20241218

contrast/event_test.py

@@ -7,7 +7,7 @@ Created on Mon Dec 16 18:56:18 2024
 import os
 import cv2
 
-from utils.event import ShoppingEvent
+from utils.event import ShoppingEvent, save_data
 
 def 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"
@@ -29,39 +29,14 @@ def main():
     events = list(set(events))
 
     '''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"    
+    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"    
     for evtpath in events:
-        evtname = os.path.basename(evtpath)
         event = ShoppingEvent(evtpath)
+        save_data(event, resultPath)
         
-        img_cat = event.draw_tracks()
+        print(event.evtname)
-        trajpath = os.path.join(resultPath, "trajectory")
-        if not os.path.exists(trajpath):
-            os.makedirs(trajpath)
-        traj_imgpath =  os.path.join(trajpath, evtname+".png")
-        cv2.imwrite(traj_imgpath, img_cat)
         
-        
-        ## 保存序列图像和轨迹子图
-        subimgpath = os.path.join(resultPath, f"{evtname}", "subimg")
-        imgspath = os.path.join(resultPath, f"{evtname}", "imgs")
-        if not os.path.exists(subimgpath):
-            os.makedirs(subimgpath)
-        if not os.path.exists(imgspath):
-            os.makedirs(imgspath)
- 
-        
-        subimgpairs = event.save_event_subimg(subimgpath)
-        for subimgName, subimg in subimgpairs:
-            spath = os.path.join(subimgpath, subimgName)
-            cv2.imwrite(spath, subimg)
 
-        imgpairs = event.plot_save_image(imgspath)
-        for imgname, img in imgpairs:
-            spath = os.path.join(imgspath, imgname)
-            cv2.imwrite(spath, img)
-
-        print(f"{evtname}")
 
     
 

contrast/one2n_contrast.py

@@ -1,498 +1,249 @@
 # -*- coding: utf-8 -*-
 """
-Created on Sat Jul ** 14:07:25 2024
+Created on Wed Dec 18 11:49:01 2024
-
-  现场测试精度、召回率分析程序，是 feat_select.py 的简化版，
-  但支持循环计算，并输出总的pr曲线
 
 @author: ym
 """
-
+import os
-
+import pickle
-
-import os.path
-import shutil
-
 import numpy as np
-import matplotlib.pyplot as plt
-import cv2
 from pathlib import Path
-import sys
+import matplotlib.pyplot as plt
-sys.path.append(r"D:\DetectTracking")
+from scipy.spatial.distance import cdist
-from tracking.utils.plotting import Annotator, colors
+from utils.event import ShoppingEvent
-from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
-from tracking.utils.plotting import draw_tracking_boxes, get_subimgs
-from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
 
 
-# =============================================================================
+def gen_eventdict(sourcePath, stype="data"):
-# def read_tracking_output(filepath):
+    '''stype: str, 
-#     boxes = []
+        'source': 由 videos 或 images 生成的 pickle 文件
-#     feats = []
+        'data': 从 data 文件中读取的现场运行数据
-#     with open(filepath, 'r', encoding='utf-8') as file:
-#         for line in file:  
-#             line = line.strip()  # 去除行尾的换行符和可能的空白字符
-#             
-#             if not line:  
-#                 continue 
-#             
-#             if line.endswith(','):  
-#                 line = line[:-1]
-#                 
-#             data = np.array([float(x) for x in line.split(",")])
-#             if data.size == 9:
-#                 boxes.append(data)
-#             if data.size == 256:
-#                 feats.append(data)
-#     
-#     return np.array(boxes), np.array(feats) 
-# =============================================================================
-
-def read_tracking_imgs(imgspath):
     '''
-    input:
+         
-        imgspath：该路径中的图像为Yolo算法的输入图像，640x512
+    k, errEvents = 0, []
-    output：
+    for source_path in sourcePath:
-        imgs_0：后摄图像，根据 frameId 进行了排序
+        evtpath, bname = os.path.split(source_path)
-        imgs_1：前摄图像，根据 frameId 进行了排序 
+        
-    '''
+        # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
-    imgs_0, frmIDs_0, imgs_1, frmIDs_1 = [], [], [], []
+        source_path = os.path.join(evtpath, bname)
-    
+
-    for filename in os.listdir(imgspath):
+        pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
-        file, ext = os.path.splitext(filename)
+        if os.path.isfile(pickpath): continue
-        flist = file.split('_')
+
-        if len(flist)==4 and ext==".jpg":
+        try:
-            camID, frmID = flist[0], int(flist[-1])
+            event = ShoppingEvent(source_path, stype)
-            imgpath = os.path.join(imgspath, filename)
+            # save_data(event, resultPath)
-            img = cv2.imread(imgpath)
             
-            if camID=='0':
+            with open(pickpath, 'wb') as f:
-                imgs_0.append(img)
+                pickle.dump(event, f)
-                frmIDs_0.append(frmID)
+            print(bname)
-            if camID=='1':
+        except Exception as e:
-                imgs_1.append(img)
+            errEvents.append(source_path)
-                frmIDs_1.append(frmID)
+            print(e)        
-                
+        # k += 1
-    if len(frmIDs_0):
+        # if k==1:
-        indice = np.argsort(np.array(frmIDs_0))
+        #     break
-        imgs_0 = [imgs_0[i] for i in indice ]
+      
-    if len(frmIDs_1):
+    errfile = os.path.join(resultPath, 'error_events.txt')
-        indice = np.argsort(np.array(frmIDs_1))
+    with open(errfile, 'w', encoding='utf-8') as f:
-        imgs_1 = [imgs_1[i] for i in indice ]
+        for line in errEvents:
-    
+            f.write(line + '\n')
-    return imgs_0, imgs_1
-    
 
-# =============================================================================
+def read_eventdict(eventDataPath):
-# def draw_tracking_boxes(imgs, tracks):
+    evtDict = {}
-#     '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+    for filename in os.listdir(eventDataPath):
-#                 0   1   2   3      4        5     6       7           8
+        evtname, ext = os.path.splitext(filename)
-#        关键：imgs中的次序和 track 中的 fid 对应
+        if ext != ".pickle": continue
-#     ''' 
-#     subimgs = []
-#     for *xyxy, tid, conf, cls, fid, bid in tracks:                  
-#         label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
-#         
-#         annotator = Annotator(imgs[int(fid-1)].copy())  
-#         if cls==0:
-#             color = colors(int(cls), True)
-#         elif tid>0 and cls!=0:
-#             color = colors(int(tid), True)
-#         else:
-#             color = colors(19, True)     # 19为调色板的最后一个元素
-#         
-#         pt2 = [p/2 for p in xyxy]
-#         annotator.box_label(pt2, label, color=color)
-#         img0 = annotator.result()
-#         
-#         subimgs.append(img0)
-#         
-#     return subimgs
-# =============================================================================
 
-def get_contrast_paths(pair, basepath):
+        evtpath = os.path.join(eventDataPath, filename)
-    assert(len(pair)==2 or len(pair)==3), "pair: seqdir, delete, barcodes"
+        with open(evtpath, 'rb') as f:  
-    
+            evtdata = pickle.load(f)
-    getout_fold = pair[0]       # 取出操作对应的文件夹
+        evtDict[evtname] = evtdata
-    relvt_barcode = pair[1]     # 取出操作对应放入操作的 Barcode
-    if len(pair)==3:
-        error_match = pair[2]       # 取出操作错误匹配的 Barcode
-    else:
-        error_match = ''
-        
-    
-    getoutpath, inputpath, errorpath = '', '', ''
-
-    day, hms = getout_fold.strip('_').split('-')
-    
-    input_folds, times = [], []
-    errmatch_folds, errmatch_times = [], []
-    for pathname in os.listdir(basepath):
-        if pathname.endswith('_'): continue
-        if os.path.isfile(os.path.join(basepath, pathname)):continue
-        infold = pathname.split('_')
-        if len(infold)!=2: continue  
-    
-        day1, hms1 = infold[0].split('-') 
-        
-        if day1==day and infold[1]==relvt_barcode and int(hms1)<int(hms):
-            input_folds.append(pathname)
-            times.append(int(hms1))
-        
-        if day1==day and len(error_match) and infold[1]==error_match and int(hms1)<int(hms):
-            errmatch_folds.append(pathname)
-            errmatch_times.append(int(hms1))
-
-    ''' 根据时间排序，选择离取出操作最近时间的文件夹，
-        作为取出操作应正确匹配的放入操作所对应的文件夹 '''
-    if len(input_folds):
-        indice = np.argsort(np.array(times)) 
-        input_fold = input_folds[indice[-1]]
-        
-        inputpath = os.path.join(basepath, input_fold)
-        
-    
-    '''取出操作错误匹配的放入操作对应的文件夹'''
-    if len(errmatch_folds):
-        indice = np.argsort(np.array(errmatch_times)) 
-        errmatch_fold = errmatch_folds[indice[-1]]
-        
-        errorpath = os.path.join(basepath, errmatch_fold)
-        
-
-    
-    '''放入事件文件夹地址、取出事件文件夹地址'''
-    getoutpath = os.path.join(basepath, getout_fold)
     
