{"action":"created","issue":{"url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/84","repository_url":"https://api.github.com/repos/jcjohnson/fast-neural-style","labels_url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/84/labels{/name}","comments_url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/84/comments","events_url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/84/events","html_url":"https://github.com/jcjohnson/fast-neural-style/issues/84","id":192971371,"number":84,"title":"would it be feasible to implement the -seed arg in fast_neural_style?","user":{"login":"tkoham","id":13039170,"avatar_url":"https://avatars.githubusercontent.com/u/13039170?v=3","gravatar_id":"","url":"https://api.github.com/users/tkoham","html_url":"https://github.com/tkoham","followers_url":"https://api.github.com/users/tkoham/followers","following_url":"https://api.github.com/users/tkoham/following{/other_user}","gists_url":"https://api.github.com/users/tkoham/gists{/gist_id}","starred_url":"https://api.github.com/users/tkoham/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tkoham/subscriptions","organizations_url":"https://api.github.com/users/tkoham/orgs","repos_url":"https://api.github.com/users/tkoham/repos","events_url":"https://api.github.com/users/tkoham/events{/privacy}","received_events_url":"https://api.github.com/users/tkoham/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2016-12-01T21:45:49Z","updated_at":"2016-12-02T19:05:38Z","closed_at":null,"body":"I'm working on video processing with fast_neural_style, and I've explored various methods of reducing noise and variance frame to frame, with mixed results. The biggest problem with using an external solution is that it slows down batch rendering to a crawl because it has to restart the process on each frame. the -seed argument used in slow_neural_style seems to be a good solution, but before I try to implement that function in the fast one, I'd like to know if it's just a wild goose-chase because I only have a basic working knowledge of lua. \r\n\r\nPS: It also occurs to me that I could write a quick-n-dirty script using pillow, g'mic or similar that would suspend the process after each frame, blend the output, and resume, but fast_neural_style doesn't do batch processing in a linear, counting up fashion. if I could force this, it would achieve a similar result, but again, I'm not sure where to start."},"comment":{"url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/comments/264535312","html_url":"https://github.com/jcjohnson/fast-neural-style/issues/84#issuecomment-264535312","issue_url":"https://api.github.com/repos/jcjohnson/fast-neural-style/issues/84","id":264535312,"user":{"login":"tkoham","id":13039170,"avatar_url":"https://avatars.githubusercontent.com/u/13039170?v=3","gravatar_id":"","url":"https://api.github.com/users/tkoham","html_url":"https://github.com/tkoham","followers_url":"https://api.github.com/users/tkoham/followers","following_url":"https://api.github.com/users/tkoham/following{/other_user}","gists_url":"https://api.github.com/users/tkoham/gists{/gist_id}","starred_url":"https://api.github.com/users/tkoham/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tkoham/subscriptions","organizations_url":"https://api.github.com/users/tkoham/orgs","repos_url":"https://api.github.com/users/tkoham/repos","events_url":"https://api.github.com/users/tkoham/events{/privacy}","received_events_url":"https://api.github.com/users/tkoham/received_events","type":"User","site_admin":false},"created_at":"2016-12-02T19:05:38Z","updated_at":"2016-12-02T19:05:38Z","body":"I figured as much. what about forcing the batch processing to work in alphabetical/numerical order? would that be relatively uncomplicated? if I can get that done, then I can pause the process, and keep everything in memory while I do a blend pass on each frame. it seems my only other option would be to incorporate image processing into the style transfer script"}}
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