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guoqing bakeup

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王庆刚
2024-10-04 12:12:44 +08:00
parent 09e92d63b3
commit 390c5d2d94
37 changed files with 1409 additions and 219 deletions
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__pycache__/track_reid.cpython-39.pyc Normal file
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contrast/__init__.py Normal file
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@ -0,0 +1,7 @@
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 26 08:53:58 2024
@author: ym
"""

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contrast/__pycache__/one2n_contrast.cpython-39.pyc Normal file
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contrast/test_ori.py → contrast/feat_inference.py
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@ -1,4 +1,10 @@
# -*- coding: utf-8 -*-
"""
@author: LiChen
"""
import os
import os.path as osp
import pdb

6
tracking/feat_select.py → contrast/feat_select.py
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@ -13,10 +13,10 @@ import matplotlib.pyplot as plt
import sys
sys.path.append(r"D:\DetectTracking")
from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output
from tracking.dotrack.dotracks import Track
# from tracking.dotrack.dotracks import Track
from tracking.contrast_analysis import compute_recall_precision, show_recall_prec
from tracking.contrast_analysis import performance_evaluate
from one2n_contrast import compute_recall_precision, show_recall_prec
from one2n_contrast import performance_evaluate
def compute_similar(feat1, feat2):

143
contrast/feat_similar.py
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@ -1,60 +1,104 @@
# -*- 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"
img = img1[:, :, ::-1].copy() # the model expects RGB inputs
patch = ReIDEncoder.transform(img)
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
# 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)
file_path = os.path.join(root, filename)
filepaths.append(file_path)
patches.append(patch)
if (d + 1) % ReIDEncoder.batch_size == 0:
patches = torch.stack(patches, dim=0)
batch_patches.append(patches)
patches = []
feature = inference_image(filepaths, conf.test_transform, model, conf.device)
feature /= np.linalg.norm(feature, axis=1)[:, None]
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
similar = 1 - np.maximum(0.0, cdist(feature, feature, metric='cosine'))
def similarity_compare(root_dir):
print("Done!")
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 = []
@ -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'))
@ -111,7 +155,6 @@ def similarity_compare(root_dir):
ha, wa = imga.shape[:2]
return
@ -119,7 +162,7 @@ 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()

3
tracking/contrast_analysis.py → contrast/one2n_contrast.py
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@ -16,9 +16,10 @@ import shutil
import numpy as np
import matplotlib.pyplot as plt
import cv2
from utils.plotting import Annotator, colors
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

103
contrast/one2one_contrast.py
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@ -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):
@ -371,9 +371,15 @@ 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
@ -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
@ -478,6 +477,7 @@ def contrast_performance_evaluate(resultPath):
evtList = [(p.stem, p.stem.split('_')[1]) for p in Path(eventFeatPath).iterdir()
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!")
@ -794,18 +796,18 @@ def ft16_to_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()

64
contrast/one2one_onsite.py
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@ -40,29 +40,22 @@ def read_one2one_data(filepath):
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])
@ -72,13 +65,11 @@ def main():
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')
@ -92,18 +83,47 @@ def main():
# plt.savefig(f'./result/{file}_pr.png') # svg, png, pdf
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()

202
contrast/result/pickle/20240911_183903.txt Normal file
View 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
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@ -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
View 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
View 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
View 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()

10
contrast/write_feature_json.py → contrast/utils/write_feature_json.py
View File

