update one2n.py

contrast/feat_extract/inference.py

@@ -72,10 +72,11 @@ class FeatsInterface:
             new_img.paste(img, (paste_x, paste_y))
             
             patch = self.transform(new_img)            
-            if str(self.device) != "cpu":
-                patch = patch.to(device=self.device).half()
-            else:
             patch = patch.to(device=self.device)
+            # if str(self.device) != "cpu":
+            #     patch = patch.to(device=self.device).half()
+            # else:
+            #     patch = patch.to(device=self.device)
             
             patches.append(patch)
             if (i + 1) % self.batch_size == 0:

contrast/genfeats.py

@@ -120,8 +120,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
     
     # imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
     # featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
-    stdBarcodeDict = {}
+
-    stdBarcodeDict_ft16 = {}
     
     Encoder = FeatsInterface(conf)
     
@@ -168,9 +167,6 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
                 # feature_uint8, _ = ft16_to_uint8(feature_ft16)
                 feature_uint8 = (feature_ft16*128).astype(np.int8)
                 
-        except Exception as e:
-            print(f"Error accured at: {filename}, with Exception is: {e}")
-        
             '''================ 保存单个barcode特征 ================'''
             ##================== float32
             stdbDict["barcode"] = barcode
@@ -182,8 +178,9 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
             with open(featpath, 'wb') as f:
                 pickle.dump(stdbDict, f)
   
-        stdBarcodeDict[barcode] = feature
+        except Exception as e:
-        stdBarcodeDict_ft16[barcode] = feature_ft16
+            print(f"Error accured at: {filename}, with Exception is: {e}")
+
         
         t2 = time.time()
         print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")

contrast/one2n_contrast.py

@@ -24,7 +24,7 @@ def init_eventdict(sourcePath, stype="data"):
         # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
         
         source_path = os.path.join(sourcePath, bname)
-        if stype=="data":
+        if stype=="data" or stype=="realtime":
             pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
             if not os.path.isdir(source_path) or os.path.isfile(pickpath):
                 continue
@@ -33,6 +33,11 @@ def init_eventdict(sourcePath, stype="data"):
             if not os.path.isfile(source_path) or os.path.isfile(pickpath):
                 continue
             
+        evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_')
+        cont =  len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
+        if not cont:
+            continue
+
         try:
             event = ShoppingEvent(source_path, stype)
             
@@ -46,10 +51,10 @@ def init_eventdict(sourcePath, stype="data"):
         # if k==1:
         #     break
       
-    errfile = os.path.join(resultPath, 'error_events.txt')
+    # errfile = os.path.join(resultPath, 'error_events.txt')
-    with open(errfile, 'a', encoding='utf-8') as f:
+    # with open(errfile, 'a', encoding='utf-8') as f:
-        for line in errEvents:
+    #     for line in errEvents:
-            f.write(line + '\n')
+    #         f.write(line + '\n')
 
 def read_eventdict(eventDataPath):
     evtDict = {}
@@ -236,14 +241,22 @@ def one2n_pr(evtDicts, pattern=1):
 def main():
     
     '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
-    init_eventdict(eventSourcePath, stype="data")
+    init_eventdict(eventSourcePath, stype="source")  # 'source', 'data', 'realtime'
+    
+    
+    # for pfile in os.listdir(eventDataPath):
+    #     evt = os.path.splitext(pfile)[0].split('_')
+    #     cont =  len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
+    #     if not cont:
+    #         continue
+    
  
     '''2. 读取事件字典 '''
     evtDicts = read_eventdict(eventDataPath)
     
     
     '''3. 1:n 比对事件评估 '''
-    fpevents = one2n_pr(evtDicts, pattern=1)
+    fpevents = one2n_pr(evtDicts, pattern=2)
 
     fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
     with open(fpErrFile, "w") as file:
@@ -253,10 +266,10 @@ def main():
             
             
 if __name__ == '__main__':
-    eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"
+    eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12\ShoppingDict_pkfile"
-    resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing"
+    resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
     
-    eventDataPath = os.path.join(resultPath, "evtobjs")
+    eventDataPath = os.path.join(resultPath, "evtobjs_data")
     if not os.path.exists(eventDataPath): 
         os.makedirs(eventDataPath) 
         

contrast/one2one_contrast.py

@@ -419,7 +419,6 @@ def one2one_simi(evtList, evtDict, stdDict):
         '''================ float32、16、int8 精度比较与存储 ============='''
         # data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
  
-    
     return rltdata
     
 
@@ -520,12 +519,12 @@ def gen_eventdict(sourcePath, saveimg=True):
         pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
         if os.path.isfile(pickpath): continue
 
