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modify pipeline.py

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王庆刚
2025-01-13 17:35:15 +08:00
parent 3d13b0d9c5
commit a50f777839
12 changed files with 322 additions and 211 deletions
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82
contrast/one2n_contrast.py
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@ -24,10 +24,15 @@ def init_eventdict(sourcePath, stype="data"):
# bname = r"20241126-135911-bdf91cf9-3e9a-426d-94e8-ddf92238e175_6923555210479"
source_path = os.path.join(sourcePath, bname)
if not os.path.isdir(source_path): continue
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if os.path.isfile(pickpath): continue
if stype=="data":
pickpath = os.path.join(eventDataPath, f"{bname}.pickle")
if not os.path.isdir(source_path) or os.path.isfile(pickpath):
continue
if stype=="source":
pickpath = os.path.join(eventDataPath, bname)
if not os.path.isfile(source_path) or os.path.isfile(pickpath):
continue
try:
event = ShoppingEvent(source_path, stype)
@ -86,20 +91,28 @@ def simi_calc(event, o2nevt, typee=None):
feat1 = event.back_feats
feat2 = o2nevt.front_feats
'''自定义事件特征选择'''
if typee==3:
feat1 = event.feats_compose
feat2 = o2nevt.feats_compose
if len(feat1) and len(feat2):
matrix = 1 - cdist(feat1[0], feat2[0], 'cosine')
simi = np.mean(matrix)
else:
simi = None
return simi
def one2n_pr(evtDicts):
def one2n_pr(evtDicts, pattern=1):
'''
pattern:
1: process.data 中记录的相似度
2: 根据 process.data 中标记的 type 选择特征计算
3: 以其它方式选择特征计算
'''
tpevents, fnevents, fpevents, tnevents = [], [], [], []
tpsimi, fnsimi, tnsimi, fpsimi = [], [], [], []
errorFile_one2n = []
@ -111,22 +124,30 @@ def one2n_pr(evtDicts):
barcode = ndict["barcode"]
similar = ndict["similar"]
typee = ndict["type"].strip()
o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
if len(o2n_evt)==1:
o2nevt = o2n_evt[0]
else:
continue
simival = simi_calc(event, o2nevt, typee)
if simival==None:
continue
evt_names.append(nname)
evt_barcodes.append(barcode)
evt_similars.append(simival)
evt_types.append(typee)
if pattern==1:
evt_similars.append(similar)
if pattern==2 or pattern==3:
o2n_evt = [evt for name, evt in evtDicts.items() if name.find(nname[:15])==0]
if len(o2n_evt)==1:
o2nevt = o2n_evt[0]
else:
continue
if pattern==2:
simival = simi_calc(event, o2nevt, typee)
if pattern==3:
simival = simi_calc(event, o2nevt, typee=pattern)
if simival==None:
continue
evt_similars.append(simival)
if len(evt_names)==len(evt_barcodes) and len(evt_barcodes)==len(evt_similars) \
and len(evt_similars)==len(evt_types) and len(evt_names)>0:
@ -205,30 +226,25 @@ def one2n_pr(evtDicts):
def main():
'''1. 生成事件字典并保存至 eventDataPath, 只需运行一次 '''
# init_eventdict(eventSourcePath)
init_eventdict(eventSourcePath, stype="source")
'''2. 读取事件字典 '''
evtDicts = read_eventdict(eventDataPath)
'''3. 1:n 比对事件评估 '''
fpevents = one2n_pr(evtDicts)
fpevents = one2n_pr(evtDicts, pattern=3)
fpErrFile = str(Path(resultPath).joinpath("one2n_fp_Error.txt"))
with open(fpErrFile, "w") as file:
for item in fpevents:
file.write(item + "\n")
if __name__ == '__main__':
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\images"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result"
eventSourcePath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\ShoppingDict_pkfile"
resultPath = r"\\192.168.1.28\share\测试视频数据以及日志\算法全流程测试\202412\result\contrast"
eventDataPath = os.path.join(resultPath, "evtobjs")
similPath = os.path.join(resultPath, "simidata")