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segmentation_network_only_preprocess_inference_data.py
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segmentation_network_only_preprocess_inference_data.py
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# --------------------------------------------------
#
# Copyright (C) {2020} {Kevin Bronik and Le Zhang}
#
# UCL Medical Physics and Biomedical Engineering
# https://www.ucl.ac.uk/medical-physics-biomedical-engineering/
# UCL Queen Square Institute of Neurology
# https://www.ucl.ac.uk/ion/
# 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/>.
#
#
#
# {Multi-Label Multi/Single-Class Image Segmentation} Copyright (C) {2020}
# This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
# This is free software, and you are welcome to redistribute it
# under certain conditions; type `show c' for details.
# This program uses piece of source code from:
# Title: nicMSlesions
# Author: Sergi Valverde
# Date: 2017
# Code version: 0.2
# Availability: https://github.com/NIC-VICOROB/nicMSlesions
# --------------------------------------------------
import click
import shutil
import argparse
import os
import sys
import platform
from timeit import time
import configparser
import numpy as np
import signal
from src.segmentation_network_preprocess import preprocess
from src.segmentation_network_postprocess import inverse_transformation
from src.segmentation_network_load_settings import load_settings, print_settings
CURRENT_PATH = CURRENT_PATH = os.path.split(os.path.realpath(__file__))[0]
sys.path.append(os.path.join(CURRENT_PATH, 'libs'))
# load settings from input
parser = argparse.ArgumentParser()
parser.add_argument('--docker',
dest='docker',
action='store_true')
parser.set_defaults(docker=False)
args = parser.parse_args()
container = args.docker
# check and remove the folder which dose not contain the necessary modalities before prepossessing step
CEND = '\33[0m'
CBOLD = '\33[1m'
CITALIC = '\33[3m'
CURL = '\33[4m'
CBLINK = '\33[5m'
CBLINK2 = '\33[6m'
CSELECTED = '\33[7m'
CBLACK = '\33[30m'
CRED = '\33[31m'
CGREEN = '\33[32m'
CYELLOW = '\33[33m'
CBLUE = '\33[34m'
CVIOLET = '\33[35m'
CBEIGE = '\33[36m'
CWHITE = '\33[37m'
CBLACKBG = '\33[40m'
CREDBG = '\33[41m'
CGREENBG = '\33[42m'
CYELLOWBG = '\33[43m'
CBLUEBG = '\33[44m'
CVIOLETBG = '\33[45m'
CBEIGEBG = '\33[46m'
CWHITEBG = '\33[47m'
CGREY = '\33[90m'
CRED2 = '\33[91m'
CGREEN2 = '\33[92m'
CYELLOW2 = '\33[93m'
CBLUE2 = '\33[94m'
CVIOLET2 = '\33[95m'
CBEIGE2 = '\33[96m'
CWHITE2 = '\33[97m'
CGREYBG = '\33[100m'
CREDBG2 = '\33[101m'
CGREENBG2 = '\33[102m'
CYELLOWBG2 = '\33[103m'
CBLUEBG2 = '\33[104m'
CVIOLETBG2 = '\33[105m'
CBEIGEBG2 = '\33[106m'
CWHITEBG2 = '\33[107m'
def check_inputs(current_folder, settings, choice):
"""
checking input errors, fixing and writing it into the Input Issue Report File
"""
erf =os.path.join(CURRENT_PATH, 'InputIssueReportfile.txt')
f = open(erf, "a")
if os.path.isdir(os.path.join(settings['train_folder'], current_folder)):
if len(os.listdir(os.path.join(settings['train_folder'], current_folder))) == 0:
print(('Directory:', current_folder, 'is empty'))
print('Warning: if the directory is not going to be removed, the Training could be later stopped!')
if click.confirm('The empty directory will be removed. Do you want to continue?', default=True):
f.write("The empty directory: %s has been removed from Training set!" % current_folder + os.linesep)
f.close()
shutil.rmtree(os.path.join(settings['train_folder'], current_folder), ignore_errors=True)
return
return
else:
pass
if choice == 'training':
modalities = settings['modalities'][:] + ['lesion']
image_mods = settings['image_mods'][:] + settings['roi_mods'][:]
else:
modalities = settings['modalities'][:]
image_mods = settings['image_mods'][:]
if settings['debug']:
print("> DEBUG:", "number of input sequences to find:", len(modalities))
print("> PRE:", current_folder, "identifying input modalities")
found_modalities = 0
if os.path.isdir(os.path.join(settings['train_folder'], current_folder)):
masks = [m for m in os.listdir(os.path.join(settings['train_folder'], current_folder)) if m.find('.nii') > 0]
pass # do your stuff here for directory
else:
# shutil.rmtree(os.path.join(settings['train_folder'], current_folder), ignore_errors=True)
print(('The file:', current_folder, 'is not part of training'))
print('Warning: if the file is not going to be removed, the Training could be later stopped!')
