diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 03ce2149..00000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml new file mode 100644 index 00000000..d68b17e1 --- /dev/null +++ b/.github/FUNDING.yml @@ -0,0 +1,12 @@ +# These are supported funding model platforms + +github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] +patreon: # Replace with a single Patreon username +open_collective: # Replace with a single Open Collective username +ko_fi: # Replace with a single Ko-fi username +tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel +community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry +liberapay: # Replace with a single Liberapay username +issuehunt: # Replace with a single IssueHunt username +otechie: # Replace with a single Otechie username +custom: ['https://www.paypal.me/Udayraj123/','https://www.buymeacoffee.com/Udayraj123'] diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..408ab293 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,34 @@ +--- +name: Bug report +about: Create a report to help us improve +title: "[Bug]" +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Use sample '...' +2. Command(s) used '....' +3. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. MacOS, Linux, Windows] + - Python version + - OpenCV version + + +**Additional context** +Add any other context about the problem here. + +Error Stack trace. Sample images used, etc diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..604341ba --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea/enhancement for this project +title: "[Feature]" +labels: enhancement +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I think implementing [...] will help everyone. + +**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 or screenshots about the feature request here. diff --git a/.github/pre-commit.yml b/.github/pre-commit.yml new file mode 100644 index 00000000..d6b9d9a6 --- /dev/null +++ b/.github/pre-commit.yml @@ -0,0 +1,13 @@ +name: Pre-Commit Hook + +on: [push, pull_request] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v3 + with: + python-version: '3.11' + - uses: pre-commit/action@v3.0.0 diff --git a/.gitignore b/.gitignore index 2778cf2c..a7d4046b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,12 +1,14 @@ -**/__pycache__ -**/CheckedOMRs -**/ignore -**/.DS_Store -OMRChecker.wiki/ +# Any directory starting with a dot +**/\.*/ +# Except .github +!.github/ + # Everything in inputs/ and outputs/ inputs/* outputs/* -# Except *.json and OMR_Files/ -# !inputs/OMR_Files/ -# !inputs/*.json -# !inputs/omr_marker.jpg \ No newline at end of file + +# Misc +**/.DS_Store +**/__pycache__ +venv/ +OMRChecker.wiki/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..2daa2af8 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,59 @@ +exclude: "__snapshots__/.*$" +default_install_hook_types: [pre-commit, pre-push] +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: check-yaml + stages: [commit] + - id: check-added-large-files + args: ['--maxkb=300'] + fail_fast: false + stages: [commit] + - id: pretty-format-json + args: ['--autofix', '--no-sort-keys'] + - id: end-of-file-fixer + exclude_types: ["csv", "json"] + stages: [commit] + - id: trailing-whitespace + stages: [commit] + - repo: https://github.com/pycqa/isort + rev: 5.12.0 + hooks: + - id: isort + args: ["--profile", "black"] + stages: [commit] + - repo: https://github.com/psf/black + rev: 23.3.0 + hooks: + - id: black + fail_fast: true + stages: [commit] + - repo: https://github.com/pycqa/flake8 + rev: 6.0.0 + hooks: + - id: flake8 + args: + - "--ignore=E501,W503,E203,E741,F541" # Line too long, Line break occurred before a binary operator, Whitespace before ':' + fail_fast: true + stages: [commit] + - repo: local + hooks: + - id: pytest-on-commit + name: Running single sample test + entry: python3 -m pytest -rfpsxEX --disable-warnings --verbose -k sample1 + language: system + pass_filenames: false + always_run: true + fail_fast: true + stages: [commit] + - repo: local + hooks: + - id: pytest-on-push + name: Running all tests before push... + entry: python3 -m pytest -rfpsxEX --disable-warnings --verbose --durations=3 + language: system + pass_filenames: false + always_run: true + fail_fast: true + stages: [push] diff --git a/.pylintrc b/.pylintrc new file mode 100644 index 00000000..405ce2e7 --- /dev/null +++ b/.pylintrc @@ -0,0 +1,43 @@ +[BASIC] +# Regular expression matching correct variable names. Overrides variable-naming-style. +# snake_case with single letter regex - +variable-rgx=[a-z0-9_]{1,30}$ + +# Good variable names which should always be accepted, separated by a comma. +good-names=x,y,pt + +[MESSAGES CONTROL] + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once). You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use "--disable=all --enable=classes +# --disable=W". +disable=import-error, + unresolved-import, + too-few-public-methods, + missing-docstring, + relative-beyond-top-level, + too-many-instance-attributes, + bad-continuation, + no-member + +# Note: bad-continuation is a false positive showing bug in pylint +# https://github.com/psf/black/issues/48 + + +[REPORTS] +# Set the output format. Available formats are text, parseable, colorized, json +# and msvs (visual studio). You can also give a reporter class, e.g. +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages. +reports=no + +# Activate the evaluation score. +score=yes diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..5a01f7a6 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,133 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official email address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +[INSERT CONTACT METHOD]. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..dbf459d2 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,32 @@ +# How to contribute +So you want to write code and get it landed in the official OMRChecker repository? +First, fork our repository into your own GitHub account, and create a local clone of it as described in the installation instructions. +The latter will be used to get new features implemented or bugs fixed. + +Once done and you have the code locally on the disk, you can get started. We advise you to not work directly on the master branch, +but to create a separate branch for each issue you are working on. That way you can easily switch between different work, +and you can update each one for the latest changes on the upstream master individually. + + +# Writing Code +For writing the code just follow the [Pep8 Python style](https://peps.python.org/pep-0008/) guide, If there is something unclear about the style, just look at existing code which might help you to understand it better. + +Also, try to use commits with [conventional messages](https://www.conventionalcommits.org/en/v1.0.0/#summary). + + +# Code Formatting +Before committing your code, make sure to run the following command to format your code according to the PEP8 style guide: +```.sh +pip install -r requirements.dev.txt && pre-commit install +``` + +Run `pre-commit` before committing your changes: +```.sh +git add . +pre-commit run -a +``` + +# Where to contribute from + +- You can pickup any open [issues](https://github.com/Udayraj123/OMRChecker/issues) to solve. +- You can also check out the [ideas list](https://github.com/users/Udayraj123/projects/2/views/1) diff --git a/Contributors.md b/Contributors.md new file mode 100644 index 00000000..3f95b245 --- /dev/null +++ b/Contributors.md @@ -0,0 +1,22 @@ +# Contributors + +- [Udayraj123](https://github.com/Udayraj123) +- [leongwaikay](https://github.com/leongwaikay) +- [deepakgouda](https://github.com/deepakgouda) +- [apurva91](https://github.com/apurva91) +- [sparsh2706](https://github.com/sparsh2706) +- [namit2saxena](https://github.com/namit2saxena) +- [Harsh-Kapoorr](https://github.com/Harsh-Kapoorr) +- [Sandeep-1507](https://github.com/Sandeep-1507) +- [SpyzzVVarun](https://github.com/SpyzzVVarun) +- [asc249](https://github.com/asc249) +- [05Alston](https://github.com/05Alston) +- [Antibodyy](https://github.com/Antibodyy) +- [infinity1729](https://github.com/infinity1729) +- [Rohan-G](https://github.com/Rohan-G) +- [UjjwalMahar](https://github.com/UjjwalMahar) +- [Kurtsley](https://github.com/Kurtsley) +- [gaursagar21](https://github.com/gaursagar21) +- [aayushibansal2001](https://github.com/aayushibansal2001) +- [ShamanthVallem](https://github.com/ShamanthVallem) +- [rudrapsc](https://github.com/rudrapsc) diff --git a/LICENSE b/LICENSE index fbbea73a..3942f30c 100644 --- a/LICENSE +++ b/LICENSE @@ -1,621 +1,22 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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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/README.md b/README.md index 8afb795e..279aaca8 100644 --- a/README.md +++ b/README.md @@ -1,60 +1,83 @@ # OMR Checker -Grade exams fast and accurately using a scanner πŸ–¨ or your phone 🀳. -[![HitCount](http://hits.dwyl.io/udayraj123/OMRchecker.svg)](http://hits.dwyl.io/udayraj123/OMRchecker) -[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-brightgreen.svg)](https://github.com/Udayraj123/OMRChecker/wiki/TODOs) +Read OMR sheets fast and accurately using a scanner πŸ–¨ or your phone 🀳. + +## What is OMR? + +OMR stands for Optical Mark Recognition, used to detect and interpret human-marked data on documents. OMR refers to the process of reading and evaluating OMR sheets, commonly used in exams, surveys, and other forms. + +#### **Quick Links** + +- [Installation](#getting-started) +- [User Guide](https://github.com/Udayraj123/OMRChecker/wiki) +- [Contributor Guide](https://github.com/Udayraj123/OMRChecker/blob/master/CONTRIBUTING.md) +- [Project Ideas List](https://github.com/users/Udayraj123/projects/2/views/1) + +
+ +[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](https://github.com/Udayraj123/OMRChecker/pull/new/master) [![GitHub pull-requests closed](https://img.shields.io/github/issues-pr-closed/Udayraj123/OMRChecker.svg)](https://github.com/Udayraj123/OMRChecker/pulls?q=is%3Aclosed) [![GitHub issues-closed](https://img.shields.io/github/issues-closed/Udayraj123/OMRChecker.svg)](https://GitHub.com/Udayraj123/OMRChecker/issues?q=is%3Aissue+is%3Aclosed) -[![GitHub contributors](https://img.shields.io/github/contributors/Udayraj123/OMRChecker.svg)](https://GitHub.com/Udayraj123/OMRChecker/graphs/contributors/) +[![Ask me](https://img.shields.io/badge/Discuss-on_Github-purple.svg?style=flat-square)](https://github.com/Udayraj123/OMRChecker/issues/5) + + [![GitHub stars](https://img.shields.io/github/stars/Udayraj123/OMRChecker.svg?style=social&label=Stars✯)](https://GitHub.com/Udayraj123/OMRChecker/stargazers/) -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](https://github.com/Udayraj123/OMRChecker/pull/new/master) +[![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FUdayraj123%2FOMRChecker&count_bg=%2379C83D&title_bg=%23555555&icon=googlecast.svg&icon_color=%23E7E7E7&title=hits%2829.09.2022%29&edge_flat=false)](https://hits.seeyoufarm.com) [![Join](https://img.shields.io/badge/Join-Discord_group-purple.svg?style=flat-square)](https://discord.gg/qFv2Vqf) -[![Ask me](https://img.shields.io/badge/Discuss-on_Github-purple.svg?style=flat-square)](https://github.com/Udayraj123/OMRChecker/issues/5) - - -#### **TLDR;** Jump to [Getting Started](#getting-started). + ## 🎯 Features -A full-fledged OMR checking software that can read and evaluate OMR sheets scanned at any angle and having any color. Support is also provided for a customisable marking scheme with section-wise marking, bonus questions, etc. +A full-fledged OMR checking software that can read and evaluate OMR sheets scanned at any angle and having any color. + +| Specs | ![Current_Speed](https://img.shields.io/badge/Speed-200+_OMRs/min-blue.svg?style=flat-square) ![Min Resolution](https://img.shields.io/badge/Min_Resolution-640x480-blue.svg?style=flat-square) | +| :--------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| πŸ’― **Accurate** | Currently nearly 100% accurate on good quality document scans; and about 90% accurate on mobile images. | +| πŸ’ͺ🏿 **Robust** | Supports low resolution, xeroxed sheets. See [**Robustness**](https://github.com/Udayraj123/OMRChecker/wiki/Robustness) for more. | +| ⏩ **Fast** | Current processing speed without any optimization is 200 OMRs/minute. | +| βœ… **Customizable** | [Easily apply](https://github.com/Udayraj123/OMRChecker/wiki/User-Guide) to custom OMR layouts, surveys, etc. | +| πŸ“Š **Visually Rich** | [Get insights](https://github.com/Udayraj123/OMRChecker/wiki/Rich-Visuals) to configure and debug easily. | +| 🎈 **Lightweight** | Very minimal core code size. | +| 🏫 **Large Scale** | Tested on a large scale at [Technothlon](https://en.wikipedia.org/wiki/Technothlon). | +| πŸ‘©πŸΏβ€πŸ’» **Dev Friendly** | [Pylinted](http://pylint.pycqa.org/) and [Black formatted](https://github.com/psf/black) code. Also has a [developer community](https://discord.gg/qFv2Vqf) on discord. | -| Specs | ![Current_Speed](https://img.shields.io/badge/Speed-200_OMRs/m-blue.svg?style=flat-square) ![Current_Size](https://img.shields.io/badge/Code_Size-500KB-blue.svg?style=flat-square) ![Min Resolution](https://img.shields.io/badge/Min_Resolution-640x480-blue.svg?style=flat-square) | -|:----------------:|-----------------------------------------------------------------------------------------------------------------------------------| -| πŸ’― **Accurate** | Currently nearly 100% accurate on good quality document scans; and about 90% accurate on mobile images. | -| πŸ’ͺ🏿 **Robust** | Supports low resolution, xeroxed sheets. See [**Robustness**](https://github.com/Udayraj123/OMRChecker/wiki/Robustness) for more. | -| ⏩ **Fast** | Current processing speed without any optimization is 200 OMRs/minute. | -| βœ… **Extensible** | [**Easily apply**](https://github.com/Udayraj123/OMRChecker/wiki/User-Guide) to different OMR layouts, surveys, etc. | -| πŸ“Š **Visually Rich Outputs** | [Get insights](https://github.com/Udayraj123/OMRChecker/wiki/Rich-Visuals) to configure and debug easily. | -| 🎈 **Extremely lightweight** | Core code size is **less than 500 KB**(Samples excluded). | -| 🏫 **Large Scale** | Used on tens of thousands of OMRs at [Technothlon](https://www.facebook.com/technothlon.techniche). | -| πŸ‘©πŸΏβ€πŸ’» **Dev Friendly** | [**Well documented**](https://github.com/Udayraj123/OMRChecker/wiki/) repository based on python and openCV with [an active discussion group](https://discord.gg/qFv2Vqf). | +Note: For solving interesting challenges, developers can check out [**TODOs**](https://github.com/Udayraj123/OMRChecker/wiki/TODOs). -Note: For solving live challenges, developers can checkout [**TODOs**](https://github.com/Udayraj123/OMRChecker/wiki/TODOs). -See all details in [Project Wiki](https://github.com/Udayraj123/OMRChecker/wiki/). +See the complete guide and details at [Project Wiki](https://github.com/Udayraj123/OMRChecker/wiki/). + ## πŸ’‘ What can OMRChecker do for me? -Once you configure the OMR layout, just throw images of the sheets at the software; and you'll get back the graded responses in an excel sheet! + +Once you configure the OMR layout, just throw images of the sheets at the software; and you'll get back the marked responses in an excel sheet! Images can be taken from various angles as shown below- +

sample_input

-### Code in action on images taken by scanner: +### Code in action on images taken by scanner: +

document_scanner

-### Code in action on images taken by a mobile phone: +### Code in action on images taken by a mobile phone: +

checking_xeroxed_mobile

-See step by step processing of any OMR sheet: +## Visuals + +### Processing steps + +See step-by-step processing of any OMR sheet: +

rotation_stack @@ -63,7 +86,9 @@ See step by step processing of any OMR sheet: *Note: This image is generated by the code itself!*

-Output: A CSV sheet containing the detected responses and evaluated scores: +### Output + +Get a CSV sheet containing the detected responses and evaluated scores:

@@ -71,29 +96,65 @@ Output: A CSV sheet containing the detected responses and evaluated scores:

-#### There are many visuals in the wiki. [Check them out!](https://github.com/Udayraj123/OMRChecker/wiki/Rich-Visuals) +We now support [colored outputs](https://github.com/Udayraj123/OMRChecker/wiki/%5Bv2%5D-About-Evaluation) as well. Here's a sample output on another image - +

+ + colored_output + +

+ +#### There are many more visuals in the wiki. Check them out [here!](https://github.com/Udayraj123/OMRChecker/wiki/Rich-Visuals) ## Getting started + ![Setup Time](https://img.shields.io/badge/Setup_Time-20_min-blue.svg) -### Operating System -Although windows is supported, **Linux** is recommended for a bug-free experience. +**Operating system:** OSX or Linux is recommended although Windows is also supported. -### 1. Install dependencies -![opencv 4.0.0](https://img.shields.io/badge/opencv-4.0.0-blue.svg) ![python 3.4](https://img.shields.io/badge/python-3.4-blue.svg) +### 1. Install global dependencies + +![opencv 4.0.0](https://img.shields.io/badge/opencv-4.0.0-blue.svg) ![python 3.5+](https://img.shields.io/badge/python-3.5+-blue.svg) + +To check if python3 and pip is already installed: + +```bash +python3 --version +python3 -m pip --version +``` + +
+ Install Python3 + +To install python3 follow instructions [here](https://www.python.org/downloads/) + +To install pip - follow instructions [here](https://pip.pypa.io/en/stable/installation/) + +
+
+Install OpenCV + +**Any installation method is fine.** + +Recommended: -_Note: To get a copy button for below commands, use [CodeCopy Chrome](https://chrome.google.com/webstore/detail/codecopy/fkbfebkcoelajmhanocgppanfoojcdmg) | [CodeCopy Firefox](https://addons.mozilla.org/en-US/firefox/addon/codecopy/)._ ```bash python3 -m pip install --user --upgrade pip python3 -m pip install --user opencv-python python3 -m pip install --user opencv-contrib-python ``` + More details on pip install openCV [here](https://www.pyimagesearch.com/2018/09/19/pip-install-opencv/). -> **Note:** On a fresh computer some of the libraries may get missing in above pip install. +
+ +
+ +Extra steps(for Linux users only) + +Installing missing libraries(if any): + +On a fresh computer, some of the libraries may get missing in event after a successful pip install. Install them using following commands[(ref)](https://www.pyimagesearch.com/2018/05/28/ubuntu-18-04-how-to-install-opencv/): -Install them using the [following commands](https://www.pyimagesearch.com/2018/05/28/ubuntu-18-04-how-to-install-opencv/): -Windows users may skip this step. ```bash sudo apt-get install -y build-essential cmake unzip pkg-config sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev @@ -101,126 +162,199 @@ sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev libv4l-dev sudo apt-get install -y libatlas-base-dev gfortran ``` -### 2. Clone the repo +
+ +### 2. Install project dependencies + +Clone the repo + ```bash -# Shallow clone - takes latest code with minimal size -git clone https://github.com/Udayraj123/OMRChecker --depth=1 +git clone https://github.com/Udayraj123/OMRChecker +cd OMRChecker/ ``` -Note: Contributors should take a full clone(without the --depth flag). -#### Install other requirements -![imutils 0.5.2](https://img.shields.io/badge/imutils-0.5.2-blue.svg) ![matplotlib 3.0.2](https://img.shields.io/badge/matplotlib-3.0.2-blue.svg) ![pandas 0.24.0](https://img.shields.io/badge/pandas-0.24.0-blue.svg) ![numpy 1.16.0](https://img.shields.io/badge/numpy-1.16.0-blue.svg) +Install pip requirements ```bash -cd OMRChecker/ python3 -m pip install --user -r requirements.txt ``` -> **Note:** If you face a distutils error, use `--ignore-installed` flag in above command. - -### 3. Run the code +_**Note:** If you face a distutils error in pip, use `--ignore-installed` flag in above command._ -1. Put your data in inputs folder. You can copy sample data as shown below: - ```bash - # Note: you may remove previous inputs if any with `mv inputs/* ~/.trash` - cp -r ./samples/sample1 inputs/ - ``` - _Note: Change the number N in sampleN to see more examples_ -2. Run OMRChecker: - **` python3 main.py `** + -These samples demonstrate different ways OMRChecker can be used. +### 3. Run the code -#### Running it on your own OMR Sheets +1. First copy and examine the sample data to know how to structure your inputs: + ```bash + cp -r ./samples/sample1 inputs/ + # Note: you may remove previous inputs (if any) with `mv inputs/* ~/.trash` + # Change the number N in sampleN to see more examples + ``` +2. Run OMRChecker: + ```bash + python3 main.py + ``` + +Alternatively you can also use `python3 main.py -i ./samples/sample1`. + +Each example in the samples folder demonstrates different ways in which OMRChecker can be used. + +### Common Issues + +
+ + 1. [Windows] ERROR: Could not open requirements file
+ +Command: python3 -m pip install --user -r requirements.txt +
+ Link to Solution: #54 +
+
+ +2. [Linux] ERROR: No module named pip
+ +Command: python3 -m pip install --user --upgrade pip +
+ Link to Solution: #70 +
+ +## OMRChecker for custom OMR Sheets + +1. First, [create your own template.json](https://github.com/Udayraj123/OMRChecker/wiki/User-Guide). +2. Configure the tuning parameters. +3. Run OMRChecker with appropriate arguments (See full usage). + + +## Full Usage -1. First [create your own template.json](https://github.com/Udayraj123/OMRChecker/wiki/User-Guide). -2. Open `globals.py` and check the tuning parameters. - -3. Run OMRChecker with appropriate arguments. - #### Full Usage - ``` - python3 main.py [--setLayout] [--noCropping] [--autoAlign] [--inputDir dir1] [--outputDir dir1] [--template path/to/template.json] - ``` - Explanation for the arguments: +``` +python3 main.py [--setLayout] [--inputDir dir1] [--outputDir dir1] +``` - `--setLayout`: Set up OMR template layout - modify your json file and run again until the template is set. +Explanation for the arguments: - `--autoAlign`: (experimental) Enables automatic template alignment - use if the scans show slight misalignments. +`--setLayout`: Set up OMR template layout - modify your json file and run again until the template is set. - `--noCropping`: Disables page contour detection - used when page boundary is not visible e.g. document scanner. +`--inputDir`: Specify an input directory. - `--inputDir`: Specify an input directory. +`--outputDir`: Specify an output directory. - `--outputDir`: Specify an output directory. +
+ + Deprecation logs + - `--template`: Specify a default template if no template file in input directories. +- The old `--noCropping` flag has been replaced with the 'CropPage' plugin in "preProcessors" of the template.json(see [samples](https://github.com/Udayraj123/OMRChecker/tree/master/samples)). +- The `--autoAlign` flag is deprecated due to low performance on a generic OMR sheet +- The `--template` flag is deprecated and instead it's recommended to keep the template file at the parent folder containing folders of different images +
-## πŸ’‘ Why is this software free? +## FAQ + +
+ +Why is this software free? + -The idea for this project began at Technothlon, which is a non-profit international school championship. After seeing it work fabulously at such a large scale, we decided to share this simple and powerful tool with the world to perhaps help revamp OMR checking processes and help greatly reduce the tediousness of the work involved. +This project was born out of a student-led organization called as [Technothlon](https://technothlon.techniche.org.in). It is a logic-based international school championship organized by students of IIT Guwahati. Being a non-profit organization, and after seeing it work fabulously at such a large scale we decided to share this tool with the world. The OMR checking processes still involves so much tediousness which we aim to reduce dramatically. -And we believe in the power of open source! Currently, OMRChecker is in its initial stage where only developers can use it. We hope to see it become more user-friendly and even more robust with exposure to different inputs from you! +We believe in the power of open source! Currently, OMRChecker is in an intermediate stage where only developers can use it. We hope to see it become more user-friendly as well as robust from exposure to different inputs from you all! [![Open Source](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/) -### Can I use this code in my work? +
+ +
+ +Can I use this code in my (public) work? + + +OMRChecker can be forked and modified. You are encouraged to play with it and we would love to see your own projects in action! + +It is published under the [MIT license](https://github.com/Udayraj123/OMRChecker/blob/master/LICENSE). + +
+ +
+ +What are the ways to contribute? + + + + +- Join the developer community on [Discord](https://discord.gg/qFv2Vqf) to fix [issues](https://github.com/Udayraj123/OMRChecker/issues) with OMRChecker. + +- If this project saved you large costs on OMR Software licenses, or saved efforts to make one. Consider donating an amount of your choice(donate section). -OMRChecker can be forked and modified. **You are encouraged to play with it and we would love to see your own projects in action!** The only requirement is **disclose usage** of this software in your code. It is published under the [**GPLv3 license**](https://github.com/Udayraj123/OMRChecker/blob/master/LICENSE) + + + -## Credits -_A Huge thanks to :_ -_The creative master **Adrian Rosebrock** for his blog :_ https://pyimagesearch.com +
-_The legendary **Harrison** aka sentdex for his [video tutorials](https://www.youtube.com/watch?v=Z78zbnLlPUA&list=PLQVvvaa0QuDdttJXlLtAJxJetJcqmqlQq)._ +## Credits -_And the james bond of computer vision **Satya Mallic** for his blog:_ https://www.learnopencv.com +_A Huge thanks to:_ +_**Adrian Rosebrock** for his exemplary blog:_ https://pyimagesearch.com -_And many other amazing people over the globe without whom this project would never have completed. Thank you!_ +_**Harrison Kinsley** aka sentdex for his [video tutorials](https://www.youtube.com/watch?v=Z78zbnLlPUA&list=PLQVvvaa0QuDdttJXlLtAJxJetJcqmqlQq) and many other resources._ -> _This project is dedicated to [Technothlon](https://www.facebook.com/technothlon.techniche) where the idea of making such solution was conceived. Technothlon is a logic-based examination organized by students of IIT Guwahati._ +_**Satya Mallic** for his resourceful blog:_ https://www.learnopencv.com - -## License -``` -Copyright Β© 2019 Udayraj Deshmukh -OMRChecker : Grade exams fast and accurately using a scanner πŸ–¨ or your phone 🀳 -This is free software, and you are welcome to redistribute it under certain conditions; -``` -For more details see [![GitHub license](https://img.shields.io/github/license/Udayraj123/OMRChecker.svg)](https://github.com/Udayraj123/OMRChecker/blob/master/LICENSE) - ## Related Projects -Here's a sneak peak of the [Android OMR Helper App(WIP)](https://github.com/Udayraj123/AndroidOMRHelper): + +Here's a snapshot of the [Android OMR Helper App (archived)](https://github.com/Udayraj123/AndroidOMRHelper): +

- +

- -### Other ways you can contribute: -- Help OMRChecker cross 560 stars ⭐ to become #1 ([Currently #4](https://github.com/topics/omr)). -Current stars: [![GitHub stars](https://img.shields.io/github/stars/Udayraj123/OMRChecker.svg?style=social&label=Stars✯)](https://GitHub.com/Udayraj123/OMRChecker/stargazers/) +## Stargazers over time -- [Buy Me A Coffee β˜•](https://www.buymeacoffee.com/Udayraj123) - To keep my brain juices flowing and help me create more such projects πŸ’‘ +[![Stargazers over time](https://starchart.cc/Udayraj123/OMRChecker.svg)](https://starchart.cc/Udayraj123/OMRChecker) -- If this project saved you large costs on OMR Software licenses, or saved efforts to make one. [![paypal](https://www.paypalobjects.com/en_GB/i/btn/btn_donate_LG.gif)](https://www.paypal.me/Udayraj123/500) +--- - - - +

