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ultralytics 8.3.38 SAM 2 video inference (#14851)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
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Laughing 2024-11-26 19:38:23 +08:00 • committed by GitHub
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16 changed files with 917 additions and 124 deletions
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ultralytics/utils/ops.py
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@ -317,11 +317,11 @@ def clip_boxes(boxes, shape):
Takes a list of bounding boxes and a shape (height, width) and clips the bounding boxes to the shape.
Args:
boxes (torch.Tensor): the bounding boxes to clip
shape (tuple): the shape of the image
boxes (torch.Tensor): The bounding boxes to clip.
shape (tuple): The shape of the image.
Returns:
(torch.Tensor | numpy.ndarray): Clipped boxes
(torch.Tensor | numpy.ndarray): The clipped boxes.
"""
if isinstance(boxes, torch.Tensor): # faster individually (WARNING: inplace .clamp_() Apple MPS bug)
boxes[..., 0] = boxes[..., 0].clamp(0, shape[1]) # x1
@ -359,9 +359,9 @@ def scale_image(masks, im0_shape, ratio_pad=None):
Takes a mask, and resizes it to the original image size.
Args:
masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
im0_shape (tuple): the original image shape
ratio_pad (tuple): the ratio of the padding to the original image.
masks (np.ndarray): Resized and padded masks/images, [h, w, num]/[h, w, 3].
im0_shape (tuple): The original image shape.
ratio_pad (tuple): The ratio of the padding to the original image.
Returns:
masks (np.ndarray): The masks that are being returned with shape [h, w, num].
@ -692,12 +692,12 @@ def process_mask_native(protos, masks_in, bboxes, shape):
Args:
protos (torch.Tensor): [mask_dim, mask_h, mask_w]
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms
bboxes (torch.Tensor): [n, 4], n is number of masks after nms
shape (tuple): the size of the input image (h,w)
masks_in (torch.Tensor): [n, mask_dim], n is number of masks after nms.
bboxes (torch.Tensor): [n, 4], n is number of masks after nms.
shape (tuple): The size of the input image (h,w).
Returns:
masks (torch.Tensor): The returned masks with dimensions [h, w, n]
masks (torch.Tensor): The returned masks with dimensions [h, w, n].
"""
c, mh, mw = protos.shape # CHW
masks = (masks_in @ protos.float().view(c, -1)).view(-1, mh, mw)