# ==============================================================================
# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung,
# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz
#
# This file is part of OAI-MRI-TKR
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero 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 Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
# ==============================================================================
import numpy as np
import pandas as pd
import h5py
import nibabel as nib
import keras
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm
import matplotlib.colorbar
import matplotlib.colors
import pandas as pd
import numpy as np
from sklearn import metrics
import os
import tensorflow as tf
from keras.models import load_model
from sklearn.metrics import roc_auc_score,auc,roc_curve,average_precision_score
from Augmentation import RandomCrop, CenterCrop, RandomFlip
from DataGenerator import DataGenerator
tf.app.flags.DEFINE_string('model_path', '/gpfs/data/denizlab/Users/hrr288/Radiology_test/COR_IW_TSE/', 'Folder with the model')
tf.app.flags.DEFINE_string('csv_path', '/gpfs/data/denizlab/Users/hrr288/Tianyu_dat/COR_TSE/', 'Folder with the fold splits')
tf.app.flags.DEFINE_string('result_path', './', 'Folder to save output csv with preds')
tf.app.flags.DEFINE_string('file_folder','/gpfs/data/denizlab/Datasets/OAI/COR_IW_TSE/', 'Path to HDF5 radiographs of test set')
FLAGS = tf.app.flags.FLAGS
def main(argv=None):
base_path = FLAGS.model_path
csv_path = FLAGS.csv_path
# Choosing the model in each folder with lowest val loss
models= {'fold_1':[],'fold_2':[],'fold_3':[],'fold_4':[],'fold_5':[],'fold_6':[],'fold_7':[]}
for fold in np.arange(1,8):
tmp_mod_list = []
for cv in np.arange(1,7):
dir_1 = 'Fold_'+str(fold)+'/CV_'+str(cv)+'/'
files_avai = os.listdir(base_path+dir_1)
cands = []
cands_score = []
for fs in files_avai:
if 'weights' not in fs:
continue
else:
cands_score.append(float(fs.split('-')[2]))
cands.append(dir_1+fs)
ind_c = int(np.argmin(cands_score))
tmp_mod_list.append(cands[ind_c])
models['fold_'+str(fold)]=tmp_mod_list
val_params = {'dim': (352,352,35),
'batch_size': 1,
'n_classes': 2,
'n_channels': 1,
'shuffle': False,
'normalize' : True,
'randomCrop' : False,
'randomFlip' : False,
'flipProbability' : -1,
'cropDim' : (352,352,35)}
dfs = []
for i in np.arange(1,8):
print("Fold_"+str(i))
validation_generator = DataGenerator(directory = csv_path+'Fold_'+str(i)+'/Fold_'+str(i)+'_test.csv',file_folder=FLAGS.file_folder, **val_params)
df = pd.read_csv(csv_path+'Fold_'+str(i)+'/Fold_'+str(i)+'_test.csv')
pred_arr = np.zeros(df.shape[0])
for j in np.arange(1,7):
model = load_model(base_path+'/'+models['fold_'+str(i)][j-1])
s = model.predict_generator(validation_generator)
pred_arr += np.squeeze(s)
pred_arr = pred_arr/6
df["Preds"] = pred_arr
dfs.append(df)
full_df = pd.concat(dfs)
full_df.to_csv(FLAGS.result_path+"OAI_T1TSE_results.csv")
if __name__ == "__main__":
tf.app.run()
Datasets
Models
