bakeup

contrast/feat_similar.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  9 10:36:45 2024
+
+@author: ym
+"""
+import os
+import cv2
+import numpy as np
+import torch
+import sys
+from scipy.spatial.distance import cdist
+sys.path.append(r"D:\DetectTracking")
+from tracking.trackers.reid.reid_interface import ReIDInterface 
+from tracking.trackers.reid.config import config as ReIDConfig
+ReIDEncoder = ReIDInterface(ReIDConfig)
+
+
+def inference_image(images):
+    batch_patches = []
+    patches = []
+    for d, img1 in enumerate(images):
+
+
+        img = img1[:, :, ::-1].copy()    # the model expects RGB inputs
+        patch = ReIDEncoder.transform(img)
+        
+        # patch = patch.to(device=self.device).half()
+        if str(ReIDEncoder.device) != "cpu":
+            patch = patch.to(device=ReIDEncoder.device).half()
+        else:
+            patch = patch.to(device=ReIDEncoder.device)
+        
+        patches.append(patch)
+        if (d + 1) % ReIDEncoder.batch_size == 0:
+            patches = torch.stack(patches, dim=0)
+            batch_patches.append(patches)
+            patches = []
+
+    if len(patches):
+        patches = torch.stack(patches, dim=0)
+        batch_patches.append(patches)
+
+    features = np.zeros((0, ReIDEncoder.embedding_size))
+    for patches in batch_patches:
+        pred = ReIDEncoder.model(patches)
+        pred[torch.isinf(pred)] = 1.0
+        feat = pred.cpu().data.numpy()
+        features = np.vstack((features, feat))
+
+    return features
+
+
+def similarity_compare(root_dir):
+    '''
+    root_dir：包含 "subimgs"字段的文件夹中图像为 subimg子图
+    功能：相邻帧子图间相似度比较
+    '''
+    
+    all_files = []
+    extensions = ['.png', '.jpg']
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        filepaths = []
+        for filename in filenames:
+            if os.path.basename(dirpath).find('subimgs') < 0: 
+                continue
+            file, ext = os.path.splitext(filename)
+            if ext in extensions:
+                imgpath = os.path.join(dirpath, filename)
+                filepaths.append(imgpath)
+        nf = len(filepaths)
+        if nf==0:
+            continue
+
+        fnma = os.path.basename(filepaths[0]).split('.')[0]
+        imga = cv2.imread(filepaths[0])
+        ha, wa = imga.shape[:2]
+        
+        for i in range(1, nf):
+            fnmb = os.path.basename(filepaths[i]).split('.')[0]
+            
+            imgb = cv2.imread(filepaths[i])
+            hb, wb = imgb.shape[:2]
+            
+            
+            feats = inference_image(((imga, imgb)))
+            
+            similar = 1 - np.maximum(0.0, cdist(feats, feats, metric='cosine')) 
+            
+            
+            h, w  = max((ha, hb)), max((wa, wb))
+            img = np.zeros(((h, 2*w, 3)), np.uint8)
+            img[0:ha, 0:wa], img[0:hb, w:(w+wb)] = imga, imgb
+
+            linewidth = max(round(((h+2*w))/2 * 0.001), 2)
+            cv2.putText(img,
+                        text=f'{similar[0,1]:.2f}',     # Text string to be drawn
+                        org=(max(w-20, 10), h-10),       # Bottom-left corner of the text string
+                        fontFace=0,                     # Font type
+                        fontScale=linewidth/3,          # Font scale factor
+                        color=(0, 0, 255),              # Text color
+                        thickness=linewidth,            # Thickness of the lines used to draw a text
+                        lineType=cv2.LINE_AA,           # Line type
+                        )                           
+            spath = os.path.join(dirpath, 's'+fnma+'-vs-'+fnmb+'.png')
+            cv2.imwrite(spath, img)
+            
+            
+            fnma = os.path.basename(filepaths[i]).split('.')[0]
+            imga = imgb.copy()
+            ha, wa = imga.shape[:2]
+                
+
+                
+    return
+
+
+def main():
+    root_dir = r"D:\contrast\dataset\result\20240723-112242_6923790709882"
+    
+    try:
+        similarity_compare(root_dir)   
+    except Exception as e:
+        print(f'Error: {e}') 
+    
+
+
+
+if __name__ == '__main__':
+    main()
+    

track_reid.py

@@ -172,7 +172,10 @@ def run(
     if is_url and is_file:
         source = check_file(source)  # download
     
-    save_dir = Path(project) / Path(source).stem
+    
+    # spth = source.split('\\')[-2] + "_" + Path(source).stem
+    save_dir = Path(project) / Path(source.split('\\')[-2] + "_" + str(Path(source).stem))
+    # save_dir = Path(project) / Path(source).stem
     if save_dir.exists():
         print(Path(source).stem)
         # return
@@ -387,6 +390,8 @@ def run(
         # Print time (inference-only)
         LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
 
+    if track_boxes.size == 0:
+        return
     
     ## ======================================================================== written by WQG
     ## track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id]
@@ -397,7 +402,7 @@ def run(
     filename = os.path.split(save_path_img)[-1]
  
     '''======================== 1. save in './run/detect/' ===================='''
-    if source.find("front") >= 0:
+    if source.find("front") >= 0 or Path(source).stem.split('_')[0] == '1':
         carttemp = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
     else:
         carttemp = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
@@ -516,10 +521,11 @@ def main_loop(opt):
     optdict = vars(opt)   
     
     # p = r"D:\datasets\ym\永辉测试数据_比对" 
-    p = r"D:\datasets\ym\广告板遮挡测试\8"
+    # p = r"D:\datasets\ym\广告板遮挡测试\8"
     # p = r"D:\datasets\ym\videos\标记视频"
     # p = r"D:\datasets\ym\实验室测试"
     # p = r"D:\datasets\ym\永辉双摄视频\新建文件夹"
+    p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_"
     
     k = 0
     if os.path.isdir(p):
@@ -531,16 +537,16 @@ def main_loop(opt):
         #           r"D:\datasets\ym\广告板遮挡测试\8\2500441577966_20240508-175946_front_addGood_70f75407b7ae_155_17788571404.mp4"
         #           ]
         
-        files = [r"D:\datasets\ym\广告板遮挡测试\8\6907149227609_20240508-174733_back_returnGood_70f754088050_425_17327712807.mp4"]
+        # files = [r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-095838_\1_seek_193.mp4"]
         
         
         for file in files:
             optdict["source"] = file
             run(**optdict)
             
