diff --git a/docs/florence2-sam2.md b/docs/florence2-sam2.md new file mode 100644 index 00000000..c3ce9be8 --- /dev/null +++ b/docs/florence2-sam2.md @@ -0,0 +1,49 @@ +# Florence2Sam2 + +This tool uses FlorenceV2 and the SAM-2 model to do text to instance segmentation on image or video inputs. + +```python +from vision_agent_tools.tools.florence2_sam2 import Florence2SAM2 +from decord import VideoReader +from decord import cpu + + +# Path to your video +video_path = "path/to/your/video.mp4" + +# Load the video +vr = VideoReader(video_path, ctx=cpu(0)) + +# Subsample frames +frame_idxs = range(0, len(vr) - 1, 20) +frames = vr.get_batch(frame_idxs).asnumpy() + +# Create the Florence2SAM2 instance +florence2_sam2 = Florence2SAM2() + +# segment all the instances of the prompt "ball" for all video frames +results = florence2_sam2(video=frames, prompts=["ball"]) + +# Returns a dictionary where the first key is the frame index then an annotation +# ID, then an object with the mask, label and possibly bbox (for images) for each +# annotation ID. For example: +# { +# 0: +# { +# 0: ImageBboxMaskLabel({"mask": np.ndarray, "label": "car"}), +# 1: ImageBboxMaskLabel({"mask", np.ndarray, "label": "person"}) +# }, +# 1: ... +# } + +print("Instance segmentation complete!") + +``` + +You can also run similarity against an image and get additionally bounding boxes doing the following: + +```python +results = florence2_sam2(image=image, prompts=["ball"]) +``` + +::: vision_agent_tools.tools.florence2_sam2 diff --git a/poetry.lock b/poetry.lock index faede16d..c81e292f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -55,28 +55,26 @@ files = [ [[package]] name = "altair" -version = "5.3.0" +version = "5.4.0" description = "Vega-Altair: A declarative statistical visualization library for Python." optional = true python-versions = ">=3.8" files = [ - {file = "altair-5.3.0-py3-none-any.whl", hash = "sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a"}, - {file = "altair-5.3.0.tar.gz", hash = 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["qreader", "transformers", "scipy", "loca", "depth-anything-v2", "timm", "einops", "controlnet-aux", "lmdeploy", "decord"] +all = ["qreader", "transformers", "scipy", "loca", "depth-anything-v2", "timm", "einops", "controlnet-aux", "lmdeploy", "decord", "sam-2"] qr-reader = ["qreader"] owlv2 = ["transformers", "scipy"] florencev2 = ["transformers", "scipy", "timm", "einops"] @@ -53,6 +54,7 @@ controlnet-aux = ["controlnet-aux"] florencev2-qa = ["transformers", "scipy", "timm", "einops"] clip-media-sim = ["transformers"] ixc-25 = ["transformers", "lmdeploy", "decord"] +florence2-sam2 = ["transformers", "scipy", "timm", "einops", "sam-2"] [build-system] diff --git a/tests/tools/test_florence2_sam2.py b/tests/tools/test_florence2_sam2.py new file mode 100644 index 00000000..ae11c736 --- /dev/null +++ b/tests/tools/test_florence2_sam2.py @@ -0,0 +1,83 @@ +import numpy as np +import pytest +from PIL import Image + +from vision_agent_tools.tools.florence2_sam2 import Florence2SAM2 + + +def test_successful_florence2_sam2_image(): + """ + This test verifies that Florence2SAM2 returns a valid iresponse when passed an image + """ + test_image = Image.open("tests/tools/data/loca/tomatoes.jpg").convert("RGB") + + florence2_sam2 = Florence2SAM2() + + results = florence2_sam2(image=test_image, prompts=["tomato"]) + + # The disctionary should have only one key: 0 + assert len(results) == 1 + # The dictionary should have 23 instances of the tomato class + assert len(results[0]) == 23 + for instance in results[0].values(): + assert len(instance.bounding_box) == 4 + assert np.all( + [ + 0 <= coord <= np.max(test_image.size[:2]) + for coord in instance.bounding_box + ] + ) + assert isinstance(instance.mask, np.ndarray) + assert instance.mask.shape == test_image.size[::-1] + assert instance.label == "tomato" + + +def test_successful_florence2_sam2_video(): + """ + This test verifies that Florence2SAM2 returns a valid iresponse when passed a video + """ + tomatoes_image = np.array( + Image.open("tests/tools/data/loca/tomatoes.jpg").convert("RGB"), dtype=np.uint8 + ) + test_video = np.array( + [tomatoes_image, np.zeros(tomatoes_image.shape, dtype=np.uint8)] + ) + + florence2_sam2 = Florence2SAM2() + + results = florence2_sam2(video=test_video, prompts=["tomato"]) + + # The disctionary should have 2 keys for the two frames in the video + assert len(results) == 2 + # The first frame should have 23 instances of the tomato class + assert len(results[0]) == 23 + assert len(results[1]) == 23 + # First frame + for instance in results[0].values(): + assert isinstance(instance.mask, np.ndarray) + assert instance.mask.shape == tomatoes_image.shape[:2] + assert instance.label == "tomato" + + # Second frame + for instance in results[1].values(): + assert isinstance(instance.mask, np.ndarray) + assert instance.mask.shape == tomatoes_image.shape[:2] + assert instance.label == "tomato" + # All masks should de empty since it's a black frame + assert np.all(instance.mask == 0) + + +def test_florence2_sam2_invalid_media(): + """ + This test verifies that Florence2SAM2 raises an error if the media is not a valid. + """ + florence2_sam2 = Florence2SAM2() + + with pytest.raises(ValueError): + florence2_sam2(image="invalid media", prompts=["tomato"]) + + with pytest.raises(ValueError): + florence2_sam2(video="invalid media", prompts=["tomato"]) + + with pytest.raises(AssertionError): + florence2_sam2(video=np.array([1, 2, 3]), prompts=["tomato"]) diff --git a/vision_agent_tools/helpers/roberta_qa.py b/vision_agent_tools/helpers/roberta_qa.py index 32045ff8..9a91bb63 100644 --- a/vision_agent_tools/helpers/roberta_qa.py +++ b/vision_agent_tools/helpers/roberta_qa.py @@ -2,7 +2,7 @@ from transformers import pipeline from pydantic import BaseModel -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool MODEL_NAME = "deepset/roberta-base-squad2" PROCESSOR_NAME = "deepset/roberta-base-squad2" diff --git a/vision_agent_tools/tools/shared_types.py b/vision_agent_tools/shared_types.py similarity index 57% rename from vision_agent_tools/tools/shared_types.py rename to vision_agent_tools/shared_types.py index 1f6e7328..6daebfb6 100644 --- a/vision_agent_tools/tools/shared_types.py +++ b/vision_agent_tools/shared_types.py @@ -1,10 +1,21 @@ +from typing import Annotated, Literal, TypeVar + from pydantic import BaseModel +import numpy as np +import numpy.typing as npt class BaseTool: pass +DType = TypeVar("DType", bound=np.generic) + +VideoNumpy = Annotated[npt.NDArray[DType], Literal["N", "N", "N", 3]] + +SegmentationBitMask = Annotated[npt.NDArray[np.bool_], Literal["N", "N"]] + + class Point(BaseModel): # X coordinate of the point x: float diff --git a/vision_agent_tools/tools/clip_media_sim.py b/vision_agent_tools/tools/clip_media_sim.py index 5b3fad83..447191df 100644 --- a/vision_agent_tools/tools/clip_media_sim.py +++ b/vision_agent_tools/tools/clip_media_sim.py @@ -8,8 +8,7 @@ from pydantic import validate_call from transformers import CLIPModel, CLIPProcessor -from vision_agent_tools.tools.shared_types import BaseTool -from vision_agent_tools.types import VideoNumpy +from vision_agent_tools.shared_types import BaseTool, VideoNumpy _HF_MODEL = "openai/clip-vit-large-patch14" diff --git a/vision_agent_tools/tools/depth_anything_v2.py b/vision_agent_tools/tools/depth_anything_v2.py