def pipeline(self, data):
if self.nimage.get('pipeline')[0] == 'STOCHASTIC':
####! swap x and z for volume coming from Slicer - do not forget to apply the inverse before to send them back
data = data.swapaxes(2, 0)
data = data.astype('float')
####
shpD = data.shape
shpV0 = numpy.zeros((4), 'uint16')
shpV0[0] = shpD[0]
shpV0[1] = shpD[1]
shpV0[2] = shpD[2]
shpV0[3] = shpD[3]
logger.info("pipeline data shape : %s:%s:%s:%s" %
(shpD[0], shpD[1], shpD[2], shpD[3]))
logger.info("pipeline data type : %s" % data.dtype)
orgS = self.nimage.get('origin')
org = [float(orgS[0]), float(orgS[1]), float(orgS[2])]
org0 = numpy.zeros((3), 'float')
org0[0] = org[0]
org0[1] = org[1]
org0[2] = org[2]
logger.info("origin : %s:%s:%s" % (org[0], org[1], org[2]))
spaS = self.nimage.get('spacing')
spa = [float(spaS[0]), float(spaS[1]), float(spaS[2])]
spa0 = numpy.zeros((3), 'float')
spa0[0] = spa[0]
spa0[1] = spa[1]
spa0[2] = spa[2]
logger.info("spacing : %s:%s:%s" % (spa[0], spa[1], spa[2]))
G = self.nimage.get('grads')
b = self.nimage.get('bval')
i2r = self.nimage.get('ijk2ras')
i2rd = self.nimage.get('ijk2rasd')
mu = self.nimage.get('mu')
dims = self.nimage.get('dimensions')
s = slicerd.slicerd()
scene = s.ls()
dscene = {}
indS = []
for i in range(len(scene)):
if scene[i].isdigit():
indS.append(i)
for j in range(len(indS)):
if j < len(indS) - 1:
tmpD2 = ''
tmpD = scene[indS[j] + 2:indS[j + 1]]
for k in range(len(tmpD)):
if k < len(tmpD) - 1:
tmpD2 += tmpD[k] + ' '
else:
tmpD2 += tmpD[k]
dscene[tmpD2] = j
else:
tmpD2 = ''
tmpD = scene[indS[j] + 2:]
for k in range(len(tmpD)):
if k < len(tmpD) - 1:
tmpD2 += tmpD[k] + ' '
else:
tmpD2 += tmpD[k]
dscene[tmpD2] = j
logger.info("scene : %s" % dscene)
# currently there is a bug in the GUI of slicer python - do not load if three times the same volume
isInRoiA = False
if self.params.hasKey('roiA'):
if dscene.has_key(self.params.get('roiA')[0]):
self.roiA = s.get(int(dscene[self.params.get('roiA')[0]]))
roiAR = numpy.fromstring(self.roiA.getImage(), 'uint16')
if roiAR.shape[0] == shpD[2] * shpD[1] * shpD[0]:
roiAR = roiAR.reshape(
shpD[2], shpD[1], shpD[0]
) # because come from Slicer - will not send them back so swap them one for all
roiAR = roiAR.swapaxes(2, 0)
roiAR[roiAR > 0] = 1
self.roiA.setImage(roiAR)
isInRoiA = True
logger.info(
"RoiA : %s:%s:%s" %
(roiAR.shape[0], roiAR.shape[1], roiAR.shape[2]))
else:
logger.info(
"RoiA has not the same dimension as the DWI : %s" %
roiAR.shape[0])
isInRoiB = False
if self.params.hasKey('roiB'):
if dscene.has_key(self.params.get('roiB')[0]):
if self.params.get('roiB')[0] != self.params.get(
'roiA')[0]:
self.roiB = s.get(
int(dscene[self.params.get('roiB')[0]]))
roiBR = numpy.fromstring(self.roiB.getImage(),
'uint16')
if roiBR.shape[0] == shpD[2] * shpD[1] * shpD[0]:
roiBR = roiBR.reshape(shpD[2], shpD[1], shpD[0])
roiBR = roiBR.swapaxes(2, 0)
roiBR[roiBR > 0] = 1
self.roiB.setImage(roiBR)
isInRoiB = True
logger.info("RoiB : %s:%s:%s" %
(roiBR.shape[0], roiBR.shape[1],
roiBR.shape[2]))
else:
logger.info(
"RoiB has not the same dimension as the DWI : %s"
% roiBR.shape[0])
isInWM = False
if self.params.hasKey('wm'):
if dscene.has_key(self.params.get('wm')[0]):
self.wm = s.get(int(dscene[self.params.get('wm')[0]]))
wmR = numpy.fromstring(self.wm.getImage(), 'uint16')
if wmR.shape[0] == shpD[2] * shpD[1] * shpD[0]:
wmR = wmR.reshape(shpD[2], shpD[1], shpD[0])
wmR = wmR.swapaxes(2, 0)
self.wm.setImage(wmR)
isInWM = True
logger.info("WM : %s:%s:%s" %
(wmR.shape[0], wmR.shape[1], wmR.shape[2]))
else:
logger.info(
"WM has not the same dimension as the DWI : %s" %
wmR.shape[0])
isInTensor = False
if self.params.hasKey('tensor'):
if dscene.has_key(self.params.get('tensor')[0]):
self.ten = s.get(int(dscene[self.params.get('tensor')[0]]))
tenR = numpy.fromstring(self.ten.getImage(), 'float32')
if tenR.shape[0] == shpD[2] * shpD[1] * shpD[0] * 7:
tenR = tenR.reshape(shpD[2], shpD[1], shpD[0], 7)
tenR = tenR.swapaxes(2, 0)
self.ten.setImage(tenR)
isInTensor = True
logger.info("Tensor : %s:%s" %
(tenR.shape[0], tenR.shape[1]))
else:
logger.info(
"Tensor has not the same dimension as the DWI : %s"
% tenR.shape[0])
logger.info("Input volumes loaded!")
# values per default
smoothEnabled = False
wmEnabled = True
infWMThres = 300
supWMThres = 900
tensEnabled = True
bLine = 0
stEnabled = True
totalTracts = 500
maxLength = 200
stepSize = 0.5
stopEnabled = True
fa = 0.0
cmEnabled = False
probMode = 0
# got from client
# special handling for bools
if self.params.hasKey('smoothEnabled'):
smoothEnabled = bool(int(self.params.get('smoothEnabled')[0]))
if self.params.hasKey('wmEnabled'):
wmEnabled = bool(int(self.params.get('wmEnabled')[0]))
if self.params.hasKey('tensEnabled'):
tensEnabled = bool(int(self.params.get('tensEnabled')[0]))
if self.params.hasKey('stEnabled'):
stEnabled = bool(int(self.params.get('stEnabled')[0]))
if self.params.hasKey('cmEnabled'):
cmEnabled = bool(int(self.params.get('cmEnabled')[0]))
if self.params.hasKey('spaceEnabled'):
spaceEnabled = bool(int(self.params.get('spaceEnabled')[0]))
if self.params.hasKey('stopEnabled'):
stopEnabled = bool(int(self.params.get('stopEnabled')[0]))
if self.params.hasKey('faEnabled'):
faEnabled = bool(int(self.params.get('faEnabled')[0]))
if self.params.hasKey('traceEnabled'):
traceEnabled = bool(int(self.params.get('traceEnabled')[0]))
if self.params.hasKey('modeEnabled'):
modeEnabled = bool(int(self.params.get('modeEnabled')[0]))
if self.params.hasKey('sphericalEnabled'):
sphericalEnabled = bool(
int(self.params.get('sphericalEnabled')[0]))
# can handle normally
FWHM = numpy.ones((3), 'float')
if self.params.hasKey('stdDev'):
FWHM[0] = float(self.params.get('stdDev')[0])
FWHM[1] = float(self.params.get('stdDev')[1])
FWHM[2] = float(self.params.get('stdDev')[2])
logger.debug("FWHM: %s:%s:%s" % (FWHM[0], FWHM[1], FWHM[2]))
if self.params.hasKey('infWMThres'):
infWMThres = int(self.params.get('infWMThres')[0])
logger.debug("infWMThres: %s" % infWMThres)
if self.params.hasKey('supWMThres'):
supWMThres = int(self.params.get('supWMThres')[0])
logger.debug("supWMThres: %s" % supWMThres)
if self.params.hasKey('bLine'):
bLine = int(self.params.get('bLine')[0])
logger.debug("bLine: %s" % bLine)
if self.params.hasKey('isIJK'):
isIJK = bool(int(self.params.get('isIJK')[0]))
logger.debug("isIJK: %s" % isIJK)
if self.params.hasKey('tensMode'):
tensMode = self.params.get('tensMode')[0]
logger.debug("tensMode: %s" % tensMode)
if self.params.hasKey('totalTracts'):
totalTracts = int(self.params.get('totalTracts')[0])
logger.debug("totalTracts: %s" % totalTracts)
if self.params.hasKey('maxLength'):
maxLength = int(self.params.get('maxLength')[0])
logger.debug("maxLength: %s" % maxLength)
if self.params.hasKey('stepSize'):
stepSize = float(self.params.get('stepSize')[0])
logger.debug("stepSize: %s" % stepSize)
if self.params.hasKey('fa'):
fa = float(self.params.get('fa')[0])
logger.debug("fa: %s" % fa)
if self.params.hasKey('probMode'):
probMode = self.params.get('probMode')[0]
logger.debug("probMode: %s" % probMode)
if self.params.hasKey('lengthEnabled'):
lengthEnabled = self.params.get('lengthEnabled')[0]
logger.debug("lengthEnabled: %s" % lengthEnabled)
if self.params.hasKey('lengthClass'):
lengthClass = self.params.get('lengthClass')[0]
logger.debug("lengthClass: %s" % lengthClass)
if self.params.hasKey('tractOffset'):
tractOffset = int(self.params.get('tractOffset')[0])
logger.debug("tractOffset: %s" % tractOffset)
if self.params.hasKey('vicinity'):
vicinity = int(self.params.get('vicinity')[0])
logger.debug("vicinity: %s" % vicinity)
if self.params.hasKey('threshold'):
threshold = float(self.params.get('threshold')[0])
logger.debug("threshold: %s" % threshold)
ngrads = shpD[3]
logger.info("Number of gradients : %s" % str(ngrads))
G = G.reshape((ngrads, 3))
b = b.reshape((ngrads, 1))
i2r = i2r.reshape((4, 4))
i2rd = i2rd.reshape((4, 4))
mu = mu.reshape((4, 4))
r2i = numpy.linalg.inv(i2r)
r2id = numpy.linalg.inv(i2rd)
# compute trafo fir IJK & RAS
# gradients in RAS
G1 = numpy.dot(G, mu[:3, :3].T)
# gradients in IJK
mu2 = numpy.dot(r2id[:3, :3], mu[:3, :3])
G2 = numpy.dot(G, mu2[:3, :3].T)
# switch to compute either in IJK or RAS
if isIJK:
G0 = G2
else:
G0 = G1
vts = vects.vectors
logger.info("Tensor flag : %s" % str(tensEnabled))
if smoothEnabled:
for k in range(shpD[3]):
timeSM0 = time.time()
data[..., k] = sm.smooth(
data[..., k], FWHM,
numpy.array([spa[0], spa[1], spa[2]], 'float'))
logger.info("Smoothing DWI volume %i in %s sec" %
(k, str(time.time() - timeSM0)))
if wmEnabled:
wm = tens.EvaluateWM0(data, bLine, infWMThres, supWMThres)
if isInRoiA: # correcting brain mask with roi A
logger.info("Correcting mask based on roiA")
tmpA = self.roiA.getImage()
wm[tmpA > 0] = 1
if isInRoiB: # correcting brain mask with roi A & B
logger.info("Correcting mask based on roiB")
tmpB = self.roiB.getImage()
wm[tmpB > 0] = 1
else: # avoid singularities in data
minVData = 10
wm = tens.EvaluateWM0(data, bLine, minVData, data[...,
bLine].max())
if isInWM:
logger.info("Using external mask")
wm = self.wm.getImage()
if tensEnabled:
logger.info("Compute tensor")
timeS1 = time.time()
if not isInTensor:
EV, lV, xVTensor, xYTensor = tens.EvaluateTensorX1(
data, G0.T, b.T, wm)
else:
