Source code for viqa.fr_metrics.vsi

"""Module for calculating the visual saliency index (VSI) between two images.

Examples
--------
    .. doctest-skip::

        >>> import numpy as np
        >>> from viqa import VSI
        >>> img_r = np.random.rand(256, 256)
        >>> img_m = np.random.rand(256, 256)
        >>> vsi = VSI()
        >>> vsi.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 vsi

from viqa._metrics import FullReferenceMetricsInterface
from viqa.utils import _check_chromatic


[docs] class VSI(FullReferenceMetricsInterface): """Calculate the visual saliency index (VSI) between two images. Attributes ---------- score_val : float VSI score value of the last calculation. parameters : dict Dictionary containing the parameters for VSI 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 VSI 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. Raises ------ ValueError If ``data_range`` is not set. Warnings -------- This metric is not yet tested. The metric should be only used for experimental purposes. .. todo:: test Notes ----- For more information on the VSI metric, see [1]_. .. note:: The original metric supports RGB images only. This implementation can work with grayscale images by copying the luminance channel 3 times. References ---------- .. [1] Zhang, L., Shen, Y., & Li, H. (2014). VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10), 4270–4281. https://doi.org/10.1109/TIP.2014.2346028 """ 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) self._name = "VSI"
[docs] def score(self, img_r, img_m, dim=None, im_slice=None, **kwargs): """Calculate the visual saliency index (VSI) 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 VSI for 3D images is calculated as mean over all slices of the given dimension. im_slice : int, optional If given, VSI is calculated only for the given slice of the 3D image. **kwargs : optional Additional parameters for VSI calculation. The keyword arguments are passed to :py:func:`piq.vsi`. See the documentation under [2]_. Other Parameters ---------------- reduction : str, default='mean' Specifies the reduction type: 'none', 'mean' or 'sum'. c1 : float, default=1.27 Coefficient to calculate saliency component. See [3]_. c2 : float, default=386.0 Coefficient to calculate gradient component. See [3]_. c3 : float, default=130.0 Coefficient to calculate color component. See [3]_. alpha : float, default=0.4 Power for gradient component. beta : float, default=0.02 Power for color component. omega_0 : float, default=0.021 Coefficient to get log Gabor filter with SDSP. See [4]_. sigma_f : float, default=1.34 Coefficient to get log Gabor filter with SDSP. See [4]_. sigma_d : float, default=145.0 Coefficient to get SDSP. See [4]_. sigma_c : float, default=0.001 Coefficient to get SDSP. See [4]_. Returns ------- score_val : float VSI 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, VSI is calculated for the full volume. Notes ----- For 3D images if ``dim`` is given, but ``im_slice`` is not, the VSI is calculated for the full volume of the 3D image. This is implemented as `mean` of the VSI values of all slices of the given dimension. If ``dim`` is given and ``im_slice`` is given, the VSI is calculated for the given slice of the given dimension (represents a 2D metric of the given slice). References ---------- .. [2] https://piq.readthedocs.io/en/latest/functions.html#piq.vsi .. [3] Zhang, L., Shen, Y., & Li, H. (2014). VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10), 4270–4281. https://doi.org/10.1109/TIP.2014.2346028 .. [4] Zhang, L., Gu, Z., & Li, H. (2013). SDSP: A novel saliency detection method by combining simple priors. 2013 IEEE International Conference on Image Processing, 171–175. https://api.semanticscholar.org/CorpusID:6028723 """ img_r, img_m = self.load_images(img_r, img_m) if img_r.ndim == 3: if ( dim is not None and type(im_slice) is int ): # if dim and im_slice are given # Calculate VSI 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 = vsi( img_r_tensor, img_m_tensor, data_range=self.parameters["data_range"], **kwargs, ) case 1: img_r_tensor, img_m_tensor = _check_chromatic( img_r[:, im_slice, :], img_m[:, im_slice, :], self.parameters["chromatic"], ) score_val = vsi( img_r_tensor, img_m_tensor, data_range=self.parameters["data_range"], **kwargs, ) case 2: img_r_tensor, img_m_tensor = _check_chromatic( img_r[:, :, im_slice], img_m[:, :, im_slice], self.parameters["chromatic"], ) score_val = vsi( img_r_tensor, img_m_tensor, data_range=self.parameters["data_range"], **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 VSI for full volume warn( "im_slice is not given. Calculating VSI for full volume.", RuntimeWarning, ) img_r_tensor, img_m_tensor = _check_chromatic( img_r, img_m, self.parameters["chromatic"], ) score_val = vsi( img_r_tensor, img_m_tensor, data_range=self.parameters["data_range"], **kwargs, ) else: if type(im_slice) is not int or 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: if dim or im_slice: warn("dim and im_slice are ignored for 2D images.", RuntimeWarning) # Calculate VSI for 2D images img_r_tensor, img_m_tensor = _check_chromatic( img_r, img_m, self.parameters["chromatic"], ) score_val = vsi( img_r_tensor, img_m_tensor, data_range=self.parameters["data_range"], **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 VSI 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("VSI: {}".format(np.round(self.score_val, decimals))) else: print("No score value for VSI. Run score() first.")