dataset_mccad.py

import scipy
import torch.utils.data
import numpy as np
import random


def CreateDatasetSynthesis(phase, input_path):

    target_file = input_path + phase+"/t1.mat"
    data_fs_s1=LoadDataSet(target_file)
    
    target_file = input_path + phase+"/t2.mat"
    data_fs_s2=LoadDataSet(target_file)


    target_file = input_path + phase+"/flair.mat"
    data_fs_s4=LoadDataSet(target_file)

    dataset=torch.utils.data.TensorDataset( torch.from_numpy(data_fs_s1), torch.from_numpy(data_fs_s2), torch.from_numpy(data_fs_s4))  
    return dataset 



#Dataset loading from load_dir and converintg to 256x256 
def LoadDataSet(load_dir, variable = 'slices', padding = True, Norm = True):
    f = scipy.io.loadmat(load_dir)
    if np.array(f[variable]).ndim==3:
        data=np.expand_dims(np.transpose(np.array(f[variable]),(0,2,1)),axis=1)
    else:
        data=np.transpose(np.array(f[variable]),(1,0,3,2))
    if Norm:
        data=data.astype(np.float32) 
    else:
        data=data.astype(np.uint8)
    if padding:
        pad_x=int((256-data.shape[2])/2)
        pad_y=int((256-data.shape[3])/2)
        print('padding in x-y with:'+str(pad_x)+'-'+str(pad_y))
        data=np.pad(data,((0,0),(0,0),(pad_x,pad_x),(pad_y,pad_y)))   
    if Norm:    
        data=(data-0.5)/0.5      
    return data