{
    "info": {
        "author": "David Caron",
        "author_email": "dcaron05@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: Microsoft :: Windows",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython"
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        "description": "\n# pclpy: PCL for python\n\n[![PyPI](https://img.shields.io/pypi/v/pclpy.svg)](https://pypi.org/project/pclpy/)\n[![PyPI Python version](https://img.shields.io/pypi/pyversions/pclpy.svg)](https://pypi.org/project/pclpy/)\n\nPython bindings for the Point Cloud Library (PCL).\nGenerated from headers using CppHeaderParser and pybind11.\n\n__This library is in active development, the api is likely to change.\nThe included modules do work, but tests are incomplete, and corner\ncases are still common.__\n\nOnly Windows and python 3.6 x64 are supported at the moment.\n\nContributions, issues, comments are welcome!\n\nGithub repository: https://www.github.com/davidcaron/pclpy\n\nPypi: https://pypi.org/project/pclpy/\n\n## Motivation\nMany other python libraries tried to bind PCL.\nThe most popular one being python-pcl, which uses Cython.\nWhile Cython is really powerful, binding C++ templates isn't one of\nits strenghts (and PCL uses templates heavily).\nThe result for python-pcl is a lot of code repetition, which is hard\nto maintain and to add features to, and incomplete bindings of PCL's classes\nand point types.\n\nUsing pybind11, we use C++ directly. Templates, boost::smart_ptr and\nthe buffer protocol are examples of things that are simpler to implement.\n\nThe results so far are very promising. A large percentage of PCL is covered.\n\n## Installing\n\n#### Windows with python 3.6 x64\n`pip install pclpy`\n\nWhen pip installs the project, `pclpy_dependencies` is installed as a requirement.\nThis simple package contains only the PCL dlls required on Windows so you don't have\nto download a PCL release or build it.\n\n#### Linux\n\nNot working for now. Contributions are welcome!\n\n## Features\n- All point types are implemented (those specified by the default msvc compile flags)\n- You can view point cloud data as numpy arrays using `cloud.x` or `cloud.xyz`\n- boost::shared_ptr is handled by pybind11 so it's completely abstracted at the python level\n- laspy integration for reading/writing las files\n\n## Example\n\nYou can use either a high level, more pythonic api, or the wrapper over the PCL api.\nThe wrapper is meant to be as close as possible to the original PCL C++ api.\n\nHere is how you would use the library to process Moving Least Squares.\nSee the PCL documentation: http://pointclouds.org/documentation/tutorials/resampling.php\n\nUsing the higher level api:\n\n```python\nimport pclpy\n\n# read a las file\npoint_cloud = pclpy.read(\"street.las\", \"PointXYZRGBA\")\n# compute mls\noutput = point_cloud.moving_least_squares(search_radius=0.05, compute_normals=True, num_threads=8)\n```\n\nOr the wrapper over the PCL api:\n\n```python\nimport pclpy\nfrom pclpy import pcl\n\npoint_cloud = pclpy.read(\"street.las\", \"PointXYZRGBA\")\nmls = pcl.surface.MovingLeastSquaresOMP.PointXYZRGBA_PointNormal()\ntree = pcl.search.KdTree.PointXYZRGBA()\nmls.setSearchRadius(0.05)\nmls.setPolynomialFit(False)\nmls.setNumberOfThreads(12)\nmls.setInputCloud(point_cloud)\nmls.setSearchMethod(tree)\nmls.setComputeNormals(True)\noutput = pcl.PointCloud.PointNormal()\nmls.process(output)\n```\n\nYou can see the wrapper is very close to the C++ version:\n\n``` c++\n// C++ version\n\npcl::PointCloud<pcl::PointXYZ>::Ptr point_cloud (new pcl::PointCloud<pcl::PointXYZ> ());\npcl::io::loadPCDFile (\"bunny.pcd\", *point_cloud);\npcl::MovingLeastSquaresOMP<pcl::PointXYZ, pcl::PointNormal> mls;\npcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);\nmls.setSearchRadius (0.05);\nmls.setPolynomialFit (false);\nmls.setNumberOfThreads (12);\nmls.setInputCloud (point_cloud);\nmls.setSearchMethod (tree);\nmls.setComputeNormals (true);\npcl::PointCloud<pcl::PointNormal> output;\nmls.process (output);\n```\n\n## Modules\n- 2d\n- common\n- geometry\n- features\n- filters\n- io\n- kdtree\n- keypoints\n- octree\n- recognition\n- sample_consensus\n- search\n- segmentation\n- stereo\n- surface\n- tracking\n- visualization\n#### These modules are skipped for now\n- ml\n- people\n- outofcore\n- registration\n- every module not in the PCL Windows release (gpu, cuda, etc.)\n\n## Not Implemented\n(see [github issues](https://github.com/davidcaron/pclpy/issues)\nand the _what to skip_ section in `generators/config.py`)\n\n## To build\n#### Windows with python 3.6 x64\n\n- Download PCL release for Windows (PCL-1.8.1-AllInOne-msvc2017-win64.exe) at:\n    https://github.com/PointCloudLibrary/pcl/releases/download/pcl-1.8.1/PCL-1.8.1-AllInOne-msvc2017-win64.exe\n- PCL_ROOT environment variable must be set to the installation directory of PCL\n- About requirements:\n    - Install pybind11 from github (2.3dev version) it includes a necessary bug fix\n    - Install CppHeaderParser from https://github.com/davidcaron/CppHeaderParser (specific bug fixes)\n- Generate modules using `generate_pybind11_bindings.py`\n- There is a missing file from the PCL release that you should get from the github repo: 2d/impl/kernel.hpp\n- Must be built with x64 version of cl.exe because of the large memory usage (see workaround in setup.py)\n- python setup.py install\n- Useful setup.py arguments:\n    - --msvc-mp-build should enable a multiprocessed build\n    - --msvc-no-code-link makes linking much faster (do not use for releases, see setup.py description)\n    - --use-clcache to cache msvc builds using clcache (must be installed)\n    - --debug to build in debug mode\n\n## Roadmap\n- Wrap as much of PCL as reasonably possible\n- More tests\n- CI on Appveyor\n- Make it work on Linux\n\n\n",
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