for bakeup

tracking/feat_select.py

@@ -0,0 +1,418 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 27 14:07:25 2024
+
+@author: ym
+"""
+import os.path
+import numpy as np
+from scipy.spatial.distance import cdist
+import matplotlib.pyplot as plt
+import sys
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
+from tracking.dotrack.dotracks import Track
+
+from tracking.contrast_analysis import performance_evaluate, compute_recall_precision, show_recall_prec
+
+def compute_similar(feat1, feat2):
+
+    if len(feat1)==0 or len(feat2)==0: 
+        return 0
+    
+    similar = 1 - np.maximum(0.0, cdist(feat1, feat2, metric = 'cosine'))
+    smean = np.mean(similar)
+    
+    return smean
+    
+    
+
+
+
+
+def update_event(datapath):
+    '''一次购物事件，包含 8 个keys'''
+    event = {}
+    # event['front_tracking_boxes'] = []
+    # event['front_tracking_feats'] = {}
+    # event['back_tracking_boxes'] = []
+    # event['back_tracking_feats'] = {}
+    
+    event['back_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
+    event['front_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
+    
+    event['back_sole_feats'] = np.empty((0, 256), dtype=np.float64)
+    event['front_sole_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)
+
+    '''读取当前事件的 data 文件'''
+    for filename in os.listdir(datapath):
+        # filename = '1_track.data'
+        fpath = os.path.join(datapath, filename)
+        CamerType = filename.split('_')[0]
+        # if os.path.isfile(fpath) and filename.find("track.data")>0:
+        #     bboxes, ffeats, trackerboxes, tracker_feat_dict, tracking_boxes, tracking_feat_dict = extract_data(fpath)
+            
+        #     if CamerType == '0':
+        #         event['back_tracking_boxes'] = tracking_boxes
+        #         event['back_tracking_feats'] = tracking_feat_dict
+        #     elif CamerType == '1':
+        #         event['front_tracking_boxes'] = tracking_boxes
+        #         event['front_tracking_feats'] = tracking_feat_dict
+            
+            
+
+        if os.path.isfile(fpath) and filename.find("tracking_output.data")>0:
+            tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath)
+            if CamerType == '0':
+                event['back_sole_boxes'] = tracking_output_boxes
+                event['back_sole_feats'] = tracking_output_feats
+            elif CamerType == '1':
+                event['front_sole_boxes'] = tracking_output_boxes
+                event['front_sole_feats'] = tracking_output_feats
+
+    
+    '''事件的特征表征方式选择'''
+    
+    fs_feats = event['front_sole_feats']
+    bs_feats = event['back_sole_feats']
+    
+    '''1. 如果前后摄均没有轨迹选择输出，返回'''
+    condt1 = len(fs_feats) + len(bs_feats) == 0
+    if condt1:
+        return event
+    
+    
+    '''2. 构造综合特征'''   
+    feats_compose = np.empty((0, 256), dtype=np.float64)
+    if len(fs_feats):
+        feats_compose = np.concatenate((feats_compose, fs_feats), axis=0)
+    if len(bs_feats):
+        feats_compose = np.concatenate((feats_compose, bs_feats), axis=0)
+    event['feats_compose'] = feats_compose
+    
+    '''3. 构造前摄特征'''
+    if len(fs_feats):
+        event['feats_select'] = fs_feats
+        return event
+
+
+    '''4. 从前摄输出轨迹中选取特定轨迹对应的特征'''
+    ftrboxes = event['front_tracking_boxes']
+    ftrfeats = event['front_tracking_feats']
+    
+    condt2 = len(ftrboxes) + len(ftrfeats) == 0
+    condt3 = len(ftrfeats) != len(ftrboxes)
+    if condt2 or condt3:
+        return event
+
+    bprops = []
+    for boxes in ftrboxes:
+        track = Track(boxes)
+        bprops.append(max(track.trajdist))
+        
+    index = bprops.index(max(bprops))
+    box_select = ftrboxes[index]
+    tid = int(box_select[0, 4])
+    
+    feat_select = ftrfeats[f"track_{tid}"]
+    feats_select = np.empty((0, 256), dtype=np.float64)
+    for fid_bid, feat in feat_select['feats'].items():
+        feats_select = np.concatenate((feats_select, feat[None, :]), axis=0)
+    event['feats_select'] = feats_select
+
+    return event
+
+
