From 5e9da704f024bf9d9604f1b511153114f6980f90 Mon Sep 17 00:00:00 2001 From: Tyler Romero Date: Tue, 3 Dec 2024 13:10:25 -0800 Subject: [PATCH] Documenation Improvements and Updates (#279) # Changes (selected) 1. Update documentation to reflect that we support Python 3.9+ 2. Document usage of `ask_ml` and `ask_confident` 3. Reorganize site to improve consistency and flow (most significantly, docs for *developing* applications are separate from docs detailing *sample* applications) 4. Document use of `framegrab` and tuning of motion detection 5. Document `Alerts`, update documentation for the `edge-endpoint` and `groundlight/stream` --------- Co-authored-by: Brandon <132288221+brandon-groundlight@users.noreply.github.com> Co-authored-by: Paulina Varshavskaya <88207457+paulina-positronix@users.noreply.github.com> --- .github/workflows/cicd.yaml | 23 +- .pylintrc | 2 +- DEVELOPING.md | 2 +- Makefile | 30 +- docs/docs/api-reference/_category_.json | 2 +- .../1-sample-applications.md | 29 - .../3-working-with-detectors.md | 99 - .../4-managing-confidence.md | 51 - .../building-applications/6-async-queries.md | 73 - docs/docs/building-applications/7-edge.md | 34 - .../building-applications/_category_.json | 4 - docs/docs/getting-started/1-api-tokens.md | 68 - docs/docs/getting-started/2-initial-setup.md | 87 + ...riting-queries.md => 4-writing-queries.md} | 27 +- docs/docs/getting-started/5-api-tokens.md | 45 + docs/docs/getting-started/5-streaming.md | 85 - docs/docs/getting-started/getting-started.mdx | 4 +- .../2-grabbing-images.md | 82 +- docs/docs/guide/3-working-with-detectors.md | 96 + docs/docs/guide/4-submitting-image-queries.md | 156 + docs/docs/guide/5-managing-confidence.md | 61 + .../6-handling-errors.md} | 23 +- docs/docs/guide/7-async-queries.md | 82 + docs/docs/guide/8-edge.md | 52 + docs/docs/guide/9-alerts.md | 27 + docs/docs/guide/_category_.json | 4 + .../guide.md} | 27 +- docs/docs/installation/1-linux.md | 32 +- docs/docs/installation/2-macos.md | 17 +- docs/docs/installation/3-windows.md | 11 +- ...l-libraries.md => 4-optional-libraries.md} | 2 +- .../{4-raspberry-pi.md => 5-raspberry-pi.md} | 12 +- ...{5-nvidia-jetson.md => 6-nvidia-jetson.md} | 12 +- .../7-monitoring-notification-server.md | 83 - docs/docs/installation/_category_.json | 2 +- docs/docs/installation/installation.md | 35 +- docs/docs/iot/_category_.json | 5 - .../other-ways-to-use/1-stream-processor.md | 82 + .../iot.md => other-ways-to-use/2-iot.md} | 2 +- .../3-monitoring-notification-server.md | 88 + docs/docs/other-ways-to-use/_category_.json | 5 + .../1-dog-on-couch.md} | 12 +- .../2-retail-analytics.md} | 18 +- .../3-streaming-with-alerts.md | 94 + .../4-industrial.md} | 4 - docs/docs/sample-applications/_category_.json | 4 + .../sample-applications.md | 45 + docs/docusaurus.config.js | 20 +- docs/package-lock.json | 21595 +++++++++------- docs/package.json | 24 +- docs/static/img/edge-endpoint-latency.png | Bin 0 -> 57389 bytes docs/static/img/edge-endpoint-throughput.png | Bin 0 -> 78231 bytes generated/.gitlab-ci.yml | 23 +- generated/.travis.yml | 7 +- package-lock.json | 933 +- pyproject.toml | 6 +- sphinx_docs/conf.py | 3 +- src/groundlight/client.py | 688 +- src/groundlight/experimental_api.py | 550 +- src/groundlight/internalapi.py | 2 +- 60 files changed, 15435 insertions(+), 10256 deletions(-) delete mode 100644 docs/docs/building-applications/1-sample-applications.md delete mode 100644 docs/docs/building-applications/3-working-with-detectors.md delete mode 100644 docs/docs/building-applications/4-managing-confidence.md delete mode 100644 docs/docs/building-applications/6-async-queries.md delete mode 100644 docs/docs/building-applications/7-edge.md delete mode 100644 docs/docs/building-applications/_category_.json delete mode 100644 docs/docs/getting-started/1-api-tokens.md create mode 100644 docs/docs/getting-started/2-initial-setup.md rename docs/docs/getting-started/{2-writing-queries.md => 4-writing-queries.md} (52%) create mode 100644 docs/docs/getting-started/5-api-tokens.md delete mode 100644 docs/docs/getting-started/5-streaming.md rename docs/docs/{building-applications => guide}/2-grabbing-images.md (50%) create mode 100644 docs/docs/guide/3-working-with-detectors.md create mode 100644 docs/docs/guide/4-submitting-image-queries.md create mode 100644 docs/docs/guide/5-managing-confidence.md rename docs/docs/{building-applications/5-handling-errors.md => guide/6-handling-errors.md} (65%) create mode 100644 docs/docs/guide/7-async-queries.md create mode 100644 docs/docs/guide/8-edge.md create mode 100644 docs/docs/guide/9-alerts.md create mode 100644 docs/docs/guide/_category_.json rename docs/docs/{building-applications/building-applications.md => guide/guide.md} (53%) rename docs/docs/installation/{6-optional-libraries.md => 4-optional-libraries.md} (93%) rename docs/docs/installation/{4-raspberry-pi.md => 5-raspberry-pi.md} (78%) rename docs/docs/installation/{5-nvidia-jetson.md => 6-nvidia-jetson.md} (79%) delete mode 100644 docs/docs/installation/7-monitoring-notification-server.md delete mode 100644 docs/docs/iot/_category_.json create mode 100644 docs/docs/other-ways-to-use/1-stream-processor.md rename docs/docs/{iot/iot.md => other-ways-to-use/2-iot.md} (97%) create mode 100644 docs/docs/other-ways-to-use/3-monitoring-notification-server.md create mode 100644 docs/docs/other-ways-to-use/_category_.json rename docs/docs/{getting-started/4-dog-on-couch.md => sample-applications/1-dog-on-couch.md} (91%) rename docs/docs/{getting-started/3-retail-analytics.md => sample-applications/2-retail-analytics.md} (96%) create mode 100644 docs/docs/sample-applications/3-streaming-with-alerts.md rename docs/docs/{building-applications/8-industrial.md => sample-applications/4-industrial.md} (98%) create mode 100644 docs/docs/sample-applications/_category_.json create mode 100644 docs/docs/sample-applications/sample-applications.md create mode 100644 docs/static/img/edge-endpoint-latency.png create mode 100644 docs/static/img/edge-endpoint-throughput.png diff --git a/.github/workflows/cicd.yaml b/.github/workflows/cicd.yaml index 431747f4..b97926d2 100644 --- a/.github/workflows/cicd.yaml +++ b/.github/workflows/cicd.yaml @@ -6,7 +6,7 @@ on: push: env: - PYTHON_VERSION: "3.10" + PYTHON_VERSION: "3.11" POETRY_VERSION: "1.7.1" jobs: # Run our linter on every push to the repository. @@ -65,7 +65,7 @@ jobs: # This is associated with the "sdk-test-prod" user, credentials on 1password GROUNDLIGHT_API_TOKEN: ${{ secrets.GROUNDLIGHT_API_TOKEN_PROD }} - # Check that the docs build. (No broken links, etc.) + # Check that the docs build. (No broken links, etc.) test-docs: runs-on: ubuntu-latest defaults: @@ -77,14 +77,14 @@ jobs: - name: Setup npm uses: actions/setup-node@v3 with: - node-version: 18 + node-version: 21 cache: npm - name: Install dependencies run: npm install - name: Build website run: npm run build - # Checks that the API reference docs built with shpinx build + # Checks that the API reference docs built with sphinx build test-api-reference-docs: runs-on: ubuntu-latest steps: @@ -94,14 +94,17 @@ jobs: uses: actions/setup-python@v4 with: python-version: ${{ env.PYTHON_VERSION }} - - name: install poetry + - name: Install poetry uses: snok/install-poetry@v1 with: version: ${{ env.POETRY_VERSION }} - + - name: Setup npm + uses: actions/setup-node@v3 + with: + node-version: 21 + cache: npm - name: Install dependencies run: make install-sphinx-deps - - name: Build API documentation run: | make apidocs @@ -135,6 +138,7 @@ jobs: "3.10", "3.11", "3.12", + "3.13", ] install_extras: [true, false] steps: @@ -257,7 +261,7 @@ jobs: - name: Setup npm uses: actions/setup-node@v3 with: - node-version: 18 + node-version: 21 cache: npm - name: Install dependencies run: npm install @@ -292,8 +296,7 @@ jobs: runs-on: ubuntu-latest needs: - test-comprehensive - # For now, we'll require the comprehensive tests to succeed, but not the linter checks. - # - lint + - lint env: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_PUBLISH_TOKEN }} steps: diff --git a/.pylintrc b/.pylintrc index 0a1fba02..8838f39f 100644 --- a/.pylintrc +++ b/.pylintrc @@ -46,7 +46,7 @@ persistent=yes # Min Python version to use for version dependend checks. Will default to the # version used to run pylint. -py-version=3.8 +py-version=3.9 # When enabled, pylint would attempt to guess common misconfiguration and emit # user-friendly hints instead of false-positive error messages. diff --git a/DEVELOPING.md b/DEVELOPING.md index 072ebbe8..43604682 100644 --- a/DEVELOPING.md +++ b/DEVELOPING.md @@ -14,7 +14,7 @@ you can install the package dependencies by running: make install ``` -Note: We support Python 3.8+ for clients of the SDK, but we recommend developing with Python 3.10+. +Note: We support Python 3.9+ for clients of the SDK, but we recommend developing with Python 3.10+. ### Run tests diff --git a/Makefile b/Makefile index 84dcf563..e264a91f 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,8 @@ -.PHONY: apidocs docs-comprehensive generate html install install-dev install-extras install-generator install-lint install-pre-commit test test-4edge test-integ test-local +.PHONY: apidocs docs-comprehensive generate html install install-dev install-extras install-generator install-lint install-pre-commit test test-4edge test-integ test-local help + +help: ## Print all targets with their descriptions + @grep -E '^[a-zA-Z_-]+:.*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {if (NF == 1) {printf "\033[36m%-30s\033[0m %s\n", $$1, ""} else {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}}' + install: ## Install the package from source poetry install @@ -16,7 +20,7 @@ install-pre-commit: install ## Install pre-commit hooks poetry run pre-commit install install-generator: install ## Install dependencies for SDK code generator - npm install --save remark-math@6 rehype-katex@7 + npm install generate: install-generator ## Generate the SDK from our public openapi spec node_modules/.bin/openapi-generator-cli generate -i spec/public-api.yaml \ @@ -73,7 +77,7 @@ format: install-lint ## Run standard python formatting ./code-quality/format ${LINT_PATHS} -# Targets for sphinx documentation +# Targets for building code.groundlight.ai and API reference documentation install-sphinx-deps: ## Only install the sphinx dependencies poetry install --no-root --only sphinx-deps @@ -86,19 +90,15 @@ SPHINXBUILD ?= sphinx-build SOURCEDIR = sphinx_docs BUILDDIR = build -sphinx-help: +sphinx-help: ## Print help for Sphinx build options (Sphinx is used to build our API reference documentation) @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -# Start an interactive server to test docs locally. -# Before running this, make sure that you have installed the node modules -# by running `node install` in the docs directory. -develop-docs-comprehensive: docs-comprehensive +## Before running this, make sure that you have installed the node modules +## by running `node install` in the docs directory. +develop-docs-comprehensive: docs-comprehensive ## Start an interactive server to test docs locally. cd docs && npm start -## Builds docs comprehensively (integrating API reference docs built -## with sphinx into the docusaurus docs). -docs-comprehensive: apidocs +docs-comprehensive: apidocs ## Builds docs comprehensively (integrating API reference docs built with sphinx into the docusaurus docs). rm -rf docs/static/api-reference-docs rm -rf docs/build/api-reference-docs mkdir docs/static/api-reference-docs @@ -106,10 +106,10 @@ docs-comprehensive: apidocs cd docs && npm run build -apidocs: - cd docs && npm install --save remark-math@6 rehype-katex@7 +apidocs: ## Installs necessary npm packages and builds API reference documentation using Sphinx. + cd docs && npm install poetry run make html -html: +html: ## Builds HTML reference documentation using Sphinx. @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(0) @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." diff --git a/docs/docs/api-reference/_category_.json b/docs/docs/api-reference/_category_.json index f9a04b2b..a820f610 100644 --- a/docs/docs/api-reference/_category_.json +++ b/docs/docs/api-reference/_category_.json @@ -1,5 +1,5 @@ { "label": "API Reference", - "position": 5, + "position": 6, "collapsed": false } \ No newline at end of file diff --git a/docs/docs/building-applications/1-sample-applications.md b/docs/docs/building-applications/1-sample-applications.md deleted file mode 100644 index 5fd84bad..00000000 --- a/docs/docs/building-applications/1-sample-applications.md +++ /dev/null @@ -1,29 +0,0 @@ -# Sample Applications - -Explore these GitHub repositories to see examples of Groundlight-powered applications: - -## Groundlight Stream Processor - -Repository: [https://github.com/groundlight/stream](https://github.com/groundlight/stream) - -The Groundlight Stream Processor is an easy-to-use Docker container for analyzing RTSP streams or common USB-based cameras. You can run it with a single Docker command, such as: - -```bash -docker run stream:local --help -``` - -## Arduino ESP32 Camera Sample App - -Repository: [https://github.com/groundlight/esp32cam](https://github.com/groundlight/esp32cam) - -This sample application allows you to build a working AI vision detector using an inexpensive WiFi camera. With a cost of under $10, you can create a powerful and affordable AI vision system. - -## Raspberry Pi - -Repository: [https://github.com/groundlight/raspberry-pi-door-lock](https://github.com/groundlight/raspberry-pi-door-lock) - -This sample application demonstrates how to set up a Raspberry Pi-based door lock system. The application monitors a door and sends a notification if the door is observed to be unlocked during non-standard business hours. - -## Industrial and Manufacturing Applications - -Groundlight can be used to [apply modern natural-language-based computer vision to industrial and manufacturing applications](/docs/building-applications/industrial). diff --git a/docs/docs/building-applications/3-working-with-detectors.md b/docs/docs/building-applications/3-working-with-detectors.md deleted file mode 100644 index dda90640..00000000 --- a/docs/docs/building-applications/3-working-with-detectors.md +++ /dev/null @@ -1,99 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Working with Detectors - -### Explicitly create a new detector - -Typically you'll use the `get_or_create_detector(name: str, query: str)` method to find an existing detector you've already created with the same name, or create a new one if it doesn't exists. But if you'd like to force creating a new detector you can also use the `create_detector(name: str, query: str)` method - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() -# highlight-start -detector = gl.create_detector(name="your_detector_name", query="is this what we want to see?") -# highlight-end -``` - -### Retrieve an existing detector - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() -# highlight-start -detector = gl.get_detector(id="YOUR_DETECTOR_ID") -# highlight-end -``` - -### List your detectors - -```python -from groundlight import Groundlight - -gl = Groundlight() -# highlight-start -# Defaults to 10 results per page -detectors = gl.list_detectors() - -# Pagination: 1st page of 5 results per page -detectors = gl.list_detectors(page=1, page_size=5) -# highlight-end -``` - -### Retrieve an image query - -In practice, you may want to check for a new result on your query. For example, after a cloud reviewer labels your query. For example, you can use the `image_query.id` after the above `submit_image_query()` call. - - - -```python notest -from groundlight import Groundlight - -gl = Groundlight() -# highlight-start -image_query = gl.get_image_query(id="iq_YOUR_IMAGE_QUERY_ID") -# highlight-end -``` - -### List your previous image queries - -```python -from groundlight import Groundlight - -gl = Groundlight() -# highlight-start -# Defaults to 10 results per page -image_queries = gl.list_image_queries() - -# Pagination: 1st page of 5 results per page -image_queries = gl.list_image_queries(page=1, page_size=5) -# highlight-end -``` - -### Adding labels to existing image queries - -Groundlight lets you start using models by making queries against your very first image, but there are a few situations where you might either have an existing dataset, or you'd like to handle the escalation response programatically in your own code but still include the label to get better responses in the future. With your `image_query` from either `submit_image_query()` or `get_image_query()` you can add the label directly. Note that if the query is already in the escalation queue due to low ML confidence or audit thresholds, it may also receive labels from another source. - -```python -from groundlight import Groundlight -from PIL import Image -import requests - -gl = Groundlight() -d = gl.get_or_create_detector(name="doorway", query="Is the doorway open?") -image_url= "https://images.selfstorage.com/large-compress/2174925f24362c479b2.jpg" -image = Image.open(requests.get(image_url, stream=True).raw) -image_query = gl.submit_image_query(detector=d, image=image) -# highlight-start -gl.add_label(image_query, 'YES') # or 'NO' -# highlight-end -``` - -The only valid labels at this time are `'YES'` and `'NO'`. diff --git a/docs/docs/building-applications/4-managing-confidence.md b/docs/docs/building-applications/4-managing-confidence.md deleted file mode 100644 index 757ce8bd..00000000 --- a/docs/docs/building-applications/4-managing-confidence.md +++ /dev/null @@ -1,51 +0,0 @@ ---- -sidebar_position: 3 ---- -# Confidence Levels - -Groundlight gives you a simple way to control the trade-off of latency against accuracy. The longer you can wait for an answer to your image query, the better accuracy you can get. In particular, if the ML models are unsure of the best response, they will escalate the image query to more intensive analysis with more complex models and real-time human monitors as needed. Your code can easily wait for this delayed response. Either way, these new results are automatically trained into your models so your next queries will get better results faster. - -The desired confidence level is set as the escalation threshold on your detector. This determines the minimum confidence score for the ML system to provide before the image query is escalated. - -For example, say you want to set your desired confidence level to 0.95, but that you're willing to wait up to 60 seconds to get a confident response. - - - -```python notest -from groundlight import Groundlight -from PIL import Image -import requests - -gl = Groundlight() -image_url = "https://www.photos-public-domain.com/wp-content/uploads/2010/11/over_flowing_garbage_can.jpg" -image = Image.open(requests.get(image_url, stream=True).raw) - -# highlight-start -d = gl.get_or_create_detector(name="trash", query="Is the trash can full?", confidence_threshold=0.95) - -# This will wait until either 60 seconds have passed or the confidence reaches 0.95 -image_query = gl.submit_image_query(detector=d, image=image, wait=60) -# highlight-end - -print(f"The answer is {image_query.result}") -``` - -:::tip - -Tuning confidence lets you balance accuracy against latency. -Higher confidence will get higher accuracy, but will generally require higher latency. -Higher confidence also requires more labels, which increases labor costs. - -::: - -Or if you want to execute `submit_image_query` as fast as possible, set `wait=0`. You will either get the ML results or a placeholder response if the ML model hasn't finished executing. Image queries which are below the desired confidence level will still be escalated for further analysis, and the results are incorporated as training data to improve your ML model, but your code will not wait for that to happen. - -```python notest continuation -image_query = gl.submit_image_query(detector=d, image=image, wait=0) -``` - -If the returned result was generated from an ML model, you can see the confidence score returned for the image query: - -```python notest continuation -print(f"The confidence is {image_query.result.confidence}") -``` diff --git a/docs/docs/building-applications/6-async-queries.md b/docs/docs/building-applications/6-async-queries.md deleted file mode 100644 index 3507f66e..00000000 --- a/docs/docs/building-applications/6-async-queries.md +++ /dev/null @@ -1,73 +0,0 @@ ---- -sidebar_position: 5 ---- - -# Asynchronous Queries - -Groundlight provides a simple interface for submitting asynchronous queries. This is useful for times in which the thread or process or machine submitting image queries is not the same thread or machine that will be retrieving and using the results. For example, you might have a forward deployed robot or camera that submits image queries to Groundlight, and a separate server that retrieves the results and takes action based on them. We will refer to these two machines as the **submitting machine** and the **retrieving machine**. - -## Setup Submitting Machine -On the **submitting machine**, you will need to install the Groundlight Python SDK. Then you can submit image queries asynchronously using the `ask_async` interface (read the full documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight.ask_async)). `ask_async` submits your query and returns as soon as the query is submitted. It does not wait for an answer to be available prior to returning to minimize the time your program spends interacting with Groundlight. As a result, the `ImageQuery` object `ask_async` returns lacks a `result` (the `result` field will be `None`). This is acceptable for this use case as the **submitting machine** is not interested in the result. Instead, the **submitting machine** just needs to communicate the `ImageQuery.id`s to the **retrieving machine** - this might be done via a database, a message queue, or some other mechanism. For this example, we assume you are using a database where you save the `ImageQuery.id` to it via `db.save(image_query.id)`. - -```python notest -from groundlight import Groundlight -import cv2 -from time import sleep - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") - -cam = cv2.VideoCapture(0) # Initialize camera (0 is the default index) - -while True: - _, image = cam.read() # Capture one frame from the camera - image_query = gl.ask_async(detector=detector, image=image) # Submit the frame to Groundlight - db.save(image_query.id) # Save the image_query.id to a database for the retrieving machine to use - sleep(10) # Sleep for 10 seconds before submitting the next query - -cam.release() # Release the camera - -``` - -## Setup Retrieving Machine -On the **retrieving machine** you will need to install the Groundlight Python SDK. Then you can retrieve the results of the image queries submitted by another machine using `get_image_query`. The **retrieving machine** can then use the `ImageQuery.result` to take action based on the result for whatever application you are building. For this example, we assume your application looks up the next image query to process from a database via `db.get_next_image_query_id()` and that this function returns `None` once all `ImageQuery`s are processed. -```python notest -from groundlight import Groundlight - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") - -image_query_id = db.get_next_image_query_id() - -while image_query_id is not None: - image_query = gl.get_image_query(id=image_query_id) # retrieve the image query from Groundlight - result = image_query.result - - # take action based on the result of the image query - if result.label == 'YES': - pass # TODO: do something based on your application - elif result.label == 'NO': - pass # TODO: do something based on your application - elif result.label == 'UNCLEAR': - pass # TODO: do something based on your application - - # update image_query_id for next iteration of the loop - image_query_id = db.get_next_image_query_id() -``` - -## Important Considerations -When you submit an image query asynchronously, ML prediction on your query is **not** instant. So attempting to retrieve the result immediately after submitting an async query will likely result in an `UNCLEAR` result as Groundlight is still processing your query. Instead, if your code needs a `result` synchronously we recommend using one of our methods with a polling mechanism to retrieve the result. You can see all of the interfaces available in the documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight). - -```python notest -from groundlight import Groundlight -from PIL import Image - -detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") -image = Image.open("/path/to/your/image.jpg") -image_query = gl.ask_async(detector=detector, image=image) # Submit async query to Groundlight -result = image_query.result # This will always be 'None' as you asked asynchronously - -image_query = gl.get_image_query(id=image_query.id) # Immediately retrieve the image query from Groundlight -result = image_query.result # This will likely be 'UNCLEAR' as Groundlight is still processing your query - -image_query = gl.wait_for_confident_result(id=image_query.id) # Poll for a confident result from Groundlight -result = image_query.result -``` \ No newline at end of file diff --git a/docs/docs/building-applications/7-edge.md b/docs/docs/building-applications/7-edge.md deleted file mode 100644 index 78329ed5..00000000 --- a/docs/docs/building-applications/7-edge.