abc

contrast/feat_select.py

@@ -16,7 +16,7 @@ from tracking.utils.read_data import extract_data, read_deletedBarcode_file, rea
 # from tracking.dotrack.dotracks import Track
 
 from one2n_contrast import compute_recall_precision, show_recall_prec
-from one2n_contrast import performance_evaluate
+from one2n_contrast import performance_evaluate, one2n_return, one2n_deleted
 
 def compute_similar(feat1, feat2):
 

contrast/genfeats.py

@@ -27,7 +27,7 @@ model.load_state_dict(torch.load(model_path, map_location=conf.device))
 model.eval()
 print('load model {} '.format(conf.testbackbone))
 
-def get_std_barcodeDict(bcdpath, savepath):
+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
@@ -42,10 +42,14 @@ def get_std_barcodeDict(bcdpath, savepath):
     '''读取数据集中 barcode 列表'''
     stdBarcodeList = []
     for filename in os.listdir(bcdpath):
-        filepath = os.path.join(bcdpath, filename)
+        # filepath = os.path.join(bcdpath, filename)
         # if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8: 
         #     continue
-        stdBarcodeList.append(filename)
+        if bcdSet is None:
+            stdBarcodeList.append(filename)
+        elif filename in bcdSet:
+            stdBarcodeList.append(filename)
+           
     
     bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList]
     
@@ -184,18 +188,15 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
 
 
 
-def genfeatures(imgpath, bcdpath, featpath):
+def genfeatures(imgpath, bcdpath, featpath, bcdSet=None):
+    ''' 生成标准特征集 '''
+    '''1. 提取 imgpath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
+          并存储于: bcdpath, 格式为 barcode.pickle'''
+    get_std_barcodeDict(imgpath, bcdpath, bcdSet)
     
-    get_std_barcodeDict(imgpath, bcdpath)
+    '''2. 特征提取，并保存至文件夹 featpath 中，也根据  bcdSet 交集执行'''
-    stdfeat_infer(bcdpath, featpath, bcdSet=None)
+    stdfeat_infer(bcdpath, featpath, bcdSet)
     
-    print(f"Features have generated, saved in: {featpath}")
-    
-    
-    
-
-
-
 def main():
     imgpath = r"\\192.168.1.28\share\展厅barcode数据\整理\zhantingBase"
     bcdpath = r"D:\exhibition\dataset\bcdpath"  

contrast/one2n_contrast.py

@@ -21,7 +21,7 @@ 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
+from tracking.utils.plotting import draw_tracking_boxes, get_subimgs
 from contrast.utils.tools import showHist, show_recall_prec, compute_recall_precision
 
 
@@ -148,13 +148,18 @@ def get_contrast_paths(pair, basepath):
     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)
@@ -163,33 +168,33 @@ def get_contrast_paths(pair, basepath):
     return getoutpath, inputpath, errorpath
 
 
-def save_tracking_imgpairs(pair, basepath, savepath):
+def save_tracking_imgpairs(pairs, savepath):
     '''
-    basepath: 原始测试数据文件夹的路径
+    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, inputpath, errorpath = get_contrast_paths(pair, basepath)
+    getoutpath = get_event_path(pairs[0])
-    
+    inputpath = get_event_path(pairs[1])
-    if len(inputpath)==0:
-        return
 
+    if len(pairs) == 3:
+        errorpath = get_event_path(pairs[2])
+    else:
+        errorpath = ''
 
-    '''==== 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 ===='''
+    ''' 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
-    '''==== 读取放入、取出事件对应的 tracking 输出：boxes, feats ===='''
+        4. 截取并保存轨迹子图
-    
+    '''
-    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)
-    
     if len(getoutpath):
         imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
         
@@ -199,9 +204,28 @@ def save_tracking_imgpairs(pair, basepath, savepath):
         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)
-    
-    if len(errorpath):
         
+        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')
@@ -211,37 +235,61 @@ def save_tracking_imgpairs(pair, basepath, savepath):
         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 = pair[0] + pair[1]
+        savedir = savedir + '+' + os.path.basename(errorpath)
-    if len(errorpath):
-        savedir = savedir + '_' + errorpath.split('_')[-1]
-    foldname = os.path.join(savepath, 'imgpairs', savedir)    
-    if not os.path.exists(foldname):
-        os.makedirs(foldname)
-
-    for i, img in enumerate(ImgsInput_0):
-        imgpath = os.path.join(foldname, f'input_0_{i}.png')
-        cv2.imwrite(imgpath, img)
-    for i, img in enumerate(ImgsInput_1):
-        imgpath = os.path.join(foldname, f'input_1_{i}.png')
-        cv2.imwrite(imgpath, img)
-    for i, img in enumerate(ImgsGetout_0):
-        imgpath = os.path.join(foldname, f'getout_0_{i}.png')
-        cv2.imwrite(imgpath, img)
-    for i, img in enumerate(ImgsGetout_1):
-        imgpath = os.path.join(foldname, f'getout_1_{i}.png')
-        cv2.imwrite(imgpath, img)
         
-    for i, img in enumerate(ImgsError_0):
+    ''' savepath\pairs\savedir\eventpairs\保存画框后的图像序列 '''
-        imgpath = os.path.join(foldname, f'errMatch_0_{i}.png')
+    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 i, img in enumerate(ImgsError_1):
+    for fid, img in ImgsInput_1:
-        imgpath = os.path.join(foldname, f'errMatch_1_{i}.png')
+        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): 
-def one2n_old(all_list): 
     corrpairs, errpairs, correct_similarity, err_similarity = [], [], [], []
     for s_list in all_list:
         seqdir = s_list['SeqDir'].strip()
@@ -277,8 +325,9 @@ def one2n_old(all_list):
 
