474 lines
16 KiB
Python
474 lines
16 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Aug 30 17:53:03 2024
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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
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2. 读取0/1_tracking_output.data 中数据，boxes、feats，len(boxes)=len(feats)
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帧ID约束
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3. 优先选择前摄
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4. 保存图像数据
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5. 一次购物事件类型
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shopEvent: {barcode:
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type: getout, input
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front_traj:[{imgpath: str,
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box: arrar(1, 9),
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feat: array(1, 256)
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}]
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back_traj: [{imgpath: str,
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box: arrar(1, 9),
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feat: array(1, 256)
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}]
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}
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@author: ym
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"""
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import numpy as np
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import cv2
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import os
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import sys
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import pickle
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import torch
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import time
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import json
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from config import config as conf
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from model import resnet18
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from inference import load_contrast_model
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from inference import featurize
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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model = load_contrast_model()
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def creat_shopping_event(basePath, savePath, subimgPath=False):
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eventList = []
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'''一、构造放入商品事件列表'''
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k = 0
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for filename in os.listdir(basePath):
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# filename = "20240723-155413_6904406215720"
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'''filename下为一次购物事件'''
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filepath = os.path.join(basePath, filename)
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'''================ 0. 检查 filename 及 filepath 正确性和有效性 ================'''
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nmlist = filename.split('_')
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if filename.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
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continue
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if not os.path.isdir(filepath): continue
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'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
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event = {}
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event['barcode'] = nmlist[1]
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event['type'] = 'input'
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event['filepath'] = filepath
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event['back_imgpaths'] = []
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event['front_imgpaths'] = []
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event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['back_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_feats'] = np.empty((0, 256), dtype=np.float64)
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event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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# event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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'''================= 2. 读取 data 文件 ============================='''
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for dataname in os.listdir(filepath):
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# filename = '1_track.data'
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datapath = os.path.join(filepath, dataname)
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if not os.path.isfile(datapath): continue
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CamerType = dataname.split('_')[0]
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''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
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# if dataname.find("_track.data")>0:
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# bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
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''' 2.2 读取 0/1_tracking_output.data 中数据'''
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if dataname.find("_tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
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if len(tracking_output_boxes) != len(tracking_output_feats): continue
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if CamerType == '0':
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event['back_boxes'] = tracking_output_boxes
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event['back_feats'] = tracking_output_feats
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elif CamerType == '1':
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event['front_boxes'] = tracking_output_boxes
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event['front_feats'] = tracking_output_feats
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'''2.3 事件的特征表征方式: 特征选择、特征集成'''
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bk_feats = event['back_feats']
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ft_feats = event['front_feats']
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'''2.3.1 特征集成'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(ft_feats):
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feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
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if len(bk_feats):
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feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
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event['feats_compose'] = feats_compose
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'''2.3.1 特征选择'''
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if len(ft_feats):
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event['feats_select'] = ft_feats
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pickpath = os.path.join(savePath, f"{filename}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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print(f"Event: {filename}")
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if subimgPath==False:
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eventList.append(event)
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continue
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'''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
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frontImgs, frontFid = [], []
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backImgs, backFid = [], []
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for imgname in os.listdir(filepath):
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name, ext = os.path.splitext(imgname)
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if ext not in IMG_FORMAT or name.find('frameId')<0: continue
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CamerType = name.split('_')[0]
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frameId = int(name.split('_')[3])
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imgpath = os.path.join(filepath, imgname)
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if CamerType == '0':
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backImgs.append(imgpath)
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backFid.append(frameId)
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if CamerType == '1':
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frontImgs.append(imgpath)
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frontFid.append(frameId)
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frontIdx = np.argsort(np.array(frontFid))
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backIdx = np.argsort(np.array(backFid))
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'''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
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frontImgs = [frontImgs[i] for i in frontIdx]
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backImgs = [backImgs[i] for i in backIdx]
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'''2.2 将前、后摄图像路径添加至事件字典'''
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bfid = event['back_boxes'][:, 7].astype(np.int64)
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ffid = event['front_boxes'][:, 7].astype(np.int64)
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if len(bfid) and max(bfid) <= len(backImgs):
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event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
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if len(ffid) and max(ffid) <= len(frontImgs):
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event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
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'''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
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condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
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condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
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if condt1 or condt2:
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print(f" Error, condt1: {condt1}, condt2: {condt2}")
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continue
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eventList.append(event)
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# k += 1
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# if k==1:
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# continue
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'''一、构造放入商品事件列表，暂不处理'''
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# delepath = os.path.join(basePath, 'deletedBarcode.txt')
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# bcdList = read_deletedBarcode_file(delepath)
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# for slist in bcdList:
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# getoutFold = slist['SeqDir'].strip()
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# getoutPath = os.path.join(basePath, getoutFold)
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# '''取出事件文件夹不存在，跳出循环'''
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# if not os.path.exists(getoutPath) and not os.path.isdir(getoutPath):
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# continue
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# ''' 生成取出事件字典 '''
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# event = {}
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# event['barcode'] = slist['Deleted'].strip()
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# event['type'] = 'getout'
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# event['basePath'] = getoutPath
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return eventList
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def get_std_barcodeDict(bcdpath, bpath):
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'''
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inputs:
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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
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(default = r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771')
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功能：
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生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}，
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bpath: 字典存储地址
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'''
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# bpath = r'\\192.168.1.28\share\测试_202406\contrast\barcodes'
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'''读取数据集中 barcode 列表'''
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stdBlist = []
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for filename in os.listdir(bcdpath):
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filepath = os.path.join(bcdpath, filename)
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if not os.path.isdir(filepath) or not filename.isdigit(): continue
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stdBlist.append(filename)
