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Adjusts the logarithmic exposure 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. inv : bool, default=False Perform inverse logarithmic correction. Returns: out : Image The adjusted image.
Python Code
import numpy as np
from flojoy import Image, flojoy
from skimage.exposure import adjust_log

@flojoy(deps={"scikit-image": "0.21.0"})
default: Image,
gain: float = 1,
inv: bool = False,
) -> Image:
"""
Adjusts the logarithmic exposure 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.
inv: bool, default=False
Perform inverse logarithmic correction.

Returns
-------
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_log(rgba_image, gain=gain, inv=inv)

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)


Find this Flojoy Block on GitHub

## Example

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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.