ElasticTransform

Description

Apply elastic deformation to images, masks, bounding boxes, and keypoints.

    This transformation introduces random elastic distortions to the input data. It's particularly
    useful for data augmentation in training deep learning models, especially for tasks like
    image segmentation or object detection where you want to maintain the relative positions of
    features while introducing realistic deformations.

    The transform works by generating random displacement fields and applying them to the input.
    These fields are smoothed using a Gaussian filter to create more natural-looking distortions.

    Args:
        alpha (float): Scaling factor for the random displacement fields. Higher values result in
            more pronounced distortions. Default: 1.0
        sigma (float): Standard deviation of the Gaussian filter used to smooth the displacement
            fields. Higher values result in smoother, more global distortions. Default: 50.0
        interpolation (int): Interpolation method to be used for image transformation. Should be one
            of the OpenCV interpolation types. Default: cv2.INTER_LINEAR
        border_mode (int): Border mode to be used for handling pixels outside the image boundaries.
            Should be one of the OpenCV border types. Default: cv2.BORDER_REFLECT_101
        value (int, float, list of int, list of float, optional): Padding value if border_mode is
            cv2.BORDER_CONSTANT. Default: None
        mask_value (int, float, list of int, list of float, optional): Padding value for mask if
            border_mode is cv2.BORDER_CONSTANT. Default: None
        approximate (bool): Whether to use an approximate version of the elastic transform. If True,
            uses a fixed kernel size for Gaussian smoothing, which can be faster but potentially
            less accurate for large sigma values. Default: False
        same_dxdy (bool): Whether to use the same random displacement field for both x and y
            directions. Can speed up the transform at the cost of less diverse distortions. Default: False
        mask_interpolation (int): Flag that is used to specify the interpolation algorithm for mask.
            Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
            Default: cv2.INTER_NEAREST.
        noise_distribution (Literal["gaussian", "uniform"]): Distribution used to generate the displacement fields.
            "gaussian" generates fields using normal distribution (more natural deformations).
            "uniform" generates fields using uniform distribution (more mechanical deformations).
            Default: "gaussian".

        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - The transform will maintain consistency across all targets (image, mask, bboxes, keypoints)
          by using the same displacement fields for all.
        - The 'approximate' parameter determines whether to use a precise or approximate method for
          generating displacement fields. The approximate method can be faster but may be less
          accurate for large sigma values.
        - Bounding boxes that end up outside the image after transformation will be removed.
        - Keypoints that end up outside the image after transformation will be removed.

    Example:
        >>> import albumentations as A
        >>> transform = A.Compose([
        ...     A.ElasticTransform(alpha=1, sigma=50, p=0.5),
        ... ])
        >>> transformed = transform(image=image, mask=mask, bboxes=bboxes, keypoints=keypoints)
        >>> transformed_image = transformed['image']
        >>> transformed_mask = transformed['mask']
        >>> transformed_bboxes = transformed['bboxes']
        >>> transformed_keypoints = transformed['keypoints']
    

Parameters

• alpha: float (default: 10)
• sigma: float (default: 10)
• interpolation: Literal['cv2.INTER_NEAREST', 'cv2.INTER_LINEAR', 'cv2.INTER_CUBIC', 'cv2.INTER_AREA', 'cv2.INTER_LANCZOS4', 'cv2.INTER_BITS', 'cv2.INTER_NEAREST_EXACT', 'cv2.INTER_MAX'] (default: 1)
• border_mode: Literal['cv2.BORDER_CONSTANT', 'cv2.BORDER_REPLICATE', 'cv2.BORDER_REFLECT', 'cv2.BORDER_WRAP', 'cv2.BORDER_DEFAULT', 'cv2.BORDER_TRANSPARENT'] (default: 4)
• value: float | Sequence[float] | None (default: null)
• mask_value: float | Sequence[float] | None (default: null)
• approximate: bool (default: false)
• same_dxdy: bool (default: false)
• mask_interpolation: Literal['cv2.INTER_NEAREST', 'cv2.INTER_LINEAR', 'cv2.INTER_CUBIC', 'cv2.INTER_AREA', 'cv2.INTER_LANCZOS4', 'cv2.INTER_BITS', 'cv2.INTER_NEAREST_EXACT', 'cv2.INTER_MAX'] (default: 0)
• noise_distribution: Literal['gaussian', 'uniform'] (default: 'gaussian')
• p: float (default: 0.5)

Targets

• Image
• Mask
• BBoxes
• Keypoints