增加了单帧入侵判断及yoloV10

contrast/event_test.py

@@ -9,17 +9,19 @@ import cv2
 import json
 import numpy as np
 import matplotlib.pyplot as plt
+from pathlib import Path
+
 from matplotlib import rcParams
 from matplotlib.font_manager import FontProperties
 from scipy.spatial.distance import cdist
 from utils.event import ShoppingEvent, save_data
-
-
+from utils.calsimi import calsimi_vs_stdfeat_new, get_topk_percent, cluster
+from utils.tools import get_evtList
+import pickle
 
 rcParams['font.sans-serif'] = ['SimHei']  # 用黑体显示中文
 rcParams['axes.unicode_minus'] = False  # 正确显示负号
 
-
 '''*********** USearch ***********'''
 def read_usearch():
     stdFeaturePath = r"D:\contrast\stdlib\v11_test.json"
@@ -35,13 +37,12 @@ def read_usearch():
         
     return stdlib
 
-def get_eventlist():
+def get_eventlist_errortxt(evtpaths):
     '''
     读取一次测试中的错误事件
-    '''
-    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"
-    text1 = "one2n_Error.txt"
-    text2 = "one2SN_Error.txt"
+    '''    
+    text1 = "one_2_Small_n_Error.txt"
+    text2 = "one_2_Big_N_Error.txt"
     events = []
     text = (text1, text2)
     for txt in text:
@@ -53,16 +54,16 @@ def get_eventlist():
                 if line:
                     fpath=os.path.join(evtpaths, line)
                     events.append(fpath)
+                    
+                    
     
     events = list(set(events))
     
     return events
     
-def single_event():
-    
-    events = get_eventlist()
-                    
-    
+def save_eventdata():
+    evtpaths = r"/home/wqg/dataset/test_dataset/performence_dataset/"
+    events = get_eventlist_errortxt(evtpaths)
 
     '''定义当前事件存储地址及生成相应文件件'''
     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\single_event"    
@@ -74,121 +75,148 @@ def single_event():
 
 
 
-def get_topk_percent(data, k):
-    """
-    获取数据中最大的 k% 的元素
-    """
-    # 将数据转换为 NumPy 数组
-    if isinstance(data, list):
-        data = np.array(data)
+# def get_topk_percent(data, k):
+#     """
+#     获取数据中最大的 k% 的元素
+#     """
+#     # 将数据转换为 NumPy 数组
+#     if isinstance(data, list):
+#         data = np.array(data)
     
-    percentile = np.percentile(data, 100-k)
-    top_k_percent = data[data >= percentile]
+#     percentile = np.percentile(data, 100-k)
+#     top_k_percent = data[data >= percentile]
     
-    return top_k_percent
-def cluster(data, thresh=0.15):
-    # data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
-    # data = np.array([0.1, 0.13, 0.2, 0.3])
-    # data = np.array([0.1])
+#     return top_k_percent
+# def cluster(data, thresh=0.15):
+#     # data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
+#     # data = np.array([0.1, 0.13, 0.2, 0.3])
+#     # data = np.array([0.1])
     
-    if isinstance(data, list):
-        data = np.array(data)
+#     if isinstance(data, list):
+#         data = np.array(data)
     
-    data1 = np.sort(data)
-    cluter, Cluters,  = [data1[0]], []
-    for i in range(1, len(data1)):
-        if data1[i] - data1[i-1]< thresh:
-            cluter.append(data1[i])
-        else:
-            Cluters.append(cluter)
-            cluter = [data1[i]]
-    Cluters.append(cluter)
+#     data1 = np.sort(data)
+#     cluter, Cluters,  = [data1[0]], []
+#     for i in range(1, len(data1)):
+#         if data1[i] - data1[i-1]< thresh:
+#             cluter.append(data1[i])
+#         else:
+#             Cluters.append(cluter)
+#             cluter = [data1[i]]
+#     Cluters.append(cluter)
     
-    clt_center = []
-    for clt in Cluters:
-        ## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中
-        # if len(clt)>=3:
-        #     clt_center.append(np.mean(clt))
-        clt_center.append(np.mean(clt))
+#     clt_center = []
+#     for clt in Cluters:
+#         ## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中
+#         # if len(clt)>=3:
+#         #     clt_center.append(np.mean(clt))
+#         clt_center.append(np.mean(clt))
     
-    # print(clt_center)
+#     # print(clt_center)
         
-    return clt_center
+#     return clt_center
 
-def calc_simil(event, stdfeat):
-    '''事件与标准库的对比策略
-       该比对策略是否可以拓展到事件与事件的比对？
-    '''
+# def calsimi_vs_stdfeat_new(event, stdfeat):
+#     '''事件与标准库的对比策略
+#        该比对策略是否可以拓展到事件与事件的比对？
+#     '''
     
     
-    def calsiml(feat1, feat2, topkp=75, cluth=0.15):
-        '''轨迹样本和标准特征集样本相似度的选择策略'''
-        matrix = 1 - cdist(feat1, feat2, 'cosine')
-        simi_max = []
-        for i in range(len(matrix)):
-            sim = np.mean(get_topk_percent(matrix[i, :], topkp))
-            simi_max.append(sim)
-        cltc_max = cluster(simi_max, cluth)
-        Simi = max(cltc_max) 
+#     def calsiml(feat1, feat2, topkp=75, cluth=0.15):
+#         '''轨迹样本和标准特征集样本相似度的选择策略'''
+#         matrix = 1 - cdist(feat1, feat2, 'cosine')
+#         simi_max = []
+#         for i in range(len(matrix)):
+#             sim = np.mean(get_topk_percent(matrix[i, :], topkp))
+#             simi_max.append(sim)
+#         cltc_max = cluster(simi_max, cluth)
+#         Simi = max(cltc_max) 
         
