guoqing bakeup
This commit is contained in:
王庆刚
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contrast/__init__.py
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7
contrast/__init__.py
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Sep 26 08:53:58 2024
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@author: ym
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"""
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contrast/__pycache__/one2n_contrast.cpython-39.pyc
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contrast/__pycache__/one2n_contrast.cpython-39.pyc
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# -*- coding: utf-8 -*-
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"""
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@author: LiChen
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"""
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import os
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import os.path as osp
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import pdb
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contrast/feat_select.py
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contrast/feat_select.py
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# -*- coding: utf-8 -*-
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"""
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Created on Sat Jul 27 14:07:25 2024
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现场测试数据，在不同特征组合情况下的精度、召回率分析程序
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@author: ym
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"""
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import os.path
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import numpy as np
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from scipy.spatial.distance import cdist
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import matplotlib.pyplot as plt
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import sys
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sys.path.append(r"D:\DetectTracking")
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from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
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# from tracking.dotrack.dotracks import Track
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from one2n_contrast import compute_recall_precision, show_recall_prec
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from one2n_contrast import performance_evaluate
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def compute_similar(feat1, feat2):
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if len(feat1)==0 or len(feat2)==0:
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return 0
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similar = 1 - np.maximum(0.0, cdist(feat1, feat2, metric = 'cosine'))
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smean = np.mean(similar)
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return smean
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def update_event(datapath):
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'''一次购物事件，包含 8 个keys
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back_sole_boxes：后摄boxes
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front_sole_boxes：前摄boxes
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back_sole_feats：后摄特征
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front_sole_feats：前摄特征
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feats_compose：将前后摄特征进行合并
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feats_select：特征选择，优先选择前摄特征
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'''
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event = {}
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# event['front_tracking_boxes'] = []
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# event['front_tracking_feats'] = {}
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# event['back_tracking_boxes'] = []
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# event['back_tracking_feats'] = {}
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event['back_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['front_sole_boxes'] = np.empty((0, 9), dtype=np.float64)
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event['back_sole_feats'] = np.empty((0, 256), dtype=np.float64)
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event['front_sole_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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# '''读取 track.data 文件中的数据'''
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# fpath_0_track = os.path.join(datapath, '0_track.data')
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# fpath_1_track = os.path.join(datapath, '1_track.data')
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# if os.path.exists(fpath_0_track) and os.path.isfile(fpath_0_track):
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# _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_0_track)
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# event['back_tracking_boxes'] = tracking_boxes
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# event['back_tracking_feats'] = tracking_feat_dict
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# if os.path.exists(fpath_1_track) and os.path.isfile(fpath_1_track):
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# _, _, _, _, tracking_boxes, tracking_feat_dict = extract_data(fpath_1_track)
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# event['front_tracking_boxes'] = tracking_boxes
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# event['front_tracking_feats'] = tracking_feat_dict
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# =============================================================================
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# '''================1. 直接指定读取，速度快======================================'''
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# '''读取 tracking_output.data 文件中的数据'''
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# fpath_0_tracking = os.path.join(datapath, '0_tracking_output.data')
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# fpath_1_tracking = os.path.join(datapath, '1_tracking_output.data')
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# if os.path.exists(fpath_0_tracking) and os.path.isfile(fpath_0_tracking):
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# tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_0_tracking)
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# event['back_sole_boxes'] = tracking_output_boxes
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# event['back_sole_feats'] = tracking_output_feats
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#
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# if os.path.exists(fpath_1_tracking) and os.path.isfile(fpath_1_tracking):
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# tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath_1_tracking)
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# event['front_sole_boxes'] = tracking_output_boxes
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# event['front_sole_feats'] = tracking_output_feats
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# =============================================================================
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'''================2. 遍历方式读取（与以上方式二选一）======================================'''
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'''读取当前事件的 data 文件'''
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for filename in os.listdir(datapath):
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# filename = '1_track.data'
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fpath = os.path.join(datapath, filename)
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CamerType = filename.split('_')[0]
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# if os.path.isfile(fpath) and filename.find("track.data")>0:
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# bboxes, ffeats, trackerboxes, tracker_feat_dict, tracking_boxes, tracking_feat_dict = extract_data(fpath)
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# if CamerType == '0':
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# event['back_tracking_boxes'] = tracking_boxes
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# event['back_tracking_feats'] = tracking_feat_dict
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# elif CamerType == '1':
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# event['front_tracking_boxes'] = tracking_boxes
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# event['front_tracking_feats'] = tracking_feat_dict
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if os.path.isfile(fpath) and filename.find("tracking_output.data")>0:
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tracking_output_boxes, tracking_output_feats = read_tracking_output(fpath)
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if CamerType == '0':
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event['back_sole_boxes'] = tracking_output_boxes
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event['back_sole_feats'] = tracking_output_feats
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elif CamerType == '1':
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event['front_sole_boxes'] = tracking_output_boxes
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event['front_sole_feats'] = tracking_output_feats
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'''事件的特征表征方式选择'''
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fs_feats = event['front_sole_feats']
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bs_feats = event['back_sole_feats']
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'''1. 如果前后摄均没有轨迹选择输出，返回'''
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condt1 = len(fs_feats) + len(bs_feats) == 0
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if condt1:
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return event
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'''2. 构造综合特征'''
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feats_compose = np.empty((0, 256), dtype=np.float64)
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if len(fs_feats):
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feats_compose = np.concatenate((feats_compose, fs_feats), axis=0)
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if len(bs_feats):
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feats_compose = np.concatenate((feats_compose, bs_feats), axis=0)
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event['feats_compose'] = feats_compose
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'''3. 构造前摄特征'''
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if len(fs_feats):
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event['feats_select'] = fs_feats
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return event
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'''4. 从前摄输出轨迹中选取特定轨迹对应的特征'''
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# =============================================================================
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# ftrboxes = event['front_tracking_boxes']
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# ftrfeats = event['front_tracking_feats']
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#
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# condt2 = len(ftrboxes) + len(ftrfeats) == 0
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# condt3 = len(ftrfeats) != len(ftrboxes)
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# if condt2 or condt3:
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# return event
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#
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# bprops = []
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# for boxes in ftrboxes:
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# track = Track(boxes)
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# bprops.append(max(track.trajdist))
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#
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# index = bprops.index(max(bprops))
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# box_select = ftrboxes[index]
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# tid = int(box_select[0, 4])
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#
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# feat_select = ftrfeats[f"track_{tid}"]
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# feats_select = np.empty((0, 256), dtype=np.float64)
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# for fid_bid, feat in feat_select['feats'].items():
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# feats_select = np.concatenate((feats_select, feat[None, :]), axis=0)
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# event['feats_select'] = feats_select
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# =============================================================================
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return event
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def creatd_deletedBarcode_front(filepath):
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'''
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生成deletedBarcodeTest.txt
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'''
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# filepath = r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt'
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basepath, _ = os.path.split(filepath)
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bcdlist = read_deletedBarcode_file(filepath)
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MatchList = []
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k = 0
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for s_list in bcdlist:
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getout_fold = s_list['SeqDir'].strip()
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getout_path = os.path.join(basepath, getout_fold)
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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(getout_path) and not os.path.isdir(getout_path):
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continue
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day, hms = getout_fold.strip('_').split('-')
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''' 生成取出事件字典 '''
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getout_event = {}
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getout_event['barcode'] = s_list['Deleted'].strip()
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getout_event['path'] = getout_path
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getout_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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getout_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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InputList = []
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barcodes = [s.strip() for s in s_list['barcode']]
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similarity = [float(s.strip()) for s in s_list['similarity']]
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for i, barcode in enumerate(barcodes):
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''' 生成放入事件字典 '''
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input_event = {}
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input_folds, times = [], []
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for pathname in os.listdir(basepath):
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if pathname.endswith('_'): continue
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if os.path.isfile(os.path.join(basepath, pathname)):continue
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infold = pathname.split('_')
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if len(infold)!=2: continue
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if len(infold[0])<=14 or len(infold[1])<=10: continue
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day1, hms1 = infold[0].split('-')
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if day1==day and infold[1]==barcode and int(hms1)<int(hms):
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input_folds.append(pathname)
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times.append(int(hms1))
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if len(input_folds)==0: continue
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''' 根据时间排序，选择离取出操作最近时间的文件夹，作为取出操作应的放入操作所对应的文件夹 '''
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input_fold = input_folds[times.index(max(times))]
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input_path = os.path.join(basepath, input_fold)
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if not os.path.exists(getout_path) and not os.path.isdir(getout_path):
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continue
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input_event['barcode'] = barcode
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input_event['path'] = input_path
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input_event['similarity'] = float(similarity[i])
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input_event['feats_compose'] = np.empty((0, 256), dtype=np.float64)
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input_event['feats_select'] = np.empty((0, 256), dtype=np.float64)
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InputList.append(input_event)
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MatchList.append((getout_event, InputList))
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# k += 1
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# if k==2:
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# break
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print('Step 1: Event init Done!')
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for getout_event, InputList in MatchList:
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getout_path = getout_event['path']
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if os.path.exists(getout_path) and os.path.isdir(getout_path):
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event = update_event(getout_path)
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getout_event.update(event)
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'''====== 放入事件是在取出事件存在的情况下分析 ======'''
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for input_event in InputList:
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input_path = input_event['path']
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if os.path.exists(input_path) and os.path.isdir(input_path):
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event = update_event(input_path)
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input_event.update(event)
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print('Step 2: Event update Done!')
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results = []
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for getout_event, InputList in MatchList:
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getout_barcode = getout_event['barcode']
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getout_feats_compose = getout_event['feats_compose']
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getout_feats_select = getout_event['feats_select']
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if len(getout_feats_select):
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outfeats_select = getout_feats_select.copy()
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else:
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outfeats_select = getout_feats_compose.copy()
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result = {}
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result['SeqDir'] = os.path.split(getout_event['path'])[1]
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result['Deleted'] = getout_barcode
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result['List'] = {}
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for input_event in InputList:
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input_barcode = input_event['barcode']
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input_feats_compose = input_event['feats_compose']
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input_feats_select = input_event['feats_select']
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if len(input_feats_select):
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infeats_select = input_feats_select.copy()
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else:
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infeats_select = input_feats_compose.copy()
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similar_comp = compute_similar(getout_feats_compose, input_feats_compose)
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similar_selt = compute_similar(outfeats_select, infeats_select)
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'''现场测试相似度，组合特征相似度，前摄选择特征相似度'''
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result['List'][f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
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# result[f'{input_barcode}'] = (input_event['similarity'], similar_comp, similar_selt)
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results.append(result)
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print('Step 3: Similarity conputation Done!')
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wpath = os.path.split(filepath)[0]
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wfile = os.path.join(wpath, 'deletedBarcodeTest.txt')
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with open(wfile, 'w', encoding='utf-8') as file:
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for result in results:
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SeqDir = result['SeqDir']
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Deleted = result['Deleted']
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file.write('\n')
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file.write(f'SeqDir: {SeqDir}\n')
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file.write(f'Deleted: {Deleted}\n')
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file.write('List:\n')
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for key, value in result['List'].items():
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file.write(f'{key}: ')
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file.write(f'{value[0]}, {value[1]:.3f}, {value[2]:.3f}\n')
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print('Step 4: File writting Done!')
