def prepareDataThread(self, dataQueue, numpyImages, numpyGT):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
keysIMG = numpyImages.keys()
nr_iter_dataAug = nr_iter * batchsize
np.random.seed()
whichDataList = np.random.randint(
len(keysIMG),
size=int(nr_iter_dataAug / self.params['ModelParams']['nProc']))
whichDataForMatchingList = np.random.randint(
len(keysIMG),
size=int(nr_iter_dataAug / self.params['ModelParams']['nProc']))
for whichData, whichDataForMatching in zip(whichDataList,
whichDataForMatchingList):
filename, ext = splitext(keysIMG[whichData])
currGtKey = filename + '_segmentation' + ext
currImgKey = filename + ext
# data agugumentation through hist matching across different examples...
ImgKeyMatching = keysIMG[whichDataForMatching]
defImg = numpyImages[currImgKey]
defLab = numpyGT[currGtKey]
# print "is image binary:"
# print ((defLab.astype(int)==0) | (defLab.astype(int)==1)).all()
defImg = utilities.hist_match(defImg, numpyImages[ImgKeyMatching])
if (np.random.rand(1)[0] >
0.5): #do not apply deformations always, just sometimes
defImg, defLab = utilities.produceRandomlyDeformedImage(
defImg, defLab,
self.params['ModelParams']['numcontrolpoints'],
self.params['ModelParams']['sigma'])
weightData = np.zeros_like(defLab, dtype=float)
weightData[defLab == 1] = np.prod(defLab.shape) / np.sum(
(defLab == 1).astype(dtype=np.float32))
weightData[defLab == 0] = np.prod(defLab.shape) / np.sum(
(defLab == 0).astype(dtype=np.float32))
dataQueue.put(tuple((defImg, defLab, weightData)))
def prepareDataThread(self, dataQueue, numpyImages, numpyGT):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
keysIMG = numpyImages.keys()
nr_iter_dataAug = nr_iter*batchsize
np.random.seed()
whichDataList = np.random.randint(1,int(self.params['ModelParams']['TrainSize'])+1, size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
whichDataForMatchingList = np.random.randint(1,int(self.params['ModelParams']['TrainSize'])+1, size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
for p,whichDataForMatching in zip(whichDataList,whichDataForMatchingList):
currGtKey = str(p) + '_segmentation' + '.nii'
currImgKey1 = str(p) +'_'+ self.params['ModelParams']['mod1'] + self.params['ModelParams']['format']
currImgKey2 = str(p) +'_'+ self.params['ModelParams']['mod2'] + self.params['ModelParams']['format']
currImgKey3 = str(p) +'_'+ self.params['ModelParams']['mod3'] + self.params['ModelParams']['format']
currImgKey4 = str(p) +'_'+ self.params['ModelParams']['mod4'] + self.params['ModelParams']['format']
defImg1= numpyImages[currImgKey1]
defImg2= numpyImages[currImgKey2]
defImg3= numpyImages[currImgKey3]
defImg4= numpyImages[currImgKey4]
defLab = numpyGT[currGtKey]
ImgKeyMatching = keysIMG[whichDataForMatching]
defImg1 = utilities.hist_match(defImg1, numpyImages[ImgKeyMatching])
defImg2 = utilities.hist_match(defImg2, numpyImages[ImgKeyMatching])
defImg3 = utilities.hist_match(defImg3, numpyImages[ImgKeyMatching])
defImg4 = utilities.hist_match(defImg4, numpyImages[ImgKeyMatching])
if(np.random.rand(1)[0]>0.5):
defImg1,defImg2,defImg3,defImg4,defLab = utilities.produceRandomlyDeformedImage(defImg1,defImg2,defImg3,defImg4,defLab,self.params['ModelParams']['numcontrolpoints'],self.params['ModelParams']['sigma'])
weightData = np.zeros_like(defLab,dtype=float)
weightData[defLab == 1] = np.prod(defLab.shape) / np.sum((defLab==1).astype(dtype=np.float32))
weightData[defLab == 0] = np.prod(defLab.shape) / np.sum((defLab == 0).astype(dtype=np.float32))
dataQueue.put(tuple((defImg1,defImg2,defImg3,defImg4,defLab,weightData)))
def prepareDataThread(self, dataQueue, numpyImages, numpyGT):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
keysIMG = numpyImages.keys()
keysGT = numpyGT.keys()
nr_iter_dataAug = nr_iter*batchsize
np.random.seed()
whichDataList = np.random.randint(len(keysIMG), size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
whichDataForMatchingList = np.random.randint(len(keysIMG), size=int(nr_iter_dataAug/self.params['ModelParams']['nProc']))
for whichData,whichDataForMatching in zip(whichDataList,whichDataForMatchingList):
filename, ext = splitext(keysIMG[whichData])
gtname, ext = splitext(keysGT[whichData])