-        # event = ShoppingEvent(source_path, stype="data")        
+        # event = ShoppingEvent(source_path, stype=source_type)        
         # with open(pickpath, 'wb') as f:
         #     pickle.dump(event, f)
 
         try:
-            event = ShoppingEvent(source_path, stype="source")
+            event = ShoppingEvent(source_path, stype=source_type)
             # save_data(event, resultPath)
             
             with open(pickpath, 'wb') as f:
@@ -541,20 +540,19 @@ def gen_eventdict(sourcePath, saveimg=True):
 
             
     errfile = os.path.join(resultPath, 'error_events.txt')
-    with open(errfile, 'w', encoding='utf-8') as f:
+    # with open(errfile, 'w', encoding='utf-8') as f:
-        for line in errEvents:
+    #     for line in errEvents:
-            f.write(line + '\n')
+    #         f.write(line + '\n')
             
 
 def init_std_evt_dict():
     '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
     bcdList, event_spath = [], []
-    for evtpath in eventSourcePath:
+    for evtname in os.listdir(eventSourcePath):
-        for evtname in os.listdir(evtpath):
         bname, ext = os.path.splitext(evtname)
         
         ## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
-            fpath = os.path.join(evtpath, evtname)
+        fpath = os.path.join(eventSourcePath, evtname)
         if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
             evt = bname.split('_')
         elif os.path.isdir(fpath):
@@ -562,17 +560,15 @@ def init_std_evt_dict():
         else:
             continue
 
-            
         if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
             bcdList.append(evt[-1])       
-                event_spath.append(os.path.join(evtpath, evtname))
+            event_spath.append(fpath)
                
     '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ===========''' 
     bcdSet = set(bcdList)
     gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
     print("stdFeats have generated and saved!")
 
-    
     '''==== 2. 生成事件字典, 只需运行一次 ===============''' 
     gen_eventdict(event_spath)
     print("eventList have generated and saved!")
@@ -584,7 +580,7 @@ def test_one2one():
     '''1:1性能评估''' 
     
     # 1. 只需运行一次，生成事件字典和相应的标准特征库字典
-    init_std_evt_dict()
+    # init_std_evt_dict()
     
     # 2. 基于事件barcode集和标准库barcode交集构造事件集合
     evtList, evtDict, stdDict = build_std_evt_dict()
@@ -598,7 +594,7 @@ def test_one2SN():
     '''1:SN性能评估''' 
     
     # 1. 只需运行一次，生成事件字典和相应的标准特征库字典
-    init_std_evt_dict()
+    # init_std_evt_dict()
     
     # 2. 事件barcode集和标准库barcode求交集
     evtList, evtDict, stdDict = build_std_evt_dict()
@@ -612,7 +608,7 @@ if __name__ == '__main__':
         (1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
         (2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
         (3) stdFeaturePath: 比对标准特征集特征存储地址
-        (4) eventSourcePath: 事件地址
+        (4) eventSourcePath: 事件地址, 包含data文件的文件夹或 Yolo-Resnet-Tracker输出的Pickle文件父文件夹
         (5) resultPath: 结果存储地址
         (6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
         (7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
@@ -622,19 +618,33 @@ if __name__ == '__main__':
     # stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
     # stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592"
     
-    # eventSourcePath  = [r'D:\exhibition\images\20241202']
+    # eventSourcePath  = r'D:\exhibition\images\20241202'
-    # eventSourcePath  = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
+    # eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"
     
+    # stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
+    # stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\bcdpath"
+    # stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\stdfeats"
     
-    stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
+    stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
-    stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\bcdpath"
+    stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\bcdpath"
-    stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\stdfeats"
+    stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\stdfeats"
 
-    eventSourcePath  = [r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"]
+    if not os.path.exists(stdBarcodePath): 
+        os.makedirs(stdBarcodePath) 
+    if not os.path.exists(stdFeaturePath): 
+        os.makedirs(stdFeaturePath)
     
-    resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\evtobjs"
+    '''
-    eventDataPath = os.path.join(resultPath, "evtobjs")
+    source_type: 
-    similPath = os.path.join(resultPath, "simidata")
+        "source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件
+        "data": eventSourcePath 为 包含 data 文件的文件夹
+    '''
+    source_type = 'realtime'   # 'source', 'data', 'realtime'
+    eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
+    
+    resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
+    eventDataPath = os.path.join(resultPath, "evtobjs_data")
+    similPath = os.path.join(resultPath, "simidata_data")
     if not os.path.exists(eventDataPath): 
         os.makedirs(eventDataPath) 
     if not os.path.exists(similPath): 

contrast/onsite_contrast_pr.py

@@ -1,6 +1,11 @@
+
 # -*- coding: utf-8 -*-
 """
 Created on Wed Sep 11 11:57:30 2024
+    contrast_pr:
+        直接利用测试数据中的 data 文件进行 1:1、1:SN、1:n 性能评估
+        
+    test_compare:
         永辉现场试验输出数据的 1:1 性能评估
         适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
 @author: ym
@@ -147,6 +152,7 @@ def contrast_pr(paths):
     
     errorFile_one2one, errorFile_one2SN, errorFile_one2n = [], [], []
     
