update for bakeup

tracking/contrast_analysis.py

@@ -1,3 +1,15 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul ** 14:07:25 2024
+
+  现场测试精度、召回率分析程序，是 feat_select.py 的简化版，
+  但支持循环计算，并输出总的pr曲线
+
+@author: ym
+"""
+
+
+
 import os.path
 import shutil
 
@@ -10,41 +22,6 @@ sys.path.append(r"D:\DetectTracking")
 from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
 from tracking.utils.plotting import draw_tracking_boxes
 
-VideoFormat = ['.mp4', '.avi']
-def video2imgs(videopath, savepath):
-    k = 0
-    have = False
-    for filename in os.listdir(videopath):
-        file, ext = os.path.splitext(filename)
-        if ext not in VideoFormat:
-            continue
-        
-        basename = os.path.basename(videopath)
-        imgbase = basename + '_' + file
-        imgdir = os.path.join(savepath, imgbase)
-        if not os.path.exists(imgdir):
-            os.mkdir(imgdir)
-
-        video = os.path.join(videopath, filename)
-        cap = cv2.VideoCapture(video)
-        i = 0
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            imgp = os.path.join(imgdir, file+f"_{i}.png")
-            i += 1
-            cv2.imwrite(imgp, frame)
-        cap.release()
-        
-        print(filename + f" haved resolved")
-        
-        k+=1
-        if k==1000:
-            break
-
-
-
 
 
 def showHist(err, correct):
@@ -309,6 +286,24 @@ def save_tracking_imgpairs(pair, basepath, savepath):
         cv2.imwrite(imgpath, img)
 
 
+# def performance_evaluate(all_list, isshow=False):
+    
+#     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 = [float(s.strip()) for s in s_list['similarity']]
+        
+#         if delete in barcodes[:1]:
+#             corrpairs.append((seqdir, delete))
+#             correct_barcode_list.append(delete)
+#             correct_similarity.append(similarity[0])
+#         else:
+#             errpairs.append((seqdir, delete, barcodes[0]))
+#             err_barcode_list.append(delete)
+#             err_similarity.append(similarity[0])
+
 def performance_evaluate(all_list, isshow=False):
     
     corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list,  err_similarity = [], [], [], [], [], []
@@ -316,17 +311,32 @@ def performance_evaluate(all_list, isshow=False):
         seqdir = s_list['SeqDir'].strip()
         delete = s_list['Deleted'].strip()
         barcodes = [s.strip() for s in s_list['barcode']]
-        similarity = [float(s.strip()) for s in s_list['similarity']]
 
-        if delete in barcodes[:1]:
+
+        similarity_comp, similarity_front = [], []
+        for simil in s_list['similarity']:
+            ss = [float(s.strip()) for s in simil.split(',')]
+            
+            similarity_comp.append(ss[0])
+            if len(ss)==3:
+                similarity_front.append(ss[2])
+
+        if len(similarity_front):
+            similarity = [s for s in similarity_front]
+        else:
+            similarity = [s for s in similarity_comp]
+
+
+        index = similarity.index(max(similarity))
+        matched_barcode = barcodes[index]
+        if matched_barcode == delete:
             corrpairs.append((seqdir, delete))
             correct_barcode_list.append(delete)
-            correct_similarity.append(similarity[0])
+            correct_similarity.append(max(similarity))
         else:
-            errpairs.append((seqdir, delete, barcodes[0]))
+            errpairs.append((seqdir, delete, matched_barcode))
             err_barcode_list.append(delete)
-            err_similarity.append(similarity[0])
-
+            err_similarity.append(max(similarity))
     
     '''3. 计算比对性能 '''
     if isshow:
@@ -338,6 +348,14 @@ def performance_evaluate(all_list, isshow=False):
     
     
       
+    return errpairs, corrpairs, err_similarity, correct_similarity
+
+
+
+
+
+
+
 def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
     '''
     del_barcode_file: 测试数据文件，利用该文件进行算法性能分析
@@ -371,11 +389,6 @@ def contrast_loop(fpath):
     if os.path.isfile(fpath):
         fpath, filename = os.path.split(fpath)
 