EV, lV, xVTensor, xYTensor = tens.EvaluateTensorK1(
self.ten.getImage(), shpD, wm)
logger.info("Compute tensor in %s sec" %
str(time.time() - timeS1))
if faEnabled:
faMap = tensC.CalculateFA0(lV)
if traceEnabled:
trMap = tensC.CalculateTrace0(lV)
if modeEnabled:
moMap = tensC.CalculateMode0(lV)
if cmEnabled:
logger.info("Track fibers")
if not stopEnabled:
fa = 0.0
if isInRoiA:
# ROI A
logger.info("Search ROI A")
roiP = cmpV.march0InVolume(self.roiA.getImage())
shpR = roiP.shape
logger.info("ROI A dimension : %s:%s" %
(str(shpR[0]), str(shpR[1])))
blocksize = totalTracts
IJKstartpoints = numpy.tile(roiP, (blocksize, 1))
logger.info("IJK start points shape : %s" %
str(IJKstartpoints.shape))
timeS2 = time.time()
logger.info("Data type : %s" % data.dtype)
if tensEnabled:
paths00, paths01, paths02, paths03, paths04 = track.TrackFiberZ40(s, wm, shpD, b.T, G0.T, vts.T, IJKstartpoints.T,\
r2i, i2r, r2id, i2rd, spa, lV, EV, xVTensor, stepSize, maxLength, fa, spaceEnabled, isIJK)
else:
paths00, paths01, paths02, paths03, paths04 = track.TrackFiberX40(s, data.flatten(), wm, shpD, b.T, G0.T, vts.T, IJKstartpoints.T,\
r2i, i2r, r2id, i2rd, spa, stepSize, maxLength, fa, spaceEnabled, isIJK)
logger.info("Track fibers in %s sec" %
str(time.time() - timeS2))
logger.info("Connect tract")
if probMode == 'binary':
cm = track.ConnectFibersX0(paths01, paths04, shpD,
lengthEnabled, lengthClass)
elif probMode == 'cumulative':
cm = track.ConnectFibersX1(paths01, paths04, shpD,
lengthEnabled, lengthClass)
else:
cm = track.ConnectFibersX2(paths01, paths04, shpD,
lengthEnabled, lengthClass)
if isInRoiB:
# ROI B
logger.info("Search ROI B")
roiP2 = cmpV.march0InVolume(self.roiB.getImage())
shpR2 = roiP2.shape
logger.info("ROI B dimension : %s:%s" %
(str(shpR2[0]), str(shpR2[1])))
blocksize = totalTracts
IJKstartpoints2 = numpy.tile(roiP2, (blocksize, 1))
logger.info("IJK start points shape : %s" %
str(IJKstartpoints2.shape))
timeS3 = time.time()
logger.info("Data type : %s" % data.dtype)
if tensEnabled:
paths10, paths11, paths12, paths13, paths14 = track.TrackFiberZ40(s, wm, shpD, b.T, G0.T, vts.T, IJKstartpoints2.T,\
r2i, i2r, r2id, i2rd, spa, lV, EV, xVTensor, stepSize, maxLength, fa, spaceEnabled, isIJK)
else:
paths10, paths11, paths12, paths13, paths14 = track.TrackFiberX40(s, data.flatten(), wm, shpD, b.T, G0.T, vts.T, IJKstartpoints2.T,\
r2i, i2r, r2id, i2rd, spa, stepSize, maxLength, fa, spaceEnabled, isIJK)
logger.info("Track fibers in %s sec" %
str(time.time() - timeS3))
logger.info("Connect tract")
if probMode == 'binary':
cm2 = track.ConnectFibersX0(paths11, paths14, shpD,
lengthEnabled, lengthClass)
elif probMode == 'cumulative':
cm2 = track.ConnectFibersX1(paths11, paths14, shpD,
lengthEnabled, lengthClass)
else:
cm2 = track.ConnectFibersX2(paths11, paths14, shpD,
lengthEnabled, lengthClass)
if isInRoiA and isInRoiB:
cm3 = track.FilterFibers0(paths00, paths01, paths02, paths03, paths04, self.roiA.getImage(), self.roiB.getImage(),\
shpD, threshold, tractOffset, vicinity, sphericalEnabled)
cm4 = track.FilterFibers0(paths10, paths11, paths12, paths13, paths14, self.roiB.getImage(), self.roiA.getImage(),\
shpD, threshold, tractOffset, vicinity, sphericalEnabled)
else:
logger.info("No tractography to execute!")
dateT = str(int(round(time.time())))
isDir = os.access('outputs', os.F_OK)
if not isDir:
os.mkdir('outputs')
tmpF = './outputs/'
i2r.tofile(tmpF + 'trafo_' + dateT + '.ijk')
if smoothEnabled:
ga = data[..., bLine]
ga = ga.swapaxes(2, 0)
tmp = 'smooth_' + dateT
if not (ga == 0).all():
ga.tofile(tmpF + tmp + '.data')
createParams(ga, tmpF + tmp)
s.putS(ga, dims, org, i2r, tmp)
#if wmEnabled:
wm = wm.swapaxes(2, 0)
tmp = 'brain_' + dateT
if not (wm == 0).all():
wm.tofile(tmpF + tmp + '.data')
createParams(wm, tmpF + tmp)
s.putS(wm, dims, org, i2r, tmp)
if tensEnabled:
if isIJK:
xVTensor = xVTensor.swapaxes(2, 0)
xVTensor = xVTensor.astype(
'float32') # slicerd do not support double type yet
xYTensor = xYTensor.swapaxes(2, 0)
xYTensor = xYTensor.astype(
'float32') # slicerd do not support double type yet
tmp = 'tensor_' + dateT
if (not (xYTensor == 0).all()) and (not (xVTensor
== 0).all()):
xYTensor.tofile(tmpF + tmp + '.data')
createParams(xYTensor, tmpF + tmp, True)
s.putD(xVTensor, dims, org, i2r, mu, tmp)
if faEnabled:
faMap = faMap.swapaxes(2, 0)
tmp = 'fa_' + dateT
if not (faMap == 0).all():
faMap.tofile(tmpF + tmp + '.data')
createParams(faMap, tmpF + tmp)
s.putS(faMap, dims, org, i2r, tmp)
if traceEnabled:
trMap = trMap.swapaxes(2, 0)
tmp = 'trace_' + dateT
if not (trMap == 0).all():
trMap.tofile(tmpF + tmp + '.data')
createParams(trMap, tmpF + tmp)
s.putS(trMap, dims, org, i2r, tmp)
if modeEnabled:
moMap = moMap.swapaxes(2, 0)
tmp = 'mode_' + dateT
if not (moMap == 0).all():
moMap.tofile(tmpF + tmp + '.data')
createParams(moMap, tmpF + tmp)
s.putS(moMap, dims, org, i2r, tmp)
if cmEnabled:
if isInRoiA:
cm = cm.swapaxes(2, 0)
tmp = 'cmA_' + dateT
if not (cm == 0).all():
cm.tofile(tmpF + tmp + '.data')
createParams(cm, tmpF + tmp)
#s.putS(cm, dims, org, i2r, tmp)
tmp = 'cmFA_' + dateT
cmf = cm / float(cm.max())
cmf.astype('float32')
if not (cmf == 0).all():
cmf.tofile(tmpF + tmp + '.data')
createParams(cmf, tmpF + tmp)
s.putS(cmf, dims, org, i2r, tmp)
if isInRoiB:
cm2 = cm2.swapaxes(2, 0)
tmp = 'cmB_' + dateT
if not (cm2 == 0).all():
cm2.tofile(tmpF + tmp + '.data')
createParams(cm2, tmpF + tmp)
#s.putS(cm2, dims, org, i2r, tmp)
tmp = 'cmFB_' + dateT
cm2f = cm2 / float(cm2.max())
cm2f.astype('float32')
if not (cm2f == 0).all():
cm2f.tofile(tmpF + tmp + '.data')
createParams(cm2f, tmpF + tmp)
s.putS(cm2f, dims, org, i2r, tmp)
if isInRoiA and isInRoiB:
cm1a2 = cm[...] * cm2[...] / 2.0
cm1a2 = cm1a2.astype('uint32')
if not (cm1a2 == 0).all():
tmp = 'cmAandB_' + dateT
cm1a2.tofile(tmpF + tmp + '.data')
createParams(cm1a2, tmpF + tmp)
#s.putS(cm1a2, dims, org, i2r, tmp)
tmp = 'cmFAandB_' + dateT
cm1a2f = cm1a2 / float(cm1a2.max())
cm1a2f.astype('float32')
cm1a2f.tofile(tmpF + tmp + '.data')
createParams(cm1a2f, tmpF + tmp)
s.putS(cm1a2f, dims, org, i2r, tmp)
cm1o2 = (cm[...] + cm2[...]) / 2.0
cm1o2 = cm1o2.astype('uint32')
if not (cm1o2 == 0).all():
tmp = 'cmAorB_' + dateT
cm1o2.tofile(tmpF + tmp + '.data')
createParams(cm1o2, tmpF + tmp)
#s.putS(cm1o2, dims, org, i2r, tmp)
tmp = 'cmFAorB_' + dateT
cm1o2f = cm1o2 / float(cm1o2.max())
cm1o2f.astype('float32')
cm1o2f.tofile(tmpF + tmp + '.data')
createParams(cm1o2f, tmpF + tmp)
s.putS(cm1o2f, dims, org, i2r, tmp)
if not (cm3 == 0).all():
tmp = 'cmA2B_' + dateT
cm3 = cm3.swapaxes(2, 0)
cm3.tofile(tmpF + tmp + '.data')
createParams(cm3, tmpF + tmp)
#s.putS(cm3, dims, org, i2r, tmp)
tmp = 'cmFA2B_' + dateT
cm3f = cm3 / float(cm3.max())
cm3f.astype('float32')
cm3f.tofile(tmpF + tmp + '.data')
createParams(cm3f, tmpF + tmp)
s.putS(cm3f, dims, org, i2r, tmp)
if not (cm4 == 0).all():
tmp = 'cmB2A_' + dateT
cm4 = cm4.swapaxes(2, 0)
cm4.tofile(tmpF + tmp + '.data')
createParams(cm4, tmpF + tmp)
#s.putS(cm4, dims, org, i2r, tmp)
tmp = 'cmFB2A_' + dateT
cm4f = cm4 / float(cm4.max())
cm4f.astype('float32')
cm4f.tofile(tmpF + tmp + '.data')
createParams(cm4f, tmpF + tmp)
s.putS(cm4f, dims, org, i2r, tmp)
logger.debug("pipeline data shape end : %s:%s:%s:%s" %
(shpD[0], shpD[1], shpD[2], shpD[3]))
return data
def pipeline(params, nimage, roiA, roiB, wm):
if params.get('pipeline')=='STOCHASTIC':
#print "Pipeline : STOCHASTIC"
ppserversL=("localhost",)
#ppserversL=("*",)
data = nimage.getImage()
####! swap x and z for volume coming from Slicer - do not forget tp apply the inverse before to send them back
data = data.swapaxes(2,0)
print 'pipeline data type : %s' % str(data.dtype)
####
shpD = data.shape
print 'pipeline data shape : %s' % str(shpD)
orgS = nimage.get('origin')
org = [float(orgS[0]), float(orgS[1]), float(orgS[2])]
print 'Origin : %s:%s:%s' % (str(org[0]), str(org[1]), str(org[2]))
spaS = nimage.get('spacing')
spa = [float(spaS[0]), float(spaS[1]), float(spaS[2])]
print 'Spacing : %s:%s:%s' % (str(spa[0]), str(spa[1]), str(spa[2]))
G = nimage.get('grads')
b = nimage.get('bval')
i2r = nimage.get('ijk2ras')
i2rd = nimage.get('ijk2rasd')
mu = nimage.get('mu')
dims = nimage.get('dimensions')
#print 'DWI : ', params.get('dwi')
isInRoiA = False
if params.hasKey('roiA'):
roiAR = numpy.fromstring(roiA.getImage(), 'uint16')
roiAR = roiAR.reshape(shpD[2], shpD[1], shpD[0]) # because come from Slicer - will not send them back so swap them one for all
roiAR = roiAR.swapaxes(2,0)
roiAR[roiAR>0]=1
roiA.setImage(roiAR)
isInRoiA = True
#print "RoiA : %s" % str(roiAR.shape)
isDir = os.access('paths0', os.F_OK)
if not isDir:
os.mkdir('paths0')
isInRoiB = False
if params.hasKey('roiB'):
if params.get('roiB') != params.get('roiA'):
roiBR = numpy.fromstring(roiB.getImage(), 'uint16')
roiBR = roiBR.reshape(shpD[2], shpD[1], shpD[0])
roiBR = roiBR.swapaxes(2,0)
roiBR[roiBR>0]=1
roiB.setImage(roiBR)
isInRoiB = True
#print "RoiB : %s" % str(roiBR.shape)
isDir = os.access('paths1', os.F_OK)
if not isDir:
os.mkdir('paths1')
isInWM = False
if params.hasKey('wm'):
wmR = numpy.fromstring(wm.getImage(), 'uint16')
wmR = wmR.reshape(shpD[2], shpD[1], shpD[0])
wmR = wmR.swapaxes(2,0)
wm.setImage(wmR)
isInWM = True
#print "WM : %s" % str(wmR.shape)
isInTensor = False
print "Input volumes loaded!"
