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#coding:utf-8
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from ultralytics import YOLO
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import cv2
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import detect_tools as tools
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from PIL import ImageFont
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from paddleocr import PaddleOCR
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"#程序中链接了多个 OpenMP 运行时库的副本
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def get_license_result(ocr,image):
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"""
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image:输入的车牌截取照片
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输出，车牌号与置信度
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"""
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result = ocr.ocr(image, cls=True)[0]
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if result:
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license_name, conf = result[0][1]
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if '·' in license_name:
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license_name = license_name.replace('·', '')
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return license_name, conf
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else:
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return None, None
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# 需要检测的图片地址
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img_path = "TestFiles/down.jpeg"
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now_img = tools.img_cvread(img_path)
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fontC = ImageFont.truetype("Font/platech.ttf", 50, 0)
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# 加载ocr模型
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cls_model_dir = 'paddleModels/whl/cls/ch_ppocr_mobile_v2.0_cls_infer'
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rec_model_dir = 'paddleModels/whl/rec/ch/ch_PP-OCRv4_rec_infer'
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ocr = PaddleOCR(use_angle_cls=False, lang="ch", det=False, cls_model_dir=cls_model_dir,rec_model_dir=rec_model_dir)
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# 所需加载的模型目录
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path = 'models/best.pt'
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# 加载预训练模型
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# conf 0.25 object confidence threshold for detection
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# iou 0.7 int.ersection over union (IoU) threshold for NMS
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model = YOLO(path, task='detect')
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# model = YOLO(path, task='detect',conf=0.5)
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# 检测图片
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results = model(img_path)[0]
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location_list = results.boxes.xyxy.tolist()
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if len(location_list) >= 1:
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location_list = [list(map(int, e)) for e in location_list]
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# 截取每个车牌区域的照片
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license_imgs = []
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for each in location_list:
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x1, y1, x2, y2 = each
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cropImg = now_img[y1:y2, x1:x2]
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license_imgs.append(cropImg)
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# cv2.imshow('111',cropImg) # cv2.waitKey(0)
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# 车牌识别结果
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lisence_res = []
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conf_list = []
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for each in license_imgs:
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license_num, conf = get_license_result(ocr, each)
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print(license_num, conf)
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if license_num:
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lisence_res.append(license_num)
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conf_list.append(conf)
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else:
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lisence_res.append('无法识别')
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conf_list.append(0)
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for text, box in zip(lisence_res, location_list):
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now_img = tools.drawRectBox(now_img, box, text, fontC)
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# now_img = cv2.resize(now_img,dsize=None,fx=0.5,fy=0.5,interpolation=cv2.INTER_LINEAR)
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# cv2.imshow("YOLOv8 Detection", now_img)
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# cv2.waitKey(0)
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