scikit-video | Video Processing in Python
kandi X-RAY | scikit-video Summary
kandi X-RAY | scikit-video Summary
Video Processing in Python
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QUESTION
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.
ANSWER
Answered 2021-Mar-29 at 21:05Getting 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 topixel_format='bgr24'
, because AVI supportsbgr24
and notrgb24
. - Select video a lossless codec for
test_2
video.
You may selectvcodec='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:
QUESTION
When I type conda env create -f environment.yml
I constantly get
...ANSWER
Answered 2021-Jan-15 at 14:57Conda 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.
QUESTION
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 00:17The key here is this:
QUESTION
Is it possible to set the path of ffmpeg for scikit-video module? I know I can set using skvideo.setFFmpegPath(path)
. Is there any environment variable that I can set, so that everytime I import skvideo.io
, it picks the defined path?
ANSWER
Answered 2019-Dec-02 at 11:32skvideo does not directly support taking the ffmpeg path from an environment variable. But you can adjust the system search path by setting the PATH environment variable:
QUESTION
I have a .mp4 video file that I would like to get into an numpy array and store on disk with numpy.memmap, but it is too large to read in all at once. I know how to process the file frame by frame with scikit video, but not how to then pump that to a single numpy file on disk. Does anyone know how this can be done?
...ANSWER
Answered 2017-Aug-10 at 16:58An uncompressed video is a large amount of data...this answer gives you some idea of how much memory you'd need ("roughly 33 GB per minute for 8-bit 24 fps").
I remember attempting to do something similar when I was getting started with openCV. I'd suggest you're probably coming at your problem wrong. But if you really want to do it, you can use numpy.save
.
This answer has some good tips on better data formats for large data sets.
QUESTION
I need to read some metadata from a lot of video files. After some research I bumped on http://www.scikit-video.org. and I used skvideo.io.ffprobe which gave me the result that I wanted. It returns a dictionary with the info I'm looking for.
It looks like this:
...ANSWER
Answered 2017-Jul-22 at 01:08You have a list as the value to the key "tag"
, so to access it you'll need to get the list out of the dictionary as so.
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