'''
An example showing nearest point queries, 
primitive volume sampling, oriented bounding boxes, 
and using PointCloud objects for visualization
'''
import trimesh 
import numpy as np

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

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

# 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:
[3.55276498e-04 1.50895244e-01 3.46190437e-02 1.92738940e-03
 3.78616616e-01 2.36062754e-02 8.27695649e-03 1.51274975e-01
 3.76258283e-02 1.60871329e-04]

# 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()