FancyPCA

Description

Apply Fancy PCA augmentation to the input image.

    This augmentation technique applies PCA (Principal Component Analysis) to the image's color channels,
    then adds multiples of the principal components to the image, with magnitudes proportional to the
    corresponding eigenvalues times a random variable drawn from a Gaussian with mean 0 and standard
    deviation 'alpha'.

    Args:
        alpha (tuple[float, float] | float): Standard deviation of the Gaussian distribution used to generate
            random noise for each principal component. If a single float is provided, it will be used for
            all channels. If a tuple of two floats (min, max) is provided, the standard deviation will be
            uniformly sampled from this range for each run. Default: 0.1.
        always_apply (bool): If True, the transform will always be applied. Default: False.
        p (float): Probability of applying the transform. Default: 0.5.

    Targets:
        image

    Image types:
        uint8, float32

    Number of channels:
        any

    Note:
        - This augmentation is particularly effective for RGB images but can work with any number of channels.
        - For grayscale images, it applies a simplified version of the augmentation.
        - The transform preserves the mean of the image while adjusting the color/intensity variation.
        - This implementation is based on the paper by Krizhevsky et al. and is similar to the one used
          in the original AlexNet paper.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.FancyPCA(alpha=0.1, p=1.0)
        >>> result = transform(image=image)
        >>> augmented_image = result["image"]

    References:
        - Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep
          convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
        - https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

    

Parameters

• alpha: float (default: 0.1)
• p: float (default: 0.5)

Targets

• Image