-        ## cltc_max为空属于编程考虑不周，应予以排查解决 
-        # if len(cltc_max):
-        #     Simi = max(cltc_max) 
-        # else:
-        #     Simi = 0   #不应该走到该处
+#         ## cltc_max为空属于编程考虑不周，应予以排查解决 
+#         # if len(cltc_max):
+#         #     Simi = max(cltc_max) 
+#         # else:
+#         #     Simi = 0   #不应该走到该处
             
         
-        return Simi
+#         return Simi
         
     
-    front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
-    front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
-    for i in range(len(event.front_boxes)):
-        front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
-        front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
+#     front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
+#     front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+#     for i in range(len(event.front_boxes)):
+#         front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
+#         front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
     
-    back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
-    back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
-    for i in range(len(event.back_boxes)):
-        back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
-        back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
+#     back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
+#     back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+#     for i in range(len(event.back_boxes)):
+#         back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
+#         back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
     
-    if len(front_feats):
-        front_simi = calsiml(front_feats, stdfeat)
-    if len(back_feats):
-        back_simi = calsiml(back_feats, stdfeat)
+#     if len(front_feats):
+#         front_simi = calsiml(front_feats, stdfeat)
+#     if len(back_feats):
+#         back_simi = calsiml(back_feats, stdfeat)
+
+#     '''前后摄相似度融合策略'''
+#     if len(front_feats) and len(back_feats):
+#         diff_simi = abs(front_simi - back_simi) 
+#         if diff_simi>0.15:
+#             Similar = max([front_simi, back_simi])
+#         else:
+#             Similar = (front_simi+back_simi)/2
+#     elif len(front_feats) and len(back_feats)==0:
+#         Similar = front_simi
+#     elif len(front_feats)==0 and len(back_feats):
+#         Similar = back_simi
+#     else:
+#         Similar = None   # 在event.front_feats和event.back_feats同时为空时
+
+#     return Similar
 
-    '''前后摄相似度融合策略'''
-    if len(front_feats) and len(back_feats):
-        diff_simi = abs(front_simi - back_simi) 
-        if diff_simi>0.15:
-            Similar = max([front_simi, back_simi])
-        else:
-            Similar = (front_simi+back_simi)/2
-    elif len(front_feats) and len(back_feats)==0:
-        Similar = front_simi
-    elif len(front_feats)==0 and len(back_feats):
-        Similar = back_simi
-    else:
-        Similar = None   # 在event.front_feats和event.back_feats同时为空时
 
-    return Similar
 
 
 def simi_matrix():
-    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\single_event" 
+    evtpaths = r"/home/wqg/dataset/pipeline/contrast/single_event_V10/evtobjs/"
     
-    stdlib = read_usearch()
-    events = get_eventlist()
-    for evtpath in events:
-        evtname = os.path.basename(evtpath)
-        _, barcode = evtname.split("_")
+    stdfeatPath = r"/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/"
+    resultPath = r"/home/wqg/dataset/performence_dataset/result/" 
+
+    evt_paths, bcdSet = get_evtList(evtpaths)
+    
+    ## read std features
+    stdDict={}
+    evtDict = {}
+    for barcode in bcdSet:          
+        stdpath = os.path.join(stdfeatPath, f"{barcode}.json")
+        if not os.path.isfile(stdpath):
+            continue
         
-        # 生成事件与相应标准特征集
-        event = ShoppingEvent(evtpath)
-        stdfeat = stdlib[barcode] 
+        with open(stdpath, 'r', encoding='utf-8') as f:
+            stddata = json.load(f)
+            feat = np.array(stddata["value"])
+            stdDict[barcode] = feat
+
+    for evtpath in evt_paths:
+        barcode = Path(evtpath).stem.split("_")[-1]
+
+        if barcode not in stdDict.keys():
+            continue
         
-        Similar = calc_simil(event, stdfeat)
+        # try:
+        #     with open(evtpath, 'rb') as f:  
+        #         evtdata = pickle.load(f)
+        # except Exception as e:
+        #     print(evtname)
+            
+        with open(evtpath, 'rb') as f:  
+            event = pickle.load(f)
+
+        stdfeat = stdDict[barcode] 
+        
+        Similar = calsimi_vs_stdfeat_new(event, stdfeat)
         
         # 构造 boxes 子图存储路径
         subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg")
@@ -217,9 +245,9 @@ def simi_matrix():
                     evtfeat = np.concatenate((evtfeat, event.back_feats[i]), axis=0)
                 imgpaths = event.back_imgpaths
                 
-            assert len(boxes)==len(evtfeat), f"Please check the Event: {evtname}"
+            assert len(boxes)==len(evtfeat), f"Please check the Event: {event.evtname}"
             if len(boxes)==0: continue
-            print(evtname)
+            print(event.evtname)
 
             matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
             simi_1d = matrix.flatten()
@@ -309,8 +337,8 @@ def simi_matrix():
             mean_diff = abs(mean_values[1]-mean_values[0])
             ax[0, 1].set_title(f"mean diff: {mean_diff:.3f}")
         if len(max_values)==2:
-            max_values = abs(max_values[1]-max_values[0])
-            ax[0, 2].set_title(f"max diff: {max_values:.3f}")
+            max_diff = abs(max_values[1]-max_values[0])
+            ax[0, 2].set_title(f"max diff: {max_diff:.3f}")
         try:
             fig.suptitle(f"Similar: {Similar:.3f}", fontsize=16)
         except Exception as e:
@@ -319,19 +347,14 @@ def simi_matrix():
         pltpath = os.path.join(subimgpath, f"hist_max_{kpercent}%_.png")
         plt.savefig(pltpath) 
         
