Metadata-Version: 2.1
Name: pymccrgb
Version: 0.1.7
Summary: A Python package for point cloud classification using color and curvature
Home-page: https://github.com/rmsare/pymccrgb
Author: ['Robert Sare', 'George Hilley']
Author-email: rmsare@stanford.edu
License: MIT
Description: # pymccrgb
        
        [![Build Status](https://travis-ci.com/rmsare/pymccrgb.svg?branch=master)](https://travis-ci.com/rmsare/pymccrgb)
        [![Documentation Status](https://readthedocs.org/projects/pymccrgb/badge/?version=latest)](https://pymccrgb.readthedocs.io/en/latest/?badge=latest)
        
        **pymccrgb** is a Python package for multiscale curvature classification of
        point clouds with color attributes. 
        
        It extends a popular classification method
        ([MCC lidar](https://sourceforge.net/p/mcclidar/wiki/Home/)) [[0]](#references) to point cloud datasets with multiple color channels, such as those
        commonly produced in surveys using drone photography or other platforms. It can be used to distinguish points from the
        ground surface and low vegetation in data produced by structure from motion photogrammetry,
        stereo photogrammetry, or multi-spectral lidar scanning, or to filter colorized lidar point clouds.
        
        The intended users are scientists in geomorphology, ecology, or planetary science
        who want to classify point clouds for topographic analysis, canopy height measurements, or other spectral classification.
        
        ### Installation
        
        This package is developed for Linux and Python 3.6+. It depends on common 
        Python packages like sklearn, numpy, the LibLAS C API, and 
        [MCC Python bindings](https://github.com/stgl/pymcc).
        
        You can install it with `conda`:
        
        ```bash
        conda env create -n pymcc
        conda activate pymcc
        conda install pymccrgb -c conda-forge
        ```
        
        ### Requirements
        
        The LibLAS C library is required for MCC and `pymccrgb`. The MCC wrapper also 
        requires Boost and the C++11 or later standard library. These are installed 
        with the conda package.
        
        Refer to the [documentation](https://pymccrgb.readthedocs.io/en/latest/installation.html)
        and the [LibLAS install guide](https://liblas.org/start.html#installation) for 
        instructions for installing LibLAS from source.
        
        ### Examples
        
        Example notebooks are available in the docs or at [docs/source/examples](docs/source/examples).
        
        #### Topography under tree cover
        
        ```python
        from pymccrgb import mcc, mcc_rgb
        from pymccrgb.datasets import load_mammoth_lidar
        from pymccrgb.plotting import plot_results
        
        # Load sample data (Mammoth Mountain, CA)
        data = load_mammoth_lidar(npoints=1e6)
        
        # MCC algorithm
        ground_mcc, labels_mcc = mcc(data)
        
        # MCC-RGB algorithm
        ground_mccrgb, labels_mccrgb = mcc_rgb(data)
        
        plot_results(data, labels_mcc, labels_mccrgb)
        ```
        
        [![MCC results](docs/img/mccrgb.png)]()
        
        Results of MCC and MCC-RGB on a forested area near Mammoth Mountain, CA. 
        
        ### Documentation
        
        Read the documentation for example use cases, an API reference, and more at [pymccrgb.readthedocs.io](https://pymccrgb.readthedocs.io). 
        
        ### Contributing
        
        #### Bug reports
        
        Bug reports are much appreciated. Please [open an issue](https://github.com/rmsare/pymccrgb/issues/new) with the `bug` label,
        and provide a minimal example illustrating the problem.
        
        #### Suggestions
        
        Feel free to [suggest new features](https://github.com/rmsare/pymccrgb/issues/new) in an issue with the `new-feature` label.
        
        #### Pull requests
        
        If you would like to add a feature or fix a bug, please fork the repository, create a feature branch, and [submit a PR](https://github.com/rmsare/pymccrgb/compare) and reference any relevant issues. There are nice guides to contributing with GitHub [here](https://akrabat.com/the-beginners-guide-to-contributing-to-a-github-project/) and [here](https://yourfirstpr.github.io/). Please include tests where appropriate and check that the test suite passes (a Travis build or `pytest pymccrgb/tests`) before submitting.
        
        ### Support and questions
        
        Please [open an issue](https://github.com/rmsare/pymccrgb/issues/new) with your question.
        
        ### References
        
        [0] Evans, J. S., & Hudak, A. T. 2007. A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 1029-1038 [doi](https://doi.org/10.1109/TGRS.2006.890412) 
        
        ### License
        
        This work is licensed under the MIT License (see [LICENSE](LICENSE)). It also
        incorporates a wrapper for the [`mcc-lidar` implementation](https://sourceforge.net/p/mcclidar),
        which is distributed under the Apache license (see [LICENSE.txt](https://sourceforge.net/p/mcclidar/code/HEAD/tree/tags/2.1/LICENSE.txt)).
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
