Near duplicates and resized images can be found with the package.

Demo: similar image clustering (deployed from).

Semantic versions:

• v1/v2 (/images),
• v3 (/images3) - this repository,
• v4 (/images4) - latest recommended.

All versions will be kept available indefinitely.

Release note (v3): (1) Hashes get proper "hashy" meaning. If you work with millions of images, do preliminary image comparison with hash tables. (2) Renamed functions. What used to be Hash now becomes Icon to reflect (1).

Func Similar gives a verdict whether 2 images are similar with well-tested default thresholds.

Func EucMetric can be used instead, when you need different precision or want to sort by similarity. Func PropMetric can be used for customization of image proportion threshold.

Func Open supports JPEG, PNG and GIF. But other image types are possible through third-party libraries, because func Icon input is image.Image.

For search in billions of images, use a hash table for preliminary filtering (see the 2nd example below).

Go doc for code reference.

The only dependency is my hyper package, which in turn does not have any dependencies. If you are not using hashes, you can remove this dependency by deleting files [hashes.go, hashes_test.go] from your fork.

package main

import (
	"fmt"
	"github.com/vitali-fedulov/images3"
)

func main() {

	// Photos to compare.
	path1 := "1.jpg"
	path2 := "2.jpg"

	// Open photos (ignoring errors here).
	img1, _ := images3.Open(path1)
	img2, _ := images3.Open(path2)

	// Icons are compact image representations.
	icon1 := images3.Icon(img1, path1)
	icon2 := images3.Icon(img2, path2)

	// Comparison.
	if images3.Similar(icon1, icon2) {
		fmt.Println("Images are similar.")
	} else {
		fmt.Println("Images are distinct.")
	}
}

Detailed explanation, also as a PDF.

Summary: Images are resized in a special way to squares of fixed size called "icons". Euclidean distance between the icons is used to give the similarity verdict. Also image proportions are used to avoid matching images of distinct shape.

To increase precision you can either use your own thresholds in func EucMetric (and PropMetric) OR generate icons for image sub-regions and compare those icons.

To speedup file processing you may want to generate icons for available image thumbnails. Specifically, many JPEG images contain EXIF thumbnails, you could considerably speedup the reads by using decoded thumbnails to feed into func Icon. External packages to read thumbnails: 1 and 2. A note of caution: in rare cases there could be issues with thumbnails not matching image content. EXIF standard specification: 1 and 2.

Hash-based comparison provides fast and RAM-friendly rough approximation of image similarity, when you need to process millions of images. After matching hashes use func Similar to get the final verdict. The demo shows only the hash-based similarity testing in its simplified form (without using actual hash table).

package main

import (
	"fmt"
	"github.com/vitali-fedulov/images3"
)

func main() {

	// Paths to photos.
	path1 := "1.jpg"
	path2 := "2.jpg"

	// Hyper space parameters.
	epsPct := 0.25
	numBuckets := 4
	
	// Open photos (skipping error handling for clarity).
	img1, _ := images3.Open(path1)
	img2, _ := images3.Open(path2)

	// Make icons. They are image representations for comparison.
	icon1 := images3.Icon(img1, path1)
	icon2 := images3.Icon(img2, path2)


	// Hash table values.

	// Value to save to the hash table as a key with corresponding
	// image ids. Table structure: map[centralHash][]imageId.
	// imageId is simply an image number in a directory tree.
	centralHash := images3.CentralHash(
		icon1, images3.HyperPoints10, epsPct, numBuckets)

	// Hash set to be used as a query to the hash table. Each hash from
	// the hashSet has to be checked against the hash table.
	// See more info in the package "hyper" README.
	hashSet := images3.HashSet(
		icon2, images3.HyperPoints10, epsPct, numBuckets)

	// Checking hash matches. In full implementation this will
	// be done on the hash table map[centralHash][]imageId.
	foundSimilarImage := false
	for _, hash := range hashSet {
		if centralHash == hash {
			foundSimilarImage = true
			break
		}
	}

	// Image comparison result.
	if foundSimilarImage {
		fmt.Println("Images are similar.")
	} else {
		fmt.Println("Images are distinct.")
	}
}