-            k += 1
+            # k += 1
-            if k == 1:
+            # if k == 10:
-                break
+            #     break
     elif os.path.isfile(p):
         optdict["source"] = p
         run(**vars(opt))

tracking/contrast_analysis.py

@@ -346,14 +346,6 @@ def performance_evaluate(all_list, isshow=False):
         
     return errpairs, corrpairs, err_similarity, correct_similarity
     
-    
-      
-    return errpairs, corrpairs, err_similarity, correct_similarity
-
-
-
-
-
 
 
 def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
@@ -417,21 +409,20 @@ 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'
+    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'    
-    
     contrast_loop(fpath)
 
 def main1():
-    del_barcode_file = 'D:/contrast/dataset/compairsonResult/deletedBarcode_20240709_pm.txt'
+    del_barcode_file = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
-    basepath = r'D:\contrast\dataset\1_to_n\709'
+    basepath = r'\\192.168.1.28\share\测试_202406\709'
     savepath = r'D:\contrast\dataset\result'
     
     try:
         relative_path = contrast_analysis(del_barcode_file, basepath, savepath)
     except Exception as e:
         print(f'Error Type: {e}')
-    
 
 
 if __name__ == '__main__':

tracking/contrast_one2one.py

@@ -0,0 +1,332 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug 30 17:53:03 2024
+
+1. 确认在相同CamerType下，track.data 中 CamerID 项数量 = 图像数 = 帧ID数 = 最大帧ID
+
+2. 读取0/1_tracking_output.data 中数据，boxes、feats，len(boxes)=len(feats)
+    帧ID约束
+
+3. 优先选择前摄
+
+4. 保存图像数据
+
+5. 一次购物事件类型
+    shopEvent: {barcode:
+                type: getout, input
+                front_traj:[{imgpath: str, 
+                             box: arrar(1, 9),
+                             feat: array(1, 256)
+                           }]
+                back_traj: [{imgpath: str, 
+                             box: arrar(1, 9),
+                             feat: array(1, 256)
+                           }]
+                }
+        
+        
+        
+@author: ym
+
+"""
+import numpy as np
+import cv2
+import os
+import sys
+import json
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file
+
+IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
+
+def creat_shopping_event(basepath):
+    eventList = []
+    
+    '''一、构造放入商品事件列表'''
+    k = 0
+    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)
+
+        '''================ 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 = {}
+        event['barcode'] = nmlist[1]
+        event['type'] = 'input'
+        event['filepath'] = filepath
+        event['back_imgpaths'] = []
+        event['front_imgpaths'] = []
+        event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
+        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_select'] = np.empty((0, 256), dtype=np.float64)
+        
+        
+        '''================= 1. 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'''
+            # 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'''
+            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
+                if CamerType == '0':
+                    event['back_boxes'] = tracking_output_boxes
+                    event['back_feats'] = tracking_output_feats
+                elif CamerType == '1':
+                    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'''
+        # bk_feats = event['back_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. 读取图像文件地址，并按照帧ID排序 ============='''
+        frontImgs, frontFid = [], []
+        backImgs, backFid = [], []
+        for imgname in os.listdir(filepath):
+            name, ext = os.path.splitext(imgname)
+            if ext not in IMG_FORMAT or name.find('frameId')<0: continue
+
+            CamerType = name.split('_')[0]
+            frameId = int(name.split('_')[3])
+            imgpath = os.path.join(filepath, imgname)
+            if CamerType == '0':
+                backImgs.append(imgpath)
+                backFid.append(frameId)
+            if CamerType == '1':
+                frontImgs.append(imgpath)
+                frontFid.append(frameId)
+        
+        frontIdx = np.argsort(np.array(frontFid))
+        backIdx = np.argsort(np.array(backFid))
+        
+        '''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
+        frontImgs = [frontImgs[i] for i in frontIdx]
+        backImgs = [backImgs[i] for i in backIdx]
+
+        '''2.2 将前、后摄图像路径添加至事件字典'''   
+        bfid = event['back_boxes'][:, 7].astype(np.int64)
+        ffid = event['front_boxes'][:, 7].astype(np.int64)
+        if len(bfid) and max(bfid) <= len(backImgs):
+            event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
+        if len(ffid) and max(ffid) <= len(frontImgs):
+            event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
+
+        
+        '''================ 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
+
+        eventList.append(event)
+        
+        # k += 1
+        # if k==1:
+        #     continue
+        
+    '''一、构造放入商品事件列表，暂不处理'''
+    # 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)
+        
+    #     '''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):
+    #         continue
+        
+    #     ''' 生成取出事件字典 '''
+    #     event = {}
+    #     event['barcode'] = slist['Deleted'].strip()
+    #     event['type'] = 'getout'   
+    #     event['basepath'] = getoutPath
+    
+
+    return eventList
+
+def get_std_barcodeDict(bcdpath):
+    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]
+    
+    stdBarcodeDict = {}
+    for barcode, bpath in bcdpaths:  
+        stdBarcodeDict[barcode] = []
+        for root, dirs, files in os.walk(bpath):
+            
+            imgpaths = []
+            if "base" in dirs:
+                broot = os.path.join(root, "base")
+                for imgname in os.listdir(broot):
+                    imgpath = os.path.join(broot, imgname)
+                    _, ext = os.path.splitext(imgpath)
+                    if ext not in IMG_FORMAT: continue
+                    imgpaths.append(imgpath)
+                
+                stdBarcodeDict[barcode].extend(imgpaths)
+                break
+            
+            else:
+                for imgname in files:
+                    imgpath = os.path.join(root, imgname)
+                    _, ext = os.path.splitext(imgpath)
+                    if ext not in IMG_FORMAT: continue
+                    imgpaths.append(imgpath)
+            stdBarcodeDict[barcode].extend(imgpaths)
+            
+    with open('stdBarcodeDict.json', 'wb') as f:
+       json.dump(stdBarcodeDict, f)
+       
+       
+       
+    return stdBarcodeDict 
+
+
+def one2one_test(filepath):
+    
+    savepath = r'\\192.168.1.28\share\测试_202406\contrast'
+    
+    '''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)
+    print("=========== eventList have generated! ===========")
+    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)
+        if not os.path.exists(spath):
+            os.makedirs(spath)
+        cameras = ('front', 'back')
+        for camera in cameras:
+            if camera == 'front':
+                boxes = event['front_boxes']
+                imgpaths = event['front_imgpaths']
+            else:
+                boxes = event['back_boxes']
+                imgpaths = event['back_imgpaths']
+
+            for i, box in enumerate(boxes):
+                x1, y1, x2, y2, tid, score, cls, fid, bid = box
+                
+                imgpath = imgpaths[i]
+                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"{camerType}_{tid}_fid({fid}, {frameID}).png"
+                subimgPath = os.path.join(spath, subimgName)
+                
+                cv2.imwrite(subimgPath, subimg)
+        print(f"Image saved: {basename}")
+            
+        
+    
+    
+    
+    
+        
+
+def main():
+    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',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_01',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_02'
+            ]
+    
+    
+    
+    for filepath in  fplist:
+        one2one_test(filepath)
+    
+    # for filepath in  fplist:
+    #     try:
+    #         one2one_test(filepath)
+            
+    #     except Exception as e:
+    #         print(f'{filepath}, Error: {e}') 
+        
+if __name__ == '__main__':
+    
+    main()

tracking/dotrack/dotracks.py

@@ -96,7 +96,7 @@ class Track:
         
         self.isCornpoint = False
         self.imgshape = imgshape
-        self.isBorder = False
+        # self.isBorder = False
         # self.state = MoveState.Unknown
         
