20241217

contrast/event_test.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Dec 16 18:56:18 2024
+
+@author: ym
+"""
+import os
+import cv2
+
+from utils.event import ShoppingEvent
+
+def main():
+    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
+    text1 = "one2n_Error.txt"
+    text2 = "one2SN_Error.txt"
+    events = []
+    text = (text1, text2)
+    for txt in text:
+        txtfile = os.path.join(evtpaths, txt)
+        with open(txtfile, "r") as f:
+            lines = f.readlines()
+            for i, line in enumerate(lines):
+                line = line.strip()
+                if line:
+                    fpath=os.path.join(evtpaths, line)
+                    events.append(fpath)
+                    
+                    
+    events = list(set(events))
+
+    '''定义当前事件存储地址及生成相应文件件'''
+    resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"    
+    for evtpath in events:
+        evtname = os.path.basename(evtpath)
+        event = ShoppingEvent(evtpath)
+        
+        img_cat = event.draw_tracks()
+        trajpath = os.path.join(resultPath, "trajectory")
+        if not os.path.exists(trajpath):
+            os.makedirs(trajpath)
+        traj_imgpath =  os.path.join(trajpath, evtname+".png")
+        cv2.imwrite(traj_imgpath, img_cat)
+        
+        
+        ## 保存序列图像和轨迹子图
+        subimgpath = os.path.join(resultPath, f"{evtname}", "subimg")
+        imgspath = os.path.join(resultPath, f"{evtname}", "imgs")
+        if not os.path.exists(subimgpath):
+            os.makedirs(subimgpath)
+        if not os.path.exists(imgspath):
+            os.makedirs(imgspath)
+ 
+        
+        subimgpairs = event.save_event_subimg(subimgpath)
+        for subimgName, subimg in subimgpairs:
+            spath = os.path.join(subimgpath, subimgName)
+            cv2.imwrite(spath, subimg)
+
+        imgpairs = event.plot_save_image(imgspath)
+        for imgname, img in imgpairs:
+            spath = os.path.join(imgspath, imgname)
+            cv2.imwrite(spath, img)
+
+        print(f"{evtname}")
+
+    
+
+if __name__ == "__main__": 
+    main()

contrast/one2one_contrast.py

@@ -84,82 +84,84 @@ def ft16_to_uint8(arr_ft16):
     return arr_uint8, arr_ft16_
 
 
-def plot_save_image(event, savepath):
+# =============================================================================
-    cameras = ('front', 'back')
+# def plot_save_image(event, savepath):
-    for camera in cameras:
+#     cameras = ('front', 'back')
-        if camera == 'front':
+#     for camera in cameras:
-            boxes = event.front_trackerboxes
+#         if camera == 'front':
-            imgpaths = event.front_imgpaths
+#             boxes = event.front_trackerboxes
-        else:
+#             imgpaths = event.front_imgpaths
-            boxes = event.back_trackerboxes
+#         else:
-            imgpaths = event.back_imgpaths
+#             boxes = event.back_trackerboxes
-            
+#             imgpaths = event.back_imgpaths
-    def array2list(bboxes):
+#             
-        '''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
+#     def array2list(bboxes):
-        frame_ids = bboxes[:, 7].astype(int)
+#         '''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
-        fID = np.unique(bboxes[:, 7].astype(int))
+#         frame_ids = bboxes[:, 7].astype(int)
-        fboxes = []
+#         fID = np.unique(bboxes[:, 7].astype(int))
-        for f_id in fID: 
+#         fboxes = []
-            idx = np.where(frame_ids==f_id)[0]
+#         for f_id in fID: 
-            box = bboxes[idx, :]  
+#             idx = np.where(frame_ids==f_id)[0]
-            fboxes.append((f_id, box))
+#             box = bboxes[idx, :]  
-        return fboxes
+#             fboxes.append((f_id, box))
-    
+#         return fboxes
-        fboxes = array2list(boxes)
+#     
-        
+#         fboxes = array2list(boxes)
-        for fid, fbox in fboxes:
+#         
-            imgpath = imgpaths[int(fid-1)]
+#         for fid, fbox in fboxes:
-            
+#             imgpath = imgpaths[int(fid-1)]
-            image = cv2.imread(imgpath)
+#             
-
+#             image = cv2.imread(imgpath)
-            annotator = Annotator(image.copy(), line_width=2)
+# 
-            for i, *xyxy, tid, score, cls, fid, bid in enumerate(fbox):
+#             annotator = Annotator(image.copy(), line_width=2)
-                label = f'{int(id), int(cls)}'
+#             for i, *xyxy, tid, score, cls, fid, bid in enumerate(fbox):
-                if tid >=0 and cls==0:
+#                 label = f'{int(id), int(cls)}'
-                    color = colors(int(cls), True)
+#                 if tid >=0 and cls==0:
-                elif tid >=0 and cls!=0:
+#                     color = colors(int(cls), True)
-                    color = colors(int(id), True)
+#                 elif tid >=0 and cls!=0:
-                else:
+#                     color = colors(int(id), True)
-                    color = colors(19, True)     # 19为调色板的最后一个元素
+#                 else:
-                annotator.box_label(xyxy, label, color=color)
+#                     color = colors(19, True)     # 19为调色板的最后一个元素
-            
+#                 annotator.box_label(xyxy, label, color=color)
-            im0 = annotator.result()
+#             
-            spath = os.path.join(savepath, Path(imgpath).name)
+#             im0 = annotator.result()
-            cv2.imwrite(spath, im0)
+#             spath = os.path.join(savepath, Path(imgpath).name)
-
+#             cv2.imwrite(spath, im0)
-
+# 
-def save_event_subimg(event, savepath):
+# 
-    '''
+# def save_event_subimg(event, savepath):
-        功能: 保存一次购物事件的轨迹子图
+#     '''
-        9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, 
+#         功能: 保存一次购物事件的轨迹子图
-                 back_boxes, front_boxes, back_feats, front_feats, 
+#         9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, 
-                 feats_compose, feats_select
+#                  back_boxes, front_boxes, back_feats, front_feats, 
-        子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
+#                  feats_compose, feats_select
-    '''
+#         子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
-    cameras = ('front', 'back')
+#     '''
-    for camera in cameras:
+#     cameras = ('front', 'back')
-        if camera == 'front':
+#     for camera in cameras:
-            boxes = event.front_boxes
+#         if camera == 'front':
-            imgpaths = event.front_imgpaths
+#             boxes = event.front_boxes
-        else:
+#             imgpaths = event.front_imgpaths
-            boxes = event.back_boxes
+#         else:
-            imgpaths = event.back_imgpaths
+#             boxes = event.back_boxes
-
+#             imgpaths = event.back_imgpaths
-        for i, box in enumerate(boxes):
+# 
-            x1, y1, x2, y2, tid, score, cls, fid, bid = box
+#         for i, box in enumerate(boxes):
-            
+#             x1, y1, x2, y2, tid, score, cls, fid, bid = box
-            imgpath = imgpaths[int(fid-1)]
+#             
-            image = cv2.imread(imgpath)
+#             imgpath = imgpaths[int(fid-1)]
-
+#             image = cv2.imread(imgpath)
-            subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
+# 
-            
+#             subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
-            camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
+#             
-            subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
+#             camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
-            spath = os.path.join(savepath, subimgName)
+#             subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
-            
+#             spath = os.path.join(savepath, subimgName)
-            cv2.imwrite(spath, subimg)
+#             
-    # basename = os.path.basename(event['filepath'])
+#             cv2.imwrite(spath, subimg)
-    print(f"Image saved: {os.path.basename(event.eventpath)}")
+#     # basename = os.path.basename(event['filepath'])
+#     print(f"Image saved: {os.path.basename(event.eventpath)}")
+# =============================================================================
 
