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model.py
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model.py
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from __future__ import division
import os
import time
from glob import glob
import tensorflow as tf
from collections import namedtuple
import numpy as np
from module import *
from utils import *
from PIL import Image
from skimage import transform
import imageio
class cyclegan(object):
def __init__(self, sess, args):
self.sess = sess
self.batch_size = args.batch_size
self.image_size0 = args.fine_size0
self.image_size1 = args.fine_size1
self.input_c_dim = args.input_nc
self.output_c_dim = args.output_nc
self.L1_lambda = args.L1_lambda
self.L2_lambda = args.L2_lambda
self.dataset_dir = args.dataset_dir
self.discriminator = discriminator
if args.use_resnet:
self.generator = generator_resnet
else:
self.generator = generator_unet
if args.use_lsgan:
self.criterionGAN = mae_criterion
else:
self.criterionGAN = sce_criterion
self.MIND = MIND
OPTIONS = namedtuple('OPTIONS', 'batch_size image_size0 image_size1 \
gf_dim df_dim output_c_dim is_training \
sigma eps neigh_size patch_size')
self.options = OPTIONS._make((args.batch_size, args.fine_size0, args.fine_size1,
args.ngf, args.ndf, args.output_nc,
args.phase == 'train',
args.sigma, args.eps, args.neigh_size, args.patch_size))
self._build_model()
self.saver = tf.train.Saver(max_to_keep = 1)
self.pool = ImagePool(args.max_size)
def _build_model(self):
self.real_data = tf.placeholder(tf.float32,
[None, self.image_size0, self.image_size1,
self.input_c_dim + self.output_c_dim],
name='real_A_and_B_images')
self.real_A = self.real_data[:, :, :, :self.input_c_dim]
self.real_B = self.real_data[:, :, :, self.input_c_dim:self.input_c_dim + self.output_c_dim]
self.fake_B = self.generator(self.real_A, self.options, False, name="generatorA2B")
self.fake_A_ = self.generator(self.fake_B, self.options, False, name="generatorB2A")
self.fake_A = self.generator(self.real_B, self.options, True, name="generatorB2A")
self.fake_B_ = self.generator(self.fake_A, self.options, True, name="generatorA2B")
self.DB_fake = self.discriminator(self.fake_B, self.options, reuse=False, name="discriminatorB")
self.DA_fake = self.discriminator(self.fake_A, self.options, reuse=False, name="discriminatorA")
self.real_A_MIND = self.MIND(self.real_A, self.options, name='realA_MIND')
self.fake_B_MIND = self.MIND(self.fake_B, self.options, name='fakeB_MIND')
self.real_B_MIND = self.MIND(self.real_B, self.options, name='realB_MIND')
self.fake_A_MIND = self.MIND(self.fake_A, self.options, name='fakeA_MIND')
self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_)
self.g_loss_MIND = abs_criterion(self.real_A_MIND, self.fake_B_MIND) \
+ abs_criterion(self.real_B_MIND, self.fake_A_MIND)
self.g_loss = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \
+ self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \
+ self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
+ self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) \
+ self.L2_lambda * abs_criterion(self.real_A_MIND, self.fake_B_MIND) \
+ self.L2_lambda * abs_criterion(self.real_B_MIND, self.fake_A_MIND)
self.fake_A_sample = tf.placeholder(tf.float32,
[None, self.image_size0, self.image_size1,
self.input_c_dim], name='fake_A_sample')
self.fake_B_sample = tf.placeholder(tf.float32,
[None, self.image_size0, self.image_size1,
self.output_c_dim], name='fake_B_sample')
self.DB_real = self.discriminator(self.real_B, self.options, reuse=True, name="discriminatorB")
self.DA_real = self.discriminator(self.real_A, self.options, reuse=True, name="discriminatorA")
self.DB_fake_sample = self.discriminator(self.fake_B_sample, self.options, reuse=True, name="discriminatorB")
self.DA_fake_sample = self.discriminator(self.fake_A_sample, self.options, reuse=True, name="discriminatorA")
self.db_loss_real = self.criterionGAN(self.DB_real, tf.ones_like(self.DB_real))
self.db_loss_fake = self.criterionGAN(self.DB_fake_sample, tf.zeros_like(self.DB_fake_sample))
self.db_loss = (self.db_loss_real + self.db_loss_fake) / 2
