Metadata-Version: 1.1
Name: Photohash
Version: 0.4.0
Summary: A Python Perceptual Image Hashing Module
Home-page: https://github.com/bunchesofdonald/photohash
Author: Chris Pickett
Author-email: chris.pickett@gmail.com
License: MIT
Description: =========
        PhotoHash
        =========
        
        .. image:: https://travis-ci.org/bunchesofdonald/django-hermes.svg?branch=master
            :target: https://travis-ci.org/bunchesofdonald/photohash
        
        This was mainly created just for my own use and education. It's a perceptual
        hash algorithm, used to find if two images are similar.
        
        Installation
        ============
        
        ::
        
            pip install PhotoHash
        
        
        Usage
        =====
        
        average_hash
        ------------
        Returns the hash of the image using an average hash algorithm. This algorithm
        compares each pixel in the image to the average value of all the pixels.::
        
            import photohash
            hash = photohash.average_hash('/path/to/myimage.jpg')
        
        distance
        --------
        Returns the hamming distance between the average_hash of the given images.::
        
            import photohash
            distance = photohash.distance('/path/to/myimage.jpg', '/path/to/myotherimage.jpg')
        
        is_look_alike
        -------------
        Returns a boolean of whether or not the photos look similar.::
        
            import photohash
            similar = photohash.is_look_alike('/path/to/myimage.jpg', '/path/to/myotherimage.jpg')
        
        is_look_alike also takes an optional tolerance argument that defines how strict
        the comparison should be.::
        
            import photohash
            similar = photohash.is_look_alike('/path/to/myimage.jpg', '/path/to/myimage.jpg', tolerance=3)
        
        hash_distance
        -------------
        Returns the hamming distance between two hashes of the same length::
        
            import photohash
            hash_one = average_hash('/path/to/myimage.jpg')
            hash_two = average_hash('/path/to/myotherimage.jpg')
            distance = photohash.hash_distance(hash_one, hash_two)
        
        hashes_are_similar
        ------------------
        Returns a boolean of whether or not the two hashes are within the given tolerance. Same as
        is_look_alike, but takes hashes instead of image paths::
        
            import photohash
            hash_one = average_hash('/path/to/myimage.jpg')
            hash_two = average_hash('/path/to/myotherimage.jpg')
            similar = photohash.hash_are_similar(hash_one, hash_two)
        
        hashes_are_similar also takes the same optional tolerance argument that is_look_alike does.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Image Recognition
