path updates

cnn_ela_test.py

@@ -78,8 +78,8 @@ test_fake_folder = 'datasets/test_set/fake/'
 
 
 
-test_real_ela_folder = 'datasets/test_set/ela_real/'
-test_fake_ela_folder = 'datasets/test_set/ela_fake/'
+test_ela_output = 'datasets/training_set/ela_output/'
+
 
 
 
@@ -91,7 +91,7 @@ Y_test = []
 
 # Preprocess test set
 for index, row in test_set.iterrows():
-    X_test.append(array(convert_to_ela_image(row[0], 90, test_real_ela_folder).resize((128, 128))).flatten() / 255.0)
+    X_test.append(array(convert_to_ela_image(row[0], 90, test_ela_output).resize((128, 128))).flatten() / 255.0)
     Y_test.append(row[1])
 
 # Convert to numpy arrays

project_cnn_ela.py

@@ -96,15 +96,10 @@ if __name__ == "__main__":
 
 
 
-    test_real_folder = 'datasets/test_set/real/'
-    test_fake_folder = 'datasets/test_set/fake/'
+    traning_ela_output = 'datasets/training_set/ela_output/'
 
 
-    traning_fake_ela_folder = 'datasets/training_set/ela_fake/'
-    traning_real_ela_folder = 'datasets/training_set/ela_real/'
 
-    test_real_ela_folder = 'datasets/test_set/ela_real/'
-    test_fake_ela_folder = 'datasets/test_set/ela_fake/'
 
 
 
@@ -115,7 +110,7 @@ if __name__ == "__main__":
     Y = []
         
     for index, row in traning_set.iterrows():
-       X.append(array(convert_to_ela_image(row[0], 90,traning_real_ela_folder).resize((128, 128))).flatten() / 255.0)
+       X.append(array(convert_to_ela_image(row[0], 90,traning_ela_output).resize((128, 128))).flatten() / 255.0)
        Y.append(row[1])