-    
-        
-    
-    
-    
     BarLists, blists = {}, []
     for filename in os.listdir(fpath):
         file = os.path.splitext(filename)[0][15:]
@@ -386,8 +399,6 @@ def contrast_loop(fpath):
         BarLists.update({file: blist})
         blists.extend(blist)
  
-        
-        
     BarLists.update({file: blist})
     BarLists.update({"Total": blists})
     for file, blist in BarLists.items():        
@@ -406,8 +417,6 @@ def contrast_loop(fpath):
         # plt2.savefig(os.path.join(savepath, file+'_hist.png'))
         # plt.close()
 
-        
-        
 def main():
     fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
     
@@ -423,17 +432,12 @@ def main1():
     except Exception as e:
         print(f'Error Type: {e}')
     
-def resolve_vidoes():
-    videopath = r"\\192.168.1.28\share\测试_202406\0719\719_1\20240719-103533_"
-    savepath = r"D:\contrast\result"
 
-    video2imgs(videopath, savepath)
 
 if __name__ == '__main__':
     main()
     
     # main1()
-    # resolve_vidoes()
     
     
 

tracking/dotrack/dotracks.py

@@ -93,14 +93,15 @@ class Track:
         self.tid = int(boxes[0, 4])
         self.cls = int(boxes[0, 6])
         self.frnum = boxes.shape[0]
-        self.imgBorder = False
+        
         self.isCornpoint = False
         self.imgshape = imgshape
-        self.state = MoveState.Unknown
+        self.isBorder = False
+        # self.state = MoveState.Unknown
         
         '''轨迹开始帧、结束帧 ID'''
-        self.start_fid = int(np.min(boxes[:, 7]))
-        self.end_fid = int(np.max(boxes[:, 7]))
+        # self.start_fid = int(np.min(boxes[:, 7]))
+        # self.end_fid = int(np.max(boxes[:, 7]))
         
         ''''''
         self.Hands = []
@@ -326,6 +327,34 @@ class Track:
         self.posState = self.Cent_isIncart+self.LB_isIncart+self.RB_isIncart
     
     
+    def is_freemove(self):
+        # if self.tid==4:
+        #     print(f"track ID: {self.tid}")
+        # boxes = self.boxes
+        # features = self.features
+        # similars = 1 - np.maximum(0.0, cdist(self.features, self.features, metric = 'cosine'))
+
+        box1 = self.boxes[0, :4]
+        box2 = self.boxes[-1, :4]
+        
+        ''' 第1帧、最后一帧subimg的相似度 '''
+        feat1 = self.features[0, :][None, :]
+        feat2 = self.features[-1, :][None, :]
+        similar = 1 - np.maximum(0.0, cdist(feat1, feat2, metric = 'cosine'))
+        condta = similar > 0.8
+
+        ''' 第1帧、最后一帧 boxes 四个角点间的距离 '''
+        ptd = box2 - box1
+        ptd1 = np.linalg.norm((ptd[0], ptd[1]))
+        ptd2 = np.linalg.norm((ptd[2], ptd[1]))
+        ptd3 = np.linalg.norm((ptd[0], ptd[3]))
+        ptd4 = np.linalg.norm((ptd[2], ptd[3]))
+        condtb = ptd1<50 and ptd2<50 and ptd3<50 and ptd4<50
+        
+        condt = condta and condtb
+        return condt
+    
+    
     def extract_hand_features(self):              
         assert self.cls == 0, "The class of traj must be HAND!"
         

tracking/dotrack/dotracks_back.py

@@ -50,6 +50,12 @@ class doBackTracks(doTracks):
         tracks = self.sub_tracks(tracks, static_tracks)
 
         
+        tracks_free = [t for t in tracks if t.frnum>1 and t.is_freemove()]
+        self.FreeMove.extend(tracks_free)
+        tracks = self.sub_tracks(tracks, tracks_free)
+        
+        
+        
         # '''购物框边界外具有运动状态的干扰目标'''
         # out_trcak = [t for t in tracks if t.is_OutTrack()]
         # tracks = self.sub_tracks(tracks, out_trcak)

tracking/dotrack/dotracks_front.py

@@ -45,6 +45,9 @@ class doFrontTracks(doTracks):
         '''剔除静止目标后的 tracks'''
         tracks = self.sub_tracks(tracks, static_tracks)
         
