Make frame selection from a video more intelligent

[danie1ll]

From my understanding, right now calling ns-process-data video just randomly samples --num-frames-target images from the video. This is suboptimal because of two reasons:

1. The random strategy is super naive, maybe you could add some algorithm to select key frames based on some difference metric between images?
2. An option to fix seed for random sampling would make processing videos with nerfstudio much more reproducible

[LucasArmand]

A good metric could be frame blurriness. Here's an example from Stack Overflow using the Laplacian to quantify image blurriness and select the "least blurry image" every second.
https://stackoverflow.com/questions/65949172/how-to-extract-clear-frames-from-video-file

[danie1ll]

@LucasArmand this looks very promising! Could also be combined with accumulated visual change between frames, so we not only select 'least blurry image' every second but 'the most important frames, which are different enough and are not blurry'

[LucasArmand]

@danie1ll I wonder how far we can go in optimizing the ns-process-data process? When our source is a video, we usually only select a low percentage of the total frames, so there are many unique combinations of frames to select for training. Some of these combinations would certainly result in higher quality trained scenes than others. Heuristics like "image blurriness" and "accumulated visual change" will probably give us a better combination of frames for training, but is it possible to find the best combination?

[msusag]

The scripts at https://github.com/SharkWipf/nerf_dataset_preprocessing_helper could perhaps be integrated into ns-process-data video?

[Anthony-Tafoya]

No matter the solution decided for "smarter" processing, I think there is room for a fixed seed option. I am working on a PR to implement it.