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DBFace | singlestage detector for face detection, with faster speed | Computer Vision library

 by   dlunion Python Version: Current License: No License

 by   dlunion Python Version: Current License: No License

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kandi X-RAY | DBFace Summary

DBFace is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow, OpenCV applications. DBFace has no bugs, it has no vulnerabilities and it has medium support. However DBFace build file is not available. You can download it from GitHub.
DBFace is a real-time, single-stage detector for face detection, with faster speed and higher accuracy.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • DBFace has a medium active ecosystem.
  • It has 1132 star(s) with 265 fork(s). There are 39 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 35 open issues and 15 have been closed. On average issues are closed in 13 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of DBFace is current.
DBFace Support
Best in #Computer Vision
Average in #Computer Vision
DBFace Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • DBFace has 0 bugs and 0 code smells.
DBFace Quality
Best in #Computer Vision
Average in #Computer Vision
DBFace Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • DBFace has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • DBFace code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
DBFace Security
Best in #Computer Vision
Average in #Computer Vision
DBFace Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • DBFace does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
DBFace License
Best in #Computer Vision
Average in #Computer Vision
DBFace License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • DBFace releases are not available. You will need to build from source code and install.
  • DBFace has no build file. You will be need to create the build yourself to build the component from source.
  • DBFace saves you 1271 person hours of effort in developing the same functionality from scratch.
  • It has 2857 lines of code, 246 functions and 20 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
DBFace Reuse
Best in #Computer Vision
Average in #Computer Vision
DBFace Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

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

  • Get an image at a given index .
  • Compute ground truth curve .
  • Interpolate a tensor .
  • Train a single epoch .
  • Augment image with crop scale
  • Loads the boxes from a text file .
  • Detects images using netout .
  • Detects the image using the given model .
  • Evaluate the prediction .
  • Transforms the given matrix .

DBFace Key Features

DBFace is a real-time, single-stage detector for face detection, with faster speed and higher accuracy

Community Discussions

Trending Discussions on Computer Vision
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  • Boxing large objects in image containing both large and small objects of similar color and in high density from a picture
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Trending Discussions on Computer Vision

QUESTION

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!)

ANSWER

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.

1-(distance/Float.greatestFiniteMagnitude)*100

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

Vulnerabilities

No vulnerabilities reported

Install DBFace

You can download it from GitHub.
You can use DBFace 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.

Support

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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