def data_generate(self):
# input
path_x = self.data_x.toPlainText()
# blur
fwhm_kernel = []
if self.fwhm4.isChecked():
fwhm_kernel.append(4)
if self.fwhm5.isChecked():
fwhm_kernel.append(5)
if self.fwhm6.isChecked():
fwhm_kernel.append(6)
if self.fwhm7.isChecked():
fwhm_kernel.append(7)
if self.fwhm8.isChecked():
fwhm_kernel.append(8)
# adjacent slice
slice_x = 1
if self.slice3.isChecked():
slice_x = 3
gbl_set_value("slice_x", slice_x)
# generate
list_X = glob.glob(path_x+'/*.nii')
X, Y = blurring_data_generator(list_X, fwhm_kernel)
QtWidgets.QMessageBox.information(self.generate, "test", list_X[0])
def main():
parser = argparse.ArgumentParser(
description=
'''This is a beta script for Partial Volume Correction in PET/MRI system. ''',
epilog="""All's well that ends well.""")
# parser.add_argument('--dir_pet', metavar='', type=str, default="breast1_pet",
# help='Name of PET subject.(breast1_pet)<str>')
# parser.add_argument('--dir_mri', metavar='', type=str, default="breast1_water",
# help='Name of MRI subject.(breast1_water)<str>')
parser.add_argument('--dir_folder',
metavar='',
type=str,
default="crohns",
help='Name of dataset.(crohns)<str>')
parser.add_argument(
'--blur_method',
metavar='',
type=str,
default="nib_smooth",
help=
'The blurring method of syn PET(nib_smooth)<str> [kernel_conv/skimage_gaus/nib_smooth]'
)
parser.add_argument('--blur_para',
metavar='',
type=str,
default="4",
help='Parameters of blurring data(4)<str>')
parser.add_argument('--slice_x',
metavar='',
type=int,
default="1",
help='Slices of input(1)<int>[1/3]')
parser.add_argument(
'--enhance_blur',
metavar='',
type=bool,
default=False,
help='Whether stack different blurring methods to train the model')
parser.add_argument('--id',
metavar='',
type=str,
default="eeVee",
help='ID of the current model.(eeVee)<str>')
parser.add_argument('--epoch',
metavar='',
type=int,
default=500,
help='Number of epoches of training(2000)<int>')
parser.add_argument('--n_filter',
metavar='',
type=int,
default=64,
help='The initial filter number(64)<int>')
parser.add_argument('--depth',
metavar='',
type=int,
default=4,
help='The depth of U-Net(4)<int>')
parser.add_argument('--batch_size',
metavar='',
type=int,
default=10,
help='The batch_size of training(10)<int>')
parser.add_argument('--model_name',
metavar='',
type=str,
default='',
help='The name of model to be predicted. ()<str>')
args = parser.parse_args()
# common setting
model_name = args.model_name
enhance_blur = args.enhance_blur
gbl_set_value("depth", args.depth)
gbl_set_value("n_epoch", args.epoch + 1)
gbl_set_value("n_filter", args.n_filter)
gbl_set_value("depth", args.depth)
gbl_set_value("batch_size", args.batch_size)
gbl_set_value("slice_x", args.slice_x)
# file-specific
dir_folder = './data/dataset/' + args.dir_folder + '/'
list_pet = glob.glob(dir_folder + 'pet/*.nii.gz')
list_mri = glob.glob(dir_folder + 'mri/*.nii.gz')
n_files = len(list_pet)
for idx in range(n_files):
filename_start = list_pet[idx].rfind('/')
filename_end = list_pet[idx].find('_')
filename = list_pet[idx][filename_start + 1:filename_end]
# print(filename)
# print(list_pet[idx])
time_stamp = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M")
model_id = filename + time_stamp
dir_pet = list_pet[idx]
dir_mri = dir_folder + 'mri/' + filename + '_water.nii.gz'
print(dir_pet)
print(dir_mri)
dir_syn = './folder_results/' + args.dir_folder + '/synthesized/'
if not os.path.exists(dir_syn):
os.makedirs(dir_syn)
dir_model = './folder_results/' + args.dir_folder + '/models/'
if not os.path.exists(dir_model):
os.makedirs(dir_model)
gbl_set_value("dir_mri", dir_mri)
gbl_set_value("dir_pet", dir_pet)
gbl_set_value('dir_syn', dir_syn)
gbl_set_value('dir_model', dir_model)
gbl_set_value("model_id", model_id)
# Load data
file_pet = nib.load(dir_pet)
file_mri = nib.load(dir_mri)
data_pet = file_pet.get_fdata()
data_mri = file_mri.get_fdata()
gbl_set_value("img_shape", data_pet.shape)
print("Loading Completed!")
