def estimateNewMonomodalDiffeomorphicField2D(moving, fixed, lambdaParam,
maxOuterIter,
previousDisplacement,
previousDisplacementInverse):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance = 1e-4
displacement = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
gradientField = np.empty(shape=(moving.shape) + (2, ), dtype=np.float64)
totalDisplacement = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
totalDisplacementInverse = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
if (previousDisplacement != None):
totalDisplacement[...] = previousDisplacement
totalDisplacementInverse[...] = previousDisplacementInverse
outerIter = 0
framesToCapture = 5
maxOuterIter = framesToCapture * (
(maxOuterIter + framesToCapture - 1) / framesToCapture)
itersPerCapture = maxOuterIter / framesToCapture
plt.figure()
while (outerIter < maxOuterIter):
outerIter += 1
print 'Outer iter:', outerIter
warped = np.array(tf.warp_image(moving, totalDisplacement, None))
if ((outerIter == 1) or (outerIter % itersPerCapture == 0)):
plt.subplot(1, framesToCapture + 1,
1 + outerIter / itersPerCapture)
rcommon.overlayImages(warped, fixed, False)
plt.title('Iter:' + str(outerIter - 1))
sigmaField = np.ones_like(warped, dtype=np.float64)
deltaField = fixed - warped
gradientField[:, :, 0], gradientField[:, :, 1] = sp.gradient(warped)
maxVariation = 1 + innerTolerance
innerIter = 0
displacement[...] = 0
maxInnerIter = 200
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, gradientField, lambdaParam,
displacement, None)
#maxDisplacement=np.max(np.abs(displacement))
expd, invexpd = tf.vector_field_exponential(displacement, True)
totalDisplacement, stats = tf.compose_vector_fields(
displacement, totalDisplacement)
#totalDisplacement=np.array(totalDisplacement)
totalDisplacementInverse, stats = tf.compose_vector_fields(
totalDisplacementInverse, invexpd)
#totalDisplacementInverse=np.array(totalDisplacementInverse)
#if(maxDisplacement<outerTolerance):
#break
print "Iter: ", innerIter, "Max variation:", maxVariation
return totalDisplacement, totalDisplacementInverse
def testInversion(lambdaParam):
fname0='data/circle.png'
fname1='data/C.png'
circleToCDisplacementName='circleToCDisplacement.npy'
circleToCDisplacementInverseName='circleToCDisplacementInverse.npy'
nib_moving=plt.imread(fname0)
nib_fixed=plt.imread(fname1)
moving=nib_moving[:,:,0]
fixed=nib_fixed[:,:,1]
moving=(moving-moving.min())/(moving.max() - moving.min())
fixed=(fixed-fixed.min())/(fixed.max() - fixed.min())
level=3
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, np.ones_like(maskMoving))]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(fixed, level, np.ones_like(maskFixed))]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
maxOuterIter=[10,50,100,100,100,100,100,100,100]
if(os.path.exists(circleToCDisplacementName)):
displacement=np.load(circleToCDisplacementName)
inverse=np.load(circleToCDisplacementInverseName)
else:
displacement, inverse=estimateMonomodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
print 'vector field exponential'
expd, invexpd=tf.vector_field_exponential(displacement, True)
print 'vector field inversion'
directInverse=tf.invert_vector_field(displacement, 1.0, 10000, 1e-7)
print 'vector field inversion'
directExpInverse=tf.invert_vector_field(expd, 1.0, 10000, 1e-7)
###Now compare inversions###
residualJoint=np.array(tf.compose_vector_fields(displacement, inverse)[0])
residualDirect=np.array(tf.compose_vector_fields(displacement, directInverse)[0])
