PiecewiseAffine

Description

Apply affine transformations that differ between local neighborhoods.
    This augmentation places a regular grid of points on an image and randomly moves the neighborhood of these point
    around via affine transformations. This leads to local distortions.

    This is mostly a wrapper around scikit-image's ``PiecewiseAffine``.
    See also ``Affine`` for a similar technique.

    Note:
        This augmenter is very slow. Try to use ``ElasticTransformation`` instead, which is at least 10x faster.

    Note:
        For coordinate-based inputs (keypoints, bounding boxes, polygons, ...),
        this augmenter still has to perform an image-based augmentation,
        which will make it significantly slower and not fully correct for such inputs than other transforms.

    Args:
        scale (float, tuple of float): Each point on the regular grid is moved around via a normal distribution.
            This scale factor is equivalent to the normal distribution's sigma.
            Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of
            the image if ``absolute_scale=False`` (default), so this scale can be the same for different sized images.
            Recommended values are in the range ``0.01`` to ``0.05`` (weak to strong augmentations).
                * If a single ``float``, then that value will always be used as the scale.
                * If a tuple ``(a, b)`` of ``float`` s, then a random value will
                  be uniformly sampled per image from the interval ``[a, b]``.
        nb_rows (int, tuple of 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``.
            You might have to then adjust scale to lower values.
                * 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.
        nb_cols (int, tuple of int): Number of columns. Analogous to `nb_rows`.
        interpolation (int): The order of interpolation. The order has to be in the range 0-5:
             - 0: Nearest-neighbor
             - 1: Bi-linear (default)
             - 2: Bi-quadratic
             - 3: Bi-cubic
             - 4: Bi-quartic
             - 5: Bi-quintic
        mask_interpolation (int): same as interpolation but for mask.
        cval (number): The constant value to use when filling in newly created pixels.
        cval_mask (number): Same as cval but only for masks.
        mode (str): {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
            Points outside the boundaries of the input are filled according
            to the given mode.  Modes match the behaviour of `numpy.pad`.
        absolute_scale (bool): Take `scale` as an absolute value rather than a relative value.
        keypoints_threshold (float): Used as threshold in conversion from distance maps to keypoints.
            The search for keypoints works by searching for the
            argmin (non-inverted) or argmax (inverted) in each channel. This
            parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit
            as a keypoint. Use ``None`` to use no min/max. Default: 0.01

    Targets:
        image, mask, keypoints, bboxes

    Image types:
        uint8, float32

    

Parameters

• p: float (default: 0.5)
• scale: int | tuple[int, int] | float | tuple[float, float] (default: (0.03, 0.05))
• nb_rows: int | tuple[int, int] (default: 4)
• nb_cols: int | tuple[int, int] (default: 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)
• keypoints_threshold: float (default: 0.01)

Targets

• Image
• Mask
• BBoxes
• Keypoints

Try it out