optical-flow | https://nanonetscom/blog/optical-flow/ | Machine Learning library

by chuanenlin Python Version: Current License: No License

X-Ray Key Features Code Snippets(2)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | optical-flow Summary

optical-flow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. optical-flow has no bugs, it has no vulnerabilities and it has low support. However optical-flow build file is not available. You can download it from GitHub.

This repository is for the article "Introduction to Motion Estimation with Optical Flow" published with Nanonets.

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optical-flow has a low active ecosystem.

It has 120 star(s) with 52 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 176 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of optical-flow is current.

Quality

optical-flow has 0 bugs and 0 code smells.

Security

optical-flow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

optical-flow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

optical-flow 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

optical-flow releases are not available. You will need to build from source code and install.

optical-flow has no build file. You will be need to create the build yourself to build the component from source.

optical-flow saves you 63 person hours of effort in developing the same functionality from scratch.

It has 165 lines of code, 0 functions and 11 files.

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

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optical-flow Key Features

No Key Features are available at this moment for optical-flow.

optical-flow Examples and Code Snippets

2d convolutional convolutional convolution .

python

Lines of Code : 147

License : Non-SPDX (Apache License 2.0)

Copy

def atrous_conv2d(value, filters, rate, padding, name=None):
  """Atrous convolution (a.k.a. convolution with holes or dilated convolution).

  This function is a simpler wrapper around the more general
  `tf.nn.convolution`, and exists only for back

Calculate the product of two images .

python

Lines of Code : 65

License : Permissive (MIT License)

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def horn_schunck(
    image0: np.ndarray,
    image1: np.ndarray,
    num_iter: SupportsIndex,
    alpha: float | None = None,
) -> tuple[np.ndarray, np.ndarray]:
    """
    This function performs the Horn-Schunck algorithm and returns the estima

Community Discussions

Trending Discussions on optical-flow

Create frame from previous frame and Optical Flow

What is the difference between L2_NORM and NORM_MINMAX implementation in cv::Normalise()?

QUESTION

Create frame from previous frame and Optical Flow

Asked 2020-Jun-07 at 15:32

I have a video and I need to simulate frames using Optical Flow; i.e. having a frame and the Optical Flow that represents the pixel translation for the next frame simulate this following resulting frame.

I am using Python and OpenCV as follows:

Generate flow between two consecutive grayscale frames

...

ANSWER

Answered 2020-Jun-07 at 15:32

The issue was solved slightly updating the code as follows:

Source https://stackoverflow.com/questions/62244550

QUESTION

What is the difference between L2_NORM and NORM_MINMAX implementation in cv::Normalise()?

Asked 2020-Jun-07 at 03:54

I was trying to replicate the Dense optical flow as discussed here : https://nanonets.com/blog/optical-flow/ .This is the snippet , I have used to process each frame for optical flow. The intution is that H->Angle of gradient,S->255,V->Norm value of Magnitude of gradient.The gradient is from output of calcOpticalFlowFarenback.

...

ANSWER

Answered 2020-Jun-07 at 03:54

cv::NORM_L2 normalises your data such that if you took the entire image and converted it into one long vector, the magnitude of this vector is such that it becomes alpha. beta is ignored in the normalisation. Therefore, once you normalise by cv::NORM_L2, after you normalise if you were to consider this normalised input as one long vector, the L₂ norm of this vector thus becomes alpha. Because you specified alpha = 0, it's not surprising that the output image is entirely 0 because you are specifying that the norm needs to be 0 after normalisation.

cv::NORM_MINMAX uses both alpha and beta such that the smallest value in the input array gets mapped to alpha and the largest value gets mapped to beta with all values in between scaled proportionally.

If you're wondering how I know this, the documentation for the function makes this very clear: https://docs.opencv.org/4.3.0/d2/de8/group__core__array.html#ga87eef7ee3970f86906d69a92cbf064bd

Source https://stackoverflow.com/questions/62230135

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

Vulnerabilities

No vulnerabilities reported

Install optical-flow

You can download it from GitHub.
You can use optical-flow 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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