Cut Pursuit Segmentation
Overview
Cut Pursuit is an efficient algorithm for segmenting point clouds by minimizing a functional over a graph. This package provides a Python interface to the C++ implementation of the Cut Pursuit algorithm.
Installation (Easy way - Recommended)
pip install cut-pursuit-py
Installation from the source (Alternative)
1. Clone the Repository
Open a terminal and clone the repo:
git clone https://github.com/truebelief/cut_pursuit_py.git
Then change into the project directory:
cd cut_pursuit_py
2. (Optional) Create and Activate a Virtual Environment
It’s a good idea to isolate your installation:
python -m venv venv
source venv/bin/activate # On Linux/macOS
venv\Scripts\activate # On Windows
3. Install Dependencies
Ensure you have a C++11–compatible compiler installed (needed to compile the C++ extension). Also install Python dependencies like NumPy (and SciPy if you plan to run the example):
pip install numpy scipy
4. Install the Package
You have two main options:
-
Editable Installation (for development):
This lets you modify the source code and see changes immediately.
pip install -e .
-
Standard Installation:
This builds and installs the package without linking directly to the source.
pip install .
Both commands use the build configuration specified in pyproject.toml and setup.py to compile the C++ extension and install the Python binder.
5. Verify the Installation
Open a Python shell and try importing the package:
import cut_pursuit
If there’s no error, your installation was successful.
Usage Example
import numpy as np
import cut_pursuit_py
from scipy.spatial import cKDTree
# Assume pcd is a numpy array of 3D points (N x 3)
def segment_point_cloud(pcd, k=7, reg_strength=1.0):
# Preprocess point cloud
pcd = pcd - np.mean(pcd, axis=0)
# Compute k-nearest neighbors
kdtree = cKDTree(pcd)
_, nn_idx = kdtree.query(pcd, k=k)
# Prepare graph structure
indices = nn_idx[:, 1:] # exclude self
n_nodes = len(pcd)
# Create edge lists
eu = np.repeat(np.arange(n_nodes), k - 1)
ev = indices.ravel()
# Edge weights
edge_weights = np.ones_like(eu, dtype=np.float32)
# Perform cut pursuit
segments = cut_pursuit_py.perform_cut_pursuit(
reg_strength=reg_strength, # Regularization strength
D=3, # Dimension of points
pc_vec=pcd.astype(np.float32), # Point cloud
edge_weights=edge_weights,
Eu=eu.astype(np.uint32),
Ev=ev.astype(np.uint32),
verbose=True,
)
return segments
# Example usage
point_cloud = np.random.rand(1000, 3)
segmentation = segment_point_cloud(point_cloud)
print(f"Number of segments: {len(np.unique(segmentation))}")
Dependencies
- NumPy
- C++11 compatible compiler (Not required if you choose to download prebuilt wheels via pip)
Citation
If you use this implementation, please cite the original paper:
Landrieu, L., & Obozinski, G. (2017). Cut Pursuit: Fast Algorithms to Learn Piecewise Constant Functions on General Weighted Graphs. SIAM Journal on Imaging Sciences, 10(4), 1724-1766.
1 maintainer
Metadata
1 maintainer
Release files for cut-pursuit-py 1.0.12
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Built distributions (wheels)
Total release size: 26.0 MB
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Transparency logRelease files / cut_pursuit_py-1.0.12-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | cut_pursuit_py-1.0.12-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 1.5 MB |
| Tags | CPython 3.6 CPython 3.6 pymalloc Linux glibc 2.17+ x86-32 |
|
SHA-256 checksum How to use checksums |
6e15c05bb07803c7482103f67387cff597e0cecc913578090d3203f55abdb529
|
|
BLAKE2b-256 checksum How to use checksums |
7c2d297c5a1ed88ff4a9787b61f1590eb1f87495ec9d2e5478bcddecf8dc07fe
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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Signed by GitHub Actions, verified by PyPI on Apr 1, 2025.
Transparency log
