"""Module for calculating the feature similarity (FSIM) between two images.
Examples
--------
.. doctest-skip::
>>> import numpy as np
>>> from viqa import FSIM
>>> img_r = np.random.rand(256, 256)
>>> img_m = np.random.rand(256, 256)
>>> fsim = FSIM()
>>> fsim.score(img_r, img_m, data_range=1)
"""
# Authors
# -------
# Author: Lukas Behammer
# Research Center Wels
# University of Applied Sciences Upper Austria, 2023
# CT Research Group
#
# Modifications
# -------------
# Original code, 2024, Lukas Behammer
#
# License
# -------
# BSD-3-Clause License
from warnings import warn
import numpy as np
from piq import fsim
from viqa._metrics import FullReferenceMetricsInterface
from viqa.utils import _check_chromatic
[docs]
class FSIM(FullReferenceMetricsInterface):
"""Calculate the feature similarity (FSIM) between two images.
Attributes
----------
score_val : float
FSIM score value of the last calculation.
parameters : dict
Dictionary containing the parameters for FSIM calculation.
Parameters
----------
data_range : {1, 255, 65535}, default=255
Data range of the returned data in data loading. Is used for image loading when
``normalize`` is True and for the FSIM calculation. Passed to
:py:func:`viqa.utils.load_data` and :py:meth:`score`.
normalize : bool, default=False
If True, the input images are normalized to the ``data_range`` argument.
**kwargs : optional
Additional parameters for data loading. The keyword arguments are passed to
:py:func:`.viqa.utils.load_data`.
Other Parameters
----------------
chromatic : bool, default False
If True, the input images are expected to be RGB images and FSIMc is
calculated. See [1]_. Passed to
:py:func:`piq.fsim`. See the documentation under [2]_.
Raises
------
ValueError
If ``data_range`` is not set.
Warnings
--------
This metric is not yet tested. The metric should be only used for experimental
purposes.
Notes
-----
For more information on the FSIM metric, see [1]_.
References
----------
.. [1] Zhang, L., Zhang, L., Mou, X., & Zhang, D. (2011). FSIM: A feature
similarity index for image quality assessment. IEEE Transactions on Image
Processing, 20(8). https://doi.org/10.1109/TIP.2011.2109730
.. [2] https://piq.readthedocs.io/en/latest/functions.html#piq.fsim
"""
def __init__(self, data_range=255, normalize=False, **kwargs):
"""Construct method."""
if data_range is None:
raise ValueError("Parameter data_range must be set.")
super().__init__(data_range=data_range, normalize=normalize, **kwargs)
if self.parameters["chromatic"]:
self._name = "FSIMc"
else:
self._name = "FSIM"
[docs]
def score(self, img_r, img_m, dim=None, im_slice=None, **kwargs):
"""Calculate the FSIM score between two images.
The metric can be calculated for 2D and 3D images. If the images are 3D, the
metric can be calculated for the full volume or for a given slice of the image
by setting ``dim`` to the desired dimension and ``im_slice`` to the desired
slice number.
Parameters
----------
img_r : np.ndarray or Tensor or str or os.PathLike
Reference image to calculate score against.
img_m : np.ndarray or Tensor or str or os.PathLike
Distorted image to calculate score of.
dim : {0, 1, 2}, optional
FSIM for 3D images is calculated as mean over all slices of the given
dimension.
im_slice : int, optional
If given, FSIM is calculated only for the given slice of the 3D image.
**kwargs : optional
Additional parameters for FSIM calculation. The keyword arguments are passed
to :py:func:`piq.fsim`. See the documentation under [3]_.
Other Parameters
----------------
reduction : str, default='mean'
Specifies the reduction type: 'none', 'mean' or 'sum'.
scales : int, default=4
Number of wavelets used for computation of phase congruency maps.
orientations : int, default=4
Number of filter orientations used for computation of phase congruency
maps.
min_length : int, default=6
Wavelength of smallest scale filter.
mult : int, default=2
Scaling factor between successive filters.
sigma_f : float, default=0.55
Ratio of the standard deviation of the Gaussian describing the log Gabor
filter’s transfer function in the frequency domain to the filter center
frequency.
delta_theta : float, default=1.2
Ratio of angular interval between filter orientations and the standard
deviation of the angular Gaussian function used to construct filters in
the frequency plane.
k : float, default=2.0
No of standard deviations of the noise energy beyond the mean at which
we set the noise threshold point, below which phase congruency values
get penalized.