     
-    return getoutpath, inputpath, errorpath
+    return evtDict
-
-
-def save_tracking_imgpairs(pairs, savepath):
-    '''
-    pairs: 匹配事件对
-    savepath: 保存的目标文件夹
-    '''
-    def get_event_path(evtpath):
-        basepath, eventname = os.path.split(evtpath)
-        evt_path = ''
-        for filename in os.listdir(basepath):
-            if filename.find(eventname)==0:
-                evt_path = os.path.join(basepath, filename)
-                break
-        return evt_path
     
-    getoutpath = get_event_path(pairs[0])
-    inputpath = get_event_path(pairs[1])
 
-    if len(pairs) == 3:
+def one2n_pr(evtDicts):
-        errorpath = get_event_path(pairs[2])
+    tpevents, fnevents, fpevents, tnevents = [], [], [], []
-    else:
+    tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
-        errorpath = ''
+    errorFile_one2n = []
-
+    for evtname, event in evtDicts.items():
-    ''' 1. 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄
+        evt_names, evt_barcodes, evt_similars, evt_types = [], [], [], [] 
-        2. 读取放入、取出事件对应的 tracking 输出：boxes, feats
-        3. boxes绘制并保存图像序列
-        4. 截取并保存轨迹子图
-    '''
-    if len(getoutpath):
-        imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
         
-        getout_data_0 = os.path.join(getoutpath, '0_tracking_output.data')
+        for ndict in event.one2n:
-        getout_data_1 = os.path.join(getoutpath, '1_tracking_output.data')
+            nname = ndict["event"]
-        boxes_output_0, feats_output_0 = read_tracking_output(getout_data_0)
+            barcode = ndict["barcode"]
-        boxes_output_1, feats_output_1 = read_tracking_output(getout_data_1)
+            similar = ndict["similar"]
-        ImgsGetout_0 = draw_tracking_boxes(imgs_getout_0, boxes_output_0)
+            typee = ndict["type"].strip()
-        ImgsGetout_1 = draw_tracking_boxes(imgs_getout_1, boxes_output_1)
-        
-        SubimgsGetout_0 = get_subimgs(imgs_getout_0, boxes_output_0)
-        SubimgsGetout_1 = get_subimgs(imgs_getout_1, boxes_output_1)
-        
-        savedir = os.path.basename(getoutpath)
-
-    if len(inputpath):
-        imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
-        
-        input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
-        input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
-        boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
-        boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
-        ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
-        ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
-        
-        SubimgsInput_0 = get_subimgs(imgs_input_0, boxes_input_0)
-        SubimgsInput_1 = get_subimgs(imgs_input_1, boxes_input_1)
-
-        savedir =  savedir + '+' + os.path.basename(inputpath)
-
-    if len(errorpath):
-        imgs_error_0, imgs_error_1 = read_tracking_imgs(errorpath)
-        
-        error_data_0 = os.path.join(errorpath, '0_tracking_output.data')
-        error_data_1 = os.path.join(errorpath, '1_tracking_output.data')
-        boxes_error_0, feats_error_0 = read_tracking_output(error_data_0)
-        boxes_error_1, feats_error_1 = read_tracking_output(error_data_1)
-        ImgsError_0 = draw_tracking_boxes(imgs_error_0, boxes_error_0)
-        ImgsError_1 = draw_tracking_boxes(imgs_error_1, boxes_error_1)
-        
-        SubimgsError_0 = get_subimgs(imgs_error_0, boxes_error_0)
-        SubimgsError_1 = get_subimgs(imgs_error_0, boxes_error_0)
-
-        savedir = savedir + '+' + os.path.basename(errorpath)
-        
-    ''' savepath\pairs\savedir\eventpairs\保存画框后的图像序列 '''
-    entpairs = os.path.join(savepath, 'pairs', savedir, 'eventpairs')    
-    if not os.path.exists(entpairs):
-        os.makedirs(entpairs)
-    for fid, img in ImgsInput_0:
-        imgpath = os.path.join(entpairs, f'input_0_{fid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, img in ImgsInput_1:
-        imgpath = os.path.join(entpairs, f'input_1_{fid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, img in ImgsGetout_0:
-        imgpath = os.path.join(entpairs, f'getout_0_{fid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, img in ImgsGetout_1:
-        imgpath = os.path.join(entpairs, f'getout_1_{fid}.png')
-        cv2.imwrite(imgpath, img)
-    if 'ImgsError_0' in vars() and 'ImgsError_1' in vars():
-        for fid, img in ImgsError_0:
-            imgpath = os.path.join(entpairs, f'errMatch_0_{fid}.png')
-            cv2.imwrite(imgpath, img)
-        for fid, img in ImgsError_1:
-            imgpath = os.path.join(entpairs, f'errMatch_1_{fid}.png')
-            cv2.imwrite(imgpath, img)
-
-    ''' savepath\pairs\savedir\subimgpairs\保存轨迹子图 '''
-    subimgpairs = os.path.join(savepath, 'pairs', savedir, 'subimgpairs')    
-    if not os.path.exists(subimgpairs):
-        os.makedirs(subimgpairs)
-    for fid, bid, img in SubimgsGetout_0:
-        imgpath = os.path.join(subimgpairs, f'getout_0_{fid}_{bid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, bid, img in SubimgsGetout_1:
-        imgpath = os.path.join(subimgpairs, f'getout_1_{fid}_{bid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, bid, img in SubimgsInput_0:
-        imgpath = os.path.join(subimgpairs, f'input_0_{fid}_{bid}.png')
-        cv2.imwrite(imgpath, img)
-    for fid, bid, img in SubimgsInput_1:
-        imgpath = os.path.join(subimgpairs, f'input_1_{fid}_{bid}.png')
-        cv2.imwrite(imgpath, img)
-    if 'SubimgsError_0' in vars() and 'SubimgsError_1' in vars():
-        for fid, bid, img in SubimgsError_0:
-            imgpath = os.path.join(subimgpairs, f'errMatch_0_{fid}_{bid}.png')
-            cv2.imwrite(imgpath, img)
-        for fid, bid, img in SubimgsError_1:
-            imgpath = os.path.join(subimgpairs, f'errMatch_1_{fid}_{bid}.png')
-            cv2.imwrite(imgpath, img)
-
-
-def one2n_deleted(all_list): 
-    corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
-    for s_list in all_list:
-        seqdir = s_list['SeqDir'].strip()
-        delete = s_list['Deleted'].strip()
-        barcodes = [s.strip() for s in s_list['barcode']]
-
-
-        similarity_comp, similarity_front = [], []
-        for simil in s_list['similarity']:
-            ss = [float(s.strip()) for s in simil.split(',')]
             
