RandomToneCurve

Description

Randomly change the relationship between bright and dark areas of the image by manipulating its tone curve.

    Args:
        scale (float): Standard deviation of the normal distribution used to sample random distances
            to move two control points that modify the image's curve. Values should be in range [0, 1]. Default: 0.1
        per_channel (bool): If `True`, the tone curve will be applied to each channel of the input image separately,
            which can lead to color distortion. Default: False.
        p (float): Probability of applying the transform. Default: 0.5

    Targets:
        image

    Image types:
        uint8, float32

    Reference:
        - "What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance"
          by Mahmoud Afifi and Michael S. Brown, ICCV 2019.
        - GitHub repository: https://github.com/mahmoudnafifi/WB_color_augmenter

    Example:
        >>> import numpy as np
        >>> from albumentations import RandomToneCurve
        >>> img = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = RandomToneCurve(scale=0.1, per_channel=True, p=1.0)
        >>> transformed_img = transform(image=img)['image']

    This transform applies a random tone curve to the input image by adjusting the relationship between bright and
    dark areas. When `per_channel` is set to True, each channel is adjusted separately, potentially causing color
    distortions. Otherwise, the same adjustment is applied to all channels, preserving the original color relationships.
    

Parameters

• p: float (default: 0.5)
• scale: float (default: 0.1)
• per_channel: bool (default: false)

Targets

• Image

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