scikit-video | Video processing routines for SciPy | Computer Vision library

by aizvorski Python Version: Current License: Non-SPDX

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

kandi X-RAY | scikit-video Summary

scikit-video is a Python library typically used in Artificial Intelligence, Computer Vision applications. scikit-video has no bugs, it has no vulnerabilities, it has build file available and it has low support. However scikit-video has a Non-SPDX License. You can download it from GitHub.

Video Processing SciKit BETA. Video processing algorithms, including I/O, quality metrics, temporal filtering, motion/object detection, motion estimation…. This is intended as a companion to scikit-image, containing all the algorithms which deal with video. There is a certain degree of overlap between image and video algorithms, for example a PSNR quality metric could be applied to pairs of images or pairs of video frames just as well. However, other algorithms are video-specific, for example a temporal denoise. This is the future home of the video-specific algorithms, as well as some of the algorithms which are not strictly video specific but are usually seen in a video context. This also has some overlap with OpenCV. Roughly, the algorithms implemented here would be easier to hack on, and more research-oriented. Rather than building on top of a C/C++ framework, this will stay Python all the way, using whichever combinaiton of Numba/Theano/etc seems best for performance. This should add flexibility and better future ability to use GPU compute.

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Quality

Security

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Reuse

Support

scikit-video has a low active ecosystem.

It has 103 star(s) with 23 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 3 have been closed. On average issues are closed in 51 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of scikit-video is current.

Quality

scikit-video has 0 bugs and 0 code smells.

Security

scikit-video has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

scikit-video code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

scikit-video has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

scikit-video 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.

scikit-video saves you 272 person hours of effort in developing the same functionality from scratch.

It has 659 lines of code, 31 functions and 21 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed scikit-video and discovered the below as its top functions. This is intended to give you an instant insight into scikit-video implemented functionality, and help decide if they suit your requirements.

Open the subtitle file
Kill the process
Returns True if the process is open
Compute the SSimulation of two images
Make a block view of A
Read data from the command line
Open the video file
Update the plot
Get the info from the device
Benchmark something
Returns the version number
Write image to stdin
Close the process
Convert from RGB to y coordinates

Get all kandi verified functions for this library.

scikit-video Key Features

No Key Features are available at this moment for scikit-video.

scikit-video Examples and Code Snippets

No Code Snippets are available at this moment for scikit-video.

Community Discussions

Trending Discussions on scikit-video

NumPy array of a video changes from the original after writing into the same video

I constantly get ResolvePackageNotFound

The conflict is caused by: The user requested tensorboard==2.1.0 tensorflow 1.15.4 depends on tensorboard<1.16.0 and >=1.15.0

QUESTION

NumPy array of a video changes from the original after writing into the same video

Asked 2021-Mar-29 at 21:05

I have a video (test.mkv) that I have converted into a 4D NumPy array - (frame, height, width, color_channel). I have even managed to convert that array back into the same video (test_2.mkv) without altering anything. However, after reading this new, test_2.mkv, back into a new NumPy array, the array of the first video is different from the second video's array i.e. their hashes don't match and the numpy.array_equal() function returns false. I have tried using both python-ffmpeg and scikit-video but cannot get the arrays to match.

Python-ffmpeg attempt: ...

ANSWER

Answered 2021-Mar-29 at 21:05

Getting the same hash requires when writing and reading a video file requires careful attention.

Before comparing the hash, try to look at the video first.

Executing your code gave me the following output (first frame of video_2):

When the input (first frame of video) is:

I suggest the following modifications:

Use AVI container (instead of MKV) for storing test_2 video in raw video format.
AVI video container is originally designed for storing raw video.
There could be a way for storing raw, or lossless RGB video in MKV container, but I am not aware of such option.
Set the input pixel format of test_2 video.
Add an argument: pixel_format='rgb24'.
Note: I modified it to pixel_format='bgr24', because AVI supports bgr24 and not rgb24.
Select video a lossless codec for test_2 video.
You may select vcodec='rawvideo' (rawvideo codec is supported by AVI but not supported by MKV).

Note:
For getting equal hash, you need to look for lossless video codec that supports rgb24 (or bgr24) pixel format.
Most of the lossless codecs, converts the pixel format from RGB to YUV.
The RGB to YUV conversion has rounding errors that prevents equal hash.
(I suppose there are ways to get around it, but it's a bit complicated).

Here is your complete code with few modifications:

Source https://stackoverflow.com/questions/66713980

QUESTION

I constantly get ResolvePackageNotFound

Asked 2021-Jan-17 at 05:16

When I type conda env create -f environment.yml

I constantly get

...

ANSWER

Answered 2021-Jan-15 at 14:57

Conda does not work well with large environments in which everything pinned to specific versions (in contrast to other ecosystems in which pinning everything is the standard). The result of conda env export, which is what this probably is, here also includes the build numbers, which are almost always too specific (and often platform-specific) for the purpose of installing the right version of the software. It's great for things like reproducibility of scientific work (specific versions and builds of everything need to be known), but not great for installing software (there is plenty of flexibility in versions that should work with any package).

I'd start by removing the build pins (dropping everything after the second = in each line) so that only the versions are pinned. After that, I'd start removing version pins.

Source https://stackoverflow.com/questions/65735532

QUESTION

The conflict is caused by: The user requested tensorboard==2.1.0 tensorflow 1.15.4 depends on tensorboard<1.16.0 and >=1.15.0

Asked 2020-Dec-10 at 00:17

I am trying to install a package VIBE from a git repo and inistally I was installing its dependencies. The code is located here: https://github.com/mkocabas/VIBE how should I fix this?

Here's the error I got:

...

ANSWER

Answered 2020-Dec-10 at 00:17

The key here is this:

Source https://stackoverflow.com/questions/65226693

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

Vulnerabilities

No vulnerabilities reported

Install scikit-video

You can download it from GitHub.
You can use scikit-video 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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