20250313

contrast/feat_infer.py

@@ -16,11 +16,6 @@ from feat_extract.inference import FeatsInterface #, inference_image
 
 Encoder = FeatsInterface(conf)
 
-
-    
-
-
-
 def main():
     imgpaths = r"D:\全实时\202502\result\Yolos_Tracking\20250228-160049-188_6921168558018_6921168558018\a"
     featDict = {}

contrast/genfeats.py

@@ -191,10 +191,19 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
     return
 
 
-def gen_bcd_features(imgpath, bcdpath, featpath, bcdSet=None):
+def gen_bcd_features(imgpath, bcdpath, featpath, eventSourcePath):
     ''' 生成标准特征集 '''
     '''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
           并存储于: bcdpath, 格式为 barcode.pickle'''
+          
+    bcdList = []
+    for evtname in os.listdir(eventSourcePath):
+        bname, ext = os.path.splitext(evtname)
+        evt = bname.split('_')
+        if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
+            bcdList.append(evt[-1])
+    
+    bcdSet = set(bcdList)   
     get_std_barcodeDict(imgpath, bcdpath, bcdSet)
     
     '''2. 特征提取，并保存至文件夹 featpath 中，也根据  bcdSet 交集执行'''

contrast/one2n_contrast.py

@@ -14,49 +14,47 @@ from scipy.spatial.distance import cdist
 from utils.event import ShoppingEvent
 
 
-def init_eventdict(sourcePath, stype="data"):
+def init_eventDict(sourcePath, eventDataPath, stype="data"):
     '''stype: str, 
         'source': 由 videos 或 images 生成的 pickle 文件
         'data': 从 data 文件中读取的现场运行数据
         "realtime": 全实时数据，从 data 文件中读取的现场运行数据
-    '''   
+    
+       sourcePath:事件文件夹，事件类型包含2种：
+       (1) pipeline生成的 pickle 文件
+       (2) 直接采集的事件文件夹
+    '''        
     k, errEvents = 0, []
-    for bname in os.listdir(sourcePath):        
-        # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
+    for evtname in os.listdir(sourcePath):        
+        bname, ext = os.path.splitext(evtname)
+        source_path = os.path.join(sourcePath, evtname)
         
-        source_path = os.path.join(sourcePath, bname)
-        if stype=="source" and not os.path.isfile(source_path): continue
+        if stype=="source" and ext not in ['.pkl', '.pickle']: continue
         if stype=="data" and os.path.isfile(source_path): continue
         if stype=="realtime" and os.path.isfile(source_path): continue
-
-        if os.path.isdir(source_path):
-            pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
-        else:
-            pickpath = os.path.join(eventDataPath, bname)
         
-        if os.path.isfile(pickpath): 
-            continue
+        evt = bname.split('_')
+        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
+        if not condt: 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
+        pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
+        if os.path.isfile(pickpath): continue
 
         # event = ShoppingEvent(source_path, stype)
         try:
             event = ShoppingEvent(source_path, stype)
             with open(pickpath, 'wb') as f:
                 pickle.dump(event, f)
-            print(bname)
+            print(evtname)
         except Exception as e:
             errEvents.append(source_path)
-            print(f"Error: {bname}, {e}")        
+            print(f"Error: {evtname}, {e}")        
         # k += 1
         # if k==1:
         #     break
-      
-    errfile = os.path.join(resultPath, 'error_events.txt')
-    with open(errfile, 'a', encoding='utf-8') as f:
+    
+    errfile = Path(eventDataPath).parent / 'error_events.txt'
+    with open(str(errfile), 'a', encoding='utf-8') as f:
         for line in errEvents:
             f.write(line + '\n')
 
