Mutex watershed

.gitignore

@@ -24,3 +24,6 @@ htmlcov/
 # Local virtual environments
 .venv/
 venv/
+
+# Data
+*.h5

CMakeLists.txt

@@ -12,7 +12,9 @@ find_package(nanobind CONFIG REQUIRED)
 nanobind_add_module(_core
     NB_STATIC
     src/bindings/module.cxx
+    src/bindings/segmentation.cxx
     src/bindings/utils.cxx
+    src/cpp/segmentation/mutex_watershed.cxx
     src/cpp/take_dict.cxx
 )
 

README.md

@@ -30,6 +30,17 @@ out = bic.utils.take_dict(relabeling, labels)
 # array([10, 30, 20, 10], dtype=uint64)
 ```
 
+```python
+affinities = np.ones((2, 4, 4), dtype=np.float32)
+offsets = [[0, 1], [1, 0]]
+
+segmentation = bic.segmentation.mutex_watershed(
+    affinities,
+    offsets,
+    number_of_attractive_channels=2,
+)
+```
+
 ## Scope
 
 The project is not a compatibility layer for `nifty`, `vigra`, or other large libraries. It keeps I/O and heavy dependencies out of the C++ core; callers should use existing Python packages for file formats and pass NumPy arrays into `bioimage-cpp`.

development/segmentation/_mutex_watershed_equivalence.py

@@ -0,0 +1,227 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+from statistics import median
+from time import perf_counter
+from typing import Callable
+
+import numpy as np
+
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+DEFAULT_DATA_PREFIX = PROJECT_ROOT / "examples" / "segmentation" / "isbi-data-"
+
+
+def load_problem(data_prefix: Path | str = DEFAULT_DATA_PREFIX):
+    from elf.segmentation.utils import load_mutex_watershed_problem
+
+    affinities, offsets = load_mutex_watershed_problem(prefix=str(data_prefix))
+    return np.ascontiguousarray(affinities), [tuple(offset) for offset in offsets]
+
+
+def prepare_2d_problem(
+    affinities: np.ndarray,
+    offsets: list[tuple[int, ...]],
+    z: int,
+    yx_shape: tuple[int, int],
+):
+    channels_2d = [i for i, offset in enumerate(offsets) if offset[0] == 0]
+    y, x = yx_shape
+    cropped = affinities[channels_2d, z, :y, :x]
+    offsets_2d = [offsets[i][1:] for i in channels_2d]
+    return np.ascontiguousarray(cropped), offsets_2d, 2
+
+
+def prepare_3d_problem(
+    affinities: np.ndarray,
+    offsets: list[tuple[int, ...]],
+    zyx_shape: tuple[int, int, int],
+):
+    z, y, x = zyx_shape
+    cropped = affinities[:, :z, :y, :x]
+    return np.ascontiguousarray(cropped), offsets, 3
+
+
+def run_bioimage_cpp(
+    affinities: np.ndarray,
+    offsets: list[tuple[int, ...]],
+    number_of_attractive_channels: int,
+) -> np.ndarray:
+    import bioimage_cpp as bic
+
+    affs = affinities.copy()
+    affs[:number_of_attractive_channels] *= -1
+    affs[:number_of_attractive_channels] += 1
+    return bic.segmentation.mutex_watershed(
+        affs,
+        offsets,
+        number_of_attractive_channels=number_of_attractive_channels,
+    )
+
+
+def run_affogato_reference(
+    affinities: np.ndarray,
+    offsets: list[tuple[int, ...]],
+    number_of_attractive_channels: int,
+) -> np.ndarray:
+    from elf.segmentation.mutex_watershed import mutex_watershed
+
+    spatial_ndim = len(offsets[0])
+    if number_of_attractive_channels != spatial_ndim:
+        raise ValueError(
+            "the elf mutex_watershed wrapper assumes one attractive channel per "
+            f"spatial axis, got ndim={spatial_ndim}, attractive channels="
+            f"{number_of_attractive_channels}"
+        )
+    return mutex_watershed(affinities.copy(), offsets, strides=[1] * spatial_ndim)
+
+
+def _load_validation_metrics():
+    try:
+        from elf.validation import rand_index, variation_of_information
+
+        return "elf.validation", rand_index, variation_of_information
+    except ImportError:
+        from elf.evaluation import rand_index, variation_of_information
+
+        return "elf.evaluation", rand_index, variation_of_information
+
+
+def compare_segmentations(
+    candidate: np.ndarray,
+    reference: np.ndarray,
+    *,
+    max_vi: float = 1.0e-10,
+    max_are: float = 1.0e-10,
+    min_rand_index: float = 1.0 - 1.0e-10,
+) -> dict[str, float | str | bool]:
+    source, rand_index, variation_of_information = _load_validation_metrics()
+
+    vi_split, vi_merge = variation_of_information(candidate, reference)
+    adapted_rand_error, ri = rand_index(candidate, reference)
+    exact_equal = bool(np.array_equal(candidate, reference))
+    equivalent = (
+        vi_split <= max_vi
+        and vi_merge <= max_vi
+        and adapted_rand_error <= max_are
+        and ri >= min_rand_index
+    )
+    metrics: dict[str, float | str | bool] = {
+        "validation_source": source,
+        "vi_split": float(vi_split),
+        "vi_merge": float(vi_merge),
+        "adapted_rand_error": float(adapted_rand_error),
+        "rand_index": float(ri),
+        "exact_label_equality": exact_equal,
+        "equivalent": equivalent,
+    }
+    if not equivalent:
+        raise AssertionError(
+            "mutex watershed results differ: "
+            f"VI split={vi_split:.6g}, VI merge={vi_merge:.6g}, "
+            f"adapted rand error={adapted_rand_error:.6g}, "
