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update one2n.py

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This commit is contained in:
王庆刚
2025-02-24 18:56:54 +08:00
parent 64248b1557
commit b657be729b
22 changed files with 279 additions and 123 deletions
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__pycache__/track_reid.cpython-39.pyc
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contrast/__pycache__/genfeats.cpython-39.pyc
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contrast/feat_extract/__pycache__/inference.cpython-39.pyc
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contrast/feat_extract/inference.py
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@ -72,10 +72,11 @@ class FeatsInterface:
new_img.paste(img, (paste_x, paste_y))
patch = self.transform(new_img)
if str(self.device) != "cpu":
patch = patch.to(device=self.device).half()
else:
patch = patch.to(device=self.device)
patch = patch.to(device=self.device)
# if str(self.device) != "cpu":
# patch = patch.to(device=self.device).half()
# else:
# patch = patch.to(device=self.device)
patches.append(patch)
if (i + 1) % self.batch_size == 0:

27
contrast/genfeats.py
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@ -120,8 +120,7 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
# imgPath = r"\\192.168.1.28\share\测试_202406\contrast\std_barcodes"
# featPath = r"\\192.168.1.28\share\测试_202406\contrast\std_features"
stdBarcodeDict = {}
stdBarcodeDict_ft16 = {}
Encoder = FeatsInterface(conf)
@ -168,22 +167,20 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
# feature_uint8, _ = ft16_to_uint8(feature_ft16)
feature_uint8 = (feature_ft16*128).astype(np.int8)
'''================ 保存单个barcode特征 ================'''
##================== float32
stdbDict["barcode"] = barcode
stdbDict["imgpaths"] = imgpaths
stdbDict["feats_ft32"] = feature_ft32
stdbDict["feats_ft16"] = feature_ft16
stdbDict["feats_uint8"] = feature_uint8
with open(featpath, 'wb') as f:
pickle.dump(stdbDict, f)
except Exception as e:
print(f"Error accured at: {filename}, with Exception is: {e}")
'''================ 保存单个barcode特征 ================'''
##================== float32
stdbDict["barcode"] = barcode
stdbDict["imgpaths"] = imgpaths
stdbDict["feats_ft32"] = feature_ft32
stdbDict["feats_ft16"] = feature_ft16
stdbDict["feats_uint8"] = feature_uint8
with open(featpath, 'wb') as f:
pickle.dump(stdbDict, f)
stdBarcodeDict[barcode] = feature
stdBarcodeDict_ft16[barcode] = feature_ft16
t2 = time.time()
print(f"Barcode: {barcode}, need time: {t2-t1:.1f} secs")

33
contrast/one2n_contrast.py
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@ -24,7 +24,7 @@ def init_eventdict(sourcePath, stype="data"):
# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
source_path = os.path.join(sourcePath, bname)
if stype=="data":
if stype=="data" or stype=="realtime":
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if not os.path.isdir(source_path) or os.path.isfile(pickpath):
continue
@ -33,6 +33,11 @@ def init_eventdict(sourcePath, stype="data"):
if not os.path.isfile(source_path) or os.path.isfile(pickpath):
continue
evt = os.path.splitext(os.path.split(pickpath)[-1])[0].split('_')
cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
if not cont:
continue
try:
event = ShoppingEvent(source_path, stype)
@ -46,10 +51,10 @@ def init_eventdict(sourcePath, stype="data"):
# if k==1:
# break
errfile = os.path.join(resultPath, 'error_events.txt')
with open(errfile, 'a', encoding='utf-8') as f:
for line in errEvents:
f.write(line + '\n')
# errfile = os.path.join(resultPath, 'error_events.txt')
# with open(errfile, 'a', encoding='utf-8') as f:
# for line in errEvents:
# f.write(line + '\n')
def read_eventdict(eventDataPath):
evtDict = {}
@ -236,14 +241,22 @@ def one2n_pr(evtDicts, pattern=1):
def main():
'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
init_eventdict(eventSourcePath, stype="data")
init_eventdict(eventSourcePath, stype="source") # 'source', 'data', 'realtime'
# for pfile in os.listdir(eventDataPath):
# evt = os.path.splitext(pfile)[0].split('_')
# cont = len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10
# if not cont:
# continue
'''2. 读取事件字典 '''
evtDicts = read_eventdict(eventDataPath)
'''3. 1:n 比对事件评估 '''
fpevents = one2n_pr(evtDicts, pattern=1)
fpevents = one2n_pr(evtDicts, pattern=2)
fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
with open(fpErrFile, "w") as file:
@ -253,10 +266,10 @@ def main():
if __name__ == '__main__':
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing"
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12\ShoppingDict_pkfile"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
eventDataPath = os.path.join(resultPath, "evtobjs")
eventDataPath = os.path.join(resultPath, "evtobjs_data")
if not os.path.exists(eventDataPath):
os.makedirs(eventDataPath)