@@ -185,7 +183,7 @@ def one2n_pr(evtDicts, pattern=1):
                 elif bcd!=event.barcode and simi!=maxsim: 
                     tnsimi.append(simi)
                     tnevents.append(evtname)
-                elif bcd!=event.barcode and simi==maxsim:
+                elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
                     fpsimi.append(simi)
                     fpevents.append(evtname)
                 else:
@@ -216,7 +214,11 @@ def one2n_pr(evtDicts, pattern=1):
     ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
+
     ax.set_title('1:n Precise & Recall')
     ax.set_xlabel(f"Event Num: {len(one2nFile)}")     
     ax.legend()
@@ -241,7 +243,7 @@ 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="realtime")  # 'source', 'data', 'realtime'
+    init_eventDict(eventSourcePath, eventDataPath, stype="realtime")  # 'source', 'data', 'realtime'
     
     # for pfile in os.listdir(eventDataPath):
     #     evt = os.path.splitext(pfile)[0].split('_')
@@ -264,7 +266,7 @@ def main():
             
             
 if __name__ == '__main__':
-    eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-27"
+    eventSourcePath  = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-4_2"
     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_wang")

contrast/one2one_contrast.py

@@ -58,6 +58,7 @@ from feat_extract.inference import FeatsInterface
 from utils.event import ShoppingEvent, save_data
 from genfeats import gen_bcd_features
 from event_test import calc_simil
+from one2n_contrast import init_eventDict
 
 
 
@@ -271,8 +272,12 @@ def build_std_evt_dict():
     evtDict = {}
     for evtname, barcode in evtList:
         evtpath = os.path.join(eventDataPath, evtname+'.pickle')
-        with open(evtpath, 'rb') as f:  
-            evtdata = pickle.load(f)
+        try:
+            with open(evtpath, 'rb') as f:  
+                evtdata = pickle.load(f)
+        except Exception as e:
+            print(evtname)
+                
         evtDict[evtname] = evtdata
         
     return evtList, evtDict, stdDict
@@ -300,7 +305,8 @@ def one2SN_pr(evtList, evtDict, stdDict):
         event = evtDict[evtname]
         ## 无轨迹判断
         if len(event.front_feats)+len(event.back_feats)==0:
-            print(evtname)
+            errorFile_one2SN.append(evtname)
+            print(f"No trajectory: {evtname}")
             continue
 
         barcodes, similars = [], []
@@ -351,10 +357,10 @@ def one2SN_pr(evtList, evtDict, stdDict):
         FNX = sum(np.array(fn_simi) < th)
         TNX = sum(np.array(tn_simi) < th)
         PPreciseX.append(TPX/(TPX+FPX+1e-6))
-        PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6))
+        PRecallX.append(TPX/(TPX+FNX+1e-6))
         
         NPreciseX.append(TNX/(TNX+FNX+1e-6))
-        NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6))
+        NRecallX.append(TNX/(TNX+FPX+1e-6))
         
     fig, ax = plt.subplots()
     ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
@@ -363,9 +369,11 @@ def one2SN_pr(evtList, evtDict, stdDict):
     ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
     ax.set_title('1:SN Precise & Recall')
-    ax.set_xlabel(f"Event Num: {len(evtList)}")     
+    ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}")     
     ax.legend()
     plt.show()
     ## ============================= 1:N 展厅 直方图'''
@@ -403,10 +411,14 @@ def one2one_simi(evtList, evtDict, stdDict):
 
     '''======2 计算事件、标准特征集相似度 =================='''
     rltdata = []  
+    errorFile_one2one = []
     for i in range(len(mergePairs)):
         evtname, stdbcd, label = mergePairs[i]
         event = evtDict[evtname]
-        if len(event.feats_compose)==0: continue
+        if len(event.feats_compose)==0: 
+            errorFile_one2one.append(evtname)
+            
+            continue
         
         stdfeat = stdDict[stdbcd]  # float32
     
@@ -418,11 +430,16 @@ 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
+    
+    errorFile_one2one = list(set(errorFile_one2one))
+    
+    return rltdata, errorFile_one2one
     
