dev(narugo): psnr examples

import random

import numpy as np

from PIL import Image

from plot import image_plot

ORIGIN_FILENAME = 'psnr/origin.jpg'

def gaussian_noise(scale=20):

    random.seed(0)

    img = Image.open(ORIGIN_FILENAME).convert('RGB')

    img_arr = np.array(img)

    noise = np.random.normal(0, scale, img_arr.shape)

    noisy_arr = img_arr + noise

    noisy_img = Image.fromarray(noisy_arr.astype('uint8'), mode='RGB')

    export_file = f'psnr/gaussian_{scale}.dat.jpg'

    noisy_img.save(export_file)

    return export_file

def low_quality():

    random.seed(0)

    img = Image.open(ORIGIN_FILENAME).convert('RGB')

    export_file = 'psnr/lq.dat.jpg'

    img.save(export_file, quality=5)

    return export_file

if __name__ == '__main__':

    image_plot(

        ORIGIN_FILENAME,

        gaussian_noise(20),

        gaussian_noise(3),

        low_quality(),

        save_as='psnr.dat.svg',

        columns=2,

        figsize=(6, 5)

    )

"""

Overview:

    Implementation of `PSNR <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_ metrics.

"""

import numpy as np

from ..data import rgb_encode

def psnr(img1, img2) -> float:

    """

    Overview:

        Psnr difference between images.

    :param img1: First image.

    :param img2: Second image.

    :return: Psnr difference of these two images.

    Example:

        Here are some images for example

        .. image:: psnr.dat.svg

           :align: center

        >>> from imgutils.metrics import psnr

        >>>

        >>> psnr('psnr/origin.jpg', 'psnr/origin.jpg')  # same image

        inf

        >>> psnr('psnr/origin.jpg', 'psnr/gaussian_20.dat.jpg')

        15.058228614646987

        >>> psnr('psnr/origin.jpg', 'psnr/gaussian_3.dat.jpg')

        27.65611098737784

        >>> psnr('psnr/origin.jpg', 'psnr/lq.dat.jpg')

        25.29589659377844

    """

    d1, d2 = rgb_encode(img1), rgb_encode(img2)

    mse = np.mean((d1 - d2) ** 2)

    return float(10 * np.log10(1. / mse))