(2018-02-25, 20:09)ssaluja16 Wrote: HI,
So I have been running into troubles while building Kodi(went from 14.04 to Windows and then to 16.04, where I am making progress so not a problem), hence, apologies for the delay.
I read up on how we may implement the project, we can try two ways, training using audio or video
1. Use the video: We see the frames being repeated in multiple episodes and hence mark the times, but more computationally complex
2. Audio: We use the audio being repeated, theme songs(Intro) or maybe an absence of music in multiple episodes(credits).

Hence, as far as I have understood, Kodi can help you pull data about your locally stored video files(movies or TV shows), hence the time duration for intros and outros needs to be stored somewhere and the model trained individually for all TV shows.
Are we considering analyzing the episodes in the background, and training it on the device itself?

(2018-02-25, 15:48)mohit-0212 Wrote: I know black screen detection isn't the best way to do this. I was trying my hands on, by implementing some of the literature available on this. As I also wrote, black screen detection doesn't work for all, in my case for ex, it failed for breaking bad whose intro scene starts with a black screen itself.
To be more accurate for each show, we would have to have a model for individual show. Your idea seems interesting where we take the title card for individual show, use it to train our model, and then apply it for further upcoming episodes. A proper training and implementing pipeline needs to be developed for that which makes the process for each new show easier.
 

Quote:Or do you think we can find a common factor that solves 90% of shows?

This depends on how we want our Kodi platform to work. Trade off between better accuracy and faster output. I think we should aim for better accuracy, i.e developing model for individual shows, and then try to make this process faster.  

(2018-02-25, 22:13)Razze Wrote: Not training on the device itself that's to expensive. Analyzing with the finished model is a must as we can't rely on intro and outro durations.

(2018-02-26, 17:31)ssaluja16 Wrote:

(2018-02-25, 22:13)Razze Wrote: Not training on the device itself that's to expensive. Analyzing with the finished model is a must as we can't rely on intro and outro durations.

So I have built Kodi on my system (Ubuntu 16.04), and have been playing with it.
About the actual implementation, so we just need to push the timestamps o intros and outros to the device and use them to skip, so I'll start looking on the implementation of Add-Ons to Kodi like you mentioned in https://kodi.wiki/view/Python_development.
Any advice to get started on implementing the function? 

(2018-02-25, 15:53)Razze Wrote: Sounds good, maybe we can even convince some metadata providers like tmdb to ship these models for us if we have a specification that's useable for everybody not just kodi. So good api design/ focusing on standards on that part would be important.

(2018-02-26, 03:39)sarbes Wrote: 1. The user marks the intro (via EDL) and sets a few parameters.

(2018-02-25, 15:29)Razze Wrote: always start with a title card that's basically japanese text (the episode name) above different backgrounds.

(2018-03-02, 20:20)mohit-0212 Wrote: Hey @Razze 

Which do you think will be better?
1. We let the user mark the frame till which they want to skip using an episode of a TV show and then store that frame locally and use that frame as a reference for getting intro skip times for the rest of the episodes.
(This will personalise the skip timings for each user, we'll need to make the addon code call and can compute the timings using the frame stored).
or
2. Make it where user doesn't have to interact and the addon does everything itself. In that case, supposing we go by the title cover approach, then we'd need to retrieve the title cover image (need to create a dataset) using the show name or some metadata associated with it and then run the code to get the timings.
3. something else?

Also I have joined the irc channel, I will post my queries there regarding the codebase.

Thanks

(2018-03-04, 23:23)mohit-0212 Wrote: Yes agreed. One title cover only works for those which have a consistent title cover for the whole series. So for the example you gave One Piece, it might not work correctly. For that, we'd need a more dense dataset. 
Do tvdb/imdb have such a model or an exhaustive set of title cover images for majority shows?

Also one other question, how can we handle a case where the user watches a show and we don't have an available pre-trained model for that?

Quote:I think we can't handle it without training a dataset, it's more likely to do the wrong thing. So do nothing instead, if you ask me.