if model_name == '':
if not enhance_blur:
X, Y = data_generator(data_mri, args.blur_method,
args.blur_para)
else:
X, Y = enhance_data_generator(data_mri)
print(X.shape)
print("Blurring Completed!")
model = train_a_unet(X, Y)
print("Training Completed!")
predict(model, data_pet)
print("Predicting Completed!")
send_emails(model_id)
print("Notification completed!")
else:
gbl_set_value("model_id", model_name[5:])
model = load_existing_model(model_name)
predict(model, data_pet)
print("Predicting Completed!")
def main():
parser = argparse.ArgumentParser(
description=
'''This is a beta script for Partial Volume Correction in PET/MRI system. ''',
epilog="""All's well that ends well.""")
parser.add_argument('--X',
metavar='',
type=str,
default="X_mnist",
help='X file name.(X_mnist)<str>')
parser.add_argument('--Y',
metavar='',
type=str,
default="Y_mnist",
help='Y file name.(Y_mnist)<str>')
parser.add_argument('--id',
metavar='',
type=str,
default="eeVee",
help='ID of the current model.(eeVee)<str>')
parser.add_argument('--epoch',
metavar='',
type=int,
default=500,
help='Number of epoches of training(2000)<int>')
parser.add_argument('--n_filter',
metavar='',
type=int,
default=64,
help='The initial filter number(64)<int>')
parser.add_argument('--depth',
metavar='',
type=int,
default=4,
help='The depth of U-Net(4)<int>')
parser.add_argument('--batch_size',
metavar='',
type=int,
default=10,
help='The batch_size of training(10)<int>')
args = parser.parse_args()
model_name = args.model_name
dir_X = './data/' + args.dir_X + '.npy'
dir_Y = './data/' + args.dir_Y + '.npy'
time_stamp = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M")
model_id = args.id + time_stamp
gbl_set_value("depth", args.depth)
gbl_set_value("dir_X", dir_X)
gbl_set_value("dir_Y", dir_Y)
gbl_set_value("model_id", model_id)
gbl_set_value("n_epoch", args.epoch + 1)
gbl_set_value("n_filter", args.n_filter)
gbl_set_value("depth", args.depth)
gbl_set_value("batch_size", args.batch_size)
gbl_set_value("slice_x", args.slice_x)
gbl_set_value("flag_bypass", False)
# Load data
file_X = np.load(dir_X)
file_Y = np.load(dir_Y)
gbl_set_value("img_shape", file_X.shape)
print("Loading Completed!")
X, Y = sa_data_generator(file_X)
print("Data Preparation Completed!")
model = train_a_unet(X, Y)
print("Training Completed!")
# predict(model, X)
# print("Predicting Completed!")
send_emails(model_id)
print("Notification completed!")
def main():
parser = argparse.ArgumentParser(
description=
'''This is a beta script for sCT generation from low filed MRI images. ''',
epilog="""All's well that ends well.""")
parser.add_argument('--slice_x',
metavar='',
type=int,
default=1,
help='channel of input(1)<int>[1/3]')
parser.add_argument('--input_size',
metavar='',
type=int,
default=512,
help='The size of the input image, eg 224 and 512')
parser.add_argument('--id',
metavar='',
type=str,
default="chansey",
help='ID of the current model.(eeVee)<str>')
parser.add_argument('--epoch',
metavar='',
type=int,
default=1000,
help='Number of epoches of training(300)<int>')
parser.add_argument('--n_filter',
metavar='',
type=int,
default=64,
help='The initial filter number of Unet(64)<int>')
parser.add_argument('--depth',
metavar='',
type=int,
default=4,
help='The depth of Unet(4)<int>')
parser.add_argument('--batch_size',
metavar='',
type=int,
default=4,
help='The batch_size of training(10)<int>')
parser.add_argument('--tv_weight',
metavar='',
type=float,
default=1e-4,
help='record the tv_weight')
parser.add_argument('--content_weight',
metavar='',
type=float,
default=2500,
help='record the content_weight')
parser.add_argument('--style_weight',
metavar='',
type=float,
default=11000,
help='record the style_weight')
parser.add_argument('--binary_weight',
metavar='',
type=float,
default=100,
help='record the style_weight')
parser.add_argument('--group_id',
metavar='',
type=int,
default=1,
help='The id of groups(1)<int>[1/2/3/4/5/6/7/8/9/10]')
parser.add_argument('--round_id',
metavar='',
type=str,
default='1st_round',
help='The id of rounds(1)<str>[1st_round/2nd_round]')
parser.add_argument('--case_id',
metavar='',
type=int,
default=10,
help='The number pf cases used for training(10)<int>')
parser.add_argument(
'--pretrained_flag',
metavar='',
type=int,
default=0,
