# -*- coding: utf-8 -*-

"""

Created on Wed Jun  6 13:38:42 2018

@author: Administrator

"""

import cv2

import numpy as np

import scipy.signal

img = cv2.imread('img_roi_4.bmp', 0)

img_double = np.array(img, dtype = np.float64)

cluster_num = 2

max_iter = 200

label  = np.random.randint(1, cluster_num + 1, size = img_double.shape)

#label = 2 * np.ones((70, 70))

iter  = 0

f_u = np.array([0,1,0,0,0,0,0,0,0]).reshape(3, 3)

f_d = np.array([0,0,0,0,0,0,0,1,0]).reshape(3, 3)

f_l = np.array([0,0,0,1,0,0,0,0,0]).reshape(3, 3)

f_r = np.array([0,0,0,0,0,1,0,0,0]).reshape(3, 3)

f_ul = np.array([1,0,0,0,0,0,0,0,0]).reshape(3, 3)

f_ur = np.array([0,0,1,0,0,0,0,0,0]).reshape(3, 3)

f_dl = np.array([0,0,0,0,0,0,1,0,0]).reshape(3, 3)

f_dr = np.array([0,0,0,0,0,0,0,0,1]).reshape(3, 3)

while iter < max_iter:

iter = iter + 1

print(iter)

label_u  = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_u)

label_d  = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_d)

label_l = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_l)

label_r = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_r)

label_ul = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_ul)

label_ur = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_ur)

label_dl = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_dl)

label_dr = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_dr)

#label_u_out = cv2.filter2D(np.array(label, dtype = np.uint8), -1 , f_u)

m, n = label.shape

p_c = np.zeros((cluster_num, m ,  n))

for i in range(cluster_num):

label_i = (i+1) * np.ones((m, n))

u_T = 1 * np.logical_not(label_i - label_u)

d_T = 1 * np.logical_not(label_i - label_d)

l_T = 1 * np.logical_not(label_i - label_l)

r_T = 1 * np.logical_not(label_i - label_r)

ul_T = 1 * np.logical_not(label_i - label_ul)

ur_T = 1 * np.logical_not(label_i - label_ur)

dl_T = 1 * np.logical_not(label_i - label_dl)

dr_T = 1 * np.logical_not(label_i - label_dr)

temp1 = u_T + d_T + l_T + r_T + ul_T + ur_T + dl_T + dr_T

#        temp = 1 * np.logical_not(label_i - label_u) + 1 * np.logical_not(label_i - label_d)

#        +1 * np.logical_not(label_i - label_l) + 1 * np.logical_not(label_i - label_r)

#        + 1 * np.logical_not(label_i - label_ul) + 1 * np.logical_not(label_i - label_ur)

#        + 1 * np.logical_not(label_i - label_dl) + 1 * np.logical_not(label_i - label_dr)

p_c[i, :] = (1.0/8) * temp1

p_c[p_c == 0] = 0.001

mu = np.zeros((1, cluster_num))

sigma = np.zeros((1, cluster_num))

for i in range(cluster_num):

index = np.where(label == (i+1))

data_c = img[index]

mu[0, i] = np.mean(data_c)

sigma[0, i] = np.var(data_c)

p_sc = np.zeros((cluster_num, m , n))

one_a = np.ones((m, n))

for j in range(cluster_num):

MU = mu[0, j]* one_a

p_sc[j, :] = (1.0/np.sqrt(2 * np.pi * sigma[0, j])) * np.exp(-1. * (( img - MU)**2) /(2 * sigma[0, j]))

X_out =  np.log(p_c) + np.log(p_sc)

label_c = X_out.reshape(2, m * n)

label_c_t = label_c.T

label_m = np.argmax(label_c_t, axis = 1)

label_m = label_m + np.ones(label_m.shape)

#label_1= label_m.reshape(m, n)

label= label_m.reshape(m, n)

label = label -  np.ones(label.shape)

lable_w = 255 * label

cv2.imwrite('label_4.bmp', lable_w)