# values per default
smoothEnabled = False
wmEnabled = True
infWMThres = 300
supWMThres = 900
tensEnabled =True
bLine = 0
stEnabled = True
totalTracts = 500
maxLength = 200
stepSize = 0.5
stopEnabled = True
fa = 0.0
cmEnabled = False
probMode = 0
# got from client
# special handling for bools
if params.hasKey('smoothEnabled'):
smoothEnabled = bool(int(params.get('smoothEnabled')))
if params.hasKey('wmEnabled'):
wmEnabled = bool(int(params.get('wmEnabled')))
if params.hasKey('tensEnabled'):
tensEnabled = bool(int(params.get('tensEnabled')))
if params.hasKey('stEnabled'):
stEnabled = bool(int(params.get('stEnabled')))
if params.hasKey('cmEnabled'):
cmEnabled = bool(int(params.get('cmEnabled')))
if params.hasKey('spaceEnabled'):
spaceEnabled = bool(int(params.get('spaceEnabled')))
if params.hasKey('stopEnabled'):
stopEnabled = bool(int(params.get('stopEnabled')))
if params.hasKey('faEnabled'):
faEnabled = bool(int(params.get('faEnabled')))
if params.hasKey('traceEnabled'):
traceEnabled = bool(int(params.get('traceEnabled')))
if params.hasKey('modeEnabled'):
modeEnabled = bool(int(params.get('modeEnabled')))
# can handle normally
FWHM = numpy.ones((3), 'float')
if params.hasKey('stdDev'):
FWHM[0] = float(params.get('stdDev')[0])
FWHM[1] = float(params.get('stdDev')[1])
FWHM[2] = float(params.get('stdDev')[2])
print "FWHM: %s:%s:%s" % (str(FWHM[0]), str(FWHM[1]), str(FWHM[2]))
if params.hasKey('infWMThres'):
infWMThres = int(params.get('infWMThres'))
print "infWMThres: %s" % str(infWMThres)
if params.hasKey('supWMThres'):
supWMThres = int(params.get('supWMThres'))
print "supWMThres: %s" % str(supWMThres)
if params.hasKey('bLine'):
bLine = int(params.get('bLine'))
print "bLine: %s" % str(bLine)
if params.hasKey('tensMode'):
tensMode = params.get('tensMode')
print "tensMode: %s" % str(tensMode)
if params.hasKey('totalTracts'):
totalTracts = int(params.get('totalTracts'))
print "totalTracts: %s" % str(totalTracts)
if params.hasKey('maxLength'):
maxLength = int(params.get('maxLength'))
print "maxLength: %s" % str(maxLength)
if params.hasKey('stepSize'):
stepSize = float(params.get('stepSize'))
print "stepSize: %s" % str(stepSize)
if params.hasKey('fa'):
fa = float(params.get('fa'))
print "fa: %s" % str(fa)
if params.hasKey('probMode'):
probMode = params.get('probMode')
print "probMode: %s" % str(probMode)
if params.hasKey('lengthEnabled'):
lengthEnabled = params.get('lengthEnabled')
print "lengthEnabled: %s" % str(lengthEnabled)
if params.hasKey('lengthClass'):
lengthClass = params.get('lengthClass')
print "lengthClass: %s" % str(lengthClass)
ngrads = shpD[3] #b.shape[0]
print "Number of gradients : %s" % str(ngrads)
G = G.reshape(ngrads,3)
b = b.reshape(ngrads,1)
i2r = i2r.reshape(4,4)
i2rd = i2rd.reshape(4,4)
mu = mu.reshape(4,4)
r2i = numpy.linalg.inv(i2r)
r2id = numpy.linalg.inv(i2rd)
mu2 = numpy.dot(r2id[:3, :3], mu[:3, :3])
G2 = numpy.dot(G, mu2[:3, :3].T)
vts = vects.vectors
print "Tensor flag : %s" % str(tensEnabled)
cm = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm2 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm3 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm4 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
if smoothEnabled:
for k in range(shpD[3]):
timeSM0 = time.time()
data[...,k] = sm.smooth(data[...,k], FWHM, numpy.array([ spa[0], spa[1], spa[2] ],'float'))
print "Smoothing DWI volume %i in %s sec" % (k, str(time.time()-timeSM0))
if wmEnabled:
wm = tensPP.EvaluateWM0(data, bLine, infWMThres, supWMThres)
if isInRoiA: # correcting brain mask with roi A
print "Correcting mask based on roiA"
tmpA = roiA.getImage()
wm[tmpA>0]=1
if isInRoiB: # correcting brain mask with roi A & B
print "Correcting mask based on roiB"
tmpB = roiB.getImage()
wm[tmpB>0]=1
else: # avoid singularities in data
minVData = 10
wm = tensPP.EvaluateWM0(data, bLine, minVData, data[..., bLine].max())
wmEnabled = True # fix
if isInWM or wmEnabled:
isDir = os.access('masks', os.F_OK)
if not isDir:
os.mkdir('masks')
tmpF = './masks/'
numpy.save(tmpF + 'wm.npy', wm)
indx = numpy.transpose(wm.nonzero())
print 'Total Number of voxels : ', shpD[0]*shpD[1]*shpD[2]*ngrads
print 'Masked voxels : ', indx.shape[0]*ngrads
print 'Index shape : ', indx.shape
dataf = data.flatten()
cId = numpy.zeros((indx.shape[0]), 'uint32')
mdata = numpy.zeros((indx.shape[0], ngrads), data.dtype)
for i in range(indx.shape[0]):
cId[i] = indx[i][0]*shpD[1]*shpD[2]*shpD[3] + indx[i][1]*shpD[2]*shpD[3] + indx[i][2]*shpD[3]
mdata[i,:]= dataf[cId[i]:cId[i]+ngrads]
numpy.save(tmpF + 'index.npy', cId)
numpy.save(tmpF + 'mdata.npy', mdata)
if cmEnabled:
print "Compute tensor"
timeS1 = time.time()
roiFilterOnly = False
monoP = False
# multiprocessing support
dataBlocks = []
wmBlocks = []
nCpu = 2 # could be set to the number of available cores
nParts = 1
if shpD[2]>0 and nCpu>0 :
job_server = pp.Server(ppservers=ppserversL)
ncpusL = job_server.get_ncpus()
print "Number of cores on local machine : %s" % str(ncpusL)
print "Number of active computing nodes : %s" % str(nCpu)
if shpD[2] >= nCpu:
nParts = nCpu
else:
nParts = shpD[2]
for i in range(nParts):
datax = data[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, :]
print "data block %i dimension : %s" % (i, str(datax.shape))
dataBlocks.append(datax)
if isInWM or wmEnabled:
wmx = wm[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts]
wmBlocks.append(wmx)
else:
monoP = True
if not monoP:
jobs = []
job_server.set_ncpus(ncpus = ncpusL)
for i in range(nParts):
jobs.append(job_server.submit(tensPP.EvaluateTensorX1, (dataBlocks[i], G2.T, b.T, wmBlocks[i],),(tensPP.ComputeAFunctional, tensPP.ComputeTensorFunctional,), ("numpy","time",) ))
tBlocks = []
for i in range(nParts):
tBlocks.append(jobs[i]())
lV = numpy.zeros((shpD[0], shpD[1], shpD[2], 3) , 'float')
EV = numpy.zeros((shpD[0], shpD[1], shpD[2], 3, 3), 'float' )
xVTensor = numpy.zeros((shpD[0], shpD[1], shpD[2], 7), 'float')
xYTensor = numpy.zeros((shpD[0], shpD[1], shpD[2], 9), 'float')
xTensor0 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'float')
for i in range(nParts):
EV[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][0]
lV[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, :]= tBlocks[i][1]
xVTensor[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][2]
xYTensor[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][3]
xTensor0[...]= xVTensor[..., 0]
lVType = lV.dtype
EVType = EV.dtype
xVTensorType = xVTensor.dtype
wmType = wm.dtype
# computation of alpha, beta, logmu0 and principal eigenvector
tdata = numpy.zeros((indx.shape[0], 6), 'float')
for i in range(indx.shape[0]):
cId[i] = indx[i][0]*shpD[1]*shpD[2]*shpD[3] + indx[i][1]*shpD[2]*shpD[3] + indx[i][2]*shpD[3]
l = lV[indx[i][0], indx[i][1], indx[i][2], :]
index = numpy.argsort(abs(l))[::-1]
l =l[index,:]
# Set point estimates in the Constrained model
E = EV[indx[i][0], indx[i][1], indx[i][2], ...]
