"""
视频检查业务类 - RTSP专用
专门处理RTSP视频流中的人脸识别和检测
"""

import cv2
import numpy as np
from typing import Optional, List, Dict, Tuple
import time
from insightface.app import FaceAnalysis
from biz.base_face_biz import BaseFaceBiz

class VideoFacePrisonBiz(BaseFaceBiz):
    """
    视频检查业务类 - RTSP专用
    专门处理RTSP视频流中的人脸识别和检测
    """

    def __init__(self, face_analysis: FaceAnalysis):
        """
        初始化视频检查业务类

        参数:
            face_analysis: 已初始化好的FaceAnalysis实例
        """
        super().__init__(face_analysis)

    def draw_detections(self, frame: np.ndarray, results: List[Dict]) -> np.ndarray:
        """
        重写绘制检测结果方法
        只在检测到黑名单匹配时用红色绘制人脸框

        参数:
            frame: 原始帧图像
            results: 检测结果列表

        返回:
            绘制后的帧图像
        """
        for result in results:
            # 只在黑名单匹配时绘制
            if result['is_match']:
                bbox = result['bbox']

                # 使用红色绘制人脸框
                x1, y1, x2, y2 = bbox
                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)

                # 添加简单的匹配信息
                best_match = result['best_match']
                similarity = result['similarity']

                # 绘制匹配信息
                text = f"{best_match}: {similarity:.3f}"
                text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)[0]

                # 绘制文本背景
                cv2.rectangle(frame, (x1, y1 - text_size[1] - 5),
                              (x1 + text_size[0], y1), (0, 0, 0), -1)

                # 绘制文本
                cv2.putText(frame, text, (x1, y1 - 5),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)

        return frame

    def process_frame(self, frame: np.ndarray) -> Tuple[np.ndarray, List[Dict], float]:
        """
        处理单帧图像

        返回:
            (原始帧, 识别结果列表, 处理时间ms)
        """
        start_time = time.time()

        # 人脸检测和识别
        faces = self.app.get(frame)

        results = []
        for face in faces:
            # 检查人脸质量是否可接受
            is_acceptable, quality_metrics = self.is_face_quality_acceptable(face, frame)

            # 查找最佳匹配
            best_name, similarity = self.find_best_match(face.embedding)

            is_match = best_name is not None and similarity >= self.similarity_threshold

            result = {
                'bbox': face.bbox.astype(int).tolist(),
                'similarity': similarity,
                'best_match': best_name,
                'is_match': is_match,
                'det_score': float(face.det_score),
                'quality_metrics': quality_metrics,
                'is_acceptable': is_acceptable
            }
            results.append(result)

        processing_time = (time.time() - start_time) * 1000
        return frame, results, processing_time