index 0a7ccf8f..21222aa2 100644 --- a/vision_agent_tools/tools/depth_anything_v2.py +++ b/vision_agent_tools/tools/depth_anything_v2.py @@ -3,7 +3,6 @@ # Run this line before loading torch os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" -import cv2 import numpy as np import os.path as osp import torch @@ -11,7 +10,7 @@ from PIL import Image from .utils import download, CHECKPOINT_DIR from typing import Union, Any -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool from depth_anything_v2.dpt import DepthAnythingV2 as DepthAnythingV2Model from pydantic import BaseModel diff --git a/vision_agent_tools/tools/florence2_sam2.py b/vision_agent_tools/tools/florence2_sam2.py new file mode 100644 index 00000000..57a58ba3 --- /dev/null +++ b/vision_agent_tools/tools/florence2_sam2.py @@ -0,0 +1,170 @@ +from dataclasses import dataclass +from typing_extensions import Annotated + +import torch +import numpy as np +from PIL import Image +from pydantic import validate_call + +from vision_agent_tools.shared_types import BaseTool, VideoNumpy, SegmentationBitMask +from vision_agent_tools.tools.florencev2 import Florencev2, PromptTask + +from sam2.sam2_video_predictor import SAM2VideoPredictor +from sam2.sam2_image_predictor import SAM2ImagePredictor + + +_HF_MODEL = "facebook/sam2-hiera-large" + + +@dataclass +class ImageBboxAndMaskLabel: + label: str + bounding_box: list[ + Annotated[float, "x_min"], + Annotated[float, "y_min"], + Annotated[float, "x_max"], + Annotated[float, "y_max"], + ] + mask: SegmentationBitMask | None + + +@dataclass +class MaskLabel: + label: str + mask: SegmentationBitMask + + +class Florence2SAM2(BaseTool): + """ + A class that receives a video or an image plus a list of text prompts and + returns the instance segmentation for the text prompts in each frame. + """ + + def __init__(self, device: str | None = None): + """ + Initializes the Florence2SAM2 object with a pre-trained Florencev2 model + and a SAM2 model. + """ + self.device = ( + device + if device in ["cuda", "mps", "cpu"] + else "cuda" + if torch.cuda.is_available() + else "mps" + if torch.backends.mps.is_available() + else "cpu" + ) + self.florence2 = Florencev2() + self.video_predictor = SAM2VideoPredictor.from_pretrained(_HF_MODEL) + self.image_predictor = SAM2ImagePredictor(self.video_predictor) + + @torch.inference_mode() + def get_bbox_and_mask( + self, image: Image.Image, prompts: list[str], return_mask: bool = True + ) -> dict[int, ImageBboxAndMaskLabel]: + objs = {} + self.image_predictor.set_image(np.array(image, dtype=np.uint8)) + annotation_id = 0 + for prompt in prompts: + with torch.autocast(device_type=self.device, dtype=torch.float16): + bboxes = self.florence2( + image, PromptTask.CAPTION_TO_PHRASE_GROUNDING, prompt + )[PromptTask.CAPTION_TO_PHRASE_GROUNDING]["bboxes"] + if return_mask: + with torch.autocast(device_type=self.device, dtype=torch.bfloat16): + masks, _, _ = self.image_predictor.predict( + point_coords=None, + point_labels=None, + box=bboxes, + multimask_output=False, + ) + for i in range(len(bboxes)): + objs[annotation_id] = ImageBboxAndMaskLabel( + bounding_box=bboxes[i], + mask=( + masks[i, 0, :, :] if len(masks.shape) == 4 else masks[i, :, :] + ) + if return_mask + else None, + label=prompt, + ) + annotation_id += 1 + return objs + + @torch.inference_mode() + def handle_image( + self, image: Image.Image, prompts: list[str] + ) -> dict[int, dict[int, ImageBboxAndMaskLabel]]: + self.image_predictor.reset_predictor() + objs = self.get_bbox_and_mask(image.convert("RGB"), prompts) + return {0: objs} + + @torch.inference_mode() + def handle_video( + self, video: VideoNumpy, prompts: list[str] + ) -> dict[int, dict[int, MaskLabel]]: + self.image_predictor.reset_predictor() + objs = self.get_bbox_and_mask( + Image.fromarray(video[0]).convert("RGB"), prompts, return_mask=False + ) + with torch.autocast(device_type=self.device, dtype=torch.bfloat16): + inference_state = self.video_predictor.init_state(video=video) + for annotation_id in objs: + _, _, out_mask_logits = self.video_predictor.add_new_points_or_box( + inference_state=inference_state, + frame_idx=0, + obj_id=annotation_id, + box=objs[annotation_id].bounding_box, + ) + + annotation_id_to_label = {} + for annotation_id in objs: + annotation_id_to_label[annotation_id] = objs[annotation_id].label + + video_segments = {} + for ( + out_frame_idx, + out_obj_ids, + out_mask_logits, + ) in self.video_predictor.propagate_in_video(inference_state): + video_segments[out_frame_idx] = { + out_obj_id: MaskLabel( + mask=(out_mask_logits[i][0] > 0.0).cpu().numpy(), + label=annotation_id_to_label[out_obj_id], + ) + for i, out_obj_id in enumerate(out_obj_ids) + } + self.video_predictor.reset_state(inference_state) + return video_segments + + @validate_call(config={"arbitrary_types_allowed": True}) + @torch.inference_mode() + def __call__( + self, + prompts: list[str], + image: Image.Image | None = None, + video: VideoNumpy | None = None, + ) -> dict[int, dict[int, ImageBboxAndMaskLabel | MaskLabel]]: + """Returns a dictionary where the first key is the frame index then an annotation + ID, then an object with the mask, label and possibly bbox (for images) for each + annotation ID. For example: + { + 0: + { + 0: ImageBboxMaskLabel({"mask": np.ndarray, "label": "car"}), + 1: ImageBboxMaskLabel({"mask", np.ndarray, "label": "person"}) + }, + 1: ... + } + """ + if image is None and video is None: + raise ValueError("Either 'image' or 'video' must be provided.") + if image is not None and video is not None: + raise ValueError("Only one of 'image' or 'video' can be provided.") + + if image is not None: + return self.handle_image(image, prompts) + elif video is not None: + assert video.ndim == 4, "Video should have 4 dimensions" + return self.handle_video(video, prompts) + # No need to raise an error here, the validatie_call decorator will take care of it diff --git a/vision_agent_tools/tools/florencev2.py b/vision_agent_tools/tools/florencev2.py index 3bb826ea..d2cd2002 100644 --- a/vision_agent_tools/tools/florencev2.py +++ b/vision_agent_tools/tools/florencev2.py @@ -4,7 +4,7 @@ from enum import Enum from PIL import Image from transformers import AutoModelForCausalLM, AutoProcessor -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool MODEL_NAME = "microsoft/Florence-2-large" PROCESSOR_NAME = "microsoft/Florence-2-large" diff --git a/vision_agent_tools/tools/florencev2_qa.py b/vision_agent_tools/tools/florencev2_qa.py index 1a1204f0..5df78beb 100644 --- a/vision_agent_tools/tools/florencev2_qa.py +++ b/vision_agent_tools/tools/florencev2_qa.py @@ -1,10 +1,9 @@ from PIL import Image -from typing import Dict import torch from vision_agent_tools.tools.florencev2 import Florencev2, PromptTask from vision_agent_tools.helpers.roberta_qa import RobertaQA -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool class FlorenceQA(BaseTool): diff --git a/vision_agent_tools/tools/internlm_xcomposer2.py b/vision_agent_tools/tools/internlm_xcomposer2.py index 74923d90..1e90cb13 100644 --- a/vision_agent_tools/tools/internlm_xcomposer2.py +++ b/vision_agent_tools/tools/internlm_xcomposer2.py @@ -1,9 +1,8 @@ import torch from PIL import Image -from vision_agent_tools.types import VideoNumpy -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool, VideoNumpy from pydantic import