feat: add count tool

.github/workflows/ci_cd.yml

@@ -1,10 +1,14 @@
 name: CI
+
 on:
  push:
  branches: [ main ]
  pull_request:
  branches: [ main ]
 
+env:
+ LANDINGAI_DEV_API_KEY: ${{ secrets.LANDINGAI_DEV_API_KEY }}
+
 jobs:
  unit_test:
  name: Test
@@ -79,6 +83,9 @@ jobs:
  - name: Test with pytest
  run: |
  poetry run pytest -v tests/integ
+ - name: Test with pytest, dev env
+ run: |
+ LANDINGAI_API_KEY=$LANDINGAI_DEV_API_KEY LANDINGAI_URL=https://api.dev.landing.ai poetry run pytest -v tests/integration_dev
 
  release:
  name: Release

tests/integration_dev/test_tools.py

@@ -0,0 +1,21 @@
+import skimage as ski
+
+from vision_agent.tools import (
+ countgd_counting,
+ countgd_example_based_counting,
+)
+
+
+def test_countgd_counting() -> None:
+ img = ski.data.coins()
+ result = countgd_counting(image=img, prompt="coin")
+ assert len(result) == 24
+
+
+def test_countgd_example_based_counting() -> None:
+ img = ski.data.coins()
+ result = countgd_example_based_counting(
+ visual_prompts=[[85, 106, 122, 145]],
+ image=img,
+ )
+ assert len(result) == 24

vision_agent/agent/vision_agent_coder_prompts.py

@@ -81,20 +81,19 @@
 - Count the number of detected objects labeled as 'person'.
 plan3:
 - Load the image from the provided file path 'image.jpg'.
-- Use the 'loca_zero_shot_counting' tool to count the dominant foreground object, which in this case is people.
+- Use the 'countgd_counting' tool to count the dominant foreground object, which in this case is people.
 
 ```python
-from vision_agent.tools import load_image, owl_v2, grounding_sam, loca_zero_shot_counting
+from vision_agent.tools import load_image, owl_v2, grounding_sam, countgd_counting
 image = load_image("image.jpg")
 owl_v2_out = owl_v2("person", image)
 
 gsam_out = grounding_sam("person", image)
 gsam_out = [{{k: v for k, v in o.items() if k != "mask"}} for o in gsam_out]
 
-loca_out = loca_zero_shot_counting(image)
-loca_out = loca_out["count"]
+cgd_out = countgd_counting(image)
 
-final_out = {{"owl_v2": owl_v2_out, "florencev2_object_detection": florencev2_out, "loca_zero_shot_counting": loca_out}}
+final_out = {{"owl_v2": owl_v2_out, "florencev2_object_detection": florencev2_out, "countgd_counting": cgd_out}}
 print(final_out)
 ```
 """

vision_agent/lmm/lmm.py

@@ -286,9 +286,6 @@ def generate_segmentor(self, question: str) -> Callable:
 
  return lambda x: T.grounding_sam(params["prompt"], x)
 
- def generate_zero_shot_counter(self, question: str) -> Callable:
- return T.loca_zero_shot_counting
-
  def generate_image_qa_tool(self, question: str) -> Callable:
  return lambda x: T.git_vqa_v2(question, x)
 

vision_agent/tools/__init__.py

@@ -37,10 +37,13 @@
  load_image,
  loca_visual_prompt_counting,
  loca_zero_shot_counting,
+ countgd_counting,
+ countgd_example_based_counting,
  ocr,
  overlay_bounding_boxes,
  overlay_heat_map,
  overlay_segmentation_masks,
+ overlay_counting_results,
  owl_v2,
  save_image,
  save_json,

vision_agent/tools/tool_utils.py

@@ -1,6 +1,6 @@
+import os
 import inspect
 import logging
-import os
 from typing import Any, Callable, Dict, List, MutableMapping, Optional, Tuple
 
 import pandas as pd
@@ -13,6 +13,7 @@
 from vision_agent.utils.exceptions import RemoteToolCallFailed
 from vision_agent.utils.execute import Error, MimeType
 from vision_agent.utils.type_defs import LandingaiAPIKey
+from vision_agent.tools.tools_types import BoundingBoxes
 
 _LOGGER = logging.getLogger(__name__)
 _LND_API_KEY = os.environ.get("LANDINGAI_API_KEY", LandingaiAPIKey().api_key)
@@ -34,61 +35,58 @@ def send_inference_request(
  files: Optional[List[Tuple[Any, ...]]] = None,
  v2: bool = False,
  metadata_payload: Optional[Dict[str, Any]] = None,
-) -> Dict[str, Any]:
+) -> Any:
  # TODO: runtime_tag and function_name should be metadata_payload and now included
  # in the service payload
- try:
- if runtime_tag := os.environ.get("RUNTIME_TAG", ""):
- payload["runtime_tag"] = runtime_tag
+ if runtime_tag := os.environ.get("RUNTIME_TAG", ""):
+ payload["runtime_tag"] = runtime_tag
+
+ url = f"{_LND_API_URL_v2 if v2 else _LND_API_URL}/{endpoint_name}"
+ if "TOOL_ENDPOINT_URL" in os.environ:
+ url = os.environ["TOOL_ENDPOINT_URL"]
+
+ headers = {"apikey": _LND_API_KEY}
+ if "TOOL_ENDPOINT_AUTH" in os.environ:
+ headers["Authorization"] = os.environ["TOOL_ENDPOINT_AUTH"]
+ headers.pop("apikey")
+
+ session = _create_requests_session(
+ url=url,
+ num_retry=3,
+ headers=headers,
+ )
 