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -sidebar_position: 6 ---- - -# Using Groundlight on the Edge - -If your account has access to edge models, you can download and install them to your edge devices. -This allows you to run your model evaluations on the edge, reducing latency, cost, network bandwidth, and energy. - -## How the Edge Endpoint works - -The Edge Endpoint runs as a set of docker containers on an "edge device". This edge device can be an NVIDIA Jetson device, rack-mounted server, or even a Raspberry Pi. The Edge Endpoint is responsible for downloading and running the models, -and for communicating with the Groundlight cloud service. - -To use the edge endpoint, simply configure the Groundlight SDK to use the edge endpoint's URL instead of the cloud endpoint. -All application logic will work seamlessly and unchanged with the Groundlight Edge Endpoint, except some ML answers will -return much faster locally. Image queries answered at the edge endpoint will not appear in the cloud dashboard unless -specifically configured to do so, in which case the edge prediction will not be reflected on the image query in the cloud. - -## Configuring the Edge Endpoint - -To configure the Groundlight SDK to use the edge endpoint, you can either pass the endpoint URL to the Groundlight constructor like: - -```python notest -from groundlight import Groundlight -gl = Groundlight(endpoint="http://localhost:6717") -``` - -or by setting the `GROUNDLIGHT_ENDPOINT` environment variable like: - -```bash -export GROUNDLIGHT_ENDPOINT=http://localhost:6717 -python your_app.py -``` \ No newline at end of file diff --git a/docs/docs/building-applications/_category_.json b/docs/docs/building-applications/_category_.json deleted file mode 100644 index dab58c1c..00000000 --- a/docs/docs/building-applications/_category_.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "label": "Building Applications", - "position": 2 -} diff --git a/docs/docs/getting-started/1-api-tokens.md b/docs/docs/getting-started/1-api-tokens.md deleted file mode 100644 index c5d31c16..00000000 --- a/docs/docs/getting-started/1-api-tokens.md +++ /dev/null @@ -1,68 +0,0 @@ -# API Tokens - -## About API Tokens - -To use the Groundlight SDK or API, you need a security token which we call an "API Token." These authenticate you to Groundlight and authorize your code to use services in your account. - -API tokens look like `api_2GdXMflhJ...` and consist of a ksuid (a kind of sortable UUID) followed by a secret string. - -## Handling API Tokens - -**You should treat API tokens like passwords.** Never check them directly into your code or share them. Please use best security practices with your API tokens, because if anybody gets your API token, they have nearly full control over your Groundlight account. - -Here are some best practices for handling API tokens: - -- Store API tokens in a secure location, such as an encrypted vault. -- Use environment variables to store API tokens, rather than hardcoding them in your application. -- Limit the number of people who have access to API tokens. -- Rotate API tokens regularly and revoke old ones when they are no longer needed. - -## Using API Tokens with the SDK - -There are a couple of ways the SDK can find your API token: - -1. Environment variable (recommended): As a best practice, we recommend storing API tokens in the environment variable `GROUNDLIGHT_API_TOKEN`. This helps avoid accidentally committing the token to your code repository. The SDK will automatically look for the API token there, so you don't have to put it in your code at all. - -```python -from groundlight import Groundlight - -# looks for API token in environment variable GROUNDLIGHT_API_TOKEN -gl = Groundlight() -``` - -2. Constructor argument: Alternatively, you can pass the API token directly to the Groundlight constructor. However, be cautious not to commit this code to your repository. - -``` -from groundlight import Groundlight - -token = get_token_from_secure_location() -gl = Groundlight(api_token=token) -``` - -## Creating and Revoking API Tokens -You can manage your API tokens from the Groundlight dashboard at [https://dashboard.groundlight.ai/reef/my-account/api-tokens](https://dashboard.groundlight.ai/reef/my-account/api-tokens). - - -### Creating API Tokens - -1. Log in to your Groundlight account and navigate to the API tokens page. -1. Click the "Create New API Token" button. -1. Give the new token a descriptive name, so you can easily identify it later. -1. Click "Create Token." -1. Copy the generated token and store it securely, as you won't be able to see it again. Groundlight does not store a copy of your API tokens. - -### Viewing and Revoking API Tokens - -On the API tokens page, you can see a list of your current tokens, along with the following information: - -- Token Name: The descriptive name you assigned when creating the token -- Snippet (prefix): A short, unique identifier for each token -- Last used: The date and time the token was last used - -### To revoke an API token - -1. Locate the token you want to revoke in the list. -1. Click the "Delete" button next to the token. -1. Confirm that you want to revoke the token. - -Note: Revoking an API token will immediately invalidate it and prevent any applications using it from accessing your Groundlight account. Be sure to update your applications with a new token before revoking an old one. diff --git a/docs/docs/getting-started/2-initial-setup.md b/docs/docs/getting-started/2-initial-setup.md new file mode 100644 index 00000000..33e77f57 --- /dev/null +++ b/docs/docs/getting-started/2-initial-setup.md @@ -0,0 +1,87 @@ +# Initial setup + +In this guide, you will set up your development environment to interact with the Groundlight API using the Groundlight SDK. You will learn how to: +1. Install the Groundlight SDK +2. Set your API token +3. Call the Groundlight API + +## Prerequisites +You will need: +1. A [Groundlight account](https://dashboard.groundlight.ai/) +2. An API token from the [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens) +3. Python 3.9+ + + +## Install the Groundlight SDK +Groundlight provides a Python (3.9+) SDK that you can use to interact with the Groundlight API. + +In your project directory, create a virtual environment. +```bash +python -m venv groundlight-env +``` + +Activate the virtual environment using +- On macOS or Linux, `source groundlight-env/bin/activate` +- On Windows, `.\groundlight-env\Scripts\activate` + +Install the Groundlight SDK using pip: +```bash +pip install groundlight +``` + +For more detailed installation instructions, see the [installation guide](../installation/). + +## Set your API token +Every request to the Groundlight API requires an API token. The Groundlight SDK is designed to pull the API token from an environment variable `GROUNDLIGHT_API_TOKEN`. + +Set the API token in your terminal: +```bash +# MacOS / Linux +export GROUNDLIGHT_API_TOKEN='your-api-token' +``` +```powershell +# Windows +setx GROUNDLIGHT_API_TOKEN "your-api-token" +``` + +## Call the Groundlight API +Call the Groundlight API by creating a `Detector` and submitting an `ImageQuery`. + +```python title="ask.py" +from groundlight import Groundlight, Detector, ImageQuery + +gl = Groundlight() + +det: Detector = gl.get_or_create_detector( + name="parking-space", + query="Is there a car in the leftmost parking space?" +) + +img = "./docs/static/img/doorway.jpg" # Image can be a file or a Python object +image_query = gl.submit_image_query(detector=det, image=img) + +print(f"The answer is {image_query.result.label}") +print(image_query) +``` + +Run the code using `python3 ask.py`. The code will submit an image to the Groundlight API and print the result: +``` +The answer is NO +ImageQuery( + id='iq_2pL5wwlefaOnFNQx1X6awTOd119', + query="Is there a car in the leftmost parking space?", + detector_id='det_2owcsT7XCsfFlu7diAKgPKR4BXY', + result=BinaryClassificationResult( + confidence=0.9995857543478209, + label= + ), + created_at=datetime.datetime(2024, 11, 25, 11, 5, 57, 38627, tzinfo=tzutc()), + patience_time=30.0, + confidence_threshold=0.9, + type=, + result_type=, + metadata=None +) +``` + +For more information on the Groundlight SDK, see the [API Reference](../api-reference/), or check out our [guide to building applications with the Groundlight SDK](../guide/). \ No newline at end of file diff --git a/docs/docs/getting-started/2-writing-queries.md b/docs/docs/getting-started/4-writing-queries.md similarity index 52% rename from docs/docs/getting-started/2-writing-queries.md rename to docs/docs/getting-started/4-writing-queries.md index 742fa1cd..83235b7c 100644 --- a/docs/docs/getting-started/2-writing-queries.md +++ b/docs/docs/getting-started/4-writing-queries.md @@ -2,9 +2,7 @@ ## Introduction -With Groundlight's `detectors`, you can ask binary questions about images — i.e., the answer -should be unambiguously "YES" or "NO". If you ask an ambiguous question, you may receive an "UNSURE" -response. +Groundlight allows you to ask a variety of questions about images. The most common type of question is a binary question that can be answered with a simple "YES" or "NO". For example, "Is there a car in the leftmost parking space?" or "Is the door open?". Ambiguity in the question can lead to "UNCLEAR" responses. ```python notest detector = gl.get_or_create_detector( @@ -13,34 +11,29 @@ detector = gl.get_or_create_detector( ) image_query = gl.submit_image_query(detector=detector, image=some_image) -# The SDK can return "YES" or "NO" (or "UNSURE") +# The SDK can return "YES" or "NO" (or "UNCLEAR") print(f"The answer is {image_query.result.label}") ``` -So, what makes a good question? Let's look at a few good ✅, moderate 🟡, and bad ❌ examples! +So, what makes a good question for a binary-mode detector? Let's look at a few good ✅, moderate 🟡, and bad ❌ examples! ## Examples ### ✅ Are there any cardboard boxes on the conveyor belt? -This question is binary and can be answered unambiguously with a simple "YES" or "NO" based on the -image content. +This question is binary and can be answered unambiguously with a simple "YES" or "NO" based on the image content. ### 🟡 Is the trash can full? -This question is okay, but it could be rephrased to be more specific. For example, "Is the black -trash can more than 80% full?" +This question is okay, but it could be rephrased to be more specific. For example, "Is the black trash can more than 80% full?" :::tip -With Groundlight, your questions may be routed to a machine learning model or a human reviewer. One -way to improve your questions is to think, "If I saw this question for the first time, would I know -precisely what the person was trying to convey?" +With Groundlight, your questions may be routed to a machine learning model or a human reviewer. One way to improve your questions is to think, "If I saw this question for the first time, would I know precisely what the person was trying to convey?" ::: ### ✅ Is the garage door completely closed? -The query is very specific about what "YES" means. According to this query, any slight / partial -opening would be considered "NO". +The query is very specific about what "YES" means. According to this query, any slight / partial opening would be considered "NO". ### 🟡 Is the weather nice out? @@ -49,10 +42,8 @@ is nice weather. Instead, you might ask "Can you see any clouds in the sky?" ### ❌ Where is the thing? -This is not a binary question — "YES" and "NO" don't make sense in this context. Also, it's -not clear what the "thing" refers to. +This is not a binary question — "YES" and "NO" don't make sense in this context. Also, it's not clear what the "thing" refers to. ### 🟡 Is the factory floor clean and organized? -While this question is binary, "cleanliness" can be somewhat subjective. An improved version could -be: "Are there any visible spills or clutter on the factory floor?" +While this question is binary, "cleanliness" can be somewhat subjective. An improved version could be: "Are there any visible spills or clutter on the factory floor?" diff --git a/docs/docs/getting-started/5-api-tokens.md b/docs/docs/getting-started/5-api-tokens.md new file mode 100644 index 00000000..4f8780dc --- /dev/null +++ b/docs/docs/getting-started/5-api-tokens.md @@ -0,0 +1,45 @@ +# Using API Tokens + +API tokens authenticate your code to access Groundlight services. They look like `api_2GdXMflhJ...` and should be treated as sensitive credentials. + +The SDK can access your token in two ways: + +1. **Environment Variable (Recommended)** +```python +from groundlight import Groundlight + +# Automatically uses GROUNDLIGHT_API_TOKEN environment variable +gl = Groundlight() +``` + +2. **Direct Configuration** +```python notest +from groundlight import Groundlight + +token = get_token_from_secure_location() +gl = Groundlight(api_token=token) +``` + +## Security Best Practices + +- Store tokens in environment variables or secure vaults +- Never commit tokens to code repositories +- Limit token access to necessary personnel +- Rotate tokens periodically +- Revoke unused tokens promptly + +## Managing Tokens + +Access token management at [dashboard.groundlight.ai/reef/my-account/api-tokens](https://dashboard.groundlight.ai/reef/my-account/api-tokens) + +### Create a Token +1. Navigate to the [API tokens page](https://dashboard.groundlight.ai/reef/my-account/api-tokens) +2. Enter a token name and click "Create API Token" +3. Save the generated token securely - it won't be shown again! + +### Revoke a Token +1. Find the token in your dashboard by name +2. Click "Delete" +3. Confirm revocation + +> **Important**: Update your applications with a new token before revoking an old one to prevent service interruption. \ No newline at end of file diff --git a/docs/docs/getting-started/5-streaming.md b/docs/docs/getting-started/5-streaming.md deleted file mode 100644 index 2b0a597b..00000000 --- a/docs/docs/getting-started/5-streaming.md +++ /dev/null @@ -1,85 +0,0 @@ -# A Quick Example: Live Stream Alert - -A quick example to get used to setting up detectors and asking good questions: set up a monitor on a live stream. - -## Requirements - -- [Groundlight SDK](/docs/installation/) with Python 3.8 or higher -- The video ID of a YouTube live stream you'd like to monitor - -## Installation - -Ensure you have Python 3.8 or higher installed, and then install the Groundlight SDK and OpenCV library: - -```bash -# MacOS -brew install ffmpeg - -# Ubuntu/Fedora linux -sudo apt install -y ffmpeg - -pip install groundlight pillow ffmpeg yt-dlp typer -``` - -## Creating the Application - -1. Save this command as a shell script `get_latest_frame.sh`: - -``` -#!/bin/bash - -ffmpeg -i "$(yt-dlp -g $1 | head -n 1)" -vframes 1 last.jpg -y -``` - -This will download the most recent frame from a YouTube live stream and save it to a local file `last.jpg`. - -2. Ensure that the script has execute permissions. You can add execute permissions using the following command: - -``` -chmod +x get_latest_frame.sh -``` - -3. Log in to the [Groundlight dashboard](https://dashboard.groundlight.ai) and create an [API Token](https://dashboard.groundlight.ai/reef/my-account/api-tokens). - -4. Next, we'll write the Python script for the application. - -```python notest -import os -import subprocess -import typer -from groundlight import Groundlight -from PIL import Image - - -def main(*, video_id: str = None, detector_name: str = None, query: str = None, confidence: float = 0.75, wait: int = 60): - """ - Run the script to get the stream's last frame as a subprocess, and submit result as an image query to a Groundlight detector - :param video_id: Video ID of the YouTube live stream (the URLs have the form https://www.youtube.com/watch?v=) - :param detector_name: Name for your Groundlight detector - :param query: Question you want to ask of the stream (we will alert on the answer of NO) - """ - gl = Groundlight() - detector = gl.get_or_create_detector(name=detector_name, query=query, confidence_threshold=confidence) - - while True: - p = subprocess.run(["./get_latest_frame.sh", video_id]) - if p.returncode != 0: - raise RuntimeError(f"Could not get image from video ID: {video_id}. Process exited with return code {p.returncode}.") - - image = Image.open("last.jpg").convert("RGB") - response = gl.submit_image_query(detector=detector, image=image, wait=wait) - - if response.result.label == "NO": - os.system("say 'Alert!'") # this may not work on all operating systems - - -if __name__ == "__main__": - typer.run(main) - -``` - -5. Save the script as `streaming_alert.py` in the same directory as `get_latest_frame.sh` above and run it: - -```bash -python streaming_alert.py --video-id= --detector-name= --query= -``` diff --git a/docs/docs/getting-started/getting-started.mdx b/docs/docs/getting-started/getting-started.mdx index ca683ce7..5da9c7b4 100644 --- a/docs/docs/getting-started/getting-started.mdx +++ b/docs/docs/getting-started/getting-started.mdx @@ -16,7 +16,7 @@ print(f"The answer is {image_query.result}") ### How does it work? -Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough [confidence](docs/building-applications/4-managing-confidence.md), that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence. +Your images are first analyzed by machine learning (ML) models which are automatically trained on your data. If those models have high enough [confidence](docs/guide/5-managing-confidence.md), that's your answer. But if the models are unsure, then the images are progressively escalated to more resource-intensive analysis methods up to real-time human review. So what you get is a computer vision system that starts working right away without even needing to first gather and label a dataset. At first it will operate with high latency, because people need to review the image queries. But over time, the ML systems will learn and improve so queries come back faster with higher confidence. ### Escalation Technology @@ -27,7 +27,7 @@ Groundlight's Escalation Technology combines the power of generative AI using ou ## Building a simple visual application -1. Install the `groundlight` SDK. Requires python version 3.8 or higher. See [prerequisites](#Prerequisites). +1. Install the `groundlight` SDK. Requires python version 3.9 or higher. ```shell pip3 install groundlight diff --git a/docs/docs/building-applications/2-grabbing-images.md b/docs/docs/guide/2-grabbing-images.md similarity index 50% rename from docs/docs/building-applications/2-grabbing-images.md rename to docs/docs/guide/2-grabbing-images.md index b8a34f60..f9c9e8f0 100644 --- a/docs/docs/building-applications/2-grabbing-images.md +++ b/docs/docs/guide/2-grabbing-images.md @@ -1,13 +1,64 @@ ---- -sidebar_position: 1 ---- - # Grabbing Images +In order to analyze images with Groundlight, you first need to capture images from a camera or other image source. This guide will show you how to capture images from different sources and formats. + +## Framegrab + +For a unified interface to many different kinds of image sources, see [framegrab](https://pypi.org/project/framegrab/), an [open-source](https://github.com/groundlight/framegrab) python library maintained by Groundlight. + +### Capturing Images +Framegrab has many useful features for working with cameras and other image sources. It provides a single interface for extracting images from many different image sources, including generic USB cameras (such as webcams), RTSP streams, HTTP live streams, YouTube live streams, Basler USB cameras, Basler GigE cameras, and Intel RealSense depth cameras. + +Installation is straightforward: +```bash +pip install framegrab[all] +``` -Groundlight's SDK accepts images in many popular formats, including PIL, OpenCV, and numpy arrays. +To capture frames, first configure a `FrameGrabber` object, specifying the image source. Then call the `grab()` method to capture a frame: +```python notest +from framegrab import FrameGrabber + +# Create a FrameGrabber for a generic USB camera (e.g., a webcam) +config = { + 'input_type': 'generic_usb', +} +grabber = FrameGrabber.create_grabber(config) + +frame = grabber.grab() +``` +Framegrab returns images as numpy arrays in BGR format, which is the standard format for OpenCV. This makes it easy to use the images with other image processing libraries, such as OpenCV. -## PIL +See the [framegrab documentation](https://github.com/groundlight/framegrab/blob/main/README.md) for more information on configuring different image sources. + +### Motion Detection +Framegrab also includes a motion detection module, which can be used to detect motion in a video stream. This can be useful for detecting when something changes in a scene, such as when a person enters a room or a car pulls into a parking space. + +To use the built-in motion detection functionality, first create a `MotionDetector` object, specifying the percentage threshold for motion detection. Then, use the motion_detected() method with every captured frame to check if motion has been detected: +```python notest +from framegrab import FrameGrabber, MotionDetector + +config = {'input_type': 'generic_usb'} +grabber = FrameGrabber.create_grabber(config) + +motion_threshold = 1.0 +mdet = MotionDetector(pct_threshold=motion_threshold) + +while True: + frame = grabber.grab() + if frame is None: + print("No frame captured!") + continue + + if mdet.motion_detected(frame): + print("Motion detected!") +``` + +In this example, `motion_threshold` specifies the sensitivity level for detecting motion based on the percentage of pixels that have changed. By default, this is set to 1.0, indicating a 1% change. To increase the sensitivity, set the threshold to a lower value, such as 0.5%. Likewise, to decrease the sensitivity, set the threshold to a higher value, such as 2%. + + +## Image Formats +Groundlight's SDK accepts images in many popular formats, including PIL, OpenCV, and numpy arrays. +### PIL The Groundlight SDK can accept PIL images directly in `submit_image_query`. Here's an example: @@ -21,7 +72,7 @@ pil_img = Image.open("./docs/static/img/doorway.jpg") gl.submit_image_query(det, pil_img) ``` -## OpenCV +### OpenCV OpenCV is a popular image processing library, with many utilities for working with images. OpenCV images are stored as numpy arrays. (Note they are stored in BGR order, not RGB order, but as of Groundlight SDK v0.8 this is the expected order.) @@ -38,7 +89,7 @@ cam.release() # Release the camera ``` -## Numpy +### Numpy The Groundlight SDK can accept images as `numpy` arrays. They should be in the standard HWN format in BGR color order, matching OpenCV standards. Pixel values should be from 0-255 (not 0.0-1.0 as floats). So `uint8` data type is preferable since it saves memory. @@ -55,12 +106,12 @@ np_img = np.random.uniform(low=0, high=255, size=(600, 800, 3)).astype(np.uint8) gl.submit_image_query(detector, np_img) ``` -### Channel order: BGR vs RGB +#### Channel order: BGR vs RGB Groundlight expects images in BGR order, because this is standard for OpenCV, which uses numpy arrays as image storage. (OpenCV uses BGR because it was originally developed decades ago for compatibility with the BGR color format used by many cameras and image processing hardware at the time of its creation.) Most other image libraries use RGB order, so if you are using images as numpy arrays which did not originate from OpenCV you likely need to reverse the channel order before sending the images to Groundlight. -Note this change was made in v0.8 of the Groundlight SDK - in previous versions, RGB order was expected. +Note this change was made in v0.8 of the Groundlight SDK - in previous versions, RGB order was expected. If you have an RGB array, you must reverse the channel order before sending it to Groundlight, like: @@ -78,14 +129,3 @@ Here's an example of a natural-scene image where you might think the color balan In industrial settings, the difference can be almost impossible to detect without prior knowledge of the scene: ![Correct color order](/img/cnc-gripper.jpg) ![Swapped color channels](/img/cnc-gripper-bgr.jpg) - - -## Framegrab - -For a unified interface to many different kinds of image sources, see the [framegrab library](https://pypi.org/project/framegrab/). -Framegrab is still an early work in progress, but has many useful features for working with cameras and other image sources. Framegrab provides a single interface for many different kinds of image sources, including: - -- USB cameras -- IP cameras -- Video files -- Image files diff --git a/docs/docs/guide/3-working-with-detectors.md b/docs/docs/guide/3-working-with-detectors.md new file mode 100644 index 00000000..30628959 --- /dev/null +++ b/docs/docs/guide/3-working-with-detectors.md @@ -0,0 +1,96 @@ +# Working with Detectors + +### Explicitly create a new detector + +Typically you'll use the `get_or_create_detector(name: str, query: str)` method to find an existing detector you've already created with the same name, or create a new one if it doesn't exists. But if you'd like to force creating a new detector you can also use the `create_detector(name: str, query: str)` method + + + +```python notest +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +detector = gl.create_detector(name="your_detector_name", query="is there a hummingbird near the feeder?") +# highlight-end +``` + +### Retrieve an existing detector +To work with a detector that you've previously created, you need to retrieve it using its unique identifier. This is typical in Groundlight applications where you want to continue to use a detector you've already created. + + + +```python notest +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +detector = gl.get_detector(id="your_detector_id") +# highlight-end +``` + +Alternatively, you can retrieve a detector by its name: + +```python notest +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +detector = gl.get_detector_by_name(name="your_detector_name") +# highlight-end +``` + +### List your detectors +To manage and interact with your detectors, you might need to list them. Groundlight provides a straightforward way to retrieve a list of detectors you've created. By default, the list is paginated to show 10 results per page, but you can customize this to suit your needs. + +```python +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +# Defaults to 10 results per page +detectors = gl.list_detectors() + +# Pagination: 1st page of 5 results per page +detectors = gl.list_detectors(page=1, page_size=5) +# highlight-end +``` + +### [BETA] Create a Counting Detector +So far, all of the detectors we've created have been binary classification detectors. But what if you want to count the number of objects in an image? You can create a counting detector to do just that. Counting detectors also return bounding boxes around the objects they count. + +:::note + +Counting Detectors are available on [Pro, Business, and Enterprise plans](https://www.groundlight.ai/pricing). + +::: + +```python notest +from groundlight import ExperimentalApi + +gl_experimental = ExperimentalApi() + +# highlight-start +detector = gl_experimental.create_counting_detector(name="your_detector_name", query="How many cars are in the parking lot?", max_count=20) +# highlight-end +``` + +### [BETA] Create a Multi-Class Detector +If you want to classify images into multiple categories, you can create a multi-class detector. + +```python notest +from groundlight import ExperimentalApi + +gl_experimental = ExperimentalApi() + +# highlight-start +class_names = ["Golden Retriever", "Labrador Retriever", "German Shepherd"] +detector = gl_experimental.create_multiclass_detector( + name, query="What kind of dog is this?", class_names=class_names +) +# highlight-end +``` \ No newline at end of file diff --git a/docs/docs/guide/4-submitting-image-queries.md b/docs/docs/guide/4-submitting-image-queries.md new file mode 100644 index 00000000..cbf9d291 --- /dev/null +++ b/docs/docs/guide/4-submitting-image-queries.md @@ -0,0 +1,156 @@ +# Submitting Image Queries +Once you have [created a `Detector`](./3-working-with-detectors.md) and [captured an image](./2-grabbing-images.md), you can submit your image to Groundlight for analysis. + +## Submit an Image Query +The primary method for submitting an image query is `submit_image_query(detector: Detector, image: Any)`. This method takes a `Detector` object and an image as input and returns an `ImageQuery` object. +```python notest +from groundlight import Groundlight + +gl = Groundlight() +detector = gl.get_detector(id="det_abcdef...") + +# highlight-start +image_query = gl.submit_image_query(detector=detector, image="path/to/image.jpg") +# highlight-end +``` + +`submit_image_query` provides fine-grained control over how the `ImageQuery` is processed. For example, a per-query confidence threshold can be set (defaults to the `Detector`'s confidence threshold), and the query can be set to wait for up to a certain amount of time for a confident response (defaults to 30s). For example: +```python notest +from groundlight import Groundlight + +gl = Groundlight() +detector = gl.get_detector(id="det_abcdef...") + +# highlight-start +image_query = gl.submit_image_query( + detector=detector, + image="path/to/image.jpg", + confidence_threshold=0.95, + wait=10.0, # seconds +) +# highlight-end +``` + +See the [API Reference](../api-reference/api-reference.md) for more information on the `submit_image_query` method. + +## Aliases for `submit_image_query` +For convenience, the `submit_image_query` method has aliases for the different patterns of usage. These aliases are `ask_confident`, `ask_ml`, and `ask_async`. + +### Get the first confident answer +`ask_confident` evaluates an image with Groundlight waiting until an answer above the confidence threshold + of the detector is reached or the wait period has passed. +```python notest +from groundlight import Groundlight + +gl = Groundlight() +detector = gl.get_detector(id="det_abcdef...") + +# highlight-start +image_query = gl.ask_confident(detector=detector, image="path/to/image.jpg") +# highlight-end +``` + +### Submit an ImageQuery asynchronously +`ask_async` is a convenience method for submitting an `ImageQuery` asynchronously. This is equivalent to calling `submit_image_query` with `want_async=True` and `wait=0`. Use `get_image_query` to retrieve the `result` of the ImageQuery. + +```python notest +from groundlight import Groundlight + +gl = Groundlight() +detector = gl.get_detector(id="det_abcdef...") + +# highlight-start +# Submit ImageQuery asynchronously +image_query = gl.ask_async(detector=detector, image="path/to/image.jpg") + +# Do other work while waiting for the result +sleep(1.0) + +# Retrieve the result of the ImageQuery. Note that the provided +# result can change over time - as the query is escalated through +# our ladder - until a confident answer is reached. +image_query = gl.get_image_query(id=image_query.id) +# highlight-end +``` + +See this [guide](./7-async-queries.md) for more information on ImageQueries submitted asynchronously. + +### (Advanced) Get the first available answer, regardless of confidence +`ask_ml` evaluates an image with Groundlight and returns the first answer Groundlight can provide, agnostic of confidence. There is no wait period when using this method. It is called `ask_ml` because our machine learning models are earliest on our escalation ladder and thus always the fastest to respond. + +:::note + +We recommend using the `ask_confident` or the `ask_async` methods whenever possible for best results. + +::: + +```python notest +from groundlight import Groundlight + +gl = Groundlight() +detector = gl.get_detector(id="det_abcdef...") + +# highlight-start +image_query = gl.ask_ml(detector=detector, image="path/to/image.jpg") +# highlight-end +``` + +When using this method, low-confidence Image Queries will still be escalated to human review - this allows our models to continue to improve over time. + +## Working with Image Queries Post-Submission + +### Retrieve an Image Query result + +In practice, you may want to check for a new result on your query. For example, after a cloud reviewer labels your query. For example, you can use the `image_query.id` after the above `submit_image_query()` call. + + + +```python notest +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +image_query = gl.get_image_query(id="iq_YOUR_IMAGE_QUERY_ID") +# highlight-end +``` + +### List your previous Image Queries + +```python +from groundlight import Groundlight + +gl = Groundlight() + +# highlight-start +# Defaults to 10 results per page +image_queries = gl.list_image_queries() + +# Pagination: 1st page of 5 results per page +image_queries = gl.list_image_queries(page=1, page_size=5) +# highlight-end +``` + +### Add a label to an Image Query + +Groundlight lets you start using models by making queries against your very first image, but there are a few situations where you might either have an existing dataset, or you'd like to handle the escalation response programatically in your own code but still include the label to get better responses in the future. + +With your `ImageQuery` from either `submit_image_query()` or `get_image_query()` you can add the label directly. Note that if the query is already in the escalation queue due to low ML confidence or audit thresholds, it may also receive labels from another source. However, user-provided labels are always treated as the most authoritative. + +```python +import requests +from PIL import Image +from groundlight import Groundlight + +gl = Groundlight() + +d = gl.get_or_create_detector(name="doorway", query="Is the doorway open?") + +image_url= "https://images.selfstorage.com/large-compress/2174925f24362c479b2.jpg" +image = Image.open(requests.get(image_url, stream=True).raw) +image_query = gl.submit_image_query(detector=d, image=image) + +# highlight-start +gl.add_label(image_query, 'YES') # or 'NO' +# highlight-end +``` diff --git a/docs/docs/guide/5-managing-confidence.md b/docs/docs/guide/5-managing-confidence.md new file mode 100644 index 00000000..00cfda31 --- /dev/null +++ b/docs/docs/guide/5-managing-confidence.md @@ -0,0 +1,61 @@ +# Setting Confidence Thresholds + +## Introduction to Confidence Thresholds +When creating a Detector or submitting an ImageQuery, you can set the necessary confidence level for your use case. We call this the `confidence_threshold`. Tuning this value allows you to balance the trade-offs between accuracy and latency / cost. + +Confidence scores represent the model's internal assessment of its prediction reliability. Groundlight models provide *calibrated* confidence scores, which means that, when a model makes a prediction with a confidence of 0.95, we expect that (under typical conditions) 95% of the time that prediction will be correct. In other words, a prediction with a confidence of 0.95 is expected to be correct 19 out of 20 times. Confidence calibration kicks in after a sufficient number of labeled images have been collected. + +Confidence thresholds represent a minimum confidence that must be achieved for Groundlight to return an answer. If a confidence above the confidence threshold is not achieved, Groundlight will escalate your query up our heirarchy to stronger models and human reviewers. Confidence thresholds should be determined based on your application's acceptable error rate and the potential impact of those errors. + +Higher confidence thresholds result in predictions that are more accurate but may take longer to process (because they are escalated to more complex/expensive models or human review). Lower confidence thresholds result in faster responses but may be less accurate. Over time, and as more human-provided labels are collected, the ML models will improve, and our fastest models will be able to provide higher confidence predictions more quickly. + +## Configuring Timeouts + +In some cases, challenging queries that require human review can take a number of seconds, so we provide both client-side and server-side timeouts to ensure that your application can continue to function even if the query takes longer than expected. + +Set a client-side timeout by configuring the `wait` parameter in the `submit_image_query` method. This simply stops the client from waiting for a response after a certain amount of time. + +Set a server-side timeout by configuring the `patience_time` parameter in the `submit_image_query` method. This tells Groundlight to deprioritize the query after a certain amount of time, which can be useful if the result of a query becomes less relevant over time. For example, if you are monitoring a live video feed, you may want to deprioritize queries that are more than a few seconds old so that our human reviewers can focus on the most recent data. + + + +```python notest +from groundlight import Groundlight +from PIL import Image +import requests + +gl = Groundlight() +image_url = "https://www.photos-public-domain.com/wp-content/uploads/2010/11/over_flowing_garbage_can.jpg" +image = Image.open(requests.get(image_url, stream=True).raw) + +d = gl.get_or_create_detector( + name="trash", + query="Is the trash can full?", +# highlight-start + confidence_threshold=0.95, # Set the confidence threshold to 0.95 +# highlight-end +) + +# This will wait until either 60 seconds have passed or the confidence reaches 0.95 +image_query = gl.submit_image_query( + detector=d, + image=image, +# highlight-start + wait=10, # tell the client to stop waiting after 10 seconds + patience_time=20, # tell Groundlight to deprioritize the query after 20 seconds +# highlight-end +) + +print(f"The answer is {image_query.result.label}") +print(f"The confidence is {image_query.result.confidence}") +``` + +:::tip + +Tuning the `confidence_threshold` allows you to balance accuracy with response time. + +Higher confidence thresholds result in more accurate predictions but can increase latency. Achieving these higher confidence levels often requires more labels, which can increase labor costs. + +As our models improve over time, they will become more confident, enabling you to receive higher-confidence answers more quickly and at a lower cost. + +::: diff --git a/docs/docs/building-applications/5-handling-errors.md b/docs/docs/guide/6-handling-errors.md similarity index 65% rename from docs/docs/building-applications/5-handling-errors.md rename to docs/docs/guide/6-handling-errors.md index 36bf8766..e618c651 100644 --- a/docs/docs/building-applications/5-handling-errors.md +++ b/docs/docs/guide/6-handling-errors.md @@ -1,14 +1,10 @@ ---- -sidebar_position: 4 ---- +# Handling Errors -# Handling Server Errors +When building applications with the Groundlight SDK, you may encounter errors during API calls. This page covers how to handle such errors and build robust code that can gracefully handle exceptions. -When building applications with the Groundlight SDK, you may encounter server errors during API calls. This page covers how to handle such errors and build robust code that can gracefully handle exceptions. +## Handling `ApiException` -## Handling ApiException - -If there is an HTTP error during an API call, the SDK will raise an `ApiException`. You can access different metadata from that exception: +In the event of an HTTP error during an API call, the Groundlight SDK raises an `ApiException`. This exception provides access to various metadata: ```python notest import traceback @@ -17,8 +13,9 @@ from groundlight import ApiException, Groundlight gl = Groundlight() try: d = gl.get_or_create_detector( - "Road Checker", - "Is the site access road blocked?") + name="Road Checker", + query="Is the site access road blocked?", + ) iq = gl.submit_image_query(d, get_image(), wait=60) except ApiException as e: # Print a traceback for debugging @@ -53,15 +50,15 @@ Consider creating custom exception classes for your application-specific errors. ### Log Exceptions -Log exceptions with appropriate log levels (e.g., error, warning, etc.) and include relevant context information. This will help you debug issues more effectively and monitor the health of your application. +Log exceptions using appropriate log levels (e.g., error, warning) and include relevant context. This practice aids in effective debugging and monitoring application health. ### Implement Retry Logic -When handling exceptions, implement retry logic with exponential backoff for transient errors, such as network issues or rate-limiting. This can help your application recover from temporary issues without manual intervention. +Incorporate retry logic with exponential backoff for transient errors, such as network issues or rate limits. This strategy allows your application to recover from temporary problems automatically. ### Handle Exceptions Gracefully -In addition to logging exceptions, handle them gracefully to ensure that your application remains functional despite errors. This might include displaying an error message to users or falling back to a default behavior. +Ensure your application remains functional despite errors by handling exceptions gracefully. This might involve displaying user-friendly error messages or reverting to default behaviors. ### Test Your Error Handling diff --git a/docs/docs/guide/7-async-queries.md b/docs/docs/guide/7-async-queries.md new file mode 100644 index 00000000..2bbf8baa --- /dev/null +++ b/docs/docs/guide/7-async-queries.md @@ -0,0 +1,82 @@ +# Using Asynchronous Queries + +Groundlight provides a simple interface for submitting asynchronous queries. This is useful for situations in which the thread or process or machine submitting image queries is not the same thread or machine that will be retrieving and using the results. + +For example, you might have a forward deployed robot or camera that submits image queries to Groundlight, and a separate server that retrieves the results and takes action based on them. We will refer to these two machines as the **submitting machine** and the **retrieving machine**. + +## Setup Submitting Machine +On the **submitting machine**, you will need to install the Groundlight Python SDK. Then you can submit image queries asynchronously using the `ask_async` interface (read the full documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight.ask_async)). + +`ask_async` submits your query and immediately returns, without waiting for an answer. This minimizes the time your program spends interacting with Groundlight. Consequently, the `ImageQuery` object returned by `ask_async` does not contain a `result` (the `result` field will be `None`). This is suitable for scenarios where the **submitting machine** does not need the result. Instead, the **submitting machine** only needs to share the `ImageQuery.id` with the **retrieving machine**. This can be done through a database, message queue, or another method. In this example, we assume you are using a database to save the `ImageQuery.id` with `db.save(image_query.id)`. + +```python notest +from time import sleep +from framegrab import FrameGrabber +from groundlight import Groundlight + +# Create a FrameGrabber for a generic USB camera (e.g., a webcam) +config = {'input_type': 'generic_usb'} +grabber = FrameGrabber.create_grabber(config) + +detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") + +while True: + image = grabber.grab() + + # highlight-start + image_query = gl.ask_async(detector=detector, image=image) + db.save(image_query.id) # Save the image_query.id to a database for the retrieving machine to use + # highlight-end + + sleep(10) # Sleep for 10 seconds before grabbing the next image + +grabber.release() +``` + +## Setup Retrieving Machine +On the **retrieving machine**, ensure the Groundlight Python SDK is installed. You can then use the `get_image_query` method to fetch results of image queries submitted by the **submitting machine**. The **retrieving machine** can utilize the `ImageQuery.result` to perform actions based on the application's requirements. In this example, we assume your application retrieves the next image query ID to process from a database using `db.get_next_image_query_id()`. This function should return `None` when all `ImageQuery` entries have been processed. + +```python notest +from groundlight import Groundlight + +detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") + +image_query_id = db.get_next_image_query_id() + +while image_query_id is not None: + # highlight-start + image_query = gl.get_image_query(id=image_query_id) # retrieve the image query from Groundlight + # highlight-end + result = image_query.result + + # take action based on the result of the image query + if result.label == 'YES': + pass # TODO: do something based on your application + elif result.label == 'NO': + pass # TODO: do something based on your application + elif result.label == 'UNCLEAR': + pass # TODO: do something based on your application + + # update image_query_id for next iteration of the loop + image_query_id = db.get_next_image_query_id() +``` + +## Important Considerations +When you submit an image query asynchronously, ML prediction on your query is **not** instant. So attempting to retrieve the result immediately after submitting an async query will likely result in an `UNCLEAR` result as Groundlight is still processing your query. Instead, if your code needs a `result` synchronously we recommend using one of our methods with a polling mechanism to retrieve the result (e.g. `ask_confident`). You can see all of the interfaces available in the documentation [here](pathname:///python-sdk/api-reference-docs/#groundlight.client.Groundlight). + +```python notest +from PIL import Image +from groundlight import Groundlight + +detector = gl.get_or_create_detector(name="your_detector_name", query="your_query") +image = Image.open("/path/to/your/image.jpg") + +image_query = gl.ask_async(detector=detector, image=image) # Submit async query to Groundlight +assert image_query.result is None # IQs returned from `ask_async` will not have a result + +image_query = gl.get_image_query(id=image_query.id) # Immediately retrieve the image query from Groundlight +result = image_query.result # This may be 'UNCLEAR' as Groundlight continues to process the query + +image_query = gl.wait_for_confident_result(id=image_query.id) # Poll for a confident result from Groundlight +result = image_query.result +``` \ No newline at end of file diff --git a/docs/docs/guide/8-edge.md b/docs/docs/guide/8-edge.md new file mode 100644 index 00000000..07d4f043 --- /dev/null +++ b/docs/docs/guide/8-edge.md @@ -0,0 +1,52 @@ +# Processing Images on the Edge + +If your account includes access to edge models, you can download and install them on your edge devices. This allows you to run Groundlight's ML models locally on your edge devices, reducing latency and increasing throughput. Additionally, inference requests handled on the edge are not counted towards your account's usage limits. + +This is achieved through a proxy service called the `edge-endpoint`, a lightweight, open-source service that runs