 
 
-def one2n_new(all_list): 
+def one2n_return(all_list, basepath): 
-    corrpairs, correct_similarity, errpairs, err_similarity = [], [], [], []
+    corrpairs, corr_similarity, errpairs, err_similarity = [], [], [], []
+
     for s_list in all_list:
         seqdir = s_list['SeqDir'].strip()
         delete = s_list['Deleted'].strip()
@@ -305,7 +354,7 @@ def one2n_new(all_list):
         matched_barcode = barcodes[index]
         if matched_barcode == delete:
             corrpairs.append((seqdir, events[index]))
-            correct_similarity.append(max(similarity))
+            corr_similarity.append(max(similarity))
         else:
             idx = [i for i, name in enumerate(events) if name.split('_')[-1] == delete]
             idxmax, simimax = -1, -1
@@ -314,49 +363,80 @@ def one2n_new(all_list):
                 if similarity[k] > simimax:
                     idxmax = k
                     simimax = similarity[k]
-
+            if idxmax>-1:
-            errpairs.append((seqdir, events[idxmax], events[index]))
+                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
-    return errpairs, corrpairs, err_similarity, correct_similarity    
 
 
-# def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
+def test_rpath_deleted():
-def get_relative_paths(del_barcode_file, basepath, savepath, saveimgs=False):
+    '''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_barcode_file: 
+    del_bfile = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
-        deletedBarcode.txt 格式的 1:n 数据结果文件
+    basepath = r'\\192.168.1.28\share\测试_202406\709'
-        returnGoods.txt格式数据文件不需要调用该函数，one2n_old() 函数返回的 errpairs
+    savepath = r'D:\DetectTracking\contrast\result' 
-        中元素为三元元组（取出，放入, 错误匹配）
+    saveimgs = True
-    '''
+    
+    
+    
     relative_paths = []
 
     '''1. 读取 deletedBarcode 文件 '''
-    all_list = read_deletedBarcode_file(del_barcode_file)
+    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 '''
-    errpairs, corrpairs, _, _ = one2n_old(all_list)
+    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)
-        relative_paths.append((GetoutPath, InputPath, ErrorPath))
         
+        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:
+        if saveimgs: 
-            save_tracking_imgpairs(errpair, basepath, savepath)
+            save_tracking_imgpairs(pairs, savepath)
 
-    return relative_paths
+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'
-def one2n_test():
+    savepath = r'D:\DetectTracking\contrast\result' 
-    fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
-    fpath = r'\\192.168.1.28\share\测试_202406\1030\images'
     
+    all_list = read_returnGoods_file(return_bfile)
+    corrpairs, errpairs, _, _ = one2n_return(all_list, basepath)
+    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\returnGoods\all'               # returnGoods.txt
     savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
-    if not os.path.exists(savepath):
-        os.mkdir(savepath)
 
     if os.path.isdir(fpath):
         filepaths = [os.path.join(fpath, f) for f in os.listdir(fpath) 
@@ -366,37 +446,27 @@ def one2n_test():
         filepaths = [fpath]
     else:
         return
-
-     
-    FileFormat = {}
     
+    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:
-            FileFormat[file] = 'deletedBarcode'  
             blist = read_deletedBarcode_file(pth)
-        elif file.find('returnGoods')>=0:
+        if file.find('returnGoods')>=0:
-            FileFormat[file] = 'returnGoods'
             blist = read_returnGoods_file(pth)
-        else:
+
-            return
-            
- 
         BarLists.update({file: blist})
         blists.extend(blist)
  
-    BarLists.update({file: blist})
+    if len(blists): BarLists.update({"Total": blists})
-    BarLists.update({"Total": blists})
-    
     for file, blist in BarLists.items():
-        if FileFormat[file] == 'deletedBarcode':
+        if all(b['filetype']=="deletedBarcode" for b in blist):
-            _, _, err_similarity, correct_similarity = one2n_old(blist)
+            _, _, correct_similarity, err_similarity = one2n_deleted(blist)
-        elif FileFormat[file] == 'returnGoods':
+        if all(b['filetype']=="returnGoods" for b in blists):
-            _, _, err_similarity, correct_similarity = one2n_new(blist)
+            _, _, correct_similarity, err_similarity = one2n_return(blist)
-        else:
-            _, _, err_similarity, correct_similarity = one2n_old(blist)
-            
         
         recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
         
@@ -413,51 +483,16 @@ def one2n_test():
 
 
 
-
-def test_getreltpath():
-    '''
-    适用于：deletedBarcode.txt，不适用于：returnGoods.txt
-    '''
-
-    del_barcode_file = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
-    basepath = r'\\192.168.1.28\share\测试_202406\709'
-    
-    # del_barcode_file = r'\\192.168.1.28\share\测试_202406\1030\images\returnGoods.txt'
-    # basepath = r'\\192.168.1.28\share\测试_202406\1030\images'
-
-    savepath = r'D:\contrast\dataset\result' 
-    saveimgs = True
-    try:
-        relative_path = get_relative_paths(del_barcode_file, basepath, savepath, saveimgs)
-    except Exception as e:
-        print(f'Error Type: {e}')
-        
-
 if __name__ == '__main__':
-    
+    # test_one2n()
-    one2n_test()
+    test_rpath_return()     # returnGoods.txt
-    
+    test_rpath_deleted()    # deleteBarcode.txt
-    # test_getreltpath()
+    
-
+    
+    # try:
+    #     test_rpath_return()
+    #     test_rpath_deleted()
+    # except Exception as e:
+    #     print(e)
     
