feat: initialize standalone image-filter

Rename the distribution to image-filter and imports to image_filter.
Keep medical fixtures local and document their setup for fresh clones.
Include composite pipelines, consistent examples, and class diagrams.

tests/test_composite.py

@@ -0,0 +1,51 @@
+"""Composite behavior: substitution, ordering, metadata, and adapters."""
+import json
+
+import numpy as np
+import pytest
+
+from image_filter import (
+    CannyFilter, GaussianFilter, ImageFilter, ImageItem, InvalidConfigurationError,
+    MeanFilter, Pipeline, SliceWise, SobelFilter, UnsupportedDimensionError,
+)
+
+
+def test_nested_pipeline_matches_explicit_execution_and_preserves_metadata():
+    image = np.arange(120, dtype=float).reshape(10, 12)
+    metadata = {"patient": {"label": "test"}}
+    first, second, third = MeanFilter(3), GaussianFilter(0.8), SobelFilter()
+    operation = Pipeline([Pipeline([first, second]), third])
+    assert isinstance(operation, ImageFilter)
+    result = operation.apply(image, metadata)
+    expected = third.apply(second.apply(first.apply(image).image).image)
+    np.testing.assert_array_equal(result.image, expected.image)
+    assert [x["filter"] for x in result.metadata["processing_history"]] == [
+        "MeanFilter", "GaussianFilter", "SobelFilter",
+    ]
+    assert metadata == {"patient": {"label": "test"}}
+    assert result.metadata["patient"] is not metadata["patient"]
+    np.testing.assert_array_equal(image, np.arange(120).reshape(10, 12))
+    for component in (first, operation):
+        batch = component.apply_batch([ImageItem(image), ImageItem(image[None])])
+        np.testing.assert_array_equal(batch[0].image, batch[1].image[0])
+
+
+def test_pipeline_in_slicewise_preserves_boolean_output_and_serializable_history():
+    volume = np.random.default_rng(12).random((10, 12, 5))
+    pipeline = Pipeline([GaussianFilter(0.8), CannyFilter(use_quantiles=True)])
+    result = SliceWise(pipeline, axis=2).apply(volume)
+    expected = np.stack([pipeline.apply(volume[:, :, i]).image for i in range(5)], axis=2)
+    np.testing.assert_array_equal(result.image, expected)
+    assert result.image.dtype == bool
+    json.dumps(dict(result.metadata))
+    assert pipeline.capabilities.spatial_dimensions == frozenset({2})
+    with pytest.raises(UnsupportedDimensionError):
+        pipeline.apply(volume)
+
+
+def test_invalid_composites_fail_at_construction():
+    for steps in ([], [object()], [Pipeline([MeanFilter()]), "invalid"]):
+        with pytest.raises(InvalidConfigurationError):
+            Pipeline(steps)
+    with pytest.raises(InvalidConfigurationError):
+        SliceWise(object(), axis=0)