+def creatd_deletedBarcode_front(filepath):
+    # filepath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt'
+    
+    basepath, _ = os.path.split(filepath)
+    
+    MatchList = []
+      
+    bcdlist = read_deletedBarcode_file(filepath)
+    
+    k = 0
+    for s_list in bcdlist:
+        getout_fold = s_list['SeqDir'].strip()
+        day, hms = getout_fold.strip('_').split('-')
+
+        ''' 生成取出事件字典 '''
+        getout_event = {}
+        getout_event['barcode'] = s_list['Deleted'].strip()
+        getout_event['path'] = os.path.join(basepath, getout_fold)
+        
+        getout_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+        getout_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+        
+
+        InputList = []
+        barcodes = [s.strip() for s in s_list['barcode']]
+        similarity = [float(s.strip()) for s in s_list['similarity']]
+        for i, barcode in enumerate(barcodes):
+            
+            ''' 生成放入事件字典 '''
+            input_event = {}
+            
+            input_folds, 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]==barcode and int(hms1)<int(hms):
+                    input_folds.append(pathname)
+                    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，作为取出操作应的放入操作所对应的文件夹 '''
+            input_path = ''
+            if len(input_folds):
+                indice = np.argsort(np.array(times)) 
+                input_fold = input_folds[indice[-1]]
+                input_path = os.path.join(basepath, input_fold)
+            
+            input_event['barcode'] = barcode
+            input_event['path'] = input_path
+            input_event['similarity'] = float(similarity[i])
+            input_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
+            input_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+            
+            
+            InputList.append(input_event)
+            
+        MatchList.append((getout_event, InputList))
+ 
+        # k += 1
+        # if k==2:
+        #     break
+    print('Step 1 Done!')
+        
+    for getout_event, InputList in MatchList:
+        getout_path = getout_event['path']
+        if os.path.exists(getout_path) and os.path.isdir(getout_path):
+            event = update_event(getout_path)
+            getout_event.update(event)
+        
+        for input_event in InputList:
+            input_path = input_event['path']
+            if os.path.exists(input_path) and os.path.isdir(input_path):
+                event = update_event(input_path)
+                input_event.update(event)
+        
+    print('Step 2 Done!') 
+        
+    results = []
+    for getout_event, InputList in MatchList:
+        getout_barcode = getout_event['barcode']
+        getout_feats_compose = getout_event['feats_compose']
+        getout_feats_select = getout_event['feats_select']
+
+        if len(getout_feats_select):
+            outfeats_select = getout_feats_select.copy()
+        else:
+            outfeats_select = getout_feats_compose.copy()
+
+        result = {}
+        result['SeqDir'] = os.path.split(getout_event['path'])[1]
+        result['Deleted'] = getout_barcode
+        result['List'] = {}
+        for input_event in InputList:
+            input_barcode = input_event['barcode']
+            
+            input_feats_compose = input_event['feats_compose']
+            input_feats_select = input_event['feats_select']
+            
+            if len(input_feats_select):
+                infeats_select = input_feats_select.copy()
+            else:
+                infeats_select = input_feats_compose.copy()
+                
+            similar_comp = compute_similar(getout_feats_compose, input_feats_compose)
+            similar_selt = compute_similar(outfeats_select, infeats_select)
+
+            '''现场测试相似度，组合特征相似度，前摄选择特征相似度'''
+            result['List'][f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
+            # result[f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
+        results.append(result)
+    
+    print('Step 3 Done!') 