@ -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
View File

95
pipeline.py Normal file
View File

@ -0,0 +1,95 @@
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 29 08:59:21 2024
@author: ym
"""
import os
import cv2
from pathlib import Path
from track_reid import parse_opt, yolo_resnet_tracker
from tracking.dotrack.dotracks_back import doBackTracks
from tracking.dotrack.dotracks_front import doFrontTracks
IMGFORMATS = '.bmp', '.jpeg', '.jpg', 'png', 'tif', 'tiff', 'webp', 'pfm'
VIDFORMATS = '.avi', '.gif', '.m4v', '.mkv', '.mov', '.mp4', '.ts', '.wmv'
std_feature_path = r"\\192.168.1.28\share\测试_202406\contrast\std_features_2192_ft32vsft16"
opt = parse_opt()
optdict = vars(opt)
def get_video_pairs(vpath):
vdieopath = []
for filename in os.listdir(vpath):
file, ext = os.path.splitext(filename)
if ext in VIDFORMATS:
vdieopath.append(os.path.join(vpath, filename))
return vdieopath
def pipeline():
eventpath = r"\\192.168.1.28\share\测试_202406\0918\images1\20240918-110913-c3a7e4d9-23d4-4a6f-a23f-a2eeee510536_6939947701616"
savepath = r"D:\contrast\detect"
optdict["project"] = savepath
eventname = os.path.basename(eventpath)
vpaths = get_video_pairs(eventpath)
event_tracks = []
for vpath in vpaths:
'''事件结果文件夹'''
save_dir_event = Path(savepath) / Path(eventname)
save_dir_img = save_dir_event / Path(str(Path(vpath).stem))
if not save_dir_img.exists():
save_dir_img.mkdir(parents=True, exist_ok=True)
'''Yolo + Resnet + Tracker'''
optdict["source"] = vpath
optdict["save_dir"] = save_dir_img
optdict["nosave"] = False
tracksdict = yolo_resnet_tracker(**optdict)
bboxes = tracksdict['TrackBoxes']
bname = os.path.basename(vpath)
if bname.split('_')[0] == "0" or bname.find('back')>=0:
vts = doFrontTracks(bboxes, tracksdict)
vts.classify()
event_tracks.append(("back", vts))
if bname.split('_')[0] == "1" or bname.find('front')>=0:
vts = doBackTracks(bboxes, tracksdict)
vts.classify()
event_tracks.append(("front", vts))
for CamerType, vts in event_tracks:
if CamerType == 'back':
pass
if CamerType == 'front':
pass
for featname in os.listdir(std_feature_path):
pass
def main():
pipeline()
if __name__ == "__main__":
main()

