PiecewiseAffine

Description

Apply piecewise affine transformations to the input image.

    This augmentation places a regular grid of points on an image and randomly moves the neighborhood of these points
    around via affine transformations. This leads to local distortions in the image.

    Args:
        scale (tuple[float, float] | float): Standard deviation of the normal distributions. These are used to sample
            the random distances of the subimage's corners from the full image's corners.
            If scale is a single float value, the range will be (0, scale).
            Recommended values are in the range (0.01, 0.05) for small distortions,
            and (0.05, 0.1) for larger distortions. Default: (0.03, 0.05).
        nb_rows (tuple[int, int] | int): Number of rows of points that the regular grid should have.
            Must be at least 2. For large images, you might want to pick a higher value than 4.
            If a single int, then that value will always be used as the number of rows.
            If a tuple (a, b), then a value from the discrete interval [a..b] will be uniformly sampled per image.
            Default: 4.
        nb_cols (tuple[int, int] | int): Number of columns of points that the regular grid should have.
            Must be at least 2. For large images, you might want to pick a higher value than 4.
            If a single int, then that value will always be used as the number of columns.
            If a tuple (a, b), then a value from the discrete interval [a..b] will be uniformly sampled per image.
            Default: 4.
        interpolation (OpenCV flag): Flag that is used to specify the interpolation algorithm.
            Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
            Default: cv2.INTER_LINEAR.
        mask_interpolation (OpenCV flag): 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.
        cval (number): The constant value to use when filling in newly created pixels.
            Default: 0.
        cval_mask (number): The constant value to use when filling in newly created pixels in masks.
            Default: 0.
        mode (str): {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
            Points outside the boundaries of the input are filled according
            to the given mode. Default: 'constant'.
        absolute_scale (bool): If set to True, the value of the scale parameter will be treated as an absolute
            pixel value. If set to False, it will be treated as a fraction of the image height and width.
            Default: False.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image, mask, keypoints, bboxes

    Image types:
        uint8, float32

    Note:
        - This augmentation is very slow. Consider using `ElasticTransform` instead, which is at least 10x faster.
        - The augmentation may not always produce visible effects, especially with small scale values.
        - For keypoints and bounding boxes, the transformation might move them outside the image boundaries.
          In such cases, the keypoints will be set to (-1, -1) and the bounding boxes will be removed.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.Compose([
        ...     A.PiecewiseAffine(scale=(0.03, 0.05), nb_rows=4, nb_cols=4, p=0.5),
        ... ])
        >>> transformed = transform(image=image)
        >>> transformed_image = transformed["image"]
    

Parameters

• scale: int | tuple[int, int] | float | tuple[float, float] (default: (0.03, 0.05))
• nb_rows: int | tuple[int, int] (default: (4, 4))
• nb_cols: int | tuple[int, int] (default: (4, 4))
• 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)
• 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)
• cval: int (default: 0)
• cval_mask: int (default: 0)
• mode: Literal['constant', 'edge', 'symmetric', 'reflect', 'wrap'] (default: 'constant')
• absolute_scale: bool (default: false)
• p: float (default: 0.5)

Targets

• Image
• Mask
• BBoxes
• Keypoints