feat: florence2-ft video support

.github/workflows/ci_cd.yml

@@ -83,9 +83,6 @@ 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/integ/test_tools.py

@@ -11,7 +11,8 @@
  dpt_hybrid_midas,
  florence2_image_caption,
  florence2_ocr,
- florence2_phrase_grounding,
+ florence2_phrase_grounding_image,
+ # florence2_phrase_grounding_video,
  florence2_roberta_vqa,
  florence2_sam2_image,
  florence2_sam2_video_tracking,
@@ -31,6 +32,8 @@
  template_match,
  vit_image_classification,
  vit_nsfw_classification,
+ countgd_counting,
+ countgd_example_based_counting,
 )
 
 FINE_TUNE_ID = "65ebba4a-88b7-419f-9046-0750e30250da"
@@ -92,19 +95,19 @@ def test_owl_v2_video():
  assert 24 <= len([res["label"] for res in result[0]]) <= 26
 
 
-def test_florence2_phrase_grounding():
+def test_florence2_phrase_grounding_image():
  img = ski.data.coins()
- result = florence2_phrase_grounding(
+ result = florence2_phrase_grounding_image(
  image=img,
  prompt="coin",
  )
  assert len(result) == 25
  assert [res["label"] for res in result] == ["coin"] * 25
 
 
-def test_florence2_phrase_grounding_fine_tune_id():
+def test_florence2_phrase_grounding_image_fine_tune_id():
  img = ski.data.coins()
- result = florence2_phrase_grounding(
+ result = florence2_phrase_grounding_image(
  prompt="coin",
  image=img,
  fine_tune_id=FINE_TUNE_ID,
@@ -114,6 +117,32 @@ def test_florence2_phrase_grounding_fine_tune_id():
  assert [res["label"] for res in result] == ["coin"] * len(result)
 
 
+# def test_florence2_phrase_grounding_video():
+# frames = [
+# np.array(Image.fromarray(ski.data.coins()).convert("RGB")) for _ in range(10)
+# ]
+# result = florence2_phrase_grounding_video(
+# prompt="coin",
+# frames=frames,
+# )
+# assert len(result) == 10
+# assert 2 <= len([res["label"] for res in result[0]]) <= 26
+
+
+# def test_florence2_phrase_grounding_video_fine_tune_id():
+# frames = [
+# np.array(Image.fromarray(ski.data.coins()).convert("RGB")) for _ in range(10)
+# ]
+# # this calls a fine-tuned florence2 model which is going to be worse at this task
+# result = florence2_phrase_grounding_video(
+# prompt="coin",
+# frames=frames,
+# fine_tune_id=FINE_TUNE_ID,
+# )
+# assert len(result) == 10
+# assert 16 <= len([res["label"] for res in result[0]]) <= 26
+
+
 def test_template_match():
  img = ski.data.coins()
  result = template_match(
@@ -360,3 +389,18 @@ def test_generate_hed():
  )
 
  assert result.shape == img.shape
+
+
+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

tests/unit/test_meta_tools.py

@@ -33,41 +33,41 @@ def test_use_object_detection_fine_tuning_none():
 
 def test_use_object_detection_fine_tuning():
  artifacts = Artifacts("test")
- code = """florence2_phrase_grounding('one', image1)
+ code = """florence2_phrase_grounding_image('one', image1)
 owl_v2_image('two', image2)
 florence2_sam2_image('three', image3)"""
- expected_code = """florence2_phrase_grounding("one", image1, "123")
+ expected_code = """florence2_phrase_grounding_image("one", image1, "123")
 owl_v2_image("two", image2, "123")
 florence2_sam2_image("three", image3, "123")"""
  artifacts["code"] = code
 
  output = use_object_detection_fine_tuning(artifacts, "code", "123")
- assert 'florence2_phrase_grounding("one", image1, "123")' in output
+ assert 'florence2_phrase_grounding_image("one", image1, "123")' in output
  assert 'owl_v2_image("two", image2, "123")' in output
  assert 'florence2_sam2_image("three", image3, "123")' in output
  assert artifacts["code"] == expected_code
 