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def precision_compare(filepath, savepath):
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'''
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1. deletedBarcode.txt 中的相似度的计算为现场算法前后摄轨迹特征合并
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2. deletedBarcodeTest.txt 中的 3 个相似度计算方式依次为：
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(1)现场算法前后摄轨迹特征合并;
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(2)本地算法前后摄轨迹特征合并；
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(3)本地算法优先选择前摄
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'''
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fpath = os.path.split(filepath)[0]
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_, basefile = os.path.split(fpath)
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'''1. 综合前后摄特征的相似度比对性能'''
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fpath1 = os.path.join(fpath, 'deletedBarcode.txt')
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blist1 = read_deletedBarcode_file(fpath1)
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errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist1)
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recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
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os.path.split(fpath1)
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plt1 = show_recall_prec(recall, prec, ths)
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# plt1.show()
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plt1.xlabel(f'threshold, Num: {len(blist1)}')
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plt1.title(basefile + ', compose')
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plt1.savefig(os.path.join(savepath, basefile+'_pr.png'))
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plt1.close()
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'''2. 优先选取前摄特征的相似度比对性能'''
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fpath2 = os.path.join(fpath, 'deletedBarcodeTest.txt')
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blist2 = read_deletedBarcode_file(fpath2)
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front_errpairs, front_corrpairs, front_err_similarity, front_correct_similarity = performance_evaluate(blist2)
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front_recall, front_prec, front_ths = compute_recall_precision(front_err_similarity, front_correct_similarity)
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plt2 = show_recall_prec(front_recall, front_prec, front_ths)
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# plt2.show()
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plt2.xlabel(f'threshold, Num: {len(blist2)}')
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plt1.title(basefile + ', front')
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plt2.savefig(os.path.join(savepath, basefile+'_pr_front.png'))
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plt2.close()
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def main():
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'''
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1. 成deletedBarcodeTest.txt
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2. 不同特征选择下的精度比对性能比较
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'''
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fplist = [#r'\\192.168.1.28\share\测试_202406\0723\0723_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0723\0723_2\deletedBarcode.txt',
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r'\\192.168.1.28\share\测试_202406\0723\0723_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0722\0722_01\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0722\0722_02\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_1\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_2\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_3\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0719\719_4\deletedBarcode.txt',
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# r'\\192.168.1.28\share\测试_202406\0718\0718-1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0718\0718-2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0717\0717-1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0717\0717-2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0717\0717-3\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0716\0716_1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0716\0716_2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0716\0716_3\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0715\0715_1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0715\0715_2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0715\0715_3\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0712\0712_1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\0712\0712_2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\711\images01\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\711\images02\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\710\images_1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\710\images_2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\705\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\703\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\702_pm_1\702_pm_1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\702_pm_3\702_pm_3\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\701_am\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\701_pm\701_pm\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\628\1\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\628\2\deletedBarcode.txt',
|
||||
# r'\\192.168.1.28\share\测试_202406\627\deletedBarcode.txt',
|
||||
]
|
||||
|
||||
savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
|
||||
for filepath in fplist:
|
||||
try:
|
||||
#1. 生成deletedBarcodeTest.txt 文件
|
||||
creatd_deletedBarcode_front(filepath)
|
||||
|
||||
#2. 确保该目录下存在deletedBarcode.txt, deletedBarcodeTest.txt 文件
|
||||
precision_compare(filepath, savepath)
|
||||
except Exception as e:
|
||||
print(f'{filepath}, Error: {e}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
main()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,62 +1,106 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Aug 9 10:36:45 2024
|
||||
分析图像对间的相似度
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
import os
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
import sys
|
||||
from scipy.spatial.distance import cdist
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
from tracking.trackers.reid.reid_interface import ReIDInterface
|
||||
from tracking.trackers.reid.config import config as ReIDConfig
|
||||
ReIDEncoder = ReIDInterface(ReIDConfig)
|
||||
|
||||
''' 加载 LC 定义的模型形式'''
|
||||
from config import config as conf
|
||||
from model import resnet18 as resnet18
|
||||
from test_ori import inference_image
|
||||
##============ load resnet mdoel
|
||||
model = resnet18().to(conf.device)
|
||||
# model = nn.DataParallel(model).to(conf.device)
|
||||
model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
|
||||
model.eval()
|
||||
print('load model {} '.format(conf.testbackbone))
|
||||
|
||||
IMG_FORMAT = ['.bmp', '.jpg', '.JPG', '.jpeg', '.png']
|
||||
|
||||
# =============================================================================
|
||||
# ''' 加载REID中定义的模型形式'''
|
||||
# sys.path.append(r"D:\DetectTracking")
|
||||
# from tracking.trackers.reid.reid_interface import ReIDInterface
|
||||
# from tracking.trackers.reid.config import config as ReIDConfig
|
||||
# ReIDEncoder = ReIDInterface(ReIDConfig)
|
||||
#
|
||||
# def inference_image_ReID(images):
|
||||
# batch_patches = []
|
||||
# patches = []
|
||||
# for d, img1 in enumerate(images):
|
||||
#
|
||||
#
|
||||
# img = img1[:, :, ::-1].copy() # the model expects RGB inputs
|
||||
# patch = ReIDEncoder.transform(img)
|
||||
#
|
||||
# # patch = patch.to(device=self.device).half()
|
||||
# if str(ReIDEncoder.device) != "cpu":
|
||||
# patch = patch.to(device=ReIDEncoder.device).half()
|
||||
# else:
|
||||
# patch = patch.to(device=ReIDEncoder.device)
|
||||
#
|
||||
# patches.append(patch)
|
||||
# if (d + 1) % ReIDEncoder.batch_size == 0:
|
||||
# patches = torch.stack(patches, dim=0)
|
||||
# batch_patches.append(patches)
|
||||
# patches = []
|
||||
#
|
||||
# if len(patches):
|
||||
# patches = torch.stack(patches, dim=0)
|
||||
# batch_patches.append(patches)
|
||||
#
|
||||
# features = np.zeros((0, ReIDEncoder.embedding_size))
|
||||
# for patches in batch_patches:
|
||||
# pred = ReIDEncoder.model(patches)
|
||||
# pred[torch.isinf(pred)] = 1.0
|
||||
# feat = pred.cpu().data.numpy()
|
||||
# features = np.vstack((features, feat))
|
||||
#
|
||||
# return features
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def inference_image(images):
|
||||
batch_patches = []
|
||||
patches = []
|
||||
for d, img1 in enumerate(images):
|
||||
def silimarity_compare():
|
||||
|
||||
imgpaths = r"D:\DetectTracking\contrast\images\2"
|
||||
|
||||
|
||||
filepaths = []
|
||||
for root, dirs, filenames in os.walk(imgpaths):
|
||||
for filename in filenames:
|
||||
file, ext = os.path.splitext(filename)
|
||||
if ext not in IMG_FORMAT: continue
|
||||
|
||||
file_path = os.path.join(root, filename)
|
||||
filepaths.append(file_path)
|
||||
|
||||
feature = inference_image(filepaths, conf.test_transform, model, conf.device)
|
||||
feature /= np.linalg.norm(feature, axis=1)[:, None]
|
||||
|
||||
similar = 1 - np.maximum(0.0, cdist(feature, feature, metric='cosine'))
|
||||
|
||||
|
||||
print("Done!")
|
||||
|
||||
|
||||
img = img1[:, :, ::-1].copy() # the model expects RGB inputs
|
||||
patch = ReIDEncoder.transform(img)
|
||||
|
||||
# patch = patch.to(device=self.device).half()
|
||||
if str(ReIDEncoder.device) != "cpu":
|
||||
patch = patch.to(device=ReIDEncoder.device).half()
|
||||
else:
|
||||
patch = patch.to(device=ReIDEncoder.device)
|
||||
|
||||
patches.append(patch)
|
||||
if (d + 1) % ReIDEncoder.batch_size == 0:
|
||||
patches = torch.stack(patches, dim=0)
|
||||
batch_patches.append(patches)
|
||||
patches = []
|
||||
|
||||
if len(patches):
|
||||
patches = torch.stack(patches, dim=0)
|
||||
batch_patches.append(patches)
|
||||
|
||||
features = np.zeros((0, ReIDEncoder.embedding_size))
|
||||
for patches in batch_patches:
|
||||
pred = ReIDEncoder.model(patches)
|
||||
pred[torch.isinf(pred)] = 1.0
|
||||
feat = pred.cpu().data.numpy()
|
||||
features = np.vstack((features, feat))
|
||||
|
||||
return features
|
||||
|
||||
|
||||
def similarity_compare(root_dir):
|
||||
def similarity_compare_sequence(root_dir):
|
||||
'''
|
||||
root_dir：包含 "subimgs"字段的文件夹中图像为 subimg子图
|
||||
功能：相邻帧子图间相似度比较
|
||||
'''
|
||||
|
||||
|
||||
'''
|
||||
|
||||
all_files = []
|
||||
extensions = ['.png', '.jpg']
|
||||
for dirpath, dirnames, filenames in os.walk(root_dir):
|
||||
@ -83,7 +127,7 @@ def similarity_compare(root_dir):
|
||||
hb, wb = imgb.shape[:2]
|
||||
|
||||
|
||||
feats = inference_image(((imga, imgb)))
|
||||
feats = inference_image_ReID(((imga, imgb)))
|
||||
|
||||
similar = 1 - np.maximum(0.0, cdist(feats, feats, metric='cosine'))
|
||||
|
||||
@ -109,17 +153,16 @@ def similarity_compare(root_dir):
|
||||
fnma = os.path.basename(filepaths[i]).split('.')[0]
|
||||
imga = imgb.copy()
|
||||
ha, wa = imga.shape[:2]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
return
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
root_dir = r"D:\contrast\dataset\result\20240723-112242_6923790709882"
|
||||
|
||||
try:
|
||||
similarity_compare(root_dir)
|
||||
similarity_compare_sequence(root_dir)
|
||||
except Exception as e:
|
||||
print(f'Error: {e}')
|
||||
|
||||
@ -127,5 +170,31 @@ def main():
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
# main()
|
||||
|
||||
silimarity_compare()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
454
contrast/one2n_contrast.py
Normal file
454
contrast/one2n_contrast.py
Normal file
@ -0,0 +1,454 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Sat Jul ** 14:07:25 2024
|
||||
|
||||
现场测试精度、召回率分析程序，是 feat_select.py 的简化版，
|
||||
但支持循环计算，并输出总的pr曲线
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
|
||||
|
||||
import os.path
|
||||
import shutil
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import cv2
|
||||
|
||||
import sys
|
||||
sys.path.append(r"D:\DetectTracking")
|
||||
from tracking.utils.plotting import Annotator, colors
|
||||
from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
|
||||
from tracking.utils.plotting import draw_tracking_boxes
|
||||
|
||||
|
||||
|
||||
def showHist(err, correct):
|
||||
err = np.array(err)
|
||||
correct = np.array(correct)
|
||||
|
||||
fig, axs = plt.subplots(2, 1)
|
||||
axs[0].hist(err, bins=50, edgecolor='black')
|
||||
axs[0].set_xlim([0, 1])
|
||||
axs[0].set_title('err')
|
||||
|
||||
axs[1].hist(correct, bins=50, edgecolor='black')
|
||||
axs[1].set_xlim([0, 1])
|
||||
axs[1].set_title('correct')
|
||||
# plt.show()
|
||||
|
||||
return plt
|
||||
|
||||
def show_recall_prec(recall, prec, ths):
|
||||
# x = np.linspace(start=-0, stop=1, num=11, endpoint=True).tolist()
|
||||
fig = plt.figure(figsize=(10, 6))
|
||||
plt.plot(ths, recall, color='red', label='recall')
|
||||
plt.plot(ths, prec, color='blue', label='PrecisePos')
|
||||
plt.legend()
|
||||
plt.xlabel(f'threshold')
|
||||
# plt.ylabel('Similarity')
|
||||
plt.grid(True, linestyle='--', alpha=0.5)
|
||||
# plt.savefig('accuracy_recall_grid.png')
|
||||
# plt.show()
|
||||
# plt.close()
|
||||
|
||||
return plt
|
||||
|
||||
|
||||
def compute_recall_precision(err_similarity, correct_similarity):
|
||||
ths = np.linspace(0, 1, 51)
|
||||
recall, prec = [], []
|
||||
for th in ths:
|
||||
TP = len([num for num in correct_similarity if num >= th])
|
||||
FP = len([num for num in err_similarity if num >= th])
|
||||
if (TP+FP) == 0:
|
||||
prec.append(1)
|
||||
recall.append(0)
|
||||
else:
|
||||
prec.append(TP / (TP + FP))
|
||||
recall.append(TP / (len(err_similarity) + len(correct_similarity)))
|
||||
return recall, prec, ths
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# def read_tracking_output(filepath):
|
||||
# boxes = []
|
||||
# feats = []
|
||||
# with open(filepath, 'r', encoding='utf-8') as file:
|
||||
# for line in file:
|
||||
# line = line.strip() # 去除行尾的换行符和可能的空白字符
|
||||
#
|
||||
# if not line:
|
||||
# continue
|
||||
#
|
||||
# if line.endswith(','):
|
||||
# line = line[:-1]
|
||||
#
|
||||
# data = np.array([float(x) for x in line.split(",")])
|
||||
# if data.size == 9:
|
||||
# boxes.append(data)
|
||||
# if data.size == 256:
|
||||
# feats.append(data)
|
||||
#
|
||||
# return np.array(boxes), np.array(feats)
|
||||
# =============================================================================
|
||||
|
||||
def read_tracking_imgs(imgspath):
|
||||
'''
|
||||
input:
|
||||
imgspath：该路径中的图像为Yolo算法的输入图像，640x512
|
||||
output：
|
||||
imgs_0：后摄图像，根据 frameId 进行了排序
|
||||
imgs_1：前摄图像，根据 frameId 进行了排序
|
||||
'''
|
||||
imgs_0, frmIDs_0, imgs_1, frmIDs_1 = [], [], [], []
|
||||
|
||||
for filename in os.listdir(imgspath):
|
||||
file, ext = os.path.splitext(filename)
|
||||
flist = file.split('_')
|
||||
if len(flist)==4 and ext==".jpg":
|
||||
camID, frmID = flist[0], int(flist[-1])
|
||||
imgpath = os.path.join(imgspath, filename)
|
||||
img = cv2.imread(imgpath)
|
||||
|
||||
if camID=='0':
|
||||
imgs_0.append(img)
|
||||
frmIDs_0.append(frmID)
|
||||
if camID=='1':
|
||||
imgs_1.append(img)
|
||||
frmIDs_1.append(frmID)
|
||||
|
||||
if len(frmIDs_0):
|
||||
indice = np.argsort(np.array(frmIDs_0))
|
||||
imgs_0 = [imgs_0[i] for i in indice ]
|
||||
if len(frmIDs_1):
|
||||
indice = np.argsort(np.array(frmIDs_1))
|
||||
imgs_1 = [imgs_1[i] for i in indice ]
|
||||
|
||||
return imgs_0, imgs_1
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# def draw_tracking_boxes(imgs, tracks):
|
||||
# '''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
|
||||
# 0 1 2 3 4 5 6 7 8
|
||||
# 关键：imgs中的次序和 track 中的 fid 对应
|
||||
# '''
|
||||
# subimgs = []
|
||||
# for *xyxy, tid, conf, cls, fid, bid in tracks:
|
||||
# label = f'id:{int(tid)}_{int(cls)}_{conf:.2f}'
|
||||
#
|
||||
# annotator = Annotator(imgs[int(fid-1)].copy())
|
||||
# if cls==0:
|
||||
# color = colors(int(cls), True)
|
||||
# elif tid>0 and cls!=0:
|
||||
# color = colors(int(tid), True)
|
||||
# else:
|
||||
# color = colors(19, True) # 19为调色板的最后一个元素
|
||||
#
|
||||
# pt2 = [p/2 for p in xyxy]
|
||||
# annotator.box_label(pt2, label, color=color)
|
||||
# img0 = annotator.result()
|
||||
#
|
||||
# subimgs.append(img0)
|
||||
#
|
||||
# return subimgs
|
||||
# =============================================================================
|
||||
|
||||
def get_contrast_paths(pair, basepath):
|
||||
assert(len(pair)==2 or len(pair)==3), "pair: seqdir, delete, barcodes"
|
||||
|
||||
getout_fold = pair[0] # 取出操作对应的文件夹
|
||||
relvt_barcode = pair[1] # 取出操作对应放入操作的 Barcode
|
||||
if len(pair)==3:
|
||||
error_match = pair[2] # 取出操作错误匹配的 Barcode
|
||||
else:
|
||||
error_match = ''
|
||||
|
||||
|
||||
getoutpath, inputpath, errorpath = '', '', ''
|
||||
|
||||
day, hms = getout_fold.strip('_').split('-')
|
||||
|
||||
input_folds, times = [], []
|
||||
errmatch_folds, errmatch_times = [], []
|
||||
for pathname in os.listdir(basepath):
|
||||
if pathname.endswith('_'): continue
|
||||
if os.path.isfile(os.path.join(basepath, pathname)):continue
|
||||
infold = pathname.split('_')
|
||||
if len(infold)!=2: continue
|
||||
|
||||
day1, hms1 = infold[0].split('-')
|
||||
|
||||
if day1==day and infold[1]==relvt_barcode and int(hms1)<int(hms):
|
||||
input_folds.append(pathname)
|
||||
times.append(int(hms1))
|
||||
|
||||
if day1==day and len(error_match) and infold[1]==error_match and int(hms1)<int(hms):
|
||||
errmatch_folds.append(pathname)
|
||||
errmatch_times.append(int(hms1))
|
||||
|
||||
''' 根据时间排序，选择离取出操作最近时间的文件夹，
|
||||
作为取出操作应正确匹配的放入操作所对应的文件夹 '''
|
||||
if len(input_folds):
|
||||
indice = np.argsort(np.array(times))
|
||||
input_fold = input_folds[indice[-1]]
|
||||
inputpath = os.path.join(basepath, input_fold)
|
||||
|
||||
'''取出操作错误匹配的放入操作对应的文件夹'''
|
||||
if len(errmatch_folds):
|
||||
indice = np.argsort(np.array(errmatch_times))
|
||||
errmatch_fold = errmatch_folds[indice[-1]]
|
||||
errorpath = os.path.join(basepath, errmatch_fold)
|
||||
|
||||
'''放入事件文件夹地址、取出事件文件夹地址'''
|
||||
getoutpath = os.path.join(basepath, getout_fold)
|
||||
|
||||
|
||||
return getoutpath, inputpath, errorpath
|
||||
|
||||
|
||||
def save_tracking_imgpairs(pair, basepath, savepath):
|
||||
'''
|
||||
basepath: 原始测试数据文件夹的路径
|
||||
savepath: 保存的目标文件夹
|
||||
'''
|
||||
|
||||
getoutpath, inputpath, errorpath = get_contrast_paths(pair, basepath)
|
||||
|
||||
if len(inputpath)==0:
|
||||
return
|
||||
|
||||
|
||||
'''==== 读取放入、取出事件对应的 Yolo输入的前后摄图像，0：后摄，1：前摄 ===='''
|
||||
|
||||
|
||||
'''==== 读取放入、取出事件对应的 tracking 输出：boxes, feats ===='''
|
||||
|
||||
if len(inputpath):
|
||||
imgs_input_0, imgs_input_1 = read_tracking_imgs(inputpath)
|
||||
|
||||
input_data_0 = os.path.join(inputpath, '0_tracking_output.data')
|
||||
input_data_1 = os.path.join(inputpath, '1_tracking_output.data')
|
||||
boxes_input_0, feats_input_0 = read_tracking_output(input_data_0)
|
||||
boxes_input_1, feats_input_1 = read_tracking_output(input_data_1)
|
||||
ImgsInput_0 = draw_tracking_boxes(imgs_input_0, boxes_input_0)
|
||||
ImgsInput_1 = draw_tracking_boxes(imgs_input_1, boxes_input_1)
|
||||
|
||||
if len(getoutpath):
|
||||
imgs_getout_0, imgs_getout_1 = read_tracking_imgs(getoutpath)
|
||||
|
||||
getout_data_0 = os.path.join(getoutpath, '0_tracking_output.data')
|
||||
getout_data_1 = os.path.join(getoutpath, '1_tracking_output.data')
|
||||
boxes_output_0, feats_output_0 = read_tracking_output(getout_data_0)
|
||||
boxes_output_1, feats_output_1 = read_tracking_output(getout_data_1)
|
||||
ImgsGetout_0 = draw_tracking_boxes(imgs_getout_0, boxes_output_0)
|
||||
ImgsGetout_1 = draw_tracking_boxes(imgs_getout_1, boxes_output_1)
|
||||
|
||||
if len(errorpath):
|
||||
|
||||
imgs_error_0, imgs_error_1 = read_tracking_imgs(errorpath)
|
||||
|
||||
error_data_0 = os.path.join(errorpath, '0_tracking_output.data')
|
||||
error_data_1 = os.path.join(errorpath, '1_tracking_output.data')
|
||||
boxes_error_0, feats_error_0 = read_tracking_output(error_data_0)
|
||||
boxes_error_1, feats_error_1 = read_tracking_output(error_data_1)
|
||||
ImgsError_0 = draw_tracking_boxes(imgs_error_0, boxes_error_0)
|
||||
ImgsError_1 = draw_tracking_boxes(imgs_error_1, boxes_error_1)
|
||||
|
||||
|
||||
savedir = pair[0] + pair[1]
|
||||
if len(errorpath):
|
||||
savedir = savedir + '_' + errorpath.split('_')[-1]
|
||||
foldname = os.path.join(savepath, 'imgpairs', savedir)
|
||||
if not os.path.exists(foldname):
|
||||
os.makedirs(foldname)
|
||||
|
||||
for i, img in enumerate(ImgsInput_0):
|
||||
imgpath = os.path.join(foldname, f'input_0_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
for i, img in enumerate(ImgsInput_1):
|
||||
imgpath = os.path.join(foldname, f'input_1_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
for i, img in enumerate(ImgsGetout_0):
|
||||
imgpath = os.path.join(foldname, f'getout_0_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
for i, img in enumerate(ImgsGetout_1):
|
||||
imgpath = os.path.join(foldname, f'getout_1_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
|
||||
for i, img in enumerate(ImgsError_0):
|
||||
imgpath = os.path.join(foldname, f'errMatch_0_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
for i, img in enumerate(ImgsError_1):
|
||||
imgpath = os.path.join(foldname, f'errMatch_1_{i}.png')
|
||||
cv2.imwrite(imgpath, img)
|
||||
|
||||
|
||||
# def performance_evaluate(all_list, isshow=False):
|
||||
|
||||
# corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list, err_similarity = [], [], [], [], [], []
|
||||
# for s_list in all_list:
|
||||
# seqdir = s_list['SeqDir'].strip()
|
||||
# delete = s_list['Deleted'].strip()
|
||||
# barcodes = [s.strip() for s in s_list['barcode']]
|
||||
# similarity = [float(s.strip()) for s in s_list['similarity']]
|
||||
|
||||
# if delete in barcodes[:1]:
|
||||
# corrpairs.append((seqdir, delete))
|
||||
# correct_barcode_list.append(delete)
|
||||
# correct_similarity.append(similarity[0])
|
||||
# else:
|
||||
# errpairs.append((seqdir, delete, barcodes[0]))
|
||||
# err_barcode_list.append(delete)
|
||||
# err_similarity.append(similarity[0])
|
||||
|
||||
def performance_evaluate(all_list, isshow=False):
|
||||
|
||||
corrpairs, correct_barcode_list, correct_similarity, errpairs, err_barcode_list, err_similarity = [], [], [], [], [], []
|
||||
for s_list in all_list:
|
||||
seqdir = s_list['SeqDir'].strip()
|
||||
delete = s_list['Deleted'].strip()
|
||||
barcodes = [s.strip() for s in s_list['barcode']]
|
||||
|
||||
|
||||
similarity_comp, similarity_front = [], []
|
||||
for simil in s_list['similarity']:
|
||||
ss = [float(s.strip()) for s in simil.split(',')]
|
||||
|
||||
similarity_comp.append(ss[0])
|
||||
if len(ss)==3:
|
||||
similarity_front.append(ss[2])
|
||||
|
||||
if len(similarity_front):
|
||||
similarity = [s for s in similarity_front]
|
||||
else:
|
||||
similarity = [s for s in similarity_comp]
|
||||
|
||||
|
||||
index = similarity.index(max(similarity))
|
||||
matched_barcode = barcodes[index]
|
||||
if matched_barcode == delete:
|
||||
corrpairs.append((seqdir, delete))
|
||||
correct_barcode_list.append(delete)
|
||||
correct_similarity.append(max(similarity))
|
||||
else:
|
||||
errpairs.append((seqdir, delete, matched_barcode))
|
||||
err_barcode_list.append(delete)
|
||||
err_similarity.append(max(similarity))
|
||||
|
||||
'''3. 计算比对性能 '''
|
||||
if isshow:
|
||||
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
|
||||
show_recall_prec(recall, prec, ths)
|
||||
showHist(err_similarity, correct_similarity)
|
||||
|
||||
return errpairs, corrpairs, err_similarity, correct_similarity
|
||||
|
||||
|
||||
|
||||
def contrast_analysis(del_barcode_file, basepath, savepath, saveimgs=False):
|
||||
'''
|
||||
del_barcode_file: 测试数据文件，利用该文件进行算法性能分析
|
||||
|
||||
'''
|
||||
|
||||
'''1. 读取 deletedBarcode 文件 '''
|
||||
all_list = read_deletedBarcode_file(del_barcode_file)
|
||||
|
||||
'''2. 算法性能评估，并输出 (取出，删除, 错误匹配) 对 '''
|
||||
errpairs, corrpairs, _, _ = performance_evaluate(all_list)
|
||||
|
||||
'''3. 获取 (取出，删除, 错误匹配) 对应路径，保存相应轨迹图像'''
|
||||
relative_paths = []
|
||||
for errpair in errpairs:
|
||||
GetoutPath, InputPath, ErrorPath = get_contrast_paths(errpair, basepath)
|
||||
relative_paths.append((GetoutPath, InputPath, ErrorPath))
|
||||
|
||||
if saveimgs:
|
||||
save_tracking_imgpairs(errpair, basepath, savepath)
|
||||
|
||||
return relative_paths
|
||||
|
||||
|
||||
def contrast_loop(fpath):
|
||||
savepath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\illustration'
|
||||
# savepath = r'D:\contrast\dataset\1_to_n\illustration'
|
||||