 
 def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
@@ -296,7 +298,11 @@ def one2one_simi():
         if not os.path.exists(pairpath):
             os.makedirs(pairpath)
             try:
-                save_event_subimg(event, pairpath)
+                subimgpairs = event.save_event_subimg(pairpath)
+                for subimgName, subimg in subimgpairs:
+                    spath = os.path.join(pairpath, subimgName)
+                    cv2.imwrite(spath, subimg)
+                
             except Exception as e:
                 error_event.append(evtname)
         
@@ -304,11 +310,17 @@ def one2one_simi():
         if not os.path.exists(img_path):
             os.makedirs(img_path)
             try:
-                plot_save_image(event, img_path)
+                imgpairs = event.plot_save_image(img_path)
+                for imgname, img in imgpairs:
+                    spath = os.path.join(img_path, imgname)
+                    cv2.imwrite(spath, img)
             except Exception as e:
                 error_event.append(evtname)
 
             
+         
+            
+                
     errfile = os.path.join(subimgPath, f'error_event.txt')
     with open(errfile, 'w', encoding='utf-8') as f:
         for line in error_event:
@@ -353,7 +365,6 @@ def one2one_simi():
         matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
         matrix[matrix < 0] = 0
         
-        
         simi_mean = np.mean(matrix)
         simi_max  = np.max(matrix)
         stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
@@ -436,7 +447,10 @@ def gen_eventdict(sourcePath, saveimg=True):
     errEvents = []
     k = 0
     for source_path in sourcePath:
-        bname = os.path.basename(source_path)
+        evtpath, bname = os.path.split(source_path)
+        
+        bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
+        source_path = os.path.join(evtpath, bname)
 
         pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
         if os.path.isfile(pickpath): continue
@@ -451,9 +465,9 @@ def gen_eventdict(sourcePath, saveimg=True):
             errEvents.append(source_path)
             print(e)
         
-        # k += 1
+        k += 1
-        # if k==10:
+        if k==1:
-        #     break
+            break
             
     errfile = os.path.join(eventDataPath, f'error_events.txt')
     with open(errfile, 'w', encoding='utf-8') as f:

contrast/one2one_onsite.py

@@ -105,27 +105,56 @@ def test_compare():
     plot_pr_curve(simiList)
     
 def one2one_pr(paths): 
+    '''
+    1:1
+    
+    '''  
+    
     paths = Path(paths)
     
-    # evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2]
+    evtpaths = []
-    evtpaths = [p for p in paths.iterdir() if p.is_dir()]
+    for p in paths.iterdir():
+        condt1 = p.is_dir()
+        condt2 = len(p.name.split('_'))>=2
+        condt3 = len(p.name.split('_')[-1])>8
+        condt4 = p.name.split('_')[-1].isdigit()
+        
+        if condt1 and condt2 and condt3 and condt4:
+            evtpaths.append(p)
+            
+        
+    
+    
+    # evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2 and len(p.name.split('_')[-1])>8]
+    # evtpaths = [p for p in paths.iterdir() if p.is_dir()]
     
     events, similars = [], []
     
