import numpy as np

import SimpleITK as sitk

def read_image(opt):

    I_pre = sitk.ReadImage(opt.pre)  

    I_post = sitk.ReadImage(opt.post)  

    img_pre = np.array(sitk.GetArrayFromImage(I_pre))

    img_post = np.array(sitk.GetArrayFromImage(I_post))

    img_sub = img_post - img_pre

    return img_pre, img_post, img_sub, np.array(I_pre.GetSpacing())

def Norm_Zscore(img):

    img= (img-np.mean(img))/np.std(img) 

    return img

def imgnorm(N_I,index1=0.001,index2=0.001):

    N_I = N_I.astype(np.float32)

    I_sort = np.sort(N_I.flatten())

    I_min = I_sort[int(index1*len(I_sort))]

    I_max = I_sort[-int(index2*len(I_sort))]

    N_I =1.0*(N_I-I_min)/(I_max-I_min)

    N_I[N_I>1.0]=1.0

    N_I[N_I<0.0]=0.0

    return N_I

def save_image(image,opt,savename):

    I = sitk.ReadImage(opt.pre)  

    Heat_image = sitk.GetImageFromArray(image, isVector=False) 

    Heat_image.SetSpacing(I.GetSpacing())

    Heat_image.SetOrigin(I.GetOrigin())

    Heat_image.SetDirection(I.GetDirection())

    sitk.WriteImage(Heat_image,opt.outfolder+'/'+savename)     

from skimage.morphology import closing  

from scipy import ndimage as nd   

def labeling_seg(seg):

    bw = closing(seg)

    struct = nd.generate_binary_structure(3, 3)    

    bw = nd.morphology.binary_dilation(bw,structure=struct,iterations=7)

    bw=bw.astype('uint8')

    label_image = nd.measurements.label(bw)

    label_image = label_image[0]

    label_image = seg*label_image  

    return label_image