on your edge devices. The `edge-endpoint` is responsible for downloading and running models and communicating with the Groundlight cloud service. You can find the source code and documentation for the `edge-endpoint` [on GitHub](https://github.com/groundlight/edge-endpoint). + +## How the Edge Endpoint Works + +The `edge-endpoint` is a [proxy service](https://en.wikipedia.org/wiki/Proxy_server) that runs on your edge devices. It intercepts requests and responses between your application and the Groundlight cloud service, enabling you to run Groundlight's ML models locally on your edge devices. + +When your application sends an image query to the Groundlight cloud service, the `edge-endpoint` intercepts the request and downloads the relevant edge-sized model from the cloud. It then runs the model locally on the edge device and returns the result to your application. By default, it will return answers without escalating to the cloud if the edge model answers above the specified confidence threshold. Otherwise, it will escalate to the cloud for a more confident answer. This process also allows Groundlight to learn from examples that are challenging for the edge model. Once a new edge model is trained to handle such examples, it will automatically be downloaded to the edge device for future queries. + +The `edge-endpoint` operates as a set of [containers](https://en.wikipedia.org/wiki/Containerization_(computing)) on an "edge device," which can be an NVIDIA Jetson device, a rack-mounted server, or even a Raspberry Pi. The main container is the `edge-endpoint` proxy service, which handles requests and manages other containers, such as the `inferencemodel` containers responsible for loading and running the ML models. + +## Installing and Running the Edge Endpoint +To set up an `edge-endpoint` manually, please refer to [the deploy README](https://github.com/groundlight/edge-endpoint/blob/main/deploy/README.md). + +Groundlight also provides managed `edge-endpoint` servers. Management is performed via [Balena](https://www.balena.io/). To received a managed `edge-endpoint`, please [contact us](mailto:info@groundlight.ai). + +## Using the Edge Endpoint +To utilize the `edge-endpoint`, set the Groundlight SDK to use the `edge-endpoint`'s URL instead of the cloud endpoint. Your application logic can remain unchanged and will work seamlessly with the Groundlight `edge-endpoint`. This setup allows some ML responses to be returned much faster, locally. + +Note that image queries processed at the `edge-endpoint` will not appear on the Groundlight cloud dashboard unless specifically configured. In such cases, the edge prediction will not be reflected in the cloud image query. Additional documentation and configuration options are available in the [edge-endpoint repository](https://github.com/groundlight/edge-endpoint#running-the-edge-endpoint). + +To set the Groundlight Python SDK to submit requests to your `edge-endpoint` proxy server, you can either pass the endpoint URL to the Groundlight constructor like this: +```python notest +from groundlight import Groundlight +gl = Groundlight(endpoint="http://localhost:30101") +``` + +or set the `GROUNDLIGHT_ENDPOINT` environment variable like: +```bash +export GROUNDLIGHT_ENDPOINT=http://localhost:30101 +python your_app.py +``` + +:::tip +In the above example, the `edge-endpoint` is running on the same machine as the application, so the endpoint URL is `http://localhost:30101`. If the `edge-endpoint` is running on a different machine, you should replace `localhost` with the IP address or hostname of the machine running the `edge-endpoint`. +::: + +## Edge Endpoint performance + +We have benchmarked the `edge-endpoint` handling 500 requests/sec at a latency of less than 50ms on an off-the-shelf [Katana 15 B13VGK-1007US](https://us.msi.com/Laptop/Katana-15-B13VX/Specification) laptop (Intel® Core™ i9-13900H CPU, NVIDIA® GeForce RTX™ 4070 Laptop GPU, 32GB DDR5 5200MHz RAM) running Ubuntu 20.04. + +The following graphs show the throughput and latency of the `edge-endpoint` running on the Katana 15 laptop. As time progresses along the x-axis, the benchmark script ramps up the number of requests per second from 1 to 500 (and the number of clients submitting requests from 1 to 60). The y-axes shows the throughput in requests per second and the latency in seconds. + +![edge-endpoint throughput](../../static/img/edge-endpoint-throughput.png) + +![edge-endpoint latency](../../static/img/edge-endpoint-latency.png) + + +The `edge-endpoint` is designed to be lightweight and efficient, and can be run on a variety of edge devices, including NVIDIA Jetson devices, Raspberry Pi, and other ARM- and x86-based devices. \ No newline at end of file diff --git a/docs/docs/guide/9-alerts.md b/docs/docs/guide/9-alerts.md new file mode 100644 index 00000000..67c6d877 --- /dev/null +++ b/docs/docs/guide/9-alerts.md @@ -0,0 +1,27 @@ +# Configuring Alerts + +Groundlight supports triggering alerts based on the results of image queries. Alerts can be configured to notify you when a specific condition is met. + +To configure an alert, navigate to [the **Alerts** tab on the Groundlight dashboard](https://dashboard.groundlight.ai/reef/alerts). Here, you can create a new alert by clicking the **Create New Alert** button. + +## Alert Configuration + +When creating a new alert, you can configure alerts for the following conditions: +1. A specific answer is returned N times in a row. +2. The answer changes from one value to another. +3. There are no changes in the answer for a specified period of time. +4. There are no queries submitted for a specified period of time. + +A snooze period can be configured to prevent the alert from triggering multiple times in quick succession. + +Optionally, you can configure the alert to include the triggering image in the alert message. + +:::tip + +Consider configuring a "no queries submitted" alert to monitor system health. If your application is expected to submit queries regularly (e.g., monitoring a camera feed), setting an alert for when no queries are received for a few minutes can help quickly identify if your system has gone offline or is experiencing connectivity issues. + +::: + +## Alert Mediums + +Groundlight supports the following alerts via Email and Text Message (SMS), with webhook support coming soon. diff --git a/docs/docs/guide/_category_.json b/docs/docs/guide/_category_.json new file mode 100644 index 00000000..2e266ee8 --- /dev/null +++ b/docs/docs/guide/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Guide", + "position": 3 +} diff --git a/docs/docs/building-applications/building-applications.md b/docs/docs/guide/guide.md similarity index 53% rename from docs/docs/building-applications/building-applications.md rename to docs/docs/guide/guide.md index ab206b5c..bbb77ea6 100644 --- a/docs/docs/building-applications/building-applications.md +++ b/docs/docs/guide/guide.md @@ -1,23 +1,16 @@ -# Building Applications +# Guide Groundlight provides a powerful "computer vision powered by natural language" system that enables you to build visual applications with minimal code. With Groundlight, you can quickly create applications for various use cases, from simple object detection to complex visual analysis. -In this page, we'll introduce you to some sample applications built using Groundlight and provide links to more detailed guides on various topics. - -## Sample Applications -- **[Sample Applications](1-sample-applications.md)**: Find repositories with examples of applications built with Groundlight - -## Further Reading - -For more in-depth guides on various aspects of building applications with Groundlight, check out the following pages: +On the following pages, we'll guide you through the process of building applications with Groundlight. - **[Grabbing images](2-grabbing-images.md)**: Understand the intricacies of how to submit images from various input sources to Groundlight. - **[Working with detectors](3-working-with-detectors.md)**: Learn how to create, configure, and use detectors in your Groundlight-powered applications. -- **[Confidence levels](4-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors. -- **[Handling server errors](5-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors that may occur while using Groundlight. -- **[Asynchronous queries](6-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later. -- **[Using Groundlight on the edge](7-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency. -- **[Industrial applications](8-industrial.md)**: Learn how to apply modern natural-language-based computer vision to your industrial and manufacturing applications. - -By exploring these resources and sample applications, you'll be well on your way to building powerful visual applications using Groundlight's computer vision and natural language capabilities. - +- **[Submitting image queries](4-submitting-image-queries.md)**: Submit images to Groundlight for analysis and retrieve the results. +- **[Confidence levels](5-managing-confidence.md)**: Master how to control the trade-off of latency against accuracy by configuring the desired confidence level for your detectors. +- **[Handling errors](6-handling-errors.md)**: Understand how to handle and troubleshoot HTTP errors (`ApiException`) that may occur while using Groundlight. +- **[Asynchronous queries](7-async-queries.md)**: Groundlight makes it easy to submit asynchronous queries. Learn how to submit queries asynchronously and retrieve the results later. +- **[Using Groundlight on the edge](8-edge.md)**: Discover how to deploy Groundlight in edge computing environments for improved performance and reduced latency. +- **[Alerts](9-alerts.md)**: Learn how to set up alerts to notify you via text (SMS) or email when specific conditions are met in your visual applications. +- **[Industrial applications](../sample-applications/4-industrial.md)**: Learn how to apply modern natural-language-based computer vision to your industrial and manufacturing applications. +By exploring these resources and sample applications, you'll be well on your way to building powerful visual applications using Groundlight's computer vision and natural language capabilities. diff --git a/docs/docs/installation/1-linux.md b/docs/docs/installation/1-linux.md index 1a7a5b06..1a240a98 100644 --- a/docs/docs/installation/1-linux.md +++ b/docs/docs/installation/1-linux.md @@ -1,17 +1,17 @@ # Installing on Linux -This guide will help you install the Groundlight SDK on Linux. The Groundlight SDK requires Python 3.8 or higher. +This guide will help you install the Groundlight SDK on Linux. The Groundlight SDK requires Python 3.9 or higher. ## Prerequisites Ensure that you have the following installed on your system: -- Python 3.8 or higher +- Python 3.9 or higher - pip (Python package installer) ## Basic Installation -Assuming you have Python 3.8 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: +Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: ### Installing Groundlight SDK @@ -37,6 +37,12 @@ To check if the Groundlight SDK is installed and to display its version, you can python -c "import groundlight; print(groundlight.__version__)" ``` +or the groundlight command line tool that comes with the SDK: + +```bash +groundlight --help +``` + ### Upgrading Groundlight SDK If you need to upgrade the Groundlight SDK to the latest version, use the following pip command: @@ -61,7 +67,7 @@ To check your installed Python version, open a terminal and run: python --version ``` -If you see a version number starting with "3.8" or higher (e.g., "3.8.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. +If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. ### Upgrading Python on Linux @@ -92,24 +98,24 @@ After upgrading, verify the Python version by running `python --version` or `pyt ### Special note about Ubuntu 18.04 -Ubuntu 18.04 still uses python 3.6 by default, which is end-of-life. We generally recommend using python 3.10. If you know how to install py3.10, please go ahead. But the easiest version of python 3 to use with Ubuntu 18.04 is python 3.8, which can be installed as follows without adding any extra repositories: +Ubuntu 18.04 still uses python 3.6 by default, which is end-of-life. We generally recommend using python 3.10. If you know how to install py3.10, please go ahead. But the easiest version of python 3 to use with Ubuntu 18.04 is python 3.9, which can be installed as follows without adding any extra repositories: ```shell # Prepare Ubuntu to install things sudo apt-get update # Install the basics -sudo apt-get install -y python3.8 python3.8-distutils curl -# Configure `python3` to run python3.8 by default -sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 10 -# Download and install pip3.8 +sudo apt-get install -y python3.9 python3.9-distutils curl +# Configure `python3` to run python3.9 by default +sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 10 +# Download and install pip3.9 curl https://bootstrap.pypa.io/get-pip.py > /tmp/get-pip.py -sudo python3.8 /tmp/get-pip.py -# Configure `pip3` to run pip3.8 -sudo update-alternatives --install /usr/bin/pip3 pip3 $(which pip3.8) 10 +sudo python3.9 /tmp/get-pip.py +# Configure `pip3` to run pip3.9 +sudo update-alternatives --install /usr/bin/pip3 pip3 $(which pip3.9) 10 # Now we can install Groundlight! pip3 install groundlight ``` ## Ready to go! -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](/docs/getting-started/api-tokens) and [Building Applications](/docs/building-applications) documentation pages. +You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/2-macos.md b/docs/docs/installation/2-macos.md index 04f843c6..4e95b110 100644 --- a/docs/docs/installation/2-macos.md +++ b/docs/docs/installation/2-macos.md @@ -1,17 +1,17 @@ # Installing on macOS -This guide will help you install the Groundlight SDK on macOS. The Groundlight SDK requires Python 3.8 or higher. +This guide will help you install the Groundlight SDK on macOS. The Groundlight SDK requires Python 3.9 or higher. ## Prerequisites Ensure that you have the following installed on your system: -- Python 3.8 or higher +- Python 3.9 or higher - pip (Python package installer) ## Basic Installation -Assuming you have Python 3.8 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: +Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: ### Installing Groundlight SDK @@ -37,6 +37,11 @@ To check if the Groundlight SDK is installed and to display its version, you can python -c "import groundlight; print(groundlight.__version__)" ``` +or the groundlight command line tool that comes with the SDK: + +```bash +groundlight --help +``` ### Upgrading Groundlight SDK If you need to upgrade the Groundlight SDK to the latest version, use the following pip command: @@ -61,7 +66,7 @@ To check your installed Python version, open a terminal and run: python --version ``` -If you see a version number starting with "3.8" or higher (e.g., "3.8.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. +If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. ### Upgrading Python on MacOS @@ -75,6 +80,4 @@ After upgrading, verify the Python version by running `python --version` or `pyt ## Ready to go! -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](/docs/getting-started/api-tokens) and [Building Applications](/docs/building-applications) documentation pages. - - +You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/3-windows.md b/docs/docs/installation/3-windows.md index bab94295..e71c0c5d 100644 --- a/docs/docs/installation/3-windows.md +++ b/docs/docs/installation/3-windows.md @@ -1,17 +1,17 @@ # Installing on Windows -This guide will help you install the Groundlight SDK on Windows. The Groundlight SDK requires Python 3.8 or higher. +This guide will help you install the Groundlight SDK on Windows. The Groundlight SDK requires Python 3.9 or higher. ## Prerequisites Ensure that you have the following installed on your system: -- Python 3.8 or higher +- Python 3.9 or higher - pip (Python package installer) ## Basic Installation -Assuming you have Python 3.8 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: +Assuming you have Python 3.9 or higher installed on your system, you can proceed with the following steps to install or upgrade the Groundlight SDK: ### Installing Groundlight SDK @@ -61,7 +61,7 @@ To check your installed Python version, open a Command Prompt and run: python --version ``` -If you see a version number starting with "3.8" or higher (e.g., "3.8.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. +If you see a version number starting with "3.9" or higher (e.g., "3.9.5" or "3.9.0"), you're good to go. If not, you might need to upgrade Python on your system. ### Upgrading Python on Windows @@ -71,5 +71,4 @@ After upgrading, verify the Python version by running `python --version` or `pyt ## Ready to go! -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](/docs/getting-started/api-tokens) and [Building Applications](/docs/building-applications) documentation pages. - +You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). diff --git a/docs/docs/installation/6-optional-libraries.md b/docs/docs/installation/4-optional-libraries.md similarity index 93% rename from docs/docs/installation/6-optional-libraries.md rename to docs/docs/installation/4-optional-libraries.md index 0ef99f40..92d1f13d 100644 --- a/docs/docs/installation/6-optional-libraries.md +++ b/docs/docs/installation/4-optional-libraries.md @@ -2,7 +2,7 @@ ## Smaller is better! -The Groundlight SDK is optimized to run on small edge devices. As such, you can use the Groundlight SDK without +The Groundlight Python SDK is optimized to run on small edge devices. As such, you can use the Groundlight SDK without installing large libraries like `numpy` or `OpenCV`. But if you're already installing them, we'll use them. Our SDK detects if these libraries are installed diff --git a/docs/docs/installation/4-raspberry-pi.md b/docs/docs/installation/5-raspberry-pi.md similarity index 78% rename from docs/docs/installation/4-raspberry-pi.md rename to docs/docs/installation/5-raspberry-pi.md index 6f10ccde..3f178059 100644 --- a/docs/docs/installation/4-raspberry-pi.md +++ b/docs/docs/installation/5-raspberry-pi.md @@ -1,17 +1,17 @@ -# Installing on Raspberry Pi +# Usage on Raspberry Pi -This guide will help you install the Groundlight SDK on Raspberry Pi. The Groundlight SDK requires Python 3.8 or higher. +This guide will help you install the Groundlight SDK on Raspberry Pi. The Groundlight SDK requires Python 3.9 or higher. ## Prerequisites Ensure that you have the following installed on your Raspberry Pi: -- Python 3.8 or higher +- Python 3.9 or higher - pip (Python package installer) ## Basic Installation -Assuming you have Python 3.8 or higher installed on your Raspberry Pi, you can proceed with the following steps to install or upgrade the Groundlight SDK: +Assuming you have Python 3.9 or higher installed on your Raspberry Pi, you can proceed with the following steps to install or upgrade the Groundlight SDK: ### Installing Groundlight SDK @@ -39,6 +39,4 @@ For a complete end-to-end example of running on a Raspberry Pi, see [this GitHub ## Ready to go! -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](/docs/getting-started/api-tokens) and [Building Applications](/docs/building-applications) documentation pages. - - +You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). \ No newline at end of file diff --git a/docs/docs/installation/5-nvidia-jetson.md b/docs/docs/installation/6-nvidia-jetson.md similarity index 79% rename from docs/docs/installation/5-nvidia-jetson.md rename to docs/docs/installation/6-nvidia-jetson.md index 3a3fde72..7478811b 100644 --- a/docs/docs/installation/5-nvidia-jetson.md +++ b/docs/docs/installation/6-nvidia-jetson.md @@ -1,17 +1,17 @@ -# Installing on NVIDIA Jetson +# Usage on NVIDIA Jetson -This guide will help you install the Groundlight SDK on NVIDIA Jetson devices. The Groundlight SDK requires Python 3.8 or higher. +This guide will help you install the Groundlight SDK on NVIDIA Jetson devices. The Groundlight SDK requires Python 3.9 or higher. ## Prerequisites Ensure that you have the following installed on your NVIDIA Jetson: -- Python 3.8 or higher +- Python 3.9 or higher - pip (Python package installer) ## Basic Installation -Assuming you have Python 3.8 or higher installed on your NVIDIA Jetson, you can proceed with the following steps to install or upgrade the Groundlight SDK: +Assuming you have Python 3.9 or higher installed on your NVIDIA Jetson, you can proceed with the following steps to install or upgrade the Groundlight SDK: ### Installing Groundlight SDK @@ -39,6 +39,4 @@ For a complete end-to-end example of running on an NVIDIA Jetson, see [this GitH ## Ready to go! -You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](/docs/getting-started/api-tokens) and [Building Applications - - +You're now ready to start using the Groundlight SDK in your projects. For more information on using the SDK, refer to the [API Tokens](../getting-started/5-api-tokens.md) documentation and the [Building Applications Guide](../guide/). \ No newline at end of file diff --git a/docs/docs/installation/7-monitoring-notification-server.md b/docs/docs/installation/7-monitoring-notification-server.md deleted file mode 100644 index 94917598..00000000 --- a/docs/docs/installation/7-monitoring-notification-server.md +++ /dev/null @@ -1,83 +0,0 @@ -# Monitoring Notification Server - -This is the easiest way to deploy your Groundlight detectors on a linux computer. All configuration is done through a web user interface, and no code development is required. - -## Prerequisites - -1. Internet- connected linux computer -2. Video source (USB camera or RTSP stream) -3. Groundlight API Key (available from [groundlight.ai](https://www.groundlight.ai/)) - -## Using the Application - -Our Monitoring Notification Server is a server you can deploy anywhere to easily build Groundlight Detectors, and configure them to pull from custom image sources and post notifications. - -The Monitoring Notification Server has a simple web interface (depected below) that allows you to configure your detector(s), and a backend that runs on your device to pull images from your camera and post notifications. - -### MNS Home Page - -![Intro Page](/img/docker-img-frontpage.png) - -### Detector Dashboard - -![Detector Dashboard](/img/docker-img-dashboard.png) - -## Running the server - -There are several ways to deploy the code: - -- Using Docker Compose -- Using AWS Greengrass -- Using Kubernetes - -### Running with Docker Compose - -1. Use the file [`docker-compose.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose.yml). - -2. Run `docker-compose up` in the same directory as the `docker-compose.yml` file. - -> If you're using Docker Compose v2, replace `docker-compose` with `docker compose`. - -### Running from Docker Compose on 32-bit ARM (armv7) - -32-bit arm requires different binary images. - -1. Use the slightly different [`docker-compose-armv7.