import numpy              as np
import numpy.linalg       as nl
import matplotlib.pyplot  as plt
import cv2
import requests

def load_image_from_url(url):
    response = requests.get(url)
    image_array = np.frombuffer(response.content, np.uint8)
    img = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
    return img

img_link = "https://jonghank.github.io/jhk-2024.jpg"

img = load_image_from_url(img_link)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img_gray_raw = cv2.resize(img, None, fx=0.25, fy=0.25)

kernel_mean_10  = 1/100 * np.ones((10,10), dtype=np.float16)
kernel_mean_20 = 1/400 * np.ones((20,20), dtype=np.float16)

img_gray_flt_10  = cv2.filter2D(img_gray_raw, -1, kernel_mean_10 )
img_gray_flt_20 = cv2.filter2D(img_gray_raw, -1, kernel_mean_20)


fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.imshow(img_gray_raw, cmap='gray')
ax2.imshow(img_gray_flt_10, cmap='gray')
ax3.imshow(img_gray_flt_20, cmap='gray')
ax1.set_title("Original")
ax2.set_title("Blured (10x10)")
ax3.set_title("Blured (20x20)")
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
plt.show()

img_gray_noisy_gausian = img_gray_raw.copy() + 100*np.random.rand(*img_gray_raw.shape)
img_gray_noisy_salt_pepper = img_gray_raw.copy()

random_mask = np.random.rand(*img_gray_raw.shape)
salt_mask = np.where(random_mask > 0.95)
pepper_mask = np.where(random_mask < 0.05)
img_gray_noisy_salt_pepper[salt_mask] = 255
img_gray_noisy_salt_pepper[pepper_mask] = 0

fig = plt.figure()
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.imshow(img_gray_raw, cmap='gray')
ax2.imshow(img_gray_noisy_gausian, cmap='gray')
ax3.imshow(img_gray_noisy_salt_pepper, cmap='gray')
ax1.set_title("Original")
ax2.set_title("Random")
ax3.set_title("Salt & pepper")
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
plt.show()

img_gray_gnoise_flt_10 = cv2.filter2D(img_gray_noisy_gausian, -1, kernel_mean_10)
img_gray_gnoise_flt_20 = cv2.filter2D(img_gray_noisy_gausian, -1, kernel_mean_20)
img_gray_spnoise_flt_10 = cv2.filter2D(img_gray_noisy_salt_pepper, -1, kernel_mean_10 )
img_gray_spnoise_flt_20 = cv2.filter2D(img_gray_noisy_salt_pepper, -1, kernel_mean_20 )

fig = plt.figure()
ax1 = fig.add_subplot(231)
ax2 = fig.add_subplot(232)
ax3 = fig.add_subplot(233)
ax4 = fig.add_subplot(234)
ax5 = fig.add_subplot(235)
ax6 = fig.add_subplot(236)

ax1.imshow(img_gray_noisy_gausian, cmap='gray')
ax2.imshow(img_gray_gnoise_flt_10, cmap='gray')
ax3.imshow(img_gray_gnoise_flt_20, cmap='gray')
ax4.imshow(img_gray_noisy_salt_pepper, cmap='gray')
ax5.imshow(img_gray_spnoise_flt_10, cmap='gray')
ax6.imshow(img_gray_spnoise_flt_20, cmap='gray')

ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
ax5.axis('off')
ax6.axis('off')

ax1.set_title("Gaussian\noriginal", fontsize=9)
ax2.set_title("Gaussian\nblured (10x10)", fontsize=9)
ax3.set_title("Gaussian\nblured (20x20)", fontsize=9)
ax4.set_title("Salt & pepper\noriginal", fontsize=9)
ax5.set_title("Salt & pepper\nblured (10x10)", fontsize=9)
ax6.set_title("Salt & pepper\nblured (20x20)", fontsize=9)

plt.show()