if not os.path.exists(savepath):
|
||||
os.mkdir(savepath)
|
||||
|
||||
if os.path.isfile(fpath):
|
||||
fpath, filename = os.path.split(fpath)
|
||||
|
||||
BarLists, blists = {}, []
|
||||
for filename in os.listdir(fpath):
|
||||
file = os.path.splitext(filename)[0][15:]
|
||||
|
||||
filepath = os.path.join(fpath, filename)
|
||||
blist = read_deletedBarcode_file(filepath)
|
||||
|
||||
BarLists.update({file: blist})
|
||||
blists.extend(blist)
|
||||
|
||||
BarLists.update({file: blist})
|
||||
BarLists.update({"Total": blists})
|
||||
for file, blist in BarLists.items():
|
||||
errpairs, corrpairs, err_similarity, correct_similarity = performance_evaluate(blist)
|
||||
|
||||
recall, prec, ths = compute_recall_precision(err_similarity, correct_similarity)
|
||||
|
||||
plt1 = show_recall_prec(recall, prec, ths)
|
||||
# plt1.show()
|
||||
plt1.xlabel(f'threshold, Num: {len(blist)}')
|
||||
plt1.savefig(os.path.join(savepath, file+'_pr.png'))
|
||||
# plt1.close()
|
||||
|
||||
# plt2 = showHist(err_similarity, correct_similarity)
|
||||
# plt2.show()
|
||||
# plt2.savefig(os.path.join(savepath, file+'_hist.png'))
|
||||
# plt.close()
|
||||
|
||||
|
||||
def main():
|
||||
fpath = r'\\192.168.1.28\share\测试_202406\deletedBarcode\other'
|
||||
contrast_loop(fpath)
|
||||
|
||||
def main1():
|
||||
del_barcode_file = r'\\192.168.1.28\share\测试_202406\709\deletedBarcode.txt'
|
||||
basepath = r'\\192.168.1.28\share\测试_202406\709'
|
||||
savepath = r'D:\contrast\dataset\result'
|
||||
|
||||
try:
|
||||
relative_path = contrast_analysis(del_barcode_file, basepath, savepath)
|
||||
except Exception as e:
|
||||
print(f'Error Type: {e}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
# main1()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -40,6 +40,7 @@ from scipy.spatial.distance import cdist
|
||||
import matplotlib.pyplot as plt
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
from openpyxl import load_workbook, Workbook
|
||||
|
||||
# Vit版resnet, 和现场特征不一致，需将resnet_vit中文件提出
|
||||
# from config import config as conf
|
||||
@ -56,7 +57,7 @@ from tracking.utils.read_data import extract_data, read_tracking_output, read_de
|
||||
|
||||
from config import config as conf
|
||||
from model import resnet18 as resnet18
|
||||
from test_ori import inference_image
|
||||
from feat_inference import inference_image
|
||||
|
||||
|
||||
IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
|
||||
@ -100,13 +101,13 @@ def creat_shopping_event(eventPath, subimgPath=False):
|
||||
|
||||
'''================ 0. 检查 filename 及 eventPath 正确性和有效性 ================'''
|
||||
nmlist = eventName.split('_')
|
||||
if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
|
||||
# if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[0])!=15 or len(nmlist[1])<11:
|
||||
# return
|
||||
if eventName.find('2024')<0 or len(nmlist)!=2 or len(nmlist[1])<11:
|
||||
return
|
||||
if not os.path.isdir(eventPath):
|
||||
return
|
||||
|
||||
|
||||
|
||||
'''================ 1. 构造事件描述字典，暂定 9 items ==============='''
|
||||
event = {}
|
||||
event['barcode'] = eventName.split('_')[1]
|
||||
@ -293,10 +294,10 @@ def get_std_barcodeDict(bcdpath, savepath):
|
||||
imgpaths.append(imgpath)
|
||||
stdBarcodeDict[barcode].extend(imgpaths)
|
||||
|
||||
# pickpath = os.path.join(savepath, f"{barcode}.pickle")
|
||||
# with open(pickpath, 'wb') as f:
|
||||
# pickle.dump(stdBarcodeDict, f)
|
||||
# print(f"Barcode: {barcode}")
|
||||
pickpath = os.path.join(savepath, f"{barcode}.pickle")
|
||||
with open(pickpath, 'wb') as f:
|
||||
pickle.dump(stdBarcodeDict, f)
|
||||
print(f"Barcode: {barcode}")
|
||||
|
||||
# k += 1
|
||||
# if k == 10:
|
||||
@ -352,7 +353,6 @@ def batch_inference(imgpaths, batch):
|
||||
feature = featurize(group, conf.test_transform, model, conf.device)
|
||||
features.append(feature)
|
||||
features = np.concatenate(features, axis=0)
|
||||
|
||||
return features
|
||||
|
||||
def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
@ -371,14 +371,20 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
stdBarcodeDict = {}
|
||||
stdBarcodeDict_ft16 = {}
|
||||
|
||||
|
||||
'''4处同名: (1)barcode原始图像文件夹; (2)imgPath中的 .pickle 文件名、该pickle文件中字典的key值'''
|
||||
|
||||
k = 0
|
||||
for filename in os.listdir(imgPath):
|
||||
bcd, ext = os.path.splitext(filename)
|
||||
pkpath = os.path.join(featPath, f"{bcd}.pickle")
|
||||
|
||||
if os.path.isfile(pkpath): continue
|
||||
if bcdSet is not None and bcd not in bcdSet:
|
||||
continue
|
||||
|
||||
filepath = os.path.join(imgPath, filename)
|
||||
|
||||
|
||||
stdbDict = {}
|
||||
stdbDict_ft16 = {}
|
||||
stdbDict_uint8 = {}
|
||||
@ -399,12 +405,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
|
||||
# uint8, 两种策略，1) 精度损失小, 2) 计算复杂度小
|
||||
# stdfeat_uint8, _ = ft16_to_uint8(feature_ft16)
|
||||
|
||||
stdfeat_uint8 = (feature_ft16*128).astype(np.int8)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error accured at: {filename}, with Exception is: {e}")
|
||||
|
||||
@ -414,33 +416,30 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
stdbDict["imgpaths"] = imgpaths
|
||||
stdbDict["feats"] = feature
|
||||
|
||||
pkpath = os.path.join(featPath, f"{barcode}.pickle")
|
||||
# pkpath = os.path.join(featPath, f"{barcode}.pickle")
|
||||
with open(pkpath, 'wb') as f:
|
||||
pickle.dump(stdbDict, f)
|
||||
pickle.dump(stdbDict, f)
|
||||
stdBarcodeDict[barcode] = feature
|
||||
|
||||
|
||||
##================== float16
|
||||
stdbDict_ft16["barcode"] = barcode
|
||||
stdbDict_ft16["imgpaths"] = imgpaths
|
||||
stdbDict_ft16["feats"] = feature_ft16
|
||||
pkpath_ft16 = os.path.join(featPath, f"{barcode}_ft16.pickle")
|
||||
with open(pkpath_ft16, 'wb') as f:
|
||||
pickle.dump(stdbDict_ft16, f)
|
||||
stdBarcodeDict_ft16[barcode] = pkpath_ft16
|
||||
# stdbDict_ft16["barcode"] = barcode
|
||||
# stdbDict_ft16["imgpaths"] = imgpaths
|
||||
# stdbDict_ft16["feats"] = feature_ft16
|
||||
# pkpath_ft16 = os.path.join(featPath, f"{barcode}_ft16.pickle")
|
||||
# with open(pkpath_ft16, 'wb') as f:
|
||||
# pickle.dump(stdbDict_ft16, f)
|
||||
# stdBarcodeDict_ft16[barcode] = pkpath_ft16
|
||||
|
||||
|
||||
|
||||
##================== uint8
|
||||
stdbDict_uint8["barcode"] = barcode
|
||||
stdbDict_uint8["imgpaths"] = imgpaths
|
||||
stdbDict_uint8["feats"] = stdfeat_uint8
|
||||
pkpath_uint8 = os.path.join(featPath, f"{barcode}_uint8.pickle")
|
||||
with open(pkpath_uint8, 'wb') as f:
|
||||
pickle.dump(stdbDict_uint8, f)
|
||||
# stdBarcodeDict_ft16[barcode] = pkpath_ft16
|
||||
|
||||
|
||||
# stdbDict_uint8["barcode"] = barcode
|
||||
# stdbDict_uint8["imgpaths"] = imgpaths
|
||||
# stdbDict_uint8["feats"] = stdfeat_uint8
|
||||
# pkpath_uint8 = os.path.join(featPath, f"{barcode}_uint8.pickle")
|
||||
# with open(pkpath_uint8, 'wb') as f:
|
||||
# pickle.dump(stdbDict_uint8, f)
|
||||
|
||||
t2 = time.time()
|
||||
print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")
|
||||
@ -448,7 +447,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
# if k == 10:
|
||||
# break
|
||||
|
||||
|
||||
##================== float32
|
||||
# pickpath = os.path.join(featPath, f"barcode_features_{k}.pickle")
|
||||
# with open(pickpath, 'wb') as f:
|
||||
# pickle.dump(stdBarcodeDict, f)
|
||||
@ -456,7 +455,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
|
||||
##================== float16
|
||||
# pickpath_ft16 = os.path.join(featPath, f"barcode_features_ft16_{k}.pickle")
|
||||
# with open(pickpath_ft16, 'wb') as f:
|
||||
# pickle.dump(stdBarcodeDict_ft16, f)
|
||||
# pickle.dump(stdBarcodeDict_ft16, f)
|
||||
|
||||
return
|
||||
|
||||
@ -477,7 +476,8 @@ def contrast_performance_evaluate(resultPath):
|
||||
# ]
|
||||
|
||||
evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir()
|
||||
if p.is_file()
|
||||
if p.is_file()
|
||||
and str(p).find('240910')>0
|
||||
and p.suffix=='.pickle'
|
||||
and len(p.stem.split('_'))==2
|
||||
and p.stem.split('_')[1].isdigit()
|
||||
@ -487,7 +487,7 @@ def contrast_performance_evaluate(resultPath):
|
||||
barcodes = set([bcd for _, bcd in evtList])
|
||||
|
||||
'''标准特征集图像样本经特征提取并保存，运行一次后无需再运行'''
|
||||
# stdfeat_infer(stdBarcodePath, stdFeaturePath, barcodes)
|
||||
stdfeat_infer(stdBarcodePath, stdFeaturePath, barcodes)
|
||||
|
||||
'''========= 构建用于比对的标准特征字典 ============='''
|
||||
stdDict = {}
|
||||
@ -639,6 +639,7 @@ def compute_precise_recall(pickpath):
|
||||
file, ext = os.path.splitext(pickfile)
|
||||
|
||||
if ext != '.pickle': return
|
||||
if file.find('ft16') < 0: return
|
||||
|
||||
with open(pickpath, 'rb') as f:
|
||||
results = pickle.load(f)
|
||||
@ -717,7 +718,8 @@ def generate_event_and_stdfeatures():
|
||||
|
||||
|
||||
'''=========================== 2. 提取并存储事件特征 ========================'''
|
||||
eventDatePath = [# r'\\192.168.1.28\share\测试_202406\0723\0723_1',
|
||||
eventDatePath = [r'\\192.168.1.28\share\测试_202406\0910\images',
|
||||
# r'\\192.168.1.28\share\测试_202406\0723\0723_1',
|
||||
# r'\\192.168.1.28\share\测试_202406\0723\0723_2',
|
||||
# r'\\192.168.1.28\share\测试_202406\0723\0723_3',
|
||||
# r'\\192.168.1.28\share\测试_202406\0722\0722_01',
|
||||
@ -751,12 +753,12 @@ def generate_event_and_stdfeatures():
|
||||
# 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)
|
||||
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!")
|
||||
|
||||
@ -789,23 +791,23 @@ def ft16_to_uint8(arr_ft16):
|
||||
return arr_uint8, arr_ft16_
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
generate_event_and_stdfeatures()
|
||||
# generate_event_and_stdfeatures()
|
||||
|
||||
# contrast_performance_evaluate(resultPath)
|
||||
# for filename in os.listdir(resultPath):
|
||||
# if filename.find('.pickle') < 0: continue
|
||||
# # if filename.find('0909') < 0: continue
|
||||
# pickpath = os.path.join(resultPath, filename)
|
||||
# compute_precise_recall(pickpath)
|
||||
contrast_performance_evaluate(resultPath)
|
||||
for filename in os.listdir(resultPath):
|
||||
if filename.find('.pickle') < 0: continue
|
||||
if filename.find('0911') < 0: continue
|
||||
pickpath = os.path.join(resultPath, filename)
|
||||
compute_precise_recall(pickpath)
|
||||
|
||||
|
||||
def main_std():
|
||||
std_sample_path = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_1979_已清洗"
|
||||
std_sample_path = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\barcode_500_2192_已清洗"
|
||||
std_barcode_path = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes_2192"
|
||||
std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16"
|
||||
|
||||
@ -824,7 +826,8 @@ def main_std():
|
||||
# print("done")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
# main()
|
||||
|
||||
|
||||
# main_std()
|
||||
|
||||
|
||||
@ -39,30 +39,23 @@ def read_one2one_data(filepath):
|
||||
if len(simi_dict): simiList.append(simi_dict)
|
||||
|
||||
return simiList
|
||||
|
||||
def main():
|
||||
filepath = r"\\192.168.1.28\share\测试_202406\0910\images\OneToOneCompare.txt"
|
||||
|
||||
def plot_pr_curve(matrix):
|
||||
|
||||
simiList = read_one2one_data(filepath)
|
||||
simimax, simimean = [], []
|
||||
small = []
|
||||
for simidict in simiList:
|
||||
need_analysis = []
|
||||
for simidict in matrix:
|
||||
simimax.append(simidict["simi_max"])
|
||||
simimean.append(simidict["simi_min"])
|
||||
if simidict["simi_max"]<0.6:
|
||||
small.append(simidict)
|
||||
|
||||
|
||||
if simidict["simi_max"]>0.6:
|
||||
need_analysis.append(simidict)
|
||||
|
||||
simimax = np.array(simimax)
|
||||
simimean = np.array(simimean)
|
||||
|
||||
|
||||
|
||||
|
||||
TPFN_max = len(simimax)
|
||||
TPFN_mean = len(simimean)
|
||||
|
||||
|
||||
|
||||
|
||||
fig, axs = plt.subplots(2, 1)
|
||||
axs[0].hist(simimax, bins=60, edgecolor='black')
|
||||
axs[0].set_xlim([-0.2, 1])
|
||||
@ -71,14 +64,12 @@ def main():
|
||||
axs[1].set_xlim([-0.2, 1])
|
||||
axs[1].set_title(f'Cross Barcode, Num: {TPFN_mean}')
|
||||
# plt.savefig(f'./result/{file}_hist.png') # svg, png, pdf
|
||||
|
||||
|
||||
|
||||
|
||||
Recall_Pos = []
|
||||
Thresh = np.linspace(-0.2, 1, 100)
|
||||
for th in Thresh:
|
||||
TP = np.sum(simimax > th)
|
||||
Recall_Pos.append(TP/TPFN_max)
|
||||
TN = np.sum(simimax < th)
|
||||
Recall_Pos.append(TN/TPFN_max)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.plot(Thresh, Recall_Pos, 'b', label='Recall_Pos: TP/TPFN')
|
||||
@ -91,19 +82,48 @@ def main():
|
||||
plt.show()
|
||||
# plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
|
||||
|
||||
print("Have done!")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
print("Have done!")
