img_hash [![Build Status](https://travis-ci.org/abonander/img_hash.svg?branch=master)](https://travis-ci.org/abonander/img_hash) [![Crates.io shield](https://img.shields.io/crates/v/img_hash.svg)](https://crates.io/crates/img_hash)
========

##### Now builds on stable Rust! (But needs nightly to bench.)

A library for getting perceptual hash values of images.

Thanks to Dr. Neal Krawetz for the outlines of the Mean (aHash), Gradient (dHash), and DCT (pHash) perceptual hash algorithms:  
http://www.hackerfactor.com/blog/?/archives/432-Looks-Like-It.html (Accessed August 2014)

Also provides an implementation of [the Blockhash.io algorithm](http://blockhash.io).

This crate can operate directly on buffers from the [PistonDevelopers/image][1] crate.

[1]: https://github.com/PistonDevelopers/image 

Usage
=====
[Documentation](https://docs.rs/img_hash)


Add `img_hash` to your `Cargo.toml`:

    [dependencies.img_hash]
    version = "3.0"
    
Example program:

```rust
 extern crate image;
 extern crate img_hash;
 
 use img_hash::{HasherConfig, HashAlg};

 fn main() {
     let image1 = image::open("image1.png").unwrap();
     let image2 = image::open("image2.png").unwrap();
     
     let hasher = HasherConfig::new().to_hasher();

     let hash1 = hasher.hash_image(&image1);
     let hash2 = hasher.hash_image(&image2);
     
     println!("Image1 hash: {}", hash1.to_base64());
     println!("Image2 hash: {}", hash2.to_base64());
     
     println!("Hamming Distance: {}", hash1.dist(&hash2));
 }
```
   
Benchmarking
============

In order to build and test on Rust stable, the benchmarks have to be placed behind a feature gate. If you have Rust nightly installed and want to run benchmarks, use the following command:

```
cargo bench --features bench
```

## License

Licensed under either of

 * Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
 * MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)

at your option.

### Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any
additional terms or conditions.