alpha = (l[1]+l[2])/2
beta = l[0] - alpha
logmu0 = xTensor0[indx[i][0], indx[i][1], indx[i][2]]
e = E[:, index[0]]
tdata[i,0]= alpha
tdata[i,1]= beta
tdata[i,2]= logmu0
tdata[i,3:]= e
numpy.save(tmpF + 'tdata.npy', tdata)
isDir = os.access('tensors', os.F_OK)
if not isDir:
os.mkdir('tensors')
tmpF = './tensors/'
numpy.save(tmpF + 'eigenv.npy', EV)
numpy.save(tmpF + 'lambda.npy', lV)
numpy.save(tmpF + 'tensor.npy', xVTensor)
numpy.save(tmpF + 'tensor0.npy', xTensor0)
numpy.save(tmpF + 'vectors.npy', vts.T)
dateT = str(int(round(time.time())))
isDir = os.access('outputs', os.F_OK)
if not isDir:
os.mkdir('outputs')
tmpF = './outputs/'
i2r.tofile(tmpF + 'trafo_' + dateT + '.ijk')
if smoothEnabled:
ga = data[..., bLine]
ga = ga.swapaxes(2,0)
tmp= 'smooth_' + dateT
ga.tofile(tmpF + tmp + '.data')
createParams(ga, tmpF + tmp)
if wmEnabled:
wm = wm.swapaxes(2,0)
tmp= 'brain_' + dateT
wm.tofile(tmpF + tmp + '.data')
createParams(wm, tmpF + tmp)
if cmEnabled:
xVTensor = xVTensor.swapaxes(2,0)
xVTensor = xVTensor.astype('float32') # slicerd do not support double type yet
xYTensor = xYTensor.swapaxes(2,0)
xYTensor = xYTensor.astype('float32') # slicerd do not support double type yet
tmp= 'tensor_' + dateT
xYTensor.tofile(tmpF + tmp + '.data')
createParams(xYTensor, tmpF + tmp, True)
if faEnabled:
faMap = tensPP.CalculateFA0(lV)
faMap = faMap.swapaxes(2,0)
tmp= 'fa_' + dateT
faMap.tofile(tmpF + tmp + '.data')
createParams(faMap, tmpF + tmp)
if traceEnabled:
trMap = tensPP.CalculateTrace0(lV)
trMap = trMap.swapaxes(2,0)
tmp= 'trace_' + dateT
trMap.tofile(tmpF + tmp + '.data')
createParams(trMap, tmpF + tmp)
if modeEnabled:
moMap = tensPP.CalculateMode0(lV)
moMap = moMap.swapaxes(2,0)
tmp= 'mode_' + dateT
moMap.tofile(tmpF + tmp + '.data')
createParams(moMap, tmpF + tmp)
del tBlocks
del dataBlocks
del wmBlocks
del lV
del EV
del xVTensor
del xYTensor
else:
pass
print "Compute tensor in %s sec" % str(time.time()-timeS1)
print "Track fibers"
if not stopEnabled:
fa = 0.0
if isInRoiA:
# ROI A
print "Search ROI A"
roiP = cmpV.march0InVolume(roiA.getImage())
shpR = roiP.shape
print "ROI A dimension : %s" % str(shpR)
blocksize = totalTracts
IJKstartpoints = []
monoP = False
nParts = 1
if shpR[0]>0 and nCpu>0 :
if shpR[0] >= nCpu:
nParts = nCpu
else:
nParts = shpR[0]
for i in range(nParts):
roiPx = roiP[i*shpR[0]/nParts:(i+1)*shpR[0]/nParts, :]
print "ROI A %i dimension : %s" % (i, str(roiPx.shape))
IJKstartpoints.append(numpy.tile(roiPx,( blocksize, 1)))
else:
IJKstartpoints.append(numpy.tile(roiP,( blocksize, 1)))
monoP = True
timeS2 = time.time()
# multiprocessing
print "Data type : %s" % str(data.dtype)
if not monoP:
jobs = []
for i in range(nParts):
jobs.append(job_server.submit(trackPP.TrackFiberYFM40, (i, 0, params.get('location'), nimage.get('fullname'), nimage.get('type'), shpD, b.T, G2.T, IJKstartpoints[i].T, r2i, i2r, r2id, i2rd, spa,\
'vectors.npy', vects.vectors.dtype, vects.vectors.T.shape, 'lambda.npy', lVType, 'eigenv.npy', EVType, 'tensor0.npy', xVTensorType, 'wm.npy' ,\
wmType, stepSize, maxLength, fa, spaceEnabled,),(), ("numpy","time",) ))
res = jobs[0]()
for i in range(nParts-1):
res = jobs[i+1]()
print "Track fibers in %s sec" % str(time.time()-timeS2)
if not roiFilterOnly:
print "Connect tract"
jobs = []
cm = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
for j in range(nParts):
print "Number of paths : %s" % str(IJKstartpoints[j].shape[0])
pathsIJKId = './paths0/' + 'unit_' + str(j) + '_IJK.npy'
pathsLENId = './paths0/' + 'unit_' + str(j) + '_LEN.npy'
if probMode=='binary':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM0, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
elif probMode=='cumulative':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM1, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
else:
jobs.append(job_server.submit(trackPP.ConnectFibersPZM2, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
cm = jobs[0]()
for j in range(nParts-1):
cm += jobs[j+1]()
else:
pass
if isInRoiB:
# ROI B
print "Search ROI B"
roiP2 = cmpV.march0InVolume(roiB.getImage())
shpR2 = roiP2.shape
print "ROI B dimension : %s" % str(shpR2)
blocksize = totalTracts
IJKstartpoints2 = []
monoP = False
nParts2 = 1
if shpR2[0]>0 and nCpu>0 :
if shpR2[0] >= nCpu:
nParts2 = nCpu
else:
nParts2 = shpR2[0]
for i in range(nParts2):
roiPx = roiP2[i*shpR2[0]/nParts2:(i+1)*shpR2[0]/nParts2, :]
print "ROI B %i dimension : %s" % (i, str(roiPx.shape))
IJKstartpoints2.append(numpy.tile(roiPx,( blocksize, 1)))
else:
IJKstartpoints2.append(numpy.tile(roiP2,( blocksize, 1)))
monoP = True
timeS3 = time.time()
# multiprocessing
print "Data type : %s" % str(data.dtype)
if not monoP:
jobs = []
for i in range(nParts2):
jobs.append(job_server.submit(trackPP.TrackFiberYFM40, (i, 1, params.get('location'), nimage.get('fullname'), nimage.get('type'), shpD, b.T, G2.T, IJKstartpoints2[i].T, r2i, i2r, r2id, i2rd, spa,\
'vectors.npy', vects.vectors.dtype, vects.vectors.T.shape, 'lambda.npy', lVType, 'eigenv.npy', EVType, 'tensor0.npy', xVTensorType, 'wm.npy' ,\
wmType, stepSize, maxLength, fa, spaceEnabled,),(), ("numpy","time",) ))
res = jobs[0]()
for i in range(nParts2-1):
res = jobs[i+1]()
print "Track fibers in %s sec" % str(time.time()-timeS3)
if not roiFilterOnly:
print "Connect tract"
jobs = []
cm2 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
for j in range(nParts2):
print "Number of paths : %s" % str(IJKstartpoints2[j].shape[0])
pathsIJKId = './paths1/' + 'unit_' + str(j) + '_IJK.npy'
pathsLENId = './paths1/' + 'unit_' + str(j) + '_LEN.npy'
if probMode=='binary':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM0, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
elif probMode=='cumulative':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM1, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
else:
jobs.append(job_server.submit(trackPP.ConnectFibersPZM2, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
cm2 = jobs[0]()
for j in range(nParts2-1):
cm2 += jobs[j+1]()
else:
pass
if isInRoiA and isInRoiB:
if not monoP:
vicinity= 1
threshold = 0.1
minLength = 4
print "Try out connecting"
jobs = []
cm3 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
counter1 = 0
counter2 = 0
counterA1 = 0
counterB1 = 0
counterA2 = 0
counterB2 = 0
Pr = 0.0
Fa = 0.0
Wa = 0.0
for i in range(nParts):
print "Number of paths : %s" % str(IJKstartpoints[i].shape[0])
#pathsRASId = './paths0/' + 'unit_' + str(i) + '_RAS.npy'
pathsIJKId = './paths0/' + 'unit_' + str(i) + '_IJK.npy'
#pathsANISId = './paths0/' + 'unit_' + str(i) + '_ANIS.npy'
pathsLOGPId = './paths0/' + 'unit_' + str(i) + '_LOGP.npy'
pathsLENId = './paths0/' + 'unit_' + str(i) + '_LEN.npy'
jobs.append(job_server.submit(trackPP.FilterFibersZM0, (params.get('location'), pathsIJKId, pathsLOGPId, pathsLENId, roiA.getImage(), roiB.getImage(), shpD,\
counter1, counter2, counterA1, counterB1, counterA2, counterB2, Pr, threshold, vicinity), (), ("numpy","time",) ))
cm3 = jobs[0]()
for i in range(nParts-1):
cm3 += jobs[i+1]()
print "Filtering of fibers done from region A to region B"
if counter1>0:
print "Number of curves connecting : %s" % str(counter1)
print "Mean probability : %s" % str(Pr/float(counter1))
jobs = []
cm4 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
counter1 = 0
counter2 = 0
counterA1 = 0
counterB1 = 0
counterA2 = 0
counterB2 = 0
Pr = 0.0
Fa = 0.0
Wa = 0.0
for i in range(nParts2):
print "Number of paths : ", str(IJKstartpoints2[i].shape[0])
#pathsRASId = './paths1/' + 'unit_' + str(i) + '_RAS.npy'
pathsIJKId = './paths1/' + 'unit_' + str(i) + '_IJK.npy'
#pathsANISId = './paths1/' + 'unit_' + str(i) + '_ANIS.npy'
pathsLOGPId = './paths1/' + 'unit_' + str(i) + '_LOGP.npy'
pathsLENId = './paths1/' + 'unit_' + str(i) + '_LEN.npy'
jobs.append(job_server.submit(trackPP.FilterFibersZM0, (params.get('location'), pathsIJKId, pathsLOGPId, pathsLENId, roiB.getImage(), roiA.getImage(), shpD,\
counter1, counter2, counterA1, counterB1, counterA2, counterB2, Pr, threshold, vicinity), (), ("numpy","time",) ))
cm4 = jobs[0]()
for i in range(nParts2-1):
cm4 += jobs[i+1]()
print "Filtering of fibers done from region B to region A"
if counter1>0:
print "Number of curves connecting : %s" % str(counter1)
print "Mean probability : %s" % str(Pr/float(counter1))
else:
pass
else:
print "No tractography to execute!"
if cmEnabled:
if isInRoiA and not roiFilterOnly:
if not (cm == 0).all():
cm = cm.swapaxes(2,0)
tmp= 'cmA_' + dateT
cm.tofile(tmpF + tmp + '.data')
createParams(cm, tmpF + tmp)
if isInRoiB and not roiFilterOnly:
if not (cm2 == 0).all():
cm2 = cm2.swapaxes(2,0)
tmp= 'cmB_' + dateT
cm2.tofile(tmpF + tmp + '.data')
createParams(cm2, tmpF + tmp)
if isInRoiA and isInRoiB and not roiFilterOnly:
cm1a2 = cm[...]*cm2[...]/2.0
cm1a2 = cm1a2.astype('uint32')
if not (cm1a2 == 0).all():
tmp= 'cmAandB_' + dateT
cm1a2.tofile(tmpF + tmp + '.data')
createParams(cm1a2, tmpF + tmp)
tmp= 'cmFAandB_' + dateT
cm1a2f = cm1a2/float(cm1a2.max())
cm1a2f.astype('float32')
cm1a2f.tofile(tmpF + tmp + '.data')
createParams(cm1a2f, tmpF + tmp)
cm1o2 = (cm[...]+cm2[...])/2.0
cm1o2 = cm1o2.astype('uint32')
if not (cm1o2 == 0).all():
tmp= 'cmAorB_' + dateT
cm1o2.tofile(tmpF + tmp + '.data')
createParams(cm1o2, tmpF + tmp)
tmp= 'cmFAorB_' + dateT
cm1o2f = cm1o2/float(cm1o2.max())
cm1o2f.astype('float32')
cm1o2f.tofile(tmpF + tmp + '.data')
createParams(cm1o2f, tmpF + tmp)
if isInRoiA and isInRoiB:
if not (cm3 == 0).all():
tmp= 'cmA2B_' + dateT
cm3 = cm3.swapaxes(2,0)
cm3.tofile(tmpF + tmp + '.data')
createParams(cm3, tmpF + tmp)
tmp= 'cmFA2B_' + dateT
cm3f = cm3/float(cm3.max())
cm3f.astype('float32')
cm3f.tofile(tmpF + tmp + '.data')
createParams(cm3f, tmpF + tmp)
if not (cm4 == 0).all():
tmp= 'cmB2A_' + dateT
cm4 = cm4.swapaxes(2,0)
cm4.tofile(tmpF + tmp + '.data')
createParams(cm4, tmpF + tmp)
tmp= 'cmFB2A_' + dateT
cm4f = cm4/float(cm4.max())
cm4f.astype('float32')
cm4f.tofile(tmpF + tmp + '.data')
createParams(cm4f, tmpF + tmp)
print "pipeline STOCHASTIC, data shape end : %s" % str(shpD)
def pipeline(host, port, params, nimage, roiA, roiB, wm):
if params.get('pipeline')=='STOCHASTIC':
#print "Pipeline : STOCHASTIC"
ppserversL=("localhost",)
#ppserversL=("*",)
data = nimage.getImage()
####! swap x and z for volume coming from Slicer - do not forget tp apply the inverse before to send them back
data = data.swapaxes(2,0)
print 'pipeline data type : %s' % str(data.dtype)
####
shpD = data.shape
print 'pipeline data shape : %s' % str(shpD)
orgS = nimage.get('origin')
org = [float(orgS[0]), float(orgS[1]), float(orgS[2])]
print 'Origin : %s:%s:%s' % (str(org[0]), str(org[1]), str(org[2]))
spaS = nimage.get('spacing')
spa = [float(spaS[0]), float(spaS[1]), float(spaS[2])]
print 'Spacing : %s:%s:%s' % (str(spa[0]), str(spa[1]), str(spa[2]))
G = nimage.get('grads')
b = nimage.get('bval')
i2r = nimage.get('ijk2ras')
i2rd = nimage.get('ijk2rasd')
mu = nimage.get('mu')
dims = nimage.get('dimensions')
s = slicerd.slicerd(host, int(port))
#print 'DWI : ', params.get('dwi')
isInRoiA = False
if params.hasKey('roiA'):
roiAR = numpy.fromstring(roiA.getImage(), 'uint16')
roiAR = roiAR.reshape(shpD[2], shpD[1], shpD[0]) # because come from Slicer - will not send them back so swap them one for all
roiAR = roiAR.swapaxes(2,0)
roiAR[roiAR>0]=1
roiA.setImage(roiAR)
isInRoiA = True
#print "RoiA : %s" % str(roiAR.shape)
isDir = os.access('paths0', os.F_OK)
if not isDir:
os.mkdir('paths0')
isInRoiB = False
if params.hasKey('roiB'):
if params.get('roiB') != params.get('roiA'):
roiBR = numpy.fromstring(roiB.getImage(), 'uint16')
roiBR = roiBR.reshape(shpD[2], shpD[1], shpD[0])
roiBR = roiBR.swapaxes(2,0)
roiBR[roiBR>0]=1
roiB.setImage(roiBR)
isInRoiB = True
#print "RoiB : %s" % str(roiBR.shape)
isDir = os.access('paths1', os.F_OK)
if not isDir:
os.mkdir('paths1')
isInWM = False
if params.hasKey('wm'):
wmR = numpy.fromstring(wm.getImage(), 'uint16')
wmR = wmR.reshape(shpD[2], shpD[1], shpD[0])
wmR = wmR.swapaxes(2,0)
wm.setImage(wmR)
isInWM = True
#print "WM : %s" % str(wmR.shape)
isInTensor = False
print "Input volumes loaded!"