-        pltpath1 = os.path.join(histpath, f"{evtname}_.png")
+        pltpath1 = os.path.join(histpath, f"{event.evtname}_.png")
         plt.savefig(pltpath1) 
         
         
         plt.close()
 
-    
-
-    
-
 
 def main():
-    
     simi_matrix()
     
     

contrast/genfeats.py

@@ -38,13 +38,13 @@ def get_std_barcodeDict(bcdpath, savepath, bcdSet):
     '''
     inputs:
         bcdpath: 已清洗的barcode样本图像，如果barcode下有'base'文件夹，只选用该文件夹下图像
-                 (default =  r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771')
+                 (default =  r'\\\\192.168.1.28\\share\\已标注数据备份\\对比数据\\barcode\\barcode_1771')
     功能：
         生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}，
         savepath: 字典存储地址，文件名格式：barcode.pickle
     '''
     
-    # savepath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes'  
+    # savepath = r'\\\\192.168.1.28\\share\\测试_202406\\contrast\\std_barcodes'  
     
     '''读取数据集中 barcode 列表'''
     stdBarcodeList = []

contrast/one2n_contrast.py

@@ -6,57 +6,11 @@ Created on Wed Dec 18 11:49:01 2024
 """
 import os
 import pickle
-import copy
 import numpy as np
 from pathlib import Path
 import matplotlib.pyplot as plt
 from scipy.spatial.distance import cdist
-from utils.event import ShoppingEvent
-
-
-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 evtname in os.listdir(sourcePath):        
-        bname, ext = os.path.splitext(evtname)
-        source_path = os.path.join(sourcePath, evtname)
-        
-        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
-        
-        evt = bname.split('_')
-        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
-        if not condt: 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(evtname)
-        except Exception as e:
-            errEvents.append(source_path)
-            print(f"Error: {evtname}, {e}")        
-        # k += 1
-        # if k==1:
-        #     break
-    
-    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')
+from utils.tools import init_eventDict
 
 def read_eventdict(eventDataPath):
     evtDict = {}

contrast/one2one_contrast.py

@@ -27,188 +27,24 @@ Created on Fri Aug 30 17:53:03 2024
 
 """
 import numpy as np
-import cv2
 import os
 import sys
 import random 
 import pickle
 import json
-import random
-import copy
-import sys
-# import torch
-import time
-# import json
+
 from pathlib import Path
-from scipy.spatial.distance import cdist
 import matplotlib.pyplot as plt
-import shutil
-from datetime import datetime
-# from openpyxl import load_workbook, Workbook  
 
-# from config import config as conf
-# from model import resnet18 as resnet18
-# from feat_inference import inference_image
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))
 
-sys.path.append(r"D:\DetectTracking")
-from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file
-from tracking.utils.plotting import Annotator, colors
-from feat_extract.config import config as conf
-from feat_extract.inference import FeatsInterface
-from utils.event import ShoppingEvent, save_data
+from utils.calsimi import calsimi_vs_stdfeat, calsimi_vs_stdfeat_new
+from utils.tools import get_evtList, init_eventDict
+from utils.databits import data_precision_compare
 from genfeats import gen_bcd_features
-from event_test import calc_simil
-from one2n_contrast import init_eventDict
-
-
-
-
-
-def int8_to_ft16(arr_uint8, amin, amax):
-    arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16)
-    
-    return arr_ft16
-    
-def ft16_to_uint8(arr_ft16):
-    # pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle"
-    
-    # with open(pickpath, 'rb') as f:  
-    #     edict = pickle.load(f)
-        
-    # arr_ft16 = edict['feats']
-    
-    amin = np.min(arr_ft16)
-    amax = np.max(arr_ft16)
-    arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin)
-    arr_uint8 = arr_ft255.astype(np.uint8)
-
-    arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax)
-
-    arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
-
-    return arr_uint8, arr_ft16_
-
-
-def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
-    evt, stdbcd, label = evtMessage
-    rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
-
-    matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
-    simi_mean = np.mean(matrix)
-    simi_max  = np.max(matrix)
-    stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
-    evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
-    simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
-    rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
-    
-    
-    ##================================================================= float16
-    stdfeat_ft16 = stdfeat.astype(np.float16)
-    evtfeat_ft16 = evtfeat.astype(np.float16)
-    stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
-    evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
-    
-    
-    matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
-    simi_mean_ft16 = np.mean(matrix_ft16)
-    simi_max_ft16  = np.max(matrix_ft16)
-    stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
-    evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
-    simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
-    rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
-    
-    '''****************** uint8 is ok!!!!!! ******************'''
-    ##=================================================================== uint8
-    # stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
-    # evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
-    
-    stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
-    evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
-    stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
-    evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
-
-    absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
-    
-    matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
-    simi_mean_ft16_ = np.mean(matrix_ft16_)
-    simi_max_ft16_  = np.max(matrix_ft16_)
-    stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
-    evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
-    simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
-    rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
-    
-    if not save:
-        return
-    
-
-    ##========================================================= save as float32
-    rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
-    with open(rppath, 'wb') as f:  
-        pickle.dump(rltdata, f)
-        
-    rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
-    with open(rtpath, 'w', encoding='utf-8') as f:
-        for result in rltdata:
-            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
-            line = ', '.join(part)
-            f.write(line + '\n')
-
-
-    ##========================================================= save as float16        
-    rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
-    with open(rppath_ft16, 'wb') as f:  
-        pickle.dump(rltdata_ft16, f)
-    
-    rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
-    with open(rtpath_ft16, 'w', encoding='utf-8') as f:
-        for result in rltdata_ft16:
-            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
-            line = ', '.join(part)
-            f.write(line + '\n')
-    
-
-    ##=========================================================== save as uint8        
-    rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
-    with open(rppath_uint8, 'wb') as f:  
-        pickle.dump(rltdata_ft16_, f)
-    
-    rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
-    with open(rtpath_uint8, 'w', encoding='utf-8') as f:
-        for result in rltdata_ft16_:
-            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
-            line = ', '.join(part)
-            f.write(line + '\n')
-
-
-
-
-def simi_calc(event, stdfeat):
-    evtfeat = event.feats_compose
-    if isinstance(event.feats_select, list):
-        if len(event.feats_select) and len(event.feats_select[0]):
-            evtfeat = event.feats_select[0]
-        else:
-            return None, None, None
-    else:
-        evtfeat = event.feats_select
-
-    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]
-
-    matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
-    matrix[matrix < 0] = 0
-    
-    simi_mean = np.mean(matrix)
-    simi_max  = np.max(matrix)
-    stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
-    evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
-    simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
-    
-    return simi_mean, simi_max, simi_mfeat[0,0]
 