 
 def test_use_object_detection_fine_tuning_twice():
  artifacts = Artifacts("test")
- code = """florence2_phrase_grounding('one', image1)
+ code = """florence2_phrase_grounding_image('one', image1)
 owl_v2_image('two', image2)
 florence2_sam2_image('three', image3)"""
- expected_code1 = """florence2_phrase_grounding("one", image1, "123")
+ expected_code1 = """florence2_phrase_grounding_image("one", image1, "123")
 owl_v2_image("two", image2, "123")
 florence2_sam2_image("three", image3, "123")"""
- expected_code2 = """florence2_phrase_grounding("one", image1, "456")
+ expected_code2 = """florence2_phrase_grounding_image("one", image1, "456")
 owl_v2_image("two", image2, "456")
 florence2_sam2_image("three", image3, "456")"""
  artifacts["code"] = code
  output = use_object_detection_fine_tuning(artifacts, "code", "123")
- assert 'florence2_phrase_grounding("one", image1, "123")' in output
+ assert 'florence2_phrase_grounding_image("one", image1, "123")' in output
  assert 'owl_v2_image("two", image2, "123")' in output
  assert 'florence2_sam2_image("three", image3, "123")' in output
  assert artifacts["code"] == expected_code1
 
  output = use_object_detection_fine_tuning(artifacts, "code", "456")
- assert 'florence2_phrase_grounding("one", image1, "456")' in output
+ assert 'florence2_phrase_grounding_image("one", image1, "456")' in output
  assert 'owl_v2_image("two", image2, "456")' in output
  assert 'florence2_sam2_image("three", image3, "456")' in output
  assert artifacts["code"] == expected_code2

vision_agent/agent/vision_agent_coder_prompts.py

@@ -101,15 +101,15 @@
 - Use the 'owl_v2_video' tool with the prompt 'person' to detect where the people are in the video.
 plan2:
 - Extract frames from 'video.mp4' at 10 FPS using the 'extract_frames_and_timestamps' tool.
-- Use the 'florence2_phrase_grounding' tool with the prompt 'person' to detect where the people are in the video.
+- Use the 'florence2_phrase_grounding_image' tool with the prompt 'person' to detect where the people are in the video.
 plan3:
 - Extract frames from 'video.mp4' at 10 FPS using the 'extract_frames_and_timestamps' tool.
 - Use the 'florence2_sam2_video_tracking' tool with the prompt 'person' to detect where the people are in the video.
 