+        tracks_free = [t for t in tracks if t.frnum>1 and t.is_freemove()]
+        self.FreeMove.extend(tracks_free)
+        
         # [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)

tracking/dotrack/track_back.py

@@ -217,6 +217,9 @@ class backTrack(Track):
         
         return condt
               
+
+    
+    
     def is_OutTrack(self):     
         if self.posState <= 1:
             isout = True

tracking/feat_select.py

@@ -2,6 +2,8 @@
 """
 Created on Sat Jul 27 14:07:25 2024
 
+现场测试数据，在不同特征组合情况下的精度、召回率分析程序
+
 @author: ym
 """
 import os.path
@@ -13,7 +15,8 @@ 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
+from tracking.contrast_analysis import compute_recall_precision, show_recall_prec
+from tracking.contrast_analysis import performance_evaluate
 
 def compute_similar(feat1, feat2):
 
@@ -26,10 +29,6 @@ def compute_similar(feat1, feat2):
     return smean
     
 
-
-
-
-
 def update_event(datapath):
     '''一次购物事件，包含 8 个keys'''
     event = {}
@@ -47,6 +46,38 @@ def update_event(datapath):
     event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
     event['feats_select'] = np.empty((0, 256), dtype=np.float64)
 
+
+    # '''读取 track.data 文件中的数据'''
+    # fpath_0_track = os.path.join(datapath, '0_track.data')
+    # fpath_1_track = os.path.join(datapath, '1_track.data')
+    # if os.path.exists(fpath_0_track) and os.path.isfile(fpath_0_track):
+    #     _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_0_track)
+    #     event['back_tracking_boxes'] = tracking_boxes
+    #     event['back_tracking_feats'] = tracking_feat_dict
+    
+    # if os.path.exists(fpath_1_track) and os.path.isfile(fpath_1_track):
+    #     _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_1_track)
+    #     event['front_tracking_boxes'] = tracking_boxes
+    #     event['front_tracking_feats'] = tracking_feat_dict
+    
+# =============================================================================
+#     '''================1. 直接指定读取，速度快======================================'''    
+#     '''读取 tracking_output.data 文件中的数据'''    
+#     fpath_0_tracking = os.path.join(datapath, '0_tracking_output.data')
+#     fpath_1_tracking = os.path.join(datapath, '1_tracking_output.data')    
+#     if os.path.exists(fpath_0_tracking) and os.path.isfile(fpath_0_tracking):
+#         tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_0_tracking)
+#         event['back_sole_boxes'] = tracking_output_boxes
+#         event['back_sole_feats'] = tracking_output_feats
+#     
+#     if os.path.exists(fpath_1_tracking) and os.path.isfile(fpath_1_tracking):
+#         tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_1_tracking)
+#         event['front_sole_boxes'] = tracking_output_boxes
+#         event['front_sole_feats'] = tracking_output_feats
+# =============================================================================
+
+
+    '''================2. 遍历方式读取（与以上方式二选一）======================================'''
     '''读取当前事件的 data 文件'''
     for filename in os.listdir(datapath):
         # filename = '1_track.data'
@@ -62,8 +93,6 @@ def update_event(datapath):
         #         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':
@@ -75,7 +104,6 @@ def update_event(datapath):
 
     
     '''事件的特征表征方式选择'''
-    
     fs_feats = event['front_sole_feats']
     bs_feats = event['back_sole_feats']
     
@@ -100,50 +128,56 @@ def update_event(datapath):
 
 
     '''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
+# =============================================================================
+#     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)
     
+    MatchList = []
     k = 0
     for s_list in bcdlist:
         getout_fold = s_list['SeqDir'].strip()
+        getout_path = os.path.join(basepath, getout_fold)
+        
+        '''取出事件文件夹不存在，跳出循环'''
+        if not os.path.exists(getout_path) and not os.path.isdir(getout_path):
+            continue
+
         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['path'] = getout_path
         
         getout_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
         getout_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
@@ -164,19 +198,20 @@ def creatd_deletedBarcode_front(filepath):
                 
                 infold = pathname.split('_')
                 if len(infold)!=2: continue  
+                if len(infold[0])<=14 or len(infold[1])<=10: 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))
+            if len(input_folds)==0: continue
                 