 
 def build_std_evt_dict():
@@ -218,18 +54,6 @@ def build_std_evt_dict():
     '''
     
     stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and (p.suffix=='.json' or p.suffix=='.pickle')]
-    
-    '''*********** USearch ***********'''
-    # stdFeaturePath = r"D:\contrast\stdlib\v11_test.json"
-    # stdBarcode = []
-    # stdlib = {}
-    # with open(stdFeaturePath, 'r', encoding='utf-8') as f:
-    #     data = json.load(f)
-    # for dic in data['total']:
-    #     barcode = dic['key']
-    #     feature = np.array(dic['value'])
-    #     stdBarcode.append(barcode)
-    #     stdlib[barcode] = feature
 
     '''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ==='''    
     evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir() 
@@ -259,10 +83,7 @@ def build_std_evt_dict():
                 stddata = pickle.load(f)
                 feat = stddata["feats_ft32"]
                 stdDict[barcode] = feat
-                
-                
-                
-    
+
     '''*********** USearch ***********'''        
     # stdDict = {}
     # for barcode in barcodes:
@@ -282,7 +103,7 @@ def build_std_evt_dict():
         
     return evtList, evtDict, stdDict
 
-def one2SN_pr(evtList, evtDict, stdDict):  
+def one2SN_pr(evtList, evtDict, stdDict, simType="simple"):  
     
     std_barcodes = set([bcd for _, bcd in evtList])
     
@@ -312,8 +133,14 @@ 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 = calc_simil(event, stdfeat)
+            
+            if simType=="typea":
+                simi_mean, simi_max, simi_mfeat = calsimi_vs_stdfeat(event, stdfeat)
+            elif simType=="typeb":
+                pass
+            else:
+                simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(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:
@@ -376,6 +203,10 @@ def one2SN_pr(evtList, evtDict, stdDict):
     ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}")     
     ax.legend()
     plt.show()
+    
+    rltpath = os.path.join(similPath, f'pr_1toSN_{simType}.png')
+    plt.savefig(rltpath)
+    
     ## ============================= 1:N 展厅 直方图'''
     fig, axes = plt.subplots(2, 2)
     axes[0, 0].hist(tp_simi, bins=60, range=(-0.2, 1), edgecolor='black')
@@ -391,11 +222,14 @@ def one2SN_pr(evtList, evtDict, stdDict):
     axes[1, 1].set_xlim([-0.2, 1])
     axes[1, 1].set_title(f'FN({len(fn_simi)})')
     plt.show()
+    
+    rltpath = os.path.join(similPath, f'hist_1toSN_{simType}.png')
+    plt.savefig(rltpath)
 
     
 
 
-def one2one_simi(evtList, evtDict, stdDict):  
+def one2one_simi(evtList, evtDict, stdDict, simType):  
     
     barcodes = set([bcd for _, bcd in evtList])
     '''======1 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
@@ -421,31 +255,50 @@ def one2one_simi(evtList, evtDict, stdDict):
             continue
         
         stdfeat = stdDict[stdbcd]  # float32
-    
-        simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
+        
+        if simType=="typea":
+            simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat)
+        elif simType=="typeb":
+            pass
+        else:
+            simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat(event, stdfeat)
+        
         if simi_mean is None:
             continue
     
-        rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat))
+        rltdata.append((label, stdbcd, evtname, simi_mean, simi_1, simi_2))
         
         '''================ float32、16、int8 精度比较与存储 ============='''
-        # data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
+        # data_precision_compare(stdfeat, evtfeat, mergePairs[i], similPath, save=True)
     
     errorFile_one2one = list(set(errorFile_one2one))
     
     return rltdata, errorFile_one2one
     
 
-def one2one_pr(evtList, evtDict, stdDict): 
+def one2one_pr(evtList, evtDict, stdDict, simType="simple"): 
     