help='The flag of pretrained model or new model(0)<int>')
parser.add_argument('--pretrained_path',
metavar='',
type=str,
default="---the pretrained path---",
help='The path of pretrained model<str>')
parser.add_argument(
'--discriminator_path',
metavar='',
type=str,
default=
'/code/data/per/gp_1/10-cases/1st_round/dis/model/loss_model_chansey-2020-07-15-23-10.hdf5',
help='the discriminator path')
args = parser.parse_args()
data_path = '/code/data/per/gp_' + str(args.group_id) + '/' + str(
args.case_id) + '-cases/data_for_gen/'
train_num = len(os.listdir(data_path + '/train/train_y/CT/'))
val_num = len(os.listdir(data_path + '/val/val_y/CT/'))
dir_syn = '/code/data/per/gp_' + str(args.group_id) + '/' + str(
args.case_id) + '-cases/' + args.round_id + '/gen/model/'
dir_model = '/code/data/per/gp_' + str(args.group_id) + '/' + str(
args.case_id) + '-cases/' + args.round_id + '/gen/model/'
if not os.path.exists(dir_syn):
os.makedirs(dir_syn)
if not os.path.exists(dir_model):
os.makedirs(dir_model)
time_stamp = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M")
model_id = args.id + time_stamp
gbl_set_value("depth", args.depth)
gbl_set_value("dir_syn", dir_syn)
gbl_set_value("dir_model", dir_model)
gbl_set_value("model_id", model_id)
gbl_set_value("n_epoch", args.epoch + 1)
gbl_set_value("n_filter", args.n_filter)
gbl_set_value("depth", args.depth)
gbl_set_value("batch_size", args.batch_size)
gbl_set_value("slice_x", args.slice_x)
gbl_set_value("n_slice_train", train_num)
gbl_set_value("n_slice_val", val_num)
gbl_set_value('tv_weight', args.tv_weight)
gbl_set_value('content_weight', args.content_weight)
gbl_set_value('style_weight', args.style_weight)
gbl_set_value('binary_weight', args.binary_weight)
gbl_set_value('discriminator_path', args.discriminator_path)
gbl_set_value('input_size', args.input_size)
gbl_set_value("pretrained_flag", args.pretrained_flag)
gbl_set_value("pretrained_path", args.pretrained_path)
model = train_a_unet(data_path)
print("Training Completed!")
def main():
parser = argparse.ArgumentParser(
description=
'''This is a beta script for Partial Volume Correction in PET/MRI system. ''',
epilog="""All's well that ends well.""")
parser.add_argument('--train_case',
metavar='',
type=int,
default=1,
help='The training dataset case(1)<int>[1,2,3,4]')
parser.add_argument('--test_case',
metavar='',
type=int,
default=2,
help='The testing dataset case(2)<int>[1,2,3,4]')
parser.add_argument('--slice_x',
metavar='',
type=int,
default="1",
help='Slices of input(1)<int>[1/3]')
parser.add_argument('--id',
metavar='',
type=str,
default="chansey",
help='ID of the current model.(eeVee)<str>')
parser.add_argument('--epoch',
metavar='',
type=int,
default=240,
help='Number of epoches of training(300)<int>')
parser.add_argument('--n_filter',
metavar='',
type=int,
default=64,
help='The initial filter number(64)<int>')
parser.add_argument('--depth',
metavar='',
type=int,
default=3,
help='The depth of U-Net(4)<int>')
parser.add_argument('--batch_size',
metavar='',
type=int,
default=5,
help='The batch_size of training(10)<int>')
args = parser.parse_args()
train_case = args.train_case
test_case = args.test_case
dir_syn = './walmart/'
dir_model = './walmart/'
if not os.path.exists(dir_syn):
os.makedirs(dir_syn)
if not os.path.exists(dir_model):
os.makedirs(dir_model)
time_stamp = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M")
model_id = args.id + time_stamp
gbl_set_value("depth", args.depth)
gbl_set_value("dir_syn", dir_syn)
gbl_set_value("dir_model", dir_model)
gbl_set_value("model_id", model_id)
gbl_set_value("n_epoch", args.epoch + 1)
gbl_set_value("n_filter", args.n_filter)
gbl_set_value("depth", args.depth)
gbl_set_value("batch_size", args.batch_size)
gbl_set_value("slice_x", args.slice_x)
# Load data
train_path = './data/MRCT/Case' + str(train_case) + '/'
test_path = './data/MRCT/Case' + str(test_case) + '/'
path_X = glob.glob(train_path + '*Align*.nii')[-1]
path_Y = glob.glob(train_path + '*CT*.nii')[-1]
file_X = nib.load(path_X)
file_Y = nib.load(path_Y)
data_X = file_X.get_fdata()
data_Y = file_Y.get_fdata()
#MaxMin-norm
data_X_norm = MaxMinNorm(data_X)
data_Y_norm = MaxMinNorm(data_Y)
gbl_set_value("img_shape", data_X.shape)
X, Y = write_XY(data_X_norm, data_Y_norm)
print(X.shape, Y.shape)
print("Loading Completed!")
