update one2one contrast module

contrast/contrast_one2one.py

@@ -51,23 +51,22 @@ IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 
 model = load_contrast_model()
 
-def creat_shopping_event(basepath):
+def creat_shopping_event(basePath, savePath, subimgPath=False):
     eventList = []
     
     '''一、构造放入商品事件列表'''
     k = 0
-    for filename in os.listdir(basepath):
+    for filename in os.listdir(basePath):
         # filename = "20240723-155413_6904406215720"
         
         '''filename下为一次购物事件'''
-        filepath = os.path.join(basepath, filename)
+        filepath = os.path.join(basePath, filename)
 
         '''================ 0. 检查 filename 及 filepath 正确性和有效性 ================'''
         nmlist = filename.split('_')
         if filename.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
             continue
         if not os.path.isdir(filepath): continue
-        print(f"Event name: {filename}")
         
         '''================ 1. 构造事件描述字典，暂定 9 items ==============='''
         event = {}
@@ -80,22 +79,22 @@ def creat_shopping_event(basepath):
         event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
         event['back_feats'] = np.empty((0, 256), dtype=np.float64)
         event['front_feats'] = np.empty((0, 256), dtype=np.float64)
-        # event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+        event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
         # event['feats_select'] = np.empty((0, 256), dtype=np.float64)
         
         
-        '''================= 1. 读取 data 文件 ============================='''
+        '''================= 2. 读取 data 文件 ============================='''
         for dataname in os.listdir(filepath):
             # filename = '1_track.data'
             datapath = os.path.join(filepath, dataname)
             if not os.path.isfile(datapath): continue
             
             CamerType = dataname.split('_')[0]
-            ''' 3.1 读取 0/1_track.data 中数据，暂不考虑'''
+            ''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
             # if dataname.find("_track.data")>0:
             #     bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
   
-            ''' 3.2 读取 0/1_tracking_output.data 中数据'''
+            ''' 2.2 读取 0/1_tracking_output.data 中数据'''
             if dataname.find("_tracking_output.data")>0:
                 tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
                 if len(tracking_output_boxes) != len(tracking_output_feats): continue
@@ -106,22 +105,30 @@ def creat_shopping_event(basepath):
                     event['front_boxes'] = tracking_output_boxes
                     event['front_feats'] = tracking_output_feats
         
-        # '''1.1 事件的特征表征方式选择'''
+        '''2.3 事件的特征表征方式: 特征选择、特征集成'''
-        # bk_feats = event['back_feats']
+        bk_feats = event['back_feats']
-        # ft_feats = event['front_feats']
+        ft_feats = event['front_feats']
-        
-        # feats_compose = np.empty((0, 256), dtype=np.float64)
-        # if len(ft_feats):
-        #     feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
-        # if len(bk_feats):
-        #     feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
-        # event['feats_compose'] = feats_compose
-        
-        # '''3. 构造前摄特征'''
-        # if len(ft_feats):
-        #     event['feats_select'] = ft_feats
         
+        '''2.3.1 特征集成'''
+        feats_compose = np.empty((0, 256), dtype=np.float64)
+        if len(ft_feats):
+            feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
+        if len(bk_feats):
+            feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
+        event['feats_compose'] = feats_compose
+                
+        '''2.3.1 特征选择'''
+        if len(ft_feats):
+            event['feats_select'] = ft_feats
 
+        pickpath = os.path.join(savePath, f"{filename}.pickle")
+        with open(pickpath, 'wb') as f:
+           pickle.dump(event, f)
+        print(f"Event: {filename}")
+        
+        if subimgPath==False:
+            eventList.append(event)
+            continue
 
         '''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
         frontImgs, frontFid = [], []
@@ -159,7 +166,6 @@ def creat_shopping_event(basepath):
         '''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
         condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
         condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
-        
         if condt1 or condt2:
             print(f"     Error, condt1: {condt1}, condt2: {condt2}")
             continue
@@ -171,11 +177,11 @@ def creat_shopping_event(basepath):
         #     continue
         
     '''一、构造放入商品事件列表，暂不处理'''
-    # delepath = os.path.join(basepath, 'deletedBarcode.txt')
+    # delepath = os.path.join(basePath, 'deletedBarcode.txt')
     # bcdList = read_deletedBarcode_file(delepath) 
     # for slist in bcdList:
     #     getoutFold = slist['SeqDir'].strip()
-    #     getoutPath = os.path.join(basepath, getoutFold)
+    #     getoutPath = os.path.join(basePath, getoutFold)
         
