"""10_blur_mean.py
Blur and mean on images with OpenCV
.. moduleauthor:: Julien VILLEMEJANE <julien.villemejane@institutoptique.fr>

@see: https://iogs-lense-training.github.io/image-processing/contents/opencv_blur.html
"""

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

# Image
image = cv2.imread('./_data/noise_vi_small.png', cv2.IMREAD_GRAYSCALE)
print(f'Shape = {image.shape}')

# Kernel
kernel_size1 = (5, 5)
kernel_size2 = (11, 11)
# Blur
blurred_image1 = cv2.GaussianBlur(image, kernel_size1, 4)
blurred_image2 = cv2.GaussianBlur(image, kernel_size2, 4)

# Slice
line_number = 300
# Display Slice
fig, ax = plt.subplots(nrows=2, ncols=3)
ax[0,0].plot(image[line_number,:])
ax[0,0].set_title('Initial Gray Image / Filter')
ax[0,1].plot(blurred_image1[line_number,:])
ax[0,1].set_title(f'Gaussian Blur - Size = {kernel_size1}')
ax[0,2].plot(blurred_image2[line_number,:])
ax[0,2].set_title(f'Gaussian Blur - Size = {kernel_size2}')

ax[1,0].imshow(image, cmap='gray')
ax[1,0].set_title('Initial Gray Image / Filter')
ax[1,1].imshow(image - blurred_image1, cmap='gray')
ax[1,1].set_title(f'Gaussian Blur - Size = {kernel_size1}')
ax[1,2].imshow(image - blurred_image2, cmap='gray')
ax[1,2].set_title(f'Gaussian Blur - Size = {kernel_size2}')
plt.show()

##
# Slice Gradient
dy1 = np.gradient(blurred_image1[line_number,:].astype(float))
dy2 = np.gradient(blurred_image2[line_number,:].astype(float))

# Display Slice Gradient
fig, ax = plt.subplots(nrows=2, ncols=3)
ax[0,0].plot(image[line_number,:])
ax[0,0].set_title('Initial Gray Image / Filter')
ax[0,1].plot(blurred_image1[line_number,:])
ax[0,1].set_title(f'Gaussian Blur - Size = {kernel_size1}')
ax[0,2].plot(blurred_image2[line_number,:])
ax[0,2].set_title(f'Gaussian Blur - Size = {kernel_size2}')

ax[1,0].imshow(image, cmap='gray')
ax[1,0].set_title('Initial Gray Image / Filter')
ax[1,1].plot(dy1)
ax[1,1].set_title(f'Gaussian Blur - Size = {kernel_size1}')
ax[1,2].plot(dy2)
ax[1,2].set_title(f'Gaussian Blur - Size = {kernel_size2}')
plt.show()