def viewImg2(img, spacing, contours):
print("In viewImg2: (min,max)=(%f,%f)"%(img.min(),img.max()))
print("contours=",contours)
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
src = mlab.pipeline.scalar_field(img)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 1]
src.update_image_data = True
# Extract some inner structures: the ventricles and the inter-hemisphere
# fibers. We define a volume of interest (VOI) that restricts the
# iso-surfaces to the inner of the brain. We do this with the ExtractGrid
# filter.
blur = mlab.pipeline.user_defined(src, filter='ImageGaussianSmooth')
#mlab.pipeline.volume(blur, vmin=0.2, vmax=0.8)
mlab.pipeline.iso_surface(src, contours=contours)
#mlab.pipeline.image_plane_widget(blur,
# plane_orientation='z_axes',
# slice_index=img.shape[0]/2,
# )
#voi = mlab.pipeline.extract_grid(blur)
#voi.set(x_min=125, x_max=193, y_min=92, y_max=125, z_min=34, z_max=75)
#mlab.pipeline.iso_surface(src, contours=[1,2], colormap='Spectral')
#mlab.pipeline.contour3d(blur)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.show()
def viewImg(img, spacing, contours):
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
src = mlab.pipeline.scalar_field(img)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 1]
src.update_image_data = True
# Extract some inner structures: the ventricles and the inter-hemisphere
# fibers. We define a volume of interest (VOI) that restricts the
# iso-surfaces to the inner of the brain. We do this with the ExtractGrid
# filter.
blur = mlab.pipeline.user_defined(blur, filter='ImageGaussianSmooth')
print("blur type is",type(blur),blur.max())
#voi = mlab.pipeline.extract_grid(blur)
#voi.set(x_min=125, x_max=193, y_min=92, y_max=125, z_min=34, z_max=75)
#mlab.pipeline.iso_surface(src, contours=[1,2], colormap='Spectral')
mlab.pipeline.contour3d(blur)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.show()
def viewImgWithNodes(img, spacing, contours,g, title=''):
mlab.figure(bgcolor=(0, 0, 0), size=(900, 900))
#src = mlab.pipeline.scalar_field(img)
## Our data is not equally spaced in all directions:
#src.spacing = [1, 1, 1]
#src.update_image_data = True
#
#mlab.pipeline.iso_surface(src, contours=contours, opacity=0.2)
nodes = np.array(g.nodes())
dsize = 4*np.ones(nodes.shape[0],dtype='float32')
print(dsize.shape,nodes.shape)
#mlab.points3d(nodes[:,0],nodes[:,1],nodes[:,2],color=(0.0,1.0,0.0))
mlab.points3d(nodes[:,2],nodes[:,1],nodes[:,0],dsize,color=(0.0,0.0,1.0), scale_factor=0.25)
for n1, n2, edge in g.edges(data=True):
path = [n1]+edge['path']+[n2]
pa = np.array(path)
#print pa
mlab.plot3d(pa[:,2],pa[:,1],pa[:,0],color=(0,1,0),tube_radius=0.25)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.title(title, height=0.1)
mlab.show()
def autoPositionCamera():
"""
Set camera position looking along the x-axis.