-    rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict)
+    rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict, simType)
     
     Same, Cross = [], []
+    
     for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
-        if label == "same":
+        if simType=="simple" and label == "same":
             Same.append(simi_max)
-        if label == "diff":
+        if simType=="simple" and label == "diff":
             Cross.append(simi_max)
+            
+        if simType=="typea" and label == "same":
+            Same.append(simi_mean)
+        if simType=="typea" and label == "diff":
+            Cross.append(simi_mean)
+        
+                 
+    # for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
+    #     if label == "same":
+    #         Same.append(simi_mean)
+    #     if label == "diff":
+    #         Cross.append(simi_mean)
 
     Same = np.array(Same)
     Cross = np.array(Cross)
@@ -508,7 +361,7 @@ def one2one_pr(evtList, evtDict, stdDict):
     ax.legend()
     plt.show() 
     
-    rltpath = os.path.join(similPath, 'pr.png')
+    rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png')
     plt.savefig(rltpath)  # svg, png, pdf
     
     
@@ -521,7 +374,7 @@ def one2one_pr(evtList, evtDict, stdDict):
     axes[1].set_xlim([-0.2, 1])
     axes[1].set_title(f'TN({len(Cross)})')
     
-    rltpath = os.path.join(similPath, 'hist.png')
+    rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png')
     plt.savefig(rltpath) 
     
 
@@ -529,158 +382,25 @@ def one2one_pr(evtList, evtDict, stdDict):
     
     
 
-def gen_eventdict(sourcePath, saveimg=True):         
-    k, errEvents = 0, []
-    for source_path in sourcePath:
-        evtpath, bname = os.path.split(source_path)
-        
-        ## 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('_')
-        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
-        if not condt: continue
 
 
-        # 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
-
-        try:
-            event = ShoppingEvent(source_path, stype=source_type)
-            # save_data(event, resultPath)
-            
-            with open(pickpath, 'wb') as f:
-                pickle.dump(event, f)
-            print(bname)
-        except Exception as e:
-            errEvents.append(source_path)
-            print(e)
-        
-        # k += 1
-        # 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 = [], []
-#     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)
-        
-        ## 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])       
-            evtpaths.append(fpath)
-    
-    bcdSet = set(bcdList)
-    
-    return evtpaths, bcdSet 
-
-
-
-# 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(): 
+def test_one2one_one2SN(simType): 
     '''1:1性能评估''' 
     
-    # evtpaths, bcdSet = get_evtList()
+    # evtpaths, bcdSet = get_evtList(eventSourcePath)
     
     '''=== 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)
     
     '''==== 2. 生成事件字典, 只需运行一次 ====================''' 
-    
-    # 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)
-        
-        
+    # init_eventDict(eventSourcePath, eventDataPath, source_type)
 
-    '''==== 2. 基于事件barcode集和标准库barcode交集构造事件集合 ========='''
+    '''==== 3. 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()
 
-    one2one_pr(evtList, evtDict, stdDict)
+    one2one_pr(evtList, evtDict, stdDict, simType)
     
-    one2SN_pr(evtList, evtDict, stdDict) 
+    one2SN_pr(evtList, evtDict, stdDict, simType) 
         
 if __name__ == '__main__':
     '''
@@ -694,21 +414,10 @@ if __name__ == '__main__':
         (7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
     '''
     
-    # stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
-    # 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"
+    stdSamplePath = "/home/wqg/dataset/total_barcode/totalBarcode"
+    stdBarcodePath = "/home/wqg/dataset/total_barcode/bcdpath"
+    stdFeaturePath = "/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/"
     
-    # 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"
-    
-    # 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\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\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): 
@@ -719,18 +428,24 @@ if __name__ == '__main__':
             "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 = '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"
+    source_type = 'source'   # 'source', 'data', 'realtime'
+    simType = "typea"  # "simple", "typea", "typeb"
     
-    eventDataPath = os.path.join(resultPath, "evtobjs_0304_1")
-    similPath = os.path.join(resultPath, "simidata_0304_1")
+    evttype = "single_event_V10"
+    # evttype = "single_event_V5"
+    # evttype = "performence_V10"
+    # evttype = "performence_V5"
+    eventSourcePath  = "/home/wqg/dataset/pipeline/yrt/{}/shopping_pkl".format(evttype)
+
+    resultPath = "/home/wqg/dataset/pipeline/contrast/{}".format(evttype)
+    eventDataPath = os.path.join(resultPath, "evtobjs")
+    similPath = os.path.join(resultPath, "simidata")
     if not os.path.exists(eventDataPath): 
         os.makedirs(eventDataPath) 
     if not os.path.exists(similPath): 
         os.makedirs(similPath)
 
-    test_one2one_one2SN()
+    test_one2one_one2SN(simType)
 
 
 

contrast/onsite_contrast_pr.py

@@ -16,7 +16,11 @@ from pathlib import Path
 import matplotlib.pyplot as plt
 import sys
 
-sys.path.append(r"D:\DetectTracking")
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))
+
 from tracking.utils.read_data import read_similar
 
 def read_one2one_data(filepath):
@@ -531,21 +535,23 @@ def contrast_pr(evtPaths):
 
     
     # bcdSet = set(bcdList)
-    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 = 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 = 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")
+    # 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\2025-3-3"
+    evtpaths = r"/home/wqg/dataset/test_base_dataset/single_event/source"
     contrast_pr(evtpaths)
     
