Perspective

Description

Perform a random four point perspective transform of the input.

    Args:
        scale: 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). Default: (0.05, 0.1).
        keep_size: Whether to resize image back to their original size after applying the perspective
            transform. If set to False, the resulting images may end up having different shapes
            and will always be a list, never an array. Default: True
        pad_mode (OpenCV flag): OpenCV border mode.
        pad_val (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
            Default: 0
        mask_pad_val (int, float, list of int, list of float): padding value for mask
            if border_mode is cv2.BORDER_CONSTANT. Default: 0
        fit_output (bool): If True, the image plane size and position will be adjusted to still capture
            the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.)
            Otherwise, parts of the transformed image may be outside of the image plane.
            This setting should not be set to True when using large scale values as it could lead to very large images.
            Default: False
        p (float): probability of applying the transform. Default: 0.5.

    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: (0.05, 0.1))
• keep_size: bool (default: true)
• pad_mode: Literal['cv2.BORDER_CONSTANT', 'cv2.BORDER_REPLICATE', 'cv2.BORDER_REFLECT', 'cv2.BORDER_WRAP', 'cv2.BORDER_DEFAULT', 'cv2.BORDER_TRANSPARENT'] (default: 0)
• pad_val: float | Sequence[float] | None (default: 0)
• mask_pad_val: float | Sequence[float] | None (default: 0)
• fit_output: bool (default: false)
• 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)

Targets

• Image
• Mask
• Keypoints
• BBoxes

Try it out