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contrast/one2one_contrast.py
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@ -419,7 +419,6 @@ def one2one_simi(evtList, evtDict, stdDict):
'''================ float32、16、int8 精度比较与存储 ============='''
# data_precision_compare(stdfeat, evtfeat, mergePairs[i], save=True)
return rltdata
@ -520,12 +519,12 @@ def gen_eventdict(sourcePath, saveimg=True):
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if os.path.isfile(pickpath): continue
# event = ShoppingEvent(source_path, stype="data")
# event = ShoppingEvent(source_path, stype=source_type)
# with open(pickpath, 'wb') as f:
# pickle.dump(event, f)
try:
event = ShoppingEvent(source_path, stype="source")
event = ShoppingEvent(source_path, stype=source_type)
# save_data(event, resultPath)
with open(pickpath, 'wb') as f:
@ -541,38 +540,35 @@ def gen_eventdict(sourcePath, saveimg=True):
errfile = os.path.join(resultPath, 'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f:
for line in errEvents:
f.write(line + '\n')
# with open(errfile, 'w', encoding='utf-8') as f:
# for line in errEvents:
# f.write(line + '\n')
def init_std_evt_dict():
'''==== 0. 生成事件列表和对应的 Barcodes列表 ==========='''
bcdList, event_spath = [], []
for evtpath in eventSourcePath:
for evtname in os.listdir(evtpath):
bname, ext = os.path.splitext(evtname)
for evtname in os.listdir(eventSourcePath):
bname, ext = os.path.splitext(evtname)
## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
fpath = os.path.join(evtpath, evtname)
if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
evt = bname.split('_')
elif os.path.isdir(fpath):
evt = evtname.split('_')
else:
continue
## 处理事件的两种情况：文件夹 和 Yolo-Resnet-Tracker 的输出
fpath = os.path.join(eventSourcePath, evtname)
if os.path.isfile(fpath) and (ext==".pkl" or ext==".pickle"):
evt = bname.split('_')
elif os.path.isdir(fpath):
evt = evtname.split('_')
else:
continue
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
bcdList.append(evt[-1])
event_spath.append(os.path.join(evtpath, evtname))
if len(evt)>=2 and evt[-1].isdigit() and len(evt[-1])>=10:
bcdList.append(evt[-1])
event_spath.append(fpath)
'''==== 1. 生成标准特征集, 只需运行一次, 在 genfeats.py 中实现 ==========='''
bcdSet = set(bcdList)
gen_bcd_features(stdSamplePath, stdBarcodePath, stdFeaturePath, bcdSet)
print("stdFeats have generated and saved!")
'''==== 2. 生成事件字典, 只需运行一次 ==============='''
gen_eventdict(event_spath)
print("eventList have generated and saved!")
@ -584,7 +580,7 @@ def test_one2one():
'''1:1性能评估'''
# 1. 只需运行一次，生成事件字典和相应的标准特征库字典
init_std_evt_dict()
# init_std_evt_dict()
# 2. 基于事件barcode集和标准库barcode交集构造事件集合
evtList, evtDict, stdDict = build_std_evt_dict()
@ -598,7 +594,7 @@ def test_one2SN():
'''1:SN性能评估'''
# 1. 只需运行一次，生成事件字典和相应的标准特征库字典
init_std_evt_dict()
# init_std_evt_dict()
# 2. 事件barcode集和标准库barcode求交集
evtList, evtDict, stdDict = build_std_evt_dict()
@ -612,7 +608,7 @@ if __name__ == '__main__':
(1) stdSamplePath: 用于生成比对标准特征集的原始图像地址
(2) stdBarcodePath: 比对标准特征集原始图像地址的pickle文件存储，{barcode: [imgpath1, imgpath1, ...]}
(3) stdFeaturePath: 比对标准特征集特征存储地址
(4) eventSourcePath: 事件地址
(4) eventSourcePath: 事件地址, 包含data文件的文件夹或 Yolo-Resnet-Tracker输出的Pickle文件父文件夹
(5) resultPath: 结果存储地址
(6) eventDataPath: 用于1:1比对的购物事件存储地址，在resultPath下
(7) similPath: 1:1比对结果存储地址(事件级)，在resultPath下
@ -622,19 +618,33 @@ if __name__ == '__main__':
# stdBarcodePath = r"D:\exhibition\dataset\bcdpath"
# stdFeaturePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\features_json\v11_barcode_11592"
# eventSourcePath = [r'D:\exhibition\images\20241202']
# eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"]
# eventSourcePath = r'D:\exhibition\images\20241202'
# eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\各模块测试记录\展厅测试\1129_展厅模型v801测试组测试"
# stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
# stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\bcdpath"
# stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对测试数据20250121_testing\stdfeats"
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\展厅数据\v2.0_abroad\比对数据\all_base_二筛"
stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\bcdpath"
stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\stdfeats"
stdSamplePath = r"\\192.168.1.28\share\数据\已完成数据\比对数据\barcode\all_totalBarocde\totalBarcode"
stdBarcodePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\bcdpath"
stdFeaturePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing\stdfeats"
eventSourcePath = [r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\比对数据"]
if not os.path.exists(stdBarcodePath):
os.makedirs(stdBarcodePath)
if not os.path.exists(stdFeaturePath):
os.makedirs(stdFeaturePath)
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\海外展厅测试数据\testing\evtobjs"
eventDataPath = os.path.join(resultPath, "evtobjs")
similPath = os.path.join(resultPath, "simidata")
'''
source_type:
"source": eventSourcePath 为 Yolo-Resnet-Tracker 输出的 pickle 文件
"data": eventSourcePath 为 包含 data 文件的文件夹
'''
source_type = 'realtime' # 'source', 'data', 'realtime'
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\testing"
eventDataPath = os.path.join(resultPath, "evtobjs_data")
similPath = os.path.join(resultPath, "simidata_data")
if not os.path.exists(eventDataPath):
os.makedirs(eventDataPath)
if not os.path.exists(similPath):

