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compute3DSSIM.py
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compute3DSSIM.py
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'''
Target: Compute structure similarity (SSIM) between two 3D volumes
Created on Jan, 22th 2018
Author: Dong Nie
reference from: http://simpleitk-prototype.readthedocs.io/en/latest/user_guide/plot_image.html
'''
import SimpleITK as sitk
from multiprocessing import Pool
import os
import h5py
import numpy as np
import scipy.io as scio
from morpologicalTransformation import denoiseImg_closing, denoiseImg_isolation
from skimage import measure
path = '/shenlab/lab_stor5/dongnie/3T7T/results/'
def main():
ids = [14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
ids = range(0, 30)
ids = [1]
for id in ids:
# datafn = os.path.join(path,'Case%02d.mhd'%id)
# outdatafn = os.path.join(path,'Case%02d.nii.gz'%id)
#
# dataOrg = sitk.ReadImage(datafn)
# dataMat = sitk.GetArrayFromImage(dataOrg)
# #gtMat=np.transpose(gtMat,(2,1,0))
# dataVol = sitk.GetImageFromArray(dataMat)
# sitk.WriteImage(dataVol,outdatafn)
# datafn = os.path.join(path, 'img1.mhd')
# dataOrg = sitk.ReadImage(datafn)
# spacing = dataOrg.GetSpacing()
# origin = dataOrg.GetOrigin()
# direction = dataOrg.GetDirection()
# dataMat = sitk.GetArrayFromImage(dataOrg)
gtfn = os.path.join(path, 'S1to1_7t.nii.gz')
gtOrg = sitk.ReadImage(gtfn)
gtMat = sitk.GetArrayFromImage(gtOrg)
# gtMat=np.transpose(gtMat,(2,1,0))
gtMat = gtMat.astype(np.float32)
gtMat = (gtMat - np.amin(gtMat))/(np.amax(gtMat)-np.amin(gtMat))
print np.amax(gtMat),',', np.amin(gtMat), 'dtype: ',gtMat.dtype, ',',gtMat.shape
prefn = os.path.join(path,'preSub1_1112_195000.nii.gz')
preOrg = sitk.ReadImage(prefn)
preMat = sitk.GetArrayFromImage(preOrg)
preMat = preMat.astype(np.float32)
preMat = (preMat - np.amin(preMat))/(np.amax(preMat)-np.amin(preMat))
# preMat = np.transpose(preMat,(2,0,1))
print np.amax(preMat),',', np.amin(preMat), 'dtype:',preMat.dtype, ',',preMat.shape
ssim_3d_sk = measure.compare_ssim(gtMat, preMat, multichannel=True, gaussian_weights=True, data_range=1.0,
use_sample_covariance=False)
print ssim_3d_sk
# ssim_3d_sk = measure.structural_similarity(gtMat, preMat, multichannel=True, gaussian_weights=True, data_range=1.0,
# use_sample_covariance=False)
# gtMat1 = denoiseImg_closing(gtMat, kernel=np.ones((20, 20, 20)))
# gtMat2 = gtMat + gtMat1
# gtMat2[np.where(gtMat2 > 1)] = 1
# gtMat = gtMat2
# gtMat = denoiseImg_isolation(gtMat, struct=np.ones((3, 3, 3)))
#
# gtMat = gtMat.astype(np.uint8)
# ind1 = np.where((gtMat==1)&(preMat==1))
# preMat[ind1] = 0
# ind2 = np.where((gtMat==2)&(preMat==2))
# preMat[ind2] = 0
# ind3 = np.where((gtMat==3)&(preMat==3))
# preMat[ind3] = 0
# errorMat = preMat
#
# outgtfn = os.path.join(path, 'sgm_errormap_sub1.nii.gz')
# errorVol = sitk.GetImageFromArray(errorMat)
# errorVol.SetSpacing(spacing)
# errorVol.SetOrigin(origin)
# errorVol.SetDirection(direction)
# sitk.WriteImage(errorVol, outgtfn)
#
# prefn='preSub%d_as32_v12.nii'%id
# preOrg=sitk.ReadImage(prefn)
# preMat=sitk.GetArrayFromImage(preOrg)
# preMat=np.transpose(preMat,(2,1,0))
# preVol=sitk.GetImageFromArra(preMat)
# sitk.WriteImage(preVol,prefn)
if __name__ == '__main__':
main()