Metadata-Version: 2.1
Name: wbia-pyhesaff
Version: 4.0.0
Summary: PyHesaff - Python wrapper for Hessian Affine
Home-page: https://github.com/WildbookOrg/hesaff
Author: Krystian Mikolajczyk, Michal Perdoch, Jon Crall, Avi Weinstock, WildMe Developers
Author-email: dev@wildme.org
License: APL 2.0
Description: ======================
        Wildbook IA - PyHesaff
        ======================
        
        |Build| |Pypi| |ReadTheDocs|
        
        Hessian Affine + SIFT keypoints in Python! - Part of the WildMe / Wildbook IA Project.
        
        This is an implementation of Hessian-Affine detector.
        
        The implementation uses a Lowe's (Lowe 1999, Lowe 2004) like pyramid
        to sample Gaussian scale-space and localizes local extrema of the
        Detetminant of Hessian Matrix operator computed on normalized
        derivatives. Then a Baumberg-Lindeberg discovery of a local affine
        shape is employed (Lindeberg 1998, Baumberg 2000, Mikolajzyk 2002) to
        compute affine shape of each det of Hessian extrema. Finally a local
        neighbourhood is normalized to a fixed size patch and SIFT
        descriptor(Lowe 1999, Lowe 2004) computed.
        
        
        IMPLEMENTATION
        --------------
        
        Implementation depends on OpenCV (2.3.1+). Although, the code is
        original, the affine iteration and normalization was derived from the
        code of Krystian Mikolajczyk.
        
        The SIFT descriptor code was patented under a US Patent 6,711,293, which
        expired on March 7th 2019, so the license is no longer required for use.
        
        
        OUTPUT
        ------
        
        NOTE THIS IS NO LONGER THE CASE. WE MAY REINSTATE THIS.
        
        The built binary rewrites output file: <input_image_name>.hesaff.sift
        
        The output format is compatible with the binaries available from the
        page "Affine Covariant Features". The geometry of an affine region is
        specified by: u,v,a,b,c in a(x-u)(x-u)+2b(x-u)(y-v)+c(y-v)(y-v)=1. The
        top left corner of the image is at (u,v)=(0,0). The geometry of an
        affine region is followed by N descriptor values (N = 128).
        
        File format:
        
        ::
        
            N
            m
            u1 v1 a1 b1 c1 d1(1) d1(2) d1(3) ... d1(N)
                  :
                  :
            um vm am bm cm dm(1) dm(2) dm(3) ... dm(N)
        
        
        PROPER USE
        ----------
        
        If you use this code, please refer to
        
        Perdoch, M. and Chum, O. and Matas, J.: Efficient Representation of
        Local Geometry for Large Scale Object Retrieval. In proceedings of
        CVPR09. June 2009.
        
        TBD: A reference to technical report describing the details and some
        retrieval results will be placed here.
        
        
        NOTES
        -----
        
        Requires opencv. On ubuntu you can: ``sudo apt-get install libopencv-dev``. You can also build / use wheels. If cmake is unable to find OpenCV, manually set the location of OpenCV to the OpenCV_DIR environment variable
        
        
        .. |Build| image:: https://img.shields.io/github/workflow/status/WildMeOrg/wbia-tpl-pyhesaff/Build%20and%20upload%20to%20PyPI/main
            :target: https://github.com/WildMeOrg/wbia-tpl-pyhesaff/actions?query=branch%3Amain+workflow%3A%22Build+and+upload+to+PyPI%22
            :alt: Build and upload to PyPI (main)
        
        .. |Pypi| image:: https://img.shields.io/pypi/v/wbia-pyhesaff.svg
           :target: https://pypi.python.org/pypi/wbia-pyhesaff
           :alt: Latest PyPI version
        
        .. |ReadTheDocs| image:: https://readthedocs.org/projects/wbia-tpl-pyhesaff/badge/?version=latest
            :target: http://wbia-tpl-pyhesaff.readthedocs.io/en/latest/
            :alt: Documentation on ReadTheDocs
        
Platform: UNKNOWN
Classifier: Development Status :: 6 - Mature
Classifier: License :: OSI Approved :: BSD 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: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Description-Content-Type: text/x-rst
Provides-Extra: all
Provides-Extra: tests
Provides-Extra: build
Provides-Extra: runtime
