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FaceKit | Implementations of PCN and other facerelated algorithms | Computer Vision library

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kandi X-RAY | FaceKit Summary

FaceKit is a C++ library typically used in Artificial Intelligence, Computer Vision, Pytorch, OpenCV applications. FaceKit has no bugs, it has no vulnerabilities and it has medium support. However FaceKit has a Non-SPDX License. You can download it from GitHub.
Implementations of PCN (an accurate real-time rotation-invariant face detector) and other face-related algorithms

kandi-support Support

  • FaceKit has a medium active ecosystem.
  • It has 1031 star(s) with 298 fork(s). There are 72 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 27 open issues and 71 have been closed. On average issues are closed in 25 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of FaceKit is current.

quality kandi Quality

  • FaceKit has no bugs reported.


  • FaceKit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

license License

  • FaceKit has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.


  • FaceKit releases are not available. You will need to build from source code and install.
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FaceKit Key Features

Implementations of PCN (an accurate real-time rotation-invariant face detector) and other face-related algorithms

FaceKit Examples and Code Snippets

No Code Snippets are available at this moment for FaceKit.Refer to component home page for details.

No Code Snippets are available at this moment for FaceKit.Refer to component home page for details.

Community Discussions

Trending Discussions on Computer Vision
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Trending Discussions on Computer Vision


Image similarity in swift

Asked 2022-Mar-25 at 11:42

The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
  try requestHandler.perform([request])
  return request.results?.first as? VNFeaturePrintObservation
} catch {
  print("Vision error: \(error)")
  return nil
 let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)


Answered 2022-Mar-25 at 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.


A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

1-(min(distance, 10)/10)*100

Here the artificial ceiling would be 10, but it can be any arbitrary number.

Source https://stackoverflow.com/questions/71615277

Community Discussions, Code Snippets contain sources that include Stack Exchange Network


No vulnerabilities reported

Install FaceKit

You can download it from GitHub.


For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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