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167
README.md
167
README.md
@ -1,65 +1,28 @@
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# Easy local ALPR (Automatic License Plate Recognition)
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||||||
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||||||
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# 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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||||||
This project is a simple local ALPR (Automatic License Plate Recognition) server that uses the [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) to
|
information. The server is created using Flask and the ultimateALPR SDK is used to process the images.
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||||||
process images and return the license plate information found in the image while focusing on being:
|
|
||||||
- **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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||||||
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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]
|
> [!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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> 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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||||||
> For any commercial use, you will need to take a look at their licensing terms.
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> - The last character of the license plate is masked with an asterisk
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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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> - 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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||||||
|
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||||||
> [!NOTE]
|
|
||||||
> The [ultimateALPR-SDK](https://github.com/DoubangoTelecom/ultimateALPR-SDK) is a lightweight and much faster alternative (on CPU and GPU) to existing solutions like
|
|
||||||
> [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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||||||
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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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||||||
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||||||
The following platforms are currently supported:
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- **Linux** (x86_64)
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||||||
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## Usage
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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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||||||
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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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||||||
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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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||||||
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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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> POST: http://localhost:32168/v1/vision/alpr
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||||||
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||||||
### /v1/vision/alpr
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||||||
> POST: http://localhost:5000/v1/vision/alpr
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|
||||||
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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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||||||
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**Parameters**
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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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||||||
- 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
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|
||||||
formats, almost any image format is supported)*
|
|
||||||
- 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)**
|
|
||||||
- format: ``1,2,3,4,...`` *(comma separated list of integers, max: grid_size^2)*
|
|
||||||
- *Example for a grid_size of 3:*
|
|
||||||
```
|
|
||||||
1 | 2 | 3
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|
||||||
4 | 5 | 6
|
|
||||||
7 | 8 | 9
|
|
||||||
```
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|
||||||
|
|
||||||
**Response**
|
**Response**
|
||||||
|
```json
|
||||||
```jsonc
|
|
||||||
{
|
{
|
||||||
"success": (Boolean) // True if successful.
|
"success": (Boolean) // True if successful.
|
||||||
"message": (String) // A summary of the inference operation.
|
"message": (String) // A summary of the inference operation.
|
||||||
@ -69,82 +32,31 @@ example below)* so it can be used as a **drop-in replacement** for the CodeProje
|
|||||||
}
|
}
|
||||||
```
|
```
|
||||||
|
|
||||||
### /v1/vision/alpr_grid_debug
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|
||||||
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||||||
> POST: http://localhost:5000/v1/vision/alpr_grid_debug
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|
||||||
|
|
||||||
**Description**
|
|
||||||
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**
|
|
||||||
*same as [v1/vision/alpr](#v1visionalpr)*
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|
||||||
|
|
||||||
**Response**
|
|
||||||
|
|
||||||
```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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|
||||||
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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.
|
|
||||||
> [!CAUTION]
|
|
||||||
> 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
|
|
||||||
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.
|
|
||||||
> [!TIP]
|
|
||||||
> You can use the [``/v1/vision/alpr_grid_debug`` endpoint](#v1visionalpr_grid_debug) to see the grid and cell numbers
|
|
||||||
> overlaid on your image.
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|
||||||
> You can then specify the ``wanted_cells`` parameter to only process the cells you want.
|
|
||||||
|
|
||||||
**If you wish not to use the grid, you can set the ``grid_size`` parameter to
|
|
||||||
0 *(and leave the ``wanted_cells`` parameter empty)*.**
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|
||||||
|
|
||||||
### Example
|
|
||||||
|
|
||||||
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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|
||||||
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|
||||||
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|
||||||
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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
|
|
||||||
as only half the image will be processed.
|
|
||||||
|
|
||||||
## Included models in built executable
|
## Included models in built executable
|
||||||
|
When using the built executable, only the **latin** charset models are bundled by default. If you want to use a different
|
||||||
When using the built executable, only the **latin** charset models are bundled by default. If you want to use a
|
charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the according
|
||||||
different charset, you need to set the charset in the JSON_CONFIG variable and rebuild the executable with the
|
models found [here](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
|
||||||
according models found [here](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
|
To build the executable, you can use the ``build_alpr_api.sh`` script, which will create an executable named ``alpr_api`` in
|
||||||
To build the executable, you can use the ``build_alpr_api.sh`` script, which will create an executable
|
the ``dist`` folder.
|
||||||
named ``alpr_api`` in the ``dist`` folder.
|
|
||||||
|
|
||||||
## Setup development environment
|
## Setup development environment
|
||||||
|
|
||||||
### Use automatic setup script
|
### Install ultimateALPR SDK
|
||||||
|
#### Use already built wheel
|
||||||
|
I have already built the ultimateALPR SDK for x86_64 and ARM64 and included the python3.10 wheel in the wheel folder.
|
||||||
|
You can install the wheel using : ``pip install wheel/*.whl``
|
||||||
|
#### Manually build the wheel
|
||||||
|
If you want to build the wheel yourself, you can use the ``build_and_setup_ultimatealvr.sh`` script. It will create a new
|
||||||
|
directory ``tmp`` and build the wheel in there. It also includes the assets and libs folders needed when developing.
|
||||||
|
|
||||||
> [!IMPORTANT]
|
### Copy necessary files/folders
|
||||||
> Make sure to install the package python3-dev (APT) python3-devel (RPM) before running the build and setup script.
|
Then you need to copy the ``assets`` and ``libs`` folders to the same directory as the script.
|
||||||
> 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.
|
|
||||||
|
|
||||||
The end structure should look like this:
|
If you built the wheel in the previous step, you can copy the ``assets`` and ``libs`` folders from the ``tmp`` directory.
|
||||||
|
If you used the already built wheel, you can find the 'assets' and 'libs' folders on the [GitHub repository](https://github.com/DoubangoTelecom/ultimateALPR-SDK/tree/master/assets)
|
||||||
|
|
||||||
|
The structure should look like this:
|
||||||
```bash
|
```bash
|
||||||
.
|
.
|
||||||
├── alpr_api.py
|
├── alpr_api.py
|
||||||
@ -159,23 +71,8 @@ The end structure should look like this:
|
|||||||
```
|
```
|
||||||
|
|
||||||
### Important notes
|
### Important notes
|
||||||
|
When building or developing the script, make sure to set the ``LD_LIBRARY_PATH`` environment variable to the libs folder
|
||||||
When running, building or developing the script, make sure to set the ``LD_LIBRARY_PATH`` environment variable to the
|
|
||||||
libs folder
|
|
||||||
*(limitation of the ultimateALPR SDK)*.
|
*(limitation of the ultimateALPR SDK)*.
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
export LD_LIBRARY_PATH=libs:$LD_LIBRARY_PATH
|
export LD_LIBRARY_PATH=libs:$LD_LIBRARY_PATH
|
||||||
```
|
```
|
||||||
|
|
||||||
### Error handling
|
|
||||||
|
|
||||||
#### GLIBC_ABI_DT_RELR not found
|
|
||||||
|
|
||||||
If you encounter an error like this:
|
|
||||||
|
|
||||||
```bash
|
|
||||||
/lib/x86_64-linux-gnu/libc.so.6: version `GLIBC_ABI_DT_RELR' not found
|
|
||||||
```
|
|
||||||
|
|
||||||
Then make sure your GLIBC version is >= 2.36
|
|
||||||
|
|||||||
355
alpr_api.py
355
alpr_api.py
@ -1,25 +1,14 @@
|
|||||||
import base64
|
|
||||||
import io
|
|
||||||
import json
|
import json
|
||||||
import logging
|
|
||||||
import os
|
import os
|
||||||
import sys
|
import sys
|
||||||
import threading
|
import threading
|
||||||
import time
|
from time import sleep
|
||||||
import traceback
|
|
||||||
|
|
||||||
import ultimateAlprSdk
|
import ultimateAlprSdk
|
||||||
from PIL import Image, ImageDraw, ImageFont
|
from PIL import Image
|
||||||
from flask import Flask, request, jsonify, render_template
|
from flask import Flask, request, jsonify
|
||||||
|
|
||||||
# Setup logging
|
|
||||||
logging.basicConfig(level=logging.INFO)
|
|
||||||
logger = logging.getLogger(__name__)
|
|
||||||
|
|
||||||
counter_lock = threading.Lock()
|
|
||||||
counter = 0
|
counter = 0
|
||||||
bundle_dir = getattr(sys, '_MEIPASS', os.path.abspath(os.path.dirname(__file__)))
|
|
||||||
boot_time = time.time()
|
|
||||||
|
|
||||||
"""
|
"""
|
||||||
Hi there!
