def _run_interface(self, runtime):
import os.path as op
import nibabel.gifti as ng
import numpy as np
import skimage.measure as sm
import nilearn.image as nimg
import slam.io as sio
import slam.differential_geometry as sdg
from nipype.utils.filemanip import split_filename
# Generate output mesh filename from the input image name
_, fname, _ = split_filename(self.inputs.image_file)
gii_file = op.abspath(op.join(runtime.cwd, fname + ".gii"))
# Load the largest connected component of the input image
img = nimg.largest_connected_component_img(self.inputs.image_file)
# TODO: check if the input image is correct (binary)
# Run the marching cube algorithm
verts, faces, normals, values = sm.marching_cubes_lewiner(
img.get_data(), self.inputs.level)
# Convert vertices coordinates to image space
# TODO: check that is correct by plotting the mesh on the image
x, y, z = nimg.coord_transform(verts[:, 0], verts[:, 1], verts[:, 2],
img.affine)
mm_verts = np.array([x, y, z]).T
# Save the mesh as Gifti
# FIXME: FreeView can not open the mesh (but anatomist do)
gii = ng.GiftiImage(darrays=[
ng.GiftiDataArray(mm_verts, intent='NIFTI_INTENT_POINTSET'),
ng.GiftiDataArray(faces, intent='NIFTI_INTENT_TRIANGLE')
])
gii.meta = ng.GiftiMetaData().from_dict({
"volume_file":
self.inputs.image_file,
"marching_cube_level":
str(self.inputs.level),
"smoothing_iterations":
str(self.inputs.smoothing_iter),
"smoothing_dt":
str(self.inputs.smoothing_dt)
})
ng.write(gii, gii_file)
# Optional: Smooth the marching cube output with SLAM
if self.inputs.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.inputs.smoothing_iter,
dt=self.inputs.smoothing_dt)
sio.write_mesh(mesh, gii_file)
return runtime
# License: BSD (3-clause)
# sphinx_gallery_thumbnail_number = 2
###############################################################################
# Importation of slam modules
import slam.distortion as sdst
import slam.differential_geometry as sdg
import slam.plot as splt
import slam.io as sio
import numpy as np
###############################################################################
# Loading an example mesh and a smoothed copy of it
mesh = sio.load_mesh('data/example_mesh.gii')
mesh_s = sdg.laplacian_mesh_smoothing(mesh, nb_iter=50, dt=0.1)
################################################################################
# Visualization of the original mesh
visb_sc = splt.visbrain_plot(mesh=mesh, caption='original mesh')
visb_sc.preview()
###############################################################################
# Visualization of the smoothed mesh
visb_sc = splt.visbrain_plot(mesh=mesh_s,
caption='smoothed mesh',
visb_sc=visb_sc)
visb_sc.preview()
###############################################################################
# Computation of the angle difference between each faces of mesh and mesh_s
if __name__ == '__main__':
# load example data
mesh = sio.load_mesh('data/example_mesh.gii')
tex = sio.load_texture('data/example_texture.gii')
# compute various types of Laplacian of the mesh
lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='fem')
print(mesh.vertices.shape)
print(lap.shape)
lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='conformal')
lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='meanvalue')
lap, lap_b = sdg.compute_mesh_laplacian(mesh, lap_type='authalic')
# smooth the mesh using Laplacian
s_mesh = sdg.laplacian_mesh_smoothing(mesh, nb_iter=100, dt=0.1)
# compute the gradient of texture tex
triangle_grad = sdg.triangle_gradient(mesh, tex.darray[0])
print(triangle_grad)
grad = sdg.gradient(mesh, tex.darray[0])
print(grad.values)
norm_grad = sdg.norm_gradient(mesh, tex.darray[0])
print(norm_grad)
# compute the depth potential function
dpf = sdg.depth_potential_function(mesh, tex.darray[0], [0.3])
# plot all that to show the results
visb_sc = splt.visbrain_plot(mesh=mesh,
tex=tex.darray[0],
"marching_cube_level": self.input_spec.level,
"smoothing_iterations": self.input_spec.smoothing_iter,
"smoothing_dt": self.input_spec.smoothing_dt
=======
"volume_file": self.inputs.image_file,
"marching_cube_level": str(self.inputs.level),
"smoothing_iterations": str(self.inputs.smoothing_iter),
"smoothing_dt": str(self.inputs.smoothing_dt)
>>>>>>> 02924efd7fd9ff93e10f0e0030a43b878812e288
})
ng.write(gii, gii_file)
# Optional: Smooth the marching cube output with SLAM
<<<<<<< HEAD
if self.input_spec.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.input_spec.smoothing_iter,
dt=self.input_spec.smoothing_dt)
sio.write_mesh(mesh, gii_file)
=======
if self.inputs.smoothing_iter > 0:
mesh = sdg.laplacian_mesh_smoothing(
sio.load_mesh(gii_file),
nb_iter=self.inputs.smoothing_iter,
dt=self.inputs.smoothing_dt)
sio.write_mesh(mesh, gii_file)
return runtime
>>>>>>> 02924efd7fd9ff93e10f0e0030a43b878812e288