def compute_clips_for_video(video_pts, num_frames, step, fps, frame_rate):
if fps is None:
# if for some reason the video doesn't have fps (because doesn't have a video stream)
# set the fps to 1. The value doesn't matter, because video_pts is empty anyway
fps = 1
if frame_rate is None:
frame_rate = fps
total_frames = len(video_pts) * (float(frame_rate) / fps)
# idxs = VideoClips._resample_video_idx(int(math.floor(total_frames)), fps, frame_rate)
idxs = VideoClipsFast._resample_video_idx(
int(math.floor(total_frames)), fps, frame_rate)
video_pts = video_pts[idxs]
clips = unfold(video_pts, num_frames, step)
if isinstance(idxs, slice):
idxs = [idxs] * len(clips)
else:
idxs = unfold(idxs, num_frames, step)
return clips, idxs
def test_unfold(self):
a = torch.arange(7)
r = unfold(a, 3, 3, 1)
expected = torch.tensor([
[0, 1, 2],
[3, 4, 5],
])
assert_equal(r, expected)
r = unfold(a, 3, 2, 1)
expected = torch.tensor([[0, 1, 2], [2, 3, 4], [4, 5, 6]])
assert_equal(r, expected)
r = unfold(a, 3, 2, 2)
expected = torch.tensor([
[0, 2, 4],
[2, 4, 6],
])
assert_equal(r, expected)
def __call__(self, frame_indices):
frame_indices = Tensor(frame_indices)
out = unfold(frame_indices, self.size, self.step, self.dilatation)
out = out[:self.n_samples].int().tolist()
return out