self.da_loss_real = self.criterionGAN(self.DA_real, tf.ones_like(self.DA_real))
self.da_loss_fake = self.criterionGAN(self.DA_fake_sample, tf.zeros_like(self.DA_fake_sample))
self.da_loss = (self.da_loss_real + self.da_loss_fake) / 2
self.d_loss = self.da_loss + self.db_loss
self.g_loss_a2b_sum = tf.summary.scalar("g_loss_a2b", self.g_loss_a2b)
self.g_loss_b2a_sum = tf.summary.scalar("g_loss_b2a", self.g_loss_b2a)
self.g_loss_MIND_sum = tf.summary.scalar("g_loss_MIND", self.g_loss_MIND)
self.g_loss_sum = tf.summary.scalar("g_loss", self.g_loss)
self.g_sum = tf.summary.merge([self.g_loss_a2b_sum, self.g_loss_b2a_sum, self.g_loss_MIND_sum, self.g_loss_sum])
self.db_loss_sum = tf.summary.scalar("db_loss", self.db_loss)
self.da_loss_sum = tf.summary.scalar("da_loss", self.da_loss)
self.d_loss_sum = tf.summary.scalar("d_loss", self.d_loss)
self.db_loss_real_sum = tf.summary.scalar("db_loss_real", self.db_loss_real)
self.db_loss_fake_sum = tf.summary.scalar("db_loss_fake", self.db_loss_fake)
self.da_loss_real_sum = tf.summary.scalar("da_loss_real", self.da_loss_real)
self.da_loss_fake_sum = tf.summary.scalar("da_loss_fake", self.da_loss_fake)
self.d_sum = tf.summary.merge(
[self.da_loss_sum, self.da_loss_real_sum, self.da_loss_fake_sum,
self.db_loss_sum, self.db_loss_real_sum, self.db_loss_fake_sum,
self.d_loss_sum]
)
self.test_A = tf.placeholder(tf.float32,
[None, self.image_size0, self.image_size1,
self.input_c_dim], name='test_A')
self.test_B = tf.placeholder(tf.float32,
[None, self.image_size0, self.image_size1,
self.output_c_dim], name='test_B')
self.testB = self.generator(self.test_A, self.options, True, name="generatorA2B")
self.testA = self.generator(self.test_B, self.options, True, name="generatorB2A")
t_vars = tf.trainable_variables()
self.d_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_vars = [var for var in t_vars if 'generator' in var.name]
for var in t_vars: print(var.name)
def train(self, args):
"""Train cyclegan"""
self.lr = tf.placeholder(tf.float32, None, name='learning_rate')
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \
.minimize(self.d_loss, var_list=self.d_vars)
self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=args.beta1) \
.minimize(self.g_loss, var_list=self.g_vars)
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
self.writer = tf.summary.FileWriter("./logs", self.sess.graph)
counter = 1
start_time = time.time()
if args.continue_train:
if self.load(args.checkpoint_dir):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
saver_all = tf.train.Saver(max_to_keep = None)
for epoch in range(args.epoch):
# A: CT
# B: T1
trdataSize = 27
lr = args.lr if epoch < args.epoch_step else args.lr * (args.epoch - epoch) / (args.epoch - args.epoch_step)
idA_all = np.arange(trdataSize) + 1
np.random.shuffle(idA_all)
dataInfoFile = open(r'dataset/trainInfo.txt','r')
sourceInLines = dataInfoFile.readlines()
dataInfoFile.close()
dataInfo = []
for line in sourceInLines:
temp1 = line.strip('\n')
temp2 = temp1.split(' ')
dataInfo.append(temp2)
randBd = 5
for idA in idA_all:
sliceNumA = int(dataInfo[idA-1][3])
sliceA_all = np.arange(sliceNumA)
np.random.shuffle(sliceA_all)
batch_idxs = sliceNumA // self.batch_size
for idx in range(0, batch_idxs):
idB_all = np.arange(trdataSize) + 1
idB_all = np.delete(idB_all, idA - 1)
np.random.shuffle(idB_all)
sliceNumB = int(dataInfo[idB_all[0] - 1][3])
dataA_idxs = []
dataB_idxs = []
for i in sliceA_all[idx * self.batch_size:(idx + 1) * self.batch_size]:
dataA_idxs.append(i + 1)
i_Bslice = round( i / (sliceNumA-1.) * (sliceNumB-1.) )
if i_Bslice >= randBd and i_Bslice <= sliceNumB-randBd-1:
i_Bslice = i_Bslice + np.random.randint((-1)*randBd,randBd)
dataB_idxs.append(i_Bslice + 1)
batch_files = list(zip(dataA_idxs, dataB_idxs))
batch_images = [
load_train_data(batch_file, idA, idB_all[0], args.load_size0, args.load_size1, args.fine_size0,
args.fine_size1) for batch_file in batch_files]
batch_images = np.array(batch_images).astype(np.float32)
if idA == idB_all[0]:
raise RuntimeError('MRI and CT should come from different subjects!')
