diff --git a/tracking/__pycache__/contrast_analysis.cpython-39.pyc b/tracking/__pycache__/contrast_analysis.cpython-39.pyc
index 8e5b16f..b5f51c6 100644
Binary files a/tracking/__pycache__/contrast_analysis.cpython-39.pyc and b/tracking/__pycache__/contrast_analysis.cpython-39.pyc differ
diff --git a/tracking/contrast_analysis.py b/tracking/contrast_analysis.py
index 058c6fd..c8e620c 100644
--- a/tracking/contrast_analysis.py
+++ b/tracking/contrast_analysis.py
@@ -397,7 +397,7 @@ def contrast_loop(fpath):
         
         plt1 = show_recall_prec(recall, prec, ths)
         # plt1.show()
-        plt.xlabel(f'threshold, Num: {len(blist)}')
+        plt1.xlabel(f'threshold, Num: {len(blist)}')
         plt1.savefig(os.path.join(savepath, file+'_pr.png'))
         # plt1.close()
         
@@ -408,13 +408,12 @@ def contrast_loop(fpath):
 
         
         
-def main1():
-    fpath = r'D:\contrast\dataset\1_to_n\0719'
-    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\good'
+def main():
+    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
     
     contrast_loop(fpath)
 
-def main():
+def main1():
     del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
     basepath = r'D:\contrast\dataset\1_to_n\709'
     savepath = r'D:\contrast\dataset\result'
@@ -431,9 +430,9 @@ def resolve_vidoes():
     video2imgs(videopath, savepath)
 
 if __name__ == '__main__':
-    # main()
-    main1()
+    main()
     
+    # main1()
     # resolve_vidoes()
     
     
diff --git a/tracking/dotrack/__pycache__/dotracks.cpython-39.pyc b/tracking/dotrack/__pycache__/dotracks.cpython-39.pyc
index 671fcc3..d34df3e 100644
Binary files a/tracking/dotrack/__pycache__/dotracks.cpython-39.pyc and b/tracking/dotrack/__pycache__/dotracks.cpython-39.pyc differ
diff --git a/tracking/dotrack/__pycache__/dotracks_back.cpython-39.pyc b/tracking/dotrack/__pycache__/dotracks_back.cpython-39.pyc
index 2c89354..d60ba11 100644
Binary files a/tracking/dotrack/__pycache__/dotracks_back.cpython-39.pyc and b/tracking/dotrack/__pycache__/dotracks_back.cpython-39.pyc differ
diff --git a/tracking/dotrack/__pycache__/dotracks_front.cpython-39.pyc b/tracking/dotrack/__pycache__/dotracks_front.cpython-39.pyc
index cc3a8f9..ddd487d 100644
Binary files a/tracking/dotrack/__pycache__/dotracks_front.cpython-39.pyc and b/tracking/dotrack/__pycache__/dotracks_front.cpython-39.pyc differ
diff --git a/tracking/dotrack/dotracks.py b/tracking/dotrack/dotracks.py
index 72305b8..df10ceb 100644
--- a/tracking/dotrack/dotracks.py
+++ b/tracking/dotrack/dotracks.py
@@ -79,7 +79,7 @@ class  ShoppingCart:
     
 class Track:
     '''抽象基类，不能实例化对象'''
-    def __init__(self, boxes, features, imgshape=(1024, 1280)):
+    def __init__(self, boxes, features=None, imgshape=(1024, 1280)):
         '''
         boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 
                 0   1   2   3      4        5     6       7           8     
diff --git a/tracking/dotrack/dotracks_back.py b/tracking/dotrack/dotracks_back.py
index 459bdf9..bf17b07 100644
--- a/tracking/dotrack/dotracks_back.py
+++ b/tracking/dotrack/dotracks_back.py
@@ -55,10 +55,14 @@ class doBackTracks(doTracks):
         # tracks = self.sub_tracks(tracks, out_trcak)
 
         
-        [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
+        
         '''轨迹循环归并'''
         # merged_tracks = self.merge_tracks(tracks)
         merged_tracks = self.merge_tracks_loop(tracks)
+        
+        [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
+        
+        
         tracks = [t for t in merged_tracks if t.frnum > 1]
         
         self.merged_tracks = merged_tracks
diff --git a/tracking/dotrack/dotracks_front.py b/tracking/dotrack/dotracks_front.py
index 6eedf3c..a4c7f54 100644
--- a/tracking/dotrack/dotracks_front.py
+++ b/tracking/dotrack/dotracks_front.py
@@ -45,10 +45,12 @@ class doFrontTracks(doTracks):
         '''剔除静止目标后的 tracks'''
         tracks = self.sub_tracks(tracks, static_tracks)
         
