import cv2
from logger_config import logger
from ocr_violation_detector import OCRViolationDetector
from yolo_violation_detector import ViolationDetector as YoloViolationDetector
from face_recognizer import FaceRecognizer


class MultiModelViolationDetector:
    """
    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
    """

    def __init__(self,
                 forbidden_words_path: str,
                 ocr_config_path: str,  # 新增OCR配置文件路径参数
                 yolo_model_path: str,
                 known_faces_dir: str,
                 ocr_confidence_threshold: float = 0.5):
        """
        初始化所有检测模型

        Args:
            forbidden_words_path: 违禁词文件路径
            ocr_config_path: OCR配置文件（1.yaml）路径
            yolo_model_path: YOLO模型文件路径
            known_faces_dir: 已知人脸目录路径
            ocr_confidence_threshold: OCR置信度阈值
        """
        # 初始化OCR检测器（传入配置文件路径）
        self.ocr_detector = OCRViolationDetector(
            forbidden_words_path=forbidden_words_path,
            ocr_config_path=ocr_config_path,  # 传递配置文件路径
            ocr_confidence_threshold=ocr_confidence_threshold
        )

        # 初始化人脸识别器
        self.face_recognizer = FaceRecognizer(
            known_faces_dir=known_faces_dir
        )

        # 初始化YOLO检测器（调整为最后初始化）
        self.yolo_detector = YoloViolationDetector(
            model_path=yolo_model_path
        )

        logger.info("多模型违规检测器初始化完成")

    def detect_violations(self, frame):
        """
        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
        Args:
            frame: 输入视频帧 (NumPy数组, BGR格式)
        Returns:
            tuple: (是否有违规, 违规类型, 违规详情)
                  违规类型: 'ocr' | 'yolo' | 'face' | None
                  违规详情: 对应模型的检测结果
        """
        # 1. 首先进行OCR违禁词检测
        try:
            ocr_has_violation, ocr_words, ocr_confs = self.ocr_detector.detect(frame)
            if ocr_has_violation:
                details = {
                    "words": ocr_words,
                    "confidences": ocr_confs
                }
                logger.warning(f"OCR检测到违禁内容: {details}")
                return (True, "ocr", details)
        except Exception as e:
            logger.error(f"OCR检测出错: {str(e)}", exc_info=True)

        # 2. 接着进行人脸识别检测
        try:
            face_has_violation, face_name, face_similarity = self.face_recognizer.recognize(frame)
            if face_has_violation:
                details = {
                    "name": face_name,
                    "similarity": face_similarity
                }
                logger.warning(f"人脸识别到违规人员: {details}")
                return (True, "face", details)
        except Exception as e:
            logger.error(f"人脸识别出错: {str(e)}", exc_info=True)

        # 3. 最后进行YOLO目标检测（调整为最后检测）
        try:
            yolo_results = self.yolo_detector.detect(frame)
            # 检查是否有检测结果（根据实际业务定义何为违规目标）
            if len(yolo_results.boxes) > 0:
                # 提取检测到的目标信息
                details = {
                    "classes": yolo_results.names,
                    "boxes": yolo_results.boxes.xyxy.tolist(),  # 边界框坐标
                    "confidences": yolo_results.boxes.conf.tolist(),  # 置信度
                    "class_ids": yolo_results.boxes.cls.tolist()  # 类别ID
                }
                logger.warning(f"YOLO检测到违规目标: {details}")
                return (True, "yolo", details)
        except Exception as e:
            logger.error(f"YOLO检测出错: {str(e)}", exc_info=True)

        # 所有检测均未发现违规
        return (False, None, None)


# # 使用示例
# if __name__ == "__main__":
#     # 配置文件路径（根据实际情况修改）
#     FORBIDDEN_WORDS_PATH = r"D:\Git\bin\video\ocr\forbidden_words.txt"
#     OCR_CONFIG_PATH = r"D:\Git\bin\video\ocr\config\1.yaml"  # 新增OCR配置文件路径
#     YOLO_MODEL_PATH = r"D:\Git\bin\video\ocr\models\best.pt"
#     KNOWN_FACES_DIR = r"D:\Git\bin\video\ocr\known_faces"
#
#     # 初始化多模型检测器
#     detector = MultiModelViolationDetector(
#         forbidden_words_path=FORBIDDEN_WORDS_PATH,
#         ocr_config_path=OCR_CONFIG_PATH,  # 传入OCR配置文件路径
#         yolo_model_path=YOLO_MODEL_PATH,
#         known_faces_dir=KNOWN_FACES_DIR,
#         ocr_confidence_threshold=0.5
#     )
#
#     # 读取测试图像（可替换为视频帧读取逻辑）
#     test_image_path = r"D:\Git\bin\video\ocr\images\img.png"
#     frame = cv2.imread(test_image_path)
#
#     if frame is not None:
#         has_violation, violation_type, details = detector.detect_violations(frame)
#         if has_violation:
#             print(f"检测到违规 - 类型: {violation_type}, 详情: {details}")
#         else:
#             print("未检测到任何违规内容")
#     else:
#         print(f"无法读取测试图像: {test_image_path}")