425 lines
16 KiB
Python
425 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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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_eval(similPath)
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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 json
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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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# 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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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_similar, read_deletedBarcode_file
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from tracking.utils.plotting import Annotator, colors
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from feat_extract.config import config as conf
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from feat_extract.inference import FeatsInterface
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from utils.event import ShoppingEvent, save_data
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from genfeats import gen_bcd_features
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def int8_to_ft16(arr_uint8, amin, amax):
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arr_ft16 = (arr_uint8 / 255 * (amax-amin) + amin).astype(np.float16)
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return arr_ft16
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def ft16_to_uint8(arr_ft16):
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# pickpath = r"\\192.168.1.28\share\测试_202406\contrast\std_features_ft32vsft16\6902265587712_ft16.pickle"
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# with open(pickpath, 'rb') as f:
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# edict = pickle.load(f)
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# arr_ft16 = edict['feats']
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amin = np.min(arr_ft16)
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amax = np.max(arr_ft16)
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arr_ft255 = (arr_ft16 - amin) * 255 / (amax-amin)
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arr_uint8 = arr_ft255.astype(np.uint8)
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arr_ft16_ = int8_to_ft16(arr_uint8, amin, amax)
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arrDistNorm = np.linalg.norm(arr_ft16_ - arr_ft16) / arr_ft16_.size
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return arr_uint8, arr_ft16_
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def data_precision_compare(stdfeat, evtfeat, evtMessage, save=True):
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evt, stdbcd, label = evtMessage
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rltdata, rltdata_ft16, rltdata_ft16_ = [], [], []
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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rltdata = [label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]]
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##================================================================= float16
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stdfeat_ft16 = stdfeat.astype(np.float16)
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evtfeat_ft16 = evtfeat.astype(np.float16)
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stdfeat_ft16 /= np.linalg.norm(stdfeat_ft16, axis=1)[:, None]
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evtfeat_ft16 /= np.linalg.norm(evtfeat_ft16, axis=1)[:, None]
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matrix_ft16 = 1 - cdist(stdfeat_ft16, evtfeat_ft16, 'cosine')
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simi_mean_ft16 = np.mean(matrix_ft16)
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simi_max_ft16 = np.max(matrix_ft16)
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stdfeatm_ft16 = np.mean(stdfeat_ft16, axis=0, keepdims=True)
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evtfeatm_ft16 = np.mean(evtfeat_ft16, axis=0, keepdims=True)
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simi_mfeat_ft16 = 1- np.maximum(0.0, cdist(stdfeatm_ft16, evtfeatm_ft16, 'cosine'))
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rltdata_ft16 = [label, stdbcd, evt, simi_mean_ft16, simi_max_ft16, simi_mfeat_ft16[0,0]]
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'''****************** uint8 is ok!!!!!! ******************'''
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##=================================================================== uint8
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# stdfeat_uint8, stdfeat_ft16_ = ft16_to_uint8(stdfeat_ft16)
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# evtfeat_uint8, evtfeat_ft16_ = ft16_to_uint8(evtfeat_ft16)
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stdfeat_uint8 = (stdfeat_ft16*128).astype(np.int8)
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evtfeat_uint8 = (evtfeat_ft16*128).astype(np.int8)
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stdfeat_ft16_ = stdfeat_uint8.astype(np.float16)/128
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evtfeat_ft16_ = evtfeat_uint8.astype(np.float16)/128
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absdiff = np.linalg.norm(stdfeat_ft16_ - stdfeat) / stdfeat.size
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matrix_ft16_ = 1 - cdist(stdfeat_ft16_, evtfeat_ft16_, 'cosine')
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simi_mean_ft16_ = np.mean(matrix_ft16_)
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simi_max_ft16_ = np.max(matrix_ft16_)
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stdfeatm_ft16_ = np.mean(stdfeat_ft16_, axis=0, keepdims=True)
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evtfeatm_ft16_ = np.mean(evtfeat_ft16_, axis=0, keepdims=True)
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simi_mfeat_ft16_ = 1- np.maximum(0.0, cdist(stdfeatm_ft16_, evtfeatm_ft16_, 'cosine'))
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rltdata_ft16_ = [label, stdbcd, evt, simi_mean_ft16_, simi_max_ft16_, simi_mfeat_ft16_[0,0]]
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if not save:
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return
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##========================================================= save as float32
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rppath = os.path.join(similPath, f'{evt}_ft32.pickle')
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with open(rppath, 'wb') as f:
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pickle.dump(rltdata, f)
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rtpath = os.path.join(similPath, f'{evt}_ft32.txt')
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with open(rtpath, 'w', encoding='utf-8') as f:
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for result in rltdata:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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##========================================================= save as float16
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rppath_ft16 = os.path.join(similPath, f'{evt}_ft16.pickle')
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with open(rppath_ft16, 'wb') as f:
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pickle.dump(rltdata_ft16, f)
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rtpath_ft16 = os.path.join(similPath, f'{evt}_ft16.txt')
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with open(rtpath_ft16, 'w', encoding='utf-8') as f:
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for result in rltdata_ft16:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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##=========================================================== save as uint8
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rppath_uint8 = os.path.join(similPath, f'{evt}_uint8.pickle')
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with open(rppath_uint8, 'wb') as f:
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pickle.dump(rltdata_ft16_, f)
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rtpath_uint8 = os.path.join(similPath, f'{evt}_uint8.txt')
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with open(rtpath_uint8, 'w', encoding='utf-8') as f:
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for result in rltdata_ft16_:
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part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
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line = ', '.join(part)
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f.write(line + '\n')
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def one2one_simi():
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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=='.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 barcode in barcodes:
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stdpath = os.path.join(stdFeaturePath, barcode+'.pickle')
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with open(stdpath, 'rb') as f:
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stddata = pickle.load(f)
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stdDict[barcode] = stddata
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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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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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evtDict[evtname] = evtdata