- url = f"{_LND_API_URL_v2 if v2 else _LND_API_URL}/{endpoint_name}"
- if "TOOL_ENDPOINT_URL" in os.environ:
- url = os.environ["TOOL_ENDPOINT_URL"]
+ function_name = "unknown"
+ if "function_name" in payload:
+ function_name = payload["function_name"]
+ elif metadata_payload is not None and "function_name" in metadata_payload:
+ function_name = metadata_payload["function_name"]
 
- tool_call_trace = ToolCallTrace(
- endpoint_url=url,
- request=payload,
- response={},
- error=None,
- )
- headers = {"apikey": _LND_API_KEY}
- if "TOOL_ENDPOINT_AUTH" in os.environ:
- headers["Authorization"] = os.environ["TOOL_ENDPOINT_AUTH"]
- headers.pop("apikey")
-
- session = _create_requests_session(
- url=url,
- num_retry=3,
- headers=headers,
- )
+ response = _call_post(url, payload, session, files, function_name)
 
- if files is not None:
- res = session.post(url, data=payload, files=files)
- else:
- res = session.post(url, json=payload)
- if res.status_code != 200:
- tool_call_trace.error = Error(
- name="RemoteToolCallFailed",
- value=f"{res.status_code} - {res.text}",
- traceback_raw=[],
- )
- _LOGGER.error(f"Request failed: {res.status_code} {res.text}")
- # TODO: function_name should be in metadata_payload
- function_name = "unknown"
- if "function_name" in payload:
- function_name = payload["function_name"]
- elif metadata_payload is not None and "function_name" in metadata_payload:
- function_name = metadata_payload["function_name"]
- raise RemoteToolCallFailed(function_name, res.status_code, res.text)
-
- resp = res.json()
- tool_call_trace.response = resp
- # TODO: consider making the response schema the same between below two sources
- return resp if "TOOL_ENDPOINT_AUTH" in os.environ else resp["data"] # type: ignore
- finally:
- trace = tool_call_trace.model_dump()
- trace["type"] = "tool_call"
- display({MimeType.APPLICATION_JSON: trace}, raw=True)
+ # TODO: consider making the response schema the same between below two sources
+ return response if "TOOL_ENDPOINT_AUTH" in os.environ else response["data"]
+
+
+def send_task_inference_request(
+ payload: Dict[str, Any],
+ task_name: str,
+ files: Optional[List[Tuple[Any, ...]]] = None,
+ metadata: Optional[Dict[str, Any]] = None,
+) -> Any:
+ url = f"{_LND_API_URL_v2}/{task_name}"
+ headers = {"apikey": _LND_API_KEY}
+ session = _create_requests_session(
+ url=url,
+ num_retry=3,
+ headers=headers,
+ )
+
+ function_name = "unknown"
+ if metadata is not None and "function_name" in metadata:
+ function_name = metadata["function_name"]
+ response = _call_post(url, payload, session, files, function_name)
+ return response["data"]
 
 
 def _create_requests_session(
@@ -195,3 +193,49 @@ def get_tools_info(funcs: List[Callable[..., Any]]) -> Dict[str, str]:
  data[func.__name__] = f"{func.__name__}{inspect.signature(func)}:\n{desc}"
 
  return data
+
+
+def _call_post(
+ url: str,
+ payload: dict[str, Any],
+ session: Session,
+ files: Optional[List[Tuple[Any, ...]]] = None,
+ function_name: str = "unknown",
+) -> Any:
+ try:
+ tool_call_trace = ToolCallTrace(
+ endpoint_url=url,
+ request=payload,
+ response={},
+ error=None,
+ )
+
+ if files is not None:
+ response = session.post(url, data=payload, files=files)
+ else:
+ response = session.post(url, json=payload)
+
+ if response.status_code != 200:
+ tool_call_trace.error = Error(
+ name="RemoteToolCallFailed",
+ value=f"{response.status_code} - {response.text}",
+ traceback_raw=[],
+ )
+ _LOGGER.error(f"Request failed: {response.status_code} {response.text}")
+ raise RemoteToolCallFailed(
+ function_name, response.status_code, response.text
+ )
+
+ result = response.json()
+ tool_call_trace.response = result
+ return result
+ finally:
+ trace = tool_call_trace.model_dump()
+ trace["type"] = "tool_call"
+ display({MimeType.APPLICATION_JSON: trace}, raw=True)
+
+
+def filter_bboxes_by_threshold(
+ bboxes: BoundingBoxes, threshold: float
+) -> BoundingBoxes:
+ return list(filter(lambda bbox: bbox.score >= threshold, bboxes))