fixed florence OD as phrase grounding

tests/integ/test_tools.py

@@ -10,7 +10,7 @@
  detr_segmentation,
  dpt_hybrid_midas,
  florence2_image_caption,
- florence2_object_detection,
+ florence2_phrase_grounding,
  florence2_ocr,
  florence2_roberta_vqa,
  florence2_sam2_image,
@@ -65,7 +65,7 @@ def test_owl():
 
 def test_object_detection():
  img = ski.data.coins()
- result = florence2_object_detection(
+ result = florence2_phrase_grounding(
  image=img,
  prompt="coin",
  )

vision_agent/tools/__init__.py

@@ -21,7 +21,7 @@
  dpt_hybrid_midas,
  extract_frames,
  florence2_image_caption,
- florence2_object_detection,
+ florence2_phrase_grounding,
  florence2_ocr,
  florence2_roberta_vqa,
  florence2_sam2_image,

vision_agent/tools/tools.py

@@ -760,10 +760,10 @@ def florence2_image_caption(image: np.ndarray, detail_caption: bool = True) -> s
  return answer[task] # type: ignore
 
 
-def florence2_object_detection(prompt: str, image: np.ndarray) -> List[Dict[str, Any]]:
- """'florencev2_object_detection' is a tool that can detect and count multiple
- objects given a text prompt such as category names or referring expressions. You
- can optionally separate the categories in the text with commas. It returns a list
+def florence2_phrase_grounding(prompt: str, image: np.ndarray) -> List[Dict[str, Any]]:
+ """'florence2_phrase_grounding' is a tool that can detect multiple
+ objects given a text prompt which can be object names or caption. You
+ can optionally separate the object names in the text with commas. It returns a list
  of bounding boxes with normalized coordinates, label names and associated
  probability scores of 1.0.
 
@@ -780,7 +780,7 @@ def florence2_object_detection(prompt: str, image: np.ndarray) -> List[Dict[str,
 
  Example
  -------
- >>> florence2_object_detection('person looking at a coyote', image)
+ >>> florence2_phrase_grounding('person looking at a coyote', image)
  [
  {'score': 1.0, 'label': 'person', 'bbox': [0.1, 0.11, 0.35, 0.4]},
  {'score': 1.0, 'label': 'coyote', 'bbox': [0.34, 0.21, 0.85, 0.5},
@@ -792,7 +792,7 @@ def florence2_object_detection(prompt: str, image: np.ndarray) -> List[Dict[str,
  "image": image_b64,
  "task": "<CAPTION_TO_PHRASE_GROUNDING>",
  "prompt": prompt,
- "function_name": "florence2_object_detection",
+ "function_name": "florence2_phrase_grounding",
  }
 
  detections = send_inference_request(data, "florence2", v2=True)
@@ -1663,7 +1663,7 @@ def florencev2_fine_tuned_object_detection(
  florence2_ocr,
  florence2_sam2_image,
  florence2_sam2_video,
- florence2_object_detection,
+ florence2_phrase_grounding,
  ixc25_image_vqa,
  ixc25_video_vqa,
  detr_segmentation,