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eval_wave_new.py
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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import h5py
import nibabel as nib
import os
import numpy as np
import pandas as pd
import keras.backend as K
import time
from skimage import measure
import logging
K.set_image_data_format("channels_first")
import tensorflow as tf
from FCN_2D import SR_UnetGAN
from MWUnet import unet_mw_2d_2
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import pywt
import pywt.data
aaa=np.array([[ -1.66666698, 0. , 0. , 299.16671753],
[ 0. , -1.66666698, 0. , 299.16671753],
[ 0. , 0. , 2.88599992, 569.75793457],
[ 0. , 0. , 0. , 1. ]])
max_img=612363.6
# max_img=1172342.5
class Evaluate():
def __init__(self):
self.domain = 'valid'
self.target_shape = [360,360,673]
def compute_metrics(self, real_input, pred_input):
real = real_input.copy()
real[real<1] = 0
pred = pred_input.copy()
pred[pred<1] = 0
mse = np.mean(np.square(real-pred))
print(mse)
nrmse = np.sqrt(mse) / (np.max(real)-np.min(real))
print('nrmse:{}'.format(nrmse))
ok_idx = np.where(real!=0)
mape = np.mean(np.abs((real[ok_idx] - pred[ok_idx]) / real[ok_idx]))
print('mape:{}'.format(mape))
PIXEL_MAX = np.max(real)
psnr = 20*np.log10(PIXEL_MAX / np.sqrt(np.mean(np.square(real-pred))))
print('psnr:{}'.format(psnr))
real_norm = real / float(np.max(real))
pred_norm = pred / float(np.max(pred))
ssim = measure.compare_ssim(real_norm, pred_norm)
return mse, nrmse, mape, psnr, ssim
def generate_result_4channel(self):
if 1:
if 1:
#data_path = '/media/data/fanxuan/data/PART2_Siemens_h5data/data_Siemens_50.h5'
#data_path_normal = '/media/data/fanxuan/data/PART2_Siemens_h5data/data_Siemens_normal_50.h5'
#data_path = '/media/data/fanxuan/data/PART2_h5data/data_50_10_wave.h5'
#data_path_normal = '/media/data/fanxuan/data/PART2_h5data/data_50_new.h5'
data_path = '/media/data/fanxuan/data/PART2_h5data/data_wave_10_haar.h5'
data_path_normal = '/media/data/fanxuan/data/PART2_h5data/data_normal_10.h5'
save_dir = '/media/data/fanxuan/result//FCN_10_wave_mw_haar/2.0'
model_path = os.path.join(save_dir, 'model/generator_epoch_300.hdf5')
save_path = os.path.join(save_dir, 'valid_300')
if not os.path.exists(save_path):
os.makedirs(save_path)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=tf.get_default_graph(), config=config) as sess:
sess.run(tf.global_variables_initializer())
K.set_session(sess)
generator = unet_mw_2d_2((4,176,176))
generator.load_weights(model_path)
data = h5py.File(data_path, 'r')
data_normal = h5py.File(data_path_normal, 'r')
filenames = np.array(data[self.domain+'_filenames']).flatten()
filenames = [x.decode('utf-8') for x in filenames]
patients = sorted(set(x.split('+')[1] for x in filenames))
count = 0
patient_list, mse_list, nrmse_list, mape_list, psnr_list, ssim_list = [],[],[],[],[],[]
ori_mse_list, ori_nrmse_list, ori_mape_list, ori_psnr_list, ori_ssim_list = [],[],[],[],[]
for patient in patients:
volume = np.zeros(shape=self.target_shape)
gd_volume = np.zeros(shape=self.target_shape)
x_volume = np.zeros(shape=self.target_shape)
print(len(filenames))
for i in range(len(filenames)):
