hough_transform | Hough Transform implementation in Python | Build Tool library

by alyssaq Python Version: Current License: No License

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kandi X-RAY | hough_transform Summary

hough_transform is a Python library typically used in Utilities, Build Tool, Numpy applications. hough_transform has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitLab, GitHub.

Hough Transform implementation in Python

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Quality

Security

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Support

hough_transform has a low active ecosystem.

It has 82 star(s) with 43 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 1 have been closed. On average issues are closed in 5 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of hough_transform is current.

Quality

hough_transform has 0 bugs and 0 code smells.

Security

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

hough_transform code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

hough_transform 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.

Reuse

hough_transform 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 are not available. Examples and code snippets are available.

hough_transform saves you 45 person hours of effort in developing the same functionality from scratch.

It has 121 lines of code, 11 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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hough_transform Key Features

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hough_transform Examples and Code Snippets

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Community Discussions

Trending Discussions on hough_transform

Hough Line Transform implementation

opencv python is faster than c++?

QUESTION

Hough Line Transform implementation

Asked 2018-Aug-16 at 17:07

I am trying to implement Hough Line Transform.

Input. I am using the following image as input. This single line is expected to produce only one intersection of sine waves in the output.

Desired behavior. my source code is expected to produce the following output as it was generated by the sample application of AForge framework.

Here, we can see:

the dimension of the output is identical to the input image.
the intersection of sine waves are seen at almost at the center.
the intersection pattern of waves is very small and simple.

Present behavior. My source code is producing the following output which is different than that of the output generated by AForge.

the intersection is not at the center.
the wave patterns are also different.

Why is my code producing a different output?

Source Code

I have written the following code myself. The following is a Minimal, Complete, and Verifiable source code.

...

ANSWER

Answered 2018-Aug-16 at 17:07

I have solved the problem from this link. The source code from this link is the best one I have ever came across.

Source https://stackoverflow.com/questions/51868362

QUESTION

opencv python is faster than c++?

Asked 2018-Mar-22 at 15:32

I am trying to time the houghcircle in python and c++ to see if c++ gives edge over processing time (intuitively it should!)

Versions

python: 3.6.4
gcc compiler: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.9) 5.4.0 20160609
cmake : 3.5.1
opencv : 3.4.1

I actually installed opencv using anaconda. Surprisingly c++ version also worked

The image I am using is given here:

Python code ...

ANSWER

Answered 2018-Mar-22 at 13:47

I wouldn't expect any difference between the two at all to be honest. The python library more than likely is a wrapper around the C++ library; meaning that once they get into the core of the opencv they will have identical performance if compiled with the same optimisation flags.

The only slight slowdown I'd EXPECT is python getting to that point; and with so little python code actually there; the difference is unlikely to be measureable. The fact that you're seeing it the other way around I don't think proves anything as you're performing a single test; and getting a difference of 0.2s which could trivially be the difference in just the hard disk seeking to the file to process.

Source https://stackoverflow.com/questions/49428298

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

Vulnerabilities

No vulnerabilities reported

Install hough_transform

You can download it from GitLab, GitHub.
You can use hough_transform 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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