if click.confirm('The file will be removed. Do you want to continue?', default=True):
f.write("The file: %s has been removed from Training set!" % current_folder + os.linesep)
f.close()
os.remove(os.path.join(settings['train_folder'], current_folder))
return
return
for t, m in zip(image_mods, modalities):
# check first the input modalities
# find tag
found_mod = [mask.find(t) if mask.find(t) >= 0
else np.Inf for mask in masks]
if found_mod[np.argmin(found_mod)] is not np.Inf:
found_modalities += 1
# check that the minimum number of modalities are used
if found_modalities < len(modalities):
print("> ERROR:", current_folder, \
"does not contain all valid input modalities")
print('Warning: if the folder is not going to be removed, the Training could be later stopped!')
if click.confirm('The folder will be removed. Do you want to continue?', default=True):
f.write("The folder: %s has been removed from Training set!" % current_folder + os.linesep)
f.close()
shutil.rmtree(os.path.join(settings['train_folder'], current_folder), ignore_errors=True)
#return True
def read_default_config():
"""
Get the CNN_GUI configuration from file
"""
default_config = configparser.SafeConfigParser()
default_config.read(os.path.join(CURRENT_PATH, 'config', 'configuration.cfg'))
# read user's configuration file
settings = load_settings(default_config)
settings['tmp_folder'] = CURRENT_PATH + '/tmp'
settings['standard_lib'] = CURRENT_PATH + '/libs/standard'
# set paths taking into account the host OS
host_os = platform.system()
if host_os == 'Linux' or 'Darwin':
settings['niftyreg_path'] = CURRENT_PATH + '/libs/linux/niftyreg'
settings['robex_path'] = CURRENT_PATH + '/libs/linux/ROBEX/runROBEX.sh'
# settings['tensorboard_path'] = CURRENT_PATH + '/libs/bin/tensorboard'
settings['test_slices'] = 256
elif host_os == 'Windows':
settings['niftyreg_path'] = os.path.normpath(
os.path.join(CURRENT_PATH,
'libs',
'win',
'niftyreg'))
settings['robex_path'] = os.path.normpath(
os.path.join(CURRENT_PATH,
'libs',
'win',
'ROBEX',
'runROBEX.bat'))
settings['test_slices'] = 256
else:
print("The OS system also here ...", host_os, "is not currently supported.")
exit()
# print settings when debugging
if settings['debug']:
print_settings(settings)
return settings
def set_library (settings):
"""
Define the library backend and write settings
"""
#
# if settings['backend'] == 'theano':
# device = 'cuda' + str(settings['gpu_number']) if settings['gpu_mode'] else 'cpu'
# os.environ['KERAS_BACKEND'] = settings['backend']
# os.environ['THEANO_FLAGS'] = 'mode=FAST_RUN,device=' + device + ',floatX=float32,optimizer=fast_compile'
# else:
# device = str(settings['gpu_number']) if settings['gpu_mode'] is not None else " "
# print "DEBUG: ", device
# os.environ['KERAS_BACKEND'] = 'tensorflow'
# os.environ["CUDA_VISIBLE_DEVICES"] = device
# forcing tensorflow
device = str(settings['gpu_number'])
print("DEBUG: ", device)
os.environ['KERAS_BACKEND'] = 'tensorflow'
os.environ["CUDA_VISIBLE_DEVICES"] = device
def check_oututs(current_folder, settings, choice='testing'):
"""
checking input errors, fixing and writing it into the Input Issue Report File
"""
erf =os.path.join(CURRENT_PATH, 'OutputIssueReportfile.txt')
f = open(erf, "a")
if os.path.isdir(os.path.join(settings['test_folder'], current_folder)):
if len(os.listdir(os.path.join(settings['test_folder'], current_folder))) == 0:
print(('Directory:', current_folder, 'is empty'))
print('Warning: if the directory is not going to be removed, the Testing could be later stopped!')
