Adding more tools (#84)

* added different verbosity levels, better json parsing

* fix typing error

* log retrieved functions

* add distance functions

* fix types

* fix types

* fix formatting

vision_agent/agent/vision_agent_v2.py

@@ -235,9 +235,11 @@ def run_plan(
  f"""
 {tabulate(tabular_data=[task], headers="keys", tablefmt="mixed_grid", maxcolwidths=_MAX_TABULATE_COL_WIDTH)}"""
  )
- tool_info = "\n".join(
- [e["doc"] for e in tool_recommender.top_k(task["instruction"])]
- )
+ tools = tool_recommender.top_k(task["instruction"])
+ tool_info = "\n".join([e["doc"] for e in tools])
+
+ if verbosity == 2:
+ _LOGGER.info(f"Tools retrieved: {[e['desc'] for e in tools]}")
 
  if long_term_memory is not None:
  retrieved_ltm = "\n".join(

vision_agent/tools/__init__.py

@@ -3,8 +3,8 @@
  CLIP,
  OCR,
  TOOLS,
- BboxStats,
  BboxIoU,
+ BboxStats,
  BoxDistance,
  Crop,
  DINOv,

vision_agent/tools/tools_v2.py

@@ -4,12 +4,13 @@
 import tempfile
 from importlib import resources
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Tuple, Union
+from typing import Any, Callable, Dict, List, Tuple, Union, cast
 
 import numpy as np
 import pandas as pd
 import requests
 from PIL import Image, ImageDraw, ImageFont
+from scipy.spatial import distance # type: ignore
 
 from vision_agent.tools.tool_utils import _send_inference_request
 from vision_agent.utils import extract_frames_from_video
@@ -233,6 +234,54 @@ def ocr(image: np.ndarray) -> List[Dict[str, Any]]:
  return output
 
 
+def closest_mask_distance(mask1: np.ndarray, mask2: np.ndarray) -> float:
+ """'closest_mask_distance' calculates the closest distance between two masks.
+
+ Parameters:
+ mask1 (np.ndarray): The first mask.
+ mask2 (np.ndarray): The second mask.
+
+ Returns:
+ float: The closest distance between the two masks.
+
+ Example
+ -------
+ >>> closest_mask_distance(mask1, mask2)
+ 0.5
+ """
+
+ mask1 = np.clip(mask1, 0, 1)
+ mask2 = np.clip(mask2, 0, 1)
+ mask1_points = np.transpose(np.nonzero(mask1))
+ mask2_points = np.transpose(np.nonzero(mask2))
+ dist_matrix = distance.cdist(mask1_points, mask2_points, "euclidean")
+ return cast(float, np.min(dist_matrix))
+
+
+def closest_box_distance(box1: List[float], box2: List[float]) -> float:
+ """'closest_box_distance' calculates the closest distance between two bounding boxes.
+
+ Parameters:
+ box1 (List[float]): The first bounding box.
+ box2 (List[float]): The second bounding box.
+
+ Returns:
+ float: The closest distance between the two bounding boxes.
+
+ Example
+ -------
+ >>> closest_box_distance([100, 100, 200, 200], [300, 300, 400, 400])
+ 141.42
+ """
+
+ x11, y11, x12, y12 = box1
+ x21, y21, x22, y22 = box2
+
+ horizontal_distance = np.max([0, x21 - x12, x11 - x22])
+ vertical_distance = np.max([0, y21 - y12, y11 - y22])
+ return cast(float, np.sqrt(horizontal_distance**2 + vertical_distance**2))
+
+
 # Utility and visualization functions
 
 
@@ -429,6 +478,8 @@ def get_tools_df(funcs: List[Callable[..., Any]]) -> pd.DataFrame:
  grounding_sam,
  extract_frames,
  ocr,
+ closest_mask_distance,
+ closest_box_distance,
  load_image,
  save_image,
  overlay_bounding_boxes,