Go implementation of multiple perceptual hash algorithms for images.

Documentation

Full documentation lives on the wiki. It covers installation, a quick start guide, a per-algorithm reference (options, defaults, references, examples) for every hasher shipped with the library, an API reference for the Hasher / Comparer interfaces and the similarity distance functions, conceptual guides on hash types and interpolation methods, and practical guides for choosing an algorithm, comparing hashes, real-world examples, and migrating from v1 to v2.

Installing

go get -u github.com/ajdnik/imghash/v2

import "github.com/ajdnik/imghash/v2"

Most consumers only need the top-level imghash package. Core types (Hash, Binary, UInt8, Float64, Distance) are re-exported there.

Quick Start

If you're unsure which hash to pick, start with PDQ.

package main

import (
  "fmt"

  "github.com/ajdnik/imghash/v2"
)

func main() {
  pdq, err := imghash.NewPDQ()
  if err != nil {
    panic(err)
  }

  h1, err := imghash.HashFile(pdq, "image1.png")
  if err != nil {
    panic(err)
  }

  h2, err := imghash.HashFile(pdq, "image2.png")
  if err != nil {
    panic(err)
  }

  dist, err := pdq.Compare(h1, h2)
  if err != nil {
    panic(err)
  }

  fmt.Printf("Distance: %v\n", dist)
}

License

Imghash is released under the MIT license. See LICENSE.