--- a
+++ b/IW-TSE/evaluate.py
@@ -0,0 +1,131 @@
+# ==============================================================================
+# 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/Tnetres_Best/lr24ch32kerne773773_strde222_new_arch/', 'Folder with the models')
+tf.app.flags.DEFINE_string('csv_path', '/gpfs/data/denizlab/Users/hrr288/TSE_dataset/', '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/SAG_IW_TSE/', 'Path to IW TSE HDF5 radiographs')
+
+
+
+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': (384,384,36),
+              'batch_size': 1,
+              'n_classes': 2,
+              'n_channels': 1,
+              'shuffle': False,
+              'normalize' : True,
+              'randomCrop' : False,
+              'randomFlip' : False,
+              'flipProbability' : -1}
+    
+
+
+    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_DESS_results.csv")
+if __name__ == "__main__":
+    tf.app.run()
+
+