def testInversion_invertible():
displacement_clean=tf.create_invertible_displacement_field(256, 256, 0.5, 8)
detJacobian=rcommon.computeJacobianField(displacement_clean)
plt.figure()
plt.imshow(detJacobian)
print 'Range:', detJacobian.min(), detJacobian.max()
X1,X0=np.mgrid[0:displacement_clean.shape[0], 0:displacement_clean.shape[1]]
CS=plt.contour(X0,X1,detJacobian,levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
displacement=displacement_clean+np.random.normal(0.0, 1.1, displacement_clean.shape)
#displacement=np.array(displacement_clean)
#inverse=rcommon.invert_vector_field_fixed_point(displacement, 100, 1e-7)
#inverse=np.array(tf.invert_vector_field(displacement, 0.1, 100, 1e-7))
lambdaParam=5.0
#########Jacobi##########
inverse=np.array(tf.invert_vector_field(displacement, lambdaParam, 100, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'Jacobi. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'Jacobi', 7)
#########Fixed point######
inverse=np.array(tf.invert_vector_field_fixed_point(displacement, 100, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'Fixed point. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'Fixed point', 7)
#########TV-L2###########
inverse=np.array(tf.invert_vector_field_tv_l2(displacement, lambdaParam, 3000, 1e-6))
residual, stats=tf.compose_vector_fields(displacement_clean, inverse)
residual=np.array(residual)
print 'TV-L2. Max:',stats[0], '. Mean:',stats[1],'Std:',stats[2]
[d,invd,res, detJ]=rcommon.plotDiffeomorphism(displacement, inverse, residual, 'TV-L2', 7)
def testCircleToCMonomodalDiffeomorphic(lambdaParam):
import numpy as np
import tensorFieldUtils as tf
import matplotlib.pyplot as plt
import registrationCommon as rcommon
fname0='data/circle.png'
#fname0='data/C_trans.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)
X1,X0=np.mgrid[0:displacement.shape[0], 0:displacement.shape[1]]
detJacobian=rcommon.computeJacobianField(displacement)
plt.figure()
plt.imshow(detJacobian)
CS=plt.contour(X0,X1,detJacobian,levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
print 'J range:', '[', detJacobian.min(), detJacobian.max(),']'
#directInverse=np.array(tf.invert_vector_field(displacement, 0.5, 1000, 1e-7))
directInverse=np.array(tf.invert_vector_field_fixed_point(displacement, 100, 1e-7))
detJacobianInverse=rcommon.computeJacobianField(directInverse)
plt.figure()
plt.imshow(detJacobianInverse)
CS=plt.contour(X0,X1,detJacobianInverse, levels=[0.0],colors='w')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement^-1))')
print 'J^-1 range:', '[', detJacobianInverse.min(), detJacobianInverse.max(),']'
#directInverse=rcommon.invert_vector_field_fixed_point(displacement, 1000, 1e-7)
residual, stats=tf.compose_vector_fields(displacement, inverse)
residual=np.array(residual)
directResidual,stats=tf.compose_vector_fields(displacement, directInverse)
directResidual=np.array(directResidual)
# warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
# rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
# rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, 'inv-joint', 7)
rcommon.plotDiffeomorphism(displacement, directInverse, directResidual, 'inv-direct', 7)
tf.write_double_buffer(displacement.reshape(-1), 'displacement.bin')
def testInversion_invertible():
displacement_clean = tf.create_invertible_displacement_field(
256, 256, 0.5, 8)
detJacobian = rcommon.computeJacobianField(displacement_clean)
plt.figure()
plt.imshow(detJacobian)
print 'Range:', detJacobian.min(), detJacobian.max()
X1, X0 = np.mgrid[0:displacement_clean.shape[0],
0:displacement_clean.shape[1]]
CS = plt.contour(X0, X1, detJacobian, levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
displacement = displacement_clean + np.random.normal(
0.0, 1.1, displacement_clean.shape)
#displacement=np.array(displacement_clean)
#inverse=rcommon.invert_vector_field_fixed_point(displacement, 100, 1e-7)
#inverse=np.array(tf.invert_vector_field(displacement, 0.1, 100, 1e-7))
lambdaParam = 5.0
#########Jacobi##########
inverse = np.array(
tf.invert_vector_field(displacement, lambdaParam, 100, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'Jacobi. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res, detJ] = rcommon.plotDiffeomorphism(displacement, inverse,
residual, 'Jacobi', 7)
#########Fixed point######
inverse = np.array(
tf.invert_vector_field_fixed_point(displacement, 100, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'Fixed point. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res,
detJ] = rcommon.plotDiffeomorphism(displacement, inverse, residual,
'Fixed point', 7)
#########TV-L2###########
inverse = np.array(
tf.invert_vector_field_tv_l2(displacement, lambdaParam, 3000, 1e-6))
residual, stats = tf.compose_vector_fields(displacement_clean, inverse)
residual = np.array(residual)
print 'TV-L2. Max:', stats[0], '. Mean:', stats[1], 'Std:', stats[2]
[d, invd, res, detJ] = rcommon.plotDiffeomorphism(displacement, inverse,
residual, 'TV-L2', 7)
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 testCircleToCMonomodalDiffeomorphic(lambdaParam):
import numpy as np
import tensorFieldUtils as tf
import matplotlib.pyplot as plt
import registrationCommon as rcommon
fname0 = 'data/circle.png'
#fname0='data/C_trans.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)
X1, X0 = np.mgrid[0:displacement.shape[0], 0:displacement.shape[1]]
detJacobian = rcommon.computeJacobianField(displacement)
plt.figure()
plt.imshow(detJacobian)
CS = plt.contour(X0, X1, detJacobian, levels=[0.0], colors='b')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement))')
print 'J range:', '[', detJacobian.min(), detJacobian.max(), ']'
#directInverse=np.array(tf.invert_vector_field(displacement, 0.5, 1000, 1e-7))
directInverse = np.array(
tf.invert_vector_field_fixed_point(displacement, 100, 1e-7))
detJacobianInverse = rcommon.computeJacobianField(directInverse)
plt.figure()
plt.imshow(detJacobianInverse)
CS = plt.contour(X0, X1, detJacobianInverse, levels=[0.0], colors='w')
plt.clabel(CS, inline=1, fontsize=10)
plt.title('det(J(displacement^-1))')
print 'J^-1 range:', '[', detJacobianInverse.min(), detJacobianInverse.max(
), ']'
#directInverse=rcommon.invert_vector_field_fixed_point(displacement, 1000, 1e-7)
residual, stats = tf.compose_vector_fields(displacement, inverse)
residual = np.array(residual)
directResidual, stats = tf.compose_vector_fields(displacement,
directInverse)
directResidual = np.array(directResidual)
# warpPyramid=[rcommon.warpImage(movingPyramid[i], displacementList[i]) for i in range(level+1)]
# rcommon.plotOverlaidPyramids(warpPyramid, fixedPyramid)
# rcommon.overlayImages(warpPyramid[0], fixedPyramid[0])
rcommon.plotDiffeomorphism(displacement, inverse, residual, 'inv-joint', 7)
rcommon.plotDiffeomorphism(displacement, directInverse, directResidual,
'inv-direct', 7)
tf.write_double_buffer(displacement.reshape(-1), 'displacement.bin')