"""
s = mlab.gcf().scene
s.disable_render = True
mlab.view(90,90,distance='auto',focalpoint='auto')
mlab.roll(0)
s.disable_render = False
def __view(self, viewargs=None, roll=None):
"""Wrapper for mlab.view()
Parameters
----------
viewargs: dict
mapping with keys corresponding to mlab.view args
roll: num
int or float to set camera roll
Returns
-------
camera settings: tuple
view settings, roll setting
"""
from enthought.mayavi import mlab
if viewargs:
viewargs['reset_roll'] = True
mlab.view(**viewargs)
if not roll is None:
mlab.roll(roll)
return mlab.view(), mlab.roll()
def viewGraph(g, sub=3,title=''):
mlab.figure(bgcolor=(0, 0, 0), size=(900, 900))
nodes = g.nodes()
random.shuffle(nodes)
nodes = np.array(nodes[0:100])
#mlab.points3d(nodes[:,2],nodes[:,1],nodes[:,0],color=(0.0,0.0,1.0))
edges = g.edges(data=True)
print(len(edges))
input('continue')
count = 0
for n1, n2, edge in edges:
count += 1
if( count % 100 == 0 ):
print(count)
path = [n1]+edge['path']+[n2]
pa = np.array(path)
#print pa
mlab.plot3d(pa[::sub,2],pa[::sub,1],pa[::sub,0],color=(0,1,0),tube_radius=0.75)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.title(title, height=0.1)
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 enthought.tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
# Create the points src = mlab.pipeline.scalar_scatter( x, y, z, s ) # Connect them src.mlab_source.dataset.lines = connections # The stripper filter cleans up connected lines lines = mlab.pipeline.stripper( src ) # Finally, display the set of lines mlab.pipeline.surface( mlab.pipeline.tube( lines, tube_sides=7, tube_radius=0.1 ), opacity=.4, colormap='Accent' ) # And choose a nice view mlab.view( 33.6, 106, 5.5, [0, 0, .05] ) mlab.roll( 125 ) mlab.show() from enthought.mayavi import mlab import numpy as np mlab.clf() # Number of lines n_lines = 200 # Number of points per line n_points = 100 # Create Example Coordinates
[np.arange(index, index + N - 1.5),
np.arange(index+1, index + N - .5)]
).T)
index += N
# Now collapse all positions, scalars and connections in big arrays
x = np.hstack(x)
y = np.hstack(y)
z = np.hstack(z)
s = np.hstack(s)
connections = np.vstack(connections)
# Create the points
src = mlab.pipeline.scalar_scatter(x, y, z, s)
# Connect them
src.mlab_source.dataset.lines = connections
# The stripper filter cleans up connected lines
lines = mlab.pipeline.stripper(src)
# Finally, display the set of lines
mlab.pipeline.surface(lines, colormap='Accent', line_width=1, opacity=.4)
# And choose a nice view
mlab.view(33.6, 106, 5.5, [0, 0, .05])
mlab.roll(125)
mlab.show()
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
m.axes(
s,
color=(0.7, 0.7, 0.7),
extent=(-1, 1, 0, 1, 0, 1),
ranges=(-0.21, 0.21, 0.1, 20, 0, max_mu_q),
xlabel="x",
ylabel="Lr",
zlabel="Force",
)
m.view(-60.0, 70.0, focalpoint=[0.0, 0.45, 0.45])
# Store the information
view = m.view()
roll = m.roll()
print "view", view
print "roll", roll
print n_mu_q_arr.shape[2]
ms = s.mlab_source
for i in range(1, n_mu_q_arr.shape[0]):
ms.scalars = n_mu_q_arr[i, :, :]
fname = "x%02d.jpg" % i
print "saving", fname
m.savefig(fname)
sleep(0.1)
# m.surf( n_e_arr[0], n_e_arr[1], n_mu_q_arr + n_std_q_arr )
# m.surf( n_e_arr[0], n_e_arr[1], n_mu_q_arr - n_std_q_arr )
m.show()
vmax=2600)
cut_plane2.implicit_plane.origin = (136, 111.5, 82)
cut_plane2.implicit_plane.widget.enabled = False
# Extract two views of the outside surface. We need to define VOIs in
# order to leave out a cut in the head.
voi2 = mlab.pipeline.extract_grid(src)
voi2.set(y_min=112)
outer = mlab.pipeline.iso_surface(voi2,
contours=[
1776,
],
color=(0.8, 0.7, 0.6))
voi3 = mlab.pipeline.extract_grid(src)
voi3.set(y_max=112, z_max=53)
outer3 = mlab.pipeline.iso_surface(voi3,
contours=[
1776,
],
color=(0.8, 0.7, 0.6))
mlab.view(-125, 54, 326, (145.5, 138, 66.5))
mlab.roll(-175)
mlab.show()
import shutil
shutil.rmtree('mri_data')
cut_plane.implicit_plane.widget.enabled = False
cut_plane2 = mlab.pipeline.scalar_cut_plane(thr,
plane_orientation='z_axes',
colormap='black-white',
vmin=1400,
vmax=2600)
cut_plane2.implicit_plane.origin = (136, 111.5, 82)
