def showNormals(s,figm=None,algo='sobel',color=(1,0,0),as2D=False):
s.getNormals(algo)
if as2D:
if figm is None:
figm = plt.figure()
I = (s.n.copy()+1.0)*0.5 # make non negative
I[np.logical_not(s.mask),0] = 0.;
I[np.logical_not(s.mask),1] = 0.;
I[np.logical_not(s.mask),2] = 0.;
plt.imshow(I)
else:
if figm is None:
figm = mlab.figure(bgcolor=(1,1,1))
M = Sphere(2)
M.plotFanzy(figm,1.0,linewidth=1)
mlab.points3d(s.n[s.mask,0],s.n[s.mask,1],s.n[s.mask,2],
scale_factor=0.01, color=color, figure=figm,
mode='point',mask_points=1)
return figm
# figm1 = rgbd.showNormals(as2D=True);
# figm1.show()
# plt.show()
# plt.show()
# figm2 = rgbd.showWeightedNormals(algo=algo)
# fig = rgbd.showAxialSigma()
# fig = rgbd.showLateralSigma(theta=30.0)
#fig = rgbd.bilateralDepthFiltering(theta=30.0)
# figm0 = rgbd.showPc(showNormals=True,algo=algo)
# figm1 = rgbd.showNormals()
# show raw normals
figm1 = mlab.figure(bgcolor=(1, 1, 1))
M = Sphere(2)
M.plotFanzy(figm1, 1.0)
from js.utils.plot.colors import colorScheme
rgbd.n[2, :] *= -1.
figm1 = rgbd.showNormals(figm=figm1,
color=colorScheme("labelMap")["orange"])
# show clustered normals
figm3 = mlab.figure(bgcolor=(1, 1, 1))
M = Sphere(2)
M.plotFanzy(figm1, 1.0)
mlab.points3d(n[0, :],
n[1, :],
n[2, :],
-z[-1, :],
colormap='jet',
mode='point')
# for k in range(K):
# figm1.show()
# plt.show()
# plt.show()
# figm2 = rgbd.showWeightedNormals(algo=algo)
# fig = rgbd.showAxialSigma()
# fig = rgbd.showLateralSigma(theta=30.0)
#fig = rgbd.bilateralDepthFiltering(theta=30.0)
# figm0 = rgbd.showPc(showNormals=True,algo=algo)
# figm1 = rgbd.showNormals()
# show raw normals
figm1 = mlab.figure(bgcolor=(1,1,1))
M = Sphere(2)
M.plotFanzy(figm1,1.0)
from js.utils.plot.colors import colorScheme
rgbd.n[2,:] *= -1.
figm1=rgbd.showNormals(figm=figm1,color=colorScheme("labelMap")["orange"])
# show clustered normals
figm3 = mlab.figure(bgcolor=(1,1,1))
M = Sphere(2)
M.plotFanzy(figm1,1.0)
mlab.points3d(n[0,:],n[1,:],n[2,:],-z[-1,:],colormap='jet',
mode='point')
# for k in range(K):
# ids = z[-1,:]==k
# if np.count_nonzero(ids) > 0:
# mlab.points3d(n[0,ids],n[1,ids],n[2,ids],color=((float(k)/K),0,0),
# mode='point')
mlab.show(stop=True)
