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data_prepare.py
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import numpy as np
import pandas as pd
import datetime
import torch.optim as optim
from scipy import io
import argparse
import os
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
import h5py
import matplotlib.pyplot as plt
import matplotlib
# print(matplotlib.__version__)
matplotlib.use('Agg')
import h5py
from PIL import Image
import math
from sklearn.metrics import confusion_matrix
import pylab as pl
import numpy as np
import itertools
import torchvision
# from ._conv import register_converters as _register_converters
# current_file_data_save = '//nfs/bisp_data_server/Linfang/PF_recon/PF55/SS/NYU_brain_new/'
current_file_data_save = '//media/bisp/New Volume/Linfang/PF_CC398_218_170_218/PF55/EMS/CC_brain_testing_code/'
data_file = '/nfs/bisp_data_server/Linfang/CC359_dataset/multi_channel/train_val_12_channel/CC398_218_170_128/'
os.makedirs(current_file_data_save+'/test/', exist_ok=True)
os.makedirs(current_file_data_save+'/validation/', exist_ok=True)
os.makedirs(current_file_data_save+'/train/', exist_ok=True)
matrix_size = int(218)
pf_line = int(np.floor(matrix_size*0.45))
pf_line_com = matrix_size-pf_line
SS_flag =0
MS_flag =0
EMS_flag =1
# for validation
# filePath ='///nfs/bisp_data_server/Linfang/PF_recon/singlecoil//validation/'
filePath =data_file +'/test/'
filename = os.listdir(filePath)
length = len(filename)
print('test')
for idx in range(0,length):
f = h5py.File(filePath+ filename[idx],'r')
# print(f.keys())
# data = f['kspace'].value
data_I = f['kspace_I'].value
data_R = f['kspace_R'].value
data = data_I*1j +data_R
sz = data.shape
freq= np.fft.ifftshift(data ,axes=0)
freq = np.fft.ifft(freq,axis =0)
data1 = np.sqrt(sz[0])*np.fft.fftshift(freq,axes=0)
# data1 = data[(sz[0]//2-8):(sz[0]//2+8),(sz[1]//2-matix_crop):(sz[1]//2+matix_crop),(sz[2]//2-matix_crop):(sz[2]//2+matix_crop)]
data2 = np.reshape(data1,(-1,1,sz[1],sz[2]))
# sz1 = data1.shape
# plt.figure(1)
# plt.imshow(np.log(abs(data2[150,0, :, :])-1e-10), cmap='gray')
# plt.savefig('k_or')
# # freq= np.fft.ifft2(data2 ,axes=(2,3))
# # img = np.fft.ifftshift(freq,axes=(2,3))
freq= np.fft.ifftshift(data2 ,axes=(2,3))
freq = np.fft.ifft2(freq,axes=(2,3))
img = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
####################################################
# plt.figure(1)
# plt.imshow((abs(img[90,0, :, :])-1e-10), cmap='gray')
# plt.savefig('img_data_or')
# plt.figure(1)
# plt.imshow((np.angle(img[90,0, :, :])-1e-10), cmap='gray')
# plt.savefig('img_angle_or')
# freq= np.fft.ifftshift(img ,axes=(2,3))
# freq= np.fft.fft2(freq ,axes=(2,3))
# freq = 1/matrix_size*np.fft.fftshift(freq,axes=(2,3))
# plt.figure(1)
# plt.imshow(np.log(abs(freq[2,0, :, :])-1e-10), cmap='gray')
# plt.savefig('k_or')
##############################################################
scale_ref = round(np.max(np.absolute(img)),15)
# imgR = np.real(img)/scale_ref
# imgI = np.imag(img)/scale_ref
img = img/scale_ref
##single slice
if SS_flag ==1:
img_ref = img[1:sz[0]-1,:,:,:]*0
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = img[k,:,:,:]
else:
img_ref = np.concatenate((img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0),axis = 1)
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = np.concatenate((img[k,:,:,:], img[k-1,:,:,:], img[k+1,:,:,:]), axis =0)
# plt.figure(2)
# plt.imshow((abs(img[91,0, :, :])), cmap='gray')
# plt.savefig('img_data_or')
##multi slice
freq= np.fft.ifftshift(img_ref ,axes=(2,3))
freq= np.fft.fft2(freq ,axes=(2,3))
test_k= 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
test_data = np.copy(test_k)
test_data[:,0,:,:pf_line] = 0
##############complementry sampling pattern