34
contrast/onsite_contrast_pr.py
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@ -1,8 +1,13 @@
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 11 11:57:30 2024
永辉现场试验输出数据的 1:1 性能评估
适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
contrast_pr:
直接利用测试数据中的 data 文件进行 1:1、1:SN、1:n 性能评估
test_compare:
永辉现场试验输出数据的 1:1 性能评估
适用于202410前数据保存版本的，需调用 OneToOneCompare.txt
@author: ym
"""
import os
@ -147,6 +152,7 @@ def contrast_pr(paths):
errorFile_one2one, errorFile_one2SN, errorFile_one2n = [], [], []
errorFile = []
for path in evtpaths:
barcode = path.stem.split('_')[-1]
datapath = path.joinpath('process.data')
@ -167,6 +173,10 @@ def contrast_pr(paths):
one2SN = SimiDict['one2SN']
one2n = SimiDict['one2n']
if len(one2one)+len(one2SN)+len(one2n) == 0:
errorFile.append(path.stem)
'''================== 0. 1:1 ==================='''
barcodes, similars = [], []
for dt in one2one:
@ -176,6 +186,8 @@ def contrast_pr(paths):
continue
barcodes.append(dt['barcode'])
similars.append(dt['similar'])
if len(barcodes)==len(similars) and len(barcodes)!=0:
## 扫A放A, 扫A放B场景
simAA = [similars[i] for i in range(len(barcodes)) if barcodes[i]==barcode]
@ -466,15 +478,15 @@ def contrast_pr(paths):
plt.show()
fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
with open(fpsnErrFile, "w") as file:
for item in fp_events:
file.write(item + "\n")
# fpsnErrFile = str(paths.joinpath("one2SN_Error.txt"))
# with open(fpsnErrFile, "w") as file:
# for item in fp_events:
# file.write(item + "\n")
fpErrFile = str(paths.joinpath("one2n_Error.txt"))
with open(fpErrFile, "w") as file:
for item in fpevents:
file.write(item + "\n")
# fpErrFile = str(paths.joinpath("one2n_Error.txt"))
# with open(fpErrFile, "w") as file:
# for item in fpevents:
# file.write(item + "\n")
@ -495,7 +507,7 @@ def contrast_pr(paths):
if __name__ == "__main__":
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
evtpaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
contrast_pr(evtpaths)