+    
+    wpath = os.path.split(filepath)[0]
+    wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
+    with open(wfile, 'w', encoding='utf-8') as file:
+        for result in results:
+            
+            SeqDir = result['SeqDir']
+            Deleted = result['Deleted']
+            
+            file.write('\n')
+            file.write(f'SeqDir: {SeqDir}\n')
+            file.write(f'Deleted: {Deleted}\n')
+            file.write('List:\n')
+            for key, value in  result['List'].items():
+                file.write(f'{key}: ')
+                file.write(f'{value[0]}, {value[1]:.3f}, {value[2]:.3f}\n')
+    
+    print('Step 4 Done!') 
+
+
+
+
+
+
+def front_performance_evaluate(all_list):
+    
+    corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list,  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, front_similarity = [], []
+        for simil in s_list['similarity']:
+            ss = [float(s.strip()) for s in simil.split(',')]
+            
+            similarity.append(ss[0])
+            if len(ss)==3:
+                front_similarity.append(ss[2])
+
+        # similarity = [float(s.strip()) for s in s_list['similarity']]
+        
+        
+        index = front_similarity.index(max(front_similarity))
+        matched_barcode = barcodes[index]
+        if matched_barcode == delete:
+            corrpairs.append((seqdir, delete))
+            correct_barcode_list.append(delete)
+            correct_similarity.append(max(front_similarity))
+        else:
+            errpairs.append((seqdir, delete, matched_barcode))
+            err_barcode_list.append(delete)
+            err_similarity.append(max(front_similarity))
+
+    
+
+        
+    return errpairs, corrpairs, err_similarity, correct_similarity
+
+
+                
+def compute_pres(filepath, savepath):
+    
+    
+    fpath = os.path.split(filepath)[0]
+    _, basefile = os.path.split(fpath)
+
+    '''1. 综合前后摄特征的相似度比对性能'''
+    fpath1 = os.path.join(fpath, 'deletedBarcode.txt')
+    blist1 = read_deletedBarcode_file(fpath1)  
+    errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist1)
+    recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
+    
+    
+    
+    os.path.split(fpath1)
+    plt1 = show_recall_prec(recall, prec, ths)
+    # plt1.show()
+    plt1.xlabel(f'threshold, Num: {len(blist1)}')
+    plt1.title(basefile + ', compose')
+    plt1.savefig(os.path.join(savepath, basefile+'_pr.png'))
+    plt1.close()
+    
+    '''2. 优先选取前摄特征的相似度比对性能'''
+    fpath2 = os.path.join(fpath, 'deletedBarcodeTest.txt')
+    blist2 = read_deletedBarcode_file(fpath2)
+    front_errpairs, front_corrpairs, front_err_similarity, front_correct_similarity = front_performance_evaluate(blist2)
+    front_recall, front_prec, front_ths = compute_recall_precision(front_err_similarity, front_correct_similarity)
+    
+    plt2 = show_recall_prec(front_recall, front_prec, front_ths)
+    # plt2.show()
+    plt2.xlabel(f'threshold, Num: {len(blist2)}')
+    plt1.title(basefile + ', front')
+    plt2.savefig(os.path.join(savepath, basefile+'_pr_front.png'))
+    plt2.close()
+            
+def main():
+    fplist = [r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_01\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0722\0722_02\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0719\0719_4\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0718\0718-1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0718\0718-2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0717\0717-3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0716\0716_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0715\0715_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0712\0712_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\0712\0712_3\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\711\images01\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\711\images02\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\710\images_1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\710\images_2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\705\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\703\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_1\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_2\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm_3\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_am\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\702_pm\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\701_am\images\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\628\1\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\628\2\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\627\deletedBarcode.txt',
+              r'\\192.168.1.28\share\测试_202406\625\deletedBarcode.txt',
+              ]
+    
+    savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
+    for filepath in  fplist:
+        print(filepath)
+        
+        try:
+            creatd_deletedBarcode_front(filepath)
+            compute_pres(filepath, savepath)
+        except Exception as e:
+            print(f'Error: {e}') 
+        
+if __name__ == '__main__':
+    main()
+
+    
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