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deepgaze | Computer Vision library for humancomputer interaction | Computer Vision library

 by   mpatacchiola Python Version: Current License: MIT

 by   mpatacchiola Python Version: Current License: MIT

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kandi X-RAY | deepgaze Summary

deepgaze is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, OpenCV applications. deepgaze has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
Deepgaze is a library for human-computer interaction, people detection and tracking which uses Convolutional Neural Networks (CNNs) for face detection, head pose estimation and classification. The focus of attention of a person can be approximately estimated finding the head orientation. This is particularly useful when the eyes are covered, or when the user is too far from the camera to grab the eye region with a good resolution. When the eye region is visible it is possible to estimate the gaze direction, which is much more informative and can give a good indication of the FOA. Deepgaze contains useful packages for:. Deepgaze is based on OpenCV and Tensorflow, some of the best libraries in computer vision and machine learning. Deepgaze is an open source project and any contribution is appreciated, feel free to fork the repository and propose integrations.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • deepgaze has a medium active ecosystem.
  • It has 1489 star(s) with 441 fork(s). There are 101 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 8 open issues and 76 have been closed. On average issues are closed in 59 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of deepgaze is current.
deepgaze Support
Best in #Computer Vision
Average in #Computer Vision
deepgaze Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • deepgaze has no bugs reported.
deepgaze Quality
Best in #Computer Vision
Average in #Computer Vision
deepgaze Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • deepgaze has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
deepgaze Security
Best in #Computer Vision
Average in #Computer Vision
deepgaze Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • deepgaze is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
deepgaze License
Best in #Computer Vision
Average in #Computer Vision
deepgaze License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • deepgaze 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, examples and code snippets are available.
deepgaze Reuse
Best in #Computer Vision
Average in #Computer Vision
deepgaze Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

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

  • Creates a csv file .
  • Create aloo file from a csv file .
  • Initialize Yaw layer .
  • Returns the roll pitch of an image .
  • Returns the face position of the input image
  • Resample the particles .
  • Find the face of the given image .
  • Calculate the histogram for each channel .
  • Creates a list of matching patterns
  • Calculates the NMS of a box .

deepgaze Key Features

Head pose estimation (Perspective-n-Point, Convolutional Neural Networks)

Face detection (Haar Cascade)

Skin and color detection (Range detection, Backprojection)

Histogram-based classification (Histogram Intersection)

Motion detection (Frame differencing, MOG, MOG2)

Motion tracking (Particle filter)

Saliency map (FASA)

Community Discussions

Trending Discussions on Computer Vision
  • Image similarity in swift
  • When using pandas_profiling: "ModuleNotFoundError: No module named 'visions.application'"
  • Classify handwritten text using Google Cloud Vision
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  • Fastest way to get the RGB average inside of a non-rectangular contour in the CMSampleBuffer
  • UIViewController can't override method from it's superclass
  • X and Y-axis swapped in Vision Framework Swift
  • Swift's Vision framework not recognizing Japanese characters
  • Boxing large objects in image containing both large and small objects of similar color and in high density from a picture
  • Create a LabVIEW IMAQ image from a binary buffer/file with and without NI Vision
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 deepgaze

ATTENTION: this version is obsolete, please check the branch 2.0 on this repository.

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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