residualExpJoint=np.array(tf.compose_vector_fields(expd, invexpd)[0])
residualExpDirect=np.array(tf.compose_vector_fields(expd, directExpInverse)[0])
rcommon.plotDiffeomorphism(displacement, inverse, residualJoint, 'D-joint')
rcommon.plotDiffeomorphism(expd, invexpd, residualExpJoint, 'expD-joint')
d,invd,res,jacobian=rcommon.plotDiffeomorphism(displacement, directInverse, residualDirect, 'D-direct')
rcommon.plotDiffeomorphism(expd, directExpInverse, residualExpDirect, 'expD-direct')
sp.misc.imsave('circleToC_deformation.png', d)
sp.misc.imsave('circleToC_inverse_deformation.png', invd)
sp.misc.imsave('circleToC_residual_deformation.png', res)
tf.write_double_buffer(np.array(displacement).reshape(-1), '../inverse/experiments/displacement.bin')
tf.write_double_buffer(np.array(displacement).reshape(-1), '../inverse/experiments/displacement_clean.bin')
def estimateNewMonomodalDiffeomorphicField2D(moving, fixed, lambdaParam, maxOuterIter, previousDisplacement, previousDisplacementInverse):
'''
Warning: in the monomodal case, the parameter lambda must be significantly lower than in the multimodal case. Try lambdaParam=1,
as opposed as lambdaParam=150 used in the multimodal case
'''
innerTolerance=1e-4
displacement =np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
gradientField =np.empty(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacement=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacementInverse=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
if(previousDisplacement!=None):
totalDisplacement[...]=previousDisplacement
totalDisplacementInverse[...]=previousDisplacementInverse
outerIter=0
framesToCapture=5
maxOuterIter=framesToCapture*((maxOuterIter+framesToCapture-1)/framesToCapture)
itersPerCapture=maxOuterIter/framesToCapture
plt.figure()
while(outerIter<maxOuterIter):
outerIter+=1
print 'Outer iter:', outerIter
warped=np.array(tf.warp_image(moving, totalDisplacement, None))
if((outerIter==1) or (outerIter%itersPerCapture==0)):
plt.subplot(1,framesToCapture+1, 1+outerIter/itersPerCapture)
rcommon.overlayImages(warped, fixed, False)
plt.title('Iter:'+str(outerIter-1))
sigmaField=np.ones_like(warped, dtype=np.float64)
deltaField=fixed-warped
gradientField[:,:,0], gradientField[:,:,1]=sp.gradient(warped)
maxVariation=1+innerTolerance
innerIter=0
displacement[...]=0
maxInnerIter=200
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, gradientField, lambdaParam, displacement, None)
#maxDisplacement=np.max(np.abs(displacement))
expd, invexpd=tf.vector_field_exponential(displacement, True)
totalDisplacement, stats=tf.compose_vector_fields(displacement, totalDisplacement)
#totalDisplacement=np.array(totalDisplacement)
totalDisplacementInverse, stats=tf.compose_vector_fields(totalDisplacementInverse, invexpd)
#totalDisplacementInverse=np.array(totalDisplacementInverse)
#if(maxDisplacement<outerTolerance):
#break
print "Iter: ",innerIter, "Max variation:",maxVariation
return totalDisplacement, totalDisplacementInverse
def update(new_displacement, current_displacement):
expd, invexpd = tf.vector_field_exponential(new_displacement, True)
updated, stats = tf.compose_vector_fields(expd, current_displacement)
return updated, stats[0]
def testInversion(lambdaParam):
fname0 = 'data/circle.png'
fname1 = 'data/C.png'
circleToCDisplacementName = 'circleToCDisplacement.npy'
circleToCDisplacementInverseName = 'circleToCDisplacementInverse.npy'
nib_moving = plt.imread(fname0)
nib_fixed = plt.imread(fname1)
moving = nib_moving[:, :, 0]
