# import matplotlib.pyplot as plt
import numpy as np
import scipy.spatial
from skimage import io
import time

def distance(points,Pc):   
    return scipy.spatial.distance.cdist(points[:,:], Pc[:,:])

def kmeans(points = [0,0], K = 1):
    # Initialisation K prototypes
    dim = points.shape[1]
    N = points.shape[0]
    iter = 0
    eps = 0.1
    Pc_index = []
    Pc_save = np.zeros([K,dim])
    clusters = []

    for i in range(0,K):
        Pc_index.append(np.random.randint(0,N))
    Pc = points[Pc_index,:]

    while (np.mean(distance(Pc,Pc_save)) > eps and iter < 10):
        iter += 1
        Pc_save = Pc
        # print(Pc)
        # print(points[:,:Pc.shape[0]])
        dist = distance(points=points[:,:Pc.shape[1]],Pc=Pc)
        clust = np.argmin(dist, axis=1)
        clust = np.expand_dims(clust, axis=0)
        points = np.append(points[:,:Pc.shape[1]], clust.T, axis=1)
        # print(points)
        Pc = np.zeros([K,dim])
        index = np.array([])

        for n in range(0,N):
            for k in range(0,K):
                index = np.append(index, (clust==k).sum())
                if points[n,-1] == k:
                    # print(points)
                    # print(Pc)
                    Pc[k,:] = np.add(Pc[k,:], points[n,:-1])

        for k in range(0,K):
            Pc[k,:] = np.divide(Pc[k,:],index[k])

        # print(Pc)
        indice = points[:,-1]
        points = points[:,:-1]
    return Pc, indice, points

def mat_2_img(mat,my_img):
    img_seg = mat.reshape(my_img.shape[0], my_img.shape[1], my_img.shape[2])
    return img_seg

def img_2_mat(my_img):
    mat = my_img.reshape(my_img.shape[0]*my_img.shape[1],my_img.shape[2])
    return mat

def kmeans_image(path_image, K):
    my_img = io.imread(path_image)
    # imgplot = plt.imshow(my_img)
    Mat = img_2_mat(my_img)
    
    Pc, index, clusters = kmeans(Mat, K)

    for k in range(Mat.shape[0]):
        Mat[k,:] = np.floor(Pc[index[k],:])

    img_seg = mat_2_img(Mat, my_img)

    io.imsave(path_image.split('.')[0] + "_%d.jpg" % K, img_seg)
    # imgplot = plt.imshow(img_seg)
    return Pc, index, img_seg

path_image = "images/fruits.jpg"

for K in range(1,21):
    start_time = time.time()
    Pc, index, img_seg = kmeans_image(path_image=path_image, K=K)
    end_time = time.time()
    print(f"It took {end_time-start_time:.2f} seconds to compute for K =", K)