feat: Add BEN2 background removal functionality and integrate into SnapForm

- Introduced BEN2 model for background removal in the panel and properties.
- Updated runtime to handle background removal commands and model downloads.
- Implemented background removal logic in worker, including image processing and saving results.
- Enhanced tests to cover new background removal features and ensure proper integration.
- Updated version to 0.9.28 to reflect new features and improvements.

tests/test_worker.py

@@ -47,6 +47,52 @@ class WorkerTest(unittest.TestCase):
             source.index("import torch"),
         )
 
+    def test_optional_background_removal_runs_before_da3_model_load(self):
+        source = inspect.getsource(self.worker.reconstruct)
+
+        self.assertLess(
+            source.index("infer_foreground("),
+            source.index("DepthAnything3.from_pretrained"),
+        )
+        args = self.worker.parser().parse_args(
+            [
+                "reconstruct",
+                "--input",
+                "input.png",
+                "--model-dir",
+                "da3",
+                "--output-dir",
+                "result",
+                "--export-format",
+                "mini_npz",
+                "--status",
+                "status.json",
+                "--remove-background-model-dir",
+                "ben2",
+                "--process-res",
+                "1024",
+                "--process-res-method",
+                "upper_bound_resize",
+                "--conf-thresh-percentile",
+                "40",
+                "--num-max-points",
+                "1000",
+                "--show-cameras",
+                "0",
+                "--gs-views-interval",
+                "1",
+                "--video-frames",
+                "16",
+                "--device",
+                "auto",
+                "--use-ray-pose",
+                "1",
+                "--ref-view-strategy",
+                "first",
+            ]
+        )
+        self.assertEqual(args.remove_background_model_dir, "ben2")
+
     def test_prepares_a_single_image_file(self):
         with tempfile.TemporaryDirectory() as directory:
             image_path = Path(directory) / "input.png"
@@ -69,6 +115,117 @@ class WorkerTest(unittest.TestCase):
             with self.assertRaisesRegex(RuntimeError, "Unsupported input type"):
                 self.worker.prepare_input(input_path, 16, directory)
 
+    def test_download_passes_optional_file_patterns(self):
+        calls = []
+        fake_hugging_face = ModuleType("huggingface_hub")
+        fake_hugging_face.snapshot_download = lambda **options: calls.append(
+            options
+        )
+        args = SimpleNamespace(
+            repository="PramaLLC/BEN2",
+            model_dir="model",
+            status="status.json",
+            allow_pattern=["config.json", "model.safetensors"],
+        )
+
+        with tempfile.TemporaryDirectory() as directory, patch.dict(
+            sys.modules,
+            {"huggingface_hub": fake_hugging_face},
+        ), patch.object(
+            self.worker,
+            "create_download_progress_class",
+            return_value=object,
+        ):
+            args.model_dir = str(Path(directory) / "model")
+            args.status = str(Path(directory) / "status.json")
+            self.worker.download_model(args)
+
+        self.assertEqual(
+            calls[0]["allow_patterns"],
+            ["config.json", "model.safetensors"],
+        )
+
+    def test_ben2_background_removal_writes_fast_transparent_png(self):
+        inference_options = []
+        save_options = []
+
+        class FakeImage:
+            def __enter__(self):
+                return self
+
+            def __exit__(self, *_args):
+                return None
+
+            def convert(self, mode):
+                self.mode = mode
+                return self
+
+            def save(self, path, **options):
+                save_options.append(options)
+                Path(path).touch()
+
+        class FakeModel:
+            @classmethod
+            def from_pretrained(cls, model_dir):
+                self.assertEqual(model_dir, "model")
+                return cls()
+
+            def to(model_self, device):
+                self.assertEqual(device.type, "cpu")
+                return model_self
+
+            def eval(self):
+                return None
+
+            def inference(self, image, **options):
+                inference_options.append((image, options))
+                return FakeImage()
+
+        fake_torch = ModuleType("torch")
+        fake_torch.cuda = SimpleNamespace(
+            is_available=lambda: False,
+            synchronize=lambda: None,
+        )
+        fake_torch.device = lambda device_type: SimpleNamespace(type=device_type)
+        fake_torch.inference_mode = nullcontext
+        fake_ben2 = ModuleType("ben2")
+        fake_ben2.BEN_Base = FakeModel
+        fake_pil = ModuleType("PIL")
+        fake_pil.Image = SimpleNamespace(open=lambda _path: FakeImage())
+
+        with tempfile.TemporaryDirectory() as directory, patch.dict(
+            sys.modules,
+            {
+                "torch": fake_torch,
+                "ben2": fake_ben2,
+                "PIL": fake_pil,
+            },
+        ):
+            root = Path(directory)
+            input_path = root / "input.png"
+            input_path.touch()
+            args = SimpleNamespace(
+                input=str(input_path),
+                model_dir="model",
+                output_dir=str(root / "result"),
+                status=str(root / "status.json"),
+                device="cpu",
+            )
+            self.worker.remove_background(args)
+            status = json.loads(Path(args.status).read_text(encoding="utf-8"))
+
+            self.assertTrue((root / "result" / "foreground.png").is_file())
+
+        self.assertEqual(
+            inference_options[0][1],
+            {"refine_foreground": False},
+        )
+        self.assertEqual(
+            save_options[0],
+            {"format": "PNG", "compress_level": 1},
+        )
+        self.assertIn("Background removed in", status["message"])
+
     def test_uses_official_da3_export_parameters_for_all_four_formats(self):
         inference_options = []
         export_calls = []