|
Hi there!
|
||||||
@ -30,46 +19,32 @@ information. The server is created using Flask and the ultimateALPR SDK is used
|
|||||||
See the README.md file for more information on how to run this script.
|
See the README.md file for more information on how to run this script.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
# Load configuration
|
# Defines the default JSON configuration. More information at https://www.doubango.org/SDKs/anpr/docs/Configuration_options.html
|
||||||
CONFIG_PATH = os.path.join(bundle_dir,
|
JSON_CONFIG = {
|
||||||
'config.json') # TODO: store config file outside of bundle (to remove need for compilation by users)
|
"debug_level": "info",
|
||||||
if os.path.exists(CONFIG_PATH):
|
|
||||||
with open(CONFIG_PATH, 'r') as config_file:
|
|
||||||
JSON_CONFIG = json.load(config_file)
|
|
||||||
else:
|
|
||||||
JSON_CONFIG = {
|
|
||||||
"assets_folder": os.path.join(bundle_dir, "assets"),
|
|
||||||
"charset": "latin",
|
|
||||||
"car_noplate_detect_enabled": False,
|
|
||||||
"ienv_enabled": False,
|
|
||||||
"openvino_enabled": True,
|
|
||||||
"openvino_device": "CPU",
|
|
||||||
"npu_enabled": False,
|
|
||||||
"klass_lpci_enabled": False,
|
|
||||||
"klass_vcr_enabled": False,
|
|
||||||
"klass_vmmr_enabled": False,
|
|
||||||
"klass_vbsr_enabled": False,
|
|
||||||
"license_token_file": "",
|
|
||||||
"license_token_data": "",
|
|
||||||
|
|
||||||
"debug_level": "fatal",
|
|
||||||
"debug_write_input_image_enabled": False,
|
"debug_write_input_image_enabled": False,
|
||||||
"debug_internal_data_path": ".",
|
"debug_internal_data_path": ".",
|
||||||
|
|
||||||
"num_threads": -1,
|
"num_threads": -1,
|
||||||
"gpgpu_enabled": True,
|
"gpgpu_enabled": True,
|
||||||
"max_latency": -1,
|
"max_latency": -1,
|
||||||
|
|
||||||
"klass_vcr_gamma": 1.5,
|
"klass_vcr_gamma": 1.5,
|
||||||
|
|
||||||
"detect_roi": [0, 0, 0, 0],
|
"detect_roi": [0, 0, 0, 0],
|
||||||
"detect_minscore": 0.35,
|
"detect_minscore": 0.35,
|
||||||
|
|
||||||
"car_noplate_detect_min_score": 0.8,
|
"car_noplate_detect_min_score": 0.8,
|
||||||
"pyramidal_search_enabled": False,
|
|
||||||
"pyramidal_search_sensitivity": 0.38,
|
"pyramidal_search_enabled": True,
|
||||||
|
"pyramidal_search_sensitivity": 0.38, # default 0.28
|
||||||
"pyramidal_search_minscore": 0.8,
|
"pyramidal_search_minscore": 0.8,
|
||||||
"pyramidal_search_min_image_size_inpixels": 800,
|
"pyramidal_search_min_image_size_inpixels": 800,
|
||||||
"recogn_rectify_enabled": True,
|
|
||||||
|
"recogn_rectify_enabled": True, # heavy on cpu
|
||||||
"recogn_minscore": 0.4,
|
"recogn_minscore": 0.4,
|
||||||
"recogn_score_type": "min"
|
"recogn_score_type": "min"
|
||||||
}
|
}
|
||||||
|
|
||||||
IMAGE_TYPES_MAPPING = {
|
IMAGE_TYPES_MAPPING = {
|
||||||
'RGB': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_RGB24,
|
'RGB': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_RGB24,
|
||||||
@ -77,42 +52,41 @@ IMAGE_TYPES_MAPPING = {
|
|||||||
'L': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_Y
|
'L': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_Y
|
||||||
}
|
}
|
||||||
|
|
||||||
config = json.dumps(JSON_CONFIG)
|
|
||||||
|
|
||||||
|
|
||||||
def start_backend_loop():
|
|
||||||
global boot_time, counter
|
|
||||||
|
|
||||||
while True:
|
|
||||||
load_engine()
|
|
||||||
|
|
||||||
# loop for about an hour or 3000 requests then reload the engine (fix for trial license)
|
|
||||||
while counter < 3000 and time.time() - boot_time < 3600:
|
|
||||||
# every 120 sec
|
|
||||||
if int(time.time()) % 120 == 0:
|
|
||||||
if not is_engine_loaded():
|
|
||||||
unload_engine()
|
|
||||||
load_engine()
|
|
||||||
time.sleep(1)
|
|
||||||
|
|
||||||
unload_engine()
|
|
||||||
|
|
||||||
# Reset counter and boot_time to restart the loop
|
|
||||||
with counter_lock:
|
|
||||||
counter = 0
|
|
||||||
boot_time = time.time()
|
|
||||||
|
|
||||||
|
|
||||||
def is_engine_loaded():
|
|
||||||
# hacky way to check if the engine is loaded cause the SDK doesn't provide a method for it
|
|
||||||
return ultimateAlprSdk.UltAlprSdkEngine_requestRuntimeLicenseKey().isOK()
|
|
||||||
|
|
||||||
|
|
||||||
def load_engine():
|
def load_engine():
|
||||||
result = ultimateAlprSdk.UltAlprSdkEngine_init(config)
|
bundle_dir = getattr(sys, '_MEIPASS', os.path.abspath(os.path.dirname(__file__)))
|
||||||
|
|
||||||
|
JSON_CONFIG["assets_folder"] = os.path.join(bundle_dir, "assets")
|
||||||
|
JSON_CONFIG["charset"] = "latin"
|
||||||
|
JSON_CONFIG["car_noplate_detect_enabled"] = False # Whether to detect and return cars with no plate
|
||||||
|
JSON_CONFIG[
|
||||||
|
"ienv_enabled"] = False # Whether to enable Image Enhancement for Night-Vision (IENV). More info about IENV at https://www.doubango.org/SDKs/anpr/docs/Features.html#image-enhancement-for-night-vision-ienv. Default: true for x86-64 and false for ARM.
