fastvideo.v1.dataset.transform

`fastvideo.v1.dataset.transform`#

`CenterCropResizeVideo`	First use the short side for cropping length, center crop video, then resize to the specified size
`Normalize255`	Convert tensor data type from uint8 to float, divide value by 255.0 and
`TemporalRandomCrop`	Temporally crop the given frame indices at a random location.

`center_crop_th_tw`
`crop`	param clip: Video clip to be cropped. Size is (T, C, H, W) type clip: torch.tensor
`normalize_video`	Convert tensor data type from uint8 to float, divide value by 255.0 and permute the dimensions of clip tensor
`resize`

class fastvideo.v1.dataset.transform.CenterCropResizeVideo(size, top_crop=False, interpolation_mode='bilinear')[source]#

First use the short side for cropping length, center crop video, then resize to the specified size

Initialization

class fastvideo.v1.dataset.transform.Normalize255[source]#

Convert tensor data type from uint8 to float, divide value by 255.0 and

Initialization

class fastvideo.v1.dataset.transform.TemporalRandomCrop(size)[source]#

Temporally crop the given frame indices at a random location.

Parameters:: size (int) – Desired length of frames will be seen in the model.

Initialization

fastvideo.v1.dataset.transform.center_crop_th_tw(clip, th, tw, top_crop) → torch.Tensor[source]#

fastvideo.v1.dataset.transform.crop(clip, i, j, h, w) → torch.Tensor[source]#

Parameters:: clip (torch.tensor) – Video clip to be cropped. Size is (T, C, H, W)

fastvideo.v1.dataset.transform.normalize_video(clip) → torch.Tensor[source]#

Convert tensor data type from uint8 to float, divide value by 255.0 and permute the dimensions of clip tensor

Parameters:: clip (torch.tensor, dtype=torch.uint8) – Size is (T, C, H, W)
Returns:: Size is (T, C, H, W)
Return type:: clip (torch.tensor, dtype=torch.float)

fastvideo.v1.dataset.transform.resize(clip, target_size, interpolation_mode) → torch.Tensor[source]#