Class LabelToContour

monai/transforms/post/array.py:589–639

Return the contour of binary input images that only compose of 0 and 1, with Laplacian kernel set as default for edge detection. Typical usage is to plot the edge of label or segmentation output. Args: kernel_type: the method applied to do edge detection, default is "Laplace".

class LabelToContour(Transform):
    """
    Return the contour of binary input images that only compose of 0 and 1, with Laplacian kernel
    set as default for edge detection. Typical usage is to plot the edge of label or segmentation output.

    Args:
        kernel_type: the method applied to do edge detection, default is "Laplace".

    Raises:
        NotImplementedError: When ``kernel_type`` is not "Laplace".

    """

    backend = [TransformBackends.TORCH]

    def __init__(self, kernel_type: str = "Laplace") -> None:
        if kernel_type != "Laplace":
            raise NotImplementedError('Currently only kernel_type="Laplace" is supported.')
        self.kernel_type = kernel_type

    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
        """
        Args:
            img: torch tensor data to extract the contour, with shape: [channels, height, width[, depth]]

        Raises:
            ValueError: When ``image`` ndim is not one of [3, 4].

        Returns:
            A torch tensor with the same shape as img, note:
                1. it's the binary classification result of whether a pixel is edge or not.
                2. in order to keep the original shape of mask image, we use padding as default.
                3. the edge detection is just approximate because it defects inherent to Laplace kernel,
                   ideally the edge should be thin enough, but now it has a thickness.

        """
        img = convert_to_tensor(img, track_meta=get_track_meta())
        img_: torch.Tensor = convert_to_tensor(img, track_meta=False)
        spatial_dims = len(img_.shape) - 1
        img_ = img_.unsqueeze(0)  # adds a batch dim
        if spatial_dims == 2:
            kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32)
        elif spatial_dims == 3:
            kernel = -1.0 * torch.ones(3, 3, 3, dtype=torch.float32)
            kernel[1, 1, 1] = 26.0
        else:
            raise ValueError(f"{self.__class__} can only handle 2D or 3D images.")
        contour_img = apply_filter(img_, kernel)
        contour_img.clamp_(min=0.0, max=1.0)
        output, *_ = convert_to_dst_type(contour_img.squeeze(0), img)
        return output


class Ensemble: