From f726105ed0822e4b87926b080801c875d4d9c635 Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 1 Jul 2018 12:59:02 +0200 Subject: padding --- run.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/run.py b/run.py index 1568a9d..b059835 100644 --- a/run.py +++ b/run.py @@ -84,15 +84,17 @@ def recurse_videos(moduleNetwork, tensorOutput, a, b, a_offset, b_offset, count, a_fn = os.path.join(a, "frame_{:0>5}.png".format(int(frame_index + a_offset))) b_fn = os.path.join(b, "frame_{:0>5}.png".format(int(frame_index + b_offset))) print("{} => {}".format(a_fn, b_fn)) + if not os.path.exists(a_fn) or not os.path.exists(b_fn): + return [ None ] a_np = load_image(a_fn) b_np = load_image(b_fn) img_np = recurse_two_frames(moduleNetwork, tensorOutput, a_np, b_np, frame_index, count / 2, count / 4) if step < 2 * dilate: - return [img_np] + return [ img_np ] else: left = recurse_videos(moduleNetwork, tensorOutput, a, b, a_offset, b_offset, count, step, frame_index - (step/2), dilate) right = recurse_videos(moduleNetwork, tensorOutput, a, b, a_offset, b_offset, count, step, frame_index + (step/2), dilate) - return left + [img_np] + right + return left + [ img_np ] + right def process_two_videos(moduleNetwork, tensorOutput, a, b, a_offset, b_offset, steps, dilate): steps *= dilate @@ -109,6 +111,8 @@ def average_two_videos(moduleNetwork, tensorOutput, a, b, a_offset, b_offset, st a_fn = os.path.join(a, "frame_{:0>5}.png".format(int(i + a_offset))) b_fn = os.path.join(b, "frame_{:0>5}.png".format(int(i + b_offset))) print("{} => {}".format(a_fn, b_fn)) + if not os.path.exists(a_fn) and not os.path.exists(b_fn): + continue a_np = load_image(a_fn) b_np = load_image(b_fn) tensorInputFirst = torch.FloatTensor(a_np) @@ -207,8 +211,8 @@ def pad_frames(writer, base_path, start, end): for index in range(start, end): fn = os.path.join(base_path, "frame_{:0>5}.png".format(int(index + start))) if os.path.exists(fn): - PIL.Image.open(fn) - writer.write_frame(np) + img = PIL.Image.open(fn) + writer.write_frame(img) def tensor_to_image(np_val): return (numpy.rollaxis(np_val, 0, 3)[:,:,::-1] * 255.0).astype(numpy.uint8) -- cgit v1.2.3-70-g09d2