# Update G network and record fake outputs
fake_A, fake_B, _, summary_str = self.sess.run(
[self.fake_A, self.fake_B, self.g_optim, self.g_sum],
feed_dict={self.real_data: batch_images, self.lr: lr})
self.writer.add_summary(summary_str, counter)
[fake_A, fake_B] = self.pool([fake_A, fake_B])
# Update D network
_, summary_str = self.sess.run(
[self.d_optim, self.d_sum],
feed_dict={self.real_data: batch_images,
self.fake_A_sample: fake_A,
self.fake_B_sample: fake_B,
self.lr: lr})
self.writer.add_summary(summary_str, counter)
counter += 1
print(("Epoch: [%2d] SubsetA: [%2d] [%4d/%4d] time: %4.4f" % (
epoch, idA, idx, batch_idxs, time.time() - start_time)))
if np.mod(counter, args.print_freq) == 1:
self.sample_model(args.sample_dir, epoch, idA, idx)
if np.mod(counter, args.save_freq) == 2:
self.save(args.checkpoint_dir, counter)
save_epoch_file = os.path.join(args.checkpoint_dir, 'MRCT_epoch')
if not os.path.exists(save_epoch_file):
os.makedirs(save_epoch_file)
saver_all.save(self.sess, os.path.join(save_epoch_file, 'cyclegans.epoch'), global_step=epoch)
def save(self, checkpoint_dir, step):
model_name = "cyclegan.model"
model_dir = "%s_%s_%s" % (self.dataset_dir, self.image_size0, self.image_size1)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
self.saver.save(self.sess,
os.path.join(checkpoint_dir, model_name),
global_step=step)
def load_valid(self, checkpoint_dir, epoch):
print(" [*] Reading checkpoint...")
model_dir = "%s_epoch" % (self.dataset_dir)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt_name = os.path.basename('cyclegans.epoch-{}'.format(epoch))
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
def load(self, checkpoint_dir):
print(" [*] Reading checkpoint...")
model_dir = "%s_%s_%s" % (self.dataset_dir, self.image_size0, self.image_size1)
checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
self.saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
return True
else:
return False
def sample_model(self, sample_dir, epoch, idA, idx):
randBd = 5
trdataSize = 27
dataInfoFile = open(r'dataset/trainInfo.txt', 'r')
sourceInLines = dataInfoFile.readlines()
dataInfoFile.close()
dataInfo = []
for line in sourceInLines:
temp1 = line.strip('\n')
temp2 = temp1.split(' ')
dataInfo.append(temp2)
idB_all = np.arange(trdataSize) + 1
idB_all = np.delete(idB_all, idA - 1)
np.random.shuffle(idB_all)
sliceNumA = int(dataInfo[idA - 1][3])
sliceNumB = int(dataInfo[idB_all[0] - 1][3])
sliceA_all = np.arange(sliceNumA)
np.random.shuffle(sliceA_all)
dataA_idxs = []
dataB_idxs = []
for i in sliceA_all[:self.batch_size]:
dataA_idxs.append(i + 1)
i_Bslice = round(i / (sliceNumA - 1.) * (sliceNumB - 1.))
if i_Bslice >= randBd and i_Bslice <= sliceNumB - randBd - 1:
i_Bslice = i_Bslice + np.random.randint((-1) * randBd, randBd)
dataB_idxs.append(i_Bslice + 1)
batch_files = list(zip(dataA_idxs, dataB_idxs))
sample_image = [load_train_data(batch_file, idA, idB_all[0], is_testing=True) for batch_file in batch_files]
sample_images = np.array(sample_image).astype(np.float32)
if idA == idB_all[0]:
raise RuntimeError('MRI and CT should come from different subjects!')