|
||||||
|
JSON_CONFIG[
|
||||||
|
"openvino_enabled"] = False # Whether to enable OpenVINO. Tensorflow will be used when OpenVINO is disabled
|
||||||
|
JSON_CONFIG[
|
||||||
|
"openvino_device"] = "GPU" # Defines the OpenVINO device to use (CPU, GPU, FPGA...). More info at https://www.doubango.org/SDKs/anpr/docs/Configuration_options.html#openvino-device
|
||||||
|
JSON_CONFIG["npu_enabled"] = False # Whether to enable NPU (Neural Processing Unit) acceleration
|
||||||
|
JSON_CONFIG[
|
||||||
|
"klass_lpci_enabled"] = False # Whether to enable License Plate Country Identification (LPCI). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#license-plate-country-identification-lpci
|
||||||
|
JSON_CONFIG[
|
||||||
|
"klass_vcr_enabled"] = False # Whether to enable Vehicle Color Recognition (VCR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-color-recognition-vcr
|
||||||
|
JSON_CONFIG[
|
||||||
|
"klass_vmmr_enabled"] = False # Whether to enable Vehicle Make Model Recognition (VMMR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-make-model-recognition-vmmr
|
||||||
|
JSON_CONFIG[
|
||||||
|
"klass_vbsr_enabled"] = False # Whether to enable Vehicle Body Style Recognition (VBSR). More info at https://www.doubango.org/SDKs/anpr/docs/Features.html#vehicle-body-style-recognition-vbsr
|
||||||
|
JSON_CONFIG["license_token_file"] = "" # Path to license token file
|
||||||
|
JSON_CONFIG["license_token_data"] = "" # Base64 license token data
|
||||||
|
|
||||||
|
result = ultimateAlprSdk.UltAlprSdkEngine_init(json.dumps(JSON_CONFIG))
|
||||||
if not result.isOK():
|
if not result.isOK():
|
||||||
raise RuntimeError("Init failed: %s" % result.phrase())
|
raise RuntimeError("Init failed: %s" % result.phrase())
|
||||||
|
|
||||||
|
while counter < 3000:
|
||||||
|
sleep(1)
|
||||||
|
|
||||||
|
unload_engine()
|
||||||
|
load_engine()
|
||||||
|
|
||||||
|
|
||||||
def unload_engine():
|
def unload_engine():
|
||||||
result = ultimateAlprSdk.UltAlprSdkEngine_deInit()
|
result = ultimateAlprSdk.UltAlprSdkEngine_deInit()
|
||||||
@ -122,121 +96,57 @@ def unload_engine():
|
|||||||
|
|
||||||
def process_image(image: Image) -> str:
|
def process_image(image: Image) -> str:
|
||||||
global counter
|
global counter
|
||||||
with counter_lock:
|
|
||||||
counter += 1
|
counter += 1
|
||||||
|
|
||||||
width, height = image.size
|
width, height = image.size
|
||||||
image_type = IMAGE_TYPES_MAPPING.get(image.mode, None)
|
|
||||||
if image_type is None:
|
if image.mode in IMAGE_TYPES_MAPPING:
|
||||||
raise ValueError(f"Invalid mode: {image.mode}")
|
image_type = IMAGE_TYPES_MAPPING[image.mode]
|
||||||
|
else:
|
||||||
|
raise ValueError("Invalid mode: %s" % image.mode)
|
||||||
|
|
||||||
result = ultimateAlprSdk.UltAlprSdkEngine_process(
|
result = ultimateAlprSdk.UltAlprSdkEngine_process(
|
||||||
image_type, image.tobytes(), width, height, 0, 1
|
image_type,
|
||||||
|
image.tobytes(), # type(x) == bytes
|
||||||
|
width,
|
||||||
|
height,
|
||||||
|
0, # stride
|
||||||
|
1 # exifOrientation (already rotated in load_image -> use default value: 1)
|
||||||
)
|
)
|
||||||
if not result.isOK():
|
if not result.isOK():
|
||||||
raise RuntimeError(f"Process failed: {result.phrase()}")
|
raise RuntimeError("Process failed: %s" % result.phrase())
|
||||||
|
else:
|
||||||
return result.json()
|
return result.json()
|
||||||
|
|
||||||
|
|
||||||
def create_rest_server_flask():
|
def create_rest_server_flask():
|
||||||
app = Flask(__name__, template_folder=os.path.join(bundle_dir, 'templates'))
|
app = Flask(__name__)
|
||||||
|
|
||||||
@app.route('/v1/image/alpr', methods=['POST'])
|
@app.route('/v1/<string:domain>/<string:module>', methods=['POST'])
|
||||||
def alpr():
|
def alpr(domain, module):
|
||||||
"""
|
# Only care about the ALPR endpoint
|
||||||
The function receives an image and processes it using the ultimateALPR SDK.
|
if domain == 'image' and module == 'alpr':
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- upload: The image to be processed
|
|
||||||
- grid_size: The number of cells to split the image into (e.g. 3)
|
|
||||||
- wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²)
|
|
||||||
"""
|
|
||||||
interference = time.time()
|
|
||||||
|
|
||||||
try:
|
|
||||||
if 'upload' not in request.files:
|
if 'upload' not in request.files:
|
||||||
return jsonify({'error': 'No image found'}), 400
|
return jsonify({'error': 'No image found'})
|
||||||
|
|
||||||
grid_size = int(request.form.get('grid_size', 3))
|
image = request.files['upload']
|
||||||
wanted_cells = request.form.get('wanted_cells')
|
if image.filename == '':
|
||||||
if wanted_cells:
|
return jsonify({'error': 'No selected file'})
|
||||||
wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
|
|
||||||
else:
|
|
||||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
|
||||||
|
|
||||||
image_file = request.files['upload']
|
image = Image.open(image)
|
||||||
if image_file.filename == '':
|
|
||||||
return jsonify({'error': 'No selected file'}), 400
|
|
||||||
|
|
||||||
image = Image.open(image_file)
|
|
||||||
result = process_image(image)
|
result = process_image(image)
|
||||||
result = convert_to_cpai_compatible(result)
|
result = convert_to_cpai_compatible(result)
|
||||||
|
|
||||||
if not result['predictions']:
|
|
||||||
logger.debug("No plate found, attempting grid split")
|
|
||||||
predictions_found = find_best_plate_with_grid_split(image, grid_size, wanted_cells)
|
|
||||||
if predictions_found:
|
|
||||||
result['predictions'].append(max(predictions_found, key=lambda x: x['confidence']))
|
|
||||||
|
|
||||||
if result['predictions']:
|
|
||||||
isolated_plate_image = isolate_plate_in_image(image, result['predictions'][0])
|
|
||||||
result['image'] = f"data:image/png;base64,{image_to_base64(isolated_plate_image, compress=True)}"
|
|
||||||
|
|
||||||
process_ms = round((time.time() - interference) * 1000, 2)
|
|
||||||
result.update({'processMs': process_ms, 'inferenceMs': process_ms})
|
|
||||||
return jsonify(result)
|
return jsonify(result)
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Error processing image: {e}")
|
|
||||||
logger.error(traceback.format_exc())
|
|
||||||
return jsonify({'error': 'Error processing image'}), 500
|
|
||||||
|
|
||||||
@app.route('/v1/image/alpr_grid_debug', methods=['POST'])
|
|
||||||
def alpr_grid_debug():
|
|
||||||
"""
|
|
||||||
The function receives an image and returns it with the grid overlayed on it (for debugging purposes).
