modify pipeline.py

contrast/event_test.py

@@ -171,16 +171,6 @@ def calc_simil(event, stdfeat):
     return Similar
     
     
-    
-        
-        
-
-
-        
-    
-    
-
-
 
 
 
@@ -345,20 +335,6 @@ def main():
     
     simi_matrix()
     
-
-        
-        
-    
-
-    
-    
-    
-    
-    
-    
-        
-
-
     
 
 if __name__ == "__main__": 

contrast/genfeats.py

@@ -136,8 +136,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
             continue
 
         featpath = os.path.join(featPath, f"{bcd}.pickle")
-        # if os.path.isfile(featpath):
+        if os.path.isfile(featpath):
-        #     continue
+            continue
 
         stdbDict = {}
         t1 = time.time()

contrast/one2n_contrast.py

@@ -24,10 +24,15 @@ def init_eventdict(sourcePath, stype="data"):
         # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
         
         source_path = os.path.join(sourcePath, bname)
-        if not os.path.isdir(source_path): continue
+        if stype=="data":
-        
+            pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
-        pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
+            if not os.path.isdir(source_path) or os.path.isfile(pickpath):
-        if os.path.isfile(pickpath): continue
+                continue
+        if stype=="source":
+            pickpath = os.path.join(eventDataPath, bname)
+            if not os.path.isfile(source_path) or os.path.isfile(pickpath):
+                continue
+    
         try:
             event = ShoppingEvent(source_path, stype)
             
@@ -86,20 +91,28 @@ def simi_calc(event, o2nevt, typee=None):
         feat1 = event.back_feats
         feat2 = o2nevt.front_feats
     
+    '''自定义事件特征选择'''    
+    if typee==3:
+        feat1 = event.feats_compose
+        feat2 = o2nevt.feats_compose
+        
+    
     if len(feat1) and len(feat2):
         matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
         simi = np.mean(matrix)
     else:
         simi = None
-        
-
-    
     return simi
-    
 
-    
 
-def one2n_pr(evtDicts):
+def one2n_pr(evtDicts, pattern=1):
+    '''
+    pattern:
+        1: process.data 中记录的相似度
+        2: 根据 process.data 中标记的 type 选择特征计算
+        3: 以其它方式选择特征计算   
+    '''
+    
     tpevents, fnevents, fpevents, tnevents = [], [], [], []
     tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
     errorFile_one2n = []
@@ -111,22 +124,30 @@ def one2n_pr(evtDicts):
             barcode = ndict["barcode"]
             similar = ndict["similar"]
             typee = ndict["type"].strip()
-            
+        
-            o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
-            if len(o2n_evt)==1:
-                o2nevt = o2n_evt[0]
-            else:
-                continue
-            
-            simival = simi_calc(event, o2nevt, typee)
-            if simival==None: 
-                continue
-
             evt_names.append(nname)
             evt_barcodes.append(barcode)
-            evt_similars.append(simival)
             evt_types.append(typee)
-        
+            
+            if pattern==1:
+                evt_similars.append(similar)
+            
+            if pattern==2 or pattern==3:
+                o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
+                if len(o2n_evt)==1:
+                    o2nevt = o2n_evt[0]
+                else:
+                    continue
+                
+                if pattern==2:
+                    simival = simi_calc(event, o2nevt, typee)
+                
+                if pattern==3:
+                    simival = simi_calc(event, o2nevt, typee=pattern)
+
+                if simival==None: 
+                    continue
+                evt_similars.append(simival)
         
         if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
             and len(evt_similars)==len(evt_types) and len(evt_names)>0:  
@@ -205,30 +226,25 @@ def one2n_pr(evtDicts):
 def main():
     
     '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
-    # init_eventdict(eventSourcePath)
+    init_eventdict(eventSourcePath, stype="source")
  
     '''2. 读取事件字典 '''
     evtDicts = read_eventdict(eventDataPath)
     
