adding reflect to be optional for cases where LMM might not be able t…

…o understand the image

README.md

@@ -122,7 +122,7 @@ you. For example:
 
 #### Custom Tools
 You can also add your own custom tools for your vision agent to use:
- 
+
 ```python
 from vision_agent.tools import Tool, register_tool
 @register_tool
@@ -160,6 +160,7 @@ find an example that creates a custom tool for template matching [here](examples
 | BboxIoU | BboxIoU returns the intersection over union of two bounding boxes normalized to 2 decimal places. |
 | SegIoU | SegIoU returns the intersection over union of two segmentation masks normalized to 2 decimal places. |
 | BoxDistance | BoxDistance returns the minimum distance between two bounding boxes normalized to 2 decimal places. |
+| MaskDistance | MaskDistance returns the minimum distance between two segmentation masks in pixel units |
 | BboxContains | BboxContains returns the intersection of two boxes over the target box area. It is good for check if one box is contained within another box. |
 | ExtractFrames | ExtractFrames extracts frames with motion from a video. |
 | ZeroShotCounting | ZeroShotCounting returns the total number of objects belonging to a single class in a given image. |

vision_agent/agent/vision_agent.py

@@ -489,6 +489,7 @@ def __call__(
  image: Optional[Union[str, Path]] = None,
  reference_data: Optional[Dict[str, str]] = None,
  visualize_output: Optional[bool] = False,
+ reflect_output: Optional[bool] = True,
  ) -> str:
  """Invoke the vision agent.
 
@@ -538,6 +539,7 @@ def chat_with_workflow(
  image: Optional[Union[str, Path]] = None,
  reference_data: Optional[Dict[str, str]] = None,
  visualize_output: Optional[bool] = False,
+ reflect_output: Optional[bool] = True,
  ) -> Tuple[str, List[Dict]]:
  """Chat with the vision agent and return the final answer and all tool results.
 
@@ -625,20 +627,25 @@ def chat_with_workflow(
  reflection_images = [image]
  else:
  reflection_images = None
- reflection = self_reflect(
- self.reflect_model,
- question,
- self.tools,
- all_tool_results,
- final_answer,
- reflection_images,
- )
- self.log_progress(f"Reflection: {reflection}")
- parsed_reflection = parse_reflect(reflection)
- if parsed_reflection["Finish"]:
- break
+
+ if reflect_output:
+ reflection = self_reflect(
+ self.reflect_model,
+ question,
+ self.tools,
+ all_tool_results,
+ final_answer,
+ reflection_images,
+ )
+ self.log_progress(f"Reflection: {reflection}")
+ parsed_reflection = parse_reflect(reflection)
+ if parsed_reflection["Finish"]:
+ break
+ else:
+ reflections += "\n" + parsed_reflection["Reflection"]
  else:
- reflections += "\n" + parsed_reflection["Reflection"]
+ self.log_progress("Reflection skipped based on user request.")
+ break
  # '<ANSWER>' is a symbol to indicate the end of the chat, which is useful for streaming logs.
  self.log_progress(
  f"The Vision Agent has concluded this chat. <ANSWER>{final_answer}</ANSWER>"
@@ -660,12 +667,14 @@ def chat(
  image: Optional[Union[str, Path]] = None,
  reference_data: Optional[Dict[str, str]] = None,
  visualize_output: Optional[bool] = False,
+ reflect_output: Optional[bool] = True,
  ) -> str:
  answer, _ = self.chat_with_workflow(
  chat,
  image=image,
  visualize_output=visualize_output,
  reference_data=reference_data,
+ reflect_output=reflect_output,
  )
  return answer