SeamCarving | Image processing method that allows to remove an object | Computer Vision library

by zavolokas C# Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | SeamCarving Summary

SeamCarving is a C# library typically used in Artificial Intelligence, Computer Vision applications. SeamCarving has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Seam carving (or liquid rescaling) is an algorithm for content-aware image resizing. It functions by establishing a number of seams (paths of least importance) in an image and automatically removes seams to reduce image size or inserts seams to extend it. Seam carving also allows manually defining areas in which pixels may not be modified, and features the ability to remove whole objects from photographs. This method is known as Image Retargeting.

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Quality

Security

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Support

SeamCarving has a low active ecosystem.

It has 95 star(s) with 6 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SeamCarving is current.

Quality

SeamCarving has 0 bugs and 0 code smells.

Security

SeamCarving has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SeamCarving code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SeamCarving is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

SeamCarving releases are not available. You will need to build from source code and install.

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SeamCarving Key Features

No Key Features are available at this moment for SeamCarving.

SeamCarving Examples and Code Snippets

No Code Snippets are available at this moment for SeamCarving.

Community Discussions

Trending Discussions on SeamCarving

How do you use Web Workers with Webpack 5?

CMake CUDA C++ Linking Error - Undefined reference to `someFunction()`

Calculating Dual-Energy Gradient using Numpy

QUESTION

How do you use Web Workers with Webpack 5?

Asked 2021-Mar-19 at 18:36

I've been struggling to find the best way to use Web Workers while utilizing Webpack 5 and typescript. I try to follow the documentation, however, when I try to create a Web Worker, I am experiencing the following error:

...

ANSWER

Answered 2021-Mar-19 at 18:36

I figured out my problem,

Incorrect output block:

Source https://stackoverflow.com/questions/66710729

QUESTION

CMake CUDA C++ Linking Error - Undefined reference to `someFunction()`

Asked 2020-Sep-25 at 09:02

I am trying to call a CUDA kernel from a C++ program. I am using CMake to manage the build process.

CMakeLists.txt

...

ANSWER

Answered 2020-Sep-25 at 09:02

MyLibrary is not a variable, it's a CMake target. The syntax ${variable} is used to substitute the contents of a variable. The correct syntax to create a dependency to MyLibrary is this:

target_link_libraries( myProject PRIVATE MyLibrary )

The syntax is probably correct in the case of ${OpenCV_LIBS}. It's common practice that packages define a variable that contains the names of the libraries that need to be included to use the package. So in your case, ${OpenCV_LIBS} might evaluate to something like "OpenCV_lib1 OpenCV_lib2".

A few other improvements you could make:

It's a bad idea to GLOB your source files. The glob will be evaluated at configure-time, and if you add files to your directory without re-running CMake configuration, they will not be taken in consideration. The canonical way AFAIK is to just list your sources explicitly.
Consider using target_include_directories instead of include_directories, to limit the effect to just the appropriate target.

Source https://stackoverflow.com/questions/64059085

QUESTION

Calculating Dual-Energy Gradient using Numpy

Asked 2018-Feb-25 at 15:47

I have the definition of "dual-energy gradient" (Reference) of pixel (x, y) is Δ^2(x, y) + Δy^2(x, y), where the square of the x-gradient Δx^2(x, y) = Rx(x, y)^2 + Gx(x, y)^2 + Bx(x, y)^2 and where the central differences Rx(x, y), Gx(x, y), and Bx(x, y) are the absolute value in differences of red, green, and blue components between pixel (x + 1, y) and pixel (x − 1, y) and similar for y-gradient.

How do I calculate this efficiently using numpy?

I am not sure if the numpy.gradient can help me out of box.

...

ANSWER

Answered 2018-Feb-25 at 13:53

Assuming I understood your formulas correctly, here's what I would suggest:

Source https://stackoverflow.com/questions/48973768

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SeamCarving

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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