if patient==filenames[i].split('+')[1]:
count += 1
print('{}: {}/{}'.format(filenames[i], count, len(filenames)))
n = int(filenames[i].split('+')[-2])
s = int(filenames[i].split('+')[-1])
#x_slice = np.array(data.get(self.domain+'_reduce_50')[i])
x_slice = np.array(data.get(self.domain+'_reduce')[i])
x_normal = np.array(data_normal.get(self.domain+'_reduce')[i])
gd_slice = np.array(data.get(self.domain+'_normal')[i])
gd_normal = np.array(data_normal.get(self.domain+'_normal')[i])
coeffs_x = x_slice[0], (x_slice[1], x_slice[2], x_slice[3])
x_re = pywt.idwt2(coeffs_x, 'haar')
x_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],x_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(x_re.shape)
x_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],x_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
x_volume[:,:,s] += x_re * max_img
coeffs_gd = gd_slice[0], (gd_slice[1], gd_slice[2], gd_slice[3])
gd_re = pywt.idwt2(coeffs_gd, 'haar')
gd_re = np.concatenate((gd_normal[0:(gd_normal.shape[0]//2-176), (gd_normal.shape[1]//2-176):(gd_normal.shape[1]//2+176)],gd_re,gd_normal[(gd_normal.shape[0]//2+176):, (gd_normal.shape[1]//2-176):(gd_normal.shape[1]//2+176)]), axis=0)
print(gd_re.shape)
gd_re = np.concatenate((gd_normal[:, :(gd_normal.shape[1]//2-176)],gd_re,gd_normal[:, (gd_normal.shape[1]//2+176):]), axis = 1)
gd_volume[:,:,s] += gd_re * max_img
# ori_mse, ori_nrmse, ori_psnr, ori_ssim = self.compute_metrics(gd_slice, x_slice)
print(np.mean(x_slice))
x_slice = np.expand_dims(x_slice, axis=0)
# x_slice = np.expand_dims(x_slice, axis=0)
out_slice = generator.predict(x_slice)[0,:,:,:]
print(np.mean(x_slice))
print(out_slice.shape)
coeffs_out = out_slice[0], (out_slice[1], out_slice[2], out_slice[3])
out_re = pywt.idwt2(coeffs_out, 'haar')
out_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],out_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(out_re.shape)
out_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],out_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
volume[:,:,s] += out_re * max_img
patient_save_path = os.path.join(save_path, 'patients')
if not os.path.exists(patient_save_path):
os.makedirs(patient_save_path)
'''nib.save(nib.Nifti1Image(volume, affine=aaa),os.path.join(patient_save_path, os.path.splitext(patient)[0]+'_source_{}_generated.nii'.format(n)))
nib.save(nib.Nifti1Image(gd_volume, affine=aaa),os.path.join(patient_save_path, os.path.splitext(patient)[0]+'_source_{}_ori.nii'.format(n)))
nib.save(nib.Nifti1Image(x_volume, affine=aaa), os.path.join(patient_save_path, os.path.splitext(patient)[0]+'_source_{}_reduce.nii'.format(n)))
'''
volume[volume < 0] = 0
x_volume[x_volume < 0] = 0
gd_volume[gd_volume < 0] = 0
#ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim = self.compute_metrics(gd_volume, x_volume)
mse, nrmse, mape, psnr, ssim = self.compute_metrics(gd_volume, volume)
'''print('ori_mse:{}, ori_nrmse:{}, ori_mape:{}, ori_psnr:{}, ori_ssim:{}'.format(ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim))'''
print('mse:{}, nrmse:{}, mape:{}, psnr:{}, ssim:{}'.format(mse, nrmse, mape, psnr, ssim))
patient_list.append(patient)
mse_list.append(mse)