             ''' 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_fold = input_folds[times.index(max(times))]
+            input_path = os.path.join(basepath, input_fold)
+            if not os.path.exists(getout_path) and not os.path.isdir(getout_path):
+                continue
 
             input_event['barcode'] = barcode
             input_event['path'] = input_path
@@ -192,22 +227,23 @@ def creatd_deletedBarcode_front(filepath):
         # k += 1
         # if k==2:
         #     break
-    print('Step 1 Done!')
+    print('Step 1: Event init 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!') 
+            '''====== 放入事件是在取出事件存在的情况下分析 ======'''
+            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: Event update Done!')   
     results = []
     for getout_event, InputList in MatchList:
         getout_barcode = getout_event['barcode']
@@ -242,10 +278,10 @@ def creatd_deletedBarcode_front(filepath):
             # result[f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
         results.append(result)
     
-    print('Step 3 Done!') 
+    print('Step 3: Similarity conputation Done!') 
     
     wpath = os.path.split(filepath)[0]
-    wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
+    wfile = os.path.join(wpath, 'deletedBarcodeTest_x.txt')
     with open(wfile, 'w', encoding='utf-8') as file:
         for result in results:
             
@@ -260,52 +296,13 @@ def creatd_deletedBarcode_front(filepath):
                 file.write(f'{key}: ')
                 file.write(f'{value[0]}, {value[1]:.3f}, {value[2]:.3f}\n')
     
-    print('Step 4 Done!') 
+    print('Step 4: File writting 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):
+def compute_precision(filepath, savepath):
     
     
     fpath = os.path.split(filepath)[0]
@@ -330,7 +327,7 @@ def compute_pres(filepath, savepath):
     '''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_errpairs, front_corrpairs, front_err_similarity, front_correct_similarity = 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)
@@ -341,56 +338,61 @@ def compute_pres(filepath, savepath):
     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',
+    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\719_1\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\0719\719_2\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\0719\719_3\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\0719\719_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_1\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\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\702_pm_1\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\702_pm_3\702_pm_3\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\701_am\deletedBarcode.txt',
+              # r'\\192.168.1.28\share\测试_202406\701_pm\701_pm\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',
+              ]
+    
+    
+    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_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcodeTest.txt',
               ]
     
     savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
     for filepath in  fplist:
         print(filepath)
+        # creatd_deletedBarcode_front(filepath)
+        compute_precision(filepath, savepath)
         
-        try:
-            creatd_deletedBarcode_front(filepath)
-            compute_pres(filepath, savepath)
-        except Exception as e:
-            print(f'Error: {e}') 
+        # try:
+        #     creatd_deletedBarcode_front(filepath)
+        #     compute_pres(filepath, savepath)
+        # except Exception as e:
+        #     print(f'{filepath}, Error: {e}') 
         
 if __name__ == '__main__':
     main()

tracking/module_analysis.py

@@ -10,6 +10,7 @@ import os
 import cv2
 import numpy as np
 from pathlib import Path
+import warnings
 import sys
 sys.path.append(r"D:\DetectTracking")
 
@@ -203,7 +204,7 @@ def do_tracker_tracking(fpath, save_dir):
     
 
 
-def do_tracking(fpath, savedir):
+def do_tracking(fpath, savedir, event_name='images'):
     '''
       fpath: 算法各模块输出的data文件地址，匹配；
       savedir: 对 fpath 各模块输出的复现；
@@ -212,53 +213,59 @@ 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]
 
+
+    '''1.1 构造 0/1_tracking_output.data 文件地址，读取文件数据''' 
     tracking_output_path = os.path.join(imgpath, CamerType + '_tracking_output.data')
+    
+    basename = os.path.basename(imgpath)
     if not os.path.isfile(fpath): 
-        print(f"Can't find {dfname} file!")
-        return
+        print(f"{basename}: Can't find {dfname} file!")
+        return None, None
     if not os.path.isfile(tracking_output_path): 
-        print(f"Can't find {CamerType}_tracking_output.data file!")
-        return
+        print(f"{basename}: Can't find {CamerType}_tracking_output.data file!")
+        return None, None
     
     bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
     tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
     
