from math import pi, exp
from PIL import Image
import numpy as np

# filtre rouge
def filtre_rouge(img_orig):
    im = np.copy(img_orig) 
    for i in range(im.shape[0]):
        for j in range(im.shape[1]):
            r, v, b = im[i, j]
            im[i, j] = (r, 0,0)
    return im

# filtre bleu
def filtre_bleu(img_orig):
    im = np.copy(img_orig) 
    for i in range(im.shape[0]):
        for j in range(im.shape[1]):
            r, v, b = im[i, j]
            im[i, j] = (0, 0,b)
    return im

# filtre vert
def filtre_vert(img_orig):
    im = np.copy(img_orig) 
    for i in range(im.shape[0]):
        for j in range(im.shape[1]):
            r, v, b = im[i, j]
            im[i, j] = (0, v,0)
    return im

# filtre divers A
def filtreA(img_orig):
    im = np.copy(img_orig) 
    for i in range(im.shape[0]):
        for j in range(im.shape[1]):
            r, v, b = im[i, j]
            im[i, j] = (99-r, 99-v,99-b)
    return im

# filtre divers B
def filtreB(img_orig):
    im = np.copy(img_orig) 
    for i in range(im.shape[0]):
        for j in range(im.shape[1]):
            r, v, b = im[i, j]
            im[i, j] = (99-r, 99-v,99-b)
    return im

# flou gaussien
def gaussKernel(r,sigma):
    kernel = [[0] * (2*r+1) for _ in range(2*r + 1)]
    if sigma > 0:
        gaussianKernelFactor, e = 0, 0
        for ky in range(-r,r):
            for kx in range(-r,r):
                e = exp(-(kx**2+ky**2)/(2*pi*sigma**2))
                gaussianKernelFactor += e
                kernel[kx+r][ky+r] = e
        
        for ky in range(-r,r):
            for kx in range(-r,r):
                kernel[kx+r][ky+r] = kernel[kx+r][ky+r] / gaussianKernelFactor
                
    return kernel
                
def convolution(img,M):
    px = int( (len(M)-1) / 2 )
    im = img.copy()
    imax = img.shape[1] - px
    for i in range(px,imax):
        for j in range(px,img.shape[0]-px):
            somme = 0.0
            for k in range(-px,px+1):
                for l in range(-px,px+1):
                    somme += img[j+l][i+k] * M[l+px][k+px]
            im[j][i] = somme
    return im

# -----

imgpil = Image.open("image.jpg")  
img = np.array(imgpil) 
H = gaussKernel(3,10)
imgpil = Image.fromarray(convolution(img,H))
imgpil.save("resultat_flou.jpg")


"""
from PIL import Image
 
im = Image.open("getit.png")
im.thumbnail((128, 128), Image.ANTIALIAS)
im.save("thumbnail.png", "PNG")
"""