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose-armv7.yml). - -2. Run `docker-compose -f docker-compose-armv7.yml up`. - -> If you're using Docker Compose v2, replace `docker-compose` with `docker compose`. - -### Running with AWS Greengrass - -Before creating the component, run `sudo usermod -aG docker ggc_user` on your Greengrass device to allow the Greengrass service to access the host's Docker daemon. - -1. Create a new Greengrass Component -2. Select "Enter recipe as YAML" -3. Paste the YAML from [greengrass-recipe.yaml](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/greengrass-recipe.yaml) into the text box -4. Click "Create component" -5. Click "Deploy" to deploy the component to your Greengrass group - -### Running with Kubernetes - -We recommend a minimal Kubernetes install like [k3s](https://k3s.io/). - -> Use [`kubernetes.yaml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/kubernetes.yaml) manifest. - -1. Create a Kubernetes cluster and install `kubectl` on your machine. -2. Run `kubectl apply -f kubernetes.yaml` in the same directory as the `kubernetes.yaml` file. - -## Building from Source - -1. Install [Node.js](https://nodejs.org/en/download/) and [Python 3.8+](https://www.python.org/downloads/). - -```bash -git clone https://github.com/groundlight/monitoring-notification-server -cd monitoring-notification-server -npm install -npm run dev -``` - -Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. - -The FastApi server will be running on [http://0.0.0.0:8000](http://0.0.0.0:8000) – feel free to change the port in `package.json` (you'll also need to update it in `next.config.js`). diff --git a/docs/docs/installation/_category_.json b/docs/docs/installation/_category_.json index 874082cc..e1112892 100644 --- a/docs/docs/installation/_category_.json +++ b/docs/docs/installation/_category_.json @@ -1,4 +1,4 @@ { "label": "Installation", - "position": 3 + "position": 2 } diff --git a/docs/docs/installation/installation.md b/docs/docs/installation/installation.md index 977d5e14..0c9f3287 100644 --- a/docs/docs/installation/installation.md +++ b/docs/docs/installation/installation.md @@ -1,17 +1,28 @@ -# Installation +# Installation Guide for Groundlight Python SDK -Welcome to the Groundlight SDK installation guide. In this guide, you'll find step-by-step instructions on how to install and set up the Groundlight SDK on various platforms. +The Groundlight Python SDK requires Python 3.9 or higher and can be installed on all major platforms. Follow the installation guide for your specific operating system or device below. -## Platform-specific Installation Guides +## Operating System Installation Guides -Choose your platform from the list below and follow the instructions in the corresponding guide: +For desktop and server installations: -- [Linux](1-linux.md) -- [macOS](2-macos.md) -- [Windows](3-windows.md) -- [Raspberry Pi](4-raspberry-pi.md) -- [NVIDIA Jetson](5-nvidia-jetson.md) -- [Linux with Monitoring Notification Server](7-monitoring-notification-server.md) -- [ESP32 Camera Device](/docs/iot) +- [Linux Installation Guide](1-linux.md) - For Ubuntu, Debian, Fedora and other Linux distributions +- [macOS Installation Guide](2-macos.md) - For Intel and Apple Silicon Macs +- [Windows Installation Guide](3-windows.md) - For Windows 10 and 11 -After completing the installation process for your platform, you'll be ready to start [building visual applications](/docs/building-applications/) using the Groundlight SDK. +## Edge Device Installation Guides + +For IoT and edge computing devices: + +- [Raspberry Pi Installation Guide](5-raspberry-pi.md) - For Raspberry Pi 4 and 5 devices +- [NVIDIA Jetson Installation Guide](6-nvidia-jetson.md) - For Jetson Nano, Xavier and Orin devices + +## Alternative Groundlight Usage Options + +Explore different ways to utilize Groundlight: + +- [Streaming Processor with Docker](../other-ways-to-use/1-stream-processor.md) +- [ESP32 Camera Integration](../other-ways-to-use/2-iot.md) +- [Linux with Monitoring and Notification Server](../other-ways-to-use/3-monitoring-notification-server.md) + +Once you've completed the installation for your platform, you can begin [developing visual applications](/docs/guide/) using the Groundlight SDK. diff --git a/docs/docs/iot/_category_.json b/docs/docs/iot/_category_.json deleted file mode 100644 index e47d4520..00000000 --- a/docs/docs/iot/_category_.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "label": "IoT", - "position": 4, - "collapsed": false -} \ No newline at end of file diff --git a/docs/docs/other-ways-to-use/1-stream-processor.md b/docs/docs/other-ways-to-use/1-stream-processor.md new file mode 100644 index 00000000..5842713b --- /dev/null +++ b/docs/docs/other-ways-to-use/1-stream-processor.md @@ -0,0 +1,82 @@ +# Low-Code Stream Processor + +The [Groundlight Stream Processor](https://github.com/groundlight/stream) is a simple containerized application for processing video streams and submitting frames to Groundlight. + +It supports a variety of input sources, including: +- Video devices (webcams) +- Video files (MP4, etc) +- RTSP streams +- HLS streams +- YouTube videos +- Image directories +- Image URLs + +The Stream Processor can be combined with [Groundlight Alerts](../guide/9-alerts.md) to create a simple video analytics system. For example, you could use the Stream Processor to process a video stream from a security camera and send an alert when a person is detected. + +## Prerequisites: +You will need: +1. A [Groundlight account](https://dashboard.groundlight.ai/) +2. An API token from the [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens) +3. [Docker](https://www.docker.com/) installed on your system + +Set your Groundlight API token as an environment variable: +```bash +export GROUNDLIGHT_API_TOKEN="" +``` + +## Create a Detector via the Groundlight Dashboard +Once signed in to [the Groundlight dashboard](https://dashboard.groundlight.ai/), create a new detector by clicking the "Create New" button. Give your detector a name, a question, and a confidence threshold, then click "Save." + +You will be redirected to the detector's page, where you can find the detector ID under the Setup tab. Note this ID for later use. + +## Processing a Stream +Processing a stream is as easy as running a Docker container. For example, the following command will process a video file: +```bash +docker run -v /path/to/video:/videos groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d “” \ + -s /videos/video.mp4 \ + -f 1 +``` +This will begin submitting frames from the video file to Groundlight. The `-f` flag specifies the frame rate in terms of frames per second. The container can be stopped by pressing `Ctrl+C`. + +A variety of input sources are supported, including RTSP streams. To process an RTSP stream, run the following command: +```bash +docker run groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d “” \ + -s "" \ + -f 0.5 \ + -v +``` + +This will begin submitting frames from the RTSP stream to Groundlight. The `-v` flag enables verbose logging. + +If you only wish to submit frames to Groundlight when there is motion detected in the video stream, you can add the `-m` flag: +```bash +docker run groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d “” \ + -s "" \ + -f 2 \ + -m +``` + +You may want the container to run in the background. To do this, add the `--detach` flag to the `docker run` command: +```bash +docker run --detach groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d “” \ + -s "" \ + -f 2 \ + -m +``` + +:::tip +The Groundlight Stream Processor is lightweight and can be run on a Raspberry Pi or other low-power devices. +::: + +## Combining with Groundlight Alerts +The Stream Processor submits frames to Groundlight, but it does not do anything with the results. + +In order to build a useful alerting system, you can combine the Stream Processor with [Groundlight Alerts](../guide/9-alerts.md). diff --git a/docs/docs/iot/iot.md b/docs/docs/other-ways-to-use/2-iot.md similarity index 97% rename from docs/docs/iot/iot.md rename to docs/docs/other-ways-to-use/2-iot.md index 26160a06..7e25f9e3 100644 --- a/docs/docs/iot/iot.md +++ b/docs/docs/other-ways-to-use/2-iot.md @@ -1,6 +1,6 @@ --- id: esp32cam -title: "Setting up an ESP32 Camera Board" +title: "No-Code deployment on an ESP32 Camera Board" sidebar_position: 1 --- diff --git a/docs/docs/other-ways-to-use/3-monitoring-notification-server.md b/docs/docs/other-ways-to-use/3-monitoring-notification-server.md new file mode 100644 index 00000000..3a337ed6 --- /dev/null +++ b/docs/docs/other-ways-to-use/3-monitoring-notification-server.md @@ -0,0 +1,88 @@ +# Low-Code Monitoring Notification Server + +Groundlight's Monitoring Notification Server (MNS) is the easiest way to deploy your Groundlight detectors on a linux computer. All configuration is done through a web user interface, and no code development is required. + +## Prerequisites + +1. Internet-connected Linux computer +2. Video source (USB camera or RTSP stream) +3. Groundlight API Key (available from [groundlight.ai](https://www.groundlight.ai/)) + +## Using the Application + +The Monitoring Notification Server is a versatile tool that can be deployed on any server to facilitate the creation and management of Groundlight Detectors. It allows you to configure detectors to retrieve images from custom sources and send notifications. + +Featuring an intuitive web interface, the Monitoring Notification Server enables easy configuration of detectors. The server operates on your device, capturing images from your camera and sending notifications as needed. + +### MNS Home Page + +![Intro Page](/img/docker-img-frontpage.png) + +### Detector Dashboard + +![Detector Dashboard](/img/docker-img-dashboard.png) + +## Running the server + +To begin, clone the GitHub repository: https://github.com/groundlight/monitoring-notification-server + +```bash +git clone https://github.com/groundlight/monitoring-notification-server.git +cd monitoring-notification-server +``` + +Deployment options include Docker Compose, AWS Greengrass, and Kubernetes. + +### Running with Docker Compose + +1. Locate the [`docker-compose.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose.yml) file. + +2. Run `docker-compose up` in the directory containing the `docker-compose.yml` file (the root of the repository). + +:::tip +If you're using Docker Compose v2, replace `docker-compose` with `docker compose`. +::: + +### Running from Docker Compose on 32-bit ARM (armv7) + +32-bit arm requires different binary images. + +1. Use the slightly different [`docker-compose-armv7.yml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/docker-compose-armv7.yml). + +2. Run `docker-compose -f docker-compose-armv7.yml up`. + +### Running with AWS Greengrass + +Before creating the component, run `sudo usermod -aG docker ggc_user` on your Greengrass device to allow the Greengrass service to access the host's Docker daemon. + +1. Create a new Greengrass Component +2. Select "Enter recipe as YAML" +3. Paste the YAML from [greengrass-recipe.yaml](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/greengrass-recipe.yaml) into the text box +4. Click "Create component" +5. Click "Deploy" to deploy the component to your Greengrass group + +### Running with Kubernetes + +For a minimal Kubernetes setup, we recommend using [k3s](https://k3s.io/). + +1. Set up a Kubernetes cluster and install `kubectl` on your machine. +2. Apply the Kubernetes configuration by running: + ```bash + kubectl apply -f kubernetes.yaml + ``` + Ensure you are in the directory containing the [`kubernetes.yaml`](https://github.com/groundlight/monitoring-notification-server/blob/main/deploy/kubernetes.yaml) file. + +## Building from Source + +1. Install [Node.js](https://nodejs.org/en/download/) and [Python 3.9+](https://www.python.org/downloads/). + +```bash +git clone https://github.com/groundlight/monitoring-notification-server +cd monitoring-notification-server +npm install +npm run dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +The FastApi server will be running on [http://0.0.0.0:8000](http://0.0.0.0:8000) – feel free to change the port in `package.json` (you'll also need to update it in `next.config.js`). diff --git a/docs/docs/other-ways-to-use/_category_.json b/docs/docs/other-ways-to-use/_category_.json new file mode 100644 index 00000000..dc7ff00c --- /dev/null +++ b/docs/docs/other-ways-to-use/_category_.json @@ -0,0 +1,5 @@ +{ + "label": "Alternative Deployment Options", + "position": 5, + "collapsed": false +} \ No newline at end of file diff --git a/docs/docs/getting-started/4-dog-on-couch.md b/docs/docs/sample-applications/1-dog-on-couch.md similarity index 91% rename from docs/docs/getting-started/4-dog-on-couch.md rename to docs/docs/sample-applications/1-dog-on-couch.md index 0305af0c..28e9272d 100644 --- a/docs/docs/getting-started/4-dog-on-couch.md +++ b/docs/docs/sample-applications/1-dog-on-couch.md @@ -4,14 +4,14 @@ Here is a whimsical example of how you could use Groundlight in your home to kee ## Requirements -- [Groundlight SDK](/docs/installation/) with Python 3.8 or higher +- [Groundlight SDK](/docs/installation/) with Python 3.9 or higher - A supported USB or network-connected camera - A pre-recorded sound file (e.g., `get_off_couch.mp3`) - A couch and a dog are recommended for proper end-to-end testing. ## Installation -Ensure you have Python 3.8 or higher installed, and then install the Groundlight SDK and OpenCV library: +Ensure you have Python 3.9 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries: ```bash pip install groundlight opencv-python pillow pyaudio @@ -24,12 +24,12 @@ pip install groundlight opencv-python pillow pyaudio 2. Next, we'll write the Python script for the application. Import the required libraries: ```python notest -import time import cv2 -from groundlight import Groundlight -from PIL import Image import pyaudio +import time import wave +from PIL import Image +from groundlight import Groundlight, ApiException ``` 3. Define a function to capture an image from the camera using OpenCV: @@ -90,7 +90,7 @@ while True: if answer == "YES": print("Dog detected on the couch!") play_sound("get_off_couch.mp3") - except Exception as e: + except ApiException as e: print(f"Error submitting image query: {e}") else: print("Failed to capture image") diff --git a/docs/docs/getting-started/3-retail-analytics.md b/docs/docs/sample-applications/2-retail-analytics.md similarity index 96% rename from docs/docs/getting-started/3-retail-analytics.md rename to docs/docs/sample-applications/2-retail-analytics.md index f64073ba..e3be7a23 100644 --- a/docs/docs/getting-started/3-retail-analytics.md +++ b/docs/docs/sample-applications/2-retail-analytics.md @@ -18,13 +18,13 @@ To implement this retail analytics solution, a store would need to install a sup ## Requirements -- [Groundlight SDK](/docs/installation/) with Python 3.8 or higher +- [Groundlight SDK](/docs/installation/) with Python 3.9 or higher - A supported USB or network-connected camera - An email account with SMTP access to send the daily log ## Installation -Ensure you have Python 3.8 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries: +Ensure you have Python 3.9 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries: ```bash pip install groundlight opencv-python pillow @@ -37,14 +37,16 @@ pip install groundlight opencv-python pillow 2. Next, we'll write the Python script for the application. Import the required libraries: ```python notest -import time -import cv2 import smtplib -from groundlight import Groundlight -from PIL import Image +import time from datetime import datetime, timedelta from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText + +import cv2 +from PIL import Image + +from groundlight import Groundlight ``` 3. Define a function to capture an image from the camera using OpenCV: @@ -102,7 +104,7 @@ gl = Groundlight() detector = gl.get_or_create_detector( name="counter-in-use", query="Is there a customer at the service counter?", - # We can get away with relatively low confidence since we're aggregating + # We can get away with relatively low confidence since we're aggregating across images confidence_threshold=0.8) DELAY = 60 @@ -161,7 +163,7 @@ while True: This application captures an image using the `capture_image` function, then submits it to the Groundlight API for analysis. If a customer is detected at the counter, it logs the event. Every hour, it prints a summary of the counter's usage percentage, and at the end of the day, it emails the daily log using the `send_email` function. -Save the script as `service_counter_monitor.py` and run it: +Save the entire script as `service_counter_monitor.py` and run it: ```bash python service_counter_monitor.py diff --git a/docs/docs/sample-applications/3-streaming-with-alerts.md b/docs/docs/sample-applications/3-streaming-with-alerts.md new file mode 100644 index 00000000..f6a30b7a --- /dev/null +++ b/docs/docs/sample-applications/3-streaming-with-alerts.md @@ -0,0 +1,94 @@ +# A Quick Example: Live Stream Monitor + +A quick example to help you get started with monitoring live streams using the [groundlight/stream](../other-ways-to-use/1-stream-processor.md) container. + +In this example, we will set up a monitor on a live stream of a bird feeder and configure Groundlight to alert us when a bird is present at the feeder. + +## Requirements + +- [Docker](https://www.docker.com/) installed on your system +- A YouTube live stream URL or video ID you'd like to monitor. For example, this live stream of a Bird Feeder in Panama hosted by the Cornell Lab of Ornithology: [`https://www.youtube.com/watch?v=WtoxxHADnGk`](https://www.youtube.com/watch?v=WtoxxHADnGk) +- A [Groundlight account](https://dashboard.groundlight.ai) + + + +## Installation + +Pull the Groundlight Stream container: + +```bash +docker pull groundlight/stream +``` + +## Creating the Monitor + +The Groundlight Stream container makes it easy to monitor video streams. Here's how to use it: + +1. First, get (or create) your API token from the [Groundlight dashboard](https://dashboard.groundlight.ai/reef/my-account/api-tokens). Set your Groundlight API token as an environment variable: + +```bash +export GROUNDLIGHT_API_TOKEN="" +``` + +2. Create a Binary-mode detector in the [dashboard](https://dashboard.groundlight.ai). Set the question to "Is there a bird at the feeder?" and the confidence threshold to 0.75. Note the detector ID for later use. + +:::tip + +We use a relatively low confidence threshold in this example because birdwatching is a fun and casual activity. For more critical applications, you may want to set a higher confidence threshold. + +::: + +3. Now, run the Groundlight Stream container to process the live stream. For example, to monitor the Cornell Lab of Ornithology's bird feeder live stream, you could use the following command: + +```bash +docker run groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d "" \ + -s "https://www.youtube.com/watch?v=WtoxxHADnGk" \ + -f 0.25 \ # 1 frame every 4 seconds + -m \ # enable motion detection to only process frames when movement occurs + -v # enable verbose logging +``` +You should see Image Queries being submitted to Groundlight as the container processes the live stream. Once you have confirmed that the container is working as expected, you can remove the `-v` flag to reduce the amount of logging, and you can also run the container in the background by adding the `--detach` flag. +```bash +docker run --detach groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d "" \ + -s "https://www.youtube.com/watch?v=WtoxxHADnGk" \ + -f 0.25 \ # 1 frame every 4 seconds + -m # enable motion detection to only process frames when movement occurs +``` + +4. Finally, let's set up an Alert to notify you when a bird visits the feeder. In the Groundlight dashboard: + + 1. Navigate to [the Alerts tab](https://dashboard.groundlight.ai/reef/alerts) and click "Create New Alert" + 2. Enter a descriptive name like for the alert, such as "Bird at feeder" + 3. Select your bird detector by name + 4. Set the alert condition to `Gives answer 'Yes' For 1 Consecutive answer(s)` + 5. Choose "Text" as the Alert Type and enter your phone number + 6. Enable "Include image in message" to receive a photo of the bird with each alert (optional) + 7. Enable a 5-minute snooze period to prevent alert fatigue (optional) + 8. Click "Create" to activate your alert + +5. Now, sit back and relax! The container will begin submitting frames from the live stream to Groundlight. You will receive alerts when a bird is detected at the feeder. + +## Additional Options + +You can also use `groundlight/stream` to process local video files, RTSP streams, and more. Here are some examples: +- Process local video files by mounting them: +```bash +docker run -v /path/to/video:/videos groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d \ + -s /videos/video.mp4 +``` + +- Connect to RTSP cameras: +```bash +docker run groundlight/stream \ + -t "$GROUNDLIGHT_API_TOKEN" \ + -d \ + -s "rtsp://username:password@camera_ip:554/stream" +``` + +See the complete documentation at https://github.com/groundlight/stream \ No newline at end of file diff --git a/docs/docs/building-applications/8-industrial.md b/docs/docs/sample-applications/4-industrial.md similarity index 98% rename from docs/docs/building-applications/8-industrial.md rename to docs/docs/sample-applications/4-industrial.md index cf50d3ed..48dd0eef 100644 --- a/docs/docs/building-applications/8-industrial.md +++ b/docs/docs/sample-applications/4-industrial.md @@ -1,7 +1,3 @@ ---- -sidebar_position: 7 ---- - # Industrial and Manufacturing Applications Modern natural language-based computer vision is transforming industrial and manufacturing applications by enabling more intuitive interaction with automation systems. Groundlight offers cutting-edge computer vision technology that can be seamlessly integrated into various industrial processes, enhancing efficiency, productivity, and quality control. diff --git a/docs/docs/sample-applications/_category_.json b/docs/docs/sample-applications/_category_.json new file mode 100644 index 00000000..f1985446 --- /dev/null +++ b/docs/docs/sample-applications/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Sample Applications", + "position": 4 + } diff --git a/docs/docs/sample-applications/sample-applications.md b/docs/docs/sample-applications/sample-applications.md new file mode 100644 index 00000000..8c5d09d4 --- /dev/null +++ b/docs/docs/sample-applications/sample-applications.md @@ -0,0 +1,45 @@ +# Sample Applications + +Explore these example applications to see Groundlight's computer vision capabilities in action: + +## Industrial and Manufacturing Applications + +Groundlight's natural language-based computer vision technology transforms industrial processes in several key areas: + +- **Machine Tending**: Automate loading/unloading of CNC machines and manufacturing equipment +- **Process Automation**: Optimize workflows and reduce manual intervention through intelligent vision systems +- **Quality Control**: Identify defects and maintain strict quality standards +- **Cobot Integration**: Enhance capabilities of collaborative robots and CNC machines + +[Learn more about industrial applications →](/docs/sample-applications/industrial) + +## Retail Analytics Solution + +Monitor customer service counter utilization with this practical retail application. Features include: + +- Real-time tracking of service counter usage +- Hourly utilization summaries +- Automated daily reports via email +- Data-driven insights for staff scheduling and store layout optimization + +[View the retail analytics implementation →](/docs/sample-applications/retail-analytics) + +## Fun Project: Dog-on-Couch Detector + +Create a playful home automation system that detects when your dog is on the couch and plays a pre-recorded message. This example demonstrates: + +- Real-time image capture and analysis +- Audio playback integration +- Continuous monitoring capabilities + +[Build your own dog detector →](/docs/sample-applications/dog-on-couch) + +## Live Stream Monitor: Bird Feeder Edition + +Monitor live streams with automated alerts using Groundlight's vision API. Features include: + +- Frame capture from live streams +- Alert system integration +- Simple command-line interface + +[Create a monitor for birds at your feeder →](/docs/sample-applications/streaming-with-alerts) \ No newline at end of file diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 8e90100b..4937bc51 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -103,6 +103,11 @@ const config = { ({ // image is expected to be a "social card". Logo for now. image: "img/gl-icon400.png", + colorMode: { + defaultMode: 'dark', + disableSwitch: false, + respectPrefersColorScheme: false, + }, navbar: { title: "Groundlight", logo: { @@ -118,7 +123,12 @@ const config = { label: "Docs", }, { - to: "/docs/building-applications", + to: "/docs/guide", + label: "Guide", + position: "left", + }, + { + to: "/docs/sample-applications", label: "Applications", position: "left", }, @@ -150,8 +160,8 @@ const config = { to: "/docs/getting-started", }, { - label: "Building Applications", - to: "/docs/building-applications", + label: "Guide", + to: "/docs/guide", }, { label: "Installation", @@ -192,11 +202,11 @@ const config = { href: "https://pypi.org/project/groundlight/", }, { - label: "Video streaming", + label: "Video Streaming", href: "https://github.com/groundlight/stream", }, { - label: "Arduino", + label: "Arduino / ESP32", href: "https://github.com/groundlight/esp32cam", }, ], diff --git a/docs/package-lock.json b/docs/package-lock.json index 006c0939..26baf099 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -1,61 +1,64 @@ { - "name": "web", + "name": "code-groundlight-ai", "version": "0.