import numpy as np
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def set_lut(img, low, high):
"""Normalize input image intensity to 0-1.
Outer intensities are set to border values.
Args:
img (numpy.ndarray): Input 2D image.
low (float): Lower bound of intensity.
high (float): Upper bound of intensity.
Returns:
numpy.ndarray: Normalized input image
"""
img = (img - low) / (high - low)
img[img > 1] = 1
img[img < 0] = 0
return img
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def norm_img_sd(img, low_sd, high_sd):
"""Normalize input image intensity using standard deviation factors.
Outer intensities are set to border values.
Args:
img (numpy.ndarray): Input 2D image.
low_sd (float): A factor to multiply the standard deviation of image
intensity. Mean intensity - S.D * low_sd is used to lower bound.
high_sd (float): A factor to multiply the standard deviation of image
intensity. Mean intensity + S.D * high_sd is used to upper bound.
Returns:
numpy.ndarray: Normalized input image
"""
ave = np.mean(img)
sd = np.std(img)
low_limit = ave - sd * low_sd
high_limit = ave + sd * high_sd
return set_lut(img, low_limit, high_limit)