# values per default
smoothEnabled = False
wmEnabled = True
infWMThres = 300
supWMThres = 900
tensEnabled =True
bLine = 0
stEnabled = True
totalTracts = 500
maxLength = 200
stepSize = 0.5
stopEnabled = True
fa = 0.0
cmEnabled = False
probMode = 0
# got from client
# special handling for bools
if params.hasKey('smoothEnabled'):
smoothEnabled = bool(int(params.get('smoothEnabled')))
if params.hasKey('wmEnabled'):
wmEnabled = bool(int(params.get('wmEnabled')))
if params.hasKey('tensEnabled'):
tensEnabled = bool(int(params.get('tensEnabled')))
if params.hasKey('stEnabled'):
stEnabled = bool(int(params.get('stEnabled')))
if params.hasKey('cmEnabled'):
cmEnabled = bool(int(params.get('cmEnabled')))
if params.hasKey('spaceEnabled'):
spaceEnabled = bool(int(params.get('spaceEnabled')))
if params.hasKey('stopEnabled'):
stopEnabled = bool(int(params.get('stopEnabled')))
if params.hasKey('faEnabled'):
faEnabled = bool(int(params.get('faEnabled')))
if params.hasKey('traceEnabled'):
traceEnabled = bool(int(params.get('traceEnabled')))
if params.hasKey('modeEnabled'):
modeEnabled = bool(int(params.get('modeEnabled')))
# can handle normally
FWHM = numpy.ones((3), 'float')
if params.hasKey('stdDev'):
FWHM[0] = float(params.get('stdDev')[0])
FWHM[1] = float(params.get('stdDev')[1])
FWHM[2] = float(params.get('stdDev')[2])
print "FWHM: %s:%s:%s" % (str(FWHM[0]), str(FWHM[1]), str(FWHM[2]))
if params.hasKey('infWMThres'):
infWMThres = int(params.get('infWMThres'))
print "infWMThres: %s" % str(infWMThres)
if params.hasKey('supWMThres'):
supWMThres = int(params.get('supWMThres'))
print "supWMThres: %s" % str(supWMThres)
if params.hasKey('bLine'):
bLine = int(params.get('bLine'))
print "bLine: %s" % str(bLine)
if params.hasKey('tensMode'):
tensMode = params.get('tensMode')
print "tensMode: %s" % str(tensMode)
if params.hasKey('totalTracts'):
totalTracts = int(params.get('totalTracts'))
print "totalTracts: %s" % str(totalTracts)
if params.hasKey('maxLength'):
maxLength = int(params.get('maxLength'))
print "maxLength: %s" % str(maxLength)
if params.hasKey('stepSize'):
stepSize = float(params.get('stepSize'))
print "stepSize: %s" % str(stepSize)
if params.hasKey('fa'):
fa = float(params.get('fa'))
print "fa: %s" % str(fa)
if params.hasKey('probMode'):
probMode = params.get('probMode')
print "probMode: %s" % str(probMode)
if params.hasKey('lengthEnabled'):
lengthEnabled = params.get('lengthEnabled')
print "lengthEnabled: %s" % str(lengthEnabled)
if params.hasKey('lengthClass'):
lengthClass = params.get('lengthClass')
print "lengthClass: %s" % str(lengthClass)
ngrads = shpD[3] #b.shape[0]
print "Number of gradients : %s" % str(ngrads)
G = G.reshape(ngrads,3)
b = b.reshape(ngrads,1)
i2r = i2r.reshape(4,4)
i2rd = i2rd.reshape(4,4)
mu = mu.reshape(4,4)
r2i = numpy.linalg.inv(i2r)
r2id = numpy.linalg.inv(i2rd)
mu2 = numpy.dot(r2id[:3, :3], mu[:3, :3])
G2 = numpy.dot(G, mu2[:3, :3].T)
vts = vects.vectors
print "Tensor flag : %s" % str(tensEnabled)
cm = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm2 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm3 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
cm4 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
if smoothEnabled:
for k in range(shpD[3]):
timeSM0 = time.time()
data[...,k] = sm.smooth(data[...,k], FWHM, numpy.array([ spa[0], spa[1], spa[2] ],'float'))
print "Smoothing DWI volume %i in %s sec" % (k, str(time.time()-timeSM0))
if wmEnabled:
wm = tensPP.EvaluateWM0(data, bLine, infWMThres, supWMThres)
if isInRoiA: # correcting brain mask with roi A
print "Correcting mask based on roiA"
tmpA = roiA.getImage()
wm[tmpA>0]=1
if isInRoiB: # correcting brain mask with roi A & B
print "Correcting mask based on roiB"
tmpB = roiB.getImage()
wm[tmpB>0]=1
else: # avoid singularities in data
minVData = 10
wm = tensPP.EvaluateWM0(data, bLine, minVData, data[..., bLine].max())
wmEnabled = True # fix
if isInWM or wmEnabled:
isDir = os.access('masks', os.F_OK)
if not isDir:
os.mkdir('masks')
tmpF = './masks/'
numpy.save(tmpF + 'wm.npy', wm)
indx = numpy.transpose(wm.nonzero())
print 'Total Number of voxels : ', shpD[0]*shpD[1]*shpD[2]*ngrads
print 'Masked voxels : ', indx.shape[0]*ngrads
print 'Index shape : ', indx.shape
dataf = data.flatten()
cId = numpy.zeros((indx.shape[0]), 'uint32')
mdata = numpy.zeros((indx.shape[0], ngrads), data.dtype)
for i in range(indx.shape[0]):
cId[i] = indx[i][0]*shpD[1]*shpD[2]*shpD[3] + indx[i][1]*shpD[2]*shpD[3] + indx[i][2]*shpD[3]
mdata[i,:]= dataf[cId[i]:cId[i]+ngrads]
numpy.save(tmpF + 'index.npy', cId)
numpy.save(tmpF + 'mdata.npy', mdata)
if cmEnabled:
print "Compute tensor"
timeS1 = time.time()
monoP = False
# multiprocessing support
dataBlocks = []
wmBlocks = []
nCpu = 2 # could be set to the number of available cores
nParts = 1
if shpD[2]>0 and nCpu>0 :
job_server = pp.Server(ppservers=ppserversL)
ncpusL = job_server.get_ncpus()
#if ncpusL == 0 : ncpusL = 1
print "Number of cores on local machine : %s" % str(ncpusL)
print "Number of active computing nodes : %s" % str(nCpu)
if shpD[2] >= nCpu:
nParts = nCpu
else:
nParts = shpD[2]
for i in range(nParts):
datax = data[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, :]
print "data block %i dimension : %s" % (i, str(datax.shape))
dataBlocks.append(datax)
if isInWM or wmEnabled:
wmx = wm[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts]
wmBlocks.append(wmx)
else:
monoP = True
if not monoP:
jobs = []
job_server.set_ncpus(ncpus = ncpusL)
for i in range(nParts):
jobs.append(job_server.submit(tensPP.EvaluateTensorX1, (dataBlocks[i], G2.T, b.T, wmBlocks[i],),(tensPP.ComputeAFunctional, tensPP.ComputeTensorFunctional,), ("numpy","time",) ))
tBlocks = []
for i in range(nParts):
tBlocks.append(jobs[i]())
lV = numpy.zeros((shpD[0], shpD[1], shpD[2], 3) , 'float')
EV = numpy.zeros((shpD[0], shpD[1], shpD[2], 3, 3), 'float' )
xVTensor = numpy.zeros((shpD[0], shpD[1], shpD[2], 7), 'float')
xYTensor = numpy.zeros((shpD[0], shpD[1], shpD[2], 9), 'float')
xTensor0 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'float')
for i in range(nParts):
EV[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][0]
lV[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, :]= tBlocks[i][1]
xVTensor[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][2]
xYTensor[:, :, i*shpD[2]/nParts:(i+1)*shpD[2]/nParts, ...]= tBlocks[i][3]
xTensor0[...]= xVTensor[..., 0]
lVType = lV.dtype
EVType = EV.dtype
xVTensorType = xVTensor.dtype
wmType = wm.dtype
# computation of alpha, beta, logmu0 and principal eigenvector
tdata = numpy.zeros((indx.shape[0], 6), 'float')
for i in range(indx.shape[0]):
cId[i] = indx[i][0]*shpD[1]*shpD[2]*shpD[3] + indx[i][1]*shpD[2]*shpD[3] + indx[i][2]*shpD[3]
l = lV[indx[i][0], indx[i][1], indx[i][2], :]
index = numpy.argsort(abs(l))[::-1]
l =l[index,:]
# Set point estimates in the Constrained model
E = EV[indx[i][0], indx[i][1], indx[i][2], ...]