-            similarity_comp.append(ss[0])
+            o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
-            if len(ss)==3:
+            if len(o2n_evt)==1:
-                similarity_front.append(ss[2])
+                o2nevt = o2n_evt[0]
-
-        if len(similarity_front):
-            similarity = [s for s in similarity_front]
-        else:
-            similarity = [s for s in similarity_comp]
-
-
-        index = similarity.index(max(similarity))
-        matched_barcode = barcodes[index]
-        if matched_barcode == delete:
-            corrpairs.append((seqdir, delete))
-            correct_similarity.append(max(similarity))
-        else:
-            errpairs.append((seqdir, delete, matched_barcode))
-            err_similarity.append(max(similarity))
-    
-        
-    return corrpairs, errpairs, correct_similarity, err_similarity
-
-
-
-def one2n_return(all_list): 
-    corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
-
-    for s_list in all_list:
-        seqdir = s_list['SeqDir'].strip()
-        delete = s_list['Deleted'].strip()
-        barcodes = [s.strip() for s in s_list['barcode']]
-        events = [s.strip() for s in s_list['event']]
-        types = [s.strip() for s in s_list['type']]
-
-        ## =================== 读入相似度值
-        similarity_comp, similarity_front = [], []
-        for simil in s_list['similarity']:
-            ss = [float(s.strip()) for s in simil.split(',')]
-            
-            similarity_comp.append(ss[0])
-            if len(ss)==3:
-                similarity_front.append(ss[2])
-
-        if len(similarity_front):
-            similarity = [s for s in similarity_front]
-        else:
-            similarity = [s for s in similarity_comp]
-
-
-        index = similarity.index(max(similarity))
-        matched_barcode = barcodes[index]
-        if matched_barcode == delete:
-            corrpairs.append((seqdir, events[index]))
-            corr_similarity.append(max(similarity))
-        else:
-            idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
-            idxmax, simimax = -1, -1
-            # idxmax, simimax = k, similarity[k] for k in idx if similarity[k] > simimax
-            for k in idx:
-                if similarity[k] > simimax:
-                    idxmax = k
-                    simimax = similarity[k]
-            if idxmax>-1:
-                input_event = events[idxmax]
             else:
-                input_event = ''
+                continue
+            if typee == "11":
+                boxes1 = event.front_trackingboxes
+                boxes2 = o2nevt.front_trackingboxes
+                
+                feat1 = event.front_trackingfeats
+                feat2 = o2nevt.front_trackingfeats
+            if typee == "10":
+                boxes1 = event.front_trackingboxes
+                boxes2 = o2nevt.back_trackingboxes
+                
+                feat1 = event.front_trackingfeats
+                feat2 = o2nevt.back_trackingfeats
+            if typee == "00":
+                boxes1 = event.back_trackingboxes
+                boxes2 = o2nevt.back_trackingboxes
+                
+                feat1 = event.back_trackingfeats
+                feat2 = o2nevt.back_trackingfeats
+            if typee == "01":
+                boxes1 = event.back_trackingboxes
+                boxes2 = o2nevt.front_trackingboxes
+                
+                feat1 = event.back_trackingfeats
+                feat2 = o2nevt.front_trackingfeats
+                
+            matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
+            simi_mean = np.mean(matrix)
+            simi_max  = np.max(matrix)
             
-            errpairs.append((seqdir, input_event, events[index]))
+            evt_names.append(nname)
-            err_similarity.append(max(similarity))
+            evt_barcodes.append(barcode)
-    
+            evt_similars.append(simi_mean)
-    return corrpairs, errpairs, corr_similarity, err_similarity
+            evt_types.append(typee)
-
-
-def test_rpath_deleted():
-    '''deletedBarcode.txt 格式的 1:n 数据结果文件, returnGoods.txt格式数据文件不需要调用该函数'''
-    
-    del_bfile = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
-    basepath = r'\\192.168.1.28\share\测试_202406\709'
-    savepath = r'D:\DetectTracking\contrast\result' 
-    saveimgs = True
-    
-    
-    
-    relative_paths = []
-
-    '''1. 读取 deletedBarcode 文件 '''
-    all_list = read_deletedBarcode_file(del_bfile)
-    
-    '''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
-    corrpairs, errpairs, _, _ = one2n_deleted(all_list)
-    
-    '''3. 构造事件组合(取出，放入并删除, 错误匹配) 对应路径 '''
-    for errpair in errpairs:
-        GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
         
-        pairs = (GetoutPath, InputPath, ErrorPath)
-        relative_paths.append(pairs)
         
-        print(InputPath)
+        if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
-        '''3. 获取 (取出，放入并删除, 错误匹配) 对应路径，保存相应轨迹图像'''
+            and len(evt_similars)==len(evt_types) and len(evt_names)>0:  
-        if saveimgs: 
+            
-            save_tracking_imgpairs(pairs, savepath)
+            maxsim = evt_similars[evt_similars.index(max(evt_similars))]
-
+            for i in range(len(evt_names)):
-def test_rpath_return():
+                bcd, simi = evt_barcodes[i], evt_similars[i]
-    return_bfile = r'\\192.168.1.28\share\测试_202406\1101\images\returnGoods.txt'
+                
-    basepath = r'\\192.168.1.28\share\测试_202406\1101\images'
+                if bcd==event.barcode and simi==maxsim:
-    savepath = r'D:\DetectTracking\contrast\result' 
+                    tpsimi.append(simi)
+                    tpevents.append(evtname)
+                elif bcd==event.barcode and simi!=maxsim:
+                    fnsimi.append(simi)
+                    fnevents.append(evtname)
+                elif bcd!=event.barcode and simi!=maxsim: 
+                    tnsimi.append(simi)
+                    tnevents.append(evtname)
+                elif bcd!=event.barcode and simi==maxsim and event.barcode in evt_barcodes:
+                    fpsimi.append(simi)
+                    fpevents.append(evtname)
+                else:
+                    errorFile_one2n.append(evtname)
+            
+            
+            
+    ''' 1:n 数据存储，需根据相似度排序'''
+    PPrecise, PRecall = [], []
+    NPrecise, NRecall = [], []
     
-    all_list = read_returnGoods_file(return_bfile)
+    Thresh = np.linspace(-0.2, 1, 100)
-    corrpairs, errpairs, _, _ = one2n_return(all_list)
+    for th in Thresh:
-    for corrpair in corrpairs:
+        '''============================= 1:n 计算'''
-        GetoutPath = os.path.join(basepath, corrpair[0])
+        TP = sum(np.array(tpsimi) >= th)
-        InputPath = os.path.join(basepath, corrpair[1])
+        FP = sum(np.array(fpsimi) >= th)
+        FN = sum(np.array(fnsimi) < th)
+        TN = sum(np.array(tnsimi) < th)
         
-        pairs = (GetoutPath, InputPath)
+        PPrecise.append(TP/(TP+FP+1e-6))
-        save_tracking_imgpairs(pairs, savepath)
+        PRecall.append(TP/(len(tpsimi)+len(fnsimi)+1e-6)) 
-    
+        NPrecise.append(TN/(TN+FN+1e-6))
-    for errpair in errpairs:
+        NRecall.append(TN/(len(tnsimi)+len(fpsimi)+1e-6))
-        GetoutPath = os.path.join(basepath, errpair[0])
-        InputPath = os.path.join(basepath, errpair[1])
-        ErrorPath = os.path.join(basepath, errpair[2])
         
-        pairs = (GetoutPath, InputPath, ErrorPath)
+        
-        save_tracking_imgpairs(pairs, savepath)
+    '''4. ============================= 1:n 曲线,''' 
-
+    fig, ax = plt.subplots()
-
+    ax.plot(Thresh, PPrecise, 'r', label='Precise_Pos: TP/TPFP')
-def test_one2n():
+    ax.plot(Thresh, PRecall, 'b', label='Recall_Pos: TP/TPFN')
-    '''
+    ax.plot(Thresh, NPrecise, 'g', label='Precise_Neg: TN/TNFP')
-    1:n 性能测试
+    ax.plot(Thresh, NRecall, 'c', label='Recall_Neg: TN/TNFN')
-    兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt
+    ax.set_xlim([0, 1])
-    fpath: 文件路径、或文件夹，其中包含多个 txt 文件
+    ax.set_ylim([0, 1])
-    savepath: pr曲线保存路径 
+    ax.grid(True)
-    '''
+    ax.set_title('1:n Precise & Recall')
-    # fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'        # deletedBarcode.txt
+    ax.set_xlabel(f"Event Num: {len(tpsimi)+len(fnsimi)}")     
-    fpath = r'\\192.168.1.28\share\测试_202406\1108_展厅模型v800测试'               # returnGoods.txt
+    ax.legend()
-    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+    plt.show()
-
+    ## ============================= 1:n 直方图'''
-    if os.path.isdir(fpath):
+    fig, axes = plt.subplots(2, 2)
-        filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath) 
+    axes[0, 0].hist(tpsimi, bins=60, edgecolor='black')
-                                            if f.find('.txt')>0 
+    axes[0, 0].set_xlim([-0.2, 1])
-                                            and (f.find('deletedBarcode')>=0 or f.find('returnGoods')>=0)]
+    axes[0, 0].set_title('TP')
-    elif os.path.isfile(fpath):
+    axes[0, 1].hist(fpsimi, bins=60, edgecolor='black')
-        filepaths = [fpath]
+    axes[0, 1].set_xlim([-0.2, 1])
-    else:
+    axes[0, 1].set_title('FP')
-        return
+    axes[1, 0].hist(tnsimi, bins=60, edgecolor='black')
+    axes[1, 0].set_xlim([-0.2, 1])
+    axes[1, 0].set_title('TN')
+    axes[1, 1].hist(fnsimi, bins=60, edgecolor='black')
+    axes[1, 1].set_xlim([-0.2, 1])
+    axes[1, 1].set_title('FN')
+    plt.show()
+      
+    return fpevents
+           
+def main():
     