     
     '''3. 1:n 比对事件评估 '''
-    fpevents = one2n_pr(evtDicts)
+    fpevents = one2n_pr(evtDicts, pattern=3)
 
     fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
     with open(fpErrFile, "w") as file:
         for item in fpevents:
             file.write(item + "\n")
-    
+       
-    
-    
-     
-        
-            
             
             
 if __name__ == '__main__':
-    eventSourcePath  = 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"
+    eventSourcePath  = 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\ShoppingDict_pkfile"
-    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"
+    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\contrast"
     
     eventDataPath = os.path.join(resultPath, "evtobjs")
     similPath = os.path.join(resultPath, "simidata")

contrast/one2one_contrast.py

@@ -194,6 +194,7 @@ def simi_calc(event, stdfeat):
     if len(evtfeat)==0 or len(stdfeat)==0:
         return None, None, None
     
+    
     evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
     stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
 
@@ -305,8 +306,8 @@ def one2SN_pr(evtList, evtDict, stdDict):
         barcodes, similars = [], []
         for stdbcd in bcd_selected:
             stdfeat = stdDict[stdbcd]
-            # simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
+            simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
-            simi_mean = calc_simil(event, stdfeat)
+            # simi_mean = calc_simil(event, stdfeat)
             
             ## 在event.front_feats和event.back_feats同时为空时，此处不需要保护
             # if simi_mean==None:
@@ -629,10 +630,8 @@ if __name__ == '__main__':
     stdBarcodePath = r"D:\全实时\source_data\bcdpath"
     stdFeaturePath = r"D:\全实时\source_data\stdfeats"
     
-    eventSourcePath  = [r"D:\全实时\result\pipeline\pipeline"]
+    eventSourcePath  = [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\ShoppingDict_pkfile"]
-    resultPath = r"D:\全实时\result\pipeline"
+    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\contrast"
-    
-
     eventDataPath = os.path.join(resultPath, "evtobjs")
     similPath = os.path.join(resultPath, "simidata")
     if not os.path.exists(eventDataPath): 
@@ -640,7 +639,7 @@ if __name__ == '__main__':
     if not os.path.exists(similPath): 
         os.makedirs(similPath)
 
-    test_one2one()
+    # test_one2one()
     
     test_one2SN()
 

contrast/utils/event.py

@@ -76,7 +76,7 @@ def array2list(bboxes):
 
 class ShoppingEvent:
     def __init__(self, eventpath, stype="data"):  
-        '''stype: str, 'source', 'data', '''
+        '''stype: str, 'source', 'data', 'realtime', 共三种 '''
         
         self.eventpath = eventpath
         self.evtname = str(Path(eventpath).stem)
@@ -167,20 +167,22 @@ class ShoppingEvent:
 
     
     def from_source_pkl(self, eventpath):
-        
         with open(eventpath, 'rb') as f:
             ShoppingDict = pickle.load(f)
-        
+          
-            
         self.eventpath = ShoppingDict["eventPath"]
         self.evtname = ShoppingDict["eventName"]
         self.barcode = ShoppingDict["barcode"]
-        
+                
+        if len(ShoppingDict["one2n"]):
+            self.one2n = ShoppingDict["one2n"]
+
         '''=========== path of image and video =========== '''
         self.back_videopath = ShoppingDict["backCamera"]["videoPath"]
         self.front_videopath = ShoppingDict["frontCamera"]["videoPath"]
         self.back_imgpaths = ShoppingDict["backCamera"]["imagePaths"]
         self.front_imgpaths = ShoppingDict["frontCamera"]["imagePaths"]
+                
         
