    -------
+    response : ndarray
+        Harris response image.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Corner_detection
+
+    Examples
+    --------
+    >>> from cucim.skimage.feature import corner_harris, corner_peaks
+    >>> square = cp.zeros([10, 10])
+    >>> square[2:8, 2:8] = 1
+    >>> square.astype(int)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> corner_peaks(corner_harris(square), min_distance=1)
+    array([[2, 2],
+           [2, 7],
+           [7, 2],
+           [7, 7]])
+
+    """
+
+    Arr, Arc, Acc = structure_tensor(image, sigma, order="rc")
+
+    # determinant
+    detA = Arr * Acc
+    detA -= Arc * Arc
+    # trace
+    traceA = Arr + Acc
+
+    if method == "k":
+        response = detA - k * traceA * traceA
+    else:
+        response = 2 * detA / (traceA + eps)
+
+    return response
+
+
+def corner_shi_tomasi(image, sigma=1):
+    """Compute Shi-Tomasi (Kanade-Tomasi) corner measure response image.
+
+    This corner detector uses information from the auto-correlation matrix A::
+
+        A = [(imx**2)   (imx*imy)] = [Axx Axy]
+            [(imx*imy)   (imy**2)]   [Axy Ayy]
+
+    Where imx and imy are first derivatives, averaged with a gaussian filter.
+    The corner measure is then defined as the smaller eigenvalue of A::
+
+        ((Axx + Ayy) - sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, which is used as
+        weighting function for the auto-correlation matrix.
+
+    Returns
+    -------
+    response : ndarray
+        Shi-Tomasi response image.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Corner_detection
+
+    Examples
+    --------
+    >>> from cucim.skimage.feature import corner_shi_tomasi, corner_peaks
+    >>> square = cp.zeros([10, 10])
+    >>> square[2:8, 2:8] = 1
+    >>> square.astype(int)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> corner_peaks(corner_shi_tomasi(square), min_distance=1)
+    array([[2, 2],
+           [2, 7],
+           [7, 2],
+           [7, 7]])
+
+    """
+
+    Arr, Arc, Acc = structure_tensor(image, sigma, order="rc")
+
+    # minimum eigenvalue of A
+
+    # response = ((Axx + Ayy) - np.sqrt((Axx - Ayy) ** 2 + 4 * Axy ** 2)) / 2
+    tmp = Arr - Acc
+    tmp *= tmp
+    tmp2 = 4 * Arc
+    tmp2 *= Arc
+    tmp += tmp2
+    cp.sqrt(tmp, out=tmp)
+    tmp /= 2
+    response = Arr + Acc
+    response -= tmp
+
+    return response
+
+
+def corner_foerstner(image, sigma=1):
+    """Compute Foerstner corner measure response image.
+
+    This corner detector uses information from the auto-correlation matrix A::
+
+        A = [(imx**2)   (imx*imy)] = [Axx Axy]
+            [(imx*imy)   (imy**2)]   [Axy Ayy]
+
+    Where imx and imy are first derivatives, averaged with a gaussian filter.
+    The corner measure is then defined as::
+
+        w = det(A) / trace(A)           (size of error ellipse)
+        q = 4 * det(A) / trace(A)**2    (roundness of error ellipse)
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    sigma : float, optional
+        Standard deviation used for the Gaussian kernel, which is used as
+        weighting function for the auto-correlation matrix.
+
+    Returns
+    -------
+    w : ndarray
+        Error ellipse sizes.
+    q : ndarray
+        Roundness of error ellipse.
+
+    References
+    ----------
+    .. [1] Förstner, W., & Gülch, E. (1987, June). A fast operator for
+           detection and precise location of distinct points, corners and
+           centres of circular features. In Proc. ISPRS intercommission
+           conference on fast processing of photogrammetric data (pp. 281-305).
+           https://cseweb.ucsd.edu/classes/sp02/cse252/foerstner/foerstner.pdf
+    .. [2] https://en.wikipedia.org/wiki/Corner_detection
+
+    Examples
+    --------
+    >>> from cucim.skimage.feature import corner_foerstner, corner_peaks
+    >>> square = cp.zeros([10, 10])
+    >>> square[2:8, 2:8] = 1
+    >>> square.astype(int)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    >>> w, q = corner_foerstner(square)
+    >>> accuracy_thresh = 0.5
+    >>> roundness_thresh = 0.3
+    >>> foerstner = (q > roundness_thresh) * (w > accuracy_thresh) * w
+    >>> corner_peaks(foerstner, min_distance=1)
+    array([[2, 2],
+           [2, 7],
+           [7, 2],
+           [7, 7]])
+
+    """
+
+    Arr, Arc, Acc = structure_tensor(image, sigma, order="rc")
+
+    # determinant
+    detA = Arr * Acc
+    detA -= Arc * Arc
+    # trace
+    traceA = Arr + Acc
+
+    w = cp.zeros_like(image, dtype=np.double)
+    q = cp.zeros_like(image, dtype=np.double)
+
+    mask = traceA != 0
+
+    w[mask] = detA[mask] / traceA[mask]
+    tsq = traceA[mask]
+    tsq *= tsq
+    q[mask] = 4 * detA[mask] / tsq
+
+    return w, q
+
+
+def corner_peaks(
+    image,
+    min_distance=1,
+    threshold_abs=None,
+    threshold_rel=None,
+    exclude_border=True,
+    indices=True,
+    num_peaks=np.inf,
+    footprint=None,
+    labels=None,
+    *,
+    num_peaks_per_label=np.inf,
+    p_norm=np.inf,
+):
+    """Find peaks in corner measure response image.
+
+    This differs from `skimage.feature.peak_local_max` in that it suppresses
+    multiple connected peaks with the same accumulator value.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    min_distance : int, optional
+        The minimal allowed distance separating peaks.
+    * : *
+        See :py:meth:`skimage.feature.peak_local_max`.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+
+    Returns
+    -------
+    output : ndarray or ndarray of bools
+
+        * If `indices = True`  : (row, column, ...) coordinates of peaks.
+        * If `indices = False` : Boolean array shaped like `image`, with peaks
+          represented by True values.
+
+    See also
+    --------
+    skimage.feature.peak_local_max
+
+    Notes
+    -----
+    .. versionchanged:: 0.18
+        The default value of `threshold_rel` has changed to None, which
+        corresponds to letting `skimage.feature.peak_local_max` decide on the
+        default. This is equivalent to `threshold_rel=0`.
+
+    The `num_peaks` limit is applied before suppression of connected peaks.
+    To limit the number of peaks after suppression, set `num_peaks=np.inf` and
+    post-process the output of this function.
+
+    Examples
+    --------
+    >>> from cucim.skimage.feature import peak_local_max
+    >>> response = cp.zeros((5, 5))
+    >>> response[2:4, 2:4] = 1
+    >>> response
+    array([[0., 0., 0., 0., 0.],
+           [0., 0., 0., 0., 0.],
+           [0., 0., 1., 1., 0.],
+           [0., 0., 1., 1., 0.],
+           [0., 0., 0., 0., 0.]])
+    >>> peak_local_max(response)
+    array([[2, 2],
+           [2, 3],
+           [3, 2],
+           [3, 3]])
+    >>> corner_peaks(response)
+    array([[2, 2]])
+
+    """
+    if cp.isinf(num_peaks):
+        num_peaks = None
+
+    # Get the coordinates of the detected peaks
+    coords = peak_local_max(
+        image,
+        min_distance=min_distance,
+        threshold_abs=threshold_abs,
+        threshold_rel=threshold_rel,
+        exclude_border=exclude_border,
+        num_peaks=np.inf,
+        footprint=footprint,
+        labels=labels,
+        num_peaks_per_label=num_peaks_per_label,
+    )
+
+    if len(coords):
+        # TODO: modify to do KDTree on the GPU (cuSpatial?)
+        coords = cp.asnumpy(coords)
+
+        # Use KDtree to find the peaks that are too close to each other
+        tree = spatial.cKDTree(coords)
+
+        rejected_peaks_indices = set()
+        for idx, point in enumerate(coords):
+            if idx not in rejected_peaks_indices:
+                candidates = tree.query_ball_point(
+                    point, r=min_distance, p=p_norm
+                )
+                candidates.remove(idx)
+                rejected_peaks_indices.update(candidates)
+
+        # Remove the peaks that are too close to each other
+        coords = np.delete(coords, tuple(rejected_peaks_indices), axis=0)[
+            :num_peaks
+        ]
+        coords = cp.asarray(coords)
+
+    if indices:
+        return coords
+
+    peaks = cp.zeros_like(image, dtype=bool)
+    peaks[tuple(coords.T)] = True
+
+    return peaks
diff --git a/python/cucim/src/cucim/skimage/feature/peak.py b/python/cucim/src/cucim/skimage/feature/peak.py
new file mode 100644
index 000000000..207be1451
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/peak.py
@@ -0,0 +1,435 @@
+from warnings import warn
+
+import cupy as cp
+import cupyx.scipy.ndimage as ndi
+import numpy as np
+from scipy.ndimage import find_objects as cpu_find_objects
+
+# from ..filters import rank_order
+from cucim.skimage import measure
+
+from .._shared.coord import ensure_spacing
+from .._shared.utils import remove_arg
+
+# TODO: update if GPU implementations of the following are completed/improved
+# skimage.measure.regionprops
+
+
+def _get_high_intensity_peaks(image, mask, num_peaks, min_distance, p_norm):
+    """
+    Return the highest intensity peak coordinates.
+    """
+    # get coordinates of peaks
+    coord = cp.nonzero(mask)
+    intensities = image[coord]
+    # Highest peak first
+    idx_maxsort = cp.argsort(-intensities)
+    coord = cp.column_stack(coord)[idx_maxsort]
+
+    coord = ensure_spacing(coord, spacing=min_distance, p_norm=p_norm)
+    if len(coord) > num_peaks:
+        coord = coord[:num_peaks]
+    return coord
+
+
+def _get_peak_mask(image, footprint, threshold, mask=None):
+    """
+    Return the mask containing all peak candidates above thresholds.
+    """
+    if footprint.size == 1 or image.size == 1:
+        return image > threshold
+
+    image_max = ndi.maximum_filter(image, footprint=footprint,
+                                   mode='constant')
+
+    out = image == image_max
+
+    # no peak for a trivial image
+    image_is_trivial = np.all(out) if mask is None else np.all(out[mask])
+    if image_is_trivial:  # synchronize
+        out[:] = False
+        if mask is not None:
+            # isolated pixels in masked area are returned as peaks
+            isolated_px = cp.logical_xor(mask, ndi.binary_opening(mask))
+            out[isolated_px] = True
+
+    out &= image > threshold
+    return out
+
+
+def _exclude_border(label, border_width):
+    """Set label border values to 0.
+    """
+    # zero out label borders
+    for i, width in enumerate(border_width):
+        if width == 0:
+            continue
+        label[(slice(None),) * i + (slice(None, width),)] = 0
+        label[(slice(None),) * i + (slice(-width, None),)] = 0
+    return label
+
+
+def _get_threshold(image, threshold_abs, threshold_rel):
+    """Return the threshold value according to an absolute and a relative
+    value.
+
+    """
+    threshold = threshold_abs if threshold_abs is not None else image.min()
+
+    if threshold_rel is not None:
+        threshold = max(threshold, threshold_rel * float(image.max()))
+    # TODO: return host or device scalar?
+    return float(threshold)
+
+
+def _get_excluded_border_width(image, min_distance, exclude_border):
+    """Return border_width values relative to a min_distance if requested.
+
+    """
+
+    if isinstance(exclude_border, bool):
+        border_width = (min_distance if exclude_border else 0,) * image.ndim
+    elif isinstance(exclude_border, int):
+        if exclude_border < 0:
+            raise ValueError("`exclude_border` cannot be a negative value")
+        border_width = (exclude_border,) * image.ndim
+    elif isinstance(exclude_border, tuple):
+        if len(exclude_border) != image.ndim:
+            raise ValueError(
+                "`exclude_border` should have the same length as the "
+                "dimensionality of the image.")
+        for exclude in exclude_border:
+            if not isinstance(exclude, int):
+                raise ValueError(
+                    "`exclude_border`, when expressed as a tuple, must only "
+                    "contain ints."
+                )
+            if exclude < 0:
+                raise ValueError(
+                    "`exclude_border` can not be a negative value")
+        border_width = exclude_border
+    else:
+        raise TypeError(
+            "`exclude_border` must be bool, int, or tuple with the same "
+            "length as the dimensionality of the image.")
+
+    return border_width
+
+
+@remove_arg("indices", changed_version="0.20")
+def peak_local_max(image, min_distance=1, threshold_abs=None,
+                   threshold_rel=None, exclude_border=True, indices=True,
+                   num_peaks=np.inf, footprint=None, labels=None,
+                   num_peaks_per_label=np.inf, p_norm=np.inf):
+    """Find peaks in an image as coordinate list or boolean mask.
+
+    Peaks are the local maxima in a region of `2 * min_distance + 1`
+    (i.e. peaks are separated by at least `min_distance`).
+
+    If both `threshold_abs` and `threshold_rel` are provided, the maximum
+    of the two is chosen as the minimum intensity threshold of peaks.
+
+    .. versionchanged:: 0.18
+        Prior to version 0.18, peaks of the same height within a radius of
+        `min_distance` were all returned, but this could cause unexpected
+        behaviour. From 0.18 onwards, an arbitrary peak within the region is
+        returned. See issue gh-2592.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    min_distance : int, optional
+        The minimal allowed distance separating peaks. To find the
+        maximum number of peaks, use `min_distance=1`.
+    threshold_abs : float, optional
+        Minimum intensity of peaks. By default, the absolute threshold is
+        the minimum intensity of the image.
+    threshold_rel : float, optional
+        Minimum intensity of peaks, calculated as `max(image) * threshold_rel`.
+    exclude_border : int, tuple of ints, or bool, optional
+        If positive integer, `exclude_border` excludes peaks from within
+        `exclude_border`-pixels of the border of the image.
+        If tuple of non-negative ints, the length of the tuple must match the
+        input array's dimensionality.  Each element of the tuple will exclude
+        peaks from within `exclude_border`-pixels of the border of the image
+        along that dimension.
+        If True, takes the `min_distance` parameter as value.
+        If zero or False, peaks are identified regardless of their distance
+        from the border.
+    indices : bool, optional
+        If True, the output will be an array representing peak
+        coordinates. The coordinates are sorted according to peaks
+        values (Larger first). If False, the output will be a boolean
+        array shaped as `image.shape` with peaks present at True
+        elements. ``indices`` is deprecated and will be removed in
+        version 0.20. Default behavior will be to always return peak
+        coordinates. You can obtain a mask as shown in the example
+        below.
+    num_peaks : int, optional
+        Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
+        return `num_peaks` peaks based on highest peak intensity.
+    footprint : ndarray of bools, optional
+        If provided, `footprint == 1` represents the local region within which
+        to search for peaks at every point in `image`.
+    labels : ndarray of ints, optional
+        If provided, each unique region `labels == value` represents a unique
+        region to search for peaks. Zero is reserved for background.
+    num_peaks_per_label : int, optional
+        Maximum number of peaks for each label.
+    p_norm : float
+        Which Minkowski p-norm to use. Should be in the range [1, inf].
+        A finite large p may cause a ValueError if overflow can occur.
+        ``inf`` corresponds to the Chebyshev distance and 2 to the
+        Euclidean distance.
+
+    Returns
+    -------
+    output : ndarray or ndarray of bools
+
+        * If `indices = True`  : (row, column, ...) coordinates of peaks.
+        * If `indices = False` : Boolean array shaped like `image`, with peaks
+          represented by True values.
+
+    Notes
+    -----
+    The peak local maximum function returns the coordinates of local peaks
+    (maxima) in an image. Internally, a maximum filter is used for finding local
+    maxima. This operation dilates the original image. After comparison of the
+    dilated and original image, this function returns the coordinates or a mask
+    of the peaks where the dilated image equals the original image.
+
+    See also
+    --------
+    skimage.feature.corner_peaks
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> img1 = cp.zeros((7, 7))
+    >>> img1[3, 4] = 1
+    >>> img1[3, 2] = 1.5
+    >>> img1
+    array([[0. , 0. , 0. , 0. , 0. , 0. , 0. ],
+           [0. , 0. , 0. , 0. , 0. , 0. , 0. ],
+           [0. , 0. , 0. , 0. , 0. , 0. , 0. ],
+           [0. , 0. , 1.5, 0. , 1. , 0. , 0. ],
+           [0. , 0. , 0. , 0. , 0. , 0. , 0. ],
+           [0. , 0. , 0. , 0. , 0. , 0. , 0. ],
+           [0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
+
+    >>> peak_local_max(img1, min_distance=1)
+    array([[3, 2],
+           [3, 4]])
+
+    >>> peak_local_max(img1, min_distance=2)
+    array([[3, 2]])
+
+    >>> img2 = cp.zeros((20, 20, 20))
+    >>> img2[10, 10, 10] = 1
+    >>> img2[15, 15, 15] = 1
+    >>> peak_idx = peak_local_max(img2, exclude_border=0)
+    >>> peak_idx
+    array([[10, 10, 10],
+           [15, 15, 15]])
+
+    >>> peak_mask = cp.zeros_like(img2, dtype=bool)
+    >>> peak_mask[tuple(peak_idx.T)] = True
+    >>> np.argwhere(peak_mask)
+    array([[10, 10, 10],
+           [15, 15, 15]])
+
+    """
+    if (footprint is None or footprint.size == 1) and min_distance < 1:
+        warn("When min_distance < 1, peak_local_max acts as finding "
+             "image > max(threshold_abs, threshold_rel * max(image)).",
+             RuntimeWarning, stacklevel=2)
+
+    border_width = _get_excluded_border_width(image, min_distance,
+                                              exclude_border)
+
+    threshold = _get_threshold(image, threshold_abs, threshold_rel)
+
+    if footprint is None:
+        size = 2 * min_distance + 1
+        footprint = cp.ones((size,) * image.ndim, dtype=bool)
+
+    if labels is None:
+        # Non maximum filter
+        mask = _get_peak_mask(image, footprint, threshold)
+
+        mask = _exclude_border(mask, border_width)
+
+        # Select highest intensities (num_peaks)
+        coordinates = _get_high_intensity_peaks(image, mask,
+                                                num_peaks,
+                                                min_distance, p_norm)
+
+    else:
+        # Backend: casting="safe" not implemented in CuPy
+        _labels = _exclude_border(labels.astype(int), border_width)
+
+        if np.issubdtype(image.dtype, np.floating):
+            bg_val = cp.finfo(image.dtype).min
+        else:
+            bg_val = cp.iinfo(image.dtype).min
+
+        # For each label, extract a smaller image enclosing the object of
+        # interest, identify num_peaks_per_label peaks
+        labels_peak_coord = []
+
+        # For each label, extract a smaller image enclosing the object of
+        # interest, identify num_peaks_per_label peaks and mark them in
+        # variable out.
+        try:
+            objects = ndi.find_objects(_labels)
+        except AttributeError:
+            # CuPy Backend: fallback to CPU implementation until implemented
+            objects = cpu_find_objects(cp.asnumpy(_labels))
+
+        for label_idx, roi in enumerate(objects):
+            if roi is None:
+                continue
+
+            # Get roi mask
+            label_mask = labels[roi] == label_idx + 1
+            # Extract image roi
+            img_object = image[roi]
+            # Ensure masked values don't affect roi's local peaks
+            img_object[np.logical_not(label_mask)] = bg_val
+
+            mask = _get_peak_mask(img_object, footprint, threshold, label_mask)
+
+            coordinates = _get_high_intensity_peaks(img_object, mask,
+                                                    num_peaks_per_label,
+                                                    min_distance,
+                                                    p_norm)
+
+            # transform coordinates in global image indices space
+            for idx, s in enumerate(roi):
+                coordinates[:, idx] += s.start
+
+            labels_peak_coord.append(coordinates)
+
+        if labels_peak_coord:
+            coordinates = cp.vstack(labels_peak_coord)
+        else:
+            coordinates = cp.empty((0, 2), dtype=int)
+
+        if len(coordinates) > num_peaks:
+            out = cp.zeros_like(image, dtype=bool)
+            out[tuple(coordinates.T)] = True
+            coordinates = _get_high_intensity_peaks(image, out,
+                                                    num_peaks,
+                                                    min_distance,
+                                                    p_norm)
+
+    if indices:
+        return coordinates
+    else:
+        out = cp.zeros_like(image, dtype=bool)
+        out[tuple(coordinates.T)] = True
+        return out
+
+
+def _prominent_peaks(image, min_xdistance=1, min_ydistance=1,
+                     threshold=None, num_peaks=np.inf):
+    """Return peaks with non-maximum suppression.
+
+    Identifies most prominent features separated by certain distances.
+    Non-maximum suppression with different sizes is applied separately
+    in the first and second dimension of the image to identify peaks.
+
+    Parameters
+    ----------
+    image : (M, N) ndarray
+        Input image.
+    min_xdistance : int
+        Minimum distance separating features in the x dimension.
+    min_ydistance : int
+        Minimum distance separating features in the y dimension.
+    threshold : float
+        Minimum intensity of peaks. Default is `0.5 * max(image)`.
+    num_peaks : int
+        Maximum number of peaks. When the number of peaks exceeds `num_peaks`,
+        return `num_peaks` coordinates based on peak intensity.
+
+    Returns
+    -------
+    intensity, xcoords, ycoords : tuple of array
+        Peak intensity values, x and y indices.
+    """
+
+    img = image.copy()
+    rows, cols = img.shape
+
+    if threshold is None:
+        threshold = 0.5 * np.max(img)
+
+    ycoords_size = 2 * min_ydistance + 1
+    xcoords_size = 2 * min_xdistance + 1
+    img_max = ndi.maximum_filter1d(img, size=ycoords_size, axis=0,
+                                   mode='constant', cval=0)
+    img_max = ndi.maximum_filter1d(img_max, size=xcoords_size, axis=1,
+                                   mode='constant', cval=0)
+    mask = (img == img_max)
+    img *= mask
+    img_t = img > threshold
+
+    label_img = measure.label(img_t)
+    props = measure.regionprops(label_img, img_max)
+
+    # Sort the list of peaks by intensity, not left-right, so larger peaks
+    # in Hough space cannot be arbitrarily suppressed by smaller neighbors
+    props = sorted(props, key=lambda x: x.max_intensity)[::-1]
+    coords = cp.asarray([np.round(p.centroid) for p in props], dtype=int)
+
+    img_peaks = []
+    ycoords_peaks = []
+    xcoords_peaks = []
+
+    # relative coordinate grid for local neighbourhood suppression
+    ycoords_ext, xcoords_ext = cp.mgrid[-min_ydistance:min_ydistance + 1,
+                                        -min_xdistance:min_xdistance + 1]
+
+    for ycoords_idx, xcoords_idx in coords:
+        accum = img_max[ycoords_idx, xcoords_idx]
+        if accum > threshold:
+            # absolute coordinate grid for local neighbourhood suppression
+            ycoords_nh = ycoords_idx + ycoords_ext
+            xcoords_nh = xcoords_idx + xcoords_ext
+
+            # no reflection for distance neighbourhood
+            ycoords_in = cp.logical_and(ycoords_nh > 0, ycoords_nh < rows)
+            ycoords_nh = ycoords_nh[ycoords_in]
+            xcoords_nh = xcoords_nh[ycoords_in]
+
+            # reflect xcoords and assume xcoords are continuous,
+            # e.g. for angles:
+            # (..., 88, 89, -90, -89, ..., 89, -90, -89, ...)
+            xcoords_low = xcoords_nh < 0
+            ycoords_nh[xcoords_low] = rows - ycoords_nh[xcoords_low]
+            xcoords_nh[xcoords_low] += cols
+            xcoords_high = xcoords_nh >= cols
+            ycoords_nh[xcoords_high] = rows - ycoords_nh[xcoords_high]
+            xcoords_nh[xcoords_high] -= cols
+
+            # suppress neighbourhood
+            img_max[ycoords_nh, xcoords_nh] = 0
+
+            # add current feature to peaks
+            img_peaks.append(accum)
+            ycoords_peaks.append(ycoords_idx)
+            xcoords_peaks.append(xcoords_idx)
+
+    img_peaks = cp.array(img_peaks)
+    ycoords_peaks = cp.array(ycoords_peaks)
+    xcoords_peaks = cp.array(xcoords_peaks)
+
+    if num_peaks < len(img_peaks):
+        idx_maxsort = cp.argsort(img_peaks)[::-1][:num_peaks]
+        img_peaks = img_peaks[idx_maxsort]
+        ycoords_peaks = ycoords_peaks[idx_maxsort]
+        xcoords_peaks = xcoords_peaks[idx_maxsort]
+
+    return img_peaks, xcoords_peaks, ycoords_peaks
diff --git a/python/cucim/src/cucim/skimage/feature/template.py b/python/cucim/src/cucim/skimage/feature/template.py
new file mode 100644
index 000000000..0edf04546
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/template.py
@@ -0,0 +1,198 @@
+import cupy as cp
+
+import cucim.skimage._vendored
+# TODO: use cupyx.scipy.signal once upstream fftconvolve and
+#       choose_conv_method for > 1d has been implemented.
+from cucim import _misc
+
+from .._shared.utils import check_nD
+
+# from cupyx.scipy import signal
+
+
+signal = cucim.skimage._vendored
+
+
+def _window_sum_2d(image, window_shape):
+
+    # TODO: remove copy in line below once the following issue is resolved
+    #       https://github.com/cupy/cupy/issues/4456
+    window_sum = cp.cumsum(image, axis=0)
+    window_sum = (window_sum[window_shape[0]:-1]
+                  - window_sum[:-window_shape[0] - 1])
+
+    window_sum = cp.cumsum(window_sum, axis=1)
+    window_sum = (window_sum[:, window_shape[1]:-1]
+                  - window_sum[:, :-window_shape[1] - 1])
+
+    return window_sum
+
+
+def _window_sum_3d(image, window_shape):
+
+    window_sum = _window_sum_2d(image, window_shape)
+
+    window_sum = cp.cumsum(window_sum, axis=2)
+    window_sum = (window_sum[:, :, window_shape[2]:-1]
+                  - window_sum[:, :, :-window_shape[2] - 1])
+
+    return window_sum
+
+
+def match_template(image, template, pad_input=False, mode='constant',
+                   constant_values=0):
+    """Match a template to a 2-D or 3-D image using normalized correlation.
+
+    The output is an array with values between -1.0 and 1.0. The value at a
+    given position corresponds to the correlation coefficient between the image
+    and the template.
+
+    For `pad_input=True` matches correspond to the center and otherwise to the
+    top-left corner of the template. To find the best match you must search for
+    peaks in the response (output) image.
+
+    Parameters
+    ----------
+    image : (M, N[, D]) array
+        2-D or 3-D input image.
+    template : (m, n[, d]) array
+        Template to locate. It must be `(m <= M, n <= N[, d <= D])`.
+    pad_input : bool
+        If True, pad `image` so that output is the same size as the image, and
+        output values correspond to the template center. Otherwise, the output
+        is an array with shape `(M - m + 1, N - n + 1)` for an `(M, N)` image
+        and an `(m, n)` template, and matches correspond to origin
+        (top-left corner) of the template.
+    mode : see `numpy.pad`, optional
+        Padding mode.
+    constant_values : see `numpy.pad`, optional
+        Constant values used in conjunction with ``mode='constant'``.
+
+    Returns
+    -------
+    output : array
+        Response image with correlation coefficients.
+
+    Notes
+    -----
+    Details on the cross-correlation are presented in [1]_. This implementation
+    uses FFT convolutions of the image and the template. Reference [2]_
+    presents similar derivations but the approximation presented in this
+    reference is not used in our implementation.
+
+    This CuPy implementation does not force the image to float64 internally,
+    but will use float32 for single-precision inputs.
+
+    References
+    ----------
+    .. [1] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light
+           and Magic.
+    .. [2] Briechle and Hanebeck, "Template Matching using Fast Normalized
+           Cross Correlation", Proceedings of the SPIE (2001).
+           :DOI:`10.1117/12.421129`
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> template = cp.zeros((3, 3))
+    >>> template[1, 1] = 1
+    >>> template
+    array([[ 0.,  0.,  0.],
+           [ 0.,  1.,  0.],
+           [ 0.,  0.,  0.]])
+    >>> image = cp.zeros((6, 6))
+    >>> image[1, 1] = 1
+    >>> image[4, 4] = -1
+    >>> image
+    array([[ 0.,  0.,  0.,  0.,  0.,  0.],
+           [ 0.,  1.,  0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0., -1.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.,  0.]])
+    >>> result = match_template(image, template)
+    >>> cp.round(result, 3)
+    array([[ 1.   , -0.125,  0.   ,  0.   ],
+           [-0.125, -0.125,  0.   ,  0.   ],
+           [ 0.   ,  0.   ,  0.125,  0.125],
+           [ 0.   ,  0.   ,  0.125, -1.   ]])
+    >>> result = match_template(image, template, pad_input=True)
+    >>> cp.round(result, 3)
+    array([[-0.125, -0.125, -0.125,  0.   ,  0.   ,  0.   ],
+           [-0.125,  1.   , -0.125,  0.   ,  0.   ,  0.   ],
+           [-0.125, -0.125, -0.125,  0.   ,  0.   ,  0.   ],
+           [ 0.   ,  0.   ,  0.   ,  0.125,  0.125,  0.125],
+           [ 0.   ,  0.   ,  0.   ,  0.125, -1.   ,  0.125],
+           [ 0.   ,  0.   ,  0.   ,  0.125,  0.125,  0.125]])
+    """
+    check_nD(image, (2, 3))
+
+    if image.ndim < template.ndim:
+        raise ValueError("Dimensionality of template must be less than or "
+                         "equal to the dimensionality of image.")
+    if any(si < st for si, st in zip(image.shape, template.shape)):
+        raise ValueError("Image must be larger than template.")
+
+    image_shape = image.shape
+
+    float_dtype = cp.promote_types(image.dtype, cp.float32)
+    image = image.astype(float_dtype, copy=False)
+    template = template.astype(float_dtype, copy=False)
+
+    pad_width = tuple((width, width) for width in template.shape)
+    if mode == 'constant':
+        image = cp.pad(image, pad_width=pad_width, mode=mode,
+                       constant_values=constant_values)
+    else:
+        image = cp.pad(image, pad_width=pad_width, mode=mode)
+
+    # Use special case for 2-D images for much better performance in
+    # computation of integral images
+    if image.ndim == 2:
+        image_window_sum = _window_sum_2d(image, template.shape)
+        image_window_sum2 = _window_sum_2d(image * image, template.shape)
+    elif image.ndim == 3:
+        image_window_sum = _window_sum_3d(image, template.shape)
+        image_window_sum2 = _window_sum_3d(image * image, template.shape)
+
+    template_mean = template.mean()
+    template_volume = _misc.prod(template.shape)
+    template_ssd = template - template_mean
+    template_ssd *= template_ssd
+    template_ssd = cp.sum(template_ssd)
+
+    if image.ndim == 2:
+        xcorr = signal.fftconvolve(image, template[::-1, ::-1],
+                                   mode="valid")[1:-1, 1:-1]
+    elif image.ndim == 3:
+        xcorr = signal.fftconvolve(image, template[::-1, ::-1, ::-1],
+                                   mode="valid")[1:-1, 1:-1, 1:-1]
+
+    numerator = xcorr - image_window_sum * template_mean
+
+    denominator = image_window_sum2
+    cp.multiply(image_window_sum, image_window_sum, out=image_window_sum)
+    cp.divide(image_window_sum, template_volume, out=image_window_sum)
+    denominator -= image_window_sum
+    denominator *= template_ssd
+    cp.maximum(denominator, 0, out=denominator)  # avoid sqrt of negative value
+    cp.sqrt(denominator, out=denominator)
+
+    response = cp.zeros_like(xcorr, dtype=float_dtype)
+
+    # avoid zero-division
+    mask = denominator > cp.finfo(response.dtype).eps
+
+    response[mask] = numerator[mask] / denominator[mask]
+
+    slices = []
+    for i in range(template.ndim):
+        if pad_input:
+            d0 = (template.shape[i] - 1) // 2
+            d1 = d0 + image_shape[i]
+        else:
+            d0 = template.shape[i] - 1
+            d1 = d0 + image_shape[i] - template.shape[i] + 1
+        slices.append(slice(d0, d1))
+
+    return response[tuple(slices)]
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_basic_features.py b/python/cucim/src/cucim/skimage/feature/tests/test_basic_features.py
new file mode 100644
index 000000000..871162e98
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_basic_features.py
@@ -0,0 +1,36 @@
+import cupy as cp
+import pytest
+
+from cucim.skimage.feature import multiscale_basic_features
+
+
+@pytest.mark.parametrize('edges', (False, True))
+@pytest.mark.parametrize('texture', (False, True))
+def test_multiscale_basic_features(edges, texture):
+    img = cp.zeros((20, 20, 3))
+    img[:10] = 1
+    img += 0.05 * cp.random.randn(*img.shape)
+    features = multiscale_basic_features(
+        img, edges=edges, texture=texture, multichannel=True
+    )
+    n_sigmas = 6
+    intensity = True
+    assert features.shape[-1] == 3 * n_sigmas * (
+        int(intensity) + int(edges) + 2 * int(texture)
+    )
+    assert features.shape[:-1] == img.shape[:-1]
+
+
+def test_multiscale_basic_features_channel():
+    img = cp.zeros((10, 10, 5))
+    img[:10] = 1
+    img += 0.05 * cp.random.randn(*img.shape)
+    n_sigmas = 2
+    features = multiscale_basic_features(img, sigma_min=1, sigma_max=2,
+                                         multichannel=True)
+    assert features.shape[-1] == 5 * n_sigmas * 4
+    assert features.shape[:-1] == img.shape[:-1]
+    # Consider last axis as spatial dimension
+    features = multiscale_basic_features(img, sigma_min=1, sigma_max=2)
+    assert features.shape[-1] == n_sigmas * 5
+    assert features.shape[:-1] == img.shape
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_canny.py b/python/cucim/src/cucim/skimage/feature/tests/test_canny.py
new file mode 100644
index 000000000..69e649f20
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_canny.py
@@ -0,0 +1,127 @@
+import unittest
+
+import cupy as cp
+from cupy.testing import assert_array_equal
+from cupyx.scipy.ndimage import binary_dilation, binary_erosion
+from skimage import data
+
+from cucim.skimage import feature
+from cucim.skimage.util import img_as_float
+
+
+class TestCanny(unittest.TestCase):
+    def test_00_00_zeros(self):
+        """Test that the Canny filter finds no points for a blank field"""
+        result = feature.canny(cp.zeros((20, 20)), 4, 0, 0, cp.ones((20, 20),
+                               bool))
+        self.assertFalse(cp.any(result))
+
+    def test_00_01_zeros_mask(self):
+        """Test that the Canny filter finds no points in a masked image"""
+        result = (feature.canny(cp.random.uniform(size=(20, 20)), 4, 0, 0,
+                                cp.zeros((20, 20), bool)))
+        self.assertFalse(cp.any(result))
+
+    def test_01_01_circle(self):
+        """Test that the Canny filter finds the outlines of a circle"""
+        i, j = cp.mgrid[-200:200, -200:200].astype(float) / 200
+        c = cp.abs(cp.sqrt(i * i + j * j) - 0.5) < 0.02
+        result = feature.canny(c.astype(float), 4, 0, 0, cp.ones(c.shape, bool))
+        #
+        # erode and dilate the circle to get rings that should contain the
+        # outlines
+        #
+        # TODO: grlee77: only implemented brute_force=True, so added that to
+        #                these tests
+        cd = binary_dilation(c, iterations=3, brute_force=True)
+        ce = binary_erosion(c, iterations=3, brute_force=True)
+        cde = cp.logical_and(cd, cp.logical_not(ce))
+        self.assertTrue(cp.all(cde[result]))
+        #
+        # The circle has a radius of 100. There are two rings here, one
+        # for the inside edge and one for the outside. So that's
+        # 100 * 2 * 2 * 3 for those places where pi is still 3.
+        # The edge contains both pixels if there's a tie, so we
+        # bump the count a little.
+        point_count = cp.sum(result)
+        self.assertTrue(point_count > 1200)
+        self.assertTrue(point_count < 1600)
+
+    def test_01_02_circle_with_noise(self):
+        """Test that the Canny filter finds the circle outlines
+        in a noisy image"""
+        cp.random.seed(0)
+        i, j = cp.mgrid[-200:200, -200:200].astype(float) / 200
+        c = cp.abs(cp.sqrt(i * i + j * j) - 0.5) < 0.02
+        cf = c.astype(float) * 0.5 + cp.random.uniform(size=c.shape) * 0.5
+        result = feature.canny(cf, 4, 0.1, 0.2, cp.ones(c.shape, bool))
+        #
+        # erode and dilate the circle to get rings that should contain the
+        # outlines
+        #
+        cd = binary_dilation(c, iterations=4, brute_force=True)
+        ce = binary_erosion(c, iterations=4, brute_force=True)
+        cde = cp.logical_and(cd, cp.logical_not(ce))
+        self.assertTrue(cp.all(cde[result]))
+        point_count = cp.sum(result)
+        self.assertTrue(point_count > 1200)
+        self.assertTrue(point_count < 1600)
+
+    def test_image_shape(self):
+        self.assertRaises(ValueError, feature.canny, cp.zeros((20, 20, 20)), 4,
+                          0, 0)
+
+    def test_mask_none(self):
+        result1 = feature.canny(cp.zeros((20, 20)), 4, 0, 0, cp.ones((20, 20),
+                                bool))
+        result2 = feature.canny(cp.zeros((20, 20)), 4, 0, 0)
+        self.assertTrue(cp.all(result1 == result2))
+
+    @cp.testing.with_requires("skimage>=1.18")
+    def test_use_quantiles(self):
+        image = img_as_float(cp.asarray(data.camera()[::100, ::100]))
+
+        # Correct output produced manually with quantiles
+        # of 0.8 and 0.6 for high and low respectively
+        correct_output = cp.asarray(
+            [[False, False, False, False, False, False],
+             [False,  True,  True,  True, False, False],   # noqa
+             [False, False, False,  True, False, False],   # noqa
+             [False, False, False,  True, False, False],   # noqa
+             [False, False,  True,  True, False, False],   # noqa
+             [False, False, False, False, False, False]])
+
+        result = feature.canny(image, low_threshold=0.6, high_threshold=0.8,
+                               use_quantiles=True)
+
+        assert_array_equal(result, correct_output)
+
+    def test_invalid_use_quantiles(self):
+        image = img_as_float(cp.array(data.camera()[::50, ::50]))
+
+        self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
+                          low_threshold=0.5, high_threshold=3.6)
+
+        self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
+                          low_threshold=-5, high_threshold=0.5)
+
+        self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
+                          low_threshold=99, high_threshold=0.9)
+
+        self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
+                          low_threshold=0.5, high_threshold=-100)
+
+        # Example from issue #4282
+        image = data.camera()
+        self.assertRaises(ValueError, feature.canny, image, use_quantiles=True,
+                          low_threshold=50, high_threshold=150)
+
+    def test_dtype(self):
+        """Check that the same output is produced regardless of image dtype."""
+        image_uint8 = cp.asarray(data.camera())
+        image_float = img_as_float(image_uint8)
+
+        result_uint8 = feature.canny(image_uint8)
+        result_float = feature.canny(image_float)
+
+        assert_array_equal(result_uint8, result_float)
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_corner.py b/python/cucim/src/cucim/skimage/feature/tests/test_corner.py
new file mode 100644
index 000000000..b725bc84d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_corner.py
@@ -0,0 +1,595 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_equal
+from skimage import data, draw
+
+from cucim.skimage import img_as_float
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.color import rgb2gray
+from cucim.skimage.feature import (corner_foerstner, corner_harris,
+                                   corner_kitchen_rosenfeld, corner_peaks,
+                                   corner_shi_tomasi, hessian_matrix,
+                                   hessian_matrix_det, hessian_matrix_eigvals,
+                                   peak_local_max, shape_index,
+                                   structure_tensor,
+                                   structure_tensor_eigenvalues,
+                                   structure_tensor_eigvals)
+from cucim.skimage.morphology import cube
+
+
+@pytest.fixture
+def im3d():
+    r = 10
+    pad = 10
+    im3 = draw.ellipsoid(r, r, r)
+    im3 = np.pad(im3, pad, mode='constant').astype(np.uint8)
+    return cp.asarray(im3)
+
+
+def test_structure_tensor():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 1
+    Arr, Arc, Acc = structure_tensor(square, sigma=0.1, order='rc')
+    # fmt: off
+    assert_array_equal(Acc, cp.asarray([[0, 0, 0, 0, 0],
+                                        [0, 1, 0, 1, 0],
+                                        [0, 4, 0, 4, 0],
+                                        [0, 1, 0, 1, 0],
+                                        [0, 0, 0, 0, 0]]))
+    assert_array_equal(Arc, cp.asarray([[0, 0, 0, 0, 0],
+                                        [0, 1, 0, -1, 0],
+                                        [0, 0, 0, -0, 0],
+                                        [0, -1, -0, 1, 0],
+                                        [0, 0, 0, 0, 0]]))
+    assert_array_equal(Arr, cp.asarray([[0, 0, 0, 0, 0],
+                                        [0, 1, 4, 1, 0],
+                                        [0, 0, 0, 0, 0],
+                                        [0, 1, 4, 1, 0],
+                                        [0, 0, 0, 0, 0]]))
+    # fmt: on
+
+
+def test_structure_tensor_3d():
+    cube = cp.zeros((5, 5, 5))
+    cube[2, 2, 2] = 1
+    A_elems = structure_tensor(cube, sigma=0.1)
+    assert_equal(len(A_elems), 6)
+    # fmt: off
+    assert_array_equal(A_elems[0][:, 1, :], cp.asarray([[0, 0, 0, 0, 0],
+                                                        [0, 1, 4, 1, 0],
+                                                        [0, 0, 0, 0, 0],
+                                                        [0, 1, 4, 1, 0],
+                                                        [0, 0, 0, 0, 0]]))
+    assert_array_equal(A_elems[0][1], cp.asarray([[0, 0, 0, 0, 0],
+                                                  [0, 1, 4, 1, 0],
+                                                  [0, 4, 16, 4, 0],
+                                                  [0, 1, 4, 1, 0],
+                                                  [0, 0, 0, 0, 0]]))
+    assert_array_equal(A_elems[3][2], cp.asarray([[0, 0, 0, 0, 0],
+                                                  [0, 4, 16, 4, 0],
+                                                  [0, 0, 0, 0, 0],
+                                                  [0, 4, 16, 4, 0],
+                                                  [0, 0, 0, 0, 0]]))
+    # fmt: on
+
+
+def test_structure_tensor_3d_rc_only():
+    cube = cp.zeros((5, 5, 5))
+    with pytest.raises(ValueError):
+        structure_tensor(cube, sigma=0.1, order='xy')
+    A_elems_rc = structure_tensor(cube, sigma=0.1, order='rc')
+    A_elems_none = structure_tensor(cube, sigma=0.1)
+    for a_rc, a_none in zip(A_elems_rc, A_elems_none):
+        assert_array_equal(a_rc, a_none)
+
+
+def test_structure_tensor_orders():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 1
+    with expected_warnings(['the default order of the structure']):
+        A_elems_default = structure_tensor(square, sigma=0.1)
+    A_elems_xy = structure_tensor(square, sigma=0.1, order='xy')
+    A_elems_rc = structure_tensor(square, sigma=0.1, order='rc')
+    for elem_xy, elem_def in zip(A_elems_xy, A_elems_default):
+        assert_array_equal(elem_xy, elem_def)
+    for elem_xy, elem_rc in zip(A_elems_xy, A_elems_rc[::-1]):
+        assert_array_equal(elem_xy, elem_rc)
+
+
+def test_hessian_matrix():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 4
+    Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order="rc")
+    # fmt: off
+    assert_array_almost_equal(Hrr, cp.asarray([[0, 0,  0, 0, 0],    # noqa
+                                               [0, 0,  0, 0, 0],    # noqa
+                                               [2, 0, -2, 0, 2],    # noqa
+                                               [0, 0,  0, 0, 0],    # noqa
+                                               [0, 0,  0, 0, 0]]))  # noqa
+
+    assert_array_almost_equal(Hrc, cp.asarray([[0,  0, 0,  0, 0],    # noqa
+                                               [0,  1, 0, -1, 0],    # noqa
+                                               [0,  0, 0,  0, 0],    # noqa
+                                               [0, -1, 0,  1, 0],    # noqa
+                                               [0,  0, 0,  0, 0]]))  # noqa
+
+    assert_array_almost_equal(Hcc, cp.asarray([[0, 0,  2, 0, 0],    # noqa
+                                               [0, 0,  0, 0, 0],    # noqa
+                                               [0, 0, -2, 0, 0],    # noqa
+                                               [0, 0,  0, 0, 0],    # noqa
+                                               [0, 0,  2, 0, 0]]))  # noqa
+    # fmt: on
+
+
+def test_hessian_matrix_3d():
+    cube = cp.zeros((5, 5, 5))
+    cube[2, 2, 2] = 4
+    Hs = hessian_matrix(cube, sigma=0.1, order='rc')
+    assert len(Hs) == 6, "incorrect number of Hessian images (%i) for 3D" % len(
+        Hs
+    )
+    # fmt: off
+    assert_array_almost_equal(
+        Hs[2][:, 2, :], cp.asarray([[0,  0,  0,  0,  0],    # noqa
+                                    [0,  1,  0, -1,  0],    # noqa
+                                    [0,  0,  0,  0,  0],    # noqa
+                                    [0, -1,  0,  1,  0],    # noqa
+                                    [0,  0,  0,  0,  0]]))  # noqa
+    # fmt: on
+
+
+def test_structure_tensor_eigenvalues():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 1
+    A_elems = structure_tensor(square, sigma=0.1, order='rc')
+    l1, l2 = structure_tensor_eigenvalues(A_elems)
+    assert_array_equal(l1, cp.asarray([[0, 0, 0, 0, 0],
+                                       [0, 2, 4, 2, 0],
+                                       [0, 4, 0, 4, 0],
+                                       [0, 2, 4, 2, 0],
+                                       [0, 0, 0, 0, 0]]))
+    assert_array_equal(l2, cp.asarray([[0, 0, 0, 0, 0],
+                                       [0, 0, 0, 0, 0],
+                                       [0, 0, 0, 0, 0],
+                                       [0, 0, 0, 0, 0],
+                                       [0, 0, 0, 0, 0]]))
+
+
+def test_structure_tensor_eigenvalues_3d():
+    image = cp.pad(cube(9), 5, mode='constant') * 1000
+    boundary = (cp.pad(cube(9), 5, mode='constant')
+                - cp.pad(cube(7), 6, mode='constant')).astype(bool)
+    A_elems = structure_tensor(image, sigma=0.1)
+    e0, e1, e2 = structure_tensor_eigenvalues(A_elems)
+    # e0 should detect facets
+    assert np.all(e0[boundary] != 0)
+
+
+def test_structure_tensor_eigvals():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 1
+    A_elems = structure_tensor(square, sigma=0.1, order='rc')
+    with expected_warnings(['structure_tensor_eigvals is deprecated']):
+        eigvals = structure_tensor_eigvals(*A_elems)
+    eigenvalues = structure_tensor_eigenvalues(A_elems)
+    for ev1, ev2 in zip(eigvals, eigenvalues):
+        assert_array_equal(ev1, ev2)
+
+
+def test_hessian_matrix_eigvals():
+    square = cp.zeros((5, 5))
+    square[2, 2] = 4
+    H = hessian_matrix(square, sigma=0.1, order='rc')
+    l1, l2 = hessian_matrix_eigvals(H)
+    # fmt: off
+    assert_array_almost_equal(l1, cp.asarray([[0, 0,  2, 0, 0],      # noqa
+                                              [0, 1,  0, 1, 0],      # noqa
+                                              [2, 0, -2, 0, 2],      # noqa
+                                              [0, 1,  0, 1, 0],      # noqa
+                                              [0, 0,  2, 0, 0]]))    # noqa
+    assert_array_almost_equal(l2, cp.asarray([[0,  0,  0,  0, 0],    # noqa
+                                              [0, -1,  0, -1, 0],    # noqa
+                                              [0,  0, -2,  0, 0],    # noqa
+                                              [0, -1,  0, -1, 0],    # noqa
+                                              [0,  0,  0,  0, 0]]))  # noqa
+
+    # fmt: on
+
+
+def test_hessian_matrix_eigvals_3d(im3d):
+    H = hessian_matrix(im3d)
+    E = hessian_matrix_eigvals(H)
+    E = cp.asnumpy(E)
+    # test descending order:
+    e0, e1, e2 = E
+    assert np.all(e0 >= e1) and np.all(e1 >= e2)
+
+    E0, E1, E2 = E[:, E.shape[1] // 2]  # cross section
+    row_center, col_center = np.asarray(E0.shape) // 2
+    circles = [
+        draw.circle_perimeter(row_center, col_center, radius, shape=E0.shape)
+        for radius in range(1, E0.shape[1] // 2 - 1)
+    ]
+    response0 = np.array([np.mean(E0[c]) for c in circles])
+    response2 = np.array([np.mean(E2[c]) for c in circles])
+
+    # eigenvalues are negative just inside the sphere, positive just outside
+    assert np.argmin(response2) < np.argmax(response0)
+    assert np.min(response2) < 0
+    assert np.max(response0) > 0
+
+
+def test_hessian_matrix_det():
+    image = cp.zeros((5, 5))
+    image[2, 2] = 1
+    # TODO: approximate=True case not implemented
+    det = hessian_matrix_det(image, 5, approximate=False)
+    assert_array_almost_equal(det, 0, decimal=3)
+
+
+def test_hessian_matrix_det_3d(im3d):
+    D = hessian_matrix_det(im3d)
+    D = cp.asnumpy(D)
+    D0 = D[D.shape[0] // 2]
+    row_center, col_center = np.asarray(D0.shape) // 2
+    # testing in 3D is hard. We test this by showing that you get the
+    # expected flat-then-low-then-high 2nd derivative response in a circle
+    # around the midplane of the sphere.
+    circles = [
+        draw.circle_perimeter(row_center, col_center, r, shape=D0.shape)
+        for r in range(1, D0.shape[1] // 2 - 1)
+    ]
+    response = np.array([np.mean(D0[c]) for c in circles])
+    lowest = np.argmin(response)
+    highest = np.argmax(response)
+    assert lowest < highest
+    assert response[lowest] < 0
+    assert response[highest] > 0
+
+
+def test_shape_index():
+    # software floating point arm doesn't raise a warning on divide by zero
+    # https://github.com/scikit-image/scikit-image/issues/3335
+    square = cp.zeros((5, 5))
+    square[2, 2] = 4
+    with expected_warnings([r"divide by zero|\A\Z", r"invalid value|\A\Z"]):
+        s = shape_index(square, sigma=0.1)
+    # fmt: off
+    assert_array_almost_equal(
+        s,
+        cp.asarray(
+            [
+                [cp.nan, cp.nan,   -0.5, cp.nan, cp.nan],  # noqa
+                [cp.nan,      0, cp.nan,      0, cp.nan],  # noqa
+                [  -0.5, cp.nan,     -1, cp.nan,   -0.5],  # noqa
+                [cp.nan,      0, cp.nan,      0, cp.nan],  # noqa
+                [cp.nan, cp.nan,   -0.5, cp.nan, cp.nan],  # noqa
+            ]
+        )
+    )
+    # fmt: on
+
+
+# @test_parallel()
+def test_square_image():
+    im = cp.zeros((50, 50)).astype(float)
+    im[:25, :25] = 1.0
+
+    # # Moravec
+    # results = peak_local_max(corner_moravec(im),
+    #                          min_distance=10, threshold_rel=0)
+    # # interest points along edge
+    # assert len(results) == 57
+
+    # Harris
+    results = peak_local_max(corner_harris(im, method='k'),
+                             min_distance=10, threshold_rel=0)
+    # interest at corner
+    assert len(results) == 1
+
+    results = peak_local_max(corner_harris(im, method='eps'),
+                             min_distance=10, threshold_rel=0)
+    # interest at corner
+    assert len(results) == 1
+
+    # Shi-Tomasi
+    results = peak_local_max(corner_shi_tomasi(im),
+                             min_distance=10, threshold_rel=0)
+    # interest at corner
+    assert len(results) == 1
+
+
+def test_noisy_square_image():
+    im = cp.zeros((50, 50)).astype(float)
+    im[:25, :25] = 1.0
+    np.random.seed(seed=1234)  # result is specic to this NumPy seed
+    im = im + cp.asarray(np.random.uniform(size=im.shape)) * 0.2
+
+    # # Moravec
+    # results = peak_local_max(corner_moravec(im),
+    #                          min_distance=10, threshold_rel=0)
+    # # undefined number of interest points
+    # assert results.any()
+
+    # Harris
+    results = peak_local_max(corner_harris(im, method='k'),
+                             min_distance=10, threshold_rel=0)
+    assert len(results) == 1
+    results = peak_local_max(corner_harris(im, method='eps'),
+                             min_distance=10, threshold_rel=0)
+    assert len(results) == 1
+
+    # Shi-Tomasi
+    results = peak_local_max(corner_shi_tomasi(im, sigma=1.5),
+                             min_distance=10, threshold_rel=0)
+    assert len(results) == 1
+
+
+def test_squared_dot():
+    im = cp.zeros((50, 50))
+    im[4:8, 4:8] = 1
+    im = img_as_float(im)
+
+    # Moravec fails
+
+    # Harris
+    results = peak_local_max(corner_harris(im),
+                             min_distance=10, threshold_rel=0)
+
+    assert (results == cp.asarray([[6, 6]])).all()
+
+    # Shi-Tomasi
+    results = peak_local_max(corner_shi_tomasi(im),
+                             min_distance=10, threshold_rel=0)
+
+    assert (results == cp.asarray([[6, 6]])).all()
+
+
+def test_rotated_img():
+    """
+    The harris filter should yield the same results with an image and it's
+    rotation.
+    """
+    im = img_as_float(cp.asarray(data.astronaut().mean(axis=2)))
+    im_rotated = im.T
+
+    # # Moravec
+    # results = peak_local_max(corner_moravec(im),
+    #                          min_distance=10, threshold_rel=0)
+    # results_rotated = peak_local_max(corner_moravec(im_rotated),
+    #                                  min_distance=10, threshold_rel=0)
+    # assert (cp.sort(results[:, 0]) == cp.sort(results_rotated[:, 1])).all()
+    # assert (cp.sort(results[:, 1]) == cp.sort(results_rotated[:, 0])).all()
+
+    # Harris
+    results = cp.nonzero(corner_harris(im))
+    results_rotated = cp.nonzero(corner_harris(im_rotated))
+
+    assert (cp.sort(results[0]) == cp.sort(results_rotated[1])).all()
+    assert (cp.sort(results[1]) == cp.sort(results_rotated[0])).all()
+
+    # Shi-Tomasi
+    results = cp.nonzero(corner_shi_tomasi(im))
+    results_rotated = cp.nonzero(corner_shi_tomasi(im_rotated))
+
+    assert (cp.sort(results[0]) == cp.sort(results_rotated[1])).all()
+    assert (cp.sort(results[1]) == cp.sort(results_rotated[0])).all()
+
+
+# def test_subpix_edge():
+#     img = cp.zeros((50, 50))
+#     img[:25, :25] = 255
+#     img[25:, 25:] = 255
+#     corner = peak_local_max(corner_harris(img),
+#                             min_distance=10, threshold_rel=0, num_peaks=1)
+#     subpix = corner_subpix(img, corner)
+#     assert_array_equal(subpix[0], (24.5, 24.5))
+
+
+# def test_subpix_dot():
+#     img = cp.zeros((50, 50))
+#     img[25, 25] = 255
+#     corner = peak_local_max(corner_harris(img),
+#                             min_distance=10, threshold_rel=0, num_peaks=1)
+#     subpix = corner_subpix(img, corner)
+#     assert_array_equal(subpix[0], (25, 25))
+
+
+# def test_subpix_no_class():
+#     img = cp.zeros((50, 50))
+#     subpix = corner_subpix(img, cp.asarray([[25, 25]]))
+#     assert_array_equal(subpix[0], (cp.nan, cp.nan))
+
+#     img[25, 25] = 1e-10
+#     corner = peak_local_max(corner_harris(img),
+#                             min_distance=10, threshold_rel=0, num_peaks=1)
+#     subpix = corner_subpix(img, cp.asarray([[25, 25]]))
+#     assert_array_equal(subpix[0], (cp.nan, cp.nan))
+
+
+# def test_subpix_border():
+#     img = cp.zeros((50, 50))
+#     img[1:25, 1:25] = 255
+#     img[25:-1, 25:-1] = 255
+#     corner = corner_peaks(corner_harris(img), threshold_rel=0)
+#     subpix = corner_subpix(img, corner, window_size=11)
+#     ref = cp.asarray([[24.5, 24.5],
+#                     [0.52040816, 0.52040816],
+#                     [0.52040816, 24.47959184],
+#                     [24.47959184, 0.52040816],
+#                     [24.52040816, 48.47959184],
+#                     [48.47959184, 24.52040816],
+#                     [48.47959184, 48.47959184]])
+
+#     assert_array_almost_equal(subpix, ref)
+
+
+def test_num_peaks():
+    """For a bunch of different values of num_peaks, check that
+    peak_local_max returns exactly the right amount of peaks. Test
+    is run on the astronaut image in order to produce a sufficient number of
+    corners
+    """
+
+    img_corners = corner_harris(rgb2gray(cp.asarray(data.astronaut())))
+
+    for i in range(20):
+        n = cp.random.randint(1, 21)
+        results = peak_local_max(img_corners,
+                                 min_distance=10, threshold_rel=0, num_peaks=n)
+        assert results.shape[0] == n
+
+
+def test_corner_peaks():
+    response = cp.zeros((10, 10))
+    response[2:5, 2:5] = 1
+    response[8:10, 0:2] = 1
+
+    corners = corner_peaks(response, exclude_border=False, min_distance=10,
+                           threshold_rel=0)
+    assert corners.shape == (1, 2)
+
+    corners = corner_peaks(response, exclude_border=False, min_distance=5,
+                           threshold_rel=0)
+    assert corners.shape == (2, 2)
+
+    corners = corner_peaks(response, exclude_border=False, min_distance=1)
+    assert corners.shape == (5, 2)
+
+    corners = corner_peaks(response, exclude_border=False, min_distance=1,
+                           indices=False)
+    assert cp.sum(corners) == 5
+
+
+def test_blank_image_nans():
+    """Some of the corner detectors had a weakness in terms of returning
+    NaN when presented with regions of constant intensity. This should
+    be fixed by now. We test whether each detector returns something
+    finite in the case of constant input"""
+
+    #    detectors = [corner_moravec, corner_harris, corner_shi_tomasi,
+    #                 corner_kitchen_rosenfeld]
+    detectors = [
+        corner_harris,
+        corner_shi_tomasi,
+        corner_kitchen_rosenfeld,
+    ]
+    constant_image = cp.zeros((20, 20))
+
+    for det in detectors:
+        response = det(constant_image)
+        assert cp.all(cp.isfinite(response))
+
+    w, q = corner_foerstner(constant_image)
+    assert cp.all(cp.isfinite(w))
+    assert cp.all(cp.isfinite(q))
+
+
+# def test_corner_fast_image_unsupported_error():
+#     img = cp.zeros((20, 20, 3))
+#     with pytest.raises(ValueError):
+#         corner_fast(img)
+
+
+# # @test_parallel()
+# def test_corner_fast_astronaut():
+#     img = rgb2gray(cp.asarray(data.astronaut()))
+#     expected = cp.asarray([[444, 310],
+#                          [374, 171],
+#                          [249, 171],
+#                          [492, 139],
+#                          [403, 162],
+#                          [496, 266],
+#                          [362, 328],
+#                          [476, 250],
+#                          [353, 172],
+#                          [346, 279],
+#                          [494, 169],
+#                          [177, 156],
+#                          [413, 181],
+#                          [213, 117],
+#                          [390, 149],
+#                          [140, 205],
+#                          [232, 266],
+#                          [489, 155],
+#                          [387, 195],
+#                          [101, 198],
+#                          [363, 192],
+#                          [364, 147],
+#                          [300, 244],
+#                          [325, 245],
+#                          [141, 242],
+#                          [401, 197],
+#                          [197, 148],
+#                          [339, 242],
+#                          [188, 113],
+#                          [362, 252],
+#                          [379, 183],
+#                          [358, 307],
+#                          [245, 137],
+#                          [369, 159],
+#                          [464, 251],
+#                          [305,  57],
+#                          [223, 375]])
+#     actual = corner_peaks(corner_fast(img, 12, 0.3),
+#                           min_distance=10, threshold_rel=0)
+#     assert_array_equal(actual, expected)
+
+
+# def test_corner_orientations_image_unsupported_error():
+#     img = cp.zeros((20, 20, 3))
+#     with pytest.raises(ValueError):
+#         corner_orientations(
+#             img,
+#             cp.asarray([[7, 7]]), cp.ones((3, 3)))
+
+
+# def test_corner_orientations_even_shape_error():
+#     img = cp.zeros((20, 20))
+#     with pytest.raises(ValueError):
+#         corner_orientations(
+#             img,
+#             cp.asarray([[7, 7]]), cp.ones((4, 4)))
+
+
+# # @test_parallel()
+# def test_corner_orientations_astronaut():
+#     img = rgb2gray(cp.asarray(data.astronaut()))
+#     corners = corner_peaks(corner_fast(img, 11, 0.35),
+#                            min_distance=10, threshold_abs=0,
+#                            threshold_rel=0.1)
+#     expected = cp.asarray([-4.40598471e-01, -1.46554357e+00,
+#                          2.39291733e+00, -1.63869275e+00,
+#                          1.45931342e+00, -1.64397304e+00,
+#                          -1.76069982e+00, 1.09650167e+00,
+#                          -1.65449964e+00, 1.19134149e+00,
+#                          5.46905279e-02, 2.17103132e+00,
+#                          8.12701702e-01, -1.22091334e-01,
+#                          -2.01162417e+00, 1.25854853e+00,
+#                          3.05330950e+00, 2.01197383e+00,
+#                          1.07812134e+00, 3.09780364e+00,
+#                          -3.49561988e-01, 2.43573659e+00,
+#                          3.14918803e-01, -9.88548213e-01,
+#                          -1.88247204e-01, 2.47305654e+00,
+#                          -2.99143370e+00, 1.47154532e+00,
+#                          -6.61151410e-01, -1.68885773e+00,
+#                          -3.09279990e-01, -2.81524886e+00,
+#                          -1.75220190e+00, -1.69230287e+00,
+#                          -7.52950306e-04])
+
+#     actual = corner_orientations(img, corners, octagon(3, 2))
+#     assert_array_almost_equal(actual, expected)
+
+
+# def test_corner_orientations_square():
+#     square = cp.zeros((12, 12))
+#     square[3:9, 3:9] = 1
+#     corners = corner_peaks(corner_fast(square, 9),
+#                            min_distance=1, threshold_rel=0)
+#     actual_orientations = corner_orientations(square, corners, octagon(3, 2))
+#     actual_orientations_degrees = cp.rad2deg(actual_orientations)
+#     expected_orientations_degree = cp.asarray([45, 135, -45, -135])
+#     assert_array_equal(actual_orientations_degrees,
+#                        expected_orientations_degree)
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_daisy.py b/python/cucim/src/cucim/skimage/feature/tests/test_daisy.py
new file mode 100644
index 000000000..9b4aa7335
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_daisy.py
@@ -0,0 +1,114 @@
+import math
+
+import cupy as cp
+import pytest
+from cupy.testing import assert_array_almost_equal
+from skimage import data
+
+from cucim.skimage import img_as_float
+from cucim.skimage.feature import daisy
+
+
+def test_daisy_color_image_unsupported_error():
+    img = cp.zeros((20, 20, 3))
+    with pytest.raises(ValueError):
+        daisy(img)
+
+
+def test_daisy_desc_dims():
+    img = img_as_float(cp.asarray(data.astronaut()[:128, :128].mean(axis=2)))
+    rings = 2
+    histograms = 4
+    orientations = 3
+    descs = daisy(
+        img, rings=rings, histograms=histograms, orientations=orientations
+    )
+    assert descs.shape[2] == (rings * histograms + 1) * orientations
+
+    rings = 4
+    histograms = 5
+    orientations = 13
+    descs = daisy(
+        img, rings=rings, histograms=histograms, orientations=orientations
+    )
+    assert descs.shape[2] == (rings * histograms + 1) * orientations
+
+
+def test_descs_shape():
+    img = img_as_float(data.astronaut()[:256, :256].mean(axis=2))
+    radius = 20
+    step = 8
+    descs = daisy(img, radius=radius, step=step)
+    assert descs.shape[0] == math.ceil(
+        (img.shape[0] - radius * 2) / float(step)
+    )
+    assert descs.shape[1] == math.ceil(
+        (img.shape[1] - radius * 2) / float(step)
+    )
+
+    img = img[:-1, :-2]
+    radius = 5
+    step = 3
+    descs = daisy(img, radius=radius, step=step)
+    assert descs.shape[0] == math.ceil(
+        (img.shape[0] - radius * 2) / float(step)
+    )
+    assert descs.shape[1] == math.ceil(
+        (img.shape[1] - radius * 2) / float(step)
+    )
+
+
+def test_daisy_sigmas_and_radii():
+    img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
+    sigmas = [1, 2, 3]
+    radii = [1, 2]
+    daisy(img, sigmas=sigmas, ring_radii=radii)
+
+
+def test_daisy_incompatible_sigmas_and_radii():
+    img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
+    sigmas = [1, 2]
+    radii = [1, 2]
+    with pytest.raises(ValueError):
+        daisy(img, sigmas=sigmas, ring_radii=radii)
+
+
+def test_daisy_normalization():
+    img = img_as_float(data.astronaut()[:64, :64].mean(axis=2))
+
+    descs = daisy(img, normalization="l1")
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            assert_array_almost_equal(cp.sum(descs[i, j, :]), 1)
+    descs_ = daisy(img)
+    assert_array_almost_equal(descs, descs_)
+
+    descs = daisy(img, normalization="l2")
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            dtmp = descs[i, j, :]
+            assert_array_almost_equal(cp.sqrt(cp.sum(dtmp * dtmp)), 1)
+
+    orientations = 8
+    descs = daisy(img, orientations=orientations, normalization="daisy")
+    desc_dims = descs.shape[2]
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            for k in range(0, desc_dims, orientations):
+                dtmp = descs[i, j, k:k + orientations]
+                assert_array_almost_equal(cp.sqrt(cp.sum(dtmp * dtmp)), 1)
+
+    img = cp.zeros((50, 50))
+    descs = daisy(img, normalization="off")
+    for i in range(descs.shape[0]):
+        for j in range(descs.shape[1]):
+            assert_array_almost_equal(cp.sum(descs[i, j, :]), 0)
+
+    with pytest.raises(ValueError):
+        daisy(img, normalization="does_not_exist")
+
+
+def test_daisy_visualization():
+    img = img_as_float(data.astronaut()[:32, :32].mean(axis=2))
+    descs, descs_img = daisy(img, visualize=True)
+    assert descs_img.shape == (32, 32, 3)
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_peak.py b/python/cucim/src/cucim/skimage/feature/tests/test_peak.py
new file mode 100644
index 000000000..b1f9496f0
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_peak.py
@@ -0,0 +1,598 @@
+import itertools
+import unittest
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from cupyx.scipy import ndimage as ndi
+from scipy import ndimage as ndimage_cpu
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.feature import peak
+
+np.random.seed(21)
+
+
+class TestPeakLocalMax:
+    def test_trivial_case(self):
+        trivial = cp.zeros((25, 25))
+        peak_indices = peak.peak_local_max(trivial, min_distance=1)
+        assert type(peak_indices) is cp.ndarray
+        assert peak_indices.size == 0
+        with expected_warnings(["indices argument is deprecated"]):
+            peaks = peak.peak_local_max(trivial, min_distance=1,
+                                        indices=False)
+        assert (peaks.astype(bool) == trivial).all()
+
+    def test_noisy_peaks(self):
+        peak_locations = [(7, 7), (7, 13), (13, 7), (13, 13)]
+
+        # image with noise of amplitude 0.8 and peaks of amplitude 1
+        image = 0.8 * cp.asarray(np.random.rand(20, 20))
+        for r, c in peak_locations:
+            image[r, c] = 1
+
+        peaks_detected = peak.peak_local_max(image, min_distance=5)
+
+        assert len(peaks_detected) == len(peak_locations)
+        for loc in peaks_detected:
+            assert tuple(loc) in peak_locations
+
+    def test_relative_threshold(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        image[1, 1] = 10
+        image[3, 3] = 20
+        peaks = peak.peak_local_max(image, min_distance=1, threshold_rel=0.5)
+        assert len(peaks) == 1
+        assert_array_almost_equal(peaks, [(3, 3)])
+
+    def test_absolute_threshold(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        image[1, 1] = 10
+        image[3, 3] = 20
+        peaks = peak.peak_local_max(image, min_distance=1, threshold_abs=10)
+        assert len(peaks) == 1
+        assert_array_almost_equal(peaks, [(3, 3)])
+
+    def test_constant_image(self):
+        image = cp.full((20, 20), 128, dtype=cp.uint8)
+        peaks = peak.peak_local_max(image, min_distance=1)
+        assert len(peaks) == 0
+
+    def test_flat_peak(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        image[1:3, 1:3] = 10
+        peaks = peak.peak_local_max(image, min_distance=1)
+        assert len(peaks) == 4
+
+    def test_sorted_peaks(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        image[1, 1] = 20
+        image[3, 3] = 10
+        peaks = peak.peak_local_max(image, min_distance=1)
+        assert peaks.tolist() == [[1, 1], [3, 3]]
+
+        image = cp.zeros((3, 10))
+        # Note: CuPy doesn't support this type of indexing
+        #       image[1, (1, 3, 5, 7)] = (1, 2, 3, 4)
+        image[1, 1] = 1
+        image[1, 3] = 2
+        image[1, 5] = 3
+        image[1, 7] = 4
+        peaks = peak.peak_local_max(image, min_distance=1)
+        assert peaks.tolist() == [[1, 7], [1, 5], [1, 3], [1, 1]]
+
+    def test_num_peaks(self):
+        image = cp.zeros((7, 7), dtype=cp.uint8)
+        image[1, 1] = 10
+        image[1, 3] = 11
+        image[1, 5] = 12
+        image[3, 5] = 8
+        image[5, 3] = 7
+        assert (
+            len(peak.peak_local_max(image, min_distance=1, threshold_abs=0))
+            == 5
+        )
+        peaks_limited = peak.peak_local_max(
+            image, min_distance=1, threshold_abs=0, num_peaks=2
+        )
+        assert len(peaks_limited) == 2
+        peaks_limited = cp.asnumpy(peaks_limited)
+        assert (1, 3) in peaks_limited
+        assert (1, 5) in peaks_limited
+
+        peaks_limited = peak.peak_local_max(
+            image, min_distance=1, threshold_abs=0, num_peaks=4
+        )
+
+        peaks_limited = cp.asnumpy(peaks_limited)
+        assert len(peaks_limited) == 4
+        assert (1, 3) in peaks_limited
+        assert (1, 5) in peaks_limited
+        assert (1, 1) in peaks_limited
+        assert (3, 5) in peaks_limited
+
+    def test_num_peaks_and_labels(self):
+        image = cp.zeros((7, 7), dtype=cp.uint8)
+        labels = cp.zeros((7, 7), dtype=cp.uint8) + 20
+        image[1, 1] = 10
+        image[1, 3] = 11
+        image[1, 5] = 12
+        image[3, 5] = 8
+        image[5, 3] = 7
+        peaks_limited = peak.peak_local_max(
+            image, min_distance=1, threshold_abs=0, labels=labels
+        )
+        assert len(peaks_limited) == 5
+        peaks_limited = peak.peak_local_max(
+            image, min_distance=1, threshold_abs=0, labels=labels, num_peaks=2
+        )
+        assert len(peaks_limited) == 2
+
+    def test_num_peaks_tot_vs_labels_4quadrants(self):
+        np.random.seed(21)
+        image = cp.asarray(np.random.uniform(size=(20, 30)))
+        i, j = cp.mgrid[0:20, 0:30]
+        labels = 1 + (i >= 10) + (j >= 15) * 2
+        result = peak.peak_local_max(image, labels=labels,
+                                     min_distance=1, threshold_rel=0,
+                                     num_peaks=cp.inf,
+                                     num_peaks_per_label=2)
+        assert len(result) == 8
+        result = peak.peak_local_max(image, labels=labels,
+                                     min_distance=1, threshold_rel=0,
+                                     num_peaks=cp.inf,
+                                     num_peaks_per_label=1)
+        assert len(result) == 4
+        result = peak.peak_local_max(image, labels=labels,
+                                     min_distance=1, threshold_rel=0,
+                                     num_peaks=2,
+                                     num_peaks_per_label=2)
+        assert len(result) == 2
+
+    def test_num_peaks3D(self):
+        # Issue 1354: the old code only hold for 2D arrays
+        # and this code would die with IndexError
+        image = cp.zeros((10, 10, 100))
+        image[5, 5, ::5] = cp.arange(20)
+        peaks_limited = peak.peak_local_max(image, min_distance=1, num_peaks=2)
+        assert len(peaks_limited) == 2
+
+    def test_reorder_labels(self):
+        image = cp.asarray(np.random.uniform(size=(40, 60)))
+        i, j = cp.mgrid[0:40, 0:60]
+        labels = 1 + (i >= 20) + (j >= 30) * 2
+        labels[labels == 4] = 5
+        i, j = cp.mgrid[-3:4, -3:4]
+        footprint = i * i + j * j <= 9
+        expected = cp.zeros(image.shape, float)
+        for imin, imax in ((0, 20), (20, 40)):
+            for jmin, jmax in ((0, 30), (30, 60)):
+                expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
+                    image[imin:imax, jmin:jmax], footprint=footprint
+                )
+        expected = expected == image
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels, min_distance=1,
+                                         threshold_rel=0, footprint=footprint,
+                                         indices=False, exclude_border=False)
+        assert (result == expected).all()
+
+    def test_indices_with_labels(self):
+        image = cp.asarray(np.random.uniform(size=(40, 60)))
+        i, j = cp.mgrid[0:40, 0:60]
+        labels = 1 + (i >= 20) + (j >= 30) * 2
+        i, j = cp.mgrid[-3:4, -3:4]
+        footprint = i * i + j * j <= 9
+        expected = cp.zeros(image.shape, float)
+        for imin, imax in ((0, 20), (20, 40)):
+            for jmin, jmax in ((0, 30), (30, 60)):
+                expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
+                    image[imin:imax, jmin:jmax], footprint=footprint
+                )
+        expected = cp.stack(cp.nonzero(expected == image), axis=-1)
+        expected = expected[cp.argsort(image[tuple(expected.T)])[::-1]]
+        result = peak.peak_local_max(image, labels=labels, min_distance=1,
+                                     threshold_rel=0, footprint=footprint,
+                                     exclude_border=False)
+        result = result[cp.argsort(image[tuple(result.T)])[::-1]]
+        assert (result == expected).all()
+
+    def test_ndarray_indices_false(self):
+        nd_image = cp.zeros((5, 5, 5))
+        nd_image[2, 2, 2] = 1
+        with expected_warnings(["indices argument is deprecated"]):
+            peaks = peak.peak_local_max(nd_image, min_distance=1,
+                                        indices=False)
+        assert (peaks == nd_image.astype(bool)).all()
+
+    def test_ndarray_exclude_border(self):
+        nd_image = cp.zeros((5, 5, 5))
+        nd_image[[1, 0, 0], [0, 1, 0], [0, 0, 1]] = 1
+        nd_image[3, 0, 0] = 1
+        nd_image[2, 2, 2] = 1
+        expected = cp.zeros_like(nd_image, dtype=bool)
+        expected[2, 2, 2] = True
+        expectedNoBorder = np.zeros_like(nd_image, dtype=bool)
+        expectedNoBorder[2, 2, 2] = True
+        expectedNoBorder[0, 0, 1] = True
+        expectedNoBorder[3, 0, 0] = True
+        expectedNoBorder = cp.asarray(expectedNoBorder)
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(nd_image, min_distance=2,
+                                         exclude_border=2, indices=False)
+            assert_array_equal(result, expected)
+            # Check that bools work as expected
+            assert_array_equal(
+                peak.peak_local_max(nd_image, min_distance=2,
+                                    exclude_border=2, indices=False),
+                peak.peak_local_max(nd_image, min_distance=2,
+                                    exclude_border=True, indices=False)
+            )
+            assert_array_equal(
+                peak.peak_local_max(nd_image, min_distance=2,
+                                    exclude_border=0, indices=False),
+                peak.peak_local_max(nd_image, min_distance=2,
+                                    exclude_border=False, indices=False)
+            )
+            # Check both versions with  no border
+            assert_array_equal(
+                peak.peak_local_max(nd_image, min_distance=2,
+                                    exclude_border=0, indices=False),
+                expectedNoBorder,
+            )
+            assert_array_equal(
+                peak.peak_local_max(nd_image,
+                                    exclude_border=False, indices=False),
+                nd_image.astype(bool)
+            )
+
+    def test_empty(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(~ result)
+
+    def test_empty_non2d_indices(self):
+        image = cp.zeros((10, 10, 10))
+        result = peak.peak_local_max(image,
+                                     footprint=cp.ones((3, 3, 3), bool),
+                                     min_distance=1, threshold_rel=0,
+                                     exclude_border=False)
+        assert result.shape == (0, image.ndim)
+
+    def test_one_point(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        labels[5, 5] = 1
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == (labels == 1))
+
+    def test_adjacent_and_same(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5:6] = 1
+        labels[5, 5:6] = 1
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == (labels == 1))
+
+    def test_adjacent_and_different(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        image[5, 6] = 0.5
+        labels[5, 5:6] = 1
+        expected = image == 1
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == expected)
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == expected)
+
+    def test_not_adjacent_and_different(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        image[5, 8] = 0.5
+        labels[image > 0] = 1
+        expected = labels == 1
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == expected)
+
+    def test_two_objects(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        image[5, 15] = 0.5
+        labels[5, 5] = 1
+        labels[5, 15] = 2
+        expected = labels > 0
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == expected)
+
+    def test_adjacent_different_objects(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        image[5, 6] = 0.5
+        labels[5, 5] = 1
+        labels[5, 6] = 2
+        expected = labels > 0
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=cp.ones((3, 3), bool),
+                                         min_distance=1, threshold_rel=0,
+                                         indices=False, exclude_border=False)
+        assert cp.all(result == expected)
+
+    def test_four_quadrants(self):
+        image = cp.asarray(np.random.uniform(size=(20, 30)))
+        i, j = cp.mgrid[0:20, 0:30]
+        labels = 1 + (i >= 10) + (j >= 15) * 2
+        i, j = cp.mgrid[-3:4, -3:4]
+        footprint = i * i + j * j <= 9
+        expected = cp.zeros(image.shape, float)
+        for imin, imax in ((0, 10), (10, 20)):
+            for jmin, jmax in ((0, 15), (15, 30)):
+                expected[imin:imax, jmin:jmax] = ndi.maximum_filter(
+                    image[imin:imax, jmin:jmax], footprint=footprint
+                )
+        expected = expected == image
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=labels,
+                                         footprint=footprint,
+                                         min_distance=1,
+                                         threshold_rel=0,
+                                         indices=False,
+                                         exclude_border=False)
+        assert cp.all(result == expected)
+
+    def test_disk(self):
+        """regression test of img-1194, footprint = [1]
+        Test peak.peak_local_max when every point is a local maximum
+        """
+        image = cp.asarray(np.random.uniform(size=(10, 20)))
+        footprint = cp.asarray([[1]])
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, labels=cp.ones((10, 20), int),
+                                         footprint=footprint,
+                                         min_distance=1, threshold_rel=0,
+                                         threshold_abs=-1, indices=False,
+                                         exclude_border=False)
+        assert cp.all(result)
+        with expected_warnings(["indices argument is deprecated"]):
+            result = peak.peak_local_max(image, footprint=footprint,
+                                         threshold_abs=-1,
+                                         indices=False,
+                                         exclude_border=False)
+        assert cp.all(result)
+
+    def test_3D(self):
+        image = cp.zeros((30, 30, 30))
+        image[15, 15, 15] = 1
+        image[5, 5, 5] = 1
+        assert_array_equal(
+            peak.peak_local_max(image, min_distance=10, threshold_rel=0),
+            [[15, 15, 15]],
+        )
+        assert_array_equal(
+            peak.peak_local_max(image, min_distance=6, threshold_rel=0),
+            [[15, 15, 15]],
+        )
+        assert sorted(peak.peak_local_max(image, min_distance=10,
+                                          threshold_rel=0,
+                                          exclude_border=False).tolist()) == \
+            [[5, 5, 5], [15, 15, 15]]
+        assert sorted(peak.peak_local_max(image, min_distance=5,
+                                          threshold_rel=0).tolist()) == \
+            [[5, 5, 5], [15, 15, 15]]
+
+    def test_4D(self):
+        image = cp.zeros((30, 30, 30, 30))
+        image[15, 15, 15, 15] = 1
+        image[5, 5, 5, 5] = 1
+        assert_array_equal(
+            peak.peak_local_max(image, min_distance=10, threshold_rel=0),
+            [[15, 15, 15, 15]],
+        )
+        assert_array_equal(
+            peak.peak_local_max(image, min_distance=6, threshold_rel=0),
+            [[15, 15, 15, 15]],
+        )
+        assert sorted(
+            peak.peak_local_max(
+                image, min_distance=10, threshold_rel=0, exclude_border=False
+            ).tolist()
+        ) == [[5, 5, 5, 5], [15, 15, 15, 15]]
+        assert sorted(
+            peak.peak_local_max(image, min_distance=5, threshold_rel=0).tolist()
+        ) == [[5, 5, 5, 5], [15, 15, 15, 15]]
+
+    def test_threshold_rel_default(self):
+        image = cp.ones((5, 5))
+
+        image[2, 2] = 1
+        assert len(peak.peak_local_max(image)) == 0
+
+        image[2, 2] = 2
+        assert_array_equal(peak.peak_local_max(image), [[2, 2]])
+
+        image[2, 2] = 0
+        with expected_warnings(["When min_distance < 1"]):
+            assert len(peak.peak_local_max(image,
+                                           min_distance=0)) == image.size - 1
+
+
+@pytest.mark.parametrize(
+    ["indices"],
+    [[indices] for indices in itertools.product(range(5), range(5))],
+)
+def test_exclude_border(indices):
+    image = cp.zeros((5, 5))
+    image[indices] = 1
+
+    # exclude_border = False, means it will always be found.
+    assert len(peak.peak_local_max(image, exclude_border=False)) == 1
+
+    # exclude_border = 0, means it will always be found.
+    assert len(peak.peak_local_max(image, exclude_border=0)) == 1
+
+    # exclude_border = True, min_distance=1 means it will be found unless it's
+    # on the edge.
+    if indices[0] in (0, 4) or indices[1] in (0, 4):
+        expected_peaks = 0
+    else:
+        expected_peaks = 1
+    assert len(peak.peak_local_max(
+        image, min_distance=1, exclude_border=True)) == expected_peaks
+
+    # exclude_border = (1, 0) means it will be found unless it's on the edge of
+    # the first dimension.
+    if indices[0] in (0, 4):
+        expected_peaks = 0
+    else:
+        expected_peaks = 1
+    assert len(peak.peak_local_max(
+        image, exclude_border=(1, 0))) == expected_peaks
+
+    # exclude_border = (0, 1) means it will be found unless it's on the edge of
+    # the second dimension.
+    if indices[1] in (0, 4):
+        expected_peaks = 0
+    else:
+        expected_peaks = 1
+    assert len(peak.peak_local_max(
+        image, exclude_border=(0, 1))) == expected_peaks
+
+
+def test_exclude_border_errors():
+    image = cp.zeros((5, 5))
+
+    # exclude_border doesn't have the right cardinality.
+    with pytest.raises(ValueError):
+        assert peak.peak_local_max(image, exclude_border=(1,))
+
+    # exclude_border doesn't have the right type
+    with pytest.raises(TypeError):
+        assert peak.peak_local_max(image, exclude_border=1.0)
+
+    # exclude_border is a tuple of the right cardinality but contains
+    # non-integer values.
+    with pytest.raises(ValueError):
+        assert peak.peak_local_max(image, exclude_border=(1, 'a'))
+
+    # exclude_border is a tuple of the right cardinality but contains a
+    # negative value.
+    with pytest.raises(ValueError):
+        assert peak.peak_local_max(image, exclude_border=(1, -1))
+
+    # exclude_border is a negative value.
+    with pytest.raises(ValueError):
+        assert peak.peak_local_max(image, exclude_border=-1)
+
+
+class TestProminentPeaks(unittest.TestCase):
+    def test_isolated_peaks(self):
+        image = cp.zeros((15, 15))
+        x0, y0, i0 = (12, 8, 1)
+        x1, y1, i1 = (2, 2, 1)
+        x2, y2, i2 = (5, 13, 1)
+        image[y0, x0] = i0
+        image[y1, x1] = i1
+        image[y2, x2] = i2
+        out = peak._prominent_peaks(image)
+        assert len(out[0]) == 3
+        for i, x, y in zip(out[0], out[1], out[2]):
+            self.assertTrue(i in (i0, i1, i2))
+            self.assertTrue(x in (x0, x1, x2))
+            self.assertTrue(y in (y0, y1, y2))
+
+    def test_threshold(self):
+        image = cp.zeros((15, 15))
+        x0, y0, i0 = (12, 8, 10)
+        x1, y1, i1 = (2, 2, 8)
+        x2, y2, i2 = (5, 13, 10)
+        image[y0, x0] = i0
+        image[y1, x1] = i1
+        image[y2, x2] = i2
+        out = peak._prominent_peaks(image, threshold=None)
+        assert len(out[0]) == 3
+        for i, x, y in zip(out[0], out[1], out[2]):
+            self.assertTrue(i in (i0, i1, i2))
+            self.assertTrue(x in (x0, x1, x2))
+        out = peak._prominent_peaks(image, threshold=9)
+        assert len(out[0]) == 2
+        for i, x, y in zip(out[0], out[1], out[2]):
+            self.assertTrue(i in (i0, i2))
+            self.assertTrue(x in (x0, x2))
+            self.assertTrue(y in (y0, y2))
+
+    def test_peaks_in_contact(self):
+        image = cp.zeros((15, 15))
+        x0, y0, i0 = (8, 8, 1)
+        x1, y1, i1 = (7, 7, 1)  # prominent peak
+        x2, y2, i2 = (6, 6, 1)
+        image[y0, x0] = i0
+        image[y1, x1] = i1
+        image[y2, x2] = i2
+        out = peak._prominent_peaks(image, min_xdistance=3, min_ydistance=3)
+        assert_array_equal(out[0], cp.asarray((i1,)))
+        assert_array_equal(out[1], cp.asarray((x1,)))
+        assert_array_equal(out[2], cp.asarray((y1,)))
+
+    def test_input_labels_unmodified(self):
+        image = cp.zeros((10, 20))
+        labels = cp.zeros((10, 20), int)
+        image[5, 5] = 1
+        labels[5, 5] = 3
+        labelsin = labels.copy()
+        with expected_warnings(["indices argument is deprecated"]):
+            peak.peak_local_max(image, labels=labels,
+                                footprint=cp.ones((3, 3), bool),
+                                min_distance=1, threshold_rel=0,
+                                indices=False, exclude_border=False)
+        assert cp.all(labels == labelsin)
+
+    def test_many_objects(self):
+        mask = np.zeros([500, 500], dtype=bool)
+        x, y = np.indices((500, 500))
+        x_c = x // 20 * 20 + 10
+        y_c = y // 20 * 20 + 10
+        mask[(x - x_c) ** 2 + (y - y_c) ** 2 < 8 ** 2] = True
+        labels, num_objs = ndimage_cpu.label(mask)
+        dist = ndimage_cpu.distance_transform_edt(mask)
+
+        dist = cp.asarray(dist)
+        labels = cp.asarray(labels)
+        local_max = peak.peak_local_max(dist, min_distance=20,
+                                        exclude_border=False, labels=labels)
+
+        assert len(local_max) == 625
diff --git a/python/cucim/src/cucim/skimage/feature/tests/test_template.py b/python/cucim/src/cucim/skimage/feature/tests/test_template.py
new file mode 100644
index 000000000..acbe76871
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/tests/test_template.py
@@ -0,0 +1,202 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_equal
+from skimage import data
+
+from cucim.skimage import img_as_float
+from cucim.skimage.feature import match_template, peak_local_max
+from cucim.skimage.morphology import diamond
+
+
+def test_template():
+    size = 100
+    # Float prefactors ensure that image range is between 0 and 1
+    image = np.full((400, 400), 0.5)
+    target = 0.1 * (np.tri(size) + np.tri(size)[::-1])
+    target_positions = [(50, 50), (200, 200)]
+    for x, y in target_positions:
+        image[x:x + size, y:y + size] = target
+    np.random.seed(1)
+    image += 0.1 * np.random.uniform(size=(400, 400))
+    image = cp.asarray(image)
+    target = cp.asarray(target)
+
+    result = match_template(image, target)
+    delta = 5
+
+    positions = peak_local_max(result, min_distance=delta)
+
+    if len(positions) > 2:
+        # Keep the two maximum peaks.
+        intensities = result[tuple(positions.T)]
+        i_maxsort = cp.argsort(intensities)[::-1]
+        positions = positions[i_maxsort][:2]
+
+    # Sort so that order matches `target_positions`.
+    positions = positions[cp.argsort(positions[:, 0])]
+
+    for xy_target, xy in zip(target_positions, positions):
+        assert_array_almost_equal(xy, xy_target)
+
+
+def test_normalization():
+    """Test that `match_template` gives the correct normalization.
+
+    Normalization gives 1 for a perfect match and -1 for an inverted-match.
+    This test adds positive and negative squares to a zero-array and matches
+    the array with a positive template.
+    """
+    n = 5
+    N = 20
+    ipos, jpos = (2, 3)
+    ineg, jneg = (12, 11)
+    image = cp.full((N, N), 0.5)
+    image[ipos:ipos + n, jpos:jpos + n] = 1
+    image[ineg:ineg + n, jneg:jneg + n] = 0
+
+    # white square with a black border
+    template = cp.zeros((n + 2, n + 2))
+    template[1:1 + n, 1:1 + n] = 1
+
+    result = match_template(image, template)
+
+    # get the max and min results.
+    sorted_result = cp.argsort(result.ravel())
+    iflat_min = cp.asnumpy(sorted_result[0])
+    iflat_max = cp.asnumpy(sorted_result[-1])
+    min_result = np.unravel_index(iflat_min, result.shape)
+    max_result = np.unravel_index(iflat_max, result.shape)
+
+    # shift result by 1 because of template border
+    assert np.all((np.array(min_result) + 1) == (ineg, jneg))
+    assert np.all((np.array(max_result) + 1) == (ipos, jpos))
+
+    assert cp.allclose(result.ravel()[iflat_min], -1)
+    assert cp.allclose(result.ravel()[iflat_max], 1)
+
+
+def test_no_nans():
+    """Test that `match_template` doesn't return NaN values.
+
+    When image values are only slightly different, floating-point errors can
+    cause a subtraction inside of a square root to go negative (without an
+    explicit check that was added to `match_template`).
+    """
+    np.random.seed(1)
+    image = 0.5 + 1e-9 * np.random.normal(size=(20, 20))
+    template = np.ones((6, 6))
+    template[:3, :] = 0
+    image = cp.asarray(image)
+    template = cp.asarray(template)
+    result = match_template(image, template)
+    assert not cp.any(cp.isnan(result))
+
+
+def test_switched_arguments():
+    image = cp.ones((5, 5))
+    template = cp.ones((3, 3))
+    with pytest.raises(ValueError):
+        match_template(template, image)
+
+
+def test_pad_input():
+    """Test `match_template` when `pad_input=True`.
+
+    This test places two full templates (one with values lower than the image
+    mean, the other higher) and two half templates, which are on the edges of
+    the image. The two full templates should score the top (positive and
+    negative) matches and the centers of the half templates should score 2nd.
+    """
+    # Float prefactors ensure that image range is between 0 and 1
+    template = 0.5 * diamond(2)
+    image = 0.5 * cp.ones((9, 19))
+    mid = slice(2, 7)
+    image[mid, :3] -= template[:, -3:]  # half min template centered at 0
+    image[mid, 4:9] += template         # full max template centered at 6
+    image[mid, -9:-4] -= template       # full min template centered at 12
+    image[mid, -3:] += template[:, :3]  # half max template centered at 18
+
+    result = match_template(image, template, pad_input=True,
+                            constant_values=float(image.mean()))
+
+    # get the max and min results.
+    sorted_result = cp.argsort(result.ravel())
+    i, j = cp.unravel_index(sorted_result[:2], result.shape)
+    assert_array_equal(j, (12, 0))
+    i, j = cp.unravel_index(sorted_result[-2:], result.shape)
+    assert_array_equal(j, (18, 6))
+
+
+def test_3d():
+    np.random.seed(1)
+    template = np.random.rand(3, 3, 3)
+    image = np.zeros((12, 12, 12))
+
+    image[3:6, 5:8, 4:7] = template
+
+    image = cp.asarray(image)
+    template = cp.asarray(template)
+
+    result = match_template(image, template)
+
+    assert_equal(result.shape, (10, 10, 10))
+    assert_equal(
+        np.unravel_index(int(result.argmax()), result.shape), (3, 5, 4)
+    )
+
+
+def test_3d_pad_input():
+    np.random.seed(1)
+    template = np.random.rand(3, 3, 3)
+    image = np.zeros((12, 12, 12))
+
+    image[3:6, 5:8, 4:7] = template
+
+    image = cp.asarray(image)
+    template = cp.asarray(template)
+
+    result = match_template(image, template, pad_input=True)
+
+    assert_equal(result.shape, (12, 12, 12))
+    assert_equal(
+        np.unravel_index(int(result.argmax()), result.shape), (4, 6, 5)
+    )
+
+
+def test_padding_reflect():
+    template = diamond(2)
+    image = cp.zeros((10, 10))
+    image[2:7, :3] = template[:, -3:]
+
+    result = match_template(image, template, pad_input=True, mode="reflect")
+
+    assert_equal(np.unravel_index(int(result.argmax()), result.shape), (4, 0))
+
+
+def test_wrong_input():
+    image = cp.ones((5, 5, 1))
+    template = cp.ones((3, 3))
+    with pytest.raises(ValueError):
+        match_template(template, image)
+
+    image = cp.ones((5, 5))
+    template = cp.ones((3, 3, 2))
+    with pytest.raises(ValueError):
+        match_template(template, image)
+
+    image = cp.ones((5, 5, 3, 3))
+    template = cp.ones((3, 3, 2))
+    with pytest.raises(ValueError):
+        match_template(template, image)
+
+
+def test_bounding_values():
+    image = img_as_float(cp.asarray(data.page()))
+    template = cp.zeros((3, 3))
+    template[1, 1] = 1
+    result = match_template(image, template)
+    print(result.max())
+    assert result.max() < 1 + 1e-7
+    assert result.min() > -1 - 1e-7
diff --git a/python/cucim/src/cucim/skimage/feature/util.py b/python/cucim/src/cucim/skimage/feature/util.py
new file mode 100644
index 000000000..4dcace31c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/feature/util.py
@@ -0,0 +1,49 @@
+import cupy as cp
+
+from .._shared.utils import check_nD
+from ..util import img_as_float
+
+
+def _prepare_grayscale_input_2D(image):
+    image = cp.squeeze(image)
+    check_nD(image, 2)
+    return img_as_float(image)
+
+
+def _prepare_grayscale_input_nD(image):
+    image = cp.squeeze(image)
+    check_nD(image, range(2, 6))
+    return img_as_float(image)
+
+
+def _mask_border_keypoints(image_shape, keypoints, distance):
+    """Mask coordinates that are within certain distance from the image border.
+
+    Parameters
+    ----------
+    image_shape : (2, ) array_like
+        Shape of the image as ``(rows, cols)``.
+    keypoints : (N, 2) array
+        Keypoint coordinates as ``(rows, cols)``.
+    distance : int
+        Image border distance.
+
+    Returns
+    -------
+    mask : (N, ) bool array
+        Mask indicating if pixels are within the image (``True``) or in the
+        border region of the image (``False``).
+
+    """
+
+    rows = image_shape[0]
+    cols = image_shape[1]
+
+    mask = (
+        ((distance - 1) < keypoints[:, 0])
+        & (keypoints[:, 0] < (rows - distance + 1))
+        & ((distance - 1) < keypoints[:, 1])
+        & (keypoints[:, 1] < (cols - distance + 1))
+    )
+
+    return mask
diff --git a/python/cucim/src/cucim/skimage/filters/__init__.py b/python/cucim/src/cucim/skimage/filters/__init__.py
new file mode 100644
index 000000000..4ea51df50
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/__init__.py
@@ -0,0 +1,67 @@
+from ._gabor import gabor, gabor_kernel
+from ._gaussian import _guess_spatial_dimensions  # noqa
+from ._gaussian import difference_of_gaussians, gaussian
+# from . import rank
+from ._median import median
+from ._rank_order import rank_order
+from ._sparse import correlate_sparse
+from ._unsharp_mask import unsharp_mask
+from ._window import window
+from .edges import (farid, farid_h, farid_v, laplace, prewitt,  # noqa
+                    prewitt_h, prewitt_v, roberts, roberts_neg_diag,
+                    roberts_pos_diag, scharr, scharr_h, scharr_v, sobel,
+                    sobel_h, sobel_v)
+from .lpi_filter import LPIFilter2D, inverse, wiener
+from .ridges import frangi, hessian, meijering, sato
+from .thresholding import (apply_hysteresis_threshold, threshold_isodata,
+                           threshold_li, threshold_local, threshold_mean,
+                           threshold_minimum, threshold_multiotsu,
+                           threshold_niblack, threshold_otsu,
+                           threshold_sauvola, threshold_triangle,
+                           threshold_yen, try_all_threshold)
+
+__all__ = [
+    "inverse",
+    "correlate_sparse",
+    "wiener",
+    "LPIFilter2D",
+    "gaussian",
+    "difference_of_gaussians",
+    "median",
+    "sobel",
+    "sobel_h",
+    "sobel_v",
+    "scharr",
+    "scharr_h",
+    "scharr_v",
+    "prewitt",
+    "prewitt_h",
+    "prewitt_v",
+    "roberts",
+    "roberts_pos_diag",
+    "roberts_neg_diag",
+    "laplace",
+    "rank_order",
+    "gabor_kernel",
+    "gabor",
+    "try_all_threshold",
+    "meijering",
+    "sato",
+    "frangi",
+    "hessian",
+    "threshold_otsu",
+    "threshold_yen",
+    "threshold_isodata",
+    "threshold_li",
+    "threshold_local",
+    "threshold_minimum",
+    "threshold_mean",
+    "threshold_niblack",
+    "threshold_sauvola",
+    "threshold_triangle",
+    "threshold_multiotsu",
+    "apply_hysteresis_threshold",
+    # "rank",
+    "unsharp_mask",
+    "window",
+]
diff --git a/python/cucim/src/cucim/skimage/filters/_gabor.py b/python/cucim/src/cucim/skimage/filters/_gabor.py
new file mode 100644
index 000000000..43769bbc6
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_gabor.py
@@ -0,0 +1,195 @@
+import math
+
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import check_nD
+
+__all__ = ["gabor_kernel", "gabor"]
+
+
+def _sigma_prefactor(bandwidth):
+    b = bandwidth
+    # See http://www.cs.rug.nl/~imaging/simplecell.html
+    return 1.0 / math.pi * math.sqrt(math.log(2) / 2.0) * \
+        (2.0 ** b + 1) / (2.0 ** b - 1)
+
+
+def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
+                 n_stds=3, offset=0, *, float_dtype=cp.float64):
+    """Return complex 2D Gabor filter kernel.
+
+    Gabor kernel is a Gaussian kernel modulated by a complex harmonic function.
+    Harmonic function consists of an imaginary sine function and a real
+    cosine function. Spatial frequency is inversely proportional to the
+    wavelength of the harmonic and to the standard deviation of a Gaussian
+    kernel. The bandwidth is also inversely proportional to the standard
+    deviation.
+
+    Parameters
+    ----------
+    frequency : float
+        Spatial frequency of the harmonic function. Specified in pixels.
+    theta : float, optional
+        Orientation in radians. If 0, the harmonic is in the x-direction.
+    bandwidth : float, optional
+        The bandwidth captured by the filter. For fixed bandwidth, ``sigma_x``
+        and ``sigma_y`` will decrease with increasing frequency. This value is
+        ignored if ``sigma_x`` and ``sigma_y`` are set by the user.
+    sigma_x, sigma_y : float, optional
+        Standard deviation in x- and y-directions. These directions apply to
+        the kernel *before* rotation. If `theta = pi/2`, then the kernel is
+        rotated 90 degrees so that ``sigma_x`` controls the *vertical*
+        direction.
+    n_stds : scalar, optional
+        The linear size of the kernel is n_stds (3 by default) standard
+        deviations
+    offset : float, optional
+        Phase offset of harmonic function in radians.
+
+    Returns
+    -------
+    g : complex array
+        Complex filter kernel.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Gabor_filter
+    .. [2] https://web.archive.org/web/20180127125930/http://mplab.ucsd.edu/tutorials/gabor.pdf
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.filters import gabor_kernel
+    >>> from skimage import io
+    >>> from matplotlib import pyplot as plt  # doctest: +SKIP
+
+    >>> gk = gabor_kernel(frequency=0.2)
+    >>> plt.figure()                    # doctest: +SKIP
+    >>> io.imshow(cp.asnumpy(gk.real))  # doctest: +SKIP
+    >>> io.show()                       # doctest: +SKIP
+
+    >>> # more ripples (equivalent to increasing the size of the
+    >>> # Gaussian spread)
+    >>> gk = gabor_kernel(frequency=0.2, bandwidth=0.1)
+    >>> plt.figure()                    # doctest: +SKIP
+    >>> io.imshow(cp.asnumpy(gk.real))  # doctest: +SKIP
+    >>> io.show()                       # doctest: +SKIP
+    """  # noqa
+    if sigma_x is None:
+        sigma_x = _sigma_prefactor(bandwidth) / frequency
+    if sigma_y is None:
+        sigma_y = _sigma_prefactor(bandwidth) / frequency
+
+    x0 = math.ceil(
+        max(
+            abs(n_stds * sigma_x * math.cos(theta)),
+            abs(n_stds * sigma_y * math.sin(theta)),
+            1,
+        )
+    )
+    y0 = math.ceil(
+        max(
+            abs(n_stds * sigma_y * math.cos(theta)),
+            abs(n_stds * sigma_x * math.sin(theta)),
+            1,
+        )
+    )
+    y, x = cp.mgrid[-y0:y0 + 1, -x0:x0 + 1]
+
+    rotx = x * math.cos(theta) + y * math.sin(theta)
+    roty = -x * math.sin(theta) + y * math.cos(theta)
+
+    cplx_dtype = cp.promote_types(float_dtype, cp.complex64)
+    g = cp.empty(y.shape, dtype=cplx_dtype)
+    g[:] = cp.exp(
+        -0.5 * ((rotx * rotx) / sigma_x ** 2 + (roty * roty) / sigma_y ** 2)
+    )
+    g /= 2 * math.pi * sigma_x * sigma_y
+    g *= cp.exp(1j * (2 * math.pi * frequency * rotx + offset))
+
+    assert g.dtype == cplx_dtype
+    return g
+
+
+def gabor(image, frequency, theta=0, bandwidth=1, sigma_x=None,
+          sigma_y=None, n_stds=3, offset=0, mode='reflect', cval=0):
+    """Return real and imaginary responses to Gabor filter.
+
+    The real and imaginary parts of the Gabor filter kernel are applied to the
+    image and the response is returned as a pair of arrays.
+
+    Gabor filter is a linear filter with a Gaussian kernel which is modulated
+    by a sinusoidal plane wave. Frequency and orientation representations of
+    the Gabor filter are similar to those of the human visual system.
+    Gabor filter banks are commonly used in computer vision and image
+    processing. They are especially suitable for edge detection and texture
+    classification.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Input image.
+    frequency : float
+        Spatial frequency of the harmonic function. Specified in pixels.
+    theta : float, optional
+        Orientation in radians. If 0, the harmonic is in the x-direction.
+    bandwidth : float, optional
+        The bandwidth captured by the filter. For fixed bandwidth, ``sigma_x``
+        and ``sigma_y`` will decrease with increasing frequency. This value is
+        ignored if ``sigma_x`` and ``sigma_y`` are set by the user.
+    sigma_x, sigma_y : float, optional
+        Standard deviation in x- and y-directions. These directions apply to
+        the kernel *before* rotation. If `theta = pi/2`, then the kernel is
+        rotated 90 degrees so that ``sigma_x`` controls the *vertical*
+        direction.
+    n_stds : scalar, optional
+        The linear size of the kernel is n_stds (3 by default) standard
+        deviations.
+    offset : float, optional
+        Phase offset of harmonic function in radians.
+    mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional
+        Mode used to convolve image with a kernel, passed to `ndi.convolve`
+    cval : scalar, optional
+        Value to fill past edges of input if ``mode`` of convolution is
+        'constant'. The parameter is passed to `ndi.convolve`.
+
+    Returns
+    -------
+    real, imag : arrays
+        Filtered images using the real and imaginary parts of the Gabor filter
+        kernel. Images are of the same dimensions as the input one.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Gabor_filter
+    .. [2] https://web.archive.org/web/20180127125930/http://mplab.ucsd.edu/tutorials/gabor.pdf
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.filters import gabor
+    >>> from skimage import data, io
+    >>> from matplotlib import pyplot as plt  # doctest: +SKIP
+
+    >>> image = cp.array(data.coins())
+    >>> # detecting edges in a coin image
+    >>> filt_real, filt_imag = gabor(image, frequency=0.6)
+    >>> plt.figure()                        # doctest: +SKIP
+    >>> io.imshow(cp.asnumpy(filt_real))    # doctest: +SKIP
+    >>> io.show()                           # doctest: +SKIP
+
+    >>> # less sensitivity to finer details with the lower frequency kernel
+    >>> filt_real, filt_imag = gabor(image, frequency=0.1)
+    >>> plt.figure()                       # doctest: +SKIP
+    >>> io.imshow(cp.asnumpy(filt_real)    # doctest: +SKIP
+    >>> io.show()                          # doctest: +SKIP
+    """  # noqa
+    check_nD(image, 2)
+    float_dtype = cp.promote_types(image.dtype, cp.float16)
+    g = gabor_kernel(frequency, theta, bandwidth, sigma_x, sigma_y, n_stds,
+                     offset, float_dtype=float_dtype)
+
+    filtered = ndi.convolve(image, g, mode=mode, cval=cval)
+
+    return filtered.real, filtered.imag
diff --git a/python/cucim/src/cucim/skimage/filters/_gaussian.py b/python/cucim/src/cucim/skimage/filters/_gaussian.py
new file mode 100644
index 000000000..99c2f2eb3
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_gaussian.py
@@ -0,0 +1,303 @@
+from collections.abc import Iterable
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import convert_to_float, warn
+from ..util import img_as_float
+
+__all__ = ['gaussian', 'difference_of_gaussians']
+
+
+def gaussian(image, sigma=1, output=None, mode='nearest', cval=0,
+             multichannel=None, preserve_range=False, truncate=4.0):
+    """Multi-dimensional Gaussian filter.
+
+    Parameters
+    ----------
+    image : array-like
+        Input image (grayscale or color) to filter.
+    sigma : scalar or sequence of scalars, optional
+        Standard deviation for Gaussian kernel. The standard
+        deviations of the Gaussian filter are given for each axis as a
+        sequence, or as a single number, in which case it is equal for
+        all axes.
+    output : array, optional
+        The ``output`` parameter passes an array in which to store the
+        filter output.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+        The ``mode`` parameter determines how the array borders are
+        handled, where ``cval`` is the value when mode is equal to
+        'constant'. Default is 'nearest'.
+    cval : scalar, optional
+        Value to fill past edges of input if ``mode`` is 'constant'. Default
+        is 0.0
+    multichannel : bool, optional (default: None)
+        Whether the last axis of the image is to be interpreted as multiple
+        channels. If True, each channel is filtered separately (channels are
+        not mixed together). Only 3 channels are supported. If ``None``,
+        the function will attempt to guess this, and raise a warning if
+        ambiguous, when the array has shape (M, N, 3).
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of ``img_as_float``.
+        Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+    truncate : float, optional
+        Truncate the filter at this many standard deviations.
+
+    Returns
+    -------
+    filtered_image : ndarray
+        the filtered array
+
+    Notes
+    -----
+    This function is a wrapper around :func:`scipy.ndi.gaussian_filter`.
+
+    Integer arrays are converted to float.
+
+    The ``output`` should be floating point data type since gaussian converts
+    to float provided ``image``. If ``output`` is not provided, another array
+    will be allocated and returned as the result.
+
+    The multi-dimensional filter is implemented as a sequence of
+    one-dimensional convolution filters. The intermediate arrays are
+    stored in the same data type as the output. Therefore, for output
+    types with a limited precision, the results may be imprecise
+    because intermediate results may be stored with insufficient
+    precision.
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> a = cp.zeros((3, 3))
+    >>> a[1, 1] = 1
+    >>> a
+    array([[0., 0., 0.],
+           [0., 1., 0.],
+           [0., 0., 0.]])
+    >>> gaussian(a, sigma=0.4)  # mild smoothing
+    array([[0.00163116, 0.03712502, 0.00163116],
+           [0.03712502, 0.84496158, 0.03712502],
+           [0.00163116, 0.03712502, 0.00163116]])
+    >>> gaussian(a, sigma=1)  # more smoothing
+    array([[0.05855018, 0.09653293, 0.05855018],
+           [0.09653293, 0.15915589, 0.09653293],
+           [0.05855018, 0.09653293, 0.05855018]])
+    >>> # Several modes are possible for handling boundaries
+    >>> gaussian(a, sigma=1, mode='reflect')
+    array([[0.08767308, 0.12075024, 0.08767308],
+           [0.12075024, 0.16630671, 0.12075024],
+           [0.08767308, 0.12075024, 0.08767308]])
+    >>> # For RGB images, each is filtered separately
+    >>> from skimage.data import astronaut
+    >>> image = cp.array(astronaut())
+    >>> filtered_img = gaussian(image, sigma=1, multichannel=True)
+
+    """
+
+    spatial_dims = None
+    try:
+        spatial_dims = _guess_spatial_dimensions(image)
+    except ValueError:
+        spatial_dims = image.ndim
+    if spatial_dims is None and multichannel is None:
+        msg = ("Images with dimensions (M, N, 3) are interpreted as 2D+RGB "
+               "by default. Use `multichannel=False` to interpret as "
+               "3D image with last dimension of length 3.")
+        warn(RuntimeWarning(msg))
+        multichannel = True
+    # CuPy Backend: refactor to avoid overhead of cp.any(cp.asarray(sigma))
+    sigma_msg = "Sigma values less than zero are not valid"
+    if not isinstance(sigma, Iterable):
+        if sigma < 0:
+            raise ValueError(sigma_msg)
+    elif any(s < 0 for s in sigma):
+        raise ValueError(sigma_msg)
+    if multichannel:
+        # do not filter across channels
+        if not isinstance(sigma, Iterable):
+            sigma = [sigma] * (image.ndim - 1)
+        if len(sigma) != image.ndim:
+            sigma = tuple(sigma) + (0,)  # zero on channels axis
+        sigma = tuple(sigma)
+    image = convert_to_float(image, preserve_range)
+    if output is None:
+        output = cp.empty_like(image)
+    elif not np.issubdtype(output.dtype, np.floating):
+        raise ValueError("Provided output data type is not float")
+    ndi.gaussian_filter(image, sigma, output=output, mode=mode, cval=cval,
+                        truncate=truncate)
+    return output
+
+
+def _guess_spatial_dimensions(image):
+    """Make an educated guess about whether an image has a channels dimension.
+
+    Parameters
+    ----------
+    image : ndarray
+        The input image.
+
+    Returns
+    -------
+    spatial_dims : int or None
+        The number of spatial dimensions of ``image``. If ambiguous, the value
+        is ``None``.
+
+    Raises
+    ------
+    ValueError
+        If the image array has less than two or more than four dimensions.
+    """
+    if image.ndim == 2:
+        return 2
+    if image.ndim == 3 and image.shape[-1] != 3:
+        return 3
+    if image.ndim == 3 and image.shape[-1] == 3:
+        return None
+    if image.ndim == 4 and image.shape[-1] == 3:
+        return 3
+    else:
+        raise ValueError("Expected 2D, 3D, or 4D array, got %iD." % image.ndim)
+
+
+def difference_of_gaussians(image, low_sigma, high_sigma=None, *,
+                            mode='nearest', cval=0, multichannel=False,
+                            truncate=4.0):
+    """Find features between ``low_sigma`` and ``high_sigma`` in size.
+
+    This function uses the Difference of Gaussians method for applying
+    band-pass filters to multi-dimensional arrays. The input array is
+    blurred with two Gaussian kernels of differing sigmas to produce two
+    intermediate, filtered images. The more-blurred image is then subtracted
+    from the less-blurred image. The final output image will therefore have
+    had high-frequency components attenuated by the smaller-sigma Gaussian, and
+    low frequency components will have been removed due to their presence in
+    the more-blurred intermediate.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input array to filter.
+    low_sigma : scalar or sequence of scalars
+        Standard deviation(s) for the Gaussian kernel with the smaller sigmas
+        across all axes. The standard deviations are given for each axis as a
+        sequence, or as a single number, in which case the single number is
+        used as the standard deviation value for all axes.
+    high_sigma : scalar or sequence of scalars, optional (default is None)
+        Standard deviation(s) for the Gaussian kernel with the larger sigmas
+        across all axes. The standard deviations are given for each axis as a
+        sequence, or as a single number, in which case the single number is
+        used as the standard deviation value for all axes. If None is given
+        (default), sigmas for all axes are calculated as 1.6 * low_sigma.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+        The ``mode`` parameter determines how the array borders are
+        handled, where ``cval`` is the value when mode is equal to
+        'constant'. Default is 'nearest'.
+    cval : scalar, optional
+        Value to fill past edges of input if ``mode`` is 'constant'. Default
+        is 0.0
+    multichannel : bool, optional (default: False)
+        Whether the last axis of the image is to be interpreted as multiple
+        channels. If True, each channel is filtered separately (channels are
+        not mixed together).
+    truncate : float, optional (default is 4.0)
+        Truncate the filter at this many standard deviations.
+
+    Returns
+    -------
+    filtered_image : ndarray
+        the filtered array.
+
+    See also
+    --------
+    skimage.feature.blog_dog
+
+    Notes
+    -----
+    This function will subtract an array filtered with a Gaussian kernel
+    with sigmas given by ``high_sigma`` from an array filtered with a
+    Gaussian kernel with sigmas provided by ``low_sigma``. The values for
+    ``high_sigma`` must always be greater than or equal to the corresponding
+    values in ``low_sigma``, or a ``ValueError`` will be raised.
+
+    When ``high_sigma`` is none, the values for ``high_sigma`` will be
+    calculated as 1.6x the corresponding values in ``low_sigma``. This ratio
+    was originally proposed by Marr and Hildreth (1980) [1]_ and is commonly
+    used when approximating the inverted Laplacian of Gaussian, which is used
+    in edge and blob detection.
+
+    Input image is converted according to the conventions of ``img_as_float``.
+
+    Except for sigma values, all parameters are used for both filters.
+
+    Examples
+    --------
+    Apply a simple Difference of Gaussians filter to a color image:
+
+    >>> from skimage.data import astronaut
+    >>> from cucim.skimage.filters import difference_of_gaussians
+    >>> astro = cp.asarray(astronaut())
+    >>> filtered_image = difference_of_gaussians(astro, 2, 10,
+    ...                                          multichannel=True)
+
+    Apply a Laplacian of Gaussian filter as approximated by the Difference
+    of Gaussians filter:
+
+    >>> filtered_image = difference_of_gaussians(astro, 2,
+    ...                                          multichannel=True)
+
+    Apply a Difference of Gaussians filter to a grayscale image using different
+    sigma values for each axis:
+
+    >>> from skimage.data import camera
+    >>> cam = cp.array(camera())
+    >>> filtered_image = difference_of_gaussians(cam, (2,5), (3,20))
+
+    References
+    ----------
+    .. [1] Marr, D. and Hildreth, E. Theory of Edge Detection. Proc. R. Soc.
+           Lond. Series B 207, 187-217 (1980).
+           https://doi.org/10.1098/rspb.1980.0020
+
+    """
+    image = img_as_float(image)
+    # CuPy Backend: refactored low_sigma and high_sigma processing
+
+    if multichannel is True:
+        spatial_dims = image.ndim - 1
+    else:
+        spatial_dims = image.ndim
+
+    if np.isscalar(low_sigma):
+        low_sigma = (low_sigma,) * spatial_dims
+
+    low_sigma = tuple(map(float, low_sigma))
+    if high_sigma is None:
+        high_sigma = tuple(low * 1.6 for low in low_sigma)
+    elif np.isscalar(high_sigma):
+        high_sigma = (high_sigma,) * spatial_dims
+    high_sigma = tuple(map(float, high_sigma))
+
+    if len(low_sigma) != 1 and len(low_sigma) != spatial_dims:
+        raise ValueError('low_sigma must have length equal to number of'
+                         ' spatial dimensions of input')
+    if len(high_sigma) != 1 and len(high_sigma) != spatial_dims:
+        raise ValueError('high_sigma must have length equal to number of'
+                         ' spatial dimensions of input')
+
+    if any(h < l for h, l in zip(high_sigma, low_sigma)):
+        raise ValueError('high_sigma must be equal to or larger than'
+                         'low_sigma for all axes')
+
+    out = gaussian(image, low_sigma, mode=mode, cval=cval,
+                   multichannel=multichannel, truncate=truncate)
+
+    out -= gaussian(image, high_sigma, mode=mode, cval=cval,
+                    multichannel=multichannel, truncate=truncate)
+
+    return out
diff --git a/python/cucim/src/cucim/skimage/filters/_median.py b/python/cucim/src/cucim/skimage/filters/_median.py
new file mode 100644
index 000000000..16d9835ac
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_median.py
@@ -0,0 +1,79 @@
+from warnings import warn
+
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+
+def median(image, selem=None, out=None, mode='nearest', cval=0.0,
+           behavior='ndimage'):
+    """Return local median of an image.
+
+    Parameters
+    ----------
+    image : array-like
+        Input image.
+    selem : ndarray, optional
+        If ``behavior=='rank'``, ``selem`` is a 2-D array of 1's and 0's.
+        If ``behavior=='ndimage'``, ``selem`` is a N-D array of 1's and 0's
+        with the same number of dimension than ``image``.
+        If None, ``selem`` will be a N-D array with 3 elements for each
+        dimension (e.g., vector, square, cube, etc.)
+    out : ndarray, (same dtype as image), optional
+        If None, a new array is allocated.
+    mode : {'reflect', 'constant', 'nearest', 'mirror','‘wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        ``cval`` is the value when mode is equal to 'constant'.
+        Default is 'nearest'.
+
+        .. versionadded:: 0.15
+           ``mode`` is used when ``behavior='ndimage'``.
+    cval : scalar, optional
+        Value to fill past edges of input if mode is 'constant'. Default is 0.0
+
+        .. versionadded:: 0.15
+           ``cval`` was added in 0.15 is used when ``behavior='ndimage'``.
+    behavior : {'ndimage', 'rank'}, optional
+        Either to use the old behavior (i.e., < 0.15) or the new behavior.
+        The old behavior will call the :func:`skimage.filters.rank.median`.
+        The new behavior will call the :func:`scipy.ndimage.median_filter`.
+        Default is 'ndimage'.
+
+        .. versionadded:: 0.15
+           ``behavior`` is introduced in 0.15
+        .. versionchanged:: 0.16
+           Default ``behavior`` has been changed from 'rank' to 'ndimage'
+
+    Returns
+    -------
+    out : 2-D array (same dtype as input image)
+        Output image.
+
+    See also
+    --------
+    skimage.filters.rank.median : Rank-based implementation of the median
+        filtering offering more flexibility with additional parameters but
+        dedicated for unsigned integer images.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage.morphology import disk
+    >>> from cucim.skimage.filters import median
+    >>> img = cp.array(data.camera())
+    >>> med = median(img, disk(5))
+
+    """
+    if behavior == 'rank':
+        if mode != 'nearest' or not np.isclose(cval, 0.0):
+            warn("Change 'behavior' to 'ndimage' if you want to use the "
+                 "parameters 'mode' or 'cval'. They will be discarded "
+                 "otherwise.")
+        raise NotImplementedError("rank behavior not currently implemented")
+        # TODO: implement median rank filter
+        # return generic.median(image, selem=selem, out=out)
+
+    if selem is None:
+        selem = ndi.generate_binary_structure(image.ndim, image.ndim)
+    return ndi.median_filter(image, footprint=selem, output=out, mode=mode,
+                             cval=cval)
diff --git a/python/cucim/src/cucim/skimage/filters/_rank_order.py b/python/cucim/src/cucim/skimage/filters/_rank_order.py
new file mode 100644
index 000000000..9ff44e6a6
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_rank_order.py
@@ -0,0 +1,59 @@
+"""rankorder.py - convert an image of any type to an image of ints whose
+pixels have an identical rank order compared to the original image
+
+Originally part of CellProfiler, code licensed under both GPL and BSD licenses.
+Website: http://www.cellprofiler.org
+Copyright (c) 2003-2009 Massachusetts Institute of Technology
+Copyright (c) 2009-2011 Broad Institute
+All rights reserved.
+Original author: Lee Kamentstky
+"""
+import cupy as cp
+
+
+def rank_order(image):
+    """Return an image of the same shape where each pixel is the
+    index of the pixel value in the ascending order of the unique
+    values of ``image``, aka the rank-order value.
+
+    Parameters
+    ----------
+    image : ndarray
+
+    Returns
+    -------
+    labels : ndarray of type np.uint32, of shape image.shape
+        New array where each pixel has the rank-order value of the
+        corresponding pixel in ``image``. Pixel values are between 0 and
+        n - 1, where n is the number of distinct unique values in
+        ``image``.
+    original_values : 1-D ndarray
+        Unique original values of ``image``
+
+    Examples
+    --------
+    >>> a = cp.asarray([[1, 4, 5], [4, 4, 1], [5, 1, 1]])
+    >>> a
+    array([[1, 4, 5],
+           [4, 4, 1],
+           [5, 1, 1]])
+    >>> rank_order(a)
+    (array([[0, 1, 2],
+           [1, 1, 0],
+           [2, 0, 0]], dtype=uint32), array([1, 4, 5]))
+    >>> b = cp.asarray([-1., 2.5, 3.1, 2.5])
+    >>> rank_order(b)
+    (array([0, 1, 2, 1], dtype=uint32), array([-1. ,  2.5,  3.1]))
+    """
+    flat_image = image.ravel()
+    sort_order = flat_image.argsort().astype(cp.uint32)
+    flat_image = flat_image[sort_order]
+    sort_rank = cp.zeros_like(sort_order)
+    is_different = flat_image[:-1] != flat_image[1:]
+    cp.cumsum(is_different, out=sort_rank[1:])
+    original_values = cp.zeros((int(sort_rank[-1]) + 1,), image.dtype)
+    original_values[0] = flat_image[0]
+    original_values[1:] = flat_image[1:][is_different]
+    int_image = cp.zeros_like(sort_order)
+    int_image[sort_order] = sort_rank
+    return (int_image.reshape(image.shape), original_values)
diff --git a/python/cucim/src/cucim/skimage/filters/_sparse.py b/python/cucim/src/cucim/skimage/filters/_sparse.py
new file mode 100644
index 000000000..2effb058b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_sparse.py
@@ -0,0 +1,139 @@
+import cupy as cp
+import numpy as np
+
+
+def _validate_window_size(axis_sizes):
+    """Ensure all sizes in ``axis_sizes`` are odd.
+
+    Parameters
+    ----------
+    axis_sizes : iterable of int
+
+    Raises
+    ------
+    ValueError
+        If any given axis size is even.
+    """
+    for axis_size in axis_sizes:
+        if axis_size % 2 == 0:
+            msg = ('Window size must not be even on any dimension. '
+                   'Got {}'.format(axis_sizes))
+            raise ValueError(msg)
+
+
+def _to_np_mode(mode):
+    """Convert padding modes from `ndi.correlate` to `np.pad`."""
+    mode_translation_dict = dict(nearest='edge', reflect='symmetric',
+                                 mirror='reflect')
+    if mode in mode_translation_dict:
+        mode = mode_translation_dict[mode]
+    return mode
+
+
+def _get_view(padded, kernel_shape, idx, val):
+    """Get a view into `padded` that is offset by `idx` and scaled by `val`.
+
+    If `padded` was created by padding the original image by `kernel_shape` as
+    in _mean_std, then the view created here will match the size of the
+    original image.
+    """
+    sl_shift = tuple([slice(c, s - (w_ - 1 - c))
+                      for c, w_, s in zip(idx, kernel_shape, padded.shape)])
+    v = padded[sl_shift]
+    if val == 1:
+        return v
+    elif val == -1:
+        return -v
+    return val * v
+
+
+def _correlate_sparse(image, kernel_shape, kernel_indices_and_values):
+    """Perform correlation with a sparse kernel.
+
+    Parameters
+    ----------
+    image : ndarray
+        The (prepadded) image to be correlated.
+    kernel_shape : tuple of int
+        The shape of the sparse filter kernel.
+    kernel_indices_and_values : list of 2-tuples
+        This is a list of 2-tuples with length equal to the number of nonzero
+        kernel entries. The first element of the tuple is the coordinate within
+        `kernel_shape` and the second element is the kernel value at that
+        coordinate.
+
+    Returns
+    -------
+    out : ndarray
+        The filtered image.
+
+    Notes
+    -----
+    This function only returns results for the 'valid' region of the
+    convolution, and thus `out` will be smaller than `image` by an amount
+    equal to the kernel size along each axis.
+    """
+    idx, val = kernel_indices_and_values[0]
+    # implementation assumes this corner is first in kernel_indices_in_values
+    assert tuple(idx) == (0, ) * image.ndim
+    out = _get_view(image, kernel_shape, idx, val)
+    if not out.flags.owndata:
+        # make out contiguous and avoid modifying image
+        out = out.copy()
+    for idx, val in kernel_indices_and_values[1:]:
+        out += _get_view(image, kernel_shape, idx, val)
+    return out
+
+
+def correlate_sparse(image, kernel, mode='reflect'):
+    """Compute valid cross-correlation of `padded_array` and `kernel`.
+
+    This function is *fast* when `kernel` is large with many zeros.
+
+    See ``scipy.ndimage.correlate`` for a description of cross-correlation.
+
+    Parameters
+    ----------
+    image : ndarray, dtype float, shape (M, N,[ ...,] P)
+        The input array. If mode is 'valid', this array should already be
+        padded, as a margin of the same shape as kernel will be stripped
+        off.
+    kernel : ndarray, dtype float shape (Q, R,[ ...,] S)
+        The kernel to be correlated. Must have the same number of
+        dimensions as `padded_array`. For high performance, it should
+        be sparse (few nonzero entries).
+    mode : string, optional
+        See `scipy.ndimage.correlate` for valid modes.
+        Additionally, mode 'valid' is accepted, in which case no padding is
+        applied and the result is the result for the smaller image for which
+        the kernel is entirely inside the original data.
+
+    Returns
+    -------
+    result : array of float, shape (M, N,[ ...,] P)
+        The result of cross-correlating `image` with `kernel`. If mode
+        'valid' is used, the resulting shape is (M-Q+1, N-R+1,[ ...,] P-S+1).
+    """
+    if mode == 'valid':
+        padded_image = image
+    else:
+        np_mode = _to_np_mode(mode)
+        _validate_window_size(kernel.shape)
+        padded_image = cp.pad(
+            image,
+            [(w // 2, w // 2) for w in kernel.shape],
+            mode=np_mode,
+        )
+
+    kernel = cp.asnumpy(kernel)
+    indices = np.nonzero(kernel)
+    values = kernel[indices].astype(padded_image.dtype, copy=False)
+    indices = list(zip(*indices))
+    kernel_indices_and_values = [(idx, v) for idx, v in zip(indices, values)]
+    if (0, ) * kernel.ndim not in indices:
+        kernel_indices_and_values = \
+            [((0,) * kernel.ndim, 0.0)] + kernel_indices_and_values
+    out = _correlate_sparse(
+        padded_image, kernel.shape, kernel_indices_and_values
+    )
+    return out
diff --git a/python/cucim/src/cucim/skimage/filters/_unsharp_mask.py b/python/cucim/src/cucim/skimage/filters/_unsharp_mask.py
new file mode 100644
index 000000000..4c638fed7
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_unsharp_mask.py
@@ -0,0 +1,139 @@
+import cupy as cp
+from cupyx.scipy.ndimage import gaussian_filter
+
+from .. import img_as_float
+from .thresholding import _float_dtype
+
+
+def _unsharp_mask_single_channel(image, radius, amount, vrange):
+    """Single channel implementation of the unsharp masking filter."""
+
+    blurred = gaussian_filter(image,
+                              sigma=radius,
+                              mode='reflect')
+
+    result = image + (image - blurred) * amount
+    if vrange is not None:
+        return cp.clip(result, vrange[0], vrange[1], out=result)
+    return result
+
+
+def unsharp_mask(image, radius=1.0, amount=1.0, multichannel=False,
+                 preserve_range=False):
+    """Unsharp masking filter.
+
+    The sharp details are identified as the difference between the original
+    image and its blurred version. These details are then scaled, and added
+    back to the original image.
+
+    Parameters
+    ----------
+    image : [P, ..., ]M[, N][, C] ndarray
+        Input image.
+    radius : scalar or sequence of scalars, optional
+        If a scalar is given, then its value is used for all dimensions.
+        If sequence is given, then there must be exactly one radius
+        for each dimension except the last dimension for multichannel images.
+        Note that 0 radius means no blurring, and negative values are
+        not allowed.
+    amount : scalar, optional
+        The details will be amplified with this factor. The factor could be 0
+        or negative. Typically, it is a small positive number, e.g. 1.0.
+    multichannel : bool, optional
+        If True, the last ``image`` dimension is considered as a color channel,
+        otherwise as spatial. Color channels are processed individually.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of ``img_as_float``.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Returns
+    -------
+    output : [P, ..., ]M[, N][, C] ndarray of float
+        Image with unsharp mask applied.
+
+    Notes
+    -----
+    Unsharp masking is an image sharpening technique. It is a linear image
+    operation, and numerically stable, unlike deconvolution which is an
+    ill-posed problem. Because of this stability, it is often
+    preferred over deconvolution.
+
+    The main idea is as follows: sharp details are identified as the
+    difference between the original image and its blurred version.
+    These details are added back to the original image after a scaling step:
+
+        enhanced image = original + amount * (original - blurred)
+
+    When applying this filter to several color layers independently,
+    color bleeding may occur. More visually pleasing result can be
+    achieved by processing only the brightness/lightness/intensity
+    channel in a suitable color space such as HSV, HSL, YUV, or YCbCr.
+
+    Unsharp masking is described in most introductory digital image
+    processing books. This implementation is based on [1]_.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> array = cp.ones(shape=(5,5), dtype=np.uint8)*100
+    >>> array[2,2] = 120
+    >>> array
+    array([[100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100],
+           [100, 100, 120, 100, 100],
+           [100, 100, 100, 100, 100],
+           [100, 100, 100, 100, 100]], dtype=uint8)
+    >>> cp.around(unsharp_mask(array, radius=0.5, amount=2),2)
+    array([[0.39, 0.39, 0.39, 0.39, 0.39],
+           [0.39, 0.39, 0.38, 0.39, 0.39],
+           [0.39, 0.38, 0.53, 0.38, 0.39],
+           [0.39, 0.39, 0.38, 0.39, 0.39],
+           [0.39, 0.39, 0.39, 0.39, 0.39]])
+
+    >>> array = cp.ones(shape=(5,5), dtype=np.int8)*100
+    >>> array[2,2] = 127
+    >>> cp.around(unsharp_mask(array, radius=0.5, amount=2),2)
+    array([[0.79, 0.79, 0.79, 0.79, 0.79],
+           [0.79, 0.78, 0.75, 0.78, 0.79],
+           [0.79, 0.75, 1.  , 0.75, 0.79],
+           [0.79, 0.78, 0.75, 0.78, 0.79],
+           [0.79, 0.79, 0.79, 0.79, 0.79]])
+
+    >>> cp.around(unsharp_mask(array, radius=0.5, amount=2,
+    ...                        preserve_range=True),
+    ...           2)
+    array([[100.  , 100.  ,  99.99, 100.  , 100.  ],
+           [100.  ,  99.39,  95.48,  99.39, 100.  ],
+           [ 99.99,  95.48, 147.59,  95.48,  99.99],
+           [100.  ,  99.39,  95.48,  99.39, 100.  ],
+           [100.  , 100.  ,  99.99, 100.  , 100.  ]])
+
+    References
+    ----------
+    .. [1]  Maria Petrou, Costas Petrou
+            "Image Processing: The Fundamentals", (2010), ed ii., page 357,
+            ISBN 13: 9781119994398  :DOI:`10.1002/9781119994398`
+    .. [2]  Wikipedia. Unsharp masking
+            https://en.wikipedia.org/wiki/Unsharp_masking
+
+    """
+    vrange = None  # Range for valid values; used for clipping.
+    if preserve_range:
+        fimg = image.astype(_float_dtype(image), copy=False)
+    else:
+        fimg = img_as_float(image)
+        negative = cp.any(fimg < 0)
+        if negative:
+            vrange = [-1.0, 1.0]
+        else:
+            vrange = [0.0, 1.0]
+
+    if multichannel:
+        result = cp.empty_like(fimg, dtype=fimg.dtype)
+        for channel in range(image.shape[-1]):
+            result[..., channel] = _unsharp_mask_single_channel(
+                fimg[..., channel], radius, amount, vrange)
+        return result
+    else:
+        return _unsharp_mask_single_channel(fimg, radius, amount, vrange)
diff --git a/python/cucim/src/cucim/skimage/filters/_window.py b/python/cucim/src/cucim/skimage/filters/_window.py
new file mode 100644
index 000000000..9b543801d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/_window.py
@@ -0,0 +1,132 @@
+import functools
+
+import cupy as cp
+import numpy as np
+from scipy.signal import get_window
+
+from .._shared.utils import safe_as_int
+from ..transform import warp
+
+
+def window(window_type, shape, warp_kwargs=None):
+    """Return an n-dimensional window of a given size and dimensionality.
+
+    Parameters
+    ----------
+    window_type : string, float, or tuple
+        The type of window to be created. Any window type supported by
+        ``scipy.signal.get_window`` is allowed here. See notes below for a
+        current list, or the SciPy documentation for the version of SciPy
+        on your machine.
+    shape : tuple of int or int
+        The shape of the window along each axis. If an integer is provided,
+        a 1D window is generated.
+    warp_kwargs : dict
+        Keyword arguments passed to `skimage.transform.warp` (e.g.,
+        ``warp_kwargs={'order':3}`` to change interpolation method).
+
+    Returns
+    -------
+    nd_window : ndarray
+        A window of the specified ``shape``. ``dtype`` is ``np.double``.
+
+    Notes
+    -----
+    This function is based on ``scipy.signal.get_window`` and thus can access
+    all of the window types available to that function
+    (e.g., ``"hann"``, ``"boxcar"``). Note that certain window types require
+    parameters that have to be supplied with the window name as a tuple
+    (e.g., ``("tukey", 0.8)``). If only a float is supplied, it is interpreted
+    as the beta parameter of the Kaiser window.
+
+    See https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.windows.get_window.html
+    for more details.
+
+    Note that this function generates a double precision array of the specified
+    ``shape`` and can thus generate very large arrays that consume a large
+    amount of available memory.
+
+    The approach taken here to create nD windows is to first calculate the
+    Euclidean distance from the center of the intended nD window to each
+    position in the array. That distance is used to sample, with
+    interpolation, from a 1D window returned from ``scipy.signal.get_window``.
+    The method of interpolation can be changed with the ``order`` keyword
+    argument passed to `skimage.transform.warp`.
+
+    Some coordinates in the output window will be outside of the original
+    signal; these will be filled in with zeros.
+
+    Window types:
+    - boxcar
+    - triang
+    - blackman
+    - hamming
+    - hann
+    - bartlett
+    - flattop
+    - parzen
+    - bohman
+    - blackmanharris
+    - nuttall
+    - barthann
+    - kaiser (needs beta)
+    - gaussian (needs standard deviation)
+    - general_gaussian (needs power, width)
+    - slepian (needs width)
+    - dpss (needs normalized half-bandwidth)
+    - chebwin (needs attenuation)
+    - exponential (needs decay scale)
+    - tukey (needs taper fraction)
+
+    Examples
+    --------
+    Return a Hann window with shape (512, 512):
+
+    >>> from cucim.skimage.filters import window
+    >>> w = window('hann', (512, 512))
+
+    Return a Kaiser window with beta parameter of 16 and shape (256, 256, 35):
+
+    >>> w = window(16, (256, 256, 35))
+
+    Return a Tukey window with an alpha parameter of 0.8 and shape (100, 300):
+
+    >>> w = window(('tukey', 0.8), (100, 300))
+
+    References
+    ----------
+    .. [1] Two-dimensional window design, Wikipedia,
+           https://en.wikipedia.org/wiki/Two_dimensional_window_design
+    """  # noqa
+
+    if np.isscalar(shape):
+        shape = (safe_as_int(shape),)
+    else:
+        shape = tuple(safe_as_int(shape))
+    if any(s < 0 for s in shape):
+        raise ValueError("invalid shape")
+
+    ndim = len(shape)
+    if ndim <= 0:
+        raise ValueError("Number of dimensions must be greater than zero")
+
+    max_size = functools.reduce(max, shape)
+    w = cp.asarray(get_window(window_type, max_size, fftbins=False))
+    w = cp.reshape(w, (-1,) + (1,) * (ndim - 1))
+
+    # Create coords for warping following `ndimage.map_coordinates` convention.
+    L = [cp.arange(s, dtype=np.float32) * (max_size / s) for s in shape]
+
+    center = cp.asarray((max_size / 2) - 0.5)
+    dist = 0
+    for g in cp.meshgrid(*L, sparse=True, indexing="ij"):
+        g -= center
+        dist = dist + g * g
+    cp.sqrt(dist, out=dist)
+    coords = cp.zeros((ndim,) + dist.shape, dtype=np.float32)
+    coords[0] = dist + center
+
+    if warp_kwargs is None:
+        warp_kwargs = {}
+
+    return warp(w, coords, mode='constant', cval=0.0, **warp_kwargs)
diff --git a/python/cucim/src/cucim/skimage/filters/edges.py b/python/cucim/src/cucim/skimage/filters/edges.py
new file mode 100644
index 000000000..25235c8b7
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/edges.py
@@ -0,0 +1,857 @@
+"""
+
+Sobel and Prewitt filters originally part of CellProfiler, code licensed under
+both GPL and BSD licenses.
+Website: http://www.cellprofiler.org
+Copyright (c) 2003-2009 Massachusetts Institute of Technology
+Copyright (c) 2009-2011 Broad Institute
+All rights reserved.
+Original author: Lee Kamentsky
+
+"""
+import math
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from .. import img_as_float
+from .._shared.utils import check_nD
+from ..restoration.uft import laplacian
+
+# n-dimensional filter weights
+SOBEL_EDGE = np.array([1, 0, -1])
+SOBEL_SMOOTH = np.array([1, 2, 1]) / 4
+HSOBEL_WEIGHTS = SOBEL_EDGE.reshape((3, 1)) * SOBEL_SMOOTH.reshape((1, 3))
+VSOBEL_WEIGHTS = HSOBEL_WEIGHTS.T
+
+SCHARR_EDGE = np.array([1, 0, -1])
+SCHARR_SMOOTH = np.array([3, 10, 3]) / 16
+HSCHARR_WEIGHTS = SCHARR_EDGE.reshape((3, 1)) * SCHARR_SMOOTH.reshape((1, 3))
+VSCHARR_WEIGHTS = HSCHARR_WEIGHTS.T
+
+PREWITT_EDGE = np.array([1, 0, -1])
+PREWITT_SMOOTH = np.full((3,), 1 / 3)
+HPREWITT_WEIGHTS = (PREWITT_EDGE.reshape((3, 1))
+                    * PREWITT_SMOOTH.reshape((1, 3)))
+VPREWITT_WEIGHTS = HPREWITT_WEIGHTS.T
+
+# 2D-only filter weights
+# fmt: off
+ROBERTS_PD_WEIGHTS = np.array([[1, 0],
+                               [0, -1]], dtype=np.double)
+ROBERTS_ND_WEIGHTS = np.array([[0, 1],
+                               [-1, 0]], dtype=np.double)
+# fmt: on
+
+# These filter weights can be found in Farid & Simoncelli (2004),
+# Table 1 (3rd and 4th row). Additional decimal places were computed
+# using the code found at https://www.cs.dartmouth.edu/farid/
+# fmt: off
+p = np.array([[0.0376593171958126, 0.249153396177344, 0.426374573253687,
+               0.249153396177344, 0.0376593171958126]])
+d1 = np.array([[0.109603762960254, 0.276690988455557, 0, -0.276690988455557,
+                -0.109603762960254]])
+# fmt: on
+HFARID_WEIGHTS = d1.T * p
+VFARID_WEIGHTS = np.copy(HFARID_WEIGHTS.T)
+
+
+def _mask_filter_result(result, mask):
+    """Return result after masking.
+
+    Input masks are eroded so that mask areas in the original image don't
+    affect values in the result.
+    """
+    if mask is not None:
+        erosion_selem = ndi.generate_binary_structure(mask.ndim, mask.ndim)
+        mask = ndi.binary_erosion(mask, erosion_selem, border_value=0)
+        result *= mask
+    return result
+
+
+def _kernel_shape(ndim, dim):
+    """Return list of `ndim` 1s except at position `dim`, where value is -1.
+
+    Parameters
+    ----------
+    ndim : int
+        The number of dimensions of the kernel shape.
+    dim : int
+        The axis of the kernel to expand to shape -1.
+
+    Returns
+    -------
+    shape : list of int
+        The requested shape.
+
+    Examples
+    --------
+    >>> _kernel_shape(2, 0)
+    [-1, 1]
+    >>> _kernel_shape(3, 1)
+    [1, -1, 1]
+    >>> _kernel_shape(4, -1)
+    [1, 1, 1, -1]
+    """
+    shape = [1] * ndim
+    shape[dim] = -1
+    return shape
+
+
+def _reshape_nd(arr, ndim, dim):
+    """Reshape a 1D array to have n dimensions, all singletons but one.
+
+    Parameters
+    ----------
+    arr : array, shape (N,)
+        Input array
+    ndim : int
+        Number of desired dimensions of reshaped array.
+    dim : int
+        Which dimension/axis will not be singleton-sized.
+
+    Returns
+    -------
+    arr_reshaped : array, shape ([1, ...], N, [1,...])
+        View of `arr` reshaped to the desired shape.
+
+    Examples
+    --------
+    >>> arr = cp.random.random(7)
+    >>> _reshape_nd(arr, 2, 0).shape
+    (7, 1)
+    >>> _reshape_nd(arr, 3, 1).shape
+    (1, 7, 1)
+    >>> _reshape_nd(arr, 4, -1).shape
+    (1, 1, 1, 7)
+    """
+    kernel_shape = _kernel_shape(ndim, dim)
+    return cp.reshape(arr, kernel_shape)
+
+
+def _generic_edge_filter(image, *, smooth_weights, edge_weights=[1, 0, -1],
+                         axis=None, mode='reflect', cval=0.0, mask=None):
+    """Apply a generic, n-dimensional edge filter.
+
+    The filter is computed by applying the edge weights along one dimension
+    and the smoothing weights along all other dimensions. If no axis is given,
+    or a tuple of axes is given the filter is computed along all axes in turn,
+    and the magnitude is computed as the square root of the average square
+    magnitude of all the axes.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    smooth_weights : array of float
+        The smoothing weights for the filter. These are applied to dimensions
+        orthogonal to the edge axis.
+    edge_weights : 1D array of float, optional
+        The weights to compute the edge along the chosen axes.
+    axis : int or sequence of int, optional
+        Compute the edge filter along this axis. If not provided, the edge
+        magnitude is computed. This is defined as::
+
+            edge_mag = np.sqrt(sum([_generic_edge_filter(image, ..., axis=i)**2
+                                    for i in range(image.ndim)]) / image.ndim)
+
+        The magnitude is also computed if axis is a sequence.
+    mode : str or sequence of str, optional
+        The boundary mode for the convolution. See `scipy.ndimage.convolve`
+        for a description of the modes. This can be either a single boundary
+        mode or one boundary mode per axis.
+    cval : float, optional
+        When `mode` is ``'constant'``, this is the constant used in values
+        outside the boundary of the image data.
+    """
+    ndim = image.ndim
+    if axis is None:
+        axes = list(range(ndim))
+    elif np.isscalar(axis):
+        axes = [axis]
+    else:
+        axes = axis
+    return_magnitude = len(axes) > 1
+
+    float_dtype = cp.promote_types(image.dtype, np.float16)
+
+    # TODO: file an upstream scikit-image PR casting weights in this manner
+    edge_weights = cp.asarray(edge_weights, dtype=float_dtype)
+    smooth_weights = cp.asarray(smooth_weights, dtype=float_dtype)
+
+    if return_magnitude:
+        edge_weights /= math.sqrt(ndim)
+
+    # CuPy Backend: Apply the smoothing and edge convolutions separably
+    #               rather than forming an n-dimensional kernel. This is
+    #               moderately faster for large 2D images and substantially
+    #               faster in 3D and higher dimensions.
+    for i, edge_dim in enumerate(axes):
+        ax_output = ndi.convolve1d(image, edge_weights, axis=edge_dim,
+                                   mode=mode, output=float_dtype)
+        smooth_axes = list(set(range(ndim)) - {edge_dim})
+        for smooth_dim in smooth_axes:
+            # TODO: why did this benchmark slower if output=ax_output was used?
+            ax_output = ndi.convolve1d(ax_output, smooth_weights,
+                                       axis=smooth_dim, mode=mode,
+                                       output=float_dtype)
+        if return_magnitude:
+            ax_output *= ax_output
+        if i == 0:
+            output = ax_output
+        else:
+            output += ax_output
+
+    if return_magnitude:
+        cp.sqrt(output, out=output)
+    return output
+
+
+def sobel(image, mask=None, *, axis=None, mode='reflect', cval=0.0):
+    """Find edges in an image using the Sobel filter.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    mask : array of bool, optional
+        Clip the output image to this mask. (Values where mask=0 will be set
+        to 0.)
+    axis : int or sequence of int, optional
+        Compute the edge filter along this axis. If not provided, the edge
+        magnitude is computed. This is defined as::
+
+            sobel_mag = np.sqrt(sum([sobel(image, axis=i)**2
+                                     for i in range(image.ndim)]) / image.ndim)
+
+        The magnitude is also computed if axis is a sequence.
+    mode : str or sequence of str, optional
+        The boundary mode for the convolution. See `scipy.ndimage.convolve`
+        for a description of the modes. This can be either a single boundary
+        mode or one boundary mode per axis.
+    cval : float, optional
+        When `mode` is ``'constant'``, this is the constant used in values
+        outside the boundary of the image data.
+
+    Returns
+    -------
+    output : array of float
+        The Sobel edge map.
+
+    See also
+    --------
+    scharr, prewitt, canny
+
+    References
+    ----------
+    .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
+           Optimization of Kernel Based Image Derivatives.
+
+    .. [2] https://en.wikipedia.org/wiki/Sobel_operator
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage import filters
+    >>> camera = cp.array(data.camera())
+    >>> edges = filters.sobel(camera)
+    """
+    image = img_as_float(image)
+    output = _generic_edge_filter(image, smooth_weights=SOBEL_SMOOTH,
+                                  axis=axis, mode=mode, cval=cval)
+    output = _mask_filter_result(output, mask)
+    return output
+
+
+def sobel_h(image, mask=None):
+    """Find the horizontal edges of an image using the Sobel transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Sobel edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      1   2   1
+      0   0   0
+     -1  -2  -1
+
+    """
+    check_nD(image, 2)
+    return sobel(image, mask=mask, axis=0)
+
+
+def sobel_v(image, mask=None):
+    """Find the vertical edges of an image using the Sobel transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Sobel edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      1   0  -1
+      2   0  -2
+      1   0  -1
+
+    """
+    check_nD(image, 2)
+    return sobel(image, mask=mask, axis=1)
+
+
+def scharr(image, mask=None, *, axis=None, mode='reflect', cval=0.0):
+    """Find the edge magnitude using the Scharr transform.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    mask : array of bool, optional
+        Clip the output image to this mask. (Values where mask=0 will be set
+        to 0.)
+    axis : int or sequence of int, optional
+        Compute the edge filter along this axis. If not provided, the edge
+        magnitude is computed. This is defined as::
+
+            sch_mag = np.sqrt(sum([scharr(image, axis=i)**2
+                                   for i in range(image.ndim)]) / image.ndim)
+
+        The magnitude is also computed if axis is a sequence.
+    mode : str or sequence of str, optional
+        The boundary mode for the convolution. See `scipy.ndimage.convolve`
+        for a description of the modes. This can be either a single boundary
+        mode or one boundary mode per axis.
+    cval : float, optional
+        When `mode` is ``'constant'``, this is the constant used in values
+        outside the boundary of the image data.
+
+    Returns
+    -------
+    output : array of float
+        The Scharr edge map.
+
+    See also
+    --------
+    sobel, prewitt, canny
+
+    Notes
+    -----
+    The Scharr operator has a better rotation invariance than
+    other edge filters such as the Sobel or the Prewitt operators.
+
+    References
+    ----------
+    .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
+           Optimization of Kernel Based Image Derivatives.
+
+    .. [2] https://en.wikipedia.org/wiki/Sobel_operator#Alternative_operators
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage import filters
+    >>> camera = cp.array(data.camera())
+    >>> edges = filters.scharr(camera)
+    """
+    image = img_as_float(image)
+    output = _generic_edge_filter(image, smooth_weights=SCHARR_SMOOTH,
+                                  axis=axis, mode=mode, cval=cval)
+    output = _mask_filter_result(output, mask)
+    return output
+
+
+def scharr_h(image, mask=None):
+    """Find the horizontal edges of an image using the Scharr transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Scharr edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      3   10   3
+      0    0   0
+     -3  -10  -3
+
+    References
+    ----------
+    .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
+           Optimization of Kernel Based Image Derivatives.
+
+    """
+    check_nD(image, 2)
+    return scharr(image, mask=mask, axis=0)
+
+
+def scharr_v(image, mask=None):
+    """Find the vertical edges of an image using the Scharr transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Scharr edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+       3   0   -3
+      10   0  -10
+       3   0   -3
+
+    References
+    ----------
+    .. [1] D. Kroon, 2009, Short Paper University Twente, Numerical
+           Optimization of Kernel Based Image Derivatives.
+    """
+    check_nD(image, 2)
+    return scharr(image, mask=mask, axis=1)
+
+
+def prewitt(image, mask=None, *, axis=None, mode='reflect', cval=0.0):
+    """Find the edge magnitude using the Prewitt transform.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    mask : array of bool, optional
+        Clip the output image to this mask. (Values where mask=0 will be set
+        to 0.)
+    axis : int or sequence of int, optional
+        Compute the edge filter along this axis. If not provided, the edge
+        magnitude is computed. This is defined as::
+
+            prw_mag = np.sqrt(sum([prewitt(image, axis=i)**2
+                                   for i in range(image.ndim)]) / image.ndim)
+
+        The magnitude is also computed if axis is a sequence.
+    mode : str or sequence of str, optional
+        The boundary mode for the convolution. See `scipy.ndimage.convolve`
+        for a description of the modes. This can be either a single boundary
+        mode or one boundary mode per axis.