-        [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
+        # [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
         '''轨迹循环归并'''
         merged_tracks = self.merge_tracks_loop(tracks)
         
+        [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in merged_tracks]
+        
         tracks = [t for t in merged_tracks if t.frnum > 1]
 
         # for gtrack in tracks:
diff --git a/tracking/dotrack/track_select.py b/tracking/dotrack/track_select.py
new file mode 100644
index 0000000..d27fd1a
--- /dev/null
+++ b/tracking/dotrack/track_select.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 29 10:28:21 2024
+    未来需将这一部分和轨迹分析代码集成
+
+@author: ym
+"""
+import numpy as np
+import cv2
+from scipy.spatial.distance import cdist
+
+class TProp:
+    def __init__(self, boxes):
+        
+        self.boxes = boxes
+        
+    
+    
+
+
+class TProp:
+    '''抽象基类，不能实例化对象'''
+    def __init__(self, boxes):
+        '''
+        boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index] 
+                0   1   2   3      4        5     6       7           8     
+        '''
+        # assert len(set(boxes[:, 4].astype(int))) == 1, "For a Track, track_id more than 1"
+        # assert len(set(boxes[:, 6].astype(int))) == 1, "For a Track, class number more than 1"
+        
+        self.boxes = boxes
+        
+        '''5个关键点（中心点、左上点、右上点、左下点、右下点 ）坐标'''
+        self.compute_cornpoints()
+        
+        '''5个关键点轨迹特征，可以在子类中实现，降低顺序处理时的计算量
+          （中心点、左上点、右上点、左下点、右下点 ）轨迹特征'''
+        self.compute_cornpts_feats()
+        
+        self.distmax = max(self.trajdist)
+        
+        
+    def compute_cornpoints(self):
+        '''
+        cornpoints 共10项，分别是个点的坐标值（x, y）
+            (center, top_left, top_right, bottom_left, bottom_right)
+        '''
+        boxes = self.boxes
+        cornpoints = np.zeros((self.frnum, 10))
+        cornpoints[:,0] = (boxes[:, 0] + boxes[:, 2]) / 2
+        cornpoints[:,1] = (boxes[:, 1] + boxes[:, 3]) / 2
+        cornpoints[:,2], cornpoints[:,3] = boxes[:, 0], boxes[:, 1]
+        cornpoints[:,4], cornpoints[:,5] = boxes[:, 2], boxes[:, 1]
+        cornpoints[:,6], cornpoints[:,7] = boxes[:, 0], boxes[:, 3]
+        cornpoints[:,8], cornpoints[:,9] = boxes[:, 2], boxes[:, 3]
+
+        self.cornpoints = cornpoints
+    def compute_cornpts_feats(self):
+        '''
+        '''
+        trajectory = []
+        trajlens = []
+        trajdist = []
+        trajrects = []
+        for k in range(5):
+            # diff_xy2 = np.power(np.diff(self.cornpoints[:, 2*k:2*(k+1)], axis = 0), 2)
+            # trajlen = np.sum(np.sqrt(np.sum(diff_xy2, axis = 1)))
+            
+            X = self.cornpoints[:, 2*k:2*(k+1)]
+            
+            traj = np.linalg.norm(np.diff(X, axis=0), axis=1)
+            trajectory.append(traj)
+            
+            trajlen = np.sum(traj)
+            trajlens.append(trajlen)
+            
+            ptdist = np.max(cdist(X, X))
+            trajdist.append(ptdist)
+            
+            '''最小外接矩形：
+                   rect[0]: 中心(x, y)
+                   rect[1]: (w, h)
+                   rect[0]: 旋转角度 (-90°, 0]  
+            '''  
+            rect = cv2.minAreaRect(X.astype(np.int64))
+            trajrects.append(rect)
+        
+        self.trajectory = trajectory
+        self.trajlens = trajlens
+        self.trajdist = trajdist
+        self.trajrects = trajrects
\ No newline at end of file
diff --git a/tracking/feat_select.py b/tracking/feat_select.py
new file mode 100644
index 0000000..8430c79
--- /dev/null
+++ b/tracking/feat_select.py
@@ -0,0 +1,418 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 27 14:07:25 2024
+
+@author: ym
+"""
+import os.path
+import numpy as np
+from scipy.spatial.distance import cdist
+import matplotlib.pyplot as plt
+import sys
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
+from tracking.dotrack.dotracks import Track
+
+from tracking.contrast_analysis import performance_evaluate, compute_recall_precision, show_recall_prec
+
+def compute_similar(feat1, feat2):
+
+    if len(feat1)==0 or len(feat2)==0: 
+        return 0
+    
+    similar = 1 - np.maximum(0.0, cdist(feat1, feat2, metric = 'cosine'))
+    smean = np.mean(similar)
+    
+    return smean
+    
+    
+
+
+
+
+def update_event(datapath):
+    '''一次购物事件，包含 8 个keys'''
+    event = {}
+    # event['front_tracking_boxes'] = []
+    # event['front_tracking_feats'] = {}
+    # event['back_tracking_boxes'] = []