 
 ```python
 import numpy as np
-from vision_agent.tools import extract_frames_and_timestamps, owl_v2_video, florence2_phrase_grounding, florence2_sam2_video_tracking
+from vision_agent.tools import extract_frames_and_timestamps, owl_v2_video, florence2_phrase_grounding_image, florence2_sam2_video_tracking
 
 # sample at 1 FPS and use the first 10 frames to reduce processing time
 frames = extract_frames_and_timestamps("video.mp4", 1)
@@ -143,7 +143,7 @@ def get_counts(preds):
 owl_v2_counts = get_counts(owl_v2_out)
 
 # plan2
-florence2_out = [florence2_phrase_grounding("person", f) for f in frames]
+florence2_out = [florence2_phrase_grounding_image("person", f) for f in frames]
 florence2_counts = get_counts(florence2_out)
 
 # plan3
@@ -153,13 +153,13 @@ def get_counts(preds):
 
 final_out = {{
  "owl_v2_video": owl_v2_out,
- "florence2_phrase_grounding": florence2_out,
+ "florence2_phrase_grounding_image": florence2_out,
  "florence2_sam2_video_tracking": f2s2_out,
 }}
 
 counts = {{
  "owl_v2_video": owl_v2_counts,
- "florence2_phrase_grounding": florence2_counts,
+ "florence2_phrase_grounding_image": florence2_counts,
  "florence2_sam2_video_tracking": f2s2_counts,
 }}
 

vision_agent/agent/vision_agent_prompts.py

@@ -131,10 +131,10 @@
 
 OBSERVATION:
 [Artifact code.py]
-0|from vision_agent.tools import load_image, florence2_phrase_grounding, closest_box_distance, overlay_bounding_boxes, save_image
+0|from vision_agent.tools import load_image, florence2_phrase_grounding_image, closest_box_distance, overlay_bounding_boxes, save_image
 1|def count_workers_with_helmets(image_path: str, output_path: str):
 2| image = load_image(image_path)
-3| detections = florence2_phrase_grounding("worker, helmet", image)
+3| detections = florence2_phrase_grounding_image("worker, helmet", image)
 4| workers = [d for d in detections if d['label'] == 'worker']
 5| helmets = [d for d in detections if d['label'] == 'helmet']
 6| count = 0
@@ -166,18 +166,18 @@
 OBSERVATION:
 [Fine tuning id: 23b3b022-5ebf-4798-9373-20ef36429abf]
 
-AGENT: {"thoughts": "The model has finished fine tuning, I will now replace the original florence2_phrase_grounding call with the fine tuning id.", "response": "I will not update the code to use the fine tuned model. <execute_python>use_object_detection_fine_tuning(artifacts, "code.py", "23b3b022-5ebf-4798-9373-20ef36429abf")</execute_python>", "let_user_respond": false}
+AGENT: {"thoughts": "The model has finished fine tuning, I will now replace the original florence2_phrase_grounding_image call with the fine tuning id.", "response": "I will not update the code to use the fine tuned model. <execute_python>use_object_detection_fine_tuning(artifacts, "code.py", "23b3b022-5ebf-4798-9373-20ef36429abf")</execute_python>", "let_user_respond": false}
 
 OBSERVATION:
 [Artifact code.py edits]
 ---
 +++
 @@ -1,7 +1,7 @@
- from vision_agent.tools import load_image, florence2_phrase_grounding, closest_box_distance, overlay_bounding_boxes, save_image
+ from vision_agent.tools import load_image, florence2_phrase_grounding_image, closest_box_distance, overlay_bounding_boxes, save_image
  def count_workers_with_helmets(image_path: str, output_path: str):
  image = load_image(image_path)
-- detections = florence2_phrase_grounding("worker, helmet", image)
-+ detections = florence2_phrase_grounding("worker, helmet", image, "23b3b022-5ebf-4798-9373-20ef36429abf")
+- detections = florence2_phrase_grounding_image("worker, helmet", image)
++ detections = florence2_phrase_grounding_image("worker, helmet", image, "23b3b022-5ebf-4798-9373-20ef36429abf")
  workers = [d for d in detections if d['label'] == 'worker']
  helmets = [d for d in detections if d['label'] == 'helmet']
  count = 0
@@ -189,5 +189,5 @@ def count_workers_with_helmets(image_path: str, output_path: str):
 ----- stdout -----
 3
 
