增加测试维度

pipline_test/read_trackdata_filter.py

@@ -49,10 +49,14 @@ def read_tracking_output(filepath):
                     break
                     
             if start_idx != -1 and end_idx != -1:
+                content = []
                 for i in range(start_idx, end_idx):
                     line = lines[i].strip()
                     if line:
-                        gift_data.append(line)
+                        content.append(line)
+                # 将所有内容合并成一行字符串
+                if content:
+                    gift_data.append(' '.join(content))
         except Exception as e:
             print(f"Error extracting gift data: {e}")
 
@@ -63,28 +67,13 @@ def read_tracking_output(filepath):
 
 
 def extract_data_realtime(datapath):
-    boxes, feats = [], []
-    tracker_feats = {}
-    with open(datapath, 'r', encoding='utf-8') as lines:
-        for line in lines:
-            line = line.strip()  # 去除行尾的换行符和可能的空白字符
-            if not line:  # 跳过空行
-                continue
-
-            if line.endswith(','):
-                line = line[:-1]
-            ftlist = [float(x) for x in line.split()]
-
-            if len(ftlist) != 265:
-                continue
-            boxes.append(ftlist[:9])
-            feats.append(ftlist[9:])
-
-    trackerboxes = np.array(boxes)
-    trackerfeats = np.array(feats)
-
-    if len(trackerboxes) == 0 or len(trackerboxes) != len(trackerfeats):
-        return np.array([]), {}
+    boxes, feats, gift_data = read_tracking_output(datapath)
+    
+    if not boxes or not feats:
+        return np.array([]), {}, []
+        
+    trackerboxes = boxes[0]  # 因为read_tracking_output返回的是list中的numpy数组
+    trackerfeats = feats[0]
 
     frmIDs = np.sort(np.unique(trackerboxes[:, 7].astype(int)))
     for fid in frmIDs:
@@ -373,9 +362,15 @@ for event in os.listdir(video_path):
         # print('imgfile_list', imgfile_list)
         for track_data in track_list:
             track_path = os.path.join(event_path, track_data)
-            boxes, feat = extract_data_realtime(track_path)
+            boxes, feat, gift_data = extract_data_realtime(track_path)
             camera_id = track_data.split('_')[0]
             imgfile = [x for x in imgfile_list if x.split('_')[0] == camera_id][0]
+            
+            # 打印gift数据
+            if gift_data:
+                print(f"\nGift data for {event}/{track_data}:")
+                print(gift_data[0])  # 现在gift_data只包含一个元素，即合并后的字符串
+                    
             if len(boxes) > 0:
                 if del_staticBox:   ##根据距离删除box
                     boxes_ = compute_box_dist(boxes)