648 lines
23 KiB
Python
648 lines
23 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_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 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 random
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import copy
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import sys
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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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from event_test import calc_simil
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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 simi_calc(event, stdfeat):
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evtfeat = event.feats_compose
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if isinstance(event.feats_select, list):
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if len(event.feats_select) and len(event.feats_select[0]):
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evtfeat = event.feats_select[0]
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else:
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return None, None, None
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else:
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evtfeat = event.feats_select
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if len(evtfeat)==0 or len(stdfeat)==0:
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return None, None, None
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evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
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stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
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matrix = 1 - cdist(evtfeat, stdfeat, '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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return simi_mean, simi_max, simi_mfeat[0,0]
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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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'''*********** USearch ***********'''
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# stdFeaturePath = r"D:\contrast\stdlib\v11_test.json"
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# stdBarcode = []
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# stdlib = {}
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# with open(stdFeaturePath, 'r', encoding='utf-8') as f:
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# data = json.load(f)
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# for dic in data['total']:
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# barcode = dic['key']
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# feature = np.array(dic['value'])
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# stdBarcode.append(barcode)
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# stdlib[barcode] = feature
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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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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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return evtList, evtDict, stdDict
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def one2SN_pr(evtList, evtDict, stdDict):
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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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print(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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simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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# simi_mean = calc_simil(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/(len(tp_simi)+len(fn_simi)+1e-6))
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NPreciseX.append(TNX/(TNX+FNX+1e-6))
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NRecallX.append(TNX/(len(tn_simi)+len(fp_simi)+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.grid(True)
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ax.set_title('1:SN Precise & Recall')
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ax.set_xlabel(f"Event Num: {len(evtList)}")
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ax.legend()
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plt.show()
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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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def one2one_simi(evtList, evtDict, stdDict):
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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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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: continue
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stdfeat = stdDict[stdbcd] # float32
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simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
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if simi_mean is None:
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||
continue
|
||
|
||
