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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import numpy as np
from scipy.spatial import cKDTree
import trimesh
import logging
logging.getLogger("trimesh").setLevel(logging.ERROR)
def save_obj_mesh(mesh_path, verts, faces):
file = open(mesh_path, 'w')
for v in verts:
file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
file.close()
def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
file = open(mesh_path, 'w')
for idx, v in enumerate(verts):
c = colors[idx]
file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' %
(v[0], v[1], v[2], c[0], c[1], c[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
file.close()
def save_ply(mesh_path, points, rgb):
'''
Save the visualization of sampling to a ply file.
Red points represent positive predictions.
Green points represent negative predictions.
:param mesh_path: File name to save
:param points: [N, 3] array of points
:param rgb: [N, 3] array of rgb values in the range [0~1]
:return:
'''
to_save = np.concatenate([points, rgb * 255], axis=-1)
return np.savetxt(
mesh_path,
to_save,
fmt='%.6f %.6f %.6f %d %d %d',
comments='',
header=(
'ply\nformat ascii 1.0\nelement vertex {:d}\n' +
'property float x\nproperty float y\nproperty float z\n' +
'property uchar red\nproperty uchar green\nproperty uchar blue\n' +
'end_header').format(points.shape[0]))
class HoppeMesh:
def __init__(self, verts, faces, vert_normals, face_normals):
'''
The HoppeSDF calculates signed distance towards a predefined oriented point cloud
http://hhoppe.com/recon.pdf
For clean and high-resolution pcl data, this is the fastest and accurate approximation of sdf
:param points: pts
:param normals: normals
'''
self.verts = verts # [n, 3]
self.faces = faces # [m, 3]
self.vert_normals = vert_normals # [n, 3]
self.face_normals = face_normals # [m, 3]
self.kd_tree = cKDTree(self.verts)
self.len = len(self.verts)
def query(self, points):
dists, idx = self.kd_tree.query(points, n_jobs=1)
# FIXME: because the eyebows are removed, cKDTree around eyebows
# are not accurate. Cause a few false-inside labels here.
dirs = points - self.verts[idx]
signs = (dirs * self.vert_normals[idx]).sum(axis=1)
signs = (signs > 0) * 2 - 1
return signs * dists
def contains(self, points):
labels = trimesh.Trimesh(vertices=self.verts,
faces=self.faces).contains(points)
return labels
def export(self, path):
if self.colors is not None:
save_obj_mesh_with_color(path, self.verts, self.faces,
self.colors[:, 0:3] / 255.0)
else:
save_obj_mesh(path, self.verts, self.faces)
def export_ply(self, path):
save_ply(path, self.verts, self.colors[:, 0:3] / 255.0)
def triangles(self):
return self.verts[self.faces] # [n, 3, 3]
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