Rotate

Description

Rotate the input by an angle selected randomly from the uniform distribution.

    Args:
        limit (float | tuple[float, float]): Range from which a random angle is picked. If limit is a single float,
            an angle is picked from (-limit, limit). Default: (-90, 90)
        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.
        border_mode (OpenCV flag): Flag that is used to specify the pixel extrapolation method. Should be one of:
            cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
            Default: cv2.BORDER_REFLECT_101
        value (int, float, list of ints, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT.
        mask_value (int, float, list of ints, list of float): Padding value if border_mode is cv2.BORDER_CONSTANT
            applied for masks.
        rotate_method (str): Method to rotate bounding boxes. Should be 'largest_box' or 'ellipse'.
            Default: 'largest_box'
        crop_border (bool): Whether to crop border after rotation. If True, the output image size might differ
            from the input. Default: False
        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.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - The rotation angle is randomly selected for each execution within the range specified by 'limit'.
        - When 'crop_border' is False, the output image will have the same size as the input, potentially
          introducing black triangles in the corners.
        - When 'crop_border' is True, the output image is cropped to remove black triangles, which may result
          in a smaller image.
        - Bounding boxes are rotated and may change size or shape.
        - Keypoints are rotated around the center of the image.

    Mathematical Details:
        1. An angle θ is randomly sampled from the range specified by 'limit'.
        2. The image is rotated around its center by θ degrees.
        3. The rotation matrix R is:
           R = [cos(θ)  -sin(θ)]
               [sin(θ)   cos(θ)]
        4. Each point (x, y) in the image is transformed to (x', y') by:
           [x']   [cos(θ)  -sin(θ)] [x - cx]   [cx]
           [y'] = [sin(θ)   cos(θ)] [y - cy] + [cy]
           where (cx, cy) is the center of the image.
        5. If 'crop_border' is True, the image is cropped to the largest rectangle that fits inside the rotated image.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.Rotate(limit=45, p=1.0)
        >>> result = transform(image=image)
        >>> rotated_image = result['image']
        # rotated_image will be the input image rotated by a random angle between -45 and 45 degrees
    

Parameters

• limit: int | tuple[int, int] | float | tuple[float, float] (default: (-90, 90))
• 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)
• rotate_method: Literal['largest_box', 'ellipse'] (default: 'largest_box')
• crop_border: 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)
• p: float (default: 0.5)

Targets

• Image
• Mask
• BBoxes
• Keypoints