Skip to main content

A Rust-based Python library for extracting pointpca2 features from Point Clouds.

Project description

PointPCA2 - Python Lib

A seamless Python integration to the Rust implementation of PointPCA2

This project aims to integrate Python to pointpca2-rs, enabling the use of Python's comprehensive data science tools combined to the performance provided by the Rust implementation of PointPCA2.

Setup

From PyPI

pip install pointpca2
# or
python -m pip install pointpca2

From source

  • Prerequisites

    • rustc == 1.77.2
    • anaconda3 >= 23.7.4
  • Build

# Clone this repository
https://github.com/akaTsunemori/pointpca2-pylib.git

# cd into the project folder
cd pointpca2-pylib

# Setup and activate the conda environment
conda env create -f environment.yml
conda activate pointpca2-pylib

# Compile the project into a python module using maturin
maturin develop -r

Usage

import open3d as o3d
import numpy as np
import pointpca2

# Load both reference and test PCs
PC_REF_PATH = "examples/pcs/amphoriskos_vox10.ply"
pc_ref = o3d.io.read_point_cloud(PC_REF_PATH)
points_a, colors_a = np.asarray(pc_ref.points), np.asarray(pc_ref.colors)
PC_TEST_PATH = "examples/pcs/tmc13_amphoriskos_vox10_dec_geom01_text01_octree-predlift.ply"
pc_test = o3d.io.read_point_cloud(PC_TEST_PATH)
points_b, colors_b = np.asarray(pc_test.points), np.asarray(pc_test.colors)

# Compute the features (predictors) through the pointpca2 function
predictors = pointpca2.compute_pointpca2(
    points_a, colors_a, points_b, colors_b, search_size=81, verbose=True
)
print(*predictors)

Contributing

Feel free to open any kind of issues and contributions related to this Python package. Issues related to the Rust implementation should be open on the pointpca2-rs repository.

Acknowledgments

License

MIT License


GitHub @akaTsunemori

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pointpca2-0.2.2.tar.gz (6.5 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pointpca2-0.2.2-cp310-cp310-manylinux_2_35_x86_64.whl (389.0 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.35+ x86-64

File details

Details for the file pointpca2-0.2.2.tar.gz.

File metadata

  • Download URL: pointpca2-0.2.2.tar.gz
  • Upload date:
  • Size: 6.5 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: maturin/1.5.1

File hashes

Hashes for pointpca2-0.2.2.tar.gz
Algorithm Hash digest
SHA256 1d8825e4616bdde6e789f42045adc815fc00e3bd3cd8982f60f02835e5a0a133
MD5 71d1418f9ec9223b3f2c11ae096da974
BLAKE2b-256 fd098ff710e05101f97caebbdd7b1f94eeaf40e3636160d68e5d5498e71a3b74

See more details on using hashes here.

File details

Details for the file pointpca2-0.2.2-cp310-cp310-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for pointpca2-0.2.2-cp310-cp310-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 b03e089ee8ac8ef5831d1405f21b3306d1fbd954050afd357d9c63d25ef84895
MD5 d72781ac6c40de50456bced765c17166
BLAKE2b-256 425d9f473a3cef8af5be81f9c11845416b719a11793f97f858bca0f101b52495

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page