img_hash [](https://travis-ci.org/cybergeek94/img-hash)
========
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)
Thanks to Emil Mikulic for the 2D Discrete Cosine Transform implementation in C, ported to Rust in `src/dct.rs`:
http://unix4lyfe.org/dct/ (Implementation: http://unix4lyfe.org/dct/listing2.c) (Accessed August 2014)
With the `rust-image` feature, this crate can operate directly on buffers from the [PistonDevelopers/image][1] crate.
[1]: https://github.com/PistonDevelopers/image
Usage
=====
[Documentation on Rust-CI](http://rust-ci.org/cybergeek94/img_hash/doc/img_hash/index.html)
Add `img_hash` to your `Cargo.toml`:
[dependencies.img_hash]
git = "https://github.com/cybergeek94/img_hash"
# For interop with `image`:
features = ["rust-image"]
Example program:
```rust
extern crate image;
extern crate img_hash;
use img_hash::{ImageHash, HashType};
fn main() {
let image1 = image::open(&Path::new("image1.png")).unwrap();
let image2 = image::open(&Path::new("image2.png")).unwrap();
// These two lines produce hashes with 64 bits (8 ** 2),
// using the Gradient hash, a good middle ground between
// the performance of Mean and the accuracy of DCT.
let hash1 = ImageHash::hash(&image1, 8, HashType::Gradient);
let hash2 = ImageHash::hash(&image2, 8, HashType::Gradient);
println!("Image1 hash: {}", hash1.to_base64());
println!("Image2 hash: {}", hash2.to_base64());
println!("% Difference: {}", hash1.dist_ratio(&hash2));
}
```