-    if not os.path.exists(savepath):
+    '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
-        os.mkdir(savepath)
+    # gen_eventdict(sourcePath)
-
-    BarLists, blists = {}, []
-    for pth in filepaths:        
-        file = str(Path(pth).stem)
-        if file.find('deletedBarcode')>=0:
-            blist = read_deletedBarcode_file(pth)
-        if file.find('returnGoods')>=0:
-            blist = read_returnGoods_file(pth)
-
-        BarLists.update({file: blist})
-        blists.extend(blist)
  
-    if len(blists): BarLists.update({"Total": blists})
+    '''2. 读取时间字典 '''
-    for file, blist in BarLists.items():
+    evtDicts = read_eventdict(eventDataPath)
-        if all(b['filetype']=="deletedBarcode" for b in blist):
+    
-            _, _, correct_similarity, err_similarity = one2n_deleted(blist)
+    
-        if all(b['filetype']=="returnGoods" for b in blists):
+    '''3. 1:n 比对事件评估 '''
-            _, _, correct_similarity, err_similarity = one2n_return(blist)
+    fpevents = one2n_pr(evtDicts)
-        
-        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
-        
-        plt1 = show_recall_prec(recall, prec, ths)
-        # plt1.show()
-        plt1.xlabel(f'threshold, Num: {len(blist)}')
-        plt1.savefig(os.path.join(savepath, file+'_pr.png'))
-        # plt1.close()
-        
-        plt2 = showHist(err_similarity, correct_similarity)
-        plt2.show()
-        plt2.savefig(os.path.join(savepath, file+'_hist.png'))
-        # plt.close()
-
-
 
+    fpErrFile = str(Path(resultPath).joinpath("one2n_Error.txt"))
+    with open(fpErrFile, "w") as file:
+        for item in fpevents:
+            file.write(item + "\n")
+    
+    
+    
+     
+        
+            
+            
+            
 if __name__ == '__main__':
-    # test_one2n()
+    
-    test_rpath_return()     # returnGoods.txt
+    eventSourcePath  = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"]
-    # test_rpath_deleted()    # deleteBarcode.txt
+    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"
+    
+    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)
+    
+    main()
     
     
-    # try:
-    #     test_rpath_return()
-    #     test_rpath_deleted()
-    # except Exception as e:
-    #     print(e)
     
