def generateTestingPair(betaGT):
betaGTRads = np.array(betaGT, dtype=np.float64)
betaGTRads[0:3] = np.copy(np.pi * betaGTRads[0:3] / 180.0)
ns = 181
nr = 217
nc = 181
left = np.fromfile('data/t2/t2_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape(ns, nr, nc)
left = left.astype(np.float64)
right = np.fromfile('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape(ns, nr, nc)
right = right.astype(np.float64)
right = rcommon.applyRigidTransformation3D(right, betaGTRads)
affine_transform = AffineTransform(
'ijk', ['aligned-z=I->S', 'aligned-y=P->A', 'aligned-x=L->R'],
np.eye(4))
left = Image(left, affine_transform)
right = Image(right, affine_transform)
nipy.save_image(left, 'moving.nii')
nipy.save_image(right, 'fixed.nii')
def changeCoords2(affine, scale, coords):
domain = coords.function_domain
outrange = coords.function_range
# Create a new matrix
mod_affine = np.zeros((4, 4))
# Set up the diagonals
mod_affine[0, 0] = affine[0, 0] * scale
mod_affine[1, 1] = affine[1, 1] * scale
mod_affine[2, 2] = affine[2, 2] * scale
mod_affine[
3,
3] = 1 # This is the new temporal dimension: each vol ume is 2s apart
# Pull the last column of the first 3 rows into the new matrix
mod_affine[0, 3] = affine[0, 3] // scale
mod_affine[1, 3] = affine[1, 3] // scale
mod_affine[2, 3] = affine[2, 3] // scale
modified = AffineTransform(domain, outrange, mod_affine)
return modified
def peelTemplateBrain():
ns = 181
nr = 217
nc = 181
gt_template = np.fromfile('data/phantom_1.0mm_normal_crisp.rawb',
dtype=np.ubyte).reshape((ns, nr, nc))
t1_template = np.fromfile('data/t1/t1_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape((ns, nr, nc))
t2_template = np.fromfile('data/t2/t2_icbm_normal_1mm_pn0_rf0.rawb',
dtype=np.ubyte).reshape((ns, nr, nc))
#t1_template*=((1<=gt_template)*(gt_template<=3)+(gt_template==8))
t1_template *= ((1 <= gt_template) * (gt_template <= 3))
t2_template *= ((1 <= gt_template) * (gt_template <= 3))
affine_transform = AffineTransform(
'ijk', ['aligned-z=I->S', 'aligned-y=P->A', 'aligned-x=L->R'],
np.eye(4))
t1_template = Image(t1_template, affine_transform)
t2_template = Image(t2_template, affine_transform)
nipy.save_image(t1_template,
'data/t1/t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz')
nipy.save_image(t2_template,
'data/t2/t2_icbm_normal_1mm_pn0_rf0_peeled.nii.gz')
def testIntersubjectRigidRegistration(fname0, fname1, level, outfname):
nib_left = nib.load(fname0)
nib_right = nib.load(fname1)
left = nib_left.get_data().astype(np.double).squeeze()
right = nib_right.get_data().astype(np.double).squeeze()
leftPyramid = [i for i in rcommon.pyramid_gaussian_3D(left, level)]
rightPyramid = [i for i in rcommon.pyramid_gaussian_3D(right, level)]
plotSlicePyramidsAxial(leftPyramid, rightPyramid)
print 'Estimation started.'
beta = estimateRigidTransformationMultiscale3D(leftPyramid, rightPyramid)
print 'Estimation finished.'
rcommon.applyRigidTransformation3D(left, beta)
sl = np.array(left.shape) // 2
sr = np.array(right.shape) // 2
rcommon.overlayImages(left[sl[0], :, :], leftPyramid[0][sr[0], :, :])
rcommon.overlayImages(left[sl[0], :, :], right[sr[0], :, :])
affine_transform = AffineTransform(
'ijk', ['aligned-z=I->S', 'aligned-y=P->A', 'aligned-x=L->R'],
np.eye(4))
left = Image(left, affine_transform)
nipy.save_image(left, outfname)
return beta
def convert3DCoordsTo4D(orig):
# Get the data from the original coordinates
domain = orig.function_domain
outrange = orig.function_range
affine = orig.affine
print(type(affine), affine)
# Add the temporal dimension
mod_domain = CoordinateSystem(domain.coord_names + ('t', ), domain.name,
domain.coord_dtype)
mod_range = CoordinateSystem(outrange.coord_names + ('t', ), outrange.name,
outrange.coord_dtype)
# Temporal is a little trickier in the affine matrix
# Create a new matrix
mod_affine = np.zeros((5, 5))
# Set up the diagonals
mod_affine[0, 0] = affine[0, 0]
mod_affine[1, 1] = affine[1, 1]
mod_affine[2, 2] = affine[2, 2]
mod_affine[
3,
3] = 2 # This is the new temporal dimension: each volume is 2s apart
mod_affine[
4,
4] = 1 # This is the last row in the matrix and should be all 0 except this column
# Pull the last column of the first 3 rows into the new matrix
mod_affine[0, 4] = affine[0, 3]
mod_affine[1, 4] = affine[1, 3]
mod_affine[2, 4] = affine[2, 3]
print(mod_affine)
modified = AffineTransform(mod_domain, mod_range, mod_affine)
return modified
def create(rootdir):
out = None
prev = None
out_is_there = False
dirs = sorted(os.listdir(rootdir))
for d in dirs:
files = os.listdir(os.path.join(rootdir, d))
for f in files:
input_image = tif.imread(os.path.join(rootdir, d, f))
# print input_image
# print type(input_image)
# print 'ccc',input_image.flatten()
if out_is_there:
#out = np.concatenate([out, input_image.flatten()])
out = np.dstack([out, input_image])
else:
# out = input_image.flatten()
out = input_image
out_is_there = True
# return
# i = 0
# for root, dirs, files in os.walk(rootdir):
# fullpaths = [(os.path.join(root, name)) for name in files]
# for f in fullpaths:
# print f
# input_image = tif.imread(f)
# # print input_image
# # print type(input_image)
# # print 'ccc',input_image.flatten()
# if out_is_there:
# #out = np.concatenate([out, input_image.flatten()])
# out = np.dstack([out, input_image])
# else:
# # out = input_image.flatten()
# out = input_image
# out_is_there = True
#>>> from nipy.io.api import save_image
#>>> data = np.zeros((91,109,91), dtype=np.uint8)
#>>> cmap = AffineTransform('kji', 'zxy', np.eye(4))
#>>> img = Image(data, cmap)
#>>> fname1 = os.path.join(tmpdir, 'img1.nii.gz')
#>>> saved_img1 = save_image(img, fname1)
# image_data = PILImage.open(os.path.join(subdir,file))
# if image_data.mode != "RGB":
# image_data = image_data.convert("RGB")
# print image_data.toString()
# break
# shutil.copy(os.path.join(subdir, file), '/tmp/'+str(i)+'.tif')
# i+=1
length = out.shape[0]
print 'aaa'
print out
# out = out.reshape((512, 512, length/512/512))
print out
cmap = AffineTransform('kji', 'zxy', np.eye(4))
img = Image(out, cmap)
save_image(img, '/tmp/out.nii.gz')