 
-def one2one_pr(rltdata): 
+def one2one_pr(evtList, evtDict, stdDict): 
+    
+    rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict)
+    
     Same, Cross = [], []
     for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
         if label == "same":
@@ -451,27 +468,41 @@ def one2one_pr(rltdata):
     Correct = []
     Thresh = np.linspace(-0.2, 1, 100)
     for th in Thresh:
-        TP = np.sum(Same > th)
-        FN = TPFN - TP
+        TP = np.sum(Same >= th)
+        FN = np.sum(Same < th)
+        # FN = TPFN - TP
+        
         TN = np.sum(Cross < th)
-        FP = TNFP - TN
+        FP = np.sum(Cross >= th)
+        # FP = TNFP - TN
 
-        Recall_Pos.append(TP/TPFN)
-        Recall_Neg.append(TN/TNFP)
+        
         Precision_Pos.append(TP/(TP+FP+1e-6))
         Precision_Neg.append(TN/(TN+FN+1e-6))
+        Recall_Pos.append(TP/(TP+FN+1e-6))
+        Recall_Neg.append(TN/(TN+FP+1e-6))
+        
+        # Recall_Pos.append(TP/TPFN)
+        # Recall_Neg.append(TN/TNFP)
+        
+        
         Correct.append((TN+TP)/(TPFN+TNFP))
         
     fig, ax = plt.subplots()  
-    ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
+    
+    ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)')
     ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
     ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
-    ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
+    ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)')
     ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
     
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
+    
     ax.set_title('PrecisePos & PreciseNeg')
     ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")     
     ax.legend()
@@ -506,23 +537,17 @@ def gen_eventdict(sourcePath, saveimg=True):
         ## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
         if os.path.isfile(source_path):
             bname, ext = os.path.splitext(bname)
-            evt = bname.split("_")
+            # evt = bname.split("_")
         
         evt = bname.split('_')
         condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
         if not condt: continue
 
-        # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
-        # source_path = os.path.join(evtpath, bname)
-        
+
         # 如果已完成事件生成，则不执行
         pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
         if os.path.isfile(pickpath): continue
 
-        # event = ShoppingEvent(source_path, stype=source_type)        
-        # with open(pickpath, 'wb') as f:
-        #     pickle.dump(event, f)
-
         try:
             event = ShoppingEvent(source_path, stype=source_type)
             # save_data(event, resultPath)
@@ -538,16 +563,44 @@ def gen_eventdict(sourcePath, saveimg=True):
         # if k==1:
         #     break
 
-            
     errfile = os.path.join(resultPath, 'error_events.txt')
     # with open(errfile, 'w', encoding='utf-8') as f:
     #     for line in errEvents:
     #         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 = [], []
+# def init_std_evt_dict():
+#     '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
+#     bcdList, event_spath = [], []
+#     for evtname in os.listdir(eventSourcePath):
+#         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(eventSourcePath, evtname)
+#         if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
+#             evt = bname.split('_')
+#         elif os.path.isdir(fpath):
+#             evt = evtname.split('_')
+#         else:
+#             continue
+
+#         if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
+#             bcdList.append(evt[-1])       
+#             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!")
+
+def get_evtList():
+
+    '''==== 0. 生成事件列表和对应的 Barcodes 集合 ==========='''
+    bcdList, evtpaths = [], []
     for evtname in os.listdir(eventSourcePath):
         bname, ext = os.path.splitext(evtname)
         
@@ -562,46 +615,73 @@ def init_std_evt_dict():
 
         if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
             bcdList.append(evt[-1])       
-            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!")
+            evtpaths.append(fpath)
     
+    bcdSet = set(bcdList)
+    
+    return evtpaths, bcdSet 
 
 
 
