Merge remote-tracking branch 'origin/uncertaintyconeimpl' into uncert…

…aintyconeimpl

# Conflicts:
#	fury/actor.py
#	fury/actors/tensor.py
#	fury/shaders/sdf/sd_cone.frag
#	fury/shaders/sdf/sd_union.frag
#	fury/tests/test_actors.py

fury/actors/tensor.py

@@ -86,10 +86,10 @@ def tensor_ellipsoid(centers, axes, lengths, colors, scales, opacity):
 
     # Normalization
     n_evals = "evalsVSOutput = evals/(max(evals.x, max(evals.y, evals.z)));"
-        
+
     # Values constraint to avoid incorrect visualizations
     evals = "evalsVSOutput = clamp(evalsVSOutput,0.05,1);"
-        
+
     # Scaling matrix
     sc_matrix = \
         """
@@ -100,7 +100,7 @@ def tensor_ellipsoid(centers, axes, lengths, colors, scales, opacity):
 
     # Rotation matrix
     rot_matrix = "mat3 R = mat3(evec1, evec2, evec3);"
-        
+
     # Tensor matrix
     t_matrix = "tensorMatrix = inverse(R) * S * R;"
 

fury/actors/tests/test_uncertainty.py

@@ -0,0 +1,98 @@
+"""
+This spript includes the implementation of dti_uncertainty actor for the
+visualization of the cones of uncertainty along with the diffusion tensors for
+comparison
+"""
+from dipy.core.gradients import gradient_table
+from dipy.reconst import dti
+from dipy.segment.mask import median_otsu
+
+import dipy.denoise.noise_estimate as ne
+
+from fury import actor, window
+
+from dipy.io.image import load_nifti
+from dipy.io.gradients import read_bvals_bvecs
+
+from dipy.data import get_fnames, read_stanford_hardi
+
+from fury.primitive import prim_sphere
+
+
+def test_uncertainty():
+    hardi_fname, hardi_bval_fname, hardi_bvec_fname =\
+        get_fnames('stanford_hardi')
+
+    data, affine = load_nifti(hardi_fname)
+
+    # load the b-values and b-vectors
+    bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
+
+    from dipy.segment.mask import median_otsu
+
+    maskdata, mask = median_otsu(data, vol_idx=range(10, 50), median_radius=3,
+                                 numpass=1, autocrop=True, dilate=2)
+
+    gtab = gradient_table(bvals, bvecs)
+
+    tenmodel = dti.TensorModel(gtab)
+    tenfit = tenmodel.fit(maskdata[13:43, 44:74, 28:29])
+
+    # Eigenvalues and eigenvectors
+    fevals = tenfit.evals
+    fevecs = tenfit.evecs
+
+    tensor_vals = dti.lower_triangular(tenfit.quadratic_form)
+    dti_params = dti.eig_from_lo_tri(tensor_vals)
+
+    # Predicted signal given tensor parameters
+    fsignal = dti.tensor_prediction(dti_params, gtab, 1.0)
+
+    # Design matrix or B matrix
+    b_matrix = dti.design_matrix(gtab)
+
+    # Standard deviation of the noise
+    sigma = ne.estimate_sigma(maskdata[13:43, 44:74, 28:29])
+
+    uncertainty_cones = actor.uncertainty_cone(evecs=fevecs, evals=fevals,
+                                               signal=fsignal, sigma=sigma,
+                                               b_matrix=b_matrix)
+
+    scene = window.Scene()
+    scene.background([255, 255, 255])
+
+    scene.add(diffusion_tensors())
+    window.show(scene, reset_camera=False)
+    scene.clear()
+
+    scene.add(uncertainty_cones)
+    window.show(scene, reset_camera=False)
+
+
+class Sphere:
+
+    vertices = None
+    faces = None
+
+def diffusion_tensors():
+    # https://dipy.org/documentation/1.0.0./examples_built/reconst_dti/
+    img, gtab = read_stanford_hardi()
+    data = img.get_data()
+
+    maskdata, mask = median_otsu(data, vol_idx=range(10, 50), median_radius=3,
+                                 numpass=1, autocrop=True, dilate=2)
+    tenmodel = dti.TensorModel(gtab)
+    tenfit = tenmodel.fit(maskdata)
+
+    evals = tenfit.evals[13:43, 44:74, 28:29]
+    evecs = tenfit.evecs[13:43, 44:74, 28:29]
+
+    vertices, faces = prim_sphere('symmetric724', True)
+    sphere = Sphere()
+    sphere.vertices = vertices
+    sphere.faces = faces
+
+    #from dipy.data import get_sphere
+    #sphere = get_sphere('symmetric724')
+
+    return actor.tensor_slicer(evals, evecs, sphere=sphere, scale=0.3)