Made with ❀️ by Awesome Contributors

+ + + + + +--- + +### License + +[![GitHub license](https://img.shields.io/github/license/Udayraj123/OMRChecker.svg)](https://github.com/Udayraj123/OMRChecker/blob/master/LICENSE) + +For more details see [LICENSE](https://github.com/Udayraj123/OMRChecker/blob/master/LICENSE). + +### Donate + +Buy Me A Coffee [![paypal](https://www.paypalobjects.com/en_GB/i/btn/btn_donate_LG.gif)](https://www.paypal.me/Udayraj123/500) + +_Find OMRChecker on_ [**_Product Hunt_**](https://www.producthunt.com/posts/omr-checker/) **|** [**_Reddit_**](https://www.reddit.com/r/computervision/comments/ccbj6f/omrchecker_grade_exams_using_python_and_opencv/) **|** [**Discord**](https://discord.gg/qFv2Vqf) **|** [**Linkedin**](https://www.linkedin.com/pulse/open-source-talks-udayraj-udayraj-deshmukh/) **|** [**goodfirstissue.dev**](https://goodfirstissue.dev/language/python) **|** [**codepeak.tech**](https://www.codepeak.tech/) **|** [**fossoverflow.dev**](https://fossoverflow.dev/projects) **|** [**Interview on Console by CodeSee**](https://console.substack.com/p/console-140) **|** [**Open Source Hub**](https://opensourcehub.io/udayraj123/omrchecker) - -*Find OMRChecker on* [***Product Hunt***](https://www.producthunt.com/posts/omr-checker/) **|** [***Hacker News***](https://news.ycombinator.com/item?id=20420602) **|** [***Reddit***](https://www.reddit.com/r/computervision/comments/ccbj6f/omrchecker_grade_exams_using_python_and_opencv/) **|** [***Swyya***](https://www.swyya.com/projects/omrchecker) **|** [![Join](https://img.shields.io/badge/Join-on_Discord-purple.svg?style=flat-square)](https://discord.gg/qFv2Vqf) + + diff --git a/docs/assets/colored_output.jpg b/docs/assets/colored_output.jpg new file mode 100644 index 00000000..3cafa473 Binary files /dev/null and b/docs/assets/colored_output.jpg differ diff --git a/globals.py b/globals.py deleted file mode 100644 index 5f9cd929..00000000 --- a/globals.py +++ /dev/null @@ -1,140 +0,0 @@ -""" - -Designed and Developed by- -Udayraj Deshmukh -https://github.com/Udayraj123 - -""" - -""" -Constants -""" -display_height = int(480) -display_width = int(640) -windowWidth = 1280 -windowHeight = 720 - -saveMarked = 1 -saveCropped = 1 -showimglvl = 4 -saveimglvl = 0 -PRELIM_CHECKS = 0 -saveImgList = {} -resetpos = [0, 0] -explain = 0 -# autorotate=1 - -BATCH_NO = 1000 -NO_MARKER_ERR = 12 -MULTI_BUBBLE_WARN = 15 - -# name of template file -TEMPLATE_FILE = 'template.json' -MARKER_FILE = "omr_marker.jpg" - -# For preProcessing -GAMMA_LOW = 0.7 -GAMMA_HIGH = 1.25 - -ERODE_SUB_OFF = 1 - -# For new ways of determining threshold -MIN_GAP, MIN_STD = 30, 25 -MIN_JUMP = 25 -# If only not confident, take help of globalTHR -CONFIDENT_JUMP = MIN_JUMP + 15 -JUMP_DELTA = 30 -# MIN_GAP : worst case gap of black and gray - -# Templ alignment parameters -ALIGN_RANGE = range(-5, 6, 1) -# TODO ^THIS SHOULD BE IN LAYOUT FILE AS ITS RELATED TO DIMENSIONS -# ALIGN_RANGE = [-6,-4,-2,-1,0,1,2,4,6] - -# max threshold difference for template matching -thresholdVar = 0.41 - -# TODO: remove unnec variables here- -thresholdCircle = 0.3 -marker_rescale_range = (35, 100) -marker_rescale_steps = 10 - -# Presentation variables -uniform_height = int(1231 / 1.5) -uniform_width = int(1000 / 1.5) -# Original dims are about (3527, 2494) - -# Any input images should be resized to this-- -uniform_width_hd = int(uniform_width * 1.5) -uniform_height_hd = int(uniform_height * 1.5) - -TEXT_SIZE = 0.95 -CLR_BLACK = (50, 150, 150) -CLR_WHITE = (250, 250, 250) -CLR_GRAY = (130, 130, 130) -# CLR_DARK_GRAY = (190,190,190) -CLR_DARK_GRAY = (100, 100, 100) - -MIN_PAGE_AREA = 80000 - -# Filepaths - - -class Paths: - def __init__(self, output): - self.output = output - self.saveMarkedDir = f'{output}/CheckedOMRs/' - self.resultDir = f'{output}/Results/' - self.manualDir = f'{output}/Manual/' - self.errorsDir = f'{self.manualDir}ErrorFiles/' - self.badRollsDir = f'{self.manualDir}BadRollNosFiles/' - self.multiMarkedDir = f'{self.manualDir}MultiMarkedFiles/' - - -""" -Variables -""" -filesMoved = 0 -filesNotMoved = 0 - -# for positioning image windows -windowX, windowY = 0, 0 - - -# TODO: move to template or similar json -Answers = { - 'J': { - 'q1': ['B'], 'q2': ['B'], 'q3': ['B'], 'q4': ['C'], 'q5': ['0', '00'], 'q6': ['0', '00'], 'q7': ['4', '04'], - 'q8': ['9', '09'], 'q9': ['11', '11'], 'q10': ['C'], 'q11': ['C'], 'q12': ['B'], 'q13': ['C'], - 'q14': ['C'], 'q15': ['B'], 'q16': ['C'], 'q17': ['BONUS'], 'q18': ['A'], 'q19': ['C'], 'q20': ['B']}, - 'H': { - 'q1': ['B'], 'q2': ['BONUS'], 'q3': ['A'], 'q4': ['B'], 'q5': ['A'], 'q6': ['B'], 'q7': ['B'], - 'q8': ['C'], 'q9': ['4', '04'], 'q10': ['4', '04'], 'q11': ['5', '05'], 'q12': ['1', '01'], 'q13': ['28'], - 'q14': ['C'], 'q15': ['B'], 'q16': ['C'], 'q17': ['C'], 'q18': ['C'], 'q19': ['B'], 'q20': ['C']}, - 'JK': { - 'q1': ['B'], 'q2': ['B'], 'q3': ['B'], 'q4': ['C'], 'q5': ['0', '00'], 'q6': ['0', '00'], 'q7': ['4', '04'], - 'q8': ['9', '09'], 'q9': ['11', '11'], 'q10': ['C'], 'q11': ['C'], 'q12': ['B'], 'q13': ['C'], - 'q14': ['C'], 'q15': ['B'], 'q16': ['C'], 'q17': ['BONUS'], 'q18': ['A'], 'q19': ['C'], 'q20': ['B']}, - 'HK': { - 'q1': ['B'], 'q2': ['BONUS'], 'q3': ['A'], 'q4': ['B'], 'q5': ['B'], 'q6': ['B'], 'q7': ['B'], - 'q8': ['C'], 'q9': ['4', '04'], 'q10': ['4', '04'], 'q11': ['5', '05'], 'q12': ['1', '01'], 'q13': ['28'], - 'q14': ['C'], 'q15': ['B'], 'q16': ['C'], 'q17': ['C'], 'q18': ['C'], 'q19': ['B'], 'q20': ['C']}, -} - -# TODO: Make this generalized and move it to samples -Sections = { - 'J': { - 'Fibo1': {'ques': [1, 2, 3, 4], '+seq': [2, 3, 5, 8], '-seq': [0, 1, 1, 2]}, - 'Power1': {'ques': [5, 6, 7, 8, 9], '+seq': [1, 2, 4, 8, 16], '-seq': [0, 0, 0, 0, 0]}, - 'Fibo2': {'ques': [10, 11, 12, 13], '+seq': [2, 3, 5, 8], '-seq': [0, 1, 1, 2]}, - 'allNone1': {'ques': [14, 15, 16], 'marks': 12}, - 'Boom1': {'ques': [17, 18, 19, 20], '+seq': [3, 3, 3, 3], '-seq': [1, 1, 1, 1]}, - }, - 'H': { - 'Boom1': {'ques': [1, 2, 3, 4], '+seq': [3, 3, 3, 3], '-seq': [1, 1, 1, 1]}, - 'Fibo1': {'ques': [5, 6, 7, 8], '+seq': [2, 3, 5, 8], '-seq': [0, 1, 1, 2]}, - 'Power1': {'ques': [9, 10, 11, 12, 13], '+seq': [1, 2, 4, 8, 16], '-seq': [0, 0, 0, 0, 0]}, - 'allNone1': {'ques': [14, 15, 16], 'marks': 12}, - 'Boom2': {'ques': [17, 18, 19, 20], '+seq': [3, 3, 3, 3], '-seq': [1, 1, 1, 1]}, - }, -} diff --git a/main.py b/main.py index eac70a50..dfecbca3 100644 --- a/main.py +++ b/main.py @@ -1,546 +1,99 @@ """ -Designed and Developed by- -Udayraj Deshmukh -https://github.com/Udayraj123 + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 """ -import re -import os -import cv2 import argparse -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt - -# from utils import * #Now imported via template -from globals import * -from template import * -from glob import glob -from csv import QUOTE_NONNUMERIC -from time import localtime, strftime, time - - -# TODO(beginner task) :- -# from colorama import init -# init() -# from colorama import Fore, Back, Style - -def process_dir(root_dir, subdir, template): - curr_dir = os.path.join(root_dir, subdir) - - # Look for template in current dir - template_file = os.path.join(curr_dir, TEMPLATE_FILE) - if os.path.exists(template_file): - template = Template(template_file) - - # look for images in current dir to process - paths = Paths(os.path.join(args['output_dir'], subdir)) - exts = ('*.png', '*.jpg') - omr_files = sorted( - [f for ext in exts for f in glob(os.path.join(curr_dir, ext))]) - - # Exclude marker image if exists - if(template and template.marker_path): - omr_files = [f for f in omr_files if f != template.marker_path] - - subfolders = sorted([file for file in os.listdir( - curr_dir) if os.path.isdir(os.path.join(curr_dir, file))]) - if omr_files: - args_local = args.copy() - if("OverrideFlags" in template.options): - args_local.update(template.options["OverrideFlags"]) - print('\n------------------------------------------------------------------') - print(f'Processing directory "{curr_dir}" with settings- ') - print("\tTotal images : %d" % (len(omr_files))) - print("\tCropping Enabled : " + str(not args_local["noCropping"])) - print("\tAuto Alignment : " + str(args_local["autoAlign"])) - print("\tUsing Template : " + str(template.path) if(template) else "N/A") - print("\tUsing Marker : " + str(template.marker_path) - if(template.marker is not None) else "N/A") - print('') - - if not template: - print(f'Error: No template file when processing {curr_dir}.') - print( - f' Place {TEMPLATE_FILE} in the directory or specify a template using -t.') - return - - setup_dirs(paths) - output_set = setup_output(paths, template) - process_files(omr_files, template, args_local, output_set) - elif(len(subfolders) == 0): - # the directory should have images or be non-leaf - print(f'Note: No valid images or subfolders found in {curr_dir}') - - # recursively process subfolders - for folder in subfolders: - process_dir(root_dir, os.path.join(subdir, folder), template) - - -def checkAndMove(error_code, filepath, filepath2): - # print("Dummy Move: "+filepath, " --> ",filepath2) - global filesNotMoved - filesNotMoved += 1 - return True - - global filesMoved - if(not os.path.exists(filepath)): - print('File already moved') - return False - if(os.path.exists(filepath2)): - print('ERROR : Duplicate file at ' + filepath2) - return False - - print("Moved: " + filepath, " --> ", filepath2) - os.rename(filepath, filepath2) - filesMoved += 1 - return True - - -def processOMR(template, omrResp): - # Note: This is a reference function. It is not part of the OMR checker - # So its implementation is completely subjective to user's requirements. - csvResp = {} - - # symbol for absent response - UNMARKED_SYMBOL = '' - - # print("omrResp",omrResp) - - # Multi-column/multi-row questions which need to be concatenated - for qNo, respKeys in template.concats.items(): - csvResp[qNo] = ''.join([omrResp.get(k, UNMARKED_SYMBOL) - for k in respKeys]) - - # Single-column/single-row questions - for qNo in template.singles: - csvResp[qNo] = omrResp.get(qNo, UNMARKED_SYMBOL) - - # Note: Concatenations and Singles together should be mutually exclusive - # and should cover all questions in the template(exhaustive) - # TODO: ^add a warning if omrResp has unused keys remaining - return csvResp - - -def report( - Status, - streak, - scheme, - qNo, - marked, - ans, - prevmarks, - currmarks, - marks): - print( - '%s \t %s \t\t %s \t %s \t %s \t %s \t %s ' % (qNo, - Status, - str(streak), - '[' + scheme + '] ', - (str(prevmarks) + ' + ' + str(currmarks) + ' =' + str(marks)), - str(marked), - str(ans))) - -# check sectionwise only. - - -def evaluate(resp, squad="H", explain=False): - # TODO: @contributors - Need help generalizing this function - global Answers, Sections - marks = 0 - answers = Answers[squad] - if(explain): - print('Question\tStatus \t Streak\tSection \tMarks_Update\tMarked:\tAnswer:') - for scheme, section in Sections[squad].items(): - sectionques = section['ques'] - prevcorrect = None - allflag = 1 - streak = 0 - for q in sectionques: - qNo = 'q' + str(q) - ans = answers[qNo] - marked = resp.get(qNo, 'X') - firstQ = sectionques[0] - lastQ = sectionques[len(sectionques) - 1] - unmarked = marked == 'X' or marked == '' - bonus = 'BONUS' in ans - correct = bonus or (marked in ans) - inrange = 0 - - if(unmarked or int(q) == firstQ): - streak = 0 - elif(prevcorrect == correct): - streak += 1 - else: - streak = 0 - - if('allNone' in scheme): - # loop on all sectionques - allflag = allflag and correct - if(q == lastQ): - # at the end check allflag - prevcorrect = correct - currmarks = section['marks'] if allflag else 0 - else: - currmarks = 0 - - elif('Proxy' in scheme): - a = int(ans[0]) - # proximity check - inrange = 1 if unmarked else ( - float(abs(int(marked) - a)) / float(a) <= 0.25) - currmarks = section['+marks'] if correct else ( - 0 if inrange else -section['-marks']) - - elif('Fibo' in scheme or 'Power' in scheme or 'Boom' in scheme): - currmarks = section['+seq'][streak] if correct else ( - 0 if unmarked else -section['-seq'][streak]) - elif('TechnoFin' in scheme): - currmarks = 0 - else: - print('Invalid Sections') - prevmarks = marks - marks += currmarks - - if(explain): - if bonus: - report('BonusQ', streak, scheme, qNo, marked, - ans, prevmarks, currmarks, marks) - elif correct: - report('Correct', streak, scheme, qNo, marked, - ans, prevmarks, currmarks, marks) - elif unmarked: - report('Unmarked', streak, scheme, qNo, marked, - ans, prevmarks, currmarks, marks) - elif inrange: - report('InProximity', streak, scheme, qNo, - marked, ans, prevmarks, currmarks, marks) - else: - report('Incorrect', streak, scheme, qNo, - marked, ans, prevmarks, currmarks, marks) - - prevcorrect = correct - - return marks - - -def setup_output(paths, template): - ns = argparse.Namespace() - print("\nChecking Files...") - - # Include current output paths - ns.paths = paths - - # custom sort: To use integer order in question names instead of - # alphabetical - avoids q1, q10, q2 and orders them q1, q2, ..., q10 - ns.respCols = sorted(list(template.concats.keys()) + template.singles, - key=lambda x: int(x[1:]) if ord(x[1]) in range(48, 58) else 0) - ns.emptyResp = [''] * len(ns.respCols) - ns.sheetCols = ['file_id', 'input_path', - 'output_path', 'score'] + ns.respCols - ns.OUTPUT_SET = [] - ns.filesObj = {} - ns.filesMap = { - "Results": paths.resultDir + 'Results_' + timeNowHrs + '.csv', - "MultiMarked": paths.manualDir + 'MultiMarkedFiles_.csv', - "Errors": paths.manualDir + 'ErrorFiles_.csv', - "BadRollNos": paths.manualDir + 'BadRollNoFiles_.csv' - } - - for fileKey, fileName in ns.filesMap.items(): - if(not os.path.exists(fileName)): - print("Note: Created new file: %s" % (fileName)) - # still append mode req [THINK!] - ns.filesObj[fileKey] = open(fileName, 'a') - # Create Header Columns - pd.DataFrame([ns.sheetCols], dtype=str).to_csv( - ns.filesObj[fileKey], quoting=QUOTE_NONNUMERIC, header=False, index=False) - else: - print('Present : appending to %s' % (fileName)) - ns.filesObj[fileKey] = open(fileName, 'a') - - return ns - - -''' TODO: Refactor into new process flow. - Currently I have no idea what this does so I left it out''' - - -def preliminary_check(): - filesCounter = 0 - # PRELIM_CHECKS for thresholding - if(PRELIM_CHECKS): - # TODO: add more using unit testing - ALL_WHITE = 255 * \ - np.ones((TEMPLATE.dims[1], TEMPLATE.dims[0]), dtype='uint8') - OMRresponseDict, final_marked, MultiMarked, multiroll = readResponse( - ALL_WHITE, name="ALL_WHITE", savedir=None, autoAlign=True) - print("ALL_WHITE", OMRresponseDict) - if(OMRresponseDict != {}): - print("Preliminary Checks Failed.") - exit(12) - ALL_BLACK = np.zeros( - (TEMPLATE.dims[1], TEMPLATE.dims[0]), dtype='uint8') - OMRresponseDict, final_marked, MultiMarked, multiroll = readResponse( - ALL_BLACK, name="ALL_BLACK", savedir=None, autoAlign=True) - print("ALL_BLACK", OMRresponseDict) - show("Confirm : All bubbles are black", final_marked, 1, 1) - - -def process_files(omr_files, template, args, out): - start_time = int(time()) - filesCounter = 0 - filesNotMoved = 0 - - for filepath in omr_files: - filesCounter += 1 - # For windows filesystem support: all '\' will be replaced by '/' - filepath = filepath.replace(os.sep, '/') - - # Prefixing a 'r' to use raw string (escape character '\' is taken - # literally) - finder = re.search(r'.*/(.*)/(.*)', filepath, re.IGNORECASE) - if(finder): - inputFolderName, filename = finder.groups() - else: - print("Error: Filepath not matching to Regex: " + filepath) - continue - # set global var for reading - - inOMR = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE) - print( - '\n[%d] Processing image: \t' % - (filesCounter), - filepath, - "\tResolution: ", - inOMR.shape) - - OMRCrop = getROI(inOMR, filename, noCropping=args["noCropping"]) - - if(OMRCrop is None): - # Error OMR - could not crop - newfilepath = out.paths.errorsDir + filename - out.OUTPUT_SET.append([filename] + out.emptyResp) - if(checkAndMove(NO_MARKER_ERR, filepath, newfilepath)): - err_line = [filename, filepath, - newfilepath, "NA"] + out.emptyResp - pd.DataFrame( - err_line, - dtype=str).T.to_csv( - out.filesObj["Errors"], - quoting=QUOTE_NONNUMERIC, - header=False, - index=False) - continue - - if template.marker is not None: - OMRCrop = handle_markers(OMRCrop, template.marker, filename) - - if(args["setLayout"]): - templateLayout = drawTemplateLayout( - OMRCrop, template, shifted=False, border=2) - show("Template Layout", templateLayout, 1, 1) - continue - - # uniquify - file_id = inputFolderName + '_' + filename - savedir = out.paths.saveMarkedDir - OMRresponseDict, final_marked, MultiMarked, multiroll = \ - readResponse(template, OMRCrop, name=file_id, - savedir=savedir, autoAlign=args["autoAlign"]) - - # concatenate roll nos, set unmarked responses, etc - resp = processOMR(template, OMRresponseDict) - print("\nRead Response: \t", resp) - - # This evaluates and returns the score attribute - score = evaluate(resp, explain=explain) - respArray = [] - for k in out.respCols: - respArray.append(resp[k]) - - out.OUTPUT_SET.append([filename] + respArray) - - # TODO: Add roll number validation here - if(MultiMarked == 0): - filesNotMoved += 1 - newfilepath = savedir + file_id - # Enter into Results sheet- - results_line = [filename, filepath, newfilepath, score] + respArray - # Write/Append to results_line file(opened in append mode) - pd.DataFrame( - results_line, - dtype=str).T.to_csv( - out.filesObj["Results"], - quoting=QUOTE_NONNUMERIC, - header=False, - index=False) - print("[%d] Graded with score: %.2f" % - (filesCounter, score), '\t file_id: ', file_id) - # print(filesCounter,file_id,resp['Roll'],'score : ',score) - else: - # MultiMarked file - print('[%d] MultiMarked, moving File: %s' % - (filesCounter, file_id)) - newfilepath = out.paths.multiMarkedDir + filename - if(checkAndMove(MULTI_BUBBLE_WARN, filepath, newfilepath)): - mm_line = [filename, filepath, newfilepath, "NA"] + respArray - pd.DataFrame( - mm_line, - dtype=str).T.to_csv( - out.filesObj["MultiMarked"], - quoting=QUOTE_NONNUMERIC, - header=False, - index=False) - # else: - # TODO: Add appropriate record handling here - # pass - - # flush after every 20 files for a live view - if(filesCounter % 20 == 0 or filesCounter == len(omr_files)): - for fileKey in out.filesMap.keys(): - out.filesObj[fileKey].flush() - - timeChecking = round(time() - start_time, 2) if filesCounter else 1 - print('') - print('Total files moved : %d ' % (filesMoved)) - print('Total files not moved : %d ' % (filesNotMoved)) - print('------------------------------') - print( - 'Total files processed : %d (%s)' % - (filesCounter, - 'Sum Tallied!' if filesCounter == ( - filesMoved + - filesNotMoved) else 'Not Tallying!')) - - if(showimglvl <= 0): - print( - '\nFinished Checking %d files in %.1f seconds i.e. ~%.1f minutes.' % - (filesCounter, timeChecking, timeChecking / 60)) - print('OMR Processing Rate :\t ~ %.2f seconds/OMR' % - (timeChecking / filesCounter)) - print('OMR Processing Speed :\t ~ %.2f OMRs/minute' % - ((filesCounter * 60) / timeChecking)) - else: - print("\nTotal script time :", timeChecking, "seconds") - - if(showimglvl <= 1): - # TODO: colorama this - print( - "\nTip: To see some awesome visuals, open globals.py and increase 'showimglvl'") - - evaluate_correctness(template, out) - - # Use this data to train as +ve feedback - if(showimglvl >= 0 and filesCounter > 10): - # TODO: Find good parameters to plot and depict image set quality - for x in [thresholdCircles]: # ,badThresholds,veryBadPoints, , mbs]: - if(x != []): - x = pd.DataFrame(x) - print(x.describe()) - plt.plot(range(len(x)), x) - plt.title("Mystery Plot") - plt.show() - else: - print(x) - - -# Evaluate accuracy based on OMRDataset file generated through moderation -# portal on the same set of images -def evaluate_correctness(template, out): - # TODO: TEST_FILE WOULD BE RELATIVE TO INPUT SUBDIRECTORY NOW- - TEST_FILE = 'inputs/OMRDataset.csv' - if(os.path.exists(TEST_FILE)): - print("\nStarting evaluation for: " + TEST_FILE) - - TEST_COLS = ['file_id'] + out.respCols - y_df = pd.read_csv( - TEST_FILE, dtype=str)[TEST_COLS].replace( - np.nan, '', regex=True).set_index('file_id') - - if(np.any(y_df.index.duplicated)): - y_df_filtered = y_df.loc[~y_df.index.duplicated(keep='first')] - print( - "WARNING: Found duplicate File-ids in file %s. Removed %d rows from testing data. Rows remaining: %d" % - (TEST_FILE, y_df.shape[0] - y_df_filtered.shape[0], y_df_filtered.shape[0])) - y_df = y_df_filtered - - x_df = pd.DataFrame( - out.OUTPUT_SET, - dtype=str, - columns=TEST_COLS).set_index('file_id') - # print("x_df",x_df.head()) - # print("\ny_df",y_df.head()) - intersection = y_df.index.intersection(x_df.index) - - # Checking if the merge is okay - if(intersection.size == x_df.index.size): - y_df = y_df.loc[intersection] - x_df['TestResult'] = (x_df == y_df).all(axis=1).astype(int) - print(x_df.head()) - print("\n\t Accuracy on the %s Dataset: %.6f" % - (TEST_FILE, (x_df['TestResult'].sum() / x_df.shape[0]))) - else: - print( - "\nERROR: Insufficient Testing Data: Have you appended MultiMarked data yet?") - print("Missing File-ids: ", - list(x_df.index.difference(intersection))) - - -timeNowHrs = strftime("%I%p", localtime()) - -# construct the argument parse and parse the arguments -argparser = argparse.ArgumentParser() -# https://docs.python.org/3/howto/argparse.html -# store_true: if the option is specified, assign the value True to -# args.verbose. Not specifying it implies False. -argparser.add_argument( - "-c", - "--noCropping", - required=False, - dest='noCropping', - action='store_true', - help="Disables page contour detection - used when page boundary is not visible e.g. document scanner.") -argparser.add_argument( - "-a", - "--autoAlign", - required=False, - dest='autoAlign', - action='store_true', - help="(experimental) Enables automatic template alignment - use if the scans show slight misalignments.") -argparser.add_argument( - "-l", - "--setLayout", - required=False, - dest='setLayout', - action='store_true', - help="Set up OMR template layout - modify your json file and run again until the template is set.") -argparser.add_argument("-i", "--inputDir", required=False, action='append', - dest='input_dir', help="Specify an input directory.") -argparser.add_argument("-o", "--outputDir", default='outputs', required=False, - dest='output_dir', help="Specify an output directory.") -argparser.add_argument( - "-t", - "--template", - required=False, - dest='template', - help="Specify a default template if no template file in input directories.") - - -args, unknown = argparser.parse_known_args() -args = vars(args) -if(len(unknown) > 0): - print("\nError: Unknown arguments:", unknown) - argparser.print_help() - exit(11) - -if args['template']: - args['template'] = Template(args['template']) - -if args['input_dir'] is None: - args['input_dir'] = ['inputs'] - -for root in args['input_dir']: - process_dir(root, '', args['template']) +import sys +from pathlib import Path + +from src.entry import entry_point +from src.logger import logger + + +def parse_args(): + # construct the argument parse and parse the arguments + argparser = argparse.ArgumentParser() + + argparser.add_argument( + "-i", + "--inputDir", + default=["inputs"], + # https://docs.python.org/3/library/argparse.html#nargs + nargs="*", + required=False, + type=str, + dest="input_paths", + help="Specify an input directory.", + ) + + argparser.add_argument( + "-d", + "--debug", + required=False, + dest="debug", + action="store_false", + help="Enables debugging mode for showing detailed errors", + ) + + argparser.add_argument( + "-o", + "--outputDir", + default="outputs", + required=False, + dest="output_dir", + help="Specify an output directory.", + ) + + argparser.add_argument( + "-a", + "--autoAlign", + required=False, + dest="autoAlign", + action="store_true", + help="(experimental) Enables automatic template alignment - \ + use if the scans show slight misalignments.", + ) + + argparser.add_argument( + "-l", + "--setLayout", + required=False, + dest="setLayout", + action="store_true", + help="Set up OMR template layout - modify your json file and \ + run again until the template is set.", + ) + + ( + args, + unknown, + ) = argparser.parse_known_args() + + args = vars(args) + + if len(unknown) > 0: + logger.warning(f"\nError: Unknown arguments: {unknown}", unknown) + argparser.print_help() + exit(11) + return args + + +def entry_point_for_args(args): + if args["debug"] is True: + # Disable tracebacks + sys.tracebacklimit = 0 + for root in args["input_paths"]: + entry_point( + Path(root), + args, + ) + + +if __name__ == "__main__": + args = parse_args() + entry_point_for_args(args) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..060309be --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,18 @@ +[tool.black] +exclude = ''' +( + /( + \.eggs # exclude a few common directories in the + | \.git # root of the project + | \.venv + | _build + | build + | dist + )/ + | foo.py # also separately exclude a file named foo.py in + # the root of the project +) +''' +include = '\.pyi?$' +line-length = 88 +target-version = ['py37'] diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..84008a21 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,6 @@ +# pytest.ini +[pytest] +minversion = 7.0 +addopts = -qq --capture=no +testpaths = + src/tests diff --git a/requirements.dev.txt b/requirements.dev.txt new file mode 100644 index 00000000..722ab0dd --- /dev/null +++ b/requirements.dev.txt @@ -0,0 +1,7 @@ +-r requirements.txt +flake8>=6.0.0 +freezegun>=1.2.2 +pre-commit>=3.3.3 +pytest-mock>=3.11.1 +pytest>=7.4.0 +syrupy>=4.0.4 diff --git a/requirements.txt b/requirements.txt index 505865c9..761d2e51 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,8 @@ -imutils>=0.5.2 -matplotlib>=3.0.2 -numpy>=1.16.0 -pandas>=0.24.0 +deepmerge>=1.1.0 +dotmap>=1.3.30 +jsonschema>=4.17.3 +matplotlib>=3.7.1 +numpy>=1.25.0 +pandas>=2.0.2 +rich>=13.4.2 +screeninfo>=0.8.1 diff --git a/samples/answer-key/using-csv/adrian_omr.png b/samples/answer-key/using-csv/adrian_omr.png new file mode 100644 index 00000000..d8db0994 Binary files /dev/null and b/samples/answer-key/using-csv/adrian_omr.png differ diff --git a/samples/answer-key/using-csv/answer_key.csv b/samples/answer-key/using-csv/answer_key.csv new file mode 100644 index 00000000..566201d1 --- /dev/null +++ b/samples/answer-key/using-csv/answer_key.csv @@ -0,0 +1,5 @@ +q1,C +q2,E +q3,A +q4,B +q5,B \ No newline at end of file diff --git a/samples/answer-key/using-csv/evaluation.json b/samples/answer-key/using-csv/evaluation.json new file mode 100644 index 00000000..14a3db25 --- /dev/null +++ b/samples/answer-key/using-csv/evaluation.json @@ -0,0 +1,14 @@ +{ + "source_type": "csv", + "options": { + "answer_key_csv_path": "answer_key.csv", + "should_explain_scoring": true + }, + "marking_schemes": { + "DEFAULT": { + "correct": "1", + "incorrect": "0", + "unmarked": "0" + } + } +} diff --git a/samples/answer-key/using-csv/template.json b/samples/answer-key/using-csv/template.json new file mode 100644 index 00000000..41ec9ffa --- /dev/null +++ b/samples/answer-key/using-csv/template.json @@ -0,0 +1,35 @@ +{ + "pageDimensions": [ + 300, + 400 + ], + "bubbleDimensions": [ + 25, + 25 + ], + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + } + ], + "fieldBlocks": { + "MCQ_Block_1": { + "fieldType": "QTYPE_MCQ5", + "origin": [ + 65, + 60 + ], + "fieldLabels": [ + "q1..5" + ], + "labelsGap": 52, + "bubblesGap": 41 + } + } +} diff --git a/samples/answer-key/weighted-answers/evaluation.json b/samples/answer-key/weighted-answers/evaluation.json new file mode 100644 index 00000000..c0daefcf --- /dev/null +++ b/samples/answer-key/weighted-answers/evaluation.json @@ -0,0 +1,35 @@ +{ + "source_type": "custom", + "options": { + "questions_in_order": [ + "q1..5" + ], + "answers_in_order": [ + "C", + "E", + [ + "A", + "C" + ], + [ + [ + "B", + 2 + ], + [ + "C", + "3/2" + ] + ], + "C" + ], + "should_explain_scoring": true + }, + "marking_schemes": { + "DEFAULT": { + "correct": "3", + "incorrect": "-1", + "unmarked": "0" + } + } +} diff --git a/samples/answer-key/weighted-answers/images/adrian_omr.png b/samples/answer-key/weighted-answers/images/adrian_omr.png new file mode 100644 index 00000000..69a53823 Binary files /dev/null and b/samples/answer-key/weighted-answers/images/adrian_omr.png differ diff --git a/samples/answer-key/weighted-answers/images/adrian_omr_2.png b/samples/answer-key/weighted-answers/images/adrian_omr_2.png new file mode 100644 index 00000000..d8db0994 Binary files /dev/null and b/samples/answer-key/weighted-answers/images/adrian_omr_2.png differ diff --git a/samples/answer-key/weighted-answers/template.json b/samples/answer-key/weighted-answers/template.json new file mode 100644 index 00000000..41ec9ffa --- /dev/null +++ b/samples/answer-key/weighted-answers/template.json @@ -0,0 +1,35 @@ +{ + "pageDimensions": [ + 300, + 400 + ], + "bubbleDimensions": [ + 25, + 25 + ], + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + } + ], + "fieldBlocks": { + "MCQ_Block_1": { + "fieldType": "QTYPE_MCQ5", + "origin": [ + 65, + 60 + ], + "fieldLabels": [ + "q1..5" + ], + "labelsGap": 52, + "bubblesGap": 41 + } + } +} diff --git a/samples/community/Antibodyy/simple_omr_sheet.jpg b/samples/community/Antibodyy/simple_omr_sheet.jpg new file mode 100644 index 00000000..661d5f4f Binary files /dev/null and b/samples/community/Antibodyy/simple_omr_sheet.jpg differ diff --git a/samples/community/Antibodyy/template.json b/samples/community/Antibodyy/template.json new file mode 100644 index 00000000..7e962bbf --- /dev/null +++ b/samples/community/Antibodyy/template.json @@ -0,0 +1,35 @@ +{ + "pageDimensions": [ + 299, + 398 + ], + "bubbleDimensions": [ + 42, + 42 + ], + "fieldBlocks": { + "MCQBlock1": { + "fieldType": "QTYPE_MCQ5", + "origin": [ + 65, + 79 + ], + "bubblesGap": 43, + "labelsGap": 50, + "fieldLabels": [ + "q1..6" + ] + } + }, + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + } + ] +} diff --git a/samples/community/Sandeep-1507/omr-1.png b/samples/community/Sandeep-1507/omr-1.png new file mode 100644 index 00000000..8c40655e Binary files /dev/null and b/samples/community/Sandeep-1507/omr-1.png differ diff --git a/samples/community/Sandeep-1507/omr-2.png b/samples/community/Sandeep-1507/omr-2.png new file mode 100644 index 00000000..aabef0a5 Binary files /dev/null and b/samples/community/Sandeep-1507/omr-2.png differ diff --git a/samples/community/Sandeep-1507/omr-3.png b/samples/community/Sandeep-1507/omr-3.png new file mode 100644 index 00000000..8f1cb5c8 Binary files /dev/null and b/samples/community/Sandeep-1507/omr-3.png differ diff --git a/samples/community/Sandeep-1507/template.json b/samples/community/Sandeep-1507/template.json new file mode 100644 index 00000000..b35404e2 --- /dev/null +++ b/samples/community/Sandeep-1507/template.json @@ -0,0 +1,234 @@ +{ + "pageDimensions": [ + 1189, + 1682 + ], + "bubbleDimensions": [ + 15, + 15 + ], + "preProcessors": [ + { + "name": "GaussianBlur", + "options": { + "kSize": [ + 3, + 3 + ], + "sigmaX": 0 + } + } + ], + "customLabels": { + "Booklet_No": [ + "b1..7" + ] + }, + "fieldBlocks": { + "Booklet_No": { + "fieldType": "QTYPE_INT", + "origin": [ + 112, + 530 + ], + "fieldLabels": [ + "b1..7" + ], + "emptyValue": "no", + "bubblesGap": 28, + "labelsGap": 26.5 + }, + "MCQBlock1a1": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q1..10" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 476, + 100 + ] + }, + "MCQBlock1a2": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q11..20" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 476, + 370 + ] + }, + "MCQBlock1a3": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q21..35" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 476, + 638 + ] + }, + "MCQBlock2a1": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q51..60" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 645, + 100 + ] + }, + "MCQBlock2a2": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q61..70" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 645, + 370 + ] + }, + "MCQBlock2a3": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q71..85" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 645, + 638 + ] + }, + "MCQBlock3a1": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q101..110" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 815, + 100 + ] + }, + "MCQBlock3a2": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q111..120" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 815, + 370 + ] + }, + "MCQBlock3a3": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q121..135" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 815, + 638 + ] + }, + "MCQBlock4a1": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q151..160" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 983, + 100 + ] + }, + "MCQBlock4a2": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q161..170" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 983, + 370 + ] + }, + "MCQBlock4a3": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q171..185" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 983, + 638 + ] + }, + "MCQBlock1a": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q36..50" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 480, + 1061 + ] + }, + "MCQBlock2a": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q86..100" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 648, + 1061 + ] + }, + "MCQBlock3a": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q136..150" + ], + "bubblesGap": 28.7, + "labelsGap": 26.7, + "origin": [ + 815, + 1061 + ] + }, + "MCQBlock4a": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q186..200" + ], + "bubblesGap": 28.7, + "labelsGap": 26.6, + "origin": [ + 986, + 1061 + ] + } + } +} diff --git a/samples/community/Shamanth/omr_sheet_01.png b/samples/community/Shamanth/omr_sheet_01.png new file mode 100644 index 00000000..ead17dd1 Binary files /dev/null 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+1,11 @@ +{ + "dimensions": { + "display_height": 1800, + "display_width": 2400, + "processing_height": 2400, + "processing_width": 1800 + }, + "outputs": { + "show_image_level": 0 + } +} diff --git a/samples/community/UPSC-mock/evaluation.json b/samples/community/UPSC-mock/evaluation.json new file mode 100644 index 00000000..42fbec8b --- /dev/null +++ b/samples/community/UPSC-mock/evaluation.json @@ -0,0 +1,18 @@ +{ + "source_type": "csv", + "options": { + "answer_key_csv_path": "answer_key.csv", + "answer_key_image_path": "answer_key.jpg", + "questions_in_order": [ + "q1..100" + ], + "should_explain_scoring": true + }, + "marking_schemes": { + "DEFAULT": { + "correct": "2", + "incorrect": "-2/3", + "unmarked": "0" + } + } +} diff --git a/samples/community/UPSC-mock/scan-angles/angle-1.jpg b/samples/community/UPSC-mock/scan-angles/angle-1.jpg new file mode 100644 index 00000000..9c75d7be Binary files /dev/null and b/samples/community/UPSC-mock/scan-angles/angle-1.jpg differ diff --git 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], - "bigGaps": [ - 128, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "q5.1", - "q5.2" - ], - [ - "q6.1", - "q6.2" - ], - [ - "q7.1", - "q7.2" - ] - ] + 282 ] }, - "Int2": { - "qType": "QTYPE_INT", - "orig": [ - 1418, - 285 - ], - "bigGaps": [ - 128, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "q8.1", - "q8.2" - ], - [ - "q9.1", - "q9.2" - ] - ] + "Int_Block_Q6": { + "fieldType": "QTYPE_INT", + "fieldLabels": [ + "q6_1", + "q6_2" + ], + "bubblesGap": 46, + "labelsGap": 60, + "origin": [ + 1077, + 282 ] }, - "Mcq1": { - "qType": "QTYPE_MCQ4", - "orig": [ - 118, - 857 - ], - "bigGaps": [ - 115, - 181 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q1", - "q2", - "q3", - "q4" - ], - [ - "q10", - "q11", - "q12", - "q13" - ] - ] + "Int_Block_Q7": { + "fieldType": "QTYPE_INT", + "fieldLabels": [ + "q7_1", + "q7_2" + ], + "bubblesGap": 46, + "labelsGap": 60, + "origin": [ + 1240, + 282 + ] + }, + "Int_Block_Q8": { + "fieldType": "QTYPE_INT", + "fieldLabels": [ + "q8_1", + "q8_2" + ], + "bubblesGap": 46, + "labelsGap": 57, + "origin": [ + 1410, + 282 + ] + }, + "Int_Block_Q9": { + "fieldType": "QTYPE_INT", + "fieldLabels": [ + "q9_1", + "q9_2" + ], + "bubblesGap": 46, + "labelsGap": 57, + "origin": [ + 1580, + 282 ] }, - "Mcq2": { - "qType": "QTYPE_MCQ4", - "orig": [ + "MCQ_Block_Q1": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q1..4" + ], + "bubblesGap": 59, + "labelsGap": 50, + "origin": [ + 121, + 860 + ] + }, + "MCQ_Block_Q10": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q10..13" + ], + "bubblesGap": 59, + "labelsGap": 50, + "origin": [ + 121, + 1195 + ] + }, + "MCQ_Block_Q14": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q14..16" + ], + "bubblesGap": 57, + "labelsGap": 50, + "origin": [ 905, 860 - ], - "bigGaps": [ - 115, - 180 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q14", - "q15", - "q16" - ] - ] ] }, - "Mcq3": { - "qType": "QTYPE_MCQ4", - "orig": [ + "MCQ_Block_Q17": { + "fieldType": "QTYPE_MCQ4", + "fieldLabels": [ + "q17..20" + ], + "bubblesGap": 57, + "labelsGap": 50, + "origin": [ 905, - 1198 - ], - "bigGaps": [ - 115, - 180 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q17", - "q18", - "q19", - "q20" - ] - ] + 1195 ] } - } -} \ No newline at end of file + }, + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + }, + { + "name": "CropOnMarkers", + "options": { + "relativePath": "omr_marker.jpg", + "sheetToMarkerWidthRatio": 17 + } + } + ] +} diff --git a/samples/sample2/AdrianSample/adrian_omr.png b/samples/sample2/AdrianSample/adrian_omr.png index dc008145..69a53823 100644 Binary files a/samples/sample2/AdrianSample/adrian_omr.png and b/samples/sample2/AdrianSample/adrian_omr.png differ diff --git a/samples/sample2/AdrianSample/adrian_omr_2.png b/samples/sample2/AdrianSample/adrian_omr_2.png new file mode 100644 index 00000000..d8db0994 Binary files /dev/null and b/samples/sample2/AdrianSample/adrian_omr_2.png differ diff --git a/samples/sample2/config.json b/samples/sample2/config.json new file mode 100644 index 00000000..a9daf449 --- /dev/null +++ b/samples/sample2/config.json @@ -0,0 +1,11 @@ +{ + "dimensions": { + "display_height": 2480, + "display_width": 1640, + "processing_height": 820, + "processing_width": 666 + }, + "outputs": { + "show_image_level": 5 + } +} diff --git a/samples/sample2/template.json b/samples/sample2/template.json index e82a9051..41ec9ffa 100644 --- a/samples/sample2/template.json +++ b/samples/sample2/template.json @@ -1,46 +1,35 @@ { - "Dimensions": [ + "pageDimensions": [ 300, 400 ], - "BubbleDimensions": [ + "bubbleDimensions": [ 25, 25 ], - "Concatenations": {}, - "Singles": [ - "q1", - "q2", - "q3", - "q4", - "q5" + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + } ], - "QBlocks": { - "MCQBlock1": { - "qType": "QTYPE_MCQ5", - "orig": [ + "fieldBlocks": { + "MCQ_Block_1": { + "fieldType": "QTYPE_MCQ5", + "origin": [ 65, 60 ], - "qNos": [ - [ - [ - "q1", - "q2", - "q3", - "q4", - "q5" - ] - ] - ], - "gaps": [ - 41, - 52 + "fieldLabels": [ + "q1..5" ], - "bigGaps": [ - 30, - 30 - ] + "labelsGap": 52, + "bubblesGap": 41 } } -} \ No newline at end of file +} diff --git a/samples/sample3/README.md b/samples/sample3/README.md new file mode 100644 index 00000000..d764c8b3 --- /dev/null +++ b/samples/sample3/README.md @@ -0,0 +1,31 @@ +## Observation +The OMR layout is slightly different on colored thick papers vs on xeroxed thin papers. The shift becomes noticible in case of OMR with large number of questions. + +We overlapped a colored OMR sheet with a xerox copy of the same OMR sheet(both printed on A4 papers) and observed that there is a great amount of layout sheet as we reach the bottom of the OMR. + +Link to an explainer with a real life example: [Google Drive](https://drive.google.com/drive/folders/1GpZTmpEhEjSALJEMjHwDafzWKgEoCTOI?usp=sharing) + +## Reasons for shifts in Template layout: +Listing out a few reasons for the above observation: +### Printer margin setting +The margin settings for different printers may be different for the same OMR layout. Thus causing the print to become elongated either horizontally, vertically or both ways. + +### The Fan-out effect +The fan-out effect is usually observed in a sheet fed offset press. Depending on how the papers are made, their dimensions have a tendency to change when they are exposed to moisture or water. + +The standard office papers(80 gsm) can easily capture moisture and change shape e.g. in case they get stored for multiple days in a place where the weather is highly humid. + +Below are some examples of the GSM ranges: + +- 74gsm to 90gsm – This is the basic standard office paper, used in your laser printers. +- 100gsm to 120gsm – This is stationary paper used for standard letterheads, complimentary slips. +- 130 to 170gsm – Mostly used for leaflets, posters, single-sided flyers, and brochures. + +## Solution + +It is recommended to scan each types of prints into different folders and use a separate template.json layout for each of the folders. The same is presented in this sample folder. + +## References + +- [Paper dimensional stability in sheet-fed offset printing](https://www.diva-portal.org/smash/get/diva2:517895/FULLTEXT01.pdf) +- An analysis of a few ["Interesting" bubble sheets](https://imgur.com/a/10qwL) diff --git a/samples/sample3/colored-thick-sheet/rgb-100-gsm.jpg b/samples/sample3/colored-thick-sheet/rgb-100-gsm.jpg new file mode 100644 index 00000000..4a34731a Binary files /dev/null and b/samples/sample3/colored-thick-sheet/rgb-100-gsm.jpg differ diff --git a/samples/sample3/colored-thick-sheet/template.json b/samples/sample3/colored-thick-sheet/template.json new file mode 100644 index 00000000..743723d6 --- /dev/null +++ b/samples/sample3/colored-thick-sheet/template.json @@ -0,0 +1,143 @@ +{ + "pageDimensions": [ + 1800, + 2400 + ], + "bubbleDimensions": [ + 23, + 20 + ], + "fieldBlocks": { + "q01block": { + "origin": [ + 504, + 927 + ], + "bubblesGap": 60.35, + "labelsGap": 31.75, + "fieldLabels": [ + 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"fieldType": "QTYPE_MCQ4" + }, + "q71block": { + "origin": [ + 811, + 1885 + ], + "bubblesGap": 62.25, + "labelsGap": 33.5, + "fieldLabels": [ + "q71..80" + ], + "fieldType": "QTYPE_MCQ4" + }, + "q81block": { + "origin": [ + 1120, + 927 + ], + "bubblesGap": 60.35, + "labelsGap": 31.75, + "fieldLabels": [ + "q81..90" + ], + "fieldType": "QTYPE_MCQ4" + }, + "q91block": { + "origin": [ + 1120, + 1242 + ], + "bubblesGap": 60.35, + "labelsGap": 31.75, + "fieldLabels": [ + "q91..100" + ], + "fieldType": "QTYPE_MCQ4" + } + }, + "preProcessors": [ + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } + } + ] +} diff --git a/samples/sample3/config.json b/samples/sample3/config.json new file mode 100644 index 00000000..a9daf449 --- /dev/null +++ b/samples/sample3/config.json @@ -0,0 +1,11 @@ +{ + "dimensions": { + "display_height": 2480, + "display_width": 1640, + "processing_height": 820, + "processing_width": 666 + }, + "outputs": { + "show_image_level": 5 + } +} diff --git a/samples/sample3/template.json b/samples/sample3/template.json deleted file mode 100644 index 2dddf37e..00000000 --- a/samples/sample3/template.json +++ /dev/null @@ -1,248 +0,0 @@ -{ - "Dimensions": [ - 1846, - 1500 - ], - "BubbleDimensions": [ - 40, - 40 - ], - "Options": { - "Marker": { - "RelativePath": "omr_marker.jpg", - "SheetToMarkerWidthRatio": 17 - }, - "OverrideFlags":{ - "noCropping": true - } - }, - "Concatenations": { - "Roll": [ - "Squad", - "Medium", - "roll0", - "roll1", - "roll2", - "roll3", - "roll4", - "roll5", - "roll6", - "roll7", - "roll8" - ], - "q6": [ - "q6.1", - "q6.2" - ], - "q7": [ - "q7.1", - "q7.2" - ], - "q8": [ - "q8.1", - "q8.2" - ], - "q9": [ - "q9.1", - "q9.2" - ], - "q10": [ - "q10.1", - "q10.2" - ] - }, - "Singles": [ - "q1", - "q2", - "q3", - "q4", - "q5", - "q11", - "q12", - "q13", - "q14", - "q15", - "q16", - "q17", - "q18", - "q19", - "q20", - "q21", - "q22" - ], - "QBlocks": { - "Medium": { - "qType": "QTYPE_MED", - "orig": [ - 208, - 205 - ], - "bigGaps": [ - 115, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "Medium" - ] - ] - ] - }, - "Roll": { - "qType": "QTYPE_ROLL", - "orig": [ - 261, - 210 - ], - "bigGaps": [ - 115, - 11 - ], - "gaps": [ - 58, - 46 - ], - "qNos": [ - [ - [ - "roll0", - "roll1", - "roll2", - "roll3", - "roll4", - "roll5", - "roll6", - "roll7", - "roll8" - ] - ] - ] - }, - "Int1": { - "qType": "QTYPE_INT", - "orig": [ - 935, - 211 - ], - "bigGaps": [ - 124, - 11 - ], - "gaps": [ - 57, - 46 - ], - "qNos": [ - [ - [ - "q6.1", - "q6.2" - ], - [ - "q7.1", - "q7.2" - ], - [ - "q8.1", - "q8.2" - ], - [ - "q9.1", - "q9.2" - ], - [ - "q10.1", - "q10.2" - ] - ] - ] - }, - "Mcq1": { - "qType": "QTYPE_MCQ4", - "orig": [ - 198, - 826 - ], - "bigGaps": [ - 115, - 183 - ], - "gaps": [ - 93, - 62 - ], - "qNos": [ - [ - [ - "q1", - "q2", - "q3", - "q4", - "q5" - ] - ] - ] - }, - "Mcq2": { - "qType": "QTYPE_MCQ4", - "orig": [ - 833, - 830 - ], - "bigGaps": [ - 127, - 254 - ], - "gaps": [ - 71, - 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+1,13 @@ +{ + "dimensions": { + "display_width": 1189, + "display_height": 1682 + }, + "threshold_params": { + "MIN_JUMP": 30 + }, + "outputs": { + "filter_out_multimarked_files": false, + "show_image_level": 5 + } +} diff --git a/samples/sample4/evaluation.json b/samples/sample4/evaluation.json new file mode 100644 index 00000000..50db0fbe --- /dev/null +++ b/samples/sample4/evaluation.json @@ -0,0 +1,34 @@ +{ + "source_type": "custom", + "options": { + "questions_in_order": [ + "q1..11" + ], + "answers_in_order": [ + "B", + "D", + "C", + "B", + "D", + "C", + [ + "B", + "C", + "BC" + ], + "A", + "C", + "D", + "C" + ], + "should_explain_scoring": true, + "enable_evaluation_table_to_csv": true + }, + "marking_schemes": { + "DEFAULT": { + "correct": "3", + "incorrect": "-1", + "unmarked": "0" + } + } +} diff --git a/samples/sample4/omr_marker.jpg b/samples/sample4/omr_marker.jpg deleted file mode 100644 index 201dab0a..00000000 Binary files a/samples/sample4/omr_marker.jpg and /dev/null differ diff --git a/samples/sample4/template.json b/samples/sample4/template.json index 958d6ef8..0d615b87 100644 --- a/samples/sample4/template.json +++ b/samples/sample4/template.json @@ -1,263 +1,45 @@ { - "Dimensions": [ - 1846, - 1500 + "pageDimensions": [ + 1189, + 1682 ], - "BubbleDimensions": [ - 40, - 40 + "bubbleDimensions": [ + 30, + 30 ], - "Options": { - "Marker": { - "RelativePath": "omr_marker.jpg", - "SheetToMarkerWidthRatio": 17 + "preProcessors": [ + { + "name": "GaussianBlur", + "options": { + "kSize": [ + 3, + 3 + ], + "sigmaX": 0 + } }, - "OverrideFlags": { - "noCropping": true + { + "name": "CropPage", + "options": { + "morphKernel": [ + 10, + 10 + ] + } } - }, - "Concatenations": { - "Roll": [ - "Squad", - "Medium", - "roll0", - "roll1", - "roll2", - "roll3", - "roll4", - "roll5", - "roll6", - "roll7", - "roll8" - ], - "q5": [ - "q5.1", - "q5.2" - ], - "q6": [ - "q6.1", - "q6.2" - ], - "q7": [ - "q7.1", - "q7.2" - ], - "q9": [ - "q9.1", - "q9.2" - ], - "q11": [ - "q8.1", - "q8.2" - ] - }, - "Singles": [ - "q1", - "q2", - "q3", - "q4", - "q10", - "q11", - "q12", - "q13", - "q14", - "q15", - "q16", - "q17", - "q18", - "q19", - "q20" ], - "QBlocks": { - "Medium": { - "qType": "QTYPE_MED", - "orig": [ - 160, - 285 - ], - "bigGaps": [ - 115, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "Medium" - ] - ] - ] - }, - "Roll": { - "qType": "QTYPE_ROLL", - "orig": [ - 218, - 285 - ], - "bigGaps": [ - 115, - 11 - ], - "gaps": [ - 58, - 46 - ], - "qNos": [ - [ - [ - "roll0", - "roll1", - "roll2", - "roll3", - "roll4", - "roll5", - "roll6", - "roll7", - "roll8" - ] - ] - ] - }, - "Int1": { - "qType": "QTYPE_INT", - "orig": [ - 903, - 285 - ], - "bigGaps": [ - 128, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "q5.1", - "q5.2" - ], - [ - "q6.1", - "q6.2" - ], - [ - "q7.1", - "q7.2" - ] - ] - ] - }, - "Int2": { - "qType": "QTYPE_INT", - "orig": [ - 1418, - 285 - ], - "bigGaps": [ - 128, - 11 - ], - "gaps": [ - 59, - 46 - ], - "qNos": [ - [ - [ - "q8.1", - "q8.2" - ], - [ - "q9.1", - "q9.2" - ] - ] - ] - }, - "Mcq1": { - "qType": "QTYPE_MCQ4", - "orig": [ - 118, - 857 - ], - "bigGaps": [ - 115, - 181 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q1", - "q2", - "q3", - "q4" - ], - [ - "q10", - "q11", - "q12", - "q13" - ] - ] - ] - }, - "Mcq2": { - "qType": "QTYPE_MCQ4", - "orig": [ - 905, - 860 - ], - "bigGaps": [ - 115, - 180 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q14", - "q15", - "q16" - ] - ] - ] - }, - "Mcq3": { - "qType": "QTYPE_MCQ4", - "orig": [ - 905, - 1198 - ], - "bigGaps": [ - 115, - 180 - ], - "gaps": [ - 59, - 53 - ], - "qNos": [ - [ - [ - "q17", - "q18", - "q19", - "q20" - ] - ] - ] + "fieldBlocks": { + "MCQBlock1": { + "fieldType": "QTYPE_MCQ4", + "origin": [ + 134, + 684 + ], + "fieldLabels": [ + "q1..11" + ], + "bubblesGap": 79, + "labelsGap": 62 } } -} \ No newline at end of file +} diff --git a/samples/sample5/README.md b/samples/sample5/README.md new file mode 100644 index 00000000..b5ed8ccb --- /dev/null +++ b/samples/sample5/README.md @@ -0,0 +1,6 @@ +### OMRChecker Sample + +This sample demonstrates multiple things, namely - +- Running OMRChecker on images scanned using popular document scanning apps +- Using a common template.json file for sub-folders (e.g. multiple scan batches) +- Using evaluation.json file with custom marking (without streak-based marking) diff --git a/samples/sample3/CamScanner/sheet2.jpg b/samples/sample5/ScanBatch1/camscanner-1.jpg similarity index 100% rename from samples/sample3/CamScanner/sheet2.jpg rename to samples/sample5/ScanBatch1/camscanner-1.jpg diff --git a/samples/sample3/CamScanner/sheet1.jpg b/samples/sample5/ScanBatch2/camscanner-2.jpg similarity index 100% rename from samples/sample3/CamScanner/sheet1.jpg rename to samples/sample5/ScanBatch2/camscanner-2.jpg diff --git a/samples/sample5/config.json b/samples/sample5/config.json new file mode 100644 index 00000000..a9daf449 --- /dev/null +++ b/samples/sample5/config.json @@ -0,0 +1,11 @@ +{ + "dimensions": { + "display_height": 2480, + "display_width": 1640, + "processing_height": 820, + "processing_width": 666 + }, + "outputs": { + "show_image_level": 5 + } +} diff --git a/samples/sample5/evaluation.json b/samples/sample5/evaluation.json new file mode 100644 index 00000000..3332fe6a --- /dev/null +++ b/samples/sample5/evaluation.json @@ -0,0 +1,93 @@ +{ + "source_type": "custom", + "options": { + "questions_in_order": [ + "q1..22" + ], + "answers_in_order": [ + "C", + "C", + "B", + "C", + "C", + [ + "1", + "01" + ], + "19", + "10", + "10", + "18", + "D", + "A", + "D", + "D", + "D", + "C", + "C", + "C", + "C", + "D", + "B", + "A" + ], + "should_explain_scoring": true + }, + "marking_schemes": { + "DEFAULT": { + "correct": "1", + "incorrect": "0", + "unmarked": "0" + }, + "BOOMERANG_1": { + "questions": [ + "q1..5" + ], + "marking": { + "correct": 4, + "incorrect": -1, + "unmarked": 0 + } + }, + "PROXIMITY_1": { + "questions": [ + "q6..10" + ], + "marking": { + "correct": 3, + "incorrect": -1, + "unmarked": 0 + } + }, + "FIBONACCI_SECTION_1": { + "questions": [ + "q11..14" + ], + "marking": { + "correct": 2, + "incorrect": -1, + "unmarked": 0 + } + }, + "POWER_SECTION_1": { + "questions": [ + "q15..18" + ], + "marking": { + "correct": 1, + "incorrect": 0, + "unmarked": 0 + } + }, + "FIBONACCI_SECTION_2": { + "questions": [ + "q19..22" + ], + "marking": { + "correct": 2, + "incorrect": -1, + "unmarked": 0 + } + } + } +} diff --git a/samples/sample5/omr_marker.jpg b/samples/sample5/omr_marker.jpg new file mode 100644 index 00000000..0929feec Binary files /dev/null and b/samples/sample5/omr_marker.jpg differ diff --git a/samples/sample5/template.json b/samples/sample5/template.json new file mode 100644 index 00000000..39f6e83d --- /dev/null +++ b/samples/sample5/template.json @@ -0,0 +1,188 @@ +{ + "pageDimensions": [ + 1846, + 1500 + ], + "bubbleDimensions": [ + 40, + 40 + ], + "preProcessors": [ + { + "name": "CropOnMarkers", + "options": { + "relativePath": "omr_marker.jpg", + "sheetToMarkerWidthRatio": 17 + } + } + ], + "customLabels": { + "Roll": [ + "Medium", + "roll1..9" + ], + "q6": [ + "q6_1", + "q6_2" + ], + "q7": [ + "q7_1", + "q7_2" + ], + "q8": [ + "q8_1", + "q8_2" + ], + "q9": [ + "q9_1", + "q9_2" + ], + "q10": [ + "q10_1", + "q10_2" + ] + }, + "fieldBlocks": { + "Medium": { + "bubbleValues": [ + "E", + "H" + ], + "direction": "vertical", + "origin": [ + 200, + 215 + ], + "bubblesGap": 46, + "labelsGap": 0, + "fieldLabels": [ + "Medium" + ] + }, + "Roll": { + "fieldType": "QTYPE_INT", + "origin": [ + 261, + 210 + ], + "bubblesGap": 46.5, + "labelsGap": 58, + "fieldLabels": [ + "roll1..9" + ] + }, + "Int1": { + "fieldType": "QTYPE_INT", + "origin": [ + 935, + 211 + ], + "bubblesGap": 46, + "labelsGap": 57, + "fieldLabels": [ + "q6_1", + "q6_2" + ] + }, + "Int2": { + "fieldType": "QTYPE_INT", + "origin": [ + 1100, + 211 + ], + "bubblesGap": 46, + "labelsGap": 57, + "fieldLabels": [ + "q7_1", + "q7_2" + ] + }, + "Int3": { + "fieldType": "QTYPE_INT", + "origin": [ + 1275, + 211 + ], + "bubblesGap": 46, + "labelsGap": 57, + "fieldLabels": [ + "q8_1", + "q8_2" + ] + }, + "Int4": { + "fieldType": "QTYPE_INT", + "origin": [ + 1449, + 211 + ], + "bubblesGap": 46, + "labelsGap": 57, + "fieldLabels": [ + "q9_1", + "q9_2" + ] + }, + "Int5": { + "fieldType": "QTYPE_INT", + "origin": [ + 1620, + 211 + ], + "bubblesGap": 46, + "labelsGap": 57, + "fieldLabels": [ + "q10_1", + "q10_2" + ] + }, + "Mcq1": { + "fieldType": "QTYPE_MCQ4", + "origin": [ + 198, + 826 + ], + "bubblesGap": 93, + "labelsGap": 62, + "fieldLabels": [ + "q1..5" + ] + }, + "Mcq2": { + "fieldType": "QTYPE_MCQ4", + "origin": [ + 833, + 830 + ], + "bubblesGap": 71, + "labelsGap": 61, + "fieldLabels": [ + "q11..14" + ] + }, + "Mcq3": { + "fieldType": "QTYPE_MCQ4", + "origin": [ + 833, + 1270 + ], + "bubblesGap": 71, + "labelsGap": 61, + "fieldLabels": [ + "q15..18" + ] + }, + "Mcq4": { + "fieldType": "QTYPE_MCQ4", + "origin": [ + 1481, + 830 + ], + "bubblesGap": 73, + "labelsGap": 61, + "fieldLabels": [ + "q19..22" + ] + } + } +} diff --git a/samples/sample6/config.json b/samples/sample6/config.json new file mode 100644 index 00000000..6f39f0ca --- /dev/null +++ b/samples/sample6/config.json @@ -0,0 +1,11 @@ +{ + "dimensions": { + "display_width": 2480, + "display_height": 3508, + "processing_width": 1653, + "processing_height": 2339 + }, + "outputs": { + "show_image_level": 5 + } +} diff --git a/samples/sample6/doc-scans/sample_roll_01.jpg b/samples/sample6/doc-scans/sample_roll_01.jpg new file mode 100644 index 00000000..25093a37 Binary files /dev/null and b/samples/sample6/doc-scans/sample_roll_01.jpg differ diff --git a/samples/sample6/doc-scans/sample_roll_02.jpg b/samples/sample6/doc-scans/sample_roll_02.jpg new file mode 100644 index 00000000..a81ee4fc Binary files /dev/null and b/samples/sample6/doc-scans/sample_roll_02.jpg differ diff --git a/samples/sample6/doc-scans/sample_roll_03.jpg b/samples/sample6/doc-scans/sample_roll_03.jpg new file mode 100644 index 00000000..94296f4e Binary files /dev/null and b/samples/sample6/doc-scans/sample_roll_03.jpg differ diff --git a/samples/sample6/readme.md b/samples/sample6/readme.md new file mode 100644 index 00000000..2a02ee90 --- /dev/null +++ b/samples/sample6/readme.md @@ -0,0 +1,21 @@ +# Demo for feature-based alignment + +## Background +OMR is used to match student roll on final exam scripts. Scripts are scanned using a document scanner and the cover pages are extracted for OMR. Even though a document scanner does not produce any warpped perspective, the alignment is not perfect, causing some rotation and translation in the scans. + +The scripts in this sample were specifically selected incorrectly marked scripts to demonstrate how feature-based alignment can correct transformation errors using a reference image. In the actual batch. 156 out of 532 scripts were incorrectly marked. With feature-based alignment, all scripts were correctly marked. + +## Usage +Two template files are given in the sample folder, one with feature-based alignment (template_fb_align), the other without (template_no_fb_align). + +## Additional Notes + +### Reference Image +When using a reference image for feature-based alignment, it is better not to have many repeated patterns as it is causes ambiguity when trying to match similar feature points. The bubbles in an OMR form are identical and should not be used for feature-extraction. + +Thus, the reference image should be cleared of any bubbles. Forms with lots of text as in this example would be effective. + +Note the reference image in this sample was generated from a vector pdf, and not from a scanned blank, producing in a perfectly aligned reference. + +### Level adjustment +The bubbles on the scripts were not shaded dark enough. Thus, a level adjustment was done to bring the black point to 70% to darken the light shading. White point was brought down to 80% to remove the light-grey background in the columns. diff --git a/samples/sample6/reference.png b/samples/sample6/reference.png new file mode 100644 index 00000000..5351267f Binary files /dev/null and b/samples/sample6/reference.png differ diff --git a/samples/sample6/template.json b/samples/sample6/template.json new file mode 100644 index 00000000..57605a6c --- /dev/null +++ b/samples/sample6/template.json @@ -0,0 +1,110 @@ +{ + "pageDimensions": [ + 2480, + 3508 + ], + "bubbleDimensions": [ + 42, + 42 + ], + "preProcessors": [ + { + "name": "Levels", + "options": { + "low": 0.7, + "high": 0.8 + } + }, + { + "name": "GaussianBlur", + "options": { + "kSize": [ + 3, + 3 + ], + "sigmaX": 0 + } + } + ], + "customLabels": { + "Roll": [ + "stu", + "roll1..7", + "check_1", + "check_2" + ] + }, + "fieldBlocks": { + "Check1": { + "origin": [ + 2033, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_1" + ], + "bubbleValues": [ + "A", + "B", + "E", + "H", + "J", + "L", + "M" + ], + "direction": "vertical" + }, + "Check2": { + "origin": [ + 2083, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_2" + ], + "bubbleValues": [ + "N", + "R", + "U", + "W", + "X", + "Y" + ], + "direction": "vertical" + }, + "Stu": { + "origin": [ + 1636, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "stu" + ], + "bubbleValues": [ + "U", + "A", + "HT", + "GT" + ], + "direction": "vertical" + }, + "Roll": { + "fieldType": "QTYPE_INT", + "origin": [ + 1685, + 1290 + ], + "bubblesGap": 50.5, + "labelsGap": 50.5, + "fieldLabels": [ + "roll1..7" + ] + } + } +} diff --git a/samples/sample6/template_fb_align.json b/samples/sample6/template_fb_align.json new file mode 100644 index 00000000..7c0c2397 --- /dev/null +++ b/samples/sample6/template_fb_align.json @@ -0,0 +1,118 @@ +{ + "pageDimensions": [ + 2480, + 3508 + ], + "bubbleDimensions": [ + 42, + 42 + ], + "preProcessors": [ + { + "name": "Levels", + "options": { + "low": 0.7, + "high": 0.8 + } + }, + { + "name": "FeatureBasedAlignment", + "options": { + "reference": "reference.png", + "maxFeatures": 1000, + "2d": true + } + }, + { + "name": "GaussianBlur", + "options": { + "kSize": [ + 3, + 3 + ], + "sigmaX": 0 + } + } + ], + "customLabels": { + "Roll": [ + "stu", + "roll1..7", + "check_1", + "check_2" + ] + }, + "fieldBlocks": { + "Check1": { + "origin": [ + 2033, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_1" + ], + "bubbleValues": [ + "A", + "B", + "E", + "H", + "J", + "L", + "M" + ], + "direction": "vertical" + }, + "Check2": { + "origin": [ + 2083, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_2" + ], + "bubbleValues": [ + "N", + "R", + "U", + "W", + "X", + "Y" + ], + "direction": "vertical" + }, + "Stu": { + "origin": [ + 1636, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "stu" + ], + "bubbleValues": [ + "U", + "A", + "HT", + "GT" + ], + "direction": "vertical" + }, + "Roll": { + "fieldType": "QTYPE_INT", + "origin": [ + 1685, + 1290 + ], + "bubblesGap": 50.5, + "labelsGap": 50.5, + "fieldLabels": [ + "roll1..7" + ] + } + } +} diff --git a/samples/sample6/template_no_fb_align.json b/samples/sample6/template_no_fb_align.json new file mode 100644 index 00000000..57605a6c --- /dev/null +++ b/samples/sample6/template_no_fb_align.json @@ -0,0 +1,110 @@ +{ + "pageDimensions": [ + 2480, + 3508 + ], + "bubbleDimensions": [ + 42, + 42 + ], + "preProcessors": [ + { + "name": "Levels", + "options": { + "low": 0.7, + "high": 0.8 + } + }, + { + "name": "GaussianBlur", + "options": { + "kSize": [ + 3, + 3 + ], + "sigmaX": 0 + } + } + ], + "customLabels": { + "Roll": [ + "stu", + "roll1..7", + "check_1", + "check_2" + ] + }, + "fieldBlocks": { + "Check1": { + "origin": [ + 2033, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_1" + ], + "bubbleValues": [ + "A", + "B", + "E", + "H", + "J", + "L", + "M" + ], + "direction": "vertical" + }, + "Check2": { + "origin": [ + 2083, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "check_2" + ], + "bubbleValues": [ + "N", + "R", + "U", + "W", + "X", + "Y" + ], + "direction": "vertical" + }, + "Stu": { + "origin": [ + 1636, + 1290 + ], + "bubblesGap": 50, + "labelsGap": 50, + "fieldLabels": [ + "stu" + ], + "bubbleValues": [ + "U", + "A", + "HT", + "GT" + ], + "direction": "vertical" + }, + "Roll": { + "fieldType": "QTYPE_INT", + "origin": [ + 1685, + 1290 + ], + "bubblesGap": 50.5, + "labelsGap": 50.5, + "fieldLabels": [ + "roll1..7" + ] + } + } +} diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 00000000..7fc528b4 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1,5 @@ +# https://docs.python.org/3/tutorial/modules.html#:~:text=The%20__init__.py,on%20the%20module%20search%20path. +from src.logger import logger + +# It takes a few seconds for the imports +logger.info(f"Loading OMRChecker modules...") diff --git a/src/constants.py b/src/constants.py new file mode 100644 index 00000000..f1cdc87b --- /dev/null +++ b/src/constants.py @@ -0,0 +1,54 @@ +""" + + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 + +""" +from dotmap import DotMap + +# Filenames +TEMPLATE_FILENAME = "template.json" +EVALUATION_FILENAME = "evaluation.json" +CONFIG_FILENAME = "config.json" + +FIELD_LABEL_NUMBER_REGEX = r"([^\d]+)(\d*)" +# +ERROR_CODES = DotMap( + { + "MULTI_BUBBLE_WARN": 1, + "NO_MARKER_ERR": 2, + }, + _dynamic=False, +) + +FIELD_TYPES = { + "QTYPE_INT": { + "bubbleValues": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], + "direction": "vertical", + }, + "QTYPE_INT_FROM_1": { + "bubbleValues": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "0"], + "direction": "vertical", + }, + "QTYPE_MCQ4": {"bubbleValues": ["A", "B", "C", "D"], "direction": "horizontal"}, + "QTYPE_MCQ5": { + "bubbleValues": ["A", "B", "C", "D", "E"], + "direction": "horizontal", + }, + # + # You can create and append custom field types here- + # +} + +# TODO: move to interaction.py +TEXT_SIZE = 0.95 +CLR_BLACK = (50, 150, 150) +CLR_WHITE = (250, 250, 250) +CLR_GRAY = (130, 130, 130) +CLR_DARK_GRAY = (100, 100, 100) + +# TODO: move to config.json +GLOBAL_PAGE_THRESHOLD_WHITE = 200 +GLOBAL_PAGE_THRESHOLD_BLACK = 100 diff --git a/src/core.py b/src/core.py new file mode 100644 index 00000000..d475105f --- /dev/null +++ b/src/core.py @@ -0,0 +1,721 @@ +import os +from collections import defaultdict +from typing import Any + +import cv2 +import matplotlib.pyplot as plt +import numpy as np + +import src.constants as constants +from src.logger import logger +from src.utils.image import CLAHE_HELPER, ImageUtils +from src.utils.interaction import InteractionUtils + + +class ImageInstanceOps: + """Class to hold fine-tuned utilities for a group of images. One instance for each processing directory.""" + + save_img_list: Any = defaultdict(list) + + def __init__(self, tuning_config): + super().__init__() + self.tuning_config = tuning_config + self.save_image_level = tuning_config.outputs.save_image_level + + def apply_preprocessors(self, file_path, in_omr, template): + tuning_config = self.tuning_config + # resize to conform to template + in_omr = ImageUtils.resize_util( + in_omr, + tuning_config.dimensions.processing_width, + tuning_config.dimensions.processing_height, + ) + + # run pre_processors in sequence + for pre_processor in template.pre_processors: + in_omr = pre_processor.apply_filter(in_omr, file_path) + return in_omr + + def read_omr_response(self, template, image, name, save_dir=None): + config = self.tuning_config + auto_align = config.alignment_params.auto_align + try: + img = image.copy() + # origDim = img.shape[:2] + img = ImageUtils.resize_util( + img, template.page_dimensions[0], template.page_dimensions[1] + ) + if img.max() > img.min(): + img = ImageUtils.normalize_util(img) + # Processing copies + transp_layer = img.copy() + final_marked = img.copy() + + morph = img.copy() + self.append_save_img(3, morph) + + if auto_align: + # Note: clahe is good for morphology, bad for thresholding + morph = CLAHE_HELPER.apply(morph) + self.append_save_img(3, morph) + # Remove shadows further, make columns/boxes darker (less gamma) + morph = ImageUtils.adjust_gamma( + morph, config.threshold_params.GAMMA_LOW + ) + # TODO: all numbers should come from either constants or config + _, morph = cv2.threshold(morph, 220, 220, cv2.THRESH_TRUNC) + morph = ImageUtils.normalize_util(morph) + self.append_save_img(3, morph) + if config.outputs.show_image_level >= 4: + InteractionUtils.show("morph1", morph, 0, 1, config) + + # Move them to data class if needed + # Overlay Transparencies + alpha = 0.65 + omr_response = {} + multi_marked, multi_roll = 0, 0 + + # TODO Make this part useful for visualizing status checks + # blackVals=[0] + # whiteVals=[255] + + if config.outputs.show_image_level >= 5: + all_c_box_vals = {"int": [], "mcq": []} + # TODO: simplify this logic + q_nums = {"int": [], "mcq": []} + + # Find Shifts for the field_blocks --> Before calculating threshold! + if auto_align: + # print("Begin Alignment") + # Open : erode then dilate + v_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 10)) + morph_v = cv2.morphologyEx( + morph, cv2.MORPH_OPEN, v_kernel, iterations=3 + ) + _, morph_v = cv2.threshold(morph_v, 200, 200, cv2.THRESH_TRUNC) + morph_v = 255 - ImageUtils.normalize_util(morph_v) + + if config.outputs.show_image_level >= 3: + InteractionUtils.show( + "morphed_vertical", morph_v, 0, 1, config=config + ) + + # InteractionUtils.show("morph1",morph,0,1,config=config) + # InteractionUtils.show("morphed_vertical",morph_v,0,1,config=config) + + self.append_save_img(3, morph_v) + + morph_thr = 60 # for Mobile images, 40 for scanned Images + _, morph_v = cv2.threshold(morph_v, morph_thr, 255, cv2.THRESH_BINARY) + # kernel best tuned to 5x5 now + morph_v = cv2.erode(morph_v, np.ones((5, 5), np.uint8), iterations=2) + + self.append_save_img(3, morph_v) + # h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 2)) + # morph_h = cv2.morphologyEx(morph, cv2.MORPH_OPEN, h_kernel, iterations=3) + # ret, morph_h = cv2.threshold(morph_h,200,200,cv2.THRESH_TRUNC) + # morph_h = 255 - normalize_util(morph_h) + # InteractionUtils.show("morph_h",morph_h,0,1,config=config) + # _, morph_h = cv2.threshold(morph_h,morph_thr,255,cv2.THRESH_BINARY) + # morph_h = cv2.erode(morph_h, np.ones((5,5),np.uint8), iterations = 2) + if config.outputs.show_image_level >= 3: + InteractionUtils.show( + "morph_thr_eroded", morph_v, 0, 1, config=config + ) + + self.append_save_img(6, morph_v) + + # template relative alignment code + for field_block in template.field_blocks: + s, d = field_block.origin, field_block.dimensions + + match_col, max_steps, align_stride, thk = map( + config.alignment_params.get, + [ + "match_col", + "max_steps", + "stride", + "thickness", + ], + ) + shift, steps = 0, 0 + while steps < max_steps: + left_mean = np.mean( + morph_v[ + s[1] : s[1] + d[1], + s[0] + shift - thk : -thk + s[0] + shift + match_col, + ] + ) + right_mean = np.mean( + morph_v[ + s[1] : s[1] + d[1], + s[0] + + shift + - match_col + + d[0] + + thk : thk + + s[0] + + shift + + d[0], + ] + ) + + # For demonstration purposes- + # if(field_block.name == "int1"): + # ret = morph_v.copy() + # cv2.rectangle(ret, + # (s[0]+shift-thk,s[1]), + # (s[0]+shift+thk+d[0],s[1]+d[1]), + # constants.CLR_WHITE, + # 3) + # appendSaveImg(6,ret) + # print(shift, left_mean, right_mean) + left_shift, right_shift = left_mean > 100, right_mean > 100 + if left_shift: + if right_shift: + break + else: + shift -= align_stride + else: + if right_shift: + shift += align_stride + else: + break + steps += 1 + + field_block.shift = shift + # print("Aligned field_block: ",field_block.name,"Corrected Shift:", + # field_block.shift,", dimensions:", field_block.dimensions, + # "origin:", field_block.origin,'\n') + # print("End Alignment") + + final_align = None + if config.outputs.show_image_level >= 2: + initial_align = self.draw_template_layout(img, template, shifted=False) + final_align = self.draw_template_layout( + img, template, shifted=True, draw_qvals=True + ) + # appendSaveImg(4,mean_vals) + self.append_save_img(2, initial_align) + self.append_save_img(2, final_align) + + if auto_align: + final_align = np.hstack((initial_align, final_align)) + self.append_save_img(5, img) + + # Get mean bubbleValues n other stats + all_q_vals, all_q_strip_arrs, all_q_std_vals = [], [], [] + total_q_strip_no = 0 + for field_block in template.field_blocks: + box_w, box_h = field_block.bubble_dimensions + q_std_vals = [] + for field_block_bubbles in field_block.traverse_bubbles: + q_strip_vals = [] + for pt in field_block_bubbles: + # shifted + x, y = (pt.x + field_block.shift, pt.y) + rect = [y, y + box_h, x, x + box_w] + q_strip_vals.append( + cv2.mean(img[rect[0] : rect[1], rect[2] : rect[3]])[0] + # detectCross(img, rect) ? 