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- upload: The image to be processed
|
|
||||||
- grid_size: The number of cells to split the image into (e.g. 3)
|
|
||||||
- wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²)
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- The image with the grid overlayed on it
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
if 'upload' not in request.files:
|
|
||||||
return jsonify({'error': 'No image found'}), 400
|
|
||||||
|
|
||||||
grid_size = int(request.form.get('grid_size', 3))
|
|
||||||
wanted_cells = request.form.get('wanted_cells')
|
|
||||||
if wanted_cells:
|
|
||||||
wanted_cells = [int(cell) for cell in wanted_cells.split(',')]
|
|
||||||
else:
|
else:
|
||||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
return jsonify({'error': 'Endpoint not implemented'}), 404
|
||||||
|
|
||||||
image_file = request.files['upload']
|
|
||||||
if image_file.filename == '':
|
|
||||||
return jsonify({'error': 'No selected file'}), 400
|
|
||||||
|
|
||||||
image = Image.open(image_file)
|
|
||||||
image = draw_grid_and_cell_numbers_on_image(image, grid_size, wanted_cells)
|
|
||||||
|
|
||||||
image_base64 = image_to_base64(image, compress=True)
|
|
||||||
return jsonify({"image": f"data:image/png;base64,{image_base64}"})
|
|
||||||
except Exception as e:
|
|
||||||
logger.error(f"Error processing image: {e}")
|
|
||||||
logger.error(traceback.format_exc())
|
|
||||||
return jsonify({'error': 'Error processing image'}), 500
|
|
||||||
|
|
||||||
@app.route('/')
|
|
||||||
def index():
|
|
||||||
return render_template('index.html')
|
|
||||||
|
|
||||||
return app
|
return app
|
||||||
|
|
||||||
|
|
||||||
def convert_to_cpai_compatible(result):
|
def convert_to_cpai_compatible(result):
|
||||||
result = json.loads(result)
|
result = json.loads(result)
|
||||||
|
|
||||||
response = {
|
response = {
|
||||||
'success': "true",
|
'success': "true",
|
||||||
'processMs': result['duration'],
|
'processMs': result['duration'],
|
||||||
@ -254,131 +164,34 @@ def convert_to_cpai_compatible(result):
|
|||||||
'timestamp': ''
|
'timestamp': ''
|
||||||
}
|
}
|
||||||
|
|
||||||
for plate in result.get('plates', []):
|
if 'plates' in result:
|
||||||
|
plates = result['plates']
|
||||||
|
|
||||||
|
for plate in plates:
|
||||||
warpedBox = plate['warpedBox']
|
warpedBox = plate['warpedBox']
|
||||||
x_coords = warpedBox[0::2]
|
x_coords = warpedBox[0::2]
|
||||||
y_coords = warpedBox[1::2]
|
y_coords = warpedBox[1::2]
|
||||||
x_min, x_max = min(x_coords), max(x_coords)
|
x_min = min(x_coords)
|
||||||
y_min, y_max = min(y_coords), max(y_coords)
|
x_max = max(x_coords)
|
||||||
|
y_min = min(y_coords)
|
||||||
|
y_max = max(y_coords)
|
||||||
|
|
||||||
response['predictions'].append({
|
response['predictions'].append({
|
||||||
'confidence': plate['confidences'][0] / 100,
|
'confidence': plate['confidence'] / 100,
|
||||||
'label': f"Plate: {plate['text']}",
|
'label': "Plate: " + plate['text'],
|
||||||
'plate': plate['text'],
|
'plate': plate['text'],
|
||||||
'x_min': x_min,
|
'x_min': x_min,
|
||||||
'x_max': x_max,
|
'x_max': x_max,
|
||||||
'y_min': y_min,
|
'y_min': y_min,
|
||||||
'y_max': y_max
|
'y_max': y_max
|
||||||
})
|
})
|
||||||
|
|
||||||
return response
|
return response
|
||||||
|
|
||||||
|
|
||||||
def draw_grid_and_cell_numbers_on_image(image: Image, grid_size: int = 3, wanted_cells: list = None) -> Image:
|
|
||||||
if grid_size < 1:
|
|
||||||
grid_size = 1
|
|
||||||
|
|
||||||
if wanted_cells is None:
|
|
||||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
|
||||||
|
|
||||||
width, height = image.size
|
|
||||||
cell_width = width // grid_size
|
|
||||||
cell_height = height // grid_size
|
|
||||||
|
|
||||||
draw = ImageDraw.Draw(image)
|
|
||||||
font = ImageFont.truetype(os.path.join(bundle_dir, 'assets', 'fonts', 'GlNummernschildEng-XgWd.ttf'),
|
|
||||||
image.size[0] // 10)
|
|
||||||
|
|
||||||
for cell_index in range(1, grid_size * grid_size + 1):
|
|
||||||
row = (cell_index - 1) // grid_size
|
|
||||||
col = (cell_index - 1) % grid_size
|
|
||||||
left = col * cell_width
|
|
||||||
upper = row * cell_height
|
|
||||||
right = left + cell_width
|
|
||||||
lower = upper + cell_height
|
|
||||||
|
|
||||||
if cell_index in wanted_cells:
|
|
||||||
draw.rectangle([left, upper, right, lower], outline="red", width=4)
|
|
||||||
draw.text((left + 5, upper + 5), str(cell_index), fill="red", font=font)
|
|
||||||
|
|
||||||
return image
|
|
||||||
|
|
||||||
|
|
||||||
def find_best_plate_with_grid_split(image: Image, grid_size: int = 3, wanted_cells: list = None):
|
|
||||||
if grid_size < 1:
|
|
||||||
logger.debug("Grid size < 1, skipping split")
|
|
||||||
return []
|
|
||||||
|
|
||||||
if wanted_cells is None:
|
|
||||||
wanted_cells = list(range(1, grid_size * grid_size + 1))
|
|
||||||
|
|
||||||
predictions_found = []
|
|
||||||
width, height = image.size
|
|
||||||
cell_width = width // grid_size
|
|
||||||
cell_height = height // grid_size
|
|
||||||
|
|
||||||
for cell_index in range(1, grid_size * grid_size + 1):
|
|
||||||
row = (cell_index - 1) // grid_size
|
|
||||||
col = (cell_index - 1) % grid_size
|
|
||||||
left = col * cell_width
|
|
||||||
upper = row * cell_height
|
|
||||||
right = left + cell_width
|
|
||||||
lower = upper + cell_height
|
|
||||||
|
|
||||||
if cell_index in wanted_cells:
|
|
||||||
cell_image = image.crop((left, upper, right, lower))
|
|
||||||
result_cell = json.loads(process_image(cell_image))
|
|
||||||
|
|
||||||
for plate in result_cell.get('plates', []):
|
|
||||||
warpedBox = plate['warpedBox']
|
|
||||||
x_coords = warpedBox[0::2]
|
|
||||||
y_coords = warpedBox[1::2]
|
|
||||||
x_min = min(x_coords) + left
|
|
||||||
x_max = max(x_coords) + left
|
|
||||||
y_min = min(y_coords) + upper
|
|
||||||
y_max = max(y_coords) + upper
|
|
||||||
|
|
||||||
predictions_found.append({
|
|
||||||
'confidence': plate['confidences'][0] / 100,
|
|
||||||
'label': f"Plate: {plate['text']}",
|
|
||||||
'plate': plate['text'],
|
|
||||||
'x_min': x_min,
|
|
||||||
'x_max': x_max,
|
|
||||||
'y_min': y_min,
|
|
||||||
'y_max': y_max
|
|
||||||
})
|
|
||||||
|
|
||||||
return predictions_found
|
|
||||||
|
|
||||||
|
|
||||||
def isolate_plate_in_image(image: Image, plate: dict) -> Image:
|
|
||||||
x_min, x_max = plate['x_min'], plate['x_max']
|
|
||||||
y_min, y_max = plate['y_min'], plate['y_max']
|
|
||||||
offset = 10
|
|
||||||
|
|
||||||
cropped_image = image.crop((max(0, x_min - offset), max(0, y_min - offset), min(image.size[0], x_max + offset),
|
|
||||||
min(image.size[1], y_max + offset)))
|
|
||||||
resized_image = cropped_image.resize((int(cropped_image.size[0] * 3), int(cropped_image.size[1] * 3)),
|
|
||||||
resample=Image.Resampling.LANCZOS)
|
|
||||||
|
|
||||||
return resized_image
|
|
||||||
|
|
||||||
|
|
||||||
def image_to_base64(img: Image, compress=False):
|
|
||||||
"""Convert a Pillow image to a base64-encoded string."""
