Add tools and tests for AnyImage model management and documentation

- Implement sync_models.py for preparing and uploading ONNX artifacts.
- Create build_node.py for building and validating Blender scripts.
- Add docs.py for building and previewing documentation with Mike or MkDocs.
- Introduce model_exports.py for exporting PyTorch models to ONNX format.
- Develop pack.py for packaging the project with dependencies for different platforms.
- Add test_sync_models.py to validate sync_models functionality.
- Add test_tool_commands.py to ensure repository tools have correct command scripts.
- Initialize tools package with __init__.py.

src/anyimage/common/depth_data.py

@@ -0,0 +1,80 @@
+"""Load and scale Depth file artifacts shared by Blender workflows."""
+
+import json
+from pathlib import Path
+
+import bpy
+
+from .image_data import depth_data_name, image_empty_bounds
+
+
+def load_depth_metadata(path):
+    """Read and validate the compact camera metadata beside a Depth texture."""
+    metadata = json.loads(Path(path).read_text(encoding="utf-8"))
+    try:
+        image_size = tuple(int(value) for value in metadata["image_size"])
+        intrinsics = tuple(
+            tuple(float(value) for value in row)
+            for row in metadata["intrinsics"]
+        )
+        reference_depth = float(metadata["reference_depth"])
+    except (KeyError, TypeError, ValueError) as error:
+        raise ValueError("Depth metadata is invalid") from error
+    if (
+        len(image_size) != 2
+        or min(image_size) <= 0
+        or len(intrinsics) != 3
+        or any(len(row) != 3 for row in intrinsics)
+        or intrinsics[0][0] <= 0.0
+        or intrinsics[1][1] <= 0.0
+        or reference_depth <= 0.0
+    ):
+        raise ValueError("Depth metadata is invalid")
+    return {
+        "image_size": image_size,
+        "intrinsics": intrinsics,
+        "reference_depth": reference_depth,
+    }
+
+
+def load_depth_result_image(path, source_object, metadata):
+    """Load, validate, name, configure, and pack a generated Depth texture."""
+    image = bpy.data.images.load(str(path), check_existing=False)
+    try:
+        if tuple(int(value) for value in image.size) != metadata["image_size"]:
+            raise ValueError("Depth texture dimensions do not match its metadata")
+        image.name = depth_data_name(source_object)
+        image.colorspace_settings.name = "Non-Color"
+        image.alpha_mode = "CHANNEL_PACKED"
+        image.pack()
+        return image
+    except Exception:
+        bpy.data.images.remove(image, do_unlink=True)
+        raise
+
+
+def camera_depth_values(image):
+    """Return top-down camera Z and validity from a loaded Depth texture."""
+    import numpy as np
+
+    width, height = (int(value) for value in image.size)
+    pixels = np.empty(width * height * 4, dtype=np.float32)
+    image.pixels.foreach_get(pixels)
+    pixels = np.flipud(pixels.reshape((height, width, 4)))
+    return pixels[..., 2], pixels[..., 3] > 0.5
+
+
+def depth_uniform_scale(source_object, metadata, bounds=None):
+    """Convert model camera units uniformly into Image Empty local distance."""
+    import numpy as np
+
+    intrinsics = metadata["intrinsics"]
+    width, height = metadata["image_size"]
+    reference_depth = metadata["reference_depth"]
+    x_min, x_max, y_min, y_max = bounds or image_empty_bounds(source_object)
+    plane_width = x_max - x_min
+    plane_height = y_max - y_min
+    fx, fy = float(intrinsics[0][0]), float(intrinsics[1][1])
+    scale_x = plane_width * fx / (max(width - 1, 1) * reference_depth)
+    scale_y = plane_height * fy / (max(height - 1, 1) * reference_depth)
+    return float(np.sqrt(scale_x * scale_y))