     #     '''取出事件文件夹不存在，跳出循环'''
     #     if not os.path.exists(getoutPath) and not os.path.isdir(getoutPath):
@@ -185,28 +191,39 @@ def creat_shopping_event(basepath):
     #     event = {}
     #     event['barcode'] = slist['Deleted'].strip()
     #     event['type'] = 'getout'   
-    #     event['basepath'] = getoutPath
+    #     event['basePath'] = getoutPath
     
 
     return eventList
 
-def get_std_barcodeDict(bcdpath):
+def get_std_barcodeDict(bcdpath, bpath):
+    '''
+    inputs:
+        bcdpath: 已清洗的barcode样本图像，如果barcode下有'base'文件夹，只选用该文件夹下图像
+                 (default =  r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771')
+    功能：
+        生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}，
+        bpath: 字典存储地址
+    '''
+    
+    # bpath = r'\\192.168.1.28\share\测试_202406\contrast\barcodes'  
+    
+    '''读取数据集中 barcode 列表'''
     stdBlist = []
     for filename in os.listdir(bcdpath):
         filepath = os.path.join(bcdpath, filename)
         if not os.path.isdir(filepath) or not filename.isdigit(): continue
-    
         stdBlist.append(filename)
         
-        
+      
     bcdpaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBlist]
     
+    '''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}'''
     k = 0
     for barcode, bpath in bcdpaths:  
         stdBarcodeDict = {}
         stdBarcodeDict[barcode] = []
         for root, dirs, files in os.walk(bpath):
-            
             imgpaths = []
             if "base" in dirs:
                 broot = os.path.join(root, "base")
@@ -227,75 +244,45 @@ def get_std_barcodeDict(bcdpath):
                     imgpaths.append(imgpath)
             stdBarcodeDict[barcode].extend(imgpaths)
         
-        jsonpath = os.path.join(r'\\192.168.1.28\share\测试_202406\contrast\barcodes', f"{barcode}.pickle")
+        pickpath = os.path.join(bpath, f"{barcode}.pickle")
-        with open(jsonpath, 'wb') as f:
+        with open(pickpath, 'wb') as f:
            pickle.dump(stdBarcodeDict, f)
-         
         print(f"Barcode: {barcode}")
-        
+
-        k += 1
+        # k += 1
-        if k == 10:
+        # if k == 10:
-            break
+        #     break
-        
+ 
-       
+    return  
-       
-       
-    return stdBarcodeDict 
 
 
-def one2one_test(filepath):
+def extract_save_trajture_subimgs(shoppingEventPath, shoppingFeatPath, subimgPath):
+    '''用于保存一次购物事件的轨迹图像子图'''
     
-    savepath = r'\\192.168.1.28\share\测试_202406\contrast'
+    shoppingFeatPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
-    
+    subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
-    '''获得 Barcode 列表'''
-    bcdpath = r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771'
-    stdBarcodeDict = get_std_barcodeDict(bcdpath)
     
     
-    eventList = creat_shopping_event(filepath)
+    eventList = creat_shopping_event(shoppingEventPath, shoppingFeatPath, subimgPath=True)
-    print("=========== eventList have generated! ===========")
+    print("======= eventList have generated and features have saved! =======")
+  
+
     barcodeDict = {}
     for event in eventList:
         '''9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, 
                     back_boxes, front_boxes, back_feats, front_feats
         '''
-        
-        barcode = event['barcode']
-        if barcode not in stdBarcodeDict.keys():
-            continue
-        
-        
         if len(event['feats_select']):
             event_feats = event['feats_select']
         elif len(event['back_feats']):
             event_feats = event['back_feats']
         else:
             continue
-        
+
-        std_bcdpath = os.path.join(bcdpath, barcode)
+
-        
-              
-        
-        for root, dirs, files in os.walk(std_bcdpath):
-            if "base" in files:
-                std_bcdpath = os.path.join(root, "base")
-                break
-        
-        
-        
-        
-        
-            
-        
-        
-        
-        
-        
-        
-        
         '''保存一次购物事件的轨迹子图'''
         basename = os.path.basename(event['filepath'])
-        spath = os.path.join(savepath, basename)
+        spath = os.path.join(subimgPath, basename)
         if not os.path.exists(spath):
             os.makedirs(spath)
         cameras = ('front', 'back')
@@ -319,6 +306,7 @@ def one2one_test(filepath):
                 subimgPath = os.path.join(spath, subimgName)
                 
                 cv2.imwrite(subimgPath, subimg)
+       
         print(f"Image saved: {basename}")
             
