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SAKE_nohank.py
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import os.path as osp
import matplotlib.pyplot as plt
import scipy.io as io
import numpy as np
from skimage.metrics import structural_similarity as SSIM
from skimage.metrics import peak_signal_noise_ratio as PSNR
import time
import math
import torch
import cv2
#from scipy import linalg
def compare_hfen(rec,ori):
operation = np.array(io.loadmat("./mask/loglvbo.mat")['h1'],dtype=np.float32)
ori = cv2.filter2D(ori.astype('float32'), -1, operation,borderType=cv2.BORDER_CONSTANT)
rec = cv2.filter2D(rec.astype('float32'), -1, operation,borderType=cv2.BORDER_CONSTANT)
hfen = np.linalg.norm(ori-rec, ord = 'fro')
return hfen
def write_images(x,image_save_path):
x = np.clip(x * 255, 0, 255).astype(np.uint8)
cv2.imwrite(image_save_path, x)
def fft2c(x):
size = (x).shape
fctr = size[0]*size[1]
Kdata = np.zeros((size),dtype=np.complex64)
for i in range(size[2]):
Kdata[:,:,i] = (1/np.sqrt(fctr))*np.fft.fftshift(np.fft.fft2(x[:,:,i]))
return Kdata
def ifft2c(kspace):
size = (kspace).shape
#fctr = size[0]*size[1]
Image = np.zeros((size),dtype=np.complex64)
for i in range(size[2]):
Image[:,:,i] = np.fft.ifft2(kspace[:,:,i])#np.sqrt(fctr)*np.fft.ifft2(kspace[:,:,i])
return Image
def im2row(im,winSize):
size = (im).shape
out = np.zeros(((size[0]-winSize[0]+1)*(size[1]-winSize[1]+1),winSize[0]*winSize[1],size[2]),dtype=np.complex64)
count = -1
for y in range(winSize[1]):
for x in range(winSize[0]):
count = count + 1
temp1 = im[x:(size[0]-winSize[0]+x+1),y:(size[1]-winSize[1]+y+1),:]
temp2 = np.reshape(temp1,[(size[0]-winSize[0]+1)*(size[1]-winSize[1]+1),1,size[2]],order = 'F')
out[:,count,:] = np.squeeze(temp2) # MATLAB reshape
return out
def row2im(mtx,size_data,winSize):
size_mtx = mtx.shape #(63001, 36, 8)
sx = size_data[0] # 256
sy = size_data[1] # 256
sz = size_mtx[2] # 8
res = np.zeros((sx,sy,sz),dtype=np.complex64)
W = np.zeros((sx,sy,sz),dtype=np.complex64)
out = np.zeros((sx,sy,sz),dtype=np.complex64)
count = -1
for y in range(winSize[1]):
for x in range(winSize[0]):
count = count + 1
res[x : sx-winSize[0]+x+1 ,y : sy-winSize[1]+y+1 ,:] = res[x : sx-winSize[0]+x+1 ,y : sy-winSize[1]+y+1 ,:] + np.reshape(np.squeeze(mtx[:,count,:]),[sx-winSize[0]+1,sy-winSize[1]+1,sz],order = 'F')
W[x : sx-winSize[0]+x+1 ,y : sy-winSize[1]+y+1 ,:] = W[x : sx-winSize[0]+x+1 ,y : sy-winSize[1]+y+1 ,:] + 1
out = np.multiply(res,1./W)
return out
def sake(K_input, Ksample, mask, ksize, wnthresh, sakeIter, ori):
PSNR_all = []
SSIM_all = []
HFEN_all = []
#size_data = (K_input).shape
Krec = np.copy(K_input)#np.zeros((Ksample.shape),dtype=np.complex64)#np.copy(Ksample)
Krec = Krec.transpose(1, 2, 0)
size_data = (Krec).shape
Ksample = Ksample.transpose(1, 2, 0)
mask = mask.transpose(1, 2, 0)
ori = ori.transpose(1, 2, 0)
for n in range(sakeIter):
#print('==sake== ',n)
#temp = im2row(Krec,ksize)
temp = np.reshape(Krec, [192*192, 16], order = 'F')
size_temp = temp.shape
#A = np.reshape(temp,[size_temp[0],size_temp[1]*size_temp[2]],order = 'F') # max: 14.925017 (matlab:14.9250) (63001, 288)
A = temp
#=============================================== SVD
A = torch.tensor(A,dtype=torch.complex64)
U,S,V = torch.svd(A)
S = torch.diag(S)
U = np.array(U,dtype=np.complex64)
S = np.array(S,dtype=np.complex64)
V = np.array(V,dtype=np.complex64)
#===============================================
uu = U[:,0:math.floor(wnthresh*ksize[0]*ksize[1])] #(63001, 64)
ss = S[0:math.floor(wnthresh*ksize[0]*ksize[1]),0:math.floor(wnthresh*ksize[0]*ksize[1])] #(64, 64)
vv = V[:,0:math.floor(wnthresh*ksize[0]*ksize[1])] #(64, 288)
A = np.dot(np.dot(uu,ss),vv.T)
#A = np.reshape(A,[size_temp[0],size_temp[1],size_temp[2]],order = 'F')
#kcomplex = row2im(A,size_data,ksize)
kcomplex = np.reshape(A, [192, 192, 16], order = 'F')
#print(np.max(abs(kcomplex)),' ',np.min(abs(kcomplex)))
for ii in range(16):
Krec[:,:,ii] = kcomplex[:,:,ii]*(1-mask[:,:,ii]) + Ksample[:,:,ii]
#Kout = np.copy(Krec)
Image_Rec = ifft2c(Krec)
#print(np.max(abs(Image_Rec)))
'''
for i in range(16):
eval_ori = (abs(ori[:, :, i]))/np.max(abs(ori[:, :, i]))
eval_rec = (abs(Image_Rec[:, :, i]))/np.max(abs(Image_Rec[:, :, i]))
psnr_ori_rec = PSNR(255*eval_ori, 255*eval_rec, data_range=255)
ssim_ori_rec = SSIM(eval_ori, eval_rec, data_range=1)
hfen_ori_rec = compare_hfen(eval_rec, eval_ori)
PSNR_all.append(psnr_ori_rec)
SSIM_all.append(ssim_ori_rec)
HFEN_all.append(hfen_ori_rec)
write_images(eval_ori, osp.join('./result/'+'ori_'+str(i)+'.png'))
write_images(eval_rec, osp.join('./result/'+'rec_'+str(i)+'.png'))
print('Average psnr: ', sum(PSNR_all)/len(PSNR_all))
print('Average ssim: ', sum(SSIM_all)/len(SSIM_all))
print('Average hfen: ', sum(HFEN_all)/len(HFEN_all))
'''
return Image_Rec, Krec