nrmse_list.append(nrmse * 1e2)
mape_list.append(mape)
psnr_list.append(psnr)
ssim_list.append(ssim)
'''ori_mse_list.append(ori_mse)
ori_nrmse_list.append(ori_nrmse * 1e2)
ori_mape_list.append(ori_mape)
ori_psnr_list.append(ori_psnr)
ori_ssim_list.append(ori_ssim)
ori_df = pd.DataFrame({'Patient_num': patient_list, 'MSE': ori_mse_list,
'NRMSE %': ori_nrmse_list, 'mape': ori_mape_list,
'PSNR': ori_psnr_list, 'SSIM': ori_ssim_list})
ori_df = ori_df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(ori_mse_list),
'NRMSE %': np.mean(ori_nrmse_list), 'mape': np.mean(ori_mape_list),
'PSNR': np.mean(ori_psnr_list), 'SSIM': np.mean(ori_ssim_list)}, ignore_index=True)
ori_df = ori_df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
ori_df.to_csv(os.path.join(patient_save_path,'ori.csv'), index=False)'''
df = pd.DataFrame({ 'Patient_num': patient_list, 'MSE': mse_list,
'NRMSE %': nrmse_list,'mape': mape_list, 'PSNR': psnr_list, 'SSIM': ssim_list})
df = df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(mse_list),
'NRMSE %': np.mean(nrmse_list), 'mape': np.mean(mape_list),
'PSNR': np.mean(psnr_list), 'SSIM': np.mean(ssim_list)}, ignore_index=True)
df = df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
df.to_csv(os.path.join(patient_save_path, 'gen.csv'), index=False)
data.close()
data_normal.close()
def generate_all_result_4channel(self):
if 1:
if 1:
#data_path = '/media/data/fanxuan/data/PART2_Siemens_h5data/data_Siemens_50.h5'
#data_path_normal = '/media/data/fanxuan/data/PART2_Siemens_h5data/data_Siemens_normal_50.h5'
#data_path = '/media/data/fanxuan/data/PART2_h5data/data_50_10_wave.h5'
#data_path_normal = '/media/data/fanxuan/data/PART2_h5data/data_50_new.h5'
data_path = '/media/data/fanxuan/data/PART2_h5data/data_wave_10_bio3_7.h5'
data_path_normal = '/media/data/fanxuan/data/PART2_h5data/data_normal_10.h5'
save_dir = '/media/data/fanxuan/result/FCN_10_wave_bior3_7'
wave_name = 'bior3.7'
len_wave = 169
start_num = 15
end_num = 40
save_path = os.path.join(save_dir, 'valid_all')
if not os.path.exists(save_path):
os.makedirs(save_path)
model_list, ave_mse_list, ave_nrmse_list, ave_mape_list, ave_psnr_list, ave_ssim_list = [],[],[],[],[],[]
for model_index in range(start_num,end_num+1):
model_num = model_index*10
model_path = os.path.join(save_dir, 'model/generator_epoch_'+str(model_num)+'.hdf5')
#save_path = os.path.join(save_dir, 'valid_300')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=tf.get_default_graph(), config=config) as sess:
sess.run(tf.global_variables_initializer())
K.set_session(sess)
generator = unet_mw_2d_2((176,176,4))
generator.load_weights(model_path)
data = h5py.File(data_path, 'r')
data_normal = h5py.File(data_path_normal, 'r')
filenames = np.array(data[self.domain+'_filenames']).flatten()
filenames = [x.decode('utf-8') for x in filenames]
patients = sorted(set(x.split('+')[1] for x in filenames))
count = 0
patient_list, mse_list, nrmse_list, mape_list, psnr_list, ssim_list = [],[],[],[],[],[]
ori_mse_list, ori_nrmse_list, ori_mape_list, ori_psnr_list, ori_ssim_list = [],[],[],[],[]
for patient in patients:
volume = np.zeros(shape=self.target_shape)
gd_volume = np.zeros(shape=self.target_shape)
x_volume = np.zeros(shape=self.target_shape)
#print(len(filenames))
for i in range(len(filenames)):
if patient==filenames[i].split('+')[1]:
count += 1
print('{}:{}: {}/{}'.format(model_num, filenames[i], count, len(filenames)))
n = int(filenames[i].split('+')[-2])
s = int(filenames[i].split('+')[-1])
x_slice = np.array(data.get(self.domain+'_reduce')[i])
x_normal = np.array(data_normal.get(self.domain+'_reduce')[i])
gd_slice = np.array(data.get(self.domain+'_normal')[i])
gd_normal = np.array(data_normal.get(self.domain+'_normal')[i])
coeffs_x = x_slice[0], (x_slice[1], x_slice[2], x_slice[3])
x_re = pywt.idwt2(coeffs_x, wave_name)
x_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-len_wave), (x_normal.shape[1]//2-len_wave):(x_normal.shape[1]//2+len_wave)],x_re,x_normal[(x_normal.shape[0]//2+len_wave):, (x_normal.shape[1]//2-len_wave):(x_normal.shape[1]//2+len_wave)]), axis=0)
x_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-len_wave)],x_re,x_normal[:, (x_normal.shape[1]//2+len_wave):]), axis = 1)
x_volume[:,:,s] += x_re * max_img
coeffs_gd = gd_slice[0], (gd_slice[1], gd_slice[2], gd_slice[3])
gd_re = pywt.idwt2(coeffs_gd, wave_name)
gd_re = np.concatenate((gd_normal[0:(gd_normal.shape[0]//2-len_wave), (gd_normal.shape[1]//2-len_wave):(gd_normal.shape[1]//2+len_wave)],gd_re,gd_normal[(gd_normal.shape[0]//2+len_wave):, (gd_normal.shape[1]//2-len_wave):(gd_normal.shape[1]//2+len_wave)]), axis=0)
gd_re = np.concatenate((gd_normal[:, :(gd_normal.shape[1]//2-len_wave)],gd_re,gd_normal[:, (gd_normal.shape[1]//2+len_wave):]), axis = 1)
gd_volume[:,:,s] += gd_re * max_img
x_slice = np.expand_dims(x_slice, axis=0)
out_slice = generator.predict(x_slice)[0,:,:,:]
coeffs_out = out_slice[0], (out_slice[1], out_slice[2], out_slice[3])
out_re = pywt.idwt2(coeffs_out, wave_name)
out_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-len_wave), (x_normal.shape[1]//2-len_wave):(x_normal.shape[1]//2+len_wave)],out_re,x_normal[(x_normal.shape[0]//2+len_wave):, (x_normal.shape[1]//2-len_wave):(x_normal.shape[1]//2+len_wave)]), axis=0)
out_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-len_wave)],out_re,x_normal[:, (x_normal.shape[1]//2+len_wave):]), axis = 1)
volume[:,:,s] += out_re * max_img
patient_save_path = save_path
if not os.path.exists(patient_save_path):
os.makedirs(patient_save_path)
volume[volume < 0] = 0
x_volume[x_volume < 0] = 0
gd_volume[gd_volume < 0] = 0
if model_index == start_num:
ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim = self.compute_metrics(gd_volume, x_volume)
print('ori_mse:{}, ori_nrmse:{}, ori_mape:{}, ori_psnr:{}, ori_ssim:{}'.format(ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim))
ori_mse_list.append(ori_mse)
ori_nrmse_list.append(ori_nrmse * 1e2)
ori_mape_list.append(ori_mape)
ori_psnr_list.append(ori_psnr)
ori_ssim_list.append(ori_ssim)
mse, nrmse, mape, psnr, ssim = self.compute_metrics(gd_volume, volume)
print('mse:{}, nrmse:{}, mape:{}, psnr:{}, ssim:{}'.format(mse, nrmse, mape, psnr, ssim))
patient_list.append(patient)
mse_list.append(mse)
nrmse_list.append(nrmse * 1e2)
mape_list.append(mape)
psnr_list.append(psnr)
ssim_list.append(ssim)
