Update app.py

app.py

@@ -59,7 +59,7 @@ def predict_image(input_image, name, age, medical_record, sex):
         class_index = highest_confidence_result.cls.item()
         if class_index == 0:
             label = "Immature"
-            color = (0, 255, 255)  # Yellow for Immature
+            color = (0, 0, 255)  # Blue for Immature
         elif class_index == 1:
             label = "Mature"
             color = (255, 0, 0)  # Red for Mature
@@ -69,9 +69,6 @@ def predict_image(input_image, name, age, medical_record, sex):
 
         confidence = highest_confidence_result.conf.item()
         xmin, ymin, xmax, ymax = map(int, highest_confidence_result.xyxy[0])
-        
-        # Draw the bounding box
-        cv2.rectangle(image_with_boxes, (xmin, ymin), (xmax, ymax), color, 2)
 
         # Calculate the average of box width and height
         box_width = xmax - xmin
@@ -85,9 +82,9 @@ def predict_image(input_image, name, age, medical_record, sex):
         center_x = int((xmin + xmax) / 2)
         center_y = int((ymin + ymax) / 2)
         
-        # Draw the white circle at the center of the bounding box
-        cv2.circle(image_with_boxes, (center_x, center_y), radius, (255, 255, 255), 2)
-        
+        # Draw the circle at the center of the bounding box with the color corresponding to the label
+        cv2.circle(image_with_boxes, (center_x, center_y), radius, color, 2)
+
         # Enlarge font scale and thickness
         font_scale = 1.0
         thickness = 2
@@ -99,7 +96,7 @@ def predict_image(input_image, name, age, medical_record, sex):
         # Put the label text with black background
         cv2.putText(image_with_boxes, f'{label} {confidence:.2f}', (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness)
         
-        raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]")
+        raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Circle Center: [{center_x}, {center_y}], Radius: {radius}")
 
     raw_predictions_str = "\n".join(raw_predictions)
     
@@ -114,7 +111,7 @@ def predict_image(input_image, name, age, medical_record, sex):
     input_image.save(uploaded_folder / image_name)
     pil_image_with_boxes.save(predicted_folder / image_name)
     
-    # Convert the predicted image to base64 for embedding in HTML
+    # Convert the predicted image to base64 for embedding in the XLSX file
     buffered = BytesIO()
     pil_image_with_boxes.save(buffered, format="PNG")
     predicted_image_base64 = base64.b64encode(buffered.getvalue()).decode()