Add new tests and functionality for image processing and geometry handling

- Introduced color_image.py for preparing standalone color images for tests.
- Added hdr_image.py to create HDR textures for testing.
- Implemented test_image_depth_panorama.py to evaluate panorama topology, UVs, and depth discontinuities.
- Created test_shadeless.py to validate the opacity and HDR color endpoints of the Shadeless group.
- Developed moge3_export.py for exporting dynamic MoGe-3 backbone and ONNX graphs.
- Added material.py to inherit material slots onto generated geometry.
- Built image_depth_panorama.py to construct a quad panorama with depth separation and sampling.

src/anyimage/server/jobs/remove_background.py

@@ -1,7 +1,8 @@
 import time
 
 from ..media import input_frames
-from ..models.ben2 import infer_foreground, infer_foreground_frames
+from ..media.input import open_image
+from ..models.ben2 import infer_alpha, infer_foreground, infer_foreground_frames
 
 
 def run(context, parameters):
@@ -15,10 +16,25 @@ def run(context, parameters):
     device = parameters["device"]
     model_manager = context.resource("model_manager")
     model_directory = model_manager.directory(parameters["model"])
+    output_kind = parameters.get("output_kind", "foreground")
+    if output_kind not in {"foreground", "alpha"}:
+        raise ValueError(f"Unknown background removal output: {output_kind}")
 
     with input_frames(context, parameters) as images:
         context.progress(0.12, f"Loading BEN2 for {device.upper()}")
-        if len(images) == 1:
+        if output_kind == "alpha":
+            import numpy as np
+
+            if len(images) != 1:
+                raise ValueError("Alpha output requires one still image")
+            with open_image(images[0]) as source:
+                alpha, load_ms, inference_ms = infer_alpha(
+                    source, model_directory, device, model_cache=model_manager,
+                    cancel_check=context.check_cancelled,
+                )
+            context.check_cancelled()
+            np.save(output_directory / "alpha.npy", alpha, allow_pickle=False)
+        elif len(images) == 1:
             context.progress(0.35, "Removing background")
             foreground, load_ms, inference_ms = infer_foreground(
                 images[0],
@@ -62,4 +78,4 @@ def run(context, parameters):
     )
     context.log(message)
     context.progress(1.0, message)
-    return {"image": "."}
+    return {"alpha": "alpha.npy"} if output_kind == "alpha" else {"image": "."}