cut_plane2.implicit_plane.widget.enabled = False
# Extract two views of the outside surface. We need to define VOIs in
# order to leave out a cut in the head.
voi2 = mlab.pipeline.extract_grid(src)
voi2.set(y_min=112)
outer = mlab.pipeline.iso_surface(voi2, contours=[1776, ],
color=(0.8, 0.7, 0.6))
voi3 = mlab.pipeline.extract_grid(src)
voi3.set(y_max=112, z_max=53)
outer3 = mlab.pipeline.iso_surface(voi3, contours=[1776, ],
color=(0.8, 0.7, 0.6))
mlab.view(-125, 54, 326, (145.5, 138, 66.5))
mlab.roll(-175)
mlab.show()
import shutil
shutil.rmtree('mri_data')
#filter.filter.source = extract_vector_norm.outputs[0]
#filter.filter.source = surface.outputs[0]
print "vectors"
vectors = mlab.pipeline.vectors(filter, mode='2darrow')
print "polishing"
vectors.glyph.color_mode = 'no_coloring'
vectors.actor.property.color = (0, 0, 0)
vectors.glyph.glyph.scale_factor = 0.25
vectors.glyph.glyph_source.glyph_source.scale = 1.033
vectors.glyph.glyph_source.glyph_source.center = array([0.5, 0, 0])
print "done"
mlab.view(0.0, 0.0, 19.6, array([5, 5, 0]))
mlab.roll(0)
#mlab.show()
for i in range(max_frame_number + 1):
print "doing:", i
vtk_file_reader.timestep = i
vectors.actor.property.color = (0, 0, 0)
vectors.glyph.glyph.scale_factor = 0.25
vectors.glyph.glyph_source.glyph_source.scale = 1.033 * 0.5
vectors.glyph.glyph_source.glyph_source.center = array([0.5, 0, 0])
mlab.savefig("output/frame_vec%04d.png" % i)
print "Files saved to output/*"
print """Create the video using:
ffmpeg -i output/frame_vec%04d.png -r 15 -vcodec copy output/output.avi
#filter.filter.source = extract_vector_norm.outputs[0]
#filter.filter.source = surface.outputs[0]
print "vectors"
vectors = mlab.pipeline.vectors(filter, mode='2darrow')
print "polishing"
vectors.glyph.color_mode = 'no_coloring'
vectors.actor.property.color = (0, 0, 0)
vectors.glyph.glyph.scale_factor = 0.25
vectors.glyph.glyph_source.glyph_source.scale = 1.033
vectors.glyph.glyph_source.glyph_source.center = array([0.5, 0, 0])
print "done"
mlab.view(0.0, 0.0, 19.6, array([5, 5, 0]))
mlab.roll(0)
#mlab.show()
for i in range(max_frame_number+1):
print "doing:", i
vtk_file_reader.timestep = i
vectors.actor.property.color = (0, 0, 0)
vectors.glyph.glyph.scale_factor = 0.25
vectors.glyph.glyph_source.glyph_source.scale = 1.033*0.5
vectors.glyph.glyph_source.glyph_source.center = array([0.5, 0, 0])
mlab.savefig("output/frame_vec%04d.png" % i)
print "Files saved to output/*"
print """Create the video using:
ffmpeg -i output/frame_vec%04d.png -r 15 -vcodec copy output/output.avi
vtk_file_reader = engine.open(u'output/frame_scal0000.vtk')
warp_scalar = WarpScalar()
engine.add_filter(warp_scalar, vtk_file_reader)
surface = Surface()
engine.add_filter(surface, warp_scalar)
warp_scalar.filter.normal = array([ 0., 0., 1.])
warp_scalar.filter.scale_factor = 10.0
module_manager = engine.scenes[0].children[0].children[0].children[0]
module_manager.scalar_lut_manager.use_default_range = False
module_manager.scalar_lut_manager.data_range = array([-1.5, 1.5])
module_manager.scalar_lut_manager.show_scalar_bar = True
mlab.view(-122, 53, 26, array([7.5, 2.5, -0.11]))
mlab.roll(40)
print " done."
for i in range(max_frame_number+1):
print "doing:", i
vtk_file_reader.timestep = i
mlab.savefig("output/frame_scal%04d.png" % i)
print "Files saved to output/*"
print """Create the video using:
ffmpeg -i output/frame_scal%04d.png -r 15 -vcodec copy output/output.avi
ffmpeg2theora output/output.avi -o output.ogv
To produce a FLV video, use:
ffmpeg -b 3600k -i output/frame_scal%04d.png -r 15 video.flv
"""