rltdata.append((label, stdbcd, evtname, simi_mean, simi_max, simi_mfeat))
|
||
|
||
'''================ float32、16、int8 精度比较与存储 ============='''
|
||
# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
|
||
|
||
|
||
return rltdata
|
||
|
||
|
||
def one2one_pr(rltdata):
|
||
Same, Cross = [], []
|
||
for label, stdbcd, evtname, simi_mean, simi_max, simi_mft in rltdata:
|
||
if label == "same":
|
||
Same.append(simi_max)
|
||
if label == "diff":
|
||
Cross.append(simi_max)
|
||
|
||
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, range=(-0.2, 1), edgecolor='black')
|
||
# axs[0].set_xlim([-0.2, 1])
|
||
# axs[0].set_title(f'Same Barcode, Num: {TPFN}')
|
||
|
||
# axs[1].hist(Cross, bins=60, range=(-0.2, 1), 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()
|
||
|
||
rltpath = os.path.join(similPath, 'pr.png')
|
||
plt.savefig(rltpath) # svg, png, pdf
|
||
|
||
|
||
fig, axes = plt.subplots(2,1)
|
||
axes[0].hist(Same, bins=60, range=(-0.2, 1), edgecolor='black')
|
||
axes[0].set_xlim([-0.2, 1])
|
||
axes[0].set_title(f'TP({len(Same)})')
|
||
|
||
axes[1].hist(Cross, bins=60, range=(-0.2, 1), edgecolor='black')
|
||
axes[1].set_xlim([-0.2, 1])
|
||
axes[1].set_title(f'TN({len(Cross)})')
|
||
|
||
rltpath = os.path.join(similPath, 'hist.png')
|
||
plt.savefig(rltpath)
|
||
|
||
|
||
plt.show()
|
||
|
||
|
||
|
||
def gen_eventdict(sourcePath, saveimg=True):
|
||
k, errEvents = 0, []
|
||
for source_path in sourcePath:
|
||
evtpath, bname = os.path.split(source_path)
|
||
|
||
## 兼容事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
|
||
if os.path.isfile(source_path):
|
||
bname, ext = os.path.splitext(bname)
|
||
evt = bname.split("_")
|
||
|
||
evt = bname.split('_')
|
||
condt = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
|
||
if not condt: continue
|
||
|
||
# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
|
||
# source_path = os.path.join(evtpath, bname)
|
||
|
||
# 如果已完成事件生成，则不执行
|
||
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
|
||
if os.path.isfile(pickpath): continue
|
||
|
||
# event = ShoppingEvent(source_path, stype="data")
|
||
# with open(pickpath, 'wb') as f:
|
||
# pickle.dump(event, f)
|
||
|
||
try:
|
||
event = ShoppingEvent(source_path, stype="source")
|
||
# save_data(event, resultPath)
|
||
|
||
with open(pickpath, 'wb') as f:
|
||
pickle.dump(event, f)
|
||
print(bname)
|
||
except Exception as e:
|
||
errEvents.append(source_path)
|
||
print(e)
|
||
|
||
# k += 1
|
||
# if k==1:
|
||
# break
|
||
|
||
|
||
errfile = os.path.join(resultPath, 'error_events.txt')
|
||
with open(errfile, 'w', encoding='utf-8') as f:
|
||
for line in errEvents:
|
||
f.write(line + '\n')
|
||
|
||
|
||
def init_std_evt_dict():
|
||
'''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
|
||
bcdList, event_spath = [], []
|
||
for evtpath in eventSourcePath:
|
||
for evtname in os.listdir(evtpath):
|
||
bname, ext = os.path.splitext(evtname)
|
||
|
||
## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
|
||
fpath = os.path.join(evtpath, evtname)
|
||
if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
|
||
evt = bname.split('_')
|
||
elif os.path.isdir(fpath):
|
||
evt = evtname.split('_')
|
||
else:
|
||
continue
|
||
|
||
|
||
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
|
||
bcdList.append(evt[-1])
|
||
event_spath.append(os.path.join(evtpath, evtname))
|
||
|
||
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
|
||
bcdSet = set(bcdList)
|
||
gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
|
||
print("stdFeats have generated and saved!")
|
||
|
||
|
||
'''==== 2. 生成事件字典, 只需运行一次 ==============='''
|
||
gen_eventdict(event_spath)
|
||
print("eventList have generated and saved!")
|
||
|
||
|
||
|
||
|
||
def test_one2one():
|
||
'''1:1性能评估'''
|
||
|
||
# 1. 只需运行一次，生成事件字典和相应的标准特征库字典
|
||
init_std_evt_dict()
|
||
|
||
# 2. 基于事件barcode集和标准库barcode交集构造事件集合
|
||
evtList, evtDict, stdDict = build_std_evt_dict()
|
||
|
||
rltdata = one2one_simi(evtList, evtDict, stdDict)
|
||
|
||
one2one_pr(rltdata)
|
||
|
||
|
||
def test_one2SN():
|
||
'''1:SN性能评估'''
|
||
|
||
# 1. 只需运行一次，生成事件字典和相应的标准特征库字典
|
||
init_std_evt_dict()
|
||
|
||
# 2. 事件barcode集和标准库barcode求交集
|
||
evtList, evtDict, stdDict = build_std_evt_dict()
|
||
|
||
one2SN_pr(evtList, evtDict, stdDict)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
'''
|
||
共7个地址：
|
||
(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
|
||
(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 = 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"
|
||
# stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
|
||
# stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592"
|
||
|
||
# eventSourcePath = [r'D:\exhibition\images\20241202']
|
||
# 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"]
|
||
|
||
|
||
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
|
||
stdBarcodePath = r"D:\全实时\source_data\bcdpath"
|
||
stdFeaturePath = r"D:\全实时\source_data\stdfeats"
|
||
|
||
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\result\ShoppingDict_pkfile"]
|
||
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\contrast"
|
||
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()
|
||
|
||
test_one2SN()
|
||
|
||
|
||
|
||
|