if EMS_flag ==1:
test_data[:,1:3,:,pf_line_com:] = 0
if MS_flag==1:
test_data[:,1:3,:,:pf_line] = 0
###########################
freq = np.fft.ifftshift(test_data,axes=(2,3))
freq = np.fft.ifft2(freq ,axes=(2,3))
img_data = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
# plt.figure(2)
# plt.imshow((abs(img_data[90,0, :, :])), cmap='gray')
# plt.savefig('img_data_zero')
# plt.figure(2)
# plt.imshow((np.angle(img_data[90,0, :, :])), cmap='gray')
# plt.savefig('angle_data_zero')
# img_label = img_ref*0
img_label =img_ref[:,0,:,:] -img_data[:,0,:,:]
img_label =img_label.reshape(sz[0]-2,-1,sz[1],sz[2])
imgdataR = np.copy(np.real(img_data))
imgdataI = np.copy(np.imag(img_data))
imgdata = np.concatenate((imgdataR,imgdataI), axis=1)
imglabelR = np.copy(np.real(img_label))
imglabelI = np.copy(np.imag(img_label))
imglabel = np.concatenate((imglabelR,imglabelI), axis=1)
imgfull=img_label[:,0,:,:] + img_data[:,0,:,:]
imgfull =imgfull.reshape(sz[0]-2,-1,sz[1],sz[2])
#########################################################
k_nor = np.fft.ifftshift(imgfull,axes=(2,3))
k_nor= np.fft.fft2(k_nor ,axes=(2,3))
k_nor = 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(k_nor,axes=(2,3))
k_zero = np.fft.ifftshift(img_data,axes=(2,3))
k_zero= np.fft.fft2(k_zero ,axes=(2,3))
k_zero = 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(k_zero,axes=(2,3))
k_label = np.fft.ifftshift(img_label,axes=(2,3))
k_label= np.fft.fft2(k_label ,axes=(2,3))
k_label = 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(k_label,axes=(2,3))
nc =0
nslice = 90
plt.figure(1)
plt.imshow(np.log(np.abs(k_nor[nslice,0,:,:])),'gray')
plt.savefig('0_original_k.png')
plt.figure(2)
plt.imshow(abs(imgfull[nslice,0,:,:]),'gray')
plt.savefig('1_original_ref.png')
plt.figure(3)
plt.imshow(np.log(np.abs(k_zero[nslice,nc,:,:])),'gray')
plt.savefig('2_zero_k.png')
plt.figure(4)
plt.imshow(abs(img_data[nslice,nc,:,:]),'gray')
plt.savefig('3_zero_img.png')
plt.figure(6)
plt.imshow(np.log(abs(k_label[nslice,0,:,:])),'gray')
plt.savefig('4_label_k.png')
plt.figure(5)
plt.imshow(abs(img_label[nslice,0,:,:]),'gray')
plt.savefig('5_label_img.png')
# ####################################################################
plt.figure(3)
plt.imshow(np.log(np.abs(k_zero[nslice,1,:,:])),'gray')
plt.savefig('2_zero_k_1.png')
plt.figure(4)
plt.imshow(abs(img_data[nslice,1,:,:]),'gray')
plt.savefig('3_zero_img_1.png')
plt.figure(3)
plt.imshow(np.log(np.abs(k_zero[nslice,2,:,:])),'gray')
plt.savefig('2_zero_k_2.png')
plt.figure(4)
plt.imshow(abs(img_data[nslice,2,:,:]),'gray')
plt.savefig('3_zero_img_2.png')
#####################################################################
D = torch.from_numpy(imgdata).float()
L = torch.from_numpy(imglabel).float()
data = {'k-space':D,'label':L}
torch.save(data,current_file_data_save +'/test/'+str(idx)+'.pth')
f.close()
# # for test
print('validation')
filePath =data_file +'/validation/'
filename = os.listdir(filePath)
length = len(filename)
for idx in range(0,length):
f = h5py.File(filePath+ filename[idx],'r')
data_I = f['kspace_I'].value
data_R = f['kspace_R'].value
data = data_I*1j +data_R
sz = data.shape
freq= np.fft.ifftshift(data ,axes=0)
freq = np.fft.ifft(freq,axis =0)
data1 = np.sqrt(sz[0])*np.fft.fftshift(freq,axes=0)
data2 = np.reshape(data1,(-1,1,sz[1],sz[2]))
freq= np.fft.ifftshift(data2 ,axes=(2,3))
freq = np.fft.ifft2(freq,axes=(2,3))
img = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
scale_ref = round(np.max(np.absolute(img)),15)
img = img/scale_ref
##single slice
if SS_flag ==1:
img_ref = img[1:sz[0]-1,:,:,:]*0
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = img[k,:,:,:]
else:
img_ref = np.concatenate((img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0),axis = 1)
