import json import os import sys import threading import time from time import sleep import ultimateAlprSdk from PIL import Image from flask import Flask, request, jsonify, render_template counter = 0 bundle_dir = getattr(sys, '_MEIPASS', os.path.abspath(os.path.dirname(__file__))) boot_time = time.time() """ 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 = { "assets_folder": os.path.join(bundle_dir, "assets"), # don't change this "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": "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": False, "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 } config = json.dumps(JSON_CONFIG) def start_backend_loop(): global counter 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 < 60 * 60: # every 120 sec if int(time.time()) % 120 == 0: if not is_engine_loaded(): unload_engine() # just in case load_engine() sleep(1) unload_engine() start_backend_loop() 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(): result = ultimateAlprSdk.UltAlprSdkEngine_init(config) if not result.isOK(): raise RuntimeError("Init failed: %s" % result.phrase()) 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) # TODO: add check for if engine still loaded result = ultimateAlprSdk.UltAlprSdkEngine_process( image_type, image.tobytes(), width, height, 0, # stride 1 # exifOrientation ) if not result.isOK(): raise RuntimeError("Process failed: %s" % result.phrase()) else: return result.json() def create_rest_server_flask(): app = Flask(__name__, template_folder=os.path.join(bundle_dir, 'templates')) @app.route('/v1/image/alpr', methods=['POST']) def alpr(): """ This function is called when a POST request is made to the /v1/image/alpr endpoint. The function receives an image and processes it using the ultimateALPR SDK. Parameters: - upload: The image to be processed - grid_size: The number of cells to split the image into (e.g. 4) - wanted_cells: The cells to process in the grid separated by commas (e.g. 1,2,3,4) (max: grid_size²) """ interference = time.time() if 'upload' not in request.files: return jsonify({'error': 'No image found'}) if 'grid_size' in request.form and request.form['grid_size'].isdigit(): grid_size = int(request.form['grid_size']) else: grid_size = None if 'wanted_cells' in request.form and request.form['wanted_cells']: wanted_cells = request.form['wanted_cells'].split(',') wanted_cells = [int(cell) for cell in wanted_cells] else: wanted_cells = None 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 not result['predictions']: print("No plate found in the image, attempting to split the image") predictions_found = find_best_plate_with_split(image, grid_size, wanted_cells) if predictions_found: result['predictions'].append(max(predictions_found, key=lambda x: x['confidence'])) result['processMs'] = round((time.time() - interference) * 1000, 2) result['inferenceMs'] = result['processMs'] return jsonify(result) @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 def find_best_plate_with_split(image: Image, grid_size: int = None, wanted_cells: str = None): if grid_size is None: grid_size = 3 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)) if 'plates' in result_cell: for plate in result_cell['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': "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 if __name__ == '__main__': engine = threading.Thread(target=start_backend_loop, daemon=True) engine.start() app = create_rest_server_flask() app.run(host='0.0.0.0', port=5000) unload_engine()