-def test_one2one(): 
+# def init_stdDict():
+#     evtpaths, bcdSet = get_evtList()
+#     gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
+#     print("stdFeats have generated and saved!")
+
+
+# def init_evtDict():
+#     '''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
+#     bcdList, event_spath = [], []
+#     for evtname in os.listdir(eventSourcePath):
+#         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(eventSourcePath, evtname)
+#         if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
+#             evt = bname.split('_')
+#         elif os.path.isdir(fpath):
+#             evt = evtname.split('_')
+#         else:
+#             continue
+
+#         if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
+#             bcdList.append(evt[-1])       
+#             event_spath.append(fpath)
+               
+#     '''==== 2. 生成事件字典, 只需运行一次 ===============''' 
+#     gen_eventdict(event_spath)
+#     print("eventList have generated and saved!")
+
+
+
+
+
+def test_one2one_one2SN(): 
     '''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()
+    # evtpaths, bcdSet = get_evtList()
     
-    # 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()
+    '''=== 1. 只需运行一次，生成事件对应的标准特征库字典,如已生成，无需运行 ===='''
+    # gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, eventSourcePath)
     
-    rltdata = one2one_simi(evtList, evtDict, stdDict)
+    '''==== 2. 生成事件字典, 只需运行一次 ====================''' 
     
-    one2one_pr(rltdata)
+    # date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2']
+    # for dt in date_:   
+    #     evtpaths = os.path.join(eventSourcePath, dt)
+    #     init_eventDict(evtpaths, eventDataPath, source_type)
     
+    init_eventDict(eventSourcePath, eventDataPath, source_type)
+        
+        
 
-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()
-    
-    # 2. 事件barcode集和标准库barcode求交集
+    '''==== 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()
-    
-    one2SN_pr(evtList, evtDict, stdDict)    
 
+    one2one_pr(evtList, evtDict, stdDict)
     
+    one2SN_pr(evtList, evtDict, stdDict) 
+        
 if __name__ == '__main__':
     '''
     共7个地址：
@@ -627,32 +707,30 @@ if __name__ == '__main__':
     
     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\全实时测试\testing\bcdpath"
-    stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\stdfeats"
+    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_0304"
 
     if not os.path.exists(stdBarcodePath): 
         os.makedirs(stdBarcodePath) 
     if not os.path.exists(stdFeaturePath): 
         os.makedirs(stdFeaturePath)
     
+    '''source_type: 
+            "source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件
+            "data": 基于事件切分的原 data 文件版本
+            "realtime": 全实时生成的 data 文件
     '''
-    source_type: 
-        "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"
-    
+    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\基准数据集\2025-3-4_1"
     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")
+    
+    eventDataPath = os.path.join(resultPath, "evtobjs_0304_1")
+    similPath = os.path.join(resultPath, "simidata_0304_1")
     if not os.path.exists(eventDataPath): 
         os.makedirs(eventDataPath) 
     if not os.path.exists(similPath): 
         os.makedirs(similPath)
 
-    test_one2one()
-    
-    test_one2SN()
+    test_one2one_one2SN()
 
 
 

contrast/onsite_contrast_pr.py

@@ -109,24 +109,31 @@ def test_compare():
 
     plot_pr_curve(simiList)
     
-def contrast_pr(paths): 
+def contrast_pr(evtPaths): 
     '''
     1:1
-    '''     
-    paths = Path(paths)
-    
+    '''
     evtpaths = []
-    for p in paths.iterdir():
+    # date_ = ['2025-3-4_1', '2025-3-5_1', '2025-3-5_2']
+    # for dt in date_:
+    #     paths = Path(evtPaths) / dt
+    abc = []    
+    for p in Path(evtPaths).iterdir():
         condt1 = p.is_dir()
         condt2 = len(p.name.split('_'))>=2
-        condt3 = len(p.name.split('_')[-1])>8
+        condt3 = len(p.name.split('_')[-1])>=8
         condt4 = p.name.split('_')[-1].isdigit() 
         if condt1 and condt2 and condt3 and condt4:
             evtpaths.append(p)
+        elif p.is_dir():
+            abc.append(p.stem)
+            
 
     # evtpaths = [p for p in paths.iterdir() if p.is_dir() and len(p.name.split('_'))>=2 and len(p.name.split('_')[-1])>8]
     # evtpaths = [p for p in paths.iterdir() if p.is_dir()]
     
