870 lines
32 KiB
Python
870 lines
32 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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功能：1:1比对性能测试程序
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1. 基于标准特征集所对应的原始图像样本，生成标准特征集并保存。
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func: generate_event_and_stdfeatures():
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(1) get_std_barcodeDict(stdSamplePath, stdBarcodePath)
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提取 stdSamplePath 中样本地址，生成字典{barcode: [imgpath1, imgpath1, ...]}
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并存储为 pickle 文件，barcode.pickle'''
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(2) stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None)
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标准特征提取，并保存至文件夹 stdFeaturePath 中，
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也可在运行过程中根据与购物事件集合 barcodes 交集执行
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2. 1:1 比对性能测试，
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func: contrast_performance_evaluate(resultPath)
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(1) 求购物事件和标准特征级 Barcode 交集，构造 evtDict、stdDict
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(2) 构造扫 A 放 A、扫 A 放 B 组合，mergePairs = AA_list + AB_list
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(3) 循环计算 mergePairs 中元素 "(A, A) 或 (A, B)" 相似度;
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对于未保存的轨迹图像或标准 barcode 图像，保存图像
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(4) 保存计算结果
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3. precise、recall等指标计算
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func: compute_precise_recall(pickpath)
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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 random
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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 pathlib import Path
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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import shutil
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from datetime import datetime
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from openpyxl import load_workbook, Workbook
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# Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出
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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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# embedding_size = conf.embedding_size
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# img_size = conf.img_size
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# device = conf.device
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# model = load_contrast_model()
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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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from config import config as conf
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from model import resnet18 as resnet18
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from feat_inference import inference_image
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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'''
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共6个地址：
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(1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
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(2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
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(3) stdFeaturePath: 比对标准特征集特征存储地址
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(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
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(5) subimgPath: 1:1比对购物事件轨迹、标准barcode所对应的 subimgs 存储地址
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(6) resultPath: 1:1比对结果存储地址
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'''
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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"
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stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
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stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32"
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eventFeatPath = 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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resultPath = r"D:\DetectTracking\contrast\result\pickle"
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if not os.path.exists(resultPath):
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os.makedirs(resultPath)
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##============ load resnet mdoel
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model = resnet18().to(conf.device)
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# model = nn.DataParallel(model).to(conf.device)
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model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
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model.eval()
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print('load model {} '.format(conf.testbackbone))
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def creat_shopping_event(eventPath, subimgPath=False):
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'''构造放入商品事件字典，这些事件需满足条件：
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1) 前后摄至少有一条轨迹输出
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2) 保存有帧图像，以便裁剪出 boxe 子图
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'''
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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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eventName = os.path.basename(eventPath)
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'''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================'''
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nmlist = eventName.split('_')
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# if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
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# return
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if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[1])<11:
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return
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if not os.path.isdir(eventPath):
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return
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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'] = eventName.split('_')[1]
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event['type'] = 'input'
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event['filepath'] = eventPath
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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(eventPath):