currGtKey = gtname + ext
currImgKey = filename + ext
# data agugumentation through hist matching across different examples...
ImgKeyMatching = keysIMG[whichDataForMatching]
defImg = numpyImages[currImgKey]
defLab = numpyGT[currGtKey]
defImg = utilities.hist_match(defImg, numpyImages[ImgKeyMatching])
if(np.random.rand(1)[0]>0.5): #do not apply deformations always, just sometimes
defImg, defLab = utilities.produceRandomlyDeformedImage(defImg, defLab,
self.params['ModelParams']['numcontrolpoints'],
self.params['ModelParams']['sigma'])
weightData = np.zeros_like(defLab,dtype=float)
for label in np.unique(defLab):
weightData[defLab == label] = np.prod(defLab.shape) / np.sum((defLab==label).astype(dtype=np.float32))
dataQueue.put(tuple((defImg,defLab, weightData)))
def prepareDataThread(self,
dataQueue,
numpyImages,
numpyGT,
numpyDmap=None,
numpyPmap=None):
nr_iter = self.params['ModelParams']['numIterations']
batchsize = self.params['ModelParams']['batchsize']
keys = numpyImages.keys()
nr_iter_dataAug = nr_iter * batchsize
np.random.seed()
whichDataList = np.random.randint(
len(keys),
size=int(nr_iter_dataAug / self.params['ModelParams']['nProc']))
whichDataForMatchingList = np.random.randint(
len(keys),
size=int(nr_iter_dataAug / self.params['ModelParams']['nProc']))
for whichData, whichDataForMatching in zip(whichDataList,
whichDataForMatchingList):
filename, ext = splitext(keys[whichData])
currKey = keys[whichData]
#currImgKey = filename + ext
#currGtKey = filename + '_segmented' + ext
# data augmentation through hist matching across different examples...
ImgKeyMatching = keys[whichDataForMatching]
defImg = numpyImages[currKey]
defLab = numpyGT[currKey]
if self.params['DataManagerParams']['dmap']:
defDmap = numpyDmap[currKey]
else:
defDmap = None
if self.params['DataManagerParams']['ProbabilityMap']:
defPmap = numpyPmap[currKey]
else:
defPmap = None
if self.params['ModelParams']['histmatching']:
defImg = utilities.hist_match(defImg,
numpyImages[ImgKeyMatching])
if (np.random.rand(1)[0] <
self.params['ModelParams']['RandomDeform']
): #do not apply deformations always, just sometimes
defImg, defLab, defDmap = utilities.produceRandomlyDeformedImage(
defImg, defLab, defDmap,
self.params['ModelParams']['numcontrolpoints'],
self.params['ModelParams']['sigma'],
self.params['DataManagerParams']['dmap'])
weightData = np.zeros_like(defLab, dtype=float)
weightData[defLab == 1] = np.prod(defLab.shape) / np.sum(
(defLab == 1).astype(dtype=np.float32))
weightData[defLab == 0] = np.prod(defLab.shape) / np.sum(
(defLab == 0).astype(dtype=np.float32))
dataQueue.put(tuple((defImg, defLab, defDmap, defPmap, currKey)))