新增从一串视频中标记黑名单方法

2025-12-21 12:43:36 +08:00
parent 96f03cd83e
commit 93f7e11f9e
1 changed files with 275 additions and 1 deletions
--- a/src/video_check_biz.py
+++ b/src/video_check_biz.py
@@ -27,6 +27,45 @@ class VideoCheckBiz(BaseFaceBiz):
        """
        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 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
@@ -215,3 +254,238 @@ class VideoCheckBiz(BaseFaceBiz):
            }
        
        return None
+    
+    def batch_process_videos_with_blacklist_detection(self, video_paths: List[str], frame_skip: int = 10, 
+                                                     suffix: str = "_processed") -> List[str]:
+        """
+        批量处理视频文件，进行黑名单检测并保存结果
+        
+        参数:
+            video_paths: 视频文件路径列表
+            frame_skip: 跳帧数，每隔多少帧处理一帧
+            suffix: 输出文件后缀
+            
+        返回:
+            处理后的视频路径列表
+        """
+        processed_paths = []
+        
+        # 确保使用黑名单模式
+        self.set_list_mode("blacklist")
+        
+        print(f"🎯 开始批量处理视频: 共{len(video_paths)}个视频")
+        print(f"🔍 检测模式: 黑名单模式, 跳帧数: {frame_skip}")
+        
+        for i, video_path in enumerate(video_paths, 1):
+            print(f"\n--- 处理第{i}/{len(video_paths)}个视频: {os.path.basename(video_path)} ---")
+            
+            if not os.path.exists(video_path):
+                print(f"❌ 视频文件不存在: {video_path}")
+                continue
+            
+            # 处理单个视频
+            output_path = self._process_single_video_with_detection(video_path, frame_skip, suffix)
+            
+            if output_path:
+                processed_paths.append(output_path)
+                print(f"✅ 视频处理完成: {os.path.basename(output_path)}")
+            else:
+                print(f"❌ 视频处理失败: {os.path.basename(video_path)}")
+        
+        print(f"\n🎉 批量处理完成: 成功{len(processed_paths)}/{len(video_paths)}个视频")
+        return processed_paths
+    
+    def _process_single_video_with_detection(self, video_path: str, frame_skip: int, suffix: str) -> Optional[str]:
+        """
+        处理单个视频，进行黑名单检测并保存结果
+        
+        参数:
+            video_path: 视频文件路径
+            frame_skip: 跳帧数
+            suffix: 输出文件后缀
+            
+        返回:
+            处理后的视频路径
+        """
+        # 打开输入视频
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print(f"❌ 无法打开视频文件: {video_path}")
+            return None
+        
+        # 获取视频信息
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        
+        # 创建输出路径
+        video_dir = os.path.dirname(video_path)
+        video_name = os.path.splitext(os.path.basename(video_path))[0]
+        output_path = os.path.join(video_dir, f"{video_name}{suffix}.mp4")
+        
+        # 创建视频写入器
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        
+        if not out.isOpened():
+            print(f"❌ 无法创建输出文件: {output_path}")
+            cap.release()
+            return None
+        
+        print(f"📹 视频信息: {width}x{height}, {fps:.1f}FPS, {total_frames}帧")
+        
+        frame_index = 0
+        processed_frames = 0
+        detection_count = 0
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # 处理帧：每隔frame_skip帧处理一次
+            if frame_index % frame_skip == 0:
+                processed_frames += 1
+                
+                # 人脸检测和识别
+                faces = self.app.get(frame)
+                results = []
+                
+                for face in faces:
+                    # 检查人脸质量
+                    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)
+                    
+                    # 黑名单检测：在黑名单中即为匹配
+                    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)
+                    
+                    if is_match:
+                        detection_count += 1
+                
+                # 绘制检测结果
+                if results:
+                    frame = self.draw_detections(frame, results)
+            
+            # 写入处理后的帧
+            out.write(frame)
+            frame_index += 1
+            
+            # 显示进度
+            if frame_index % 100 == 0:
+                print(f"  进度: {frame_index}/{total_frames}帧 ({frame_index/total_frames*100:.1f}%)")
+        
+        # 释放资源
+        cap.release()
+        out.release()
+        
+        print(f"📊 处理统计: 处理{processed_frames}帧, 检测到{detection_count}次黑名单匹配")
+        
+        return output_path
+    
+    def process_video_with_custom_detection(self, video_path: str, frame_skip: int = 10, 
+                                           suffix: str = "_detected", 
+                                           custom_draw_callback=None) -> Optional[str]:
+        """
+        处理视频并进行自定义检测（可扩展版本）
+        
+        参数:
+            video_path: 视频文件路径
+            frame_skip: 跳帧数
+            suffix: 输出文件后缀
+            custom_draw_callback: 自定义绘制回调函数
+            
+        返回:
+            处理后的视频路径
+        """
+        # 打开输入视频
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print(f"❌ 无法打开视频文件: {video_path}")
+            return None
+        
+        # 获取视频信息
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        
+        # 创建输出路径
+        video_dir = os.path.dirname(video_path)
+        video_name = os.path.splitext(os.path.basename(video_path))[0]
+        output_path = os.path.join(video_dir, f"{video_name}{suffix}.mp4")
+        
+        # 创建视频写入器
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        
+        if not out.isOpened():
+            print(f"❌ 无法创建输出文件: {output_path}")
+            cap.release()
+            return None
+        
+        frame_index = 0
+        
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            # 处理帧：每隔frame_skip帧处理一次
+            if frame_index % frame_skip == 0:
+                # 人脸检测和识别
+                faces = self.app.get(frame)
+                results = []
+                
+                for face in faces:
+                    # 检查人脸质量
+                    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)
+                    
+                    # 根据名单模式判断是否匹配
+                    if self.list_mode == "blacklist":
+                        is_match = best_name is not None and similarity >= self.similarity_threshold
+                    else:  # whitelist
+                        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)
+                
+                # 使用自定义绘制或默认绘制
+                if custom_draw_callback:
+                    frame = custom_draw_callback(frame, results, self)
+                else:
+                    frame = self.draw_detections(frame, results)
+            
+            # 写入处理后的帧
+            out.write(frame)
+            frame_index += 1
+        
+        # 释放资源
+        cap.release()
+        out.release()
+        
+        print(f"✅ 视频处理完成: {output_path}")
+        return output_path