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { - "name": "web", + "name": "code-groundlight-ai", "version": "0.0.0", "dependencies": { - "@docusaurus/core": "3.0.0", - "@docusaurus/plugin-client-redirects": "^3.0.0", - "@docusaurus/preset-classic": "3.0.0", - "@easyops-cn/docusaurus-search-local": "^0.38.0", - "@mdx-js/react": "^3.0.0", - "clsx": "^1.2.1", - "prism-react-renderer": "^2.3.0", + "@docusaurus/core": "^3.6.3", + "@docusaurus/plugin-client-redirects": "^3.6.3", + "@docusaurus/preset-classic": "^3.6.3", + "@easyops-cn/docusaurus-search-local": "^0.46.1", + "@mdx-js/react": "^3.1.0", + "clsx": "^2.1.1", + "prism-react-renderer": "^2.4.0", "react": "^18.0.0", "react-dom": "^18.0.0", "rehype-katex": "^7.0.1", "remark-math": "^6.0.0" }, "devDependencies": { - "@docusaurus/module-type-aliases": "3.0.0", - "@docusaurus/tsconfig": "3.0.0", - "@docusaurus/types": "3.0.0", + "@docusaurus/module-type-aliases": "^3.6.3", + "@docusaurus/tsconfig": "^3.6.3", + "@docusaurus/types": "^3.6.3", "typescript": "~5.2.2" }, "engines": { - 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zY;1IQw4v8Iiswz9f*(;{w8;Y?qj2s3?{gO?iLXVu`|Vmys&)tF8N(;IU$=; z$;XdG#Clr_%px5U?|5CQr`7D*^bHIUs(SIfwx>x&)c7WB99(%%!u!QyqTsO;6efcw zCvGWLz1P>!NrI0NcJ5!bMEUrdSyH-A_pZSDbbGr`Ng8z@%PY%9Dnr|Y83mhE_Wdp_ zi$iIZ)U;oUA8lDNCCrlFHoeK?F7@4kQ0q=(Zbu`~=a1(e+>nJYWi*0+Cq5-W|3jxF zR9fY(cV>IOb~fj^9AO4}h0o6;J=Je|R0tcK70J>(Hoqwq!*6~mBm4go{!gC6>AgkM W)_~KWl(Kig-xGPY$0f2b|Nj6tj*DCX literal 0 HcmV?d00001 diff --git a/generated/.gitlab-ci.yml b/generated/.gitlab-ci.yml index 03ea3147..4bd9b626 100644 --- a/generated/.gitlab-ci.yml +++ b/generated/.gitlab-ci.yml @@ -10,15 +10,22 @@ stages: - pip install -r test-requirements.txt - pytest --cov=groundlight_openapi_client -test-3.6: +test-3.9: extends: .tests - image: python:3.6-alpine -test-3.7: + image: python:3.9-alpine + +test-3.10: extends: .tests - image: python:3.7-alpine -test-3.8: + image: python:3.10-alpine + +test-3.11: extends: .tests - image: python:3.8-alpine -test-3.9: + image: python:3.11-alpine + +test-3.12: extends: .tests - image: python:3.9-alpine + image: python:3.12-alpine + +test-3.13: + extends: .tests + image: python:3.13-alpine \ No newline at end of file diff --git a/generated/.travis.yml b/generated/.travis.yml index 7a48c12c..346a348b 100644 --- a/generated/.travis.yml +++ b/generated/.travis.yml @@ -1,10 +1,11 @@ # ref: https://docs.travis-ci.com/user/languages/python language: python python: - - "3.6" - - "3.7" - - "3.8" - "3.9" + - "3.10" + - "3.11" + - "3.12" + - "3.13" # command to install dependencies install: - "pip install -r requirements.txt" diff --git a/package-lock.json b/package-lock.json index c8c16bf4..e5928619 100644 --- a/package-lock.json +++ b/package-lock.json @@ -11,18 +11,32 @@ "remark-math": "^6.0.0" } }, + "node_modules/@babel/runtime": { + "version": "7.26.0", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.26.0.tgz", + "integrity": "sha512-FDSOghenHTiToteC/QRlv2q3DhPZ/oOXTBoirfWNx1Cx3TMVcGWQtMMmQcSvb/JjpNeGzx8Pq/b4fKEJuWm1sw==", + "license": "MIT", + "dependencies": { + "regenerator-runtime": "^0.14.0" + }, + "engines": { + "node": ">=6.9.0" + } + }, "node_modules/@lukeed/csprng": { "version": "1.1.0", "resolved": "https://registry.npmjs.org/@lukeed/csprng/-/csprng-1.1.0.tgz", "integrity": "sha512-Z7C/xXCiGWsg0KuKsHTKJxbWhpI3Vs5GwLfOean7MGyVFGqdRgBbAjOCh6u4bbjPc/8MJ2pZmK/0DLdCbivLDA==", + "license": "MIT", "engines": { "node": ">=8" } }, "node_modules/@nestjs/axios": { - "version": "3.0.3", - "resolved": "https://registry.npmjs.org/@nestjs/axios/-/axios-3.0.3.tgz", - "integrity": "sha512-h6TCn3yJwD6OKqqqfmtRS5Zo4E46Ip2n+gK1sqwzNBC+qxQ9xpCu+ODVRFur6V3alHSCSBxb3nNtt73VEdluyA==", + "version": "3.1.1", + "resolved": "https://registry.npmjs.org/@nestjs/axios/-/axios-3.1.1.tgz", + "integrity": "sha512-ySoxrzqX80P1q6LKLKGcgyBd2utg4gbC+4FsJNpXYvILorMlxss/ECNogD9EXLCE4JS5exVFD5ez0nK5hXcNTQ==", + "license": "MIT", "peerDependencies": { "@nestjs/common": "^7.0.0 || ^8.0.0 || ^9.0.0 || ^10.0.0", "axios": "^1.3.1", @@ -30,9 +44,10 @@ } }, 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"license": "MIT", "dependencies": { "ansi-styles": "^4.0.0", @@ -2050,18 +2696,13 @@ "strip-ansi": "^6.0.0" }, "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/chalk/wrap-ansi?sponsor=1" + "node": ">=8" } }, - "node_modules/wrappy": { - "version": "1.0.2", - "license": "ISC" - }, "node_modules/y18n": { "version": "5.0.8", + "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz", + "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==", "license": "ISC", "engines": { "node": ">=10" @@ -2069,6 +2710,8 @@ }, "node_modules/yargs": { "version": "16.2.0", + "resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz", + "integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==", "license": "MIT", "dependencies": { "cliui": "^7.0.2", @@ -2085,6 +2728,8 @@ }, "node_modules/yargs-parser": { "version": "20.2.9", + "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-20.2.9.tgz", + "integrity": "sha512-y11nGElTIV+CT3Zv9t7VKl+Q3hTQoT9a1Qzezhhl6Rp21gJ/IVTW7Z3y9EWXhuUBC2Shnf+DX0antecpAwSP8w==", "license": "ISC", "engines": { "node": ">=10" @@ -2092,6 +2737,8 @@ }, "node_modules/zwitch": { "version": "2.0.4", + "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", + "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", "license": "MIT", "funding": { "type": "github", diff --git a/pyproject.toml b/pyproject.toml index 908316e1..11c7fbc9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,19 +58,19 @@ groundlight = "groundlight.cli:groundlight" [tool.black] line-length = 120 preview = true -target-version = ["py37"] +target-version = ["py39"] # https://mypy.readthedocs.io/en/stable/config_file.html [tool.mypy] ignore_missing_imports = true -python_version = "3.8" +python_version = "3.9" # https://beta.ruff.rs/docs/configuration/#using-pyprojecttoml [tool.ruff] exclude = ["__init__.py"] line-length = 120 select = ["E", "F", "I", "N", "PL"] # shorthand names for plugins -target-version = "py37" +target-version = "py39" # https://toml-sort.readthedocs.io/en/latest/#configuration-file [tool.tomlsort] diff --git a/sphinx_docs/conf.py b/sphinx_docs/conf.py index c9db0e27..fd94dfa8 100644 --- a/sphinx_docs/conf.py +++ b/sphinx_docs/conf.py @@ -9,6 +9,7 @@ import os import sys +from datetime import datetime import toml @@ -26,7 +27,7 @@ def get_version_name() -> str: project = "Groundlight Python SDK" -copyright = "2023, Groundlight AI " +copyright = f"{datetime.now().year}, Groundlight AI " author = "Groundlight AI " version = get_version_name() release = version diff --git a/src/groundlight/client.py b/src/groundlight/client.py index 3275b939..14ab15af 100644 --- a/src/groundlight/client.py +++ b/src/groundlight/client.py @@ -1,3 +1,4 @@ +# pylint: disable=too-many-lines import logging import os import time @@ -56,7 +57,8 @@ class ApiTokenError(GroundlightClientError): class Groundlight: # pylint: disable=too-many-instance-attributes """ - Client for accessing the Groundlight cloud service. + Client for accessing the Groundlight cloud service. Provides methods to create visual detectors, + submit images for analysis, and retrieve predictions. The API token (auth) is specified through the **GROUNDLIGHT_API_TOKEN** environment variable by default. @@ -64,27 +66,45 @@ class Groundlight: # pylint: disable=too-many-instance-attributes gl = Groundlight() detector = gl.get_or_create_detector( - name="door", - query="Is the door locked?", - confidence_threshold=0.9 - ) + name="door_detector", + query="Is the door open?", + confidence_threshold=0.9 + ) + + # Submit image and get prediction image_query = gl.submit_image_query( - detector=detector, - image="path/to/image.jpeg", - wait=0.0, - human_review="ALWAYS" - ) - print(f"Image query confidence = {image_query.result.confidence}") - - # Poll the backend service for a confident answer + detector=detector, + image="path/to/image.jpg", + wait=30.0 + ) + print(f"Answer: {image_query.result.label}") + + # Async submission with human review + image_query = gl.ask_async( + detector=detector, + image="path/to/image.jpg", + human_review="ALWAYS" + ) + + # Later, get the result image_query = gl.wait_for_confident_result( - image_query=image_query, - confidence_threshold=0.9, - timeout_sec=60.0 - ) + image_query=image_query, + confidence_threshold=0.95, + timeout_sec=60.0 + ) - # Examine new confidence after a continuously trained ML model has re-evaluated the image query - print(f"Image query confidence = {image_query.result.confidence}") + :param endpoint: Optional custom API endpoint URL. If not specified, uses the default Groundlight endpoint. + :param api_token: Authentication token for API access. If not provided, will attempt to read from + the "GROUNDLIGHT_API_TOKEN" environment variable. + :param disable_tls_verification: If True, disables SSL/TLS certificate verification for API calls. + When not specified, checks the "DISABLE_TLS_VERIFY" environment variable (1=disable, 0=enable). + Certificate verification is enabled by default. + + Warning: Only disable verification when connecting to a Groundlight Edge Endpoint using + self-signed certificates. For security, always keep verification enabled when using the + Groundlight cloud service. + + :return: Groundlight client instance """ DEFAULT_WAIT: float = 30.0 @@ -98,24 +118,20 @@ def __init__( endpoint: Optional[str] = None, api_token: Optional[str] = None, disable_tls_verification: Optional[bool] = None, - ) -> None: + ): """ - Constructs a Groundlight client. + Initialize a new Groundlight client instance. - :param endpoint: optionally specify a different endpoint + :param endpoint: Optional custom API endpoint URL. If not specified, uses the default Groundlight endpoint. + :param api_token: Authentication token for API access. + If not provided, will attempt to read from the "GROUNDLIGHT_API_TOKEN" environment variable. + :param disable_tls_verification: If True, disables SSL/TLS certificate verification for API calls. + When not specified, checks the "DISABLE_TLS_VERIFY" environment variable + (1=disable, 0=enable). Certificate verification is enabled by default. - :param api_token: use this API token for your API calls. - If unset, fallback to the environment variable "GROUNDLIGHT_API_TOKEN". - - :param disable_tls_verification: Set this to `True` to skip verifying SSL/TLS certificates - when calling API from https server. If unset, the fallback will check - the environment variable "DISABLE_TLS_VERIFY" for `1` or `0`. By default, - certificates are verified. - - Disable verification if using a self-signed TLS certificate with a Groundlight - Edge Endpoint. It is unadvised to disable verification if connecting directly - to the Groundlight cloud service. - :type disable_tls_verification: Optional[bool] + Warning: Only disable verification when connecting to a Groundlight Edge + Endpoint using self-signed certificates. For security, always keep + verification enabled when using the Groundlight cloud service. :return: Groundlight client """ @@ -203,9 +219,20 @@ def _fixup_image_query(iq: ImageQuery) -> ImageQuery: def whoami(self) -> str: """ - Return the username associated with the API token. + Return the username (email address) associated with the current API token. + + This method verifies that the API token is valid and returns the email address of the authenticated user. + It can be used to confirm that authentication is working correctly. - :return: str + **Example usage**:: + + gl = Groundlight() + username = gl.whoami() + print(f"Authenticated as {username}") + + :return: The email address of the authenticated user + :raises ApiTokenError: If the API token is invalid + :raises GroundlightClientError: If there are connectivity issues with the Groundlight service """ obj = self.user_api.who_am_i(_request_timeout=DEFAULT_REQUEST_TIMEOUT) return obj["email"] @@ -220,7 +247,13 @@ def _user_is_privileged(self) -> bool: def get_detector(self, id: Union[str, Detector]) -> Detector: # pylint: disable=redefined-builtin """ - Get a detector by id + Get a Detector by id. + + **Example usage**:: + + gl = Groundlight() + detector = gl.get_detector(id="det_12345") + print(detector) :param id: the detector id @@ -238,7 +271,13 @@ def get_detector(self, id: Union[str, Detector]) -> Detector: # pylint: disable def get_detector_by_name(self, name: str) -> Detector: """ - Get a detector by name + Get a Detector by name. + + **Example usage**:: + + gl = Groundlight() + detector = gl.get_detector_by_name(name="door_detector") + print(detector) :param name: the detector name @@ -248,13 +287,23 @@ def get_detector_by_name(self, name: str) -> Detector: def list_detectors(self, page: int = 1, page_size: int = 10) -> PaginatedDetectorList: """ - List out detectors you own + Retrieve a paginated list of detectors associated with your account. - :param page: the page number + **Example usage**:: + + gl = Groundlight() - :param page_size: the page size + # Get first page of 5 detectors + detectors = gl.list_detectors(page=1, page_size=5) - :return: PaginatedDetectorList + for detector in detectors.items: + print(detector) + + :param page: The page number to retrieve (1-based indexing). Use this parameter to navigate + through multiple pages of detectors. + :param page_size: The number of detectors to return per page. + + :return: PaginatedDetectorList containing the requested page of detectors and pagination metadata """ obj = self.detectors_api.list_detectors( page=page, page_size=page_size, _request_timeout=DEFAULT_REQUEST_TIMEOUT @@ -305,27 +354,54 @@ def create_detector( # noqa: PLR0913 metadata: Union[dict, str, None] = None, ) -> Detector: """ - Create a new detector with a given name and query - - :param name: the detector name - - :param query: the detector query - - :param group_name: the detector group that the new detector should belong to. If none, - defaults to default_group + Create a new Detector with a given name and query. - :param confidence_threshold: the confidence threshold + **Example usage**:: - :param patience_time: the patience time, or how long Groundlight should work to generate a - confident answer. Defaults to 30 seconds. + gl = Groundlight() - :param pipeline_config: the pipeline config + # Create a basic binary detector + detector = gl.create_detector( + name="dog-on-couch-detector", + query="Is there a dog on the couch?", + confidence_threshold=0.9, + patience_time=30.0 + ) - :param metadata: A dictionary or JSON string of custom key/value metadata to associate with - the detector (limited to 1KB). You can retrieve this metadata later by calling - `get_detector()`. + # Create a detector with metadata + detector = gl.create_detector( + name="door-monitor", + query="Is the door open?", + metadata={"location": "front-entrance", "building": "HQ"}, + confidence_threshold=0.95 + ) - :return: Detector + # Create a detector in a specific group + detector = gl.create_detector( + name="vehicle-counter", + query="How many vehicles are in the parking lot?", + group_name="parking-monitoring", + patience_time=60.0 + ) + :param name: A short, descriptive name for the detector. This name should be unique within your account + and help identify the detector's purpose. + :param query: The question that the detector will answer about images. For binary classification, + this should be a yes/no question (e.g. "Is there a person in the image?"). + :param group_name: Optional name of a group to organize related detectors together. If not specified, + the detector will be placed in the default group. + :param confidence_threshold: A value between 0.5 and 1 that sets the minimum confidence level required + for the ML model's predictions. If confidence is below this threshold, + the query may be sent for human review. + :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a + confident prediction before falling back to human review. Defaults to 30 seconds. + :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific + prediction pipeline for this detector. + :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with + the detector (limited to 1KB). This metadata can be used to store additional + information like location, purpose, or related system IDs. You can retrieve this + metadata later by calling `get_detector()`. + + :return: The created Detector object """ detector_creation_input = self._prep_create_detector( @@ -351,25 +427,48 @@ def get_or_create_detector( # noqa: PLR0913 metadata: Union[dict, str, None] = None, ) -> Detector: """ - Tries to look up the detector by name. If a detector with that name, query, and - confidence exists, return it. Otherwise, create a detector with the specified query and + Tries to look up the Detector by name. If a Detector with that name, query, and + confidence exists, return it. Otherwise, create a Detector with the specified query and config. - :param name: the detector name - - :param query: the detector query - - :param group_name: the detector group that the new detector should belong to - - :param confidence_threshold: the confidence threshold + **Example usage**:: - :param pipeline_config: the pipeline config + gl = Groundlight() - :param metadata: A dictionary or JSON string of custom key/value metadata to associate with - the detector (limited to 1KB). You can retrieve this metadata later by calling - `get_detector()`. + detector = gl.get_or_create_detector( + name="service-counter-usage", + query="Is there a customer at the service counter?", + group_name="retail-analytics", + confidence_threshold=0.95, + metadata={"location": "store-123"} + ) - :return: Detector + :param name: A short, descriptive name for the detector. This name should be unique within your account + and help identify the detector's purpose. + :param query: The question that the detector will answer about images. For binary classification, + this should be a yes/no question (e.g. "Is there a person in the image?"). + :param group_name: Optional name of a group to organize related detectors together. If not specified, + the detector will be placed in the default group. + :param confidence_threshold: A value between 0.5 and 1 that sets the minimum confidence level required + for the ML model's predictions. If confidence is below this threshold, + the query may be sent for human review. + :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific + prediction pipeline for this detector. + :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with + the detector (limited to 1KB). This metadata can be used to store additional + information like location, purpose, or related system IDs. You can retrieve this + metadata later by calling `get_detector()`. + + :return: Detector with the specified configuration + :raises ValueError: If an existing detector is found but has different configuration + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors + + .. note:: + If a detector with the given name exists, this method verifies that its + configuration (query, group_name, etc.) matches what was requested. If + there are any mismatches, it raises ValueError rather than modifying the + existing detector. """ try: existing_detector = self.get_detector_by_name(name) @@ -405,11 +504,25 @@ def get_or_create_detector( # noqa: PLR0913 def get_image_query(self, id: str) -> ImageQuery: # pylint: disable=redefined-builtin """ - Get an image query by id + Get an ImageQuery by its ID. This is useful for retrieving the status and results of a + previously submitted query. - :param id: the image query id + **Example Usage:** - :return: ImageQuery + gl = Groundlight() + + # Get an existing image query by ID + image_query = gl.get_image_query("iq_abc123") + + # Get the result if available + if image_query.result is not None: + print(f"Answer: {image_query.result.label}") + print(f"Source: {image_query.result.source}") + print(f"Confidence: {image_query.result.confidence}") # e.g. 0.98 + + :param id: The ImageQuery ID to look up. This ID is returned when submitting a new ImageQuery. + + :return: ImageQuery object containing the query details and results """ obj = self.image_queries_api.get_image_query(id=id, _request_timeout=DEFAULT_REQUEST_TIMEOUT) iq = ImageQuery.parse_obj(obj.to_dict()) @@ -417,13 +530,25 @@ def get_image_query(self, id: str) -> ImageQuery: # pylint: disable=redefined-b def list_image_queries(self, page: int = 1, page_size: int = 10) -> PaginatedImageQueryList: """ - List out image queries you own + List all image queries associated with your account, with pagination support. + + **Example Usage**:: - :param page: the page number + gl = Groundlight() + + # Get first page of 10 image queries + queries = gl.list_image_queries(page=1, page_size=10) - :param page_size: the page size + # Access results + for query in queries.results: + print(f"Query ID: {query.id}") + print(f"Result: {query.result.label if query.result else 'No result yet'}") - :return: PaginatedImageQueryList + :param page: The page number to retrieve (1-based indexing). Use this parameter to navigate + through multiple pages of image queries. + :param page_size: Number of image queries to return per page. Default is 10. + :return: PaginatedImageQueryList containing the requested page of image queries and pagination metadata + like total count and links to next/previous pages. """ obj = self.image_queries_api.list_image_queries( page=page, page_size=page_size, _request_timeout=DEFAULT_REQUEST_TIMEOUT @@ -447,49 +572,90 @@ def submit_image_query( # noqa: PLR0913 # pylint: disable=too-many-arguments, t image_query_id: Optional[str] = None, ) -> ImageQuery: """ - Evaluates an image with Groundlight. + Evaluates an image with Groundlight. This is the core method for getting predictions about images. - :param detector: the Detector object, or string id of a detector like `det_12345` + **Example Usage**:: - :param image: The image, in several possible formats: - - filename (string) of a jpeg file - - byte array or BytesIO or BufferedReader with jpeg bytes - - numpy array with values 0-255 and dimensions (H,W,3) in BGR order - (Note OpenCV uses BGR not RGB. `img[:, :, ::-1]` will reverse the channels) - - PIL Image: Any binary format must be JPEG-encoded already. - Any pixel format will get converted to JPEG at high quality before sending to service. + from groundlight import Groundlight + from PIL import Image - :param wait: How long to poll (in seconds) for a confident answer. This is a client-side timeout. + gl = Groundlight() + det = gl.get_or_create_detector( + name="parking-space", + query="Is there a car in the leftmost parking space?" + ) + + # Basic synchronous usage + image = "path/to/image.jpg" + image_query = gl.submit_image_query(detector=det, image=image) + print(f"The answer is {image_query.result.label}") + + # Asynchronous usage with custom confidence + image = Image.open("path/to/image.jpg") + image_query = gl.submit_image_query( + detector=det, + image=image, + wait=0, # Don't wait for result + confidence_threshold=0.95, + want_async=True + ) + print(f"Submitted image_query {image_query.id}") + + # With metadata and mandatory human review + image_query = gl.submit_image_query( + detector=det, + image=image, + metadata={"location": "entrance", "camera": "cam1"}, + human_review="ALWAYS" + ) + + .. note:: + This method supports both synchronous and asynchronous workflows, configurable confidence thresholds, + and optional human review. + .. seealso:: + :meth:`ask_confident` for a simpler synchronous workflow + + :meth:`ask_async` for a simpler asynchronous workflow + + :meth:`ask_ml` for faster ML predictions without waiting for a confident answer + + :param detector: the Detector object, or string id of a detector like `det_12345` + :param image: The image, in several possible formats: + - filename (string) of a jpeg file + - byte array or BytesIO or BufferedReader with jpeg bytes + - numpy array with values 0-255 and dimensions (H,W,3) in BGR order + (Note OpenCV uses BGR not RGB. `img[:, :, ::-1]` will reverse the channels) + - PIL Image: Any binary format must be JPEG-encoded already. + Any pixel format will get converted to JPEG at high quality before sending to service. + :param wait: How long to poll (in seconds) for a confident answer. This is a client-side timeout. + Default is 30.0. Set to 0 for async operation. :param patience_time: How long to wait (in seconds) for a confident answer for this image query. The longer the patience_time, the more likely Groundlight will arrive at a confident answer. Within patience_time, Groundlight will update ML predictions based on stronger findings, and, additionally, Groundlight will prioritize human review of the image query if necessary. This is a soft server-side timeout. If not set, use the detector's patience_time. - :param confidence_threshold: The confidence threshold to wait for. - If not set, use the detector's confidence threshold. - + If not set, use the detector's confidence threshold. :param human_review: If `None` or `DEFAULT`, send the image query for human review - only if the ML prediction is not confident. - If set to `ALWAYS`, always send the image query for human review. - If set to `NEVER`, never send the image query for human review. - - :param want_async: If True, the client will return as soon as the image query is submitted and will not wait for - an ML/human prediction. The returned `ImageQuery` will have a `result` of None. Must set `wait` to 0 to use - want_async. - + only if the ML prediction is not confident. + If set to `ALWAYS`, always send the image query for human review. + If set to `NEVER`, never send the image query for human review. + :param want_async: If True, return immediately without waiting for result. + Must set `wait=0` when using this option. :param inspection_id: Most users will omit this. For accounts with Inspection Reports enabled, - this is the ID of the inspection to associate with the image query. - + this is the ID of the inspection to associate with the image query. :param metadata: A dictionary or JSON string of custom key/value metadata to associate with - the image query (limited to 1KB). You can retrieve this metadata later by calling - `get_image_query()`. - - :param image_query_id: The ID for the image query. This is to enable specific functionality and is not intended - for general external use. If not set, a random ID will be generated. - - :return: ImageQuery + the image query (limited to 1KB). You can retrieve this metadata later by calling + `get_image_query()`. + :param image_query_id: The ID for the image query. This is to enable specific functionality + and is not intended for general external use. If not set, a random ID + will be generated. + + :return: ImageQuery with query details and result (if wait > 0) + :raises ValueError: If wait > 0 when want_async=True + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors """ if wait is None: wait = self.DEFAULT_WAIT @@ -549,11 +715,24 @@ def ask_confident( # noqa: PLR0913 # pylint: disable=too-many-arguments inspection_id: Optional[str] = None, ) -> ImageQuery: """ - Evaluates an image with Groundlight waiting until an answer above the confidence threshold - of the detector is reached or the wait period has passed. + Evaluates an image with Groundlight, waiting until an answer above the confidence threshold + is reached or the wait period has passed. - :param detector: the Detector object, or string id of a detector like `det_12345` + **Example usage**:: + + gl = Groundlight() + image_query = gl.ask_confident( + detector="det_12345", + image="path/to/image.jpg", + confidence_threshold=0.9, + wait=30.0 + ) + if image_query.result.confidence >= 0.9: + print(f"Confident answer: {image_query.result.label}") + else: + print("Could not get confident answer within timeout") + :param detector: the Detector object, or string id of a detector like `det_12345` :param image: The image, in several possible formats: - filename (string) of a jpeg file - byte array or BytesIO or BufferedReader with jpeg bytes @@ -562,17 +741,24 @@ def ask_confident( # noqa: PLR0913 # pylint: disable=too-many-arguments - PIL Image Any binary format must be JPEG-encoded already. Any pixel format will get converted to JPEG at high quality before sending to service. - :param confidence_threshold: The confidence threshold to wait for. If not set, use the detector's confidence threshold. - :param wait: How long to wait (in seconds) for a confident answer. - :param metadata: A dictionary or JSON string of custom key/value metadata to associate with the image query (limited to 1KB). You can retrieve this metadata later by calling `get_image_query()`. + :param inspection_id: Most users will omit this. For accounts with Inspection Reports enabled, + this is the ID of the inspection to associate with the image query. + + :return: ImageQuery containing the prediction result + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors + + .. seealso:: + :meth:`ask_ml` for getting the first ML prediction without waiting an answer above the confidence threshold + :meth:`ask_async` for submitting queries asynchronously + :meth:`submit_image_query` for submitting queries with more control over the process - :return: ImageQuery """ return self.submit_image_query( detector, @@ -594,10 +780,29 @@ def ask_ml( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-loca inspection_id: Optional[str] = None, ) -> ImageQuery: """ - Evaluates an image with Groundlight, getting the first answer Groundlight can provide. + Evaluates an image with Groundlight, getting the first ML prediction without waiting + for high confidence or human review. - :param detector: the Detector object, or string id of a detector like `det_12345` + **Example usage**:: + gl = Groundlight() + detector = gl.get_detector("det_12345") # or create one with create_detector() + + # Get quick ML prediction for an image + image_query = gl.ask_ml(detector, "path/to/image.jpg") + + # The image_query may have low confidence since we're never waiting for human review + print(f"Quick ML prediction: {image_query.result.label}") + print(f"Confidence: {image_query.result.confidence}") + + # You can also pass metadata to track additional information + image_query = gl.ask_ml( + detector=detector, + image="path/to/image.jpg", + metadata={"camera_id": "front_door", "timestamp": "2023-01-01T12:00:00Z"} + ) + + :param detector: the Detector object, or string id of a detector like `det_12345` :param image: The image, in several possible formats: - filename (string) of a jpeg file - byte array or BytesIO or BufferedReader with jpeg bytes @@ -606,14 +811,25 @@ def ask_ml( # noqa: PLR0913 # pylint: disable=too-many-arguments, too-many-loca - PIL Image Any binary format must be JPEG-encoded already. Any pixel format will get converted to JPEG at high quality before sending to service. - - :param wait: How long to wait (in seconds) for any answer. - + :param wait: How long to wait (in seconds) for any ML prediction. + Default is 30.0 seconds. :param metadata: A dictionary or JSON string of custom key/value metadata to associate with the image query (limited to 1KB). You can retrieve this metadata later by calling `get_image_query()`. + :param inspection_id: Most users will omit this. For accounts with Inspection Reports enabled, + this is the ID of the inspection to associate with the image query. - :return: ImageQuery + :return: ImageQuery containing the ML prediction + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors + + .. note:: + This method returns the first available ML prediction, which may have low confidence. + For answers above a configured confidence_threshold, use :meth:`ask_confident` instead. + + .. seealso:: + :meth:`ask_confident` for waiting until a high-confidence prediction is available + :meth:`ask_async` for submitting queries asynchronously """ iq = self.submit_image_query( detector, @@ -638,76 +854,74 @@ def ask_async( # noqa: PLR0913 # pylint: disable=too-many-arguments inspection_id: Optional[str] = None, ) -> ImageQuery: """ - Convenience method for submitting an `ImageQuery` asynchronously. This is equivalent to calling - `submit_image_query` with `want_async=True` and `wait=0`. Use `get_image_query` to retrieve the `result` of the - ImageQuery. + Submit an image query asynchronously. This is equivalent to calling `submit_image_query` + with `want_async=True` and `wait=0`. - :param detector: the Detector object, or string id of a detector like `det_12345` + .. note:: + The returned ImageQuery will have result=None since the prediction is computed + asynchronously. Use :meth:`wait_for_confident_result`, :meth:`wait_for_ml_result`, + or :meth:`get_image_query` to retrieve the result later. - :param image: The image, in several possible formats: + **Example usage**:: - - filename (string) of a jpeg file - - byte array or BytesIO or BufferedReader with jpeg bytes - - numpy array with values 0-255 and dimensions (H,W,3) in BGR order - (Note OpenCV uses BGR not RGB. `img[:, :, ::-1]` will reverse the channels) - - PIL Image: Any binary format must be JPEG-encoded already. - Any pixel format will get converted to JPEG at high quality before sending to service. + gl = Groundlight() + # Submit query asynchronously + image_query = gl.ask_async( + detector="det_12345", + image="path/to/image.jpg", + confidence_threshold=0.9, + human_review="ALWAYS" + ) + print(f"Query submitted with ID: {query.id}") - :param patience_time: How long to wait (in seconds) for a confident answer for this image query. - The longer the patience_time, the more likely Groundlight will arrive at a confident answer. - Within patience_time, Groundlight will update ML predictions based on stronger findings, - and, additionally, Groundlight will prioritize human review of the image query if necessary. - This is a soft server-side timeout. If not set, use the detector's patience_time. + # Later, retrieve the result + image_query = gl.wait_for_confident_result( + image_query=query, + confidence_threshold=0.9, + timeout_sec=60.0 + ) + print(f"Answer: {image_query.result.label}") + + # Alternatively, check if result is ready without blocking + image_query = gl.get_image_query(image_query.id) + if image_query.result: + print(f"Result ready: {image_query.result.label}") + else: + print("Still processing...") + :param detector: the Detector object, or string id of a detector like `det_12345` + :param image: The image, in several possible formats: + - filename (string) of a jpeg file + - byte array or BytesIO or BufferedReader with jpeg bytes + - numpy array with values 0-255 and dimensions (H,W,3) in BGR order + (Note OpenCV uses BGR not RGB. `img[:, :, ::-1]` will reverse the channels) + - PIL Image: Any binary format must be JPEG-encoded already. + Any pixel format will get converted to JPEG at high quality before sending to service. + :param patience_time: How long to wait (in seconds) for a confident answer for this image query. + The longer the patience_time, the more likely Groundlight will arrive at a + confident answer. This is a soft server-side timeout. If not set, use the + detector's patience_time. :param confidence_threshold: The confidence threshold to wait for. If not set, use the detector's confidence threshold. - :param human_review: If `None` or `DEFAULT`, send the image query for human review only if the ML prediction is not confident. If set to `ALWAYS`, always send the image query for human review. If set to `NEVER`, never send the image query for human review. - - :param inspection_id: Most users will omit this. For accounts with Inspection Reports enabled, - this is the ID of the inspection to associate with the image query. - :param metadata: A dictionary or JSON string of custom key/value metadata to associate with the image query (limited to 1KB). You can retrieve this metadata later by calling `get_image_query()`. + :param inspection_id: Most users will omit this. For accounts with Inspection Reports enabled, + this is the ID of the inspection to associate with the image query. - :return: ImageQuery - - - **Example usage**:: - - gl = Groundlight() - detector = gl.get_or_create_detector( - name="door", - query="Is the door locked?", - confidence_threshold=0.9 - ) - - image_query = gl.ask_async( - detector=detector, - image="path/to/image.jpeg") - - # the image_query will have an id for later retrieval - assert image_query.id is not None - - # Do not attempt to access the result of this query as the result for all async queries - # will be None. Your result is being computed asynchronously and will be available later - assert image_query.result is None - - # retrieve the result later or on another machine by calling gl.wait_for_confident_result() - # with the id of the image_query above. This will block until the result is available. - image_query = gl.wait_for_confident_result(image_query.id) - - # now the result will be available for your use - assert image_query.result is not None + :return: ImageQuery with result set to None (result will be computed asynchronously) + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors - # alternatively, you can check if the result is available (without blocking) by calling - # gl.get_image_query() with the id of the image_query above. - image_query = gl.get_image_query(image_query.id) + .. seealso:: + :meth:`wait_for_confident_result` for waiting until a confident result is available + :meth:`wait_for_ml_result` for waiting until the first ML result is available + :meth:`get_image_query` for checking if a result is ready without blocking """ return self.submit_image_query( detector, @@ -728,35 +942,91 @@ def wait_for_confident_result( timeout_sec: float = 30.0, ) -> ImageQuery: """ - Waits for an image query result's confidence level to reach the specified value. - Currently this is done by polling with an exponential back-off. + Waits for an image query result's confidence level to reach the specified confidence_threshold. + Uses polling with exponential back-off to check for results. - :param image_query: An ImageQuery object to poll - - :param confidence_threshold: The confidence threshold to wait for. - If not set, use the detector's confidence threshold. + .. note:: + This method blocks until either: + 1. A result with confidence >= confidence_threshold is available + 2. The timeout_sec is reached + 3. An error occurs - :param timeout_sec: The maximum number of seconds to wait. + **Example usage**:: - :return: ImageQuery + gl = Groundlight() + query = gl.ask_async( + detector="det_12345", + image="path/to/image.jpg" + ) + try: + result = gl.wait_for_confident_result( + query, + confidence_threshold=0.9, + timeout_sec=60.0 + ) + print(f"Got confident answer: {result.result.label}") + except TimeoutError: + print("Timed out waiting for confident result") + + :param image_query: An ImageQuery object or query ID string to poll + :param confidence_threshold: The confidence threshold to wait for. + If not set, use the detector's confidence threshold. + :param timeout_sec: The maximum number of seconds to wait. Default is 30.0 seconds. + + :return: ImageQuery with confident result + :raises TimeoutError: If no confident result is available within timeout_sec + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors + + .. seealso:: + :meth:`ask_async` for submitting queries asynchronously + :meth:`get_image_query` for checking result status without blocking + :meth:`wait_for_ml_result` for waiting until the first ML result is available """ - if confidence_threshold is None: - if isinstance(image_query, str): - image_query = self.get_image_query(image_query) + if isinstance(image_query, str): + image_query = self.get_image_query(image_query) confidence_threshold = self.get_detector(image_query.detector_id).confidence_threshold - confidence_above_thresh = partial(iq_is_confident, confidence_threshold=confidence_threshold) + confidence_above_thresh = partial(iq_is_confident, confidence_threshold=confidence_threshold) # type: ignore return self._wait_for_result(image_query, condition=confidence_above_thresh, timeout_sec=timeout_sec) def wait_for_ml_result(self, image_query: Union[ImageQuery, str], timeout_sec: float = 30.0) -> ImageQuery: - """Waits for the first ml result to be returned. - Currently this is done by polling with an exponential back-off. + """ + Waits for the first ML result to be returned for an image query. + Uses polling with exponential back-off to check for results. - :param image_query: An ImageQuery object to poll + .. note:: + This method blocks until either: + 1. An ML result is available + 2. The timeout_sec is reached + 3. An error occurs - :param timeout_sec: The maximum number of seconds to wait. + **Example usage**:: - :return: ImageQuery + gl = Groundlight() + query = gl.ask_async( + detector="det_12345", + image="path/to/image.jpg" + ) + + try: + result = gl.wait_for_ml_result(query, timeout_sec=3.0) + print(f"Got ML result: {result.result.label}") + except TimeoutError: + print("Timed out waiting for ML result") + + :param image_query: An ImageQuery object or ImageQuery ID string to poll + :param timeout_sec: The maximum number of seconds to wait. Default is 30.0 seconds. + + :return: ImageQuery with ML result + :raises TimeoutError: If no ML result is available within timeout_sec + :raises ApiTokenError: If API token is invalid + :raises GroundlightClientError: For other API errors + + .. seealso:: + :meth:`ask_async` for submitting queries asynchronously + :meth:`get_image_query` for checking result status without blocking + :meth:`wait_for_confident_result` for waiting until a confident result is available """ return self._wait_for_result(image_query, condition=iq_is_answered, timeout_sec=timeout_sec) @@ -766,10 +1036,8 @@ def _wait_for_result( """Performs polling with exponential back-off until the condition is met for the image query. :param image_query: An ImageQuery object to poll - :param condition: A callable that takes an ImageQuery and returns True or False whether to keep waiting for a better result. - :param timeout_sec: The maximum number of seconds to wait. :return: ImageQuery @@ -802,17 +1070,30 @@ def add_label( self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None ): """ - Add a new label to an image query. This answers the detector's - question. + Provide a new label (annotation) for an image query. This is used to provide ground-truth labels + for training detectors, or to correct the results of detectors. + + **Example usage**:: + + gl = Groundlight() + + # Using an ImageQuery object + image_query = gl.ask_ml(detector_id, image_data) + gl.add_label(image_query, "YES") + + # Using an image query ID string directly + gl.add_label("iq_abc123", "NO") - :param image_query: Either an ImageQuery object (returned from - `ask_ml` or similar method) or an image_query id as a - string. + # With regions of interest (ROIs) + rois = [ROI(x=100, y=100, width=50, height=50)] + gl.add_label(image_query, "YES", rois=rois) - :param label: The string "YES" or the string "NO" in answer to the - query. - :param rois: An option list of regions of interest (ROIs) to associate - with the label. (This feature experimental) + :param image_query: Either an ImageQuery object (returned from methods like :meth:`ask_ml`) or an image query ID + string starting with "iq_". + :param label: The label value to assign, typically "YES" or "NO" for binary classification detectors. + For multi-class detectors, use one of the defined class names. + :param rois: Optional list of ROI objects defining regions of interest in the image. + Each ROI specifies a bounding box with x, y coordinates and width, height. :return: None """ @@ -823,7 +1104,6 @@ def add_label( else: image_query_id = str(image_query) # Some old imagequery id's started with "chk_" - # TODO: handle iqe_ for image_queries returned from edge endpoints if not image_query_id.startswith(("chk_", "iq_")): raise ValueError(f"Invalid image query id {image_query_id}") api_label = convert_display_label_to_internal(image_query_id, label) diff --git a/src/groundlight/experimental_api.py b/src/groundlight/experimental_api.py index 69f35c47..aac00fa5 100644 --- a/src/groundlight/experimental_api.py +++ b/src/groundlight/experimental_api.py @@ -40,13 +40,55 @@ class ExperimentalApi(Groundlight): - def __init__(self, endpoint: Union[str, None] = None, api_token: Union[str, None] = None): + def __init__( + self, + endpoint: Union[str, None] = None, + api_token: Union[str, None] = None, + disable_tls_verification: Optional[bool] = None, + ): """ - Constructs an experimental groundlight client. The experimental client inherits all the functionality of the - base groundlight client, but also includes additional functionality that is still in development. Experimental - functionality is subject to change. + Constructs an experimental Groundlight client. + + This client extends the base Groundlight client with additional experimental functionality that is still in + development. Note that experimental features may undergo significant changes or be removed in future releases. + + **Example usage**:: + + from groundlight import ExperimentalApi + + # Create an experimental API client + gl = ExperimentalApi() + + # Create a notification rule + rule = gl.create_rule( + detector="door_detector", + rule_name="Door Open Alert", + channel="EMAIL", + recipient="alerts@company.com", + alert_on="CHANGED_TO", + include_image=True, + condition_parameters={"label": "YES"} + ) + + # Create a detector group + group = gl.create_detector_group( + name="Security Detectors", + description="Detectors monitoring security-related conditions", + detectors=["door_detector", "motion_detector"] + ) + + :param endpoint: Optional custom API endpoint URL. If not specified, uses the default Groundlight endpoint. + :param api_token: Authentication token for API access. If not provided, will attempt to read from + the "GROUNDLIGHT_API_TOKEN" environment variable. + :param disable_tls_verification: If True, disables SSL/TLS certificate verification for API calls. + When not specified, checks the "DISABLE_TLS_VERIFY" environment variable (1=disable, 0=enable). + Certificate verification is enabled by default. + + Warning: Only disable verification when connecting to a Groundlight Edge Endpoint using + self-signed certificates. For security, always keep verification enabled when using the + Groundlight cloud service. """ - super().__init__(endpoint=endpoint, api_token=api_token) + super().