alpha = (l[1]+l[2])/2
beta = l[0] - alpha
logmu0 = xTensor0[indx[i][0], indx[i][1], indx[i][2]]
e = E[:, index[0]]
tdata[i,0]= alpha
tdata[i,1]= beta
tdata[i,2]= logmu0
tdata[i,3:]= e
numpy.save(tmpF + 'tdata.npy', tdata)
isDir = os.access('tensors', os.F_OK)
if not isDir:
os.mkdir('tensors')
tmpF = './tensors/'
numpy.save(tmpF + 'eigenv.npy', EV)
numpy.save(tmpF + 'lambda.npy', lV)
numpy.save(tmpF + 'tensor.npy', xVTensor)
numpy.save(tmpF + 'tensor0.npy', xTensor0)
numpy.save(tmpF + 'vectors.npy', vts.T)
else:
pass
print "Compute tensor in %s sec" % str(time.time()-timeS1)
if faEnabled:
faMap = tensC.CalculateFA0(lV)
if traceEnabled:
trMap = tensC.CalculateTrace0(lV)
if modeEnabled:
moMap = tensC.CalculateMode0(lV)
print "Track fibers"
if not stopEnabled:
fa = 0.0
if isInRoiA:
# ROI A
print "Search ROI A"
roiP = cmpV.march0InVolume(roiA.getImage())
shpR = roiP.shape
print "ROI A dimension : %s" % str(shpR)
blocksize = totalTracts
IJKstartpoints = []
monoP = False
nParts = 1
if shpR[0]>0 and nCpu>0 :
if shpR[0] >= nCpu:
nParts = nCpu
else:
nParts = shpR[0]
for i in range(nParts):
roiPx = roiP[i*shpR[0]/nParts:(i+1)*shpR[0]/nParts, :]
print "ROI A %i dimension : %s" % (i, str(roiPx.shape))
IJKstartpoints.append(numpy.tile(roiPx,( blocksize, 1)))
else:
IJKstartpoints.append(numpy.tile(roiP,( blocksize, 1)))
monoP = True
timeS2 = time.time()
# multiprocessing
print "Data type : %s" % str(data.dtype)
if not monoP:
jobs = []
for i in range(nParts):
jobs.append(job_server.submit(trackPP.TrackFiberYFM40, (i, 0, params.get('location'), nimage.get('fullname'), nimage.get('type'), shpD, b.T, G2.T, IJKstartpoints[i].T, r2i, i2r, r2id, i2rd, spa,\
'vectors.npy', vects.vectors.dtype, vects.vectors.T.shape, 'lambda.npy', lVType, 'eigenv.npy', EVType, 'tensor0.npy', xVTensorType, 'wm.npy' ,\
wmType, stepSize, maxLength, fa, spaceEnabled,),(), ("numpy","time",) ))
res = jobs[0]()
for i in range(nParts-1):
res = jobs[i+1]()
print "Track fibers in %s sec" % str(time.time()-timeS2)
print "Connect tract"
jobs = []
cm = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
for j in range(nParts):
print "Number of paths : %s" % str(IJKstartpoints[j].shape[0])
pathsIJKId = './paths0/' + 'unit_' + str(j) + '_IJK.npy'
pathsLENId = './paths0/' + 'unit_' + str(j) + '_LEN.npy'
if probMode=='binary':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM0, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
elif probMode=='cumulative':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM1, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
else:
jobs.append(job_server.submit(trackPP.ConnectFibersPZM2, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
cm = jobs[0]()
for j in range(nParts-1):
cm += jobs[j+1]()
else:
pass
if isInRoiB:
# ROI B
print "Search ROI B"
roiP2 = cmpV.march0InVolume(roiB.getImage())
shpR2 = roiP2.shape
print "ROI B dimension : %s" % str(shpR2)
blocksize = totalTracts
IJKstartpoints2 = []
monoP = False
nParts2 = 1
if shpR2[0]>0 and nCpu>0 :
if shpR2[0] >= nCpu:
nParts2 = nCpu
else:
nParts2 = shpR2[0]
for i in range(nParts2):
roiPx = roiP2[i*shpR2[0]/nParts2:(i+1)*shpR2[0]/nParts2, :]
print "ROI B %i dimension : %s" % (i, str(roiPx.shape))
IJKstartpoints2.append(numpy.tile(roiPx,( blocksize, 1)))
else:
IJKstartpoints2.append(numpy.tile(roiP2,( blocksize, 1)))
monoP = True
timeS3 = time.time()
# multiprocessing
print "Data type : %s" % str(data.dtype)
if not monoP:
jobs = []
for i in range(nParts2):
#if isInWM or wmEnabled:
jobs.append(job_server.submit(trackPP.TrackFiberYFM40, (i, 1, params.get('location'), nimage.get('fullname'), nimage.get('type'), shpD, b.T, G2.T, IJKstartpoints2[i].T, r2i, i2r, r2id, i2rd, spa,\
'vectors.npy', vects.vectors.dtype, vects.vectors.T.shape, 'lambda.npy', lVType, 'eigenv.npy', EVType, 'tensor0.npy', xVTensorType, 'wm.npy' ,\
wmType, stepSize, maxLength, fa, spaceEnabled,),(), ("numpy","time",) ))
res = jobs[0]()
for i in range(nParts2-1):
res = jobs[i+1]()
print "Track fibers in %s sec" % str(time.time()-timeS3)
print "Connect tract"
jobs = []
cm2 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
for j in range(nParts2):
print "Number of paths : %s" % str(IJKstartpoints2[j].shape[0])
pathsIJKId = './paths1/' + 'unit_' + str(j) + '_IJK.npy'
pathsLENId = './paths1/' + 'unit_' + str(j) + '_LEN.npy'
if probMode=='binary':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM0, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
elif probMode=='cumulative':
jobs.append(job_server.submit(trackPP.ConnectFibersPZM1, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
else:
jobs.append(job_server.submit(trackPP.ConnectFibersPZM2, ( params.get('location'), pathsIJKId, pathsLENId, shpD, lengthEnabled),\
(trackPP.ComputeConnectFibersFunctionalP0, trackPP.ComputeConnectFibersFunctionalP1, trackPP.ComputeConnectFibersFunctionalP2,),\
("numpy","time",) ))
cm2 = jobs[0]()
for j in range(nParts2-1):
cm2 += jobs[j+1]()
else:
pass
if isInRoiA and isInRoiB:
if not monoP:
vicinity= 1
threshold = 0.1
minLength = 4
print "Try out connecting"
jobs = []
cm3 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
counter1 = 0
counter2 = 0
counterA1 = 0
counterB1 = 0
counterA2 = 0
counterB2 = 0
Pr = 0.0
Fa = 0.0
Wa = 0.0
for i in range(nParts):
print "Number of paths : %s" % str(IJKstartpoints[i].shape[0])
#pathsRASId = './paths0/' + 'unit_' + str(i) + '_RAS.npy'
pathsIJKId = './paths0/' + 'unit_' + str(i) + '_IJK.npy'
#pathsANISId = './paths0/' + 'unit_' + str(i) + '_ANIS.npy'
pathsLOGPId = './paths0/' + 'unit_' + str(i) + '_LOGP.npy'
pathsLENId = './paths0/' + 'unit_' + str(i) + '_LEN.npy'
jobs.append(job_server.submit(trackPP.FilterFibersZM0, (params.get('location'), pathsIJKId, pathsLOGPId, pathsLENId, roiA.getImage(), roiB.getImage(), shpD,\
counter1, counter2, counterA1, counterB1, counterA2, counterB2, Pr, threshold, vicinity, minLength), (), ("numpy","time",) ))
cm3 = jobs[0]()
for i in range(nParts-1):
cm3 += jobs[i+1]()
print "Filtering of fibers done from region A to region B"
if counter1>0:
print "Number of curves connecting : %s" % str(counter1)
print "Mean probability : %s" % str(Pr/float(counter1))
#print "Mean FA : %s" % str(Fa/float(counter1))
#print "Mean WA : %s" % str(Wa/float(counter1))
jobs = []
cm4 = numpy.zeros((shpD[0], shpD[1], shpD[2]), 'uint32')
counter1 = 0
counter2 = 0
counterA1 = 0
counterB1 = 0
counterA2 = 0
counterB2 = 0
Pr = 0.0
Fa = 0.0
Wa = 0.0
for i in range(nParts2):
print "Number of paths : ", str(IJKstartpoints2[i].shape[0])
#pathsRASId = './paths1/' + 'unit_' + str(i) + '_RAS.npy'
pathsIJKId = './paths1/' + 'unit_' + str(i) + '_IJK.npy'
#pathsANISId = './paths1/' + 'unit_' + str(i) + '_ANIS.npy'
pathsLOGPId = './paths1/' + 'unit_' + str(i) + '_LOGP.npy'
pathsLENId = './paths1/' + 'unit_' + str(i) + '_LEN.npy'
jobs.append(job_server.submit(trackPP.FilterFibersZM0, (params.get('location'), pathsIJKId, pathsLOGPId, pathsLENId, roiB.getImage(), roiA.getImage(), shpD,\
counter1, counter2, counterA1, counterB1, counterA2, counterB2, Pr, threshold, vicinity, minLength), (), ("numpy","time",) ))
cm4 = jobs[0]()
for i in range(nParts2-1):
cm4 += jobs[i+1]()
print "Filtering of fibers done from region B to region A"
if counter1>0:
print "Number of curves connecting : %s" % str(counter1)
print "Mean probability : %s" % str(Pr/float(counter1))
else:
pass
else:
print "No tractography to execute!"
dateT = str(int(round(time.time())))
isDir = os.access('outputs', os.F_OK)
if not isDir:
os.mkdir('outputs')
tmpF = './outputs/'
i2r.tofile(tmpF + 'trafo_' + dateT + '.ijk')
if smoothEnabled:
ga = data[..., bLine]
ga = ga.swapaxes(2,0)
tmp= 'smooth_' + dateT
ga.tofile(tmpF + tmp + '.data')
createParams(ga, tmpF + tmp)
s.putS(ga, dims, org, i2r, tmp)
if wmEnabled:
wm = wm.swapaxes(2,0)
tmp= 'brain_' + dateT
wm.tofile(tmpF + tmp + '.data')
createParams(wm, tmpF + tmp)
s.putS(wm, dims, org, i2r, tmp)
if cmEnabled:
xVTensor = xVTensor.swapaxes(2,0)
xVTensor = xVTensor.astype('float32') # slicerd do not support double type yet
xYTensor = xYTensor.swapaxes(2,0)
xYTensor = xYTensor.astype('float32') # slicerd do not support double type yet
tmp= 'tensor_' + dateT
xYTensor.tofile(tmpF + tmp + '.data')
createParams(xYTensor, tmpF + tmp, True)
s.putD(xVTensor, dims, org, i2r, mu, tmp)
if faEnabled:
faMap = faMap.swapaxes(2,0)
tmp= 'fa_' + dateT
faMap.tofile(tmpF + tmp + '.data')
createParams(faMap, tmpF + tmp)
s.putS(faMap, dims, org, i2r, tmp)
if traceEnabled:
trMap = trMap.swapaxes(2,0)
tmp= 'trace_' + dateT
trMap.tofile(tmpF + tmp + '.data')
createParams(trMap, tmpF + tmp)
s.putS(trMap, dims, org, i2r, tmp)
if modeEnabled:
moMap = moMap.swapaxes(2,0)
tmp= 'mode_' + dateT
moMap.tofile(tmpF + tmp + '.data')
createParams(moMap, tmpF + tmp)
s.putS(moMap, dims, org, i2r, tmp)
if isInRoiA:
if not (cm == 0).all():
cm = cm.swapaxes(2,0)
tmp= 'cmA_' + dateT
cm.tofile(tmpF + tmp + '.data')
createParams(cm, tmpF + tmp)
#s.putS(cm, dims, org, i2r, tmp)
tmp= 'cmFA_' + dateT
cmf = cm/float(cm.max())
cmf.astype('float32')
cmf.tofile(tmpF + tmp + '.data')
createParams(cmf, tmpF + tmp)
s.putS(cmf, dims, org, i2r, tmp)
if isInRoiB:
if not (cm2 == 0).all():
cm2 = cm2.swapaxes(2,0)
tmp= 'cmB_' + dateT
cm2.tofile(tmpF + tmp + '.data')
createParams(cm2, tmpF + tmp)
#s.putS(cm2, dims, org, i2r, tmp)
tmp= 'cmFB_' + dateT
cm2f = cm2/float(cm2.max())
cm2f.astype('float32')
cm2f.tofile(tmpF + tmp + '.data')
createParams(cm2f, tmpF + tmp)
s.putS(cm2f, dims, org, i2r, tmp)
if isInRoiA and isInRoiB:
cm1a2 = cm[...]*cm2[...]/2.0
cm1a2 = cm1a2.astype('uint32')
if not (cm1a2 == 0).all():
tmp= 'cmAandB_' + dateT
cm1a2.tofile(tmpF + tmp + '.data')
createParams(cm1a2, tmpF + tmp)
#s.putS(cm1a2, dims, org, i2r, tmp)
tmp= 'cmFAandB_' + dateT
cm1a2f = cm1a2/float(cm1a2.max())
cm1a2f.astype('float32')
cm1a2f.tofile(tmpF + tmp + '.data')
createParams(cm1a2f, tmpF + tmp)
s.putS(cm1a2f, dims, org, i2r, tmp)
cm1o2 = (cm[...]+cm2[...])/2.0
cm1o2 = cm1o2.astype('uint32')
if not (cm1o2 == 0).all():
tmp= 'cmAorB_' + dateT
cm1o2.tofile(tmpF + tmp + '.data')
createParams(cm1o2, tmpF + tmp)
#s.putS(cm1o2, dims, org, i2r, tmp)
tmp= 'cmFAorB_' + dateT
cm1o2f = cm1o2/float(cm1o2.max())
cm1o2f.astype('float32')
cm1o2f.tofile(tmpF + tmp + '.data')
createParams(cm1o2f, tmpF + tmp)
s.putS(cm1o2f, dims, org, i2r, tmp)
if not (cm3 == 0).all():
tmp= 'cmA2B_' + dateT
cm3 = cm3.swapaxes(2,0)
cm3.tofile(tmpF + tmp + '.data')
createParams(cm3, tmpF + tmp)
#s.putS(cm3, dims, org, i2r, tmp)
tmp= 'cmFA2B_' + dateT
cm3f = cm3/float(cm3.max())
cm3f.astype('float32')
cm3f.tofile(tmpF + tmp + '.data')
createParams(cm3f, tmpF + tmp)
s.putS(cm3f, dims, org, i2r, tmp)
if not (cm4 == 0).all():
tmp= 'cmB2A_' + dateT
cm4 = cm4.swapaxes(2,0)
cm4.tofile(tmpF + tmp + '.data')
createParams(cm4, tmpF + tmp)
#s.putS(cm4, dims, org, i2r, tmp)
tmp= 'cmFB2A_' + dateT
cm4f = cm4/float(cm4.max())
cm4f.astype('float32')
cm4f.tofile(tmpF + tmp + '.data')
createParams(cm4f, tmpF + tmp)
s.putS(cm4f, dims, org, i2r, tmp)
print "pipeline STOCHASTIC, data shape end : %s" % str(shpD)
def pipeline(self, data):
if self.nimage.get('pipeline')[0]=='STOCHASTIC':
####! swap x and z for volume coming from Slicer - do not forget to apply the inverse before to send them back
data = data.swapaxes(2,0)
data = data.astype('float')
####
shpD = data.shape
shpV0 = numpy.zeros((4), 'uint16')
shpV0[0]= shpD[0]
shpV0[1]= shpD[1]
shpV0[2]= shpD[2]
shpV0[3]= shpD[3]
logger.info("pipeline data shape : %s:%s:%s:%s" % (shpD[0], shpD[1], shpD[2], shpD[3]))
logger.info("pipeline data type : %s" % data.dtype)
orgS = self.nimage.get('origin')
org = [float(orgS[0]), float(orgS[1]), float(orgS[2])]
org0 = numpy.zeros((3), 'float')
org0[0]=org[0]
org0[1]=org[1]
org0[2]=org[2]
logger.info("origin : %s:%s:%s" % (org[0], org[1], org[2]))
spaS = self.nimage.get('spacing')
spa = [float(spaS[0]), float(spaS[1]), float(spaS[2])]
spa0 = numpy.zeros((3), 'float')
spa0[0]=spa[0]
spa0[1]=spa[1]
spa0[2]=spa[2]
logger.info("spacing : %s:%s:%s" % (spa[0], spa[1], spa[2]))
G = self.nimage.get('grads')
b = self.nimage.get('bval')
i2r = self.nimage.get('ijk2ras')
i2rd = self.nimage.get('ijk2rasd')
mu = self.nimage.get('mu')
dims = self.nimage.get('dimensions')
s = slicerd.slicerd()
scene = s.ls()
dscene = {}
indS = []
for i in range(len(scene)):
if scene[i].isdigit():
indS.append(i)
for j in range(len(indS)):
if j<len(indS)-1:
tmpD2 = ''
tmpD = scene[indS[j]+2:indS[j+1]]
for k in range(len(tmpD)):
if k <len(tmpD)-1:
tmpD2 += tmpD[k] + ' '
else:
tmpD2 += tmpD[k]
dscene[tmpD2]= j
else:
tmpD2 = ''
tmpD = scene[indS[j]+2:]
for k in range(len(tmpD)):
if k <len(tmpD)-1:
tmpD2 += tmpD[k] + ' '
else:
tmpD2 += tmpD[k]
dscene[tmpD2]= j
logger.info("scene : %s" % dscene)
# currently there is a bug in the GUI of slicer python - do not load if three times the same volume
isInRoiA = False
if self.params.hasKey('roiA'):
if dscene.has_key(self.params.get('roiA')[0]):
self.roiA = s.get(int(dscene[self.params.get('roiA')[0]]))
roiAR = numpy.fromstring(self.roiA.getImage(), 'uint16')
if roiAR.shape[0] == shpD[2]*shpD[1]*shpD[0]:
roiAR = roiAR.reshape(shpD[2], shpD[1], shpD[0]) # because come from Slicer - will not send them back so swap them one for all
roiAR = roiAR.swapaxes(2,0)
roiAR[roiAR>0]=1
self.roiA.setImage(roiAR)
isInRoiA = True
logger.info("RoiA : %s:%s:%s" % (roiAR.shape[0], roiAR.shape[1], roiAR.shape[2]))
else:
logger.info("RoiA has not the same dimension as the DWI : %s" % roiAR.shape[0])
isInRoiB = False
if self.params.hasKey('roiB'):
if dscene.has_key(self.params.get('roiB')[0]):
if self.params.get('roiB')[0] != self.params.get('roiA')[0]:
self.roiB = s.get(int(dscene[self.params.get('roiB')[0]]))
roiBR = numpy.fromstring(self.roiB.getImage(), 'uint16')
if roiBR.shape[0] == shpD[2]*shpD[1]*shpD[0]:
roiBR = roiBR.reshape(shpD[2], shpD[1], shpD[0])
roiBR = roiBR.swapaxes(2,0)
roiBR[roiBR>0]=1
self.roiB.setImage(roiBR)
isInRoiB = True
logger.info("RoiB : %s:%s:%s" % (roiBR.shape[0], roiBR.shape[1], roiBR.shape[2]))
else:
logger.info("RoiB has not the same dimension as the DWI : %s" % roiBR.shape[0])
isInWM = False
if self.params.hasKey('wm'):
if dscene.has_key(self.params.get('wm')[0]):
self.wm = s.get(int(dscene[self.params.get('wm')[0]]))
wmR = numpy.fromstring(self.wm.getImage(), 'uint16')
if wmR.shape[0] == shpD[2]*shpD[1]*shpD[0]:
wmR = wmR.reshape(shpD[2], shpD[1], shpD[0])
wmR = wmR.swapaxes(2,0)
self.wm.setImage(wmR)
isInWM = True
logger.info("WM : %s:%s:%s" % (wmR.shape[0], wmR.shape[1], wmR.shape[2]))
else:
logger.info("WM has not the same dimension as the DWI : %s" % wmR.shape[0])
isInTensor = False
if self.params.hasKey('tensor'):
if dscene.has_key(self.params.get('tensor')[0]):
self.ten = s.get(int(dscene[self.params.get('tensor')[0]]))
tenR = numpy.fromstring(self.ten.getImage(), 'float32')
if tenR.shape[0] == shpD[2]*shpD[1]*shpD[0]*7:
tenR = tenR.reshape(shpD[2], shpD[1], shpD[0], 7)
tenR = tenR.swapaxes(2,0)
self.ten.setImage(tenR)
isInTensor = True
logger.info("Tensor : %s:%s" % (tenR.shape[0], tenR.shape[1]))
else:
logger.info("Tensor has not the same dimension as the DWI : %s" % tenR.shape[0])
logger.info("Input volumes loaded!")
# values per default
smoothEnabled = False
wmEnabled = True
infWMThres = 300
supWMThres = 900
tensEnabled =True
bLine = 0
stEnabled = True
totalTracts = 500
maxLength = 200
stepSize = 0.5
stopEnabled = True
fa = 0.0
cmEnabled = False
probMode = 0
# got from client
# special handling for bools
if self.params.hasKey('smoothEnabled'):
smoothEnabled = bool(int(self.params.get('smoothEnabled')[0]))
if self.params.hasKey('wmEnabled'):
wmEnabled = bool(int(self.params.get('wmEnabled')[0]))
if self.params.hasKey('tensEnabled'):
tensEnabled = bool(int(self.params.get('tensEnabled')[0]))
if self.params.hasKey('stEnabled'):
stEnabled = bool(int(self.params.get('stEnabled')[0]))
if self.params.hasKey('cmEnabled'):
cmEnabled = bool(int(self.params.get('cmEnabled')[0]))
if self.params.hasKey('spaceEnabled'):
spaceEnabled = bool(int(self.params.get('spaceEnabled')[0]))
if self.params.hasKey('stopEnabled'):
stopEnabled = bool(int(self.params.get('stopEnabled')[0]))
if self.params.hasKey('faEnabled'):
faEnabled = bool(int(self.params.get('faEnabled')[0]))
if self.params.hasKey('traceEnabled'):
traceEnabled = bool(int(self.params.get('traceEnabled')[0]))
if self.params.hasKey('modeEnabled'):
modeEnabled = bool(int(self.params.get('modeEnabled')[0]))
if self.params.hasKey('sphericalEnabled'):
sphericalEnabled = bool(int(self.params.get('sphericalEnabled')[0]))
# can handle normally
FWHM = numpy.ones((3), 'float')
if self.params.hasKey('stdDev'):
FWHM[0] = float(self.params.get('stdDev')[0])
FWHM[1] = float(self.params.get('stdDev')[1])
FWHM[2] = float(self.params.get('stdDev')[2])
logger.debug("FWHM: %s:%s:%s" % (FWHM[0], FWHM[1], FWHM[2]) )
if self.params.hasKey('infWMThres'):
infWMThres = int(self.params.get('infWMThres')[0])
logger.debug("infWMThres: %s" % infWMThres)
if self.params.hasKey('supWMThres'):
supWMThres = int(self.params.get('supWMThres')[0])
logger.debug("supWMThres: %s" % supWMThres)
if self.params.hasKey('bLine'):
bLine = int(self.params.get('bLine')[0])
logger.debug("bLine: %s" % bLine)
if self.params.hasKey('isIJK'):
isIJK = bool(int(self.params.get('isIJK')[0]))
logger.debug("isIJK: %s" % isIJK)
if self.params.hasKey('tensMode'):
tensMode = self.params.get('tensMode')[0]
logger.debug("tensMode: %s" % tensMode)
if self.params.hasKey('totalTracts'):
totalTracts = int(self.params.get('totalTracts')[0])
logger.debug("totalTracts: %s" % totalTracts)
if self.params.hasKey('maxLength'):
maxLength = int(self.params.get('maxLength')[0])
logger.debug("maxLength: %s" % maxLength)
if self.params.hasKey('stepSize'):
stepSize = float(self.params.get('stepSize')[0])
logger.debug("stepSize: %s" % stepSize)
if self.params.hasKey('fa'):
fa = float(self.params.get('fa')[0])
logger.debug("fa: %s" % fa)
if self.params.hasKey('probMode'):
probMode = self.params.get('probMode')[0]
logger.debug("probMode: %s" % probMode)
if self.params.hasKey('lengthEnabled'):
lengthEnabled = self.params.get('lengthEnabled')[0]
logger.debug("lengthEnabled: %s" % lengthEnabled)
if self.params.hasKey('lengthClass'):
lengthClass = self.params.get('lengthClass')[0]
logger.debug("lengthClass: %s" % lengthClass)