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contrast/utils/event.py
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@ -17,6 +17,12 @@ from tracking.utils.drawtracks import drawTrack
from tracking.utils.read_data import extract_data, read_tracking_output, read_similar
from tracking.utils.read_data import extract_data_realtime, read_tracking_output_realtime
# import platform
# import pathlib
# plt = platform.system()
IMG_FORMAT = ['.bmp', '.jpg', '.jpeg', '.png']
VID_FORMAT = ['.mp4', '.avi']
@ -167,6 +173,8 @@ class ShoppingEvent:
def from_source_pkl(self, eventpath):
# if plt == 'Windows':
# pathlib.PosixPath = pathlib.WindowsPath
with open(eventpath, 'rb') as f:
ShoppingDict = pickle.load(f)
@ -202,10 +210,10 @@ class ShoppingEvent:
self.front_trackingfeats = frontdata[5]
'''===========对应于 0/1_tracking_output.data ============================='''
self.back_boxes = back_outdata
self.back_feats = back_outdata
self.front_boxes = front_outdata
self.front_feats = front_outdata
self.back_boxes = back_outdata[0]
self.back_feats = back_outdata[1]
self.front_boxes = front_outdata[0]
self.front_feats = front_outdata[1]
def from_datafile(self, eventpath):
@ -296,13 +304,13 @@ class ShoppingEvent:
self.front_feats = tracking_output_feats
def from_realtime_datafile(self, eventpath):
# evtList = self.evtname.split('_')
# if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
# self.barcode = evtList[-1]
# if len(evtList)==3 and evtList[-1]== evtList[-2]:
# self.evtType = 'input'
# else:
# self.evtType = 'other'
evtList = self.evtname.split('_')
if len(evtList)>=2 and len(evtList[-1])>=10 and evtList[-1].isdigit():
self.barcode = evtList[-1]
if len(evtList)==3 and evtList[-1]== evtList[-2]:
self.evtType = 'input'
else:
self.evtType = 'other'
'''================ path of video ============='''
for vidname in os.listdir(eventpath):
@ -330,7 +338,7 @@ class ShoppingEvent:
if not os.path.isfile(datapath): continue
CamerType = dataname.split('_')[0]
'''========== 0/1_track.data =========='''
if dataname.find("_track.data")>0:
if dataname.find("_tracker.data")>0:
trackerboxes, trackerfeats = extract_data_realtime(datapath)
if CamerType == '0':
self.back_trackerboxes = trackerboxes

