Changes

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..fb7bbf5
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+__pycache__/
+*.py[cod]
+*.egg-info/
+.venv/
+build/
+dist/
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..6068928
--- /dev/null
+++ b/README.md
@@ -0,0 +1,64 @@
+# rmbg-mask
+
+`rmbg-mask` generates an 8-bit grayscale foreground mask from an image using
+a local copy of the RMBG 2.0 model. It never downloads model files.
+
+RMBG 2.0's downloadable weights are restricted to non-commercial use unless
+you have a separate commercial agreement with BRIA. Review the model license
+before using this program.
+
+## Installation
+
+For development with uv:
+
+```sh
+uv sync
+uv run rmbg-mask --help
+```
+
+Alternatively, install the project with pip:
+
+```sh
+python -m pip install .
+rmbg-mask --help
+```
+
+## Usage
+
+Download RMBG 2.0 separately and place all of its repository files in one
+local directory. Then run:
+
+```sh
+rmbg-mask input.jpg mask.png --model /path/to/RMBG-2.0
+```
+
+The output mask has the same dimensions as the input image. White represents
+foreground, black represents background, and intermediate values preserve the
+model's soft edges.
+
+Use `--processing-resolution` (or `-r`) to control the square resolution used
+for inference:
+
+```sh
+rmbg-mask input.jpg mask.png \
+    --model /path/to/RMBG-2.0 \
+    --processing-resolution 1536
+```
+
+Higher resolutions may retain more detail, but use substantially more memory
+and processing time. The default is 1024.
+
+The device defaults to CUDA when available, then MPS, then CPU. It can be
+selected explicitly:
+
+```sh
+rmbg-mask input.jpg mask.png --model ./RMBG-2.0 --device cpu
+```
+
+## Testing
+
+The unit tests do not load the model:
+
+```sh
+uv run python -m unittest discover -s tests
+```
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..d891157
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,24 @@
+[build-system]
+requires = ["hatchling>=1.27"]
+build-backend = "hatchling.build"
+
+[project]
+name = "rmbg-mask"
+version = "0.1.0"
+description = "Generate grayscale masks with a local RMBG 2.0 model"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "kornia>=0.7",
+    "pillow>=10",
+    "timm>=1.0,<2",
+    "torch>=2.2",
+    "torchvision>=0.17",
+    "transformers>=4.47,<4.49",
+]
+
+[project.scripts]
+rmbg-mask = "rmbg_mask.cli:main"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/rmbg_mask"]
diff --git a/src/rmbg_mask/__init__.py b/src/rmbg_mask/__init__.py
new file mode 100644
index 0000000..3be87d2
--- /dev/null
+++ b/src/rmbg_mask/__init__.py
@@ -0,0 +1,3 @@
+"""Generate foreground masks with a locally installed RMBG 2.0 model."""
+
+__version__ = "0.1.0"
diff --git a/src/rmbg_mask/__main__.py b/src/rmbg_mask/__main__.py
new file mode 100644
index 0000000..5b080b4
--- /dev/null
+++ b/src/rmbg_mask/__main__.py
@@ -0,0 +1,7 @@
+"""Run the RMBG mask command-line interface as a module."""
+
+from rmbg_mask.cli import main
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/rmbg_mask/cli.py b/src/rmbg_mask/cli.py
new file mode 100644
index 0000000..37a3b47
--- /dev/null
+++ b/src/rmbg_mask/cli.py
@@ -0,0 +1,89 @@
+"""Command-line interface for local RMBG 2.0 inference."""
+
+import argparse
+from collections.abc import Sequence
+from pathlib import Path
+import sys
+
+from rmbg_mask.inference import RmbgError, generateMask
+
+
+DEFAULT_PROCESSING_RESOLUTION = 1024
+
+
+def positiveInt(value: str) -> int:
+    """Parse a positive integer for an argparse option."""
+    try:
+        parsed_value = int(value)
+    except ValueError as error:
+        raise argparse.ArgumentTypeError(
+            f"expected a positive integer, got {value!r}"
+        ) from error
+
+    if parsed_value <= 0:
+        raise argparse.ArgumentTypeError(
+            f"expected a positive integer, got {value!r}"
+        )
+    return parsed_value
+
+
+def buildParser() -> argparse.ArgumentParser:
+    """Build and return the command-line argument parser."""
+    parser = argparse.ArgumentParser(
+        prog="rmbg-mask",
+        description=(
+            "Generate an original-size grayscale mask using a local "
+            "RMBG 2.0 model."
