modified for site test

tracking/utils/drawtracks.py

@@ -80,15 +80,32 @@ def plot_frameID_y2(vts):
     return plt
 
 
-def draw_all_trajectories(vts, edgeline, save_dir, filename):
+def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
     '''显示四种类型结果'''
-    file, ext = os.path.splitext(filename)
+    # file, ext = os.path.splitext(filename)
     # edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
     # edgeline2 = edgeline1.copy()
     # edgeline = np.concatenate((edgeline1, edgeline2), exis = 1)
     
-    '''1. tracks 5点轨迹'''
-    trackpth = save_dir.parent /Path("trajectory")/ Path(f"{file}")
+    if not isinstance(save_dir, Path): save_dir = Path(save_dir)
+    
+    ''' all tracks 中心轨迹'''
+    img1, img2 = edgeline.copy(), edgeline.copy()
+    img1 = drawTrack(vts.tracks, img1)
+    img2 = drawTrack(vts.Residual, img2)
+    
+    img = np.concatenate((img1, img2), axis = 1)
+    H, W = img.shape[:2]
+    cv2.line(img, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
+    
+    imgpth = save_dir.joinpath(f"{file}_show.png")
+    cv2.imwrite(str(imgpth), img)
+
+    if not draw5p:
+        return
+        
+    ''' tracks 5点轨迹'''
+    trackpth = save_dir / Path("trajectory") / Path(f"{file}")
     if not trackpth.exists():
         trackpth.mkdir(parents=True, exist_ok=True)
     for track in vts.tracks:
@@ -106,23 +123,9 @@ def draw_all_trajectories(vts, edgeline, save_dir, filename):
 
         pth = trackpth.joinpath(f"{track.tid}_.png")        
         cv2.imwrite(str(pth), img)
+
     
     
-    
-    
-    '''2. all tracks 中心轨迹'''
-    img1, img2 = edgeline.copy(), edgeline.copy()
-    
-    img1 = drawTrack(vts.tracks, img1)
-    img2 = drawTrack(vts.Residual, img2)
-    
-    img = np.concatenate((img1, img2), axis = 1)
-    H, W = img.shape[:2]
-    cv2.line(img, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
-    
-    pth = save_dir.joinpath(f"{file}_show.png")
-    cv2.imwrite(str(pth), img)
-    
 # =============================================================================
 #     '''3. moving tracks 中心轨迹'''
 #     filename2 = f"{file}_show_r.png"
@@ -134,13 +137,11 @@ def draw_all_trajectories(vts, edgeline, save_dir, filename):
     
 
     
-# =============================================================================
-#     '''5. tracks 时序：trajmin、trajmax、arearate、incartrate'''
-#     plt = drawtracefeat(vts)
-#     pth = save_dir.joinpath(f"{file}_x.png")
-#     plt.savefig(pth)
-#     plt.close('all')
-# =============================================================================
+    '''5. tracks 时序：trajmin、trajmax、arearate、incartrate'''
+    # plt = drawtracefeat(vts)
+    # pth = save_dir.joinpath(f"{file}_x.png")
+    # plt.savefig(pth)
+    # plt.close('all')
     
 
 

tracking/utils/mergetrack.py

@@ -15,17 +15,18 @@ def readDict(boxes, TracksDict):
     for i in range(boxes.shape[0]):   
         tid, fid, bid = int(boxes[i, 4]), int(boxes[i, 7]), int(boxes[i, 8])
         
-        feat = TracksDict[f"frame_{fid}"]["feats"][bid]
-        img = TracksDict[f"frame_{fid}"]["imgs"][bid]
+        trackdict = TracksDict[f"frame_{fid}"]
+        if "feats" in trackdict:
+            feat = trackdict["feats"][bid]
+            feats.append(feat)
         
-        box = TracksDict[f"frame_{fid}"]["boxes"][bid]
+        if "boxes" in trackdict:
+            box = trackdict["boxes"][bid]
+            assert (box[:4].astype(int) == boxes[i, :4].astype(int)).all(), f"Please check: frame_{fid}"
         
