import umage as um
from math import sqrt, atan2

def greyscale(mat_img):
    gray_img = []
    for ligne in mat_img:
        lig = []
        for r,g,b in ligne:
            v = int(r*0.2125 + g*0.7154 + b*0.0721)
            lig.append((v,)*3)
        gray_img.append(lig)
    return gray_img

def convolution(mat_img, mat):
    return_img = []
    for j in range(len(mat_img)):
        ligne = []
        for i in range(len(mat_img[0])):
            val = appliquer_convolution(mat_img, mat, i, j)
            ligne.append((val,)*3)
        return_img.append(ligne)
    return return_img

def filtre_sobel(img):

    def calcul_norme(pixel1, pixel2):
        valeur = pixel1[0]**2 + pixel2[0]**2
        norm = round(sqrt(valeur))
        norm = int(min(norm, 255))
        return norm

    def application_norme(im_x, im_y):
        result_image = []
        for j in range(len(im_x)):
            ligne = []
            for i in range(len(im_x[0])):
                pixel1 = im_x[j][i]
                pixel2 = im_y[j][i]
                norme = calcul_norme(pixel1, pixel2)
                ligne.append((norme,)*3)
            result_image.append(ligne)
        return result_image

    if not is_greyscale(img):
        img = greyscale(img)

    mat_x = [[-1,0,1],[-2,0,2],[-1,0,1]]
    mat_y = [[-1,-2,-1],[0,0,0],[1,2,1]]
    Gx = convolution(img, mat_x)
    Gy = convolution(img, mat_y)
    
    filtred_image = application_norme(Gx,Gy)
    return filtred_image



#########################################################################
########################Exercices Supplémentaires########################
#########################################################################

def is_greyscale(img):
    _greyscale = True
    for ligne in img:
        for r,g,b in ligne:
            if not (r==g and g==b):
                _greyscale = False
                break
        if not _greyscale:
            break
    return _greyscale

def invert(img):
    result_image = []
    for ligne in img:
        result_ligne = []
        for r,g,b in ligne:
            result_ligne.append((255-r, 255-g, 255-b))
        result_image.append(result_ligne)
    return result_image

def pixel(img, i, j, default=(0,0,0)):
    #i la colone et j la ligne
    if 0 <= i < len(img[0]) and 0 <= j < len(img):
        return img[j][i]
    else:
        return default

def appliquer_convolution(img, mat, i, j):
    somme = 0
    for y in range(len(mat)):
        for x in range(len(mat[0])):
            pixel_i = i - (len(mat[0]) // 2) + x
            pixel_j = j - (len(mat) // 2) + y
            pix = pixel(img, pixel_i, pixel_j)
            somme += pix[0]*mat[y][x]
    return min(max(somme,0), 255)



######################################################################
########################Exercices personnelles########################
######################################################################
def convolution_gauss(mat_img, mat):
    return_img = []
    for j in range(len(mat_img)):
        ligne = []
        for i in range(len(mat_img[0])):
            val = reduction_bruit(mat_img, mat, i, j)
            ligne.append((val,)*3)
        return_img.append(ligne)
    return return_img

def reduction_bruit(img, mat, i, j):
    somme = 0
    for y in range(len(mat)):
        for x in range(len(mat[0])):
            pixel_i = i - (len(mat[0]) // 2) + x
            pixel_j = j - (len(mat) // 2) + y
            pix = pixel(img, pixel_i, pixel_j)
            somme += pix[0]*mat[y][x]
    normalise = int(round(somme / (1/159)))
    return min(max(normalise,0), 255)

def filtre_canny(img):

    def norme_gradient(pixel1, pixel2):
        color_x = pixel1[0]
        color_y = pixel2[0]
        
        norm = round(sqrt(color_x**2 + color_y**2))
        norm = int(min(norm, 255))

        grad = atan2(color_y, color_x)
        return norm, grad

    def liste_normGrad(im1, im2):
        liste = []
        for j in range(len(im1)):
            ligne = []
            for i in range(len(im1[0])):
                normGrad = norme_gradient(im1[j][i], im2[j][i])
                ligne.append(normGrad)
            liste.append(ligne)
        return liste

    if not is_greyscale(img):
        img = greyscale(img)
    
    mat_gauss = [
        [2, 4, 5, 4,2],
        [4, 9,12, 9,4],
        [5,12,15,12,5],
        [4, 9,12, 9,4],
        [2, 4, 5, 4,2]
    ]
    mat_x = [[-1,0,1]]
    mat_y = [[1],[0],[-1]]

    #lissage
    img = convolution_gauss(img, mat_gauss)
    Jx = convolution(img, mat_x)
    Jy = convolution(img, mat_y)
    normGrad = liste_normGrad(Jx, Jy)