def viewImgMaya(arr):
src = mlab.pipeline.scalar_field(arr)
#mlab.pipeline.iso_surface(src, name='bone', contours=[500, 1200], opacity=1.0, color=(1, 1, 1))
#mlab.pipeline.iso_surface(src, name='lung', contours=[-700, -600], opacity=0.3)
# mlab.pipeline.iso_surface(src, name='fat', contours=[-100, -50], opacity=0.3, color=(1,0.5,0))
mlab.pipeline.iso_surface(src, opacity=0.7)
mlab.show(stop=False)
mlab.show_pipeline(engine=None, rich_view=True)
def do_mlab():
gas = readfile_tex_vic(sys.argv[1])
size = int(1920), int(1080)
fig = mlab.figure('Viz', size=size,bgcolor=(0,0,0))
#fig.scene.anti_aliasing_frames = 0
#print "Drawing metal"
#metp = mlab.points3d(metal.x,metal.y,metal.z,metal.v,scale_mode="none",scale_factor=metal.d, colormap="copper")
# print "Drawing gas"
print gas.x
scatter_g = mlab.pipeline.scalar_scatter(gas.x,gas.y,gas.z,gas.t, scale_mode="none",scale_factor=gas.d)
gasp = mlab.points3d(gas.x, gas.y, gas.z, gas.t, scale_mode="none",scale_factor=gas.d)
print gas.x.min()
print gas.x.max()
mlab.outline()
mlab.colorbar(title='T', orientation='vertical', nb_labels=3)
#print "saving to ./vid/{num:02d}.png".format(num=i)
#mlab.savefig("./vid/{num:02d}.png".format(num=i))
#mlab.clf()
mlab.show_pipeline()
mlab.show()
connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4)
connect = mlab.pipeline.user_defined(smooth, filter=connect_)
# Compute normals for shading the surface
compute_normals = mlab.pipeline.poly_data_normals(connect)
compute_normals.filter.feature_angle = 80
surf = mlab.pipeline.surface(compute_normals,
color=(0.9, 0.72, 0.62))
#----------------------------------------------------------------------
# Display a cut plane of the raw data
ipw = mlab.pipeline.image_plane_widget(src, colormap='bone',
plane_orientation='z_axes',
slice_index=55)
mlab.view(-165, 32, 350, [143, 133, 73])
mlab.roll(180)
fig.scene.disable_render = False
#----------------------------------------------------------------------
# To make the link between the Mayavi pipeline and the much more
# complex VTK pipeline, we display both:
mlab.show_pipeline(rich_view=False)
from tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4)
connect = mlab.pipeline.user_defined(smooth, filter=connect_)
# Compute normals for shading the surface
compute_normals = mlab.pipeline.poly_data_normals(connect)
compute_normals.filter.feature_angle = 80
surf = mlab.pipeline.surface(compute_normals,
color=(0.9, 0.72, 0.62))
#----------------------------------------------------------------------
# Display a cut plane of the raw data
ipw = mlab.pipeline.image_plane_widget(src, colormap='bone',
plane_orientation='z_axes',
slice_index=55)
mlab.view(-165, 32, 350, [143, 133, 73])
mlab.roll(180)
fig.scene.disable_render = False
#----------------------------------------------------------------------
# To make the link between the Mayavi pipeline and the much more
# complex VTK pipeline, we display both:
mlab.show_pipeline(rich_view=False)
from tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
#vector = mlab.pipeline.vector_field(Bx, By)
densscalar = mlab.pipeline.scalar_field(dens)
#### Visualize the field ####################################################
fig = mlab.figure(1, size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# Vector field first for seeing the field lines
#magnitude = mlab.pipeline.extract_vector_norm(vector)
#field_lines = mlab.pipeline.streamline(magnitude)
# Scalar field for visualisation image planes
# First the photosphere
mlab.pipeline.image_plane_widget(densscalar,
plane_orientation='z_axes',
slice_index=0,
colormap='RdBu',
transparent=False,
opacity=1)
# Upper layers
#for i in range(0, max_slice):
# mlab.pipeline.image_plane_widget(densscalar, plane_orientation='z_axes', slice_index=50, colormap='RdBu', transparent=True, opacity=0.5)
mlab.show_pipeline()
mlab.show()
