dart_imagehash #

A Dart package for perceptual image hashing, inspired by the Python imagehash library.

🌐 Project Website

Image hashing algorithms generate compact, fixed-length fingerprints from images that allow you to:

• Find visually similar images (even with small transformations)
• Detect duplicate or near-duplicate images
• Perform content-based image retrieval

Features #

This package provides multiple image hashing algorithms:

• Average Hash (aHash)
• Perceptual Hash (pHash)
• Difference Hash (dHash) (both horizontal and vertical)
• Wavelet Hash (wHash)

Getting started #

Add dart_imagehash to your project:

dart pub add dart_imagehash

This will automatically add the latest version to your pubspec.yaml file.

Usage #

import 'dart:io';
import 'package:image/image.dart' as img;
import 'package:dart_imagehash/dart_imagehash.dart';

void main() {
  // Load images
  final image1 = img.decodeImage(File('image1.jpg').readAsBytesSync())!;
  final image2 = img.decodeImage(File('image2.jpg').readAsBytesSync())!;

  // Calculate hashes using the ImageHasher utility class
  final hash1 = ImageHasher.averageHash(image1);
  final hash2 = ImageHasher.averageHash(image2);

  // Calculate the similarity (0-1, where 1 is identical)
  final hashDistance = hash1 - hash2;
  final similarity = 1.0 - (hashDistance / hash1.length);

  print('Hash 1: ${hash1}');
  print('Hash 2: ${hash2}');
  print('Distance: $hashDistance');
  print('Similarity: ${(similarity * 100).toStringAsFixed(2)}%');

  // Try other hashing algorithms
  final pHash1 = ImageHasher.perceptualHash(image1);
  final pHash2 = ImageHasher.perceptualHash(image2);
  print('Perceptual Hash distance: ${pHash1 - pHash2}');

  // Calculate hashes directly from bytes
  final bytes1 = File('image1.jpg').readAsBytesSync();
  final bytes2 = File('image2.jpg').readAsBytesSync();

  final hashFromBytes1 = ImageHasher.averageHashFromBytes(bytes1);
  final hashFromBytes2 = ImageHasher.averageHashFromBytes(bytes2);
  print('Hash from bytes distance: ${hashFromBytes1 - hashFromBytes2}');
}

For a more comprehensive example that demonstrates comparing images with all four hash algorithms, check out the example included in this package. You can also try the interactive demo to see the algorithms in action. The example demonstrates how to:

• Calculate and compare hashes for similar and different images
• Calculate hashes directly from image bytes
• Display similarity percentages for each algorithm
• Handle file loading with relative paths

Hash Comparison #

Image hashes are compared using the Hamming distance - the number of bits that differ between two hashes. A smaller distance indicates more similar images.

// Get the Hamming distance
int distance = hash1 - hash2;

// Boolean comparison (only true if exactly equal)
bool identical = hash1 == hash2;

// Convert hash to hex string
String hexString = hash1.toString(); // or hash1.toHex()

// Create hash from hex string
var hash = ImageHash.fromHex('f8e0a060c020f8e0');

Additional information #

This package is a Dart implementation of the algorithms found in the Python imagehash library. For more information about the theory behind perceptual hashing, see:

• Perceptual Hashing
• pHash.org
• Difference Hashing