Source code for viqa.fr_metrics.psnr

"""Module for calculating the peak signal-to-noise ratio (PSNR) between two images.

Examples
--------
    .. doctest-requires:: numpy

        >>> import numpy as np
        >>> from viqa import PSNR
        >>> img_r = np.zeros((256, 256))
        >>> img_m = np.ones((256, 256))
        >>> psnr = PSNR(data_range=1, normalize=False)
        >>> psnr
        PSNR(score_val=None)
        >>> score = psnr.score(img_r, img_m)
        >>> score
        0.0
        >>> psnr.print_score()
        PSNR: 0.0
        >>> img_r = np.zeros((256, 256))
        >>> img_m = np.zeros((256, 256))
        >>> psnr.score(img_r, img_m)
        inf
        >>> img_r = np.random.rand(256, 256)
        >>> img_m = np.random.rand(128, 128)
        >>> psnr.score(img_r, img_m)
        Traceback (most recent call last):
            ...
        ValueError: Image shapes do not match
"""

# 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 skimage.metrics import peak_signal_noise_ratio

from viqa._metrics import FullReferenceMetricsInterface


[docs] class PSNR(FullReferenceMetricsInterface): """Class to calculate the peak signal-to-noise ratio (PSNR) between two images. Attributes ---------- score_val : float PSNR score value of the last calculation. parameters : dict Dictionary containing the parameters for PSNR 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 PSNR 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. If False, the input images are converted to grayscale images if necessary. Raises ------ ValueError If ``data_range`` is not set. Notes ----- ``data_range`` for image loading is also used for the PSNR calculation and therefore must be set. The parameter is set through the constructor of the class and is passed to :py:meth:`score`. """ def __init__(self, data_range=255, normalize=False, **kwargs) -> None: """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 = "PSNRc" else: self._name = "PSNR"
[docs] def score(self, img_r, img_m): """Calculate the peak signal-to-noise ratio (PSNR) between two images. 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. Returns ------- score_val : float PSNR score value. """ img_r, img_m = self.load_images(img_r, img_m) # Calculate score if np.array_equal(img_r, img_m): score_val = np.inf # PSNR of identical images is infinity else: score_val = peak_signal_noise_ratio( img_r, img_m, data_range=self.parameters["data_range"] ) self.score_val = score_val return score_val
[docs] def print_score(self, decimals=2): """Print the PSNR 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("PSNR: {}".format(np.round(self.score_val, decimals))) else: warn("No score value for PSNR. Run score() first.", RuntimeWarning)