trimesh.points¶

points.py¶

Functions dealing with (n, d) points.

class trimesh.points.PointCloud(vertices, colors=None, metadata=None, **kwargs)¶

Bases: trimesh.parent.Geometry3D

Hold 3D points in an object which can be visualized in a scene.

__init__(vertices, colors=None, metadata=None, **kwargs)¶

Load an array of points into a PointCloud object.

Parameters

vertices ((n, 3) float) – Points in space
colors ((n, 4) uint8 or None) – RGBA colors for each point
metadata (dict or None) – Metadata about points

apply_transform(transform)¶

Apply a homogeneous transformation to the PointCloud object in- place.

Parameters: transform ((4, 4) float) – Homogeneous transformation to apply to PointCloud

property bounds¶

The axis aligned bounds of the PointCloud

Returns: bounds – Minimum, Maximum verteex
Return type: (2, 3) float

property centroid¶

The mean vertex position

Returns: centroid – Mean vertex position
Return type: (3,) float

property colors¶

Stored per- point color

Returns: colors – Per- point RGBA color
Return type: (len(self.vertices), 4) np.uint8

property convex_hull¶

A convex hull of every point.

Returns: convex_hull – A watertight mesh of the hull of the points
Return type: trimesh.Trimesh

copy()¶

Safely get a copy of the current point cloud.

Copied objects will have emptied caches to avoid memory issues and so may be slow on initial operations until caches are regenerated.

Current object will not have its cache cleared.

Returns: copied – Copy of current point cloud
Return type: trimesh.PointCloud

crc()¶

Get a CRC hash of the current vertices.

Returns: crc – Hash of self.vertices
Return type: int

export(file_obj=None, file_type=None, **kwargs)¶

Export the current pointcloud to a file object. If file_obj is a filename, file will be written there. Supported formats are xyz :param file_obj: str, file name where to save the pointcloud

None, if you would like this function to return the export blob

Parameters: file_type (str) – Which file type to export as. If file name is passed this is not required

property extents¶

The size of the axis aligned bounds

Returns: extents – Edge length of axis aligned bounding box
Return type: (3,) float

property is_empty¶

Are there any vertices defined or not.

Returns: empty – True if no vertices defined
Return type: bool

md5()¶

Get an MD5 hash of the current vertices.

Returns: md5 – Hash of self.vertices
Return type: str

merge_vertices()¶: Merge vertices closer than tol.merge (default: 1e-8)

scene()¶

A scene containing just the PointCloud

Returns: scene – Scene object containing this PointCloud
Return type: trimesh.Scene

property shape¶

Get the shape of the pointcloud

Returns: shape – Shape of vertex array
Return type: (2,) int

show(**kwargs)¶: Open a viewer window displaying the current PointCloud

property vertices¶

Vertices of the PointCloud

Returns: vertices – Points in the PointCloud
Return type: (n, 3) float

trimesh.points.k_means(points, k, **kwargs)¶

Find k centroids that attempt to minimize the k- means problem: https://en.wikipedia.org/wiki/Metric_k-center

Parameters

points ((n, d) float) – Points in space
k (int) – Number of centroids to compute
**kwargs (dict) – Passed directly to scipy.cluster.vq.kmeans

Returns

centroids ((k, d) float) – Points in some space
labels ((n) int) – Indexes for which points belong to which centroid

trimesh.points.major_axis(points)¶

Returns an approximate vector representing the major axis of the passed points.

Parameters: points ((n, dimension) float) – Points in space
Returns: axis – Vector along approximate major axis
Return type: (dimension,) float

trimesh.points.plane_fit(points)¶

Fit a plane to points using SVD.

Parameters

points ((n, 3) float) – 3D points in space

Returns

C ((3,) float) – Point on the plane
N ((3,) float) – Unit normal vector of plane

trimesh.points.plot_points(points, show=True)¶

Plot an (n, 3) list of points using matplotlib

Parameters

points ((n, 3) float) – Points in space
show (bool) – If False, will not show until plt.show() is called

trimesh.points.point_plane_distance(points, plane_normal, plane_origin=[0.0, 0.0, 0.0])¶

The minimum perpendicular distance of a point to a plane.

Parameters

points ((n, 3) float) – Points in space
plane_normal ((3,) float) – Unit normal vector
plane_origin ((3,) float) – Plane origin in space

Returns

distances – Distance from point to plane

Return type

(n,) float

trimesh.points.project_to_plane(points, plane_normal=[0, 0, 1], plane_origin=[0, 0, 0], transform=None, return_transform=False, return_planar=True)¶

Project (n, 3) points onto a plane.

Parameters

points ((n, 3) float) – Points in space.
plane_normal ((3,) float) – Unit normal vector of plane
plane_origin ((3,)) – Origin point of plane
transform (None or (4, 4) float) – Homogeneous transform, if specified, normal+origin are overridden
return_transform (bool) – Returns the (4, 4) matrix used or not
return_planar (bool) – Return (n, 2) points rather than (n, 3) points

trimesh.points.radial_sort(points, origin, normal)¶

Sorts a set of points radially (by angle) around an an axis specified by origin and normal vector.

Parameters

points ((n, 3) float) – Points in space
origin ((3,) float) – Origin to sort around
normal ((3,) float) – Vector to sort around

Returns

ordered – Same as input points but reordered

Return type

(n, 3) float

trimesh.points.remove_close(points, radius)¶

Given an (n, m) array of points return a subset of points where no point is closer than radius.

Parameters

points ((n, dimension) float) – Points in space
radius (float) – Minimum radius between result points

Returns

culled ((m, dimension) float) – Points in space
mask ((n,) bool) – Which points from the original points were returned

trimesh.points.tsp(points, start=0)¶

Find an ordering of points where each is visited and the next point is the closest in euclidean distance, and if there are multiple points with equal distance go to an arbitrary one.

Assumes every point is visitable from every other point, i.e. the travelling salesman problem on a fully connected graph. It is not a MINIMUM traversal; rather it is a “not totally goofy traversal, quickly.” On random points this traversal is often ~20x shorter than random ordering, and executes on 1000 points in around 29ms on a 2014 i7.

Parameters

points ((n, dimension) float) – ND points in space
start (int) – The index of points we should start at

Returns

traversal ((n,) int) – Ordered traversal visiting every point
distances ((n - 1,) float) – The euclidean distance between points in traversal