__init__(endpoint=endpoint, api_token=api_token, disable_tls_verification=disable_tls_verification) self.actions_api = ActionsApi(self.api_client) self.images_api = ImageQueriesApi(self.api_client) self.notes_api = NotesApi(self.api_client) @@ -72,25 +114,63 @@ def create_rule( # pylint: disable=too-many-locals # noqa: PLR0913 human_review_required: bool = False, ) -> Rule: """ - Adds a notification rule to the given detector - - :param detector: the detector to add the action to - :param rule_name: a name to uniquely identify the rule - :param channel: what channel to send the notification over. Currently EMAIL or TEXT - :param recipient: the address or number to send the notification to + Creates a notification rule for a detector that will send alerts based on specified conditions. + + A notification rule allows you to configure automated alerts when certain conditions are met, + such as when a detector's prediction changes or maintains a particular state. + + .. note:: + Currently, only binary mode detectors (YES/NO answers) are supported for notification rules. + + **Example usage**:: + + gl = ExperimentalApi() + + # Create a rule to send email alerts when door is detected as open + rule = gl.create_rule( + detector="door_detector", + rule_name="Door Open Alert", + channel="EMAIL", + recipient="alerts@company.com", + alert_on="CHANGED_TO", + condition_parameters={"label": "YES"}, + include_image=True + ) + + # Create a rule for consecutive motion detections via SMS + rule = gl.create_rule( + detector="motion_detector", + rule_name="Repeated Motion Alert", + channel="TEXT", + recipient="+1234567890", + alert_on="ANSWERED_CONSECUTIVELY", + condition_parameters={ + "num_consecutive_labels": 3, + "label": "YES" + }, + snooze_time_enabled=True, + snooze_time_value=1, + snooze_time_unit="HOURS" + ) + + :param detector: The detector ID or Detector object to add the rule to + :param rule_name: A unique name to identify this rule + :param channel: Notification channel - either "EMAIL" or "TEXT" + :param recipient: Email address or phone number to receive notifications :param alert_on: what to alert on. One of ANSWERED_CONSECUTIVELY, ANSWERED_WITHIN_TIME, CHANGED_TO, NO_CHANGE, NO_QUERIES - :param enabled: whether the rule is enabled initially - :param include_image: whether to include the image in the notification - :param condition_parameters: additional information needed for the condition. i.e. if the - condition is ANSWERED_CONSECUTIVELY, we specify num_consecutive_labels and label here - :param snooze_time_enabled: Whether notifications wil be snoozed, no repeat notification - will be delivered until the snooze time has passed - :param snooze_time_value: The value of the snooze time - :param snooze_time_unit: The unit of the snooze time - :param huamn_review_required: If true, a cloud labeler will review and confirm alerts before they are sent - - :return: a Rule object corresponding to the new rule + :param enabled: Whether the rule should be active when created (default True) + :param include_image: Whether to attach the triggering image to notifications (default False) + :param condition_parameters: Additional parameters for the alert condition: + - For ANSWERED_CONSECUTIVELY: {"num_consecutive_labels": N, "label": "YES/NO"} + - For CHANGED_TO: {"label": "YES/NO"} + - For time-based conditions: {"time_value": N, "time_unit": "MINUTES/HOURS/DAYS"} + :param snooze_time_enabled: Enable notification snoozing to prevent alert spam (default False) + :param snooze_time_value: Duration of snooze period (default 3600) + :param snooze_time_unit: Unit for snooze duration - "SECONDS", "MINUTES", "HOURS", or "DAYS" (default "SECONDS") + :param human_review_required: Require human verification before sending alerts (default False) + + :return: The created Rule object """ if condition_parameters is None: condition_parameters = {} @@ -122,35 +202,85 @@ def create_rule( # pylint: disable=too-many-locals # noqa: PLR0913 def get_rule(self, action_id: int) -> Rule: """ - Gets the action with the given id + Gets the rule with the given id. - :param action_id: the id of the action to get - :return: the action with the given id + **Example usage**:: + + gl = ExperimentalApi() + + # Get an existing rule by ID + rule = gl.get_rule(action_id=123) + print(f"Rule name: {rule.name}") + print(f"Rule enabled: {rule.enabled}") + + :param action_id: the id of the rule to get + :return: the Rule object with the given id """ return Rule.model_validate(self.actions_api.get_rule(action_id).to_dict()) def delete_rule(self, action_id: int) -> None: """ - Deletes the action with the given id + Deletes the rule with the given id. + + **Example usage**:: + + gl = ExperimentalApi() + + # Delete a specific rule + gl.delete_rule(action_id=123) - :param action_id: the id of the action to delete + :param action_id: the id of the rule to delete """ self.actions_api.delete_rule(action_id) def list_rules(self, page=1, page_size=10) -> PaginatedRuleList: """ - Gets a list of all rules + Gets a paginated list of all rules. + + **Example usage**:: + + gl = ExperimentalApi() + + # Get first page of rules + rules = gl.list_rules(page=1, page_size=10) + print(f"Total rules: {rules.count}") + + # Iterate through rules on current page + for rule in rules.results: + print(f"Rule {rule.id}: {rule.name}") + + # Get next page + next_page = gl.list_rules(page=2, page_size=10) - :return: a list of all rules + :param page: Page number to retrieve (default: 1) + :param page_size: Number of rules per page (default: 10) + :return: PaginatedRuleList containing the rules and pagination info """ obj = self.actions_api.list_rules(page=page, page_size=page_size) return PaginatedRuleList.parse_obj(obj.to_dict()) def delete_all_rules(self, detector: Union[None, str, Detector] = None) -> int: """ - Deletes all rules associated with the given detector + Deletes all rules associated with the given detector. If no detector is specified, + deletes all rules in the account. - :param detector: the detector to delete the rules from + WARNING: If no detector is specified, this will delete ALL rules in your account. + This action cannot be undone. Use with caution. + + **Example usage**:: + + gl = ExperimentalApi() + + # Delete all rules for a specific detector + detector = gl.get_detector("my_detector") + num_deleted = gl.delete_all_rules(detector) + print(f"Deleted {num_deleted} rules") + + # Delete all rules in the account + num_deleted = gl.delete_all_rules() + print(f"Deleted {num_deleted} rules") + + :param detector: the detector to delete the rules from. If None, deletes all rules. :return: the number of rules deleted """ @@ -170,22 +300,58 @@ def delete_all_rules(self, detector: Union[None, str, Detector] = None) -> int: def get_image(self, iq_id: str) -> bytes: """ - Get the image associated with the given image ID - If you have PIL installed, you can instantiate the pill image as PIL.Image.open(gl.get_image(iq.id)) + Get the image associated with the given image query ID. + + **Example usage**:: + + gl = ExperimentalApi() + + # Get image from an image query + iq = gl.get_image_query("iq_123") + image_bytes = gl.get_image(iq.id) + + # Open with PIL - returns RGB order + from PIL import Image + image = Image.open(gl.get_image(iq.id)) # Returns RGB image + + # Open with numpy via PIL - returns RGB order + import numpy as np + from io import BytesIO + image = np.array(Image.open(gl.get_image(iq.id))) # Returns RGB array + + # Open with OpenCV - returns BGR order + import cv2 + import numpy as np + nparr = np.frombuffer(image_bytes, np.uint8) + image = cv2.imdecode(nparr, cv2.IMREAD_COLOR) # Returns BGR array + # To convert to RGB if needed: + # image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - :param image_id: the ID of the image to get - :return: the image as a byte array + :param iq_id: The ID of the image query to get the image from + :return: The image as a byte array that can be used with PIL or other image libraries """ + # TODO: support taking an ImageQuery object return self.images_api.get_image(iq_id) def get_notes(self, detector: Union[str, Detector]) -> Dict[str, Any]: """ - Gets the notes for a given detector + Retrieves all notes associated with a detector. - :param detector: the detector to get the notes for + **Example usage**:: - :return: a dictionary with two keys "CUSTOMER" and "GL" to indicate who added the note to - the detector, and values that are lists of notes + gl = ExperimentalApi() + + detector = gl.get_detector("det_123") + notes = gl.get_notes(detector) + # notes = { + # "CUSTOMER": ["Customer note 1", "Customer note 2"], + # "GL": ["Groundlight note 1"] + # } + + :param detector: The detector object or ID string to retrieve notes for + + :return: A dictionary containing notes organized by source ("CUSTOMER" or "GL"), + where each source maps to a list of note strings """ det_id = detector.id if isinstance(detector, Detector) else detector return self.notes_api.get_notes(det_id) @@ -197,53 +363,127 @@ def create_note( image: Union[str, bytes, Image.Image, BytesIO, BufferedReader, np.ndarray, None] = None, ) -> None: """ - Adds a note to a given detector + Adds a note to a given detector. + + **Example usage**:: + + gl = ExperimentalApi() + + detector = gl.get_detector("det_123") + gl.create_note(detector, "Please label doors that are slightly ajar as 'YES'") - :param detector: the detector to add the note to - :param note: the text content of the note - :param image: a path to an image to attach to the note + # With an image attachment + gl.create_note( + detector, + "Door that is slightly ajar and should be labeled 'YES'", + image="path/to/image.jpg" + ) + + :param detector: The detector object or detector ID string to add the note to + :param note: The text content of the note to add + :param image: Optional image to attach to the note. """ det_id = detector.id if isinstance(detector, Detector) else detector + + # Initialize img_bytes to None + img_bytes = None if image is not None: img_bytes = parse_supported_image_types(image) + # TODO: The openapi generator doesn't handle file submissions well at the moment, so we manually implement this # kwargs = {"image": img_bytes} # self.notes_api.create_note(det_id, note, **kwargs) url = f"{self.endpoint}/v1/notes" - files = {"image": ("image.jpg", img_bytes, "image/jpeg")} if image is not None else None + files = {"image": ("image.jpg", img_bytes, "image/jpeg")} if img_bytes is not None else None data = {"content": note} params = {"detector_id": det_id} headers = {"x-api-token": self.configuration.api_key["ApiToken"]} - requests.post(url, headers=headers, data=data, files=files, params=params) # type: ignore + + response = requests.post(url, headers=headers, data=data, files=files, params=params) # type: ignore + response.raise_for_status() # Raise an exception for error status codes def create_detector_group(self, name: str) -> DetectorGroup: """ - Creates a detector group with the given name - Note: you can specify a detector group when creating a detector without the need to create it ahead of time + Creates a detector group with the given name. A detector group allows you to organize + related detectors together. + + .. note:: + You can specify a detector group when creating a detector without the need to create it ahead of time. + The group will be created automatically if it doesn't exist. - :param name: the name of the detector group + **Example usage**:: - :return: a Detector object corresponding to the new detector group + gl = ExperimentalApi() + + # Create a group for all door-related detectors + door_group = gl.create_detector_group("door-detectors") + + # Later, create detectors in this group + door_open_detector = gl.create_detector( + name="front-door-open", + query="Is the front door open?", + detector_group=door_group + ) + + :param name: The name of the detector group. This should be descriptive and unique within your organization. + :type name: str + :return: A DetectorGroup object corresponding to the newly created detector group + :rtype: DetectorGroup """ return DetectorGroup(**self.detector_group_api.create_detector_group(DetectorGroupRequest(name=name)).to_dict()) def list_detector_groups(self) -> List[DetectorGroup]: """ - Gets a list of all detector groups + Gets a list of all detector groups in your account. + + **Example usage**:: + + gl = ExperimentalApi() + + # Get all detector groups + groups = gl.list_detector_groups() - :return: a list of all detector groups + # Print information about each group + for group in groups: + print(f"Group name: {group.name}") + print(f"Group ID: {group.id}") + + :return: A list of DetectorGroup objects representing all detector groups in your account """ return [DetectorGroup(**det.to_dict()) for det in self.detector_group_api.get_detector_groups()] def create_roi(self, label: str, top_left: Tuple[float, float], bottom_right: Tuple[float, float]) -> ROI: """ - Adds a region of interest to the given detector - NOTE: This feature is only available to Pro tier and higher - If you would like to learn more, reach out to us at https://groundlight.ai + Creates a Region of Interest (ROI) object that can be used to specify areas of interest in images. Certain + detectors (such as Count-mode detectors) may emit ROIs as part of their output. Providing an ROI can help + improve the accuracy of such detectors. + + .. note:: + ROI functionality is only available to Pro tier and higher. + If you would like to learn more, reach out to us at https://groundlight.ai + + **Example usage**:: + + gl = ExperimentalApi() + + # Create an ROI for a door in the image + door_roi = gl.create_roi( + label="door", + top_left=(0.2, 0.3), # Coordinates are normalized (0-1) + bottom_right=(0.4, 0.8) # Coordinates are normalized (0-1) + ) - :param label: the label of the item in the roi - :param top_left: the top left corner of the roi - :param bottom_right: the bottom right corner of the roi + # Use the ROI when submitting an image query + query = gl.submit_image_query( + detector="door-detector", + image=image_bytes, + rois=[door_roi] + ) + + :param label: A descriptive label for the object or area contained in the ROI + :param top_left: Tuple of (x, y) coordinates for the top-left corner, normalized to [0,1] + :param bottom_right: Tuple of (x, y) coordinates for the bottom-right corner, normalized to [0,1] + :return: An ROI object that can be used in image queries """ return ROI( @@ -259,22 +499,40 @@ def create_roi(self, label: str, top_left: Tuple[float, float], bottom_right: Tu ), ) + # TODO: remove duplicate method on subclass # pylint: disable=duplicate-code def add_label( self, image_query: Union[ImageQuery, str], label: Union[Label, str], rois: Union[List[ROI], str, None] = None ): """ - Experimental version of add_label. Add a new label to an image query. - This answers the detector's question. + Provide a new label (annotation) for an image query. This is used to provide ground-truth labels + for training detectors, or to correct the results of detectors. + + **Example usage**:: + + gl = ExperimentalApi() + + # Using an ImageQuery object + image_query = gl.ask_ml(detector_id, image_data) + gl.add_label(image_query, "YES") + + # Using an image query ID string directly + gl.add_label("iq_abc123", "NO") + + # With regions of interest (ROIs) + rois = [ROI(x=100, y=100, width=50, height=50)] + gl.add_label(image_query, "YES", rois=rois) - :param image_query: Either an ImageQuery object (returned from - `submit_image_query`) or an image_query id as a - string. + :param image_query: Either an ImageQuery object (returned from methods like + `ask_ml`) or an image query ID string starting with "iq_". - :param label: The string "YES" or the string "NO" in answer to the - query. - :param rois: An option list of regions of interest (ROIs) to associate - with the label. (This feature experimental) + :param label: The label value to assign, typically "YES" or "NO" for binary + classification detectors. For multi-class detectors, use one of + the defined class names. + + :param rois: Optional list of ROI objects defining regions of interest in the + image. Each ROI specifies a bounding box with x, y coordinates + and width, height. :return: None """ @@ -303,9 +561,27 @@ def add_label( def reset_detector(self, detector: Union[str, Detector]) -> None: """ - Removes all image queries for the given detector + Removes all image queries and training data for the given detector. This effectively resets + the detector to its initial state, allowing you to start fresh with new training data. + + .. warning:: + This operation cannot be undone. All image queries and training data will be deleted. + + **Example usage**:: + + gl = ExperimentalApi() + + # Using a detector object + detector = gl.get_detector("det_abc123") + gl.reset_detector(detector) - :param detector_id: the id of the detector to reset + # Using a detector ID string directly + gl.reset_detector("det_abc123") + + :param detector: Either a Detector object or a detector ID string starting with "det_". + The detector whose data should be reset. + + :return: None """ if isinstance(detector, Detector): detector = detector.id @@ -315,8 +591,20 @@ def update_detector_name(self, detector: Union[str, Detector], name: str) -> Non """ Updates the name of the given detector - :param detector: the detector to update - :param name: the new name + **Example usage**:: + + gl = ExperimentalApi() + + # Using a detector object + detector = gl.get_detector("det_abc123") + gl.update_detector_name(detector, "new_detector_name") + + # Using a detector ID string directly + gl.update_detector_name("det_abc123", "new_detector_name") + + :param detector: Either a Detector object or a detector ID string starting with "det_". + The detector whose name should be updated. + :param name: The new name to assign to the detector :return: None """ @@ -326,10 +614,24 @@ def update_detector_name(self, detector: Union[str, Detector], name: str) -> Non def update_detector_status(self, detector: Union[str, Detector], enabled: bool) -> None: """ - Updates the status of the given detector. If the detector is disabled, it will not receive new image queries + Updates the status of the given detector. When a detector is disabled (enabled=False), + it will not accept or process any new image queries. Existing queries will not be affected. + + **Example usage**:: + + gl = ExperimentalApi() + + # Using a detector object + detector = gl.get_detector("det_abc123") + gl.update_detector_status(detector, enabled=False) # Disable the detector + + # Using a detector ID string directly + gl.update_detector_status("det_abc123", enabled=True) # Enable the detector - :param detector: the detector to update - :param enabled: whether the detector is enabled, can be either True or False + :param detector: Either a Detector object or a detector ID string starting with "det_". + The detector whose status should be updated. + :param enabled: Boolean indicating whether the detector should be enabled (True) or + disabled (False). When disabled, the detector will not process new queries. :return: None """ @@ -342,16 +644,34 @@ def update_detector_status(self, detector: Union[str, Detector], enabled: bool) def update_detector_escalation_type(self, detector: Union[str, Detector], escalation_type: str) -> None: """ - Updates the escalation type of the given detector + Updates the escalation type of the given detector, controlling whether queries can be + sent to human labelers when ML confidence is low. - This is particularly useful for turning off human labeling for billing or security purposes. - By setting a detector to "NO_HUMAN_LABELING", no image queries sent to this detector will be - sent to human labelers. + This is particularly useful for controlling costs. When set to "NO_HUMAN_LABELING", + queries will only receive ML predictions, even if confidence is low. + When set to "STANDARD", low-confidence queries may be sent to human labelers for verification. - :param detector: the detector to update - :param escalation_type: the new escalation type, can be "STANDARD" or "NO_HUMAN_LABELING" + **Example usage**:: + + gl = ExperimentalApi() + + # Using a detector object + detector = gl.get_detector("det_abc123") + + # Disable human labeling + gl.update_detector_escalation_type(detector, "NO_HUMAN_LABELING") + + # Re-enable standard human labeling + gl.update_detector_escalation_type("det_abc123", "STANDARD") + + :param detector: Either a Detector object or a detector ID string starting with "det_". + The detector whose escalation type should be updated. + :param escalation_type: The new escalation type setting. Must be one of: + - "STANDARD": Allow human labeling for low-confidence queries + - "NO_HUMAN_LABELING": Never send queries to human labelers :return: None + :raises ValueError: If escalation_type is not one of the allowed values """ if isinstance(detector, Detector): detector = detector.id @@ -376,7 +696,42 @@ def create_counting_detector( # noqa: PLR0913 # pylint: disable=too-many-argume metadata: Union[dict, str, None] = None, ) -> Detector: """ - Creates a counting detector with the given name and query + Creates a counting detector that can count objects in images up to a specified maximum count. + + **Example usage**:: + + gl = ExperimentalApi() + + # Create a detector that counts people up to 5 + detector = gl.create_counting_detector( + name="people_counter", + query="How many people are in the image?", + max_count=5, + confidence_threshold=0.9, + patience_time=30.0 + ) + + # Use the detector to count people in an image + image_query = gl.ask_ml(detector, "path/to/image.jpg") + print(f"Counted {image_query.result.count} people") + print(f"Confidence: {image_query.result.confidence}") + + :param name: A short, descriptive name for the detector. + :param query: A question about the count of an object in the image. + :param max_count: Maximum number of objects to count (default: 10) + :param group_name: Optional name of a group to organize related detectors together. + :param confidence_threshold: A value that sets the minimum confidence level required for the ML model's + predictions. If confidence is below this threshold, the query may be sent for human review. + :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a + confident prediction before falling back to human review. Defaults to 30 seconds. + :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific + prediction pipeline for this detector. + :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with + the detector (limited to 1KB). This metadata can be used to store additional + information like location, purpose, or related system IDs. You can retrieve this + metadata later by calling `get_detector()`. + + :return: The created Detector object """ detector_creation_input = self._prep_create_detector( @@ -410,7 +765,40 @@ def create_multiclass_detector( # noqa: PLR0913 # pylint: disable=too-many-argu metadata: Union[dict, str, None] = None, ) -> Detector: """ - Creates a multiclass detector with the given name and query + Creates a multiclass detector with the given name and query. + + **Example usage**:: + + gl = ExperimentalApi() + + detector = gl.create_multiclass_detector( + name="Traffic Light Detector", + query="What color is the traffic light?", + class_names=["Red", "Yellow", "Green"] + ) + + # Use the detector to classify a traffic light + image_query = gl.ask_ml(detector, "path/to/image.jpg") + print(f"Traffic light is {image_query.result.label}") + print(f"Confidence: {image_query.result.confidence}") + + :param name: A short, descriptive name for the detector. + :param query: A question about classifying objects in the image. + :param class_names: List of possible class labels for classification. + :param group_name: Optional name of a group to organize related detectors together. + :param confidence_threshold: A value between 1/num_classes and 1 that sets the minimum confidence level required + for the ML model's predictions. If confidence is below this threshold, + the query may be sent for human review. + :param patience_time: The maximum time in seconds that Groundlight will attempt to generate a + confident prediction before falling back to human review. Defaults to 30 seconds. + :param pipeline_config: Advanced usage only. Configuration string needed to instantiate a specific + prediction pipeline for this detector. + :param metadata: A dictionary or JSON string containing custom key/value pairs to associate with + the detector (limited to 1KB). This metadata can be used to store additional + information like location, purpose, or related system IDs. You can retrieve this + metadata later by calling `get_detector()`. + + :return: The created Detector object """ detector_creation_input = self._prep_create_detector( diff --git a/src/groundlight/internalapi.py b/src/groundlight/internalapi.py index 91fea60c..f5a2e45b 100644 --- a/src/groundlight/internalapi.py +++ b/src/groundlight/internalapi.py @@ -65,7 +65,7 @@ def iq_is_confident(iq: ImageQuery, confidence_threshold: float) -> bool: The only subtlety here is that currently confidence of None means human label, which is treated as confident. """ - return iq.result.confidence >= confidence_threshold + return iq.result.confidence >= confidence_threshold # type: ignore def iq_is_answered(iq: ImageQuery) -> bool: