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# Copyright CNRS/Inria/UNS
# Contributor(s): Eric Debreuve (since 2019)
#
# 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.
from type import array_t
import multiprocessing as pl_
# from ctypes import c_bool as c_bool_t
from ctypes import c_float as c_float_t
from multiprocessing.sharedctypes import Array as shared_array_t
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# from multiprocessing.sharedctypes import Value as shared_value_t
from typing import List, Tuple
import numpy as np_
from scipy import ndimage as im_
def FrangiEnhancement(
img: array_t,
scale_range: Tuple[float, float] = (1.0e-10, 10.0),
scale_step: float = 2.0,
alpha: float = 0.5,
beta: float = 0.5,
frangi_c: float = 500.0,
bright_on_dark: bool = True,
in_parallel: bool = False,
) -> Tuple[array_t, array_t]:
#
enhanced = None
scale_map = None
img = img.astype(np_.float32, copy=False)
scales = np_.linspace(
scale_range[0],
scale_range[1],
num=np_.ceil((scale_range[1] - scale_range[0]) / scale_step) + 1,
)
if in_parallel:
# initializer = np_.zeros_like(img, dtype = c_float_t)
# shared_not_init = shared_value_t(c_bool_t, True)
# shared_enhanced = shared_array_t(c_float_t, img.size)
# shared_scale_map = shared_array_t(c_float_t, img.size)
# kwargs = {
# "alpha": alpha,
# "beta": beta,
# "frangi_c": frangi_c,
# "bright_on_dark": bright_on_dark,
# "not_init": shared_not_init,
# "enhanced": shared_enhanced,
# "scale_map": shared_scale_map,
# }
shared_enhanced_s = tuple(shared_array_t(c_float_t, img.size) for ___ in scales)
processes = tuple(
pl_.Process(
target=FrangiEnhancementOneScale,
args=(img, scale),
kwargs={
"alpha": alpha,
"beta": beta,
"frangi_c": frangi_c,
"bright_on_dark": bright_on_dark,
"shared_enhanced": shared_enhanced,
},
)
for scale, shared_enhanced in zip(scales, shared_enhanced_s)
)
for process in processes:
process.start()
for process in processes:
process.join()
# enhanced = np_.array(shared_enhanced).astype(np_.float32, copy = False).reshape(img.shape)
# scale_map = np_.array(shared_scale_map).astype(np_.float32, copy = False).reshape(img.shape)
enhanced = np_.array(shared_enhanced_s[0])
scale_map = np_.full(img.size, scales[0], dtype=np_.float32)
for s_idx, shared_enhanced in enumerate(shared_enhanced_s[1:], start=1):
local_enhanced = np_.array(shared_enhanced)
larger_map = local_enhanced > enhanced
enhanced[larger_map] = local_enhanced[larger_map]
scale_map[larger_map] = scales[s_idx]
enhanced = enhanced.astype(np_.float32, copy=False).reshape(img.shape)
scale_map = scale_map.astype(np_.float32, copy=False).reshape(img.shape)
else:
for s_idx, scale in enumerate(scales):
local_enhanced = FrangiEnhancementOneScale(
img,
scale,
alpha=alpha,
beta=beta,
frangi_c=frangi_c,
bright_on_dark=bright_on_dark,
)
if s_idx > 0:
larger_map = local_enhanced > enhanced
enhanced[larger_map] = local_enhanced[larger_map]
scale_map[larger_map] = scale
else:
enhanced = local_enhanced
