Source code for viqa.fr_metrics.gsm

"""Module for calculating the Gradient Similarity Metric (GSM) metric.

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

        >>> import numpy as np
        >>> from viqa import GSM
        >>> img_r = np.random.rand(256, 256)
        >>> img_m = np.random.rand(256, 256)
        >>> gsm = GSM()
        >>> gsm.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
import scipy.ndimage as ndi
from tqdm.autonotebook import trange

from viqa._metrics import FullReferenceMetricsInterface
from viqa.kernels import *
from viqa.utils import _to_float

# Load the kernels as constants
KERNELS_3D = [
    gsm_kernel_x(),
    gsm_kernel_y(),
    gsm_kernel_z(),
    gsm_kernel_xy1(),
    gsm_kernel_xy2(),
    gsm_kernel_yz1(),
    gsm_kernel_yz2(),
    gsm_kernel_xz1(),
    gsm_kernel_xz2(),
    gsm_kernel_xyz1(),
    gsm_kernel_xyz2(),
    gsm_kernel_xyz3(),
    gsm_kernel_xyz4(),
]

KERNELS_2D = [
    gsm_kernel_2d_x(),
    gsm_kernel_2d_y(),
    gsm_kernel_2d_xy(),
    gsm_kernel_2d_yx(),
]


[docs] class GSM(FullReferenceMetricsInterface): """Calculate the gradient similarity (GSM) between two images. Attributes ---------- score_val : float GSM score value of the last calculation. parameters : dict Dictionary containing the parameters for GSM 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 GSM calculation. Passed to :py:func:`viqa.utils.load_data` and :py:func:`viqa.fr_metrics.gsm.gradient_similarity`. 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. If False, the input images are converted to grayscale images if necessary. 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 ----- GSM is a full reference IQA metric based on gradient similarity. It combines luminosity information and contrast-structural information. For further details, see [1]_. ``data_range`` for image loading is also used for the GSM calculation and therefore must be set. The parameter is set through the constructor of the class and is passed to :py:meth:`score`. References ---------- .. [1] Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4), 1500–1512. https://doi.org/10.1109/TIP.2011.2175935 """ 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 = "GSM"
[docs] def score(self, img_r, img_m, dim=None, im_slice=None, **kwargs): """Calculate the gradient similarity (GSM) 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 GSM for 3D images is calculated as mean over all slices of the given dimension. im_slice : int, optional If given, GSM is calculated only for the given slice of the 3D image. **kwargs : optional Additional parameters for GSM calculation. The keyword arguments are passed to :py:func:`.viqa.fr_metrics.gsm.gradient_similarity`. Returns ------- score_val : float GSM 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, GSM is calculated for the full volume. Notes ----- For 3D images if ``dim`` is given, but ``im_slice`` is not, the GSM is calculated for the full volume of the 3D image. This is implemented as `mean` of the GSM values of all slices of the given dimension. If ``dim`` is given and ``im_slice`` is given, the GSM is calculated for the given slice of the given dimension (represents a 2D metric of the given slice). This implementation is adapted for 3D images if ``experimental=True``. Therefore, 12 kernels are used instead of the original 4. The gradient is calculated by .. math:: \\max_{n=1,2,...,N} \\lbrace \\pmb{I} * \\mathcal{K}_{n} \\rbrace \\text{ instead of } \\max_{n=1,2,...,N} \\lbrace \\operatorname{mean2} (\\lvert \\pmb{X} \\cdot \\mathcal{K}_{n} \\rvert) \\rbrace with :math:`\\pmb{I}` denoting the Image, :math:`\\mathcal{K}_{n}` denoting the Kernel `n` and :math:`\\pmb{X}` denoting an image block. """ 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 GSM for given slice of given dimension match dim: case 0: score_val = gradient_similarity( img_r[im_slice, :, :], img_m[im_slice, :, :], data_range=self.parameters["data_range"], **kwargs, ) case 1: score_val = gradient_similarity( img_r[:, im_slice, :], img_m[:, im_slice, :], data_range=self.parameters["data_range"], **kwargs, ) case 2: score_val = gradient_similarity( img_r[:, :, im_slice], img_m[:, :, im_slice], 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 GSM for full volume warn( "im_slice is not given. Calculating GSM for full volume.", RuntimeWarning, ) score_val = gradient_similarity_3d( img_r, img_m, data_range=self.parameters["data_range"], dim=dim, **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 GSM for 2D images score_val = gradient_similarity( img_r, img_m, data_range=self.parameters["data_range"], **kwargs ) else: raise ValueError("Images must be 2D or 3D.") self.score_val = score_val return score_val
[docs] def print_score(self, decimals=2): """Print the GSM 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("GSM: {}".format(np.round(self.score_val, decimals))) else: warn("No score value for GSM. Run score() first.", RuntimeWarning)
[docs] def gradient_similarity_3d(img_r, img_m, dim=0, experimental=False, **kwargs): """Calculate the gradient similarity (GSM) between two 3D images. Parameters ---------- img_r : np.ndarray Reference image to calculate score against img_m : np.ndarray Distorted image to calculate score of dim : {0, 1, 2}, default=2 Dimension on which the slices are iterated. experimental : bool, default=False If ``True``, calculate GSM for the full volume with experimental 3D kernels. If ``False``, calculate GSM for all slices of the given dimension and calculate mean over all single slice values. .. attention:: This is experimental and the resulting values are not validated. **kwargs : optional Additional parameters for GSM calculation. The keyword arguments are passed to :py:func:`viqa.fr_metrics.gsm.gradient_similarity`. Returns ------- gsm_score : float GSM score value. Raises ------ ValueError If the ``dim`` is not an integer of 0, 1 or 2. Warnings -------- This metric is not yet tested. The metric should be only used for experimental purposes. .. todo:: test See Also -------- viqa.fr_metrics.gsm.gradient_similarity : Calculate the gradient similarity between two images. References ---------- .. [1] Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4), 1500–1512. https://doi.org/10.1109/TIP.2011.2175935 """ if not experimental: x, y, z = img_r.shape # get image dimensions scores = [] # Calculate GSM for all slices of the given dimension match dim: case 0: for slice_ in trange(x): scores.append( gradient_similarity( img_r[slice_, :, :], img_m[slice_, :, :], **kwargs ) ) case 1: for slice_ in trange(y): scores.append( gradient_similarity( img_r[:, slice_, :], img_m[:, slice_, :], **kwargs ) ) case 2: for slice_ in trange(z): scores.append( gradient_similarity( img_r[:, :, slice_], img_m[:, :, slice_], **kwargs ) ) case _: raise ValueError("Invalid dim value. Must be integer of 0, 1 or 2.") return np.mean(np.array(scores)) else: return gradient_similarity(img_r, img_m, **kwargs)
[docs] def gradient_similarity(img_r, img_m, data_range=255, c=200, p=0.1): """Calculate the gradient similarity between two images. Parameters ---------- img_r : np.ndarray Reference image to calculate score against img_m : np.ndarray Distorted image to calculate score of data_range : {1, 255, 65535} Data range of the input images c : int, default=200 Constant as masking parameter. Typically, :math:`200 \\leq c \\leq 1000`. See [1] for details. p : float, default=0.1 Constant for weighting between luminance and structure similarity. Can be :math:`0 \\leq p \\leq 1`. Higher `p` means more accentuation of luminance. Should be :math:`p \\ll 0.5`. See [1]_ for details. Returns ------- gsm_score : float GSM score value. Raises ------ ValueError If the images are neither 2D nor 3D. Warnings -------- This metric is not yet tested. The metric should be only used for experimental purposes. .. todo:: test See Also -------- viqa.fr_metrics.gsm.gradient_similarity_3d : Calculate the gradient similarity between two 3D images. References ---------- .. [1] Liu, A., Lin, W., & Narwaria, M. (2012). Image quality assessment based on gradient similarity. IEEE Transactions on Image Processing, 21(4), 1500–1512. https://doi.org/10.1109/TIP.2011.2175935 """ gradients_r = [] gradients_m = [] if img_r.ndim == 3: kernels = KERNELS_3D elif img_r.ndim == 2: kernels = KERNELS_2D else: raise ValueError("Images must be 2D or 3D.") for kernel in kernels: gradients_r.append(ndi.correlate(img_r, kernel)) gradients_m.append(ndi.correlate(img_m, kernel)) # key for sorting the gradients by their mean of absolute values def _mean_of_abs(input_array): return np.mean(np.abs(input_array)) img_r_gradient = sorted(gradients_r, key=_mean_of_abs, reverse=True)[0] img_m_gradient = sorted(gradients_m, key=_mean_of_abs, reverse=True)[0] img_r_gradient = _to_float(img_r_gradient) img_m_gradient = _to_float(img_m_gradient) k = c / max(np.max(img_r_gradient), np.max(img_m_gradient)) r = np.abs(img_r_gradient - img_m_gradient) / max( img_r_gradient, img_m_gradient, key=_mean_of_abs ) con_struc_sim = ((2 * (1 - r)) + k) / (1 + (1 - r) ** 2 + k) lum_sim = 1 - ((img_r - img_m) / data_range) ** 2 weight = p * con_struc_sim quality = (1 - weight) * con_struc_sim + weight * lum_sim gsm_score = np.nanmean(quality) return gsm_score