+    errorFile = []
     for path in evtpaths:
         barcode = path.stem.split('_')[-1]
         datapath = path.joinpath('process.data')
@@ -167,6 +173,10 @@ def contrast_pr(paths):
         one2SN = SimiDict['one2SN']
         one2n = SimiDict['one2n']
         
+        if len(one2one)+len(one2SN)+len(one2n) == 0:
+            errorFile.append(path.stem)
+            
+        
         '''================== 0.  1:1 ==================='''
         barcodes, similars = [], []
         for dt in one2one:
@@ -176,6 +186,8 @@ def contrast_pr(paths):
                 continue
             barcodes.append(dt['barcode'])
             similars.append(dt['similar'])
+        
+        
         if len(barcodes)==len(similars) and len(barcodes)!=0:
             ## 扫A放A, 扫A放B场景    
             simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode] 
@@ -466,15 +478,15 @@ def contrast_pr(paths):
     plt.show()
     
     
-    fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
+    # fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
-    with open(fpsnErrFile, "w") as file:
+    # with open(fpsnErrFile, "w") as file:
-        for item in fp_events:
+    #     for item in fp_events:
-            file.write(item + "\n")
+    #         file.write(item + "\n")
             
-    fpErrFile = str(paths.joinpath("one2n_Error.txt"))
+    # fpErrFile = str(paths.joinpath("one2n_Error.txt"))
-    with open(fpErrFile, "w") as file:
+    # with open(fpErrFile, "w") as file:
-        for item in fpevents:
+    #     for item in fpevents:
-            file.write(item + "\n")
+    #         file.write(item + "\n")
     
     
     
@@ -495,7 +507,7 @@ def contrast_pr(paths):
 
 
 if __name__ == "__main__":
-    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
+    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
     contrast_pr(evtpaths)
     
     

contrast/utils/event.py

@@ -17,6 +17,12 @@ from tracking.utils.drawtracks import drawTrack
 from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
 from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime
 
+
+# import platform
+# import pathlib
+# plt = platform.system()
+
+
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 VID_FORMAT = ['.mp4', '.avi']
 
@@ -167,6 +173,8 @@ class ShoppingEvent:
 
     
     def from_source_pkl(self, eventpath): 
+        # if plt == 'Windows':
+        #    pathlib.PosixPath = pathlib.WindowsPath
         with open(eventpath, 'rb') as f:
             ShoppingDict = pickle.load(f)
           
@@ -202,10 +210,10 @@ class ShoppingEvent:
         self.front_trackingfeats = frontdata[5]
 
         '''===========对应于 0/1_tracking_output.data ============================='''
-        self.back_boxes = back_outdata
+        self.back_boxes = back_outdata[0]
-        self.back_feats = back_outdata
+        self.back_feats = back_outdata[1]
-        self.front_boxes = front_outdata
+        self.front_boxes = front_outdata[0]
-        self.front_feats = front_outdata
+        self.front_feats = front_outdata[1]
 
 
     def from_datafile(self, eventpath):
@@ -296,13 +304,13 @@ class ShoppingEvent:
                     self.front_feats = tracking_output_feats        
     
     def from_realtime_datafile(self, eventpath):
-        # evtList = self.evtname.split('_')
+        evtList = self.evtname.split('_')
-        # if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
+        if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
-        #     self.barcode = evtList[-1]
+            self.barcode = evtList[-1]
-        # if len(evtList)==3 and evtList[-1]== evtList[-2]:
+        if len(evtList)==3 and evtList[-1]== evtList[-2]:
-        #     self.evtType = 'input'
+            self.evtType = 'input'
-        # else:
+        else:
-        #     self.evtType = 'other'
+            self.evtType = 'other'
             
