wehub-resource-sync
77 lines
3.4 KiB
Markdown
77 lines
3.4 KiB
Markdown
<!--[metadata]
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title = "Live camera edge detection"
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tags = ["2D", "Canny", "Live", "OpenCV"]
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thumbnail = "https://static.rerun.io/live-camera-edge-detection/f747bcf9ff3039c895f0bf0290e2dea0a72631ea/480w.png"
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thumbnail_dimensions = [480, 480]
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-->
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Visualize the [OpenCV Canny Edge Detection](https://docs.opencv.org/4.x/da/d22/tutorial_py_canny.html) results from a live camera stream.
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<picture>
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/live_camera_edge_detection/bf877bffd225f6c62cae3b87eecbc8e247abb202/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/live_camera_edge_detection/bf877bffd225f6c62cae3b87eecbc8e247abb202/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/live_camera_edge_detection/bf877bffd225f6c62cae3b87eecbc8e247abb202/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/live_camera_edge_detection/bf877bffd225f6c62cae3b87eecbc8e247abb202/1200w.png">
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<img src="https://static.rerun.io/live_camera_edge_detection/bf877bffd225f6c62cae3b87eecbc8e247abb202/full.png" alt="Live Camera Edge Detection example screenshot">
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</picture>
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## Used Rerun types
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[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image)
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## Background
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In this example, the results of the [OpenCV Canny Edge Detection](https://docs.opencv.org/4.x/da/d22/tutorial_py_canny.html) algorithm are visualized.
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Canny Edge Detection is a popular edge detection algorithm, and can efficiently extract important structural information from visual objects while notably reducing the computational load.
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The process in this example involves converting the input image to RGB, then to grayscale, and finally applying the Canny Edge Detector for precise edge detection.
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## Logging and visualizing with Rerun
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The visualization in this example were created with the following Rerun code:
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### RGB image
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The original image is read and logged in RGB format under the entity "image/rgb".
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```python
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# Log the original image
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rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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rr.log("image/rgb", rr.Image(rgb))
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```
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### Grayscale image
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The input image is converted from BGR color space to grayscale, and the resulting grayscale image is logged under the entity "image/gray".
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```python
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# Convert to grayscale
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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rr.log("image/gray", rr.Image(gray))
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```
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### Canny edge detection image
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The Canny edge detector is applied to the grayscale image, and the resulting edge-detected image is logged under the entity "image/canny".
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```python
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# Run the canny edge detector
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canny = cv2.Canny(gray, 50, 200)
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rr.log("image/canny", rr.Image(canny))
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```
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## Run the code
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To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
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```bash
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pip install --upgrade rerun-sdk # install the latest Rerun SDK
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git clone git@github.com:rerun-io/rerun.git # Clone the repository
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cd rerun
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git checkout latest # Check out the commit matching the latest SDK release
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```
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Install the necessary libraries specified in the requirements file:
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```bash
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pip install -e examples/python/live_camera_edge_detection
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```
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To experiment with the provided example, simply execute the main Python script:
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```bash
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python -m live_camera_edge_detection # run the example
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```
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If you wish to customize it, explore additional features, or save it use the CLI with the `--help` option for guidance:
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```bash
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python -m live_camera_edge_detection --help
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```
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