709 lines
24 KiB
Python
709 lines
24 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Aug 30 17:53:03 2024
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1. 确认在相同CamerType下，track.data 中 CamerID 项数量 = 图像数 = 帧ID数 = 最大帧ID
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2. 读取0/1_tracking_output.data 中数据，boxes、feats，len(boxes)=len(feats)
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帧ID约束
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3. 优先选择前摄
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4. 保存图像数据
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5. 一次购物事件类型
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shopEvent: {barcode:
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type: getout, input
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front_traj:[{imgpath: str,
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box: arrar(1, 9),
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feat: array(1, 256)
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}]
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back_traj: [{imgpath: str,
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box: arrar(1, 9),
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feat: array(1, 256)
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}]
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}
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@author: ym
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"""
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import numpy as np
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import cv2
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import os
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import sys
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import random
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import pickle
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import torch
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import time
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import json
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from pathlib import Path
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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import shutil
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from datetime import datetime
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# Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出
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# from config import config as conf
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# from model import resnet18
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# from inference import load_contrast_model
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# from inference import featurize
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# embedding_size = conf.embedding_size
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# img_size = conf.img_size
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# device = conf.device
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# model = load_contrast_model()
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_tracking_output, read_deletedBarcode_file
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from config import config as conf
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from model import resnet18 as resnet18
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from test_ori import inference_image
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IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
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model = resnet18().to(conf.device)
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print('load model {} '.format(conf.testbackbone))
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# model = nn.DataParallel(model).to(conf.device)
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model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
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model.eval()
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def creat_shopping_event(eventPath, subimgPath=False):
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'''构造放入商品事件字典，这些事件需满足条件：
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1) 前后摄至少有一条轨迹输出
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2) 保存有帧图像，以便裁剪出 boxe 子图
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'''
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# filename = "20240723-155413_6904406215720"
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'''filename下为一次购物事件'''
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eventName = os.path.basename(eventPath)
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'''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================'''
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nmlist = eventName.split('_')
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if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
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return
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if not os.path.isdir(eventPath):
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return
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'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
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event = {}
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event['barcode'] = eventName.split('_')[1]
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event['type'] = 'input'
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event['filepath'] = eventPath
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event['back_imgpaths'] = []
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event['front_imgpaths'] = []
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event['back_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['front_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['back_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_feats'] = np.empty((0, 256), dtype=np.float64)
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event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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# event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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'''================= 2. 读取 data 文件 ============================='''
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for dataname in os.listdir(eventPath):
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# filename = '1_track.data'
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datapath = os.path.join(eventPath, dataname)
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if not os.path.isfile(datapath): continue
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CamerType = dataname.split('_')[0]
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''' 2.1 读取 0/1_track.data 中数据，暂不考虑'''
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# if dataname.find("_track.data")>0:
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# bboxes, ffeats, trackerboxes, tracker_feat_dict, trackingboxes, tracking_feat_dict = extract_data(datapath)
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''' 2.2 读取 0/1_tracking_output.data 中数据'''
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if dataname.find("_tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(datapath)
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if len(tracking_output_boxes) != len(tracking_output_feats): continue
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if CamerType == '0':
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event['back_boxes'] = tracking_output_boxes
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event['back_feats'] = tracking_output_feats
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elif CamerType == '1':