         '''================ path of video ============='''
         for vidname in os.listdir(eventpath):
@@ -330,7 +338,7 @@ class ShoppingEvent:
             if not os.path.isfile(datapath): continue
             CamerType = dataname.split('_')[0]    
             '''========== 0/1_track.data =========='''
-            if dataname.find("_track.data")>0:                
+            if dataname.find("_tracker.data")>0:                
                 trackerboxes, trackerfeats = extract_data_realtime(datapath)
                 if CamerType == '0':
                     self.back_trackerboxes = trackerboxes

pipeline.py

@@ -136,7 +136,7 @@ def pipeline(
             bname = os.path.basename(vpath[0])
         if not isinstance(vpath, list):
             CameraEvent["videoPath"] = vpath
-            bname = os.path.basename(vpath)
+            bname = os.path.basename(vpath).split('.')[0]
         if bname.split('_')[0] == "0" or bname.find('back')>=0:
             CameraEvent["cameraType"] = "back"
         if  bname.split('_')[0] == "1" or bname.find('front')>=0:
@@ -265,18 +265,17 @@ def main():
     '''
     函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
     '''     
-       
     parmDict = {}
-    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 = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
     parmDict["SourceType"] = "video"   # video, image
-    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["savepath"] = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12"
     parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt' 
 
     evtdir = Path(evtdir) 
     k, errEvents = 0, []
     for item in evtdir.iterdir():
         if item.is_dir():            
-            # item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
+            item = evtdir/Path("20250221-160936-893_6942506204855_6942506204855")
             parmDict["eventpath"] = item
             # pipeline(**parmDict)
             
@@ -284,9 +283,9 @@ def main():
                 pipeline(**parmDict)
             except Exception as e:
                 errEvents.append(str(item))
-        # k+=1
+        k+=1
-        # if k==100:
+        if k==1:
-        #     break
+            break
             
     errfile = os.path.join(parmDict["savepath"], f'error_events.txt')
     with open(errfile, 'w', encoding='utf-8') as f:

realtime/draw_traj.py

@@ -0,0 +1,86 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 21 14:28:59 2025
+
+@author: ym
+"""
+
+import os
+import numpy as np
+from pathlib import Path
+
+import sys
+sys.path.append(r"D:\DetectTracking")
+from contrast.utils.event import ShoppingEvent
+from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
+from tracking.utils.read_data import read_process
+
+
+
+def read_tracker_data(filepath):
+    pass
+
+
+def read_tracking_output_data(filepath):
+    pass
+
+
+def read_process_data(filepath):
+    path
+
+
+
+
+
+
+
+
+
+def main():
+    evtPaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\persist"
+    evtPaths = Path(evtPaths)
+    
+    for evtpath in evtPaths.iterdir():
+        
+        ## 1. 读取重力数据
+        if evtpath.name.find("Weight")>=0 and evtpath.name.find(".txt")>0:
+            weight_data = read_weight_sensor(evtpath)
+        
+        if not evtpath.is_dir():
+            continue
+        
+        ## 2. 读取事件data数据
+        for fpath in evtpath.iterdir():
+            
+            fname = fpath.name
+            if fname.find("tracker.data"):
+                pass
+                
+            if fname.find("tracking_output.data"):
+                pass
+            
+                
+            if fname.find("process.data") >=0:
+                pass
+                
+            
+            fpath = str(fpath)
+            
+            
+            
+        
+        
+        
+        
+        
+        
+        
+    
+    
+    
+    
+    pass
+
+
+if __name__ == "__main__":
+    main()

realtime/event_time_specify.py

@@ -9,11 +9,10 @@ import numpy as np
 # from matplotlib.pylab import mpl 
 # mpl.use('Qt5Agg')
 import matplotlib.pyplot as plt
-from move_detect import MoveDetect
 
 import sys
 sys.path.append(r"D:\DetectTracking")
-
+from move_detect import MoveDetect
 # from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_weight_timeConsuming
 from tracking.utils.read_data import read_weight_timeConsuming
 

realtime/full_realtime.py

@@ -9,9 +9,10 @@ import sys
 import numpy as np
 import matplotlib.pyplot as plt
 from datetime import datetime
-from contrast.utils.event import ShoppingEvent
 
 sys.path.append(r"D:\DetectTracking")
+
+from contrast.utils.event import ShoppingEvent
 from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
 from tracking.utils.read_data import read_process
 

realtime/time_devide.py

@@ -13,10 +13,14 @@ from pathlib import Path
 import glob
 import numpy as np
 import copy
-
 import matplotlib.pyplot as plt
+from collections import OrderedDict
+
+from event_time_specify import devide_motion_state #, state_measure
+
+import sys
+sys.path.append(r"D:\DetectTracking")
 from imgs_inference import run_yolo
-from event_time_specify import devide_motion_state#, state_measure
 from tracking.utils.read_data import read_weight_sensor
 
 # IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
@@ -400,8 +404,8 @@ def splitevent(imgpath, MotionSlice):
 
     
 def runyolo():
-    eventdirs = r"\\192.168.1.28\share\realtime\eventdata"
+    eventdirs = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata"
-    savedir = r"\\192.168.1.28\share\realtime\result"
+    savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\result"
     
     k = 0
     for edir in os.listdir(eventdirs):
@@ -419,12 +423,40 @@ def run_tracking(trackboxes, MotionSlice):
     pass
             
 
+def read_wsensor(filepath):
+    WeightDict = OrderedDict()
+    with open(filepath, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+        clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]  
+        for i, line in enumerate(clean_lines):
+            line = line.strip()
             
+            line = line.strip()
+            
+            if line.find(':') < 0: continue
+            # if line.find("Weight") >= 0: 
+            #     label = "Weight"
+            #     continue
+ 
+            
+            keyword = line.split(':')[0]
+            value = line.split(':')[1]
+            
+            # if label == "Weight":
+            if len(keyword) and len(value):
+                vdata = [float(s) for s in value.split(',') if len(s)]
+                WeightDict[keyword] = vdata[-1]
+                
+                
+    weights = [(float(t), w) for t, w in WeightDict.items()]
+    weights = np.array(weights).astype(np.int64)
+    
+    return weights
 
             
 def show_seri():    
     datapath = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata\1731316835560"
-    savedir = r"D:\DetectTracking\realtime\1"
+    savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\1"
     
     
     imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
@@ -450,7 +482,7 @@ def show_seri():
         
     '''===============读取重力信号数据==================='''
     seneorfile = os.path.join(datapath, 'sensor.txt')
-    weights = read_weight_sensor(seneorfile)
+    weights = read_wsensor(seneorfile)
     
     # weights = [(float(t), w) for t, w in WeightDict.items()]
     # weights = np.array(weights)
@@ -471,10 +503,8 @@ def show_seri():
 
 def main():
     # runyolo()
-    
     show_seri()
  
-    
 if __name__ == '__main__':
     main()
     

tracking/dotrack/dotracks_back.py

@@ -153,8 +153,8 @@ class doBackTracks(doTracks):
         
         hand_ious = []
         
-        hboxes = np.empty(shape=(0, 9), dtype = np.float)
+        hboxes = np.empty(shape=(0, 9), dtype = np.float64)
-        gboxes = np.empty(shape=(0, 9), dtype = np.float)
+        gboxes = np.empty(shape=(0, 9), dtype = np.float64)
 
         
         # start, end 为索引值，需要 start:(end+1)

tracking/dotrack/dotracks_front.py

@@ -113,8 +113,8 @@ class doFrontTracks(doTracks):
         '''
         assert htrack.cls==0 and gtrack.cls!=0 and gtrack.cls!=9, 'Track cls is Error!'
         
-        hboxes = np.empty(shape=(0, 9), dtype = np.float)
+        hboxes = np.empty(shape=(0, 9), dtype = np.float64)
-        gboxes = np.empty(shape=(0, 9), dtype = np.float)
+        gboxes = np.empty(shape=(0, 9), dtype = np.float64)
         
         # start, end 为索引值，需要 start:(end+1)
         for start, end in htrack.dynamic_y2:

tracking/utils/read_data.py

@@ -321,6 +321,8 @@ def read_process(filePath):
 
 
 def read_similar(filePath):
+    '''1:n时 Dict['type']字段提取和非全实时不一致，无 "=" 字符 '''
+    
     SimiDict = {}
     SimiDict['one2one'] = []
     SimiDict['one2SN'] = []
@@ -386,7 +388,7 @@ def read_similar(filePath):
                 Dict['event'] = label
                 Dict['barcode'] = bcd
                 Dict['similar'] = float(value.split(',')[0])
-                Dict['type'] = value.split('=')[-1]
+                Dict['type'] = value.split(',')[1]
                 one2n_list.append(Dict)
 
         if len(one2one_list): SimiDict['one2one'] = one2one_list
@@ -403,8 +405,6 @@ def read_weight_sensor(filepath):
         for i, line in enumerate(clean_lines):
             line = line.strip()
             
-            line = line.strip()
-            
             if line.find(':') < 0: continue
             if line.find("Weight") >= 0: 
                 label = "Weight"
@@ -415,7 +415,7 @@ def read_weight_sensor(filepath):
             value = line.split(':')[1]
             
             if label == "Weight":    
-                vdata = [float(s) for s in value.split(',') if len(s)]
+                vdata = [float(s) for s in value.split(',') if len(s) and s.isdigit()]
                 WeightDict[keyword] = vdata[-1]