def display3D(img, list_remove=[], dictionary=None, lut=black_and_white, lut_range = False, cell_separation = False, verbose=False, labels=False):
"""
paramètres :
- img (SpatialImage ou SpatialImageAnalysis) : segmented tissu
- list_remove (list) : une liste des cellules à enlever lors de l'affichage
- dictionary (dict) : dictionnaire cells->scalar
- lut : -Look-Up Table- disponibles dans 'colormaps.py'
- lut_range (list) : list of 2 values defing the range (min, max) of the lut.
- cell_separation (bool) : if True add a "separation" between cells.
- verbose (bool) : pour afficher ou non les progressions
- labels (bool) : display labels of cell in 3D.
Examples :
im1 = imread('/home/vince/softs/vplants/vplants/trunk/vtissue/imaging/mars_alt/test/data/segmentation/imgSeg.inr.gz')
im1a=SpatialImageAnalysis(im1)
dictionary=dict(zip(im1a.labels(), im1a.volume()))
labs=im1a.labels()
L1=im1a.L1()[1]
filtre=[i for i in labs if i not in L1]
display3D(im1, verbose=True)
Fonction maitre en deux parties :
1. Gestion du format de l'objet donné en paramètre;
2. Gestion de l'affichage.
"""
if cell_separation:
# -- Management of file format
if isinstance(img,SpatialImageAnalysis3D):
im = compute_cell_separation(img.image)
elif isinstance(img,SpatialImage):
im = compute_cell_separation(img)
else:
print "for now this file format is not managed by display3D"
return None
# -- Management of file format
if isinstance(img,SpatialImageAnalysis3D):
if list_remove == None:
list_remove = img._ignoredlabels
a, a2, sc, m, m2 = rootSpI(img.image, list_remove = list_remove, sc = dictionary, lut_range = lut_range, verbose=verbose)
elif isinstance(img,SpatialImage):
a, a2, sc, m, m2 = rootSpI(img, list_remove = list_remove, sc = dictionary, lut_range = lut_range, verbose=verbose)
else:
print "for now this file format is not managed by display3D"
return None
# -- choice of colormap
m.lookup_table = lut(m.lookup_table)
from openalea.image.all import black_and_white
m2.lookup_table = black_and_white(m2.lookup_table)
# -- switching on the viewer and loading the object and the scalarbar
viewer = ivtk.viewer()
viewer.scene.add_actor(a)
viewer.scene.add_actor(a2)
if dictionary != None and lut_range != None:
viewer.scene.add_actor(sc)
if labels:
if isinstance(img,SpatialImageAnalysis3D):
lab=create_labels(img, viewer.scene.renderer)
viewer.scene.add_actor(lab)
def main(instantiate_gui=True):
"""Simple test case."""
from tvtk.tools import ivtk
v = ivtk.viewer(browser=False, instantiate_gui=instantiate_gui)
cs = tvtk.ConeSource()
m = tvtk.PolyDataMapper(input=cs.output)
a = tvtk.Actor(mapper=m)
v.scene.add_actor(a)
v.scene.reset_zoom()
b = PipelineBrowser(v.scene)
b.show()
return v, b, a
def main(instantiate_gui=True):
"""Simple test case."""
from tvtk.tools import ivtk
v = ivtk.viewer(browser=False, instantiate_gui=instantiate_gui)
cs = tvtk.ConeSource()
m = tvtk.PolyDataMapper(input_connection=cs.output_port)
a = tvtk.Actor(mapper=m)
v.scene.add_actor(a)
v.scene.reset_zoom()
b = PipelineBrowser(v.scene)
b.show()
return v, b, a
def figure(outline=True, browser=True):
"""Simple helper function that returns a usable figure.
Parameters
----------
- outline : `bool` (default: True)
If True, create an outline bounding box along with an axes
marker for the scene.
- browser : `bool` (default, True)
If True, creates an IVTK scene with an embedded PipelineBrowser.
If False, does not create it.
"""
v = ivtk.viewer(browser)
f = Figure(v.scene)
if outline:
o = Outline()
f.add(o)
v.scene.reset_zoom()
return f
def figure(outline=True, browser=True):
"""Simple helper function that returns a usable figure.
Parameters
----------
- outline : `bool` (default: True)
If True, create an outline bounding box along with an axes
marker for the scene.
- browser : `bool` (default, True)
If True, creates an IVTK scene with an embedded PipelineBrowser.
If False, does not create it.