     

contrast/trail2trail.py

@@ -0,0 +1,172 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+  取出再放回场景下商品轨迹特征比对方式与性能分析
+  
+Created on Tue Apr  1 17:17:47 2025
+@author: wqg
+"""
+import os
+import pickle
+import random
+import numpy as np
+from pathlib import Path
+import matplotlib.pyplot as plt
+from scipy.spatial.distance import cdist
+from utils.calsimi import calsiml, calsimi_vs_evts
+
+def read_eventdict(evtpaths):
+    evtDict = {}
+    for filename in os.listdir(evtpaths):
+        evtname, ext = os.path.splitext(filename)
+        if ext != ".pickle": continue
+
+        evtpath = os.path.join(evtpaths, filename)
+        with open(evtpath, 'rb') as f:  
+            evtdata = pickle.load(f)
+        evtDict[evtname] = evtdata
+    
+    
+    return evtDict
+
+
+    
+def compute_show_pr(Same, Cross):
+    TPFN = len(Same)
+    TNFP = len(Cross)
+
+    Recall_Pos, Recall_Neg = [], []
+    Precision_Pos, Precision_Neg = [], []
+    Correct = []
+    Thresh = np.linspace(-0.2, 1, 100)
+    for th in Thresh:
+        TP = np.sum(Same >= th)
+        FN = np.sum(Same < th)
+        # FN = TPFN - TP
+        
+        TN = np.sum(Cross < th)
+        FP = np.sum(Cross >= th)
+        # FP = TNFP - TN
+
+        
+        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, 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, 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.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()
+    plt.show() 
+    
+    # rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png')
+    # plt.savefig(rltpath)  # svg, png, pdf
+    
+    
+    fig, axes = plt.subplots(2,1)
+    axes[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black')
+    axes[0].set_xlim([-0.2, 1])
+    axes[0].set_title(f'TP({len(Same)})')
+
+    axes[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black')
+    axes[1].set_xlim([-0.2, 1])
+    axes[1].set_title(f'TN({len(Cross)})')
+    
+    # rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png')
+    # plt.savefig(rltpath) 
+    
+
+    plt.show()
+
+
+
+def trail_to_trail(evtpaths, rltpaths):
+    # select the method type of how to calculate the feat similarity of trail
+    simType = 2
+    
+    ##1. read all the ShoppingEvent object in the dir 'evtpaths'
+    evtDicts = read_eventdict(evtpaths)
+    
+    ##2. Combine event object with the same barcode
+    barcodes, evtpairDict = [], {}
+    for k in evtDicts.keys():
+        evt = k.split('_')
+        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
+        if not condt: continue
+        
+        barcode = evt[-1]
+        if barcode not in evtpairDict.keys():
+            evtpairDict[barcode] = []
+            barcodes.append(barcode)
+        
+        evtpairDict[barcode].append(evtDicts[k])
+    barcodes = set(barcodes)
+    
+    AA_list, AB_list = [], []
+    for barcode in evtpairDict.keys():
+        events = evtpairDict[barcode]
+        if len(events)>1:
+            evta, evtb = random.sample(events, 2)
+            AA_list.append((evta, evtb, "same"))
+        
+        evtc = random.sample(events, 1)[0]
+        
+        dset = list(barcodes.symmetric_difference(set([barcode]))) 
+        bcd = random.sample(dset, 1)[0]
+        evtd = random.sample(evtpairDict[bcd], 1)[0]
+        AB_list.append((evtc, evtd, "diff"))
+        
+    mergePairs = AA_list + AB_list
+    
+    ##3. calculate the similar of two event: evta, evtb
+    new_pirs = []
+    for evta, evtb, label in mergePairs:
+        similar = calsimi_vs_evts(evta, evtb, simType)
+        if similar is None: 
+            continue
+        new_pirs.append((label, round(similar, 3), evta.evtname[:15], evtb.evtname[:15]))
+
+    ##4. compute PR and showing
+    Same = np.array([s for label, s, _, _ in new_pirs if label=="same"])
+    Cross = np.array([s for label, s, _, _ in new_pirs if label=="diff"])
+    compute_show_pr(Same, Cross)
+
+
+def main():
+    evttypes = ["single_event_V10", "single_event_V5", "performence_V10", "performence_V5"]
+    # evttypes = ["single_event_V10"]
+    
+    for evttype in evttypes:
+        evtpaths  = "/home/wqg/dataset/pipeline/contrast/{}/evtobjs/".format(evttype)
+        rltpaths = "/home/wqg/dataset/pipeline/yrt/{}/yolos_tracking".format(evttype)
+    
+        trail_to_trail(evtpaths, rltpaths)
+
+
+
+if __name__ == '__main__':
+    main()
+    
+    
+    

