fixing test cases for grounding tools as the output format changed

tests/test_tools.py

@@ -18,9 +18,8 @@ def test_grounding_dino():
  prompt="coin",
  image=img,
  )
- assert result["labels"] == ["coin"] * 24
- assert len(result["bboxes"]) == 24
- assert len(result["scores"]) == 24
+ assert len(result) == 24
+ assert [res["label"] for res in result] == ["coin"] * 24
 
 
 def test_grounding_sam():
@@ -29,10 +28,9 @@ def test_grounding_sam():
  prompt="coin",
  image=img,
  )
- assert result["labels"] == ["coin"] * 24
- assert len(result["bboxes"]) == 24
- assert len(result["scores"]) == 24
- assert len(result["masks"]) == 24
+ assert len(result) == 24
+ assert [res["label"] for res in result] == ["coin"] * 24
+ assert len([res["mask"] for res in result]) == 24
 
 
 def test_clip():

vision_agent/tools/tools_v2.py

@@ -68,12 +68,13 @@ def grounding_dino(
  box_threshold (float, optional): The threshold for the box detection. Defaults
  to 0.20.
  iou_threshold (float, optional): The threshold for the Intersection over Union
- (IoU). Defaults to 0.75.
+ (IoU). Defaults to 0.20.
 
  Returns:
  List[Dict[str, Any]]: A list of dictionaries containing the score, label, and
  bounding box of the detected objects with normalized coordinates
- (x1, y1, x2, y2).
+ (xmin, ymin, xmax, ymax). xmin and ymin are the coordinates of the top-left and
+ xmax and ymax are the coordinates of the bottom-right of the bounding box.
 
  Example
  -------
@@ -120,12 +121,15 @@ def grounding_sam(
  box_threshold (float, optional): The threshold for the box detection. Defaults
  to 0.20.
  iou_threshold (float, optional): The threshold for the Intersection over Union
- (IoU). Defaults to 0.75.
+ (IoU). Defaults to 0.20.
 
  Returns:
  List[Dict[str, Any]]: A list of dictionaries containing the score, label,
  bounding box, and mask of the detected objects with normalized coordinates
- (x1, y1, x2, y2).
+ (xmin, ymin, xmax, ymax). xmin and ymin are the coordinates of the top-left and
+ xmax and ymax are the coordinates of the bottom-right of the bounding box.
+ The mask is binary 2D numpy array where 1 indicates the object and 0 indicates
+ the background.
 
  Example
  -------