176
track_reid.py
View File

@ -127,6 +127,176 @@ def init_trackers(tracker_yaml = None, bs=1):
return trackers
@smart_inference_mode()
def yolo_resnet_tracker(
weights=ROOT / 'yolov5s.pt', # model path or triton URL
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
save_dir = '',
tracker_yaml = "./tracking/trackers/cfg/botsort.yaml",
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_csv=False, # save results in CSV format
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidencesL
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
vid_stride=1, # video frame-rate stride
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
):
source = str(source)
save_img = not nosave and not source.endswith('.txt') # save inference images
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
stride, names, pt = model.stride, model.names, model.pt
imgsz = check_img_size(imgsz, s=stride) # check image size
# Dataloader
bs = 1 # batch_size
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
tracker = init_trackers(tracker_yaml, bs)[0]
dt = (Profile(), Profile(), Profile())
track_boxes = np.empty((0, 9), dtype = np.float32)
TracksDict = {}
for path, im, im0s, vid_cap, s in dataset:
with dt[0]:
im = torch.from_numpy(im).to(model.device)
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
# Inference
with dt[1]:
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
# NMS
with dt[2]:
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
# Process predictions
for i, det in enumerate(pred): # per image
im0 = im0s.copy()
save_path = str(save_dir / Path(path).name) # im.jpg
s += '%gx%g ' % im.shape[2:] # print string
annotator = Annotator(im0.copy(), line_width=line_thickness, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
# det = det.cpu().numpy()
## ================================================================ writed by WQG
'''tracks: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
0 1 2 3 4 5 6 7 8
这里，frame_index 也可以用视频的 帧ID 代替, box_index 保持不变
'''
det_tracking = Boxes(det, im0.shape).cpu().numpy()
tracks = tracker.update(det_tracking, im0)
if len(tracks) == 0:
continue
tracks[:, 7] = dataset.frame
'''================== 1. 存储 dets/subimgs/features Dict ============='''
imgs, features = inference_image(im0, tracks)
# TrackerFeats = np.concatenate([TrackerFeats, features], axis=0)
imgdict = {}
boxdict = {}
featdict = {}
for ii, bid in enumerate(tracks[:, 8]):
imgdict.update({int(bid): imgs[ii]}) # [f"img_{int(bid)}"] = imgs[i]
boxdict.update({int(bid): tracks[ii, :]}) # [f"box_{int(bid)}"] = tracks[i, :]
featdict.update({int(bid): features[ii, :]}) # [f"feat_{int(bid)}"] = features[i, :]
TracksDict[f"frame_{int(dataset.frame)}"] = {"imgs":imgdict, "boxes":boxdict, "feats":featdict}
track_boxes = np.concatenate([track_boxes, tracks], axis=0)
'''================== 2. 提取手势位置 ==================='''
for *xyxy, id, conf, cls, fid, bid in reversed(tracks):
name = ('' if id==-1 else f'id:{int(id)} ') + names[int(cls)]
label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
if id >=0 and cls==0:
color = colors(int(cls), True)
elif id >=0 and cls!=0:
color = colors(int(id), True)
else:
color = colors(19, True) # 19为调色板的最后一个元素
annotator.box_label(xyxy, label, color=color)
# Save results (image and video with tracking)
im0 = annotator.result()
save_path_img, ext = os.path.splitext(save_path)
if save_img:
if dataset.mode == 'image':
imgpath = save_path_img + f"_{dataset}.png"
else:
imgpath = save_path_img + f"_{dataset.frame}.png"
cv2.imwrite(Path(imgpath), im0)
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release() # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
## track_boxes: Array, [x1, y1, x2, y2, track_id, score, cls, frame_index, box_id]
TracksDict.update({"TrackBoxes": track_boxes})
return TracksDict
@smart_inference_mode()
def run(
weights=ROOT / 'yolov5s.pt', # model path or triton URL
@ -438,7 +608,8 @@ def run(
def parse_opt():
modelpath = ROOT / 'ckpts/best_yolov5m_250000.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt'
modelpath = ROOT / 'ckpts/best_cls10_0906.pt' # 'ckpts/best_15000_0908.pt', 'ckpts/yolov5s.pt', 'ckpts/best_20000_cls30.pt, best_yolov5m_250000'
'''datapath为视频文件目录或视频文件'''
datapath = r"D:/datasets/ym/videos/标记视频/" # ROOT/'data/videos', ROOT/'data/images' images
@ -522,7 +693,8 @@ def main_loop(opt):
# p = r"D:\datasets\ym\videos\标记视频"
# p = r"D:\datasets\ym\实验室测试"
# p = r"D:\datasets\ym\永辉双摄视频\新建文件夹"
p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_"
# p = r"\\192.168.1.28\share\测试_202406\0723\0723_2\20240723-112522_"
p = r"D:\datasets\ym\联华中环"
k = 0
if os.path.isdir(p):

1
tracking/contrast_one2one.py → tracking/deprecated/contrast_one2one.py
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@ -1,6 +1,7 @@
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 30 17:53:03 2024
have Deprecated!
1. 确认在相同CamerType下，track.data 中 CamerID 项数量 = 图像数 = 帧ID数 = 最大帧ID

5
tracking/eventsmatch.py → tracking/deprecated/eventsmatch.py
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@ -15,12 +15,15 @@ import pandas as pd
import shutil
import random
import math
import sys
from scipy.spatial.distance import cdist
import matplotlib.pyplot as plt
from pathlib import Path
from utils.gen import Profile
sys.path.append(r"D:\DetectTracking\tracking")
from dotrack.dotracks_back import doBackTracks
from dotrack.dotracks_front import doFrontTracks
from utils.drawtracks import plot_frameID_y2, draw_all_trajectories

