size = 100. n_points = 11 n_layers = 2 img = sphere_tissue_image(size=size, n_points=n_points, n_layers=n_layers) world.add(img,"segmented_image",colormap='glasbey',alphamap='constant',bg_id=1,alpha=0.25) draco = DracoMesh(image=img) draco.delaunay_adjacency_complex(surface_cleaning_criteria=[]) world.add(draco.triangulation_topomesh,'adjacency_complex') world['adjacency_complex']['display_3'] = False world['adjacency_complex']['display_0'] = True world['adjacency_complex_vertices']['display_colorbar'] = False world['adjacency_complex_vertices']['polydata_colormap'] = load_colormaps()['glasbey'] world['adjacency_complex_vertices']['point_radius'] = 1.5 draco.adjacency_complex_optimization(n_iterations=3) world['adjacency_complex']['coef_3'] = 0.9 world['adjacency_complex']['display_3'] = True world['adjacency_complex_cells']['display_colorbar'] = False world['adjacency_complex_cells']['polydata_colormap'] = load_colormaps()['grey'] world['adjacency_complex_cells']['intensity_range'] = (-1,0) world['adjacency_complex_cells']['preserve_faces'] = True world['adjacency_complex_cells']['x_slice'] = (0,90) triangular = ['star','remeshed','projected','flat'] image_dual_topomesh = draco.dual_reconstruction(reconstruction_triangulation = triangular, adjacency_complex_degree=3, maximal_edge_length=5.1)
pi_img = image_dict['PI']
world.add(pi_img,
'membrane_image' + suffix,
colormap='Reds',
voxelsize=microscope_orientation *
np.array(image_dict[reference_name].voxelsize))
world['membrane_image']['intensity_range'] = (5000, 30000)
L1_cells = np.array(list(
topomesh.wisps(0)))[topomesh.wisp_property('layer', 0).values() == 1]
non_L1_cells = np.array(list(
topomesh.wisps(0)))[topomesh.wisp_property('layer', 0).values() != 1]
world.add(topomesh, "detected_nuclei" + suffix)
world["detected_nuclei_vertices"]["polydata_colormap"] = load_colormaps(
)['jet']
world["detected_nuclei"]["property_name_0"] = "layer"
world["detected_nuclei_vertices"]["intensity_range"] = (-0.5, 2.5)
world["detected_nuclei_vertices"]["display_colorbar"] = False
world['nuclei_image']['x_plane_position'] = 0
world['nuclei_image']['y_plane_position'] = 0
world['nuclei_image']['z_plane_position'] = 10
world['nuclei_image']['cut_planes_alpha'] = 0.5
user_input = ""
while user_input != 'next':
user_input = raw_input("Type 'next' to continue...")
# Start of post-edition
edited_L1_points = np.array(
world["detected_nuclei_vertices"].data.points.values())
(3, [])])
save_ply_property_topomesh(expert_topomesh,
xp_topomesh_fname,
properties_to_save=ppty2ply,
color_faces=False)
# -- Edit 'expert_topomesh' (ground truth) for potential labels at the stack margins:
margin_cells = [
k for k, v in expert_topomesh.wisp_property('marginal', 0).items() if v
]
non_margin_cells = list(set(expert_topomesh.wisps(0)) - set(margin_cells))
expert_topomesh = filter_topomesh_vertices(expert_topomesh, non_margin_cells)
# --- Update EXPERT topomesh display:
world.add(expert_topomesh, "expert_seeds")
world["expert_seeds"]["property_name_0"] = 'layer'
world["expert_seeds_vertices"]["polydata_colormap"] = load_colormaps(
)['Greens']
# -- Create a 'detected_topomesh' out of L1 cells only:
L1_detected_topomesh = filter_topomesh_vertices(detected_topomesh, "L1")
suffix = "_expert"
# world.add(L1_detected_topomesh,"L1_detected_seed"+suffix)
# world["L1_detected_seed"+ suffix]["property_name_0"] = 'layer'
# world["L1_detected_seed{}_vertices".format(suffix)]["polydata_colormap"] = load_colormaps()['Reds']
# -- Create a 'L1_expert_topomesh' (L1 ground truth) out of L1 cells only:
L1_expert_topomesh = filter_topomesh_vertices(expert_topomesh, "L1")
world.add(L1_expert_topomesh, "L1_expert_seeds")
world["L1_expert_seeds"]["property_name_0"] = 'layer'
world["L1_expert_seeds_vertices"]["polydata_colormap"] = load_colormaps(
)['Greens']
def _draco_control_changed(self, control_name, old, new):
print control_name, " changed! : ", new
control_names = [c['name'] for c in self._controls]
self._controls[control_names.index(control_name)]['value'] = new
if control_name == 'img_filename':
img_file = new
try:
img = imread(img_file)
assert img.ndim == 3
except:
print "Image type not recognized! Please choose a different file..."
