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# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019), Morgane Nadal (2020)
#
# eric.debreuve@cnrs.fr
#
# This software is governed by the CeCILL license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
import numpy as np_
from brick.general.type import array_t
def ImageVerification(image: array_t, channel: str) -> array_t:
"""
Verification of the dimension of the image and of the coherence with the entered 'channel' parameter.
"""
#
# The image must not be constant
if np_.amax(image) == np_.amin(image):
raise ValueError("The input image should not be constant.")
# Verification of the dimension of the image and its coherence with the parameters (channel)
elif image.ndim == 3:
print("1 CHANNEL")
if channel is not None:
print(
"Warning: The image has only 3 dimensions. Give the channel the value None in the parameters."
)
return image
elif image.ndim == 4:
if channel == "R" or channel == "G" or channel == "B":
# not changed into if --channel in 'RGB'-- because if channel='RG' => True.
print("MULTIPLE CHANNELS: ", channel, " specified in the parameters.")
image = image[:, :, :, "RGB".find(channel)]
# The obtained image must not be constant
if image.max() == image.min():
raise ValueError(
"The input image should not be constant. Verify the channel parameter."
)
return image
else:
raise ValueError(
"The image has multiple color channels. Error in the value of the parameter channel."
)
elif image.ndim != 4 and image.ndim != 3:
raise ValueError(
f"The image dimensions are not correct: {image.ndim}, instead of 3 or 4."
)
def IntensityNormalizedImage(image: array_t) -> array_t:
"""
Relative normalization of the image between 0 and 1.
No division per 0 since the image should not be constant (ImageVerification function).
"""
#
# print('Relative Intensity Normalization between 0 and 1.')
value_max = np_.amax(image)
value_min = np_.amin(image)
result = (image.astype(np_.float32) - value_min) / float(value_max - value_min)
return result
# print(
# "This normalization does not bring anything; left as is for now to avoid the need for changing prms"
# )
# nonextreme_values = image[np_.logical_and(image > 0.0, image < image.max())]
#
# if nonextreme_values.size > 0:
# nonextreme_avg = nonextreme_values.mean()
# result = image.astype(np_.float32) / nonextreme_avg
# else:
# result = image.astype(np_.float32)
#
# return result