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 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 estimateNewMonomodalSyNField2D(moving, fixed, fWarp, fInv, mWarp, mInv,
lambdaParam, maxOuterIter):
'''
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
outerTolerance = 1e-3
if (mWarp != None):
totalM = mWarp
totalMInv = mInv
else:
totalM = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
totalMInv = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
if (fWarp != None):
totalF = fWarp
totalFInv = fInv
else:
totalF = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
totalFInv = np.zeros(shape=(moving.shape) + (2, ), dtype=np.float64)
outerIter = 0
framesToCapture = 5
maxOuterIter = framesToCapture * (
(maxOuterIter + framesToCapture - 1) / framesToCapture)
itersPerCapture = maxOuterIter / framesToCapture
plt.figure()
while (outerIter < maxOuterIter):
outerIter += 1
print 'Outer iter:', outerIter
wmoving = np.array(tf.warp_image(moving, totalMInv))
wfixed = np.array(tf.warp_image(fixed, totalFInv))
if ((outerIter == 1) or (outerIter % itersPerCapture == 0)):
plt.subplot(1, framesToCapture + 1,
1 + outerIter / itersPerCapture)
rcommon.overlayImages(wmoving, wfixed, False)
plt.title('Iter:' + str(outerIter - 1))
#Compute forward update
sigmaField = np.ones_like(wmoving, dtype=np.float64)
deltaField = wfixed - wmoving
movingGradient = np.empty(shape=(wmoving.shape) + (2, ),
dtype=np.float64)
movingGradient[:, :, 0], movingGradient[:, :, 1] = sp.gradient(wmoving)
maxVariation = 1 + innerTolerance
innerIter = 0
fw = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
maxInnerIter = 1000
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, movingGradient, lambdaParam, fw, None)
#fw*=0.5
totalF, stats = tf.compose_vector_fields(fw, totalF)
totalF = np.array(totalF)
meanDispF = np.mean(np.abs(fw))
#Compute backward field
sigmaField = np.ones_like(wfixed, dtype=np.float64)
deltaField = wmoving - wfixed
fixedGradient = np.empty(shape=(wfixed.shape) + (2, ),
dtype=np.float64)
fixedGradient[:, :, 0], fixedGradient[:, :, 1] = sp.gradient(wfixed)
maxVariation = 1 + innerTolerance
innerIter = 0
mw = np.zeros(shape=(fixed.shape) + (2, ), dtype=np.float64)
maxInnerIter = 1000
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField2DCYTHON(
deltaField, sigmaField, fixedGradient, lambdaParam, mw, None)
#mw*=0.5
totalM, stats = tf.compose_vector_fields(mw, totalM)
totalM = np.array(totalM)
meanDispM = np.mean(np.abs(mw))
totalFInv = np.array(
tf.invert_vector_field_fixed_point(totalF, None, 20, 1e-3, None))
totalMInv = np.array(
tf.invert_vector_field_fixed_point(totalM, None, 20, 1e-3, None))
totalF = np.array(
tf.invert_vector_field_fixed_point(totalFInv, None, 20, 1e-3,
None))
totalM = np.array(
tf.invert_vector_field_fixed_point(totalMInv, None, 20, 1e-3,
None))
# totalFInv=np.array(tf.invert_vector_field(totalF, 0.75, 100, 1e-6))
# totalMInv=np.array(tf.invert_vector_field(totalM, 0.75, 100, 1e-6))
# totalF=np.array(tf.invert_vector_field(totalFInv, 0.75, 100, 1e-6))
# totalM=np.array(tf.invert_vector_field(totalMInv, 0.75, 100, 1e-6))
if (meanDispM + meanDispF < 2 * outerTolerance):
break
print "Iter: ", innerIter, "Mean lateral displacement:", 0.5 * (
meanDispM + meanDispF), "Max variation:", maxVariation
return totalF, totalFInv, totalM, totalMInv
def estimateNewMonomodalSyNField2D(moving, fixed, fWarp, fInv, mWarp, mInv, lambdaParam, maxOuterIter):
'''
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
outerTolerance=1e-3
if(mWarp!=None):
totalM=mWarp
totalMInv=mInv
else:
totalM=np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
totalMInv=np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
if(fWarp!=None):
totalF=fWarp
totalFInv=fInv
else:
totalF=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
totalFInv=np.zeros(shape=(moving.shape)+(2,), dtype=np.float64)
outerIter=0
framesToCapture=5
maxOuterIter=framesToCapture*((maxOuterIter+framesToCapture-1)/framesToCapture)
itersPerCapture=maxOuterIter/framesToCapture
plt.figure()
while(outerIter<maxOuterIter):
outerIter+=1
print 'Outer iter:', outerIter
wmoving=np.array(tf.warp_image(moving, totalMInv))
wfixed=np.array(tf.warp_image(fixed, totalFInv))
if((outerIter==1) or (outerIter%itersPerCapture==0)):
plt.subplot(1,framesToCapture+1, 1+outerIter/itersPerCapture)
rcommon.overlayImages(wmoving, wfixed, False)
plt.title('Iter:'+str(outerIter-1))
#Compute forward update
sigmaField=np.ones_like(wmoving, dtype=np.float64)
deltaField=wfixed-wmoving
movingGradient =np.empty(shape=(wmoving.shape)+(2,), dtype=np.float64)
movingGradient[:,:,0], movingGradient[:,:,1]=sp.gradient(wmoving)
maxVariation=1+innerTolerance
innerIter=0
fw =np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
maxInnerIter=1000
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, movingGradient, lambdaParam, fw, None)
#fw*=0.5
totalF, stats=tf.compose_vector_fields(fw, totalF)
totalF=np.array(totalF);
meanDispF=np.mean(np.abs(fw))
#Compute backward field
sigmaField=np.ones_like(wfixed, dtype=np.float64)
deltaField=wmoving-wfixed
fixedGradient =np.empty(shape=(wfixed.shape)+(2,), dtype=np.float64)
fixedGradient[:,:,0], fixedGradient[:,:,1]=sp.gradient(wfixed)
maxVariation=1+innerTolerance
innerIter=0
mw =np.zeros(shape=(fixed.shape)+(2,), dtype=np.float64)
maxInnerIter=1000
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField2DCYTHON(deltaField, sigmaField, fixedGradient, lambdaParam, mw, None)
#mw*=0.5
totalM, stats=tf.compose_vector_fields(mw, totalM)
totalM=np.array(totalM);
meanDispM=np.mean(np.abs(mw))
totalFInv=np.array(tf.invert_vector_field_fixed_point(totalF, None, 20, 1e-3, None))
totalMInv=np.array(tf.invert_vector_field_fixed_point(totalM, None, 20, 1e-3, None))
totalF=np.array(tf.invert_vector_field_fixed_point(totalFInv, None, 20, 1e-3, None))
totalM=np.array(tf.invert_vector_field_fixed_point(totalMInv, None, 20, 1e-3, None))
# totalFInv=np.array(tf.invert_vector_field(totalF, 0.75, 100, 1e-6))
# totalMInv=np.array(tf.invert_vector_field(totalM, 0.75, 100, 1e-6))
# totalF=np.array(tf.invert_vector_field(totalFInv, 0.75, 100, 1e-6))
# totalM=np.array(tf.invert_vector_field(totalMInv, 0.75, 100, 1e-6))
if(meanDispM+meanDispF<2*outerTolerance):
break
print "Iter: ",innerIter, "Mean lateral displacement:", 0.5*(meanDispM+meanDispF), "Max variation:",maxVariation
return totalF, totalFInv, totalM, totalMInv
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 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
