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add box segmenter solution
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@ -21,6 +21,8 @@ ______________________________________________________________________
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## Latest News 🔥
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## Latest News 🔥
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- Added the Box Segmenter all-in-one solution ([model](https://huggingface.co/finegrain/finegrain-box-segmenter), [HF Space](https://huggingface.co/spaces/finegrain/finegrain-object-cutter))
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- Added [MVANet](https://arxiv.org/abs/2404.07445) for high resolution segmentation
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- Added [IC-Light](https://github.com/lllyasviel/IC-Light) to manipulate the illumination of images
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- Added [IC-Light](https://github.com/lllyasviel/IC-Light) to manipulate the illumination of images
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- Added Multi Upscaler for high-resolution image generation, inspired from [Clarity Upscaler](https://github.com/philz1337x/clarity-upscaler) ([HF Space](https://huggingface.co/spaces/finegrain/enhancer))
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- Added Multi Upscaler for high-resolution image generation, inspired from [Clarity Upscaler](https://github.com/philz1337x/clarity-upscaler) ([HF Space](https://huggingface.co/spaces/finegrain/enhancer))
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- Added [HQ-SAM](https://arxiv.org/abs/2306.01567) for high quality mask prediction with Segment Anything
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- Added [HQ-SAM](https://arxiv.org/abs/2306.01567) for high quality mask prediction with Segment Anything
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@ -67,6 +67,9 @@ doc = [
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"mkdocstrings[python]>=0.24.0",
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"mkdocstrings[python]>=0.24.0",
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"mkdocs-literate-nav>=0.6.1",
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"mkdocs-literate-nav>=0.6.1",
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]
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]
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solutions = [
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"huggingface-hub>=0.24.6",
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]
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[build-system]
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[build-system]
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requires = ["hatchling"]
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requires = ["hatchling"]
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@ -100,9 +100,10 @@ gitpython==3.1.43
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# via wandb
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# via wandb
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griffe==0.48.0
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griffe==0.48.0
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# via mkdocstrings-python
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# via mkdocstrings-python
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huggingface-hub==0.24.5
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huggingface-hub==0.24.6
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# via datasets
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# via datasets
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# via diffusers
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# via diffusers
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# via refiners
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# via timm
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# via timm
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# via tokenizers
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# via tokenizers
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# via transformers
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# via transformers
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@ -466,6 +466,15 @@ def download_mvanet():
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check_hash(dest_filename, "b915d492")
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check_hash(dest_filename, "b915d492")
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def download_box_segmenter():
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download_file(
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"https://huggingface.co/finegrain/finegrain-box-segmenter/resolve/v0.1/model.safetensors",
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dest_folder=test_weights_dir,
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filename="finegrain-box-segmenter-v0-1.safetensors",
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expected_hash="e0450e8c",
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)
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def printg(msg: str):
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def printg(msg: str):
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"""print in green color"""
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"""print in green color"""
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print("\033[92m" + msg + "\033[0m")
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print("\033[92m" + msg + "\033[0m")
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@ -861,6 +870,7 @@ def download_all():
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download_sdxl_lightning_lora()
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download_sdxl_lightning_lora()
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download_ic_light()
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download_ic_light()
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download_mvanet()
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download_mvanet()
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download_box_segmenter()
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def convert_all():
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def convert_all():
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3
src/refiners/solutions/__init__.py
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3
src/refiners/solutions/__init__.py
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from .box_segmenter import BoxSegmenter
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__all__ = ["BoxSegmenter"]
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79
src/refiners/solutions/box_segmenter.py
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79
src/refiners/solutions/box_segmenter.py
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from pathlib import Path
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import torch
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from PIL import Image
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from refiners.fluxion.utils import image_to_tensor, no_grad, normalize, tensor_to_image
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from refiners.foundationals.swin.mvanet import MVANet
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BoundingBox = tuple[int, int, int, int]
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class BoxSegmenter:
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def __init__(
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self,
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*,
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margin: float = 0.05,
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weights: Path | str | dict[str, torch.Tensor] | None = None,
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device: torch.device | str = "cpu",
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):
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assert margin >= 0
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self.margin = margin
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self.device = torch.device(device)
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self.model = MVANet(device=self.device).eval()
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if weights is None:
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from huggingface_hub.file_download import hf_hub_download # type: ignore[reportUnknownVariableType]
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weights = hf_hub_download(
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repo_id="finegrain/finegrain-box-segmenter",
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filename="model.safetensors",
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revision="v0.1",
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)
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if isinstance(weights, dict):
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self.model.load_state_dict(weights)