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event['front_boxes'] = tracking_output_boxes
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event['front_feats'] = tracking_output_feats
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if len(event['back_boxes'])==0 or len(event['front_boxes'])==0:
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return None
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'''2.3 事件的特征表征方式: 特征选择、特征集成'''
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bk_feats = event['back_feats']
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ft_feats = event['front_feats']
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'''2.3.1 特征集成'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(ft_feats):
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feats_compose = np.concatenate((feats_compose, ft_feats), axis=0)
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if len(bk_feats):
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feats_compose = np.concatenate((feats_compose, bk_feats), axis=0)
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event['feats_compose'] = feats_compose
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'''2.3.1 特征选择'''
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if len(ft_feats):
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event['feats_select'] = ft_feats
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# pickpath = os.path.join(savePath, f"{filename}.pickle")
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# with open(pickpath, 'wb') as f:
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# pickle.dump(event, f)
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# print(f"Event: {filename}")
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# if subimgPath==False:
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# eventList.append(event)
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# continue
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'''================ 2. 读取图像文件地址，并按照帧ID排序 ============='''
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frontImgs, frontFid = [], []
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backImgs, backFid = [], []
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for imgname in os.listdir(eventPath):
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name, ext = os.path.splitext(imgname)
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if ext not in IMG_FORMAT or name.find('frameId')<0: continue
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CamerType = name.split('_')[0]
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frameId = int(name.split('_')[3])
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imgpath = os.path.join(eventPath, imgname)
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if CamerType == '0':
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backImgs.append(imgpath)
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backFid.append(frameId)
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if CamerType == '1':
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frontImgs.append(imgpath)
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frontFid.append(frameId)
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frontIdx = np.argsort(np.array(frontFid))
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backIdx = np.argsort(np.array(backFid))
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'''2.1 生成依据帧 ID 排序的前后摄图像地址列表'''
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frontImgs = [frontImgs[i] for i in frontIdx]
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backImgs = [backImgs[i] for i in backIdx]
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'''2.2 将前、后摄图像路径添加至事件字典'''
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bfid = event['back_boxes'][:, 7].astype(np.int64)
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ffid = event['front_boxes'][:, 7].astype(np.int64)
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if len(bfid) and max(bfid) <= len(backImgs):
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event['back_imgpaths'] = [backImgs[i-1] for i in bfid]
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if len(ffid) and max(ffid) <= len(frontImgs):
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event['front_imgpaths'] = [frontImgs[i-1] for i in ffid]
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'''================ 3. 判断当前事件有效性，并添加至事件列表 =========='''
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condt1 = len(event['back_imgpaths'])==0 or len(event['front_imgpaths'])==0
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condt2 = len(event['front_feats'])==0 and len(event['back_feats'])==0
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if condt1 or condt2:
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print(f"Event: {eventName}, Error, condt1: {condt1}, condt2: {condt2}")
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return None
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'''构造放入商品事件列表，暂不处理'''
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# delepath = os.path.join(basePath, 'deletedBarcode.txt')
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# bcdList = read_deletedBarcode_file(delepath)
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# for slist in bcdList:
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# getoutFold = slist['SeqDir'].strip()
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# getoutPath = os.path.join(basePath, getoutFold)
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# '''取出事件文件夹不存在，跳出循环'''
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# if not os.path.exists(getoutPath) and not os.path.isdir(getoutPath):
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# continue
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# ''' 生成取出事件字典 '''
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# event = {}
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# event['barcode'] = slist['Deleted'].strip()
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# event['type'] = 'getout'
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# event['basePath'] = getoutPath
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return event
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def get_std_barcodeDict(bcdpath, bpath):
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'''
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inputs:
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bcdpath: 已清洗的barcode样本图像，如果barcode下有'base'文件夹，只选用该文件夹下图像
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(default = r'\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771')
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功能：
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生成并保存只有一个key值的字典 {barcode: [imgpath1, imgpath1, ...]}，
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bpath: 字典存储地址
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'''
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# bpath = r'\\192.168.1.28\share\测试_202406\contrast\std_barcodes'
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'''读取数据集中 barcode 列表'''
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stdBlist = []
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for filename in os.listdir(bcdpath):
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filepath = os.path.join(bcdpath, filename)
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if not os.path.isdir(filepath) or not filename.isdigit(): continue
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stdBlist.append(filename)
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bcdpaths = [(barcode, os.path.join(bcdpath, barcode)) for barcode in stdBlist]
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'''遍历数据集，针对每一个barcode，生成并保存字典{barcode: [imgpath1, imgpath1, ...]}'''