+    cval : float, optional
+        When `mode` is ``'constant'``, this is the constant used in values
+        outside the boundary of the image data.
+
+    Returns
+    -------
+    output : array of float
+        The Prewitt edge map.
+
+    See also
+    --------
+    sobel, scharr
+
+    Notes
+    -----
+    The edge magnitude depends slightly on edge directions, since the
+    approximation of the gradient operator by the Prewitt operator is not
+    completely rotation invariant. For a better rotation invariance, the Scharr
+    operator should be used. The Sobel operator has a better rotation
+    invariance than the Prewitt operator, but a worse rotation invariance than
+    the Scharr operator.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> from cucim.skimage import filters
+    >>> camera = cp.array(data.camera())
+    >>> edges = filters.prewitt(camera)
+    """
+    image = img_as_float(image)
+    output = _generic_edge_filter(image, smooth_weights=PREWITT_SMOOTH,
+                                  axis=axis, mode=mode, cval=cval)
+    output = _mask_filter_result(output, mask)
+    return output
+
+
+def prewitt_h(image, mask=None):
+    """Find the horizontal edges of an image using the Prewitt transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Prewitt edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      1/3   1/3   1/3
+       0     0     0
+     -1/3  -1/3  -1/3
+
+    """
+    check_nD(image, 2)
+    return prewitt(image, mask=mask, axis=0)
+
+
+def prewitt_v(image, mask=None):
+    """Find the vertical edges of an image using the Prewitt transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Prewitt edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      1/3   0  -1/3
+      1/3   0  -1/3
+      1/3   0  -1/3
+
+    """
+    check_nD(image, 2)
+    return prewitt(image, mask=mask, axis=1)
+
+
+def roberts(image, mask=None):
+    """Find the edge magnitude using Roberts' cross operator.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Roberts' Cross edge map.
+
+    See also
+    --------
+    sobel, scharr, prewitt, feature.canny
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> camera = cp.array(data.camera())
+    >>> from cucim.skimage import filters
+    >>> edges = filters.roberts(camera)
+
+    """
+    check_nD(image, 2)
+    # CuPy Backend: refactored this section slightly for efficiency with CuPy
+    pos_diag_sq = roberts_pos_diag(image, mask)
+    pos_diag_sq *= pos_diag_sq
+
+    out = roberts_neg_diag(image, mask)
+    out *= out
+    out += pos_diag_sq
+
+    cp.sqrt(out, out=out)
+    out /= math.sqrt(2)
+    return out
+
+
+def roberts_pos_diag(image, mask=None):
+    """Find the cross edges of an image using Roberts' cross operator.
+
+    The kernel is applied to the input image to produce separate measurements
+    of the gradient component one orientation.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Robert's edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      1   0
+      0  -1
+
+    """
+    check_nD(image, 2)
+    image = img_as_float(image)
+    # CuPy Backend: allow float16 & float32 filtering
+    weights = cp.array(ROBERTS_PD_WEIGHTS, dtype=image.dtype)
+    result = ndi.convolve(image, weights)
+    return _mask_filter_result(result, mask)
+
+
+def roberts_neg_diag(image, mask=None):
+    """Find the cross edges of an image using the Roberts' Cross operator.
+
+    The kernel is applied to the input image to produce separate measurements
+    of the gradient component one orientation.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Robert's edge map.
+
+    Notes
+    -----
+    We use the following kernel::
+
+      0   1
+     -1   0
+
+    """
+    check_nD(image, 2)
+    image = img_as_float(image)
+    # CuPy Backend: allow float16 & float32 filtering
+    weights = cp.array(ROBERTS_ND_WEIGHTS, dtype=image.dtype)
+    result = ndi.convolve(image, weights)
+    return _mask_filter_result(result, mask)
+
+
+def laplace(image, ksize=3, mask=None):
+    """Find the edges of an image using the Laplace operator.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image to process.
+    ksize : int, optional
+        Define the size of the discrete Laplacian operator such that it
+        will have a size of (ksize,) * image.ndim.
+    mask : ndarray, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : ndarray
+        The Laplace edge map.
+
+    Notes
+    -----
+    The Laplacian operator is generated using the function
+    skimage.restoration.uft.laplacian().
+
+    """
+    image = img_as_float(image)
+
+    # TODO: File an upstream bug for scikit-image. ksize does not appear to
+    #       actually be used and is hard-coded to 3 in `laplacian`.
+    if ksize != 3:
+        raise NotImplementedError("only ksize=3 is supported")
+
+    # Create the discrete Laplacian operator - We keep only the real part of
+    # the filter
+    laplace_op = laplacian(image.ndim, None, dtype=image.dtype)
+    result = ndi.convolve(image, laplace_op)
+    return _mask_filter_result(result, mask)
+
+
+def farid(image, *, mask=None):
+    """Find the edge magnitude using the Farid transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Farid edge map.
+
+    See also
+    --------
+    sobel, prewitt, canny
+
+    Notes
+    -----
+    Take the square root of the sum of the squares of the horizontal and
+    vertical derivatives to get a magnitude that is somewhat insensitive to
+    direction. Similar to the Scharr operator, this operator is designed with
+    a rotation invariance constraint.
+
+    References
+    ----------
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
+    .. [2] Wikipedia, "Farid and Simoncelli Derivatives." Available at:
+           <https://en.wikipedia.org/wiki/Image_derivatives#Farid_and_Simoncelli_Derivatives>
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import data
+    >>> camera = cp.array(data.camera())
+    >>> from cucim.skimage import filters
+    >>> edges = filters.farid(camera)
+    """
+    check_nD(image, 2)
+    # CuPy Backend: refactored this section slightly for efficiency with CuPy
+    h_sq = farid_h(image, mask=mask)
+    h_sq *= h_sq
+
+    out = farid_v(image, mask=mask)
+    out *= out
+    out += h_sq
+    cp.sqrt(out, out=out)
+    out /= math.sqrt(2)
+    return out
+
+
+def farid_h(image, *, mask=None):
+    """Find the horizontal edges of an image using the Farid transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Farid edge map.
+
+    Notes
+    -----
+    The kernel was constructed using the 5-tap weights from [1].
+
+    References
+    ----------
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
+    .. [2] Farid, H. and Simoncelli, E. P. "Optimally rotation-equivariant
+           directional derivative kernels", In: 7th International Conference on
+           Computer Analysis of Images and Patterns, Kiel, Germany. Sep, 1997.
+    """
+    check_nD(image, 2)
+    image = img_as_float(image)
+    result = ndi.convolve(image, cp.array(HFARID_WEIGHTS, dtype=image.dtype))
+    return _mask_filter_result(result, mask)
+
+
+def farid_v(image, *, mask=None):
+    """Find the vertical edges of an image using the Farid transform.
+
+    Parameters
+    ----------
+    image : 2-D array
+        Image to process.
+    mask : 2-D array, optional
+        An optional mask to limit the application to a certain area.
+        Note that pixels surrounding masked regions are also masked to
+        prevent masked regions from affecting the result.
+
+    Returns
+    -------
+    output : 2-D array
+        The Farid edge map.
+
+    Notes
+    -----
+    The kernel was constructed using the 5-tap weights from [1].
+
+    References
+    ----------
+    .. [1] Farid, H. and Simoncelli, E. P., "Differentiation of discrete
+           multidimensional signals", IEEE Transactions on Image Processing
+           13(4): 496-508, 2004. :DOI:`10.1109/TIP.2004.823819`
+    """
+    check_nD(image, 2)
+    image = img_as_float(image)
+    result = ndi.convolve(image, cp.array(VFARID_WEIGHTS, dtype=image.dtype))
+    return _mask_filter_result(result, mask)
diff --git a/python/cucim/src/cucim/skimage/filters/lpi_filter.py b/python/cucim/src/cucim/skimage/filters/lpi_filter.py
new file mode 100644
index 000000000..f67cb782d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/lpi_filter.py
@@ -0,0 +1,251 @@
+"""
+:author: Stefan van der Walt, 2008
+:license: modified BSD
+"""
+
+import cupy as cp
+import numpy as np
+
+from .._shared.fft import fftmodule as fft
+from .._shared.utils import check_nD
+
+eps = np.finfo(float).eps
+
+
+def _min_limit(x, val=eps):
+    mask = cp.abs(x) < eps
+    x[mask] = cp.sign(x[mask]) * eps
+
+
+def _centre(x, oshape):
+    """Return an array of oshape from the centre of x."""
+    start = (np.array(x.shape) - np.array(oshape)) // 2 + 1
+    out = x[tuple(slice(s, s + n) for s, n in zip(start, oshape))]
+    return out
+
+
+def _pad(data, shape):
+    """Pad the data to the given shape with zeros.
+
+    Parameters
+    ----------
+    data : 2-d ndarray
+        Input data
+    shape : (2,) tuple
+
+    """
+    out = cp.zeros(shape)
+    out[tuple(slice(0, n) for n in data.shape)] = data
+    return out
+
+
+class LPIFilter2D(object):
+    """Linear Position-Invariant Filter (2-dimensional)"""
+
+    def __init__(self, impulse_response, **filter_params):
+        """
+        Parameters
+        ----------
+        impulse_response : callable `f(r, c, **filter_params)`
+            Function that yields the impulse response.  ``r`` and ``c`` are
+            1-dimensional vectors that represent row and column positions, in
+            other words coordinates are (r[0],c[0]),(r[0],c[1]) etc.
+            `**filter_params` are passed through.
+
+            In other words, ``impulse_response`` would be called like this:
+
+            >>> def impulse_response(r, c, **filter_params):
+            ...     pass
+            >>>
+            >>> r = [0,0,0,1,1,1,2,2,2]
+            >>> c = [0,1,2,0,1,2,0,1,2]
+            >>> filter_params = {'kw1': 1, 'kw2': 2, 'kw3': 3}
+            >>> impulse_response(r, c, **filter_params)
+
+
+        Examples
+        --------
+        Gaussian filter: Use a 1-D gaussian in each direction without
+        normalization coefficients.
+
+        >>> def filt_func(r, c, sigma = 1):
+        ...     return cp.exp(-cp.hypot(r, c)/sigma)
+        >>> filter = LPIFilter2D(filt_func)
+
+        """
+        if not callable(impulse_response):
+            raise ValueError("Impulse response must be a callable.")
+
+        self.impulse_response = impulse_response
+        self.filter_params = filter_params
+        self._cache = None
+
+    def _prepare(self, data):
+        """Calculate filter and data FFT in preparation for filtering."""
+        dshape = np.array(data.shape)
+        dshape += dshape % 2 == 0  # all filter dimensions must be uneven
+        oshape = np.array(data.shape) * 2 - 1
+
+        if self._cache is None or np.any(self._cache.shape != oshape):
+            coords = cp.mgrid[[slice(0, float(n)) for n in dshape]]
+            # this steps over two sets of coordinates,
+            # not over the coordinates individually
+            for k, coord in enumerate(coords):
+                coord -= (dshape[k] - 1) / 2.0
+            coords = coords.reshape(2, -1).T  # coordinate pairs (r,c)
+
+            f = self.impulse_response(coords[:, 0], coords[:, 1],
+                                      **self.filter_params).reshape(dshape)
+
+            f = _pad(f, oshape)
+            F = fft.fftn(f)
+            self._cache = F
+        else:
+            F = self._cache
+
+        data = _pad(data, oshape)
+        G = fft.fftn(data)
+
+        return F, G
+
+    def __call__(self, data):
+        """Apply the filter to the given data.
+
+        Parameters
+        ----------
+        data : (M,N) ndarray
+
+        """
+        check_nD(data, 2, 'data')
+        F, G = self._prepare(data)
+        out = fft.ifftn(F * G)
+        out = cp.abs(_centre(out, data.shape))
+        return out
+
+
+def forward(data, impulse_response=None, filter_params={},
+            predefined_filter=None):
+    """Apply the given filter to data.
+
+    Parameters
+    ----------
+    data : (M,N) ndarray
+        Input data.
+    impulse_response : callable `f(r, c, **filter_params)`
+        Impulse response of the filter.  See LPIFilter2D.__init__.
+    filter_params : dict
+        Additional keyword parameters to the impulse_response function.
+
+    Other Parameters
+    ----------------
+    predefined_filter : LPIFilter2D
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
+
+    Examples
+    --------
+
+    Gaussian filter:
+
+    >>> import cupy as cp
+    >>> def filt_func(r, c):
+    ...     return cp.exp(-cp.hypot(r, c)/1)
+    >>>
+    >>> from skimage import data
+    >>> filtered = forward(cp.array(data.coins()), filt_func)
+
+    """
+    check_nD(data, 2, 'data')
+    if predefined_filter is None:
+        predefined_filter = LPIFilter2D(impulse_response, **filter_params)
+    return predefined_filter(data)
+
+
+def inverse(data, impulse_response=None, filter_params={}, max_gain=2,
+            predefined_filter=None):
+    """Apply the filter in reverse to the given data.
+
+    Parameters
+    ----------
+    data : (M,N) ndarray
+        Input data.
+    impulse_response : callable `f(r, c, **filter_params)`
+        Impulse response of the filter.  See LPIFilter2D.__init__.
+    filter_params : dict
+        Additional keyword parameters to the impulse_response function.
+    max_gain : float
+        Limit the filter gain.  Often, the filter contains zeros, which would
+        cause the inverse filter to have infinite gain.  High gain causes
+        amplification of artefacts, so a conservative limit is recommended.
+
+    Other Parameters
+    ----------------
+    predefined_filter : LPIFilter2D
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
+
+    """
+    check_nD(data, 2, 'data')
+    if predefined_filter is None:
+        filt = LPIFilter2D(impulse_response, **filter_params)
+    else:
+        filt = predefined_filter
+
+    F, G = filt._prepare(data)
+    _min_limit(F)
+
+    F = 1 / F
+    mask = cp.abs(F) > max_gain
+    F[mask] = cp.sign(F[mask]) * max_gain
+
+    return _centre(cp.abs(fft.ifftshift(fft.ifftn(G * F))), data.shape)
+
+
+def wiener(data, impulse_response=None, filter_params={}, K=0.25,
+           predefined_filter=None):
+    """Minimum Mean Square Error (Wiener) inverse filter.
+
+    Parameters
+    ----------
+    data : (M,N) ndarray
+        Input data.
+    K : float or (M,N) ndarray
+        Ratio between power spectrum of noise and undegraded
+        image.
+    impulse_response : callable `f(r, c, **filter_params)`
+        Impulse response of the filter.  See LPIFilter2D.__init__.
+    filter_params : dict
+        Additional keyword parameters to the impulse_response function.
+
+    Other Parameters
+    ----------------
+    predefined_filter : LPIFilter2D
+        If you need to apply the same filter multiple times over different
+        images, construct the LPIFilter2D and specify it here.
+
+    """
+    check_nD(data, 2, 'data')
+
+    if not isinstance(K, float):
+        check_nD(K, 2, 'K')
+
+    if predefined_filter is None:
+        filt = LPIFilter2D(impulse_response, **filter_params)
+    else:
+        filt = predefined_filter
+
+    F, G = filt._prepare(data)
+    _min_limit(F)
+
+    H_mag_sqr = cp.abs(F)
+    H_mag_sqr *= H_mag_sqr
+    F = 1 / F * H_mag_sqr / (H_mag_sqr + K)
+
+    tmp = fft.ifftn(G * F)
+    tmp = fft.ifftshift(tmp)
+    return _centre(cp.abs(tmp), data.shape)
+
+
+def constrained_least_squares(data, lam, impulse_response=None,
+                              filter_params={}):
+    raise NotImplementedError
diff --git a/python/cucim/src/cucim/skimage/filters/ridges.py b/python/cucim/src/cucim/skimage/filters/ridges.py
new file mode 100644
index 000000000..8bbd9993a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/ridges.py
@@ -0,0 +1,595 @@
+"""
+Ridge filters.
+
+Ridge filters can be used to detect continuous edges, such as vessels,
+neurites, wrinkles, rivers, and other tube-like structures. The present
+class of ridge filters relies on the eigenvalues of the Hessian matrix of
+image intensities to detect tube-like structures where the intensity changes
+perpendicular but not along the structure.
+"""
+
+from functools import reduce
+from warnings import warn
+
+import cupy as cp
+import numpy as np
+
+from .._shared.utils import check_nD
+from ..util import img_as_float, invert
+from .thresholding import _float_dtype
+
+
+def _divide_nonzero(array1, array2, cval=1e-10):
+    """
+    Divides two arrays.
+
+    Denominator is set to small value where zero to avoid ZeroDivisionError and
+    return finite float array.
+
+    Parameters
+    ----------
+    array1 : (N, ..., M) ndarray
+        Array 1 in the enumerator.
+    array2 : (N, ..., M) ndarray
+        Array 2 in the denominator.
+    cval : float, optional
+        Value used to replace zero entries in the denominator.
+
+    Returns
+    -------
+    array : (N, ..., M) ndarray
+        Quotient of the array division.
+    """
+
+    # Copy denominator
+    denominator = cp.copy(array2)
+
+    # Set zero entries of denominator to small value
+    denominator[denominator == 0] = cval
+
+    # Return quotient
+    return cp.divide(array1, denominator)
+
+
+def _sortbyabs(array, axis=0):
+    """
+    Sort array along a given axis by absolute values.
+
+    Parameters
+    ----------
+    array : (N, ..., M) ndarray
+        Array with input image data.
+    axis : int
+        Axis along which to sort.
+
+    Returns
+    -------
+    array : (N, ..., M) ndarray
+        Array sorted along a given axis by absolute values.
+
+    Notes
+    -----
+    Modified from: http://stackoverflow.com/a/11253931/4067734
+    """
+
+    # Create auxiliary array for indexing
+    index = list(cp.ix_(*[cp.arange(i) for i in array.shape]))
+
+    # Get indices of abs sorted array
+    index[axis] = cp.abs(array).argsort(axis)
+
+    # Return abs sorted array
+    return array[tuple(index)]
+
+
+def _check_sigmas(sigmas):
+    """Check sigma values for ridges filters.
+
+    Parameters
+    ----------
+    sigmas : iterable of floats
+        Sigmas argument to be checked
+
+    Returns
+    -------
+    sigmas : ndarray
+        input iterable converted to ndarray
+
+    Raises
+    ------
+    ValueError if any input value is negative
+
+    """
+    if np.isscalar(sigmas):
+        sigmas = (sigmas,)
+    if any(s < 0.0 for s in sigmas):
+        raise ValueError('Sigma values should be equal to or greater '
+                         'than zero.')
+    return np.asarray(sigmas)
+
+
+def compute_hessian_eigenvalues(image, sigma, sorting='none',
+                                mode='constant', cval=0):
+    """
+    Compute Hessian eigenvalues of nD images.
+
+    For 2D images, the computation uses a more efficient, skimage-based
+    algorithm.
+
+    Parameters
+    ----------
+    image : (N, ..., M) ndarray
+        Array with input image data.
+    sigma : float
+        Smoothing factor of image for detection of structures at different
+        (sigma) scales.
+    sorting : {'val', 'abs', 'none'}, optional
+        Sorting of eigenvalues by values ('val') or absolute values ('abs'),
+        or without sorting ('none'). Default is 'none'.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    eigenvalues : (D, N, ..., M) ndarray
+        Array with (sorted) eigenvalues of Hessian eigenvalues for each pixel
+        of the input image.
+    """
+
+    # Import moved here to avoid circular import error
+    from ..feature import hessian_matrix, hessian_matrix_eigvals
+
+    # Convert image to float
+    image = img_as_float(image)
+
+    # Make nD hessian
+    hessian_elements = hessian_matrix(image, sigma=sigma, order='rc',
+                                      mode=mode, cval=cval)
+
+    # Correct for scale
+    var = sigma * sigma
+    hessian_elements = [var * e for e in hessian_elements]
+
+    # Compute Hessian eigenvalues
+    # hessian_eigenvalues = np.array(hessian_matrix_eigvals(hessian_elements))
+    hessian_eigenvalues = hessian_matrix_eigvals(hessian_elements)
+
+    if sorting == 'abs':
+
+        # Sort eigenvalues by absolute values in ascending order
+        hessian_eigenvalues = _sortbyabs(hessian_eigenvalues, axis=0)
+
+    elif sorting == 'val':
+
+        # Sort eigenvalues by values in ascending order
+        hessian_eigenvalues = cp.sort(hessian_eigenvalues, axis=0)
+
+    # Return Hessian eigenvalues
+    return hessian_eigenvalues
+
+
+def meijering(image, sigmas=range(1, 10, 2), alpha=None,
+              black_ridges=True, mode='reflect', cval=0):
+    """
+    Filter an image with the Meijering neuriteness filter.
+
+    This filter can be used to detect continuous ridges, e.g. neurites,
+    wrinkles, rivers. It can be used to calculate the fraction of the
+    whole image containing such objects.
+
+    Calculates the eigenvectors of the Hessian to compute the similarity of
+    an image region to neurites, according to the method described in [1]_.
+
+    Parameters
+    ----------
+    image : (N, M[, ..., P]) ndarray
+        Array with input image data.
+    sigmas : iterable of floats, optional
+        Sigmas used as scales of filter
+    alpha : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to deviation from a plate-like structure.
+    black_ridges : boolean, optional
+        When True (the default), the filter detects black ridges; when
+        False, it detects white ridges.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    out : (N, M[, ..., P]) ndarray
+        Filtered image (maximum of pixels across all scales).
+
+    See also
+    --------
+    sato
+    frangi
+    hessian
+
+    References
+    ----------
+    .. [1] Meijering, E., Jacob, M., Sarria, J. C., Steiner, P., Hirling, H.,
+        Unser, M. (2004). Design and validation of a tool for neurite tracing
+        and analysis in fluorescence microscopy images. Cytometry Part A,
+        58(2), 167-176.
+        :DOI:`10.1002/cyto.a.20022`
+    """
+
+    # Check (sigma) scales
+    sigmas = _check_sigmas(sigmas)
+
+    # Get image dimensions
+    ndim = image.ndim
+
+    # Set parameters
+    if alpha is None:
+        alpha = 1.0 / ndim
+
+    # Invert image to detect dark ridges on bright background
+    if black_ridges:
+        image = invert(image)
+
+    # Generate empty (n+1)D arrays for storing auxiliary images filtered at
+    # different (sigma) scales
+    float_dtype = _float_dtype(image)
+    filtered_array = cp.empty(sigmas.shape + image.shape, dtype=float_dtype)
+
+    # Filtering for all (sigma) scales
+    for i, sigma in enumerate(sigmas):
+
+        # Calculate (sorted) eigenvalues
+        eigenvalues = compute_hessian_eigenvalues(image, sigma, sorting='abs',
+                                                  mode=mode, cval=cval)
+        # CuPy Backend: do intermediate computations with tiny arrays on the
+        #               CPU
+        # TODO: refactor to avoid host-device transfer
+        eigenvalues = cp.asnumpy(eigenvalues)
+
+        if ndim > 1:
+
+            # Set coefficients for scaling eigenvalues
+            coefficients = [alpha] * ndim
+            coefficients[0] = 1
+
+            # Compute normalized eigenvalues l_i = e_i + sum_{j!=i} alpha * e_j
+            auxiliary = [np.sum([eigenvalues[i] * np.roll(coefficients, j)[i]
+                         for j in range(ndim)], axis=0) for i in range(ndim)]
+
+            # Get maximum eigenvalues by magnitude
+            auxiliary = auxiliary[-1]
+            auxiliary = cp.asarray(auxiliary)
+
+            # Rescale image intensity and avoid ZeroDivisionError
+            filtered = _divide_nonzero(auxiliary, cp.min(auxiliary))
+
+            # Remove background
+            filtered = cp.where(auxiliary < 0, filtered, 0)
+
+            # Store results in (n+1)D matrices
+            filtered_array[i] = filtered
+
+    # Return for every pixel the maximum value over all (sigma) scales
+    return cp.max(filtered_array, axis=0)
+
+
+def sato(image, sigmas=range(1, 10, 2), black_ridges=True,
+         mode=None, cval=0):
+    """
+    Filter an image with the Sato tubeness filter.
+
+    This filter can be used to detect continuous ridges, e.g. tubes,
+    wrinkles, rivers. It can be used to calculate the fraction of the
+    whole image containing such objects.
+
+    Defined only for 2-D and 3-D images. Calculates the eigenvectors of the
+    Hessian to compute the similarity of an image region to tubes, according to
+    the method described in [1]_.
+
+    Parameters
+    ----------
+    image : (N, M[, P]) ndarray
+        Array with input image data.
+    sigmas : iterable of floats, optional
+        Sigmas used as scales of filter.
+    black_ridges : boolean, optional
+        When True (the default), the filter detects black ridges; when
+        False, it detects white ridges.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    out : (N, M[, P]) ndarray
+        Filtered image (maximum of pixels across all scales).
+
+    See also
+    --------
+    meijering
+    frangi
+    hessian
+
+    References
+    ----------
+    .. [1] Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S.,
+        Koller, T., ..., Kikinis, R. (1998). Three-dimensional multi-scale line
+        filter for segmentation and visualization of curvilinear structures in
+        medical images. Medical image analysis, 2(2), 143-168.
+        :DOI:`10.1016/S1361-8415(98)80009-1`
+    """
+
+    # Check image dimensions
+    check_nD(image, [2, 3])
+
+    # Check (sigma) scales
+    sigmas = _check_sigmas(sigmas)
+
+    if mode is None:
+        warn("Previously, sato implicitly used 'constant' as the "
+             "border mode when dealing with the edge of the array. The new "
+             "behavior is 'reflect'. To recover the old behavior, use "
+             "mode='constant'. To avoid this warning, please explicitly "
+             "set the mode.", category=FutureWarning, stacklevel=2)
+        mode = 'reflect'
+
+    # Invert image to detect bright ridges on dark background
+    if not black_ridges:
+        image = invert(image)
+
+    # Generate empty (n+1)D arrays for storing auxiliary images filtered
+    # at different (sigma) scales
+    float_dtype = _float_dtype(image)
+    filtered_array = cp.empty(sigmas.shape + image.shape, dtype=float_dtype)
+
+    # Filtering for all (sigma) scales
+    for i, sigma in enumerate(sigmas):
+
+        # Calculate (sorted) eigenvalues
+        lamba1, *lambdas = compute_hessian_eigenvalues(image, sigma,
+                                                       sorting='val',
+                                                       mode=mode, cval=cval)
+
+        # Compute tubeness, see  equation (9) in reference [1]_.
+        # cp.abs(lambda2) in 2D, cp.sqrt(cp.abs(lambda2 * lambda3)) in 3D
+
+        # CuPy Backend: cp.multiply does not have a reduce method
+        # filtered = cp.abs(cp.multiply.reduce(lambdas)) ** (1 / len(lambdas))
+        filtered = cp.abs(reduce(cp.multiply, lambdas)) ** (1 / len(lambdas))
+
+        # Remove background and store results in (n+1)D matrices
+        filtered_array[i] = cp.where(lambdas[-1] > 0, filtered, 0)
+
+    # Return for every pixel the maximum value over all (sigma) scales
+    return cp.max(filtered_array, axis=0)
+
+
+def frangi(image, sigmas=range(1, 10, 2), scale_range=None,
+           scale_step=None, alpha=0.5, beta=0.5, gamma=15,
+           black_ridges=True, mode='reflect', cval=0):
+    """
+    Filter an image with the Frangi vesselness filter.
+
+    This filter can be used to detect continuous ridges, e.g. vessels,
+    wrinkles, rivers. It can be used to calculate the fraction of the
+    whole image containing such objects.
+
+    Defined only for 2-D and 3-D images. Calculates the eigenvectors of the
+    Hessian to compute the similarity of an image region to vessels, according
+    to the method described in [1]_.
+
+    Parameters
+    ----------
+    image : (N, M[, P]) ndarray
+        Array with input image data.
+    sigmas : iterable of floats, optional
+        Sigmas used as scales of filter, i.e.,
+        np.arange(scale_range[0], scale_range[1], scale_step)
+    scale_range : 2-tuple of floats, optional
+        The range of sigmas used.
+    scale_step : float, optional
+        Step size between sigmas.
+    alpha : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to deviation from a plate-like structure.
+    beta : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to deviation from a blob-like structure.
+    gamma : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to areas of high variance/texture/structure.
+    black_ridges : boolean, optional
+        When True (the default), the filter detects black ridges; when
+        False, it detects white ridges.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    out : (N, M[, P]) ndarray
+        Filtered image (maximum of pixels across all scales).
+
+    Notes
+    -----
+    Written by Marc Schrijver, November 2001
+    Re-Written by D. J. Kroon, University of Twente, May 2009, [2]_
+    Adoption of 3D version from D. G. Ellis, Januar 20017, [3]_
+
+    See also
+    --------
+    meijering
+    sato
+    hessian
+
+    References
+    ----------
+    .. [1] Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A.
+        (1998,). Multiscale vessel enhancement filtering. In International
+        Conference on Medical Image Computing and Computer-Assisted
+        Intervention (pp. 130-137). Springer Berlin Heidelberg.
+        :DOI:`10.1007/BFb0056195`
+    .. [2] Kroon, D. J.: Hessian based Frangi vesselness filter.
+    .. [3] Ellis, D. G.: https://github.com/ellisdg/frangi3d/tree/master/frangi
+    """
+    if scale_range is not None and scale_step is not None:
+        warn('Use keyword parameter `sigmas` instead of `scale_range` and '
+             '`scale_range` which will be removed in version 0.17.',
+             stacklevel=2)
+        sigmas = np.arange(scale_range[0], scale_range[1], scale_step)
+
+    # Check image dimensions
+    check_nD(image, [2, 3])
+
+    # Check (sigma) scales
+    sigmas = _check_sigmas(sigmas)
+
+    # Rescale filter parameters
+    alpha_sq = 2 * alpha ** 2
+    beta_sq = 2 * beta ** 2
+    gamma_sq = 2 * gamma ** 2
+
+    # Get image dimensions
+    ndim = image.ndim
+
+    # Invert image to detect dark ridges on light background
+    if black_ridges:
+        image = invert(image)
+
+    # Generate empty (n+1)D arrays for storing auxiliary images filtered
+    # at different (sigma) scales
+    float_dtype = _float_dtype(image)
+    filtered_array = cp.empty(sigmas.shape + image.shape, dtype=float_dtype)
+    lambdas_array = cp.empty_like(filtered_array)
+
+    # Filtering for all (sigma) scales
+    for i, sigma in enumerate(sigmas):
+
+        # Calculate (abs sorted) eigenvalues
+        lambda1, *lambdas = compute_hessian_eigenvalues(image, sigma,
+                                                        sorting='abs',
+                                                        mode=mode, cval=cval)
+
+        # Compute sensitivity to deviation from a plate-like
+        # structure see equations (11) and (15) in reference [1]_
+        r_a = np.inf if ndim == 2 else _divide_nonzero(*lambdas) ** 2
+
+        # Compute sensitivity to deviation from a blob-like structure,
+        # see equations (10) and (15) in reference [1]_,
+        # np.abs(lambda2) in 2D, np.sqrt(np.abs(lambda2 * lambda3)) in 3D
+        # CuPy Backend: cp.multiply does not have a reduce method
+        # filtered_raw = np.abs(np.multiply.reduce(lambdas))**(1/len(lambdas))
+        filtered_raw = cp.abs(reduce(cp.multiply, lambdas)) ** (
+            1 / len(lambdas)
+        )
+        r_b = _divide_nonzero(lambda1, filtered_raw)
+        r_b *= r_b
+
+        # Compute sensitivity to areas of high variance/texture/structure,
+        # see equation (12)in reference [1]_
+        r_g = sum([lambda1 * lambda1] +
+                  [lambdai * lambdai for lambdai in lambdas])
+
+        # Compute output image for given (sigma) scale and store results in
+        # (n+1)D matrices, see equations (13) and (15) in reference [1]_
+        filtered_array[i] = ((1 - cp.exp(-r_a / alpha_sq))
+                             * cp.exp(-r_b / beta_sq)
+                             * (1 - cp.exp(-r_g / gamma_sq)))
+
+        lambdas_array[i] = cp.max(cp.asarray(lambdas), axis=0)
+
+    # Remove background
+    filtered_array[lambdas_array > 0] = 0
+
+    # Return for every pixel the maximum value over all (sigma) scales
+    return cp.max(filtered_array, axis=0)
+
+
+def hessian(image, sigmas=range(1, 10, 2), scale_range=None, scale_step=None,
+            alpha=0.5, beta=0.5, gamma=15, black_ridges=True, mode=None,
+            cval=0):
+    """Filter an image with the Hybrid Hessian filter.
+
+    This filter can be used to detect continuous edges, e.g. vessels,
+    wrinkles, rivers. It can be used to calculate the fraction of the whole
+    image containing such objects.
+
+    Defined only for 2-D and 3-D images. Almost equal to Frangi filter, but
+    uses alternative method of smoothing. Refer to [1]_ to find the differences
+    between Frangi and Hessian filters.
+
+    Parameters
+    ----------
+    image : (N, M[, P]) ndarray
+        Array with input image data.
+    sigmas : iterable of floats, optional
+        Sigmas used as scales of filter, i.e.,
+        np.arange(scale_range[0], scale_range[1], scale_step)
+    scale_range : 2-tuple of floats, optional
+        The range of sigmas used.
+    scale_step : float, optional
+        Step size between sigmas.
+    beta : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to deviation from a blob-like structure.
+    gamma : float, optional
+        Frangi correction constant that adjusts the filter's
+        sensitivity to areas of high variance/texture/structure.
+    black_ridges : boolean, optional
+        When True (the default), the filter detects black ridges; when
+        False, it detects white ridges.
+    mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
+        How to handle values outside the image borders.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+
+    Returns
+    -------
+    out : (N, M[, P]) ndarray
+        Filtered image (maximum of pixels across all scales).
+
+    Notes
+    -----
+    Written by Marc Schrijver (November 2001)
+    Re-Written by D. J. Kroon University of Twente (May 2009) [2]_
+
+    See also
+    --------
+    meijering
+    sato
+    frangi
+
+    References
+    ----------
+    .. [1] Ng, C. C., Yap, M. H., Costen, N., & Li, B. (2014,). Automatic
+        wrinkle detection using hybrid Hessian filter. In Asian Conference on
+        Computer Vision (pp. 609-622). Springer International Publishing.
+        :DOI:`10.1007/978-3-319-16811-1_40`
+    .. [2] Kroon, D. J.: Hessian based Frangi vesselness filter.
+    """
+
+    if mode is None:
+        warn("Previously, hessian implicitly used 'constant' as the "
+             "border mode when dealing with the edge of the array. The new "
+             "behavior is 'reflect'. To recover the old behavior, use "
+             "mode='constant'. To avoid this warning, please explicitly "
+             "set the mode.", category=FutureWarning, stacklevel=2)
+        mode = 'reflect'
+
+    filtered = frangi(image, sigmas=sigmas, scale_range=scale_range,
+                      scale_step=scale_step, alpha=alpha, beta=beta,
+                      gamma=gamma, black_ridges=black_ridges, mode=mode,
+                      cval=cval)
+
+    filtered[filtered <= 0] = 1
+    return filtered
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_correlate.py b/python/cucim/src/cucim/skimage/filters/tests/test_correlate.py
new file mode 100644
index 000000000..2bc5e888b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_correlate.py
@@ -0,0 +1,51 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_array_equal
+from cupyx.scipy import ndimage as ndi
+
+from cucim.skimage._shared import testing
+from cucim.skimage.filters import correlate_sparse
+
+
+def test_correlate_sparse_valid_mode():
+    image = cp.array([[0, 0, 1, 3, 5],
+                      [0, 1, 4, 3, 4],
+                      [1, 2, 5, 4, 1],
+                      [2, 4, 5, 2, 1],
+                      [4, 5, 1, 0, 0]], dtype=float)
+
+    kernel = cp.array([0, 1, 2, 4, 8, 16, 32, 64, 128]).reshape((3, 3))
+
+    cs_output = correlate_sparse(image, kernel, mode="valid")
+    ndi_output = ndi.correlate(image, kernel, mode='wrap')
+    ndi_output = ndi_output[1:4, 1:4]
+
+    assert_array_equal(cs_output, ndi_output)
+
+
+@pytest.mark.parametrize("mode", ["nearest", "reflect", "mirror"])
+def test_correlate_sparse(mode):
+    image = cp.array([[0, 0, 1, 3, 5],
+                      [0, 1, 4, 3, 4],
+                      [1, 2, 5, 4, 1],
+                      [2, 4, 5, 2, 1],
+                      [4, 5, 1, 0, 0]], dtype=float)
+
+    kernel = cp.array([0, 1, 2, 4, 8, 16, 32, 64, 128]).reshape((3, 3))
+
+    cs_output = correlate_sparse(image, kernel, mode=mode)
+    ndi_output = ndi.correlate(image, kernel, mode=mode)
+    assert_array_equal(cs_output, ndi_output)
+
+
+@pytest.mark.parametrize("mode", ["nearest", "reflect", "mirror"])
+def test_correlate_sparse_invalid_kernel(mode):
+    image = cp.array([[0, 0, 1, 3, 5],
+                      [0, 1, 4, 3, 4],
+                      [1, 2, 5, 4, 1],
+                      [2, 4, 5, 2, 1],
+                      [4, 5, 1, 0, 0]], dtype=float)
+    # invalid kernel size
+    invalid_kernel = cp.array([0, 1, 2, 4]).reshape((2, 2))
+    with testing.raises(ValueError):
+        correlate_sparse(image, invalid_kernel, mode=mode)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_edges.py b/python/cucim/src/cucim/skimage/filters/tests/test_edges.py
new file mode 100644
index 000000000..a866fe73c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_edges.py
@@ -0,0 +1,587 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_allclose, assert_array_almost_equal
+from numpy.testing import assert_
+
+
+from cucim.skimage import filters
+from cucim.skimage.data import binary_blobs
+from cucim.skimage.filters.edges import _mask_filter_result
+
+
+def test_roberts_zeros():
+    """Roberts' filter on an array of all zeros."""
+    result = filters.roberts(cp.zeros((10, 10)), cp.ones((10, 10), bool))
+    assert cp.all(result == 0)
+
+
+def test_roberts_diagonal1():
+    """Roberts' filter on a diagonal edge should be a diagonal line."""
+    image = cp.tri(10, 10, 0)
+    expected = ~(cp.tri(10, 10, -1).astype(bool) |
+                 cp.tri(10, 10, -2).astype(bool).transpose())
+    expected[-1, -1] = 0  # due to 'reflect' & image shape, last pixel not edge
+    result = filters.roberts(image).astype(bool)
+    assert_array_almost_equal(result, expected)
+
+
+def test_roberts_diagonal2():
+    """Roberts' filter on a diagonal edge should be a diagonal line."""
+    image = cp.rot90(cp.tri(10, 10, 0), 3)
+    expected = ~cp.rot90(cp.tri(10, 10, -1).astype(bool) |
+                         cp.tri(10, 10, -2).astype(bool).transpose())
+    expected = _mask_filter_result(expected, None)
+    result = filters.roberts(image).astype(bool)
+    assert_array_almost_equal(result, expected)
+
+
+def test_sobel_zeros():
+    """Sobel on an array of all zeros."""
+    result = filters.sobel(cp.zeros((10, 10)), cp.ones((10, 10), bool))
+    assert cp.all(result == 0)
+
+
+@pytest.mark.parametrize('function', ['sobel', 'prewitt', 'scharr'])
+@pytest.mark.parametrize('dtype', [cp.float16, cp.float32, cp.float64])
+def test_sobel_float_output_dtype(function, dtype):
+    """Sobel on an array of all zeros."""
+    x = cp.ones((4, 4), dtype=dtype)
+    filter_func = getattr(filters, function)
+    result = filter_func(x)
+    assert result.dtype == dtype
+
+
+def test_sobel_mask():
+    """Sobel on a masked array should be zero."""
+    result = filters.sobel(cp.random.uniform(size=(10, 10)),
+                           cp.zeros((10, 10), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_sobel_horizontal():
+    """Sobel on a horizontal edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.sobel(image) * np.sqrt(2)
+    # Check if result match transform direction
+
+    assert_allclose(result[i == 0], 1)
+    assert_allclose(result[cp.abs(i) > 1], 0)
+
+
+def test_sobel_vertical():
+    """Sobel on a vertical edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.sobel(image) * np.sqrt(2)
+    assert_allclose(result[j == 0], 1)
+    assert cp.all(result[cp.abs(j) > 1] == 0)
+
+
+def test_sobel_h_zeros():
+    """Horizontal sobel on an array of all zeros."""
+    result = filters.sobel_h(cp.zeros((10, 10)), cp.ones((10, 10), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_sobel_h_mask():
+    """Horizontal Sobel on a masked array should be zero."""
+    result = filters.sobel_h(cp.random.uniform(size=(10, 10)),
+                             cp.zeros((10, 10), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_sobel_h_horizontal():
+    """Horizontal Sobel on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.sobel_h(image)
+    # Check if result match transform direction
+    assert cp.all(result[i == 0] == 1)
+    assert cp.all(result[cp.abs(i) > 1] == 0)
+
+
+def test_sobel_h_vertical():
+    """Horizontal Sobel on a vertical edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float) * np.sqrt(2)
+    result = filters.sobel_h(image)
+    assert_allclose(result, 0, atol=1e-10)
+
+
+def test_sobel_v_zeros():
+    """Vertical sobel on an array of all zeros."""
+    result = filters.sobel_v(cp.zeros((10, 10)), cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_sobel_v_mask():
+    """Vertical Sobel on a masked array should be zero."""
+    result = filters.sobel_v(cp.random.uniform(size=(10, 10)),
+                             cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_sobel_v_vertical():
+    """Vertical Sobel on an edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.sobel_v(image)
+    # Check if result match transform direction
+    assert cp.all(result[j == 0] == 1)
+    assert cp.all(result[cp.abs(j) > 1] == 0)
+
+
+def test_sobel_v_horizontal():
+    """vertical Sobel on a horizontal edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.sobel_v(image)
+    assert_allclose(result, 0)
+
+
+def test_scharr_zeros():
+    """Scharr on an array of all zeros."""
+    result = filters.scharr(cp.zeros((10, 10)), cp.ones((10, 10), dtype=bool))
+    assert cp.all(result < 1e-16)
+
+
+def test_scharr_mask():
+    """Scharr on a masked array should be zero."""
+    result = filters.scharr(cp.random.uniform(size=(10, 10)),
+                            cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_scharr_horizontal():
+    """Scharr on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.scharr(image) * np.sqrt(2)
+    # Check if result match transform direction
+    assert_allclose(result[i == 0], 1)
+    assert cp.all(result[cp.abs(i) > 1] == 0)
+
+
+def test_scharr_vertical():
+    """Scharr on a vertical edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.scharr(image) * np.sqrt(2)
+    assert_allclose(result[j == 0], 1)
+    assert cp.all(result[cp.abs(j) > 1] == 0)
+
+
+def test_scharr_h_zeros():
+    """Horizontal Scharr on an array of all zeros."""
+    result = filters.scharr_h(cp.zeros((10, 10)),
+                              cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_scharr_h_mask():
+    """Horizontal Scharr on a masked array should be zero."""
+    result = filters.scharr_h(cp.random.uniform(size=(10, 10)),
+                              cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_scharr_h_horizontal():
+    """Horizontal Scharr on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.scharr_h(image)
+    # Check if result match transform direction
+    assert cp.all(result[i == 0] == 1)
+    assert cp.all(result[cp.abs(i) > 1] == 0)
+
+
+def test_scharr_h_vertical():
+    """Horizontal Scharr on a vertical edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.scharr_h(image)
+    assert_allclose(result, 0)
+
+
+def test_scharr_v_zeros():
+    """Vertical Scharr on an array of all zeros."""
+    result = filters.scharr_v(cp.zeros((10, 10)),
+                              cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_scharr_v_mask():
+    """Vertical Scharr on a masked array should be zero."""
+    result = filters.scharr_v(cp.random.uniform(size=(10, 10)),
+                              cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_scharr_v_vertical():
+    """Vertical Scharr on an edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.scharr_v(image)
+    # Check if result match transform direction
+    assert cp.all(result[j == 0] == 1)
+    assert cp.all(result[cp.abs(j) > 1] == 0)
+
+
+def test_scharr_v_horizontal():
+    """vertical Scharr on a horizontal edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.scharr_v(image)
+    assert_allclose(result, 0)
+
+
+def test_prewitt_zeros():
+    """Prewitt on an array of all zeros."""
+    result = filters.prewitt(cp.zeros((10, 10)),
+                             cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_prewitt_mask():
+    """Prewitt on a masked array should be zero."""
+    result = filters.prewitt(cp.random.uniform(size=(10, 10)),
+                             cp.zeros((10, 10), dtype=bool))
+    assert_allclose(cp.abs(result), 0)
+
+
+def test_prewitt_horizontal():
+    """Prewitt on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.prewitt(image) * np.sqrt(2)
+    # Check if result match transform direction
+    assert_allclose(result[i == 0], 1)
+    assert_allclose(result[cp.abs(i) > 1], 0, atol=1e-10)
+
+
+def test_prewitt_vertical():
+    """Prewitt on a vertical edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.prewitt(image) * np.sqrt(2)
+    assert_allclose(result[j == 0], 1)
+    assert_allclose(result[cp.abs(j) > 1], 0, atol=1e-10)
+
+
+def test_prewitt_h_zeros():
+    """Horizontal prewitt on an array of all zeros."""
+    result = filters.prewitt_h(cp.zeros((10, 10)),
+                               cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_prewitt_h_mask():
+    """Horizontal prewitt on a masked array should be zero."""
+    result = filters.prewitt_h(cp.random.uniform(size=(10, 10)),
+                               cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_prewitt_h_horizontal():
+    """Horizontal prewitt on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.prewitt_h(image)
+    # Check if result match transform direction
+    assert cp.all(result[i == 0] == 1)
+    assert_allclose(result[cp.abs(i) > 1], 0, atol=1e-10)
+
+
+def test_prewitt_h_vertical():
+    """Horizontal prewitt on a vertical edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.prewitt_h(image)
+    assert_allclose(result, 0, atol=1e-10)
+
+
+def test_prewitt_v_zeros():
+    """Vertical prewitt on an array of all zeros."""
+    result = filters.prewitt_v(cp.zeros((10, 10)),
+                               cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_prewitt_v_mask():
+    """Vertical prewitt on a masked array should be zero."""
+    result = filters.prewitt_v(cp.random.uniform(size=(10, 10)),
+                               cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_prewitt_v_vertical():
+    """Vertical prewitt on an edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.prewitt_v(image)
+    # Check if result match transform direction
+    assert cp.all(result[j == 0] == 1)
+    assert_allclose(result[cp.abs(j) > 1], 0, atol=1e-10)
+
+
+def test_prewitt_v_horizontal():
+    """Vertical prewitt on a horizontal edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.prewitt_v(image)
+    assert_allclose(result, 0)
+
+
+def test_laplace_zeros():
+    """Laplace on a square image."""
+    # Create a synthetic 2D image
+    image = cp.zeros((9, 9))
+    image[3:-3, 3:-3] = 1
+    result = filters.laplace(image)
+    # fmt: off
+    check_result = cp.array([[0., 0., 0., 0., 0., 0., 0., 0., 0.],
+                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
+                             [0., 0., 0., -1., -1., -1., 0., 0., 0.],
+                             [0., 0., -1., 2., 1., 2., -1., 0., 0.],
+                             [0., 0., -1., 1., 0., 1., -1., 0., 0.],
+                             [0., 0., -1., 2., 1., 2., -1., 0., 0.],
+                             [0., 0., 0., -1., -1., -1., 0., 0., 0.],
+                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
+                             [0., 0., 0., 0., 0., 0., 0., 0., 0.]])
+    # fmt: on
+    assert_allclose(result, check_result)
+
+
+def test_laplace_mask():
+    """Laplace on a masked array should be zero."""
+    # Create a synthetic 2D image
+    image = cp.zeros((9, 9))
+    image[3:-3, 3:-3] = 1
+    # Define the mask
+    result = filters.laplace(image, ksize=3, mask=cp.zeros((9, 9), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_farid_zeros():
+    """Farid on an array of all zeros."""
+    result = filters.farid(cp.zeros((10, 10)),
+                           mask=cp.ones((10, 10), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_farid_mask():
+    """Farid on a masked array should be zero."""
+    result = filters.farid(cp.random.uniform(size=(10, 10)),
+                           mask=cp.zeros((10, 10), dtype=bool))
+    assert (cp.all(result == 0))
+
+
+def test_farid_horizontal():
+    """Farid on a horizontal edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.farid(image) * np.sqrt(2)
+    # Check if result match transform direction
+    assert cp.all(result[i == 0] == result[i == 0][0])
+    assert_allclose(result[cp.abs(i) > 2], 0, atol=1e-10)
+
+
+def test_farid_vertical():
+    """Farid on a vertical edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.farid(image) * np.sqrt(2)
+    assert cp.all(result[j == 0] == result[j == 0][0])
+    assert_allclose(result[cp.abs(j) > 2], 0, atol=1e-10)
+
+
+def test_farid_h_zeros():
+    """Horizontal Farid on an array of all zeros."""
+    result = filters.farid_h(cp.zeros((10, 10)),
+                             mask=cp.ones((10, 10), dtype=bool))
+    assert (cp.all(result == 0))
+
+
+def test_farid_h_mask():
+    """Horizontal Farid on a masked array should be zero."""
+    result = filters.farid_h(cp.random.uniform(size=(10, 10)),
+                             mask=cp.zeros((10, 10), dtype=bool))
+    assert cp.all(result == 0)
+
+
+def test_farid_h_horizontal():
+    """Horizontal Farid on an edge should be a horizontal line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.farid_h(image)
+    # Check if result match transform direction
+    assert cp.all(result[i == 0] == result[i == 0][0])
+    assert_allclose(result[cp.abs(i) > 2], 0, atol=1e-10)
+
+
+def test_farid_h_vertical():
+    """Horizontal Farid on a vertical edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float) * np.sqrt(2)
+    result = filters.farid_h(image)
+    assert_allclose(result, 0, atol=1e-10)
+
+
+def test_farid_v_zeros():
+    """Vertical Farid on an array of all zeros."""
+    result = filters.farid_v(cp.zeros((10, 10)),
+                             mask=cp.ones((10, 10), dtype=bool))
+    assert_allclose(result, 0, atol=1e-10)
+
+
+def test_farid_v_mask():
+    """Vertical Farid on a masked array should be zero."""
+    result = filters.farid_v(cp.random.uniform(size=(10, 10)),
+                             mask=cp.zeros((10, 10), dtype=bool))
+    assert_allclose(result, 0)
+
+
+def test_farid_v_vertical():
+    """Vertical Farid on an edge should be a vertical line."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (j >= 0).astype(float)
+    result = filters.farid_v(image)
+    # Check if result match transform direction
+    assert cp.all(result[j == 0] == result[j == 0][0])
+    assert_allclose(result[cp.abs(j) > 2], 0, atol=1e-10)
+
+
+def test_farid_v_horizontal():
+    """vertical Farid on a horizontal edge should be zero."""
+    i, j = cp.mgrid[-5:6, -5:6]
+    image = (i >= 0).astype(float)
+    result = filters.farid_v(image)
+    assert_allclose(result, 0, atol=1e-10)
+
+
+@pytest.mark.parametrize(
+    "grad_func", (filters.prewitt_h, filters.sobel_h, filters.scharr_h)
+)
+def test_horizontal_mask_line(grad_func):
+    """Horizontal edge filters mask pixels surrounding input mask."""
+    vgrad, _ = cp.mgrid[:1:11j, :1:11j]  # vertical gradient with spacing 0.1
+    vgrad[5, :] = 1  # bad horizontal line
+
+    mask = cp.ones_like(vgrad)
+    mask[5, :] = 0  # mask bad line
+
+    expected = cp.zeros_like(vgrad)
+    expected[1:-1, 1:-1] = 0.2  # constant gradient for most of image,
+    expected[4:7, 1:-1] = 0  # but line and neighbors masked
+
+    result = grad_func(vgrad, mask)
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize(
+    "grad_func", (filters.prewitt_v, filters.sobel_v, filters.scharr_v)
+)
+def test_vertical_mask_line(grad_func):
+    """Vertical edge filters mask pixels surrounding input mask."""
+    _, hgrad = cp.mgrid[:1:11j, :1:11j]  # horizontal gradient with spacing 0.1
+    hgrad[:, 5] = 1  # bad vertical line
+
+    mask = cp.ones_like(hgrad)
+    mask[:, 5] = 0  # mask bad line
+
+    expected = cp.zeros_like(hgrad)
+    expected[1:-1, 1:-1] = 0.2  # constant gradient for most of image,
+    expected[1:-1, 4:7] = 0  # but line and neighbors masked
+
+    result = grad_func(hgrad, mask)
+    assert_allclose(result, expected)
+
+
+# The below three constant 3x3x3 cubes were empirically found to maximise the
+# output of each of their respective filters. We use them to test that the
+# output of the filter on the blobs image matches expectation in terms of
+# scale.
+
+# maximum Sobel 3D edge on axis 0
+# fmt: off
+MAX_SOBEL_0 = cp.array([
+    [[0, 0, 0],
+     [0, 0, 0],
+     [0, 0, 0]],
+    [[0, 0, 0],
+     [0, 0, 0],
+     [0, 0, 0]],
+    [[1, 1, 1],
+     [1, 1, 1],
+     [1, 1, 1]],
+]).astype(float)
+
+# maximum Sobel 3D edge in magnitude
+MAX_SOBEL_ND = cp.array([
+    [[1, 0, 0],
+     [1, 0, 0],
+     [1, 0, 0]],
+
+    [[1, 0, 0],
+     [1, 1, 0],
+     [1, 1, 0]],
+
+    [[1, 1, 0],
+     [1, 1, 0],
+     [1, 1, 0]]
+]).astype(float)
+
+# maximum Scharr 3D edge in magnitude. This illustrates the better rotation
+# invariance of the Scharr filter!
+MAX_SCHARR_ND = cp.array([
+    [[0, 0, 0],
+     [0, 0, 1],
+     [0, 1, 1]],
+    [[0, 0, 1],
+     [0, 1, 1],
+     [0, 1, 1]],
+    [[0, 0, 1],
+     [0, 1, 1],
+     [1, 1, 1]]
+]).astype(float)
+# fmt: on
+
+
+@pytest.mark.parametrize(
+    ('func', 'max_edge'),
+    [(filters.prewitt, MAX_SOBEL_ND),
+     (filters.sobel, MAX_SOBEL_ND),
+     (filters.scharr, MAX_SCHARR_ND)]
+)
+def test_3d_edge_filters(func, max_edge):
+    blobs = binary_blobs(length=128, n_dim=3)
+    edges = func(blobs)
+    assert_allclose(cp.max(edges), func(max_edge)[1, 1, 1])
+
+
+@pytest.mark.parametrize(
+    'func', (filters.prewitt, filters.sobel, filters.scharr)
+)
+def test_3d_edge_filters_single_axis(func):
+    blobs = binary_blobs(length=128, n_dim=3)
+    edges0 = func(blobs, axis=0)
+    assert_allclose(cp.max(edges0), func(MAX_SOBEL_0, axis=0)[1, 1, 1])
+
+
+@pytest.mark.parametrize(
+    'detector',
+    [filters.sobel, filters.scharr, filters.prewitt,
+     filters.roberts, filters.farid]
+)
+def test_range(detector):
+    """Output of edge detection should be in [0, 1]"""
+    image = cp.random.random((100, 100))
+    out = detector(image)
+    assert_(
+        out.min() >= 0, f'Minimum of `{detector.__name__}` is smaller than 0.'
+    )
+    assert_(
+        out.max() <= 1, f'Maximum of `{detector.__name__}` is larger than 1.'
+    )
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py b/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py
new file mode 100644
index 000000000..f7b239fdc
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_gabor.py
@@ -0,0 +1,84 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal
+from numpy.testing import assert_almost_equal
+
+from cucim.skimage.filters._gabor import _sigma_prefactor, gabor, gabor_kernel
+
+
+def test_gabor_kernel_size():
+    sigma_x = 5
+    sigma_y = 10
+    # Sizes cut off at +/- three sigma + 1 for the center
+    size_x = sigma_x * 6 + 1
+    size_y = sigma_y * 6 + 1
+
+    kernel = gabor_kernel(0, theta=0, sigma_x=sigma_x, sigma_y=sigma_y)
+    assert kernel.shape == (size_y, size_x)
+
+    kernel = gabor_kernel(0, theta=np.pi / 2, sigma_x=sigma_x, sigma_y=sigma_y)
+    assert kernel.shape == (size_x, size_y)
+
+
+def test_gabor_kernel_bandwidth():
+    kernel = gabor_kernel(1, bandwidth=1)
+    assert kernel.shape == (5, 5)
+
+    kernel = gabor_kernel(1, bandwidth=0.5)
+    assert kernel.shape == (9, 9)
+
+    kernel = gabor_kernel(0.5, bandwidth=1)
+    assert kernel.shape == (9, 9)
+
+
+def test_sigma_prefactor():
+    assert_almost_equal(_sigma_prefactor(1), 0.56, 2)
+    assert_almost_equal(_sigma_prefactor(0.5), 1.09, 2)
+
+
+def test_gabor_kernel_sum():
+    for sigma_x in range(1, 10, 2):
+        for sigma_y in range(1, 10, 2):
+            for frequency in range(0, 10, 2):
+                kernel = gabor_kernel(frequency + 0.1, theta=0,
+                                      sigma_x=sigma_x, sigma_y=sigma_y)
+                # make sure gaussian distribution is covered nearly 100%
+                assert_almost_equal(float(cp.abs(kernel).sum()), 1, 2)
+
+
+def test_gabor_kernel_theta():
+    for sigma_x in range(1, 10, 2):
+        for sigma_y in range(1, 10, 2):
+            for frequency in range(0, 10, 2):
+                for theta in range(0, 10, 2):
+                    kernel0 = gabor_kernel(frequency + 0.1, theta=theta,
+                                           sigma_x=sigma_x, sigma_y=sigma_y)
+                    kernel180 = gabor_kernel(frequency, theta=theta + np.pi,
+                                             sigma_x=sigma_x, sigma_y=sigma_y)
+
+                    assert_array_almost_equal(cp.abs(kernel0),
+                                              cp.abs(kernel180))
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_gabor(dtype):
+    Y, X = cp.mgrid[:40, :40]
+    frequencies = (0.1, 0.3)
+    wave_images = [cp.sin(2 * np.pi * X * f) for f in frequencies]
+
+    wave_images = [w.astype(dtype, copy=False) for w in wave_images]
+
+    def match_score(image, frequency):
+        gabor_responses = gabor(image, frequency)
+        assert all(r.dtype == dtype for r in gabor_responses)
+        return float(cp.mean(cp.hypot(*gabor_responses)))
+
+    # Gabor scores: diagonals are frequency-matched, off-diagonals are not.
+    responses = np.array(
+        [[match_score(image, f) for f in frequencies] for image in wave_images]
+    )
+    assert responses[0, 0] > responses[0, 1]
+    assert responses[1, 1] > responses[0, 1]
+    assert responses[0, 0] > responses[1, 0]
+    assert responses[1, 1] > responses[1, 0]
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_gaussian.py b/python/cucim/src/cucim/skimage/filters/tests/test_gaussian.py
new file mode 100644
index 000000000..dbf51c3f3
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_gaussian.py
@@ -0,0 +1,144 @@
+import cupy as cp
+import numpy as np
+import pytest
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.filters._gaussian import (_guess_spatial_dimensions,
+                                             difference_of_gaussians, gaussian)
+
+
+def test_negative_sigma():
+    a = cp.zeros((3, 3))
+    a[1, 1] = 1.0
+    with pytest.raises(ValueError):
+        gaussian(a, sigma=-1.0)
+    with pytest.raises(ValueError):
+        gaussian(a, sigma=[-1.0, 1.0])
+    with pytest.raises(ValueError):
+        gaussian(a, sigma=cp.asarray([-1.0, 1.0]))
+
+
+def test_null_sigma():
+    a = cp.zeros((3, 3))
+    a[1, 1] = 1.0
+    assert cp.all(gaussian(a, 0) == a)
+
+
+def test_default_sigma():
+    a = cp.zeros((3, 3))
+    a[1, 1] = 1.0
+    assert cp.all(gaussian(a) == gaussian(a, sigma=1))
+
+
+def test_energy_decrease():
+    a = cp.zeros((3, 3))
+    a[1, 1] = 1.0
+    gaussian_a = gaussian(a, sigma=1, mode="reflect")
+    assert gaussian_a.std() < a.std()
+
+
+def test_multichannel():
+    a = cp.zeros((5, 5, 3))
+    a[1, 1] = cp.arange(1, 4)
+    gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect', multichannel=True)
+    # Check that the mean value is conserved in each channel
+    # (color channels are not mixed together)
+    assert cp.allclose([a[..., i].mean() for i in range(3)],
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
+    # Test multichannel = None
+    with expected_warnings(["multichannel"]):
+        gaussian_rgb_a = gaussian(a, sigma=1, mode="reflect")
+    # Check that the mean value is conserved in each channel
+    # (color channels are not mixed together)
+    assert cp.allclose([a[..., i].mean() for i in range(3)],
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
+    # Iterable sigma
+    gaussian_rgb_a = gaussian(a, sigma=[1, 2], mode='reflect',
+                              multichannel=True)
+    assert cp.allclose([a[..., i].mean() for i in range(3)],
+                       [gaussian_rgb_a[..., i].mean() for i in range(3)])
+
+
+def test_preserve_range():
+    img = cp.array([[10.0, -10.0], [-4, 3]], dtype=cp.float32)
+    gaussian(img, 1, preserve_range=True)
+
+
+def test_4d_ok():
+    img = cp.zeros((5,) * 4)
+    img[2, 2, 2, 2] = 1
+    res = gaussian(img, 1, mode="reflect")
+    assert cp.allclose(res.sum(), 1)
+
+
+def test_guess_spatial_dimensions():
+    im1 = cp.zeros((5, 5))
+    im2 = cp.zeros((5, 5, 5))
+    im3 = cp.zeros((5, 5, 3))
+    im4 = cp.zeros((5, 5, 5, 3))
+    im5 = cp.zeros((5,))
+    assert _guess_spatial_dimensions(im1) == 2
+    assert _guess_spatial_dimensions(im2) == 3
+    assert _guess_spatial_dimensions(im3) is None
+    assert _guess_spatial_dimensions(im4) == 3
+    with pytest.raises(ValueError):
+        _guess_spatial_dimensions(im5)
+
+
+@pytest.mark.parametrize(
+    "dtype", [cp.float32, cp.float64]
+)
+def test_preserve_output(dtype):
+    image = cp.arange(9, dtype=dtype).reshape((3, 3))
+    output = cp.zeros_like(image, dtype=dtype)
+    gaussian_image = gaussian(image, sigma=1, output=output,
+                              preserve_range=True)
+    assert gaussian_image is output
+
+
+def test_output_error():
+    image = cp.arange(9, dtype=cp.float32).reshape((3, 3))
+    output = cp.zeros_like(image, dtype=cp.uint8)
+    with pytest.raises(ValueError):
+        gaussian(image, sigma=1, output=output,
+                 preserve_range=True)
+
+
+@pytest.mark.parametrize("s", [1, (2, 3)])
+@pytest.mark.parametrize("s2", [4, (5, 6)])
+def test_difference_of_gaussians(s, s2):
+    image = cp.random.rand(10, 10)
+    im1 = gaussian(image, s)
+    im2 = gaussian(image, s2)
+    dog = im1 - im2
+    dog2 = difference_of_gaussians(image, s, s2)
+    assert cp.allclose(dog, dog2)
+
+
+@pytest.mark.parametrize("s", [1, (1, 2)])
+def test_auto_sigma2(s):
+    image = cp.random.rand(10, 10)
+    im1 = gaussian(image, s)
+    s2 = 1.6 * np.array(s)
+    im2 = gaussian(image, s2)
+    dog = im1 - im2
+    dog2 = difference_of_gaussians(image, s, s2)
+    assert cp.allclose(dog, dog2)
+
+
+def test_dog_invalid_sigma_dims():
+    image = cp.ones((5, 5, 3))
+    with pytest.raises(ValueError):
+        difference_of_gaussians(image, (1, 2))
+    with pytest.raises(ValueError):
+        difference_of_gaussians(image, 1, (3, 4))
+    with pytest.raises(ValueError):
+        difference_of_gaussians(image, (1, 2, 3), multichannel=True)
+
+
+def test_dog_invalid_sigma2():
+    image = cp.ones((3, 3))
+    with pytest.raises(ValueError):
+        difference_of_gaussians(image, 3, 2)
+    with pytest.raises(ValueError):
+        difference_of_gaussians(image, (1, 5), (2, 4))
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_lpi_filter.py b/python/cucim/src/cucim/skimage/filters/tests/test_lpi_filter.py
new file mode 100644
index 000000000..1a4649568
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_lpi_filter.py
@@ -0,0 +1,58 @@
+import unittest
+
+import cupy as cp
+import pytest
+from skimage import data
+
+from cucim.skimage.filters import LPIFilter2D, inverse, wiener
+
+
+class TestLPIFilter2D(unittest.TestCase):
+    img = cp.array(data.camera()[:50, :50])
+
+    def filt_func(self, r, c):
+        return cp.exp(-cp.hypot(r, c) / 1)
+
+    def setUp(self):
+        self.f = LPIFilter2D(self.filt_func)
+
+    def tst_shape(self, x):
+        X = self.f(x)
+        assert X.shape == x.shape
+
+    def test_ip_shape(self):
+        rows, columns = self.img.shape[:2]
+
+        for c_slice in [slice(0, columns), slice(0, columns - 5),
+                        slice(0, columns - 20)]:
+            yield (self.tst_shape, self.img[:, c_slice])
+
+    def test_inverse(self):
+        F = self.f(self.img)
+        g = inverse(F, predefined_filter=self.f)
+        assert g.shape == self.img.shape
+
+        g1 = inverse(F[::-1, ::-1], predefined_filter=self.f)
+        assert (g - g1[::-1, ::-1]).sum() < 55
+
+        # test cache
+        g1 = inverse(F[::-1, ::-1], predefined_filter=self.f)
+        assert (g - g1[::-1, ::-1]).sum() < 55
+
+        g1 = inverse(F[::-1, ::-1], self.filt_func)
+        assert (g - g1[::-1, ::-1]).sum() < 55
+
+    def test_wiener(self):
+        F = self.f(self.img)
+        g = wiener(F, predefined_filter=self.f)
+        assert g.shape == self.img.shape
+
+        g1 = wiener(F[::-1, ::-1], predefined_filter=self.f)
+        assert (g - g1[::-1, ::-1]).sum() < 1
+
+        g1 = wiener(F[::-1, ::-1], self.filt_func)
+        assert (g - g1[::-1, ::-1]).sum() < 1
+
+    def test_non_callable(self):
+        with pytest.raises(ValueError):
+            LPIFilter2D(None)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_median.py b/python/cucim/src/cucim/skimage/filters/tests/test_median.py
new file mode 100644
index 000000000..d8f45173f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_median.py
@@ -0,0 +1,74 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_allclose
+from cupyx.scipy import ndimage
+
+from cucim.skimage.filters import median
+
+# from cucim.skimage.filters import rank
+
+
+@pytest.fixture
+def image():
+    return cp.array([[1, 2, 3, 2, 1],
+                     [1, 1, 2, 2, 3],
+                     [3, 2, 1, 2, 1],
+                     [3, 2, 1, 1, 1],
+                     [1, 2, 1, 2, 3]],
+                    dtype=np.uint8)
+
+
+# TODO: mode='rank' disabled until it has been implmented
+@pytest.mark.parametrize(
+    "mode, cval, behavior, n_warning, warning_type",
+    [('nearest', 0.0, 'ndimage', 0, []),
+     # ('constant', 0.0, 'rank', 1, (UserWarning,)),
+     # ('nearest', 0.0, 'rank', 0, []),
+     ('nearest', 0.0, 'ndimage', 0, [])]
+)
+def test_median_warning(image, mode, cval, behavior,
+                        n_warning, warning_type):
+
+    with pytest.warns(None) as records:
+        median(image, mode=mode, behavior=behavior)
+
+    assert len(records) == n_warning
+    for rec in records:
+        assert isinstance(rec.message, warning_type)
+
+
+# TODO: update if rank.median implemented
+@pytest.mark.parametrize(
+    "behavior, func, params",
+    [('ndimage', ndimage.median_filter, {'size': (3, 3)})]
+    # ('rank', rank.median, {'selem': np.ones((3, 3), dtype=np.uint8)})]
+)
+def test_median_behavior(image, behavior, func, params):
+    assert_allclose(median(image, behavior=behavior), func(image, **params))
+
+
+@pytest.mark.parametrize(
+    "dtype", [np.uint8, np.uint16, np.float32, np.float64]
+)
+def test_median_preserve_dtype(image, dtype):
+    median_image = median(image.astype(dtype), behavior='ndimage')
+    assert median_image.dtype == dtype
+
+
+# TODO: update if rank.median implemented
+# def test_median_error_ndim():
+#     img = cp.random.randint(0, 10, size=(5, 5, 5), dtype=np.uint8)
+#     with pytest.raises(ValueError):
+#         median(img, behavior='rank')
+
+
+# TODO: update if rank.median implemented
+@pytest.mark.parametrize(
+    "img, behavior",
+    # (np.random.randint(0, 10, size=(3, 3), dtype=np.uint8), 'rank'),
+    [(cp.random.randint(0, 10, size=(3, 3), dtype=np.uint8), 'ndimage'),
+     (cp.random.randint(0, 10, size=(3, 3, 3), dtype=np.uint8), 'ndimage')]
+)
+def test_median(img, behavior):
+    median(img, behavior=behavior)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_rank_order.py b/python/cucim/src/cucim/skimage/filters/tests/test_rank_order.py
new file mode 100644
index 000000000..0adad9203
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_rank_order.py
@@ -0,0 +1,14 @@
+import cupy as cp
+import skimage.data
+import skimage.filters
+
+from cucim.skimage.filters import rank_order
+
+img = cp.asarray(skimage.data.camera())
+
+
+def test_rank_order():
+    expected, ov_expected = skimage.filters.rank_order(img.get())
+    r, ov = rank_order(img)
+    cp.testing.assert_allclose(r, expected)
+    cp.testing.assert_allclose(ov, ov_expected)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_ridges.py b/python/cucim/src/cucim/skimage/filters/tests/test_ridges.py
new file mode 100644
index 000000000..58ab5c9dd
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_ridges.py
@@ -0,0 +1,263 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_allclose, assert_array_equal, assert_array_less
+from skimage.data import camera, retina
+
+from cucim.skimage._shared.testing import expected_warnings
+from cucim.skimage.color import rgb2gray
+from cucim.skimage.filters import frangi, hessian, meijering, sato
+from cucim.skimage.util import crop, invert
+
+
+def test_2d_null_matrix():
+
+    a_black = cp.zeros((3, 3)).astype(cp.uint8)
+    a_white = invert(a_black)
+
+    zeros = cp.zeros((3, 3))
+    ones = cp.ones((3, 3))
+
+    assert_array_equal(meijering(a_black, black_ridges=True), zeros)
+    assert_array_equal(meijering(a_white, black_ridges=False), zeros)
+
+    assert_array_equal(sato(a_black, black_ridges=True, mode='reflect'),
+                       zeros)
+    assert_array_equal(sato(a_white, black_ridges=False, mode='reflect'),
+                       zeros)
+    assert_allclose(frangi(a_black, black_ridges=True), zeros, atol=1e-3)
+    assert_allclose(frangi(a_white, black_ridges=False), zeros, atol=1e-3)
+
+    assert_array_equal(hessian(a_black, black_ridges=False, mode='reflect'),
+                       ones)
+    assert_array_equal(hessian(a_white, black_ridges=True, mode='reflect'),
+                       ones)
+
+
+def test_3d_null_matrix():
+
+    a_black = cp.zeros((3, 3, 3)).astype(cp.uint8)
+    a_white = invert(a_black)
+
+    zeros = cp.zeros((3, 3, 3))
+    ones = cp.ones((3, 3, 3))
+
+    assert_allclose(meijering(a_black, black_ridges=True), zeros, atol=1e-1)
+    assert_allclose(meijering(a_white, black_ridges=False), zeros, atol=1e-1)
+
+    assert_array_equal(sato(a_black, black_ridges=True, mode='reflect'),
+                       zeros)
+    assert_array_equal(sato(a_white, black_ridges=False, mode='reflect'),
+                       zeros)
+
+    assert_allclose(frangi(a_black, black_ridges=True), zeros, atol=1e-3)
+    assert_allclose(frangi(a_white, black_ridges=False), zeros, atol=1e-3)
+
+    assert_array_equal(hessian(a_black, black_ridges=False, mode='reflect'),
+                       ones)
+    assert_array_equal(hessian(a_white, black_ridges=True, mode='reflect'),
+                       ones)
+
+
+def test_2d_energy_decrease():
+
+    a_black = cp.zeros((5, 5)).astype(np.uint8)
+    a_black[2, 2] = 255
+    a_white = invert(a_black)
+
+    assert_array_less(meijering(a_black, black_ridges=True).std(),
+                      a_black.std())
+    assert_array_less(meijering(a_white, black_ridges=False).std(),
+                      a_white.std())
+
+    assert_array_less(sato(a_black, black_ridges=True, mode='reflect').std(),
+                      a_black.std())
+    assert_array_less(sato(a_white, black_ridges=False, mode='reflect').std(),
+                      a_white.std())
+
+    assert_array_less(frangi(a_black, black_ridges=True).std(),
+                      a_black.std())
+    assert_array_less(frangi(a_white, black_ridges=False).std(),
+                      a_white.std())
+
+    assert_array_less(hessian(a_black, black_ridges=True,
+                              mode='reflect').std(), a_black.std())
+    assert_array_less(hessian(a_white, black_ridges=False,
+                              mode='reflect').std(), a_white.std())
+
+
+def test_3d_energy_decrease():
+
+    a_black = cp.zeros((5, 5, 5)).astype(np.uint8)
+    a_black[2, 2, 2] = 255
+    a_white = invert(a_black)
+
+    assert_array_less(meijering(a_black, black_ridges=True).std(),
+                      a_black.std())
+    assert_array_less(meijering(a_white, black_ridges=False).std(),
+                      a_white.std())
+
+    assert_array_less(sato(a_black, black_ridges=True, mode='reflect').std(),
+                      a_black.std())
+    assert_array_less(sato(a_white, black_ridges=False, mode='reflect').std(),
+                      a_white.std())
+
+    assert_array_less(frangi(a_black, black_ridges=True).std(),
+                      a_black.std())
+    assert_array_less(frangi(a_white, black_ridges=False).std(),
+                      a_white.std())
+
+    assert_array_less(hessian(a_black, black_ridges=True,
+                              mode='reflect').std(), a_black.std())
+    assert_array_less(hessian(a_white, black_ridges=False,
+                              mode='reflect').std(), a_white.std())
+
+
+def test_2d_linearity():
+
+    a_black = cp.ones((3, 3)).astype(np.uint8)
+    a_white = invert(a_black)
+
+    assert_allclose(meijering(1 * a_black, black_ridges=True),
+                    meijering(10 * a_black, black_ridges=True), atol=1e-3)
+    assert_allclose(meijering(1 * a_white, black_ridges=False),
+                    meijering(10 * a_white, black_ridges=False), atol=1e-3)
+
+    assert_allclose(sato(1 * a_black, black_ridges=True, mode='reflect'),
+                    sato(10 * a_black, black_ridges=True, mode='reflect'),
+                    atol=1e-3)
+    assert_allclose(sato(1 * a_white, black_ridges=False, mode='reflect'),
+                    sato(10 * a_white, black_ridges=False, mode='reflect'),
+                    atol=1e-3)
+
+    assert_allclose(frangi(1 * a_black, black_ridges=True),
+                    frangi(10 * a_black, black_ridges=True), atol=1e-3)
+    assert_allclose(frangi(1 * a_white, black_ridges=False),
+                    frangi(10 * a_white, black_ridges=False), atol=1e-3)
+
+    assert_allclose(hessian(1 * a_black, black_ridges=True, mode='reflect'),
+                    hessian(10 * a_black, black_ridges=True, mode='reflect'),
+                    atol=1e-3)
+    assert_allclose(hessian(1 * a_white, black_ridges=False, mode='reflect'),
+                    hessian(10 * a_white, black_ridges=False, mode='reflect'),
+                    atol=1e-3)
+
+
+def test_3d_linearity():
+
+    a_black = cp.ones((3, 3, 3)).astype(np.uint8)
+    a_white = invert(a_black)
+
+    assert_allclose(meijering(1 * a_black, black_ridges=True),
+                    meijering(10 * a_black, black_ridges=True), atol=1e-3)
+    assert_allclose(meijering(1 * a_white, black_ridges=False),
+                    meijering(10 * a_white, black_ridges=False), atol=1e-3)
+
+    assert_allclose(sato(1 * a_black, black_ridges=True, mode='reflect'),
+                    sato(10 * a_black, black_ridges=True, mode='reflect'),
+                    atol=1e-3)
+    assert_allclose(sato(1 * a_white, black_ridges=False, mode='reflect'),
+                    sato(10 * a_white, black_ridges=False, mode='reflect'),
+                    atol=1e-3)
+
+    assert_allclose(frangi(1 * a_black, black_ridges=True),
+                    frangi(10 * a_black, black_ridges=True), atol=1e-3)
+    assert_allclose(frangi(1 * a_white, black_ridges=False),
+                    frangi(10 * a_white, black_ridges=False), atol=1e-3)
+
+    assert_allclose(hessian(1 * a_black, black_ridges=True, mode='reflect'),
+                    hessian(10 * a_black, black_ridges=True, mode='reflect'),
+                    atol=1e-3)
+    assert_allclose(hessian(1 * a_white, black_ridges=False, mode='reflect'),
+                    hessian(10 * a_white, black_ridges=False, mode='reflect'),
+                    atol=1e-3)
+
+
+def test_2d_cropped_camera_image():
+
+    a_black = crop(cp.array(camera()), ((200, 212), (100, 312)))
+    a_white = invert(a_black)
+
+    zeros = cp.zeros((100, 100))
+    ones = cp.ones((100, 100))
+
+    assert_allclose(meijering(a_black, black_ridges=True),
+                    meijering(a_white, black_ridges=False))
+
+    assert_allclose(sato(a_black, black_ridges=True, mode='mirror'),
+                    sato(a_white, black_ridges=False, mode='mirror'))
+
+    assert_allclose(frangi(a_black, black_ridges=True), zeros, atol=1e-3)
+    assert_allclose(frangi(a_white, black_ridges=False), zeros, atol=1e-3)
+
+    assert_allclose(hessian(a_black, black_ridges=True, mode='mirror'),
+                    ones, atol=1 - 1e-7)
+    assert_allclose(hessian(a_white, black_ridges=False, mode='mirror'),
+                    ones, atol=1 - 1e-7)
+
+
+def test_3d_cropped_camera_image():
+
+    a_black = crop(cp.asarray(camera()), ((200, 212), (100, 312)))
+    a_black = cp.dstack([a_black, a_black, a_black])
+    a_white = invert(a_black)
+
+    zeros = cp.zeros((100, 100, 3))
+    ones = cp.ones((100, 100, 3))
+
+    # TODO: determine why the following allclose checks occassionally fail
+    assert_allclose(meijering(a_black, black_ridges=True),
+                    meijering(a_white, black_ridges=False))
+
+    assert_allclose(sato(a_black, black_ridges=True, mode='mirror'),
+                    sato(a_white, black_ridges=False, mode='mirror'))
+
+    assert_allclose(frangi(a_black, black_ridges=True), zeros, atol=1e-3)
+    assert_allclose(frangi(a_white, black_ridges=False), zeros, atol=1e-3)
+
+    assert_allclose(hessian(a_black, black_ridges=True, mode='mirror'),
+                    ones, atol=1 - 1e-7)
+    assert_allclose(hessian(a_white, black_ridges=False, mode='mirror'),
+                    ones, atol=1 - 1e-7)
+
+
+@pytest.mark.parametrize('func, tol', [(frangi, 1e-7),
+                                       (meijering, 2e-2),
+                                       (sato, 1e-3),
+                                       (hessian, 2e-2)])
+def test_border_management(func, tol):
+    img = rgb2gray(cp.array(retina()[300:500, 700:900]))
+    out = func(img, sigmas=[1], mode='mirror')
+
+    full_std = out.std()
+    full_mean = out.mean()
+    inside_std = out[4:-4, 4:-4].std()
+    inside_mean = out[4:-4, 4:-4].mean()
+    border_std = cp.stack([out[:4, :], out[-4:, :],
+                           out[:, :4].T, out[:, -4:].T]).std()
+    border_mean = cp.stack([out[:4, :], out[-4:, :],
+                            out[:, :4].T, out[:, -4:].T]).mean()
+
+    assert abs(full_std - inside_std) < tol
+    assert abs(full_std - border_std) < tol
+    assert abs(inside_std - border_std) < tol
+    assert abs(full_mean - inside_mean) < tol
+    assert abs(full_mean - border_mean) < tol
+    assert abs(inside_mean - border_mean) < tol
+
+
+@pytest.mark.parametrize('func', [sato, hessian])
+def test_border_warning(func):
+    img = rgb2gray(cp.array(retina()[300:500, 700:900]))
+
+    with expected_warnings(["implicitly used 'constant' as the border mode"]):
+        func(img, sigmas=[1])
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+@pytest.mark.parametrize('func', [sato, hessian, meijering, frangi])
+def test_output_dtype(func, dtype):
+    img = rgb2gray(cp.array(retina()[300:500, 700:900], dtype=dtype))
+
+    out = func(img, sigmas=[1], mode='reflect')
+    assert out.dtype == dtype
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py b/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py
new file mode 100644
index 000000000..d9c380935
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_thresholding.py
@@ -0,0 +1,731 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from skimage import data
+from skimage.draw import disk
+from skimage.filters._multiotsu import (_get_multiotsu_thresh_indices,
+                                        _get_multiotsu_thresh_indices_lut)
+
+# from cupyx.scipy import ndimage as ndi
+from cucim.skimage import util
+from cucim.skimage._shared import testing
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.color import rgb2gray
+from cucim.skimage.exposure import histogram
+from cucim.skimage.filters.thresholding import _cross_entropy  # _mean_std,
+from cucim.skimage.filters.thresholding import (threshold_isodata,
+                                                threshold_li, threshold_local,
+                                                threshold_mean,
+                                                threshold_minimum,
+                                                threshold_multiotsu,
+                                                threshold_niblack,
+                                                threshold_otsu,
+                                                threshold_sauvola,
+                                                threshold_triangle,
+                                                threshold_yen,
+                                                try_all_threshold)
+
+# transfer images to GPU
+astronautd = cp.array(data.astronaut())
+camerad = cp.array(data.camera())
+celld = cp.array(data.cell())
+coinsd = cp.array(data.coins())
+moond = cp.array(data.moon())
+
+
+class TestSimpleImage:
+    def setup(self):
+        # fmt: off
+        self.image = cp.array([[0, 0, 1, 3, 5],
+                               [0, 1, 4, 3, 4],
+                               [1, 2, 5, 4, 1],
+                               [2, 4, 5, 2, 1],
+                               [4, 5, 1, 0, 0]], dtype=int)
+        # fmt: on
+
+    def test_minimum(self):
+        with pytest.raises(RuntimeError):
+            threshold_minimum(self.image)
+
+    def test_try_all_threshold(self):
+        fig, ax = try_all_threshold(self.image)
+        all_texts = [axis.texts for axis in ax if axis.texts != []]
+        text_content = [text.get_text() for x in all_texts for text in x]
+        assert 'RuntimeError' in text_content
+
+    def test_otsu(self):
+        assert threshold_otsu(self.image) == 2
+
+    def test_otsu_negative_int(self):
+        image = self.image - 2
+        assert threshold_otsu(image) == 0
+
+    def test_otsu_float_image(self):
+        image = self.image.astype(cp.float64)
+        assert 2 <= threshold_otsu(image) < 3
+
+    def test_li(self):
+        assert 2 < threshold_li(self.image) < 3
+
+    def test_li_negative_int(self):
+        image = self.image - 2
+        assert 0 < threshold_li(image) < 1
+
+    def test_li_float_image(self):
+        image = self.image.astype(float)
+        assert 2 < threshold_li(image) < 3
+
+    def test_li_constant_image(self):
+        assert threshold_li(cp.ones((10, 10))) == 1.0
+
+    def test_yen(self):
+        assert threshold_yen(self.image) == 2
+
+    def test_yen_negative_int(self):
+        image = self.image - 2
+        assert threshold_yen(image) == 0
+
+    def test_yen_float_image(self):
+        image = self.image.astype(cp.float64)
+        assert 2 <= threshold_yen(image) < 3
+
+    def test_yen_arange(self):
+        image = cp.arange(256)
+        assert threshold_yen(image) == 127
+
+    def test_yen_binary(self):
+        image = cp.zeros([2, 256], dtype=cp.uint8)
+        image[0] = 255
+        assert threshold_yen(image) < 1
+
+    def test_yen_blank_zero(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        assert threshold_yen(image) == 0
+
+    def test_yen_blank_max(self):
+        image = cp.empty((5, 5), dtype=cp.uint8)
+        image.fill(255)
+        assert threshold_yen(image) == 255
+
+    def test_isodata(self):
+        assert threshold_isodata(self.image) == 2
+        assert_array_equal(threshold_isodata(self.image, return_all=True), [2])
+
+    def test_isodata_blank_zero(self):
+        image = cp.zeros((5, 5), dtype=cp.uint8)
+        assert threshold_isodata(image) == 0
+        assert_array_equal(threshold_isodata(image, return_all=True), [0])
+
+    def test_isodata_linspace(self):
+        image = cp.linspace(-127, 0, 256)
+        assert -63.8 < threshold_isodata(image) < -63.6
+        assert_array_almost_equal(
+            threshold_isodata(image, return_all=True),
+            [-63.74804688, -63.25195312],
+        )
+
+    def test_isodata_16bit(self):
+        np.random.seed(0)
+        imfloat = cp.array(np.random.rand(256, 256))
+        assert 0.49 < threshold_isodata(imfloat, nbins=1024) < 0.51
+        assert all(0.49 < threshold_isodata(imfloat, nbins=1024,
+                                            return_all=True))
+
+    def test_threshold_local_gaussian(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, False,  True],  # noqa
+             [False, False,  True, False,  True],  # noqa
+             [False, False,  True,  True, False],  # noqa
+             [False,  True,  True, False, False],  # noqa
+             [ True,  True, False, False, False]]  # noqa
+        )
+        out = threshold_local(self.image, 3, method='gaussian')
+        assert_array_equal(ref, self.image > out)
+
+        out = threshold_local(self.image, 3, method='gaussian',
+                              param=1.0 / 3.0)
+        assert_array_equal(ref, self.image > out)
+
+    def test_threshold_local_mean(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, False,  True],  # noqa
+             [False, False,  True, False,  True],  # noqa
+             [False, False,  True,  True, False],  # noqa
+             [False,  True,  True, False, False],  # noqa
+             [ True,  True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        out = threshold_local(self.image, 3, method="mean")
+        assert_array_equal(ref, self.image > out)
+
+    def test_threshold_local_median(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, False,  True],  # noqa
+             [False, False,  True, False, False],  # noqa
+             [False, False,  True, False, False],  # noqa
+             [False, False,  True,  True, False],  # noqa
+             [False,  True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        out = threshold_local(self.image, 3, method="median")
+        assert_array_equal(ref, self.image > out)
+
+    def test_threshold_local_median_constant_mode(self):
+        out = threshold_local(self.image, 3, method='median',
+                              mode='constant', cval=20)
+
+        # fmt: off
+        expected = cp.array(
+            [[20.,  1.,  3.,  4., 20.],   # noqa
+             [ 1.,  1.,  3.,  4.,  4.],   # noqa
+             [ 2.,  2.,  4.,  4.,  4.],   # noqa
+             [ 4.,  4.,  4.,  1.,  2.],   # noqa
+             [20.,  5.,  5.,  2., 20.]])  # noqa
+        # fmt: on
+        assert_array_equal(expected, out)
+
+    def test_threshold_niblack(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, True, True],  # noqa
+             [False, True, True, True, True],    # noqa
+             [False, True, True, True, False],   # noqa
+             [False, True, True, True, True],    # noqa
+             [True, True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        thres = threshold_niblack(self.image, window_size=3, k=0.5)
+        out = self.image > thres
+        assert_array_equal(ref, out)
+
+    def test_threshold_sauvola(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, True, True],  # noqa
+             [False, False, True, True, True],   # noqa
+             [False, False, True, True, False],  # noqa
+             [False, True, True, True, False],   # noqa
+             [True, True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        thres = threshold_sauvola(self.image, window_size=3, k=0.2, r=128)
+        out = self.image > thres
+        assert_array_equal(ref, out)
+
+    def test_threshold_niblack_iterable_window_size(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, True, True],  # noqa
+             [False, False, True, True, True],   # noqa
+             [False, True, True, True, False],   # noqa
+             [False, True, True, True, False],   # noqa
+             [True, True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        thres = threshold_niblack(self.image, window_size=[3, 5], k=0.5)
+        out = self.image > thres
+        assert_array_equal(ref, out)
+
+    def test_threshold_sauvola_iterable_window_size(self):
+        # fmt: off
+        ref = cp.array(
+            [[False, False, False, True, True],  # noqa
+             [False, False, True, True, True],   # noqa
+             [False, False, True, True, False],  # noqa
+             [False, True, True, True, False],   # noqa
+             [True, True, False, False, False]]  # noqa
+        )
+        # fmt: on
+        thres = threshold_sauvola(self.image, window_size=(3, 5), k=0.2, r=128)
+        out = self.image > thres
+        assert_array_equal(ref, out)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_otsu_camera_image():
+    camera = util.img_as_ubyte(camerad)
+    assert 101 < threshold_otsu(camera) < 103
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_otsu_camera_image_histogram():
+    camera = util.img_as_ubyte(camerad)
+    hist = histogram(camera.ravel(), 256, source_range="image")
+    assert 101 < threshold_otsu(hist=hist) < 103
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_otsu_camera_image_counts():
+    camera = util.img_as_ubyte(camerad)
+    counts, bin_centers = histogram(camera.ravel(), 256, source_range="image")
+    assert 101 < threshold_otsu(hist=counts) < 103
+
+
+def test_otsu_coins_image():
+    coins = util.img_as_ubyte(coinsd)
+    assert 106 < threshold_otsu(coins) < 108
+
+
+def test_otsu_coins_image_as_float():
+    coins = util.img_as_float(coinsd)
+    assert 0.41 < threshold_otsu(coins) < 0.42
+
+
+def test_otsu_astro_image():
+    img = util.img_as_ubyte(astronautd)
+    with expected_warnings(['grayscale']):
+        assert 109 < threshold_otsu(img) < 111
+
+
+def test_otsu_one_color_image():
+    img = cp.ones((10, 10), dtype=np.uint8)
+    assert threshold_otsu(img) == 1
+
+
+def test_otsu_one_color_image_3d():
+    img = cp.ones((10, 10, 10), dtype=np.uint8)
+    assert threshold_otsu(img) == 1
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_li_camera_image():
+    image = util.img_as_ubyte(camerad)
+    threshold = threshold_li(image)
+    ce_actual = _cross_entropy(image, threshold)
+    assert 78 < threshold_li(image) < 79
+    assert ce_actual < _cross_entropy(image, threshold + 1)
+    assert ce_actual < _cross_entropy(image, threshold - 1)
+
+
+def test_li_coins_image():
+    image = util.img_as_ubyte(coinsd)
+    threshold = threshold_li(image)
+    ce_actual = _cross_entropy(image, threshold)
+    assert 94 < threshold_li(image) < 95
+    assert ce_actual < _cross_entropy(image, threshold + 1)
+    # in the case of the coins image, the minimum cross-entropy is achieved one
+    # threshold below that found by the iterative method. Not sure why that is
+    # but `threshold_li` does find the stationary point of the function (ie the
+    # tolerance can be reduced arbitrarily but the exact same threshold is
+    # found), so my guess is some kind of histogram binning effect.
+    assert ce_actual < _cross_entropy(image, threshold - 2)
+
+
+def test_li_coins_image_as_float():
+    coins = util.img_as_float(coinsd)
+    assert 94 / 255 < threshold_li(coins) < 95 / 255
+
+
+def test_li_astro_image():
+    image = util.img_as_ubyte(astronautd)
+    threshold = threshold_li(image)
+    ce_actual = _cross_entropy(image, threshold)
+    assert 64 < threshold < 65
+    assert ce_actual < _cross_entropy(image, threshold + 1)
+    assert ce_actual < _cross_entropy(image, threshold - 1)
+
+
+def test_li_nan_image():
+    image = cp.full((5, 5), cp.nan)
+    assert cp.isnan(threshold_li(image))
+
+
+def test_li_inf_image():
+    image = cp.array([cp.inf, cp.nan])
+    assert threshold_li(image) == cp.inf
+
+
+def test_li_inf_minus_inf():
+    image = cp.array([cp.inf, -cp.inf])
+    assert threshold_li(image) == 0
+
+
+def test_li_constant_image_with_nan():
+    image = cp.asarray([8, 8, 8, 8, cp.nan])
+    assert threshold_li(image) == 8
+
+
+def test_li_arbitrary_start_point():
+    cell = celld
+    max_stationary_point = threshold_li(cell)
+    low_stationary_point = threshold_li(
+        cell, initial_guess=float(cp.percentile(cell, 5))
+    )
+    optimum = threshold_li(cell, initial_guess=float(cp.percentile(cell, 95)))
+    assert 67 < max_stationary_point < 68
+    assert 48 < low_stationary_point < 49
+    assert 111 < optimum < 112
+
+
+def test_li_negative_inital_guess():
+    with testing.raises(ValueError):
+        threshold_li(coinsd, initial_guess=-5)
+
+
+def test_li_pathological_arrays():
+    # See https://github.com/scikit-image/scikit-image/issues/4140
+    a = cp.array([0, 0, 1, 0, 0, 1, 0, 1])
+    b = cp.array([0, 0, 0.1, 0, 0, 0.1, 0, 0.1])
+    c = cp.array([0, 0, 0.1, 0, 0, 0.1, 0.01, 0.1])
+    d = cp.array([0, 0, 1, 0, 0, 1, 0.5, 1])
+    e = cp.array([1, 1])
+    f = cp.asarray([1, 2])
+    arrays = [a, b, c, d, e, f]
+    with np.errstate(divide='ignore'):
+        # ignoring "divide by zero encountered in log" error from np.log(0)
+        thresholds = cp.array([float(threshold_li(arr)) for arr in arrays])
+    assert cp.all(cp.isfinite(thresholds))
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_yen_camera_image():
+    camera = util.img_as_ubyte(camerad)
+    assert 145 < threshold_yen(camera) < 147
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_yen_camera_image_histogram():
+    camera = util.img_as_ubyte(camerad)
+    hist = histogram(camera.ravel(), 256, source_range="image")
+    assert 145 < threshold_yen(hist=hist) < 147
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_yen_camera_image_counts():
+    camera = util.img_as_ubyte(camerad)
+    counts, bin_centers = histogram(camera.ravel(), 256, source_range='image')
+    assert 145 < threshold_yen(hist=counts) < 147
+
+
+def test_yen_coins_image():
+    coins = util.img_as_ubyte(coinsd)
+    assert 109 < threshold_yen(coins) < 111
+
+
+def test_yen_coins_image_as_float():
+    coins = util.img_as_float(coinsd)
+    assert 0.43 < threshold_yen(coins) < 0.44
+
+
+def test_local_even_block_size_error():
+    img = camerad
+    with testing.raises(ValueError):
+        threshold_local(img, block_size=4)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_isodata_camera_image():
+    camera = util.img_as_ubyte(camerad)
+
+    threshold = threshold_isodata(camera)
+    assert np.floor((camera[camera <= threshold].mean() +
+                     camera[camera > threshold].mean()) / 2.0) == threshold
+    assert threshold == 102
+
+    assert_array_equal(threshold_isodata(camera, return_all=True), [102, 103])
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_isodata_camera_image_histogram():
+    camera = util.img_as_ubyte(data.camera())
+    hist = histogram(camera.ravel(), 256, source_range='image')
+    threshold = threshold_isodata(hist=hist)
+    assert threshold == 102
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_isodata_camera_image_counts():
+    camera = util.img_as_ubyte(data.camera())
+    counts, bin_centers = histogram(camera.ravel(), 256, source_range='image')
+    threshold = threshold_isodata(hist=counts)
+    assert threshold == 102
+
+
+def test_isodata_coins_image():
+    coins = util.img_as_ubyte(coinsd)
+
+    threshold = threshold_isodata(coins)
+    assert np.floor((coins[coins <= threshold].mean() +
+                     coins[coins > threshold].mean()) / 2.0) == threshold
+    assert threshold == 107
+
+    assert_array_equal(threshold_isodata(coins, return_all=True), [107])
+
+
+def test_isodata_moon_image():
+    moon = util.img_as_ubyte(moond)
+
+    threshold = threshold_isodata(moon)
+    assert np.floor((moon[moon <= threshold].mean() +
+                     moon[moon > threshold].mean()) / 2.0) == threshold
+    assert threshold == 86
+
+    thresholds = threshold_isodata(moon, return_all=True)
+    for threshold in thresholds:
+        assert np.floor((moon[moon <= threshold].mean() +
+                         moon[moon > threshold].mean()) / 2.0) == threshold
+    assert_array_equal(thresholds, [86, 87, 88, 122, 123, 124, 139, 140])
+
+
+def test_isodata_moon_image_negative_int():
+    moon = util.img_as_ubyte(moond).astype(cp.int32)
+    moon -= 100
+
+    threshold = threshold_isodata(moon)
+    assert np.floor((moon[moon <= threshold].mean() +
+                     moon[moon > threshold].mean()) / 2.0) == threshold
+    assert threshold == -14
+
+    thresholds = threshold_isodata(moon, return_all=True)
+    for threshold in thresholds:
+        assert np.floor((moon[moon <= threshold].mean() +
+                         moon[moon > threshold].mean()) / 2.0) == threshold
+    assert_array_equal(thresholds, [-14, -13, -12, 22, 23, 24, 39, 40])
+
+
+def test_isodata_moon_image_negative_float():
+    moon = util.img_as_ubyte(moond).astype(cp.float64)
+    moon -= 100
+
+    assert -14 < threshold_isodata(moon) < -13
+
+    thresholds = threshold_isodata(moon, return_all=True)
+    # fmt: off
+    assert_array_almost_equal(thresholds,
+        [-13.83789062, -12.84179688, -11.84570312, 22.02148438,   # noqa
+          23.01757812,  24.01367188,  38.95507812, 39.95117188])  # noqa
+    # fmt: on
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_threshold_minimum():
+    camera = util.img_as_ubyte(camerad)
+
+    threshold = threshold_minimum(camera)
+    assert_array_equal(threshold, 85)
+
+    astronaut = util.img_as_ubyte(astronautd)
+    threshold = threshold_minimum(astronaut)
+    assert_array_equal(threshold, 114)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_threshold_minimum_histogram():
+    camera = util.img_as_ubyte(camerad)
+    hist = histogram(camera.ravel(), 256, source_range='image')
+    threshold = threshold_minimum(hist=hist)
+    assert_array_equal(threshold, 85)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_threshold_minimum_counts():
+    camera = util.img_as_ubyte(camerad)
+    counts, bin_centers = histogram(camera.ravel(), 256, source_range='image')
+    threshold = threshold_minimum(hist=counts)
+    assert_array_equal(threshold, 85)
+
+
+def test_threshold_minimum_synthetic():
+    img = cp.arange(25 * 25, dtype=cp.uint8).reshape((25, 25))
+    img[0:9, :] = 50
+    img[14:25, :] = 250
+
+    threshold = threshold_minimum(img)
+    assert_array_equal(threshold, 95)
+
+
+def test_threshold_minimum_failure():
+    img = cp.zeros((16 * 16), dtype=cp.uint8)
+    with testing.raises(RuntimeError):
+        threshold_minimum(img)
+
+
+def test_mean():
+    img = cp.zeros((2, 6))
+    img[:, 2:4] = 1
+    img[:, 4:] = 2
+    assert threshold_mean(img) == 1.0
+
+
+def test_triangle_uint_images():
+    text = cp.array(data.text())
+    assert threshold_triangle(cp.invert(text)) == 151
+    assert threshold_triangle(text) == 104
+    assert threshold_triangle(coinsd) == 80
+    assert threshold_triangle(cp.invert(coinsd)) == 175
+
+
+def test_triangle_float_images():
+    text = cp.array(data.text())
+    int_bins = int(text.max() - text.min() + 1)
+    # Set nbins to match the uint case and threshold as float.
+    assert round(float(threshold_triangle(
+        text.astype(float), nbins=int_bins))) == 104
+    # Check that rescaling image to floats in unit interval is equivalent.
+    assert (
+        round(float(threshold_triangle(text / 255.0, nbins=int_bins) * 255))
+        == 104
+    )
+    # Repeat for inverted image.
+    assert round(float(threshold_triangle(
+        cp.invert(text).astype(float), nbins=int_bins))) == 151
+    assert round(float(threshold_triangle(
+        cp.invert(text) / 255, nbins=int_bins) * 255)) == 151
+
+
+def test_triangle_flip():
+    # Depending on the skewness, the algorithm flips the histogram.
+    # We check that the flip doesn't affect too much the result.
+    img = camerad
+    inv_img = cp.invert(img)
+    t = threshold_triangle(inv_img)
+    t_inv_img = inv_img > t
+    t_inv_inv_img = cp.invert(t_inv_img)
+
+    t = threshold_triangle(img)
+    t_img = img > t
+
+    # Check that most of the pixels are identical
+    # See numpy #7685 for a future cp.testing API
+    unequal_pos = cp.where(t_img.ravel() != t_inv_inv_img.ravel())
+    assert len(unequal_pos[0]) / t_img.size < 1e-2
+
+
+# TODO: need generic_filter
+# @pytest.mark.parametrize(
+#     "window_size, mean_kernel",
+#     [(11, cp.full((11,) * 2,  1 / 11 ** 2)),
+#      ((11, 11), cp.full((11, 11), 1 / 11 ** 2)),
+#      ((9, 13), cp.full((9, 13), 1 / np.prod((9, 13)))),
+#      ((13, 9), cp.full((13, 9), 1 / np.prod((13, 9)))),
+#      ((1, 9), cp.full((1, 9), 1 / np.prod((1, 9))))
+#      ]
+# )
+# def test_mean_std_2d(window_size, mean_kernel):
+#     image = cp.asarray(np.random.rand(256, 256))
+#     m, s = _mean_std(image, w=window_size)
+#     expected_m = ndi.convolve(image, mean_kernel, mode='mirror')
+#     cp.testing.assert_allclose(m, expected_m)
+#     expected_s = ndi.generic_filter(image, cp.std, size=window_size,
+#                                     mode='mirror')
+#     cp.testing.assert_allclose(s, expected_s)
+
+# TODO: need generic_filter
+# @pytest.mark.parametrize(
+#     "window_size, mean_kernel", [
+#         (5, cp.full((5,) * 3, 1 / 5) ** 3),
+#         ((5, 5, 5), cp.full((5, 5, 5), 1 / 5 ** 3)),
+#         ((1, 5, 5), cp.full((1, 5, 5), 1 / 5 ** 2)),
+#         ((3, 5, 7), cp.full((3, 5, 7), 1 / np.prod((3, 5, 7))))]
+# )
+# def test_mean_std_3d(window_size, mean_kernel):
+#     image = cp.asarray(np.random.rand(40, 40, 40))
+#     m, s = _mean_std(image, w=window_size)
+#     expected_m = ndi.convolve(image, mean_kernel, mode='mirror')
+#     cp.testing.assert_allclose(m, expected_m)
+#     expected_s = ndi.generic_filter(image, cp.std, size=window_size,
+#                                     mode='mirror')
+#     cp.testing.assert_allclose(s, expected_s)
+
+
+def test_niblack_sauvola_pathological_image():
+    # For certain values, floating point error can cause
+    # E(X^2) - (E(X))^2 to be negative, and taking the square root of this
+    # resulted in NaNs. Here we check that these are safely caught.
+    # see https://github.com/scikit-image/scikit-image/issues/3007
+    value = 0.03082192 + 2.19178082e-09
+    src_img = cp.full((4, 4), value).astype(cp.float64)
+    assert not cp.any(cp.isnan(threshold_niblack(src_img)))
+
+
+def test_bimodal_multiotsu_hist():
+    for name in ['camera', 'moon', 'coins', 'text', 'clock', 'page']:
+        img = cp.array(getattr(data, name)())
+        assert threshold_otsu(img) == threshold_multiotsu(img, 2)
+
+    for name in ['chelsea', 'coffee', 'astronaut', 'rocket']:
+        img = rgb2gray(cp.array(getattr(data, name)()))
+        assert threshold_otsu(img) == threshold_multiotsu(img, 2)
+
+
+def test_check_multiotsu_results():
+    # fmt: off
+    image = 0.25 * cp.array([[0, 1, 2, 3, 4],
+                             [0, 1, 2, 3, 4],
+                             [0, 1, 2, 3, 4],
+                             [0, 1, 2, 3, 4]])
+    # fmt: on
+    for idx in range(3, 6):
+        thr_multi = threshold_multiotsu(image,
+                                        classes=idx)
+        assert len(thr_multi) == idx - 1
+
+
+def test_multiotsu_output():
+    image = cp.zeros((100, 100), dtype='int')
+    coords = [(25, 25), (50, 50), (75, 75)]
+    values = [64, 128, 192]
+    for coor, val in zip(coords, values):
+        rr, cc = disk(coor, 20)
+        rr, cc = cp.asarray(rr), cp.asarray(cc)
+        image[rr, cc] = val
+    thresholds = [0, 64, 128]
+    assert_array_equal(thresholds, threshold_multiotsu(image, classes=4))
+
+
+def test_multiotsu_astro_image():
+    img = util.img_as_ubyte(astronautd)
+    with expected_warnings(['grayscale']):
+        assert_array_almost_equal(threshold_multiotsu(img), [58, 149])
+
+
+def test_multiotsu_more_classes_then_values():
+    img = cp.ones((10, 10), dtype=cp.uint8)
+    with testing.raises(ValueError):
+        threshold_multiotsu(img, classes=2)
+    img[:, 3:] = 2
+    with testing.raises(ValueError):
+        threshold_multiotsu(img, classes=3)
+    img[:, 6:] = 3
+    with testing.raises(ValueError):
+        threshold_multiotsu(img, classes=4)
+
+
+# @testing.with_requires("skimage>=0.18")
+# @pytest.mark.parametrize(
+#     "thresholding, lower, upper",
+#     [
+#         (threshold_otsu, 86, 88),
+#         (threshold_yen, 197, 199),
+#         (threshold_isodata, 86, 88),
+#         (threshold_mean, 117, 119),
+#         (threshold_triangle, 21, 23),
+#         (threshold_minimum, 75, 77),
+#     ],
+# )
+# def test_thresholds_dask_compatibility(thresholding, lower, upper):
+#     pytest.importorskip('dask', reason="dask python library is not installed")
+#     import dask.array as da
+#     dask_camera = da.from_array(camera, chunks=(256, 256))
+#     assert lower < float(thresholding(dask_camera)) < upper
+
+
+@pytest.mark.skip("_get_multiotsu_thresh_indices functions not implemented yet")
+def test_multiotsu_lut():
+    for classes in [2, 3, 4]:
+        for name in ['camera', 'moon', 'coins', 'text', 'clock', 'page']:
+            img = cp.array(getattr(data, name)())
+            prob, bin_centers = histogram(img.ravel(),
+                                          nbins=256,
+                                          source_range='image',
+                                          normalize=True)
+            prob = prob.astype('float32')
+
+            result_lut = _get_multiotsu_thresh_indices_lut(prob, classes - 1)
+            result = _get_multiotsu_thresh_indices(prob, classes - 1)
+
+            assert_array_equal(result_lut, result)
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_unsharp_mask.py b/python/cucim/src/cucim/skimage/filters/tests/test_unsharp_mask.py
new file mode 100644
index 000000000..7eeb195cf
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_unsharp_mask.py
@@ -0,0 +1,74 @@
+import cupy as cp
+import numpy as np
+import pytest
+
+from cucim.skimage.filters import unsharp_mask
+
+
+@pytest.mark.parametrize("shape,multichannel",
+                         [((29,), False),
+                          ((40, 4), True),
+                          ((32, 32), False),
+                          ((29, 31, 3), True),
+                          ((13, 17, 4, 8), False)])
+@pytest.mark.parametrize("dtype", [np.uint8, np.int8,
+                                   np.uint16, np.int16,
+                                   np.uint32, np.int32,
+                                   np.uint64, np.int64,
+                                   np.float16, np.float32, np.float64])
+@pytest.mark.parametrize("radius", [0, 0.1, 2.0])
+@pytest.mark.parametrize("amount", [0.0, 0.5, 2.0, -1.0])
+@pytest.mark.parametrize("offset", [-1.0, 0.0, 1.0])
+@pytest.mark.parametrize("preserve", [False, True])
+def test_unsharp_masking_output_type_and_shape(
+        radius, amount, shape, multichannel, dtype, offset, preserve):
+    array = cp.random.random(shape)
+    array = ((array + offset) * 128).astype(dtype)
+    if (preserve is False) and (dtype in [np.float32, np.float64]):
+        array /= max(cp.abs(array).max(), 1.0)
+    output = unsharp_mask(array, radius, amount, multichannel, preserve)
+    assert output.dtype in [np.float16, np.float32, np.float64]
+    if np.dtype(dtype).kind == 'f':
+        assert output.dtype == dtype
+    assert output.shape == shape
+
+
+@pytest.mark.parametrize("shape,multichannel",
+                         [((32, 32), False),
+                          ((15, 15, 2), True),
+                          ((17, 19, 3), True)])
+@pytest.mark.parametrize("radius", [(0.0, 0.0), (1.0, 1.0), (2.0, 1.5)])
+@pytest.mark.parametrize("preserve", [False, True])
+def test_unsharp_masking_with_different_radii(radius, shape,
+                                              multichannel, preserve):
+    amount = 1.0
+    dtype = np.float64
+    array = (cp.random.random(shape) * 96).astype(dtype)
+    if preserve is False:
+        array /= max(cp.abs(array).max(), 1.0)
+    output = unsharp_mask(array, radius, amount, multichannel, preserve)
+    assert output.dtype in [np.float32, np.float64]
+    assert output.shape == shape
+
+
+@pytest.mark.parametrize("shape,multichannel",
+                         [((16, 16), False),
+                          ((15, 15, 2), True),
+                          ((13, 17, 3), True)])
+@pytest.mark.parametrize("offset", [-5, 0, 5])
+@pytest.mark.parametrize("preserve", [False, True])
+def test_unsharp_masking_with_different_ranges(shape, offset,
+                                               multichannel, preserve):
+    radius = 2.0
+    amount = 1.0
+    dtype = np.int16
+    array = (cp.random.random(shape) * 5 + offset).astype(dtype)
+    negative = cp.any(array < 0)
+    output = unsharp_mask(array, radius, amount, multichannel, preserve)
+    if preserve is False:
+        assert cp.any(output <= 1)
+        assert cp.any(output >= -1)
+        if negative is False:
+            assert cp.any(output >= 0)
+    assert output.dtype in [np.float32, np.float64]
+    assert output.shape == shape
diff --git a/python/cucim/src/cucim/skimage/filters/tests/test_window.py b/python/cucim/src/cucim/skimage/filters/tests/test_window.py
new file mode 100644
index 000000000..0341c9844
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/tests/test_window.py
@@ -0,0 +1,54 @@
+import cupy as cp
+import pytest
+from scipy.signal import get_window
+
+from cucim.skimage.filters import window
+
+
+@pytest.mark.parametrize("size", [5, 6])
+@pytest.mark.parametrize("ndim", [2, 3, 4])
+def test_window_shape_isotropic(size, ndim):
+    w = window('hann', (size,) * ndim)
+    assert w.ndim == ndim
+    assert w.shape[1:] == w.shape[:-1]
+    for i in range(1, ndim - 1):
+        assert cp.allclose(w.sum(axis=0), w.sum(axis=i))
+
+
+@pytest.mark.parametrize("shape", [(8, 16), (16, 8), (2, 3, 4)])
+def test_window_shape_anisotropic(shape):
+    w = window('hann', shape)
+    assert w.shape == shape
+
+
+@pytest.mark.parametrize("shape", [[17, 33], [17, 97]])
+def test_window_anisotropic_amplitude(shape):
+    w = window(('tukey', 0.8), shape)
+
+    # The shape is stretched to give approximately the same range on each axis,
+    # so the center profile should have a similar mean value.
+    profile_w = w[w.shape[0] // 2, :]
+    profile_h = w[:, w.shape[1] // 2]
+    assert abs(profile_w.mean() - profile_h.mean()) < 0.01
+
+
+@pytest.mark.parametrize("wintype", [16, "triang", ("tukey", 0.8)])
+def test_window_type(wintype):
+    w = window(wintype, (9, 9))
+    assert w.ndim == 2
+    assert w.shape[1:] == w.shape[:-1]
+    assert cp.allclose(w.sum(axis=0), w.sum(axis=1))
+
+
+@pytest.mark.parametrize("size", [10, 11])
+def test_window_1d(size):
+    w = window('hann', size)
+    w1 = get_window('hann', size, fftbins=False)
+    assert cp.allclose(w, w1)
+
+
+def test_window_invalid_shape():
+    with pytest.raises(ValueError):
+        window(10, shape=(-5, 10))
+    with pytest.raises(ValueError):
+        window(10, shape=(1.3, 2.0))
diff --git a/python/cucim/src/cucim/skimage/filters/thresholding.py b/python/cucim/src/cucim/skimage/filters/thresholding.py
new file mode 100644
index 000000000..328d11803
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/filters/thresholding.py
@@ -0,0 +1,1293 @@
+import itertools
+import math
+from collections import OrderedDict
+from collections.abc import Iterable
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from cucim import _misc
+
+from .._shared.utils import check_nD, warn
+from ..exposure import histogram
+from ..transform import integral_image
+from ..util import dtype_limits
+from ._sparse import _correlate_sparse, _validate_window_size
+
+__all__ = ['try_all_threshold',
+           'threshold_otsu',
+           'threshold_yen',
+           'threshold_isodata',
+           'threshold_li',
+           'threshold_local',
+           'threshold_minimum',
+           'threshold_mean',
+           'threshold_niblack',
+           'threshold_sauvola',
+           'threshold_triangle',
+           'apply_hysteresis_threshold',
+           'threshold_multiotsu']
+
+
+def _cumsum(x, *args, **kwargs):
+    return cp.cumsum(x, *args, **kwargs)
+
+
+def _try_all(image, methods=None, figsize=None, num_cols=2, verbose=True):
+    """Returns a figure comparing the outputs of different methods.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        Input image.
+    methods : dict, optional
+        Names and associated functions.
+        Functions must take and return an image.
+    figsize : tuple, optional
+        Figure size (in inches).
+    num_cols : int, optional
+        Number of columns.
+    verbose : bool, optional
+        Print function name for each method.
+
+    Returns
+    -------
+    fig, ax : tuple
+        Matplotlib figure and axes.
+    """
+    from matplotlib import pyplot as plt
+
+    # Handle default value
+    methods = methods or {}
+
+    num_rows = math.ceil((len(methods) + 1.0) / num_cols)
+    fig, ax = plt.subplots(num_rows, num_cols, figsize=figsize,
+                           sharex=True, sharey=True)
+    ax = ax.ravel()
+
+    ax[0].imshow(cp.asnumpy(image), cmap=plt.cm.gray)
+    ax[0].set_title('Original')
+
+    i = 1
+    for name, func in methods.items():
+        ax[i].set_title(name)
+        try:
+            ax[i].imshow(cp.asnumpy(func(image)), cmap=plt.cm.gray)
+        except Exception as e:
+            ax[i].text(0.5, 0.5, "%s" % type(e).__name__,
+                       ha="center", va="center", transform=ax[i].transAxes)
+        i += 1
+        if verbose:
+            print(func.__orifunc__)
+
+    for a in ax:
+        a.axis('off')
+
+    fig.tight_layout()
+    return fig, ax
+
+
+def try_all_threshold(image, figsize=(8, 5), verbose=True):
+    """Returns a figure comparing the outputs of different thresholding methods.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        Input image.
+    figsize : tuple, optional
+        Figure size (in inches).
+    verbose : bool, optional
+        Print function name for each method.
+
+    Returns
+    -------
+    fig, ax : tuple
+        Matplotlib figure and axes.
+
+    Notes
+    -----
+    The following algorithms are used:
+
+    * isodata
+    * li
+    * mean
+    * minimum
+    * otsu
+    * triangle
+    * yen
+
+    Examples
+    --------
+    >>> from skimage.data import text
+    >>> fig, ax = try_all_threshold(text(), figsize=(10, 6), verbose=False)
+    """
+
+    def thresh(func):
+        """
+        A wrapper function to return a thresholded image.
+        """
+
+        def wrapper(im):
+            return im > func(im)
+
+        try:
+            wrapper.__orifunc__ = func.__orifunc__
+        except AttributeError:
+            wrapper.__orifunc__ = func.__module__ + "." + func.__name__
+        return wrapper
+
+    # Global algorithms.
+    methods = OrderedDict({'Isodata': thresh(threshold_isodata),
+                           'Li': thresh(threshold_li),
+                           'Mean': thresh(threshold_mean),
+                           'Minimum': thresh(threshold_minimum),
+                           'Otsu': thresh(threshold_otsu),
+                           'Triangle': thresh(threshold_triangle),
+                           'Yen': thresh(threshold_yen)})
+
+    return _try_all(image, figsize=figsize,
+                    methods=methods, verbose=verbose)
+
+
+def _float_dtype(image):
+    if image.dtype.kind == 'f':
+        return image.dtype
+    return cp.float64
+
+
+def threshold_local(image, block_size, method='gaussian', offset=0,
+                    mode='reflect', param=None, cval=0):
+    """Compute a threshold mask image based on local pixel neighborhood.
+
+    Also known as adaptive or dynamic thresholding. The threshold value is
+    the weighted mean for the local neighborhood of a pixel subtracted by a
+    constant. Alternatively the threshold can be determined dynamically by a
+    given function, using the 'generic' method.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        Input image.
+    block_size : int
+        Odd size of pixel neighborhood which is used to calculate the
+        threshold value (e.g. 3, 5, 7, ..., 21, ...).
+    method : {'generic', 'gaussian', 'mean', 'median'}, optional
+        Method used to determine adaptive threshold for local neighbourhood in
+        weighted mean image.
+
+        * 'generic': use custom function (see ``param`` parameter)
+        * 'gaussian': apply gaussian filter (see ``param`` parameter for custom\
+                      sigma value)
+        * 'mean': apply arithmetic mean filter
+        * 'median': apply median rank filter
+
+        By default the 'gaussian' method is used.
+    offset : float, optional
+        Constant subtracted from weighted mean of neighborhood to calculate
+        the local threshold value. Default offset is 0.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        cval is the value when mode is equal to 'constant'.
+        Default is 'reflect'.
+    param : {int, function}, optional
+        Either specify sigma for 'gaussian' method or function object for
+        'generic' method. This functions takes the flat array of local
+        neighbourhood as a single argument and returns the calculated
+        threshold for the centre pixel.
+    cval : float, optional
+        Value to fill past edges of input if mode is 'constant'.
+
+    Returns
+    -------
+    threshold : (N, M) ndarray
+        Threshold image. All pixels in the input image higher than the
+        corresponding pixel in the threshold image are considered foreground.
+
+    References
+    ----------
+    .. [1] https://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html?highlight=threshold#adaptivethreshold
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()[:50, :50]
+    >>> binary_image1 = image > threshold_local(image, 15, 'mean')
+    >>> func = lambda arr: arr.mean()
+    >>> binary_image2 = image > threshold_local(image, 15, 'generic',
+    ...                                         param=func)
+    """  # noqa
+    if block_size % 2 == 0:
+        raise ValueError("The kwarg ``block_size`` must be odd! Given "
+                         "``block_size`` {0} is even.".format(block_size))
+    check_nD(image, 2)
+    thresh_image = cp.zeros(image.shape, _float_dtype(image))
+    if method == 'generic':
+        raise NotImplementedError("TODO: implement generic_filter")
+        ndi.generic_filter(
+            image, param, block_size, output=thresh_image, mode=mode, cval=cval
+        )
+    elif method == 'gaussian':
+        if param is None:
+            # automatically determine sigma which covers > 99% of distribution
+            sigma = (block_size - 1) / 6.0
+        else:
+            sigma = param
+        ndi.gaussian_filter(image, sigma, output=thresh_image, mode=mode,
+                            cval=cval)
+    elif method == 'mean':
+        mask = 1.0 / block_size * cp.ones((block_size,))
+        # separation of filters to speedup convolution
+        ndi.convolve1d(image, mask, axis=0, output=thresh_image, mode=mode,
+                       cval=cval)
+        ndi.convolve1d(thresh_image, mask, axis=1, output=thresh_image,
+                       mode=mode, cval=cval)
+    elif method == 'median':
+        ndi.median_filter(image, block_size, output=thresh_image, mode=mode,
+                          cval=cval)
+    else:
+        raise ValueError("Invalid method specified. Please use `generic`, "
+                         "`gaussian`, `mean`, or `median`.")
+
+    return thresh_image - offset
+
+
+def _validate_image_histogram(image, hist, nbins=None):
+    """Ensure that either image or hist were given, return valid histogram.
+
+    If hist is given, image is ignored.
+
+    Parameters
+    ----------
+    image : array or None
+        Grayscale image.
+    hist : array, 2-tuple of array, or None
+        Histogram, either a 1D counts array, or an array of counts together
+        with an array of bin centers.
+    nbins : int, optional
+        The number of bins with which to compute the histogram, if `hist` is
+        None.
+
+    Returns
+    -------
+    counts : 1D array of float
+        Each element is the number of pixels falling in each intensity bin.
+    bin_centers : 1D array
+        Each element is the value corresponding to the center of each intensity
+        bin.
+
+    Raises
+    ------
+    ValueError : if image and hist are both None
+    """
+    if image is None and hist is None:
+        raise Exception("Either image or hist must be provided.")
+
+    if hist is not None:
+        if isinstance(hist, (tuple, list)):
+            counts, bin_centers = hist
+        else:
+            counts = hist
+            bin_centers = cp.arange(counts.size)
+    else:
+        counts, bin_centers = histogram(
+            image.ravel(), nbins, source_range="image"
+        )
+    return counts.astype(float), bin_centers
+
+
+def threshold_otsu(image=None, nbins=256, *, hist=None):
+    """Return threshold value based on Otsu's method.
+
+    Either image or hist must be provided. If hist is provided, the actual
+    histogram of the image is ignored.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray, optional
+        Grayscale input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+    hist : array, or 2-tuple of arrays, optional
+        Histogram from which to determine the threshold, and optionally a
+        corresponding array of bin center intensities.
+        An alternative use of this function is to pass it only hist.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    References
+    ----------
+    .. [1] Wikipedia, https://en.wikipedia.org/wiki/Otsu's_Method
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_otsu(image)
+    >>> binary = image <= thresh
+
+    Notes
+    -----
+    The input image must be grayscale.
+    """
+    if image is not None and image.ndim > 2 and image.shape[-1] in (3, 4):
+        msg = "threshold_otsu is expected to work correctly only for " \
+              "grayscale images; image shape {0} looks like an RGB image"
+        warn(msg.format(image.shape))
+
+    # Check if the image is multi-colored or not
+    # Check if the image has more than one intensity value; if not, return that
+    # value
+    if image is not None:
+        first_pixel = image.ravel()[0]
+        if cp.all(image == first_pixel):  # device synchronization!
+            return first_pixel
+
+    counts, bin_centers = _validate_image_histogram(image, hist, nbins)
+
+    # class probabilities for all possible thresholds
+    weight1 = _cumsum(counts)
+    weight2 = _cumsum(counts[::-1])[::-1]
+    # class means for all possible thresholds
+    mean1 = _cumsum(counts * bin_centers) / weight1
+    mean2 = (_cumsum((counts * bin_centers)[::-1]) / weight2[::-1])[::-1]
+
+    # Clip ends to align class 1 and class 2 variables:
+    # The last value of ``weight1``/``mean1`` should pair with zero values in
+    # ``weight2``/``mean2``, which do not exist.
+    variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:]) ** 2
+
+    idx = cp.argmax(variance12)
+    threshold = bin_centers[idx]
+
+    return threshold
+
+
+def threshold_yen(image=None, nbins=256, *, hist=None):
+    """Return threshold value based on Yen's method.
+    Either image or hist must be provided. In case hist is given, the actual
+    histogram of the image is ignored.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray, optional
+        Input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+    hist : array, or 2-tuple of arrays, optional
+        Histogram from which to determine the threshold, and optionally a
+        corresponding array of bin center intensities.
+        An alternative use of this function is to pass it only hist.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    References
+    ----------
+    .. [1] Yen J.C., Chang F.J., and Chang S. (1995) "A New Criterion
+           for Automatic Multilevel Thresholding" IEEE Trans. on Image
+           Processing, 4(3): 370-378. :DOI:`10.1109/83.366472`
+    .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
+           Techniques and Quantitative Performance Evaluation" Journal of
+           Electronic Imaging, 13(1): 146-165, :DOI:`10.1117/1.1631315`
+           http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf
+    .. [3] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_yen(image)
+    >>> binary = image <= thresh
+    """  # noqa
+    counts, bin_centers = _validate_image_histogram(image, hist, nbins)
+
+    # On blank images (e.g. filled with 0) with int dtype, `histogram()`
+    # returns ``bin_centers`` containing only one value. Speed up with it.
+    if bin_centers.size == 1:
+        return bin_centers[0]
+
+    # Calculate probability mass function
+    pmf = counts.astype(cp.float32) / counts.sum()
+    P1 = _cumsum(pmf)  # Cumulative normalized histogram
+    P1_sq = _cumsum(pmf * pmf)
+    # Get cumsum calculated from end of squared array:
+    P2_sq = _cumsum(pmf[::-1] ** 2)[::-1]
+    # P2_sq indexes is shifted +1. I assume, with P1[:-1] it's help avoid
+    # '-inf' in crit. ImageJ Yen implementation replaces those values by zero.
+    crit = cp.log(((P1_sq[:-1] * P2_sq[1:]) ** -1) *
+                  (P1[:-1] * (1.0 - P1[:-1])) ** 2)
+    return bin_centers[crit.argmax()]
+
+
+def threshold_isodata(image=None, nbins=256, return_all=False, *, hist=None):
+    """Return threshold value(s) based on ISODATA method.
+
+    Histogram-based threshold, known as Ridler-Calvard method or inter-means.
+    Threshold values returned satisfy the following equality::
+
+        threshold = (image[image <= threshold].mean() +
+                     image[image > threshold].mean()) / 2.0
+
+    That is, returned thresholds are intensities that separate the image into
+    two groups of pixels, where the threshold intensity is midway between the
+    mean intensities of these groups.
+
+    For integer images, the above equality holds to within one; for floating-
+    point images, the equality holds to within the histogram bin-width.
+
+    Either image or hist must be provided. In case hist is given, the actual
+    histogram of the image is ignored.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray, optional
+        Input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+    return_all : bool, optional
+        If False (default), return only the lowest threshold that satisfies
+        the above equality. If True, return all valid thresholds.
+    hist : array, or 2-tuple of arrays, optional
+        Histogram to determine the threshold from and a corresponding array
+        of bin center intensities. Alternatively, only the histogram can be
+        passed.
+
+    Returns
+    -------
+    threshold : float or int or array
+        Threshold value(s).
+
+    References
+    ----------
+    .. [1] Ridler, TW & Calvard, S (1978), "Picture thresholding using an
+           iterative selection method"
+           IEEE Transactions on Systems, Man and Cybernetics 8: 630-632,
+           :DOI:`10.1109/TSMC.1978.4310039`
+    .. [2] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
+           Techniques and Quantitative Performance Evaluation" Journal of
+           Electronic Imaging, 13(1): 146-165,
+           http://www.busim.ee.boun.edu.tr/~sankur/SankurFolder/Threshold_survey.pdf
+           :DOI:`10.1117/1.1631315`
+    .. [3] ImageJ AutoThresholder code,
+           http://fiji.sc/wiki/index.php/Auto_Threshold
+
+    Examples
+    --------
+    >>> from skimage.data import coins
+    >>> image = coins()
+    >>> thresh = threshold_isodata(image)
+    >>> binary = image > thresh
+    """
+    counts, bin_centers = _validate_image_histogram(image, hist, nbins)
+
+    # image only contains one unique value
+    if len(bin_centers) == 1:
+        if return_all:
+            return bin_centers
+        else:
+            return bin_centers[0]
+
+    counts = counts.astype(cp.float32)
+
+    # csuml and csumh contain the count of pixels in that bin or lower, and
+    # in all bins strictly higher than that bin, respectively
+    csuml = _cumsum(counts)
+    csumh = csuml[-1] - csuml
+
+    # intensity_sum contains the total pixel intensity from each bin
+    intensity_sum = counts * bin_centers
+
+    # l and h contain average value of all pixels in that bin or lower, and
+    # in all bins strictly higher than that bin, respectively.
+    # Note that since exp.histogram does not include empty bins at the low or
+    # high end of the range, csuml and csumh are strictly > 0, except in the
+    # last bin of csumh, which is zero by construction.
+    # So no worries about division by zero in the following lines, except
+    # for the last bin, but we can ignore that because no valid threshold
+    # can be in the top bin.
+    # To avoid the division by zero, we simply skip over the last element in
+    # all future computation.
+    csum_intensity = _cumsum(intensity_sum)
+    lower = csum_intensity[:-1] / csuml[:-1]
+    higher = (csum_intensity[-1] - csum_intensity[:-1]) / csumh[:-1]
+
+    # isodata finds threshold values that meet the criterion t = (l + m)/2
+    # where l is the mean of all pixels <= t and h is the mean of all pixels
+    # > t, as calculated above. So we are looking for places where
+    # (l + m) / 2 equals the intensity value for which those l and m figures
+    # were calculated -- which is, of course, the histogram bin centers.
+    # We only require this equality to be within the precision of the bin
+    # width, of course.
+    all_mean = (lower + higher) / 2.0
+    bin_width = bin_centers[1] - bin_centers[0]
+
+    # Look only at thresholds that are below the actual all_mean value,
+    # for consistency with the threshold being included in the lower pixel
+    # group. Otherwise can get thresholds that are not actually fixed-points
+    # of the isodata algorithm. For float images, this matters less, since
+    # there really can't be any guarantees anymore anyway.
+    distances = all_mean - bin_centers[:-1]
+    thresholds = bin_centers[:-1][(distances >= 0) & (distances < bin_width)]
+
+    if return_all:
+        return thresholds
+    else:
+        return thresholds[0]
+
+
+# Computing a histogram using cp.histogram on a uint8 image with bins=256
+# doesn't work and results in aliasing problems. We use a fully specified set
+# of bins to ensure that each uint8 value false into its own bin.
+_DEFAULT_ENTROPY_BINS = tuple(np.arange(-0.5, 255.51, 1))
+
+
+def _cross_entropy(image, threshold, bins=_DEFAULT_ENTROPY_BINS):
+    """Compute cross-entropy between distributions above and below a threshold.
+
+    Parameters
+    ----------
+    image : array
+        The input array of values.
+    threshold : float
+        The value dividing the foreground and background in ``image``.
+    bins : int or array of float, optional
+        The number of bins or the bin edges. (Any valid value to the ``bins``
+        argument of ``cp.histogram`` will work here.) For an exact calculation,
+        each unique value should have its own bin. The default value for bins
+        ensures exact handling of uint8 images: ``bins=256`` results in
+        aliasing problems due to bin width not being equal to 1.
+
+    Returns
+    -------
+    nu : float
+        The cross-entropy target value as defined in [1]_.
+
+    Notes
+    -----
+    See Li and Lee, 1993 [1]_; this is the objective function ``threshold_li``
+    minimizes. This function can be improved but this implementation most
+    closely matches equation 8 in [1]_ and equations 1-3 in [2]_.
+
+    References
+    ----------
+    .. [1] Li C.H. and Lee C.K. (1993) "Minimum Cross Entropy Thresholding"
+           Pattern Recognition, 26(4): 617-625
+           :DOI:`10.1016/0031-3203(93)90115-D`
+    .. [2] Li C.H. and Tam P.K.S. (1998) "An Iterative Algorithm for Minimum
+           Cross Entropy Thresholding" Pattern Recognition Letters, 18(8): 771-776
+           :DOI:`10.1016/S0167-8655(98)00057-9`
+    """  # noqa
+    bins = cp.asarray(bins)  # required for _DEFAULT_ENTROPY_BINS tuple
+    histogram, bin_edges = cp.histogram(image, bins=bins, density=True)
+    bin_centers = cp.convolve(bin_edges, cp.array([0.5, 0.5]), mode='valid')
+    t = cp.flatnonzero(bin_centers > threshold)[0]
+    m0a = cp.sum(histogram[:t])  # 0th moment, background
+    m0b = cp.sum(histogram[t:])
+    m1a = cp.sum(histogram[:t] * bin_centers[:t])  # 1st moment, background
+    m1b = cp.sum(histogram[t:] * bin_centers[t:])
+    mua = m1a / m0a  # mean value, background
+    mub = m1b / m0b
+    nu = -m1a * cp.log(mua) - m1b * cp.log(mub)
+    return nu
+
+
+def threshold_li(image, *, tolerance=None, initial_guess=None,
+                 iter_callback=None):
+    """Compute threshold value by Li's iterative Minimum Cross Entropy method.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+
+    tolerance : float, optional
+        Finish the computation when the change in the threshold in an iteration
+        is less than this value. By default, this is half the smallest
+        difference between intensity values in ``image``.
+
+    initial_guess : float or Callable[[array[float]], float], optional
+        Li's iterative method uses gradient descent to find the optimal
+        threshold. If the image intensity histogram contains more than two
+        modes (peaks), the gradient descent could get stuck in a local optimum.
+        An initial guess for the iteration can help the algorithm find the
+        globally-optimal threshold. A float value defines a specific start
+        point, while a callable should take in an array of image intensities
+        and return a float value. Example valid callables include
+        ``numpy.mean`` (default), ``lambda arr: numpy.quantile(arr, 0.95)``,
+        or even :func:`skimage.filters.threshold_otsu`.
+
+    iter_callback : Callable[[float], Any], optional
+        A function that will be called on the threshold at every iteration of
+        the algorithm.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    References
+    ----------
+    .. [1] Li C.H. and Lee C.K. (1993) "Minimum Cross Entropy Thresholding"
+           Pattern Recognition, 26(4): 617-625
+           :DOI:`10.1016/0031-3203(93)90115-D`
+    .. [2] Li C.H. and Tam P.K.S. (1998) "An Iterative Algorithm for Minimum
+           Cross Entropy Thresholding" Pattern Recognition Letters, 18(8): 771-776
+           :DOI:`10.1016/S0167-8655(98)00057-9`
+    .. [3] Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
+           Techniques and Quantitative Performance Evaluation" Journal of
+           Electronic Imaging, 13(1): 146-165
+           :DOI:`10.1117/1.1631315`
+    .. [4] ImageJ AutoThresholder code, http://fiji.sc/wiki/index.php/Auto_Threshold
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_li(image)
+    >>> binary = image > thresh
+    """  # noqa
+    # Remove nan:
+    image = image[~cp.isnan(image)]
+    if image.size == 0:
+        return cp.nan
+
+    # Make sure image has more than one value; otherwise, return that value
+    # This works even for cp.inf
+    val0 = image.ravel()[0]
+    if cp.all(image == val0):  # device synchronize!
+        return val0
+
+    # At this point, the image only contains cp.inf, -cp.inf, or valid numbers
+    image = image[cp.isfinite(image)]
+    # if there are no finite values in the image, return 0. This is because
+    # at this point we *know* that there are *both* inf and -inf values,
+    # because inf == inf evaluates to True. We might as well separate them.
+    if image.size == 0:
+        return 0.0
+
+    # Li's algorithm requires positive image (because of log(mean))
+    image_min = cp.min(image)
+    image -= image_min
+    tolerance = tolerance or cp.min(cp.diff(cp.unique(image))) / 2
+
+    # Initial estimate for iteration. See "initial_guess" in the parameter list
+    if initial_guess is None:
+        t_next = cp.mean(image)
+    elif callable(initial_guess):
+        t_next = initial_guess(image)
+    elif cp.isscalar(initial_guess):  # convert to new, positive image range
+        t_next = initial_guess - image_min
+        image_max = cp.max(image) + image_min
+        if not 0 < t_next < cp.max(image):
+            msg = ('The initial guess for threshold_li must be within the '
+                   'range of the image. Got {} for image min {} and max {} '
+                   .format(initial_guess, image_min, image_max))
+            raise ValueError(msg)
+    else:
+        raise TypeError('Incorrect type for `initial_guess`; should be '
+                        'a floating point value, or a function mapping an '
+                        'array to a floating point value.')
+
+    # initial value for t_curr must be different from t_next by at
+    # least the tolerance. Since the image is positive, we ensure this
+    # by setting to a large-enough negative number
+    t_curr = -2 * tolerance
+
+    # Callback on initial iterations
+    if iter_callback is not None:
+        iter_callback(t_next + image_min)
+
+    # Stop the iterations when the difference between the
+    # new and old threshold values is less than the tolerance
+    while abs(t_next - t_curr) > tolerance:
+        t_curr = t_next
+        foreground = image > t_curr
+        mean_fore = cp.mean(image[foreground])
+        mean_back = cp.mean(image[~foreground])
+
+        t_next = ((mean_back - mean_fore) /
+                  (cp.log(mean_back) - cp.log(mean_fore)))
+
+        if iter_callback is not None:
+            iter_callback(t_next + image_min)
+
+    threshold = t_next + image_min
+    return threshold
+
+
+def threshold_minimum(image=None, nbins=256, max_iter=10000, *, hist=None):
+    """Return threshold value based on minimum method.
+
+    The histogram of the input ``image`` is computed if not provided and
+    smoothed until there are only two maxima. Then the minimum in between is
+    the threshold value.
+
+    Either image or hist must be provided. In case hist is given, the actual
+    histogram of the image is ignored.
+
+    Parameters
+    ----------
+    image : (M, N) ndarray, optional
+        Input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+    max_iter : int, optional
+        Maximum number of iterations to smooth the histogram.
+    hist : array, or 2-tuple of arrays, optional
+        Histogram to determine the threshold from and a corresponding array
+        of bin center intensities. Alternatively, only the histogram can be
+        passed.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    Raises
+    ------
+    RuntimeError
+        If unable to find two local maxima in the histogram or if the
+        smoothing takes more than 1e4 iterations.
+
+    References
+    ----------
+    .. [1] C. A. Glasbey, "An analysis of histogram-based thresholding
+           algorithms," CVGIP: Graphical Models and Image Processing,
+           vol. 55, pp. 532-537, 1993.
+    .. [2] Prewitt, JMS & Mendelsohn, ML (1966), "The analysis of cell
+           images", Annals of the New York Academy of Sciences 128: 1035-1053
+           :DOI:`10.1111/j.1749-6632.1965.tb11715.x`
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_minimum(image)
+    >>> binary = image > thresh
+    """
+
+    def find_local_maxima_idx(hist):
+        # We can't use scipy.signal.argrelmax
+        # as it fails on plateaus
+        maximum_idxs = list()
+        direction = 1
+
+        # TODO: better to transfer hist back to cpu?
+        hist = cp.asnumpy(hist)  # device synchronize
+
+        for i in range(hist.shape[0] - 1):
+            if direction > 0:
+                if hist[i + 1] < hist[i]:
+                    direction = -1
+                    maximum_idxs.append(i)
+            else:
+                if hist[i + 1] > hist[i]:
+                    direction = 1
+
+        return maximum_idxs
+
+    counts, bin_centers = _validate_image_histogram(image, hist, nbins)
+
+    smooth_hist = counts.astype(cp.float64, copy=False)
+
+    for counter in range(max_iter):
+        smooth_hist = ndi.uniform_filter1d(smooth_hist, 3)
+        maximum_idxs = find_local_maxima_idx(smooth_hist)
+        if len(maximum_idxs) < 3:
+            break
+
+    if len(maximum_idxs) != 2:
+        raise RuntimeError('Unable to find two maxima in histogram')
+    elif counter == max_iter - 1:
+        raise RuntimeError('Maximum iteration reached for histogram'
+                           'smoothing')
+
+    # Find lowest point between the maxima
+    threshold_idx = cp.argmin(
+        smooth_hist[maximum_idxs[0]:maximum_idxs[1] + 1]
+    )
+
+    return bin_centers[maximum_idxs[0] + int(threshold_idx)]
+
+
+def threshold_mean(image):
+    """Return threshold value based on the mean of grayscale values.
+
+    Parameters
+    ----------
+    image : (N, M[, ..., P]) ndarray
+        Grayscale input image.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    References
+    ----------
+    .. [1] C. A. Glasbey, "An analysis of histogram-based thresholding
+        algorithms," CVGIP: Graphical Models and Image Processing,
+        vol. 55, pp. 532-537, 1993.
+        :DOI:`10.1006/cgip.1993.1040`
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_mean(image)
+    >>> binary = image > thresh
+    """
+    return cp.mean(image)
+
+
+def threshold_triangle(image, nbins=256):
+    """Return threshold value based on the triangle algorithm.
+
+    Parameters
+    ----------
+    image : (N, M[, ..., P]) ndarray
+        Grayscale input image.
+    nbins : int, optional
+        Number of bins used to calculate histogram. This value is ignored for
+        integer arrays.
+
+    Returns
+    -------
+    threshold : float
+        Upper threshold value. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    References
+    ----------
+    .. [1] Zack, G. W., Rogers, W. E. and Latt, S. A., 1977,
+       Automatic Measurement of Sister Chromatid Exchange Frequency,
+       Journal of Histochemistry and Cytochemistry 25 (7), pp. 741-753
+       :DOI:`10.1177/25.7.70454`
+    .. [2] ImageJ AutoThresholder code,
+       http://fiji.sc/wiki/index.php/Auto_Threshold
+
+    Examples
+    --------
+    >>> from skimage.data import camera
+    >>> image = camera()
+    >>> thresh = threshold_triangle(image)
+    >>> binary = image > thresh
+    """
+    # nbins is ignored for integer arrays
+    # so, we recalculate the effective nbins.
+    hist, bin_centers = histogram(image.ravel(), nbins, source_range='image')
+    nbins = len(hist)
+
+    # Find peak, lowest and highest gray levels.
+    arg_peak_height = cp.argmax(hist)
+    peak_height = hist[arg_peak_height]
+    arg_low_level, arg_high_level = cp.where(hist > 0)[0][[0, -1]]
+
+    # Flip is True if left tail is shorter.
+    flip = arg_peak_height - arg_low_level < arg_high_level - arg_peak_height
+    if flip:
+        hist = hist[::-1]
+        arg_low_level = nbins - arg_high_level - 1
+        arg_peak_height = nbins - arg_peak_height - 1
+
+    # If flip == True, arg_high_level becomes incorrect
+    # but we don't need it anymore.
+    del arg_high_level
+
+    # Set up the coordinate system.
+    width = arg_peak_height - arg_low_level
+    x1 = cp.arange(width)
+    y1 = hist[x1 + arg_low_level]
+
+    # Normalize.
+    norm = cp.sqrt(peak_height ** 2 + width ** 2)
+    try:
+        peak_height /= norm
+        width /= norm
+    except TypeError:
+        # TODO: CuPy bug?
+        # workaround for:
+        #    TypeError: output (typecode 'd') could not be coerced to provided
+        #    output parameter (typecode 'l') according to the casting rule
+        #    "same_kind"
+        peak_height = peak_height / norm
+        width = width / norm
+
+    # Maximize the length.
+    # The ImageJ implementation includes an additional constant when calculating
+    # the length, but here we omit it as it does not affect the location of the
+    # minimum.
+    length = peak_height * x1 - width * y1
+    arg_level = cp.argmax(length) + arg_low_level
+
+    if flip:
+        arg_level = nbins - arg_level - 1
+
+    return bin_centers[arg_level]
+
+
+def _mean_std(image, w):
+    """Return local mean and standard deviation of each pixel using a
+    neighborhood defined by a rectangular window size ``w``.
+    The algorithm uses integral images to speedup computation. This is
+    used by :func:`threshold_niblack` and :func:`threshold_sauvola`.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    w : int, or iterable of int
+        Window size specified as a single odd integer (3, 5, 7, …),
+        or an iterable of length ``image.ndim`` containing only odd
+        integers (e.g. ``(1, 5, 5)``).
+
+    Returns
+    -------
+    m : ndarray of float, same shape as ``image``
+        Local mean of the image.
+    s : ndarray of float, same shape as ``image``
+        Local standard deviation of the image.
+
+    References
+    ----------
+    .. [1] F. Shafait, D. Keysers, and T. M. Breuel, "Efficient
+           implementation of local adaptive thresholding techniques
+           using integral images." in Document Recognition and
+           Retrieval XV, (San Jose, USA), Jan. 2008.
+           :DOI:`10.1117/12.767755`
+    """
+    if not isinstance(w, Iterable):
+        w = (w,) * image.ndim
+    _validate_window_size(w)
+
+    pad_width = tuple((k // 2 + 1, k // 2) for k in w)
+    padded = cp.pad(image.astype('float'), pad_width,
+                    mode='reflect')
+
+    integral = integral_image(padded)
+    padded *= padded
+    integral_sq = integral_image(padded)
+
+    # Store the kernel as a list 2-tuples where:
+    #     - The first element is an index into the kernel (of shape w).
+    #     - The second element is the value at that index.
+    kernel_indices_and_values = []
+    for indices in itertools.product(*tuple([(0, _w) for _w in w])):
+        kernel_indices_and_values.append(
+            (indices, (-1) ** (image.ndim % 2 != np.sum(indices) % 2))
+        )
+
+    total_window_size = _misc.prod(w)
+    kernel_shape = tuple(_w + 1 for _w in w)
+    m = _correlate_sparse(integral, kernel_shape, kernel_indices_and_values)
+    m /= total_window_size
+    g2 = _correlate_sparse(integral_sq, kernel_shape,
+                           kernel_indices_and_values)
+    g2 /= total_window_size
+    # Note: we use np.clip because g2 is not guaranteed to be greater than
+    # m*m when floating point error is considered
+    s = cp.clip(g2 - m * m, 0, None)
+    cp.sqrt(s, out=s)
+    return m, s
+
+
+def threshold_niblack(image, window_size=15, k=0.2):
+    """Applies Niblack local threshold to an array.
+
+    A threshold T is calculated for every pixel in the image using the
+    following formula::
+
+        T = m(x,y) - k * s(x,y)
+
+    where m(x,y) and s(x,y) are the mean and standard deviation of
+    pixel (x,y) neighborhood defined by a rectangular window with size w
+    times w centered around the pixel. k is a configurable parameter
+    that weights the effect of standard deviation.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    window_size : int, or iterable of int, optional
+        Window size specified as a single odd integer (3, 5, 7, …),
+        or an iterable of length ``image.ndim`` containing only odd
+        integers (e.g. ``(1, 5, 5)``).
+    k : float, optional
+        Value of parameter k in threshold formula.
+
+    Returns
+    -------
+    threshold : (N, M) ndarray
+        Threshold mask. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    Notes
+    -----
+    This algorithm is originally designed for text recognition.
+
+    The Bradley threshold is a particular case of the Niblack
+    one, being equivalent to
+
+    >>> from skimage import data
+    >>> image = data.page()
+    >>> q = 1
+    >>> threshold_image = threshold_niblack(image, k=0) * q
+
+    for some value ``q``. By default, Bradley and Roth use ``q=1``.
+
+
+    References
+    ----------
+    .. [1] W. Niblack, An introduction to Digital Image Processing,
+           Prentice-Hall, 1986.
+    .. [2] D. Bradley and G. Roth, "Adaptive thresholding using Integral
+           Image", Journal of Graphics Tools 12(2), pp. 13-21, 2007.
+           :DOI:`10.1080/2151237X.2007.10129236`
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> image = data.page()
+    >>> threshold_image = threshold_niblack(image, window_size=7, k=0.1)
+    """
+    m, s = _mean_std(image, window_size)
+    return m - k * s
+
+
+def threshold_sauvola(image, window_size=15, k=0.2, r=None):
+    """Applies Sauvola local threshold to an array. Sauvola is a
+    modification of Niblack technique.
+
+    In the original method a threshold T is calculated for every pixel
+    in the image using the following formula::
+
+        T = m(x,y) * (1 + k * ((s(x,y) / R) - 1))
+
+    where m(x,y) and s(x,y) are the mean and standard deviation of
+    pixel (x,y) neighborhood defined by a rectangular window with size w
+    times w centered around the pixel. k is a configurable parameter
+    that weights the effect of standard deviation.
+    R is the maximum standard deviation of a greyscale image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    window_size : int, or iterable of int, optional
+        Window size specified as a single odd integer (3, 5, 7, …),
+        or an iterable of length ``image.ndim`` containing only odd
+        integers (e.g. ``(1, 5, 5)``).
+    k : float, optional
+        Value of the positive parameter k.
+    r : float, optional
+        Value of R, the dynamic range of standard deviation.
+        If None, set to the half of the image dtype range.
+
+    Returns
+    -------
+    threshold : (N, M) ndarray
+        Threshold mask. All pixels with an intensity higher than
+        this value are assumed to be foreground.
+
+    Notes
+    -----
+    This algorithm is originally designed for text recognition.
+
+    References
+    ----------
+    .. [1] J. Sauvola and M. Pietikainen, "Adaptive document image
+           binarization," Pattern Recognition 33(2),
+           pp. 225-236, 2000.
+           :DOI:`10.1016/S0031-3203(99)00055-2`
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> image = data.page()
+    >>> t_sauvola = threshold_sauvola(image, window_size=15, k=0.2)
+    >>> binary_image = image > t_sauvola
+    """
+    if r is None:
+        imin, imax = dtype_limits(image, clip_negative=False)
+        r = 0.5 * (imax - imin)
+    m, s = _mean_std(image, window_size)
+    return m * (1 + k * ((s / r) - 1))
+
+
+def apply_hysteresis_threshold(image, low, high):
+    """Apply hysteresis thresholding to ``image``.
+
+    This algorithm finds regions where ``image`` is greater than ``high``
+    OR ``image`` is greater than ``low`` *and* that region is connected to
+    a region greater than ``high``.
+
+    Parameters
+    ----------
+    image : array, shape (M,[ N, ..., P])
+        Grayscale input image.
+    low : float, or array of same shape as ``image``
+        Lower threshold.
+    high : float, or array of same shape as ``image``
+        Higher threshold.
+
+    Returns
+    -------
+    thresholded : array of bool, same shape as ``image``
+        Array in which ``True`` indicates the locations where ``image``
+        was above the hysteresis threshold.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.filters import apply_hysteresis_threshold
+    >>> image = cp.asarray([1, 2, 3, 2, 1, 2, 1, 3, 2])
+    >>> apply_hysteresis_threshold(image, 1.5, 2.5).astype(int)
+    array([0, 1, 1, 1, 0, 0, 0, 1, 1])
+
+    References
+    ----------
+    .. [1] J. Canny. A computational approach to edge detection.
+           IEEE Transactions on Pattern Analysis and Machine Intelligence.
+           1986; vol. 8, pp.679-698.
+           :DOI:`10.1109/TPAMI.1986.4767851`
+    """
+    low = cp.asarray(low)  # asarray to allow scalar low
+    # ensure low always below high
+    low = cp.clip(low, a_min=None, a_max=high)
+    mask_low = image > low
+    mask_high = image > high
+    # Connected components of mask_low
+    labels_low, num_labels = ndi.label(mask_low)
+    # Check which connected components contain pixels from mask_high
+
+    # CuPy Backend: refactored in the same style as features.canny to avoid
+    #               slow call to cupyx.scipy.ndimage.sum
+    nonzero_sums = cp.unique(labels_low[mask_high])
+    connected_to_high = cp.zeros((num_labels + 1,), bool)
+    connected_to_high[nonzero_sums] = True
+    thresholded = connected_to_high[labels_low]
+    return thresholded
+
+
+def threshold_multiotsu(image, classes=3, nbins=256):
+    r"""Generate `classes`-1 threshold values to divide gray levels in `image`.