     
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

contrast/one2one_contrast.py

@@ -11,7 +11,7 @@ Created on Fri Aug 30 17:53:03 2024
             标准特征提取，并保存至文件夹 stdFeaturePath 中，
             也可在运行过程中根据与购物事件集合 barcodes 交集执行
     2. 1:1 比对性能测试，
-        func: contrast_performance_evaluate(resultPath)
+        func: one2one_eval(resultPath)
         (1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict
         (2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list
         (3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度;
@@ -32,86 +32,83 @@ import os
 import sys
 import random 
 import pickle
-import torch
+# import torch
 import time
-import json
+# 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 openpyxl import load_workbook, Workbook  
 
-# Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出
 # from config import config as conf
-# from model import resnet18
+# from model import resnet18 as resnet18
-# from inference import load_contrast_model
+# from feat_inference import inference_image
-# from inference import featurize
-# embedding_size = conf.embedding_size
-# img_size = conf.img_size
-# device = conf.device
-# model = load_contrast_model()
 
 sys.path.append(r"D:\DetectTracking")
-from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file
+from tracking.utils.read_data import extract_data, read_tracking_output, read_one2one_simi, read_deletedBarcode_file
-
-from config import config as conf
-from model import resnet18 as resnet18
-from feat_inference import inference_image
 
+from genfeats import genfeatures, stdfeat_infer
 
 IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
 
-'''
-共6个地址：
-    (1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
-    (2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
-    (3) stdFeaturePath: 比对标准特征集特征存储地址
-    (4) eventFeatPath: 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
-    (5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
-    (6) resultPath: 1:1比对结果存储地址
-'''
-
-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"
-eventFeatPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
-subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
-resultPath = r"D:\DetectTracking\contrast\result\pickle"
-if not os.path.exists(resultPath): 
-    os.makedirs(resultPath)
-
-##============ load resnet mdoel 
-model = resnet18().to(conf.device)
-# model = nn.DataParallel(model).to(conf.device)
-model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
-model.eval()
-print('load model {} '.format(conf.testbackbone))
 
 
-def creat_shopping_event(eventPath, subimgPath=False):
+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 creat_shopping_event(eventPath):
     '''构造放入商品事件字典，这些事件需满足条件：
         1) 前后摄至少有一条轨迹输出
         2) 保存有帧图像，以便裁剪出 boxe 子图
     '''
-    # filename = "20240723-155413_6904406215720"
-    
-    '''filename下为一次购物事件'''    
-    eventName = os.path.basename(eventPath)
 
-    '''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================'''
+    '''evtName 为一次购物事件'''    
-    nmlist = eventName.split('_')
+    evtName = os.path.basename(eventPath)
-    # if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
+    evtList = evtName.split('_')
-    #     return
+
-    if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[1])<11:
+    '''================ 0. 检查 evtName 及 eventPath 正确性和有效性 ================'''
+    if evtName.find('2024')<0 and len(evtList[0])!=15:
         return
     if not os.path.isdir(eventPath): 
         return
+    
+    if len(evtList)==1 or (len(evtList)==2 and len(evtList[1])==0):
+        barcode = ''
+    else:
+        barcode = evtList[-1]
+    
+    if len(evtList)==3 and evtList[-1]== evtList[-2]:
+        evtType = 'input'
+    else:
+        evtType = 'other'
 
     '''================ 1. 构造事件描述字典，暂定 9 items ==============='''
     event = {}
-    event['barcode'] = eventName.split('_')[1]
+    event['barcode'] = barcode
-    event['type'] = 'input'
+    event['type'] = evtType
     event['filepath'] = eventPath
     event['back_imgpaths'] = []
     event['front_imgpaths'] = []
@@ -120,7 +117,8 @@ def creat_shopping_event(eventPath, subimgPath=False):
     event['back_feats'] = np.empty((0, 256), dtype=np.float64)
     event['front_feats'] = np.empty((0, 256), dtype=np.float64)
     event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
-    # event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+    event['one2one_simi'] = None
+    event['feats_select'] = np.empty((0, 256), dtype=np.float64)
     
     
     '''================= 2. 读取 data 文件 ============================='''
@@ -144,8 +142,12 @@ def creat_shopping_event(eventPath, subimgPath=False):
             elif CamerType == '1':
                 event['front_boxes'] = tracking_output_boxes
                 event['front_feats'] = tracking_output_feats
-    
+                
-    
+        if dataname.find("process.data")==0:
+            simiDict = read_one2one_simi(datapath)
+            event['one2one_simi'] = simiDict
+
+
     if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
         return None
     
@@ -165,16 +167,8 @@ def creat_shopping_event(eventPath, subimgPath=False):
     if len(ft_feats):
         event['feats_select'] = ft_feats
 
-    # pickpath = os.path.join(savePath, f"{filename}.pickle")
-    # with open(pickpath, 'wb') as f:
-    #    pickle.dump(event, f)
-    # print(f"Event: {filename}")
-    
-    # if subimgPath==False:
-    #     eventList.append(event)
-    #     continue
 
