def __init__ (self) : args = config.parse_arguments() self.roi_model=args.roi_model self.main_model=args.main_model self.load_weights_roi=args.load_weights_roi self.load_weights_main=args.load_weights_main self.store_model=args.store_txt self.batch_size=args.batch_size
def __init__(self):
args = config.parse_arguments()
self.store_model_path = args.store_txt
self.classes = args.classes
self.loss_main = args.loss_main
self.loss_roi = args.loss_roi
self.width = args.width
self.height = args.height
def __init__(self, init1, init2):
args = config.parse_arguments()
self.roi_activation = args.roi_activation
self.init_f = init1
self.init_b = init2
self.loss_pre = args.loss_pre
self.roi_shape = args.roi_shape_roi
self.roi_optimizer = args.pre_optimizer
self.get_history = False
self.verbose = 1
self.units_ed = 64
self.pretrain_window = args.pretrain_window
self.epoch_pre = args.epochs_pre
self.monitor = args.monitor_callbacks
self.store_model = args.store_txt
self.weight_name = 'first'
def __init__(self, X, Y):
self.mask = Y
self.images = X
args = config.parse_arguments()
self.max = args.max_loops
self.featurewise_center = args.featurewise_center
self.samplewise_center = args.samplewise_center
self.featurewise_std_normalization = args.featurewise_std_normalization
self.samplewise_std_normalization = args.samplewise_std_normalization
self.zca_whitening = args.zca_whitening
self.rotation_range = args.rotation_range
self.width_shift_range = args.width_shift_range
self.height_shift_range = args.height_shift_range
self.horizontal_flip = args.horizontal_flip
self.vertical_flip = args.vertical_flip
self.data_augm = args.data_augm_classic
self.alpha = args.alpha
self.sigma = args.sigma
self.normilize = args.normalize
self.shuffle = args.shuffle
self.batch_size = args.batch_size
self.index = np.arange(len(self.images))
self.noise = args.noise
self.random_apply_in_batch = args.random_apply_in_batch
if self.data_augm == 'True':
self.datagen = ImageDataGenerator(
featurewise_center=self.
featurewise_center, # set input mean to 0 over the dataset
samplewise_center=self.
samplewise_center, # set each sample mean o 0
featurewise_std_normalization=self.
featurewise_std_normalization, # divide inputs by std of the dataset
samplewise_std_normalization=self.
samplewise_std_normalization, # divide each input by its std
zca_whitening=self.zca_whitening, # apply ZCA whitening
rotation_range=self.
rotation_range, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=self.
width_shift_range, # randomly shift images horizontally (fraction of total width)
height_shift_range=self.
height_shift_range, # randomly shift images vertically (fraction of total height)
horizontal_flip=self.horizontal_flip, # randomly flip images
vertical_flip=self.vertical_flip,
zca_epsilon=1e-6)
def __init__(self, init1, init2, height, channels, classes, width):
args = config.parse_arguments()
self.height = height
self.channels = channels
self.classes = classes
self.main_activation = args.main_activation
self.init_w = init1
self.init_b = init2
self.features = args.features
self.depth = args.depth
self.padding = args.padding
self.batchnorm = args.batchnorm
self.dropout = args.dropout
self.width = width
self.temperature = 1.0
self.max_norm_const = args.max_norm_const
self.max_norm_value = args.max_norm_value
self.im_length = args.height
def __init__(self, rmn, mmn, data_return='off', save_mode='off'):
args = config.parse_arguments()
self.roi_model_name = rmn
self.main_model_name = mmn
self.data_return = data_return
self.save_mode = save_mode
self.STORE_PATH = args.store_data_test
self.STORE_PATH1 = self.STORE_PATH + '/ROI/train/'
self.STORE_PATH2 = self.STORE_PATH + '/ROI/test/'
self.STORE_PATH_main1 = self.STORE_PATH + '/MAIN/train/'
self.STORE_PATH_main2 = self.STORE_PATH + '/MAIN/test/'