fixed = nib_fixed[:, :, 1]
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 3
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level,
np.ones_like(maskMoving))
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_2D(fixed, level,
np.ones_like(maskFixed))
]
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
maxOuterIter = [10, 50, 100, 100, 100, 100, 100, 100, 100]
if (os.path.exists(circleToCDisplacementName)):
displacement = np.load(circleToCDisplacementName)
inverse = np.load(circleToCDisplacementInverseName)
else:
displacement, inverse = estimateMonomodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
np.save(circleToCDisplacementName, displacement)
np.save(circleToCDisplacementInverseName, inverse)
print 'vector field exponential'
expd, invexpd = tf.vector_field_exponential(displacement, True)
print 'vector field inversion'
directInverse = tf.invert_vector_field(displacement, 1.0, 10000, 1e-7)
print 'vector field inversion'
directExpInverse = tf.invert_vector_field(expd, 1.0, 10000, 1e-7)
###Now compare inversions###
residualJoint = np.array(
tf.compose_vector_fields(displacement, inverse)[0])
residualDirect = np.array(
tf.compose_vector_fields(displacement, directInverse)[0])
residualExpJoint = np.array(tf.compose_vector_fields(expd, invexpd)[0])
residualExpDirect = np.array(
tf.compose_vector_fields(expd, directExpInverse)[0])
rcommon.plotDiffeomorphism(displacement, inverse, residualJoint, 'D-joint')
rcommon.plotDiffeomorphism(expd, invexpd, residualExpJoint, 'expD-joint')
d, invd, res, jacobian = rcommon.plotDiffeomorphism(
displacement, directInverse, residualDirect, 'D-direct')
rcommon.plotDiffeomorphism(expd, directExpInverse, residualExpDirect,
'expD-direct')
sp.misc.imsave('circleToC_deformation.png', d)
sp.misc.imsave('circleToC_inverse_deformation.png', invd)
sp.misc.imsave('circleToC_residual_deformation.png', res)
tf.write_double_buffer(
np.array(displacement).reshape(-1),
'../inverse/experiments/displacement.bin')
tf.write_double_buffer(
np.array(displacement).reshape(-1),
'../inverse/experiments/displacement_clean.bin')
def runArcesExperiment(rootDir, lambdaParam, maxOuterIter):
#---Load displacement field---
dxName = rootDir + 'Vx.dat'
dyName = rootDir + 'Vy.dat'
dx = np.loadtxt(dxName)
dy = np.loadtxt(dyName)
GT_in = np.ndarray(shape=dx.shape + (2, ), dtype=np.float64)
GT_in[..., 0] = dy
GT_in[..., 1] = dx
GT, GTinv = tf.vector_field_exponential(GT_in)
GTres = tf.compose_vector_fields(GT, GTinv)
#---Load input images---
fnameT1 = rootDir + 't1.jpg'
fnameT2 = rootDir + 't2.jpg'
fnamePD = rootDir + 'pd.jpg'
fnameMask = rootDir + 'Mascara.bmp'
t1 = plt.imread(fnameT1)[..., 0].astype(np.float64)
t2 = plt.imread(fnameT2)[..., 0].astype(np.float64)
pd = plt.imread(fnamePD)[..., 0].astype(np.float64)
t1 = (t1 - t1.min()) / (t1.max() - t1.min())
t2 = (t2 - t2.min()) / (t2.max() - t2.min())
pd = (pd - pd.min()) / (pd.max() - pd.min())
mask = plt.imread(fnameMask).astype(np.float64)
fixed = t1
moving = t2
maskMoving = mask > 0
maskFixed = mask > 0
fixed *= mask
moving *= mask
plt.figure()
plt.subplot(1, 4, 1)
plt.imshow(t1, cmap=plt.cm.gray)
plt.title('Input T1')
plt.subplot(1, 4, 2)
plt.imshow(t2, cmap=plt.cm.gray)
plt.title('Input T2')
plt.subplot(1, 4, 3)
plt.imshow(pd, cmap=plt.cm.gray)
plt.title('Input PD')
plt.subplot(1, 4, 4)
plt.imshow(mask, cmap=plt.cm.gray)
plt.title('Input Mask')
#-------------------------
warpedFixed = rcommon.warpImage(fixed, GT)
print 'Registering T2 (template) to deformed T1 (template)...'