         '''轨迹开始帧、结束帧 ID'''
@@ -157,10 +157,12 @@ class Track:
     def compute_cornpts_feats(self):
         '''
         '''
+        # print(f"TrackID: {self.tid}")
         trajectory = []
         trajlens = []
         trajdist = []
         trajrects = []
+        trajrects_wh = []
         for k in range(5):
             # diff_xy2 = np.power(np.diff(self.cornpoints[:, 2*k:2*(k+1)], axis = 0), 2)
             # trajlen = np.sum(np.sqrt(np.sum(diff_xy2, axis = 1)))
@@ -182,12 +184,17 @@ class Track:
                    rect[0]: 旋转角度 (-90°, 0]  
             '''  
             rect = cv2.minAreaRect(X.astype(np.int64))
+            rect_wh = max(rect[1])
+            
+            
+            trajrects_wh.append(rect_wh)
             trajrects.append(rect)
         
         self.trajectory = trajectory
         self.trajlens = trajlens
         self.trajdist = trajdist
         self.trajrects = trajrects
+        self.trajrects_wh = trajrects_wh
     
 
         
@@ -198,12 +205,17 @@ class Track:
           -最小轨迹长度：trajlen_min
           -最小轨迹欧氏距离：trajdist_max
         '''
-        idx1 = self.trajlens.index(max(self.trajlens))
+        
+        # idx1 = self.trajlens.index(max(self.trajlens))
+        idx1 = self.trajrects_wh.index(max(self.trajrects_wh))
+
         trajmax = self.trajectory[idx1]
         trajlen_max = self.trajlens[idx1]
         trajdist_max = self.trajdist[idx1]
         if not self.isCornpoint:
-            idx2 = self.trajlens.index(min(self.trajlens))            
+            # idx2 = self.trajlens.index(min(self.trajlens)) 
+            idx2 = self.trajrects_wh.index(min(self.trajrects_wh))
+            
             trajmin = self.trajectory[idx2]
             trajlen_min = self.trajlens[idx2]
             trajdist_min = self.trajdist[idx2]
@@ -284,7 +296,7 @@ class Track:
         camerType: back, 后置摄像头
                    front, 前置摄像头
         '''
-        if camerType=="front":
+        if camerType=="back":
             incart = cv2.imread("./shopcart/cart_tempt/incart.png", cv2.IMREAD_GRAYSCALE)
             outcart = cv2.imread("./shopcart/cart_tempt/outcart.png", cv2.IMREAD_GRAYSCALE)
         else:
@@ -487,6 +499,14 @@ class doTracks:
             blist = [b for b in alist]
             alist = []
             for btrack in blist:
+                # afids = []
+                # for track in cur_list:
+                #     afids.extend(list(track.boxes[:, 7].astype(np.int_)))          
+                # bfids = btrack.boxes[:, 7].astype(np.int_)
+                # interfid = set(afids).intersection(set(bfids))
+                # if len(interfid):
+                #     print("wait!!!")
+                # if track_equal_track(atrack, btrack) and len(interfid)==0:
                 if track_equal_track(atrack, btrack):
                     cur_list.append(btrack)
                 else:

tracking/dotrack/dotracks_back.py

@@ -155,6 +155,7 @@ class doBackTracks(doTracks):
     def merge_tracks(self, Residual):
         """
         对不同id，但可能是同一商品的目标进行归并
+        和 dotrack_front.py中函数相同，可以合并，可以合并至基类
         """ 
         mergedTracks = self.base_merge_tracks(Residual)
         

tracking/dotrack/dotracks_front.py

@@ -47,6 +47,7 @@ class doFrontTracks(doTracks):
         
         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)
         
         # [self.associate_with_hand(htrack, gtrack) for htrack in hand_tracks for gtrack in tracks]
         '''轨迹循环归并'''
@@ -126,6 +127,7 @@ class doFrontTracks(doTracks):
     def merge_tracks(self, Residual):
         """
         对不同id，但可能是同一商品的目标进行归并
+        和 dotrack_back.py中函数相同，可以合并至基类
         """ 
         mergedTracks = self.base_merge_tracks(Residual)
         

tracking/dotrack/track_front.py

@@ -165,7 +165,7 @@ class frontTrack(Track):
 
         '''情况2：中心点向上 '''
         ## 商品中心点向上移动，但没有关联的Hand轨迹，也不是左右边界点
-        condt_b =  condt0 and len(self.Hands)==0 and  y0[-1] < y0[0] and (not self.is_edge_cornpoint())
+        condt_b =  condt0 and len(self.Hands)==0 and  y0[-1] < y0[0] and (not self.is_edge_cornpoint()) and min(y0)>self.CART_HIGH_THRESH1
 
 
         '''情况3: 商品在购物车内，但运动方向无序'''

tracking/eventsmatch.py

@@ -619,7 +619,6 @@ def match_evaluate(filename = r'./matching/featdata/MatchDict.pkl'):
     
 
 def have_tracked():   
-    featdir = r"./data/trackfeats"
     trackdir = r"./data/tracks"
     
 # =============================================================================
@@ -634,35 +633,25 @@ def have_tracked():
     
     MatchingDict = {}
     k, gt = 0, Profile()
-    for filename in os.listdir(featdir):
+    for filename in os.listdir(trackdir):
         file, ext = os.path.splitext(filename)
         
         # if file not in FileList: continue
         if file.find('20240508')<0: continue
-        if file.find('17327712807')<0: continue
+        filepath = os.path.join(trackdir, filename)
-    
+
-        trackpath = os.path.join(trackdir, file + ".npy")
+                
-        featpath = os.path.join(featdir, filename)
+        tracksDict = np.load(filepath, allow_pickle=True)
-        
+        bboxes = tracksDict['TrackBoxes'] 
-        bboxes = np.load(trackpath)
-        features_dict = np.load(featpath, allow_pickle=True)
         with gt:
             if filename.find("front") >= 0:
-                vts = doFrontTracks(bboxes, features_dict)
+                vts = doFrontTracks(bboxes, tracksDict)
                 vts.classify()
                 
-                plt = plot_frameID_y2(vts)
-                
-                savedir = save_dir.joinpath(f'{file}_y2.png') 
-                
-                plt.savefig(savedir)  
-                plt.close()
             elif filename.find("back") >= 0:
-                vts = doBackTracks(bboxes, features_dict)
+                vts = doBackTracks(bboxes, tracksDict)
                 vts.classify()
-                
+
-                edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
-                draw_all_trajectories(vts, edgeline, save_dir, filename)
         print(file+f" need time: {gt.dt:.2f}s")
         
         elements = file.split('_')
@@ -691,7 +680,7 @@ def have_tracked():
                 box = boxes[i, :]
                 tid, fid, bid = int(box[4]), int(box[7]), int(box[8])
                 