-    '''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
+    '''================ 3. 读取图像文件地址，并按照帧ID排序 ============='''
     frontImgs, frontFid = [], []
     backImgs, backFid = [], []
     for imgname in os.listdir(eventPath):
@@ -194,11 +188,11 @@ def creat_shopping_event(eventPath, subimgPath=False):
     frontIdx = np.argsort(np.array(frontFid))
     backIdx = np.argsort(np.array(backFid))
     
-    '''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
+    '''3.1 生成依据帧 ID 排序的前后摄图像地址列表'''
     frontImgs = [frontImgs[i] for i in frontIdx]
     backImgs = [backImgs[i] for i in backIdx]
 
-    '''2.2 将前、后摄图像路径添加至事件字典'''   
+    '''3.2 将前、后摄图像路径添加至事件字典'''   
     
     
     bfid = event['back_boxes'][:, 7].astype(np.int64)
@@ -209,101 +203,16 @@ def creat_shopping_event(eventPath, subimgPath=False):
         event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
 
     
-    '''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
+    '''================ 4. 判断当前事件有效性，并添加至事件列表 =========='''
     condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
     condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
     
     if condt1 or condt2:
-        print(f"Event: {eventName}, Error, condt1: {condt1}, condt2: {condt2}")
+        print(f"Event: {evtName}, Error, condt1: {condt1}, condt2: {condt2}")
         return None
-    
-    
-
- 
-    '''构造放入商品事件列表，暂不处理'''
-    # delepath = os.path.join(basePath, 'deletedBarcode.txt')
-    # bcdList = read_deletedBarcode_file(delepath) 
-    # for slist in bcdList:
-    #     getoutFold = slist['SeqDir'].strip()
-    #     getoutPath = os.path.join(basePath, getoutFold)
-        
-    #     '''取出事件文件夹不存在，跳出循环'''
-    #     if not os.path.exists(getoutPath) and not os.path.isdir(getoutPath):
-    #         continue
-        
-    #     ''' 生成取出事件字典 '''
-    #     event = {}
-    #     event['barcode'] = slist['Deleted'].strip()
-    #     event['type'] = 'getout'   
-    #     event['basePath'] = getoutPath
 
     return event
 
-# def get_std_barcodeDict(bcdpath, savepath):
-#     '''
-#     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')
-#     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'  
-    
-#     '''读取数据集中 barcode 列表'''
-#     stdBarcodeList = []
-#     for filename in os.listdir(bcdpath):
-#         filepath = os.path.join(bcdpath, filename)
-#         # if not os.path.isdir(filepath) or not filename.isdigit() or len(filename)<8: 
-#         #     continue
-#         stdBarcodeList.append(filename)
-    
-#     bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList]
-    
-#     '''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}'''
-#     k = 0
-#     errbarcodes = []
-#     for barcode, bpath in bcdPaths:  
-#         pickpath = os.path.join(savepath, f"{barcode}.pickle")
-#         if os.path.isfile(pickpath):
-#             continue
-        
-#         stdBarcodeDict = {}
-#         stdBarcodeDict[barcode] = []
-#         for root, dirs, files in os.walk(bpath):
-#             imgpaths = []
-#             if "base" in dirs:
-#                 broot = os.path.join(root, "base")
-#                 for imgname in os.listdir(broot):
-#                     imgpath = os.path.join(broot, imgname)
-#                     file, ext = os.path.splitext(imgpath)
-                    
-#                     if ext not in IMG_FORMAT: 
-#                         continue
-#                     imgpaths.append(imgpath)
-                
-#                 stdBarcodeDict[barcode].extend(imgpaths)
-#                 break
-            
-#             else:
-#                 for imgname in files:
-#                     imgpath = os.path.join(root, imgname)
-#                     _, ext = os.path.splitext(imgpath)
-#                     if ext not in IMG_FORMAT: continue
-#                     imgpaths.append(imgpath)
-#             stdBarcodeDict[barcode].extend(imgpaths)
-        
-#         pickpath = os.path.join(savepath, f"{barcode}.pickle")
-#         with open(pickpath, 'wb') as f:
-#             pickle.dump(stdBarcodeDict, f)
-#         print(f"Barcode: {barcode}")
-
-#         # k += 1
-#         # if k == 10:
-#         #     break
-#     print(f"Len of errbarcodes: {len(errbarcodes)}")
-#     return  
 
 def save_event_subimg(event, savepath):
     '''
@@ -340,131 +249,20 @@ def save_event_subimg(event, savepath):
     print(f"Image saved: {os.path.basename(event['filepath'])}")
     
       
-    
-def batch_inference(imgpaths, batch):
-    size = len(imgpaths)
-    groups = []
-    for i in range(0, size, batch):
-        end = min(batch + i, size)
-        groups.append(imgpaths[i: end])
 