if click.confirm('The empty directory will be removed. Do you want to continue?', default=True):
f.write("The empty directory: %s has been removed from Testing set!" % current_folder + os.linesep)
f.close()
shutil.rmtree(os.path.join(settings['test_folder'], current_folder), ignore_errors=True)
return
return
else:
pass
if choice == 'training':
modalities = settings['modalities'][:] + ['lesion']
image_mods = settings['image_mods'][:] + settings['roi_mods'][:]
else:
modalities = settings['modalities'][:]
image_mods = settings['image_mods'][:]
if settings['debug']:
print("> DEBUG:", "number of input sequences to find:", len(modalities))
print("> PRE:", current_folder, "identifying input modalities")
found_modalities = 0
if os.path.isdir(os.path.join(settings['test_folder'], current_folder)):
masks = [m for m in os.listdir(os.path.join(settings['test_folder'], current_folder)) if m.find('.nii') > 0]
pass # do your stuff here for directory
else:
# shutil.rmtree(os.path.join(settings['train_folder'], current_folder), ignore_errors=True)
print(('The file:', current_folder, 'is not part of testing'))
print('Warning: if the file is not going to be removed, the Testing could be later stopped!')
if click.confirm('The file will be removed. Do you want to continue?', default=True):
f.write("The file: %s has been removed from Testing set!" % current_folder + os.linesep)
f.close()
os.remove(os.path.join(settings['test_folder'], current_folder))
return
return
for t, m in zip(image_mods, modalities):
# check first the input modalities
# find tag
found_mod = [mask.find(t) if mask.find(t) >= 0
else np.Inf for mask in masks]
if found_mod[np.argmin(found_mod)] is not np.Inf:
found_modalities += 1
# check that the minimum number of modalities are used
if found_modalities < len(modalities):
print("> ERROR:", current_folder, \
"does not contain all valid input modalities")
print('Warning: if the folder is not going to be removed, the Testing could be later stopped!')
if click.confirm('The folder will be removed. Do you want to continue?', default=True):
f.write("The folder: %s has been removed from Testing set!" % current_folder + os.linesep)
f.close()
shutil.rmtree(os.path.join(settings['test_folder'], current_folder), ignore_errors=True)
def pre_segmentation(settings):
"""
Infer segmentation given the input settings passed as parameters
"""
# define the training backend
set_library (settings)
# from src.main_script import testing_models
# from src.build_model import build_and_compile_models
# --------------------------------------------------
# net configuration
# take into account if the pretrained models have to be used
# all images share the same network model
# --------------------------------------------------
settings['full_train'] = True
settings['load_weights'] = True
settings['weight_paths'] = os.path.join(CURRENT_PATH, 'nets')
settings['net_verbose'] = 0
# model = build_and_compile_models(settings)
# --------------------------------------------------
# process each of the scans
# - image identification
# - image registration
# - skull-stripping
# - WM segmentation
# --------------------------------------------------
# scan_list = os.listdir(settings['test_folder'])
# scan_list.sort()
# # check and remove the folder which dose not contain the necessary modalities before prepossessing step
# for check in scan_list:
# check_oututs(check, settings)
# update scan list after removing the unnecessary folders before prepossessing step
settings['task'] = 'testing'
scan_list = os.listdir(settings['test_folder'])
scan_list.sort()
for scan in scan_list:
total_time = time.time()
settings['tmp_scan'] = scan
# --------------------------------------------------
# move things to a tmp folder before starting
# --------------------------------------------------
current_folder = os.path.join(settings['test_folder'], scan)
settings['tmp_folder'] = os.path.normpath(
os.path.join(current_folder, 'tmp'))
# --------------------------------------------------
# preprocess scans
# --------------------------------------------------
preprocess(current_folder, settings, CURRENT_PATH)
if __name__ == '__main__':
#
try:
print("##################################################")
print('\x1b[6;30;45m' + 'Multi/Single-Class Lesion Segmentation ' + '\x1b[0m')
print('\x1b[6;30;42m' + 'Medical Physics and Biomedical Engineering' + '\x1b[0m')
print('\x1b[6;30;44m' + 'UCL - 2020 ' + '\x1b[0m')
print('\x1b[6;30;41m' + 'Kevin Bronik and Le Zhang ' + '\x1b[0m')
print("##################################################")
print('\x1b[6;30;42m' + '.............................................................' + '\x1b[0m')
print('\x1b[6;30;42m' + 'preprocessing of testing data started.......................' + '\x1b[0m')
print('\x1b[6;30;42m' + '.............................................................' + '\x1b[0m')
settings = read_default_config()
pre_segmentation(settings)
print('\x1b[6;30;42m' + '.............................................................' + '\x1b[0m')
print('\x1b[6;30;42m' + 'preprocessing of testing data completed.....................' + '\x1b[0m')
print('\x1b[6;30;42m' + '.............................................................' + '\x1b[0m')
except KeyboardInterrupt:
print("KeyboardInterrupt has been caught.")
time.sleep(1)
os.kill(os.getpid(), signal.SIGTERM)