     
     '''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) 
-     
-    subimg_dir = os.path.join(save_dir, basename.split('_')[0] + '_subimgs')
+    save_dir = os.path.join(savedir, event_name)
+    subimg_dir = os.path.join(savedir, event_name + '_subimgs')
+    if not os.path.exists(save_dir):
+         os.makedirs(save_dir)
     if not os.path.exists(subimg_dir):
-        os.makedirs(subimg_dir)
+         os.makedirs(subimg_dir)
+
 
-    '''1.3 读取 fpath 中 track.data 文件对应的图像 imgs '''
-    imgs = read_imgs(imgpath, CamerType)
     
 
     '''2. 执行轨迹分析, 保存轨迹分析前后的对比图示'''
-    traj_graphic = basename + '_' + CamerType
+    traj_graphic = event_name + '_' + CamerType
     if  CamerType == '1':
         vts = doFrontTracks(trackerboxes, tracker_feat_dict)
         vts.classify()
         
         plt = plot_frameID_y2(vts)
-        # ftpath = os.path.join(save_dir, f"{traj_graphic}_front_y2.png")
+        # ftpath = os.path.join(savedir, f"{traj_graphic}_front_y2.png")
         # plt.savefig(ftpath)  
         plt.close()
+        
+        edgeline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png")
+        img_tracking = draw_all_trajectories(vts, edgeline, savedir, CamerType, draw5p=True)
+        
+        
     elif CamerType == '0':
         vts = doBackTracks(trackerboxes, tracker_feat_dict)
         vts.classify()
 
         edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
-        img = draw_all_trajectories(vts, edgeline, save_dir, traj_graphic)
+        img_tracking = draw_all_trajectories(vts, edgeline, savedir, CamerType, draw5p=True)
         
-        # imgpth = os.path.join(save_dir, f"{traj_graphic}_.png")
+        # imgpth = os.path.join(savedir, f"{traj_graphic}_.png")
         # cv2.imwrite(str(imgpth), img)
     else:
         print("Please check data file!")
@@ -291,56 +298,131 @@ 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 = os.path.join(save_dir, f"{traj_graphic}_alg.png")
+    # algpath = os.path.join(savedir, 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'''
+    '''4.0 读取 fpath 中对应的图像 imgs '''
+    imgs = read_imgs(imgpath, CamerType)
+
     '''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")
+            img_savepath = os.path.join(save_dir, 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
+        print(f"{basename}: len(imgs) = {len(imgs)} < Tracker max(fid) = {int(np.max(trackerboxes[:,7]))}, 无法匹配画框")
+        return img_tracking, abimg
         
     '''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")
+        img_savepath = os.path.join(save_dir, 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:
+    # for track in tracking_output_boxes:
+    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)
+    # 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)}_x.png' )
+    #         cv2.imwrite(subimg_path, subimg)
 