+
+    The threshold values are chosen to maximize the total sum of pairwise
+    variances between the thresholded graylevel classes. See Notes and [1]_
+    for more details.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        Grayscale input image.
+    classes : int, optional
+        Number of classes to be thresholded, i.e. the number of resulting
+        regions.
+    nbins : int, optional
+        Number of bins used to calculate the histogram. This value is ignored
+        for integer arrays.
+
+    Returns
+    -------
+    thresh : array
+        Array containing the threshold values for the desired classes.
+
+    Raises
+    ------
+    ValueError
+         If ``image`` contains less grayscale value then the desired
+         number of classes.
+
+    Notes
+    -----
+    This implementation relies on a Cython function whose complexity
+    is :math:`O\left(\frac{Ch^{C-1}}{(C-1)!}\right)`, where :math:`h`
+    is the number of histogram bins and :math:`C` is the number of
+    classes desired.
+
+    The input image must be grayscale.
+
+    References
+    ----------
+    .. [1] Liao, P-S., Chen, T-S. and Chung, P-C., "A fast algorithm for
+           multilevel thresholding", Journal of Information Science and
+           Engineering 17 (5): 713-727, 2001. Available at:
+           <https://ftp.iis.sinica.edu.tw/JISE/2001/200109_01.pdf>
+           :DOI:`10.6688/JISE.2001.17.5.1`
+    .. [2] Tosa, Y., "Multi-Otsu Threshold", a java plugin for ImageJ.
+           Available at:
+           <http://imagej.net/plugins/download/Multi_OtsuThreshold.java>
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.color import label2rgb
+    >>> from skimage import data
+    >>> image = cp.asarray(data.camera())
+    >>> thresholds = threshold_multiotsu(image)
+    >>> regions = cp.digitize(image, bins=thresholds)
+    >>> regions_colorized = label2rgb(regions)
+
+    """
+    try:
+        from skimage.filters._multiotsu import (
+            _get_multiotsu_thresh_indices, _get_multiotsu_thresh_indices_lut)
+    except ImportError:
+        raise ImportError(
+            "could not the required (private) multi-otsu helper functions "
+            "from scikit-image"
+        )
+
+    if len(image.shape) > 2 and image.shape[-1] in (3, 4):
+        msg = ("threshold_multiotsu is expected to work correctly only for "
+               "grayscale images; image shape {0} looks like an RGB image")
+        warn(msg.format(image.shape))
+
+    # calculating the histogram and the probability of each gray level.
+    prob, bin_centers = histogram(image.ravel(),
+                                  nbins=nbins,
+                                  source_range='image',
+                                  normalize=True)
+
+    nvalues = cp.count_nonzero(prob)
+    if nvalues < classes:
+        msg = ("The input image has only {} different values. "
+               "It can not be thresholded in {} classes")
+        raise ValueError(msg.format(nvalues, classes))
+    elif nvalues == classes:
+        thresh_idx = cp.where(prob > 0)[0][:-1]
+    else:
+        # Need probabilities on the CPU to use the Cython code
+        # CuPy Backend: prob is small, so CPU computations should be faster
+        prob = cp.asnumpy(prob)  # synchronization!
+        prob = prob.astype("float32")
+
+        # Get threshold indices
+        try:
+            thresh_idx = _get_multiotsu_thresh_indices_lut(prob, classes - 1)
+        except MemoryError:
+            # Don't use LUT if the number of bins is too large (if the
+            # image is uint16 for example): in this case, the
+            # allocated memory is too large.
+            thresh_idx = _get_multiotsu_thresh_indices(prob, classes - 1)
+        # transfer indices back to the GPU
+        thresh_idx = cp.asarray(thresh_idx)  # synchronization!
+
+    thresh = bin_centers[thresh_idx]
+
+    return thresh
diff --git a/python/cucim/src/cucim/skimage/measure/__init__.py b/python/cucim/src/cucim/skimage/measure/__init__.py
new file mode 100644
index 000000000..38937c96c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/__init__.py
@@ -0,0 +1,30 @@
+from ._label import label
+from ._moments import (centroid, inertia_tensor, inertia_tensor_eigvals,
+                       moments, moments_central, moments_coords,
+                       moments_coords_central, moments_hu, moments_normalized)
+from ._polygon import approximate_polygon, subdivide_polygon
+from ._regionprops import perimeter, regionprops, regionprops_table
+from .block import block_reduce
+from .entropy import shannon_entropy
+from .profile import profile_line
+
+__all__ = [
+    "regionprops",
+    "regionprops_table",
+    "perimeter",
+    "approximate_polygon",
+    "subdivide_polygon",
+    "block_reduce",
+    "centroid",
+    "moments",
+    "moments_central",
+    "moments_coords",
+    "moments_coords_central",
+    "moments_normalized",
+    "moments_hu",
+    "inertia_tensor",
+    "inertia_tensor_eigvals",
+    "profile_line",
+    "label",
+    "shannon_entropy",
+]
diff --git a/python/cucim/src/cucim/skimage/measure/_label.py b/python/cucim/src/cucim/skimage/measure/_label.py
new file mode 100644
index 000000000..8d5351d86
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_label.py
@@ -0,0 +1,125 @@
+import cupy as cp
+import scipy.ndimage as cpu_ndi
+
+from ._label_kernels import _label
+
+
+def _get_structure(ndim, connectivity):
+    if connectivity is None:
+        # use the full connectivity by default
+        connectivity = ndim
+    if not 1 <= connectivity <= ndim:
+        raise ValueError("Connectivity below 1 or above %d is illegal." % ndim)
+    return cpu_ndi.generate_binary_structure(ndim, connectivity)
+
+
+# TODO: currently uses int32 for the labels. should add int64 option as well
+def label(input, background=None, return_num=False, connectivity=None):
+    r"""Label connected regions of an integer array.
+
+    Two pixels are connected when they are neighbors and have the same value.
+    In 2D, they can be neighbors either in a 1- or 2-connected sense.
+    The value refers to the maximum number of orthogonal hops to consider a
+    pixel/voxel a neighbor::
+
+      1-connectivity     2-connectivity     diagonal connection close-up
+
+           [ ]           [ ]  [ ]  [ ]             [ ]
+            |               \  |  /                 |  <- hop 2
+      [ ]--[x]--[ ]      [ ]--[x]--[ ]        [x]--[ ]
+            |               /  |  \             hop 1
+           [ ]           [ ]  [ ]  [ ]
+
+    Parameters
+    ----------
+    input : ndarray of dtype int
+        Image to label.
+    background : int, optional
+        Consider all pixels with this value as background pixels, and label
+        them as 0. By default, 0-valued pixels are considered as background
+        pixels.
+    return_num : bool, optional
+        Whether to return the number of assigned labels.
+    connectivity : int, optional
+        Maximum number of orthogonal hops to consider a pixel/voxel
+        as a neighbor.
+        Accepted values are ranging from  1 to input.ndim. If ``None``, a full
+        connectivity of ``input.ndim`` is used.
+
+    Returns
+    -------
+    labels : ndarray of dtype int
+        Labeled array, where all connected regions are assigned the
+        same integer value.
+    num : int, optional
+        Number of labels, which equals the maximum label index and is only
+        returned if return_num is `True`.
+
+    See Also
+    --------
+    regionprops
+    regionprops_table
+
+    References
+    ----------
+    .. [1] Christophe Fiorio and Jens Gustedt, "Two linear time Union-Find
+           strategies for image processing", Theoretical Computer Science
+           154 (1996), pp. 165-181.
+    .. [2] Kensheng Wu, Ekow Otoo and Arie Shoshani, "Optimizing connected
+           component labeling algorithms", Paper LBNL-56864, 2005,
+           Lawrence Berkeley National Laboratory (University of California),
+           http://repositories.cdlib.org/lbnl/LBNL-56864
+
+    Notes
+    -----
+    Currently the cucim implementation of this function always uses 32-bit
+    integers for the label array. This is done for performance. In the future
+    64-bit integer support may also be added for better skimage compatibility.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> x = cp.eye(3).astype(int)
+    >>> print(x)
+    [[1 0 0]
+     [0 1 0]
+     [0 0 1]]
+    >>> print(label(x, connectivity=1))
+    [[1 0 0]
+     [0 2 0]
+     [0 0 3]]
+    >>> print(label(x, connectivity=2))
+    [[1 0 0]
+     [0 1 0]
+     [0 0 1]]
+    >>> print(label(x, background=-1))
+    [[1 2 2]
+     [2 1 2]
+     [2 2 1]]
+    >>> x = cp.asarray([[1, 0, 0],
+    ...                 [1, 1, 5],
+    ...                 [0, 0, 0]])
+    >>> print(label(x))
+    [[1 0 0]
+     [1 1 2]
+     [0 0 0]]
+    """
+    ndim = input.ndim
+    structure = _get_structure(ndim, connectivity)
+    if background is None:
+        background = 0
+    elif background != 0:
+        # offset so that background becomes 0 as expected by _label below
+        input = input - background
+
+    if input.dtype.kind not in "bui":
+        # skimage always copies the input into a np.intp dtype array so do the
+        # same here for non-integer dtypes.
+        input = input.astype(cp.intp)
+
+    labels = cp.empty(input.shape, order="C", dtype=cp.int32)
+    num = _label(input, structure, labels, greyscale_mode=True)
+
+    if return_num:
+        return labels, num
+    return labels
diff --git a/python/cucim/src/cucim/skimage/measure/_label_kernels.py b/python/cucim/src/cucim/skimage/measure/_label_kernels.py
new file mode 100644
index 000000000..ce4199e8f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_label_kernels.py
@@ -0,0 +1,194 @@
+"""Kernels for scikit-image label.
+
+These are copied from CuPy, with modification to add a greyscale_mode
+parameter as needed for scikit-image.
+
+"""
+import cupy
+import numpy
+
+
+def _label(x, structure, y, greyscale_mode=False):
+    elems = numpy.where(structure != 0)
+    vecs = [elems[dm] - 1 for dm in range(x.ndim)]
+    offset = vecs[0]
+    for dm in range(1, x.ndim):
+        offset = offset * 3 + vecs[dm]
+    indxs = numpy.where(offset < 0)[0]
+    dirs = [[vecs[dm][dr] for dm in range(x.ndim)] for dr in indxs]
+    dirs = cupy.array(dirs, dtype=numpy.int32)
+    ndirs = indxs.shape[0]
+    y_shape = cupy.array(y.shape, dtype=numpy.int32)
+    count = cupy.zeros(2, dtype=numpy.int32)
+    _kernel_init()(x, y)
+    try:
+        int_t = int_types[y.dtype.char]
+    except KeyError:
+        raise ValueError("y must have int32, uint16, uint32 or uint64 dtype")
+    if int_t != "int":
+        raise NotImplementedError(
+            "Currently only 32-bit integer case is implemented"
+        )
+    if greyscale_mode:
+        _kernel_connect(True, int_t)(
+            x, y_shape, dirs, ndirs, x.ndim, y, size=y.size
+        )
+    else:
+        _kernel_connect(False, int_t)(
+            y_shape, dirs, ndirs, x.ndim, y, size=y.size
+        )
+    _kernel_count()(y, count, size=y.size)
+    maxlabel = int(count[0])  # synchronize
+    labels = cupy.empty(maxlabel, dtype=numpy.int32)
+    _kernel_labels()(y, count, labels, size=y.size)
+    _kernel_finalize()(maxlabel, cupy.sort(labels), y, size=y.size)
+    return maxlabel
+
+
+"""
+Elementwise kernels for use by label
+"""
+
+
+def _kernel_init():
+    return cupy.ElementwiseKernel(
+        "X x",
+        "Y y",
+        "if (x == 0) { y = -1; } else { y = i; }",
+        "cucim_nd_label_init",
+    )
+
+
+def _kernel_connect(greyscale_mode=False, int_t="int"):
+    """
+    Notes
+    -----
+    dirs is a (n_neig//2, ndim) of relative offsets to the neighboring voxels.
+    For example, for structure = np.ones((3, 3)):
+        dirs = array([[-1, -1],
+                      [-1,  0],
+                      [-1,  1],
+                      [ 0, -1]], dtype=int32)
+    (Implementation assumes a centro-symmetric structure)
+    ndirs = dirs.shape[0]
+
+    In the dirs loop below, there is a loop over the ndim neighbors:
+        Here, index j corresponds to the current pixel and k is the current
+        neighbor location.
+    """
+    in_params = "raw int32 shape, raw int32 dirs, int32 ndirs, int32 ndim"
+    if greyscale_mode:
+        # greyscale mode -> different values receive different labels
+        x_condition = "if (x[k] != x[j]) continue;"
+        in_params = "raw X x, " + in_params
+    else:
+        # binary mode -> all non-background voxels treated the same
+        x_condition = ""
+
+    # Note: atomicCAS is implemented for int, unsigned short, unsigned int, and
+    # unsigned long long
+
+    code = """
+        if (y[i] < 0) continue;
+        for (int dr = 0; dr < ndirs; dr++) {{
+            {int_t} j = i;
+            {int_t} rest = j;
+            {int_t} stride = 1;
+            {int_t} k = 0;
+            for (int dm = ndim-1; dm >= 0; dm--) {{
+                int pos = rest % shape[dm] + dirs[dm + dr * ndim];
+                if (pos < 0 || pos >= shape[dm]) {{
+                    k = -1;
+                    break;
+                }}
+                k += pos * stride;
+                rest /= shape[dm];
+                stride *= shape[dm];
+            }}
+            if (k < 0) continue;
+            if (y[k] < 0) continue;
+            {x_condition}
+            while (1) {{
+                while (j != y[j]) {{ j = y[j]; }}
+                while (k != y[k]) {{ k = y[k]; }}
+                if (j == k) break;
+                if (j < k) {{
+                    {int_t} old = atomicCAS( &y[k], (Y)k, (Y)j );
+                    if (old == k) break;
+                    k = old;
+                }}
+                else {{
+                    {int_t} old = atomicCAS( &y[j], (Y)j, (Y)k );
+                    if (old == j) break;
+                    j = old;
+                }}
+            }}
+        }}
+        """.format(
+        x_condition=x_condition, int_t=int_t
+    )
+
+    return cupy.ElementwiseKernel(
+        in_params, "raw Y y", code, "cucim_nd_label_connect",
+    )
+
+
+def _kernel_count():
+    return cupy.ElementwiseKernel(
+        "",
+        "raw Y y, raw int32 count",
+        """
+        if (y[i] < 0) continue;
+        int j = i;
+        while (j != y[j]) { j = y[j]; }
+        if (j != i) y[i] = j;
+        else atomicAdd(&count[0], 1);
+        """,
+        "cucim_nd_label_count",
+    )
+
+
+def _kernel_labels():
+    return cupy.ElementwiseKernel(
+        "",
+        "raw Y y, raw int32 count, raw int32 labels",
+        """
+        if (y[i] != i) continue;
+        int j = atomicAdd(&count[1], 1);
+        labels[j] = i;
+        """,
+        "cucim_nd_label_labels",
+    )
+
+
+def _kernel_finalize():
+    return cupy.ElementwiseKernel(
+        "int32 maxlabel",
+        "raw int32 labels, raw Y y",
+        """
+        if (y[i] < 0) {
+            y[i] = 0;
+            continue;
+        }
+        int yi = y[i];
+        int j_min = 0;
+        int j_max = maxlabel - 1;
+        int j = (j_min + j_max) / 2;
+        while (j_min < j_max) {
+            if (yi == labels[j]) break;
+            if (yi < labels[j]) j_max = j - 1;
+            else j_min = j + 1;
+            j = (j_min + j_max) / 2;
+        }
+        y[i] = j + 1;
+        """,
+        "cucim_nd_label_finalize",
+    )
+
+
+int_types = {
+    "i": "int",
+    "H": "unsigned short",
+    "I": "unsigned int",
+    "L": "unsigned long long",
+}
diff --git a/python/cucim/src/cucim/skimage/measure/_moments.py b/python/cucim/src/cucim/skimage/measure/_moments.py
new file mode 100644
index 000000000..c6d2b9708
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_moments.py
@@ -0,0 +1,537 @@
+import itertools
+
+import cupy as cp
+import numpy as np
+
+from .._shared.utils import check_nD
+
+
+def moments_coords(coords, order=3):
+    """Calculate all raw image moments up to a certain order.
+
+    The following properties can be calculated from raw image moments:
+     * Area as: ``M[0, 0]``.
+     * Centroid as: {``M[1, 0] / M[0, 0]``, ``M[0, 1] / M[0, 0]``}.
+
+    Note that raw moments are neither translation, scale nor rotation
+    invariant.
+
+    Parameters
+    ----------
+    coords : (N, D) floating point or uint8 array
+        Array of N points that describe an image of D dimensionality in
+        Cartesian space.
+    order : int, optional
+        Maximum order of moments. Default is 3.
+
+    Returns
+    -------
+    M : (``order + 1``, ``order + 1``, ...) array
+        Raw image moments. (D dimensions)
+
+    References
+    ----------
+    .. [1] Johannes Kilian. Simple Image Analysis By Moments. Durham
+           University, version 0.2, Durham, 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import moments_coords
+    >>> coords = cp.array([[row, col]
+    ...                    for row in range(13, 17)
+    ...                    for col in range(14, 18)], dtype=cp.float64)
+    >>> M = moments_coords(coords)
+    >>> centroid = (M[1, 0] / M[0, 0], M[0, 1] / M[0, 0])
+    >>> centroid
+    (14.5, 15.5)
+    """
+    return moments_coords_central(coords, 0, order=order)
+
+
+def moments_coords_central(coords, center=None, order=3):
+    """Calculate all central image moments up to a certain order.
+
+    The following properties can be calculated from raw image moments:
+     * Area as: ``M[0, 0]``.
+     * Centroid as: {``M[1, 0] / M[0, 0]``, ``M[0, 1] / M[0, 0]``}.
+
+    Note that raw moments are neither translation, scale nor rotation
+    invariant.
+
+    Parameters
+    ----------
+    coords : (N, D) floating point or uint8 array
+        Array of N points that describe an image of D dimensionality in
+        Cartesian space. A tuple of coordinates as returned by
+        ``cp.nonzero`` is also accepted as input.
+    center : tuple of float, optional
+        Coordinates of the image centroid. This will be computed if it
+        is not provided.
+    order : int, optional
+        Maximum order of moments. Default is 3.
+
+    Returns
+    -------
+    Mc : (``order + 1``, ``order + 1``, ...) array
+        Central image moments. (D dimensions)
+
+    References
+    ----------
+    .. [1] Johannes Kilian. Simple Image Analysis By Moments. Durham
+           University, version 0.2, Durham, 2001.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import moments_coords_central
+    >>> coords = cp.array([[row, col]
+    ...                    for row in range(13, 17)
+    ...                    for col in range(14, 18)])
+    >>> moments_coords_central(coords)
+    array([[16.,  0., 20.,  0.],
+           [ 0.,  0.,  0.,  0.],
+           [20.,  0., 25.,  0.],
+           [ 0.,  0.,  0.,  0.]])
+
+    As seen above, for symmetric objects, odd-order moments (columns 1 and 3,
+    rows 1 and 3) are zero when centered on the centroid, or center of mass,
+    of the object (the default). If we break the symmetry by adding a new
+    point, this no longer holds:
+
+    >>> coords2 = cp.concatenate((coords, [[17, 17]]), axis=0)
+    >>> cp.round(moments_coords_central(coords2),
+    ...          decimals=2)  # doctest: +NORMALIZE_WHITESPACE
+    array([[17.  ,  0.  , 22.12, -2.49],
+           [ 0.  ,  3.53,  1.73,  7.4 ],
+           [25.88,  6.02, 36.63,  8.83],
+           [ 4.15, 19.17, 14.8 , 39.6 ]])
+
+    Image moments and central image moments are equivalent (by definition)
+    when the center is (0, 0):
+
+    >>> cp.allclose(moments_coords(coords),
+    ...             moments_coords_central(coords, (0, 0)))
+    True
+    """
+    if isinstance(coords, tuple):
+        # This format corresponds to coordinate tuples as returned by
+        # e.g. cp.nonzero: (row_coords, column_coords).
+        # We represent them as an npoints x ndim array.
+        coords = cp.stack(coords, axis=-1)
+    float_dtype = coords.dtype if coords.dtype.kind == 'f' else cp.float64
+    check_nD(coords, 2)
+    ndim = coords.shape[1]
+    if center is None:
+        center = cp.mean(coords, axis=0)
+    else:
+        center = cp.asarray(center, dtype=float_dtype)
+
+    # center the coordinates
+    coords = coords.astype(float_dtype, copy=False)
+    coords -= center
+
+    # CuPy backend: for efficiency, sum over the last axis
+    #               (which is memory contiguous)
+    # generate all possible exponents for each axis in the given set of points
+    # produces a matrix of shape (order + 1, D, N)
+    coords = coords.T
+    powers = cp.arange(order + 1, dtype=float_dtype)[:, np.newaxis, np.newaxis]
+    coords = coords[cp.newaxis, ...] ** powers
+
+    # add extra dimensions for proper broadcasting
+    coords = coords.reshape((1,) * (ndim - 1) + coords.shape)
+
+    calc = cp.moveaxis(coords[..., 0, :], -2, 0)
+
+    for axis in range(1, ndim):
+        # isolate each point's axis
+        isolated_axis = coords[..., axis, :]
+
+        # rotate orientation of matrix for proper broadcasting
+        isolated_axis = cp.moveaxis(isolated_axis, -2, axis)
+
+        # calculate the moments for each point, one axis at a time
+        calc = calc * isolated_axis
+    # sum all individual point moments to get our final answer
+    Mc = cp.sum(calc, axis=-1)
+
+    return Mc
+
+
+def moments(image, order=3):
+    """Calculate all raw image moments up to a certain order.
+
+    The following properties can be calculated from raw image moments:
+     * Area as: ``M[0, 0]``.
+     * Centroid as: {``M[1, 0] / M[0, 0]``, ``M[0, 1] / M[0, 0]``}.
+
+    Note that raw moments are neither translation, scale nor rotation
+    invariant.
+
+    Parameters
+    ----------
+    image : nD floating point or uint8 array
+        Rasterized shape as image.
+    order : int, optional
+        Maximum order of moments. Default is 3.
+
+    Returns
+    -------
+    m : (``order + 1``, ``order + 1``) array
+        Raw image moments.
+
+    References
+    ----------
+    .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
+           Core Algorithms. Springer-Verlag, London, 2009.
+    .. [2] B. Jähne. Digital Image Processing. Springer-Verlag,
+           Berlin-Heidelberg, 6. edition, 2005.
+    .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
+           Features, from Lecture notes in computer science, p. 676. Springer,
+           Berlin, 1993.
+    .. [4] https://en.wikipedia.org/wiki/Image_moment
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import moments
+    >>> image = cp.zeros((20, 20), dtype=cp.float64)
+    >>> image[13:17, 13:17] = 1
+    >>> M = moments(image)
+    >>> centroid = (M[1, 0] / M[0, 0], M[0, 1] / M[0, 0])
+    >>> centroid
+    (14.5, 14.5)
+    """
+    return moments_central(image, (0,) * image.ndim, order=order)
+
+
+def moments_central(image, center=None, order=3, **kwargs):
+    """Calculate all central image moments up to a certain order.
+
+    The center coordinates (cr, cc) can be calculated from the raw moments as:
+    {``M[1, 0] / M[0, 0]``, ``M[0, 1] / M[0, 0]``}.
+
+    Note that central moments are translation invariant but not scale and
+    rotation invariant.
+
+    Parameters
+    ----------
+    image : nD floating point or uint8 array
+        Rasterized shape as image.
+    center : tuple of float, optional
+        Coordinates of the image centroid. This will be computed if it
+        is not provided.
+    order : int, optional
+        The maximum order of moments computed.
+
+    Returns
+    -------
+    mu : (``order + 1``, ``order + 1``) array
+        Central image moments.
+
+    References
+    ----------
+    .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
+           Core Algorithms. Springer-Verlag, London, 2009.
+    .. [2] B. Jähne. Digital Image Processing. Springer-Verlag,
+           Berlin-Heidelberg, 6. edition, 2005.
+    .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
+           Features, from Lecture notes in computer science, p. 676. Springer,
+           Berlin, 1993.
+    .. [4] https://en.wikipedia.org/wiki/Image_moment
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import moments, moments_central
+    >>> image = cp.zeros((20, 20), dtype=cp.float64)
+    >>> image[13:17, 13:17] = 1
+    >>> M = moments(image)
+    >>> centroid = (M[1, 0] / M[0, 0], M[0, 1] / M[0, 0])
+    >>> moments_central(image, centroid)
+    array([[16.,  0., 20.,  0.],
+           [ 0.,  0.,  0.,  0.],
+           [20.,  0., 25.,  0.],
+           [ 0.,  0.,  0.,  0.]])
+    """
+    if center is None:
+        center = centroid(image)
+    float_dtype = image.dtype if image.dtype.kind == 'f' else cp.float64
+    calc = image.astype(float_dtype, copy=False)
+    powers = cp.arange(order + 1, dtype=float_dtype)
+    for dim, dim_length in enumerate(image.shape):
+        delta = cp.arange(dim_length, dtype=float_dtype) - center[dim]
+        powers_of_delta = delta[:, cp.newaxis] ** powers
+        calc = cp.rollaxis(calc, dim, image.ndim)
+        calc = cp.dot(calc, powers_of_delta)
+        calc = cp.rollaxis(calc, -1, dim)
+    return calc
+
+
+def moments_normalized(mu, order=3):
+    """Calculate all normalized central image moments up to a certain order.
+
+    Note that normalized central moments are translation and scale invariant
+    but not rotation invariant.
+
+    Parameters
+    ----------
+    mu : (M,[ ...,] M) array
+        Central image moments, where M must be greater than or equal
+        to ``order``.
+    order : int, optional
+        Maximum order of moments. Default is 3.
+
+    Returns
+    -------
+    nu : (``order + 1``,[ ...,] ``order + 1``) array
+        Normalized central image moments.
+
+    References
+    ----------
+    .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
+           Core Algorithms. Springer-Verlag, London, 2009.
+    .. [2] B. Jähne. Digital Image Processing. Springer-Verlag,
+           Berlin-Heidelberg, 6. edition, 2005.
+    .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
+           Features, from Lecture notes in computer science, p. 676. Springer,
+           Berlin, 1993.
+    .. [4] https://en.wikipedia.org/wiki/Image_moment
+
+    Notes
+    -----
+    Due to the small array sizes, this function should be faster on the CPU.
+    Consider transfering ``mu`` to the host and running
+    ``skimage.measure.moments_normalized``.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import (moments, moments_central,
+    ...                                      moments_normalized)
+    >>> image = cp.zeros((20, 20), dtype=cp.float64)
+    >>> image[13:17, 13:17] = 1
+    >>> m = moments(image)
+    >>> centroid = (m[0, 1] / m[0, 0], m[1, 0] / m[0, 0])
+    >>> mu = moments_central(image, centroid)
+    >>> moments_normalized(mu)
+    array([[       nan,        nan, 0.078125  , 0.        ],
+           [       nan, 0.        , 0.        , 0.        ],
+           [0.078125  , 0.        , 0.00610352, 0.        ],
+           [0.        , 0.        , 0.        , 0.        ]])
+    """
+    if any(s <= order for s in mu.shape):
+        raise ValueError("Shape of image moments must be >= `order`")
+    # CuPy Backend: For the tiny mu and nu arrays, it is faster to run this
+    #               computation on the host and then transfer back to the GPU.
+    mu = cp.asnumpy(mu)
+    nu = np.empty_like(mu)
+    mu0 = mu.ravel()[0]
+    for powers in itertools.product(range(order + 1), repeat=mu.ndim):
+        if sum(powers) < 2:
+            nu[powers] = cp.nan
+        else:
+            nu[powers] = mu[powers] / (mu0 ** (sum(powers) / nu.ndim + 1))
+    return cp.array(nu)
+
+
+def moments_hu(nu):
+    """Calculate Hu's set of image moments (2D-only).
+
+    Note that this set of moments is proofed to be translation, scale and
+    rotation invariant.
+
+    Parameters
+    ----------
+    nu : (M, M) array
+        Normalized central image moments, where M must be >= 4.
+
+    Returns
+    -------
+    nu : (7,) array
+        Hu's set of image moments.
+
+    Notes
+    -----
+    Due to the small array sizes, this function will be faster on the CPU.
+    Consider transfering ``nu`` to the host and running
+    ``skimage.measure.moments_hu`` if the moments are not needed on the
+    device.
+
+    References
+    ----------
+    .. [1] M. K. Hu, "Visual Pattern Recognition by Moment Invariants",
+           IRE Trans. Info. Theory, vol. IT-8, pp. 179-187, 1962
+    .. [2] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
+           Core Algorithms. Springer-Verlag, London, 2009.
+    .. [3] B. Jähne. Digital Image Processing. Springer-Verlag,
+           Berlin-Heidelberg, 6. edition, 2005.
+    .. [4] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
+           Features, from Lecture notes in computer science, p. 676. Springer,
+           Berlin, 1993.
+    .. [5] https://en.wikipedia.org/wiki/Image_moment
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import (moments_central, moments_hu,
+    ...                                      moments_normalized)
+    >>> image = cp.zeros((20, 20), dtype=np.float64)
+    >>> image[13:17, 13:17] = 0.5
+    >>> image[10:12, 10:12] = 1
+    >>> mu = moments_central(image)
+    >>> nu = moments_normalized(mu)
+    >>> moments_hu(nu)
+    array([7.45370370e-01, 3.51165981e-01, 1.04049179e-01, 4.06442107e-02,
+           2.64312299e-03, 2.40854582e-02, 4.33680869e-19])
+    """
+    try:
+        from skimage.measure import moments_hu
+    except ImportError:
+        raise ImportError("moments_hu requires scikit-image.")
+
+    # CuPy Backend: TODO: Due to small arrays involved, just transfer to/from
+    #                     the CPU implementation.
+    float_dtype = nu.dtype if nu.dtype.kind == 'f' else cp.float64
+    return cp.asarray(moments_hu(cp.asnumpy(nu)), dtype=float_dtype)
+
+
+def centroid(image):
+    """Return the (weighted) centroid of an image.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+
+    Returns
+    -------
+    center : tuple of float, length ``image.ndim``
+        The centroid of the (nonzero) pixels in ``image``.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.measure import centroid
+    >>> image = cp.zeros((20, 20), dtype=np.float64)
+    >>> image[13:17, 13:17] = 0.5
+    >>> image[10:12, 10:12] = 1
+    >>> centroid(image)
+    array([13.16666667, 13.16666667])
+    """
+    M = moments_central(image, center=(0,) * image.ndim, order=1)
+    center = (
+        M[tuple(cp.eye(image.ndim, dtype=int))]  # array of weighted sums
+        # for each axis
+        / M[(0,) * image.ndim]
+    )  # weighted sum of all points
+    return center
+
+
+def inertia_tensor(image, mu=None, *, xp=cp):
+    """Compute the inertia tensor of the input image.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    mu : array, optional
+        The pre-computed central moments of ``image``. The inertia tensor
+        computation requires the central moments of the image. If an
+        application requires both the central moments and the inertia tensor
+        (for example, `skimage.measure.regionprops`), then it is more
+        efficient to pre-compute them and pass them to the inertia tensor
+        call.
+
+    Additional Parameters
+    ---------------------
+    xp : {numpy, cupy}
+        This setting determines whether the tensor returned is on the host or
+        GPU. Note that this option does not exist in the scikit-image
+        implementation.
+
+    Returns
+    -------
+    T : array, shape ``(image.ndim, image.ndim)``
+        The inertia tensor of the input image. :math:`T_{i, j}` contains
+        the covariance of image intensity along axes :math:`i` and :math:`j`.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Moment_of_inertia#Inertia_tensor
+    .. [2] Bernd Jähne. Spatio-Temporal Image Processing: Theory and
+           Scientific Applications. (Chapter 8: Tensor Methods) Springer, 1993.
+    """
+    if mu is None:
+        mu = moments_central(image, order=2)  # don't need higher-order moments
+    # CuPy Backend: mu and result are tiny, so faster on the CPU
+    mu = cp.asnumpy(mu)
+    mu0 = mu[(0,) * image.ndim]
+    # nD expression to get coordinates ([2, 0], [0, 2]) (2D),
+    # ([2, 0, 0], [0, 2, 0], [0, 0, 2]) (3D), etc.
+    corners2 = tuple(2 * np.eye(image.ndim, dtype=int))
+    # See https://ocw.mit.edu/courses/aeronautics-and-astronautics/
+    #             16-07-dynamics-fall-2009/lecture-notes/MIT16_07F09_Lec26.pdf
+    # Iii is the sum of second-order moments of every axis *except* i, not the
+    # second order moment of axis i.
+    # See also https://github.com/scikit-image/scikit-image/issues/3229
+    result = np.diag((np.sum(mu[corners2]) - mu[corners2]) / mu0)
+
+    for dims in itertools.combinations(range(image.ndim), 2):
+        mu_index = np.zeros(image.ndim, dtype=int)
+        mu_index[list(dims)] = 1
+        result[dims] = -mu[tuple(mu_index)] / mu0
+        result.T[dims] = -mu[tuple(mu_index)] / mu0
+    return xp.asarray(result)
+
+
+def inertia_tensor_eigvals(image, mu=None, T=None, *, xp=cp):
+    """Compute the eigenvalues of the inertia tensor of the image.
+
+    The inertia tensor measures covariance of the image intensity along
+    the image axes. (See `inertia_tensor`.) The relative magnitude of the
+    eigenvalues of the tensor is thus a measure of the elongation of a
+    (bright) object in the image.
+
+    Parameters
+    ----------
+    image : array
+        The input image.
+    mu : array, optional
+        The pre-computed central moments of ``image``.
+    T : array, shape ``(image.ndim, image.ndim)``
+        The pre-computed inertia tensor. If ``T`` is given, ``mu`` and
+        ``image`` are ignored.
+
+    Additional Parameters
+    ---------------------
+    xp : {numpy, cupy}
+        This setting determines whether the tensor returned is on the host or
+        GPU. Note that this option does not exist in the scikit-image
+        implementation.
+
+    Returns
+    -------
+    eigvals : list of float, length ``image.ndim``
+        The eigenvalues of the inertia tensor of ``image``, in descending
+        order.
+
+    Notes
+    -----
+    Computing the eigenvalues requires the inertia tensor of the input image.
+    This is much faster if the central moments (``mu``) are provided, or,
+    alternatively, one can provide the inertia tensor (``T``) directly.
+    """
+    # For such tiny arrays it is best to perform the computation on the CPU.
+    if T is None:
+        T = inertia_tensor(image, mu, xp=np)
+    else:
+        T = cp.asnumpy(T)
+    eigvals = np.linalg.eigvalsh(T)
+    # Floating point precision problems could make a positive
+    # semidefinite matrix have an eigenvalue that is very slightly
+    # negative. This can cause problems down the line, so set values
+    # very near zero to zero.
+    eigvals = np.clip(eigvals, 0, None, out=eigvals)
+    return xp.asarray(sorted(eigvals, reverse=True))
diff --git a/python/cucim/src/cucim/skimage/measure/_polygon.py b/python/cucim/src/cucim/skimage/measure/_polygon.py
new file mode 100644
index 000000000..63e375e30
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_polygon.py
@@ -0,0 +1,176 @@
+# TODO: use cupyx.scipy.signal once upstream fftconvolve and
+#       choose_conv_method for > 1d has been implemented.
+import cupy as cp
+
+import cucim.skimage._vendored
+
+# from cupyx.scipy import signal
+
+signal = cucim.skimage._vendored
+
+
+def approximate_polygon(coords, tolerance):
+    """Approximate a polygonal chain with the specified tolerance.
+
+    It is based on the Douglas-Peucker algorithm.
+
+    Note that the approximated polygon is always within the convex hull of the
+    original polygon.
+
+    Parameters
+    ----------
+    coords : (N, 2) array
+        Coordinate array.
+    tolerance : float
+        Maximum distance from original points of polygon to approximated
+        polygonal chain. If tolerance is 0, the original coordinate array
+        is returned.
+
+    Returns
+    -------
+    coords : (M, 2) array
+        Approximated polygonal chain where M <= N.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
+    """
+    if tolerance <= 0:
+        return coords
+
+    chain = cp.zeros(coords.shape[0], "bool")
+    # pre-allocate distance array for all points
+    dists = cp.zeros(coords.shape[0])
+    chain[0] = True
+    chain[-1] = True
+    pos_stack = [(0, chain.shape[0] - 1)]
+    end_of_chain = False
+
+    while not end_of_chain:
+        start, end = pos_stack.pop()
+        # determine properties of current line segment
+        r0, c0 = cp.asnumpy(coords[start, :])
+        r1, c1 = cp.asnumpy(coords[end, :])
+        dr = r1 - r0
+        dc = c1 - c0
+        segment_angle = -cp.arctan2(dr, dc)
+        segment_dist = c0 * cp.sin(segment_angle) + r0 * cp.cos(segment_angle)
+
+        # select points in-between line segment
+        segment_coords = coords[start + 1:end, :]
+        segment_dists = dists[start + 1:end]
+
+        # check whether to take perpendicular or euclidean distance with
+        # inner product of vectors
+
+        # vectors from points -> start and end
+        dr0 = segment_coords[:, 0] - r0
+        dc0 = segment_coords[:, 1] - c0
+        dr1 = segment_coords[:, 0] - r1
+        dc1 = segment_coords[:, 1] - c1
+        # vectors points -> start and end projected on start -> end vector
+        projected_lengths0 = dr0 * dr + dc0 * dc
+        projected_lengths1 = -dr1 * dr - dc1 * dc
+        perp = cp.logical_and(projected_lengths0 > 0,
+                              projected_lengths1 > 0)
+        eucl = cp.logical_not(perp)
+        segment_dists[perp] = cp.abs(
+            segment_coords[perp, 0] * cp.cos(segment_angle)
+            + segment_coords[perp, 1] * cp.sin(segment_angle)
+            - segment_dist
+        )
+        segment_dists[eucl] = cp.minimum(
+            # distance to start point
+            cp.sqrt(dc0[eucl] ** 2 + dr0[eucl] ** 2),
+            # distance to end point
+            cp.sqrt(dc1[eucl] ** 2 + dr1[eucl] ** 2),
+        )
+
+        if cp.any(segment_dists > tolerance):
+            # select point with maximum distance to line
+            new_end = start + cp.argmax(segment_dists) + 1
+            pos_stack.append((new_end, end))
+            pos_stack.append((start, new_end))
+            chain[new_end] = True
+
+        if len(pos_stack) == 0:
+            end_of_chain = True
+
+    return coords[chain, :]
+
+
+# B-Spline subdivision
+_SUBDIVISION_MASKS = {
+    # degree: (mask_even, mask_odd)
+    #         extracted from (degree + 2)th row of Pascal's triangle
+    1: ([1, 1], [1, 1]),
+    2: ([3, 1], [1, 3]),
+    3: ([1, 6, 1], [0, 4, 4]),
+    4: ([5, 10, 1], [1, 10, 5]),
+    5: ([1, 15, 15, 1], [0, 6, 20, 6]),
+    6: ([7, 35, 21, 1], [1, 21, 35, 7]),
+    7: ([1, 28, 70, 28, 1], [0, 8, 56, 56, 8]),
+}
+
+
+def subdivide_polygon(coords, degree=2, preserve_ends=False):
+    """Subdivision of polygonal curves using B-Splines.
+
+    Note that the resulting curve is always within the convex hull of the
+    original polygon. Circular polygons stay closed after subdivision.
+
+    Parameters
+    ----------
+    coords : (N, 2) array
+        Coordinate array.
+    degree : {1, 2, 3, 4, 5, 6, 7}, optional
+        Degree of B-Spline. Default is 2.
+    preserve_ends : bool, optional
+        Preserve first and last coordinate of non-circular polygon. Default is
+        False.
+
+    Returns
+    -------
+    coords : (M, 2) array
+        Subdivided coordinate array.
+
+    References
+    ----------
+    .. [1] http://mrl.nyu.edu/publications/subdiv-course2000/coursenotes00.pdf
+    """
+    if degree not in _SUBDIVISION_MASKS:
+        raise ValueError("Invalid B-Spline degree. Only degree 1 - 7 is "
+                         "supported.")
+
+    circular = cp.all(coords[0, :] == coords[-1, :])
+
+    method = 'valid'
+    if circular:
+        # remove last coordinate because of wrapping
+        coords = coords[:-1, :]
+        # circular convolution by wrapping boundaries
+        method = 'same'
+
+    mask_even, mask_odd = _SUBDIVISION_MASKS[degree]
+    # divide by total weight
+    float_dtype = coords.dtype if coords.dtype.kind == 'f' else cp.float64
+    mask_even = cp.array(mask_even, float_dtype) / (2 ** degree)
+    mask_odd = cp.array(mask_odd, float_dtype) / (2 ** degree)
+
+    even = signal.convolve2d(coords.T, cp.atleast_2d(mask_even), mode=method,
+                             boundary='wrap')
+    odd = signal.convolve2d(coords.T, cp.atleast_2d(mask_odd), mode=method,
+                            boundary='wrap')
+
+    out = cp.empty((even.shape[1] + odd.shape[1], 2), dtype=float_dtype)
+    out[1::2] = even.T
+    out[::2] = odd.T
+
+    if circular:
+        # close polygon
+        out = cp.vstack([out, out[0, :]])
+
+    if preserve_ends and not circular:
+        out = cp.vstack([coords[0, :], out, coords[-1, :]])
+
+    return out
diff --git a/python/cucim/src/cucim/skimage/measure/_regionprops.py b/python/cucim/src/cucim/skimage/measure/_regionprops.py
new file mode 100644
index 000000000..e9b0d73ab
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_regionprops.py
@@ -0,0 +1,1216 @@
+import inspect
+from functools import wraps
+from math import atan2
+from math import pi as PI
+from math import sqrt
+from warnings import warn
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+from scipy.ndimage import find_objects as cpu_find_objects
+
+from . import _moments
+from ._regionprops_utils import euler_number, perimeter, perimeter_crofton
+
+__all__ = ['regionprops', 'euler_number', 'perimeter', 'perimeter_crofton']
+
+
+PROPS = {
+    'Area': 'area',
+    'BoundingBox': 'bbox',
+    'BoundingBoxArea': 'bbox_area',
+    'CentralMoments': 'moments_central',
+    'Centroid': 'centroid',
+    'ConvexArea': 'convex_area',
+    # 'ConvexHull',
+    'ConvexImage': 'convex_image',
+    'Coordinates': 'coords',
+    'Eccentricity': 'eccentricity',
+    'EquivDiameter': 'equivalent_diameter',
+    'EulerNumber': 'euler_number',
+    'Extent': 'extent',
+    # 'Extrema',
+    'FeretDiameterMax': 'feret_diameter_max',
+    'FilledArea': 'filled_area',
+    'FilledImage': 'filled_image',
+    'HuMoments': 'moments_hu',
+    'Image': 'image',
+    'InertiaTensor': 'inertia_tensor',
+    'InertiaTensorEigvals': 'inertia_tensor_eigvals',
+    'IntensityImage': 'intensity_image',
+    'Label': 'label',
+    'LocalCentroid': 'local_centroid',
+    'MajorAxisLength': 'major_axis_length',
+    'MaxIntensity': 'max_intensity',
+    'MeanIntensity': 'mean_intensity',
+    'MinIntensity': 'min_intensity',
+    'MinorAxisLength': 'minor_axis_length',
+    'Moments': 'moments',
+    'NormalizedMoments': 'moments_normalized',
+    'Orientation': 'orientation',
+    'Perimeter': 'perimeter',
+    'CroftonPerimeter': 'perimeter_crofton',
+    # 'PixelIdxList',
+    # 'PixelList',
+    'Slice': 'slice',
+    'Solidity': 'solidity',
+    # 'SubarrayIdx'
+    'WeightedCentralMoments': 'weighted_moments_central',
+    'WeightedCentroid': 'weighted_centroid',
+    'WeightedHuMoments': 'weighted_moments_hu',
+    'WeightedLocalCentroid': 'weighted_local_centroid',
+    'WeightedMoments': 'weighted_moments',
+    'WeightedNormalizedMoments': 'weighted_moments_normalized'
+}
+
+OBJECT_COLUMNS = {
+    'image', 'coords', 'convex_image', 'slice',
+    'filled_image', 'intensity_image'
+}
+
+COL_DTYPES = {
+    'area': int,
+    'bbox': int,
+    'bbox_area': int,
+    'moments_central': float,
+    'centroid': float,
+    'convex_area': int,
+    'convex_image': object,
+    'coords': object,
+    'eccentricity': float,
+    'equivalent_diameter': float,
+    'euler_number': int,
+    'extent': float,
+    'feret_diameter_max': float,
+    'filled_area': int,
+    'filled_image': object,
+    'moments_hu': float,
+    'image': object,
+    'inertia_tensor': float,
+    'inertia_tensor_eigvals': float,
+    'intensity_image': object,
+    'label': int,
+    'local_centroid': float,
+    'major_axis_length': float,
+    'max_intensity': int,
+    'mean_intensity': float,
+    'min_intensity': int,
+    'minor_axis_length': float,
+    'moments': float,
+    'moments_normalized': float,
+    'orientation': float,
+    'perimeter': float,
+    'perimeter_crofton': float,
+    'slice': object,
+    'solidity': float,
+    'weighted_moments_central': float,
+    'weighted_centroid': float,
+    'weighted_moments_hu': float,
+    'weighted_local_centroid': float,
+    'weighted_moments': float,
+    'weighted_moments_normalized': float
+}
+
+PROP_VALS = set(PROPS.values())
+
+
+def _infer_number_of_required_args(func):
+    """Infer the number of required arguments for a function
+
+    Parameters
+    ----------
+    func : callable
+        The function that is being inspected.
+
+    Returns
+    -------
+    n_args : int
+        The number of required arguments of func.
+    """
+    argspec = inspect.getfullargspec(func)
+    n_args = len(argspec.args)
+    if argspec.defaults is not None:
+        n_args -= len(argspec.defaults)
+    return n_args
+
+
+def _infer_regionprop_dtype(func, *, intensity, ndim):
+    """Infer the dtype of a region property calculated by func.
+
+    If a region property function always returns the same shape and type of
+    output regardless of input size, then the dtype is the dtype of the
+    returned array. Otherwise, the property has object dtype.
+
+    Parameters
+    ----------
+    func : callable
+        Function to be tested. The signature should be array[bool] -> Any if
+        intensity is False, or *(array[bool], array[float]) -> Any otherwise.
+    intensity : bool
+        Whether the regionprop is calculated on an intensity image.
+    ndim : int
+        The number of dimensions for which to check func.
+
+    Returns
+    -------
+    dtype : NumPy data type
+        The data type of the returned property.
+    """
+    labels = [1, 2]
+    sample = cp.zeros((3,) * ndim, dtype=np.intp)
+    sample[(0,) * ndim] = labels[0]
+    sample[(slice(1, None),) * ndim] = labels[1]
+    propmasks = [(sample == n) for n in labels]
+    if intensity and _infer_number_of_required_args(func) == 2:
+
+        def _func(mask):
+            return func(mask, cp.random.random(sample.shape))
+
+    else:
+        _func = func
+    props1, props2 = map(_func, propmasks)
+    if (
+        cp.isscalar(props1)
+        and cp.isscalar(props2)
+        or cp.asarray(props1).shape == cp.asarray(props2).shape
+    ):
+        dtype = cp.asarray(props1).dtype.type
+    else:
+        dtype = np.object_
+    return dtype
+
+
+def _cached(f):
+    @wraps(f)
+    def wrapper(obj):
+        cache = obj._cache
+        prop = f.__name__
+
+        if not ((prop in cache) and obj._cache_active):
+            cache[prop] = f(obj)
+
+        return cache[prop]
+
+    return wrapper
+
+
+def only2d(method):
+    @wraps(method)
+    def func2d(self, *args, **kwargs):
+        if self._ndim > 2:
+            raise NotImplementedError('Property %s is not implemented for '
+                                      '3D images' % method.__name__)
+        return method(self, *args, **kwargs)
+    return func2d
+
+
+class RegionProperties:
+    """Please refer to `skimage.measure.regionprops` for more information
+    on the available region properties.
+    """
+
+    def __init__(self, slice, label, label_image, intensity_image,
+                 cache_active, *, extra_properties=None):
+
+        if intensity_image is not None:
+            ndim = label_image.ndim
+            if not (intensity_image.shape[:ndim] == label_image.shape
+                    and intensity_image.ndim in [ndim, ndim + 1]):
+                raise ValueError('Label and intensity image shapes must match,'
+                                 ' except for channel (last) axis.')
+            multichannel = label_image.shape < intensity_image.shape
+        else:
+            multichannel = False
+
+        self.label = label
+
+        self._slice = slice
+        self.slice = slice
+        self._label_image = label_image
+        self._intensity_image = intensity_image
+
+        self._cache_active = cache_active
+        self._cache = {}
+        self._ndim = label_image.ndim
+        self._multichannel = multichannel
+        self._spatial_axes = tuple(range(self._ndim))
+
+        self._extra_properties = {}
+        if extra_properties is None:
+            extra_properties = []
+        for func in extra_properties:
+            name = func.__name__
+            if hasattr(self, name):
+                msg = (
+                    f"Extra property '{name}' is shadowed by existing "
+                    "property and will be inaccessible. Consider renaming it."
+                )
+                warn(msg)
+        self._extra_properties = {
+            func.__name__: func for func in extra_properties
+        }
+
+    def __getattr__(self, attr):
+        if attr in self._extra_properties:
+            func = self._extra_properties[attr]
+            n_args = _infer_number_of_required_args(func)
+            # determine whether func requires intensity image
+            if n_args == 2:
+                if self._intensity_image is not None:
+                    return func(self.image, self.intensity_image)
+                else:
+                    raise AttributeError(
+                        f"intensity image required to calculate {attr}"
+                    )
+            elif n_args == 1:
+                return func(self.image)
+            else:
+                raise AttributeError(
+                    "Custom regionprop function's number of arguments must be "
+                    f" 1 or 2 but {attr} takes {n_args} arguments."
+                )
+        else:
+            raise AttributeError(
+                f"'{type(self)}' object has no attribute '{attr}'"
+            )
+
+    @property
+    @_cached
+    def area(self):
+        return cp.sum(self.image)
+
+    @property
+    def bbox(self):
+        """
+        Returns
+        -------
+        A tuple of the bounding box's start coordinates for each dimension,
+        followed by the end coordinates for each dimension
+        """
+        return tuple([self.slice[i].start for i in range(self._ndim)] +
+                     [self.slice[i].stop for i in range(self._ndim)])
+
+    @property
+    def bbox_area(self):
+        return self.image.size
+
+    @property
+    def centroid(self):
+        return tuple(cp.asnumpy(self.coords.mean(axis=0)))
+
+    @property
+    @_cached
+    def convex_area(self):
+        return cp.sum(self.convex_image)
+
+    @property
+    @_cached
+    def convex_image(self):
+        # TODO: grlee77: avoid host/device transfers
+        # from ..morphology.convex_hull import convex_hull_image
+        from skimage.morphology.convex_hull import convex_hull_image
+
+        # CuPy Backend: copy required here to avoid unexpected behavior
+        #               reported in https://github.com/cupy/cupy/issues/4354
+        return cp.asarray(convex_hull_image(cp.asnumpy(self.image))).copy()
+
+    @property
+    def coords(self):
+        indices = cp.nonzero(self.image)
+        return cp.vstack([indices[i] + self.slice[i].start
+                          for i in range(self._ndim)]).T
+
+    @property
+    @only2d
+    def eccentricity(self):
+        l1, l2 = self.inertia_tensor_eigvals
+        if l1 == 0:
+            return 0
+        return sqrt(1 - l2 / l1)
+
+    @property
+    def equivalent_diameter(self):
+        if self._ndim == 2:
+            return sqrt(4 * self.area / PI)
+        return (2 * self._ndim * self.area / PI) ** (1 / self._ndim)
+
+    @property
+    def euler_number(self):
+        if self._ndim not in [2, 3]:
+            raise NotImplementedError('Euler number is implemented for '
+                                      '2D or 3D images only')
+        return euler_number(self.image, self._ndim)
+
+    @property
+    def extent(self):
+        return self.area / self.image.size
+
+    @property
+    def feret_diameter_max(self):
+        from scipy.spatial.distance import pdist
+        from skimage.measure import find_contours, marching_cubes
+
+        # TODO: implement marching cubes, etc.
+        warn("feret diameter_max currently not implemented on GPU.")
+        identity_convex_hull = cp.pad(
+            self.convex_image, 2, mode="constant", constant_values=0
+        )
+        identity_convex_hull = cp.asnumpy(identity_convex_hull)
+        if self._ndim == 2:
+            coordinates = np.vstack(find_contours(identity_convex_hull, 0.5,
+                                                  fully_connected='high'))
+        elif self._ndim == 3:
+            coordinates, _, _, _ = marching_cubes(identity_convex_hull,
+                                                  level=0.5)
+        distances = pdist(coordinates, 'sqeuclidean')
+        return sqrt(np.max(distances))
+
+    @property
+    def filled_area(self):
+        return cp.sum(self.filled_image)
+
+    @property
+    @_cached
+    def filled_image(self):
+        structure = cp.ones((3,) * self._ndim)
+        return ndi.binary_fill_holes(self.image, structure)
+
+    @property
+    @_cached
+    def image(self):
+        return self._label_image[self.slice] == self.label
+
+    @property
+    @_cached
+    def inertia_tensor(self):
+        mu = self.moments_central
+        return _moments.inertia_tensor(self.image, mu)
+
+    @property
+    @_cached
+    def inertia_tensor_eigvals(self):
+        return _moments.inertia_tensor_eigvals(self.image,
+                                               T=self.inertia_tensor)
+
+    @property
+    @_cached
+    def intensity_image(self):
+        if self._intensity_image is None:
+            raise AttributeError("No intensity image specified.")
+        image = (
+            self.image
+            if not self._multichannel
+            else cp.expand_dims(self.image, self._ndim)
+        )
+        return self._intensity_image[self.slice] * image
+
+    def _intensity_image_double(self):
+        return self.intensity_image.astype(cp.double, copy=False)
+
+    @property
+    def local_centroid(self):
+        M = self.moments
+        return tuple(M[tuple(cp.eye(self._ndim, dtype=int))] /
+                     M[(0,) * self._ndim])
+
+    @property
+    def max_intensity(self):
+        return cp.max(self.intensity_image[self.image], axis=0)
+
+    @property
+    def mean_intensity(self):
+        return cp.mean(self.intensity_image[self.image], axis=0)
+
+    @property
+    def min_intensity(self):
+        return cp.min(self.intensity_image[self.image], axis=0)
+
+    @property
+    def major_axis_length(self):
+        l1 = self.inertia_tensor_eigvals[0]
+        return 4 * sqrt(l1)
+
+    @property
+    def minor_axis_length(self):
+        l2 = self.inertia_tensor_eigvals[-1]
+        return 4 * sqrt(l2)
+
+    @property
+    @_cached
+    def moments(self):
+        M = _moments.moments(self.image.astype(cp.uint8), 3)
+        return M
+
+    @property
+    @_cached
+    def moments_central(self):
+        mu = _moments.moments_central(self.image.astype(cp.uint8),
+                                      self.local_centroid, order=3)
+        return mu
+
+    @property
+    @only2d
+    def moments_hu(self):
+        return _moments.moments_hu(self.moments_normalized)
+
+    @property
+    @_cached
+    def moments_normalized(self):
+        return _moments.moments_normalized(self.moments_central, 3)
+
+    @property
+    @only2d
+    def orientation(self):
+        a, b, b, c = self.inertia_tensor.ravel()
+        if a - c == 0:
+            if b < 0:
+                return -PI / 4.0
+            else:
+                return PI / 4.0
+        else:
+            return 0.5 * atan2(-2 * b, c - a)
+
+    @property
+    @only2d
+    def perimeter(self):
+        return perimeter(self.image, 4)
+
+    @property
+    @only2d
+    def perimeter_crofton(self):
+        return perimeter_crofton(self.image, 4)
+
+    @property
+    def solidity(self):
+        return self.area / self.convex_area
+
+    @property
+    def weighted_centroid(self):
+        ctr = cp.asnumpy(self.weighted_local_centroid)
+        return tuple(idx + slc.start
+                     for idx, slc in zip(ctr, self.slice))
+
+    @property
+    def weighted_local_centroid(self):
+        M = self.weighted_moments
+        return (M[tuple(cp.eye(self._ndim, dtype=int))] /
+                M[(0,) * self._ndim])
+
+    @property
+    @_cached
+    def weighted_moments(self):
+        image = self._intensity_image_double()
+        if self._multichannel:
+            moments = cp.stack(
+                [_moments.moments(image[..., i], order=3)
+                    for i in range(image.shape[-1])],
+                axis=-1
+            )
+        else:
+            moments = _moments.moments(image, order=3)
+        return moments
+
+    @property
+    @_cached
+    def weighted_moments_central(self):
+        ctr = self.weighted_local_centroid
+        image = self._intensity_image_double()
+        if self._multichannel:
+            moments_list = [
+                _moments.moments_central(
+                    image[..., i], center=ctr[..., i], order=3
+                )
+                for i in range(image.shape[-1])
+            ]
+            moments = cp.stack(moments_list, axis=-1)
+        else:
+            moments = _moments.moments_central(image, ctr, order=3)
+        return moments
+
+    @property
+    @only2d
+    def weighted_moments_hu(self):
+        nu = self.weighted_moments_normalized
+        if self._multichannel:
+            nchannels = self._intensity_image.shape[-1]
+            return cp.stack(
+                [_moments.moments_hu(nu[..., i]) for i in range(nchannels)],
+                axis=-1,
+            )
+        else:
+            return _moments.moments_hu(nu)
+
+    @property
+    @_cached
+    def weighted_moments_normalized(self):
+        mu = self.weighted_moments_central
+        if self._multichannel:
+            nchannels = self._intensity_image.shape[-1]
+            return cp.stack(
+                [_moments.moments_normalized(mu[..., i], order=3)
+                 for i in range(nchannels)],
+                axis=-1,
+            )
+        else:
+            return _moments.moments_normalized(mu, order=3)
+        return _moments.moments_normalized(self.weighted_moments_central, 3)
+
+    def __iter__(self):
+        props = PROP_VALS
+
+        if self._intensity_image is None:
+            unavailable_props = ('intensity_image',
+                                 'max_intensity',
+                                 'mean_intensity',
+                                 'min_intensity',
+                                 'weighted_moments',
+                                 'weighted_moments_central',
+                                 'weighted_centroid',
+                                 'weighted_local_centroid',
+                                 'weighted_moments_hu',
+                                 'weighted_moments_normalized')
+
+            props = props.difference(unavailable_props)
+
+        return iter(sorted(props))
+
+    def __getitem__(self, key):
+        value = getattr(self, key, None)
+        if value is not None:
+            return value
+        else:  # backwards compatibility
+            return getattr(self, PROPS[key])
+
+    def __eq__(self, other):
+        if not isinstance(other, RegionProperties):
+            return False
+
+        for key in PROP_VALS:
+            try:
+                v1 = getattr(self, key, None)
+                v2 = getattr(other, key, None)
+                if isinstance(v1, tuple):
+                    np.testing.assert_equal(v1, v2)
+                else:
+                    # so that NaNs are equal
+                    cp.testing.assert_array_equal(getattr(self, key, None),
+                                                  getattr(other, key, None))
+            except AssertionError:
+                return False
+
+        return True
+
+
+# For compatibility with code written prior to 0.16
+_RegionProperties = RegionProperties
+
+
+def _props_to_dict(regions, properties=('label', 'bbox'), separator='-'):
+    """Convert image region properties list into a column dictionary.
+
+    Parameters
+    ----------
+    regions : (N,) list
+        List of RegionProperties objects as returned by :func:`regionprops`.
+    properties : tuple or list of str, optional
+        Properties that will be included in the resulting dictionary
+        For a list of available properties, please see :func:`regionprops`.
+        Users should remember to add "label" to keep track of region
+        identities.
+    separator : str, optional
+        For non-scalar properties not listed in OBJECT_COLUMNS, each element
+        will appear in its own column, with the index of that element separated
+        from the property name by this separator. For example, the inertia
+        tensor of a 2D region will appear in four columns:
+        ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``,
+        and ``inertia_tensor-1-1`` (where the separator is ``-``).
+
+        Object columns are those that cannot be split in this way because the
+        number of columns would change depending on the object. For example,
+        ``image`` and ``coords``.
+
+    Returns
+    -------
+    out_dict : dict
+        Dictionary mapping property names to an array of values of that
+        property, one value per region. This dictionary can be used as input to
+        pandas ``DataFrame`` to map property names to columns in the frame and
+        regions to rows.
+
+    Notes
+    -----
+    Each column contains either a scalar property, an object property, or an
+    element in a multidimensional array.
+
+    Properties with scalar values for each region, such as "eccentricity", will
+    appear as a float or int array with that property name as key.
+
+    Multidimensional properties *of fixed size* for a given image dimension,
+    such as "centroid" (every centroid will have three elements in a 3D image,
+    no matter the region size), will be split into that many columns, with the
+    name {property_name}{separator}{element_num} (for 1D properties),
+    {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D
+    properties), and so on.
+
+    For multidimensional properties that don't have a fixed size, such as
+    "image" (the image of a region varies in size depending on the region
+    size), an object array will be used, with the corresponding property name
+    as the key.
+
+    Examples
+    --------
+    >>> from skimage import data, util, measure
+    >>> image = data.coins()
+    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
+    >>> proplist = regionprops(label_image, image)
+    >>> props = _props_to_dict(proplist, properties=['label', 'inertia_tensor',
+    ...                                              'inertia_tensor_eigvals'])
+    >>> props  # doctest: +ELLIPSIS +SKIP
+    {'label': array([ 1,  2, ...]), ...
+     'inertia_tensor-0-0': array([  4.012...e+03,   8.51..., ...]), ...
+     ...,
+     'inertia_tensor_eigvals-1': array([  2.67...e+02,   2.83..., ...])}
+
+    The resulting dictionary can be directly passed to pandas, if installed, to
+    obtain a clean DataFrame:
+
+    >>> import pandas as pd  # doctest: +SKIP
+    >>> data = pd.DataFrame(props)  # doctest: +SKIP
+    >>> data.head()  # doctest: +SKIP
+       label  inertia_tensor-0-0  ...  inertia_tensor_eigvals-1
+    0      1         4012.909888  ...                267.065503
+    1      2            8.514739  ...                  2.834806
+    2      3            0.666667  ...                  0.000000
+    3      4            0.000000  ...                  0.000000
+    4      5            0.222222  ...                  0.111111
+
+    """
+
+    out = {}
+    n = len(regions)
+    for prop in properties:
+        r = regions[0]
+        rp = getattr(r, prop)
+        if prop in COL_DTYPES:
+            dtype = COL_DTYPES[prop]
+        else:
+            func = r._extra_properties[prop]
+            dtype = _infer_regionprop_dtype(
+                func,
+                intensity=r._intensity_image is not None,
+                ndim=r.image.ndim,
+            )
+        column_buffer = cp.zeros(n, dtype=dtype)
+
+        is_0dim_array = isinstance(rp, cp.ndarray) and rp.ndim == 0
+        # scalars and objects are dedicated one column per prop
+        # array properties are raveled into multiple columns
+        # for more info, refer to notes 1
+        if (
+            cp.isscalar(rp)
+            or is_0dim_array
+            or prop in OBJECT_COLUMNS
+            or dtype is np.object_
+        ):
+            for i in range(n):
+                column_buffer[i] = regions[i][prop]
+            out[prop] = cp.copy(column_buffer)
+        else:
+            if isinstance(rp, cp.ndarray):
+                shape = rp.shape
+            else:
+                shape = (len(rp),)
+
+            for ind in np.ndindex(shape):
+                for k in range(n):
+                    loc = ind if len(ind) > 1 else ind[0]
+                    column_buffer[k] = regions[k][prop][loc]
+                modified_prop = separator.join(map(str, (prop,) + ind))
+                out[modified_prop] = cp.copy(column_buffer)
+    return out
+
+
+def regionprops_table(label_image, intensity_image=None,
+                      properties=('label', 'bbox'),
+                      *,
+                      cache=True, separator='-', extra_properties=None):
+    """Compute image properties and return them as a pandas-compatible table.
+
+    The table is a dictionary mapping column names to value arrays. See Notes
+    section below for details.
+
+    .. versionadded:: 0.16
+
+    Parameters
+    ----------
+    label_image : (N, M[, P]) ndarray
+        Labeled input image. Labels with value 0 are ignored.
+    intensity_image : (M, N[, P][, C]) ndarray, optional
+        Intensity (i.e., input) image with same size as labeled image, plus
+        optionally an extra dimension for multichannel data.
+        Default is None.
+
+        .. versionchanged:: 0.18.0
+            The ability to provide an extra dimension for channels was added.
+    properties : tuple or list of str, optional
+        Properties that will be included in the resulting dictionary
+        For a list of available properties, please see :func:`regionprops`.
+        Users should remember to add "label" to keep track of region
+        identities.
+    cache : bool, optional
+        Determine whether to cache calculated properties. The computation is
+        much faster for cached properties, whereas the memory consumption
+        increases.
+    separator : str, optional
+        For non-scalar properties not listed in OBJECT_COLUMNS, each element
+        will appear in its own column, with the index of that element separated
+        from the property name by this separator. For example, the inertia
+        tensor of a 2D region will appear in four columns:
+        ``inertia_tensor-0-0``, ``inertia_tensor-0-1``, ``inertia_tensor-1-0``,
+        and ``inertia_tensor-1-1`` (where the separator is ``-``).
+
+        Object columns are those that cannot be split in this way because the
+        number of columns would change depending on the object. For example,
+        ``image`` and ``coords``.
+    extra_properties : Iterable of callables
+        Add extra property computation functions that are not included with
+        skimage. The name of the property is derived from the function name,
+        the dtype is inferred by calling the function on a small sample.
+        If the name of an extra property clashes with the name of an existing
+        property the extra property wil not be visible and a UserWarning is
+        issued. A property computation function must take a region mask as its
+        first argument. If the property requires an intensity image, it must
+        accept the intensity image as the second argument.
+
+    Returns
+    -------
+    out_dict : dict
+        Dictionary mapping property names to an array of values of that
+        property, one value per region. This dictionary can be used as input to
+        pandas ``DataFrame`` to map property names to columns in the frame and
+        regions to rows. If the image has no regions,
+        the arrays will have length 0, but the correct type.
+
+    Notes
+    -----
+    Each column contains either a scalar property, an object property, or an
+    element in a multidimensional array.
+
+    Properties with scalar values for each region, such as "eccentricity", will
+    appear as a float or int array with that property name as key.
+
+    Multidimensional properties *of fixed size* for a given image dimension,
+    such as "centroid" (every centroid will have three elements in a 3D image,
+    no matter the region size), will be split into that many columns, with the
+    name {property_name}{separator}{element_num} (for 1D properties),
+    {property_name}{separator}{elem_num0}{separator}{elem_num1} (for 2D
+    properties), and so on.
+
+    For multidimensional properties that don't have a fixed size, such as
+    "image" (the image of a region varies in size depending on the region
+    size), an object array will be used, with the corresponding property name
+    as the key.
+
+    Examples
+    --------
+    >>> from skimage import data, util, measure
+    >>> image = data.coins()
+    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
+    >>> props = measure.regionprops_table(label_image, image,
+    ...                           properties=['label', 'inertia_tensor',
+    ...                                       'inertia_tensor_eigvals'])
+    >>> props  # doctest: +ELLIPSIS +SKIP
+    {'label': array([ 1,  2, ...]), ...
+     'inertia_tensor-0-0': array([  4.012...e+03,   8.51..., ...]), ...
+     ...,
+     'inertia_tensor_eigvals-1': array([  2.67...e+02,   2.83..., ...])}
+
+    The resulting dictionary can be directly passed to pandas, if installed, to
+    obtain a clean DataFrame:
+
+    >>> import pandas as pd  # doctest: +SKIP
+    >>> data = pd.DataFrame(props)  # doctest: +SKIP
+    >>> data.head()  # doctest: +SKIP
+       label  inertia_tensor-0-0  ...  inertia_tensor_eigvals-1
+    0      1         4012.909888  ...                267.065503
+    1      2            8.514739  ...                  2.834806
+    2      3            0.666667  ...                  0.000000
+    3      4            0.000000  ...                  0.000000
+    4      5            0.222222  ...                  0.111111
+
+    [5 rows x 7 columns]
+
+    If we want to measure a feature that does not come as a built-in
+    property, we can define custom functions and pass them as
+    ``extra_properties``. For example, we can create a custom function
+    that measures the intensity quartiles in a region:
+
+    >>> from skimage import data, util, measure
+    >>> import numpy as np
+    >>> def quartiles(regionmask, intensity):
+    ...     return np.percentile(intensity[regionmask], q=(25, 50, 75))
+    >>>
+    >>> image = data.coins()
+    >>> label_image = measure.label(image > 110, connectivity=image.ndim)
+    >>> props = measure.regionprops_table(label_image, intensity_image=image,
+    ...                                   properties=('label',),
+    ...                                   extra_properties=(quartiles,))
+    >>> import pandas as pd # doctest: +SKIP
+    >>> pd.DataFrame(props).head() # doctest: +SKIP
+           label  quartiles-0  quartiles-1  quartiles-2
+    0      1       117.00        123.0        130.0
+    1      2       111.25        112.0        114.0
+    2      3       111.00        111.0        111.0
+    3      4       111.00        111.5        112.5
+    4      5       112.50        113.0        114.0
+
+    """
+    regions = regionprops(label_image, intensity_image=intensity_image,
+                          cache=cache, extra_properties=extra_properties)
+    if extra_properties is not None:
+        properties = (
+            list(properties) + [prop.__name__ for prop in extra_properties]
+        )
+    if len(regions) == 0:
+        ndim = label_image.ndim
+        label_image = np.zeros((3,) * ndim, dtype=int)
+        label_image[(1,) * ndim] = 1
+        label_image = cp.asarray(label_image)
+        if intensity_image is not None:
+            intensity_image = cp.zeros(
+                label_image.shape + intensity_image.shape[ndim:],
+                dtype=intensity_image.dtype,
+            )
+        regions = regionprops(label_image, intensity_image=intensity_image,
+                              cache=cache, extra_properties=extra_properties)
+
+        out_d = _props_to_dict(regions, properties=properties,
+                               separator=separator)
+        return {k: v[:0] for k, v in out_d.items()}
+
+    return _props_to_dict(
+        regions, properties=properties, separator=separator
+    )
+
+
+def regionprops(label_image, intensity_image=None, cache=True,
+                coordinates=None, *, extra_properties=None):
+    r"""Measure properties of labeled image regions.
+
+    Parameters
+    ----------
+    label_image : (M, N[, P]) ndarray
+        Labeled input image. Labels with value 0 are ignored.
+
+        .. versionchanged:: 0.14.1
+            Previously, ``label_image`` was processed by ``numpy.squeeze`` and
+            so any number of singleton dimensions was allowed. This resulted in
+            inconsistent handling of images with singleton dimensions. To
+            recover the old behaviour, use
+            ``regionprops(np.squeeze(label_image), ...)``.
+    intensity_image : (M, N[, P][, C]) ndarray, optional
+        Intensity (i.e., input) image with same size as labeled image, plus
+        optionally an extra dimension for multichannel data.
+        Default is None.
+
+        .. versionchanged:: 0.18.0
+            The ability to provide an extra dimension for channels was added.
+    cache : bool, optional
+        Determine whether to cache calculated properties. The computation is
+        much faster for cached properties, whereas the memory consumption
+        increases.
+    coordinates : DEPRECATED
+        This argument is deprecated and will be removed in a future version
+        of scikit-image.
+
+        See `Coordinate conventions
+        <https://scikit-image.org/docs/0.18.x/user_guide/numpy_images.html#coordinate-conventions>`_
+        for more details.
+
+        .. deprecated:: 0.16.0
+            Use "rc" coordinates everywhere. It may be sufficient to call
+            ``numpy.transpose`` on your label image to get the same values as
+            0.15 and earlier. However, for some properties, the transformation
+            will be less trivial. For example, the new orientation is
+            :math:`\frac{\pi}{2}` plus the old orientation.
+    extra_properties : Iterable of callables
+        Add extra property computation functions that are not included with
+        skimage. The name of the property is derived from the function name,
+        the dtype is inferred by calling the function on a small sample.
+        If the name of an extra property clashes with the name of an existing
+        property the extra property wil not be visible and a UserWarning is
+        issued. A property computation function must take a region mask as its
+        first argument. If the property requires an intensity image, it must
+        accept the intensity image as the second argument.
+
+    Returns
+    -------
+    properties : list of RegionProperties
+        Each item describes one labeled region, and can be accessed using the
+        attributes listed below.
+
+    Notes
+    -----
+    The following properties can be accessed as attributes or keys:
+
+    **area** : int
+        Number of pixels of the region.
+    **bbox** : tuple
+        Bounding box ``(min_row, min_col, max_row, max_col)``.
+        Pixels belonging to the bounding box are in the half-open interval
+        ``[min_row; max_row)`` and ``[min_col; max_col)``.
+    **bbox_area** : int
+        Number of pixels of bounding box.
+    **centroid** : array
+        Centroid coordinate tuple ``(row, col)``.
+    **convex_area** : int
+        Number of pixels of convex hull image, which is the smallest convex
+        polygon that encloses the region.
+    **convex_image** : (H, J) ndarray
+        Binary convex hull image which has the same size as bounding box.
+    **coords** : (N, 2) ndarray
+        Coordinate list ``(row, col)`` of the region.
+    **eccentricity** : float
+        Eccentricity of the ellipse that has the same second-moments as the
+        region. The eccentricity is the ratio of the focal distance
+        (distance between focal points) over the major axis length.
+        The value is in the interval [0, 1).
+        When it is 0, the ellipse becomes a circle.
+    **equivalent_diameter** : float
+        The diameter of a circle with the same area as the region.
+    **euler_number** : int
+        Euler characteristic of the set of non-zero pixels.
+        Computed as number of connected components subtracted by number of
+        holes (input.ndim connectivity). In 3D, number of connected
+        components plus number of holes subtracted by number of tunnels.
+    **extent** : float
+        Ratio of pixels in the region to pixels in the total bounding box.
+        Computed as ``area / (rows * cols)``
+    **feret_diameter_max** : float
+        Maximum Feret's diameter computed as the longest distance between
+        points around a region's convex hull contour as determined by
+        ``find_contours``. [5]_
+    **filled_area** : int
+        Number of pixels of the region will all the holes filled in. Describes
+        the area of the filled_image.
+    **filled_image** : (H, J) ndarray
+        Binary region image with filled holes which has the same size as
+        bounding box.
+    **image** : (H, J) ndarray
+        Sliced binary region image which has the same size as bounding box.
+    **inertia_tensor** : ndarray
+        Inertia tensor of the region for the rotation around its mass.
+    **inertia_tensor_eigvals** : tuple
+        The eigenvalues of the inertia tensor in decreasing order.
+    **intensity_image** : ndarray
+        Image inside region bounding box.
+    **label** : int
+        The label in the labeled input image.
+    **local_centroid** : array
+        Centroid coordinate tuple ``(row, col)``, relative to region bounding
+        box.
+    **major_axis_length** : float
+        The length of the major axis of the ellipse that has the same
+        normalized second central moments as the region.
+    **max_intensity** : float
+        Value with the greatest intensity in the region.
+    **mean_intensity** : float
+        Value with the mean intensity in the region.
+    **min_intensity** : float
+        Value with the least intensity in the region.
+    **minor_axis_length** : float
+        The length of the minor axis of the ellipse that has the same
+        normalized second central moments as the region.
+    **moments** : (3, 3) ndarray
+        Spatial moments up to 3rd order::
+
+            m_ij = sum{ array(row, col) * row^i * col^j }
+
+        where the sum is over the `row`, `col` coordinates of the region.
+    **moments_central** : (3, 3) ndarray
+        Central moments (translation invariant) up to 3rd order::
+
+            mu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j }
+
+        where the sum is over the `row`, `col` coordinates of the region,
+        and `row_c` and `col_c` are the coordinates of the region's centroid.
+    **moments_hu** : tuple
+        Hu moments (translation, scale and rotation invariant).
+    **moments_normalized** : (3, 3) ndarray
+        Normalized moments (translation and scale invariant) up to 3rd order::
+
+            nu_ij = mu_ij / m_00^[(i+j)/2 + 1]
+
+        where `m_00` is the zeroth spatial moment.
+    **orientation** : float
+        Angle between the 0th axis (rows) and the major
+        axis of the ellipse that has the same second moments as the region,
+        ranging from `-pi/2` to `pi/2` counter-clockwise.
+    **perimeter** : float
+        Perimeter of object which approximates the contour as a line
+        through the centers of border pixels using a 4-connectivity.
+    **perimeter_crofton** : float
+        Perimeter of object approximated by the Crofton formula in 4
+        directions.
+    **slice** : tuple of slices
+        A slice to extract the object from the source image.
+    **solidity** : float
+        Ratio of pixels in the region to pixels of the convex hull image.
+    **weighted_centroid** : array
+        Centroid coordinate tuple ``(row, col)`` weighted with intensity
+        image.
+    **weighted_local_centroid** : array
+        Centroid coordinate tuple ``(row, col)``, relative to region bounding
+        box, weighted with intensity image.
+    **weighted_moments** : (3, 3) ndarray
+        Spatial moments of intensity image up to 3rd order::
+
+            wm_ij = sum{ array(row, col) * row^i * col^j }
+
+        where the sum is over the `row`, `col` coordinates of the region.
+    **weighted_moments_central** : (3, 3) ndarray
+        Central moments (translation invariant) of intensity image up to
+        3rd order::
+
+            wmu_ij = sum{ array(row, col) * (row - row_c)^i * (col - col_c)^j }
+
+        where the sum is over the `row`, `col` coordinates of the region,
+        and `row_c` and `col_c` are the coordinates of the region's weighted
+        centroid.
+    **weighted_moments_hu** : tuple
+        Hu moments (translation, scale and rotation invariant) of intensity
+        image.
+    **weighted_moments_normalized** : (3, 3) ndarray
+        Normalized moments (translation and scale invariant) of intensity
+        image up to 3rd order::
+
+            wnu_ij = wmu_ij / wm_00^[(i+j)/2 + 1]
+
+        where ``wm_00`` is the zeroth spatial moment (intensity-weighted area).
+
+    Each region also supports iteration, so that you can do::
+
+      for prop in region:
+          print(prop, region[prop])
+
+    See Also
+    --------
+    label
+
+    References
+    ----------
+    .. [1] Wilhelm Burger, Mark Burge. Principles of Digital Image Processing:
+           Core Algorithms. Springer-Verlag, London, 2009.
+    .. [2] B. Jähne. Digital Image Processing. Springer-Verlag,
+           Berlin-Heidelberg, 6. edition, 2005.
+    .. [3] T. H. Reiss. Recognizing Planar Objects Using Invariant Image
+           Features, from Lecture notes in computer science, p. 676. Springer,
+           Berlin, 1993.
+    .. [4] https://en.wikipedia.org/wiki/Image_moment
+    .. [5] W. Pabst, E. Gregorová. Characterization of particles and particle
+           systems, pp. 27-28. ICT Prague, 2007.
+           https://old.vscht.cz/sil/keramika/Characterization_of_particles/CPPS%20_English%20version_.pdf
+
+    Examples
+    --------
+    >>> from skimage import data, util
+    >>> from cucim.skimage.measure import label, regionprops
+    >>> img = cp.asarray(util.img_as_ubyte(data.coins()) > 110)
+    >>> label_img = label(img, connectivity=img.ndim)
+    >>> props = regionprops(label_img)
+    >>> # centroid of first labeled object
+    >>> props[0].centroid
+    (22.72987986048314, 81.91228523446583)
+    >>> # centroid of first labeled object
+    >>> props[0]['centroid']
+    (22.72987986048314, 81.91228523446583)
+
+    Add custom measurements by passing functions as ``extra_properties``
+
+    >>> from skimage import data, util
+    >>> from cucim.skimage.measure import label, regionprops
+    >>> import numpy as np
+    >>> img = cp.asarray(util.img_as_ubyte(data.coins()) > 110)
+    >>> label_img = label(img, connectivity=img.ndim)
+    >>> def pixelcount(regionmask):
+    ...     return np.sum(regionmask)
+    >>> props = regionprops(label_img, extra_properties=(pixelcount,))
+    >>> props[0].pixelcount
+    7741
+    >>> props[1]['pixelcount']
+    42
+
+    """
+
+    if label_image.ndim not in (2, 3):
+        raise TypeError('Only 2-D and 3-D images supported.')
+
+    if not cp.issubdtype(label_image.dtype, cp.integer):
+        if cp.issubdtype(label_image.dtype, bool):
+            raise TypeError(
+                'Non-integer image types are ambiguous: '
+                'use skimage.measure.label to label the connected'
+                'components of label_image,'
+                'or label_image.astype(np.uint8) to interpret'
+                'the True values as a single label.')
+        else:
+            raise TypeError(
+                'Non-integer label_image types are ambiguous')
+
+    if coordinates is not None:
+        if coordinates == 'rc':
+            msg = ('The coordinates keyword argument to skimage.measure.'
+                   'regionprops is deprecated. All features are now computed '
+                   'in rc (row-column) coordinates. Please remove '
+                   '`coordinates="rc"` from all calls to regionprops before '
+                   'updating scikit-image.')
+            warn(msg, stacklevel=2, category=FutureWarning)
+        else:
+            msg = ('Values other than "rc" for the "coordinates" argument '
+                   'to skimage.measure.regionprops are no longer supported. '
+                   'You should update your code to use "rc" coordinates and '
+                   'stop using the "coordinates" argument, or use skimage '
+                   'version 0.15.x or earlier.')
+            raise ValueError(msg)
+
+    regions = []
+
+    # CuPy Backend: ndimage.find_objects not implemented
+    objects = cpu_find_objects(cp.asnumpy(label_image))  # synchronize!
+    for i, sl in enumerate(objects):
+        if sl is None:
+            continue
+
+        label = i + 1
+
+        props = RegionProperties(sl, label, label_image, intensity_image,
+                                 cache, extra_properties=extra_properties)
+        regions.append(props)
+
+    return regions
+
+
+def _parse_docs():
+    import re
+    import textwrap
+
+    doc = regionprops.__doc__ or ''
+    matches = re.finditer(r'\*\*(\w+)\*\* \:.*?\n(.*?)(?=\n    [\*\S]+)',
+                          doc, flags=re.DOTALL)
+    prop_doc = {m.group(1): textwrap.dedent(m.group(2)) for m in matches}
+
+    return prop_doc
+
+
+def _install_properties_docs():
+    prop_doc = _parse_docs()
+
+    for p in [member for member in dir(RegionProperties)
+              if not member.startswith('_')]:
+        getattr(RegionProperties, p).__doc__ = prop_doc[p]
+
+
+if __debug__:
+    # don't install docstrings when in optimized/non-debug mode
+    _install_properties_docs()
diff --git a/python/cucim/src/cucim/skimage/measure/_regionprops_utils.py b/python/cucim/src/cucim/skimage/measure/_regionprops_utils.py
new file mode 100644
index 000000000..b5453503e
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/_regionprops_utils.py
@@ -0,0 +1,328 @@
+import math
+
+import cupy as cp
+import cupyx.scipy.ndimage as ndi
+import numpy as np
+
+# Don't allocate STREL_* on GPU as we don't know in advance which device
+# fmt: off
+STREL_4 = np.array([[0, 1, 0],
+                    [1, 1, 1],
+                    [0, 1, 0]], dtype=np.uint8)
+STREL_8 = np.ones((3, 3), dtype=np.uint8)
+# fmt: on
+
+# Coefficients from
+# Ohser J., Nagel W., Schladitz K. (2002) The Euler Number of Discretized Sets
+# - On the Choice of Adjacency in Homogeneous Lattices.
+# In: Mecke K., Stoyan D. (eds) Morphology of Condensed Matter. Lecture Notes
+# in Physics, vol 600. Springer, Berlin, Heidelberg.
+# The value of coefficients correspond to the contributions to the Euler number
+# of specific voxel configurations, which are themselves encoded thanks to a
+# LUT. Computing the Euler number from the addition of the contributions of
+# local configurations is possible thanks to an integral geometry formula
+# (see the paper by Ohser et al. for more details).
+EULER_COEFS2D_4 = [0, 1, 0, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, 0, 0]
+EULER_COEFS2D_8 = [0, 0, 0, 0, 0, 0, -1, 0, 1, 0, 0, 0, 0, 0, -1, 0]
+# fmt: off
+EULER_COEFS3D_26 = np.array([0, 1, 1, 0, 1, 0, -2, -1,
+                            1, -2, 0, -1, 0, -1, -1, 0,
+                            1, 0, -2, -1, -2, -1, -1, -2,
+                            -6, -3, -3, -2, -3, -2, 0, -1,
+                            1, -2, 0, -1, -6, -3, -3, -2,
+                            -2, -1, -1, -2, -3, 0, -2, -1,
+                            0, -1, -1, 0, -3, -2, 0, -1,
+                            -3, 0, -2, -1, 0, 1, 1, 0,
+                            1, -2, -6, -3, 0, -1, -3, -2,
+                            -2, -1, -3, 0, -1, -2, -2, -1,
+                            0, -1, -3, -2, -1, 0, 0, -1,
+                            -3, 0, 0, 1, -2, -1, 1, 0,
+                            -2, -1, -3, 0, -3, 0, 0, 1,
+                            -1, 4, 0, 3, 0, 3, 1, 2,
+                            -1, -2, -2, -1, -2, -1, 1,
+                            0, 0, 3, 1, 2, 1, 2, 2, 1,
+                            1, -6, -2, -3, -2, -3, -1, 0,
+                            0, -3, -1, -2, -1, -2, -2, -1,
+                            -2, -3, -1, 0, -1, 0, 4, 3,
+                            -3, 0, 0, 1, 0, 1, 3, 2,
+                            0, -3, -1, -2, -3, 0, 0, 1,
+                            -1, 0, 0, -1, -2, 1, -1, 0,
+                            -1, -2, -2, -1, 0, 1, 3, 2,
+                            -2, 1, -1, 0, 1, 2, 2, 1,
+                            0, -3, -3, 0, -1, -2, 0, 1,
+                            -1, 0, -2, 1, 0, -1, -1, 0,
+                            -1, -2, 0, 1, -2, -1, 3, 2,
+                            -2, 1, 1, 2, -1, 0, 2, 1,
+                            -1, 0, -2, 1, -2, 1, 1, 2,
+                            -2, 3, -1, 2, -1, 2, 0, 1,
+                            0, -1, -1, 0, -1, 0, 2, 1,
+                            -1, 2, 0, 1, 0, 1, 1, 0, ])
+# fmt: on
+
+
+def euler_number(image, connectivity=None):
+    """Calculate the Euler characteristic in binary image.
+
+    For 2D objects, the Euler number is the number of objects minus the number
+    of holes. For 3D objects, the Euler number is obtained as the number of
+    objects plus the number of holes, minus the number of tunnels, or loops.
+
+    Parameters
+    ----------
+    image: (N, M) ndarray or (N, M, D) ndarray.
+        2D or 3D images.
+        If image is not binary, all values strictly greater than zero
+        are considered as the object.
+    connectivity : int, optional
+        Maximum number of orthogonal hops to consider a pixel/voxel
+        as a neighbor.
+        Accepted values are ranging from  1 to input.ndim. If ``None``, a full
+        connectivity of ``input.ndim`` is used.
+        4 or 8 neighborhoods are defined for 2D images (connectivity 1 and 2,
+        respectively).
+        6 or 26 neighborhoods are defined for 3D images, (connectivity 1 and 3,
+        respectively). Connectivity 2 is not defined.
+
+    Returns
+    -------
+    euler_number : int
+        Euler characteristic of the set of all objects in the image.
+
+    Notes
+    -----
+    The Euler characteristic is an integer number that describes the
+    topology of the set of all objects in the input image. If object is
+    4-connected, then background is 8-connected, and conversely.
+
+    The computation of the Euler characteristic is based on an integral
+    geometry formula in discretized space. In practice, a neighbourhood
+    configuration is constructed, and a LUT is applied for each
+    configuration. The coefficients used are the ones of Ohser et al.
+
+    It can be useful to compute the Euler characteristic for several
+    connectivities. A large relative difference between results
+    for different connectivities suggests that the image resolution
+    (with respect to the size of objects and holes) is too low.
+
+    References
+    ----------
+    .. [1] S. Rivollier. Analyse d’image geometrique et morphometrique par
+           diagrammes de forme et voisinages adaptatifs generaux. PhD thesis,
+           2010. Ecole Nationale Superieure des Mines de Saint-Etienne.
+           https://tel.archives-ouvertes.fr/tel-00560838
+    .. [2] Ohser J., Nagel W., Schladitz K. (2002) The Euler Number of
+           Discretized Sets - On the Choice of Adjacency in Homogeneous
+           Lattices. In: Mecke K., Stoyan D. (eds) Morphology of Condensed
+           Matter. Lecture Notes in Physics, vol 600. Springer, Berlin,
+           Heidelberg.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> SAMPLE = np.zeros((100,100,100));
+    >>> SAMPLE[40:60, 40:60, 40:60]=1
+    >>> euler_number(SAMPLE) # doctest: +ELLIPSIS
+    1...
+    >>> SAMPLE[45:55,45:55,45:55] = 0;
+    >>> euler_number(SAMPLE) # doctest: +ELLIPSIS
+    2...
+    >>> SAMPLE = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+    ...                    [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
+    ...                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1],
+    ...                    [0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]])
+    >>> euler_number(SAMPLE)  # doctest:
+    0
+    >>> euler_number(SAMPLE, connectivity=1)  # doctest:
+    2
+    """  # noqa
+
+    # as image can be a label image, transform it to binary
+    image = (image > 0).astype(int)
+    image = cp.pad(image, pad_width=1, mode='constant')
+
+    # check connectivity
+    if connectivity is None:
+        connectivity = image.ndim
+
+    # config variable is an adjacency configuration. A coefficient given by
+    # variable coefs is attributed to each configuration in order to get
+    # the Euler characteristic.
+    if image.ndim == 2:
+
+        config = cp.array([[0, 0, 0], [0, 1, 4], [0, 2, 8]])
+        if connectivity == 1:
+            coefs = EULER_COEFS2D_4
+        else:
+            coefs = EULER_COEFS2D_8
+        bins = 16
+    else:  # 3D images
+        if connectivity == 2:
+            raise NotImplementedError(
+                'For 3D images, Euler number is implemented '
+                'for connectivities 1 and 3 only')
+
+        # fmt: off
+        config = cp.array([[[0, 0, 0], [0, 0, 0], [0, 0, 0]],
+                           [[0, 0, 0], [0, 1, 4], [0, 2, 8]],
+                           [[0, 0, 0], [0, 16, 64], [0, 32, 128]]])
+        # fmt: on
+        if connectivity == 1:
+            coefs = EULER_COEFS3D_26[::-1]
+        else:
+            coefs = EULER_COEFS3D_26
+        bins = 256
+
+    # XF has values in the 0-255 range in 3D, and in the 0-15 range in 2D,
+    # with one unique value for each binary configuration of the
+    # 27-voxel cube in 3D / 8-pixel square in 2D, up to symmetries
+    XF = ndi.convolve(image, config, mode='constant', cval=0)
+    h = cp.bincount(XF.ravel(), minlength=bins)
+
+    coefs = cp.asarray(coefs)
+    if image.ndim == 2:
+        return coefs @ h
+    else:
+        return int(0.125 * coefs @ h)
+
+
+def perimeter(image, neighbourhood=4):
+    """Calculate total perimeter of all objects in binary image.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        2D binary image.
+    neighbourhood : 4 or 8, optional
+        Neighborhood connectivity for border pixel determination. It is used to
+        compute the contour. A higher neighbourhood widens the border on which
+        the perimeter is computed.
+
+    Returns
+    -------
+    perimeter : float
+        Total perimeter of all objects in binary image.
+
+    References
+    ----------
+    .. [1] K. Benkrid, D. Crookes. Design and FPGA Implementation of
+           a Perimeter Estimator. The Queen's University of Belfast.
+           http://www.cs.qub.ac.uk/~d.crookes/webpubs/papers/perimeter.doc
+
+    Examples
+    --------
+    >>> from skimage import data, util
+    >>> from skimage.measure import label
+    >>> # coins image (binary)
+    >>> img_coins = data.coins() > 110
+    >>> # total perimeter of all objects in the image
+    >>> perimeter(img_coins, neighbourhood=4)  # doctest: +ELLIPSIS
+    7796.867...
+    >>> perimeter(img_coins, neighbourhood=8)  # doctest: +ELLIPSIS
+    8806.268...
+
+    """
+    if image.ndim != 2:
+        raise NotImplementedError('`perimeter` supports 2D images only')
+
+    if neighbourhood == 4:
+        strel = STREL_4
+    else:
+        strel = STREL_8
+    strel = cp.asarray(strel)
+    image = image.astype(cp.uint8)
+    eroded_image = ndi.binary_erosion(image, strel, border_value=0)
+    border_image = image - eroded_image
+
+    perimeter_weights = cp.zeros(50, dtype=cp.double)
+    perimeter_weights[[5, 7, 15, 17, 25, 27]] = 1
+    perimeter_weights[[21, 33]] = math.sqrt(2)
+    perimeter_weights[[13, 23]] = (1 + math.sqrt(2)) / 2
+
+    perimeter_image = ndi.convolve(border_image, cp.array([[10, 2, 10],
+                                                           [2, 1, 2],
+                                                           [10, 2, 10]]),
+                                   mode='constant', cval=0)
+
+    # You can also write
+    # return perimeter_weights[perimeter_image].sum()
+    # but that was measured as taking much longer than bincount + cp.dot (5x
+    # as much time)
+    perimeter_histogram = cp.bincount(perimeter_image.ravel(), minlength=50)
+    total_perimeter = perimeter_histogram @ perimeter_weights
+    return total_perimeter
+
+
+def perimeter_crofton(image, directions=4):
+    """Calculate total Crofton perimeter of all objects in binary image.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        2D image. If image is not binary, all values strictly greater than zero
+        are considered as the object.
+    directions : 2 or 4, optional
+        Number of directions used to approximate the Crofton perimeter. By
+        default, 4 is used: it should be more accurate than 2.
+        Computation time is the same in both cases.
+
+    Returns
+    -------
+    perimeter : float
+        Total perimeter of all objects in binary image.
+
+    Notes
+    -----
+    This measure is based on Crofton formula [1], which is a measure from
+    integral geometry. It is defined for general curve length evaluation via
+    a double integral along all directions. In a discrete
+    space, 2 or 4 directions give a quite good approximation, 4 being more
+    accurate than 2 for more complex shapes.
+
+    Similar to :func:`~.measure.perimeter`, this function returns an
+    approximation of the perimeter in continuous space.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Crofton_formula
+    .. [2] S. Rivollier. Analyse d’image geometrique et morphometrique par
+           diagrammes de forme et voisinages adaptatifs generaux. PhD thesis,
+           2010.
+           Ecole Nationale Superieure des Mines de Saint-Etienne.
+           https://tel.archives-ouvertes.fr/tel-00560838
+    """
+    if image.ndim != 2:
+        raise NotImplementedError(
+            '`perimeter_crofton` supports 2D images only')
+
+    # as image could be a label image, transform it to binary image
+    image = (image > 0).astype(cp.uint8)
+    image = cp.pad(image, pad_width=1, mode="constant")
+    XF = ndi.convolve(image, cp.array([[0, 0, 0], [0, 1, 4], [0, 2, 8]]),
+                      mode='constant', cval=0)
+
+    h = cp.bincount(XF.ravel(), minlength=16)
+
+    # definition of the LUT
+    # fmt: off
+    if directions == 2:
+        coefs = [0, np.pi / 2, 0, 0, 0, np.pi / 2, 0, 0,
+                 np.pi / 2, np.pi, 0, 0, np.pi / 2, np.pi, 0, 0]
+    else:
+        sq2 = math.sqrt(2)
+        coefs = [0, np.pi / 4 * (1 + 1 / sq2),
+                 np.pi / (4 * sq2),
+                 np.pi / (2 * sq2), 0,
+                 np.pi / 4 * (1 + 1 / sq2),
+                 0, np.pi / (4 * sq2), np.pi / 4, np.pi / 2,
+                 np.pi / (4 * sq2), np.pi / (4 * sq2),
+                 np.pi / 4, np.pi / 2, 0, 0]
+    # fmt: on
+
+    total_perimeter = cp.asarray(coefs) @ h
+    return total_perimeter
diff --git a/python/cucim/src/cucim/skimage/measure/block.py b/python/cucim/src/cucim/skimage/measure/block.py
new file mode 100644
index 000000000..2e011027c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/block.py
@@ -0,0 +1,89 @@
+import cupy as cp
+
+from ..util import view_as_blocks
+
+
+def block_reduce(image, block_size, func=cp.sum, cval=0, func_kwargs=None):
+    """Downsample image by applying function `func` to local blocks.
+
+    This function is useful for max and mean pooling, for example.
+
+    Parameters
+    ----------
+    image : ndarray
+        N-dimensional input image.
+    block_size : array_like
+        Array containing down-sampling integer factor along each axis.
+    func : callable
+        Function object which is used to calculate the return value for each
+        local block. This function must implement an ``axis`` parameter.
+        Primary functions are ``numpy.sum``, ``numpy.min``, ``numpy.max``,
+        ``numpy.mean`` and ``numpy.median``.  See also `func_kwargs`.
+    cval : float
+        Constant padding value if image is not perfectly divisible by the
+        block size.
+    func_kwargs : dict
+        Keyword arguments passed to `func`. Notably useful for passing dtype
+        argument to ``np.mean``. Takes dictionary of inputs, e.g.:
+        ``func_kwargs={'dtype': np.float16})``.
+
+    Returns
+    -------
+    image : ndarray
+        Down-sampled image with same number of dimensions as input image.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage.measure import block_reduce
+    >>> image = cp.arange(3*3*4).reshape(3, 3, 4)
+    >>> image # doctest: +NORMALIZE_WHITESPACE
+    array([[[ 0,  1,  2,  3],
+            [ 4,  5,  6,  7],
+            [ 8,  9, 10, 11]],
+           [[12, 13, 14, 15],
+            [16, 17, 18, 19],
+            [20, 21, 22, 23]],
+           [[24, 25, 26, 27],
+            [28, 29, 30, 31],
+            [32, 33, 34, 35]]])
+    >>> block_reduce(image, block_size=(3, 3, 1), func=cp.mean)
+    array([[[16., 17., 18., 19.]]])
+    >>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=cp.max)
+    >>> image_max1 # doctest: +NORMALIZE_WHITESPACE
+    array([[[11]],
+           [[23]],
+           [[35]]])
+    >>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=cp.max)
+    >>> image_max2 # doctest: +NORMALIZE_WHITESPACE
+    array([[[27],
+            [31],
+            [35]]])
+    """
+
+    if len(block_size) != image.ndim:
+        raise ValueError("`block_size` must have the same length "
+                         "as `image.shape`.")
+
+    if func_kwargs is None:
+        func_kwargs = {}
+
+    pad_width = []
+    for i in range(len(block_size)):
+        if block_size[i] < 1:
+            raise ValueError("Down-sampling factors must be >= 1. Use "
+                             "`skimage.transform.resize` to up-sample an "
+                             "image.")
+        if image.shape[i] % block_size[i] != 0:
+            after_width = block_size[i] - (image.shape[i] % block_size[i])
+        else:
+            after_width = 0
+        pad_width.append((0, after_width))
+
+    image = cp.pad(image, pad_width=pad_width, mode='constant',
+                   constant_values=cval)
+
+    blocked = view_as_blocks(image, block_size)
+
+    return func(blocked, axis=tuple(range(image.ndim, blocked.ndim)),
+                **func_kwargs)
diff --git a/python/cucim/src/cucim/skimage/measure/entropy.py b/python/cucim/src/cucim/skimage/measure/entropy.py
new file mode 100644
index 000000000..5b53d65a5
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/entropy.py
@@ -0,0 +1,35 @@
+import cupy as cp
+from cupyx.scipy.stats import entropy as scipy_entropy
+
+
+def shannon_entropy(image, base=2):
+    """Calculate the Shannon entropy of an image.
+
+    The Shannon entropy is defined as S = -sum(pk * log(pk)),
+    where pk are frequency/probability of pixels of value k.
+
+    Parameters
+    ----------
+    image : (N, M) ndarray
+        Grayscale input image.
+    base : float, optional
+        The logarithmic base to use.
+
+    Returns
+    -------
+    entropy : 0-dimensional float cupy.ndarray
+
+    Notes
+    -----
+    The returned value is measured in bits or shannon (Sh) for base=2, natural
+    unit (nat) for base=np.e and hartley (Hart) for base=10.
+
+    References
+    ----------
+    .. [1] `https://en.wikipedia.org/wiki/Entropy_(information_theory) <https://en.wikipedia.org/wiki/Entropy_(information_theory)>`_
+    .. [2] https://en.wiktionary.org/wiki/Shannon_entropy
+
+    """  # noqa
+
+    _, counts = cp.unique(image, return_counts=True)
+    return scipy_entropy(counts, base=base)
diff --git a/python/cucim/src/cucim/skimage/measure/profile.py b/python/cucim/src/cucim/skimage/measure/profile.py
new file mode 100644
index 000000000..7ec707a8d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/profile.py
@@ -0,0 +1,178 @@
+import math
+from warnings import warn
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import _validate_interpolation_order
+
+
+def profile_line(image, src, dst, linewidth=1,
+                 order=None, mode=None, cval=0.0,
+                 *, reduce_func=cp.mean):
+    """Return the intensity profile of an image measured along a scan line.
+
+    Parameters
+    ----------
+    image : ndarray, shape (M, N[, C])
+        The image, either grayscale (2D array) or multichannel
+        (3D array, where the final axis contains the channel
+        information).
+    src : array_like, shape (2, )
+        The coordinates of the start point of the scan line.
+    dst : array_like, shape (2, )
+        The coordinates of the end point of the scan
+        line. The destination point is *included* in the profile, in
+        contrast to standard numpy indexing.
+    linewidth : int, optional
+        Width of the scan, perpendicular to the line
+    order : int in {0, 1, 2, 3, 4, 5}, optional
+        The order of the spline interpolation, default is 0 if
+        image.dtype is bool and 1 otherwise. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'nearest', 'reflect', 'mirror', 'wrap'}, optional
+        How to compute any values falling outside of the image.
+    cval : float, optional
+        If `mode` is 'constant', what constant value to use outside the image.
+    reduce_func : callable, optional
+        Function used to calculate the aggregation of pixel values
+        perpendicular to the profile_line direction when `linewidth` > 1.
+        If set to None the unreduced array will be returned.
+
+    Returns
+    -------
+    return_value : array
+        The intensity profile along the scan line. The length of the profile
+        is the ceil of the computed length of the scan line.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> x = cp.asarray([[1, 1, 1, 2, 2, 2]])
+    >>> img = cp.vstack([cp.zeros_like(x), x, x, x, cp.zeros_like(x)])
+    >>> img
+    array([[0, 0, 0, 0, 0, 0],
+           [1, 1, 1, 2, 2, 2],
+           [1, 1, 1, 2, 2, 2],
+           [1, 1, 1, 2, 2, 2],
+           [0, 0, 0, 0, 0, 0]])
+    >>> profile_line(img, (2, 1), (2, 4))
+    array([1., 1., 2., 2.])
+    >>> profile_line(img, (1, 0), (1, 6), cval=4)
+    array([1., 1., 1., 2., 2., 2., 4.])
+
+    The destination point is included in the profile, in contrast to
+    standard numpy indexing.
+    For example:
+
+    >>> profile_line(img, (1, 0), (1, 6))  # The final point is out of bounds
+    array([1., 1., 1., 2., 2., 2., 0.])
+    >>> profile_line(img, (1, 0), (1, 5))  # This accesses the full first row
+    array([1., 1., 1., 2., 2., 2.])
+
+    For different reduce_func inputs:
+
+    >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=cp.mean)
+    array([0.66666667, 0.66666667, 0.66666667, 1.33333333])
+    >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=cp.max)
+    array([1, 1, 1, 2])
+    >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=cp.sum)
+    array([2, 2, 2, 4])
+
+    The unreduced array will be returned when `reduce_func` is None or when
+    `reduce_func` acts on each pixel value individually.
+
+    >>> profile_line(img, (1, 2), (4, 2), linewidth=3, order=0,
+    ...     reduce_func=None)
+    array([[1, 1, 2],
+           [1, 1, 2],
+           [1, 1, 2],
+           [0, 0, 0]])
+    >>> profile_line(img, (1, 0), (1, 3), linewidth=3, reduce_func=cp.sqrt)
+    array([[1.        , 1.        , 0.        ],
+           [1.        , 1.        , 0.        ],
+           [1.        , 1.        , 0.        ],
+           [1.41421356, 1.41421356, 0.        ]])
+    """
+
+    order = _validate_interpolation_order(image.dtype, order)
+
+    if mode is None:
+        warn("Default out of bounds interpolation mode 'constant' is "
+             "deprecated. In version 0.19 it will be set to 'reflect'. "
+             "To avoid this warning, set `mode=` explicitly.",
+             FutureWarning, stacklevel=2)
+        mode = 'constant'
+
+    perp_lines = _line_profile_coordinates(src, dst, linewidth=linewidth)
+    if image.ndim == 3:
+        pixels = [ndi.map_coordinates(image[..., i], perp_lines,
+                                      prefilter=order > 1,
+                                      order=order, mode=mode,
+                                      cval=cval) for i in
+                  range(image.shape[2])]
+        pixels = cp.transpose(cp.stack(pixels, axis=0), (1, 2, 0))
+    else:
+        pixels = ndi.map_coordinates(image, perp_lines, prefilter=order > 1,
+                                     order=order, mode=mode, cval=cval)
+    # The outputted array with reduce_func=None gives an array where the
+    # row values (axis=1) are flipped. Here, we make this consistent.
+    pixels = np.flip(pixels, axis=1)
+
+    if reduce_func is None:
+        intensities = pixels
+    else:
+        try:
+            intensities = reduce_func(pixels, axis=1)
+        except TypeError:  # function doesn't allow axis kwarg
+            intensities = cp.apply_along_axis(reduce_func, arr=pixels, axis=1)
+
+    return intensities
+
+
+def _line_profile_coordinates(src, dst, linewidth=1):
+    """Return the coordinates of the profile of an image along a scan line.
+
+    Parameters
+    ----------
+    src : 2-tuple of numeric scalar (float or int)
+        The start point of the scan line.
+    dst : 2-tuple of numeric scalar (float or int)
+        The end point of the scan line.
+    linewidth : int, optional
+        Width of the scan, perpendicular to the line
+
+    Returns
+    -------
+    coords : array, shape (2, N, C), float
+        The coordinates of the profile along the scan line. The length of the
+        profile is the ceil of the computed length of the scan line.
+
+    Notes
+    -----
+    This is a utility method meant to be used internally by skimage functions.
+    The destination point is included in the profile, in contrast to
+    standard numpy indexing.
+    """
+    src_row, src_col = src
+    dst_row, dst_col = dst
+    d_row, d_col = (d - s for d, s in zip(dst, src))
+    theta = math.atan2(d_row, d_col)
+
+    length = math.ceil(math.hypot(d_row, d_col) + 1)
+    # we add one above because we include the last point in the profile
+    # (in contrast to standard numpy indexing)
+    line_col = cp.linspace(src_col, dst_col, length)
+    line_row = cp.linspace(src_row, dst_row, length)
+
+    # we subtract 1 from linewidth to change from pixel-counting
+    # (make this line 3 pixels wide) to point distances (the
+    # distance between pixel centers)
+    col_width = (linewidth - 1) * cp.sin(-theta) / 2
+    row_width = (linewidth - 1) * cp.cos(theta) / 2
+    perp_rows = cp.stack([cp.linspace(row_i - row_width, row_i + row_width,
+                                      linewidth) for row_i in line_row])
+    perp_cols = cp.stack([cp.linspace(col_i - col_width, col_i + col_width,
+                                      linewidth) for col_i in line_col])
+    return cp.stack([perp_rows, perp_cols])
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_block.py b/python/cucim/src/cucim/skimage/measure/tests/test_block.py
new file mode 100644
index 000000000..d357bba17
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_block.py
@@ -0,0 +1,145 @@
+import cupy as cp
+import pytest
+
+from cucim.skimage.measure import block_reduce
+
+assert_equal = cp.testing.assert_array_equal
+
+
+def test_block_reduce_sum():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3))
+    # fmt: off
+    expected1 = cp.array([[24,  42],
+                          [96, 114]])
+    # fmt: on
+    assert_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (3, 3))
+    # fmt: off
+    expected2 = cp.array([[ 81, 108,  87],
+                          [174, 192, 138]])
+    # fmt: on
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_mean():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=cp.mean)
+    # fmt: off
+    expected1 = cp.array([[ 4.,  7.],
+                          [16., 19.]])
+    # fmt: on
+    assert_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=cp.mean)
+    # fmt: off
+    expected2 = cp.array([[14. , 10.8],   # noqa
+                          [ 8.5,  5.7]])
+    # fmt: on
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_median():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=cp.median)
+    # fmt: off
+    expected1 = cp.array([[ 4.,  7.],
+                          [16., 19.]])
+    # fmt: on
+    assert_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=cp.median)
+    # fmt: off
+    expected2 = cp.array([[14., 6.5],
+                          [ 0., 0. ]])
+    # fmt: on
+    assert_equal(expected2, out2)
+
+    image3 = cp.array([[1, 5, 5, 5], [5, 5, 5, 1000]])
+    out3 = block_reduce(image3, (2, 4), func=cp.median)
+    assert_equal(5, out3)
+
+
+def test_block_reduce_min():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=cp.min)
+    # fmt: off
+    expected1 = cp.array([[ 0, 3],
+                          [12, 15]])
+    # fmt: on
+    assert_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=cp.min)
+    # fmt: off
+    expected2 = cp.array([[0, 0],
+                          [0, 0]])
+    # fmt: on
+    assert_equal(expected2, out2)
+
+
+def test_block_reduce_max():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = block_reduce(image1, (2, 3), func=cp.max)
+    # fmt: off
+    expected1 = cp.array([[ 8, 11],
+                          [20, 23]])
+    # fmt: on
+    assert_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = block_reduce(image2, (4, 5), func=cp.max)
+    # fmt: off
+    expected2 = cp.array([[28, 31],
+                          [36, 39]])
+    # fmt: on
+    assert_equal(expected2, out2)
+
+
+def test_invalid_block_size():
+    image = cp.arange(4 * 6).reshape(4, 6)
+
+    with pytest.raises(ValueError):
+        block_reduce(image, [1, 2, 3])
+    with pytest.raises(ValueError):
+        block_reduce(image, [1, 0.5])
+
+
+@pytest.mark.skip(reason="cupy.mean doesn't support setting dtype=cupy.uint8")
+def test_func_kwargs_same_dtype():
+    # fmt: off
+    image = cp.array([[97, 123, 173, 227],
+                     [217, 241, 221, 214],
+                     [211,  11, 170,  53],
+                     [214, 205, 101,  57]], dtype=cp.uint8)
+    # fmt: on
+
+    out = block_reduce(
+        image, (2, 2), func=cp.mean, func_kwargs={"dtype": cp.uint8}
+    )
+    expected = cp.array([[41, 16], [32, 31]], dtype=cp.uint8)
+
+    assert_equal(out, expected)
+    assert out.dtype == expected.dtype
+
+
+def test_func_kwargs_different_dtype():
+    # fmt: off
+    image = cp.array([[0.45745366, 0.67479345, 0.20949775, 0.3147348],
+                      [0.7209286, 0.88915504, 0.66153409, 0.07919526],
+                      [0.04640037, 0.54008495, 0.34664343, 0.56152301],
+                      [0.58085003, 0.80144708, 0.87844473, 0.29811511]],
+                     dtype=cp.float64)
+    # fmt: on
+
+    out = block_reduce(image, (2, 2), func=cp.mean,
+                       func_kwargs={'dtype': cp.float16})
+    expected = cp.array([[0.6855, 0.3164], [0.4922, 0.521]], dtype=cp.float16)
+
+    # Note: had to set decimal=3 for float16 to pass here when using CuPy
+    cp.testing.assert_array_almost_equal(out, expected, decimal=3)
+    assert out.dtype == expected.dtype
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_ccomp.py b/python/cucim/src/cucim/skimage/measure/tests/test_ccomp.py
new file mode 100644
index 000000000..ab65af21d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_ccomp.py
@@ -0,0 +1,319 @@
+# Note: These test cases originated in skimage/morphology/tests/test_ccomp.py
+
+import cupy as cp
+# import numpy as np
+from cupy.testing import assert_array_equal
+
+from cucim.skimage.measure import label
+
+# import pytest
+
+# import cucim.skimage.measure._ccomp as ccomp
+
+
+BG = 0  # background value
+
+
+class TestConnectedComponents:
+    def setup(self):
+        # fmt: off
+        self.x = cp.array([
+            [0, 0, 3, 2, 1, 9],
+            [0, 1, 1, 9, 2, 9],
+            [0, 0, 1, 9, 9, 9],
+            [3, 1, 1, 5, 3, 0]])
+
+        self.labels = cp.array([
+            [0, 0, 1, 2, 3, 4],
+            [0, 5, 5, 4, 2, 4],
+            [0, 0, 5, 4, 4, 4],
+            [6, 5, 5, 7, 8, 0]])
+        # fmt: on
+
+        # No background - there is no label 0, instead, labelling starts with 1
+        # and all labels are incremented by 1.
+        self.labels_nobg = self.labels + 1
+        # The 0 at lower right corner is isolated, so it should get a new label
+        self.labels_nobg[-1, -1] = 10
+
+        # We say that background value is 9 (and bg label is 0)
+        self.labels_bg_9 = self.labels_nobg.copy()
+        self.labels_bg_9[self.x == 9] = 0
+        # Then, where there was the label 5, we now expect 4 etc.
+        # (we assume that the label of value 9 would normally be 5)
+        self.labels_bg_9[self.labels_bg_9 > 5] -= 1
+
+    def test_basic(self):
+        assert_array_equal(label(self.x), self.labels)
+
+        # Make sure data wasn't modified
+        assert self.x[0, 2] == 3
+
+        # Check that everything works if there is no background
+        assert_array_equal(label(self.x, background=99), self.labels_nobg)
+        # Check that everything works if background value != 0
+        assert_array_equal(label(self.x, background=9), self.labels_bg_9)
+
+    def test_random(self):
+        x = (cp.random.rand(20, 30) * 5).astype(cp.int)
+        labels = label(x)
+
+        n = int(labels.max())
+        for i in range(n):
+            values = x[labels == i]
+            assert cp.all(values == values[0])
+
+    def test_diag(self):
+        # fmt: off
+        x = cp.array([[0, 0, 1],
+                      [0, 1, 0],
+                      [1, 0, 0]])
+        assert_array_equal(label(x), x)
+        # fmt: on
+
+    def test_4_vs_8(self):
+        # fmt: off
+        x = cp.array([[0, 1],
+                      [1, 0]], dtype=int)
+
+        assert_array_equal(label(x, connectivity=1),
+                           [[0, 1],
+                            [2, 0]])
+        assert_array_equal(label(x, connectivity=2),
+                           [[0, 1],
+                            [1, 0]])
+        # fmt: on
+
+    def test_background(self):
+        # fmt: off
+        x = cp.array([[1, 0, 0],
+                      [1, 1, 5],
+                      [0, 0, 0]])
+
+        assert_array_equal(label(x), [[1, 0, 0],
+                                      [1, 1, 2],
+                                      [0, 0, 0]])
+
+        assert_array_equal(label(x, background=0),
+                           [[1, 0, 0],
+                            [1, 1, 2],
+                            [0, 0, 0]])
+        # fmt: on
+
+    def test_background_two_regions(self):
+        # fmt: off
+        x = cp.array([[0, 0, 6],
+                      [0, 0, 6],
+                      [5, 5, 5]])
+
+        res = label(x, background=0)
+        assert_array_equal(res,
+                           [[0, 0, 1],
+                            [0, 0, 1],
+                            [2, 2, 2]])
+        # fmt: on
+
+    def test_background_one_region_center(self):
+        # fmt: off
+        x = cp.array([[0, 0, 0],
+                      [0, 1, 0],
+                      [0, 0, 0]])
+
+        assert_array_equal(label(x, connectivity=1, background=0),
+                           [[0, 0, 0],
+                            [0, 1, 0],
+                            [0, 0, 0]])
+        # fmt: on
+
+    def test_return_num(self):
+        # fmt: off
+        x = cp.array([[1, 0, 6],
+                      [0, 0, 6],
+                      [5, 5, 5]])
+        # fmt: on
+        assert_array_equal(label(x, return_num=True)[1], 3)
+
+        assert_array_equal(label(x, background=-1, return_num=True)[1], 4)
+
+
+class TestConnectedComponents3d:
+    def setup(self):
+        self.x = cp.zeros((3, 4, 5), int)
+        # fmt: off
+        self.x[0] = cp.array([[0, 3, 2, 1, 9],
+                              [0, 1, 9, 2, 9],
+                              [0, 1, 9, 9, 9],
+                              [3, 1, 5, 3, 0]])
+
+        self.x[1] = cp.array([[3, 3, 2, 1, 9],
+                              [0, 3, 9, 2, 1],
+                              [0, 3, 3, 1, 1],
+                              [3, 1, 3, 3, 0]])
+
+        self.x[2] = cp.array([[3, 3, 8, 8, 0],
+                              [2, 3, 9, 8, 8],
+                              [2, 3, 0, 8, 0],
+                              [2, 1, 0, 0, 0]])
+
+        self.labels = cp.zeros((3, 4, 5), int)
+
+        self.labels[0] = cp.array([[0, 1, 2, 3, 4],
+                                   [0, 5, 4, 2, 4],
+                                   [0, 5, 4, 4, 4],
+                                   [1, 5, 6, 1, 0]])
+
+        self.labels[1] = cp.array([[1, 1, 2, 3, 4],
+                                   [0, 1, 4, 2, 3],
+                                   [0, 1, 1, 3, 3],
+                                   [1, 5, 1, 1, 0]])
+
+        self.labels[2] = cp.array([[1, 1, 7, 7, 0],
+                                   [8, 1, 4, 7, 7],
+                                   [8, 1, 0, 7, 0],
+                                   [8, 5, 0, 0, 0]])
+        # fmt: on
+
+    def test_basic(self):
+        labels = label(self.x)
+        assert_array_equal(labels, self.labels)
+
+        assert self.x[0, 0, 2] == 2, "Data was modified!"
+
+    def test_random(self):
+        x = (cp.random.rand(20, 30) * 5).astype(cp.int)
+        labels = label(x)
+
+        n = int(labels.max())
+        for i in range(n):
+            values = x[labels == i]
+            assert cp.all(values == values[0])
+
+    def test_diag(self):
+        x = cp.zeros((3, 3, 3), int)
+        x[0, 2, 2] = 1
+        x[1, 1, 1] = 1
+        x[2, 0, 0] = 1
+        assert_array_equal(label(x), x)
+
+    def test_4_vs_8(self):
+        x = cp.zeros((2, 2, 2), int)
+        x[0, 1, 1] = 1
+        x[1, 0, 0] = 1
+        label4 = x.copy()
+        label4[1, 0, 0] = 2
+        assert_array_equal(label(x, connectivity=1), label4)
+        assert_array_equal(label(x, connectivity=3), x)
+
+    def test_connectivity_1_vs_2(self):
+        x = cp.zeros((2, 2, 2), int)
+        x[0, 1, 1] = 1
+        x[1, 0, 0] = 1
+        label1 = x.copy()
+        label1[1, 0, 0] = 2
+        assert_array_equal(label(x, connectivity=1), label1)
+        assert_array_equal(label(x, connectivity=3), x)
+
+    def test_background(self):
+        x = cp.zeros((2, 3, 3), int)
+        # fmt: off
+        x[0] = cp.array([[1, 0, 0],
+                         [1, 0, 0],
+                         [0, 0, 0]])
+        x[1] = cp.array([[0, 0, 0],
+                         [0, 1, 5],
+                         [0, 0, 0]])
+
+        lnb = x.copy()
+        lnb[0] = cp.array([[1, 2, 2],
+                           [1, 2, 2],
+                           [2, 2, 2]])
+        lnb[1] = cp.array([[2, 2, 2],
+                           [2, 1, 3],
+                           [2, 2, 2]])
+        lb = x.copy()
+        lb[0] = cp.array([[1,  BG, BG],  # noqa
+                          [1,  BG, BG],  # noqa
+                          [BG, BG, BG]])
+        lb[1] = cp.array([[BG, BG, BG],
+                          [BG, 1,   2],  # noqa
+                          [BG, BG, BG]])
+        # fmt: on
+        assert_array_equal(label(x), lb)
+        assert_array_equal(label(x, background=-1), lnb)
+
+    def test_background_two_regions(self):
+        x = cp.zeros((2, 3, 3), int)
+        # fmt: off
+        x[0] = cp.array([[0, 0, 6],
+                         [0, 0, 6],
+                         [5, 5, 5]])
+        x[1] = cp.array([[6, 6, 0],
+                         [5, 0, 0],
+                         [0, 0, 0]])
+        lb = x.copy()
+        lb[0] = cp.array([[BG, BG, 1],
+                          [BG, BG, 1],
+                          [2,  2,  2]])  # noqa
+        lb[1] = cp.array([[1,  1,  BG],  # noqa
+                          [2,  BG, BG],  # noqa
+                          [BG, BG, BG]])
+        # fmt: on
+        res = label(x, background=0)
+        assert_array_equal(res, lb)
+
+    def test_background_one_region_center(self):
+        x = cp.zeros((3, 3, 3), int)
+        x[1, 1, 1] = 1
+
+        lb = cp.ones_like(x) * BG
+        lb[1, 1, 1] = 1
+
+        assert_array_equal(label(x, connectivity=1, background=0), lb)
+
+    def test_return_num(self):
+        # fmt: off
+        x = cp.array([[1, 0, 6],
+                      [0, 0, 6],
+                      [5, 5, 5]])
+        # fmt: on
+        assert_array_equal(label(x, return_num=True)[1], 3)
+        assert_array_equal(label(x, background=-1, return_num=True)[1], 4)
+
+    def test_1D(self):
+        x = cp.array((0, 1, 2, 2, 1, 1, 0, 0))
+        xlen = len(x)
+        y = cp.array((0, 1, 2, 2, 3, 3, 0, 0))
+        reshapes = ((xlen,),
+                    (1, xlen), (xlen, 1),
+                    (1, xlen, 1), (xlen, 1, 1), (1, 1, xlen))
+        for reshape in reshapes:
+            x2 = x.reshape(reshape)
+            labelled = label(x2)
+            assert_array_equal(y, labelled.flatten())
+
+# CuPy Backend: unlike scikit-image, the CUDA implementation is nD
+#    def test_nd(self):
+#        x = cp.ones((1, 2, 3, 4))
+#        with testing.raises(NotImplementedError):
+#            label(x)
+
+
+# @pytest.mark.skip("ccomp not yet implemented")
+# class TestSupport:
+#     def test_reshape(self):
+#         shapes_in = ((3, 1, 2), (1, 4, 5), (3, 1, 1), (2, 1), (1,))
+#         for shape in shapes_in:
+#             shape = np.array(shape)
+#             numones = sum(shape == 1)
+#             inp = np.random.random(shape)
+#             inp = cp.asarray(inp)
+
+#             fixed, swaps = ccomp.reshape_array(inp)
+#             shape2 = fixed.shape
+#             # now check that all ones are at the beginning
+#             for i in range(numones):
+#                 assert shape2[i] == 1
+
+#             back = ccomp.undo_reshape_array(fixed, swaps)
+#             # check that the undo works as expected
+#             assert_array_equal(inp, back)
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_entropy.py b/python/cucim/src/cucim/skimage/measure/tests/test_entropy.py
new file mode 100644
index 000000000..63244b43e
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_entropy.py
@@ -0,0 +1,17 @@
+import cupy as cp
+import numpy as np
+from numpy.testing import assert_almost_equal
+
+from cucim.skimage.measure import shannon_entropy
+
+
+def test_shannon_ones():
+    img = cp.ones((10, 10))
+    res = shannon_entropy(img, base=np.e)
+    assert_almost_equal(float(res), 0.0)
+
+
+def test_shannon_all_unique():
+    img = cp.arange(64)
+    res = shannon_entropy(img, base=2)
+    assert_almost_equal(float(res), np.log(64) / np.log(2))
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_moments.py b/python/cucim/src/cucim/skimage/measure/tests/test_moments.py
new file mode 100644
index 000000000..2fdbc8cf7
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_moments.py
@@ -0,0 +1,225 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import (assert_allclose, assert_array_almost_equal,
+                          assert_array_equal)
+from cupyx.scipy import ndimage as ndi
+from numpy.testing import assert_almost_equal
+from skimage import draw
+
+from cucim.skimage.measure import (centroid, inertia_tensor,
+                                   inertia_tensor_eigvals, moments,
+                                   moments_central, moments_coords,
+                                   moments_coords_central, moments_normalized)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[14, 14] = 1
+    image[15, 15] = 1
+    image[14, 15] = 0.5
+    image[15, 14] = 0.5
+    m = moments(image)
+    assert m.dtype == dtype
+    assert_array_equal(m[0, 0], 3)
+    assert_almost_equal(m[1, 0] / m[0, 0], 14.5)
+    assert_almost_equal(m[0, 1] / m[0, 0], 14.5)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments_central(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[14, 14] = 1
+    image[15, 15] = 1
+    image[14, 15] = 0.5
+    image[15, 14] = 0.5
+    mu = moments_central(image, (14.5, 14.5))
+    assert mu.dtype == dtype
+
+    # check for proper centroid computation
+    mu_calc_centroid = moments_central(image)
+    assert_array_equal(mu, mu_calc_centroid)
+
+    # shift image by dx=2, dy=2
+    image2 = cp.zeros((20, 20), dtype=dtype)
+    image2[16, 16] = 1
+    image2[17, 17] = 1
+    image2[16, 17] = 0.5
+    image2[17, 16] = 0.5
+    mu2 = moments_central(image2, (14.5 + 2, 14.5 + 2))
+    assert mu2.dtype == dtype
+    # central moments must be translation invariant
+    assert_array_equal(mu, mu2)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments_coords(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[13:17, 13:17] = 1
+    mu_image = moments(image)
+    assert mu_image.dtype == dtype
+
+    coords = cp.asarray(
+        np.array([[r, c] for r in range(13, 17)
+                  for c in range(13, 17)], dtype=dtype)
+    )
+    mu_coords = moments_coords(coords)
+    assert mu_coords.dtype == dtype
+    assert_array_almost_equal(mu_coords, mu_image)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments_central_coords(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[13:17, 13:17] = 1
+    mu_image = moments_central(image, (14.5, 14.5))
+    assert mu_image.dtype == dtype
+
+    coords = cp.asarray(
+        np.array(
+            [[r, c] for r in range(13, 17) for c in range(13, 17)],
+            dtype=dtype,
+        )
+    )
+    mu_coords = moments_coords_central(coords, (14.5, 14.5))
+    assert mu_coords.dtype == dtype
+    assert_array_almost_equal(mu_coords, mu_image)
+
+    # ensure that center is being calculated normally
+    mu_coords_calc_centroid = moments_coords_central(coords)
+    assert_array_almost_equal(mu_coords_calc_centroid, mu_coords)
+
+    # shift image by dx=3 dy=3
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[16:20, 16:20] = 1
+    mu_image = moments_central(image, (14.5, 14.5))
+    assert mu_image.dtype == dtype
+
+    coords = cp.asarray(
+        np.array([[r, c] for r in range(16, 20)
+                  for c in range(16, 20)], dtype=dtype)
+    )
+    mu_coords = moments_coords_central(coords, (14.5, 14.5))
+    assert mu_coords.dtype == dtype
+    decimal = 3 if dtype == np.float32 else 6
+    assert_array_almost_equal(mu_coords, mu_image, decimal=decimal)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments_normalized(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[13:17, 13:17] = 1
+    mu = moments_central(image, (14.5, 14.5))
+    nu = moments_normalized(mu)
+    # shift image by dx=-3, dy=-3 and scale by 0.5
+    image2 = cp.zeros((20, 20), dtype=dtype)
+    image2[11:13, 11:13] = 1
+    mu2 = moments_central(image2, (11.5, 11.5))
+    nu2 = moments_normalized(mu2)
+    # central moments must be translation and scale invariant
+    assert_array_almost_equal(nu, nu2, decimal=1)
+
+
+def test_moments_normalized_3d():
+    image = cp.asarray(draw.ellipsoid(1, 1, 10))
+    mu_image = moments_central(image)
+    nu = moments_normalized(mu_image)
+    assert nu[0, 0, 2] > nu[0, 2, 0]
+    assert_almost_equal(nu[0, 2, 0], nu[2, 0, 0])
+
+    coords = cp.where(image)
+    mu_coords = moments_coords_central(coords)
+    assert_array_almost_equal(mu_coords, mu_image)
+
+
+def test_moments_normalized_invalid():
+    with pytest.raises(ValueError):
+        moments_normalized(cp.zeros((3, 3)), 3)
+    with pytest.raises(ValueError):
+        moments_normalized(cp.zeros((3, 3)), 4)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_moments_hu(dtype):
+    moments_hu = pytest.importorskip("skimage.measure.moments_hu")
+
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[13:15, 13:17] = 1
+    mu = moments_central(image, (13.5, 14.5))
+    nu = moments_normalized(mu)
+    hu = moments_hu(nu)
+    assert hu.dtype == image.dtype
+    # shift image by dx=2, dy=3, scale by 0.5 and rotate by 90deg
+    image2 = cp.zeros((20, 20), dtype=dtype)
+    image2[11, 11:13] = 1
+    image2 = image2.T
+    mu2 = moments_central(image2, (11.5, 11))
+    nu2 = moments_normalized(mu2)
+    hu2 = moments_hu(nu2)
+    assert hu2.dtype == image2.dtype
+    # central moments must be translation and scale invariant
+    assert_array_almost_equal(hu, hu2, decimal=1)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_centroid(dtype):
+    image = cp.zeros((20, 20), dtype=dtype)
+    image[14, 14:16] = 1
+    image[15, 14:16] = 1 / 3
+    image_centroid = centroid(image)
+    assert image_centroid.dtype == image.dtype
+    assert_allclose(image_centroid, (14.25, 14.5))
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_inertia_tensor_2d(dtype):
+    image = cp.zeros((40, 40), dtype=dtype)
+    image[15:25, 5:35] = 1  # big horizontal rectangle (aligned with axis 1)
+    T = inertia_tensor(image)
+    assert T.dtype == image.dtype
+    assert T[0, 0] > T[1, 1]
+    cp.testing.assert_allclose(T[0, 1], 0)
+    v0, v1 = inertia_tensor_eigvals(image, T=T)
+    assert v0.dtype == image.dtype
+    cp.testing.assert_allclose(cp.sqrt(v0 / v1), 3, rtol=0.01, atol=0.05)
+
+
+def test_inertia_tensor_3d():
+    image = cp.asarray(draw.ellipsoid(10, 5, 3))
+    T0 = inertia_tensor(image)
+    eig0, V0 = np.linalg.eig(cp.asnumpy(T0))
+    # principal axis of ellipse = eigenvector of smallest eigenvalue
+    v0 = cp.asarray(V0[:, np.argmin(eig0)])
+
+    assert cp.allclose(v0, [1, 0, 0]) or cp.allclose(-v0, [1, 0, 0])
+
+    imrot = ndi.rotate(image.astype(float), 30, axes=(0, 1), order=1)
+    Tr = inertia_tensor(imrot)
+    eigr, Vr = np.linalg.eig(cp.asnumpy(Tr))
+    vr = cp.asarray(Vr[:, np.argmin(eigr)])
+
+    # Check that axis has rotated by expected amount
+    pi, cos, sin = np.pi, np.cos, np.sin
+    # fmt: off
+    R = cp.array([[cos(pi/6), -sin(pi/6), 0],   # noqa
+                  [sin(pi/6),  cos(pi/6), 0],   # noqa
+                  [        0,          0, 1]])  # noqa
+    # fmt: on
+    expected_vr = R @ v0
+    assert (cp.allclose(vr, expected_vr, atol=1e-3, rtol=0.01) or
+            cp.allclose(-vr, expected_vr, atol=1e-3, rtol=0.01))
+
+
+def test_inertia_tensor_eigvals():
+    # Floating point precision problems could make a positive
+    # semidefinite matrix have an eigenvalue that is very slightly
+    # negative.  Check that we have caught and fixed this problem.
+    # fmt: off
+    image = cp.array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])
+    # fmt: on
+    # mu = np.array([[3, 0, 98], [0, 14, 0], [2, 0, 98]])
+    eigvals = inertia_tensor_eigvals(image=image)
+    assert min(eigvals) >= 0
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_polygon.py b/python/cucim/src/cucim/skimage/measure/tests/test_polygon.py
new file mode 100644
index 000000000..ffd7c13b8
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_polygon.py
@@ -0,0 +1,74 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_array_equal
+from numpy.testing import assert_equal
+
+from cucim.skimage.measure import approximate_polygon, subdivide_polygon
+from cucim.skimage.measure._polygon import _SUBDIVISION_MASKS
+
+_square = cp.array([
+    [0, 0], [0, 1], [0, 2], [0, 3],
+    [1, 3], [2, 3], [3, 3],
+    [3, 2], [3, 1], [3, 0],
+    [2, 0], [1, 0], [0, 0]
+])
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_approximate_polygon(dtype):
+    square = _square.astype(dtype, copy=False)
+    out = approximate_polygon(square, 0.1)
+    assert out.dtype == dtype
+    assert_array_equal(out, square[cp.asarray((0, 3, 6, 9, 12)), :])
+
+    out = approximate_polygon(square, 2.2)
+    assert_array_equal(out, square[cp.asarray((0, 6, 12)), :])
+
+    out = approximate_polygon(
+        square[cp.asarray((0, 1, 3, 4, 5, 6, 7, 9, 11, 12)), :], 0.1
+    )
+    assert_array_equal(out, square[cp.asarray((0, 3, 6, 9, 12)), :])
+
+    out = approximate_polygon(square, -1)
+    assert_array_equal(out, square)
+    out = approximate_polygon(square, 0)
+    assert_array_equal(out, square)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_subdivide_polygon(dtype):
+    square = _square.astype(dtype, copy=False)
+    new_square1 = square
+    new_square2 = square[:-1]
+    new_square3 = square[:-1]
+    # test iterative subdvision
+    for _ in range(10):
+        square1, square2, square3 = new_square1, new_square2, new_square3
+        # test different B-Spline degrees
+        for degree in range(1, 7):
+            mask_len = len(_SUBDIVISION_MASKS[degree][0])
+            # test circular
+            new_square1 = subdivide_polygon(square1, degree)
+            assert new_square1.dtype == dtype
+            assert_array_equal(new_square1[-1], new_square1[0])
+            assert_equal(new_square1.shape[0],
+                         2 * square1.shape[0] - 1)
+            # test non-circular
+            new_square2 = subdivide_polygon(square2, degree)
+            assert new_square3.dtype == dtype
+            assert_equal(new_square2.shape[0],
+                         2 * (square2.shape[0] - mask_len + 1))
+            # test non-circular, preserve_ends
+            new_square3 = subdivide_polygon(square3, degree, True)
+            assert new_square3.dtype == dtype
+            assert_array_equal(new_square3[0], square3[0])
+            assert_array_equal(new_square3[-1], square3[-1])
+
+            assert_equal(new_square3.shape[0],
+                         2 * (square3.shape[0] - mask_len + 2))
+
+    # not supported B-Spline degree
+    with pytest.raises(ValueError):
+        subdivide_polygon(square, 0)
+    with pytest.raises(ValueError):
+        subdivide_polygon(square, 8)
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_profile.py b/python/cucim/src/cucim/skimage/measure/tests/test_profile.py
new file mode 100644
index 000000000..eee10dd56
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_profile.py
@@ -0,0 +1,215 @@
+import cupy as cp
+import numpy as np
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+
+from cucim.skimage.measure import profile_line
+
+image = cp.arange(100).reshape((10, 10)).astype(cp.float)
+
+
+def test_horizontal_rightward():
+    prof = profile_line(image, (0, 2), (0, 8), order=0, mode="constant")
+    expected_prof = cp.arange(2, 9)
+    assert_array_equal(prof, expected_prof)
+
+
+def test_horizontal_leftward():
+    prof = profile_line(image, (0, 8), (0, 2), order=0, mode="constant")
+    expected_prof = cp.arange(8, 1, -1)
+    assert_array_equal(prof, expected_prof)
+
+
+def test_vertical_downward():
+    prof = profile_line(image, (2, 5), (8, 5), order=0, mode="constant")
+    expected_prof = cp.arange(25, 95, 10)
+    assert_array_equal(prof, expected_prof)
+
+
+def test_vertical_upward():
+    prof = profile_line(image, (8, 5), (2, 5), order=0, mode="constant")
+    expected_prof = cp.arange(85, 15, -10)
+    assert_array_equal(prof, expected_prof)
+
+
+def test_45deg_right_downward():
+    prof = profile_line(image, (2, 2), (8, 8), order=0, mode="constant")
+    expected_prof = cp.array([22, 33, 33, 44, 55, 55, 66, 77, 77, 88])
+    # repeats are due to aliasing using nearest neighbor interpolation.
+    # to see this, imagine a diagonal line with markers every unit of
+    # length traversing a checkerboard pattern of squares also of unit
+    # length. Because the line is diagonal, sometimes more than one
+    # marker will fall on the same checkerboard box.
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_45deg_right_downward_interpolated():
+    prof = profile_line(image, (2, 2), (8, 8), order=1, mode="constant")
+    expected_prof = cp.linspace(22, 88, 10)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_45deg_right_upward():
+    prof = profile_line(image, (8, 2), (2, 8), order=1, mode="constant")
+    expected_prof = cp.arange(82, 27, -6)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_45deg_left_upward():
+    prof = profile_line(image, (8, 8), (2, 2), order=1, mode="constant")
+    expected_prof = cp.arange(88, 21, -22.0 / 3)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_45deg_left_downward():
+    prof = profile_line(image, (2, 8), (8, 2), order=1, mode="constant")
+    expected_prof = cp.arange(28, 83, 6)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_pythagorean_triangle_right_downward():
+    prof = profile_line(image, (1, 1), (7, 9), order=0, mode="constant")
+    expected_prof = cp.array([11, 22, 23, 33, 34, 45, 56, 57, 67, 68, 79])
+    assert_array_equal(prof, expected_prof)
+
+
+def test_pythagorean_triangle_right_downward_interpolated():
+    prof = profile_line(image, (1, 1), (7, 9), order=1, mode="constant")
+    expected_prof = cp.linspace(11, 79, 11)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+pyth_image = np.zeros((6, 7), float)
+line = ((1, 2, 2, 3, 3, 4), (1, 2, 3, 3, 4, 5))
+below = ((2, 2, 3, 4, 4, 5), (0, 1, 2, 3, 4, 4))
+above = ((0, 1, 1, 2, 3, 3), (2, 2, 3, 4, 5, 6))
+pyth_image[line] = 1.8
+pyth_image[below] = 0.6
+pyth_image[above] = 0.6
+pyth_image = cp.asarray(pyth_image)
+
+
+def test_pythagorean_triangle_right_downward_linewidth():
+    prof = profile_line(pyth_image, (1, 1), (4, 5), linewidth=3, order=0,
+                        mode='constant')
+    expected_prof = cp.ones(6)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_pythagorean_triangle_right_upward_linewidth():
+    prof = profile_line(pyth_image[::-1, :], (4, 1), (1, 5),
+                        linewidth=3, order=0, mode='constant')
+    expected_prof = cp.ones(6)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_pythagorean_triangle_transpose_left_down_linewidth():
+    prof = profile_line(pyth_image.T[:, ::-1], (1, 4), (5, 1),
+                        linewidth=3, order=0, mode='constant')
+    expected_prof = np.ones(6)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_mean():
+    prof = profile_line(pyth_image, (0, 1), (3, 1), linewidth=3, order=0,
+                        reduce_func=np.mean, mode='reflect')
+    expected_prof = pyth_image[:4, :3].mean(1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_max():
+    prof = profile_line(pyth_image, (0, 1), (3, 1), linewidth=3, order=0,
+                        reduce_func=np.max, mode='reflect')
+    expected_prof = pyth_image[:4, :3].max(1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_sum():
+    prof = profile_line(pyth_image, (0, 1), (3, 1), linewidth=3, order=0,
+                        reduce_func=np.sum, mode='reflect')
+    expected_prof = pyth_image[:4, :3].sum(1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_mean_linewidth_1():
+    prof = profile_line(pyth_image, (0, 1), (3, 1), linewidth=1, order=0,
+                        reduce_func=np.mean, mode='constant')
+    expected_prof = pyth_image[:4, 1]
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_None_linewidth_1():
+    prof = profile_line(pyth_image, (1, 2), (4, 2), linewidth=1,
+                        order=0, reduce_func=None, mode='constant')
+    expected_prof = pyth_image[1:5, 2, np.newaxis]
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_None_linewidth_3():
+    prof = profile_line(pyth_image, (1, 2), (4, 2), linewidth=3,
+                        order=0, reduce_func=None, mode='constant')
+    expected_prof = pyth_image[1:5, 1:4]
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_lambda_linewidth_3():
+    def reduce_func(x):
+        return x + x ** 2
+    prof = profile_line(pyth_image, (1, 2), (4, 2), linewidth=3, order=0,
+                        reduce_func=reduce_func, mode='constant')
+    expected_prof = cp.apply_along_axis(reduce_func,
+                                        arr=pyth_image[1:5, 1:4], axis=1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_sqrt_linewidth_3():
+    def reduce_func(x):
+        return x ** 0.5
+    prof = profile_line(pyth_image, (1, 2), (4, 2), linewidth=3,
+                        order=0, reduce_func=reduce_func,
+                        mode='constant')
+    expected_prof = cp.apply_along_axis(reduce_func,
+                                        arr=pyth_image[1:5, 1:4], axis=1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_reduce_func_sumofsqrt_linewidth_3():
+    def reduce_func(x):
+        return np.sum(x ** 0.5)
+    prof = profile_line(pyth_image, (1, 2), (4, 2), linewidth=3, order=0,
+                        reduce_func=reduce_func, mode='constant')
+    expected_prof = cp.apply_along_axis(reduce_func,
+                                        arr=pyth_image[1:5, 1:4], axis=1)
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_oob_coodinates():
+    offset = 2
+    idx = pyth_image.shape[0] + offset
+    prof = profile_line(pyth_image, (-offset, 2), (idx, 2), linewidth=1,
+                        order=0, reduce_func=None, mode='constant')
+    expected_prof = cp.vstack([cp.zeros((offset, 1)),
+                               pyth_image[:, 2, cp.newaxis],
+                               cp.zeros((offset + 1, 1))])
+    assert_array_almost_equal(prof, expected_prof)
+
+
+def test_bool_array_input():
+
+    shape = (200, 200)
+    center_x, center_y = (140, 150)
+    radius = 20
+    x, y = cp.meshgrid(cp.arange(shape[1]), cp.arange(shape[0]))
+    mask = (y - center_y) ** 2 + (x - center_x) ** 2 < radius ** 2
+    src = (center_y, center_x)
+    phi = 4 * np.pi / 9.0
+    dy = 31 * np.cos(phi)
+    dx = 31 * np.sin(phi)
+    dst = (center_y + dy, center_x + dx)
+
+    profile_u8 = profile_line(mask.astype(cp.uint8), src, dst, mode='reflect')
+    assert int(cp.all(profile_u8[:radius] == 1))
+
+    profile_b = profile_line(mask, src, dst, mode='constant')
+    assert int(cp.all(profile_b[:radius] == 1))
+
+    assert int(cp.all(profile_b == profile_u8))
diff --git a/python/cucim/src/cucim/skimage/measure/tests/test_regionprops.py b/python/cucim/src/cucim/skimage/measure/tests/test_regionprops.py
new file mode 100644
index 000000000..b04e95118
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/measure/tests/test_regionprops.py
@@ -0,0 +1,755 @@
+import math
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from numpy.testing import assert_almost_equal, assert_equal
+from skimage import data
+from skimage.segmentation import slic
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.measure._regionprops import (COL_DTYPES, OBJECT_COLUMNS,
+                                                PROPS, _parse_docs,
+                                                _props_to_dict, euler_number,
+                                                perimeter, perimeter_crofton,
+                                                regionprops, regionprops_table)
+
+# fmt: off
+SAMPLE = cp.array(
+    [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0],
+     [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+     [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+     [1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0],
+     [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1],
+     [0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]]
+)
+# fmt: on
+INTENSITY_SAMPLE = SAMPLE.copy()
+INTENSITY_SAMPLE[1, 9:11] = 2
+
+SAMPLE_MULTIPLE = cp.eye(10, dtype=np.int32)
+SAMPLE_MULTIPLE[3:5, 7:8] = 2
+INTENSITY_SAMPLE_MULTIPLE = SAMPLE_MULTIPLE.copy() * 2.0
+
+SAMPLE_3D = cp.zeros((6, 6, 6), dtype=cp.uint8)
+SAMPLE_3D[1:3, 1:3, 1:3] = 1
+SAMPLE_3D[3, 2, 2] = 1
+INTENSITY_SAMPLE_3D = SAMPLE_3D.copy()
+
+
+def test_all_props():
+    region = regionprops(SAMPLE, INTENSITY_SAMPLE)[0]
+    for prop in PROPS:
+        try:
+            assert_array_almost_equal(
+                region[prop], getattr(region, PROPS[prop])
+            )
+        except TypeError:  # the `slice` property causes this
+            pass
+
+
+def test_all_props_3d():
+    region = regionprops(SAMPLE_3D, INTENSITY_SAMPLE_3D)[0]
+    for prop in PROPS:
+        try:
+            assert_array_almost_equal(
+                region[prop], getattr(region, PROPS[prop])
+            )
+        except (NotImplementedError, TypeError):
+            pass
+
+
+def test_dtype():
+    regionprops(cp.zeros((10, 10), dtype=cp.int))
+    regionprops(cp.zeros((10, 10), dtype=cp.uint))
+    with pytest.raises(TypeError):
+        regionprops(cp.zeros((10, 10), dtype=cp.float))
+    with pytest.raises(TypeError):
+        regionprops(cp.zeros((10, 10), dtype=cp.double))
+    with pytest.raises(TypeError):
+        regionprops(cp.zeros((10, 10), dtype=bool))
+
+
+def test_ndim():
+    regionprops(cp.zeros((10, 10), dtype=cp.int))
+    regionprops(cp.zeros((10, 10, 1), dtype=cp.int))
+    regionprops(cp.zeros((10, 10, 10), dtype=cp.int))
+    regionprops(cp.zeros((1, 1), dtype=cp.int))
+    regionprops(cp.zeros((1, 1, 1), dtype=cp.int))
+    with pytest.raises(TypeError):
+        regionprops(cp.zeros((10, 10, 10, 2), dtype=cp.int))
+
+
+@pytest.mark.skip('feret_diameter_max not implmented on the GPU')
+def test_feret_diameter_max():
+    # comparator result is based on SAMPLE from manually-inspected computations
+    comparator_result = 18
+    test_result = regionprops(SAMPLE)[0].feret_diameter_max
+    assert cp.abs(test_result - comparator_result) < 1
+    # square, test that Feret diameter is sqrt(2) * square side
+    img = cp.zeros((20, 20), dtype=cp.uint8)
+    img[2:-2, 2:-2] = 1
+    feret_diameter_max = regionprops(img)[0].feret_diameter_max
+    assert cp.abs(feret_diameter_max - 16 * math.sqrt(2)) < 1
+
+
+@pytest.mark.skip('feret_diameter_max not implmented on the GPU')
+def test_feret_diameter_max_3d():
+    img = cp.zeros((20, 20), dtype=cp.uint8)
+    img[2:-2, 2:-2] = 1
+    img_3d = cp.dstack((img,) * 3)
+    feret_diameter_max = regionprops(img_3d)[0].feret_diameter_max
+    assert cp.abs(feret_diameter_max - 16 * math.sqrt(2)) < 1
+
+
+def test_area():
+    area = regionprops(SAMPLE)[0].area
+    assert area == cp.sum(SAMPLE)
+    area = regionprops(SAMPLE_3D)[0].area
+    assert area == cp.sum(SAMPLE_3D)
+
+
+def test_bbox():
+    bbox = regionprops(SAMPLE)[0].bbox
+    assert_array_almost_equal(bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1]))
+
+    SAMPLE_mod = SAMPLE.copy()
+    SAMPLE_mod[:, -1] = 0
+    bbox = regionprops(SAMPLE_mod)[0].bbox
+    assert_array_almost_equal(
+        bbox, (0, 0, SAMPLE.shape[0], SAMPLE.shape[1] - 1)
+    )
+
+    bbox = regionprops(SAMPLE_3D)[0].bbox
+    assert_array_almost_equal(bbox, (1, 1, 1, 4, 3, 3))
+
+
+def test_bbox_area():
+    padded = cp.pad(SAMPLE, 5, mode='constant')
+    bbox_area = regionprops(padded)[0].bbox_area
+    assert_array_almost_equal(bbox_area, SAMPLE.size)
+
+
+def test_moments_central():
+    mu = regionprops(SAMPLE)[0].moments_central
+    # determined with OpenCV
+    assert_almost_equal(mu[2, 0], 436.00000000000045)
+    # different from OpenCV results, bug in OpenCV
+    assert_almost_equal(mu[3, 0], -737.333333333333)
+    assert_almost_equal(mu[1, 1], -87.33333333333303)
+    assert_almost_equal(mu[2, 1], -127.5555555555593)
+    assert_almost_equal(mu[0, 2], 1259.7777777777774)
+    assert_almost_equal(mu[1, 2], 2000.296296296291)
+    assert_almost_equal(mu[0, 3], -760.0246913580195)
+
+
+def test_centroid():
+    centroid = regionprops(SAMPLE)[0].centroid
+    # determined with MATLAB
+    assert_almost_equal(centroid, (5.66666666666666, 9.444444444444444))
+
+
+def test_centroid_3d():
+    centroid = regionprops(SAMPLE_3D)[0].centroid
+    # determined by mean along axis 1 of SAMPLE_3D.nonzero()
+    assert_almost_equal(centroid, (1.66666667, 1.55555556, 1.55555556))
+
+
+def test_convex_area():
+    area = regionprops(SAMPLE)[0].convex_area
+    assert area == 125
+
+
+def test_convex_image():
+    img = regionprops(SAMPLE)[0].convex_image
+    # fmt: off
+    ref = cp.array(
+        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+         [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+         [0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+         [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+         [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+         [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
+    )
+    # fmt: on
+    assert_array_equal(img, ref)
+
+
+def test_coordinates():
+    sample = cp.zeros((10, 10), dtype=cp.int8)
+    coords = cp.array([[3, 2], [3, 3], [3, 4]])
+    sample[coords[:, 0], coords[:, 1]] = 1
+    prop_coords = regionprops(sample)[0].coords
+    assert_array_equal(prop_coords, coords)
+
+    sample = cp.zeros((6, 6, 6), dtype=cp.int8)
+    coords = cp.array([[1, 1, 1], [1, 2, 1], [1, 3, 1]])
+    sample[coords[:, 0], coords[:, 1], coords[:, 2]] = 1
+    prop_coords = regionprops(sample)[0].coords
+    assert_array_equal(prop_coords, coords)
+
+
+def test_slice():
+    padded = cp.pad(SAMPLE, ((2, 4), (5, 2)), mode="constant")
+    nrow, ncol = SAMPLE.shape
+    result = regionprops(padded)[0].slice
+    expected = (slice(2, 2 + nrow), slice(5, 5 + ncol))
+    assert_array_equal(result, expected)
+
+
+def test_eccentricity():
+    eps = regionprops(SAMPLE)[0].eccentricity
+    assert_almost_equal(eps, 0.814629313427)
+
+    img = cp.zeros((5, 5), dtype=cp.int)
+    img[2, 2] = 1
+    eps = regionprops(img)[0].eccentricity
+    assert_almost_equal(eps, 0)
+
+
+def test_equiv_diameter():
+    diameter = regionprops(SAMPLE)[0].equivalent_diameter
+    # determined with MATLAB
+    assert_almost_equal(diameter, 9.57461472963)
+
+
+def test_euler_number():
+    en = regionprops(SAMPLE)[0].euler_number
+    assert en == 0
+
+    SAMPLE_mod = SAMPLE.copy()
+    SAMPLE_mod[7, -3] = 0
+    en = regionprops(SAMPLE_mod)[0].euler_number
+    assert en == -1
+
+    en = euler_number(SAMPLE, 1)
+    assert en == 2
+
+    en = euler_number(SAMPLE_mod, 1)
+    assert en == 1
+
+    en = euler_number(SAMPLE_3D, 1)
+    assert en == 1
+
+    en = euler_number(SAMPLE_3D, 3)
+    assert en == 1
+
+    # for convex body, Euler number is 1
+    SAMPLE_3D_2 = cp.zeros((100, 100, 100))
+    SAMPLE_3D_2[40:60, 40:60, 40:60] = 1
+    en = euler_number(SAMPLE_3D_2, 3)
+    assert en == 1
+
+    SAMPLE_3D_2[45:55, 45:55, 45:55] = 0
+    en = euler_number(SAMPLE_3D_2, 3)
+    assert en == 2
+
+
+def test_extent():
+    extent = regionprops(SAMPLE)[0].extent
+    assert_almost_equal(extent, 0.4)
+
+
+def test_moments_hu():
+    hu = regionprops(SAMPLE)[0].moments_hu
+    # fmt: off
+    ref = cp.array([
+        3.27117627e-01,
+        2.63869194e-02,
+        2.35390060e-02,
+        1.23151193e-03,
+        1.38882330e-06,
+        -2.72586158e-05,
+        -6.48350653e-06
+    ])
+    # fmt: on
+    # bug in OpenCV caused in Central Moments calculation?