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15
tracking/dotrack/dotracks.py
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@ -8,10 +8,11 @@ import numpy as np
import cv2
from pathlib import Path
from scipy.spatial.distance import cdist
from utils.mergetrack import track_equal_track, readDict
from tracking.utils.mergetrack import track_equal_track, readDict
curpath = Path(__file__).resolve().parents[0]
curpath = Path(curpath)
parpath = curpath.parent
class MoveState:
"""商品运动状态标志"""
@ -297,11 +298,15 @@ class Track:
front, 前置摄像头
'''
if camerType=="back":
incart = cv2.imread("./shopcart/cart_tempt/incart.png", cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread("./shopcart/cart_tempt/outcart.png", cv2.IMREAD_GRAYSCALE)
incart = cv2.imread(str(parpath/'shopcart/cart_tempt/incart.png'), cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread(str(parpath/'shopcart/cart_tempt/outcart.png'), cv2.IMREAD_GRAYSCALE)
else:
incart = cv2.imread("./shopcart/cart_tempt/incart_ftmp.png", cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread("./shopcart/cart_tempt/outcart_ftmp.png", cv2.IMREAD_GRAYSCALE)
incart = cv2.imread(str(parpath/'shopcart/cart_tempt/incart_ftmp.png'), cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread(str(parpath/'shopcart/cart_tempt/outcart_ftmp.png'), cv2.IMREAD_GRAYSCALE)
# incart = cv2.imread('./cart_tempt/incart_ftmp.png', cv2.IMREAD_GRAYSCALE)
# outcart = cv2.imread('./cart_tempt/outcart_ftmp.png', cv2.IMREAD_GRAYSCALE)
xc, yc = self.cornpoints[:,0].clip(0,self.imgshape[0]-1).astype(np.int64), self.cornpoints[:,1].clip(0,self.imgshape[1]-1).astype(np.int64)
x1, y1 = self.cornpoints[:,6].clip(0,self.imgshape[0]-1).astype(np.int64), self.cornpoints[:,7].clip(0,self.imgshape[1]-1).astype(np.int64)

2
tracking/dotrack/dotracks_back.py
View File

@ -5,7 +5,7 @@ Created on Mon Mar 4 18:36:31 2024
@author: ym
"""
import numpy as np
from utils.mergetrack import track_equal_track
from tracking.utils.mergetrack import track_equal_track
from scipy.spatial.distance import cdist
from .dotracks import doTracks, ShoppingCart
from .track_back import backTrack

2
tracking/dotrack/dotracks_front.py
View File

@ -5,7 +5,7 @@ Created on Mon Mar 4 18:38:20 2024
@author: ym
"""
import numpy as np
from utils.mergetrack import track_equal_track
# from tracking.utils.mergetrack import track_equal_track
from .dotracks import doTracks
from .track_front import frontTrack

10
tracking/dotrack/track_back.py
View File

@ -10,6 +10,10 @@ from scipy.spatial.distance import cdist
from sklearn.decomposition import PCA
from .dotracks import MoveState, Track
from pathlib import Path
curpath = Path(__file__).resolve().parents[0]
curpath = Path(curpath)
parpath = curpath.parent
class backTrack(Track):
# boxes: [x1, y1, x2, y2, track_id, score, cls, frame_index, box_index]
@ -93,9 +97,9 @@ class backTrack(Track):
maxbox_iou, minbox_iou：track中最大、最小 box 和boxes流的iou，二者差值越小，越接近 1，表明track的运动型越小。
incartrates: 各box和incart的iou时序，由小变大，反应的是置入过程，由大变小，反应的是取出过程
'''
incart = cv2.imread("./shopcart/cart_tempt/incart.png", cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread("./shopcart/cart_tempt/outcart.png", cv2.IMREAD_GRAYSCALE)
cartboarder = cv2.imread("./shopcart/cart_tempt/cartboarder.png", cv2.IMREAD_GRAYSCALE)
incart = cv2.imread(str(parpath/"shopcart/cart_tempt/incart.png"), cv2.IMREAD_GRAYSCALE)
outcart = cv2.imread(str(parpath/"shopcart/cart_tempt/outcart.png"), cv2.IMREAD_GRAYSCALE)
cartboarder = cv2.imread(str(parpath/"shopcart/cart_tempt/cartboarder.png"), cv2.IMREAD_GRAYSCALE)
incartrates = []
temp = np.zeros(incart.shape, np.uint8)

