新增video check biz类，新增从视频获取特征值方法，未测试

src/video_check_biz.py

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+"""
+视频检查业务类
+继承自BaseFaceBiz，专门处理视频中的人脸特征提取
+"""
+
+import cv2
+import numpy as np
+from typing import Optional, List, Dict, Tuple
+import os
+from insightface.app import FaceAnalysis
+
+from src.base_face_biz import BaseFaceBiz
+
+
+class VideoCheckBiz(BaseFaceBiz):
+    """
+    视频检查业务类
+    专门处理视频中的人脸特征提取和质量评估
+    """
+
+    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 extract_best_face_from_video(self, video_path: str, frame_skip: int = 10) -> Optional[np.ndarray]:
+        """
+        从视频中提取最佳人脸特征
+        
+        参数:
+            video_path: 视频文件路径
+            frame_skip: 跳帧数，每隔多少帧取一帧
+            
+        返回:
+            最佳人脸的特征向量，如果未找到合适的人脸则返回None
+        """
+        if not os.path.exists(video_path):
+            print(f"❌ 视频文件不存在: {video_path}")
+            return None
+        
+        # 打开视频文件
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print(f"❌ 无法打开视频文件: {video_path}")
+            return None
+        
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        
+        print(f"📹 视频信息: {total_frames}帧, {fps:.1f}FPS")
+        print(f"🔍 跳帧设置: 每{frame_skip}帧取一帧")
+        
+        best_quality_score = -1.0
+        best_feature = None
+        best_frame_info = None
+        processed_frames = 0
+        valid_frames = 0
+        
+        frame_index = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+                
+            # 跳过指定帧数
+            if frame_index % frame_skip != 0:
+                frame_index += 1
+                continue
+            
+            processed_frames += 1
+            
+            # 人脸检测
+            faces = self.app.get(frame)
+            
+            # 检查人脸数量
+            if len(faces) == 0:
+                # 无人脸，跳过
+                frame_index += 1
+                continue
+            elif len(faces) > 1:
+                # 超过1个人脸，舍弃该帧
+                frame_index += 1
+                continue
+            
+            # 只有1个人脸
+            face = faces[0]
+            
+            # 检查人脸质量
+            is_acceptable, quality_metrics = self.is_face_quality_acceptable(face, frame)
+            
+            if not is_acceptable:
+                # 质量不可接受，跳过
+                frame_index += 1
+                continue
+            
+            valid_frames += 1
+            
+            # 计算综合质量得分
+            quality_score = self._calculate_face_quality_score(face, quality_metrics)
+            
+            # 更新最佳人脸
+            if quality_score > best_quality_score:
+                best_quality_score = quality_score
+                best_feature = face.embedding
+                best_frame_info = {
+                    'frame_index': frame_index,
+                    'quality_score': quality_score,
+                    'quality_metrics': quality_metrics
+                }
+            
+            frame_index += 1
+        
+        cap.release()
+        
+        # 输出结果统计
+        print(f"📊 处理统计: 共处理{processed_frames}帧, 有效帧{valid_frames}帧")
+        
+        if best_feature is not None:
+            print(f"✅ 找到最佳人脸: 帧{best_frame_info['frame_index']}, 质量得分: {best_quality_score:.3f}")
+            print(f"   检测得分: {best_frame_info['quality_metrics']['det_score']:.3f}")
+            print(f"   清晰度: {best_frame_info['quality_metrics']['clarity_score']:.1f}")
+            print(f"   姿态角度: pitch={best_frame_info['quality_metrics']['pitch']:.1f}°, yaw={best_frame_info['quality_metrics']['yaw']:.1f}°")
+            print(f"   人脸尺寸: {best_frame_info['quality_metrics']['bbox_width']}x{best_frame_info['quality_metrics']['bbox_height']}")
+        else:
+            print("❌ 未找到合适的人脸")
+        
+        return best_feature
+    
+    def _calculate_face_quality_score(self, face, quality_metrics: Dict) -> float:
+        """
+        计算人脸的综合质量得分
+        
+        参数:
+            face: 人脸检测结果
+            quality_metrics: 质量指标字典
+            
+        返回:
+            综合质量得分 (0-1)
+        """
+        # 基础得分：检测置信度
+        base_score = quality_metrics['det_score']
+        
+        # 清晰度得分 (归一化到0-1)
+        clarity_score = min(quality_metrics['clarity_score'] / self.clarity_threshold, 1.0)
+        
+        # 姿态得分 (基于角度偏差)
+        pitch_score = 1.0 - min(abs(quality_metrics['pitch']) / self.pitch_threshold, 1.0)
+        yaw_score = 1.0 - min(abs(quality_metrics['yaw']) / self.yaw_threshold, 1.0)
+        
+        # 尺寸得分 (基于最小尺寸要求)
+        min_dim = min(quality_metrics['bbox_width'], quality_metrics['bbox_height'])
+        size_score = min(min_dim / self.min_face_size, 1.0)
+        
+        # 综合得分权重
+        weights = {
+            'base': 0.3,      # 检测置信度
+            'clarity': 0.3,   # 清晰度
+            'pose': 0.2,      # 姿态
+            'size': 0.2       # 尺寸
+        }
+        
+        # 计算综合得分
+        pose_score = (pitch_score + yaw_score) / 2
+        
+        total_score = (
+            base_score * weights['base'] +
+            clarity_score * weights['clarity'] +
+            pose_score * weights['pose'] +
+            size_score * weights['size']
+        )
+        
+        return total_score
+    
+    def extract_best_face_from_video_with_details(self, video_path: str, frame_skip: int = 10) -> Optional[Dict]:
+        """
+        从视频中提取最佳人脸特征（带详细信息）
+        
+        参数:
+            video_path: 视频文件路径
+            frame_skip: 跳帧数
+            
+        返回:
+            包含特征向量和详细信息的字典，如果未找到合适的人脸则返回None
+        """
+        best_feature = self.extract_best_face_from_video(video_path, frame_skip)
+        
+        if best_feature is not None:
+            # 重新处理视频获取详细信息
+            cap = cv2.VideoCapture(video_path)
+            if not cap.isOpened():
+                return {
+                    'feature': best_feature,
+                    'frame_info': None,
+                    'video_info': {
+                        'path': video_path,
+                        'frame_skip': frame_skip
+                    }
+                }
+            
+            # 这里可以添加更多详细信息的提取逻辑
+            cap.release()
+            
+            return {
+                'feature': best_feature,
+                'frame_info': {
+                    'frame_skip': frame_skip
+                },
+                'video_info': {
+                    'path': video_path
+                }
+            }
+        
+        return None