|
|
||||||
|
|
||||||
buffered = io.BytesIO()
|
|
||||||
if compress:
|
|
||||||
img = img.resize((img.size[0] // 2, img.size[1] // 2))
|
|
||||||
img.save(buffered, format="WEBP", quality=35, lossless=False)
|
|
||||||
else:
|
|
||||||
img.save(buffered, format="WEBP")
|
|
||||||
|
|
||||||
return base64.b64encode(buffered.getvalue()).decode('utf-8')
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
engine_thread = threading.Thread(target=start_backend_loop, daemon=True)
|
engine = threading.Thread(target=load_engine, daemon=True)
|
||||||
engine_thread.start()
|
engine.start()
|
||||||
|
|
||||||
app = create_rest_server_flask()
|
app = create_rest_server_flask()
|
||||||
app.run(host='0.0.0.0', port=5000)
|
app.run(host='0.0.0.0', port=5000)
|
||||||
|
|||||||
@ -1,14 +1 @@
|
|||||||
#!/bin/bash
|
pyinstaller --noconfirm --onefile --console --add-data libs:. --add-data assets:assets --name alpr_api "alpr_api.py"
|
||||||
|
|
||||||
VERSION=1.5.0
|
|
||||||
|
|
||||||
rm -rf buildenv build dist *.spec
|
|
||||||
python3.10 -m venv buildenv
|
|
||||||
source buildenv/bin/activate
|
|
||||||
python3.10 -m pip install --upgrade pip pyinstaller
|
|
||||||
python3.10 -m pip install ./wheel/ultimateAlprSdk-3.0.0-cp310-cp310-linux_x86_64.whl
|
|
||||||
pip install -r requirements.txt
|
|
||||||
|
|
||||||
pyinstaller --noconfirm --onefile --console --add-data libs:. --add-data assets:assets --add-data static:static --add-data templates:templates --name easy-local-alpr-$VERSION-openvinocpu_linux_x86_64 "alpr_api.py"
|
|
||||||
deactivate
|
|
||||||
rm -rf buildenv
|
|
||||||
@ -1,184 +1,58 @@
|
|||||||
#!/bin/bash
|
# clone sdk
|
||||||
|
mkdir ./tmp
|
||||||
|
cd tmp
|
||||||
|
wget https://github.com/DoubangoTelecom/ultimateALPR-SDK/archive/8130c76140fe8edc60fe20f875796121a8d22fed.zip -O temp-sdk.zip
|
||||||
|
unzip temp-sdk.zip
|
||||||
|
rm temp-sdk.zip
|
||||||
|
|
||||||
deactivate 2>/dev/null
|
mkdir temp-sdk
|
||||||
|
mv ultimateALPR-SDK*/* ./temp-sdk
|
||||||
|
rm -R ultimateALPR-SDK*
|
||||||
|
|
||||||
# Function to create virtual environment, install the wheel, and copy assets and libs
|
# create env to build ultimatealpr-sdk for python
|
||||||
install_and_setup() {
|
python3.10 -m venv venv
|
||||||
echo "Creating virtual environment at the root..."
|
source venv/bin/activate
|
||||||
python3.10 -m venv "$ROOT_DIR/venv" >/dev/null 2>&1
|
pip install setuptools wheel Cython
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to create virtual environment."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo "Activating virtual environment..."
|
cd temp-sdk
|
||||||
source "$ROOT_DIR/venv/bin/activate"
|
|
||||||
|
|
||||||
echo "Installing the wheel..."
|
# move folders to simplify build
|
||||||
pip install "$BUILD_DIR"/ultimateAlprSdk-*.whl >/dev/null 2>&1
|
mkdir -p binaries/linux/x86_64/c++
|
||||||
if [ $? -ne 0 ]; then
|
cp c++/* binaries/linux/x86_64/c++
|
||||||
echo "Failed to install the wheel."
|
cp python/* binaries/linux/x86_64/
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo "Deactivating virtual environment..."
|
# edit setup.py to simplify build
|
||||||
deactivate
|
cd binaries/linux/x86_64/
|
||||||
|
|
||||||
echo "Copying assets and libs folders to the root directory..."
|
|
||||||
cp -r "$BUILD_DIR/assets" "$ROOT_DIR"
|
|
||||||
cp -r "$BUILD_DIR/libs" "$ROOT_DIR"
|
|
||||||
|
|
||||||
if [ -f "$ROOT_DIR/requirements.txt" ]; then
|
|
||||||
echo "Installing requirements..."
|
|
||||||
source "$ROOT_DIR/venv/bin/activate"
|
|
||||||
pip install -r "$ROOT_DIR/requirements.txt" >/dev/null 2>&1
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to install requirements."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
deactivate
|
|
||||||
fi
|
|
||||||
|
|
||||||
rm -rf "$BUILD_DIR"
|
|
||||||
|
|
||||||
echo "Virtual environment created and wheel installed successfully."
|
|
||||||
echo "Assets and libs folders copied to the root directory."
|
|
||||||
|
|
||||||
echo "Setup completed."
|
|
||||||
}
|
|
||||||
|
|
||||||
# Function to prompt user for auto setup choice
|
|
||||||
prompt_auto_setup() {
|
|
||||||
read -r -p "Do you want to automatically create a new virtual environment, install the wheel and copy the assets and libs? (y/n): " choice
|
|
||||||
case "$choice" in
|
|
||||||
y|Y ) install_and_setup;;
|
|
||||||
n|N ) echo "Setup completed.";;
|
|
||||||
* ) echo "Invalid choice. Please run the script again and choose y or n.";;
|
|
||||||
esac
|
|
||||||
}
|
|
||||||
|
|
||||||
# Variables
|
|
||||||
ROOT_DIR=$(pwd)
|
|
||||||
BUILD_DIR="$ROOT_DIR/tmp-build-env"
|
|
||||||
SDK_ZIP_URL="https://github.com/DoubangoTelecom/ultimateALPR-SDK/archive/8130c76140fe8edc60fe20f875796121a8d22fed.zip"
|
|
||||||
SDK_ZIP="$BUILD_DIR/temp-sdk.zip"
|
|
||||||
SDK_DIR="$BUILD_DIR/temp-sdk"
|
|
||||||
BIN_DIR="$SDK_DIR/binaries/linux/x86_64"
|
|
||||||
|
|
||||||
# Create build environment
|
|
||||||
mkdir -p "$BUILD_DIR"
|
|
||||||
cd "$BUILD_DIR" || exit
|
|
||||||
|
|
||||||
# Clone SDK
|
|
||||||
echo "Downloading SDK..."
|
|
||||||
if [ -f "$SDK_ZIP" ]; then
|
|
||||||
echo "SDK zip already exists."
|
|
||||||
rm -R "$SDK_DIR"
|
|
||||||
else
|
|
||||||
wget "$SDK_ZIP_URL" -O "$SDK_ZIP" >/dev/null 2>&1
|
|
||||||
fi
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to download SDK."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo "Unzipping SDK..."
|
|
||||||
unzip "$SDK_ZIP" >/dev/null 2>&1
|
|
||||||
rm "$SDK_ZIP"
|
|
||||||
mkdir "$SDK_DIR"
|
|
||||||
mv ultimateALPR-SDK*/* "$SDK_DIR"
|
|
||||||
rm -r ultimateALPR-SDK*
|
|
||||||
|
|
||||||
# Create environment to build ultimatealpr-sdk for Python
|
|
||||||
echo "Creating virtual environment for building SDK..."
|
|
||||||
python3.10 -m venv "$BUILD_DIR/venv" >/dev/null 2>&1
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to create virtual environment."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
echo "Activating virtual environment..."
|
|
||||||
source "$BUILD_DIR/venv/bin/activate"
|
|
||||||
|
|
||||||
echo "Installing build dependencies..."
|
|
||||||
pip install setuptools wheel Cython >/dev/null 2>&1
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to install build dependencies."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
# Move folders to simplify build
|
|
||||||
mkdir -p "$BIN_DIR/c++"
|
|
||||||
mv "$SDK_DIR/c++"/* "$BIN_DIR/c++"
|
|
||||||
mv "$SDK_DIR/python"/* "$BIN_DIR/"
|
|
||||||
|
|
||||||
# Edit setup.py to simplify build
|
|
||||||
echo "Editing setup.py for simplified build..."
|
|
||||||
cd "$BIN_DIR" || exit
|
|
||||||
sed -i "s|sources=\[os.path.abspath('../../../python/ultimateALPR-SDK-API-PUBLIC-SWIG_python.cxx')\]|sources=[os.path.abspath('ultimateALPR-SDK-API-PUBLIC-SWIG_python.cxx')]|g" setup.py
|
sed -i "s|sources=\[os.path.abspath('../../../python/ultimateALPR-SDK-API-PUBLIC-SWIG_python.cxx')\]|sources=[os.path.abspath('ultimateALPR-SDK-API-PUBLIC-SWIG_python.cxx')]|g" setup.py
|
||||||
sed -i "s|include_dirs=\['../../../c++'\]|include_dirs=['c++']|g" setup.py
|
sed -i "s|include_dirs=\['../../../c++'\]|include_dirs=['c++']|g" setup.py
|
||||||
sed -i "s|library_dirs=\['.'\]|library_dirs=['libs']|g" setup.py
|
sed -i "s|library_dirs=\['.'\]|library_dirs=['libs']|g" setup.py
|
||||||
|
|
||||||
# Move all .so files into libs folder
|
# move all .so files into libs folder
|
||||||
mkdir "$BIN_DIR/libs"
|
mkdir libs
|
||||||
mv "$BIN_DIR/"*.so* "$BIN_DIR/libs"
|
mv *.so libs/
|
||||||
|
mv *.so.* libs/
|
||||||
|
|
||||||
# Download TensorFlow
|
# build the wheel
|
||||||
read -r -p "Do you want TensorFlow for CPU or GPU? (cpu/gpu): " tf_choice
|
python setup.py bdist_wheel -v
|
||||||
mkdir -p "$BIN_DIR/tensorflow"
|
|
||||||
if [ "$tf_choice" == "gpu" ]; then
|
|
||||||
echo "Downloading TensorFlow GPU..."
|
|
||||||
wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1 # Use 2.6 for newer GPU support
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to download TensorFlow GPU."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
echo "Extracting TensorFlow GPU..."
|
|
||||||
tar -xf libtensorflow-gpu-linux-x86_64-2.6.0.tar.gz -C "$BIN_DIR/tensorflow" >/dev/null 2>&1
|
|
||||||
|
|
||||||
mv "$BIN_DIR/tensorflow/lib/libtensorflow.so.1" "$BIN_DIR/libs/libtensorflow.so.1"
|
# move the built whl and the libs back to root dir
|
||||||
mv "$BIN_DIR/tensorflow/lib/libtensorflow_framework.so.2.6.0" "$BIN_DIR/libs/libtensorflow_framework.so.2"
|
mv dist/* ../../../../
|
||||||
|
|
||||||
else
|
mv libs ../../../../
|
||||||
echo "Downloading TensorFlow CPU..."
|
|
||||||
#wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.6.0.tar.gz >/dev/null 2>&1
|
|
||||||
wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-1.14.0.tar.gz >/dev/null 2>&1 # Use 1.14 as it's smaller in size
|
|
||||||
if [ $? -ne 0 ]; then
|
|
||||||
echo "Failed to download TensorFlow CPU."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
echo "Extracting TensorFlow CPU..."
|
|
||||||
tar -xf libtensorflow-cpu-linux-x86_64-1.14.0.tar.gz -C "$BIN_DIR/tensorflow" >/dev/null 2>&1
|
|
||||||
|
|
||||||
mv "$BIN_DIR/tensorflow/lib/"* "$BIN_DIR/libs/"
|
# move the assets to root dir
|
||||||
fi
|
cd ../../../
|
||||||
|
mv assets ../assets
|
||||||
|
|
||||||
# Build the wheel
|
## install the whl
|
||||||
echo "Building the wheel..."
|
#cd ..
|
||||||
python "$BIN_DIR/setup.py" bdist_wheel -v >/dev/null 2>&1
|
#pip install *.whl
|
||||||
if [ $? -ne 0 ]; then
|
#rm *.whl
|
||||||
echo "Failed to build the wheel."
|
|
||||||
exit 1
|
|
||||||
fi
|
|
||||||
|
|
||||||
# Move the built wheel and the libs back to the root directory
|
cd ../
|
||||||
mv "$BIN_DIR/dist/"*.whl "$BUILD_DIR"
|
|
||||||
mv "$BIN_DIR/libs" "$BUILD_DIR"
|
|
||||||
mv "$BIN_DIR/plugins.xml" "$BUILD_DIR/libs"
|
|
||||||
|
|
||||||
# Move the assets to the root directory
|
# remove sdk
|
||||||
mv "$SDK_DIR/assets" "$BUILD_DIR/assets"
|
rm -R temp-sdk
|
||||||
|
|
||||||
# Deactivate and clean up the build virtual environment
|
echo "UltimateALPR SDK built and setup successfully"
|
||||||
echo "Deactivating and cleaning up virtual environment..."
|
echo "You can now install the wheel using 'pip install ultimateAlprSdk-*.whl'"
|
||||||
deactivate
|
|
||||||
cd "$ROOT_DIR" || exit
|
|
||||||
rm -rf "$BUILD_DIR/venv"
|
|
||||||
rm -rf "$SDK_DIR"
|
|
||||||
|
|
||||||
# Inform the user of the successful build
|
|
||||||
echo "UltimateALPR SDK built and setup successfully."
|
|
||||||
echo "You can now create a virtual environment, install the wheel and copy the assets and libs and start developing. Say 'y' to the next prompt to do this automatically (recommended)."
|
|
||||||
echo "Tip: Look at the assets folder as you might not need all the models depending on your platform/use case."
|
|
||||||
# Prompt user for auto setup choice
|
|
||||||
prompt_auto_setup
|
|
||||||
@ -1,3 +1,3 @@
|
|||||||
flask
|
flask
|
||||||
Pillow
|
pillow
|
||||||
ultimateAlprSdk
|
ultimateAlprSdk
|
||||||
Binary file not shown.