+    # event['back_tracking_feats'] = {}
+    
+    event['back_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
+    event['front_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
+    
+    event['back_sole_feats'] = np.empty((0, 256), dtype=np.float64)
+    event['front_sole_feats'] = np.empty((0, 256), dtype=np.float64)
+
+    event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+    event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+
+    '''读取当前事件的 data 文件'''
+    for filename in os.listdir(datapath):
+        # filename = '1_track.data'
+        fpath = os.path.join(datapath, filename)
+        CamerType = filename.split('_')[0]
+        # if os.path.isfile(fpath) and filename.find("track.data")>0:
+        #     bboxes, ffeats, trackerboxes, tracker_feat_dict, tracking_boxes, tracking_feat_dict = extract_data(fpath)
+            
+        #     if CamerType == '0':
+        #         event['back_tracking_boxes'] = tracking_boxes
+        #         event['back_tracking_feats'] = tracking_feat_dict
+        #     elif CamerType == '1':
+        #         event['front_tracking_boxes'] = tracking_boxes
+        #         event['front_tracking_feats'] = tracking_feat_dict
+            
+            
+
+        if os.path.isfile(fpath) and filename.find("tracking_output.data")>0:
+            tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath)
+            if CamerType == '0':
+                event['back_sole_boxes'] = tracking_output_boxes
+                event['back_sole_feats'] = tracking_output_feats
+            elif CamerType == '1':
+                event['front_sole_boxes'] = tracking_output_boxes
+                event['front_sole_feats'] = tracking_output_feats
+
+    
+    '''事件的特征表征方式选择'''
+    
+    fs_feats = event['front_sole_feats']
+    bs_feats = event['back_sole_feats']
+    
+    '''1. 如果前后摄均没有轨迹选择输出，返回'''
+    condt1 = len(fs_feats) + len(bs_feats) == 0
+    if condt1:
+        return event
+    
+    
+    '''2. 构造综合特征'''   
+    feats_compose = np.empty((0, 256), dtype=np.float64)
+    if len(fs_feats):
+        feats_compose = np.concatenate((feats_compose, fs_feats), axis=0)
+    if len(bs_feats):
+        feats_compose = np.concatenate((feats_compose, bs_feats), axis=0)
+    event['feats_compose'] = feats_compose
+    
+    '''3. 构造前摄特征'''
+    if len(fs_feats):
+        event['feats_select'] = fs_feats
+        return event
+
+
+    '''4. 从前摄输出轨迹中选取特定轨迹对应的特征'''
+    ftrboxes = event['front_tracking_boxes']
+    ftrfeats = event['front_tracking_feats']
+    
+    condt2 = len(ftrboxes) + len(ftrfeats) == 0
+    condt3 = len(ftrfeats) != len(ftrboxes)
+    if condt2 or condt3:
+        return event
+
+    bprops = []
+    for boxes in ftrboxes:
+        track = Track(boxes)
+        bprops.append(max(track.trajdist))
+        
+    index = bprops.index(max(bprops))
+    box_select = ftrboxes[index]
+    tid = int(box_select[0, 4])
+    
+    feat_select = ftrfeats[f"track_{tid}"]
+    feats_select = np.empty((0, 256), dtype=np.float64)
+    for fid_bid, feat in feat_select['feats'].items():
+        feats_select = np.concatenate((feats_select, feat[None, :]), axis=0)
+    event['feats_select'] = feats_select
+
+    return event
+
+
+def creatd_deletedBarcode_front(filepath):
+    # filepath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt'
+    
+    basepath, _ = os.path.split(filepath)
+    
+    MatchList = []
+      
+    bcdlist = read_deletedBarcode_file(filepath)
+    
+    k = 0
+    for s_list in bcdlist:
+        getout_fold = s_list['SeqDir'].strip()
+        day, hms = getout_fold.strip('_').split('-')
+
+        ''' 生成取出事件字典 '''
+        getout_event = {}
+        getout_event['barcode'] = s_list['Deleted'].strip()
+        getout_event['path'] = os.path.join(basepath, getout_fold)
+        
+        getout_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+        getout_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+        
+
+        InputList = []
+        barcodes = [s.strip() for s in s_list['barcode']]
+        similarity = [float(s.strip()) for s in s_list['similarity']]
+        for i, barcode in enumerate(barcodes):
+            
+            ''' 生成放入事件字典 '''
+            input_event = {}
+            
+            input_folds, times = [], []            
+            for pathname in os.listdir(basepath):
+                if pathname.endswith('_'): continue
+                if os.path.isfile(os.path.join(basepath, pathname)):continue