model = train_a_unet(X, Y)
print("Training Completed!")
predict_MRCT(model, test_path)
print("Predicting Completed!")
def main():
parser = argparse.ArgumentParser(
description=
'''This is a beta script for Partial Volume Correction in PET/MRI system. ''',
epilog="""All's well that ends well.""")
parser.add_argument('--dir_pet',
metavar='',
type=str,
default="breast1_pet",
help='Name of PET subject.(breast1_pet)<str>')
parser.add_argument('--dir_mri',
metavar='',
type=str,
default="breast1_water",
help='Name of MRI subject.(breast1_water)<str>')
parser.add_argument(
'--blur_method',
metavar='',
type=str,
default="nib_smooth",
help=
'The blurring method of synthesizing PET(nib_smooth)<str> [kernel_conv/skimage_gaus/nib_smooth]'
)
parser.add_argument('--blur_para',
metavar='',
type=str,
default="4",
help='Parameters of blurring data(4)<str>')
parser.add_argument('--slice_x',
metavar='',
type=int,
default="1",
help='Slices of input(1)<int>[1/3]')
parser.add_argument(
'--enhance_blur',
metavar='',
type=bool,
default=False,
help='Whether stack different blurring methods to train the model')
parser.add_argument('--id',
metavar='',
type=str,
default="eeVee",
help='ID of the current model.(eeVee)<str>')
parser.add_argument('--epoch',
metavar='',
type=int,
default=500,
help='Number of epoches of training(2000)<int>')
parser.add_argument('--n_filter',
metavar='',
type=int,
default=64,
help='The initial filter number(64)<int>')
parser.add_argument('--depth',
metavar='',
type=int,
default=4,
help='The depth of U-Net(4)<int>')
parser.add_argument('--batch_size',
metavar='',
type=int,
default=10,
help='The batch_size of training(10)<int>')
parser.add_argument('--model_name',
metavar='',
type=str,
default='',
help='The name of model to be predicted. ()<str>')
parser.add_argument('--run_aim',
metavar='',
type=str,
default='train',
help='Why do you run this program? (train)<str>')
args = parser.parse_args()
model_name = args.model_name
dir_mri = './data/' + args.dir_mri + '.nii'
dir_pet = './data/' + args.dir_pet + '.nii'
time_stamp = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M")
model_id = args.id + time_stamp
enhance_blur = args.enhance_blur
gbl_set_value("depth", args.depth)
gbl_set_value("dir_mri", dir_mri)
gbl_set_value("dir_pet", dir_pet)
gbl_set_value("model_id", model_id)
gbl_set_value("n_epoch", args.epoch + 1)
gbl_set_value("n_filter", args.n_filter)
gbl_set_value("depth", args.depth)
gbl_set_value("batch_size", args.batch_size)
gbl_set_value("slice_x", args.slice_x)
gbl_set_value("run_aim", args.run_aim)
# Load data
file_pet = nib.load(dir_pet)
file_mri = nib.load(dir_mri)
data_pet = file_pet.get_fdata()
data_mri = file_mri.get_fdata()
gbl_set_value("img_shape", data_pet.shape)
print("Loading Completed!")
if model_name == '':
if not enhance_blur:
X, Y = data_generator(data_mri, args.blur_method, args.blur_para)
else:
X, Y = enhance_data_generator(data_mri)
print(X.shape)
print("Blurring Completed!")
model = train_a_unet(X, Y)
print("Training Completed!")
predict(model, data_pet)
print("Predicting Completed!")
# send_emails(model_id)
# print("Notification completed!")
else:
gbl_set_value("model_id", model_name[5:])
model = load_existing_model(model_name)
predict(model, data_pet)
print("Predicting Completed!")