fake_A, fake_B = self.sess.run(
[self.fake_A, self.fake_B],
feed_dict={self.real_data: sample_images}
)
name_inputB = r'dataset/T1/InputNorm_id{:0>2d}_slice{:0>3d}_T1.npy'.format(
idB_all[0], int(dataB_idxs[0]))
name_real_A = r'dataset/CT/InputNorm_id{:0>2d}_slice{:0>3d}_CT.npy'.format(
idB_all[0], int(dataB_idxs[0]))
inputB = np.load(name_inputB)
real_A = np.load(name_real_A)
inputB = (inputB + 1.) * 127.5
real_A = (real_A + 1.) * 127.5
pad_inputB_size = self.image_size0 - inputB.shape[0]
pad_real_A_size = self.image_size0 - real_A.shape[0]
inputB = np.pad(inputB, ((int(pad_inputB_size // 2), int(pad_inputB_size) - int(pad_inputB_size // 2)), (0, 0)),
mode='constant',
constant_values=0)
real_A = np.pad(real_A, ((int(pad_real_A_size // 2), int(pad_real_A_size) - int(pad_real_A_size // 2)), (0, 0)),
mode='constant',
constant_values=0)
name_inputA = r'dataset/CT/InputNorm_id{:0>2d}_slice{:0>3d}_CT.npy'.format(
idA, int(dataA_idxs[0]))
name_real_B = r'dataset/T1/InputNorm_id{:0>2d}_slice{:0>3d}_T1.npy'.format(
idA, int(dataA_idxs[0]))
inputA = np.load(name_inputA)
real_B = np.load(name_real_B)
inputA = (inputA + 1.) * 127.5
real_B = (real_B + 1.) * 127.5
pad_inputA_size = self.image_size0 - inputA.shape[0]
pad_real_B_size = self.image_size0 - real_B.shape[0]
inputA = np.pad(inputA, ((int(pad_inputA_size // 2), int(pad_inputA_size) - int(pad_inputA_size // 2)), (0, 0)),
mode='constant',
constant_values=0)
real_B = np.pad(real_B, ((int(pad_real_B_size // 2), int(pad_real_B_size) - int(pad_real_B_size // 2)), (0, 0)),
mode='constant',
constant_values=0)
save_images(fake_A, [self.batch_size, 1],
'./{}/A_{:02d}_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, idA, idx))
imageio.imwrite('./{}/A_{:02d}_{:02d}_{:04d}_input.jpg'.format(sample_dir, epoch, idA, idx),inputB)
imageio.imwrite('./{}/A_{:02d}_{:02d}_{:04d}_areal.jpg'.format(sample_dir, epoch, idA, idx),real_A)
save_images(fake_B, [self.batch_size, 1],
'./{}/B_{:02d}_{:02d}_{:04d}.jpg'.format(sample_dir, epoch, idA, idx))
imageio.imwrite('./{}/B_{:02d}_{:02d}_{:04d}_input.jpg'.format(sample_dir, epoch, idA, idx),inputA)
imageio.imwrite('./{}/B_{:02d}_{:02d}_{:04d}_areal.jpg'.format(sample_dir, epoch, idA, idx),real_B)
def valid_CT(self, args):
"""valid cyclegan"""
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
val_id_vec = [28, 29, 30]
sample_files = r'dataset/InputNorm_{:0>2d}_T1.nii'
gt_name = r'dataset/InputNorm_{:0>2d}_CT.nii'
isCT = False
namehd = 'synCT'
input_val = 0
output_val = -1000
epochNum = 10
epochVec = np.arange(epochNum)
dataInfoFile = open(r'dataset/trainInfo.txt', 'r')
sourceInLines = dataInfoFile.readlines()
dataInfoFile.close()
dataInfo = []
for line in sourceInLines:
temp1 = line.strip('\n')
temp2 = temp1.split(' ')
dataInfo.append(temp2)
valid_dir = './valid'
if not os.path.exists(valid_dir):
os.makedirs(valid_dir)
for epoch in epochVec:
self.load_valid(args.checkpoint_dir, epoch)
for val_id in val_id_vec:
sliceNum = int(dataInfo[val_id-1][3])
sliceVec = np.arange(sliceNum)
imgTemp = nib.load(sample_files.format(val_id))
teResults = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(output_val)
inputImage = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(input_val)
gtImage = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(output_val)
for iSlicet in sliceVec:
iSlice = iSlicet + int(dataInfo[val_id - 1][1]) - int(dataInfo[val_id - 1][5])
print('Processing image: id ' + str(val_id) + 'slice' + str(iSlicet))
sample_image = [load_test_data(sample_files.format(val_id), isCT, iSlice, args.fine_size0, args.fine_size1)]
