Filtering and resampling

# to run in google colab
import sys

if "google.colab" in sys.modules:

    def download_from_web(url):
        import requests

        response = requests.get(url)
        if response.status_code == 200:
            with open(url.split("/")[-1], "wb") as file:
                file.write(response.content)
        else:
            raise Exception(
                f"Failed to download the image. Status code: {response.status_code}"
            )

    download_from_web(
        "https://github.com/YoniChechik/AI_is_Math/raw/master/c_02b_filtering_and_resampling/Tour_Eiffel.jpg"
    )

import cv2
import numpy as np
from matplotlib import pyplot as plt

figsize = (10, 10)

def plot_im(img, title):
    plt.figure(figsize=figsize)
    plt.imshow(img)
    plt.title(title)
    plt.xticks([])
    plt.yticks([])
    plt.show()


img = cv2.imread("Tour_Eiffel.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plot_im(img, "original image")

def mean_kernel_smoothing(img, sz):
    kernel = np.ones((sz, sz)) / (sz**2)
    dst = cv2.filter2D(img, -1, kernel)

    plot_im(dst, str(sz) + "X" + str(sz) + " mean kernel")


mean_kernel_smoothing(img, 5)
mean_kernel_smoothing(img, 10)
mean_kernel_smoothing(img, 20)

def gauss_blur(img, k_sz, sigma=-1, is_plot_kernel=False):
    blur = cv2.GaussianBlur(img, (k_sz, k_sz), sigma)
    plot_im(
        blur,
        "gaussian kernel with kernel_size=" + str(k_sz) + r", $\sigma$=" + str(sigma),
    )
    if is_plot_kernel:
        # sigma=-1 will set the sigma size automatically
        gauss_ker = cv2.getGaussianKernel(k_sz, sigma)
        plt.figure(figsize=(figsize[0] / 2, figsize[1] / 2))
        plt.plot(gauss_ker)
        plt.title("corresponding gaussian kernel")
        plt.show()


gauss_blur(img, 5, is_plot_kernel=True)
gauss_blur(img, 21, is_plot_kernel=True)
gauss_blur(img, 21, 1, is_plot_kernel=True)

def median_filter(img, k_sz):
    res = cv2.medianBlur(img, k_sz)
    plot_im(res, "median filter with kernel_size=" + str(k_sz))


median_filter(img, 5)

def gauss_noise(image, gauss_var=1000):
    mean = 0
    sigma = gauss_var**0.5
    gauss = np.random.normal(mean, sigma, image.shape)

    res = image + gauss
    noisy = np.clip(res, 0, 255).astype(np.uint8)
    return noisy


def s_and_p_noise(image, s_p_ratio=0.04):
    # this implementation is not entirely correct because it assumes that
    # only 0 OR 255 values are S&P noise.
    out = np.copy(image)

    # Salt mode
    mask = np.random.rand(image.shape[0], image.shape[1]) <= s_p_ratio / 2
    out[mask] = 255

    # Pepper mode
    mask = np.random.rand(image.shape[0], image.shape[1]) <= s_p_ratio / 2
    out[mask] = 0
    return out

np.random.seed(1234)

gauss_noise_im = gauss_noise(img, gauss_var=70)
plt.figure(figsize=figsize)
plt.imshow(gauss_noise_im)
plt.title("original image + gaussian noise")
plt.show()

gauss_blur(gauss_noise_im, 3)
gauss_blur(gauss_noise_im, 5)
gauss_blur(gauss_noise_im, 11)
median_filter(gauss_noise_im, 5)

s_p_noise_im = s_and_p_noise(img, s_p_ratio=0.04)
plt.figure(figsize=figsize)
plt.imshow(s_p_noise_im)
plt.title("original image + s&p noise")
plt.show()

gauss_blur(s_p_noise_im, 5)
median_filter(s_p_noise_im, 3)
median_filter(s_p_noise_im, 5)