ipfml/ipfml/iqa/fr.py

from skimage.measure import compare_ssim, compare_psnr
from ipfml.exceptions import NumpyShapeComparisonException

import numpy as np
"""
Full-reference Image Quality Assessment (FR-IQA) methods
"""


def _prepare_arrays(img_true, img_test):
    """
    Prepare image data

    Raises:
        NumpyShapeComparisonException: if shape of images are not the same
    """

    img_true = np.asarray(img_true, dtype='float32')
    img_test = np.asarray(img_test, dtype='float32')

    if img_true.shape != img_test.shape:
        raise NumpyShapeComparisonException

    return img_true, img_test


def mse(img_true, img_test):
    """Returns Mean-Squared Error score between two Numpy arrays

    Args:
        img_true: Image, numpy array of any dimension
        img_test: Image, numpy array of any dimension

    Returns:
        Computed MSE score

    Raises:
        NumpyShapeComparisonException: if shape of images are not the same

    Example:
        >>> from ipfml.iqa import fr
        >>> import numpy as np
        >>> arr1 = np.arange(10)
        >>> arr2 = np.arange(5, 15)
        >>> mse_score = fr.mse(arr1, arr2)
        >>> mse_score
        25.0
    """

    img_true, img_test = _prepare_arrays(img_true, img_test)

    return np.mean(np.square(img_true - img_test), dtype=np.float64)


def rmse(img_true, img_test):
    """Returns Root Mean-Squared Error score between two Numpy arrays

    Args:
        img_true: Image, numpy array of any dimension
        img_test: Image, numpy array of any dimension

    Returns:
        Computed RMSE score

    Raises:
        NumpyShapeComparisonException: if shape of images are not the same

    Example:
        >>> from ipfml.iqa import fr
        >>> import numpy as np
        >>> arr1 = np.arange(10)
        >>> arr2 = np.arange(5, 15)
        >>> rmse_score = fr.rmse(arr1, arr2)
        >>> rmse_score
        5.0
    """

    return np.sqrt(mse(img_true, img_test))


def mae(img_true, img_test):
    """Returns Mean Absolute Error between two Numpy arrays

    Args:
        img_true: Image, numpy array of any dimension
        img_test: Image, numpy array of any dimension

    Returns:
        Computed MAE score

    Raises:
        NumpyShapeComparisonException: if shape of images are not the same

    Example:
        >>> from ipfml.iqa import fr
        >>> import numpy as np
        >>> arr1 = np.arange(10)
        >>> arr2 = np.arange(5, 15)
        >>> mae_score = fr.mae(arr1, arr2)
        >>> mae_score
        5.0
    """

    img_true, img_test = _prepare_arrays(img_true, img_test)

    return np.mean(np.absolute(img_true - img_test), dtype=np.float64)


def pnsr(img_true, img_test):
    """Returns the computed Peak Signal to Noise Ratio (PSNR) between two images

    Args:
        img_true: Image, numpy array of any dimension
        img_test: Image, numpy array of any dimension

    Returns:
        Computed PSNR score

    Example:
        >>> from ipfml.iqa import fr
        >>> import numpy as np
        >>> arr1 = np.arange(10)
        >>> arr2 = np.arange(5, 15)
        >>> pnsr_score = fr.pnsr(arr1, arr2)
        >>> int(pnsr_score)
        365
    """

    return compare_psnr(img_true, img_test)


def ms_ssim(img_true, img_test):
    """
    Implemented later..
    """
    pass


def vif(img_true, img_test):
    """
    Implemented later..
    """
    pass