========= 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.hashes_are_similar(hash_one, hash_two)

hashes_are_similar also takes the same optional tolerance argument that is_look_alike does.