 
-    return abimg 
+    return img_tracking, abimg 
 
 
+def tracking_simulate(eventpath, savepath):
+    '''args:
+        eventpath: 时间文件夹
+        savepath: 存储文件夹
+    '''
+
+    '''1. 获取事件名'''
+    event_names = os.path.basename(eventpath).strip().split('_')
+    if len(event_names)==2 and len(event_names[1])>=8:
+        enent_name = event_names[1]
+    elif len(event_names)==2 and len(event_names[1])==0:
+        enent_name = event_names[0]
+    else:
+        return
+    
+    '''2. 依次读取 0/1_track.data 中数据，进行仿真'''
+    illu_tracking, illu_select = [], []
+    for filename in os.listdir(eventpath):
+        # filename = '1_track.data'
+        if filename.find("track.data") <= 0: continue
+        fpath = os.path.join(eventpath, filename)
+        if not os.path.isfile(fpath): continue
+        
+        img_tracking, img_select = do_tracking(fpath, savepath, enent_name)
+        
+        if img_select is not None: 
+            illu_select.append(img_select)
+        if img_tracking is not None: 
+            illu_tracking.append(img_tracking)        
+
+    '''3. 前、后摄，原始轨迹、本地tracking输出、现场算法轨迹选择前、后，共幅8图'''    
+    if len(illu_select)==2:
+        Img_s = np.concatenate((illu_select[0], illu_select[1]), axis = 0)
+        H, W = Img_s.shape[:2]
+        cv2.line(Img_s, (0, int(H/2)), (int(W), int(H/2)), (128, 255, 128), 2)
+    elif len(illu_select)==1:
+        Img_s = illu_select[0]
+    else:
+        Img_s = None
+
+    if len(illu_tracking)==2:
+        Img_t = np.concatenate((illu_tracking[0], illu_tracking[1]), axis = 0)
+        H, W = Img_t.shape[:2]
+        cv2.line(Img_t, (0, int(H/2)), (int(W), int(H/2)), (128, 255, 128), 2)
+    elif len(illu_tracking)==1:
+        Img_t = illu_tracking[0]
+    else:
+        Img_t = None      
+        
+    
+    
+    '''3.1 单独另存保存完好的 8 轨迹图'''
+    basepath, _ = os.path.split(savepath)
+    trajpath = os.path.join(basepath, 'trajs')
+    if not os.path.exists(trajpath):
+        os.makedirs(trajpath) 
+    traj_path = os.path.join(trajpath, enent_name+'.png')
+    imgpath_tracking = os.path.join(savepath, enent_name + '_ing.png')  
+    imgpath_select = os.path.join(savepath, enent_name + '_slt.png')
+    imgpath_ts = os.path.join(savepath, enent_name + '_ts.png')
+    
+    if Img_t is not None and Img_s is not None and np.all(Img_s.shape==Img_t.shape):
+        Img_ts = np.concatenate((Img_t, Img_s), axis = 1)
+        H, W = Img_ts.shape[:2]
+        cv2.line(Img_ts, (int(W/2), 0), (int(W/2), int(H)), (0, 0, 255), 4)
+        cv2.imwrite(imgpath_ts, Img_ts)
+        cv2.imwrite(traj_path, Img_ts)
+    else:
+        if Img_s: cv2.imwrite(imgpath_select, Img_s)
+        if Img_t: cv2.imwrite(imgpath_tracking, Img_t)
+        
+
+
+
+# warnings.simplefilter("error", category=np.VisibleDeprecationWarning)   
+
 def main_loop():
-    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
+    del_barcode_file = r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt'
+    basepath = r'\\192.168.1.28\share\测试_202406\0723\0723_3'   # 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_"]
-    
+    # prefix = ["getout_", "input_", "error_"]
     
     '''获取性能测试数据相关路径'''
     relative_paths = contrast_analysis(del_barcode_file, basepath, SavePath)
@@ -349,7 +431,7 @@ def main_loop():
     k = 0
     for tuple_paths in relative_paths:
         
-        '''生成文件夹存储结果图像的文件夹'''
+        '''1. 生成存储结果图像的文件夹'''
         namedirs = []
         for data_path in tuple_paths:
             base_name = os.path.basename(data_path).strip().split('_')
@@ -358,76 +440,56 @@ def main_loop():
             else:
                 name = base_name[0]
             namedirs.append(name)
-
         sdir = "_".join(namedirs)
         savepath = os.path.join(SavePath, sdir)        
+
+        # if os.path.exists(savepath):
+        #     continue
         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 = ''
-                    
-                    '''构建结果保存文件名前缀'''
-                    for i, name in enumerate(namedirs):
-                        if fpath.find(name)>0:
-                            enent_name = prefix[i] + name
-                            break
-                    spath = os.path.join(savepath, enent_name)
-                    
-                    # 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}')
-
-            
+        '''2. 循环执行操作事件：取出、放入、错误匹配'''
+        for eventpath in tuple_paths: 
+            try:
+             tracking_simulate(eventpath, savepath)
+            except Exception as e:
+                print(f'Error! {eventpath}, {e}')
 
         # k +=1
         # if k==1:
         #     break
 
 
-
-
 def main():
     '''
-    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。
+        eventpath: data文件地址，该 data 文件包括 Pipeline 各模块输出
+        savepath: 包含二级目录，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。
     '''
+    EventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_2'
+    SavePath = r'D:\contrast\dataset\result'
+    k=0
+    for pathname in os.listdir(EventPaths):
+        # pathname = "20240723-094731_6903148242797"
         