+        ),
+    )
+    parser.add_argument(
+        "input",
+        type=Path,
+        help="input image path",
+    )
+    parser.add_argument(
+        "output",
+        type=Path,
+        help="output grayscale mask path (PNG recommended)",
+    )
+    parser.add_argument(
+        "--model",
+        type=Path,
+        required=True,
+        help="directory containing the already-downloaded model",
+    )
+    parser.add_argument(
+        "-r",
+        "--processing-resolution",
+        type=positiveInt,
+        default=DEFAULT_PROCESSING_RESOLUTION,
+        metavar="PIXELS",
+        help=(
+            "square inference resolution "
+            f"(default: {DEFAULT_PROCESSING_RESOLUTION})"
+        ),
+    )
+    parser.add_argument(
+        "--device",
+        choices=("auto", "cpu", "cuda", "mps"),
+        default="auto",
+        help="inference device (default: auto)",
+    )
+    return parser
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    """Run the command-line program and return its process exit status."""
+    arguments = buildParser().parse_args(argv)
+    try:
+        generateMask(
+            model_path=arguments.model,
+            input_path=arguments.input,
+            output_path=arguments.output,
+            processing_resolution=arguments.processing_resolution,
+            device_name=arguments.device,
+        )
+    except RmbgError as error:
+        print(f"rmbg-mask: error: {error}", file=sys.stderr)
+        return 1
+    return 0
diff --git a/src/rmbg_mask/inference.py b/src/rmbg_mask/inference.py
new file mode 100644
index 0000000..4813d92
--- /dev/null
+++ b/src/rmbg_mask/inference.py
@@ -0,0 +1,137 @@
+"""RMBG 2.0 model loading and mask generation."""
+
+from pathlib import Path
+from typing import Any
+
+from PIL import Image, ImageOps, UnidentifiedImageError
+
+
+class RmbgError(Exception):
+    """Report an expected model, image, device, or output error."""
+
+
+def validateModelPath(model_path: Path) -> Path:
+    """Validate and resolve a local Hugging Face model directory."""
+    if not model_path.exists():
+        raise RmbgError(f"model directory does not exist: {model_path}")
+    if not model_path.is_dir():
+        raise RmbgError(f"model path is not a directory: {model_path}")
+
+    config_path = model_path / "config.json"
+    if not config_path.is_file():
+        raise RmbgError(
+            f"model directory does not contain config.json: {model_path}"
+        )
+    return model_path.resolve()
+
+
+def selectDevice(torch_module: Any, device_name: str) -> str:
+    """Select an available PyTorch inference device."""
+    if device_name == "auto":
+        if torch_module.cuda.is_available():
+            return "cuda"
+        mps = getattr(torch_module.backends, "mps", None)
+        if mps is not None and mps.is_available():
+            return "mps"
+        return "cpu"
+
+    if device_name == "cuda" and not torch_module.cuda.is_available():
+        raise RmbgError("CUDA was requested but is not available")
+    if device_name == "mps":
+        mps = getattr(torch_module.backends, "mps", None)
+        if mps is None or not mps.is_available():
+            raise RmbgError("MPS was requested but is not available")
+    return device_name
+
+
+def loadModel(model_path: Path, device: str) -> Any:
+    """Load RMBG from a validated local directory without network access."""
+    from transformers import AutoModelForImageSegmentation
+
+    try:
+        model = AutoModelForImageSegmentation.from_pretrained(
+            str(model_path),
+            local_files_only=True,
+            trust_remote_code=True,
+        )
+    except Exception as error:
+        raise RmbgError(f"could not load local model: {error}") from error
+
+    return model.eval().to(device)
+
+
+def openImage(input_path: Path) -> Image.Image:
+    """Open an image, apply EXIF orientation, and convert it to RGB."""
+    try:
+        with Image.open(input_path) as source_image:
+            return ImageOps.exif_transpose(source_image).convert("RGB")
+    except FileNotFoundError as error:
+        raise RmbgError(f"input image does not exist: {input_path}") from error
+    except (OSError, UnidentifiedImageError) as error:
+        raise RmbgError(f"could not read input image: {error}") from error
+
+
+def inferMask(
+    model: Any,
+    image: Image.Image,
+    processing_resolution: int,
+    device: str,
+) -> Image.Image:
+    """Infer a soft grayscale mask and restore the source dimensions."""