-    features = []
+def one2one_eval(resultPath):   
-    for group in groups:
-        feature = featurize(group, conf.test_transform, model, conf.device)
-        features.append(feature)
-    features = np.concatenate(features, axis=0)
-    return features    
-    
-# def stdfeat_infer(imgPath, featPath, bcdSet=None):
-#     '''
-#     inputs: 
-#         imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
-#         featPath: imgPath图像对应特征的存储地址
-#     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
-    
-#     '''
-    
-#     # imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
-#     # featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
-#     stdBarcodeDict = {}
-#     stdBarcodeDict_ft16 = {}
-    
-    
-#     '''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名、该pickle文件中字典的key值'''
-    
-#     k = 0
-#     for filename in os.listdir(imgPath):
-#         bcd, ext = os.path.splitext(filename)
-#         pkpath = os.path.join(featPath, f"{bcd}.pickle")
-        
-#         if os.path.isfile(pkpath): continue
-#         if bcdSet is not None and bcd not in bcdSet:
-#             continue
-
-#         filepath = os.path.join(imgPath, filename)
-
-#         stdbDict = {}
-#         stdbDict_ft16 = {}
-#         stdbDict_uint8 = {}
-        
-#         t1 = time.time()
-        
-#         try:
-#             with open(filepath, 'rb') as f:  
-#                 bpDict = pickle.load(f)    
-#             for barcode, imgpaths in bpDict.items():
-#                 # feature = batch_inference(imgpaths, 8) #from vit distilled model of LiChen
-#                 feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
-#                 feature /= np.linalg.norm(feature, axis=1)[:, None]
-                
-#                 # float16
-#                 feature_ft16 = feature.astype(np.float16)
-#                 feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
-                
-#                 # uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小
-#                 # feature_uint8, _ = ft16_to_uint8(feature_ft16)
-#                 feature_uint8 = (feature_ft16*128).astype(np.int8)
-  
-#         except Exception as e:
-#             print(f"Error accured at: {filename}, with Exception is: {e}")
-        
-#         '''================ 保存单个barcode特征 ================'''
-#         ##================== float32
-#         stdbDict["barcode"] = barcode
-#         stdbDict["imgpaths"] = imgpaths
-#         stdbDict["feats_ft32"] = feature
-#         stdbDict["feats_ft16"] = feature_ft16
-#         stdbDict["feats_uint8"] = feature_uint8
-        
-#         with open(pkpath, 'wb') as f:
-#             pickle.dump(stdbDict, f)
-        
-#         stdBarcodeDict[barcode] = feature
-#         stdBarcodeDict_ft16[barcode] = feature_ft16
-        
-#         t2 = time.time()
-#         print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
-#         # k += 1
-#         # if k == 10:
-#         #     break
-    
-#     ##================== float32 
-#     # pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
-#     # with open(pickpath, 'wb') as f:
-#     #     pickle.dump(stdBarcodeDict, f)  
-       
-#     ##================== float16   
-#     # pickpath_ft16 = os.path.join(featPath, f"barcode_features_ft16_{k}.pickle")
-#     # with open(pickpath_ft16, 'wb') as f:
-#     #     pickle.dump(stdBarcodeDict_ft16, f)  
-    
-#     return
-
-
-def contrast_performance_evaluate(resultPath):   
     
     # stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and p.suffix=='.pickle']
     stdBarcode = [p.stem for p in Path(stdBarcodePath).iterdir() if p.is_file() and p.suffix=='.pickle']
     
     
-    '''购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内'''
+    '''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(eventFeatPath).iterdir() 
+    evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventFeatPath).iterdir() 
-    #               if p.is_file() 
-    #               and p.suffix=='.pickle' 
-    #               and len(p.stem.split('_'))==2
-    #               and p.stem.split('_')[1].isdigit()
-    #               and p.stem.split('_')[1] in stdBarcode
-    #               ]
-    
-    evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir() 
                   if p.is_file()
-                  and str(p).find('240910')>0
                   and p.suffix=='.pickle' 
-                  and len(p.stem.split('_'))==2
+                  and (len(p.stem.split('_'))==2 or len(p.stem.split('_'))==3)
-                  and p.stem.split('_')[1].isdigit()
+                  and p.stem.split('_')[-1].isdigit()
-                  and p.stem.split('_')[1] in stdBarcode
+                  and p.stem.split('_')[-1] in stdBarcode
                   ]
     
     barcodes = set([bcd for _, bcd in evtList])
@@ -612,7 +410,7 @@ def contrast_performance_evaluate(resultPath):
             f.write(line + '\n')
 
     
-    print("func: contrast_performance_evaluate(), have finished!")
+    print("func: one2one_eval(), have finished!")
             
 
  
@@ -684,44 +482,16 @@ def compute_precise_recall(pickpath):
     plt.savefig(f'./result/{file}_pr.png')  # svg, png, pdf
     
   
-    
-  
 
-def generate_event_and_stdfeatures():    
+def gen_eventdict(eventDatePath, saveimg=True):         
-    '''=========================== 1. 生成标准特征集 ========================'''
-    '''1.1 提取 stdSamplePath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
-           并存储为 pickle 文件，barcode.pickle'''
-    # get_std_barcodeDict(stdSamplePath, stdBarcodePath)
-    # print("standard imgpath have extracted and saved")
-    
-    
-    '''1.2 特征提取，并保存至文件夹 stdFeaturePath 中，也可在运行过程中根据 barcodes 交集执行'''
-    # stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None)
-    # print("standard features have generated!")
-    
-    
-    '''=========================== 2. 提取并存储事件特征 ========================'''
-    eventDatePath  = [r'\\192.168.1.28\share\测试_202406\0910\images',
-                      # r'\\192.168.1.28\share\测试_202406\0723\0723_1',
-                      # r'\\192.168.1.28\share\测试_202406\0723\0723_2',
-                      # r'\\192.168.1.28\share\测试_202406\0723\0723_3',
-                      # r'\\192.168.1.28\share\测试_202406\0722\0722_01',
-                      # r'\\192.168.1.28\share\测试_202406\0722\0722_02' 
-                      # r'\\192.168.1.28\share\测试_202406\0719\719_3',    
-                      # r'\\192.168.1.28\share\测试_202406\0716\0716_1',
-                      # r'\\192.168.1.28\share\测试_202406\0716\0716_2',
-                      # r'\\192.168.1.28\share\测试_202406\0716\0716_3',
-                      # r'\\192.168.1.28\share\测试_202406\0712\0712_1', # 无帧图像
-                      # r'\\192.168.1.28\share\测试_202406\0712\0712_2', # 无帧图像
-                     ]    
     eventList = []
     # k = 0
     for datePath in eventDatePath: 
         for eventName in os.listdir(datePath):
+            
             pickpath = os.path.join(eventFeatPath, f"{eventName}.pickle")
             if os.path.isfile(pickpath):
                 continue
-
             eventPath = os.path.join(datePath, eventName)
             
