基于YOLO和EasyOCR从视频中识别车牌

pip install opencv-python ultralytics easyocr Pillow numpy

import cv2from ultralytics import YOLOimport easyocrfrom PIL import Imageimport numpy as np
# Initialize EasyOCR readerreader = easyocr.Reader(['en'], gpu=False)
# Load your YOLO model (replace with your model's path)model = YOLO('best_float32.tflite', task='detect')
# Open the video file (replace with your video file path)video_path = 'sample4.mp4'cap = cv2.VideoCapture(video_path)
# Create a VideoWriter object (optional, if you want to save the output)output_path = 'output_video.mp4'fourcc = cv2.VideoWriter_fourcc(*'mp4v')out = cv2.VideoWriter(output_path, fourcc, 30.0, (640, 480))  # Adjust frame size if necessary

# Frame skipping factor (adjust as needed for performance)frame_skip = 3  # Skip every 3rd frameframe_count = 0
while cap.isOpened():    ret, frame = cap.read()  # Read a frame from the video    if not ret:        break  # Exit loop if there are no frames left
    # Skip frames    if frame_count % frame_skip != 0:        frame_count += 1        continue  # Skip processing this frame
    # Resize the frame (optional, adjust size as needed)    frame = cv2.resize(frame, (640, 480))  # Resize to 640x480
    # Make predictions on the current frame    results = model.predict(source=frame)
    # Iterate over results and draw predictions    for result in results:        boxes = result.boxes  # Get the boxes predicted by the model        for box in boxes:            class_id = int(box.cls)  # Get the class ID            confidence = box.conf.item()  # Get confidence score            coordinates = box.xyxy[0]  # Get box coordinates as a tensor
            # Extract and convert box coordinates to integers            x1, y1, x2, y2 = map(int, coordinates.tolist())  # Convert tensor to list and then to int
            # Draw the box on the frame            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw rectangle
            # Try to apply OCR on detected region            try:                # Ensure coordinates are within frame bounds                r0 = max(0, x1)                r1 = max(0, y1)                r2 = min(frame.shape[1], x2)                r3 = min(frame.shape[0], y2)
                # Crop license plate region                plate_region = frame[r1:r3, r0:r2]
                # Convert to format compatible with EasyOCR                plate_image = Image.fromarray(cv2.cvtColor(plate_region, cv2.COLOR_BGR2RGB))                plate_array = np.array(plate_image)
                # Use EasyOCR to read text from plate                plate_number = reader.readtext(plate_array)                concat_number = ' '.join([number[1] for number in plate_number])                number_conf = np.mean([number[2] for number in plate_number])
                # Draw the detected text on the frame                cv2.putText(                    img=frame,                    text=f"Plate: {concat_number} ({number_conf:.2f})",                    org=(r0, r1 - 10),                    fontFace=cv2.FONT_HERSHEY_SIMPLEX,                    fontScale=0.7,                    color=(0, 0, 255),                    thickness=2                )
            except Exception as e:                print(f"OCR Error: {e}")                pass
    # Show the frame with detections    cv2.imshow('Detections', frame)
    # Write the frame to the output video (optional)    out.write(frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):        break  # Exit loop if 'q' is pressed
    frame_count += 1  # Increment frame count
# Release resourcescap.release()out.release()  # Release the VideoWriter object if usedcv2.destroyAllWindows()