level = 3
movingPyramid = [
img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)
]
fixedPyramid = [
img
for img in rcommon.pyramid_gaussian_2D(warpedFixed, level, maskFixed)
]
plt.figure()
plt.subplot(1, 2, 1)
plt.imshow(moving, cmap=plt.cm.gray)
plt.title('Moving')
plt.subplot(1, 2, 2)
plt.imshow(warpedFixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList = []
displacement, inverse = estimateMultimodalDiffeomorphicField2DMultiScale(
movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0,
displacementList)
residual = tf.compose_vector_fields(displacement, inverse)
warpPyramid = [
rcommon.warpImage(movingPyramid[i], displacementList[i])
for i in range(level + 1)
]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
displacement[..., 0] *= (maskFixed)
displacement[..., 1] *= (maskFixed)
#----plot deformations---
rcommon.plotDiffeomorphism(GT, GTinv, GTres, 7)
rcommon.plotDiffeomorphism(displacement, inverse, residual, 7)
#----statistics---
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2)
nrm *= maskFixed
maxNorm = np.max(nrm)
residual = ((displacement - GT))**2
meanDisplacementError = np.sqrt(residual.sum(2) * (maskFixed)).mean()
stdevDisplacementError = np.sqrt(residual.sum(2) * (maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError, '(', stdevDisplacementError, ')'
def estimateNewMultimodalDiffeomorphicField2D(moving, fixed,
lambdaDisplacement,
quantizationLevels, maxOuterIter,
previousDisplacement,
previousDisplacementInverse):
innerTolerance = 1e-4
outerTolerance = 1e-3
sh = moving.shape
X0, X1 = np.mgrid[0:sh[0], 0:sh[1]]
displacement = np.empty(shape=(moving.shape) + (2, ), dtype=np.float64)
residuals = np.zeros(shape=(moving.shape), dtype=np.float64)
gradientField = np.empty(shape=(moving.shape) + (2, ), dtype=np.float64)
totalDisplacement = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
totalDisplacementInverse = np.zeros(shape=(moving.shape) + (2, ),
dtype=np.float64)
if (previousDisplacement != None):
totalDisplacement[...] = previousDisplacement
totalDisplacementInverse[...] = previousDisplacementInverse
fixedQ = None
grayLevels = None
fixedQ, grayLevels, hist = tf.quantizePositiveImageCYTHON(
fixed, quantizationLevels)
fixedQ = np.array(fixedQ, dtype=np.int32)
finished = False
outerIter = 0
maxDisplacement = None
maxVariation = None
maxResidual = 0
while ((not finished) and (outerIter < maxOuterIter)):
outerIter += 1
#---E step---
warped = ndimage.map_coordinates(
moving,
[X0 + totalDisplacement[..., 0], X1 + totalDisplacement[..., 1]],
prefilter=True)
movingMask = ((moving > 0) * 1.0) * ((fixed > 0) * 1.0)
warpedMovingMask = ndimage.map_coordinates(
movingMask,
[X0 + totalDisplacement[..., 0], X1 + totalDisplacement[..., 1]],
order=0,
prefilter=False)
warpedMovingMask = warpedMovingMask.astype(np.int32)
means, variances = tf.computeMaskedImageClassStatsCYTHON(
warpedMovingMask, warped, quantizationLevels, fixedQ)