set_default_control(self._controls, 'initialize')
else:
self.name = os.path.split(img_file)[-1].split('.')[0]
set_default_control(self._controls,
'initialize',
value=self._init_button_pressed)
elif control_name == 'display_img':
if new:
self.world.add(self.draco.segmented_image,
self.name + '_segmented_image',
colormap='glasbey',
alphamap='constant',
bg_id=1)
else:
if self.world.has_key(self.name + '_segmented_image'):
self.world.remove(self.name + '_segmented_image')
elif control_name == 'display_cells':
if new:
self.world.add(self.draco.point_topomesh,
self.name + '_image_cells')
self.world[self.name + '_image_cells_vertices'].set_attribute(
'point_radius', self.draco.segmented_image.max())
self.world[self.name + '_image_cells_vertices'].set_attribute(
'display_colorbar', False)
else:
if self.world.has_key(self.name + '_image_cells'):
self.world.remove(self.name + '_image_cells')
elif control_name == 'display_adjacency':
control_names = [c['name'] for c in self._controls]
adjacency_complex = self._controls[control_names.index(
'adjacency_complex')]['value']
if adjacency_complex in ['delaunay', 'L1-L2']:
degree = 3
else:
degree = 2
if new:
self.world.add(self.draco.triangulation_topomesh,
self.name + '_adjacency_complex')
if degree == 3:
self.world[self.name +
'_adjacency_complex_cells'].set_attribute(
'polydata_colormap',
load_colormaps()['grey'])
self.world[self.name +
'_adjacency_complex_cells'].set_attribute(
'intensity_range', (-1, 0))
self.world[self.name + '_adjacency_complex'].set_attribute(
'coef_3', 0.95)
self.world[self.name +
'_adjacency_complex_cells'].set_attribute(
'display_colorbar', False)
else:
self.world[self.name + '_adjacency_complex'].set_attribute(
'display_3', False)
self.world[self.name + '_adjacency_complex'].set_attribute(
'display_2', True)
self.world[self.name +
'_adjacency_complex_faces'].set_attribute(
'polydata_colormap',
load_colormaps()['grey'])
self.world[self.name +
'_adjacency_complex_faces'].set_attribute(
'intensity_range', (-1, 0))
self.world[self.name + '_adjacency_complex'].set_attribute(
'coef_2', 0.98)
self.world[self.name +
'_adjacency_complex_faces'].set_attribute(
'display_colorbar', False)
else:
if self.world.has_key(self.name + '_adjacency_complex'):
self.world.remove(self.name + '_adjacency_complex')
elif control_name == 'display_dual':
if new:
self.world.add(self.draco.dual_reconstruction_topomesh,
self.name + '_dual_reconstruction')
else:
if self.world.has_key(self.name + '_dual_reconstruction'):
self.world.remove(self.name + '_dual_reconstruction')
elif control_name == 'adjacency_complex':
if new == '':
print "Please define a mode for adjacency complex computation"
set_default_control(self._controls, 'compute_adjacency')
else:
set_default_control(
self._controls,
'compute_adjacency',
value=self._compute_adjacency_button_pressed)
self.refresh_manager()
#world.add(draco.segmented_image-(draco.segmented_image==1),'segmented_image',colormap='glasbey',alphamap='constant',bg_id=0)
world.add(draco.point_topomesh,'image_cells')
world['image_cells_vertices'].set_attribute('point_radius',img.max())
world.add(draco.layer_edge_topomesh['L1'],'L1_adjacency')
#world.add(draco.image_cell_vertex_topomesh,'image_cell_vertex')