scale_map = np_.full(img.shape, scale, dtype=np_.float32)
return enhanced, scale_map
def FrangiEnhancementOneScale(
img: array_t,
scale: float,
alpha: float = 0.5,
beta: float = 0.5,
frangi_c: float = 500.0,
bright_on_dark: bool = True,
shared_enhanced=None,
# not_init = None,
# enhanced =None,
# scale_map =None,
) -> array_t:
#
print(f"scale={scale}")
Dxx, Dxy, Dxz, Dyy, Dyz, Dzz = HessianMatrix(img, scale)
eig_val_1, eig_val_2, eig_val_3 = EigenValues(Dxx, Dxy, Dxz, Dyy, Dyz, Dzz)
# Absolute plate, blob, and background maps
abs_eig_val_1 = np_.fabs(eig_val_1)
abs_eig_val_2 = np_.fabs(eig_val_2)
abs_eig_val_3 = np_.fabs(eig_val_3)
plate_map = CarefullDivision(abs_eig_val_2, abs_eig_val_3)
blob_map = CarefullDivision(abs_eig_val_1, np_.sqrt(abs_eig_val_2 * abs_eig_val_3))
bckgnd = np_.sqrt(abs_eig_val_1 ** 2 + abs_eig_val_2 ** 2 + abs_eig_val_3 ** 2)
# Normalized plate, blob, and background maps
plate_factor = -2.0 * alpha ** 2
blob_factor = -2.0 * beta ** 2
bckgnd_factor = -2.0 * frangi_c ** 2
plate_map = 1.0 - np_.exp(plate_map ** 2 / plate_factor)
blob_map = np_.exp(blob_map ** 2 / blob_factor)
bckgnd = 1.0 - np_.exp(bckgnd ** 2 / bckgnd_factor)
local_enhanced = plate_map * blob_map * bckgnd
if bright_on_dark:
local_enhanced[eig_val_2 > 0.0] = 0.0
local_enhanced[eig_val_3 > 0.0] = 0.0
else:
local_enhanced[eig_val_2 < 0.0] = 0.0
local_enhanced[eig_val_3 < 0.0] = 0.0
local_enhanced[
np_.logical_or(np_.isnan(local_enhanced), np_.isinf(local_enhanced))
] = 0.0
# if enhanced is None:
if shared_enhanced is None:
return local_enhanced
else:
shared_enhanced[:] = local_enhanced.flatten()[:]
def HessianMatrix(
img: array_t, scale: float
) -> Tuple[array_t, array_t, array_t, array_t, array_t, array_t]:
#
if scale > 0.0:
img = scale ** 2 * im_.gaussian_filter(img, scale)
else:
img = scale ** 2 * img
Dx, Dy, Dz = np_.gradient(img)
Dxx, Dxy, Dxz = np_.gradient(Dx, axis=(0, 1, 2))
Dyy, Dyz = np_.gradient(Dy, axis=(1, 2))
Dzz = np_.gradient(Dz, axis=2)
return Dxx, Dxy, Dxz, Dyy, Dyz, Dzz
def EigenValues(
Dxx: array_t, Dxy: array_t, Dxz: array_t, Dyy: array_t, Dyz: array_t, Dzz: array_t
) -> List[array_t]:
#
hessians = np_.stack((Dxx, Dxy, Dxz, Dxy, Dyy, Dyz, Dxz, Dyz, Dzz), axis=-1)
hessians = np_.reshape(hessians, (*Dxx.shape, 3, 3))
eigenvalues = np_.linalg.eigvalsh(hessians)
sorted_eigenvalues = SortedByAbsValues(eigenvalues, axis=-1)
return [
np_.squeeze(eigenvalue, axis=-1)
for eigenvalue in np_.split(
sorted_eigenvalues, sorted_eigenvalues.shape[-1], axis=-1
)
]
def SortedByAbsValues(array: array_t, axis: int = 0) -> array_t:
#
index = list(np_.ix_(*[np_.arange(size) for size in array.shape]))
index[axis] = np_.fabs(array).argsort(axis=axis)
return array[tuple(index)]
def CarefullDivision(array_1: array_t, array_2: array_t) -> array_t:
#
null_map = array_2 == 0.0
if null_map.any():
denominator = np_.copy(array_2)
denominator[null_map] = 1.0e-10
else:
denominator = array_2
return array_1 / denominator