means[0] = 0
means = np.array(means)
variances = np.array(variances)
sigmaField = variances[fixedQ]
deltaField = means[
fixedQ] - warped #########Delta-field using Arce's rule
#--M step--
g0, g1 = sp.gradient(warped)
gradientField[:, :, 0] = g0
gradientField[:, :, 1] = g1
maxVariation = 1 + innerTolerance
innerIter = 0
maxInnerIter = 1000
displacement[...] = 0
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, gradientField, lambdaDisplacement,
totalDisplacement, displacement, residuals)
opt = np.max(residuals)
if (maxResidual < opt):
maxResidual = opt
#--accumulate displacement--
expd, invexpd = tf.vector_field_exponential(displacement)
totalDisplacement = tf.compose_vector_fields(expd, totalDisplacement)
totalDisplacementInverse = tf.compose_vector_fields(
totalDisplacementInverse, invexpd)
#--check stop condition--
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement = np.mean(nrm)
if ((maxDisplacement < outerTolerance) or (outerIter >= maxOuterIter)):
finished = True
# plt.figure()
# plt.subplot(1,3,1)
# plt.imshow(means[fixedQ],cmap=plt.cm.gray)
# plt.title("Estimated warped modality")
# plt.subplot(1,3,2)
# plt.imshow(fixedQ,cmap=plt.cm.gray)
# plt.title("Quantized")
# plt.subplot(1,3,3)
# plt.plot(means)
# plt.title("Means")
print "Iter: ", outerIter, "Mean displacement:", maxDisplacement, "Max variation:", maxVariation, "Max residual:", maxResidual
if (previousDisplacement != None):
return totalDisplacement - previousDisplacement, totalDisplacementInverse
return totalDisplacement, totalDisplacementInverse
def runArcesExperiment(rootDir, lambdaParam, maxOuterIter):
#---Load displacement field---
dxName=rootDir+'Vx.dat'
dyName=rootDir+'Vy.dat'
dx=np.loadtxt(dxName)
dy=np.loadtxt(dyName)
GT_in=np.ndarray(shape=dx.shape+(2,), dtype=np.float64)
GT_in[...,0]=dy
GT_in[...,1]=dx
GT, GTinv=tf.vector_field_exponential(GT_in)
GTres=tf.compose_vector_fields(GT, GTinv)
#---Load input images---
fnameT1=rootDir+'t1.jpg'
fnameT2=rootDir+'t2.jpg'
fnamePD=rootDir+'pd.jpg'
fnameMask=rootDir+'Mascara.bmp'
t1=plt.imread(fnameT1)[...,0].astype(np.float64)
t2=plt.imread(fnameT2)[...,0].astype(np.float64)
pd=plt.imread(fnamePD)[...,0].astype(np.float64)
t1=(t1-t1.min())/(t1.max()-t1.min())
t2=(t2-t2.min())/(t2.max()-t2.min())
pd=(pd-pd.min())/(pd.max()-pd.min())
mask=plt.imread(fnameMask).astype(np.float64)
fixed=t1
moving=t2
maskMoving=mask>0
maskFixed=mask>0
fixed*=mask
moving*=mask
plt.figure()
plt.subplot(1,4,1)
plt.imshow(t1, cmap=plt.cm.gray)
plt.title('Input T1')
plt.subplot(1,4,2)
plt.imshow(t2, cmap=plt.cm.gray)
plt.title('Input T2')
plt.subplot(1,4,3)
plt.imshow(pd, cmap=plt.cm.gray)
plt.title('Input PD')
plt.subplot(1,4,4)
plt.imshow(mask, cmap=plt.cm.gray)
plt.title('Input Mask')
#-------------------------
warpedFixed=rcommon.warpImage(fixed,GT)
print 'Registering T2 (template) to deformed T1 (template)...'
