added test images

app.py

@@ -10,6 +10,8 @@ from IMVIP_Supplementary_Material.scripts import dfutils #methods used for DF-Ne
 DESCRIPTION = """# DF-Net
 The Digital Forensics Network is designed and trained to detect and locate image manipulations. 
 More information can be found in this [publication](https://zenodo.org/record/8214996)
+
+#### Select example image or upload your own image:
 """
 
 IMG_SIZE=256
@@ -17,7 +19,8 @@ IMG_SIZE=256
 tf.experimental.numpy.experimental_enable_numpy_behavior()
 #np.warnings.filterwarnings('error', category=np.VisibleDeprecationWarning) 
 
-
+model_M1 = None
+model_M2 = None
 
 def check_forgery_df(img):
     shape_original = img.shape
@@ -45,18 +48,74 @@ def check_forgery_df(img):
     return resized_image
 
 
-
 def evaluate(img):
     pre_t = check_forgery_df(img)
     st.image(pre_t, caption="White area indicates potential image manipulations.")
 
+def start_evaluation(uploaded_file):    
+    #load models
+    model_path1 = "IMVIP_Supplementary_Material/models/model1/"
+    model_path2 = "IMVIP_Supplementary_Material/models/model2/"
+    
+    #tfsm_layer1 = tf.keras.layers.TFSMLayer(model_path1, call_endpoint='serving_default')
+    #tfsm_layer2 = tf.keras.layers.TFSMLayer(model_path2, call_endpoint='serving_default')
+    #
+    #input_shape = (256, 256, 3)
+    #inputs = Input(shape=input_shape)
+
+    ##create the model
+    #outputs1 = tfsm_layer1(inputs)
+    #model_M1 = Model(inputs, outputs1)
+
+    #outputs2 = tfsm_layer2(inputs)
+    #model_M2 = Model(inputs, outputs2)
+    
+    model_M1 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/") #tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model1/")
+    model_M2 = tf.keras.models.load_model("IMVIP_Supplementary_Material/models/model2/")
+
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    opencv_image = cv2.imdecode(file_bytes, 1)
+    reversed_image = opencv_image[:, :, ::-1]
+    st.image(reversed_image, caption="Input Image")
+    evaluate(reversed_image)
 
 
 st.markdown(DESCRIPTION)
 
-uploaded_file = st.file_uploader("Please upload an image", type=["jpeg", "jpg", "png"])
 
-if uploaded_file is not None:
+img_path1 = "example_images/Sp_D_NRD_A_nat0095_art0058_0582"
+img_path2 = "example_images/Sp_D_NRN_A_nat0083_arc0080_0445"
+img_path3 = "example_images/Sp_D_NRN_A_ani0088_cha0044_0441"
+image_paths = [img_path1+".jpg", img_path2+".jpg", img_path3+".jpg"]
+gt_paths = [img_path1+"_gt.png", img_path2+"_gt.png", img_path3+"_gt.png"]
+# Display images in a table format
+img = None
+for idx, image_path in enumerate(image_paths):
+    cols = st.columns([2, 2, 2, 2])  # Define column widths
+    
+    # Place the button in the first column
+    if cols[0].button(f"Select Image {idx+1}", key=idx):
+        img = Image.open(image_path)
+        
+    # Place the image in the second column
+    with cols[1]:
+        st.image(image_path, use_column_width=True, caption="Example Image "+str(idx+1))
+
+    # Place the ground truth in the third column
+    with cols[2]:
+        st.image(gt_paths[idx], use_column_width=True, caption="Ground Truth")
+
+if img is not None:
+    start_evaluation(img)
+
+def reset_image_select():
+    img = None
+
+
+
+
+def start_evaluation(uploaded_file):    
     #load models
     model_path1 = "IMVIP_Supplementary_Material/models/model1/"
     model_path2 = "IMVIP_Supplementary_Material/models/model2/"
@@ -85,4 +144,8 @@ if uploaded_file is not None:
     evaluate(reversed_image)
 
 
+uploaded_file= None
+uploaded_file = st.file_uploader("Please upload an image", type=["jpeg", "jpg", "png"], on_change=reset_image_select)
+if (uploaded_file is not None) and (img is None):
+    start_evaluation(uploaded_file)