feat(video_processor): 优化镜头边界检测和关键帧提取功能

- 将镜头边界检测的阈值从 30 调整到 70，提高检测精度
- 添加 tqdm 进度条，增强处理过程的可视化
- 优化内存管理，提高程序运行效率
- 调整关键帧提取日志输出，增加处理进度信息

app/utils/video_processor.py

@@ -6,6 +6,7 @@ import re
 from typing import List, Tuple, Generator
 from loguru import logger
 import gc
+from tqdm import tqdm
 
 
 class VideoProcessor:
@@ -61,7 +62,7 @@ class VideoProcessor:
             if frame_idx % 1000 == 0:
                 gc.collect()
 
-    def detect_shot_boundaries(self, threshold: int = 30) -> List[int]:
+    def detect_shot_boundaries(self, threshold: int = 70) -> List[int]:
         """
         使用批处理方式检测镜头边界
         
@@ -75,20 +76,24 @@ class VideoProcessor:
         prev_frame = None
         prev_idx = -1
         
-        for frame_idx, curr_frame in self.preprocess_video():
+        pbar = tqdm(self.preprocess_video(), 
+                   total=self.total_frames,
+                   desc="检测镜头边界",
+                   unit="帧")
+        
+        for frame_idx, curr_frame in pbar:
             if prev_frame is not None:
-                # 转换为灰度图并降低分辨率以提高性能
                 prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
                 curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_BGR2GRAY)
                 
                 diff = np.mean(np.abs(curr_gray.astype(float) - prev_gray.astype(float)))
                 if diff > threshold:
                     shot_boundaries.append(frame_idx)
+                    pbar.set_postfix({"检测到边界": len(shot_boundaries)})
             
             prev_frame = curr_frame.copy()
             prev_idx = frame_idx
             
-            # 释放不需要的内存
             del curr_frame
             if frame_idx % 100 == 0:
                 gc.collect()
@@ -108,20 +113,20 @@ class VideoProcessor:
         if not shot_frames:
             return None, -1
             
-        # 提取特征
         frame_features = []
         frame_indices = []
         
-        for idx, frame in shot_frames:
+        for idx, frame in tqdm(shot_frames, 
+                             desc="处理镜头帧",
+                             unit="帧",
+                             leave=False):
             gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            # 降低特征维度以节省内存
             resized_gray = cv2.resize(gray, (32, 32))
             frame_features.append(resized_gray.flatten())
             frame_indices.append(idx)
             
         frame_features = np.array(frame_features)
         
-        # 使用MiniBatchKMeans替代KMeans以减少内存使用
         kmeans = MiniBatchKMeans(n_clusters=1, batch_size=min(len(frame_features), 100),
                                 random_state=0).fit(frame_features)
         
@@ -195,7 +200,11 @@ class VideoProcessor:
             logger.info("开始提取关键帧...")
             frame_count = 0
             
-            for keyframe, frame_idx in self.extract_keyframes(shot_boundaries):
+            pbar = tqdm(self.extract_keyframes(shot_boundaries),
+                       desc="提取关键帧",
+                       unit="帧")
+            
+            for keyframe, frame_idx in pbar:
                 if frame_idx < skip_frames:
                     continue
                     
@@ -212,7 +221,8 @@ class VideoProcessor:
                 cv2.imwrite(output_path, keyframe)
                 frame_count += 1
                 
-                # 定期清理内存
+                pbar.set_postfix({"已保存": frame_count})
+                
                 if frame_count % 10 == 0:
                     gc.collect()