-    ##===================================== 扫A放A, 扫A放B场景
+    ##===================================== 扫A放A, 扫A放B场景()
     one2oneAA, one2oneAB = [], []
+    one2SNAA, one2SNAB = [], []
     
-    ##===================================== 应用于展厅 1：N
+    ##===================================== 应用于 1：1
+    
+    _tp_events, _fn_events, _fp_events, _tn_events = [], [], [], []
+    _tp_simi, _fn_simi, _tn_simi, _fp_simi = [], [], [], []
+    
+    ##===================================== 应用于 1：SN
     tp_events, fn_events, fp_events, tn_events = [], [], [], []
     tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], []
     
     ##===================================== 应用于1:n
     tpevents, fnevents, fpevents, tnevents = [], [], [], []
     tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
-    other_event, other_simi = [], []
     
     ##===================================== barcodes总数、比对错误事件
-    bcdList, one2onePath = [], []
+    bcdList = []
+    one2onePath, one2onePath1 = [], [] 
+    one2SNPath, one2SNPath1 = [], []
+    one2nPath = []
+    
+    errorFile_one2one, errorFile_one2SN, errorFile_one2n = [], [], []
+    
     for path in evtpaths:
         barcode = path.stem.split('_')[-1]
         datapath = path.joinpath('process.data')
@@ -140,35 +169,74 @@ def one2one_pr(paths):
         except Exception as e:
             print(f"{path.stem}, Error: {e}")
         
+        
+        '''放入为 1:1，相似度取最大值；取出时为 1:SN, 相似度取均值'''
         one2one = SimiDict['one2one']
+        one2SN = SimiDict['one2SN']
         one2n = SimiDict['one2n']
         
+        '''================== 0.  1:1 ==================='''
         barcodes, similars = [], []
         for dt in one2one:
+            one2onePath.append((path.stem))
+            if dt['similar']==0: 
+                one2onePath1.append((path.stem))
+                continue
             barcodes.append(dt['barcode'])
             similars.append(dt['similar'])
-        
+        if len(barcodes)==len(similars) and len(barcodes)!=0:
-        if len(barcodes)!=len(similars) or len(barcodes)==0:
+            ## 扫A放A, 扫A放B场景    
-            continue
-            
-        ##===================================== 扫A放A, 扫A放B场景    
             simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] 
             simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
             
             one2oneAA.extend(simAA)
             one2oneAB.extend(simAB)
-        one2onePath.append(path.stem)
             
-        ##===================================== 以下应用适用于展厅 1：N
+            ## 相似度排序，barcode相等且排名第一为TP，适用于多的barcode相似度比较
             max_idx = similars.index(max(similars))
             max_sim = similars[max_idx]
             # max_bcd = barcodes[max_idx]
-        
-        if path.stem.find('100321')>0:
-            print("hhh")
-        
-        
             for i in range(len(one2one)):
+                bcd, simi = barcodes[i], similars[i]
+                if bcd==barcode and simi==max_sim:
+                    _tp_simi.append(simi)
+                    _tp_events.append(path.stem)
+                elif bcd==barcode and simi!=max_sim:
+                    _fn_simi.append(simi)
+                    _fn_events.append(path.stem)
+                elif bcd!=barcode and simi!=max_sim: 
+                    _tn_simi.append(simi)
+                    _tn_events.append(path.stem)
+                elif bcd!=barcode and simi==max_sim and barcode in barcodes:
+                    _fp_simi.append(simi)
+                    _fp_events.append(path.stem)
+                else:
+                    errorFile_one2one.append(path.stem)
+
+        
+        '''================== 2. 取出场景下的 1 : Small N ==================='''
+        barcodes, similars = [], []
+        for dt in one2SN:
+            barcodes.append(dt['barcode'])
+            similars.append(dt['similar'])
+        
+        if len(barcodes)==len(similars) and len(barcodes)!=0:            
+            ## 扫A放A, 扫A放B场景    
+            simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] 
+            simAB = [similars[i] for i in range(len(barcodes)) if barcodes[i]!=barcode]
+            
+            one2SNAA.extend(simAA)
+            one2SNAB.extend(simAB)
+            one2SNPath.append(path.stem)
+            if len(simAA)==0:
+                one2SNPath1.append(path.stem)
+                
+  
+            ## 相似度排序，barcode相等且排名第一为TP，适用于多的barcode相似度比较
+            max_idx = similars.index(max(similars))
+            max_sim = similars[max_idx]
+            # max_bcd = barcodes[max_idx]
+            for i in range(len(one2SN)):
                 bcd, simi = barcodes[i], similars[i]
                 if bcd==barcode and simi==max_sim:
                     tp_simi.append(simi)
@@ -179,12 +247,15 @@ def one2one_pr(paths):
                 elif bcd!=barcode and simi!=max_sim: 
                     tn_simi.append(simi)
                     tn_events.append(path.stem)
-            else:
+                elif bcd!=barcode and simi==max_sim and barcode in barcodes:
                     fp_simi.append(simi)
                     fp_events.append(path.stem)
+                else:
+                    errorFile_one2SN.append(path.stem)
                     
         
-        ##===================================== 以下应用适用1:n 
+            
+        '''===================== 3. 取出场景下的 1:n ========================''' 
         events, evt_barcodes, evt_similars, evt_types = [], [], [], []        
         for dt in one2n:
             events.append(dt["event"])
@@ -192,9 +263,10 @@ def one2one_pr(paths):
             evt_similars.append(dt["similar"])
             evt_types.append(dt["type"])
         
-        if len(events)!=len(evt_barcodes) or len(evt_barcodes)!=len(evt_similars) \
+        if len(events)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
-            or len(evt_barcodes)!=len(evt_similars) or len(events)==0: continue 
+            and len(evt_similars)==len(evt_types) and len(events)>0:  
             
+            one2nPath.append(path.stem)
             maxsim = evt_similars[evt_similars.index(max(evt_similars))]
             for i in range(len(one2n)):
                 bcd, simi = evt_barcodes[i], evt_similars[i]
@@ -208,90 +280,102 @@ def one2one_pr(paths):
                 elif bcd!=barcode and simi!=maxsim: 
                     tnsimi.append(simi)
                     tnevents.append(path.stem)
-            elif bcd!=barcode and simi==maxsim:
+                elif bcd!=barcode and simi==maxsim and barcode in evt_barcodes:
                     fpsimi.append(simi)
                     fpevents.append(path.stem)
                 else:
-                other_simi.append(simi)
+                    errorFile_one2n.append(path.stem)
-                other_event.append(path.stem)
                 
             
     '''命名规则:
-          1:1              1:n                1:N
+          1:1 (max)   1:1 (max)            1:n                1:N
-          TP_              TP                 TPX
+            _TP           TP_              TP                 TPX
-        PPrecise_       PPrecise           PPreciseX
+          _PPrecise     PPrecise_       PPrecise           PPreciseX
                                          tpsimi             tp_simi  
     '''
     
-    ''' 1:1 数据存储'''
+    ''' 1:1 数据存储, 相似度计算方式：最大值、均值'''
+    _PPrecise, _PRecall = [], []
+    _NPrecise, _NRecall = [], []
     PPrecise_, PRecall_ = [], []
     NPrecise_, NRecall_ = [], []
     
-    ''' 1:n 数据存储'''
+    ''' 1:SN 数据存储，需根据相似度排序'''
-    PPrecise, PRecall = [], []
-    NPrecise, NRecall = [], []
-    
-    ''' 展厅 1:N 数据存储'''
     PPreciseX, PRecallX = [], []
     NPreciseX, NRecallX = [], []
     
+    ''' 1:n 数据存储，需根据相似度排序'''
+    PPrecise, PRecall = [], []
+    NPrecise, NRecall = [], []
+    
+    
+    
     Thresh = np.linspace(-0.2, 1, 100)
     for th in Thresh:
-        '''============================= 1:1'''
+        '''(Precise, Recall) 计算方式, 若 1:1 与 1:SN 相似度选择方式相同，则可以合并'''
-        TP_ = sum(np.array(one2oneAA) >= th)
+        '''===================================== 1:1 最大值'''
-        FP_ = sum(np.array(one2oneAB) >= th)
+        _TP = sum(np.array(one2oneAA) >= th)
-        FN_ = sum(np.array(one2oneAA) < th)
+        _FP = sum(np.array(one2oneAB) >= th)
-        TN_ = sum(np.array(one2oneAB) < th)
+        _FN = sum(np.array(one2oneAA) < th)
+        _TN = sum(np.array(one2oneAB) < th)
+        
+        _PPrecise.append(_TP/(_TP+_FP+1e-6))
+        _PRecall.append(_TP/(len(one2oneAA)+1e-6))  
+        _NPrecise.append(_TN/(_TN+_FN+1e-6))
+        _NRecall.append(_TN/(len(one2oneAB)+1e-6))
+
+        '''===================================== 1:SN 均值'''
+        TP_ = sum(np.array(one2SNAA) >= th)
+        FP_ = sum(np.array(one2SNAB) >= th)
+        FN_ = sum(np.array(one2SNAA) < th)
+        TN_ = sum(np.array(one2SNAB) < th)
+        
         PPrecise_.append(TP_/(TP_+FP_+1e-6))
-        # PRecall_.append(TP_/(TP_+FN_+1e-6))
+        PRecall_.append(TP_/(len(one2SNAA)+1e-6))
-        PRecall_.append(TP_/(len(one2oneAA)+1e-6))
-        
         NPrecise_.append(TN_/(TN_+FN_+1e-6))
-        # NRecall_.append(TN_/(TN_+FP_+1e-6))
+        NRecall_.append(TN_/(len(one2SNAB)+1e-6))
-        NRecall_.append(TN_/(len(one2oneAB)+1e-6))
 
-        '''============================= 1:n'''
+        '''适用于 (Precise, Recall) 计算方式：多个相似度计算并排序，barcode相等且排名第一为 TP '''
-        TP = sum(np.array(tpsimi) >= th)
+        '''===================================== 1:SN '''
-        FP = sum(np.array(fpsimi) >= th)
-        FN = sum(np.array(fnsimi) < th)
-        TN = sum(np.array(tnsimi) < th)
-        PPrecise.append(TP/(TP+FP+1e-6))
-        # PRecall.append(TP/(TP+FN+1e-6))
-        PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6))
-        
-        NPrecise.append(TN/(TN+FN+1e-6))
-        # NRecall.append(TN/(TN+FP+1e-6))
-        NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
-        
-        
-        '''============================= 1:N 展厅'''
         TPX = sum(np.array(tp_simi) >= th)
         FPX = sum(np.array(fp_simi) >= th)
         FNX = sum(np.array(fn_simi) < th)
         TNX = sum(np.array(tn_simi) < th)
         PPreciseX.append(TPX/(TPX+FPX+1e-6))
-        # PRecallX.append(TPX/(TPX+FNX+1e-6))
         PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6))
         
         NPreciseX.append(TNX/(TNX+FNX+1e-6))
-        # NRecallX.append(TNX/(TNX+FPX+1e-6))
         NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6))
         
-    '''============================= 1:1 曲线'''
+        
+        '''===================================== 1:n'''
+        TP = sum(np.array(tpsimi) >= th)
+        FP = sum(np.array(fpsimi) >= th)
+        FN = sum(np.array(fnsimi) < th)
+        TN = sum(np.array(tnsimi) < th)
+        
+        PPrecise.append(TP/(TP+FP+1e-6))
+        PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6)) 
+        NPrecise.append(TN/(TN+FN+1e-6))
+        NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
+
+        
+    
+    
+    '''1. ============================= 1:1 最大值方案 曲线'''
     fig, ax = plt.subplots()
-    ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, _PPrecise, 'r', label='Precise_Pos: TP/TPFP')
-    ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, _PRecall, 'b', label='Recall_Pos: TP/TPFN')
-    ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, _NPrecise, 'g', label='Precise_Neg: TN/TNFP')
-    ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
+    ax.plot(Thresh, _NRecall, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
     ax.grid(True)
     ax.set_title('1:1 Precise & Recall')
-    ax.set_xlabel(f"Event Num: {len(one2oneAA)}")     
+    ax.set_xlabel(f"Event Num: {len(one2oneAA)+len(one2oneAB)}")     
     ax.legend()
     plt.show()
-    
+    ## ============================= 1:1 最大值方案 直方图'''
-    '''============================= 1:1 直方图'''
     fig, axes = plt.subplots(2, 1)
     axes[0].hist(np.array(one2oneAA), bins=60, edgecolor='black')
     axes[0].set_xlim([-0.2, 1])
@@ -301,7 +385,63 @@ def one2one_pr(paths):
     axes[1].set_title('BB')
     plt.show()
     
-    '''============================= 1:n 曲线''' 
+    
+    
+    
+    '''2. ============================= 1:1 均值方案 曲线'''
+    fig, ax = plt.subplots()
+    ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
+    ax.set_xlim([0, 1])
+    ax.set_ylim([0, 1])
+    ax.grid(True)
+    ax.set_title('1:1 Precise & Recall')
+    ax.set_xlabel(f"Event Num: {len(one2SNAA)}")     
+    ax.legend()
+    plt.show()
+    ## ============================= 1:1 均值方案 直方图'''
+    fig, axes = plt.subplots(2, 1)
+    axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black')
+    axes[0].set_xlim([-0.2, 1])
+    axes[0].set_title('AA')
+    axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black')
+    axes[1].set_xlim([-0.2, 1])
+    axes[1].set_title('BB')
+    plt.show()
+    
+    ''''3. ============================= 1:SN 曲线''' 
+    fig, ax = plt.subplots()
+    ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
+    ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
+    ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
+    ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
+    ax.set_xlim([0, 1])
+    ax.set_ylim([0, 1])
+    ax.grid(True)
+    ax.set_title('1:SN Precise & Recall')
+    ax.set_xlabel(f"Event Num: {len(one2SNAA)}")     
+    ax.legend()
+    plt.show()
+    ## ============================= 1:N 展厅 直方图'''
+    fig, axes = plt.subplots(2, 2)
+    axes[0, 0].hist(tp_simi, bins=60, edgecolor='black')
+    axes[0, 0].set_xlim([-0.2, 1])
+    axes[0, 0].set_title('TP')
+    axes[0, 1].hist(fp_simi, bins=60, edgecolor='black')
+    axes[0, 1].set_xlim([-0.2, 1])
+    axes[0, 1].set_title('FP')
+    axes[1, 0].hist(tn_simi, bins=60, edgecolor='black')
+    axes[1, 0].set_xlim([-0.2, 1])
+    axes[1, 0].set_title('TN')
+    axes[1, 1].hist(fn_simi, bins=60, edgecolor='black')
+    axes[1, 1].set_xlim([-0.2, 1])
+    axes[1, 1].set_title('FN')
+    plt.show()
+    
+        
+    '''4. ============================= 1:n 曲线,''' 
     fig, ax = plt.subplots()
     ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
     ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
@@ -311,11 +451,10 @@ def one2one_pr(paths):
     ax.set_ylim([0, 1])
     ax.grid(True)
     ax.set_title('1:n Precise & Recall')
-    ax.set_xlabel(f"Event Num: {len(one2oneAA)}")     
+    ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")     
     ax.legend()
     plt.show()
-    
+    ## ============================= 1:n 直方图'''
-    '''============================= 1:n 直方图'''
     fig, axes = plt.subplots(2, 2)
     axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
     axes[0, 0].set_xlim([-0.2, 1])
@@ -332,35 +471,18 @@ def one2one_pr(paths):
     plt.show()
     
     
-    '''============================= 1:N 展厅 曲线''' 
+    fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
-    fig, ax = plt.subplots()
+    with open(fpsnErrFile, "w") as file:
-    ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
+        for item in fp_events:
-    ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
+            file.write(item + "\n")
-    ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
+            
-    ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
+    fpErrFile = str(paths.joinpath("one2n_Error.txt"))
-    ax.set_xlim([0, 1])
+    with open(fpErrFile, "w") as file:
-    ax.set_ylim([0, 1])
+        for item in fpevents:
-    ax.grid(True)
+            file.write(item + "\n")
-    ax.set_title('1:N Precise & Recall')
+    
-    ax.set_xlabel(f"Event Num: {len(one2oneAA)}")     
+    
-    ax.legend()
-    plt.show()
     
