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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: [email protected] | |
| 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] | |