用 YOLO v7 和 EasyOCR 做車牌辨識

calendar Aug 1, 2023 · 3 分鐘閱讀 · YOLO  ·
分享到: linkedin copy

文章目錄

資料

資料來源: https://www.kaggle.com/datasets/bomaich/vnlicenseplate (1000 images of Vietnamese License Plate) 用這個資料集的主要原因是, 他有提供 bounding box, 直接標出車牌的座標, 這樣就不用花人力去標記.

YOLO

參考: https://www.kaggle.com/code/bomaich/yolo-v7-license-plate-detection

  1. 設定檔 data_LP.yaml:

    1names:
2  - LP
3nc: 1
4train: c/train
5val: c/valid

  2. 訓練指令:

    1pip install -r requirements.txt
2python train.py --batch 16 --cfg cfg/training/yolov7.yaml --epochs 30 --data data_LP.yaml --weights 'yolov7.pt' --device 0

  3. 最後產生模型於這個位置 runs/train/exp/weights/best.pt

EasyOCR

OCR 的部分直接使用 EasyOCR 產生的 pretrained model, 參數如下:

  • ocr.py: 自己寫的程式 (程式碼在文章最後面), 目的是呼叫 EasyOCR 判讀 bbox.txt 指定的範圍, 然後把 OCR 結果寫回照片 (檔名會是 xxx_tagged.jpg)
  • image.jpg: 要判讀的照片 (直接拿這個資料集的任一張即可)
  • bbox.txt: bounding box (直接拿這張照片對應的 .txr 即可, 他會放在 labels 目錄下)

  1. 指令:

    1$ pip install easyocr
2$ python ocr.py path/to/image.jpg path/to/bbox.txt
3License plate: AR02BP2454 (Score: 0.8545837298766594)

  2. 他會輸出判讀的車牌號碼還有信心值, 另外也會產生標記車牌的照片在 xxx_tagged.jpg, 如下.

  3. 照片中可以看到, 他把車牌中的 P 判讀成 R 了, 實際上應該還是要把車牌給 EasyOCR 重做一次 training, 可能會比較準.

  4. ocr.py 程式內容如下, 邏輯其實並不複雜, 就是把照片和 bounding box 傳給 EasyOCR, 並附上一些參數 (如合法的字元等等), 然後再使用 cv2 把辨識出來的車牌寫回影像檔. (實際上, 這程式絕大多數都是 ChatGPT 寫的)

 1import argparse
 2import easyocr
 3import cv2
 4import os
 5
 6# Parse command-line arguments
 7parser = argparse.ArgumentParser(description='Read text from an image within bounding boxes')
 8parser.add_argument('image', help='Path to the image file')
 9parser.add_argument('bbox_file', help='Path to the bounding box file')
10parser.add_argument('--writecrop', action='store_true', help='Write cropped images')
11args = parser.parse_args()
12
13# Load the image
14image = cv2.imread(args.image)
15
16# Initialize the OCR reader
17reader = easyocr.Reader(['en'])
18
19# Read bounding box coordinates from the file
20with open(args.bbox_file, 'r') as f:
21    lines = f.readlines()
22    for i, line in enumerate(lines):
23        class_index, x, y, width, height = map(float, line.strip().split())
24        x_min = int((x - width / 2) * image.shape[1])
25        y_min = int((y - height / 2) * image.shape[0])
26        x_max = int((x + width / 2) * image.shape[1])
27        y_max = int((y + height / 2) * image.shape[0])
28
29        # Crop the image within the bounding box
30        cropped_image = image[y_min:y_max, x_min:x_max]
31
32        # Read text from the cropped image
33        result = reader.readtext(cropped_image, allowlist='ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789', decoder='wordbeamsearch', beamWidth=100)
34
35        # Check if text is detected
36        if len(result) > 0:
37            
38            # Print the OCR result
39            text_to_write = ""  # Initialize an empty string
40            for text in result:
41                text_to_write += text[1] + " "  # Concatenate the detected texts with a space
42
43            text_to_write = text_to_write.strip()  # Remove leading/trailing whitespace
44
45            if text_to_write != "":
46                print(f'License plate: {text_to_write} (Score: {text[2]})')
47
48                # Write the selected text on the original image
49                cv2.putText(
50                    image,
51                    text_to_write,
52                    (x_min + 50, y_min - 10),
53                    cv2.FONT_HERSHEY_SIMPLEX,
54                    0.9,
55                    (0, 0, 128),
56                    4
57                )
58
59        # Write the cropped image if the writecrop parameter is set
60        if args.writecrop:
61            # Get the original filename without extension
62            filename = os.path.splitext(os.path.basename(args.image))[0]
63            # Construct the output filename for the cropped image
64            cropped_filename = f'{filename}_cropped{i+1}.jpg'
65            # Write the cropped image
66            cv2.imwrite(cropped_filename, cropped_image)
67
68# Save the modified image with the original filename appended with '_tagged'
69filename, extension = os.path.splitext(args.image)
70output_filename = f'{filename}_tagged{extension}'
71cv2.imwrite(output_filename, image)

Posts in this Series