+    import torch
+    from torchvision import transforms
+
+    transform_image = transforms.Compose(
+        [
+            transforms.Resize(
+                (processing_resolution, processing_resolution),
+                antialias=True,
+            ),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                [0.485, 0.456, 0.406],
+                [0.229, 0.224, 0.225],
+            ),
+        ]
+    )
+    input_tensor = transform_image(image).unsqueeze(0).to(device)
+
+    try:
+        with torch.inference_mode():
+            prediction = model(input_tensor)[-1].sigmoid().cpu()
+    except RuntimeError as error:
+        raise RmbgError(f"model inference failed: {error}") from error
+
+    mask_tensor = prediction[0].squeeze().clamp(0, 1)
+    processing_mask = transforms.ToPILImage()(mask_tensor).convert("L")
+    return processing_mask.resize(image.size, Image.Resampling.BILINEAR)
+
+
+def saveMask(mask: Image.Image, output_path: Path) -> None:
+    """Save an 8-bit grayscale mask to the requested path."""
+    try:
+        mask.save(output_path)
+    except (OSError, ValueError) as error:
+        raise RmbgError(f"could not save output mask: {error}") from error
+
+
+def generateMask(
+    model_path: Path,
+    input_path: Path,
+    output_path: Path,
+    processing_resolution: int = 1024,
+    device_name: str = "auto",
+) -> None:
+    """Generate and save a soft foreground mask with a local RMBG model."""
+    if processing_resolution <= 0:
+        raise RmbgError("processing resolution must be positive")
+
+    local_model_path = validateModelPath(model_path)
+
+    import torch
+
+    device = selectDevice(torch, device_name)
+    image = openImage(input_path)
+    model = loadModel(local_model_path, device)
+    mask = inferMask(model, image, processing_resolution, device)
+    saveMask(mask, output_path)
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 0000000..2675e05
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,76 @@
+"""Tests for command-line parsing and dispatch."""
+
+from pathlib import Path
+import tempfile
+import unittest
+from unittest.mock import patch
+
+from rmbg_mask.cli import buildParser, main
+
+
+class CliTest(unittest.TestCase):
+    """Verify CLI defaults, options, and inference dispatch."""
+
+    def testDefaultProcessingResolution(self) -> None:
+        """Use 1024 pixels when no processing resolution is supplied."""
+        arguments = buildParser().parse_args(
+            ["input.jpg", "mask.png", "--model", "model"]
+        )
+        self.assertEqual(arguments.processing_resolution, 1024)
+
+    def testCustomProcessingResolution(self) -> None:
+        """Accept a positive custom processing resolution."""
+        arguments = buildParser().parse_args(
+            [
+                "input.jpg",
+                "mask.png",
+                "--model",
+                "model",
+                "--processing-resolution",
+                "1536",
+            ]
+        )
+        self.assertEqual(arguments.processing_resolution, 1536)
+
+    def testMainDispatchesArguments(self) -> None:
+        """Pass parsed paths and options to mask generation."""
+        with patch("rmbg_mask.cli.generateMask") as generate_mask:
+            status = main(
+                [
+                    "input.jpg",
+                    "mask.png",
+                    "--model",
+                    "model",
+                    "-r",
+                    "512",
+                    "--device",
+                    "cpu",
+                ]
+            )
+
+        self.assertEqual(status, 0)
+        generate_mask.assert_called_once_with(
+            model_path=Path("model"),
+            input_path=Path("input.jpg"),
+            output_path=Path("mask.png"),
+            processing_resolution=512,
+            device_name="cpu",
+        )
+
+    def testMainReportsExpectedErrors(self) -> None:
+        """Return a failure status for an expected inference error."""
+        from rmbg_mask.inference import RmbgError
+
+        with patch(
+            "rmbg_mask.cli.generateMask",
+            side_effect=RmbgError("test failure"),
+        ):
+            status = main(
+                ["input.jpg", "mask.png", "--model", "model"]
+            )
+
+        self.assertEqual(status, 1)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_inference.py b/tests/test_inference.py
new file mode 100644
index 0000000..e15daff
--- /dev/null
+++ b/tests/test_inference.py
@@ -0,0 +1,138 @@
+"""Tests for inference validation and device selection."""
+
+from pathlib import Path
+from types import SimpleNamespace
+import tempfile
+import unittest
+from unittest.mock import patch
+
+from PIL import Image
+import torch
+
+from rmbg_mask.inference import (
+    RmbgError,
+    inferMask,
+    loadModel,
+    selectDevice,
+    validateModelPath,
+)
+
+
+class Availability:
+    """Provide a configurable is_available method for device tests."""
