everestpipkin
commented
4 years ago Hi! Glad to have you hop on. I've done a few tests for video but haven't had time to implement it, as it is likely almost as large of a task as the whole tool up to this point. but if you want to take on the task of video I'd fully encourage you to make a PR.
IMO facetracking is both unreliable and a little bit too heavy on mobile architecture, but the 'tap mode' I added a few days ago is an attempt to do a quicker, tap-based blur for crowds. My sketch for video processing would mean recording the tap-blur location each frame and compositing them into the video loop. My guess is this should cover most bases - the trouble will be that if there are 100s of faces in a long video, it'll take a lot of long loops to cover everyone. This could be partly addressed by doing multi-touch handling, so you can blur 5~ points at once. Still not ideal but likely still faster and more reliable than facetracking on an old phone.
raisingdibar
Considering the fact that video has become the best way for individuals to easily document dangerous situations (not having to focus on a subject, audio helps accountability, etc) and the fact that video is the majority of non-professional content emerging from these public spaces, has anyone considered trying to apply the existing processes to video?
I know images are the basis for video, so getting the image-processing tools ironed out is definitely a must.
I have a very basic understanding of image processing myself, so please chime in if I'm missing something that makes video unfeasible; but something tells me that there must be a way to track faces (either using ML, or gesture interactions) across the timeline of a video and maintain blur by blurring each frame according to where a face is at during that frame.
Thoughts?