     
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    

contrast/one2n_contrast_old.py

@@ -0,0 +1,498 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul ** 14:07:25 2024
+
+  现场测试精度、召回率分析程序，是 feat_select.py 的简化版，
+  但支持循环计算，并输出总的pr曲线
+
+@author: ym
+"""
+
+
+
+import os.path
+import shutil
+
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+from pathlib import Path
+import sys
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.plotting import Annotator, colors
+from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_returnGoods_file
+from tracking.utils.plotting import draw_tracking_boxes, get_subimgs
+from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
+
+
+# =============================================================================
+# def read_tracking_output(filepath):
+#     boxes = []
+#     feats = []
+#     with open(filepath, 'r', encoding='utf-8') as file:
+#         for line in file:  
+#             line = line.strip()  # 去除行尾的换行符和可能的空白字符
+#             
+#             if not line:  
+#                 continue 
+#             
+#             if line.endswith(','):  
+#                 line = line[:-1]
+#                 
+#             data = np.array([float(x) for x in line.split(",")])
+#             if data.size == 9:
+#                 boxes.append(data)
+#             if data.size == 256:
+#                 feats.append(data)
+#     
+#     return np.array(boxes), np.array(feats) 
+# =============================================================================
+
+def read_tracking_imgs(imgspath):
+    '''
+    input:
+        imgspath：该路径中的图像为Yolo算法的输入图像，640x512
+    output：
+        imgs_0：后摄图像，根据 frameId 进行了排序
+        imgs_1：前摄图像，根据 frameId 进行了排序 
+    '''
+    imgs_0, frmIDs_0, imgs_1, frmIDs_1 = [], [], [], []
+    
+    for filename in os.listdir(imgspath):
+        file, ext = os.path.splitext(filename)
+        flist = file.split('_')
+        if len(flist)==4 and ext==".jpg":
+            camID, frmID = flist[0], int(flist[-1])
+            imgpath = os.path.join(imgspath, filename)
+            img = cv2.imread(imgpath)
+            
+            if camID=='0':
+                imgs_0.append(img)
+                frmIDs_0.append(frmID)
+            if camID=='1':
+                imgs_1.append(img)
+                frmIDs_1.append(frmID)
+                
+    if len(frmIDs_0):
+        indice = np.argsort(np.array(frmIDs_0))
+        imgs_0 = [imgs_0[i] for i in indice ]
+    if len(frmIDs_1):
+        indice = np.argsort(np.array(frmIDs_1))
+        imgs_1 = [imgs_1[i] for i in indice ]
+    
+    return imgs_0, imgs_1
+    
+
+# =============================================================================
+# def draw_tracking_boxes(imgs, tracks):
+#     '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
+#                 0   1   2   3      4        5     6       7           8
+#        关键：imgs中的次序和 track 中的 fid 对应
+#     ''' 
+#     subimgs = []
+#     for *xyxy, tid, conf, cls, fid, bid in tracks:                  
+#         label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
+#         
+#         annotator = Annotator(imgs[int(fid-1)].copy())  
+#         if cls==0:
+#             color = colors(int(cls), True)
+#         elif tid>0 and cls!=0:
+#             color = colors(int(tid), True)
+#         else:
+#             color = colors(19, True)     # 19为调色板的最后一个元素
+#         
+#         pt2 = [p/2 for p in xyxy]
+#         annotator.box_label(pt2, label, color=color)
+#         img0 = annotator.result()
+#         
+#         subimgs.append(img0)
+#         
+#     return subimgs
+# =============================================================================
+
+def get_contrast_paths(pair, basepath):
+    assert(len(pair)==2 or len(pair)==3), "pair: seqdir, delete, barcodes"
+    
+    getout_fold = pair[0]       # 取出操作对应的文件夹
+    relvt_barcode = pair[1]     # 取出操作对应放入操作的 Barcode
+    if len(pair)==3:
+        error_match = pair[2]       # 取出操作错误匹配的 Barcode
+    else:
+        error_match = ''
+        
+    
+    getoutpath, inputpath, errorpath = '', '', ''
+
+    day, hms = getout_fold.strip('_').split('-')
+    
+    input_folds, times = [], []
+    errmatch_folds, errmatch_times = [], []
+    for pathname in os.listdir(basepath):
+        if pathname.endswith('_'): continue
+        if os.path.isfile(os.path.join(basepath, pathname)):continue
+        infold = pathname.split('_')
+        if len(infold)!=2: continue  
+    
+        day1, hms1 = infold[0].split('-') 
+        
+        if day1==day and infold[1]==relvt_barcode and int(hms1)<int(hms):
+            input_folds.append(pathname)
+            times.append(int(hms1))
+        
+        if day1==day and len(error_match) and infold[1]==error_match and int(hms1)<int(hms):
+            errmatch_folds.append(pathname)
+            errmatch_times.append(int(hms1))
+
+    ''' 根据时间排序，选择离取出操作最近时间的文件夹，
+        作为取出操作应正确匹配的放入操作所对应的文件夹 '''
+    if len(input_folds):
+        indice = np.argsort(np.array(times)) 
+        input_fold = input_folds[indice[-1]]
+        
+        inputpath = os.path.join(basepath, input_fold)
+        
+    
+    '''取出操作错误匹配的放入操作对应的文件夹'''
+    if len(errmatch_folds):
+        indice = np.argsort(np.array(errmatch_times)) 
+        errmatch_fold = errmatch_folds[indice[-1]]
+        
+        errorpath = os.path.join(basepath, errmatch_fold)
+        
+
+    
+    '''放入事件文件夹地址、取出事件文件夹地址'''
+    getoutpath = os.path.join(basepath, getout_fold)
+    
+    
+    return getoutpath, inputpath, errorpath
+
+
+def save_tracking_imgpairs(pairs, savepath):
+    '''
+    pairs: 匹配事件对
+    savepath: 保存的目标文件夹
+    '''
+    def get_event_path(evtpath):
+        basepath, eventname = os.path.split(evtpath)
+        evt_path = ''
+        for filename in os.listdir(basepath):
+            if filename.find(eventname)==0:
+                evt_path = os.path.join(basepath, filename)
+                break
+        return evt_path
+    
+    getoutpath = get_event_path(pairs[0])
+    inputpath = get_event_path(pairs[1])
+
+    if len(pairs) == 3:
+        errorpath = get_event_path(pairs[2])
+    else:
+        errorpath = ''
+
+    ''' 1. 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄
+        2. 读取放入、取出事件对应的 tracking 输出：boxes, feats
+        3. boxes绘制并保存图像序列
+        4. 截取并保存轨迹子图
+    '''
+    if len(getoutpath):
+        imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
+        
+        getout_data_0 = os.path.join(getoutpath, '0_tracking_output.data')
+        getout_data_1 = os.path.join(getoutpath, '1_tracking_output.data')
+        boxes_output_0, feats_output_0 = read_tracking_output(getout_data_0)
+        boxes_output_1, feats_output_1 = read_tracking_output(getout_data_1)
+        ImgsGetout_0 = draw_tracking_boxes(imgs_getout_0, boxes_output_0)
+        ImgsGetout_1 = draw_tracking_boxes(imgs_getout_1, boxes_output_1)
+        
+        SubimgsGetout_0 = get_subimgs(imgs_getout_0, boxes_output_0)
+        SubimgsGetout_1 = get_subimgs(imgs_getout_1, boxes_output_1)
+        
+        savedir = os.path.basename(getoutpath)
+
+    if len(inputpath):
+        imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
+        
+        input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
+        input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
+        boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
+        boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
+        ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
+        ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
+        
+        SubimgsInput_0 = get_subimgs(imgs_input_0, boxes_input_0)
+        SubimgsInput_1 = get_subimgs(imgs_input_1, boxes_input_1)
+
+        savedir =  savedir + '+' + os.path.basename(inputpath)
+
+    if len(errorpath):
+        imgs_error_0, imgs_error_1 = read_tracking_imgs(errorpath)
+        
+        error_data_0 = os.path.join(errorpath, '0_tracking_output.data')
+        error_data_1 = os.path.join(errorpath, '1_tracking_output.data')
+        boxes_error_0, feats_error_0 = read_tracking_output(error_data_0)
+        boxes_error_1, feats_error_1 = read_tracking_output(error_data_1)
+        ImgsError_0 = draw_tracking_boxes(imgs_error_0, boxes_error_0)
+        ImgsError_1 = draw_tracking_boxes(imgs_error_1, boxes_error_1)
+        
+        SubimgsError_0 = get_subimgs(imgs_error_0, boxes_error_0)
+        SubimgsError_1 = get_subimgs(imgs_error_0, boxes_error_0)
+
+        savedir = savedir + '+' + os.path.basename(errorpath)
+        
+    ''' savepath\pairs\savedir\eventpairs\保存画框后的图像序列 '''
+    entpairs = os.path.join(savepath, 'pairs', savedir, 'eventpairs')    
+    if not os.path.exists(entpairs):
+        os.makedirs(entpairs)
+    for fid, img in ImgsInput_0:
+        imgpath = os.path.join(entpairs, f'input_0_{fid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, img in ImgsInput_1:
+        imgpath = os.path.join(entpairs, f'input_1_{fid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, img in ImgsGetout_0:
+        imgpath = os.path.join(entpairs, f'getout_0_{fid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, img in ImgsGetout_1:
+        imgpath = os.path.join(entpairs, f'getout_1_{fid}.png')
+        cv2.imwrite(imgpath, img)
+    if 'ImgsError_0' in vars() and 'ImgsError_1' in vars():
+        for fid, img in ImgsError_0:
+            imgpath = os.path.join(entpairs, f'errMatch_0_{fid}.png')
+            cv2.imwrite(imgpath, img)
+        for fid, img in ImgsError_1:
+            imgpath = os.path.join(entpairs, f'errMatch_1_{fid}.png')
+            cv2.imwrite(imgpath, img)
+
+    ''' savepath\pairs\savedir\subimgpairs\保存轨迹子图 '''
+    subimgpairs = os.path.join(savepath, 'pairs', savedir, 'subimgpairs')    
+    if not os.path.exists(subimgpairs):
+        os.makedirs(subimgpairs)
+    for fid, bid, img in SubimgsGetout_0:
+        imgpath = os.path.join(subimgpairs, f'getout_0_{fid}_{bid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, bid, img in SubimgsGetout_1:
+        imgpath = os.path.join(subimgpairs, f'getout_1_{fid}_{bid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, bid, img in SubimgsInput_0:
+        imgpath = os.path.join(subimgpairs, f'input_0_{fid}_{bid}.png')
+        cv2.imwrite(imgpath, img)
+    for fid, bid, img in SubimgsInput_1:
+        imgpath = os.path.join(subimgpairs, f'input_1_{fid}_{bid}.png')
+        cv2.imwrite(imgpath, img)
+    if 'SubimgsError_0' in vars() and 'SubimgsError_1' in vars():
+        for fid, bid, img in SubimgsError_0:
+            imgpath = os.path.join(subimgpairs, f'errMatch_0_{fid}_{bid}.png')
+            cv2.imwrite(imgpath, img)
+        for fid, bid, img in SubimgsError_1:
+            imgpath = os.path.join(subimgpairs, f'errMatch_1_{fid}_{bid}.png')
+            cv2.imwrite(imgpath, img)
+
+
+def one2n_deleted(all_list): 
+    corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
+    for s_list in all_list:
+        seqdir = s_list['SeqDir'].strip()
+        delete = s_list['Deleted'].strip()
+        barcodes = [s.strip() for s in s_list['barcode']]
+
+
+        similarity_comp, similarity_front = [], []
+        for simil in s_list['similarity']:
+            ss = [float(s.strip()) for s in simil.split(',')]
+            
+            similarity_comp.append(ss[0])
+            if len(ss)==3:
+                similarity_front.append(ss[2])
+
+        if len(similarity_front):
+            similarity = [s for s in similarity_front]
+        else:
+            similarity = [s for s in similarity_comp]
+
+
+        index = similarity.index(max(similarity))
+        matched_barcode = barcodes[index]
+        if matched_barcode == delete:
+            corrpairs.append((seqdir, delete))
+            correct_similarity.append(max(similarity))
+        else:
+            errpairs.append((seqdir, delete, matched_barcode))
+            err_similarity.append(max(similarity))
+    
+        
+    return corrpairs, errpairs, correct_similarity, err_similarity
+
+
+
+def one2n_return(all_list): 
+    corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
+
+    for s_list in all_list:
+        seqdir = s_list['SeqDir'].strip()
+        delete = s_list['Deleted'].strip()
+        barcodes = [s.strip() for s in s_list['barcode']]
+        events = [s.strip() for s in s_list['event']]
+        types = [s.strip() for s in s_list['type']]
+
+        ## =================== 读入相似度值
+        similarity_comp, similarity_front = [], []
+        for simil in s_list['similarity']:
+            ss = [float(s.strip()) for s in simil.split(',')]
+            
+            similarity_comp.append(ss[0])
+            if len(ss)==3:
+                similarity_front.append(ss[2])
+
+        if len(similarity_front):
+            similarity = [s for s in similarity_front]
+        else:
+            similarity = [s for s in similarity_comp]
+
+
+        index = similarity.index(max(similarity))
+        matched_barcode = barcodes[index]
+        if matched_barcode == delete:
+            corrpairs.append((seqdir, events[index]))
+            corr_similarity.append(max(similarity))
+        else:
+            idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
+            idxmax, simimax = -1, -1
+            # idxmax, simimax = k, similarity[k] for k in idx if similarity[k] > simimax
+            for k in idx:
+                if similarity[k] > simimax:
+                    idxmax = k
+                    simimax = similarity[k]
+            if idxmax>-1:
+                input_event = events[idxmax]
+            else:
+                input_event = ''
+            
+            errpairs.append((seqdir, input_event, events[index]))
+            err_similarity.append(max(similarity))
+    
+    return corrpairs, errpairs, corr_similarity, err_similarity
+
+
+def test_rpath_deleted():
+    '''deletedBarcode.txt 格式的 1:n 数据结果文件, returnGoods.txt格式数据文件不需要调用该函数'''
+    
+    del_bfile = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
+    basepath = r'\\192.168.1.28\share\测试_202406\709'
+    savepath = r'D:\DetectTracking\contrast\result' 
+    saveimgs = True
+    
+    
+    
+    relative_paths = []
+
+    '''1. 读取 deletedBarcode 文件 '''
+    all_list = read_deletedBarcode_file(del_bfile)
+    
+    '''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
+    corrpairs, errpairs, _, _ = one2n_deleted(all_list)
+    
+    '''3. 构造事件组合(取出，放入并删除, 错误匹配) 对应路径 '''
+    for errpair in errpairs:
+        GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
+        
+        pairs = (GetoutPath, InputPath, ErrorPath)
+        relative_paths.append(pairs)
+        
+        print(InputPath)
+        '''3. 获取 (取出，放入并删除, 错误匹配) 对应路径，保存相应轨迹图像'''
+        if saveimgs: 
+            save_tracking_imgpairs(pairs, savepath)
+
+def test_rpath_return():
+    return_bfile = r'\\192.168.1.28\share\测试_202406\1101\images\returnGoods.txt'
+    basepath = r'\\192.168.1.28\share\测试_202406\1101\images'
+    savepath = r'D:\DetectTracking\contrast\result' 
+    
+    all_list = read_returnGoods_file(return_bfile)
+    corrpairs, errpairs, _, _ = one2n_return(all_list)
+    for corrpair in corrpairs:
+        GetoutPath = os.path.join(basepath, corrpair[0])
+        InputPath = os.path.join(basepath, corrpair[1])
+        
+        pairs = (GetoutPath, InputPath)
+        save_tracking_imgpairs(pairs, savepath)
+    
+    for errpair in errpairs:
+        GetoutPath = os.path.join(basepath, errpair[0])
+        InputPath = os.path.join(basepath, errpair[1])
+        ErrorPath = os.path.join(basepath, errpair[2])
+        
+        pairs = (GetoutPath, InputPath, ErrorPath)
+        save_tracking_imgpairs(pairs, savepath)
+
+
+def test_one2n():
+    '''
+    1:n 性能测试
+    兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt
+    fpath: 文件路径、或文件夹，其中包含多个 txt 文件
+    savepath: pr曲线保存路径 
+    '''
+    # fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'        # deletedBarcode.txt
+    fpath = r'\\192.168.1.28\share\测试_202406\1108_展厅模型v800测试'               # returnGoods.txt
+    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+
+    if os.path.isdir(fpath):
+        filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath) 
+                                            if f.find('.txt')>0 
+                                            and (f.find('deletedBarcode')>=0 or f.find('returnGoods')>=0)]
+    elif os.path.isfile(fpath):
+        filepaths = [fpath]
+    else:
+        return
+    
+    if not os.path.exists(savepath):
+        os.mkdir(savepath)
+
+    BarLists, blists = {}, []
+    for pth in filepaths:        
+        file = str(Path(pth).stem)
+        if file.find('deletedBarcode')>=0:
+            blist = read_deletedBarcode_file(pth)
+        if file.find('returnGoods')>=0:
+            blist = read_returnGoods_file(pth)
+
+        BarLists.update({file: blist})
+        blists.extend(blist)
+ 
+    if len(blists): BarLists.update({"Total": blists})
+    for file, blist in BarLists.items():
+        if all(b['filetype']=="deletedBarcode" for b in blist):
+            _, _, correct_similarity, err_similarity = one2n_deleted(blist)
+        if all(b['filetype']=="returnGoods" for b in blists):
+            _, _, correct_similarity, err_similarity = one2n_return(blist)
+        
+        recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+        
+        plt1 = show_recall_prec(recall, prec, ths)
+        # plt1.show()
+        plt1.xlabel(f'threshold, Num: {len(blist)}')
+        plt1.savefig(os.path.join(savepath, file+'_pr.png'))
+        # plt1.close()
+        
+        plt2 = showHist(err_similarity, correct_similarity)
+        plt2.show()
+        plt2.savefig(os.path.join(savepath, file+'_hist.png'))
+        # plt.close()
+
+
+
+if __name__ == '__main__':
+    # test_one2n()
+    test_rpath_return()     # returnGoods.txt
+    # test_rpath_deleted()    # deleteBarcode.txt
+    
+    
+    # try:
+    #     test_rpath_return()
+    #     test_rpath_deleted()
+    # except Exception as e:
+    #     print(e)
+    
+    