-                feat_dict = features_dict[fid]
+                feat_dict = tracksDict[fid]
                 feature = feat_dict[bid]
                 img = feat_dict[f'{bid}_img']
                 

tracking/feat_select.py

@@ -30,7 +30,14 @@ def compute_similar(feat1, feat2):
     
 
 def update_event(datapath):
-    '''一次购物事件，包含 8 个keys'''
+    '''一次购物事件，包含 8 个keys
+    back_sole_boxes：后摄boxes
+    front_sole_boxes：前摄boxes
+    back_sole_feats：后摄特征
+    front_sole_feats：前摄特征
+    feats_compose：将前后摄特征进行合并
+    feats_select：特征选择，优先选择前摄特征
+    '''
     event = {}
     # event['front_tracking_boxes'] = []
     # event['front_tracking_feats'] = {}
@@ -157,6 +164,10 @@ def update_event(datapath):
 
 
 def creatd_deletedBarcode_front(filepath):
+    '''
+    生成deletedBarcodeTest.txt
+    '''
+
     # filepath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt'
     basepath, _ = os.path.split(filepath)
       
@@ -281,7 +292,7 @@ def creatd_deletedBarcode_front(filepath):
     print('Step 3: Similarity conputation Done!') 
     
     wpath = os.path.split(filepath)[0]
-    wfile = os.path.join(wpath, 'deletedBarcodeTest_x.txt')
+    wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
     with open(wfile, 'w', encoding='utf-8') as file:
         for result in results:
             
@@ -299,11 +310,14 @@ def creatd_deletedBarcode_front(filepath):
     print('Step 4: File writting Done!') 
 
 
-
+def precision_compare(filepath, savepath):
-
+    '''
-                
+    1. deletedBarcode.txt 中的相似度的计算为现场算法前后摄轨迹特征合并
-def compute_precision(filepath, savepath):
+    2. deletedBarcodeTest.txt 中的 3 个相似度计算方式依次为：
-    
+          (1)现场算法前后摄轨迹特征合并;
+          (2)本地算法前后摄轨迹特征合并；
+          (3)本地算法优先选择前摄
+    '''
     
     fpath = os.path.split(filepath)[0]
     _, basefile = os.path.split(fpath)
@@ -336,11 +350,16 @@ def compute_precision(filepath, savepath):
     plt1.title(basefile + ', front')
     plt2.savefig(os.path.join(savepath, basefile+'_pr_front.png'))
     plt2.close()
-            
+   
 def main():
+    '''
+    1. 成deletedBarcodeTest.txt
+    2. 不同特征选择下的精度比对性能比较
+    '''
+    
     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\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',
@@ -376,25 +395,19 @@ def main():
               # 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)
+        try:
-        # creatd_deletedBarcode_front(filepath)
+            #1. 生成deletedBarcodeTest.txt 文件
-        compute_precision(filepath, savepath)
+            creatd_deletedBarcode_front(filepath)
-        
+                
-        # try:
+            #2. 确保该目录下存在deletedBarcode.txt, deletedBarcodeTest.txt 文件
-        #     creatd_deletedBarcode_front(filepath)
+            precision_compare(filepath, savepath)
-        #     compute_pres(filepath, savepath)
+        except Exception as e:
-        # except Exception as e:
+            print(f'{filepath}, Error: {e}') 
-        #     print(f'{filepath}, Error: {e}') 
         
 if __name__ == '__main__':
+    
     main()
 
     

tracking/module_analysis.py

@@ -25,110 +25,14 @@ from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
 from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
 
 from contrast_analysis import contrast_analysis
-
 from tracking.utils.annotator import TrackAnnotator
 
 W, H = 1024, 1280
 Mode = 'front'  #'back'
 ImgFormat = ['.jpg', '.jpeg', '.png', '.bmp']
 
-def video2imgs(path):
-    vpath = os.path.join(path, "videos")
-    
-    k = 0
-    have = False
-    for filename in os.listdir(vpath):
-        file, ext = os.path.splitext(filename)
-        imgdir = os.path.join(path, file)
-        if os.path.exists(imgdir): 
-            continue          
-        else:
-            os.mkdir(imgdir)
-
-        vfile = os.path.join(vpath, filename)
-        cap = cv2.VideoCapture(vfile)
-        i = 0
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
-            
-            i += 1
-            imgp = os.path.join(imgdir, file+f"_{i}.png")
-            cv2.imwrite(imgp, frame)
-        
-            print(filename+f": {i}")
-        
-        
-        cap.release()
-        
-        k+=1
-        if k==1000:
-            break
-
-def draw_boxes():
-    datapath = r'D:\datasets\ym\videos_test\20240530\1_tracker_inout(1).data'
-    VideosData = read_tracker_input(datapath)
-    
-    bboxes = VideosData[0][0]
-    ffeats = VideosData[0][1]
-    
-    videopath = r"D:\datasets\ym\videos_test\20240530\134458234-1cd970cf-f8b9-4e80-9c2e-7ca3eec83b81-1_seek0.10415589124891511.mp4"
-    
-    cap = cv2.VideoCapture(videopath)
-    i = 0
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            break
-        
-        
-        annotator = Annotator(frame.copy(), line_width=3)
-        
-        
-        boxes = bboxes[i]
-            
-        for *xyxy, conf, cls in reversed(boxes):
-            label = f'{int(cls)}: {conf:.2f}'                   
-   
-            color = colors(int(cls), True)
-            annotator.box_label(xyxy, label, color=color)
-            
-        img = annotator.result()    
-    
-        imgpath = r"D:\datasets\ym\videos_test\20240530\result\int8_front\{}.png".format(i+1)
-        cv2.imwrite(imgpath, img)
-    
-        print(f"Output: {i}")
-        i += 1
-    cap.release()
-    
-def read_imgs(imgspath, CamerType):
-    imgs, frmIDs = [], []
-    for filename in os.listdir(imgspath):
-        file, ext = os.path.splitext(filename)
-        flist = file.split('_')
-        if len(flist)==4 and ext in ImgFormat:
-            camID, frmID = flist[0], int(flist[-1])
-            imgpath = os.path.join(imgspath, filename)
-            img = cv2.imread(imgpath)
-            
-            if camID==CamerType:
-                imgs.append(img)
-                frmIDs.append(frmID)
-
-    if len(frmIDs):
-        indice = np.argsort(np.array(frmIDs))
-        imgs = [imgs[i] for i in indice]
-        
-    return imgs
-    
-    
-    
-    pass
-    
-    
 