         '''===========对应于 0/1_track.data ============================='''
         backdata, back_outdata = self.kerndata(ShoppingDict, "backCamera")

events/evt_rename.py

@@ -0,0 +1,111 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Jan  9 14:34:41 2025
+
+@author: ym
+"""
+import os
+def read_deletedBarcode_file(filePath):
+    with open(filePath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+
+        split_flag, all_list = False, []
+        dict, barcode_list, similarity_list =  {}, [], []
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
+        
+        for i, line in enumerate(clean_lines):
+            if line.endswith(','):  
+                line = line[:-1]
+                
+            stripped_line = line.strip()
+            if not stripped_line:
+                if len(barcode_list): dict['barcode'] = barcode_list
+                if len(similarity_list): dict['similarity'] = similarity_list
+                if len(dict): all_list.append(dict)
+                
+                split_flag = False
+                dict, barcode_list, similarity_list =  {}, [], []
+                continue
+            
+            if line.find(':')<0: continue
+            label = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            if label == 'SeqDir':
+                dict['SeqDir'] = value
+                dict['filetype'] = "deletedBarcode"
+            if label == 'Deleted':
+                dict['Deleted'] = value
+            if label == 'List':
+                split_flag = True
+                continue
+            if split_flag:
+                barcode_list.append(label)
+                similarity_list.append(value)
+        
+        if len(barcode_list): dict['barcode'] = barcode_list
+        if len(similarity_list): dict['similarity'] = similarity_list
+        if len(dict): all_list.append(dict)
+    return all_list
+
+
+def event_rename(path):
+    for filename in  os.listdir(path):
+        fpath = os.path.join(path, filename)
+        if os.path.isfile(fpath): continue
+        flist = filename.split("_")
+        
+        if len(flist)==3 and flist[-1].isdigit() and len(flist[-1])>=8:
+            print(f"{path}: renamed!")
+            return
+
+    dpath = os.path.join(path, "deletedBarcode.txt")
+    alist = read_deletedBarcode_file(dpath)
+    
+    input_events, getout_events = [], []
+    for filename in  os.listdir(path):
+        fpath = os.path.join(path, filename)
+        if os.path.isfile(fpath): continue
+        flist = filename.split("_")
+        
+        bcd = flist[-1]
+        if len(flist)==2 and flist[0].find("2024")==0 and len(bcd)==0:
+            bcds = [dt['Deleted'] for dt in  alist if dt['SeqDir'].strip()==filename]
+            if len(bcds)==1:
+                getout_events.append((filename, filename + bcds[0].strip()))
+                
+        if len(flist)==2 and flist[0].find("2024")==0 and len(bcd)>=10:            
+            input_events.append((filename, filename + '_' + bcd))
+    
+    events = (input_events, getout_events)
+    for evts in events:
+        for name_old, name_new in evts:
+            path_old = os.path.join(path, name_old)
+            path_new = os.path.join(path, name_new)
+            try:
+                os.rename(path_old, path_new)
+            except Exception as e:
+                print(f"发生错误：{e}")
+
+
+
+
+def main():
+    
+    path = r"\\192.168.1.28\share\测试视频数据以及日志\测试_202406\0910\images"
+    event_rename(path)
+    
+
+
+if __name__ == "__main__": 
+    main()
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

pipeline.py

@@ -5,67 +5,48 @@ Created on Sun Sep 29 08:59:21 2024
 @author: ym
 """
 import os
-import sys
+# import sys
 import cv2
 import pickle
-import argparse
 import numpy as np
 from pathlib import Path
-from track_reid import parse_opt
 from track_reid import yolo_resnet_tracker
-# FILE = Path(__file__).resolve()
-# ROOT = FILE.parents[0]  # YOLOv5 root directory
-# if str(ROOT) not in sys.path:
-#     sys.path.append(str(ROOT))  # add ROOT to PATH
-# ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
 from tracking.dotrack.dotracks_back import doBackTracks
 from tracking.dotrack.dotracks_front import doFrontTracks 
 from tracking.utils.drawtracks import plot_frameID_y2, draw_all_trajectories
 from utils.getsource import get_image_pairs, get_video_pairs
+from tracking.utils.read_data import read_similar
 
