nearest

In [1]:

"""
An example showing nearest point queries, sampling the
volume of box primitives generated from the oriented bounds
and using PointCloud objects for visualization.
"""

Out[1]:

'\nAn example showing nearest point queries, sampling the\nvolume of box primitives generated from the oriented bounds\nand using PointCloud objects for visualization.\n'

In [2]:

import trimesh 
import numpy as np

In [3]:

# load a large- ish PLY model with colors    
mesh = trimesh.load('../models/cycloidal.ply')   

In [4]:

# we can sample the volume of Box primitives
points = mesh.bounding_box_oriented.sample_volume(count=10)

In [5]:

# find the closest point on the mesh to each random point
(closest_points,
 distances,
 triangle_id) = mesh.nearest.on_surface(points)
print('Distance from point to surface of mesh:\n{}'.format(distances))

Distance from point to surface of mesh:
[0.02273162 0.00362908 0.02801399 0.27195312 0.00199928 0.02354396
 0.00113454 0.03076838 0.39018792 0.05434157]

In [6]:

# create a PointCloud object out of each (n,3) list of points
cloud_original = trimesh.points.PointCloud(points)
cloud_close    = trimesh.points.PointCloud(closest_points)

# create a unique color for each point
cloud_colors = np.array([trimesh.visual.random_color() for i in points])

# set the colors on the random point and its nearest point to be the same
cloud_original.vertices_color = cloud_colors
cloud_close.vertices_color    = cloud_colors

# create a scene containing the mesh and two sets of points
scene = trimesh.Scene([mesh,
                       cloud_original,
                       cloud_close])

# show the scene wusing 
scene.show()

Out[6]: