8.2

'''
Koristeći zašumljenu sliku iz prethodnog primera, ukloniti šum upotrebom aritmetičkog usrednjivača veličina:
3x3
5x5
11x11
Koristeći PSNR odrediti koji filtar je dao objektivno najbolje otklanjanje.
'''
import plotly.express as px

fig = px.imshow(img_noisy, zmin=0, zmax=255, color_continuous_scale='gray')
fig.show()

from scipy import ndimage
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# 3x3 aritmeticki usrednjivac
kernel = np.ones((3,3))/9

img_f = ndimage.convolve(img_noisy.astype('float'), kernel, mode = 'mirror')

fig = px.imshow(img_f, zmin=0, zmax=255, color_continuous_scale='gray')
fig.show()

psnr = 10*np.log10(255**2/((img - img_f)**2).mean())
print("PSNR za box filtar dimenzije {} je {}".format(kernel.shape, psnr))

# 5x5 aritmeticki usrednjivac
kernel = np.ones((5,5))/25

img_f = ndimage.convolve(img_noisy.astype('float'), kernel, mode = 'mirror')

fig = px.imshow(img_f, zmin=0, zmax=255, color_continuous_scale='gray')
fig.show()

psnr = 10*np.log10(255**2/((img - img_f)**2).mean())
print("PSNR za box filtar dimenzije {} je {}".format(kernel.shape, psnr))

# 11x11 aritmeticki usrednjivac
kernel = np.ones((11,11))/121

img_f = ndimage.convolve(img_noisy.astype('float'), kernel, mode = 'mirror')

fig = px.imshow(img_f, zmin=0, zmax=255, color_continuous_scale='gray')
fig.show()

psnr = 10*np.log10(255**2/((img - img_f)**2).mean())
print("PSNR za box filtar dimenzije {} je {}".format(kernel.shape, psnr))