90 lines
4.1 KiB
Markdown
90 lines
4.1 KiB
Markdown
# Easy local ALPR (Automatic License Plate Recognition)
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This script is a REST API server that uses [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) to process images and return the license plate
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information. The server is created using Flask and the ultimateALPR SDK is used to process the images.
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This script is intended to be used as a faster local alternative to the large and resource heavy [CodeProject AI](https://www.codeproject.com/AI/docs) software.
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> [!IMPORTANT]
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> The ultimateALPR SDK is a lightweight and much faster alternative (on CPU and GPU) to the CodeProject AI software but it has **a few limitations** with it's free version:
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> - The last character of the license plate is masked with an asterisk
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> - The SDK supposedly has a limit of requests per program execution *(never encountered yet)* **but I have implemented a workaround for this by restarting the SDK after 3000 requests just in case.**
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## Usage
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The server listens on port 5000 and has one endpoint: /v1/image/alpr. The endpoint accepts POST requests with an
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image file in the 'upload' field. The image is processed using the ultimateALPR SDK and the license plate
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information is returned in JSON format. The reponse follows the CodeProject AI ALPR API format. So it can be used
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as a drop-in replacement for the [CodeProject AI ALPR API](https://www.codeproject.com/AI/docs/api/api_reference.html#license-plate-reader).
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> POST: http://localhost:5000/v1/vision/alpr
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**Parameters**
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- upload: (File) The image file to process. (see [Pillow.Image.open()](https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open) for supported formats)
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- grid_size: (Integer) The grid size to use when no match have been found on the whole image (default: 4)
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- wanted_cells: (String) The wanted cells to use when no match have been found on the whole image (default: all cells)
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- format: ``1,2,3,4,...`` *(comma separated list of integers, max: grid_size^2)*
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- *Example for a grid_size of 3:*
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```
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1 | 2 | 3
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4 | 5 | 6
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7 | 8 | 9
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```
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**Response**
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```json
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{
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"success": (Boolean) // True if successful.
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"message": (String) // A summary of the inference operation.
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"error": (String) // (Optional) An description of the error if success was false.
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"predictions": (Object[]) // An array of objects with the x_max, x_min, max, y_min bounds of the plate, label, the plate chars and confidence.
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"processMs": (Integer) // The time (ms) to process the image (includes inference and image manipulation operations).
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}
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```
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## Included models in built executable
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When using the built executable, only the **latin** charset models are bundled by default. If you want to use a different
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charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the according
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models found [here](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
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To build the executable, you can use the ``build_alpr_api.sh`` script, which will create an executable named ``alpr_api`` in
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the ``dist`` folder.
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## Setup development environment
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### Use automatic setup script
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You can use the ``build_and_setup_ultimatealvr.sh`` script to automatically install the necessary packages and build the ultimateALPR SDK wheel, copy the assets and the libs.
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> [!IMPORTANT]
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> Make sure to install the package python3-dev (APT) python3-devel (RPM) before running the build and setup script.
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The end structure should look like this:
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```bash
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.
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├── alpr_api.py
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├── assets
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│ ├── fonts
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│ └── models
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├── libs
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│ ├── libxxxxxx.so
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│ ├── ...
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│ └── libxxxxxx.so
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└── ...
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```
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### Important notes
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When running, building or developing the script, make sure to set the ``LD_LIBRARY_PATH`` environment variable to the libs folder
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*(limitation of the ultimateALPR SDK)*.
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```bash
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export LD_LIBRARY_PATH=libs:$LD_LIBRARY_PATH
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```
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### Error handling
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#### GLIBC_ABI_DT_RELR not found
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If you encounter an error like this:
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```bash
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/lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_ABI_DT_RELR' not found
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```
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Then make sure your GLIBC version is >= 2.36
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