133 lines
5.4 KiB
Python
133 lines
5.4 KiB
Python
import cv2
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from logger_config import logger
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from ocr_violation_detector import OCRViolationDetector
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from yolo_violation_detector import ViolationDetector as YoloViolationDetector
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from face_recognizer import FaceRecognizer
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class MultiModelViolationDetector:
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"""
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多模型违规检测封装类，串行调用OCR、人脸识别和YOLO模型（调整为YOLO最后检测），任一模型检测到违规即返回结果
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"""
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def __init__(self,
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forbidden_words_path: str,
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ocr_config_path: str, # 新增OCR配置文件路径参数
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yolo_model_path: str,
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known_faces_dir: str,
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ocr_confidence_threshold: float = 0.5):
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"""
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初始化所有检测模型
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Args:
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forbidden_words_path: 违禁词文件路径
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ocr_config_path: OCR配置文件（1.yaml）路径
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yolo_model_path: YOLO模型文件路径
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known_faces_dir: 已知人脸目录路径
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ocr_confidence_threshold: OCR置信度阈值
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"""
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# 初始化OCR检测器（传入配置文件路径）
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self.ocr_detector = OCRViolationDetector(
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forbidden_words_path=forbidden_words_path,
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ocr_config_path=ocr_config_path, # 传递配置文件路径
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ocr_confidence_threshold=ocr_confidence_threshold
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)
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# 初始化人脸识别器
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self.face_recognizer = FaceRecognizer(
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known_faces_dir=known_faces_dir
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)
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# 初始化YOLO检测器（调整为最后初始化）
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self.yolo_detector = YoloViolationDetector(
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model_path=yolo_model_path
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)
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logger.info("多模型违规检测器初始化完成")
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def detect_violations(self, frame):
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"""
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串行调用三个检测模型（OCR → 人脸识别 → YOLO），任一检测到违规即返回结果
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Args:
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frame: 输入视频帧 (NumPy数组, BGR格式)
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Returns:
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tuple: (是否有违规, 违规类型, 违规详情)
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违规类型: 'ocr' | 'yolo' | 'face' | None
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违规详情: 对应模型的检测结果
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"""
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# 1. 首先进行OCR违禁词检测
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try:
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ocr_has_violation, ocr_words, ocr_confs = self.ocr_detector.detect(frame)
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if ocr_has_violation:
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details = {
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"words": ocr_words,
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"confidences": ocr_confs
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}
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logger.warning(f"OCR检测到违禁内容: {details}")
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return (True, "ocr", details)
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except Exception as e:
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logger.error(f"OCR检测出错: {str(e)}", exc_info=True)
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# 2. 接着进行人脸识别检测
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try:
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face_has_violation, face_name, face_similarity = self.face_recognizer.recognize(frame)
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if face_has_violation:
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details = {
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"name": face_name,
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"similarity": face_similarity
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}
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logger.warning(f"人脸识别到违规人员: {details}")
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return (True, "face", details)
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except Exception as e:
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logger.error(f"人脸识别出错: {str(e)}", exc_info=True)
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# 3. 最后进行YOLO目标检测（调整为最后检测）
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try:
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yolo_results = self.yolo_detector.detect(frame)
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# 检查是否有检测结果（根据实际业务定义何为违规目标）
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if len(yolo_results.boxes) > 0:
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# 提取检测到的目标信息
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details = {
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"classes": yolo_results.names,
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"boxes": yolo_results.boxes.xyxy.tolist(), # 边界框坐标
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"confidences": yolo_results.boxes.conf.tolist(), # 置信度
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"class_ids": yolo_results.boxes.cls.tolist() # 类别ID
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}
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logger.warning(f"YOLO检测到违规目标: {details}")
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return (True, "yolo", details)
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except Exception as e:
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logger.error(f"YOLO检测出错: {str(e)}", exc_info=True)
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# 所有检测均未发现违规
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return (False, None, None)
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# # 使用示例
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# if __name__ == "__main__":
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# # 配置文件路径（根据实际情况修改）
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# FORBIDDEN_WORDS_PATH = r"D:\Git\bin\video\ocr\forbidden_words.txt"
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# OCR_CONFIG_PATH = r"D:\Git\bin\video\ocr\config\1.yaml" # 新增OCR配置文件路径
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# YOLO_MODEL_PATH = r"D:\Git\bin\video\ocr\models\best.pt"
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# KNOWN_FACES_DIR = r"D:\Git\bin\video\ocr\known_faces"
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#
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# # 初始化多模型检测器
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# detector = MultiModelViolationDetector(
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# forbidden_words_path=FORBIDDEN_WORDS_PATH,
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# ocr_config_path=OCR_CONFIG_PATH, # 传入OCR配置文件路径
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# yolo_model_path=YOLO_MODEL_PATH,
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# known_faces_dir=KNOWN_FACES_DIR,
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# ocr_confidence_threshold=0.5
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# )
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#
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# # 读取测试图像（可替换为视频帧读取逻辑）
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# test_image_path = r"D:\Git\bin\video\ocr\images\img.png"
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# frame = cv2.imread(test_image_path)
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#
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# if frame is not None:
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# has_violation, violation_type, details = detector.detect_violations(frame)
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# if has_violation:
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# print(f"检测到违规 - 类型: {violation_type}, 详情: {details}")
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# else:
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# print("未检测到任何违规内容")
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# else:
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# print(f"无法读取测试图像: {test_image_path}") |