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stereo_sparse_depth_fusion | ICRA 2019 Repository for Real Time Dense | Computer Vision library

 by   ShreyasSkandanS C++ Version: Current License: GPL-3.0

 by   ShreyasSkandanS C++ Version: Current License: GPL-3.0

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kandi X-RAY | stereo_sparse_depth_fusion Summary

stereo_sparse_depth_fusion is a C++ library typically used in Artificial Intelligence, Computer Vision applications. stereo_sparse_depth_fusion has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.
ICRA 2019 | Repository for "Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements" | OpenCV, C++
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Security
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kandi-support Support

  • stereo_sparse_depth_fusion has a low active ecosystem.
  • It has 136 star(s) with 40 fork(s). There are 9 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 5 have been closed. On average issues are closed in 3 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of stereo_sparse_depth_fusion is current.
This Library - Support
Best in #Computer Vision
Average in #Computer Vision
This Library - Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • stereo_sparse_depth_fusion has no bugs reported.
This Library - Quality
Best in #Computer Vision
Average in #Computer Vision
This Library - Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • stereo_sparse_depth_fusion has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
This Library - Security
Best in #Computer Vision
Average in #Computer Vision
This Library - Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • stereo_sparse_depth_fusion is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
This Library - License
Best in #Computer Vision
Average in #Computer Vision
This Library - License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • stereo_sparse_depth_fusion releases are not available. You will need to build from source code and install.
  • Installation instructions are not available. Examples and code snippets are available.
This Library - Reuse
Best in #Computer Vision
Average in #Computer Vision
This Library - Reuse
Best in #Computer Vision
Average in #Computer Vision
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stereo_sparse_depth_fusion Key Features

ICRA 2019 | Repository for "Real Time Dense Depth Estimation by Fusing Stereo with Sparse Depth Measurements" | OpenCV, C++

stereo_sparse_depth_fusion Examples and Code Snippets

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Reference

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@inproceedings{shivakumar2019real,
  title={Real time dense depth estimation by fusing stereo with sparse depth measurements},
  author={Shivakumar, Shreyas S and Mohta, Kartik and Pfrommer, Bernd and Kumar, Vijay and Taylor, Camillo J},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={6482--6488},
  year={2019},
  organization={IEEE}
}

Dependencies

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sudo apt-get install libpng++-dev

Testing Code

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mkdir build
cd build
cmake ..
make -j

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Community Discussions

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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 stereo_sparse_depth_fusion

You can download it from GitHub.

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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