from itertools import zip_longest from pathlib import Path from typing import Literal import numpy as np import torch from PIL import Image from pydantic import BaseModel, Field, model_validator from transformers.models.sam import SamModel from transformers.models.sam.processing_sam import SamProcessor from transformers.models.sam2 import Sam2Model from transformers.models.sam2.processing_sam2 import Sam2Processor from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation from invokeai.app.invocations.fields import BoundingBoxField, ImageField, InputField, TensorField from invokeai.app.invocations.primitives import MaskOutput from invokeai.app.services.shared.invocation_context import InvocationContext from invokeai.backend.image_util.segment_anything.mask_refinement import mask_to_polygon, polygon_to_mask from invokeai.backend.image_util.segment_anything.segment_anything_2_pipeline import SegmentAnything2Pipeline from invokeai.backend.image_util.segment_anything.segment_anything_pipeline import SegmentAnythingPipeline from invokeai.backend.image_util.segment_anything.shared import SAMInput, SAMPoint SegmentAnythingModelKey = Literal[ "segment-anything-base", "segment-anything-large", "segment-anything-huge", "segment-anything-2-tiny", "segment-anything-2-small", "segment-anything-2-base", "segment-anything-2-large", ] SEGMENT_ANYTHING_MODEL_IDS: dict[SegmentAnythingModelKey, str] = { "segment-anything-base": "facebook/sam-vit-base", "segment-anything-large": "facebook/sam-vit-large", "segment-anything-huge": "facebook/sam-vit-huge", "segment-anything-2-tiny": "facebook/sam2.1-hiera-tiny", "segment-anything-2-small": "facebook/sam2.1-hiera-small", "segment-anything-2-base": "facebook/sam2.1-hiera-base-plus", "segment-anything-2-large": "facebook/sam2.1-hiera-large", } class SAMPointsField(BaseModel): points: list[SAMPoint] = Field(..., description="The points of the object", min_length=1) def to_list(self) -> list[list[float]]: return [[point.x, point.y, point.label.value] for point in self.points] @invocation( "segment_anything", title="Segment Anything", tags=["prompt", "segmentation", "sam", "sam2"], category="segmentation", version="1.3.0", ) class SegmentAnythingInvocation(BaseInvocation): """Runs a Segment Anything Model (SAM or SAM2).""" # Reference: # - https://arxiv.org/pdf/2304.02643 # - https://huggingface.co/docs/transformers/v4.43.3/en/model_doc/grounding-dino#grounded-sam # - https://github.com/NielsRogge/Transformers-Tutorials/blob/a39f33ac1557b02ebfb191ea7753e332b5ca933f/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb model: SegmentAnythingModelKey = InputField(description="The Segment Anything model to use (SAM or SAM2).") image: ImageField = InputField(description="The image to segment.") bounding_boxes: list[BoundingBoxField] | None = InputField( default=None, description="The bounding boxes to prompt the model with." ) point_lists: list[SAMPointsField] | None = InputField( default=None, description="The list of point lists to prompt the model with. Each list of points represents a single object.", ) apply_polygon_refinement: bool = InputField( description="Whether to apply polygon refinement to the masks. This will smooth the edges of the masks slightly and ensure that each mask consists of a single closed polygon (before merging).", default=True, ) mask_filter: Literal["all", "largest", "highest_box_score"] = InputField( description="The filtering to apply to the detected masks before merging them into a final output.", default="all", ) @model_validator(mode="after") def validate_points_and_boxes_len(self): if self.point_lists is not None and self.bounding_boxes is not None: if len(self.point_lists) != len(self.bounding_boxes): raise ValueError("If both point_lists and bounding_boxes are provided, they must have the same length.") return self @torch.no_grad() def invoke(self, context: InvocationContext) -> MaskOutput: # The models expect a 3-channel RGB image. image_pil = context.images.get_pil(self.image.image_name, mode="RGB") if (not self.bounding_boxes or len(self.bounding_boxes) == 0) and ( not self.point_lists or len(self.point_lists) == 0 ): combined_mask = torch.zeros(image_pil.size[::-1], dtype=torch.bool) else: masks = self._segment(context=context, image=image_pil) masks = self._filter_masks(masks=masks, bounding_boxes=self.bounding_boxes) # masks contains bool values, so we merge them via max-reduce. combined_mask, _ = torch.stack(masks).max(dim=0) # Unsqueeze the channel dimension. combined_mask = combined_mask.unsqueeze(0) mask_tensor_name = context.tensors.save(combined_mask) _, height, width = combined_mask.shape return MaskOutput(mask=TensorField(tensor_name=mask_tensor_name), width=width, height=height) @staticmethod def _load_sam_model(model_path: Path): sam_model = SamModel.from_pretrained( model_path, local_files_only=True, # TODO(ryand): Setting the torch_dtype here doesn't work. Investigate whether fp16 is supported by the # model, and figure out how to make it work in the pipeline. # torch_dtype=TorchDevice.choose_torch_dtype(), ) sam_processor = SamProcessor.from_pretrained(model_path, local_files_only=True) return SegmentAnythingPipeline(sam_model=sam_model, sam_processor=sam_processor) @staticmethod def _load_sam_2_model(model_path: Path): sam2_model = Sam2Model.from_pretrained(model_path, local_files_only=True) sam2_processor = Sam2Processor.from_pretrained(model_path, local_files_only=True) return SegmentAnything2Pipeline(sam2_model=sam2_model, sam2_processor=sam2_processor) def _segment(self, context: InvocationContext, image: Image.Image) -> list[torch.Tensor]: """Use Segment Anything (SAM or SAM2) to generate masks given an image + a set of bounding boxes.""" source = SEGMENT_ANYTHING_MODEL_IDS[self.model] inputs: list[SAMInput] = [] for bbox_field, point_field in zip_longest(self.bounding_boxes or [], self.point_lists or [], fillvalue=None): inputs.append( SAMInput( bounding_box=bbox_field, points=point_field.points if point_field else None, ) ) if "sam2" in source: loader = SegmentAnythingInvocation._load_sam_2_model with context.models.load_remote_model(source=source, loader=loader) as pipeline: assert isinstance(pipeline, SegmentAnything2Pipeline) masks = pipeline.segment(image=image, inputs=inputs) else: loader = SegmentAnythingInvocation._load_sam_model with context.models.load_remote_model(source=source, loader=loader) as pipeline: assert isinstance(pipeline, SegmentAnythingPipeline) masks = pipeline.segment(image=image, inputs=inputs) masks = self._process_masks(masks) if self.apply_polygon_refinement: masks = self._apply_polygon_refinement(masks) return masks def _process_masks(self, masks: torch.Tensor) -> list[torch.Tensor]: """Convert the tensor output from the Segment Anything model from a tensor of shape [num_masks, channels, height, width] to a list of tensors of shape [height, width]. """ assert masks.dtype == torch.bool # [num_masks, channels, height, width] -> [num_masks, height, width] masks, _ = masks.max(dim=1) # Split the first dimension into a list of masks. return list(masks.cpu().unbind(dim=0)) def _apply_polygon_refinement(self, masks: list[torch.Tensor]) -> list[torch.Tensor]: """Apply polygon refinement to the masks. Convert each mask to a polygon, then back to a mask. This has the following effect: - Smooth the edges of the mask slightly. - Ensure that each mask consists of a single closed polygon - Removes small mask pieces. - Removes holes from the mask. """ # Convert tensor masks to np masks. np_masks = [mask.cpu().numpy().astype(np.uint8) for mask in masks] # Apply polygon refinement. for idx, mask in enumerate(np_masks): shape = mask.shape assert len(shape) == 2 # Assert length to satisfy type checker. polygon = mask_to_polygon(mask) mask = polygon_to_mask(polygon, shape) np_masks[idx] = mask # Convert np masks back to tensor masks. masks = [torch.tensor(mask, dtype=torch.bool) for mask in np_masks] return masks def _filter_masks( self, masks: list[torch.Tensor], bounding_boxes: list[BoundingBoxField] | None ) -> list[torch.Tensor]: """Filter the detected masks based on the specified mask filter.""" if self.mask_filter == "all": return masks elif self.mask_filter == "largest": # Find the largest mask. return [max(masks, key=lambda x: float(x.sum()))] elif self.mask_filter == "highest_box_score": assert bounding_boxes is not None, ( "Bounding boxes must be provided to use the 'highest_box_score' mask filter." ) assert len(masks) == len(bounding_boxes) # Find the index of the bounding box with the highest score. # Note that we fallback to -1.0 if the score is None. This is mainly to satisfy the type checker. In most # cases the scores should all be non-None when using this filtering mode. That being said, -1.0 is a # reasonable fallback since the expected score range is [0.0, 1.0]. max_score_idx = max(range(len(bounding_boxes)), key=lambda i: bounding_boxes[i].score or -1.0) return [masks[max_score_idx]] else: raise ValueError(f"Invalid mask filter: {self.mask_filter}")