def ncut(self):
# dims = (3,3,3)
dims = (91, 109, 91)
graphconn = self.graph.compute_conn()
total = np.shape(graphconn)[0]
print "1"
print np.shape(graphconn)
print np.max(self.ncluster)
pdb.set_trace()
eigenval, eigenvec = pyncut.ncut(graphconn, np.max(self.ncluster))
# Discretize to label
# self.labelcut = np.array(eigenvec.shape[0], len(self.ncluster))
# for c in range(self.ncluster):
# eigk = eigenvec[:,:c]
# print eigk
eigenvec_discrete = pyncut.discretisation(eigenvec)
img_outfile_1d = eigenvec_discrete[:, 0]
for i in range(self.ncluster):
img_outfile_1d = img_outfile_1d + (i + 1) * eigenvec_discrete[:, i]
img_outfile_1d = list(img_outfile_1d.todense())
img_outfile_3d = np.reshape(img_outfile_1d, dims)
print "ok"
# name = "mytest.nii.gz"
# nib.save(img_outfile_3d,name)
return img_outfile_3d
def ncut(self):
# dims = (3,3,3)
dims = (91, 109, 91)
graphconn = self.graph.compute_conn()
total = np.shape(graphconn)[0]
print "1"
print np.shape(graphconn)
print np.max(self.ncluster)
pdb.set_trace()
eigenval, eigenvec = pyncut.ncut(graphconn, np.max(self.ncluster))
# Discretize to label
# self.labelcut = np.array(eigenvec.shape[0], len(self.ncluster))
# for c in range(self.ncluster):
# eigk = eigenvec[:,:c]
# print eigk
eigenvec_discrete = pyncut.discretisation(eigenvec)
img_outfile_1d = eigenvec_discrete[:, 0]
for i in range(self.ncluster):
img_outfile_1d = img_outfile_1d + (i + 1) * eigenvec_discrete[:, i]
img_outfile_1d = list(img_outfile_1d.todense())
img_outfile_3d = np.reshape(img_outfile_1d, dims)
print "ok"
# name = "mytest.nii.gz"
# nib.save(img_outfile_3d,name)
return img_outfile_3d
def _ncut(self,graph,ncluster,dims): total = np.prod(dims) eigenval, eigenvec = pyncut.ncut(graph,ncluster) eigenvec_discrete= pyncut.descretisation(eigenvec) markers = eigenvec_discrete[:,0] for i in range(total): markers = markers + (i + 1) * eigenvec_discrete[:,i] markers = markers.todense() return markers