182 lines
7.2 KiB
Markdown
182 lines
7.2 KiB
Markdown
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# Easy Local ALPR (Automatic License Plate Recognition)
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This project is a simple local ALPR (Automatic License Plate Recognition) server that uses the [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) to
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process images and return the license plate information found in the image while focusing on being:
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- **Fast** *(~100ms per image on decent CPU)*
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- **Lightweight** *(~100MB of RAM)*
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- **Easy to use** *(REST API)*
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- **Easy to setup** *(one command setup)*
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- **Offline** *(no internet connection required)*
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> [!IMPORTANT]
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> This project relies on the [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK), which is a commercial product but has a free version with a few limitations.
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> For any commercial use, you will need to take a look at their licensing terms.
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> **I am not affiliated with ultimateALPR-SDK in any way, and I am not responsible for any misuse of the software.**
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> [!NOTE]
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> The [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) is a lightweight and much faster alternative (on CPU and GPU) to existing solutions like
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> [CodeProject AI](https://www.codeproject.com/AI/docs/index.html) but it has **one important restriction** with it's free version:
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> - The last character of the license plate is masked with an asterisk *(e.g. ``ABC1234`` -> ``ABC123*``)*
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## Installation
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Simply download the latest release from the [releases page](/releases) and run the executable.
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The following platforms are currently supported:
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- **Linux** (x86_64)
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## Usage
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The server listens on port 5000 and has a few endpoints documented below, the most important one being [``/v1/image/alpr``](#v1visionalpr).
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### /v1/vision/alpr
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> POST: http://localhost:5000/v1/vision/alpr
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**Description**
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This endpoint processes an image and returns the license plate information (if any) found in the image.
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This endpoint follows
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the [CodeProject AI ALPR API](https://www.codeproject.com/AI/docs/api/api_reference.html#license-plate-reader) format *(
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example below)* so it can be used as a **drop-in replacement** for the CodeProject AI software.
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**Parameters**
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- upload: (File) The image file to process. *(
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see [Pillow.Image.open()](https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open) for supported
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formats, almost any image format is supported)*
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- grid_size: (Integer, optional) Size of grid to divide the image into and retry on each cell when no match have been
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found on the whole image *(default: 3)* **[(more info)](#more-information-about-the-grid-parameter)**
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- wanted_cells: (String, optional) The cells you want to process *(default: all cells)* *
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*[(see here)](#v1visionalpr_grid_debug)**
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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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```jsonc
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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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### /v1/vision/alpr_grid_debug
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> POST: http://localhost:5000/v1/vision/alpr_grid_debug
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**Description**
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This endpoint displays the grid and each cell's number on the image.
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It is intended to be used for debugging purposes to see which cells are being processed.
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**Parameters**
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*same as [v1/vision/alpr](#v1visionalpr)*
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**Response**
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```jsonc
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{
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"image": (Base64) // The image with the grid and cell numbers drawn on it.
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}
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```
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## More information about the grid parameter
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When you send an image to the server, sometimes the ALPR software cannot find any plate because the image is too big or
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the plate is too small in the image.
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To solve this problem, if no plate is found on the whole image, the server will divide the image into a grid of cells
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and retry the ALPR software on each cell.
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You can specify the size of the grid with the ``grid_size`` parameter in each of your requests.
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> [!CAUTION]
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> The higher the grid size, the longer the processing time will be. It is recommended to keep the grid size between 3
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> and 4.
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> Note: The processing time is in no way multiplied by the grid size (usually takes 2x the time)
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You can speed up the processing time by specifying the ``wanted_cells`` parameter. This parameter allows you to specify
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which cells you want to run plate detection on.
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This can be useful if you know the plates can only be in certain areas of the image.
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> [!TIP]
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> You can use the [``/v1/vision/alpr_grid_debug`` endpoint](#v1visionalpr_grid_debug) to see the grid and cell numbers
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> overlaid on your image.
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> You can then specify the ``wanted_cells`` parameter to only process the cells you want.
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**If you wish not to use the grid, you can set the ``grid_size`` parameter to
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0 *(and leave the ``wanted_cells`` parameter empty)*.**
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### Example
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Let's say your driveway camera looks something like this:
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If you set the ``grid_size`` parameter to 2, the image will be divided into a 2x2 grid like this:
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You can see that cell 1 and 2 are empty and cells 3 and 4 might contain license plates.
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You can then set the ``wanted_cells`` parameter to ``3,4`` to only process cells 3 and 4, reducing the processing time
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as only half the image will be processed.
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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
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different charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the
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according 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
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named ``alpr_api`` in the ``dist`` folder.
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## Setup development environment
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### Use automatic setup script
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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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> You can use the ``build_and_setup_ultimatealvr.sh`` script to automatically install the necessary packages and build
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> the
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> ultimateALPR SDK wheel, copy the assets and the libs.
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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
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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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