"""
v = ivtk.viewer(browser)
f = Figure(v.scene)
if outline:
o = Outline()
f.add(o)
v.scene.reset_zoom()
return f
mbg, mb, mbc = TutorialTree()
q = map(list, mbc.q)
q[1] = [np.pi/2.]
q[2] = [-np.pi/4.]
q[3] = [-np.pi/2.]
q[4] = [0.5]
mbc.q = q
rbd.forwardKinematics(mb, mbc)
X_s = sva.PTransformd(sva.RotY(-np.pi/2.), e.Vector3d(0.1, 0., 0.))
mbv = MultiBodyViz(mbg, mb, endEffectorDict={'b4':(X_s, 0.1, (0., 1., 0.))})
# test MultiBodyViz
from tvtk.tools import ivtk
viewer = ivtk.viewer()
mbv.addActors(viewer.scene)
mbv.display(mb, mbc)
# test axis
from axis import Axis
a1 = Axis(text='test', length=0.2)
a1.addActors(viewer.scene)
a1.X = sva.PTransformd(sva.RotX(np.pi/2.), e.Vector3d.UnitX())
# test vector6d
from vector6d import ForceVecViz, MotionVecViz
M = sva.MotionVecd(e.Vector3d(0.2, 0.1, 0.), e.Vector3d(0.1, 0., 0.2))
F = sva.ForceVecd(e.Vector3d(-0.2, -0.1, 0.), e.Vector3d(-0.1, 0., -0.2))
MV = MotionVecViz(M, a1.X)
FV = ForceVecViz(F, sva.PTransformd(sva.RotX(np.pi/2.), e.Vector3d.UnitX()*1.4))
def display3D(img,
list_remove=[],
dictionary=None,
lut=black_and_white,
lut_range=False,
cell_separation=False,
verbose=False,
labels=False):
"""
paramètres :
- img (SpatialImage ou SpatialImageAnalysis) : segmented tissu
- list_remove (list) : une liste des cellules à enlever lors de l'affichage
- dictionary (dict) : dictionnaire cells->scalar
- lut : -Look-Up Table- disponibles dans 'colormaps.py'
- lut_range (list) : list of 2 values defing the range (min, max) of the lut.
- cell_separation (bool) : if True add a "separation" between cells.
- verbose (bool) : pour afficher ou non les progressions
- labels (bool) : display labels of cell in 3D.
Examples :
im1 = imread('/home/vince/softs/vplants/vplants/trunk/vtissue/imaging/mars_alt/test/data/segmentation/imgSeg.inr.gz')
im1a=SpatialImageAnalysis(im1)
dictionary=dict(zip(im1a.labels(), im1a.volume()))
labs=im1a.labels()
L1=im1a.L1()[1]
filtre=[i for i in labs if i not in L1]
display3D(im1, verbose=True)
Fonction maitre en deux parties :
1. Gestion du format de l'objet donné en paramètre;
2. Gestion de l'affichage.
"""
if cell_separation:
# -- Management of file format
if isinstance(img, SpatialImageAnalysis3D):
im = compute_cell_separation(img.image)
elif isinstance(img, SpatialImage):
im = compute_cell_separation(img)
else:
print "for now this file format is not managed by display3D"
return None
# -- Management of file format
if isinstance(img, SpatialImageAnalysis3D):
if list_remove == None:
list_remove = img._ignoredlabels
a, a2, sc, m, m2 = rootSpI(img.image,
list_remove=list_remove,
sc=dictionary,
lut_range=lut_range,
verbose=verbose)
elif isinstance(img, SpatialImage):
a, a2, sc, m, m2 = rootSpI(img,
list_remove=list_remove,
sc=dictionary,
lut_range=lut_range,
verbose=verbose)
else:
print "for now this file format is not managed by display3D"
return None
# -- choice of colormap
m.lookup_table = lut(m.lookup_table)
from openalea.image.all import black_and_white
m2.lookup_table = black_and_white(m2.lookup_table)
# -- switching on the viewer and loading the object and the scalarbar
viewer = ivtk.viewer()
viewer.scene.add_actor(a)
viewer.scene.add_actor(a2)
if dictionary != None and lut_range != None:
viewer.scene.add_actor(sc)
if labels:
if isinstance(img, SpatialImageAnalysis3D):
lab = create_labels(img, viewer.scene.renderer)
viewer.scene.add_actor(lab)
#!/usr/bin/env python
from IPython.display import Image
from tvtk.tools import ivtk
#from graph import Axis
viewer = ivtk.viewer()
viewer.size = (640, 480)
viewer.scene.camera.focal_point = (0.175, 0.321, 0.0)
viewer.scene.camera.position = (0.175, 0.321, 3.3)
display_number = 1
def display():
global display_number
path = 'img/%s.png' % display_number
display_number += 1
viewer.scene.save(path)