100 : 0 + ) + q_std_vals.append(round(np.std(q_strip_vals), 2)) + all_q_strip_arrs.append(q_strip_vals) + # _, _, _ = get_global_threshold(q_strip_vals, "QStrip Plot", + # plot_show=False, sort_in_plot=True) + # hist = getPlotImg() + # InteractionUtils.show("QStrip "+field_block_bubbles[0].field_label, hist, 0, 1,config=config) + all_q_vals.extend(q_strip_vals) + # print(total_q_strip_no, field_block_bubbles[0].field_label, q_std_vals[len(q_std_vals)-1]) + total_q_strip_no += 1 + all_q_std_vals.extend(q_std_vals) + + global_std_thresh, _, _ = self.get_global_threshold( + all_q_std_vals + ) # , "Q-wise Std-dev Plot", plot_show=True, sort_in_plot=True) + # plt.show() + # hist = getPlotImg() + # InteractionUtils.show("StdHist", hist, 0, 1,config=config) + + # Note: Plotting takes Significant times here --> Change Plotting args + # to support show_image_level + # , "Mean Intensity Histogram",plot_show=True, sort_in_plot=True) + global_thr, _, _ = self.get_global_threshold(all_q_vals, looseness=4) + + logger.info( + f"Thresholding: \tglobal_thr: {round(global_thr, 2)} \tglobal_std_THR: {round(global_std_thresh, 2)}\t{'(Looks like a Xeroxed OMR)' if (global_thr == 255) else ''}" + ) + # plt.show() + # hist = getPlotImg() + # InteractionUtils.show("StdHist", hist, 0, 1,config=config) + + # if(config.outputs.show_image_level>=1): + # hist = getPlotImg() + # InteractionUtils.show("Hist", hist, 0, 1,config=config) + # appendSaveImg(4,hist) + # appendSaveImg(5,hist) + # appendSaveImg(2,hist) + + per_omr_threshold_avg, total_q_strip_no, total_q_box_no = 0, 0, 0 + for field_block in template.field_blocks: + block_q_strip_no = 1 + box_w, box_h = field_block.bubble_dimensions + shift = field_block.shift + s, d = field_block.origin, field_block.dimensions + key = field_block.name[:3] + # cv2.rectangle(final_marked,(s[0]+shift,s[1]),(s[0]+shift+d[0], + # s[1]+d[1]),CLR_BLACK,3) + for field_block_bubbles in field_block.traverse_bubbles: + # All Black or All White case + no_outliers = all_q_std_vals[total_q_strip_no] < global_std_thresh + # print(total_q_strip_no, field_block_bubbles[0].field_label, + # all_q_std_vals[total_q_strip_no], "no_outliers:", no_outliers) + per_q_strip_threshold = self.get_local_threshold( + all_q_strip_arrs[total_q_strip_no], + global_thr, + no_outliers, + f"Mean Intensity Histogram for {key}.{field_block_bubbles[0].field_label}.{block_q_strip_no}", + config.outputs.show_image_level >= 6, + ) + # print(field_block_bubbles[0].field_label,key,block_q_strip_no, "THR: ", + # round(per_q_strip_threshold,2)) + per_omr_threshold_avg += per_q_strip_threshold + + # Note: Little debugging visualization - view the particular Qstrip + # if( + # 0 + # # or "q17" in (field_block_bubbles[0].field_label) + # # or (field_block_bubbles[0].field_label+str(block_q_strip_no))=="q15" + # ): + # st, end = qStrip + # InteractionUtils.show("QStrip: "+key+"-"+str(block_q_strip_no), + # img[st[1] : end[1], st[0]+shift : end[0]+shift],0,config=config) + + # TODO: get rid of total_q_box_no + detected_bubbles = [] + for bubble in field_block_bubbles: + bubble_is_marked = ( + per_q_strip_threshold > all_q_vals[total_q_box_no] + ) + total_q_box_no += 1 + if bubble_is_marked: + detected_bubbles.append(bubble) + x, y, field_value = ( + bubble.x + field_block.shift, + bubble.y, + bubble.field_value, + ) + cv2.rectangle( + final_marked, + (int(x + box_w / 12), int(y + box_h / 12)), + ( + int(x + box_w - box_w / 12), + int(y + box_h - box_h / 12), + ), + constants.CLR_DARK_GRAY, + 3, + ) + + cv2.putText( + final_marked, + str(field_value), + (x, y), + cv2.FONT_HERSHEY_SIMPLEX, + constants.TEXT_SIZE, + (20, 20, 10), + int(1 + 3.5 * constants.TEXT_SIZE), + ) + else: + cv2.rectangle( + final_marked, + (int(x + box_w / 10), int(y + box_h / 10)), + ( + int(x + box_w - box_w / 10), + int(y + box_h - box_h / 10), + ), + constants.CLR_GRAY, + -1, + ) + + for bubble in detected_bubbles: + field_label, field_value = ( + bubble.field_label, + bubble.field_value, + ) + # Only send rolls multi-marked in the directory + multi_marked_local = field_label in omr_response + omr_response[field_label] = ( + (omr_response[field_label] + field_value) + if multi_marked_local + else field_value + ) + # TODO: generalize this into identifier + # multi_roll = multi_marked_local and "Roll" in str(q) + multi_marked = multi_marked or multi_marked_local + + if len(detected_bubbles) == 0: + field_label = field_block_bubbles[0].field_label + omr_response[field_label] = field_block.empty_val + + if config.outputs.show_image_level >= 5: + if key in all_c_box_vals: + q_nums[key].append(f"{key[:2]}_c{str(block_q_strip_no)}") + all_c_box_vals[key].append( + all_q_strip_arrs[total_q_strip_no] + ) + + block_q_strip_no += 1 + total_q_strip_no += 1 + # /for field_block + + per_omr_threshold_avg /= total_q_strip_no + per_omr_threshold_avg = round(per_omr_threshold_avg, 2) + # Translucent + cv2.addWeighted( + final_marked, alpha, transp_layer, 1 - alpha, 0, final_marked + ) + # Box types + if config.outputs.show_image_level >= 6: + # plt.draw() + f, axes = plt.subplots(len(all_c_box_vals), sharey=True) + f.canvas.manager.set_window_title(name) + ctr = 0 + type_name = { + "int": "Integer", + "mcq": "MCQ", + "med": "MED", + "rol": "Roll", + } + for k, boxvals in all_c_box_vals.items(): + axes[ctr].title.set_text(type_name[k] + " Type") + axes[ctr].boxplot(boxvals) + # thrline=axes[ctr].axhline(per_omr_threshold_avg,color='red',ls='--') + # thrline.set_label("Average THR") + axes[ctr].set_ylabel("Intensity") + axes[ctr].set_xticklabels(q_nums[k]) + # axes[ctr].legend() + ctr += 1 + # imshow will do the waiting + plt.tight_layout(pad=0.5) + plt.show() + + if config.outputs.show_image_level >= 3 and final_align is not None: + final_align = ImageUtils.resize_util_h( + final_align, int(config.dimensions.display_height) + ) + # [final_align.shape[1],0]) + InteractionUtils.show( + "Template Alignment Adjustment", final_align, 0, 0, config=config + ) + + if config.outputs.save_detections and save_dir is not None: + if multi_roll: + save_dir = save_dir.joinpath("_MULTI_") + image_path = str(save_dir.joinpath(name)) + ImageUtils.save_img(image_path, final_marked) + + self.append_save_img(2, final_marked) + + if save_dir is not None: + for i in range(config.outputs.save_image_level): + self.save_image_stacks(i + 1, name, save_dir) + + return omr_response, final_marked, multi_marked, multi_roll + + except Exception as e: + raise e + + @staticmethod + def draw_template_layout(img, template, shifted=True, draw_qvals=False, border=-1): + img = ImageUtils.resize_util( + img, template.page_dimensions[0], template.page_dimensions[1] + ) + final_align = img.copy() + for field_block in template.field_blocks: + s, d = field_block.origin, field_block.dimensions + box_w, box_h = field_block.bubble_dimensions + shift = field_block.shift + if shifted: + cv2.rectangle( + final_align, + (s[0] + shift, s[1]), + (s[0] + shift + d[0], s[1] + d[1]), + constants.CLR_BLACK, + 3, + ) + else: + cv2.rectangle( + final_align, + (s[0], s[1]), + (s[0] + d[0], s[1] + d[1]), + constants.CLR_BLACK, + 3, + ) + for field_block_bubbles in field_block.traverse_bubbles: + for pt in field_block_bubbles: + x, y = (pt.x + field_block.shift, pt.y) if shifted else (pt.x, pt.y) + cv2.rectangle( + final_align, + (int(x + box_w / 10), int(y + box_h / 10)), + (int(x + box_w - box_w / 10), int(y + box_h - box_h / 10)), + constants.CLR_GRAY, + border, + ) + if draw_qvals: + rect = [y, y + box_h, x, x + box_w] + cv2.putText( + final_align, + f"{int(cv2.mean(img[rect[0] : rect[1], rect[2] : rect[3]])[0])}", + (rect[2] + 2, rect[0] + (box_h * 2) // 3), + cv2.FONT_HERSHEY_SIMPLEX, + 0.6, + constants.CLR_BLACK, + 2, + ) + if shifted: + text_in_px = cv2.getTextSize( + field_block.name, cv2.FONT_HERSHEY_SIMPLEX, constants.TEXT_SIZE, 4 + ) + cv2.putText( + final_align, + field_block.name, + (int(s[0] + d[0] - text_in_px[0][0]), int(s[1] - text_in_px[0][1])), + cv2.FONT_HERSHEY_SIMPLEX, + constants.TEXT_SIZE, + constants.CLR_BLACK, + 4, + ) + return final_align + + def get_global_threshold( + self, + q_vals_orig, + plot_title=None, + plot_show=True, + sort_in_plot=True, + looseness=1, + ): + """ + Note: Cannot assume qStrip has only-gray or only-white bg + (in which case there is only one jump). + So there will be either 1 or 2 jumps. + 1 Jump : + ...... + |||||| + |||||| <-- risky THR + |||||| <-- safe THR + ....|||||| + |||||||||| + + 2 Jumps : + ...... + |||||| <-- wrong THR + ....|||||| + |||||||||| <-- safe THR + ..|||||||||| + |||||||||||| + + The abstract "First LARGE GAP" is perfect for this. + Current code is considering ONLY TOP 2 jumps(>= MIN_GAP) to be big, + gives the smaller one + + """ + config = self.tuning_config + PAGE_TYPE_FOR_THRESHOLD, MIN_JUMP, JUMP_DELTA = map( + config.threshold_params.get, + [ + "PAGE_TYPE_FOR_THRESHOLD", + "MIN_JUMP", + "JUMP_DELTA", + ], + ) + + global_default_threshold = ( + constants.GLOBAL_PAGE_THRESHOLD_WHITE + if PAGE_TYPE_FOR_THRESHOLD == "white" + else constants.GLOBAL_PAGE_THRESHOLD_BLACK + ) + + # Sort the Q bubbleValues + # TODO: Change var name of q_vals + q_vals = sorted(q_vals_orig) + # Find the FIRST LARGE GAP and set it as threshold: + ls = (looseness + 1) // 2 + l = len(q_vals) - ls + max1, thr1 = MIN_JUMP, global_default_threshold + for i in range(ls, l): + jump = q_vals[i + ls] - q_vals[i - ls] + if jump > max1: + max1 = jump + thr1 = q_vals[i - ls] + jump / 2 + + # NOTE: thr2 is deprecated, thus is JUMP_DELTA + # Make use of the fact that the JUMP_DELTA(Vertical gap ofc) between + # values at detected jumps would be atleast 20 + max2, thr2 = MIN_JUMP, global_default_threshold + # Requires atleast 1 gray box to be present (Roll field will ensure this) + for i in range(ls, l): + jump = q_vals[i + ls] - q_vals[i - ls] + new_thr = q_vals[i - ls] + jump / 2 + if jump > max2 and abs(thr1 - new_thr) > JUMP_DELTA: + max2 = jump + thr2 = new_thr + # global_thr = min(thr1,thr2) + global_thr, j_low, j_high = thr1, thr1 - max1 // 2, thr1 + max1 // 2 + + # # For normal images + # thresholdRead = 116 + # if(thr1 > thr2 and thr2 > thresholdRead): + # print("Note: taking safer thr line.") + # global_thr, j_low, j_high = thr2, thr2 - max2//2, thr2 + max2//2 + + if plot_title: + _, ax = plt.subplots() + ax.bar(range(len(q_vals_orig)), q_vals if sort_in_plot else q_vals_orig) + ax.set_title(plot_title) + thrline = ax.axhline(global_thr, color="green", ls="--", linewidth=5) + thrline.set_label("Global Threshold") + thrline = ax.axhline(thr2, color="red", ls=":", linewidth=3) + thrline.set_label("THR2 Line") + # thrline=ax.axhline(j_low,color='red',ls='-.', linewidth=3) + # thrline=ax.axhline(j_high,color='red',ls='-.', linewidth=3) + # thrline.set_label("Boundary Line") + # ax.set_ylabel("Mean Intensity") + ax.set_ylabel("Values") + ax.set_xlabel("Position") + ax.legend() + if plot_show: + plt.title(plot_title) + plt.show() + + return global_thr, j_low, j_high + + def get_local_threshold( + self, q_vals, global_thr, no_outliers, plot_title=None, plot_show=True + ): + """ + TODO: Update this documentation too- + //No more - Assumption : Colwise background color is uniformly gray or white, + but not alternating. In this case there is atmost one jump. + + 0 Jump : + <-- safe THR? + ....... + ...||||||| + |||||||||| <-- safe THR? + // How to decide given range is above or below gray? + -> global q_vals shall absolutely help here. Just run same function + on total q_vals instead of colwise _// + How to decide it is this case of 0 jumps + + 1 Jump : + ...... + |||||| + |||||| <-- risky THR + |||||| <-- safe THR + ....|||||| + |||||||||| + + """ + config = self.tuning_config + # Sort the Q bubbleValues + q_vals = sorted(q_vals) + + # Small no of pts cases: + # base case: 1 or 2 pts + if len(q_vals) < 3: + thr1 = ( + global_thr + if np.max(q_vals) - np.min(q_vals) < config.threshold_params.MIN_GAP + else np.mean(q_vals) + ) + else: + # qmin, qmax, qmean, qstd = round(np.min(q_vals),2), round(np.max(q_vals),2), + # round(np.mean(q_vals),2), round(np.std(q_vals),2) + # GVals = [round(abs(q-qmean),2) for q in q_vals] + # gmean, gstd = round(np.mean(GVals),2), round(np.std(GVals),2) + # # DISCRETION: Pretty critical factor in reading response + # # Doesn't work well for small number of values. + # DISCRETION = 2.7 # 2.59 was closest hit, 3.0 is too far + # L2MaxGap = round(max([abs(g-gmean) for g in GVals]),2) + # if(L2MaxGap > DISCRETION*gstd): + # no_outliers = False + + # # ^Stackoverflow method + # print(field_label, no_outliers,"qstd",round(np.std(q_vals),2), "gstd", gstd, + # "Gaps in gvals",sorted([round(abs(g-gmean),2) for g in GVals],reverse=True), + # '\t',round(DISCRETION*gstd,2), L2MaxGap) + + # else: + # Find the LARGEST GAP and set it as threshold: //(FIRST LARGE GAP) + l = len(q_vals) - 1 + max1, thr1 = config.threshold_params.MIN_JUMP, 255 + for i in range(1, l): + jump = q_vals[i + 1] - q_vals[i - 1] + if jump > max1: + max1 = jump + thr1 = q_vals[i - 1] + jump / 2 + # print(field_label,q_vals,max1) + + confident_jump = ( + config.threshold_params.MIN_JUMP + + config.threshold_params.CONFIDENT_SURPLUS + ) + # If not confident, then only take help of global_thr + if max1 < confident_jump: + if no_outliers: + # All Black or All White case + thr1 = global_thr + else: + # TODO: Low confidence parameters here + pass + + # if(thr1 == 255): + # print("Warning: threshold is unexpectedly 255! (Outlier Delta issue?)",plot_title) + + # Make a common plot function to show local and global thresholds + if plot_show and plot_title is not None: + _, ax = plt.subplots() + ax.bar(range(len(q_vals)), q_vals) + thrline = ax.axhline(thr1, color="green", ls=("-."), linewidth=3) + thrline.set_label("Local Threshold") + thrline = ax.axhline(global_thr, color="red", ls=":", linewidth=5) + thrline.set_label("Global Threshold") + ax.set_title(plot_title) + ax.set_ylabel("Bubble Mean Intensity") + ax.set_xlabel("Bubble Number(sorted)") + ax.legend() + # TODO append QStrip to this plot- + # appendSaveImg(6,getPlotImg()) + if plot_show: + plt.show() + return thr1 + + def append_save_img(self, key, img): + if self.save_image_level >= int(key): + self.save_img_list[key].append(img.copy()) + + def save_image_stacks(self, key, filename, save_dir): + config = self.tuning_config + if self.save_image_level >= int(key) and self.save_img_list[key] != []: + name = os.path.splitext(filename)[0] + result = np.hstack( + tuple( + [ + ImageUtils.resize_util_h(img, config.dimensions.display_height) + for img in self.save_img_list[key] + ] + ) + ) + result = ImageUtils.resize_util( + result, + min( + len(self.save_img_list[key]) * config.dimensions.display_width // 3, + int(config.dimensions.display_width * 2.5), + ), + ) + ImageUtils.save_img(f"{save_dir}stack/{name}_{str(key)}_stack.jpg", result) + + def reset_all_save_img(self): + for i in range(self.save_image_level): + self.save_img_list[i + 1] = [] diff --git a/src/defaults/__init__.py b/src/defaults/__init__.py new file mode 100644 index 00000000..62ed2e6f --- /dev/null +++ b/src/defaults/__init__.py @@ -0,0 +1,5 @@ +# https://docs.python.org/3/tutorial/modules.html#:~:text=The%20__init__.py,on%20the%20module%20search%20path. +# Use all imports relative to root directory +# (https://chrisyeh96.github.io/2017/08/08/definitive-guide-python-imports.html) +from src.defaults.config import * # NOQA +from src.defaults.template import * # NOQA diff --git a/src/defaults/config.py b/src/defaults/config.py new file mode 100644 index 00000000..1c6cad3f --- /dev/null +++ b/src/defaults/config.py @@ -0,0 +1,35 @@ +from dotmap import DotMap + +CONFIG_DEFAULTS = DotMap( + { + "dimensions": { + "display_height": 2480, + "display_width": 1640, + "processing_height": 820, + "processing_width": 666, + }, + "threshold_params": { + "GAMMA_LOW": 0.7, + "MIN_GAP": 30, + "MIN_JUMP": 25, + "CONFIDENT_SURPLUS": 5, + "JUMP_DELTA": 30, + "PAGE_TYPE_FOR_THRESHOLD": "white", + }, + "alignment_params": { + # Note: 'auto_align' enables automatic template alignment, use if the scans show slight misalignments. + "auto_align": False, + "match_col": 5, + "max_steps": 20, + "stride": 1, + "thickness": 3, + }, + "outputs": { + "show_image_level": 0, + "save_image_level": 0, + "save_detections": True, + "filter_out_multimarked_files": False, + }, + }, + _dynamic=False, +) diff --git a/src/defaults/template.py b/src/defaults/template.py new file mode 100644 index 00000000..d0a2a831 --- /dev/null +++ b/src/defaults/template.py @@ -0,0 +1,6 @@ +TEMPLATE_DEFAULTS = { + "preProcessors": [], + "emptyValue": "", + "customLabels": {}, + "outputColumns": [], +} diff --git a/src/entry.py b/src/entry.py new file mode 100644 index 00000000..5cfcfd95 --- /dev/null +++ b/src/entry.py @@ -0,0 +1,370 @@ +""" + + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 + +""" +import os +from csv import QUOTE_NONNUMERIC +from pathlib import Path +from time import time + +import cv2 +import pandas as pd +from rich.table import Table + +from src import constants +from src.defaults import CONFIG_DEFAULTS +from src.evaluation import EvaluationConfig, evaluate_concatenated_response +from src.logger import console, logger +from src.template import Template +from src.utils.file import Paths, setup_dirs_for_paths, setup_outputs_for_template +from src.utils.image import ImageUtils +from src.utils.interaction import InteractionUtils, Stats +from src.utils.parsing import get_concatenated_response, open_config_with_defaults + +# Load processors +STATS = Stats() + + +def entry_point(input_dir, args): + if not os.path.exists(input_dir): + raise Exception(f"Given input directory does not exist: '{input_dir}'") + curr_dir = input_dir + return process_dir(input_dir, curr_dir, args) + + +def print_config_summary( + curr_dir, + omr_files, + template, + tuning_config, + local_config_path, + evaluation_config, + args, +): + logger.info("") + table = Table(title="Current Configurations", show_header=False, show_lines=False) + table.add_column("Key", style="cyan", no_wrap=True) + table.add_column("Value", style="magenta") + table.add_row("Directory Path", f"{curr_dir}") + table.add_row("Count of Images", f"{len(omr_files)}") + table.add_row("Set Layout Mode ", "ON" if args["setLayout"] else "OFF") + table.add_row( + "Markers Detection", + "ON" if "CropOnMarkers" in template.pre_processors else "OFF", + ) + table.add_row("Auto Alignment", f"{tuning_config.alignment_params.auto_align}") + table.add_row("Detected Template Path", f"{template}") + if local_config_path: + table.add_row("Detected Local Config", f"{local_config_path}") + if evaluation_config: + table.add_row("Detected Evaluation Config", f"{evaluation_config}") + + table.add_row( + "Detected pre-processors", + f"{[pp.__class__.__name__ for pp in template.pre_processors]}", + ) + console.print(table, justify="center") + + +def process_dir( + root_dir, + curr_dir, + args, + template=None, + tuning_config=CONFIG_DEFAULTS, + evaluation_config=None, +): + # Update local tuning_config (in current recursion stack) + local_config_path = curr_dir.joinpath(constants.CONFIG_FILENAME) + if os.path.exists(local_config_path): + tuning_config = open_config_with_defaults(local_config_path) + + # Update local template (in current recursion stack) + local_template_path = curr_dir.joinpath(constants.TEMPLATE_FILENAME) + local_template_exists = os.path.exists(local_template_path) + if local_template_exists: + template = Template( + local_template_path, + tuning_config, + ) + # Look for subdirectories for processing + subdirs = [d for d in curr_dir.iterdir() if d.is_dir()] + + output_dir = Path(args["output_dir"], curr_dir.relative_to(root_dir)) + paths = Paths(output_dir) + + # look for images in current dir to process + exts = ("*.[pP][nN][gG]", "*.[jJ][pP][gG]", "*.[jJ][pP][eE][gG]") + omr_files = sorted([f for ext in exts for f in curr_dir.glob(ext)]) + + # Exclude images (take union over all pre_processors) + excluded_files = [] + if template: + for pp in template.pre_processors: + excluded_files.extend(Path(p) for p in pp.exclude_files()) + + local_evaluation_path = curr_dir.joinpath(constants.EVALUATION_FILENAME) + if not args["setLayout"] and os.path.exists(local_evaluation_path): + if not local_template_exists: + logger.warning( + f"Found an evaluation file without a parent template file: {local_evaluation_path}" + ) + evaluation_config = EvaluationConfig( + curr_dir, + local_evaluation_path, + template, + tuning_config, + ) + + excluded_files.extend( + Path(exclude_file) for exclude_file in evaluation_config.get_exclude_files() + ) + + omr_files = [f for f in omr_files if f not in excluded_files] + + if omr_files: + if not template: + logger.error( + f"Found images, but no template in the directory tree \ + of '{curr_dir}'. \nPlace {constants.TEMPLATE_FILENAME} in the \ + appropriate directory." + ) + raise Exception( + f"No template file found in the directory tree of {curr_dir}" + ) + + setup_dirs_for_paths(paths) + outputs_namespace = setup_outputs_for_template(paths, template) + + print_config_summary( + curr_dir, + omr_files, + template, + tuning_config, + local_config_path, + evaluation_config, + args, + ) + if args["setLayout"]: + show_template_layouts(omr_files, template, tuning_config) + else: + process_files( + omr_files, + template, + tuning_config, + evaluation_config, + outputs_namespace, + ) + + elif not subdirs: + # Each subdirectory should have images or should be non-leaf + logger.info( + f"No valid images or sub-folders found in {curr_dir}.\ + Empty directories not allowed." + ) + + # recursively process sub-folders + for d in subdirs: + process_dir( + root_dir, + d, + args, + template, + tuning_config, + evaluation_config, + ) + + +def show_template_layouts(omr_files, template, tuning_config): + for file_path in omr_files: + file_name = file_path.name + file_path = str(file_path) + in_omr = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE) + in_omr = template.image_instance_ops.apply_preprocessors( + file_path, in_omr, template + ) + template_layout = template.image_instance_ops.draw_template_layout( + in_omr, template, shifted=False, border=2 + ) + InteractionUtils.show( + f"Template Layout: {file_name}", template_layout, 1, 1, config=tuning_config + ) + + +def process_files( + omr_files, + template, + tuning_config, + evaluation_config, + outputs_namespace, +): + start_time = int(time()) + files_counter = 0 + STATS.files_not_moved = 0 + + for file_path in omr_files: + files_counter += 1 + file_name = file_path.name + + in_omr = cv2.imread(str(file_path), cv2.IMREAD_GRAYSCALE) + + logger.info("") + logger.info( + f"({files_counter}) Opening image: \t'{file_path}'\tResolution: {in_omr.shape}" + ) + + template.image_instance_ops.reset_all_save_img() + + template.image_instance_ops.append_save_img(1, in_omr) + + in_omr = template.image_instance_ops.apply_preprocessors( + file_path, in_omr, template + ) + + if in_omr is None: + # Error OMR case + new_file_path = outputs_namespace.paths.errors_dir.joinpath(file_name) + outputs_namespace.OUTPUT_SET.append( + [file_name] + outputs_namespace.empty_resp + ) + if check_and_move( + constants.ERROR_CODES.NO_MARKER_ERR, file_path, new_file_path + ): + err_line = [ + file_name, + file_path, + new_file_path, + "NA", + ] + outputs_namespace.empty_resp + pd.DataFrame(err_line, dtype=str).T.to_csv( + outputs_namespace.files_obj["Errors"], + mode="a", + quoting=QUOTE_NONNUMERIC, + header=False, + index=False, + ) + continue + + # uniquify + file_id = str(file_name) + save_dir = outputs_namespace.paths.save_marked_dir + ( + response_dict, + final_marked, + multi_marked, + _, + ) = template.image_instance_ops.read_omr_response( + template, image=in_omr, name=file_id, save_dir=save_dir + ) + + # TODO: move inner try catch here + # concatenate roll nos, set unmarked responses, etc + omr_response = get_concatenated_response(response_dict, template) + + if ( + evaluation_config is None + or not evaluation_config.get_should_explain_scoring() + ): + logger.info(f"Read Response: \n{omr_response}") + + score = 0 + if evaluation_config is not None: + score = evaluate_concatenated_response( + omr_response, evaluation_config, file_path, outputs_namespace.paths.evaluation_dir + ) + logger.info( + f"(/{files_counter}) Graded with score: {round(score, 2)}\t for file: '{file_id}'" + ) + else: + logger.info(f"(/{files_counter}) Processed file: '{file_id}'") + + if tuning_config.outputs.show_image_level >= 2: + InteractionUtils.show( + f"Final Marked Bubbles : '{file_id}'", + ImageUtils.resize_util_h( + final_marked, int(tuning_config.dimensions.display_height * 1.3) + ), + 1, + 1, + config=tuning_config, + ) + + resp_array = [] + for k in template.output_columns: + resp_array.append(omr_response[k]) + + outputs_namespace.OUTPUT_SET.append([file_name] + resp_array) + + if multi_marked == 0 or not tuning_config.outputs.filter_out_multimarked_files: + STATS.files_not_moved += 1 + new_file_path = save_dir.joinpath(file_id) + # Enter into Results sheet- + results_line = [file_name, file_path, new_file_path, score] + resp_array + # Write/Append to results_line file(opened in append mode) + pd.DataFrame(results_line, dtype=str).T.to_csv( + outputs_namespace.files_obj["Results"], + mode="a", + quoting=QUOTE_NONNUMERIC, + header=False, + index=False, + ) + else: + # multi_marked file + logger.info(f"[{files_counter}] Found multi-marked file: '{file_id}'") + new_file_path = outputs_namespace.paths.multi_marked_dir.joinpath(file_name) + if check_and_move( + constants.ERROR_CODES.MULTI_BUBBLE_WARN, file_path, new_file_path + ): + mm_line = [file_name, file_path, new_file_path, "NA"] + resp_array + pd.DataFrame(mm_line, dtype=str).T.to_csv( + outputs_namespace.files_obj["MultiMarked"], + mode="a", + quoting=QUOTE_NONNUMERIC, + header=False, + index=False, + ) + # else: + # TODO: Add appropriate record handling here + # pass + + print_stats(start_time, files_counter, tuning_config) + + +def check_and_move(error_code, file_path, filepath2): + # TODO: fix file movement into error/multimarked/invalid etc again + STATS.files_not_moved += 1 + return True + + +def print_stats(start_time, files_counter, tuning_config): + time_checking = max(1, round(time() - start_time, 2)) + log = logger.info + log("") + log(f"{'Total file(s) moved': <27}: {STATS.files_moved}") + log(f"{'Total file(s) not moved': <27}: {STATS.files_not_moved}") + log("--------------------------------") + log( + f"{'Total file(s) processed': <27}: {files_counter} ({'Sum Tallied!' if files_counter == (STATS.files_moved + STATS.files_not_moved) else 'Not Tallying!'})" + ) + + if tuning_config.outputs.show_image_level <= 0: + log( + f"\nFinished Checking {files_counter} file(s) in {round(time_checking, 1)} seconds i.e. ~{round(time_checking / 60, 1)} minute(s)." + ) + log( + f"{'OMR Processing Rate': <27}: \t ~ {round(time_checking / files_counter, 2)} seconds/OMR" + ) + log( + f"{'OMR Processing Speed': <27}: \t ~ {round((files_counter * 60) / time_checking, 2)} OMRs/minute" + ) + else: + log(f"\n{'Total script time': <27}: {time_checking} seconds") + + if tuning_config.outputs.show_image_level <= 1: + log( + "\nTip: To see some awesome visuals, open config.json and increase 'show_image_level'" + ) diff --git a/src/evaluation.py b/src/evaluation.py new file mode 100644 index 00000000..0b567169 --- /dev/null +++ b/src/evaluation.py @@ -0,0 +1,546 @@ +import ast +import os +import re +from copy import deepcopy +from csv import QUOTE_NONNUMERIC + +import cv2 +import pandas as pd +from rich.table import Table + +from src.logger import console, logger +from src.schemas.constants import ( + BONUS_SECTION_PREFIX, + DEFAULT_SECTION_KEY, + MARKING_VERDICT_TYPES, +) +from src.utils.parsing import ( + get_concatenated_response, + open_evaluation_with_validation, + parse_fields, + parse_float_or_fraction, +) + + +class AnswerMatcher: + def __init__(self, answer_item, section_marking_scheme): + self.section_marking_scheme = section_marking_scheme + self.answer_item = answer_item + self.answer_type = self.validate_and_get_answer_type(answer_item) + self.set_defaults_from_scheme(section_marking_scheme) + + @staticmethod + def is_a_marking_score(answer_element): + # Note: strict type checking is already done at schema validation level, + # Here we focus on overall struct type + return type(answer_element) == str or type(answer_element) == int + + @staticmethod + def is_standard_answer(answer_element): + return type(answer_element) == str and len(answer_element) >= 1 + + def validate_and_get_answer_type(self, answer_item): + if self.is_standard_answer(answer_item): + return "standard" + elif type(answer_item) == list: + if ( + # Array of answer elements: ['A', 'B', 'AB'] + len(answer_item) >= 2 + and all( + self.is_standard_answer(answers_or_score) + for answers_or_score in answer_item + ) + ): + return "multiple-correct" + elif ( + # Array of two-tuples: [['A', 1], ['B', 1], ['C', 3], ['AB', 2]] + len(answer_item) >= 1 + and all( + type(answer_and_score) == list and len(answer_and_score) == 2 + for answer_and_score in answer_item + ) + and all( + self.is_standard_answer(allowed_answer) + and self.is_a_marking_score(answer_score) + for allowed_answer, answer_score in answer_item + ) + ): + return "multiple-correct-weighted" + + logger.critical( + f"Unable to determine answer type for answer item: {answer_item}" + ) + raise Exception("Unable to determine answer type") + + def set_defaults_from_scheme(self, section_marking_scheme): + answer_type = self.answer_type + self.empty_val = section_marking_scheme.empty_val + answer_item = self.answer_item + self.marking = deepcopy(section_marking_scheme.marking) + # TODO: reuse part of parse_scheme_marking here - + if answer_type == "standard": + # no local overrides + pass + elif answer_type == "multiple-correct": + # override marking scheme scores for each allowed answer + for allowed_answer in answer_item: + self.marking[f"correct-{allowed_answer}"] = self.marking["correct"] + elif answer_type == "multiple-correct-weighted": + # Note: No override using marking scheme as answer scores are provided in answer_item + for allowed_answer, answer_score in answer_item: + self.marking[f"correct-{allowed_answer}"] = parse_float_or_fraction( + answer_score + ) + + def get_marking_scheme(self): + return self.section_marking_scheme + + def get_section_explanation(self): + answer_type = self.answer_type + if answer_type in ["standard", "multiple-correct"]: + return self.section_marking_scheme.section_key + elif answer_type == "multiple-correct-weighted": + return f"Custom: {self.marking}" + + def get_verdict_marking(self, marked_answer): + answer_type = self.answer_type + question_verdict = "incorrect" + if answer_type == "standard": + question_verdict = self.get_standard_verdict(marked_answer) + elif answer_type == "multiple-correct": + question_verdict = self.get_multiple_correct_verdict(marked_answer) + elif answer_type == "multiple-correct-weighted": + question_verdict = self.get_multiple_correct_weighted_verdict(marked_answer) + return question_verdict, self.marking[question_verdict] + + def get_standard_verdict(self, marked_answer): + allowed_answer = self.answer_item + if marked_answer == self.empty_val: + return "unmarked" + elif marked_answer == allowed_answer: + return "correct" + else: + return "incorrect" + + def get_multiple_correct_verdict(self, marked_answer): + allowed_answers = self.answer_item + if marked_answer == self.empty_val: + return "unmarked" + elif marked_answer in allowed_answers: + return f"correct-{marked_answer}" + else: + return "incorrect" + + def get_multiple_correct_weighted_verdict(self, marked_answer): + allowed_answers = [ + allowed_answer for allowed_answer, _answer_score in self.answer_item + ] + if marked_answer == self.empty_val: + return "unmarked" + elif marked_answer in allowed_answers: + return f"correct-{marked_answer}" + else: + return "incorrect" + + def __str__(self): + return f"{self.answer_item}" + + +class SectionMarkingScheme: + def __init__(self, section_key, section_scheme, empty_val): + # TODO: get local empty_val from qblock + self.empty_val = empty_val + self.section_key = section_key + # DEFAULT marking scheme follows a shorthand + if section_key == DEFAULT_SECTION_KEY: + self.questions = None + self.marking = self.parse_scheme_marking(section_scheme) + else: + self.questions = parse_fields(section_key, section_scheme["questions"]) + self.marking = self.parse_scheme_marking(section_scheme["marking"]) + + def __str__(self): + return self.section_key + + def parse_scheme_marking(self, marking): + parsed_marking = {} + for verdict_type in MARKING_VERDICT_TYPES: + verdict_marking = parse_float_or_fraction(marking[verdict_type]) + if ( + verdict_marking > 0 + and verdict_type == "incorrect" + and not self.section_key.startswith(BONUS_SECTION_PREFIX) + ): + logger.warning( + f"Found positive marks({round(verdict_marking, 2)}) for incorrect answer in the schema '{self.section_key}'. For Bonus sections, add a prefix 'BONUS_' to them." + ) + parsed_marking[verdict_type] = verdict_marking + + return parsed_marking + + def match_answer(self, marked_answer, answer_matcher): + question_verdict, verdict_marking = answer_matcher.get_verdict_marking( + marked_answer + ) + + return verdict_marking, question_verdict + + +class EvaluationConfig: + """Note: this instance will be reused for multiple omr sheets""" + + def __init__(self, curr_dir, evaluation_path, template, tuning_config): + self.path = evaluation_path + evaluation_json = open_evaluation_with_validation(evaluation_path) + options, marking_schemes, source_type = map( + evaluation_json.get, ["options", "marking_schemes", "source_type"] + ) + self.should_explain_scoring = options.get("should_explain_scoring", False) + self.has_non_default_section = False + self.exclude_files = [] + self.enable_evaluation_table_to_csv = options.get( + "enable_evaluation_table_to_csv", False + ) + + if source_type == "csv": + csv_path = curr_dir.joinpath(options["answer_key_csv_path"]) + if not os.path.exists(csv_path): + logger.warning(f"Answer key csv does not exist at: '{csv_path}'.") + + answer_key_image_path = options.get("answer_key_image_path", None) + if os.path.exists(csv_path): + # TODO: CSV parsing/validation for each row with a (qNo, ) pair + answer_key = pd.read_csv( + csv_path, + header=None, + names=["question", "answer"], + converters={"question": str, "answer": self.parse_answer_column}, + ) + + self.questions_in_order = answer_key["question"].to_list() + answers_in_order = answer_key["answer"].to_list() + elif not answer_key_image_path: + raise Exception(f"Answer key csv not found at '{csv_path}'") + else: + image_path = str(curr_dir.joinpath(answer_key_image_path)) + if not os.path.exists(image_path): + raise Exception(f"Answer key image not found at '{image_path}'") + + # self.exclude_files.append(image_path) + + logger.debug( + f"Attempting to generate answer key from image: '{image_path}'" + ) + # TODO: use a common function for below changes? + in_omr = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) + in_omr = template.image_instance_ops.apply_preprocessors( + image_path, in_omr, template + ) + if in_omr is None: + raise Exception( + f"Could not read answer key from image {image_path}" + ) + ( + response_dict, + _final_marked, + _multi_marked, + _multi_roll, + ) = template.image_instance_ops.read_omr_response( + template, + image=in_omr, + name=image_path, + save_dir=None, + ) + omr_response = get_concatenated_response(response_dict, template) + + empty_val = template.global_empty_val + empty_answer_regex = ( + rf"{re.escape(empty_val)}+" if empty_val != "" else r"^$" + ) + + if "questions_in_order" in options: + self.questions_in_order = self.parse_questions_in_order( + options["questions_in_order"] + ) + empty_answered_questions = [ + question + for question in self.questions_in_order + if re.search(empty_answer_regex, omr_response[question]) + ] + if len(empty_answered_questions) > 0: + logger.error( + f"Found empty answers for questions: {empty_answered_questions}, empty value used: '{empty_val}'" + ) + raise Exception( + f"Found empty answers in file '{image_path}'. Please check your template again in the --setLayout mode." + ) + else: + logger.warning( + f"questions_in_order not provided, proceeding to use non-empty values as answer key" + ) + self.questions_in_order = sorted( + question + for (question, answer) in omr_response.items() + if not re.search(empty_answer_regex, answer) + ) + answers_in_order = [ + omr_response[question] for question in self.questions_in_order + ] + # TODO: save the CSV + else: + self.questions_in_order = self.parse_questions_in_order( + options["questions_in_order"] + ) + answers_in_order = options["answers_in_order"] + + self.validate_questions(answers_in_order) + + self.section_marking_schemes, self.question_to_scheme = {}, {} + for section_key, section_scheme in marking_schemes.items(): + section_marking_scheme = SectionMarkingScheme( + section_key, section_scheme, template.global_empty_val + ) + if section_key != DEFAULT_SECTION_KEY: + self.section_marking_schemes[section_key] = section_marking_scheme + for q in section_marking_scheme.questions: + # TODO: check the answer key for custom scheme here? + self.question_to_scheme[q] = section_marking_scheme + self.has_non_default_section = True + else: + self.default_marking_scheme = section_marking_scheme + + self.validate_marking_schemes() + + self.question_to_answer_matcher = self.parse_answers_and_map_questions( + answers_in_order + ) + self.validate_answers(answers_in_order, tuning_config) + + def __str__(self): + return str(self.path) + + # Externally called methods have higher abstraction level. + def prepare_and_validate_omr_response(self, omr_response): + self.reset_explanation_table() + + omr_response_questions = set(omr_response.keys()) + all_questions = set(self.questions_in_order) + missing_questions = sorted(all_questions.difference(omr_response_questions)) + if len(missing_questions) > 0: + logger.critical(f"Missing OMR response for: {missing_questions}") + raise Exception( + f"Some questions are missing in the OMR response for the given answer key" + ) + + prefixed_omr_response_questions = set( + [k for k in omr_response.keys() if k.startswith("q")] + ) + missing_prefixed_questions = sorted( + prefixed_omr_response_questions.difference(all_questions) + ) + if len(missing_prefixed_questions) > 0: + logger.warning( + f"No answer given for potential questions in OMR response: {missing_prefixed_questions}" + ) + + def match_answer_for_question(self, current_score, question, marked_answer): + answer_matcher = self.question_to_answer_matcher[question] + question_verdict, delta = answer_matcher.get_verdict_marking(marked_answer) + self.conditionally_add_explanation( + answer_matcher, + delta, + marked_answer, + question_verdict, + question, + current_score, + ) + return delta + + def conditionally_print_explanation(self): + if self.should_explain_scoring: + console.print(self.explanation_table, justify="center") + + # Explanation Table to CSV + def conditionally_save_explanation_csv(self, file_path, evaluation_output_dir): + if self.enable_evaluation_table_to_csv: + data = {col.header: col._cells for col in self.explanation_table.columns} + + output_path = os.path.join( + evaluation_output_dir, + f"{file_path.stem}_evaluation.csv", + ) + + pd.DataFrame(data, dtype=str).to_csv( + output_path, + mode="a", + quoting=QUOTE_NONNUMERIC, + index=False, + ) + + def get_should_explain_scoring(self): + return self.should_explain_scoring + + def get_exclude_files(self): + return self.exclude_files + + @staticmethod + def parse_answer_column(answer_column): + if answer_column[0] == "[": + # multiple-correct-weighted or multiple-correct + parsed_answer = ast.literal_eval(answer_column) + elif "," in answer_column: + # multiple-correct + parsed_answer = answer_column.split(",") + else: + # single-correct + parsed_answer = answer_column + return parsed_answer + + def parse_questions_in_order(self, questions_in_order): + return parse_fields("questions_in_order", questions_in_order) + + def validate_answers(self, answers_in_order, tuning_config): + answer_matcher_map = self.question_to_answer_matcher + if tuning_config.outputs.filter_out_multimarked_files: + multi_marked_answer = False + for question, answer_item in zip(self.questions_in_order, answers_in_order): + answer_type = answer_matcher_map[question].answer_type + if answer_type == "standard": + if len(answer_item) > 1: + multi_marked_answer = True + if answer_type == "multiple-correct": + for single_answer in answer_item: + if len(single_answer) > 1: + multi_marked_answer = True + break + if answer_type == "multiple-correct-weighted": + for single_answer, _answer_score in answer_item: + if len(single_answer) > 1: + multi_marked_answer = True + + if multi_marked_answer: + raise Exception( + f"Provided answer key contains multiple correct answer(s), but config.filter_out_multimarked_files is True. Scoring will get skipped." + ) + + def validate_questions(self, answers_in_order): + questions_in_order = self.questions_in_order + len_questions_in_order, len_answers_in_order = len(questions_in_order), len( + answers_in_order + ) + if len_questions_in_order != len_answers_in_order: + logger.critical( + f"questions_in_order({len_questions_in_order}): {questions_in_order}\nanswers_in_order({len_answers_in_order}): {answers_in_order}" + ) + raise Exception( + f"Unequal lengths for questions_in_order and answers_in_order ({len_questions_in_order} != {len_answers_in_order})" + ) + + def validate_marking_schemes(self): + section_marking_schemes = self.section_marking_schemes + section_questions = set() + for section_key, section_scheme in section_marking_schemes.items(): + if section_key == DEFAULT_SECTION_KEY: + continue + current_set = set(section_scheme.questions) + if not section_questions.isdisjoint(current_set): + raise Exception( + f"Section '{section_key}' has overlapping question(s) with other sections" + ) + section_questions = section_questions.union(current_set) + + all_questions = set(self.questions_in_order) + missing_questions = sorted(section_questions.difference(all_questions)) + if len(missing_questions) > 0: + logger.critical(f"Missing answer key for: {missing_questions}") + raise Exception( + f"Some questions are missing in the answer key for the given marking scheme" + ) + + def parse_answers_and_map_questions(self, answers_in_order): + question_to_answer_matcher = {} + for question, answer_item in zip(self.questions_in_order, answers_in_order): + section_marking_scheme = self.get_marking_scheme_for_question(question) + answer_matcher = AnswerMatcher(answer_item, section_marking_scheme) + question_to_answer_matcher[question] = answer_matcher + if ( + answer_matcher.answer_type == "multiple-correct-weighted" + and section_marking_scheme.section_key != DEFAULT_SECTION_KEY + ): + logger.warning( + f"The custom scheme '{section_marking_scheme}' will not apply to question '{question}' as it will use the given answer weights f{answer_item}" + ) + return question_to_answer_matcher + + # Then unfolding lower abstraction levels + def reset_explanation_table(self): + self.explanation_table = None + self.prepare_explanation_table() + + def prepare_explanation_table(self): + # TODO: provide a way to export this as csv/pdf + if not self.should_explain_scoring: + return + table = Table(title="Evaluation Explanation Table", show_lines=True) + table.add_column("Question") + table.add_column("Marked") + table.add_column("Answer(s)") + table.add_column("Verdict") + table.add_column("Delta") + table.add_column("Score") + # TODO: Add max and min score in explanation (row-wise and total) + if self.has_non_default_section: + table.add_column("Section") + self.explanation_table = table + + def get_marking_scheme_for_question(self, question): + return self.question_to_scheme.get(question, self.default_marking_scheme) + + def conditionally_add_explanation( + self, + answer_matcher, + delta, + marked_answer, + question_verdict, + question, + current_score, + ): + if self.should_explain_scoring: + next_score = current_score + delta + # Conditionally add cells + row = [ + item + for item in [ + question, + marked_answer, + str(answer_matcher), + str.title(question_verdict), + str(round(delta, 2)), + str(round(next_score, 2)), + ( + answer_matcher.get_section_explanation() + if self.has_non_default_section + else None + ), + ] + if item is not None + ] + self.explanation_table.add_row(*row) + + +def evaluate_concatenated_response( + concatenated_response, evaluation_config, file_path, evaluation_output_dir +): + evaluation_config.prepare_and_validate_omr_response(concatenated_response) + current_score = 0.0 + for question in evaluation_config.questions_in_order: + marked_answer = concatenated_response[question] + delta = evaluation_config.match_answer_for_question( + current_score, question, marked_answer + ) + current_score += delta + + evaluation_config.conditionally_print_explanation() + evaluation_config.conditionally_save_explanation_csv(file_path, evaluation_output_dir) + + return current_score diff --git a/src/logger.py b/src/logger.py new file mode 100644 index 00000000..27f44b44 --- /dev/null +++ b/src/logger.py @@ -0,0 +1,68 @@ +import logging +from typing import Union + +from rich.console import Console +from rich.logging import RichHandler + +FORMAT = "%(message)s" + +# TODO: set logging level from config.json dynamically +logging.basicConfig( + level=logging.INFO, + format="%(message)s", + datefmt="[%X]", + handlers=[RichHandler(rich_tracebacks=True)], +) + + +class Logger: + def __init__( + self, + name, + level: Union[int, str] = logging.NOTSET, + message_format="%(message)s", + date_format="[%X]", + ): + self.log = logging.getLogger(name) + self.log.setLevel(level) + self.log.__format__ = message_format + self.log.__date_format__ = date_format + + def debug(self, *msg: object, sep=" ", end="\n") -> None: + return self.logutil("debug", *msg, sep=sep) + + def info(self, *msg: object, sep=" ", end="\n") -> None: + return self.logutil("info", *msg, sep=sep) + + def warning(self, *msg: object, sep=" ", end="\n") -> None: + return self.logutil("warning", *msg, sep=sep) + + def error(self, *msg: object, sep=" ", end="\n") -> None: + return self.logutil("error", *msg, sep=sep) + + def critical(self, *msg: object, sep=" ", end="\n") -> None: + return self.logutil("critical", *msg, sep=sep) + + def stringify(func): + def inner(self, method_type: str, *msg: object, sep=" "): + nmsg = [] + for v in msg: + if not isinstance(v, str): + v = str(v) + nmsg.append(v) + return func(self, method_type, *nmsg, sep=sep) + + return inner + + # set stack level to 3 so that the caller of this function is logged, not this function itself. + # stack-frame - self.log.debug - logutil - stringify - log method - caller + @stringify + def logutil(self, method_type: str, *msg: object, sep=" ") -> None: + func = getattr(self.log, method_type, None) + if not func: + raise AttributeError(f"Logger has no method {method_type}") + return func(sep.join(msg), stacklevel=4) + + +logger = Logger(__name__) +console = Console() diff --git a/src/processors/CropOnMarkers.py b/src/processors/CropOnMarkers.py new file mode 100644 index 00000000..4867e038 --- /dev/null +++ b/src/processors/CropOnMarkers.py @@ -0,0 +1,233 @@ +import os + +import cv2 +import numpy as np + +from src.logger import logger +from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor +from src.utils.image import ImageUtils +from src.utils.interaction import InteractionUtils + + +class CropOnMarkers(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + config = self.tuning_config + marker_ops = self.options + self.threshold_circles = [] + # img_utils = ImageUtils() + + # options with defaults + self.marker_path = os.path.join( + self.relative_dir, marker_ops.get("relativePath", "omr_marker.jpg") + ) + self.min_matching_threshold = marker_ops.get("min_matching_threshold", 0.3) + self.max_matching_variation = marker_ops.get("max_matching_variation", 0.41) + self.marker_rescale_range = tuple( + int(r) for r in marker_ops.get("marker_rescale_range", (35, 100)) + ) + self.marker_rescale_steps = int(marker_ops.get("marker_rescale_steps", 10)) + self.apply_erode_subtract = marker_ops.get("apply_erode_subtract", True) + self.marker = self.load_marker(marker_ops, config) + + def __str__(self): + return self.marker_path + + def exclude_files(self): + return [self.marker_path] + + def apply_filter(self, image, file_path): + config = self.tuning_config + image_instance_ops = self.image_instance_ops + image_eroded_sub = ImageUtils.normalize_util( + image + if self.apply_erode_subtract + else (image - cv2.erode(image, kernel=np.ones((5, 5)), iterations=5)) + ) + # Quads on warped image + quads = {} + h1, w1 = image_eroded_sub.shape[:2] + midh, midw = h1 // 3, w1 // 2 + origins = [[0, 0], [midw, 0], [0, midh], [midw, midh]] + quads[0] = image_eroded_sub[0:midh, 0:midw] + quads[1] = image_eroded_sub[0:midh, midw:w1] + quads[2] = image_eroded_sub[midh:h1, 0:midw] + quads[3] = image_eroded_sub[midh:h1, midw:w1] + + # Draw Quadlines + image_eroded_sub[:, midw : midw + 2] = 255 + image_eroded_sub[midh : midh + 2, :] = 255 + + best_scale, all_max_t = self.getBestMatch(image_eroded_sub) + if best_scale is None: + if config.outputs.show_image_level >= 1: + InteractionUtils.show("Quads", image_eroded_sub, config=config) + return None + + optimal_marker = ImageUtils.resize_util_h( + self.marker, u_height=int(self.marker.shape[0] * best_scale) + ) + _h, w = optimal_marker.shape[:2] + centres = [] + sum_t, max_t = 0, 0 + quarter_match_log = "Matching Marker: " + for k in range(0, 4): + res = cv2.matchTemplate(quads[k], optimal_marker, cv2.TM_CCOEFF_NORMED) + max_t = res.max() + quarter_match_log += f"Quarter{str(k + 1)}: {str(round(max_t, 3))}\t" + if ( + max_t < self.min_matching_threshold + or abs(all_max_t - max_t) >= self.max_matching_variation + ): + logger.error( + file_path, + "\nError: No circle found in Quad", + k + 1, + "\n\t min_matching_threshold", + self.min_matching_threshold, + "\t max_matching_variation", + self.max_matching_variation, + "\t max_t", + max_t, + "\t all_max_t", + all_max_t, + ) + if config.outputs.show_image_level >= 1: + InteractionUtils.show( + f"No markers: {file_path}", + image_eroded_sub, + 0, + config=config, + ) + InteractionUtils.show( + f"res_Q{str(k + 1)} ({str(max_t)})", + res, + 1, + config=config, + ) + return None + + pt = np.argwhere(res == max_t)[0] + pt = [pt[1], pt[0]] + pt[0] += origins[k][0] + pt[1] += origins[k][1] + # print(">>",pt) + image = cv2.rectangle( + image, tuple(pt), (pt[0] + w, pt[1] + _h), (150, 150, 150), 2 + ) + # display: + image_eroded_sub = cv2.rectangle( + image_eroded_sub, + tuple(pt), + (pt[0] + w, pt[1] + _h), + (50, 50, 50) if self.apply_erode_subtract else (155, 155, 155), + 4, + ) + centres.append([pt[0] + w / 2, pt[1] + _h / 2]) + sum_t += max_t + + logger.info(quarter_match_log) + logger.info(f"Optimal Scale: {best_scale}") + # analysis data + self.threshold_circles.append(sum_t / 4) + + image = ImageUtils.four_point_transform(image, np.array(centres)) + # appendSaveImg(1,image_eroded_sub) + # appendSaveImg(1,image_norm) + + image_instance_ops.append_save_img(2, image_eroded_sub) + # Debugging image - + # res = cv2.matchTemplate(image_eroded_sub,optimal_marker,cv2.TM_CCOEFF_NORMED) + # res[ : , midw:midw+2] = 255 + # res[ midh:midh+2, : ] = 255 + # show("Markers Matching",res) + if config.outputs.show_image_level >= 2 and config.outputs.show_image_level < 4: + image_eroded_sub = ImageUtils.resize_util_h( + image_eroded_sub, image.shape[0] + ) + image_eroded_sub[:, -5:] = 0 + h_stack = np.hstack((image_eroded_sub, image)) + InteractionUtils.show( + f"Warped: {file_path}", + ImageUtils.resize_util( + h_stack, int(config.dimensions.display_width * 1.6) + ), + 0, + 0, + [0, 0], + config=config, + ) + # iterations : Tuned to 2. + # image_eroded_sub = image_norm - cv2.erode(image_norm, kernel=np.ones((5,5)),iterations=2) + return image + + def load_marker(self, marker_ops, config): + if not os.path.exists(self.marker_path): + logger.error( + "Marker not found at path provided in template:", + self.marker_path, + ) + exit(31) + + marker = cv2.imread(self.marker_path, cv2.IMREAD_GRAYSCALE) + + if "sheetToMarkerWidthRatio" in marker_ops: + marker = ImageUtils.resize_util( + marker, + config.dimensions.processing_width + / int(marker_ops["sheetToMarkerWidthRatio"]), + ) + marker = cv2.GaussianBlur(marker, (5, 5), 0) + marker = cv2.normalize( + marker, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX + ) + + if self.apply_erode_subtract: + marker -= cv2.erode(marker, kernel=np.ones((5, 5)), iterations=5) + + return marker + + # Resizing the marker within scaleRange at rate of descent_per_step to + # find the best match. + def getBestMatch(self, image_eroded_sub): + config = self.tuning_config + descent_per_step = ( + self.marker_rescale_range[1] - self.marker_rescale_range[0] + ) // self.marker_rescale_steps + _h, _w = self.marker.shape[:2] + res, best_scale = None, None + all_max_t = 0 + + for r0 in np.arange( + self.marker_rescale_range[1], + self.marker_rescale_range[0], + -1 * descent_per_step, + ): # reverse order + s = float(r0 * 1 / 100) + if s == 0.0: + continue + rescaled_marker = ImageUtils.resize_util_h( + self.marker, u_height=int(_h * s) + ) + # res is the black image with white dots + res = cv2.matchTemplate( + image_eroded_sub, rescaled_marker, cv2.TM_CCOEFF_NORMED + ) + + max_t = res.max() + if all_max_t < max_t: + # print('Scale: '+str(s)+', Circle Match: '+str(round(max_t*100,2))+'%') + best_scale, all_max_t = s, max_t + + if all_max_t < self.min_matching_threshold: + logger.warning( + "\tTemplate matching too low! Consider rechecking preProcessors applied before this." + ) + if config.outputs.show_image_level >= 1: + InteractionUtils.show("res", res, 1, 0, config=config) + + if best_scale is None: + logger.warning( + "No matchings for given scaleRange:", self.marker_rescale_range + ) + return best_scale, all_max_t diff --git a/src/processors/CropPage.py b/src/processors/CropPage.py new file mode 100644 index 00000000..25629e41 --- /dev/null +++ b/src/processors/CropPage.py @@ -0,0 +1,112 @@ +""" +https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/ +""" +import cv2 +import numpy as np + +from src.logger import logger +from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor +from src.utils.image import ImageUtils +from src.utils.interaction import InteractionUtils + +MIN_PAGE_AREA = 80000 + + +def normalize(image): + return cv2.normalize(image, 0, 255, norm_type=cv2.NORM_MINMAX) + + +def check_max_cosine(approx): + # assumes 4 pts present + max_cosine = 0 + min_cosine = 1.5 + for i in range(2, 5): + cosine = abs(angle(approx[i % 4], approx[i - 2], approx[i - 1])) + max_cosine = max(cosine, max_cosine) + min_cosine = min(cosine, min_cosine) + + if max_cosine >= 0.35: + logger.warning("Quadrilateral is not a rectangle.") + return False + return True + + +def validate_rect(approx): + return len(approx) == 4 and check_max_cosine(approx.reshape(4, 2)) + + +def angle(p_1, p_2, p_0): + dx1 = float(p_1[0] - p_0[0]) + dy1 = float(p_1[1] - p_0[1]) + dx2 = float(p_2[0] - p_0[0]) + dy2 = float(p_2[1] - p_0[1]) + return (dx1 * dx2 + dy1 * dy2) / np.sqrt( + (dx1 * dx1 + dy1 * dy1) * (dx2 * dx2 + dy2 * dy2) + 1e-10 + ) + + +class CropPage(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + cropping_ops = self.options + self.morph_kernel = tuple( + int(x) for x in cropping_ops.get("morphKernel", [10, 10]) + ) + + def apply_filter(self, image, file_path): + image = normalize(cv2.GaussianBlur(image, (3, 3), 0)) + + # Resize should be done with another preprocessor is needed + sheet = self.find_page(image, file_path) + if len(sheet) == 0: + logger.error( + f"\tError: Paper boundary not found for: '{file_path}'\nHave you accidentally included CropPage preprocessor?" + ) + return None + + logger.info(f"Found page corners: \t {sheet.tolist()}") + + # Warp layer 1 + image = ImageUtils.four_point_transform(image, sheet) + + # Return preprocessed image + return image + + def find_page(self, image, file_path): + config = self.tuning_config + + image = normalize(image) + + _ret, image = cv2.threshold(image, 200, 255, cv2.THRESH_TRUNC) + image = normalize(image) + + kernel = cv2.getStructuringElement(cv2.MORPH_RECT, self.morph_kernel) + + # Close the small holes, i.e. Complete the edges on canny image + closed = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel) + + edge = cv2.Canny(closed, 185, 55) + + if config.outputs.show_image_level >= 5: + InteractionUtils.show("edge", edge, config=config) + + # findContours returns outer boundaries in CW and inner ones, ACW. + cnts = ImageUtils.grab_contours( + cv2.findContours(edge, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + ) + # convexHull to resolve disordered curves due to noise + cnts = [cv2.convexHull(c) for c in cnts] + cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5] + sheet = [] + for c in cnts: + if cv2.contourArea(c) < MIN_PAGE_AREA: + continue + peri = cv2.arcLength(c, True) + approx = cv2.approxPolyDP(c, epsilon=0.025 * peri, closed=True) + if validate_rect(approx): + sheet = np.reshape(approx, (4, -1)) + cv2.drawContours(image, [approx], -1, (0, 255, 0), 2) + cv2.drawContours(edge, [approx], -1, (255, 255, 255), 10) + break + + return sheet diff --git a/src/processors/FeatureBasedAlignment.py b/src/processors/FeatureBasedAlignment.py new file mode 100644 index 00000000..be872924 --- /dev/null +++ b/src/processors/FeatureBasedAlignment.py @@ -0,0 +1,94 @@ +""" +Image based feature alignment +Credits: https://www.learnopencv.com/image-alignment-feature-based-using-opencv-c-python/ +""" +import cv2 +import numpy as np + +from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor +from src.utils.image import ImageUtils +from src.utils.interaction import InteractionUtils + + +class FeatureBasedAlignment(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + options = self.options + config = self.tuning_config + + # process reference image + self.ref_path = self.relative_dir.joinpath(options["reference"]) + ref_img = cv2.imread(str(self.ref_path), cv2.IMREAD_GRAYSCALE) + self.ref_img = ImageUtils.resize_util( + ref_img, + config.dimensions.processing_width, + config.dimensions.processing_height, + ) + # get options with defaults + self.max_features = int(options.get("maxFeatures", 500)) + self.good_match_percent = options.get("goodMatchPercent", 0.15) + self.transform_2_d = options.get("2d", False) + # Extract keypoints and description of source image + self.orb = cv2.ORB_create(self.max_features) + self.to_keypoints, self.to_descriptors = self.orb.detectAndCompute( + self.ref_img, None + ) + + def __str__(self): + return self.ref_path.name + + def exclude_files(self): + return [self.ref_path] + + def apply_filter(self, image, _file_path): + config = self.tuning_config + # Convert images to grayscale + # im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY) + # im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY) + + image = cv2.normalize(image, 0, 255, norm_type=cv2.NORM_MINMAX) + + # Detect ORB features and compute descriptors. + from_keypoints, from_descriptors = self.orb.detectAndCompute(image, None) + + # Match features. + matcher = cv2.DescriptorMatcher_create( + cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING + ) + + # create BFMatcher object (alternate matcher) + # matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) + + matches = np.array(matcher.match(from_descriptors, self.to_descriptors, None)) + + # Sort matches by score + matches = sorted(matches, key=lambda x: x.distance, reverse=False) + + # Remove not so good matches + num_good_matches = int(len(matches) * self.good_match_percent) + matches = matches[:num_good_matches] + + # Draw top matches + if config.outputs.show_image_level > 2: + im_matches = cv2.drawMatches( + image, from_keypoints, self.ref_img, self.to_keypoints, matches, None + ) + InteractionUtils.show("Aligning", im_matches, resize=True, config=config) + + # Extract location of good matches + points1 = np.zeros((len(matches), 2), dtype=np.float32) + points2 = np.zeros((len(matches), 2), dtype=np.float32) + + for i, match in enumerate(matches): + points1[i, :] = from_keypoints[match.queryIdx].pt + points2[i, :] = self.to_keypoints[match.trainIdx].pt + + # Find homography + height, width = self.ref_img.shape + if self.transform_2_d: + m, _inliers = cv2.estimateAffine2D(points1, points2) + return cv2.warpAffine(image, m, (width, height)) + + # Use homography + h, _mask = cv2.findHomography(points1, points2, cv2.RANSAC) + return cv2.warpPerspective(image, h, (width, height)) diff --git a/src/processors/builtins.py b/src/processors/builtins.py new file mode 100644 index 00000000..faa6288f --- /dev/null +++ b/src/processors/builtins.py @@ -0,0 +1,54 @@ +import cv2 +import numpy as np + +from src.processors.interfaces.ImagePreprocessor import ImagePreprocessor + + +class Levels(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + options = self.options + + def output_level(value, low, high, gamma): + if value <= low: + return 0 + if value >= high: + return 255 + inv_gamma = 1.0 / gamma + return (((value - low) / (high - low)) ** inv_gamma) * 255 + + self.gamma = np.array( + [ + output_level( + i, + int(255 * options.get("low", 0)), + int(255 * options.get("high", 1)), + options.get("gamma", 1.0), + ) + for i in np.arange(0, 256) + ] + ).astype("uint8") + + def apply_filter(self, image, _file_path): + return cv2.LUT(image, self.gamma) + + +class MedianBlur(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + options = self.options + self.kSize = int(options.get("kSize", 5)) + + def apply_filter(self, image, _file_path): + return cv2.medianBlur(image, self.kSize) + + +class GaussianBlur(ImagePreprocessor): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + options = self.options + self.kSize = tuple(int(x) for x in options.get("kSize", (3, 3))) + self.sigmaX = int(options.get("sigmaX", 0)) + + def apply_filter(self, image, _file_path): + return cv2.GaussianBlur(image, self.kSize, self.sigmaX) diff --git a/src/processors/interfaces/ImagePreprocessor.py b/src/processors/interfaces/ImagePreprocessor.py new file mode 100644 index 00000000..a5cf09a4 --- /dev/null +++ b/src/processors/interfaces/ImagePreprocessor.py @@ -0,0 +1,18 @@ +# Use all imports relative to root directory +from src.processors.manager import Processor + + +class ImagePreprocessor(Processor): + """Base class for an extension that applies some preprocessing to the input image""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def apply_filter(self, image, filename): + """Apply filter to the image and returns modified image""" + raise NotImplementedError + + @staticmethod + def exclude_files(): + """Returns a list of file paths that should be excluded from processing""" + return [] diff --git a/src/processors/manager.py b/src/processors/manager.py new file mode 100644 index 00000000..c41e0713 --- /dev/null +++ b/src/processors/manager.py @@ -0,0 +1,80 @@ +""" +Processor/Extension framework +Adapated from https://github.com/gdiepen/python_processor_example +""" +import inspect +import pkgutil + +from src.logger import logger + + +class Processor: + """Base class that each processor must inherit from.""" + + def __init__( + self, + options=None, + relative_dir=None, + image_instance_ops=None, + ): + self.options = options + self.relative_dir = relative_dir + self.image_instance_ops = image_instance_ops + self.tuning_config = image_instance_ops.tuning_config + self.description = "UNKNOWN" + + +class ProcessorManager: + """Upon creation, this class will read the processors package for modules + that contain a class definition that is inheriting from the Processor class + """ + + def __init__(self, processors_dir="src.processors"): + """Constructor that initiates the reading of all available processors + when an instance of the ProcessorCollection object is created + """ + self.processors_dir = processors_dir + self.reload_processors() + + @staticmethod + def get_name_filter(processor_name): + def filter_function(member): + return inspect.isclass(member) and member.__module__ == processor_name + + return filter_function + + def reload_processors(self): + """Reset the list of all processors and initiate the walk over the main + provided processor package to load all available processors + """ + self.processors = {} + self.seen_paths = [] + + logger.info(f'Loading processors from "{self.processors_dir}"...') + self.walk_package(self.processors_dir) + + def walk_package(self, package): + """walk the supplied package to retrieve all processors""" + imported_package = __import__(package, fromlist=["blah"]) + loaded_packages = [] + for _, processor_name, ispkg in pkgutil.walk_packages( + imported_package.__path__, imported_package.__name__ + "." + ): + if not ispkg and processor_name != __name__: + processor_module = __import__(processor_name, fromlist=["blah"]) + # https://stackoverflow.com/a/46206754/6242649 + clsmembers = inspect.getmembers( + processor_module, + ProcessorManager.get_name_filter(processor_name), + ) + for _, c in clsmembers: + # Only add classes that are a sub class of Processor, but NOT Processor itself + if issubclass(c, Processor) & (c is not Processor): + self.processors[c.__name__] = c + loaded_packages.append(c.__name__) + + logger.info(f"Loaded processors: {loaded_packages}") + + +# Singleton export +PROCESSOR_MANAGER = ProcessorManager() diff --git a/src/schemas/__init__.py b/src/schemas/__init__.py new file mode 100644 index 00000000..3ccb298d --- /dev/null +++ b/src/schemas/__init__.py @@ -0,0 +1,18 @@ +# https://docs.python.org/3/tutorial/modules.html#:~:text=The%20__init__.py,on%20the%20module%20search%20path. +from jsonschema import Draft202012Validator + +from src.schemas.config_schema import CONFIG_SCHEMA +from src.schemas.evaluation_schema import EVALUATION_SCHEMA +from src.schemas.template_schema import TEMPLATE_SCHEMA + +SCHEMA_JSONS = { + "config": CONFIG_SCHEMA, + "evaluation": EVALUATION_SCHEMA, + "template": TEMPLATE_SCHEMA, +} + +SCHEMA_VALIDATORS = { + "config": Draft202012Validator(CONFIG_SCHEMA), + "evaluation": Draft202012Validator(EVALUATION_SCHEMA), + "template": Draft202012Validator(TEMPLATE_SCHEMA), +} diff --git a/src/schemas/config_schema.py b/src/schemas/config_schema.py new file mode 100644 index 00000000..6eb815f8 --- /dev/null +++ b/src/schemas/config_schema.py @@ -0,0 +1,57 @@ +CONFIG_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://github.com/Udayraj123/OMRChecker/tree/master/src/schemas/config-schema.json", + "title": "Config Schema", + "description": "OMRChecker config schema for custom tuning", + "type": "object", + "additionalProperties": False, + "properties": { + "dimensions": { + "type": "object", + "additionalProperties": False, + "properties": { + "display_height": {"type": "integer"}, + "display_width": {"type": "integer"}, + "processing_height": {"type": "integer"}, + "processing_width": {"type": "integer"}, + }, + }, + "threshold_params": { + "type": "object", + "additionalProperties": False, + "properties": { + "GAMMA_LOW": {"type": "number", "minimum": 0, "maximum": 1}, + "MIN_GAP": {"type": "integer", "minimum": 10, "maximum": 100}, + "MIN_JUMP": {"type": "integer", "minimum": 10, "maximum": 100}, + "CONFIDENT_SURPLUS": {"type": "integer", "minimum": 0, "maximum": 20}, + "JUMP_DELTA": {"type": "integer", "minimum": 10, "maximum": 100}, + "PAGE_TYPE_FOR_THRESHOLD": { + "enum": ["white", "black"], + "type": "string", + }, + }, + }, + "alignment_params": { + "type": "object", + "additionalProperties": False, + "properties": { + "auto_align": {"type": "boolean"}, + "match_col": {"type": "integer", "minimum": 0, "maximum": 10}, + "max_steps": {"type": "integer", "minimum": 1, "maximum": 100}, + "stride": {"type": "integer", "minimum": 1, "maximum": 10}, + "thickness": {"type": "integer", "minimum": 1, "maximum": 10}, + }, + }, + "outputs": { + "type": "object", + "additionalProperties": False, + "properties": { + "show_image_level": {"type": "integer", "minimum": 0, "maximum": 6}, + "save_image_level": {"type": "integer", "minimum": 0, "maximum": 6}, + "save_detections": {"type": "boolean"}, + # This option moves multimarked files into a separate folder for manual checking, skipping evaluation + "filter_out_multimarked_files": {"type": "boolean"}, + }, + }, + }, +} diff --git a/src/schemas/constants.py b/src/schemas/constants.py new file mode 100644 index 00000000..ccaa6f38 --- /dev/null +++ b/src/schemas/constants.py @@ -0,0 +1,17 @@ +DEFAULT_SECTION_KEY = "DEFAULT" + +BONUS_SECTION_PREFIX = "BONUS" + +MARKING_VERDICT_TYPES = ["correct", "incorrect", "unmarked"] + +ARRAY_OF_STRINGS = { + "type": "array", + "items": {"type": "string"}, +} + +FIELD_STRING_TYPE = { + "type": "string", + "pattern": "^([^\\.]