-AGENT: {"thoughts": "Three workers with helmets are detected, I will show this to the user and ask them if the result looks good.", "response": "I have updated the code using the fine tuned florence2_phrase_grounding model to count the workers wearing helmets in code.py and saved the visualization under 'workers_viz.png'.", "let_user_respond": true}
+AGENT: {"thoughts": "Three workers with helmets are detected, I will show this to the user and ask them if the result looks good.", "response": "I have updated the code using the fine tuned florence2_phrase_grounding_image model to count the workers wearing helmets in code.py and saved the visualization under 'workers_viz.png'.", "let_user_respond": true}
 """

vision_agent/tools/__init__.py

@@ -24,7 +24,7 @@
  extract_frames_and_timestamps,
  florence2_image_caption,
  florence2_ocr,
- florence2_phrase_grounding,
+ florence2_phrase_grounding_image,
  florence2_roberta_vqa,
  florence2_sam2_image,
  florence2_sam2_video_tracking,

vision_agent/tools/meta_tools.py

@@ -665,8 +665,12 @@ def use_object_detection_fine_tuning(
 
  patterns_with_fine_tune_id = [
  (
- r'florence2_phrase_grounding\(\s*["\']([^"\']+)["\']\s*,\s*([^,]+)(?:,\s*["\'][^"\']+["\'])?\s*\)',
- lambda match: f'florence2_phrase_grounding("{match.group(1)}", {match.group(2)}, "{fine_tune_id}")',
+ r'florence2_phrase_grounding_image\(\s*["\']([^"\']+)["\']\s*,\s*([^,]+)(?:,\s*["\'][^"\']+["\'])?\s*\)',
+ lambda match: f'florence2_phrase_grounding_image("{match.group(1)}", {match.group(2)}, "{fine_tune_id}")',
+ ),
+ (
+ r'florence2_phrase_grounding_video\(\s*["\']([^"\']+)["\']\s*,\s*([^,]+)(?:,\s*["\'][^"\']+["\'])?\s*\)',
+ lambda match: f'florence2_phrase_grounding_video("{match.group(1)}", {match.group(2)}, "{fine_tune_id}")',
  ),
  (
  r'owl_v2_image\(\s*["\']([^"\']+)["\']\s*,\s*([^,]+)(?:,\s*["\'][^"\']+["\'])?\s*\)',

vision_agent/tools/tool_utils.py

@@ -1,6 +1,6 @@
+import os
 import inspect
 import logging
-import os
 from base64 import b64encode
 from typing import Any, Callable, Dict, List, MutableMapping, Optional, Tuple
 
@@ -37,8 +37,9 @@ def send_inference_request(
  files: Optional[List[Tuple[Any, ...]]] = None,
  v2: bool = False,
  metadata_payload: Optional[Dict[str, Any]] = None,
+ is_form: bool = False,
 ) -> Any:
- # TODO: runtime_tag and function_name should be metadata_payload and now included
+ # TODO: runtime_tag and function_name should be metadata_payload and not included
  # in the service payload
  if runtime_tag := os.environ.get("RUNTIME_TAG", ""):
  payload["runtime_tag"] = runtime_tag
@@ -64,7 +65,7 @@ def send_inference_request(
  elif metadata_payload is not None and "function_name" in metadata_payload:
  function_name = metadata_payload["function_name"]
 
- response = _call_post(url, payload, session, files, function_name)
+ response = _call_post(url, payload, session, files, function_name, is_form)
 
  # TODO: consider making the response schema the same between below two sources
  return response if "TOOL_ENDPOINT_AUTH" in os.environ else response["data"]
@@ -75,6 +76,7 @@ def send_task_inference_request(
  task_name: str,
  files: Optional[List[Tuple[Any, ...]]] = None,
  metadata: Optional[Dict[str, Any]] = None,
+ is_form: bool = False,
 ) -> Any:
  url = f"{_LND_API_URL_v2}/{task_name}"
  headers = {"apikey": _LND_API_KEY}
@@ -87,7 +89,7 @@ def send_task_inference_request(
  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)
+ response = _call_post(url, payload, session, files, function_name, is_form)
  return response["data"]
 
 
@@ -203,13 +205,16 @@ def _call_post(
  session: Session,
  files: Optional[List[Tuple[Any, ...]]] = None,
  function_name: str = "unknown",
+ is_form: bool = False,
 ) -> Any:
  files_in_b64 = None
  if files:
  files_in_b64 = [(file[0], b64encode(file[1]).decode("utf-8")) for file in files]
  try:
  if files is not None:
  response = session.post(url, data=payload, files=files)
+ elif is_form:
+ response = session.post(url, data=payload)
  else:
  response = session.post(url, json=payload)