30
tracking/module_analysis.py
View File

@ -98,13 +98,10 @@ def read_imgs(imgspath, CamerType):
flist = file.split('_')
if len(flist)==4 and ext in ImgFormat:
camID, frmID = flist[0], int(flist[-1])
imgpath = os.path.join(imgspath, filename)
img = cv2.imread(imgpath)
if camID==CamerType:
img = cv2.imread(os.path.join(imgspath, filename))
imgs.append(img)
frmIDs.append(frmID)
if len(frmIDs):
indice = np.argsort(np.array(frmIDs))
imgs = [imgs[i] for i in indice]
@ -227,7 +224,7 @@ def do_tracking(fpath, savedir, event_name='images'):
'''4.2 在 imgs 上画框并保存'''
imgs_dw = draw_tracking_boxes(imgs, trackerboxes)
for fid, img in imgs_dw:
img_savepath = os.path.join(save_dir, CamerType + "_fid_" + f"{fid}.png")
img_savepath = os.path.join(save_dir, CamerType + "_fid_" + f"{int(fid)}.png")
cv2.imwrite(img_savepath, img)
'''4.3.2 保存轨迹选择对应的子图'''
@ -238,7 +235,7 @@ def do_tracking(fpath, savedir, event_name='images'):
x1, y1, x2, y2 = int(xyxy[0]/2), int(xyxy[1]/2), int(xyxy[2]/2), int(xyxy[3]/2)
subimg = img[y1:y2, x1:x2]
subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid-1)}_{int(bid)}.png' )
subimg_path = os.path.join(subimg_dir, f'{CamerType}_tid{int(tid)}_{int(fid)}_{int(bid)}.png' )
cv2.imwrite(subimg_path, subimg)
# for track in tracking_output_boxes:
# for *xyxy, tid, conf, cls, fid, bid in track:
@ -270,8 +267,9 @@ def tracking_simulate(eventpath, savepath):
# else:
# return
# =============================================================================
enent_name = os.path.basename(eventpath)[:15]
bname = os.path.basename(eventpath)
idx = bname.find('2024')
enent_name = bname[idx:(idx+15)]
'''2. 依次读取 0/1_track.data 中数据，进行仿真'''
illu_tracking, illu_select = [], []
@ -289,7 +287,9 @@ def tracking_simulate(eventpath, savepath):
if img_tracking is not None:
illu_tracking.append(img_tracking)
'''3. 前、后摄，原始轨迹、本地tracking输出、现场算法轨迹选择前、后，共幅8图'''
'''3. 共幅8图，上下子图显示的是前后摄，每一行4个子图，分别为：
(1) tracker输出原始轨迹; (2)本地tracking输出; (3)现场算法轨迹选择前轨迹; (4)现场算法轨迹选择后的轨迹
'''
if len(illu_select)==2:
Img_s = np.concatenate((illu_select[0], illu_select[1]), axis = 0)
H, W = Img_s.shape[:2]
@ -309,13 +309,13 @@ def tracking_simulate(eventpath, savepath):
Img_t = None
'''3.1 单独另存保存完好的 8 轨迹图'''
basepath, _ = os.path.split(savepath)
trajpath = os.path.join(basepath, 'trajs')
if not os.path.exists(trajpath):
os.makedirs(trajpath)
traj_path = os.path.join(trajpath, enent_name+'.png')
imgpath_tracking = os.path.join(savepath, enent_name + '_ing.png')
imgpath_select = os.path.join(savepath, enent_name + '_slt.png')
imgpath_ts = os.path.join(savepath, enent_name + '_ts.png')
@ -327,8 +327,8 @@ def tracking_simulate(eventpath, savepath):
cv2.imwrite(imgpath_ts, Img_ts)
cv2.imwrite(traj_path, Img_ts)
else:
if Img_s: cv2.imwrite(imgpath_select, Img_s)
if Img_t: cv2.imwrite(imgpath_tracking, Img_t)
if Img_s: cv2.imwrite(imgpath_select, Img_s) # 不会执行到该处
if Img_t: cv2.imwrite(imgpath_tracking, Img_t) # 不会执行到该处
@ -382,11 +382,13 @@ def main():
eventPaths: data文件地址，该 data 文件包括 Pipeline 各模块输出
SavePath: 包含二级目录，一级目录为轨迹图像;二级目录为与data文件对应的序列图像存储地址。
'''
eventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_3'
# eventPaths = r'\\192.168.1.28\share\测试_202406\0723\0723_3'
eventPaths = r"D:\DetectTracking\tracking\images"
savePath = r'D:\contrast\dataset\result'
k=0
for pathname in os.listdir(eventPaths):
pathname = "20240723-163121_6925282237668"
pathname = "20240925-142635-3e3cb61a-8bbe-45f2-aed7-a40de7f2d624_6924743924161"
eventpath = os.path.join(eventPaths, pathname)
savepath = os.path.join(savePath, pathname)