if self.params.hasKey('tractOffset'):
tractOffset = int(self.params.get('tractOffset')[0])
logger.debug("tractOffset: %s" % tractOffset)
if self.params.hasKey('vicinity'):
vicinity = int(self.params.get('vicinity')[0])
logger.debug("vicinity: %s" % vicinity)
if self.params.hasKey('threshold'):
threshold = float(self.params.get('threshold')[0])
logger.debug("threshold: %s" % threshold)
ngrads = shpD[3]
logger.info("Number of gradients : %s" % str(ngrads) )
G = G.reshape((ngrads,3))
b = b.reshape((ngrads,1))
i2r = i2r.reshape((4,4))
i2rd = i2rd.reshape((4,4))
mu = mu.reshape((4,4))
r2i = numpy.linalg.inv(i2r)
r2id = numpy.linalg.inv(i2rd)
# compute trafo fir IJK & RAS
# gradients in RAS
G1 = numpy.dot(G, mu[:3, :3].T)
# gradients in IJK
mu2 = numpy.dot(r2id[:3, :3], mu[:3, :3])
G2 = numpy.dot(G, mu2[:3, :3].T)
# switch to compute either in IJK or RAS
if isIJK:
G0 = G2
else:
G0 = G1
vts = vects.vectors
logger.info("Tensor flag : %s" % str(tensEnabled))
if smoothEnabled:
for k in range(shpD[3]):
timeSM0 = time.time()
data[...,k] = sm.smooth(data[...,k], FWHM, numpy.array([ spa[0], spa[1], spa[2] ],'float'))
logger.info("Smoothing DWI volume %i in %s sec" % (k, str(time.time()-timeSM0)))
if wmEnabled:
wm = tens.EvaluateWM0(data, bLine, infWMThres, supWMThres)
if isInRoiA: # correcting brain mask with roi A
logger.info("Correcting mask based on roiA")
tmpA = self.roiA.getImage()
wm[tmpA>0]=1
if isInRoiB: # correcting brain mask with roi A & B
logger.info("Correcting mask based on roiB")
tmpB = self.roiB.getImage()
wm[tmpB>0]=1
else: # avoid singularities in data
minVData = 10
wm = tens.EvaluateWM0(data, bLine, minVData, data[..., bLine].max())
if isInWM:
logger.info("Using external mask")
wm = self.wm.getImage()
if tensEnabled:
logger.info("Compute tensor")
timeS1 = time.time()
if not isInTensor:
EV, lV, xVTensor, xYTensor = tens.EvaluateTensorX1(data, G0.T, b.T, wm)
else:
EV, lV, xVTensor, xYTensor = tens.EvaluateTensorK1(self.ten.getImage(), shpD, wm)
logger.info("Compute tensor in %s sec" % str(time.time()-timeS1))
if faEnabled:
faMap = tensC.CalculateFA0(lV)
if traceEnabled:
trMap = tensC.CalculateTrace0(lV)
if modeEnabled:
moMap = tensC.CalculateMode0(lV)
if cmEnabled:
logger.info("Track fibers")
if not stopEnabled:
fa = 0.0
if isInRoiA:
# ROI A
logger.info("Search ROI A")
roiP = cmpV.march0InVolume(self.roiA.getImage())
shpR = roiP.shape
logger.info("ROI A dimension : %s:%s" % (str(shpR[0]), str(shpR[1])))
blocksize = totalTracts
IJKstartpoints= numpy.tile(roiP,( blocksize, 1))
logger.info("IJK start points shape : %s" % str(IJKstartpoints.shape))
timeS2 = time.time()
logger.info("Data type : %s" % data.dtype)
if tensEnabled:
paths00, paths01, paths02, paths03, paths04 = track.TrackFiberZ40(s, wm, shpD, b.T, G0.T, vts.T, IJKstartpoints.T,\
r2i, i2r, r2id, i2rd, spa, lV, EV, xVTensor, stepSize, maxLength, fa, spaceEnabled, isIJK)
else:
paths00, paths01, paths02, paths03, paths04 = track.TrackFiberX40(s, data.flatten(), wm, shpD, b.T, G0.T, vts.T, IJKstartpoints.T,\
r2i, i2r, r2id, i2rd, spa, stepSize, maxLength, fa, spaceEnabled, isIJK)
logger.info("Track fibers in %s sec" % str(time.time()-timeS2))
logger.info("Connect tract")
if probMode=='binary':
cm = track.ConnectFibersX0(paths01, paths04, shpD, lengthEnabled, lengthClass)
elif probMode=='cumulative':
cm = track.ConnectFibersX1(paths01, paths04, shpD, lengthEnabled, lengthClass)
else:
cm = track.ConnectFibersX2(paths01, paths04, shpD, lengthEnabled, lengthClass)
if isInRoiB:
# ROI B
logger.info("Search ROI B")
roiP2 = cmpV.march0InVolume(self.roiB.getImage())
shpR2 = roiP2.shape
logger.info("ROI B dimension : %s:%s" % (str(shpR2[0]), str(shpR2[1])))
blocksize = totalTracts
IJKstartpoints2 = numpy.tile(roiP2,( blocksize, 1))
logger.info("IJK start points shape : %s" % str(IJKstartpoints2.shape))
timeS3 = time.time()
logger.info("Data type : %s" % data.dtype)
if tensEnabled:
paths10, paths11, paths12, paths13, paths14 = track.TrackFiberZ40(s, wm, shpD, b.T, G0.T, vts.T, IJKstartpoints2.T,\
r2i, i2r, r2id, i2rd, spa, lV, EV, xVTensor, stepSize, maxLength, fa, spaceEnabled, isIJK)
else:
paths10, paths11, paths12, paths13, paths14 = track.TrackFiberX40(s, data.flatten(), wm, shpD, b.T, G0.T, vts.T, IJKstartpoints2.T,\
r2i, i2r, r2id, i2rd, spa, stepSize, maxLength, fa, spaceEnabled, isIJK)
logger.info("Track fibers in %s sec" % str(time.time()-timeS3))
logger.info("Connect tract")
if probMode=='binary':
cm2 = track.ConnectFibersX0(paths11, paths14, shpD, lengthEnabled, lengthClass)
elif probMode=='cumulative':
cm2 = track.ConnectFibersX1(paths11, paths14, shpD, lengthEnabled, lengthClass)
else:
cm2 = track.ConnectFibersX2(paths11, paths14, shpD, lengthEnabled, lengthClass)
if isInRoiA and isInRoiB:
cm3 = track.FilterFibers0(paths00, paths01, paths02, paths03, paths04, self.roiA.getImage(), self.roiB.getImage(),\
shpD, threshold, tractOffset, vicinity, sphericalEnabled)
cm4 = track.FilterFibers0(paths10, paths11, paths12, paths13, paths14, self.roiB.getImage(), self.roiA.getImage(),\
shpD, threshold, tractOffset, vicinity, sphericalEnabled)
else:
logger.info("No tractography to execute!")
dateT = str(int(round(time.time())))
isDir = os.access('outputs', os.F_OK)
if not isDir:
os.mkdir('outputs')
tmpF = './outputs/'
i2r.tofile(tmpF + 'trafo_' + dateT + '.ijk')
if smoothEnabled:
ga = data[..., bLine]
ga = ga.swapaxes(2,0)
tmp= 'smooth_' + dateT
if not (ga == 0).all():
ga.tofile(tmpF + tmp + '.data')
createParams(ga, tmpF + tmp)
s.putS(ga, dims, org, i2r, tmp)
#if wmEnabled:
wm = wm.swapaxes(2,0)
tmp= 'brain_' + dateT
if not (wm == 0).all():
wm.tofile(tmpF + tmp + '.data')
createParams(wm, tmpF + tmp)
s.putS(wm, dims, org, i2r, tmp)
if tensEnabled:
if isIJK:
xVTensor = xVTensor.swapaxes(2,0)
xVTensor = xVTensor.astype('float32') # slicerd do not support double type yet
xYTensor = xYTensor.swapaxes(2,0)
xYTensor = xYTensor.astype('float32') # slicerd do not support double type yet
tmp= 'tensor_' + dateT
if (not (xYTensor == 0).all()) and (not (xVTensor == 0).all()):
xYTensor.tofile(tmpF + tmp + '.data')
createParams(xYTensor, tmpF + tmp, True)
s.putD(xVTensor, dims, org, i2r, mu, tmp)
if faEnabled:
faMap = faMap.swapaxes(2,0)
tmp= 'fa_' + dateT
if not (faMap == 0).all():
faMap.tofile(tmpF + tmp + '.data')
createParams(faMap, tmpF + tmp)
s.putS(faMap, dims, org, i2r, tmp)
if traceEnabled:
trMap = trMap.swapaxes(2,0)
tmp= 'trace_' + dateT
if not (trMap == 0).all():
trMap.tofile(tmpF + tmp + '.data')
createParams(trMap, tmpF + tmp)
s.putS(trMap, dims, org, i2r, tmp)
if modeEnabled:
moMap = moMap.swapaxes(2,0)
tmp= 'mode_' + dateT
if not (moMap == 0).all():
moMap.tofile(tmpF + tmp + '.data')
createParams(moMap, tmpF + tmp)
s.putS(moMap, dims, org, i2r, tmp)
if cmEnabled:
if isInRoiA:
cm = cm.swapaxes(2,0)
tmp= 'cmA_' + dateT
if not (cm == 0).all():
cm.tofile(tmpF + tmp + '.data')
createParams(cm, tmpF + tmp)
#s.putS(cm, dims, org, i2r, tmp)
tmp= 'cmFA_' + dateT
cmf = cm/float(cm.max())
cmf.astype('float32')
if not (cmf == 0).all():
cmf.tofile(tmpF + tmp + '.data')
createParams(cmf, tmpF + tmp)
s.putS(cmf, dims, org, i2r, tmp)
if isInRoiB:
cm2 = cm2.swapaxes(2,0)
tmp= 'cmB_' + dateT
if not (cm2 == 0).all():
cm2.tofile(tmpF + tmp + '.data')
createParams(cm2, tmpF + tmp)
#s.putS(cm2, dims, org, i2r, tmp)
tmp= 'cmFB_' + dateT
cm2f = cm2/float(cm2.max())
cm2f.astype('float32')
if not (cm2f == 0).all():
cm2f.tofile(tmpF + tmp + '.data')
createParams(cm2f, tmpF + tmp)
s.putS(cm2f, dims, org, i2r, tmp)
if isInRoiA and isInRoiB:
cm1a2 = cm[...]*cm2[...]/2.0
cm1a2 = cm1a2.astype('uint32')
if not (cm1a2 == 0).all():
tmp= 'cmAandB_' + dateT
cm1a2.tofile(tmpF + tmp + '.data')
createParams(cm1a2, tmpF + tmp)
#s.putS(cm1a2, dims, org, i2r, tmp)
tmp= 'cmFAandB_' + dateT
cm1a2f = cm1a2/float(cm1a2.max())
cm1a2f.astype('float32')
cm1a2f.tofile(tmpF + tmp + '.data')
createParams(cm1a2f, tmpF + tmp)
s.putS(cm1a2f, dims, org, i2r, tmp)
cm1o2 = (cm[...]+cm2[...])/2.0
cm1o2 = cm1o2.astype('uint32')
if not (cm1o2 == 0).all():
tmp= 'cmAorB_' + dateT
cm1o2.tofile(tmpF + tmp + '.data')
createParams(cm1o2, tmpF + tmp)
#s.putS(cm1o2, dims, org, i2r, tmp)
tmp= 'cmFAorB_' + dateT
cm1o2f = cm1o2/float(cm1o2.max())
cm1o2f.astype('float32')
cm1o2f.tofile(tmpF + tmp + '.data')
createParams(cm1o2f, tmpF + tmp)
s.putS(cm1o2f, dims, org, i2r, tmp)
if not (cm3 == 0).all():
tmp= 'cmA2B_' + dateT
cm3 = cm3.swapaxes(2,0)
cm3.tofile(tmpF + tmp + '.data')
createParams(cm3, tmpF + tmp)
#s.putS(cm3, dims, org, i2r, tmp)
tmp= 'cmFA2B_' + dateT
cm3f = cm3/float(cm3.max())
cm3f.astype('float32')
cm3f.tofile(tmpF + tmp + '.data')
createParams(cm3f, tmpF + tmp)
s.putS(cm3f, dims, org, i2r, tmp)
if not (cm4 == 0).all():
tmp= 'cmB2A_' + dateT
cm4 = cm4.swapaxes(2,0)
cm4.tofile(tmpF + tmp + '.data')
createParams(cm4, tmpF + tmp)
#s.putS(cm4, dims, org, i2r, tmp)
tmp= 'cmFB2A_' + dateT
cm4f = cm4/float(cm4.max())
cm4f.astype('float32')
cm4f.tofile(tmpF + tmp + '.data')
createParams(cm4f, tmpF + tmp)
s.putS(cm4f, dims, org, i2r, tmp)
logger.debug("pipeline data shape end : %s:%s:%s:%s" % (shpD[0], shpD[1], shpD[2], shpD[3]))
return data