15
pipeline.py
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@ -136,7 +136,7 @@ def pipeline(
bname = os.path.basename(vpath[0])
if not isinstance(vpath, list):
CameraEvent["videoPath"] = vpath
bname = os.path.basename(vpath)
bname = os.path.basename(vpath).split('.')[0]
if bname.split('_')[0] == "0" or bname.find('back')>=0:
CameraEvent["cameraType"] = "back"
if bname.split('_')[0] == "1" or bname.find('front')>=0:
@ -265,18 +265,17 @@ def main():
'''
函数：pipeline()，遍历事件文件夹，选择类型 image 或 video,
'''
parmDict = {}
evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
evtdir = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\比对\video"
parmDict["SourceType"] = "video" # video, image
parmDict["savepath"] = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
parmDict["savepath"] = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\result_V12"
parmDict["weights"] = r'D:\DetectTracking\ckpts\best_cls10_0906.pt'
evtdir = Path(evtdir)
k, errEvents = 0, []
for item in evtdir.iterdir():
if item.is_dir():
# item = evtdir/Path("20241209-160201-b97f7a0e-7322-4375-9f17-c475500097e9_6926265317292")
item = evtdir/Path("20250221-160936-893_6942506204855_6942506204855")
parmDict["eventpath"] = item
# pipeline(**parmDict)
@ -284,9 +283,9 @@ def main():
pipeline(**parmDict)
except Exception as e:
errEvents.append(str(item))
# k+=1
# if k==100:
# break
k+=1
if k==1:
break
errfile = os.path.join(parmDict["savepath"], f'error_events.txt')
with open(errfile, 'w', encoding='utf-8') as f:

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realtime/__pycache__/event_time_specify.cpython-39.pyc Normal file
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realtime/draw_traj.py Normal file
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@ -0,0 +1,86 @@
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 21 14:28:59 2025
@author: ym
"""
import os
import numpy as np
from pathlib import Path
import sys
sys.path.append(r"D:\DetectTracking")
from contrast.utils.event import ShoppingEvent
from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
from tracking.utils.read_data import read_process
def read_tracker_data(filepath):
pass
def read_tracking_output_data(filepath):
pass
def read_process_data(filepath):
path
def main():
evtPaths = r"\\192.168.1.28\share\测试视频数据以及日志\全实时测试\V12\2025-2-21\persist"
evtPaths = Path(evtPaths)
for evtpath in evtPaths.iterdir():
## 1. 读取重力数据
if evtpath.name.find("Weight")>=0 and evtpath.name.find(".txt")>0:
weight_data = read_weight_sensor(evtpath)
if not evtpath.is_dir():
continue
## 2. 读取事件data数据
for fpath in evtpath.iterdir():
fname = fpath.name
if fname.find("tracker.data"):
pass
if fname.find("tracking_output.data"):
pass
if fname.find("process.data") >=0:
pass
fpath = str(fpath)
pass
if __name__ == "__main__":
main()

3
realtime/event_time_specify.py
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@ -9,11 +9,10 @@ import numpy as np
# from matplotlib.pylab import mpl
# mpl.use('Qt5Agg')
import matplotlib.pyplot as plt
from move_detect import MoveDetect
import sys
sys.path.append(r"D:\DetectTracking")
from move_detect import MoveDetect
# from tracking.utils.read_data import extract_data, read_deletedBarcode_file, read_tracking_output, read_weight_timeConsuming
from tracking.utils.read_data import read_weight_timeConsuming

3
realtime/full_realtime.py
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@ -9,9 +9,10 @@ import sys
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from contrast.utils.event import ShoppingEvent
sys.path.append(r"D:\DetectTracking")
from contrast.utils.event import ShoppingEvent
from tracking.utils.read_data import read_weight_sensor, extract_data_realtime, read_tracking_output_realtime
from tracking.utils.read_data import read_process

