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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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contrast/__pycache__/genfeats.cpython-39.pyc
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24
contrast/event_test.py
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@ -171,16 +171,6 @@ def calc_simil(event, stdfeat):
return Similar
@ -345,20 +335,6 @@ def main():
simi_matrix()
if __name__ == "__main__":

4
contrast/genfeats.py
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@ -136,8 +136,8 @@ def stdfeat_infer(imgPath, featPath, bcdSet=None):
continue
featpath = os.path.join(featPath, f"{bcd}.pickle")
# if os.path.isfile(featpath):
# continue
if os.path.isfile(featpath):
continue
stdbDict = {}
t1 = time.time()

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")

13
contrast/one2one_contrast.py
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@ -194,6 +194,7 @@ def simi_calc(event, stdfeat):
if len(evtfeat)==0 or len(stdfeat)==0:
return None, None, None
evtfeat /= np.linalg.norm(evtfeat, axis=1)[:, None]
stdfeat /= np.linalg.norm(stdfeat, axis=1)[:, None]
@ -305,8 +306,8 @@ def one2SN_pr(evtList, evtDict, stdDict):
barcodes, similars = [], []
for stdbcd in bcd_selected:
stdfeat = stdDict[stdbcd]
# simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
simi_mean = calc_simil(event, stdfeat)
simi_mean, simi_max, simi_mfeat = simi_calc(event, stdfeat)
# simi_mean = calc_simil(event, stdfeat)
## 在event.front_feats和event.back_feats同时为空时，此处不需要保护
# if simi_mean==None:
@ -629,10 +630,8 @@ if __name__ == '__main__':
stdBarcodePath = r"D:\全实时\source_data\bcdpath"
stdFeaturePath = r"D:\全实时\source_data\stdfeats"
eventSourcePath = [r"D:\全实时\result\pipeline\pipeline"]
resultPath = r"D:\全实时\result\pipeline"
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")
if not os.path.exists(eventDataPath):
@ -640,7 +639,7 @@ if __name__ == '__main__':
if not os.path.exists(similPath):
os.makedirs(similPath)
test_one2one()
# test_one2one()
test_one2SN()

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contrast/utils/__pycache__/event.cpython-39.pyc
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12
contrast/utils/event.py
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@ -76,7 +76,7 @@ def array2list(bboxes):
class ShoppingEvent:
def __init__(self, eventpath, stype="data"):
'''stype: str, 'source', 'data', '''
'''stype: str, 'source', 'data', 'realtime', 共三种 '''
self.eventpath = eventpath
self.evtname = str(Path(eventpath).stem)
@ -167,20 +167,22 @@ class ShoppingEvent:
def from_source_pkl(self, eventpath):
with open(eventpath, 'rb') as f:
ShoppingDict = pickle.load(f)
self.eventpath = ShoppingDict["eventPath"]
self.evtname = ShoppingDict["eventName"]
self.barcode = ShoppingDict["barcode"]
if len(ShoppingDict["one2n"]):
self.one2n = ShoppingDict["one2n"]
'''=========== path of image and video =========== '''
self.back_videopath = ShoppingDict["backCamera"]["videoPath"]
self.front_videopath = ShoppingDict["frontCamera"]["videoPath"]
self.back_imgpaths = ShoppingDict["backCamera"]["imagePaths"]
self.front_imgpaths = ShoppingDict["frontCamera"]["imagePaths"]
'''===========对应于 0/1_track.data ============================='''
backdata, back_outdata = self.kerndata(ShoppingDict, "backCamera")