Metadata-Version: 2.1
Name: wildbook-ia
Version: 3.3.1
Summary: Wildbook IA (WBIA) - Machine learning service for the WildBook project
Home-page: https://github.com/WildbookOrg/wildbook-ia
Author: Jason Parham, Dr. Jon Crall, Dr. Charles Stewart, Drew Blount, Ben Scheiner, Wild Me Developers, Karen Chan, Michael Mulich, Hendrik Weideman, A. Batbouta, A. Beard, Z. Jablons, D. Lowe, Z. Rutfield, K. Southerland, A. Weinstock, J. Wrona
Author-email: dev@wildme.org
License: Apache License 2.0
Project-URL: Bug Reports, https://github.com/WildbookOrg/wildbook-ia/issues
Project-URL: Funding, https://www.wildme.org/donate/
Project-URL: Say Thanks!, https://community.wildbook.org
Project-URL: Source, https://github.com/WildbookOrg/wildbook-ia
Description: ==================
        WBIA - WildBook IA
        ==================
        
        |Build| |Pypi| |ReadTheDocs| |Downloads|
        
        .. image:: http://i.imgur.com/TNCiEBe.png
            :alt: "(Note: the rhino and wildebeest matches may be dubious. Other species do work well though")
        
        WBIA program for the storage and management of images and derived data for
        use in computer vision algorithms. It aims to compute who an animal is, what
        species an animal is, and where an animal is with the ultimate goal being to
        ask important why biological questions.
        
        This project is the Machine Learning (ML) / computer vision component of the WildBook project: See https://github.com/WildbookOrg/.  This project is an actively maintained fork of the popular IBEIS (Image Based Ecological Information System) software suite for wildlife conservation.  The original IBEIS project is maintained by Jon Crall (@Erotemic) at https://github.com/Erotemic/ibeis.  The IBEIS toolkit originally was a wrapper around HotSpotter, which original binaries can be downloaded from: http://cs.rpi.edu/hotspotter/
        
        Currently the system is build around and SQLite database, a web GUI,
        and matplotlib visualizations. Algorithms employed are: convolutional neural network
        detection and localization and classification, hessian-affine keypoint detection, SIFT keypoint
        description, LNBNN identification using approximate nearest neighbors.
        
        Requirements
        ------------
        
        * Python 3.5+
        * OpenCV 3.4.10
        * Python dependencies listed in requirements.txt
        
        Installation Instructions
        -------------------------
        
        PyPI
        ~~~~
        
        The WBIA software is now available on `pypi
        <https://pypi.org/project/wbia/>`_ for Linux systems. This means if you have
        `Python installed
        <https://xdoctest.readthedocs.io/en/latest/installing_python.html>`_. You can
        simply run:
        
        .. code:: bash
        
            pip install wbia
        
        to install the software. Then the command to run the GUI is:
        
        .. code:: bash
        
            wbia
        
        We highly recommend using a Python virtual environment: https://docs.python-guide.org/dev/virtualenvs/#lower-level-virtualenv
        
        Docker
        ~~~~~~
        
        The WBIA software is built and deployed as a Docker image `wildme/wbia`.  You can download and run the pre-configured instance from the command line using:
        
        .. code:: bash
        
            # Install Docker - https://docs.docker.com/engine/install/
            docker pull wildme/wbia:latest
            docker container run -p <external port>:5000 --name wildbook-ia -v /path/to/local/database/:/data/docker/ wildme/wbia:latest
        
        This image is built using the multi-stage Dockerfiles in `devops/`.
        
        Source
        ~~~~~~
        
        To be updated soon.
        
        This project depends on an array of other repositories for functionality.
        
        First Party Toolkits (Required)
        
        * https://github.com/WildbookOrg/wbia-utool
        
        * https://github.com/WildbookOrg/wbia-vtool
        
        First Party Dependencies for Third Party Libraries (Required)
        
        * https://github.com/WildbookOrg/wbia-tpl-pyhesaff
        
        * https://github.com/WildbookOrg/wbia-tpl-pyflann
        
        * https://github.com/WildbookOrg/wbia-tpl-pydarknet
        
        * https://github.com/WildbookOrg/wbia-tpl-pyrf
        
        First Party Plug-ins (Optional)
        
        * https://github.com/WildbookOrg/wbia-plugin-cnn
        
        * https://github.com/WildbookOrg/wbia-plugin-flukematch
        
        * https://github.com/WildbookOrg/wbia-plugin-deepsense
        
        * https://github.com/WildbookOrg/wbia-plugin-finfindr
        
        * https://github.com/WildbookOrg/wbia-plugin-curvrank
        
            + https://github.com/WildbookOrg/wbia-tpl-curvrank
        
        * https://github.com/WildbookOrg/wbia-plugin-kaggle7
        
            + https://github.com/WildbookOrg/wbia-tpl-kaggle7
        
        * https://github.com/WildbookOrg/wbia-plugin-2d-orientation
        
            + https://github.com/WildbookOrg/wbia-tpl-2d-orientation
        
        * https://github.com/WildbookOrg/wbia-plugin-lca
        
            + https://github.com/WildbookOrg/wbia-tpl-lca
        
        Deprecated Toolkits (Deprecated)
        * https://github.com/WildbookOrg/wbia-deprecate-ubelt
        
        * https://github.com/WildbookOrg/wbia-deprecate-dtool
        
        * https://github.com/WildbookOrg/wbia-deprecate-guitool
        
        * https://github.com/WildbookOrg/wbia-deprecate-plottool
        
        * https://github.com/WildbookOrg/wbia-deprecate-detecttools
        
        * https://github.com/WildbookOrg/wbia-deprecate-plugin-humpbacktl
        
        * https://github.com/WildbookOrg/wbia-deprecate-tpl-lightnet
        
        * https://github.com/WildbookOrg/wbia-deprecate-tpl-brambox
        
        Plug-in Templates (Reference)
        
