Function load_video_frames_from_data

sam2_train/utils/misc.py:215–244

Load the video frames from a directory of JPEG files (" .jpg" format). The frames are resized to image_size x image_size and are loaded to GPU if `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`. You can load a frame asynchronously by se

(
    imgs_tensor,
    offload_video_to_cpu,
    img_mean=(0.485, 0.456, 0.406),
    img_std=(0.229, 0.224, 0.225),
    async_loading_frames=False,
)

    return images, video_height, video_width

def load_video_frames_from_data(
    imgs_tensor,
    offload_video_to_cpu,
    img_mean=(0.485, 0.456, 0.406),
    img_std=(0.229, 0.224, 0.225),
    async_loading_frames=False,
):
    """
    Load the video frames from a directory of JPEG files ("<frame_index>.jpg" format).

    The frames are resized to image_size x image_size and are loaded to GPU if
    `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`.

    You can load a frame asynchronously by setting `async_loading_frames` to `True`.
    """

    num_frames = imgs_tensor.shape[0]

    img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None]
    img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None]

    images = imgs_tensor / 255.0
    if not offload_video_to_cpu:
        images = images.cuda()
        img_mean = img_mean.cuda()
        img_std = img_std.cuda()
    # normalize by mean and std
    images -= img_mean
    images /= img_std
    return images


def fill_holes_in_mask_scores(mask, max_area):