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monodepth-cpp | Tensorflow C implementation for single-image depth | Computer Vision library

 by   yan99033 C++ Version: Current License: MIT

 by   yan99033 C++ Version: Current License: MIT

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kandi X-RAY | monodepth-cpp Summary

monodepth-cpp is a C++ library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. monodepth-cpp has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
Tensorflow C++ implementation for single-image depth estimation (Monodepth)
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • monodepth-cpp has a low active ecosystem.
  • It has 89 star(s) with 31 fork(s). There are 6 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 18 have been closed. On average issues are closed in 99 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of monodepth-cpp is current.
monodepth-cpp Support
Best in #Computer Vision
Average in #Computer Vision
monodepth-cpp Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • monodepth-cpp has 0 bugs and 0 code smells.
monodepth-cpp Quality
Best in #Computer Vision
Average in #Computer Vision
monodepth-cpp Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

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

license License

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

buildReuse

  • monodepth-cpp releases are not available. You will need to build from source code and install.
  • Installation instructions, examples and code snippets are available.
  • It has 215 lines of code, 15 functions and 2 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
monodepth-cpp Reuse
Best in #Computer Vision
Average in #Computer Vision
monodepth-cpp Reuse
Best in #Computer Vision
Average in #Computer Vision
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monodepth-cpp Key Features

Tensorflow C++ implementation for single-image depth estimation (Monodepth)

monodepth-cpp Examples and Code Snippets

See all related Code Snippets

Option 1

copy iconCopydownload iconDownload
chmod +x run_tf_docker.sh
./run_tf_docker.sh

Build (static/shared) library

copy iconCopydownload iconDownload
mkdir build && mkdir install
cd build
cmake -DCMAKE_INSTALL_PREFIX=../install ..
make && make install

Use (static/shared) library

copy iconCopydownload iconDownload
set(monodepth_INCLUDE_DIRS /path/to/monodepth-cpp/install/include)
INCLUDE_DIRECTORIES(
  ...
  monodepth_INCLUDE_DIRS
  ...
)

TARGET_LINK_LIBRARIES(awesome_exe /path/to/tensorflow/library/libtensorflow_cc.so) # Only if you are using the provided instructions
TARGET_LINK_LIBRARIES(awesome_exe /path/to/monodepth-cpp/install/lib/libmonodepth_static.a) # if you are using static library

See all related Code Snippets

Community Discussions

Trending Discussions on Computer Vision
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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 monodepth-cpp

NOTE: If you use the Docker container, this github repo will be mounted in /monodepth. Therefore, run cd monodepth in the container first. You will be seeing 'include' and 'lib' folders in the 'install' folder, import them in your project. To test if Monodepth C++ is working properly,. NOTE: Select either static or shared library in CMakeLists.txt, unless you want both of them.

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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