A Python Perceptual Image Hashing Module

## Project description

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.

## Project details

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