-    '''============================= 1:N 展厅 直方图'''
-    fig, axes = plt.subplots(2, 2)
-    axes[0, 0].hist(tp_simi, bins=60, edgecolor='black')
-    axes[0, 0].set_xlim([-0.2, 1])
-    axes[0, 0].set_title('TP')
-    axes[0, 1].hist(fp_simi, bins=60, edgecolor='black')
-    axes[0, 1].set_xlim([-0.2, 1])
-    axes[0, 1].set_title('FP')
-    axes[1, 0].hist(tn_simi, bins=60, edgecolor='black')
-    axes[1, 0].set_xlim([-0.2, 1])
-    axes[1, 0].set_title('TN')
-    axes[1, 1].hist(fn_simi, bins=60, edgecolor='black')
-    axes[1, 1].set_xlim([-0.2, 1])
-    axes[1, 1].set_title('FN')
-    plt.show()
     
     # bcdSet = set(bcdList)
     # one2nErrFile = str(paths.joinpath("one_2_Small_n_Error.txt"))
@@ -378,7 +500,7 @@ def one2one_pr(paths):
 
 
 if __name__ == "__main__":
-    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"
+    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
     one2one_pr(evtpaths)
     
     

contrast/utils/event.py

@@ -5,17 +5,43 @@ Created on Tue Nov 26 17:35:05 2024
 @author: ym
 """
 import os
+import cv2
 import pickle
 import numpy as np
 from pathlib import Path
 
 import sys
 sys.path.append(r"D:\DetectTracking")
+from tracking.utils.plotting import Annotator, colors
+from tracking.utils.drawtracks import drawTrack
 from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
 
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 VID_FORMAT = ['.mp4', '.avi']
 
+
+def array2list(bboxes):
+    '''
+        将 bboxes 变换为 track 列表
+        bboxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+    Return：
+        lboxes：列表，列表中元素具有同一 track_id，x1y1x2y2 格式
+                [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]        
+    '''
+    lboxes = []
+    if len(bboxes)==0:
+        return []
+
+    trackID = np.unique(bboxes[:, 4].astype(int))
+    track_ids = bboxes[:, 4].astype(int)
+    for t_id in trackID: 
+        idx = np.where(track_ids == t_id)[0]
+        box = bboxes[idx, :]  
+        lboxes.append(box)
+    
+    return lboxes
+
+
 class ShoppingEvent:
     def __init__(self, eventpath, stype="data"):  
         '''stype: str, 'pickle', 'data', '''
@@ -253,14 +279,218 @@ class ShoppingEvent:
         elif len(self.back_feats):
             self.feats_select = self.back_feats
             
-def main():
+    def plot_save_image(self, savepath):
-    pklpath = r"D:\DetectTracking\evtresult\images2\ShoppingDict.pkl"
         