-def get_interbcd_inputenents():
-    bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
-    eventpath = r"\\192.168.1.28\share\测试_202406\0918"
 
-    barcodes = []
+def save_subimgs(imgdict, boxes, spath, ctype):
-    eventpaths = []
+    for i in range(len(boxes)):
-    for featname in os.listdir(bcdpath):
+        fid, bid = int(boxes[i, 7]), int(boxes[i, 8])
-        barcode, ext = os.path.splitext(featname)
+        if f"{fid}_{bid}" in imgdict.keys():
-        barcodes.append(barcode)
+            img = imgdict[f"{fid}_{bid}"]
-    
+            imgpath = spath / f"{ctype}_{fid}_{bid}.png"
-    input_enents = []    
+            cv2.imwrite(imgpath, img)
-    for root, dirs, files in os.walk(eventpath):
-        input_enent = [os.path.join(root, d) for d in dirs if d.split('_')[-1] in barcodes]
         
-        input_enents.extend(input_enent)
-        
-        
-    return input_enents
 
 def pipeline(        
         eventpath,
-        savepath = '',
+        savepath,
-        SourceType = "image",   # video
+        SourceType,
-        stdfeat_path = None 
+        weights
         ):
-    if SourceType == "video":
-        vpaths = get_video_pairs(eventpath)
-    elif SourceType == "image":
-        vpaths = get_image_pairs(eventpath)
     '''
     eventpath: 单个事件的存储路径
     
     '''
     
-    '''======== 函数 yolo_resnet_tracker() 的参数字典 ========'''
+    if SourceType == "video":
-    opt = parse_opt()
+        vpaths = get_video_pairs(eventpath)
-    optdict = vars(opt)
+    elif SourceType == "image":
+        vpaths = get_image_pairs(eventpath)
     
-    
+    optdict = {}
-    optdict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt' 
+    optdict["weights"] = weights
-    optdict["is_save_img"] = True
+
-    optdict["is_save_video"] = True
     
     event_tracks = []
     
@@ -76,21 +57,35 @@ def pipeline(
                                      and evtname.split('_')[-1].isdigit() else ''
     '''事件结果存储文件夹'''
     if not savepath:
-        savepath = Path(__file__).resolve().parents[0] / "evtresult"
+        savepath = Path(__file__).resolve().parents[0] / "events_result"
     
-    save_dir_event = Path(savepath) / evtname
+    savepath_pipeline = Path(savepath) / Path("Yolos_Tracking") / evtname
-    
+
-    pickpath = Path(savepath)/"pickfile"
-    if not pickpath.exists():
-        pickpath.mkdir(parents=True, exist_ok=True)
     
+    """ShoppingDict pickle 文件保存地址 """
+    savepath_spdict = Path(savepath) / "ShoppingDict_pkfile"
+    if not savepath_spdict.exists():
+        savepath_spdict.mkdir(parents=True, exist_ok=True)
+    pf_path = Path(savepath_spdict) / Path(str(evtname)+".pickle")
+
+    # if pf_path.exists():
+    #     return
 
     ShoppingDict = {"eventPath": eventpath, 
                     "eventName": evtname,
                     "barcode": barcode,
                     "eventType": '',   # "input", "output", "other"
                     "frontCamera": {}, 
-                    "backCamera": {}}
+                    "backCamera": {},
+                    "one2n": []
+                    }
+    
+    
+    procpath = Path(eventpath).joinpath('process.data')
+    if procpath.is_file():
+        SimiDict = read_similar(procpath)
+        ShoppingDict["one2n"] = SimiDict['one2n']
+
 
     for vpath in vpaths:
         '''相机事件字典构造'''
@@ -115,16 +110,20 @@ def pipeline(
         '''事件结果存储文件夹'''
         
