def test_fvtk_ellipsoid():
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(mevals, mevecs, sphere=sphere))
fvtk.add(ren, fvtk.tensor(mevals, mevecs, np.ones(mevals.shape), sphere=sphere))
assert_equal(ren.GetActors().GetNumberOfItems(), 2)
def test_fvtk_ellipsoid():
evals = np.array([1.4, .35, .35]) * 10**(-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(mevals, mevecs, sphere=sphere))
fvtk.add(ren,
fvtk.tensor(mevals, mevecs, np.ones(mevals.shape), sphere=sphere))
assert_equal(ren.GetActors().GetNumberOfItems(), 2)
def plot_as_dti(gtab, data, params, dipy_sph, output_dir="."):
logging.info("Fitting data to DTI model...")
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data[params['slice']])
FA = dti.fractional_anisotropy(tenfit.evals)
FA = np.clip(FA, 0, 1)
RGB = dti.color_fa(FA, tenfit.evecs)
cfa = RGB
cfa /= cfa.max()
logging.info("Recording DTI plot...")
camera_params, long_ax, view_ax, stack_ax = \
prepare_plot_camera(data[params['slice']].shape[:-1], scale=2.2)
r = fvtk.ren()
fvtk.add(r, fvtk.tensor(tenfit.evals, tenfit.evecs, cfa, dipy_sph))
r.set_camera(**camera_params)
fname = os.path.join(output_dir, "plot-dti-0.png")
fvtk.snapshot(r, size=(1500, 1500), offscreen=True, fname=fname)
establish a baseline, using the data as it is:
"""
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.get_sphere('symmetric724')
"""
We visualize the ODFs in the ROI using fvtk:
"""
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(evals1, evecs1, cfa1, sphere))
print('Saving illustration as tensor_ellipsoids_wls.png')
fvtk.record(ren,
n_frames=1,
out_path='tensor_ellipsoids_wls.png',
size=(600, 600))
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
Tensor Ellipsoids.
"""
fvtk.clear(ren)
"""
Next, we corrupt the data with some noise. To simulate a subject that moves
from dipy.viz import fvtk
ren = fvtk.ren()
evals = tenfit.evals[13:43, 44:74, 28:29]
evecs = tenfit.evecs[13:43, 44:74, 28:29]
"""
We can color the ellipsoids using the ``color_fa`` values that we calculated
above. In this example we additionally normalize the values to increase the
contrast.
"""
cfa = RGB[13:43, 44:74, 28:29]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(evals, evecs, cfa, sphere))
print('Saving illustration as tensor_ellipsoids.png')
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
"""
.. figure:: tensor_ellipsoids.png
:align: center
**Tensor Ellipsoids**.
"""
fvtk.clear(ren)
"""
Finally, we can visualize the tensor orientation distribution functions
RGB = color_fa(FA, tensor_fit.evecs)
nib.save(nib.Nifti1Image(np.array(255*RGB, 'uint8'), image.get_affine()), 'tensor_rgb.nii.gz')
print('Computing tensor ellipsoids in a part of the splenium of the CC')
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.viz import fvtk
ren = fvtk.ren()
eigenvalues = tensor_fit.evals[13:43, 44:74, 28:29]
eigenvectors = tensor_fit.evecs[13:43, 44:74, 28:29]
color_fa = RGB[13:43, 44:74, 28:29]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(eigenvalues, eigenvectors, color_fa, sphere))
print('Saving illustration as tensor_ellipsoids.png')
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png')
fvtk.clear(ren)
tensor_odfs = tensor_model.fit(data[20:50, 55:85, 38:39]).odf(sphere)
fvtk.add(ren, fvtk.sphere_funcs(tensor_odfs, sphere, colormap=None))
#fvtk.show(r)
print('Saving illustration as tensor_odfs.png')
fvtk.record(ren, n_frames=1, out_path='tensor_odfs.png', size=(600, 600))
md1 = dti.mean_diffusivity(tenfit.evals)
nibabel.save(nibabel.Nifti1Image(md1.astype(numpy.float32), img.affine),
'output_md.nii')
shutil.move('output_md.nii', args.output_nifti1_md)
move_directory_files(input_dir, args.output_nifti1_md_files_path, copy=True)
fa = numpy.clip(fa, 0, 1)
rgb = color_fa(fa, tenfit.evecs)
nibabel.save(nibabel.Nifti1Image(numpy.array(255 * rgb, 'uint8'), img.affine),