-    evt = ShoppingEvent(pklpath, stype='pickle')
+        def array2list(bboxes):
+            '''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
+            frame_ids = bboxes[:, 7].astype(int)
+            fID = np.unique(bboxes[:, 7].astype(int))
+            fboxes = []
+            for f_id in fID: 
+                idx = np.where(frame_ids==f_id)[0]
+                box = bboxes[idx, :]  
+                fboxes.append((f_id, box))
+            return fboxes
+        
+        imgpairs = []
+        cameras = ('front', 'back')
+        for camera in cameras:
+            if camera == 'front':
+                boxes = self.front_trackerboxes
+                imgpaths = self.front_imgpaths
+            else:
+                boxes = self.back_trackerboxes
+                imgpaths = self.back_imgpaths
+
+            fboxes = array2list(boxes)
+            for fid, fbox in fboxes:
+                imgpath = imgpaths[int(fid-1)]
+                
+                image = cv2.imread(imgpath)
+    
+                annotator = Annotator(image.copy(), line_width=2)
+                for i, box in enumerate(fbox):
+                    x1, y1, x2, y2, tid, score, cls, fid, bid = box
+                    label = f'{int(tid), int(cls)}'
+                    if tid >=0 and 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
+                    xyxy = (x1/2, y1/2, x2/2, y2/2)
+                    annotator.box_label(xyxy, label, color=color)
+                
+                im0 = annotator.result()
+                
+                imgpairs.append((Path(imgpath).name, im0))
+                
+                # spath = os.path.join(savepath, Path(imgpath).name)
+                
+                
+                # cv2.imwrite(spath, im0)
+        return imgpairs
+
+
+    def save_event_subimg(self, savepath):
+        '''
+            功能: 保存一次购物事件的轨迹子图
+            9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, 
+                     back_boxes, front_boxes, back_feats, front_feats, 
+                     feats_compose, feats_select
+            子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
+        '''
+        imgpairs = []
+        cameras = ('front', 'back')
+        for camera in cameras:
+            boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
+            if camera == 'front':
+                for b in self.front_boxes:
+                    boxes = np.concatenate((boxes, b), axis=0)
+                imgpaths = self.front_imgpaths
+            else:             
+                for b in self.back_boxes:
+                    boxes = np.concatenate((boxes, b), axis=0)
+                imgpaths = self.back_imgpaths
+
+            for i, box in enumerate(boxes):
+                x1, y1, x2, y2, tid, score, cls, fid, bid = box
+                
+                imgpath = imgpaths[int(fid-1)]
+                image = cv2.imread(imgpath)
+
+                subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
+                
+                camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
+                subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
+                
+                imgpairs.append((subimgName, subimg))
+                
+                # spath = os.path.join(savepath, subimgName)
+                
+                # cv2.imwrite(spath, subimg)
+        return imgpairs
+        # basename = os.path.basename(event['filepath'])
+        print(f"Image saved: {os.path.basename(self.eventpath)}")
+        
+    def draw_tracks(self):
+        front_edge = cv2.imread(r"D:\DetectTracking\tracking\shopcart\cart_tempt\board_ftmp_line.png")
+        back_edge = cv2.imread(r"D:\DetectTracking\tracking\shopcart\cart_tempt\edgeline.png")
+        
+        front_trackerboxes = array2list(self.front_trackerboxes)
+        back_trackerboxes = array2list(self.back_trackerboxes)
+
+        # img1, img2 = edgeline.copy(), edgeline.copy()
+        img1 = drawTrack(front_trackerboxes, front_edge.copy())
+        img2 = drawTrack(self.front_trackingboxes, front_edge.copy())
+        
+        img3 = drawTrack(back_trackerboxes, back_edge.copy())
+        img4 = drawTrack(self.back_trackingboxes, back_edge.copy())
+        
+        
+        
+        imgcat1 = np.concatenate((img1, img2), axis = 1)
+        H, W = imgcat1.shape[:2]
+        cv2.line(imgcat1, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
+        
+        imgcat2 = np.concatenate((img3, img4), axis = 1)
+        H, W = imgcat2.shape[:2]
+        cv2.line(imgcat2, (int(W/2), 0), (int(W/2), H), (128, 255, 128), 2)
+        
+        
+        illus = [imgcat1, imgcat2]
+        if len(illus):
+            img_cat = np.concatenate(illus, axis = 1)
+            if len(illus)==2:
+                H, W = img_cat.shape[:2]
+                cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
+        
+        return img_cat
+
+        
+        
+        
+
+def main():
+    
+    
+    # pklpath = r"D:\DetectTracking\evtresult\images2\ShoppingDict.pkl"
+    # evt = ShoppingEvent(pklpath, stype='pickle')
+    
+    
+    
+    evtpath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images\20241209-160248-08edd5f6-1806-45ad-babf-7a4dd11cea60_6973226721445"   
+    evt = ShoppingEvent(evtpath, stype='data')
+    
+    img_cat = evt.draw_tracks()
+    
+    cv2.imwrite("a.png", img_cat)
+    
+    
+    
+# =============================================================================
+# def main1():
+#     evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
+#     text1 = "one2n_Error.txt"
+#     text2 = "one2SN_Error.txt"
+#     events = []
+#     text = (text1, text2)
+#     for txt in text:
+#         txtfile = os.path.join(evtpaths, txt)
+#         with open(txtfile, "r") as f:
+#             lines = f.readlines()
+#             for i, line in enumerate(lines):
+#                 line = line.strip()
+#                 if line:
+#                     fpath=os.path.join(evtpaths, line)
+#                     events.append(fpath)
+#                     
+#                     
+#     events = list(set(events))
+# 
+#     '''定义当前事件存储地址及生成相应文件件'''
+#     resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
+#     # eventDataPath = os.path.join(resultPath, "evtobjs")
+#     # subimgPath = os.path.join(resultPath, "subimgs")
+#     # imagePath = os.path.join(resultPath, "image")
+#     
+#     # if not os.path.exists(eventDataPath): 
+#     #     os.makedirs(eventDataPath) 
+#     # if not os.path.exists(subimgPath): 
+#     #     os.makedirs(subimgPath)
+#     # if not os.path.exists(imagePath): 
+#     #     os.makedirs(imagePath)
+# 
+#     
+#     for evtpath in events:
+#         event = ShoppingEvent(evtpath)
+#         
+#         
+#         evtname = os.path.basename(evtpath)
+#         subimgpath = os.path.join(resultPath, f"{evtname}", "subimg")
+#         imgspath = os.path.join(resultPath, f"{evtname}", "imgs")
+#         if not os.path.exists(subimgpath):
+#             os.makedirs(subimgpath)
+#         if not os.path.exists(imgspath):
+#             os.makedirs(imgspath)
+#         
+#         subimgpairs = event.save_event_subimg(subimgpath)
+#         
+#         for subimgName, subimg in subimgpairs:
+#             spath = os.path.join(subimgpath, subimgName)
+#             cv2.imwrite(spath, subimg)
+# 
+#         imgpairs = event.plot_save_image(imgspath)
+#         for imgname, img in imgpairs:
+#             spath = os.path.join(imgspath, imgname)
+#             cv2.imwrite(spath, img)
+# 
+# =============================================================================
 
 
 if __name__ == "__main__": 
     main()
+    # main1()
     
 
 

pipeline.py

@@ -77,8 +77,13 @@ def pipeline(
     '''事件结果存储文件夹'''
     if not savepath:
         savepath = Path(__file__).resolve().parents[0] / "evtresult"
+    
     save_dir_event = Path(savepath) / evtname
     
+    pickpath = Path(savepath)/"pickfile"
+    if not pickpath.exists():
+        pickpath.mkdir(parents=True, exist_ok=True)
+    
 
     ShoppingDict = {"eventPath": eventpath, 
                     "eventName": evtname,
@@ -117,6 +122,8 @@ def pipeline(
             save_dir_video.mkdir(parents=True, exist_ok=True)
         
