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import torch
import igl
import numpy as np
@torch.no_grad()
def igl_flips(
vertices:np.array, #V,3
faces:np.array, #F,3
target_vertices:np.array, #VT,3
target_faces:np.array, #FT,3
)->tuple[np.array,np.array]:
full_vertices = vertices[faces] #F,C=3,3
face_centers = full_vertices.mean(axis=1) #F,3
_,ind,points = igl.point_mesh_squared_distance(face_centers,target_vertices,target_faces)
target_faces = target_faces[ind] #F,3
corners = target_vertices[target_faces] #F,3,3
bary = igl.barycentric_coordinates_tri(points,corners[:,0].copy(),corners[:,1].copy(),corners[:,2].copy()) #P,3
target_normals = igl.per_vertex_normals(target_vertices,target_faces,igl.PER_VERTEX_NORMALS_WEIGHTING_TYPE_AREA)
corner_normals = target_normals[target_faces] #P,3,3
ref_normals = (bary[:,:,None] * corner_normals).sum(axis=1) #F,3
face_normals = igl.per_face_normals(vertices,faces,np.array([0,0,0],dtype=np.float32)) #F,3 not normalized
flip = np.sum(ref_normals * face_normals, axis=-1)<0 #F
flipped_area = np.sum(flip * np.linalg.norm(face_normals,axis=-1))
total_area = np.sum(np.linalg.norm(face_normals,axis=-1))
ratio = flipped_area / total_area
return flip, ratio
@torch.no_grad()
def igl_distance(
vertices:np.array, #V,3
faces:np.array, #F,3
target_vertices:np.array, #VT,3
target_faces:np.array, #FT,3
):
dist1_sq,_,_ = igl.point_mesh_squared_distance(vertices,target_vertices,target_faces)
dist2_sq,_,_ = igl.point_mesh_squared_distance(target_vertices,vertices,faces)
vertex_distance = np.sqrt(dist1_sq)
rms_distance = ((dist1_sq.mean()+dist2_sq.mean())/2)**.5
max_distance = max(dist1_sq.max(),dist2_sq.max())**.5
return vertex_distance,rms_distance,max_distance |