return Image(path)
import numpy as np
import eigen3 as e
import spacevecalg as sva
def render(self, filename=None, crop=True, add_actors=[]):
renderer = tvtk.Renderer(background = (1, 1, 1))
subset = tvtk.ExtractVOI(
sample_rate = (self.sample_rate, self.sample_rate, 1),
voi = (self._x_min, self._x_max, self._y_min, self._y_max, 0, 0))
configure_input(subset, self._dtm_reader)
cropped = tvtk.Threshold()
configure_input(cropped, subset)
cropped.threshold_by_upper(-9998)
geom = tvtk.GeometryFilter()
configure_input(geom, cropped)
surface1 = tvtk.WarpScalar(scale_factor = self.z_exaggeration)
configure_input(surface1, geom)
triangles = tvtk.TriangleFilter()
configure_input(triangles, surface1)
reduced = tvtk.DecimatePro(target_reduction = 0.5, preserve_topology = True)
configure_input(reduced, triangles)
strips = tvtk.Stripper()
configure_input(strips, reduced)
texture_plane = tvtk.TextureMapToPlane(
origin = (0, self._y_max, 0),
point1 = (self._x_max, self._y_max, 0),
point2 = (0, 0, 0))
configure_input(texture_plane, strips)
map_normals = tvtk.PolyDataNormals()
configure_input(map_normals, texture_plane)
surface = tvtk.PolyDataMapper(scalar_visibility = False)
configure_input(surface, map_normals)
#map_image = tvtk.PNGReader(file_name = overlay_file)
map_texture = tvtk.Texture(interpolate = True)
configure_input(map_texture, self._overlay)
geomap_actor = tvtk.Actor(mapper = surface, texture = map_texture, visibility = True)
renderer.add_actor(geomap_actor)
overhead_light = tvtk.Light()
overhead_light.set_color(1, 1, 1)
overhead_light.focal_point = ((self._x_max - self._x_min) / 2, (self._y_max - self._y_min) / 2, 0)
overhead_light.position = ((self._x_max - self._x_min) / 2, (self._y_max - self._y_min) / 2, 2000)
overhead_light.intensity = 0.5
renderer.add_light(overhead_light)
headlight = tvtk.Light(light_type = 'headlight')
renderer.add_light(headlight)
renderer.active_camera.set(**self.camera_settings)
## add axes indicator:
#axes = tvtk.AxesActor()
#axes.total_length = (300,) * 3
#text_property = tvtk.TextProperty()
#text_property.font_family = 'arial'
#text_property.font_size = 40
#text_property.color = (0, 0, 0)
#axes.x_axis_caption_actor2d.text_actor.text_scale_mode = False
#axes.y_axis_caption_actor2d.text_actor.text_scale_mode = False
#axes.z_axis_caption_actor2d.text_actor.text_scale_mode = False
#axes.x_axis_caption_actor2d.caption_text_property = text_property
#axes.y_axis_caption_actor2d.caption_text_property = text_property
#axes.z_axis_caption_actor2d.caption_text_property = text_property
#axes.x_axis_caption_actor2d.position = [10, 0]
#axes.y_axis_caption_actor2d.position = [20, -20]
#axes.z_axis_caption_actor2d.position = [20, 0]
#axes.x_axis_shaft_property.line_width = 3
#axes.y_axis_shaft_property.line_width = 3
#axes.z_axis_shaft_property.line_width = 3
#renderer.add_actor(axes)
## shift it a little bit:
#transform = tvtk.Transform()
#transform.translate((400, 400, 0))
#axes.user_transform = transform
# add additional actors:
for actor in add_actors:
renderer.add_actor(actor)
if filename is None:
v = ivtk.viewer()
v.scene.add_actors(renderer.actors)
cam = v.scene.camera
else:
render_window = tvtk.RenderWindow(size = (840, 700), position = (100, 100))
render_window.add_renderer(renderer)
cam = renderer.active_camera
if filename is not None:
render_window.render()
render_large = tvtk.RenderLargeImage(input = renderer, magnification = 2)
writer = tvtk.PNGWriter(file_name = filename)
configure_input(writer, render_large)
writer.write()
if crop:
# crop image to non-white pixels:
image = Image.open(filename)
bg = Image.new(image.mode, image.size, (255, 255, 255))
diff = ImageChops.difference(image, bg)
bbox = diff.getbbox()
image = image.crop(bbox)
image.save(filename)
