Open def preprocess_image(image) again.

app.py

@@ -52,12 +52,12 @@ def load_model_detection():
     )
     return model
 
-# def preprocess_image(image):
-#     """ Preprocess the image to the required size and normalization. """
-#     image = cv2.resize(image, (W, H))
-#     image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
-#     image = np.expand_dims(image, axis=0).astype(np.float32)
-#     return image
+def preprocess_image(image):
+    """ Preprocess the image to the required size and normalization. """
+    image = cv2.resize(image, (W, H))
+    image = (image - 127.5) / 127.5  # Normalize to [-1, +1]
+    image = np.expand_dims(image, axis=0).astype(np.float32)
+    return image
 
 
 
@@ -469,9 +469,9 @@ if uploaded_file is not None:
             st.write("Processing...")
             input_image = preprocess_image(image)
             # input_image = enhancement_type
-            input_image = cv2.resize(enhanced_image, (W, H))  # Resize the enhanced image to the required input size
-            input_image = (input_image - 127.5) / 127.5  # Normalize to [-1, +1]
-            input_image = np.expand_dims(input_image, axis=0).astype(np.float32)  # Expand dimensions and convert to float32
+            # input_image = cv2.resize(enhanced_image, (W, H))  # Resize the enhanced image to the required input size
+            # input_image = (input_image - 127.5) / 127.5  # Normalize to [-1, +1]
+            # input_image = np.expand_dims(input_image, axis=0).astype(np.float32)  # Expand dimensions and convert to float32
             
             pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image)