CocoaImageHashing

To use Carthage, create a Cartfile with the following entry:

github "ameingast/cocoaimagehashing" ~> 1.9.0

Then follow the standard Carthage workflow:

1. Run carthage update.
2. Drag the framework from Carthage/Build into Linked Frameworks on the General tab of your target.
3. Add carthage copy-frameworks to a Run Scripts phase.

To integrate CocoaImageHashing into your project using CocoaPods, add the following dependency to your Podfile:

pod 'CocoaImageHashing', :git => 'https://github.com/ameingast/cocoaimagehashing.git'

You can sort an NSArray of image data based on how similar each image is to a provided base image using sortedArray(usingImageSimilartyComparator:for:for:).

import CocoaImageHashing

let baseImage = Data()
let images = [Data(), Data(), Data()]
let sortedImages = imageHashing.sortedArray(usingImageSimilartyComparator: baseImage, 
                                            for: images, 
                                            for: .pHash)
print("Sorted images", sortedImages)

The OSImageHashing class is the primary entrypoint. You can compare two images (provided as NSData, NSImage, or UIImage) to see if they are perceptually similar using a specific hashing algorithm (e.g., .pHash).

import CocoaImageHashing

let firstImageData = Data()
let secondImageData = Data()
let result = OSImageHashing.sharedInstance().compareImageData(firstImageData,
                                                              to: secondImageData,
                                                              with: .pHash)
print("Match", result)

The framework provides methods to concurrently find similar elements within an array or a data stream. This is useful for de-duplication or finding matches in large datasets.

import CocoaImageHashing

var imageData = [Data(), Data(), Data()]
let similarImages = imageHashing.similarImages(withProvider: .pHash) { 
    if imageData.count > 0 {
        let data = imageData.removeFirst()
        return OSTuple<NSString, NSData>(first: name as NSString, 
                                         andSecond: data as NSData)
    } else {
        return nil
    }
}
print("Similar Images", similarImages)

You can calculate the perceptual distance between two existing fingerprints (OSHashType) using hashDistance. This is useful for quantifying how similar two images are.

import CocoaImageHashing

let lhsData = OSImageHashing.sharedInstance().hashImageData(Data(), with: .pHash)
let rhsData = OSImageHashing.sharedInstance().hashImageData(Data(), with: .pHash)
let result = OSImageHashing.sharedInstance().hashDistance(lhsData, to: rhsData, with: .pHash)
print("Distance", result)

The framework provides three main hashing functions. Choose based on your requirements for speed vs. accuracy:

The framework uses the following fundamental types for its API: