AdjustSigmoid.py

import os.path
import numpy as np
from skimage.io import imread, imsave
from skimage.exposure import adjust_sigmoid
from skimage.util import img_as_uint, img_as_ubyte

np.seterr(divide='ignore', invalid='ignore')

"""
See: https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.adjust_sigmoid

Performs a Sigmoid contrast adjustment. Think of the "cutoff" being a number 0 - 1,
representing a percentile of the histogram, above and below which the histogram is
"squished" to its bounds. The gain controls the amount of "squishing".

In mathematical terms, O = 1 / (1 + exp*(gain*(cutoff - I))).

Note that this transform is prone to returning a wildly different dynamic range than
the input image and should be parameterized carefully.

Requirements
------------
numpy (comes with Aivia installer)
scikit-image (comes with Aivia installer)

Parameters
----------
Input Image : Aivia channel
    Input channel to use for the transform.

Cutoff : double
    Percentile of the histogram from which to center the transform.

Gain : double
    Scaling factor that controls the strength of the transform.

Returns
-------
Aivia channel
    Result of the transform
"""

# [INPUT Name:inputImagePath Type:string DisplayName:'Input Image']
# [INPUT Name:cutoff Type:double DisplayName:'Cutoff [0-1]' Default:0.5 Min:0.0 Max:1.0]
# [INPUT Name:gain Type:double DisplayName:'Gain' Default:10.0 Min:0.0 Max:20.0]
# [OUTPUT Name:resultPath Type:string DisplayName:'Sigmoid']
def run(params):
    image_location = params['inputImagePath']
    result_location = params['resultPath']
    cutoff = float(params['cutoff'])
    gain = float(params['gain'])
    
    if not os.path.exists(image_location):
        print(f'Error: {image_location} does not exist')
        return;
        
    image_data = imread(image_location)
    output_data = np.empty_like(image_data)
    sigmoid_image = adjust_sigmoid(image_data, cutoff=cutoff, gain=gain, inv=False)
    if image_data.dtype == np.uint16:
        output_data = img_as_uint(sigmoid_image)
    else:
        output_data = img_as_ubyte(sigmoid_image)
    imsave(result_location, output_data)


if __name__ == '__main__':
    params = {}
    params['inputImagePath'] = 'test.png'
    params['resultPath'] = 'testResult.png'
    params['cutoff'] = 0.5
    params['gain'] = 10.0
    
    run(params)