add spacing

src/super_gradients/training/utils/visualization/classification.py

@@ -16,13 +16,16 @@ def draw_label(image: np.ndarray, label: str, confidence: float) -> np.ndarray:
     fontScale = 0.8
     thickness = 1
 
+    # Define additional spacing between the two lines
+    line_spacing = 5
+
     # Determine the size of the label and confidence text
     label_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, fontScale, thickness)[0]
     confidence_size = cv2.getTextSize(confidence_str, cv2.FONT_HERSHEY_SIMPLEX, fontScale, thickness)[0]
 
     # Determine the size of the bounding rectangle
     text_width = max(label_size[0], confidence_size[0])
-    text_height = label_size[1] + confidence_size[1] + thickness * 3
+    text_height = label_size[1] + confidence_size[1] + thickness * 3 + line_spacing
 
     # Calculate the position to draw the label, centered horizontally and at the top
     start_x = (image.shape[1] - text_width) // 2
@@ -38,7 +41,7 @@ def draw_label(image: np.ndarray, label: str, confidence: float) -> np.ndarray:
     alpha = 0.6
     cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
 
-    # Center the label and confidence text within the bounding rectangle
+    # Center the label and confidence text within the bounding rectangle, with additional spacing
     text_color = (0, 0, 0)  # Black
     cv2.putText(
         image,
@@ -53,7 +56,7 @@ def draw_label(image: np.ndarray, label: str, confidence: float) -> np.ndarray:
     cv2.putText(
         image,
         confidence_str,
-        (start_x + (text_width - confidence_size[0]) // 2, start_y + label_size[1] + confidence_size[1] + thickness),
+        (start_x + (text_width - confidence_size[0]) // 2, start_y + label_size[1] + confidence_size[1] + thickness + line_spacing),
         cv2.FONT_HERSHEY_SIMPLEX,
         fontScale,
         text_color,