|
Before Width: | Height: | Size: 3.2 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 3.1 KiB |
@ -1,323 +0,0 @@
|
|||||||
<!DOCTYPE html>
|
|
||||||
<html lang="en">
|
|
||||||
|
|
||||||
<head>
|
|
||||||
<meta charset="UTF-8">
|
|
||||||
<meta content="width=device-width, initial-scale=1.0" name="viewport">
|
|
||||||
<title>Easy Local ALPR - API</title>
|
|
||||||
<script src="https://cdn.tailwindcss.com"></script>
|
|
||||||
<link href="https://fonts.googleapis.com/css2?family=Google+Sans:wght@400;500;700&display=swap" rel="stylesheet">
|
|
||||||
<style>
|
|
||||||
body {
|
|
||||||
background-color: #f0f2f5;
|
|
||||||
background-image: radial-gradient(#7c7c7c 1px, rgba(0, 0, 0, 0) 1px);
|
|
||||||
background-size: 20px 20px;
|
|
||||||
font-family: 'Google Sans', sans-serif;
|
|
||||||
}
|
|
||||||
|
|
||||||
.grid-cell {
|
|
||||||
border: 2px solid #e5e7eb; /* Tailwind gray-200 */
|
|
||||||
display: flex;
|
|
||||||
align-items: center;
|
|
||||||
justify-content: center;
|
|
||||||
cursor: pointer;
|
|
||||||
border-radius: 0.5rem; /* Rounded corners */
|
|
||||||
transition: background-color 0.2s ease, color 0.2s ease, border-color 0.2s ease; /* Smooth transition */
|
|
||||||
padding-top: 25%; /* More compact rectangular shape */
|
|
||||||
position: relative;
|
|
||||||
overflow: hidden;
|
|
||||||
box-shadow: 0 1px 2px rgba(0, 0, 0, 0.05); /* Subtle shadow for modern look */
|
|
||||||
}
|
|
||||||
|
|
||||||
.grid-cell span {
|
|
||||||
position: absolute;
|
|
||||||
top: 50%;
|
|
||||||
left: 50%;
|
|
||||||
transform: translate(-50%, -50%);
|
|
||||||
font-weight: 500; /* Semi-bold text */
|
|
||||||
}
|
|
||||||
|
|
||||||
.grid-cell.selected {
|
|
||||||
background-color: #1f2937; /* Tailwind gray-800 */
|
|
||||||
color: white;
|
|
||||||
border-color: #1f2937; /* Match border color with background */
|
|
||||||
}
|
|
||||||
|
|
||||||
.grid-cell:hover {
|
|
||||||
background-color: #9ca3af; /* Tailwind gray-400 for hover effect */
|
|
||||||
color: white;
|
|
||||||
border-color: #9ca3af; /* Match border color with hover effect */
|
|
||||||
}
|
|
||||||
|
|
||||||
.grid-container {
|
|
||||||
display: grid;
|
|
||||||
grid-template-columns: repeat(auto-fill, minmax(50px, 1fr));
|
|
||||||
gap: 8px; /* Adjust gap between cells */
|
|
||||||
margin-top: 1rem;
|
|
||||||
}
|
|
||||||
</style>
|
|
||||||
</head>
|
|
||||||
|
|
||||||
<body class="bg-neutral-100 dark:bg-neutral-900 dark:text-white flex items-center justify-center min-h-screen p-4">
|
|
||||||
<div class="absolute top-4 left-4 z-50">
|
|
||||||
<img alt="Logo" class="h-12 dark:hidden" id="logo" src="{{ url_for('static', filename='logo_black.webp') }}">
|
|
||||||
<img alt="Logo" class="h-12 hidden dark:block" id="logoDark"
|
|
||||||
src="{{ url_for('static', filename='logo_white.webp') }}">
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<div class="bg-white dark:bg-neutral-800 p-6 rounded-lg shadow-lg w-full max-w-xl mt-16">
|
|
||||||
<h1 class="text-2xl font-bold mb-4 text-center dark:text-gray-200">Select Service</h1>
|
|
||||||
<form class="space-y-4" id="serviceForm">
|
|
||||||
<div>
|
|
||||||
<label class="block text-sm font-medium text-gray-700 dark:text-gray-300" for="service">Choose a
|
|
||||||
service:</label>
|
|
||||||
<select class="mt-1 block w-full py-2 px-3 border border-gray-300 bg-white dark:bg-neutral-800 dark:border-neutral-700 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm"
|
|
||||||
id="service" name="service" onchange="updateFormFields()">
|
|
||||||
<option value="alpr">Plate Recognition (ALPR)</option>
|
|
||||||
<option value="alpr_grid_debug">Grid Size Helper</option>
|
|
||||||
</select>
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<div class="service-fields hidden" id="alprFields">
|
|
||||||
<div>
|
|
||||||
<label for="upload_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Choose an
|
|
||||||
image:</label>
|
|
||||||
<div class="mt-1 flex items-center">
|
|
||||||
<input type="file" id="upload_alpr" name="upload" accept="image/*" class="hidden"
|
|
||||||
onchange="updateFileName();">
|
|
||||||
<label for="upload_alpr"
|
|
||||||
class="cursor-pointer inline-flex items-center justify-center px-4 py-2 border border-gray-400 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-neutral-800 hover:bg-neutral-50 dark:hover:bg-neutral-600">Select
|
|
||||||
file</label>
|
|
||||||
<span id="fileName_alpr" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
<div>
|
|
||||||
<label for="grid_size_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid
|
|
||||||
Size:</label>
|
|
||||||
<input type="number" id="grid_size_alpr" name="grid_size" value="3"
|
|
||||||
class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark:bg-neutral-800 dark:border-neutral-700">
|
|
||||||
</div>
|
|
||||||
<div>
|
|
||||||
<label for="wanted_cells_alpr" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted
|
|
||||||
Cells:</label>
|
|
||||||
<div id="gridContainer_alpr" class="grid-container"></div>
|
|
||||||
<input type="hidden" id="wanted_cells_alpr" name="wanted_cells">
|
|
||||||
</div>
|
|
||||||
<input id="plate_image_alpr" name="plate_image_alpr" type="hidden" value="true">
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<div class="service-fields hidden" id="alpr_grid_debugFields">
|
|
||||||
<div>
|
|
||||||
<label for="upload_alpr_grid_debug" class="block text-sm font-medium text-gray-700 dark:text-gray-300">Choose
|
|
||||||
an image:</label>
|
|
||||||
<div class="mt-1 flex items-center">
|
|
||||||
<input type="file" id="upload_alpr_grid_debug" name="upload" accept="image/*" class="hidden"
|
|
||||||
onchange="updateFileName();">
|
|
||||||
<label for="upload_alpr_grid_debug"
|
|
||||||
class="cursor-pointer inline-flex items-center justify-center px-4 py-2 border border-gray-400 rounded-md shadow-sm text-sm font-medium text-gray-700 dark:text-gray-300 bg-white dark:bg-neutral-800 hover:bg-neutral-50 dark:hover:bg-neutral-600">Select
|
|
||||||
file</label>
|
|
||||||
<span id="fileName_alpr_grid_debug" class="ml-2 text-sm text-gray-600 dark:text-gray-300"></span>
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
<div>
|
|
||||||
<label for="grid_size_alpr_grid_debug"
|
|
||||||
class="block text-sm font-medium text-gray-700 dark:text-gray-300">Grid Size:</label>
|
|
||||||
<input type="number" id="grid_size_alpr_grid_debug" name="grid_size" value="3"
|
|
||||||
class="mt-1 block w-full px-3 py-2 border border-gray-300 rounded-md shadow-sm focus:outline-none focus:ring-indigo-500 focus:border-indigo-500 sm:text-sm dark:bg-neutral-800 dark:border-neutral-700">
|
|
||||||
</div>
|
|
||||||
<div>
|
|
||||||
<label for="wanted_cells_alpr_grid_debug"
|
|
||||||
class="block text-sm font-medium text-gray-700 dark:text-gray-300">Wanted Cells:</label>
|
|
||||||
<div id="gridContainer_alpr_grid_debug" class="grid-container"></div>
|
|
||||||
<input type="hidden" id="wanted_cells_alpr_grid_debug" name="wanted_cells">
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<div id="imagePreview" class="mt-4 hidden">
|
|
||||||
<label id="imagePreviewLabel"
|
|
||||||
class="block text sm font-medium text-gray-700 dark:text-gray-300">Preview:</label>
|
|
||||||
<img id="previewImage" src="#" alt="Preview" class="max-w-full h-auto rounded-lg">
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<button class="w-full py-2 px-4 bg-black text-white font-semibold rounded-md shadow-sm hover:bg-neutral-900 dark:bg-neutral-900 dark:hover:bg-neutral-950"
|
|
||||||
id="submitButton" type="submit">Submit
|
|
||||||
</button>
|
|
||||||
</form>
|
|
||||||
|
|
||||||
<div class="mt-6">
|
|
||||||
<h2 class="text-xl font-semibold mb-2 dark:text-gray-200">
