RandomResizedCrop

Description

Crop a random part of the input and rescale it to a specified size.

    This transform first crops a random portion of the input image (or mask, bounding boxes, keypoints)
    and then resizes the crop to a specified size. It's particularly useful for training neural networks
    on images of varying sizes and aspect ratios.

    Args:
        size (tuple[int, int]): Target size for the output image, i.e. (height, width) after crop and resize.
        scale (tuple[float, float]): Range of the random size of the crop relative to the input size.
            For example, (0.08, 1.0) means the crop size will be between 8% and 100% of the input size.
            Default: (0.08, 1.0)
        ratio (tuple[float, float]): Range of aspect ratios of the random crop.
            For example, (0.75, 1.3333) allows crop aspect ratios from 3:4 to 4:3.
            Default: (0.75, 1.3333333333333333)
        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
        p (float): Probability of applying the transform. Default: 1.0

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - This transform attempts to crop a random area with an aspect ratio and relative size
          specified by 'ratio' and 'scale' parameters. If it fails to find a suitable crop after
          10 attempts, it will return a crop from the center of the image.
        - The crop's aspect ratio is defined as width / height.
        - Bounding boxes that end up fully outside the cropped area will be removed.
        - Keypoints that end up outside the cropped area will be removed.
        - After cropping, the result is resized to the specified size.

    Mathematical Details:
        1. A target area A is sampled from the range [scale[0] * input_area, scale[1] * input_area].
        2. A target aspect ratio r is sampled from the range [ratio[0], ratio[1]].
        3. The crop width and height are computed as:
           w = sqrt(A * r)
           h = sqrt(A / r)
        4. If w and h are within the input image dimensions, the crop is accepted.
           Otherwise, steps 1-3 are repeated (up to 10 times).
        5. If no valid crop is found after 10 attempts, a centered crop is taken.
        6. The crop is then resized to the specified size.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.RandomResizedCrop(size=80, scale=(0.5, 1.0), ratio=(0.75, 1.33), p=1.0)
        >>> result = transform(image=image)
        >>> transformed_image = result['image']
        # transformed_image will be a 80x80 crop from a random location in the original image,
        # with the crop's size between 50% and 100% of the original image size,
        # and the crop's aspect ratio between 3:4 and 4:3.
    

Parameters

• size: int | tuple[int, int] | None (default: (512, 320))
• scale: tuple[float, float] (default: (0.08, 1))
• ratio: tuple[float, float] (default: (0.75, 1.3333333333333333))
• 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)
• p: float (default: 1)

Targets

• Image
• Mask
• BBoxes
• Keypoints