# if the paths does not excist create automatic
# if not os.path.exists(self.STORE_PATH1):
# os.makedirs(self.STORE_PATH1)
# if not os.path.exists(self.STORE_PATH2):
# os.makedirs(self.STORE_PATH2)
# if not os.path.exists(self.STORE_PATH_main1):
# os.makedirs(self.STORE_PATH_main1)
# if not os.path.exists(self.STORE_PATH_main2):
# os.makedirs(self.STORE_PATH_main2)
self.data_extention = args.data_extention
self.counter_extention = args.counter_extention
self.restore_from_jpg_tif = args.restore_image
self.label_classes = args.label_classes
if self.label_classes == 'both':
self.classes = 2
if self.label_classes == 'three':
self.classes = 3
if self.label_classes == 'four':
self.classes = 4
if self.label_classes == 'first':
self.classes = 1
if self.label_classes == 'second':
self.classes = 1
if self.label_classes == 'third':
self.classes = 1
if self.label_classes == 'fourth':
self.classes = 1
def __init__(self, X, Y, case):
args = config.parse_arguments()
self.case = case
self.gan_train_directory = args.gan_train_directory
self.data_augm = args.data_augm
self.batch_size = args.batch_size
self.batch_size_test = args.batch_size_test
self.num_cores = args.num_cores
self.validation_split = args.validation_split
self.validation = args.validation
self.shuffle = args.shuffle
self.normalize_image = args.normalize
self.gancheckpoint = 'checkpoint'
self.gan_synthetic = args.gan_synthetic
self.num_synthetic_images = args.num_synthetic_images
self.batch_size = args.batch_size
self.X = X
self.Y = Y
self.fourier = args.fft_convert_data
self.STORE_PATH = args.store_data_test
self.STORE_PATH10 = self.STORE_PATH + '/ROI/train/'
self.STORE_PATH1 = self.STORE_PATH + '/FFT/train/'
self.data_extention = "jpeg"
boolt = 'False'
self.main_model = args.main_model
if (args.main_model == 'rgu_net' and self.case == 'main'):
A, nodes_coordinates = grid_graph(args.height)
coarsening_levels = args.depth
u_shape, u_rows, u_cols, u_val, perm = coarsen_mnist(
A, coarsening_levels, nodes_coordinates)
self.X = convert_train_data(X, perm, args.height)
print(
'the modified shape of X input because of RGMM structure is: ')
print(self.X.shape)
if boolt == 'True':
self.threshold_bin = 0.8
else:
self.threshold_bin = 0.05
import sys
import cv2
import matplotlib.pyplot as plt
from auto_segm import run_model, config
from keras.losses import mean_squared_error
import numpy as np
import itertools
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from keras.wrappers.scikit_learn import KerasClassifier
class vision_outputs:
def __init__(self, case, history=None):
args = config.parse_arguments()
self.h = history
self.STORE_TXT = args.store_txt
self.store_results = args.store_results
self.validation = args.validation
self.metrics = args.metrics
self.metrics1 = args.metrics1
self.metrics2 = args.metrics2
self.case = case
def plot_loss(self):
'''
plot the loss results
'''
print(self.h.history.keys())
if (self.validation == "on"):
def __init__(self, path_case):
args = config.parse_arguments()
self.model_name = args.main_model
self.ram = args.ram
self.cross_validation_number = args.crossval_cycle
self.ngpu = args.ngpu
self.metrics = args.metrics
self.metrics1 = args.metrics1
self.metrics2 = args.metrics2
self.batch_size = args.batch_size
self.batch_size_test = args.batch_size_test
self.epochs_roi = args.epochs_roi
self.epochs_main = args.epochs_main
self.num_cores = args.num_cores
self.path_case = path_case
self.validation_split = args.validation_split
self.validation = args.validation
self.shuffle = args.shuffle
self.optimizer = args.pre_optimizer
self.weights = args.loss_weights
self.normalize_image = args.normalize
self.roi_shape = args.roi_shape_roi
self.store_model = args.store_txt
self.weight_name = 'first'
self.early_stopping = args.early_stop
self.monitor = args.monitor_callbacks
self.mode = args.mode_convert
self.label_classes = args.label_classes
self.exponential_decay = 'False'
self.lrate = args.learning_rate
self.store_model_path = args.store_txt
self.fourier = args.fft_convert_data