-        assert (box[:4].astype(int) == boxes[i, :4].astype(int)).all(), f"Please check: frame_{fid}"
-        
-        feats.append(feat)
-        
-        # img = TracksDict[fid][f'{bid}_img']
-        # cv2.imwrite(f'./data/imgs/{tid}_{fid}_{bid}.png', img)
+        if "imgs" in trackdict:
+            img = trackdict["imgs"][bid]
+            cv2.imwrite(f'./data/imgs/{tid}_{fid}_{bid}.png', img)
     
     return np.asarray(feats, dtype=np.float32)
 
@@ -59,13 +60,12 @@ def track_equal_track(atrack, btrack):
     ''' 2. 轨迹特征相似度判断'''
     feat = np.concatenate((afeat, bfeat), axis=0)
     
-    emb_simil = 1-np.maximum(0.0, cdist(feat, feat, 'cosine'))
-    emb_ = 1-cdist(np.mean(afeat, axis=0)[None, :], np.mean(bfeat, axis=0)[None, :], 'cosine')
+    emb_simil = 1 - np.maximum(0.0, cdist(feat, feat, 'cosine'))
+    emb_ = 1 - np.maximum(0.0, cdist(np.mean(afeat, axis=0)[None, :], np.mean(bfeat, axis=0)[None, :], 'cosine'))/2
     
     if emb_[0, 0]<0.66:
         return False
     
-    
     ''' 3. 轨迹空间iou'''
     alabel = np.array([0] * afids.size, dtype=np.int_)
     blabel = np.array([1] * bfids.size, dtype=np.int_)
@@ -93,7 +93,7 @@ def track_equal_track(atrack, btrack):
         
         af, bf = afeat[a, :], bfeat[b, :]
         
-        emb_ab = 1-cdist(af[None, :], bf[None, :], 'cosine')
+        emb_ab = 1 - np.maximum(0.0, cdist(af[None, :], bf[None, :], 'cosine'))
         
         
         xa1, ya1 = abox[0] - abox[2]/2, abox[1] - abox[3]/2 
@@ -113,7 +113,22 @@ def track_equal_track(atrack, btrack):
 
         ious.append(inter/union)
         embs.append(emb_ab[0, 0])
-
+    
+    ''' 4. 和同一手部关联，如何将该代码和 iou 部分相融合,需进一步完善'''   
+    # ahands = np.array(atrack.Hands)
+    # bhands = np.array(btrack.Hands)
+    # ahids = ahands[:, 0]
+    # bhids = bhands[:, 0]
+    # interhid = set(ahids).intersection(set(bhids))
+    # for hid in interhid:
+    #     aidx = ahands[:, 0] == hid
+    #     bidx = bhands[:, 0] == hid
+        
+    #     ahfids = ahids[aidx, 1]
+    #     bhfids = bhids[bidx, 1]
+    
+    
+    
 
     cont = False if len(interfid) else True    # fid 无交集
     cont1 = all(emb > 0.5 for emb in embs)

tracking/utils/plotting.py

@@ -1,4 +1,4 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import contextlib
 import math
@@ -284,5 +284,59 @@ def boxing_img(det, img, line_width=3):
     imgx = annotator.result()
     
