GAMMA_ADJUSTMENT

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Adjusts the gamma of an input image and outputs the adjusted image Params: default : Image The input image gain : float, default=1 The gain of the logarithmic correction. gamma : float, default=1 The gamma to correct to. Returns: out : Image The adjusted image.

Python Code

import numpy as np
from flojoy import Image, flojoy
from skimage.exposure import adjust_gamma


@flojoy(deps={"scikit-image": "0.21.0"})
def GAMMA_ADJUSTMENT(
    default: Image,
    gain: float = 1,
    gamma: float = 1,
) -> Image:
    """
    Adjusts the gamma of an input image and outputs the adjusted image

    Parameters
    ----------
    default: Image
        The input image
    gain: float, default=1
        The gain of the logarithmic correction.
    gamma: float, default=1
        The gamma to correct to.

    Returns
    -------
    Image
        The adjusted image.
    """

    r = default.r
    g = default.g
    b = default.b
    a = default.a

    if a is not None:
        rgba_image = np.stack((r, g, b, a), axis=2)
    else:
        rgba_image = np.stack((r, g, b), axis=2)

    # Check if the image is in the appropriate format
    if rgba_image.dtype != np.uint8:
        raise ValueError("Image must be in uint8 format")

    # Apply logarithmic correction
    corrected_image = adjust_gamma(rgba_image, gain=gain, gamma=gamma)

    r = corrected_image[:, :, 0]
    g = corrected_image[:, :, 1]
    b = corrected_image[:, :, 2]
    if a is not None:
        a = corrected_image[:, :, 3]

    return Image(r=r, g=g, b=b, a=a)

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Example App

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In this example, contrast adjustments were performed on an example image.

First the necessary blocks were added: SKIMAGE to fetch the example image, 3 IMAGE blocks to view the images, LOGARITHMIC_ADJUSTMENT, AND GAMMA_ADJUSTMENT.

The gamma parameter for GAMMA_ADJUSTMENT was set to 1.5, and the SKLEARN image was set to moon. The remaining parameters were left at default values.

The blocks were connected as shown and the app was run.