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@ -321,7 +321,7 @@ def scale_image(masks, im0_shape, ratio_pad=None):
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Takes a mask, and resizes it to the original image size
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Takes a mask, and resizes it to the original image size
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Args:
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Args:
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masks (torch.Tensor): resized and padded masks/images, [h, w, num]/[h, w, 3].
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masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
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im0_shape (tuple): the original image shape
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im0_shape (tuple): the original image shape
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ratio_pad (tuple): the ratio of the padding to the original image.
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ratio_pad (tuple): the ratio of the padding to the original image.
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@ -344,9 +344,6 @@ def scale_image(masks, im0_shape, ratio_pad=None):
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if len(masks.shape) < 2:
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if len(masks.shape) < 2:
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raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
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raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
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masks = masks[top:bottom, left:right]
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masks = masks[top:bottom, left:right]
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# masks = masks.permute(2, 0, 1).contiguous()
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# masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
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# masks = masks.permute(1, 2, 0).contiguous()
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masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
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masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
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if len(masks.shape) == 2:
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if len(masks.shape) == 2:
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masks = masks[:, :, None]
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masks = masks[:, :, None]
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