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ms_wrapper.py
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ms_wrapper.py
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# Copyright 2021 Population Health Sciences and Image Analysis, German Center for Neurodegenerative Diseases(DZNE)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import subprocess
import shlex
import sys
import argparse
sys.path.append('../')
sys.path.append('../../')
from utils import stats
def run_cmd(cmd):
"""
execute the comand
"""
print('#@# Command: ' + cmd + '\n')
args = shlex.split(cmd)
try:
subprocess.check_call(args)
except subprocess.CalledProcessError as e:
print('ERROR: ' + 'cannot run command')
# sys.exit(1)
raise
print('\n')
def option_parse():
parser = argparse.ArgumentParser(
description='Wrapper for running the olfactory bulb pipeline in multiple scans',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-slist", "--sublist", help="Subject List", required=True)
parser.add_argument("-indir", "--data_dir", type=str, help="data directory", required=True)
parser.add_argument("-out", "--output_dir", help="Main output directory where pipeline results are going to be store", required=True)
parser.add_argument('-batch', "--batch_size", type=int,
help='Batch size for inference by default is 8', required=False, default=8)
parser.add_argument('-gpu', "--gpu_id", type=int,
help='GPU device name to run model', required=False, default=0)
parser.add_argument('-ncuda', "--no_cuda", action='store_true',
help='Disable CUDA (no GPU usage, inference on CPU)', required=False)
parser.add_argument('-ninter', "--no_interpolate", action='store_true',
help='No interpolate input scans to the default training resolution of 0.8mm isotropic', required=False)
parser.add_argument('-order', "--order", type=int,
help='interpolation order to used if input scan is interpolated (0=nearest,1=linear(default),2=quadratic,3=cubic)', required=False, default=1)
parser.add_argument('-logits', "--save_logits", action='store_true',
help='Save segmentation logits maps as a h5 file', required=False)
parser.add_argument('-model', "--model", type=int,
help='AttFastSurferCNN model to be run by default the pipeline runs all 4 AttFastSurferCNN models;\n'
'(1 = model 1,2 = model 2,3 = model 3, 4 = model 4, 5= all models(default))', required=False, default=5)
parser.add_argument('-ores', '--orig_res', action='store_true', help='Upsample or downsample OB segmentation to the input image resolution;\n'
' by default the pipeline produces a segmentation with a 0.8mm isotropic resolution', required=False)
parser.add_argument('-loc_dir','--loc_dir',help='Localization weights directory',required=False,default='./LocModels')
parser.add_argument('-seg_dir','--seg_dir',help='Segmentation weights directory',required=False,default='./SegModels')
args = parser.parse_args()
return args
def check_paths(sublist,root_dir):
import pandas as pd
import os
import numpy as np
from utils import misc
df=pd.read_csv(sublist,sep=',')
arr=df.values
new_arr=np.zeros(arr.shape,dtype=object)
idx=0
for i in range(arr.shape[0]):
sub=str(arr[i,0])
t2_prefix=arr[i,1]
t2_path=misc.locate_file('*'+t2_prefix,os.path.join(root_dir,sub))
if t2_path:
if os.path.isfile(t2_path[0]):
new_arr[idx,0]=sub
new_arr[idx,1]=t2_path[0]
idx += 1
else:
print('--'*30)
print('ERROR: path {} is not a file '.format(t2_path[0]))
else:
print('--' * 30)
print('ERROR image {} not found at directory {}'.format(t2_prefix,os.path.join(root_dir,sub)))
return new_arr[:idx,:]
if __name__=='__main__':
args= option_parse()
sub_list=check_paths(args.sublist,args.data_dir)
for i in range(sub_list.shape[0]):
cmd='python3 ./run_pipeline.py -in {} -out {} -sid {} -batch {} -gpu {} ' \
'-loc_dir {} -seg_dir {} -model {} -order {}'.format(sub_list[i,1],args.output_dir,
sub_list[i,0],args.batch_size,
args.gpu_id,args.loc_dir,
args.seg_dir,args.model,args.order)
if args.no_cuda:
cmd =cmd +' -ncuda'
if args.save_logits:
cmd = cmd + ' -logits'
if args.no_interpolate:
cmd = cmd + ' -ninter'
if args.orig_res:
cmd = cmd + ' -ores'
run_cmd(cmd)
stats.obstats2table(args.output_dir,sub_list[:,0])
sys.exit(0)