         if isinstance(vpath, list):
-            save_dir_video = save_dir_event / Path("images") 
+            savepath_pipeline_imgs = savepath_pipeline / Path("images") 
         else:
-            save_dir_video = save_dir_event / Path(str(Path(vpath).stem))
+            savepath_pipeline_imgs = savepath_pipeline / Path(str(Path(vpath).stem))
-        if not save_dir_video.exists():
+        if not savepath_pipeline_imgs.exists():
-            save_dir_video.mkdir(parents=True, exist_ok=True)
+            savepath_pipeline_imgs.mkdir(parents=True, exist_ok=True)
+            
+        savepath_pipeline_subimgs = savepath_pipeline / Path("subimgs") 
+        if not savepath_pipeline_subimgs.exists():
+            savepath_pipeline_subimgs.mkdir(parents=True, exist_ok=True)
 
         
         '''Yolo + Resnet + Tracker'''
         optdict["source"] = vpath
-        optdict["save_dir"] = save_dir_video        
+        optdict["save_dir"] = savepath_pipeline_imgs        
         
         yrtOut = yolo_resnet_tracker(**optdict)
         
@@ -164,13 +163,30 @@ def pipeline(
             CameraEvent["tracking"] = vts
             ShoppingDict["frontCamera"] = CameraEvent
     
-    # pklpath = save_dir_event / "ShoppingDict.pkl"
+   
-    # with open(str(pklpath), 'wb') as f:
-    #     pickle.dump(ShoppingDict, f)    
-    pf_path = Path(pickpath) / Path(str(evtname)+".pkl")
     with open(str(pf_path), 'wb') as f:
         pickle.dump(ShoppingDict, f)
         
+        
+    for CamerType, vts in event_tracks:
+        if len(vts.tracks)==0: continue
+        if CamerType == 'front':
+            yolos = ShoppingDict["frontCamera"]["yoloResnetTracker"]
+            ctype = 1
+        if CamerType == 'back':
+            yolos = ShoppingDict["backCamera"]["yoloResnetTracker"]
+            ctype = 0
+            
+        imgdict = {}
+        for y in yolos:
+            imgdict.update(y["imgs"])
+ 
+        for track in vts.Residual:
+            if isinstance(track, np.ndarray):
+                save_subimgs(imgdict, track, savepath_pipeline_subimgs, ctype)
+            else:
+                save_subimgs(imgdict, track.boxes, savepath_pipeline_subimgs,  ctype)
+  
 
     '''轨迹显示模块'''
     illus = [None, None]
@@ -181,24 +197,23 @@ def pipeline(
             edgeline = cv2.imread("./tracking/shopcart/cart_tempt/board_ftmp_line.png")
             
             h, w = edgeline.shape[:2]
-            nh, nw = h//2, w//2
+            # nh, nw = h//2, w//2
-            edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
             
-            img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
+            img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True)
             illus[0] = img_tracking
-            
+
-            
             plt = plot_frameID_y2(vts)
-            plt.savefig(os.path.join(save_dir_event, "front_y2.png"))
+            plt.savefig(os.path.join(savepath_pipeline, "front_y2.png"))
-        
+            
         if CamerType == 'back':
             edgeline = cv2.imread("./tracking/shopcart/cart_tempt/edgeline.png")
             
             h, w = edgeline.shape[:2]
-            nh, nw = h//2, w//2
+            # nh, nw = h//2, w//2
-            edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
+            # edgeline = cv2.resize(edgeline, (nw, nh), interpolation=cv2.INTER_AREA)
             
-            img_tracking = draw_all_trajectories(vts, edgeline, save_dir_event, CamerType, draw5p=True)
+            img_tracking = draw_all_trajectories(vts, edgeline, savepath_pipeline, CamerType, draw5p=True)
             illus[1] = img_tracking
     
     illus = [im for im in illus if im is not None]
@@ -208,64 +223,25 @@ def pipeline(
             H, W = img_cat.shape[:2]
             cv2.line(img_cat, (int(W/2), 0), (int(W/2), int(H)), (128, 128, 255), 3)
             