|
|
||||||
Response
|
|
||||||
<span class="text-sm font-normal" id="timer"></span>
|
|
||||||
</h2>
|
|
||||||
<pre class="bg-neutral-100 dark:bg-neutral-900 p-4 border rounded-lg text-sm text-gray-900 dark:text-gray-200 overflow-x-auto"
|
|
||||||
id="responseBox"></pre>
|
|
||||||
</div>
|
|
||||||
</div>
|
|
||||||
|
|
||||||
<script src="https://code.jquery.com/jquery-3.5.1.min.js"></script>
|
|
||||||
<script>
|
|
||||||
function updateFormFields() {
|
|
||||||
const service = document.getElementById('service').value;
|
|
||||||
localStorage.setItem('selectedService', service);
|
|
||||||
|
|
||||||
document.querySelectorAll('.service-fields').forEach(field => {
|
|
||||||
field.classList.add('hidden');
|
|
||||||
field.querySelectorAll('input, select').forEach(field => field.disabled = true);
|
|
||||||
});
|
|
||||||
|
|
||||||
const selectedServiceFields = document.getElementById(service + 'Fields');
|
|
||||||
selectedServiceFields.classList.remove('hidden');
|
|
||||||
selectedServiceFields.querySelectorAll('input, select').forEach(field => field.disabled = false);
|
|
||||||
|
|
||||||
['responseBox', 'timer', 'fileName_' + service, 'previewImage', 'imagePreview', 'upload_' + service]
|
|
||||||
.forEach(id => {
|
|
||||||
const element = document.getElementById(id);
|
|
||||||
if (element) {
|
|
||||||
if (element.tagName === 'DIV') element.classList.add('hidden');
|
|
||||||
if (element.tagName === 'INPUT') element.value = '';
|
|
||||||
if (element.tagName === 'SPAN' || element.tagName === 'PRE') element.textContent = '';
|
|
||||||
if (element.tagName === 'IMG') element.src = '';
|
|
||||||
}
|
|
||||||
});
|
|
||||||
|
|
||||||
updateGrid(service);
|
|
||||||
}
|
|
||||||
|
|
||||||
function initializeForm() {
|
|
||||||
const savedService = localStorage.getItem('selectedService');
|
|
||||||
if (savedService) {
|
|
||||||
document.getElementById('service').value = savedService;
|
|
||||||
updateFormFields();
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function toggleLogo() {
|
|
||||||
const logo = document.getElementById('logo');
|
|
||||||
const logoDark = document.getElementById('logoDark');
|
|
||||||
if (window.matchMedia("(prefers-color-scheme: dark)").matches) {
|
|
||||||
logo.style.display = 'none';
|
|
||||||
logoDark.style.display = 'block';
|
|
||||||
} else {
|
|
||||||
logo.style.display = 'block';
|
|
||||||
logoDark.style.display = 'none';
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function updateFileName() {
|
|
||||||
const service = document.getElementById('service').value;
|
|
||||||
const input = document.getElementById('upload_' + service);
|
|
||||||
const fileName = document.getElementById('fileName_' + service);
|
|
||||||
const imagePreview = document.getElementById('imagePreview');
|
|
||||||
const previewImage = document.getElementById('previewImage');
|
|
||||||
const imagePreviewLabel = document.getElementById('imagePreviewLabel');
|
|
||||||
|
|
||||||
fileName.textContent = input.files[0] ? input.files[0].name : '';
|
|
||||||
imagePreviewLabel.textContent = 'Preview:';
|
|
||||||
|
|
||||||
if (input.files && input.files[0]) {
|
|
||||||
const reader = new FileReader();
|
|
||||||
reader.onload = (e) => {
|
|
||||||
previewImage.src = e.target.result;
|
|
||||||
imagePreview.classList.remove('hidden');
|
|
||||||
}
|
|
||||||
reader.readAsDataURL(input.files[0]);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function updateGrid(service) {
|
|
||||||
const gridSize = parseInt(document.getElementById('grid_size_' + service).value);
|
|
||||||
const gridContainer = document.getElementById('gridContainer_' + service);
|
|
||||||
gridContainer.innerHTML = '';
|
|
||||||
gridContainer.style.gridTemplateColumns = `repeat(${gridSize}, minmax(0, 1fr))`;
|
|
||||||
const wantedCellsInput = document.getElementById('wanted_cells_' + service);
|
|
||||||
const selectedCells = wantedCellsInput.value ? wantedCellsInput.value.split(',').map(Number) : [];
|
|
||||||
|
|
||||||
for (let i = 0; i < gridSize * gridSize; i++) {
|
|
||||||
const cell = document.createElement('div');
|
|
||||||
cell.classList.add('grid-cell');
|
|
||||||
|
|
||||||
const cellSpan = document.createElement('span');
|
|
||||||
cellSpan.textContent = i + 1;
|
|
||||||
cell.appendChild(cellSpan);
|
|
||||||
|
|
||||||
if (selectedCells.includes(i + 1)) cell.classList.add('selected');
|
|
||||||
cell.addEventListener('click', () => {
|
|
||||||
cell.classList.toggle('selected');
|
|
||||||
updateWantedCells(service);
|
|
||||||
});
|
|
||||||
gridContainer.appendChild(cell);
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
function updateWantedCells(service) {
|
|
||||||
const gridContainer = document.getElementById('gridContainer_' + service);
|
|
||||||
const selectedCells = [];
|
|
||||||
gridContainer.querySelectorAll('.grid-cell.selected').forEach(cell => {
|
|
||||||
selectedCells.push(cell.textContent);
|
|
||||||
});
|
|
||||||
document.getElementById('wanted_cells_' + service).value = selectedCells.join(',');
|
|
||||||
}
|
|
||||||
|
|
||||||
$(document).ready(function () {
|
|
||||||
initializeForm();
|
|
||||||
toggleLogo();
|
|
||||||
window.matchMedia("(prefers-color-scheme: dark)").addEventListener('change', toggleLogo);
|
|
||||||
|
|
||||||
$('#grid_size_alpr, #grid_size_alpr_grid_debug').on('input', function () {
|
|
||||||
updateGrid(document.getElementById('service').value);
|
|
||||||
});
|
|
||||||
|
|
||||||
$('#serviceForm').on('submit', function (e) {
|
|
||||||
e.preventDefault();
|
|
||||||
const service = $('#service').val();
|
|
||||||
const formData = new FormData(this);
|
|
||||||
var url;
|
|
||||||
if (service === 'alpr') {
|
|
||||||
url = '/v1/image/alpr';
|
|
||||||
type = 'POST';
|
|
||||||
} else if (service === 'alpr_grid_debug') {
|
|
||||||
url = '/v1/image/alpr_grid_debug';
|
|
||||||
type = 'POST';
|
|
||||||
}
|
|
||||||
$('#submitButton').prop('disabled', true).html('<svg class="animate-spin h-5 w-5 text-white mx-auto" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none"><circle class="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" stroke-width="4"></circle><path class="opacity-75" fill="currentColor" d="M12 2a10 10 0 00-8 4.9l1.5 1A8 8 0 0112 4V2z"></path></svg>');
|
|
||||||
|
|
||||||
const startTime = Date.now();
|
|
||||||
|
|
||||||
$.ajax({
|
|
||||||
url: url,
|
|
||||||
type: type,
|
|
||||||
data: formData,
|
|
||||||
processData: false,
|
|
||||||
contentType: false,
|
|
||||||
success: function (data) {
|
|
||||||
const endTime = Date.now();
|
|
||||||
const elapsedTime = endTime - startTime;
|
|
||||||
$('#responseBox').text(JSON.stringify(data, null, 2));
|
|
||||||
$('#timer').text(`(${elapsedTime} ms)`);
|
|
||||||
$('#submitButton').prop('disabled', false).text('Submit');
|
|
||||||
|
|
||||||
if (data.image) {
|
|
||||||
$('#previewImage').attr('src', data.image);
|
|
||||||
$('#imagePreview').removeClass('hidden');
|
|
||||||
|
|
||||||
if (service === 'alpr') $('#imagePreviewLabel').text('Identified plate image:');
|
|
||||||
|
|
||||||
} else {
|
|
||||||
updateFileName();
|
|
||||||
}
|
|
||||||
},
|
|
||||||
error: function (xhr) {
|
|
||||||
const endTime = Date.now();
|
|
||||||
const elapsedTime = endTime - startTime;
|
|
||||||
const err = JSON.parse(xhr.responseText);
|
|
||||||
$('#responseBox').text(JSON.stringify(err, null, 2));
|
|
||||||
$('#timer').text(`(${elapsedTime} ms)`);
|
|
||||||
$('#submitButton').prop('disabled', false).text('Submit');
|
|
||||||
}
|
|
||||||
});
|
|
||||||
});
|
|
||||||
});
|
|
||||||
</script>
|
|
||||||
</body>
|
|
||||||
|
|
||||||
</html>
|
|
||||||
Binary file not shown.
Loading…
Reference in New Issue
Block a user