def evaluate_topomesh_quality(topomesh,quality_criteria=["Mesh Complexity","Triangle Area Deviation","Triangle Eccentricity","Cell Volume Error","Vertex Distance","Cell Convexity","Epidermis Cell Angle","Vertex Valence","Cell 2 Adjacency"],image=None,image_cell_vertex=None,image_labels=None,image_cell_volumes=None,**kwargs):
"""
"""
quality_data = {}
if "Mesh Complexity" in quality_criteria:
start_time = time()
print "--> Computing mesh complexity"
quality_data["Mesh Complexity"] = np.minimum(1.0,(152.*topomesh.nb_wisps(3))/np.sum([len(list(topomesh.border_neighbors(3,c))) for c in topomesh.wisps(3)]))
end_time = time()
print "<-- Computing mesh complexity [",end_time-start_time,"s]"
compute_topomesh_property(topomesh,'length',degree=1)
triangular = kwargs.get('triangular',True)
display = kwargs.get('display',False)
if triangular:
compute_topomesh_triangle_properties(topomesh)
compute_topomesh_property(topomesh,'normal',degree=2)
compute_topomesh_property(topomesh,'angles',degree=2)
if "Triangle Area Deviation" in quality_criteria:
start_time = time()
print "--> Computing triangle area deviation"
# area_deviation = np.nanmean(np.abs(topomesh.wisp_property('area',degree=2).values()-np.nanmean(topomesh.wisp_property('area',degree=2).values())))/np.nanmean(topomesh.wisp_property('area',degree=2).values())
area_deviation = np.nanstd(topomesh.wisp_property('area',degree=2).values())/np.nanmean(topomesh.wisp_property('area',degree=2).values())
# quality_data["Triangle Area Deviation"] = np.minimum(1.0,1.0-area_deviation/np.sqrt(2))
# quality_data["Triangle Area Deviation"] = np.minimum(1.0,1.0-area_deviation/2.)
quality_data["Triangle Area Deviation"] = np.minimum(1.0,np.sqrt(2)-area_deviation/np.sqrt(2))
end_time = time()
print "<-- Computing triangle area deviation [",end_time-start_time,"s]"
if "Triangle Eccentricity" in quality_criteria:
start_time = time()
print "--> Computing triangle eccentricity"
quality_data["Triangle Eccentricity"] = 1.-2.*np.nanmean(topomesh.wisp_property('eccentricity',degree=2).values())
end_time = time()
print "<-- Computing triangle eccentricity [",end_time-start_time,"s]"
compute_topomesh_property(topomesh,'borders',degree=3)
compute_topomesh_property(topomesh,'borders',degree=2)
compute_topomesh_property(topomesh,'borders',degree=1)
compute_topomesh_property(topomesh,'vertices',degree=3)
compute_topomesh_property(topomesh,'vertices',degree=2)
compute_topomesh_property(topomesh,'barycenter',degree=3)
compute_topomesh_property(topomesh,'barycenter',degree=2)
compute_topomesh_property(topomesh,'barycenter',degree=1)
compute_topomesh_property(topomesh,'triangles',degree=0)
compute_topomesh_property(topomesh,'cells',degree=0)
compute_topomesh_property(topomesh,'cells',degree=1)
compute_topomesh_property(topomesh,'cells',degree=2)
compute_topomesh_property(topomesh,'epidermis',degree=0)
compute_topomesh_property(topomesh,'epidermis',degree=1)
compute_topomesh_property(topomesh,'epidermis',degree=3)
if "Cell Volume Error" in quality_criteria or "Cell Convexity" in quality_criteria:
compute_topomesh_property(topomesh,'volume',degree=3)
compute_topomesh_property(topomesh,'convexhull_volume',degree=3)
img_graph = kwargs.get('image_graph',None)
if "Cell Volume Error" in quality_criteria:
start_time = time()
print "--> Computing cell volume error"
from vplants.tissue_analysis.temporal_graph_from_image import graph_from_image
if (image_cell_volumes == None) or (image_labels == None) or (image_cell_vertex == None):
img_graph = graph_from_image(image, spatio_temporal_properties=['volume','barycenter'],background=0,ignore_cells_at_stack_margins = False,property_as_real=True)
image_labels = np.array(list(img_graph.vertices()))
image_cell_volumes = np.array([img_graph.vertex_property('volume')[v] for v in image_labels])
else:
img_graph = None
img_volumes = array_dict(image_cell_volumes,image_labels)
if triangular:
volume_error = array_dict([(c,(topomesh.wisp_property('volume',degree=3)[c] - img_volumes[c])/img_volumes[c]) for c in topomesh.wisps(3)])
else:
volume_error = array_dict([(c,(topomesh.wisp_property('convexhull_volume',degree=3)[c] - img_volumes[c])/img_volumes[c]) for c in topomesh.wisps(3)])
quality_data["Cell Volume Error"] = np.maximum(1.-abs(volume_error.values()).mean(),0.0)
end_time = time()
print "<-- Computing cell volume error [",end_time-start_time,"s]"
if "Image Accuracy" in quality_criteria:
start_time = time()
print "--> Computing image accuracy"
from openalea.mesh.utils.image_tools import compute_topomesh_image
topomesh_img = compute_topomesh_image(topomesh,image)
cells_img = deepcopy(image)
for c in set(np.unique(image)).difference(set(topomesh.wisps(3))):