-    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'
+        eventpath = os.path.join(EventPaths, pathname)
+        savepath = os.path.join(SavePath, pathname)
+        if not os.path.exists(savepath):
+            os.makedirs(savepath) 
         
-    do_tracking(fpath, save_dir)
+        # tracking_simulate(eventpath, savepath)
+        try:
+            tracking_simulate(eventpath, savepath)
+        except Exception as e:
+            print(f'Error! {eventpath}, {e}')
+        
+        # k += 1
+        # if k==10:
+        #     break
         
     
 if __name__ == "__main__":  
-    
-    main_loop()
-    # main()
+    # main_loop()
+    main()
     # try:
     #     main_loop()   
     # except Exception as e:

tracking/utils/annotator.py

@@ -25,6 +25,8 @@ class TrackAnnotator(Annotator):
         cls：类别编号，从 0 开始计数，用作 names 的 key 值
         """
         
+        if track.size==0: return
+    
         id, cls = track[0, 4],  track[0, 6]
         if id >=0 and cls==0:
             color = colors(int(cls), True)
@@ -51,6 +53,8 @@ class TrackAnnotator(Annotator):
         """
         绘制选定 track 的轨迹
         """
+        if track.size==0: return
+        
         x, y = int((track[0]+track[2])/2), int((track[1]+track[3])/2)
         
         cv2.circle(self.im, (x, y), 6, color, 2)

tracking/utils/drawtracks.py

@@ -94,19 +94,19 @@ def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
     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)
+    imgcat = np.concatenate((img1, img2), axis = 1)
+    H, W = imgcat.shape[:2]
+    cv2.line(imgcat, (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 img
+        return imgcat
         
     ''' tracks 5点轨迹'''
-    trackpth = save_dir / Path("trajectory") / Path(f"{file}")
+    trackpth = save_dir / Path("trajectory")
     if not trackpth.exists():
         trackpth.mkdir(parents=True, exist_ok=True)
     for track in vts.tracks:
@@ -114,17 +114,18 @@ def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
         img = edgeline.copy()
         img = draw5points(track, img)
 
-        pth = trackpth.joinpath(f"{track.tid}.png")        
+        pth = trackpth.joinpath(f"{file}_{track.tid}.png")        
         cv2.imwrite(str(pth), img)
         
-    for track in vts.merged_tracks:
-        # if track.cls != 0:
-        img = edgeline.copy()
-        img = draw5points(track, img)
+    # for track in vts.Residual:
+    #     # if track.cls != 0:
+    #     img = edgeline.copy()
+    #     img = draw5points(track, img)
 
-        pth = trackpth.joinpath(f"{track.tid}_.png")        
-        cv2.imwrite(str(pth), img)
+    #     pth = trackpth.joinpath(f"{file}_{track.tid}_.png")        
+    #     cv2.imwrite(str(pth), img)
         
+    return imgcat
     
 
 # =============================================================================
@@ -306,7 +307,7 @@ def draw5points(track, img):
     
     
     '''=============== 最小轨迹长度索引 ===================='''
-    if track.imgBorder:
+    if track.isBorder:
         idx = 0
     else:
         idx = trajlens.index(min(trajlens))

tracking/utils/read_data.py

@@ -30,6 +30,8 @@ def find_samebox_in_array(arr, target):
             return i
     return -1
 
+import warnings
+
 
 def extract_data(datapath):
     bboxes, ffeats = [], []
@@ -47,6 +49,17 @@ def extract_data(datapath):
             if line.find("CameraId")>=0:
                 if len(boxes): bboxes.append(np.array(boxes))
                 if len(feats): ffeats.append(np.array(feats))
+                
+                # with warnings.catch_warnings(record=True) as w:
+                #     if len(boxes): bboxes.append(np.array(boxes))
+                #     if len(feats): ffeats.append(np.array(feats))
+                # if w:
+                #     print(f"捕获到 {len(w)} 个警告:")
+                #     for warning in w:
+                #         print(f"警告类型: {warning.category}")
+                #         print(f"警告消息: {warning.message}")
+                #         print(f"警告发生的地方: {warning.filename}:{warning.lineno}")
+                
                 if len(tboxes):  
                     trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))
                 if len(tfeats):
@@ -56,16 +69,20 @@ def extract_data(datapath):
             