15
tracking/tracking_test.py
View File

@ -80,14 +80,14 @@ def save_subimgs(vts, file, TracksDict):
cv2.imwrite(str(imgdir) + f"/{tid}_{fid}_{bid}.png", img)
def have_tracked():
trackdict = r'./data/trackdicts_20240608'
trackdict = r'./data/trackdicts'
alltracks = []
k = 0
gt = Profile()
for filename in os.listdir(trackdict):
# filename = 'test_20240402-173935_6920152400975_back_174037372.pkl'
filename = '6907149227609_20240508-174733_back_returnGood_70f754088050_425_17327712807.pkl'
filename = '6907149227609_20240508-174733_front_returnGood_70f754088050_425_17327712807.pkl'
# filename = '6907149227609_20240508-174733_back_returnGood_70f754088050_425_17327712807.pkl'
# filename = '6907149227609_20240508-174733_front_returnGood_70f754088050_425_17327712807.pkl'
file, ext = os.path.splitext(filename)
filepath = os.path.join(trackdict, filename)
@ -119,11 +119,14 @@ def have_tracked():
save_subimgs(vts, file, TracksDict)
edgeline = cv2.imread("./shopcart/cart_tempt/edgeline.png")
img_tracking = draw_all_trajectories(vts, edgeline, save_dir, file)
trackpath = save_dir.joinpath(f'{file}.png')
cv2.imwrite(str(trackpath), img_tracking)
print(file+f" need time: {gt.dt:.2f}s")
k += 1
if k==1:
break
# k += 1
# if k==1:
# break
if len(alltracks):
drawFeatures(alltracks, save_dir)

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2
tracking/utils/plotting.py
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@ -333,7 +333,7 @@ def draw_tracking_boxes(imgs, tracks, scale=2):
annotator.box_label(pt2, label, color=color)
img = annotator.result()
subimgs.append((fid-1, img))
subimgs.append((fid, img))
return subimgs

