# Copyright or © or Copr. IETR/INSA Rennes (2019)
#
# Contributors :
# Eduardo Fernandes-Montesuma eduardo.fernandes-montesuma@insa-rennes.fr (2019)
# Florian Lemarchand florian.lemarchand@insa-rennes.fr (2019)
#
#
# OpenDenoising is a computer program whose purpose is to benchmark image
# restoration algorithms.
#
# This software is governed by the CeCILL-C 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-C
# 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-C license and that you accept its terms.
from OpenDenoising.model import AbstractDenoiser
[docs]class FilteringModel(AbstractDenoiser):
"""FilteringModel represents a Denoising Model that does not depend on Neural Networks.
Attributes
----------
model_function : :class:`function`
Filtering denoising function. It should accept at least one argument, a image batch :class:`numpy.ndarray`.
It should also have only one return, another :class:`numpy.ndarray` with same shape, corresponding to the
denoising result.
"""
[docs] def __init__(self, model_name="FilteringModel"):
self.model_function = None
super().__init__(model_name=model_name)
[docs] def charge_model(self, model_function, **kwargs):
"""Charges the denoising function into the class wrapper.
Parameters
----------
model_function : :class:`function`
Filtering denoising function. It should accept at least one argument, a image batch :class:`numpy.ndarray`.
It should also have only one return, another :class:`numpy.ndarray` with same shape, corresponding to the
denoising result. Notice that, if your function needs more arguments than the noisy image batch, these can
be passed through keyword arguments to charge_model (see examples section).
"""
def partial_func(image):
return model_function(image, **kwargs)
self.model_function = partial_func
[docs] def __call__(self, image):
"""Denoises a batch of images noised_image
Parameters
----------
image : :class:`numpy.ndarray`
batch of images with shape (batch_size, height, width, channels)
Returns
-------
:class:`numpy.ndarray`
batch of images denoised by model_func, with shape (b, h ,w , c)
"""
denoised_image = self.model_function(image)
return denoised_image
[docs] def __repr__(self):
return "Filtering model name: {}".format(self)
def __len__(self):
return 1