             eventDict = creat_shopping_event(eventPath)
@@ -736,52 +506,61 @@ def generate_event_and_stdfeatures():
             #     break
             
     ## 保存轨迹中 boxes 子图
+    if not saveimg:
+        return
     for event in eventList: 
         basename = os.path.basename(event['filepath'])
         savepath = os.path.join(subimgPath, basename)
         if not os.path.exists(savepath):
             os.makedirs(savepath)
         save_event_subimg(event, savepath)
+            
+    
+
+    
+
+def test_one2one(): 
+    eventDatePath  = [r'\\192.168.1.28\share\测试_202406\1101\images',
+                      # r'\\192.168.1.28\share\测试_202406\0910\images',
+                      # r'\\192.168.1.28\share\测试_202406\0723\0723_1',
+                      # r'\\192.168.1.28\share\测试_202406\0723\0723_2',
+                      # r'\\192.168.1.28\share\测试_202406\0723\0723_3',
+                      # r'\\192.168.1.28\share\测试_202406\0722\0722_01',
+                      # r'\\192.168.1.28\share\测试_202406\0722\0722_02' 
+                      # r'\\192.168.1.28\share\测试_202406\0719\719_3',    
+                      # r'\\192.168.1.28\share\测试_202406\0716\0716_1',
+                      # r'\\192.168.1.28\share\测试_202406\0716\0716_2',
+                      # r'\\192.168.1.28\share\测试_202406\0716\0716_3',
+                      # r'\\192.168.1.28\share\测试_202406\0712\0712_1', # 无帧图像
+                      # r'\\192.168.1.28\share\测试_202406\0712\0712_2', # 无帧图像
+                     ]
+    bcdList = []
+    for evtpath in eventDatePath:
+        for evtname in os.listdir(evtpath):
+            evt = evtname.split('_')
+            if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
+                bcdList.append(evt[-1])
+                
+    bcdSet = set(bcdList)         
         
-    print("eventList have generated and features have saved!")
-
-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)
+    
+    '''==== 1. 生成标准特征集, 只需运行一次 ==============='''    
+    genfeatures(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
+    print("stdFeats have generated and saved!")
     
     
-    arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
+    '''==== 2. 生成事件字典, 只需运行一次 ===============''' 
-    
+     
-    
+    gen_eventdict(eventDatePath)
-    return arr_uint8, arr_ft16_
+    print("eventList have generated and saved!")
     
 
-    
+    '''==== 3. 1:1性能评估 ===============''' 
-
+    one2one_eval(resultPath)
-
-
-def main():  
-    # generate_event_and_stdfeatures()
-    
-    contrast_performance_evaluate(resultPath)
     for filename in os.listdir(resultPath):
         if filename.find('.pickle') < 0: continue
         if filename.find('0911') < 0: continue 
@@ -789,63 +568,29 @@ def main():
         compute_precise_recall(pickpath)
 
 
-# def main_std():
-#     std_sample_path = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_2192_已清洗"
-#     std_barcode_path = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"  
-#     std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16"
-    
-    
-#     get_std_barcodeDict(std_sample_path, std_barcode_path)
-#     stdfeat_infer(std_barcode_path, std_feature_path, bcdSet=None)
-    
-#     # fileList = []
-#     # for filename in os.listdir(std_barcode_path):
-#     #     filepath = os.path.join(std_barcode_path, filename)        
-#     #     with open(filepath, 'rb') as f:  
-#     #         bpDict = pickle.load(f) 
-            
-#     #     for v in bpDict.values():
-#     #         fileList.append(len(v))
-#     # print("done")
     
 if __name__ == '__main__':
-    main()
+    '''
-    # main_std()
+    共6个地址：
+        (1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
+        (2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
+        (3) stdFeaturePath: 比对标准特征集特征存储地址
+        (4) eventFeatPath: 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
+        (5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
+        (6) resultPath: 1:1比对结果存储地址
+    '''
+
+    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"
+    eventFeatPath  = r"\\192.168.1.28\share\测试_202406\contrast\events"
+    subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
+    resultPath = r"D:\DetectTracking\contrast\result\pickle"
+    if not os.path.exists(resultPath): 
+        os.makedirs(resultPath)
+
+    test_one2one()
 
 
 