+    assert_array_almost_equal(hu, ref)
+
+
+def test_image():
+    img = regionprops(SAMPLE)[0].image
+    assert_array_equal(img, SAMPLE)
+
+    img = regionprops(SAMPLE_3D)[0].image
+    assert_array_equal(img, SAMPLE_3D[1:4, 1:3, 1:3])
+
+
+def test_label():
+    label = regionprops(SAMPLE)[0].label
+    assert_array_equal(label, 1)
+
+    label = regionprops(SAMPLE_3D)[0].label
+    assert_array_equal(label, 1)
+
+
+def test_filled_area():
+    area = regionprops(SAMPLE)[0].filled_area
+    assert area == cp.sum(SAMPLE)
+
+    SAMPLE_mod = SAMPLE.copy()
+    SAMPLE_mod[7, -3] = 0
+    area = regionprops(SAMPLE_mod)[0].filled_area
+    assert area == cp.sum(SAMPLE)
+
+
+def test_filled_image():
+    img = regionprops(SAMPLE)[0].filled_image
+    assert_array_equal(img, SAMPLE)
+
+
+def test_major_axis_length():
+    length = regionprops(SAMPLE)[0].major_axis_length
+    # MATLAB has different interpretation of ellipse than found in literature,
+    # here implemented as found in literature
+    assert_almost_equal(length, 16.7924234999)
+
+
+def test_max_intensity():
+    intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                            )[0].max_intensity
+    assert_almost_equal(intensity, 2)
+
+
+def test_mean_intensity():
+    intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                            )[0].mean_intensity
+    assert_almost_equal(intensity, 1.02777777777777)
+
+
+def test_min_intensity():
+    intensity = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                            )[0].min_intensity
+    assert_almost_equal(intensity, 1)
+
+
+def test_minor_axis_length():
+    length = regionprops(SAMPLE)[0].minor_axis_length
+    # MATLAB has different interpretation of ellipse than found in literature,
+    # here implemented as found in literature
+    assert_almost_equal(length, 9.739302807263)
+
+
+def test_moments():
+    m = regionprops(SAMPLE)[0].moments
+    # determined with OpenCV
+    assert_almost_equal(m[0, 0], 72.0)
+    assert_almost_equal(m[0, 1], 680.0)
+    assert_almost_equal(m[0, 2], 7682.0)
+    assert_almost_equal(m[0, 3], 95588.0)
+    assert_almost_equal(m[1, 0], 408.0)
+    assert_almost_equal(m[1, 1], 3766.0)
+    assert_almost_equal(m[1, 2], 43882.0)
+    assert_almost_equal(m[2, 0], 2748.0)
+    assert_almost_equal(m[2, 1], 24836.0)
+    assert_almost_equal(m[3, 0], 19776.0)
+
+
+def test_moments_normalized():
+    nu = regionprops(SAMPLE)[0].moments_normalized
+
+    # determined with OpenCV
+    assert_almost_equal(nu[0, 2], 0.24301268861454037)
+    assert_almost_equal(nu[0, 3], -0.017278118992041805)
+    assert_almost_equal(nu[1, 1], -0.016846707818929982)
+    assert_almost_equal(nu[1, 2], 0.045473992910668816)
+    assert_almost_equal(nu[2, 0], 0.08410493827160502)
+    assert_almost_equal(nu[2, 1], -0.002899800614433943)
+
+
+def test_orientation():
+    orient = regionprops(SAMPLE)[0].orientation
+    # determined with MATLAB
+    assert_almost_equal(orient, -1.4663278802756865)
+    # test diagonal regions
+    diag = cp.eye(10, dtype=int)
+    orient_diag = regionprops(diag)[0].orientation
+    assert_almost_equal(orient_diag, -math.pi / 4)
+    orient_diag = regionprops(cp.flipud(diag))[0].orientation
+    assert_almost_equal(orient_diag, math.pi / 4)
+    orient_diag = regionprops(cp.fliplr(diag))[0].orientation
+    assert_almost_equal(orient_diag, math.pi / 4)
+    orient_diag = regionprops(cp.fliplr(cp.flipud(diag)))[0].orientation
+    assert_almost_equal(orient_diag, -math.pi / 4)
+
+
+def test_perimeter():
+    per = regionprops(SAMPLE)[0].perimeter
+    assert_almost_equal(per, 55.2487373415)
+
+    per = perimeter(SAMPLE.astype('double'), neighbourhood=8)
+    assert_almost_equal(per, 46.8284271247)
+
+
+def test_perimeter_crofton():
+    per = regionprops(SAMPLE)[0].perimeter_crofton
+    assert_almost_equal(per, 61.0800637973)
+
+    per = perimeter_crofton(SAMPLE.astype('double'), directions=2)
+    assert_almost_equal(per, 64.4026493985)
+
+
+def test_solidity():
+    solidity = regionprops(SAMPLE)[0].solidity
+    assert_almost_equal(solidity, 0.576)
+
+
+def test_weighted_moments_central():
+    wmu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                      )[0].weighted_moments_central
+    # fmt: off
+    ref = cp.array(
+        [[7.4000000000e+01, 3.7303493627e-14, 1.2602837838e+03,
+          -7.6561796932e+02],
+         [-2.1316282073e-13, -8.7837837838e+01, 2.1571526662e+03,
+          -4.2385971907e+03],
+         [4.7837837838e+02, -1.4801314828e+02, 6.6989799420e+03,
+          -9.9501164076e+03],
+         [-7.5943608473e+02, -1.2714707125e+03, 1.5304076361e+04,
+          -3.3156729271e+04]])
+    # fmt: on
+    np.set_printoptions(precision=10)
+    assert_array_almost_equal(wmu, ref)
+
+
+def test_weighted_centroid():
+    centroid = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                           )[0].weighted_centroid
+    assert_almost_equal(centroid, (5.540540540540, 9.445945945945))
+
+
+def test_weighted_moments_hu():
+    whu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                      )[0].weighted_moments_hu
+    # fmt: off
+    ref = cp.array([
+        3.1750587329e-01,
+        2.1417517159e-02,
+        2.3609322038e-02,
+        1.2565683360e-03,
+        8.3014209421e-07,
+        -3.5073773473e-05,
+        -6.7936409056e-06
+    ])
+    # fmt: on
+    assert_array_almost_equal(whu, ref)
+
+
+def test_weighted_moments():
+    wm = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                     )[0].weighted_moments
+    # fmt: off
+    ref = cp.array(
+        [[7.4000000e+01, 6.9900000e+02, 7.8630000e+03, 9.7317000e+04],
+         [4.1000000e+02, 3.7850000e+03, 4.4063000e+04, 5.7256700e+05],
+         [2.7500000e+03, 2.4855000e+04, 2.9347700e+05, 3.9007170e+06],
+         [1.9778000e+04, 1.7500100e+05, 2.0810510e+06, 2.8078871e+07]]
+    )
+    # fmt: on
+    assert_array_almost_equal(wm, ref)
+
+
+def test_weighted_moments_normalized():
+    wnu = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE
+                      )[0].weighted_moments_normalized
+    # fmt: off
+    ref = np.array(
+        [[np.nan,        np.nan, 0.2301467830, -0.0162529732],         # noqa
+         [np.nan, -0.0160405109, 0.0457932622, -0.0104598869],         # noqa
+         [0.0873590903, -0.0031421072, 0.0165315478, -0.0028544152],   # noqa
+         [-0.0161217406, -0.0031376984, 0.0043903193, -0.0011057191]]  # noqa
+    )
+    # fmt: on
+    assert_array_almost_equal(wnu, ref)
+
+
+def test_label_sequence():
+    a = cp.empty((2, 2), dtype=cp.int)
+    a[:, :] = 2
+    ps = regionprops(a)
+    assert len(ps) == 1
+    assert ps[0].label == 2
+
+
+def test_pure_background():
+    a = cp.zeros((2, 2), dtype=cp.int)
+    ps = regionprops(a)
+    assert len(ps) == 0
+
+
+def test_invalid():
+    ps = regionprops(SAMPLE)
+
+    def get_intensity_image():
+        ps[0].intensity_image
+
+    with pytest.raises(AttributeError):
+        get_intensity_image()
+
+
+def test_invalid_size():
+    wrong_intensity_sample = cp.array([[1], [1]])
+    with pytest.raises(ValueError):
+        regionprops(SAMPLE, wrong_intensity_sample)
+
+
+def test_equals():
+    arr = cp.zeros((100, 100), dtype=cp.int)
+    arr[0:25, 0:25] = 1
+    arr[50:99, 50:99] = 2
+
+    regions = regionprops(arr)
+    r1 = regions[0]
+
+    regions = regionprops(arr)
+    r2 = regions[0]
+    r3 = regions[1]
+
+    assert_equal(r1 == r2, True, "Same regionprops are not equal")
+    assert_equal(r1 != r3, True, "Different regionprops are equal")
+
+
+def test_iterate_all_props():
+    region = regionprops(SAMPLE)[0]
+    p0 = {p: region[p] for p in region}
+
+    region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE)[0]
+    p1 = {p: region[p] for p in region}
+
+    assert len(p0) < len(p1)
+
+
+def test_cache():
+    SAMPLE_mod = SAMPLE.copy()
+    region = regionprops(SAMPLE_mod)[0]
+    f0 = region.filled_image
+    region._label_image[:10] = 1
+    f1 = region.filled_image
+
+    # Changed underlying image, but cache keeps result the same
+    assert_array_equal(f0, f1)
+
+    # Now invalidate cache
+    region._cache_active = False
+    f1 = region.filled_image
+
+    assert cp.any(f0 != f1)
+
+
+def test_docstrings_and_props():
+    def foo():
+        """foo"""
+
+    has_docstrings = bool(foo.__doc__)
+
+    region = regionprops(SAMPLE)[0]
+
+    docs = _parse_docs()
+    props = [m for m in dir(region) if not m.startswith('_')]
+
+    nr_docs_parsed = len(docs)
+    nr_props = len(props)
+    if has_docstrings:
+        assert_equal(nr_docs_parsed, nr_props)
+        ds = docs['weighted_moments_normalized']
+        assert 'iteration' not in ds
+        assert len(ds.split('\n')) > 3
+    else:
+        assert_equal(nr_docs_parsed, 0)
+
+
+def test_props_to_dict():
+    regions = regionprops(SAMPLE)
+    out = _props_to_dict(regions)
+    assert out == {'label': cp.array([1]),
+                   'bbox-0': cp.array([0]), 'bbox-1': cp.array([0]),
+                   'bbox-2': cp.array([10]), 'bbox-3': cp.array([18])}
+
+    regions = regionprops(SAMPLE)
+    out = _props_to_dict(regions, properties=('label', 'area', 'bbox'),
+                         separator='+')
+    assert out == {'label': cp.array([1]), 'area': cp.array([72]),
+                   'bbox+0': cp.array([0]), 'bbox+1': cp.array([0]),
+                   'bbox+2': cp.array([10]), 'bbox+3': cp.array([18])}
+
+
+def test_regionprops_table():
+    out = regionprops_table(SAMPLE)
+    assert out == {'label': cp.array([1]),
+                   'bbox-0': cp.array([0]), 'bbox-1': cp.array([0]),
+                   'bbox-2': cp.array([10]), 'bbox-3': cp.array([18])}
+
+    out = regionprops_table(SAMPLE, properties=('label', 'area', 'bbox'),
+                            separator='+')
+    assert out == {'label': cp.array([1]), 'area': cp.array([72]),
+                   'bbox+0': cp.array([0]), 'bbox+1': cp.array([0]),
+                   'bbox+2': cp.array([10]), 'bbox+3': cp.array([18])}
+
+
+def test_regionprops_table_no_regions():
+    out = regionprops_table(cp.zeros((2, 2), dtype=int),
+                            properties=('label', 'area', 'bbox'),
+                            separator='+')
+    assert len(out) == 6
+    assert len(out['label']) == 0
+    assert len(out['area']) == 0
+    assert len(out['bbox+0']) == 0
+    assert len(out['bbox+1']) == 0
+    assert len(out['bbox+2']) == 0
+    assert len(out['bbox+3']) == 0
+
+
+def test_props_dict_complete():
+    region = regionprops(SAMPLE)[0]
+    properties = [s for s in dir(region) if not s.startswith('_')]
+    assert set(properties) == set(PROPS.values())
+
+
+def test_column_dtypes_complete():
+    assert set(COL_DTYPES.keys()).union(OBJECT_COLUMNS) == set(PROPS.values())
+
+
+def test_column_dtypes_correct():
+    msg = 'mismatch with expected type,'
+    region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE)[0]
+    for col in COL_DTYPES:
+        r = region[col]
+
+        if col in OBJECT_COLUMNS:
+            assert COL_DTYPES[col] == object
+            continue
+
+        # TODO: grlee77: check desired types for returned.
+        #       e.g. currently inertia_tensor_eigvals returns a list of 0-dim
+        #       arrays
+        if isinstance(r, (tuple, list)):
+            r0 = r[0]
+            if isinstance(r0, cp.ndarray) and r0.ndim == 0:
+                r0 = r0.item()
+            t = type(r0)
+        elif cp.isscalar(r):
+            t = type(r)
+        else:
+            t = type(r.ravel()[0].item())
+
+        if cp.issubdtype(t, cp.floating):
+            assert COL_DTYPES[col] == float, (
+                f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
+            )
+        elif cp.issubdtype(t, cp.integer):
+            assert COL_DTYPES[col] == int, (
+                f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
+            )
+        else:
+            assert False, (
+                f'{col} dtype {t} {msg} {COL_DTYPES[col]}'
+            )
+
+
+def test_deprecated_coords_argument():
+    with expected_warnings(['coordinates keyword argument']):
+        regionprops(SAMPLE, coordinates='rc')
+    with pytest.raises(ValueError):
+        regionprops(SAMPLE, coordinates='xy')
+
+
+def pixelcount(regionmask):
+    """a short test for an extra property"""
+    return cp.sum(regionmask)
+
+
+def median_intensity(regionmask, intensity_image):
+    return cp.median(intensity_image[regionmask])
+
+
+def too_many_args(regionmask, intensity_image, superfluous):
+    return 1
+
+
+def too_few_args():
+    return 1
+
+
+def test_extra_properties():
+    region = regionprops(SAMPLE, extra_properties=(pixelcount,))[0]
+    assert region.pixelcount == cp.sum(SAMPLE == 1)
+
+
+def test_extra_properties_intensity():
+    region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE,
+                         extra_properties=(median_intensity,)
+                         )[0]
+    assert region.median_intensity == cp.median(INTENSITY_SAMPLE[SAMPLE == 1])
+
+
+def test_extra_properties_no_intensity_provided():
+    with pytest.raises(AttributeError):
+        region = regionprops(SAMPLE, extra_properties=(median_intensity,))[0]
+        _ = region.median_intensity
+
+
+def test_extra_properties_nr_args():
+    with pytest.raises(AttributeError):
+        region = regionprops(SAMPLE, extra_properties=(too_few_args,))[0]
+        _ = region.too_few_args
+    with pytest.raises(AttributeError):
+        region = regionprops(SAMPLE, extra_properties=(too_many_args,))[0]
+        _ = region.too_many_args
+
+
+def test_extra_properties_mixed():
+    # mixed properties, with and without intensity
+    region = regionprops(SAMPLE, intensity_image=INTENSITY_SAMPLE,
+                         extra_properties=(median_intensity, pixelcount)
+                         )[0]
+    assert region.median_intensity == cp.median(INTENSITY_SAMPLE[SAMPLE == 1])
+    assert region.pixelcount == cp.sum(SAMPLE == 1)
+
+
+def test_extra_properties_table():
+    out = regionprops_table(SAMPLE_MULTIPLE,
+                            intensity_image=INTENSITY_SAMPLE_MULTIPLE,
+                            properties=('label',),
+                            extra_properties=(median_intensity, pixelcount)
+                            )
+    assert_array_almost_equal(out['median_intensity'], np.array([2.0, 4.0]))
+    assert_array_equal(out['pixelcount'], np.array([10, 2]))
+
+
+def test_multichannel():
+    """Test that computing multichannel properties works."""
+    astro = data.astronaut()[::4, ::4]
+    labels = slic(astro.astype(float), start_label=1)
+
+    astro = cp.asarray(astro)
+    astro_green = astro[..., 1]
+    labels = cp.asarray(labels)
+
+    segment_idx = int(cp.max(labels) // 2)
+    region = regionprops(labels, astro_green)[segment_idx]
+    region_multi = regionprops(labels, astro)[segment_idx]
+    for prop in PROPS:
+        p = region[prop]
+        p_multi = region_multi[prop]
+        if isinstance(p, (list, tuple)):
+            p = tuple([cp.asnumpy(p_) for p_ in p])
+            p = np.stack(p)
+        if isinstance(p_multi, (list, tuple)):
+            p_multi = tuple([cp.asnumpy(p_) for p_ in p_multi])
+            p_multi = np.stack(p_multi)
+        if np.shape(p) == np.shape(p_multi):
+            # property does not depend on multiple channels
+            assert_array_equal(p, p_multi)
+        else:
+            # property uses multiple channels, returns props stacked along
+            # final axis
+            assert_array_equal(p, p_multi[..., 1])
diff --git a/python/cucim/src/cucim/skimage/metrics/__init__.py b/python/cucim/src/cucim/skimage/metrics/__init__.py
new file mode 100644
index 000000000..46345ea1d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/metrics/__init__.py
@@ -0,0 +1,10 @@
+from ._structural_similarity import structural_similarity
+from .simple_metrics import (mean_squared_error, normalized_root_mse,
+                             peak_signal_noise_ratio)
+
+__all__ = [
+    "mean_squared_error",
+    "normalized_root_mse",
+    "peak_signal_noise_ratio",
+    "structural_similarity",
+]
diff --git a/python/cucim/src/cucim/skimage/metrics/_structural_similarity.py b/python/cucim/src/cucim/skimage/metrics/_structural_similarity.py
new file mode 100644
index 000000000..2adccf8dd
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/metrics/_structural_similarity.py
@@ -0,0 +1,235 @@
+import cupy as cp
+from cupyx.scipy.ndimage import gaussian_filter, uniform_filter
+
+from .._shared.utils import check_shape_equality, warn
+from ..util.arraycrop import crop
+from ..util.dtype import dtype_range
+
+__all__ = ['structural_similarity']
+
+
+# CuPy Backend: added float32 support
+#               TODO: make upstream PR to scikit-image to enabled float32
+def structural_similarity(im1, im2,
+                          *,
+                          win_size=None, gradient=False, data_range=None,
+                          multichannel=False, gaussian_weights=False,
+                          full=False, **kwargs):
+    """
+    Compute the mean structural similarity index between two images.
+
+    Parameters
+    ----------
+    im1, im2 : ndarray
+        Images. Any dimensionality with same shape.
+    win_size : int or None, optional
+        The side-length of the sliding window used in comparison. Must be an
+        odd value. If `gaussian_weights` is True, this is ignored and the
+        window size will depend on `sigma`.
+    gradient : bool, optional
+        If True, also return the gradient with respect to im2.
+    data_range : float, optional
+        The data range of the input image (distance between minimum and
+        maximum possible values). By default, this is estimated from the image
+        data-type.
+    multichannel : bool, optional
+        If True, treat the last dimension of the array as channels. Similarity
+        calculations are done independently for each channel then averaged.
+    gaussian_weights : bool, optional
+        If True, each patch has its mean and variance spatially weighted by a
+        normalized Gaussian kernel of width sigma=1.5.
+    full : bool, optional
+        If True, also return the full structural similarity image.
+
+    Other Parameters
+    ----------------
+    use_sample_covariance : bool
+        If True, normalize covariances by N-1 rather than, N where N is the
+        number of pixels within the sliding window.
+    K1 : float
+        Algorithm parameter, K1 (small constant, see [1]_).
+    K2 : float
+        Algorithm parameter, K2 (small constant, see [1]_).
+    sigma : float
+        Standard deviation for the Gaussian when `gaussian_weights` is True.
+
+    Returns
+    -------
+    mssim : float
+        The mean structural similarity index over the image.
+    grad : ndarray
+        The gradient of the structural similarity between im1 and im2 [2]_.
+        This is only returned if `gradient` is set to True.
+    S : ndarray
+        The full SSIM image.  This is only returned if `full` is set to True.
+
+    Notes
+    -----
+    To match the implementation of Wang et. al. [1]_, set `gaussian_weights`
+    to True, `sigma` to 1.5, and `use_sample_covariance` to False.
+
+    .. versionchanged:: 0.16
+        This function was renamed from ``skimage.measure.compare_ssim`` to
+        ``skimage.metrics.structural_similarity``.
+
+    References
+    ----------
+    .. [1] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P.
+       (2004). Image quality assessment: From error visibility to
+       structural similarity. IEEE Transactions on Image Processing,
+       13, 600-612.
+       https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf,
+       :DOI:`10.1109/TIP.2003.819861`
+
+    .. [2] Avanaki, A. N. (2009). Exact global histogram specification
+       optimized for structural similarity. Optical Review, 16, 613-621.
+       :arxiv:`0901.0065`
+       :DOI:`10.1007/s10043-009-0119-z`
+
+    """
+    check_shape_equality(im1, im2)
+    float_dtype = im1.dtype if im1.dtype.kind == 'f' else cp.float64
+
+    if multichannel:
+        # loop over channels
+        args = dict(win_size=win_size,
+                    gradient=gradient,
+                    data_range=data_range,
+                    multichannel=False,
+                    gaussian_weights=gaussian_weights,
+                    full=full)
+        args.update(kwargs)
+        nch = im1.shape[-1]
+        mssim = cp.empty(nch, dtype=float_dtype)
+        if gradient:
+            G = cp.empty(im1.shape, dtype=float_dtype)
+        if full:
+            S = cp.empty(im1.shape, dtype=float_dtype)
+        for ch in range(nch):
+            ch_result = structural_similarity(im1[..., ch],
+                                              im2[..., ch], **args)
+            if gradient and full:
+                mssim[..., ch], G[..., ch], S[..., ch] = ch_result
+            elif gradient:
+                mssim[..., ch], G[..., ch] = ch_result
+            elif full:
+                mssim[..., ch], S[..., ch] = ch_result
+            else:
+                mssim[..., ch] = ch_result
+        mssim = mssim.mean()
+        if gradient and full:
+            return mssim, G, S
+        elif gradient:
+            return mssim, G
+        elif full:
+            return mssim, S
+        else:
+            return mssim
+
+    K1 = kwargs.pop('K1', 0.01)
+    K2 = kwargs.pop('K2', 0.03)
+    sigma = kwargs.pop('sigma', 1.5)
+    if K1 < 0:
+        raise ValueError("K1 must be positive")
+    if K2 < 0:
+        raise ValueError("K2 must be positive")
+    if sigma < 0:
+        raise ValueError("sigma must be positive")
+    use_sample_covariance = kwargs.pop('use_sample_covariance', True)
+
+    if gaussian_weights:
+        # Set to give an 11-tap filter with the default sigma of 1.5 to match
+        # Wang et. al. 2004.
+        truncate = 3.5
+
+    if win_size is None:
+        if gaussian_weights:
+            # set win_size used by crop to match the filter size
+            r = int(truncate * sigma + 0.5)  # radius as in ndimage
+            win_size = 2 * r + 1
+        else:
+            win_size = 7  # backwards compatibility
+
+    if any(s < win_size for s in im1.shape):
+        raise ValueError(
+            "win_size exceeds image extent.  If the input is a multichannel "
+            "(color) image, set multichannel=True.")
+
+    if not (win_size % 2 == 1):
+        raise ValueError('Window size must be odd.')
+
+    if data_range is None:
+        if im1.dtype != im2.dtype:
+            warn("Inputs have mismatched dtype.  Setting data_range based on "
+                 "im1.dtype.", stacklevel=2)
+        dmin, dmax = dtype_range[im1.dtype.type]
+        data_range = dmax - dmin
+
+    ndim = im1.ndim
+
+    filter_args = dict(mode='reflect')
+    if gaussian_weights:
+        filter_func = gaussian_filter
+        filter_args = {'sigma': sigma, 'truncate': truncate}
+    else:
+        filter_func = uniform_filter
+        filter_args = {'size': win_size}
+
+    # ndimage filters need floating point data
+    im1 = im1.astype(float_dtype, copy=False)
+    im2 = im2.astype(float_dtype, copy=False)
+
+    NP = win_size ** ndim
+
+    # filter has already normalized by NP
+    if use_sample_covariance:
+        cov_norm = NP / (NP - 1)  # sample covariance
+    else:
+        cov_norm = 1.0  # population covariance to match Wang et. al. 2004
+
+    # compute (weighted) means
+    ux = filter_func(im1, **filter_args)
+    uy = filter_func(im2, **filter_args)
+
+    # compute (weighted) variances and covariances
+    uxx = filter_func(im1 * im1, **filter_args)
+    uyy = filter_func(im2 * im2, **filter_args)
+    uxy = filter_func(im1 * im2, **filter_args)
+    vx = cov_norm * (uxx - ux * ux)
+    vy = cov_norm * (uyy - uy * uy)
+    vxy = cov_norm * (uxy - ux * uy)
+
+    R = data_range
+    C1 = (K1 * R) ** 2
+    C2 = (K2 * R) ** 2
+
+    A1, A2, B1, B2 = ((2 * ux * uy + C1,
+                       2 * vxy + C2,
+                       ux ** 2 + uy ** 2 + C1,
+                       vx + vy + C2))
+    D = B1 * B2
+    S = (A1 * A2) / D
+
+    # to avoid edge effects will ignore filter radius strip around edges
+    pad = (win_size - 1) // 2
+
+    # compute (weighted) mean of ssim
+    mssim = crop(S, pad).mean()
+
+    if gradient:
+        # The following is Eqs. 7-8 of Avanaki 2009.
+        grad = filter_func(A1 / D, **filter_args) * im1
+        grad += filter_func(-S / B2, **filter_args) * im2
+        grad += filter_func((ux * (A2 - A1) - uy * (B2 - B1) * S) / D,
+                            **filter_args)
+        grad *= (2 / im1.size)
+
+        if full:
+            return mssim, grad, S
+        else:
+            return mssim, grad
+    else:
+        if full:
+            return mssim, S
+        else:
+            return mssim
diff --git a/python/cucim/src/cucim/skimage/metrics/simple_metrics.py b/python/cucim/src/cucim/skimage/metrics/simple_metrics.py
new file mode 100644
index 000000000..890b94755
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/metrics/simple_metrics.py
@@ -0,0 +1,165 @@
+import functools
+
+import cupy as cp
+
+from cucim.skimage._shared.utils import check_shape_equality, warn
+from cucim.skimage.util.dtype import dtype_range
+
+__all__ = ['mean_squared_error',
+           'normalized_root_mse',
+           'peak_signal_noise_ratio']
+
+
+def _as_floats(image0, image1):
+    """
+    Promote im1, im2 to nearest appropriate floating point precision.
+    """
+    float_type = functools.reduce(
+        cp.promote_types, [image0.dtype, image1.dtype, cp.float32]
+    )
+    image0 = image0.astype(float_type, copy=False)
+    image1 = image1.astype(float_type, copy=False)
+    return image0, image1
+
+
+def mean_squared_error(image0, image1):
+    """
+    Compute the mean-squared error between two images.
+
+    Parameters
+    ----------
+    image0, image1 : ndarray
+        Images.  Any dimensionality, must have same shape.
+
+    Returns
+    -------
+    mse : float
+        The mean-squared error (MSE) metric.
+
+    Notes
+    -----
+    .. versionchanged:: 0.16
+        This function was renamed from ``skimage.measure.compare_mse`` to
+        ``skimage.metrics.mean_squared_error``.
+
+    """
+    check_shape_equality(image0, image1)
+    image0, image1 = _as_floats(image0, image1)
+    diff = image0 - image1
+    return cp.mean(diff * diff, dtype=cp.float64)
+
+
+def normalized_root_mse(image_true, image_test, *, normalization='euclidean'):
+    """
+    Compute the normalized root mean-squared error (NRMSE) between two
+    images.
+
+    Parameters
+    ----------
+    image_true : ndarray
+        Ground-truth image, same shape as im_test.
+    image_test : ndarray
+        Test image.
+    normalization : {'euclidean', 'min-max', 'mean'}, optional
+        Controls the normalization method to use in the denominator of the
+        NRMSE.  There is no standard method of normalization across the
+        literature [1]_.  The methods available here are as follows:
+
+        - 'euclidean' : normalize by the averaged Euclidean norm of
+          ``im_true``::
+
+              NRMSE = RMSE * sqrt(N) / || im_true ||
+
+          where || . || denotes the Frobenius norm and ``N = im_true.size``.
+          This result is equivalent to::
+
+              NRMSE = || im_true - im_test || / || im_true ||.
+
+        - 'min-max'   : normalize by the intensity range of ``im_true``.
+        - 'mean'      : normalize by the mean of ``im_true``
+
+    Returns
+    -------
+    nrmse : float
+        The NRMSE metric.
+
+    Notes
+    -----
+    .. versionchanged:: 0.16
+        This function was renamed from ``skimage.measure.compare_nrmse`` to
+        ``skimage.metrics.normalized_root_mse``.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Root-mean-square_deviation
+
+    """
+    check_shape_equality(image_true, image_test)
+    image_true, image_test = _as_floats(image_true, image_test)
+
+    # Ensure that both 'Euclidean' and 'euclidean' match
+    normalization = normalization.lower()
+    if normalization == 'euclidean':
+        denom = cp.sqrt(cp.mean((image_true * image_true), dtype=cp.float64))
+    elif normalization == 'min-max':
+        denom = image_true.max() - image_true.min()
+    elif normalization == 'mean':
+        denom = image_true.mean()
+    else:
+        raise ValueError("Unsupported norm_type")
+    return cp.sqrt(mean_squared_error(image_true, image_test)) / denom
+
+
+def peak_signal_noise_ratio(image_true, image_test, *, data_range=None):
+    """
+    Compute the peak signal to noise ratio (PSNR) for an image.
+
+    Parameters
+    ----------
+    image_true : ndarray
+        Ground-truth image, same shape as im_test.
+    image_test : ndarray
+        Test image.
+    data_range : int, optional
+        The data range of the input image (distance between minimum and
+        maximum possible values).  By default, this is estimated from the image
+        data-type.
+
+    Returns
+    -------
+    psnr : float
+        The PSNR metric.
+
+    Notes
+    -----
+    .. versionchanged:: 0.16
+        This function was renamed from ``skimage.measure.compare_psnr`` to
+        ``skimage.metrics.peak_signal_noise_ratio``.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio
+
+    """
+    check_shape_equality(image_true, image_test)
+
+    if data_range is None:
+        if image_true.dtype != image_test.dtype:
+            warn("Inputs have mismatched dtype.  Setting data_range based on "
+                 "im_true.", stacklevel=2)
+        dmin, dmax = dtype_range[image_true.dtype.type]
+        true_min, true_max = cp.min(image_true), cp.max(image_true)
+        if true_max > dmax or true_min < dmin:
+            raise ValueError(
+                "im_true has intensity values outside the range expected for "
+                "its data type.  Please manually specify the data_range")
+        if true_min >= 0:
+            # most common case (255 for uint8, 1 for float)
+            data_range = dmax
+        else:
+            data_range = dmax - dmin
+
+    image_true, image_test = _as_floats(image_true, image_test)
+
+    err = mean_squared_error(image_true, image_test)
+    return 10 * cp.log10((data_range * data_range) / err)
diff --git a/python/cucim/src/cucim/skimage/metrics/tests/test_simple_metrics.py b/python/cucim/src/cucim/skimage/metrics/tests/test_simple_metrics.py
new file mode 100644
index 000000000..f611f9998
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/metrics/tests/test_simple_metrics.py
@@ -0,0 +1,114 @@
+import cupy as cp
+import numpy as np
+import pytest
+from skimage import data
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.metrics import (mean_squared_error, normalized_root_mse,
+                                   peak_signal_noise_ratio)
+
+np.random.seed(
+    5
+)  # need exact NumPy seed here. (Don't use CuPy as it won't be identical)
+cam = cp.asarray(data.camera())
+sigma = 20.0
+noise = cp.asarray(sigma * np.random.randn(*cam.shape))
+cam_noisy = cp.clip(cam + noise, 0, 255)
+cam_noisy = cam_noisy.astype(cam.dtype)
+
+assert_equal = cp.testing.assert_array_equal
+assert_almost_equal = cp.testing.assert_array_almost_equal
+
+
+@pytest.mark.parametrize('dtype', [cp.uint8, cp.float32, cp.float64])
+@cp.testing.with_requires("skimage>=1.18")
+def test_PSNR_vs_IPOL(dtype):
+    """Tests vs. imdiff result from the following IPOL article and code:
+    https://www.ipol.im/pub/art/2011/g_lmii/.
+
+    Notes
+    -----
+    To generate p_IPOL, we need a local copy of cam_noisy:
+
+    >>> from skimage import io
+    >>> io.imsave('/tmp/cam_noisy.png', cam_noisy)
+
+    Then, we use the following command:
+    $ ./imdiff -m psnr <path to camera.png>/camera.png /tmp/cam_noisy.png
+
+    Values for current data.camera() calculated by Gregory Lee on Sep, 2020.
+    Available at:
+    https://github.com/scikit-image/scikit-image/pull/4913#issuecomment-700653165
+    """
+    p_IPOL = 22.409353363576034
+    p = peak_signal_noise_ratio(cam.astype(dtype), cam_noisy.astype(dtype))
+    if np.dtype.kind(dtype) == 'f':
+        assert p.dtype == dtype
+    else:
+        assert p.dtype == cp.float64
+    assert_almost_equal(p, p_IPOL, decimal=4)
+
+
+def test_PSNR_float():
+    p_uint8 = peak_signal_noise_ratio(cam, cam_noisy)
+    p_float64 = peak_signal_noise_ratio(cam / 255.0, cam_noisy / 255.0,
+                                        data_range=1)
+    assert_almost_equal(p_uint8, p_float64, decimal=5)
+
+    # mixed precision inputs
+    p_mixed = peak_signal_noise_ratio(cam / 255.0,
+                                      cam_noisy.astype(np.float32) / 255.0,
+                                      data_range=1)
+    assert_almost_equal(p_mixed, p_float64, decimal=5)
+
+    # mismatched dtype results in a warning if data_range is unspecified
+    with expected_warnings(["Inputs have mismatched dtype"]):
+        p_mixed = peak_signal_noise_ratio(
+            cam / 255.0, cam_noisy.astype(np.float32) / 255.0
+        )
+    assert_almost_equal(p_mixed, p_float64, decimal=5)
+
+
+def test_PSNR_errors():
+    # shape mismatch
+    with pytest.raises(ValueError):
+        peak_signal_noise_ratio(cam, cam[:-1, :])
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_NRMSE(dtype):
+    x = cp.ones(4, dtype=dtype)
+    y = cp.array([0.0, 2.0, 2.0, 2.0], dtype=dtype)
+    assert_almost_equal(
+        normalized_root_mse(y, x, normalization='mean'),
+        1 / np.mean(y))
+    assert_almost_equal(
+        normalized_root_mse(y, x, normalization='euclidean'),
+        1 / np.sqrt(3))
+    assert_almost_equal(
+        normalized_root_mse(y, x, normalization='min-max'),
+        1 / (y.max() - y.min()))
+
+    # mixed precision inputs are allowed
+    assert_almost_equal(
+        normalized_root_mse(y, x.astype(cp.float32), normalization='min-max'),
+        1 / (y.max() - y.min()))
+
+
+def test_NRMSE_no_int_overflow():
+    camf = cam.astype(cp.float32)
+    cam_noisyf = cam_noisy.astype(cp.float32)
+    assert_almost_equal(mean_squared_error(cam, cam_noisy),
+                        mean_squared_error(camf, cam_noisyf))
+    assert_almost_equal(normalized_root_mse(cam, cam_noisy),
+                        normalized_root_mse(camf, cam_noisyf))
+
+
+def test_NRMSE_errors():
+    x = cp.ones(4)
+    # shape mismatch
+    with pytest.raises(ValueError):
+        normalized_root_mse(x[:-1], x)
+    # invalid normalization name
+    with pytest.raises(ValueError):
+        normalized_root_mse(x, x, normalization="foo")
diff --git a/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py b/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py
new file mode 100644
index 000000000..10871a90f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/metrics/tests/test_structural_similarity.py
@@ -0,0 +1,239 @@
+import cupy as cp
+import numpy as np
+import pytest
+from skimage import data
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.metrics import structural_similarity
+
+# need exact NumPy seed here. (CuPy as it won't be identical)
+np.random.seed(5)
+cam = cp.asarray(data.camera())
+sigma = 20.0
+noise = cp.asarray(sigma * np.random.randn(*cam.shape))
+cam_noisy = cp.clip(cam + noise, 0, 255)
+cam_noisy = cam_noisy.astype(cam.dtype)
+
+
+cp.random.seed(1234)
+
+assert_equal = cp.testing.assert_array_equal
+assert_almost_equal = cp.testing.assert_array_almost_equal
+assert_array_almost_equal = cp.testing.assert_array_almost_equal
+
+
+def test_structural_similarity_patch_range():
+    N = 51
+    rstate = cp.random.RandomState(1234)
+    X = (rstate.rand(N, N) * 255).astype(cp.uint8)
+    Y = (rstate.rand(N, N) * 255).astype(cp.uint8)
+
+    assert structural_similarity(X, Y, win_size=N) < 0.1
+    assert_equal(structural_similarity(X, X, win_size=N), 1)
+
+
+def test_structural_similarity_image():
+    N = 100
+    rstate = cp.random.RandomState(1234)
+    X = (rstate.rand(N, N) * 255).astype(cp.uint8)
+    Y = (rstate.rand(N, N) * 255).astype(cp.uint8)
+
+    S0 = structural_similarity(X, X, win_size=3)
+    assert_equal(S0, 1)
+
+    S1 = structural_similarity(X, Y, win_size=3)
+    assert S1 < 0.3
+
+    S2 = structural_similarity(X, Y, win_size=11, gaussian_weights=True)
+    assert S2 < 0.3
+
+    mssim0, S3 = structural_similarity(X, Y, full=True)
+    assert_equal(S3.shape, X.shape)
+    mssim = structural_similarity(X, Y)
+    assert_equal(mssim0, mssim)
+
+    # structural_similarity of image with itself should be 1.0
+    assert_equal(structural_similarity(X, X), 1.0)
+
+
+# Because we are forcing a random seed state, it is probably good to test
+# against a few seeds in case on seed gives a particularly bad example
+@pytest.mark.parametrize('seed', [1, 2, 3, 5, 8, 13])
+def test_structural_similarity_grad(seed):
+    N = 30
+    # NOTE: This test is known to randomly fail on some systems (Mac OS X 10.6)
+    #       And when testing tests in parallel. Therefore, we choose a few
+    #       seeds that are known to work.
+    #       The likely cause of this failure is that we are setting a hard
+    #       threshold on the value of the gradient. Often the computed gradient
+    #       is only slightly larger than what was measured.
+    # X = cp.random.rand(N, N) * 255
+    # Y = cp.random.rand(N, N) * 255
+    rnd = np.random.RandomState(seed)
+    X = cp.array(rnd.rand(N, N) * 255)
+    Y = cp.array(rnd.rand(N, N) * 255)
+
+    f = structural_similarity(X, Y, data_range=255)
+    g = structural_similarity(X, Y, data_range=255, gradient=True)
+
+    assert f < 0.05
+
+    assert g[0] < 0.05
+    assert cp.all(g[1] < 0.05)
+
+    mssim, grad, s = structural_similarity(
+        X, Y, data_range=255, gradient=True, full=True
+    )
+    assert cp.all(grad < 0.05)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_structural_similarity_dtype(dtype):
+    N = 30
+    rstate = cp.random.RandomState(1234)
+    X = rstate.rand(N, N).astype(dtype, copy=False)
+    Y = rstate.rand(N, N).astype(dtype, copy=False)
+
+    S1 = structural_similarity(X, Y)
+    assert S1.dtype == dtype
+
+    X = (X * 255).astype(cp.uint8)
+    Y = (X * 255).astype(cp.uint8)
+
+    S2 = structural_similarity(X, Y)
+    assert S1 < 0.15  # grlee77: increase value from 0.1
+    assert S2 < 0.15  # grlee77: increase value from 0.1
+
+
+def test_structural_similarity_multichannel():
+    N = 100
+    X = (cp.random.rand(N, N) * 255).astype(cp.uint8)
+    Y = (cp.random.rand(N, N) * 255).astype(cp.uint8)
+
+    S1 = structural_similarity(X, Y, win_size=3)
+
+    # replicate across three channels.  should get identical value
+    Xc = cp.tile(X[..., cp.newaxis], (1, 1, 3))
+    Yc = cp.tile(Y[..., cp.newaxis], (1, 1, 3))
+    S2 = structural_similarity(Xc, Yc, multichannel=True, win_size=3)
+    assert_almost_equal(S1, S2)
+
+    # full case should return an image as well
+    m, S3 = structural_similarity(Xc, Yc, multichannel=True, full=True)
+    assert_equal(S3.shape, Xc.shape)
+
+    # gradient case
+    m, grad = structural_similarity(Xc, Yc, multichannel=True, gradient=True)
+    assert_equal(grad.shape, Xc.shape)
+
+    # full and gradient case
+    m, grad, S3 = structural_similarity(
+        Xc, Yc, multichannel=True, full=True, gradient=True
+    )
+    assert_equal(grad.shape, Xc.shape)
+    assert_equal(S3.shape, Xc.shape)
+
+    # fail if win_size exceeds any non-channel dimension
+    with pytest.raises(ValueError):
+        structural_similarity(Xc, Yc, win_size=7, multichannel=False)
+
+
+@pytest.mark.parametrize('dtype', [cp.uint8, cp.float32, cp.float64])
+def test_structural_similarity_nD(dtype):
+    # test 1D through 4D on small random arrays
+    N = 10
+    for ndim in range(1, 5):
+        xsize = [N] * 5
+        X = (cp.random.rand(*xsize) * 255).astype(dtype)
+        Y = (cp.random.rand(*xsize) * 255).astype(dtype)
+
+        mssim = structural_similarity(X, Y, win_size=3)
+        assert mssim < 0.05
+        if np.dtype(dtype).kind == 'f':
+            assert mssim.dtype == X.dtype
+        else:
+            assert mssim.dtype == cp.float64
+
+
+def test_structural_similarity_multichannel_chelsea():
+    # color image example
+    Xc = cp.asarray(data.chelsea())
+    sigma = 15.0
+    Yc = cp.clip(Xc + sigma * cp.random.randn(*Xc.shape), 0, 255)
+    Yc = Yc.astype(Xc.dtype)
+
+    # multichannel result should be mean of the individual channel results
+    mssim = structural_similarity(Xc, Yc, multichannel=True)
+    mssim_sep = [
+        float(structural_similarity(Yc[..., c], Xc[..., c]))
+        for c in range(Xc.shape[-1])
+    ]
+    assert_almost_equal(mssim, np.mean(mssim_sep))
+
+    # structural_similarity of image with itself should be 1.0
+    assert_equal(structural_similarity(Xc, Xc, multichannel=True), 1.0)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_gaussian_structural_similarity_vs_IPOL():
+    """Tests vs. imdiff result from the following IPOL article and code:
+    https://www.ipol.im/pub/art/2011/g_lmii/.
+
+    Notes
+    -----
+    To generate mssim_IPOL, we need a local copy of cam_noisy:
+
+    >>> from skimage import io
+    >>> io.imsave('/tmp/cam_noisy.png', cam_noisy)
+
+    Then, we use the following command:
+    $ ./imdiff -m mssim <path to camera.png>/camera.png /tmp/cam_noisy.png
+
+    Values for current data.camera() calculated by Gregory Lee on Sep, 2020.
+    Available at:
+    https://github.com/scikit-image/scikit-image/pull/4913#issuecomment-700653165
+    """
+    mssim_IPOL = 0.357959091663361
+    mssim = structural_similarity(
+        cam, cam_noisy, gaussian_weights=True, use_sample_covariance=False
+    )
+    assert_almost_equal(mssim, mssim_IPOL, decimal=3)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_mssim_vs_legacy():
+    # check that ssim with default options matches skimage 0.11 result
+    mssim_skimage_0pt17 = 0.3674518327910367
+    mssim = structural_similarity(cam, cam_noisy)
+    assert_almost_equal(mssim, mssim_skimage_0pt17)
+
+
+def test_mssim_mixed_dtype():
+    mssim = structural_similarity(cam, cam_noisy)
+    with expected_warnings(["Inputs have mismatched dtype"]):
+        mssim_mixed = structural_similarity(cam, cam_noisy.astype(cp.float32))
+    assert_almost_equal(mssim, mssim_mixed)
+
+    # no warning when user supplies data_range
+    mssim_mixed = structural_similarity(
+        cam, cam_noisy.astype(cp.float32), data_range=255
+    )
+    assert_almost_equal(mssim, mssim_mixed)
+
+
+def test_invalid_input():
+    # size mismatch
+    X = cp.zeros((9, 9), dtype=cp.double)
+    Y = cp.zeros((8, 8), dtype=cp.double)
+    with pytest.raises(ValueError):
+        structural_similarity(X, Y)
+    # win_size exceeds image extent
+    with pytest.raises(ValueError):
+        structural_similarity(X, X, win_size=X.shape[0] + 1)
+    # some kwarg inputs must be non-negative
+    with pytest.raises(ValueError):
+        structural_similarity(X, X, K1=-0.1)
+    with pytest.raises(ValueError):
+        structural_similarity(X, X, K2=-0.1)
+    with pytest.raises(ValueError):
+        structural_similarity(X, X, sigma=-1.0)
diff --git a/python/cucim/src/cucim/skimage/morphology/__init__.py b/python/cucim/src/cucim/skimage/morphology/__init__.py
new file mode 100644
index 000000000..499698268
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/__init__.py
@@ -0,0 +1,33 @@
+from .binary import (binary_closing, binary_dilation, binary_erosion,
+                     binary_opening)
+from .grey import (black_tophat, closing, dilation, erosion, opening,
+                   white_tophat)
+from .greyreconstruct import reconstruction
+from .misc import remove_small_holes, remove_small_objects
+from .selem import (ball, cube, diamond, disk, octagon, octahedron, rectangle,
+                    square, star)
+
+__all__ = [
+    "binary_erosion",
+    "binary_dilation",
+    "binary_opening",
+    "binary_closing",
+    "erosion",
+    "dilation",
+    "opening",
+    "closing",
+    "white_tophat",
+    "black_tophat",
+    "square",
+    "rectangle",
+    "diamond",
+    "disk",
+    "cube",
+    "octahedron",
+    "ball",
+    "octagon",
+    "star",
+    "reconstruction",
+    "remove_small_objects",
+    "remove_small_holes",
+]
diff --git a/python/cucim/src/cucim/skimage/morphology/binary.py b/python/cucim/src/cucim/skimage/morphology/binary.py
new file mode 100644
index 000000000..c51a6d036
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/binary.py
@@ -0,0 +1,147 @@
+"""
+Binary morphological operations
+"""
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from .misc import default_selem
+
+
+# The default_selem decorator provides a diamond structuring element as default
+# with the same dimension as the input image and size 3 along each axis.
+@default_selem
+def binary_erosion(image, selem=None, out=None):
+    """Return fast binary morphological erosion of an image.
+
+    This function returns the same result as greyscale erosion but performs
+    faster for binary images.
+
+    Morphological erosion sets a pixel at ``(i,j)`` to the minimum over all
+    pixels in the neighborhood centered at ``(i,j)``. Erosion shrinks bright
+    regions and enlarges dark regions.
+
+    Parameters
+    ----------
+    image : ndarray
+        Binary input image.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use a cross-shaped structuring element (connectivity=1).
+    out : ndarray of bool, optional
+        The array to store the result of the morphology. If None is
+        passed, a new array will be allocated.
+
+    Returns
+    -------
+    eroded : ndarray of bool or uint
+        The result of the morphological erosion taking values in
+        ``[False, True]``.
+
+    """
+    if out is None:
+        out = cp.empty(image.shape, dtype=cp.bool)
+    ndi.binary_erosion(image, structure=selem, output=out, border_value=True)
+    return out
+
+
+@default_selem
+def binary_dilation(image, selem=None, out=None):
+    """Return fast binary morphological dilation of an image.
+
+    This function returns the same result as greyscale dilation but performs
+    faster for binary images.
+
+    Morphological dilation sets a pixel at ``(i,j)`` to the maximum over all
+    pixels in the neighborhood centered at ``(i,j)``. Dilation enlarges bright
+    regions and shrinks dark regions.
+
+    Parameters
+    ----------
+
+    image : ndarray
+        Binary input image.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use a cross-shaped structuring element (connectivity=1).
+    out : ndarray of bool, optional
+        The array to store the result of the morphology. If None is
+        passed, a new array will be allocated.
+
+    Returns
+    -------
+    dilated : ndarray of bool or uint
+        The result of the morphological dilation with values in
+        ``[False, True]``.
+    """
+    if out is None:
+        out = cp.empty(image.shape, dtype=cp.bool)
+    ndi.binary_dilation(image, structure=selem, output=out)
+    return out
+
+
+@default_selem
+def binary_opening(image, selem=None, out=None):
+    """Return fast binary morphological opening of an image.
+
+    This function returns the same result as greyscale opening but performs
+    faster for binary images.
+
+    The morphological opening on an image is defined as an erosion followed by
+    a dilation. Opening can remove small bright spots (i.e. "salt") and connect
+    small dark cracks. This tends to "open" up (dark) gaps between (bright)
+    features.
+
+    Parameters
+    ----------
+    image : ndarray
+        Binary input image.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use a cross-shaped structuring element (connectivity=1).
+    out : ndarray of bool, optional
+        The array to store the result of the morphology. If None
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    opening : ndarray of bool
+        The result of the morphological opening.
+
+    """
+    eroded = binary_erosion(image, selem)
+    out = binary_dilation(eroded, selem, out=out)
+    return out
+
+
+@default_selem
+def binary_closing(image, selem=None, out=None):
+    """Return fast binary morphological closing of an image.
+
+    This function returns the same result as greyscale closing but performs
+    faster for binary images.
+
+    The morphological closing on an image is defined as a dilation followed by
+    an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
+    small bright cracks. This tends to "close" up (dark) gaps between (bright)
+    features.
+
+    Parameters
+    ----------
+    image : ndarray
+        Binary input image.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use a cross-shaped structuring element (connectivity=1).
+    out : ndarray of bool, optional
+        The array to store the result of the morphology. If None,
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    closing : ndarray of bool
+        The result of the morphological closing.
+
+    """
+    dilated = binary_dilation(image, selem)
+    out = binary_erosion(dilated, selem, out=out)
+    return out
diff --git a/python/cucim/src/cucim/skimage/morphology/grey.py b/python/cucim/src/cucim/skimage/morphology/grey.py
new file mode 100644
index 000000000..c69d7549b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/grey.py
@@ -0,0 +1,490 @@
+"""
+Grayscale morphological operations
+"""
+import functools
+
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from ..util import crop
+from .misc import default_selem
+
+__all__ = ['erosion', 'dilation', 'opening', 'closing', 'white_tophat',
+           'black_tophat']
+
+
+def _shift_selem(selem, shift_x, shift_y):
+    """Shift the binary image `selem` in the left and/or up.
+
+    This only affects 2D structuring elements with even number of rows
+    or columns.
+
+    Parameters
+    ----------
+    selem : 2D array, shape (M, N)
+        The input structuring element.
+    shift_x, shift_y : bool
+        Whether to move `selem` along each axis.
+
+    Returns
+    -------
+    out : 2D array, shape (M + int(shift_x), N + int(shift_y))
+        The shifted structuring element.
+    """
+    if selem.ndim != 2:
+        # do nothing for 1D or 3D or higher structuring elements
+        return selem
+    m, n = selem.shape
+    if m % 2 == 0:
+        extra_row = cp.zeros((1, n), selem.dtype)
+        if shift_x:
+            selem = cp.vstack((selem, extra_row))
+        else:
+            selem = cp.vstack((extra_row, selem))
+        m += 1
+    if n % 2 == 0:
+        extra_col = cp.zeros((m, 1), selem.dtype)
+        if shift_y:
+            selem = cp.hstack((selem, extra_col))
+        else:
+            selem = cp.hstack((extra_col, selem))
+    return selem
+
+
+def _invert_selem(selem):
+    """Change the order of the values in `selem`.
+
+    This is a patch for the *weird* footprint inversion in
+    `ndi.grey_morphology` [1]_.
+
+    Parameters
+    ----------
+    selem : array
+        The input structuring element.
+
+    Returns
+    -------
+    inverted : array, same shape and type as `selem`
+        The structuring element, in opposite order.
+
+    Examples
+    --------
+    >>> selem = cp.asarray([[0, 0, 0], [0, 1, 1], [0, 1, 1]], cp.uint8)
+    >>> _invert_selem(selem)
+    array([[1, 1, 0],
+           [1, 1, 0],
+           [0, 0, 0]], dtype=uint8)
+
+    References
+    ----------
+    .. [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285
+    """  # noqa
+    inverted = selem[(slice(None, None, -1),) * selem.ndim]
+    return inverted
+
+
+def pad_for_eccentric_selems(func):
+    """Pad input images for certain morphological operations.
+
+    Parameters
+    ----------
+    func : callable
+        A morphological function, either opening or closing, that
+        supports eccentric structuring elements. Its parameters must
+        include at least `image`, `selem`, and `out`.
+
+    Returns
+    -------
+    func_out : callable
+        The same function, but correctly padding the input image before
+        applying the input function.
+
+    See Also
+    --------
+    opening, closing.
+    """
+
+    @functools.wraps(func)
+    def func_out(image, selem, out=None, *args, **kwargs):
+        pad_widths = []
+        padding = False
+        if out is None:
+            out = cp.empty_like(image)
+        for axis_len in selem.shape:
+            if axis_len % 2 == 0:
+                axis_pad_width = axis_len - 1
+                padding = True
+            else:
+                axis_pad_width = 0
+            pad_widths.append((axis_pad_width,) * 2)
+        if padding:
+            image = cp.pad(image, pad_widths, mode='edge')
+            out_temp = cp.empty_like(image)
+        else:
+            out_temp = out
+        out_temp = func(image, selem, out=out_temp, *args, **kwargs)
+        if padding:
+            out[:] = crop(out_temp, pad_widths)
+        else:
+            out = out_temp
+        return out
+    return func_out
+
+
+@default_selem
+def erosion(image, selem=None, out=None, shift_x=False, shift_y=False):
+    """Return greyscale morphological erosion of an image.
+
+    Morphological erosion sets a pixel at (i,j) to the minimum over all pixels
+    in the neighborhood centered at (i,j). Erosion shrinks bright regions and
+    enlarges dark regions.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as an array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarrays, optional
+        The array to store the result of the morphology. If None is
+        passed, a new array will be allocated.
+    shift_x, shift_y : bool, optional
+        shift structuring element about center point. This only affects
+        eccentric structuring elements (i.e. selem with even numbered sides).
+
+    Returns
+    -------
+    eroded : array, same shape as `image`
+        The result of the morphological erosion.
+
+    Notes
+    -----
+    For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the
+    lower algorithm complexity makes the `skimage.filters.rank.minimum`
+    function more efficient for larger images and structuring elements.
+
+    Examples
+    --------
+    >>> # Erosion shrinks bright regions
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> bright_square = cp.asarray([[0, 0, 0, 0, 0],
+    ...                             [0, 1, 1, 1, 0],
+    ...                             [0, 1, 1, 1, 0],
+    ...                             [0, 1, 1, 1, 0],
+    ...                             [0, 0, 0, 0, 0]], dtype=cp.uint8)
+    >>> erosion(bright_square, square(3))
+    array([[0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0],
+           [0, 0, 1, 0, 0],
+           [0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    selem = _shift_selem(selem, shift_x, shift_y)
+    if out is None:
+        out = cp.empty_like(image)
+    ndi.grey_erosion(image, footprint=selem, output=out)
+    return out
+
+
+@default_selem
+def dilation(image, selem=None, out=None, shift_x=False, shift_y=False):
+    """Return greyscale morphological dilation of an image.
+
+    Morphological dilation sets a pixel at (i,j) to the maximum over all pixels
+    in the neighborhood centered at (i,j). Dilation enlarges bright regions
+    and shrinks dark regions.
+
+    Parameters
+    ----------
+
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarray, optional
+        The array to store the result of the morphology. If None, is
+        passed, a new array will be allocated.
+    shift_x, shift_y : bool, optional
+        shift structuring element about center point. This only affects
+        eccentric structuring elements (i.e. selem with even numbered sides).
+
+    Returns
+    -------
+    dilated : uint8 array, same shape and type as `image`
+        The result of the morphological dilation.
+
+    Notes
+    -----
+    For `uint8` (and `uint16` up to a certain bit-depth) data, the lower
+    algorithm complexity makes the `skimage.filters.rank.maximum` function more
+    efficient for larger images and structuring elements.
+
+    Examples
+    --------
+    >>> # Dilation enlarges bright regions
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> bright_pixel = cp.asarray([[0, 0, 0, 0, 0],
+    ...                            [0, 0, 0, 0, 0],
+    ...                            [0, 0, 1, 0, 0],
+    ...                            [0, 0, 0, 0, 0],
+    ...                            [0, 0, 0, 0, 0]], dtype=cp.uint8)
+    >>> dilation(bright_pixel, square(3))
+    array([[0, 0, 0, 0, 0],
+           [0, 1, 1, 1, 0],
+           [0, 1, 1, 1, 0],
+           [0, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    selem = _shift_selem(selem, shift_x, shift_y)
+    # Inside ndimage.grey_dilation, the structuring element is inverted,
+    # eg. `selem = selem[::-1, ::-1]` for 2D [1]_, for reasons unknown to
+    # this author (@jni). To "patch" this behaviour, we invert our own
+    # selem before passing it to `ndi.grey_dilation`.
+    # [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285    # noqa
+    selem = _invert_selem(selem)
+    if out is None:
+        out = cp.empty_like(image)
+    ndi.grey_dilation(image, footprint=selem, output=out)
+    return out
+
+
+@default_selem
+@pad_for_eccentric_selems
+def opening(image, selem=None, out=None):
+    """Return greyscale morphological opening of an image.
+
+    The morphological opening on an image is defined as an erosion followed by
+    a dilation. Opening can remove small bright spots (i.e. "salt") and connect
+    small dark cracks. This tends to "open" up (dark) gaps between (bright)
+    features.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as an array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarray, optional
+        The array to store the result of the morphology. If None
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    opening : array, same shape and type as `image`
+        The result of the morphological opening.
+
+    Examples
+    --------
+    >>> # Open up gap between two bright regions (but also shrink regions)
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> bad_connection = cp.asarray([[1, 0, 0, 0, 1],
+    ...                              [1, 1, 0, 1, 1],
+    ...                              [1, 1, 1, 1, 1],
+    ...                              [1, 1, 0, 1, 1],
+    ...                              [1, 0, 0, 0, 1]], dtype=cp.uint8)
+    >>> opening(bad_connection, square(3))
+    array([[0, 0, 0, 0, 0],
+           [1, 1, 0, 1, 1],
+           [1, 1, 0, 1, 1],
+           [1, 1, 0, 1, 1],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    eroded = erosion(image, selem)
+    # note: shift_x, shift_y do nothing if selem side length is odd
+    out = dilation(eroded, selem, out=out, shift_x=True, shift_y=True)
+    return out
+
+
+@default_selem
+@pad_for_eccentric_selems
+def closing(image, selem=None, out=None):
+    """Return greyscale morphological closing of an image.
+
+    The morphological closing on an image is defined as a dilation followed by
+    an erosion. Closing can remove small dark spots (i.e. "pepper") and connect
+    small bright cracks. This tends to "close" up (dark) gaps between (bright)
+    features.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as an array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarray, optional
+        The array to store the result of the morphology. If None,
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    closing : array, same shape and type as `image`
+        The result of the morphological closing.
+
+    Examples
+    --------
+    >>> # Close a gap between two bright lines
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> broken_line = cp.asarray([[0, 0, 0, 0, 0],
+    ...                           [0, 0, 0, 0, 0],
+    ...                           [1, 1, 0, 1, 1],
+    ...                           [0, 0, 0, 0, 0],
+    ...                           [0, 0, 0, 0, 0]], dtype=cp.uint8)
+    >>> closing(broken_line, square(3))
+    array([[0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0],
+           [1, 1, 1, 1, 1],
+           [0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    dilated = dilation(image, selem)
+    # note: shift_x, shift_y do nothing if selem side length is odd
+    out = erosion(dilated, selem, out=out, shift_x=True, shift_y=True)
+    return out
+
+
+@default_selem
+def white_tophat(image, selem=None, out=None):
+    """Return white top hat of an image.
+
+    The white top hat of an image is defined as the image minus its
+    morphological opening. This operation returns the bright spots of the image
+    that are smaller than the structuring element.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as an array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarray, optional
+        The array to store the result of the morphology. If None
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    out : array, same shape and type as `image`
+        The result of the morphological white top hat.
+
+    See also
+    --------
+    black_tophat
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Top-hat_transform
+
+    Examples
+    --------
+    >>> # Subtract grey background from bright peak
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> bright_on_grey = cp.asarray([[2, 3, 3, 3, 2],
+    ...                              [3, 4, 5, 4, 3],
+    ...                              [3, 5, 9, 5, 3],
+    ...                              [3, 4, 5, 4, 3],
+    ...                              [2, 3, 3, 3, 2]], dtype=cp.uint8)
+    >>> white_tophat(bright_on_grey, square(3))
+    array([[0, 0, 0, 0, 0],
+           [0, 0, 1, 0, 0],
+           [0, 1, 5, 1, 0],
+           [0, 0, 1, 0, 0],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    if out is image:
+        opened = opening(image, selem)
+        if cp.issubdtype(opened.dtype, cp.bool_):
+            cp.logical_xor(out, opened, out=out)
+        else:
+            out -= opened
+        return out
+    elif out is None:
+        out = cp.empty_like(image)
+    # work-around for NumPy deprecation warning for arithmetic
+    # operations on bool arrays
+    if isinstance(image, cp.ndarray) and image.dtype == bool:
+        image_ = image.view(dtype=cp.uint8)
+    else:
+        image_ = image
+    if isinstance(out, cp.ndarray) and out.dtype == bool:
+        out_ = out.view(dtype=cp.uint8)
+    else:
+        out_ = out
+    out_ = ndi.white_tophat(image_, footprint=selem, output=out_)
+    return out
+
+
+@default_selem
+def black_tophat(image, selem=None, out=None):
+    """Return black top hat of an image.
+
+    The black top hat of an image is defined as its morphological closing minus
+    the original image. This operation returns the dark spots of the image that
+    are smaller than the structuring element. Note that dark spots in the
+    original image are bright spots after the black top hat.
+
+    Parameters
+    ----------
+    image : ndarray
+        Image array.
+    selem : ndarray, optional
+        The neighborhood expressed as a 2-D array of 1's and 0's.
+        If None, use cross-shaped structuring element (connectivity=1).
+    out : ndarray, optional
+        The array to store the result of the morphology. If None
+        is passed, a new array will be allocated.
+
+    Returns
+    -------
+    out : array, same shape and type as `image`
+        The result of the morphological black top hat.
+
+    See also
+    --------
+    white_tophat
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Top-hat_transform
+
+    Examples
+    --------
+    >>> # Change dark peak to bright peak and subtract background
+    >>> import cupy as cp
+    >>> from cucim.skimage.morphology import square
+    >>> dark_on_grey = cp.asarray([[7, 6, 6, 6, 7],
+    ...                            [6, 5, 4, 5, 6],
+    ...                            [6, 4, 0, 4, 6],
+    ...                            [6, 5, 4, 5, 6],
+    ...                            [7, 6, 6, 6, 7]], dtype=cp.uint8)
+    >>> black_tophat(dark_on_grey, square(3))
+    array([[0, 0, 0, 0, 0],
+           [0, 0, 1, 0, 0],
+           [0, 1, 5, 1, 0],
+           [0, 0, 1, 0, 0],
+           [0, 0, 0, 0, 0]], dtype=uint8)
+
+    """
+    if out is image:
+        original = image.copy()
+    else:
+        original = image
+    out = closing(image, selem, out=out)
+    if cp.issubdtype(out.dtype, bool):
+        cp.logical_xor(out, original, out=out)
+    else:
+        out -= original
+    return out
diff --git a/python/cucim/src/cucim/skimage/morphology/greyreconstruct.py b/python/cucim/src/cucim/skimage/morphology/greyreconstruct.py
new file mode 100644
index 000000000..7ef77fcda
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/greyreconstruct.py
@@ -0,0 +1,233 @@
+"""
+This morphological reconstruction routine was adapted from CellProfiler, code
+licensed under both GPL and BSD licenses.
+
+Website: http://www.cellprofiler.org
+Copyright (c) 2003-2009 Massachusetts Institute of Technology
+Copyright (c) 2009-2011 Broad Institute
+All rights reserved.
+Original author: Lee Kamentsky
+
+"""
+import cupy as cp
+import numpy as np
+
+from ..filters._rank_order import rank_order
+
+
+def reconstruction(seed, mask, method='dilation', selem=None, offset=None):
+    """Perform a morphological reconstruction of an image.
+
+    Morphological reconstruction by dilation is similar to basic morphological
+    dilation: high-intensity values will replace nearby low-intensity values.
+    The basic dilation operator, however, uses a structuring element to
+    determine how far a value in the input image can spread. In contrast,
+    reconstruction uses two images: a "seed" image, which specifies the values
+    that spread, and a "mask" image, which gives the maximum allowed value at
+    each pixel. The mask image, like the structuring element, limits the spread
+    of high-intensity values. Reconstruction by erosion is simply the inverse:
+    low-intensity values spread from the seed image and are limited by the mask
+    image, which represents the minimum allowed value.
+
+    Alternatively, you can think of reconstruction as a way to isolate the
+    connected regions of an image. For dilation, reconstruction connects
+    regions marked by local maxima in the seed image: neighboring pixels
+    less-than-or-equal-to those seeds are connected to the seeded region.
+    Local maxima with values larger than the seed image will get truncated to
+    the seed value.
+
+    Parameters
+    ----------
+    seed : ndarray
+        The seed image (a.k.a. marker image), which specifies the values that
+        are dilated or eroded.
+    mask : ndarray
+        The maximum (dilation) / minimum (erosion) allowed value at each pixel.
+    method : {'dilation'|'erosion'}, optional
+        Perform reconstruction by dilation or erosion. In dilation (or
+        erosion), the seed image is dilated (or eroded) until limited by the
+        mask image. For dilation, each seed value must be less than or equal
+        to the corresponding mask value; for erosion, the reverse is true.
+        Default is 'dilation'.
+    selem : ndarray, optional
+        The neighborhood expressed as an n-D array of 1's and 0's.
+        Default is the n-D square of radius equal to 1 (i.e. a 3x3 square
+        for 2D images, a 3x3x3 cube for 3D images, etc.)
+    offset : ndarray, optional
+        The coordinates of the center of the structuring element.
+        Default is located on the geometrical center of the selem, in that case
+        selem dimensions must be odd.
+
+    Returns
+    -------
+    reconstructed : ndarray
+       The result of morphological reconstruction.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from skimage.morphology import reconstruction
+
+    First, we create a sinusoidal mask image with peaks at middle and ends.
+
+    >>> x = np.linspace(0, 4 * np.pi)
+    >>> y_mask = np.cos(x)
+
+    Then, we create a seed image initialized to the minimum mask value (for
+    reconstruction by dilation, min-intensity values don't spread) and add
+    "seeds" to the left and right peak, but at a fraction of peak value (1).
+
+    >>> y_seed = y_mask.min() * np.ones_like(x)
+    >>> y_seed[0] = 0.5
+    >>> y_seed[-1] = 0
+    >>> y_rec = reconstruction(y_seed, y_mask)
+
+    The reconstructed image (or curve, in this case) is exactly the same as the
+    mask image, except that the peaks are truncated to 0.5 and 0. The middle
+    peak disappears completely: Since there were no seed values in this peak
+    region, its reconstructed value is truncated to the surrounding value (-1).
+
+    As a more practical example, we try to extract the bright features of an
+    image by subtracting a background image created by reconstruction.
+
+    >>> y, x = np.mgrid[:20:0.5, :20:0.5]
+    >>> bumps = np.sin(x) + np.sin(y)
+
+    To create the background image, set the mask image to the original image,
+    and the seed image to the original image with an intensity offset, `h`.
+
+    >>> h = 0.3
+    >>> seed = bumps - h
+    >>> background = reconstruction(seed, bumps)
+
+    The resulting reconstructed image looks exactly like the original image,
+    but with the peaks of the bumps cut off. Subtracting this reconstructed
+    image from the original image leaves just the peaks of the bumps
+
+    >>> hdome = bumps - background
+
+    This operation is known as the h-dome of the image and leaves features
+    of height `h` in the subtracted image.
+
+    Notes
+    -----
+    The algorithm is taken from [1]_. Applications for greyscale reconstruction
+    are discussed in [2]_ and [3]_.
+
+    References
+    ----------
+    .. [1] Robinson, "Efficient morphological reconstruction: a downhill
+           filter", Pattern Recognition Letters 25 (2004) 1759-1767.
+    .. [2] Vincent, L., "Morphological Grayscale Reconstruction in Image
+           Analysis: Applications and Efficient Algorithms", IEEE Transactions
+           on Image Processing (1993)
+    .. [3] Soille, P., "Morphological Image Analysis: Principles and
+           Applications", Chapter 6, 2nd edition (2003), ISBN 3540429883.
+    """
+    assert tuple(seed.shape) == tuple(mask.shape)
+    if method == 'dilation' and cp.any(seed > mask):  # synchronize!
+        raise ValueError("Intensity of seed image must be less than that "
+                         "of the mask image for reconstruction by dilation.")
+
+    elif method == 'erosion' and cp.any(seed < mask):  # synchronize!
+        raise ValueError("Intensity of seed image must be greater than that "
+                         "of the mask image for reconstruction by erosion.")
+
+    try:
+        from skimage.morphology._greyreconstruct import reconstruction_loop
+    except ImportError:
+        raise ImportError("reconstruction requires scikit-image")
+
+    if selem is None:
+        selem = np.ones([3] * seed.ndim, dtype=bool)
+    else:
+        if isinstance(selem, cp.ndarray):
+            selem = cp.asnumpy(selem)
+        selem = selem.astype(bool)
+
+    if offset is None:
+        if not all([d % 2 == 1 for d in selem.shape]):
+            raise ValueError("Footprint dimensions must all be odd")
+        offset = np.array([d // 2 for d in selem.shape])
+    else:
+        if isinstance(offset, cp.ndarray):
+            offset = cp.asnumpy(offset)
+        if offset.ndim != selem.ndim:
+            raise ValueError("Offset and selem ndims must be equal.")
+        if not all([(0 <= o < d) for o, d in zip(offset, selem.shape)]):
+            raise ValueError("Offset must be included inside selem")
+
+    # Cross out the center of the selem
+    selem[tuple(slice(d, d + 1) for d in offset)] = False
+
+    # Make padding for edges of reconstructed image so we can ignore boundaries
+    dims = (2, ) + \
+        tuple(s1 + s2 - 1 for s1, s2 in zip(seed.shape, selem.shape))
+    inside_slices = tuple(slice(o, o + s) for o, s in zip(offset, seed.shape))
+    # Set padded region to minimum image intensity and mask along first axis so
+    # we can interleave image and mask pixels when sorting.
+    if method == 'dilation':
+        pad_value = cp.min(seed).item()
+    elif method == 'erosion':
+        pad_value = cp.max(seed).item()
+    else:
+        raise ValueError("Reconstruction method can be one of 'erosion' "
+                         "or 'dilation'. Got '%s'." % method)
+
+    # TODO: potentially allow int64 if seed image is too large for int32
+    #       skimage currently only supports int32, though
+    int_dtype = np.int32
+
+    # CuPy Backend: modified to allow images_dtype based on input dtype
+    #               instead of float64
+    images_dtype = np.promote_types(seed.dtype, mask.dtype)
+    images = cp.full(dims, pad_value, dtype=images_dtype)
+    images[(0, *inside_slices)] = seed
+    images[(1, *inside_slices)] = mask
+
+    # Create a list of strides across the array to get the neighbors within
+    # a flattened array
+    value_stride = np.array(images.strides[1:]) // images.dtype.itemsize
+    image_stride = images.strides[0] // images.dtype.itemsize
+    selem_mgrid = np.mgrid[[slice(-o, d - o)
+                            for d, o in zip(selem.shape, offset)]]
+    selem_offsets = selem_mgrid[:, selem].transpose()
+    nb_strides = [
+        np.sum(value_stride * selem_offset) for selem_offset in selem_offsets
+    ]
+    nb_strides = np.array(nb_strides, int_dtype)
+
+    # CuPy Backend: changed flatten to ravel to avoid copy
+    images = images.ravel()
+
+    # Erosion goes smallest to largest; dilation goes largest to smallest.
+    index_sorted = cp.argsort(images).astype(int_dtype, copy=False)
+    if method == 'dilation':
+        index_sorted = index_sorted[::-1]
+
+    # Make a linked list of pixels sorted by value. -1 is the list terminator.
+    index_sorted = cp.asnumpy(index_sorted)
+    prev = np.full(len(images), -1, np.int32)
+    next = np.full(len(images), -1, np.int32)
+    prev[index_sorted[1:]] = index_sorted[:-1]
+    next[index_sorted[:-1]] = index_sorted[1:]
+
+    # Cython inner-loop compares the rank of pixel values.
+    if method == 'dilation':
+        value_rank, value_map = rank_order(images)
+    elif method == 'erosion':
+        value_rank, value_map = rank_order(-images)
+        value_map = -value_map
+
+    # TODO: implement reconstruction_loop on the GPU? For now, run it on host.
+    start = index_sorted[0]
+
+    value_rank = cp.asnumpy(value_rank)
+    reconstruction_loop(value_rank, prev, next, nb_strides, start, image_stride)
+
+    # Reshape reconstructed image to original image shape and remove padding.
+    value_rank = cp.asarray(value_rank[:image_stride])
+
+    rec_img = value_map[value_rank]
+    rec_img.shape = dims[1:]
+    return rec_img[inside_slices]
diff --git a/python/cucim/src/cucim/skimage/morphology/misc.py b/python/cucim/src/cucim/skimage/morphology/misc.py
new file mode 100644
index 000000000..e4b2cb3bf
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/misc.py
@@ -0,0 +1,232 @@
+"""Miscellaneous morphology functions."""
+import functools
+
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import warn
+from .selem import _default_selem
+
+# Our function names don't exactly correspond to ndimages.
+# This dictionary translates from our names to scipy's.
+funcs = ('erosion', 'dilation', 'opening', 'closing')
+skimage2ndimage = {x: 'grey_' + x for x in funcs}
+
+# These function names are the same in ndimage.
+funcs = ('binary_erosion', 'binary_dilation', 'binary_opening',
+         'binary_closing', 'black_tophat', 'white_tophat')
+skimage2ndimage.update({x: x for x in funcs})
+
+
+def default_selem(func):
+    """Decorator to add a default structuring element to morphology functions.
+
+    Parameters
+    ----------
+    func : function
+        A morphology function such as erosion, dilation, opening, closing,
+        white_tophat, or black_tophat.
+
+    Returns
+    -------
+    func_out : function
+        The function, using a default structuring element of same dimension
+        as the input image with connectivity 1.
+
+    """
+
+    @functools.wraps(func)
+    def func_out(image, selem=None, *args, **kwargs):
+        if selem is None:
+            selem = _default_selem(image.ndim)
+        return func(image, selem=selem, *args, **kwargs)
+
+    return func_out
+
+
+def _check_dtype_supported(ar):
+    # Should use `issubdtype` for bool below, but there's a bug in numpy 1.7
+    if not (ar.dtype == bool or cp.issubdtype(ar.dtype, cp.integer)):
+        raise TypeError("Only bool or integer image types are supported. "
+                        "Got %s." % ar.dtype)
+
+
+def remove_small_objects(ar, min_size=64, connectivity=1, in_place=False):
+    """Remove objects smaller than the specified size.
+
+    Expects ar to be an array with labeled objects, and removes objects
+    smaller than min_size. If `ar` is bool, the image is first labeled.
+    This leads to potentially different behavior for bool and 0-and-1
+    arrays.
+
+    Parameters
+    ----------
+    ar : ndarray (arbitrary shape, int or bool type)
+        The array containing the objects of interest. If the array type is
+        int, the ints must be non-negative.
+    min_size : int, optional (default: 64)
+        The smallest allowable object size.
+    connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1)
+        The connectivity defining the neighborhood of a pixel. Used during
+        labelling if `ar` is bool.
+    in_place : bool, optional (default: False)
+        If ``True``, remove the objects in the input array itself.
+        Otherwise, make a copy.
+
+    Raises
+    ------
+    TypeError
+        If the input array is of an invalid type, such as float or string.
+    ValueError
+        If the input array contains negative values.
+
+    Returns
+    -------
+    out : ndarray, same shape and type as input `ar`
+        The input array with small connected components removed.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import morphology
+    >>> a = cp.array([[0, 0, 0, 1, 0],
+    ...               [1, 1, 1, 0, 0],
+    ...               [1, 1, 1, 0, 1]], bool)
+    >>> b = morphology.remove_small_objects(a, 6)
+    >>> b
+    array([[False, False, False, False, False],
+           [ True,  True,  True, False, False],
+           [ True,  True,  True, False, False]])
+    >>> c = morphology.remove_small_objects(a, 7, connectivity=2)
+    >>> c
+    array([[False, False, False,  True, False],
+           [ True,  True,  True, False, False],
+           [ True,  True,  True, False, False]])
+    >>> d = morphology.remove_small_objects(a, 6, in_place=True)
+    >>> d is a
+    True
+
+    """
+    # Raising type error if not int or bool
+    _check_dtype_supported(ar)
+
+    if in_place:
+        out = ar
+    else:
+        out = ar.copy()
+
+    if min_size == 0:  # shortcut for efficiency
+        return out
+
+    if out.dtype == bool:
+        selem = ndi.generate_binary_structure(ar.ndim, connectivity)
+        ccs = cp.zeros_like(ar, dtype=cp.int32)
+        ndi.label(ar, selem, output=ccs)
+    else:
+        ccs = out
+
+    try:
+        component_sizes = cp.bincount(ccs.ravel())
+    except ValueError:
+        raise ValueError("Negative value labels are not supported. Try "
+                         "relabeling the input with `scipy.ndimage.label` or "
+                         "`skimage.morphology.label`.")
+
+    if len(component_sizes) == 2 and out.dtype != bool:
+        warn("Only one label was provided to `remove_small_objects`. "
+             "Did you mean to use a boolean array?")
+
+    too_small = component_sizes < min_size
+    too_small_mask = too_small[ccs]
+    out[too_small_mask] = 0
+
+    return out
+
+
+def remove_small_holes(ar, area_threshold=64, connectivity=1, in_place=False):
+    """Remove contiguous holes smaller than the specified size.
+
+    Parameters
+    ----------
+    ar : ndarray (arbitrary shape, int or bool type)
+        The array containing the connected components of interest.
+    area_threshold : int, optional (default: 64)
+        The maximum area, in pixels, of a contiguous hole that will be filled.
+        Replaces `min_size`.
+    connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1)
+        The connectivity defining the neighborhood of a pixel.
+    in_place : bool, optional (default: False)
+        If `True`, remove the connected components in the input array itself.
+        Otherwise, make a copy.
+
+    Raises
+    ------
+    TypeError
+        If the input array is of an invalid type, such as float or string.
+    ValueError
+        If the input array contains negative values.
+
+    Returns
+    -------
+    out : ndarray, same shape and type as input `ar`
+        The input array with small holes within connected components removed.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import morphology
+    >>> a = cp.array([[1, 1, 1, 1, 1, 0],
+    ...               [1, 1, 1, 0, 1, 0],
+    ...               [1, 0, 0, 1, 1, 0],
+    ...               [1, 1, 1, 1, 1, 0]], bool)
+    >>> b = morphology.remove_small_holes(a, 2)
+    >>> b
+    array([[ True,  True,  True,  True,  True, False],
+           [ True,  True,  True,  True,  True, False],
+           [ True, False, False,  True,  True, False],
+           [ True,  True,  True,  True,  True, False]])
+    >>> c = morphology.remove_small_holes(a, 2, connectivity=2)
+    >>> c
+    array([[ True,  True,  True,  True,  True, False],
+           [ True,  True,  True, False,  True, False],
+           [ True, False, False,  True,  True, False],
+           [ True,  True,  True,  True,  True, False]])
+    >>> d = morphology.remove_small_holes(a, 2, in_place=True)
+    >>> d is a
+    True
+
+    Notes
+    -----
+    If the array type is int, it is assumed that it contains already-labeled
+    objects. The labels are not kept in the output image (this function always
+    outputs a bool image). It is suggested that labeling is completed after
+    using this function.
+
+    """
+    _check_dtype_supported(ar)
+
+    # Creates warning if image is an integer image
+    if ar.dtype != bool:
+        warn("Any labeled images will be returned as a boolean array. "
+             "Did you mean to use a boolean array?", UserWarning)
+
+    if in_place:
+        out = ar
+    else:
+        out = ar.copy()
+
+    # Creating the inverse of ar
+    if in_place:
+        cp.logical_not(out, out=out)
+    else:
+        out = cp.logical_not(out)
+
+    # removing small objects from the inverse of ar
+    out = remove_small_objects(out, area_threshold, connectivity, in_place)
+
+    if in_place:
+        cp.logical_not(out, out=out)
+    else:
+        out = cp.logical_not(out)
+
+    return out
diff --git a/python/cucim/src/cucim/skimage/morphology/selem.py b/python/cucim/src/cucim/skimage/morphology/selem.py
new file mode 100644
index 000000000..536d618f7
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/selem.py
@@ -0,0 +1,386 @@
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import deprecate_kwarg
+
+
+def square(width, dtype=np.uint8):
+    """Generates a flat, square-shaped structuring element.
+
+    Every pixel along the perimeter has a chessboard distance
+    no greater than radius (radius=floor(width/2)) pixels.
+
+    Parameters
+    ----------
+    width : int
+        The width and height of the square.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        A structuring element consisting only of ones, i.e. every
+        pixel belongs to the neighborhood.
+
+    """
+    return cp.ones((width, width), dtype=dtype)
+
+
+@deprecate_kwarg({"height": "ncols", "width": "nrows"},
+                 removed_version="0.20.0")
+def rectangle(nrows, ncols, dtype=np.uint8):
+    """Generates a flat, rectangular-shaped structuring element.
+
+    Every pixel in the rectangle generated for a given width and given height
+    belongs to the neighborhood.
+
+    Parameters
+    ----------
+    nrows : int
+        The number of rows of the rectangle.
+    ncols : int
+        The number of columns of the rectangle.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        A structuring element consisting only of ones, i.e. every
+        pixel belongs to the neighborhood.
+
+    Notes
+    -----
+    - The use of ``width`` and ``height`` has been deprecated in
+      scikit-image 0.18.0. Use ``nrows`` and ``ncols`` instead.
+    """
+
+    return cp.ones((nrows, ncols), dtype=dtype)
+
+
+def diamond(radius, dtype=np.uint8):
+    """Generates a flat, diamond-shaped structuring element.
+
+    A pixel is part of the neighborhood (i.e. labeled 1) if
+    the city block/Manhattan distance between it and the center of
+    the neighborhood is no greater than radius.
+
+    Parameters
+    ----------
+    radius : int
+        The radius of the diamond-shaped structuring element.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+    """
+    # CuPy Backend: grid is usually small -> faster to generate it in NumPy
+    L = np.arange(0, radius * 2 + 1)
+    I, J = np.meshgrid(L, L, sparse=True)
+    return cp.asarray(
+        np.abs(I - radius) + np.abs(J - radius) <= radius, dtype=dtype
+    )
+
+
+def disk(radius, dtype=np.uint8):
+    """Generates a flat, disk-shaped structuring element.
+
+    A pixel is within the neighborhood if the Euclidean distance between
+    it and the origin is no greater than radius.
+
+    Parameters
+    ----------
+    radius : int
+        The radius of the disk-shaped structuring element.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+    """
+    # CuPy Backend: grid is usually small -> faster to generate it in NumPy
+    L = np.arange(-radius, radius + 1)
+    X, Y = np.meshgrid(L, L, sparse=True)
+    return cp.asarray((X * X + Y * Y) <= radius * radius, dtype=dtype)
+
+
+def ellipse(width, height, dtype=np.uint8):
+    """Generates a flat, ellipse-shaped structuring element.
+
+    Every pixel along the perimeter of ellipse satisfies
+    the equation ``(x/width+1)**2 + (y/height+1)**2 = 1``.
+
+    Parameters
+    ----------
+    width : int
+        The width of the ellipse-shaped structuring element.
+    height : int
+        The height of the ellipse-shaped structuring element.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+
+    Examples
+    --------
+    >>> from cucim.skimage.morphology import selem
+    >>> selem.ellipse(5, 3)
+    array([[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]], dtype=uint8)
+
+    """
+    from skimage import draw
+
+    selem = np.zeros((2 * height + 1, 2 * width + 1), dtype=dtype)
+    rows, cols = draw.ellipse(height, width, height + 1, width + 1)
+    selem[rows, cols] = 1
+    # Note: no CUDA counterpart for draw.ellipse so compute in NumPy
+    # CuPy Backend: grid is usually small -> faster to generate it in NumPy
+    return cp.asarray(selem)
+
+
+def cube(width, dtype=np.uint8):
+    """Generates a cube-shaped structuring element.
+
+    This is the 3D equivalent of a square.
+    Every pixel along the perimeter has a chessboard distance
+    no greater than radius (radius=floor(width/2)) pixels.
+
+    Parameters
+    ----------
+    width : int
+        The width, height and depth of the cube.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        A structuring element consisting only of ones, i.e. every
+        pixel belongs to the neighborhood.
+
+    """
+    return cp.ones((width, width, width), dtype=dtype)
+
+
+def octahedron(radius, dtype=np.uint8):
+    """Generates a octahedron-shaped structuring element.
+
+    This is the 3D equivalent of a diamond.
+    A pixel is part of the neighborhood (i.e. labeled 1) if
+    the city block/Manhattan distance between it and the center of
+    the neighborhood is no greater than radius.
+
+    Parameters
+    ----------
+    radius : int
+        The radius of the octahedron-shaped structuring element.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+    """
+    # note that in contrast to diamond(), this method allows non-integer radii
+    n = 2 * radius + 1
+    Z, Y, X = np.ogrid[
+        -radius:radius:n * 1j,
+        -radius:radius:n * 1j,
+        -radius:radius:n * 1j,
+    ]
+    s = np.abs(X) + np.abs(Y) + np.abs(Z)
+    return cp.array(s <= radius, dtype=dtype)
+
+
+def ball(radius, dtype=np.uint8):
+    """Generates a ball-shaped structuring element.
+
+    This is the 3D equivalent of a disk.
+    A pixel is within the neighborhood if the Euclidean distance between
+    it and the origin is no greater than radius.
+
+    Parameters
+    ----------
+    radius : int
+        The radius of the ball-shaped structuring element.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+    """
+    n = 2 * radius + 1
+    Z, Y, X = np.ogrid[
+        -radius:radius:n * 1j,
+        -radius:radius:n * 1j,
+        -radius:radius:n * 1j,
+    ]
+    s = X * X + Y * Y + Z * Z
+    return cp.array(s <= radius * radius, dtype=dtype)
+
+
+def octagon(m, n, dtype=np.uint8):
+    """Generates an octagon shaped structuring element.
+
+    For a given size of (m) horizontal and vertical sides
+    and a given (n) height or width of slanted sides octagon is generated.
+    The slanted sides are 45 or 135 degrees to the horizontal axis
+    and hence the widths and heights are equal.
+
+    Parameters
+    ----------
+    m : int
+        The size of the horizontal and vertical sides.
+    n : int
+        The height or width of the slanted sides.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+
+    """
+    try:
+        from skimage.morphology import convex_hull_image
+    except ImportError:
+        raise ImportError("octagon requires scikit-image")
+
+    selem = np.zeros((m + 2 * n, m + 2 * n))
+    selem[0, n] = 1
+    selem[n, 0] = 1
+    selem[0, m + n - 1] = 1
+    selem[m + n - 1, 0] = 1
+    selem[-1, n] = 1
+    selem[n, -1] = 1
+    selem[-1, m + n - 1] = 1
+    selem[m + n - 1, -1] = 1
+    selem = convex_hull_image(selem).astype(dtype)
+    return cp.array(selem)
+
+
+def star(a, dtype=np.uint8):
+    """Generates a star shaped structuring element.
+
+    Start has 8 vertices and is an overlap of square of size `2*a + 1`
+    with its 45 degree rotated version.
+    The slanted sides are 45 or 135 degrees to the horizontal axis.
+
+    Parameters
+    ----------
+    a : int
+        Parameter deciding the size of the star structural element. The side
+        of the square array returned is `2*a + 1 + 2*floor(a / 2)`.
+
+    Other Parameters
+    ----------------
+    dtype : data-type
+        The data type of the structuring element.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+
+    """
+    try:
+        from skimage.morphology import convex_hull_image
+    except ImportError:
+        raise ImportError("star requires scikit-image")
+
+    if a == 1:
+        bfilter = np.zeros((3, 3), dtype)
+        bfilter[:] = 1
+        return bfilter
+
+    m = 2 * a + 1
+    n = a // 2
+    selem_square = np.zeros((m + 2 * n, m + 2 * n))
+    selem_square[n:m + n, n:m + n] = 1
+
+    c = (m + 2 * n - 1) // 2
+    selem_rotated = np.zeros((m + 2 * n, m + 2 * n))
+    selem_rotated[0, c] = selem_rotated[-1, c] = 1
+    selem_rotated[c, 0] = selem_rotated[c, -1] = 1
+    selem_rotated = convex_hull_image(selem_rotated).astype(int)
+
+    selem = selem_square + selem_rotated
+    selem[selem > 0] = 1
+
+    return cp.array(selem.astype(dtype, copy=False))
+
+
+def _default_selem(ndim):
+    """Generates a cross-shaped structuring element (connectivity=1).
+
+    This is the default structuring element (selem) if no selem was specified.
+
+    Parameters
+    ----------
+    ndim : int
+        Number of dimensions of the image.
+
+    Returns
+    -------
+    selem : ndarray
+        The structuring element where elements of the neighborhood
+        are 1 and 0 otherwise.
+
+    """
+    return ndi.generate_binary_structure(ndim, 1)
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_binary.py b/python/cucim/src/cucim/skimage/morphology/tests/test_binary.py
new file mode 100755
index 000000000..383446b9a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_binary.py
@@ -0,0 +1,182 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy import testing
+from cupyx.scipy import ndimage as ndi
+from skimage import data
+
+from cucim.skimage import color
+from cucim.skimage.morphology import binary, grey, selem
+from cucim.skimage.util import img_as_bool
+
+img = color.rgb2gray(cp.array(data.astronaut()))
+bw_img = img > 100 / 255.0
+
+
+def test_non_square_image():
+    strel = selem.square(3)
+    binary_res = binary.binary_erosion(bw_img[:100, :200], strel)
+    grey_res = img_as_bool(grey.erosion(bw_img[:100, :200], strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_binary_erosion():
+    strel = selem.square(3)
+    binary_res = binary.binary_erosion(bw_img, strel)
+    grey_res = img_as_bool(grey.erosion(bw_img, strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_binary_dilation():
+    strel = selem.square(3)
+    binary_res = binary.binary_dilation(bw_img, strel)
+    grey_res = img_as_bool(grey.dilation(bw_img, strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_binary_closing():
+    strel = selem.square(3)
+    binary_res = binary.binary_closing(bw_img, strel)
+    grey_res = img_as_bool(grey.closing(bw_img, strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_binary_opening():
+    strel = selem.square(3)
+    binary_res = binary.binary_opening(bw_img, strel)
+    grey_res = img_as_bool(grey.opening(bw_img, strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_selem_overflow():
+    strel = cp.ones((17, 17), dtype=cp.uint8)
+    img = cp.zeros((20, 20), dtype=bool)
+    img[2:19, 2:19] = True
+    binary_res = binary.binary_erosion(img, strel)
+    grey_res = img_as_bool(grey.erosion(img, strel))
+    testing.assert_array_equal(binary_res, grey_res)
+
+
+def test_out_argument():
+    for func in (binary.binary_erosion, binary.binary_dilation):
+        strel = cp.ones((3, 3), dtype=cp.uint8)
+        img = cp.ones((10, 10))
+        out = cp.zeros_like(img)
+        out_saved = out.copy()
+        func(img, strel, out=out)
+        assert cp.any(out != out_saved)
+        testing.assert_array_equal(out, func(img, strel))
+
+
+binary_functions = [binary.binary_erosion, binary.binary_dilation,
+                    binary.binary_opening, binary.binary_closing]
+
+
+@pytest.mark.parametrize("function", binary_functions)
+def test_default_selem(function):
+    strel = selem.diamond(radius=1)
+    # fmt: off
+    image = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], cp.uint8)
+    # fmt: on
+    im_expected = function(image, strel)
+    im_test = function(image)
+    testing.assert_array_equal(im_expected, im_test)
+
+
+def test_3d_fallback_default_selem():
+    # 3x3x3 cube inside a 7x7x7 image:
+    image = cp.zeros((7, 7, 7), bool)
+    image[2:-2, 2:-2, 2:-2] = 1
+
+    opened = binary.binary_opening(image)
+
+    # expect a "hyper-cross" centered in the 5x5x5:
+    image_expected = cp.zeros((7, 7, 7), dtype=bool)
+    image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1)
+    testing.assert_array_equal(opened, image_expected)
+
+
+binary_3d_fallback_functions = [binary.binary_opening, binary.binary_closing]
+
+
+@pytest.mark.parametrize("function", binary_3d_fallback_functions)
+def test_3d_fallback_cube_selem(function):
+    # 3x3x3 cube inside a 7x7x7 image:
+    image = cp.zeros((7, 7, 7), bool)
+    image[2:-2, 2:-2, 2:-2] = 1
+
+    cube = cp.ones((3, 3, 3), dtype=cp.uint8)
+
+    new_image = function(image, cube)
+    testing.assert_array_equal(new_image, image)
+
+
+def test_2d_ndimage_equivalence():
+    image = cp.zeros((9, 9), cp.uint16)
+    image[2:-2, 2:-2] = 2 ** 14
+    image[3:-3, 3:-3] = 2 ** 15
+    image[4, 4] = 2 ** 16 - 1
+
+    bin_opened = binary.binary_opening(image)
+    bin_closed = binary.binary_closing(image)
+
+    selem = ndi.generate_binary_structure(2, 1)
+    ndimage_opened = ndi.binary_opening(image, structure=selem)
+    ndimage_closed = ndi.binary_closing(image, structure=selem)
+
+    testing.assert_array_equal(bin_opened, ndimage_opened)
+    testing.assert_array_equal(bin_closed, ndimage_closed)
+
+
+def test_binary_output_2d():
+    image = cp.zeros((9, 9), cp.uint16)
+    image[2:-2, 2:-2] = 2 ** 14
+    image[3:-3, 3:-3] = 2 ** 15
+    image[4, 4] = 2 ** 16 - 1
+
+    bin_opened = binary.binary_opening(image)
+    bin_closed = binary.binary_closing(image)
+
+    int_opened = cp.empty_like(image, dtype=cp.uint8)
+    int_closed = cp.empty_like(image, dtype=cp.uint8)
+    binary.binary_opening(image, out=int_opened)
+    binary.binary_closing(image, out=int_closed)
+
+    np.testing.assert_equal(bin_opened.dtype, bool)
+    np.testing.assert_equal(bin_closed.dtype, bool)
+
+    np.testing.assert_equal(int_opened.dtype, np.uint8)
+    np.testing.assert_equal(int_closed.dtype, np.uint8)
+
+
+def test_binary_output_3d():
+    image = cp.zeros((9, 9, 9), cp.uint16)
+    image[2:-2, 2:-2, 2:-2] = 2 ** 14
+    image[3:-3, 3:-3, 3:-3] = 2 ** 15
+    image[4, 4, 4] = 2 ** 16 - 1
+
+    bin_opened = binary.binary_opening(image)
+    bin_closed = binary.binary_closing(image)
+
+    int_opened = cp.empty_like(image, dtype=cp.uint8)
+    int_closed = cp.empty_like(image, dtype=cp.uint8)
+    binary.binary_opening(image, out=int_opened)
+    binary.binary_closing(image, out=int_closed)
+
+    np.testing.assert_equal(bin_opened.dtype, bool)
+    np.testing.assert_equal(bin_closed.dtype, bool)
+
+    np.testing.assert_equal(int_opened.dtype, np.uint8)
+    np.testing.assert_equal(int_closed.dtype, np.uint8)
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_grey.py b/python/cucim/src/cucim/skimage/morphology/tests/test_grey.py
new file mode 100755
index 000000000..1015b86cf
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_grey.py
@@ -0,0 +1,292 @@
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+from skimage import data
+
+from cucim.skimage import color, transform
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage._shared.testing import TestCase, fetch, parametrize
+from cucim.skimage.morphology import grey, selem
+from cucim.skimage.util import img_as_ubyte, img_as_uint
+
+
+class TestMorphology(TestCase):
+
+    # These expected outputs were generated with skimage v0.12.1
+    # using:
+    #
+    #   from skimage.morphology.tests.test_grey import TestMorphology
+    #   import numpy as np
+    #   output = TestMorphology()._build_expected_output()
+    #   np.savez_compressed('gray_morph_output.npz', **output)
+
+    def _build_expected_output(self):
+        funcs = (grey.erosion, grey.dilation, grey.opening, grey.closing,
+                 grey.white_tophat, grey.black_tophat)
+        selems_2D = (selem.square, selem.diamond,
+                     selem.disk, selem.star)
+
+        image = img_as_ubyte(transform.downscale_local_mean(
+            color.rgb2gray(cp.array(data.coffee())), (20, 20)))
+
+        output = {}
+        for n in range(1, 4):
+            for strel in selems_2D:
+                for func in funcs:
+                    key = '{0}_{1}_{2}'.format(
+                        strel.__name__, n, func.__name__)
+                    output[key] = func(image, strel(n))
+
+        return output
+
+    def test_gray_morphology(self):
+        expected = dict(np.load(fetch('data/gray_morph_output.npz')))
+        calculated = self._build_expected_output()
+        for k, v in calculated.items():
+            cp.testing.assert_array_equal(cp.asarray(expected[k]), v)
+
+
+class TestEccentricStructuringElements(TestCase):
+    def setUp(self):
+        self.black_pixel = 255 * cp.ones((4, 4), dtype=cp.uint8)
+        self.black_pixel[1, 1] = 0
+        self.white_pixel = 255 - self.black_pixel
+        self.selems = [
+            selem.square(2),
+            selem.rectangle(2, 2),
+            selem.rectangle(2, 1),
+            selem.rectangle(1, 2),
+        ]
+
+    def test_dilate_erode_symmetry(self):
+        for s in self.selems:
+            c = grey.erosion(self.black_pixel, s)
+            d = grey.dilation(self.white_pixel, s)
+            assert cp.all(c == (255 - d))
+
+    def test_open_black_pixel(self):
+        for s in self.selems:
+            grey_open = grey.opening(self.black_pixel, s)
+            assert cp.all(grey_open == self.black_pixel)
+
+    def test_close_white_pixel(self):
+        for s in self.selems:
+            grey_close = grey.closing(self.white_pixel, s)
+            assert cp.all(grey_close == self.white_pixel)
+
+    def test_open_white_pixel(self):
+        for s in self.selems:
+            assert cp.all(grey.opening(self.white_pixel, s) == 0)
+
+    def test_close_black_pixel(self):
+        for s in self.selems:
+            assert cp.all(grey.closing(self.black_pixel, s) == 255)
+
+    def test_white_tophat_white_pixel(self):
+        for s in self.selems:
+            tophat = grey.white_tophat(self.white_pixel, s)
+            cp.testing.assert_array_equal(tophat, self.white_pixel)
+
+    def test_black_tophat_black_pixel(self):
+        for s in self.selems:
+            tophat = grey.black_tophat(self.black_pixel, s)
+            cp.testing.assert_array_equal(tophat, 255 - self.black_pixel)
+
+    def test_white_tophat_black_pixel(self):
+        for s in self.selems:
+            tophat = grey.white_tophat(self.black_pixel, s)
+            assert cp.all(tophat == 0)
+
+    def test_black_tophat_white_pixel(self):
+        for s in self.selems:
+            tophat = grey.black_tophat(self.white_pixel, s)
+            assert cp.all(tophat == 0)
+
+
+grey_functions = [grey.erosion, grey.dilation,
+                  grey.opening, grey.closing,
+                  grey.white_tophat, grey.black_tophat]
+
+
+@parametrize("function", grey_functions)
+def test_default_selem(function):
+    strel = selem.diamond(radius=1)
+    # fmt: off
+    image = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 0, 0, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                      [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], cp.uint8)
+    # fmt: on
+    im_expected = function(image, strel)
+    im_test = function(image)
+    cp.testing.assert_array_equal(im_expected, im_test)
+
+
+def test_3d_fallback_default_selem():
+    # 3x3x3 cube inside a 7x7x7 image:
+    image = cp.zeros((7, 7, 7), bool)
+    image[2:-2, 2:-2, 2:-2] = 1
+
+    opened = grey.opening(image)
+
+    # expect a "hyper-cross" centered in the 5x5x5:
+    image_expected = cp.zeros((7, 7, 7), dtype=bool)
+    image_expected[2:5, 2:5, 2:5] = ndi.generate_binary_structure(3, 1)
+    cp.testing.assert_array_equal(opened, image_expected)
+
+
+grey_3d_fallback_functions = [grey.closing, grey.opening]
+
+
+@parametrize("function", grey_3d_fallback_functions)
+def test_3d_fallback_cube_selem(function):
+    # 3x3x3 cube inside a 7x7x7 image:
+    image = cp.zeros((7, 7, 7), bool)
+    image[2:-2, 2:-2, 2:-2] = 1
+
+    cube = cp.ones((3, 3, 3), dtype=cp.uint8)
+
+    new_image = function(image, cube)
+    cp.testing.assert_array_equal(new_image, image)
+
+
+def test_3d_fallback_white_tophat():
+    image = cp.zeros((7, 7, 7), dtype=bool)
+    image[2, 2:4, 2:4] = 1
+    image[3, 2:5, 2:5] = 1
+    image[4, 3:5, 3:5] = 1
+
+    with expected_warnings([r'operator.*deprecated|\A\Z']):
+        new_image = grey.white_tophat(image)
+    footprint = ndi.generate_binary_structure(3, 1)
+    with expected_warnings([r'operator.*deprecated|\A\Z']):
+        image_expected = ndi.white_tophat(
+            image.view(dtype=cp.uint8), footprint=footprint
+        )
+    cp.testing.assert_array_equal(new_image, image_expected)
+
+
+def test_3d_fallback_black_tophat():
+    image = cp.ones((7, 7, 7), dtype=bool)
+    image[2, 2:4, 2:4] = 0
+    image[3, 2:5, 2:5] = 0
+    image[4, 3:5, 3:5] = 0
+
+    with expected_warnings([r'operator.*deprecated|\A\Z']):
+        new_image = grey.black_tophat(image)
+    footprint = ndi.generate_binary_structure(3, 1)
+    with expected_warnings([r'operator.*deprecated|\A\Z']):
+        image_expected = ndi.black_tophat(
+            image.view(dtype=cp.uint8), footprint=footprint
+        )
+    cp.testing.assert_array_equal(new_image, image_expected)
+
+
+def test_2d_ndimage_equivalence():
+    image = cp.zeros((9, 9), cp.uint8)
+    image[2:-2, 2:-2] = 128
+    image[3:-3, 3:-3] = 196
+    image[4, 4] = 255
+
+    opened = grey.opening(image)
+    closed = grey.closing(image)
+
+    selem = ndi.generate_binary_structure(2, 1)
+    ndimage_opened = ndi.grey_opening(image, footprint=selem)
+    ndimage_closed = ndi.grey_closing(image, footprint=selem)
+
+    cp.testing.assert_array_equal(opened, ndimage_opened)
+    cp.testing.assert_array_equal(closed, ndimage_closed)
+
+
+# float test images
+# fmt: off
+im = cp.array([[0.55, 0.72, 0.6 , 0.54, 0.42],   # noqa
+               [0.65, 0.44, 0.89, 0.96, 0.38],
+               [0.79, 0.53, 0.57, 0.93, 0.07],
+               [0.09, 0.02, 0.83, 0.78, 0.87],
+               [0.98, 0.8 , 0.46, 0.78, 0.12]])  # noqa
+
+eroded = cp.array([[0.55, 0.44, 0.54, 0.42, 0.38],
+                   [0.44, 0.44, 0.44, 0.38, 0.07],
+                   [0.09, 0.02, 0.53, 0.07, 0.07],
+                   [0.02, 0.02, 0.02, 0.78, 0.07],
+                   [0.09, 0.02, 0.46, 0.12, 0.12]])
+
+dilated = cp.array([[0.72, 0.72, 0.89, 0.96, 0.54],
+                    [0.79, 0.89, 0.96, 0.96, 0.96],
+                    [0.79, 0.79, 0.93, 0.96, 0.93],
+                    [0.98, 0.83, 0.83, 0.93, 0.87],
+                    [0.98, 0.98, 0.83, 0.78, 0.87]])
+
+opened = cp.array([[0.55, 0.55, 0.54, 0.54, 0.42],
+                   [0.55, 0.44, 0.54, 0.44, 0.38],
+                   [0.44, 0.53, 0.53, 0.78, 0.07],
+                   [0.09, 0.02, 0.78, 0.78, 0.78],
+                   [0.09, 0.46, 0.46, 0.78, 0.12]])
+
+closed = cp.array([[0.72, 0.72, 0.72, 0.54, 0.54],
+                   [0.72, 0.72, 0.89, 0.96, 0.54],
+                   [0.79, 0.79, 0.79, 0.93, 0.87],
+                   [0.79, 0.79, 0.83, 0.78, 0.87],
+                   [0.98, 0.83, 0.78, 0.78, 0.78]])
+# fmt: on
+
+
+def test_float():
+    cp.testing.assert_allclose(grey.erosion(im), eroded)
+    cp.testing.assert_allclose(grey.dilation(im), dilated)
+    cp.testing.assert_allclose(grey.opening(im), opened)
+    cp.testing.assert_allclose(grey.closing(im), closed)
+
+
+def test_uint16():
+    im16, eroded16, dilated16, opened16, closed16 = map(
+        img_as_uint, [im, eroded, dilated, opened, closed]
+    )
+    cp.testing.assert_allclose(grey.erosion(im16), eroded16)
+    cp.testing.assert_allclose(grey.dilation(im16), dilated16)
+    cp.testing.assert_allclose(grey.opening(im16), opened16)
+    cp.testing.assert_allclose(grey.closing(im16), closed16)
+
+
+def test_discontiguous_out_array():
+    # fmt: off
+    image = cp.array([[5, 6, 2],
+                      [7, 2, 2],
+                      [3, 5, 1]], cp.uint8)
+    # fmt: on
+    out_array_big = cp.zeros((5, 5), cp.uint8)
+    out_array = out_array_big[::2, ::2]
+    # fmt: off
+    expected_dilation = cp.array([[7, 0, 6, 0, 6],
+                                  [0, 0, 0, 0, 0],
+                                  [7, 0, 7, 0, 2],
+                                  [0, 0, 0, 0, 0],
+                                  [7, 0, 5, 0, 5]], cp.uint8)
+    expected_erosion = cp.array([[5, 0, 2, 0, 2],
+                                 [0, 0, 0, 0, 0],
+                                 [2, 0, 2, 0, 1],
+                                 [0, 0, 0, 0, 0],
+                                 [3, 0, 1, 0, 1]], cp.uint8)
+    # fmt: on
+    grey.dilation(image, out=out_array)
+    cp.testing.assert_array_equal(out_array_big, expected_dilation)
+    grey.erosion(image, out=out_array)
+    cp.testing.assert_array_equal(out_array_big, expected_erosion)
+
+
+def test_1d_erosion():
+    image = cp.array([1, 2, 3, 2, 1])
+    expected = cp.array([1, 1, 2, 1, 1])
+    eroded = grey.erosion(image)
+    cp.testing.assert_array_equal(eroded, expected)
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_misc.py b/python/cucim/src/cucim/skimage/morphology/tests/test_misc.py
new file mode 100755
index 000000000..edacc68cb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_misc.py
@@ -0,0 +1,215 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_array_equal
+from numpy.testing import assert_equal
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.morphology import remove_small_holes, remove_small_objects
+
+# fmt: off
+test_image = cp.array([[0, 0, 0, 1, 0],
+                       [1, 1, 1, 0, 0],
+                       [1, 1, 1, 0, 1]], bool)
+# fmt: on
+
+
+def test_one_connectivity():
+    # fmt: off
+    expected = cp.array([[0, 0, 0, 0, 0],
+                         [1, 1, 1, 0, 0],
+                         [1, 1, 1, 0, 0]], bool)
+    # fmt: on
+    observed = remove_small_objects(test_image, min_size=6)
+    assert_array_equal(observed, expected)
+
+
+def test_two_connectivity():
+    # fmt: off
+    expected = cp.array([[0, 0, 0, 1, 0],
+                         [1, 1, 1, 0, 0],
+                         [1, 1, 1, 0, 0]], bool)
+    # fmt: on
+    observed = remove_small_objects(test_image, min_size=7, connectivity=2)
+    assert_array_equal(observed, expected)
+
+
+def test_in_place():
+    image = test_image.copy()
+    observed = remove_small_objects(image, min_size=6, in_place=True)
+    assert_equal(observed is image, True,
+                 "remove_small_objects in_place argument failed.")
+
+
+def test_labeled_image():
+    # fmt: off
+    labeled_image = cp.array([[2, 2, 2, 0, 1],
+                              [2, 2, 2, 0, 1],
+                              [2, 0, 0, 0, 0],
+                              [0, 0, 3, 3, 3]], dtype=int)
+    expected = cp.array([[2, 2, 2, 0, 0],
+                         [2, 2, 2, 0, 0],
+                         [2, 0, 0, 0, 0],
+                         [0, 0, 3, 3, 3]], dtype=int)
+    # fmt: on
+    observed = remove_small_objects(labeled_image, min_size=3)
+    assert_array_equal(observed, expected)
+
+
+def test_uint_image():
+    # fmt: off
+    labeled_image = cp.array([[2, 2, 2, 0, 1],
+                              [2, 2, 2, 0, 1],
+                              [2, 0, 0, 0, 0],
+                              [0, 0, 3, 3, 3]], dtype=cp.uint8)
+    expected = cp.array([[2, 2, 2, 0, 0],
+                         [2, 2, 2, 0, 0],
+                         [2, 0, 0, 0, 0],
+                         [0, 0, 3, 3, 3]], dtype=cp.uint8)
+    # fmt: on
+    observed = remove_small_objects(labeled_image, min_size=3)
+    assert_array_equal(observed, expected)
+
+
+def test_single_label_warning():
+    # fmt: off
+    image = cp.array([[0, 0, 0, 1, 0],
+                      [1, 1, 1, 0, 0],
+                      [1, 1, 1, 0, 0]], int)
+    # fmt: on
+    with expected_warnings(['use a boolean array?']):
+        remove_small_objects(image, min_size=6)
+
+
+def test_float_input():
+    float_test = cp.random.rand(5, 5)
+    with pytest.raises(TypeError):
+        remove_small_objects(float_test)
+
+
+def test_negative_input():
+    negative_int = cp.random.randint(-4, -1, size=(5, 5))
+    with pytest.raises(ValueError):
+        remove_small_objects(negative_int)
+
+
+# fmt: off
+test_holes_image = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                             [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                             [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+                             [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
+                             [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                             [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                             [0, 0, 0, 0, 0, 0, 0, 1, 0, 1],
+                             [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], bool)
+# fmt: on
+
+
+def test_one_connectivity_holes():
+    # fmt: off
+    expected = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], bool)
+    # fmt: on
+    observed = remove_small_holes(test_holes_image, area_threshold=3)
+    assert_array_equal(observed, expected)
+
+
+def test_two_connectivity_holes():
+    # fmt: off
+    expected = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], bool)
+    # fmt: on
+    observed = remove_small_holes(test_holes_image, area_threshold=3,
+                                  connectivity=2)
+    assert_array_equal(observed, expected)
+
+
+def test_in_place_holes():
+    image = test_holes_image.copy()
+    observed = remove_small_holes(image, area_threshold=3, in_place=True)
+    assert_equal(observed is image, True,
+                 "remove_small_holes in_place argument failed.")
+
+
+def test_labeled_image_holes():
+    # fmt: off
+    labeled_holes_image = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2]],
+                                   dtype=int)
+    expected = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], dtype=bool)
+    # fmt: on
+    with expected_warnings(['returned as a boolean array']):
+        observed = remove_small_holes(labeled_holes_image, area_threshold=3)
+    assert_array_equal(observed, expected)
+
+
+def test_uint_image_holes():
+    # fmt: off
+    labeled_holes_image = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2]],
+                                   dtype=cp.uint8)
+    expected = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1],
+                         [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], dtype=bool)
+    # fmt: on
+    with expected_warnings(['returned as a boolean array']):
+        observed = remove_small_holes(labeled_holes_image, area_threshold=3)
+    assert_array_equal(observed, expected)
+
+
+def test_label_warning_holes():
+    # fmt: off
+    labeled_holes_image = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 0, 0, 1, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
+                                    [0, 1, 1, 1, 1, 1, 0, 0, 0, 0],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 0, 2],
+                                    [0, 0, 0, 0, 0, 0, 0, 2, 2, 2]],
+                                   dtype=int)
+    # fmt: on
+    with expected_warnings(['use a boolean array?']):
+        remove_small_holes(labeled_holes_image, area_threshold=3)
+    remove_small_holes(labeled_holes_image.astype(bool), area_threshold=3)
+
+
+def test_float_input_holes():
+    float_test = cp.random.rand(5, 5)
+    with pytest.raises(TypeError):
+        remove_small_holes(float_test)
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_reconstruction.py b/python/cucim/src/cucim/skimage/morphology/tests/test_reconstruction.py
new file mode 100644
index 000000000..2bf5cda49
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_reconstruction.py
@@ -0,0 +1,162 @@
+"""
+These tests are originally part of CellProfiler, code licensed under both GPL
+and BSD licenses.
+
+Website: http://www.cellprofiler.org
+Copyright (c) 2003-2009 Massachusetts Institute of Technology
+Copyright (c) 2009-2011 Broad Institute
+All rights reserved.
+Original author: Lee Kamentsky
+"""
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal
+
+from cucim.skimage.morphology import reconstruction
+from skimage.morphology import reconstruction as reconstruction_cpu
+
+
+def test_zeros():
+    """Test reconstruction with image and mask of zeros"""
+    assert_array_almost_equal(
+        reconstruction(cp.zeros((5, 7)), cp.zeros((5, 7))), 0)
+
+
+def test_image_equals_mask():
+    """Test reconstruction where the image and mask are the same"""
+    assert_array_almost_equal(
+        reconstruction(cp.ones((7, 5)), cp.ones((7, 5))), 1)
+
+
+def test_image_less_than_mask():
+    """Test reconstruction where the image is uniform and less than mask"""
+    image = cp.ones((5, 5))
+    mask = cp.ones((5, 5)) * 2
+    assert_array_almost_equal(reconstruction(image, mask), 1)
+
+
+def test_one_image_peak():
+    """Test reconstruction with one peak pixel"""
+    image = cp.ones((5, 5))
+    image[2, 2] = 2
+    mask = cp.ones((5, 5)) * 3
+    assert_array_almost_equal(reconstruction(image, mask), 2)
+
+
+def test_two_image_peaks():
+    """Test reconstruction with two peak pixels isolated by the mask"""
+    # fmt: off
+    image = cp.array([[1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 2, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 3, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1]])
+
+    mask = cp.array([[4, 4, 4, 1, 1, 1, 1, 1],
+                     [4, 4, 4, 1, 1, 1, 1, 1],
+                     [4, 4, 4, 1, 1, 1, 1, 1],
+                     [1, 1, 1, 1, 1, 4, 4, 4],
+                     [1, 1, 1, 1, 1, 4, 4, 4],
+                     [1, 1, 1, 1, 1, 4, 4, 4]])
+
+    expected = cp.array([[2, 2, 2, 1, 1, 1, 1, 1],
+                         [2, 2, 2, 1, 1, 1, 1, 1],
+                         [2, 2, 2, 1, 1, 1, 1, 1],
+                         [1, 1, 1, 1, 1, 3, 3, 3],
+                         [1, 1, 1, 1, 1, 3, 3, 3],
+                         [1, 1, 1, 1, 1, 3, 3, 3]])
+    # fmt: on
+    assert_array_almost_equal(reconstruction(image, mask), expected)
+
+
+def test_zero_image_one_mask():
+    """Test reconstruction with an image of all zeros and a mask that's not"""
+    result = reconstruction(cp.zeros((10, 10)), cp.ones((10, 10)))
+    assert_array_almost_equal(result, 0)
+
+
+def test_fill_hole():
+    """Test reconstruction by erosion, which should fill holes in mask."""
+    seed = cp.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0])
+    mask = cp.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
+    result = reconstruction(seed, mask, method="erosion")
+    assert_array_almost_equal(
+        result, cp.array([0, 3, 6, 4, 4, 4, 4, 4, 2, 0])
+    )
+
+
+def test_invalid_seed():
+    seed = cp.ones((5, 5))
+    mask = cp.ones((5, 5))
+    with pytest.raises(ValueError):
+        reconstruction(seed * 2, mask, method='dilation')
+    with pytest.raises(ValueError):
+        reconstruction(seed * 0.5, mask, method='erosion')
+
+
+def test_invalid_selem():
+    seed = cp.ones((5, 5))
+    mask = cp.ones((5, 5))
+    with pytest.raises(ValueError):
+        reconstruction(seed, mask, selem=np.ones((4, 4)))
+    with pytest.raises(ValueError):
+        reconstruction(seed, mask, selem=np.ones((3, 4)))
+    reconstruction(seed, mask, selem=np.ones((3, 3)))
+
+
+def test_invalid_method():
+    seed = cp.array([0, 8, 8, 8, 8, 8, 8, 8, 8, 0])
+    mask = cp.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
+    with pytest.raises(ValueError):
+        reconstruction(seed, mask, method="foo")
+
+
+def test_invalid_offset_not_none():
+    """Test reconstruction with invalid not None offset parameter"""
+    # fmt: off
+    image = cp.array([[1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 2, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1],
+                      [1, 1, 1, 1, 1, 1, 3, 1],
+                      [1, 1, 1, 1, 1, 1, 1, 1]])
+
+    mask = cp.array([[4, 4, 4, 1, 1, 1, 1, 1],
+                     [4, 4, 4, 1, 1, 1, 1, 1],
+                     [4, 4, 4, 1, 1, 1, 1, 1],
+                     [1, 1, 1, 1, 1, 4, 4, 4],
+                     [1, 1, 1, 1, 1, 4, 4, 4],
+                     [1, 1, 1, 1, 1, 4, 4, 4]])
+    # fmt: on
+    with pytest.raises(ValueError):
+        reconstruction(image, mask, method='dilation',
+                       selem=cp.ones((3, 3)), offset=cp.array([3, 0]))
+
+
+def test_offset_not_none():
+    """Test reconstruction with valid offset parameter"""
+    seed = cp.array([0, 3, 6, 2, 1, 1, 1, 4, 2, 0])
+    mask = cp.array([0, 8, 6, 8, 8, 8, 8, 4, 4, 0])
+    expected = cp.array([0, 3, 6, 6, 6, 6, 6, 4, 4, 0])
+
+    assert_array_almost_equal(
+        reconstruction(seed, mask, method='dilation',
+                       selem=cp.ones(3), offset=cp.array([0])), expected)
+
+
+def test_reconstruction_float_inputs():
+    """Verifies fix for: https://github.com/rapidsai/cuci/issues/36
+
+    Run the 2D example from the reconstruction docstring and compare the output
+    to scikit-image.
+    """
+
+    y, x = np.mgrid[:20:0.5, :20:0.5]
+    bumps = np.sin(x) + np.sin(y)
+    h = 0.3
+    seed = bumps - h
+    background_cpu = reconstruction_cpu(seed, bumps)
+    background = reconstruction(cp.asarray(seed), cp.asarray(bumps))
+    cp.testing.assert_allclose(background, background_cpu)
diff --git a/python/cucim/src/cucim/skimage/morphology/tests/test_selem.py b/python/cucim/src/cucim/skimage/morphology/tests/test_selem.py
new file mode 100644
index 000000000..37a6f076a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/morphology/tests/test_selem.py
@@ -0,0 +1,156 @@
+"""
+Tests for Morphological structuring elements
+(skimage.morphology.selem)
+
+Author: Damian Eads
+"""
+
+
+import cupy as cp
+import numpy as np
+from cupy.testing import assert_array_equal
+
+from cucim.skimage._shared.testing import fetch
+from cucim.skimage.morphology import selem
+
+
+class TestSElem:
+    def test_square_selem(self):
+        """Test square structuring elements"""
+        for k in range(0, 5):
+            actual_mask = selem.square(k)
+            expected_mask = cp.ones((k, k), dtype='uint8')
+            assert_array_equal(expected_mask, actual_mask)
+
+    def test_rectangle_selem(self):
+        """Test rectangle structuring elements"""
+        for i in range(0, 5):
+            for j in range(0, 5):
+                actual_mask = selem.rectangle(i, j)
+                expected_mask = cp.ones((i, j), dtype='uint8')
+                assert_array_equal(expected_mask, actual_mask)
+
+    def test_cube_selem(self):
+        """Test cube structuring elements"""
+        for k in range(0, 5):
+            actual_mask = selem.cube(k)
+            expected_mask = cp.ones((k, k, k), dtype='uint8')
+            assert_array_equal(expected_mask, actual_mask)
+
+    def strel_worker(self, fn, func):
+        matlab_masks = np.load(fetch(fn))
+        k = 0
+        for arrname in sorted(matlab_masks):
+            expected_mask = matlab_masks[arrname]
+            actual_mask = func(k)
+            if expected_mask.shape == (1,):
+                expected_mask = expected_mask[:, np.newaxis]
+            assert_array_equal(expected_mask, actual_mask)
+            k = k + 1
+
+    def strel_worker_3d(self, fn, func):
+        matlab_masks = np.load(fetch(fn))
+        k = 0
+        for arrname in sorted(matlab_masks):
+            expected_mask = matlab_masks[arrname]
+            actual_mask = func(k)
+            if expected_mask.shape == (1,):
+                expected_mask = expected_mask[:, np.newaxis]
+            # Test center slice for each dimension. This gives a good
+            # indication of validity without the need for a 3D reference
+            # mask.
+            c = int(expected_mask.shape[0] / 2)
+            assert_array_equal(expected_mask, actual_mask[c, :, :])
+            assert_array_equal(expected_mask, actual_mask[:, c, :])
+            assert_array_equal(expected_mask, actual_mask[:, :, c])
+            k = k + 1
+
+    def test_selem_disk(self):
+        """Test disk structuring elements"""
+        self.strel_worker("data/disk-matlab-output.npz", selem.disk)
+
+    def test_selem_diamond(self):
+        """Test diamond structuring elements"""
+        self.strel_worker("data/diamond-matlab-output.npz", selem.diamond)
+
+    def test_selem_ball(self):
+        """Test ball structuring elements"""
+        self.strel_worker_3d("data/disk-matlab-output.npz", selem.ball)
+
+    def test_selem_octahedron(self):
+        """Test octahedron structuring elements"""
+        self.strel_worker_3d("data/diamond-matlab-output.npz",
+                             selem.octahedron)
+
+    def test_selem_octagon(self):
+        """Test octagon structuring elements"""
+        # fmt: off
+        expected_mask1 = cp.array([[0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0]],
+                                  dtype=cp.uint8)
+        actual_mask1 = selem.octagon(5, 3)
+        expected_mask2 = cp.array([[0, 1, 0],
+                                   [1, 1, 1],
+                                   [0, 1, 0]], dtype=cp.uint8)
+
+        # fmt: on
+        actual_mask2 = selem.octagon(1, 1)
+        assert_array_equal(expected_mask1, actual_mask1)
+        assert_array_equal(expected_mask2, actual_mask2)
+
+    def test_selem_ellipse(self):
+        """Test ellipse structuring elements"""
+        # fmt: off
+        expected_mask1 = cp.array([[0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]],
+                                  dtype=cp.uint8)
+        actual_mask1 = selem.ellipse(5, 3)
+        expected_mask2 = cp.array([[1, 1, 1],
+                                   [1, 1, 1],
+                                   [1, 1, 1]], dtype=cp.uint8)
+        # fmt: on
+        actual_mask2 = selem.ellipse(1, 1)
+        assert_array_equal(expected_mask1, actual_mask1)
+        assert_array_equal(expected_mask2, actual_mask2)
+        assert_array_equal(expected_mask1, selem.ellipse(3, 5).T)
+        assert_array_equal(expected_mask2, selem.ellipse(1, 1).T)
+
+    def test_selem_star(self):
+        """Test star structuring elements"""
+        # fmt: off
+        expected_mask1 = cp.array([[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0],
+                                   [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+                                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                                   [0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                                   [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0],
+                                   [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]],
+                                  dtype=cp.uint8)
+        actual_mask1 = selem.star(4)
+        expected_mask2 = cp.array([[1, 1, 1],
+                                   [1, 1, 1],
+                                   [1, 1, 1]], dtype=cp.uint8)
+        # fmt: on
+        actual_mask2 = selem.star(1)
+        assert_array_equal(expected_mask1, actual_mask1)
+        assert_array_equal(expected_mask2, actual_mask2)
diff --git a/python/cucim/src/cucim/skimage/registration/__init__.py b/python/cucim/src/cucim/skimage/registration/__init__.py
new file mode 100644
index 000000000..5734f266f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/__init__.py
@@ -0,0 +1,4 @@
+from ._optical_flow import optical_flow_ilk, optical_flow_tvl1  # noqa
+from ._phase_cross_correlation import phase_cross_correlation  # noqa
+
+__all__ = ["optical_flow_ilk", "optical_flow_tvl1", "phase_cross_correlation"]
diff --git a/python/cucim/src/cucim/skimage/registration/_masked_phase_cross_correlation.py b/python/cucim/src/cucim/skimage/registration/_masked_phase_cross_correlation.py
new file mode 100644
index 000000000..b34a58ff5
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/_masked_phase_cross_correlation.py
@@ -0,0 +1,302 @@
+"""
+Implementation of the masked normalized cross-correlation.
+
+Based on the following publication:
+D. Padfield. Masked object registration in the Fourier domain.
+IEEE Transactions on Image Processing (2012)
+
+and the author's original MATLAB implementation, available on this website:
+http://www.dirkpadfield.com/
+"""
+
+from functools import partial
+
+import cupy as cp
+import numpy as np
+
+from .._shared.fft import fftmodule, next_fast_len
+
+
+def _masked_phase_cross_correlation(reference_image, moving_image,
+                                    reference_mask, moving_mask=None,
+                                    overlap_ratio=0.3):
+    """Masked image translation registration by masked normalized
+    cross-correlation.
+
+    Parameters
+    ----------
+    reference_image : ndarray
+        Reference image.
+    moving_image : ndarray
+        Image to register. Must be same dimensionality as ``reference_image``,
+        but not necessarily the same size.
+    reference_mask : ndarray
+        Boolean mask for ``reference_image``. The mask should evaluate
+        to ``True`` (or 1) on valid pixels. ``reference_mask`` should
+        have the same shape as ``reference_image``.
+    moving_mask : ndarray or None, optional
+        Boolean mask for ``moving_image``. The mask should evaluate to ``True``
+        (or 1) on valid pixels. ``moving_mask`` should have the same shape
+        as ``moving_image``. If ``None``, ``reference_mask`` will be used.
+    overlap_ratio : float, optional
+        Minimum allowed overlap ratio between images. The correlation for
+        translations corresponding with an overlap ratio lower than this
+        threshold will be ignored. A lower `overlap_ratio` leads to smaller
+        maximum translation, while a higher `overlap_ratio` leads to greater
+        robustness against spurious matches due to small overlap between
+        masked images.
+
+    Returns
+    -------
+    shifts : ndarray
+        Shift vector (in pixels) required to register ``moving_image``
+        with ``reference_image``. Axis ordering is consistent with
+        numpy (e.g. Z, Y, X)
+
+    References
+    ----------
+    .. [1] Dirk Padfield. Masked Object Registration in the Fourier Domain.
+           IEEE Transactions on Image Processing, vol. 21(5),
+           pp. 2706-2718 (2012). :DOI:`10.1109/TIP.2011.2181402`
+    .. [2] D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
+           Pattern Recognition, pp. 2918-2925 (2010).
+           :DOI:`10.1109/CVPR.2010.5540032`
+    """
+    if moving_mask is None:
+        if reference_image.shape != moving_image.shape:
+            raise ValueError(
+                "Input images have different shapes, moving_mask must "
+                "be explicitely set.")
+        moving_mask = cp.array(reference_mask, dtype=bool, copy=True)
+
+    # We need masks to be of the same size as their respective images
+    for (im, mask) in [(reference_image, reference_mask),
+                       (moving_image, moving_mask)]:
+        if im.shape != mask.shape:
+            raise ValueError(
+                "Image sizes must match their respective mask sizes.")
+
+    xcorr = cross_correlate_masked(moving_image, reference_image, moving_mask,
+                                   reference_mask, axes=(0, 1), mode='full',
+                                   overlap_ratio=overlap_ratio)
+
+    # Generalize to the average of multiple equal maxima
+    maxima = cp.stack(cp.nonzero(xcorr == xcorr.max()), axis=1)
+    center = cp.mean(maxima, axis=0)
+    shifts = center - cp.asarray(reference_image.shape) + 1
+
+    # The mismatch in size will impact the center location of the
+    # cross-correlation
+    size_mismatch = [
+        t - s for t, s in zip(moving_image.shape, reference_image.shape)
+    ]
+    size_mismatch = cp.asarray(size_mismatch)
+
+    return -shifts + (size_mismatch / 2)
+
+
+def cross_correlate_masked(arr1, arr2, m1, m2, mode='full', axes=(-2, -1),
+                           overlap_ratio=0.3):
+    """
+    Masked normalized cross-correlation between arrays.
+
+    Parameters
+    ----------
+    arr1 : ndarray
+        First array.
+    arr2 : ndarray
+        Seconds array. The dimensions of `arr2` along axes that are not
+        transformed should be equal to that of `arr1`.
+    m1 : ndarray
+        Mask of `arr1`. The mask should evaluate to `True`
+        (or 1) on valid pixels. `m1` should have the same shape as `arr1`.
+    m2 : ndarray
+        Mask of `arr2`. The mask should evaluate to `True`
+        (or 1) on valid pixels. `m2` should have the same shape as `arr2`.
+    mode : {'full', 'same'}, optional
+        'full':
+            This returns the convolution at each point of overlap. At
+            the end-points of the convolution, the signals do not overlap
+            completely, and boundary effects may be seen.
+        'same':
+            The output is the same size as `arr1`, centered with respect
+            to the `‘full’` output. Boundary effects are less prominent.
+    axes : tuple of ints, optional
+        Axes along which to compute the cross-correlation.
+    overlap_ratio : float, optional
+        Minimum allowed overlap ratio between images. The correlation for
+        translations corresponding with an overlap ratio lower than this
+        threshold will be ignored. A lower `overlap_ratio` leads to smaller
+        maximum translation, while a higher `overlap_ratio` leads to greater
+        robustness against spurious matches due to small overlap between
+        masked images.
+
+    Returns
+    -------
+    out : ndarray
+        Masked normalized cross-correlation.
+
+    Raises
+    ------
+    ValueError : if correlation `mode` is not valid, or array dimensions along
+        non-transformation axes are not equal.
+
+    References
+    ----------
+    .. [1] Dirk Padfield. Masked Object Registration in the Fourier Domain.
+           IEEE Transactions on Image Processing, vol. 21(5),
+           pp. 2706-2718 (2012). :DOI:`10.1109/TIP.2011.2181402`
+    .. [2] D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
+           Pattern Recognition, pp. 2918-2925 (2010).
+           :DOI:`10.1109/CVPR.2010.5540032`
+    """
+    if mode not in {'full', 'same'}:
+        raise ValueError("Correlation mode '{}' is not valid.".format(mode))
+
+    if arr1.dtype.kind == "c" or arr2.dtype.kind == "c":
+        raise ValueError("complex-valued arr1, arr2 are not supported")
+
+    # CuPy Backend: use float_dtype instead of forcing cp.float64
+    float_dtype = np.result_type(arr1.dtype, arr2.dtype, cp.float32)
+
+    fixed_image = arr1.astype(float_dtype, copy=False)
+    fixed_mask = m1.astype(bool, copy=False)
+    moving_image = arr2.astype(float_dtype, copy=False)
+    moving_mask = m2.astype(bool, copy=False)
+    eps = np.finfo(float_dtype).eps
+
+    # Array dimensions along non-transformation axes should be equal.
+    all_axes = set(range(fixed_image.ndim))
+    for axis in (all_axes - set(axes)):
+        if fixed_image.shape[axis] != moving_image.shape[axis]:
+            raise ValueError(
+                "Array shapes along non-transformation axes should be "
+                "equal, but dimensions along axis {a} are not".format(a=axis))
+
+    # Determine final size along transformation axes
+    # Note that it might be faster to compute Fourier transform in a slightly
+    # larger shape (`fast_shape`). Then, after all fourier transforms are done,
+    # we slice back to`final_shape` using `final_slice`.
+    final_shape = list(arr1.shape)
+    for axis in axes:
+        final_shape[axis] = fixed_image.shape[axis] + \
+            moving_image.shape[axis] - 1
+    final_shape = tuple(final_shape)
+    final_slice = tuple([slice(0, int(sz)) for sz in final_shape])
+
+    # Extent transform axes to the next fast length (i.e. multiple of 3, 5, or
+    # 7)
+    fast_shape = tuple([next_fast_len(final_shape[ax]) for ax in axes])
+
+    # We use numpy.fft or the new scipy.fft because they allow leaving the
+    # transform axes unchanged which was not possible with scipy.fftpack's
+    # fftn/ifftn in older versions of SciPy.
+    # E.g. arr shape (2, 3, 7), transform along axes (0, 1) with shape (4, 4)
+    # results in arr_fft shape (4, 4, 7)
+    fft = partial(fftmodule.fftn, s=fast_shape, axes=axes)
+    ifft = partial(fftmodule.ifftn, s=fast_shape, axes=axes)
+
+    fixed_image[cp.logical_not(fixed_mask)] = 0.0
+    moving_image[cp.logical_not(moving_mask)] = 0.0
+
+    # N-dimensional analog to rotation by 180deg is flip over all relevant axes.
+    # See [1] for discussion.
+    rotated_moving_image = _flip(moving_image, axes=axes)
+    rotated_moving_mask = _flip(moving_mask, axes=axes)
+
+    fixed_fft = fft(fixed_image)
+    rotated_moving_fft = fft(rotated_moving_image)
+    fixed_mask_fft = fft(fixed_mask.astype(dtype=float_dtype))
+    rotated_moving_mask_fft = fft(rotated_moving_mask.astype(dtype=float_dtype))
+
+    # Calculate overlap of masks at every point in the convolution.
+    # Locations with high overlap should not be taken into account.
+    number_overlap_masked_px = cp.real(
+        ifft(rotated_moving_mask_fft * fixed_mask_fft)
+    )
+    number_overlap_masked_px[:] = cp.around(number_overlap_masked_px)
+    number_overlap_masked_px[:] = cp.fmax(number_overlap_masked_px, eps)
+    masked_correlated_fixed_fft = ifft(rotated_moving_mask_fft * fixed_fft)
+    masked_correlated_rotated_moving_fft = ifft(
+        fixed_mask_fft * rotated_moving_fft)
+
+    numerator = ifft(rotated_moving_fft * fixed_fft)
+    numerator -= masked_correlated_fixed_fft * \
+        masked_correlated_rotated_moving_fft / number_overlap_masked_px
+
+    fixed_squared_fft = fft(cp.square(fixed_image))
+    fixed_denom = ifft(rotated_moving_mask_fft * fixed_squared_fft)
+    fixed_denom -= cp.square(masked_correlated_fixed_fft) / \
+        number_overlap_masked_px
+
+    fixed_denom[:] = cp.fmax(fixed_denom, 0.0)
+
+    rotated_moving_squared_fft = fft(cp.square(rotated_moving_image))
+    moving_denom = ifft(fixed_mask_fft * rotated_moving_squared_fft)
+    moving_denom -= cp.square(masked_correlated_rotated_moving_fft) / \
+        number_overlap_masked_px
+
+    moving_denom[:] = cp.fmax(moving_denom, 0.0)
+
+    denom = cp.sqrt(fixed_denom * moving_denom)
+
+    # Slice back to expected convolution shape.
+    numerator = numerator[final_slice]
+    denom = denom[final_slice]
+    number_overlap_masked_px = number_overlap_masked_px[final_slice]
+
+    if mode == 'same':
+        _centering = partial(_centered,
+                             newshape=fixed_image.shape, axes=axes)
+        denom = _centering(denom)
+        numerator = _centering(numerator)
+        number_overlap_masked_px = _centering(number_overlap_masked_px)
+
+    # Pixels where `denom` is very small will introduce large
+    # numbers after division. To get around this problem,
+    # we zero-out problematic pixels.
+    tol = 1e3 * eps * cp.max(cp.abs(denom), axis=axes, keepdims=True)
+    nonzero_indices = denom > tol
+
+    # TODO: Added a cast to real here.
+    #       probably it should be real earlier?
+    numerator = numerator.real
+    denom = denom.real
+    out = cp.zeros_like(denom)
+    out[nonzero_indices] = numerator[nonzero_indices] / denom[nonzero_indices]
+    cp.clip(out, a_min=-1, a_max=1, out=out)
+
+    # Apply overlap ratio threshold
+    number_px_threshold = overlap_ratio * np.max(number_overlap_masked_px,
+                                                 axis=axes, keepdims=True)
+    out[number_overlap_masked_px < number_px_threshold] = 0.0
+
+    return out
+
+
+def _centered(arr, newshape, axes):
+    """Return the center `newshape` portion of `arr`, leaving axes not
+    in `axes` untouched."""
+    currshape = arr.shape
+
+    slices = [slice(None, None)] * arr.ndim
+
+    for ax in axes:
+        startind = (currshape[ax] - newshape[ax]) // 2
+        endind = startind + newshape[ax]
+        slices[ax] = slice(startind, endind)
+
+    return arr[tuple(slices)]
+
+
+def _flip(arr, axes=None):
+    """Reverse array over many axes. Generalization of arr[::-1] for many
+    dimensions. If `axes` is `None`, flip along all axes."""
+    if axes is None:
+        reverse = [slice(None, None, -1)] * arr.ndim
+    else:
+        reverse = [slice(None, None, None)] * arr.ndim
+        for axis in axes:
+            reverse[axis] = slice(None, None, -1)
+
+    return arr[tuple(reverse)]
diff --git a/python/cucim/src/cucim/skimage/registration/_optical_flow.py b/python/cucim/src/cucim/skimage/registration/_optical_flow.py
new file mode 100644
index 000000000..7e4018121
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/_optical_flow.py
@@ -0,0 +1,366 @@
+# coding: utf-8
+"""TV-L1 optical flow algorithm implementation.
+
+"""
+
+from functools import partial
+from itertools import combinations_with_replacement
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from cucim.skimage.transform import warp
+
+from ._optical_flow_utils import coarse_to_fine, get_warp_points
+
+
+def _tvl1(reference_image, moving_image, flow0, attachment, tightness,
+          num_warp, num_iter, tol, prefilter):
+    """TV-L1 solver for optical flow estimation.
+
+    Parameters
+    ----------
+    reference_image : ndarray, shape (M, N[, P[, ...]])
+        The first gray scale image of the sequence.
+    moving_image : ndarray, shape (M, N[, P[, ...]])
+        The second gray scale image of the sequence.
+    flow0 : ndarray, shape (image0.ndim, M, N[, P[, ...]])
+        Initialization for the vector field.
+    attachment : float
+        Attachment parameter. The smaller this parameter is,
+        the smoother is the solutions.
+    tightness : float
+        Tightness parameter. It should have a small value in order to
+        maintain attachement and regularization parts in
+        correspondence.
+    num_warp : int
+        Number of times image1 is warped.
+    num_iter : int
+        Number of fixed point iteration.
+    tol : float
+        Tolerance used as stopping criterion based on the L² distance
+        between two consecutive values of (u, v).
+    prefilter : bool
+        Whether to prefilter the estimated optical flow before each
+        image warp.
+
+    Returns
+    -------
+    flow : ndarray, shape ((image0.ndim, M, N[, P[, ...]])
+        The estimated optical flow components for each axis.
+
+    """
+
+    dtype = reference_image.dtype
+    grid = cp.meshgrid(*[cp.arange(n, dtype=dtype)
+                         for n in reference_image.shape],
+                       indexing='ij', sparse=True)
+
+    dt = 0.5 / reference_image.ndim
+    reg_num_iter = 2
+    f0 = attachment * tightness
+    f1 = dt / tightness
+    tol *= reference_image.size
+
+    flow_current = flow_previous = flow0
+
+    g = cp.zeros((reference_image.ndim,) + reference_image.shape, dtype=dtype)
+    proj = cp.zeros((reference_image.ndim, reference_image.ndim,)
+                    + reference_image.shape, dtype=dtype)
+
+    s_g = [slice(None)] * g.ndim
+    s_p = [slice(None)] * proj.ndim
+    s_d = [slice(None)] * (proj.ndim - 2)
+
+    for _ in range(num_warp):
+        if prefilter:
+            flow_current = ndi.median_filter(flow_current,
+                                             [1] + reference_image.ndim * [3])
+
+        image1_warp = warp(moving_image, get_warp_points(grid, flow_current),
+                           mode='nearest')
+        grad = cp.stack(cp.gradient(image1_warp))
+        NI = (grad * grad).sum(0)
+        NI[NI == 0] = 1
+
+        rho_0 = image1_warp - reference_image - (grad * flow_current).sum(0)
+
+        for _ in range(num_iter):
+
+            # Data term
+
+            rho = rho_0 + (grad * flow_current).sum(0)
+
+            idx = abs(rho) <= f0 * NI
+
+            flow_auxiliary = flow_current
+
+            flow_auxiliary[:, idx] -= rho[idx] * grad[:, idx] / NI[idx]
+
+            idx = ~idx
+            srho = f0 * cp.sign(rho[idx])
+            flow_auxiliary[:, idx] -= srho * grad[:, idx]
+
+            # Regularization term
+            flow_current = flow_auxiliary.copy()
+
+            for idx in range(reference_image.ndim):
+                s_p[0] = idx
+                for _ in range(reg_num_iter):
+                    for ax in range(reference_image.ndim):
+                        s_g[0] = ax
+                        s_g[ax + 1] = slice(0, -1)
+                        g[tuple(s_g)] = cp.diff(flow_current[idx], axis=ax)
+                        s_g[ax + 1] = slice(None)
+
+                    norm = cp.sqrt((g * g).sum(0, keepdims=True))
+                    norm *= f1
+                    norm += 1.0
+                    proj[idx] -= dt * g
+                    proj[idx] /= norm
+
+                    # d will be the (negative) divergence of proj[idx]
+                    d = -proj[idx].sum(0)
+                    for ax in range(reference_image.ndim):
+                        s_p[1] = ax
+                        s_p[ax + 2] = slice(0, -1)
+                        s_d[ax] = slice(1, None)
+                        d[tuple(s_d)] += proj[tuple(s_p)]
+                        s_p[ax + 2] = slice(None)
+                        s_d[ax] = slice(None)
+
+                    flow_current[idx] = flow_auxiliary[idx] + d
+
+        flow_previous -= flow_current  # The difference as stopping criteria
+        if (flow_previous * flow_previous).sum() < tol:
+            break
+
+        flow_previous = flow_current
+
+    return flow_current
+
+
+def optical_flow_tvl1(reference_image, moving_image,
+                      *,
+                      attachment=15, tightness=0.3, num_warp=5, num_iter=10,
+                      tol=1e-4, prefilter=False, dtype=np.float32):
+    r"""Coarse to fine optical flow estimator.
+
+    The TV-L1 solver is applied at each level of the image
+    pyramid. TV-L1 is a popular algorithm for optical flow estimation
+    introduced by Zack et al. [1]_, improved in [2]_ and detailed in [3]_.
+
+    Parameters
+    ----------
+    reference_image : ndarray, shape (M, N[, P[, ...]])
+        The first gray scale image of the sequence.
+    moving_image : ndarray, shape (M, N[, P[, ...]])
+        The second gray scale image of the sequence.
+    attachment : float, optional
+        Attachment parameter (:math:`\lambda` in [1]_). The smaller
+        this parameter is, the smoother the returned result will be.
+    tightness : float, optional
+        Tightness parameter (:math:`\tau` in [1]_). It should have
+        a small value in order to maintain attachement and
+        regularization parts in correspondence.
+    num_warp : int, optional
+        Number of times image1 is warped.
+    num_iter : int, optional
+        Number of fixed point iteration.
+    tol : float, optional
+        Tolerance used as stopping criterion based on the L² distance
+        between two consecutive values of (u, v).
+    prefilter : bool, optional
+        Whether to prefilter the estimated optical flow before each
+        image warp. When True, a median filter with window size 3
+        along each axis is applied. This helps to remove potential
+        outliers.
+    dtype : dtype, optional
+        Output data type: must be floating point. Single precision
+        provides good results and saves memory usage and computation
+        time compared to double precision.
+
+    Returns
+    -------
+    flow : ndarray, shape ((image0.ndim, M, N[, P[, ...]])
+        The estimated optical flow components for each axis.
+
+    Notes
+    -----
+    Color images are not supported.
+
+    References
+    ----------
+    .. [1] Zach, C., Pock, T., & Bischof, H. (2007, September). A
+       duality based approach for realtime TV-L 1 optical flow. In Joint
+       pattern recognition symposium (pp. 214-223). Springer, Berlin,
+       Heidelberg. :DOI:`10.1007/978-3-540-74936-3_22`
+    .. [2] Wedel, A., Pock, T., Zach, C., Bischof, H., & Cremers,
+       D. (2009). An improved algorithm for TV-L 1 optical flow. In
+       Statistical and geometrical approaches to visual motion analysis
+       (pp. 23-45). Springer, Berlin, Heidelberg.
+       :DOI:`10.1007/978-3-642-03061-1_2`
+    .. [3] Pérez, J. S., Meinhardt-Llopis, E., & Facciolo,
+       G. (2013). TV-L1 optical flow estimation. Image Processing On
+       Line, 2013, 137-150. :DOI:`10.5201/ipol.2013.26`
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.color import rgb2gray
+    >>> from skimage.data import stereo_motorcycle
+    >>> from cucim.skimage.registration import optical_flow_tvl1
+    >>> image0, image1, disp = [cp.array(a) for a in stereo_motorcycle()]
+    >>> # --- Convert the images to gray level: color is not supported.
+    >>> image0 = rgb2gray(image0)
+    >>> image1 = rgb2gray(image1)
+    >>> flow = optical_flow_tvl1(image1, image0)
+
+    """
+
+    solver = partial(_tvl1, attachment=attachment,
+                     tightness=tightness, num_warp=num_warp, num_iter=num_iter,
+                     tol=tol, prefilter=prefilter)
+
+    return coarse_to_fine(reference_image, moving_image, solver, dtype=dtype)
+
+
+def _ilk(reference_image, moving_image, flow0, radius, num_warp, gaussian,
+         prefilter):
+    """Iterative Lucas-Kanade (iLK) solver for optical flow estimation.
+
+    Parameters
+    ----------
+    reference_image : ndarray, shape (M, N[, P[, ...]])
+        The first gray scale image of the sequence.
+    moving_image : ndarray, shape (M, N[, P[, ...]])
+        The second gray scale image of the sequence.
+    flow0 : ndarray, shape (reference_image.ndim, M, N[, P[, ...]])
+        Initialization for the vector field.
+    radius : int
+        Radius of the window considered around each pixel.
+    num_warp : int
+        Number of times moving_image is warped.
+    gaussian : bool
+        if True, a gaussian kernel is used for the local
+        integration. Otherwise, a uniform kernel is used.
+    prefilter : bool
+        Whether to prefilter the estimated optical flow before each
+        image warp. This helps to remove potential outliers.
+
+    Returns
+    -------
+    flow : ndarray, shape ((reference_image.ndim, M, N[, P[, ...]])
+        The estimated optical flow components for each axis.
+
+    """
+    dtype = reference_image.dtype
+    ndim = reference_image.ndim
+    size = 2 * radius + 1
+
+    if gaussian:
+        sigma = ndim * (size / 4,)
+        filter_func = partial(ndi.gaussian_filter, sigma=sigma, mode='mirror')
+    else:
+        filter_func = partial(ndi.uniform_filter, size=ndim * (size,),
+                              mode='mirror')
+
+    flow = flow0
+    # For each pixel location (i, j), the optical flow X = flow[:, i, j]
+    # is the solution of the ndim x ndim linear system
+    # A[i, j] * X = b[i, j]
+    A = cp.zeros(reference_image.shape + (ndim, ndim), dtype=dtype)
+    b = cp.zeros(reference_image.shape + (ndim,), dtype=dtype)
+
+    grid = cp.meshgrid(*[cp.arange(n, dtype=dtype)
+                         for n in reference_image.shape],
+                       indexing='ij', sparse=True)
+
+    for _ in range(num_warp):
+        if prefilter:
+            flow = ndi.median_filter(flow, (1,) + ndim * (3,))
+
+        moving_image_warp = warp(moving_image, get_warp_points(grid, flow),
+                                 mode='nearest')
+        grad = cp.stack(cp.gradient(moving_image_warp), axis=0)
+        error_image = ((grad * flow).sum(axis=0)
+                       + reference_image - moving_image_warp)
+
+        # Local linear systems creation
+        for i, j in combinations_with_replacement(range(ndim), 2):
+            A[..., i, j] = A[..., j, i] = filter_func(grad[i] * grad[j])
+
+        for i in range(ndim):
+            b[..., i] = filter_func(grad[i] * error_image)
+
+        # Don't consider badly conditioned linear systems
+        idx = abs(cp.linalg.det(A)) < 1e-14
+        A[idx] = cp.eye(ndim, dtype=dtype)
+        b[idx] = 0
+
+        # Solve the local linear systems
+        flow = cp.moveaxis(cp.linalg.solve(A, b), ndim, 0)
+
+    return flow
+
+
+def optical_flow_ilk(reference_image, moving_image, *,
+                     radius=7, num_warp=10, gaussian=False,
+                     prefilter=False, dtype=np.float32):
+    """Coarse to fine optical flow estimator.