cells_img[image==c] = 1
true_positives = ((cells_img != 1)&(cells_img == topomesh_img)).sum()
false_positives = ((cells_img == 1) & (topomesh_img != 1)).sum() + ((cells_img != 1)&(topomesh_img != 1)&(cells_img != topomesh_img)).sum()
false_negatives = ((cells_img != 1) & (topomesh_img == 1)).sum() + ((cells_img != 1)&(topomesh_img != 1)&(cells_img != topomesh_img)).sum()
true_negatives = ((cells_img == 1)&(cells_img == topomesh_img)).sum()
estimators = {}
estimators['Precision'] = float(true_positives/float(true_positives+false_positives))
estimators['Recall'] = float(true_positives/float(true_positives+false_negatives))
estimators['Dice'] = float(2*true_positives/float(2*true_positives+false_positives+false_negatives))
estimators['Jaccard'] = float(true_positives/float(true_positives+false_positives+false_negatives))
estimators['Accuracy'] = float(true_positives+true_negatives)/float(true_positives+true_negatives+false_positives+false_negatives)
estimators['Identity'] = float((cells_img == topomesh_img).sum())/np.prod(cells_img.shape)
print estimators
quality_data["Image Accuracy"] = estimators['Dice']
end_time = time()
print "<-- Computing image accuracy [",end_time-start_time,"s]"
vertex_cell_neighbours = topomesh.wisp_property('cells',degree=0)
vertex_cell_degree = np.array(map(len,vertex_cell_neighbours.values()))
epidermis_vertices = np.array(list(topomesh.wisps(0)))[np.where(topomesh.wisp_property('epidermis',degree=0).values())]
start_time = time()
print "--> Computing mesh cell vertices"
mesh_cell_vertex = {}
for v in topomesh.wisps(0):
if len(vertex_cell_neighbours[v]) == 5:
for k in xrange(5):
vertex_cell_labels = tuple([c for c in vertex_cell_neighbours[v]][:k])+tuple([c for c in vertex_cell_neighbours[v]][k+1:])
if not mesh_cell_vertex.has_key(vertex_cell_labels):
mesh_cell_vertex[vertex_cell_labels] = v
if len(vertex_cell_neighbours[v]) == 4:
vertex_cell_labels = tuple([c for c in vertex_cell_neighbours[v]])
mesh_cell_vertex[vertex_cell_labels] = v
if v in epidermis_vertices:
for k in xrange(4):
vertex_cell_labels = (1,) + tuple([c for c in vertex_cell_neighbours[v]][:k])+tuple([c for c in vertex_cell_neighbours[v]][k+1:])
if not mesh_cell_vertex.has_key(vertex_cell_labels):
mesh_cell_vertex[vertex_cell_labels] = v
if (len(vertex_cell_neighbours[v]) == 3) and (v in epidermis_vertices):
vertex_cell_labels = (1,) + tuple([c for c in vertex_cell_neighbours[v]])
mesh_cell_vertex[vertex_cell_labels] = v
end_time = time()
print "<-- Computing mesh cell vertices [",end_time-start_time,"s]"
cell_vertex = np.unique(mesh_cell_vertex.values())
mesh_cell_vertices = topomesh.wisp_property('barycenter',degree=0).values(cell_vertex)
if "Vertex Distance" in quality_criteria:
start_time = time()
print "--> Computing vertex distance"
if image_cell_vertex is None:
image_cell_vertex = cell_vertex_extraction(image,hollow_out=False,verbose=False)
if img_graph is None:
img_graph = graph_from_image(image, spatio_temporal_properties=['volume','barycenter'],background=0,ignore_cells_at_stack_margins = False,property_as_real=True)
img_edges = np.array([img_graph.edge_vertices(e) for e in img_graph.edges()])
tetra_edge_index_list = np.array([[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]])
img_vertex_edges = np.concatenate(np.sort(image_cell_vertex.keys())[:,tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges,img_edges)[1] > 0).reshape(len(image_cell_vertex),6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(vertex_edge_matching==0)[0]]
image_cell_vertex = array_dict(np.array([image_cell_vertex[tuple(vertex)] for vertex in img_cell_vertex]),np.sort(img_cell_vertex))
else:
image_cell_vertex = deepcopy(image_cell_vertex)
# for v in image_cell_vertex.keys():
# image_cell_vertex[v] = image_cell_vertex[v]*np.array(image.resolution)
image_cell_vertices = np.array(image_cell_vertex.values())
print mesh_cell_vertices[np.where(1-np.isnan(mesh_cell_vertices)[:,0])]*image.resolution
print image_cell_vertices*image.resolution
vertex_distances_mesh = array_dict(vq(mesh_cell_vertices[np.where(1-np.isnan(mesh_cell_vertices)[:,0])]*image.resolution,image_cell_vertices*image.resolution)[1],cell_vertex)
vertex_distances_image = vq(image_cell_vertices[np.where(1-np.isnan(image_cell_vertices)[:,0])],mesh_cell_vertices)[1]
# quality_data["Vertex Distance"] = np.sqrt(3)/(np.maximum(np.sqrt(3),vertex_distances_image.mean()))
quality_data["Vertex Distance"] = (0.25*np.sqrt(3))/(np.maximum((0.25*np.sqrt(3)),vertex_distances_image.mean()))
end_time = time()
print "<-- Computing vertex distance [",end_time-start_time,"s]"
if "Cell Convexity" in quality_criteria:
start_time = time()
print "--> Computing cell convexity"
