视频检索的处理后视频，只保留处理帧，不保留未处理帧，帧率控制在15-23

src/video_check_biz.py

@@ -279,7 +279,7 @@ class VideoCheckBiz(BaseFaceBiz):
         results = []
         
         # 确保使用黑名单模式
-        self.set_list_mode("blacklist")
+        self.set_list_mode("0")
         
         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}")
@@ -332,6 +332,7 @@ class VideoCheckBiz(BaseFaceBiz):
     def _process_single_video_with_detection(self, video_path: str, frame_skip: int, suffix: str) -> Optional[Dict]:
         """
         处理单个视频，进行黑名单检测并保存结果
+        只保留处理的帧，帧率控制在15-23帧之间
         
         参数:
             video_path: 视频文件路径
@@ -356,30 +357,36 @@ class VideoCheckBiz(BaseFaceBiz):
             return None
         
         # 获取视频信息
-        fps = cap.get(cv2.CAP_PROP_FPS)
+        original_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))
         
+        # 计算输出视频的帧率
+        # 目标帧率在15-23帧之间
+        target_fps = min(max(original_fps / frame_skip, 15), 23)
+        
         # 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
+        # 创建视频写入器，使用计算出的目标帧率
         fourcc = cv2.VideoWriter_fourcc(*'mp4v')
-        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+        out = cv2.VideoWriter(output_path, fourcc, target_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")
+        print(f"📹 原视频信息: {width}x{height}, {original_fps:.1f}FPS, {total_frames}帧")
+        print(f"🎯 输出视频帧率: {target_fps:.1f}FPS (目标范围: 15-23FPS)")
         
         frame_index = 0
         processed_frames = 0
         detection_count = 0
+        processed_frames_list = []  # 记录处理过的帧
         
         while True:
             ret, frame = cap.read()
@@ -421,9 +428,15 @@ class VideoCheckBiz(BaseFaceBiz):
                 # 绘制检测结果
                 if results:
                     frame = self.draw_detections(frame, results)
+                
+                # 只写入处理过的帧
+                out.write(frame)
+                processed_frames_list.append({
+                    'frame_index': frame_index,
+                    'has_detection': len(results) > 0,
+                    'has_blacklist_match': any(r['is_match'] for r in results)
+                })
             
-            # 写入处理后的帧
-            out.write(frame)
             frame_index += 1
             
             # 显示进度
@@ -434,7 +447,11 @@ class VideoCheckBiz(BaseFaceBiz):
         cap.release()
         out.release()
         
+        # 统计处理结果
+        frames_with_blacklist = sum(1 for f in processed_frames_list if f['has_blacklist_match'])
+        
         print(f"📊 处理统计: 处理{processed_frames}帧, 检测到{detection_count}次黑名单匹配")
+        print(f"🎬 输出视频: {len(processed_frames_list)}帧, {target_fps:.1f}FPS")
         
         # 返回详细结果
         return {
@@ -443,6 +460,8 @@ class VideoCheckBiz(BaseFaceBiz):
             'has_blacklist_match': detection_count > 0,
             'detection_count': detection_count,
             'total_frames_processed': processed_frames,
+            'output_fps': target_fps,
+            'output_frame_count': len(processed_frames_list),
             'blacklist_matches': [{
                 'frame_index': frame_index,
                 'best_match': result['best_match'],
@@ -512,7 +531,7 @@ class VideoCheckBiz(BaseFaceBiz):
                     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":
+                    if self.list_mode == "0":
                         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