# 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
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# 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.
from brick.general.type import array_t
import numpy as np_
import sys as sy_
from PIL import Image
from PIL.ExifTags import TAGS
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('Your image has only one color channel.')
if channel is not None:
raise ValueError('The image has only 3 dimensions. However, a value for the "channel" parameter is '
'specified. Give the channel the value None')
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('The image has multiple color channels. The channel', channel,
'is 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.')
# if channel != 'RGB' and image.ndim == 4:
# print('The image has multiple color channels. The channel', channel, 'was specified in the parameters.')
# for idx, color in enumerate('RGB'):
# if channel == color:
# image = image[:, :, :, idx]
# elif channel == 'RGB' and image.ndim == 3:
# print('WARNING. The 3 RGB color channels were selected in the parameters but the image has only one channel.')
def IntensityNormalizedImage(image: array_t) -> array_t:
#
print(
'Relative Intensity Normalization between 0 and 1 (Not a standardization). Need to reevaluate the parameters !!!')
value_max = image.astype(np_.float32).max()
value_min = image.astype(np_.float32).min()
result = (image.astype(np_.float32) - value_min) / (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 VoxelDimensionInMicrons(data_path: str) -> array_t:
# TODO Verify if metadata are in the same format for all the images - if not, add a raise error ?
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 idaxe, axe in enumerate('XYZ'):
idvox = metadata.find('dblVoxel' + axe) # Get the index of the voxel size for the axe X, Y, Z
idvox += 15
while metadata[idvox] != '\n':
voxel_size[idaxe] += metadata[idvox]
idvox += 1
voxel_size = np_.asarray(list(map(float, voxel_size)))
voxel_size_microns = 1.0e6 * voxel_size
print('Voxel dimension in the image is [X Y Z] =', voxel_size_microns, 'in microns. WARNING this method highly '
'depends on the format of the metadata.')
return voxel_size_microns
def ToPixels(micron: float, voxel_size_microns: array_t) -> int:
# Conversion of pixels into microns. Used in morphomath (disk structuring element mp_.disk(n_pixel)).
# Assumes that the axes X and Y have the same ratio pixel:microns.
return round(micron/voxel_size_microns[0])
def ToMicrons(pixel: int, voxel_size_microns: array_t) -> float:
# May not be used. if not used, delete.
# Conversion of microns into pixels.
# Assumes that the axes X and Y have the same ratio pixel:microns.
return float(pixel * voxel_size_microns[0])
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