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# filename = '1_track.data'
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datapath = os.path.join(eventPath, 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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if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
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return None
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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(eventPath):
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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(eventPath, 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"Event: {eventName}, Error, condt1: {condt1}, condt2: {condt2}")
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return None
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'''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 event
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def get_std_barcodeDict(bcdpath, savepath):
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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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savepath: 字典存储地址，文件名格式：barcode.pickle
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'''
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# savepath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes'
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'''读取数据集中 barcode 列表'''
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stdBarcodeList = []
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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() or len(filename)<8:
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# continue
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stdBarcodeList.append(filename)
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bcdPaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBarcodeList]
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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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errbarcodes = []
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for barcode, bpath in bcdPaths:
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pickpath = os.path.join(savepath, f"{barcode}.pickle")
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if os.path.isfile(pickpath):
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continue
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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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file, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT:
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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(savepath, 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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print(f"Len of errbarcodes: {len(errbarcodes)}")
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return
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def save_event_subimg(event, savepath):
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'''
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功能: 保存一次购物事件的轨迹子图
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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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feats_compose, feats_select
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子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
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'''
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cameras = ('front', 'back')
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k = 0
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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"{k}_cam-{camerType}_tid-{int(tid)}_fid-({int(fid)}, {frameID}).png"
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spath = os.path.join(savepath, subimgName)
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cv2.imwrite(spath, subimg)
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k += 1
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# basename = os.path.basename(event['filepath'])
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print(f"Image saved: {os.path.basename(event['filepath'])}")
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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, bcdSet=None):
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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\std_barcodes"
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# featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
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stdBarcodeDict = {}
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stdBarcodeDict_ft16 = {}
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'''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'''
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k = 0
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for filename in os.listdir(imgPath):
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bcd, ext = os.path.splitext(filename)
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pkpath = os.path.join(featPath, f"{bcd}.pickle")
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if os.path.isfile(pkpath): continue
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if bcdSet is not None and bcd not in bcdSet:
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continue
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filepath = os.path.join(imgPath, filename)
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stdbDict = {}
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stdbDict_ft16 = {}
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stdbDict_uint8 = {}
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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) #from vit distilled model of LiChen
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feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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# float16
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feature_ft16 = feature.astype(np.float16)
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feature_ft16 /= np.linalg.norm(feature_ft16, axis=1)[:, None]
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# uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小
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# stdfeat_uint8, _ = ft16_to_uint8(feature_ft16)
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stdfeat_uint8 = (feature_ft16*128).astype(np.int8)
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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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##================== float32
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stdbDict["barcode"] = barcode
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stdbDict["imgpaths"] = imgpaths
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stdbDict["feats"] = feature
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# pkpath = os.path.join(featPath, f"{barcode}.pickle")
|
||
with open(pkpath, 'wb') as f:
|
||
pickle.dump(stdbDict, f)
|
||
stdBarcodeDict[barcode] = feature
|
||
|
||
|
||
##================== float16
|
||
# stdbDict_ft16["barcode"] = barcode
|
||
# stdbDict_ft16["imgpaths"] = imgpaths
|
||
# stdbDict_ft16["feats"] = feature_ft16
|
||
# pkpath_ft16 = os.path.join(featPath, f"{barcode}_ft16.pickle")
|
||
# with open(pkpath_ft16, 'wb') as f:
|
||
# pickle.dump(stdbDict_ft16, f)
|
||
# stdBarcodeDict_ft16[barcode] = pkpath_ft16
|
||
|
||
|
||
|
||
##================== uint8
|
||
# stdbDict_uint8["barcode"] = barcode
|
||
# stdbDict_uint8["imgpaths"] = imgpaths
|
||
# stdbDict_uint8["feats"] = stdfeat_uint8
|
||
# pkpath_uint8 = os.path.join(featPath, f"{barcode}_uint8.pickle")
|
||
# with open(pkpath_uint8, 'wb') as f:
|
||
# pickle.dump(stdbDict_uint8, f)
|
||
|
||
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'''
|
||
# 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 p.stem.split('_')[1].isdigit()
|
||
and p.stem.split('_')[1] in stdBarcode
|
||
]
|
||
|
||
barcodes = set([bcd for _, bcd in evtList])
|
||
|
||
'''标准特征集图像样本经特征提取并保存，运行一次后无需再运行'''
|
||
stdfeat_infer(stdBarcodePath, stdFeaturePath, barcodes)
|
||
|
||
'''========= 构建用于比对的标准特征字典 ============='''
|
||
stdDict = {}
|
||
for barcode in barcodes:
|
||
stdpath = os.path.join(stdFeaturePath, barcode+'.pickle')
|
||
with open(stdpath, 'rb') as f:
|
||
stddata = pickle.load(f)
|
||
stdDict[barcode] = stddata
|
||
|
||
'''========= 构建用于比对的操作事件字典 ============='''
|
||
evtDict = {}
|
||
for event, barcode in evtList:
|
||
evtpath = os.path.join(eventFeatPath, event+'.pickle')
|
||
with open(evtpath, 'rb') as f:
|
||
evtdata = pickle.load(f)
|
||
evtDict[event] = evtdata
|
||
|
||
|
||
'''===== 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
|
||
AA_list = [(event, barcode, "same") for event, barcode in evtList]
|
||
AB_list = []
|
||
for event, barcode in evtList:
|
||
dset = list(barcodes.symmetric_difference(set([barcode])))
|
||
idx = random.randint(0, len(dset)-1)
|
||
AB_list.append((event, dset[idx], "diff"))
|
||
|
||
mergePairs = AA_list + AB_list
|
||
|
||
'''读取事件、标准特征文件中数据，以 AA_list 和 AB_list 中关键字为 key 生成字典'''
|
||
rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
|
||
for evt, stdbcd, label in mergePairs:
|
||
event = evtDict[evt]
|
||
|
||
## 判断是否存在轨迹图像文件夹，不存在则创建文件夹并保存轨迹图像
|
||
pairpath = os.path.join(subimgPath, f"{evt}")
|
||
if not os.path.exists(pairpath):
|
||
os.makedirs(pairpath)
|
||
save_event_subimg(event, pairpath)
|
||
|
||
## 判断是否存在 barcode 标准样本集图像文件夹，不存在则创建文件夹并存储 barcode 样本集图像
|
||
stdImgpath = stdDict[stdbcd]["imgpaths"]
|
||
pstdpath = os.path.join(subimgPath, f"{stdbcd}")
|
||
if not os.path.exists(pstdpath):
|
||
os.makedirs(pstdpath)
|
||
ii = 1
|
||
for filepath in stdImgpath:
|
||
stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
|
||
shutil.copy2(filepath, stdpath)
|
||
ii += 1
|
||
|
||
##============================================ float32
|
||
stdfeat = stdDict[stdbcd]["feats"]
|
||
evtfeat = event["feats_compose"]
|
||
|
||
matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
|
||
simi_mean = np.mean(matrix)
|
||
simi_max = np.max(matrix)
|
||
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
|
||
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
|
||
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
|
||
rltdata.append((label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]))
|
||
|
||
|
||
##============================================ float16
|
||
stdfeat_ft16 = stdfeat.astype(np.float16)
|
||
evtfeat_ft16 = evtfeat.astype(np.float16)
|
||
stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
|
||
evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
|
||
|
||
|
||
matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
|
||
simi_mean_ft16 = np.mean(matrix_ft16)
|
||
simi_max_ft16 = np.max(matrix_ft16)
|
||
stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
|
||
evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
|
||
simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
|
||
rltdata_ft16.append((label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]))
|
||
|
||
'''****************** uint8 is ok!!!!!! ******************'''
|
||
##============================================ uint8
|
||
# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
|
||
# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
|
||
|
||
stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
|
||
evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
|
||
stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
|
||
evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
|
||
|
||
|
||
absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
|
||
|
||
matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
|
||
simi_mean_ft16_ = np.mean(matrix_ft16_)
|
||
simi_max_ft16_ = np.max(matrix_ft16_)
|
||
stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
|
||
evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
|
||
simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
|
||
rltdata_ft16_.append((label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]))
|
||
|
||
|
||
|
||
tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
|
||
##================================================ save as float32,
|
||
rppath = os.path.join(resultPath, f'{tm}.pickle')
|
||
with open(rppath, 'wb') as f:
|
||
pickle.dump(rltdata, f)
|
||
|
||
rtpath = os.path.join(resultPath, f'{tm}.txt')
|
||
with open(rtpath, 'w', encoding='utf-8') as f:
|
||
for result in rltdata:
|
||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||
line = ', '.join(part)
|
||
f.write(line + '\n')
|
||
|
||
|
||
##================================================ save as float16,
|
||
rppath_ft16 = os.path.join(resultPath, f'{tm}_ft16.pickle')
|
||
with open(rppath_ft16, 'wb') as f:
|
||
pickle.dump(rltdata_ft16, f)
|
||
|
||
rtpath_ft16 = os.path.join(resultPath, f'{tm}_ft16.txt')
|
||
with open(rtpath_ft16, 'w', encoding='utf-8') as f:
|
||
for result in rltdata_ft16:
|
||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||
line = ', '.join(part)
|
||
f.write(line + '\n')
|
||
|
||
|
||
##================================================ save as uint8,
|
||
rppath_uint8 = os.path.join(resultPath, f'{tm}_uint8.pickle')
|
||
with open(rppath_uint8, 'wb') as f:
|
||
pickle.dump(rltdata_ft16_, f)
|
||
|
||
rtpath_uint8 = os.path.join(resultPath, f'{tm}_uint8.txt')
|
||
with open(rtpath_uint8, 'w', encoding='utf-8') as f:
|
||
for result in rltdata_ft16_:
|
||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||
line = ', '.join(part)
|
||
f.write(line + '\n')
|
||
|
||
|
||
print("func: contrast_performance_evaluate(), have finished!")