if model_index == start_num:
ori_df = pd.DataFrame({'Patient_num': patient_list, 'MSE': ori_mse_list,
'NRMSE %': ori_nrmse_list, 'mape': ori_mape_list,
'PSNR': ori_psnr_list, 'SSIM': ori_ssim_list})
ori_df = ori_df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(ori_mse_list),
'NRMSE %': np.mean(ori_nrmse_list), 'mape': np.mean(ori_mape_list),
'PSNR': np.mean(ori_psnr_list), 'SSIM': np.mean(ori_ssim_list)}, ignore_index=True)
ori_df = ori_df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
ori_df.to_csv(os.path.join(patient_save_path,'ori.csv'), index=False)
model_list.append(0)
ave_mse_list.append(np.mean(ori_mse_list))
ave_nrmse_list.append(np.mean(ori_nrmse_list))
ave_mape_list.append(np.mean(ori_mape_list))
ave_psnr_list.append(np.mean(ori_psnr_list))
ave_ssim_list.append(np.mean(ori_ssim_list))
df = pd.DataFrame({ 'Patient_num': patient_list, 'MSE': mse_list,
'NRMSE %': nrmse_list,'mape': mape_list, 'PSNR': psnr_list, 'SSIM': ssim_list})
df = df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(mse_list),
'NRMSE %': np.mean(nrmse_list), 'mape': np.mean(mape_list),
'PSNR': np.mean(psnr_list), 'SSIM': np.mean(ssim_list)}, ignore_index=True)
df = df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
df.to_csv(os.path.join(patient_save_path, 'gen_'+str(model_num)+'.csv'), index=False)
model_list.append(model_num)
ave_mse_list.append(np.mean(mse_list))
ave_nrmse_list.append(np.mean(nrmse_list))
ave_mape_list.append(np.mean(mape_list))
ave_psnr_list.append(np.mean(psnr_list))
ave_ssim_list.append(np.mean(ssim_list))
data.close()
data_normal.close()
df_all = pd.DataFrame({ 'Model_epoch_num': model_list, 'MSE': ave_mse_list,'NRMSE %': ave_nrmse_list,'mape': ave_mape_list, 'PSNR': ave_psnr_list, 'SSIM': ave_ssim_list})
df_all = df_all[['Model_epoch_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
df_all.to_csv(os.path.join(patient_save_path, 'all_from'+str(start_num)+'to'+str(end_num)+'.csv'), index=False)
def generate_result_4channel_multi(self):
if 1:
if 1:
data_path = '/media/data/fanxuan/data/PART2_h5data/data_50_10_wave.h5'
data_path_normal = '/media/data/fanxuan/data/PART2_h5data/data_50_new.h5'
save_dir = '/media/data/fanxuan/result/FCN_10_50_wave_new'
model1_path = os.path.join(save_dir, 'model/generator1_epoch_100.hdf5')
model2_path = os.path.join(save_dir, 'model/generator2_epoch_100.hdf5')
save_path = os.path.join(save_dir, 'valid_100')
if not os.path.exists(save_path):
os.makedirs(save_path)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(graph=tf.get_default_graph(), config=config) as sess:
sess.run(tf.global_variables_initializer())
K.set_session(sess)
generator1 = unet_model_2d((4,176,176))
generator1.load_weights(model1_path)
generator2 = unet_model_2d((8,176,176))
generator2.load_weights(model2_path)
data = h5py.File(data_path, 'r')
data_normal = h5py.File(data_path_normal, 'r')
filenames = np.array(data[self.domain+'_filenames']).flatten()
filenames = [x.decode('utf-8') for x in filenames]
patients = sorted(set(x.split('+')[1] for x in filenames))
count = 0
patient_list, mse_list, nrmse_list, mape_list, psnr_list, ssim_list = [],[],[],[],[],[]