11
tracking/utils/read_data.py
View File

@ -79,18 +79,21 @@ def extract_data(datapath):
feats.append(str_to_float_arr(feat))
if line.find("output_box:") >= 0:
box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
tboxes.append(box) # 去掉'output_box:'并去除可能的空白字符
index = find_samebox_in_array(boxes, box)
assert(len(boxes)>=0 and len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
assert(len(boxes)==len(feats)), f"{datapath}, {datapath}, len(boxes)!=len(feats)"
box = str_to_float_arr(line[line.find("output_box:") + 11:].strip())
index = find_samebox_in_array(boxes, box)
if index >= 0:
tboxes.append(box) # 去掉'output_box:'并去除可能的空白字符
# feat_f = str_to_float_arr(input_feats[index])
feat_f = feats[index]
norm_f = np.linalg.norm(feat_f)
feat_f = feat_f / norm_f
tfeats.append(feat_f)
if len(boxes): bboxes.append(np.array(boxes))
if len(feats): ffeats.append(np.array(feats))
if len(tboxes): trackerboxes = np.concatenate((trackerboxes, np.array(tboxes)))

7
tracking/utils/videot.py
View File

@ -80,13 +80,12 @@ def videosave(bboxes, videopath="100_1688009697927.mp4"):
cap.release()
def main():
videopath = r'\\192.168.1.28\share\测试_202406\0822\A_1724314806144'
videopath = r'D:\videos'
savepath = r'D:\videos'
videopath = r'D:\datasets\ym'
savepath = r'D:\datasets\ym'
# video2imgs(videopath, savepath)
k = 0
for filename in os.listdir(videopath):
# filename = "20240822-163506_88e6409d-f19b-4e97-9f01-b3fde259cbff.ts"
filename = "20240929-155533.ts"
file, ext = os.path.splitext(filename)
if ext not in VideoFormat:

36
tracking/说明文档.txt
View File

@ -1,36 +0,0 @@
tracking_test.py
have_tracked():
轨迹分析测试。遍历track_reid.py输出的文件夹trackdict下的所有.pkl文件。
time_test.py
统计Pipeline整体流程中各模块耗时
module_analysis.py
main():
遍历文件夹下的每一个子文件夹，对子文件夹执行tracking_simulate() 函数；
main_loop()：
(1) 根据 deletedBarcode.txt 生成事件对，并利用事件对生成存储地址
(2) 调用 tracking_simulate() 函数
tracking_simulate(eventpath, savepath)：
(1) 根据event_names获取事件名enent_name
(2) 遍历并执行 eventpath 文件夹下的 0_track.data、1_track.data 文件，并调用do_tracking() 执行
(3) 将前后摄、本地与现场，工8幅子图合并为1幅大图。
do_tracking(fpath, savedir, event_name='images')
enentmatch.py
1:n 模拟测试，have Deprecated!
contrast_analysis.py
1:n 现场测试评估。
main():
循环读取不同文件夹中的 deletedBarcode.txt，合并评估。
main1():
指定deletedBarcode.txt进行1:n性能评估
feat_select.py
以下两种特征选择策略下的比对性能比较
(1) 现场算法前后摄特征组合；
(2) 本地算法优先选择前摄特征；

