# 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".
#
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# 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
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# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL license and that you accept its terms.
from brick.general.type import array_t
import numpy as np_
import math as mt_
import sys as sy_
from PIL import Image
from PIL.ExifTags import TAGS
import re as re_
def ImageVerification(image: array_t, channel: str) -> array_t:
#
# The image must not be constant
if image.max() == image.min():
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. However, a value for the "channel" parameter is specified. Give the channel the value None')
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.')
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 = image.max()
value_min = image.min()
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
def FindVoxelDimensionInMicron(data_path: str, size_voxel_in_micron: list = None) -> array_t:
'''
Find Voxel dimension in micron from the image metadata.
'''
#
if size_voxel_in_micron is not None:
print('VOXEL DIM: [X Y Z] =', size_voxel_in_micron, 'micron.')
return np_.array(size_voxel_in_micron)
else:
print('Warning: The size of a voxel is not specified in the parameters.')
try:
with Image.open(data_path) as img: # Find the voxels dimensions in micron in the metadata.
meta_dict = {TAGS.get(key, 'missing'): img.tag[key] for key in
img.tag} # Use the exif tags into the image metadata
# Decode the tags text
metadata = meta_dict['missing'].decode('utf8') # Decode the tags text
metadata = metadata.replace('\x00', '')
voxel_size = [] # Initialize the list of voxel size in str
for axe in 'XYZ':
pattern = 'Voxel' + axe + '.+\= (\d.+E.\d.)' # Regular expression
voxel_size.append(re_.findall(pattern, metadata)[0])
voxel_size = np_.array(list(map(float, voxel_size)))
voxel_size_micron = 1.0e6 * voxel_size # Conversion meters in micron
print('VOXEL DIM: [X Y Z] =', voxel_size_micron, 'micron.')
return voxel_size_micron
except:
raise('/!\ Unable to find the voxel dimensions in micron in the metadata. Please specify it in the parameters.')
def ToPixel(micron: float, voxel_size_micron: array_t, dimension: tuple = (0,), decimals: int = None) -> int:
'''
Dimension correspond to the axis (X,Y,Z) = (0,1,2). Can be used for distance, area and volumes.
'''
# Conversion of pixels into micron.
return round(micron/(mt_.prod(voxel_size_micron[axis] for axis in dimension)), decimals)
def ToMicron(pixel: float, voxel_size_micron: array_t, dimension: tuple = (0,), decimals: int = None) -> float:
'''
Dimension correspond to the axis (X,Y,Z) = (0,1,2). Can be used for distance, area and volumes.
'''
# May not be used. if not used, delete.
# Conversion of micron into pixels.
return round(pixel * (mt_.prod(voxel_size_micron[axis] for axis in dimension)), decimals)
def DijkstraCosts(image: array_t, som_map: array_t, ext_map: array_t) -> array_t:
#
# TODO: Ideally, the extension part should be dilated
# but in ext-ext connections, there must not be dilation around the current or the other exts
# (current ext plays the role of a soma in soma-ext step)
#
dijkstra_costs = 1.0 / (image + 1.0)
dijkstra_costs[np_.logical_or(som_map > 0, ext_map > 0)] = np_.inf
return dijkstra_costs