+    alg_times = []
+    
     events, similars = [], []
     ##===================================== 扫A放A, 扫A放B场景()
     one2oneAA, one2oneAB = [], []
@@ -157,7 +164,7 @@ def contrast_pr(paths):
         barcode = path.stem.split('_')[-1]
         datapath = path.joinpath('process.data')
         
-        if not barcode.isdigit() or len(barcode)<10: continue
+        if not barcode.isdigit() or len(barcode)<8: continue
         if not datapath.is_file(): continue
         
         bcdList.append(barcode)
@@ -175,10 +182,15 @@ def contrast_pr(paths):
         
         if len(one2one)+len(one2SN)+len(one2n) == 0:
             errorFile.append(path.stem)
+        
+        dtime = SimiDict["algroStartToEnd"]
+        if dtime >= 0:
+            alg_times.append((dtime, path.stem))
             
         
         '''================== 0.  1:1 ==================='''
         barcodes, similars = [], []
+        barcodes_ = []
         for dt in one2one:
             one2onePath.append((path.stem))
             if dt['similar']==0: 
@@ -186,6 +198,12 @@ def contrast_pr(paths):
                 continue
             barcodes.append(dt['barcode'])
             similars.append(dt['similar'])
+            
+            
+            barcodes_.append(path.stem)
+        
+        
+        
         
         
         if len(barcodes)==len(similars) and len(barcodes)!=0:
@@ -216,6 +234,8 @@ def contrast_pr(paths):
                     _fp_events.append(path.stem)
                 else:
                     errorFile_one2one.append(path.stem)
+        elif len(one2SN)+len(one2n) == 0:
+            errorFile_one2one.append(path.stem)
 
         
         '''================== 2. 取出场景下的 1 : Small N ==================='''
@@ -223,6 +243,7 @@ def contrast_pr(paths):
         for dt in one2SN:
             barcodes.append(dt['barcode'])
             similars.append(dt['similar'])
+            
         
         if len(barcodes)==len(similars) and len(barcodes)!=0:            
             ## 扫A放A, 扫A放B场景    
@@ -231,11 +252,11 @@ def contrast_pr(paths):
             
             one2SNAA.extend(simAA)
             one2SNAB.extend(simAB)
+            
             one2SNPath.append(path.stem)
             if len(simAA)==0:
-                one2SNPath1.append(path.stem)
-                
-  
+                errorFile_one2SN.append(path.stem)
+
             ## 相似度排序，barcode相等且排名第一为TP，适用于多的barcode相似度比较
             max_idx = similars.index(max(similars))
             max_sim = similars[max_idx]
@@ -256,6 +277,7 @@ def contrast_pr(paths):
                     fp_events.append(path.stem)
                 else:
                     errorFile_one2SN.append(path.stem)
+            
                     
         
             
@@ -266,10 +288,17 @@ def contrast_pr(paths):
             evt_barcodes.append(dt["barcode"])
             evt_similars.append(dt["similar"])
             evt_types.append(dt["type"])
-        
-        if len(events)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
-            and len(evt_similars)==len(evt_types) and len(events)>0:  
             
+        
+        
+        if len(events)==len(evt_barcodes)==len(evt_similars)==len(evt_types) and len(events)>0:
+            if not barcode in evt_barcodes:
+                errorFile_one2n.append(path.stem)
+                continue
+        
+            if len(barcodes_):
+                print("do")
+          
             one2nPath.append(path.stem)
             maxsim = evt_similars[evt_similars.index(max(evt_similars))]
             for i in range(len(one2n)):
@@ -324,9 +353,9 @@ def contrast_pr(paths):
         _TN = sum(np.array(one2oneAB) < th)
         
         _PPrecise.append(_TP/(_TP+_FP+1e-6))
-        _PRecall.append(_TP/(len(one2oneAA)+1e-6))  
+        _PRecall.append(_TP/(_TP+_FN+1e-6))  
         _NPrecise.append(_TN/(_TN+_FN+1e-6))
-        _NRecall.append(_TN/(len(one2oneAB)+1e-6))
+        _NRecall.append(_TN/(_TN+_FP+1e-6))
 