        * https://github.com/WildbookOrg/wbia-plugin-template
        
        * https://github.com/WildbookOrg/wbia-plugin-id-example
        
        Miscellaneous (Reference)
        
        * https://github.com/WildbookOrg/wbia-pypkg-build
        
        * https://github.com/WildbookOrg/wbia-project-website
        
        * https://github.com/WildbookOrg/wbia-aws-codedeploy
        
        Citation
        --------
        
        If you use this code or its models in your research, please cite:
        
        .. code:: text
        
            @inproceedings{crall2013hotspotter,
                title={Hotspotter — patterned species instance recognition},
                author={Crall, Jonathan P and Stewart, Charles V and Berger-Wolf, Tanya Y and Rubenstein, Daniel I and Sundaresan, Siva R},
                booktitle={2013 IEEE workshop on applications of computer vision (WACV)},
                pages={230--237},
                year={2013},
                organization={IEEE}
            }
        
            @inproceedings{parham2018animal,
                title={An animal detection pipeline for identification},
                author={Parham, Jason and Stewart, Charles and Crall, Jonathan and Rubenstein, Daniel and Holmberg, Jason and Berger-Wolf, Tanya},
                booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
                pages={1075--1083},
                year={2018},
                organization={IEEE}
            }
        
            @inproceedings{berger2015ibeis,
                title={IBEIS: Image-based ecological information system: From pixels to science and conservation},
                author={Berger-Wolf, TY and Rubenstein, DI and Stewart, CV and Holmberg, J and Parham, J and Crall, J},
                booktitle={Bloomberg Data for Good Exchange Conference, New York, NY, USA},
                volume={2},
                year={2015}
            }
        
            @article{berger2017wildbook,
                title={Wildbook: Crowdsourcing, computer vision, and data science for conservation},
                author={Berger-Wolf, Tanya Y and Rubenstein, Daniel I and Stewart, Charles V and Holmberg, Jason A and Parham, Jason and Menon, Sreejith and Crall, Jonathan and Van Oast, Jon and Kiciman, Emre and Joppa, Lucas},
                journal={arXiv preprint arXiv:1710.08880},
                year={2017}
            }
        
        Documentation
        -------------------------
        
        The WBIA API Documentation can be found here: https://wildbook-ia.readthedocs.io/en/latest/
        
        Code Style and Development Guidelines
        -------------------------------------
        
        Contributing
        ~~~~~~~~~~~~
        
        It's recommended that you use ``pre-commit`` to ensure linting procedures are run
        on any commit you make. (See also `pre-commit.com <https://pre-commit.com/>`_)
        
        Reference `pre-commit's installation instructions <https://pre-commit.com/#install>`_ for software installation on your OS/platform. After you have the software installed, run ``pre-commit install`` on the command line. Now every time you commit to this project's code base the linter procedures will automatically run over the changed files.  To run pre-commit on files preemtively from the command line use:
        
        .. code:: bash
        
            git add .
            pre-commit run
        
            # or
        
            pre-commit run --all-files
        
        Brunette
        ~~~~~~~~
        
        Our code base has been formatted by Brunette, which is a fork and more configurable version of Black (https://black.readthedocs.io/en/stable/).
        
        Flake8
        ~~~~~~
        
        Try to conform to PEP8.  You should set up your preferred editor to use flake8 as its Python linter, but pre-commit will ensure compliance before a git commit is completed.
        
        To run flake8 from the command line use:
        
        .. code:: bash
        
            flake8
        
        
        This will use the flake8 configuration within ``setup.cfg``,
        which ignores several errors and stylistic considerations.
        See the ``setup.cfg`` file for a full and accurate listing of stylistic codes to ignore.
        
        PyTest
        ~~~~~~
        
        Our code uses Google-style documentation tests (doctests) that uses pytest and xdoctest to enable full support.  To run the tests from the command line use:
        
        .. code:: bash
        
            pytest
        
        To run doctests with `+REQUIRES(--web-tests)` do:
        
        .. code:: bash
        
            pytest --web-tests
        
        .. |Build| image:: https://img.shields.io/github/workflow/status/WildbookOrg/wildbook-ia/Build%20and%20upload%20to%20PyPI/master
            :target: https://github.com/WildbookOrg/wildbook-ia/actions?query=branch%3Amaster+workflow%3A%22Build+and+upload+to+PyPI%22
            :alt: Build and upload to PyPI (master)
        
        .. |Pypi| image:: https://img.shields.io/pypi/v/wildbook-ia.svg
           :target: https://pypi.python.org/pypi/wildbook-ia
           :alt: Latest PyPI version
        
        .. |ReadTheDocs| image:: https://readthedocs.org/projects/wildbook-ia/badge/?version=latest
            :target: http://wildbook-ia.readthedocs.io/en/latest/
            :alt: Documentation on ReadTheDocs
        
        .. |Downloads| image:: https://img.shields.io/pypi/dm/wildbook-ia.svg
           :target: https://pypistats.org/packages/wildbook-ia
        
Keywords: wildbook,wildme,ibeis,ecological,wildlife,conservation,machine learning,ai,hotspotter,detection,classification,animal ID,re-id,re-identification,flukebook
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Environment :: GPU
Classifier: Environment :: GPU :: NVIDIA CUDA :: 11.0
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Unix
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Utilities
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6, <4
Description-Content-Type: text/x-rst
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: runtime
Provides-Extra: postgres