# quality_data["Cell Convexity"] = (topomesh.wisp_property('volume',degree=3).values()/topomesh.wisp_property('convexhull_volume',degree=3).values()).mean()
quality_data["Cell Convexity"] = 1 - (((topomesh.wisp_property('convexhull_volume',3).values() - topomesh.wisp_property('volume',3).values()).mean())/topomesh.wisp_property('volume',3).values().mean())
end_time = time()
print "<-- Computing cell convexity [",end_time-start_time,"s]"
if "Epidermis Cell Angle" in quality_criteria:
start_time = time()
print "--> Computing epidermis cell angle"
epidermis_vertex_cell_angles=np.array([])
epidermis_vertex_angles = array_dict()
angle_keys = np.concatenate([np.concatenate([[(t,v)] for v in topomesh.wisp_property('vertices',2)[t]],axis=0) for t in topomesh.wisps(2)],axis=0)
angle_dict = dict([(tuple(k),0.) for k in angle_keys])
for v in cell_vertex:
if topomesh.wisp_property('epidermis',0)[v]:
vertex_triangles = np.array([t for t in topomesh.wisp_property('triangles',0)[v] if topomesh.wisp_property('epidermis',2)[t]])
vertex_triangle_cells = np.concatenate(topomesh.wisp_property('cells',degree=2).values(vertex_triangles))
vertex_cells = np.unique(vertex_triangle_cells)
vertex_triangle_cell_directions = topomesh.wisp_property('barycenter',2).values(vertex_triangles) - topomesh.wisp_property('barycenter',3).values(vertex_triangle_cells)
vertex_triangle_normals = topomesh.wisp_property('normal',2).values(vertex_triangles)
reversed_normals = np.where(np.einsum('ij,ij->i',vertex_triangle_normals,vertex_triangle_cell_directions) < 0)[0]
vertex_triangle_normals[reversed_normals] = -vertex_triangle_normals[reversed_normals]
vertex_normal = np.mean(vertex_triangle_normals,axis=0)
vertex_normal = vertex_normal/np.linalg.norm(vertex_normal)
triangle_vertices = topomesh.wisp_property('vertices',degree=2).values(vertex_triangles)
triangle_positions = topomesh.wisp_property('barycenter',degree=0).values(triangle_vertices)
triangle_proj_vectors = triangle_positions - topomesh.wisp_property('barycenter',degree=0)[v]
triangle_proj_dot = np.einsum('...ij,...j->...i',triangle_proj_vectors,vertex_normal)
triangle_proj_vectors = -triangle_proj_dot[...,np.newaxis]*vertex_normal
triangle_proj_positions = triangle_positions + triangle_proj_vectors
edge_index_list = np.array([[1, 2],[0, 1],[0, 2]])
triangle_edge_positions = triangle_proj_positions[:,edge_index_list]
triangle_edge_vectors = triangle_edge_positions[:,:,1] - triangle_edge_positions[:,:,0]
triangle_edge_lengths = np.linalg.norm(triangle_edge_vectors,axis=2)
triangle_cosines = np.zeros_like(triangle_edge_lengths,np.float32)
triangle_cosines[:,0] = (triangle_edge_lengths[:,1]**2+triangle_edge_lengths[:,2]**2-triangle_edge_lengths[:,0]**2)/(2.0*triangle_edge_lengths[:,1]*triangle_edge_lengths[:,2])
triangle_cosines[:,2] = (triangle_edge_lengths[:,2]**2+triangle_edge_lengths[:,0]**2-triangle_edge_lengths[:,1]**2)/(2.0*triangle_edge_lengths[:,2]*triangle_edge_lengths[:,0])
triangle_cosines[:,1] = (triangle_edge_lengths[:,0]**2+triangle_edge_lengths[:,1]**2-triangle_edge_lengths[:,2]**2)/(2.0*triangle_edge_lengths[:,0]*triangle_edge_lengths[:,1])
triangle_angles = 180.*np.arccos(triangle_cosines)/np.pi
vertex_triangle_angles = triangle_angles[np.where(triangle_vertices==v)]
vertex_cell_angles = array_dict(nd.sum(vertex_triangle_angles,vertex_triangle_cells,index=vertex_cells),vertex_cells)
for t,c in zip(vertex_triangles,vertex_triangle_cells):
angle_dict[(t,v)] = vertex_cell_angles[c]
epidermis_vertex_angles[v] = np.sum(vertex_cell_angles.values())
if abs(epidermis_vertex_angles[v]-360)<10:
epidermis_vertex_cell_angles = np.concatenate([epidermis_vertex_cell_angles,vertex_cell_angles.values()])
# quality_data["Epidermis Cell Angle"] = 1.0 - ((epidermis_vertex_cell_angles>180).sum()+(epidermis_vertex_cell_angles<90).sum())/(float(epidermis_vertex_cell_angles.shape[0]))
quality_data["Epidermis Cell Angle"] = 1 - (np.sum(1 - np.exp(-np.power(np.maximum(100-epidermis_vertex_cell_angles,0.0)/20.,2.0))) + np.sum(1 - np.exp(-np.power(np.maximum(epidermis_vertex_cell_angles-170,0.0)/20.,2.0))))/(float(epidermis_vertex_cell_angles.shape[0]))
# np.sqrt(np.sum(np.power(np.maximum(90-epidermis_vertex_cell_angles,0.0)/30.,2.0))) + np.sum(np.maximum(epidermis_vertex_cell_angles-180,0.0)/30.)
# quality_data["Epidermis Cell Angle"] = np.minimum(1.0,3-np.sqrt(np.power(epidermis_vertex_cell_angles-120.,2.0).mean())/30.)
# quality_data["Epidermis Cell Angle"] = np.minimum(1.0,1.0-np.sqrt(np.power(epidermis_vertex_cell_angles-120.,2.0).mean())/120.)
# quality_data["Epidermis Cell Angle"] = 1.0-np.power(np.power(np.abs(epidermis_vertex_cell_angles-120.),1.0).mean(),1.0)/120.