46
realtime/time_devide.py
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@ -13,10 +13,14 @@ from pathlib import Path
import glob
import numpy as np
import copy
import matplotlib.pyplot as plt
from collections import OrderedDict
from event_time_specify import devide_motion_state #, state_measure
import sys
sys.path.append(r"D:\DetectTracking")
from imgs_inference import run_yolo
from event_time_specify import devide_motion_state#, state_measure
from tracking.utils.read_data import read_weight_sensor
# IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
@ -400,8 +404,8 @@ def splitevent(imgpath, MotionSlice):
def runyolo():
eventdirs = r"\\192.168.1.28\share\realtime\eventdata"
savedir = r"\\192.168.1.28\share\realtime\result"
eventdirs = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata"
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\result"
k = 0
for edir in os.listdir(eventdirs):
@ -419,12 +423,40 @@ def run_tracking(trackboxes, MotionSlice):
pass
def read_wsensor(filepath):
WeightDict = OrderedDict()
with open(filepath, 'r', encoding='utf-8') as f:
lines = f.readlines()
clean_lines = [line.strip().replace("'", '').replace('"', '') for line in lines]
for i, line in enumerate(clean_lines):
line = line.strip()
line = line.strip()
if line.find(':') < 0: continue
# if line.find("Weight") >= 0:
# label = "Weight"
# continue
keyword = line.split(':')[0]
value = line.split(':')[1]
# if label == "Weight":
if len(keyword) and len(value):
vdata = [float(s) for s in value.split(',') if len(s)]
WeightDict[keyword] = vdata[-1]
weights = [(float(t), w) for t, w in WeightDict.items()]
weights = np.array(weights).astype(np.int64)
return weights
def show_seri():
datapath = r"\\192.168.1.28\share\个人文件\wqg\realtime\eventdata\1731316835560"
savedir = r"D:\DetectTracking\realtime\1"
savedir = r"\\192.168.1.28\share\个人文件\wqg\realtime\1"
imgdir = datapath.split('\\')[-2] + "_" + datapath.split('\\')[-1]
@ -450,7 +482,7 @@ def show_seri():
'''===============读取重力信号数据==================='''
seneorfile = os.path.join(datapath, 'sensor.txt')
weights = read_weight_sensor(seneorfile)
weights = read_wsensor(seneorfile)
# weights = [(float(t), w) for t, w in WeightDict.items()]
# weights = np.array(weights)
@ -471,10 +503,8 @@ def show_seri():
def main():
# runyolo()
show_seri()
if __name__ == '__main__':
main()

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tracking/dotrack/__pycache__/dotracks_front.cpython-39.pyc
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4
tracking/dotrack/dotracks_back.py
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@ -153,8 +153,8 @@ class doBackTracks(doTracks):
hand_ious = []
hboxes = np.empty(shape=(0, 9), dtype = np.float)
gboxes = np.empty(shape=(0, 9), dtype = np.float)
hboxes = np.empty(shape=(0, 9), dtype = np.float64)
gboxes = np.empty(shape=(0, 9), dtype = np.float64)
# start, end 为索引值，需要 start:(end+1)

4
tracking/dotrack/dotracks_front.py
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@ -113,8 +113,8 @@ class doFrontTracks(doTracks):
'''
assert htrack.cls==0 and gtrack.cls!=0 and gtrack.cls!=9, 'Track cls is Error!'
hboxes = np.empty(shape=(0, 9), dtype = np.float)
gboxes = np.empty(shape=(0, 9), dtype = np.float)
hboxes = np.empty(shape=(0, 9), dtype = np.float64)
gboxes = np.empty(shape=(0, 9), dtype = np.float64)
# start, end 为索引值，需要 start:(end+1)
for start, end in htrack.dynamic_y2:

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tracking/utils/__pycache__/read_data.cpython-39.pyc
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8
tracking/utils/read_data.py
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@ -321,6 +321,8 @@ def read_process(filePath):
def read_similar(filePath):
'''1:n时 Dict['type']字段提取和非全实时不一致，无 "=" 字符 '''
SimiDict = {}
SimiDict['one2one'] = []
SimiDict['one2SN'] = []
@ -386,7 +388,7 @@ def read_similar(filePath):
Dict['event'] = label
Dict['barcode'] = bcd
Dict['similar'] = float(value.split(',')[0])
Dict['type'] = value.split('=')[-1]
Dict['type'] = value.split(',')[1]
one2n_list.append(Dict)
if len(one2one_list): SimiDict['one2one'] = one2one_list
@ -403,8 +405,6 @@ def read_weight_sensor(filepath):
for i, line in enumerate(clean_lines):
line = line.strip()
line = line.strip()
if line.find(':') < 0: continue
if line.find("Weight") >= 0:
label = "Weight"
@ -415,7 +415,7 @@ def read_weight_sensor(filepath):
value = line.split(':')[1]
if label == "Weight":
vdata = [float(s) for s in value.split(',') if len(s)]
vdata = [float(s) for s in value.split(',') if len(s) and s.isdigit()]
WeightDict[keyword] = vdata[-1]