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.

pip install PhotoHash

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')

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')

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)

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)

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.