+'''调用tracking()函数，利用本地跟踪算法获取各目标轨迹，可以比较本地跟踪算法与现场跟踪算法的区别。'''
 def init_tracker(tracker_yaml = None, bs=1):
     """
     Initialize tracker for object tracking during prediction.
@@ -177,38 +81,45 @@ def tracking(bboxes, ffeats):
             
     return TrackBoxes, TracksDict
 
+def read_imgs(imgspath, CamerType):
+    '''
+    inputs:
+        imgspath；序列图像地址
+        CamerType：相机类型，0：后摄，1：前摄
+    outputs：
+        imgs：图像序列
+    功能：
+        根据CamerType类型读取imgspath文件夹中的图像，并根据帧索引进行排序。
+        do_tracking()中调用该函数，实现（1）读取imgs并绘制各目标轨迹框；（2）获取subimgs
+    '''
+    imgs, frmIDs = [], []
+    for filename in os.listdir(imgspath):
+        file, ext = os.path.splitext(filename)
+        flist = file.split('_')
+        if len(flist)==4 and ext in ImgFormat:
+            camID, frmID = flist[0], int(flist[-1])
+            imgpath = os.path.join(imgspath, filename)
+            img = cv2.imread(imgpath)
+            
+            if camID==CamerType:
+                imgs.append(img)
+                frmIDs.append(frmID)
 
-
+    if len(frmIDs):
-def do_tracker_tracking(fpath, save_dir):  
+        indice = np.argsort(np.array(frmIDs))
-    bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
+        imgs = [imgs[i] for i in indice]
-    tboxes, feats_dict = tracking(bboxes, ffeats)
-    
-    CamerType = os.path.basename(fpath).split('_')[0]
-    dirname  = os.path.split(os.path.split(fpath)[0])[1]
-    if  CamerType == '1':
-        vts = doFrontTracks(tboxes, feats_dict)
-        vts.classify()
         
-        plt = plot_frameID_y2(vts)
+    return imgs
-        plt.savefig('front_y2.png')  
-        # plt.close()
-    elif CamerType == '0':
-        vts = doBackTracks(tboxes, feats_dict)
-        vts.classify()
-        
-        filename = dirname+'_' + CamerType
-        edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
-        draw_all_trajectories(vts, edgeline, save_dir, filename)
-    else:
-        print("Please check data file!")
-    
-
 
 def do_tracking(fpath, savedir, event_name='images'):
     '''
-      fpath: 算法各模块输出的data文件地址，匹配；
+    args:
-      savedir: 对 fpath 各模块输出的复现；
+        fpath: 算法各模块输出的data文件地址，匹配；
-      分析具体视频时，需指定 fpath 和 savedir
+        savedir: 对 fpath 各模块输出的复现；
+        分析具体视频时，需指定 fpath 和 savedir
+    outputs:
+        img_tracking：目标跟踪轨迹、本地轨迹分析算法的轨迹对比图
+        abimg：现场轨迹分析算法、轨迹选择输出的对比图
     '''
     # 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'
@@ -231,8 +142,10 @@ def do_tracking(fpath, savedir, event_name='images'):
     bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(fpath)
     tracking_output_boxes, _ = read_tracking_output(tracking_output_path)
     
+    '''1.2 利用本地跟踪算法生成各商品轨迹'''
+    # trackerboxes, tracker_feat_dict = tracking(bboxes, ffeats)
     
-    '''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'''
+    '''1.3 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 = os.path.join(savedir, event_name)
     subimg_dir = os.path.join(savedir, event_name + '_subimgs')
     if not os.path.exists(save_dir):
@@ -241,8 +154,6 @@ def do_tracking(fpath, savedir, event_name='images'):
          os.makedirs(subimg_dir)
 
 
-    
-
     '''2. 执行轨迹分析, 保存轨迹分析前后的对比图示'''
     traj_graphic = event_name + '_' + CamerType
     if  CamerType == '1':
@@ -344,24 +255,30 @@ def do_tracking(fpath, savedir, event_name='images'):
 
 def tracking_simulate(eventpath, savepath):
     '''args:
-        eventpath: 时间文件夹
+        eventpath: 事件文件夹
         savepath: 存储文件夹
+        遍历eventpath
     '''
 
-    '''1. 获取事件名'''
+# =============================================================================
-    event_names = os.path.basename(eventpath).strip().split('_')
+#     '''1. 获取事件名'''
-    if len(event_names)==2 and len(event_names[1])>=8:
+#     event_names = os.path.basename(eventpath).strip().split('_')
-        enent_name = event_names[1]
+#     if len(event_names)==2 and len(event_names[1])>=8:
-    elif len(event_names)==2 and len(event_names[1])==0:
+#         enent_name = event_names[1]
-        enent_name = event_names[0]
+#     elif len(event_names)==2 and len(event_names[1])==0:
-    else:
+#         enent_name = event_names[0]
-        return
+#     else:
+#         return
+# =============================================================================
+    
+    enent_name =  os.path.basename(eventpath)[:15]
     
     '''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
+        if filename.find("track.data") < 0: continue
+        
         fpath = os.path.join(eventpath, filename)
         if not os.path.isfile(fpath): continue
         
@@ -451,7 +368,7 @@ def main_loop():
         '''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)
+                tracking_simulate(eventpath, savepath)
             except Exception as e:
                 print(f'Error! {eventpath}, {e}')
 
@@ -462,29 +379,29 @@ def main_loop():
 
 def main():
     '''
-        eventpath: data文件地址，该 data 文件包括 Pipeline 各模块输出
+        eventPaths: 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。
+        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'
+    eventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_3'
-    SavePath = r'D:\contrast\dataset\result'
+    savePath = r'D:\contrast\dataset\result'
     k=0
-    for pathname in os.listdir(EventPaths):
+    for pathname in os.listdir(eventPaths):
-        # pathname = "20240723-094731_6903148242797"
+        pathname = "20240723-163121_6925282237668"
-        
+                
-        eventpath = os.path.join(EventPaths, pathname)
+        eventpath = os.path.join(eventPaths, pathname)
-        savepath = os.path.join(SavePath, pathname)
+        savepath = os.path.join(savePath, pathname)
         if not os.path.exists(savepath):
             os.makedirs(savepath) 
         
-        # tracking_simulate(eventpath, savepath)
+        tracking_simulate(eventpath, savepath)
-        try:
+        # try:
-            tracking_simulate(eventpath, savepath)
+        #     tracking_simulate(eventpath, savepath)
-        except Exception as e:
+        # except Exception as e:
-            print(f'Error! {eventpath}, {e}')
+        #     print(f'Error! {eventpath}, {e}')
         
-        # k += 1
+        k += 1
-        # if k==10:
+        if k==1:
-        #     break
+            break
         
     
 if __name__ == "__main__":  

tracking/shopcart/cart_tempt/说明.txt

@@ -0,0 +1,6 @@
+5幅图：
+incart.png
+outcart.png
+incart_ftmp.png
+outcart_ftmp.png
+cartboarder.png

tracking/shopcart/carttempt.py

@@ -36,10 +36,10 @@ def temp_add_boarder():
 
     
 def create_front_temp():
-    image = cv2.imread("image_front.png")
+    image = cv2.imread("./iCart4/b.png")
     Height, Width = image.shape[:2]
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    thresh, binary = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY_INV)
+    thresh, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
     board = cv2.bitwise_not(binary)
     contours, _ = cv2.findContours(board, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
     
@@ -48,12 +48,12 @@ def create_front_temp():
         img = np.zeros((Height, Width), dtype=np.uint8)
         cv2.drawContours(img, [cnt], -1, 255, 3)
         k += 1
-        cv2.imwrite(f"fronttemp_{k}.png", img)
+        cv2.imwrite(f"./iCart4/back{k}.png", img)
     
     imgshow = cv2.drawContours(image, contours, -1, (0,255,0), 3)
-    cv2.imwrite("board_ftmp_line.png", imgshow)
+    cv2.imwrite("./iCart4/board_back_line.png", imgshow)
     
-    # cv2.imwrite("4.png", board)
+    # cv2.imwrite("./iCart4/4.png", board)
     # cv2.imwrite("1.png", gray)
     # cv2.imwrite("2.png", binary)
 

tracking/time_test.py

@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug 13 09:39:42 2024
+
+@author: ym
+"""
+import os
+import time
+import datetime
+import numpy as np
+import sys
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import extract_data, read_weight_timeConsuming
+
+
+
+def main():
+    directory = r"\\192.168.1.28\share\测试_202406\0821\images"
+    
+    TimeConsuming = []
+    DayHMS = []
+    for root, dirs, files in os.walk(directory):   
+        if root.find('20240821') == -1: continue       
+        for name in files:
+            if name.find('process.data') == -1: continue
+            datename = os.path.basename(root)[:15]
+
+            fpath = os.path.join(root, name)
+            WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(fpath)
+            try:
+                t1 = ProcessTimeDict['algroDoStart']           # 算法处理的第一帧图像时间
+                t2 = ProcessTimeDict['breakinFirst']           # 第一次入侵时间
+                t3 = ProcessTimeDict['algroLastFrame']         # 算法处理的最后一帧图像时间
+                t4 = ProcessTimeDict['breakinLast']            # 最后一次入侵时间
+                t5 = ProcessTimeDict['weightStablityTime']     # 重力稳定时间
+                wv = ProcessTimeDict['weightValue']            # 重力值
+                t6 = ProcessTimeDict['YoloResnetTrackerEnd']   # Yolo、Resnet、tracker执行结束时间
+                t7 = ProcessTimeDict['trackingEnd']            # 轨迹分析结束时间
+                t8 = ProcessTimeDict['contrastEnd']            # 比对结束时间
+                t9 = ProcessTimeDict['algroStartToEnd']        # 算法从开始至结束时间 
+                t10 = ProcessTimeDict['weightstablityToEnd']   # 重力稳定至算法结束时间
+                t11 = ProcessTimeDict['frameEndToEnd']         # 最后一帧图像至算法结束时间
+                
+                TimeConsuming.append((t1, t2, t3, t4, t5, wv, t6, t7, t8, t9, t10, t11))
+                DayHMS.append(datename)
+            except Exception as e:
+                print(f'Error! {datename}, {e}')
+    
+    TimeConsuming = np.array(TimeConsuming, dtype = np.int64)
+    
+    TimeTotal = np.concatenate((TimeConsuming, 
+                                TimeConsuming[:,4][:, None] - TimeConsuming[:,0][:, None], 
+                                TimeConsuming[:,4][:, None] - TimeConsuming[:,2][:, None]), axis=1)
+    
+    tt = TimeTotal[:, 3]==0
+    
+    TimeTotal0 = TimeTotal[tt]
+    DayHMS0 = [DayHMS[ti] for i, ti in enumerate(tt) if ti]  
+    
+    TimeTotalMinus = TimeTotal[TimeTotal[:, 5]<0]
+    TimeTotalAdd = TimeTotal[TimeTotal[:, 5]>=0]
+    
+    TimeTotalAdd0 = TimeTotalAdd[TimeTotalAdd[:,3] == 0]
+    TimeTotalAdd1 = TimeTotalAdd[TimeTotalAdd[:,3] != 0]
+    
+    TimeTotalMinus0 = TimeTotalMinus[TimeTotalMinus[:,3] == 0]
+    TimeTotalMinus1 = TimeTotalMinus[TimeTotalMinus[:,3] != 0]
+    
+    print(f"Total number is {len(TimeConsuming)}")
+
+    
+if __name__ == "__main__":
+    main()
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

tracking/trackers/bot_sort.py

@@ -163,7 +163,7 @@ class BOTSORT(BYTETracker):
             '''1. reid 相似度阈值，低于该值的两 boxes 图像不可能是同一对象，需要确定一个合理的可信阈值
                2. iou 的约束为若约束，故 iou_dists 应设置为较大的值
             '''
-            emb_dists_mask = (emb_dists > 0.65)
+            emb_dists_mask = (emb_dists > 0.9)
             iou_dists[emb_dists_mask] = 1
             emb_dists[iou_dists_mask] = 1
   

