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 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')