+
+    The iterative Lucas-Kanade (iLK) solver is applied at each level
+    of the image pyramid. iLK [1]_ is a fast and robust alternative to
+    TVL1 algorithm although less accurate for rendering flat surfaces
+    and object boundaries (see [2]_).
+
+    Parameters
+    ----------
+    reference_image : ndarray, shape (M, N[, P[, ...]])
+        The first gray scale image of the sequence.
+    moving_image : ndarray, shape (M, N[, P[, ...]])
+        The second gray scale image of the sequence.
+    radius : int, optional
+        Radius of the window considered around each pixel.
+    num_warp : int, optional
+        Number of times moving_image is warped.
+    gaussian : bool, optional
+        If True, a Gaussian kernel is used for the local
+        integration. Otherwise, a uniform kernel is used.
+    prefilter : bool, optional
+        Whether to prefilter the estimated optical flow before each
+        image warp. When True, a median filter with window size 3
+        along each axis is applied. This helps to remove potential
+        outliers.
+    dtype : dtype, optional
+        Output data type: must be floating point. Single precision
+        provides good results and saves memory usage and computation
+        time compared to double precision.
+
+    Returns
+    -------
+    flow : ndarray, shape ((reference_image.ndim, M, N[, P[, ...]])
+        The estimated optical flow components for each axis.
+
+    Notes
+    -----
+    - The implemented algorithm is described in **Table2** of [1]_.
+    - Color images are not supported.
+
+    References
+    ----------
+    .. [1] Le Besnerais, G., & Champagnat, F. (2005, September). Dense
+       optical flow by iterative local window registration. In IEEE
+       International Conference on Image Processing 2005 (Vol. 1,
+       pp. I-137). IEEE. :DOI:`10.1109/ICIP.2005.1529706`
+    .. [2] Plyer, A., Le Besnerais, G., & Champagnat,
+       F. (2016). Massively parallel Lucas Kanade optical flow for
+       real-time video processing applications. Journal of Real-Time
+       Image Processing, 11(4), 713-730. :DOI:`10.1007/s11554-014-0423-0`
+    """
+
+    solver = partial(_ilk, radius=radius, num_warp=num_warp, gaussian=gaussian,
+                     prefilter=prefilter)
+
+    return coarse_to_fine(reference_image, moving_image, solver, dtype=dtype)
diff --git a/python/cucim/src/cucim/skimage/registration/_optical_flow_utils.py b/python/cucim/src/cucim/skimage/registration/_optical_flow_utils.py
new file mode 100644
index 000000000..7bdab9ac2
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/_optical_flow_utils.py
@@ -0,0 +1,152 @@
+# coding: utf-8
+"""Common tools to optical flow algorithms.
+
+"""
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from cucim.skimage.transform import pyramid_reduce
+from cucim.skimage.util.dtype import _convert
+
+
+def get_warp_points(grid, flow):
+    """Compute warp point coordinates.
+
+    Parameters
+    ----------
+    grid : iterable
+        The sparse grid to be warped (optained using
+        ``np.meshgrid(..., sparse=True)).``)
+    flow : ndarray
+        The warping motion field.
+
+    Returns
+    -------
+    out : ndarray
+        The warp point coordinates.
+
+    """
+    out = flow.copy()
+    for idx, g in enumerate(grid):
+        out[idx, ...] += g
+    return out
+
+
+def resize_flow(flow, shape):
+    """Rescale the values of the vector field (u, v) to the desired shape.
+
+    The values of the output vector field are scaled to the new
+    resolution.
+
+    Parameters
+    ----------
+    flow : ndarray
+        The motion field to be processed.
+    shape : iterable
+        Couple of integers representing the output shape.
+
+    Returns
+    -------
+    rflow : ndarray
+        The resized and rescaled motion field.
+
+    """
+
+    scale = [n / o for n, o in zip(shape, flow.shape[1:])]
+    scale_factor = cp.asarray(scale, dtype=flow.dtype)
+
+    for _ in shape:
+        scale_factor = scale_factor[..., cp.newaxis]
+
+    rflow = scale_factor * ndi.zoom(flow, [1] + scale, order=0,
+                                    mode='nearest', prefilter=False)
+
+    return rflow
+
+
+def get_pyramid(I, downscale=2.0, nlevel=10, min_size=16):  # noqa
+    """Construct image pyramid.
+
+    Parameters
+    ----------
+    I : ndarray
+        The image to be preprocessed (Gray scale or RGB).
+    downscale : float
+        The pyramid downscale factor.
+    nlevel : int
+        The maximum number of pyramid levels.
+    min_size : int
+        The minimum size for any dimension of the pyramid levels.
+
+    Returns
+    -------
+    pyramid : list[ndarray]
+        The coarse to fine images pyramid.
+
+    """
+
+    pyramid = [I]
+    size = min(I.shape)
+    count = 1
+
+    while (count < nlevel) and (size > downscale * min_size):
+        J = pyramid_reduce(pyramid[-1], downscale, multichannel=False)
+        pyramid.append(J)
+        size = min(J.shape)
+        count += 1
+
+    return pyramid[::-1]
+
+
+def coarse_to_fine(I0, I1, solver, downscale=2, nlevel=10, min_size=16,
+                   dtype=np.float32):
+    """Generic coarse to fine solver.
+
+    Parameters
+    ----------
+    I0 : ndarray
+        The first gray scale image of the sequence.
+    I1 : ndarray
+        The second gray scale image of the sequence.
+    solver : callable
+        The solver applyed at each pyramid level.
+    downscale : float
+        The pyramid downscale factor.
+    nlevel : int
+        The maximum number of pyramid levels.
+    min_size : int
+        The minimum size for any dimension of the pyramid levels.
+    dtype : dtype
+        Output data type.
+
+    Returns
+    -------
+    flow : ndarray
+        The estimated optical flow components for each axis.
+
+    """
+
+    if I0.shape != I1.shape:
+        raise ValueError("Input images should have the same shape")
+
+    if np.dtype(dtype).char not in 'efdg':
+        raise ValueError("Only floating point data type are valid"
+                         " for optical flow")
+
+    pyramid = list(zip(get_pyramid(_convert(I0, dtype),
+                                   downscale, nlevel, min_size),
+                       get_pyramid(_convert(I1, dtype),
+                                   downscale, nlevel, min_size)))
+
+    # Initialization to 0 at coarsest level.
+    flow = cp.zeros((pyramid[0][0].ndim, ) + pyramid[0][0].shape,
+                    dtype=dtype)
+
+    flow = solver(pyramid[0][0], pyramid[0][1], flow)
+
+    for J0, J1 in pyramid[1:]:
+        flow = solver(J0, J1, resize_flow(flow, J0.shape))
+
+    return flow
diff --git a/python/cucim/src/cucim/skimage/registration/_phase_cross_correlation.py b/python/cucim/src/cucim/skimage/registration/_phase_cross_correlation.py
new file mode 100644
index 000000000..5ba999b1a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/_phase_cross_correlation.py
@@ -0,0 +1,294 @@
+"""
+Port of Manuel Guizar's code from:
+http://www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation
+"""
+
+import math
+
+import cupy as cp
+import numpy as np
+
+from .._shared.fft import fftmodule as fft
+from ._masked_phase_cross_correlation import _masked_phase_cross_correlation
+
+
+def _upsampled_dft(data, upsampled_region_size,
+                   upsample_factor=1, axis_offsets=None):
+    """
+    Upsampled DFT by matrix multiplication.
+
+    This code is intended to provide the same result as if the following
+    operations were performed:
+        - Embed the array "data" in an array that is ``upsample_factor`` times
+          larger in each dimension.  ifftshift to bring the center of the
+          image to (1,1).
+        - Take the FFT of the larger array.
+        - Extract an ``[upsampled_region_size]`` region of the result, starting
+          with the ``[axis_offsets+1]`` element.
+
+    It achieves this result by computing the DFT in the output array without
+    the need to zeropad. Much faster and memory efficient than the zero-padded
+    FFT approach if ``upsampled_region_size`` is much smaller than
+    ``data.size * upsample_factor``.
+
+    Parameters
+    ----------
+    data : array
+        The input data array (DFT of original data) to upsample.
+    upsampled_region_size : integer or tuple of integers, optional
+        The size of the region to be sampled.  If one integer is provided, it
+        is duplicated up to the dimensionality of ``data``.
+    upsample_factor : integer, optional
+        The upsampling factor.  Defaults to 1.
+    axis_offsets : tuple of integers, optional
+        The offsets of the region to be sampled.  Defaults to None (uses
+        image center)
+
+    Returns
+    -------
+    output : ndarray
+            The upsampled DFT of the specified region.
+    """
+    # if people pass in an integer, expand it to a list of equal-sized sections
+    if not hasattr(upsampled_region_size, "__iter__"):
+        upsampled_region_size = [upsampled_region_size] * data.ndim
+    else:
+        if len(upsampled_region_size) != data.ndim:
+            raise ValueError("shape of upsampled region sizes must be equal "
+                             "to input data's number of dimensions.")
+
+    if axis_offsets is None:
+        axis_offsets = [0] * data.ndim
+    else:
+        if len(axis_offsets) != data.ndim:
+            raise ValueError("number of axis offsets must be equal to input "
+                             "data's number of dimensions.")
+
+    im2pi = 1j * 2 * np.pi
+
+    dim_properties = list(zip(data.shape, upsampled_region_size, axis_offsets))
+
+    for (n_items, ups_size, ax_offset) in dim_properties[::-1]:
+        kernel = ((cp.arange(ups_size) - ax_offset)[:, None]
+                  * fft.fftfreq(n_items, upsample_factor))
+        kernel = cp.exp(-im2pi * kernel)
+        # CuPy Backend: use kernel of same precision as the data
+        kernel = kernel.astype(data.dtype, copy=False)
+
+        # Equivalent to:
+        #   data[i, j, k] = kernel[i, :] @ data[j, k].T
+        data = cp.tensordot(kernel, data, axes=(1, -1))
+    return data
+
+
+def _compute_phasediff(cross_correlation_max):
+    """
+    Compute global phase difference between the two images (should be
+        zero if images are non-negative).
+
+    Parameters
+    ----------
+    cross_correlation_max : complex
+        The complex value of the cross correlation at its maximum point.
+    """
+    return cp.arctan2(cross_correlation_max.imag, cross_correlation_max.real)
+
+
+def _compute_error(cross_correlation_max, src_amp, target_amp):
+    """
+    Compute RMS error metric between ``src_image`` and ``target_image``.
+
+    Parameters
+    ----------
+    cross_correlation_max : complex
+        The complex value of the cross correlation at its maximum point.
+    src_amp : float
+        The normalized average image intensity of the source image
+    target_amp : float
+        The normalized average image intensity of the target image
+    """
+    error = 1.0 - cross_correlation_max * cross_correlation_max.conj() /\
+        (src_amp * target_amp)
+
+    return cp.sqrt(cp.abs(error))
+
+
+def phase_cross_correlation(reference_image, moving_image, *,
+                            upsample_factor=1, space="real",
+                            return_error=True, reference_mask=None,
+                            moving_mask=None, overlap_ratio=0.3):
+    """Efficient subpixel image translation registration by cross-correlation.
+
+    This code gives the same precision as the FFT upsampled cross-correlation
+    in a fraction of the computation time and with reduced memory requirements.
+    It obtains an initial estimate of the cross-correlation peak by an FFT and
+    then refines the shift estimation by upsampling the DFT only in a small
+    neighborhood of that estimate by means of a matrix-multiply DFT.
+
+    Parameters
+    ----------
+    reference_image : array
+        Reference image.
+    moving_image : array
+        Image to register. Must be same dimensionality as
+        ``reference_image``.
+    upsample_factor : int, optional
+        Upsampling factor. Images will be registered to within
+        ``1 / upsample_factor`` of a pixel. For example
+        ``upsample_factor == 20`` means the images will be registered
+        within 1/20th of a pixel. Default is 1 (no upsampling).
+        Not used if any of ``reference_mask`` or ``moving_mask`` is not None.
+    space : string, one of "real" or "fourier", optional
+        Defines how the algorithm interprets input data. "real" means
+        data will be FFT'd to compute the correlation, while "fourier"
+        data will bypass FFT of input data. Case insensitive. Not
+        used if any of ``reference_mask`` or ``moving_mask`` is not
+        None.
+    return_error : bool, optional
+        Returns error and phase difference if on, otherwise only
+        shifts are returned. Has noeffect if any of ``reference_mask`` or
+        ``moving_mask`` is not None. In this case only shifts is returned.
+    reference_mask : ndarray
+        Boolean mask for ``reference_image``. The mask should evaluate
+        to ``True`` (or 1) on valid pixels. ``reference_mask`` should
+        have the same shape as ``reference_image``.
+    moving_mask : ndarray or None, optional
+        Boolean mask for ``moving_image``. The mask should evaluate to ``True``
+        (or 1) on valid pixels. ``moving_mask`` should have the same shape
+        as ``moving_image``. If ``None``, ``reference_mask`` will be used.
+    overlap_ratio : float, optional
+        Minimum allowed overlap ratio between images. The correlation for
+        translations corresponding with an overlap ratio lower than this
+        threshold will be ignored. A lower `overlap_ratio` leads to smaller
+        maximum translation, while a higher `overlap_ratio` leads to greater
+        robustness against spurious matches due to small overlap between
+        masked images. Used only if one of ``reference_mask`` or
+        ``moving_mask`` is None.
+
+    Returns
+    -------
+    shifts : ndarray
+        Shift vector (in pixels) required to register ``moving_image``
+        with ``reference_image``. Axis ordering is consistent with
+        numpy (e.g. Z, Y, X)
+    error : float
+        Translation invariant normalized RMS error between
+        ``reference_image`` and ``moving_image``.
+    phasediff : float
+        Global phase difference between the two images (should be
+        zero if images are non-negative).
+
+    References
+    ----------
+    .. [1] Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup,
+           "Efficient subpixel image registration algorithms,"
+           Optics Letters 33, 156-158 (2008). :DOI:`10.1364/OL.33.000156`
+    .. [2] James R. Fienup, "Invariant error metrics for image reconstruction"
+           Optics Letters 36, 8352-8357 (1997). :DOI:`10.1364/AO.36.008352`
+    .. [3] Dirk Padfield. Masked Object Registration in the Fourier Domain.
+           IEEE Transactions on Image Processing, vol. 21(5),
+           pp. 2706-2718 (2012). :DOI:`10.1109/TIP.2011.2181402`
+    .. [4] D. Padfield. "Masked FFT registration". In Proc. Computer Vision and
+           Pattern Recognition, pp. 2918-2925 (2010).
+           :DOI:`10.1109/CVPR.2010.5540032`
+    """
+    if (reference_mask is not None) or (moving_mask is not None):
+        return _masked_phase_cross_correlation(reference_image, moving_image,
+                                               reference_mask, moving_mask,
+                                               overlap_ratio)
+
+    # images must be the same shape
+    if reference_image.shape != moving_image.shape:
+        raise ValueError("images must be same shape")
+
+    # assume complex data is already in Fourier space
+    if space.lower() == 'fourier':
+        src_freq = reference_image
+        target_freq = moving_image
+    # real data needs to be fft'd.
+    elif space.lower() == 'real':
+        src_freq = fft.fftn(reference_image)
+        target_freq = fft.fftn(moving_image)
+    else:
+        raise ValueError('space argument must be "real" of "fourier"')
+
+    # Whole-pixel shift - Compute cross-correlation by an IFFT
+    shape = src_freq.shape
+    image_product = src_freq * target_freq.conj()
+    cross_correlation = fft.ifftn(image_product)
+
+    # Locate maximum
+    maxima = cp.unravel_index(
+        cp.argmax(cp.abs(cross_correlation)), cross_correlation.shape
+    )
+    midpoints = cp.asarray([np.fix(axis_size / 2) for axis_size in shape])
+
+    float_dtype = image_product.real.dtype
+    shifts = cp.stack([m.astype(float_dtype, copy=False) for m in maxima])
+    shifts[shifts > midpoints] -= cp.asarray(shape)[shifts > midpoints]
+
+    if upsample_factor == 1:
+        if return_error:
+            sabs = cp.abs(src_freq)
+            sabs *= sabs
+            tabs = cp.abs(target_freq)
+            tabs *= tabs
+            src_amp = np.sum(sabs) / src_freq.size
+            target_amp = np.sum(tabs) / target_freq.size
+            CCmax = cross_correlation[maxima]
+    # If upsampling > 1, then refine estimate with matrix multiply DFT
+    else:
+        # Initial shift estimate in upsampled grid
+        shifts = cp.around(shifts * upsample_factor) / upsample_factor
+        upsampled_region_size = math.ceil(upsample_factor * 1.5)
+        # Center of output array at dftshift + 1
+        dftshift = np.fix(upsampled_region_size / 2.0)
+        upsample_factor = float(upsample_factor)
+        # Matrix multiply DFT around the current shift estimate
+        sample_region_offset = dftshift - shifts * upsample_factor
+        cross_correlation = _upsampled_dft(image_product.conj(),
+                                           upsampled_region_size,
+                                           upsample_factor,
+                                           sample_region_offset).conj()
+
+        # Locate maximum and map back to original pixel grid
+        maxima = cp.unravel_index(cp.argmax(cp.abs(cross_correlation)),
+                                  cross_correlation.shape)
+        CCmax = cross_correlation[maxima]
+
+        maxima = (
+            cp.stack([m.astype(float_dtype, copy=False) for m in maxima])
+            - dftshift
+        )
+
+        shifts = shifts + maxima / upsample_factor
+
+        if return_error:
+            src_amp = cp.abs(src_freq)
+            src_amp *= src_amp
+            src_amp = cp.sum(src_amp)
+            target_amp = cp.abs(target_freq)
+            target_amp *= target_amp
+            target_amp = cp.sum(target_amp)
+
+    # If its only one row or column the shift along that dimension has no
+    # effect. We set to zero.
+    for dim in range(src_freq.ndim):
+        if shape[dim] == 1:
+            shifts[dim] = 0
+
+    if return_error:
+        # Redirect user to masked_phase_cross_correlation if NaNs are observed
+        if cp.isnan(CCmax) or cp.isnan(src_amp) or cp.isnan(target_amp):
+            raise ValueError(
+                "NaN values found, please remove NaNs from your "
+                "input data or use the `reference_mask`/`moving_mask` "
+                "keywords, eg: "
+                "phase_cross_correlation(reference_image, moving_image, "
+                "reference_mask=~np.isnan(reference_image), "
+                "moving_mask=~np.isnan(moving_image))")
+
+        return shifts, _compute_error(CCmax, src_amp, target_amp),\
+            _compute_phasediff(CCmax)
+    else:
+        return shifts
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX-130Y130.png b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX-130Y130.png
new file mode 100644
index 000000000..23ef2b095
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX-130Y130.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX130Y130.png b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX130Y130.png
new file mode 100644
index 000000000..dab7efcd8
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX130Y130.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX75Y75.png b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX75Y75.png
new file mode 100644
index 000000000..51071fda6
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/OriginalX75Y75.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX-130Y130.png b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX-130Y130.png
new file mode 100644
index 000000000..0ab2357dd
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX-130Y130.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX130Y130.png b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX130Y130.png
new file mode 100644
index 000000000..bff2a18ff
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX130Y130.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX75Y75.png b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX75Y75.png
new file mode 100644
index 000000000..34402aa57
Binary files /dev/null and b/python/cucim/src/cucim/skimage/registration/tests/data/TransformedX75Y75.png differ
diff --git a/python/cucim/src/cucim/skimage/registration/tests/test_ilk.py b/python/cucim/src/cucim/skimage/registration/tests/test_ilk.py
new file mode 100644
index 000000000..bfb38d809
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/tests/test_ilk.py
@@ -0,0 +1,130 @@
+import cupy as cp
+import numpy as np
+import pytest
+
+from cucim.skimage._shared import testing
+from cucim.skimage.registration import optical_flow_ilk
+from cucim.skimage.transform import warp
+
+
+def _sin_flow_gen(image0, max_motion=4.5, npics=5):
+    """Generate a synthetic ground truth optical flow with a sinusoid as
+      first component.
+
+    Parameters:
+    ----
+    image0: ndarray
+        The base image to be warped.
+    max_motion: float
+        Maximum flow magnitude.
+    npics: int
+        Number of sinusoid pics.
+
+    Returns
+    -------
+    flow, image1 : ndarray
+        The synthetic ground truth optical flow with a sinusoid as
+        first component and the corresponding warped image.
+
+    """
+    grid = cp.meshgrid(*[cp.arange(n) for n in image0.shape], indexing='ij')
+    grid = cp.stack(grid)
+    # TODO: make upstream scikit-image PR changing gt_flow dtype to float
+    gt_flow = cp.zeros_like(grid, dtype=float)
+    gt_flow[0, ...] = max_motion * cp.sin(
+        grid[0] / grid[0].max() * npics * np.pi
+    )
+    image1 = warp(image0, grid - gt_flow, mode="nearest")
+    return gt_flow, image1
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+@pytest.mark.parametrize('gaussian', [True, False])
+@pytest.mark.parametrize('prefilter', [True, False])
+def test_2d_motion(dtype, gaussian, prefilter):
+    # Generate synthetic data
+    rnd = np.random.RandomState(0)
+    image0 = rnd.normal(size=(256, 256))
+    image0 = cp.asarray(image0, dtype=dtype)
+    gt_flow, image1 = _sin_flow_gen(image0)
+    image1 = image1.astype(dtype, copy=False)
+    # Estimate the flow
+    flow = optical_flow_ilk(image0, image1,
+                            gaussian=gaussian, prefilter=prefilter,
+                            dtype=dtype)
+    assert flow.dtype == dtype
+    # Assert that the average absolute error is less then half a pixel
+    assert abs(flow - gt_flow).mean() < 0.5
+
+
+@pytest.mark.parametrize('gaussian', [True, False])
+@pytest.mark.parametrize('prefilter', [True, False])
+def test_3d_motion(gaussian, prefilter):
+    # Generate synthetic data
+    rnd = np.random.RandomState(0)
+    image0 = rnd.normal(size=(50, 55, 60))
+    image0 = cp.asarray(image0)
+    gt_flow, image1 = _sin_flow_gen(image0, npics=3)
+    # Estimate the flow
+    flow = optical_flow_ilk(image0, image1, radius=5,
+                            gaussian=gaussian, prefilter=prefilter)
+
+    # Assert that the average absolute error is less then half a pixel
+    assert abs(flow - gt_flow).mean() < 0.5
+
+
+def test_no_motion_2d():
+    rnd = np.random.RandomState(0)
+    img = rnd.normal(size=(256, 256))
+    img = cp.asarray(img)
+
+    flow = optical_flow_ilk(img, img)
+
+    assert cp.all(flow == 0)
+
+
+def test_no_motion_3d():
+    rnd = np.random.RandomState(0)
+    img = rnd.normal(size=(64, 64, 64))
+    img = cp.asarray(img)
+
+    flow = optical_flow_ilk(img, img)
+
+    assert cp.all(flow == 0)
+
+
+def test_optical_flow_dtype():
+    # Generate synthetic data
+    rnd = np.random.RandomState(0)
+    image0 = rnd.normal(size=(256, 256))
+    image0 = cp.asarray(image0)
+    gt_flow, image1 = _sin_flow_gen(image0)
+    # Estimate the flow at double precision
+    flow_f64 = optical_flow_ilk(image0, image1, dtype='float64')
+
+    assert flow_f64.dtype == 'float64'
+
+    # Estimate the flow at single precision
+    flow_f32 = optical_flow_ilk(image0, image1, dtype='float32')
+
+    assert flow_f32.dtype == 'float32'
+
+    # Assert that floating point precision does not affect the quality
+    # of the estimated flow
+
+    assert cp.abs(flow_f64 - flow_f32).mean() < 1e-3
+
+
+def test_incompatible_shapes():
+    rnd = cp.random.RandomState(0)
+    I0 = rnd.normal(size=(256, 256))
+    I1 = rnd.normal(size=(255, 256))
+    with testing.raises(ValueError):
+        u, v = optical_flow_ilk(I0, I1)
+
+
+def test_wrong_dtype():
+    rnd = cp.random.RandomState(0)
+    img = rnd.normal(size=(256, 256))
+    with testing.raises(ValueError):
+        u, v = optical_flow_ilk(img, img, dtype='int')
diff --git a/python/cucim/src/cucim/skimage/registration/tests/test_masked_phase_cross_correlation.py b/python/cucim/src/cucim/skimage/registration/tests/test_masked_phase_cross_correlation.py
new file mode 100644
index 000000000..b225799ba
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/tests/test_masked_phase_cross_correlation.py
@@ -0,0 +1,302 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupyx.scipy.ndimage import fourier_shift
+from numpy.testing import assert_almost_equal
+from skimage.data import camera, stereo_motorcycle
+from skimage.io import imread
+
+from cucim.skimage._shared.fft import fftmodule as fft
+from cucim.skimage._shared.testing import expected_warnings, fetch
+from cucim.skimage.feature import masked_register_translation as _deprecated
+from cucim.skimage.registration._masked_phase_cross_correlation import \
+    _masked_phase_cross_correlation as masked_register_translation
+from cucim.skimage.registration._masked_phase_cross_correlation import \
+    cross_correlate_masked
+from cucim.skimage.registration._phase_cross_correlation import \
+    phase_cross_correlation
+
+
+def test_detrecated_masked_register_translation():
+    reference_image, moving_image, _ = stereo_motorcycle()
+    ref_mask = np.random.choice(
+        [True, False], reference_image.shape, p=[3 / 4, 1 / 4]
+    )
+    ref_mask = cp.asarray(ref_mask)
+    reference_image = cp.asarray(reference_image)
+    moving_image = cp.asarray(moving_image)
+    with expected_warnings(["Function ``masked_register_translation``"]):
+        cp.testing.assert_array_equal(
+            _deprecated(reference_image, moving_image, ref_mask),
+            phase_cross_correlation(
+                reference_image, moving_image, reference_mask=ref_mask
+            ),
+        )
+
+
+def test_masked_registration_vs_phase_cross_correlation():
+    """masked_register_translation should give the same results as
+    phase_cross_correlation in the case of trivial masks."""
+    reference_image = cp.array(camera())
+    shift = (-7, 12)
+    shifted = cp.real(fft.ifft2(fourier_shift(
+        fft.fft2(reference_image), shift)))
+    trivial_mask = cp.ones_like(reference_image)
+
+    nonmasked_result, *_ = phase_cross_correlation(reference_image, shifted)
+    masked_result = masked_register_translation(reference_image,
+                                                shifted,
+                                                reference_mask=trivial_mask,
+                                                overlap_ratio=1 / 10)
+
+    cp.testing.assert_array_equal(nonmasked_result, masked_result)
+
+
+def test_masked_registration_random_masks():
+    """masked_register_translation should be able to register translations
+    between images even with random masks."""
+    # See random number generator for reproducible results
+    np.random.seed(23)
+
+    reference_image = cp.array(camera())
+    shift = (-7, 12)
+    shifted = cp.real(fft.ifft2(fourier_shift(
+        fft.fft2(reference_image), shift)))
+
+    # Random masks with 75% of pixels being valid
+    ref_mask = np.random.choice(
+        [True, False], reference_image.shape, p=[3 / 4, 1 / 4])
+    shifted_mask = np.random.choice(
+        [True, False], shifted.shape, p=[3 / 4, 1 / 4])
+
+    ref_mask = cp.asarray(ref_mask)
+    shifted_mask = cp.asarray(shifted_mask)
+
+    measured_shift = masked_register_translation(reference_image,
+                                                 shifted,
+                                                 reference_mask=ref_mask,
+                                                 moving_mask=shifted_mask)
+
+    cp.testing.assert_array_equal(measured_shift, -cp.asarray(shift))
+
+
+def test_masked_registration_random_masks_non_equal_sizes():
+    """masked_register_translation should be able to register
+    translations between images that are not the same size even
+    with random masks."""
+    # See random number generator for reproducible results
+    np.random.seed(23)
+
+    reference_image = cp.array(camera())
+    shift = (-7, 12)
+    shifted = cp.real(fft.ifft2(fourier_shift(
+        fft.fft2(reference_image), shift)))
+
+    # Crop the shifted image
+    shifted = shifted[64:-64, 64:-64]
+
+    # Random masks with 75% of pixels being valid
+    ref_mask = np.random.choice(
+        [True, False], reference_image.shape, p=[3 / 4, 1 / 4])
+    shifted_mask = np.random.choice(
+        [True, False], shifted.shape, p=[3 / 4, 1 / 4])
+
+    reference_image = cp.asarray(reference_image)
+    shifted = cp.asarray(shifted)
+    measured_shift = masked_register_translation(
+        reference_image,
+        shifted,
+        reference_mask=cp.ones(ref_mask.shape, dtype=ref_mask.dtype),
+        moving_mask=cp.ones(shifted_mask.shape, dtype=shifted_mask.dtype))
+    cp.testing.assert_array_equal(measured_shift, -cp.asarray(shift))
+
+
+def test_masked_registration_padfield_data():
+    """Masked translation registration should behave like in the original
+    publication"""
+    # Test translated from MATLABimplementation `MaskedFFTRegistrationTest`
+    # file. You can find the source code here:
+    # http://www.dirkpadfield.com/Home/MaskedFFTRegistrationCode.zip
+
+    shifts = [(75, 75), (-130, 130), (130, 130)]
+    for xi, yi in shifts:
+
+        fixed_image = cp.array(imread(
+            fetch('registration/tests/data/OriginalX{:d}Y{:d}.png'
+                  ''.format(xi, yi))))
+        moving_image = cp.array(imread(
+            fetch('registration/tests/data/TransformedX{:d}Y{:d}.png'
+                  ''.format(xi, yi))))
+
+        # Valid pixels are 1
+        fixed_mask = fixed_image != 0
+        moving_mask = moving_image != 0
+
+        # Note that shifts in x and y and shifts in cols and rows
+        shift_y, shift_x = cp.asnumpy(masked_register_translation(
+            fixed_image, moving_image, reference_mask=fixed_mask,
+            moving_mask=moving_mask, overlap_ratio=0.1))
+        # Note: by looking at the test code from Padfield's
+        # MaskedFFTRegistrationCode repository, the
+        # shifts were not xi and yi, but xi and -yi
+        np.testing.assert_array_equal((shift_x, shift_y), (-xi, yi))
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_cross_correlate_masked_output_shape(dtype):
+    """Masked normalized cross-correlation should return a shape
+    of N + M + 1 for each transform axis."""
+    shape1 = (15, 4, 5)
+    shape2 = (6, 12, 7)
+    expected_full_shape = tuple(np.array(shape1) + np.array(shape2) - 1)
+    expected_same_shape = shape1
+
+    arr1 = cp.zeros(shape1, dtype=dtype)
+    arr2 = cp.zeros(shape2, dtype=dtype)
+    # Trivial masks
+    m1 = cp.ones_like(arr1)
+    m2 = cp.ones_like(arr2)
+
+    full_xcorr = cross_correlate_masked(
+        arr1, arr2, m1, m2, axes=(0, 1, 2), mode='full')
+    assert full_xcorr.dtype.kind != "c"  # grlee77: output should be real
+    assert full_xcorr.shape == expected_full_shape
+    assert full_xcorr.dtype == dtype
+
+    same_xcorr = cross_correlate_masked(
+        arr1, arr2, m1, m2, axes=(0, 1, 2), mode='same')
+    assert same_xcorr.shape == expected_same_shape
+    assert same_xcorr.dtype == dtype
+
+
+def test_cross_correlate_masked_test_against_mismatched_dimensions():
+    """Masked normalized cross-correlation should raise an error if array
+    dimensions along non-transformation axes are mismatched."""
+    shape1 = (23, 1, 1)
+    shape2 = (6, 2, 2)
+
+    arr1 = cp.zeros(shape1)
+    arr2 = cp.zeros(shape2)
+
+    # Trivial masks
+    m1 = cp.ones_like(arr1)
+    m2 = cp.ones_like(arr2)
+
+    with pytest.raises(ValueError):
+        cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
+
+
+def test_cross_correlate_masked_output_range():
+    """Masked normalized cross-correlation should return between 1 and -1."""
+    # See random number generator for reproducible results
+    np.random.seed(23)
+
+    # Array dimensions must match along non-transformation axes, in
+    # this case
+    # axis 0
+    shape1 = (15, 4, 5)
+    shape2 = (15, 12, 7)
+
+    # Initial array ranges between -5 and 5
+    arr1 = 10 * np.random.random(shape1) - 5
+    arr2 = 10 * np.random.random(shape2) - 5
+
+    # random masks
+    m1 = np.random.choice([True, False], arr1.shape)
+    m2 = np.random.choice([True, False], arr2.shape)
+
+    arr1 = cp.asarray(arr1)
+    arr2 = cp.asarray(arr2)
+    m1 = cp.asarray(m1)
+    m2 = cp.asarray(m2)
+    xcorr = cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
+
+    # No assert array less or equal, so we add an eps
+    # Also could not find an `assert_array_greater`, Use (-xcorr) instead
+    eps = np.finfo(float).eps
+    cp.testing.assert_array_less(xcorr, 1 + eps)
+    cp.testing.assert_array_less(-xcorr, 1 + eps)
+
+
+def test_cross_correlate_masked_side_effects():
+    """Masked normalized cross-correlation should not modify the inputs."""
+    shape1 = (2, 2, 2)
+    shape2 = (2, 2, 2)
+
+    arr1 = cp.zeros(shape1)
+    arr2 = cp.zeros(shape2)
+
+    # Trivial masks
+    m1 = cp.ones_like(arr1)
+    m2 = cp.ones_like(arr2)
+
+    # CuPy Backed: had to refactor (cannot set write=False)
+    # for arr in (arr1, arr2, m1, m2):
+    #    arr.setflags(write=False)
+    arr1c, arr2c, m1c, m2c = [a.copy() for a in (arr1, arr2, m1, m2)]
+
+    cross_correlate_masked(arr1, arr2, m1, m2)
+
+    cp.testing.assert_array_equal(arr1, arr1c)
+    cp.testing.assert_array_equal(arr2, arr2c)
+    cp.testing.assert_array_equal(m1, m1c)
+    cp.testing.assert_array_equal(m2, m2c)
+
+
+def test_cross_correlate_masked_over_axes():
+    """Masked normalized cross-correlation over axes should be
+    equivalent to a loop over non-transform axes."""
+    # See random number generator for reproducible results
+    np.random.seed(23)
+
+    arr1 = np.random.random((8, 8, 5))
+    arr2 = np.random.random((8, 8, 5))
+
+    m1 = np.random.choice([True, False], arr1.shape)
+    m2 = np.random.choice([True, False], arr2.shape)
+
+    arr1 = cp.asarray(arr1)
+    arr2 = cp.asarray(arr2)
+    m1 = cp.asarray(m1)
+    m2 = cp.asarray(m2)
+
+    # Loop over last axis
+    with_loop = cp.empty_like(arr1, dtype=np.complex128)
+    for index in range(arr1.shape[-1]):
+        with_loop[:, :, index] = cross_correlate_masked(arr1[:, :, index],
+                                                        arr2[:, :, index],
+                                                        m1[:, :, index],
+                                                        m2[:, :, index],
+                                                        axes=(0, 1),
+                                                        mode='same')
+
+    over_axes = cross_correlate_masked(
+        arr1, arr2, m1, m2, axes=(0, 1), mode='same')
+
+    cp.testing.assert_array_almost_equal(with_loop, over_axes)
+
+
+def test_cross_correlate_masked_autocorrelation_trivial_masks():
+    """Masked normalized cross-correlation between identical arrays
+    should reduce to an autocorrelation even with random masks."""
+    # See random number generator for reproducible results
+    np.random.seed(23)
+
+    arr1 = cp.asarray(camera())
+
+    # Random masks with 75% of pixels being valid
+    m1 = np.random.choice([True, False], arr1.shape, p=[3 / 4, 1 / 4])
+    m2 = np.random.choice([True, False], arr1.shape, p=[3 / 4, 1 / 4])
+    m1 = cp.asarray(m1)
+    m2 = cp.asarray(m2)
+
+    xcorr = cross_correlate_masked(arr1, arr1, m1, m2, axes=(0, 1),
+                                   mode='same', overlap_ratio=0).real
+    max_index = cp.unravel_index(cp.argmax(xcorr), xcorr.shape)
+    max_index = tuple(map(int, max_index))
+
+    # Autocorrelation should have maximum in center of array
+    # CuPy Backend: uint8 inputs will be processed in float32, so reduce
+    #               decimal to 5
+    assert_almost_equal(float(xcorr.max()), 1, decimal=5)
+    np.testing.assert_array_equal(max_index, np.array(arr1.shape) / 2)
diff --git a/python/cucim/src/cucim/skimage/registration/tests/test_phase_cross_correlation.py b/python/cucim/src/cucim/skimage/registration/tests/test_phase_cross_correlation.py
new file mode 100644
index 000000000..ee3319f82
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/tests/test_phase_cross_correlation.py
@@ -0,0 +1,150 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_allclose
+from cupyx.scipy.ndimage import fourier_shift
+from skimage.data import camera
+
+from cucim.skimage import img_as_float
+from cucim.skimage.data import binary_blobs
+from cucim.skimage._shared.fft import fftmodule as fft
+from cucim.skimage.registration._phase_cross_correlation import (
+    _upsampled_dft, phase_cross_correlation)
+
+
+def test_correlation():
+    reference_image = fft.fftn(cp.array(camera()))
+    shift = (-7, 12)
+    shifted_image = fourier_shift(reference_image, shift)
+
+    # pixel precision
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       space="fourier")
+    assert_allclose(result[:2], -cp.array(shift))
+
+
+def test_subpixel_precision():
+    reference_image = fft.fftn(cp.array(camera()))
+    subpixel_shift = (-2.4, 1.32)
+    shifted_image = fourier_shift(reference_image, subpixel_shift)
+
+    # subpixel precision
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       upsample_factor=100,
+                                                       space="fourier")
+    assert_allclose(result[:2], -cp.array(subpixel_shift), atol=0.05)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_real_input(dtype):
+    reference_image = cp.array(camera()).astype(dtype, copy=False)
+    subpixel_shift = (-2.4, 1.32)
+    shifted_image = fourier_shift(fft.fftn(reference_image), subpixel_shift)
+    shifted_image = fft.ifftn(shifted_image).real.astype(dtype, copy=False)
+
+    # subpixel precision
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       upsample_factor=100)
+    assert result.dtype == dtype
+    assert_allclose(result[:2], -cp.array(subpixel_shift), atol=0.05)
+
+
+def test_size_one_dimension_input():
+    # take a strip of the input image
+    reference_image = fft.fftn(cp.array(camera())[:, 15]).reshape((-1, 1))
+    subpixel_shift = (-2.4, 4)
+    shifted_image = fourier_shift(reference_image, subpixel_shift)
+
+    # subpixel precision
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       upsample_factor=20,
+                                                       space="fourier")
+    assert_allclose(result[:2], -cp.array((-2.4, 0)), atol=0.05)
+
+
+def test_3d_input():
+    phantom = img_as_float(binary_blobs(length=32, n_dim=3))
+    reference_image = fft.fftn(phantom)
+    shift = (-2.0, 1.0, 5.0)
+    shifted_image = fourier_shift(reference_image, shift)
+
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       space="fourier")
+    assert_allclose(result, -cp.array(shift), atol=0.05)
+
+    # subpixel precision now available for 3-D data
+
+    subpixel_shift = (-2.3, 1.7, 5.4)
+    shifted_image = fourier_shift(reference_image, subpixel_shift)
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       upsample_factor=100,
+                                                       space="fourier")
+    assert_allclose(result, -cp.array(subpixel_shift), atol=0.05)
+
+
+def test_unknown_space_input():
+    image = cp.ones((5, 5))
+    with pytest.raises(ValueError):
+        phase_cross_correlation(image, image, space="frank")
+
+
+def test_wrong_input():
+    # Dimensionality mismatch
+    image = cp.ones((5, 5, 1))
+    template = cp.ones((5, 5))
+    with pytest.raises(ValueError):
+        phase_cross_correlation(template, image)
+
+    # Size mismatch
+    image = cp.ones((5, 5))
+    template = cp.ones((4, 4))
+    with pytest.raises(ValueError):
+        phase_cross_correlation(template, image)
+
+    # NaN values in data
+    image = cp.ones((5, 5))
+    image[0][0] = cp.nan
+    template = cp.ones((5, 5))
+    with pytest.raises(ValueError):
+        phase_cross_correlation(template, image, return_error=True)
+
+
+def test_4d_input_pixel():
+    phantom = img_as_float(binary_blobs(length=32, n_dim=4))
+    reference_image = fft.fftn(phantom)
+    shift = (-2.0, 1.0, 5.0, -3)
+    shifted_image = fourier_shift(reference_image, shift)
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       space="fourier")
+    assert_allclose(result, -cp.array(shift), atol=0.05)
+
+
+def test_4d_input_subpixel():
+    phantom = img_as_float(binary_blobs(length=32, n_dim=4))
+    reference_image = fft.fftn(phantom)
+    subpixel_shift = (-2.3, 1.7, 5.4, -3.2)
+    shifted_image = fourier_shift(reference_image, subpixel_shift)
+    result, error, diffphase = phase_cross_correlation(reference_image,
+                                                       shifted_image,
+                                                       upsample_factor=10,
+                                                       space="fourier")
+    assert_allclose(result, -cp.array(subpixel_shift), atol=0.05)
+
+
+def test_mismatch_upsampled_region_size():
+    with pytest.raises(ValueError):
+        _upsampled_dft(
+            cp.ones((4, 4)),
+            upsampled_region_size=[3, 2, 1, 4])
+
+
+def test_mismatch_offsets_size():
+    with pytest.raises(ValueError):
+        _upsampled_dft(cp.ones((4, 4)), 3,
+                       axis_offsets=[3, 2, 1, 4])
diff --git a/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py b/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py
new file mode 100644
index 000000000..2027f4bf1
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/registration/tests/test_tvl1.py
@@ -0,0 +1,121 @@
+import cupy as cp
+import numpy as np
+import pytest
+
+from cucim.skimage.registration import optical_flow_tvl1
+from cucim.skimage.transform import warp
+
+
+def _sin_flow_gen(image0, max_motion=4.5, npics=5):
+    """Generate a synthetic ground truth optical flow with a sinusoid as
+      first component.
+
+    Parameters:
+    ----
+    image0: ndarray
+        The base image to be warped.
+    max_motion: float
+        Maximum flow magnitude.
+    npics: int
+        Number of sinusoid pics.
+
+    Returns
+    -------
+    flow, image1 : ndarray
+        The synthetic ground truth optical flow with a sinusoid as
+        first component and the corresponding warped image.
+
+    """
+    grid = cp.meshgrid(*[cp.arange(n) for n in image0.shape], indexing='ij')
+    grid = cp.stack(grid)
+    # TODO: make upstream scikit-image PR changing gt_flow dtype to float
+    gt_flow = cp.zeros_like(grid, dtype=float)
+    gt_flow[0, ...] = max_motion * cp.sin(
+        grid[0] / grid[0].max() * npics * np.pi
+    )
+    image1 = warp(image0, grid - gt_flow, mode="nearest")
+    return gt_flow, image1
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_2d_motion(dtype):
+    # Generate synthetic data
+    rnd = cp.random.RandomState(0)
+    image0 = rnd.normal(size=(256, 256)).astype(dtype)
+    gt_flow, image1 = _sin_flow_gen(image0)
+    image1 = image1.astype(dtype, copy=False)
+    # Estimate the flow
+    flow = optical_flow_tvl1(image0, image1, attachment=5, dtype=dtype)
+    assert flow.dtype == dtype
+    # Assert that the average absolute error is less then half a pixel
+    assert abs(flow - gt_flow).mean() < 0.5
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_3d_motion(dtype):
+    # Generate synthetic data
+    rnd = np.random.RandomState(0)
+    image0 = cp.array(rnd.normal(size=(100, 100, 100))).astype(dtype)
+    gt_flow, image1 = _sin_flow_gen(image0)
+    image1 = image1.astype(dtype, copy=False)
+    # Estimate the flow
+    # TODO: note: when changing _sin_flow_gen to use a float deformation field
+    #             had to increase attachment here from 5 to pass the tolerance.
+    flow = optical_flow_tvl1(image0, image1, attachment=10, dtype=dtype)
+    assert flow.dtype == dtype
+    # Assert that the average absolute error is less then half a pixel
+    assert abs(flow - gt_flow).mean() < 0.5
+
+
+def test_no_motion_2d():
+    rnd = cp.random.RandomState(0)
+    img = rnd.normal(size=(256, 256))
+
+    flow = optical_flow_tvl1(img, img)
+
+    assert cp.all(flow == 0)
+
+
+def test_no_motion_3d():
+    rnd = np.random.RandomState(0)
+    img = cp.array(rnd.normal(size=(64, 64, 64)))
+
+    flow = optical_flow_tvl1(img, img)
+
+    assert cp.all(flow == 0)
+
+
+def test_optical_flow_dtype():
+    # Generate synthetic data
+    rnd = cp.random.RandomState(0)
+    image0 = rnd.normal(size=(256, 256))
+    gt_flow, image1 = _sin_flow_gen(image0)
+    # Estimate the flow at double precision
+    flow_f64 = optical_flow_tvl1(image0, image1, attachment=5, dtype=np.float64)
+
+    assert flow_f64.dtype == np.float64
+
+    # Estimate the flow at single precision
+    flow_f32 = optical_flow_tvl1(image0, image1, attachment=5, dtype=np.float32)
+
+    assert flow_f32.dtype == np.float32
+
+    # Assert that floating point precision does not affect the quality
+    # of the estimated flow
+
+    assert cp.abs(flow_f64 - flow_f32).mean() < 1e-3
+
+
+def test_incompatible_shapes():
+    rnd = cp.random.RandomState(0)
+    I0 = rnd.normal(size=(256, 256))
+    I1 = rnd.normal(size=(128, 256))
+    with pytest.raises(ValueError):
+        u, v = optical_flow_tvl1(I0, I1)
+
+
+def test_wrong_dtype():
+    rnd = cp.random.RandomState(0)
+    img = rnd.normal(size=(256, 256))
+    with pytest.raises(ValueError):
+        u, v = optical_flow_tvl1(img, img, dtype=np.int64)
diff --git a/python/cucim/src/cucim/skimage/restoration/__init__.py b/python/cucim/src/cucim/skimage/restoration/__init__.py
new file mode 100644
index 000000000..aa0b63ecb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/__init__.py
@@ -0,0 +1,11 @@
+from ._denoise import denoise_tv_chambolle
+from .deconvolution import richardson_lucy, unsupervised_wiener, wiener
+from .j_invariant import calibrate_denoiser
+
+__all__ = [
+    "wiener",
+    "unsupervised_wiener",
+    "richardson_lucy",
+    "denoise_tv_chambolle",
+    "calibrate_denoiser",
+]
diff --git a/python/cucim/src/cucim/skimage/restoration/_denoise.py b/python/cucim/src/cucim/skimage/restoration/_denoise.py
new file mode 100644
index 000000000..516ba446f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/_denoise.py
@@ -0,0 +1,172 @@
+import cupy as cp
+
+from .. import img_as_float
+
+
+def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.0e-4, n_iter_max=200):
+    """Perform total-variation denoising on n-dimensional images.
+
+    Parameters
+    ----------
+    image : ndarray
+        n-D input data to be denoised.
+    weight : float, optional
+        Denoising weight. The greater `weight`, the more denoising (at
+        the expense of fidelity to `input`).
+    eps : float, optional
+        Relative difference of the value of the cost function that determines
+        the stop criterion. The algorithm stops when:
+
+            (E_(n-1) - E_n) < eps * E_0
+
+    n_iter_max : int, optional
+        Maximal number of iterations used for the optimization.
+
+    Returns
+    -------
+    out : ndarray
+        Denoised array of floats.
+
+    Notes
+    -----
+    Rudin, Osher and Fatemi algorithm.
+
+    """
+
+    ndim = image.ndim
+    p = cp.zeros((image.ndim,) + image.shape, dtype=image.dtype)
+    g = cp.zeros_like(p)
+    d = cp.zeros_like(image)
+    i = 0
+    slices_g = [slice(None)] * (ndim + 1)
+    slices_d = [slice(None)] * ndim
+    slices_p = [slice(None)] * (ndim + 1)
+    while i < n_iter_max:
+        if i > 0:
+            # d will be the (negative) divergence of p
+            d = -p.sum(0)
+            for ax in range(ndim):
+                slices_d[ax] = slice(1, None)
+                slices_p[ax + 1] = slice(0, -1)
+                slices_p[0] = ax
+                d[tuple(slices_d)] += p[tuple(slices_p)]
+                slices_d[ax] = slice(None)
+                slices_p[ax + 1] = slice(None)
+            out = image + d
+            E = (d * d).sum()
+        else:
+            out = image
+            E = 0.0
+
+        # g stores the gradients of out along each axis
+        # e.g. g[0] is the first order finite difference along axis 0
+        for ax in range(ndim):
+            slices_g[ax + 1] = slice(0, -1)
+            slices_g[0] = ax
+            g[tuple(slices_g)] = cp.diff(out, axis=ax)
+            slices_g[ax + 1] = slice(None)
+
+        norm = (g * g).sum(axis=0, keepdims=True)
+        cp.sqrt(norm, out=norm)
+        E += weight * norm.sum()
+        tau = 1.0 / (2.0 * ndim)
+        norm *= tau / weight
+        norm += 1.0
+        p -= tau * g
+        p /= norm
+        E /= float(image.size)
+        if i == 0:
+            E_init = E
+            E_previous = E
+        else:
+            if abs(E_previous - E) < eps * E_init:
+                break
+            else:
+                E_previous = E
+        i += 1
+    return out
+
+
+def denoise_tv_chambolle(image, weight=0.1, eps=2.0e-4, n_iter_max=200,
+                         multichannel=False):
+    """Perform total-variation denoising on n-dimensional images.
+
+    Parameters
+    ----------
+    image : ndarray of ints, uints or floats
+        Input data to be denoised. `image` can be of any numeric type,
+        but it is cast into an ndarray of floats for the computation
+        of the denoised image.
+    weight : float, optional
+        Denoising weight. The greater `weight`, the more denoising (at
+        the expense of fidelity to `input`).
+    eps : float, optional
+        Relative difference of the value of the cost function that
+        determines the stop criterion. The algorithm stops when:
+
+            (E_(n-1) - E_n) < eps * E_0
+
+    n_iter_max : int, optional
+        Maximal number of iterations used for the optimization.
+    multichannel : bool, optional
+        Apply total-variation denoising separately for each channel. This
+        option should be true for color images, otherwise the denoising is
+        also applied in the channels dimension.
+
+    Returns
+    -------
+    out : ndarray
+        Denoised image.
+
+    Notes
+    -----
+    Make sure to set the multichannel parameter appropriately for color images.
+
+    The principle of total variation denoising is explained in
+    https://en.wikipedia.org/wiki/Total_variation_denoising
+
+    The principle of total variation denoising is to minimize the
+    total variation of the image, which can be roughly described as
+    the integral of the norm of the image gradient. Total variation
+    denoising tends to produce "cartoon-like" images, that is,
+    piecewise-constant images.
+
+    This code is an implementation of the algorithm of Rudin, Fatemi and Osher
+    that was proposed by Chambolle in [1]_.
+
+    References
+    ----------
+    .. [1] A. Chambolle, An algorithm for total variation minimization and
+           applications, Journal of Mathematical Imaging and Vision,
+           Springer, 2004, 20, 89-97.
+
+    Examples
+    --------
+    2D example on astronaut image:
+
+    >>> from skimage import color, data
+    >>> img = color.rgb2gray(data.astronaut())[:50, :50]
+    >>> img += 0.5 * img.std() * np.random.randn(*img.shape)
+    >>> denoised_img = denoise_tv_chambolle(img, weight=60)
+
+    3D example on synthetic data:
+
+    >>> x, y, z = np.ogrid[0:20, 0:20, 0:20]
+    >>> mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
+    >>> mask = mask.astype(np.float)
+    >>> mask += 0.2*np.random.randn(*mask.shape)
+    >>> res = denoise_tv_chambolle(mask, weight=100)
+
+    """
+    im_type = image.dtype
+    if not im_type.kind == 'f':
+        image = img_as_float(image)
+
+    if multichannel:
+        out = cp.zeros_like(image)
+        for c in range(image.shape[-1]):
+            out[..., c] = _denoise_tv_chambolle_nd(image[..., c], weight, eps,
+                                                   n_iter_max)
+    else:
+        out = _denoise_tv_chambolle_nd(image, weight, eps, n_iter_max)
+    return out
diff --git a/python/cucim/src/cucim/skimage/restoration/deconvolution.py b/python/cucim/src/cucim/skimage/restoration/deconvolution.py
new file mode 100644
index 000000000..2427017c4
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/deconvolution.py
@@ -0,0 +1,434 @@
+"""Implementations restoration functions"""
+
+import math
+
+import cupy as cp
+import cupy.random as npr
+import numpy as np
+
+# TODO: use cupyx.scipy.signal once upstream fftconvolve and
+#       choose_conv_method for > 1d has been implemented.
+import cucim.skimage._vendored
+
+from . import uft
+
+# from cupyx.scipy.signal import convolve
+# from cupyx.scipy import signal
+
+
+signal = cucim.skimage._vendored
+
+
+__keywords__ = "restoration, image, deconvolution"
+
+
+def _float_dtype(image):
+    if image.dtype.kind != 'f':
+        return cp.float64
+    return cp.result_type(image.dtype, cp.float32)
+
+
+def wiener(image, psf, balance, reg=None, is_real=True, clip=True):
+    r"""Wiener-Hunt deconvolution
+
+    Return the deconvolution with a Wiener-Hunt approach (i.e. with
+    Fourier diagonalisation).
+
+    Parameters
+    ----------
+    image : (M, N) ndarray
+       Input degraded image
+    psf : ndarray
+       Point Spread Function. This is assumed to be the impulse
+       response (input image space) if the data-type is real, or the
+       transfer function (Fourier space) if the data-type is
+       complex. There is no constraints on the shape of the impulse
+       response. The transfer function must be of shape `(M, N)` if
+       `is_real is True`, `(M, N // 2 + 1)` otherwise (see
+       `cupy.fft.rfftn`).
+    balance : float
+       The regularisation parameter value that tunes the balance
+       between the data adequacy that improve frequency restoration
+       and the prior adequacy that reduce frequency restoration (to
+       avoid noise artifacts).
+    reg : ndarray, optional
+       The regularisation operator. The Laplacian by default. It can
+       be an impulse response or a transfer function, as for the
+       psf. Shape constraint is the same as for the `psf` parameter.
+    is_real : boolean, optional
+       True by default. Specify if ``psf`` and ``reg`` are provided
+       with hermitian hypothesis, that is only half of the frequency
+       plane is provided (due to the redundancy of Fourier transform
+       of real signal). It's apply only if ``psf`` and/or ``reg`` are
+       provided as transfer function.  For the hermitian property see
+       ``uft`` module or ``cupy.fft.rfftn``.
+    clip : boolean, optional
+       True by default. If True, pixel values of the result above 1 or
+       under -1 are thresholded for skimage pipeline compatibility.
+
+    Returns
+    -------
+    im_deconv : (M, N) ndarray
+       The deconvolved image.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import color, data, restoration
+    >>> img = color.rgb2gray(data.astronaut())
+    >>> from scipy.signal import convolve2d
+    >>> psf = cp.ones((5, 5)) / 25
+    >>> img = convolve2d(img, psf, 'same')
+    >>> img += 0.1 * img.std() * cp.random.standard_normal(img.shape)
+    >>> deconvolved_img = restoration.wiener(img, psf, 1100)
+
+    Notes
+    -----
+    This function applies the Wiener filter to a noisy and degraded
+    image by an impulse response (or PSF). If the data model is
+
+    .. math:: y = Hx + n
+
+    where :math:`n` is noise, :math:`H` the PSF and :math:`x` the
+    unknown original image, the Wiener filter is
+
+    .. math::
+       \hat x = F^\dagger (|\Lambda_H|^2 + \lambda |\Lambda_D|^2)
+       \Lambda_H^\dagger F y
+
+    where :math:`F` and :math:`F^\dagger` are the Fourier and inverse
+    Fourier transforms respectively, :math:`\Lambda_H` the transfer
+    function (or the Fourier transform of the PSF, see [Hunt] below)
+    and :math:`\Lambda_D` the filter to penalize the restored image
+    frequencies (Laplacian by default, that is penalization of high
+    frequency). The parameter :math:`\lambda` tunes the balance
+    between the data (that tends to increase high frequency, even
+    those coming from noise), and the regularization.
+
+    These methods are then specific to a prior model. Consequently,
+    the application or the true image nature must corresponds to the
+    prior model. By default, the prior model (Laplacian) introduce
+    image smoothness or pixel correlation. It can also be interpreted
+    as high-frequency penalization to compensate the instability of
+    the solution with respect to the data (sometimes called noise
+    amplification or "explosive" solution).
+
+    Finally, the use of Fourier space implies a circulant property of
+    :math:`H`, see [Hunt].
+
+    References
+    ----------
+    .. [1] François Orieux, Jean-François Giovannelli, and Thomas
+           Rodet, "Bayesian estimation of regularization and point
+           spread function parameters for Wiener-Hunt deconvolution",
+           J. Opt. Soc. Am. A 27, 1593-1607 (2010)
+
+           https://www.osapublishing.org/josaa/abstract.cfm?URI=josaa-27-7-1593
+
+           http://research.orieux.fr/files/papers/OGR-JOSA10.pdf
+
+    .. [2] B. R. Hunt "A matrix theory proof of the discrete
+           convolution theorem", IEEE Trans. on Audio and
+           Electroacoustics, vol. au-19, no. 4, pp. 285-288, dec. 1971
+    """
+    if reg is None:
+        reg, _ = uft.laplacian(image.ndim, image.shape, is_real=is_real)
+    if not cp.iscomplexobj(reg):
+        reg = uft.ir2tf(reg, image.shape, is_real=is_real)
+    float_dtype = _float_dtype(image)
+    image = image.astype(float_dtype, copy=False)
+    psf = psf.real.astype(float_dtype, copy=False)
+    reg = reg.real.astype(float_dtype, copy=False)
+
+    if psf.shape != reg.shape:
+        trans_func = uft.ir2tf(psf, image.shape, is_real=is_real)
+    else:
+        trans_func = psf
+
+    atf2 = cp.abs(trans_func)
+    atf2 *= atf2
+    areg2 = cp.abs(reg)
+    areg2 *= areg2
+    wiener_filter = cp.conj(trans_func) / (atf2 + balance * areg2)
+    if is_real:
+        deconv = uft.uirfft2(wiener_filter * uft.urfft2(image),
+                             shape=image.shape)
+    else:
+        deconv = uft.uifft2(wiener_filter * uft.ufft2(image))
+
+    if clip:
+        deconv[deconv > 1] = 1
+        deconv[deconv < -1] = -1
+
+    return deconv
+
+
+def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True,
+                        clip=True):
+    """Unsupervised Wiener-Hunt deconvolution.
+
+    Return the deconvolution with a Wiener-Hunt approach, where the
+    hyperparameters are automatically estimated. The algorithm is a
+    stochastic iterative process (Gibbs sampler) described in the
+    reference below. See also ``wiener`` function.
+
+    Parameters
+    ----------
+    image : (M, N) ndarray
+       The input degraded image.
+    psf : ndarray
+       The impulse response (input image's space) or the transfer
+       function (Fourier space). Both are accepted. The transfer
+       function is automatically recognized as being complex
+       (``cupy.iscomplexobj(psf)``).
+    reg : ndarray, optional
+       The regularisation operator. The Laplacian by default. It can
+       be an impulse response or a transfer function, as for the psf.
+    user_params : dict, optional
+       Dictionary of parameters for the Gibbs sampler. See below.
+    clip : boolean, optional
+       True by default. If true, pixel values of the result above 1 or
+       under -1 are thresholded for skimage pipeline compatibility.
+
+    Returns
+    -------
+    x_postmean : (M, N) ndarray
+       The deconvolved image (the posterior mean).
+    chains : dict
+       The keys ``noise`` and ``prior`` contain the chain list of
+       noise and prior precision respectively.
+
+    Other parameters
+    ----------------
+    The keys of ``user_params`` are:
+
+    threshold : float
+       The stopping criterion: the norm of the difference between to
+       successive approximated solution (empirical mean of object
+       samples, see Notes section). 1e-4 by default.
+    burnin : int
+       The number of sample to ignore to start computation of the
+       mean. 15 by default.
+    min_iter : int
+       The minimum number of iterations. 30 by default.
+    max_iter : int
+       The maximum number of iterations if ``threshold`` is not
+       satisfied. 200 by default.
+    callback : callable (None by default)
+       A user provided callable to which is passed, if the function
+       exists, the current image sample for whatever purpose. The user
+       can store the sample, or compute other moments than the
+       mean. It has no influence on the algorithm execution and is
+       only for inspection.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from skimage import color, data, restoration
+    >>> img = color.rgb2gray(data.astronaut())
+    >>> from scipy.signal import convolve2d
+    >>> psf = cp.ones((5, 5)) / 25
+    >>> img = convolve2d(img, psf, 'same')
+    >>> img += 0.1 * img.std() * cp.random.standard_normal(img.shape)
+    >>> deconvolved_img = restoration.unsupervised_wiener(img, psf)
+
+    Notes
+    -----
+    The estimated image is design as the posterior mean of a
+    probability law (from a Bayesian analysis). The mean is defined as
+    a sum over all the possible images weighted by their respective
+    probability. Given the size of the problem, the exact sum is not
+    tractable. This algorithm use of MCMC to draw image under the
+    posterior law. The practical idea is to only draw highly probable
+    images since they have the biggest contribution to the mean. At the
+    opposite, the less probable images are drawn less often since
+    their contribution is low. Finally the empirical mean of these
+    samples give us an estimation of the mean, and an exact
+    computation with an infinite sample set.
+
+    References
+    ----------
+    .. [1] François Orieux, Jean-François Giovannelli, and Thomas
+           Rodet, "Bayesian estimation of regularization and point
+           spread function parameters for Wiener-Hunt deconvolution",
+           J. Opt. Soc. Am. A 27, 1593-1607 (2010)
+
+           https://www.osapublishing.org/josaa/abstract.cfm?URI=josaa-27-7-1593
+
+           http://research.orieux.fr/files/papers/OGR-JOSA10.pdf
+    """
+    params = {'threshold': 1e-4, 'max_iter': 200,
+              'min_iter': 30, 'burnin': 15, 'callback': None}
+    params.update(user_params or {})
+
+    if reg is None:
+        reg, _ = uft.laplacian(image.ndim, image.shape, is_real=is_real)
+    if not cp.iscomplexobj(reg):
+        reg = uft.ir2tf(reg, image.shape, is_real=is_real)
+    float_dtype = _float_dtype(image)
+    image = image.astype(float_dtype, copy=False)
+    psf = psf.real.astype(float_dtype, copy=False)
+    reg = reg.real.astype(float_dtype, copy=False)
+
+    if psf.shape != reg.shape:
+        trans_fct = uft.ir2tf(psf, image.shape, is_real=is_real)
+    else:
+        trans_fct = psf
+
+    # The mean of the object
+    x_postmean = cp.zeros(trans_fct.shape, dtype=float_dtype)
+    # The previous computed mean in the iterative loop
+    prev_x_postmean = cp.zeros(trans_fct.shape, dtype=float_dtype)
+
+    # Difference between two successive mean
+    delta = np.NAN
+
+    # Initial state of the chain
+    gn_chain, gx_chain = [1], [1]
+
+    # The correlation of the object in Fourier space (if size is big,
+    # this can reduce computation time in the loop)
+    areg2 = cp.abs(reg)
+    areg2 *= areg2
+    atf2 = cp.abs(trans_fct)
+    atf2 *= atf2
+
+    # The Fourier transform may change the image.size attribute, so we
+    # store it.
+    if is_real:
+        data_spectrum = uft.urfft2(image)
+    else:
+        data_spectrum = uft.ufft2(image)
+    # Gibbs sampling
+    for iteration in range(params["max_iter"]):
+        # Sample of Eq. 27 p(circX^k | gn^k-1, gx^k-1, y).
+
+        # weighting (correlation in direct space)
+        precision = gn_chain[-1] * atf2 + gx_chain[-1] * areg2  # Eq. 29
+        excursion = (
+            math.sqrt(0.5)
+            / cp.sqrt(precision)
+            * (
+                cp.random.standard_normal(
+                    data_spectrum.shape).astype(float_dtype, copy=False)
+                + 1j * cp.random.standard_normal(
+                    data_spectrum.shape).astype(float_dtype, copy=False)
+            )
+        )
+
+        # mean Eq. 30 (RLS for fixed gn, gamma0 and gamma1 ...)
+        wiener_filter = gn_chain[-1] * cp.conj(trans_fct) / precision
+
+        # sample of X in Fourier space
+        x_sample = wiener_filter * data_spectrum + excursion
+        if params["callback"]:
+            params["callback"](x_sample)
+
+        # sample of Eq. 31 p(gn | x^k, gx^k, y)
+        gn_chain.append(
+            npr.gamma(
+                image.size / 2,
+                2 / uft.image_quad_norm(data_spectrum -
+                                        x_sample *
+                                        trans_fct)
+            ).astype(float_dtype, copy=False)
+        )
+
+        # sample of Eq. 31 p(gx | x^k, gn^k-1, y)
+        gx_chain.append(
+            npr.gamma(
+                (image.size - 1) / 2,
+                2 / uft.image_quad_norm(x_sample * reg)
+            ).astype(float_dtype, copy=False)
+        )
+
+        # current empirical average
+        if iteration > params['burnin']:
+            x_postmean = prev_x_postmean + x_sample
+
+        if iteration > (params['burnin'] + 1):
+            current = x_postmean / (iteration - params['burnin'])
+            previous = prev_x_postmean / (iteration - params['burnin'] - 1)
+            delta = cp.sum(cp.abs(current - previous)) / \
+                cp.sum(cp.abs(x_postmean)) / (iteration - params['burnin'])
+
+        prev_x_postmean = x_postmean
+
+        # stop of the algorithm
+        if (iteration > params['min_iter']) and (delta < params['threshold']):
+            break
+
+    # Empirical average \approx POSTMEAN Eq. 44
+    x_postmean = x_postmean / (iteration - params['burnin'])
+    if is_real:
+        x_postmean = uft.uirfft2(x_postmean, shape=image.shape)
+    else:
+        x_postmean = uft.uifft2(x_postmean)
+
+    if clip:
+        x_postmean[x_postmean > 1] = 1
+        x_postmean[x_postmean < -1] = -1
+
+    return (x_postmean, {'noise': gn_chain, 'prior': gx_chain})
+
+
+def richardson_lucy(image, psf, iterations=50, clip=True,
+                    filter_epsilon=None):
+    """Richardson-Lucy deconvolution.
+
+    Parameters
+    ----------
+    image : ndarray
+       Input degraded image (can be N dimensional).
+    psf : ndarray
+       The point spread function.
+    iterations : int, optional
+       Number of iterations. This parameter plays the role of
+       regularisation.
+    clip : boolean, optional
+       True by default. If true, pixel value of the result above 1 or
+       under -1 are thresholded for skimage pipeline compatibility.
+    filter_epsilon: float, optional
+       Value below which intermediate results become 0 to avoid division
+       by small numbers.
+
+    Returns
+    -------
+    im_deconv : ndarray
+       The deconvolved image.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import img_as_float, restoration
+    >>> from skimage import data
+    >>> camera = cp.asarray(img_as_float(data.camera()))
+    >>> from cupyx.scipy.signal import convolve2d
+    >>> psf = cp.ones((5, 5)) / 25
+    >>> camera = convolve2d(camera, psf, 'same')
+    >>> camera += 0.1 * camera.std() * cp.random.standard_normal(camera.shape)
+    >>> deconvolved = restoration.richardson_lucy(camera, psf, 5)
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Richardson%E2%80%93Lucy_deconvolution
+    """
+    float_type = _float_dtype(image)
+    image = image.astype(float_type, copy=False)
+    psf = psf.astype(float_type, copy=False)
+    im_deconv = cp.full(image.shape, 0.5, dtype=float_type)
+    psf_mirror = cp.ascontiguousarray(psf[::-1, ::-1])
+
+    for _ in range(iterations):
+        conv = signal.convolve(im_deconv, psf, mode='same')
+        if filter_epsilon:
+            relative_blur = cp.where(conv < filter_epsilon, 0, image / conv)
+        else:
+            relative_blur = image / conv
+        im_deconv *= signal.convolve(relative_blur, psf_mirror, mode='same')
+
+    if clip:
+        im_deconv[im_deconv > 1] = 1
+        im_deconv[im_deconv < -1] = -1
+
+    return im_deconv
diff --git a/python/cucim/src/cucim/skimage/restoration/j_invariant.py b/python/cucim/src/cucim/skimage/restoration/j_invariant.py
new file mode 100644
index 000000000..7e00312a8
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/j_invariant.py
@@ -0,0 +1,319 @@
+import functools
+import itertools
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from ..metrics import mean_squared_error
+from ..util import img_as_float
+
+
+def _interpolate_image(image, *, multichannel=False):
+    """Replacing each pixel in ``image`` with the average of its neighbors.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input data to be interpolated.
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+
+    Returns
+    -------
+    interp : ndarray
+        Interpolated version of `image`.
+    """
+    spatialdims = image.ndim if not multichannel else image.ndim - 1
+    conv_filter = ndi.generate_binary_structure(spatialdims, 1).astype(
+        image.dtype
+    )
+    conv_filter.ravel()[conv_filter.size // 2] = 0
+    conv_filter /= conv_filter.sum()
+
+    # CuPy Backend: refactored below to avoid for loop
+    if multichannel:
+        conv_filter = conv_filter[..., np.newaxis]
+    interp = ndi.convolve(image, conv_filter, mode='mirror')
+    return interp
+
+
+def _generate_grid_slice(shape, *, offset, stride=3):
+    """Generate slices of uniformly-spaced points in an array.
+
+    Parameters
+    ----------
+    shape : tuple of int
+        Shape of the mask.
+    offset : int
+        The offset of the grid of ones. Iterating over ``offset`` will cover
+        the entire array. It should be between 0 and ``stride ** ndim``, not
+        inclusive, where ``ndim = len(shape)``.
+    stride : int, optional
+        The spacing between ones, used in each dimension.
+
+    Returns
+    -------
+    mask : ndarray
+        The mask.
+
+    Examples
+    --------
+    >>> shape = (4, 4)
+    >>> array = cp.zeros(shape, dtype=int)
+    >>> grid_slice = _generate_grid_slice(shape, offset=0, stride=2)
+    >>> array[grid_slice] = 1
+    >>> print(array)
+    [[1 0 1 0]
+     [0 0 0 0]
+     [1 0 1 0]
+     [0 0 0 0]]
+
+    Changing the offset moves the location of the 1s:
+
+    >>> array = cp.zeros(shape, dtype=int)
+    >>> grid_slice = _generate_grid_slice(shape, offset=3, stride=2)
+    >>> array[grid_slice] = 1
+    >>> print(array)
+    [[0 0 0 0]
+     [0 1 0 1]
+     [0 0 0 0]
+     [0 1 0 1]]
+    """
+    phases = np.unravel_index(offset, (stride,) * len(shape))
+    mask = tuple(slice(p, None, stride) for p in phases)
+
+    return mask
+
+
+def _invariant_denoise(image, denoise_function, *, stride=4,
+                       masks=None, denoiser_kwargs=None):
+    """Apply a J-invariant version of `denoise_function`.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input data to be denoised (converted using `img_as_float`).
+    denoise_function : function
+        Original denoising function.
+    stride : int, optional
+        Stride used in masking procedure that converts `denoise_function`
+        to J-invariance.
+    masks : list of ndarray, optional
+        Set of masks to use for computing J-invariant output. If `None`,
+        a full set of masks covering the image will be used.
+    denoiser_kwargs:
+        Keyword arguments passed to `denoise_function`.
+
+    Returns
+    -------
+    output : ndarray
+        Denoised image, of same shape as `image`.
+    """
+    image = img_as_float(image)
+    if denoiser_kwargs is None:
+        denoiser_kwargs = {}
+
+    if 'multichannel' in denoiser_kwargs:
+        multichannel = denoiser_kwargs['multichannel']
+    else:
+        multichannel = False
+    interp = _interpolate_image(image, multichannel=multichannel)
+    output = cp.zeros_like(image)
+
+    if masks is None:
+        spatialdims = image.ndim if not multichannel else image.ndim - 1
+        n_masks = stride ** spatialdims
+        masks = (_generate_grid_slice(image.shape[:spatialdims],
+                                      offset=idx, stride=stride)
+                 for idx in range(n_masks))
+
+    for mask in masks:
+        input_image = image.copy()
+        input_image[mask] = interp[mask]
+        output[mask] = denoise_function(input_image, **denoiser_kwargs)[mask]
+    return output
+
+
+def _product_from_dict(dictionary):
+    """Utility function to convert parameter ranges to parameter combinations.
+
+    Converts a dict of lists into a list of dicts whose values consist of the
+    cartesian product of the values in the original dict.
+
+    Parameters
+    ----------
+    dictionary : dict of lists
+        Dictionary of lists to be multiplied.
+
+    Yields
+    ------
+    selections : dicts of values
+        Dicts containing individual combinations of the values in the input
+        dict.
+    """
+    keys = dictionary.keys()
+    for element in itertools.product(*dictionary.values()):
+        yield dict(zip(keys, element))
+
+
+def calibrate_denoiser(image, denoise_function, denoise_parameters, *,
+                       stride=4, approximate_loss=True,
+                       extra_output=False):
+    """Calibrate a denoising function and return optimal J-invariant version.
+
+    The returned function is partially evaluated with optimal parameter values
+    set for denoising the input image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input data to be denoised (converted using `img_as_float`).
+    denoise_function : function
+        Denoising function to be calibrated.
+    denoise_parameters : dict of list
+        Ranges of parameters for `denoise_function` to be calibrated over.
+    stride : int, optional
+        Stride used in masking procedure that converts `denoise_function`
+        to J-invariance.
+    approximate_loss : bool, optional
+        Whether to approximate the self-supervised loss used to evaluate the
+        denoiser by only computing it on one masked version of the image.
+        If False, the runtime will be a factor of `stride**image.ndim` longer.
+    extra_output : bool, optional
+        If True, return parameters and losses in addition to the calibrated
+        denoising function
+
+    Returns
+    -------
+    best_denoise_function : function
+        The optimal J-invariant version of `denoise_function`.
+
+    If `extra_output` is True, the following tuple is also returned:
+
+    (parameters_tested, losses) : tuple (list of dict, list of int)
+        List of parameters tested for `denoise_function`, as a dictionary of
+        kwargs
+        Self-supervised loss for each set of parameters in `parameters_tested`.
+
+
+    Notes
+    -----
+
+    The calibration procedure uses a self-supervised mean-square-error loss
+    to evaluate the performance of J-invariant versions of `denoise_function`.
+    The minimizer of the self-supervised loss is also the minimizer of the
+    ground-truth loss (i.e., the true MSE error) [1]. The returned function
+    can be used on the original noisy image, or other images with similar
+    characteristics.
+
+    Increasing the stride increases the performance of `best_denoise_function`
+     at the expense of increasing its runtime. It has no effect on the runtime
+     of the calibration.
+
+    References
+    ----------
+    .. [1] J. Batson & L. Royer. Noise2Self: Blind Denoising by Self-Supervision,
+           International Conference on Machine Learning, p. 524-533 (2019).
+
+    Examples
+    --------
+
+    >>> import cupy as cp
+    >>> from cucim.skimage import color
+    >>> from skimage import data
+    >>> from cucim.skimage.restoration import (denoise_tv_chambolle,
+    ...                                          calibrate_denoiser)
+    >>> img = color.rgb2gray(cp.array(data.astronaut()[:50, :50]))
+    >>> noisy = img + 0.5 * img.std() * cp.random.randn(*img.shape)
+    >>> parameters = {'weight': cp.arange(0.01, 0.5, 0.05)}
+    >>> denoising_function = calibrate_denoiser(noisy, denoise_tv_chambolle,
+    ...                                         denoise_parameters=parameters)
+    >>> denoised_img = denoising_function(img)
+
+    """  # noqa
+    parameters_tested, losses = _calibrate_denoiser_search(
+        image, denoise_function,
+        denoise_parameters=denoise_parameters,
+        stride=stride,
+        approximate_loss=approximate_loss
+    )
+
+    idx = np.argmin(losses)
+    best_parameters = parameters_tested[idx]
+
+    best_denoise_function = functools.partial(
+        _invariant_denoise,
+        denoise_function=denoise_function,
+        stride=stride,
+        denoiser_kwargs=best_parameters,
+    )
+
+    if extra_output:
+        return best_denoise_function, (parameters_tested, losses)
+    else:
+        return best_denoise_function
+
+
+def _calibrate_denoiser_search(image, denoise_function, denoise_parameters, *,
+                               stride=4, approximate_loss=True):
+    """Return a parameter search history with losses for a denoise function.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input data to be denoised (converted using `img_as_float`).
+    denoise_function : function
+        Denoising function to be calibrated.
+    denoise_parameters : dict of list
+        Ranges of parameters for `denoise_function` to be calibrated over.
+    stride : int, optional
+        Stride used in masking procedure that converts `denoise_function`
+        to J-invariance.
+    approximate_loss : bool, optional
+        Whether to approximate the self-supervised loss used to evaluate the
+        denoiser by only computing it on one masked version of the image.
+        If False, the runtime will be a factor of `stride**image.ndim` longer.
+
+    Returns
+    -------
+    parameters_tested : list of dict
+        List of parameters tested for `denoise_function`, as a dictionary of
+        kwargs.
+    losses : list of int
+        Self-supervised loss for each set of parameters in `parameters_tested`.
+    """
+    image = img_as_float(image)
+    parameters_tested = list(_product_from_dict(denoise_parameters))
+    losses = []
+
+    for denoiser_kwargs in parameters_tested:
+        if 'multichannel' in denoiser_kwargs:
+            multichannel = denoiser_kwargs['multichannel']
+        else:
+            multichannel = False
+        if not approximate_loss:
+            denoised = _invariant_denoise(
+                image, denoise_function,
+                stride=stride,
+                denoiser_kwargs=denoiser_kwargs
+            )
+            loss = mean_squared_error(image, denoised)
+        else:
+            spatialdims = image.ndim if not multichannel else image.ndim - 1
+            n_masks = stride ** spatialdims
+            mask = _generate_grid_slice(image.shape[:spatialdims],
+                                        offset=n_masks // 2, stride=stride)
+
+            masked_denoised = _invariant_denoise(
+                image, denoise_function,
+                masks=[mask],
+                denoiser_kwargs=denoiser_kwargs
+            )
+
+            loss = mean_squared_error(image[mask], masked_denoised[mask])
+
+        losses.append(float(loss))
+
+    return parameters_tested, losses
diff --git a/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py b/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py
new file mode 100644
index 000000000..e159941d4
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/tests/test_denoise.py
@@ -0,0 +1,130 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_equal
+from scipy import ndimage as ndi
+from skimage import color, data, img_as_float
+
+from cucim.skimage import restoration
+from cucim.skimage.metrics import structural_similarity
+
+cp.random.seed(1234)
+
+
+astro = img_as_float(data.astronaut()[:128, :128])
+astro_gray = color.rgb2gray(astro)
+checkerboard_gray = img_as_float(data.checkerboard())
+checkerboard = color.gray2rgb(checkerboard_gray)
+# versions with one odd-sized dimension
+astro_gray_odd = astro_gray[:, :-1]
+astro_odd = astro[:, :-1]
+
+# transfer test images to the GPU
+astro = cp.asarray(astro)
+astro_gray = cp.asarray(astro_gray)
+astro_gray_odd = cp.asarray(astro_gray_odd)
+astro_odd = cp.asarray(astro_odd)
+checkerboard = cp.asarray(checkerboard)
+checkerboard_gray = cp.asarray(checkerboard_gray)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_denoise_tv_chambolle_2d(dtype):
+    # astronaut image
+    img = astro_gray.astype(dtype, copy=True)
+    # add noise to astronaut
+    img += 0.5 * img.std() * cp.random.rand(*img.shape)
+    # clip noise so that it does not exceed allowed range for float images.
+    img = cp.clip(img, 0, 1)
+    # denoise
+    denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1)
+    # which dtype?
+    assert denoised_astro.dtype == dtype
+
+    # TODO: remove device to host transfers if cuda
+    #       morphological_gradient is implemented
+    grad = ndi.morphological_gradient(cp.asnumpy(img), size=((3, 3)))
+    grad_denoised = ndi.morphological_gradient(
+        cp.asnumpy(denoised_astro), size=((3, 3)))
+    # test if the total variation has decreased
+    assert grad_denoised.dtype == dtype
+    assert np.sqrt((grad_denoised ** 2).sum()) < np.sqrt((grad ** 2).sum())
+
+
+def test_denoise_tv_chambolle_multichannel():
+    denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1)
+    denoised = restoration.denoise_tv_chambolle(astro, weight=0.1,
+                                                multichannel=True)
+    assert_array_equal(denoised[..., 0], denoised0)
+
+    # tile astronaut subset to generate 3D+channels data
+    astro3 = cp.tile(astro[:64, :64, cp.newaxis, :], [1, 1, 2, 1])
+    # modify along tiled dimension to give non-zero gradient on 3rd axis
+    astro3[:, :, 0, :] = 2 * astro3[:, :, 0, :]
+    denoised0 = restoration.denoise_tv_chambolle(astro3[..., 0], weight=0.1)
+    denoised = restoration.denoise_tv_chambolle(astro3, weight=0.1,
+                                                multichannel=True)
+    assert_array_equal(denoised[..., 0], denoised0)
+
+
+def test_denoise_tv_chambolle_float_result_range():
+    # astronaut image
+    img = astro_gray
+    int_astro = cp.multiply(img, 255).astype(np.uint8)
+    assert cp.max(int_astro) > 1
+    denoised_int_astro = restoration.denoise_tv_chambolle(int_astro,
+                                                          weight=0.1)
+    # test if the value range of output float data is within [0.0:1.0]
+    assert denoised_int_astro.dtype == float
+    assert cp.max(denoised_int_astro) <= 1.0
+    assert cp.min(denoised_int_astro) >= 0.0
+
+
+def test_denoise_tv_chambolle_3d():
+    """Apply the TV denoising algorithm on a 3D image representing a sphere."""
+    x, y, z = cp.ogrid[0:40, 0:40, 0:40]
+    mask = (x - 22) ** 2 + (y - 20) ** 2 + (z - 17) ** 2 < 8 ** 2
+    mask = 100 * mask.astype(float)
+    mask += 60
+    mask += 20 * cp.random.rand(*mask.shape)
+    mask[mask < 0] = 0
+    mask[mask > 255] = 255
+    res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1)
+    assert res.dtype == float
+    assert res.std() * 255 < mask.std()
+
+
+def test_denoise_tv_chambolle_1d():
+    """Apply the TV denoising algorithm on a 1D sinusoid."""
+    x = 125 + 100 * cp.sin(cp.linspace(0, 8 * cp.pi, 1000))
+    x += 20 * cp.random.rand(x.size)
+    x = cp.clip(x, 0, 255)
+    res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
+    assert res.dtype == float
+    assert res.std() * 255 < x.std()
+
+
+def test_denoise_tv_chambolle_4d():
+    """ TV denoising for a 4D input."""
+    im = 255 * cp.random.rand(8, 8, 8, 8)
+    res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1)
+    assert res.dtype == float
+    assert res.std() * 255 < im.std()
+
+
+def test_denoise_tv_chambolle_weighting():
+    # make sure a specified weight gives consistent results regardless of
+    # the number of input image dimensions
+    rstate = cp.random.RandomState(1234)
+    img2d = astro_gray.copy()
+    img2d += 0.15 * rstate.standard_normal(img2d.shape)
+    img2d = cp.clip(img2d, 0, 1)
+
+    # generate 4D image by tiling
+    img4d = cp.tile(img2d[..., None, None], (1, 1, 2, 2))
+
+    w = 0.2
+    denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
+    denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
+    assert (structural_similarity(denoised_2d,
+                                  denoised_4d[:, :, 0, 0]) > 0.99)
diff --git a/python/cucim/src/cucim/skimage/restoration/tests/test_j_invariant.py b/python/cucim/src/cucim/skimage/restoration/tests/test_j_invariant.py
new file mode 100644
index 000000000..4d095f2ca
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/tests/test_j_invariant.py
@@ -0,0 +1,111 @@
+import cupy as cp
+import numpy as np
+import pytest
+from skimage.data import camera, chelsea
+# from cucim.skimage.restoration import denoise_wavelet
+from skimage.restoration import denoise_wavelet
+
+from cucim.skimage.data import binary_blobs
+from cucim.skimage.metrics import mean_squared_error as mse
+from cucim.skimage.restoration import calibrate_denoiser, denoise_tv_chambolle
+from cucim.skimage.restoration.j_invariant import _invariant_denoise
+from cucim.skimage.util import img_as_float, random_noise
+
+test_img = img_as_float(cp.asarray(camera()))
+test_img_color = img_as_float(cp.asarray(chelsea()))
+test_img_3d = img_as_float(binary_blobs(64, n_dim=3)) / 2
+noisy_img = random_noise(test_img, mode="gaussian", var=0.01)
+noisy_img_color = random_noise(test_img_color, mode="gaussian", var=0.01)
+noisy_img_3d = random_noise(test_img_3d, mode="gaussian", var=0.1)
+
+
+# TODO: replace with CuPy version once completed
+def _denoise_wavelet(image, rescale_sigma=True, **kwargs):
+    return cp.asarray(
+        denoise_wavelet(
+            cp.asnumpy(image), rescale_sigma=rescale_sigma, **kwargs
+        )
+    )
+
+
+def test_invariant_denoise():
+    # denoised_img = _invariant_denoise(noisy_img, _denoise_wavelet)
+    denoised_img = _invariant_denoise(noisy_img, denoise_tv_chambolle)
+
+    denoised_mse = mse(denoised_img, test_img)
+    original_mse = mse(noisy_img, test_img)
+    assert denoised_mse < original_mse
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_invariant_denoise_color(dtype):
+    denoised_img_color = _invariant_denoise(
+        noisy_img_color.astype(dtype),
+        _denoise_wavelet,
+        denoiser_kwargs=dict(multichannel=True),
+    )
+    assert denoised_img_color.dtype == dtype
+
+    denoised_mse = mse(denoised_img_color, test_img_color)
+    original_mse = mse(noisy_img_color, test_img_color)
+    assert denoised_mse < original_mse
+
+
+def test_invariant_denoise_3d():
+    denoised_img_3d = _invariant_denoise(noisy_img_3d, _denoise_wavelet)
+
+    denoised_mse = mse(denoised_img_3d, test_img_3d)
+    original_mse = mse(noisy_img_3d, test_img_3d)
+    assert denoised_mse < original_mse
+
+
+def test_calibrate_denoiser_extra_output():
+    parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
+    _, (parameters_tested, losses) = calibrate_denoiser(
+        noisy_img,
+        _denoise_wavelet,
+        denoise_parameters=parameter_ranges,
+        extra_output=True
+    )
+
+    all_denoised = [_invariant_denoise(noisy_img, _denoise_wavelet,
+                                       denoiser_kwargs=denoiser_kwargs)
+                    for denoiser_kwargs in parameters_tested]
+
+    ground_truth_losses = [float(mse(img, test_img)) for img in all_denoised]
+    assert np.argmin(losses) == np.argmin(ground_truth_losses)
+
+
+def test_calibrate_denoiser():
+    parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
+
+    denoiser = calibrate_denoiser(
+        noisy_img, _denoise_wavelet, denoise_parameters=parameter_ranges
+    )
+
+    denoised_mse = mse(denoiser(noisy_img), test_img)
+    original_mse = mse(noisy_img, test_img)
+    assert denoised_mse < original_mse
+
+
+def test_calibrate_denoiser_tv():
+    parameter_ranges = {"weight": np.linspace(0.01, 0.4, 10)}
+
+    denoiser = calibrate_denoiser(
+        noisy_img, denoise_tv_chambolle, denoise_parameters=parameter_ranges
+    )
+
+    denoised_mse = mse(denoiser(noisy_img), test_img)
+    original_mse = mse(noisy_img, test_img)
+    assert denoised_mse < original_mse
+
+
+def test_input_image_not_modified():
+    input_image = noisy_img.copy()
+
+    parameter_ranges = {'sigma': np.random.random(5) / 2}
+    calibrate_denoiser(
+        input_image, _denoise_wavelet, denoise_parameters=parameter_ranges
+    )
+
+    assert cp.all(noisy_img == input_image)
diff --git a/python/cucim/src/cucim/skimage/restoration/tests/test_restoration.py b/python/cucim/src/cucim/skimage/restoration/tests/test_restoration.py
new file mode 100644
index 000000000..1caeac176
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/tests/test_restoration.py
@@ -0,0 +1,159 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy import testing
+from cupyx.scipy import ndimage as ndi
+from scipy import signal
+
+from cucim.skimage import restoration
+from cucim.skimage._shared.testing import fetch
+from cucim.skimage.color import rgb2gray
+from cucim.skimage.restoration import uft
+
+
+def camera():
+    import skimage
+    import skimage.data
+
+    return cp.asarray(skimage.img_as_float(skimage.data.camera()))
+
+
+def astronaut():
+    import skimage
+    import skimage.data
+
+    return cp.asarray(skimage.img_as_float(skimage.data.astronaut()))
+
+
+test_img = camera()
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_wiener(dtype):
+    psf = np.ones((5, 5)) / 25
+    data = signal.convolve2d(cp.asnumpy(test_img), psf, "same")
+    np.random.seed(0)
+    data += 0.1 * data.std() * np.random.standard_normal(data.shape)
+
+    psf = cp.asarray(psf, dtype=dtype)
+    data = cp.asarray(data, dtype=dtype)
+
+    deconvolved = restoration.wiener(data, psf, 0.05)
+    assert deconvolved.dtype == dtype
+
+    path = fetch('restoration/tests/camera_wiener.npy')
+    rtol = 1e-5 if dtype == np.float32 else 1e-12
+    atol = rtol
+    cp.testing.assert_allclose(
+        deconvolved, np.load(path), rtol=rtol, atol=atol)
+
+    _, laplacian = uft.laplacian(2, data.shape)
+    otf = uft.ir2tf(psf, data.shape, is_real=False)
+    deconvolved = restoration.wiener(data, otf, 0.05,
+                                     reg=laplacian,
+                                     is_real=False)
+    cp.testing.assert_allclose(cp.real(deconvolved),
+                               np.load(path),
+                               rtol=rtol, atol=atol)
+
+
+@pytest.mark.parametrize('dtype', [cp.float32, cp.float64])
+def test_unsupervised_wiener(dtype):
+    psf = np.ones((5, 5)) / 25
+    data = signal.convolve2d(cp.asnumpy(test_img), psf, 'same')
+    np.random.seed(0)
+    data += 0.1 * data.std() * np.random.standard_normal(data.shape)
+
+    psf = cp.asarray(psf, dtype=dtype)
+    data = cp.asarray(data, dtype=dtype)
+    deconvolved, _ = restoration.unsupervised_wiener(data, psf)
+    assert deconvolved.dtype == dtype
+
+    # CuPy Backend: Cannot use the following comparison to scikit-image data
+    #               due to different random values generated by cp.random
+    #               within unsupervised_wiener.
+    #               Verified similar appearance qualitatively.
+    # path = fetch("restoration/tests/camera_unsup.npy")
+    # cp.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
+
+    _, laplacian = uft.laplacian(2, data.shape)
+    otf = uft.ir2tf(psf, data.shape, is_real=False)
+
+    np.random.seed(0)
+    deconvolved = restoration.unsupervised_wiener(  # noqa
+        data,
+        otf,
+        reg=laplacian,
+        is_real=False,
+        user_params={"callback": lambda x: None},
+    )[0]
+
+    # CuPy Backend: Cannot use the following comparison to scikit-image data
+    #               due to different random values generated by cp.random
+    #               within unsupervised_wiener.
+    #               Verified similar appearance qualitatively.
+    # path = fetch("restoration/tests/camera_unsup2.npy")
+    # cp.testing.assert_allclose(cp.real(deconvolved), np.load(path), rtol=1e-3)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_image_shape():
+    """Test that shape of output image in deconvolution is same as input.
+
+    This addresses issue #1172.
+    """
+    point = cp.zeros((5, 5), np.float)
+    point[2, 2] = 1.0
+    psf = ndi.gaussian_filter(point, sigma=1.0)
+    # image shape: (45, 45), as reported in #1172
+    image = cp.asarray(test_img[65:165, 215:315])  # just the face
+    image_conv = ndi.convolve(image, psf)
+    deconv_sup = restoration.wiener(image_conv, psf, 1)
+    deconv_un = restoration.unsupervised_wiener(image_conv, psf)[0]
+    # test the shape
+    assert image.shape == deconv_sup.shape
+    assert image.shape == deconv_un.shape
+    # test the reconstruction error
+    sup_relative_error = cp.abs(deconv_sup - image) / image
+    un_relative_error = cp.abs(deconv_un - image) / image
+    cp.testing.assert_array_less(cp.median(sup_relative_error), 0.1)
+    cp.testing.assert_array_less(cp.median(un_relative_error), 0.1)
+
+
+def test_richardson_lucy():
+    rstate = np.random.RandomState(0)
+    psf = np.ones((5, 5)) / 25
+    data = signal.convolve2d(cp.asnumpy(test_img), psf, 'same')
+    np.random.seed(0)
+    data += 0.1 * data.std() * rstate.standard_normal(data.shape)
+
+    data = cp.asarray(data)
+    psf = cp.asarray(psf)
+    deconvolved = restoration.richardson_lucy(data, psf, 5)
+
+    path = fetch('restoration/tests/camera_rl.npy')
+    cp.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-5)
+
+
+@pytest.mark.parametrize('dtype_image', [np.float32, np.float64])
+@pytest.mark.parametrize('dtype_psf', [np.float32, np.float64])
+@testing.with_requires("scikit-image>=0.18")
+def test_richardson_lucy_filtered(dtype_image, dtype_psf):
+    if dtype_image == np.float64:
+        atol = 1e-8
+    else:
+        atol = 1e-4
+
+    test_img_astro = rgb2gray(astronaut())
+
+    psf = cp.ones((5, 5), dtype=dtype_psf) / 25
+    data = cp.array(
+        signal.convolve2d(cp.asnumpy(test_img_astro), cp.asnumpy(psf), 'same'),
+        dtype=dtype_image)
+    deconvolved = restoration.richardson_lucy(data, psf, 5,
+                                              filter_epsilon=1e-6)
+    assert deconvolved.dtype == data.dtype
+
+    path = fetch('restoration/tests/astronaut_rl.npy')
+    cp.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3,
+                               atol=atol)
diff --git a/python/cucim/src/cucim/skimage/restoration/uft.py b/python/cucim/src/cucim/skimage/restoration/uft.py
new file mode 100644
index 000000000..1a6b7478b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/restoration/uft.py
@@ -0,0 +1,478 @@
+r"""Function of unitary fourier transform (uft) and utilities
+
+This module implements the unitary fourier transform, also known as
+the ortho-normal transform. It is especially useful for convolution
+[1], as it respects the Parseval equality. The value of the null
+frequency is equal to
+
+.. math::  \frac{1}{\sqrt{n}} \sum_i x_i
+
+so the Fourier transform has the same energy as the original image
+(see ``image_quad_norm`` function). The transform is applied from the
+last axis for performance (assuming a C-order array input).
+
+References
+----------
+.. [1] B. R. Hunt "A matrix theory proof of the discrete convolution
+       theorem", IEEE Trans. on Audio and Electroacoustics,
+       vol. au-19, no. 4, pp. 285-288, dec. 1971
+
+"""
+
+
+import math
+
+import cupy as cp
+import numpy as np
+
+from .._shared.fft import fftmodule as fft
+
+__keywords__ = "fft, Fourier Transform, orthonormal, unitary"
+
+
+def ufftn(inarray, dim=None):
+    """N-dimensional unitary Fourier transform.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        The array to transform.
+    dim : int, optional
+        The last axis along which to compute the transform. All
+        axes by default.
+
+    Returns
+    -------
+    outarray : ndarray (same shape than inarray)
+        The unitary N-D Fourier transform of ``inarray``.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((3, 3, 3))
+    >>> output = ufftn(input)
+    >>> cp.allclose(cp.sum(input) / cp.sqrt(input.size), output[0, 0, 0])
+    True
+    >>> output.shape
+    (3, 3, 3)
+    """
+    if dim is None:
+        dim = inarray.ndim
+    outarray = fft.fftn(inarray, axes=range(-dim, 0), norm="ortho")
+    return outarray
+
+
+def uifftn(inarray, dim=None):
+    """N-dimensional unitary inverse Fourier transform.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        The array to transform.
+    dim : int, optional
+        The last axis along which to compute the transform. All
+        axes by default.
+
+    Returns
+    -------
+    outarray : ndarray (same shape than inarray)
+        The unitary inverse N-D Fourier transform of ``inarray``.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((3, 3, 3))
+    >>> output = uifftn(input)
+    >>> cp.allclose(cp.sum(input) / cp.sqrt(input.size), output[0, 0, 0])
+    True
+    >>> output.shape
+    (3, 3, 3)
+    """
+    if dim is None:
+        dim = inarray.ndim
+    outarray = fft.ifftn(inarray, axes=range(-dim, 0), norm='ortho')
+    return outarray
+
+
+def urfftn(inarray, dim=None):
+    """N-dimensional real unitary Fourier transform.
+
+    This transform considers the Hermitian property of the transform on
+    real-valued input.
+
+    Parameters
+    ----------
+    inarray : ndarray, shape (M, N, ..., P)
+        The array to transform.
+    dim : int, optional
+        The last axis along which to compute the transform. All
+        axes by default.
+
+    Returns
+    -------
+    outarray : ndarray, shape (M, N, ..., P / 2 + 1)
+        The unitary N-D real Fourier transform of ``inarray``.
+
+    Notes
+    -----
+    The ``urfft`` functions assume an input array of real
+    values. Consequently, the output has a Hermitian property and
+    redundant values are not computed or returned.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((5, 5, 5))
+    >>> output = urfftn(input)
+    >>> cp.allclose(cp.sum(input) / cp.sqrt(input.size), output[0, 0, 0])
+    True
+    >>> output.shape
+    (5, 5, 3)
+    """
+    if dim is None:
+        dim = inarray.ndim
+    outarray = fft.rfftn(inarray, axes=range(-dim, 0), norm='ortho')
+    return outarray
+
+
+def uirfftn(inarray, dim=None, shape=None):
+    """N-dimensional inverse real unitary Fourier transform.
+
+    This transform considers the Hermitian property of the transform
+    from complex to real input.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        The array to transform.
+    dim : int, optional
+        The last axis along which to compute the transform. All
+        axes by default.
+    shape : tuple of int, optional
+        The shape of the output. The shape of ``rfft`` is ambiguous in
+        case of odd-valued input shape. In this case, this parameter
+        should be provided. See ``cupy.fft.irfftn``.
+
+    Returns
+    -------
+    outarray : ndarray
+        The unitary N-D inverse real Fourier transform of ``inarray``.
+
+    Notes
+    -----
+    The ``uirfft`` function assumes that the output array is
+    real-valued. Consequently, the input is assumed to have a Hermitian
+    property and redundant values are implicit.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((5, 5, 5))
+    >>> output = uirfftn(urfftn(input), shape=input.shape)
+    >>> cp.allclose(input, output)
+    True
+    >>> output.shape
+    (5, 5, 5)
+    """
+    if dim is None:
+        dim = inarray.ndim
+    outarray = fft.irfftn(inarray, shape, axes=range(-dim, 0), norm='ortho')
+    return outarray
+
+
+def ufft2(inarray):
+    """2-dimensional unitary Fourier transform.
+
+    Compute the Fourier transform on the last 2 axes.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        The array to transform.
+
+    Returns
+    -------
+    outarray : ndarray (same shape as inarray)
+        The unitary 2-D Fourier transform of ``inarray``.
+
+    See Also
+    --------
+    uifft2, ufftn, urfftn
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((10, 128, 128))
+    >>> output = ufft2(input)
+    >>> cp.allclose(cp.sum(input[1, ...]) / cp.sqrt(input[1, ...].size),
+    ...               output[1, 0, 0])
+    True
+    >>> output.shape
+    (10, 128, 128)
+    """
+    return ufftn(inarray, 2)
+
+
+def uifft2(inarray):
+    """2-dimensional inverse unitary Fourier transform.
+
+    Compute the inverse Fourier transform on the last 2 axes.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        The array to transform.
+
+    Returns
+    -------
+    outarray : ndarray (same shape as inarray)
+        The unitary 2-D inverse Fourier transform of ``inarray``.
+
+    See Also
+    --------
+    uifft2, uifftn, uirfftn
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((10, 128, 128))
+    >>> output = uifft2(input)
+    >>> cp.allclose(cp.sum(input[1, ...]) / cp.sqrt(input[1, ...].size),
+    ...               output[0, 0, 0])
+    True
+    >>> output.shape
+    (10, 128, 128)
+    """
+    return uifftn(inarray, 2)
+
+
+def urfft2(inarray):
+    """2-dimensional real unitary Fourier transform
+
+    Compute the real Fourier transform on the last 2 axes. This
+    transform considers the Hermitian property of the transform from
+    complex to real-valued input.
+
+    Parameters
+    ----------
+    inarray : ndarray, shape (M, N, ..., P)
+        The array to transform.
+
+    Returns
+    -------
+    outarray : ndarray, shape (M, N, ..., 2 * (P - 1))
+        The unitary 2-D real Fourier transform of ``inarray``.
+
+    See Also
+    --------
+    ufft2, ufftn, urfftn
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((10, 128, 128))
+    >>> output = urfft2(input)
+    >>> cp.allclose(cp.sum(input[1,...]) / cp.sqrt(input[1,...].size),
+    ...               output[1, 0, 0])
+    True
+    >>> output.shape
+    (10, 128, 65)
+    """
+    return urfftn(inarray, 2)
+
+
+def uirfft2(inarray, shape=None):
+    """2-dimensional inverse real unitary Fourier transform.
+
+    Compute the real inverse Fourier transform on the last 2 axes.
+    This transform considers the Hermitian property of the transform
+    from complex to real-valued input.
+
+    Parameters
+    ----------
+    inarray : ndarray, shape (M, N, ..., P)
+        The array to transform.
+    shape : tuple of int, optional
+        The shape of the output. The shape of ``rfft`` is ambiguous in
+        case of odd-valued input shape. In this case, this parameter
+        should be provided. See ``cupy.fft.irfftn``.
+
+    Returns
+    -------
+    outarray : ndarray, shape (M, N, ..., 2 * (P - 1))
+        The unitary 2-D inverse real Fourier transform of ``inarray``.
+
+    See Also
+    --------
+    urfft2, uifftn, uirfftn
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((10, 128, 128))
+    >>> output = uirfftn(urfftn(input), shape=input.shape)
+    >>> cp.allclose(input, output)
+    True
+    >>> output.shape
+    (10, 128, 128)
+    """
+    return uirfftn(inarray, 2, shape=shape)
+
+
+def image_quad_norm(inarray):
+    """Return the quadratic norm of images in Fourier space.
+
+    This function detects whether the input image satisfies the
+    Hermitian property.
+
+    Parameters
+    ----------
+    inarray : ndarray
+        Input image. The image data should reside in the final two
+        axes.
+
+    Returns
+    -------
+    norm : float
+        The quadratic norm of ``inarray``.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> input = cp.ones((5, 5))
+    >>> image_quad_norm(ufft2(input)) == cp.sum(cp.abs(input)**2)
+    True
+    >>> image_quad_norm(ufft2(input)) == image_quad_norm(urfft2(input))
+    True
+    """
+    # If there is a Hermitian symmetry
+    abs_sq = cp.abs(inarray)
+    abs_sq *= abs_sq
+    if inarray.shape[-1] != inarray.shape[-2]:
+        return 2 * cp.sum(cp.sum(abs_sq, axis=-1), axis=-1) - cp.sum(
+            cp.abs(inarray[..., 0]) ** 2, axis=-1
+        )
+    else:
+        return cp.sum(cp.sum(abs_sq, axis=-1), axis=-1)
+
+
+def ir2tf(imp_resp, shape, dim=None, is_real=True):
+    """Compute the transfer function of an impulse response (IR).
+
+    This function makes the necessary correct zero-padding, zero
+    convention, correct fft2, etc... to compute the transfer function
+    of IR. To use with unitary Fourier transform for the signal (ufftn
+    or equivalent).
+
+    Parameters
+    ----------
+    imp_resp : ndarray
+        The impulse responses.
+    shape : tuple of int
+        A tuple of integer corresponding to the target shape of the
+        transfer function.
+    dim : int, optional
+        The last axis along which to compute the transform. All
+        axes by default.
+    is_real : boolean, optional
+       If True (default), imp_resp is supposed real and the Hermitian property
+       is used with rfftn Fourier transform.
+
+    Returns
+    -------
+    y : complex ndarray
+       The transfer function of shape ``shape``.
+
+    See Also
+    --------
+    ufftn, uifftn, urfftn, uirfftn
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> cp.all(cp.array([[4, 0], [0, 0]]) == ir2tf(cp.ones((2, 2)), (2, 2)))
+    True
+    >>> ir2tf(cp.ones((2, 2)), (512, 512)).shape == (512, 257)
+    True
+    >>> ir2tf(cp.ones((2, 2)), (512, 512), is_real=False).shape == (512, 512)
+    True
+
+    Notes
+    -----
+    The input array can be composed of multiple-dimensional IR with
+    an arbitrary number of IR. The individual IR must be accessed
+    through the first axes. The last ``dim`` axes contain the space
+    definition.
+    """
+    if not dim:
+        dim = imp_resp.ndim
+    # Zero padding and fill
+    irpadded_dtype = imp_resp.dtype if imp_resp.dtype.kind == 'f' else float
+    irpadded = cp.zeros(shape, dtype=irpadded_dtype)
+    irpadded[tuple([slice(0, s) for s in imp_resp.shape])] = imp_resp
+    # Roll for zero convention of the fft to avoid the phase
+    # problem. Work with odd and even size.
+    for axis, axis_size in enumerate(imp_resp.shape):
+        if axis >= imp_resp.ndim - dim:
+            irpadded = cp.roll(irpadded,
+                               shift=-math.floor(axis_size / 2),
+                               axis=axis)
+    if is_real:
+        return fft.rfftn(irpadded, axes=range(-dim, 0))
+    else:
+        return fft.fftn(irpadded, axes=range(-dim, 0))
+
+
+def laplacian(ndim, shape, is_real=True, *, dtype=None):
+    """Return the transfer function of the Laplacian.
+
+    Laplacian is the second order difference, on row and column.
+
+    Parameters
+    ----------
+    ndim : int
+        The dimension of the Laplacian.
+    shape : tuple
+        The support on which to compute the transfer function.
+    is_real : boolean, optional
+       If True (default), imp_resp is assumed to be real-valued and
+       the Hermitian property is used with rfftn Fourier transform
+       to return the transfer function.
+
+    Returns
+    -------
+    tf : array_like, complex
+        The transfer function.
+    impr : array_like, real
+        The Laplacian.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> tf, ir = laplacian(2, (32, 32))
+    >>> cp.all(ir == cp.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]]))
+    True
+    >>> cp.all(tf == ir2tf(ir, (32, 32)))
+    True
+    """
+    if dtype is None:
+        dtype = cp.float64 if is_real else cp.complex128
+    elif np.dtype(dtype).kind != 'f':
+        raise ValueError("dtype must be a floating point dtype")
+
+    # CuPy Backend: assemble the small kernel on the host and then transfer it
+    impr = np.zeros([3] * ndim)
+    for dim in range(ndim):
+        idx = tuple(
+            [slice(1, 2)] * dim
+            + [slice(None)]
+            + [slice(1, 2)] * (ndim - dim - 1)
+        )
+        impr[idx] = np.array([-1.0, 0.0, -1.0]).reshape(
+            [-1 if i == dim else 1 for i in range(ndim)]
+        )
+    impr[(slice(1, 2),) * ndim] = 2.0 * ndim
+    impr = cp.array(impr, dtype=dtype)
+    if shape is None:  # filters.laplace only uses the spatial kernel
+        return impr
+    return ir2tf(impr, shape, is_real=is_real), impr
diff --git a/python/cucim/src/cucim/skimage/segmentation/__init__.py b/python/cucim/src/cucim/skimage/segmentation/__init__.py
new file mode 100644
index 000000000..504ccb24e
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/__init__.py
@@ -0,0 +1,22 @@
+from ._join import join_segmentations, relabel_sequential
+from .boundaries import find_boundaries, mark_boundaries
+from .morphsnakes import (checkerboard_level_set, circle_level_set,
+                          disk_level_set, inverse_gaussian_gradient,
+                          morphological_chan_vese,
+                          morphological_geodesic_active_contour)
+from .random_walker_segmentation import random_walker
+
+__all__ = [
+    "random_walker",
+    "find_boundaries",
+    "mark_boundaries",
+    "clear_border",
+    "join_segmentations",
+    "relabel_sequential",
+    "morphological_geodesic_active_contour",
+    "morphological_chan_vese",
+    "inverse_gaussian_gradient",
+    "circle_level_set",
+    "disk_level_set",
+    "checkerboard_level_set",
+]
diff --git a/python/cucim/src/cucim/skimage/segmentation/_join.py b/python/cucim/src/cucim/skimage/segmentation/_join.py
new file mode 100644
index 000000000..793190227
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/_join.py
@@ -0,0 +1,173 @@
+import cupy as cp
+
+from ..util._map_array import ArrayMap, map_array
+
+
+def join_segmentations(s1, s2):
+    """Return the join of the two input segmentations.
+
+    The join J of S1 and S2 is defined as the segmentation in which two
+    voxels are in the same segment if and only if they are in the same
+    segment in *both* S1 and S2.
+
+    Parameters
+    ----------
+    s1, s2 : numpy arrays
+        s1 and s2 are label fields of the same shape.
+
+    Returns
+    -------
+    j : numpy array
+        The join segmentation of s1 and s2.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.segmentation import join_segmentations
+    >>> s1 = cp.array([[0, 0, 1, 1],
+    ...                [0, 2, 1, 1],
+    ...                [2, 2, 2, 1]])
+    >>> s2 = cp.array([[0, 1, 1, 0],
+    ...                [0, 1, 1, 0],
+    ...                [0, 1, 1, 1]])
+    >>> join_segmentations(s1, s2)
+    array([[0, 1, 3, 2],
+           [0, 5, 3, 2],
+           [4, 5, 5, 3]])
+    """
+    if s1.shape != s2.shape:
+        raise ValueError("Cannot join segmentations of different shape. " +
+                         "s1.shape: %s, s2.shape: %s" % (s1.shape, s2.shape))
+    s1 = relabel_sequential(s1)[0]
+    s2 = relabel_sequential(s2)[0]
+    j = (s2.max() + 1) * s1 + s2
+    j = relabel_sequential(j)[0]
+    return j
+
+
+def relabel_sequential(label_field, offset=1):
+    """Relabel arbitrary labels to {`offset`, ... `offset` + number_of_labels}.
+
+    This function also returns the forward map (mapping the original labels to
+    the reduced labels) and the inverse map (mapping the reduced labels back
+    to the original ones).
+
+    Parameters
+    ----------
+    label_field : numpy array of int, arbitrary shape
+        An array of labels, which must be non-negative integers.
+    offset : int, optional
+        The return labels will start at `offset`, which should be
+        strictly positive.
+
+    Returns
+    -------
+    relabeled : numpy array of int, same shape as `label_field`
+        The input label field with labels mapped to
+        {offset, ..., number_of_labels + offset - 1}.
+        The data type will be the same as `label_field`, except when
+        offset + number_of_labels causes overflow of the current data type.
+    forward_map : ArrayMap
+        The map from the original label space to the returned label
+        space. Can be used to re-apply the same mapping. See examples
+        for usage. The output data type will be the same as `relabeled`.
+    inverse_map : ArrayMap
+        The map from the new label space to the original space. This
+        can be used to reconstruct the original label field from the
+        relabeled one. The output data type will be the same as `label_field`.
+
+    Notes
+    -----
+    The label 0 is assumed to denote the background and is never remapped.
+
+    The forward map can be extremely big for some inputs, since its
+    length is given by the maximum of the label field. However, in most
+    situations, ``label_field.max()`` is much smaller than
+    ``label_field.size``, and in these cases the forward map is
+    guaranteed to be smaller than either the input or output images.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.segmentation import relabel_sequential
+    >>> label_field = cp.array([1, 1, 5, 5, 8, 99, 42])
+    >>> relab, fw, inv = relabel_sequential(label_field)
+    >>> relab
+    array([1, 1, 2, 2, 3, 5, 4])
+    >>> print(fw)
+    ArrayMap:
+      1 → 1
+      5 → 2
+      8 → 3
+      42 → 4
+      99 → 5
+    >>> cp.array(fw)
+    array([0, 1, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+           0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5])
+    >>> cp.array(inv)
+    array([ 0,  1,  5,  8, 42, 99])
+    >>> (fw[label_field] == relab).all()
+    True
+    >>> (inv[relab] == label_field).all()
+    True
+    >>> relab, fw, inv = relabel_sequential(label_field, offset=5)
+    >>> relab
+    array([5, 5, 6, 6, 7, 9, 8])
+    """
+    if offset <= 0:
+        raise ValueError("Offset must be strictly positive.")
+    if label_field.min() < 0:
+        raise ValueError("Cannot relabel array that contains negative values.")
+    offset = int(offset)
+    in_vals = cp.unique(label_field)
+    # Cupy Backend: currently only int32
+    if len(in_vals) > cp.iinfo(cp.int32).max:
+        raise ValueError(
+            "Too many unique values in label_field (current implementation "
+            "uses 32-bit indexing)."
+        )
+
+    out_val_dtype = cp.min_scalar_type(offset + len(in_vals))
+    if int(in_vals[0]) == 0:
+        # always map 0 to 0
+        out_vals = cp.concatenate(
+            [
+                cp.asarray([0], dtype=out_val_dtype),
+                cp.arange(
+                    offset, offset + len(in_vals) - 1, dtype=out_val_dtype
+                ),
+            ]
+        )
+    else:
+        out_vals = cp.arange(offset, offset + len(in_vals), dtype=out_val_dtype)
+    input_type = label_field.dtype
+    if input_type.kind not in "iu":
+        raise TypeError("label_field must have an integer dtype")
+
+    # Some logic to determine the output type:
+    #  - we don't want to return a smaller output type than the input type,
+    #  ie if we get uint32 as labels input, don't return a uint8 array.
+    #  - but, in some cases, using the input type could result in overflow. The
+    #  input type could be a signed integer (e.g. int32) but
+    #  `np.min_scalar_type` will always return an unsigned type. We check for
+    #  that by casting the largest output value to the input type. If it is
+    #  unchanged, we use the input type, else we use the unsigned minimum
+    #  required type
+    out_max = int(out_vals[-1])
+    required_type = cp.min_scalar_type(out_max)
+    if input_type.itemsize < required_type.itemsize:
+        output_type = required_type
+    else:
+        if out_max <= cp.iinfo(input_type).max:
+            output_type = input_type
+        else:
+            output_type = required_type
+    out_array = cp.empty(label_field.shape, dtype=output_type)
+    out_vals = out_vals.astype(output_type, copy=False)
+    map_array(label_field, in_vals, out_vals, out=out_array)
+    fw_map = ArrayMap(in_vals, out_vals)
+    inv_map = ArrayMap(out_vals, in_vals)
+    return out_array, fw_map, inv_map
diff --git a/python/cucim/src/cucim/skimage/segmentation/boundaries.py b/python/cucim/src/cucim/skimage/segmentation/boundaries.py
new file mode 100644
index 000000000..6655c4c7e
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/boundaries.py
@@ -0,0 +1,238 @@
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from ..color import gray2rgb
+from ..morphology import dilation, erosion, square
+from ..util import img_as_float
+
+
+def _find_boundaries_subpixel(label_img):
+    """See ``find_boundaries(..., mode='subpixel')``.
+
+    Notes
+    -----
+    This function puts in an empty row and column between each *actual*
+    row and column of the image, for a corresponding shape of ``2s - 1``
+    for every image dimension of size ``s``. These "interstitial" rows
+    and columns are filled as ``True`` if they separate two labels in
+    `label_img`, ``False`` otherwise.
+    """
+    ndim = label_img.ndim
+    max_label = cp.iinfo(label_img.dtype).max
+
+    label_img_expanded = cp.full([(2 * s - 1) for s in label_img.shape],
+                                 max_label, label_img.dtype)
+    pixels = (slice(None, None, 2),) * ndim
+    label_img_expanded[pixels] = label_img
+
+    # CuPy Backend: TODO: Refactor all rank filtering below into a single
+    #                     ElementwiseKernel that counts # of unique values.
+
+    # at most 2**ndim non max_label pixels in a 3**ndim shape neighborhood
+    max_possible_unique = 2 ** ndim
+
+    # Count the number of unique values aside from max_label or
+    # the background.
+    n_unique = cp.zeros(label_img_expanded.shape, dtype=cp.uint8)
+    rank_prev = ndi.minimum_filter(label_img_expanded, size=3)
+    for n in range(1, max_possible_unique + 1):
+        rank = ndi.rank_filter(label_img_expanded, n, size=3)
+        n_unique += (rank != rank_prev)
+        rank_prev = rank
+
+    # Boundaries occur where there is more than 1 unique value
+    return n_unique > 1
+
+
+def find_boundaries(label_img, connectivity=1, mode="thick", background=0):
+    """Return bool array where boundaries between labeled regions are True.
+
+    Parameters
+    ----------
+    label_img : array of int or bool
+        An array in which different regions are labeled with either different
+        integers or boolean values.
+    connectivity : int in {1, ..., `label_img.ndim`}, optional
+        A pixel is considered a boundary pixel if any of its neighbors
+        has a different label. `connectivity` controls which pixels are
+        considered neighbors. A connectivity of 1 (default) means
+        pixels sharing an edge (in 2D) or a face (in 3D) will be
+        considered neighbors. A connectivity of `label_img.ndim` means
+        pixels sharing a corner will be considered neighbors.
+    mode : string in {'thick', 'inner', 'outer', 'subpixel'}
+        How to mark the boundaries:
+
+        - thick: any pixel not completely surrounded by pixels of the
+          same label (defined by `connectivity`) is marked as a boundary.
+          This results in boundaries that are 2 pixels thick.
+        - inner: outline the pixels *just inside* of objects, leaving
+          background pixels untouched.
+        - outer: outline pixels in the background around object
+          boundaries. When two objects touch, their boundary is also
+          marked.
+        - subpixel: return a doubled image, with pixels *between* the
+          original pixels marked as boundary where appropriate.
+    background : int, optional
+        For modes 'inner' and 'outer', a definition of a background
+        label is required. See `mode` for descriptions of these two.
+
+    Returns
+    -------
+    boundaries : array of bool, same shape as `label_img`
+        A bool image where ``True`` represents a boundary pixel. For
+        `mode` equal to 'subpixel', ``boundaries.shape[i]`` is equal
+        to ``2 * label_img.shape[i] - 1`` for all ``i`` (a pixel is
+        inserted in between all other pairs of pixels).
+
+    Examples
+    --------
+    >>> labels = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 1, 1, 1, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 5, 5, 5, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+    ...                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=cp.uint8)
+    >>> find_boundaries(labels, mode='thick').astype(cp.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
+           [0, 1, 1, 0, 1, 1, 0, 1, 1, 0],
+           [0, 1, 1, 1, 1, 1, 0, 1, 1, 0],
+           [0, 0, 1, 1, 1, 1, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels, mode='inner').astype(cp.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
+           [0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels, mode='outer').astype(cp.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 1, 0, 0, 1, 1, 0, 0, 1, 0],
+           [0, 0, 1, 1, 1, 1, 0, 0, 1, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> labels_small = labels[::2, ::3]
+    >>> labels_small
+    array([[0, 0, 0, 0],
+           [0, 0, 5, 0],
+           [0, 1, 5, 0],
+           [0, 0, 5, 0],
+           [0, 0, 0, 0]], dtype=uint8)
+    >>> find_boundaries(labels_small, mode='subpixel').astype(cp.uint8)
+    array([[0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 1, 1, 1, 0],
+           [0, 0, 0, 1, 0, 1, 0],
+           [0, 1, 1, 1, 0, 1, 0],
+           [0, 1, 0, 1, 0, 1, 0],
+           [0, 1, 1, 1, 0, 1, 0],
+           [0, 0, 0, 1, 0, 1, 0],
+           [0, 0, 0, 1, 1, 1, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> bool_image = cp.array([[False, False, False, False, False],
+    ...                        [False, False, False, False, False],
+    ...                        [False, False,  True,  True,  True],
+    ...                        [False, False,  True,  True,  True],
+    ...                        [False, False,  True,  True,  True]],
+    ...                       dtype=cp.bool)
+    >>> find_boundaries(bool_image)
+    array([[False, False, False, False, False],
+           [False, False,  True,  True,  True],
+           [False,  True,  True,  True,  True],
+           [False,  True,  True, False, False],
+           [False,  True,  True, False, False]])
+    """
+    if label_img.dtype == 'bool':
+        label_img = label_img.astype(cp.uint8)
+    ndim = label_img.ndim
+    selem = ndi.generate_binary_structure(ndim, connectivity)
+    if mode != 'subpixel':
+        boundaries = dilation(label_img, selem) != erosion(label_img, selem)
+        if mode == 'inner':
+            foreground_image = label_img != background
+            boundaries &= foreground_image
+        elif mode == 'outer':
+            max_label = cp.iinfo(label_img.dtype).max
+            background_image = label_img == background
+            selem = ndi.generate_binary_structure(ndim, ndim)
+            inverted_background = cp.array(label_img, copy=True)
+            inverted_background[background_image] = max_label
+            adjacent_objects = ((dilation(label_img, selem) !=
+                                 erosion(inverted_background, selem)) &
+                                ~background_image)
+            boundaries &= (background_image | adjacent_objects)
+        return boundaries
+    else:
+        boundaries = _find_boundaries_subpixel(label_img)
+        return boundaries
+
+
+# Cupy Backend: added order keyword-only parameter
+def mark_boundaries(image, label_img, color=(1, 1, 0),
+                    outline_color=None, mode='outer', background_label=0,
+                    *, order=3):
+    """Return image with boundaries between labeled regions highlighted.
+
+    Parameters
+    ----------
+    image : (M, N[, 3]) array
+        Grayscale or RGB image.
+    label_img : (M, N) array of int
+        Label array where regions are marked by different integer values.
+    color : length-3 sequence, optional
+        RGB color of boundaries in the output image.
+    outline_color : length-3 sequence, optional
+        RGB color surrounding boundaries in the output image. If None, no
+        outline is drawn.
+    mode : string in {'thick', 'inner', 'outer', 'subpixel'}, optional
+        The mode for finding boundaries.
+    background_label : int, optional
+        Which label to consider background (this is only useful for
+        modes ``inner`` and ``outer``).
+
+    Additional Parameters
+    ---------------------
+    order : int
+       The spline interpolation order to use when ``mode="subpixel"``.
+       Unused by other modes.
+
+    Returns
+    -------
+    marked : (M, N, 3) array of float
+        An image in which the boundaries between labels are
+        superimposed on the original image.
+
+    See Also
+    --------
+    find_boundaries
+    """
+    marked = img_as_float(image, force_copy=True)
+    if marked.ndim == 2:
+        marked = gray2rgb(marked)
+    if mode == 'subpixel':
+        # Here, we want to interpose an extra line of pixels between
+        # each original line - except for the last axis which holds
+        # the RGB information. ``ndi.zoom`` then performs the (cubic)
+        # interpolation, filling in the values of the interposed pixels
+        marked = ndi.zoom(marked, [2 - 1 / s for s in marked.shape[:-1]] + [1],
+                          mode='mirror', order=order)
+    boundaries = find_boundaries(label_img, mode=mode,
+                                 background=background_label)
+    if outline_color is not None:
+        outlines = dilation(boundaries, square(3))
+        marked[outlines] = outline_color
+    marked[boundaries] = color
+    return marked
diff --git a/python/cucim/src/cucim/skimage/segmentation/morphsnakes.py b/python/cucim/src/cucim/skimage/segmentation/morphsnakes.py
new file mode 100644
index 000000000..9590e175b
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/morphsnakes.py
@@ -0,0 +1,496 @@
+import functools
+import warnings
+from itertools import cycle
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from cucim import _misc
+
+from .._shared.utils import check_nD
+
+__all__ = ['morphological_chan_vese',
+           'morphological_geodesic_active_contour',
+           'inverse_gaussian_gradient',
+           'circle_level_set',
+           'disk_level_set',
+           'checkerboard_level_set'
+           ]
+
+
+class _fcycle(object):
+
+    def __init__(self, iterable):
+        """Call functions from the iterable each time it is called."""
+        self.funcs = cycle(iterable)
+
+    def __call__(self, *args, **kwargs):
+        f = next(self.funcs)
+        return f(*args, **kwargs)
+
+
+# SI and IS operators for 2D and 3D.
+_P2 = [np.eye(3),
+       np.array([[0, 1, 0]] * 3),
+       np.flipud(np.eye(3)),
+       np.rot90([[0, 1, 0]] * 3)]
+_P3 = [np.zeros((3, 3, 3)) for i in range(9)]
+
+_P3[0][:, :, 1] = 1
+_P3[1][:, 1, :] = 1
+_P3[2][1, :, :] = 1
+_P3[3][:, [0, 1, 2], [0, 1, 2]] = 1
+_P3[4][:, [0, 1, 2], [2, 1, 0]] = 1
+_P3[5][[0, 1, 2], :, [0, 1, 2]] = 1
+_P3[6][[0, 1, 2], :, [2, 1, 0]] = 1
+_P3[7][[0, 1, 2], [0, 1, 2], :] = 1
+_P3[8][[0, 1, 2], [2, 1, 0], :] = 1
+
+
+def sup_inf(u):
+    """SI operator."""
+
+    if _misc.ndim(u) == 2:
+        P = _P2
+    elif _misc.ndim(u) == 3:
+        P = _P3
+    else:
+        raise ValueError("u has an invalid number of dimensions "
+                         "(should be 2 or 3)")
+
+    erosions = []
+    for P_i in P:
+        e = ndi.binary_erosion(u, cp.asarray(P_i)).astype(np.int8, copy=False)
+        erosions.append(e)
+
+    return cp.stack(erosions, axis=0).max(0)
+
+
+def inf_sup(u):
+    """IS operator."""
+
+    if _misc.ndim(u) == 2:
+        P = _P2
+    elif _misc.ndim(u) == 3:
+        P = _P3
+    else:
+        raise ValueError("u has an invalid number of dimensions "
+                         "(should be 2 or 3)")
+
+    dilations = []
+    for P_i in P:
+        d = ndi.binary_dilation(u, cp.asarray(P_i)).astype(np.int8,
+                                                           copy=False)
+        dilations.append(d)
+
+    return cp.stack(dilations, axis=0).min(0)
+
+
+_curvop = _fcycle([lambda u: sup_inf(inf_sup(u)),   # SIoIS
+                   lambda u: inf_sup(sup_inf(u))])  # ISoSI
+
+
+def _check_input(image, init_level_set):
+    """Check that shapes of `image` and `init_level_set` match."""
+    check_nD(image, [2, 3])
+
+    if len(image.shape) != len(init_level_set.shape):
+        raise ValueError("The dimensions of the initial level set do not "
+                         "match the dimensions of the image.")
+
+
+def _init_level_set(init_level_set, image_shape):
+    """Auxiliary function for initializing level sets with a string.
+
+    If `init_level_set` is not a string, it is returned as is.
+    """
+    if isinstance(init_level_set, str):
+        if init_level_set == 'checkerboard':
+            res = checkerboard_level_set(image_shape)
+        # TODO: remove me in 0.19.0
+        elif init_level_set == 'circle':
+            res = circle_level_set(image_shape)
+        elif init_level_set == 'disk':
+            res = disk_level_set(image_shape)
+        else:
+            raise ValueError("`init_level_set` not in "
+                             "['checkerboard', 'circle', 'disk']")
+    else:
+        res = init_level_set
+    return res
+
+
+def circle_level_set(image_shape, center=None, radius=None):
+    """Create a circle level set with binary values.
+
+    Parameters
+    ----------
+    image_shape : tuple of positive integers
+        Shape of the image
+    center : tuple of positive integers, optional
+        Coordinates of the center of the circle given in (row, column). If not
+        given, it defaults to the center of the image.
+    radius : float, optional
+        Radius of the circle. If not given, it is set to the 75% of the
+        smallest image dimension.
+
+    Returns
+    -------
+    out : array with shape `image_shape`
+        Binary level set of the circle with the given `radius` and `center`.
+
+    Warns
+    -----
+    Deprecated:
+        .. versionadded:: 0.17
+
+            This function is deprecated and will be removed in scikit-image
+            0.19. Please use the function named ``disk_level_set`` instead.
+
+    See Also
+    --------
+    checkerboard_level_set
+    """
+    warnings.warn("circle_level_set is deprecated in favor of "
+                  "disk_level_set."
+                  "circle_level_set will be removed in version 0.19",
+                  FutureWarning, stacklevel=2)
+
+    return disk_level_set(image_shape, center=center, radius=radius)
+
+
+def disk_level_set(image_shape, *, center=None, radius=None):
+    """Create a disk level set with binary values.
+
+    Parameters
+    ----------
+    image_shape : tuple of positive integers
+        Shape of the image
+    center : tuple of positive integers, optional
+        Coordinates of the center of the disk given in (row, column). If not
+        given, it defaults to the center of the image.
+    radius : float, optional
+        Radius of the disk. If not given, it is set to the 75% of the
+        smallest image dimension.
+
+    Returns
+    -------
+    out : array with shape `image_shape`
+        Binary level set of the disk with the given `radius` and `center`.
+
+    See Also
+    --------
+    checkerboard_level_set
+    """
+
+    if center is None:
+        center = tuple(i // 2 for i in image_shape)
+
+    if radius is None:
+        radius = min(image_shape) * 3.0 / 8.0
+
+    grid = cp.mgrid[[slice(i) for i in image_shape]]
+    grid = (grid.T - cp.asarray(center)).T
+    grid *= grid
+    phi = radius - cp.sqrt(cp.sum(grid, axis=0))
+    res = (phi > 0).astype(cp.int8)
+    return res
+
+
+def checkerboard_level_set(image_shape, square_size=5):
+    """Create a checkerboard level set with binary values.
+
+    Parameters
+    ----------
+    image_shape : tuple of positive integers
+        Shape of the image.
+    square_size : int, optional
+        Size of the squares of the checkerboard. It defaults to 5.
+
+    Returns
+    -------
+    out : array with shape `image_shape`
+        Binary level set of the checkerboard.
+
+    See Also
+    --------
+    circle_level_set
+    """
+
+    grid = cp.mgrid[[slice(i) for i in image_shape]]
+    grid = grid // square_size
+
+    # Alternate 0/1 for even/odd numbers.
+    grid = grid & 1
+
+    # CuPy Backend: use functools.reduce instead of cp.bitwise_xor.reduce
+    #     checkerboard = cp.bitwise_xor.reduce(grid, axis=0)
+    checkerboard = functools.reduce(cp.bitwise_xor, [g for g in grid])
+    res = checkerboard.astype(cp.int8)
+    return res
+
+
+def inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0):
+    """Inverse of gradient magnitude.
+
+    Compute the magnitude of the gradients in the image and then inverts the
+    result in the range [0, 1]. Flat areas are assigned values close to 1,
+    while areas close to borders are assigned values close to 0.
+
+    This function or a similar one defined by the user should be applied over
+    the image as a preprocessing step before calling
+    `morphological_geodesic_active_contour`.
+
+    Parameters
+    ----------
+    image : (M, N) or (L, M, N) array
+        Grayscale image or volume.
+    alpha : float, optional
+        Controls the steepness of the inversion. A larger value will make the
+        transition between the flat areas and border areas steeper in the
+        resulting array.
+    sigma : float, optional
+        Standard deviation of the Gaussian filter applied over the image.
+
+    Returns
+    -------
+    gimage : (M, N) or (L, M, N) array
+        Preprocessed image (or volume) suitable for
+        `morphological_geodesic_active_contour`.
+    """
+    gradnorm = ndi.gaussian_gradient_magnitude(image, sigma, mode='nearest')
+    return 1.0 / cp.sqrt(1.0 + alpha * gradnorm)
+
+
+def morphological_chan_vese(image, iterations, init_level_set='checkerboard',
+                            smoothing=1, lambda1=1, lambda2=1,
+                            iter_callback=lambda x: None):
+    """Morphological Active Contours without Edges (MorphACWE)
+
+    Active contours without edges implemented with morphological operators. It
+    can be used to segment objects in images and volumes without well defined
+    borders. It is required that the inside of the object looks different on
+    average than the outside (i.e., the inner area of the object should be
+    darker or lighter than the outer area on average).
+
+    Parameters
+    ----------
+    image : (M, N) or (L, M, N) array
+        Grayscale image or volume to be segmented.
+    iterations : uint
+        Number of iterations to run
+    init_level_set : str, (M, N) array, or (L, M, N) array
+        Initial level set. If an array is given, it will be binarized and used
+        as the initial level set. If a string is given, it defines the method
+        to generate a reasonable initial level set with the shape of the
+        `image`. Accepted values are 'checkerboard' and 'circle'. See the
+        documentation of `checkerboard_level_set` and `circle_level_set`
+        respectively for details about how these level sets are created.
+    smoothing : uint, optional
+        Number of times the smoothing operator is applied per iteration.
+        Reasonable values are around 1-4. Larger values lead to smoother
+        segmentations.
+    lambda1 : float, optional
+        Weight parameter for the outer region. If `lambda1` is larger than
+        `lambda2`, the outer region will contain a larger range of values than
+        the inner region.
+    lambda2 : float, optional
+        Weight parameter for the inner region. If `lambda2` is larger than
+        `lambda1`, the inner region will contain a larger range of values than
+        the outer region.
+    iter_callback : function, optional
+        If given, this function is called once per iteration with the current
+        level set as the only argument. This is useful for debugging or for
+        plotting intermediate results during the evolution.
+
+    Returns
+    -------
+    out : (M, N) or (L, M, N) array
+        Final segmentation (i.e., the final level set)
+
+    See Also
+    --------
+    circle_level_set, checkerboard_level_set
+
+    Notes
+    -----
+
+    This is a version of the Chan-Vese algorithm that uses morphological
+    operators instead of solving a partial differential equation (PDE) for the
+    evolution of the contour. The set of morphological operators used in this
+    algorithm are proved to be infinitesimally equivalent to the Chan-Vese PDE
+    (see [1]_). However, morphological operators are do not suffer from the
+    numerical stability issues typically found in PDEs (it is not necessary to
+    find the right time step for the evolution), and are computationally
+    faster.
+
+    The algorithm and its theoretical derivation are described in [1]_.
+
+    References
+    ----------
+    .. [1] A Morphological Approach to Curvature-based Evolution of Curves and
+           Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
+           Transactions on Pattern Analysis and Machine Intelligence (PAMI),
+           2014, :DOI:`10.1109/TPAMI.2013.106`
+    """
+
+    init_level_set = _init_level_set(init_level_set, image.shape)
+
+    _check_input(image, init_level_set)
+
+    u = (init_level_set > 0).astype(cp.int8)
+
+    iter_callback(u)
+
+    for _ in range(iterations):
+
+        # inside = u > 0
+        # outside = u <= 0
+        c0 = (image * (1 - u)).sum() / float((1 - u).sum() + 1e-8)
+        c1 = (image * u).sum() / float(u.sum() + 1e-8)
+
+        # Image attachment
+        du = cp.gradient(u)
+        abs_du = cp.abs(cp.stack(du, axis=0)).sum(0)
+        aux = abs_du * (
+            lambda1 * (image - c1) ** 2 - lambda2 * (image - c0) ** 2
+        )
+
+        u[aux < 0] = 1
+        u[aux > 0] = 0
+
+        # Smoothing
+        for _ in range(smoothing):
+            u = _curvop(u)
+
+        iter_callback(u)
+
+    return u
+
+
+def morphological_geodesic_active_contour(gimage, iterations,
+                                          init_level_set='circle', smoothing=1,
+                                          threshold='auto', balloon=0,
+                                          iter_callback=lambda x: None):
+    """Morphological Geodesic Active Contours (MorphGAC).
+
+    Geodesic active contours implemented with morphological operators. It can
+    be used to segment objects with visible but noisy, cluttered, broken
+    borders.
+
+    Parameters
+    ----------
+    gimage : (M, N) or (L, M, N) array
+        Preprocessed image or volume to be segmented. This is very rarely the
+        original image. Instead, this is usually a preprocessed version of the
+        original image that enhances and highlights the borders (or other
+        structures) of the object to segment.
+        `morphological_geodesic_active_contour` will try to stop the contour
+        evolution in areas where `gimage` is small. See
+        `morphsnakes.inverse_gaussian_gradient` as an example function to
+        perform this preprocessing. Note that the quality of
+        `morphological_geodesic_active_contour` might greatly depend on this
+        preprocessing.
+    iterations : uint
+        Number of iterations to run.
+    init_level_set : str, (M, N) array, or (L, M, N) array
+        Initial level set. If an array is given, it will be binarized and used
+        as the initial level set. If a string is given, it defines the method
+        to generate a reasonable initial level set with the shape of the
+        `image`. Accepted values are 'checkerboard' and 'circle'. See the
+        documentation of `checkerboard_level_set` and `circle_level_set`
+        respectively for details about how these level sets are created.
+    smoothing : uint, optional
+        Number of times the smoothing operator is applied per iteration.
+        Reasonable values are around 1-4. Larger values lead to smoother
+        segmentations.
+    threshold : float, optional
+        Areas of the image with a value smaller than this threshold will be
+        considered borders. The evolution of the contour will stop in this
+        areas.
+    balloon : float, optional
+        Balloon force to guide the contour in non-informative areas of the
+        image, i.e., areas where the gradient of the image is too small to push
+        the contour towards a border. A negative value will shrink the contour,
+        while a positive value will expand the contour in these areas. Setting
+        this to zero will disable the balloon force.
+    iter_callback : function, optional
+        If given, this function is called once per iteration with the current
+        level set as the only argument. This is useful for debugging or for
+        plotting intermediate results during the evolution.
+
+    Returns
+    -------
+    out : (M, N) or (L, M, N) array
+        Final segmentation (i.e., the final level set)
+
+    See Also
+    --------
+    inverse_gaussian_gradient, circle_level_set, checkerboard_level_set
+
+    Notes
+    -----
+
+    This is a version of the Geodesic Active Contours (GAC) algorithm that uses
+    morphological operators instead of solving partial differential equations
+    (PDEs) for the evolution of the contour. The set of morphological operators
+    used in this algorithm are proved to be infinitesimally equivalent to the
+    GAC PDEs (see [1]_). However, morphological operators are do not suffer
+    from the numerical stability issues typically found in PDEs (e.g., it is
+    not necessary to find the right time step for the evolution), and are
+    computationally faster.
+
+    The algorithm and its theoretical derivation are described in [1]_.
+
+    References
+    ----------
+    .. [1] A Morphological Approach to Curvature-based Evolution of Curves and
+           Surfaces, Pablo Márquez-Neila, Luis Baumela, Luis Álvarez. In IEEE
+           Transactions on Pattern Analysis and Machine Intelligence (PAMI),
+           2014, :DOI:`10.1109/TPAMI.2013.106`
+    """
+
+    image = gimage
+    init_level_set = _init_level_set(init_level_set, image.shape)
+
+    _check_input(image, init_level_set)
+
+    if threshold == 'auto':
+        threshold = cp.percentile(image, 40)
+
+    structure = cp.ones((3,) * len(image.shape), dtype=cp.int8)
+    dimage = cp.gradient(image)
+    # threshold_mask = image > threshold
+    if balloon != 0:
+        threshold_mask_balloon = image > threshold / cp.abs(balloon)
+
+    u = (init_level_set > 0).astype(cp.int8)
+
+    iter_callback(u)
+
+    for _ in range(iterations):
+
+        # Balloon
+        if balloon > 0:
+            aux = ndi.binary_dilation(u, structure)
+        elif balloon < 0:
+            aux = ndi.binary_erosion(u, structure)
+        if balloon != 0:
+            u[threshold_mask_balloon] = aux[threshold_mask_balloon]
+
+        # Image attachment
+        aux = cp.zeros_like(image)
+        du = cp.gradient(u)
+        for el1, el2 in zip(dimage, du):
+            aux += el1 * el2
+        u[aux > 0] = 1
+        u[aux < 0] = 0
+
+        # Smoothing
+        for _ in range(smoothing):
+            u = _curvop(u)
+
+        iter_callback(u)
+
+    return u
diff --git a/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py b/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py
new file mode 100644
index 000000000..c7e3f8348
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/random_walker_segmentation.py
@@ -0,0 +1,508 @@
+"""Random walker segmentation algorithm.
+
+from *Random walks for image segmentation*, Leo Grady, IEEE Trans
+Pattern Anal Mach Intell. 2006 Nov;28(11):1768-83.
+"""
+
+import functools
+import math
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+from cupyx.scipy import sparse
+from cupyx.scipy.sparse.linalg import cg, spsolve
+
+from .._shared.utils import warn
+from ..util import img_as_float
+
+# TODO: Implemented multigrid solver option, 'cg_mg'
+#    NVIDIA's AMGX library has a Ruge-Stüben algebraic multigrid solver
+#    The following package has python wrappers for it:
+#        https://github.com/shwina/pyamgx
+
+amg_loaded = False
+
+cg = functools.partial(cg, atol=0)
+
+
+def _make_graph_edges_3d(n_x, n_y, n_z):
+    """Returns a list of edges for a 3D image.
+
+    Parameters
+    ----------
+    n_x: integer
+        The size of the grid in the x direction.
+    n_y: integer
+        The size of the grid in the y direction
+    n_z: integer
+        The size of the grid in the z direction
+
+    Returns
+    -------
+    edges : (2, N) ndarray
+        with the total number of edges::
+
+            N = n_x * n_y * (nz - 1) +
+                n_x * (n_y - 1) * nz +
+                (n_x - 1) * n_y * nz
+
+        Graph edges with each column describing a node-id pair.
+    """
+    vertices = cp.arange(n_x * n_y * n_z).reshape((n_x, n_y, n_z))
+    edges_deep = cp.stack((vertices[..., :-1].ravel(),
+                           vertices[..., 1:].ravel()), axis=0)
+    edges_right = cp.stack((vertices[:, :-1].ravel(),
+                            vertices[:, 1:].ravel()), axis=0)
+    edges_down = cp.stack((vertices[:-1].ravel(), vertices[1:].ravel()),
+                          axis=0)
+    edges = cp.concatenate((edges_deep, edges_right, edges_down), axis=1)
+    return edges
+
+
+def _compute_weights_3d(data, spacing, beta, eps, multichannel):
+    # Weight calculation is main difference in multispectral version
+    # Original gradient**2 replaced with sum of gradients ** 2
+    gradients = cp.concatenate(
+        [cp.diff(data[..., 0], axis=ax).ravel() / spacing[ax]
+         for ax in [2, 1, 0] if data.shape[ax] > 1], axis=0)
+    gradients *= gradients
+    for channel in range(1, data.shape[-1]):
+        grad = cp.concatenate(
+            [cp.diff(data[..., channel], axis=ax).ravel() / spacing[ax]
+             for ax in [2, 1, 0] if data.shape[ax] > 1], axis=0)
+        grad *= grad
+        gradients += grad
+
+    # All channels considered together in this standard deviation
+    scale_factor = -beta / (10 * data.std())
+    if multichannel:
+        # New final term in beta to give == results in trivial case where
+        # multiple identical spectra are passed.
+        scale_factor /= math.sqrt(data.shape[-1])
+    weights = cp.exp(scale_factor * gradients)
+    weights += eps
+    return -weights
+
+
+def _build_laplacian(data, spacing, mask, beta, multichannel):
+    l_x, l_y, l_z = data.shape[:3]
+    edges = _make_graph_edges_3d(l_x, l_y, l_z)
+    weights = _compute_weights_3d(data, spacing, beta=beta, eps=1.e-10,
+                                  multichannel=multichannel)
+    assert weights.dtype == data.dtype
+    if mask is not None:
+        # Remove edges of the graph connected to masked nodes, as well
+        # as corresponding weights of the edges.
+        mask0 = cp.concatenate([mask[..., :-1].ravel(), mask[:, :-1].ravel(),
+                                mask[:-1].ravel()])
+        mask1 = cp.concatenate([mask[..., 1:].ravel(), mask[:, 1:].ravel(),
+                                mask[1:].ravel()])
+        ind_mask = cp.logical_and(mask0, mask1)
+        edges, weights = edges[:, ind_mask], weights[ind_mask]
+
+        # Reassign edges labels to 0, 1, ... edges_number - 1
+        _, inv_idx = cp.unique(edges, return_inverse=True)
+        edges = inv_idx.reshape(edges.shape)
+
+    # Build the sparse linear system
+    pixel_nb = l_x * l_y * l_z
+    i_indices = edges.ravel()
+    j_indices = edges[::-1].ravel()
+    data = cp.concatenate((weights, weights))
+    lap = sparse.coo_matrix((data, (i_indices, j_indices)),
+                            shape=(pixel_nb, pixel_nb))
+    # need CSR instead of COO for indexing used later in _build_linear_system
+    lap = lap.tocsr()
+    lap.setdiag(-cp.ravel(lap.sum(axis=0)))
+    return lap
+
+
+def _build_linear_system(data, spacing, labels, nlabels, mask,
+                         beta, multichannel):
+    """
+    Build the matrix A and rhs B of the linear system to solve.
+    A and B are two block of the laplacian of the image graph.
+    """
+    if mask is None:
+        labels = labels.ravel()
+    else:
+        labels = labels[mask]
+
+    indices = cp.arange(labels.size)
+    seeds_mask = labels > 0
+    unlabeled_indices = indices[~seeds_mask]
+    seeds_indices = indices[seeds_mask]
+
+    lap_sparse = _build_laplacian(data, spacing, mask=mask,
+                                  beta=beta, multichannel=multichannel)
+
+    rows = lap_sparse[unlabeled_indices, :]
+    lap_sparse = rows[:, unlabeled_indices]
+    B = -rows[:, seeds_indices]
+
+    seeds = labels[seeds_mask]
+    # CuPy Backend: sparse matrices are only implemented for floating point
+    #               dtypes, so have to convert bool->float32 here
+    seeds_mask = sparse.csc_matrix(
+        cp.stack([(seeds == lab) for lab in range(1, nlabels + 1)],
+                 axis=-1).astype(np.float32)
+    )
+    rhs = B.dot(seeds_mask)
+
+    return lap_sparse, rhs
+
+
+def _solve_linear_system(lap_sparse, B, tol, mode):
+
+    if mode is None:
+        mode = 'cg_j'
+
+    if mode == 'cg_mg' and not amg_loaded:
+        warn('"cg_mg" not available. The "cg_j" mode will be used instead.',
+             stacklevel=2)
+        mode = 'cg_j'
+
+    if mode == 'bf':
+        # toarray will give a C contiguous output as desired
+        B = B.T.toarray()
+        X = cp.zeros_like(B)
+        for n, b in enumerate(B):
+            X[n, :] = spsolve(lap_sparse, b)
+    else:
+        maxiter = None
+        if mode == 'cg':
+            M = None
+        elif mode == 'cg_j':
+            M = sparse.diags(1.0 / lap_sparse.diagonal())
+        else:
+            raise NotImplementedError("cg_mg not implemented")
+            # # mode == 'cg_mg'
+            # lap_sparse = lap_sparse.tocsr()
+            # ml = ruge_stuben_solver(lap_sparse)
+            # M = ml.aspreconditioner(cycle='V')
+            # maxiter = 30
+        cg_out = [
+            cg(lap_sparse, B[:, i].toarray(), tol=tol, M=M, maxiter=maxiter)
+            for i in range(B.shape[1])]
+        if any([info > 0 for _, info in cg_out]):
+            warn("Conjugate gradient convergence to tolerance not achieved. "
+                 "Consider decreasing beta to improve system conditionning.",
+                 stacklevel=2)
+        X = cp.stack([x for x, _ in cg_out], axis=0)
+
+    return X
+
+
+def _preprocess(labels):
+
+    label_values, inv_idx = cp.unique(labels, return_inverse=True)
+    if not (label_values == 0).any():
+        warn('Random walker only segments unlabeled areas, where '
+             'labels == 0. No zero valued areas in labels were '
+             'found. Returning provided labels.',
+             stacklevel=2)
+
+        return labels, None, None, None, None
+
+    # If some labeled pixels are isolated inside pruned zones, prune them
+    # as well and keep the labels for the final output
+
+    null_mask = labels == 0
+    pos_mask = labels > 0
+    mask = labels >= 0
+
+    fill = ndi.binary_propagation(null_mask, mask=mask)
+    isolated = cp.logical_and(pos_mask, cp.logical_not(fill))
+
+    pos_mask[isolated] = False
+
+    # If the array has pruned zones, be sure that no isolated pixels
+    # exist between pruned zones (they could not be determined)
+    if label_values[0] < 0 or cp.any(isolated):  # synchronize!
+        isolated = cp.logical_and(
+            cp.logical_not(ndi.binary_propagation(pos_mask, mask=mask)),
+            null_mask)
+
+        labels[isolated] = -1
+        if cp.all(isolated[null_mask]):
+            warn('All unlabeled pixels are isolated, they could not be '
+                 'determined by the random walker algorithm.',
+                 stacklevel=2)
+            return labels, None, None, None, None
+
+        mask[isolated] = False
+        mask = cp.atleast_3d(mask)
+
+    else:
+        mask = None
+
+    # Reorder label values to have consecutive integers (no gaps)
+    zero_idx = cp.searchsorted(label_values, cp.array(0))
+    labels = cp.atleast_3d(inv_idx.reshape(labels.shape) - zero_idx)
+
+    nlabels = label_values[zero_idx + 1:].shape[0]
+
+    inds_isolated_seeds = cp.nonzero(isolated)
+    isolated_values = labels[inds_isolated_seeds]
+
+    return labels, nlabels, mask, inds_isolated_seeds, isolated_values
+
+
+def random_walker(data, labels, beta=130, mode='cg_j', tol=1.e-3, copy=True,
+                  multichannel=False, return_full_prob=False, spacing=None,
+                  *, prob_tol=1e-3):
+    """Random walker algorithm for segmentation from markers.
+
+    Random walker algorithm is implemented for gray-level or multichannel
+    images.
+
+    Parameters
+    ----------
+    data : array_like
+        Image to be segmented in phases. Gray-level `data` can be two- or
+        three-dimensional; multichannel data can be three- or four-
+        dimensional (multichannel=True) with the highest dimension denoting
+        channels. Data spacing is assumed isotropic unless the `spacing`
+        keyword argument is used.
+    labels : array of ints, of same shape as `data` without channels dimension
+        Array of seed markers labeled with different positive integers
+        for different phases. Zero-labeled pixels are unlabeled pixels.
+        Negative labels correspond to inactive pixels that are not taken
+        into account (they are removed from the graph). If labels are not
+        consecutive integers, the labels array will be transformed so that
+        labels are consecutive. In the multichannel case, `labels` should have
+        the same shape as a single channel of `data`, i.e. without the final
+        dimension denoting channels.