-
-
-
-
-
-
-
-    
- 
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
     

time_devide.py

@@ -17,7 +17,6 @@ import copy
 
 import matplotlib.pyplot as plt
 from imgs_inference import run_yolo
-
 from event_time_specify import devide_motion_state#, state_measure
 from tracking.utils.read_data import read_seneor
 

tracking/utils/plotting.py

@@ -285,6 +285,38 @@ def boxing_img(det, img, line_width=3):
     
     return imgx
 
+def array2list(bboxes):
+    track_fids = np.unique(bboxes[:, 7].astype(int))
+    track_fids.sort()
+    
+    lboxes = []
+    for f_id in track_fids:
+        # print(f"The ID is: {t_id}")
+        idx = np.where(bboxes[:, 7] == f_id)[0]
+        box = bboxes[idx, :]  
+        lboxes.append(box)
+        
+        assert len(set(box[:, 4])) == len(box), "Please check!!!"
+        
+    return lboxes
+
+
+def get_subimgs(imgs, tracks, scale=2):
+    bboxes = []
+    if len(tracks):
+        bboxes = array2list(tracks)
+    
+    subimgs = []
+    for i, boxes in enumerate(bboxes):
+        fid = int(boxes[0, 7])
+        
+        for *xyxy, tid, conf, cls, fid, bid in boxes:
+            pt2 = [p/scale for p in xyxy]
+            x1, y1, x2, y2 = (int(pt2[0]), int(pt2[1])), (int(pt2[2]), int(pt2[3]))
+            subimgs.append((int(fid), int(bid), imgs[fid-1][y1:y2, x1:x2]))
+    
+    return subimgs
+
 def draw_tracking_boxes(imgs, tracks, scale=2):
     '''需要确保 imgs 覆盖tracks中的帧ID数
         tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
@@ -295,27 +327,15 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
        
     '''
     
-    def array2list(bboxes):
+
-        track_fids = np.unique(bboxes[:, 7].astype(int))
+    bboxes = []
-        track_fids.sort()
+    if len(tracks):
-        
+        bboxes = array2list(tracks)
-        lboxes = []
-        for f_id in track_fids:
-            # print(f"The ID is: {t_id}")
-            idx = np.where(bboxes[:, 7] == f_id)[0]
-            box = bboxes[idx, :]  
-            lboxes.append(box)
-            
-            assert len(set(box[:, 4])) == len(box), "Please check!!!"
-            
-        return lboxes
-    
-    bboxes = array2list(tracks)
     
     # if len(bboxes)!=len(imgs):
     #     return False, imgs
     
-    subimgs = []
+    annimgs = []
     for i, boxes in enumerate(bboxes):
         fid = int(boxes[0, 7])
         annotator = Annotator(imgs[fid-1].copy())
@@ -331,11 +351,11 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
             
             pt2 = [p/scale for p in xyxy]
             annotator.box_label(pt2, label, color=color)
-        
+
         img = annotator.result()
-        subimgs.append((fid, img))
+        annimgs.append((int(fid), img))
         
-    return subimgs
+    return annimgs
 
 
 

tracking/utils/read_data.py

@@ -37,6 +37,9 @@ def find_samebox_in_array(arr, target):
 
 
 def extract_data(datapath):
+    '''
+    0/1_track.data 数据读取
+    '''
     bboxes, ffeats = [], []
     
     trackerboxes = np.empty((0, 9), dtype=np.float64)
@@ -147,8 +150,15 @@ def extract_data(datapath):
     return bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict
 
 def read_tracking_output(filepath):
+    '''
+    0/1_tracking_output.data 数据读取
+    '''
+    
     boxes = []
     feats = []
+    if not os.path.isfile(filepath):
+        return np.array(boxes), np.array(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
@@ -176,7 +186,6 @@ def read_deletedBarcode_file(filePath):
 
         split_flag, all_list = False, []
         dict, barcode_list, similarity_list =  {}, [], []
-  
         clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
         
         for i, line in enumerate(clean_lines):
@@ -199,6 +208,7 @@ def read_deletedBarcode_file(filePath):
             
             if label == 'SeqDir':
                 dict['SeqDir'] = value
+                dict['filetype'] = "deletedBarcode"
             if label == 'Deleted':
                 dict['Deleted'] = value
             if label == 'List':
@@ -259,15 +269,19 @@ def read_returnGoods_file(filePath):
             if label == 'SeqDir':
                 dict['SeqDir'] = value
                 dict['Deleted'] = value.split('_')[-1]
+                dict['filetype'] = "returnGoods"
             if label == 'List':
                 split_flag = True
                 continue
             if split_flag:
+                bcd = label.split('_')[-1]
+                # event_list.append(label + '_' + bcd)
                 event_list.append(label)
-                barcode_list.append(label.split('_')[-1])
+                barcode_list.append(bcd)
                 similarity_list.append(value.split(',')[0])
                 type_list.append(value.split('=')[-1])
         
+            
         if len(barcode_list): dict['barcode'] = barcode_list
         if len(similarity_list): dict['similarity'] = similarity_list
         if len(event_list): dict['event'] = event_list
@@ -279,33 +293,51 @@ def read_returnGoods_file(filePath):
         
     
     
+
+# =============================================================================
+# def read_seneor(filepath):
+#     WeightDict = OrderedDict()
+#     with open(filepath, 'r', encoding='utf-8') as f:
+#         lines = f.readlines()
+#         for i, line in enumerate(lines):
+#             line = line.strip()
+#             
+#             keyword = line.split(':')[0]
+#             value = line.split(':')[1]
+#             
+#             vdata = [float(s) for s in value.split(',') if len(s)]
+#             
+#             WeightDict[keyword] = vdata[-1]
+#     
+#     return WeightDict
+# =============================================================================
+
+def read_one2one_simi(filePath):
     
