import settings
from settings import process_options, perform_setup
(options, args) = process_options()
IMG_DTYPE, SEG_DTYPE, _globalnpfile, _globalexpectedpixel, _nx, _ny = perform_setup(
options)
print('database file: %s ' % settings._globalnpfile)
from setupmodel import GetDataDictionary, BuildDB
from trainmodel import TrainModel
from predictmodel import PredictModel
from kfolds import OneKfold, Kfold
if options.builddb:
BuildDB()
if options.kfolds > 1:
if options.idfold > -1:
databaseinfo = GetDataDictionary(options.dbfile)
OneKfold(i=options.idfold, datadict=databaseinfo)
else:
Kfold()
if options.trainmodel and options.kfolds == 1: # no kfolds, i.e. k=1
TrainModel()
if options.predictmodel:
PredictModel()
if ((not options.builddb) and (not options.trainmodel)
and (not options.predictmodel) and (options.kfolds == 1)):
print("parser error")
saveloclist = options.datafiles_liver
elif options.tumor:
if not options.datafiles_tumor:
print('no list of tumor .npy files given for training')
quit()
else:
saveloclist = options.datafiles_tumor
else:
print('not specified liver vs tumor')
quit()
print('files already generated: using', saveloclist)
if options.kfolds > 1:
if options.idfold > -1:
OneKfold(i=options.idfold, saveloclist=saveloclist)
else:
Kfold(saveloclist=saveloclist)
else:
TrainModel(saveloclist=saveloclist)
if options.predictmodel:
if options.predictfromcsv:
PredictCSV(modelloc=options.predictmodel,
outdir=options.outdir,
indir=options.predictfromcsv)
else:
PredictNifti(model,
options.outdir + '/predictions/pred',
options.predictimage,
segloc=None)