draco.delaunay_adjacency_complex(surface_cleaning_criteria = [])
#draco.delaunay_adjacency_complex(surface_cleaning_criteria = ['surface','sliver','distance'])
draco.adjacency_complex_optimization(n_iterations=2)
from copy import deepcopy
triangulation_topomesh = deepcopy(draco.triangulation_topomesh)
world.add(triangulation_topomesh,'cell_adjacency_complex')
world['cell_adjacency_complex_cells'].set_attribute('polydata_colormap',load_colormaps()['grey'])
world['cell_adjacency_complex_cells'].set_attribute('intensity_range',(-1,0))
world['cell_adjacency_complex'].set_attribute('coef_3',0.95)#
#world['cell_adjacency_complex_cells'].set_attribute('x_slice',(50,100))
world['cell_adjacency_complex_cells'].set_attribute('display_colorbar',False)
#triangular = ['star','remeshed','regular','projected']
triangular = ['star','split']
image_dual_topomesh = draco.dual_reconstruction(reconstruction_triangulation = triangular, adjacency_complex_degree=3)
#image_dual_topomesh = draco.draco_topomesh(reconstruction_triangulation = triangular)
world.add(image_dual_topomesh ,'dual_reconstuction')
from openalea.mesh.property_topomesh_extraction import epidermis_topomesh
L1_topomesh = epidermis_topomesh(image_dual_topomesh)
from openalea.oalab.colormap.colormap_def import load_colormaps
filename = "/Users/gcerutti/Developpement/openalea/openalea_meshing_data/share/data/output_meshes/propTopomesh_withCurv_persoZones_T00/propTopomesh_withCurv_persoZones_T00_star_split_topomesh.pkl"
topomesh = pickle.load(open(filename,'r'))
compute_topomesh_property(topomesh,'triangles',1)
compute_topomesh_property(topomesh,'vertices',1)
topomesh.update_wisp_property('boundary',1,array_dict((np.array(map(len,topomesh.wisp_property('triangles',1).values()))==1).astype(int),list(topomesh.wisps(1))))
world.add(topomesh,"topomesh")
world['topomesh'].set_attribute('display_3',False)
world['topomesh'].set_attribute('property_name_1','boundary')
world['topomesh'].set_attribute('display_1',True)
world['topomesh_edges'].set_attribute('intensity_range',(0,1))
world['topomesh_edges'].set_attribute('polydata_colormap',load_colormaps()['Reds'])
world['topomesh_edges'].set_attribute('linewidth',3)
world['topomesh_edges'].set_attribute('polydata_alpha',0.5)
raw_input()
from scipy.cluster.vq import vq
from time import sleep
distance_threshold = 0.5
boundary_edges = np.array(list(topomesh.wisps(1)))[topomesh.wisp_property('boundary',1).values()==1]
boundary_edge_centers = topomesh.wisp_property('barycenter',0).values(topomesh.wisp_property('vertices',1).values(boundary_edges)).mean(axis=1)
sorted_boundary_edges = boundary_edges[np.argsort(-boundary_edge_centers[:,2])]
sorted_boundary_edge_centers = boundary_edge_centers[np.argsort(-boundary_edge_centers[:,2])]
boundary_vertices = list(np.unique(topomesh.wisp_property('vertices',1).values(boundary_edges)))
def _draco_control_changed(self, control_name, old, new):
print control_name," changed! : ",new
control_names = [c['name'] for c in self._controls]
self._controls[control_names.index(control_name)]['value'] = new
if control_name == 'img_filename':
img_file = new
try:
img = imread(img_file)
assert img.ndim == 3
except:
print "Image type not recognized! Please choose a different file..."