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = np.concatenate((img[k,:,:,:], img[k-1,:,:,:], img[k+1,:,:,:]), axis =0)
##multi slice
freq= np.fft.ifftshift(img_ref ,axes=(2,3))
freq= np.fft.fft2(freq ,axes=(2,3))
test_k= 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
test_data = np.copy(test_k)
test_data[:,0,:,:pf_line] = 0
##############complementry sampling pattern
if EMS_flag ==1:
test_data[:,1:3,:,pf_line_com:] = 0
if MS_flag==1:
test_data[:,1:3,:,:pf_line] = 0
###########################
freq = np.fft.ifftshift(test_data,axes=(2,3))
freq = np.fft.ifft2(freq ,axes=(2,3))
img_data = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
img_label =img_ref[:,0,:,:] -img_data[:,0,:,:]
img_label =img_label.reshape(sz[0]-2,-1,sz[1],sz[2])
imgdataR = np.copy(np.real(img_data))
imgdataI = np.copy(np.imag(img_data))
imgdata = np.concatenate((imgdataR,imgdataI), axis=1)
imglabelR = np.copy(np.real(img_label))
imglabelI = np.copy(np.imag(img_label))
imglabel = np.concatenate((imglabelR,imglabelI), axis=1)
imgfull=img_label[:,0,:,:] + img_data[:,0,:,:]
imgfull =imgfull.reshape(sz[0]-2,-1,sz[1],sz[2])
for i in range(16,int(sz[0]-20)):
D = torch.from_numpy(imgdata[i,:,:,:]).float()
L = torch.from_numpy(imglabel[i,:,:,:]).float()
data = {'k-space':D,'label':L}
torch.save(data,current_file_data_save +'/validation/'+str(idx)+'_'+str(i)+'.pth')
f.close()
# # for train
print('train')
# filePath ='///nfs/bisp_data_server/Linfang/PF_recon/singlecoil//train/'
filePath =data_file +'/train/'
filename = os.listdir(filePath)
length = len(filename)
for idx in range(0,length):
f = h5py.File(filePath+ filename[idx],'r')
data_I = f['kspace_I'].value
data_R = f['kspace_R'].value
data = data_I*1j +data_R
sz = data.shape
freq= np.fft.ifftshift(data ,axes=0)
freq = np.fft.ifft(freq,axis =0)
data1 = np.sqrt(sz[0])*np.fft.fftshift(freq,axes=0)
data2 = np.reshape(data1,(-1,1,sz[1],sz[2]))
freq= np.fft.ifftshift(data2 ,axes=(2,3))
freq = np.fft.ifft2(freq,axes=(2,3))
img = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
scale_ref = round(np.max(np.absolute(img)),15)
img = img/scale_ref
##single slice
if SS_flag ==1:
img_ref = img[1:sz[0]-1,:,:,:]*0
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = img[k,:,:,:]
else:
img_ref = np.concatenate((img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0,img[1:sz[0]-1,:,:,:]*0),axis = 1)
for k in range(1,sz[0]-1):
img_ref[k-1,:,:,:] = np.concatenate((img[k,:,:,:], img[k-1,:,:,:], img[k+1,:,:,:]), axis =0)
##multi slice
freq= np.fft.ifftshift(img_ref ,axes=(2,3))
freq= np.fft.fft2(freq ,axes=(2,3))
test_k= 1/np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
test_data = np.copy(test_k)
test_data[:,0,:,:pf_line] = 0
##############complementry sampling pattern
if EMS_flag ==1:
test_data[:,1:3,:,pf_line_com:] = 0
if MS_flag==1:
test_data[:,1:3,:,:pf_line] = 0
###########################
freq = np.fft.ifftshift(test_data,axes=(2,3))
freq = np.fft.ifft2(freq ,axes=(2,3))
img_data = np.sqrt(sz[1]*sz[2])*np.fft.fftshift(freq,axes=(2,3))
img_label =img_ref[:,0,:,:] -img_data[:,0,:,:]
img_label =img_label.reshape(sz[0]-2,-1,sz[1],sz[2])
imgdataR = np.copy(np.real(img_data))
imgdataI = np.copy(np.imag(img_data))
imgdata = np.concatenate((imgdataR,imgdataI), axis=1)
imglabelR = np.copy(np.real(img_label))
imglabelI = np.copy(np.imag(img_label))
imglabel = np.concatenate((imglabelR,imglabelI), axis=1)
imgfull=img_label[:,0,:,:] + img_data[:,0,:,:]
imgfull =imgfull.reshape(sz[0]-2,-1,sz[1],sz[2])
for i in range(16,int(sz[0]-20)):
D = torch.from_numpy(imgdata[i,:,:,:]).float()
L = torch.from_numpy(imglabel[i,:,:,:]).float()
data = {'k-space':D,'label':L}
torch.save(data,current_file_data_save +'/train/'+str(idx)+'_'+str(i)+'.pth')
f.close()
os.system('python CNN_train.py')