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k = 0
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for barcode, bpath in bcdpaths:
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stdBarcodeDict = {}
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stdBarcodeDict[barcode] = []
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for root, dirs, files in os.walk(bpath):
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imgpaths = []
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if "base" in dirs:
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broot = os.path.join(root, "base")
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for imgname in os.listdir(broot):
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imgpath = os.path.join(broot, imgname)
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_, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT: continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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break
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else:
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for imgname in files:
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imgpath = os.path.join(root, imgname)
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_, ext = os.path.splitext(imgpath)
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if ext not in IMG_FORMAT: continue
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imgpaths.append(imgpath)
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stdBarcodeDict[barcode].extend(imgpaths)
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pickpath = os.path.join(bpath, f"{barcode}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(stdBarcodeDict, f)
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print(f"Barcode: {barcode}")
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# k += 1
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# if k == 10:
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# break
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return
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def save_event_subimg(event, savepath):
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'''
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功能: 保存一次购物事件的轨迹子图
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9 items: barcode, type, filepath, back_imgpaths, front_imgpaths,
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back_boxes, front_boxes, back_feats, front_feats,
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feats_compose, feats_select
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子图保存次序：先前摄、后后摄，以 k 为编号，和 "feats_compose" 中次序相同
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'''
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cameras = ('front', 'back')
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k = 0
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for camera in cameras:
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if camera == 'front':
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boxes = event['front_boxes']
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imgpaths = event['front_imgpaths']
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else:
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boxes = event['back_boxes']
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imgpaths = event['back_imgpaths']
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for i, box in enumerate(boxes):
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x1, y1, x2, y2, tid, score, cls, fid, bid = box
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imgpath = imgpaths[i]
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image = cv2.imread(imgpath)
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subimg = image[int(y1/2):int(y2/2), int(x1/2):int(x2/2), :]
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camerType, timeTamp, _, frameID = os.path.basename(imgpath).split('.')[0].split('_')
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subimgName = f"{k}_cam-{camerType}_tid-{int(tid)}_fid-({int(fid)}, {frameID}).png"
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spath = os.path.join(savepath, subimgName)
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cv2.imwrite(spath, subimg)
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k += 1
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# basename = os.path.basename(event['filepath'])
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print(f"Image saved: {os.path.basename(event['filepath'])}")
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def batch_inference(imgpaths, batch):
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size = len(imgpaths)
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groups = []
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for i in range(0, size, batch):
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end = min(batch + i, size)
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groups.append(imgpaths[i: end])
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features = []
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for group in groups:
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feature = featurize(group, conf.test_transform, model, conf.device)
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features.append(feature)
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features = np.concatenate(features, axis=0)
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return features
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def stdfeat_infer(imgPath, featPath, bcdSet=None):
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'''
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inputs:
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imgPath: 该文件夹下的 pickle 文件格式 {barcode: [imgpath1, imgpath1, ...]}
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featPath: imgPath图像对应特征的存储地址
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功能:
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对 imgPath中图像进行特征提取，生成只有一个key值的字典，
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{barcode: features}，features.shape=(nsample, 256)，并保存至 featPath 中
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'''
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# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
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# featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
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stdBarcodeDict = {}
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k = 0
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for filename in os.listdir(imgPath):
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bcd, ext = os.path.splitext(filename)
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if bcdSet is not None and bcd not in bcdSet:
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continue
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filepath = os.path.join(imgPath, filename)
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stdbDict = {}
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t1 = time.time()
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try:
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with open(filepath, 'rb') as f:
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bpDict = pickle.load(f)
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for barcode, imgpaths in bpDict.items():
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# feature = batch_inference(imgpaths, 8) #from vit distilled model of Lichen
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feature = inference_image(imgpaths, conf.test_transform, model, conf.device)
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feature /= np.linalg.norm(feature, axis=1)[:, None]
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except Exception as e:
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print(f"Error accured at: {filename}, with Exception is: {e}")
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'''================ 保存单个barcode特征 ================'''
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stdbDict["barcode"] = barcode
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stdbDict["imgpaths"] = imgpaths
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stdbDict["feats"] = feature
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pkpath = os.path.join(featPath, f"{barcode}.pickle")
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with open(pkpath, 'wb') as f:
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pickle.dump(stdbDict, f)
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stdBarcodeDict[barcode] = feature
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t2 = time.time()
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print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
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# k += 1
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# if k == 10:
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# break
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pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
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with open(pickpath, 'wb') as f:
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pickle.dump(stdBarcodeDict, f)
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def contrast_performance_evaluate():
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eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
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stdBcdPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
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stdFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
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subimgPath = r"\\192.168.1.28\share\测试_202406\contrast\subimgs"
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# stdBarcode = [p.stem for p in Path(stdFeatPath).iterdir() if p.is_file() and p.suffix=='.pickle']
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stdBarcode = [p.stem for p in Path(stdBcdPath).iterdir() if p.is_file() and p.suffix=='.pickle']
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'''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(eventFeatPath).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
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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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evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).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
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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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'''标准特征集图像样本经特征提取并保存，运行一次后无需再运行'''
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# stdfeat_infer(stdBcdPath, stdFeatPath, barcodes)
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'''========= 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(stdFeatPath, 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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'''========= 构建用于比对的操作事件字典 ============='''
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evtDict = {}
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for event, barcode in evtList:
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evtpath = os.path.join(eventFeatPath, event+'.pickle')
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with open(evtpath, 'rb') as f:
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evtdata = pickle.load(f)
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evtDict[event] = evtdata
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|
||
|
||
'''===== 构造 3 个事件对: 扫 A 放 A, 扫 A 放 B, 合并 ===================='''
|
||
AA_list = [(event, barcode, "same") for event, barcode in evtList]
|
||
AB_list = []
|
||
for event, barcode in evtList:
|
||
dset = list(barcodes.symmetric_difference(set([barcode])))
|
||
idx = random.randint(0, len(dset)-1)
|
||
AB_list.append((event, dset[idx], "diff"))
|
||
|
||
mergePairs = AA_list + AB_list
|
||
|
||
'''读取事件、标准特征文件中数据，以 AA_list 和 AB_list 中关键字为 key 生成字典'''
|
||
results = []
|
||
for evt, stdbcd, label in mergePairs:
|
||
## 标准特征字典的构造方式不合适，需改进，不能用具体的barcode做key值
|
||
|
||
|
||
event = evtDict[evt]
|
||
|
||
|
||
## 判断是否存在轨迹图像文件夹，不存在则创建文件夹并保存轨迹图像
|
||
pairpath = os.path.join(subimgPath, f"{evt}")
|
||
if not os.path.exists(pairpath):
|
||
os.makedirs(pairpath)
|
||
save_event_subimg(event, pairpath)
|
||
|
||
## 判断是否存在 barcode 样本集图像文件夹，不存在则创建文件夹并存储 barcode 样本集图像
|
||
stdImgpath = stdDict[stdbcd]["imgpaths"]
|
||
pstdpath = os.path.join(subimgPath, f"{stdbcd}")
|
||
if not os.path.exists(pstdpath):
|
||
os.makedirs(pstdpath)
|
||
ii = 1
|
||
for filepath in stdImgpath:
|
||
stdpath = os.path.join(pstdpath, f"{stdbcd}_{ii}.png")
|
||
shutil.copy2(filepath, stdpath)
|
||
ii += 1
|
||
|
||
|
||
stdfeat = stdDict[stdbcd]["feats"]
|
||
evtfeat = event["feats_compose"]
|
||
|
||
# 比较特征归一化前后的相似度变化情况
|
||
|
||
matrix = 1 - cdist(stdfeat, evtfeat, 'cosine')
|
||
simi_mean = np.mean(matrix)
|
||
simi_max = np.max(matrix)
|
||
|
||
stdfeatm = np.mean(stdfeat, axis=0, keepdims=True)
|
||
evtfeatm = np.mean(evtfeat, axis=0, keepdims=True)
|
||
simi_mfeat = 1- np.maximum(0.0, cdist(stdfeatm, evtfeatm, 'cosine'))
|
||
|
||
results.append((label, stdbcd, evt, simi_mean, simi_max, simi_mfeat[0,0]))
|
||
|
||
print("contrast performance evaluate have done!")
|
||
|
||
tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
|
||
with open(f'{tm}.pickle', 'wb') as f:
|
||
pickle.dump(results, f)
|
||
|
||
with open(f'{tm}.txt', 'w', encoding='utf-8') as f:
|
||
for result in results:
|
||
part = [f"{x:.3f}" if isinstance(x, float) else str(x) for x in result]
|
||
line = ', '.join(part)
|
||
f.write(line + '\n')
|
||
|
||
def compute_contrast_accuracy(pickpath):
|
||
|
||
pickfile = os.path.basename(pickpath)
|
||
file, _ = os.path.splitext(pickfile)
|
||
|
||
# tm = datetime.fromtimestamp(time.time()).strftime('%Y%m%d_%H%M%S')
|
||
|
||
|
||
with open(pickpath, 'rb') as f:
|
||
results = pickle.load(f)
|
||
|
||
Same, Cross = [], []
|
||
for label, stdbcd, evt, simi_mean, simi_max, simi_mft in results:
|
||
if label == "same":
|
||
Same.append(simi_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, edgecolor='black')
|
||
axs[0].set_xlim([-0.2, 1])
|
||
axs[0].set_title(f'Same Barcode, Num: {TPFN}')
|
||
|
||
axs[1].hist(Cross, bins=60, edgecolor='black')
|
||
axs[1].set_xlim([-0.2, 1])
|
||
axs[1].set_title(f'Cross Barcode, Num: {TNFP}')
|
||
plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
|
||
|
||
|
||
Recall_Pos, Recall_Neg = [], []
|
||
Precision_Pos, Precision_Neg = [], []
|
||
Correct = []
|
||
Thresh = np.linspace(-0.2, 1, 100)
|
||
for th in Thresh:
|
||
TP = np.sum(Same > th)
|
||
FN = TPFN - TP
|
||
TN = np.sum(Cross < th)
|
||
FP = TNFP - TN
|
||
|
||
Recall_Pos.append(TP/TPFN)
|
||
Recall_Neg.append(TN/TNFP)
|
||
Precision_Pos.append(TP/(TP+FP+1e-6))
|
||
Precision_Neg.append(TN/(TN+FN+1e-6))
|
||
Correct.append((TN+TP)/(TPFN+TNFP))
|
||
|
||
fig, ax = plt.subplots()
|
||
ax.plot(Thresh, Correct, 'r', label='Correct: (TN+TP)/(TPFN+TNFP)')
|
||
ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
|
||
ax.plot(Thresh, Recall_Neg, 'g', label='Recall_Neg: TN/TNFP')
|
||
ax.plot(Thresh, Precision_Pos, 'c', label='Precision_Pos: TP/(TP+FP)')
|
||
ax.plot(Thresh, Precision_Neg, 'm', label='Precision_Neg: TN/(TN+FN)')
|
||
|
||
ax.set_xlim([0, 1])
|
||
ax.set_ylim([0, 1])
|
||
ax.grid(True)
|
||
ax.set_title('PrecisePos & PreciseNeg')
|
||
ax.set_xlabel(f"Same Num: {TPFN}, Cross Num: {TNFP}")
|
||
ax.legend()
|
||
plt.show()
|
||
plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
|
||
|
||
print("Haved done!!!")