contrast/one2one_contrast.py

@@ -52,7 +52,7 @@ from tracking.utils.read_data import extract_data, read_tracking_output, read_si
 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 
+from utils.event import ShoppingEvent, save_data
 from genfeats import gen_bcd_features
 
 
@@ -84,86 +84,6 @@ def ft16_to_uint8(arr_ft16):
     return arr_uint8, arr_ft16_
 
 
-# =============================================================================
-# def plot_save_image(event, savepath):
-#     cameras = ('front', 'back')
-#     for camera in cameras:
-#         if camera == 'front':
-#             boxes = event.front_trackerboxes
-#             imgpaths = event.front_imgpaths
-#         else:
-#             boxes = event.back_trackerboxes
-#             imgpaths = event.back_imgpaths
-#             
-#     def array2list(bboxes):
-#         '''[x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]'''
-#         frame_ids = bboxes[:, 7].astype(int)
-#         fID = np.unique(bboxes[:, 7].astype(int))
-#         fboxes = []
-#         for f_id in fID: 
-#             idx = np.where(frame_ids==f_id)[0]
-#             box = bboxes[idx, :]  
-#             fboxes.append((f_id, box))
-#         return fboxes
-#     
-#         fboxes = array2list(boxes)
-#         
-#         for fid, fbox in fboxes:
-#             imgpath = imgpaths[int(fid-1)]
-#             
-#             image = cv2.imread(imgpath)
-# 
-#             annotator = Annotator(image.copy(), line_width=2)
-#             for i, *xyxy, tid, score, cls, fid, bid in enumerate(fbox):
-#                 label = f'{int(id), int(cls)}'
-#                 if tid >=0 and cls==0:
-#                     color = colors(int(cls), True)
-#                 elif tid >=0 and cls!=0:
-#                     color = colors(int(id), True)
-#                 else:
-#                     color = colors(19, True)     # 19为调色板的最后一个元素
-#                 annotator.box_label(xyxy, label, color=color)
-#             
-#             im0 = annotator.result()
-#             spath = os.path.join(savepath, Path(imgpath).name)
-#             cv2.imwrite(spath, im0)
-# 
-# 
-# def save_event_subimg(event, savepath):
-#     '''
-#         功能: 保存一次购物事件的轨迹子图
-#         9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, 
-#                  back_boxes, front_boxes, back_feats, front_feats, 
-#                  feats_compose, feats_select
-#         子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
-#     '''
-#     cameras = ('front', 'back')
-#     for camera in cameras:
-#         if camera == 'front':
-#             boxes = event.front_boxes
-#             imgpaths = event.front_imgpaths
-#         else:
-#             boxes = event.back_boxes
-#             imgpaths = event.back_imgpaths
-# 
-#         for i, box in enumerate(boxes):
-#             x1, y1, x2, y2, tid, score, cls, fid, bid = box
-#             
-#             imgpath = imgpaths[int(fid-1)]
-#             image = cv2.imread(imgpath)
-# 
-#             subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
-#             
-#             camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
-#             subimgName = f"cam{camerType}_{i}_tid{int(tid)}_fid({int(fid)}, {frameID}).png"
-#             spath = os.path.join(savepath, subimgName)
-#             
-#             cv2.imwrite(spath, subimg)
-#     # basename = os.path.basename(event['filepath'])
-#     print(f"Image saved: {os.path.basename(event.eventpath)}")
-# =============================================================================
-
-
 def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
     evt, stdbcd, label = evtMessage
     rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
@@ -289,43 +209,7 @@ def one2one_simi():
         with open(evtpath, 'rb') as f:  
             evtdata = pickle.load(f)
         evtDict[evtname] = evtdata
-    
-    
-    '''======4.1 事件轨迹子图保存 ======================'''
-    error_event = []
-    for evtname, event in evtDict.items():
-        pairpath = os.path.join(subimgPath, f"{evtname}")
-        if not os.path.exists(pairpath):
-            os.makedirs(pairpath)
-            try:
-                subimgpairs = event.save_event_subimg(pairpath)
-                for subimgName, subimg in subimgpairs:
-                    spath = os.path.join(pairpath, subimgName)
-                    cv2.imwrite(spath, subimg)
-                
-            except Exception as e:
-                error_event.append(evtname)
-        
-        img_path = os.path.join(imagePath, f"{evtname}")
-        if not os.path.exists(img_path):
-            os.makedirs(img_path)
-            try:
-                imgpairs = event.plot_save_image(img_path)
-                for imgname, img in imgpairs:
-                    spath = os.path.join(img_path, imgname)
-                    cv2.imwrite(spath, img)
-            except Exception as e:
-                error_event.append(evtname)
 
