回传数据解析，兼容v5和v10

contrast/utils/dotest.py

@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec 10 14:30:16 2024
+
+@author: ym
+"""
+import os
+import sys
+import numpy as np
+sys.path.append(r"D:\DetectTracking")
+from tracking.utils.read_data import read_tracking_output, read_similar #, extract_data, read_deletedBarcode_file
+
+
+IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
+
+
+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
+    '''
+
+    '''evtName 为一次购物事件'''    
+    evtName = os.path.basename(eventPath)
+    evtList = evtName.split('_')
+
+    '''================ 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'] = barcode
+    event['type'] = evtType
+    event['filepath'] = eventPath
+    event['back_imgpaths'] = []
+    event['front_imgpaths'] = []
+    event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
+    event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
+    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['one2one'] = None
+    event['one2n'] = None
+    event['feats_select'] = np.empty((0, 256), dtype=np.float64)
+    
+    
+    '''================= 2. 读取 data 文件 ============================='''
+    for dataname in os.listdir(eventPath):
+        # filename = '1_track.data'
+        datapath = os.path.join(eventPath, dataname)
+        if not os.path.isfile(datapath): continue
+        
+        CamerType = dataname.split('_')[0]
+        ''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
+        # if dataname.find("_track.data")>0:
+        #     bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
+  
+        ''' 2.2 读取 0/1_tracking_output.data 中数据'''
+        if dataname.find("_tracking_output.data")>0:
+            tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
+            if len(tracking_output_boxes) != len(tracking_output_feats): continue
+            if CamerType == '0':
+                event['back_boxes'] = tracking_output_boxes
+                event['back_feats'] = tracking_output_feats
+            elif CamerType == '1':
+                event['front_boxes'] = tracking_output_boxes
+                event['front_feats'] = tracking_output_feats
+                
+        if dataname.find("process.data")==0:
+            simiDict = read_similar(datapath)
+            event['one2one'] = simiDict['one2one']
+            event['one2n'] = simiDict['one2n']
+
+
+    if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
+        return None
+    
+    '''2.3 事件的特征表征方式: 特征选择、特征集成'''
+    bk_feats = event['back_feats']
+    ft_feats = event['front_feats']
+    
+    '''2.3.1 特征集成'''
+    feats_compose = np.empty((0, 256), dtype=np.float64)
+    if len(ft_feats):
+        feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
+    if len(bk_feats):
+        feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
+    event['feats_compose'] = feats_compose
+            
+    '''2.3.1 特征选择'''
+    if len(ft_feats):
+        event['feats_select'] = ft_feats
+
+
+    '''================ 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):
+        name, ext = os.path.splitext(imgname)
+        if ext not in IMG_FORMAT or name.find('frameId')<0: continue
+
+        CamerType = name.split('_')[0]
+        frameId = int(name.split('_')[3])
+        imgpath = os.path.join(eventPath, imgname)
+        if CamerType == '0':
+            backImgs.append(imgpath)
+            backFid.append(frameId)
+        if CamerType == '1':
+            frontImgs.append(imgpath)
+            frontFid.append(frameId)
+    
+    frontIdx = np.argsort(np.array(frontFid))
+    backIdx = np.argsort(np.array(backFid))
+    
+    '''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]
+
+    '''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)
+    ffid = event['front_boxes'][:, 7].astype(np.int64)
+    if len(bfid) and max(bfid) <= len(backImgs):
+        event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
+    if len(ffid) and max(ffid) <= len(frontImgs):
+        event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
+
+    
+    '''================ 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: {evtName}, Error, condt1: {condt1}, condt2: {condt2}")
+        return None
+
+    return event