sample_image = np.array(sample_image).astype(np.float32)
sample_image = sample_image.reshape([1, args.fine_size0, args.fine_size1, 1])
if epoch == 0:
gt_imageAll = nib.load(gt_name.format(val_id))
gt_image = gt_imageAll.get_data()[int(iSlice), :, :].astype('int16')
gtzm = gt_imageAll.get_header().get_zooms()
gtsz = gt_imageAll.shape
gt_resize_1 = args.fine_size1
gt_resize_0 = round(gtsz[1] * gtzm[1] * gt_resize_1 / (gtsz[2] * gtzm[2]))
gt_pad_size = int(args.fine_size0) - gt_resize_0
gt_image = transform.resize(gt_image, (gt_resize_0, gt_resize_1), preserve_range=True)
gt_image = np.pad(gt_image, ((int(gt_pad_size // 2), int(gt_pad_size) - int(gt_pad_size // 2)), (0, 0)), mode='constant',
constant_values=output_val)
gtImage[int(iSlice), :, :] = np.array(gt_image).astype('int16')
input_imageAll = nib.load(sample_files.format(val_id))
input_image = input_imageAll.get_data()[int(iSlice), :, :].astype('int16')
inputzm = input_imageAll.get_header().get_zooms()
inputsz = input_imageAll.shape
input_resize_1 = args.fine_size1
input_resize_0 = round(inputsz[1] * inputzm[1] * input_resize_1 / (inputsz[2] * inputzm[2]))
input_pad_size = int(args.fine_size0) - input_resize_0
input_image = transform.resize(input_image, (input_resize_0, input_resize_1), preserve_range=True)
input_image = np.pad(input_image, ((int(input_pad_size // 2), int(input_pad_size) - int(input_pad_size // 2)), (0, 0)), mode='constant',
constant_values=input_val)
inputImage[int(iSlice), :, :] = np.array(input_image).astype('int16')
fake_img = self.sess.run(self.testA, feed_dict={self.test_B: sample_image})
#fake_img_255 = np.exp((fake_img + 1.) * 4. * np.log(2)) - 1.
fake_img_255 = (fake_img + 1.) * 127.5
if isCT:
temp = fake_img_255 / 255. * (3500. - 0.) + 0.
else:
temp = fake_img_255 / 255. * (3500. + 1000.) - 1000.
teResults[int(iSlice), :, :] = np.array(temp).astype('int16').reshape([args.fine_size0,args.fine_size1])
head_output = imgTemp.get_header()
head_output.set_zooms([head_output.get_zooms()[0] * args.fine_size1 / (head_output.get_zooms()[2]*imgTemp.shape[2]),1.0,1.0])
affine_output = imgTemp.affine
affine_output[1][1] = np.sign(affine_output[1][1])
affine_output[0][0] = np.sign(affine_output[0][0]) * head_output.get_zooms()[0]
saveResults = nib.Nifti1Image(teResults, affine_output, head_output)
nib.save(saveResults, '{}/{}_{:0>2d}_epoch{}.nii'.format(valid_dir,namehd,val_id,epoch))
if epoch == 0:
gtResults = nib.Nifti1Image(gtImage, affine_output, head_output)
gt_path = os.path.join(valid_dir, '{}'.format(os.path.basename(gt_name).format(val_id)))
nib.save(gtResults, gt_path)
inputResults = nib.Nifti1Image(inputImage, affine_output, head_output)
input_path = os.path.join(valid_dir, '{}'.format(os.path.basename(sample_files).format(val_id)))
nib.save(inputResults, input_path)
def test(self, args):
"""Test cyclegan"""
init_op = tf.global_variables_initializer()
self.sess.run(init_op)
if args.which_direction == 'AtoB':
sample_files = r'dataset/InputNorm_T{:0>2d}_CT.nii'
gt_name = r'dataset/InputNorm_T{:0>2d}_T1.nii'
isCT = True
namehd = 'synT1'
input_val = -1000
output_val = 0
elif args.which_direction == 'BtoA':
sample_files = r'dataset/InputNorm_T{:0>2d}_T1.nii'
gt_name = r'dataset/InputNorm_T{:0>2d}_CT.nii'
isCT = False
namehd = 'synCT'
input_val = 0
output_val = -1000
else:
raise Exception('--which_direction must be AtoB or BtoA')
epoch = 2
self.load_valid(args.checkpoint_dir, epoch)
out_var, in_var = (self.testB, self.test_A) if args.which_direction == 'AtoB' else (
self.testA, self.test_B)
tedataSize = 15
teIdVec = np.arange(tedataSize) + 1
dataInfoFile = open(r'dataset/testInfo.txt', 'r')
