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ssim_test.py
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#############################################################################################
# Import Packages
#############################################################################################
import torch
import torch.nn as nn
import numpy as np
from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
from scipy.io import savemat
#####################Plot the images##########################################################
total_val_images = 1153
val_data_tensor = torch.load('/......./FusionINN/val_data_tensor.pt')
val_data_tensor = val_data_tensor.cpu().squeeze()
count = 0
##############################################################################################
# FusionINN
##############################################################################################
fused_val_tensor = torch.load('/......./FusionINN/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
#print(fused_val_tensor.shape)
recon_val_tensor = torch.load('/......./FusionINN/val_recon_tensor.pt')
recon_val_tensor = recon_val_tensor.cpu().squeeze()
#print(recon_val_tensor.shape)
ssim_fusion = torch.zeros(total_val_images)
ssim_recon = torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_fusion_t1ce = ssim(fused_val_tensor[i:i+1, None, :,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_fusion_flair = ssim(fused_val_tensor[i:i+1, None, :,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_fusion[i]=(ssim_fusion_t1ce + ssim_fusion_flair)/2
ssim_recon_t1ce = ssim(recon_val_tensor[i:i+1, 0:1, :,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_recon_flair = ssim(recon_val_tensor[i:i+1, 1:, :,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_recon[i]=(ssim_recon_t1ce + ssim_recon_flair)/2
mdic = {"ssim_fusion": ssim_fusion, "ssim_recon": ssim_recon}
savemat("/......./FusionINN/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# DDFM
##############################################################################################
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
fused_val_tensor = torchvision.io.read_image('/......./DDFM/output/recon/' + str(i) + '.png', torchvision.io.ImageReadMode.GRAY)/255
ssim_t1ce = ssim(fused_val_tensor[:, None, :,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[:, None, :,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./DDFM/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# DeepFuse
##############################################################################################
fused_val_tensor = torch.load('/......./DeepFuse/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None, :,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None, :,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./DeepFuse/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# FunFuseAn
##############################################################################################
fused_val_tensor = torch.load('/......./FunFuseAn/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./FunFuseAn/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# Half-UNET
##############################################################################################
fused_val_tensor = torch.load('/......./Half_UNET/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./Half_UNET/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# UNET
##############################################################################################
fused_val_tensor = torch.load('/......./UNET/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./UNET/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# UNET++
##############################################################################################
fused_val_tensor = torch.load('/......./UNET++/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./UNET++/ssim.mat", mdic)
count += 1
print(count)
##############################################################################################
# UNET3+
##############################################################################################
fused_val_tensor = torch.load('/......./UNET3+/val_fused_tensor.pt')
fused_val_tensor = fused_val_tensor.cpu().squeeze()
ssim_average=torch.zeros(total_val_images)
for i in range(total_val_images):
ssim_t1ce = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 2:3, :, :], data_range=1)
ssim_flair = ssim(fused_val_tensor[i:i+1, None,:,:], val_data_tensor[i:i+1, 3:, :, :], data_range=1)
ssim_average[i]=(ssim_t1ce + ssim_flair)/2
mdic = {"ssim": ssim_average}
savemat("/......./UNET3+/ssim.mat", mdic)
count += 1
print(count)
###############################################################################################