-        trajpath = os.path.join(save_dir_event, "traj.png")
+        trajpath = os.path.join(savepath_pipeline, "trajectory.png")
         cv2.imwrite(trajpath, img_cat)
-
-
-def main_loop():
-    bcdpath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
-    eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1"
-    SourceType = "image"   # video, image
-
-    barcodes = []
-    input_enents = []  
-    output_events = []
-
-
-    '''1. 获得barcode标准特征集列表'''    
-    for featname in os.listdir(bcdpath):
-        barcode, ext = os.path.splitext(featname)
-        if not barcode.isdigit() or len(barcode)<=8 or ext != ".pickle" :
-            continue
-        barcodes.append(barcode)
     
-    '''2. 构造（放入事件，标准特征）对'''
-    for filename in os.listdir(eventpath):
-        '''barcode为时间文件夹的最后一个字段'''
-        bcd = filename.split('_')[-1]
-        
-        event_path = os.path.join(eventpath, filename)
-        stdfeat_path = None
-        if bcd in barcodes:
-            stdfeat_path = os.path.join(bcdpath, f"{bcd}.pickle")            
-            input_enents.append((event_path, stdfeat_path))
-
-    parmDict = {}
-    parmDict["SourceType"] = "image"
-    parmDict["savepath"] = r"D:\contrast\detect"
-    for eventpath, stdfeat_path in input_enents:
-        parmDict["eventpath"] = eventpath
-        parmDict["stdfeat_path"] = stdfeat_path
-        
-        pipeline(**parmDict)
-    
-
 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\算法全流程测试\202412\images"
+       
-    evtdir = Path(evtdir)    
     parmDict = {}
-    parmDict["savepath"] = r"D:\contrast\202412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"
     parmDict["SourceType"] = "video"   # video, image
-    parmDict["stdfeat_path"] = None
+    parmDict["savepath"] = 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"
-    
+    parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt' 
-    k = 0
+
-    errEvents = []
+    evtdir = Path(evtdir) 
+    k, errEvents = 0, []
     for item in evtdir.iterdir():
-        if item.is_dir():
+        if item.is_dir():            
-            # item = r"D:\exhibition\images\images2\images2"
+            # item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
             parmDict["eventpath"] = item
             # pipeline(**parmDict)
             
@@ -273,10 +249,9 @@ def main():
                 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:

track_reid.py

@@ -62,6 +62,7 @@ from hands.hand_inference import hand_pose
 
 from contrast.feat_extract.config import config as conf
 from contrast.feat_extract.inference import FeatsInterface
+
 ReIDEncoder = FeatsInterface(conf)
 
 IMG_FORMATS = '.bmp', '.dng', '.jpeg', '.jpg', '.mpo', '.png', '.tif', '.tiff', '.webp', '.pfm'  # include image suffixes
@@ -139,12 +140,8 @@ def init_trackers(tracker_yaml = None, bs=1):
 def yolo_resnet_tracker(
         weights=ROOT / 'yolov5s.pt',  # model path or triton URL
         source=ROOT / 'data/images',  # file/dir/URL/glob/screen/0(webcam)
-        
-        project=ROOT / 'runs/detect',  # save results to project/name
-        name='exp',  # save results to project/name
         save_dir = '',
-        
+        is_save_img = True,
-        is_save_img = False,
         is_save_video = True,
         
         tracker_yaml = "./tracking/trackers/cfg/botsort.yaml",
@@ -154,19 +151,9 @@ def yolo_resnet_tracker(
         max_det=1000,  # maximum detections per image
         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
 