contrast/utils/calsimi.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 31 16:25:43 2025
+
+@author: wqg
+"""
+import numpy as np
+from scipy.spatial.distance import cdist
+
+
+def get_topk_percent(data, k):
+    """
+    获取数据中最大的 k% 的元素
+    """
+    # 将数据转换为 NumPy 数组
+    if isinstance(data, list):
+        data = np.array(data)
+    
+    percentile = np.percentile(data, 100-k)
+    top_k_percent = data[data >= percentile]
+    
+    return top_k_percent
+def cluster(data, thresh=0.15):
+    # data = np.array([0.1, 0.13, 0.7, 0.2, 0.8, 0.52, 0.3, 0.7, 0.85, 0.58])
+    # data = np.array([0.1, 0.13, 0.2, 0.3])
+    # data = np.array([0.1])
+    
+    if isinstance(data, list):
+        data = np.array(data)
+    
+    data1 = np.sort(data)
+    cluter, Cluters,  = [data1[0]], []
+    for i in range(1, len(data1)):
+        if data1[i] - data1[i-1]< thresh:
+            cluter.append(data1[i])
+        else:
+            Cluters.append(cluter)
+            cluter = [data1[i]]
+    Cluters.append(cluter)
+    
+    clt_center = []
+    for clt in Cluters:
+        ## 是否应该在此处限制一个聚类中的最小轨迹样本数，应该将该因素放在轨迹分析中
+        # if len(clt)>=3:
+        #     clt_center.append(np.mean(clt))
+        clt_center.append(np.mean(clt))
+    
+    # print(clt_center)
+        
+    return clt_center
+
+def calsiml(feat1, feat2, topkp=75, cluth=0.15):
+    '''轨迹样本和标准特征集样本相似度的选择策略'''
+    matrix = 1 - cdist(feat1, feat2, 'cosine')
+    simi_max = []
+    for i in range(len(matrix)):
+        sim = np.mean(get_topk_percent(matrix[i, :], topkp))
+        simi_max.append(sim)
+    cltc_max = cluster(simi_max, cluth)
+    Simi = max(cltc_max) 
+    
+    ## cltc_max为空属于编程考虑不周，应予以排查解决 
+    # if len(cltc_max):
+    #     Simi = max(cltc_max) 
+    # else:
+    #     Simi = 0   #不应该走到该处
+
+    return Simi
+
+
+def calsimi_vs_stdfeat_new(event, stdfeat):
+    '''事件与标准库的对比策略
+       该比对策略是否可以拓展到事件与事件的比对？
+    '''
+    front_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
+    front_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(event.front_boxes)):
+        front_boxes = np.concatenate((front_boxes, event.front_boxes[i]), axis=0)
+        front_feats = np.concatenate((front_feats, event.front_feats[i]), axis=0)
+    
+    back_boxes = np.empty((0, 9), dtype=np.float64) ##和类doTracks兼容 
+    back_feats = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(event.back_boxes)):
+        back_boxes = np.concatenate((back_boxes, event.back_boxes[i]), axis=0)
+        back_feats = np.concatenate((back_feats, event.back_feats[i]), axis=0)
+    
+    front_simi, back_simi = None, None
+    if len(front_feats):
+        front_simi = calsiml(front_feats, stdfeat)
+    if len(back_feats):
+        back_simi = calsiml(back_feats, stdfeat)
+
+    '''前后摄相似度融合策略'''
+    if len(front_feats) and len(back_feats):
+        diff_simi = abs(front_simi - back_simi) 
+        if diff_simi>0.15:
+            Similar = max([front_simi, back_simi])
+        else:
+            Similar = (front_simi+back_simi)/2
+    elif len(front_feats) and len(back_feats)==0:
+        Similar = front_simi
+    elif len(front_feats)==0 and len(back_feats):
+        Similar = back_simi
+    else:
+        Similar = None   # 在event.front_feats和event.back_feats同时为空时
+
+    return Similar, front_simi, back_simi
+
+
+
+
+
+def calsimi_vs_stdfeat(event, stdfeat):
+    evtfeat = event.feats_compose
+    if isinstance(event.feats_select, list):
+        if len(event.feats_select) and len(event.feats_select[0]):
+            evtfeat = event.feats_select[0]
+        else:
+            return None, None, None
+    else:
+        evtfeat = event.feats_select
+
+    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]
+
+    matrix = 1 - cdist(evtfeat, stdfeat, 'cosine')
+    matrix[matrix < 0] = 0
+    
+    simi_mean = np.mean(matrix)
+    simi_max  = np.max(matrix)
+    stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
+    evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
+    simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
+    
+    return simi_mean, simi_max, simi_mfeat[0,0]
+
+
+def calsimi_vs_evts(evta, evtb, simType=1):
+    if simType==1:
+        if len(evta.feats_compose) and len(evtb.feats_compose):
+            feata = evta.feats_compose
+            featb = evtb.feats_compose
+            matrix = 1 - cdist(feata, featb, 'cosine')
+            similar = np.mean(matrix)
+        else:
+            similar = None
+        return similar
+    
+    if simType==2:
+        if len(evta.feats_compose) and len(evtb.feats_compose):
+            feata = evta.feats_compose
+            featb = evtb.feats_compose
+            matrix = 1 - cdist(feata, featb, 'cosine')
+            similar = np.max(matrix)
+        else:
+            similar = None
+        return similar
+
+    if simType==3:
+        if len(evta.feats_compose) and len(evtb.feats_compose):
+            feata = evta.feats_compose
+            featb = evtb.feats_compose
+            similar = calsiml(feata, featb)
+        else:
+            similar = None
+        return similar
+            
+    
+    ##1. the front feats of evta, evtb 
+    fr_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(evta.front_feats)):
+        fr_feata = np.concatenate((fr_feata, evta.front_feats[i]), axis=0)
+        
+    fr_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(evtb.front_feats)):
+        fr_featb = np.concatenate((fr_featb, evtb.front_feats[i]), axis=0)
+        
+    ##2. the back feats of evta, evtb     
+    bk_feata = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(evta.back_feats)):
+        bk_feata = np.concatenate((bk_feata, evta.back_feats[i]), axis=0)
+   
+    bk_featb = np.empty((0, 256), dtype=np.float64) ##和类doTracks兼容 
+    for i in range(len(evtb.back_feats)):
+        bk_featb = np.concatenate((bk_featb, evtb.back_feats[i]), axis=0)
+    
+    
+    front_simi, back_simi = None, None
+    if len(fr_feata) and len(fr_featb):
+        front_simi = calsiml(fr_feata, fr_featb)
+    
+    if len(bk_feata) and len(bk_featb):
+        back_simi = calsiml(bk_feata, bk_featb)
+
+    '''前后摄相似度融合策略'''
+    if front_simi is not None and back_simi is not None:
+        diff_simi = abs(front_simi - back_simi) 
+        if diff_simi>0.15:
+            similar = max([front_simi, back_simi])
+        else:
+            similar = (front_simi+back_simi)/2
+    elif front_simi is not None and back_simi is None:
+        similar = front_simi
+    elif front_simi is None and back_simi is not None:
+        similar = back_simi
+    else:
+        similar = None   # 在event.front_feats和event.back_feats同时为空时
+
+    return similar
+
+

