Add Count tools

vision_agent/tools/__init__.py

@@ -10,6 +10,8 @@
  GroundingDINO,
  GroundingSAM,
  ImageCaption,
+ ZeroShotCounting,
+ VisualPromptCounting,
  SegArea,
  SegIoU,
  Tool,

vision_agent/tools/tools.py

@@ -395,6 +395,127 @@ def __call__(
  return rets
 
 
+class ZeroShotCounting(Tool):
+ r"""ZeroShotCounting is a tool that can count total number of instances of an object
+ present in an image belonging to same class without a text or visual prompt.
+
+ Example
+ -------
+ >>> import vision_agent as va
+ >>> zshot_count = va.tools.ZeroShotCounting()
+ >>> zshot_count("image1.jpg")
+ {'count': 45}
+ """
+
+ name = "zero_shot_counting_"
+ description = """'zero_shot_counting_' is a tool that can count total number of instances of an object present in an image belonging to the same class without a text or visual prompt.
+ It returns the total count of the objects."""
+ usage = {
+ "required_parameters": [
+ {"name": "image", "type": "str"},
+ ],
+ "examples": [
+ {
+ "scenario": "Can you count the lids in the image ? Image name: lids.jpg",
+ "parameters": {"image": "lids.jpg"},
+ },
+ {
+ "scenario": "Can you count the total number of objects in this image ? Image name: tray.jpg",
+ "parameters": {"image": "tray.jpg"},
+ },
+ {
+ "scenario": "Can you build me an object counting tool ? Image name: shirts.jpg",
+ "parameters": {
+ "image": "shirts.jpg",
+ },
+ },
+ ],
+ }
+
+ # TODO: Add support for input multiple images, which aligns with the output type.
+ def __call__(self, image: Union[str, ImageType]) -> Dict:
+ """Invoke the Image captioning model.
+
+ Parameters:
+ image: the input image.
+
+ Returns:
+ A dictionary containing the key 'count' and the count as value. E.g. {count: 12}
+ """
+ image_b64 = convert_to_b64(image)
+ data = {
+ "image": image_b64,
+ "tool": "zero_shot_counting",
+ }
+ return _send_inference_request(data, "tools")
+
+
+class VisualPromptCounting(Tool):
+ r"""VisualPromptCounting is a tool that can count total number of instances of an object
+ present in an image belonging to same class with help of an visual prompt which is a bounding box.
+
+ Example
+ -------
+ >>> import vision_agent as va
+ >>> prompt_count = va.tools.VisualPromptCounting()
+ >>> prompt_count(image="image1.jpg", prompt="100, 100, 200, 250")
+ {'count': 23}
+ """
+
+ name = "visual_prompt_counting_"
+ description = """'visual_prompt_counting_' is a tool that can count total number of instances of an object present in an image belonging to the same class given an
+ example bounding box around a single instance. It returns the total count of the objects."""
+
+ usage = {
+ "required_parameters": [
+ {"name": "image", "type": "str"},
+ {"name": "prompt", "type": "str"},
+ ],
+ "examples": [
+ {
+ "scenario": "Here is an example of a lid '200, 200, 250, 300', Can you count the lids in the image ? Image name: lids.jpg",
+ "parameters": {"image": "lids.jpg", "prompt": "200, 200, 250, 300"},
+ },
+ {
+ "scenario": "Can you count the total number of objects in this image ? Image name: tray.jpg",
+ "parameters": {"image": "tray.jpg", "prompt": "100, 100, 200, 250"},
+ },
+ {
+ "scenario": "Can you build me a few shot object counting tool ? Image name: shirts.jpg",
+ "parameters": {
+ "image": "shirts.jpg",
+ "prompt": "100, 100, 200, 250",
+ },
+ },
+ {
+ "scenario": "Can you build me a counting tool based on an example prompt ? Image name: shoes.jpg",
+ "parameters": {
+ "image": "shoes.jpg",
+ "prompt": "150, 100, 500, 550",
+ },
+ },
+ ],
+ }
+
+ # TODO: Add support for input multiple images, which aligns with the output type.
+ def __call__(self, image: Union[str, ImageType], prompt: str) -> Dict:
+ """Invoke the Image captioning model.
+
+ Parameters:
+ image: the input image.
+
+ Returns:
+ A dictionary containing the key 'count' and the count as value. E.g. {count: 12}
+ """
+ image_b64 = convert_to_b64(image)
+ data = {
+ "image": image_b64,
+ "prompt": prompt,
+ "tool": "few_shot_counting",
+ }
+ return _send_inference_request(data, "tools")
+
+
 class Crop(Tool):
  r"""Crop crops an image given a bounding box and returns a file name of the cropped image."""
 
@@ -652,6 +773,8 @@ def __call__(self, equation: str) -> float:
  ImageCaption,
  GroundingDINO,
  AgentGroundingSAM,
+ ZeroShotCounting,
+ VisualPromptCounting,
  ExtractFrames,
  Crop,
  BboxArea,