tracking/trackers/reid/resnet_pre_lc.py

@@ -0,0 +1,462 @@
+import torch
+import torch.nn as nn
+from tools.config import config as conf
+
+try:
+    from torch.hub import load_state_dict_from_url
+except ImportError:
+    from torch.utils.model_zoo import load_url as load_state_dict_from_url
+# from .utils import load_state_dict_from_url
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
+           'wide_resnet50_2', 'wide_resnet101_2']
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
+    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
+    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class SpatialAttention(nn.Module):
+    def __init__(self, kernel_size=7):
+        super(SpatialAttention, self).__init__()
+
+        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
+        padding = 3 if kernel_size == 7 else 1
+
+        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = torch.mean(x, dim=1, keepdim=True)
+        max_out, _ = torch.max(x, dim=1, keepdim=True)
+        x = torch.cat([avg_out, max_out], dim=1)
+        x = self.conv1(x)
+        return self.sigmoid(x)
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None, cam=False, bam=False):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        self.cam = cam
+        self.bam = bam
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+        if self.cam:
+            if planes == 64:
+                self.globalAvgPool = nn.AvgPool2d(56, stride=1)
+            elif planes == 128:
+                self.globalAvgPool = nn.AvgPool2d(28, stride=1)
+            elif planes == 256:
+                self.globalAvgPool = nn.AvgPool2d(14, stride=1)
+            elif planes == 512:
+                self.globalAvgPool = nn.AvgPool2d(7, stride=1)
+
+            self.fc1 = nn.Linear(in_features=planes, out_features=round(planes / 16))
+            self.fc2 = nn.Linear(in_features=round(planes / 16), out_features=planes)
+            self.sigmod = nn.Sigmoid()
+        if self.bam:
+            self.bam = SpatialAttention()
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        if self.cam:
+            ori_out = self.globalAvgPool(out)
+            out = out.view(out.size(0), -1)
+            out = self.fc1(out)
+            out = self.relu(out)
+            out = self.fc2(out)
+            out = self.sigmod(out)
+            out = out.view(out.size(0), out.size(-1), 1, 1)
+            out = out * ori_out
+
+        if self.bam:
+            out = out*self.bam(out)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
+    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
+    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
+    # This variant is also known as ResNet V1.5 and improves accuracy according to
+    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None, cam=False, bam=False):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.)) * groups
+        self.cam = cam
+        self.bam = bam
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+        if self.cam:
+            if planes == 64:
+                self.globalAvgPool = nn.AvgPool2d(56, stride=1)
+            elif planes == 128:
+                self.globalAvgPool = nn.AvgPool2d(28, stride=1)
+            elif planes == 256:
+                self.globalAvgPool = nn.AvgPool2d(14, stride=1)
+            elif planes == 512:
+                self.globalAvgPool = nn.AvgPool2d(7, stride=1)
+
+            self.fc1 = nn.Linear(planes * self.expansion, round(planes / 4))
+            self.fc2 = nn.Linear(round(planes / 4), planes * self.expansion)
+            self.sigmod = nn.Sigmoid()
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        if self.cam:
+            ori_out = self.globalAvgPool(out)
+            out = out.view(out.size(0), -1)
+            out = self.fc1(out)
+            out = self.relu(out)
+            out = self.fc2(out)
+            out = self.sigmod(out)
+            out = out.view(out.size(0), out.size(-1), 1, 1)
+            out = out * ori_out
+        out += identity
+        out = self.relu(out)
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers, num_classes=conf.embedding_size, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
+                 norm_layer=None, scale=0.75):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, int(64*scale), layers[0])
+        self.layer2 = self._make_layer(block, int(128*scale), layers[1], stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block, int(256*scale), layers[2], stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block, int(512*scale), layers[3], stride=2,
+                                       dilate=replace_stride_with_dilation[2])
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(int(512 * block.expansion*scale), num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+                            self.base_width, previous_dilation, norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=self.groups,
+                                base_width=self.base_width, dilation=self.dilation,
+                                norm_layer=norm_layer))
+        return nn.Sequential(*layers)
+
+    def _forward_impl(self, x):
+        # See note [TorchScript super()]
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        # print('poolBefore', x.shape)
+        x = self.avgpool(x)
+        # print('poolAfter', x.shape)
+        x = torch.flatten(x, 1)
+        # print('fcBefore',x.shape)
+        x = self.fc(x)
+
+        # print('fcAfter',x.shape)
+
+        return x
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+
+# def _resnet(arch, block, layers, pretrained, progress, **kwargs):
+#     model = ResNet(block, layers, **kwargs)
+#     if pretrained:
+#         state_dict = load_state_dict_from_url(model_urls[arch],
+#                                               progress=progress)
+#         model.load_state_dict(state_dict, strict=False)
+#     return model
+def _resnet(arch, block, layers, pretrained, progress, **kwargs):
+    model = ResNet(block, layers, **kwargs)
+    if pretrained:
+        state_dict = load_state_dict_from_url(model_urls[arch],
+                                              progress=progress)
+
+        src_state_dict = state_dict
+        target_state_dict = model.state_dict()
+        skip_keys = []
+        # skip mismatch size tensors in case of pretraining
+        for k in src_state_dict.keys():
+            if k not in target_state_dict:
+                continue
+            if src_state_dict[k].size() != target_state_dict[k].size():
+                skip_keys.append(k)
+        for k in skip_keys:
+            del src_state_dict[k]
+        missing_keys, unexpected_keys = model.load_state_dict(src_state_dict, strict=False)
+
+    return model
+
+
+def resnet14(pretrained=True, progress=True, **kwargs):
+    r"""ResNet-14 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet18', BasicBlock, [2, 1, 1, 2], pretrained, progress,
+                   **kwargs)
+
+
+def resnet18(pretrained=True, progress=True, **kwargs):
+    r"""ResNet-18 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
+                   **kwargs)
+
+
+def resnet34(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-34 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet50(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-50 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet101(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-101 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnet152(pretrained=False, progress=True, **kwargs):
+    r"""ResNet-152 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
+                   **kwargs)
+
+
+def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
+    r"""ResNeXt-50 32x4d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 4
+    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
+                   pretrained, progress, **kwargs)
+
+
+def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
+    r"""ResNeXt-101 32x8d model from
+    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['groups'] = 32
+    kwargs['width_per_group'] = 8
+    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
+                   pretrained, progress, **kwargs)
+
+
+def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
+    r"""Wide ResNet-50-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
+                   pretrained, progress, **kwargs)
+
+
+def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
+    r"""Wide ResNet-101-2 model from
+    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
+
+    The model is the same as ResNet except for the bottleneck number of channels
+    which is twice larger in every block. The number of channels in outer 1x1
+    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
+    channels, and in Wide ResNet-50-2 has 2048-1024-2048.
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    kwargs['width_per_group'] = 64 * 2
+    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
+                   pretrained, progress, **kwargs)

tracking/tracking_test.py

@@ -107,6 +107,10 @@ def have_tracked():
                 
                 plt.savefig(savedir)  
                 plt.close()
+                
+                edgeline = cv2.imread("./shopcart/cart_tempt/board_ftmp_line.png")
+                img_tracking = draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=True)
+                
             else:
                 vts = doBackTracks(bboxes, TracksDict)
                 vts.classify()
@@ -114,7 +118,7 @@ def have_tracked():
                 
                 save_subimgs(vts, file, TracksDict)
                 edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
-                draw_all_trajectories(vts, edgeline, save_dir, filename)
+                img_tracking = draw_all_trajectories(vts, edgeline, save_dir, file)
         print(file+f" need time: {gt.dt:.2f}s")
 
         k += 1

tracking/utils/drawtracks.py

@@ -114,7 +114,7 @@ def draw_all_trajectories(vts, edgeline, save_dir, file, draw5p=False):
         img = edgeline.copy()
         img = draw5points(track, img)
 
-        pth = trackpth.joinpath(f"{file}_{track.tid}.png")        
+        pth = trackpth.joinpath(f"{file}_{track.tid}_.png")        
         cv2.imwrite(str(pth), img)
         
     # for track in vts.Residual:
@@ -307,11 +307,13 @@ def draw5points(track, img):
     
     
     '''=============== 最小轨迹长度索引 ===================='''
-    if track.isBorder:
+    trajlens = [int(t) for t in track.trajrects_wh]
+    if track.isCornpoint:
         idx = 0
     else:
         idx = trajlens.index(min(trajlens))
     
+    
     '''=============== PCA ===================='''
     if trajlens[idx] > 12:
         X = cornpoints[:, 2*idx:2*(idx+1)]

tracking/utils/read_data.py

@@ -9,7 +9,8 @@ func: extract_data()
 import numpy as np
 import re
 import os
-
+from collections import OrderedDict
+import matplotlib.pyplot as plt
 
 
 
@@ -206,19 +207,130 @@ def read_deletedBarcode_file(filePth):
     return all_list
 