contrast/utils/databits.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr  1 16:43:04 2025
+
+@author: wqg
+"""
+import os
+import pickle
+import numpy as np
+from scipy.spatial.distance import cdist
+
+
+def int8_to_ft16(arr_uint8, amin, amax):
+    arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16)
+    
+    return arr_ft16
+    
+def ft16_to_uint8(arr_ft16):
+    # pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle"
+    
+    # with open(pickpath, 'rb') as f:  
+    #     edict = pickle.load(f)
+        
+    # arr_ft16 = edict['feats']
+    
+    amin = np.min(arr_ft16)
+    amax = np.max(arr_ft16)
+    arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin)
+    arr_uint8 = arr_ft255.astype(np.uint8)
+
+    arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax)
+
+    arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
+
+    return arr_uint8, arr_ft16_
+
+
+def data_precision_compare(stdfeat, evtfeat, evtMessage, similPath='', save=True):
+    evt, stdbcd, label = evtMessage
+    rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
+
+    matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
+    simi_mean = np.mean(matrix)
+    simi_max  = np.max(matrix)
+    stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
+    evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
+    simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
+    rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
+    
+    
+    ##================================================================= float16
+    stdfeat_ft16 = stdfeat.astype(np.float16)
+    evtfeat_ft16 = evtfeat.astype(np.float16)
+    stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
+    evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
+    
+    
+    matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
+    simi_mean_ft16 = np.mean(matrix_ft16)
+    simi_max_ft16  = np.max(matrix_ft16)
+    stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
+    evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
+    simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
+    rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
+    
+    '''****************** uint8 is ok!!!!!! ******************'''
+    ##=================================================================== uint8
+    # stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
+    # evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
+    
+    stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
+    evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
+    stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
+    evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
+
+    absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
+    
+    matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
+    simi_mean_ft16_ = np.mean(matrix_ft16_)
+    simi_max_ft16_  = np.max(matrix_ft16_)
+    stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
+    evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
+    simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
+    rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
+    
+    if not save:
+        return
+    
+
+    ##========================================================= save as float32
+    rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
+    with open(rppath, 'wb') as f:  
+        pickle.dump(rltdata, f)
+        
+    rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
+    with open(rtpath, 'w', encoding='utf-8') as f:
+        for result in rltdata:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')
+
+
+    ##========================================================= save as float16        
+    rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
+    with open(rppath_ft16, 'wb') as f:  
+        pickle.dump(rltdata_ft16, f)
+    
+    rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
+    with open(rtpath_ft16, 'w', encoding='utf-8') as f:
+        for result in rltdata_ft16:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')
+    
+
+    ##=========================================================== save as uint8        
+    rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
+    with open(rppath_uint8, 'wb') as f:  
+        pickle.dump(rltdata_ft16_, f)
+    
+    rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
+    with open(rtpath_uint8, 'w', encoding='utf-8') as f:
+        for result in rltdata_ft16_:
+            part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
+            line = ', '.join(part)
+            f.write(line + '\n')

contrast/utils/event.py

@@ -5,19 +5,25 @@ Created on Tue Nov 26 17:35:05 2024
 @author: ym
 """
 import os
+import sys
 import cv2
 import pickle
 import numpy as np
 from pathlib import Path
 
-import sys
-sys.path.append(r"D:\DetectTracking")
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[2]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))
+    
 from tracking.utils.plotting import Annotator, colors
 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()

contrast/utils/tools.py

@@ -4,8 +4,81 @@ Created on Thu Oct 31 15:17:01 2024
 
 @author: ym
 """
+import os
 import numpy as np
+import pickle
+from pathlib import Path
 import matplotlib.pyplot as plt
+from .event import ShoppingEvent
+
+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 evtname in os.listdir(sourcePath):        
+        bname, ext = os.path.splitext(evtname)
+        source_path = os.path.join(sourcePath, evtname)
+        
+        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
+        
+        evt = bname.split('_')
+        condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
+        if not condt: 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(evtname)
+        except Exception as e:
+            errEvents.append(source_path)
+            print(f"Error: {evtname}, {e}")        
+        # k += 1
+        # if k==1:
+        #     break
+    
+    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')
+
+
+def get_evtList(evtpath):
+    '''==== 0. 生成事件列表和对应的 Barcodes 集合 ==========='''
+    bcdList, evtpaths = [], []
+    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)
+        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])       
+            evtpaths.append(fpath)
+    
+    bcdSet = set(bcdList)
+    
+    return evtpaths, bcdSet