draco.triangulation_topomesh = triangulation_topomesh
triangular= ['star','flat']
triangular = ['star','remeshed','projected','exact','flat']
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')
import openalea.draco_stem.stem.tissue_mesh_quality
reload(openalea.draco_stem.stem.tissue_mesh_quality)
from openalea.draco_stem.stem.tissue_mesh_quality import evaluate_topomesh_quality
quality_criteria=["Mesh Complexity","Triangle Area Deviation","Triangle Eccentricity","Vertex Valence","Image Accuracy","Vertex Distance","Cell 2 Adjacency","Cell Convexity","Epidermis Cell Angle","Cell Cliques"]
dual_quality = evaluate_topomesh_quality(image_dual_topomesh,quality_criteria,image=draco.segmented_image,image_cell_vertex=draco.image_cell_vertex,image_labels=draco.image_labels,image_cell_volumes=draco.image_cell_volumes,image_graph=draco.image_graph)
from vplants.meshing.cute_plot import spider_plot
import matplotlib.pyplot as plt
figure = plt.figure(2)
figure.clf()
spider_plot(figure,np.array([dual_quality[c] for c in quality_criteria]),color1=np.array([0.3,0.6,1.]),color2=np.array([1.,0.,0.]),xlabels=quality_criteria,ytargets=0.8 * np.ones_like(quality_criteria,float),n_points=100*len(quality_criteria),linewidth=2,smooth_factor=0.0,spline_order=1)
plt.show(block=False)
raw_input()
triangular_string = ""
for t in triangular:
triangular_string += t+"_"
topomesh_filename = dirname+"/output_meshes/"+filename+"/"+filename+"_"+triangular_string+"topomesh.ply"
topomesh_img = compute_topomesh_image(image_topomesh, idra.segmented_image)
world.add(topomesh_img,
"CGAL_image",
colormap="glasbey",
alphamap="constant",
bg_id=1)
quality_criteria = [
"Mesh Complexity", "Triangle Area Deviation", "Triangle Eccentricity",
"Vertex Valence", "Image Accuracy", "Vertex Distance", "Cell 2 Adjacency",
"Cell Convexity", "Epidermis Cell Angle", "Cell Cliques"
]
cgal_quality = evaluate_topomesh_quality(
image_topomesh,
quality_criteria,
image=idra.segmented_image,
image_cell_vertex=image_cell_vertex,
image_labels=idra.image_labels,
image_cell_volumes=idra.image_cell_volumes)
from vplants.meshing.cute_plot import spider_plot
import matplotlib.pyplot as plt
figure = plt.figure(0)
figure.clf()
spider_plot(figure,
np.array([cgal_quality[c] for c in quality_criteria]),
color1=np.array([0.3, 0.6, 1.]),
color2=np.array([1., 0., 0.]),
xlabels=quality_criteria,
ytargets=0.8 * np.ones_like(quality_criteria, float),
world.add(image_dual_topomesh, 'dual_reconstuction')
import openalea.draco_stem.stem.tissue_mesh_quality
reload(openalea.draco_stem.stem.tissue_mesh_quality)
from openalea.draco_stem.stem.tissue_mesh_quality import evaluate_topomesh_quality
quality_criteria = [
"Mesh Complexity", "Triangle Area Deviation", "Triangle Eccentricity",
"Vertex Valence", "Image Accuracy", "Vertex Distance", "Cell 2 Adjacency",
"Cell Convexity", "Epidermis Cell Angle", "Cell Cliques"
]
dual_quality = evaluate_topomesh_quality(
image_dual_topomesh,
quality_criteria,
image=draco.segmented_image,
image_cell_vertex=draco.image_cell_vertex,
image_labels=draco.image_labels,
image_cell_volumes=draco.image_cell_volumes,
image_graph=draco.image_graph)
from vplants.meshing.cute_plot import spider_plot
import matplotlib.pyplot as plt
figure = plt.figure(2)
figure.clf()
spider_plot(figure,
np.array([dual_quality[c] for c in quality_criteria]),
color1=np.array([0.3, 0.6, 1.]),
color2=np.array([1., 0., 0.]),
xlabels=quality_criteria,
ytargets=0.8 * np.ones_like(quality_criteria, float),
idra = IdraMesh(image_file=inputfile)
world.add(idra.segmented_image,filename,colormap="glasbey",alphamap="constant",bg_id=1)
raw_input()
image_topomesh = idra.idra_topomesh(mesh_fineness=1.0)
#world.add(image_topomesh,"CGAL_topomesh")
cell_vertex_file = dirname+"/output_meshes/"+filename+"/image_cell_vertex.dict"
image_cell_vertex = pickle.load(open(cell_vertex_file,'rb'))
image_cell_vertex = dict(zip(image_cell_vertex.keys(),np.array(image_cell_vertex.values())*idra.resolution))
from openalea.mesh.utils.image_tools import compute_topomesh_image
topomesh_img = compute_topomesh_image(image_topomesh,idra.segmented_image)
world.add(topomesh_img,"CGAL_image",colormap="glasbey",alphamap="constant",bg_id=1)
quality_criteria=["Mesh Complexity","Triangle Area Deviation","Triangle Eccentricity","Vertex Valence","Image Accuracy","Vertex Distance","Cell 2 Adjacency","Cell Convexity","Epidermis Cell Angle","Cell Cliques"]
cgal_quality = evaluate_topomesh_quality(image_topomesh,quality_criteria,image=idra.segmented_image,image_cell_vertex=image_cell_vertex,image_labels=idra.image_labels,image_cell_volumes=idra.image_cell_volumes)
from vplants.meshing.cute_plot import spider_plot
import matplotlib.pyplot as plt
figure = plt.figure(0)
figure.clf()
spider_plot(figure,np.array([cgal_quality[c] for c in quality_criteria]),color1=np.array([0.3,0.6,1.]),color2=np.array([1.,0.,0.]),xlabels=quality_criteria,ytargets=0.8 * np.ones_like(quality_criteria,float),n_points=100*len(quality_criteria),linewidth=2,smooth_factor=0.0,spline_order=1)
plt.show(block=False)
raw_input()
optimized_topomesh = optimize_topomesh(image_topomesh,omega_forces={'regularization':0.01,'laplacian':1.0,'planarization':0.27,'epidermis_planarization':0.27},cell_vertex_motion=True,image_cell_vertex=image_cell_vertex,edge_flip=True,iterations=20)
world.add(optimized_topomesh,"STEM_topomesh")