+                
+                infold = pathname.split('_')
+                if len(infold)!=2: continue  
+            
+                day1, hms1 = infold[0].split('-') 
+    
+                if day1==day and infold[1]==barcode and int(hms1)<int(hms):
+                    input_folds.append(pathname)
+                    times.append(int(hms1))
+
+            ''' 根据时间排序，选择离取出操作最近时间的文件夹，作为取出操作应的放入操作所对应的文件夹 '''
+            input_path = ''
+            if len(input_folds):
+                indice = np.argsort(np.array(times)) 
+                input_fold = input_folds[indice[-1]]
+                input_path = os.path.join(basepath, input_fold)
+            
+            input_event['barcode'] = barcode
+            input_event['path'] = input_path
+            input_event['similarity'] = float(similarity[i])
+            input_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+            input_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+            
+            
+            InputList.append(input_event)
+            
+        MatchList.append((getout_event, InputList))
+ 
+        # k += 1
+        # if k==2:
+        #     break
+    print('Step 1 Done!')
+        
+    for getout_event, InputList in MatchList:
+        getout_path = getout_event['path']
+        if os.path.exists(getout_path) and os.path.isdir(getout_path):
+            event = update_event(getout_path)
+            getout_event.update(event)
+        
+        for input_event in InputList:
+            input_path = input_event['path']
+            if os.path.exists(input_path) and os.path.isdir(input_path):
+                event = update_event(input_path)
+                input_event.update(event)
+        
+    print('Step 2 Done!') 
+        
+    results = []
+    for getout_event, InputList in MatchList:
+        getout_barcode = getout_event['barcode']
+        getout_feats_compose = getout_event['feats_compose']
+        getout_feats_select = getout_event['feats_select']
+
+        if len(getout_feats_select):
+            outfeats_select = getout_feats_select.copy()
+        else:
+            outfeats_select = getout_feats_compose.copy()
+
+        result = {}
+        result['SeqDir'] = os.path.split(getout_event['path'])[1]
+        result['Deleted'] = getout_barcode
+        result['List'] = {}
+        for input_event in InputList:
+            input_barcode = input_event['barcode']
+            
+            input_feats_compose = input_event['feats_compose']
+            input_feats_select = input_event['feats_select']
+            
+            if len(input_feats_select):
+                infeats_select = input_feats_select.copy()
+            else:
+                infeats_select = input_feats_compose.copy()
+                
+            similar_comp = compute_similar(getout_feats_compose, input_feats_compose)
+            similar_selt = compute_similar(outfeats_select, infeats_select)
+
+            '''现场测试相似度，组合特征相似度，前摄选择特征相似度'''
+            result['List'][f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
+            # result[f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
+        results.append(result)
+    
+    print('Step 3 Done!') 
+    
+    wpath = os.path.split(filepath)[0]
+    wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
+    with open(wfile, 'w', encoding='utf-8') as file:
+        for result in results:
+            
+            SeqDir = result['SeqDir']
+            Deleted = result['Deleted']
+            
+            file.write('\n')
+            file.write(f'SeqDir: {SeqDir}\n')
+            file.write(f'Deleted: {Deleted}\n')
+            file.write('List:\n')
+            for key, value in  result['List'].items():
+                file.write(f'{key}: ')
+                file.write(f'{value[0]}, {value[1]:.3f}, {value[2]:.3f}\n')
+    
+    print('Step 4 Done!') 
+
+
+
+
+
+
+def front_performance_evaluate(all_list):
+    
+    corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list,  err_similarity = [], [], [], [], [], []
+    
+    for s_list in all_list:
+        seqdir = s_list['SeqDir'].strip()
+        delete = s_list['Deleted'].strip()
+        barcodes = [s.strip() for s in s_list['barcode']]
+        
+        similarity, front_similarity = [], []
+        for simil in s_list['similarity']:
+            ss = [float(s.strip()) for s in simil.split(',')]
+            
+            similarity.append(ss[0])
+            if len(ss)==3:
+                front_similarity.append(ss[2])
+
+        # similarity = [float(s.strip()) for s in s_list['similarity']]
+        
+        
+        index = front_similarity.index(max(front_similarity))
+        matched_barcode = barcodes[index]
+        if matched_barcode == delete:
+            corrpairs.append((seqdir, delete))
+            correct_barcode_list.append(delete)
+            correct_similarity.append(max(front_similarity))
+        else:
+            errpairs.append((seqdir, delete, matched_barcode))
+            err_barcode_list.append(delete)
+            err_similarity.append(max(front_similarity))
+
+    
+
+        
+    return errpairs, corrpairs, err_similarity, correct_similarity
+
+
+                
+def compute_pres(filepath, savepath):
+    
+    
+    fpath = os.path.split(filepath)[0]
+    _, basefile = os.path.split(fpath)
+
+    '''1. 综合前后摄特征的相似度比对性能'''
+    fpath1 = os.path.join(fpath, 'deletedBarcode.txt')
+    blist1 = read_deletedBarcode_file(fpath1)  
+    errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist1)
+    recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+    
+    
+    
+    os.path.split(fpath1)
+    plt1 = show_recall_prec(recall, prec, ths)
+    # plt1.show()
+    plt1.xlabel(f'threshold, Num: {len(blist1)}')
+    plt1.title(basefile + ', compose')
+    plt1.savefig(os.path.join(savepath, basefile+'_pr.png'))
+    plt1.close()
+    
+    '''2. 优先选取前摄特征的相似度比对性能'''
+    fpath2 = os.path.join(fpath, 'deletedBarcodeTest.txt')
+    blist2 = read_deletedBarcode_file(fpath2)
+    front_errpairs, front_corrpairs, front_err_similarity, front_correct_similarity = front_performance_evaluate(blist2)
+    front_recall, front_prec, front_ths = compute_recall_precision(front_err_similarity, front_correct_similarity)
+    
+    plt2 = show_recall_prec(front_recall, front_prec, front_ths)
+    # plt2.show()
+    plt2.xlabel(f'threshold, Num: {len(blist2)}')
+    plt1.title(basefile + ', front')
+    plt2.savefig(os.path.join(savepath, basefile+'_pr_front.png'))
+    plt2.close()
+            
+def main():
+    fplist = [r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_01\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_02\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_4\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0718\0718-1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0718\0718-2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0712\0712_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0712\0712_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\711\images01\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\711\images02\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\710\images_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\710\images_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\705\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\703\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_1\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_2\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_3\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_am\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\701_am\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\628\1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\628\2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\627\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\625\deletedBarcode.txt',
+              ]
+    
+    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+    for filepath in  fplist:
+        print(filepath)
+        
+        try:
+            creatd_deletedBarcode_front(filepath)
+            compute_pres(filepath, savepath)
+        except Exception as e:
+            print(f'Error: {e}') 
+        
+if __name__ == '__main__':
+    main()
+
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
\ No newline at end of file
diff --git a/tracking/module_analysis.py b/tracking/module_analysis.py
index a71131a..b0c31dd 100644
--- a/tracking/module_analysis.py
+++ b/tracking/module_analysis.py
@@ -211,59 +211,45 @@ def do_tracking(fpath, savedir):
     '''
     # fpath = r'D:\contrast\dataset\1_to_n\709\20240709-102758_6971558612189\1_track.data'
     # savedir = r'D:\contrast\dataset\result\20240709-102843_6958770005357_6971558612189\error_6971558612189'
-
+    
+    '''1.1 构造 0/1_tracking_output.data 文件地址，读取文件数据'''
     imgpath, dfname = os.path.split(fpath)
     CamerType = dfname.split('_')[0]
-
-    bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
     
