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# 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.
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import numpy as np_
import skimage.measure as ms_
import sklearn.decomposition as dc_
def Soma_parameters(image, final_soma_label):
#
(w, n, m) = image.shape
soma_label = final_soma_label
soma_p1 = []
soma_p2 = []
soma_p3 = []
soma_p4 = []
soma_p5 = []
area = ms_.regionprops(soma_label)
# area paramter
for region in area:
soma_p1.append(region.centroid)
soma_p2.append(region.area)
soma_p3.append(region.coords)
soma_p4.append(region.moments)
# compute eccentricity using PCA
pca = dc_.PCA()
for l in np_.unique(soma_label):
if l != 0:
region = np_.zeros([w, n, m])
loc = np_.where(soma_label == l)
region[loc] = image[loc]
loc = np_.asarray(loc)
# Estimation, calcul des composantes principales for each soma
C = pca.fit(loc).transform(loc)
egv1 = (C[0][:]).max()
egv2 = (C[1][:]).max()
egv3 = (C[2][:]).max()
egv_vect = np_.array([egv1, egv2, egv3])
egv_max = egv_vect.max()
ration1 = egv1 / egv_max
ration2 = egv2 / egv_max
ration3 = egv3 / egv_max
loc = np_.where(egv_vect == egv_max)
vect_ration = np_.array([ration1, ration2, ration3])
ration = np_.delete(vect_ration, loc)
soma_p5.append(ration)
soma_p1 = np_.asarray(soma_p1)
soma_p2 = np_.asarray(soma_p2)
soma_p3 = np_.asarray(soma_p3)
soma_p4 = np_.asarray(soma_p4)
soma_p5 = np_.asarray(soma_p5)
parameters_soma = np_.array(
[soma_p1, soma_p2, soma_p3, soma_p4, soma_p5[:, 0], soma_p5[:, 1]]
)
parameters_soma = parameters_soma.T
return parameters_soma