from keras.initializers import Constant
import nibabel as nib
from scipy import ndimage
from sklearn.model_selection import KFold
import skimage.transform
import tensorflow as tf
import matplotlib as mptlib
#mptlib.use('TkAgg')
import matplotlib.pyplot as plt
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()
import numpy as np
import csv
import sys
import os
import settings
from settings import process_options, perform_setup
(options, args) = process_options()
IMG_DTYPE, SEG_DTYPE, _nx, _ny = perform_setup(options)
# from setupmodel import GetDataDictionary, BuildDB
from trainmodel import TrainModel
from predictmodel import PredictCSV, PredictNifti
from kfolds import OneKfold, Kfold
from generator import *
if ((not options.trainmodel) and (not options.predictmodel)):
print("parser error")
quit()
if options.trainmodel:
if options.liver:
if not options.datafiles_liver:
print('no list of liver .npy files given for training')
quit()
else:
saveloclist = options.datafiles_liver
elif options.tumor:
if not options.datafiles_tumor:
print('no list of tumor .npy files given for training')