+
+    def __init__(self, available: bool) -> None:
+        """Store the availability result."""
+        self.available = available
+
+    def is_available(self) -> bool:
+        """Return the configured availability result."""
+        return self.available
+
+
+class FakeModel:
+    """Return predictable logits while recording the model input shape."""
+
+    def __init__(self) -> None:
+        """Initialize without a recorded input shape."""
+        self.input_shape: tuple[int, ...] | None = None
+
+    def __call__(self, input_tensor: torch.Tensor) -> list[torch.Tensor]:
+        """Return a gradient of logits matching the input dimensions."""
+        self.input_shape = tuple(input_tensor.shape)
+        height, width = input_tensor.shape[-2:]
+        logits = torch.linspace(-4, 4, height * width)
+        return [logits.reshape(1, 1, height, width)]
+
+
+class LoadedModel:
+    """Record evaluation and device placement by the model loader."""
+
+    def __init__(self) -> None:
+        """Initialize model state flags."""
+        self.evaluated = False
+        self.device: str | None = None
+
+    def eval(self) -> "LoadedModel":
+        """Record evaluation mode and return this model."""
+        self.evaluated = True
+        return self
+
+    def to(self, device: str) -> "LoadedModel":
+        """Record device placement and return this model."""
+        self.device = device
+        return self
+
+
+class InferenceTest(unittest.TestCase):
+    """Verify local model validation and automatic device selection."""
+
+    def testModelDirectoryRequiresConfig(self) -> None:
+        """Reject a directory that is not a complete model directory."""
+        with tempfile.TemporaryDirectory() as directory:
+            with self.assertRaisesRegex(RmbgError, "config.json"):
+                validateModelPath(Path(directory))
+
+    def testModelDirectoryIsResolved(self) -> None:
+        """Resolve a model directory containing its configuration."""
+        with tempfile.TemporaryDirectory() as directory:
+            model_path = Path(directory)
+            (model_path / "config.json").touch()
+            validated_path = validateModelPath(model_path)
+            self.assertEqual(validated_path, model_path.resolve())
+
+    def testAutoDevicePrefersCuda(self) -> None:
+        """Prefer CUDA over other available accelerators."""
+        torch_module = SimpleNamespace(
+            cuda=Availability(True),
+            backends=SimpleNamespace(mps=Availability(True)),
+        )
+        self.assertEqual(selectDevice(torch_module, "auto"), "cuda")
+
+    def testAutoDeviceFallsBackToCpu(self) -> None:
+        """Use CPU when no supported accelerator is available."""
+        torch_module = SimpleNamespace(
+            cuda=Availability(False),
+            backends=SimpleNamespace(mps=Availability(False)),
+        )
+        self.assertEqual(selectDevice(torch_module, "auto"), "cpu")
+
+    def testInferenceRestoresArbitraryImageSize(self) -> None:
+        """Return a soft grayscale mask at the original image dimensions."""
+        image = Image.new("RGB", (37, 19), "white")
+        model = FakeModel()
+
+        mask = inferMask(model, image, 64, "cpu")
+
+        self.assertEqual(model.input_shape, (1, 3, 64, 64))
+        self.assertEqual(mask.mode, "L")
+        self.assertEqual(mask.size, image.size)
+        populated_values = sum(count > 0 for count in mask.histogram())
+        self.assertGreater(populated_values, 2)
+
+    def testModelLoadingIsStrictlyLocal(self) -> None:
+        """Tell Transformers to load custom code and weights locally only."""
+        loaded_model = LoadedModel()
+        auto_model = SimpleNamespace()
+        auto_model.from_pretrained = unittest.mock.Mock(
+            return_value=loaded_model
+        )
+        transformers = SimpleNamespace(
+            AutoModelForImageSegmentation=auto_model
+        )
+
+        with patch.dict("sys.modules", {"transformers": transformers}):
+            result = loadModel(Path("/local/model"), "cpu")
+
+        auto_model.from_pretrained.assert_called_once_with(
+            "/local/model",
+            local_files_only=True,
+            trust_remote_code=True,
+        )
+        self.assertIs(result, loaded_model)
+        self.assertTrue(loaded_model.evaluated)
+        self.assertEqual(loaded_model.device, "cpu")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..d224629
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1493 @@
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+    "python_full_version < '3.11'",
+]
+
+[[package]]
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