129
说明文档.txt Normal file
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@ -0,0 +1,129 @@
三个功能模块
1. Yolo + Tracker + Resnet, 其中 Resnet 的实现在./contrast中
track_reid.py
2. 轨迹分析模块，目录为：./tracking
(1) 基于模块（Yolo + Tracker + Resnet）的输出
tracking_test.py
(2) 基于测试过程数据（track.data, tracking_output.data）的输出
module_analysis.py
3. 比对分析模块，目录为：./contrast
2个场景：1:1，1:n
1:1场景：
(1) OneToOneCompare.txt
one2one_onsite.py
(2) 利用本地算法进行特征提取
one2one_contrast.py
1:n场景：
(1) 直接利用 deletedBarcode.txt 中数据
one2n_contrast.py
(2) 构造取出、放入事件，设计不同的特征，
feat_select.py
具体实现：
./tracking
tracking_test.py
have_tracked():
轨迹分析测试。遍历track_reid.py输出的文件夹trackdict下的所有.pkl文件。
time_test.py
统计Pipeline整体流程中各模块耗时
module_analysis.py
该模块中需要借助 try...except... 捕获data文件中的异常
main():
遍历文件夹下的每一个子文件夹，对子文件夹执行tracking_simulate() 函数；
main_loop()：
(1) 根据 deletedBarcode.txt 生成事件对，并利用事件对生成存储地址
(2) 调用 tracking_simulate() 函数
tracking_simulate(eventpath, savepath)：
(1) 根据event_names获取事件名enent_name
(2) 遍历并执行 eventpath 文件夹下的 0_track.data、1_track.data 文件，并调用do_tracking() 执行
(3) 将前后摄、本地与现场，工8幅子图合并为1幅大图。
上下子图分别显示的是前后摄，每一行4个子图，分别为：
(a) tracker输出原始轨迹;
(b) 本地tracking输出;
(c) 现场算法轨迹选择前轨迹;
(d) 现场算法轨迹选择后的轨迹
do_tracking(fpath, savedir, event_name)
inputs：
fpath: 0/1_track.data文件，并核验是否存在 0/1_tracking_output.data，若不存在该文件，直接返回 None, None
savedir: 在该文件夹下会建立3个子文件夹及一个png轨迹图
./savedir/event_name
./savedir/event_name_subimgs
./savedir/trajectory
./savedir/event_name_ts.png
outputs:
img_tracking：本机tracker、tracking 输出的结果比较图
abimg: 部署算法的tracking、轨迹选择分析比较图
./utils/read_data.py
0/1_track.data 文件保存：yolo、Resnet、tracker、tracking模块的输出
函数: extract_data(datapath)
异常排除：
(1) assert len(boxes)==len(feats)，确保每一帧内boxes数和feats数相等
(2) assert(len(bboxes)==len(ffeats)), 确保关于bboxes的帧数和关于ffeats的帧数相等
(3) assert(len(trackerboxes)==len(trackerfeats))，确保tracker输出的boxes可以对应到相应的feats上
这里未对 len(box)!=9、len(feat)!=256, 的情况做出约束
输出：
bboxes
ffeats
trackerboxes
tracker_feat_dict[f"frame_{fid}"]["feats"]{{bid}: (256,)
}
trackingboxes
tracking_feat_dict[f"track_{tid}"]["feats"]{f"{fid}_{bid}": tracker_feat_dict[f"frame_{fid}"]["feats"][bid]})
0/1_tracking_output.data 文件保存用于比对的boxes、features
函数: read_tracking_output(filepath)
异常排除：
(1) assert len(feats)==len(boxes)
(2) box.size==9、feat.size=256
./deprecated/contrast_one2one.py
1:1 比对评估。have Deprecated!
./enentmatch.py
1:n 模拟测试，have Deprecated!
./contrast
feat_similar.py
similarity_compare_sequence(root_dir)
inputs:
root_dir：文件夹，包含"subimgs"字段，对该文件夹中的相邻图像进行相似度比较
silimarity_compare()
功能：对imgpaths文件夹中的图像进行相似度比较
feat_select.py
creatd_deletedBarcode_front(filepath)
(1) 基于 deletedBarcode.txt, 构造取出事件和相应的放入事件，构成列表并更新这些列表。
MatchList = [(getout_event, InputList), ...]
(2) 设计不同的特征选择方式，计算 getout 事件和各 input 事件的相似度，并保存于文件：
deletedBarcodeTest.txt
precision_compare(filepath, savepath)
读取 deletedBarcode.txt 和 deletedBarcodeTest.txt 中的数据，进行相似度比较
one2n_contrast.py
1:n 比对，读取 deletedBarcode.txt，实现现场测试评估。
main():
循环读取不同文件夹中的 deletedBarcode.txt，合并评估。
main1():
指定deletedBarcode.txt进行1:n性能评估