Update README.md

README.md

@@ -68,11 +68,17 @@ segmentation = img_proc.post_process_semantic_segmentation(
     output,
     target_sizes=[img.size[::-1]]
 )
+```
+
+Here is a simple method for detecting bounding boxes from semantic segmentation. This is the method used to calculate the model's performance in object
+detection, as described in the "Performance" section. The method is provided without any additional post-processing.
+
+```python
+import cv2
 
 def detect_bboxes(masks: np.ndarray):
     r"""
-    A simple bounding box detection function that was used to calculate
-    the performance metrics in the "Performance" section.
+    A simple bounding box detection function
     """
     detected_blocks = []
     contours, _ = cv2.findContours(
@@ -88,7 +94,7 @@ def detect_bboxes(masks: np.ndarray):
             detected_blocks.append(bounding_box)
     return detected_blocks
 
-pred_bbox = []
+bbox_pred = []
 for segment in segmentation:
     bboxes, labels = [], []
     for ii in range(1, len(model.config.label2id)):
@@ -97,7 +103,7 @@ for segment in segmentation:
             bbx, lab = detect_bboxes(mm.numpy())
             bboxes.extend(bbx)
             labels.extend([ii]*len(bbx))
-    pred_bbox.append(dict(bboxes=bboxes, labels=lables))
+    bbox_pred.append(dict(bboxes=bboxes, labels=lables))
 ```
 
 ### Citation