Get out from integration detection.

app.py

@@ -57,10 +57,10 @@ def load_model_detection():
 
 def preprocess_image(image):
     """ Preprocess the image to the required size and normalization. """
-    # image = cv2.resize(image, (W_detection, H_detection))
-    # image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
-    # image = np.expand_dims(image, axis=0).astype(np.float32)
-    # return image
+    image = cv2.resize(image, (W_detection, H_detection))
+    image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+    return image
 
 def predict(model_detection, image):
     """ Predict bounding box and label for the input image. """
@@ -85,13 +85,41 @@ def draw_bbox(image, bbox):
 
 model_detection = load_model_detection()
 
-# uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, 1)
+
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+    if st.button('Detect'):
+        st.write("Processing...")
+        input_image = preprocess_image(image)
+        pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
+
+        # Updated label mapping based on the dataset
+        label_mapping = {
+            0: 'Atelectasis',
+            1: 'Cardiomegaly',
+            2: 'Effusion',
+            3: 'Infiltrate',
+            4: 'Mass',
+            5: 'Nodule',
+            6: 'Pneumonia',
+            7: 'Pneumothorax'
+        }
 
-# if uploaded_file is not None:
-#     file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-#     image = cv2.imdecode(file_bytes, 1)
+        if pred_label_confidence < 0.2:
+            st.write("May not detect a disease.")
+        else:
+            pred_label_name = label_mapping[pred_label]
+            st.write(f"Prediction Label: {pred_label_name}")
+            st.write(f"Prediction Bounding Box: {pred_bbox}")
+            st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
 
-#     st.image(image, caption='Uploaded Image.', use_column_width=True)
+            output_image = draw_bbox(image.copy(), pred_bbox)
+            st.image(output_image, caption='Detected Image.', use_column_width=True)
 
 @st.cache_resource
 def load_gradcam_model():
@@ -494,35 +522,35 @@ if uploaded_file is not None:
         redirect_button("https://new-ohif-viewer-k7c3gdlxua-et.a.run.app/")
 
     with col2:
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        image = cv2.imdecode(file_bytes, 1)
+        # file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+        # image = cv2.imdecode(file_bytes, 1)
         if st.button('Auto Detect'):
-            st.write("Processing...")
-            input_image = preprocess_image(image)
-            pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
+            # st.write("Processing...")
+            # input_image = preprocess_image(image)
+            # pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)
     
-            # Updated label mapping based on the dataset
-            label_mapping = {
-                0: 'Atelectasis',
-                1: 'Cardiomegaly',
-                2: 'Effusion',
-                3: 'Infiltrate',
-                4: 'Mass',
-                5: 'Nodule',
-                6: 'Pneumonia',
-                7: 'Pneumothorax'
-            }
+            # # Updated label mapping based on the dataset
+            # label_mapping = {
+            #     0: 'Atelectasis',
+            #     1: 'Cardiomegaly',
+            #     2: 'Effusion',
+            #     3: 'Infiltrate',
+            #     4: 'Mass',
+            #     5: 'Nodule',
+            #     6: 'Pneumonia',
+            #     7: 'Pneumothorax'
+            # }
     
-            if pred_label_confidence < 0.2:
-                st.write("May not detect a disease.")
-            else:
-                pred_label_name = label_mapping[pred_label]
-                st.write(f"Prediction Label: {pred_label_name}")
-                st.write(f"Prediction Bounding Box: {pred_bbox}")
-                st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
+            # if pred_label_confidence < 0.2:
+            #     st.write("May not detect a disease.")
+            # else:
+            #     pred_label_name = label_mapping[pred_label]
+            #     st.write(f"Prediction Label: {pred_label_name}")
+            #     st.write(f"Prediction Bounding Box: {pred_bbox}")
+            #     st.write(f"Prediction Confidence: {pred_label_confidence:.2f}")
     
-                output_image = draw_bbox(image.copy(), pred_bbox)
-                st.image(output_image, caption='Detected Image.', use_column_width=True)
+            #     output_image = draw_bbox(image.copy(), pred_bbox)
+            #     st.image(output_image, caption='Detected Image.', use_column_width=True)
 
 
         # if st.button('Auto Detect'):