         
@@ -334,28 +322,93 @@ def batch_inference(imgpaths, batch):
     for group in groups:
         feature = featurize(group, conf.test_transform, model, conf.device)
         features.append(feature)
+    features = np.concatenate(features, axis=0)
         
     return features    
     
-def main_infer():
+def stdfeat_infer(imgPath, featPath):
+    '''
+    inputs: 
+        imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
+        featPath: imgPath图像对应特征的存储地址
+    功能:
+        对 imgPath中图像进行特征提取，生成只有一个key值的字典，
+        {barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中
     
+    '''
     
-    
+    # imgPath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
-    bpath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
+    # featPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
-    for filename in os.listdir(bpath):
+    stdBarcodeDict = {}
-        filepath = os.path.join(bpath, filename)
+    k = 0
+    for filename in os.listdir(imgPath):
+        filepath = os.path.join(imgPath, filename)
         
-        with open(filepath, 'rb') as f:  
+        stdbDict = {}
-            bpDict = pickle.load(f)
+        t1 = time.time()
-            
+        
-        for barcode, imgpaths in bpDict.items():
+        try:
-            feature = batch_inference(imgpaths, 8)
+            with open(filepath, 'rb') as f:  
-            
+                bpDict = pickle.load(f)    
-        print("Done!!!")
+            for barcode, imgpaths in bpDict.items():
+                feature = batch_inference(imgpaths, 8) 
+        except Exception as e:
+            print(f"Error accured at: {filename}, with Exception is: {e}")
+        
+        '''================ 保存单个barcode特征 ================'''
+        stdbDict[barcode] = feature
+        pkpath = os.path.join(featPath, f"{barcode}.pickle")
+        with open(pkpath, 'wb') as f:
+           pickle.dump(stdbDict, f)
+        
+        stdBarcodeDict[barcode] = feature
+        t2 = time.time()
+        print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
+        k += 1
+        if k == 10:
+            break
+    
+    pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
+    with open(pickpath, 'wb') as f:
+       pickle.dump(stdBarcodeDict, f)   
+
+def contrast_performance_evaluate():
+    eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
+    stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
+    
+
+        
+
+def generate_event_and_standard_features():
+    stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771"
+    stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
+    stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\features"
+    
+    '''=========================== 1. 生成标准特征集 ========================'''
+    '''1.1 提取并保存标准特征库原始图像文件地址字典'''
+    # get_std_barcodeDict(stdSamplePath, stdBarcodePath)
+    # print("standard imgpath have extracted and saved")
+    
+    
+    '''1.2 特征提取，并保存至文件夹 stdFeaturePath 中'''
+    stdfeat_infer(stdBarcodePath, stdFeaturePath)
+    # print("standard features have generated!")
+    
+    
+    '''=========================== 2. 提取并存储事件特征 ========================'''
+    shoppingEventPath  = [r'\\192.168.1.28\share\测试_202406\0723\0723_1',
+                          r'\\192.168.1.28\share\测试_202406\0723\0723_2',
+                          r'\\192.168.1.28\share\测试_202406\0723\0723_3',
+                          r'\\192.168.1.28\share\测试_202406\0722\0722_01',
+                          r'\\192.168.1.28\share\测试_202406\0722\0722_02']
+    shoppingFeatPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
+    # for sPath in shoppingEventPath:
+    #     eventList = creat_shopping_event(sPath, shoppingFeatPath)
     
         
 
-def main():
+
+def shopping_event_test():
     fplist = [#r'\\192.168.1.28\share\测试_202406\0723\0723_1',
               #r'\\192.168.1.28\share\测试_202406\0723\0723_2',
               r'\\192.168.1.28\share\测试_202406\0723\0723_3',
@@ -363,18 +416,59 @@ def main():
               #r'\\192.168.1.28\share\测试_202406\0722\0722_02'
             ]
     
-    
+    shoppingFeatPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
+    subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
     
     for filepath in  fplist:
-        one2one_test(filepath)
+        '''用于保存一次购物事件的轨迹轨迹特征、及对应的图像子图'''
-    
+        extract_save_trajture_subimgs(filepath, shoppingFeatPath, subimgPath)
-    # for filepath in  fplist:
-    #     try:
-    #         one2one_test(filepath)
             
-    #     except Exception as e:
-    #         print(f'{filepath}, Error: {e}') 
         
+
+def main():    
+    generate_event_and_standard_features()
+    # shopping_event_test()
+    
+    pass
+
+
 if __name__ == '__main__':
-    # main()
+    main()
-    main_infer()
+
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