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else:
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self.model.load_from_safetensors(weights)
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def __call__(self, img: Image.Image, box_prompt: BoundingBox | None = None) -> Image.Image:
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return self.run(img, box_prompt)
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def add_margin(self, box: BoundingBox) -> BoundingBox:
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x0, y0, x1, y1 = box
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mx = int((x1 - x0) * self.margin)
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my = int((y1 - y0) * self.margin)
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return (x0 - mx, y0 - my, x1 + mx, y1 + my)
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@staticmethod
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def crop_pad(img: Image.Image, box: BoundingBox) -> Image.Image:
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img = img.convert("RGB")
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x0, y0, x1, y1 = box
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px0, py0, px1, py1 = (max(0, -x0), max(0, -y0), max(0, x1 - img.width), max(0, y1 - img.height))
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if (px0, py0, px1, py1) == (0, 0, 0, 0):
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return img.crop(box)
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padded = Image.new("RGB", (img.width + px0 + px1, img.height + py0 + py1))
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padded.paste(img, (px0, py0))
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return padded.crop((x0 + px0, y0 + py0, x1 + px0, y1 + py0))
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def predict(self, img: Image.Image) -> Image.Image:
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in_t = image_to_tensor(img.resize((1024, 1024), Image.Resampling.BILINEAR)).squeeze()
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in_t = normalize(in_t, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]).unsqueeze(0)
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with no_grad():
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prediction: torch.Tensor = self.model(in_t.to(self.device)).sigmoid()
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return tensor_to_image(prediction).resize(img.size, Image.Resampling.BILINEAR)
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def run(self, img: Image.Image, box_prompt: BoundingBox | None = None) -> Image.Image:
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if box_prompt is None:
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box_prompt = (0, 0, img.width, img.height)
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box = self.add_margin(box_prompt)
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cropped = self.crop_pad(img, box)
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prediction = self.predict(cropped)
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out = Image.new("L", (img.width, img.height))
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out.paste(prediction, box)
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return out
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72
tests/e2e/test_solutions.py
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tests/e2e/test_solutions.py
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from pathlib import Path
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from warnings import warn
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import pytest
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import torch
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from PIL import Image
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from tests.utils import ensure_similar_images
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from refiners.solutions import BoxSegmenter
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def _img_open(path: Path) -> Image.Image:
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return Image.open(path) # type: ignore
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@pytest.fixture(scope="module")
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def ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_solutions_ref"
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@pytest.fixture(scope="module")
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def ref_shelves(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "shelves.jpg").convert("RGB")
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@pytest.fixture
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def expected_box_segmenter_plant_mask(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_box_segmenter_plant_mask.png")
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@pytest.fixture
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def expected_box_segmenter_spray_mask(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_box_segmenter_spray_mask.png")
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@pytest.fixture
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def expected_box_segmenter_spray_cropped_mask(ref_path: Path) -> Image.Image:
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return _img_open(ref_path / "expected_box_segmenter_spray_cropped_mask.png")
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@pytest.fixture(scope="module")
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def box_segmenter_weights(test_weights_path: Path) -> Path:
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weights = test_weights_path / "finegrain-box-segmenter-v0-1.safetensors"
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if not weights.is_file():
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warn(f"could not find weights at {test_weights_path}, skipping")
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pytest.skip(allow_module_level=True)
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return weights
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def test_box_segmenter(
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box_segmenter_weights: Path,
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ref_shelves: Image.Image,
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expected_box_segmenter_plant_mask: Image.Image,
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expected_box_segmenter_spray_mask: Image.Image,
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expected_box_segmenter_spray_cropped_mask: Image.Image,
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test_device: torch.device,
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):
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segmenter = BoxSegmenter(weights=box_segmenter_weights, device=test_device)
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plant_mask = segmenter(ref_shelves, box_prompt=(504, 82, 754, 368))
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ensure_similar_images(plant_mask.convert("RGB"), expected_box_segmenter_plant_mask.convert("RGB"))
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spray_box = (461, 542, 594, 823)
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spray_mask = segmenter(ref_shelves, box_prompt=spray_box)
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ensure_similar_images(spray_mask.convert("RGB"), expected_box_segmenter_spray_mask.convert("RGB"))
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# Test left and bottom padding.
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off_l, off_b = 11, 7
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shelves_cropped = ref_shelves.crop((spray_box[0] - off_l, 0, ref_shelves.width, spray_box[3] + off_b))
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spray_cropped_box = (off_l, spray_box[1], spray_box[2] - spray_box[0] + off_l, spray_box[3])
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spray_cropped_mask = segmenter(shelves_cropped, box_prompt=spray_cropped_box)
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ensure_similar_images(spray_cropped_mask.convert("RGB"), expected_box_segmenter_spray_cropped_mask.convert("RGB"))
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3
tests/e2e/test_solutions_ref/README.md
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3
tests/e2e/test_solutions_ref/README.md
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`shelves.jpg` is found here: https://www.freepik.com/free-photo/front-view-shelves-with-plants_6446859.htm
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`expected_box_segmenter_plant_mask.png`, `expected_box_segmenter_spray_mask.png` and `expected_box_segmenter_spray_cropped_mask.png` are generated with Refiners.
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tests/e2e/test_solutions_ref/shelves.jpg
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tests/e2e/test_solutions_ref/shelves.jpg
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