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SAM/HQSAMAdapter: docstring examples
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from refiners.foundationals.segment_anything.hq_sam import HQSAMAdapter
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from refiners.foundationals.segment_anything.model import SegmentAnything, SegmentAnythingH
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__all__ = ["SegmentAnything", "SegmentAnythingH"]
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__all__ = ["SegmentAnything", "SegmentAnythingH", "HQSAMAdapter"]
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@ -291,6 +291,18 @@ class HQSAMAdapter(fl.Chain, Adapter[SegmentAnything]):
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"""Adapter for SAM introducing HQ features.
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See [[arXiv:2306.01567] Segment Anything in High Quality](https://arxiv.org/abs/2306.01567) for details.
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Example:
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```py
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from refiners.fluxion.utils import load_from_safetensors
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# Tips: run scripts/prepare_test_weights.py to download the weights
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tensor_path = "./tests/weights/refiners-sam-hq-vit-h.safetensors"
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weights = load_from_safetensors(tensor_path)
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hq_sam_adapter = HQSAMAdapter(sam_h, weights=weights)
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hq_sam_adapter.inject() # then use SAM as usual
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```
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"""
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_adapter_modules: dict[str, fl.Module] = {}
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@ -25,6 +25,8 @@ class SegmentAnything(fl.Chain):
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See [[arXiv:2304.02643] Segment Anything](https://arxiv.org/abs/2304.02643)
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E.g. see [`SegmentAnythingH`][refiners.foundationals.segment_anything.model.SegmentAnythingH] for usage.
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Attributes:
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mask_threshold (float): 0.0
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"""
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@ -262,6 +264,32 @@ class SegmentAnythingH(SegmentAnything):
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multimask_output: Whether to use multimask output.
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device: The PyTorch device to use.
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dtype: The PyTorch data type to use.
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Example:
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```py
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device="cuda" if torch.cuda.is_available() else "cpu"
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# multimask_output=True is recommended for ambiguous prompts such as a single point.
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# Below, a box prompt is passed, so just use multimask_output=False which will return a single mask
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sam_h = SegmentAnythingH(multimask_output=False, device=device)
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# Tips: run scripts/prepare_test_weights.py to download the weights
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tensors_path = "./tests/weights/segment-anything-h.safetensors"
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sam_h.load_from_safetensors(tensors_path=tensors_path)
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from PIL import Image
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image = Image.open("image.png")
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masks, *_ = sam_h.predict(image, box_points=[[(x1, y1), (x2, y2)]])
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assert masks.shape == (1, 1, image.height, image.width)
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assert masks.dtype == torch.bool
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# convert it to [0,255] uint8 ndarray of shape (H, W)
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mask = masks[0, 0].cpu().numpy().astype("uint8") * 255
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Image.fromarray(mask).save("mask_image.png")
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```
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"""
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image_encoder = image_encoder or SAMViTH()
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point_encoder = point_encoder or PointEncoder()
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