Field, validate_call -from typing import Annotated, Optional +from typing import Annotated from lmdeploy import GenerationConfig, TurbomindEngineConfig, pipeline from transformers.dynamic_module_utils import get_class_from_dynamic_module @@ -74,18 +73,18 @@ def __init__(self) -> None: def __call__( self, prompt: str, - image: Optional[Image.Image] = None, - video: Optional[VideoNumpy] = None, - frames: Optional[Frames] = MAX_NUMBER_OF_FRAMES, + image: Image.Image | None = None, + video: VideoNumpy | None = None, + frames: Frames = MAX_NUMBER_OF_FRAMES, ) -> str: """ InternLMXComposer2 model answers questions about a video or image. Args: prompt (str): The prompt with the question to be answered. - image (Optional[Image.Image]): The image to be analyzed. - video (Optional[VideoNumpy]): A numpy array containing the different images, representing the video. - frames (Optional[int]): The number of frames to be used from the video. + image (Image.Image | None): The image to be analyzed. + video (VideoNumpy | None): A numpy array containing the different images, representing the video. + frames (int): The number of frames to be used from the video. Returns: str: The answer to the prompt. diff --git a/vision_agent_tools/tools/nsfw_classification.py b/vision_agent_tools/tools/nsfw_classification.py index dca7ca2b..9203072c 100644 --- a/vision_agent_tools/tools/nsfw_classification.py +++ b/vision_agent_tools/tools/nsfw_classification.py @@ -3,7 +3,7 @@ from PIL import Image from pydantic import BaseModel from transformers import AutoModelForImageClassification, ViTImageProcessor -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool CHECKPOINT = "Falconsai/nsfw_image_detection" diff --git a/vision_agent_tools/tools/nshot_counting.py b/vision_agent_tools/tools/nshot_counting.py index 9713eccc..ecbc04e7 100644 --- a/vision_agent_tools/tools/nshot_counting.py +++ b/vision_agent_tools/tools/nshot_counting.py @@ -9,11 +9,11 @@ from PIL import Image from loca.loca import LOCA from .utils import download, CHECKPOINT_DIR -from typing import Union, Optional, Any +from typing import Optional, Any from torch import nn from torchvision import transforms as T from pydantic import BaseModel -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool class CountingDetection(BaseModel): diff --git a/vision_agent_tools/tools/owlv2.py b/vision_agent_tools/tools/owlv2.py index 04fb5302..8d77454f 100644 --- a/vision_agent_tools/tools/owlv2.py +++ b/vision_agent_tools/tools/owlv2.py @@ -5,7 +5,7 @@ from pydantic import BaseModel from transformers import Owlv2ForObjectDetection, Owlv2Processor -from vision_agent_tools.tools.shared_types import BaseTool +from vision_agent_tools.shared_types import BaseTool MODEL_NAME = "google/owlv2-large-patch14-ensemble" PROCESSOR_NAME = "google/owlv2-large-patch14-ensemble" diff --git a/vision_agent_tools/tools/qr_reader.py b/vision_agent_tools/tools/qr_reader.py index 184519c0..e9265da9 100644 --- a/vision_agent_tools/tools/qr_reader.py +++ b/vision_agent_tools/tools/qr_reader.py @@ -4,7 +4,7 @@ from qreader import QReader -from vision_agent_tools.tools.shared_types import BaseTool, Polygon, Point, BoundingBox +from vision_agent_tools.shared_types import BaseTool, Polygon, Point, BoundingBox class QRCodeDetection(BaseModel): diff --git a/vision_agent_tools/types.py b/vision_agent_tools/types.py deleted file mode 100644 index 251192d7..00000000 --- a/vision_agent_tools/types.py +++ /dev/null @@ -1,8 +0,0 @@ -from typing import Annotated, Literal, TypeVar -import numpy as np -import numpy.typing as npt - - -DType = TypeVar("DType", bound=np.generic) - -VideoNumpy = Annotated[npt.NDArray[DType], Literal["N", "N", "N", 3]]