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'''======4.2 barcode 标准图像保存 =================='''
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# for stdbcd in barcodes:
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# stdImgpath = stdDict[stdbcd]["imgpaths"]
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# pstdpath = os.path.join(subimgPath, f"{stdbcd}")
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# if not os.path.exists(pstdpath):
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# os.makedirs(pstdpath)
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# ii = 1
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# for filepath in stdImgpath:
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# stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
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# shutil.copy2(filepath, stdpath)
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# ii += 1
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'''======5 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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'''======6 计算事件、标准特征集相似度 =================='''
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rltdata = []
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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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##============================================ float32
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stdfeat = stdDict[stdbcd]["feats_ft32"]
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evtfeat = event.feats_compose
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if len(evtfeat)==0: continue
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matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
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matrix[matrix < 0] = 0
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simi_mean = np.mean(matrix)
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simi_max = np.max(matrix)
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stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
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evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
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simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
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rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat[0,0]))
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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], save=True)
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print("func: one2one_eval(), have finished!")
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return rltdata
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def compute_precise_recall(rltdata):
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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 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, 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, 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 = TPFN - TP
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TN = np.sum(Cross < th)
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FP = TNFP - TN
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Recall_Pos.append(TP/TPFN)
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Recall_Neg.append(TN/TNFP)
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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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Correct.append((TN+TP)/(TPFN+TNFP))
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fig, ax = plt.subplots()
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ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
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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, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
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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.grid(True)
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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, 'pr.png')
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plt.savefig(rltpath) # svg, png, pdf
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def gen_eventdict(sourcePath, saveimg=True):
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k, errEvents = 0, []
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for source_path in sourcePath:
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evtpath, bname = os.path.split(source_path)
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# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
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source_path = os.path.join(evtpath, bname)
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pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
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if os.path.isfile(pickpath): continue
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try:
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event = ShoppingEvent(source_path, stype="data")
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# save_data(event, resultPath)
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with open(pickpath, 'wb') as f:
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pickle.dump(event, f)
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print(bname)
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except Exception as e:
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errEvents.append(source_path)
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print(e)
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# k += 1
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# if k==1:
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# break
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errfile = os.path.join(resultPath, 'error_events.txt')
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with open(errfile, 'w', encoding='utf-8') as f:
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for line in errEvents:
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f.write(line + '\n')
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def test_one2one():
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'''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
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bcdList, event_spath = [], []
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for evtpath in eventSourcePath:
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for evtname in os.listdir(evtpath):
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evt = evtname.split('_')
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dirpath = os.path.join(evtpath, evtname)
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if os.path.isfile(dirpath): continue
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if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
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bcdList.append(evt[-1])
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event_spath.append(os.path.join(evtpath, evtname))
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'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
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# bcdSet = set(bcdList)
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# gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
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print("stdFeats have generated and saved!")
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'''==== 2. 生成事件字典, 只需运行一次 ==============='''
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gen_eventdict(event_spath)
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print("eventList have generated and saved!")
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'''==== 3. 1:1性能评估 ==============='''
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rltdata = one2one_simi()
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compute_precise_recall(rltdata)
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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
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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) eventSourcePath: 事件地址
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(5) resultPath: 结果存储地址
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(6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
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(7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
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'''
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stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v1.0\比对数据\整理\zhantingBase"
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stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
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stdFeaturePath = r"D:\exhibition\dataset\feats"
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# eventSourcePath = [r'D:\exhibition\images\20241202']
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# eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
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||
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"]
|
||
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
|
||
|
||
|
||
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()
|
||
|
||
|
||
|
||
|