set_default_control(self._controls,'initialize')
else:
self.name = os.path.split(img_file)[-1].split('.')[0]
set_default_control(self._controls,'initialize',value=self._init_button_pressed)
elif control_name == 'display_img':
if new:
self.world.add(self.draco.segmented_image,self.name+'_segmented_image',colormap='glasbey',alphamap='constant',bg_id=1)
else:
if self.world.has_key(self.name+'_segmented_image'):
self.world.remove(self.name+'_segmented_image')
elif control_name == 'display_cells':
if new:
self.world.add(self.draco.point_topomesh,self.name+'_image_cells')
self.world[self.name+'_image_cells_vertices'].set_attribute('point_radius',self.draco.segmented_image.max())
self.world[self.name+'_image_cells_vertices'].set_attribute('display_colorbar',False)
else:
if self.world.has_key(self.name+'_image_cells'):
self.world.remove(self.name+'_image_cells')
elif control_name == 'display_adjacency':
control_names = [c['name'] for c in self._controls]
adjacency_complex = self._controls[control_names.index('adjacency_complex')]['value']
if adjacency_complex in ['delaunay','L1-L2']:
degree = 3
else:
degree = 2
if new:
self.world.add(self.draco.triangulation_topomesh,self.name+'_adjacency_complex')
if degree == 3:
self.world[self.name+'_adjacency_complex_cells'].set_attribute('polydata_colormap',load_colormaps()['grey'])
self.world[self.name+'_adjacency_complex_cells'].set_attribute('intensity_range',(-1,0))
self.world[self.name+'_adjacency_complex'].set_attribute('coef_3',0.95)
self.world[self.name+'_adjacency_complex_cells'].set_attribute('display_colorbar',False)
else:
self.world[self.name+'_adjacency_complex'].set_attribute('display_3',False)
self.world[self.name+'_adjacency_complex'].set_attribute('display_2',True)
self.world[self.name+'_adjacency_complex_faces'].set_attribute('polydata_colormap',load_colormaps()['grey'])
self.world[self.name+'_adjacency_complex_faces'].set_attribute('intensity_range',(-1,0))
self.world[self.name+'_adjacency_complex'].set_attribute('coef_2',0.98)
self.world[self.name+'_adjacency_complex_faces'].set_attribute('display_colorbar',False)
else:
if self.world.has_key(self.name+'_adjacency_complex'):
self.world.remove(self.name+'_adjacency_complex')
elif control_name == 'display_dual':
if new:
self.world.add(self.draco.dual_reconstruction_topomesh,self.name+'_dual_reconstruction')
else:
if self.world.has_key(self.name+'_dual_reconstruction'):
self.world.remove(self.name+'_dual_reconstruction')
elif control_name == 'adjacency_complex':
if new == '':
print "Please define a mode for adjacency complex computation"
set_default_control(self._controls,'compute_adjacency')
else:
set_default_control(self._controls,'compute_adjacency',value=self._compute_adjacency_button_pressed)
self.refresh_manager()
primordia_colors[-1] = "#0065ff"
primordia_colors[0] = "#64bca4"
primordia_colors[1] = "#e30d00"
primordia_colors[2] = "#ffa200"
primordia_colors[3] = "#cccccc"
primordia_colors[4] = "#dddddd"
primordia_colors[5] = "#eeeeee"
clv3_color = "#c94389"
cmaps = {}
for cmap_name in [
'1Flashy_green', '1Flashy_purple', '1Flashy_orange',
'1Flashy_turquoise', '1Flashy_red', 'geo_jet', 'curvature'