end_time = time()
print "<-- Computing epidermis cell angle [",end_time-start_time,"s]"
if "Vertex Valence" in quality_criteria:
start_time = time()
print "--> Computing vertex degree"
compute_topomesh_property(topomesh,'region_neighbors',degree=0)
compute_topomesh_property(topomesh,'border_neighbors',degree=3)
# interface_vertices = np.array(list(topomesh.wisps(0)))[np.where((np.array(map(len,topomesh.wisp_property('cells',0).values())) == 1)
# | ((np.array(map(len,topomesh.wisp_property('cells',0).values())) == 2)
# & (1- topomesh.wisp_property('epidermis',0).values())))]
# interface_vertices_degree = np.array(map(len,topomesh.wisp_property('neighbors',0).values(interface_vertices)))
target_neighborhood = array_dict((np.array(map(len,topomesh.wisp_property('cells',0).values())) + topomesh.wisp_property('epidermis',0).values())*3,list(topomesh.wisps(0)))
vertices_neighborhood = array_dict(map(len,topomesh.wisp_property('neighbors',0).values()),list(topomesh.wisps(0)))
interface_vertices = target_neighborhood.keys_where("==6")
# np.power(np.power(target_neighborhood.values(interface_vertices)-vertices_neighborhood.values(interface_vertices),2.0).mean(),0.5)/6.0
quality_data["Vertex Valence"] = np.minimum(1.0,1.0-np.abs(target_neighborhood.values(interface_vertices)-vertices_neighborhood.values(interface_vertices)).mean()/6.0)
# quality_data["Vertex Valence"] = np.minimum(1.0,1.0-np.nanmean(np.abs(interface_vertices_degree-6.0)))
# quality_data["Vertex Valence"] = np.minimum(1.0,1.0-np.power(interface_vertices_degree-6.0,2.0).mean()/6.0)
end_time = time()
print "<-- Computing vertex degree [",end_time-start_time,"s]"
if "Cell 4 Adjacency" in quality_criteria:
start_time = time()
print "--> Computing cell adjacency"
if image_cell_vertex==None:
image_cell_vertex = cell_vertex_extraction(img,hollow_out=False,verbose=False)
if img_graph == None:
img_graph = graph_from_image(img, spatio_temporal_properties=['volume','barycenter'],background=0,ignore_cells_at_stack_margins = False,property_as_real=False,min_contact_surface=0.5)
img_edges = np.array([img_graph.edge_vertices(e) for e in img_graph.edges()])
tetra_edge_index_list = np.array([[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]])
img_vertex_edges = np.concatenate(np.sort(image_cell_vertex.keys())[:,tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges,img_edges)[1] > 0).reshape(len(image_cell_vertex),6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(vertex_edge_matching==0)[0]]
image_cell_vertex = array_dict(np.array([image_cell_vertex[tuple(vertex)] for vertex in img_cell_vertex]),np.sort(img_cell_vertex))
cell_vertex_VP = (vq(np.sort(array_dict(mesh_cell_vertex).keys()),np.sort(array_dict(image_cell_vertex).keys()))[1]==0).sum()
cell_vertex_FP = (vq(np.sort(array_dict(mesh_cell_vertex).keys()),np.sort(array_dict(image_cell_vertex).keys()))[1]>0).sum()
cell_vertex_FN = (vq(np.sort(array_dict(image_cell_vertex).keys()),np.sort(array_dict(mesh_cell_vertex).keys()))[1]>0).sum()
cell_vertex_jaccard = cell_vertex_VP/float(cell_vertex_VP+cell_vertex_FP+cell_vertex_FN)
cell_vertex_dice = 2*cell_vertex_VP/float(2*cell_vertex_VP+cell_vertex_FP+cell_vertex_FN)
quality_data["Cell 4 Adjacency"] = cell_vertex_jaccard
end_time = time()
print "<-- Computing cell adjacency [",end_time-start_time,"s]"
if "Cell 2 Adjacency" in quality_criteria:
start_time = time()
print "--> Computing cell adjacency"
tetra_edge_index_list = np.array([[0,1],[0,2],[0,3],[1,2],[1,3],[2,3]])
if image_cell_vertex==None:
image_cell_vertex = cell_vertex_extraction(img,hollow_out=False,verbose=False)
if img_graph == None:
img_graph = graph_from_image(img, spatio_temporal_properties=['volume','barycenter'],background=0,ignore_cells_at_stack_margins = False,property_as_real=False,min_contact_surface=0.5)
img_edges = np.array([img_graph.edge_vertices(e) for e in img_graph.edges()])
img_vertex_edges = np.concatenate(np.sort(image_cell_vertex.keys())[:,tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges,img_edges)[1] > 0).reshape(len(image_cell_vertex),6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(vertex_edge_matching==0)[0]]
image_cell_vertex = array_dict(np.array([image_cell_vertex[tuple(vertex)] for vertex in img_cell_vertex]),np.sort(img_cell_vertex))
from vplants.meshing.array_tools import array_unique
mesh_cell_edges = array_unique(np.concatenate(np.sort(array_dict(mesh_cell_vertex).keys())[:,tetra_edge_index_list]))
image_cell_edges = array_unique(np.concatenate(np.sort(array_dict(image_cell_vertex).keys())[:,tetra_edge_index_list]))
from vplants.meshing.evaluation_tools import jaccard_index
cell_edge_jaccard = jaccard_index(image_cell_edges,mesh_cell_edges)
quality_data["Cell 2 Adjacency"] = cell_edge_jaccard
end_time = time()
print "<-- Computing cell adjacency [",end_time-start_time,"s]"
if "Cell Cliques" in quality_criteria:
vertex_cells = np.array([len(list(topomesh.regions(0,v,3))) for v in topomesh.wisps(0)])
vertex_epidermis = topomesh.wisp_property('epidermis',degree=0).values()
cell_vertices = ((vertex_cells>=3)*(vertex_epidermis)+(vertex_cells>=4)*(True-vertex_epidermis)).sum()
clique_cell_vertices = ((vertex_cells>3)*(vertex_epidermis)+(vertex_cells>4)*(True-vertex_epidermis)).sum()
quality_data["Cell Cliques"] = 1.0 - float(clique_cell_vertices)/float(cell_vertices)
print quality_data
# if display:
# spider_figure = plt.figure(kwargs.get('figure_title',"Topomesh Quality"))
# spider_figure.clf()
# spider_data = np.array([quality_data[c] for c in quality_criteria])
# spider_fields= quality_criteria
# # spider_targets = [0.8,0.7,0.8,0.8,0.9,0.8,0.7]
# spider_targets = 0.8 * np.ones_like(quality_criteria,float)
# spider_plot(spider_figure,spider_data,color1=np.array([0.3,0.6,1.]),color2=np.array([1.,0.,0.]),xlabels=spider_fields,ytargets=spider_targets,n_points=100*len(quality_criteria),linewidth=2,smooth_factor=0.0,spline_order=1)
# plt.show(block=False)
return quality_data
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.voxelsize))
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,
def evaluate_topomesh_quality(topomesh,
quality_criteria=[
"Mesh Complexity", "Triangle Area Deviation",
"Triangle Eccentricity", "Cell Volume Error",
"Vertex Distance", "Cell Convexity",
"Epidermis Cell Angle", "Vertex Valence",
"Cell 2 Adjacency"
],
image=None,
image_cell_vertex=None,
image_labels=None,
image_cell_volumes=None,
**kwargs):
"""
"""
quality_data = {}
if "Mesh Complexity" in quality_criteria:
start_time = time()
print "--> Computing mesh complexity"
quality_data["Mesh Complexity"] = np.minimum(
1.0, (152. * topomesh.nb_wisps(3)) / np.sum([
len(list(topomesh.border_neighbors(3, c)))
for c in topomesh.wisps(3)
]))
end_time = time()
print "<-- Computing mesh complexity [", end_time - start_time, "s]"
compute_topomesh_property(topomesh, 'length', degree=1)
triangular = kwargs.get('triangular', True)
display = kwargs.get('display', False)
if triangular:
compute_topomesh_triangle_properties(topomesh)
compute_topomesh_property(topomesh, 'normal', degree=2)
compute_topomesh_property(topomesh, 'angles', degree=2)
if "Triangle Area Deviation" in quality_criteria:
start_time = time()
print "--> Computing triangle area deviation"
# area_deviation = np.nanmean(np.abs(topomesh.wisp_property('area',degree=2).values()-np.nanmean(topomesh.wisp_property('area',degree=2).values())))/np.nanmean(topomesh.wisp_property('area',degree=2).values())
area_deviation = np.nanstd(
topomesh.wisp_property('area', degree=2).values()) / np.nanmean(
topomesh.wisp_property('area', degree=2).values())