         '''===================================== 1:SN 均值'''
         TP_ = sum(np.array(one2SNAA) >= th)
@@ -346,10 +375,10 @@ def contrast_pr(paths):
         FNX = sum(np.array(fn_simi) < th)
         TNX = sum(np.array(tn_simi) < th)
         PPreciseX.append(TPX/(TPX+FPX+1e-6))
-        PRecallX.append(TPX/(len(tp_simi)+len(fn_simi)+1e-6))
+        PRecallX.append(TPX/(TPX+FNX+1e-6))
         
         NPreciseX.append(TNX/(TNX+FNX+1e-6))
-        NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+1e-6))
+        NRecallX.append(TNX/(TNX+FPX+1e-6))
         
         
         '''===================================== 1:n'''
@@ -359,13 +388,19 @@ def contrast_pr(paths):
         TN = sum(np.array(tnsimi) < th)
         
         PPrecise.append(TP/(TP+FP+1e-6))
-        PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6)) 
+        PRecall.append(TP/(TP+FN+1e-6)) 
         NPrecise.append(TN/(TN+FN+1e-6))
-        NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
+        NRecall.append(TN/(TN+FP+1e-6))
+
+    algtime = []
+    for tm, _ in alg_times:
+        algtime.append(tm)
+    fig, ax = plt.subplots()
+    ax.hist(np.array(algtime), bins=100, edgecolor='black')
+    ax.set_title('Algorthm Spend Time')
+    ax.set_xlabel(f"Event Num: {len(alg_times)}")    
+    plt.show()
 
-        
-    
-    
     '''1. ============================= 1:1 最大值方案 曲线'''
     fig, ax = plt.subplots()
     ax.plot(Thresh, _PPrecise, 'r', label='Precise_Pos: TP/TPFP')
@@ -374,7 +409,9 @@ def contrast_pr(paths):
     ax.plot(Thresh, _NRecall, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
     ax.set_title('1:1 Precise & Recall')
     ax.set_xlabel(f"Event Num: {len(one2oneAA)+len(one2oneAB)}")     
     ax.legend()
@@ -393,30 +430,30 @@ def contrast_pr(paths):
     
     
     '''2. ============================= 1:1 均值方案 曲线'''
-    fig, ax = plt.subplots()
-    ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
-    ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
-    ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
-    ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
-    ax.set_xlim([0, 1])
-    ax.set_ylim([0, 1])
-    ax.grid(True)
-    ax.set_title('1:1 Precise & Recall')
-    ax.set_xlabel(f"Event Num: {len(one2SNAA)}")     
-    ax.legend()
-    plt.show()
-    ## ============================= 1:1 均值方案 直方图'''
-    fig, axes = plt.subplots(2, 1)
-    axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black')
-    axes[0].set_xlim([-0.2, 1])
-    axes[0].set_title('AA')
-    axes[0].set_xlabel(f"Event Num: {len(one2SNAA)}")
+    # fig, ax = plt.subplots()
+    # ax.plot(Thresh, PPrecise_, 'r', label='Precise_Pos: TP/TPFP')
+    # ax.plot(Thresh, PRecall_, 'b', label='Recall_Pos: TP/TPFN')
+    # ax.plot(Thresh, NPrecise_, 'g', label='Precise_Neg: TN/TNFP')
+    # ax.plot(Thresh, NRecall_, 'c', label='Recall_Neg: TN/TNFN')
+    # ax.set_xlim([0, 1])
+    # ax.set_ylim([0, 1])
+    # ax.grid(True)
+    # ax.set_title('1:1 Precise & Recall')
+    # ax.set_xlabel(f"Event Num: {len(one2SNAA)}")     
+    # ax.legend()
+    # plt.show()
+    # ## ============================= 1:1 均值方案 直方图'''
+    # fig, axes = plt.subplots(2, 1)
+    # axes[0].hist(np.array(one2SNAA), bins=60, edgecolor='black')
+    # axes[0].set_xlim([-0.2, 1])
+    # axes[0].set_title('AA')
+    # axes[0].set_xlabel(f"Event Num: {len(one2SNAA)}")
     
