Hello, This is to announce the very early version of scikit-video, a collection of algorithms for all your video-processing needs :) Code: https://github.com/aizvorski/scikit-video Status: There is functional video reading code, which is close to a replacement for cv2.VideoCapture. Otherwise, mostly a placeholder. Motivation: This scikit is intended as a companion to scikit-image: the two are complementary and not competitive. What code is better in video than in image? Roughly, if the code *only* makes sense when applied to a sequence of images, then it may be better placed in scikit-video. An example of this would be temporal denoise. Other code which would make sense to have in here is code which is much more often seen or used in a video context, even though it may in principle be used for both images or video. An example of this would be the SSIM quality metric. It is a goal that scikit-image and scikit-video would "just work" together, for example you might read a video file using scikit-video, use a scikit-image filter on each frame and write out the filtered frames to another file. Roadmap: - Skeleton project from scikit-example - DONE - Video IO by wrapping ffmpeg/avconv - reading DONE, writing not yet - Video metrics - Unit tests and CI (beyond this point are just possible future directions) - (?) Pyramid motion estimation - (?) TLD algorithm (http://personal.ee.surrey.ac.uk/Personal/Z.Kalal/) - (?) 3D denoise - (?) OpenGL display and a mini video player - (?) Any existing off-the-shelf projects that would be good to merge into this I expect that if this gathers enough interest it will transition to a group-maintained project similar to skimage or sklearn. Your suggestions, ideas, and code are most welcome. Regards, Alex Izvorski