 
+def read_weight_timeConsuming(filePth):
+    WeightDict, SensorDict, ProcessTimeDict = OrderedDict(), OrderedDict(), OrderedDict()
+    
+    with open(filePth, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        for i, line in enumerate(lines):
+            line = line.strip()
+            
+            if line.find(':') < 0: continue
+            if line.find("Weight") >= 0: 
+                label = "Weight"
+                continue
+            if line.find("Sensor") >= 0: 
+                label = "Sensor"
+                continue
+            if line.find("processTime") >= 0: 
+                label = "ProcessTime"
+                continue
+            
+            keyword = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            if label == "Weight":
+                WeightDict[keyword] = float(value.strip(','))
+            if label == "Sensor":
+                SensorDict[keyword] = [float(s) for s in value.split(',') if len(s)]
+            if label == "ProcessTime":
+                ProcessTimeDict[keyword] = float(value.strip(','))
+  
+    # print("Done!")
+    return WeightDict, SensorDict, ProcessTimeDict
+            
+                
+def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
+    
+    wtime, wdata = [], []
+    stime, sdata = [], []
+    for key, value in WeightDict.items():
+        wtime.append(int(key))
+        wdata.append(value)
+    
+    for key, value in SensorDict.items():
+        if len(value) != 9: continue
+        
+        stime.append(int(key))
+        sdata.append(np.array(value))
+        
+    static_range = []
+    dynamic_range = []
+    windth = 8
+    nw = len(wdata)
+    assert(nw) >= 8, "The num of weight data is less than 8!"
+    
+    i1, i2 = 0, 7
+    while i2 < nw:
+        data = wdata[i1:(i2+1)]
+        max(data) - min(data)
 
+        if i2<7:
+            i1 = 0
+        else:
+            i1 = i2-windth
+
+    min_t = min(wtime + stime)
+    wtime = [t-min_t for t in wtime]
+    stime = [t-min_t for t in stime]
+    
+    max_t = max(wtime + stime)
+    
+    fig = plt.figure(figsize=(16, 12))
+    gs = fig.add_gridspec(2, 1,  left=0.1, right=0.9, bottom=0.1, top=0.9,
+                      wspace=0.05, hspace=0.15)
+    # ax1, ax2 = axs
+    
+    ax1 = fig.add_subplot(gs[0,0])
+    ax2 = fig.add_subplot(gs[1,0])
+    
+    ax1.plot(wtime, wdata, 'b--', linewidth=2 )
+    for i in range(9):
+        ydata = [s[i] for s in sdata]
+        ax2.plot(stime, ydata, linewidth=2 )
+    
+    ax1.grid(True), ax1.set_xlim(0, max_t), ax1.set_title('Weight')
+    ax1.set_label("(Time: ms)")
+    # ax1.legend()
+    
+    ax2.grid(True), ax2.set_xlim(0, max_t), ax2.set_title('IMU')
+    # ax2.legend()
+    
+    plt.show()
+    
+    
+    
+        
+    
+    
+def main(file_path):
+    WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
+    plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
+
+           
+            
 
 if __name__ == "__main__":    
-    files_path = 'D:/contrast/dataset/1_to_n/709/20240709-112658_6903148351833/'  
     
-    # 遍历目录下的所有文件和目录  
+    files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'  
+    k = 0
     for filename in os.listdir(files_path):    
-        filename = '1_track.data'
+        filename = 'process.data'
+        
         file_path = os.path.join(files_path, filename)
         if os.path.isfile(file_path) and filename.find("track.data")>0:
             extract_data(file_path)
- 
+        
-            print("Done")
+        if os.path.isfile(file_path) and filename.find("process.data")>=0:
+            main(file_path)
+        
+        k += 1
+        if k == 1:
+            break
+            
+            
+            
+        # print("Done")
 
     
     

tracking/utils/videot.py

@@ -14,38 +14,23 @@ import cv2
 # import sys
 # from scipy.spatial.distance import cdist
 
-VideoFormat = ['.mp4', '.avi']
+VideoFormat = ['.mp4', '.avi', '.ts']
-def video2imgs(videopath, savepath):
+def video2imgs(videof, imgdir):
-    k = 0
+    cap = cv2.VideoCapture(videof)
-    have = False
+    i = 0
-    for filename in os.listdir(videopath):
+    while True:
-        file, ext = os.path.splitext(filename)
+        ret, frame = cap.read()
-        if ext not in VideoFormat:
+        if not ret:
-            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
+        imgp = os.path.join(imgdir, f"{i}.png")
+        i += 1
+        cv2.imwrite(imgp, frame)
+        
+        if i == 400:
+            break
+    cap.release()
+    
+    print(os.path.basename(videof) + f" haved resolved")
 
 def videosave(bboxes, videopath="100_1688009697927.mp4"):
 
@@ -95,10 +80,30 @@ def videosave(bboxes, videopath="100_1688009697927.mp4"):
     cap.release()
     
 def main():
-    videopath = r'C:\Users\ym\Desktop'
+    videopath = r'\\192.168.1.28\share\测试_202406\0822\A_1724314806144'
-    savepath = r'C:\Users\ym\Desktop'
+    savepath = r'D:\badvideo'
-    video2imgs(videopath, savepath)
+    # video2imgs(videopath, savepath)
-    
+    k = 0
+    for filename in os.listdir(videopath):
+        filename = "20240822-163506_88e6409d-f19b-4e97-9f01-b3fde259cbff.ts"
+        
+        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)
+
+        videof = os.path.join(videopath, filename)
+        video2imgs(videof, imgdir)
+        
+        k += 1
+        if k == 1:
+            break
+
     
     
 if __name__ == '__main__':

tracking/说明文档.txt

@@ -0,0 +1,35 @@
+tracking_test.py
+	have_tracked():
+		轨迹分析测试。遍历track_reid.py输出的文件夹trackdict下的所有.pkl文件。
+
+time_test.py
+    统计Pipeline整体流程中各模块耗时
+	
+module_analysis.py
+	main(): 
+		遍历文件夹下的每一个子文件夹，对子文件夹执行tracking_simulate() 函数；
+	
+	main_loop()：
+		(1) 根据 deletedBarcode.txt 生成事件对，并利用事件对生成存储地址
+		(2) 调用 tracking_simulate() 函数
+	
+	tracking_simulate(eventpath, savepath)：
+		(1) 根据event_names获取事件名enent_name
+		(2) 遍历并执行 eventpath 文件夹下的 0_track.data、1_track.data 文件，并调用do_tracking() 执行
+		(3) 将前后摄、本地与现场，工8幅子图合并为1幅大图。
+	
+	do_tracking(fpath, savedir, event_name='images')
+	
+enentmatch.py
+	1:n 模拟测试，have Deprecated! 
+contrast_analysis.py
+	1:n 现场测试评估。
+	main():
+		循环读取不同文件夹中的 deletedBarcode.txt，合并评估。
+	main1():
+		指定deletedBarcode.txt进行1:n性能评估
+		
+feat_select.py
+	以下两种特征选择策略下的比对性能比较
+	(1) 现场算法前后摄特征组合；
+	(2) 本地算法优先选择前摄特征；