-    axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black')
-    axes[1].set_xlim([-0.2, 1])
-    axes[1].set_title('BB')
-    axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}")
-    plt.show()
+    # axes[1].hist(np.array(one2SNAB), bins=60, edgecolor='black')
+    # axes[1].set_xlim([-0.2, 1])
+    # axes[1].set_title('BB')
+    # axes[1].set_xlabel(f"Event Num: {len(one2SNAB)}")
+    # plt.show()
     
     ''''3. ============================= 1:SN 曲线''' 
     fig, ax = plt.subplots()
@@ -426,7 +463,9 @@ def contrast_pr(paths):
     ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
     ax.set_title('1:SN Precise & Recall')
     ax.set_xlabel(f"Event Num: {len(one2SNAA)}")     
     ax.legend()
@@ -456,7 +495,9 @@ def contrast_pr(paths):
     ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
     ax.set_xlim([0, 1])
     ax.set_ylim([0, 1])
-    ax.grid(True)
+    ax.set_xticks(np.arange(0, 1, 0.1))
+    ax.set_yticks(np.arange(0, 1, 0.1))
+    ax.grid(True, linestyle='--')
     ax.set_title('1:n Precise & Recall')
     ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")     
     ax.legend()
@@ -473,11 +514,11 @@ def contrast_pr(paths):
     axes[1, 0].set_xlim([-0.2, 1])
     axes[1, 0].set_title(f'TN({len(tnsimi)})')
     axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
+    
     axes[1, 1].set_xlim([-0.2, 1])
     axes[1, 1].set_title(f'FN({len(fnsimi)})')
     plt.show()
     
-    
     # fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
     # with open(fpsnErrFile, "w") as file:
     #     for item in fp_events:
@@ -487,27 +528,24 @@ def contrast_pr(paths):
     # with open(fpErrFile, "w") as file:
     #     for item in fpevents:
     #         file.write(item + "\n")
-    
-    
-    
+
     
     # bcdSet = set(bcdList)
-    # one2nErrFile = str(paths.joinpath("one_2_Small_n_Error.txt"))
-    # with open(one2nErrFile, "w") as file:
-    #     for item in fnevents:
-    #         file.write(item + "\n")
+    one2nErrFile = os.path.join(evtPaths, "one_2_Small_n_Error.txt")
+    with open(one2nErrFile, "w") as file:
+        for item in fnevents:
+            file.write(item + "\n")
             
-    # one2NErrFile = str(paths.joinpath("one_2_Big_N_Error.txt"))
-    # with open(one2NErrFile, "w") as file:
-    #     for item in fn_events:
-    #         file.write(item + "\n")
+    one2NErrFile = os.path.join(evtPaths, "one_2_Big_N_Error.txt")
+    with open(one2NErrFile, "w") as file:
+        for item in fn_events:
+            file.write(item + "\n")
     
     print('Done!')
 
 
-
 if __name__ == "__main__":
-    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-26_2"
+    evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-3-3"
     contrast_pr(evtpaths)
     
     

contrast/utils/event.py

@@ -82,7 +82,11 @@ def array2list(bboxes):
 
 class ShoppingEvent:
     def __init__(self, eventpath, stype="data"):  
-        '''stype: str, 'source', 'data', 'realtime', 共三种 '''
+        '''stype: str, 'source', 'data', 'realtime', 共三种 
+            source: 前后摄视频经 pipeline 生成的文件
+            data: 基于事件切分的原 data 文件版本
+            realtime: 全实时生成的 data 文件
+        '''
         
         self.eventpath = eventpath
         self.evtname = str(Path(eventpath).stem)