]:
cmap = load_colormaps()[cmap_name]
color_dict = dict(red=[], green=[], blue=[])
for p in np.sort(cmap._color_points.keys()):
for k, c in enumerate(['red', 'green', 'blue']):
color_dict[c] += [(p, cmap._color_points[p][k],
cmap._color_points[p][k])]
for c in ['red', 'green', 'blue']:
color_dict[c] = tuple(color_dict[c])
cm.register_cmap(cmap_name,
mpl.colors.LinearSegmentedColormap(cmap.name, color_dict))
color_dict = dict(red=[], green=[], blue=[])
for p in np.sort(cmap._color_points.keys()):
for k, c in enumerate(['red', 'green', 'blue']):
color_dict[c] = [
image_dual_topomesh = draco.dual_reconstruction(
reconstruction_triangulation=triangular,
adjacency_complex_degree=3,
maximal_edge_length=5.1)
#image_dual_topomesh = draco.draco_topomesh(reconstruction_triangulation = triangular)
from openalea.cellcomplex.property_topomesh.property_topomesh_optimization import property_topomesh_vertices_deformation
#property_topomesh_vertices_deformation(image_dual_topomesh,iterations=15)
world.add(image_dual_topomesh, 'dual_reconstuction')
draco_file = dirname + "/output_meshes/" + filename + "/" + filename + "_DRACO_mesh.ply"
save_ply_property_topomesh(image_dual_topomesh,
draco_file,
color_faces=True,
colormap=load_colormaps()['glasbey'])
optimized_dual_topomesh = optimize_topomesh(
image_dual_topomesh,
omega_forces={
'regularization': 0.00,
'neighborhood': 0.65,
'taubin_smoothing': 0.65,
'laplacian': 0.7,
'planarization': 0.27,
'epidermis_planarization': 0.05,
'convexity': 0.06
},
omega_regularization_max=0.01,
edge_flip=True,
cell_vertex_motion=True,
'layer', 0).values() == 1]
non_L1_cells = np.array(list(seed_topomesh.wisps(0)))[
seed_topomesh.wisp_property('layer', 0).values() != 1]
# from copy import deepcopy
# ## L1-seed :
# L1_seed_topomesh = deepcopy(seed_topomesh)
# L1_seed_cells = np.array(list(L1_seed_topomesh.wisps(0)))[L1_seed_topomesh.wisp_property('layer',0).values()==1]
# non_L1_seed_cells = [c for c in L1_seed_topomesh.wisps(0) if not c in L1_seed_cells]
# for c in non_L1_seed_cells:
# L1_seed_topomesh.remove_wisp(0,c)
# for property_name in L1_seed_topomesh.wisp_property_names(0):
# d_ppty = dict(zip(list(L1_seed_topomesh.wisps(0)), L1_seed_topomesh.wisp_property(property_name,0).values(list(L1_seed_topomesh.wisps(0)))))
# L1_seed_topomesh.update_wisp_property(property_name,0,d_ppty)
world.add(seed_topomesh, "detected_seed")
world["detected_seed_vertices"]["polydata_colormap"] = load_colormaps()['jet']
world["detected_seed"]["property_name_0"] = "layer"
world["detected_seed_vertices"]["intensity_range"] = (-0.5, 2.5)
world["detected_seed_vertices"]["display_colorbar"] = False
world.add(L1_corrected_topomesh, "L1_corrected_nuclei" + suffix)
world["L1_corrected_nuclei"]["property_name_0"] = 'layer'
world["L1_corrected_nuclei_vertices"]["polydata_colormap"] = load_colormaps(
)['Greens']
# evaluation = evaluate_nuclei_detection(seed_topomesh, eq_seed_topomesh, max_matching_distance=2.0, outlying_distance=4.0, max_distance=np.linalg.norm(size*voxelsize))
# print(evaluation)
# Create a seed image from the nuclei barycenters:
seed_img = seed_image_from_points(img.shape,
img.voxelsize,