.. seealso::
See :py:func:`.viqa.fr_metrics.mad.most_apparent_distortion` and
:py:func:`.viqa.utils.gabor_convolve`.
Returns
-------
score_val : float
FSIM score value.
Raises
------
ValueError
If invalid dimension given in ``dim``. \n
If images are neither 2D nor 3D. \n
If images are 3D, but dim is not given. \n
If ``im_slice`` is given, but not an integer.
Warns
-----
RuntimeWarning
If ``dim`` or ``im_slice`` is given for 2D images. \n
If ``im_slice`` is not given, but ``dim`` is given for 3D images, FSIM is
calculated for the full volume.
Notes
-----
For 3D images if ``dim`` is given, but ``im_slice`` is not, the FSIM is
calculated for the full volume of the 3D image. This is implemented as `mean` of
the FSIM values of all slices of the given dimension. If ``dim`` is given and
``im_slice`` is given, the FSIM is calculated for the given slice of the given
dimension (represents a 2D metric of the given slice).
References
----------
.. [3] https://piq.readthedocs.io/en/latest/functions.html#piq.fsim
"""
img_r, img_m = self.load_images(img_r, img_m)
if img_r.ndim == 3 and img_r.shape[-1] != 3:
if (
dim is not None and type(im_slice) is int
): # if dim and im_slice are given
# Calculate FSIM for given slice of given dimension
match dim:
case 0:
img_r_tensor, img_m_tensor = _check_chromatic(
img_r[im_slice, :, :],
img_m[im_slice, :, :],
self.parameters["chromatic"],
)
score_val = fsim(
img_r_tensor,
img_m_tensor,
data_range=self.parameters["data_range"],
chromatic=self.parameters["chromatic"],
**kwargs,
)
case 1:
img_r_tensor, img_m_tensor = _check_chromatic(
img_r[:, im_slice, :],
img_m[:, im_slice, :],
self.parameters["chromatic"],
)
score_val = fsim(
img_r_tensor,
img_m_tensor,
data_range=self.parameters["data_range"],
chromatic=self.parameters["chromatic"],
**kwargs,
)
case 2:
img_r_tensor, img_m_tensor = _check_chromatic(
img_r[:, :, im_slice],
img_m[:, :, im_slice],
self.parameters["chromatic"],
)
score_val = fsim(
img_r_tensor,
img_m_tensor,
data_range=self.parameters["data_range"],
chromatic=self.parameters["chromatic"],
**kwargs,
)
case _:
raise ValueError(
"Invalid dim value. Must be integer of 0, 1 or 2."
)
elif (
dim is not None and im_slice is None
): # if dim is given, but im_slice is not, calculate FSIM for full volume
warn(
"im_slice is not given. Calculating FSIM for full volume.",
RuntimeWarning,
)
img_r_tensor, img_m_tensor = _check_chromatic(
img_r,
img_m,
self.parameters["chromatic"],
)
score_val = fsim(
img_r_tensor,
img_m_tensor,
data_range=self.parameters["data_range"],
chromatic=self.parameters["chromatic"],
**kwargs,
)
else:
if type(im_slice) is not int and im_slice is not None:
raise ValueError("im_slice must be an integer.")
raise ValueError(
"If images are 3D, dim and im_slice (optional) must be given."
)
elif img_r.ndim == 2 or (img_r.ndim == 3 and img_r.shape[-1] == 3):
if dim or im_slice:
warn("dim and im_slice are ignored for 2D images.", RuntimeWarning)
# Calculate FSIM for 2D images
img_r_tensor, img_m_tensor = _check_chromatic(
img_r,
img_m,
self.parameters["chromatic"],
)
score_val = fsim(
img_r_tensor,
img_m_tensor,
data_range=self.parameters["data_range"],
chromatic=self.parameters["chromatic"],
**kwargs,
)
else:
raise ValueError("Images must be 2D or 3D.")
self.score_val = float(score_val)
return score_val
[docs]
def print_score(self, decimals=2):
"""Print the FSIM score value of the last calculation.
Parameters
----------
decimals : int, default=2
Number of decimal places to print the score value.
Warns
-----
RuntimeWarning
If :py:attr:`score_val` is not available.
"""
if self.score_val is not None:
print("FSIM: {}".format(np.round(self.score_val, decimals)))
else:
warn("No score value for FSIM. Run score() first.", RuntimeWarning)