             if line.find("box:") >= 0 and line.find("output_box:") < 0:
                 box = line[line.find("box:") + 4:].strip()
+                # if len(box)==6:
                 boxes.append(str_to_float_arr(box))
                 
             if line.find("feat:") >= 0:
                 feat = line[line.find("feat:") + 5:].strip()
+                # if len(feat)==256:
                 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)
+                
+                assert(len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
                 if index >= 0:
                     # feat_f = str_to_float_arr(input_feats[index])
                     feat_f = feats[index]
@@ -120,7 +137,7 @@ def extract_data(datapath):
                     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}') 
+        print(f'Path: {datapath}, tracking_feat_dict can not be structured correcttly, Error: {e}') 
 
     return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
 
@@ -143,6 +160,8 @@ def read_tracking_output(filepath):
             if data.size == 256:
                 feats.append(data)
         
+        assert(len(feats)==len(boxes)), f"{filepath}, len(feats)!=len(boxes)"
+    
     return np.array(boxes), np.array(feats) 
     
 
@@ -166,16 +185,9 @@ def read_deletedBarcode_file(filePth):
                 dict, barcode_list, similarity_list =  {}, [], []
                 continue
             
-            # print(line)
-            try:
-                label = line.split(':')[0]
-                value = line.split(':')[1]
-            except Exception as e:
-                print(f'Error: {e}')    
-            
-            
-            
-            
+            if line.find(':')<0: continue
+            label = line.split(':')[0]
+            value = line.split(':')[1]
             
             if label == 'SeqDir':
                 dict['SeqDir'] = value

tracking/utils/videot.py

@@ -14,31 +14,39 @@ import cv2
 # import sys
 # from scipy.spatial.distance import cdist
 
-
-def video2imgs(videopath):
-# =============================================================================
-#     videopath：视频文件地址，在该地址的 "/file_imgs/" 文件加下存储视频帧图像
-# =============================================================================
-
-    path, filename = os.path.split(videopath)
-    file, ext = os.path.splitext(filename)
-    
-    savepath = os.path.join(path, "{}_imgs".format(file))
-    if not os.path.exists(savepath):
-        os.makedirs(savepath)
-
-    cap = cv2.VideoCapture(videopath)
+VideoFormat = ['.mp4', '.avi']
+def video2imgs(videopath, savepath):
     k = 0
-    while True:
-        ret, frame = cap.read()
-        if not ret:
+    have = False
+    for filename in os.listdir(videopath):
+        file, ext = os.path.splitext(filename)
+        if ext not in VideoFormat:
+            continue
+        
+        basename = os.path.basename(videopath)
+        imgbase = basename + '_' + file
+        imgdir = os.path.join(savepath, imgbase)
+        if not os.path.exists(imgdir):
+            os.mkdir(imgdir)
+
+        video = os.path.join(videopath, filename)
+        cap = cv2.VideoCapture(video)
+        i = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            imgp = os.path.join(imgdir, file+f"_{i}.png")
+            i += 1
+            cv2.imwrite(imgp, frame)
+        cap.release()
+        
+        print(filename + f" haved resolved")
+        
+        k+=1
+        if k==1000:
             break
 
-        k += 1
-        cv2.imwrite(os.path.join(savepath, "{}.png".format(k)), frame)
-        
-
-
 def videosave(bboxes, videopath="100_1688009697927.mp4"):
 
     cap = cv2.VideoCapture(videopath)
@@ -85,3 +93,14 @@ def videosave(bboxes, videopath="100_1688009697927.mp4"):
     
     vid_writer.release()
     cap.release()
+    
+def main():
+    videopath = r'C:\Users\ym\Desktop'
+    savepath = r'C:\Users\ym\Desktop'
+    video2imgs(videopath, savepath)
+    
+    
+    
+if __name__ == '__main__':
+    main()
+