ori_mse_list, ori_nrmse_list, ori_mape_list, ori_psnr_list, ori_ssim_list = [],[],[],[],[]
mse_10_list, nrmse_10_list, mape_10_list, psnr_10_list, ssim_10_list = [],[],[],[],[]
for patient in patients:
volume1 = np.zeros(shape=self.target_shape)
volume2 = np.zeros(shape=self.target_shape)
gd_volume = np.zeros(shape=self.target_shape)
x1_volume = np.zeros(shape=self.target_shape)
x2_volume = np.zeros(shape=self.target_shape)
print(len(filenames))
for i in range(len(filenames)):
if patient==filenames[i].split('+')[1]:
count += 1
print('{}: {}/{}'.format(filenames[i], count, len(filenames)))
n = int(filenames[i].split('+')[-2])
s = int(filenames[i].split('+')[-1])
x1_slice = np.array(data.get(self.domain+'_reduce_50')[i])
x2_slice = np.array(data.get(self.domain+'_reduce_10')[i])
x_normal = np.array(data_normal.get(self.domain+'_reduce')[i])
gd_slice = np.array(data.get(self.domain+'_normal')[i])
gd_normal = np.array(data_normal.get(self.domain+'_normal')[i])
coeffs_x = x1_slice[0], (x1_slice[1], x1_slice[2], x1_slice[3])
x_re = pywt.idwt2(coeffs_x, 'haar')
x_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],x_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(x_re.shape)
x_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],x_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
x1_volume[:,:,s] += x_re * max_img
coeffs_x = x2_slice[0], (x2_slice[1], x2_slice[2], x2_slice[3])
x_re = pywt.idwt2(coeffs_x, 'haar')
x_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],x_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(x_re.shape)
x_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],x_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
x2_volume[:,:,s] += x_re * max_img
coeffs_gd = gd_slice[0], (gd_slice[1], gd_slice[2], gd_slice[3])
gd_re = pywt.idwt2(coeffs_gd, 'haar')
gd_re = np.concatenate((gd_normal[0:(gd_normal.shape[0]//2-176), (gd_normal.shape[1]//2-176):(gd_normal.shape[1]//2+176)],gd_re,gd_normal[(gd_normal.shape[0]//2+176):, (gd_normal.shape[1]//2-176):(gd_normal.shape[1]//2+176)]), axis=0)
print(gd_re.shape)
gd_re = np.concatenate((gd_normal[:, :(gd_normal.shape[1]//2-176)],gd_re,gd_normal[:, (gd_normal.shape[1]//2+176):]), axis = 1)
gd_volume[:,:,s] += gd_re * max_img
# ori_mse, ori_nrmse, ori_psnr, ori_ssim = self.compute_metrics(gd_slice, x_slice)
# print(np.mean(x1_slice))
x1_slice = np.expand_dims(x1_slice, axis=0)
# x_slice = np.expand_dims(x_slice, axis=0)
out1_slice = generator1.predict(x1_slice)
con_slice = np.concatenate((out1_slice,x1_slice), axis=1)
out2_slice = generator2.predict(con_slice)[0,:,:,:]
print(out2_slice.shape)
coeffs_out = out2_slice[0], (out2_slice[1], out2_slice[2], out2_slice[3])
out_re = pywt.idwt2(coeffs_out, 'haar')
out_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],out_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(out_re.shape)
out_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],out_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
volume2[:,:,s] += out_re * max_img
out1_slice = out1_slice[0,:,:,:]
coeffs_out = out1_slice[0], (out1_slice[1], out1_slice[2], out1_slice[3])
out_re = pywt.idwt2(coeffs_out, 'haar')
