Breakthrough
Joined 14 years ago
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Member since
Aug 10
Recent submissions
Comment · 1 day ago
I had one of these as well... What a trip down memory lane, thanks for sharing.
Comment · 2 months ago
> Why would you destroy your trade relation with the U.S. for decades to come when you can just ride out a bad deal for a few years?
We aren't the ones who are destroying it, and we have a backbone strong enough to say this isn't good enough.
If what you say is true, then the next administration should understand how one sided the damage done to the relationship has been. No economic trade partner should have to tolerate attacks on their sovereignty and unreliable trade negotiators.
Comment · 2 months ago
You have my empathy for this kind of sentiment. Personally this seems somewhat rare in practice. That being said I'm curious if anyone has anecdotes they can share about these kinds of things?
Comment · 11 months ago
If you're willing, could you try using AdaptiveDetector? It should have better defaults for handling fast camera movements a bit better.
The threshold values themselves can be tuned if you generate a statsfile and plot the result, but that can sometimes be tedious if you have a lot of files (thus the huge interest in methods like TransNetV2). Glad to see the real world applications of those in action. You can always just increase/decrease the threshold by 5-10% depending on if you find it's too sensitive or not sensitive enough as well.
Thanks for the response!
Comment · 11 months ago
Awesome to see this! I actually wrote PySceneDetect, was great to see it getting some use here. Would you be willing to share what parameters you were using? I'm curious why the accuracy was so low.
PySceneDetect only uses basic heuristic methods right now so it does require some degree of tuning to get things working for certain data sets. Your post inspired me to look into maybe integrating TransNetV2 as a detector in the future!
Comment · 4 years ago
Are there keyboard controls? Someone I was just playing with seemed to always lock on each "note" or "half note" exactly, wasn't sure how they were doing that.
Comment · 7 years ago
Thanks for giving PySceneDetect a try. Can you share what you're using as the command line arguments?
For detecting fades-to-black, you want to make sure that you're using the detect-threshold command (not detect-content). For example:
scenedetect -i somevideo.mp4 detect-threshold -t 12 list-scenes
https://pyscenedetect-manual.readthedocs.io/en/latest/cli/de...
Comment · 7 years ago
This is still relevant I think :) What you mentioned is very similar to an edit decision list (EDL [1]), of which I only learned recently. I had a feature request [2] to support EDL as an ouptut format, and upon further investigation, it seems like the format is very similar to what you're talking about. The Wikipedia page also indicates that VLC supports xspf files, but I haven't done much research into that yet ("XML Shareable Playlist Format").
[1] https://en.wikipedia.org/wiki/Edit_decision_list
[2] https://github.com/Breakthrough/PySceneDetect/issues/101
Comment · 7 years ago
Author of PySceneDetect here: Thank you all for the thought-provoking discussions, and the attention you've given my side project. There are some specific cases where PySceneDetect achieves great accuracy (like fast cuts or fades), and some where it's currently not so good at (like sudden flashes or large obstructions). That being said I do want to track these things and come up with solutions to improve the robustness of the content detection algorithm over time.
I'm most open to any feedback or feature requests/ideas/suggestions; feel free to checkout the issue tracker on Github, or create a new entry:
https://github.com/Breakthrough/PySceneDetect/issues
Some ideas being considered/researched for future releases:
- looking at changes to image histograms
- using edge detection to improve robustness
- camera flash / foreground object suppression
- automatic threshold detection using statistical methods (currently is just a heuristic)
Comment · 7 years ago
Funny you should mention that - initially home videos were what I was doing this with, as I was unable to find any existing programs that could handle them reliably (or had various shortcomings, or weren't hackable/extensible, etc...). Then I realized that what I came up with actually competed pretty well with the existing solutions (benchmarks below [1]), so decided to open source it and share it with the world.
Of course, the project is still very basic in it's current form, but does offer a good platform for testing more advanced scene detection methodologies (and has also been used as a baseline in various research/academic contexts). There's been plenty of suggestions for other detection methods (e.g. using histograms) which I'm very interested in looking into adding for future releases.
Lastly, one thing I do want to address going forwards is the performance, to make the algorithms more suitable for real-time systems (have decided to rewrite the core algorithms in C++ when I have some time, and interface that with the Python library/CLI). That being said, I'm always open to feedback or different ways of approaching things. If you or anyone else has any feedback or suggestions, feel free to create a new issue on the Github issue tracker [2] and I would be happy to discuss the possibility of including it in a future release of PySceneDetect.
Comment · 7 years ago
Great idea, will give that a shot - thanks for the suggestion! :)
Comment · 7 years ago
This is indeed correct, I'm just using HSV instead of YUV, but the primary source of information is the luma/brightness component (although currently all 3 of the HSV components are averaged, so perhaps a better weighting may improve precision).
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