     tracking_output_path = os.path.join(imgpath, CamerType + '_tracking_output.data')
+    if not os.path.isfile(fpath): 
+        print(f"Can't find {dfname} file!")
+        return
+    if not os.path.isfile(tracking_output_path): 
+        print(f"Can't find {CamerType}_tracking_output.data file!")
+        return
     
-    if not os.path.isfile(tracking_output_path): return
-    
+    bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
     tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
     
-
     
-    '''存储画框后的 img'''
+    '''1.2 分别构造 2 个文件夹，(1) 存储画框后的图像; (2) 运动轨迹对应的 boxes子图'''
     save_dir, basename  = os.path.split(savedir)
     if not os.path.exists(savedir):
-        os.makedirs(savedir) 
-    
-    '''存储轨迹对应的 boxes子图'''
+         os.makedirs(savedir) 
+     
     subimg_dir = os.path.join(save_dir, basename.split('_')[0] + '_subimgs')
     if not os.path.exists(subimg_dir):
         os.makedirs(subimg_dir)
-    
-        
-    ''' 读取 fpath 中 track.data 文件对应的图像 '''
+
+    '''1.3 读取 fpath 中 track.data 文件对应的图像 imgs '''
     imgs = read_imgs(imgpath, CamerType)
-    
-    ''' 在 imgs 上画框并保存，如果 trackerboxes 的帧数和 imgs 数不匹配，返回原图'''
-    imgs_dw = draw_tracking_boxes(imgs, trackerboxes)
-    
-    if len(imgs_dw)==0:
-        imgs_dw = [img for img in imgs]
-        print(f"fpath: {imgpath}, savedir: {savedir}。Tracker输出的图像数和 imgs 中图像数不相等，无法一一匹配并画框")
 
-    for i in range(len(imgs_dw)):
-        img_savepath = os.path.join(savedir, CamerType + "_" + f"{i}.png")
-        # img = imgs_dw[i]
-        cv2.imwrite(img_savepath, imgs_dw[i])
-    
-    if not isinstance(savedir, Path): 
-        savedir = Path(savedir)
-    save_dir = savedir.parent
-    
 
+    '''2. 执行轨迹分析, 保存轨迹分析前后的对比图示'''
     traj_graphic = basename + '_' + CamerType
     if  CamerType == '1':
         vts = doFrontTracks(trackerboxes, tracker_feat_dict)
         vts.classify()
         
         plt = plot_frameID_y2(vts)
-        ftpath = save_dir.joinpath(f"{traj_graphic}_front_y2.png")
-        plt.savefig(str(ftpath))  
+        # ftpath = os.path.join(save_dir, f"{traj_graphic}_front_y2.png")
+        # plt.savefig(ftpath)  
         plt.close()
     elif CamerType == '0':
         vts = doBackTracks(trackerboxes, tracker_feat_dict)
@@ -272,28 +258,13 @@ def do_tracking(fpath, savedir):
         edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
         img = draw_all_trajectories(vts, edgeline, save_dir, traj_graphic)
         
-        imgpth = save_dir.joinpath(f"{traj_graphic}_show.png")
-        cv2.imwrite(str(imgpth), img)
+        # imgpth = os.path.join(save_dir, f"{traj_graphic}_.png")
+        # cv2.imwrite(str(imgpth), img)
     else:
         print("Please check data file!")
-        
-    for track in vts.Residual:
-        for *xyxy, tid, conf, cls, fid, bid in track.boxes:
-            img = imgs[int(fid-1)]
-            x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
-            subimg = img[y1:y2, x1:x2]
-            
-            subimg_path = os.path.join(subimg_dir, f'{CamerType}_{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
-            
-            cv2.imwrite(subimg_path, subimg)
-            
-            
-        
-        
     