     return imgx
+
+def draw_tracking_boxes(imgs, tracks, scale=2):
+    '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+                0   1   2   3      4        5     6       7           8
+       关键：
+         (1) imgs中的次序和 track 中的 fid 对应
+         (2) img 尺度小对于xyxy减半
+       
+    '''
+    
+    def array2list(bboxes):
+        track_fids = np.unique(bboxes[:, 7].astype(int))
+        track_fids.sort()
+        
+        lboxes = []
+        for f_id in track_fids:
+            # print(f"The ID is: {t_id}")
+            idx = np.where(bboxes[:, 7] == f_id)[0]
+            box = bboxes[idx, :]  
+            lboxes.append(box)
+            
+            assert len(set(box[:, 4])) == len(box), "Please check!!!"
+            
+        return lboxes
+    
+    bboxes = array2list(tracks)
+    
+    if len(bboxes)!=len(imgs):
+        return []
+    
+    subimgs = []
+    for i, boxes in enumerate(bboxes):
+        annotator = Annotator(imgs[i].copy())
+        for *xyxy, tid, conf, cls, fid, bid in boxes:
+            label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
+            
+            if cls==0:
+                color = colors(int(cls), True)
+            elif tid>0 and cls!=0:
+                color = colors(int(tid), True)
+            else:
+                color = colors(19, True)     # 19为调色板的最后一个元素
+            
+            pt2 = [p/scale for p in xyxy]
+            annotator.box_label(pt2, label, color=color)
+        
+        img = annotator.result()
+        subimgs.append(img)
+        
+    return subimgs
+
+
+
+
     
 

tracking/utils/proBoxes.py

@@ -12,10 +12,12 @@ class Boxes:
         """Initialize the Boxes class."""
         if boxes.ndim == 1:
             boxes = boxes[None, :]
-        n = boxes.shape[-1]
-        assert n in (6, 7, 8), f'expected `n` in [6, 7], but got {n}'  # xyxyb, track_id, conf, cls
+        m, n = boxes.shape
+        assert n in (6, 7), f'expected `n` in [6, 7], but got {n}'  # xyxy, track_id, conf, cls
+        
+        '''对每一个box进行编号，利用该编号可以索引对应 feature'''
+        self.data = np.concatenate([boxes[:, :4], np.arange(m).reshape(-1, 1), boxes[:, 4:]], axis=-1)
         
-        self.data = boxes
         self.orig_shape = orig_shape
     
     def cpu(self):
@@ -30,10 +32,9 @@ class Boxes:
         """Return the boxes in xyxy format."""
         return self.data[:, :4]
     
-    
     @property
     def xyxyb(self):
-        """Return the boxes in xyxyb format."""
+        """Return the boxes in xyxyb format."""    
         return self.data[:, :5]
 