fat: -100 to -50
water: 0
CSF +15
Kidney +30
Blood +30 to +45
Muscle +10 to +40
Grey matter +37 to +45
White matter +20 to +30
Liver +40 to +60
Soft Tissue, Contrast +100 to +300
Bone +700 (cancellous bone) to +3000 (cortical bone)
'''
src = mlab.pipeline.scalar_field(crop)
mlab.pipeline.iso_surface(src,
name='bone',
contours=[500, 1200],
opacity=1.0,
color=(1, 1, 1))
mlab.pipeline.iso_surface(src,
name='lung',
contours=[-700, -600],
opacity=0.3)
#mlab.pipeline.iso_surface(src, name='fat', contours=[-100, -50], opacity=0.3, color=(1,0.5,0))
#mlab.pipeline.iso_surface(src, name='other', contours=[50, 300], opacity=0.7, color=(0,1.0,0))
mlab.show(stop=False)
mlab.show_pipeline(engine=None, rich_view=True)
#response = raw_input("hit enter to continue or ctrl-c to quit")
#time.sleep(3)
# mlab.pipeline.iso_surface(src, contours=[s.max()-0.1*s.ptp(), ],)
dens=alldat[1,:,:]
#scalar = mlab.pipeline.scalar_field(Bz)
#vector = mlab.pipeline.vector_field(Bx, By)
densscalar = mlab.pipeline.scalar_field(dens)
#### Visualize the field ####################################################
fig = mlab.figure(1, size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# Vector field first for seeing the field lines
#magnitude = mlab.pipeline.extract_vector_norm(vector)
#field_lines = mlab.pipeline.streamline(magnitude)
# Scalar field for visualisation image planes
# First the photosphere
mlab.pipeline.image_plane_widget(densscalar, plane_orientation='z_axes', slice_index=0, colormap='RdBu', transparent=False, opacity=1)
# Upper layers
#for i in range(0, max_slice):
# mlab.pipeline.image_plane_widget(densscalar, plane_orientation='z_axes', slice_index=50, colormap='RdBu', transparent=True, opacity=0.5)
mlab.show_pipeline()
mlab.show()
def isosurfacing(data):
"""data should be a 3d array with channel last."""
# Heuristic for finding the threshold for the brain
# Exctract the percentile 20 and 80 (without using
# scipy.stats.scoreatpercentile)
# sorted_data = np.sort(data.ravel())
# l = len(sorted_data)
# lower_thr = sorted_data[int(0.2 * l)]
# upper_thr = sorted_data[int(0.8 * l)]
# The white matter boundary: find the densest part of the upper half
# of histogram, and take a value 10% higher, to cut _in_ the white matter
# hist, bins = np.histogram(data[data > np.mean(data)], bins=50)
# brain_thr_idx = np.argmax(hist)
# brain_thr = bins[brain_thr_idx + 4]
# del hist, bins, brain_thr_idx
# Display the data #############################################################
fig = mlab.figure(bgcolor=(0, 0, 0), size=(400, 500))
# to speed things up
fig.scene.disable_render = True
src = mlab.pipeline.scalar_field(data)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 20]
src.update_image_data = True
#----------------------------------------------------------------------
# Brain extraction pipeline
# In the following, we create a Mayavi pipeline that strongly
# relies on VTK filters. For this, we make heavy use of the
# mlab.pipeline.user_defined function, to include VTK filters in
# the Mayavi pipeline.
# Apply image-based filters to clean up noise
# thresh_filter = tvtk.ImageThreshold()
# thresh_filter.threshold_between(lower_thr, upper_thr)
# thresh = mlab.pipeline.user_defined(src, filter=thresh_filter)
median_filter = tvtk.ImageMedian3D()
median_filter.kernel_size = [3, 3, 3]
median = mlab.pipeline.user_defined(src, filter=median_filter)
diffuse_filter = tvtk.ImageAnisotropicDiffusion3D(
diffusion_factor=1.0,
diffusion_threshold=100.0,
number_of_iterations=5, )
diffuse = mlab.pipeline.user_defined(median, filter=diffuse_filter)
# Extract brain surface
contour = mlab.pipeline.contour(diffuse, )
contour.filter.contours = [0.5, ]
# Apply mesh filter to clean up the mesh (decimation and smoothing)
dec = mlab.pipeline.decimate_pro(mlab.pipeline.triangle_filter(contour))
dec.filter.feature_angle = 60.