-        view_img=False,  # show results
-        save_txt=False,  # save results to *.txt
-        save_csv=False,  # save results in CSV format
-        save_conf=False,  # save confidences in --save-txt labels
-        save_crop=False,  # save cropped prediction boxes
-        nosave=False,  # do not save images/videos
-        update=False,  # update all models
-        exist_ok=False,  # existing project/name ok, do not increment
-
         classes=None,  # filter by class: --class 0, or --class 0 2 3
         agnostic_nms=False,  # class-agnostic NMS
         augment=False,  # augmented inference
-        visualize=False,  # visualize features
         
         line_thickness=3,  # bounding box thickness (pixels)
         hide_labels=False,  # hide labels
@@ -208,7 +195,7 @@ def yolo_resnet_tracker(
 
         # Inference
         with dt[1]:
-            visualize = increment_path(project / Path(path).stem, mkdir=True) if visualize else False
+            # visualize = increment_path(project / Path(path).stem, mkdir=True) if visualize else False
             pred = model(im, augment=augment, visualize=False)
 
         # NMS
@@ -242,15 +229,16 @@ def yolo_resnet_tracker(
                     # trackerBoxes = np.concatenate([trackerBoxes, tracks], axis=0)
                     '''================== 1. 存储 dets/subimgs/features Dict ============='''
                     imgs, features = ReIDEncoder.inference(im0, tracks)
-                    featdict = {}
+                    imgdict, featdict = {}, {}
                     for ii, bid in enumerate(tracks[:, 8]):
                         featdict.update({f"{int(frameId)}_{int(bid)}": features[ii, :]})            # [f"feat_{int(bid)}"] = features[i, :]
+                        imgdict.update({f"{int(frameId)}_{int(bid)}": imgs[ii]})
     
                     frameDict = {"path": path, 
                                  "fid": int(frameId),
                                  "bboxes": det,
                                  "tboxes": tracks,
-                                 "imgs": imgs,
+                                 "imgs": imgdict,
                                  "feats": featdict} 
                     yoloResnetTracker.append(frameDict)
     

说明文档.txt

@@ -26,12 +26,56 @@
 
 4. 整体流程仿真
 	pipeline.py
-		SourceType: "image", "video", yolo+resent+tracker模块输入数据类型
+		需要指定是个变量：
+	(1) evtdir: 保存各个事件的文件夹
+	(2) SourceType: "image", "video", yolo+resent+tracker模块输入数据类型
+	(3) savepath: 结果数据保存位置
+	(4) weights: YoloV5 模型权重
+	
+	savepath
+		(1) Yolos_Tracking
+			eventname文件夹，保存yolo+resent+tracker模块输出，包括：
+				(1) images 或 {videoname}
+				(2) subimgs
+				(3) trajectory
+				(4) front_y2.png
+				(5) trajectory.png
+		(2) ShoppingDict_pkfile
+			保存 ShoppingDict 字典的 pickle 文件，可用于构造 ShoppingEvent 对象。
+			
+		(3) contrast
+			保存1:1、1:SN、1:n数据
+			
+		(4) error_events.txt
+	
+	事件字典结构：
+		ShoppingDict = {"eventPath": eventpath, 
+						"eventName": evtname,
+						"barcode": barcode,
+						"eventType": '',   # "input", "output", "other"
+						"frontCamera": {}, 
+						"backCamera": {},
+						"one2n": []
+						}
 		
-	保存为 pickle 文件，该文件可用于 ShoppingEvent 对象构建。
+			相机字典：			
-
+			CameraEvent = {"cameraType": '',   # "front", "back"
-
+						   "videoPath": '',
-
+						   "imagePaths": [],
+						   "yoloResnetTracker": [],
+						   "tracking": [],
+						   }
+				
+				yoloResnetTracker，列表，元素 "frameDict" 为有关帧数据的字典结构
+				frameDict = {"path": path, 
+							 "fid": int(frameId),
+							 "bboxes": det,
+							 "tboxes": tracks,
+							 "imgs": imgdict,
+							 "feats": featdict}
+				
+			
+