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Face-Track-Detect-Extract | Detect , track and extract the optimal face | Computer Vision library

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kandi X-RAY | Face-Track-Detect-Extract Summary

Face-Track-Detect-Extract is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, OpenCV applications. Face-Track-Detect-Extract has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
πŸ’Ž Detect , track and extract the optimal face in multi-target faces (exclude side face and select the optimal face).

kandi-support Support

  • Face-Track-Detect-Extract has a low active ecosystem.
  • It has 450 star(s) with 176 fork(s). There are 24 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 8 open issues and 16 have been closed. On average issues are closed in 9 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Face-Track-Detect-Extract is current.

quality kandi Quality

  • Face-Track-Detect-Extract has 0 bugs and 0 code smells.


  • Face-Track-Detect-Extract has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Face-Track-Detect-Extract code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.

license License

  • Face-Track-Detect-Extract is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.


  • Face-Track-Detect-Extract releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA

kandi has reviewed Face-Track-Detect-Extract and discovered the below as its top functions. This is intended to give you an instant insight into Face-Track-Detect-Extract implemented functionality, and help decide if they suit your requirements.

  • Bulk detect faces .
  • Run the main function .
  • r Detects the face of a given image .
  • Update the matchers .
  • Associate detections to trackers and trackers .
  • Convolutional convolution .
  • Pad boxes to square
  • Computes the NMS of the given boxes .
  • Initialize Kalman filter .
  • Parse command line arguments .

Face-Track-Detect-Extract Key Features

πŸ’Ž Detect , track and extract the optimal face in multi-target faces (exclude side face and select the optimal face).

Face-Track-Detect-Extract Examples and Code Snippets

  • Run


python3 start.py

Community Discussions

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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 Face-Track-Detect-Extract

You can download it from GitHub.
You can use Face-Track-Detect-Extract like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


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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