-
-
-    '''================== 现场测试的 tracking() 算法输出 =================='''
+    
+    '''3 tracking() 算法输出后多轨迹选择问题分析'''
     if CamerType == '1':
         aline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png")
     elif CamerType == '0':
@@ -320,16 +291,54 @@ def do_tracking(fpath, savedir):
     abH, abW = abimg.shape[:2]
     cv2.line(abimg, (int(abW/2), 0), (int(abW/2), abH), (128, 255, 128), 2)
     
-    algpath = save_dir.joinpath(f"{traj_graphic}_Alg.png")
-    cv2.imwrite(str(algpath), abimg)
-    
-    return    
+    # algpath = os.path.join(save_dir, f"{traj_graphic}_alg.png")
+    # cv2.imwrite(str(algpath), abimg)
+
+    '''4. 画框后的图像和子图保存，若imgs数与tracker中fid数不匹配，只保存原图，不保存子图'''
+    '''4.1 imgs数 < trackerboxes 的 max(fid)，返回原图'''
+    if len(imgs) < np.max(trackerboxes[:,7]):
+        for i in range(len(imgs)):
+            img_savepath = os.path.join(savedir, CamerType + "_" + f"{i}.png")
+            cv2.imwrite(img_savepath, imgs[i])
+        print(f"fpath: {fpath}, len(imgs) = {len(imgs)} < Tracker max(fid) = {int(np.max(trackerboxes[:,7]))}, 无法匹配画框")
+        return
+        
+    '''4.2 在 imgs 上画框并保存'''
+    imgs_dw = draw_tracking_boxes(imgs, trackerboxes)
+    for fid, img in imgs_dw:
+        img_savepath = os.path.join(savedir, CamerType + "_fid_" + f"{fid}.png")
+        cv2.imwrite(img_savepath, img)
+        
+# =============================================================================
+#     '''4.3.1 保存轨迹分析对应的子图''' 
+#     for track in vts.Residual:
+#         for *xyxy, tid, conf, cls, fid, bid in track.boxes:
+#             img = imgs[int(fid-1)]
+#             x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
+#             subimg = img[y1:y2, x1:x2]
+#             
+#             subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
+#             cv2.imwrite(subimg_path, subimg)
+# =============================================================================
+
+    '''4.3.2 保存轨迹选择对应的子图''' 
+    for track in tracking_output_boxes:
+        for *xyxy, tid, conf, cls, fid, bid in track:
+            img = imgs[int(fid-1)]
+            x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
+            subimg = img[y1:y2, x1:x2]
+            
+            subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
+            cv2.imwrite(subimg_path, subimg)
+
+
+    return abimg 
     
 
 def main_loop():
-    del_barcode_file = r'\\192.168.1.28\share\测试_202406\deletedBarcode\bad\deletedBarcode_0719_4.txt'
-    basepath = r'\\192.168.1.28\share\测试_202406\0719\719_4'   # 测试数据文件夹地址
-    SavePath = r'D:\contrast\dataset\result'       # 结果保存地址
+    del_barcode_file = r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt'
+    basepath = r'\\192.168.1.28\share\测试_202406\0723\0723_2'   # 测试数据文件夹地址
+    SavePath = r'D:\contrast\dataset\resultx'       # 结果保存地址
     prefix = ["getout_", "input_", "error_"]
     
     
@@ -354,11 +363,16 @@ def main_loop():
         savepath = os.path.join(SavePath, sdir)        
         if not os.path.exists(savepath):
             os.makedirs(savepath)    
- 
+
         for path in tuple_paths:
+            '''============= 分别指定指定存储、读取对应的文件夹 ============='''
+            # if sdir.find('094631_6904724022444_6976075000082') < 0: continue
+            # if path.find('094631_') < 0: continue
+            
+            imgs = []
             for filename in os.listdir(path):
+                # filename = '1_track.data'
                 fpath = os.path.join(path, filename)
-                
                 if os.path.isfile(fpath) and filename.find("track.data")>0:
                     enent_name = ''
                     
@@ -368,35 +382,56 @@ def main_loop():
                             enent_name = prefix[i] + name
                             break
                     spath = os.path.join(savepath, enent_name)
-                    do_tracking(fpath, spath)
-        
-        k +=1
-        if k==1:
-            break
-                       
+                    
+                    # abimg = do_tracking(fpath, spath)
+                    # imgs.append(abimg)
+                    try:
+                        abimg = do_tracking(fpath, spath)
+                        imgs.append(abimg)
+                        if len(imgs) == 2:
+                            Img = np.concatenate((imgs[0], imgs[1]), axis = 0)
+                            
+                            H, W = Img.shape[:2]
+                            cv2.line(Img, (0, int(H/2)), (int(W), int(H/2)), (128, 255, 128), 2)
+                        else:
+                            Img = imgs[0] 
+
+                        imgpath = os.path.join(savepath, enent_name + '_alg.png')
+                        cv2.imwrite(imgpath, Img)
+    
+                    except Exception as e:
+                        print(f'Error! {fpath}, {e}')
+
+            
+
+        # k +=1
+        # if k==1:
+        #     break
+                           
 
 
 
 def main():
     '''
-    fpath: data文件，包括 Pipeline 各模块输出
+    fpath: data文件地址，该 data 文件包括 Pipeline 各模块输出
     save_dir：需包含二级目录，其中一级目录为轨迹图像；
               二级目录为与data文件对应的序列图像存储地址。
     '''
     
-    fpath = r'\\192.168.1.28\share\测试_202406\0719\719_4\20240719-164209_\0_track.data'
-    save_dir = r'D:\contrast\dataset\result\20240719-164209_6971284204320_6902890247777\getout'
+    fpath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\20240723-101506_6906839615771\1_track.data'
+    save_dir = r'D:\contrast\dataset\result\20240723-101506_\images'
     
     do_tracking(fpath, save_dir)
     
     
 if __name__ == "__main__":
-    try:
-        # main()
-        main_loop()
-        
-    except Exception as e:
-        print(f'Error: {e}')   
+    
+    main_loop()
+    # main()
+    # try:
+    #     main_loop()   
+    # except Exception as e:
+    #     print(f'Error: {e}')   
 
 
 
diff --git a/tracking/utils/__pycache__/plotting.cpython-39.pyc b/tracking/utils/__pycache__/plotting.cpython-39.pyc
index 2a0d87b..751d408 100644
Binary files a/tracking/utils/__pycache__/plotting.cpython-39.pyc and b/tracking/utils/__pycache__/plotting.cpython-39.pyc differ
diff --git a/tracking/utils/__pycache__/read_data.cpython-39.pyc b/tracking/utils/__pycache__/read_data.cpython-39.pyc
index fc7e663..2c54c25 100644
Binary files a/tracking/utils/__pycache__/read_data.cpython-39.pyc and b/tracking/utils/__pycache__/read_data.cpython-39.pyc differ
diff --git a/tracking/utils/plotting.py b/tracking/utils/plotting.py
index b0b528b..c5d50f0 100644
--- a/tracking/utils/plotting.py
+++ b/tracking/utils/plotting.py
@@ -286,7 +286,8 @@ def boxing_img(det, img, line_width=3):
     return imgx
 
 def draw_tracking_boxes(imgs, tracks, scale=2):
-    '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+    '''需要确保 imgs 覆盖tracks中的帧ID数
+        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
@@ -311,12 +312,13 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
     
     bboxes = array2list(tracks)
     
-    if len(bboxes)!=len(imgs):
-        return []
+    # if len(bboxes)!=len(imgs):
+    #     return False, imgs
     
     subimgs = []
     for i, boxes in enumerate(bboxes):
-        annotator = Annotator(imgs[i].copy())
+        fid = int(boxes[0, 7])
+        annotator = Annotator(imgs[fid-1].copy())
         for *xyxy, tid, conf, cls, fid, bid in boxes:
             label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
             
@@ -331,7 +333,7 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
             annotator.box_label(pt2, label, color=color)
         
         img = annotator.result()
-        subimgs.append(img)
+        subimgs.append((fid-1, img))
         
     return subimgs
 
diff --git a/tracking/utils/read_data.py b/tracking/utils/read_data.py
index a46510e..6877fd3 100644
--- a/tracking/utils/read_data.py
+++ b/tracking/utils/read_data.py
@@ -110,14 +110,17 @@ def extract_data(datapath):
     
     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]})
+    try:
+        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]})
+    except Exception as e:
+        print(f'Path: {datapath}, Error: {e}') 
 
     return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict