Algorithm for oversampling point clouds
Project description
densify
densify
is an algorithm for oversampling point clouds. It creates synthetic data points that "fill the gaps" in the cloud, making it more dense. This can be a useful technique for reducing overfitting when training machine learning models on point cloud datasets.
Installation
You can install densify
from PyPi:
$ pip install densify
Usage
densify
is simple to use. The function expects an array of points representing your cloud and a "radius" that dictates the minimum distance each synthetic point must be from all other points. The smaller the radius, the higher the density.
import numpy as np
from densify import densify
point_cloud = np.array([[4.6, 6.5],
[1.5, 4.1],
[6.1, 5.0],
[1.1, 2.9],
[10.0, 5.0]])
new_points, iter_results = densify(point_cloud, radius=0.15)
The function returns new_points
, a numpy array of the synthetic points, and iter_results
, a list of algorithm outputs to plug into visualize_densify
.
Constrained Point Generation
By default, densify
acts within the convex hull of the point cloud and will not create points outside that boundary. But if the point cloud is non-convex, you can define a boundary to generate points within. To do this, pass in a list of points in the cloud representing the boundary:
point_cloud = np.array([[0, 0],
[4, 0],
[4, -3],
[6, -3],
[6, 3],
[3, 5],
[2, 1],
[3, 3],
[5, 0],
[4, 1]])
hull = np.array([[0, 0],
[4, 0],
[4, -3],
[6, -3],
[6, 3],
[3, 5]])
new_points, iter_results = densify(point_cloud, radis=0.15, exterior_hull=hull)
Note that these points must define a simple polygon that encloses all the points in the cloud.
Visualizing Point Generation
densify
lets you visualize the point generation process for 2D point clouds. Simply plug the point_cloud
and iter_results
objects into animate_densify
:
from densify import animate_densify
animate_densify(point_cloud, iter_results, dark=True, filename="ani.gif")
How it Works
densify
computes a Delaunay triangulation of the point cloud and creates synthetic points from the centroids of each simplex in the triangulation. These points are added to the cloud, and the process is repeated recursively until no new points can be created.
If a boundary is given, densify
enforces it by using the winding number algorithm to identify simplices that contain edges outside of the boundary, and then dropping them.
Authors
densify
was created by Jonathan Shobrook with the help of Paul C. Bogdan as part of our research in the Dolcos Lab at the Beckman Institute for Advanced Science and Technology.
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