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bcdpaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBlist]
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'''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}'''
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k = 0
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for barcode, bpath in bcdpaths:
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stdBarcodeDict = {}
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stdBarcodeDict[barcode] = []
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for root, dirs, files in os.walk(bpath):
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imgpaths = []
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if "base" in dirs:
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broot = os.path.join(root, "base")
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for imgname in os.listdir(broot):
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imgpath = os.path.join(broot, imgname)
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_, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT: continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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break
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else:
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for imgname in files:
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imgpath = os.path.join(root, imgname)
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_, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT: continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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pickpath = os.path.join(bpath, f"{barcode}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(stdBarcodeDict, f)
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print(f"Barcode: {barcode}")
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# k += 1
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# if k == 10:
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# break
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return
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def extract_save_trajture_subimgs(shoppingEventPath, shoppingFeatPath, subimgPath):
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'''用于保存一次购物事件的轨迹图像子图'''
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shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
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subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
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eventList = creat_shopping_event(shoppingEventPath, shoppingFeatPath, subimgPath=True)
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print("======= eventList have generated and features have saved! =======")
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barcodeDict = {}
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for event in eventList:
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'''9 items: barcode, type, filepath, back_imgpaths, front_imgpaths,
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back_boxes, front_boxes, back_feats, front_feats
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'''
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if len(event['feats_select']):
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event_feats = event['feats_select']
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elif len(event['back_feats']):
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event_feats = event['back_feats']
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else:
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continue
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'''保存一次购物事件的轨迹子图'''
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basename = os.path.basename(event['filepath'])
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spath = os.path.join(subimgPath, basename)
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if not os.path.exists(spath):
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os.makedirs(spath)
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cameras = ('front', 'back')
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for camera in cameras:
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if camera == 'front':
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boxes = event['front_boxes']
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imgpaths = event['front_imgpaths']
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else:
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boxes = event['back_boxes']
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imgpaths = event['back_imgpaths']
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for i, box in enumerate(boxes):
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x1, y1, x2, y2, tid, score, cls, fid, bid = box
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imgpath = imgpaths[i]
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image = cv2.imread(imgpath)
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subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
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camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
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subimgName = f"{camerType}_{tid}_fid({fid}, {frameID}).png"
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subimgPath = os.path.join(spath, subimgName)
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cv2.imwrite(subimgPath, subimg)
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print(f"Image saved: {basename}")
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def batch_inference(imgpaths, batch):
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size = len(imgpaths)
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groups = []
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for i in range(0, size, batch):
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end = min(batch + i, size)
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groups.append(imgpaths[i: end])
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features = []
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for group in groups:
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feature = featurize(group, conf.test_transform, model, conf.device)
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features.append(feature)
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features = np.concatenate(features, axis=0)
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return features
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def stdfeat_infer(imgPath, featPath):
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'''
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inputs:
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imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
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featPath: imgPath图像对应特征的存储地址
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功能:
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对 imgPath中图像进行特征提取，生成只有一个key值的字典，
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{barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中
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'''
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# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
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# featPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
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stdBarcodeDict = {}
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k = 0
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for filename in os.listdir(imgPath):
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filepath = os.path.join(imgPath, filename)
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stdbDict = {}
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t1 = time.time()
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try:
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with open(filepath, 'rb') as f:
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bpDict = pickle.load(f)
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for barcode, imgpaths in bpDict.items():
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feature = batch_inference(imgpaths, 8)
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except Exception as e:
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print(f"Error accured at: {filename}, with Exception is: {e}")
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'''================ 保存单个barcode特征 ================'''
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stdbDict[barcode] = feature
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pkpath = os.path.join(featPath, f"{barcode}.pickle")
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with open(pkpath, 'wb') as f:
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pickle.dump(stdbDict, f)
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stdBarcodeDict[barcode] = feature
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t2 = time.time()
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print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
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k += 1
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if k == 10:
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break
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pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(stdBarcodeDict, f)
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def contrast_performance_evaluate():
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eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
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stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\features"
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def generate_event_and_standard_features():
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stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771"
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stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\barcodes"
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stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\features"
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'''=========================== 1. 生成标准特征集 ========================'''
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'''1.1 提取并保存标准特征库原始图像文件地址字典'''
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# get_std_barcodeDict(stdSamplePath, stdBarcodePath)
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# print("standard imgpath have extracted and saved")
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'''1.2 特征提取，并保存至文件夹 stdFeaturePath 中'''
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stdfeat_infer(stdBarcodePath, stdFeaturePath)
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# print("standard features have generated!")
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'''=========================== 2. 提取并存储事件特征 ========================'''
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shoppingEventPath = [r'\\192.168.1.28\share\测试_202406\0723\0723_1',
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r'\\192.168.1.28\share\测试_202406\0723\0723_2',
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r'\\192.168.1.28\share\测试_202406\0723\0723_3',
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r'\\192.168.1.28\share\测试_202406\0722\0722_01',
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r'\\192.168.1.28\share\测试_202406\0722\0722_02']
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shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
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# for sPath in shoppingEventPath:
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# eventList = creat_shopping_event(sPath, shoppingFeatPath)
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def shopping_event_test():
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fplist = [#r'\\192.168.1.28\share\测试_202406\0723\0723_1',
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#r'\\192.168.1.28\share\测试_202406\0723\0723_2',
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r'\\192.168.1.28\share\测试_202406\0723\0723_3',
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#r'\\192.168.1.28\share\测试_202406\0722\0722_01',
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#r'\\192.168.1.28\share\测试_202406\0722\0722_02'
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]
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shoppingFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
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subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
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for filepath in fplist:
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'''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'''
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extract_save_trajture_subimgs(filepath, shoppingFeatPath, subimgPath)
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def main():
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generate_event_and_standard_features()
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# shopping_event_test()
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pass
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if __name__ == '__main__':
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main()
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