#==============================================================================#

#  Author:       Dominik Müller                                                #

#  Copyright:    2020 IT-Infrastructure for Translational Medical Research,    #

#                University of Augsburg                                        #

#                                                                              #

#  This program is free software: you can redistribute it and/or modify        #

#  it under the terms of the GNU General Public License as published by        #

#  the Free Software Foundation, either version 3 of the License, or           #

#  (at your option) any later version.                                         #

#                                                                              #

#  This program is distributed in the hope that it will be useful,             #

#  but WITHOUT ANY WARRANTY; without even the implied warranty of              #

#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the               #

#  GNU General Public License for more details.                                #

#                                                                              #

#  You should have received a copy of the GNU General Public License           #

#  along with this program.  If not, see <http://www.gnu.org/licenses/>.       #

#==============================================================================#

#-----------------------------------------------------#

#                   Library imports                   #

#-----------------------------------------------------#

import tensorflow as tf

from miscnn.data_loading.interfaces import NIFTI_interface

from miscnn import Data_IO, Preprocessor, Neural_Network

from miscnn.processing.subfunctions import Normalization, Clipping, Resampling

from miscnn.neural_network.architecture.unet.standard import Architecture

from miscnn.neural_network.metrics import tversky_crossentropy, dice_soft, \

                                          dice_crossentropy, tversky_loss

import argparse

import os

#-----------------------------------------------------#

#                      Argparser                      #

#-----------------------------------------------------#

parser = argparse.ArgumentParser(description="Automated COVID-19 Segmentation")

parser.add_argument("--model", help="Path to model", required=True, type=str, dest="model")

parser.add_argument("--output", help="Path to the output directory",

                    required=True, type=str, dest="output")

parser.add_argument("-g", "--gpu", help="GPU ID selection for multi cluster",

                    required=False, type=int, dest="gpu", default=0)

args = parser.parse_args()

path_model = args.model

path_preds = args.output

os.environ["CUDA_VISIBLE_DEVICES"] = str(int(args.gpu))

#-----------------------------------------------------#

#               Setup of MIScnn Pipeline              #

#-----------------------------------------------------#

# Initialize Data IO Interface for NIfTI data

## We are using 4 classes due to [background, lung_left, lung_right, covid-19]

interface = NIFTI_interface(channels=1, classes=4)

# Create Data IO object to load and write samples in the file structure

data_io = Data_IO(interface, input_path="data.testing", output_path=path_preds,

                  delete_batchDir=False)

# Access all available samples in our file structure

sample_list = data_io.get_indiceslist()

sample_list.sort()

# Create a clipping Subfunction to the lung window of CTs (-1250 and 250)

sf_clipping = Clipping(min=-1250, max=250)

# Create a pixel value normalization Subfunction to scale between 0-255

sf_normalize = Normalization(mode="grayscale")

# Create a resampling Subfunction to voxel spacing 1.58 x 1.58 x 2.70

sf_resample = Resampling((1.58, 1.58, 2.70))

# Create a pixel value normalization Subfunction for z-score scaling

sf_zscore = Normalization(mode="z-score")

# Assemble Subfunction classes into a list

sf = [sf_clipping, sf_normalize, sf_resample, sf_zscore]

# Create and configure the Preprocessor class

pp = Preprocessor(data_io, data_aug=None, batch_size=2, subfunctions=sf,

                  prepare_subfunctions=True, prepare_batches=False,

                  analysis="patchwise-crop", patch_shape=(160, 160, 80),

                  use_multiprocessing=True)

# Adjust the patch overlap for predictions

pp.patchwise_overlap = (80, 80, 30)

pp.mp_threads = 16

# Initialize the Architecture

unet_standard = Architecture(depth=4, activation="softmax",

                             batch_normalization=True)

# Create the Neural Network model

model = Neural_Network(preprocessor=pp, architecture=unet_standard,

                       loss=tversky_crossentropy,

                       metrics=[tversky_loss, dice_soft, dice_crossentropy],

                       batch_queue_size=3, workers=3, learninig_rate=0.001)

# Load best model weights during fitting

model.load(path_model)

# Compute predictions

model.predict(sample_list, return_output=False)