+|[^\\.\\d]+\\d+\\.{2,3}\\d+)$", +} + +FIELD_STRING_REGEX_GROUPS = r"([^\.\d]+)(\d+)\.{2,3}(\d+)" diff --git a/src/schemas/evaluation_schema.py b/src/schemas/evaluation_schema.py new file mode 100644 index 00000000..5f2237fd --- /dev/null +++ b/src/schemas/evaluation_schema.py @@ -0,0 +1,151 @@ +from src.schemas.constants import ( + ARRAY_OF_STRINGS, + DEFAULT_SECTION_KEY, + FIELD_STRING_TYPE, +) + +marking_score_regex = "-?(\\d+)(/(\\d+))?" + +marking_score = { + "oneOf": [ + {"type": "string", "pattern": marking_score_regex}, + {"type": "number"}, + ] +} + +marking_object_properties = { + "additionalProperties": False, + "required": ["correct", "incorrect", "unmarked"], + "type": "object", + "properties": { + # TODO: can support streak marking if we allow array of marking_scores here + "correct": marking_score, + "incorrect": marking_score, + "unmarked": marking_score, + }, +} + +EVALUATION_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://github.com/Udayraj123/OMRChecker/tree/master/src/schemas/evaluation-schema.json", + "title": "Evaluation Schema", + "description": "OMRChecker evaluation schema i.e. the marking scheme", + "type": "object", + "additionalProperties": True, + "required": ["source_type", "options", "marking_schemes"], + "properties": { + "additionalProperties": False, + "source_type": {"type": "string", "enum": ["csv", "custom"]}, + "options": {"type": "object"}, + "marking_schemes": { + "type": "object", + "required": [DEFAULT_SECTION_KEY], + "patternProperties": { + f"^{DEFAULT_SECTION_KEY}$": marking_object_properties, + f"^(?!{DEFAULT_SECTION_KEY}$).*": { + "additionalProperties": False, + "required": ["marking", "questions"], + "type": "object", + "properties": { + "questions": { + "oneOf": [ + FIELD_STRING_TYPE, + { + "type": "array", + "items": FIELD_STRING_TYPE, + }, + ] + }, + "marking": marking_object_properties, + }, + }, + }, + }, + }, + "allOf": [ + { + "if": {"properties": {"source_type": {"const": "csv"}}}, + "then": { + "properties": { + "options": { + "additionalProperties": False, + "required": ["answer_key_csv_path"], + "dependentRequired": { + "answer_key_image_path": [ + "answer_key_csv_path", + "questions_in_order", + ] + }, + "type": "object", + "properties": { + "should_explain_scoring": {"type": "boolean"}, + "answer_key_csv_path": {"type": "string"}, + "answer_key_image_path": {"type": "string"}, + "questions_in_order": ARRAY_OF_STRINGS, + }, + } + } + }, + }, + { + "if": {"properties": {"source_type": {"const": "custom"}}}, + "then": { + "properties": { + "options": { + "additionalProperties": False, + "required": ["answers_in_order", "questions_in_order"], + "type": "object", + "properties": { + "should_explain_scoring": {"type": "boolean"}, + "answers_in_order": { + "oneOf": [ + { + "type": "array", + "items": { + "oneOf": [ + # "standard": single correct, multi-marked single-correct + # Example: "q1" --> '67' + {"type": "string"}, + # "multiple-correct": multiple-correct (for ambiguous/bonus questions) + # Example: "q1" --> [ 'A', 'B' ] + { + "type": "array", + "items": {"type": "string"}, + "minItems": 2, + }, + # "multiple-correct-weighted": array of answer-wise weights (marking scheme not applicable) + # Example 1: "q1" --> [['A', 1], ['B', 2], ['C', 3]] or + # Example 2: "q2" --> [['A', 1], ['B', 1], ['AB', 2]] + { + "type": "array", + "items": { + "type": "array", + "items": False, + "minItems": 2, + "maxItems": 2, + "prefixItems": [ + {"type": "string"}, + marking_score, + ], + }, + }, + # Multiple-correct with custom marking scheme + # ["A", ["1", "2", "3"]], + # [["A", "B", "AB"], ["1", "2", "3"]] + ], + }, + }, + ] + }, + "questions_in_order": ARRAY_OF_STRINGS, + "enable_evaluation_table_to_csv": { + "type": "boolean", + "default": False, + }, + }, + } + } + }, + }, + ], +} diff --git a/src/schemas/template_schema.py b/src/schemas/template_schema.py new file mode 100644 index 00000000..451d499d --- /dev/null +++ b/src/schemas/template_schema.py @@ -0,0 +1,226 @@ +from src.constants import FIELD_TYPES +from src.schemas.constants import ARRAY_OF_STRINGS, FIELD_STRING_TYPE + +positive_number = {"type": "number", "minimum": 0} +positive_integer = {"type": "integer", "minimum": 0} +two_positive_integers = { + "type": "array", + "prefixItems": [ + positive_integer, + positive_integer, + ], + "maxItems": 2, + "minItems": 2, +} +two_positive_numbers = { + "type": "array", + "prefixItems": [ + positive_number, + positive_number, + ], + "maxItems": 2, + "minItems": 2, +} +zero_to_one_number = { + "type": "number", + "minimum": 0, + "maximum": 1, +} + +TEMPLATE_SCHEMA = { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://github.com/Udayraj123/OMRChecker/tree/master/src/schemas/template-schema.json", + "title": "Template Validation Schema", + "description": "OMRChecker input template schema", + "type": "object", + "required": [ + "bubbleDimensions", + "pageDimensions", + "preProcessors", + "fieldBlocks", + ], + "additionalProperties": False, + "properties": { + "bubbleDimensions": { + **two_positive_integers, + "description": "The dimensions of the overlay bubble area: [width, height]", + }, + "customLabels": { + "description": "The customLabels contain fields that need to be joined together before generating the results sheet", + "type": "object", + "patternProperties": { + "^.*$": {"type": "array", "items": FIELD_STRING_TYPE} + }, + }, + "outputColumns": { + "type": "array", + "items": FIELD_STRING_TYPE, + "description": "The ordered list of columns to be contained in the output csv(default order: alphabetical)", + }, + "pageDimensions": { + **two_positive_integers, + "description": "The dimensions(width, height) to which the page will be resized to before applying template", + }, + "preProcessors": { + "description": "Custom configuration values to use in the template's directory", + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "enum": [ + "CropOnMarkers", + "CropPage", + "FeatureBasedAlignment", + "GaussianBlur", + "Levels", + "MedianBlur", + ], + }, + }, + "required": ["name", "options"], + "allOf": [ + { + "if": {"properties": {"name": {"const": "CropOnMarkers"}}}, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": { + "apply_erode_subtract": {"type": "boolean"}, + "marker_rescale_range": two_positive_numbers, + "marker_rescale_steps": {"type": "number"}, + "max_matching_variation": {"type": "number"}, + "min_matching_threshold": {"type": "number"}, + "relativePath": {"type": "string"}, + "sheetToMarkerWidthRatio": {"type": "number"}, + }, + "required": ["relativePath"], + } + } + }, + }, + { + "if": { + "properties": {"name": {"const": "FeatureBasedAlignment"}} + }, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": { + "2d": {"type": "boolean"}, + "goodMatchPercent": {"type": "number"}, + "maxFeatures": {"type": "integer"}, + "reference": {"type": "string"}, + }, + "required": ["reference"], + } + } + }, + }, + { + "if": {"properties": {"name": {"const": "Levels"}}}, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": { + "gamma": zero_to_one_number, + "high": zero_to_one_number, + "low": zero_to_one_number, + }, + } + } + }, + }, + { + "if": {"properties": {"name": {"const": "MedianBlur"}}}, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": {"kSize": {"type": "integer"}}, + } + } + }, + }, + { + "if": {"properties": {"name": {"const": "GaussianBlur"}}}, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": { + "kSize": two_positive_integers, + "sigmaX": {"type": "number"}, + }, + } + } + }, + }, + { + "if": {"properties": {"name": {"const": "CropPage"}}}, + "then": { + "properties": { + "options": { + "type": "object", + "additionalProperties": False, + "properties": { + "morphKernel": two_positive_integers + }, + } + } + }, + }, + ], + }, + }, + "fieldBlocks": { + "description": "The fieldBlocks denote small groups of adjacent fields", + "type": "object", + "patternProperties": { + "^.*$": { + "type": "object", + "required": [ + "origin", + "bubblesGap", + "labelsGap", + "fieldLabels", + ], + "oneOf": [ + {"required": ["fieldType"]}, + {"required": ["bubbleValues", "direction"]}, + ], + "properties": { + "bubbleDimensions": two_positive_numbers, + "bubblesGap": positive_number, + "bubbleValues": ARRAY_OF_STRINGS, + "direction": { + "type": "string", + "enum": ["horizontal", "vertical"], + }, + "emptyValue": {"type": "string"}, + "fieldLabels": {"type": "array", "items": FIELD_STRING_TYPE}, + "labelsGap": positive_number, + "origin": two_positive_integers, + "fieldType": { + "type": "string", + "enum": list(FIELD_TYPES.keys()), + }, + }, + } + }, + }, + "emptyValue": { + "description": "The value to be used in case of empty bubble detected at global level.", + "type": "string", + }, + }, +} diff --git a/src/template.py b/src/template.py new file mode 100644 index 00000000..0acc4672 --- /dev/null +++ b/src/template.py @@ -0,0 +1,327 @@ +""" + + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 + +""" +from src.constants import FIELD_TYPES +from src.core import ImageInstanceOps +from src.logger import logger +from src.processors.manager import PROCESSOR_MANAGER +from src.utils.parsing import ( + custom_sort_output_columns, + open_template_with_defaults, + parse_fields, +) + + +class Template: + def __init__(self, template_path, tuning_config): + self.path = template_path + self.image_instance_ops = ImageInstanceOps(tuning_config) + + json_object = open_template_with_defaults(template_path) + ( + custom_labels_object, + field_blocks_object, + output_columns_array, + pre_processors_object, + self.bubble_dimensions, + self.global_empty_val, + self.options, + self.page_dimensions, + ) = map( + json_object.get, + [ + "customLabels", + "fieldBlocks", + "outputColumns", + "preProcessors", + "bubbleDimensions", + "emptyValue", + "options", + "pageDimensions", + ], + ) + + self.parse_output_columns(output_columns_array) + self.setup_pre_processors(pre_processors_object, template_path.parent) + self.setup_field_blocks(field_blocks_object) + self.parse_custom_labels(custom_labels_object) + + non_custom_columns, all_custom_columns = ( + list(self.non_custom_labels), + list(custom_labels_object.keys()), + ) + + if len(self.output_columns) == 0: + self.fill_output_columns(non_custom_columns, all_custom_columns) + + self.validate_template_columns(non_custom_columns, all_custom_columns) + + def parse_output_columns(self, output_columns_array): + self.output_columns = parse_fields(f"Output Columns", output_columns_array) + + def setup_pre_processors(self, pre_processors_object, relative_dir): + # load image pre_processors + self.pre_processors = [] + for pre_processor in pre_processors_object: + ProcessorClass = PROCESSOR_MANAGER.processors[pre_processor["name"]] + pre_processor_instance = ProcessorClass( + options=pre_processor["options"], + relative_dir=relative_dir, + image_instance_ops=self.image_instance_ops, + ) + self.pre_processors.append(pre_processor_instance) + + def setup_field_blocks(self, field_blocks_object): + # Add field_blocks + self.field_blocks = [] + self.all_parsed_labels = set() + for block_name, field_block_object in field_blocks_object.items(): + self.parse_and_add_field_block(block_name, field_block_object) + + def parse_custom_labels(self, custom_labels_object): + all_parsed_custom_labels = set() + self.custom_labels = {} + for custom_label, label_strings in custom_labels_object.items(): + parsed_labels = parse_fields(f"Custom Label: {custom_label}", label_strings) + parsed_labels_set = set(parsed_labels) + self.custom_labels[custom_label] = parsed_labels + + missing_custom_labels = sorted( + parsed_labels_set.difference(self.all_parsed_labels) + ) + if len(missing_custom_labels) > 0: + logger.critical( + f"For '{custom_label}', Missing labels - {missing_custom_labels}" + ) + raise Exception( + f"Missing field block label(s) in the given template for {missing_custom_labels} from '{custom_label}'" + ) + + if not all_parsed_custom_labels.isdisjoint(parsed_labels_set): + # Note: this can be made a warning, but it's a choice + logger.critical( + f"field strings overlap for labels: {label_strings} and existing custom labels: {all_parsed_custom_labels}" + ) + raise Exception( + f"The field strings for custom label '{custom_label}' overlap with other existing custom labels" + ) + + all_parsed_custom_labels.update(parsed_labels) + + self.non_custom_labels = self.all_parsed_labels.difference( + all_parsed_custom_labels + ) + + def fill_output_columns(self, non_custom_columns, all_custom_columns): + all_template_columns = non_custom_columns + all_custom_columns + # Typical case: sort alpha-numerical (natural sort) + self.output_columns = sorted( + all_template_columns, key=custom_sort_output_columns + ) + + def validate_template_columns(self, non_custom_columns, all_custom_columns): + output_columns_set = set(self.output_columns) + all_custom_columns_set = set(all_custom_columns) + + missing_output_columns = sorted( + output_columns_set.difference(all_custom_columns_set).difference( + self.all_parsed_labels + ) + ) + if len(missing_output_columns) > 0: + logger.critical(f"Missing output columns: {missing_output_columns}") + raise Exception( + f"Some columns are missing in the field blocks for the given output columns" + ) + + all_template_columns_set = set(non_custom_columns + all_custom_columns) + missing_label_columns = sorted( + all_template_columns_set.difference(output_columns_set) + ) + if len(missing_label_columns) > 0: + logger.warning( + f"Some label columns are not covered in the given output columns: {missing_label_columns}" + ) + + def parse_and_add_field_block(self, block_name, field_block_object): + field_block_object = self.pre_fill_field_block(field_block_object) + block_instance = FieldBlock(block_name, field_block_object) + self.field_blocks.append(block_instance) + self.validate_parsed_labels(field_block_object["fieldLabels"], block_instance) + + def pre_fill_field_block(self, field_block_object): + if "fieldType" in field_block_object: + field_block_object = { + **field_block_object, + **FIELD_TYPES[field_block_object["fieldType"]], + } + else: + field_block_object = {**field_block_object, "fieldType": "__CUSTOM__"} + + return { + "direction": "vertical", + "emptyValue": self.global_empty_val, + "bubbleDimensions": self.bubble_dimensions, + **field_block_object, + } + + def validate_parsed_labels(self, field_labels, block_instance): + parsed_field_labels, block_name = ( + block_instance.parsed_field_labels, + block_instance.name, + ) + field_labels_set = set(parsed_field_labels) + if not self.all_parsed_labels.isdisjoint(field_labels_set): + # Note: in case of two fields pointing to same column, use a custom column instead of same field labels. + logger.critical( + f"An overlap found between field string: {field_labels} in block '{block_name}' and existing labels: {self.all_parsed_labels}" + ) + raise Exception( + f"The field strings for field block {block_name} overlap with other existing fields" + ) + self.all_parsed_labels.update(field_labels_set) + + page_width, page_height = self.page_dimensions + block_width, block_height = block_instance.dimensions + [block_start_x, block_start_y] = block_instance.origin + + block_end_x, block_end_y = ( + block_start_x + block_width, + block_start_y + block_height, + ) + + if ( + block_end_x >= page_width + or block_end_y >= page_height + or block_start_x < 0 + or block_start_y < 0 + ): + raise Exception( + f"Overflowing field block '{block_name}' with origin {block_instance.origin} and dimensions {block_instance.dimensions} in template with dimensions {self.page_dimensions}" + ) + + def __str__(self): + return str(self.path) + + +class FieldBlock: + def __init__(self, block_name, field_block_object): + self.name = block_name + self.shift = 0 + self.setup_field_block(field_block_object) + + def setup_field_block(self, field_block_object): + # case mapping + ( + bubble_dimensions, + bubble_values, + bubbles_gap, + direction, + field_labels, + field_type, + labels_gap, + origin, + self.empty_val, + ) = map( + field_block_object.get, + [ + "bubbleDimensions", + "bubbleValues", + "bubblesGap", + "direction", + "fieldLabels", + "fieldType", + "labelsGap", + "origin", + "emptyValue", + ], + ) + self.parsed_field_labels = parse_fields( + f"Field Block Labels: {self.name}", field_labels + ) + self.origin = origin + self.bubble_dimensions = bubble_dimensions + self.calculate_block_dimensions( + bubble_dimensions, + bubble_values, + bubbles_gap, + direction, + labels_gap, + ) + self.generate_bubble_grid( + bubble_values, + bubbles_gap, + direction, + field_type, + labels_gap, + ) + + def calculate_block_dimensions( + self, + bubble_dimensions, + bubble_values, + bubbles_gap, + direction, + labels_gap, + ): + _h, _v = (1, 0) if (direction == "vertical") else (0, 1) + + values_dimension = int( + bubbles_gap * (len(bubble_values) - 1) + bubble_dimensions[_h] + ) + fields_dimension = int( + labels_gap * (len(self.parsed_field_labels) - 1) + bubble_dimensions[_v] + ) + self.dimensions = ( + [fields_dimension, values_dimension] + if (direction == "vertical") + else [values_dimension, fields_dimension] + ) + + def generate_bubble_grid( + self, + bubble_values, + bubbles_gap, + direction, + field_type, + labels_gap, + ): + _h, _v = (1, 0) if (direction == "vertical") else (0, 1) + self.traverse_bubbles = [] + # Generate the bubble grid + lead_point = [float(self.origin[0]), float(self.origin[1])] + for field_label in self.parsed_field_labels: + bubble_point = lead_point.copy() + field_bubbles = [] + for bubble_value in bubble_values: + field_bubbles.append( + Bubble(bubble_point.copy(), field_label, field_type, bubble_value) + ) + bubble_point[_h] += bubbles_gap + self.traverse_bubbles.append(field_bubbles) + lead_point[_v] += labels_gap + + +class Bubble: + """ + Container for a Point Box on the OMR + + field_label is the point's property- field to which this point belongs to + It can be used as a roll number column as well. (eg roll1) + It can also correspond to a single digit of integer type Q (eg q5d1) + """ + + def __init__(self, pt, field_label, field_type, field_value): + self.x = round(pt[0]) + self.y = round(pt[1]) + self.field_label = field_label + self.field_type = field_type + self.field_value = field_value + + def __str__(self): + return str([self.x, self.y]) diff --git a/src/tests/__init__.py b/src/tests/__init__.py new file mode 100644 index 00000000..855c16a5 --- /dev/null +++ b/src/tests/__init__.py @@ -0,0 +1 @@ +# https://stackoverflow.com/a/50169991/6242649 diff --git a/src/tests/__snapshots__/test_all_samples.ambr b/src/tests/__snapshots__/test_all_samples.ambr new file mode 100644 index 00000000..3049cd3c --- /dev/null +++ b/src/tests/__snapshots__/test_all_samples.ambr @@ -0,0 +1,302 @@ +# serializer version: 1 +# name: test_run_answer_key_using_csv + dict({ + 'Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + "adrian_omr.png","samples/answer-key/using-csv/adrian_omr.png","outputs/answer-key/using-csv/CheckedOMRs/adrian_omr.png","5.0","C","E","A","B","B" + + ''', + }) +# --- +# name: test_run_answer_key_weighted_answers + dict({ + 'images/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'images/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'images/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + "adrian_omr.png","samples/answer-key/weighted-answers/images/adrian_omr.png","outputs/answer-key/weighted-answers/images/CheckedOMRs/adrian_omr.png","5.5","B","E","A","C","B" + "adrian_omr_2.png","samples/answer-key/weighted-answers/images/adrian_omr_2.png","outputs/answer-key/weighted-answers/images/CheckedOMRs/adrian_omr_2.png","10.0","C","E","A","B","B" + + ''', + }) +# --- +# name: test_run_community_Antibodyy + dict({ + 'Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6" + + ''', + 'Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6" + + ''', + 'Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6" + "simple_omr_sheet.jpg","samples/community/Antibodyy/simple_omr_sheet.jpg","outputs/community/Antibodyy/CheckedOMRs/simple_omr_sheet.jpg","0","A","C","B","D","E","B" + + ''', + }) +# --- +# name: test_run_community_Sandeep_1507 + dict({ + 'Manual/ErrorFiles.csv': ''' + 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+ + ''', + 'Manual/MultiMarkedFiles.csv': ''' + 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+ + ''', + 'Results/Results_05AM.csv': ''' + 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"file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + "yes_no_questionnarie.jpg","samples/community/ibrahimkilic/yes_no_questionnarie.jpg","outputs/community/ibrahimkilic/CheckedOMRs/yes_no_questionnarie.jpg","0","no","no","no","no","no" + + ''', + }) +# --- +# name: test_run_sample1 + dict({ + 'MobileCamera/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20" + + ''', + 'MobileCamera/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20" + + ''', + 'MobileCamera/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20" + "sheet1.jpg","samples/sample1/MobileCamera/sheet1.jpg","outputs/sample1/MobileCamera/CheckedOMRs/sheet1.jpg","0","E503110026","B","","D","B","6","11","20","7","16","B","D","C","D","A","D","B","A","C","C","D" + + ''', + }) +# --- +# name: test_run_sample2 + dict({ + 'AdrianSample/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'AdrianSample/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + + ''', + 'AdrianSample/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5" + "adrian_omr.png","samples/sample2/AdrianSample/adrian_omr.png","outputs/sample2/AdrianSample/CheckedOMRs/adrian_omr.png","0","B","E","A","C","B" + "adrian_omr_2.png","samples/sample2/AdrianSample/adrian_omr_2.png","outputs/sample2/AdrianSample/CheckedOMRs/adrian_omr_2.png","0","C","E","A","B","B" + + ''', + }) +# --- +# name: test_run_sample3 + dict({ + 'colored-thick-sheet/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + + ''', + 'colored-thick-sheet/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + + ''', + 'colored-thick-sheet/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + "rgb-100-gsm.jpg","samples/sample3/colored-thick-sheet/rgb-100-gsm.jpg","outputs/sample3/colored-thick-sheet/CheckedOMRs/rgb-100-gsm.jpg","0","D","D","A","","C","C","B","","A","C","C","D","A","D","A","C","A","D","B","D","D","C","D","D","D","D","","B","A","D","D","C","","B","","C","D","","","A","","A","C","C","B","C","A","A","C","","C","","D","B","C","","B","C","D","","","C","C","","C","A","B","C","","","","","D","D","C","D","A","","","B","","B","D","C","C","","D","","D","C","D","A","","A","","","A","C","B","A" + + ''', + 'xeroxed-thin-sheet/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + + ''', + 'xeroxed-thin-sheet/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + + ''', + 'xeroxed-thin-sheet/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22","q23","q24","q25","q26","q27","q28","q29","q30","q31","q32","q33","q34","q35","q36","q37","q38","q39","q40","q41","q42","q43","q44","q45","q46","q47","q48","q49","q50","q51","q52","q53","q54","q55","q56","q57","q58","q59","q60","q61","q62","q63","q64","q65","q66","q67","q68","q69","q70","q71","q72","q73","q74","q75","q76","q77","q78","q79","q80","q81","q82","q83","q84","q85","q86","q87","q88","q89","q90","q91","q92","q93","q94","q95","q96","q97","q98","q99","q100" + "grayscale-80-gsm.jpg","samples/sample3/xeroxed-thin-sheet/grayscale-80-gsm.jpg","outputs/sample3/xeroxed-thin-sheet/CheckedOMRs/grayscale-80-gsm.jpg","0","C","D","A","C","C","C","B","A","C","C","B","D","B","D","C","C","B","D","B","D","C","C","C","B","D","D","D","B","A","D","D","C","A","B","C","A","D","A","A","A","D","D","B","A","B","C","B","A","C","D","C","D","A","B","C","A","C","C","C","D","B","C","C","C","C","A","D","A","D","A","D","C","C","D","C","D","A","A","C","B","C","D","C","A","B","C","B","D","A","A","C","A","B","D","C","D","A","C","B","A" + + ''', + }) +# --- +# name: test_run_sample4 + dict({ + 'Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11" + + ''', + 'Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11" + + ''', + 'Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11" + "IMG_20201116_143512.jpg","samples/sample4/IMG_20201116_143512.jpg","outputs/sample4/CheckedOMRs/IMG_20201116_143512.jpg","33.0","B","D","C","B","D","C","BC","A","C","D","C" + "IMG_20201116_150717658.jpg","samples/sample4/IMG_20201116_150717658.jpg","outputs/sample4/CheckedOMRs/IMG_20201116_150717658.jpg","33.0","B","D","C","B","D","C","BC","A","C","D","C" + "IMG_20201116_150750830.jpg","samples/sample4/IMG_20201116_150750830.jpg","outputs/sample4/CheckedOMRs/IMG_20201116_150750830.jpg","-2.0","A","","D","C","AC","A","D","B","C","D","D" + + ''', + }) +# --- +# name: test_run_sample5 + dict({ + 'ScanBatch1/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + + ''', + 'ScanBatch1/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + + ''', + 'ScanBatch1/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + "camscanner-1.jpg","samples/sample5/ScanBatch1/camscanner-1.jpg","outputs/sample5/ScanBatch1/CheckedOMRs/camscanner-1.jpg","-4.0","E204420102","D","C","A","C","B","08","52","21","85","36","B","C","A","A","D","C","C","AD","A","A","D","" + + ''', + 'ScanBatch2/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + + ''', + 'ScanBatch2/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + + ''', + 'ScanBatch2/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","Roll","q1","q2","q3","q4","q5","q6","q7","q8","q9","q10","q11","q12","q13","q14","q15","q16","q17","q18","q19","q20","q21","q22" + "camscanner-2.jpg","samples/sample5/ScanBatch2/camscanner-2.jpg","outputs/sample5/ScanBatch2/CheckedOMRs/camscanner-2.jpg","55.0","E204420109","C","C","B","C","C","01","19","10","10","18","D","A","D","D","D","C","C","C","C","D","B","A" + + ''', + }) +# --- +# name: test_run_sample6 + dict({ + 'Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","Roll" + + ''', + 'Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","Roll" + + ''', + 'Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","Roll" + "reference.png","samples/sample6/reference.png","outputs/sample6/CheckedOMRs/reference.png","0","A" + + ''', + 'doc-scans/Manual/ErrorFiles.csv': ''' + "file_id","input_path","output_path","score","Roll" + + ''', + 'doc-scans/Manual/MultiMarkedFiles.csv': ''' + "file_id","input_path","output_path","score","Roll" + + ''', + 'doc-scans/Results/Results_05AM.csv': ''' + "file_id","input_path","output_path","score","Roll" + "sample_roll_01.jpg","samples/sample6/doc-scans/sample_roll_01.jpg","outputs/sample6/doc-scans/CheckedOMRs/sample_roll_01.jpg","0","A0188877Y" + "sample_roll_02.jpg","samples/sample6/doc-scans/sample_roll_02.jpg","outputs/sample6/doc-scans/CheckedOMRs/sample_roll_02.jpg","0","A0203959W" + "sample_roll_03.jpg","samples/sample6/doc-scans/sample_roll_03.jpg","outputs/sample6/doc-scans/CheckedOMRs/sample_roll_03.jpg","0","A0204729A" + + ''', + }) +# --- diff --git a/src/tests/test_all_samples.py b/src/tests/test_all_samples.py new file mode 100644 index 00000000..9efe94ff --- /dev/null +++ b/src/tests/test_all_samples.py @@ -0,0 +1,112 @@ +import os +import shutil +from glob import glob + +from src.tests.utils import run_entry_point, setup_mocker_patches + + +def read_file(path): + with open(path) as file: + return file.read() + + +def run_sample(mocker, sample_path): + setup_mocker_patches(mocker) + + input_path = os.path.join("samples", sample_path) + output_dir = os.path.join("outputs", sample_path) + if os.path.exists(output_dir): + print( + f"Warning: output directory already exists: {output_dir}. This may affect the test execution." + ) + + run_entry_point(input_path, output_dir) + + sample_outputs = extract_sample_outputs(output_dir) + + print(f"Note: removing output directory: {output_dir}") + shutil.rmtree(output_dir) + + return sample_outputs + + +EXT = "*.csv" + + +def extract_sample_outputs(output_dir): + sample_outputs = {} + for _dir, _subdir, _files in os.walk(output_dir): + for file in glob(os.path.join(_dir, EXT)): + relative_path = os.path.relpath(file, output_dir) + sample_outputs[relative_path] = read_file(file) + return sample_outputs + + +def test_run_answer_key_using_csv(mocker, snapshot): + sample_outputs = run_sample(mocker, "answer-key/using-csv") + assert snapshot == sample_outputs + + +def test_run_answer_key_weighted_answers(mocker, snapshot): + sample_outputs = run_sample(mocker, "answer-key/weighted-answers") + assert snapshot == sample_outputs + + +def test_run_sample1(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample1") + assert snapshot == sample_outputs + + +def test_run_sample2(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample2") + assert snapshot == sample_outputs + + +def test_run_sample3(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample3") + assert snapshot == sample_outputs + + +def test_run_sample4(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample4") + assert snapshot == sample_outputs + + +def test_run_sample5(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample5") + assert snapshot == sample_outputs + + +def test_run_sample6(mocker, snapshot): + sample_outputs = run_sample(mocker, "sample6") + assert snapshot == sample_outputs + + +def test_run_community_Antibodyy(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/Antibodyy") + assert snapshot == sample_outputs + + +def test_run_community_ibrahimkilic(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/ibrahimkilic") + assert snapshot == sample_outputs + + +def test_run_community_Sandeep_1507(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/Sandeep-1507") + assert snapshot == sample_outputs + + +def test_run_community_Shamanth(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/Shamanth") + assert snapshot == sample_outputs + + +def test_run_community_UmarFarootAPS(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/UmarFarootAPS") + assert snapshot == sample_outputs + + +def test_run_community_UPSC_mock(mocker, snapshot): + sample_outputs = run_sample(mocker, "community/UPSC-mock") + assert snapshot == sample_outputs diff --git a/src/tests/test_edge_cases.py b/src/tests/test_edge_cases.py new file mode 100644 index 00000000..f3d6fd45 --- /dev/null +++ b/src/tests/test_edge_cases.py @@ -0,0 +1,95 @@ +import os +from pathlib import Path + +import pandas as pd + +from src.tests.test_samples.sample2.boilerplate import ( + CONFIG_BOILERPLATE, + TEMPLATE_BOILERPLATE, +) +from src.tests.utils import ( + generate_write_jsons_and_run, + remove_file, + run_entry_point, + setup_mocker_patches, +) + +FROZEN_TIMESTAMP = "1970-01-01" +CURRENT_DIR = Path("src/tests") +BASE_SAMPLE_PATH = CURRENT_DIR.joinpath("test_samples", "sample2") +BASE_RESULTS_CSV_PATH = os.path.join( + "outputs", BASE_SAMPLE_PATH, "Results", "Results_05AM.csv" +) +BASE_MULTIMARKED_CSV_PATH = os.path.join( + "outputs", BASE_SAMPLE_PATH, "Manual", "MultiMarkedFiles.csv" +) + + +def run_sample(mocker, input_path): + setup_mocker_patches(mocker) + output_dir = os.path.join("outputs", input_path) + run_entry_point(input_path, output_dir) + + +def extract_output_data(path): + output_data = pd.read_csv(path, keep_default_na=False) + return output_data + + +write_jsons_and_run = generate_write_jsons_and_run( + run_sample, + sample_path=BASE_SAMPLE_PATH, + template_boilerplate=TEMPLATE_BOILERPLATE, + config_boilerplate=CONFIG_BOILERPLATE, +) + + +def test_config_low_dimensions(mocker): + def modify_config(config): + config["dimensions"]["processing_height"] = 1000 + config["dimensions"]["processing_width"] = 1000 + + exception = write_jsons_and_run(mocker, modify_config=modify_config) + + assert str(exception) == "No Error" + + +def test_different_bubble_dimensions(mocker): + # Prevent appending to output csv: + remove_file(BASE_RESULTS_CSV_PATH) + remove_file(BASE_MULTIMARKED_CSV_PATH) + + exception = write_jsons_and_run(mocker) + assert str(exception) == "No Error" + original_output_data = extract_output_data(BASE_RESULTS_CSV_PATH) + + def modify_template(template): + # Incorrect global bubble size + template["bubbleDimensions"] = [5, 5] + # Correct bubble size for MCQBlock1a1 + template["fieldBlocks"]["MCQBlock1a1"]["bubbleDimensions"] = [32, 32] + # Incorrect bubble size for MCQBlock1a11 + template["fieldBlocks"]["MCQBlock1a11"]["bubbleDimensions"] = [10, 10] + + remove_file(BASE_RESULTS_CSV_PATH) + remove_file(BASE_MULTIMARKED_CSV_PATH) + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert str(exception) == "No Error" + + results_output_data = extract_output_data(BASE_RESULTS_CSV_PATH) + + assert results_output_data.empty + + output_data = extract_output_data(BASE_MULTIMARKED_CSV_PATH) + + equal_columns = [f"q{i}" for i in range(1, 18)] + assert ( + output_data[equal_columns].iloc[0].to_list() + == original_output_data[equal_columns].iloc[0].to_list() + ) + + unequal_columns = [f"q{i}" for i in range(168, 185)] + assert not ( + output_data[unequal_columns].iloc[0].to_list() + == original_output_data[unequal_columns].iloc[0].to_list() + ) diff --git a/src/tests/test_samples/sample1/boilerplate.py b/src/tests/test_samples/sample1/boilerplate.py new file mode 100644 index 00000000..74dda465 --- /dev/null +++ b/src/tests/test_samples/sample1/boilerplate.py @@ -0,0 +1,14 @@ +TEMPLATE_BOILERPLATE = { + "pageDimensions": [300, 400], + "bubbleDimensions": [25, 25], + "preProcessors": [{"name": "CropPage", "options": {"morphKernel": [10, 10]}}], + "fieldBlocks": { + "MCQ_Block_1": { + "fieldType": "QTYPE_MCQ5", + "origin": [65, 60], + "fieldLabels": ["q1..5"], + "labelsGap": 52, + "bubblesGap": 41, + } + }, +} diff --git a/src/tests/test_samples/sample1/sample.png b/src/tests/test_samples/sample1/sample.png new file mode 100644 index 00000000..d8db0994 Binary files /dev/null and b/src/tests/test_samples/sample1/sample.png differ diff --git a/src/tests/test_samples/sample2/boilerplate.py b/src/tests/test_samples/sample2/boilerplate.py new file mode 100644 index 00000000..f43c3093 --- /dev/null +++ b/src/tests/test_samples/sample2/boilerplate.py @@ -0,0 +1,39 @@ +TEMPLATE_BOILERPLATE = { + "pageDimensions": [2550, 3300], + "bubbleDimensions": [32, 32], + "preProcessors": [ + { + "name": "CropOnMarkers", + "options": { + "relativePath": "omr_marker.jpg", + "sheetToMarkerWidthRatio": 17, + }, + } + ], + "fieldBlocks": { + "MCQBlock1a1": { + "fieldType": "QTYPE_MCQ4", + "origin": [197, 300], + "bubblesGap": 92, + "labelsGap": 59.6, + "fieldLabels": ["q1..17"], + }, + "MCQBlock1a11": { + "fieldType": "QTYPE_MCQ4", + "origin": [1770, 1310], + "bubblesGap": 92, + "labelsGap": 59.6, + "fieldLabels": ["q168..184"], + }, + }, +} + +CONFIG_BOILERPLATE = { + "dimensions": { + "display_height": 960, + "display_width": 1280, + "processing_height": 1640, + "processing_width": 1332, + }, + "outputs": {"show_image_level": 0, "filter_out_multimarked_files": True}, +} diff --git a/src/tests/test_samples/sample2/omr_marker.jpg b/src/tests/test_samples/sample2/omr_marker.jpg new file mode 100644 index 00000000..0929feec Binary files /dev/null and b/src/tests/test_samples/sample2/omr_marker.jpg differ diff --git a/src/tests/test_samples/sample2/sample.jpg b/src/tests/test_samples/sample2/sample.jpg new file mode 100644 index 00000000..2eef7c32 Binary files /dev/null and b/src/tests/test_samples/sample2/sample.jpg differ diff --git a/src/tests/test_template_validations.py b/src/tests/test_template_validations.py new file mode 100644 index 00000000..bd321a72 --- /dev/null +++ b/src/tests/test_template_validations.py @@ -0,0 +1,159 @@ +import os +from pathlib import Path + +from src.tests.test_samples.sample1.boilerplate import TEMPLATE_BOILERPLATE +from src.tests.utils import ( + generate_write_jsons_and_run, + run_entry_point, + setup_mocker_patches, +) + +FROZEN_TIMESTAMP = "1970-01-01" +CURRENT_DIR = Path("src/tests") +BASE_SAMPLE_PATH = CURRENT_DIR.joinpath("test_samples", "sample1") +BASE_SAMPLE_TEMPLATE_PATH = BASE_SAMPLE_PATH.joinpath("template.json") + + +def run_sample(mocker, input_path): + setup_mocker_patches(mocker) + output_dir = os.path.join("outputs", input_path) + run_entry_point(input_path, output_dir) + + +write_jsons_and_run = generate_write_jsons_and_run( + run_sample, + sample_path=BASE_SAMPLE_PATH, + template_boilerplate=TEMPLATE_BOILERPLATE, +) + + +def test_no_input_dir(mocker): + try: + run_sample(mocker, "X") + except Exception as e: + assert str(e) == "Given input directory does not exist: 'X'" + + +def test_no_template(mocker): + if os.path.exists(BASE_SAMPLE_TEMPLATE_PATH): + os.remove(BASE_SAMPLE_TEMPLATE_PATH) + try: + run_sample(mocker, BASE_SAMPLE_PATH) + except Exception as e: + assert ( + str(e) + == "No template file found in the directory tree of src/tests/test_samples/sample1" + ) + + +def test_empty_template(mocker): + def modify_template(_): + return {} + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == f"Provided Template JSON is Invalid: '{BASE_SAMPLE_TEMPLATE_PATH}'" + ) + + +def test_invalid_field_type(mocker): + def modify_template(template): + template["fieldBlocks"]["MCQ_Block_1"]["fieldType"] = "X" + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == f"Provided Template JSON is Invalid: '{BASE_SAMPLE_TEMPLATE_PATH}'" + ) + + +def test_overflow_labels(mocker): + def modify_template(template): + template["fieldBlocks"]["MCQ_Block_1"]["fieldLabels"] = ["q1..100"] + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == "Overflowing field block 'MCQ_Block_1' with origin [65, 60] and dimensions [189, 5173] in template with dimensions [300, 400]" + ) + + +def test_overflow_safe_dimensions(mocker): + def modify_template(template): + template["pageDimensions"] = [255, 400] + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert str(exception) == "No Error" + + +def test_field_strings_overlap(mocker): + def modify_template(template): + template["fieldBlocks"] = { + **template["fieldBlocks"], + "New_Block": { + **template["fieldBlocks"]["MCQ_Block_1"], + "fieldLabels": ["q5"], + }, + } + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert str(exception) == ( + "The field strings for field block New_Block overlap with other existing fields" + ) + + +def test_custom_label_strings_overlap_single(mocker): + def modify_template(template): + template["customLabels"] = { + "label1": ["q1..2", "q2..3"], + } + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == "Given field string 'q2..3' has overlapping field(s) with other fields in 'Custom Label: label1': ['q1..2', 'q2..3']" + ) + + +def test_custom_label_strings_overlap_multiple(mocker): + def modify_template(template): + template["customLabels"] = { + "label1": ["q1..2"], + "label2": ["q2..3"], + } + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == "The field strings for custom label 'label2' overlap with other existing custom labels" + ) + + +def test_missing_field_block_labels(mocker): + def modify_template(template): + template["customLabels"] = {"Combined": ["qX", "qY"]} + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert ( + str(exception) + == "Missing field block label(s) in the given template for ['qX', 'qY'] from 'Combined'" + ) + + +def test_missing_output_columns(mocker): + def modify_template(template): + template["outputColumns"] = ["qX", "q1..5"] + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert str(exception) == ( + "Some columns are missing in the field blocks for the given output columns" + ) + + +def test_safe_missing_label_columns(mocker): + def modify_template(template): + template["outputColumns"] = ["q1..4"] + + exception = write_jsons_and_run(mocker, modify_template=modify_template) + assert str(exception) == "No Error" diff --git a/src/tests/utils.py b/src/tests/utils.py new file mode 100644 index 00000000..f8e5bfab --- /dev/null +++ b/src/tests/utils.py @@ -0,0 +1,97 @@ +import json +import os +from copy import deepcopy + +from freezegun import freeze_time + +from main import entry_point_for_args + +FROZEN_TIMESTAMP = "1970-01-01" + + +def setup_mocker_patches(mocker): + mock_imshow = mocker.patch("cv2.imshow") + mock_imshow.return_value = True + + mock_destroy_all_windows = mocker.patch("cv2.destroyAllWindows") + mock_destroy_all_windows.return_value = True + + mock_wait_key = mocker.patch("cv2.waitKey") + mock_wait_key.return_value = ord("q") + + +def run_entry_point(input_path, output_dir): + args = { + "autoAlign": False, + "debug": False, + "input_paths": [input_path], + "output_dir": output_dir, + "setLayout": False, + "silent": True, + } + with freeze_time(FROZEN_TIMESTAMP): + entry_point_for_args(args) + + +def write_modified(modify_content, boilerplate, sample_json_path): + if boilerplate is None: + return + + content = deepcopy(boilerplate) + + if modify_content is not None: + returned_value = modify_content(content) + if returned_value is not None: + content = returned_value + + with open(sample_json_path, "w") as f: + json.dump(content, f) + + +def remove_file(path): + if os.path.exists(path): + os.remove(path) + + +def generate_write_jsons_and_run( + run_sample, + sample_path, + template_boilerplate=None, + config_boilerplate=None, + evaluation_boilerplate=None, +): + if (template_boilerplate or config_boilerplate or evaluation_boilerplate) is None: + raise Exception( + f"No boilerplates found. Provide atleast one boilerplate to write json." + ) + + def write_jsons_and_run( + mocker, + modify_template=None, + modify_config=None, + modify_evaluation=None, + ): + sample_template_path, sample_config_path, sample_evaluation_path = ( + sample_path.joinpath("template.json"), + sample_path.joinpath("config.json"), + sample_path.joinpath("evaluation.json"), + ) + write_modified(modify_template, template_boilerplate, sample_template_path) + write_modified(modify_config, config_boilerplate, sample_config_path) + write_modified( + modify_evaluation, evaluation_boilerplate, sample_evaluation_path + ) + + exception = "No Error" + try: + run_sample(mocker, sample_path) + except Exception as e: + exception = e + + remove_file(sample_template_path) + remove_file(sample_config_path) + remove_file(sample_evaluation_path) + + return exception + + return write_jsons_and_run diff --git a/src/utils/__init__.py b/src/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/utils/file.py b/src/utils/file.py new file mode 100644 index 00000000..08536ff1 --- /dev/null +++ b/src/utils/file.py @@ -0,0 +1,95 @@ +import argparse +import json +import os +from csv import QUOTE_NONNUMERIC +from time import localtime, strftime + +import pandas as pd + +from src.logger import logger + + +def load_json(path, **rest): + try: + with open(path, "r") as f: + loaded = json.load(f, **rest) + except json.decoder.JSONDecodeError as error: + logger.critical(f"Error when loading json file at: '{path}'\n{error}") + exit(1) + return loaded + + +class Paths: + def __init__(self, output_dir): + self.output_dir = output_dir + self.save_marked_dir = output_dir.joinpath("CheckedOMRs") + self.results_dir = output_dir.joinpath("Results") + self.manual_dir = output_dir.joinpath("Manual") + self.evaluation_dir = output_dir.joinpath("Evaluation") + self.errors_dir = self.manual_dir.joinpath("ErrorFiles") + self.multi_marked_dir = self.manual_dir.joinpath("MultiMarkedFiles") + + +def setup_dirs_for_paths(paths): + logger.info("Checking Directories...") + for save_output_dir in [paths.save_marked_dir]: + if not os.path.exists(save_output_dir): + logger.info(f"Created : {save_output_dir}") + os.makedirs(save_output_dir) + os.mkdir(save_output_dir.joinpath("stack")) + os.mkdir(save_output_dir.joinpath("_MULTI_")) + os.mkdir(save_output_dir.joinpath("_MULTI_", "stack")) + + for save_output_dir in [paths.manual_dir, paths.results_dir, paths.evaluation_dir]: + if not os.path.exists(save_output_dir): + logger.info(f"Created : {save_output_dir}") + os.makedirs(save_output_dir) + + for save_output_dir in [paths.multi_marked_dir, paths.errors_dir]: + if not os.path.exists(save_output_dir): + logger.info(f"Created : {save_output_dir}") + os.makedirs(save_output_dir) + + +def setup_outputs_for_template(paths, template): + # TODO: consider moving this into a class instance + ns = argparse.Namespace() + logger.info("Checking Files...") + + # Include current output paths + ns.paths = paths + + ns.empty_resp = [""] * len(template.output_columns) + ns.sheetCols = [ + "file_id", + "input_path", + "output_path", + "score", + ] + template.output_columns + ns.OUTPUT_SET = [] + ns.files_obj = {} + TIME_NOW_HRS = strftime("%I%p", localtime()) + ns.filesMap = { + "Results": os.path.join(paths.results_dir, f"Results_{TIME_NOW_HRS}.csv"), + "MultiMarked": os.path.join(paths.manual_dir, "MultiMarkedFiles.csv"), + "Errors": os.path.join(paths.manual_dir, "ErrorFiles.csv"), + } + + for file_key, file_name in ns.filesMap.items(): + if not os.path.exists(file_name): + logger.info(f"Created new file: '{file_name}'") + # moved handling of files to pandas csv writer + ns.files_obj[file_key] = file_name + # Create Header Columns + pd.DataFrame([ns.sheetCols], dtype=str).to_csv( + ns.files_obj[file_key], + mode="a", + quoting=QUOTE_NONNUMERIC, + header=False, + index=False, + ) + else: + logger.info(f"Present : appending to '{file_name}'") + ns.files_obj[file_key] = open(file_name, "a") + + return ns diff --git a/src/utils/image.py b/src/utils/image.py new file mode 100644 index 00000000..bba6f6d1 --- /dev/null +++ b/src/utils/image.py @@ -0,0 +1,155 @@ +""" + + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 + +""" +import cv2 +import matplotlib.pyplot as plt +import numpy as np + +from src.logger import logger + +plt.rcParams["figure.figsize"] = (10.0, 8.0) +CLAHE_HELPER = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8)) + + +class ImageUtils: + """A Static-only Class to hold common image processing utilities & wrappers over OpenCV functions""" + + @staticmethod + def save_img(path, final_marked): + logger.info(f"Saving Image to '{path}'") + cv2.imwrite(path, final_marked) + + @staticmethod + def resize_util(img, u_width, u_height=None): + if u_height is None: + h, w = img.shape[:2] + u_height = int(h * u_width / w) + return cv2.resize(img, (int(u_width), int(u_height))) + + @staticmethod + def resize_util_h(img, u_height, u_width=None): + if u_width is None: + h, w = img.shape[:2] + u_width = int(w * u_height / h) + return cv2.resize(img, (int(u_width), int(u_height))) + + @staticmethod + def grab_contours(cnts): + # source: imutils package + + # if the length the contours tuple returned by cv2.findContours + # is '2' then we are using either OpenCV v2.4, v4-beta, or + # v4-official + if len(cnts) == 2: + cnts = cnts[0] + + # if the length of the contours tuple is '3' then we are using + # either OpenCV v3, v4-pre, or v4-alpha + elif len(cnts) == 3: + cnts = cnts[1] + + # otherwise OpenCV has changed their cv2.findContours return + # signature yet again and I have no idea WTH is going on + else: + raise Exception( + ( + "Contours tuple must have length 2 or 3, " + "otherwise OpenCV changed their cv2.findContours return " + "signature yet again. Refer to OpenCV's documentation " + "in that case" + ) + ) + + # return the actual contours array + return cnts + + @staticmethod + def normalize_util(img, alpha=0, beta=255): + return cv2.normalize(img, alpha, beta, norm_type=cv2.NORM_MINMAX) + + @staticmethod + def auto_canny(image, sigma=0.93): + # compute the median of the single channel pixel intensities + v = np.median(image) + + # apply automatic Canny edge detection using the computed median + lower = int(max(0, (1.0 - sigma) * v)) + upper = int(min(255, (1.0 + sigma) * v)) + edged = cv2.Canny(image, lower, upper) + + # return the edged image + return edged + + @staticmethod + def adjust_gamma(image, gamma=1.0): + # build a lookup table mapping the pixel values [0, 255] to + # their adjusted gamma values + inv_gamma = 1.0 / gamma + table = np.array( + [((i / 255.0) ** inv_gamma) * 255 for i in np.arange(0, 256)] + ).astype("uint8") + + # apply gamma correction using the lookup table + return cv2.LUT(image, table) + + @staticmethod + def four_point_transform(image, pts): + # obtain a consistent order of the points and unpack them + # individually + rect = ImageUtils.order_points(pts) + (tl, tr, br, bl) = rect + + # compute the width of the new image, which will be the + width_a = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + width_b = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + + max_width = max(int(width_a), int(width_b)) + # max_width = max(int(np.linalg.norm(br-bl)), int(np.linalg.norm(tr-tl))) + + # compute the height of the new image, which will be the + height_a = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + height_b = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + max_height = max(int(height_a), int(height_b)) + # max_height = max(int(np.linalg.norm(tr-br)), int(np.linalg.norm(tl-br))) + + # now that we have the dimensions of the new image, construct + # the set of destination points to obtain a "birds eye view", + # (i.e. top-down view) of the image, again specifying points + # in the top-left, top-right, bottom-right, and bottom-left + # order + dst = np.array( + [ + [0, 0], + [max_width - 1, 0], + [max_width - 1, max_height - 1], + [0, max_height - 1], + ], + dtype="float32", + ) + + transform_matrix = cv2.getPerspectiveTransform(rect, dst) + warped = cv2.warpPerspective(image, transform_matrix, (max_width, max_height)) + + # return the warped image + return warped + + @staticmethod + def order_points(pts): + rect = np.zeros((4, 2), dtype="float32") + + # the top-left point will have the smallest sum, whereas + # the bottom-right point will have the largest sum + s = pts.sum(axis=1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis=1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + + # return the ordered coordinates + return rect diff --git a/src/utils/interaction.py b/src/utils/interaction.py new file mode 100644 index 00000000..aba5e754 --- /dev/null +++ b/src/utils/interaction.py @@ -0,0 +1,107 @@ +from dataclasses import dataclass + +import cv2 +from screeninfo import get_monitors + +from src.logger import logger +from src.utils.image import ImageUtils + +monitor_window = get_monitors()[0] + + +@dataclass +class ImageMetrics: + # TODO: Move TEXT_SIZE, etc here and find a better class name + window_width, window_height = monitor_window.width, monitor_window.height + # for positioning image windows + window_x, window_y = 0, 0 + reset_pos = [0, 0] + + +class InteractionUtils: + """Perform primary functions such as displaying images and reading responses""" + + image_metrics = ImageMetrics() + + @staticmethod + def show(name, origin, pause=1, resize=False, reset_pos=None, config=None): + image_metrics = InteractionUtils.image_metrics + if origin is None: + logger.info(f"'{name}' - NoneType image to show!") + if pause: + cv2.destroyAllWindows() + return + if resize: + if not config: + raise Exception("config not provided for resizing the image to show") + img = ImageUtils.resize_util(origin, config.dimensions.display_width) + else: + img = origin + + if not is_window_available(name): + cv2.namedWindow(name) + + cv2.imshow(name, img) + + if reset_pos: + image_metrics.window_x = reset_pos[0] + image_metrics.window_y = reset_pos[1] + + cv2.moveWindow( + name, + image_metrics.window_x, + image_metrics.window_y, + ) + + h, w = img.shape[:2] + + # Set next window position + margin = 25 + w += margin + h += margin + + w, h = w // 2, h // 2 + if image_metrics.window_x + w > image_metrics.window_width: + image_metrics.window_x = 0 + if image_metrics.window_y + h > image_metrics.window_height: + image_metrics.window_y = 0 + else: + image_metrics.window_y += h + else: + image_metrics.window_x += w + + if pause: + logger.info( + f"Showing '{name}'\n\t Press Q on image to continue. Press Ctrl + C in terminal to exit" + ) + + wait_q() + InteractionUtils.image_metrics.window_x = 0 + InteractionUtils.image_metrics.window_y = 0 + + +@dataclass +class Stats: + # TODO Fill these for stats + # Move qbox_vals here? + # badThresholds = [] + # veryBadPoints = [] + files_moved = 0 + files_not_moved = 0 + + +def wait_q(): + esc_key = 27 + while cv2.waitKey(1) & 0xFF not in [ord("q"), esc_key]: + pass + cv2.destroyAllWindows() + + +def is_window_available(name: str) -> bool: + """Checks if a window is available""" + try: + cv2.getWindowProperty(name, cv2.WND_PROP_VISIBLE) + return True + except Exception as e: + print(e) + return False diff --git a/src/utils/parsing.py b/src/utils/parsing.py new file mode 100644 index 00000000..cb6901c6 --- /dev/null +++ b/src/utils/parsing.py @@ -0,0 +1,113 @@ +import re +from copy import deepcopy +from fractions import Fraction + +from deepmerge import Merger +from dotmap import DotMap + +from src.constants import FIELD_LABEL_NUMBER_REGEX +from src.defaults import CONFIG_DEFAULTS, TEMPLATE_DEFAULTS +from src.schemas.constants import FIELD_STRING_REGEX_GROUPS +from src.utils.file import load_json +from src.utils.validations import ( + validate_config_json, + validate_evaluation_json, + validate_template_json, +) + +OVERRIDE_MERGER = Merger( + # pass in a list of tuples,with the + # strategies you are looking to apply + # to each type. + [ + # (list, ["prepend"]), + (dict, ["merge"]) + ], + # next, choose the fallback strategies, + # applied to all other types: + ["override"], + # finally, choose the strategies in + # the case where the types conflict: + ["override"], +) + + +def get_concatenated_response(omr_response, template): + # Multi-column/multi-row questions which need to be concatenated + concatenated_response = {} + for field_label, concatenate_keys in template.custom_labels.items(): + custom_label = "".join([omr_response[k] for k in concatenate_keys]) + concatenated_response[field_label] = custom_label + + for field_label in template.non_custom_labels: + concatenated_response[field_label] = omr_response[field_label] + + return concatenated_response + + +def open_config_with_defaults(config_path): + user_tuning_config = load_json(config_path) + user_tuning_config = OVERRIDE_MERGER.merge( + deepcopy(CONFIG_DEFAULTS), user_tuning_config + ) + validate_config_json(user_tuning_config, config_path) + # https://github.com/drgrib/dotmap/issues/74 + return DotMap(user_tuning_config, _dynamic=False) + + +def open_template_with_defaults(template_path): + user_template = load_json(template_path) + user_template = OVERRIDE_MERGER.merge(deepcopy(TEMPLATE_DEFAULTS), user_template) + validate_template_json(user_template, template_path) + return user_template + + +def open_evaluation_with_validation(evaluation_path): + user_evaluation_config = load_json(evaluation_path) + validate_evaluation_json(user_evaluation_config, evaluation_path) + return user_evaluation_config + + +def parse_fields(key, fields): + parsed_fields = [] + fields_set = set() + for field_string in fields: + fields_array = parse_field_string(field_string) + current_set = set(fields_array) + if not fields_set.isdisjoint(current_set): + raise Exception( + f"Given field string '{field_string}' has overlapping field(s) with other fields in '{key}': {fields}" + ) + fields_set.update(current_set) + parsed_fields.extend(fields_array) + return parsed_fields + + +def parse_field_string(field_string): + if "." in field_string: + field_prefix, start, end = re.findall(FIELD_STRING_REGEX_GROUPS, field_string)[ + 0 + ] + start, end = int(start), int(end) + if start >= end: + raise Exception( + f"Invalid range in fields string: '{field_string}', start: {start} is not less than end: {end}" + ) + return [ + f"{field_prefix}{field_number}" for field_number in range(start, end + 1) + ] + else: + return [field_string] + + +def custom_sort_output_columns(field_label): + label_prefix, label_suffix = re.findall(FIELD_LABEL_NUMBER_REGEX, field_label)[0] + return [label_prefix, int(label_suffix) if len(label_suffix) > 0 else 0] + + +def parse_float_or_fraction(result): + if type(result) == str and "/" in result: + result = float(Fraction(result)) + else: + result = float(result) + return result diff --git a/src/utils/validations.py b/src/utils/validations.py new file mode 100644 index 00000000..0c2ec839 --- /dev/null +++ b/src/utils/validations.py @@ -0,0 +1,115 @@ +""" + + OMRChecker + + Author: Udayraj Deshmukh + Github: https://github.com/Udayraj123 + +""" +import re + +import jsonschema +from jsonschema import validate +from rich.table import Table + +from src.logger import console, logger +from src.schemas import SCHEMA_JSONS, SCHEMA_VALIDATORS + + +def validate_evaluation_json(json_data, evaluation_path): + logger.info(f"Loading evaluation.json: {evaluation_path}") + try: + validate(instance=json_data, schema=SCHEMA_JSONS["evaluation"]) + except jsonschema.exceptions.ValidationError as _err: # NOQA + table = Table(show_lines=True) + table.add_column("Key", style="cyan", no_wrap=True) + table.add_column("Error", style="magenta") + + errors = sorted( + SCHEMA_VALIDATORS["evaluation"].iter_errors(json_data), + key=lambda e: e.path, + ) + for error in errors: + key, validator, msg = parse_validation_error(error) + if validator == "required": + requiredProperty = re.findall(r"'(.*?)'", msg)[0] + table.add_row( + f"{key}.{requiredProperty}", + msg + ". Make sure the spelling of the key is correct", + ) + else: + table.add_row(key, msg) + console.print(table, justify="center") + raise Exception( + f"Provided Evaluation JSON is Invalid: '{evaluation_path}'" + ) from None + + +def validate_template_json(json_data, template_path): + logger.info(f"Loading template.json: {template_path}") + try: + validate(instance=json_data, schema=SCHEMA_JSONS["template"]) + except jsonschema.exceptions.ValidationError as _err: # NOQA + table = Table(show_lines=True) + table.add_column("Key", style="cyan", no_wrap=True) + table.add_column("Error", style="magenta") + + errors = sorted( + SCHEMA_VALIDATORS["template"].iter_errors(json_data), + key=lambda e: e.path, + ) + for error in errors: + key, validator, msg = parse_validation_error(error) + + # Print preProcessor name in case of options error + if key == "preProcessors": + preProcessorName = json_data["preProcessors"][error.path[1]]["name"] + preProcessorKey = error.path[2] + table.add_row(f"{key}.{preProcessorName}.{preProcessorKey}", msg) + elif validator == "required": + requiredProperty = re.findall(r"'(.*?)'", msg)[0] + table.add_row( + f"{key}.{requiredProperty}", + f"{msg}. Check for spelling errors and make sure it is in camelCase", + ) + else: + table.add_row(key, msg) + console.print(table, justify="center") + raise Exception( + f"Provided Template JSON is Invalid: '{template_path}'" + ) from None + + +def validate_config_json(json_data, config_path): + logger.info(f"Loading config.json: {config_path}") + try: + validate(instance=json_data, schema=SCHEMA_JSONS["config"]) + except jsonschema.exceptions.ValidationError as _err: # NOQA + table = Table(show_lines=True) + table.add_column("Key", style="cyan", no_wrap=True) + table.add_column("Error", style="magenta") + errors = sorted( + SCHEMA_VALIDATORS["config"].iter_errors(json_data), + key=lambda e: e.path, + ) + for error in errors: + key, validator, msg = parse_validation_error(error) + + if validator == "required": + requiredProperty = re.findall(r"'(.*?)'", msg)[0] + table.add_row( + f"{key}.{requiredProperty}", + f"{msg}. Check for spelling errors and make sure it is in camelCase", + ) + else: + table.add_row(key, msg) + console.print(table, justify="center") + raise Exception(f"Provided config JSON is Invalid: '{config_path}'") from None + + +def parse_validation_error(error): + return ( + (error.path[0] if len(error.path) > 0 else "$root"), + error.validator, + error.message, + ) diff --git a/template.py b/template.py deleted file mode 100644 index cdfdb276..00000000 --- a/template.py +++ /dev/null @@ -1,355 +0,0 @@ -""" - -Designed and Developed by- -Udayraj Deshmukh -https://github.com/Udayraj123 - -""" -import cv2 -import os -import json -import numpy as np -from globals import * -from utils import * - -### Coordinates Part ### - - -class Pt(): - """ - Container for a Point Box on the OMR - """ - """ - qNo is the point's property- question to which this point belongs to - It can be used as a roll number column as well. (eg roll1) - It can also correspond to a single digit of integer type Q (eg q5d1) - """ - - def __init__(self, pt, qNo, qType, val): - self.x = round(pt[0]) - self.y = round(pt[1]) - self.qNo = qNo - self.qType = qType - self.val = val - - -class QBlock(): - def __init__(self, dims, key, orig, traverse_pts): - # dims = (width, height) - self.dims = tuple(round(x) for x in dims) - self.key = key - self.orig = orig - self.traverse_pts = traverse_pts - # will be set when using - self.shift = 0 - - -qtype_data = { - 'QTYPE_MED': { - 'vals': ['E', 'H'], - 'orient': 'V' - }, - 'QTYPE_ROLL': { - 'vals': range(10), - 'orient': 'V' - }, - 'QTYPE_INT': { - 'vals': range(10), - 'orient': 'V' - }, - 'QTYPE_MCQ4': { - 'vals': ['A', 'B', 'C', 'D'], - 'orient': 'H' - }, - 'QTYPE_MCQ5': { - 'vals': ['A', 'B', 'C', 'D', 'E'], - 'orient': 'H' - }, - # Add custom question types here- - # , - # 'QTYPE_MCQ_COL_5A':{'vals' : ['A']*5, 'orient':'V'}, - # 'QTYPE_MCQ_COL_5B':{'vals' : ['B']*5, 'orient':'V'}, - # 'QTYPE_MCQ_COL_5C':{'vals' : ['C']*5, 'orient':'V'}, - # 'QTYPE_MCQ_COL_5D':{'vals' : ['D']*5, 'orient':'V'}, - # 'QTYPE_MCQ_COL_4A':{'vals' : ['A']*4, 'orient':'V'}, - # 'QTYPE_MCQ_COL_4B':{'vals' : ['B']*4, 'orient':'V'}, - # 'QTYPE_MCQ_COL_4C':{'vals' : ['C']*4, 'orient':'V'}, - # 'QTYPE_MCQ_COL_4D':{'vals' : ['D']*4, 'orient':'V'}, -} - - -class Template(): - def __init__(self, path): - with open(path, "r") as f: - json_obj = json.load(f) - self.path = path - self.QBlocks = [] - # throw exception on key not exist - self.dims = json_obj["Dimensions"] - self.bubbleDims = json_obj["BubbleDimensions"] - self.concats = json_obj["Concatenations"] - self.singles = json_obj["Singles"] - - # Add new qTypes from template - if "qTypes" in json_obj: - qtype_data.update(json_obj["qTypes"]) - - # process local options - self.options = json_obj.get("Options", {}) - - self.marker = None - self.marker_path = None - # process markers - if "Marker" in self.options: - markerOps = self.options["Marker"] - self.marker_path = os.path.join( - os.path.dirname(path), markerOps.get( - "RelativePath", MARKER_FILE)) - if(not os.path.exists(self.marker_path)): - print( - "Error: Marker not found at path provided in template:", - self.marker_path) - exit(31) - - marker = cv2.imread(self.marker_path, cv2.IMREAD_GRAYSCALE) - if("SheetToMarkerWidthRatio" in markerOps): - marker = resize_util(marker, uniform_width / - int(markerOps["SheetToMarkerWidthRatio"])) - marker = cv2.GaussianBlur(marker, (5, 5), 0) - marker = cv2.normalize( - marker, - None, - alpha=0, - beta=255, - norm_type=cv2.NORM_MINMAX) - # marker_eroded_sub = marker-cv2.erode(marker,None) - self.marker = marker - \ - cv2.erode(marker, kernel=np.ones((5, 5)), iterations=5) - - # Allow template to override globals - # TODO: This is a hack as there should no be any global configuration - # All template configuration should be local. Global config should - # be via command args. - self.globals = json_obj.get("Globals") - self.update_globals() - - # Add QBlocks - for name, block in json_obj["QBlocks"].items(): - self.addQBlocks(name, block) - - def update_globals(self): - if self.globals: - globals().update(self.globals) - - # Expects bubbleDims to be set already - def addQBlocks(self, key, rect): - assert(self.bubbleDims != [-1, -1]) - # For qType defined in QBlocks - if 'qType' in rect: - rect.update(**qtype_data[rect['qType']]) - else: - rect['qType'] = {'vals': rect['vals'], 'orient': rect['orient']} - # keyword arg unpacking followed by named args - self.QBlocks += genGrid(self.bubbleDims, key, **rect) - # self.QBlocks.append(QBlock(rect.orig, calcQBlockDims(rect), maketemplate(rect))) - - -def genQBlock( - bubbleDims, - QBlockDims, - key, - orig, - qNos, - gaps, - vals, - qType, - orient, - col_orient): - """ - Input: - orig - start point - qNos - a tuple of qNos - gaps - (gapX,gapY) are the gaps between rows and cols in a block - vals - a 1D array of values of each alternative for a question - - Output: - // Returns set of coordinates of a rectangular grid of points - Returns a QBlock containing array of Qs and some metadata?! - - Ref: - 1 2 3 4 - 1 2 3 4 - 1 2 3 4 - - (q1, q2, q3) - - 00 - 11 - 22 - 33 - 44 - - (q1.1,q1.2) - - """ - H, V = (0, 1) if(orient == 'H') else (1, 0) - # orig[0] += np.random.randint(-6,6)*2 # test random shift - traverse_pts = [] - o = [float(i) for i in orig] - - if(col_orient == orient): - for q in range(len(qNos)): - pt = o.copy() - pts = [] - for v in range(len(vals)): - pts.append(Pt(pt.copy(), qNos[q], qType, vals[v])) - pt[H] += gaps[H] - # For diagonalal endpoint of QBlock - pt[H] += bubbleDims[H] - gaps[H] - pt[V] += bubbleDims[V] - # TODO- make a mini object for this - traverse_pts.append(([o.copy(), pt.copy()], pts)) - o[V] += gaps[V] - else: - for v in range(len(vals)): - pt = o.copy() - pts = [] - for q in range(len(qNos)): - pts.append(Pt(pt.copy(), qNos[q], qType, vals[v])) - pt[V] += gaps[V] - # For diagonalal endpoint of QBlock - pt[V] += bubbleDims[V] - gaps[V] - pt[H] += bubbleDims[H] - # TODO- make a mini object for this - traverse_pts.append(([o.copy(), pt.copy()], pts)) - o[H] += gaps[H] - # Pass first three args as is. only append 'traverse_pts' - return QBlock(QBlockDims, key, orig, traverse_pts) - - -def genGrid( - bubbleDims, - key, - qType, - orig, - bigGaps, - gaps, - qNos, - vals, - orient='V', - col_orient='V'): - """ - Input(Directly passable from JSON parameters): - bubbleDims - dimesions of single QBox - orig- start point - qNos - an array of qNos tuples(see below) that align with dimension of the big grid (gridDims extracted from here) - bigGaps - (bigGapX,bigGapY) are the gaps between blocks - gaps - (gapX,gapY) are the gaps between rows and cols in a block - vals - a 1D array of values of each alternative for a question - orient - The way of arranging the vals (vertical or horizontal) - - Output: - // Returns an array of Q objects (having their points) arranged in a rectangular grid - Returns grid of QBlock objects - - 00 00 00 00 - Q1 1 2 3 4 1 2 3 4 11 11 11 11 - Q2 1 2 3 4 1 2 3 4 22 22 22 22 1234567 - Q3 1 2 3 4 1 2 3 4 33 33 33 33 1234567 - 44 44 44 44 - , 55 55 55 55 , 1234567 and many more possibilities! - Q7 1 2 3 4 1 2 3 4 66 66 66 66 1234567 - Q8 1 2 3 4 1 2 3 4 77 77 77 77 - Q9 1 2 3 4 1 2 3 4 88 88 88 88 - 99 99 99 99 - -TODO: Update this part, add more examples like- - Q1 1 2 3 4 - - Q2 1 2 3 4 - Q3 1 2 3 4 - - Q4 1 2 3 4 - Q5 1 2 3 4 - - MCQ type (orient='H')- - [ - [(q1,q2,q3),(q4,q5,q6)] - [(q7,q8,q9),(q10,q11,q12)] - ] - - INT type (orient='V')- - [ - [(q1d1,q1d2),(q2d1,q2d2),(q3d1,q3d2),(q4d1,q4d2)] - ] - - ROLL type- - [ - [(roll1,roll2,roll3,...,roll10)] - ] - - """ - gridData = np.array(qNos) - # print(gridData.shape, gridData) - if(0 and len(gridData.shape) != 3 or gridData.size == 0): # product of shape is zero - print( - "Error(genGrid): Invalid qNos array given:", - gridData.shape, - gridData) - exit(32) - - orig = np.array(orig) - numQsMax = max([max([len(qb) for qb in row]) for row in gridData]) - - numDims = [numQsMax, len(vals)] - - QBlocks = [] - - # **Simple is powerful** - # H and V are named with respect to orient == 'H', reverse their meaning - # when orient = 'V' - H, V = (0, 1) if(orient == 'H') else (1, 0) - - # print(orig, numDims, gridData.shape, gridData) - # orient is also the direction of making QBlocks - - # print(key, numDims, orig, gaps, bigGaps, origGap ) - qStart = orig.copy() - - origGap = [0, 0] - - # Usually single row - for row in gridData: - qStart[V] = orig[V] - - # Usually multiple qTuples - for qTuple in row: - # Update numDims and origGaps - numDims[0] = len(qTuple) - # bigGaps is indep of orientation - origGap[0] = bigGaps[0] + (numDims[V] - 1) * gaps[H] - origGap[1] = bigGaps[1] + (numDims[H] - 1) * gaps[V] - # each qTuple will have qNos - QBlockDims = [ - # width x height in pixels - gaps[0] * (numDims[V] - 1) + bubbleDims[H], - gaps[1] * (numDims[H] - 1) + bubbleDims[V] - ] - # WATCH FOR BLUNDER(use .copy()) - qStart was getting passed by - # reference! (others args read-only) - QBlocks.append( - genQBlock( - bubbleDims, - QBlockDims, - key, - qStart.copy(), - qTuple, - gaps, - vals, - qType, - orient, - col_orient)) - # Goes vertically down first - qStart[V] += origGap[V] - qStart[H] += origGap[H] - return QBlocks diff --git a/utils.py b/utils.py deleted file mode 100644 index 25f6df64..00000000 --- a/utils.py +++ /dev/null @@ -1,1168 +0,0 @@ -""" - -Designed and Developed by- -Udayraj Deshmukh -https://github.com/Udayraj123 - -""" -from time import localtime, strftime, time -from random import randint -from template import * -from globals import * - -# In[62]: -import re -import os -import sys -import cv2 -import glob -from imutils import grab_contours -import numpy as np -import pandas as pd -import matplotlib.pyplot as plt -plt.rcParams['figure.figsize'] = (10.0, 8.0) - - -def setup_dirs(paths): - print('\nChecking Directories...') - for _dir in [paths.saveMarkedDir]: - if(not os.path.exists(_dir)): - print('Created : ' + _dir) - os.makedirs(_dir) - os.mkdir(_dir + '/stack') - os.mkdir(_dir + '/_MULTI_') - os.mkdir(_dir + '/_MULTI_' + '/stack') - # os.mkdir(_dir+sl+'/_BADSCAN_') - # os.mkdir(_dir+sl+'/_BADSCAN_'+'/stack') - else: - print('Present : ' + _dir) - - for _dir in [paths.manualDir, paths.resultDir]: - if(not os.path.exists(_dir)): - print('Created : ' + _dir) - os.makedirs(_dir) - else: - print('Present : ' + _dir) - - for _dir in [paths.multiMarkedDir, paths.errorsDir, paths.badRollsDir]: - if(not os.path.exists(_dir)): - print('Created : ' + _dir) - os.makedirs(_dir) - else: - print('Present : ' + _dir) - - -def waitQ(): - ESC_KEY = 27 - while(cv2.waitKey(1) & 0xFF not in [ord('q'), ESC_KEY]): - pass - cv2.destroyAllWindows() - - -def normalize_util(img, alpha=0, beta=255): - return cv2.normalize(img, alpha, beta, norm_type=cv2.NORM_MINMAX) - - -def normalize_hist(img): - hist, bins = np.histogram(img.flatten(), 256, [0, 256]) - cdf = hist.cumsum() - cdf_m = np.ma.masked_equal(cdf, 0) - cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min()) - cdf = np.ma.filled(cdf_m, 0).astype('uint8') - return cdf[img] - - -def resize_util(img, u_width, u_height=None): - if u_height is None: - h, w = img.shape[:2] - u_height = int(h * u_width / w) - return cv2.resize(img, (int(u_width), int(u_height))) - - -def resize_util_h(img, u_height, u_width=None): - if u_width is None: - h, w = img.shape[:2] - u_width = int(w * u_height / h) - return cv2.resize(img, (int(u_width), int(u_height))) - - -def show(name, orig, pause=1, resize=False, resetpos=None): - global windowX, windowY, display_width - if(type(orig) == type(None)): - print(name, " NoneType image to show!") - if(pause): - cv2.destroyAllWindows() - return - origDim = orig.shape[:2] - img = resize_util(orig, display_width, display_height) if resize else orig - cv2.imshow(name, img) - if(resetpos): - windowX = resetpos[0] - windowY = resetpos[1] - cv2.moveWindow(name, windowX, windowY) - - h, w = img.shape[:2] - - # Set next window position - margin = 25 - w += margin - h += margin - if(windowX + w > windowWidth): - windowX = 0 - if(windowY + h > windowHeight): - windowY = 0 - else: - windowY += h - else: - windowX += w - - if(pause): - print( - "Showing '" + - name + - "'\n\tPress Q on image to continue Press Ctrl + C in terminal to exit") - waitQ() - - -def putLabel(img, label, size): - scale = img.shape[1] / display_width - bgVal = int(np.mean(img)) - pos = (int(scale * 80), int(scale * 30)) - clr = (255 - bgVal,) * 3 - img[(pos[1] - size * 30):(pos[1] + size * 2), :] = bgVal - cv2.putText(img, label, pos, cv2.FONT_HERSHEY_SIMPLEX, size, clr, 3) - - -def drawTemplateLayout( - img, - template, - shifted=True, - draw_qvals=False, - border=-1): - img = resize_util(img, template.dims[0], template.dims[1]) - final_align = img.copy() - boxW, boxH = template.bubbleDims - for QBlock in template.QBlocks: - s, d = QBlock.orig, QBlock.dims - shift = QBlock.shift - if(shifted): - cv2.rectangle( - final_align, - (s[0] + shift, - s[1]), - (s[0] + shift + d[0], - s[1] + d[1]), - CLR_BLACK, - 3) - else: - cv2.rectangle( - final_align, (s[0], s[1]), (s[0] + d[0], s[1] + d[1]), CLR_BLACK, 3) - for qStrip, qBoxPts in QBlock.traverse_pts: - for pt in qBoxPts: - x, y = (pt.x + QBlock.shift, pt.y) if shifted else (pt.x, pt.y) - cv2.rectangle(final_align, - (int(x + boxW / 10), - int(y + boxH / 10)), - (int(x + boxW - boxW / 10), - int(y + boxH - boxH / 10)), - CLR_GRAY, - border) - if(draw_qvals): - rect = [y, y + boxH, x, x + boxW] - cv2.putText(final_align, '%d' % (cv2.mean(img[rect[0]:rect[1], rect[2]:rect[3]])[ - 0]), (rect[2] + 2, rect[0] + (boxH * 2) // 3), cv2.FONT_HERSHEY_SIMPLEX, 0.6, CLR_BLACK, 2) - if(shifted): - cv2.putText(final_align, 's%s' % - (shift), tuple(s - - [template.dims[0] // - 20, - - d[1] // - 2]), cv2.FONT_HERSHEY_SIMPLEX, TEXT_SIZE, CLR_BLACK, 4) - return final_align - - -def getPlotImg(): - plt.savefig('tmp.png') - # img = cv2.imread('tmp.png',cv2.IMREAD_COLOR) - img = cv2.imread('tmp.png', cv2.IMREAD_GRAYSCALE) - os.remove("tmp.png") - # plt.cla() - # plt.clf() - plt.close() - return img - - -def order_points(pts): - rect = np.zeros((4, 2), dtype="float32") - - # the top-left point will have the smallest sum, whereas - # the bottom-right point will have the largest sum - s = pts.sum(axis=1) - rect[0] = pts[np.argmin(s)] - rect[2] = pts[np.argmax(s)] - diff = np.diff(pts, axis=1) - rect[1] = pts[np.argmin(diff)] - rect[3] = pts[np.argmax(diff)] - - # return the ordered coordinates - return rect - - -def four_point_transform(image, pts): - # obtain a consistent order of the points and unpack them - # individually - rect = order_points(pts) - (tl, tr, br, bl) = rect - - # compute the width of the new image, which will be the - widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) - widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) - - maxWidth = max(int(widthA), int(widthB)) - # maxWidth = max(int(np.linalg.norm(br-bl)), int(np.linalg.norm(tr-tl))) - - # compute the height of the new image, which will be the - heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) - heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) - maxHeight = max(int(heightA), int(heightB)) - # maxHeight = max(int(np.linalg.norm(tr-br)), int(np.linalg.norm(tl-br))) - - # now that we have the dimensions of the new image, construct - # the set of destination points to obtain a "birds eye view", - # (i.e. top-down view) of the image, again specifying points - # in the top-left, top-right, bottom-right, and bottom-left - # order - dst = np.array([ - [0, 0], - [maxWidth - 1, 0], - [maxWidth - 1, maxHeight - 1], - [0, maxHeight - 1]], dtype="float32") - - # compute the perspective transform matrix and then apply it - M = cv2.getPerspectiveTransform(rect, dst) - warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) - - # return the warped image - return warped - - -def dist(p1, p2): - return np.linalg.norm(np.array(p1) - np.array(p2)) - - -def get_reflection(pt, pt1, pt2): - pt, pt1, pt2 = tuple( - map(lambda x: np.array(x, dtype=float), [pt, pt1, pt2])) - return (pt1 + pt2) - pt - - -def printbuf(x): - sys.stdout.write(str(x)) - sys.stdout.write('\r') - - -def get_fourth_pt(three_pts): - m = [] - for i in range(3): - m.append(dist(three_pts[i], three_pts[(i + 1) % 3])) - - v = max(m) - for i in range(3): - if(m[i] != v and m[(i + 1) % 3] != v): - refl = (i + 1) % 3 - break - fourth_pt = get_reflection( - three_pts[refl], three_pts[(refl + 1) % 3], three_pts[(refl + 2) % 3]) - return fourth_pt - - -def angle(p1, p2, p0): - dx1 = float(p1[0] - p0[0]) - dy1 = float(p1[1] - p0[1]) - dx2 = float(p2[0] - p0[0]) - dy2 = float(p2[1] - p0[1]) - return (dx1 * dx2 + dy1 * dy2) / \ - np.sqrt((dx1 * dx1 + dy1 * dy1) * (dx2 * dx2 + dy2 * dy2) + 1e-10) - - -def checkMaxCosine(approx): - # assumes 4 pts present - maxCosine = 0 - minCosine = 1.5 - for i in range(2, 5): - cosine = abs(angle(approx[i % 4], approx[i - 2], approx[i - 1])) - maxCosine = max(cosine, maxCosine) - minCosine = min(cosine, minCosine) - # TODO add to plot dict - # print(maxCosine) - if(maxCosine >= 0.35): - print('Quadrilateral is not a rectangle.') - return False - return True - - -def validateRect(approx): - # TODO: add logic from app?! - return len(approx) == 4 and checkMaxCosine(approx.reshape(4, 2)) - - -def auto_canny(image, sigma=0.93): - # compute the median of the single channel pixel intensities - v = np.median(image) - - # apply automatic Canny edge detection using the computed median - lower = int(max(0, (1.0 - sigma) * v)) - upper = int(min(255, (1.0 + sigma) * v)) - edged = cv2.Canny(image, lower, upper) - - # return the edged image - return edged - - -def resetSaveImg(key): - global saveImgList - saveImgList[key] = [] - - -def appendSaveImg(key, img): - if(saveimglvl >= int(key)): - global saveImgList - if(key not in saveImgList): - saveImgList[key] = [] - saveImgList[key].append(img.copy()) - - -def findPage(image_norm): - # Done: find ORIGIN for the quadrants - # Done, Auto tune! : Get canny parameters tuned - # (https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/) - - image_norm = normalize_util(image_norm) - ret, image_norm = cv2.threshold(image_norm, 200, 255, cv2.THRESH_TRUNC) - image_norm = normalize_util(image_norm) - - appendSaveImg(1, image_norm) - - # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 10)) - kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)) - """ - # Closing is reverse of Opening, Dilation followed by Erosion. - A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels - under the kernel is 1, otherwise it is eroded (made to zero). - """ - # Close the small holes, i.e. Complete the edges on canny image - closed = cv2.morphologyEx(image_norm, cv2.MORPH_CLOSE, kernel) - - appendSaveImg(1, closed) - - edge = cv2.Canny(closed, 185, 55) - - # findContours returns outer boundaries in CW and inner boundaries in ACW - # order. - cnts = grab_contours( - cv2.findContours( - edge, - cv2.RETR_LIST, - cv2.CHAIN_APPROX_SIMPLE)) - # hullify to resolve disordered curves due to noise - cnts = [cv2.convexHull(c) for c in cnts] - cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5] - sheet = [] - for c in cnts: - if cv2.contourArea(c) < MIN_PAGE_AREA: - continue - peri = cv2.arcLength(c, True) - # ez algo - - # https://en.wikipedia.org/wiki/Ramer–Douglas–Peucker_algorithm - approx = cv2.approxPolyDP(c, epsilon=0.025 * peri, closed=True) - # print("Area",cv2.contourArea(c), "Peri", peri) - - # check its rectangle-ness: - if(validateRect(approx)): - sheet = np.reshape(approx, (4, -1)) - cv2.drawContours(image_norm, [approx], -1, (0, 255, 0), 2) - cv2.drawContours(edge, [approx], -1, (255, 255, 255), 10) - break - # box = perspective.order_points(box) - # sobel = cv2.addWeighted(cv2.Sobel(edge, cv2.CV_64F, 1, 0, ksize=3),0.5,cv2.Sobel(edge, cv2.CV_64F, 0, 1, ksize=3),0.5,0,edge) - # ExcessDo : make it work on killer images - # edge2 = auto_canny(image_norm) - # show('Morphed Edges',np.hstack((closed,edge)),1,1) - - appendSaveImg(1, edge) - return sheet - - -# Resizing the marker within scaleRange at rate of descent_per_step to -# find the best match. -def getBestMatch(image_eroded_sub, marker): - - descent_per_step = ( - marker_rescale_range[1] - marker_rescale_range[0]) // marker_rescale_steps - h, w = marker.shape[:2] - res, best_scale = None, None - allMaxT = 0 - - for r0 in np.arange( - marker_rescale_range[1], marker_rescale_range[0], -1 * descent_per_step): # reverse order - s = float(r0 * 1 / 100) - if(s == 0.0): - continue - rescaled_marker = resize_util_h( - marker if ERODE_SUB_OFF else marker, - u_height=int( - h * s)) - # res is the black image with white dots - res = cv2.matchTemplate( - image_eroded_sub, - rescaled_marker, - cv2.TM_CCOEFF_NORMED) - - maxT = res.max() - if(allMaxT < maxT): - # print('Scale: '+str(s)+', Circle Match: '+str(round(maxT*100,2))+'%') - best_scale, allMaxT = s, maxT - - if(allMaxT < thresholdCircle): - print("\tWarning: Template matching too low! Should you pass --noCropping flag?") - if(showimglvl >= 1): - show("res", res, 1, 0) - - if(best_scale is None): - print("No matchings for given scaleRange:", marker_rescale_range) - return best_scale, allMaxT - - -def adjust_gamma(image, gamma=1.0): - # build a lookup table mapping the pixel values [0, 255] to - # their adjusted gamma values - invGamma = 1.0 / gamma - table = np.array([((i / 255.0) ** invGamma) * 255 - for i in np.arange(0, 256)]).astype("uint8") - - # apply gamma correction using the lookup table - return cv2.LUT(image, table) - - -clahe = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8)) -# TODO Fill these for stats -thresholdCircles = [] -badThresholds = [] -veryBadPoints = [] - - -def getROI(image, filename, noCropping=False): - global clahe - for i in range(saveimglvl): - resetSaveImg(i + 1) - - appendSaveImg(1, image) - """ - TODO later Autorotate: - - Rotate 90 : check page width:height, CW/ACW? - do CW, then pass to 180 check. - - Rotate 180 : - Nope, OMR specific, paper warping may be imperfect. - check markers centroid - Nope - OCR check - Match logo - can work, but 'lon' too big and may unnecessarily rotate? - but you know the scale - Check roll field morphed - """ - - # TODO: (remove noCropping bool) Automate the case of close up scan(incorrect page)- - # ^Note: App rejects croppeds along with others - - # image = resize_util(image, uniform_width, uniform_height) - - # Preprocessing the image - img = image.copy() - # TODO: need to detect if image is too blurry already! (M1: check - # noCropping dimensions b4 resizing coz it won't be blurry otherwise _/) - img = cv2.GaussianBlur(img, (3, 3), 0) - image_norm = normalize_util(img) - - if(noCropping == False): - # Need this resize for arbitrary high res images: before passing to - # findPage - if(image_norm.shape[1] > uniform_width * 2): - image_norm = resize_util(image_norm, uniform_width * 2) - sheet = findPage(image_norm) - if sheet == []: - print("\tError: Paper boundary not found! Should you pass --noCropping flag?") - return None - else: - print("Found page corners: \t", sheet.tolist()) - - # Warp layer 1 - image_norm = four_point_transform(image_norm, sheet) - - # Resize only after cropping the page for clarity as well as uniformity - # for non noCropping images - image_norm = resize_util(image_norm, uniform_width, uniform_height) - image = resize_util(image, uniform_width, uniform_height) - appendSaveImg(1, image_norm) - - # Return preprocessed image - return image_norm - - -def handle_markers(image_norm, marker, curr_filename): - - if ERODE_SUB_OFF: - image_eroded_sub = normalize_util(image_norm) - else: - image_eroded_sub = normalize_util(image_norm - - cv2.erode(image_norm, - kernel=np.ones((5, 5)), - iterations=5)) - # Quads on warped image - quads = {} - h1, w1 = image_eroded_sub.shape[:2] - midh, midw = h1 // 3, w1 // 2 - origins = [[0, 0], [midw, 0], [0, midh], [midw, midh]] - quads[0] = image_eroded_sub[0:midh, 0:midw] - quads[1] = image_eroded_sub[0:midh, midw:w1] - quads[2] = image_eroded_sub[midh:h1, 0:midw] - quads[3] = image_eroded_sub[midh:h1, midw:w1] - - # Draw Quadlines - image_eroded_sub[:, midw:midw + 2] = 255 - image_eroded_sub[midh:midh + 2, :] = 255 - - best_scale, allMaxT = getBestMatch(image_eroded_sub, marker) - if(best_scale is None): - # TODO: Plot and see performance of marker_rescale_range - if(showimglvl >= 1): - show('Quads', image_eroded_sub) - return None - - optimal_marker = resize_util_h( - marker if ERODE_SUB_OFF else marker, u_height=int( - marker.shape[0] * best_scale)) - h, w = optimal_marker.shape[:2] - centres = [] - sumT, maxT = 0, 0 - print("Matching Marker:\t", end=" ") - for k in range(0, 4): - res = cv2.matchTemplate(quads[k], optimal_marker, cv2.TM_CCOEFF_NORMED) - maxT = res.max() - print("Q" + str(k + 1) + ": maxT", round(maxT, 3), end="\t") - if(maxT < thresholdCircle or abs(allMaxT - maxT) >= thresholdVar): - # Warning - code will stop in the middle. Keep Threshold low to - # avoid. - print( - curr_filename, - "\nError: No circle found in Quad", - k + 1, - "\n\tthresholdVar", - thresholdVar, - "maxT", - maxT, - "allMaxT", - allMaxT, - "Should you pass --noCropping flag?") - if(showimglvl >= 1): - show("no_pts_" + curr_filename, image_eroded_sub, 0) - show("res_Q" + str(k + 1), res, 1) - return None - - pt = np.argwhere(res == maxT)[0] - pt = [pt[1], pt[0]] - pt[0] += origins[k][0] - pt[1] += origins[k][1] - # print(">>",pt) - image_norm = cv2.rectangle(image_norm, tuple( - pt), (pt[0] + w, pt[1] + h), (150, 150, 150), 2) - # display: - image_eroded_sub = cv2.rectangle( - image_eroded_sub, - tuple(pt), - (pt[0] + w, - pt[1] + h), - (50, - 50, - 50) if ERODE_SUB_OFF else ( - 155, - 155, - 155), - 4) - centres.append([pt[0] + w / 2, pt[1] + h / 2]) - sumT += maxT - print("Optimal Scale:", best_scale) - # analysis data - thresholdCircles.append(sumT / 4) - - image_norm = four_point_transform(image_norm, np.array(centres)) - # appendSaveImg(1,image_eroded_sub) - # appendSaveImg(1,image_norm) - - appendSaveImg(2, image_eroded_sub) - # Debugging image - - # res = cv2.matchTemplate(image_eroded_sub,optimal_marker,cv2.TM_CCOEFF_NORMED) - # res[ : , midw:midw+2] = 255 - # res[ midh:midh+2, : ] = 255 - # show("Markers Matching",res) - if(showimglvl >= 2 and showimglvl < 4): - image_eroded_sub = resize_util_h(image_eroded_sub, image_norm.shape[0]) - image_eroded_sub[:, -5:] = 0 - h_stack = np.hstack((image_eroded_sub, image_norm)) - show("Warped: " + curr_filename, resize_util(h_stack, - int(display_width * 1.6)), 0, 0, [0, 0]) - # iterations : Tuned to 2. - # image_eroded_sub = image_norm - cv2.erode(image_norm, kernel=np.ones((5,5)),iterations=2) - return image_norm - - -def getGlobalThreshold( - QVals_orig, - plotTitle=None, - plotShow=True, - sortInPlot=True, - looseness=1): - """ - Note: Cannot assume qStrip has only-gray or only-white bg (in which case there is only one jump). - So there will be either 1 or 2 jumps. - 1 Jump : - ...... - |||||| - |||||| <-- risky THR - |||||| <-- safe THR - ....|||||| - |||||||||| - - 2 Jumps : - ...... - |||||| <-- wrong THR - ....|||||| - |||||||||| <-- safe THR - ..|||||||||| - |||||||||||| - - The abstract "First LARGE GAP" is perfect for this. - Current code is considering ONLY TOP 2 jumps(>= MIN_GAP) to be big, gives the smaller one - - """ - # Sort the Q vals - QVals = sorted(QVals_orig) - # Find the FIRST LARGE GAP and set it as threshold: - ls = (looseness + 1) // 2 - l = len(QVals) - ls - max1, thr1 = MIN_JUMP, 255 - for i in range(ls, l): - jump = QVals[i + ls] - QVals[i - ls] - if(jump > max1): - max1 = jump - thr1 = QVals[i - ls] + jump / 2 - -# NOTE: thr2 is deprecated, thus is JUMP_DELTA - # Make use of the fact that the JUMP_DELTA(Vertical gap ofc) between - # values at detected jumps would be atleast 20 - max2, thr2 = MIN_JUMP, 255 - # Requires atleast 1 gray box to be present (Roll field will ensure this) - for i in range(ls, l): - jump = QVals[i + ls] - QVals[i - ls] - newThr = QVals[i - ls] + jump / 2 - if(jump > max2 and abs(thr1 - newThr) > JUMP_DELTA): - max2 = jump - thr2 = newThr - # globalTHR = min(thr1,thr2) - globalTHR, j_low, j_high = thr1, thr1 - max1 // 2, thr1 + max1 // 2 - - # # For normal images - # thresholdRead = 116 - # if(thr1 > thr2 and thr2 > thresholdRead): - # print("Note: taking safer thr line.") - # globalTHR, j_low, j_high = thr2, thr2 - max2//2, thr2 + max2//2 - - if(plotTitle is not None): - f, ax = plt.subplots() - ax.bar(range(len(QVals_orig)), QVals if sortInPlot else QVals_orig) - ax.set_title(plotTitle) - thrline = ax.axhline(globalTHR, color='green', ls='--', linewidth=5) - thrline.set_label("Global Threshold") - thrline = ax.axhline(thr2, color='red', ls=':', linewidth=3) - thrline.set_label("THR2 Line") - # thrline=ax.axhline(j_low,color='red',ls='-.', linewidth=3) - # thrline=ax.axhline(j_high,color='red',ls='-.', linewidth=3) - # thrline.set_label("Boundary Line") - # ax.set_ylabel("Mean Intensity") - ax.set_ylabel("Values") - ax.set_xlabel("Position") - ax.legend() - if(plotShow): - plt.title(plotTitle) - plt.show() - - return globalTHR, j_low, j_high - - -def getLocalThreshold( - qNo, - QVals, - globalTHR, - noOutliers, - plotTitle=None, - plotShow=True): - """ - TODO: Update this documentation too- - //No more - Assumption : Colwise background color is uniformly gray or white, but not alternating. In this case there is atmost one jump. - - 0 Jump : - <-- safe THR? - ....... - ...||||||| - |||||||||| <-- safe THR? - // How to decide given range is above or below gray? - -> global QVals shall absolutely help here. Just run same function on total QVals instead of colwise _// - How to decide it is this case of 0 jumps - - 1 Jump : - ...... - |||||| - |||||| <-- risky THR - |||||| <-- safe THR - ....|||||| - |||||||||| - - """ - # Sort the Q vals - QVals = sorted(QVals) - - # Small no of pts cases: - # base case: 1 or 2 pts - if(len(QVals) < 3): - thr1 = globalTHR if np.max( - QVals) - np.min(QVals) < MIN_GAP else np.mean(QVals) - else: - # qmin, qmax, qmean, qstd = round(np.min(QVals),2), round(np.max(QVals),2), round(np.mean(QVals),2), round(np.std(QVals),2) - # GVals = [round(abs(q-qmean),2) for q in QVals] - # gmean, gstd = round(np.mean(GVals),2), round(np.std(GVals),2) - # # DISCRETION: Pretty critical factor in reading response - # # Doesn't work well for small number of values. - # DISCRETION = 2.7 # 2.59 was closest hit, 3.0 is too far - # L2MaxGap = round(max([abs(g-gmean) for g in GVals]),2) - # if(L2MaxGap > DISCRETION*gstd): - # noOutliers = False - - # # ^Stackoverflow method - # print(qNo, noOutliers,"qstd",round(np.std(QVals),2), "gstd", gstd,"Gaps in gvals",sorted([round(abs(g-gmean),2) for g in GVals],reverse=True), '\t',round(DISCRETION*gstd,2), L2MaxGap) - - # else: - # Find the LARGEST GAP and set it as threshold: //(FIRST LARGE GAP) - l = len(QVals) - 1 - max1, thr1 = MIN_JUMP, 255 - for i in range(1, l): - jump = QVals[i + 1] - QVals[i - 1] - if(jump > max1): - max1 = jump - thr1 = QVals[i - 1] + jump / 2 - # print(qNo,QVals,max1) - - # If not confident, then only take help of globalTHR - if(max1 < CONFIDENT_JUMP): - if(noOutliers): - # All Black or All White case - thr1 = globalTHR - else: - # TODO: Low confidence parameters here - pass - - # if(thr1 == 255): - # print("Warning: threshold is unexpectedly 255! (Outlier Delta issue?)",plotTitle) - - if(plotShow and plotTitle is not None): - f, ax = plt.subplots() - ax.bar(range(len(QVals)), QVals) - thrline = ax.axhline(thr1, color='green', ls=('-.'), linewidth=3) - thrline.set_label("Local Threshold") - thrline = ax.axhline(globalTHR, color='red', ls=':', linewidth=5) - thrline.set_label("Global Threshold") - ax.set_title(plotTitle) - ax.set_ylabel("Bubble Mean Intensity") - ax.set_xlabel("Bubble Number(sorted)") - ax.legend() - # TODO append QStrip to this plot- - # appendSaveImg(6,getPlotImg()) - if(plotShow): - plt.show() - return thr1 - -# from matplotlib.ticker import MaxNLocator -# def plotArray(QVals, plotTitle, sort = False, plot=True ): -# f, ax = plt.subplots() -# if(sort): -# QVals = sorted(QVals) -# ax.bar(range(len(QVals)),QVals) -# ax.set_title(plotTitle) -# ax.set_ylabel("Values") -# ax.set_xlabel("Position") -# ax.xaxis.set_major_locator(MaxNLocator(integer=True)) -# if(plot): -# plt.show() -# # else: they will call this -# # appendSaveImg(appendImgLvl,getPlotImg()) - - -def saveImg(path, final_marked): - print('Saving Image to ' + path) - cv2.imwrite(path, final_marked) - - -def readResponse(template, image, name, savedir=None, autoAlign=False): - global clahe - - try: - img = image.copy() - origDim = img.shape[:2] - # print("noCropping dim", origDim) - img = resize_util(img, template.dims[0], template.dims[1]) - # print("Resized dim", img.shape[:2]) - - if(img.max() > img.min()): - img = normalize_util(img) - # Processing copies - transp_layer = img.copy() - final_marked = img.copy() - # putLabel(final_marked,"Crop Size: " + str(origDim[0])+"x"+str(origDim[1]) + " "+name, size=1) - - morph = img.copy() - appendSaveImg(3, morph) - - # TODO: evaluate if CLAHE is really req - if(autoAlign): - # Note: clahe is good for morphology, bad for thresholding - morph = clahe.apply(morph) - appendSaveImg(3, morph) - # Remove shadows further, make columns/boxes darker (less gamma) - morph = adjust_gamma(morph, GAMMA_LOW) - ret, morph = cv2.threshold(morph, 220, 220, cv2.THRESH_TRUNC) - morph = normalize_util(morph) - appendSaveImg(3, morph) - if(showimglvl >= 4): - show("morph1", morph, 0, 1) - - # Overlay Transparencies - alpha = 0.65 - alpha1 = 0.55 - - boxW, boxH = template.bubbleDims - lang = ['E', 'H'] - OMRresponse = {} - - multimarked, multiroll = 0, 0 - - # TODO Make this part useful for visualizing status checks - # blackVals=[0] - # whiteVals=[255] - - if(showimglvl >= 5): - # "QTYPE_ROLL":[]}#,"QTYPE_MED":[]} - allCBoxvals = {"Int": [], "Mcq": []} - # ,"QTYPE_ROLL":[]}#,"QTYPE_MED":[]} - qNums = {"Int": [], "Mcq": []} - - # Find Shifts for the QBlocks --> Before calculating threshold! - if(autoAlign): - # print("Begin Alignment") - # Open : erode then dilate - # Vertical kernel - v_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 10)) - morph_v = cv2.morphologyEx( - morph, cv2.MORPH_OPEN, v_kernel, iterations=3) - ret, morph_v = cv2.threshold(morph_v, 200, 200, cv2.THRESH_TRUNC) - morph_v = 255 - normalize_util(morph_v) - - if(showimglvl >= 3): - show("morphed_vertical", morph_v, 0, 1) - - # show("morph1",morph,0,1) - # show("morphed_vertical",morph_v,0,1) - - appendSaveImg(3, morph_v) - - morphTHR = 60 # for Mobile images - # morphTHR = 40 # for scan Images - # best tuned to 5x5 now - _, morph_v = cv2.threshold( - morph_v, morphTHR, 255, cv2.THRESH_BINARY) - morph_v = cv2.erode( - morph_v, np.ones( - (5, 5), np.uint8), iterations=2) - - appendSaveImg(3, morph_v) - # h_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 2)) - # morph_h = cv2.morphologyEx(morph, cv2.MORPH_OPEN, h_kernel, iterations=3) - # ret, morph_h = cv2.threshold(morph_h,200,200,cv2.THRESH_TRUNC) - # morph_h = 255 - normalize_util(morph_h) - # show("morph_h",morph_h,0,1) - # _, morph_h = cv2.threshold(morph_h,morphTHR,255,cv2.THRESH_BINARY) - # morph_h = cv2.erode(morph_h, np.ones((5,5),np.uint8), iterations = 2) - if(showimglvl >= 3): - show("morph_thr_eroded", morph_v, 0, 1) - - appendSaveImg(6, morph_v) - - # template alignment code - # OUTPUT : each QBlock.shift is updated - for QBlock in template.QBlocks: - s, d = QBlock.orig, QBlock.dims - # internal constants - wont need change much - # TODO - ALIGN_STRIDE would depend on template's Dimensions - ALIGN_STRIDE, MATCH_COL, ALIGN_STEPS = 1, 5, int(boxW * 2 / 3) - shift, steps = 0, 0 - THK = 3 - while steps < ALIGN_STEPS: - L = np.mean(morph_v[s[1]:s[1]+d[1],s[0]+shift-THK:-THK+s[0]+shift+MATCH_COL]) - R = np.mean(morph_v[s[1]:s[1]+d[1],s[0]+shift-MATCH_COL+d[0]+THK:THK+s[0]+shift+d[0]]) - - # For demonstration purposes- - if(QBlock.key == "Int1"): - ret = morph_v.copy() - cv2.rectangle(ret,(s[0]+shift-THK,s[1]),(s[0]+shift+THK+d[0],s[1]+d[1]),CLR_WHITE,3) - appendSaveImg(6, ret) - # print(shift, L, R) - LW, RW = L > 100, R > 100 - if(LW): - if(RW): - break - else: - shift -= ALIGN_STRIDE - else: - if(RW): - shift += ALIGN_STRIDE - else: - break - steps += 1 - - QBlock.shift = shift - # print("Aligned QBlock: ",QBlock.key,"Corrected Shift:", QBlock.shift,", Dimensions:", QBlock.dims, "orig:", QBlock.orig,'\n') - # print("End Alignment") - - final_align = None - if(showimglvl >= 2): - initial_align = drawTemplateLayout(img, template, shifted=False) - final_align = drawTemplateLayout( - img, template, shifted=True, draw_qvals=True) - # appendSaveImg(4,mean_vals) - appendSaveImg(2, initial_align) - appendSaveImg(2, final_align) - appendSaveImg(5, img) - if(autoAlign): - final_align = np.hstack((initial_align, final_align)) - - # Get mean vals n other stats - allQVals, allQStripArrs, allQStdVals = [], [], [] - totalQStripNo = 0 - for QBlock in template.QBlocks: - QStdVals = [] - for qStrip, qBoxPts in QBlock.traverse_pts: - QStripvals = [] - for pt in qBoxPts: - # shifted - x, y = (pt.x + QBlock.shift, pt.y) - rect = [y, y + boxH, x, x + boxW] - QStripvals.append( - cv2.mean(img[rect[0]:rect[1], rect[2]:rect[3]])[0]) - QStdVals.append(round(np.std(QStripvals), 2)) - allQStripArrs.append(QStripvals) - # _, _, _ = getGlobalThreshold(QStripvals, "QStrip Plot", plotShow=False, sortInPlot=True) - # hist = getPlotImg() - # show("QStrip "+qBoxPts[0].qNo, hist, 0, 1) - allQVals.extend(QStripvals) - # print(totalQStripNo, qBoxPts[0].qNo, QStdVals[len(QStdVals)-1]) - totalQStripNo += 1 - allQStdVals.extend(QStdVals) - # print("Begin getGlobalThresholdStd") - globalStdTHR, jstd_low, jstd_high = getGlobalThreshold(allQStdVals)# , "Q-wise Std-dev Plot", plotShow=True, sortInPlot=True) - # print("End getGlobalThresholdStd") - # print("Begin getGlobalThreshold") - # plt.show() - # hist = getPlotImg() - # show("StdHist", hist, 0, 1) - - # Note: Plotting takes Significant times here --> Change Plotting args - # to support showimglvl - # , "Mean Intensity Histogram",plotShow=True, sortInPlot=True) - globalTHR, j_low, j_high = getGlobalThreshold(allQVals, looseness=4) - - # TODO colorama - print( - "Thresholding:\t\t globalTHR: ", round( - globalTHR, 2), "\tglobalStdTHR: ", round( - globalStdTHR, 2), "\t(Looks like a Xeroxed OMR)" if( - globalTHR == 255) else "") - # plt.show() - # hist = getPlotImg() - # show("StdHist", hist, 0, 1) - - # print("End getGlobalThreshold") - - # if(showimglvl>=1): - # hist = getPlotImg() - # show("Hist", hist, 0, 1) - # appendSaveImg(4,hist) - # appendSaveImg(5,hist) - # appendSaveImg(2,hist) - - perOMRThresholdAvg, totalQStripNo, totalQBoxNo = 0, 0, 0 - for QBlock in template.QBlocks: - blockQStripNo = 1 # start from 1 is fine here - shift = QBlock.shift - s, d = QBlock.orig, QBlock.dims - key = QBlock.key[:3] - # cv2.rectangle(final_marked,(s[0]+shift,s[1]),(s[0]+shift+d[0],s[1]+d[1]),CLR_BLACK,3) - for qStrip, qBoxPts in QBlock.traverse_pts: - # All Black or All White case - noOutliers = allQStdVals[totalQStripNo] < globalStdTHR - # print(totalQStripNo, qBoxPts[0].qNo, allQStdVals[totalQStripNo], "noOutliers:", noOutliers) - perQStripThreshold = getLocalThreshold(qBoxPts[0].qNo, allQStripArrs[totalQStripNo], - globalTHR, noOutliers, - "Mean Intensity Histogram for " + key + "." + - qBoxPts[0].qNo + '.' + - str(blockQStripNo), - showimglvl >= 6) - # print(qBoxPts[0].qNo,key,blockQStripNo, "THR: ",round(perQStripThreshold,2)) - perOMRThresholdAvg += perQStripThreshold - - # Note: Little debugging visualization - view the particular Qstrip - # if( - # 0 - # # or "q17" in (qBoxPts[0].qNo) - # # or (qBoxPts[0].qNo+str(blockQStripNo))=="q15" - # ): - # st, end = qStrip - # show("QStrip: "+key+"-"+str(blockQStripNo), img[st[1] : end[1], st[0]+shift : end[0]+shift],0) - - for pt in qBoxPts: - # shifted - x, y = (pt.x + QBlock.shift, pt.y) - boxval0 = allQVals[totalQBoxNo] - detected = perQStripThreshold > boxval0 - - # TODO: add an option to select PLUS SIGN RESPONSE READING - # extra_check_rects = [] - # # [y,y+boxH,x,x+boxW] - # for rect in extra_check_rects: - # # Note: This is NOT pixel-based thresholding, It is boxed mean-thresholding - # boxval = cv2.mean(img[ rect[0]:rect[1] , rect[2]:rect[3] ])[0] - # if(perQStripThreshold > boxval): - # # for critical analysis - # boxval0 = max(boxval,boxval0) - # detected=True - # break - - if (detected): - cv2.rectangle(final_marked,(int(x+boxW/12),int(y+boxH/12)),(int(x+boxW-boxW/12),int(y+boxH-boxH/12)), CLR_DARK_GRAY, 3) - else: - cv2.rectangle(final_marked,(int(x+boxW/10),int(y+boxH/10)),(int(x+boxW-boxW/10),int(y+boxH-boxH/10)), CLR_GRAY,-1) - - # TODO Make this part useful! (Abstract visualizer to check - # status) - if (detected): - q, val = pt.qNo, str(pt.val) - cv2.putText( - final_marked, val, (x, y), cv2.FONT_HERSHEY_SIMPLEX, TEXT_SIZE, (20, 20, 10), int( - 1 + 3.5 * TEXT_SIZE)) - # Only send rolls multi-marked in the directory - multimarkedL = q in OMRresponse - multimarked = multimarkedL or multimarked - OMRresponse[q] = ( - OMRresponse[q] + val) if multimarkedL else val - multiroll = multimarkedL and 'roll' in str(q) - # blackVals.append(boxval0) - # else: - # whiteVals.append(boxval0) - - totalQBoxNo += 1 - # /for qBoxPts - # /for qStrip - - if(showimglvl >= 5): - if(key in allCBoxvals): - qNums[key].append(key[:2] + '_c' + str(blockQStripNo)) - allCBoxvals[key].append(allQStripArrs[totalQStripNo]) - - blockQStripNo += 1 - totalQStripNo += 1 - # /for QBlock - - # TODO: move this validation into template.py - - if(totalQStripNo == 0): - print( - "\n\t UNEXPECTED Template Incorrect Error: totalQStripNo is zero! QBlocks: ", - template.QBlocks) - exit(21) - - perOMRThresholdAvg /= totalQStripNo - perOMRThresholdAvg = round(perOMRThresholdAvg, 2) - # Translucent - cv2.addWeighted( - final_marked, - alpha, - transp_layer, - 1 - alpha, - 0, - final_marked) - # Box types - if(showimglvl >= 5): - # plt.draw() - f, axes = plt.subplots(len(allCBoxvals), sharey=True) - f.canvas.set_window_title(name) - ctr = 0 - typeName = { - "Int": "Integer", - "Mcq": "MCQ", - "Med": "MED", - "Rol": "Roll"} - for k, boxvals in allCBoxvals.items(): - axes[ctr].title.set_text(typeName[k] + " Type") - axes[ctr].boxplot(boxvals) - # thrline=axes[ctr].axhline(perOMRThresholdAvg,color='red',ls='--') - # thrline.set_label("Average THR") - axes[ctr].set_ylabel("Intensity") - axes[ctr].set_xticklabels(qNums[k]) - # axes[ctr].legend() - ctr += 1 - # imshow will do the waiting - plt.tight_layout(pad=0.5) - plt.show() - - if(showimglvl >= 3 and final_align is not None): - final_align = resize_util_h(final_align, int(display_height)) - # [final_align.shape[1],0]) - show("Template Alignment Adjustment", final_align, 0, 0) - - # TODO: refactor "type(savedir) != type(None) " - if (saveMarked and type(savedir) != type(None)): - if(multiroll): - savedir = savedir + '_MULTI_/' - saveImg(savedir + name, final_marked) - - if(showimglvl >= 1): - show("Final Marked Bubbles : " + name, - resize_util_h(final_marked, int(display_height * 1.3)), 1, 1) - - appendSaveImg(2, final_marked) - - for i in range(saveimglvl): - saveOrShowStacks(i + 1, name, savedir) - - return OMRresponse, final_marked, multimarked, multiroll - - except Exception as e: - exc_type, exc_obj, exc_tb = sys.exc_info() - fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1] - print("Error from readResponse: ", e) - print(exc_type, fname, exc_tb.tb_lineno) - - -def saveOrShowStacks(key, name, savedir=None, pause=1): - global saveImgList - if(saveimglvl >= int(key) and saveImgList[key] != []): - result = np.hstack( - tuple([resize_util_h(img, uniform_height) for img in saveImgList[key]])) - result = resize_util(result, - min(len(saveImgList[key]) * uniform_width // 3, - int(uniform_width * 2.5))) - if (type(savedir) != type(None)): - saveImg(savedir+'stack/'+name+'_'+str(key)+'_stack.jpg', result) - else: - show(name + '_' + str(key), result, pause, 0)