dec.filter.target_reduction = 0.5
smooth_ = tvtk.SmoothPolyDataFilter(
number_of_iterations=10,
relaxation_factor=0.1,
feature_angle=60,
feature_edge_smoothing=False,
boundary_smoothing=False,
convergence=0.,
)
smooth = mlab.pipeline.user_defined(dec, filter=smooth_)
# Get the largest connected region
connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4)
connect = mlab.pipeline.user_defined(smooth, filter=connect_)
# Compute normals for shading the surface
compute_normals = mlab.pipeline.poly_data_normals(connect)
compute_normals.filter.feature_angle = 80
surf = mlab.pipeline.surface(compute_normals,
color=(1, 1, 1))
#----------------------------------------------------------------------
# Display a cut plane of the raw data
ipw = mlab.pipeline.image_plane_widget(src, colormap='bone',
plane_orientation='z_axes',
slice_index=55)
# mlab.view(-165, 32, 350, [143, 133, 73])
# mlab.roll(180)
fig.scene.disable_render = False
#----------------------------------------------------------------------
# To make the link between the Mayavi pipeline and the much more
# complex VTK pipeline, we display both:
mlab.show_pipeline(rich_view=False)
from tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
# clipActor.backface_property=backProp
##bmp1 = tvtk.JPEGReader()
##bmp1.file_name=r"C:\Program Files\Cut3D Trial\Textures\1_Materials\SeamlessPine_rotated.jpg"
# my_texture=tvtk.Texture()
# my_texture.interpolate=1
# my_texture.set_input(0,bmp1.get_output())
my_scene = e1.new_scene()
# my_scene.scene.add_actor(clipActor)
src1 = VTKDataSource(data=clipper.get_output())
e1.add_source(src1)
asurf = p3d.pipeline.surface(
src1, opacity=1.0, name="main_cow", color=colors.white)
asurf.actor.actor.backface_property = backProp
p3d.show_pipeline()
# asurf.actor.actor.texture=my_texture
# Here we are cutting the cow. Cutting creates lines where the cut
# function intersects the model. (Clipping removes a portion of the
# model but the dimension of the data does not change.)
#
# The reason we are cutting is to generate a closed polygon at the
# boundary of the clipping process. The cutter generates line
# segments, the stripper then puts them together into polylines. We
# then pull a trick and define polygons using the closed line
# segements that the stripper created.
cutEdges = tvtk.Cutter()
cutEdges.set_input(cowNormals.get_output())
cutEdges.cut_function = plane
cutEdges.generate_cut_scalars = 1
def show_pipeline(self):
"""Show the Mayavi pipeline editor window."""
mlab.show_pipeline()
def _mayavi_dialog_fired(self):
mlab.show_pipeline()
import mayavi.mlab as m
import numpy as np
def test_plot3d():
"""Generates a pretty set of lines."""
n_mer, n_long = 6, 11
pi = np.pi
dphi = pi / 1000.0
phi = np.arange(0.0, 2 * pi + 0.5 * dphi, dphi)
mu = phi * n_mer
x = np.cos(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5)
y = np.sin(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5)
z = np.sin(n_long * mu / n_mer) * 0.5
l = m.plot3d(x, y, z, np.sin(mu), tube_radius=0.025, colormap='Spectral')
return l
if __name__ == '__main__':
m.text3d(1, 0, 0, 'function', orient_to_camera=False,
orientation=(0, 0, 90), scale=0.4)
m.show_pipeline()
m.pipeline
# test_plot3d()
m.show()
def showpip(self):
mlab.show_pipeline()