|
||
|
||
|
||
|
||
def generate_event_and_stdfeatures():
|
||
stdSamplePath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_1771"
|
||
stdBarcodePath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
|
||
stdFeaturePath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
|
||
|
||
'''=========================== 1. 生成标准特征集 ========================'''
|
||
'''1.1 提取并保存标准特征库原始图像文件地址字典'''
|
||
# get_std_barcodeDict(stdSamplePath, stdBarcodePath)
|
||
# print("standard imgpath have extracted and saved")
|
||
|
||
|
||
'''1.2 特征提取，并保存至文件夹 stdFeaturePath 中，也可在运行过程中根据barcodes交集执行'''
|
||
# stdfeat_infer(stdBarcodePath, stdFeaturePath, bcdSet=None)
|
||
# print("standard features have generated!")
|
||
|
||
|
||
'''=========================== 2. 提取并存储事件特征 ========================'''
|
||
eventDatePath = [# r'\\192.168.1.28\share\测试_202406\0723\0723_1',
|
||
# r'\\192.168.1.28\share\测试_202406\0723\0723_2',
|
||
# r'\\192.168.1.28\share\测试_202406\0723\0723_3',
|
||
# r'\\192.168.1.28\share\测试_202406\0722\0722_01',
|
||
# r'\\192.168.1.28\share\测试_202406\0722\0722_02'
|
||
# r'\\192.168.1.28\share\测试_202406\0719\719_3',
|
||
# r'\\192.168.1.28\share\测试_202406\0716\0716_1',
|
||
# r'\\192.168.1.28\share\测试_202406\0716\0716_2',
|
||
# r'\\192.168.1.28\share\测试_202406\0716\0716_3',
|
||
# r'\\192.168.1.28\share\测试_202406\0712\0712_1', # 无帧图像
|
||
# r'\\192.168.1.28\share\测试_202406\0712\0712_2', # 无帧图像
|
||
]
|
||
eventFeatPath = r"\\192.168.1.28\share\测试_202406\contrast\events"
|
||
subimgPath = r'\\192.168.1.28\share\测试_202406\contrast\subimgs'
|
||
|
||
eventList = []
|
||
# k = 0
|
||
for datePath in eventDatePath:
|
||
for eventName in os.listdir(datePath):
|
||
pickpath = os.path.join(eventFeatPath, f"{eventName}.pickle")
|
||
if os.path.isfile(pickpath):
|
||
continue
|
||
|
||
eventPath = os.path.join(datePath, eventName)
|
||
|
||
eventDict = creat_shopping_event(eventPath)
|
||
if eventDict:
|
||
eventList.append(eventDict)
|
||
with open(pickpath, 'wb') as f:
|
||
pickle.dump(eventDict, f)
|
||
print(f"Event: {eventName}, have saved!")
|
||
|
||
# k += 1
|
||
# if k==1:
|
||
# break
|
||
|
||
## 保存轨迹中 boxes 子图
|
||
# for event in eventList:
|
||
# basename = os.path.basename(event['filepath'])
|
||
# savepath = os.path.join(subimgPath, basename)
|
||
# if not os.path.exists(savepath):
|
||
# os.makedirs(savepath)
|
||
# save_event_subimg(event, savepath)
|
||
|
||
print("eventList have generated and features have saved!")
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
def main():
|
||
# generate_event_and_stdfeatures()
|
||
|
||
contrast_performance_evaluate()
|
||
|
||
ppath = r"D:\DetectTracking\contrast"
|
||
for filename in os.listdir(ppath):
|
||
if filename.find('.pickle') < 0:
|
||
continue
|
||
|
||
pickpath = os.path.join(ppath, filename)
|
||
compute_contrast_accuracy(pickpath)
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|