level=3
movingPyramid=[img for img in rcommon.pyramid_gaussian_2D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_2D(warpedFixed, level, maskFixed)]
plt.figure()
plt.subplot(1,2,1)
plt.imshow(moving, cmap=plt.cm.gray)
plt.title('Moving')
plt.subplot(1,2,2)
plt.imshow(warpedFixed, cmap=plt.cm.gray)
plt.title('Fixed')
rcommon.plotOverlaidPyramids(movingPyramid, fixedPyramid)
displacementList=[]
displacement, inverse=estimateMultimodalDiffeomorphicField2DMultiScale(movingPyramid, fixedPyramid, lambdaParam, maxOuterIter, 0, displacementList)
residual=tf.compose_vector_fields(displacement, inverse)
warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
displacement[...,0]*=(maskFixed)
displacement[...,1]*=(maskFixed)
#----plot deformations---
rcommon.plotDiffeomorphism(GT, GTinv, GTres, 7)
rcommon.plotDiffeomorphism(displacement, inverse, residual, 7)
#----statistics---
nrm=np.sqrt(displacement[...,0]**2 + displacement[...,1]**2)
nrm*=maskFixed
maxNorm=np.max(nrm)
residual=((displacement-GT))**2
meanDisplacementError=np.sqrt(residual.sum(2)*(maskFixed)).mean()
stdevDisplacementError=np.sqrt(residual.sum(2)*(maskFixed)).std()
print 'Max global displacement: ', maxNorm
print 'Mean displacement error: ', meanDisplacementError,'(',stdevDisplacementError,')'
def estimateNewMultimodalDiffeomorphicField2D(moving, fixed, lambdaDisplacement, quantizationLevels, maxOuterIter, previousDisplacement, previousDisplacementInverse):
innerTolerance=1e-4
outerTolerance=1e-3
sh=moving.shape
X0,X1=np.mgrid[0:sh[0], 0:sh[1]]
displacement =np.empty(shape=(moving.shape)+(2,), dtype=np.float64)
residuals=np.zeros(shape=(moving.shape), dtype=np.float64)
gradientField =np.empty(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacement=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
totalDisplacementInverse=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
if(previousDisplacement!=None):
totalDisplacement[...]=previousDisplacement
totalDisplacementInverse[...]=previousDisplacementInverse
fixedQ=None
grayLevels=None
fixedQ, grayLevels, hist=tf.quantizePositiveImageCYTHON(fixed, quantizationLevels)
fixedQ=np.array(fixedQ, dtype=np.int32)
finished=False
outerIter=0
maxDisplacement=None
maxVariation=None
maxResidual=0
while((not finished) and (outerIter<maxOuterIter)):
outerIter+=1
#---E step---
warped=ndimage.map_coordinates(moving, [X0+totalDisplacement[...,0], X1+totalDisplacement[...,1]], prefilter=True)
movingMask=((moving>0)*1.0)*((fixed>0)*1.0)
warpedMovingMask=ndimage.map_coordinates(movingMask, [X0+totalDisplacement[...,0], X1+totalDisplacement[...,1]], order=0, prefilter=False)
warpedMovingMask=warpedMovingMask.astype(np.int32)
means, variances=tf.computeMaskedImageClassStatsCYTHON(warpedMovingMask, warped, quantizationLevels, fixedQ)
means[0]=0
means=np.array(means)
variances=np.array(variances)
sigmaField=variances[fixedQ]
deltaField=means[fixedQ]-warped#########Delta-field using Arce's rule
#--M step--
g0, g1=sp.gradient(warped)
gradientField[:,:,0]=g0
gradientField[:,:,1]=g1
maxVariation=1+innerTolerance
innerIter=0
maxInnerIter=1000
displacement[...]=0
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, gradientField, lambdaDisplacement, totalDisplacement, displacement, residuals)
opt=np.max(residuals)
if(maxResidual<opt):
maxResidual=opt
#--accumulate displacement--
expd, invexpd=tf.vector_field_exponential(displacement)
totalDisplacement=tf.compose_vector_fields(expd, totalDisplacement)
totalDisplacementInverse=tf.compose_vector_fields(totalDisplacementInverse, invexpd)
#--check stop condition--
nrm=np.sqrt(displacement[...,0]**2+displacement[...,1]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement=np.mean(nrm)
if((maxDisplacement<outerTolerance)or(outerIter>=maxOuterIter)):
finished=True
# plt.figure()
# plt.subplot(1,3,1)
# plt.imshow(means[fixedQ],cmap=plt.cm.gray)
# plt.title("Estimated warped modality")
# plt.subplot(1,3,2)
# plt.imshow(fixedQ,cmap=plt.cm.gray)
# plt.title("Quantized")
# plt.subplot(1,3,3)
# plt.plot(means)
# plt.title("Means")
print "Iter: ",outerIter, "Mean displacement:", maxDisplacement, "Max variation:",maxVariation, "Max residual:", maxResidual
if(previousDisplacement!=None):
return totalDisplacement-previousDisplacement, totalDisplacementInverse
return totalDisplacement, totalDisplacementInverse