|
||||
pass
|
||||
|
||||
|
||||
def main():
|
||||
filepaths = [r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_1\OneToOneCompare.txt",
|
||||
r"\\192.168.1.28\share\测试_202406\0913_扫A放B\0913_2\OneToOneCompare.txt",
|
||||
r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_1\OneToOneCompare.txt",
|
||||
r"\\192.168.1.28\share\测试_202406\0914_扫A放B\0914_2\OneToOneCompare.txt"
|
||||
]
|
||||
|
||||
simiList = []
|
||||
for fp in filepaths:
|
||||
slist = read_one2one_data(fp)
|
||||
|
||||
simiList.extend(slist)
|
||||
|
||||
|
||||
|
||||
|
||||
plot_pr_curve(simiList)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
202
contrast/result/pickle/20240911_183903.txt
Normal file
202
contrast/result/pickle/20240911_183903.txt
Normal file
@ -0,0 +1,202 @@
|
||||
same, 6901668936684, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.268, 0.659, 0.506
|
||||
same, 6902088131437, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.582, 0.979, 0.806
|
||||
same, 6904682300226, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.173, 0.830, 0.372
|
||||
same, 6970399922365, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, 0.226, 0.774, 0.597
|
||||
same, 6902265202318, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, 0.557, 0.922, 0.803
|
||||
same, 6907992517780, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.354, 0.761, 0.848
|
||||
same, 6902132084337, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.406, 0.774, 0.850
|
||||
same, 6901668934888, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.290, 0.598, 0.621
|
||||
same, 8000500023976, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, 0.495, 0.825, 0.792
|
||||
same, 6904682300219, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, 0.278, 0.782, 0.551
|
||||
same, 6903148231623, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.320, 0.870, 0.718
|
||||
same, 6904682300219, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.217, 0.697, 0.418
|
||||
same, 6902890218470, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, 0.198, 0.690, 0.538
|
||||
same, 6901668934888, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.325, 0.710, 0.689
|
||||
same, 6902088131437, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, 0.450, 0.983, 0.784
|
||||
same, 6901070600142, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, 0.295, 0.728, 0.668
|
||||
same, 8993175540667, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, 0.418, 0.859, 0.687
|
||||
same, 6901668929730, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, 0.549, 0.853, 0.888
|
||||
same, 6970399922365, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.330, 0.766, 0.817
|
||||
same, 6901668929730, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.529, 0.849, 0.864
|
||||
same, 6903148048801, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.444, 0.865, 0.769
|
||||
same, 6901668934628, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, 0.489, 0.930, 0.758
|
||||
same, 6902890218470, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.251, 0.738, 0.652
|
||||
same, 6949909050041, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.384, 0.870, 0.714
|
||||
same, 6901668934888, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.336, 0.778, 0.751
|
||||
same, 6901668936271, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, 0.121, 0.604, 0.257
|
||||
same, 6904682300226, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.297, 0.847, 0.651
|
||||
same, 6903148126677, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, 0.422, 0.814, 0.717
|
||||
same, 6924743915848, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, 0.285, 0.697, 0.640
|
||||
same, 6902132084337, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.350, 0.819, 0.857
|
||||
same, 8993175537322, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, 0.349, 0.832, 0.611
|
||||
same, 6902265202318, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, 0.392, 0.860, 0.695
|
||||
same, 6907992517780, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.405, 0.815, 0.865
|
||||
same, 6902265160502, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.162, 0.703, 0.531
|
||||
same, 6903148347409, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, 0.156, 0.693, 0.470
|
||||
same, 6902265202318, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.378, 0.865, 0.694
|
||||
same, 6903148126677, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.496, 0.879, 0.796
|
||||
same, 6901668936295, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.170, 0.631, 0.325
|
||||
same, 6958104102516, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.235, 0.731, 0.550
|
||||
same, 6901668936684, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, 0.230, 0.638, 0.450
|
||||
same, 6902265150022, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, 0.362, 0.927, 0.794
|
||||
same, 6902890232216, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.255, 0.761, 0.626
|
||||
same, 6902890232216, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.296, 0.695, 0.585
|
||||
same, 6901668929730, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, 0.503, 0.848, 0.823
|
||||
same, 6903148231623, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.256, 0.720, 0.506
|
||||
same, 6902265150022, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, 0.428, 0.940, 0.823
|
||||
same, 6901668929518, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.361, 0.853, 0.721
|
||||
same, 6901668934628, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.444, 0.882, 0.690
|
||||
same, 6974158892364, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.119, 0.684, 0.439
|
||||
same, 6902890218470, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.281, 0.689, 0.666
|
||||
same, 6902265150022, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, 0.308, 0.899, 0.682
|
||||
same, 6901668929518, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, 0.260, 0.821, 0.586
|
||||
same, 6901668936271, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.156, 0.617, 0.315
|
||||
same, 6903148126677, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, 0.420, 0.891, 0.749
|
||||
same, 6901668936684, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.212, 0.675, 0.445
|
||||
same, 6901668936295, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.130, 0.630, 0.254
|
||||
same, 8993175540667, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.565, 0.872, 0.821
|
||||
same, 6901668929518, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.341, 0.826, 0.726
|
||||
same, 6902132084337, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, 0.438, 0.794, 0.887
|
||||
same, 6904682300219, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.365, 0.804, 0.643
|
||||
same, 6901668934628, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.489, 0.894, 0.770
|
||||
same, 6902088131437, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.536, 0.980, 0.829
|
||||
same, 9421903892324, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.421, 0.892, 0.755
|
||||
same, 6901668936684, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.289, 0.672, 0.569
|
||||
same, 8000500023976, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.286, 0.872, 0.660
|
||||
same, 6901668929518, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, 0.446, 0.847, 0.833
|
||||
same, 6902265160502, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.212, 0.857, 0.611
|
||||
same, 6901668936684, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.149, 0.614, 0.344
|
||||
same, 6901668934628, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, 0.275, 0.870, 0.521
|
||||
same, 6949909050041, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.401, 0.849, 0.792
|
||||
same, 6907992517780, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, 0.391, 0.848, 0.838
|
||||
same, 6902890218470, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, 0.281, 0.737, 0.774
|
||||
same, 6904682300219, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.424, 0.892, 0.792
|
||||
same, 6904682300226, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.257, 0.725, 0.636
|
||||
same, 6903148048801, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.422, 0.826, 0.784
|
||||
same, 6902132084337, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, 0.379, 0.831, 0.792
|
||||
same, 9421903892324, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.304, 0.877, 0.548
|
||||
same, 6904682300219, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.254, 0.770, 0.477
|
||||
same, 6902890232216, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, 0.264, 0.786, 0.593
|
||||
same, 6901668936295, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, 0.139, 0.542, 0.239
|
||||
same, 6903148126677, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, 0.351, 0.861, 0.602
|
||||
same, 6901668929518, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.365, 0.821, 0.731
|
||||
same, 6903148231623, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.176, 0.688, 0.359
|
||||
same, 6901668929518, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, 0.437, 0.874, 0.772
|
||||
same, 6901668929730, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, 0.461, 0.852, 0.797
|
||||
same, 6903148080085, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, 0.370, 0.860, 0.827
|
||||
same, 6901070600142, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.201, 0.672, 0.442
|
||||
same, 6958104102516, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.234, 0.866, 0.583
|
||||
same, 6901070600142, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, 0.269, 0.727, 0.591
|
||||
same, 8993175537322, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.450, 0.790, 0.785
|
||||
same, 6975682480393, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.448, 0.835, 0.828
|
||||
same, 6903148080085, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.351, 0.838, 0.766
|
||||
same, 6903148231623, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, 0.423, 0.845, 0.782
|
||||
same, 6949909050041, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, 0.494, 0.893, 0.885
|
||||
same, 6907992517780, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, 0.338, 0.737, 0.823
|
||||
same, 6902265160502, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, 0.239, 0.833, 0.706
|
||||
same, 6901668936271, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.230, 0.615, 0.390
|
||||
same, 8993175537322, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, 0.456, 0.783, 0.719
|
||||
same, 8993175537322, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.455, 0.766, 0.717
|
||||
same, 6901668929518, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.406, 0.861, 0.759
|
||||
same, 8000500023976, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.350, 0.853, 0.686
|
||||
diff, 8993175537322, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.017, 0.341, 0.030
|
||||
diff, 6904682300226, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.007, 0.348, 0.013
|
||||
diff, 8993175540667, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.038, 0.309, 0.067
|
||||
diff, 6901668934628, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, -0.003, 0.302, -0.006
|
||||
diff, 6901668929518, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, -0.023, 0.273, -0.038
|
||||
diff, 6903148080085, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.026, 0.408, 0.061
|
||||
diff, 6970399922365, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.090, 0.479, 0.207
|
||||
diff, 6904682300226, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.072, 0.383, 0.142
|
||||
diff, 6974158892364, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, -0.044, 0.340, -0.117
|
||||
diff, 6901668934888, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, -0.017, 0.459, -0.042
|
||||
diff, 6907992517780, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.019, 0.391, 0.051
|
||||
diff, 6901668934628, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.033, 0.331, 0.063
|
||||
diff, 6901668936684, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, -0.072, 0.270, -0.163
|
||||
diff, 6907992517780, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.141, 0.460, 0.292
|
||||
diff, 6958104102516, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, -0.022, 0.373, -0.053
|
||||
diff, 8993175537322, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, -0.018, 0.293, -0.033
|
||||
diff, 6903148126677, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, -0.044, 0.356, -0.082
|
||||
diff, 8993175540667, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, -0.021, 0.349, -0.032
|
||||
diff, 9421903892324, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.033, 0.383, 0.062
|
||||
diff, 6902890232216, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.076, 0.420, 0.151
|
||||
diff, 6903148231623, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.012, 0.309, 0.019
|
||||
diff, 6924743915848, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, -0.069, 0.326, -0.147
|
||||
diff, 6975682480393, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.122, 0.628, 0.274
|
||||
diff, 6975682480393, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.094, 0.647, 0.188
|
||||
diff, 6907992517780, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.157, 0.646, 0.343
|
||||
diff, 6902265202318, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, -0.006, 0.286, -0.011
|
||||
diff, 6902890232216, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.066, 0.491, 0.157
|
||||
diff, 9421903892324, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, -0.038, 0.450, -0.061
|
||||
diff, 6902132084337, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, -0.061, 0.267, -0.125
|
||||
diff, 9421903892324, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.105, 0.454, 0.213
|
||||
diff, 6901668934628, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, -0.089, 0.186, -0.148
|
||||
diff, 6901668934888, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, -0.038, 0.352, -0.087
|
||||
diff, 6902265202318, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.025, 0.325, 0.043
|
||||
diff, 6902890232216, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.077, 0.540, 0.241
|
||||
diff, 6903148126677, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, -0.047, 0.247, -0.113
|
||||
diff, 6903148347409, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.019, 0.312, 0.049
|
||||
diff, 6904682300219, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.022, 0.340, 0.033
|
||||
diff, 6974158892364, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.035, 0.446, 0.108
|
||||
diff, 6901070600142, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.016, 0.385, 0.042
|
||||
diff, 6901668934628, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, -0.045, 0.563, -0.079
|
||||
diff, 6924743915848, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, -0.096, 0.342, -0.249
|
||||
diff, 6903148126677, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.053, 0.326, 0.112
|
||||
diff, 6904682300226, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.063, 0.430, 0.115
|
||||
diff, 9421903892324, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, -0.066, 0.306, -0.107
|
||||
diff, 6901668936684, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.062, 0.403, 0.131
|
||||
diff, 6970399922365, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, -0.044, 0.355, -0.101
|
||||
diff, 6903148048801, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.077, 0.498, 0.147
|
||||
diff, 6901668934888, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.001, 0.441, 0.001
|
||||
diff, 6970399922365, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.072, 0.537, 0.208
|
||||
diff, 6975682480393, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.112, 0.660, 0.231
|
||||
diff, 6901668929518, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, -0.067, 0.359, -0.146
|
||||
diff, 6901070600142, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, -0.033, 0.306, -0.085
|
||||
diff, 6903148126677, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.008, 0.361, 0.018
|
||||
diff, 6903148347409, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, -0.008, 0.348, -0.017
|
||||
diff, 6901668936271, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.064, 0.555, 0.128
|
||||
diff, 6901070600142, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.189, 0.600, 0.448
|
||||
diff, 6902265150022, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.036, 0.300, 0.064
|
||||
diff, 6901668934888, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.047, 0.373, 0.112
|
||||