-    
+    SimiDict = {}
-
+    with open(filePath, 'r', encoding='utf-8') as f:
-
-
-
-
-
-
-
-
-
-def read_seneor(filepath):
-    WeightDict = OrderedDict()
-    with open(filepath, 'r', encoding='utf-8') as f:
         lines = f.readlines()
+        flag = False
         for i, line in enumerate(lines):
             line = line.strip()
+            if line.find('barcode:')<0 and not flag:
+                continue
+            if line.find('barcode:')==0 :   
+                flag = True
+                continue
             
-            keyword = line.split(':')[0]
+            # if line.endswith(','):  
-            value = line.split(':')[1]
+            #     line = line[:-1]   
-            
+            if flag:
-            vdata = [float(s) for s in value.split(',') if len(s)]
+                barcode = line.split(',')[0].strip()
-            
+                value = line.split(',')[1].split(':')[1].strip()
-            WeightDict[keyword] = vdata[-1]
+                SimiDict[barcode] = float(value)
-    
+
-    return WeightDict
+            if flag and not line:
+                flag = False
+
+    return SimiDict
+
 
 
 def read_weight_timeConsuming(filePth):
@@ -362,15 +394,14 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
     nw = len(wdata)
     assert(nw) >= 8, "The num of weight data is less than 8!"
     
-    i1, i2 = 0, 7
+    # i1, i2 = 0, 7
-    while i2 < nw:
+    # while i2 < nw:
-        data = wdata[i1:(i2+1)]
+    #     data = wdata[i1:(i2+1)]
-        max(data) - min(data)
+    #     max(data) - min(data)
-
+    #     if i2<7:
-        if i2<7:
+    #         i1 = 0
-            i1 = 0
+    #     else:
-        else:
+    #         i1 = i2-windth
-            i1 = i2-windth
 
     min_t = min(wtime + stime)
     wtime = [t-min_t for t in wtime]
@@ -405,15 +436,12 @@ def plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict):
         
     
     
-def main(file_path):
+def test_process(file_path):
     WeightDict, SensorDict, ProcessTimeDict = read_weight_timeConsuming(file_path)
     plot_sensor_curve(WeightDict, SensorDict, ProcessTimeDict)
-
-           
-            
-
-if __name__ == "__main__":    
     
+    
+def main():
     files_path = r'\\192.168.1.28\share\测试_202406\0814\0814\20240814-102227-62264578-a720-4eb9-b95e-cb8be009aa98_null'  
     k = 0
     for filename in os.listdir(files_path):    
@@ -424,42 +452,21 @@ if __name__ == "__main__":
             extract_data(file_path)
         
         if os.path.isfile(file_path) and filename.find("process.data")>=0:
-            main(file_path)
+            test_process(file_path)
         
         k += 1
         if k == 1:
             break
-            
+        
-            
+def main1():
-            
+    fpath = r'\\192.168.1.28\share\测试_202406\1101\images\20241101-140456-44dc75b5-c406-4cb2-8317-c4660bb727a3_6922130101355_6922130101355\process.data'
-        # print("Done")
+    simidct = read_one2one_simi(fpath)
-
+    print(simidct)
-    
+          
-    
-    
-    
-    
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 
+if __name__ == "__main__":
+    # main()
+    main1()
 
 
 

说明文档.txt

@@ -144,15 +144,124 @@
 		
 		precision_compare(filepath, savepath)
 			读取 deletedBarcode.txt 和 deletedBarcodeTest.txt 中的数据，进行相似度比较
-			
+	
+	genfeats.py
+		get_std_barcodeDict(bcdpath, savepath)
+		功能: 生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}
+		
+		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
-		1:n 比对，读取 deletedBarcode.txt，实现现场测试评估。
+		test_one2n()
-		main():
+			1:n 现场测试性能评估，输出 PR 曲线
-			循环读取不同文件夹中的 deletedBarcode.txt，合并评估。
+			兼容 2 种 txt 文件格式：returnGoods.txt, deletedBarcode.txt, 
-		main1():
+			分别对应不同的文件读取函数：
-			指定deletedBarcode.txt进行1:n性能评估		
+			 - 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()
+	
+	
+	one2one_contrast.py
+		共6个地址：
+			(1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
+			(2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
+			(3) stdFeaturePath: 比对标准特征集特征存储地址
+			(4) eventFeatPath: 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
+			(5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
+			(6) resultPath: 1:1比对结果存储地址
+		
+		(1), (2), (3): 保存标准特征集向量，只需运行一次
+		(4): 保存测试的事件字典，只需运行一次
+	
+	
+		test_one2one()
+			(1) 生成标准特征集, 只需运行一次
+				genfeatures()
+			(2) 生成事件字典, 只需运行一次
+				gen_eventdict(eventDatePath, saveimg)
+				参数: 
+					eventDatePath: 事件集列表，其中每个元素均为事件的集合;
+					saveimg: 是否保存事件子图
+					
+			(3) 1:1性能评估
+			(4) 计算PR曲线
+		
+
+	
+		creat_shopping_event(eventPath, subimgPath=False)
+			构造一次购物事件字典, 共12个关键字。
 			
+		save_event_subimg(event, savepath)
+			保存一次购物事件的子图
+		
+
+		one2one_eval()
+		
+		
+		
+		compute_precise_recall()
+		
+
+		
+		
+		int8_to_ft16()
+		
+		
+		ft16_to_uint8()
+		
+		
+		
+
+
+
+
+	
+	
 	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
@@ -161,11 +270,13 @@
 		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
+		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