def doClassifyFile(self, tpath, fname):
rstParms = np.array([])
tpath = "%s/%s" % (tpath, fname)
if os.path.exists(tpath):
tdata1 = np.load(tpath)
timgs32 = tdata1['imgs']
parms = tdata1['parms']
#fs2n = 1.087/props[:,12].astype(np.float)
if timgs32.shape[0] > 0:
timgs = getImgStamp(timgs32,
size=self.imgSize,
padding=1,
transMethod='none')
preY = self.model.predict(timgs, batch_size=128)
timgs2 = getImgStamp(timgs32,
size=self.imgSize2,
padding=1,
transMethod='none')
preY2 = self.model2.predict(timgs2, batch_size=128)
predProbs = preY[:, 1]
predProbs2 = preY2[:, 1]
predProbsJoin = ~((predProbs > self.pbb_threshold) &
(predProbs2 > self.pbb_threshold))
predProbs[predProbsJoin] = 0
predProbs = predProbs.reshape([predProbs.shape[0], 1])
rstParms = np.concatenate((parms, predProbs), axis=1)
return rstParms
def doClassifyFile(self, tpath, fname):
rstParms = np.array([])
tpath0 = "%s/%s" % (tpath, fname)
if len(fname) > 0 and os.path.exists(tpath0):
tdata1 = np.load(tpath0)
timgs32 = tdata1['imgs']
parms = tdata1['parms']
#fs2n = 1.087/props[:,12].astype(np.float)
if timgs32.shape[0] > 0:
timgs = getImgStamp(timgs32,
size=self.imgSize,
padding=1,
transMethod='none')
preY = self.model.predict(timgs, batch_size=128)
predProbs = preY[:, 1]
predProbs = predProbs.reshape([predProbs.shape[0], 1])
rstParms = np.concatenate((parms, predProbs), axis=1)
self.log.info("total %d subImgs, %d is valid and classified" %
(timgs32.shape[0], timgs.shape[0]))
return rstParms
def realDataTest():
imgSize = 8
pbb_threshold = 0.5
workPath = "/home/xy/Downloads/myresource/deep_data2/simot/train_20190102"
model = load_model("%s/model_128_5_RealFOT_8.h5"%(workPath))
#dataPath = '/home/xy/Downloads/myresource/deep_data2/simot/G181208_C03490'
dataPath = '/home/xy/Downloads/myresource/deep_data2/simot/data_190107/subImg'
flist = os.listdir(dataPath)
flist.sort()
for tfilename in flist:
#G034_mon_objt_181208T10463819
#G044_mon_objt_181208T10391696
#G024_mon_objt_181206T12154991_otimg
if tfilename.find('G044_mon_objt_180416T12263444_totimg')==-1:
continue
print(tfilename)
tpath = "%s/%s"%(dataPath, tfilename)
tdata1 = np.load(tpath)
#timg32s = tdata1['fot']
timg32s = tdata1['imgs']
props = tdata1['parms']
#fs2n = 1.087/props[:,12].astype(np.float)
timgs = getImgStamp(timg32s, size=imgSize, padding = 0, transMethod='none')
print(timgs.shape)
preY = model.predict(timgs, batch_size=128)
trueIdx = preY[:, 1] > pbb_threshold
falseIdx = preY[:, 1] <= pbb_threshold
trueNum = np.array(trueIdx, dtype = "int").sum()
falseNum = np.array(falseIdx, dtype = "int").sum()
print("total %d, true %d, false %d"%(timgs.shape[0], trueNum, falseNum))
trueImgs = timg32s[trueIdx]
trueProps = props[trueIdx]
truePreYs = preY[trueIdx]
falseImgs = timg32s[falseIdx]
falseProps = props[falseIdx]
falsePreYs = preY[falseIdx]
print("\n\n***********************")
print("%d images classified as False"%(trueNum))
showImage(trueImgs, trueProps, truePreYs, 'OT', showNum=1000)
print("\n\n***********************")
print("%d images classified as False"%(falseNum))
showImage(falseImgs, falseProps, falsePreYs, 'FOT', showNum=100)
def doClassifyFile(self, tpath, fname):
try:
#print("start doClassifyFile")
rstParms = np.array([])
obsUtc = ''
tpath0 = "%s/%s" % (tpath, fname)
#print(tpath0)
if len(fname) > 0 and os.path.exists(tpath0):
#print("start doClassifyFile 1")
tdata1 = np.load(tpath0)
timgs32 = tdata1['imgs']
parms = tdata1['parms']
obsUtc = tdata1['obsUtc']
#print(timgs32.shape)
#fs2n = 1.087/props[:,12].astype(np.float)
if timgs32.shape[0] > 0:
timgs = getImgStamp(timgs32,
size=self.imgSize,
padding=1,
transMethod='none')
#print(timgs.shape)
preY = self.model.predict(timgs, batch_size=128)
#model = load_model(self.modelPath,custom_objects={'concatenate':keras.layers.concatenate})
#preY = model.predict(timgs, batch_size=128)
#print(preY.shape)
predProbs = preY[:, 1]
predProbs = predProbs.reshape([predProbs.shape[0], 1])
rstParms = np.concatenate((parms, predProbs), axis=1)
tstr = "total %d subImgs, %d is valid and classified" % (
timgs32.shape[0], timgs.shape[0])
self.log.info(tstr)
print(tstr)
#print("end doClassifyFile")
except Exception as e:
tstr = traceback.format_exc()
print("doClassifyFile")
print(tstr)
self.log.error(tstr)
return rstParms, obsUtc