# quality_data["Triangle Area Deviation"] = np.minimum(1.0,1.0-area_deviation/np.sqrt(2))
# quality_data["Triangle Area Deviation"] = np.minimum(1.0,1.0-area_deviation/2.)
quality_data["Triangle Area Deviation"] = np.minimum(
1.0,
np.sqrt(2) - area_deviation / np.sqrt(2))
end_time = time()
print "<-- Computing triangle area deviation [", end_time - start_time, "s]"
if "Triangle Eccentricity" in quality_criteria:
start_time = time()
print "--> Computing triangle eccentricity"
quality_data["Triangle Eccentricity"] = 1. - 2. * np.nanmean(
topomesh.wisp_property('eccentricity', degree=2).values())
end_time = time()
print "<-- Computing triangle eccentricity [", end_time - start_time, "s]"
compute_topomesh_property(topomesh, 'borders', degree=3)
compute_topomesh_property(topomesh, 'borders', degree=2)
compute_topomesh_property(topomesh, 'borders', degree=1)
compute_topomesh_property(topomesh, 'vertices', degree=3)
compute_topomesh_property(topomesh, 'vertices', degree=2)
compute_topomesh_property(topomesh, 'barycenter', degree=3)
compute_topomesh_property(topomesh, 'barycenter', degree=2)
compute_topomesh_property(topomesh, 'barycenter', degree=1)
compute_topomesh_property(topomesh, 'triangles', degree=0)
compute_topomesh_property(topomesh, 'cells', degree=0)
compute_topomesh_property(topomesh, 'cells', degree=1)
compute_topomesh_property(topomesh, 'cells', degree=2)
compute_topomesh_property(topomesh, 'epidermis', degree=0)
compute_topomesh_property(topomesh, 'epidermis', degree=1)
compute_topomesh_property(topomesh, 'epidermis', degree=3)
if "Cell Volume Error" in quality_criteria or "Cell Convexity" in quality_criteria:
compute_topomesh_property(topomesh, 'volume', degree=3)
compute_topomesh_property(topomesh, 'convexhull_volume', degree=3)
img_graph = kwargs.get('image_graph', None)
if "Cell Volume Error" in quality_criteria:
start_time = time()
print "--> Computing cell volume error"
from vplants.tissue_analysis.temporal_graph_from_image import graph_from_image
if (image_cell_volumes == None) or (image_labels
== None) or (image_cell_vertex
== None):
img_graph = graph_from_image(
image,
spatio_temporal_properties=['volume', 'barycenter'],
background=0,
ignore_cells_at_stack_margins=False,
property_as_real=True)
image_labels = np.array(list(img_graph.vertices()))
image_cell_volumes = np.array(
[img_graph.vertex_property('volume')[v] for v in image_labels])
else:
img_graph = None
img_volumes = array_dict(image_cell_volumes, image_labels)
if triangular:
volume_error = array_dict([
(c, (topomesh.wisp_property('volume', degree=3)[c] -
img_volumes[c]) / img_volumes[c])
for c in topomesh.wisps(3)
])
else:
volume_error = array_dict([
(c, (topomesh.wisp_property('convexhull_volume', degree=3)[c] -
img_volumes[c]) / img_volumes[c])
for c in topomesh.wisps(3)
])
quality_data["Cell Volume Error"] = np.maximum(
1. - abs(volume_error.values()).mean(), 0.0)
end_time = time()
print "<-- Computing cell volume error [", end_time - start_time, "s]"
if "Image Accuracy" in quality_criteria:
start_time = time()
print "--> Computing image accuracy"
from openalea.mesh.utils.image_tools import compute_topomesh_image
topomesh_img = compute_topomesh_image(topomesh, image)
cells_img = deepcopy(image)
for c in set(np.unique(image)).difference(set(topomesh.wisps(3))):
cells_img[image == c] = 1
true_positives = ((cells_img != 1) & (cells_img == topomesh_img)).sum()
false_positives = ((cells_img == 1) & (topomesh_img != 1)).sum() + (
(cells_img != 1) & (topomesh_img != 1) &
(cells_img != topomesh_img)).sum()
false_negatives = ((cells_img != 1) & (topomesh_img == 1)).sum() + (
(cells_img != 1) & (topomesh_img != 1) &
(cells_img != topomesh_img)).sum()
true_negatives = ((cells_img == 1) & (cells_img == topomesh_img)).sum()
estimators = {}
estimators['Precision'] = float(
true_positives / float(true_positives + false_positives))
estimators['Recall'] = float(true_positives /
float(true_positives + false_negatives))
estimators['Dice'] = float(
2 * true_positives /
float(2 * true_positives + false_positives + false_negatives))
estimators['Jaccard'] = float(
true_positives /
float(true_positives + false_positives + false_negatives))
estimators['Accuracy'] = float(true_positives +
true_negatives) / float(
true_positives + true_negatives +
false_positives + false_negatives)
estimators['Identity'] = float(
(cells_img == topomesh_img).sum()) / np.prod(cells_img.shape)
print estimators
quality_data["Image Accuracy"] = estimators['Dice']
end_time = time()
print "<-- Computing image accuracy [", end_time - start_time, "s]"
vertex_cell_neighbours = topomesh.wisp_property('cells', degree=0)
vertex_cell_degree = np.array(map(len, vertex_cell_neighbours.values()))
epidermis_vertices = np.array(list(topomesh.wisps(0)))[np.where(
topomesh.wisp_property('epidermis', degree=0).values())]
start_time = time()
print "--> Computing mesh cell vertices"
mesh_cell_vertex = {}
for v in topomesh.wisps(0):
if len(vertex_cell_neighbours[v]) == 5:
for k in xrange(5):
vertex_cell_labels = tuple([
c for c in vertex_cell_neighbours[v]
][:k]) + tuple([c for c in vertex_cell_neighbours[v]][k + 1:])
if not mesh_cell_vertex.has_key(vertex_cell_labels):
mesh_cell_vertex[vertex_cell_labels] = v
if len(vertex_cell_neighbours[v]) == 4:
vertex_cell_labels = tuple([c for c in vertex_cell_neighbours[v]])
mesh_cell_vertex[vertex_cell_labels] = v
if v in epidermis_vertices:
for k in xrange(4):
vertex_cell_labels = (1, ) + tuple(
[c for c in vertex_cell_neighbours[v]][:k]) + tuple(
[c for c in vertex_cell_neighbours[v]][k + 1:])
if not mesh_cell_vertex.has_key(vertex_cell_labels):
mesh_cell_vertex[vertex_cell_labels] = v
if (len(vertex_cell_neighbours[v]) == 3) and (v in epidermis_vertices):
vertex_cell_labels = (1, ) + tuple(
[c for c in vertex_cell_neighbours[v]])
mesh_cell_vertex[vertex_cell_labels] = v
end_time = time()
print "<-- Computing mesh cell vertices [", end_time - start_time, "s]"
cell_vertex = np.unique(mesh_cell_vertex.values())
mesh_cell_vertices = topomesh.wisp_property('barycenter',
degree=0).values(cell_vertex)
if "Vertex Distance" in quality_criteria:
start_time = time()
print "--> Computing vertex distance"
if image_cell_vertex is None:
image_cell_vertex = cell_vertex_extraction(image,
hollow_out=False,
verbose=False)
if img_graph is None:
img_graph = graph_from_image(
image,
spatio_temporal_properties=['volume', 'barycenter'],
background=0,
ignore_cells_at_stack_margins=False,
property_as_real=True)
img_edges = np.array(
[img_graph.edge_vertices(e) for e in img_graph.edges()])
tetra_edge_index_list = np.array([[0, 1], [0, 2], [0, 3], [1, 2],
[1, 3], [2, 3]])
img_vertex_edges = np.concatenate(
np.sort(image_cell_vertex.keys())[:, tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges, img_edges)[1] >
0).reshape(len(image_cell_vertex),
6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(
vertex_edge_matching == 0)[0]]
image_cell_vertex = array_dict(
np.array([
image_cell_vertex[tuple(vertex)]
for vertex in img_cell_vertex
]), np.sort(img_cell_vertex))
else:
image_cell_vertex = deepcopy(image_cell_vertex)
# for v in image_cell_vertex.keys():
# image_cell_vertex[v] = image_cell_vertex[v]*np.array(image.voxelsize)
image_cell_vertices = np.array(image_cell_vertex.values())
print mesh_cell_vertices[np.where(
1 - np.isnan(mesh_cell_vertices)[:, 0])] * image.voxelsize
print image_cell_vertices * image.voxelsize
vertex_distances_mesh = array_dict(
vq(
mesh_cell_vertices[np.where(
1 - np.isnan(mesh_cell_vertices)[:, 0])] * image.voxelsize,
image_cell_vertices * image.voxelsize)[1], cell_vertex)
vertex_distances_image = vq(
image_cell_vertices[np.where(1 -
np.isnan(image_cell_vertices)[:, 0])],
mesh_cell_vertices)[1]
# quality_data["Vertex Distance"] = np.sqrt(3)/(np.maximum(np.sqrt(3),vertex_distances_image.mean()))
quality_data["Vertex Distance"] = (0.25 * np.sqrt(3)) / (np.maximum(
(0.25 * np.sqrt(3)), vertex_distances_image.mean()))
end_time = time()
print "<-- Computing vertex distance [", end_time - start_time, "s]"
if "Cell Convexity" in quality_criteria:
start_time = time()
print "--> Computing cell convexity"
# quality_data["Cell Convexity"] = (topomesh.wisp_property('volume',degree=3).values()/topomesh.wisp_property('convexhull_volume',degree=3).values()).mean()
quality_data["Cell Convexity"] = 1 - (
((topomesh.wisp_property('convexhull_volume', 3).values() -
topomesh.wisp_property('volume', 3).values()).mean()) /
topomesh.wisp_property('volume', 3).values().mean())
end_time = time()
print "<-- Computing cell convexity [", end_time - start_time, "s]"
if "Epidermis Cell Angle" in quality_criteria:
start_time = time()
print "--> Computing epidermis cell angle"
epidermis_vertex_cell_angles = np.array([])
epidermis_vertex_angles = array_dict()
angle_keys = np.concatenate([
np.concatenate([[(t, v)]
for v in topomesh.wisp_property('vertices', 2)[t]],
axis=0) for t in topomesh.wisps(2)
],
axis=0)
angle_dict = dict([(tuple(k), 0.) for k in angle_keys])
for v in cell_vertex:
if topomesh.wisp_property('epidermis', 0)[v]:
vertex_triangles = np.array([
t for t in topomesh.wisp_property('triangles', 0)[v]
if topomesh.wisp_property('epidermis', 2)[t]
])
vertex_triangle_cells = np.concatenate(
topomesh.wisp_property('cells',
degree=2).values(vertex_triangles))
vertex_cells = np.unique(vertex_triangle_cells)
vertex_triangle_cell_directions = topomesh.wisp_property(
'barycenter',
2).values(vertex_triangles) - topomesh.wisp_property(
'barycenter', 3).values(vertex_triangle_cells)
vertex_triangle_normals = topomesh.wisp_property(
'normal', 2).values(vertex_triangles)
reversed_normals = np.where(
np.einsum('ij,ij->i', vertex_triangle_normals,
vertex_triangle_cell_directions) < 0)[0]
vertex_triangle_normals[
reversed_normals] = -vertex_triangle_normals[
reversed_normals]
vertex_normal = np.mean(vertex_triangle_normals, axis=0)
vertex_normal = vertex_normal / np.linalg.norm(vertex_normal)
triangle_vertices = topomesh.wisp_property(
'vertices', degree=2).values(vertex_triangles)
triangle_positions = topomesh.wisp_property(
'barycenter', degree=0).values(triangle_vertices)
triangle_proj_vectors = triangle_positions - topomesh.wisp_property(
'barycenter', degree=0)[v]
triangle_proj_dot = np.einsum('...ij,...j->...i',
triangle_proj_vectors,
vertex_normal)
triangle_proj_vectors = -triangle_proj_dot[
..., np.newaxis] * vertex_normal
triangle_proj_positions = triangle_positions + triangle_proj_vectors
edge_index_list = np.array([[1, 2], [0, 1], [0, 2]])
triangle_edge_positions = triangle_proj_positions[:,
edge_index_list]
triangle_edge_vectors = triangle_edge_positions[:, :,
1] - triangle_edge_positions[:, :,
0]
triangle_edge_lengths = np.linalg.norm(triangle_edge_vectors,
axis=2)
triangle_cosines = np.zeros_like(triangle_edge_lengths,
np.float32)
triangle_cosines[:,
0] = (triangle_edge_lengths[:, 1]**2 +
triangle_edge_lengths[:, 2]**2 -
triangle_edge_lengths[:, 0]**2) / (
2.0 * triangle_edge_lengths[:, 1] *
triangle_edge_lengths[:, 2])
triangle_cosines[:,
2] = (triangle_edge_lengths[:, 2]**2 +
triangle_edge_lengths[:, 0]**2 -
triangle_edge_lengths[:, 1]**2) / (
2.0 * triangle_edge_lengths[:, 2] *
triangle_edge_lengths[:, 0])
triangle_cosines[:,
1] = (triangle_edge_lengths[:, 0]**2 +
triangle_edge_lengths[:, 1]**2 -
triangle_edge_lengths[:, 2]**2) / (
2.0 * triangle_edge_lengths[:, 0] *
triangle_edge_lengths[:, 1])
triangle_angles = 180. * np.arccos(triangle_cosines) / np.pi
vertex_triangle_angles = triangle_angles[np.where(
triangle_vertices == v)]
vertex_cell_angles = array_dict(
nd.sum(vertex_triangle_angles,
vertex_triangle_cells,
index=vertex_cells), vertex_cells)
for t, c in zip(vertex_triangles, vertex_triangle_cells):
angle_dict[(t, v)] = vertex_cell_angles[c]
epidermis_vertex_angles[v] = np.sum(
vertex_cell_angles.values())
if abs(epidermis_vertex_angles[v] - 360) < 10:
epidermis_vertex_cell_angles = np.concatenate([
epidermis_vertex_cell_angles,
vertex_cell_angles.values()
])
# quality_data["Epidermis Cell Angle"] = 1.0 - ((epidermis_vertex_cell_angles>180).sum()+(epidermis_vertex_cell_angles<90).sum())/(float(epidermis_vertex_cell_angles.shape[0]))
quality_data["Epidermis Cell Angle"] = 1 - (np.sum(1 - np.exp(-np.power(
np.maximum(100 - epidermis_vertex_cell_angles, 0.0) /
20., 2.0))) + np.sum(1 - np.exp(-np.power(
np.maximum(epidermis_vertex_cell_angles - 170, 0.0) /
20., 2.0)))) / (float(epidermis_vertex_cell_angles.shape[0]))
# np.sqrt(np.sum(np.power(np.maximum(90-epidermis_vertex_cell_angles,0.0)/30.,2.0))) + np.sum(np.maximum(epidermis_vertex_cell_angles-180,0.0)/30.)
# quality_data["Epidermis Cell Angle"] = np.minimum(1.0,3-np.sqrt(np.power(epidermis_vertex_cell_angles-120.,2.0).mean())/30.)
# quality_data["Epidermis Cell Angle"] = np.minimum(1.0,1.0-np.sqrt(np.power(epidermis_vertex_cell_angles-120.,2.0).mean())/120.)
# quality_data["Epidermis Cell Angle"] = 1.0-np.power(np.power(np.abs(epidermis_vertex_cell_angles-120.),1.0).mean(),1.0)/120.
end_time = time()
print "<-- Computing epidermis cell angle [", end_time - start_time, "s]"
if "Vertex Valence" in quality_criteria:
start_time = time()
print "--> Computing vertex degree"
compute_topomesh_property(topomesh, 'region_neighbors', degree=0)
compute_topomesh_property(topomesh, 'border_neighbors', degree=3)
# interface_vertices = np.array(list(topomesh.wisps(0)))[np.where((np.array(map(len,topomesh.wisp_property('cells',0).values())) == 1)
# | ((np.array(map(len,topomesh.wisp_property('cells',0).values())) == 2)
# & (1- topomesh.wisp_property('epidermis',0).values())))]
# interface_vertices_degree = np.array(map(len,topomesh.wisp_property('neighbors',0).values(interface_vertices)))
target_neighborhood = array_dict(
(np.array(map(len,
topomesh.wisp_property('cells', 0).values())) +
topomesh.wisp_property('epidermis', 0).values()) * 3,
list(topomesh.wisps(0)))
vertices_neighborhood = array_dict(
map(len,
topomesh.wisp_property('neighbors', 0).values()),
list(topomesh.wisps(0)))
interface_vertices = target_neighborhood.keys_where("==6")
# np.power(np.power(target_neighborhood.values(interface_vertices)-vertices_neighborhood.values(interface_vertices),2.0).mean(),0.5)/6.0
quality_data["Vertex Valence"] = np.minimum(
1.0, 1.0 - np.abs(
target_neighborhood.values(interface_vertices) -
vertices_neighborhood.values(interface_vertices)).mean() / 6.0)
# quality_data["Vertex Valence"] = np.minimum(1.0,1.0-np.nanmean(np.abs(interface_vertices_degree-6.0)))
# quality_data["Vertex Valence"] = np.minimum(1.0,1.0-np.power(interface_vertices_degree-6.0,2.0).mean()/6.0)
end_time = time()
print "<-- Computing vertex degree [", end_time - start_time, "s]"
if "Cell 4 Adjacency" in quality_criteria:
start_time = time()
print "--> Computing cell adjacency"
if image_cell_vertex == None:
image_cell_vertex = cell_vertex_extraction(img,
hollow_out=False,
verbose=False)
if img_graph == None:
img_graph = graph_from_image(
img,
spatio_temporal_properties=['volume', 'barycenter'],
background=0,
ignore_cells_at_stack_margins=False,
property_as_real=False,
min_contact_surface=0.5)
img_edges = np.array(
[img_graph.edge_vertices(e) for e in img_graph.edges()])
tetra_edge_index_list = np.array([[0, 1], [0, 2], [0, 3], [1, 2],
[1, 3], [2, 3]])
img_vertex_edges = np.concatenate(
np.sort(image_cell_vertex.keys())[:, tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges, img_edges)[1] >
0).reshape(len(image_cell_vertex),
6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(
vertex_edge_matching == 0)[0]]
image_cell_vertex = array_dict(
np.array([
image_cell_vertex[tuple(vertex)]
for vertex in img_cell_vertex
]), np.sort(img_cell_vertex))
cell_vertex_VP = (vq(
np.sort(array_dict(mesh_cell_vertex).keys()),
np.sort(array_dict(image_cell_vertex).keys()))[1] == 0).sum()
cell_vertex_FP = (vq(np.sort(array_dict(mesh_cell_vertex).keys()),
np.sort(array_dict(image_cell_vertex).keys()))[1]
> 0).sum()
cell_vertex_FN = (vq(np.sort(array_dict(image_cell_vertex).keys()),
np.sort(array_dict(mesh_cell_vertex).keys()))[1] >
0).sum()
cell_vertex_jaccard = cell_vertex_VP / float(cell_vertex_VP +
cell_vertex_FP +
cell_vertex_FN)
cell_vertex_dice = 2 * cell_vertex_VP / float(2 * cell_vertex_VP +
cell_vertex_FP +
cell_vertex_FN)
quality_data["Cell 4 Adjacency"] = cell_vertex_jaccard
end_time = time()
print "<-- Computing cell adjacency [", end_time - start_time, "s]"
if "Cell 2 Adjacency" in quality_criteria:
start_time = time()
print "--> Computing cell adjacency"
tetra_edge_index_list = np.array([[0, 1], [0, 2], [0, 3], [1, 2],
[1, 3], [2, 3]])
if image_cell_vertex == None:
image_cell_vertex = cell_vertex_extraction(img,
hollow_out=False,
verbose=False)
if img_graph == None:
img_graph = graph_from_image(
img,
spatio_temporal_properties=['volume', 'barycenter'],
background=0,
ignore_cells_at_stack_margins=False,
property_as_real=False,
min_contact_surface=0.5)
img_edges = np.array(
[img_graph.edge_vertices(e) for e in img_graph.edges()])
img_vertex_edges = np.concatenate(
np.sort(image_cell_vertex.keys())[:, tetra_edge_index_list])
vertex_edge_matching = (vq(img_vertex_edges, img_edges)[1] >
0).reshape(len(image_cell_vertex),
6).sum(axis=1)
img_cell_vertex = np.array(image_cell_vertex.keys())[np.where(
vertex_edge_matching == 0)[0]]
image_cell_vertex = array_dict(
np.array([
image_cell_vertex[tuple(vertex)]
for vertex in img_cell_vertex
]), np.sort(img_cell_vertex))
from vplants.meshing.array_tools import array_unique
mesh_cell_edges = array_unique(
np.concatenate(
np.sort(array_dict(mesh_cell_vertex).keys())
[:, tetra_edge_index_list]))
image_cell_edges = array_unique(
np.concatenate(
np.sort(array_dict(image_cell_vertex).keys())
[:, tetra_edge_index_list]))
from vplants.meshing.evaluation_tools import jaccard_index
cell_edge_jaccard = jaccard_index(image_cell_edges, mesh_cell_edges)
quality_data["Cell 2 Adjacency"] = cell_edge_jaccard
end_time = time()
print "<-- Computing cell adjacency [", end_time - start_time, "s]"
if "Cell Cliques" in quality_criteria:
vertex_cells = np.array(
[len(list(topomesh.regions(0, v, 3))) for v in topomesh.wisps(0)])
vertex_epidermis = topomesh.wisp_property('epidermis',
degree=0).values()
cell_vertices = ((vertex_cells >= 3) * (vertex_epidermis) +
(vertex_cells >= 4) *
(True - vertex_epidermis)).sum()
clique_cell_vertices = ((vertex_cells > 3) * (vertex_epidermis) +
(vertex_cells > 4) *
(True - vertex_epidermis)).sum()
quality_data["Cell Cliques"] = 1.0 - float(
clique_cell_vertices) / float(cell_vertices)
print quality_data
# if display:
# spider_figure = plt.figure(kwargs.get('figure_title',"Topomesh Quality"))
# spider_figure.clf()
# spider_data = np.array([quality_data[c] for c in quality_criteria])
# spider_fields= quality_criteria
# # spider_targets = [0.8,0.7,0.8,0.8,0.9,0.8,0.7]
# spider_targets = 0.8 * np.ones_like(quality_criteria,float)
# spider_plot(spider_figure,spider_data,color1=np.array([0.3,0.6,1.]),color2=np.array([1.,0.,0.]),xlabels=spider_fields,ytargets=spider_targets,n_points=100*len(quality_criteria),linewidth=2,smooth_factor=0.0,spline_order=1)
# plt.show(block=False)
return quality_data
meshing_dirname = dirname.parent.parent inputfile = dirname+"/segmented_images/"+filename+".inr.gz" 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()