sourceInLines = dataInfoFile.readlines()
dataInfoFile.close()
dataInfo = []
for line in sourceInLines:
temp1 = line.strip('\n')
temp2 = temp1.split(' ')
dataInfo.append(temp2)
for teId in teIdVec:
sliceNum = int(dataInfo[teId-1][3])
sliceVec = np.arange(sliceNum)
imgTemp = nib.load(sample_files.format(teId))
teResults = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(output_val)
inputImage = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(input_val)
gtImage = np.ones([imgTemp.shape[0], args.fine_size0, args.fine_size1], dtype=np.int16)*int(output_val)
for iSlicet in sliceVec:
iSlice = iSlicet + int(dataInfo[teId - 1][1]) - int(dataInfo[teId - 1][5])
print('Processing image: id ' + str(teId) + 'slice' + str(iSlicet))
sample_image = [load_test_data(sample_files.format(teId), isCT, iSlice,args.fine_size0, args.fine_size1)]
sample_image = np.array(sample_image).astype(np.float32)
sample_image = sample_image.reshape([1, args.fine_size0, args.fine_size1, 1])
gt_imageAll = nib.load(gt_name.format(teId))
gt_image = gt_imageAll.get_data()[int(iSlice), :, :].astype('int16')
gtzm = gt_imageAll.get_header().get_zooms()
gtsz = gt_imageAll.shape
gt_resize_1 = args.fine_size1
gt_resize_0 = round(gtsz[1] * gtzm[1] * gt_resize_1 / (gtsz[2] * gtzm[2]))
gt_pad_size = int(args.fine_size0) - gt_resize_0
gt_image = transform.resize(gt_image, (gt_resize_0, gt_resize_1), preserve_range=True)
gt_image = np.pad(gt_image, ((int(gt_pad_size // 2), int(gt_pad_size) - int(gt_pad_size // 2)), (0, 0)), mode='constant',
constant_values=output_val)
gtImage[int(iSlice), :, :] = np.array(gt_image).astype('int16')
input_imageAll = nib.load(sample_files.format(teId))
input_image = input_imageAll.get_data()[int(iSlice), :, :].astype('int16')
inputzm = input_imageAll.get_header().get_zooms()
inputsz = input_imageAll.shape
input_resize_1 = args.fine_size1
input_resize_0 = round(inputsz[1] * inputzm[1] * input_resize_1 / (inputsz[2] * inputzm[2]))
input_pad_size = int(args.fine_size0) - input_resize_0
input_image = transform.resize(input_image, (input_resize_0, input_resize_1), preserve_range=True)
input_image = np.pad(input_image, ((int(input_pad_size // 2), int(input_pad_size) - int(input_pad_size // 2)), (0, 0)), mode='constant',
constant_values=input_val)
inputImage[int(iSlice), :, :] = np.array(input_image).astype('int16')
fake_img = self.sess.run(out_var, feed_dict={in_var: sample_image})
#fake_img_255 = np.exp((fake_img + 1.) * 4. * np.log(2)) - 1.
fake_img_255 = (fake_img + 1.) * 127.5
if isCT:
temp = fake_img_255 / 255. * (3500. - 0.) + 0.
else:
temp = fake_img_255 / 255. * (3500. + 1000.) - 1000.
teResults[int(iSlice), :, :] = np.array(temp).astype('int16').reshape([args.fine_size0,args.fine_size1])
head_output = imgTemp.get_header()
head_output.set_zooms([head_output.get_zooms()[0] * args.fine_size1 / (head_output.get_zooms()[2]*imgTemp.shape[2]),1.0,1.0])
affine_output = imgTemp.affine
affine_output[1][1] = np.sign(affine_output[1][1])
affine_output[0][0] = np.sign(affine_output[0][0]) * head_output.get_zooms()[0]
saveResults = nib.Nifti1Image(teResults, affine_output, head_output)
nib.save(saveResults, '{}/{}_T{:0>2d}.nii'.format(args.test_dir,namehd,teId))
gtResults = nib.Nifti1Image(gtImage, affine_output, head_output)
gt_path = os.path.join(args.test_dir, '{}'.format(os.path.basename(gt_name).format(teId)))
nib.save(gtResults, gt_path)
inputResults = nib.Nifti1Image(inputImage, affine_output, head_output)
input_path = os.path.join(args.test_dir, '{}'.format(os.path.basename(sample_files).format(teId)))
nib.save(inputResults, input_path)