     @property

tracking/utils/read_data.py

@@ -0,0 +1,236 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul  5 13:59:21 2024
+func: extract_data()
+  读取 Pipeline 各模块的数据，在 read_pipeline_data.py（马晓慧）的基础上完成接口改造
+
+@author: ym
+"""
+import numpy as np
+import re
+import os
+
+
+
+
+def str_to_float_arr(s):
+    # 移除字符串末尾的逗号（如果存在）  
+    if s.endswith(','):  
+        s = s[:-1]  
+      
+    # 使用split()方法分割字符串，然后将每个元素转化为float  
+    float_array = [float(x) for x in s.split(",")]
+    return float_array        
+
+
+def find_samebox_in_array(arr, target):
+
+    for i, st in enumerate(arr):
+        if st[:4] == target[:4]:
+            return i
+    return -1
+
+
+def extract_data(datapath):
+    bboxes, ffeats = [], []
+    
+    trackerboxes = np.empty((0, 9), dtype=np.float64)
+    trackerfeats = np.empty((0, 256), dtype=np.float64)
+    
+    boxes, feats, tboxes, tfeats = [], [], [], []
+    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.find("CameraId")>=0:
+                if len(boxes): bboxes.append(np.array(boxes))
+                if len(feats): ffeats.append(np.array(feats))
+                if len(tboxes):  
+                    trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
+                if len(tfeats):
+                    trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+     
+                boxes, feats, tboxes, tfeats = [], [], [], []
+            
+            if line.find("box:") >= 0 and line.find("output_box:") < 0:
+                box = line[line.find("box:") + 4:].strip()
+                boxes.append(str_to_float_arr(box))
+                
+            if line.find("feat:") >= 0:
+                feat = line[line.find("feat:") + 5:].strip()
+                feats.append(str_to_float_arr(feat))
+                    
+            if line.find("output_box:") >= 0:
+                box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
+                tboxes.append(box)  # 去掉'output_box:'并去除可能的空白字符  
+                index = find_samebox_in_array(boxes, box)
+                if index >= 0:
+                    # feat_f = str_to_float_arr(input_feats[index])
+                    feat_f = feats[index]
+                    norm_f = np.linalg.norm(feat_f) 
+                    feat_f = feat_f / norm_f
+                    tfeats.append(feat_f)
+    
+    if len(boxes): bboxes.append(np.array(boxes))
+    if len(feats): ffeats.append(np.array(feats))
+    if len(tboxes): trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
+    if len(tfeats): trackerfeats = np.concatenate((trackerfeats, np.array(tfeats)))
+    
+    assert(len(bboxes)==len(ffeats)), "Error at Yolo output!"
+    assert(len(trackerboxes)==len(trackerfeats)), "Error at tracker output!"
+    
+    tracker_feat_dict = {}
+    for i in range(len(trackerboxes)):
+        tid, fid, bid = int(trackerboxes[i, 4]), int(trackerboxes[i, 7]), int(trackerboxes[i, 8])
+        if f"frame_{fid}" not in tracker_feat_dict:
+            tracker_feat_dict[f"frame_{fid}"]= {"feats": {}}
+        tracker_feat_dict[f"frame_{fid}"]["feats"].update({bid: trackerfeats[i, :]})
+    
+    
+    boxes, trackingboxes= [], []
+    tracking_flag = False    
+    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 tracking_flag:
+                if line.find("tracking_") >= 0:
+                    tracking_flag = False
+                else:
+                    box = str_to_float_arr(line)
+                    boxes.append(box)
+            if line.find("tracking_") >= 0:
+                tracking_flag = True
+                if len(boxes):
+                    trackingboxes.append(np.array(boxes))
+                boxes = []
+    
+    if len(boxes): 
+        trackingboxes.append(np.array(boxes))
+    
+    tracking_feat_dict = {}
+    for i, boxes in enumerate(trackingboxes):
+        for box in boxes:
+            tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
+            if f"track_{tid}" not in tracking_feat_dict:
+                tracking_feat_dict[f"track_{tid}"]= {"feats": {}}
+            tracking_feat_dict[f"track_{tid}"]["feats"].update({f"{fid}_{bid}": tracker_feat_dict[f"frame_{fid}"]["feats"][bid]})
+
+    return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
+
+def read_tracking_output(filepath):
+    boxes = []
+    feats = []
+    with open(filepath, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符
+            
+            if not line:  
+                continue 
+            
+            if line.endswith(','):  
+                line = line[:-1]
+                
+            data = np.array([float(x) for x in line.split(",")])
+            if data.size == 9:
+                boxes.append(data)
+            if data.size == 256:
+                feats.append(data)
+    
+    return np.array(boxes), np.array(feats) 
+    
+
+def read_deletedBarcode_file(filePth):
+    with open(filePth, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+
+        split_flag, all_list = False, []
+        dict, barcode_list, similarity_list =  {}, [], []
+  
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+        for line in clean_lines:
+            stripped_line = line.strip()
+            if not stripped_line:
+                if len(barcode_list): dict['barcode'] = barcode_list
+                if len(similarity_list): dict['similarity'] = similarity_list
+                if len(dict): all_list.append(dict)
+                
+                split_flag = False
+                dict, barcode_list, similarity_list =  {}, [], []
+                continue
+            
+            # print(line)
+            label = line.split(':')[0]
+            value = line.split(':')[1]
+            if label == 'SeqDir':
+                dict['SeqDir'] = value
+            if label == 'Deleted':
+                dict['Deleted'] = value
+            if label == 'List':
+                split_flag = True
+                continue
+            if split_flag:
+                barcode_list.append(label)
+                similarity_list.append(value)
+        
+        if len(barcode_list): dict['barcode'] = barcode_list
+        if len(similarity_list): dict['similarity'] = similarity_list
+        if len(dict): all_list.append(dict)
+    return all_list
+
+
+
+
+if __name__ == "__main__":    
+    files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
+    
+    # 遍历目录下的所有文件和目录  
+    for filename in os.listdir(files_path):    
+        filename = '1_track.data'
+        file_path = os.path.join(files_path, filename)
+        if os.path.isfile(file_path) and filename.find("track.data")>0:
+            extract_data(file_path)
+ 
+            print("Done")
+
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

