import json import os import sys import threading from time import sleep import ultimateAlprSdk from PIL import Image from flask import Flask, request, jsonify, render_template counter = 0 """ Hi there! This script is a REST API server that uses the ultimateALPR SDK to process images and return the license plate information. The server is created using Flask and the ultimateALPR SDK is used to process the images. See the README.md file for more information on how to run this script. """ # Defines the default JSON configuration. More information at https://www.doubango.org/SDKs/anpr/docs/Configuration_options.html JSON_CONFIG = { "debug_level": "info", "debug_write_input_image_enabled": False, "debug_internal_data_path": ".", "num_threads": -1, "gpgpu_enabled": True, "max_latency": -1, "klass_vcr_gamma": 1.5, "detect_roi": [0, 0, 0, 0], "detect_minscore": 0.35, "car_noplate_detect_min_score": 0.8, "pyramidal_search_enabled": True, "pyramidal_search_sensitivity": 0.38, # default 0.28 "pyramidal_search_minscore": 0.8, "pyramidal_search_min_image_size_inpixels": 800, "recogn_rectify_enabled": True, # heavy on cpu "recogn_minscore": 0.4, "recogn_score_type": "min" } IMAGE_TYPES_MAPPING = { 'RGB': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_RGB24, 'RGBA': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_RGBA32, 'L': ultimateAlprSdk.ULTALPR_SDK_IMAGE_TYPE_Y } def load_engine(): 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(): raise RuntimeError("Init failed: %s" % result.phrase()) while counter < 3000: sleep(1) unload_engine() load_engine() def unload_engine(): result = ultimateAlprSdk.UltAlprSdkEngine_deInit() if not result.isOK(): raise RuntimeError("DeInit failed: %s" % result.phrase()) def process_image(image: Image) -> str: global counter counter += 1 width, height = image.size if image.mode in IMAGE_TYPES_MAPPING: image_type = IMAGE_TYPES_MAPPING[image.mode] else: raise ValueError("Invalid mode: %s" % image.mode) result = ultimateAlprSdk.UltAlprSdkEngine_process( 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(): raise RuntimeError("Process failed: %s" % result.phrase()) else: return result.json() def create_rest_server_flask(): app = Flask(__name__) @app.route('/v1//', methods=['POST']) def alpr(domain, module): # Only care about the ALPR endpoint if domain == 'image' and module == 'alpr': if 'upload' not in request.files: return jsonify({'error': 'No image found'}) image = request.files['upload'] if image.filename == '': return jsonify({'error': 'No selected file'}) image = Image.open(image) result = process_image(image) result = convert_to_cpai_compatible(result) if len(result['predictions']) == 0: print("No plate found in the image, trying to split the image") width, height = image.size cell_width = width // 3 cell_height = height // 3 # Define which cells to process (2, 4, 5, 6, 8, 9) cells_to_process = [2, 4, 5, 6, 8, 9] # Loop through each cell for cell_index in range(1, 10): # Calculate row and column of the cell row = (cell_index - 1) // 3 col = (cell_index - 1) % 3 # Calculate bounding box of the cell left = col * cell_width upper = row * cell_height right = left + cell_width lower = upper + cell_height # Check if this cell should be processed if cell_index in cells_to_process: # Extract the cell as a new image cell_image = image.crop((left, upper, right, lower)) cell_image.show() return jsonify(result) else: return jsonify({'error': 'Endpoint not implemented'}), 404 @app.route('/') def index(): return render_template('index.html') return app def convert_to_cpai_compatible(result): result = json.loads(result) response = { 'success': "true", 'processMs': result['duration'], 'inferenceMs': result['duration'], 'predictions': [], 'message': '', 'moduleId': 'ALPR', 'moduleName': 'License Plate Reader', 'code': 200, 'command': 'alpr', 'requestId': 'null', 'inferenceDevice': 'none', 'analysisRoundTripMs': 0, 'processedBy': 'none', 'timestamp': '' } if 'plates' in result: plates = result['plates'] for plate in plates: warpedBox = plate['warpedBox'] x_coords = warpedBox[0::2] y_coords = warpedBox[1::2] x_min = min(x_coords) x_max = max(x_coords) y_min = min(y_coords) y_max = max(y_coords) response['predictions'].append({ 'confidence': plate['confidences'][0] / 100, 'label': "Plate: " + plate['text'], 'plate': plate['text'], 'x_min': x_min, 'x_max': x_max, 'y_min': y_min, 'y_max': y_max }) return response if __name__ == '__main__': engine = threading.Thread(target=load_engine, daemon=True) engine.start() app = create_rest_server_flask() app.run(host='0.0.0.0', port=5000) unload_engine()