verification.py

import argparse
import cv2
from ultralytics import YOLO
from deep_text_recognition_benchmark.dtrb import DTRB
from difflib import SequenceMatcher
from database.Creating_data import Database

parser = argparse.ArgumentParser()
parser.add_argument('--workers', type=int, help='number of data loading workers', default=0)
parser.add_argument('--batch_size', type=int, default=192, help='input batch size')
""" Data processing """
parser.add_argument('--batch_max_length', type=int, default=25, help='maximum-label-length')
parser.add_argument('--imgH', type=int, default=32, help='the height of the input image')
parser.add_argument('--imgW', type=int, default=100, help='the width of the input image')
parser.add_argument('--rgb', action='store_true', help='use rgb input')
parser.add_argument('--character', type=str, default='0123456789abcdefghijklmnopqrstuvwxyz', help='character label')
parser.add_argument('--sensitive', action='store_true', help='for sensitive character mode')
parser.add_argument('--PAD', action='store_true', help='whether to keep ratio then pad for image resize')
""" Model Architecture """
parser.add_argument('--Transformation', type=str, default="TPS", help='Transformation stage. None|TPS')
parser.add_argument('--FeatureExtraction', type=str, default="ResNet", help='FeatureExtraction stage. VGG|RCNN|ResNet')
parser.add_argument('--SequenceModeling', type=str, default="BiLSTM", help='SequenceModeling stage. None|BiLSTM')
parser.add_argument('--Prediction', type=str, default="Attn", help='Prediction stage. CTC|Attn')
parser.add_argument('--num_fiducial', type=int, default=20, help='number of fiducial points of TPS-STN')
parser.add_argument('--input_channel', type=int, default=1, help='the number of input channel of Feature extractor')
parser.add_argument('--output_channel', type=int, default=512,
                    help='the number of output channel of Feature extractor')
parser.add_argument('--hidden_size', type=int, default=256, help='the size of the LSTM hidden state')
parser.add_argument('--detector-weights', type=str, default="weigths/yolov8-detector/yolov8-s-license-plate-detector.pt")
parser.add_argument('--recognizer-weights', type=str, default="weigths/dtrb-recoginzer/dtrb-None-VGG-BiLSTM-CTC-license-plate-recognizer.pth")
parser.add_argument('--input-image', type=str, default="io/input/1.jpg")
parser.add_argument('--threshold', type=float, default=0.7)

opt = parser.parse_args()

def sequenceMatcher(plake , plakes):
                return SequenceMatcher(None,plake,plakes).ratio()

database = Database()

plate_detector = YOLO(opt.detector_weights)
plates = database.get_Plake_text()
plate_recognizer = DTRB(opt.recognizer_weights , opt)
image = cv2.imread(opt.input_image)
results = plate_detector.predict(image)

for result in results:
    for i in range(len(result.boxes.xyxy)):
        if result.boxes.conf[i] > opt.threshold:
                    bbox_tensor = result.boxes.xyxy[i]
                    bbox_ndarray = bbox_tensor.cpu().detach().numpy().astype(int)
                    print(bbox_ndarray)
                    x1, y1, x2, y2 = bbox_ndarray
                    plate_image = image[y1:y2, x1:x2].copy()
                    cv2.imwrite(f"io/output/plate_image_result_{i}.jpg", plate_image)
                    cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 4)
                    plate_image = cv2.resize(plate_image, (100, 32))
                    plate_image = cv2.cvtColor(plate_image, cv2.COLOR_BGR2GRAY)
                    labal = plate_recognizer.predict(plate_image, opt)
                    for database_plate in plates:
                        if sequenceMatcher(database_plate[2], labal) > 0.8:
                            print('True ✅️ ')
                            break
                    else:
                            print('False ❌ ')
                            break
                            

cv2.imwrite("io/output/image_result.jpg", image)