回传数据解析，兼容v5和v10
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jiajie555
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contrast/one2one_contrast.py
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452
contrast/one2one_contrast.py
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# -*- 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: one2one_simi()
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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_one2one_pr(pickpath)
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@author: ym
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"""
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import numpy as np
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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 json
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from pathlib import Path
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import matplotlib.pyplot as plt
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT))
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from utils.calsimi import calsimi_vs_stdfeat, calsimi_vs_stdfeat_new
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from utils.tools import get_evtList, init_eventDict
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from utils.databits import data_precision_compare
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from genfeats import gen_bcd_features
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def build_std_evt_dict():
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'''
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stdFeaturePath: 标准特征集地址
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eventDataPath: Event对象地址
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'''
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stdBarcode = [p.stem for p in Path(stdFeaturePath).iterdir() if p.is_file() and (p.suffix=='.json' or p.suffix=='.pickle')]
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'''======1. 购物事件列表，该列表中的 Barcode 存在于标准的 stdBarcode 内 ==='''
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evtList = [(p.stem, p.stem.split('_')[-1]) for p in Path(eventDataPath).iterdir()
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if p.is_file()
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and p.suffix=='.pickle'
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and (len(p.stem.split('_'))==2 or len(p.stem.split('_'))==3)
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and p.stem.split('_')[-1].isdigit()
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and p.stem.split('_')[-1] in stdBarcode
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]
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barcodes = set([bcd for _, bcd in evtList])
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'''======2. 构建用于比对的标准特征字典 ============='''
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stdDict = {}
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for stdfile in os.listdir(stdFeaturePath):
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barcode, ext = os.path.splitext(stdfile)
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if barcode not in barcodes:
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continue
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stdpath = os.path.join(stdFeaturePath, stdfile)
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if ext == ".json":
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with open(stdpath, 'r', encoding='utf-8') as f:
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stddata = json.load(f)
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feat = np.array(stddata["value"])
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stdDict[barcode] = feat
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if ext == ".pickle":
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with open(stdpath, 'rb') as f:
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stddata = pickle.load(f)
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feat = stddata["feats_ft32"]
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stdDict[barcode] = feat
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'''*********** USearch ***********'''
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# stdDict = {}
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# for barcode in barcodes:
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# stdDict[barcode] = stdlib[barcode]
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'''======3. 构建用于比对的操作事件字典 ============='''
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evtDict = {}
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for evtname, barcode in evtList:
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evtpath = os.path.join(eventDataPath, evtname+'.pickle')
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try:
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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except Exception as e:
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print(evtname)
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evtDict[evtname] = evtdata
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return evtList, evtDict, stdDict
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def one2SN_pr(evtList, evtDict, stdDict, simType="simple"):
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std_barcodes = set([bcd for _, bcd in evtList])
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tp_events, fn_events, fp_events, tn_events = [], [], [], []
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tp_simi, fn_simi, tn_simi, fp_simi = [], [], [], []
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errorFile_one2SN = []
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SN = 9
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for evtname, barcode in evtList:
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bcd_selected = [barcode]
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dset = list(std_barcodes - set([barcode]))
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if len(dset) > SN:
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random.shuffle(dset)
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bcd_selected.extend(dset[:SN])
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else:
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bcd_selected.extend(dset)
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event = evtDict[evtname]
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## 无轨迹判断
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if len(event.front_feats)+len(event.back_feats)==0:
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errorFile_one2SN.append(evtname)
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print(f"No trajectory: {evtname}")
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continue
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barcodes, similars = [], []
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for stdbcd in bcd_selected:
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stdfeat = stdDict[stdbcd]
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if simType=="typea":
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simi_mean, simi_max, simi_mfeat = calsimi_vs_stdfeat(event, stdfeat)
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elif simType=="typeb":
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pass
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else:
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat)
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## 在event.front_feats和event.back_feats同时为空时，此处不需要保护
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# if simi_mean==None:
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# continue
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barcodes.append(stdbcd)
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similars.append(simi_mean)
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## 此处不需要保护
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# if len(similars)==0:
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# print(evtname)
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# continue
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max_idx = similars.index(max(similars))
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max_sim = similars[max_idx]
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for i in range(len(barcodes)):
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bcd, simi = barcodes[i], similars[i]
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if bcd==barcode and simi==max_sim:
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tp_simi.append(simi)
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tp_events.append(evtname)
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elif bcd==barcode and simi!=max_sim:
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fn_simi.append(simi)
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fn_events.append(evtname)
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elif bcd!=barcode and simi!=max_sim:
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tn_simi.append(simi)
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tn_events.append(evtname)
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elif bcd!=barcode and simi==max_sim and barcode in barcodes:
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fp_simi.append(simi)
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fp_events.append(evtname)
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else:
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errorFile_one2SN.append(evtname)
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PPreciseX, PRecallX = [], []
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NPreciseX, NRecallX = [], []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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'''适用于 (Precise, Recall) 计算方式：多个相似度计算并排序，barcode相等且排名第一为 TP '''
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'''===================================== 1:SN '''
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TPX = sum(np.array(tp_simi) >= th)
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FPX = sum(np.array(fp_simi) >= th)
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FNX = sum(np.array(fn_simi) < th)
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TNX = sum(np.array(tn_simi) < th)
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PPreciseX.append(TPX/(TPX+FPX+1e-6))
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PRecallX.append(TPX/(TPX+FNX+1e-6))
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NPreciseX.append(TNX/(TNX+FNX+1e-6))
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NRecallX.append(TNX/(TNX+FPX+1e-6))
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fig, ax = plt.subplots()
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ax.plot(Thresh, PPreciseX, 'r', label='Precise_Pos: TP/TPFP')
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ax.plot(Thresh, PRecallX, 'b', label='Recall_Pos: TP/TPFN')
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ax.plot(Thresh, NPreciseX, 'g', label='Precise_Neg: TN/TNFP')
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ax.plot(Thresh, NRecallX, 'c', label='Recall_Neg: TN/TNFN')
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ax.set_xlim([0, 1])
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ax.set_ylim([0, 1])
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ax.set_xticks(np.arange(0, 1, 0.1))
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ax.set_yticks(np.arange(0, 1, 0.1))
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ax.grid(True, linestyle='--')
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ax.set_title('1:SN Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(tp_events) + len(fn_events)}")
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ax.legend()
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plt.show()
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rltpath = os.path.join(similPath, f'pr_1toSN_{simType}.png')
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plt.savefig(rltpath)
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## ============================= 1:N 展厅 直方图'''
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fig, axes = plt.subplots(2, 2)
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axes[0, 0].hist(tp_simi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 0].set_xlim([-0.2, 1])
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axes[0, 0].set_title(f'TP({len(tp_simi)})')
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axes[0, 1].hist(fp_simi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0, 1].set_xlim([-0.2, 1])
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axes[0, 1].set_title(f'FP({len(fp_simi)})')
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axes[1, 0].hist(tn_simi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 0].set_xlim([-0.2, 1])
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axes[1, 0].set_title(f'TN({len(tn_simi)})')
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axes[1, 1].hist(fn_simi, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1, 1].set_xlim([-0.2, 1])
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axes[1, 1].set_title(f'FN({len(fn_simi)})')
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plt.show()
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rltpath = os.path.join(similPath, f'hist_1toSN_{simType}.png')
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plt.savefig(rltpath)
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def one2one_simi(evtList, evtDict, stdDict, simType):
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barcodes = set([bcd for _, bcd in evtList])
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'''======1 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
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AA_list = [(evtname, barcode, "same") for evtname, barcode in evtList]
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AB_list = []
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for evtname, barcode in evtList:
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dset = list(barcodes.symmetric_difference(set([barcode])))
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if len(dset):
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idx = random.randint(0, len(dset)-1)
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AB_list.append((evtname, dset[idx], "diff"))
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mergePairs = AA_list + AB_list
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'''======2 计算事件、标准特征集相似度 =================='''
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rltdata = []
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errorFile_one2one = []
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for i in range(len(mergePairs)):
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evtname, stdbcd, label = mergePairs[i]
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event = evtDict[evtname]
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if len(event.feats_compose)==0:
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errorFile_one2one.append(evtname)
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continue
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stdfeat = stdDict[stdbcd] # float32
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if simType=="typea":
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat_new(event, stdfeat)
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elif simType=="typeb":
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pass
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else:
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simi_mean, simi_1, simi_2 = calsimi_vs_stdfeat(event, stdfeat)
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if simi_mean is None:
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continue
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rltdata.append((label, stdbcd, evtname, simi_mean, simi_1, simi_2))
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'''================ float32、16、int8 精度比较与存储 ============='''
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# data_precision_compare(stdfeat, evtfeat, mergePairs[i], similPath, save=True)
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errorFile_one2one = list(set(errorFile_one2one))
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return rltdata, errorFile_one2one
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def one2one_pr(evtList, evtDict, stdDict, simType="simple"):
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rltdata, errorFile_one2one = one2one_simi(evtList, evtDict, stdDict, simType)
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Same, Cross = [], []
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for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
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if simType=="simple" and label == "same":
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Same.append(simi_max)
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if simType=="simple" and label == "diff":
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Cross.append(simi_max)