diff, 6958104102516, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, -0.068, 0.247, -0.130
|
||||
diff, 6902265160502, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.046, 0.467, 0.106
|
||||
diff, 6970399922365, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.023, 0.376, 0.049
|
||||
diff, 6902265202318, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.017, 0.314, 0.030
|
||||
diff, 6907992517780, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.118, 0.551, 0.254
|
||||
diff, 6901668936271, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.137, 0.498, 0.255
|
||||
diff, 6901668934628, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.061, 0.324, 0.135
|
||||
diff, 6903148126677, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, -0.026, 0.332, -0.047
|
||||
diff, 6903148048801, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.030, 0.370, 0.070
|
||||
diff, 6902132084337, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.043, 0.375, 0.112
|
||||
diff, 6902890232216, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, -0.067, 0.258, -0.164
|
||||
diff, 6903148048801, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.118, 0.397, 0.235
|
||||
diff, 6970399922365, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, -0.043, 0.339, -0.101
|
||||
diff, 6903148048801, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, -0.001, 0.482, -0.002
|
||||
diff, 6904682300226, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.270, 0.813, 0.583
|
||||
diff, 6901668936271, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.026, 0.369, 0.057
|
||||
diff, 6949909050041, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.104, 0.443, 0.192
|
||||
diff, 6902890232216, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, -0.018, 0.254, -0.040
|
||||
diff, 6924743915848, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.076, 0.444, 0.182
|
||||
diff, 6901070600142, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.010, 0.482, 0.024
|
||||
diff, 6924743915848, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, -0.025, 0.380, -0.061
|
||||
diff, 6902265160502, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, -0.042, 0.280, -0.088
|
||||
diff, 6902088131437, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, -0.019, 0.228, -0.026
|
||||
diff, 6903148080085, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.064, 0.486, 0.135
|
||||
diff, 6901668934888, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.014, 0.325, 0.036
|
||||
diff, 6901668929730, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, -0.066, 0.282, -0.106
|
||||
diff, 6901070600142, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, -0.068, 0.414, -0.148
|
||||
diff, 6974158892364, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, -0.033, 0.303, -0.107
|
||||
diff, 6901668936295, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.008, 0.417, 0.015
|
||||
diff, 6975682480393, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.031, 0.405, 0.075
|
||||
diff, 6903148080085, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, -0.015, 0.311, -0.030
|
||||
diff, 6901668929730, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.020, 0.303, 0.035
|
||||
diff, 6902890218470, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.184, 0.633, 0.393
|
||||
diff, 6902890232216, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.023, 0.348, 0.053
|
||||
diff, 6902890232216, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, -0.080, 0.324, -0.182
|
||||
diff, 6901668936271, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, -0.011, 0.324, -0.019
|
||||
diff, 6902265160502, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, -0.094, 0.358, -0.244
|
||||
diff, 6902132084337, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, -0.007, 0.319, -0.020
|
||||
diff, 6970399922365, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.048, 0.361, 0.105
|
||||
diff, 6904682300219, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, -0.014, 0.472, -0.021
|
||||
diff, 6901668936271, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.009, 0.332, 0.014
|
||||
diff, 6901668936271, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.089, 0.483, 0.153
|
||||
diff, 6901668929730, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.110, 0.465, 0.216
|
||||
202
contrast/result/pickle/20240911_183903_ft16.txt
Normal file
202
contrast/result/pickle/20240911_183903_ft16.txt
Normal file
@ -0,0 +1,202 @@
|
||||
same, 6901668936684, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.268, 0.659, 0.506
|
||||
same, 6902088131437, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.582, 0.979, 0.806
|
||||
same, 6904682300226, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.173, 0.830, 0.372
|
||||
same, 6970399922365, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, 0.226, 0.774, 0.597
|
||||
same, 6902265202318, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, 0.557, 0.922, 0.803
|
||||
same, 6907992517780, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.354, 0.761, 0.848
|
||||
same, 6902132084337, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.406, 0.774, 0.850
|
||||
same, 6901668934888, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.290, 0.598, 0.621
|
||||
same, 8000500023976, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, 0.495, 0.825, 0.792
|
||||
same, 6904682300219, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, 0.278, 0.782, 0.551
|
||||
same, 6903148231623, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.320, 0.870, 0.718
|
||||
same, 6904682300219, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.217, 0.697, 0.418
|
||||
same, 6902890218470, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, 0.198, 0.690, 0.538
|
||||
same, 6901668934888, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.325, 0.710, 0.690
|
||||
same, 6902088131437, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, 0.450, 0.983, 0.784
|
||||
same, 6901070600142, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, 0.295, 0.728, 0.668
|
||||
same, 8993175540667, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, 0.418, 0.859, 0.687
|
||||
same, 6901668929730, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, 0.549, 0.853, 0.888
|
||||
same, 6970399922365, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.330, 0.766, 0.817
|
||||
same, 6901668929730, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.529, 0.849, 0.864
|
||||
same, 6903148048801, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.444, 0.865, 0.769
|
||||
same, 6901668934628, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, 0.489, 0.930, 0.758
|
||||
same, 6902890218470, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.251, 0.738, 0.652
|
||||
same, 6949909050041, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.384, 0.870, 0.714
|
||||
same, 6901668934888, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.336, 0.778, 0.751
|
||||
same, 6901668936271, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, 0.121, 0.604, 0.257
|
||||
same, 6904682300226, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.297, 0.847, 0.651
|
||||
same, 6903148126677, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, 0.422, 0.814, 0.717
|
||||
same, 6924743915848, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, 0.285, 0.697, 0.640
|
||||
same, 6902132084337, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.350, 0.819, 0.857
|
||||
same, 8993175537322, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, 0.349, 0.832, 0.611
|
||||
same, 6902265202318, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, 0.392, 0.859, 0.695
|
||||
same, 6907992517780, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.405, 0.815, 0.865
|
||||
same, 6902265160502, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.162, 0.703, 0.531
|
||||
same, 6903148347409, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, 0.156, 0.693, 0.470
|
||||
same, 6902265202318, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.378, 0.865, 0.694
|
||||
same, 6903148126677, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.496, 0.879, 0.796
|
||||
same, 6901668936295, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.170, 0.631, 0.325
|
||||
same, 6958104102516, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.235, 0.731, 0.550
|
||||
same, 6901668936684, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, 0.230, 0.638, 0.450
|
||||
same, 6902265150022, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, 0.362, 0.927, 0.794
|
||||
same, 6902890232216, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.255, 0.761, 0.626
|
||||
same, 6902890232216, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.296, 0.695, 0.585
|
||||
same, 6901668929730, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, 0.503, 0.848, 0.823
|
||||
same, 6903148231623, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.256, 0.720, 0.506
|
||||
same, 6902265150022, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, 0.428, 0.940, 0.823
|
||||
same, 6901668929518, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.361, 0.853, 0.721
|
||||
same, 6901668934628, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.444, 0.882, 0.690
|
||||
same, 6974158892364, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.119, 0.684, 0.439
|
||||
same, 6902890218470, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.281, 0.689, 0.666
|
||||
same, 6902265150022, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, 0.308, 0.899, 0.682
|
||||
same, 6901668929518, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, 0.260, 0.821, 0.586
|
||||
same, 6901668936271, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.156, 0.617, 0.315
|
||||
same, 6903148126677, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, 0.420, 0.891, 0.749
|
||||
same, 6901668936684, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.212, 0.675, 0.445
|
||||
same, 6901668936295, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.130, 0.630, 0.254
|
||||
same, 8993175540667, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.565, 0.872, 0.821
|
||||
same, 6901668929518, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.341, 0.826, 0.725
|
||||
same, 6902132084337, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, 0.438, 0.794, 0.887
|
||||
same, 6904682300219, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.365, 0.804, 0.643
|
||||
same, 6901668934628, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.489, 0.894, 0.770
|
||||
same, 6902088131437, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.536, 0.980, 0.829
|
||||
same, 9421903892324, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.421, 0.892, 0.755
|
||||
same, 6901668936684, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.289, 0.672, 0.569
|
||||
same, 8000500023976, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.286, 0.872, 0.660
|
||||
same, 6901668929518, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, 0.446, 0.847, 0.834
|
||||
same, 6902265160502, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.212, 0.857, 0.611
|
||||
same, 6901668936684, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.149, 0.614, 0.344
|
||||
same, 6901668934628, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, 0.275, 0.870, 0.521
|
||||
same, 6949909050041, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.401, 0.849, 0.792
|
||||
same, 6907992517780, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, 0.391, 0.848, 0.838
|
||||
same, 6902890218470, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, 0.281, 0.737, 0.774
|
||||
same, 6904682300219, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.424, 0.892, 0.792
|
||||
same, 6904682300226, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.257, 0.725, 0.636
|
||||
same, 6903148048801, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.422, 0.826, 0.784
|
||||
same, 6902132084337, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, 0.379, 0.831, 0.792
|
||||
same, 9421903892324, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.304, 0.877, 0.548
|
||||
same, 6904682300219, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.254, 0.769, 0.477
|
||||
same, 6902890232216, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, 0.264, 0.786, 0.593
|
||||
same, 6901668936295, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, 0.139, 0.542, 0.239
|
||||
same, 6903148126677, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, 0.351, 0.861, 0.602
|
||||
same, 6901668929518, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.365, 0.821, 0.731
|
||||
same, 6903148231623, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.176, 0.688, 0.359
|
||||
same, 6901668929518, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, 0.437, 0.874, 0.772
|
||||
same, 6901668929730, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, 0.461, 0.852, 0.797
|
||||
same, 6903148080085, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, 0.370, 0.860, 0.827
|
||||
same, 6901070600142, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.201, 0.672, 0.442
|
||||
same, 6958104102516, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.234, 0.866, 0.583
|
||||
same, 6901070600142, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, 0.269, 0.727, 0.591
|
||||
same, 8993175537322, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.450, 0.790, 0.785
|
||||
same, 6975682480393, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.448, 0.835, 0.828
|
||||
same, 6903148080085, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.351, 0.838, 0.766
|
||||
same, 6903148231623, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, 0.423, 0.845, 0.782
|
||||
same, 6949909050041, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, 0.494, 0.893, 0.885
|
||||
same, 6907992517780, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, 0.338, 0.737, 0.823
|
||||
same, 6902265160502, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, 0.239, 0.833, 0.706
|
||||
same, 6901668936271, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.230, 0.615, 0.390
|
||||
same, 8993175537322, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, 0.456, 0.783, 0.719
|
||||
same, 8993175537322, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.455, 0.766, 0.717
|
||||
same, 6901668929518, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.406, 0.861, 0.759
|
||||
same, 8000500023976, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.350, 0.853, 0.686
|
||||
diff, 8993175537322, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.017, 0.341, 0.030
|
||||
diff, 6904682300226, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.007, 0.348, 0.013
|
||||
diff, 8993175540667, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.038, 0.309, 0.067
|
||||
diff, 6901668934628, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, -0.003, 0.302, -0.006
|
||||
diff, 6901668929518, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, -0.023, 0.273, -0.038
|
||||
diff, 6903148080085, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.026, 0.408, 0.061
|
||||
diff, 6970399922365, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.090, 0.479, 0.207
|
||||
diff, 6904682300226, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.072, 0.383, 0.142
|
||||
diff, 6974158892364, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, -0.044, 0.340, -0.117
|
||||
diff, 6901668934888, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, -0.017, 0.459, -0.042
|
||||
diff, 6907992517780, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.019, 0.391, 0.051
|
||||
diff, 6901668934628, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.033, 0.331, 0.063
|
||||
diff, 6901668936684, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, -0.072, 0.270, -0.163
|
||||
diff, 6907992517780, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.141, 0.461, 0.292
|
||||
diff, 6958104102516, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, -0.022, 0.373, -0.053
|
||||
diff, 8993175537322, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, -0.018, 0.293, -0.033
|
||||
diff, 6903148126677, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, -0.044, 0.356, -0.082
|
||||
diff, 8993175540667, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, -0.021, 0.349, -0.032
|
||||
diff, 9421903892324, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.033, 0.383, 0.062
|
||||
diff, 6902890232216, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.076, 0.419, 0.151
|
||||
diff, 6903148231623, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.012, 0.309, 0.019
|
||||
diff, 6924743915848, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, -0.069, 0.326, -0.147