out_re = np.concatenate((x_normal[0:(x_normal.shape[0]//2-176), (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)],out_re,x_normal[(x_normal.shape[0]//2+176):, (x_normal.shape[1]//2-176):(x_normal.shape[1]//2+176)]), axis=0)
print(out_re.shape)
out_re = np.concatenate((x_normal[:, :(x_normal.shape[1]//2-176)],out_re,x_normal[:, (x_normal.shape[1]//2+176):]), axis = 1)
volume1[:,:,s] += out_re * max_img
patient_save_path = os.path.join(save_path, 'patients')
if not os.path.exists(patient_save_path):
os.makedirs(patient_save_path)
nib.save(nib.Nifti1Image(volume1, affine=aaa),os.path.join(patient_save_path, os.path.splitext(patient)[0]+'_source_{}_generated10.nii'.format(n)))
nib.save(nib.Nifti1Image(volume2, affine=aaa),os.path.join(patient_save_path, os.path.splitext(patient)[0]+'_source_{}_generated.nii'.format(n)))
ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim = self.compute_metrics(gd_volume, x1_volume)
mse_10, nrmse_10, mape_10, psnr_10, ssim_10 = self.compute_metrics(x2_volume, volume1)
mse, nrmse, mape, psnr, ssim = self.compute_metrics(gd_volume, volume2)
print('ori_mse:{}, ori_nrmse:{}, ori_mape:{}, ori_psnr:{}, ori_ssim:{}'.format(ori_mse, ori_nrmse, ori_mape, ori_psnr, ori_ssim))
print('mse:{}, nrmse:{}, mape:{}, psnr:{}, ssim:{}'.format(mse, nrmse, mape, psnr, ssim))
patient_list.append(patient)
mse_list.append(mse)
nrmse_list.append(nrmse * 1e2)
mape_list.append(mape)
psnr_list.append(psnr)
ssim_list.append(ssim)
mse_10_list.append(mse_10)
nrmse_10_list.append(nrmse_10 * 1e2)
mape_10_list.append(mape_10)
psnr_10_list.append(psnr_10)
ssim_10_list.append(ssim_10)
ori_mse_list.append(ori_mse)
ori_nrmse_list.append(ori_nrmse * 1e2)
ori_mape_list.append(ori_mape)
ori_psnr_list.append(ori_psnr)
ori_ssim_list.append(ori_ssim)
df = pd.DataFrame({ 'Patient_num': patient_list, 'MSE': mse_list,
'NRMSE %': nrmse_list,'mape': mape_list, 'PSNR': psnr_list, 'SSIM': ssim_list,
'MSE_10': mse_10_list, 'NRMSE_10 %': nrmse_10_list,'mape_10': mape_10_list,
'PSNR_10': psnr_10_list, 'SSIM_10': ssim_10_list})
df = df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(mse_list),
'NRMSE %': np.mean(nrmse_list), 'mape': np.mean(mape_list),
'PSNR': np.mean(psnr_list), 'SSIM': np.mean(ssim_list),
'MSE_10': np.mean(mse_10_list),
'NRMSE_10 %': np.mean(nrmse_10_list), 'mape_10': np.mean(mape_10_list),
'PSNR_10': np.mean(psnr_10_list), 'SSIM_10': np.mean(ssim_10_list)}, ignore_index=True)
df = df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM', 'MSE_10', 'NRMSE_10 %','mape_10', 'PSNR_10', 'SSIM_10']]
ori_df = pd.DataFrame({'Patient_num': patient_list, 'MSE': ori_mse_list,
'NRMSE %': ori_nrmse_list, 'mape': ori_mape_list,
'PSNR': ori_psnr_list, 'SSIM': ori_ssim_list})
ori_df = ori_df.append({'Patient_num': 'Mean Value', 'MSE': np.mean(ori_mse_list),
'NRMSE %': np.mean(ori_nrmse_list), 'mape': np.mean(ori_mape_list),
'PSNR': np.mean(ori_psnr_list), 'SSIM': np.mean(ori_ssim_list)}, ignore_index=True)
ori_df = ori_df[['Patient_num', 'MSE', 'NRMSE %','mape', 'PSNR', 'SSIM']]
df.to_csv(os.path.join(patient_save_path, 'gen.csv'), index=False)
ori_df.to_csv(os.path.join(patient_save_path,'ori.csv'), index=False)
data.close()
eva = Evaluate()
eva.generate_all_result_4channel()