-            
-         
-            
-                
-    errfile = os.path.join(subimgPath, f'error_event.txt')
-    with open(errfile, 'w', encoding='utf-8') as f:
-        for line in error_event:
-            f.write(line + '\n')
-            
     
     '''======4.2 barcode 标准图像保存 =================='''
     # for stdbcd in barcodes:    
@@ -440,16 +324,13 @@ def compute_precise_recall(rltdata):
     rltpath = os.path.join(similPath, 'pr.png')
     plt.savefig(rltpath)  # svg, png, pdf
     
-  
 
 def gen_eventdict(sourcePath, saveimg=True):         
-    eventList = []
+    k, errEvents = 0, []
-    errEvents = []
-    k = 0
     for source_path in sourcePath:
         evtpath, bname = os.path.split(source_path)
         
-        bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
+        # bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
         source_path = os.path.join(evtpath, bname)
 
         pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
@@ -457,7 +338,8 @@ def gen_eventdict(sourcePath, saveimg=True):
 
         try:
             event = ShoppingEvent(source_path, stype="data")
-            eventList.append(event)
+            # save_data(event, resultPath)
+            
             with open(pickpath, 'wb') as f:
                 pickle.dump(event, f)
             print(bname)
@@ -465,11 +347,12 @@ def gen_eventdict(sourcePath, saveimg=True):
             errEvents.append(source_path)
             print(e)
         
-        k += 1
+        # k += 1
-        if k==1:
+        # if k==1:
-            break
+        #     break
+
             
-    errfile = os.path.join(eventDataPath, f'error_events.txt')
+    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')
@@ -477,6 +360,8 @@ def gen_eventdict(sourcePath, saveimg=True):
 
 
 def test_one2one(): 
+    
+    '''==== 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(evtpath):
@@ -486,9 +371,9 @@ def test_one2one():
             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))
-           
+               
-    bcdSet = set(bcdList)         
+    '''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ===========''' 
-    '''==== 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!")
     
@@ -511,40 +396,25 @@ if __name__ == '__main__':
         (3) stdFeaturePath: 比对标准特征集特征存储地址
         (4) eventSourcePath: 事件地址
         (5) resultPath: 结果存储地址
-        (6) eventDataPath: 用于1:1比对的购物事件特征存储地址、对应子图存储地址
+        (6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
-        (7) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
+        (7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
-        (8) similPath: 1:1比对结果存储地址(事件级)
     '''
-    # stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_1979_已清洗"
-    # stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
-    # stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32"
-    # eventDataPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
-    # subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
-    # similPath = r"D:\DetectTracking\contrast\result\pickle"
-    # eventSourcePath  = [r'\\192.168.1.28\share\测试_202406\1101\images']
     
     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"D:\exhibition\dataset\feats"
-    resultPath = r"D:\exhibition\result\events"
+    
     # 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\1126_展厅模型v801测试"]
     
+    eventSourcePath  = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"]
+    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"
     
-    
+
-    '''定义当前事件存储地址及生成相应文件件'''
+    eventDataPath = os.path.join(resultPath, "evtobjs")
-    eventDataPath = os.path.join(resultPath, "1126", "evtobjs")
+    similPath = os.path.join(resultPath, "simidata")
-    subimgPath = os.path.join(resultPath, "1126", "subimgs")
-    imagePath = os.path.join(resultPath, "1126", "image")
-    similPath = os.path.join(resultPath, "1126", "simidata")
-    
     if not os.path.exists(eventDataPath): 
         os.makedirs(eventDataPath) 
-    if not os.path.exists(subimgPath): 
-        os.makedirs(subimgPath)
-    if not os.path.exists(imagePath): 
-        os.makedirs(imagePath)
     if not os.path.exists(similPath): 
         os.makedirs(similPath)
 

contrast/onsite_contrast_pr.py

@@ -104,7 +104,7 @@ def test_compare():
 
     plot_pr_curve(simiList)
     
-def one2one_pr(paths): 
+def contrast_pr(paths): 
     '''
     1:1
     
@@ -501,7 +501,7 @@ def one2one_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"
-    one2one_pr(evtpaths)
+    contrast_pr(evtpaths)
     
     
     

contrast/utils/event.py

@@ -19,6 +19,37 @@ from tracking.utils.read_data import extract_data, read_tracking_output, read_si
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 VID_FORMAT = ['.mp4', '.avi']
 
+def save_data(event, resultPath=None):
+    '''事件轨迹子图保存'''
+    if resultPath is None:
+        resultPath = os.path.dirname(os.path.abspath(__file__))
+        
+    
+    subimgpath = os.path.join(resultPath, f"{event.evtname}", "subimg")
+    imgspath = os.path.join(resultPath, f"{event.evtname}", "imgs")
+    if not os.path.exists(subimgpath):
+        os.makedirs(subimgpath)
+    if not os.path.exists(imgspath):
+        os.makedirs(imgspath)
+    ##(2) 保存轨迹中的子图
+    subimgpairs = event.save_event_subimg(subimgpath)
+    for subimgName, subimg in subimgpairs:
+        spath = os.path.join(subimgpath, subimgName)
+        cv2.imwrite(spath, subimg)
+    
+    ##(3) 保存序列图像
+    imgpairs = event.plot_save_image(imgspath)
+    for imgname, img in imgpairs:
+        spath = os.path.join(imgspath, imgname)
+        cv2.imwrite(spath, img)
+    ##(4) 保存轨迹散点图
+    img_cat = event.draw_tracks()
+    trajpath = os.path.join(resultPath, "trajectory")
+    if not os.path.exists(trajpath):
+        os.makedirs(trajpath)
+    traj_imgpath =  os.path.join(trajpath, event.evtname+".png")
+    cv2.imwrite(traj_imgpath, img_cat)
+
 
 def array2list(bboxes):
     '''
@@ -44,7 +75,7 @@ def array2list(bboxes):
 
 class ShoppingEvent:
     def __init__(self, eventpath, stype="data"):  
-        '''stype: str, 'pickle', 'data', '''
+        '''stype: str, 'source', 'data', '''
         
         self.eventpath = eventpath
         self.evtname = str(Path(eventpath).stem)
@@ -60,6 +91,7 @@ class ShoppingEvent:
         '''=========== process.data ==============================='''
         self.one2one = None
         self.one2n = None
+        self.one2SN = None
         
         '''=========== 0/1_track.data ============================='''
         self.back_yolobboxes = []
@@ -85,8 +117,8 @@ class ShoppingEvent:
 
         if stype=="data":
             self.from_datafile(eventpath)
-        if stype=="pickle":
+        if stype=="source":
-            self.from_pklfile(eventpath)
+            self.from_source_pkl(eventpath)
             
         self.feats_select = []
         self.feats_compose = np.empty((0, 256), dtype=np.float64)
@@ -131,7 +163,7 @@ class ShoppingEvent:
         return kdata, outdata
 
     
-    def from_pklfile(self, eventpath):
+    def from_source_pkl(self, eventpath):
         
         with open(eventpath, 'rb') as f:
             ShoppingDict = pickle.load(f)
@@ -222,7 +254,7 @@ class ShoppingEvent:
             SimiDict = read_similar(procpath)
             self.one2one = SimiDict['one2one']
             self.one2n = SimiDict['one2n']
-            
+            self.one2SN = SimiDict['one2SN']
 
         '''=========== 0/1_track.data & 0/1_tracking_output.data ======='''
         for dataname in os.listdir(eventpath):
@@ -411,14 +443,10 @@ class ShoppingEvent:
         
         
 
-def main():
+def main():  
-    
-    
     # pklpath = r"D:\DetectTracking\evtresult\images2\ShoppingDict.pkl"
-    # evt = ShoppingEvent(pklpath, stype='pickle')
+    # evt = ShoppingEvent(pklpath, stype='source')
-    
+
-    
-    
     evtpath = 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\20241209-160248-08edd5f6-1806-45ad-babf-7a4dd11cea60_6973226721445"   
     evt = ShoppingEvent(evtpath, stype='data')
     
@@ -427,65 +455,7 @@ def main():
     cv2.imwrite("a.png", img_cat)
     