|
||
|
||
|
||
|
||
def compute_precise_recall(pickpath):
|
||
|
||
pickfile = os.path.basename(pickpath)
|
||
file, ext = os.path.splitext(pickfile)
|
||
|
||
if ext != '.pickle': return
|
||
if file.find('ft16') < 0: return
|
||
|
||
with open(pickpath, 'rb') as f:
|
||
results = pickle.load(f)
|
||
|
||
Same, Cross = [], []
|
||
for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results:
|
||
if label == "same":
|
||
Same.append(simi_mean)
|
||
if label == "diff":
|
||
Cross.append(simi_mean)
|
||
|
||
|
||
Same = np.array(Same)
|
||
Cross = np.array(Cross)
|
||
TPFN = len(Same)
|
||
TNFP = len(Cross)
|
||
|
||
# fig, axs = plt.subplots(2, 1)
|
||
# axs[0].hist(Same, bins=60, edgecolor='black')
|
||
# axs[0].set_xlim([-0.2, 1])
|
||
# axs[0].set_title(f'Same Barcode, Num: {TPFN}')
|
||
|
||
# axs[1].hist(Cross, bins=60, edgecolor='black')
|
||
# axs[1].set_xlim([-0.2, 1])
|
||
# axs[1].set_title(f'Cross Barcode, Num: {TNFP}')
|
||
# plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
|
||
|
||
|
||
Recall_Pos, Recall_Neg = [], []
|
||
Precision_Pos, Precision_Neg = [], []
|
||
Correct = []
|
||
Thresh = np.linspace(-0.2, 1, 100)
|
||
for th in Thresh:
|
||
TP = np.sum(Same > th)
|
||
FN = TPFN - TP
|
||
TN = np.sum(Cross < th)
|
||
FP = TNFP - TN
|
||
|
||
Recall_Pos.append(TP/TPFN)
|
||
Recall_Neg.append(TN/TNFP)
|
||
Precision_Pos.append(TP/(TP+FP+1e-6))
|
||
Precision_Neg.append(TN/(TN+FN+1e-6))
|
||
Correct.append((TN+TP)/(TPFN+TNFP))
|
||
|
||
fig, ax = plt.subplots()
|
||
ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
|
||
ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
|
||
ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
|
||
ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
|
||
ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
|
||
|
||
ax.set_xlim([0, 1])
|
||
ax.set_ylim([0, 1])
|
||
ax.grid(True)
|
||
ax.set_title('PrecisePos & PreciseNeg')
|
||
ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
|
||
ax.legend()
|
||
plt.show()
|
||
plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
|
||
|
||
|
||
|
||
|
||
|
||
def generate_event_and_stdfeatures():
|
||
'''=========================== 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)
|
||
if eventDict:
|
||
eventList.append(eventDict)
|
||
with open(pickpath, 'wb') as f:
|
||
pickle.dump(eventDict, f)
|
||
print(f"Event: {eventName}, have saved!")
|
||
|
||
# k += 1
|
||
# if k==1:
|
||
# break
|
||
|
||
## 保存轨迹中 boxes 子图
|
||
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)
|
||
|
||
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)
|
||
|
||
|
||
arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
|
||
|
||
|
||
return arr_uint8, arr_ft16_
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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
|
||
pickpath = os.path.join(resultPath, filename)
|
||
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")
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if __name__ == '__main__':
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# main()
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# main_std()
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