remove temporal localization

tests/integ/test_tools.py

@@ -24,7 +24,6 @@
  grounding_dino,
  grounding_sam,
  ixc25_image_vqa,
- ixc25_temporal_localization,
  ixc25_video_vqa,
  loca_visual_prompt_counting,
  loca_zero_shot_counting,
@@ -363,17 +362,6 @@ def test_ixc25_video_vqa():
  assert "cat" in result.strip()
 
 
-def test_ixc25_temporal_localization():
- frames = [
- np.array(Image.fromarray(ski.data.cat()).convert("RGB")) for _ in range(10)
- ]
- result = ixc25_temporal_localization(
- prompt="What animal is in this video?",
- frames=frames,
- )
- assert result == [True] * 10
-
-
 def test_ocr():
  img = ski.data.page()
  result = ocr(

vision_agent/tools/__init__.py

@@ -37,7 +37,6 @@
  grounding_dino,
  grounding_sam,
  ixc25_image_vqa,
- ixc25_temporal_localization,
  ixc25_video_vqa,
  load_image,
  loca_visual_prompt_counting,

vision_agent/tools/tools.py

@@ -882,47 +882,6 @@ def ixc25_video_vqa(prompt: str, frames: List[np.ndarray]) -> str:
  return cast(str, data["answer"])
 
 
-def ixc25_temporal_localization(prompt: str, frames: List[np.ndarray]) -> List[bool]:
- """'ixc25_temporal_localization' uses ixc25_video_vqa to temporally segment a video
- given a prompt that can be other an object or a phrase. It returns a list of
- boolean values indicating whether the object or phrase is present in the
- corresponding frame.
-
- Parameters:
- prompt (str): The question about the video
- frames (List[np.ndarray]): The reference frames used for the question
-
- Returns:
- List[bool]: A list of boolean values indicating whether the object or phrase is
- present in the corresponding frame.
-
- Example
- -------
- >>> output = ixc25_temporal_localization('soccer goal', frames)
- >>> print(output)
- [False, False, False, True, True, True, False, False, False, False]
- >>> save_video([f for i, f in enumerate(frames) if output[i]], 'output.mp4')
- """
-
- buffer_bytes = frames_to_bytes(frames)
- files = [("video", buffer_bytes)]
- payload = {
- "prompt": prompt,
- "chunk_length": 2,
- "function_name": "ixc25_temporal_localization",
- }
- data: List[int] = send_inference_request(
- payload,
- "video-temporal-localization?model=internlm-xcomposer",
- files=files,
- v2=True,
- )
- chunk_size = round(len(frames) / len(data))
- data_explode = [[elt] * chunk_size for elt in data]
- data_bool = [bool(elt) for sublist in data_explode for elt in sublist]
- return data_bool[: len(frames)]
-
-
 def gpt4o_image_vqa(prompt: str, image: np.ndarray) -> str:
  """'gpt4o_image_vqa' is a tool that can answer any questions about arbitrary images
  including regular images or images of documents or presentations. It returns text