if (args.decay == 666):
self.exponential_decay = 'True'
args.decay = 0
optimizer_args_roi = {
'lr': args.roi_learning_rate,
'momentum': args.roi_momentum,
'decay': args.roi_decay,
'seed': args.roi_seed
}
optimizer_args = {
'lr': args.learning_rate,
'momentum': args.momentum,
'decay': args.decay,
'seed': args.seed
}
if self.path_case == 'roi':
for k in list(optimizer_args_roi):
if optimizer_args_roi[k] is None:
del optimizer_args_roi[k]
optimizer = self.pass_optimizer(args.roi_optimizer,
optimizer_args_roi)
self.optimizer = optimizer
self.epochs = self.epochs_roi
if self.path_case == 'main':
for k in list(optimizer_args):
if optimizer_args[k] is None:
del optimizer_args[k]
optimizer = self.pass_optimizer(args.m_optimizer, optimizer_args)
self.optimizer = optimizer
self.epochs = self.epochs_main
def __init__ (self,analysis,path_case) :
"""
Initializare of the config file
"""
args = config.parse_arguments()
self.path_case=path_case
self.restore_from_jpg_tif=args.restore_image
self.rotated="False"
if self.path_case=='roi':
self.image_shape=args.image_shape_roi
self.original_image_shape=args.original_image_shape_roi
self.roi_shape=args.roi_shape_roi
self.data_path=args.datapath
self.data_path2=args.datapath
self.STORE_TXT=args.store_txt
self.counter_path='/contour/'
self.data_extention = args.data_extention_roi
self.counter_extention = args.counter_extention_roi
self.PATH_IMAGES='/image'
self.PATH_IMAGES2='/image'
if self.path_case=='main':
if self.restore_from_jpg_tif=='off':
self.image_shape=args.image_shape
self.original_image_shape=args.original_image_shape
self.roi_shape=args.roi_shape
self.data_path=args.datapath
self.data_path2=args.datapath
self.STORE_TXT=args.store_txt
self.counter_path='/contour/'
self.data_extention = args.data_extention_roi
self.counter_extention = args.counter_extention_roi
self.PATH_IMAGES='/image'
self.PATH_IMAGES2='/image'
else:
self.image_shape=args.image_shape
self.original_image_shape=args.original_image_shape
self.roi_shape=args.roi_shape
self.data_path=args.store_data_test
self.data_path2=args.datapath
self.STORE_TXT=args.store_txt
self.counter_path='/contour_main/'
self.data_extention = args.data_extention
self.counter_extention = args.counter_extention
self.PATH_IMAGES='/ROI/train'
self.PATH_IMAGES2='/ROI/test'
if self.path_case=='pre':
self.image_shape=args.image_shape_pre
self.original_image_shape=args.original_image_shape_pre
self.roi_shape=args.roi_shape_pre
self.data_path=args.datapath
self.data_path2=args.datapath
self.STORE_TXT=args.store_txt
self.counter_path='/contour/'
self.data_extention = args.data_extention_pre
self.counter_extention = args.counter_extention_pre
self.PATH_IMAGES='/image'
self.PATH_IMAGES2='/image'
self.pretrain_window=args.pretrain_window
# seperate the train of ROI with the train set for the u_net. Thus take the epi and endo seperate in u_net and train the ROI detection in both
if (analysis=='train' or analysis=='test') :
self.n_set_pre=analysis
self.n_set=analysis
else:
self.n_set_pre='train_prediction'
self.n_set='train'
self.patient_list=args.patient_list
self.store_contour=args.store_data_test
self.image_part = np.zeros([self.original_image_shape,self.original_image_shape])
self.shuffle=args.shuffle
self.num_preprocess_threads=args.num_cores
self.batch_size=args.batch_size
self.patient_store_style=args.patient_store_style
self.STORE_PATH=args.store_data_test
#if MICCAI_2009:
self.label_case1=args.label_case_extension_1
self.label_case2=args.label_case_extension_2
self.label_classes=args.label_classes
if self.label_classes=='both':
self.classes=2
elif self.label_classes=='three':
self.classes=3
elif self.label_classes=='four':
self.classes=4
elif self.label_classes=='first':
self.classes=1
elif self.label_classes=='second':
self.classes=1
elif self.label_classes=='third':
self.classes=1
elif self.label_classes=='fourth':
self.classes=1
else:
print("No accebtable label_classes!! ")