     
-    
+
-# =============================================================================
-# def main1():
-#     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"
-#     events = []
-#     text = (text1, text2)
-#     for txt in text:
-#         txtfile = os.path.join(evtpaths, txt)
-#         with open(txtfile, "r") as f:
-#             lines = f.readlines()
-#             for i, line in enumerate(lines):
-#                 line = line.strip()
-#                 if line:
-#                     fpath=os.path.join(evtpaths, line)
-#                     events.append(fpath)
-#                     
-#                     
-#     events = list(set(events))
-# 
-#     '''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"
-#     # eventDataPath = os.path.join(resultPath, "evtobjs")
-#     # subimgPath = os.path.join(resultPath, "subimgs")
-#     # imagePath = os.path.join(resultPath, "image")
-#     
-#     # if not os.path.exists(eventDataPath): 
-#     #     os.makedirs(eventDataPath) 
-#     # if not os.path.exists(subimgPath): 
-#     #     os.makedirs(subimgPath)
-#     # if not os.path.exists(imagePath): 
-#     #     os.makedirs(imagePath)
-# 
-#     
-#     for evtpath in events:
-#         event = ShoppingEvent(evtpath)
-#         
-#         
-#         evtname = os.path.basename(evtpath)
-#         subimgpath = os.path.join(resultPath, f"{evtname}", "subimg")
-#         imgspath = os.path.join(resultPath, f"{evtname}", "imgs")
-#         if not os.path.exists(subimgpath):
-#             os.makedirs(subimgpath)
-#         if not os.path.exists(imgspath):
-#             os.makedirs(imgspath)
-#         
-#         subimgpairs = event.save_event_subimg(subimgpath)
-#         
-#         for subimgName, subimg in subimgpairs:
-#             spath = os.path.join(subimgpath, subimgName)
-#             cv2.imwrite(spath, subimg)
-# 
-#         imgpairs = event.plot_save_image(imgspath)
-#         for imgname, img in imgpairs:
-#             spath = os.path.join(imgspath, imgname)
-#             cv2.imwrite(spath, img)
-# 
-# =============================================================================
 
 
 if __name__ == "__main__": 

说明文档.txt

@@ -155,58 +155,15 @@
 		stdfeat_infer(imgPath, featPath, bcdSet=None)
 		功能: 对 imgPath 中图像进行特征提取，生成只有一个key值的字典。
 			  {barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中
-	
+			  
-			
+			  
-	one2n_contrast.py
+	onsite_contrast_pr.py
-		test_one2n()
+		one2one_pr()
-			1:n 现场测试性能评估，输出 PR 曲线
+			现场试验输出数据的性能评估，输出1:1, 1:SN, 1:n的PR曲线和相似度直方图分布。
-			兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt, 
-			分别对应不同的文件读取函数：
-			 - read_deletedBarcode_file()
-			 - read_returnGoods_file()
-
-		one2n_return(all_list)
-			输入：从returnGoods.txt读取的数据
-			输出：
-				corrpairs：(取出事件, 正确匹配的放入事件)
-				errpairs：(取出事件, 放入事件, 错误匹配的放入事件)
-				corr_similarity: (正确匹配时的相似度)
-				err_similarity: (错误匹配时的相似度)
 		
 		
-		one2n_deleted(all_list)
+		test_compare()
-			输入: 从deletedBarcode.txt读取的数据
+			适用于202410前数据保存版本的，需调用 OneToOneCompare.txt		  
-			输出：
-				corrpairs：(取出事件, 取出的barcode)
-				errpairs：(取出事件, 取出的barcode, 错误匹配的barcode)
-				corr_similarity: (正确匹配时的相似度)
-				err_similarity: (错误匹配时的相似度)
-		
-		save_tracking_imgpairs(pairs, savepath)
-			输入： 
-				pairs：匹配时间对，len(2)=2 or 3, 对应正确匹配与错误匹配
-				savepath：结果保存地址，其中图像文件的命名为：取出事件 + 放入事件 + 错误匹配时间
-			子函数 get_event_path(), 扫码放入的对齐名
-				对于 returnGoods.txt, 放入事件的事件名和对应的文件夹名不一致，需要对齐
-		
-		test_rpath_deleted()
-			功能：
-				针对 eletedBarcode.txt 格式的 1:n 数据结果文件
-				获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
-				匹配事件分析, 实现函数：save_tracking_imgpairs()
-			重要参数：
-				del_barcode_file: 
-				basepath: 对应事件路径
-				savepath: 存储路径, 是函数 save_tracking_imgpairs() 的输入
-				saveimgs: Ture, False, 是否保存错误匹配的事件对
-		
-			get_contrast_paths()
-				针对 eletedBarcode.txt 格式的 1:n 数据结果文件,返回三元时间元组（getoutpath, inputpath, errorpath）
-		
-		test_rpath_return()
-			针对 returnGoods.txt 格式 1:n 数据文件，不需要调用函数get_contrast_paths()
-			获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
-			匹配事件分析, 实现函数：save_tracking_imgpairs()
 	
 	
 	one2one_contrast.py
@@ -258,7 +215,20 @@
 		
 		ft16_to_uint8()
 		
-		
+	one2n_contrast.py
+		执行1:n共需要3步：
+	    '''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
+    # gen_eventdict(sourcePath)
+ 
+    '''2. 读取时间字典 '''
+    evtDicts = read_eventdict(eventDataPath)
+    
+    
+    '''3. 1:n 比对事件评估 '''
+	
+
+
+	
 		
 
 
@@ -266,26 +236,67 @@
 
 	
 	
-	one2one_onsite.py
-		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
-		
-		标准特征向量生成
-		std_sample_path：图像样本的存储地址
-		std_barcode_path：对 std_sample_path 中文件列表进行遍历，形成{barcode: 图像样本地址}形式字典并进行存储
-		std_feature_path：调用 inference_image(), 对每一个barcode，生成字典并进行存储
-	
 
 	
-	genfeats.py
-		genfeatures(imgpath, bcdpath, featpath)
-		功能：生成标准特征向量的字典, 并保存为: barcode.pickle
-			  keys:  barcode, imgpaths, feats_ft32, feats_ft16, feats_uint8
-		参数：
-			(1) imgpath：图像样本的存储地址
-			(2) bcdpath：对 imgpath 中文件列表进行遍历，形成{barcode: 图像样本地址}形式字典并进行存储
-			(3) featpath：调用 inference_image(), 对每一个barcode，生成字典并进行存储
 			
+	one2n_contrast_old.py (disused)
+		test_one2n()
+			1:n 现场测试性能评估，输出 PR 曲线
+			兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt, 
+			分别对应不同的文件读取函数：
+			 - read_deletedBarcode_file()
+			 - read_returnGoods_file()
+
+		one2n_return(all_list)
+			输入：从returnGoods.txt读取的数据
+			输出：
+				corrpairs：(取出事件, 正确匹配的放入事件)
+				errpairs：(取出事件, 放入事件, 错误匹配的放入事件)
+				corr_similarity: (正确匹配时的相似度)
+				err_similarity: (错误匹配时的相似度)
+		
+		
+		one2n_deleted(all_list)
+			输入: 从deletedBarcode.txt读取的数据
+			输出：
+				corrpairs：(取出事件, 取出的barcode)
+				errpairs：(取出事件, 取出的barcode, 错误匹配的barcode)
+				corr_similarity: (正确匹配时的相似度)
+				err_similarity: (错误匹配时的相似度)
+		
+		save_tracking_imgpairs(pairs, savepath)
+			输入： 
+				pairs：匹配时间对，len(2)=2 or 3, 对应正确匹配与错误匹配
+				savepath：结果保存地址，其中图像文件的命名为：取出事件 + 放入事件 + 错误匹配时间
+			子函数 get_event_path(), 扫码放入的对齐名
+				对于 returnGoods.txt, 放入事件的事件名和对应的文件夹名不一致，需要对齐
+		
+		test_rpath_deleted()
+			功能：
+				针对 eletedBarcode.txt 格式的 1:n 数据结果文件
+				获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
+				匹配事件分析, 实现函数：save_tracking_imgpairs()
+			重要参数：
+				del_barcode_file: 
+				basepath: 对应事件路径
+				savepath: 存储路径, 是函数 save_tracking_imgpairs() 的输入
+				saveimgs: Ture, False, 是否保存错误匹配的事件对
+		
+			get_contrast_paths()
+				针对 eletedBarcode.txt 格式的 1:n 数据结果文件,返回三元时间元组（getoutpath, inputpath, errorpath）
+		
+		test_rpath_return()
+			针对 returnGoods.txt 格式 1:n 数据文件，不需要调用函数get_contrast_paths()
+			获得 1:n 情况下正确或匹配事件对（取出事件、放入事件、错误匹配事件）
+			匹配事件分析, 实现函数：save_tracking_imgpairs()
+	
+	
+	
+	
+	
+
+	
+
 			
 time_devide.py
 	runyolo()