tracking/utils/read_pipeline_data.py

@@ -0,0 +1,250 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue May 21 15:25:23 2024
+读取 Pipeline 各模块的数据，主代码由 马晓慧 完成
+
+@author: ieemoo-zl003
+"""
+
+import os
+import numpy as np
+  
+# 替换为你的目录路径  
+files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
+
+def str_to_float_arr(s):
+    # 移除字符串末尾的逗号（如果存在）  
+    if s.endswith(','):  
+        s = s[:-1]  
+      
+    # 使用split()方法分割字符串，然后将每个元素转化为float  
+    float_array = np.array([float(x) for x in s.split(",")]) 
+    return float_array
+
+def extract_tracker_input_boxes_feats(file_name):
+    boxes = []
+    feats = []
+    with open(file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+              
+            # 检查是否以'box:'或'feat:'开始  
+            if line.find("box:") >= 0 and line.find("output_box:") < 0:  
+                box = line[line.find("box:") + 4:].strip()
+                boxes.append(str_to_float_arr(box))  # 去掉'box:'并去除可能的空白字符  
+
+            if line.find("feat:") >= 0:
+                feat = line[line.find("feat:") + 5:].strip()
+                feats.append(str_to_float_arr(feat))  # 去掉'box:'并去除可能的空白字符 
+            
+    return np.array(boxes), np.array(feats)
+
+def find_string_in_array(arr, target):  
+    """  
+    在字符串数组中找到目标字符串对应的行（索引）。  
+  
+    参数:  
+    arr -- 字符串数组  
+    target -- 要查找的目标字符串  
+  
+    返回:  
+    目标字符串在数组中的索引。如果未找到，则返回-1。  
+    """  
+    tg = [float(t) for k, t in enumerate(target.split(',')) if k<4][:4]
+    for i, st in enumerate(arr): 
+        st = [float(s) for k, s in enumerate(target.split(',')) if k<4][:4]
+        
+        if st == tg:
+            return i
+        
+        # if st[:20] == target[:20]:  
+        #     return i  
+    return -1 
+
+def find_samebox_in_array(arr, target):
+
+    for i, st in enumerate(arr):
+        if all(st[:4] == target[:4]):
+            return i
+    return -1
+    
+    
+    
+
+
+
+def extract_tracker_output_boxes_feats(read_file_name):
+    
+    input_boxes, input_feats = extract_tracker_input_boxes_feats(read_file_name)
+    
+    boxes = []
+    feats = []
+    with open(read_file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+              
+            # 检查是否以'output_box:'开始  
+            if line.find("output_box:") >= 0:
+                box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
+                boxes.append(box)  # 去掉'output_box:'并去除可能的空白字符  
+                index = find_samebox_in_array(input_boxes, box)
+                if index >= 0:
+                    # feat_f = str_to_float_arr(input_feats[index])
+                    feat_f = input_feats[index]
+                    norm_f = np.linalg.norm(feat_f) 
+                    feat_f = feat_f / norm_f
+                    feats.append(feat_f)
+    return input_boxes, input_feats, np.array(boxes), np.array(feats)
+
+def extract_tracking_output_boxes_feats(read_file_name):
+    tracker_boxes, tracker_feats, input_boxes, input_feats = extract_tracker_output_boxes_feats(read_file_name)
+    boxes = []
+    feats = []
+    
+    tracking_flag = False
+    with open(read_file_name, 'r', encoding='utf-8') as file:
+        for line in file:  
+            line = line.strip()  # 去除行尾的换行符和可能的空白字符  
+              
+            # 跳过空行  
+            if not line:  
+                continue  
+            
+            if tracking_flag:
+                if line.find("tracking_") >= 0:
+                    tracking_flag = False
+                else:
+                    box = str_to_float_arr(line)
+                    boxes.append(box)
+                    index = find_samebox_in_array(input_boxes, box)
+                    if index >= 0:
+                        feats.append(input_feats[index])
+            # 检查是否以tracking_'开始  
+            if line.find("tracking_") >= 0:
+                tracking_flag = True
+
+    assert(len(tracker_boxes)==len(tracker_feats)), "Error at Yolo output"
+    assert(len(input_boxes)==len(input_feats)), "Error at tracker output"
+    assert(len(boxes)==len(feats)), "Error at tracking output"            
+
+    return tracker_boxes, tracker_feats, input_boxes, input_feats, np.array(boxes), np.array(feats)
+
+def read_tracking_input(datapath):
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    
+    data = []
+    for line in lines:
+        data.append([s for s in line.split(',') if len(s)>=3])
+        # data.append([float(s) for s in line.split(',') if len(s)>=3])
+
+    # data = np.array(data, dtype = np.float32)
+    try:    
+        data = np.array(data, dtype = np.float32)
+    except Exception as e:
+        data = np.array([], dtype = np.float32)
+        print('DataError for func: read_tracking_input()')
+        
+    
+    return data
+
+
+
+def read_tracker_input(datapath):
+    with open(datapath, 'r') as file:  
+        lines = file.readlines()
+    Videos = []
+    FrameBoxes, FrameFeats = [], []
+    boxes, feats = [], []
+    
+    timestamp = []
+    t1 = None
+    for line in lines: 
+        if line.find('CameraId') >= 0: 
+            t = int(line.split(',')[1].split(':')[1])
+            timestamp.append(t)
+     
+            if len(boxes) and len(feats):
+                FrameBoxes.append(np.array(boxes, dtype = np.float32))
+                FrameFeats.append(np.array(feats, dtype = np.float32))
+                boxes, feats = [], []
+            
+            if t1 and t - t1 > 1e3:
+                Videos.append((FrameBoxes, FrameFeats))    
+                FrameBoxes, FrameFeats = [], []
+            t1 = int(line.split(',')[1].split(':')[1])
+            
+        if line.find('box') >= 0:
+            box = line.split(':', )[1].split(',')[:-1]
+            boxes.append(box)
+            
+        
+        if line.find('feat') >= 0:
+            feat = line.split(':', )[1].split(',')[:-1]
+            feats.append(feat)
+
+    FrameBoxes.append(np.array(boxes, dtype = np.float32))
+    FrameFeats.append(np.array(feats, dtype = np.float32))  
+    Videos.append((FrameBoxes, FrameFeats)) 
+    
+    # TimeStamp = np.array(timestamp, dtype = np.int64) 
+    # DimesDiff = np.diff((TimeStamp))
+    # sorted_indices = np.argsort(TimeStamp)
+    # TimeStamp_sorted = TimeStamp[sorted_indices]
+    # DimesDiff_sorted = np.diff((TimeStamp_sorted))
+    
+    return Videos
+
+
+
+def main():
+    files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
+    
+    # 遍历目录下的所有文件和目录  
+    for filename in os.listdir(files_path):  
+        # 构造完整的文件路径  
+        file_path = os.path.join(files_path, filename)
+        if os.path.isfile(file_path) and filename.find("track.data")>0:
+            tracker_boxes, tracker_feats, tracking_boxes, tracking_feats, output_boxes, output_feats = extract_tracking_output_boxes_feats(file_path)
+ 
+            print("Done")
+            
+        
+if __name__ == "__main__":
+    main()     
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+