|
||||
diff, 6975682480393, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.122, 0.628, 0.274
|
||||
diff, 6975682480393, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.094, 0.647, 0.188
|
||||
diff, 6907992517780, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.157, 0.646, 0.343
|
||||
diff, 6902265202318, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, -0.006, 0.286, -0.011
|
||||
diff, 6902890232216, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.066, 0.491, 0.157
|
||||
diff, 9421903892324, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, -0.038, 0.450, -0.061
|
||||
diff, 6902132084337, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, -0.061, 0.267, -0.125
|
||||
diff, 9421903892324, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.105, 0.454, 0.213
|
||||
diff, 6901668934628, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, -0.089, 0.186, -0.148
|
||||
diff, 6901668934888, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, -0.038, 0.352, -0.087
|
||||
diff, 6902265202318, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.025, 0.325, 0.043
|
||||
diff, 6902890232216, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.077, 0.540, 0.241
|
||||
diff, 6903148126677, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, -0.047, 0.247, -0.113
|
||||
diff, 6903148347409, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.019, 0.312, 0.049
|
||||
diff, 6904682300219, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.022, 0.340, 0.033
|
||||
diff, 6974158892364, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.035, 0.446, 0.108
|
||||
diff, 6901070600142, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.016, 0.385, 0.042
|
||||
diff, 6901668934628, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, -0.045, 0.563, -0.079
|
||||
diff, 6924743915848, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, -0.096, 0.342, -0.249
|
||||
diff, 6903148126677, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.053, 0.326, 0.112
|
||||
diff, 6904682300226, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.063, 0.430, 0.115
|
||||
diff, 9421903892324, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, -0.066, 0.306, -0.107
|
||||
diff, 6901668936684, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.062, 0.403, 0.131
|
||||
diff, 6970399922365, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, -0.044, 0.355, -0.101
|
||||
diff, 6903148048801, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.077, 0.498, 0.147
|
||||
diff, 6901668934888, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.001, 0.441, 0.001
|
||||
diff, 6970399922365, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.072, 0.537, 0.208
|
||||
diff, 6975682480393, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.112, 0.660, 0.231
|
||||
diff, 6901668929518, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, -0.067, 0.359, -0.146
|
||||
diff, 6901070600142, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, -0.033, 0.306, -0.085
|
||||
diff, 6903148126677, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.008, 0.361, 0.018
|
||||
diff, 6903148347409, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, -0.008, 0.348, -0.017
|
||||
diff, 6901668936271, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.064, 0.555, 0.128
|
||||
diff, 6901070600142, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.189, 0.600, 0.448
|
||||
diff, 6902265150022, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.036, 0.300, 0.064
|
||||
diff, 6901668934888, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.047, 0.373, 0.112
|
||||
diff, 6958104102516, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, -0.068, 0.247, -0.130
|
||||
diff, 6902265160502, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.046, 0.467, 0.106
|
||||
diff, 6970399922365, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.023, 0.376, 0.049
|
||||
diff, 6902265202318, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.017, 0.314, 0.030
|
||||
diff, 6907992517780, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.118, 0.551, 0.254
|
||||
diff, 6901668936271, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.137, 0.498, 0.255
|
||||
diff, 6901668934628, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.061, 0.324, 0.135
|
||||
diff, 6903148126677, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, -0.026, 0.332, -0.047
|
||||
diff, 6903148048801, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.030, 0.370, 0.070
|
||||
diff, 6902132084337, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.043, 0.375, 0.112
|
||||
diff, 6902890232216, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, -0.067, 0.258, -0.164
|
||||
diff, 6903148048801, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.118, 0.397, 0.235
|
||||
diff, 6970399922365, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, -0.043, 0.339, -0.101
|
||||
diff, 6903148048801, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, -0.001, 0.482, -0.002
|
||||
diff, 6904682300226, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.270, 0.813, 0.583
|
||||
diff, 6901668936271, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.026, 0.369, 0.057
|
||||
diff, 6949909050041, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.104, 0.443, 0.192
|
||||
diff, 6902890232216, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, -0.018, 0.254, -0.040
|
||||
diff, 6924743915848, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.076, 0.444, 0.182
|
||||
diff, 6901070600142, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.010, 0.482, 0.024
|
||||
diff, 6924743915848, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, -0.025, 0.380, -0.061
|
||||
diff, 6902265160502, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, -0.042, 0.280, -0.088
|
||||
diff, 6902088131437, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, -0.019, 0.228, -0.026
|
||||
diff, 6903148080085, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.064, 0.486, 0.135
|
||||
diff, 6901668934888, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.014, 0.325, 0.036
|
||||
diff, 6901668929730, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, -0.066, 0.282, -0.106
|
||||
diff, 6901070600142, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, -0.068, 0.414, -0.148
|
||||
diff, 6974158892364, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, -0.033, 0.303, -0.107
|
||||
diff, 6901668936295, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.008, 0.417, 0.015
|
||||
diff, 6975682480393, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.031, 0.405, 0.075
|
||||
diff, 6903148080085, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, -0.015, 0.311, -0.030
|
||||
diff, 6901668929730, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.020, 0.303, 0.035
|
||||
diff, 6902890218470, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.184, 0.633, 0.393
|
||||
diff, 6902890232216, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.023, 0.348, 0.053
|
||||
diff, 6902890232216, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, -0.080, 0.324, -0.182
|
||||
diff, 6901668936271, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, -0.011, 0.324, -0.019
|
||||
diff, 6902265160502, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, -0.094, 0.358, -0.244
|
||||
diff, 6902132084337, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, -0.007, 0.319, -0.020
|
||||
diff, 6970399922365, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.048, 0.361, 0.105
|
||||
diff, 6904682300219, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, -0.014, 0.472, -0.021
|
||||
diff, 6901668936271, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.009, 0.332, 0.014
|
||||
diff, 6901668936271, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.089, 0.483, 0.153
|
||||
diff, 6901668929730, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.110, 0.465, 0.216
|
||||
202
contrast/result/pickle/20240911_183903_uint8.txt
Normal file
202
contrast/result/pickle/20240911_183903_uint8.txt
Normal file
@ -0,0 +1,202 @@
|
||||
same, 6901668936684, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.268, 0.655, 0.507
|
||||
same, 6902088131437, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.581, 0.977, 0.805
|
||||
same, 6904682300226, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.173, 0.831, 0.372
|
||||
same, 6970399922365, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, 0.226, 0.774, 0.596
|
||||
same, 6902265202318, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, 0.554, 0.918, 0.802
|
||||
same, 6907992517780, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.353, 0.753, 0.849
|
||||
same, 6902132084337, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.405, 0.765, 0.850
|
||||
same, 6901668934888, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.289, 0.595, 0.620
|
||||
same, 8000500023976, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, 0.492, 0.826, 0.792
|
||||
same, 6904682300219, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, 0.279, 0.786, 0.554
|
||||
same, 6903148231623, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.319, 0.870, 0.718
|
||||
same, 6904682300219, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.218, 0.692, 0.419
|
||||
same, 6902890218470, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, 0.198, 0.688, 0.541
|
||||
same, 6901668934888, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.322, 0.713, 0.687
|
||||
same, 6902088131437, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, 0.448, 0.981, 0.782
|
||||
same, 6901070600142, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, 0.294, 0.724, 0.666
|
||||
same, 8993175540667, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, 0.419, 0.856, 0.690
|
||||
same, 6901668929730, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, 0.549, 0.847, 0.889
|
||||
same, 6970399922365, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.328, 0.767, 0.815
|
||||
same, 6901668929730, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.529, 0.850, 0.865
|
||||
same, 6903148048801, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.444, 0.861, 0.771
|
||||
same, 6901668934628, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, 0.486, 0.926, 0.755
|
||||
same, 6902890218470, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.251, 0.737, 0.653
|
||||
same, 6949909050041, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.384, 0.866, 0.715
|
||||
same, 6901668934888, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.335, 0.776, 0.751
|
||||
same, 6901668936271, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, 0.118, 0.603, 0.253
|
||||
same, 6904682300226, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.297, 0.845, 0.653
|
||||
same, 6903148126677, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, 0.420, 0.817, 0.717
|
||||
same, 6924743915848, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, 0.286, 0.706, 0.642
|
||||
same, 6902132084337, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.348, 0.818, 0.856
|
||||
same, 8993175537322, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, 0.348, 0.829, 0.611
|
||||
same, 6902265202318, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, 0.391, 0.858, 0.695
|
||||
same, 6907992517780, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.405, 0.816, 0.865
|
||||
same, 6902265160502, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.161, 0.697, 0.530
|
||||
same, 6903148347409, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, 0.156, 0.691, 0.470
|
||||
same, 6902265202318, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.376, 0.863, 0.692
|
||||
same, 6903148126677, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.494, 0.875, 0.795
|
||||
same, 6901668936295, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.170, 0.632, 0.325
|
||||
same, 6958104102516, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.235, 0.726, 0.550
|
||||
same, 6901668936684, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, 0.228, 0.638, 0.446
|
||||
same, 6902265150022, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, 0.362, 0.927, 0.794
|
||||
same, 6902890232216, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.254, 0.761, 0.625
|
||||
same, 6902890232216, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.295, 0.692, 0.584
|
||||
same, 6901668929730, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, 0.501, 0.852, 0.823
|
||||
same, 6903148231623, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.255, 0.713, 0.505
|
||||
same, 6902265150022, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, 0.427, 0.940, 0.823
|
||||
same, 6901668929518, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.361, 0.849, 0.721
|
||||
same, 6901668934628, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.442, 0.879, 0.689
|
||||
same, 6974158892364, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.118, 0.681, 0.437
|
||||
same, 6902890218470, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.281, 0.689, 0.668
|
||||
same, 6902265150022, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, 0.306, 0.901, 0.680
|
||||
same, 6901668929518, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, 0.260, 0.821, 0.586
|
||||
same, 6901668936271, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.153, 0.609, 0.311
|
||||
same, 6903148126677, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, 0.418, 0.890, 0.749
|
||||
same, 6901668936684, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.211, 0.672, 0.444
|
||||
same, 6901668936295, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.128, 0.628, 0.251
|
||||
same, 8993175540667, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.565, 0.870, 0.822
|
||||
same, 6901668929518, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.341, 0.823, 0.726
|
||||
same, 6902132084337, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, 0.438, 0.795, 0.888
|
||||
same, 6904682300219, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.364, 0.800, 0.643
|
||||
same, 6901668934628, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.487, 0.889, 0.769
|
||||
same, 6902088131437, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.533, 0.980, 0.827
|
||||
same, 9421903892324, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.420, 0.892, 0.755
|
||||
same, 6901668936684, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.289, 0.659, 0.568
|
||||
same, 8000500023976, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.286, 0.867, 0.660
|
||||
same, 6901668929518, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, 0.445, 0.846, 0.833
|
||||
same, 6902265160502, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.212, 0.857, 0.610
|
||||
same, 6901668936684, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.149, 0.612, 0.343
|
||||
same, 6901668934628, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, 0.274, 0.868, 0.521
|
||||
same, 6949909050041, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.400, 0.845, 0.791
|
||||
same, 6907992517780, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, 0.391, 0.844, 0.837
|
||||
same, 6902890218470, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, 0.281, 0.738, 0.774
|
||||
same, 6904682300219, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.423, 0.894, 0.794
|
||||
same, 6904682300226, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.257, 0.724, 0.634
|
||||
same, 6903148048801, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.421, 0.825, 0.785
|
||||
same, 6902132084337, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, 0.379, 0.832, 0.794
|
||||
same, 9421903892324, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.301, 0.875, 0.544
|
||||
same, 6904682300219, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.254, 0.772, 0.481
|
||||
same, 6902890232216, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, 0.264, 0.781, 0.592
|
||||
same, 6901668936295, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, 0.138, 0.542, 0.237
|
||||
same, 6903148126677, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, 0.351, 0.861, 0.603
|
||||
same, 6901668929518, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.364, 0.825, 0.731
|
||||
same, 6903148231623, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.174, 0.689, 0.357
|
||||
same, 6901668929518, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, 0.436, 0.872, 0.772
|
||||
same, 6901668929730, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, 0.461, 0.851, 0.797
|
||||
same, 6903148080085, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, 0.369, 0.859, 0.826
|
||||