+    beta : float, optional
+        Penalization coefficient for the random walker motion
+        (the greater `beta`, the more difficult the diffusion).
+    mode : string, available options {'cg', 'cg_j', 'cg_mg', 'bf'}
+        Mode for solving the linear system in the random walker algorithm.
+
+        - 'bf' (brute force): an LU factorization of the Laplacian is
+          computed. This is fast for small images (<1024x1024), but very slow
+          and memory-intensive for large images (e.g., 3-D volumes).
+        - 'cg' (conjugate gradient): the linear system is solved iteratively
+          using the Conjugate Gradient method from scipy.sparse.linalg. This is
+          less memory-consuming than the brute force method for large images,
+          but it is quite slow.
+        - 'cg_j' (conjugate gradient with Jacobi preconditionner): the
+          Jacobi preconditionner is applyed during the Conjugate
+          gradient method iterations. This may accelerate the
+          convergence of the 'cg' method.
+        - 'cg_mg' (conjugate gradient with multigrid preconditioner): a
+          preconditioner is computed using a multigrid solver, then the
+          solution is computed with the Conjugate Gradient method. This mode
+          requires that the pyamg module is installed.
+    tol : float, optional
+        Tolerance to achieve when solving the linear system using
+        the conjugate gradient based modes ('cg', 'cg_j' and 'cg_mg').
+    copy : bool, optional
+        If copy is False, the `labels` array will be overwritten with
+        the result of the segmentation. Use copy=False if you want to
+        save on memory.
+    multichannel : bool, optional
+        If True, input data is parsed as multichannel data (see 'data' above
+        for proper input format in this case).
+    return_full_prob : bool, optional
+        If True, the probability that a pixel belongs to each of the
+        labels will be returned, instead of only the most likely
+        label.
+    spacing : iterable of floats, optional
+        Spacing between voxels in each spatial dimension. If `None`, then
+        the spacing between pixels/voxels in each dimension is assumed 1.
+    prob_tol : float, optional
+        Tolerance on the resulting probability to be in the interval [0, 1].
+        If the tolerance is not satisfied, a warning is displayed.
+
+    Returns
+    -------
+    output : ndarray
+        * If `return_full_prob` is False, array of ints of same shape
+          and data type as `labels`, in which each pixel has been
+          labeled according to the marker that reached the pixel first
+          by anisotropic diffusion.
+        * If `return_full_prob` is True, array of floats of shape
+          `(nlabels, labels.shape)`. `output[label_nb, i, j]` is the
+          probability that label `label_nb` reaches the pixel `(i, j)`
+          first.
+
+    See Also
+    --------
+    skimage.morphology.watershed : watershed segmentation
+        A segmentation algorithm based on mathematical morphology
+        and "flooding" of regions from markers.
+
+    Notes
+    -----
+    Multichannel inputs are scaled with all channel data combined. Ensure all
+    channels are separately normalized prior to running this algorithm.
+
+    The `spacing` argument is specifically for anisotropic datasets, where
+    data points are spaced differently in one or more spatial dimensions.
+    Anisotropic data is commonly encountered in medical imaging.
+
+    The algorithm was first proposed in [1]_.
+
+    The algorithm solves the diffusion equation at infinite times for
+    sources placed on markers of each phase in turn. A pixel is labeled with
+    the phase that has the greatest probability to diffuse first to the pixel.
+
+    The diffusion equation is solved by minimizing x.T L x for each phase,
+    where L is the Laplacian of the weighted graph of the image, and x is
+    the probability that a marker of the given phase arrives first at a pixel
+    by diffusion (x=1 on markers of the phase, x=0 on the other markers, and
+    the other coefficients are looked for). Each pixel is attributed the label
+    for which it has a maximal value of x. The Laplacian L of the image
+    is defined as:
+
+       - L_ii = d_i, the number of neighbors of pixel i (the degree of i)
+       - L_ij = -w_ij if i and j are adjacent pixels
+
+    The weight w_ij is a decreasing function of the norm of the local gradient.
+    This ensures that diffusion is easier between pixels of similar values.
+
+    When the Laplacian is decomposed into blocks of marked and unmarked
+    pixels::
+
+        L = M B.T
+            B A
+
+    with first indices corresponding to marked pixels, and then to unmarked
+    pixels, minimizing x.T L x for one phase amount to solving::
+
+        A x = - B x_m
+
+    where x_m = 1 on markers of the given phase, and 0 on other markers.
+    This linear system is solved in the algorithm using a direct method for
+    small images, and an iterative method for larger images.
+
+    References
+    ----------
+    .. [1] Leo Grady, Random walks for image segmentation, IEEE Trans Pattern
+        Anal Mach Intell. 2006 Nov;28(11):1768-83.
+        :DOI:`10.1109/TPAMI.2006.233`.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> cp.random.seed(0)
+    >>> a = cp.zeros((10, 10)) + 0.2 * cp.random.rand(10, 10)
+    >>> a[5:8, 5:8] += 1
+    >>> b = cp.zeros_like(a, dtype=cp.int32)
+    >>> b[3, 3] = 1  # Marker for first phase
+    >>> b[6, 6] = 2  # Marker for second phase
+    >>> random_walker(a, b)
+    array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 2, 2, 2, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=int32)
+
+    """
+    # Parse input data
+    if mode not in ('cg_mg', 'cg', 'bf', 'cg_j', None):
+        raise ValueError(
+            "{mode} is not a valid mode. Valid modes are 'cg_mg',"
+            " 'cg', 'cg_j', 'bf' and None".format(mode=mode))
+
+    if data.dtype.kind == 'f':
+        float_dtype = cp.promote_types(data.dtype, cp.float32)
+    else:
+        float_dtype = cp.float64
+
+    # Spacing kwarg checks
+    if spacing is None:
+        spacing = cp.ones(3, dtype=float_dtype)
+    elif len(spacing) == labels.ndim:
+        if len(spacing) == 2:
+            # Need a dummy spacing for singleton 3rd dim
+            spacing = cp.r_[spacing, 1.]
+        spacing = cp.asarray(spacing, dtype=float_dtype)
+    else:
+        raise ValueError('Input argument `spacing` incorrect, should be an '
+                         'iterable with one number per spatial dimension.')
+
+    # This algorithm expects 4-D arrays of floats, where the first three
+    # dimensions are spatial and the final denotes channels. 2-D images have
+    # a singleton placeholder dimension added for the third spatial dimension,
+    # and single channel images likewise have a singleton added for channels.
+    # The following block ensures valid input and coerces it to the correct
+    # form.
+    if not multichannel:
+        if data.ndim not in (2, 3):
+            raise ValueError('For non-multichannel input, data must be of '
+                             'dimension 2 or 3.')
+        if data.shape != labels.shape:
+            raise ValueError('Incompatible data and labels shapes.')
+        data = cp.atleast_3d(img_as_float(data))[..., cp.newaxis]
+    else:
+        if data.ndim not in (3, 4):
+            raise ValueError('For multichannel input, data must have 3 or 4 '
+                             'dimensions.')
+        if data.shape[:-1] != labels.shape:
+            raise ValueError('Incompatible data and labels shapes.')
+        data = img_as_float(data)
+        if data.ndim == 3:  # 2D multispectral, needs singleton in 3rd axis
+            data = data[:, :, cp.newaxis, :]
+
+    labels_shape = labels.shape
+    labels_dtype = labels.dtype
+
+    if copy:
+        labels = cp.copy(labels)
+
+    (labels, nlabels, mask,
+     inds_isolated_seeds, isolated_values) = _preprocess(labels)
+
+    if isolated_values is None:
+        # No non isolated zero valued areas in labels were
+        # found. Returning provided labels.
+        if return_full_prob:
+            # Return the concatenation of the masks of each unique label
+            unique_labels = cp.unique(labels)
+            labels = cp.atleast_3d(labels)
+            return cp.concatenate([labels == lab
+                                   for lab in unique_labels if lab > 0],
+                                  axis=-1)
+        return labels
+
+    # Build the linear system (lap_sparse, B)
+    lap_sparse, B = _build_linear_system(data, spacing, labels, nlabels, mask,
+                                         beta, multichannel)
+
+    # Solve the linear system lap_sparse X = B
+    # where X[i, j] is the probability that a marker of label i arrives
+    # first at pixel j by anisotropic diffusion.
+    X = _solve_linear_system(lap_sparse, B, tol, mode)
+
+    if X.min() < -prob_tol or X.max() > 1 + prob_tol:
+        warn('The probability range is outside [0, 1] given the tolerance '
+             '`prob_tol`. Consider decreasing `beta` and/or decreasing '
+             '`tol`.')
+
+    # Build the output according to return_full_prob value
+    # Put back labels of isolated seeds
+    labels[inds_isolated_seeds] = isolated_values
+    labels = labels.reshape(labels_shape)
+
+    mask = labels == 0
+    mask[inds_isolated_seeds] = False
+
+    if return_full_prob:
+        out = cp.zeros((nlabels,) + labels_shape)
+        for lab, (label_prob, prob) in enumerate(zip(out, X), start=1):
+            label_prob[mask] = prob
+            label_prob[labels == lab] = 1
+    else:
+        X = cp.argmax(X, axis=0) + 1
+        out = labels.astype(labels_dtype)
+        out[mask] = X
+
+    return out
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_boundaries.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_boundaries.py
new file mode 100644
index 000000000..f0c79d0b2
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_boundaries.py
@@ -0,0 +1,141 @@
+import cupy as cp
+import numpy as np
+from cupy.testing import assert_allclose, assert_array_equal
+
+from cucim.skimage.segmentation import find_boundaries, mark_boundaries
+
+white = (1, 1, 1)
+
+
+def test_find_boundaries():
+    image = cp.zeros((10, 10), dtype=cp.uint8)
+    image[2:7, 2:7] = 1
+
+    # fmt: off
+    ref = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    # fmt: on
+    result = find_boundaries(image)
+    assert_array_equal(result, ref)
+
+
+def test_find_boundaries_bool():
+    image = cp.zeros((5, 5), dtype=bool)
+    image[2:5, 2:5] = True
+
+    # fmt: off
+    ref = cp.array([[False, False, False, False, False],  # noqa
+                    [False, False,  True,  True,  True],  # noqa
+                    [False,  True,  True,  True,  True],  # noqa
+                    [False,  True,  True, False, False],  # noqa
+                    [False,  True,  True, False, False]], dtype=bool)  # noqa
+    # fmt: on
+    result = find_boundaries(image)
+    assert_array_equal(result, ref)
+
+
+def test_mark_boundaries():
+    image = cp.zeros((10, 10))
+    label_image = cp.zeros((10, 10), dtype=cp.uint8)
+    label_image[2:7, 2:7] = 1
+
+    # fmt: off
+    ref = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    # fmt: on
+    marked = mark_boundaries(image, label_image, color=white, mode='thick')
+    result = cp.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+    # fmt: off
+    ref = cp.array([[0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
+                    [2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
+                    [2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
+                    [2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
+                    [2, 1, 1, 2, 0, 2, 1, 1, 2, 0],
+                    [2, 1, 1, 2, 2, 2, 1, 1, 2, 0],
+                    [2, 1, 1, 1, 1, 1, 1, 1, 2, 0],
+                    [2, 2, 1, 1, 1, 1, 1, 2, 2, 0],
+                    [0, 2, 2, 2, 2, 2, 2, 2, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    # fmt: on
+    marked = mark_boundaries(image, label_image, color=white,
+                             outline_color=(2, 2, 2), mode='thick')
+    result = cp.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+
+
+def test_mark_boundaries_bool():
+    image = cp.zeros((10, 10), dtype=bool)
+    label_image = cp.zeros((10, 10), dtype=cp.uint8)
+    label_image[2:7, 2:7] = 1
+    # fmt: off
+    ref = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 0, 0, 0, 1, 1, 0, 0],
+                    [0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
+    # fmt: on
+    marked = mark_boundaries(image, label_image, color=white, mode='thick')
+    result = cp.mean(marked, axis=-1)
+    assert_array_equal(result, ref)
+
+
+@cp.testing.with_requires('cupy>=9.0.0b2')
+def test_mark_boundaries_subpixel():
+    # fmt: off
+    labels = cp.array([[0, 0, 0, 0],
+                       [0, 0, 5, 0],
+                       [0, 1, 5, 0],
+                       [0, 0, 5, 0],
+                       [0, 0, 0, 0]], dtype=np.uint8)
+    np.random.seed(0)
+    # fmt: on
+    # Note: use np.random to have same seed as NumPy
+    # Note: use np.round until cp.around is fixed upstream
+    image = cp.asarray(np.round(np.random.rand(*labels.shape), 2))
+    marked = mark_boundaries(image, labels, color=white, mode='subpixel')
+    marked_proj = cp.asarray(cp.around(cp.mean(marked, axis=-1), 2))
+
+    # fmt: off
+    ref_result = cp.array(
+        [[0.55, 0.63, 0.72, 0.69, 0.6 , 0.55, 0.54],   # noqa
+         [0.45, 0.58, 0.72, 1.  , 1.  , 1.  , 0.69],   # noqa
+         [0.42, 0.54, 0.65, 1.  , 0.44, 1.  , 0.89],   # noqa
+         [0.69, 1.  , 1.  , 1.  , 0.69, 1.  , 0.83],   # noqa
+         [0.96, 1.  , 0.38, 1.  , 0.79, 1.  , 0.53],   # noqa
+         [0.89, 1.  , 1.  , 1.  , 0.38, 1.  , 0.16],   # noqa
+         [0.57, 0.78, 0.93, 1.  , 0.07, 1.  , 0.09],   # noqa
+         [0.2 , 0.52, 0.92, 1.  , 1.  , 1.  , 0.54],   # noqa
+         [0.02, 0.35, 0.83, 0.9 , 0.78, 0.81, 0.87]])  # noqa
+    # fmt: on
+
+    # TODO: get fully equivalent interpolation/boundary as skimage
+    #       I think this requires fixing mode='reflect' upstream in SciPy
+    if False:
+        assert_allclose(marked_proj, ref_result, atol=0.01)
+    else:
+        # Note: grlee77: only test locations of ones, due to different default
+        #                interpolation settings in CuPy version of mark
+        #                 boundaries
+        assert_allclose(marked_proj == 1, ref_result == 1, atol=0.01)
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_join.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_join.py
new file mode 100644
index 000000000..dde4b5584
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_join.py
@@ -0,0 +1,211 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_equal
+
+from cucim.skimage.segmentation import join_segmentations, relabel_sequential
+
+
+def test_join_segmentations():
+    # fmt: off
+    s1 = cp.array([[0, 0, 1, 1],
+                   [0, 2, 1, 1],
+                   [2, 2, 2, 1]])
+    s2 = cp.array([[0, 1, 1, 0],
+                   [0, 1, 1, 0],
+                   [0, 1, 1, 1]])
+    # test correct join
+    # NOTE: technically, equality to j_ref is not required, only that there
+    # is a one-to-one mapping between j and j_ref. I don't know of an easy way
+    # to check this (i.e. not as error-prone as the function being tested)
+    j = join_segmentations(s1, s2)
+    j_ref = np.array([[0, 1, 3, 2],
+                      [0, 5, 3, 2],
+                      [4, 5, 5, 3]])
+    assert_array_equal(j, j_ref)
+    # fmt: on
+    # test correct exception when arrays are different shapes
+    s3 = cp.array([[0, 0, 1, 1], [0, 2, 2, 1]])
+    with pytest.raises(ValueError):
+        join_segmentations(s1, s3)
+
+
+def _check_maps(ar, ar_relab, fw, inv):
+    assert_array_equal(fw[ar], ar_relab)
+    assert_array_equal(inv[ar_relab], ar)
+
+
+def test_relabel_sequential_offset1():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42])
+    ar_relab, fw, inv = relabel_sequential(ar)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([1, 1, 2, 2, 3, 5, 4])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 1
+    fw_ref[5] = 2
+    fw_ref[8] = 3
+    fw_ref[42] = 4
+    fw_ref[99] = 5
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_offset5():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42])
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_offset5_with0():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0])
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar, ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_dtype():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=cp.uint8)
+    ar_relab, fw, inv = relabel_sequential(ar, offset=5)
+    _check_maps(ar.astype(int), ar_relab, fw, inv)
+    ar_relab_ref = np.array([5, 5, 6, 6, 7, 9, 8, 0])
+    assert_array_equal(ar_relab, ar_relab_ref)
+    fw_ref = np.zeros(100, int)
+    fw_ref[1] = 5
+    fw_ref[5] = 6
+    fw_ref[8] = 7
+    fw_ref[42] = 8
+    fw_ref[99] = 9
+    assert_array_equal(fw, fw_ref)
+    inv_ref = np.array([0, 0, 0, 0, 0, 1, 5, 8, 42, 99])
+    assert_array_equal(inv, inv_ref)
+
+
+def test_relabel_sequential_signed_overflow():
+    imax = cp.iinfo(cp.int32).max
+    labels = cp.array([0, 1, 99, 42, 42], dtype=cp.int32)
+    output, fw, inv = relabel_sequential(labels, offset=imax)
+    reference = cp.array([0, imax, imax + 2, imax + 1, imax + 1],
+                         dtype=cp.uint32)
+    assert_array_equal(output, reference)
+    assert output.dtype == reference.dtype
+
+
+def test_very_large_labels():
+    imax = cp.iinfo(cp.int64).max
+    labels = cp.asarray([0, 1, imax, 42, 42], dtype=cp.int64)
+    output, fw, inv = relabel_sequential(labels, offset=imax)
+    assert int(cp.max(output)) == imax + 2
+
+
+@pytest.mark.parametrize('dtype', (np.byte, np.short, np.intc, int,
+                                   np.longlong, np.ubyte, np.ushort,
+                                   np.uintc, np.uint, np.ulonglong))
+@pytest.mark.parametrize('data_already_sequential', (False, True))
+def test_relabel_sequential_int_dtype_stability(data_already_sequential,
+                                                dtype):
+    if data_already_sequential:
+        ar = cp.asarray([1, 3, 0, 2, 5, 4], dtype=dtype)
+    else:
+        ar = cp.asarray([1, 1, 5, 5, 8, 99, 42, 0], dtype=dtype)
+    assert all(a.dtype == dtype for a in relabel_sequential(ar))
+
+
+def test_relabel_sequential_int_dtype_overflow():
+    ar = cp.asarray([1, 3, 0, 2, 5, 4], dtype=cp.uint8)
+    offset = 254
+    ar_relab, fw, inv = relabel_sequential(ar, offset=offset)
+    _check_maps(ar, ar_relab, fw, inv)
+    assert all(a.dtype == cp.uint16 for a in (ar_relab, fw))
+    assert inv.dtype == ar.dtype
+    ar_relab_ref = cp.where(ar > 0, ar.astype(cp.int) + offset - 1, 0)
+    assert_array_equal(ar_relab, ar_relab_ref)
+
+
+def test_relabel_sequential_negative_values():
+    ar = cp.array([1, 1, 5, -5, 8, 99, 42, 0])
+    with pytest.raises(ValueError):
+        relabel_sequential(ar)
+
+
+@pytest.mark.parametrize('offset', (0, -3))
+@pytest.mark.parametrize('data_already_sequential', (False, True))
+def test_relabel_sequential_nonpositive_offset(data_already_sequential,
+                                               offset):
+    if data_already_sequential:
+        ar = cp.array([1, 3, 0, 2, 5, 4])
+    else:
+        ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0])
+    with pytest.raises(ValueError):
+        relabel_sequential(ar, offset=offset)
+
+
+@pytest.mark.parametrize('offset', (1, 5))
+@pytest.mark.parametrize('with0', (False, True))
+@pytest.mark.parametrize('input_starts_at_offset', (False, True))
+def test_relabel_sequential_already_sequential(offset, with0,
+                                               input_starts_at_offset):
+    if with0:
+        ar = cp.array([1, 3, 0, 2, 5, 4])
+    else:
+        ar = cp.array([1, 3, 2, 5, 4])
+    if input_starts_at_offset:
+        ar[ar > 0] += offset - 1
+    ar_relab, fw, inv = relabel_sequential(ar, offset=offset)
+    _check_maps(ar, ar_relab, fw, inv)
+    if input_starts_at_offset:
+        ar_relab_ref = ar
+    else:
+        ar_relab_ref = cp.where(ar > 0, ar + offset - 1, 0)
+    assert_array_equal(ar_relab, ar_relab_ref)
+
+
+def test_incorrect_input_dtype():
+    labels = cp.array([0, 2, 2, 1, 1, 8], dtype=float)
+    with pytest.raises(TypeError):
+        relabel_sequential(labels)
+
+
+def test_arraymap_call():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=cp.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    assert_array_equal(relabeled, fw(ar))
+    assert_array_equal(ar, inv(relabeled))
+
+
+def test_arraymap_len():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=cp.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    assert len(fw) == 100
+    assert len(fw) == len(cp.asarray(fw))
+    assert len(inv) == 6
+    assert len(inv) == len(cp.asarray(inv))
+
+
+def test_arraymap_set():
+    ar = cp.array([1, 1, 5, 5, 8, 99, 42, 0], dtype=cp.intp)
+    relabeled, fw, inv = relabel_sequential(ar)
+    fw[72] = 6
+    assert fw[72] == 6
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_morphsnakes.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_morphsnakes.py
new file mode 100644
index 000000000..66d1549b9
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_morphsnakes.py
@@ -0,0 +1,154 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_array_equal
+
+from cucim.skimage._shared.testing import expected_warnings
+from cucim.skimage.segmentation import (circle_level_set, disk_level_set,
+                                        inverse_gaussian_gradient,
+                                        morphological_chan_vese,
+                                        morphological_geodesic_active_contour)
+
+
+def gaussian_blob():
+    coords = cp.mgrid[-5:6, -5:6]
+    sqrdistances = (coords ** 2).sum(0)
+    return cp.exp(-sqrdistances / 10)
+
+
+def test_morphsnakes_incorrect_image_shape():
+    img = cp.zeros((10, 10, 3))
+    ls = cp.zeros((10, 9))
+
+    with pytest.raises(ValueError):
+        morphological_chan_vese(img, iterations=1, init_level_set=ls)
+    with pytest.raises(ValueError):
+        morphological_geodesic_active_contour(img, iterations=1,
+                                              init_level_set=ls)
+
+
+def test_morphsnakes_incorrect_ndim():
+    img = cp.zeros((4, 4, 4, 4))
+    ls = cp.zeros((4, 4, 4, 4))
+
+    with pytest.raises(ValueError):
+        morphological_chan_vese(img, iterations=1, init_level_set=ls)
+    with pytest.raises(ValueError):
+        morphological_geodesic_active_contour(img, iterations=1,
+                                              init_level_set=ls)
+
+
+def test_morphsnakes_black():
+    img = cp.zeros((11, 11))
+    ls = disk_level_set(img.shape, center=(5, 5), radius=3)
+
+    ref_zeros = cp.zeros(img.shape, dtype=cp.int8)
+    ref_ones = cp.ones(img.shape, dtype=cp.int8)
+
+    acwe_ls = morphological_chan_vese(img, iterations=6, init_level_set=ls)
+    assert_array_equal(acwe_ls, ref_zeros)
+
+    gac_ls = morphological_geodesic_active_contour(img, iterations=6,
+                                                   init_level_set=ls)
+
+    assert_array_equal(gac_ls, ref_zeros)
+
+    gac_ls2 = morphological_geodesic_active_contour(img, iterations=6,
+                                                    init_level_set=ls,
+                                                    balloon=1, threshold=-1,
+                                                    smoothing=0)
+
+    assert_array_equal(gac_ls2, ref_ones)
+
+    assert acwe_ls.dtype == gac_ls.dtype == gac_ls2.dtype == cp.int8
+
+
+def test_morphsnakes_simple_shape_chan_vese():
+    img = gaussian_blob()
+    ls1 = disk_level_set(img.shape, center=(5, 5), radius=3)
+    ls2 = disk_level_set(img.shape, center=(5, 5), radius=6)
+
+    acwe_ls1 = morphological_chan_vese(img, iterations=10, init_level_set=ls1)
+    acwe_ls2 = morphological_chan_vese(img, iterations=10, init_level_set=ls2)
+
+    assert_array_equal(acwe_ls1, acwe_ls2)
+
+    assert acwe_ls1.dtype == acwe_ls2.dtype == cp.int8
+
+
+def test_morphsnakes_simple_shape_geodesic_active_contour():
+    img = disk_level_set((11, 11), center=(5, 5), radius=3.5).astype(float)
+    gimg = inverse_gaussian_gradient(img, alpha=10.0, sigma=1.0)
+    ls = disk_level_set(img.shape, center=(5, 5), radius=6)
+
+    # fmt: off
+    ref = cp.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0],
+                    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
+                   dtype=cp.int8)
+    # fmt: on
+    gac_ls = morphological_geodesic_active_contour(gimg, iterations=10,
+                                                   init_level_set=ls,
+                                                   balloon=-1)
+
+    assert_array_equal(gac_ls, ref)
+    assert gac_ls.dtype == cp.int8
+
+
+def test_deprecated_circle_level_set():
+    img = cp.array(gaussian_blob())
+    with expected_warnings(["circle_level_set is deprecated"]):
+        circle_level_set(img.shape, (5, 5), 3)
+
+
+def test_init_level_sets():
+    image = cp.zeros((6, 6))
+    checkerboard_ls = morphological_chan_vese(image, 0, 'checkerboard')
+    # fmt: off
+    checkerboard_ref = cp.array([[0, 0, 0, 0, 0, 1],
+                                 [0, 0, 0, 0, 0, 1],
+                                 [0, 0, 0, 0, 0, 1],
+                                 [0, 0, 0, 0, 0, 1],
+                                 [0, 0, 0, 0, 0, 1],
+                                 [1, 1, 1, 1, 1, 0]], dtype=cp.int8)
+
+    disk_ls = morphological_geodesic_active_contour(image, 0, "disk")
+    disk_ref = cp.array([[0, 0, 0, 0, 0, 0],
+                         [0, 0, 1, 1, 1, 0],
+                         [0, 1, 1, 1, 1, 1],
+                         [0, 1, 1, 1, 1, 1],
+                         [0, 1, 1, 1, 1, 1],
+                         [0, 0, 1, 1, 1, 0]], dtype=cp.int8)
+    # fmt: on
+
+    assert_array_equal(checkerboard_ls, checkerboard_ref)
+    assert_array_equal(disk_ls, disk_ref)
+
+
+def test_morphsnakes_3d():
+    image = cp.zeros((7, 7, 7))
+
+    evolution = []
+
+    def callback(x):
+        evolution.append(x.sum())
+
+    ls = morphological_chan_vese(image, 5, 'disk',
+                                 iter_callback=callback)
+
+    # Check that the initial disk level set is correct
+    assert evolution[0] == 81
+
+    # Check that the final level set is correct
+    assert ls.sum() == 0
+
+    # Check that the contour is shrinking at every iteration
+    for v1, v2 in zip(evolution[:-1], evolution[1:]):
+        assert v1 >= v2
diff --git a/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py b/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py
new file mode 100644
index 000000000..ba0434fcb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/segmentation/tests/test_random_walker.py
@@ -0,0 +1,457 @@
+import cupy as cp
+import numpy as np
+
+from cucim.skimage._shared import testing
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.segmentation import random_walker
+from cucim.skimage.transform import resize
+
+
+def make_2d_syntheticdata(lx, ly=None):
+    if ly is None:
+        ly = lx
+    np.random.seed(1234)
+    data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly)
+    small_l = int(lx // 5)
+    data[lx // 2 - small_l:lx // 2 + small_l,
+         ly // 2 - small_l:ly // 2 + small_l] = 1
+    data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
+         ly // 2 - small_l + 1:ly // 2 + small_l - 1] = (
+        0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2))
+    data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0
+    seeds = np.zeros_like(data)
+    seeds[lx // 5, ly // 5] = 1
+    seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2
+    return cp.array(data), cp.array(seeds)
+
+
+def make_3d_syntheticdata(lx, ly=None, lz=None):
+    if ly is None:
+        ly = lx
+    if lz is None:
+        lz = lx
+    np.random.seed(1234)
+    data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz)
+    small_l = int(lx // 5)
+    data[lx // 2 - small_l:lx // 2 + small_l,
+         ly // 2 - small_l:ly // 2 + small_l,
+         lz // 2 - small_l:lz // 2 + small_l] = 1
+    data[lx // 2 - small_l + 1:lx // 2 + small_l - 1,
+         ly // 2 - small_l + 1:ly // 2 + small_l - 1,
+         lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0
+    # make a hole
+    hole_size = np.max([1, small_l // 8])
+    data[lx // 2 - small_l,
+         ly // 2 - hole_size:ly // 2 + hole_size,
+         lz // 2 - hole_size:lz // 2 + hole_size] = 0
+    seeds = np.zeros_like(data)
+    seeds[lx // 5, ly // 5, lz // 5] = 1
+    seeds[lx // 2 + small_l // 4,
+          ly // 2 - small_l // 4,
+          lz // 2 - small_l // 4] = 2
+    return cp.array(data), cp.array(seeds)
+
+
+def test_2d_bf():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels_bf = random_walker(data, labels, beta=90, mode='bf')
+    assert (labels_bf[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    full_prob_bf = random_walker(data, labels, beta=90, mode='bf',
+                                 return_full_prob=True)
+    assert (full_prob_bf[1, 25:45, 40:60] >=
+            full_prob_bf[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+    # Now test with more than two labels
+    labels[55, 80] = 3
+    full_prob_bf = random_walker(data, labels, beta=90, mode='bf',
+                                 return_full_prob=True)
+    assert (full_prob_bf[1, 25:45, 40:60] >=
+            full_prob_bf[0, 25:45, 40:60]).all()
+    assert len(full_prob_bf) == 3
+    assert data.shape == labels.shape
+
+
+def test_2d_cg():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels_cg = random_walker(data, labels, beta=90, mode='cg')
+    assert (labels_cg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    full_prob = random_walker(data, labels, beta=90, mode='cg',
+                              return_full_prob=True)
+    assert (full_prob[1, 25:45, 40:60] >=
+            full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+    return data, labels_cg
+
+
+def test_2d_cg_mg():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    with expected_warnings(['"cg_mg" not available']):
+        labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
+    assert (labels_cg_mg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    with expected_warnings(['"cg_mg" not available']):
+        full_prob = random_walker(data, labels, beta=90, mode='cg_mg',
+                                  return_full_prob=True)
+    assert (full_prob[1, 25:45, 40:60] >=
+            full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+    return data, labels_cg_mg
+
+
+def test_2d_cg_j():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels_cg = random_walker(data, labels, beta=90, mode='cg_j')
+    assert (labels_cg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    full_prob = random_walker(data, labels, beta=90, mode='cg_j',
+                              return_full_prob=True)
+    assert (full_prob[1, 25:45, 40:60]
+            >= full_prob[0, 25:45, 40:60]).all()
+    assert data.shape == labels.shape
+
+
+def test_types():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = 255 * (data - data.min()) // (data.max() - data.min())
+    data = data.astype(np.uint8)
+    with expected_warnings(['"cg_mg" not available']):
+        labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg')
+    assert (labels_cg_mg[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels_cg_mg
+
+
+def test_reorder_labels():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels[labels == 2] = 4
+    labels_bf = random_walker(data, labels, beta=90, mode='bf')
+    assert (labels_bf[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels_bf
+
+
+def test_reorder_labels_cg():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels[labels == 2] = 4
+    labels_bf = random_walker(data, labels, beta=90, mode='cg')
+    assert (labels_bf[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels_bf
+
+
+def test_2d_inactive():
+    lx = 70
+    ly = 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    labels[10:20, 10:20] = -1
+    labels[46:50, 33:38] = -2
+    labels = random_walker(data, labels, beta=90)
+    assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels
+
+
+def test_3d():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    labels = random_walker(data, labels, mode='cg')
+    assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels
+
+
+def test_3d_inactive():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    old_labels = cp.copy(labels)
+    labels[5:25, 26:29, 26:29] = -1
+    after_labels = cp.copy(labels)
+    labels = random_walker(data, labels, mode='cg')
+    assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data.shape == labels.shape
+    return data, labels, old_labels, after_labels
+
+
+def test_multispectral_2d():
+    lx, ly = 70, 100
+    data, labels = make_2d_syntheticdata(lx, ly)
+    data = data[..., cp.newaxis].repeat(2, axis=-1)  # Expect identical output
+    with expected_warnings(['The probability range is outside']):
+        multi_labels = random_walker(data, labels, mode='cg',
+                                     multichannel=True)
+    assert data[..., 0].shape == labels.shape
+    single_labels = random_walker(data[..., 0], labels, mode='cg')
+    assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all()
+    assert data[..., 0].shape == labels.shape
+    return data, multi_labels, single_labels, labels
+
+
+def test_multispectral_3d():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, labels = make_3d_syntheticdata(lx, ly, lz)
+    data = data[..., cp.newaxis].repeat(2, axis=-1)  # Expect identical output
+    multi_labels = random_walker(data, labels, mode='cg',
+                                 multichannel=True)
+    assert data[..., 0].shape == labels.shape
+    single_labels = random_walker(data[..., 0], labels, mode='cg')
+    assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all()
+    assert data[..., 0].shape == labels.shape
+    return data, multi_labels, single_labels, labels
+
+
+def test_spacing_0():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, _ = make_3d_syntheticdata(lx, ly, lz)
+
+    # Rescale `data` along Z axis
+    data_aniso = cp.zeros((n, n, n // 2))
+    for i, yz in enumerate(data):
+        data_aniso[i, :, :] = resize(yz, (n, n // 2),
+                                     mode='constant',
+                                     anti_aliasing=False)
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso = cp.zeros_like(data_aniso)
+    labels_aniso[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso[lx // 2 + small_l // 4,
+                 ly // 2 - small_l // 4,
+                 lz // 4 - small_l // 8] = 2
+
+    # Test with `spacing` kwarg
+    labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
+                                 spacing=cp.array((1.0, 1.0, 0.5)))
+
+    assert (labels_aniso[13:17, 13:17, 7:9] == 2).all()
+
+
+def test_spacing_1():
+    n = 30
+    lx, ly, lz = n, n, n
+    data, _ = make_3d_syntheticdata(lx, ly, lz)
+
+    # Rescale `data` along Y axis
+    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
+    data_aniso = cp.zeros((n, n * 2, n))
+    for i, yz in enumerate(data):
+        data_aniso[i, :, :] = resize(yz, (n * 2, n),
+                                     mode='constant',
+                                     anti_aliasing=False)
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso = cp.zeros_like(data_aniso)
+    labels_aniso[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso[lx // 2 + small_l // 4,
+                 ly - small_l // 2,
+                 lz // 2 - small_l // 4] = 2
+
+    # Test with `spacing` kwarg
+    # First, anisotropic along Y
+    labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg',
+                                 spacing=cp.array((1., 2., 1.)))
+    assert (labels_aniso[13:17, 26:34, 13:17] == 2).all()
+
+    # Rescale `data` along X axis
+    # `resize` is not yet 3D capable, so this must be done by looping in 2D.
+    data_aniso = cp.zeros((n, n * 2, n))
+    for i in range(data.shape[1]):
+        data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n),
+                                     mode='constant',
+                                     anti_aliasing=False)
+
+    # Generate new labels
+    small_l = int(lx // 5)
+    labels_aniso2 = cp.zeros_like(data_aniso)
+    labels_aniso2[lx // 5, ly // 5, lz // 5] = 1
+    labels_aniso2[lx - small_l // 2,
+                  ly // 2 + small_l // 4,
+                  lz // 2 - small_l // 4] = 2
+
+    # Anisotropic along X
+    labels_aniso2 = random_walker(data_aniso,
+                                  labels_aniso2,
+                                  mode='cg', spacing=cp.array((2., 1., 1.)))
+    assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all()
+
+
+def test_trivial_cases():
+    # When all voxels are labeled
+    img = cp.ones((10, 10))
+    labels = cp.ones((10, 10))
+
+    with expected_warnings(["Returning provided labels"]):
+        pass_through = random_walker(img, labels)
+    cp.testing.assert_array_equal(pass_through, labels)
+
+    # When all voxels are labeled AND return_full_prob is True
+    labels[:, :5] = 3
+    expected = cp.concatenate(((labels == 1)[..., cp.newaxis],
+                               (labels == 3)[..., cp.newaxis]), axis=2)
+    with expected_warnings(["Returning provided labels"]):
+        test = random_walker(img, labels, return_full_prob=True)
+    cp.testing.assert_array_equal(test, expected)
+
+    # Unlabeled voxels not connected to seed, so nothing can be done
+    img = cp.full((10, 10), False)
+    object_A = np.array([(6, 7), (6, 8), (7, 7), (7, 8)])
+    object_B = np.array([(3, 1), (4, 1), (2, 2), (3, 2), (4, 2), (2, 3),
+                         (3, 3)])
+    for x, y in np.vstack((object_A, object_B)):
+        img[y][x] = True
+
+    markers = cp.zeros((10, 10), dtype=cp.int8)
+    for x, y in object_B:
+        markers[y][x] = 1
+
+    markers[img == 0] = -1
+    with expected_warnings(["All unlabeled pixels are isolated"]):
+        output_labels = random_walker(img, markers)
+    assert cp.all(output_labels[markers == 1] == 1)
+    # Here 0-labeled pixels could not be determined (no connexion to seed)
+    assert cp.all(output_labels[markers == 0] == -1)
+    with expected_warnings(["All unlabeled pixels are isolated"]):
+        test = random_walker(img, markers, return_full_prob=True)
+
+
+def test_length2_spacing():
+    # If this passes without raising an exception (warnings OK), the new
+    #   spacing code is working properly.
+    cp.random.seed(42)
+    img = cp.ones((10, 10)) + 0.2 * cp.random.normal(size=(10, 10))
+    labels = cp.zeros((10, 10), dtype=cp.uint8)
+    labels[2, 4] = 1
+    labels[6, 8] = 4
+    random_walker(img, labels, spacing=cp.array((1., 2.)))
+
+
+def test_bad_inputs():
+    # Too few dimensions
+    img = cp.ones(10)
+    labels = cp.arange(10)
+    with testing.raises(ValueError):
+        random_walker(img, labels)
+    with testing.raises(ValueError):
+        random_walker(img, labels, multichannel=True)
+
+    # Too many dimensions
+    np.random.seed(42)
+    img = cp.array(np.random.normal(size=(3, 3, 3, 3, 3)))
+    labels = cp.arange(3 ** 5).reshape(img.shape)
+    with testing.raises(ValueError):
+        random_walker(img, labels)
+    with testing.raises(ValueError):
+        random_walker(img, labels, multichannel=True)
+
+    # Spacing incorrect length
+    img = cp.array(np.random.normal(size=(10, 10)))
+    labels = cp.zeros((10, 10))
+    labels[2, 4] = 2
+    labels[6, 8] = 5
+    with testing.raises(ValueError):
+        random_walker(img, labels, spacing=cp.array((1,)))
+
+    # Invalid mode
+    img = cp.array(np.random.normal(size=(10, 10)))
+    labels = cp.zeros((10, 10))
+    with testing.raises(ValueError):
+        random_walker(img, labels, mode='bad')
+
+
+def test_isolated_seeds():
+    np.random.seed(0)
+    a = cp.array(np.random.random((7, 7)))
+    mask = - np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 1
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+    mask = cp.array(mask)
+
+    # Test that no error is raised, and that labels of isolated seeds are OK
+    with expected_warnings(['The probability range is outside']):
+        res = random_walker(a, mask)
+    assert res[1, 1] == 1
+    with expected_warnings(['The probability range is outside']):
+        res = random_walker(a, mask, return_full_prob=True)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+
+def test_isolated_area():
+    np.random.seed(0)
+    a = cp.array(np.random.random((7, 7)))
+    mask = - np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 0
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+    mask = cp.array(mask)
+
+    # Test that no error is raised, and that labels of isolated seeds are OK
+    with expected_warnings(['The probability range is outside']):
+        res = random_walker(a, mask)
+    assert res[1, 1] == 0
+    with expected_warnings(['The probability range is outside']):
+        res = random_walker(a, mask, return_full_prob=True)
+    assert res[0, 1, 1] == 0
+    assert res[1, 1, 1] == 0
+
+
+def test_prob_tol():
+    np.random.seed(0)
+    a = cp.array(np.random.random((7, 7)))
+    mask = - np.ones(a.shape)
+    # This pixel is an isolated seed
+    mask[1, 1] = 1
+    # Unlabeled pixels
+    mask[3:, 3:] = 0
+    # Seeds connected to unlabeled pixels
+    mask[4, 4] = 2
+    mask[6, 6] = 1
+    mask = cp.array(mask)
+
+    with expected_warnings(['The probability range is outside']):
+        res = random_walker(a, mask, return_full_prob=True)
+
+    # Lower beta, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, beta=10)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+    # Being more prob_tol tolerant, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, prob_tol=1e-1)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
+
+    # Reduced tol, no warning is expected.
+    res = random_walker(a, mask, return_full_prob=True, tol=1e-9)
+    assert res[0, 1, 1] == 1
+    assert res[1, 1, 1] == 0
diff --git a/python/cucim/src/cucim/skimage/transform/__init__.py b/python/cucim/src/cucim/skimage/transform/__init__.py
new file mode 100644
index 000000000..02527dd22
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/__init__.py
@@ -0,0 +1,37 @@
+from ._geometric import (AffineTransform, EssentialMatrixTransform,
+                         EuclideanTransform, FundamentalMatrixTransform,
+                         PiecewiseAffineTransform, PolynomialTransform,
+                         ProjectiveTransform, SimilarityTransform,
+                         estimate_transform, matrix_transform)
+from ._warps import (downscale_local_mean, rescale, resize, rotate, swirl,
+                     warp, warp_coords, warp_polar)
+from .integral import integral_image, integrate
+from .pyramids import (pyramid_expand, pyramid_gaussian, pyramid_laplacian,
+                       pyramid_reduce)
+
+__all__ = [
+    "integral_image",
+    "integrate",
+    "warp",
+    "warp_coords",
+    "warp_polar",
+    "estimate_transform",
+    "matrix_transform",
+    "EuclideanTransform",
+    "SimilarityTransform",
+    "AffineTransform",
+    "ProjectiveTransform",
+    "EssentialMatrixTransform",
+    "FundamentalMatrixTransform",
+    "PolynomialTransform",
+    "PiecewiseAffineTransform",
+    "swirl",
+    "resize",
+    "rotate",
+    "rescale",
+    "downscale_local_mean",
+    "pyramid_reduce",
+    "pyramid_expand",
+    "pyramid_gaussian",
+    "pyramid_laplacian",
+]
diff --git a/python/cucim/src/cucim/skimage/transform/_geometric.py b/python/cucim/src/cucim/skimage/transform/_geometric.py
new file mode 100644
index 000000000..fd8f06821
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/_geometric.py
@@ -0,0 +1,1701 @@
+# TODO: not yet converted for GPU use
+
+import math
+import textwrap
+
+import cupy as cp
+import numpy as np
+from scipy import spatial
+
+from .._shared.utils import get_bound_method_class, safe_as_int
+
+_sin, _cos = math.sin, math.cos
+
+
+def _to_ndimage_mode(mode):
+    """Convert from `numpy.pad` mode name to the corresponding ndimage mode."""
+    mode_translation_dict = dict(
+        edge="nearest", symmetric="reflect", reflect="mirror"
+    )
+    if mode in mode_translation_dict:
+        mode = mode_translation_dict[mode]
+    return mode
+
+
+def _center_and_normalize_points(points):
+    """Center and normalize image points.
+
+    The points are transformed in a two-step procedure that is expressed
+    as a transformation matrix. The matrix of the resulting points is usually
+    better conditioned than the matrix of the original points.
+
+    Center the image points, such that the new coordinate system has its
+    origin at the centroid of the image points.
+
+    Normalize the image points, such that the mean distance from the points
+    to the origin of the coordinate system is sqrt(D).
+
+    Parameters
+    ----------
+    points : (N, D) array
+        The coordinates of the image points.
+
+    Returns
+    -------
+    matrix : (D+1, D+1) array
+        The transformation matrix to obtain the new points.
+    new_points : (N, D) array
+        The transformed image points.
+    has_nan : bool
+        Indicates if all points were identical causing rms=0.
+
+    References
+    ----------
+    .. [1] Hartley, Richard I. "In defense of the eight-point algorithm."
+           Pattern Analysis and Machine Intelligence, IEEE Transactions on 19.6
+           (1997): 580-593.
+
+    """
+    # TODO: grlee77: exclude numpy arrays?
+    xp = cp.get_array_module(points)
+    n, d = points.shape
+
+    centroid = xp.mean(points, axis=0)
+
+    diff = points - centroid
+    rms = math.sqrt(xp.sum(diff * diff) / points.shape[0])
+
+    if rms == 0:
+        return xp.full((d, d), np.nan), points, True
+
+    norm_factor = math.sqrt(d) / rms
+
+    matrix = xp.concatenate(
+        (
+            norm_factor
+            * xp.concatenate((xp.eye(d), -centroid[:, xp.newaxis]), axis=1),
+            xp.asarray([[0] * d + [1]]),
+        ),
+        axis=0,
+    )
+
+    points_h = xp.concatenate([points.T, xp.ones((1, n))], axis=0)
+    new_points_h = (matrix @ points_h).T
+
+    new_points = new_points_h[:, :d]
+    new_points /= new_points_h[:, d:]
+
+    return matrix, new_points, False
+
+
+def _umeyama(src, dst, estimate_scale):
+    """Estimate N-D similarity transformation with or without scaling.
+
+    Parameters
+    ----------
+    src : (M, N) array
+        Source coordinates.
+    dst : (M, N) array
+        Destination coordinates.
+    estimate_scale : bool
+        Whether to estimate scaling factor.
+
+    Returns
+    -------
+    T : (N + 1, N + 1)
+        The homogeneous similarity transformation matrix. The matrix contains
+        NaN values only if the problem is not well-conditioned.
+
+    References
+    ----------
+    .. [1] "Least-squares estimation of transformation parameters between two
+            point patterns", Shinji Umeyama, PAMI 1991, :DOI:`10.1109/34.88573`
+
+    """
+
+    num = src.shape[0]
+    dim = src.shape[1]
+
+    # TODO: grlee77: exclude numpy arrays?
+    xp = cp.get_array_module(src)
+
+    # Compute mean of src and dst.
+    src_mean = src.mean(axis=0)
+    dst_mean = dst.mean(axis=0)
+
+    # Subtract mean from src and dst.
+    src_demean = src - src_mean
+    dst_demean = dst - dst_mean
+
+    # Eq. (38).
+    A = dst_demean.T @ src_demean / num
+
+    # Eq. (39).
+    d = xp.ones((dim,), dtype=xp.double)
+    if xp.linalg.det(A) < 0:
+        d[dim - 1] = -1
+
+    T = xp.eye(dim + 1, dtype=xp.double)
+
+    U, S, V = xp.linalg.svd(A)
+
+    # Eq. (40) and (43).
+    rank = xp.linalg.matrix_rank(A)
+    if rank == 0:
+        return xp.nan * T
+    elif rank == dim - 1:
+        if xp.linalg.det(U) * xp.linalg.det(V) > 0:
+            T[:dim, :dim] = U @ V
+        else:
+            s = d[dim - 1]
+            d[dim - 1] = -1
+            T[:dim, :dim] = U @ xp.diag(d) @ V
+            d[dim - 1] = s
+    else:
+        T[:dim, :dim] = U @ xp.diag(d) @ V
+
+    if estimate_scale:
+        # Eq. (41) and (42).
+        scale = 1.0 / src_demean.var(axis=0).sum() * (S @ d)
+    else:
+        scale = 1.0
+
+    T[:dim, dim] = dst_mean - scale * (T[:dim, :dim] @ src_mean.T)
+    T[:dim, :dim] *= scale
+
+    return T
+
+
+class GeometricTransform(object):
+    """Base class for geometric transformations."""
+
+    def __call__(self, coords):
+        """Apply forward transformation.
+
+        Parameters
+        ----------
+        coords : (N, 2) array
+            Source coordinates.
+
+        Returns
+        -------
+        coords : (N, 2) array
+            Destination coordinates.
+
+        """
+        raise NotImplementedError()
+
+    def inverse(self, coords):
+        """Apply inverse transformation.
+
+        Parameters
+        ----------
+        coords : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        coords : (N, 2) array
+            Source coordinates.
+
+        """
+        raise NotImplementedError()
+
+    def residuals(self, src, dst):
+        """Determine residuals of transformed destination coordinates.
+
+        For each transformed source coordinate the euclidean distance to the
+        respective destination coordinate is determined.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        residuals : (N, ) array
+            Residual for coordinate.
+
+        """
+        xp = cp.get_array_module(src)
+        return xp.sqrt(xp.sum((self(src) - dst) ** 2, axis=1))
+
+    def __add__(self, other):
+        """Combine this transformation with another."""
+        raise NotImplementedError()
+
+
+class FundamentalMatrixTransform(GeometricTransform):
+    """Fundamental matrix transformation.
+
+    The fundamental matrix relates corresponding points between a pair of
+    uncalibrated images. The matrix transforms homogeneous image points in one
+    image to epipolar lines in the other image.
+
+    The fundamental matrix is only defined for a pair of moving images. In the
+    case of pure rotation or planar scenes, the homography describes the
+    geometric relation between two images (`ProjectiveTransform`). If the
+    intrinsic calibration of the images is known, the essential matrix describes
+    the metric relation between the two images (`EssentialMatrixTransform`).
+
+    References
+    ----------
+    .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in
+           computer vision. Cambridge university press, 2003.
+
+    Parameters
+    ----------
+    matrix : (3, 3) array, optional
+        Fundamental matrix.
+
+    Attributes
+    ----------
+    params : (3, 3) array
+        Fundamental matrix.
+
+    """
+
+    # CuPy Backend: if matrix is None cannot infer array module from it
+    #               added explicit xp module argument for now
+    def __init__(self, matrix=None, *, dimensionality=2, xp=cp):
+        if matrix is None:
+            # default to an identity transform
+            matrix = xp.eye(dimensionality + 1)
+        else:
+            dimensionality = matrix.shape[0] - 1
+        if matrix.shape != (dimensionality + 1, dimensionality + 1):
+            raise ValueError("Invalid shape of transformation matrix")
+        self.params = matrix
+        if dimensionality != 2:
+            raise NotImplementedError(
+                f"{self.__class__} is only implemented for 2D coordinates "
+                "(i.e. 3D transformation matrices)."
+            )
+
+    def __call__(self, coords):
+        """Apply forward transformation.
+
+        Parameters
+        ----------
+        coords : (N, 2) array
+            Source coordinates.
+
+        Returns
+        -------
+        coords : (N, 3) array
+            Epipolar lines in the destination image.
+
+        """
+        xp = cp.get_array_module(coords)
+        coords_homogeneous = xp.column_stack([coords, xp.ones(coords.shape[0])])
+        return coords_homogeneous @ self.params.T
+
+    def inverse(self, coords):
+        """Apply inverse transformation.
+
+        Parameters
+        ----------
+        coords : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        coords : (N, 3) array
+            Epipolar lines in the source image.
+
+        """
+        xp = cp.get_array_module(coords)
+        coords_homogeneous = xp.column_stack([coords, xp.ones(coords.shape[0])])
+        return coords_homogeneous @ self.params
+
+    def _setup_constraint_matrix(self, src, dst):
+        """Setup and solve the homogeneous epipolar constraint matrix::
+
+            dst' * F * src = 0.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        F_normalized : (3, 3) array
+            The normalized solution to the homogeneous system. If the system
+            is not well-conditioned, this matrix contains NaNs.
+        src_matrix : (3, 3) array
+            The transformation matrix to obtain the normalized source
+            coordinates.
+        dst_matrix : (3, 3) array
+            The transformation matrix to obtain the normalized destination
+            coordinates.
+
+        """
+        if src.shape != dst.shape:
+            raise ValueError("src and dst shapes must be identical.")
+        if src.shape[0] < 8:
+            raise ValueError("src.shape[0] must be equal or larger than 8.")
+        xp = cp.get_array_module(src)
+
+        # Center and normalize image points for better numerical stability.
+        src_matrix, src, has_nan1 = _center_and_normalize_points(src)
+        dst_matrix, dst, has_nan2 = _center_and_normalize_points(dst)
+        if has_nan1 or has_nan2:
+            self.params = xp.full((3, 3), xp.nan)
+            return 3 * [xp.full((3, 3), xp.nan)]
+
+        # Setup homogeneous linear equation as dst' * F * src = 0.
+        A = xp.ones((src.shape[0], 9))
+        A[:, :2] = src
+        A[:, :3] *= dst[:, 0, xp.newaxis]
+        A[:, 3:5] = src
+        A[:, 3:6] *= dst[:, 1, xp.newaxis]
+        A[:, 6:8] = src
+
+        # Solve for the nullspace of the constraint matrix.
+        _, _, V = xp.linalg.svd(A)
+        F_normalized = V[-1, :].reshape(3, 3)
+
+        return F_normalized, src_matrix, dst_matrix
+
+    def estimate(self, src, dst):
+        """Estimate fundamental matrix using 8-point algorithm.
+
+        The 8-point algorithm requires at least 8 corresponding point pairs for
+        a well-conditioned solution, otherwise the over-determined solution is
+        estimated.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+        xp = cp.get_array_module(src)
+
+        F_normalized, src_matrix, dst_matrix = self._setup_constraint_matrix(
+            src, dst
+        )
+
+        # Enforcing the internal constraint that two singular values must be
+        # non-zero and one must be zero.
+        U, S, V = xp.linalg.svd(F_normalized)
+        S[2] = 0
+        F = U @ xp.diag(S) @ V
+
+        self.params = dst_matrix.T @ F @ src_matrix
+
+        return True
+
+    def residuals(self, src, dst):
+        """Compute the Sampson distance.
+
+        The Sampson distance is the first approximation to the geometric error.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        residuals : (N, ) array
+            Sampson distance.
+
+        """
+        xp = cp.get_array_module(src)
+        src_homogeneous = xp.column_stack([src, xp.ones(src.shape[0])])
+        dst_homogeneous = xp.column_stack([dst, xp.ones(dst.shape[0])])
+
+        F_src = self.params @ src_homogeneous.T
+        Ft_dst = self.params.T @ dst_homogeneous.T
+
+        dst_F_src = xp.sum(dst_homogeneous * F_src.T, axis=1)
+
+        return xp.abs(dst_F_src) / xp.sqrt(
+            F_src[0] ** 2 + F_src[1] ** 2 + Ft_dst[0] ** 2 + Ft_dst[1] ** 2
+        )
+
+
+class EssentialMatrixTransform(FundamentalMatrixTransform):
+    """Essential matrix transformation.
+
+    The essential matrix relates corresponding points between a pair of
+    calibrated images. The matrix transforms normalized, homogeneous image
+    points in one image to epipolar lines in the other image.
+
+    The essential matrix is only defined for a pair of moving images capturing a
+    non-planar scene. In the case of pure rotation or planar scenes, the
+    homography describes the geometric relation between two images
+    (`ProjectiveTransform`). If the intrinsic calibration of the images is
+    unknown, the fundamental matrix describes the projective relation between
+    the two images (`FundamentalMatrixTransform`).
+
+    References
+    ----------
+    .. [1] Hartley, Richard, and Andrew Zisserman. Multiple view geometry in
+           computer vision. Cambridge university press, 2003.
+
+    Parameters
+    ----------
+    rotation : (3, 3) array, optional
+        Rotation matrix of the relative camera motion.
+    translation : (3, 1) array, optional
+        Translation vector of the relative camera motion. The vector must
+        have unit length.
+    matrix : (3, 3) array, optional
+        Essential matrix.
+
+    Attributes
+    ----------
+    params : (3, 3) array
+        Essential matrix.
+
+    """
+
+    # CuPy Backend: if matrix is None cannot infer array module from it
+    #               added explicit xp module argument for now
+    def __init__(
+        self,
+        rotation=None,
+        translation=None,
+        matrix=None,
+        *,
+        dimensionality=2,
+        xp=cp,
+    ):
+        super().__init__(matrix=matrix, dimensionality=dimensionality)
+        if rotation is not None:
+            if translation is None:
+                raise ValueError("Both rotation and translation required")
+            if rotation.shape != (3, 3):
+                raise ValueError("Invalid shape of rotation matrix")
+            if abs(xp.linalg.det(rotation) - 1) > 1e-6:
+                raise ValueError("Rotation matrix must have unit determinant")
+            if translation.size != 3:
+                raise ValueError("Invalid shape of translation vector")
+            if abs(xp.linalg.norm(translation) - 1) > 1e-6:
+                raise ValueError("Translation vector must have unit length")
+            # Matrix representation of the cross product for t.
+            if isinstance(translation, cp.ndarray):
+                translation = cp.asnumpy(translation)
+            # CuPy Backend: TODO: always keep t_x, rotation, etc. on host?
+            # fmt: off
+            t_x = xp.array([0, -translation[2], translation[1],
+                            translation[2], 0, -translation[0],
+                            -translation[1], translation[0], 0]).reshape(3, 3)
+            # fmt: on
+            self.params = t_x @ rotation
+        elif matrix is not None:
+            if matrix.shape != (3, 3):
+                raise ValueError("Invalid shape of transformation matrix")
+            self.params = matrix
+        else:
+            # default to an identity transform
+            self.params = xp.eye(3)
+
+    def estimate(self, src, dst):
+        """Estimate essential matrix using 8-point algorithm.
+
+        The 8-point algorithm requires at least 8 corresponding point pairs for
+        a well-conditioned solution, otherwise the over-determined solution is
+        estimated.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+
+        xp = cp.get_array_module(src)
+        E_normalized, src_matrix, dst_matrix = self._setup_constraint_matrix(
+            src, dst
+        )
+
+        # Enforcing the internal constraint that two singular values must be
+        # equal and one must be zero.
+        U, S, V = xp.linalg.svd(E_normalized)
+        S[0] = (S[0] + S[1]) / 2.0
+        S[1] = S[0]
+        S[2] = 0
+        E = U @ xp.diag(S) @ V
+
+        self.params = dst_matrix.T @ E @ src_matrix
+
+        return True
+
+
+class ProjectiveTransform(GeometricTransform):
+    r"""Projective transformation.
+
+    Apply a projective transformation (homography) on coordinates.
+
+    For each homogeneous coordinate :math:`\mathbf{x} = [x, y, 1]^T`, its
+    target position is calculated by multiplying with the given matrix,
+    :math:`H`, to give :math:`H \mathbf{x}`::
+
+      [[a0 a1 a2]
+       [b0 b1 b2]
+       [c0 c1 1 ]].
+
+    E.g., to rotate by theta degrees clockwise, the matrix should be::
+
+      [[cos(theta) -sin(theta) 0]
+       [sin(theta)  cos(theta) 0]
+       [0            0         1]]
+
+    or, to translate x by 10 and y by 20::
+
+      [[1 0 10]
+       [0 1 20]
+       [0 0 1 ]].
+
+    Parameters
+    ----------
+    matrix : (D+1, D+1) array, optional
+        Homogeneous transformation matrix.
+    dimensionality : int, optional
+        The number of dimensions of the transform. This is ignored if
+        ``matrix`` is not None.
+
+    Attributes
+    ----------
+    params : (D+1, D+1) array
+        Homogeneous transformation matrix.
+
+    """
+
+    def __init__(self, matrix=None, *, dimensionality=2, xp=cp):
+        if matrix is not None:
+            dimensionality = matrix.shape[0] - 1
+        if matrix is None:
+            # default to an identity transform
+            matrix = xp.eye(dimensionality + 1)
+        if matrix.shape != (dimensionality + 1, dimensionality + 1):
+            raise ValueError("invalid shape of transformation matrix")
+        self.params = matrix
+        self._coeffs = range(matrix.size - 1)
+
+    @property
+    def _inv_matrix(self):
+        xp = cp.get_array_module(self.params)
+        return xp.linalg.inv(self.params)
+
+    def _apply_mat(self, coords, matrix):
+        xp = cp.get_array_module(coords)
+        ndim = matrix.shape[0] - 1
+        coords = xp.array(coords, copy=False, ndmin=2)
+
+        src = xp.concatenate([coords, xp.ones((coords.shape[0], 1))], axis=1)
+        dst = src @ matrix.T
+
+        # below, we will divide by the last dimension of the homogeneous
+        # coordinate matrix. In order to avoid division by zero,
+        # we replace exact zeros in this column with a very small number.
+        if xp is np:
+            dst[dst[:, ndim] == 0, ndim] = np.finfo(float).eps
+        else:
+            # indexing as above not supported by CuPy
+            tmp = dst[:, ndim]
+            idx = cp.where(tmp == 0)  # synchronize
+            tmp[idx] = np.finfo(float).eps
+            dst[:, ndim] = tmp
+
+        # rescale to homogeneous coordinates
+        dst[:, :ndim] /= dst[:, ndim:ndim + 1]
+
+        return dst[:, :ndim]
+
+    def __array__(self, dtype=None):
+        if dtype is None:
+            return self.params
+        else:
+            return self.params.astype(dtype)
+
+    def __call__(self, coords):
+        """Apply forward transformation.
+
+        Parameters
+        ----------
+        coords : (N, D) array
+            Source coordinates.
+
+        Returns
+        -------
+        coords_out : (N, D) array
+            Destination coordinates.
+
+        """
+        return self._apply_mat(coords, self.params)
+
+    def inverse(self, coords):
+        """Apply inverse transformation.
+
+        Parameters
+        ----------
+        coords : (N, D) array
+            Destination coordinates.
+
+        Returns
+        -------
+        coords_out : (N, D) array
+            Source coordinates.
+
+        """
+        return self._apply_mat(coords, self._inv_matrix)
+
+    def estimate(self, src, dst):
+        """Estimate the transformation from a set of corresponding points.
+
+        You can determine the over-, well- and under-determined parameters
+        with the total least-squares method.
+
+        Number of source and destination coordinates must match.
+
+        The transformation is defined as::
+
+            X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1)
+            Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1)
+
+        These equations can be transformed to the following form::
+
+            0 = a0*x + a1*y + a2 - c0*x*X - c1*y*X - X
+            0 = b0*x + b1*y + b2 - c0*x*Y - c1*y*Y - Y
+
+        which exist for each set of corresponding points, so we have a set of
+        N * 2 equations. The coefficients appear linearly so we can write
+        A x = 0, where::
+
+            A   = [[x y 1 0 0 0 -x*X -y*X -X]
+                   [0 0 0 x y 1 -x*Y -y*Y -Y]
+                    ...
+                    ...
+                  ]
+            x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3]
+
+        In case of total least-squares the solution of this homogeneous system
+        of equations is the right singular vector of A which corresponds to the
+        smallest singular value normed by the coefficient c3.
+
+        In case of the affine transformation the coefficients c0 and c1 are 0.
+        Thus the system of equations is::
+
+            A   = [[x y 1 0 0 0 -X]
+                   [0 0 0 x y 1 -Y]
+                    ...
+                    ...
+                  ]
+            x.T = [a0 a1 a2 b0 b1 b2 c3]
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+
+        xp = cp.get_array_module(src)
+        n, d = src.shape
+        src_matrix, src, has_nan1 = _center_and_normalize_points(src)
+        dst_matrix, dst, has_nan2 = _center_and_normalize_points(dst)
+        if has_nan1 or has_nan2:
+            self.params = xp.full((d, d), np.nan)
+            return False
+        # params: a0, a1, a2, b0, b1, b2, c0, c1
+        A = xp.zeros((n * d, (d + 1) ** 2))
+        for ddim in range(d):
+            A[
+                ddim * n:(ddim + 1) * n, ddim * (d + 1):ddim * (d + 1) + d
+            ] = src
+            A[ddim * n:(ddim + 1) * n, ddim * (d + 1) + d] = 1
+            A[ddim * n:(ddim + 1) * n, -d - 1:-1] = src
+            A[ddim * n:(ddim + 1) * n, -1] = -1
+            A[ddim * n:(ddim + 1) * n, -d - 1:] *= -dst[:, ddim:(ddim + 1)]
+
+        # Select relevant columns, depending on params
+        A = A[:, list(self._coeffs) + [-1]]
+
+        _, _, V = xp.linalg.svd(A)
+        # if the last element of the vector corresponding to the smallest
+        # singular value is close to zero, this implies a degenerate case
+        # because it is a rank-defective transform, which would map points
+        # to a line rather than a plane.
+        if xp.isclose(V[-1, -1], 0):
+            return False
+
+        H = np.zeros(
+            (d + 1, d + 1)
+        )  # np here because .flat not implemented in CuPy
+        # solution is right singular vector that corresponds to smallest
+        # singular value
+        try:
+            H.flat[list(self._coeffs.get()) + [-1]] = -V[-1, :-1] / V[-1, -1]
+        except AttributeError:
+            try:
+                V = V.get()
+            except AttributeError:
+                pass
+            H.flat[list(self._coeffs) + [-1]] = -V[-1, :-1] / V[-1, -1]
+        H[d, d] = 1
+        H = xp.asarray(H)
+
+        # De-center and de-normalize
+        H = xp.linalg.inv(dst_matrix) @ H @ src_matrix
+
+        self.params = H
+
+        return True
+
+    def __add__(self, other):
+        """Combine this transformation with another."""
+        if isinstance(other, ProjectiveTransform):
+            # combination of the same types result in a transformation of this
+            # type again, otherwise use general projective transformation
+            if type(self) == type(other):
+                tform = self.__class__
+            else:
+                tform = ProjectiveTransform
+            return tform(other.params @ self.params)
+        elif (
+            hasattr(other, "__name__")
+            and other.__name__ == "inverse"
+            and hasattr(get_bound_method_class(other), "_inv_matrix")
+        ):
+            return ProjectiveTransform(other.__self__._inv_matrix @ self.params)
+        else:
+            raise TypeError(
+                "Cannot combine transformations of differing " "types."
+            )
+
+    def __nice__(self):
+        """common 'paramstr' used by __str__ and __repr__"""
+        npstring = np.array2string(cp.asnumpy(self.params), separator=', ')
+        paramstr = 'matrix=\n' + textwrap.indent(npstring, '    ')
+        return paramstr
+
+    def __repr__(self):
+        """Add standard repr formatting around a __nice__ string"""
+        paramstr = self.__nice__()
+        classname = self.__class__.__name__
+        classstr = classname
+        return '<{}({}) at {}>'.format(classstr, paramstr, hex(id(self)))
+
+    def __str__(self):
+        """Add standard str formatting around a __nice__ string"""
+        paramstr = self.__nice__()
+        classname = self.__class__.__name__
+        classstr = classname
+        return '<{}({})>'.format(classstr, paramstr)
+
+    @property
+    def dimensionality(self):
+        """The dimensionality of the transformation."""
+        return self.params.shape[0] - 1
+
+
+class AffineTransform(ProjectiveTransform):
+    """Affine transformation.
+
+    Has the following form::
+
+        X = a0*x + a1*y + a2 =
+          = sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2
+
+        Y = b0*x + b1*y + b2 =
+          = sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2
+
+    where ``sx`` and ``sy`` are scale factors in the x and y directions,
+    and the homogeneous transformation matrix is::
+
+        [[a0  a1  a2]
+         [b0  b1  b2]
+         [0   0    1]]
+
+    In 2D, the transformation parameters can be given as the homogeneous
+    transformation matrix, above, or as the implicit parameters, scale,
+    rotation, shear, and translation in x (a2) and y (b2). For 3D and higher,
+    only the matrix form is allowed.
+
+    In narrower transforms, such as the Euclidean (only rotation and
+    translation) or Similarity (rotation, translation, and a global scale
+    factor) transforms, it is possible to specify 3D transforms using implicit
+    parameters also.
+
+    Parameters
+    ----------
+    matrix : (D+1, D+1) array, optional
+        Homogeneous transformation matrix. If this matrix is provided, it is an
+        error to provide any of scale, rotation, shear, or translation.
+    scale : {s as float or (sx, sy) as array, list or tuple}, optional
+        Scale factor(s). If a single value, it will be assigned to both
+        sx and sy. Only available for 2D.
+
+        .. versionadded:: 0.17
+           Added support for supplying a single scalar value.
+    rotation : float, optional
+        Rotation angle in counter-clockwise direction as radians. Only
+        available for 2D.
+    shear : float, optional
+        Shear angle in counter-clockwise direction as radians. Only available
+        for 2D.
+    translation : (tx, ty) as array, list or tuple, optional
+        Translation parameters. Only available for 2D.
+    dimensionality : int, optional
+        The dimensionality of the transform. This is not used if any other
+        parameters are provided.
+
+    Attributes
+    ----------
+    params : (D+1, D+1) array
+        Homogeneous transformation matrix.
+
+    Raises
+    ------
+    ValueError
+        If both ``matrix`` and any of the other parameters are provided.
+    """
+
+    def __init__(self, matrix=None, scale=None, rotation=None, shear=None,
+                 translation=None, *, dimensionality=2, xp=cp):
+        params = any(param is not None
+                     for param in (scale, rotation, shear, translation))
+
+        # these parameters get overwritten if a higher-D matrix is given
+        self._coeffs = range(dimensionality * (dimensionality + 1))
+
+        if params and matrix is not None:
+            raise ValueError("You cannot specify the transformation matrix and"
+                             " the implicit parameters at the same time.")
+
+        if params and dimensionality > 2:
+            raise ValueError("Parameter input is only supported in 2D.")
+        elif matrix is not None:
+            if matrix.ndim == 1:  # linearized (d, d + 1) homogeneous matrix
+                nparam = matrix.size
+                # solve for d in: d * (d - 1) = nparam
+                d = (1 + np.sqrt(1 + 4 * nparam)) / 2 - 1
+                dimensionality = int(d)
+                if d != dimensionality:
+                    raise ValueError(
+                        "Invalid number of elements for "
+                        "linearized matrix: {}".format(nparam)
+                    )
+                matrix = xp.concatenate(
+                    (
+                        matrix.reshape((dimensionality, dimensionality + 1)),
+                        [0] * d + [1],
+                    ),
+                    axis=0,
+                )
+            elif matrix.shape[0] != matrix.shape[1]:
+                raise ValueError("Invalid shape of transformation matrix.")
+            else:
+                dimensionality = matrix.shape[0] - 1
+                nparam = dimensionality * (dimensionality + 1)
+            self._coeffs = range(nparam)
+            self.params = matrix
+        elif params:  # note: 2D only
+            if scale is None:
+                scale = (1, 1)
+            if rotation is None:
+                rotation = 0
+            if shear is None:
+                shear = 0
+            if translation is None:
+                translation = (0, 0)
+
+            if np.isscalar(scale):
+                sx = sy = scale
+            else:
+                sx, sy = scale
+
+            # fmt: off
+            self.params = np.array(
+                [
+                    [sx * _cos(rotation), -sy * _sin(rotation + shear), 0],  # NOQA
+                    [sx * _sin(rotation),  sy * _cos(rotation + shear), 0],  # NOQA
+                    [                  0,                            0, 1],  # NOQA
+                ]
+            )
+            # fmt: on
+            self.params[0:2, 2] = translation
+            self.params = xp.asarray(self.params)
+        else:
+            # default to an identity transform
+            self.params = xp.eye(dimensionality + 1)
+
+    @property
+    def scale(self):
+        xp = cp.get_array_module(self.params)
+        return xp.sqrt(xp.sum(self.params * self.params, axis=0))[
+            : self.dimensionality
+        ]
+
+    @property
+    def rotation(self):
+        if self.dimensionality != 2:
+            raise NotImplementedError(
+                "The rotation property is only implemented for 2D transforms."
+            )
+        return math.atan2(self.params[1, 0], self.params[0, 0])
+
+    @property
+    def shear(self):
+        if self.dimensionality != 2:
+            raise NotImplementedError(
+                "The shear property is only implemented for 2D transforms."
+            )
+        beta = math.atan2(-self.params[0, 1], self.params[1, 1])
+        return beta - self.rotation
+
+    @property
+    def translation(self):
+        return self.params[0:self.dimensionality, self.dimensionality]
+
+
+# CuPy Backend: TODO: PiecewiseAffineTransform is inefficient currently
+#                     (It only operates via transfer to/from CPU).
+class PiecewiseAffineTransform(GeometricTransform):
+    """Piecewise affine transformation.
+
+    Control points are used to define the mapping. The transform is based on
+    a Delaunay triangulation of the points to form a mesh. Each triangle is
+    used to find a local affine transform.
+
+    Attributes
+    ----------
+    affines : list of AffineTransform objects
+        Affine transformations for each triangle in the mesh.
+    inverse_affines : list of AffineTransform objects
+        Inverse affine transformations for each triangle in the mesh.
+
+    """
+
+    def __init__(self):
+        self._tesselation = None
+        self._inverse_tesselation = None
+        self.affines = None
+        self.inverse_affines = None
+
+    def estimate(self, src, dst):
+        """Estimate the transformation from a set of corresponding points.
+
+        Number of source and destination coordinates must match.
+
+        Parameters
+        ----------
+        src : (N, D) array
+            Source coordinates.
+        dst : (N, D) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+
+        ndim = src.shape[1]
+        # forward piecewise affine
+        # triangulate input positions into mesh
+        xp = cp.get_array_module(src)
+        if xp is cp:
+            # TODO: grlee77 :update if spatial.Delaunay is implemented for GPU
+            # transfer to CPU for use of spatial.Delaunay
+            src = cp.asnumpy(src)
+            dst = cp.asnumpy(dst)
+
+        self._tesselation = spatial.Delaunay(src)
+        # find affine mapping from source positions to destination
+        self.affines = []
+        for tri in self._tesselation.vertices:
+            affine = AffineTransform(dimensionality=ndim)
+            affine.estimate(src[tri, :], dst[tri, :])
+            self.affines.append(affine)
+
+        # inverse piecewise affine
+        # triangulate input positions into mesh
+        self._inverse_tesselation = spatial.Delaunay(dst)
+        # find affine mapping from source positions to destination
+        self.inverse_affines = []
+        for tri in self._inverse_tesselation.vertices:
+            affine = AffineTransform(dimensionality=ndim)
+            affine.estimate(dst[tri, :], src[tri, :])
+            self.inverse_affines.append(affine)
+
+        return True
+
+    def __call__(self, coords):
+        """Apply forward transformation.
+
+        Coordinates outside of the mesh will be set to `- 1`.
+
+        Parameters
+        ----------
+        coords : (N, D) array
+            Source coordinates.
+
+        Returns
+        -------
+        coords : (N, D) array
+            Transformed coordinates.
+
+        """
+
+        xp = cp.get_array_module(coords)
+        if xp == cp:
+            coords = coords.get()
+        out = np.empty_like(coords, np.double)
+
+        # determine triangle index for each coordinate
+        simplex = self._tesselation.find_simplex(coords)
+
+        # coordinates outside of mesh
+        out[simplex == -1, :] = -1
+
+        for index in range(len(self._tesselation.vertices)):
+            # affine transform for triangle
+            affine = self.affines[index]
+            # all coordinates within triangle
+            index_mask = simplex == index
+
+            out[index_mask, :] = affine(coords[index_mask, :])
+
+        if xp == cp:
+            out = xp.asarray(out)
+        return out
+
+    def inverse(self, coords):
+        """Apply inverse transformation.
+
+        Coordinates outside of the mesh will be set to `- 1`.
+
+        Parameters
+        ----------
+        coords : (N, D) array
+            Source coordinates.
+
+        Returns
+        -------
+        coords : (N, D) array
+            Transformed coordinates.
+
+        """
+
+        xp = cp.get_array_module(coords)
+        if xp == cp:
+            coords = coords.get()
+        out = np.empty_like(coords, np.double)
+
+        # determine triangle index for each coordinate
+        simplex = self._inverse_tesselation.find_simplex(coords)
+
+        # coordinates outside of mesh
+        out[simplex == -1, :] = -1
+
+        for index in range(len(self._inverse_tesselation.vertices)):
+            # affine transform for triangle
+            affine = self.inverse_affines[index]
+            # all coordinates within triangle
+            index_mask = simplex == index
+
+            out[index_mask, :] = affine(coords[index_mask, :])
+        if xp == cp:
+            out = xp.asarray(out)
+        return out
+
+
+def _euler_rotation(axis, angle):
+    """Produce a single-axis Euler rotation matrix.
+
+    Parameters
+    ----------
+    axis : int in {0, 1, 2}
+        The axis of rotation.
+    angle : float
+        The angle of rotation in radians.
+
+    Returns
+    -------
+    Ri : array of float, shape (3, 3)
+        The rotation matrix along axis `axis`.
+    """
+    i = axis
+    s, c = _sin(angle), _cos(angle)
+    R2 = np.array([[c, (-1) ** (i + 1) * s], [(-1) ** i * s, c]])
+    Ri = np.eye(3)
+    axes = sorted({0, 1, 2} - {axis})
+    Ri[axes][:, axes] = R2
+    return Ri
+
+
+def _euler_rotation_matrix(angles):
+    """Produce an Euler rotation matrix from the given angles.
+
+    The matrix will have dimension equal to the number of angles given.
+
+    Parameters
+    ----------
+    angles : array of float, shape (3,)
+        The transformation angles in radians.
+
+    Returns
+    -------
+    R : array of float, shape (3, 3)
+        The Euler rotation matrix.
+    """
+    dim = len(angles)
+    R = np.eye(dim)
+    for i, angle in enumerate(angles):
+        R @= _euler_rotation(i, angle)
+    return R
+
+
+class EuclideanTransform(ProjectiveTransform):
+    """Euclidean transformation, also known as a rigid transform.
+
+    Has the following form::
+
+        X = a0 * x - b0 * y + a1 =
+          = x * cos(rotation) - y * sin(rotation) + a1
+
+        Y = b0 * x + a0 * y + b1 =
+          = x * sin(rotation) + y * cos(rotation) + b1
+
+    where the homogeneous transformation matrix is::
+
+        [[a0  b0  a1]
+         [b0  a0  b1]
+         [0   0    1]]
+
+    The Euclidean transformation is a rigid transformation with rotation and
+    translation parameters. The similarity transformation extends the Euclidean
+    transformation with a single scaling factor.
+
+    Parameters
+    ----------
+    matrix : (D+1, D+1) array, optional
+        Homogeneous transformation matrix.
+    rotation : float or sequence of float, optional
+        Rotation angle in counter-clockwise direction as radians. If given as
+        a vector, it is interpreted as Euler rotation angles [1]_. Only 2D
+        (single rotation) and 3D (Euler rotations) values are supported. For
+        higher dimensions, you must provide or estimate the transformation
+        matrix.
+    translation : sequence of float, length D, optional
+        Translation parameters for each axis.
+    dimensionality : int, optional
+        The dimensionality of the transform.
+
+    Attributes
+    ----------
+    params : (D+1, D+1) array
+        Homogeneous transformation matrix.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Rotation_matrix#In_three_dimensions
+    """
+
+    def __init__(self, matrix=None, rotation=None, translation=None, *,
+                 dimensionality=2, xp=cp,):
+        params_given = rotation is not None or translation is not None
+
+        if params_given and matrix is not None:
+            raise ValueError("You cannot specify the transformation matrix and"
+                             " the implicit parameters at the same time.")
+        elif matrix is not None:
+            if matrix.shape[0] != matrix.shape[1]:
+                raise ValueError("Invalid shape of transformation matrix.")
+            self.params = matrix
+        elif params_given:
+            if rotation is None:
+                dimensionality = len(translation)
+                if dimensionality == 2:
+                    rotation = 0
+                elif dimensionality == 3:
+                    rotation = np.zeros(3)
+                else:
+                    raise ValueError(
+                        "Parameters cannot be specified for dimension "
+                        f"{dimensionality} transforms"
+                    )
+            else:
+                if not np.isscalar(rotation) and len(rotation) != 3:
+                    raise ValueError(
+                        "Parameters cannot be specified for dimension "
+                        f"{dimensionality} transforms"
+                    )
+            if translation is None:
+                translation = (0,) * dimensionality
+
+            if dimensionality == 2:
+                # fmt: off
+                self.params = np.array([
+                    [math.cos(rotation), - math.sin(rotation), 0],  # NOQA
+                    [math.sin(rotation),   math.cos(rotation), 0],  # NOQA
+                    [                 0,                    0, 1],  # NOQA
+                ])
+                # fmt: on
+
+            elif dimensionality == 3:
+                self.params = xp.eye(dimensionality + 1)
+                self.params[:dimensionality, :dimensionality] = xp.asarray(
+                    _euler_rotation_matrix(rotation)
+                )
+            self.params[0:dimensionality, dimensionality] = translation
+        else:
+            # default to an identity transform
+            self.params = xp.eye(dimensionality + 1)
+
+    def estimate(self, src, dst):
+        """Estimate the transformation from a set of corresponding points.
+
+        You can determine the over-, well- and under-determined parameters
+        with the total least-squares method.
+
+        Number of source and destination coordinates must match.
+
+        Parameters
+        ----------
+        src : (N, D) array
+            Source coordinates.
+        dst : (N, D) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+
+        self.params = _umeyama(src, dst, False)
+
+        return True
+
+    @property
+    def rotation(self):
+        return math.atan2(self.params[1, 0], self.params[1, 1])
+
+    @property
+    def translation(self):
+        return self.params[0:2, 2]
+
+
+class SimilarityTransform(EuclideanTransform):
+    """2D similarity transformation.
+
+    Has the following form::
+
+        X = a0 * x - b0 * y + a1 =
+          = s * x * cos(rotation) - s * y * sin(rotation) + a1
+
+        Y = b0 * x + a0 * y + b1 =
+          = s * x * sin(rotation) + s * y * cos(rotation) + b1
+
+    where ``s`` is a scale factor and the homogeneous transformation matrix is::
+
+        [[a0  b0  a1]
+         [b0  a0  b1]
+         [0   0    1]]
+
+    The similarity transformation extends the Euclidean transformation with a
+    single scaling factor in addition to the rotation and translation
+    parameters.
+
+    Parameters
+    ----------
+    matrix : (dim+1, dim+1) array, optional
+        Homogeneous transformation matrix.
+    scale : float, optional
+        Scale factor. Implemented only for 2D and 3D.
+    rotation : float, optional
+        Rotation angle in counter-clockwise direction as radians.
+        Implemented only for 2D and 3D. For 3D, this is given in XZX Euler
+        angles.
+    translation : (dim,) array-like, optional
+        x, y[, z] translation parameters. Implemented only for 2D and 3D.
+
+    Attributes
+    ----------
+    params : (dim+1, dim+1) array
+        Homogeneous transformation matrix.
+
+    """
+
+    def __init__(self, matrix=None, scale=None, rotation=None,
+                 translation=None, *, dimensionality=2, xp=cp):
+        self.params = None
+        params = any(param is not None
+                     for param in (scale, rotation, translation))
+
+        if params and matrix is not None:
+            raise ValueError("You cannot specify the transformation matrix and"
+                             " the implicit parameters at the same time.")
+        elif matrix is not None:
+            if matrix.ndim == 1:  # parameter vector: scale, rot, translation
+                if dimensionality > 3:
+                    raise ValueError(
+                        "Parameter vectors are only supported for 2D and 3D."
+                    )
+                scale = matrix[0]
+                rotation = matrix[1:-dimensionality]
+                translation = matrix[-dimensionality:]
+                params = True
+            elif matrix.shape[0] != matrix.shape[1] or matrix.ndim > 2:
+                raise ValueError("Invalid shape of transformation matrix.")
+            else:
+                self.params = matrix
+                dimensionality = matrix.shape[0] - 1
+        if params:
+            if dimensionality == 2:
+                axes = ((0, 1),)
+            elif dimensionality == 3:
+                axes = ((1, 2), (0, 1), (1, 2))  # XZX Euler angles
+            else:
+                raise ValueError("Parameters only supported for 2D and 3D.")
+            matrix = xp.eye(dimensionality + 1, dtype=float)
+            if scale is None:
+                scale = 1
+            if rotation is None:
+                rotation = (0,) if dimensionality == 2 else (0, 0, 0)
+            if np.isscalar(rotation):
+                rotation = [rotation]
+            if translation is None:
+                translation = (0,) * dimensionality
+            translation = xp.asarray(translation)
+            for rot, ax in zip(rotation, axes):
+                R = np.eye(dimensionality + 1)
+                c, s = np.cos(rot), np.sin(rot)
+                R[ax, ax] = c
+                R[ax, ax[::-1]] = -s, s
+                R = xp.asarray(R)
+                matrix = xp.asarray(R @ matrix)
+
+            matrix[:dimensionality, :dimensionality] *= scale
+            matrix[:dimensionality, dimensionality] = translation
+            self.params = matrix
+        elif self.params is None:
+            # default to an identity transform
+            self.params = xp.eye(dimensionality + 1)
+
+    def estimate(self, src, dst):
+        """Estimate the transformation from a set of corresponding points.
+
+        You can determine the over-, well- and under-determined parameters
+        with the total least-squares method.
+
+        Number of source and destination coordinates must match.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+
+        self.params = _umeyama(src, dst, estimate_scale=True)
+
+        return True
+
+    @property
+    def scale(self):
+        # det = scale**(# of dimensions), therefore scale = det**(1/2)
+        return math.sqrt(np.linalg.det(cp.asnumpy(self.params)))
+
+
+class PolynomialTransform(GeometricTransform):
+    """2D polynomial transformation.
+
+    Has the following form::
+
+        X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
+        Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))
+
+    Parameters
+    ----------
+    params : (2, N) array, optional
+        Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So,
+        a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`.
+
+    Attributes
+    ----------
+    params : (2, N) array
+        Polynomial coefficients where `N * 2 = (order + 1) * (order + 2)`. So,
+        a_ji is defined in `params[0, :]` and b_ji in `params[1, :]`.
+
+    """
+
+    def __init__(self, params=None, *, dimensionality=2, xp=cp):
+        if dimensionality != 2:
+            raise NotImplementedError(
+                "Polynomial transforms are only implemented for 2D."
+            )
+        if params is None:
+            # default to transformation which preserves original coordinates
+            params = xp.asarray(np.array([[0, 1, 0], [0, 0, 1]]))
+        if params.shape[0] != 2:
+            raise ValueError("invalid shape of transformation parameters")
+        self.params = params
+
+    def estimate(self, src, dst, order=2):
+        """Estimate the transformation from a set of corresponding points.
+
+        You can determine the over-, well- and under-determined parameters
+        with the total least-squares method.
+
+        Number of source and destination coordinates must match.
+
+        The transformation is defined as::
+
+            X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i ))
+            Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i ))
+
+        These equations can be transformed to the following form::
+
+            0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) - X
+            0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) - Y
+
+        which exist for each set of corresponding points, so we have a set of
+        N * 2 equations. The coefficients appear linearly so we can write
+        A x = 0, where::
+
+            A   = [[1 x y x**2 x*y y**2 ... 0 ...             0 -X]
+                   [0 ...                 0 1 x y x**2 x*y y**2 -Y]
+                    ...
+                    ...
+                  ]
+            x.T = [a00 a10 a11 a20 a21 a22 ... ann
+                   b00 b10 b11 b20 b21 b22 ... bnn c3]
+
+        In case of total least-squares the solution of this homogeneous system
+        of equations is the right singular vector of A which corresponds to the
+        smallest singular value normed by the coefficient c3.
+
+        Parameters
+        ----------
+        src : (N, 2) array
+            Source coordinates.
+        dst : (N, 2) array
+            Destination coordinates.
+        order : int, optional
+            Polynomial order (number of coefficients is order + 1).
+
+        Returns
+        -------
+        success : bool
+            True, if model estimation succeeds.
+
+        """
+        xp = cp.get_array_module(src)
+        xs = src[:, 0]
+        ys = src[:, 1]
+        xd = dst[:, 0]
+        yd = dst[:, 1]
+        rows = src.shape[0]
+
+        # number of unknown polynomial coefficients
+        order = safe_as_int(order)
+        u = (order + 1) * (order + 2)
+
+        A = xp.zeros((rows * 2, u + 1))
+        pidx = 0
+        for j in range(order + 1):
+            for i in range(j + 1):
+                A[:rows, pidx] = xs ** (j - i) * ys ** i
+                A[rows:, pidx + u // 2] = xs ** (j - i) * ys ** i
+                pidx += 1
+
+        A[:rows, -1] = xd
+        A[rows:, -1] = yd
+
+        _, _, V = xp.linalg.svd(A)
+
+        # solution is right singular vector that corresponds to smallest
+        # singular value
+        params = -V[-1, :-1] / V[-1, -1]
+
+        self.params = params.reshape((2, u // 2))
+
+        return True
+
+    def __call__(self, coords):
+        """Apply forward transformation.
+
+        Parameters
+        ----------
+        coords : (N, 2) array
+            source coordinates
+
+        Returns
+        -------
+        coords : (N, 2) array
+            Transformed coordinates.
+
+        """
+        x = coords[:, 0]
+        y = coords[:, 1]
+        xp = cp.get_array_module(coords)
+        u = len(self.params.ravel())
+        # number of coefficients -> u = (order + 1) * (order + 2)
+        order = int((-3 + math.sqrt(9 - 4 * (2 - u))) / 2)
+        dst = xp.zeros(coords.shape)
+
+        pidx = 0
+        for j in range(order + 1):
+            for i in range(j + 1):
+                dst[:, 0] += self.params[0, pidx] * x ** (j - i) * y ** i
+                dst[:, 1] += self.params[1, pidx] * x ** (j - i) * y ** i
+                pidx += 1
+
+        return dst
+
+    def inverse(self, coords):
+        raise Exception(
+            'There is no explicit way to do the inverse polynomial '
+            'transformation. Instead, estimate the inverse transformation '
+            'parameters by exchanging source and destination coordinates,'
+            'then apply the forward transformation.')
+
+
+TRANSFORMS = {
+    'euclidean': EuclideanTransform,
+    'similarity': SimilarityTransform,
+    'affine': AffineTransform,
+    'piecewise-affine': PiecewiseAffineTransform,
+    'projective': ProjectiveTransform,
+    'fundamental': FundamentalMatrixTransform,
+    'essential': EssentialMatrixTransform,
+    'polynomial': PolynomialTransform,
+}
+
+
+def estimate_transform(ttype, src, dst, **kwargs):
+    """Estimate 2D geometric transformation parameters.
+
+    You can determine the over-, well- and under-determined parameters
+    with the total least-squares method.
+
+    Number of source and destination coordinates must match.
+
+    Parameters
+    ----------
+    ttype : {'euclidean', similarity', 'affine', 'piecewise-affine', \
+             'projective', 'polynomial'}
+        Type of transform.
+    kwargs : array or int
+        Function parameters (src, dst, n, angle)::
+
+            NAME / TTYPE        FUNCTION PARAMETERS
+            'euclidean'         `src, `dst`
+            'similarity'        `src, `dst`
+            'affine'            `src, `dst`
+            'piecewise-affine'  `src, `dst`
+            'projective'        `src, `dst`
+            'polynomial'        `src, `dst`, `order` (polynomial order,
+                                                      default order is 2)
+
+        Also see examples below.
+
+    Returns
+    -------
+    tform : :class:`GeometricTransform`
+        Transform object containing the transformation parameters and providing
+        access to forward and inverse transformation functions.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage import transform
+
+    >>> # estimate transformation parameters
+    >>> src = cp.array([0, 0, 10, 10]).reshape((2, 2))
+    >>> dst = cp.array([12, 14, 1, -20]).reshape((2, 2))
+
+    >>> tform = transform.estimate_transform('similarity', src, dst)
+
+    >>> cp.allclose(tform.inverse(tform(src)), src)
+    True
+
+    >>> # warp image using the estimated transformation
+    >>> from skimage import data
+    >>> image = cp.array(data.camera())
+
+    >>> transform.warp(image, inverse_map=tform.inverse) # doctest: +SKIP
+
+    >>> # create transformation with explicit parameters
+    >>> tform2 = transform.SimilarityTransform(scale=1.1, rotation=1,
+    ...     translation=(10, 20))
+
+    >>> # unite transformations, applied in order from left to right
+    >>> tform3 = tform + tform2
+    >>> cp.allclose(tform3(src), tform2(tform(src)))
+    True
+
+    """
+    ttype = ttype.lower()
+    if ttype not in TRANSFORMS:
+        raise ValueError('the transformation type \'%s\' is not'
+                         'implemented' % ttype)
+
+    tform = TRANSFORMS[ttype](dimensionality=src.shape[1])
+    tform.estimate(src, dst, **kwargs)
+
+    return tform
+
+
+def matrix_transform(coords, matrix):
+    """Apply 2D matrix transform.
+
+    Parameters
+    ----------
+    coords : (N, 2) array
+        x, y coordinates to transform
+    matrix : (3, 3) array
+        Homogeneous transformation matrix.
+
+    Returns
+    -------
+    coords : (N, 2) array
+        Transformed coordinates.
+
+    """
+    return ProjectiveTransform(matrix)(coords)
diff --git a/python/cucim/src/cucim/skimage/transform/_warps.py b/python/cucim/src/cucim/skimage/transform/_warps.py
new file mode 100644
index 000000000..05b1f275a
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/_warps.py
@@ -0,0 +1,1107 @@
+import math
+
+import cupy as cp
+import numpy as np
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import (_validate_interpolation_order, convert_to_float,
+                             safe_as_int, warn)
+from ..measure import block_reduce
+from ._geometric import (AffineTransform, ProjectiveTransform,
+                         SimilarityTransform, _to_ndimage_mode)
+
+# from .._shared.utils import get_bound_method_class
+
+HOMOGRAPHY_TRANSFORMS = (
+    SimilarityTransform,
+    AffineTransform,
+    ProjectiveTransform,
+)
+
+
+def resize(image, output_shape, order=None, mode='reflect', cval=0, clip=True,
+           preserve_range=False, anti_aliasing=None, anti_aliasing_sigma=None):
+    """Resize image to match a certain size.
+
+    Performs interpolation to up-size or down-size N-dimensional images. Note
+    that anti-aliasing should be enabled when down-sizing images to avoid
+    aliasing artifacts. For down-sampling with an integer factor also see
+    `skimage.transform.downscale_local_mean`.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    output_shape : tuple or ndarray
+        Size of the generated output image `(rows, cols[, ...][, dim])`. If
+        `dim` is not provided, the number of channels is preserved. In case the
+        number of input channels does not equal the number of output channels a
+        n-dimensional interpolation is applied.
+
+    Returns
+    -------
+    resized : ndarray
+        Resized version of the input.
+
+    Other parameters
+    ----------------
+    order : int, optional
+        The order of the spline interpolation, default is 0 if
+        image.dtype is bool and 1 otherwise. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode.  Modes match the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+    anti_aliasing : bool, optional
+        Whether to apply a Gaussian filter to smooth the image prior
+        to down-scaling. It is crucial to filter when down-sampling
+        the image to avoid aliasing artifacts. If input image data
+        type is bool, no anti-aliasing is applied.
+    anti_aliasing_sigma : {float, tuple of floats}, optional
+        Standard deviation for Gaussian filtering to avoid aliasing artifacts.
+        By default, this value is chosen as (s - 1) / 2 where s is the
+        down-scaling factor, where s > 1. For the up-size case, s < 1, no
+        anti-aliasing is performed prior to rescaling.
+
+    Notes
+    -----
+    Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
+    pixels are duplicated during the reflection.  As an example, if an array
+    has values [0, 1, 2] and was padded to the right by four values using
+    symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it
+    would be [0, 1, 2, 1, 0, 1, 2].
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.transform import resize
+    >>> image = cp.array(data.camera())
+    >>> resize(image, (100, 100)).shape
+    (100, 100)
+
+    """
+    output_shape = tuple(output_shape)
+    output_ndim = len(output_shape)
+    input_shape = image.shape
+    if output_ndim > image.ndim:
+        # append dimensions to input_shape
+        input_shape = input_shape + (1,) * (output_ndim - image.ndim)
+        image = cp.reshape(image, input_shape)
+    elif output_ndim == image.ndim - 1:
+        # multichannel case: append shape of last axis
+        output_shape = output_shape + (image.shape[-1],)
+    elif output_ndim < image.ndim - 1:
+        raise ValueError("len(output_shape) cannot be smaller than the image "
+                         "dimensions")
+
+    if anti_aliasing is None:
+        anti_aliasing = not image.dtype == bool
+
+    if image.dtype == bool and anti_aliasing:
+        warn("Input image dtype is bool. Gaussian convolution is not defined "
+             "with bool data type. Please set anti_aliasing to False or "
+             "explicitely cast input image to another data type. Starting "
+             "from version 0.19 a ValueError will be raised instead of this "
+             "warning.", FutureWarning, stacklevel=2)
+
+    factors = tuple(si / so for si, so in zip(input_shape, output_shape))
+
+    if anti_aliasing and any(f > 1 for f in factors):
+        if anti_aliasing_sigma is None:
+            anti_aliasing_sigma = tuple([max(0, (f - 1) / 2) for f in factors])
+        else:
+            if np.isscalar(anti_aliasing_sigma):
+                anti_aliasing_sigma = (anti_aliasing_sigma,) * len(factors)
+            elif len(anti_aliasing_sigma) != len(factors):
+                raise ValueError("invalid anti_aliasing_sigma length")
+            if any(sigma < 0 for sigma in anti_aliasing_sigma):
+                raise ValueError("Anti-aliasing standard deviation must be "
+                                 "greater than or equal to zero")
+            elif any(((sigma > 0) & (factor <= 1))
+                     for factor, sigma in zip(factors, anti_aliasing_sigma)):
+                warn("Anti-aliasing standard deviation greater than zero but "
+                     "not down-sampling along all axes")
+
+        # Translate modes used by np.pad to those used by ndi.gaussian_filter
+        np_pad_to_ndimage = {
+            'constant': 'constant',
+            'edge': 'nearest',
+            'symmetric': 'reflect',
+            'reflect': 'mirror',
+            'wrap': 'wrap'
+        }
+        try:
+            ndi_mode = np_pad_to_ndimage[mode]
+        except KeyError:
+            raise ValueError("Unknown mode, or cannot translate mode. The "
+                             "mode should be one of 'constant', 'edge', "
+                             "'symmetric', 'reflect', or 'wrap'. See the "
+                             "documentation of numpy.pad for more info.")
+
+        image = ndi.gaussian_filter(image, anti_aliasing_sigma,
+                                    cval=cval, mode=ndi_mode)
+
+    order = _validate_interpolation_order(image.dtype, order)
+    ndi_mode = _to_ndimage_mode(mode)
+    # TODO: move the following conversion into _to_ndimage_mode
+    if ndi_mode == 'constant':
+        ndi_mode = 'grid-constant'
+    elif ndi_mode == 'wrap':
+        ndi_mode = 'grid-wrap'
+    zoom_factors = [1 / f for f in factors]
+    image = convert_to_float(image, preserve_range)
+    out = ndi.zoom(image, zoom_factors, order=order, mode=ndi_mode,
+                   cval=cval, grid_mode=True)
+    _clip_warp_output(image, out, order, mode, cval, clip)
+    return out
+
+
+def rescale(image, scale, order=None, mode='reflect', cval=0, clip=True,
+            preserve_range=False, multichannel=False,
+            anti_aliasing=None, anti_aliasing_sigma=None):
+    """Scale image by a certain factor.
+
+    Performs interpolation to up-scale or down-scale N-dimensional images.
+    Note that anti-aliasing should be enabled when down-sizing images to avoid
+    aliasing artifacts. For down-sampling with an integer factor also see
+    `skimage.transform.downscale_local_mean`.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    scale : {float, tuple of floats}
+        Scale factors. Separate scale factors can be defined as
+        `(rows, cols[, ...][, dim])`.
+
+    Returns
+    -------
+    scaled : ndarray
+        Scaled version of the input.
+
+    Other parameters
+    ----------------
+    order : int, optional
+        The order of the spline interpolation, default is 0 if
+        image.dtype is bool and 1 otherwise. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode.  Modes match the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+    anti_aliasing : bool, optional
+        Whether to apply a Gaussian filter to smooth the image prior
+        to down-scaling. It is crucial to filter when down-sampling
+        the image to avoid aliasing artifacts. If input image data
+        type is bool, no anti-aliasing is applied.
+    anti_aliasing_sigma : {float, tuple of floats}, optional
+        Standard deviation for Gaussian filtering to avoid aliasing artifacts.
+        By default, this value is chosen as (s - 1) / 2 where s is the
+        down-scaling factor.
+
+    Notes
+    -----
+    Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
+    pixels are duplicated during the reflection.  As an example, if an array
+    has values [0, 1, 2] and was padded to the right by four values using
+    symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it
+    would be [0, 1, 2, 1, 0, 1, 2].
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.transform import rescale
+    >>> image = cp.array(data.camera())
+    >>> rescale(image, 0.1).shape
+    (51, 51)
+    >>> rescale(image, 0.5).shape
+    (256, 256)
+
+    """
+    scale = np.atleast_1d(scale)
+    if len(scale) > 1:
+        if ((not multichannel and len(scale) != image.ndim) or
+                (multichannel and len(scale) != image.ndim - 1)):
+            raise ValueError("Supply a single scale, or one value per spatial "
+                             "axis")
+        if multichannel:
+            scale = np.concatenate((scale, [1]))
+    orig_shape = np.asarray(image.shape)
+    output_shape = np.round(scale * orig_shape)
+    if multichannel:  # don't scale channel dimension
+        output_shape[-1] = orig_shape[-1]
+
+    return resize(image, output_shape, order=order, mode=mode, cval=cval,
+                  clip=clip, preserve_range=preserve_range,
+                  anti_aliasing=anti_aliasing,
+                  anti_aliasing_sigma=anti_aliasing_sigma)
+
+
+def _ndimage_affine(image, matrix, output_shape, order, mode, cval, clip,
+                    preserve_range):
+    """Thin wrapper around scipy.ndimage.affine_transform
+
+    Validates input and handles clipping of output in the same way as ``warp``.
+    """
+    if image.size == 0:
+        raise ValueError("Cannot warp empty image with dimensions",
+                         image.shape)
+
+    order = _validate_interpolation_order(image.dtype, order)
+
+    if image.dtype.kind == "c":
+        if not preserve_range:
+            raise NotImplementedError("TODO")
+    else:
+        image = convert_to_float(image, preserve_range)
+
+    input_shape = image.shape
+
+    if output_shape is None:
+        output_shape = input_shape
+    else:
+        output_shape = safe_as_int(output_shape)
+
+    # Pre-filtering not necessary for order 0, 1 interpolation
+    prefilter = order > 1
+
+    ndi_mode = _to_ndimage_mode(mode)
+    warped = ndi.affine_transform(image, matrix, prefilter=prefilter,
+                                  mode=ndi_mode, order=order, cval=cval,
+                                  output_shape=tuple(output_shape))
+
+    _clip_warp_output(image, warped, order, mode, cval, clip)
+
+    return warped
+
+
+def _ndimage_rotate(image, angle, resize, order, mode, cval, clip,
+                    preserve_range):
+    """Thin wrapper around scipy.ndimage.rotate
+
+    Validates input and handles clipping of output in the same way as ``warp``.
+    """
+    if image.size == 0:
+        raise ValueError("Cannot warp empty image with dimensions",
+                         image.shape)
+
+    order = _validate_interpolation_order(image.dtype, order)
+
+    if image.dtype.kind == "c":
+        if not preserve_range:
+            raise NotImplementedError("TODO")
+    else:
+        image = convert_to_float(image, preserve_range)
+
+    # Pre-filtering not necessary for order 0, 1 interpolation
+    prefilter = order > 1
+
+    ndi_mode = _to_ndimage_mode(mode)
+    warped = ndi.rotate(image, angle, reshape=resize, prefilter=prefilter,
+                        mode=ndi_mode, order=order, cval=cval)
+    _clip_warp_output(image, warped, order, mode, cval, clip)
+    return warped
+
+
+def rotate(image, angle, resize=False, center=None, order=None,
+           mode='constant', cval=0, clip=True, preserve_range=False):
+    """Rotate image by a certain angle around its center.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    angle : float
+        Rotation angle in degrees in counter-clockwise direction.
+    resize : bool, optional
+        Determine whether the shape of the output image will be automatically
+        calculated, so the complete rotated image exactly fits. Default is
+        False.
+    center : iterable of length 2
+        The rotation center. If ``center=None``, the image is rotated around
+        its center, i.e. ``center=(cols / 2 - 0.5, rows / 2 - 0.5)``.  Please
+        note that this parameter is (cols, rows), contrary to normal skimage
+        ordering.
+
+    Returns
+    -------
+    rotated : ndarray
+        Rotated version of the input.
+
+    Other parameters
+    ----------------
+    order : int, optional
+        The order of the spline interpolation, default is 0 if
+        image.dtype is bool and 1 otherwise. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode.  Modes match the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Notes
+    -----
+    Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge
+    pixels are duplicated during the reflection.  As an example, if an array
+    has values [0, 1, 2] and was padded to the right by four values using
+    symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it
+    would be [0, 1, 2, 1, 0, 1, 2].
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> from cucim.skimage.transform import rotate
+    >>> image = cp.array(data.camera())
+    >>> rotate(image, 2).shape
+    (512, 512)
+    >>> rotate(image, 2, resize=True).shape
+    (530, 530)
+    >>> rotate(image, 90, resize=True).shape
+    (512, 512)
+
+    """
+
+    rows, cols = image.shape[0], image.shape[1]
+    img_center = np.array((cols, rows)) / 2.0 - 0.5
+    if center is None:
+        center = img_center
+        centered = True
+    else:
+        center = np.asarray(center)
+        centered = np.array_equal(center, img_center)
+
+    if centered and not resize:
+        # can use cupyx.scipy.ndimage.rotate
+        return _ndimage_rotate(
+            image, angle, resize, order=order, mode=mode, cval=cval, clip=clip,
+            preserve_range=preserve_range
+        )
+
+    # rotation around center
+    tform1 = SimilarityTransform(translation=center, xp=np)
+    tform2 = SimilarityTransform(rotation=np.deg2rad(angle), xp=np)
+    tform3 = SimilarityTransform(translation=-center, xp=np)
+    tform = tform3 + tform2 + tform1
+
+    output_shape = None
+    if resize:
+        # determine shape of output image
+        # fmt: off
+        corners = np.array([
+            [0, 0],
+            [0, rows - 1],
+            [cols - 1, rows - 1],
+            [cols - 1, 0]
+        ])
+        # fmt: on
+        corners = tform.inverse(corners)
+        minc = corners[:, 0].min()
+        minr = corners[:, 1].min()
+        maxc = corners[:, 0].max()
+        maxr = corners[:, 1].max()
+        out_rows = maxr - minr + 1
+        out_cols = maxc - minc + 1
+        output_shape = np.around((out_rows, out_cols))
+
+        # fit output image in new shape
+        translation = (minc, minr)
+        tform4 = SimilarityTransform(translation=translation, xp=np)
+        tform = tform4 + tform
+
+    # Make sure the transform is exactly affine, to ensure fast warping.
+    tform.params[2] = (0, 0, 1)
+
+    # swap axes in the matrix to match cupyx.scipy.ndimage.affine_transform
+    tform.params[:2, :2] = tform.params[:2, :2].T
+    tform.params[:2, 2] = tform.params[1::-1, 2]
+
+    # transfer the coordinate transform to the GPU
+    tform.params = cp.asarray(tform.params)
+
+    return _ndimage_affine(
+        image, tform.params, output_shape=output_shape, order=order,
+        mode=mode, cval=cval, clip=clip, preserve_range=preserve_range
+    )
+
+
+def downscale_local_mean(image, factors, cval=0, clip=True):
+    """Down-sample N-dimensional image by local averaging.
+
+    The image is padded with `cval` if it is not perfectly divisible by the
+    integer factors.
+
+    In contrast to interpolation in `skimage.transform.resize` and
+    `skimage.transform.rescale` this function calculates the local mean of
+    elements in each block of size `factors` in the input image.
+
+    Parameters
+    ----------
+    image : ndarray
+        N-dimensional input image.
+    factors : array_like
+        Array containing down-sampling integer factor along each axis.
+    cval : float, optional
+        Constant padding value if image is not perfectly divisible by the
+        integer factors.
+    clip : bool, optional
+        Unused, but kept here for API consistency with the other transforms
+        in this module. (The local mean will never fall outside the range
+        of values in the input image, assuming the provided `cval` also
+        falls within that range.)
+
+    Returns
+    -------
+    image : ndarray
+        Down-sampled image with same number of dimensions as input image.
+        For integer inputs, the output dtype will be ``float64``.
+        See :func:`numpy.mean` for details.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> a = cp.arange(15).reshape(3, 5)
+    >>> a
+    array([[ 0,  1,  2,  3,  4],
+           [ 5,  6,  7,  8,  9],
+           [10, 11, 12, 13, 14]])
+    >>> downscale_local_mean(a, (2, 3))
+    array([[3.5, 4. ],
+           [5.5, 4.5]])
+
+    """
+    return block_reduce(image, factors, cp.mean, cval)
+
+
+def _swirl_mapping(xy, center, rotation, strength, radius):
+    x, y = xy.T
+    x0, y0 = center
+    xdiff = x - x0
+    ydiff = y - y0
+    rho = cp.sqrt(xdiff * xdiff + ydiff * ydiff)
+
+    # Ensure that the transformation decays to approximately 1/1000-th
+    # within the specified radius.
+    radius = radius / 5 * math.log(2)
+
+    theta = rotation + strength * cp.exp(-rho / radius)
+    theta += cp.arctan2(ydiff, xdiff)
+
+    xy[..., 0] = x0 + rho * cp.cos(theta)
+    xy[..., 1] = y0 + rho * cp.sin(theta)
+
+    return xy
+
+
+def swirl(image, center=None, strength=1, radius=100, rotation=0,
+          output_shape=None, order=None, mode='reflect', cval=0, clip=True,
+          preserve_range=False):
+    """Perform a swirl transformation.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    center : (column, row) tuple or (2,) ndarray, optional
+        Center coordinate of transformation.
+    strength : float, optional
+        The amount of swirling applied.
+    radius : float, optional
+        The extent of the swirl in pixels.  The effect dies out
+        rapidly beyond `radius`.
+    rotation : float, optional
+        Additional rotation applied to the image.
+
+    Returns
+    -------
+    swirled : ndarray
+        Swirled version of the input.
+
+    Other parameters
+    ----------------
+    output_shape : tuple (rows, cols), optional
+        Shape of the output image generated. By default the shape of the input
+        image is preserved.
+    order : int, optional
+        The order of the spline interpolation, default is 0 if
+        image.dtype is bool and 1 otherwise. The order has to be in
+        the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode, with 'constant' used as the default. Modes match
+        the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    """
+    if center is None:
+        center = np.array(image.shape)[:2][::-1] / 2
+
+    warp_args = {'center': center,
+                 'rotation': rotation,
+                 'strength': strength,
+                 'radius': radius}
+
+    return warp(image, _swirl_mapping, map_args=warp_args,
+                output_shape=output_shape, order=order, mode=mode, cval=cval,
+                clip=clip, preserve_range=preserve_range)
+
+
+def _stackcopy(a, b):
+    """Copy b into each color layer of a, such that::
+
+      a[:,:,0] = a[:,:,1] = ... = b
+
+    Parameters
+    ----------
+    a : (M, N) or (M, N, P) ndarray
+        Target array.
+    b : (M, N)
+        Source array.
+
+    Notes
+    -----
+    Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays.
+
+    """
+    if a.ndim == 3:
+        a[:] = b[:, :, np.newaxis]
+    else:
+        a[:] = b
+
+
+def warp_coords(coord_map, shape, dtype=np.float64):
+    """Build the source coordinates for the output of a 2-D image warp.
+
+    Parameters
+    ----------
+    coord_map : callable like GeometricTransform.inverse
+        Return input coordinates for given output coordinates.
+        Coordinates are in the shape (P, 2), where P is the number
+        of coordinates and each element is a ``(row, col)`` pair.
+    shape : tuple
+        Shape of output image ``(rows, cols[, bands])``.
+    dtype : np.dtype or string
+        dtype for return value (sane choices: float32 or float64).
+
+    Returns
+    -------
+    coords : (ndim, rows, cols[, bands]) array of dtype `dtype`
+            Coordinates for `scipy.ndimage.map_coordinates`, that will yield
+            an image of shape (orows, ocols, bands) by drawing from source
+            points according to the `coord_transform_fn`.
+
+    Notes
+    -----
+
+    This is a lower-level routine that produces the source coordinates for 2-D
+    images used by `warp()`.
+
+    It is provided separately from `warp` to give additional flexibility to
+    users who would like, for example, to re-use a particular coordinate
+    mapping, to use specific dtypes at various points along the the
+    image-warping process, or to implement different post-processing logic
+    than `warp` performs after the call to `ndi.map_coordinates`.
+
+
+    Examples
+    --------
+    Produce a coordinate map that shifts an image up and to the right:
+
+    >>> import cupy as cp
+    >>> from cucim.skimage.transform import warp_coords
+    >>> from skimage import data
+    >>> from cupyx.scipy.ndimage import map_coordinates
+    >>>
+    >>> def shift_up10_left20(xy):
+    ...     return xy - cp.array([-20, 10])[None, :]
+    >>>
+    >>> image = cp.array(data.astronaut().astype(cp.float32))
+    >>> coords = warp_coords(shift_up10_left20, image.shape)
+    >>> warped_image = map_coordinates(image, coords)
+
+    """
+    shape = safe_as_int(shape)
+    rows, cols = shape[0], shape[1]
+    coords_shape = [len(shape), rows, cols]
+    if len(shape) == 3:
+        coords_shape.append(shape[2])
+    coords = cp.empty(coords_shape, dtype=dtype)
+
+    # Reshape grid coordinates into a (P, 2) array of (row, col) pairs
+    tf_coords = cp.indices((cols, rows), dtype=dtype).reshape(2, -1).T
+
+    # Map each (row, col) pair to the source image according to
+    # the user-provided mapping
+    tf_coords = coord_map(tf_coords)
+
+    # Reshape back to a (2, M, N) coordinate grid
+    tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
+
+    # Place the y-coordinate mapping
+    _stackcopy(coords[1, ...], tf_coords[0, ...])
+
+    # Place the x-coordinate mapping
+    _stackcopy(coords[0, ...], tf_coords[1, ...])
+
+    if len(shape) == 3:
+        coords[2, ...] = cp.arange(shape[2], dtype=coords.dtype)
+
+    return coords
+
+
+def _clip_warp_output(input_image, output_image, order, mode, cval, clip):
+    """Clip output image to range of values of input image.
+
+    Note that this function modifies the values of `output_image` in-place
+    and it is only modified if ``clip=True``.
+
+    Parameters
+    ----------
+    input_image : ndarray
+        Input image.
+    output_image : ndarray
+        Output image, which is modified in-place.
+
+    Other parameters
+    ----------------
+    order : int, optional
+        The order of the spline interpolation, default is 1. The order has to
+        be in the range 0-5. See `skimage.transform.warp` for detail.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode.  Modes match the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+
+    """
+    if clip and order != 0:
+        min_val = input_image.min()
+        max_val = input_image.max()
+
+        preserve_cval = (mode == 'constant' and not
+                         (min_val <= cval <= max_val))
+
+        if preserve_cval:
+            cval_mask = output_image == cval
+
+        cp.clip(output_image, min_val, max_val, out=output_image)
+
+        if preserve_cval:
+            output_image[cval_mask] = cval
+
+
+def warp(image, inverse_map, map_args={}, output_shape=None, order=None,
+         mode='constant', cval=0., clip=True, preserve_range=False):
+    """Warp an image according to a given coordinate transformation.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    inverse_map : transformation object, callable ``cr = f(cr, **kwargs)``, or ndarray
+        Inverse coordinate map, which transforms coordinates in the output
+        images into their corresponding coordinates in the input image.
+
+        There are a number of different options to define this map, depending
+        on the dimensionality of the input image. A 2-D image can have 2
+        dimensions for gray-scale images, or 3 dimensions with color
+        information.
+
+         - For 2-D images, you can directly pass a transformation object,
+           e.g. `skimage.transform.SimilarityTransform`, or its inverse.
+         - For 2-D images, you can pass a ``(3, 3)`` homogeneous
+           transformation matrix, e.g.
+           `skimage.transform.SimilarityTransform.params`.
+         - For 2-D images, a function that transforms a ``(M, 2)`` array of
+           ``(col, row)`` coordinates in the output image to their
+           corresponding coordinates in the input image. Extra parameters to
+           the function can be specified through `map_args`.
+         - For N-D images, you can directly pass an array of coordinates.
+           The first dimension specifies the coordinates in the input image,
+           while the subsequent dimensions determine the position in the
+           output image. E.g. in case of 2-D images, you need to pass an array
+           of shape ``(2, rows, cols)``, where `rows` and `cols` determine the
+           shape of the output image, and the first dimension contains the
+           ``(row, col)`` coordinate in the input image.
+           See `scipy.ndimage.map_coordinates` for further documentation.
+
+        Note, that a ``(3, 3)`` matrix is interpreted as a homogeneous
+        transformation matrix, so you cannot interpolate values from a 3-D
+        input, if the output is of shape ``(3,)``.
+
+        See example section for usage.
+    map_args : dict, optional
+        Keyword arguments passed to `inverse_map`.
+    output_shape : tuple (rows, cols), optional
+        Shape of the output image generated. By default the shape of the input
+        image is preserved.  Note that, even for multi-band images, only rows
+        and columns need to be specified.
+    order : int, optional
+        The order of interpolation. The order has to be in the range 0-5:
+         - 0: Nearest-neighbor
+         - 1: Bi-linear (default)
+         - 2: Bi-quadratic
+         - 3: Bi-cubic
+         - 4: Bi-quartic
+         - 5: Bi-quintic
+
+         Default is 0 if image.dtype is bool and 1 otherwise.
+    mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
+        Points outside the boundaries of the input are filled according
+        to the given mode.  Modes match the behaviour of `numpy.pad`.
+    cval : float, optional
+        Used in conjunction with mode 'constant', the value outside
+        the image boundaries.
+    clip : bool, optional
+        Whether to clip the output to the range of values of the input image.
+        This is enabled by default, since higher order interpolation may
+        produce values outside the given input range.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see
+        https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Returns
+    -------
+    warped : double ndarray
+        The warped input image.
+
+    Notes
+    -----
+    - The input image is converted to a `double` image.
+    - In case of a `SimilarityTransform`, `AffineTransform` and
+      `ProjectiveTransform` and `order` in [0, 3] this function uses the
+      underlying transformation matrix to warp the image with a much faster
+      routine.
+
+    Examples
+    --------
+    >>> from cucim.skimage.transform import warp
+    >>> from skimage import data
+    >>> image = cp.array(data.camera())
+
+    The following image warps are all equal but differ substantially in
+    execution time. The image is shifted to the bottom.
+
+    Use a geometric transform to warp an image (fast):
+
+    >>> from cucim.skimage.transform import SimilarityTransform
+    >>> tform = SimilarityTransform(translation=(0, -10))
+    >>> warped = warp(image, tform)
+
+    Use a callable (slow):
+
+    >>> def shift_down(xy):
+    ...     xy[:, 1] -= 10
+    ...     return xy
+    >>> warped = warp(image, shift_down)
+
+    Use a transformation matrix to warp an image (fast):
+
+    >>> import cupy as cp
+    >>> matrix = cp.asarray([[1, 0, 0], [0, 1, -10], [0, 0, 1]])
+    >>> warped = warp(image, matrix)
+    >>> from cucim.skimage.transform import ProjectiveTransform, warp
+    >>> warped = warp(image, ProjectiveTransform(matrix=matrix))
+
+    You can also use the inverse of a geometric transformation (fast):
+
+    >>> warped = warp(image, tform.inverse)
+
+    For N-D images you can pass a coordinate array, that specifies the
+    coordinates in the input image for every element in the output image. E.g.
+    if you want to rescale a 3-D cube, you can do:
+
+    >>> cube_shape = (30, 30, 30)
+    >>> cube = cp.random.rand(*cube_shape)
+
+    Setup the coordinate array, that defines the scaling:
+
+    >>> scale = 0.1
+    >>> output_shape = tuple(int(scale * s) for s in cube_shape)
+    >>> coords0, coords1, coords2 = cp.mgrid[:output_shape[0],
+    ...                    :output_shape[1], :output_shape[2]]
+    >>> coords = cp.asarray([coords0, coords1, coords2])
+
+    Assume that the cube contains spatial data, where the first array element
+    center is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to
+    account for this extra offset when scaling the image:
+
+    >>> coords = (coords + 0.5) / scale - 0.5
+    >>> warped = warp(cube, coords)
+
+    """  # noqa
+    if image.size == 0:
+        raise ValueError("Cannot warp empty image with dimensions",
+                         image.shape)
+
+    order = _validate_interpolation_order(image.dtype, order)
+
+    if image.dtype.kind == "c":
+        if not preserve_range:
+            raise NotImplementedError("TODO")
+    else:
+        image = convert_to_float(image, preserve_range)
+
+    input_shape = np.array(image.shape)
+
+    if output_shape is None:
+        output_shape = input_shape
+    else:
+        output_shape = safe_as_int(output_shape)
+
+    if isinstance(inverse_map, cp.ndarray) and inverse_map.shape == (3, 3,):
+        # inverse_map is a transformation matrix as numpy array,
+        # this is only used for order >= 4.
+        inverse_map = ProjectiveTransform(matrix=inverse_map)
+
+    if isinstance(inverse_map, cp.ndarray):
+        # inverse_map is directly given as coordinates
+        coords = inverse_map
+    else:
+        # inverse_map is given as function, that transforms (N, 2)
+        # destination coordinates to their corresponding source
+        # coordinates. This is only supported for 2(+1)-D images.
+
+        if image.ndim < 2 or image.ndim > 3:
+            raise ValueError("Only 2-D images (grayscale or color) are "
+                             "supported, when providing a callable "
+                             "`inverse_map`.")
+
+        def coord_map(*args):
+            return inverse_map(*args, **map_args)
+
+        if len(input_shape) == 3 and len(output_shape) == 2:
+            # Input image is 2D and has color channel, but output_shape is
+            # given for 2-D images. Automatically add the color channel
+            # dimensionality.
+            output_shape = (output_shape[0], output_shape[1],
+                            input_shape[2])
+
+        coords = warp_coords(coord_map, output_shape)
+
+    # Pre-filtering not necessary for order 0, 1 interpolation
+    prefilter = order > 1
+
+    ndi_mode = _to_ndimage_mode(mode)
+    warped = ndi.map_coordinates(image, coords, prefilter=prefilter,
+                                 mode=ndi_mode, order=order, cval=cval)
+
+    _clip_warp_output(image, warped, order, mode, cval, clip)
+
+    return warped
+
+
+def _linear_polar_mapping(output_coords, k_angle, k_radius, center):
+    """Inverse mapping function to convert from Cartesian to polar coordinates
+
+    Parameters
+    ----------
+    output_coords : ndarray
+        `(M, 2)` array of `(col, row)` coordinates in the output image
+    k_angle : float
+        Scaling factor that relates the intended number of rows in the output
+        image to angle: ``k_angle = nrows / (2 * np.pi)``
+    k_radius : float
+        Scaling factor that relates the radius of the circle bounding the
+        area to be transformed to the intended number of columns in the output
+        image: ``k_radius = ncols / radius``
+    center : tuple (row, col)
+        Coordinates that represent the center of the circle that bounds the
+        area to be transformed in an input image.
+
+    Returns
+    -------
+    coords : ndarray
+        `(M, 2)` array of `(col, row)` coordinates in the input image that
+        correspond to the `output_coords` given as input.
+    """
+    angle = output_coords[:, 1] / k_angle
+    rr = ((output_coords[:, 0] / k_radius) * cp.sin(angle)) + center[0]
+    cc = ((output_coords[:, 0] / k_radius) * cp.cos(angle)) + center[1]
+    coords = cp.column_stack((cc, rr))
+    return coords
+
+
+def _log_polar_mapping(output_coords, k_angle, k_radius, center):
+    """Inverse mapping function to convert from Cartesian to polar coordinates
+
+    Parameters
+    ----------
+    output_coords : ndarray
+        `(M, 2)` array of `(col, row)` coordinates in the output image
+    k_angle : float
+        Scaling factor that relates the intended number of rows in the output
+        image to angle: ``k_angle = nrows / (2 * np.pi)``
+    k_radius : float
+        Scaling factor that relates the radius of the circle bounding the
+        area to be transformed to the intended number of columns in the output
+        image: ``k_radius = width / math.log(radius)``
+    center : tuple (row, col)
+        Coordinates that represent the center of the circle that bounds the
+        area to be transformed in an input image.
+
+    Returns
+    -------
+    coords : ndarray
+        `(M, 2)` array of `(col, row)` coordinates in the input image that
+        correspond to the `output_coords` given as input.
+    """
+    angle = output_coords[:, 1] / k_angle
+    rr = ((cp.exp(output_coords[:, 0] / k_radius)) * cp.sin(angle)) + center[0]
+    cc = ((cp.exp(output_coords[:, 0] / k_radius)) * cp.cos(angle)) + center[1]
+    coords = cp.column_stack((cc, rr))
+    return coords
+
+
+def warp_polar(image, center=None, *, radius=None, output_shape=None,
+               scaling='linear', multichannel=False, **kwargs):
+    """Remap image to polar or log-polar coordinates space.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image. Only 2-D arrays are accepted by default. If
+        `multichannel=True`, 3-D arrays are accepted and the last axis is
+        interpreted as multiple channels.
+    center : tuple (row, col), optional
+        Point in image that represents the center of the transformation (i.e.,
+        the origin in cartesian space). Values can be of type `float`.
+        If no value is given, the center is assumed to be the center point
+        of the image.
+    radius : float, optional
+        Radius of the circle that bounds the area to be transformed.
+    output_shape : tuple (row, col), optional
+    scaling : {'linear', 'log'}, optional
+        Specify whether the image warp is polar or log-polar. Defaults to
+        'linear'.
+    multichannel : bool, optional
+        Whether the image is a 3-D array in which the third axis is to be
+        interpreted as multiple channels. If set to `False` (default), only 2-D
+        arrays are accepted.
+    **kwargs : keyword arguments
+        Passed to `transform.warp`.
+
+    Returns
+    -------
+    warped : ndarray
+        The polar or log-polar warped image.
+
+    Examples
+    --------
+    Perform a basic polar warp on a grayscale image:
+
+    >>> from skimage import data
+    >>> from cucim.skimage.transform import warp_polar
+    >>> image = cp.array(data.checkerboard())
+    >>> warped = warp_polar(image)
+
+    Perform a log-polar warp on a grayscale image:
+
+    >>> warped = warp_polar(image, scaling='log')
+
+    Perform a log-polar warp on a grayscale image while specifying center,
+    radius, and output shape:
+
+    >>> warped = warp_polar(image, (100,100), radius=100,
+    ...                     output_shape=image.shape, scaling='log')
+
+    Perform a log-polar warp on a color image:
+
+    >>> image = data.astronaut()
+    >>> warped = warp_polar(image, scaling='log', multichannel=True)
+    """
+    if image.ndim != 2 and not multichannel:
+        raise ValueError("Input array must be 2 dimensions "
+                         "when `multichannel=False`,"
+                         " got {}".format(image.ndim))
+
+    if image.ndim != 3 and multichannel:
+        raise ValueError("Input array must be 3 dimensions "
+                         "when `multichannel=True`,"
+                         " got {}".format(image.ndim))
+
+    if center is None:
+        center = (np.array(image.shape)[:2] / 2) - 0.5
+
+    if radius is None:
+        w, h = np.array(image.shape)[:2] / 2
+        radius = np.sqrt(w ** 2 + h ** 2)
+
+    if output_shape is None:
+        height = 360
+        width = int(np.ceil(radius))
+        output_shape = (height, width)
+    else:
+        output_shape = safe_as_int(output_shape)
+        height = output_shape[0]
+        width = output_shape[1]
+
+    if scaling == 'linear':
+        k_radius = width / radius
+        map_func = _linear_polar_mapping
+    elif scaling == 'log':
+        k_radius = width / math.log(radius)
+        map_func = _log_polar_mapping
+    else:
+        raise ValueError("Scaling value must be in {'linear', 'log'}")
+
+    k_angle = height / (2 * np.pi)
+    warp_args = {'k_angle': k_angle, 'k_radius': k_radius, 'center': center}
+
+    warped = warp(image, map_func, map_args=warp_args,
+                  output_shape=output_shape, **kwargs)
+
+    return warped
diff --git a/python/cucim/src/cucim/skimage/transform/integral.py b/python/cucim/src/cucim/skimage/transform/integral.py
new file mode 100644
index 000000000..23f08eb32
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/integral.py
@@ -0,0 +1,140 @@
+import cupy as cp
+import numpy as np
+
+
+def integral_image(image):
+    r"""Integral image / summed area table.
+
+    The integral image contains the sum of all elements above and to the
+    left of it, i.e.:
+
+    .. math::
+
+       S[m, n] = \sum_{i \leq m} \sum_{j \leq n} X[i, j]
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+
+    Returns
+    -------
+    S : ndarray
+        Integral image/summed area table of same shape as input image.
+
+    References
+    ----------
+    .. [1] F.C. Crow, "Summed-area tables for texture mapping,"
+           ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207-212.
+
+    """
+    S = image
+    for i in range(image.ndim):
+        S = S.cumsum(axis=i)
+    return S
+
+
+def integrate(ii, start, end):
+    """Use an integral image to integrate over a given window.
+
+    Parameters
+    ----------
+    ii : ndarray
+        Integral image.
+    start : List of tuples, each tuple of length equal to dimension of `ii`
+        Coordinates of top left corner of window(s).
+        Each tuple in the list contains the starting row, col, ... index
+        i.e `[(row_win1, col_win1, ...), (row_win2, col_win2,...), ...]`.
+    end : List of tuples, each tuple of length equal to dimension of `ii`
+        Coordinates of bottom right corner of window(s).
+        Each tuple in the list containing the end row, col, ... index i.e
+        `[(row_win1, col_win1, ...), (row_win2, col_win2, ...), ...]`.
+
+    Returns
+    -------
+    S : scalar or ndarray
+        Integral (sum) over the given window(s).
+
+
+    Examples
+    --------
+    >>> arr = np.ones((5, 6), dtype=np.float)
+    >>> ii = integral_image(arr)
+    >>> integrate(ii, (1, 0), (1, 2))  # sum from (1, 0) to (1, 2)
+    array([3.])
+    >>> integrate(ii, [(3, 3)], [(4, 5)])  # sum from (3, 3) to (4, 5)
+    array([6.])
+    >>> # sum from (1, 0) to (1, 2) and from (3, 3) to (4, 5)
+    >>> integrate(ii, [(1, 0), (3, 3)], [(1, 2), (4, 5)])
+    array([3., 6.])
+    """
+    start = np.atleast_2d(np.array(start))
+    end = np.atleast_2d(np.array(end))
+    rows = start.shape[0]
+
+    total_shape = ii.shape
+    total_shape = np.tile(total_shape, [rows, 1])
+
+    # convert negative indices into equivalent positive indices
+    start_negatives = start < 0
+    end_negatives = end < 0
+    start = (start + total_shape) * start_negatives + \
+             start * ~(start_negatives)  # noqa
+    end = (end + total_shape) * end_negatives + \
+           end * ~(end_negatives)  # noqa
+
+    if np.any((end - start) < 0):
+        raise IndexError('end coordinates must be greater or equal to start')
+
+    # bit_perm is the total number of terms in the expression
+    # of S. For example, in the case of a 4x4 2D image
+    # sum of image from (1,1) to (2,2) is given by
+    # S = + ii[2, 2]
+    #     - ii[0, 2] - ii[2, 0]
+    #     + ii[0, 0]
+    # The total terms = 4 = 2 ** 2(dims)
+
+    scalar_output = rows == 0
+    if scalar_output:
+        S = 0
+    else:
+        S = cp.zeros(rows)
+    bit_perm = 2 ** ii.ndim
+    width = len(bin(bit_perm - 1)[2:])
+
+    # Sum of a (hyper)cube, from an integral image is computed using
+    # values at the corners of the cube. The corners of cube are
+    # selected using binary numbers as described in the following example.
+    # In a 3D cube there are 8 corners. The corners are selected using
+    # binary numbers 000 to 111. Each number is called a permutation, where
+    # perm(000) means, select end corner where none of the coordinates
+    # is replaced, i.e ii[end_row, end_col, end_depth]. Similarly, perm(001)
+    # means replace last coordinate by start - 1, i.e
+    # ii[end_row, end_col, start_depth - 1], and so on.
+    # Sign of even permutations is positive, while those of odd is negative.
+    # If 'start_coord - 1' is -ve it is labeled bad and not considered in
+    # the final sum.
+
+    for i in range(bit_perm):  # for all permutations
+        # boolean permutation array eg [True, False] for '10'
+        binary = bin(i)[2:].zfill(width)
+        bool_mask = [bit == '1' for bit in binary]
+
+        sign = (-1) ** sum(bool_mask)  # determine sign of permutation
+
+        bad = [np.any(((start[r] - 1) * bool_mask) < 0)
+               for r in range(rows)]  # find out bad start rows
+
+        # find corner for each row
+        corner_points = (end * (np.invert(bool_mask))) + \
+                         ((start - 1) * bool_mask)  # noqa
+
+        # CuPy Backend: TODO: check efficiency here
+        if scalar_output:
+            S += sign * ii[tuple(corner_points[0])] if (not bad[0]) else 0
+        else:
+            for r in range(rows):
+                # add only good rows
+                if not bad[r]:
+                    S[r] += sign * ii[tuple(corner_points[r])]
+    return S
diff --git a/python/cucim/src/cucim/skimage/transform/pyramids.py b/python/cucim/src/cucim/skimage/transform/pyramids.py
new file mode 100644
index 000000000..05af2ecf6
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/pyramids.py
@@ -0,0 +1,320 @@
+import math
+from functools import reduce
+
+import cupy as cp
+from cupyx.scipy import ndimage as ndi
+
+from .._shared.utils import convert_to_float
+from ..transform import resize
+
+
+def _smooth(image, sigma, mode, cval, multichannel=None):
+    """Return image with each channel smoothed by the Gaussian filter."""
+    smoothed = cp.empty_like(image)
+
+    # apply Gaussian filter to all channels independently
+    if multichannel:
+        sigma = (sigma,) * (image.ndim - 1) + (0,)
+    ndi.gaussian_filter(image, sigma, output=smoothed,
+                        mode=mode, cval=cval)
+    return smoothed
+
+
+def _check_factor(factor):
+    if factor <= 1:
+        raise ValueError('scale factor must be greater than 1')
+
+
+def pyramid_reduce(image, downscale=2, sigma=None, order=1,
+                   mode='reflect', cval=0, multichannel=False,
+                   preserve_range=False):
+    """Smooth and then downsample image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    downscale : float, optional
+        Downscale factor.
+    sigma : float, optional
+        Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
+        corresponds to a filter mask twice the size of the scale factor that
+        covers more than 99% of the Gaussian distribution.
+    order : int, optional
+        Order of splines used in interpolation of downsampling. See
+        `skimage.transform.warp` for detail.
+    mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        cval is the value when mode is equal to 'constant'.
+    cval : float, optional
+        Value to fill past edges of input if mode is 'constant'.
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Returns
+    -------
+    out : array
+        Smoothed and downsampled float image.
+
+    References
+    ----------
+    .. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
+
+    """
+    _check_factor(downscale)
+
+    image = convert_to_float(image, preserve_range)
+
+    out_shape = tuple([math.ceil(d / float(downscale)) for d in image.shape])
+    if multichannel:
+        out_shape = out_shape[:-1]
+
+    if sigma is None:
+        # automatically determine sigma which covers > 99% of distribution
+        sigma = 2 * downscale / 6.0
+
+    smoothed = _smooth(image, sigma, mode, cval, multichannel)
+    out = resize(smoothed, out_shape, order=order, mode=mode, cval=cval,
+                 anti_aliasing=False)
+
+    return out
+
+
+def pyramid_expand(image, upscale=2, sigma=None, order=1,
+                   mode='reflect', cval=0, multichannel=False,
+                   preserve_range=False):
+    """Upsample and then smooth image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    upscale : float, optional
+        Upscale factor.
+    sigma : float, optional
+        Sigma for Gaussian filter. Default is `2 * upscale / 6.0` which
+        corresponds to a filter mask twice the size of the scale factor that
+        covers more than 99% of the Gaussian distribution.
+    order : int, optional
+        Order of splines used in interpolation of upsampling. See
+        `skimage.transform.warp` for detail.
+    mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        cval is the value when mode is equal to 'constant'.
+    cval : float, optional
+        Value to fill past edges of input if mode is 'constant'.
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Returns
+    -------
+    out : array
+        Upsampled and smoothed float image.
+
+    References
+    ----------
+    .. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
+
+    """
+    _check_factor(upscale)
+
+    image = convert_to_float(image, preserve_range)
+
+    out_shape = tuple([math.ceil(upscale * d) for d in image.shape])
+    if multichannel:
+        out_shape = out_shape[:-1]
+
+    if sigma is None:
+        # automatically determine sigma which covers > 99% of distribution
+        sigma = 2 * upscale / 6.0
+
+    resized = resize(image, out_shape, order=order,
+                     mode=mode, cval=cval, anti_aliasing=False)
+    out = _smooth(resized, sigma, mode, cval, multichannel)
+
+    return out
+
+
+def pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1,
+                     mode='reflect', cval=0, multichannel=False,
+                     preserve_range=False):
+    """Yield images of the Gaussian pyramid formed by the input image.
+
+    Recursively applies the `pyramid_reduce` function to the image, and yields
+    the downscaled images.
+
+    Note that the first image of the pyramid will be the original, unscaled
+    image. The total number of images is `max_layer + 1`. In case all layers
+    are computed, the last image is either a one-pixel image or the image where
+    the reduction does not change its shape.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    max_layer : int, optional
+        Number of layers for the pyramid. 0th layer is the original image.
+        Default is -1 which builds all possible layers.
+    downscale : float, optional
+        Downscale factor.
+    sigma : float, optional
+        Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
+        corresponds to a filter mask twice the size of the scale factor that
+        covers more than 99% of the Gaussian distribution.
+    order : int, optional
+        Order of splines used in interpolation of downsampling. See
+        `skimage.transform.warp` for detail.
+    mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        cval is the value when mode is equal to 'constant'.
+    cval : float, optional
+        Value to fill past edges of input if mode is 'constant'.
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+    Returns
+    -------
+    pyramid : generator
+        Generator yielding pyramid layers as float images.
+
+    References
+    ----------
+    .. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
+
+    """
+    _check_factor(downscale)
+
+    # cast to float for consistent data type in pyramid
+    image = convert_to_float(image, preserve_range)
+
+    layer = 0
+    current_shape = image.shape
+
+    prev_layer_image = image
+    yield image
+
+    # build downsampled images until max_layer is reached or downscale process
+    # does not change image size
+    while layer != max_layer:
+        layer += 1
+
+        layer_image = pyramid_reduce(prev_layer_image, downscale, sigma, order,
+                                     mode, cval, multichannel=multichannel)
+
+        prev_shape = current_shape
+        prev_layer_image = layer_image
+        current_shape = layer_image.shape
+
+        # no change to previous pyramid layer
+        if current_shape == prev_shape:
+            break
+
+        yield layer_image
+
+
+def pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1,
+                      mode='reflect', cval=0, multichannel=False,
+                      preserve_range=False):
+    """Yield images of the laplacian pyramid formed by the input image.
+
+    Each layer contains the difference between the downsampled and the
+    downsampled, smoothed image::
+
+        layer = resize(prev_layer) - smooth(resize(prev_layer))
+
+    Note that the first image of the pyramid will be the difference between the
+    original, unscaled image and its smoothed version. The total number of
+    images is `max_layer + 1`. In case all layers are computed, the last image
+    is either a one-pixel image or the image where the reduction does not
+    change its shape.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    max_layer : int, optional
+        Number of layers for the pyramid. 0th layer is the original image.
+        Default is -1 which builds all possible layers.
+    downscale : float, optional
+        Downscale factor.
+    sigma : float, optional
+        Sigma for Gaussian filter. Default is `2 * downscale / 6.0` which
+        corresponds to a filter mask twice the size of the scale factor that
+        covers more than 99% of the Gaussian distribution.
+    order : int, optional
+        Order of splines used in interpolation of downsampling. See
+        `skimage.transform.warp` for detail.
+    mode : {'reflect', 'constant', 'edge', 'symmetric', 'wrap'}, optional
+        The mode parameter determines how the array borders are handled, where
+        cval is the value when mode is equal to 'constant'.
+    cval : float, optional
+        Value to fill past edges of input if mode is 'constant'.
+    multichannel : bool, optional
+        Whether the last axis of the image is to be interpreted as multiple
+        channels or another spatial dimension.
+    preserve_range : bool, optional
+        Whether to keep the original range of values. Otherwise, the input
+        image is converted according to the conventions of `img_as_float`.
+        Also see https://scikit-image.org/docs/dev/user_guide/data_types.html
+
+
+    Returns
+    -------
+    pyramid : generator
+        Generator yielding pyramid layers as float images.
+
+    References
+    ----------
+    .. [1] http://persci.mit.edu/pub_pdfs/pyramid83.pdf
+    .. [2] http://sepwww.stanford.edu/data/media/public/sep/morgan/texturematch/paper_html/node3.html
+
+    """  # noqa
+    _check_factor(downscale)
+
+    # cast to float for consistent data type in pyramid
+    image = convert_to_float(image, preserve_range)
+
+    if sigma is None:
+        # automatically determine sigma which covers > 99% of distribution
+        sigma = 2 * downscale / 6.0
+
+    current_shape = image.shape
+
+    smoothed_image = _smooth(image, sigma, mode, cval, multichannel)
+    yield image - smoothed_image
+
+    # build downsampled images until max_layer is reached or downscale process
+    # does not change image size
+    if max_layer == -1:
+        max_layer = math.ceil(math.log(reduce(max, current_shape), downscale))
+
+    for layer in range(max_layer):
+
+        out_shape = tuple(
+            [math.ceil(d / float(downscale)) for d in current_shape])
+
+        if multichannel:
+            out_shape = out_shape[:-1]
+
+        resized_image = resize(smoothed_image, out_shape, order=order,
+                               mode=mode, cval=cval, anti_aliasing=False)
+        smoothed_image = _smooth(resized_image, sigma, mode, cval,
+                                 multichannel)
+        current_shape = cp.asarray(resized_image.shape)
+
+        yield resized_image - smoothed_image
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_geometric.py b/python/cucim/src/cucim/skimage/transform/tests/test_geometric.py
new file mode 100644
index 000000000..cf5e0cfc6
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_geometric.py
@@ -0,0 +1,569 @@
+import re
+import textwrap
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+
+from cucim.skimage.transform import (AffineTransform, EssentialMatrixTransform,
+                                     EuclideanTransform,
+                                     FundamentalMatrixTransform,
+                                     PiecewiseAffineTransform,
+                                     PolynomialTransform, ProjectiveTransform,
+                                     SimilarityTransform, estimate_transform,
+                                     matrix_transform)
+from cucim.skimage.transform._geometric import GeometricTransform
+
+# fmt: off
+SRC = cp.array([
+    [-12.3705, -10.5075],
+    [-10.7865, 15.4305],
+    [8.6985, 10.8675],
+    [11.4975, -9.5715],
+    [7.8435, 7.4835],
+    [-5.3325, 6.5025],
+    [6.7905, -6.3765],
+    [-6.1695, -0.8235],
+])
+DST = cp.array([
+    [0, 0],
+    [0, 5800],
+    [4900, 5800],
+    [4900, 0],
+    [4479, 4580],
+    [1176, 3660],
+    [3754, 790],
+    [1024, 1931],
+])
+# fmt: on
+
+
+def test_estimate_transform():
+    for tform in ('euclidean', 'similarity', 'affine', 'projective',
+                  'polynomial'):
+        estimate_transform(tform, SRC[:2, :], DST[:2, :])
+    with pytest.raises(ValueError):
+        estimate_transform('foobar', SRC[:2, :], DST[:2, :])
+
+
+def test_matrix_transform():
+    tform = AffineTransform(scale=(0.1, 0.5), rotation=2)
+    assert_array_equal(tform(SRC), matrix_transform(SRC, tform.params))
+
+
+def test_euclidean_estimation():
+    # exact solution
+    tform = estimate_transform('euclidean', SRC[:2, :], SRC[:2, :] + 10)
+    assert_array_almost_equal(tform(SRC[:2, :]), SRC[:2, :] + 10)
+    assert_array_almost_equal(tform.params[0, 0], tform.params[1, 1])
+    assert_array_almost_equal(tform.params[0, 1], -tform.params[1, 0])
+
+    # over-determined
+    tform2 = estimate_transform('euclidean', SRC, DST)
+    assert_array_almost_equal(tform2.inverse(tform2(SRC)), SRC)
+    assert_array_almost_equal(tform2.params[0, 0], tform2.params[1, 1])
+    assert_array_almost_equal(tform2.params[0, 1], -tform2.params[1, 0])
+
+    # via estimate method
+    tform3 = EuclideanTransform()
+    tform3.estimate(SRC, DST)
+    assert_array_almost_equal(tform3.params, tform2.params)
+
+
+def test_euclidean_init():
+    # init with implicit parameters
+    rotation = 1
+    translation = (1, 1)
+    tform = EuclideanTransform(rotation=rotation, translation=translation)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # init with transformation matrix
+    tform2 = EuclideanTransform(tform.params)
+    assert_array_almost_equal(tform2.rotation, rotation)
+    assert_array_almost_equal(tform2.translation, translation)
+
+    # test special case for scale if rotation=0
+    rotation = 0
+    translation = (1, 1)
+    tform = EuclideanTransform(rotation=rotation, translation=translation)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # test special case for scale if rotation=90deg
+    rotation = np.pi / 2
+    translation = (1, 1)
+    tform = EuclideanTransform(rotation=rotation, translation=translation)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+
+def test_similarity_estimation():
+    # exact solution
+    tform = estimate_transform('similarity', SRC[:2, :], DST[:2, :])
+    assert_array_almost_equal(tform(SRC[:2, :]), DST[:2, :])
+    assert_array_almost_equal(tform.params[0, 0], tform.params[1, 1])
+    assert_array_almost_equal(tform.params[0, 1], -tform.params[1, 0])
+
+    # over-determined
+    tform2 = estimate_transform('similarity', SRC, DST)
+    assert_array_almost_equal(tform2.inverse(tform2(SRC)), SRC)
+    assert_array_almost_equal(tform2.params[0, 0], tform2.params[1, 1])
+    assert_array_almost_equal(tform2.params[0, 1], -tform2.params[1, 0])
+
+    # via estimate method
+    tform3 = SimilarityTransform()
+    tform3.estimate(SRC, DST)
+    assert_array_almost_equal(tform3.params, tform2.params)
+
+
+def test_similarity_init():
+    # init with implicit parameters
+    scale = 0.1
+    rotation = 1
+    translation = (1, 1)
+    tform = SimilarityTransform(scale=scale, rotation=rotation,
+                                translation=translation)
+    assert_array_almost_equal(tform.scale, scale)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # init with transformation matrix
+    tform2 = SimilarityTransform(tform.params)
+    assert_array_almost_equal(tform2.scale, scale)
+    assert_array_almost_equal(tform2.rotation, rotation)
+    assert_array_almost_equal(tform2.translation, translation)
+
+    # test special case for scale if rotation=0
+    scale = 0.1
+    rotation = 0
+    translation = (1, 1)
+    tform = SimilarityTransform(scale=scale, rotation=rotation,
+                                translation=translation)
+    assert_array_almost_equal(tform.scale, scale)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # test special case for scale if rotation=90deg
+    scale = 0.1
+    rotation = np.pi / 2
+    translation = (1, 1)
+    tform = SimilarityTransform(scale=scale, rotation=rotation,
+                                translation=translation)
+    assert_array_almost_equal(tform.scale, scale)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # test special case for scale where the rotation isn't exactly 90deg,
+    # but very close
+    scale = 1.0
+    rotation = np.pi / 2
+    translation = (0, 0)
+    params = np.array([[0, -1, 1.33226763e-15],
+                       [1, 2.22044605e-16, -1.33226763e-15],
+                       [0, 0, 1]])
+    tform = SimilarityTransform(params)
+    assert_array_almost_equal(tform.scale, scale)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.translation, translation)
+
+
+def test_affine_estimation():
+    # exact solution
+    tform = estimate_transform('affine', SRC[:3, :], DST[:3, :])
+    assert_array_almost_equal(tform(SRC[:3, :]), DST[:3, :])
+
+    # over-determined
+    tform2 = estimate_transform('affine', SRC, DST)
+    assert_array_almost_equal(tform2.inverse(tform2(SRC)), SRC)
+
+    # via estimate method
+    tform3 = AffineTransform()
+    tform3.estimate(SRC, DST)
+    assert_array_almost_equal(tform3.params, tform2.params)
+
+
+def test_affine_init():
+    # init with implicit parameters
+    scale = (0.1, 0.13)
+    rotation = 1
+    shear = 0.1
+    translation = (1, 1)
+    tform = AffineTransform(scale=scale, rotation=rotation, shear=shear,
+                            translation=translation)
+    assert_array_almost_equal(tform.scale, scale)
+    assert_array_almost_equal(tform.rotation, rotation)
+    assert_array_almost_equal(tform.shear, shear)
+    assert_array_almost_equal(tform.translation, translation)
+
+    # init with transformation matrix
+    tform2 = AffineTransform(tform.params)
+    assert_array_almost_equal(tform2.scale, scale)
+    assert_array_almost_equal(tform2.rotation, rotation)
+    assert_array_almost_equal(tform2.shear, shear)
+    assert_array_almost_equal(tform2.translation, translation)
+
+    # scalar vs. tuple scale arguments
+    assert_array_almost_equal(AffineTransform(scale=0.5).scale,
+                              AffineTransform(scale=(0.5, 0.5)).scale)
+
+
+def test_piecewise_affine():
+    tform = PiecewiseAffineTransform()
+    tform.estimate(SRC, DST)
+    # make sure each single affine transform is exactly estimated
+    assert_array_almost_equal(tform(SRC), DST)
+    assert_array_almost_equal(tform.inverse(DST), SRC)
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_fundamental_matrix_estimation(xp):
+    # fmt: off
+    src = xp.array([1.839035, 1.924743, 0.543582,  0.375221,  # noqa
+                    0.473240, 0.142522, 0.964910,  0.598376,  # noqa
+                    0.102388, 0.140092, 15.994343, 9.622164,  # noqa
+                    0.285901, 0.430055, 0.091150,  0.254594]).reshape(-1, 2)  # noqa
+    dst = xp.array([1.002114, 1.129644, 1.521742, 1.846002,  # noqa
+                    1.084332, 0.275134, 0.293328, 0.588992,  # noqa
+                    0.839509, 0.087290, 1.779735, 1.116857,  # noqa
+                    0.878616, 0.602447, 0.642616, 1.028681]).reshape(-1, 2)  # noqa
+    # fmt: on
+
+    tform = estimate_transform('fundamental', src, dst)
+
+    # Reference values obtained using COLMAP SfM library.
+    # fmt: off
+    tform_ref = xp.array([[-0.217859,  0.419282, -0.0343075],   # noqa
+                          [-0.0717941, 0.0451643, 0.0216073],   # noqa
+                          [ 0.248062, -0.429478,  0.0221019]])  # noqa
+
+    # fmt: on
+    if xp == cp:
+        # TODO: grlee77: why is there a sign difference here for CuPy?
