# -*- coding: utf-8 -*-
##########################################################################
# pySAP - Copyright (C) CEA, 2017 - 2018
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################
"""
Module with usefull plottinting utility functions.
"""
# Package import
import pysap
# Third party import
import numpy as np
from skimage import data, img_as_float
from skimage import exposure
[docs]def scaling(image, method="stretching"):
"""
Change the image dynamic.
Parameters
----------
image: Image
the image to be transformed.
method: str, default 'stretching'
the normalization method: 'stretching', 'equalization' or 'adaptive'.
Returns
-------
normalize_image: Image
the normalized image.
"""
# Contrast stretching
if method == "stretching":
p2, p98 = np.percentile(image.data, (2, 98))
norm_data = exposure.rescale_intensity(image.data, in_range=(p2, p98))
# Equalization
elif method == "equalization":
norm_data = exposure.equalize_hist(image.data)
# Adaptive Equalization
elif method == "adaptive":
norm_data = exposure.equalize_adapthist(image.data, clip_limit=0.03)
# Unknown method
else:
raise ValueError("Unknown normalization '{0}'.".format(method))
normalize_image = pysap.Image(data=norm_data)
return normalize_image
[docs]def histogram(image, nbins=256, lower_cut=0., cumulate=0):
"""
Compute the histogram of an input dataset.
Parameters
----------
image: Image
the image that contains the dataset to be analysed.
nbins: int, default 256
the histogram number of bins.
lower_cut: float, default 0
do not consider the intensities under this threshold.
cumulate: bool, default False
if set compute the cumulate histogram.
Returns
-------
hist_im: Image
the generated histogram.
"""
hist, bins = np.histogram(image.data[image.data > lower_cut], nbins)
if cumulate:
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max() / cdf.max()
hist_im = pysap.Image(data=cdf_normalized)
else:
hist_im = pysap.Image(data=hist)
return hist_im