+            f"rand index={ri:.12g}, exact labels={exact_equal}"
+        )
+    return metrics
+
+
+def time_function(
+    run: Callable[[np.ndarray, list[tuple[int, ...]], int], np.ndarray],
+    affinities: np.ndarray,
+    offsets: list[tuple[int, ...]],
+    number_of_attractive_channels: int,
+    repeats: int,
+) -> tuple[list[float], np.ndarray]:
+    timings = []
+    result = None
+    for _ in range(repeats):
+        start = perf_counter()
+        result = run(affinities, offsets, number_of_attractive_channels)
+        timings.append(perf_counter() - start)
+    assert result is not None
+    return timings, result
+
+
+def print_report(
+    *,
+    ndim: int,
+    affinities: np.ndarray,
+    metrics: dict[str, float | str | bool],
+    bic_timings: list[float],
+    ref_timings: list[float],
+):
+    bic_median = median(bic_timings)
+    ref_median = median(ref_timings)
+    speedup = ref_median / bic_median if bic_median > 0 else float("inf")
+
+    print(f"Mutex watershed {ndim}D equivalence check")
+    print(f"affinities shape: {affinities.shape}, dtype: {affinities.dtype}")
+    print(f"validation metrics: {metrics['validation_source']}")
+    print(
+        "VI split/merge: "
+        f"{metrics['vi_split']:.6g} / {metrics['vi_merge']:.6g}"
+    )
+    print(
+        "adapted rand error / rand index: "
+        f"{metrics['adapted_rand_error']:.6g} / {metrics['rand_index']:.12g}"
+    )
+    print(f"exact label equality: {metrics['exact_label_equality']}")
+    print(f"bioimage-cpp median runtime: {bic_median:.6f} s")
+    print(f"affogato reference median runtime: {ref_median:.6f} s")
+    print(f"reference / bioimage-cpp runtime ratio: {speedup:.3f}x")
+
+
+def run_check(
+    *,
+    ndim: int,
+    repeats: int,
+    data_prefix: Path | str,
+    z: int,
+    yx_shape: tuple[int, int],
+    zyx_shape: tuple[int, int, int],
+):
+    affinities, offsets = load_problem(data_prefix)
+    if ndim == 2:
+        affs, used_offsets, attractive_channels = prepare_2d_problem(
+            affinities, offsets, z=z, yx_shape=yx_shape
+        )
+    elif ndim == 3:
+        affs, used_offsets, attractive_channels = prepare_3d_problem(
+            affinities, offsets, zyx_shape=zyx_shape
+        )
+    else:
+        raise ValueError(f"ndim must be 2 or 3, got {ndim}")
+
+    ref_timings, ref_seg = time_function(
+        run_affogato_reference, affs, used_offsets, attractive_channels, repeats
+    )
+    bic_timings, bic_seg = time_function(
+        run_bioimage_cpp, affs, used_offsets, attractive_channels, repeats
+    )
+    metrics = compare_segmentations(bic_seg, ref_seg)
+    print_report(
+        ndim=ndim,
+        affinities=affs,
+        metrics=metrics,
+        bic_timings=bic_timings,
+        ref_timings=ref_timings,
+    )
+
+
+def add_common_arguments(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument(
+        "--data-prefix",
+        type=Path,
+        default=DEFAULT_DATA_PREFIX,
+        help=(
+            "Path prefix for the ISBI mutex watershed data. The loader expects "
+            "'test.h5' and 'train.h5' suffixes."
+        ),
+    )
+    parser.add_argument(
+        "--repeats",
+        type=int,
+        default=3,
+        help="Number of timed runs for each implementation.",
+    )

development/segmentation/check_mutex_watershed_2d.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+import argparse
+
+from _mutex_watershed_equivalence import add_common_arguments, run_check
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Compare bioimage-cpp and affogato mutex watershed in 2D."
+    )
+    add_common_arguments(parser)
+    parser.add_argument(
+        "--z",
+        type=int,
+        default=0,
+        help="Z slice from the example affinities used for the 2D check.",
+    )
+    parser.add_argument(
+        "--shape",
+        type=int,
+        nargs=2,
+        metavar=("Y", "X"),
+        default=(128, 128),
+        help="Spatial crop shape for the 2D check.",
+    )
+    args = parser.parse_args()
+
+    run_check(
+        ndim=2,
+        repeats=args.repeats,
+        data_prefix=args.data_prefix,
+        z=args.z,
+        yx_shape=tuple(args.shape),
+        zyx_shape=(0, 0, 0),
+    )
+
+
+if __name__ == "__main__":
+    main()

development/segmentation/check_mutex_watershed_3d.py

@@ -0,0 +1,34 @@
+from __future__ import annotations
+
+import argparse
+
+from _mutex_watershed_equivalence import add_common_arguments, run_check
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Compare bioimage-cpp and affogato mutex watershed in 3D."
+    )
+    add_common_arguments(parser)
+    parser.add_argument(
+        "--shape",
+        type=int,
+        nargs=3,
+        metavar=("Z", "Y", "X"),
+        default=(6, 96, 96),
+        help="Spatial crop shape for the 3D check.",
+    )
+    args = parser.parse_args()
+
+    run_check(
+        ndim=3,
+        repeats=args.repeats,
+        data_prefix=args.data_prefix,
+        z=0,
+        yx_shape=(0, 0),
+        zyx_shape=tuple(args.shape),
+    )
+
+
+if __name__ == "__main__":
+    main()