# -*- coding: utf-8 -*- """ Created on Fri Aug 30 17:53:03 2024 1. 确认在相同CamerType下，track.data 中 CamerID 项数量 = 图像数 = 帧ID数 = 最大帧ID 2. 读取0/1_tracking_output.data 中数据，boxes、feats，len(boxes)=len(feats) 帧ID约束 3. 优先选择前摄 4. 保存图像数据 5. 一次购物事件类型 shopEvent: {barcode: type: getout, input front_traj:[{imgpath: str, box: arrar(1, 9), feat: array(1, 256) }] back_traj: [{imgpath: str, box: arrar(1, 9), feat: array(1, 256) }] } @author: ym """ import numpy as np import cv2 import os import sys import pickle import torch import time import json from config import config as conf from model import resnet18 from inference import load_contrast_model from inference import featurize sys.path.append(r"D:\DetectTracking") from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png'] model = load_contrast_model() def creat_shopping_event(basePath, savePath, subimgPath=False): eventList = [] '''一、构造放入商品事件列表''' k = 0 for filename in os.listdir(basePath): # filename = "20240723-155413_6904406215720" '''filename下为一次购物事件''' filepath = os.path.join(basePath, filename) '''================ 0. 检查 filename 及 filepath 正确性和有效性 ================''' nmlist = filename.split('_') if filename.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11: continue if not os.path.isdir(filepath): continue '''================ 1. 构造事件描述字典，暂定 9 items ===============''' event = {} event['barcode'] = nmlist[1] event['type'] = 'input' event['filepath'] = filepath 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['feats_select'] = np.empty((0, 256), dtype=np.float64) '''================= 2. 读取 data 文件 =============================''' for dataname in os.listdir(filepath): # filename = '1_track.data' datapath = os.path.join(filepath, 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 '''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 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 =============''' frontImgs, frontFid = [], [] backImgs, backFid = [], [] for imgname in os.listdir(filepath): 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(filepath, 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)) '''2.1 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''' 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] '''================ 3. 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" Error, condt1: {condt1}, condt2: {condt2}") continue eventList.append(event) # k += 1 # if k==1: # continue '''一、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 eventList def get_std_barcodeDict(bcdpath, bpath): ''' 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, ...]}， bpath: 字典存储地址 ''' # bpath = r'\\192.168.1.28\share\测试_202406\contrast\barcodes' '''读取数据集中 barcode 列表''' stdBlist = [] for filename in os.listdir(bcdpath): filepath = os.path.join(bcdpath, filename) if not os.path.isdir(filepath) or not filename.isdigit(): continue stdBlist.append(filename) bcdpaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBlist] '''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}''' k = 0 for barcode, bpath in bcdpaths: 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) _, 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(bpath, f"{barcode}.pickle") with open(pickpath, 'wb') as f: pickle.dump(stdBarcodeDict, f) print(f"Barcode: {barcode}") # k += 1 # if k == 10: # break return def extract_save_trajture_subimgs(shoppingEventPath, shoppingFeatPath, subimgPath): '''用于保存一次购物事件的轨迹图像子图''' shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events" subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs' eventList = creat_shopping_event(shoppingEventPath, shoppingFeatPath, subimgPath=True) print("======= eventList have generated and features have saved! =======") barcodeDict = {} for event in eventList: '''9 items: barcode, type, filepath, back_imgpaths, front_imgpaths, back_boxes, front_boxes, back_feats, front_feats ''' if len(event['feats_select']): event_feats = event['feats_select'] elif len(event['back_feats']): event_feats = event['back_feats'] else: continue '''保存一次购物事件的轨迹子图''' basename = os.path.basename(event['filepath']) spath = os.path.join(subimgPath, basename) if not os.path.exists(spath): os.makedirs(spath) 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[i] 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"{camerType}_{tid}_fid({fid}, {frameID}).png" subimgPath = os.path.join(spath, subimgName) cv2.imwrite(subimgPath, subimg) print(f"Image saved: {basename}") 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 = [] 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): ''' 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\barcodes" # featPath = r"\\192.168.1.28\share\测试_202406\contrast\features" stdBarcodeDict = {} k = 0 for filename in os.listdir(imgPath): filepath = os.path.join(imgPath, filename) stdbDict = {} 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) except Exception as e: print(f"Error accured at: {filename}, with Exception is: {e}") '''================ 保存单个barcode特征 ================''' stdbDict[barcode] = feature pkpath = os.path.join(featPath, f"{barcode}.pickle") with open(pkpath, 'wb') as f: pickle.dump(stdbDict, f) stdBarcodeDict[barcode] = feature t2 = time.time() print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs") k += 1 if k == 10: break pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle") with open(pickpath, 'wb') as f: pickle.dump(stdBarcodeDict, f) def contrast_performance_evaluate(): eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events" stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\features" def generate_event_and_standard_features(): stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771" stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes" stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\features" '''=========================== 1. 生成标准特征集 ========================''' '''1.1 提取并保存标准特征库原始图像文件地址字典''' # 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''' stdfeat_infer(stdBarcodePath, stdFeaturePath) # print("standard features have generated!") '''=========================== 2. 提取并存储事件特征 ========================''' shoppingEventPath = [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'] shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events" # for sPath in shoppingEventPath: # eventList = creat_shopping_event(sPath, shoppingFeatPath) def shopping_event_test(): fplist = [#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' ] shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events" subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs' for filepath in fplist: '''用于保存一次购物事件的轨迹轨迹特征、及对应的图像子图''' extract_save_trajture_subimgs(filepath, shoppingFeatPath, subimgPath) def main(): generate_event_and_standard_features() # shopping_event_test() pass if __name__ == '__main__': main()