'output_rgb.nii')
shutil.move('output_rgb.nii', args.output_nifti1_rgb)
move_directory_files(input_dir, args.output_nifti1_rgb_files_path, copy=True)
sphere = get_sphere('symmetric724')
ren = fvtk.ren()
evals = tenfit.evals[13:43, 44:74, 28:29]
evecs = tenfit.evecs[13:43, 44:74, 28:29]
cfa = rgb[13:43, 44:74, 28:29]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(evals, evecs, cfa, sphere))
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
shutil.move('tensor_ellipsoids.png', args.output_png_ellipsoids)
fvtk.clear(ren)
tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
fvtk.add(ren, fvtk.sphere_funcs(tensor_odfs, sphere, colormap=None))
fvtk.record(ren, n_frames=1, out_path='tensor_odfs.png', size=(600, 600))
shutil.move('tensor_odfs.png', args.output_png_odfs)
plt.figure('Showing the datasets')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
plt.show()
plt.savefig('data.png', bbox_inches='tight')
from dipy.data import get_sphere
sphere = get_sphere('symmetric642')
from dipy.viz import fvtk
ren = fvtk.ren()
evals=tenfit.evals[55:85,80:90,40:45]
evecs=tenfit.evecs[55:85,80:90,40:45]
cfa = Rgbv[55:80, 80:90, 40:45]
#print cfa[1,1,1]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(evals, evecs, cfa))
fvtk.show(ren)
print('Saving illustration as fa.png')
fvtk.record(ren, n_frames=1, out_path='fa.png', size=(600, 600))
print "Hello World!"
"""
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.get_sphere('symmetric724')
"""
We visualize the ODFs in the ROI using fvtk:
"""
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(evals1, evecs1, cfa1, sphere))
print('Saving illustration as tensor_ellipsoids_wls.png')
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids_wls.png',
size=(600, 600))
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
**Tensor Ellipsoids**.
"""
fvtk.clear(ren)
"""
Next, we corrupt the data with some noise. To simulate a subject that moves
axial_middle = data.shape[2] / 2
plt.figure('Showing the datasets')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
plt.show()
plt.savefig('data.png', bbox_inches='tight')
from dipy.data import get_sphere
sphere = get_sphere('symmetric642')
from dipy.viz import fvtk
ren = fvtk.ren()
evals = tenfit.evals[55:85, 80:90, 40:45]
evecs = tenfit.evecs[55:85, 80:90, 40:45]
cfa = Rgbv[55:80, 80:90, 40:45]
#print cfa[1,1,1]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(evals, evecs, cfa))
fvtk.show(ren)
print('Saving illustration as fa.png')
fvtk.record(ren, n_frames=1, out_path='fa.png', size=(600, 600))
print "Hello World!"
mask=mask,
sphere=sphere,
scale=.5)
ren.add(odf_slicer_actor)
slider(odf_slicer_actor, None)
data_small = data[:, :, 38:39]
dti_wls = dti.TensorModel(gtab)
fit_wls = dti_wls.fit(data_small)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(evals1, evecs1, cfa1, sphere))
#fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png',
# size=(600, 600))
fvtk.show(ren)
print('Started CSD')
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
print('Finished response')
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
def run(rawargs):
arguments = docopt(doc, argv=rawargs, version='Orientation Check v{0}'.format(Version))
inputs = [{"Value":"image file", "Flag": "--image"}, {"Value":"bvec file", "Flag": "--bvecs"}, {"Value":"bvec file", "Flag": "--bvecs"}]
for inputinfo in inputs:
if not exists(arguments[inputinfo["Flag"]]):
print("The {0} specified does not exist!".format(inputinfo["Value"]))
sys.exit(1)
rawimage = nib.load(arguments["--image"])
bvals, bvecs = read_bvals_bvecs(arguments['--bvals'], arguments['--bvecs'])
print("Generating gradient table.")
gtab = gradient_table(bvals, bvecs)
#Define the tensor model
print("Generating the tensor model.")
dti_wls = dti.TensorModel(gtab, fit_method="NLLS")
image_data = rawimage.get_data()
print("Masking the brain.")