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if simType=="typea" and label == "same":
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Same.append(simi_mean)
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if simType=="typea" and label == "diff":
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Cross.append(simi_mean)
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# for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
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# if label == "same":
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# Same.append(simi_mean)
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# if label == "diff":
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# Cross.append(simi_mean)
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Same = np.array(Same)
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Cross = np.array(Cross)
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TPFN = len(Same)
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TNFP = len(Cross)
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# fig, axs = plt.subplots(2, 1)
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# axs[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black')
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# axs[0].set_xlim([-0.2, 1])
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# axs[0].set_title(f'Same Barcode, Num: {TPFN}')
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# axs[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black')
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# axs[1].set_xlim([-0.2, 1])
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# axs[1].set_title(f'Cross Barcode, Num: {TNFP}')
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# plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
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Recall_Pos, Recall_Neg = [], []
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Precision_Pos, Precision_Neg = [], []
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Correct = []
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Thresh = np.linspace(-0.2, 1, 100)
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for th in Thresh:
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TP = np.sum(Same >= th)
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FN = np.sum(Same < th)
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# FN = TPFN - TP
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TN = np.sum(Cross < th)
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FP = np.sum(Cross >= th)
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# FP = TNFP - TN
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Precision_Pos.append(TP/(TP+FP+1e-6))
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Precision_Neg.append(TN/(TN+FN+1e-6))
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Recall_Pos.append(TP/(TP+FN+1e-6))
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Recall_Neg.append(TN/(TN+FP+1e-6))
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# Recall_Pos.append(TP/TPFN)
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# Recall_Neg.append(TN/TNFP)
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Correct.append((TN+TP)/(TPFN+TNFP))
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fig, ax = plt.subplots()
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ax.plot(Thresh, Precision_Pos, 'r', label='Precision_Pos: TP/(TP+FP)')
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ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
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ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
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ax.plot(Thresh, Correct, 'c', label='Correct: (TN+TP)/(TPFN+TNFP)')
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ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
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ax.set_xlim([0, 1])
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ax.set_ylim([0, 1])
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ax.set_xticks(np.arange(0, 1, 0.1))
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ax.set_yticks(np.arange(0, 1, 0.1))
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ax.grid(True, linestyle='--')
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ax.set_title('PrecisePos & PreciseNeg')
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ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
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ax.legend()
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plt.show()
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rltpath = os.path.join(similPath, f'pr_1to1_{simType}.png')
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plt.savefig(rltpath) # svg, png, pdf
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fig, axes = plt.subplots(2,1)
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axes[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[0].set_xlim([-0.2, 1])
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axes[0].set_title(f'TP({len(Same)})')
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axes[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black')
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axes[1].set_xlim([-0.2, 1])
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axes[1].set_title(f'TN({len(Cross)})')
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rltpath = os.path.join(similPath, f'hist_1to1_{simType}.png')
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plt.savefig(rltpath)
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plt.show()
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def test_one2one_one2SN(simType):
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'''1:1性能评估'''
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# evtpaths, bcdSet = get_evtList(eventSourcePath)
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'''=== 1. 只需运行一次，生成事件对应的标准特征库字典,如已生成，无需运行 ===='''
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# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, eventSourcePath)
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'''==== 2. 生成事件字典, 只需运行一次 ===================='''
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# init_eventDict(eventSourcePath, eventDataPath, source_type)
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'''==== 3. 基于事件barcode集和标准库barcode交集构造事件集合 ========='''
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evtList, evtDict, stdDict = build_std_evt_dict()
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one2one_pr(evtList, evtDict, stdDict, simType)
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one2SN_pr(evtList, evtDict, stdDict, simType)
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if __name__ == '__main__':
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'''
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共7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
|
||||
(2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
|
||||
(3) stdFeaturePath: 比对标准特征集特征存储地址
|
||||
(4) eventSourcePath: 事件地址, 包含data文件的文件夹或 Yolo-Resnet-Tracker输出的Pickle文件父文件夹
|
||||
(5) resultPath: 结果存储地址
|
||||
(6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
|
||||
(7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
|
||||
'''
|
||||
|
||||
stdSamplePath = "/home/wqg/dataset/total_barcode/totalBarcode"
|
||||
stdBarcodePath = "/home/wqg/dataset/total_barcode/bcdpath"
|
||||
stdFeaturePath = "/home/wqg/dataset/test_dataset/total_barcode/features_json/v11_barcode_0304/"
|
||||
|
||||
if not os.path.exists(stdBarcodePath):
|
||||
os.makedirs(stdBarcodePath)
|
||||
if not os.path.exists(stdFeaturePath):
|
||||
os.makedirs(stdFeaturePath)
|
||||
|
||||
'''source_type:
|
||||
"source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件
|
||||
"data": 基于事件切分的原 data 文件版本
|
||||
"realtime": 全实时生成的 data 文件
|
||||
'''
|
||||
source_type = 'source' # 'source', 'data', 'realtime'
|
||||
simType = "typea" # "simple", "typea", "typeb"
|
||||
|
||||
evttype = "single_event_V10"
|
||||
# evttype = "single_event_V5"
|
||||
# evttype = "performence_V10"
|
||||
# evttype = "performence_V5"
|
||||
eventSourcePath = "/home/wqg/dataset/pipeline/yrt/{}/shopping_pkl".format(evttype)
|
||||
|
||||
resultPath = "/home/wqg/dataset/pipeline/contrast/{}".format(evttype)
|
||||
eventDataPath = os.path.join(resultPath, "evtobjs")
|
||||
similPath = os.path.join(resultPath, "simidata")
|
||||
if not os.path.exists(eventDataPath):
|
||||
os.makedirs(eventDataPath)
|
||||
if not os.path.exists(similPath):
|
||||
os.makedirs(similPath)
|
||||
|
||||
test_one2one_one2SN(simType)
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user