same, 6901070600142, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.200, 0.674, 0.441
|
||||
same, 6958104102516, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.233, 0.868, 0.582
|
||||
same, 6901070600142, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, 0.267, 0.725, 0.590
|
||||
same, 8993175537322, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.448, 0.787, 0.784
|
||||
same, 6975682480393, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.447, 0.832, 0.830
|
||||
same, 6903148080085, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.350, 0.836, 0.766
|
||||
same, 6903148231623, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, 0.422, 0.843, 0.780
|
||||
same, 6949909050041, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, 0.493, 0.891, 0.884
|
||||
same, 6907992517780, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, 0.338, 0.738, 0.824
|
||||
same, 6902265160502, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, 0.238, 0.826, 0.706
|
||||
same, 6901668936271, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.228, 0.610, 0.388
|
||||
same, 8993175537322, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, 0.454, 0.780, 0.718
|
||||
same, 8993175537322, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.454, 0.763, 0.718
|
||||
same, 6901668929518, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.405, 0.855, 0.759
|
||||
same, 8000500023976, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.347, 0.851, 0.684
|
||||
diff, 8993175537322, 20240910-173355-1bbf290e-1f14-4ba8-b666-82c990c4eea3_6901668936684, 0.016, 0.342, 0.029
|
||||
diff, 6904682300226, 20240910-173847-9eedb2ac-e3a5-4d07-94fe-f7e881d67418_6902088131437, 0.006, 0.344, 0.011
|
||||
diff, 8993175540667, 20240910-171800-76a062fd-409c-480f-94f4-fd0e65d72467_6904682300226, 0.038, 0.307, 0.066
|
||||
diff, 6901668934628, 20240910-172352-9b79a4d9-092f-477d-a7a4-8af079d1538d_6970399922365, -0.003, 0.305, -0.006
|
||||
diff, 6901668929518, 20240910-170331-e3ee7cf5-dda2-4d0b-b8c9-4fb411fe78ec_6902265202318, -0.022, 0.268, -0.036
|
||||
diff, 6903148080085, 20240910-163802-6b9f0129-8497-467f-a506-5708eda436a4_6907992517780, 0.026, 0.413, 0.060
|
||||
diff, 6970399922365, 20240910-172403-dbc9de02-2811-449c-961f-23e7a16877d7_6902132084337, 0.090, 0.478, 0.206
|
||||
diff, 6904682300226, 20240910-164315-38c640ba-cdf3-4ac1-8bff-55fe5d0560bb_6901668934888, 0.071, 0.384, 0.141
|
||||
diff, 6974158892364, 20240910-173323-78dc658e-e4ef-49e1-a2ff-9ada34c27a85_8000500023976, -0.044, 0.335, -0.118
|
||||
diff, 6901668934888, 20240910-164323-8e9a882a-a502-4a6e-bd99-70deb2130f57_6904682300219, -0.016, 0.459, -0.041
|
||||
diff, 6907992517780, 20240910-163750-8e13e800-21d0-4bd9-b686-18ed213460cd_6903148231623, 0.018, 0.399, 0.049
|
||||
diff, 6901668934628, 20240910-170920-dc16c149-06a3-4c2d-9bec-e930274b55ce_6904682300219, 0.033, 0.332, 0.062
|
||||
diff, 6901668936684, 20240910-172802-0dbe3709-bd0c-45e7-ad36-0cfc9781ef1b_6902890218470, -0.072, 0.269, -0.162
|
||||
diff, 6907992517780, 20240910-165620-0b870f0d-88a5-4286-bcbf-b0ebb41ddcfc_6901668934888, 0.141, 0.457, 0.292
|
||||
diff, 6958104102516, 20240910-163846-7793e886-9f09-4744-9e24-eb47d65c09f5_6902088131437, -0.023, 0.370, -0.056
|
||||
diff, 8993175537322, 20240910-170742-f78b59da-e242-42c9-ac7a-bba23ff11aff_6901070600142, -0.017, 0.289, -0.030
|
||||
diff, 6903148126677, 20240910-172814-d17bd016-b8e5-4a21-a137-6bce693e0cb0_8993175540667, -0.044, 0.360, -0.083
|
||||
diff, 8993175540667, 20240910-162930-ec2bb380-53fe-483f-9aab-9038643ebd1f_6901668929730, -0.021, 0.359, -0.031
|
||||
diff, 9421903892324, 20240910-173332-55f8124d-7ab0-4a7a-8b08-f4dd9ba06502_6970399922365, 0.033, 0.376, 0.063
|
||||
diff, 6902890232216, 20240910-173214-5b86868f-cb5b-4b7f-8f3a-aff08d89900d_6901668929730, 0.075, 0.424, 0.151
|
||||
diff, 6903148231623, 20240910-172904-5462ad91-2a07-4116-898f-ff1d2021e6af_6903148048801, 0.013, 0.311, 0.021
|
||||
diff, 6924743915848, 20240910-171838-c77a6d0d-185b-48e7-9af9-05de561f1172_6901668934628, -0.069, 0.327, -0.147
|
||||
diff, 6975682480393, 20240910-170934-74c137ee-0689-42d0-9994-da8ba59fd5db_6902890218470, 0.121, 0.624, 0.273
|
||||
diff, 6975682480393, 20240910-162952-f6ec3a40-9d64-4f20-b122-0b81eb4a2134_6949909050041, 0.092, 0.646, 0.185
|
||||
diff, 6907992517780, 20240910-172841-9d7b16fb-4200-4089-b4b2-925da10208ed_6901668934888, 0.158, 0.637, 0.344
|
||||
diff, 6902265202318, 20240910-165632-a1e22655-d9ad-47f5-a467-55718bd1e23e_6901668936271, -0.006, 0.285, -0.013
|
||||
diff, 6902890232216, 20240910-163718-e1e09ad9-7a7e-4b43-beb7-47080c0a312e_6904682300226, 0.066, 0.486, 0.157
|
||||
diff, 9421903892324, 20240910-173233-81246d1d-bbf3-4ee2-b6c1-7f8fe5818266_6903148126677, -0.038, 0.451, -0.061
|
||||
diff, 6902132084337, 20240910-162836-186bdf15-5ebb-4b55-a3a4-47edea86a7ee_6924743915848, -0.060, 0.275, -0.123
|
||||
diff, 9421903892324, 20240910-173222-8abca736-4b5d-4b8e-8e53-206809f37082_6902132084337, 0.105, 0.456, 0.213
|
||||
diff, 6901668934628, 20240910-170945-c5a587f8-925a-46c2-b2f4-b8fe0fa41c90_8993175537322, -0.088, 0.187, -0.148
|
||||
diff, 6901668934888, 20240910-162848-b0d67358-6f68-482a-94cb-d7de7414e32f_6902265202318, -0.038, 0.355, -0.088
|
||||
diff, 6902265202318, 20240910-173730-c51d9d00-65a2-4212-99f3-701092810919_6907992517780, 0.026, 0.318, 0.044
|
||||
diff, 6902890232216, 20240910-170318-706146af-c203-459a-b642-da428ce6426a_6902265160502, 0.077, 0.548, 0.244
|
||||
diff, 6903148126677, 20240910-162902-3de7f2a9-9068-4f61-a150-0bcc47194a43_6903148347409, -0.047, 0.245, -0.115
|
||||
diff, 6903148347409, 20240910-172023-a9b8c8b4-8030-4aa5-85fe-54cba57e745f_6902265202318, 0.019, 0.319, 0.050
|
||||
diff, 6904682300219, 20240910-171920-0a6490ce-547f-493d-b76a-4c849ae12a93_6903148126677, 0.022, 0.342, 0.033
|
||||
diff, 6974158892364, 20240910-164334-09d4e20e-68c8-48ca-b931-50e58428ef2a_6901668936295, 0.035, 0.449, 0.109
|
||||
diff, 6901070600142, 20240910-165604-0f805f9d-24f7-4729-923a-bff489a09323_6958104102516, 0.016, 0.382, 0.041
|
||||
diff, 6901668934628, 20240910-164409-053f810b-7369-4a3e-b91b-b7ba99fa5b9c_6901668936684, -0.046, 0.570, -0.081
|
||||
diff, 6924743915848, 20240910-170349-b357333c-e939-4ce5-8019-7762799a9097_6902265150022, -0.097, 0.344, -0.250
|
||||
diff, 6903148126677, 20240910-172828-0a20bffd-ede3-4b0c-977b-8652f52518f9_6902890232216, 0.051, 0.327, 0.109
|
||||
diff, 6904682300226, 20240910-170807-7bc77832-4cf1-4cd8-aa54-994ff164dcc7_6902890232216, 0.062, 0.423, 0.113
|
||||
diff, 9421903892324, 20240910-171715-a8fc6d8a-87bd-4fbd-b378-85e34193266f_6901668929730, -0.067, 0.299, -0.108
|
||||
diff, 6901668936684, 20240910-170258-38579506-3874-4d71-b9d2-ac6e47ca75dd_6903148231623, 0.062, 0.401, 0.131
|
||||
diff, 6970399922365, 20240910-172010-035f68e4-9b7c-40f7-961c-aa8c0f154252_6902265150022, -0.043, 0.358, -0.099
|
||||
diff, 6903148048801, 20240910-173344-258d27a2-b2e1-468e-8f32-40edcda94486_6901668929518, 0.079, 0.502, 0.151
|
||||
diff, 6901668934888, 20240910-170431-722e7de7-c7ef-4825-8080-be019c7f4602_6901668934628, 0.001, 0.440, 0.001
|
||||
diff, 6970399922365, 20240910-163028-418ab174-5722-4e8a-ae12-e8d3c33f70b5_6974158892364, 0.071, 0.539, 0.207
|
||||
diff, 6975682480393, 20240910-164251-a2a38e17-5532-49a5-9372-5a3ed8dc6972_6902890218470, 0.112, 0.662, 0.232
|
||||
diff, 6901668929518, 20240910-163814-9fc0324d-134a-46ee-bb79-6b2dfb6388f9_6902265150022, -0.067, 0.361, -0.147
|
||||
diff, 6901070600142, 20240910-170417-1ac149e8-4ecb-447c-a8b7-8d5b96e77ffa_6901668929518, -0.033, 0.302, -0.086
|
||||
diff, 6903148126677, 20240910-172745-96dc9808-4157-4806-856f-c7013452f302_6901668936271, 0.007, 0.366, 0.016
|
||||
diff, 6903148347409, 20240910-163857-736e50b8-eae8-4a6d-af26-ce3a57a073b8_6903148126677, -0.007, 0.349, -0.017
|
||||
diff, 6901668936271, 20240910-172445-4f28474f-5463-4b19-bc2d-671105764e27_6901668936684, 0.062, 0.556, 0.123
|
||||
diff, 6901070600142, 20240910-172754-d034ab2f-1b18-4d6a-a936-9fa538066253_6901668936295, 0.188, 0.602, 0.448
|
||||
diff, 6902265150022, 20240910-165644-2e79a878-caf1-44ca-851c-287848800d35_8993175540667, 0.037, 0.303, 0.064
|
||||
diff, 6901668934888, 20240910-172039-ebd2a496-c407-4450-b122-0e8f33e07de2_6901668929518, 0.048, 0.374, 0.114
|
||||
diff, 6958104102516, 20240910-162817-18813894-397a-4c94-8b90-2d7a46319793_6902132084337, -0.068, 0.248, -0.130
|
||||
diff, 6902265160502, 20240910-172257-9169e95d-ff11-4d31-98af-13df3f071840_6904682300219, 0.046, 0.473, 0.106
|
||||
diff, 6970399922365, 20240910-173306-a1409202-ea3d-47c4-aa39-9d17dae711cf_6901668934628, 0.022, 0.368, 0.047
|
||||
diff, 6902265202318, 20240910-172427-781eb94d-efb6-403c-b88f-f4b9df82fee0_6902088131437, 0.016, 0.316, 0.029
|
||||
diff, 6907992517780, 20240910-173757-b4ed1c60-a96b-48ad-a451-3caecd61c327_9421903892324, 0.118, 0.554, 0.257
|
||||
diff, 6901668936271, 20240910-170907-0e74383f-0341-4b90-b333-910e5a184296_6901668936684, 0.136, 0.493, 0.253
|
||||
diff, 6901668934628, 20240910-171014-ee1e7d74-0d89-4014-a125-7c9cdebb15fd_8000500023976, 0.061, 0.321, 0.135
|
||||
diff, 6903148126677, 20240910-164347-47377bae-2ca6-4d75-a076-e7f6c03d0f2e_6901668929518, -0.026, 0.325, -0.048
|
||||
diff, 6903148048801, 20240910-173409-55dd7611-7394-4783-9f4e-4639401078ea_6902265160502, 0.030, 0.373, 0.071
|
||||
diff, 6902132084337, 20240910-165525-e17864c9-e965-4531-be14-be551dad88fb_6901668936684, 0.045, 0.370, 0.116
|
||||
diff, 6902890232216, 20240910-162805-592cff06-4acb-420f-bc36-bb00f3e0efbb_6901668934628, -0.066, 0.262, -0.161
|
||||
diff, 6903148048801, 20240910-172919-ab2efd9a-a776-420f-95f5-2f8188f719e4_6949909050041, 0.118, 0.403, 0.235
|
||||
diff, 6970399922365, 20240910-171723-2f8a7ece-99cb-4d91-b484-67b486599f26_6907992517780, -0.043, 0.340, -0.102
|
||||
diff, 6903148048801, 20240910-165443-48bad32d-9f2b-499b-907d-c602cf563ee3_6902890218470, -0.002, 0.480, -0.004
|
||||
diff, 6904682300226, 20240910-165455-d0e36365-f7f2-4f2e-84a7-1ffc24ccc1c7_6904682300219, 0.270, 0.808, 0.584
|
||||
diff, 6901668936271, 20240910-170231-21568a27-641b-448d-8b8c-9eff4dfe7294_6904682300226, 0.025, 0.367, 0.055
|
||||
diff, 6949909050041, 20240910-163740-851d23c1-e90f-4947-abc3-f463991c5505_6903148048801, 0.102, 0.445, 0.189
|
||||
diff, 6902890232216, 20240910-170730-76626a74-34fb-486d-b889-4276552edb0e_6902132084337, -0.019, 0.245, -0.041
|
||||
diff, 6924743915848, 20240910-172316-ffa74ee4-46d5-4266-b362-ebfebed0c572_9421903892324, 0.078, 0.441, 0.186
|
||||
diff, 6901070600142, 20240910-173807-afdeec3a-0d6e-4db8-9baf-826b7d6b4660_6904682300219, 0.009, 0.483, 0.021
|
||||
diff, 6924743915848, 20240910-163838-9e6f0b38-2ffe-4727-9ec7-a02435b8f629_6902890232216, -0.025, 0.388, -0.059
|
||||
diff, 6902265160502, 20240910-165424-5d55263c-e523-495e-b673-fc53eaa68b05_6901668936295, -0.041, 0.280, -0.087
|
||||
diff, 6902088131437, 20240910-170403-c1b9db80-7ee0-4508-8858-1e3e1b924648_6903148126677, -0.019, 0.230, -0.025
|
||||
diff, 6903148080085, 20240910-172500-509a2d1e-e665-4fe6-8ffe-b69117d7b09f_6901668929518, 0.064, 0.492, 0.136
|
||||
diff, 6901668934888, 20240910-171824-2d3edfcd-c169-4c6e-9734-9325b72cf9fe_6903148231623, 0.014, 0.328, 0.034
|
||||
diff, 6901668929730, 20240910-173839-e4b3b834-c695-4917-b2f4-7cfaaebb98dc_6901668929518, -0.066, 0.278, -0.107
|
||||
diff, 6901070600142, 20240910-170447-3b37f76f-5e21-400b-a8a8-2376c0796ae6_6901668929730, -0.069, 0.411, -0.149
|
||||
diff, 6974158892364, 20240910-173314-d6ac3740-20f2-4aa7-a392-80a96b7607c3_6903148080085, -0.034, 0.302, -0.111
|
||||
diff, 6901668936295, 20240910-172734-8c23b385-99f7-4e01-819a-78c86611ff48_6901070600142, 0.007, 0.424, 0.013
|
||||
diff, 6975682480393, 20240910-164452-0f365052-2e4a-4d00-9cf7-0407d731d07e_6958104102516, 0.030, 0.402, 0.074
|
||||
diff, 6903148080085, 20240910-162749-ab186eb8-6777-489b-8ad0-c1c6e66b285d_6901070600142, -0.014, 0.308, -0.027
|
||||
diff, 6901668929730, 20240910-164432-008357d7-7ee6-49b9-8d08-3f3a6081c4e1_8993175537322, 0.020, 0.298, 0.036
|
||||
diff, 6902890218470, 20240910-163007-6dfc085b-42b9-432d-9c41-7bfd294526b6_6975682480393, 0.185, 0.635, 0.394
|
||||
diff, 6902890232216, 20240910-163825-e4de18e2-fe7c-4ff6-8b51-7ef2a7db7ed3_6903148080085, 0.024, 0.351, 0.056
|
||||
diff, 6902890232216, 20240910-172854-5fb70036-3089-4258-9346-de25d415f120_6903148231623, -0.079, 0.318, -0.181
|
||||
diff, 6901668936271, 20240910-170817-c2f8c500-3aa5-4bd2-bf82-787d0cd22585_6949909050041, -0.011, 0.325, -0.019
|
||||
diff, 6902265160502, 20240910-170246-e773b037-a712-4d78-accd-71c24b675365_6907992517780, -0.094, 0.362, -0.245
|
||||
diff, 6902132084337, 20240910-163907-1ac881ec-cac4-4811-9cab-1826731e77bd_6902265160502, -0.008, 0.321, -0.022
|
||||
diff, 6970399922365, 20240910-164239-e4d8f615-8cf3-483d-bc6e-03e470e2110c_6901668936271, 0.047, 0.366, 0.105
|
||||
diff, 6904682300219, 20240910-172328-48a512b9-4fb1-4abf-bca9-8b3443ce8f2b_8993175537322, -0.012, 0.472, -0.019
|
||||
diff, 6901668936271, 20240910-173819-226cc352-acdc-4419-9159-c97ae0eb58af_8993175537322, 0.010, 0.340, 0.016
|
||||
diff, 6901668936271, 20240910-165517-a0000cdf-aa15-42c8-a6be-dbce8cf7cb32_6901668929518, 0.090, 0.480, 0.155
|
||||
diff, 6901668929730, 20240910-172417-e9d563b9-74e2-4ec1-8f34-331424b48e72_8000500023976, 0.108, 0.463, 0.214
|
||||
7
contrast/utils/__init__.py
Normal file
7
contrast/utils/__init__.py
Normal file
@ -0,0 +1,7 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Sep 26 08:53:58 2024
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
|
||||
83
contrast/utils/barcode_set_operate.py
Normal file
83
contrast/utils/barcode_set_operate.py
Normal file
@ -0,0 +1,83 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Sep 13 16:49:05 2024
|
||||
|
||||
比较 stdBcdpath 和 filepath 中的 barcodes 列表，求出二者的并集和为包含在
|
||||
stdBcdpath 中的 barcodes 清单
|
||||
|
||||
@author: ym
|
||||
"""
|
||||
import os
|
||||
from openpyxl import load_workbook, Workbook
|
||||
|
||||
def read_xlsx():
|
||||
stdBcdpath = r"\\192.168.1.28\share\已标注数据备份\对比数据\barcode\total_barcode_6588"
|
||||
filepath = r"\\192.168.1.28\share\联华中环店\中环店商品信息.xlsx"
|
||||
|
||||
existingPath = r'\\192.168.1.28\share\联华中环店\中环店商品信息_已有商品.xlsx'
|
||||
lackingPath = r'\\192.168.1.28\share\联华中环店\中环店商品信息_未包含商品.xlsx'
|
||||
|
||||
workbook = load_workbook(filename=filepath)
|
||||
sheet = workbook['Sheet1']
|
||||
barcodeCol = [sheet.cell(row=r, column=1).value for r in range(1, sheet.max_row+1)]
|
||||
|
||||
zhBarcodeList = [barcodeCol[i] for i in range(1, len(barcodeCol))]
|
||||
|
||||
stdBarcodeList = []
|
||||
for filename in os.listdir(stdBcdpath):
|
||||
filepath = os.path.join(stdBcdpath, filename)
|
||||
if not os.path.isdir(filepath) or not filename.isdigit():
|
||||
continue
|
||||
stdBarcodeList.append(int(filename))
|
||||
|
||||
|
||||
stdBarcodeSet = set(stdBarcodeList)
|
||||
zhBarcodeSet = set(zhBarcodeList)
|
||||
interBarcodes = list(zhBarcodeSet.intersection(stdBarcodeSet))
|
||||
|
||||
print(len(interBarcodes))
|
||||
|
||||
dest_wb1 = Workbook()
|
||||
dest_sheet1 = dest_wb1.active
|
||||
for row in sheet.iter_rows(min_row=1, max_col=sheet.max_column, values_only=True):
|
||||
if str(row[0]).find("商品条码")>=0:
|
||||
dest_sheet1.append(row)
|
||||
|
||||
if row[0] in interBarcodes:
|
||||
dest_sheet1.append(row)
|
||||
|
||||
dest_wb1.save(filename=existingPath)
|
||||
dest_wb1.close()
|
||||
|
||||
|
||||
diffBarcodes = list(zhBarcodeSet.difference(stdBarcodeSet))
|
||||
|
||||
dest_wb2 = Workbook()
|
||||
dest_sheet2 = dest_wb2.active
|
||||
for row in sheet.iter_rows(min_row=1, max_col=sheet.max_column, values_only=True):
|
||||
if str(row[0]).find("商品条码")>=0:
|
||||
dest_sheet2.append(row)
|
||||
|
||||
if row[0] in diffBarcodes:
|
||||
dest_sheet2.append(row)
|
||||
|
||||
dest_wb2.save(filename=lackingPath)
|
||||
dest_wb2.close()
|
||||
|
||||
|
||||
workbook.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# main()
|
||||
|
||||
read_xlsx()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,7 +1,16 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
|
||||
@author: LiChen
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import pickle
|
||||
import numpy as np
|
||||
|
||||
import sys
|
||||
sys.path.append(r"D:\DetectTracking\contrast")
|
||||
|
||||
from config import config as conf
|
||||
# from img_data import library_imgs, temp_imgs, main_library_imgs, main_imgs_2
|
||||
# from test_logic import initModel,getFeatureList
|
||||
@ -11,7 +20,6 @@ from PIL import Image
|
||||
|
||||
device = conf.device
|
||||
|
||||
|
||||
def initModel():
|
||||
model = resnet18().to(device)
|
||||
model.load_state_dict(torch.load(conf.test_model, map_location=conf.device))
|
||||
0
contrast/说明文档.txt
Normal file
0
contrast/说明文档.txt
Normal file
Reference in New Issue
Block a user