image_masked, mask = median_otsu(image_data, 3, 1, autocrop=True, dilate=2)
#print(image_masked)
#image_masked_data = nib.nifti1.Nifti1Image(image_masked.astype(np.float32), image_data.get_affine())
#print("Saving masked brain image")
#nib.nifti1.save(image_masked_data, "./imagemasked.nii.gz")
print("Resampling the brain to a standard resolution.")
image, affine1 = reslice(image_masked, rawimage.get_affine(), rawimage.get_header().get_zooms()[:3], (3.0,3.0,3.0))
mask, maskaffine1 = reslice(mask.astype(numpy.int), rawimage.get_affine(), rawimage.get_header().get_zooms()[:3], (3.0,3.0,3.0))
#print(len([type(mask) for i in range(0,image.shape[3])]))
#mask = numpy.expand_dims(mask,3)
#print(mask)
#print(mask.shape)
#image=image*mask
print(image[0][0][0])
print("Checking the image dimensions")
Xsize, Ysize, Zsize, directions = image.shape
print("X: {0}\nY: {1}\nZ: {2}".format(Xsize, Ysize, Zsize))
#Define Image Scopes
print("Defining the image scopes.")
imagedict = {"axial": {"dropdim": [0,1], "scope": (slice(0,Xsize), slice(0,Ysize), slice(math.floor(Zsize/2),math.floor(Zsize/2)+1))},
"coronal": {"dropdim": [0,2], "scope": (slice(0,Xsize), slice(math.floor(Ysize/2),math.floor(Ysize/2)+1), slice(0, Zsize))},
"sagittal": {"dropdim": [1,2], "scope": (slice(math.floor(Xsize/2),math.floor(Xsize/2)+1), slice(0,Ysize), slice(0, Zsize))}}
#roi_idx = (slice(0,image.shape[0]), slice(0,image.shape[1]), slice(middleslice,middleslice+1))#(slice(0,image.shape[0]), slice(0,image.shape[1]), slice(int(image.shape[2]/2),int(image.shape[2]/2)+1))
print("Defining sphere.")
sphere = get_sphere('symmetric724')
#sphere = dpd.get_sphere('symmetric362')
#Slice the whole dataset by the scope
print("Slicing the dataset with the scopes.")
for view in ["sagittal", "coronal", "axial"]:
imagedict[view]["image"] = image[imagedict[view]["scope"]]
imagedict[view]["mask"] = mask[imagedict[view]["scope"]]
print("Fitting {0} data.".format(view))
fit_wls = dti_wls.fit(imagedict[view]["image"])
print("Extracting {0} FA.".format(view))
fa1 = fit_wls.fa * imagedict[view]["mask"]
print("Extracting {0} EVALS.".format(view))
evals1 = fit_wls.evals
print("Extracting {0} EVECS.".format(view))
evecs1 = fit_wls.evecs
print("Extracting {0} Color FA.".format(view))
cfa1 = dti.color_fa(fa1, evecs1)
cfa1 = cfa1/cfa1.max()
print("Defining {0} renderer.".format(view))
render = fvtk.ren()
print("Generating {0} image.".format(view))
x =cfa1.shape[imagedict[view]["dropdim"][0]]
y =cfa1.shape[imagedict[view]["dropdim"][1]]
#print(x, y, 1, 3)
cfa2 = cfa1.reshape(x, y, 1, 3)
evals2 = evals1.reshape(x, y, 1, 3)*1.25
evecs2 = evecs1.reshape(x, y, 1, 3, 3)*1.25
print("Adding render.")
fvtk.add(render, fvtk.tensor(evals2, evecs2, cfa2, sphere))
print("Recording render.")
with Xvfb() as xvfb:
fvtk.record(render, out_path=arguments["--out"+view], size=(800,800), magnification=2)
print("Image Saved")
sys.exit(0)
def run(rawargs):
arguments = docopt(doc,
argv=rawargs,
version='Orientation Check v{0}'.format(Version))
inputs = [{
"Value": "image file",
"Flag": "--image"
}, {
"Value": "bvec file",
"Flag": "--bvecs"
}, {
"Value": "bvec file",
"Flag": "--bvecs"
}]
for inputinfo in inputs:
if not exists(arguments[inputinfo["Flag"]]):
print("The {0} specified does not exist!".format(
inputinfo["Value"]))
sys.exit(1)
try:
rawimage = nib.load(arguments["--image"])
bvals, bvecs = read_bvals_bvecs(arguments['--bvals'],
arguments['--bvecs'])
#print(bvals)
#print(bvecs)
print("Generating gradient table.")
