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def test_mlab_show():
"""Test mlab.show()"""
run_mlab_examples()
# Automatically close window in 100 msecs.
GUI.invoke_after(100, close)
mlab.show()
fig = mlab.gcf()
mlab.clf()
src = mlab.pipeline.scalar_field(msnake.data)
mlab.pipeline.image_plane_widget(
src, plane_orientation='x_axes', colormap='gray')
cnt = mlab.contour3d(msnake.levelset, contours=[0.5])
@mlab.animate(ui=True)
def anim():
for i in range(num_iters):
msnake.step()
cnt.mlab_source.scalars = msnake.levelset
yield
anim()
mlab.show()
# Return the last levelset.
return msnake.levelset
def viewer_pointcloud(pointcloud):
mlab.figure(bgcolor=(1, 1, 1))
mlab.points3d(pointcloud[:, 0], pointcloud[:, 1], pointcloud[:, 2], color=(0, 0, 0), mode='point')
mlab.show()
return
def viewer_pointcloud(pointcloud):
mlab.figure(bgcolor=(1, 1, 1))
mlab.points3d(pointcloud[:, 0], pointcloud[:, 1], pointcloud[:, 2], color=(0, 1, 0), mode='sphere', scale_factor = 0.025)
mlab.show()
return
xmin,xmax,ymin,ymax,zmin,zmax = mol.bbox()
x, y, z = np.mgrid[xmin:xmax:(npts*1j),ymin:ymax:(npts*1j),zmin:zmax:(npts*1j)]
fxyz = np.zeros((npts, npts, npts))
for c,bf in zip(orb,bfs):
fxyz += c*bf(x, y, z)
src = mlab.pipeline.scalar_field(x, y, z, fxyz)
mlab.pipeline.iso_surface(src, contours=[-posval,posval], opacity=0.6)
for d,ind in planes:
if not ind: ind = npts//2
mlab.pipeline.image_plane_widget(src,
plane_orientation='%s_axes' % d,
slice_index=ind)
if doshow: mlab.show()
return
ax.set_ylim(0, ceil)
ax.set_ylabel('length (mm)')
ax.set_zlim(0, ceil)
ax.set_zlabel('length (mm)')
plt.tight_layout()
plt.show()
elif backend == 'mayavi':
try:
from mayavi import mlab
sf = mlab.pipeline.scalar_field(mask.astype(np.float))
sf.spacing = [rij, rij, rk]
mlab.pipeline.iso_surface(sf, contours=[0.5])
mlab.show()
except ImportError:
print("Mayavi could not be imported. Is it installed?")
draw_lidar(pc_velo, fig=fig)
for obj in objects:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
# Draw heading arrow
ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1,y1,z1 = ori3d_pts_3d_velo[0,:]
x2,y2,z2 = ori3d_pts_3d_velo[1,:]
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig)
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5),
tube_radius=None, line_width=1, figure=fig)
mlab.show(1)
fgcolor=None, engine=None, size=(1000, 500))
draw_lidar(backprojected_pc_velo, fig=fig)
raw_input()
# Only display those points that fall into 2d box
print(' -------- LiDAR points in a frustum from a 2D box --------')
xmin,ymin,xmax,ymax = \
objects[0].xmin, objects[0].ymin, objects[0].xmax, objects[0].ymax
boxfov_pc_velo = \
get_lidar_in_image_fov(pc_velo, calib, xmin, ymin, xmax, ymax)
print('2d box FOV point num: ', boxfov_pc_velo.shape[0])
fig = mlab.figure(figure=None, bgcolor=(0,0,0),
fgcolor=None, engine=None, size=(1000, 500))
draw_lidar(boxfov_pc_velo, fig=fig)
mlab.show(1)
raw_input()
def viewer_original_vs_ransac_pointcloud_vs_plane(ransac_pcl, original_pcl, plane_model):
sensor_range = 120.0
mlab.figure(bgcolor=(1, 1, 1))
x, y = np.ogrid[-sensor_range+50:sensor_range+50:1, -sensor_range:sensor_range:1]
mlab.points3d(original_pcl[:, 0], original_pcl[:, 1], original_pcl[:, 2], color=(0, 0, 0), mode='point')
mlab.points3d(ransac_pcl[:, 0], ransac_pcl[:, 1], ransac_pcl[:, 2], color=(1, 0, 0), mode='point')
mlab.surf(x, y, (-plane_model[3] - (plane_model[0]*x) - (plane_model[1]*y)) / plane_model[2],
color=(0.8, 0.8, 1), opacity=0.3)
mlab.show()
return
all_ds[example_id]
plt.imshow(image)
mlab.figure()
vis_mesh(gt_mesh, color=(0, 0, 1))
mlab.figure()
vis_mesh(mesh, color=(0, 1, 0))
mlab.figure()
vis_mesh(template_mesh, color=(1, 0, 0))
mlab.figure()
vis_point_cloud(
np.array(cloud), scale_factor=0.01, color=(0, 1, 0))
mlab.figure()
vis_voxels(voxels.data, color=(0, 1, 0))
plt.show(block=False)
mlab.show()
plt.close()