+        tform_ref = -tform_ref
+    assert_array_almost_equal(tform.params, tform_ref, 6)
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_fundamental_matrix_residuals(xp):
+    essential_matrix_tform = EssentialMatrixTransform(
+        rotation=xp.eye(3), translation=xp.array([1, 0, 0]), xp=xp)
+    tform = FundamentalMatrixTransform()
+    tform.params = essential_matrix_tform.params
+    src = xp.array([[0, 0], [0, 0], [0, 0]])
+    dst = xp.array([[2, 0], [2, 1], [2, 2]])
+    assert_array_almost_equal(
+        tform.residuals(src, dst) ** 2, xp.array([0, 0.5, 2]))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_fundamental_matrix_forward(xp):
+    essential_matrix_tform = EssentialMatrixTransform(
+        rotation=xp.eye(3), translation=xp.array([1, 0, 0]), xp=xp)
+    tform = FundamentalMatrixTransform()
+    tform.params = essential_matrix_tform.params
+    src = xp.array([[0, 0], [0, 1], [1, 1]])
+    assert_array_almost_equal(
+        tform(src), xp.array([[0, -1, 0], [0, -1, 1], [0, -1, 1]]))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_fundamental_matrix_inverse(xp):
+    essential_matrix_tform = EssentialMatrixTransform(
+        rotation=xp.eye(3), translation=xp.array([1, 0, 0]), xp=xp)
+    tform = FundamentalMatrixTransform()
+    tform.params = essential_matrix_tform.params
+    src = xp.array([[0, 0], [0, 1], [1, 1]])
+    assert_array_almost_equal(
+        tform.inverse(src), xp.array([[0, 1, 0], [0, 1, -1], [0, 1, -1]]))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_essential_matrix_init(xp):
+    tform = EssentialMatrixTransform(rotation=xp.eye(3),
+                                     translation=xp.array([0, 0, 1]), xp=xp)
+
+    assert_array_equal(
+        tform.params, xp.array([0, -1, 0, 1, 0, 0, 0, 0, 0]).reshape(3, 3))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_essential_matrix_estimation(xp):
+    # fmt: off
+    src = xp.array([1.839035, 1.924743, 0.543582,  0.375221,  # noqa
+                    0.473240, 0.142522, 0.964910,  0.598376,  # noqa
+                    0.102388, 0.140092, 15.994343, 9.622164,  # noqa
+                    0.285901, 0.430055, 0.091150,  0.254594]).reshape(-1, 2)  # noqa
+    dst = xp.array([1.002114, 1.129644, 1.521742, 1.846002,  # noqa
+                    1.084332, 0.275134, 0.293328, 0.588992,  # noqa
+                    0.839509, 0.087290, 1.779735, 1.116857,  # noqa
+                    0.878616, 0.602447, 0.642616, 1.028681]).reshape(-1, 2)  # noqa
+    # fmt: on
+    tform = estimate_transform('essential', src, dst)
+
+    # Reference values obtained using COLMAP SfM library.
+    # fmt: off
+    tform_ref = xp.array([[-0.0811666, 0.255449, -0.0478999],  # noqa
+                          [-0.192392, -0.0531675, 0.119547],  # noqa
+                          [ 0.177784, -0.22008,  -0.015203]])  # noqa
+
+    # fmt: on
+    if xp == cp:
+        # TODO: grlee77: why is there a sign difference here for CuPy?
+        tform_ref = -tform_ref
+    assert_array_almost_equal(tform.params, tform_ref, 6)
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_essential_matrix_forward(xp):
+    tform = EssentialMatrixTransform(rotation=xp.eye(3),
+                                     translation=xp.array([1, 0, 0]), xp=xp)
+    src = xp.array([[0, 0], [0, 1], [1, 1]])
+    assert_array_almost_equal(
+        tform(src), xp.array([[0, -1, 0], [0, -1, 1], [0, -1, 1]]))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_essential_matrix_inverse(xp):
+    tform = EssentialMatrixTransform(rotation=xp.eye(3),
+                                     translation=xp.array([1, 0, 0]), xp=xp)
+    src = xp.array([[0, 0], [0, 1], [1, 1]])
+    assert_array_almost_equal(tform.inverse(src),
+                              xp.array([[0, 1, 0], [0, 1, -1], [0, 1, -1]]))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_essential_matrix_residuals(xp):
+    tform = EssentialMatrixTransform(rotation=xp.eye(3),
+                                     translation=xp.array([1, 0, 0]), xp=xp)
+    src = xp.array([[0, 0], [0, 0], [0, 0]])
+    dst = xp.array([[2, 0], [2, 1], [2, 2]])
+    assert_array_almost_equal(
+        tform.residuals(src, dst) ** 2, xp.array([0, 0.5, 2]))
+
+
+def test_projective_estimation():
+    # exact solution
+    tform = estimate_transform('projective', SRC[:4, :], DST[:4, :])
+    assert_array_almost_equal(tform(SRC[:4, :]), DST[:4, :])
+
+    # over-determined
+    tform2 = estimate_transform('projective', SRC, DST)
+    assert_array_almost_equal(tform2.inverse(tform2(SRC)), SRC)
+
+    # via estimate method
+    tform3 = ProjectiveTransform()
+    tform3.estimate(SRC, DST)
+    assert_array_almost_equal(tform3.params, tform2.params)
+
+
+def test_projective_init():
+    tform = estimate_transform('projective', SRC, DST)
+    # init with transformation matrix
+    tform2 = ProjectiveTransform(tform.params)
+    assert_array_almost_equal(tform2.params, tform.params)
+
+
+def test_polynomial_estimation():
+    # over-determined
+    tform = estimate_transform('polynomial', SRC, DST, order=10)
+    assert_array_almost_equal(tform(SRC), DST, 6)
+
+    # via estimate method
+    tform2 = PolynomialTransform()
+    tform2.estimate(SRC, DST, order=10)
+    assert_array_almost_equal(tform2.params, tform.params)
+
+
+def test_polynomial_init():
+    tform = estimate_transform('polynomial', SRC, DST, order=10)
+    # init with transformation parameters
+    tform2 = PolynomialTransform(tform.params)
+    assert_array_almost_equal(tform2.params, tform.params)
+
+
+def test_polynomial_default_order():
+    tform = estimate_transform('polynomial', SRC, DST)
+    tform2 = estimate_transform('polynomial', SRC, DST, order=2)
+    assert_array_almost_equal(tform2.params, tform.params)
+
+
+def test_polynomial_inverse():
+    with pytest.raises(Exception):
+        PolynomialTransform().inverse(0)
+
+
+def test_union():
+    tform1 = SimilarityTransform(scale=0.1, rotation=0.3)
+    tform2 = SimilarityTransform(scale=0.1, rotation=0.9)
+    tform3 = SimilarityTransform(scale=0.1 ** 2, rotation=0.3 + 0.9)
+    tform = tform1 + tform2
+    assert_array_almost_equal(tform.params, tform3.params)
+
+    tform1 = AffineTransform(scale=(0.1, 0.1), rotation=0.3)
+    tform2 = SimilarityTransform(scale=0.1, rotation=0.9)
+    tform3 = SimilarityTransform(scale=0.1 ** 2, rotation=0.3 + 0.9)
+    tform = tform1 + tform2
+    assert_array_almost_equal(tform.params, tform3.params)
+    assert tform.__class__ == ProjectiveTransform
+
+    tform = AffineTransform(scale=(0.1, 0.1), rotation=0.3)
+    assert_array_almost_equal((tform + tform.inverse).params, cp.eye(3))
+
+    tform1 = SimilarityTransform(scale=0.1, rotation=0.3)
+    tform2 = SimilarityTransform(scale=0.1, rotation=0.9)
+    tform3 = SimilarityTransform(scale=0.1 * 1 / 0.1, rotation=0.3 - 0.9)
+    tform = tform1 + tform2.inverse
+    assert_array_almost_equal(tform.params, tform3.params)
+
+
+def test_union_differing_types():
+    tform1 = SimilarityTransform()
+    tform2 = PolynomialTransform()
+    with pytest.raises(TypeError):
+        tform1.__add__(tform2)
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_geometric_tform(xp):
+    tform = GeometricTransform()
+    with pytest.raises(NotImplementedError):
+        tform(0)
+    with pytest.raises(NotImplementedError):
+        tform.inverse(0)
+    with pytest.raises(NotImplementedError):
+        tform.__add__(0)
+
+    # See gh-3926 for discussion details
+    for i in range(20):
+        # Generate random Homography
+        H = np.random.rand(3, 3) * 100
+        H[2, H[2] == 0] += np.finfo(float).eps
+        H /= H[2, 2]
+
+        # Craft some src coords
+        # fmt: off
+        src = np.array([
+            [(H[2, 1] + 1) / -H[2, 0], 1],
+            [1, (H[2, 0] + 1) / -H[2, 1]],
+            [1, 1],
+        ])
+        # fmt: on
+        H = xp.asarray(H)
+        src = xp.asarray(src)
+
+        # Prior to gh-3926, under the above circumstances,
+        # destination coordinates could be returned with nan/inf values.
+        tform = ProjectiveTransform(H)  # Construct the transform
+        dst = tform(src)  # Obtain the dst coords
+        # Ensure dst coords are finite numeric values
+        assert xp.isfinite(dst).all()
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_invalid_input(xp):
+    with pytest.raises(ValueError):
+        ProjectiveTransform(xp.zeros((2, 3)))
+    with pytest.raises(ValueError):
+        AffineTransform(xp.zeros((2, 3)))
+    with pytest.raises(ValueError):
+        SimilarityTransform(xp.zeros((2, 3)))
+    with pytest.raises(ValueError):
+        EuclideanTransform(xp.zeros((2, 3)))
+    with pytest.raises(ValueError):
+        AffineTransform(matrix=xp.zeros((2, 3)), scale=1)
+    with pytest.raises(ValueError):
+        SimilarityTransform(matrix=xp.zeros((2, 3)), scale=1)
+    with pytest.raises(ValueError):
+        EuclideanTransform(matrix=xp.zeros((2, 3)), translation=(0, 0))
+    with pytest.raises(ValueError):
+        PolynomialTransform(xp.zeros((3, 3)))
+    with pytest.raises(ValueError):
+        FundamentalMatrixTransform(matrix=xp.zeros((3, 2)))
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(matrix=xp.zeros((3, 2)))
+
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.zeros((3, 2)))
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.zeros((3, 3)))
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.eye(3))
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.eye(3),
+                                 translation=xp.zeros((2,)), xp=xp)
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.eye(3),
+                                 translation=xp.zeros((2,)), xp=xp)
+    with pytest.raises(ValueError):
+        EssentialMatrixTransform(rotation=xp.eye(3),
+                                 translation=xp.zeros((3,)), xp=xp)
+
+
+def test_degenerate(xp=cp):
+    src = dst = xp.zeros((10, 2))
+
+    tform = SimilarityTransform()
+    tform.estimate(src, dst)
+    assert xp.all(xp.isnan(tform.params))
+
+    tform = AffineTransform()
+    tform.estimate(src, dst)
+    assert xp.all(xp.isnan(tform.params))
+
+    tform = ProjectiveTransform()
+    tform.estimate(src, dst)
+    assert xp.all(xp.isnan(tform.params))
+
+    # See gh-3926 for discussion details
+    tform = ProjectiveTransform()
+    for i in range(20):
+        # Some random coordinates
+        src = xp.random.rand(4, 2) * 100
+        dst = xp.random.rand(4, 2) * 100
+
+        # Degenerate the case by arranging points on a single line
+        src[:, 1] = xp.random.rand()
+        # Prior to gh-3926, under the above circumstances,
+        # a transform could be returned with nan values.
+        assert not tform.estimate(src, dst) or xp.isfinite(tform.params).all()
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_projective_repr(xp):
+    tform = ProjectiveTransform(xp=xp)
+    # fmt: off
+    want = re.escape(textwrap.dedent(
+        '''
+        <ProjectiveTransform(matrix=
+            [[1., 0., 0.],
+             [0., 1., 0.],
+             [0., 0., 1.]]) at
+        ''').strip()) + ' 0x[a-f0-9]+' + re.escape('>')
+    # fmt: on
+    # Hack the escaped regex to allow whitespace before each number for
+    # compatibility with different numpy versions.
+    want = want.replace('0\\.', ' *0\\.')
+    want = want.replace('1\\.', ' *1\\.')
+    assert re.match(want, repr(tform))
+
+
+@pytest.mark.parametrize("xp", [np, cp])
+def test_projective_str(xp):
+    tform = ProjectiveTransform(xp=xp)
+    # fmt: off
+    want = re.escape(textwrap.dedent(
+        '''
+        <ProjectiveTransform(matrix=
+            [[1., 0., 0.],
+             [0., 1., 0.],
+             [0., 0., 1.]])>
+        ''').strip())
+    # fmt: on
+    # Hack the escaped regex to allow whitespace before each number for
+    # compatibility with different numpy versions.
+    want = want.replace('0\\.', ' *0\\.')
+    want = want.replace('1\\.', ' *1\\.')
+    print(want)
+    assert re.match(want, str(tform))
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_integral.py b/python/cucim/src/cucim/skimage/transform/tests/test_integral.py
new file mode 100644
index 000000000..994556b1f
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_integral.py
@@ -0,0 +1,48 @@
+import cupy as cp
+import numpy as np
+from cupy.testing import assert_array_equal
+
+from cucim.skimage.transform import integral_image, integrate
+
+cp.random.seed(0)
+x = (cp.random.rand(50, 50) * 255).astype(np.uint8)
+s = integral_image(x)
+
+
+def test_validity():
+    y = cp.arange(12).reshape((4, 3))
+
+    y = (cp.random.rand(50, 50) * 255).astype(np.uint8)
+    assert_array_equal(integral_image(y)[-1, -1], y.sum())
+
+
+def test_basic():
+    assert_array_equal(x[12:24, 10:20].sum(), integrate(s, (12, 10), (23, 19)))
+    assert_array_equal(x[:20, :20].sum(), integrate(s, (0, 0), (19, 19)))
+    assert_array_equal(x[:20, 10:20].sum(), integrate(s, (0, 10), (19, 19)))
+    assert_array_equal(x[10:20, :20].sum(), integrate(s, (10, 0), (19, 19)))
+
+
+def test_single():
+    assert_array_equal(x[0, 0], integrate(s, (0, 0), (0, 0)))
+    assert_array_equal(x[10, 10], integrate(s, (10, 10), (10, 10)))
+
+
+def test_vectorized_integrate():
+    r0 = np.array([12, 0, 0, 10, 0, 10, 30])
+    c0 = np.array([10, 0, 10, 0, 0, 10, 31])
+    r1 = np.array([23, 19, 19, 19, 0, 10, 49])
+    c1 = np.array([19, 19, 19, 19, 0, 10, 49])
+    # fmt: off
+    x_cpu = cp.asnumpy(x)
+    expected = np.array([x_cpu[12:24, 10:20].sum(),
+                         x_cpu[:20, :20].sum(),
+                         x_cpu[:20, 10:20].sum(),
+                         x_cpu[10:20, :20].sum(),
+                         x_cpu[0, 0],
+                         x_cpu[10, 10],
+                         x_cpu[30:, 31:].sum()])
+    # fmt: on
+    start_pts = [(r0[i], c0[i]) for i in range(len(r0))]
+    end_pts = [(r1[i], c1[i]) for i in range(len(r0))]
+    assert_array_equal(expected, integrate(s, start_pts, end_pts))
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_pyramids.py b/python/cucim/src/cucim/skimage/transform/tests/test_pyramids.py
new file mode 100644
index 000000000..eb70cf65c
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_pyramids.py
@@ -0,0 +1,149 @@
+import math
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_equal
+from skimage import data
+
+from cucim.skimage._shared.testing import assert_almost_equal
+from cucim.skimage.transform import pyramids
+
+image = cp.asarray(data.astronaut())
+image_gray = image[..., 0]
+
+
+def test_pyramid_reduce_rgb():
+    rows, cols, dim = image.shape
+    out = pyramids.pyramid_reduce(image, downscale=2, multichannel=True)
+    assert_array_equal(out.shape, (rows / 2, cols / 2, dim))
+
+
+def test_pyramid_reduce_gray():
+    rows, cols = image_gray.shape
+    out1 = pyramids.pyramid_reduce(image_gray, downscale=2,
+                                   multichannel=False)
+    assert_array_equal(out1.shape, (rows / 2, cols / 2))
+    assert_almost_equal(float(out1.ptp()), 1.0, decimal=2)
+    out2 = pyramids.pyramid_reduce(image_gray, downscale=2,
+                                   multichannel=False, preserve_range=True)
+    assert_almost_equal(float(out2.ptp()) / float(image_gray.ptp()), 1.0,
+                        decimal=2)
+
+
+def test_pyramid_reduce_nd():
+    for ndim in [1, 2, 3, 4]:
+        img = cp.random.randn(*((8, ) * ndim))
+        out = pyramids.pyramid_reduce(img, downscale=2,
+                                      multichannel=False)
+        expected_shape = cp.asarray(img.shape) / 2
+        assert_array_equal(out.shape, expected_shape)
+
+
+def test_pyramid_expand_rgb():
+    rows, cols, dim = image.shape
+    out = pyramids.pyramid_expand(image, upscale=2,
+                                  multichannel=True)
+    assert_array_equal(out.shape, (rows * 2, cols * 2, dim))
+
+
+def test_pyramid_expand_gray():
+    rows, cols = image_gray.shape
+    out = pyramids.pyramid_expand(image_gray, upscale=2,
+                                  multichannel=False)
+    assert_array_equal(out.shape, (rows * 2, cols * 2))
+
+
+def test_pyramid_expand_nd():
+    for ndim in [1, 2, 3, 4]:
+        img = cp.random.randn(*((4,) * ndim))
+        out = pyramids.pyramid_expand(img, upscale=2,
+                                      multichannel=False)
+        expected_shape = cp.asarray(img.shape) * 2
+        assert_array_equal(out.shape, expected_shape)
+
+
+def test_build_gaussian_pyramid_rgb():
+    rows, cols, dim = image.shape
+    pyramid = pyramids.pyramid_gaussian(image, downscale=2,
+                                        multichannel=True)
+    for layer, out in enumerate(pyramid):
+        layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim)
+        assert_array_equal(out.shape, layer_shape)
+
+
+def test_build_gaussian_pyramid_gray():
+    rows, cols = image_gray.shape
+    pyramid = pyramids.pyramid_gaussian(image_gray, downscale=2,
+                                        multichannel=False)
+    for layer, out in enumerate(pyramid):
+        layer_shape = (rows / 2 ** layer, cols / 2 ** layer)
+        assert_array_equal(out.shape, layer_shape)
+
+
+def test_build_gaussian_pyramid_nd():
+    for ndim in [1, 2, 3, 4]:
+        img = cp.random.randn(*((8,) * ndim))
+        original_shape = cp.asarray(img.shape)
+        pyramid = pyramids.pyramid_gaussian(img, downscale=2,
+                                            multichannel=False)
+        for layer, out in enumerate(pyramid):
+            layer_shape = original_shape / 2 ** layer
+            assert_array_equal(out.shape, layer_shape)
+
+
+def test_build_laplacian_pyramid_rgb():
+    rows, cols, dim = image.shape
+    pyramid = pyramids.pyramid_laplacian(image, downscale=2,
+                                         multichannel=True)
+    for layer, out in enumerate(pyramid):
+        layer_shape = (rows / 2 ** layer, cols / 2 ** layer, dim)
+        assert_array_equal(out.shape, layer_shape)
+
+
+def test_build_laplacian_pyramid_nd():
+    for ndim in [1, 2, 3, 4]:
+        img = cp.random.randn(*(16,) * ndim)
+        original_shape = cp.asarray(img.shape)
+        pyramid = pyramids.pyramid_laplacian(img, downscale=2,
+                                             multichannel=False)
+
+        for layer, out in enumerate(pyramid):
+            # print(out.shape)
+            layer_shape = original_shape / 2 ** layer
+            assert_array_equal(out.shape, layer_shape)
+
+
+def test_laplacian_pyramid_max_layers():
+    for downscale in [2, 3, 5, 7]:
+        img = cp.random.randn(32, 8)
+        pyramid = pyramids.pyramid_laplacian(img, downscale=downscale,
+                                             multichannel=False)
+        max_layer = int(np.ceil(math.log(np.max(img.shape), downscale)))
+        for layer, out in enumerate(pyramid):
+            if layer < max_layer:
+                # should not reach all axes as size 1 prior to final level
+                assert np.max(out.shape) > 1
+
+        # total number of images is max_layer + 1
+        assert max_layer == layer
+
+        # final layer should be size 1 on all axes
+        assert out.shape == (1, 1)
+
+
+def test_check_factor():
+    with pytest.raises(ValueError):
+        pyramids._check_factor(0.99)
+    with pytest.raises(ValueError):
+        pyramids._check_factor(-2)
+
+
+@pytest.mark.parametrize('dtype, expected',
+                         zip(['float32', 'float64', 'uint8', 'int64'],
+                             ['float32', 'float64', 'float64', 'float64']))
+def test_pyramid_gaussian_dtype_support(dtype, expected):
+    img = cp.random.randn(32, 8).astype(dtype)
+    pyramid = pyramids.pyramid_gaussian(img)
+
+    assert all([im.dtype == expected for im in pyramid])
diff --git a/python/cucim/src/cucim/skimage/transform/tests/test_warps.py b/python/cucim/src/cucim/skimage/transform/tests/test_warps.py
new file mode 100644
index 000000000..e948db4e8
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/transform/tests/test_warps.py
@@ -0,0 +1,691 @@
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_almost_equal, assert_array_equal
+from cupyx.scipy.ndimage import map_coordinates
+from numpy.testing import assert_almost_equal, assert_equal
+from skimage.color.colorconv import rgb2gray
+from skimage.data import astronaut, checkerboard
+from skimage.draw.draw import circle_perimeter_aa
+
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.feature.peak import peak_local_max
+from cucim.skimage.transform._geometric import (AffineTransform,
+                                                ProjectiveTransform,
+                                                SimilarityTransform)
+from cucim.skimage.transform._warps import (_linear_polar_mapping,
+                                            _log_polar_mapping, _stackcopy,
+                                            downscale_local_mean, rescale,
+                                            resize, rotate, swirl, warp,
+                                            warp_coords, warp_polar)
+from cucim.skimage.util.dtype import img_as_float
+
+# from skimage._shared.testing import test_parallel
+
+
+cp.random.seed(0)
+
+
+def test_stackcopy():
+    layers = 4
+    x = cp.empty((3, 3, layers))
+    y = cp.eye(3, 3)
+    _stackcopy(x, y)
+    for i in range(layers):
+        assert_array_almost_equal(x[..., i], y)
+
+
+def test_warp_tform():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+    theta = -np.pi / 2
+    tform = SimilarityTransform(
+        scale=1, rotation=theta, translation=(0, 4), xp=cp
+    )
+
+    x90 = warp(x, tform, order=1)
+    assert_array_almost_equal(x90, cp.rot90(x))
+
+    x90 = warp(x, tform.inverse, order=1)
+    assert_array_almost_equal(x90, cp.rot90(x))
+
+
+def test_warp_callable():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+    refx = cp.zeros((5, 5), dtype=np.double)
+    refx[1, 1] = 1
+
+    def shift(xy):
+        return xy + 1
+
+    outx = warp(x, shift, order=1)
+    assert_array_almost_equal(outx, refx)
+
+
+@cp.testing.with_requires('cupy>=9.0.0b2')
+def test_warp_matrix():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+    refx = cp.zeros((5, 5), dtype=np.double)
+    refx[1, 1] = 1
+
+    matrix = cp.asarray([[1, 0, 1], [0, 1, 1], [0, 0, 1]])
+
+    # _warp_fast
+    outx = warp(x, matrix, order=1)
+    assert_array_almost_equal(outx, refx)
+    # check for ndimage.map_coordinates
+    outx = warp(x, matrix, order=5)
+
+
+def test_warp_nd():
+    for dim in range(2, 8):
+        shape = dim * (5,)
+
+        x = cp.zeros(shape, dtype=np.double)
+        x_c = dim * (2,)
+        x[x_c] = 1
+        refx = cp.zeros(shape, dtype=np.double)
+        refx_c = dim * (1,)
+        refx[refx_c] = 1
+
+        coord_grid = dim * (slice(0, 5, 1),)
+        coords = cp.array(cp.mgrid[coord_grid]) + 1
+
+        outx = warp(x, coords, order=0, cval=0)
+
+        assert_array_almost_equal(outx, refx)
+
+
+@cp.testing.with_requires('cupy>=9.0.0b2')
+def test_warp_clip():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[2, 2] = 1
+
+    outx = rescale(x, 3, order=3, clip=False,
+                   multichannel=False, anti_aliasing=False, mode='constant')
+    assert outx.min() < 0
+
+    outx = rescale(x, 3, order=3, clip=True,
+                   multichannel=False, anti_aliasing=False, mode='constant')
+    assert_almost_equal(float(outx.min()), 0)
+    assert_almost_equal(float(outx.max()), 1)
+
+
+def test_homography():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    theta = -np.pi / 2
+    # fmt: off
+    M = cp.array([[np.cos(theta), - np.sin(theta), 0],
+                  [np.sin(theta),   np.cos(theta), 4],
+                  [0,               0,             1]])
+    # fmt: on
+
+    x90 = warp(x,
+               inverse_map=ProjectiveTransform(M).inverse,
+               order=1)
+    assert_array_almost_equal(x90, cp.rot90(x))
+
+
+def test_rotate():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    x90 = rotate(x, 90)
+    assert_array_almost_equal(x90, cp.rot90(x))
+
+
+def test_rotate_resize():
+    x = cp.zeros((10, 10), dtype=np.double)
+
+    x45 = rotate(x, 45, resize=False)
+    assert x45.shape == (10, 10)
+
+    x45 = rotate(x, 45, resize=True)
+    # new dimension should be d = sqrt(2 * (10/2)^2)
+    assert x45.shape == (14, 14)
+
+
+def test_rotate_center():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[4, 4] = 1
+    refx = cp.zeros((10, 10), dtype=np.double)
+    refx[2, 5] = 1
+    x20 = rotate(x, 20, order=0, center=(0, 0))
+    assert_array_almost_equal(x20, refx)
+    x0 = rotate(x20, -20, order=0, center=(0, 0))
+    assert_array_almost_equal(x0, x)
+
+
+def test_rotate_resize_center():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[0, 0] = 1
+
+    ref_x45 = cp.zeros((14, 14), dtype=np.double)
+    ref_x45[6, 0] = 1
+    ref_x45[7, 0] = 1
+
+    x45 = rotate(x, 45, resize=True, center=(3, 3), order=0)
+    # new dimension should be d = sqrt(2 * (10/2)^2)
+    assert x45.shape == (14, 14)
+    if False:
+        # CuPy Backend: ubtle difference in rounding somewhere.
+        # (visually rotations of real images look reasonable)
+        assert_array_equal(x45, ref_x45)
+
+
+def test_rotate_resize_90():
+    x90 = rotate(cp.zeros((470, 230), dtype=np.double), 90, resize=True)
+    assert x90.shape == (230, 470)
+
+
+def test_rescale():
+    # same scale factor
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    scaled = rescale(x, 2, order=0,
+                     multichannel=False, anti_aliasing=False, mode='constant')
+    ref = cp.zeros((10, 10))
+    ref[2:4, 2:4] = 1
+    assert_array_almost_equal(scaled, ref)
+
+    # different scale factors
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+
+    scaled = rescale(x, (2, 1), order=0,
+                     multichannel=False, anti_aliasing=False, mode='constant')
+    ref = cp.zeros((10, 5))
+    ref[2:4, 1] = 1
+    assert_array_almost_equal(scaled, ref)
+
+
+def test_rescale_invalid_scale():
+    x = cp.zeros((10, 10, 3))
+    with pytest.raises(ValueError):
+        rescale(x, (2, 2),
+                multichannel=False, anti_aliasing=False, mode='constant')
+    with pytest.raises(ValueError):
+        rescale(x, (2, 2, 2),
+                multichannel=True, anti_aliasing=False, mode='constant')
+
+
+def test_rescale_multichannel():
+    # 1D + channels
+    x = cp.zeros((8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
+                     mode='constant')
+    assert_equal(scaled.shape, (16, 3))
+    # 2D
+    scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
+                     mode='constant')
+    assert_array_equal(scaled.shape, (16, 6))
+
+    # 2D + channels
+    x = cp.zeros((8, 8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
+                     mode='constant')
+    assert_equal(scaled.shape, (16, 16, 3))
+    # 3D
+    scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
+                     mode='constant')
+    assert_equal(scaled.shape, (16, 16, 6))
+
+    # 3D + channels
+    x = cp.zeros((8, 8, 8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0, multichannel=True, anti_aliasing=False,
+                     mode='constant')
+    assert_array_equal(scaled.shape, (16, 16, 16, 3))
+    # 4D
+    scaled = rescale(x, 2, order=0, multichannel=False, anti_aliasing=False,
+                     mode='constant')
+    assert_equal(scaled.shape, (16, 16, 16, 6))
+
+
+def test_rescale_multichannel_multiscale():
+    x = cp.zeros((5, 5, 3), dtype=np.double)
+    scaled = rescale(x, (2, 1), order=0, multichannel=True,
+                     anti_aliasing=False, mode='constant')
+    assert_equal(scaled.shape, (10, 5, 3))
+
+
+def test_rescale_multichannel_defaults():
+    x = cp.zeros((8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
+    assert_equal(scaled.shape, (16, 6))
+
+    x = cp.zeros((8, 8, 3), dtype=np.double)
+    scaled = rescale(x, 2, order=0, anti_aliasing=False, mode='constant')
+    assert_equal(scaled.shape, (16, 16, 6))
+
+
+def test_resize2d():
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    resized = resize(x, (10, 10), order=0, anti_aliasing=False,
+                     mode='constant')
+    ref = cp.zeros((10, 10))
+    ref[2:4, 2:4] = 1
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize3d_keep():
+    # keep 3rd dimension
+    x = cp.zeros((5, 5, 3), dtype=np.double)
+    x[1, 1, :] = 1
+    resized = resize(x, (10, 10), order=0, anti_aliasing=False,
+                     mode='constant')
+    with pytest.raises(ValueError):
+        # output_shape too short
+        resize(x, (10,), order=0, anti_aliasing=False, mode='constant')
+    ref = cp.zeros((10, 10, 3))
+    ref[2:4, 2:4, :] = 1
+    assert_array_almost_equal(resized, ref)
+    resized = resize(x, (10, 10, 3), order=0, anti_aliasing=False,
+                     mode='constant')
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize3d_resize():
+    # resize 3rd dimension
+    x = cp.zeros((5, 5, 3), dtype=np.double)
+    x[1, 1, :] = 1
+    resized = resize(x, (10, 10, 1), order=0, anti_aliasing=False,
+                     mode='constant')
+    ref = cp.zeros((10, 10, 1))
+    ref[2:4, 2:4] = 1
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize3d_2din_3dout():
+    # 3D output with 2D input
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    resized = resize(x, (10, 10, 1), order=0, anti_aliasing=False,
+                     mode='constant')
+    ref = cp.zeros((10, 10, 1))
+    ref[2:4, 2:4] = 1
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize2d_4d():
+    # resize with extra output dimensions
+    x = cp.zeros((5, 5), dtype=np.double)
+    x[1, 1] = 1
+    out_shape = (10, 10, 1, 1)
+    resized = resize(x, out_shape, order=0, anti_aliasing=False,
+                     mode='constant')
+    ref = cp.zeros(out_shape)
+    ref[2:4, 2:4, ...] = 1
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize_nd():
+    for dim in range(1, 6):
+        shape = 2 + np.arange(dim) * 2
+        x = cp.ones(tuple(shape))
+        out_shape = np.asarray(shape) * 1.5
+        resized = resize(x, out_shape, order=0, mode='reflect',
+                         anti_aliasing=False)
+        expected_shape = 1.5 * shape
+        assert_equal(resized.shape, expected_shape)
+        assert cp.all(resized == 1)
+
+
+def test_resize3d_bilinear():
+    # bilinear 3rd dimension
+    x = cp.zeros((5, 5, 2), dtype=np.double)
+    x[1, 1, 0] = 0
+    x[1, 1, 1] = 1
+    resized = resize(x, (10, 10, 1), order=1, mode='constant',
+                     anti_aliasing=False)
+    ref = np.zeros((10, 10, 1))
+    ref[1:5, 1:5, :] = 0.03125
+    ref[1:5, 2:4, :] = 0.09375
+    ref[2:4, 1:5, :] = 0.09375
+    ref[2:4, 2:4, :] = 0.28125
+    assert_array_almost_equal(resized, ref)
+
+
+def test_resize_dtype():
+    x = cp.zeros((5, 5))
+    x_f32 = x.astype(np.float32)
+    x_u8 = x.astype(np.uint8)
+    x_b = x.astype(bool)
+
+    assert resize(x, (10, 10), preserve_range=False).dtype == x.dtype
+    assert resize(x, (10, 10), preserve_range=True).dtype == x.dtype
+    assert resize(x_u8, (10, 10), preserve_range=False).dtype == np.double
+    assert resize(x_u8, (10, 10), preserve_range=True).dtype == np.double
+    assert resize(x_b, (10, 10), preserve_range=False).dtype == np.double
+    assert resize(x_b, (10, 10), preserve_range=True).dtype == np.double
+    assert resize(x_f32, (10, 10), preserve_range=False).dtype == x_f32.dtype
+    assert resize(x_f32, (10, 10), preserve_range=True).dtype == x_f32.dtype
+
+
+@cp.testing.with_requires('cupy>=9.0.0b2')
+def test_swirl():
+    image = img_as_float(cp.array(checkerboard()))
+
+    swirl_params = {'radius': 80, 'rotation': 0, 'order': 2, 'mode': 'reflect'}
+
+    # with expected_warnings(["Bi-quadratic.*bug"]):
+    swirled = swirl(image, strength=10, **swirl_params)
+    unswirled = swirl(swirled, strength=-10, **swirl_params)
+
+    assert cp.mean(cp.abs(image - unswirled)) < 0.01
+
+    swirl_params.pop('mode')
+
+    # with expected_warnings(["Bi-quadratic.*bug"]):
+    swirled = swirl(image, strength=10, **swirl_params)
+    unswirled = swirl(swirled, strength=-10, **swirl_params)
+
+    assert cp.mean(cp.abs(image[1:-1, 1:-1] - unswirled[1:-1, 1:-1])) < 0.01
+
+
+def test_const_cval_out_of_range():
+    img = cp.random.randn(100, 100)
+    cval = -10
+    warped = warp(img, AffineTransform(translation=(10, 10)), cval=cval)
+    assert cp.sum(warped == cval) == (2 * 100 * 10 - 10 * 10)
+
+
+def test_warp_identity():
+    img = img_as_float(cp.array(rgb2gray(astronaut())))
+    assert len(img.shape) == 2
+    assert cp.allclose(img, warp(img, AffineTransform(rotation=0)))
+    assert not cp.allclose(img, warp(img, AffineTransform(rotation=0.1)))
+
+    img = cp.asnumpy(img)
+    rgb_img = cp.transpose(
+        cp.asarray(np.array([img, np.zeros_like(img), img])), (1, 2, 0)
+    )
+    warped_rgb_img = warp(rgb_img, AffineTransform(rotation=0.1))
+    assert cp.allclose(rgb_img, warp(rgb_img, AffineTransform(rotation=0)))
+    assert not cp.allclose(rgb_img, warped_rgb_img)
+    # assert no cross-talk between bands
+    assert cp.all(0 == warped_rgb_img[:, :, 1])
+
+
+@cp.testing.with_requires('cupy>=9.0.0b2')
+def test_warp_coords_example():
+    image = cp.array(astronaut().astype(np.float32))
+    assert 3 == image.shape[2]
+    tform = SimilarityTransform(translation=(0, -10), xp=cp)
+    coords = warp_coords(tform, (30, 30, 3))
+    map_coordinates(image[:, :, 0], coords[:2])
+
+
+def test_downsize():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[2:4, 2:4] = 1
+    scaled = resize(x, (5, 5), order=0, anti_aliasing=False, mode='constant')
+    assert_equal(scaled.shape, (5, 5))
+    assert_equal(float(scaled[1, 1]), 1)
+    assert_equal(float(scaled[2:, :].sum()), 0)
+    assert_equal(float(scaled[:, 2:].sum()), 0)
+
+
+def test_downsize_anti_aliasing():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[2, 2] = 1
+    scaled = resize(x, (5, 5), order=1, anti_aliasing=True, mode='constant')
+    assert_equal(scaled.shape, (5, 5))
+    assert np.all(scaled[:3, :3] > 0)
+    assert_equal(float(scaled[3:, :].sum()), 0)
+    assert_equal(float(scaled[:, 3:].sum()), 0)
+
+    sigma = 0.125
+    out_size = (5, 5)
+    resize(x, out_size, order=1, mode='constant',
+           anti_aliasing=True, anti_aliasing_sigma=sigma)
+    resize(x, out_size, order=1, mode='edge',
+           anti_aliasing=True, anti_aliasing_sigma=sigma)
+    resize(x, out_size, order=1, mode='symmetric',
+           anti_aliasing=True, anti_aliasing_sigma=sigma)
+    resize(x, out_size, order=1, mode='reflect',
+           anti_aliasing=True, anti_aliasing_sigma=sigma)
+    resize(x, out_size, order=1, mode='wrap',
+           anti_aliasing=True, anti_aliasing_sigma=sigma)
+
+    with pytest.raises(ValueError):  # Unknown mode, or cannot translate mode
+        resize(x, out_size, order=1, mode='non-existent',
+               anti_aliasing=True, anti_aliasing_sigma=sigma)
+
+
+def test_downsize_anti_aliasing_invalid_stddev():
+    x = cp.zeros((10, 10), dtype=np.double)
+    with pytest.raises(ValueError):
+        resize(x, (5, 5), order=0, anti_aliasing=True, anti_aliasing_sigma=-1,
+               mode='constant')
+    with expected_warnings(["Anti-aliasing standard deviation greater"]):
+        resize(x, (5, 15), order=0, anti_aliasing=True,
+               anti_aliasing_sigma=(1, 1), mode="reflect")
+        resize(x, (5, 15), order=0, anti_aliasing=True,
+               anti_aliasing_sigma=(0, 1), mode="reflect")
+
+
+def test_downscale():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[2:4, 2:4] = 1
+    scaled = rescale(x, 0.5, order=0, anti_aliasing=False,
+                     multichannel=False, mode='constant')
+    assert_equal(scaled.shape, (5, 5))
+    assert_equal(float(scaled[1, 1]), 1)
+    assert_equal(float(scaled[2:, :].sum()), 0)
+    assert_equal(float(scaled[:, 2:].sum()), 0)
+
+
+def test_downscale_anti_aliasing():
+    x = cp.zeros((10, 10), dtype=np.double)
+    x[2, 2] = 1
+    scaled = rescale(x, 0.5, order=1, anti_aliasing=True,
+                     multichannel=False, mode='constant')
+    assert_equal(scaled.shape, (5, 5))
+    assert np.all(scaled[:3, :3] > 0)
+    assert_equal(float(scaled[3:, :].sum()), 0)
+    assert_equal(float(scaled[:, 3:].sum()), 0)
+
+
+def test_downscale_local_mean():
+    image1 = cp.arange(4 * 6).reshape(4, 6)
+    out1 = downscale_local_mean(image1, (2, 3))
+    expected1 = np.array([[4., 7.],
+                          [16., 19.]])
+    assert_array_equal(expected1, out1)
+
+    image2 = cp.arange(5 * 8).reshape(5, 8)
+    out2 = downscale_local_mean(image2, (4, 5))
+    expected2 = np.array([[14., 10.8],
+                          [8.5, 5.7]])
+    assert_array_equal(expected2, out2)
+
+
+def test_invalid():
+    with pytest.raises(ValueError):
+        warp(cp.ones((4, 3, 3, 3)), SimilarityTransform())
+
+
+def test_inverse():
+    tform = SimilarityTransform(scale=0.5, rotation=0.1)
+    inverse_tform = SimilarityTransform(matrix=cp.linalg.inv(tform.params))
+    image = cp.arange(10 * 10).reshape(10, 10).astype(cp.double)
+    assert_array_equal(warp(image, inverse_tform), warp(image, tform.inverse))
+
+
+def test_slow_warp_nonint_oshape():
+    image = cp.random.rand(5, 5)
+
+    with pytest.raises(ValueError):
+        warp(image, lambda xy: xy, output_shape=(13.1, 19.5))
+
+    warp(image, lambda xy: xy, output_shape=(13.0001, 19.9999))
+
+
+def test_keep_range():
+    image = cp.linspace(0, 2, 25).reshape(5, 5)
+    out = rescale(image, 2, preserve_range=False, clip=True, order=0,
+                  mode='constant', multichannel=False, anti_aliasing=False)
+    assert out.min() == 0
+    assert out.max() == 2
+
+    out = rescale(image, 2, preserve_range=True, clip=True, order=0,
+                  mode='constant', multichannel=False, anti_aliasing=False)
+    assert out.min() == 0
+    assert out.max() == 2
+
+    out = rescale(image.astype(np.uint8), 2, preserve_range=False,
+                  mode='constant', multichannel=False, anti_aliasing=False,
+                  clip=True, order=0)
+    assert out.min() == 0
+    assert out.max() == 2 / 255.0
+
+
+def test_zero_image_size():
+    with pytest.raises(ValueError):
+        warp(cp.zeros(0), SimilarityTransform())
+    with pytest.raises(ValueError):
+        warp(cp.zeros((0, 10)), SimilarityTransform())
+    with pytest.raises(ValueError):
+        warp(cp.zeros((10, 0)), SimilarityTransform())
+    with pytest.raises(ValueError):
+        warp(cp.zeros((10, 10, 0)), SimilarityTransform())
+
+
+def test_linear_polar_mapping():
+    # fmt: off
+    output_coords = cp.array([[0, 0],
+                             [0, 90],
+                             [0, 180],
+                             [0, 270],
+                             [99, 0],
+                             [99, 180],
+                             [99, 270],
+                             [99, 45]])
+    ground_truth = cp.array([[100, 100],
+                             [100, 100],
+                             [100, 100],
+                             [100, 100],
+                             [199, 100],
+                             [1, 100],
+                             [100, 1],
+                             [170.00357134, 170.00357134]])
+    # fmt: on
+    k_angle = 360 / (2 * np.pi)
+    k_radius = 1
+    center = (100, 100)
+    coords = _linear_polar_mapping(output_coords, k_angle, k_radius, center)
+    assert cp.allclose(coords, ground_truth)
+
+
+def test_log_polar_mapping():
+    # fmt: off
+    output_coords = cp.array([[0, 0],
+                              [0, 90],
+                              [0, 180],
+                              [0, 270],
+                              [99, 0],
+                              [99, 180],
+                              [99, 270],
+                              [99, 45]])
+    ground_truth = cp.array([[101, 100],
+                             [100, 101],
+                             [99, 100],
+                             [100, 99],
+                             [195.4992586, 100],
+                             [4.5007414, 100],
+                             [100, 4.5007414],
+                             [167.52817336, 167.52817336]])
+    # fmt: on
+    k_angle = 360 / (2 * np.pi)
+    k_radius = 100 / cp.log(100)
+    center = (100, 100)
+    coords = _log_polar_mapping(output_coords, k_angle, k_radius, center)
+    assert cp.allclose(coords, ground_truth)
+
+
+def test_linear_warp_polar():
+    radii = [5, 10, 15, 20]
+    image = cp.zeros([51, 51])
+    for rad in radii:
+        rr, cc, val = circle_perimeter_aa(25, 25, rad)
+        image[rr, cc] = val
+    warped = warp_polar(image, radius=25)
+    profile = warped.mean(axis=0)
+    peaks = cp.asnumpy(peak_local_max(profile))
+    assert np.alltrue([peak in radii for peak in peaks])
+
+
+def test_log_warp_polar():
+    radii = [np.exp(2), np.exp(3), np.exp(4), np.exp(5),
+             np.exp(5) - 1, np.exp(5) + 1]
+    radii = [int(x) for x in radii]
+    image = cp.zeros([301, 301])
+    for rad in radii:
+        rr, cc, val = circle_perimeter_aa(150, 150, rad)
+        image[rr, cc] = val
+    warped = warp_polar(image, radius=200, scaling='log')
+    profile = warped.mean(axis=0)
+    peaks_coord = peak_local_max(profile)
+    peaks_coord.sort(axis=0)
+    gaps = cp.asnumpy(peaks_coord[1:] - peaks_coord[:-1])
+    assert np.alltrue([x >= 38 and x <= 40 for x in gaps])
+
+
+def test_invalid_scaling_polar():
+    with pytest.raises(ValueError):
+        warp_polar(cp.zeros((10, 10)), (5, 5), scaling="invalid")
+    with pytest.raises(ValueError):
+        warp_polar(cp.zeros((10, 10)), (5, 5), scaling=None)
+
+
+def test_invalid_dimensions_polar():
+    with pytest.raises(ValueError):
+        warp_polar(cp.zeros((10, 10, 3)), (5, 5))
+    with pytest.raises(ValueError):
+        warp_polar(cp.zeros((10, 10)), (5, 5), multichannel=True)
+    with pytest.raises(ValueError):
+        warp_polar(cp.zeros((10, 10, 10, 3)), (5, 5), multichannel=True)
+
+
+def test_bool_img_rescale():
+    img = cp.ones((12, 18), dtype=bool)
+    img[2:-2, 4:-4] = False
+    res = rescale(img, 0.5)
+
+    expected = cp.ones((6, 9))
+    expected[1:-1, 2:-2] = False
+
+    assert_array_equal(res, expected)
+
+
+def test_bool_img_resize():
+    img = cp.ones((12, 18), dtype=bool)
+    img[2:-2, 4:-4] = False
+    res = resize(img, (6, 9))
+
+    expected = cp.ones((6, 9))
+    expected[1:-1, 2:-2] = False
+
+    assert_array_equal(res, expected)
+
+
+def test_bool_array_warnings():
+    img = cp.zeros((10, 10), dtype=bool)
+
+    with expected_warnings(['Input image dtype is bool']):
+        rescale(img, 0.5, anti_aliasing=True)
+
+    with expected_warnings(['Input image dtype is bool']):
+        resize(img, (5, 5), anti_aliasing=True)
+
+    with expected_warnings(['Input image dtype is bool']):
+        rescale(img, 0.5, order=1)
+
+    with expected_warnings(['Input image dtype is bool']):
+        resize(img, (5, 5), order=1)
+
+    with expected_warnings(['Input image dtype is bool']):
+        warp(img, cp.eye(3), order=1)
diff --git a/python/cucim/src/cucim/skimage/util/__init__.py b/python/cucim/src/cucim/skimage/util/__init__.py
new file mode 100644
index 000000000..cbfd2e608
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/__init__.py
@@ -0,0 +1,24 @@
+from ._invert import invert
+from ._map_array import map_array
+from .arraycrop import crop
+from .dtype import (dtype_limits, img_as_bool, img_as_float, img_as_float32,
+                    img_as_float64, img_as_int, img_as_ubyte, img_as_uint)
+from .noise import random_noise
+from .shape import view_as_blocks, view_as_windows
+
+__all__ = [
+    "img_as_float32",
+    "img_as_float64",
+    "img_as_float",
+    "img_as_int",
+    "img_as_uint",
+    "img_as_ubyte",
+    "img_as_bool",
+    "dtype_limits",
+    "view_as_blocks",
+    "view_as_windows",
+    "crop",
+    "map_array",
+    "random_noise",
+    "invert",
+]
diff --git a/python/cucim/src/cucim/skimage/util/_invert.py b/python/cucim/src/cucim/skimage/util/_invert.py
new file mode 100644
index 000000000..c75beadfb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/_invert.py
@@ -0,0 +1,77 @@
+import cupy as cp
+import numpy as np
+
+from .dtype import dtype_limits
+
+
+def invert(image, signed_float=False):
+    """Invert an image.
+
+    Invert the intensity range of the input image, so that the dtype maximum
+    is now the dtype minimum, and vice-versa. This operation is
+    slightly different depending on the input dtype:
+
+    - unsigned integers: subtract the image from the dtype maximum
+    - signed integers: subtract the image from -1 (see Notes)
+    - floats: subtract the image from 1 (if signed_float is False, so we
+      assume the image is unsigned), or from 0 (if signed_float is True).
+
+    See the examples for clarification.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    signed_float : bool, optional
+        If True and the image is of type float, the range is assumed to
+        be [-1, 1]. If False and the image is of type float, the range is
+        assumed to be [0, 1].
+
+    Returns
+    -------
+    inverted : ndarray
+        Inverted image.
+
+    Notes
+    -----
+    Ideally, for signed integers we would simply multiply by -1. However,
+    signed integer ranges are asymmetric. For example, for np.int8, the range
+    of possible values is [-128, 127], so that -128 * -1 equals -128! By
+    subtracting from -1, we correctly map the maximum dtype value to the
+    minimum.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> img = cp.asarray([[100,  0, 200],
+    ...                     [  0, 50,   0],
+    ...                     [ 30,  0, 255]], np.uint8)
+    >>> invert(img)
+    array([[155, 255,  55],
+           [255, 205, 255],
+           [225, 255,   0]], dtype=uint8)
+    >>> img2 = cp.asarray([[ -2, 0, -128],
+    ...                      [127, 0,    5]], np.int8)
+    >>> invert(img2)
+    array([[   1,   -1,  127],
+           [-128,   -1,   -6]], dtype=int8)
+    >>> img3 = cp.asarray([[ 0., 1., 0.5, 0.75]])
+    >>> invert(img3)
+    array([[1.  , 0.  , 0.5 , 0.25]])
+    >>> img4 = cp.asarray([[ 0., 1., -1., -0.25]])
+    >>> invert(img4, signed_float=True)
+    array([[-0.  , -1.  ,  1.  ,  0.25]])
+    """
+    if image.dtype == 'bool':
+        inverted = ~image
+    elif np.issubdtype(image.dtype, np.unsignedinteger):
+        max_val = dtype_limits(image, clip_negative=False)[1]
+        inverted = cp.subtract(max_val, image, dtype=image.dtype)
+    elif np.issubdtype(image.dtype, np.signedinteger):
+        inverted = cp.subtract(-1, image, dtype=image.dtype)
+    else:  # float dtype
+        if signed_float:
+            inverted = -image
+        else:
+            inverted = cp.subtract(1, image, dtype=image.dtype)
+    return inverted
diff --git a/python/cucim/src/cucim/skimage/util/_map_array.py b/python/cucim/src/cucim/skimage/util/_map_array.py
new file mode 100644
index 000000000..e3382a11d
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/_map_array.py
@@ -0,0 +1,224 @@
+import cupy as cp
+
+# TODO: scikit-image Cython code uses unordered_map, but here we use a simple
+#       for loop over in_vals. On my hardware, for large arrays, when
+#       nvals < 2900 or so, the GPU implementation is faster. For small nvals
+#       (e.g. 10-100) it is much faster.
+_map_array = cp.ElementwiseKernel(
+    in_params="raw X x, raw X in_vals, raw Y out_vals, int32 nvals",
+    out_params="Y y",
+    operation="""
+    int j;
+    Y out_val = 0;  // missing values default to zero
+    for (j=0; j<nvals; j++)
+    {
+        if (in_vals[j] == x[i])
+        {
+            out_val = out_vals[j];
+            break;
+        }
+    }
+    y = out_val;
+    """,
+    name="cucim_skimage_map_array",
+)
+
+
+def map_array(input_arr, input_vals, output_vals, out=None):
+    """Map values from input array from input_vals to output_vals.
+
+    Parameters
+    ----------
+    input_arr : array of int, shape (M[, N][, P][, ...])
+        The input label image.
+    input_vals : array of int, shape (N,)
+        The values to map from.
+    output_vals : array, shape (N,)
+        The values to map to.
+    out: array, same shape as `input_arr`
+        The output array. Will be created if not provided. It should
+        have the same dtype as `output_vals`.
+
+    Returns
+    -------
+    out : array, same shape as `input_arr`
+        The array of mapped values.
+    """
+
+    if not cp.issubdtype(input_arr.dtype, cp.integer):
+        raise TypeError(
+            'The dtype of an array to be remapped should be integer.'
+        )
+    # We ravel the input array for simplicity of iteration in Cython:
+    orig_shape = input_arr.shape
+    # NumPy docs for `np.ravel()` says:
+    # "When a view is desired in as many cases as possible,
+    # arr.reshape(-1) may be preferable."
+    input_arr = input_arr.reshape(-1)
+    if out is None:
+        out = cp.empty(orig_shape, dtype=output_vals.dtype)
+    elif out.shape != orig_shape:
+        raise ValueError(
+            'If out array is provided, it should have the same shape as '
+            f'the input array. Input array has shape {orig_shape}, provided '
+            f'output array has shape {out.shape}.'
+        )
+    try:
+        out_view = out.view()
+        out_view.shape = (-1,)  # no-copy reshape/ravel
+    except AttributeError:  # if out strides are not compatible with 0-copy
+        raise ValueError(
+            'If out array is provided, it should be either contiguous '
+            f'or 1-dimensional. Got array with shape {out.shape} and '
+            f'strides {out.strides}.'
+        )
+
+    # ensure all arrays have matching types before sending to Cython
+    input_vals = input_vals.astype(input_arr.dtype, copy=False)
+    output_vals = output_vals.astype(out.dtype, copy=False)
+    _map_array(input_arr, input_vals, output_vals, input_vals.size, out_view)
+    return out
+
+
+class ArrayMap:
+    """Class designed to mimic mapping by NumPy array indexing.
+
+    This class is designed to replicate the use of NumPy arrays for mapping
+    values with indexing:
+
+    >>> import cupy as cp
+    >>> values = cp.asarray([0.25, 0.5, 1.0])
+    >>> indices = cp.asarray([[0, 0, 1], [2, 2, 1]])
+    >>> values[indices]
+    array([[0.25, 0.25, 0.5 ],
+           [1.  , 1.  , 0.5 ]])
+
+    The issue with this indexing is that you need a very large ``values``
+    array if the values in the ``indices`` array are large.
+
+    >>> values = cp.asarray([0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0])
+    >>> indices = cp.asarray([[0, 0, 10], [0, 10, 10]])
+    >>> values[indices]
+    array([[0.25, 0.25, 1.  ],
+           [0.25, 1.  , 1.  ]])
+
+    Using this class, the approach is similar, but there is no need to
+    create a large values array:
+
+    >>> in_indices = cp.asarray([0, 10])
+    >>> out_values = cp.asarray([0.25, 1.0])
+    >>> values = ArrayMap(in_indices, out_values)
+    >>> values
+    ArrayMap(array([ 0, 10]), array([0.25, 1.  ]))
+    >>> print(values)
+    ArrayMap:
+      0 → 0.25
+      10 → 1.0
+    >>> indices = cp.asarray([[0, 0, 10], [0, 10, 10]])
+    >>> values[indices]
+    array([[0.25, 0.25, 1.  ],
+           [0.25, 1.  , 1.  ]])
+
+    Parameters
+    ----------
+    in_values : array of int, shape (N,)
+        The source values from which to map.
+    out_values : array, shape (N,)
+        The destination values from which to map.
+    """
+
+    def __init__(self, in_values, out_values):
+        self.in_values = in_values
+        self.out_values = out_values
+        self._max_str_lines = 4
+        self._array = None
+        # cache max value to avoid repeated device->host transfer
+        self._max_label = int(cp.max(self.in_values))
+
+    def __len__(self):
+        """Return one more than the maximum label value being remapped."""
+        return self._max_label + 1
+
+    # TODO: probably don't need to make this _ascupy method public?
+    def _ascupy(self, dtype=None):
+        """Return a CuPy array that behaves like the arraymap when indexed.
+
+        This array can be very large: it is the size of the largest value
+        in the ``in_vals`` array, plus one.
+        """
+        if dtype is None:
+            dtype = self.out_values.dtype
+        output = cp.zeros(self._max_label + 1, dtype=dtype)
+        output[self.in_values] = self.out_values
+        return output
+
+    # This array method is mainly just here for use in the tests
+    def __array__(self, dtype=None):
+        """Return a NumPy array that behaves like the arraymap when indexed.
+
+        This array can be very large: it is the size of the largest value
+        in the ``in_vals`` array, plus one.
+        """
+        return cp.asnumpy(self._ascupy(dtype))
+
+    @property
+    def dtype(self):
+        return self.out_values.dtype
+
+    def __repr__(self):
+        return f'ArrayMap({repr(self.in_values)}, {repr(self.out_values)})'
+
+    def __str__(self):
+        if len(self.in_values) <= self._max_str_lines + 1:
+            rows = range(len(self.in_values))
+            string = '\n'.join(
+                ['ArrayMap:'] +
+                [f'  {self.in_values[i]} → {self.out_values[i]}' for i in rows]
+            )
+        else:
+            rows0 = list(range(0, self._max_str_lines // 2))
+            rows1 = list(range(-self._max_str_lines // 2, 0))
+            string = '\n'.join(
+                ['ArrayMap:'] +
+                [f'  {self.in_values[i]} → {self.out_values[i]}'
+                 for i in rows0] +
+                ['  ...'] +
+                [f'  {self.in_values[i]} → {self.out_values[i]}'
+                 for i in rows1]
+            )
+        return string
+
+    def __call__(self, arr):
+        return self.__getitem__(arr)
+
+    def __getitem__(self, index):
+        scalar = cp.isscalar(index)
+        if scalar:
+            index = cp.asarray([index])
+        elif isinstance(index, slice):
+            start = index.start or 0  # treat None or 0 the same way
+            stop = (index.stop
+                    if index.stop is not None
+                    else len(self))
+            step = index.step
+            index = cp.arange(start, stop, step)
+        if index.dtype == bool:
+            index = cp.flatnonzero(index)
+
+        out = map_array(
+            index,
+            self.in_values.astype(index.dtype, copy=False),
+            self.out_values,
+        )
+
+        if scalar:
+            out = out[0]  # TODO: transfer 0-dim array to host?
+        return out
+
+    def __setitem__(self, indices, values):
+        if self._array is None:
+            self._array = self._ascupy()
+        self._array[indices] = values
+        self.in_values = cp.flatnonzero(self._array)
+        self._max_label = int(cp.max(self.in_values))
+        self.out_values = self._array[self.in_values]
diff --git a/python/cucim/src/cucim/skimage/util/arraycrop.py b/python/cucim/src/cucim/skimage/util/arraycrop.py
new file mode 100644
index 000000000..59893dddb
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/arraycrop.py
@@ -0,0 +1,72 @@
+"""
+The arraycrop module contains functions to crop values from the edges of an
+n-dimensional array.
+"""
+
+import cupy as cp
+
+__all__ = ['crop']
+
+
+def crop(ar, crop_width, copy=False, order='K'):
+    """Crop array `ar` by `crop_width` along each dimension.
+
+    Parameters
+    ----------
+    ar : array-like of rank N
+        Input array.
+    crop_width : {sequence, int}
+        Number of values to remove from the edges of each axis.
+        ``((before_1, after_1),`` ... ``(before_N, after_N))`` specifies
+        unique crop widths at the start and end of each axis.
+        ``((before, after),) or (before, after)`` specifies
+        a fixed start and end crop for every axis.
+        ``(n,)`` or ``n`` for integer ``n`` is a shortcut for
+        before = after = ``n`` for all axes.
+    copy : bool, optional
+        If `True`, ensure the returned array is a contiguous copy. Normally,
+        a crop operation will return a discontiguous view of the underlying
+        input array.
+    order : {'C', 'F', 'A', 'K'}, optional
+        If ``copy==True``, control the memory layout of the copy. See
+        ``np.copy``.
+
+    Returns
+    -------
+    cropped : array
+        The cropped array. If ``copy=False`` (default), this is a sliced
+        view of the input array.
+    """
+    ar = cp.array(ar, copy=False)
+
+    if isinstance(crop_width, int):
+        crops = [[crop_width, crop_width]] * ar.ndim
+    elif isinstance(crop_width[0], int):
+        if len(crop_width) == 1:
+            crops = [[crop_width[0], crop_width[0]]] * ar.ndim
+        elif len(crop_width) == 2:
+            crops = [crop_width] * ar.ndim
+        else:
+            raise ValueError(
+                f"crop_width has an invalid length: {len(crop_width)}\n"
+                "crop_width should be a sequence of N pairs, "
+                "a single pair, or a single integer"
+            )
+    elif len(crop_width) == 1:
+        crops = [crop_width[0]] * ar.ndim
+    elif len(crop_width) == ar.ndim:
+        crops = crop_width
+    else:
+        raise ValueError(
+            f"crop_width has an invalid length: {len(crop_width)}\n"
+            "crop_width should be a sequence of N pairs, "
+            "a single pair, or a single integer"
+        )
+
+    slices = tuple(slice(a, ar.shape[i] - b)
+                   for i, (a, b) in enumerate(crops))
+    if copy:
+        cropped = cp.array(ar[slices], order=order, copy=True)
+    else:
+        cropped = ar[slices]
+    return cropped
diff --git a/python/cucim/src/cucim/skimage/util/dtype.py b/python/cucim/src/cucim/skimage/util/dtype.py
new file mode 100644
index 000000000..827d9b3a1
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/dtype.py
@@ -0,0 +1,555 @@
+import math
+from warnings import warn
+
+import cupy as cp
+
+__all__ = ['img_as_float32', 'img_as_float64', 'img_as_float',
+           'img_as_int', 'img_as_uint', 'img_as_ubyte',
+           'img_as_bool', 'dtype_limits']
+
+# For integers Numpy uses `_integer_types` basis internally, and builds a leaky
+# `cupy.XintYY` abstraction on top of it. This leads to situations when, for
+# example, there are two cupy.Xint64 dtypes with the same attributes but
+# different object references. In order to avoid any potential issues,
+# we use the basis dtypes here. For more information, see:
+# - https://github.com/scikit-image/scikit-image/issues/3043
+# For convenience, for these dtypes we indicate also the possible bit depths
+# (some of them are platform specific). For the details, see:
+# http://www.unix.org/whitepapers/64bit.html
+_integer_types = (cp.byte, cp.ubyte,          # 8 bits
+                  cp.short, cp.ushort,        # 16 bits
+                  cp.intc, cp.uintc,          # 16 or 32 or 64 bits
+                  int, cp.int_, cp.uint,      # 32 or 64 bits
+                  cp.longlong, cp.ulonglong)  # 64 bits
+_integer_ranges = {t: (cp.iinfo(t).min, cp.iinfo(t).max)
+                   for t in _integer_types}
+dtype_range = {bool: (False, True),
+               cp.bool_: (False, True),
+               cp.bool8: (False, True),
+               float: (-1, 1),
+               cp.float_: (-1, 1),
+               cp.float16: (-1, 1),
+               cp.float32: (-1, 1),
+               cp.float64: (-1, 1)}
+dtype_range.update(_integer_ranges)
+
+_supported_types = list(dtype_range.keys())
+
+
+def dtype_limits(image, clip_negative=False):
+    """Return intensity limits, i.e. (min, max) tuple, of the image's dtype.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    clip_negative : bool, optional
+        If True, clip the negative range (i.e. return 0 for min intensity)
+        even if the image dtype allows negative values.
+
+    Returns
+    -------
+    imin, imax : tuple
+        Lower and upper intensity limits.
+    """
+    imin, imax = dtype_range[image.dtype.type]
+    if clip_negative:
+        imin = 0
+    return imin, imax
+
+
+def _dtype_itemsize(itemsize, *dtypes):
+    """Return first of `dtypes` with itemsize greater than `itemsize`
+
+    Parameters
+    ----------
+    itemsize: int
+        The data type object element size.
+
+    Other Parameters
+    ----------------
+    *dtypes:
+        Any Object accepted by `cp.dtype` to be converted to a data
+        type object
+
+    Returns
+    -------
+    dtype: data type object
+        First of `dtypes` with itemsize greater than `itemsize`.
+
+    """
+    return next(dt for dt in dtypes if cp.dtype(dt).itemsize >= itemsize)
+
+
+def _dtype_bits(kind, bits, itemsize=1):
+    """Return dtype of `kind` that can store a `bits` wide unsigned int
+
+    Parameters:
+    kind: str
+        Data type kind.
+    bits: int
+        Desired number of bits.
+    itemsize: int
+        The data type object element size.
+
+    Returns
+    -------
+    dtype: data type object
+        Data type of `kind` that can store a `bits` wide unsigned int
+
+    """
+
+    s = next(i for i in (itemsize, ) + (2, 4, 8) if
+             bits < (i * 8) or (bits == (i * 8) and kind == 'u'))
+
+    return cp.dtype(kind + str(s))
+
+
+def _scale(a, n, m, copy=True):
+    """Scale an array of unsigned/positive integers from `n` to `m` bits.
+
+    Numbers can be represented exactly only if `m` is a multiple of `n`.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input image array.
+    n : int
+        Number of bits currently used to encode the values in `a`.
+    m : int
+        Desired number of bits to encode the values in `out`.
+    copy : bool, optional
+        If True, allocates and returns new array. Otherwise, modifies
+        `a` in place.
+
+    Returns
+    -------
+    out : array
+        Output image array. Has the same kind as `a`.
+    """
+    kind = a.dtype.kind
+    if n > m and a.max() < 2 ** m:
+        mnew = math.ceil(m / 2) * 2
+        if mnew > m:
+            dtype = "int{}".format(mnew)
+        else:
+            dtype = "uint{}".format(mnew)
+        n = math.ceil(n / 2) * 2
+        warn("Downcasting {} to {} without scaling because max "
+             "value {} fits in {}".format(a.dtype, dtype, a.max(), dtype),
+             stacklevel=3)
+        return a.astype(_dtype_bits(kind, m))
+    elif n == m:
+        return a.copy() if copy else a
+    elif n > m:
+        # downscale with precision loss
+        if copy:
+            b = cp.empty(a.shape, _dtype_bits(kind, m))
+            cp.floor_divide(a, 2**(n - m), out=b, dtype=a.dtype,
+                            casting='unsafe')
+            return b
+        else:
+            a //= 2**(n - m)
+            return a
+    elif m % n == 0:
+        # exact upscale to a multiple of `n` bits
+        if copy:
+            b = cp.empty(a.shape, _dtype_bits(kind, m))
+            cp.multiply(a, (2 ** m - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
+            return b
+        else:
+            a = a.astype(_dtype_bits(kind, m, a.dtype.itemsize), copy=False)
+            a *= (2**m - 1) // (2**n - 1)
+            return a
+    else:
+        # upscale to a multiple of `n` bits,
+        # then downscale with precision loss
+        o = (m // n + 1) * n
+        if copy:
+            b = cp.empty(a.shape, _dtype_bits(kind, o))
+            cp.multiply(a, (2 ** o - 1) // (2 ** n - 1), out=b, dtype=b.dtype)
+            b //= 2**(o - m)
+            return b
+        else:
+            a = a.astype(_dtype_bits(kind, o, a.dtype.itemsize), copy=False)
+            a *= (2**o - 1) // (2**n - 1)
+            a //= 2**(o - m)
+            return a
+
+
+def _convert(image, dtype, force_copy=False, uniform=False):
+    """
+    Convert an image to the requested data-type.
+
+    Warnings are issued in case of precision loss, or when negative values
+    are clipped during conversion to unsigned integer types (sign loss).
+
+    Floating point values are expected to be normalized and will be clipped
+    to the range [0.0, 1.0] or [-1.0, 1.0] when converting to unsigned or
+    signed integers respectively.
+
+    Numbers are not shifted to the negative side when converting from
+    unsigned to signed integer types. Negative values will be clipped when
+    converting to unsigned integers.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    dtype : dtype
+        Target data-type.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+    uniform : bool, optional
+        Uniformly quantize the floating point range to the integer range.
+        By default (uniform=False) floating point values are scaled and
+        rounded to the nearest integers, which minimizes back and forth
+        conversion errors.
+
+    .. versionchanged :: 0.15
+        ``_convert`` no longer warns about possible precision or sign
+        information loss. See discussions on these warnings at:
+        https://github.com/scikit-image/scikit-image/issues/2602
+        https://github.com/scikit-image/scikit-image/issues/543#issuecomment-208202228
+        https://github.com/scikit-image/scikit-image/pull/3575
+
+    References
+    ----------
+    .. [1] DirectX data conversion rules.
+           https://msdn.microsoft.com/en-us/library/windows/desktop/dd607323%28v=vs.85%29.aspx
+    .. [2] Data Conversions. In "OpenGL ES 2.0 Specification v2.0.25",
+           pp 7-8. Khronos Group, 2010.
+    .. [3] Proper treatment of pixels as integers. A.W. Paeth.
+           In "Graphics Gems I", pp 249-256. Morgan Kaufmann, 1990.
+    .. [4] Dirty Pixels. J. Blinn. In "Jim Blinn's corner: Dirty Pixels",
+           pp 47-57. Morgan Kaufmann, 1998.
+
+    """
+    dtypeobj_in = image.dtype
+    if dtype is cp.floating:
+        dtypeobj_out = cp.dtype("float64")
+    else:
+        dtypeobj_out = cp.dtype(dtype)
+    dtype_in = dtypeobj_in.type
+    dtype_out = dtypeobj_out.type
+    kind_in = dtypeobj_in.kind
+    kind_out = dtypeobj_out.kind
+    itemsize_in = dtypeobj_in.itemsize
+    itemsize_out = dtypeobj_out.itemsize
+
+    # Below, we do an `issubdtype` check.  Its purpose is to find out
+    # whether we can get away without doing any image conversion.  This happens
+    # when:
+    #
+    # - the output and input dtypes are the same or
+    # - when the output is specified as a type, and the input dtype
+    #   is a subclass of that type (e.g. `cp.floating` will allow
+    #   `float32` and `float64` arrays through)
+
+    if cp.issubdtype(dtype_in, cp.obj2sctype(dtype)):
+        if force_copy:
+            image = image.copy()
+        return image
+
+    if not (dtype_in in _supported_types and dtype_out in _supported_types):
+        raise ValueError("Can not convert from {} to {}."
+                         .format(dtypeobj_in, dtypeobj_out))
+
+    if kind_in in 'ui':
+        imin_in = cp.iinfo(dtype_in).min
+        imax_in = cp.iinfo(dtype_in).max
+    if kind_out in 'ui':
+        imin_out = cp.iinfo(dtype_out).min
+        imax_out = cp.iinfo(dtype_out).max
+
+    # any -> binary
+    if kind_out == 'b':
+        return image > dtype_in(dtype_range[dtype_in][1] / 2)
+
+    # binary -> any
+    if kind_in == 'b':
+        result = image.astype(dtype_out)
+        if kind_out != 'f':
+            result *= dtype_out(dtype_range[dtype_out][1])
+        return result
+
+    # float -> any
+    if kind_in == 'f':
+        if kind_out == 'f':
+            # float -> float
+            return image.astype(dtype_out)
+
+        if cp.min(image) < -1.0 or cp.max(image) > 1.0:
+            raise ValueError("Images of type float must be between -1 and 1.")
+        # floating point -> integer
+        # use float type that can represent output integer type
+        computation_type = _dtype_itemsize(itemsize_out, dtype_in,
+                                           cp.float32, cp.float64)
+
+        if not uniform:
+            if kind_out == 'u':
+                image_out = cp.multiply(image, imax_out,
+                                        dtype=computation_type)
+            else:
+                image_out = cp.multiply(image, (imax_out - imin_out) / 2,
+                                        dtype=computation_type)
+                image_out -= 1.0 / 2.0
+            cp.rint(image_out, out=image_out)
+            cp.clip(image_out, imin_out, imax_out, out=image_out)
+        elif kind_out == 'u':
+            image_out = cp.multiply(image, imax_out + 1,
+                                    dtype=computation_type)
+            cp.clip(image_out, 0, imax_out, out=image_out)
+        else:
+            image_out = cp.multiply(image, (imax_out - imin_out + 1.0) / 2.0,
+                                    dtype=computation_type)
+            cp.floor(image_out, out=image_out)
+            cp.clip(image_out, imin_out, imax_out, out=image_out)
+        return image_out.astype(dtype_out)
+
+    # signed/unsigned int -> float
+    if kind_out == 'f':
+        # use float type that can exactly represent input integers
+        computation_type = _dtype_itemsize(itemsize_in, dtype_out,
+                                           cp.float32, cp.float64)
+
+        if kind_in == 'u':
+            # using cp.divide or cp.multiply doesn't copy the data
+            # until the computation time
+            image = cp.multiply(image, 1. / imax_in,
+                                dtype=computation_type)
+            # DirectX uses this conversion also for signed ints
+            # if imin_in:
+            #     cp.maximum(image, -1.0, out=image)
+        else:
+            image = cp.add(image, 0.5, dtype=computation_type)
+            image *= 2 / (imax_in - imin_in)
+
+        return image.astype(dtype_out, copy=False)
+
+    # unsigned int -> signed/unsigned int
+    if kind_in == 'u':
+        if kind_out == 'i':
+            # unsigned int -> signed int
+            image = _scale(image, 8 * itemsize_in, 8 * itemsize_out - 1)
+            return image.view(dtype_out)
+        else:
+            # unsigned int -> unsigned int
+            return _scale(image, 8 * itemsize_in, 8 * itemsize_out)
+
+    # signed int -> unsigned int
+    if kind_out == 'u':
+        image = _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out)
+        result = cp.empty(image.shape, dtype_out)
+        cp.maximum(image, 0, out=result, dtype=image.dtype, casting='unsafe')
+        return result
+
+    # signed int -> signed int
+    if itemsize_in > itemsize_out:
+        return _scale(image, 8 * itemsize_in - 1, 8 * itemsize_out - 1)
+
+    image = image.astype(_dtype_bits('i', itemsize_out * 8))
+    image -= imin_in
+    image = _scale(image, 8 * itemsize_in, 8 * itemsize_out, copy=False)
+    image += imin_out
+    return image.astype(dtype_out)
+
+
+def convert(image, dtype, force_copy=False, uniform=False):
+    warn("The use of this function is discouraged as its behavior may change "
+         "dramatically in scikit-image 1.0. This function will be removed"
+         "in scikit-image 1.0.", FutureWarning, stacklevel=2)
+    return _convert(image=image, dtype=dtype,
+                    force_copy=force_copy, uniform=uniform)
+
+
+if _convert.__doc__ is not None:
+    convert.__doc__ = _convert.__doc__ + """
+
+    Warns
+    -----
+    FutureWarning:
+        .. versionadded:: 0.17
+
+        The use of this function is discouraged as its behavior may change
+        dramatically in scikit-image 1.0. This function will be removed
+        in scikit-image 1.0.
+    """
+
+
+def img_as_float32(image, force_copy=False):
+    """Convert an image to single-precision (32-bit) floating point format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of float32
+        Output image.
+
+    Notes
+    -----
+    The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
+    converting from unsigned or signed datatypes, respectively.
+    If the input image has a float type, intensity values are not modified
+    and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
+
+    """
+    return _convert(image, cp.float32, force_copy)
+
+
+def img_as_float64(image, force_copy=False):
+    """Convert an image to double-precision (64-bit) floating point format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of float64
+        Output image.
+
+    Notes
+    -----
+    The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
+    converting from unsigned or signed datatypes, respectively.
+    If the input image has a float type, intensity values are not modified
+    and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
+
+    """
+    return _convert(image, cp.float64, force_copy)
+
+
+def img_as_float(image, force_copy=False):
+    """Convert an image to floating point format.
+
+    This function is similar to `img_as_float64`, but will not convert
+    lower-precision floating point arrays to `float64`.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of float
+        Output image.
+
+    Notes
+    -----
+    The range of a floating point image is [0.0, 1.0] or [-1.0, 1.0] when
+    converting from unsigned or signed datatypes, respectively.
+    If the input image has a float type, intensity values are not modified
+    and can be outside the ranges [0.0, 1.0] or [-1.0, 1.0].
+
+    """
+    return _convert(image, cp.floating, force_copy)
+
+
+def img_as_uint(image, force_copy=False):
+    """Convert an image to 16-bit unsigned integer format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of uint16
+        Output image.
+
+    Notes
+    -----
+    Negative input values will be clipped.
+    Positive values are scaled between 0 and 65535.
+
+    """
+    return _convert(image, cp.uint16, force_copy)
+
+
+def img_as_int(image, force_copy=False):
+    """Convert an image to 16-bit signed integer format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of int16
+        Output image.
+
+    Notes
+    -----
+    The values are scaled between -32768 and 32767.
+    If the input data-type is positive-only (e.g., uint8), then
+    the output image will still only have positive values.
+
+    """
+    return _convert(image, cp.int16, force_copy)
+
+
+def img_as_ubyte(image, force_copy=False):
+    """Convert an image to 8-bit unsigned integer format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of ubyte (uint8)
+        Output image.
+
+    Notes
+    -----
+    Negative input values will be clipped.
+    Positive values are scaled between 0 and 255.
+
+    """
+    return _convert(image, cp.uint8, force_copy)
+
+
+def img_as_bool(image, force_copy=False):
+    """Convert an image to boolean format.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image.
+    force_copy : bool, optional
+        Force a copy of the data, irrespective of its current dtype.
+
+    Returns
+    -------
+    out : ndarray of bool (`bool_`)
+        Output image.
+
+    Notes
+    -----
+    The upper half of the input dtype's positive range is True, and the lower
+    half is False. All negative values (if present) are False.
+
+    """
+    return _convert(image, bool, force_copy)
diff --git a/python/cucim/src/cucim/skimage/util/noise.py b/python/cucim/src/cucim/skimage/util/noise.py
new file mode 100644
index 000000000..1e21bc746
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/noise.py
@@ -0,0 +1,197 @@
+import cupy as cp
+
+from .dtype import img_as_float
+
+__all__ = ['random_noise']
+
+
+def random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs):
+    """
+    Function to add random noise of various types to a floating-point image.
+
+    Parameters
+    ----------
+    image : ndarray
+        Input image data. Will be converted to float.
+    mode : str, optional
+        One of the following strings, selecting the type of noise to add:
+
+        - 'gaussian'  Gaussian-distributed additive noise.
+        - 'localvar'  Gaussian-distributed additive noise, with specified
+                      local variance at each point of `image`.
+        - 'poisson'   Poisson-distributed noise generated from the data.
+        - 'salt'      Replaces random pixels with 1.
+        - 'pepper'    Replaces random pixels with 0 (for unsigned images) or
+                      -1 (for signed images).
+        - 's&p'       Replaces random pixels with either 1 or `low_val`, where
+                      `low_val` is 0 for unsigned images or -1 for signed
+                      images.
+        - 'speckle'   Multiplicative noise using out = image + n*image, where
+                      n is Gaussian noise with specified mean & variance.
+    seed : int, optional
+        If provided, this will set the random seed before generating noise,
+        for valid pseudo-random comparisons.
+    clip : bool, optional
+        If True (default), the output will be clipped after noise applied
+        for modes `'speckle'`, `'poisson'`, and `'gaussian'`. This is
+        needed to maintain the proper image data range. If False, clipping
+        is not applied, and the output may extend beyond the range [-1, 1].
+    mean : float, optional
+        Mean of random distribution. Used in 'gaussian' and 'speckle'.
+        Default : 0.
+    var : float, optional
+        Variance of random distribution. Used in 'gaussian' and 'speckle'.
+        Note: variance = (standard deviation) ** 2. Default : 0.01
+    local_vars : ndarray, optional
+        Array of positive floats, same shape as `image`, defining the local
+        variance at every image point. Used in 'localvar'.
+    amount : float, optional
+        Proportion of image pixels to replace with noise on range [0, 1].
+        Used in 'salt', 'pepper', and 'salt & pepper'. Default : 0.05
+    salt_vs_pepper : float, optional
+        Proportion of salt vs. pepper noise for 's&p' on range [0, 1].
+        Higher values represent more salt. Default : 0.5 (equal amounts)
+
+    Returns
+    -------
+    out : ndarray
+        Output floating-point image data on range [0, 1] or [-1, 1] if the
+        input `image` was unsigned or signed, respectively.
+
+    Notes
+    -----
+    Speckle, Poisson, Localvar, and Gaussian noise may generate noise outside
+    the valid image range. The default is to clip (not alias) these values,
+    but they may be preserved by setting `clip=False`. Note that in this case
+    the output may contain values outside the ranges [0, 1] or [-1, 1].
+    Use this option with care.
+
+    Because of the prevalence of exclusively positive floating-point images in
+    intermediate calculations, it is not possible to intuit if an input is
+    signed based on dtype alone. Instead, negative values are explicitly
+    searched for. Only if found does this function assume signed input.
+    Unexpected results only occur in rare, poorly exposes cases (e.g. if all
+    values are above 50 percent gray in a signed `image`). In this event,
+    manually scaling the input to the positive domain will solve the problem.
+
+    The Poisson distribution is only defined for positive integers. To apply
+    this noise type, the number of unique values in the image is found and
+    the next round power of two is used to scale up the floating-point result,
+    after which it is scaled back down to the floating-point image range.
+
+    To generate Poisson noise against a signed image, the signed image is
+    temporarily converted to an unsigned image in the floating point domain,
+    Poisson noise is generated, then it is returned to the original range.
+
+    """
+    mode = mode.lower()
+
+    # Detect if a signed image was input
+    if image.min() < 0:
+        low_clip = -1.0
+    else:
+        low_clip = 0.0
+
+    image = img_as_float(image)
+    if seed is not None:
+        cp.random.seed(seed=seed)
+
+    allowedtypes = {
+        'gaussian': 'gaussian_values',
+        'localvar': 'localvar_values',
+        'poisson': 'poisson_values',
+        'salt': 'sp_values',
+        'pepper': 'sp_values',
+        's&p': 's&p_values',
+        'speckle': 'gaussian_values'}
+
+    kwdefaults = {
+        'mean': 0.0,
+        'var': 0.01,
+        'amount': 0.05,
+        'salt_vs_pepper': 0.5,
+        'local_vars': cp.zeros_like(image) + 0.01}
+
+    allowedkwargs = {
+        'gaussian_values': ['mean', 'var'],
+        'localvar_values': ['local_vars'],
+        'sp_values': ['amount'],
+        's&p_values': ['amount', 'salt_vs_pepper'],
+        'poisson_values': []}
+
+    for key in kwargs:
+        if key not in allowedkwargs[allowedtypes[mode]]:
+            raise ValueError('%s keyword not in allowed keywords %s' %
+                             (key, allowedkwargs[allowedtypes[mode]]))
+
+    # Set kwarg defaults
+    for kw in allowedkwargs[allowedtypes[mode]]:
+        kwargs.setdefault(kw, kwdefaults[kw])
+
+    if mode == 'gaussian':
+        noise = cp.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
+                                 image.shape)
+        out = image + noise
+
+    elif mode == 'localvar':
+        # Ensure local variance input is correct
+        if (kwargs['local_vars'] <= 0).any():
+            raise ValueError('All values of `local_vars` must be > 0.')
+
+        # Safe shortcut usage broadcasts kwargs['local_vars'] as a ufunc
+
+        # CuPy Backend: Must supply size argument to get around a CuPy bug
+        #       https://github.com/cupy/cupy/pull/4457
+        out = image + cp.random.normal(
+            0, kwargs["local_vars"] ** 0.5, kwargs["local_vars"].shape
+        )
+
+    elif mode == 'poisson':
+        # Determine unique values in image & calculate the next power of two
+        vals = len(cp.unique(image))
+        vals = 2 ** cp.ceil(cp.log2(vals))
+
+        # Ensure image is exclusively positive
+        if low_clip == -1.0:
+            old_max = image.max()
+            image = (image + 1.0) / (old_max + 1.0)
+
+        # Generating noise for each unique value in image.
+        out = cp.random.poisson(image * vals) / float(vals)
+
+        # Return image to original range if input was signed
+        if low_clip == -1.0:
+            out = out * (old_max + 1.0) - 1.0
+
+    elif mode == 'salt':
+        # Re-call function with mode='s&p' and p=1 (all salt noise)
+        out = random_noise(image, mode='s&p', seed=seed,
+                           amount=kwargs['amount'], salt_vs_pepper=1.)
+
+    elif mode == 'pepper':
+        # Re-call function with mode='s&p' and p=1 (all pepper noise)
+        out = random_noise(image, mode='s&p', seed=seed,
+                           amount=kwargs['amount'], salt_vs_pepper=0.)
+
+    elif mode == 's&p':
+        out = image.copy()
+        p = kwargs['amount']
+        q = kwargs['salt_vs_pepper']
+        flipped = cp.random.choice([True, False], size=image.shape,
+                                   p=[p, 1 - p])
+        salted = cp.random.choice([True, False], size=image.shape,
+                                  p=[q, 1 - q])
+        peppered = ~salted
+        out[flipped & salted] = 1
+        out[flipped & peppered] = low_clip
+
+    elif mode == 'speckle':
+        noise = cp.random.normal(kwargs['mean'], kwargs['var'] ** 0.5,
+                                 image.shape)
+        out = image + image * noise
+
+    # Clip back to original range, if necessary
+    if clip:
+        out = cp.clip(out, low_clip, 1.0)
+
+    return out
diff --git a/python/cucim/src/cucim/skimage/util/shape.py b/python/cucim/src/cucim/skimage/util/shape.py
new file mode 100644
index 000000000..ad6dbf204
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/shape.py
@@ -0,0 +1,264 @@
+import numbers
+from warnings import warn
+
+import cupy as cp
+from cupy.lib.stride_tricks import as_strided
+
+__all__ = ["view_as_blocks", "view_as_windows"]
+
+
+def view_as_blocks(arr_in, block_shape):
+    """Block view of the input n-dimensional array (using re-striding).
+
+    Blocks are non-overlapping views of the input array.
+
+    Parameters
+    ----------
+    arr_in : ndarray
+        N-d input array.
+    block_shape : tuple
+        The shape of the block. Each dimension must divide evenly into the
+        corresponding dimensions of `arr_in`.
+
+    Returns
+    -------
+    arr_out : ndarray
+        Block view of the input array.  If `arr_in` is non-contiguous, a
+        copy is made.
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.util.shape import view_as_blocks
+    >>> A = cp.arange(4*4).reshape(4,4)
+    >>> A
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+    >>> B = view_as_blocks(A, block_shape=(2, 2))
+    >>> B[0, 0]
+    array([[0, 1],
+           [4, 5]])
+    >>> B[0, 1]
+    array([[2, 3],
+           [6, 7]])
+    >>> B[1, 0, 1, 1]
+    13
+
+    >>> A = cp.arange(4*4*6).reshape(4,4,6)
+    >>> A  # doctest: +NORMALIZE_WHITESPACE
+    array([[[ 0,  1,  2,  3,  4,  5],
+            [ 6,  7,  8,  9, 10, 11],
+            [12, 13, 14, 15, 16, 17],
+            [18, 19, 20, 21, 22, 23]],
+           [[24, 25, 26, 27, 28, 29],
+            [30, 31, 32, 33, 34, 35],
+            [36, 37, 38, 39, 40, 41],
+            [42, 43, 44, 45, 46, 47]],
+           [[48, 49, 50, 51, 52, 53],
+            [54, 55, 56, 57, 58, 59],
+            [60, 61, 62, 63, 64, 65],
+            [66, 67, 68, 69, 70, 71]],
+           [[72, 73, 74, 75, 76, 77],
+            [78, 79, 80, 81, 82, 83],
+            [84, 85, 86, 87, 88, 89],
+            [90, 91, 92, 93, 94, 95]]])
+    >>> B = view_as_blocks(A, block_shape=(1, 2, 2))
+    >>> B.shape
+    (4, 2, 3, 1, 2, 2)
+    >>> B[2:, 0, 2]  # doctest: +NORMALIZE_WHITESPACE
+    array([[[[52, 53],
+             [58, 59]]],
+           [[[76, 77],
+             [82, 83]]]])
+    """
+    if not isinstance(block_shape, tuple):
+        raise TypeError("block needs to be a tuple")
+
+    if any(s <= 0 for s in block_shape):
+        raise ValueError("'block_shape' elements must be strictly positive")
+
+    if len(block_shape) != arr_in.ndim:
+        raise ValueError(
+            "'block_shape' must have the same length as 'arr_in.shape'"
+        )
+
+    if any(s % bs for s, bs in zip(arr_in.shape, block_shape)):
+        raise ValueError("'block_shape' is not compatible with 'arr_in'")
+
+    # TODO: This C-contiguous check and call to ascontiguousarray is not in
+    #       skimage. Remove it?
+    if not arr_in.flags.c_contiguous:  # c_contiguous:
+        warn(
+            RuntimeWarning(
+                "Cannot provide views on a non-contiguous "
+                "input array without copying."
+            )
+        )
+    arr_in = cp.ascontiguousarray(arr_in)
+
+    # -- restride the array to build the block view
+    new_shape = tuple(
+        [s // bs for s, bs in zip(arr_in.shape, block_shape)]
+    ) + tuple(block_shape)
+    new_strides = (
+        tuple(s * bs for s, bs in zip(arr_in.strides, block_shape))
+        + arr_in.strides
+    )
+
+    arr_out = as_strided(arr_in, shape=new_shape, strides=new_strides)
+
+    return arr_out
+
+
+def view_as_windows(arr_in, window_shape, step=1):
+    """Rolling window view of the input n-dimensional array.
+
+    Windows are overlapping views of the input array, with adjacent windows
+    shifted by a single row or column (or an index of a higher dimension).
+
+    Parameters
+    ----------
+    arr_in : ndarray
+        N-d input array.
+    window_shape : integer or tuple of length arr_in.ndim
+        Defines the shape of the elementary n-dimensional orthotope
+        (better know as hyperrectangle [1]_) of the rolling window view.
+        If an integer is given, the shape will be a hypercube of
+        sidelength given by its value.
+    step : integer or tuple of length arr_in.ndim
+        Indicates step size at which extraction shall be performed.
+        If integer is given, then the step is uniform in all dimensions.
+
+    Returns
+    -------
+    arr_out : ndarray
+        (rolling) window view of the input array.
+
+    Notes
+    -----
+    One should be very careful with rolling views when it comes to
+    memory usage.  Indeed, although a 'view' has the same memory
+    footprint as its base array, the actual array that emerges when this
+    'view' is used in a computation is generally a (much) larger array
+    than the original, especially for 2-dimensional arrays and above.
+
+    For example, let us consider a 3 dimensional array of size (100,
+    100, 100) of ``float64``. This array takes about 8*100**3 Bytes for
+    storage which is just 8 MB. If one decides to build a rolling view
+    on this array with a window of (3, 3, 3) the hypothetical size of
+    the rolling view (if one was to reshape the view for example) would
+    be 8*(100-3+1)**3*3**3 which is about 203 MB! The scaling becomes
+    even worse as the dimension of the input array becomes larger.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Hyperrectangle
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> from cucim.skimage.util.shape import view_as_windows
+    >>> A = cp.arange(4*4).reshape(4,4)
+    >>> A
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15]])
+    >>> window_shape = (2, 2)
+    >>> B = view_as_windows(A, window_shape)
+    >>> B[0, 0]
+    array([[0, 1],
+           [4, 5]])
+    >>> B[0, 1]
+    array([[1, 2],
+           [5, 6]])
+
+    >>> A = cp.arange(10)
+    >>> A
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+    >>> window_shape = (3,)
+    >>> B = view_as_windows(A, window_shape)
+    >>> B.shape
+    (8, 3)
+    >>> B
+    array([[0, 1, 2],
+           [1, 2, 3],
+           [2, 3, 4],
+           [3, 4, 5],
+           [4, 5, 6],
+           [5, 6, 7],
+           [6, 7, 8],
+           [7, 8, 9]])
+
+    >>> A = cp.arange(5*4).reshape(5, 4)
+    >>> A
+    array([[ 0,  1,  2,  3],
+           [ 4,  5,  6,  7],
+           [ 8,  9, 10, 11],
+           [12, 13, 14, 15],
+           [16, 17, 18, 19]])
+    >>> window_shape = (4, 3)
+    >>> B = view_as_windows(A, window_shape)
+    >>> B.shape
+    (2, 2, 4, 3)
+    >>> B  # doctest: +NORMALIZE_WHITESPACE
+    array([[[[ 0,  1,  2],
+             [ 4,  5,  6],
+             [ 8,  9, 10],
+             [12, 13, 14]],
+            [[ 1,  2,  3],
+             [ 5,  6,  7],
+             [ 9, 10, 11],
+             [13, 14, 15]]],
+           [[[ 4,  5,  6],
+             [ 8,  9, 10],
+             [12, 13, 14],
+             [16, 17, 18]],
+            [[ 5,  6,  7],
+             [ 9, 10, 11],
+             [13, 14, 15],
+             [17, 18, 19]]]])
+    """
+
+    # -- basic checks on arguments
+    if not isinstance(arr_in, cp.ndarray):
+        raise TypeError("`arr_in` must be a cupy ndarray")
+
+    ndim = arr_in.ndim
+
+    if isinstance(window_shape, numbers.Number):
+        window_shape = (window_shape,) * ndim
+    if not (len(window_shape) == ndim):
+        raise ValueError("`window_shape` is incompatible with `arr_in.shape`")
+
+    if isinstance(step, numbers.Number):
+        if step < 1:
+            raise ValueError("`step` must be >= 1")
+        step = (step,) * ndim
+    if len(step) != ndim:
+        raise ValueError("`step` is incompatible with `arr_in.shape`")
+
+    arr_shape = arr_in.shape
+    window_shape = tuple([int(w) for w in window_shape])
+
+    if any(s < ws for s, ws in zip(arr_shape, window_shape)):
+        raise ValueError("`window_shape` is too large")
+
+    if any(ws < 0 for ws in window_shape):
+        raise ValueError("`window_shape` is too small")
+
+    # -- build rolling window view
+    slices = tuple(slice(None, None, st) for st in step)
+    win_indices_shape = tuple(
+        [(s - ws) // st + 1 for s, ws, st in zip(arr_shape, window_shape, step)]
+    )
+    new_shape = win_indices_shape + window_shape
+
+    window_strides = arr_in.strides
+    indexing_strides = arr_in[slices].strides
+    strides = indexing_strides + window_strides
+
+    arr_out = as_strided(arr_in, shape=new_shape, strides=strides)
+    return arr_out
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_arraycrop.py b/python/cucim/src/cucim/skimage/util/tests/test_arraycrop.py
new file mode 100644
index 000000000..3af519c36
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_arraycrop.py
@@ -0,0 +1,64 @@
+import cupy as cp
+from cupy.testing import assert_array_equal
+
+from cucim.skimage.util import crop
+
+
+def test_multi_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, ((1, 2), (2, 1)))
+    assert_array_equal(out[0], [7, 8])
+    assert_array_equal(out[-1], [32, 33])
+    assert out.shape == (6, 2)
+
+
+def test_pair_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, (1, 2))
+    assert_array_equal(out[0], [6, 7])
+    assert_array_equal(out[-1], [31, 32])
+    assert out.shape == (6, 2)
+
+
+def test_pair_tuple_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, ((1, 2),))
+    assert_array_equal(out[0], [6, 7])
+    assert_array_equal(out[-1], [31, 32])
+    assert out.shape == (6, 2)
+
+
+def test_int_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, 1)
+    assert_array_equal(out[0], [6, 7, 8])
+    assert_array_equal(out[-1], [36, 37, 38])
+    assert out.shape == (7, 3)
+
+
+def test_int_tuple_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, (1,))
+    assert_array_equal(out[0], [6, 7, 8])
+    assert_array_equal(out[-1], [36, 37, 38])
+    assert out.shape == (7, 3)
+
+
+def test_copy_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out0 = crop(arr, 1, copy=True)
+    assert out0.flags.c_contiguous
+    out0[0, 0] = 100
+    assert not cp.any(arr == 100)
+    assert not cp.may_share_memory(arr, out0)
+
+    out1 = crop(arr, 1)
+    out1[0, 0] = 100
+    assert arr[1, 1] == 100
+    assert cp.may_share_memory(arr, out1)
+
+
+def test_zero_crop():
+    arr = cp.arange(45).reshape(9, 5)
+    out = crop(arr, 0)
+    assert out.shape == (9, 5)
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_dtype.py b/python/cucim/src/cucim/skimage/util/tests/test_dtype.py
new file mode 100644
index 000000000..835e208b8
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_dtype.py
@@ -0,0 +1,192 @@
+import itertools
+
+import cupy as cp
+import numpy as np
+import pytest
+from cupy.testing import assert_array_equal
+
+from cucim.skimage import (img_as_float, img_as_float32, img_as_float64,
+                           img_as_int, img_as_ubyte, img_as_uint)
+from cucim.skimage._shared._warnings import expected_warnings
+from cucim.skimage.util.dtype import _convert, convert
+
+dtype_range = {cp.uint8: (0, 255),
+               cp.uint16: (0, 65535),
+               cp.int8: (-128, 127),
+               cp.int16: (-32768, 32767),
+               cp.float32: (-1.0, 1.0),
+               cp.float64: (-1.0, 1.0)}
+
+
+img_funcs = (img_as_int, img_as_float64, img_as_float32,
+             img_as_uint, img_as_ubyte)
+dtypes_for_img_funcs = (cp.int16, cp.float64, cp.float32, cp.uint16, cp.ubyte)
+img_funcs_and_types = zip(img_funcs, dtypes_for_img_funcs)
+
+
+def _verify_range(msg, x, vmin, vmax, dtype):
+    assert x[0] == vmin
+    assert x[-1] == vmax
+    assert x.dtype == dtype
+
+
+@pytest.mark.parametrize(
+    "dtype, f_and_dt", itertools.product(dtype_range, img_funcs_and_types)
+)
+def test_range(dtype, f_and_dt):
+    imin, imax = dtype_range[dtype]
+    x = cp.linspace(imin, imax, 10).astype(dtype)
+
+    f, dt = f_and_dt
+
+    y = f(x)
+
+    omin, omax = dtype_range[dt]
+
+    if imin == 0 or omin == 0:
+        omin = 0
+        imin = 0
+
+    _verify_range("From %s to %s" % (cp.dtype(dtype), cp.dtype(dt)),
+                  y, omin, omax, np.dtype(dt))
+
+
+# Add non-standard data types that are allowed by the `_convert` function.
+dtype_range_extra = dtype_range.copy()
+dtype_range_extra.update({cp.int32: (-2147483648, 2147483647),
+                          cp.uint32: (0, 4294967295)})
+
+dtype_pairs = [(cp.uint8, cp.uint32),
+               (cp.int8, cp.uint32),
+               (cp.int8, cp.int32),
+               (cp.int32, cp.int8),
+               (cp.float64, cp.float32),
+               (cp.int32, cp.float32)]
+
+
+@pytest.mark.parametrize("dtype_in, dt", dtype_pairs)
+def test_range_extra_dtypes(dtype_in, dt):
+    """Test code paths that are not skipped by `test_range`"""
+
+    imin, imax = dtype_range_extra[dtype_in]
+    x = cp.linspace(imin, imax, 10).astype(dtype_in)
+
+    y = _convert(x, dt)
+
+    omin, omax = dtype_range_extra[dt]
+    _verify_range("From %s to %s" % (cp.dtype(dtype_in), cp.dtype(dt)),
+                  y, omin, omax, cp.dtype(dt))
+
+
+def test_downcast():
+    x = cp.arange(10).astype(cp.uint64)
+    with expected_warnings(['Downcasting']):
+        y = img_as_int(x)
+    assert cp.allclose(y, x.astype(cp.int16))
+    assert y.dtype == cp.int16, y.dtype
+
+
+def test_float_out_of_range():
+    too_high = cp.array([2], dtype=cp.float32)
+    with pytest.raises(ValueError):
+        img_as_int(too_high)
+    too_low = cp.array([-2], dtype=cp.float32)
+    with pytest.raises(ValueError):
+        img_as_int(too_low)
+
+
+def test_float_float_all_ranges():
+    arr_in = cp.array([[-10.0, 10.0, 1e20]], dtype=cp.float32)
+    cp.testing.assert_array_equal(img_as_float(arr_in), arr_in)
+
+
+def test_copy():
+    x = cp.array([1], dtype=cp.float64)
+    y = img_as_float(x)
+    z = img_as_float(x, force_copy=True)
+
+    assert y is x
+    assert z is not x
+
+
+def test_bool():
+    img_ = cp.zeros((10, 10), bool)
+    img8 = cp.zeros((10, 10), cp.bool8)
+    img_[1, 1] = True
+    img8[1, 1] = True
+    for (func, dt) in [(img_as_int, cp.int16),
+                       (img_as_float, cp.float64),
+                       (img_as_uint, cp.uint16),
+                       (img_as_ubyte, cp.ubyte)]:
+        converted_ = func(img_)
+        assert cp.sum(converted_) == dtype_range[dt][1]
+        converted8 = func(img8)
+        assert cp.sum(converted8) == dtype_range[dt][1]
+
+
+def test_clobber():
+    # The `img_as_*` functions should never modify input arrays.
+    for func_input_type in img_funcs:
+        for func_output_type in img_funcs:
+            img = cp.random.rand(5, 5)
+
+            img_in = func_input_type(img)
+            img_in_before = img_in.copy()
+            func_output_type(img_in)
+
+            assert_array_equal(img_in, img_in_before)
+
+
+def test_signed_scaling_float32():
+    x = cp.array([-128, 127], dtype=cp.int8)
+    y = img_as_float32(x)
+    assert y.max().get() == 1
+
+
+def test_float32_passthrough():
+    x = cp.array([-1, 1], dtype=cp.float32)
+    y = img_as_float(x)
+    assert y.dtype == x.dtype
+
+
+float_dtype_list = [float, float, cp.double, cp.single, cp.float32,
+                    cp.float64, 'float32', 'float64']
+
+
+def test_float_conversion_dtype():
+    """Test any convertion from a float dtype to an other."""
+    x = cp.array([-1, 1])
+
+    # Test all combinations of dtypes convertions
+    dtype_combin = np.array(np.meshgrid(float_dtype_list,
+                                        float_dtype_list)).T.reshape(-1, 2)
+
+    for dtype_in, dtype_out in dtype_combin:
+        x = x.astype(dtype_in)
+        y = _convert(x, dtype_out)
+        assert y.dtype == cp.dtype(dtype_out)
+
+
+def test_float_conversion_dtype_warns():
+    """Test that convert issues a warning when called"""
+    x = np.array([-1, 1])
+
+    # Test all combinations of dtypes convertions
+    dtype_combin = np.array(np.meshgrid(float_dtype_list,
+                                        float_dtype_list)).T.reshape(-1, 2)
+
+    for dtype_in, dtype_out in dtype_combin:
+        x = x.astype(dtype_in)
+        with expected_warnings(["The use of this function is discouraged"]):
+            y = convert(x, dtype_out)
+        assert y.dtype == cp.dtype(dtype_out)
+
+
+def test_subclass_conversion():
+    """Check subclass conversion behavior"""
+    x = cp.array([-1, 1])
+
+    for dtype in float_dtype_list:
+        x = x.astype(dtype)
+        y = _convert(x, cp.floating)
+        assert y.dtype == x.dtype
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_invert.py b/python/cucim/src/cucim/skimage/util/tests/test_invert.py
new file mode 100644
index 000000000..f97976146
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_invert.py
@@ -0,0 +1,77 @@
+import cupy as cp
+from cupy.testing import assert_array_equal
+
+from cucim.skimage import dtype_limits
+from cucim.skimage.util import invert
+from cucim.skimage.util.dtype import dtype_range
+
+
+def test_invert_bool():
+    dtype = 'bool'
+    image = cp.zeros((3, 3), dtype=dtype)
+    upper_dtype_limit = dtype_limits(image, clip_negative=False)[1]
+    image[1, :] = upper_dtype_limit
+    expected = cp.zeros((3, 3), dtype=dtype) + upper_dtype_limit
+    expected[1, :] = 0
+    result = invert(image)
+    assert_array_equal(expected, result)
+
+
+def test_invert_uint8():
+    dtype = 'uint8'
+    image = cp.zeros((3, 3), dtype=dtype)
+    upper_dtype_limit = dtype_limits(image, clip_negative=False)[1]
+    image[1, :] = upper_dtype_limit
+    expected = cp.zeros((3, 3), dtype=dtype) + upper_dtype_limit
+    expected[1, :] = 0
+    result = invert(image)
+    assert_array_equal(expected, result)
+
+
+def test_invert_int8():
+    dtype = 'int8'
+    image = cp.zeros((3, 3), dtype=dtype)
+    lower_dtype_limit, upper_dtype_limit = \
+        dtype_limits(image, clip_negative=False)
+    image[1, :] = lower_dtype_limit
+    image[2, :] = upper_dtype_limit
+    expected = cp.zeros((3, 3), dtype=dtype)
+    expected[2, :] = lower_dtype_limit
+    expected[1, :] = upper_dtype_limit
+    expected[0, :] = -1
+    result = invert(image)
+    assert_array_equal(expected, result)
+
+
+def test_invert_float64_signed():
+    dtype = 'float64'
+    image = cp.zeros((3, 3), dtype=dtype)
+    lower_dtype_limit, upper_dtype_limit = \
+        dtype_limits(image, clip_negative=False)
+    image[1, :] = lower_dtype_limit
+    image[2, :] = upper_dtype_limit
+    expected = cp.zeros((3, 3), dtype=dtype)
+    expected[2, :] = lower_dtype_limit
+    expected[1, :] = upper_dtype_limit
+    result = invert(image, signed_float=True)
+    assert_array_equal(expected, result)
+
+
+def test_invert_float64_unsigned():
+    dtype = 'float64'
+    image = cp.zeros((3, 3), dtype=dtype)
+    lower_dtype_limit, upper_dtype_limit = \
+        dtype_limits(image, clip_negative=True)
+    image[2, :] = upper_dtype_limit
+    expected = cp.zeros((3, 3), dtype=dtype)
+    expected[0, :] = upper_dtype_limit
+    expected[1, :] = upper_dtype_limit
+    result = invert(image)
+    assert_array_equal(expected, result)
+
+
+def test_invert_roundtrip():
+    for t, limits in dtype_range.items():
+        image = cp.array(limits, dtype=t)
+        expected = invert(invert(image))
+        assert_array_equal(image, expected)
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_map_array.py b/python/cucim/src/cucim/skimage/util/tests/test_map_array.py
new file mode 100644
index 000000000..95db44806
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_map_array.py
@@ -0,0 +1,52 @@
+import cupy as cp
+import pytest
+
+from cucim.skimage.util._map_array import ArrayMap, map_array
+
+
+def test_map_array_incorrect_output_shape():
+    labels = cp.random.randint(0, 5, size=(24, 25))
+    out = cp.empty((24, 24))
+    in_values = cp.unique(labels)
+    out_values = cp.random.random(in_values.shape).astype(out.dtype)
+    with pytest.raises(ValueError):
+        map_array(labels, in_values, out_values, out=out)
+
+
+def test_map_array_non_contiguous_output_array():
+    labels = cp.random.randint(0, 5, size=(24, 25))
+    out = cp.empty((24 * 3, 25 * 2))[::3, ::2]
+    in_values = cp.unique(labels)
+    out_values = cp.random.random(in_values.shape).astype(out.dtype)
+    with pytest.raises(ValueError):
+        map_array(labels, in_values, out_values, out=out)
+
+
+def test_arraymap_long_str():
+    labels = cp.random.randint(0, 40, size=(24, 25))
+    in_values = cp.unique(labels)
+    out_values = cp.random.random(in_values.shape)
+    m = ArrayMap(in_values, out_values)
+    assert len(str(m).split('\n')) == m._max_str_lines + 2
+
+
+def test_arraymap_update():
+    in_values = cp.unique(cp.random.randint(0, 200, size=5))
+    out_values = cp.random.random(len(in_values))
+    m = ArrayMap(in_values, out_values)
+    image = cp.random.randint(1, len(m), size=(512, 512))
+    assert cp.all(m[image] < 1)  # missing values map to 0.
+    m[1:] += 1
+    assert cp.all(m[image] >= 1)
+
+
+def test_arraymap_bool_index():
+    in_values = cp.unique(cp.random.randint(0, 200, size=5))
+    out_values = cp.random.random(len(in_values))
+    m = ArrayMap(in_values, out_values)
+    image = cp.random.randint(1, len(in_values), size=(512, 512))
+    assert cp.all(m[image] < 1)  # missing values map to 0.
+    positive = cp.ones(len(m), dtype=bool)
+    positive[0] = False
+    m[positive] += 1
+    assert cp.all(m[image] >= 1)
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_random_noise.py b/python/cucim/src/cucim/skimage/util/tests/test_random_noise.py
new file mode 100644
index 000000000..40887b755
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_random_noise.py
@@ -0,0 +1,218 @@
+import cupy as cp
+import pytest
+from cupy.testing import assert_allclose, assert_array_equal
+from skimage.data import camera
+
+from cucim.skimage.util import img_as_float, random_noise
+
+camerad = cp.asarray(camera())
+
+
+def test_set_seed():
+    seed = 42
+    cam = cp.asarray(camerad)
+    test = random_noise(cam, seed=seed)
+    assert_array_equal(test, random_noise(cam, seed=seed))
+
+
+def test_salt():
+    seed = 42
+    cam = img_as_float(camerad)
+    cam_noisy = random_noise(cam, seed=seed, mode='salt', amount=0.15)
+    saltmask = cam != cam_noisy
+
+    # Ensure all changes are to 1.0
+    assert_allclose(cam_noisy[saltmask], cp.ones(int(saltmask.sum())))
+
+    # Ensure approximately correct amount of noise was added
+    proportion = float(saltmask.sum()) / (cam.shape[0] * cam.shape[1])
+    assert 0.11 < proportion <= 0.15
+
+
+def test_salt_p1():
+    image = cp.random.rand(2, 3)
+    noisy = random_noise(image, mode='salt', amount=1)
+    assert_array_equal(noisy, [[1, 1, 1], [1, 1, 1]])
+
+
+def test_singleton_dim():
+    """Ensure images where size of a given dimension is 1 work correctly."""
+    image = cp.random.rand(1, 20)
+    noisy = random_noise(image, mode='salt', amount=0.1, seed=42)
+    assert cp.sum(noisy == 1) == 3  # GRL: modified to match value for CuPy
+
+
+def test_pepper():
+    seed = 42
+    cam = img_as_float(camerad)
+    data_signed = cam * 2.0 - 1.0  # Same image, on range [-1, 1]
+
+    cam_noisy = random_noise(cam, seed=seed, mode='pepper', amount=0.15)
+    peppermask = cam != cam_noisy
+
+    # Ensure all changes are to 1.0
+    assert_allclose(cam_noisy[peppermask], cp.zeros(int(peppermask.sum())))
+
+    # Ensure approximately correct amount of noise was added
+    proportion = float(peppermask.sum()) / (cam.shape[0] * cam.shape[1])
+    assert 0.11 < proportion <= 0.15
+
+    # Check to make sure pepper gets added properly to signed images
+    orig_zeros = (data_signed == -1).sum()
+    cam_noisy_signed = random_noise(data_signed, seed=seed, mode='pepper',
+                                    amount=.15)
+
+    proportion = (float((cam_noisy_signed == -1).sum() - orig_zeros) /
+                  (cam.shape[0] * cam.shape[1]))
+    assert 0.11 < proportion <= 0.15
+
+
+def test_salt_and_pepper():
+    seed = 42
+    cam = img_as_float(camerad)
+    cam_noisy = random_noise(cam, seed=seed, mode='s&p', amount=0.15,
+                             salt_vs_pepper=0.25)
+    saltmask = cp.logical_and(cam != cam_noisy, cam_noisy == 1.)
+    peppermask = cp.logical_and(cam != cam_noisy, cam_noisy == 0.)
+
+    # Ensure all changes are to 0. or 1.
+    assert_allclose(cam_noisy[saltmask], cp.ones(int(saltmask.sum())))
+    assert_allclose(cam_noisy[peppermask], cp.zeros(int(peppermask.sum())))
+
+    # Ensure approximately correct amount of noise was added
+    proportion = float(
+        saltmask.sum() + peppermask.sum()) / (cam.shape[0] * cam.shape[1])
+    assert 0.11 < proportion <= 0.18
+
+    # Verify the relative amount of salt vs. pepper is close to expected
+    assert 0.18 < saltmask.sum() / peppermask.sum() < 0.35
+
+
+def test_gaussian():
+    seed = 42
+    data = cp.zeros((128, 128)) + 0.5
+    data_gaussian = random_noise(data, seed=seed, var=0.01)
+    assert 0.008 < data_gaussian.var() < 0.012
+
+    data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015)
+    assert 0.28 < data_gaussian.mean() - 0.5 < 0.32
+    assert 0.012 < data_gaussian.var() < 0.018
+
+
+def test_localvar():
+    seed = 42
+    data = cp.zeros((128, 128)) + 0.5
+    local_vars = cp.zeros((128, 128)) + 0.001
+    local_vars[:64, 64:] = 0.1
+    local_vars[64:, :64] = 0.25
+    local_vars[64:, 64:] = 0.45
+
+    data_gaussian = random_noise(data, mode='localvar', seed=seed,
+                                 local_vars=local_vars, clip=False)
+    assert 0.0 < data_gaussian[:64, :64].var() < 0.002
+    assert 0.095 < data_gaussian[:64, 64:].var() < 0.105
+    assert 0.245 < data_gaussian[64:, :64].var() < 0.255
+    assert 0.445 < data_gaussian[64:, 64:].var() < 0.455
+
+    # Ensure local variance bounds checking works properly
+    bad_local_vars = cp.zeros_like(data)
+    with pytest.raises(ValueError):
+        random_noise(data, mode='localvar', seed=seed,
+                     local_vars=bad_local_vars)
+
+    bad_local_vars += 0.1
+    bad_local_vars[0, 0] = -1
+    with pytest.raises(ValueError):
+        random_noise(data, mode='localvar', seed=seed,
+                     local_vars=bad_local_vars)
+
+
+def test_speckle():
+    seed = 42
+    data = cp.zeros((128, 128)) + 0.1
+    cp.random.seed(seed=seed)
+    noise = cp.random.normal(0.1, 0.02 ** 0.5, (128, 128))
+    expected = cp.clip(data + data * noise, 0, 1)
+
+    data_speckle = random_noise(data, mode='speckle', seed=seed, mean=0.1,
+                                var=0.02)
+    assert_allclose(expected, data_speckle)
+
+
+def test_poisson():
+    seed = 42
+    data = camerad  # 512x512 grayscale uint8
+    cam_noisy = random_noise(data, mode='poisson', seed=seed)
+    cam_noisy2 = random_noise(data, mode='poisson', seed=seed, clip=False)
+
+    cp.random.seed(seed=seed)
+    expected = cp.random.poisson(img_as_float(data) * 256) / 256.0
+    assert_allclose(cam_noisy, cp.clip(expected, 0.0, 1.0))
+    assert_allclose(cam_noisy2, expected)
+
+
+def test_clip_poisson():
+    seed = 42
+    data = camerad  # 512x512 grayscale uint8
+    data_signed = img_as_float(data) * 2.0 - 1.0  # Same image, on range [-1, 1]
+
+    # Signed and unsigned, clipped
+    cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=True)
+    cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed,
+                                clip=True)
+    assert (cam_poisson.max() == 1.0) and (cam_poisson.min() == 0.0)
+    assert (cam_poisson2.max() == 1.0) and (cam_poisson2.min() == -1.0)
+
+    # Signed and unsigned, unclipped
+    cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=False)
+    cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed,
+                                clip=False)
+    assert (cam_poisson.max() > 1.15) and (cam_poisson.min() == 0.0)
+    assert (cam_poisson2.max() > 1.3) and (cam_poisson2.min() == -1.0)
+
+
+@cp.testing.with_requires("skimage>=1.18")
+def test_clip_gaussian():
+    seed = 42
+    data = camerad  # 512x512 grayscale uint8
+    data_signed = img_as_float(data) * 2.0 - 1.0  # Same image, on range [-1, 1]
+
+    # Signed and unsigned, clipped
+    cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=True)
+    cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed,
+                              clip=True)
+    assert (cam_gauss.max() == 1.0) and (cam_gauss.min() == 0.0)
+    assert (cam_gauss2.max() == 1.0) and (cam_gauss2.min() == -1.0)
+
+    # Signed and unsigned, unclipped
+    cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=False)
+    cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed,
+                              clip=False)
+    assert (cam_gauss.max() > 1.22) and (cam_gauss.min() < -0.33)
+    assert (cam_gauss2.max() > 1.219) and (cam_gauss2.min() < -1.219)
+
+
+def test_clip_speckle():
+    seed = 42
+    data = camerad  # 512x512 grayscale uint8
+    data_signed = img_as_float(data) * 2.0 - 1.0  # Same image, on range [-1, 1]
+
+    # Signed and unsigned, clipped
+    cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=True)
+    cam_speckle_sig = random_noise(data_signed, mode='speckle', seed=seed,
+                                   clip=True)
+    assert (cam_speckle.max() == 1.0) and (cam_speckle.min() == 0.0)
+    assert (cam_speckle_sig.max() == 1.0) and (cam_speckle_sig.min() == -1.0)
+
+    # Signed and unsigned, unclipped
+    cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=False)
+    cam_speckle_sig = random_noise(data_signed, mode='speckle', seed=seed,
+                                   clip=False)
+    assert (cam_speckle.max() > 1.219) and (cam_speckle.min() == 0.0)
+    assert (cam_speckle_sig.max() > 1.219) and (cam_speckle_sig.min() < -1.219)
+
+
+def test_bad_mode():
+    data = cp.zeros((64, 64))
+    with pytest.raises(KeyError):
+        random_noise(data, 'perlin')
diff --git a/python/cucim/src/cucim/skimage/util/tests/test_shape.py b/python/cucim/src/cucim/skimage/util/tests/test_shape.py
new file mode 100644
index 000000000..b2a208d94
--- /dev/null
+++ b/python/cucim/src/cucim/skimage/util/tests/test_shape.py
@@ -0,0 +1,219 @@
+import cupy as cp
+import pytest
+
+from cucim.skimage._shared.testing import expected_warnings
+from cucim.skimage.util.shape import view_as_blocks, view_as_windows
+
+
+def test_view_as_blocks_block_not_a_tuple():
+    A = cp.arange(10)
+    with pytest.raises(TypeError):
+        view_as_blocks(A, [5])
+
+
+def test_view_as_blocks_negative_shape():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_blocks(A, (-2,))
+
+
+def test_view_as_blocks_block_too_large():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_blocks(A, (11,))
+
+
+def test_view_as_blocks_wrong_block_dimension():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_blocks(A, (2, 2))
+
+
+def test_view_as_blocks_1D_array_wrong_block_shape():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_blocks(A, (3,))
+
+
+def test_view_as_blocks_1D_array():
+    A = cp.arange(10)
+    B = view_as_blocks(A, (5,))
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B, cp.array([[0, 1, 2, 3, 4],
+                     [5, 6, 7, 8, 9]])
+    )
+    # fmt: on
+
+
+def test_view_as_blocks_2D_array():
+    A = cp.arange(4 * 4).reshape(4, 4)
+    B = view_as_blocks(A, (2, 2))
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B[0, 1], cp.array([[2, 3],
+                           [6, 7]])
+    )
+    # fmt: on
+    assert B[1, 0, 1, 1] == 13
+
+
+def test_view_as_blocks_3D_array():
+    A = cp.arange(4 * 4 * 6).reshape(4, 4, 6)
+    B = view_as_blocks(A, (1, 2, 2))
+    assert B.shape == (4, 2, 3, 1, 2, 2)
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B[2:, 0, 2], cp.array([[[[52, 53],
+                                 [58, 59]]],
+                               [[[76, 77],
+                                 [82, 83]]]])
+    )
+    # fmt: on
+
+
+def test_view_as_windows_input_not_array():
+    A = [1, 2, 3, 4, 5]
+    with pytest.raises(TypeError):
+        view_as_windows(A, (2,))
+
+
+def test_view_as_windows_wrong_window_dimension():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_windows(A, (2, 2))
+
+
+def test_view_as_windows_negative_window_length():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_windows(A, (-1,))
+
+
+def test_view_as_windows_window_too_large():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_windows(A, (11,))
+
+
+def test_view_as_windows_step_below_one():
+    A = cp.arange(10)
+    with pytest.raises(ValueError):
+        view_as_windows(A, (11,), step=0.9)
+
+
+def test_view_as_windows_1D():
+    A = cp.arange(10)
+    window_shape = (3,)
+    B = view_as_windows(A, window_shape)
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B, cp.array([[0, 1, 2],
+                     [1, 2, 3],
+                     [2, 3, 4],
+                     [3, 4, 5],
+                     [4, 5, 6],
+                     [5, 6, 7],
+                     [6, 7, 8],
+                     [7, 8, 9]])
+    )
+    # fmt: on
+
+
+def test_view_as_windows_2D():
+    A = cp.arange(5 * 4).reshape(5, 4)
+    window_shape = (4, 3)
+    B = view_as_windows(A, window_shape)
+    assert B.shape == (2, 2, 4, 3)
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B, cp.array([[[[0,  1,  2],
+                       [4,  5,  6],
+                       [8,  9, 10],
+                       [12, 13, 14]],
+                      [[1,  2,  3],
+                       [5,  6,  7],
+                       [9, 10, 11],
+                       [13, 14, 15]]],
+                     [[[4,  5,  6],
+                       [8,  9, 10],
+                       [12, 13, 14],
+                       [16, 17, 18]],
+                      [[5,  6,  7],
+                       [9, 10, 11],
+                       [13, 14, 15],
+                       [17, 18, 19]]]]))
+    # fmt: on
+
+
+def test_view_as_windows_with_skip():
+    A = cp.arange(20).reshape((5, 4))
+    B = view_as_windows(A, 2, step=2)
+    # fmt: off
+    cp.testing.assert_array_equal(
+        B, [[[[0, 1],
+              [4, 5]],
+             [[2, 3],
+              [6, 7]]],
+            [[[8, 9],
+              [12, 13]],
+             [[10, 11],
+              [14, 15]]]]
+    )
+    # fmt: on
+    C = view_as_windows(A, 2, step=4)
+    assert C.shape == (1, 1, 2, 2)
+
+
+def test_views_non_contiguous():
+    A = cp.arange(16).reshape((4, 4))
+    A = A[::2, :]
+
+    with expected_warnings(["Cannot provide views"]):
+        res_b = view_as_blocks(A, (2, 2))
+    res_w = view_as_windows(A, (2, 2))
+    print(res_b)
+    print(res_w)
+    # fmt: off
+    expected_b = [[[[0,  1],
+                    [8,  9]],
+                   [[2,  3],
+                    [10, 11]]]]
+
+    expected_w = [[[[ 0,  1],
+                    [ 8,  9]],
+                   [[ 1,  2],
+                    [ 9, 10]],
+                   [[ 2,  3],
+                    [10, 11]]]]
+    # fmt: on
+    cp.testing.assert_array_equal(res_b, expected_b)
+    cp.testing.assert_array_equal(res_w, expected_w)
+
+
+def test_view_as_windows_step_tuple():
+    A = cp.arange(24).reshape((6, 4))
+    B = view_as_windows(A, (3, 2), step=3)
+    assert B.shape == (2, 1, 3, 2)
+    assert B.size != A.size
+
+    C = view_as_windows(A, (3, 2), step=(3, 2))
+    assert C.shape == (2, 2, 3, 2)
+    assert C.size == A.size
+
+    # fmt: off
+    cp.testing.assert_array_equal(
+        C, [[[[0,  1],
+              [4,  5],
+              [8,  9]],
+             [[2,  3],
+              [6,  7],
+              [10, 11]]],
+            [[[12, 13],
+              [16, 17],
+              [20, 21]],
+             [[14, 15],
+              [18, 19],
+              [22, 23]]]]
+    )
+    # fmt: on
diff --git a/python/cucim/src/cucim/time.py b/python/cucim/src/cucim/time.py
new file mode 100644
index 000000000..54395da5a
--- /dev/null
+++ b/python/cucim/src/cucim/time.py
@@ -0,0 +1,4 @@
+from .skimage._vendored.time import repeat
+
+
+__all__ = ['repeat']
diff --git a/python/cucim/src/localtest.py b/python/cucim/src/localtest.py
new file mode 100644
index 000000000..324013205
--- /dev/null
+++ b/python/cucim/src/localtest.py
@@ -0,0 +1,155 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import concurrent.futures
+import json
+from contextlib import ContextDecorator
+from time import perf_counter
+
+from openslide import OpenSlide
+
+from cucim import CuImage
+
+img = CuImage("notebooks/0486052bb.tiff")
+# True if image data is loaded & available.
+print(img.is_loaded)
+# A device type.
+print(img.device)
+# The number of dimensions.
+print(img.ndim)
+# A string containing a list of dimensions being requested.
+print(img.dims)
+# A tuple of dimension sizes (in the order of `dims`).
+print(img.shape)
+# Returns size as a tuple for the given dimension order.
+print(img.size('XYC'))
+# The data type of the image.
+print(img.dtype)
+# A channel name list.
+print(img.channel_names)
+# Returns physical size in tuple.
+print(img.spacing())
+# Units for each spacing element (size is same with `ndim`).
+print(img.spacing_units())
+# Physical location of (0, 0, 0) (size is always 3).
+print(img.origin)
+# Direction cosines (size is always 3x3).
+print(img.direction)
+# Coordinate frame in which the direction cosines are measured.
+# Available Coordinate frame is not finalized yet.
+print(img.coord_sys)
+# Returns a set of associated image names.
+print(img.associated_images)
+# Returns a dict that includes resolution information.
+print(json.dumps(img.resolutions, indent=2))
+# A metadata object as `dict`
+print(json.dumps(img.metadata, indent=2))
+# A raw metadata string.
+print(img.raw_metadata)
+
+# a = np.asarray(img.read_region((10000, 10000), (1000, 1000), 0))
+# print(a.shape)
+b = img.read_region((10000, 10000), (1000, 1000), 0)
+print(b.metadata)
+# import PIL
+# from PIL import Image
+# b = Image.fromarray(a[:,:,:3])
+# b.save("output.jpg", "JPEG", quality=100)
+# import sys
+# sys.exit(1)
+
+
+class Timer(ContextDecorator):
+    def __init__(self, message):
+        self.message = message
+        self.end = None
+
+    def elapsed_time(self):
+        self.end = perf_counter()
+        return self.end - self.start
+
+    def __enter__(self):
+        self.start = perf_counter()
+        return self
+
+    def __exit__(self, exc_type, exc, exc_tb):
+        if not self.end:
+            self.elapsed_time()
+        print("{} : {}".format(self.message, self.end - self.start))
+
+
+num_threads = 1  # os.cpu_count()
+
+input_file = "notebooks/0486052bb.tiff"
+start_location = 0
+tile_size = 256
+
+
+def load_tile_openslide(slide, start_loc, tile_size):
+    _ = slide.read_region(start_loc, 0, [tile_size, tile_size])
+
+
+def load_tile_cucim(slide, start_loc, tile_size):
+    _ = slide.read_region(start_loc, [tile_size, tile_size], 0)
+
+
+openslide_tot_time = 0
+cucim_tot_time = 0
+for num_workers in range(1, num_threads + 1):
+    with OpenSlide(input_file) as slide:
+        width, height = slide.dimensions
+
+        count = 0
+        for h in range(start_location, height, tile_size):
+            for w in range(start_location, width, tile_size):
+                count += 1
+        start_loc_iter = ((w, h)
+                          for h in range(start_location, height, tile_size)
+                              for w in range(start_location, width, tile_size))
+        with Timer("  Thread elapsed time (OpenSlide)") as timer:
+            with concurrent.futures.ThreadPoolExecutor(
+                max_workers=num_workers
+            ) as executor:
+                executor.map(
+                    lambda start_loc: load_tile_openslide(
+                        slide, start_loc, tile_size),
+                    start_loc_iter,
+                )
+            openslide_time = timer.elapsed_time()
+            openslide_tot_time += openslide_time
+
+    cucim_time = 0
+    slide = CuImage(input_file)
+    start_loc_iter = ((w, h)
+                      for h in range(start_location, height, tile_size)
+                          for w in range(start_location, width, tile_size))
+    with Timer("  Thread elapsed time (cuCIM)") as timer:
+        with concurrent.futures.ThreadPoolExecutor(
+            max_workers=num_workers
+        ) as executor:
+            executor.map(
+                lambda start_loc: load_tile_cucim(slide, start_loc, tile_size),
+                start_loc_iter,
+            )
+        cucim_time = timer.elapsed_time()
+        cucim_tot_time += cucim_time
+    print("  Performance gain (OpenSlide/cuCIM): {}".format(
+        openslide_time / cucim_time))
+
+print("Total time (OpenSlide):", openslide_tot_time)
+print("Total time (cuCIM):", cucim_tot_time)
+print("Average performance gain (OpenSlide/cuCIM): {}".format(
+    openslide_tot_time / cucim_tot_time))
diff --git a/python/cucim/tests/test_cucim.py b/python/cucim/tests/test_cucim.py
new file mode 100644
index 000000000..a948f7aa4
--- /dev/null
+++ b/python/cucim/tests/test_cucim.py
@@ -0,0 +1,26 @@
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from click.testing import CliRunner
+
+from cucim.clara.cli import main
+
+
+def test_main():
+    runner = CliRunner()
+    result = runner.invoke(main, [])
+
+    # assert result.output == '()\n'
+    assert result.exit_code == 0
diff --git a/python/cucim/tox.ini b/python/cucim/tox.ini
new file mode 100644
index 000000000..522be886a
--- /dev/null
+++ b/python/cucim/tox.ini
@@ -0,0 +1,109 @@
+[testenv:bootstrap]
+deps =
+    jinja2
+    matrix
+    tox
+skip_install = true
+commands =
+    python ci/bootstrap.py --no-env
+passenv =
+    *
+; a generative tox configuration, see: https://tox.readthedocs.io/en/latest/config.html#generative-envlist
+
+[tox]
+envlist =
+    clean,
+    check,
+    docs,
+    docs-dev,
+    release,
+    {py35,py36,py37,py38,py39,pypy,pypy3},
+    report
+ignore_basepython_conflict = true
+
+[testenv]
+basepython =
+    pypy: {env:TOXPYTHON:pypy}
+    pypy3: {env:TOXPYTHON:pypy3}
+    py35: {env:TOXPYTHON:python3.5}
+    py36: {env:TOXPYTHON:python3.6}
+    py37: {env:TOXPYTHON:python3.7}
+    py38: {env:TOXPYTHON:python3.8}
+    py39: {env:TOXPYTHON:python3.9}
+    {bootstrap,clean,check,report,docs,docs-dev,release,codecov}: {env:TOXPYTHON:python3}
+setenv =
+    PYTHONPATH={toxinidir}/tests
+    PYTHONUNBUFFERED=yes
+passenv =
+    *
+usedevelop = false
+deps =
+    pytest
+    pytest-travis-fold
+    pytest-cov
+commands =
+    {posargs:pytest --cov --cov-report=term-missing -vv tests}
+
+[testenv:check]
+deps =
+    docutils
+    check-manifest
+    flake8
+    readme-renderer
+    pygments
+    isort
+    twine
+skip_install = true
+
+; https://packaging.python.org/guides/making-a-pypi-friendly-readme/#validating-restructuredtext-markup
+commands =
+    twine check dist/*.whl
+    check-manifest {toxinidir}
+    flake8
+    isort --verbose --check-only --diff --filter-files .
+
+[testenv:docs]
+; Installing from `sdist` package instead of `setup.py develop` (https://tox.readthedocs.io/en/latest/config.html#conf-usedevelop)
+usedevelop = false
+deps =
+    -r{toxinidir}/docs/requirements.txt
+commands =
+    sphinx-build -E -b doctest docs {posargs:-dist/docs}
+    sphinx-build -E -b html docs {posargs:-dist/docs}
+    sphinx-build -b linkcheck docs {posargs:-dist/docs}
+
+[testenv:docs-dev]
+; Installing from `sdist` package instead of `setup.py develop` (https://tox.readthedocs.io/en/latest/config.html#conf-usedevelop)
+usedevelop = false
+deps =
+    -r{toxinidir}/docs/requirements.txt
+commands =
+    ; https://pypi.org/project/sphinx-autobuild/
+    sphinx-autobuild {posargs:---host 0.0.0.0 --port 9999 docs dist/docs}
+
+[testenv:release]
+usedevelop = false
+allowlist_externals = /bin/bash
+commands =
+    /bin/bash -c "{posargs}"
+
+[testenv:codecov]
+deps =
+    codecov
+skip_install = true
+commands =
+    codecov []
+
+[testenv:report]
+deps =
+    coverage
+skip_install = true
+commands =
+    coverage report
+    coverage html
+
+[testenv:clean]
+commands = coverage erase
+skip_install = true
+deps =
+    coverage
diff --git a/python/cucim/versioneer.py b/python/cucim/versioneer.py
new file mode 100644
index 000000000..3aae392db
--- /dev/null
+++ b/python/cucim/versioneer.py
@@ -0,0 +1,1825 @@
+
+# Version: 0.18
+
+"""The Versioneer - like a rocketeer, but for versions.
+
+The Versioneer
+==============
+
+* like a rocketeer, but for versions!
+* https://github.com/warner/python-versioneer
+* Brian Warner
+* License: Public Domain
+* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, 3.5, 3.6, and pypy
+* [![Latest Version]
+(https://pypip.in/version/versioneer/badge.svg?style=flat)
+](https://pypi.python.org/pypi/versioneer/)
+* [![Build Status]
+(https://travis-ci.org/warner/python-versioneer.png?branch=master)
+](https://travis-ci.org/warner/python-versioneer)
+
+This is a tool for managing a recorded version number in distutils-based
+python projects. The goal is to remove the tedious and error-prone "update
+the embedded version string" step from your release process. Making a new
+release should be as easy as recording a new tag in your version-control
+system, and maybe making new tarballs.
+
+
+## Quick Install
+
+* `pip install versioneer` to somewhere to your $PATH
+* add a `[versioneer]` section to your setup.cfg (see below)
+* run `versioneer install` in your source tree, commit the results
+
+## Version Identifiers
+
+Source trees come from a variety of places:
+
+* a version-control system checkout (mostly used by developers)
+* a nightly tarball, produced by build automation
+* a snapshot tarball, produced by a web-based VCS browser, like github's
+  "tarball from tag" feature
+* a release tarball, produced by "setup.py sdist", distributed through PyPI
+
+Within each source tree, the version identifier (either a string or a number,
+this tool is format-agnostic) can come from a variety of places:
+
+* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
+  about recent "tags" and an absolute revision-id
+* the name of the directory into which the tarball was unpacked
+* an expanded VCS keyword ($Id$, etc)
+* a `_version.py` created by some earlier build step
+
+For released software, the version identifier is closely related to a VCS
+tag. Some projects use tag names that include more than just the version
+string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
+needs to strip the tag prefix to extract the version identifier. For
+unreleased software (between tags), the version identifier should provide
+enough information to help developers recreate the same tree, while also
+giving them an idea of roughly how old the tree is (after version 1.2, before
+version 1.3). Many VCS systems can report a description that captures this,
+for example `git describe --tags --dirty --always` reports things like
+"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
+0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
+uncommitted changes.
+
+The version identifier is used for multiple purposes:
+
+* to allow the module to self-identify its version: `myproject.__version__`
+* to choose a name and prefix for a 'setup.py sdist' tarball
+
+## Theory of Operation
+
+Versioneer works by adding a special `_version.py` file into your source
+tree, where your `__init__.py` can import it. This `_version.py` knows how to
+dynamically ask the VCS tool for version information at import time.
+
+`_version.py` also contains `$Revision$` markers, and the installation
+process marks `_version.py` to have this marker rewritten with a tag name
+during the `git archive` command. As a result, generated tarballs will
+contain enough information to get the proper version.
+
+To allow `setup.py` to compute a version too, a `versioneer.py` is added to
+the top level of your source tree, next to `setup.py` and the `setup.cfg`
+that configures it. This overrides several distutils/setuptools commands to
+compute the version when invoked, and changes `setup.py build` and `setup.py
+sdist` to replace `_version.py` with a small static file that contains just
+the generated version data.
+
+## Installation
+
+See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
+
+## Version-String Flavors
+
+Code which uses Versioneer can learn about its version string at runtime by
+importing `_version` from your main `__init__.py` file and running the
+`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
+import the top-level `versioneer.py` and run `get_versions()`.
+
+Both functions return a dictionary with different flavors of version
+information:
+
+* `['version']`: A condensed version string, rendered using the selected
+  style. This is the most commonly used value for the project's version
+  string. The default "pep440" style yields strings like `0.11`,
+  `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
+  below for alternative styles.
+
+* `['full-revisionid']`: detailed revision identifier. For Git, this is the
+  full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
+
+* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
+  commit date in ISO 8601 format. This will be None if the date is not
+  available.
+
+* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
+  this is only accurate if run in a VCS checkout, otherwise it is likely to
+  be False or None
+
+* `['error']`: if the version string could not be computed, this will be set
+  to a string describing the problem, otherwise it will be None. It may be
+  useful to throw an exception in setup.py if this is set, to avoid e.g.
+  creating tarballs with a version string of "unknown".
+
+Some variants are more useful than others. Including `full-revisionid` in a
+bug report should allow developers to reconstruct the exact code being tested
+(or indicate the presence of local changes that should be shared with the
+developers). `version` is suitable for display in an "about" box or a CLI
+`--version` output: it can be easily compared against release notes and lists
+of bugs fixed in various releases.
+
+The installer adds the following text to your `__init__.py` to place a basic
+version in `YOURPROJECT.__version__`:
+
+    from ._version import get_versions
+    __version__ = get_versions()['version']
+    del get_versions
+
+## Styles
+
+The setup.cfg `style=` configuration controls how the VCS information is
+rendered into a version string.
+
+The default style, "pep440", produces a PEP440-compliant string, equal to the
+un-prefixed tag name for actual releases, and containing an additional "local
+version" section with more detail for in-between builds. For Git, this is
+TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
+--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
+tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
+that this commit is two revisions ("+2") beyond the "0.11" tag. For released
+software (exactly equal to a known tag), the identifier will only contain the
+stripped tag, e.g. "0.11".
+
+Other styles are available. See [details.md](details.md) in the Versioneer
+source tree for descriptions.
+
+## Debugging
+
+Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
+to return a version of "0+unknown". To investigate the problem, run `setup.py
+version`, which will run the version-lookup code in a verbose mode, and will
+display the full contents of `get_versions()` (including the `error` string,
+which may help identify what went wrong).
+
+## Known Limitations
+
+Some situations are known to cause problems for Versioneer. This details the
+most significant ones. More can be found on Github
+[issues page](https://github.com/warner/python-versioneer/issues).
+
+### Subprojects
+
+Versioneer has limited support for source trees in which `setup.py` is not in
+the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
+two common reasons why `setup.py` might not be in the root:
+
+* Source trees which contain multiple subprojects, such as
+  [Buildbot](https://github.com/buildbot/buildbot), which contains both
+  "master" and "slave" subprojects, each with their own `setup.py`,
+  `setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
+  distributions (and upload multiple independently-installable tarballs).
+* Source trees whose main purpose is to contain a C library, but which also
+  provide bindings to Python (and perhaps other langauges) in subdirectories.
+
+Versioneer will look for `.git` in parent directories, and most operations
+should get the right version string. However `pip` and `setuptools` have bugs
+and implementation details which frequently cause `pip install .` from a
+subproject directory to fail to find a correct version string (so it usually
+defaults to `0+unknown`).
+
+`pip install --editable .` should work correctly. `setup.py install` might
+work too.
+
+Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
+some later version.
+
+[Bug #38](https://github.com/warner/python-versioneer/issues/38) is tracking
+this issue. The discussion in
+[PR #61](https://github.com/warner/python-versioneer/pull/61) describes the
+issue from the Versioneer side in more detail.
+[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
+[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
+pip to let Versioneer work correctly.
+
+Versioneer-0.16 and earlier only looked for a `.git` directory next to the
+`setup.cfg`, so subprojects were completely unsupported with those releases.
+
+### Editable installs with setuptools <= 18.5
+
+`setup.py develop` and `pip install --editable .` allow you to install a
+project into a virtualenv once, then continue editing the source code (and
+test) without re-installing after every change.
+
+"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
+convenient way to specify executable scripts that should be installed along
+with the python package.
+
+These both work as expected when using modern setuptools. When using
+setuptools-18.5 or earlier, however, certain operations will cause
+`pkg_resources.DistributionNotFound` errors when running the entrypoint
+script, which must be resolved by re-installing the package. This happens
+when the install happens with one version, then the egg_info data is
+regenerated while a different version is checked out. Many setup.py commands
+cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
+a different virtualenv), so this can be surprising.
+
+[Bug #83](https://github.com/warner/python-versioneer/issues/83) describes
+this one, but upgrading to a newer version of setuptools should probably
+resolve it.
+
+### Unicode version strings
+
+While Versioneer works (and is continually tested) with both Python 2 and
+Python 3, it is not entirely consistent with bytes-vs-unicode distinctions.
+Newer releases probably generate unicode version strings on py2. It's not
+clear that this is wrong, but it may be surprising for applications when then
+write these strings to a network connection or include them in bytes-oriented
+APIs like cryptographic checksums.
+
+[Bug #71](https://github.com/warner/python-versioneer/issues/71) investigates
+this question.
+
+
+## Updating Versioneer
+
+To upgrade your project to a new release of Versioneer, do the following:
+
+* install the new Versioneer (`pip install -U versioneer` or equivalent)
+* edit `setup.cfg`, if necessary, to include any new configuration settings
+  indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
+* re-run `versioneer install` in your source tree, to replace
+  `SRC/_version.py`
+* commit any changed files
+
+## Future Directions
+
+This tool is designed to make it easily extended to other version-control
+systems: all VCS-specific components are in separate directories like
+src/git/ . The top-level `versioneer.py` script is assembled from these
+components by running make-versioneer.py . In the future, make-versioneer.py
+will take a VCS name as an argument, and will construct a version of
+`versioneer.py` that is specific to the given VCS. It might also take the
+configuration arguments that are currently provided manually during
+installation by editing setup.py . Alternatively, it might go the other
+direction and include code from all supported VCS systems, reducing the
+number of intermediate scripts.
+
+
+## License
+
+To make Versioneer easier to embed, all its code is dedicated to the public
+domain. The `_version.py` that it creates is also in the public domain.
+Specifically, both are released under the Creative Commons "Public Domain
+Dedication" license (CC0-1.0), as described in
+https://creativecommons.org/publicdomain/zero/1.0/ .
+
+"""
+
+from __future__ import print_function
+
+try:
+    import configparser
+except ImportError:
+    import ConfigParser as configparser
+
+import errno
+import json
+import os
+import re
+import subprocess
+import sys
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_root():
+    """Get the project root directory.
+
+    We require that all commands are run from the project root, i.e. the
+    directory that contains setup.py, setup.cfg, and versioneer.py .
+    """
+    root = os.path.realpath(os.path.abspath(os.getcwd()))
+    setup_py = os.path.join(root, "setup.py")
+    versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        # allow 'python path/to/setup.py COMMAND'
+        root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
+        setup_py = os.path.join(root, "setup.py")
+        versioneer_py = os.path.join(root, "versioneer.py")
+    if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
+        err = ("Versioneer was unable to run the project root directory. "
+               "Versioneer requires setup.py to be executed from "
+               "its immediate directory (like 'python setup.py COMMAND'), "
+               "or in a way that lets it use sys.argv[0] to find the root "
+               "(like 'python path/to/setup.py COMMAND').")
+        raise VersioneerBadRootError(err)
+    try:
+        # Certain runtime workflows (setup.py install/develop in a setuptools
+        # tree) execute all dependencies in a single python process, so
+        # "versioneer" may be imported multiple times, and python's shared
+        # module-import table will cache the first one. So we can't use
+        # os.path.dirname(__file__), as that will find whichever
+        # versioneer.py was first imported, even in later projects.
+        me = os.path.realpath(os.path.abspath(__file__))
+        me_dir = os.path.normcase(os.path.splitext(me)[0])
+        vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
+        if me_dir != vsr_dir:
+            print("Warning: build in %s is using versioneer.py from %s"
+                  % (os.path.dirname(me), versioneer_py))
+    except NameError:
+        pass
+    return root
+
+
+def get_config_from_root(root):
+    """Read the project setup.cfg file to determine Versioneer config."""
+    # This might raise EnvironmentError (if setup.cfg is missing), or
+    # configparser.NoSectionError (if it lacks a [versioneer] section), or
+    # configparser.NoOptionError (if it lacks "VCS="). See the docstring at
+    # the top of versioneer.py for instructions on writing your setup.cfg .
+    setup_cfg = os.path.join(root, "setup.cfg")
+    parser = configparser.SafeConfigParser()
+    with open(setup_cfg, "r") as f:
+        parser.readfp(f)
+    VCS = parser.get("versioneer", "VCS")  # mandatory
+
+    def get(parser, name):
+        if parser.has_option("versioneer", name):
+            return parser.get("versioneer", name)
+        return None
+    cfg = VersioneerConfig()
+    cfg.VCS = VCS
+    cfg.style = get(parser, "style") or ""
+    cfg.versionfile_source = get(parser, "versionfile_source")
+    cfg.versionfile_build = get(parser, "versionfile_build")
+    cfg.tag_prefix = get(parser, "tag_prefix")
+    if cfg.tag_prefix in ("''", '""'):
+        cfg.tag_prefix = ""
+    cfg.parentdir_prefix = get(parser, "parentdir_prefix")
+    cfg.verbose = get(parser, "verbose")
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+# these dictionaries contain VCS-specific tools
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Decorator to mark a method as the handler for a particular VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            dispcmd = str([c] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
+                                 stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %s" % dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %s" % (commands,))
+        return None, None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %s (error)" % dispcmd)
+            print("stdout was %s" % stdout)
+        return None, p.returncode
+    return stdout, p.returncode
+
+
+LONG_VERSION_PY['git'] = '''
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain. Generated by
+# versioneer-0.18 (https://github.com/warner/python-versioneer)
+
+"""Git implementation of _version.py."""
+
+import errno
+import os
+import re
+import subprocess
+import sys
+
+
+def get_keywords():
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
+    git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
+    git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+
+def get_config():
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "%(STYLE)s"
+    cfg.tag_prefix = "%(TAG_PREFIX)s"
+    cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
+    cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY = {}
+HANDLERS = {}
+
+
+def register_vcs_handler(vcs, method):  # decorator
+    """Decorator to mark a method as the handler for a particular VCS."""
+    def decorate(f):
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+    return decorate
+
+
+def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
+                env=None):
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    p = None
+    for c in commands:
+        try:
+            dispcmd = str([c] + args)
+            # remember shell=False, so use git.cmd on windows, not just git
+            p = subprocess.Popen([c] + args, cwd=cwd, env=env,
+                                 stdout=subprocess.PIPE,
+                                 stderr=(subprocess.PIPE if hide_stderr
+                                         else None))
+            break
+        except EnvironmentError:
+            e = sys.exc_info()[1]
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print("unable to run %%s" %% dispcmd)
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print("unable to find command, tried %%s" %% (commands,))
+        return None, None
+    stdout = p.communicate()[0].strip()
+    if sys.version_info[0] >= 3:
+        stdout = stdout.decode()
+    if p.returncode != 0:
+        if verbose:
+            print("unable to run %%s (error)" %% dispcmd)
+            print("stdout was %%s" %% stdout)
+        return None, p.returncode
+    return stdout, p.returncode
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for i in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        else:
+            rootdirs.append(root)
+            root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %%s but none started with prefix %%s" %%
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+            if line.strip().startswith("git_date ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["date"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if not keywords:
+        raise NotThisMethod("no keywords at all, weird")
+    date = keywords.get("date")
+    if date is not None:
+        # git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %%d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%%s', no digits" %% ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %%s" %% ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %%s" %% r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                          hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %%s not under git control" %% root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
+                                          "--always", "--long",
+                                          "--match", "%%s*" %% tag_prefix],
+                                   cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%%s'"
+                               %% describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%%s' doesn't start with prefix '%%s'"
+                print(fmt %% (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
+                               %% (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                    cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = run_command(GITS, ["show", "-s", "--format=%%ci", "HEAD"],
+                       cwd=root)[0].strip()
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post.dev%%d" %% pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post.dev%%d" %% pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%%s" %% pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%%s" %% pieces["short"]
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Eexceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%%d" %% pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%%d" %% pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%%s'" %% style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+def get_versions():
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
+                                          verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for i in cfg.versionfile_source.split('/'):
+            root = os.path.dirname(root)
+    except NameError:
+        return {"version": "0+unknown", "full-revisionid": None,
+                "dirty": None,
+                "error": "unable to find root of source tree",
+                "date": None}
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to compute version", "date": None}
+'''
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs):
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords = {}
+    try:
+        f = open(versionfile_abs, "r")
+        for line in f.readlines():
+            if line.strip().startswith("git_refnames ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["refnames"] = mo.group(1)
+            if line.strip().startswith("git_full ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["full"] = mo.group(1)
+            if line.strip().startswith("git_date ="):
+                mo = re.search(r'=\s*"(.*)"', line)
+                if mo:
+                    keywords["date"] = mo.group(1)
+        f.close()
+    except EnvironmentError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(keywords, tag_prefix, verbose):
+    """Get version information from git keywords."""
+    if not keywords:
+        raise NotThisMethod("no keywords at all, weird")
+    date = keywords.get("date")
+    if date is not None:
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = set([r.strip() for r in refnames.strip("()").split(",")])
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)])
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = set([r for r in refs if re.search(r'\d', r)])
+        if verbose:
+            print("discarding '%s', no digits" % ",".join(refs - tags))
+    if verbose:
+        print("likely tags: %s" % ",".join(sorted(tags)))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix):]
+            if verbose:
+                print("picking %s" % r)
+            return {"version": r,
+                    "full-revisionid": keywords["full"].strip(),
+                    "dirty": False, "error": None,
+                    "date": date}
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {"version": "0+unknown",
+            "full-revisionid": keywords["full"].strip(),
+            "dirty": False, "error": "no suitable tags", "date": None}
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    out, rc = run_command(GITS, ["rev-parse", "--git-dir"], cwd=root,
+                          hide_stderr=True)
+    if rc != 0:
+        if verbose:
+            print("Directory %s not under git control" % root)
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = run_command(GITS, ["describe", "--tags", "--dirty",
+                                          "--always", "--long",
+                                          "--match", "%s*" % tag_prefix],
+                                   cwd=root)
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = run_command(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[:git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
+        if not mo:
+            # unparseable. Maybe git-describe is misbehaving?
+            pieces["error"] = ("unable to parse git-describe output: '%s'"
+                               % describe_out)
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
+                               % (full_tag, tag_prefix))
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix):]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        count_out, rc = run_command(GITS, ["rev-list", "HEAD", "--count"],
+                                    cwd=root)
+        pieces["distance"] = int(count_out)  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = run_command(GITS, ["show", "-s", "--format=%ci", "HEAD"],
+                       cwd=root)[0].strip()
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def do_vcs_install(manifest_in, versionfile_source, ipy):
+    """Git-specific installation logic for Versioneer.
+
+    For Git, this means creating/changing .gitattributes to mark _version.py
+    for export-subst keyword substitution.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+    files = [manifest_in, versionfile_source]
+    if ipy:
+        files.append(ipy)
+    try:
+        me = __file__
+        if me.endswith(".pyc") or me.endswith(".pyo"):
+            me = os.path.splitext(me)[0] + ".py"
+        versioneer_file = os.path.relpath(me)
+    except NameError:
+        versioneer_file = "versioneer.py"
+    files.append(versioneer_file)
+    present = False
+    try:
+        f = open(".gitattributes", "r")
+        for line in f.readlines():
+            if line.strip().startswith(versionfile_source):
+                if "export-subst" in line.strip().split()[1:]:
+                    present = True
+        f.close()
+    except EnvironmentError:
+        pass
+    if not present:
+        f = open(".gitattributes", "a+")
+        f.write("%s export-subst\n" % versionfile_source)
+        f.close()
+        files.append(".gitattributes")
+    run_command(GITS, ["add", "--"] + files)
+
+
+def versions_from_parentdir(parentdir_prefix, root, verbose):
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for i in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {"version": dirname[len(parentdir_prefix):],
+                    "full-revisionid": None,
+                    "dirty": False, "error": None, "date": None}
+        else:
+            rootdirs.append(root)
+            root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print("Tried directories %s but none started with prefix %s" %
+              (str(rootdirs), parentdir_prefix))
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+SHORT_VERSION_PY = """
+# This file was generated by 'versioneer.py' (0.18) from
+# revision-control system data, or from the parent directory name of an
+# unpacked source archive. Distribution tarballs contain a pre-generated copy
+# of this file.
+
+import json
+
+version_json = '''
+%s
+'''  # END VERSION_JSON
+
+
+def get_versions():
+    return json.loads(version_json)
+"""
+
+
+def versions_from_file(filename):
+    """Try to determine the version from _version.py if present."""
+    try:
+        with open(filename) as f:
+            contents = f.read()
+    except EnvironmentError:
+        raise NotThisMethod("unable to read _version.py")
+    mo = re.search(r"version_json = '''\n(.*)'''  # END VERSION_JSON",
+                   contents, re.M | re.S)
+    if not mo:
+        mo = re.search(r"version_json = '''\r\n(.*)'''  # END VERSION_JSON",
+                       contents, re.M | re.S)
+    if not mo:
+        raise NotThisMethod("no version_json in _version.py")
+    return json.loads(mo.group(1))
+
+
+def write_to_version_file(filename, versions):
+    """Write the given version number to the given _version.py file."""
