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random_resized_crop

RandomResizedCrop3D

Bases: Transform

Combines monai's random spatial crop followed by resize to the desired size.

Modifications: 1. The spatial crop is done with the same dimensions for all the axes. 2. Handles cases where the image_size is less than the crop_size by choosing the smallest dimension as the random scale.

Source code in fmcib/transforms/random_resized_crop.py 
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class RandomResizedCrop3D(Transform):
    """
    Combines monai's random spatial crop followed by resize to the desired size.

    Modifications:
    1. The spatial crop is done with the same dimensions for all the axes.
    2. Handles cases where the image_size is less than the crop_size by choosing the smallest dimension as the random scale.
    """

    def __init__(self, prob: float = 1, size: int = 50, scale: List[float] = [0.5, 1.0]):
        """
        Args:
            scale (List[int]): Specifies the lower and upper bounds for the random area of the crop,
             before resizing. The scale is defined with respect to the area of the original image.
        """
        super().__init__()
        self.prob = prob
        self.scale = scale
        self.size = [size] * 3

    def __call__(self, image):
        """
        Call method to apply random scale cropping and resizing to an image.

        Args:
            image (torch.Tensor): The input image.

        Returns:
            torch.Tensor: The transformed image.
        """
        if torch.rand(1) < self.prob:
            random_scale = torch.empty(1).uniform_(*self.scale).item()
            rand_cropper = RandScaleCrop(random_scale, random_size=False)
            resizer = Resize(self.size, mode="trilinear")

            for transform in [rand_cropper, resizer]:
                image = transform(image)

        return image

__call__(image)

Call method to apply random scale cropping and resizing to an image.

Parameters:

Name Type Description Default
image Tensor

The input image.

 required

Returns:

Type Description

torch.Tensor: The transformed image.
Source code in fmcib/transforms/random_resized_crop.py 
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def __call__(self, image):
    """
    Call method to apply random scale cropping and resizing to an image.

    Args:
        image (torch.Tensor): The input image.

    Returns:
        torch.Tensor: The transformed image.
    """
    if torch.rand(1) < self.prob:
        random_scale = torch.empty(1).uniform_(*self.scale).item()
        rand_cropper = RandScaleCrop(random_scale, random_size=False)
        resizer = Resize(self.size, mode="trilinear")

        for transform in [rand_cropper, resizer]:
            image = transform(image)

    return image

__init__(prob=1, size=50, scale=[0.5, 1.0])

Parameters:

Name Type Description Default
scale List[int]

Specifies the lower and upper bounds for the random area of the crop, before resizing. The scale is defined with respect to the area of the original image.

 [0.5, 1.0]
Source code in fmcib/transforms/random_resized_crop.py 
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def __init__(self, prob: float = 1, size: int = 50, scale: List[float] = [0.5, 1.0]):
    """
    Args:
        scale (List[int]): Specifies the lower and upper bounds for the random area of the crop,
         before resizing. The scale is defined with respect to the area of the original image.
    """
    super().__init__()
    self.prob = prob
    self.scale = scale
    self.size = [size] * 3