         
+        
+        
         '''Yolo + Resnet + Tracker'''
         optdict["source"] = vpath
         optdict["save_dir"] = save_dir_video        
@@ -162,14 +169,16 @@ def pipeline(
     # pklpath = save_dir_event / "ShoppingDict.pkl"
     # with open(str(pklpath), 'wb') as f:
     #     pickle.dump(ShoppingDict, f)    
-    pklpath = Path(savepath) / evtname+".pkl"
+    pf_path = Path(pickpath) / Path(str(evtname)+".pkl")
-    with open(str(pklpath), 'wb') as f:
+    with open(str(pf_path), 'wb') as f:
         pickle.dump(ShoppingDict, f)
         
 
     '''轨迹显示模块'''
     illus = [None, None]
     for CamerType, vts in event_tracks:
+        if len(vts.tracks)==0: continue
+        
         if CamerType == 'front':
             edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
             
@@ -255,7 +264,7 @@ def main():
     '''
     函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
     '''
-    evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1209永辉超市测试"
+    evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
     evtdir = Path(evtdir) 
     
     parmDict = {}
@@ -263,25 +272,22 @@ def main():
     parmDict["SourceType"] = "image"   # video, image
     parmDict["stdfeat_path"] = None
     
-    k = 1
+    k = 0
     errEvents = []
     for item in evtdir.iterdir():
         if item.is_dir():
-            item = r"D:\exhibition\images\images2\images2"
+            # item = r"D:\exhibition\images\images2\images2"
-            
-            
             parmDict["eventpath"] = item
-            
-            
-            try:
             pipeline(**parmDict)
-            except Exception as e:
-                errEvents.append(item)
             
+            # try:
+            #     pipeline(**parmDict)
+            # except Exception as e:
+            #     errEvents.append(str(item))
 
-        # k+=1
+        k+=1
-        # if k==1:
+        if k==1:
-        #     break
+            break
             
     errfile = os.path.join(parmDict["savepath"], f'error_events.txt')
     with open(errfile, 'w', encoding='utf-8') as f:

shopper.py

@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec 17 10:45:10 2024
+
+@author: ym
+"""
+import os
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+
+
+
+
+xpath = r"\\192.168.1.28\share\模型\部署相关\永辉金源广场店商品信息.xlsx"
+xroot, xfilename = os.path.split(xpath)
+xfile, ext = os.path.splitext(xfilename)
+spath = os.path.join(xroot, xfile+'_diff.xlsx')
+
+
+df = pd.read_excel(xpath)
+barcodes = df["商品条码"].tolist()
+names_caojq = df["商品名称"].tolist()
+
+stdpath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
+
+stdpath = Path(stdpath)
+stdBarcodes = [int(f.stem) for f in stdpath.iterdir() if f.is_dir() and f.stem.isdigit() and len(f.stem)>=8]
+
+
+barcodes_s = set(barcodes)
+stdBarcodes_s = set(stdBarcodes)
+
+A = barcodes_s - stdBarcodes_s
+
+record_bcd, record_name = [], []
+for bcd in A:
+    if np.isnan(bcd): continue
+    try:
+        index = barcodes.index(bcd)
+        name = names_caojq[index]
+        
+        record_bcd.append(bcd)
+        record_name.append(name)
+    except ValueError:
+        print(f"元素 {bcd} 不在列表中")
+        
+df_save = pd.DataFrame({
+    '商品条码': record_bcd,
+    '商品名称': record_name
+})
+
+df.to_excel(spath, index=False)
+
+print("Done")
+    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+    
+    

tracking/utils/drawtracks.py

@@ -364,6 +364,9 @@ def drawTrack(tracks, img):
     annotator = TrackAnnotator(img, line_width=2)
     
     for track in tracks:
+        if isinstance(track, np.ndarray):
+            annotator.plotting_track(track)
+        else:
             annotator.plotting_track(track.boxes)
     img = annotator.result()    
     

tracking/utils/read_data.py

@@ -202,9 +202,9 @@ def read_tracking_output(filepath):
 
 
     if len(feats) != len(boxes):
-        return np.array([]), np.array([])
+        return [np.array([])], [np.array([])]
     
-    return np.array(boxes), np.array(feats) 
+    return [np.array(boxes)], [np.array(feats)] 
     
 
 def read_deletedBarcode_file(filePath):
@@ -408,6 +408,7 @@ def read_similar(filePath):
 
         if len(one2one_list): SimiDict['one2one'] = one2one_list
         if len(one2n_list): SimiDict['one2n'] = one2n_list
+        if len(one2SN_list): SimiDict['one2SN'] = one2SN_list
     
     return SimiDict
 
@@ -532,13 +533,12 @@ def main():
             break
         
 def main1():
-    fpath = r'\\192.168.1.28\share\测试_202406\1101\images\20241101-140456-44dc75b5-c406-4cb2-8317-c4660bb727a3_6922130101355_6922130101355\process.data'
+    fpath = r'\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\比对测试\1209永辉超市测试\20241209-155924-117e1941-70f8-4287-8de1-4866868548a6_6926475209967\process.data'
-    simidct = read_one2one_simi(fpath)
+    simidct = read_similar(fpath)
     print(simidct)
           
 
 if __name__ == "__main__":
-    # main()
     main1()
 
 
@@ -552,3 +552,4 @@ if __name__ == "__main__":
 
 
 
+