gtab = gradient_table(bvals, bvecs)
#Define the tensor model
print("Generating the tensor model.")
dti_wls = dti.TensorModel(gtab, fit_method="NLLS")
image_data = rawimage.get_data()
print("Masking the brain.")
image_masked, mask = median_otsu(image_data,
3,
1,
autocrop=True,
dilate=2)
#print(image_masked)
#image_masked_data = nib.nifti1.Nifti1Image(image_masked.astype(np.float32), image_data.get_affine())
#print("Saving masked brain image")
#nib.nifti1.save(image_masked_data, "./imagemasked.nii.gz")
print("Resampling the brain to a standard resolution.")
image, affine1 = reslice(image_masked, rawimage.get_affine(),
rawimage.get_header().get_zooms()[:3],
(3.0, 3.0, 3.0))
mask, maskaffine1 = reslice(mask.astype(numpy.int),
rawimage.get_affine(),
rawimage.get_header().get_zooms()[:3],
(3.0, 3.0, 3.0))
#print(len([type(mask) for i in range(0,image.shape[3])]))
#mask = numpy.expand_dims(mask,3)
#print(mask)
#print(mask.shape)
#image=image*mask
print(image[0][0][0])
print("Checking the image dimensions")
Xsize, Ysize, Zsize, directions = image.shape
print("X: {0}\nY: {1}\nZ: {2}".format(Xsize, Ysize, Zsize))
#Define Image Scopes
print("Defining the image scopes.")
imagedict = {
"axial": {
"dropdim": [0, 1],
"scope": (slice(0, Xsize), slice(0, Ysize),
slice(math.floor(Zsize / 2),
math.floor(Zsize / 2) + 1))
},
"coronal": {
"dropdim": [0, 2],
"scope": (slice(0, Xsize),
slice(math.floor(Ysize / 2),
math.floor(Ysize / 2) + 1), slice(0, Zsize))
},
"sagittal": {
"dropdim": [1, 2],
"scope": (slice(math.floor(Xsize / 2),
math.floor(Xsize / 2) + 1), slice(0, Ysize),
slice(0, Zsize))
}
}
#roi_idx = (slice(0,image.shape[0]), slice(0,image.shape[1]), slice(middleslice,middleslice+1))#(slice(0,image.shape[0]), slice(0,image.shape[1]), slice(int(image.shape[2]/2),int(image.shape[2]/2)+1))
print("Defining sphere.")
#sphere = dpd.get_sphere('symmetric724')
sphere = dpd.get_sphere('symmetric362')
#Slice the whole dataset by the scope
print("Slicing the dataset with the scopes.")
for view in ["sagittal", "coronal", "axial"]:
imagedict[view]["image"] = image[imagedict[view]["scope"]]
imagedict[view]["mask"] = mask[imagedict[view]["scope"]]
print("Fitting {0} data.".format(view))
fit_wls = dti_wls.fit(imagedict[view]["image"])
print("Extracting {0} FA.".format(view))
fa1 = fit_wls.fa * imagedict[view]["mask"]
print("Extracting {0} EVALS.".format(view))
evals1 = fit_wls.evals
print("Extracting {0} EVECS.".format(view))
evecs1 = fit_wls.evecs
print("Extracting {0} Color FA.".format(view))
cfa1 = dti.color_fa(fa1, evecs1)
cfa1 = cfa1 / cfa1.max()
print("Defining {0} renderer.".format(view))
render = fvtk.ren()
print("Generating {0} image.".format(view))
x = cfa1.shape[imagedict[view]["dropdim"][0]]
y = cfa1.shape[imagedict[view]["dropdim"][1]]
#print(x, y, 1, 3)
cfa2 = cfa1.reshape(x, y, 1, 3)
evals2 = evals1.reshape(x, y, 1, 3) * 1.25
evecs2 = evecs1.reshape(x, y, 1, 3, 3) * 1.25
print("Adding render.")
fvtk.add(render, fvtk.tensor(evals2, evecs2, cfa2, sphere))
print("Recording render.")
with Xvfb() as xvfb:
fvtk.record(render,
out_path=arguments["--out" + view],
size=(800, 800),
magnification=2)
print("Image Saved")
except:
print("Could not check orientation of output image.")
sys.exit(1)
sys.exit(0)