GlassBlur

Description

Apply a glass blur effect to the input image.

    This transform simulates the effect of looking through textured glass by locally
    shuffling pixels in the image. It creates a distorted, frosted glass-like appearance.

    Args:
        sigma (float): Standard deviation for the Gaussian kernel used in the process.
            Higher values increase the blur effect. Must be non-negative.
            Default: 0.7

        max_delta (int): Maximum distance in pixels for shuffling.
            Determines how far pixels can be moved. Larger values create more distortion.
            Must be a positive integer.
            Default: 4

        iterations (int): Number of times to apply the glass blur effect.
            More iterations create a stronger effect but increase computation time.
            Must be a positive integer.
            Default: 2

        mode (Literal["fast", "exact"]): Mode of computation. Options are:
            - "fast": Uses a faster but potentially less accurate method.
            - "exact": Uses a slower but more precise method.
            Default: "fast"

        p (float): Probability of applying the transform. Should be in the range [0, 1].
            Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        Any

    Note:
        - This transform is particularly effective for creating a 'looking through
          glass' effect or simulating the view through a frosted window.
        - The 'fast' mode is recommended for most use cases as it provides a good
          balance between effect quality and computation speed.
        - Increasing 'iterations' will strengthen the effect but also increase the
          processing time linearly.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.GlassBlur(sigma=0.7, max_delta=4, iterations=3, mode="fast", p=1)
        >>> result = transform(image=image)
        >>> glass_blurred_image = result["image"]

    References:
        - This implementation is based on the technique described in:
          "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness"
          https://arxiv.org/abs/1903.12261
        - Original implementation:
          https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py
    

Parameters

• p: float (default: 0.5)
• sigma: float (default: 0.7)
• max_delta: int (default: 4)
• iterations: int (default: 2)
• mode: Literal['fast', 'exact'] (default: 'fast')

Targets

• Image

Try it out