+    os.unlink(filename)
+    contents = json.dumps(versions, sort_keys=True,
+                          indent=1, separators=(",", ": "))
+    with open(filename, "w") as f:
+        f.write(SHORT_VERSION_PY % contents)
+
+    print("set %s to '%s'" % (filename, versions["version"]))
+
+
+def plus_or_dot(pieces):
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces):
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"],
+                                          pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_pre(pieces):
+    """TAG[.post.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += ".post.dev%d" % pieces["distance"]
+    else:
+        # exception #1
+        rendered = "0.post.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces):
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g%s" % pieces["short"]
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g%s" % pieces["short"]
+    return rendered
+
+
+def render_pep440_old(pieces):
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Eexceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces):
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces):
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces, style):
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {"version": "unknown",
+                "full-revisionid": pieces.get("long"),
+                "dirty": None,
+                "error": pieces["error"],
+                "date": None}
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError("unknown style '%s'" % style)
+
+    return {"version": rendered, "full-revisionid": pieces["long"],
+            "dirty": pieces["dirty"], "error": None,
+            "date": pieces.get("date")}
+
+
+class VersioneerBadRootError(Exception):
+    """The project root directory is unknown or missing key files."""
+
+
+def get_versions(verbose=False):
+    """Get the project version from whatever source is available.
+
+    Returns dict with two keys: 'version' and 'full'.
+    """
+    if "versioneer" in sys.modules:
+        # see the discussion in cmdclass.py:get_cmdclass()
+        del sys.modules["versioneer"]
+
+    root = get_root()
+    cfg = get_config_from_root(root)
+
+    assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
+    handlers = HANDLERS.get(cfg.VCS)
+    assert handlers, "unrecognized VCS '%s'" % cfg.VCS
+    verbose = verbose or cfg.verbose
+    assert cfg.versionfile_source is not None, \
+        "please set versioneer.versionfile_source"
+    assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
+
+    versionfile_abs = os.path.join(root, cfg.versionfile_source)
+
+    # extract version from first of: _version.py, VCS command (e.g. 'git
+    # describe'), parentdir. This is meant to work for developers using a
+    # source checkout, for users of a tarball created by 'setup.py sdist',
+    # and for users of a tarball/zipball created by 'git archive' or github's
+    # download-from-tag feature or the equivalent in other VCSes.
+
+    get_keywords_f = handlers.get("get_keywords")
+    from_keywords_f = handlers.get("keywords")
+    if get_keywords_f and from_keywords_f:
+        try:
+            keywords = get_keywords_f(versionfile_abs)
+            ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
+            if verbose:
+                print("got version from expanded keyword %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        ver = versions_from_file(versionfile_abs)
+        if verbose:
+            print("got version from file %s %s" % (versionfile_abs, ver))
+        return ver
+    except NotThisMethod:
+        pass
+
+    from_vcs_f = handlers.get("pieces_from_vcs")
+    if from_vcs_f:
+        try:
+            pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
+            ver = render(pieces, cfg.style)
+            if verbose:
+                print("got version from VCS %s" % ver)
+            return ver
+        except NotThisMethod:
+            pass
+
+    try:
+        if cfg.parentdir_prefix:
+            ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+            if verbose:
+                print("got version from parentdir %s" % ver)
+            return ver
+    except NotThisMethod:
+        pass
+
+    if verbose:
+        print("unable to compute version")
+
+    return {"version": "0+unknown", "full-revisionid": None,
+            "dirty": None, "error": "unable to compute version",
+            "date": None}
+
+
+def get_version():
+    """Get the short version string for this project."""
+    return get_versions()["version"]
+
+
+def get_cmdclass():
+    """Get the custom setuptools/distutils subclasses used by Versioneer."""
+    if "versioneer" in sys.modules:
+        del sys.modules["versioneer"]
+        # this fixes the "python setup.py develop" case (also 'install' and
+        # 'easy_install .'), in which subdependencies of the main project are
+        # built (using setup.py bdist_egg) in the same python process. Assume
+        # a main project A and a dependency B, which use different versions
+        # of Versioneer. A's setup.py imports A's Versioneer, leaving it in
+        # sys.modules by the time B's setup.py is executed, causing B to run
+        # with the wrong versioneer. Setuptools wraps the sub-dep builds in a
+        # sandbox that restores sys.modules to it's pre-build state, so the
+        # parent is protected against the child's "import versioneer". By
+        # removing ourselves from sys.modules here, before the child build
+        # happens, we protect the child from the parent's versioneer too.
+        # Also see https://github.com/warner/python-versioneer/issues/52
+
+    cmds = {}
+
+    # we add "version" to both distutils and setuptools
+    from distutils.core import Command
+
+    class cmd_version(Command):
+        description = "report generated version string"
+        user_options = []
+        boolean_options = []
+
+        def initialize_options(self):
+            pass
+
+        def finalize_options(self):
+            pass
+
+        def run(self):
+            vers = get_versions(verbose=True)
+            print("Version: %s" % vers["version"])
+            print(" full-revisionid: %s" % vers.get("full-revisionid"))
+            print(" dirty: %s" % vers.get("dirty"))
+            print(" date: %s" % vers.get("date"))
+            if vers["error"]:
+                print(" error: %s" % vers["error"])
+    cmds["version"] = cmd_version
+
+    # we override "build_py" in both distutils and setuptools
+    #
+    # most invocation pathways end up running build_py:
+    #  distutils/build -> build_py
+    #  distutils/install -> distutils/build ->..
+    #  setuptools/bdist_wheel -> distutils/install ->..
+    #  setuptools/bdist_egg -> distutils/install_lib -> build_py
+    #  setuptools/install -> bdist_egg ->..
+    #  setuptools/develop -> ?
+    #  pip install:
+    #   copies source tree to a tempdir before running egg_info/etc
+    #   if .git isn't copied too, 'git describe' will fail
+    #   then does setup.py bdist_wheel, or sometimes setup.py install
+    #  setup.py egg_info -> ?
+
+    # we override different "build_py" commands for both environments
+    if "setuptools" in sys.modules:
+        from setuptools.command.build_py import build_py as _build_py
+    else:
+        from distutils.command.build_py import build_py as _build_py
+
+    class cmd_build_py(_build_py):
+        def run(self):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            versions = get_versions()
+            _build_py.run(self)
+            # now locate _version.py in the new build/ directory and replace
+            # it with an updated value
+            if cfg.versionfile_build:
+                target_versionfile = os.path.join(self.build_lib,
+                                                  cfg.versionfile_build)
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+    cmds["build_py"] = cmd_build_py
+
+    if "cx_Freeze" in sys.modules:  # cx_freeze enabled?
+        from cx_Freeze.dist import build_exe as _build_exe
+
+        # nczeczulin reports that py2exe won't like the pep440-style string
+        # as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
+        # setup(console=[{
+        #   "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
+        #   "product_version": versioneer.get_version(),
+        #   ...
+
+        class cmd_build_exe(_build_exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _build_exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["build_exe"] = cmd_build_exe
+        del cmds["build_py"]
+
+    if 'py2exe' in sys.modules:  # py2exe enabled?
+        try:
+            from py2exe.distutils_buildexe import py2exe as _py2exe  # py3
+        except ImportError:
+            from py2exe.build_exe import py2exe as _py2exe  # py2
+
+        class cmd_py2exe(_py2exe):
+            def run(self):
+                root = get_root()
+                cfg = get_config_from_root(root)
+                versions = get_versions()
+                target_versionfile = cfg.versionfile_source
+                print("UPDATING %s" % target_versionfile)
+                write_to_version_file(target_versionfile, versions)
+
+                _py2exe.run(self)
+                os.unlink(target_versionfile)
+                with open(cfg.versionfile_source, "w") as f:
+                    LONG = LONG_VERSION_PY[cfg.VCS]
+                    f.write(LONG %
+                            {"DOLLAR": "$",
+                             "STYLE": cfg.style,
+                             "TAG_PREFIX": cfg.tag_prefix,
+                             "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                             "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                             })
+        cmds["py2exe"] = cmd_py2exe
+
+    # we override different "sdist" commands for both environments
+    if "setuptools" in sys.modules:
+        from setuptools.command.sdist import sdist as _sdist
+    else:
+        from distutils.command.sdist import sdist as _sdist
+
+    class cmd_sdist(_sdist):
+        def run(self):
+            versions = get_versions()
+            self._versioneer_generated_versions = versions
+            # unless we update this, the command will keep using the old
+            # version
+            self.distribution.metadata.version = versions["version"]
+            return _sdist.run(self)
+
+        def make_release_tree(self, base_dir, files):
+            root = get_root()
+            cfg = get_config_from_root(root)
+            _sdist.make_release_tree(self, base_dir, files)
+            # now locate _version.py in the new base_dir directory
+            # (remembering that it may be a hardlink) and replace it with an
+            # updated value
+            target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
+            print("UPDATING %s" % target_versionfile)
+            write_to_version_file(target_versionfile,
+                                  self._versioneer_generated_versions)
+    cmds["sdist"] = cmd_sdist
+
+    return cmds
+
+
+CONFIG_ERROR = """
+setup.cfg is missing the necessary Versioneer configuration. You need
+a section like:
+
+ [versioneer]
+ VCS = git
+ style = pep440
+ versionfile_source = src/myproject/_version.py
+ versionfile_build = myproject/_version.py
+ tag_prefix =
+ parentdir_prefix = myproject-
+
+You will also need to edit your setup.py to use the results:
+
+ import versioneer
+ setup(version=versioneer.get_version(),
+       cmdclass=versioneer.get_cmdclass(), ...)
+
+Please read the docstring in ./versioneer.py for configuration instructions,
+edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
+"""
+
+SAMPLE_CONFIG = """
+# See the docstring in versioneer.py for instructions. Note that you must
+# re-run 'versioneer.py setup' after changing this section, and commit the
+# resulting files.
+
+[versioneer]
+#VCS = git
+#style = pep440
+#versionfile_source =
+#versionfile_build =
+#tag_prefix =
+#parentdir_prefix =
+
+"""
+
+INIT_PY_SNIPPET = """
+from ._version import get_versions
+__version__ = get_versions()['version']
+del get_versions
+"""
+
+
+def do_setup():
+    """Main VCS-independent setup function for installing Versioneer."""
+    root = get_root()
+    try:
+        cfg = get_config_from_root(root)
+    except (EnvironmentError, configparser.NoSectionError,
+            configparser.NoOptionError) as e:
+        if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
+            print("Adding sample versioneer config to setup.cfg",
+                  file=sys.stderr)
+            with open(os.path.join(root, "setup.cfg"), "a") as f:
+                f.write(SAMPLE_CONFIG)
+        print(CONFIG_ERROR, file=sys.stderr)
+        return 1
+
+    print(" creating %s" % cfg.versionfile_source)
+    with open(cfg.versionfile_source, "w") as f:
+        LONG = LONG_VERSION_PY[cfg.VCS]
+        f.write(LONG % {"DOLLAR": "$",
+                        "STYLE": cfg.style,
+                        "TAG_PREFIX": cfg.tag_prefix,
+                        "PARENTDIR_PREFIX": cfg.parentdir_prefix,
+                        "VERSIONFILE_SOURCE": cfg.versionfile_source,
+                        })
+
+    ipy = os.path.join(os.path.dirname(cfg.versionfile_source),
+                       "__init__.py")
+    if os.path.exists(ipy):
+        try:
+            with open(ipy, "r") as f:
+                old = f.read()
+        except EnvironmentError:
+            old = ""
+        if INIT_PY_SNIPPET not in old:
+            print(" appending to %s" % ipy)
+            with open(ipy, "a") as f:
+                f.write(INIT_PY_SNIPPET)
+        else:
+            print(" %s unmodified" % ipy)
+    else:
+        print(" %s doesn't exist, ok" % ipy)
+        ipy = None
+
+    # Make sure both the top-level "versioneer.py" and versionfile_source
+    # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
+    # they'll be copied into source distributions. Pip won't be able to
+    # install the package without this.
+    manifest_in = os.path.join(root, "MANIFEST.in")
+    simple_includes = set()
+    try:
+        with open(manifest_in, "r") as f:
+            for line in f:
+                if line.startswith("include "):
+                    for include in line.split()[1:]:
+                        simple_includes.add(include)
+    except EnvironmentError:
+        pass
+    # That doesn't cover everything MANIFEST.in can do
+    # (http://docs.python.org/2/distutils/sourcedist.html#commands), so
+    # it might give some false negatives. Appending redundant 'include'
+    # lines is safe, though.
+    if "versioneer.py" not in simple_includes:
+        print(" appending 'versioneer.py' to MANIFEST.in")
+        with open(manifest_in, "a") as f:
+            f.write("include versioneer.py\n")
+    else:
+        print(" 'versioneer.py' already in MANIFEST.in")
+    if cfg.versionfile_source not in simple_includes:
+        print(" appending versionfile_source ('%s') to MANIFEST.in" %
+              cfg.versionfile_source)
+        with open(manifest_in, "a") as f:
+            f.write("include %s\n" % cfg.versionfile_source)
+    else:
+        print(" versionfile_source already in MANIFEST.in")
+
+    # Make VCS-specific changes. For git, this means creating/changing
+    # .gitattributes to mark _version.py for export-subst keyword
+    # substitution.
+    do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
+    return 0
+
+
+def scan_setup_py():
+    """Validate the contents of setup.py against Versioneer's expectations."""
+    found = set()
+    setters = False
+    errors = 0
+    with open("setup.py", "r") as f:
+        for line in f.readlines():
+            if "import versioneer" in line:
+                found.add("import")
+            if "versioneer.get_cmdclass()" in line:
+                found.add("cmdclass")
+            if "versioneer.get_version()" in line:
+                found.add("get_version")
+            if "versioneer.VCS" in line:
+                setters = True
+            if "versioneer.versionfile_source" in line:
+                setters = True
+    if len(found) != 3:
+        print("")
+        print("Your setup.py appears to be missing some important items")
+        print("(but I might be wrong). Please make sure it has something")
+        print("roughly like the following:")
+        print("")
+        print(" import versioneer")
+        print(" setup( version=versioneer.get_version(),")
+        print("        cmdclass=versioneer.get_cmdclass(),  ...)")
+        print("")
+        errors += 1
+    if setters:
+        print("You should remove lines like 'versioneer.VCS = ' and")
+        print("'versioneer.versionfile_source = ' . This configuration")
+        print("now lives in setup.cfg, and should be removed from setup.py")
+        print("")
+        errors += 1
+    return errors
+
+
+if __name__ == "__main__":
+    cmd = sys.argv[1]
+    if cmd == "setup":
+        errors = do_setup()
+        errors += scan_setup_py()
+        if errors:
+            sys.exit(1)
diff --git a/python/pybind11/cucim_py.cpp b/python/pybind11/cucim_py.cpp
new file mode 100644
index 000000000..d336bc5e7
--- /dev/null
+++ b/python/pybind11/cucim_py.cpp
@@ -0,0 +1,334 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cucim_py.h"
+#include "cucim_pydoc.h"
+#include "io/init.h"
+#include "filesystem/init.h"
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/numpy.h>
+
+#include <fmt/format.h>
+#include <fmt/ranges.h>
+
+using namespace pybind11::literals;
+namespace py = pybind11;
+
+namespace cucim
+{
+
+static const std::unordered_map<uint8_t, const char*> g_dldata_typecode{
+    { kDLInt, "DLInt" },
+    { kDLUInt, "DLUInt" },
+    { kDLFloat, "DLFloat" },
+    { kDLBfloat, "DLBfloat" },
+};
+
+PYBIND11_MODULE(_cucim, m)
+{
+
+    // clang-format off
+#ifdef CUCIM_VERSION
+#  define XSTR(x) STR(x)
+#  define STR(x) #x
+    // Set version
+    m.attr("__version__") = XSTR(CUCIM_VERSION);
+#endif
+    // clang-format on
+    // Get/set plugin root path
+    m.def("_get_plugin_root", &get_plugin_root);
+    m.def("_set_plugin_root", &set_plugin_root);
+
+    // Submodule: io
+    auto m_io = m.def_submodule("io");
+    io::init_io(m_io);
+
+    // Submodule: filesystem
+    auto m_fs = m.def_submodule("filesystem");
+    filesystem::init_filesystem(m_fs);
+
+    // Data structures
+    py::enum_<DLDataTypeCode>(m, "DLDataTypeCode") //
+        .value("DLInt", kDLInt) //
+        .value("DLUInt", kDLUInt) //
+        .value("DLFloat", kDLFloat) //
+        .value("DLBfloat", kDLBfloat);
+
+    py::class_<DLDataType>(m, "DLDataType") //
+        .def(py::init([](DLDataTypeCode code, uint8_t bits, uint16_t lanes) {
+                 auto ctr = std::make_unique<DLDataType>();
+                 ctr->code = static_cast<uint8_t>(code);
+                 ctr->bits = bits;
+                 ctr->lanes = lanes;
+                 return ctr;
+             }),
+             doc::DLDataType::doc_DLDataType, py::call_guard<py::gil_scoped_release>())
+        .def_readonly("code", &DLDataType::code, doc::DLDataType::doc_code, py::call_guard<py::gil_scoped_release>()) //
+        .def_readonly("bits", &DLDataType::bits, doc::DLDataType::doc_bits, py::call_guard<py::gil_scoped_release>()) //
+        .def_readonly("lanes", &DLDataType::lanes, doc::DLDataType::doc_lanes, py::call_guard<py::gil_scoped_release>()) //
+        .def(
+            "__repr__",
+            [](const DLDataType& dtype) {
+                return fmt::format("<cucim.DLDataType code:{}({}) bits:{} lanes:{}>", g_dldata_typecode.at(dtype.code),
+                                   dtype.code, dtype.bits, dtype.lanes);
+            },
+            py::call_guard<py::gil_scoped_release>());
+
+    py::class_<CuImage, std::shared_ptr<CuImage>>(m, "CuImage") //
+        .def(py::init<const std::string&>(), doc::CuImage::doc_CuImage, py::call_guard<py::gil_scoped_release>(), //
+             py::arg("path")) //
+        .def_property("path", &CuImage::path, nullptr, doc::CuImage::doc_path, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("is_loaded", &CuImage::is_loaded, nullptr, doc::CuImage::doc_is_loaded,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def_property(
+            "device", &CuImage::device, nullptr, doc::CuImage::doc_device, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("raw_metadata", &CuImage::raw_metadata, nullptr, doc::CuImage::doc_raw_metadata,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def_property(
+            "metadata", &py_metadata, nullptr, doc::CuImage::doc_metadata, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("ndim", &CuImage::ndim, nullptr, doc::CuImage::doc_ndim, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("dims", &CuImage::dims, nullptr, doc::CuImage::doc_dims, py::call_guard<py::gil_scoped_release>()) //
+        .def_property(
+            "shape", &CuImage::shape, nullptr, doc::CuImage::doc_shape, py::call_guard<py::gil_scoped_release>()) //
+        .def("size", &CuImage::size, doc::CuImage::doc_size, py::call_guard<py::gil_scoped_release>(), //
+             py::arg("dim_order") = "" //
+             ) //
+        .def_property(
+            "dtype", &CuImage::dtype, nullptr, doc::CuImage::doc_dtype, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("channel_names", &CuImage::channel_names, nullptr, doc::CuImage::doc_channel_names,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def("spacing", &CuImage::spacing, doc::CuImage::doc_spacing, py::call_guard<py::gil_scoped_release>(), //
+             py::arg("dim_order") = "" //
+             ) //
+        .def("spacing_units", &CuImage::spacing_units, doc::CuImage::doc_spacing_units,
+             py::call_guard<py::gil_scoped_release>(), //
+             py::arg("dim_order") = "" //
+             ) //
+        .def_property(
+            "origin", &CuImage::origin, nullptr, doc::CuImage::doc_origin, py::call_guard<py::gil_scoped_release>()) //
+        .def_property("direction", &CuImage::direction, nullptr, doc::CuImage::doc_direction,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def_property("coord_sys", &CuImage::coord_sys, nullptr, doc::CuImage::doc_coord_sys,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def_property("resolutions", &py_resolutions, nullptr, doc::CuImage::doc_resolutions,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def("read_region", &py_read_region, doc::CuImage::doc_read_region, py::call_guard<py::gil_scoped_release>(), //
+             py::arg("location") = py::list{}, //
+             py::arg("size") = py::list{}, //
+             py::arg("level") = 0, //
+             py::arg("device") = io::Device(), //
+             py::arg("buf") = py::none(), //
+             py::arg("shm_name") = "") //
+        .def_property("associated_images", &CuImage::associated_images, nullptr, doc::CuImage::doc_associated_images,
+                      py::call_guard<py::gil_scoped_release>()) //
+        .def("associated_image", &CuImage::associated_image, doc::CuImage::doc_associated_image,
+             py::call_guard<py::gil_scoped_release>(), //
+             py::arg("name") = "") //
+        .def("save", &CuImage::save, doc::CuImage::doc_save, py::call_guard<py::gil_scoped_release>()) //
+        .def("__bool__", &CuImage::operator bool, py::call_guard<py::gil_scoped_release>()) //
+        .def(
+            "__repr__", //
+            [](const CuImage& cuimg) { //
+                return fmt::format("<cucim.CuImage path:{}>", cuimg.path());
+            },
+            py::call_guard<py::gil_scoped_release>())
+        .def_property("__array_interface__", &get_array_interface, nullptr, doc::CuImage::doc_get_array_interface,
+                      py::call_guard<py::gil_scoped_release>());
+
+    // We can use `"cpu"` instead of `Device("cpu")`
+    py::implicitly_convertible<const char*, io::Device>();
+}
+
+std::string get_plugin_root()
+{
+    return CuImage::get_framework()->get_plugin_root();
+}
+
+void set_plugin_root(std::string path)
+{
+    CuImage::get_framework()->set_plugin_root(path.c_str());
+}
+
+template <typename PT, typename T>
+pybind11::tuple vector2pytuple(const std::vector<T>& vec)
+{
+    py::tuple result(vec.size());
+    std::vector<pybind11::object> args;
+    args.reserve(vec.size());
+    for (auto& arg_value : vec)
+    {
+        args.emplace_back(pybind11::reinterpret_steal<pybind11::object>(pybind11::detail::make_caster<PT>::cast(
+            std::forward<PT>(arg_value), pybind11::return_value_policy::automatic_reference, nullptr)));
+    }
+    int counter = 0;
+    for (auto& arg_value : args)
+    {
+        PyTuple_SET_ITEM(result.ptr(), counter++, arg_value.release().ptr());
+    }
+    return result;
+}
+
+json py_metadata(const CuImage& cuimg)
+{
+    auto metadata = cuimg.metadata();
+    auto json_obj = json::parse(metadata.empty() ? "{}" : metadata);
+
+    // Append basic metadata for the image
+    auto item_iter = json_obj.emplace("cucim", json::object());
+    json& cucim_metadata = *(item_iter.first);
+
+    cucim_metadata.emplace("path", cuimg.path());
+    cucim_metadata.emplace("ndim", cuimg.ndim());
+    cucim_metadata.emplace("dims", cuimg.dims());
+    cucim_metadata.emplace("shape", cuimg.shape());
+    {
+        const auto& dtype = cuimg.dtype();
+        cucim_metadata.emplace(
+            "dtype", json::object({ { "code", dtype.code }, { "bits", dtype.bits }, { "lanes", dtype.lanes } }));
+    }
+    cucim_metadata.emplace("channel_names", cuimg.channel_names());
+    cucim_metadata.emplace("spacing", cuimg.spacing());
+    cucim_metadata.emplace("spacing_units", cuimg.spacing_units());
+    cucim_metadata.emplace("origin", cuimg.origin());
+    cucim_metadata.emplace("direction", cuimg.direction());
+    cucim_metadata.emplace("coord_sys", cuimg.coord_sys());
+    {
+        const auto& resolutions = cuimg.resolutions();
+        auto resolutions_iter = cucim_metadata.emplace("resolutions", json::object());
+        json& resolutions_metadata = *(resolutions_iter.first);
+        auto level_count = resolutions.level_count();
+        resolutions_metadata.emplace("level_count", level_count);
+        std::vector<std::vector<int64_t>> level_dimensions_vec;
+        level_dimensions_vec.reserve(level_count);
+        for (int level = 0; level < level_count; ++level)
+        {
+            level_dimensions_vec.emplace_back(resolutions.level_dimension(level));
+        }
+        resolutions_metadata.emplace("level_dimensions", level_dimensions_vec);
+        resolutions_metadata.emplace("level_downsamples", resolutions.level_downsamples());
+    }
+    cucim_metadata.emplace("associated_images", cuimg.associated_images());
+    return json_obj;
+}
+
+py::dict py_resolutions(const CuImage& cuimg)
+{
+    const auto& resolutions = cuimg.resolutions();
+    auto level_count = resolutions.level_count();
+    if (resolutions.level_count() == 0)
+    {
+        return py::dict{
+            "level_count"_a = pybind11::int_(0), //
+            "level_dimensions"_a = pybind11::tuple(), //
+            "level_downsamples"_a = pybind11::tuple() //
+        };
+    }
+
+    std::vector<py::tuple> level_dimensions_vec;
+    level_dimensions_vec.reserve(level_count);
+    for (int level = 0; level < level_count; ++level)
+    {
+        level_dimensions_vec.emplace_back(vector2pytuple<pybind11::int_>(resolutions.level_dimension(level)));
+    }
+
+    py::tuple level_dimensions = vector2pytuple<const pybind11::tuple&>(level_dimensions_vec);
+    py::tuple level_downsamples = vector2pytuple<pybind11::float_>(resolutions.level_downsamples());
+
+    return py::dict{
+        "level_count"_a = pybind11::int_(level_count), //
+        "level_dimensions"_a = level_dimensions, //
+        "level_downsamples"_a = level_downsamples //
+    };
+}
+
+
+CuImage py_read_region(CuImage& cuimg,
+                       std::vector<int64_t> location,
+                       std::vector<int64_t> size,
+                       int16_t level,
+                       io::Device device,
+                       py::object buf,
+                       const std::string& shm_name,
+                       py::kwargs kwargs)
+{
+    cucim::DimIndices indices;
+    if (kwargs)
+    {
+        std::vector<std::pair<char, int64_t>> indices_args;
+
+        for (auto item : kwargs)
+        {
+            auto key = std::string(py::str(item.first));
+            auto value = py::cast<int>(item.second);
+
+            if (key.size() != 1)
+            {
+                throw std::invalid_argument(
+                    fmt::format("Argument name for Dimension should be a single character but '{}' is used.", key));
+            }
+            char key_char = key[0] & ~32;
+            if (key_char < 'A' || key_char > 'Z')
+            {
+                throw std::invalid_argument(
+                    fmt::format("Dimension character should be an alphabet but '{}' is used.", key));
+            }
+
+            indices_args.emplace_back(std::make_pair(key_char, value));
+
+            //            fmt::print("k:{} v:{}\n", std::string(py::str(item.first)),
+            //            std::string(py::str(item.second)));
+        }
+        indices = cucim::DimIndices(indices_args);
+    }
+    else
+    {
+        indices = cucim::DimIndices{};
+    }
+    cucim::CuImage region = cuimg.read_region(location, size, level, indices, device, nullptr, "");
+    return region;
+}
+
+py::dict get_array_interface(const CuImage& cuimg)
+{
+    // TODO: using __array_struct__, access to array interface could be faster
+    //       (https://numpy.org/doc/stable/reference/arrays.interface.html#c-struct-access)
+    // TODO: check the performance difference between python int vs python long later.
+    const DLTensor* tensor = static_cast<DLTensor*>(cuimg.container());
+    if (!tensor)
+    {
+        return pybind11::dict();
+    }
+    const char* type_str = cuimg.container().numpy_dtype();
+
+    py::list descr;
+    descr.append(py::make_tuple(""_s, py::str(type_str)));
+
+    py::tuple shape = vector2pytuple<pybind11::int_>(cuimg.shape());
+
+    // Reference: https://numpy.org/doc/stable/reference/arrays.interface.html
+    return py::dict{ "data"_a =
+                         pybind11::make_tuple(py::int_(reinterpret_cast<uint64_t>(tensor->data)), py::bool_(false)),
+                     "strides"_a = py::none(),
+                     "descr"_a = descr,
+                     "typestr"_a = py::str(type_str),
+                     "shape"_a = shape,
+                     "version"_a = py::int_(3) };
+}
+
+
+} // namespace cucim
diff --git a/python/pybind11/cucim_py.h b/python/pybind11/cucim_py.h
new file mode 100644
index 000000000..1910624bf
--- /dev/null
+++ b/python/pybind11/cucim_py.h
@@ -0,0 +1,62 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PYCUCIM_CUIMAGE_PY_H
+#define PYCUCIM_CUIMAGE_PY_H
+
+#include <pybind11_json/pybind11_json.hpp>
+#include <nlohmann/json.hpp>
+#include <vector>
+
+#include <cucim/cuimage.h>
+
+using json = nlohmann::json;
+
+namespace cucim
+{
+
+std::string get_plugin_root();
+void set_plugin_root(std::string path);
+
+/**
+ * Converts an object with std::vector type to one with pybind11::tuple type.
+ *
+ * The code is derived from `make_tuple()` method in pybind11/cast.h which is under BSD-3-Clause License.
+ * Please see LICENSE-3rdparty.md for the detail.
+ * (https://github.com/pybind/pybind11/blob/993495c96c869c5d3f3266c3ed3b1b8439340fd2/include/pybind11/cast.h#L1817)
+ *
+ * @tparam PT Python type
+ * @tparam T Vector type
+ * @param vec A vector object to convert
+ * @return An object of pybind11::tuple type to which `vec` is converted
+ */
+template<typename PT, typename T>
+pybind11::tuple vector2pytuple(const std::vector<T>& vec);
+
+json py_metadata(const CuImage& cuimg);
+py::dict py_resolutions(const CuImage& cuimg);
+CuImage py_read_region(CuImage& cuimg,
+                    std::vector<int64_t> location,
+                    std::vector<int64_t> size,
+                    int16_t level,
+                    io::Device device,
+                    py::object buf,
+                    const std::string& shm_name,
+                    py::kwargs kwargs);
+py::dict get_array_interface(const CuImage& cuimg);
+} // namespace cucim
+
+#endif // PYCUCIM_CUIMAGE_PY_H
diff --git a/python/pybind11/cucim_pydoc.h b/python/pybind11/cucim_pydoc.h
new file mode 100644
index 000000000..c90f47330
--- /dev/null
+++ b/python/pybind11/cucim_pydoc.h
@@ -0,0 +1,233 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PYCUCIM_CUCIM_PYDOC_H
+#    define PYCUCIM_CUCIM_PYDOC_H
+
+#    include "macros.h"
+
+#    include <string>
+
+namespace cucim::doc
+{
+
+namespace DLDataType
+{
+
+//  Constructor
+PYDOC(DLDataType, R"doc(
+Constructor for `DLDataType`.
+)doc")
+
+//  uint8_t code;
+PYDOC(code, R"doc(
+Type code of base types.
+)doc")
+
+//  uint8_t bits;
+PYDOC(bits, R"doc(
+Number of bits, common choices are 8, 16, 32.
+)doc")
+
+//  uint16_t lanes;
+PYDOC(lanes, R"doc(
+Number of lanes in the type, used for vector types.
+)doc")
+
+} // namespace DLDataType
+
+
+namespace CuImage
+{
+
+// CuImage(const filesystem::Path& path);
+PYDOC(CuImage, R"doc(
+Constructor of CuImage.
+)doc")
+
+// CuImage(const filesystem::Path& path, const std::string& plugin_name);
+// CuImage(const CuImage& cuimg) = delete;
+// CuImage(CuImage&& cuimg);
+//
+// ~CuImage();
+
+// filesystem::Path path() const;
+PYDOC(path, R"doc(
+Underlying file path for this object.
+)doc")
+
+// bool is_loaded() const;
+PYDOC(is_loaded, R"doc(
+True if image data is loaded & available.
+)doc")
+
+// io::Device device() const;
+PYDOC(device, R"doc(
+A device type.
+
+By default t is `cpu` (It will be changed since v0.19.0).
+)doc")
+
+// Metadata metadata() const;
+PYDOC(raw_metadata, R"doc(
+A raw metadata string.
+)doc")
+
+// Metadata metadata() const;
+PYDOC(metadata, R"doc(
+A metadata object as `dict`.
+
+It would be a dictionary(key-value pair) in general but can be a complex object (e.g., OME-TIFF metadata).
+)doc")
+
+// uint16_t ndim() const;
+PYDOC(ndim, R"doc(
+The number of dimensions.
+)doc")
+
+// std::string dims() const;
+PYDOC(dims, R"doc(
+A string containing a list of dimensions being requested.
+
+The default is to return the six standard dims ('STCZYX') unless it is a DP multi-resolution image.
+  [sites, time, channel(or wavelength), z, y, x]. S - Sites or multiposition locations.
+
+NOTE: in OME-TIFF's metadata, dimension order would be specified as 'XYZCTS' (first one is fast-iterating dimension).
+)doc")
+
+// Shape shape() const;
+PYDOC(shape, R"doc(
+A tuple of dimension sizes (in the order of `dims`)
+)doc")
+
+// std::vector<int64_t> size(std::string dim_order) const;
+PYDOC(size, R"doc(
+Returns size as a tuple for the given dimension order.
+)doc")
+
+// DLDataType dtype() const;
+PYDOC(dtype, R"doc(
+The data type of the image.
+)doc")
+
+// std::vector<std::string> channel_names() const;
+PYDOC(channel_names, R"doc(
+A channel name list.
+)doc")
+
+// std::vector<float> spacing(std::string dim_order = std::string{}) const;
+PYDOC(spacing, R"doc(
+Returns physical size in tuple.
+
+If `dim_order` is specified, it returns phisical size for the dimensions.
+If a dimension given by the `dim_order` doesn't exist, it returns 1.0 by default for the missing dimension.
+
+Args:
+  dim_order: A dimension string (e.g., 'XYZ')
+
+Returns:
+  A tuple with physical size for each dimension
+)doc")
+
+// std::vector<std::string> spacing_units(std::string dim_order = std::string{}) const;
+PYDOC(spacing_units, R"doc(
+Units for each spacing element (size is same with `ndim`).
+)doc")
+
+// std::array<float, 3> origin() const;
+PYDOC(origin, R"doc(
+Physical location of (0, 0, 0) (size is always 3).
+)doc")
+
+// std::array<std::array<float, 3>, 3> direction() const;
+PYDOC(direction, R"doc(
+Direction cosines (size is always 3x3).
+)doc")
+
+// std::string coord_sys() const;
+PYDOC(coord_sys, R"doc(
+Coordinate frame in which the direction cosines are measured.
+
+Available Coordinate frame names are not finalized yet.
+)doc") //  (either `LPS`(ITK/DICOM) or `RAS`(NIfTI/3D Slicer)). (either `LPS`(ITK/DICOM) or `RAS`(NIfTI/3D Slicer)).
+
+// ResolutionInfo resolutions() const;
+PYDOC(resolutions, R"doc(
+Returns a dict that includes resolution information.
+
+- level_count: The number of levels
+- level_dimensions: A tuple of dimension tuples (width, height)
+- level_downsamples: A tuple of down-sample factors
+)doc")
+
+// dlpack::DLTContainer container() const;
+
+// CuImage read_region(std::vector<int64_t> location,
+//                     std::vector<int64_t> size,
+//                     uint16_t level=0,
+//                     DimIndices region_dim_indices={},
+//                     io::Device device="cpu",
+//                     DLTensor* buf=nullptr,
+//                     std::string shm_name="");
+PYDOC(read_region, R"doc(
+Returns a subresolution image.
+
+- `location` and `size`'s dimension order is reverse of image's dimension order.
+- Need to specify (X,Y) and (Width, Height) instead of (Y,X) and (Height, Width).
+- If location is not specified, location would be (0, 0) if Z=0. Otherwise, location would be (0, 0, 0)
+- Like OpenSlide, location is level-0 based coordinates (using the level-0 reference frame)
+- If `size` is not specified, size would be (width, height) of the image at the specified `level`.
+- `<not supported yet>` Additional parameters (S,T,C,Z) are similar to
+<https://allencellmodeling.github.io/aicsimageio/aicsimageio.html#aicsimageio.aics_image.AICSImage.get_image_data>
+  - We may not want to support indices/ranges for (S,T,C,Z) for the first release.
+  - Default value for level, S, T, Z are zero.
+  - Default value for C is -1 (whole channels)
+- `<not supported yet>` `device` could be one of the following strings or Device object: e.g., `'cpu'`, `'cuda'`, `'cuda:0'` (use index 0), `cucim.clara.io.Device(cucim.clara.io.CUDA,0)`.
+- `<not supported yet>` If `buf` is specified (buf's type can be either numpy object that implements `__array_interface__`, or cupy-compatible object that implements `__cuda_array_interface__`), the read image would be saved into buf object without creating CPU/GPU memory.
+- `<not supported yet>` If `shm_name` is specified, shared memory would be created and data would be read in the shared memory.
+
+)doc")
+
+// std::set<std::string> associated_images() const;
+PYDOC(associated_images, R"doc(
+Returns a set of associated image names.
+
+Digital Pathology image usually has a label/thumbnail or a macro image(low-power snapshot of the entire glass slide).
+Names of those images (such as 'macro' and 'label') are in `associated_images`.
+)doc")
+
+// CuImage associated_image(const std::string& name) const;
+PYDOC(associated_image, R"doc(
+Returns an associated image for the given name, as a CuImage object.
+)doc")
+
+// void save(std::string file_path) const;
+PYDOC(save, R"doc(
+Saves image data to the file path.
+
+Currently it supports only .ppm file format that can be viewed by `eog` command in Ubuntu.
+)doc")
+
+// py::dict get_array_interface(const CuImage& cuimg);
+PYDOC(get_array_interface, R"doc(
+Get an array interface for Python.
+)doc")
+
+}; // namespace CuImage
+
+} // namespace cucim::doc
+
+#endif // PYCUCIM_CUCIM_PYDOC_H
diff --git a/python/pybind11/filesystem/cufile_py.cpp b/python/pybind11/filesystem/cufile_py.cpp
new file mode 100644
index 000000000..c42dc1ae9
--- /dev/null
+++ b/python/pybind11/filesystem/cufile_py.cpp
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "cufile_py.h"
+#include "cufile_pydoc.h"
+#include "../memory/init.h"
+
+#include <pybind11/pybind11.h>
+#include <cucim/filesystem/cufile_driver.h>
+
+namespace py = pybind11;
+
+namespace cucim::filesystem
+{
+
+ssize_t fd_pread(const CuFileDriver& fd, py::object obj, size_t count, off_t file_offset, off_t buf_offset)
+{
+    void* buf = nullptr;
+    size_t memory_size = 0;
+    bool readonly = false;
+
+    cucim::memory::get_memory_info(obj, &buf, nullptr, &memory_size, &readonly);
+
+    if (buf == nullptr)
+    {
+        throw std::runtime_error("Cannot Recognize the array object!");
+    }
+    if (readonly)
+    {
+        throw std::runtime_error("The buffer is readonly so cannot be used for pread!");
+    }
+    if (memory_size && count > memory_size) {
+        throw std::runtime_error(fmt::format("[Error] 'count' ({}) is larger than the size of the array object ({})!", count, memory_size));
+    }
+
+    py::call_guard<py::gil_scoped_release>();
+    return fd.pread(buf, count, file_offset, buf_offset);
+}
+ssize_t fd_pwrite(CuFileDriver& fd, py::object obj, size_t count, off_t file_offset, off_t buf_offset)
+{
+    void* buf = nullptr;
+    size_t memory_size = 0;
+
+    cucim::memory::get_memory_info(obj, &buf, nullptr, &memory_size, nullptr);
+
+    if (buf == nullptr)
+    {
+        throw std::runtime_error("Cannot Recognize the array object!");
+    }
+    if (memory_size && count > memory_size) {
+        throw std::runtime_error(fmt::format("[Error] 'count' ({}) is larger than the size of the array object ({})!", count, memory_size));
+    }
+
+    py::call_guard<py::gil_scoped_release>();
+    return fd.pwrite(buf, count, file_offset, buf_offset);
+}
+} // namespace cucim::filesystem
\ No newline at end of file
diff --git a/python/pybind11/filesystem/cufile_py.h b/python/pybind11/filesystem/cufile_py.h
new file mode 100644
index 000000000..2bb847dde
--- /dev/null
+++ b/python/pybind11/filesystem/cufile_py.h
@@ -0,0 +1,33 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_CUFILE_PY_H
+#define PYCUCIM_CUFILE_PY_H
+
+#include "cucim/filesystem/cufile_driver.h"
+
+#include <cstdio>
+#include <pybind11/pytypes.h>
+
+namespace py = pybind11;
+
+namespace cucim::filesystem
+{
+// Note: there would be name conflict with pread/pwrite in cufile_driver.h so prefixed 'fd_'.
+ssize_t fd_pread(const CuFileDriver& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+ssize_t fd_pwrite(CuFileDriver& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+} // namespace cucim::filesystem
+
+#endif // PYCUCIM_CUFILE_PY_H
diff --git a/python/pybind11/filesystem/cufile_pydoc.h b/python/pybind11/filesystem/cufile_pydoc.h
new file mode 100644
index 000000000..af109168c
--- /dev/null
+++ b/python/pybind11/filesystem/cufile_pydoc.h
@@ -0,0 +1,70 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_CUFILE_PYDOC_H
+#define PYCUCIM_CUFILE_PYDOC_H
+
+#include "../macros.h"
+
+namespace cucim::filesystem::doc::CuFileDriver
+{
+
+// CuFileDriver(int fd, bool no_gds = false, bool use_mmap = false, const char* file_path = "");
+PYDOC(CuFileDriver, R"doc(
+Constructor of CuFileDriver.
+
+Args:
+    fd: A file descriptor (in `int` type) which is available through `os.open()` method.
+    no_gds: If True, use POSIX APIs only even when GDS can be supported for the file.
+    use_mmap: If True, use memory-mapped IO. This flag is supported only for the read-only file descriptor. Default value is `False`.
+    file_path: A file path for the file descriptor. It would retrieve the absolute file path of the file descriptor if not specified.
+)doc")
+
+// ssize_t pread(void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+PYDOC(pread, R"doc(
+Reads up to `count` bytes from the file driver at offset `file_offset` (from the start of the file) into the buffer
+`buf` starting at offset `buf_offset`. The file offset is not changed.
+
+Args:
+    buf: A buffer where read bytes are stored. Buffer can be either in CPU memory or (CUDA) GPU memory.
+    count: The number of bytes to read.
+    file_offset: An offset from the start of the file.
+    buf_offset: An offset from the start of the buffer. Default value is 0.
+Returns:
+    The number of bytes read if succeed, -1 otherwise.
+)doc")
+
+// ssize_t pwrite(const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+PYDOC(pwrite, R"doc(
+Reads up to `count` bytes from the file driver at offset `file_offset` (from the start of the file) into the buffer
+`buf` starting at offset `buf_offset`. The file offset is not changed.
+
+Args:
+    buf: A buffer where write bytes come from. Buffer can be either in CPU memory or (CUDA) GPU memory.
+    count: The number of bytes to write.
+    file_offset: An offset from the start of the file.
+    buf_offset: An offset from the start of the buffer. Default value is 0.
+Returns:
+    The number of bytes written if succeed, -1 otherwise.
+)doc")
+
+// bool close();
+PYDOC(close, R"doc(
+Closes opened file if not closed.
+)doc")
+
+} // namespace cucim::filesystem::doc::CuFileDriver
+
+#endif // PYCUCIM_CUFILE_PYDOC_H
diff --git a/python/pybind11/filesystem/filesystem_py.cpp b/python/pybind11/filesystem/filesystem_py.cpp
new file mode 100644
index 000000000..69767ad69
--- /dev/null
+++ b/python/pybind11/filesystem/filesystem_py.cpp
@@ -0,0 +1,115 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "init.h"
+#include "filesystem_pydoc.h"
+#include "cufile_py.h"
+#include "cufile_pydoc.h"
+
+#include <pybind11/pybind11.h>
+#include <cucim/filesystem/cufile_driver.h>
+
+namespace py = pybind11;
+
+namespace cucim::filesystem
+{
+
+void init_filesystem(py::module& fs)
+{
+    py::enum_<FileHandleType>(fs, "FileHandleType") //
+        .value("Unknown", FileHandleType::kUnknown) //
+        .value("Posix", FileHandleType::kPosix) //
+        .value("PosixODirect", FileHandleType::kPosixODirect) //
+        .value("MemoryMapped", FileHandleType::kMemoryMapped) //
+        .value("GPUDirect", FileHandleType::kGPUDirect);
+
+    py::class_<CuFileDriver, std::shared_ptr<CuFileDriver>>(fs, "CuFileDriver")
+        .def(py::init<int, bool, bool, const char*>(), doc::CuFileDriver::doc_CuFileDriver, //
+             py::arg("fd"), //
+             py::arg("no_gds") = false, //
+             py::arg("use_mmap") = false, //
+             py::arg("file_path") = py::str(""), //
+             py::call_guard<py::gil_scoped_release>())
+        .def("pread", &fd_pread, doc::CuFileDriver::doc_pread, // Do not release GIL as it would access properties of
+                                                               // python object
+             py::arg("buf"), //
+             py::arg("count"), //
+             py::arg("file_offset"), //
+             py::arg("buf_offset") = 0) //
+        .def("pwrite", &fd_pwrite, doc::CuFileDriver::doc_pwrite, // Do not release GIL as it would access properties of
+                                                                  // python object
+             py::arg("buf"), //
+             py::arg("count"), //
+             py::arg("file_offset"), //
+             py::arg("buf_offset") = 0) //
+        .def("close", &CuFileDriver::close, doc::CuFileDriver::doc_close, py::call_guard<py::gil_scoped_release>()) //
+        .def("__repr__", [](const CuFileDriver& fd) {
+            return fmt::format("<cucim.clara.filesystem.CuFileDriver path:{}>", fd.path());
+        });
+
+    fs.def("open", &py_open, doc::doc_open,
+           py::arg("file_path"), //
+           py::arg("flags"), //
+           py::arg("mode") = 0644, //
+           py::call_guard<py::gil_scoped_release>())
+        .def("pread", &py_pread, doc::doc_pread, // Do not release GIL as it would access properties of python object
+             py::arg("fd"), //
+             py::arg("buf"), //
+             py::arg("count"), //
+             py::arg("file_offset"), //
+             py::arg("buf_offset") = 0) //
+        .def("pwrite", &py_pwrite, doc::doc_pwrite, // Do not release GIL as it would access properties of python object
+             py::arg("fd"), //
+             py::arg("buf"), //
+             py::arg("count"), //
+             py::arg("file_offset"), //
+             py::arg("buf_offset") = 0) //
+        .def("close", &close, doc::doc_close, py::call_guard<py::gil_scoped_release>())
+        .def("discard_page_cache", &discard_page_cache, doc::doc_discard_page_cache,
+             py::arg("file_path"), //
+             py::call_guard<py::gil_scoped_release>());
+}
+
+std::shared_ptr<CuFileDriver> py_open(const char* file_path, const char* flags, mode_t mode)
+{
+    return open(file_path, flags, mode);
+}
+ssize_t py_pread(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset)
+{
+    if (fd != nullptr)
+    {
+        return fd_pread(*fd, buf, count, file_offset, buf_offset);
+    }
+    else
+    {
+        fmt::print(stderr, "fd (CuFileDriver) is None!");
+        return -1;
+    }
+}
+ssize_t py_pwrite(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset)
+{
+    if (fd != nullptr)
+    {
+        return fd_pwrite(*fd, buf, count, file_offset, buf_offset);
+    }
+    else
+    {
+        fmt::print(stderr, "fd (CuFileDriver) is None!");
+        return -1;
+    }
+}
+
+} // namespace cucim::filesystem
\ No newline at end of file
diff --git a/python/pybind11/filesystem/filesystem_pydoc.h b/python/pybind11/filesystem/filesystem_pydoc.h
new file mode 100644
index 000000000..a79e08f29
--- /dev/null
+++ b/python/pybind11/filesystem/filesystem_pydoc.h
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_FILESYSTEM_PYDOC_H
+#define PYCUCIM_FILESYSTEM_PYDOC_H
+
+#include "../macros.h"
+
+namespace cucim::filesystem::doc
+{
+// std::shared_ptr<CuFileDriver> open(const char* file_path, const char* flags = "r", mode_t mode = 0644);
+PYDOC(open, R"doc(
+Open file with specific flags and mode.
+
+'flags' can be one of the following flag string:
+
+- "r": os.O_RDONLY
+
+- "r+": os.O_RDWR
+
+- "w": os.O_RDWR | os.O_CREAT | os.O_TRUNC
+
+- "a": os.O_RDWR | os.O_CREAT
+
+In addition to above flags, the method append os.O_CLOEXEC and os.O_DIRECT by default.
+
+The following is optional flags that can be added to above string:
+
+- 'p': Use POSIX APIs only (first try to open with O_DIRECT). It does not use GDS.
+
+- 'n': Do not add O_DIRECT flag.
+
+- 'm': Use memory-mapped file. This flag is supported only for the read-only file descriptor.
+
+When 'm' is used, `PROT_READ` and `MAP_SHARED` are used for the parameter of mmap() function.
+
+Args:
+    file_path: A file path to open.
+    flags: File flags in string. Default value is "r".
+    mode: A file mode. Default value is '0o644'.
+Returns:
+    An object of CuFileDriver.
+)doc")
+
+
+// bool close(const std::shared_ptr<CuFileDriver>& fd);
+PYDOC(close, R"doc(
+Closes the given file driver.
+
+Args:
+    fd: An CuFileDriver object.
+Returns:
+    True if succeed, False otherwise.
+)doc")
+
+// ssize_t pread(void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+PYDOC(pread, R"doc(
+Reads up to `count` bytes from file driver `fd` at offset `offset` (from the start of the file) into the buffer
+`buf` starting at offset `buf_offset`. The file offset is not changed.
+
+Args:
+    fd: An object of CuFileDriver.
+    buf: A buffer where read bytes are stored. Buffer can be either in CPU memory or (CUDA) GPU memory.
+    count: The number of bytes to read.
+    file_offset: An offset from the start of the file.
+    buf_offset: An offset from the start of the buffer. Default value is 0.
+Returns:
+    The number of bytes read if succeed, -1 otherwise.
+)doc")
+
+// ssize_t pread(const std::shared_ptr<CuFileDriver>& fd, const void* buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+PYDOC(pwrite, R"doc(
+Write up to `count` bytes from the buffer `buf` starting at offset `buf_offset` to the file driver `fd` at offset
+`offset` (from the start of the file). The file offset is not changed.
+
+Args:
+    fd: An object of CuFileDriver.
+    buf: A buffer where write bytes come from. Buffer can be either in CPU memory or (CUDA) GPU memory.
+    count: The number of bytes to write.
+    file_offset: An offset from the start of the file.
+    buf_offset: An offset from the start of the buffer. Default value is 0.
+Returns:
+    The number of bytes written if succeed, -1 otherwise.
+)doc")
+
+// bool discard_page_cache(const char* file_path);
+PYDOC(discard_page_cache, R"doc(
+Discards a system (page) cache for the given file path.
+
+Args:
+    file_path: A file path to drop system cache.
+Returns:
+    True if succeed, False otherwise.
+)doc")
+} // namespace cucim::filesystem::doc
+#endif // PYCUCIM_FILESYSTEM_PYDOC_H
diff --git a/python/pybind11/filesystem/init.h b/python/pybind11/filesystem/init.h
new file mode 100644
index 000000000..354359abd
--- /dev/null
+++ b/python/pybind11/filesystem/init.h
@@ -0,0 +1,39 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_FILESYSTEM_INIT_H
+#define PYCUCIM_FILESYSTEM_INIT_H
+
+#include "cucim/filesystem/cufile_driver.h"
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+namespace cucim::filesystem
+{
+
+void init_filesystem(py::module& m);
+
+std::shared_ptr<CuFileDriver> py_open(const char* file_path, const char* flags, mode_t mode);
+ssize_t py_pread(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+ssize_t py_pwrite(const std::shared_ptr<CuFileDriver>& fd, py::object buf, size_t count, off_t file_offset, off_t buf_offset = 0);
+//bool py_close(const std::shared_ptr<CuFileDriver>& fd);
+//bool py_discard_page_cache(const char* file_path);
+
+
+}
+
+#endif // PYCUCIM_FILESYSTEM_INIT_H
diff --git a/python/pybind11/io/device_py.cpp b/python/pybind11/io/device_py.cpp
new file mode 100644
index 000000000..3858d7a7f
--- /dev/null
+++ b/python/pybind11/io/device_py.cpp
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "device_pydoc.h"
+
+#include <pybind11/pybind11.h>
+#include <cucim/io/device.h>
+
+namespace py = pybind11;
+
+namespace cucim::io
+{
+
+} // namespace cucim::io
\ No newline at end of file
diff --git a/python/pybind11/io/device_pydoc.h b/python/pybind11/io/device_pydoc.h
new file mode 100644
index 000000000..80dc52509
--- /dev/null
+++ b/python/pybind11/io/device_pydoc.h
@@ -0,0 +1,55 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PYCUCIM_DEVICE_PYDOC_H
+#define PYCUCIM_DEVICE_PYDOC_H
+
+#include "../macros.h"
+
+namespace cucim::io::doc::Device
+{
+
+// explicit Device();
+
+// explicit Device(const std::string& device_name);
+PYDOC(Device, R"doc(
+Constructor for `Device`.
+)doc")
+
+// Device(const char* device_name);
+// Device(DeviceType type, DeviceIndex index);
+// Device(DeviceType type, DeviceIndex index, const std::string& param);
+
+// static DeviceType parse_type(const std::string& device_name);
+PYDOC(parse_type, R"doc(
+Create `DeviceType` object from the device name string.
+)doc")
+
+// explicit operator std::string() const;
+
+// DeviceType type() const;
+PYDOC(type, R"doc(
+Device type.
+)doc")
+
+// DeviceIndex index() const;
+PYDOC(index, R"doc(
+Device index.
+)doc")
+
+} // namespace cucim::io::doc::Device
+
+#endif // PYCUCIM_DEVICE_PYDOC_H
diff --git a/python/pybind11/io/init.h b/python/pybind11/io/init.h
new file mode 100644
index 000000000..40c8f70ee
--- /dev/null
+++ b/python/pybind11/io/init.h
@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PYCUCIM_IO_INIT_H
+#define PYCUCIM_IO_INIT_H
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+namespace cucim::io
+{
+
+void init_io(py::module& m);
+
+}
+
+
+#endif // PYCUCIM_IO_INIT_H
diff --git a/python/pybind11/io/io_py.cpp b/python/pybind11/io/io_py.cpp
new file mode 100644
index 000000000..3dfd6c951
--- /dev/null
+++ b/python/pybind11/io/io_py.cpp
@@ -0,0 +1,43 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "io_pydoc.h"
+#include "device_pydoc.h"
+
+#include <pybind11/pybind11.h>
+#include <cucim/io/device.h>
+
+namespace py = pybind11;
+
+namespace cucim::io
+{
+void init_io(py::module& io)
+{
+    py::enum_<DeviceType>(io, "DeviceType") //
+        .value("CPU", DeviceType::kCPU) //
+        .value("CUDA", DeviceType::kCUDA) //
+        .value("Pinned", DeviceType::kPinned) //
+        .value("CPUShared", DeviceType::kCPUShared) //
+        .value("CUDAShared", DeviceType::kCUDAShared);
+
+    py::class_<Device>(io, "Device") //
+        .def(py::init<const std::string&>(), doc::Device::doc_Device) //
+        .def_static("parse_type", &Device::parse_type, doc::Device::doc_parse_type) //
+        .def_property("type", &Device::type, nullptr, doc::Device::doc_type) //
+        .def_property("index", &Device::index, nullptr, doc::Device::doc_index)
+        .def("__repr__", [](const Device& device) { return std::string(device); });
+}
+} // namespace cucim::io
\ No newline at end of file
diff --git a/python/pybind11/io/io_pydoc.h b/python/pybind11/io/io_pydoc.h
new file mode 100644
index 000000000..35a25a717
--- /dev/null
+++ b/python/pybind11/io/io_pydoc.h
@@ -0,0 +1,26 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_IO_PYDOC_H
+#define PYCUCIM_IO_PYDOC_H
+
+#include "../macros.h"
+
+namespace cucim::io::doc
+{
+
+}
+
+#endif // PYCUCIM_IO_PYDOC_H
diff --git a/python/pybind11/macros.h b/python/pybind11/macros.h
new file mode 100644
index 000000000..515c07dda
--- /dev/null
+++ b/python/pybind11/macros.h
@@ -0,0 +1,31 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef PYCUCIM_MACRO_H
+#define PYCUCIM_MACRO_H
+
+#include <string>
+
+#include <fmt/format.h>
+
+constexpr const char* remove_leading_spaces(const char* str)
+{
+    return *str == '\0' ? str : ((*str == ' ' || *str == '\n') ? remove_leading_spaces(str + 1) : str);
+}
+
+#define PYDOC(method, doc) static constexpr const char* doc_##method = remove_leading_spaces(doc);
+
+#endif // PYCUCIM_MACRO_H
diff --git a/python/pybind11/memory/init.h b/python/pybind11/memory/init.h
new file mode 100644
index 000000000..5d7260c5b
--- /dev/null
+++ b/python/pybind11/memory/init.h
@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_MEMORY_INIT_H
+#define PYCUCIM_MEMORY_INIT_H
+
+#include <pybind11/pybind11.h>
+#include <cucim/io/device.h>
+
+namespace py = pybind11;
+
+namespace cucim::memory
+{
+
+void init_memory(py::module& m);
+
+void get_memory_info(py::object& buf_obj,
+                     void** out_buf,
+                     cucim::io::Device* out_device = nullptr,
+                     size_t* out_memory_size = 0,
+                     bool* out_readonly = nullptr);
+
+} // namespace cucim::memory
+
+
+#endif // PYCUCIM_MEMORY_INIT_H
diff --git a/python/pybind11/memory/memory_py.cpp b/python/pybind11/memory/memory_py.cpp
new file mode 100644
index 000000000..e840dc59c
--- /dev/null
+++ b/python/pybind11/memory/memory_py.cpp
@@ -0,0 +1,138 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "memory_pydoc.h"
+#include "init.h"
+
+#include <pybind11/pybind11.h>
+#include <cucim/memory/memory_manager.h>
+
+namespace py = pybind11;
+
+namespace cucim::memory
+{
+
+void init_memory(py::module& memory)
+{
+}
+
+static size_t calculate_memory_size(std::vector<size_t> shape, const char* dtype_str)
+{
+    // TODO: implement method to calculate size
+    //       https://github.com/pytorch/pytorch/blob/master/torch/tensor.py#L733  (we can take digit part)
+    return 0;
+}
+
+void get_memory_info(
+    py::object& buf_obj, void** out_buf, cucim::io::Device* out_device, size_t* out_memory_size, bool* out_readonly)
+{
+    if (out_buf == nullptr)
+    {
+        throw std::runtime_error("[Error] out_buf shouldn't be nullptr!");
+    }
+
+    void* buf = nullptr;
+    size_t memory_size = 0;
+
+    if (py::hasattr(buf_obj, "__array_interface__"))
+    {
+        auto attr = py::getattr(buf_obj, "__array_interface__");
+        if (py::isinstance<py::dict>(attr))
+        {
+            auto dict = py::cast<py::dict>(attr);
+            if (dict.contains("data"))
+            {
+                auto data = dict["data"];
+                if (py::isinstance<py::tuple>(data))
+                {
+                    auto data_tuple = data.cast<py::tuple>();
+                    if (data_tuple.size() == 2)
+                    {
+                        if (out_readonly)
+                        {
+                            *out_readonly = data_tuple[1].cast<bool>();
+                        }
+                        buf = reinterpret_cast<void*>(data_tuple[0].cast<uint64_t>());
+                        if (py::hasattr(buf_obj, "nbytes"))
+                        {
+                            memory_size = py::getattr(buf_obj, "nbytes").cast<size_t>();
+                        }
+                        else
+                        {
+                            // TODO: implement method to calculate size
+                        }
+                    }
+                }
+            }
+        }
+    }
+    else if (py::hasattr(buf_obj, "__cuda_array_interface__"))
+    {
+        auto attr = py::getattr(buf_obj, "__cuda_array_interface__");
+        if (py::isinstance<py::dict>(attr))
+        {
+            auto dict = py::cast<py::dict>(attr);
+            if (dict.contains("data"))
+            {
+                auto data = dict["data"];
+                if (py::isinstance<py::tuple>(data))
+                {
+                    auto data_tuple = data.cast<py::tuple>();
+                    if (data_tuple.size() == 2)
+                    {
+                        if (out_readonly)
+                        {
+                            *out_readonly = data_tuple[1].cast<bool>();
+                        }
+                        buf = reinterpret_cast<void*>(data_tuple[0].cast<uint64_t>());
+                        if (py::hasattr(buf_obj, "nbytes"))
+                        {
+                            memory_size = py::getattr(buf_obj, "nbytes").cast<size_t>();
+                        }
+                        else
+                        {
+                            // TODO: implement method to calculate size
+                        }
+                    }
+                }
+            }
+        }
+    }
+    else if (py::isinstance<py::int_>(buf_obj))
+    {
+        buf = reinterpret_cast<void*>(buf_obj.cast<uint64_t>());
+    }
+
+    *out_buf = buf;
+    if (out_memory_size)
+    {
+        *out_memory_size = memory_size;
+    }
+
+    if (buf == nullptr)
+    {
+        return;
+    }
+
+    if (out_device)
+    {
+        cucim::memory::PointerAttributes attributes;
+        cucim::memory::get_pointer_attributes(attributes, buf);
+        *out_device = attributes.device;
+    }
+}
+
+} // namespace cucim::memory
\ No newline at end of file
diff --git a/python/pybind11/memory/memory_pydoc.h b/python/pybind11/memory/memory_pydoc.h
new file mode 100644
index 000000000..e817b7f84
--- /dev/null
+++ b/python/pybind11/memory/memory_pydoc.h
@@ -0,0 +1,27 @@
+/*
+ * Copyright (c) 2020, NVIDIA CORPORATION.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef PYCUCIM_MEMORY_PYDOC_H
+#define PYCUCIM_MEMORY_PYDOC_H
+
+#include "../macros.h"
+
+namespace cucim::memory::doc
+{
+
+} // namespace cucim::memory::doc
+
+
+#endif // PYCUCIM_MEMORY_PYDOC_H
diff --git a/run b/run
new file mode 100755
index 000000000..9305c7ce2
--- /dev/null
+++ b/run
@@ -0,0 +1,1235 @@
+#!/bin/bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+init_globals() {
+    if [ "$0" != "/bin/bash" ]; then
+        SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+        export RUN_SCRIPT_FILE="$(readlink -f "$0")"
+    else
+        export RUN_SCRIPT_FILE="$(readlink -f "${BASH_SOURCE[0]}")"
+    fi
+
+    export TOP=$(git rev-parse --show-toplevel || $(dirname "${RUN_SCRIPT_FILE}"))
+}
+
+################################################################################
+# Utility functions
+################################################################################
+
+#######################################
+# Get list of available commands from a given input file.
+#
+# Available commands and command summary are extracted by checking a pattern
+# "_desc() { echo '".
+# Section title is extracted by checking a pattern "# Section: ".
+# This command is used for listing available commands in CLI.
+#
+# e.g.)
+#   "# Section: String/IO functions"
+#     => "# String/IO functions"
+#   "to_lower_desc() { echo 'Convert to lower case"
+#     => "to_lower ----------------- Convert to lower case"
+#
+# Arguments:
+#   $1 - input file that defines commands
+# Returns:
+#   Print list of available commands from $1
+#######################################
+get_list_of_available_commands() {
+    local file_name="$1"
+    if [ ! -e "$1" ]; then
+        echo "$1 doesn't exist!"
+    fi
+
+    local line_str='--------------------------------'
+    local IFS= cmd_lines="$(IFS= cat "$1" | grep -E -e "^(([[:alpha:]_[:digit:]]+)_desc\(\)|# Section: )" | sed "s/_desc() *{ *echo '/ : /")"
+    local line
+    while IFS= read -r line; do
+        local cmd=$(echo "$line" | cut -d":" -f1)
+        local desc=$(echo "$line" | cut -d":" -f2-)
+        if [ "$cmd" = "# Section" ]; then
+            c_echo B "${desc}"
+        else
+            # there is no substring operation in 'sh' so use 'cut'
+            local dash_line="$(echo "${line_str}" | cut -c ${#cmd}-)"  #  = "${line_str:${#cmd}}"
+             c_echo Y "   ${cmd}" w " ${dash_line} ${desc}"
+        fi
+        # use <<EOF, not '<<<"$cmd_lines"' to be executable in sh
+    done <<EOF
+$cmd_lines
+EOF
+}
+
+my_cat_prefix() {
+    local IFS
+    local prefix="$1"
+    local line
+    while IFS= read -r line; do
+        echo "${prefix}${line}" # -e option doesn't work in 'sh' so disallow escaped characters
+    done <&0
+}
+
+
+c_str() {
+    local old_color=39
+    local old_attr=0
+    local color=39
+    local attr=0
+    local text=""
+    #local no_change=0
+    for i in "$@"; do
+        case "$i" in
+            r|R)
+                color=31
+                ;;
+            g|G)
+                color=32
+                ;;
+            y|Y)
+                color=33
+                ;;
+            b|B)
+                color=34
+                ;;
+            p|P)
+                color=35
+                ;;
+            c|C)
+                color=36
+                ;;
+            w|W)
+                color=37
+                ;;
+
+            z|Z)
+                color=0
+                ;;
+        esac
+        case "$i" in
+            l|L|R|G|Y|B|P|C|W)
+                attr=1
+                ;;
+            n|N|r|g|y|b|p|c|w)
+                attr=0
+                ;;
+            z|Z)
+                attr=0
+                ;;
+            *)
+                text="${text}$i"
+        esac
+        if [ ${old_color} -ne ${color} ] || [ ${old_attr} -ne ${attr} ]; then
+            text="${text}\033[${attr};${color}m"
+            old_color=$color
+            old_attr=$attr
+        fi
+    done
+    /bin/echo -en "$text"
+}
+
+c_echo() {
+    local old_opt="$(shopt -op xtrace)" # save old xtrace option
+    set +x # unset xtrace
+    local text="$(c_str "$@")"
+    /bin/echo -e "$text\033[0m"
+    eval "${old_opt}" # restore old xtrace option
+}
+
+
+echo_err() {
+    >&2 echo "$@"
+}
+
+c_echo_err() {
+    >&2 c_echo "$@"
+}
+
+printf_err() {
+    >&2 printf "$@"
+}
+
+get_item_ranges() {
+    local indexes="$1"
+    local list="$2"
+    echo -n "$(echo "${list}" | xargs | cut -d " " -f "${indexes}")"
+    return $?
+}
+
+get_unused_ports() {
+    local num_of_ports=${1:-1}
+    local start=${2:-49152}
+    local end=${3:-61000}
+    comm -23 \
+    <(seq ${start} ${end} | sort) \
+    <(ss -tan | awk '{print $4}' | while read line; do echo ${line##*\:}; done | grep '[0-9]\{1,5\}' | sort -u) \
+    | shuf | tail -n ${num_of_ports} # use tail instead head to avoid broken pipe in VSCode terminal
+}
+
+newline() {
+    echo
+}
+
+info() {
+    c_echo W "$(date -u '+%Y-%m-%d %H:%M:%S') [INFO] " Z "$@"
+}
+
+error() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [ERROR] " Z "$@"
+}
+
+fatal() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [FATAL] " Z "$@"
+    echo
+    if [ -n "${SCRIPT_DIR}" ]; then
+        exit 1
+    fi
+}
+
+run_command() {
+    local status=0
+    local cmd="$*"
+
+    c_echo B "$(date -u '+%Y-%m-%d %H:%M:%S') " W "\$ " G "${cmd}"
+
+    [ "$(echo -n "$@")" = "" ] && return 1 # return 1 if there is no command available
+
+    "$@"
+    status=$?
+
+    unset IFS
+
+    return $status
+}
+
+retry() {
+    local retries=$1
+    shift
+
+    local count=0
+    until run_command "$@"; do
+        exit=$?
+        wait=$((2 ** count))
+        count=$((count + 1))
+        if [ $count -lt $retries ]; then
+            info "Retry $count/$retries. Exit code=$exit, Retrying in $wait seconds..."
+            sleep $wait
+        else
+            fatal "Retry $count/$retries. Exit code=$exit, no more retries left."
+            return 1
+        fi
+    done
+    return 0
+}
+
+#==================================================================================
+# Section: Build
+#==================================================================================
+
+build_manylinux2014_desc() { echo 'Build manylinux2014 image
+
+Arguments:
+  $1 - cuda version (e.g., 110, 111)
+'
+}
+build_manylinux2014() {
+    local cuda_version="${1:-110}"
+    run_command docker build -f ${TOP}/Dockerfile-cuda${cuda_version} -t gigony/manylinux2014-x64:cuda${cuda_version} ${TOP}
+
+    read -n 1 -r -p "$(c_str R "Do you want to update dockcross-manylinux2014-x64 with " G "cuda${cuda_version}" R " (y/n)?")"
+    echo
+    if [[ $REPLY =~ ^[Yy]$ ]]; then
+        sed -i -e "s/manylinux2014-x64:cuda.../manylinux2014-x64:cuda${cuda_version}/g" ${TOP}/dockcross-manylinux2014-x64
+        c_echo W "Done"
+    fi
+}
+
+build_local_libcucim_() {
+    local source_folder=${1:-${TOP}}
+    local build_type=${2:-debug}
+    local build_type_str=${3:-Debug}
+    local prefix=${4:-}
+    local build_folder=${source_folder}/build-${build_type}
+    local CMAKE_CMD=${CMAKE_CMD:-cmake}
+
+    pushd ${source_folder} > /dev/null
+
+    # Copy cufile SDK from host system to temp/gds
+    copy_gds_files_ $source_folder
+
+    ${CMAKE_CMD} -S ${source_folder} -B ${build_folder} -G "Unix Makefiles" \
+        -DCMAKE_EXPORT_COMPILE_COMMANDS:BOOL=TRUE \
+        -DCMAKE_PREFIX_PATH=${prefix} \
+        -DCMAKE_BUILD_TYPE=${build_type_str} \
+        -DCMAKE_INSTALL_PREFIX=${source_folder}/install
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target cucim -- -j $(nproc)
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target install -- -j $(nproc)
+
+    popd
+}
+
+build_local_cuslide_() {
+    local source_folder=${1:-${TOP}/cpp/plugins/cucim.kit.cuslide}
+    local build_type=${2:-debug}
+    local build_type_str=${3:-Debug}
+    local prefix=${4:-}
+    local build_folder=${source_folder}/build-${build_type}
+    local CMAKE_CMD=${CMAKE_CMD:-cmake}
+
+    pushd ${source_folder} > /dev/null
+
+    ${CMAKE_CMD} -S ${source_folder} -B ${build_folder} -G "Unix Makefiles" \
+        -DCMAKE_EXPORT_COMPILE_COMMANDS:BOOL=TRUE \
+        -DCMAKE_BUILD_TYPE=${build_type_str} \
+        -DCMAKE_PREFIX_PATH=${prefix} \
+        -DCMAKE_INSTALL_PREFIX=${source_folder}/install
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target cucim.kit.cuslide -- -j $(nproc)
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target install -- -j $(nproc)
+
+    popd
+}
+
+build_local_cucim_() {
+    local source_folder=${1:-${TOP}/python}
+    local build_type=${2:-debug}
+    local build_type_str=${3:-Debug}
+    local prefix=${4:-}
+    local build_folder=${source_folder}/build-${build_type}
+    local CMAKE_CMD=${CMAKE_CMD:-cmake}
+
+    pushd ${source_folder} > /dev/null
+
+    local python_library=$(python3 -c "import distutils.sysconfig as sysconfig, os; print(os.path.join(sysconfig.get_config_var('LIBDIR'), sysconfig.get_config_var('LDLIBRARY')))")
+    local python_include_dir=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
+
+    ${CMAKE_CMD} -S ${source_folder} -B ${build_folder} -G "Unix Makefiles" \
+        -DCMAKE_EXPORT_COMPILE_COMMANDS:BOOL=TRUE \
+        -DCMAKE_BUILD_TYPE=${build_type_str} \
+        -DCMAKE_PREFIX_PATH=${prefix} \
+        -DCMAKE_INSTALL_PREFIX=${source_folder}/install \
+        -DPYTHON_EXECUTABLE=$(which python3) \
+        -DPYTHON_LIBRARY=${python_library} \
+        -DPYTHON_INCLUDE_DIR=${python_include_dir}
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target cucim -- -j $(nproc)
+    ${CMAKE_CMD} --build ${build_folder} --config ${build_type_str} --target install -- -j $(nproc)
+
+    popd
+}
+
+build_local_desc() { echo 'Build locally
+
+Compile binaries locally
+
+Arguments:
+  $1 - subcommand [all|libcucim|cuslide|cucim] (default: all)
+  $2 - build type [debug|release|rel-debug] (default: debug)
+'
+}
+build_local() {
+    local subcommand="${1:-all}"
+    local build_type="${2:-debug}"
+    local build_type_str="Debug"
+    local prefix=${3:-}
+
+    [ "$build_type" = "debug" ] && build_type_str="Debug"
+    [ "$build_type" = "release" ] && build_type_str="Release"
+    [ "$build_type" = "rel-debug" ] && build_type_str="RelWithDebInfo"
+
+    local old_opt="$(shopt -op errexit);$(shopt -op nounset)" # save old shopts
+    set -eu
+
+    if [ "$subcommand" = "all" ] || [ "$subcommand" = "libcucim" ]; then
+        build_local_libcucim_ ${TOP} ${build_type} ${build_type_str} ${prefix}
+    fi
+
+    if [ "$subcommand" = "all" ] || [ "$subcommand" = "cuslide" ]; then
+        build_local_cuslide_ ${TOP}/cpp/plugins/cucim.kit.cuslide ${build_type} ${build_type_str} ${prefix}
+    fi
+
+    if [ "$subcommand" = "all" ] || [ "$subcommand" = "cucim" ]; then
+        build_local_cucim_ ${TOP}/python ${build_type} ${build_type_str} ${prefix}
+    fi
+
+    # Remove existing library files at python/cucim/src/cucim/clara
+    rm -f ${TOP}/python/cucim/src/cucim/clara/*.so*
+
+    if [ "$subcommand" = "all" ] || [ "$subcommand" = "cucim" ]; then
+        # We don't need to copy binary if executed by conda-build
+        if [ "${CONDA_BUILD:-}" != "1" ]; then
+            # Copy .so files to pybind's build folder
+            # we don't need to copy symbolic links. Instead copy only libcucim.so.0 (without symbolic link)
+            cp ${TOP}/build-$build_type/lib*/libcucim.so.0 ${TOP}/python/cucim/src/cucim/clara/
+            cp -P ${TOP}/cpp/plugins/cucim.kit.cuslide/build-$build_type/lib*/cucim* ${TOP}/python/cucim/src/cucim/clara/
+
+            # Copy .so files from pybind's build folder to cucim Python source folder
+            # Since wheel file doesn't support symbolic link (https://github.com/pypa/wheel/issues/203),
+            # we don't need to copy symbolic links. Instead copy only libcucim.so.0 (without symbolic link)
+            #find ${TOP}/python/install/lib -maxdepth 1 -type f -exec cp {} ${TOP}/python/cucimrc/cucim/clara\;
+            cp ${TOP}/python/build-$build_type/lib/cucim/_cucim.*.so ${TOP}/python/cucim/src/cucim/clara/
+            # cp ${TOP}/python/build-$build_type/lib/cucim.*.so ${TOP}/python/cucim/src/cucim/clara/
+            # cp ${TOP}/python/build-$build_type/lib/libcucim.so.0 ${TOP}/python/cucim/src/cucim/clara/
+            # find ${TOP}/python/build-$build_type/lib -maxdepth 1 -type f -name "lib*.so" -exec cp {} ${TOP}/python/cucim/src/cucim/clara \;
+        fi
+    fi
+
+    eval "${old_opt}" # restore old shopts
+}
+
+copy_gds_files_() {
+    local root_folder=${1:-${TOP}}
+
+    run_command rm -rf ${root_folder}/temp/gds
+    run_command mkdir -p ${root_folder}/temp/gds/lib64
+
+    if [ ! -f /usr/local/cuda/lib64/cufile.h ] && [ ! -f /usr/local/cuda/lib64/libcufile.so ]; then
+        c_echo_err Y "GDS is not available at /usr/local/cuda/gds! Downloading internal package to get cufile.h"
+
+        local temp_deb_dir=$(mktemp -d)
+        pushd ${temp_deb_dir}
+        run_command wget https://developer.download.nvidia.com/gds/redist/ # TODO: update this once the official file is available.
+        run_command ar x libcufile-dev-11-0_0.95.0.34-1_amd64.deb
+        run_command tar xvf *.tar.xz
+        run_command cp usr/local/cuda-11.0/targets/x86_64-linux/lib/cufile.h ${root_folder}/temp/gds/lib64/
+        popd > /dev/null
+        run_command rm -r ${temp_deb_dir}
+    else
+        run_command cp -P -r /usr/local/cuda/gds/* ${root_folder}/temp/gds/ || true
+        run_command cp -P /usr/local/cuda/lib64/cufile.h /usr/local/cuda/lib64/libcufile* ${root_folder}/temp/gds/lib64/ || true
+    fi
+}
+
+build_python_package_desc() { echo 'Build Python package
+
+Note: This command does not remove `dist` folder before building.
+'
+}
+build_python_package() {
+    local ret=0
+    local old_opt="$(shopt -op errexit);$(shopt -op nounset)" # save old shopts
+    set -eu
+
+    # Copy cufile SDK from host system to temp/gds
+    copy_gds_files_
+
+    run_command ${TOP}/dockcross-manylinux2014-x64 ./run build_python_package_
+    ret=$?
+
+    eval "${old_opt}" # restore old shopts
+
+    return ${ret}
+}
+
+repair_wheel_() {
+    local wheel="$1"
+    local dest="${2:-./}"
+    local PLAT="${3:-manylinux2014-x86_64}"
+
+
+    if ! auditwheel show "$wheel"; then
+        echo "Skipping non-platform wheel ${wheel}"
+    else
+        $(head -1 $(which auditwheel) | cut -d'!' -f2) $TOP/scripts/auditwheel_repair.py repair --plat "${PLAT}" -w ${dest} "${wheel}"
+    fi
+}
+
+build_python_package_() {
+    local SRC_ROOT=${1:-${TOP}}
+    local BUILD_ROOT=${2:-${TOP}/temp}
+    local DEST_ROOT=${3:-${TOP}/dist}
+    local CUCIM_SDK_PATH=${4:-${BUILD_ROOT}/libcucim}
+
+    local old_opt="$(shopt -op errexit);$(shopt -op nounset)" # save old shopts
+    set -eu
+
+    TEMP_PYPKG_DIR=${BUILD_ROOT}/py_pkg # $(mktemp -d)
+    c_echo b "TEMP_PYPKG_DIR=${TEMP_PYPKG_DIR}"
+    mkdir -p $TEMP_PYPKG_DIR
+    rm -rf $TEMP_PYPKG_DIR/*
+    # trap 'rm -rf ${TEMP_PYPKG_DIR}' EXIT
+
+    local CMAKE_CMD=${CMAKE_CMD:-cmake}
+    local CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release}
+    local NUM_THREADS=${NUM_THREADS:-$(nproc)}
+
+    local PLAT=manylinux2014_x86_64
+
+    local pybins
+
+    mkdir -p ${DEST_ROOT}
+
+    # Remove existing library files at build root
+    rm -rf ${BUILD_ROOT}/libcucim/lib/*
+    rm -rf ${BUILD_ROOT}/libcucim/install/lib*/lib*
+    rm -rf ${BUILD_ROOT}/cuslide/lib/*
+    rm -rf ${BUILD_ROOT}/cuslide/install/lib*/cucim*
+    rm -rf ${BUILD_ROOT}/cucim/lib/cucim/*.so*
+
+    # Build libcucim
+    ${CMAKE_CMD} -S ${SRC_ROOT} -B ${BUILD_ROOT}/libcucim \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+        -DCMAKE_INSTALL_PREFIX=${CUCIM_SDK_PATH}/install
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/libcucim --config ${CMAKE_BUILD_TYPE} --target cucim -- -j ${NUM_THREADS}
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/libcucim --config ${CMAKE_BUILD_TYPE} --target install -- -j ${NUM_THREADS}
+
+    # Build cuslide plugin
+    ${CMAKE_CMD} -S ${SRC_ROOT}/cpp/plugins/cucim.kit.cuslide -B ${BUILD_ROOT}/cuslide \
+        -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+        -DCMAKE_INSTALL_PREFIX=${BUILD_ROOT}/cuslide/install \
+        -DCUCIM_SDK_PATH=${CUCIM_SDK_PATH}
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/cuslide --config ${CMAKE_BUILD_TYPE} --target cucim.kit.cuslide -- -j ${NUM_THREADS}
+    ${CMAKE_CMD} --build ${BUILD_ROOT}/cuslide --config ${CMAKE_BUILD_TYPE} --target install -- -j ${NUM_THREADS}
+
+    # Build Python bind
+    pybins="$(echo /opt/python/*/bin)"
+    if [ "${pybins}" = "/opt/python/*/bin" ]; then
+        pybins="$(dirname $(which python3))" # for building at host
+    fi
+    for PYBIN in ${pybins}; do
+        local python_library=$(${PYBIN}/python3 -c "import distutils.sysconfig as sysconfig; import os; print(os.path.join(sysconfig.get_config_var('LIBDIR'), sysconfig.get_config_var('LDLIBRARY')))")
+        local python_include_dir=$(${PYBIN}/python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())")
+
+        ${CMAKE_CMD} -S ${SRC_ROOT}/python -B ${BUILD_ROOT}/cucim \
+            -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} \
+            -DCMAKE_INSTALL_PREFIX=${BUILD_ROOT}/cucim/install \
+            -DCUCIM_SDK_PATH=${CUCIM_SDK_PATH} \
+            -DPYTHON_EXECUTABLE=${PYBIN}/python3 \
+            -DPYTHON_LIBRARY=${python_library} \
+            -DPYTHON_INCLUDE_DIR=${python_include_dir}
+        ${CMAKE_CMD} --build ${BUILD_ROOT}/cucim --config ${CMAKE_BUILD_TYPE} --target cucim -- -j ${NUM_THREADS}
+        ${CMAKE_CMD} --build ${BUILD_ROOT}/cucim --config ${CMAKE_BUILD_TYPE} --target install -- -j ${NUM_THREADS}
+    done
+
+    # Remove existing library files at python/cucim/src/cucim/clara
+    rm -f ${SRC_ROOT}/python/cucim/src/cucim/clara/*.so*
+
+    # Copy .so files to pybind's build folder
+    # (it uses -P to copy symbolic links as they are)
+    cp -P ${BUILD_ROOT}/libcucim/install/lib*/lib* ${BUILD_ROOT}/cucim/install/lib/
+    cp -P ${BUILD_ROOT}/cuslide/install/lib*/cucim* ${BUILD_ROOT}/cucim/install/lib/
+
+    # Copy .so files from pybind's build folder to cucim Python source folder
+    # Since wheel file doesn't support symbolic link (https://github.com/pypa/wheel/issues/203),
+    # we don't need to copy symbolic links. Instead copy only libcucim.so.0 (without symbolic link)
+      #find ${BUILD_ROOT}/cucim/install/lib -maxdepth 1 -type f -exec cp {} ${SRC_ROOT}/python/cucim/src/cucim/clara/ \;
+    cp ${BUILD_ROOT}/cucim/install/lib/_cucim.*.so ${SRC_ROOT}/python/cucim/src/cucim/clara/
+    cp ${BUILD_ROOT}/cucim/install/lib/cucim.*.so ${SRC_ROOT}/python/cucim/src/cucim/clara/
+    cp ${BUILD_ROOT}/cucim/install/lib/libcucim.so.0 ${SRC_ROOT}/python/cucim/src/cucim/clara/
+    find ${BUILD_ROOT}/cucim/install/lib -maxdepth 1 -type f -name "lib*.so" -exec cp {} ${SRC_ROOT}/python/cucim/src/cucim/clara/ \;
+
+    cd ${SRC_ROOT}/python/cucim
+
+    # Remove build folder
+    rm -rf ${SRC_ROOT}/python/cucim/build
+    # Compile wheels (one python binary is enough)
+    pybins="$(echo /opt/python/*/bin)"
+    [ ! -e /opt/python/cp36-cp36m/bin ] && pybins="$(dirname $(which python3))" # for building at host
+
+    pybins="$(echo /opt/python/*/bin)"
+    if [ "${pybins}" = "/opt/python/*/bin" ]; then # if multiple python binaries not found
+        pybins="$(dirname $(which python3))" # for building at host
+    else
+        pybins=/opt/python/cp36-cp36m/bin # use Python 3.6 for executing setup.py
+    fi
+
+    for PYBIN in ${pybins}; do # /opt/python/*/bin
+        run_command "${PYBIN}/python3" setup.py bdist_wheel -p $PLAT -d ${TEMP_PYPKG_DIR}
+    done
+
+    # Do not bundle external shared libraries for now.
+    # (CUDA-related libraries cannot be redistributed without EULA messages confirmed by user)
+    # Here, we just copy to dist folder.
+    # cp ${TEMP_PYPKG_DIR}/*.whl ${DEST_ROOT}/
+
+    # Bundle external shared libraries into the wheels
+    for whl in ${TEMP_PYPKG_DIR}/*.whl; do
+        repair_wheel_ "$whl" "${DEST_ROOT}/" "${PLAT}"
+    done
+
+    # run_command rm -rf ${TEMP_PYPKG_DIR}
+    # trap -- EXIT
+
+    # Copy cpp packages and examples
+    mkdir -p ${DEST_ROOT}/examples/cpp
+    cp -P -r ${BUILD_ROOT}/libcucim/install ${DEST_ROOT}/
+    cp -P -r ${BUILD_ROOT}/cuslide/install/lib/*.so ${DEST_ROOT}/install/lib/
+    cp -r ${SRC_ROOT}/examples/cpp/tiff_image ${DEST_ROOT}/examples/cpp/
+
+    cp ${BUILD_ROOT}/libcucim/CMakeLists.txt.examples.release ${DEST_ROOT}/examples/cpp/CMakeLists.txt
+
+    # # Install packages and test
+    # for PYBIN in /opt/python/*/bin/; do
+    #     "${PYBIN}/pip" install python-manylinux-demo --no-index -f /io/wheelhouse
+    #     (cd "$HOME"; "${PYBIN}/nosetests" pymanylinuxdemo)
+    # done
+    # python setup.py bdist_wheel -p manylinux2014-x86_64
+
+    eval "${old_opt}" # restore old shopts
+}
+
+build_package_desc() { echo 'Build package for release (& gen_docs)
+'
+}
+build_package() {
+    local SRC_ROOT=${1:-${TOP}}
+    local BUILD_ROOT=${2:-${TOP}/temp}
+    local DEST_ROOT=${3:-${TOP}/dist}
+    local CUCIM_SDK_PATH=${4:-${BUILD_ROOT}/libcucim}
+
+    # Clean dist folder
+    mkdir -p ${DEST_ROOT}
+    [ -n "${DEST_ROOT}" ] && run_command sudo rm -rf ${DEST_ROOT}/*
+
+    build_python_package ${SRC_ROOT} ${BUILD_ROOT} ${DEST_ROOT} ${CUCIM_SDK_PATH}
+    gen_docs ${DEST_ROOT}/docs
+    copy_data_ ${SRC_ROOT} ${BUILD_ROOT} ${DEST_ROOT}
+
+    # Copy the built wheel file into ${TOP}/notebooks
+    run_command cp ${DEST_ROOT}/*.whl ${TOP}/notebooks/
+}
+
+copy_data_() {
+    c_echo W "Copy necessary files for packaging..."
+    local SRC_ROOT=${1:-${TOP}}
+    local BUILD_ROOT=${2:-${TOP}/temp}
+    local DEST_ROOT=${3:-${TOP}/dist}
+
+    # Create notebooks folder (add notebooks and scripts)
+    run_command mkdir -p ${DEST_ROOT}/notebooks/static_images
+    run_command cp $(git ls-files ${SRC_ROOT}/notebooks/*.ipynb) ${DEST_ROOT}/notebooks/
+    run_command cp ${SRC_ROOT}/notebooks/static_images/*.png ${DEST_ROOT}/notebooks/static_images/
+
+    # Create docker folder
+    run_command mkdir -p ${DEST_ROOT}/docker
+    run_command cp ${SRC_ROOT}/docker/*-jupyter{,-gds,.txt} ${DEST_ROOT}/docker/
+    run_command cp ${SRC_ROOT}/docker/*-claratrain{,.txt} ${DEST_ROOT}/docker/
+    run_command cp ${SRC_ROOT}/docker/*-cmake ${DEST_ROOT}/docker/
+    run_command cp ${SRC_ROOT}/docker/cufile.json ${DEST_ROOT}/docker/ # Copy cufile.json
+
+    # Copy main script
+    run_command cp ${SRC_ROOT}/scripts/run-dist ${DEST_ROOT}/run
+
+    # Create .dockerignore to speed up docker run
+    echo "notebooks" > ${DEST_ROOT}/.dockerignore
+
+    # Copy license files
+    run_command cp -r ${SRC_ROOT}/3rdparty ${SRC_ROOT}/LICENSE* ${DEST_ROOT}/
+}
+
+#==================================================================================
+# Section: Example
+#==================================================================================
+
+download_testdata_desc() { echo 'Download test data from Docker Hub
+'
+}
+download_testdata() {
+    c_echo W "Downloading test data..."
+    run_command mkdir -p ${TOP}/notebooks/input
+    if [ ! -e ${TOP}/notebooks/input/README.md ]; then
+        run_command rm -rf ${TOP}/notebooks/input
+        id=$(docker create gigony/svs-testdata:little-big)
+        run_command docker cp $id:/input ${TOP}/notebooks
+        run_command docker rm -v $id
+        c_echo G "Test data is downloaded to ${TOP}/notebooks/input!"
+    else
+        c_echo G "Test data already exists at ${TOP}/notebooks/input!"
+    fi
+}
+
+launch_notebooks_desc() { echo 'Launch jupyter notebooks
+
+Arguments:
+  -p <port> - port number
+  -h <host> - hostname to serve documentation on (default: 0.0.0.0)
+  -g        - launch GDS-enabled container
+'
+}
+launch_notebooks() {
+    local OPTIND
+    local port=$(get_unused_ports 1 10000 10030)
+    local host='0.0.0.0'
+    local gds_postfix=''
+    local gds_nvme_path=''
+
+    while getopts 'p:h:g:' option;
+    do
+        case "${option}" in
+            p)
+                port="$OPTARG"
+                ;;
+            h)
+                host="$OPTARG"
+                ;;
+            g)
+                gds_postfix='-gds'
+                [ -z "$OPTARG" ] && c_echo_err R "Please specify NVMe path!" && return 1
+                gds_nvme_path=$(readlink -f "$OPTARG")
+                [ ! -d "$gds_nvme_path" ] && c_echo_err R "Folder $gds_nvme_path doesn't exist!" && return 1
+
+                # Copy cufile SDK from host system to temp/gds
+                copy_gds_files_
+                ;;
+            *)
+                return 1
+       esac
+    done
+    shift $((OPTIND-1))
+
+    download_testdata
+
+    run_command cp ${TOP}/dist/*.whl ${TOP}/notebooks
+
+    run_command nvidia-docker build -t cucim-jupyter${gds_postfix} -f ${TOP}/docker/Dockerfile-jupyter${gds_postfix}-dev ${TOP}
+
+    [ $? -ne 0 ] && return 1
+
+    c_echo W "Port " G "$port" W " would be used...(" B "http://$(hostname -I | cut -d' ' -f 1):${port}" W ")"
+
+    if [ -z "${gds_postfix}" ]; then
+        run_command nvidia-docker run --gpus all -it --rm \
+            -v ${TOP}/notebooks:/notebooks \
+            -p ${port}:${port} \
+            cucim-jupyter \
+            -c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
+    else
+        local MNT_PATH=/nvme
+        local GDS_IMAGE=cucim-jupyter${gds_postfix}
+
+        local BUILD_VER=`uname -r`
+        local NV_DRIVER=`nvidia-smi -q -i 0 | sed -n 's/Driver Version.*: *\(.*\) *$/\1/p'`
+        echo "using nvidia driver version $NV_DRIVER on kernel $BUILD_VER"
+
+        local ofed_version=$(ofed_info -s | grep MLNX)
+        if [ $? -eq 0 ]; then
+            local rdma_core=$(dpkg -s libibverbs-dev | grep "Source: rdma-core")
+            if [ $? -eq 0 ]; then
+                local CONFIG_MOFED_VERSION=$(echo $ofed_version | cut -d '-' -f 2)
+                echo "Found MOFED version $CONFIG_MOFED_VERSION"
+            fi
+            local MLNX_SRCS="--volume /usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:/usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:ro"
+            local MOFED_DEVS="--net=host --volume /sys/class/infiniband_verbs:/sys/class/infiniband_verbs/ "
+        fi
+
+        docker run \
+            --ipc host \
+            -it \
+            --rm \
+            --gpus all \
+            -v ${TOP}/notebooks:/notebooks \
+            -p ${port}:${port} \
+            --volume /run/udev:/run/udev:ro \
+            --volume /sys/kernel/config:/sys/kernel/config/ \
+            --volume /usr/src/nvidia-$NV_DRIVER:/usr/src/nvidia-$NV_DRIVER:ro  ${MLNX_SRCS}\
+            --volume /dev:/dev:ro \
+            --privileged \
+            --env NV_DRIVER=${NV_DRIVER} \
+            --volume /lib/modules/$BUILD_VER/:/lib/modules/$BUILD_VER \
+            --volume "${MNT_PATH}:/notebooks/nvme:rw" \
+            ${MOFED_DEVS} \
+            ${GDS_IMAGE} \
+            -c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
+    fi
+
+}
+
+#==================================================================================
+# Section: Documentation
+#==================================================================================
+
+gen_docs_desc() { echo 'Generate document
+
+Generated docs would be avaialable at ${TOP}/dist/docs.
+
+Returns:
+  None
+
+  Exit code:
+    exit code returned from generating document
+'
+}
+gen_docs() {
+    local OUTPUT_FOLDER=${1:-${TOP}/dist/docs}
+    local ret=0
+    pushd ${TOP}/python/cucim > /dev/null
+
+    # Remove existing files in dist/docs
+    run_command rm -rf ${OUTPUT_FOLDER}/*
+
+    # Copy notebook files to python/cucim/dist/docs/notebooks
+    run_command mkdir -p ${TOP}/python/cucim/docs/notebooks
+    run_command rm -rf ${TOP}/python/cucim/docs/notebooks/*
+    run_command mkdir -p ${TOP}/python/cucim/docs/notebooks/static_images
+    run_command cp -r $(git ls-files ${TOP}/notebooks/*.ipynb) ${TOP}/python/cucim/docs/notebooks/
+    run_command cp -r ${TOP}/notebooks/static_images/*.png ${TOP}/python/cucim/docs/notebooks/static_images/
+
+    tox -e docs -- ${OUTPUT_FOLDER}
+    ret=$?
+    # Remove jupyter_execute folder explicitly until the issue is solved
+    #   https://github.com/executablebooks/MyST-NB/issues/129
+    rm -rf $(dirname ${OUTPUT_FOLDER})/jupyter_execute
+
+    popd > /dev/null
+    return $ret
+}
+
+gen_docs_dev_desc() { echo 'Generate document
+
+Launch dev-server for sphinx.
+
+Generated docs would be avaialable at ${TOP}/python/cucim/dist/docs.
+
+Arguments:
+  -p <port> - port number
+  -h <host> - hostname to serve documentation on (default: 0.0.0.0)
+Returns:
+  None
+
+  Exit code:
+    exit code returned from generating document
+'
+}
+gen_docs_dev() {
+    local OPTIND
+    local port=9999
+    local host=0.0.0.0
+
+    while getopts 'p:h:' option;
+    do
+        case "${option}" in
+            p)
+                port="$OPTARG"
+                ;;
+            h)
+                host="$OPTARG"
+                ;;
+            *)
+                echo_err R "Invalid option!"
+                return 1
+        esac
+    done
+
+    pushd ${TOP}/python/cucim > /dev/null
+
+    # Remove existing files in python/cucim/dist/docs
+    run_command rm -rf ${TOP}/python/cucim/dist/docs/*
+    # Copy notebook files to python/cucim/dist/docs/notebooks
+    run_command mkdir -p ${TOP}/python/cucim/docs/notebooks/static_images
+    run_command rm -rf ${TOP}/python/cucim/docs/notebooks/*
+    run_command cp -r $(git ls-files ${TOP}/notebooks/*.ipynb) ${TOP}/python/cucim/docs/notebooks/
+    run_command cp -r ${TOP}/notebooks/static_images/*.png ${TOP}/python/cucim/docs/notebooks/static_images/
+
+    run_command tox -e docs-dev -- --port ${port} --host ${host} docs dist/docs
+    popd > /dev/null
+}
+
+publish_docs_desc() { echo 'Publish generated documents to the server
+
+Publish generated documents to $GITLAB_PUBLISH_PROJECT_URL
+The web page is available at the followings:
+- $GITLAB_PUBLISH_PROJECT_URL
+
+Arguments:
+  $1 - If specified, force creating a tag with the specified tag name.
+       Use "latest" tag to make public documents up to date.
+
+Returns:
+  None
+
+  Exit code:
+    exit code returned from publishing documents
+'
+}
+publish_docs() {
+    local release_version="$(cat ${TOP}/VERSION)"
+    local release_tag="${1:-v${release_version}}"
+
+    # Import secrets
+    import_env_vars_ || return 1
+
+    c_echo W "Publishing documents to Gitlab Pages..."
+    TEMP_DOCS_DIR=$(mktemp -d)
+    c_echo r "TEMP_DOCS_DIR=${TEMP_DOCS_DIR}"
+    trap 'rm -rf ${TEMP_DOCS_DIR}' EXIT
+
+    pushd ${TEMP_DOCS_DIR} > /dev/null
+
+    git clone ${GITLAB_PUBLISH_GIT_URL} --branch init --single-branch
+    cd ${GITLAB_PUBLISH_PROJECT_NAME}
+    mkdir -p dist/docs
+    cp -rf ${TOP}/dist/docs/* dist/docs/
+    git checkout -b pages
+    git add dist/docs
+    git commit -am "Upload home page v$(cat ${TOP}/VERSION)"
+    git tag -f ${release_tag}
+    git push -f origin pages
+    git push -f origin ${release_tag}
+    popd > /dev/null
+
+    # Create tag if custom (such as 'latest') tag is specified
+    if [ -n "${1:-}" ]; then
+        git tag -f "$1"
+        git push -f origin "$1"
+    fi
+
+    run_command rm -rf ${TEMP_DOCS_DIR}
+    trap -- EXIT
+
+    c_echo W "Checkout the published webpage!"
+}
+
+#==================================================================================
+# Section: Release
+#==================================================================================
+
+bump_version_desc() { echo 'Bump version (internal use)
+
+Executes bump2version(https://github.com/c4urself/bump2version) with
+a post-processing.
+`bump2version` package would be installed if not available.
+
+Post-processing includes:
+- Updating version in *.ipynb files
+
+Returns:
+  Outputs executed by bump2version
+
+  Exit code:
+    exit code returned from bump2version
+'
+}
+bump_version() {
+    local part=${1:-}
+    local ret=0
+
+    if ! command -v bump2version > /dev/null; then
+        c_echo G "bump2version" W " doesn't exists. Installing " G "bump2version" W "..."
+        run_command pip3 install --user bump2version
+        hash -r
+    fi
+
+    pushd $TOP/python/cucim
+
+    case "$part" in
+        major|minor|patch)
+            local current_version="$(bump2version --dry-run --list --allow-dirty $part | grep -Po 'current_version=\K[\d\.]+')"
+            local new_version="$(bump2version --dry-run --list --allow-dirty $part | grep -Po 'new_version=\K[\d\.]+')"
+            c_echo W "current_version=" G "${current_version}"
+            c_echo W "new_version=" G "${new_version}"
+
+            local version_from_tag="$(git tag | grep -xE 'v[0-9\.]+' | sort --version-sort | tail -n 1 | tr -d 'v')"
+
+            # Print error if versions are not in sync.
+            if [ "${current_version}" != "${version_from_tag}" ]; then
+                c_echo_err R "version (" W "${current_version}" R ") from VERSION file doesn't match with version (" W "${version_from_tag}" R ") from tag."
+                c_echo_err W "Please sync those two versions!"
+                popd
+                return 1
+            fi
+
+            bump2version "$@"
+            ret=$?
+            if [ $ret -eq 0 ]; then
+                local file_name
+                for file_name in ${TOP}/notebooks/*.ipynb; do
+                    c_echo b "processing " g "${file_name} ..."
+                    sed -ie "s#cucim-${current_version}#cucim-${new_version}#" "${file_name}"
+                    sed -ie "s#cucim ${current_version}#cucim ${new_version}#" "${file_name}"
+                    sed -ie "s#cucim==${current_version}#cucim==${new_version}#" "${file_name}"
+                    sed -ie "s#cuclara-image ${current_version}#cuclara-image ${new_version}#" "${file_name}"
+                    sed -ie "s#cuclara-image-${current_version}#cuclara-image-${new_version}#" "${file_name}"
+                done
+            fi
+            ;;
+        *)
+            bump2version "$@"
+            ret=$?
+            ;;
+    esac
+
+    popd
+
+    return $ret
+}
+
+update_version_desc() { echo 'Update version
+
+Executes ci/release/update-version.sh which updates some version-related
+files based on the tag.
+
+ci/release/update-version.sh internally call
+  ./run bump_version [major|minor|patch]
+to update other version-realted files including VERSION files.
+
+Returns:
+  Outputs executed by update-version.sh
+
+  Exit code:
+    exit code returned from update-version.sh
+'
+}
+update_version() {
+    local ret=0
+    $TOP/ci/release/update-version.sh "$@"
+    ret=$?
+
+    return $ret
+}
+
+import_env_vars_() {
+    if [ ! -e ${TOP}/.env ]; then
+        c_echo_err "File " G "${TOP}/.env " Z "is not found.\n" R \
+        "Please create the file with the following environment variable!"
+        c_echo_err " - " W "GITLAB_ACCESS_TOKEN"
+        c_echo_err " - " W "GITLAB_PUBLISH_SERVER"
+        c_echo_err " - " W "GITLAB_PUBLISH_PROJECT_NAME"
+        c_echo_err " - " W "GITLAB_PUBLISH_PROJECT_ID"
+        c_echo_err " - " W "GITLAB_PUBLISH_PROJECT_URL"
+        c_echo_err " - " W "GITLAB_PUBLISH_GIT_URL"
+        return 1
+    fi
+
+    # Import environment variables
+    . ${TOP}/.env
+}
+release_package_desc() { echo 'Release generated packages to the server
+
+Release generated package files to $GITLAB_PUBLISH_PROJECT_URL
+This leverages "release" environment in tox and uses
+  https://gitlab.com/alelec/gitlab-release
+for uploading files to the server.
+
+Returns:
+  None
+
+  Exit code:
+    exit code returned from releasing packages
+'
+}
+release_package() {
+    local release_version="$(cat ${TOP}/VERSION)"
+    local release_tag="v${release_version}"
+    local wheel_file_name="cucim-${release_version}-py3-none-manylinux2014_x86_64.whl"
+    local package_file_name="cuCIM-${release_tag}-linux.tar.gz"
+    local package_data
+    local release_note_md="${TOP}/python/cucim/docs/release_notes/${release_tag}.md"
+
+    # Import secrets
+    import_env_vars_ || return 1
+
+    # Publish docs first to create a release tag
+    publish_docs
+
+    old_opt="$(shopt -op errexit);$(shopt -op nounset)" # save old shopts
+    set -eu
+    trap 'eval "${old_opt}"; unset old_opt' EXIT
+
+    pushd ${TOP}/python/cucim/ > /dev/null
+
+    # https://github.com/gitlabhq/gitlabhq/blob/master/doc/api/releases/index.md
+    # https://docs.gitlab.com/ee/api/releases/index.html#delete-a-release
+
+    # install jq if not exists
+    if ! command -v jq > /dev/null; then
+        c_echo G "jq" W " doesn't exists. Installing " G "jq" W "..."
+        sudo apt-get install -y jq
+        hash -r
+    fi
+
+    if curl --silent --header "PRIVATE-TOKEN: ${GITLAB_ACCESS_TOKEN}" "${GITLAB_PUBLISH_SERVER}/api/v4/projects/${GITLAB_PUBLISH_PROJECT_ID}/releases/${release_tag}" | jq '.name' -e > /dev/null; then
+        read -n 1 -r -p "$(c_str R "Do you want to delete existing release " G "${release_tag}" R " (y/n)?")"
+        echo
+        if [[ $REPLY =~ ^[Yy]$ ]]
+        then
+            c_echo W "Removing release " G "${release_tag}..."
+            curl --request DELETE --header "PRIVATE-TOKEN: ${GITLAB_ACCESS_TOKEN}" "${GITLAB_PUBLISH_SERVER}/api/v4/projects/${GITLAB_PUBLISH_PROJECT_ID}/releases/${release_tag}" | jq
+        else
+            return
+        fi
+    fi
+
+    run_command cd ${TOP}/dist
+    run_command rm -rf ${package_file_name}
+    run_command tar -czvf ${package_file_name} --exclude='*.gz' ./* ./.dockerignore  # --exclude="*.whl"
+    package_url="${GITLAB_PUBLISH_SERVER}$(curl --request POST \
+        --header "PRIVATE-TOKEN: ${GITLAB_ACCESS_TOKEN}" \
+        --form "file=@${package_file_name}" \
+        "${GITLAB_PUBLISH_SERVER}/api/v4/projects/${GITLAB_PUBLISH_PROJECT_ID}/uploads" \
+        | jq '.full_path' -r)"
+
+    wheel_url="${GITLAB_PUBLISH_SERVER}$(curl --request POST \
+        --header "PRIVATE-TOKEN: ${GITLAB_ACCESS_TOKEN}" \
+        --form "file=@${wheel_file_name}" \
+        "${GITLAB_PUBLISH_SERVER}/api/v4/projects/${GITLAB_PUBLISH_PROJECT_ID}/uploads" \
+        | jq '.full_path' -r)"
+
+    package_data="$(echo "
+import json
+release_note = open('${release_note_md}').read()
+data = {'tag_name': '${release_tag}',
+        'description': release_note,
+        'assets': {
+            'links': [
+                {'name': '${package_file_name}',
+                 'url': '${package_url}'
+                },
+                {'name': '${wheel_file_name}',
+                 'url': '${wheel_url}'
+                }
+            ]
+        }
+    }
+print(json.dumps(data))
+" | python -)"
+    c_echo b "${package_data}"
+
+    # Force-push tag
+    git tag -f ${release_tag} && git push -f origin ${release_tag}
+
+    # https://docs.gitlab.com/ee/api/releases/#create-a-release
+    curl --header 'Content-Type: application/json' --header "PRIVATE-TOKEN: ${GITLAB_ACCESS_TOKEN}" \
+        --data "${package_data}" \
+        --request POST "${GITLAB_PUBLISH_SERVER}/api/v4/projects/${GITLAB_PUBLISH_PROJECT_ID}/releases" | jq
+
+    popd > /dev/null
+
+    # tox -c ${TOP}/python/cucim -e release -- ${TOP}/run release_package_to_gitlab
+
+    eval "${old_opt}" # restore old shopts
+    trap -- EXIT
+    unset old_opt
+}
+
+parse_args() {
+    local OPTIND
+    while getopts 'yh' option;
+    do
+        case "${option}" in
+            # a)
+            #     VALUE=${OPTARG}
+            #     ;;
+            y)
+                ALWAYS_YES=true;
+                ;;
+            h)
+                print_usage
+                exit 1
+                ;;
+            *)
+                ;;
+        esac
+    done
+    shift $((OPTIND-1))
+
+    CMD="$1"
+    shift
+
+    ARGS=("$@")
+}
+
+print_usage() {
+    set +x
+    echo_err
+    echo_err "USAGE: $0 [command] [arguments]..."
+    echo_err ""
+    c_echo_err W "Global Arguments"
+    echo_err
+    c_echo_err W "Command List"
+    c_echo_err Y "    help  " w "----------------------------  Print detailed description for a given argument (command name)"
+    echo_err "$(get_list_of_available_commands "${RUN_SCRIPT_FILE}" | my_cat_prefix " ")"
+    echo_err
+}
+
+print_cmd_help_messages() {
+    local cmd="$1"
+    if [ -n "${cmd}" ]; then
+        if type ${cmd}_desc > /dev/null 2>&1; then
+            ${cmd}_desc
+            exit 0
+        else
+            c_echo_err R "Command '${cmd}' doesn't exist!"
+            exit 1
+        fi
+    fi
+    print_usage
+    return 0
+}
+
+main() {
+    local ret=0
+    parse_args "$@"
+
+    case "$CMD" in
+        help)
+            print_cmd_help_messages "${ARGS[@]}"
+            exit 0
+            ;;
+        package)
+            build_package "${ARGS[@]}"
+            ;;
+        notebooks)
+            launch_notebooks "${ARGS[@]}"
+            ;;
+        docs)
+            gen_docs "${ARGS[@]}"
+            ;;
+        docs:dev)
+            gen_docs_dev "${ARGS[@]}"
+            ;;
+        release)
+            release_package "${ARGS[@]}"
+            ;;
+        ''|main)
+            print_usage
+            ;;
+        *)
+            if type ${CMD} > /dev/null 2>&1; then
+                "$CMD" "${ARGS[@]}"
+            else
+                print_usage
+                exit 1
+            fi
+            ;;
+    esac
+    ret=$?
+    if [ -n "${SCRIPT_DIR}" ]; then
+        exit $ret
+    fi
+}
+
+init_globals
+
+if [ -n "${SCRIPT_DIR}" ]; then
+    main "$@"
+fi
+
+
+# Description template
+
+# Globals:
+#   CS_OS
+#   CS_TARGET
+#   CS_USER (used if CS_OS is "l4t")
+#   CS_HOST (used if CS_OS is "l4t")
+#   CS_OBSCURA_MODE (used in Obscura server)
+# Arguments:
+#   Command line to execute
+# Returns:
+#   Outputs print messages during the execution (stdout->stdout, stderr->stderr).
+
+#   Note:
+#     This command removes "\r" characters from stdout.
+
+#   Exit code:
+#     exit code returned from executing a given command
diff --git a/run_gds.sh b/run_gds.sh
new file mode 100644
index 000000000..74b0d235a
--- /dev/null
+++ b/run_gds.sh
@@ -0,0 +1,51 @@
+#!/bin/bash
+#
+# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+MNT_PATH=/nvme
+GDS_IMAGE=cucim-gds
+
+BUILD_VER=`uname -r`
+NV_DRIVER=`nvidia-smi -q -i 0 | sed -n 's/Driver Version.*: *\(.*\) *$/\1/p'`
+echo "using nvidia driver version $NV_DRIVER on kernel $BUILD_VER"
+
+
+ofed_version=$(ofed_info -s | grep MLNX)
+if [ $? -eq 0 ]; then
+    rdma_core=$(dpkg -s libibverbs-dev | grep "Source: rdma-core")
+    if [ $? -eq 0 ]; then
+        CONFIG_MOFED_VERSION=$(echo $ofed_version | cut -d '-' -f 2)
+        echo "Found MOFED version $CONFIG_MOFED_VERSION"
+    fi
+    MLNX_SRCS="--volume /usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:/usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:ro"
+    MOFED_DEVS="--net=host --volume /sys/class/infiniband_verbs:/sys/class/infiniband_verbs/ "
+fi
+
+docker run \
+    --ipc host \
+    -it
+    --rm
+    --gpus all \
+    --volume /run/udev:/run/udev:ro \
+    --volume /sys/kernel/config:/sys/kernel/config/ \
+    --volume /usr/src/nvidia-$NV_DRIVER:/usr/src/nvidia-$NV_DRIVER:ro  ${MLNX_SRCS}\
+    --volume /dev:/dev:ro \
+    --privileged \
+    --env NV_DRIVER=${NV_DRIVER} \
+    --volume 	/lib/modules/$BUILD_VER/:/lib/modules/$BUILD_VER \
+    --volume "${MNT_PATH}/data:/data:rw" \
+    --volume "${MNT_PATH}/results:/results:rw"  ${MOFED_DEVS} \
+    -itd ${REPO_URI}/${GDS_IMAGE} \
+    /bin/bash
diff --git a/scripts/auditwheel_repair.py b/scripts/auditwheel_repair.py
new file mode 100644
index 000000000..fec5f55bf
--- /dev/null
+++ b/scripts/auditwheel_repair.py
@@ -0,0 +1,99 @@
+# Apache License, Version 2.0
+# Copyright 2020 NVIDIA Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import functools
+from os.path import join
+import glob
+from unittest.mock import patch
+from auditwheel.main import main
+import auditwheel.elfutils
+from auditwheel.wheeltools import InWheelCtx
+
+# How auditwheel repair works?
+#
+# From https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/wheel_abi.py#L38
+# 1) Find a Python extension libraries(.so)
+#      if so ==> A, else ==> B
+# 2) `needed_libs` <== external libraries needed by A and B
+# 3) From b in B,
+#      if b is not in `needed_libs`, b is added to A
+# 4) Only external libraries in A are patched to use locally copied .so files
+#    - external libraries that exists under wheel path are discarded
+#      - https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/policy/external_references.py#L61
+#    - https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/repair.py#L62
+#
+# With current implementation,
+# - `cucim/_cucim.cpython-XX-x86_64-linux-gnu.so` files are in A by 1)
+# - `cucim/libcucim.so.0` is in B by 1)
+# - `cucim/libcucim.so.0` and `libcudart.so.11.0` are in `needed_libs` by 2)
+# - `cucim/cucim.kit.cuslide@0.1.1.so` is in A by 3)
+#
+# And only libz and libcudart are considered as external libraries.
+
+# To work with cuCIM, we need to
+# 1) make `cucim/libcucim.so.0` as Python extension library
+#   - Patch elf_is_python_extension : https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/elfutils.py#L81
+# 2) control how to copy external libraries
+#   - Patch copylib: https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/repair.py#L108
+#   - Need for libnvjpeg library
+# 3) preprocess wheel metadata
+#   - patch InWheelCtx.__enter__ : https://github.com/pypa/auditwheel/blob/3.2.0/auditwheel/wheeltools.py#L158
+#   - `Root-Is-Purelib: true` -> `Root-Is-Purelib: false` from WHEEL file
+
+
+# Parameters
+PYTHON_EXTENSION_LIBRARIES = {
+    'cucim/libcucim.so.0'
+}
+
+# 1) auditwheel.elfutils.elf_is_python_extension replacement
+orig_elf_is_python_extension = auditwheel.elfutils.elf_is_python_extension
+@functools.wraps(orig_elf_is_python_extension)
+def elf_is_python_extension(fn, elf):
+    if fn in PYTHON_EXTENSION_LIBRARIES:
+        print("[cuCIM] Consider {} as a python extension.".format(fn))
+        return True, 3
+    return orig_elf_is_python_extension(fn, elf)
+
+# 3) auditwheel.wheeltools.InWheelCtx.__enter__ replacement
+orig_inwheelctx_enter = InWheelCtx.__enter__
+@functools.wraps(orig_inwheelctx_enter)
+def inwheelctx_enter(self):
+    rtn = orig_inwheelctx_enter(self)
+
+    # `self.path` is a path that extracted files from the wheel file exists
+
+    # base_dir = glob.glob(join(self.path, '*.dist-info'))
+    # wheel_path = join(base_dir[0], 'WHEEL')
+    # with open(wheel_path, 'r') as f:
+    #     wheel_text = f.read()
+
+    # wheel_text = wheel_text.replace('Root-Is-Purelib: true', 'Root-Is-Purelib: false')
+
+    # with open(wheel_path, 'w') as f:
+    #     f.write(wheel_text)
+
+    return rtn
+
+# # sys.argv replacement
+# testargs = ["auditwheel_repair.py", "repair", "--plat", "manylinux2014_x86_64", "-w", "wherehouse", "cuclara_image-0.1.1-py3-none-manylinux2014_x86_64.whl"]
+# with patch.object(sys, 'argv', testargs):
+
+if __name__ == '__main__':
+    # Patch
+    with patch.object(auditwheel.elfutils, 'elf_is_python_extension', elf_is_python_extension):
+        with patch.object(InWheelCtx, '__enter__', inwheelctx_enter):
+            main()
diff --git a/scripts/run-dist b/scripts/run-dist
new file mode 100755
index 000000000..af26406f4
--- /dev/null
+++ b/scripts/run-dist
@@ -0,0 +1,500 @@
+#!/bin/bash
+#
+# Copyright (c) 2020, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+init_globals() {
+    if [ "$0" != "/bin/bash" ]; then
+        SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+        export RUN_SCRIPT_FILE="$(readlink -f "$0")"
+    else
+        export RUN_SCRIPT_FILE="$(readlink -f "${BASH_SOURCE[0]}")"
+    fi
+
+    export TOP=$(dirname "${RUN_SCRIPT_FILE}")
+}
+
+################################################################################
+# Utility functions
+################################################################################
+
+#######################################
+# Get list of available commands from a given input file.
+#
+# Available commands and command summary are extracted by checking a pattern
+# "_desc() { echo '".
+# Section title is extracted by checking a pattern "# Section: ".
+# This command is used for listing available commands in CLI.
+#
+# e.g.)
+#   "# Section: String/IO functions"
+#     => "# String/IO functions"
+#   "to_lower_desc() { echo 'Convert to lower case"
+#     => "to_lower ----------------- Convert to lower case"
+#
+# Arguments:
+#   $1 - input file that defines commands
+# Returns:
+#   Print list of available commands from $1
+#######################################
+get_list_of_available_commands() {
+    local file_name="$1"
+    if [ ! -e "$1" ]; then
+        echo "$1 doesn't exist!"
+    fi
+
+    local line_str='--------------------------------'
+    local IFS= cmd_lines="$(IFS= cat "$1" | grep -E -e "^(([[:alpha:]_[:digit:]]+)_desc\(\)|# Section: )" | sed "s/_desc() *{ *echo '/ : /")"
+    local line
+    while IFS= read -r line; do
+        local cmd=$(echo "$line" | cut -d":" -f1)
+        local desc=$(echo "$line" | cut -d":" -f2-)
+        if [ "$cmd" = "# Section" ]; then
+            c_echo B "${desc}"
+        else
+            # there is no substring operation in 'sh' so use 'cut'
+            local dash_line="$(echo "${line_str}" | cut -c ${#cmd}-)"  #  = "${line_str:${#cmd}}"
+             c_echo Y "   ${cmd}" w " ${dash_line} ${desc}"
+        fi
+        # use <<EOF, not '<<<"$cmd_lines"' to be executable in sh
+    done <<EOF
+$cmd_lines
+EOF
+}
+
+my_cat_prefix() {
+    local IFS
+    local prefix="$1"
+    local line
+    while IFS= read -r line; do
+        echo "${prefix}${line}" # -e option doesn't work in 'sh' so disallow escaped characters
+    done <&0
+}
+
+
+c_str() {
+    local old_color=39
+    local old_attr=0
+    local color=39
+    local attr=0
+    local text=""
+    #local no_change=0
+    for i in "$@"; do
+        case "$i" in
+            r|R)
+                color=31
+                ;;
+            g|G)
+                color=32
+                ;;
+            y|Y)
+                color=33
+                ;;
+            b|B)
+                color=34
+                ;;
+            p|P)
+                color=35
+                ;;
+            c|C)
+                color=36
+                ;;
+            w|W)
+                color=37
+                ;;
+
+            z|Z)
+                color=0
+                ;;
+        esac
+        case "$i" in
+            l|L|R|G|Y|B|P|C|W)
+                attr=1
+                ;;
+            n|N|r|g|y|b|p|c|w)
+                attr=0
+                ;;
+            z|Z)
+                attr=0
+                ;;
+            *)
+                text="${text}$i"
+        esac
+        if [ ${old_color} -ne ${color} ] || [ ${old_attr} -ne ${attr} ]; then
+            text="${text}\033[${attr};${color}m"
+            old_color=$color
+            old_attr=$attr
+        fi
+    done
+    /bin/echo -en "$text"
+}
+
+c_echo() {
+    local old_opt="$(shopt -op xtrace)" # save old xtrace option
+    set +x # unset xtrace
+    local text="$(c_str "$@")"
+    /bin/echo -e "$text\033[0m"
+    eval "${old_opt}" # restore old xtrace option
+}
+
+
+echo_err() {
+    >&2 echo "$@"
+}
+
+c_echo_err() {
+    >&2 c_echo "$@"
+}
+
+printf_err() {
+    >&2 printf "$@"
+}
+
+get_item_ranges() {
+    local indexes="$1"
+    local list="$2"
+    echo -n "$(echo "${list}" | xargs | cut -d " " -f "${indexes}")"
+    return $?
+}
+
+get_unused_ports() {
+    local num_of_ports=${1:-1}
+    local start=${2:-49152}
+    local end=${3:-61000}
+    comm -23 \
+    <(seq ${start} ${end} | sort) \
+    <(ss -tan | awk '{print $4}' | while read line; do echo ${line##*\:}; done | grep '[0-9]\{1,5\}' | sort -u) \
+    | shuf | tail -n ${num_of_ports} # use tail instead head to avoid broken pipe in VSCode terminal
+}
+
+newline() {
+    echo
+}
+
+info() {
+    c_echo W "$(date -u '+%Y-%m-%d %H:%M:%S') [INFO] " Z "$@"
+}
+
+error() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [ERROR] " Z "$@"
+}
+
+fatal() {
+    echo R "$(date -u '+%Y-%m-%d %H:%M:%S') [FATAL] " Z "$@"
+    echo
+    if [ -n "${SCRIPT_DIR}" ]; then
+        exit 1
+    fi
+}
+
+run_command() {
+    local status=0
+    local cmd="$*"
+
+    c_echo B "$(date -u '+%Y-%m-%d %H:%M:%S') " W "\$ " G "${cmd}"
+
+    [ "$(echo -n "$@")" = "" ] && return 1 # return 1 if there is no command available
+
+    "$@"
+    status=$?
+
+    unset IFS
+
+    return $status
+}
+
+retry() {
+    local retries=$1
+    shift
+
+    local count=0
+    until run_command "$@"; do
+        exit=$?
+        wait=$((2 ** count))
+        count=$((count + 1))
+        if [ $count -lt $retries ]; then
+            info "Retry $count/$retries. Exit code=$exit, Retrying in $wait seconds..."
+            sleep $wait
+        else
+            fatal "Retry $count/$retries. Exit code=$exit, no more retries left."
+            return 1
+        fi
+    done
+    return 0
+}
+
+#==================================================================================
+# Section: Example
+#==================================================================================
+
+download_testdata_desc() { echo 'Download test data from Docker Hub
+'
+}
+download_testdata() {
+    c_echo W "Downloading test data..."
+    run_command mkdir -p ${TOP}/notebooks/input
+    if [ ! -e ${TOP}/notebooks/input/README.md ]; then
+        run_command rm -rf ${TOP}/notebooks/input
+        id=$(docker create gigony/svs-testdata:little-big)
+        run_command docker cp $id:/input ${TOP}/notebooks
+        run_command docker rm -v $id
+        c_echo G "Test data is downloaded to ${TOP}/notebooks/input!"
+    else
+        c_echo G "Test data already exists at ${TOP}/notebooks/input!"
+    fi
+}
+
+copy_gds_files_() {
+    [ ! -d /usr/local/cuda/gds ] && c_echo_err R "GDS is not available at /usr/local/cuda/gds !" && return 1
+
+    rm -rf ${TOP}/temp/gds
+    mkdir -p ${TOP}/temp/gds/lib64
+    cp -P -r /usr/local/cuda/gds/* ${TOP}/temp/gds/
+    cp -P /usr/local/cuda/lib64/cufile.h /usr/local/cuda/lib64/libcufile* ${TOP}/temp/gds/lib64/
+}
+
+launch_notebooks_desc() { echo 'Launch jupyter notebooks
+
+Arguments:
+  -p <port> - port number
+  -h <host> - hostname to serve documentation on (default: 0.0.0.0)
+  -g        - launch GDS-enabled container
+'
+}
+launch_notebooks() {
+    local OPTIND
+    local port=$(get_unused_ports 1 10000 10030)
+    local host='0.0.0.0'
+    local gds_postfix=''
+    local gds_nvme_path=''
+
+    while getopts 'p:h:g:' option;
+    do
+        case "${option}" in
+            p)
+                port="$OPTARG"
+                ;;
+            h)
+                host="$OPTARG"
+                ;;
+            g)
+                gds_postfix='-gds'
+                echo "# OPTARG:$OPTARG"
+                [ -z "$OPTARG" ] && c_echo_err R "Please specify NVMe path!" && return 1
+                gds_nvme_path=$(readlink -f "$OPTARG")
+                [ ! -d "$gds_nvme_path" ] && c_echo_err R "Folder $gds_nvme_path doesn't exist!" && return 1
+
+                # Copy cufile SDK from host system to temp/gds
+                copy_gds_files_
+                ;;
+            *)
+                return 1
+       esac
+    done
+
+    download_testdata
+
+    run_command cp ${TOP}/*.whl ${TOP}/notebooks
+
+    run_command nvidia-docker build -t cucim-jupyter${gds_postfix} -f ${TOP}/docker/Dockerfile-jupyter${gds_postfix} ${TOP}
+
+    [ $? -ne 0 ] && return 1
+
+    c_echo W "Port " G "$port" W " would be used...(" B "http://$(hostname -I | cut -d' ' -f 1):${port}" W ")"
+
+    if [ -z "${gds_postfix}" ]; then
+        run_command nvidia-docker run --gpus all -it --rm \
+            -v ${TOP}/notebooks:/notebooks \
+            -p ${port}:${port} \
+            cucim-jupyter \
+            -c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
+    else
+        local MNT_PATH=/nvme
+        local GDS_IMAGE=cucim-jupyter${gds_postfix}
+
+        local BUILD_VER=`uname -r`
+        local NV_DRIVER=`nvidia-smi -q -i 0 | sed -n 's/Driver Version.*: *\(.*\) *$/\1/p'`
+        echo "using nvidia driver version $NV_DRIVER on kernel $BUILD_VER"
+
+        local ofed_version=$(ofed_info -s | grep MLNX)
+        if [ $? -eq 0 ]; then
+            local rdma_core=$(dpkg -s libibverbs-dev | grep "Source: rdma-core")
+            if [ $? -eq 0 ]; then
+                local CONFIG_MOFED_VERSION=$(echo $ofed_version | cut -d '-' -f 2)
+                echo "Found MOFED version $CONFIG_MOFED_VERSION"
+            fi
+            local MLNX_SRCS="--volume /usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:/usr/src/mlnx-ofed-kernel-${CONFIG_MOFED_VERSION}:ro"
+            local MOFED_DEVS="--net=host --volume /sys/class/infiniband_verbs:/sys/class/infiniband_verbs/ "
+        fi
+
+        docker run \
+            --ipc host \
+            -it \
+            --rm \
+            --gpus all \
+            -v ${TOP}/notebooks:/notebooks \
+            -p ${port}:${port} \
+            --volume /run/udev:/run/udev:ro \
+            --volume /sys/kernel/config:/sys/kernel/config/ \
+            --volume /usr/src/nvidia-$NV_DRIVER:/usr/src/nvidia-$NV_DRIVER:ro  ${MLNX_SRCS}\
+            --volume /dev:/dev:ro \
+            --privileged \
+            --env NV_DRIVER=${NV_DRIVER} \
+            --volume /lib/modules/$BUILD_VER/:/lib/modules/$BUILD_VER \
+            --volume "${MNT_PATH}:/notebooks/nvme:rw" \
+            ${MOFED_DEVS} \
+            ${GDS_IMAGE} \
+            -c "echo -n 'Enter New Password: '; jupyter lab --ServerApp.password=\"\$(python3 -u -c \"from jupyter_server.auth import passwd;pw=input();print(passwd(pw));\" | egrep 'sha|argon')\" --ServerApp.root_dir=/notebooks --allow-root --port=${port} --ip=${host} --no-browser"
+    fi
+}
+
+#==================================================================================
+# Section: Build
+#==================================================================================
+
+build_train() {
+    local image_name=${1:-cucim-train}
+    run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-claratrain ${TOP}/docker
+}
+
+build_train_desc() { echo 'Build Clara Train Docker image with cuCIM (& OpenSlide)
+
+Build image from docker/Dockerfile-claratrain
+
+Arguments:
+  $1 - docker image name (default:cucim-train)
+'
+}
+build_train() {
+    local image_name=${1:-cucim-train}
+    run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-claratrain ${TOP}
+}
+
+build_examples_desc() { echo 'Build cuCIM C++ examples
+'
+}
+build_examples() {
+    local image_name=cucim-cmake
+    run_command docker build -t ${image_name} -f ${TOP}/docker/Dockerfile-cmake ${TOP}/docker
+    run_command docker run -it --rm \
+        -v ${TOP}:/workspace \
+        ${image_name} \
+            -c "
+                mkdir -p /workspace/examples/cpp/build;
+                rm -rf /workspace/examples/cpp/build/*;
+                cd /workspace/examples/cpp/build;
+                cmake .. && make"
+    c_echo W "Copying binary files to ${TOP}/bin folder..."
+    run_command mkdir -p ${TOP}/bin
+    run_command cp ${TOP}/examples/cpp/build/bin/* ${TOP}/bin
+
+    download_testdata
+
+    c_echo W "Execute the binary with the following commands:"
+    c_echo   "  # Set library path"
+    c_echo B "  export LD_LIBRARY_PATH=${TOP}/install/lib:\$LD_LIBRARY_PATH"
+    c_echo   "  # Execute"
+    c_echo B "  ./bin/tiff_image notebooks/input/image.tif ."
+}
+
+parse_args() {
+    local OPTIND
+    while getopts 'yh' option;
+    do
+        case "${option}" in
+            y)
+                ALWAYS_YES=true;
+                ;;
+            h)
+                print_usage
+                exit 1
+                ;;
+            *)
+                ;;
+        esac
+    done
+    shift $((OPTIND-1))
+
+    CMD="$1"
+    shift
+
+    ARGS=("$@")
+}
+
+print_usage() {
+    set +x
+    echo_err
+    echo_err "USAGE: $0 [command] [arguments]..."
+    echo_err ""
+    c_echo_err W "Global Arguments"
+    echo_err
+    c_echo_err W "Command List"
+    c_echo_err Y "    help  " w "----------------------------  Print detailed description for a given argument (command name)"
+    echo_err "$(get_list_of_available_commands "${RUN_SCRIPT_FILE}" | my_cat_prefix " ")"
+    echo_err
+}
+
+print_cmd_help_messages() {
+    local cmd="$1"
+    if [ -n "${cmd}" ]; then
+        if type ${cmd}_desc > /dev/null 2>&1; then
+            ${cmd}_desc
+            exit 0
+        else
+            c_echo_err R "Command '${cmd}' doesn't exist!"
+            exit 1
+        fi
+    fi
+    print_usage
+    return 0
+}
+
+main() {
+    local ret=0
+    parse_args "$@"
+
+    case "$CMD" in
+        help)
+            print_cmd_help_messages "${ARGS[@]}"
+            exit 0
+            ;;
+        build)
+            build_examples "${ARGS[@]}"
+            ;;
+        notebooks)
+            launch_notebooks "${ARGS[@]}"
+            ;;
+        ''|main)
+            print_usage
+            ;;
+        *)
+            if type ${CMD} > /dev/null 2>&1; then
+                "$CMD" "${ARGS[@]}"
+            else
+                print_usage
+                exit 1
+            fi
+            ;;
+    esac
+    ret=$?
+    if [ -n "${SCRIPT_DIR}" ]; then
+        exit $ret
+    fi
+}
+
+init_globals
+
+if [ -n "${SCRIPT_DIR}" ]; then
+    main "$@"
+fi
diff --git a/test_data/.gitignore b/test_data/.gitignore
new file mode 100644
index 000000000..f74f5f4a6
--- /dev/null
+++ b/test_data/.gitignore
@@ -0,0 +1 @@
+input/*.svs
diff --git a/test_data/Dockerfile b/test_data/Dockerfile
new file mode 100644
index 000000000..180b2259a
--- /dev/null
+++ b/test_data/Dockerfile
@@ -0,0 +1,4 @@
+FROM scratch
+WORKDIR /input
+COPY ./input /input
+CMD ["dummy"]
diff --git a/test_data/README.md b/test_data/README.md
new file mode 100644
index 000000000..575398bea
--- /dev/null
+++ b/test_data/README.md
@@ -0,0 +1,13 @@
+# Test Data Folder (`test_data` folder)
+
+Symbolic links for for this folder is available under the following subfolders:
+
+- `cpp/plugins/cucim.kit.cuslide
+- `python/cucim`
+
+## Private Test Data (`test_data/private` folder)
+
+- `generic_tiff_000.tif` : 256x256 multi-resolution/tiled TIF conversion of `TUPAC-TR-467.svs` from the dataset
+of [Tumor Proliferation Assessment Challenge 2016](http://tupac.tue-image.nl/node/3) (TUPAC16 | MICCAI Grand Challenge) which are publicly
+available through [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) under [CC BY 3.0 License](https://creativecommons.org/licenses/by/3.0/)
+- `philips_tiff_000.tif` : `patient_100_node_0.tif` from the [Camelyon16 challenge data](https://drive.google.com/drive/folders/0BzsdkU4jWx9BUzVXeUg0dUNOR1U) (another [link](https://camelyon17.grand-challenge.org/Data/)) which is under [CC0 License](https://choosealicense.com/licenses/cc0-1.0/)
diff --git a/test_data/input/LICENSE-3rdparty b/test_data/input/LICENSE-3rdparty
new file mode 100644
index 000000000..bd32fa847
--- /dev/null
+++ b/test_data/input/LICENSE-3rdparty
@@ -0,0 +1,319 @@
+Creative Commons Legal Code
+
+Attribution 3.0 Unported
+
+    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
+    LEGAL SERVICES. DISTRIBUTION OF THIS LICENSE DOES NOT CREATE AN
+    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
+    INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
+    REGARDING THE INFORMATION PROVIDED, AND DISCLAIMS LIABILITY FOR
+    DAMAGES RESULTING FROM ITS USE.
+
+License
+
+THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE
+COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY
+COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS
+AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.
+
+BY EXERCISING ANY RIGHTS TO THE WORK PROVIDED HERE, YOU ACCEPT AND AGREE
+TO BE BOUND BY THE TERMS OF THIS LICENSE. TO THE EXTENT THIS LICENSE MAY
+BE CONSIDERED TO BE A CONTRACT, THE LICENSOR GRANTS YOU THE RIGHTS
+CONTAINED HERE IN CONSIDERATION OF YOUR ACCEPTANCE OF SUCH TERMS AND
+CONDITIONS.
+
+1. Definitions
+
+ a. "Adaptation" means a work based upon the Work, or upon the Work and
+    other pre-existing works, such as a translation, adaptation,
+    derivative work, arrangement of music or other alterations of a
+    literary or artistic work, or phonogram or performance and includes
+    cinematographic adaptations or any other form in which the Work may be
+    recast, transformed, or adapted including in any form recognizably
+    derived from the original, except that a work that constitutes a
+    Collection will not be considered an Adaptation for the purpose of
+    this License. For the avoidance of doubt, where the Work is a musical
+    work, performance or phonogram, the synchronization of the Work in
+    timed-relation with a moving image ("synching") will be considered an
+    Adaptation for the purpose of this License.
+ b. "Collection" means a collection of literary or artistic works, such as
+    encyclopedias and anthologies, or performances, phonograms or
+    broadcasts, or other works or subject matter other than works listed
+    in Section 1(f) below, which, by reason of the selection and
+    arrangement of their contents, constitute intellectual creations, in
+    which the Work is included in its entirety in unmodified form along
+    with one or more other contributions, each constituting separate and
+    independent works in themselves, which together are assembled into a
+    collective whole. A work that constitutes a Collection will not be
+    considered an Adaptation (as defined above) for the purposes of this
+    License.
+ c. "Distribute" means to make available to the public the original and
+    copies of the Work or Adaptation, as appropriate, through sale or
+    other transfer of ownership.
+ d. "Licensor" means the individual, individuals, entity or entities that
+    offer(s) the Work under the terms of this License.
+ e. "Original Author" means, in the case of a literary or artistic work,
+    the individual, individuals, entity or entities who created the Work
+    or if no individual or entity can be identified, the publisher; and in
+    addition (i) in the case of a performance the actors, singers,
+    musicians, dancers, and other persons who act, sing, deliver, declaim,
+    play in, interpret or otherwise perform literary or artistic works or
+    expressions of folklore; (ii) in the case of a phonogram the producer
+    being the person or legal entity who first fixes the sounds of a
+    performance or other sounds; and, (iii) in the case of broadcasts, the
+    organization that transmits the broadcast.
+ f. "Work" means the literary and/or artistic work offered under the terms
+    of this License including without limitation any production in the
+    literary, scientific and artistic domain, whatever may be the mode or
+    form of its expression including digital form, such as a book,
+    pamphlet and other writing; a lecture, address, sermon or other work
+    of the same nature; a dramatic or dramatico-musical work; a
+    choreographic work or entertainment in dumb show; a musical
+    composition with or without words; a cinematographic work to which are
+    assimilated works expressed by a process analogous to cinematography;
+    a work of drawing, painting, architecture, sculpture, engraving or
+    lithography; a photographic work to which are assimilated works
+    expressed by a process analogous to photography; a work of applied
+    art; an illustration, map, plan, sketch or three-dimensional work
+    relative to geography, topography, architecture or science; a
+    performance; a broadcast; a phonogram; a compilation of data to the
+    extent it is protected as a copyrightable work; or a work performed by
+    a variety or circus performer to the extent it is not otherwise
+    considered a literary or artistic work.
+ g. "You" means an individual or entity exercising rights under this
+    License who has not previously violated the terms of this License with
+    respect to the Work, or who has received express permission from the
+    Licensor to exercise rights under this License despite a previous
+    violation.
+ h. "Publicly Perform" means to perform public recitations of the Work and
+    to communicate to the public those public recitations, by any means or
+    process, including by wire or wireless means or public digital
+    performances; to make available to the public Works in such a way that
+    members of the public may access these Works from a place and at a
+    place individually chosen by them; to perform the Work to the public
+    by any means or process and the communication to the public of the
+    performances of the Work, including by public digital performance; to
+    broadcast and rebroadcast the Work by any means including signs,
+    sounds or images.
+ i. "Reproduce" means to make copies of the Work by any means including
+    without limitation by sound or visual recordings and the right of
+    fixation and reproducing fixations of the Work, including storage of a
+    protected performance or phonogram in digital form or other electronic
+    medium.
+
+2. Fair Dealing Rights. Nothing in this License is intended to reduce,
+limit, or restrict any uses free from copyright or rights arising from
+limitations or exceptions that are provided for in connection with the
+copyright protection under copyright law or other applicable laws.
+
+3. License Grant. Subject to the terms and conditions of this License,
+Licensor hereby grants You a worldwide, royalty-free, non-exclusive,
+perpetual (for the duration of the applicable copyright) license to
+exercise the rights in the Work as stated below:
+
+ a. to Reproduce the Work, to incorporate the Work into one or more
+    Collections, and to Reproduce the Work as incorporated in the
+    Collections;
+ b. to create and Reproduce Adaptations provided that any such Adaptation,
+    including any translation in any medium, takes reasonable steps to
+    clearly label, demarcate or otherwise identify that changes were made
+    to the original Work. For example, a translation could be marked "The
+    original work was translated from English to Spanish," or a
+    modification could indicate "The original work has been modified.";
+ c. to Distribute and Publicly Perform the Work including as incorporated
+    in Collections; and,
+ d. to Distribute and Publicly Perform Adaptations.
+ e. For the avoidance of doubt:
+
+     i. Non-waivable Compulsory License Schemes. In those jurisdictions in
+        which the right to collect royalties through any statutory or
+        compulsory licensing scheme cannot be waived, the Licensor
+        reserves the exclusive right to collect such royalties for any
+        exercise by You of the rights granted under this License;
+    ii. Waivable Compulsory License Schemes. In those jurisdictions in
+        which the right to collect royalties through any statutory or
+        compulsory licensing scheme can be waived, the Licensor waives the
+        exclusive right to collect such royalties for any exercise by You
+        of the rights granted under this License; and,
+   iii. Voluntary License Schemes. The Licensor waives the right to
+        collect royalties, whether individually or, in the event that the
+        Licensor is a member of a collecting society that administers
+        voluntary licensing schemes, via that society, from any exercise
+        by You of the rights granted under this License.
+
+The above rights may be exercised in all media and formats whether now
+known or hereafter devised. The above rights include the right to make
+such modifications as are technically necessary to exercise the rights in
+other media and formats. Subject to Section 8(f), all rights not expressly
+granted by Licensor are hereby reserved.
+
+4. Restrictions. The license granted in Section 3 above is expressly made
+subject to and limited by the following restrictions:
+
+ a. You may Distribute or Publicly Perform the Work only under the terms
+    of this License. You must include a copy of, or the Uniform Resource
+    Identifier (URI) for, this License with every copy of the Work You
+    Distribute or Publicly Perform. You may not offer or impose any terms
+    on the Work that restrict the terms of this License or the ability of
+    the recipient of the Work to exercise the rights granted to that
+    recipient under the terms of the License. You may not sublicense the
+    Work. You must keep intact all notices that refer to this License and
+    to the disclaimer of warranties with every copy of the Work You
+    Distribute or Publicly Perform. When You Distribute or Publicly
+    Perform the Work, You may not impose any effective technological
+    measures on the Work that restrict the ability of a recipient of the
+    Work from You to exercise the rights granted to that recipient under
+    the terms of the License. This Section 4(a) applies to the Work as
+    incorporated in a Collection, but this does not require the Collection
+    apart from the Work itself to be made subject to the terms of this
+    License. If You create a Collection, upon notice from any Licensor You
+    must, to the extent practicable, remove from the Collection any credit
+    as required by Section 4(b), as requested. If You create an
+    Adaptation, upon notice from any Licensor You must, to the extent
+    practicable, remove from the Adaptation any credit as required by
+    Section 4(b), as requested.
+ b. If You Distribute, or Publicly Perform the Work or any Adaptations or
+    Collections, You must, unless a request has been made pursuant to
+    Section 4(a), keep intact all copyright notices for the Work and
+    provide, reasonable to the medium or means You are utilizing: (i) the
+    name of the Original Author (or pseudonym, if applicable) if supplied,
+    and/or if the Original Author and/or Licensor designate another party
+    or parties (e.g., a sponsor institute, publishing entity, journal) for
+    attribution ("Attribution Parties") in Licensor's copyright notice,
+    terms of service or by other reasonable means, the name of such party
+    or parties; (ii) the title of the Work if supplied; (iii) to the
+    extent reasonably practicable, the URI, if any, that Licensor
+    specifies to be associated with the Work, unless such URI does not
+    refer to the copyright notice or licensing information for the Work;
+    and (iv) , consistent with Section 3(b), in the case of an Adaptation,
+    a credit identifying the use of the Work in the Adaptation (e.g.,
+    "French translation of the Work by Original Author," or "Screenplay
+    based on original Work by Original Author"). The credit required by
+    this Section 4 (b) may be implemented in any reasonable manner;
+    provided, however, that in the case of a Adaptation or Collection, at
+    a minimum such credit will appear, if a credit for all contributing
+    authors of the Adaptation or Collection appears, then as part of these
+    credits and in a manner at least as prominent as the credits for the
+    other contributing authors. For the avoidance of doubt, You may only
+    use the credit required by this Section for the purpose of attribution
+    in the manner set out above and, by exercising Your rights under this
+    License, You may not implicitly or explicitly assert or imply any
+    connection with, sponsorship or endorsement by the Original Author,
+    Licensor and/or Attribution Parties, as appropriate, of You or Your
+    use of the Work, without the separate, express prior written
+    permission of the Original Author, Licensor and/or Attribution
+    Parties.
+ c. Except as otherwise agreed in writing by the Licensor or as may be
+    otherwise permitted by applicable law, if You Reproduce, Distribute or
+    Publicly Perform the Work either by itself or as part of any
+    Adaptations or Collections, You must not distort, mutilate, modify or
+    take other derogatory action in relation to the Work which would be
+    prejudicial to the Original Author's honor or reputation. Licensor
+    agrees that in those jurisdictions (e.g. Japan), in which any exercise
+    of the right granted in Section 3(b) of this License (the right to
+    make Adaptations) would be deemed to be a distortion, mutilation,
+    modification or other derogatory action prejudicial to the Original
+    Author's honor and reputation, the Licensor will waive or not assert,
+    as appropriate, this Section, to the fullest extent permitted by the
+    applicable national law, to enable You to reasonably exercise Your
+    right under Section 3(b) of this License (right to make Adaptations)
+    but not otherwise.
+
+5. Representations, Warranties and Disclaimer
+
+UNLESS OTHERWISE MUTUALLY AGREED TO BY THE PARTIES IN WRITING, LICENSOR
+OFFERS THE WORK AS-IS AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY
+KIND CONCERNING THE WORK, EXPRESS, IMPLIED, STATUTORY OR OTHERWISE,
+INCLUDING, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTIBILITY,
+FITNESS FOR A PARTICULAR PURPOSE, NONINFRINGEMENT, OR THE ABSENCE OF
+LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OF ABSENCE OF ERRORS,
+WHETHER OR NOT DISCOVERABLE. SOME JURISDICTIONS DO NOT ALLOW THE EXCLUSION
+OF IMPLIED WARRANTIES, SO SUCH EXCLUSION MAY NOT APPLY TO YOU.
+
+6. Limitation on Liability. EXCEPT TO THE EXTENT REQUIRED BY APPLICABLE
+LAW, IN NO EVENT WILL LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY FOR
+ANY SPECIAL, INCIDENTAL, CONSEQUENTIAL, PUNITIVE OR EXEMPLARY DAMAGES
+ARISING OUT OF THIS LICENSE OR THE USE OF THE WORK, EVEN IF LICENSOR HAS
+BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
+
+7. Termination
+
+ a. This License and the rights granted hereunder will terminate
+    automatically upon any breach by You of the terms of this License.
+    Individuals or entities who have received Adaptations or Collections
+    from You under this License, however, will not have their licenses
+    terminated provided such individuals or entities remain in full
+    compliance with those licenses. Sections 1, 2, 5, 6, 7, and 8 will
+    survive any termination of this License.
+ b. Subject to the above terms and conditions, the license granted here is
+    perpetual (for the duration of the applicable copyright in the Work).
+    Notwithstanding the above, Licensor reserves the right to release the
+    Work under different license terms or to stop distributing the Work at
+    any time; provided, however that any such election will not serve to
+    withdraw this License (or any other license that has been, or is
+    required to be, granted under the terms of this License), and this
+    License will continue in full force and effect unless terminated as
+    stated above.
+
+8. Miscellaneous
+
+ a. Each time You Distribute or Publicly Perform the Work or a Collection,
+    the Licensor offers to the recipient a license to the Work on the same
+    terms and conditions as the license granted to You under this License.
+ b. Each time You Distribute or Publicly Perform an Adaptation, Licensor
+    offers to the recipient a license to the original Work on the same
+    terms and conditions as the license granted to You under this License.
+ c. If any provision of this License is invalid or unenforceable under
+    applicable law, it shall not affect the validity or enforceability of
+    the remainder of the terms of this License, and without further action
+    by the parties to this agreement, such provision shall be reformed to
+    the minimum extent necessary to make such provision valid and
+    enforceable.
+ d. No term or provision of this License shall be deemed waived and no
+    breach consented to unless such waiver or consent shall be in writing
+    and signed by the party to be charged with such waiver or consent.
+ e. This License constitutes the entire agreement between the parties with
+    respect to the Work licensed here. There are no understandings,
+    agreements or representations with respect to the Work not specified
+    here. Licensor shall not be bound by any additional provisions that
+    may appear in any communication from You. This License may not be
+    modified without the mutual written agreement of the Licensor and You.
+ f. The rights granted under, and the subject matter referenced, in this
+    License were drafted utilizing the terminology of the Berne Convention
+    for the Protection of Literary and Artistic Works (as amended on
+    September 28, 1979), the Rome Convention of 1961, the WIPO Copyright
+    Treaty of 1996, the WIPO Performances and Phonograms Treaty of 1996
+    and the Universal Copyright Convention (as revised on July 24, 1971).
+    These rights and subject matter take effect in the relevant
+    jurisdiction in which the License terms are sought to be enforced
+    according to the corresponding provisions of the implementation of
+    those treaty provisions in the applicable national law. If the
+    standard suite of rights granted under applicable copyright law
+    includes additional rights not granted under this License, such
+    additional rights are deemed to be included in the License; this
+    License is not intended to restrict the license of any rights under
+    applicable law.
+
+
+Creative Commons Notice
+
+    Creative Commons is not a party to this License, and makes no warranty
+    whatsoever in connection with the Work. Creative Commons will not be
+    liable to You or any party on any legal theory for any damages
+    whatsoever, including without limitation any general, special,
+    incidental or consequential damages arising in connection to this
+    license. Notwithstanding the foregoing two (2) sentences, if Creative
+    Commons has expressly identified itself as the Licensor hereunder, it
+    shall have all rights and obligations of Licensor.
+
+    Except for the limited purpose of indicating to the public that the
+    Work is licensed under the CCPL, Creative Commons does not authorize
+    the use by either party of the trademark "Creative Commons" or any
+    related trademark or logo of Creative Commons without the prior
+    written consent of Creative Commons. Any permitted use will be in
+    compliance with Creative Commons' then-current trademark usage
+    guidelines, as may be published on its website or otherwise made
+    available upon request from time to time. For the avoidance of doubt,
+    this trademark restriction does not form part of this License.
+
+    Creative Commons may be contacted at https://creativecommons.org/.
\ No newline at end of file
diff --git a/test_data/input/README.md b/test_data/input/README.md
new file mode 100644
index 000000000..6251b5b75
--- /dev/null
+++ b/test_data/input/README.md
@@ -0,0 +1,18 @@
+
+# Test Dataset
+
+TUPAC-TR-488.svs and TUPAC-TR-467.svs are breast cancer cases (TCGA-BRCA) from the dataset
+of Tumor Proliferation Assessment Challenge 2016 (TUPAC16 | MICCAI Grand Challenge) which are publicly
+available through [The Cancer Genome Atlas (TCGA)](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) under [CC BY 3.0 License](https://creativecommons.org/licenses/by/3.0/)
+
+- Website: http://tupac.tue-image.nl/node/3
+- Data link: https://drive.google.com/drive/u/0/folders/0B--ztKW0d17XYlBqOXppQmw0M2M
+  - TUPAC-TR-467.svs : https://portal.gdc.cancer.gov/files/575c0465-c4bc-4ea7-ab63-ba48aa5e374b
+  - TUPAC-TR-488.svs : https://portal.gdc.cancer.gov/files/e27c87c9-e163-4d55-8f27-4cc7dfca08d8
+- License: CC BY 3.0 (https://wiki.cancerimagingarchive.net/display/Public/TCGA-BRCA#3539225f58e64731d8e47d588cedd99d300d5d6)
+  - See LICENSE-3rdparty file
+
+## Converted files
+
+- image.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-467.svs
+- image2.tif : 256x256 multi-resolution/tiled TIF conversion of TUPAC-TR-488.svs
diff --git a/test_data/push_testdata.sh b/test_data/push_testdata.sh
new file mode 100755
index 000000000..fae33a732
--- /dev/null
+++ b/test_data/push_testdata.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+
+docker build -t gigony/svs-testdata:little-big ${SCRIPT_DIR}
+docker push gigony/svs-testdata:little-big