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perf(sizeshape): scatter-based spatial moments + inertia, replacing regionprops einsum (~1.6x) #77

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perf(sizeshape): scatter-based spatial moments + inertia, replacing regionprops einsum (~1.6x) #77

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303cd48
perf(sizeshape): scatter-based spatial moments + inertia, replacing r…
timtreis Jun 6, 2026
f7b32be
perf(sizeshape): option B — derive axis/ecc/orientation from scatter …
timtreis Jun 6, 2026
6d961eb
refactor(sizeshape): slim moment assembly, fix eigenvalue clip, share…
timtreis Jun 9, 2026
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124 changes: 41 additions & 83 deletions src/cp_measure/core/measureobjectsizeshape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,13 @@
import numpy
import scipy.ndimage
import skimage.measure
from cp_measure.primitives._moments import (
axes_eccentricity_orientation,
inertia_2d,
moment_feature_dict,
spatial_moments_2d,
)
from cp_measure.primitives.segment import label_to_idx_lut
from cp_measure.utils import masks_to_ijv

__doc__ = """\
Expand Down Expand Up @@ -585,39 +592,25 @@ def get_sizeshape(
"centroid",
"euler_number",
"extent",
"axis_major_length",
"axis_minor_length",
]

# Features not in CellProfiler 4
if new_features:
desired_properties += ["area_filled"]

# 2d specific properties
if masks.ndim == 2:
desired_properties += [
"eccentricity",
"orientation",
"perimeter",
"solidity",
]
# 2D requests NOTHING moment-related from regionprops: the spatial / central / normalized
# / Hu moments, the inertia tensor, AND the axis lengths / eccentricity / orientation are
# all derived from the `spatial_moments_2d` central moments below (option B), so
# regionprops never runs its per-region moment einsum.
desired_properties += ["perimeter", "solidity"]
if new_features:
desired_properties += ["perimeter_crofton"]

if calculate_advanced:
if masks.ndim == 2:
desired_properties += [
"inertia_tensor",
"inertia_tensor_eigvals",
"moments",
"moments_hu",
"moments_central",
"moments_normalized",
]

else:
else:
# 3D: axis lengths still come from regionprops (3D moments are out of scope here).
desired_properties += ["axis_major_length", "axis_minor_length"]
if calculate_advanced:
desired_properties += ["solidity"]

# These advanced props were not in CP4 for 3D images
if new_features:
desired_properties += [
Expand All @@ -629,13 +622,25 @@ def get_sizeshape(
]

labels = masks
nobjects = (numpy.unique(masks) > 0).sum()
nobjects = label_to_idx_lut(masks)[1]
results: dict[str, NDArray[numpy.floating]] = {}
if labels.ndim == 2:
props = skimage.measure.regionprops_table(
labels, pixels, properties=desired_properties
)

# Option B: every moment-derived 2D feature (spatial / central / normalized / Hu moments,
# the inertia tensor, AND axis lengths / eccentricity / orientation) comes from this one
# scatter pass, so regionprops above ran no moment einsum. The normalized / Hu moments are
# only needed under calculate_advanced, so the scatter skips them otherwise.
raw, central, normalized, hu = spatial_moments_2d(
labels, advanced=calculate_advanced
)
inertia = inertia_2d(central)
axis_major, axis_minor, eccentricity, orientation = (
axes_eccentricity_orientation(inertia)
)

formfactor = 4.0 * numpy.pi * props["area"] / props["perimeter"] ** 2
denom = [max(x, 1) for x in 4.0 * numpy.pi * props["area"]]
compactness = props["perimeter"] ** 2 / denom
Expand All @@ -659,10 +664,10 @@ def get_sizeshape(
F_CONVEX_AREA: props["area_convex"],
F_EQUIVALENT_DIAMETER: props["equivalent_diameter_area"],
F_PERIMETER: props["perimeter"],
F_MAJOR_AXIS_LENGTH: props["axis_major_length"],
F_MINOR_AXIS_LENGTH: props["axis_minor_length"],
F_ECCENTRICITY: props["eccentricity"],
F_ORIENTATION: props["orientation"] * (180 / numpy.pi),
F_MAJOR_AXIS_LENGTH: axis_major,
F_MINOR_AXIS_LENGTH: axis_minor,
F_ECCENTRICITY: eccentricity,
F_ORIENTATION: orientation * (180 / numpy.pi),
F_CENTER_X: props["centroid-1"],
F_CENTER_Y: props["centroid-0"],
F_MIN_X: props["bbox-1"],
Expand All @@ -686,61 +691,14 @@ def get_sizeshape(
}

if calculate_advanced:
results |= {
F_SPATIAL_MOMENT_0_0: props["moments-0-0"],
F_SPATIAL_MOMENT_0_1: props["moments-0-1"],
F_SPATIAL_MOMENT_0_2: props["moments-0-2"],
F_SPATIAL_MOMENT_0_3: props["moments-0-3"],
F_SPATIAL_MOMENT_1_0: props["moments-1-0"],
F_SPATIAL_MOMENT_1_1: props["moments-1-1"],
F_SPATIAL_MOMENT_1_2: props["moments-1-2"],
F_SPATIAL_MOMENT_1_3: props["moments-1-3"],
F_SPATIAL_MOMENT_2_0: props["moments-2-0"],
F_SPATIAL_MOMENT_2_1: props["moments-2-1"],
F_SPATIAL_MOMENT_2_2: props["moments-2-2"],
F_SPATIAL_MOMENT_2_3: props["moments-2-3"],
F_CENTRAL_MOMENT_0_0: props["moments_central-0-0"],
F_CENTRAL_MOMENT_0_1: props["moments_central-0-1"],
F_CENTRAL_MOMENT_0_2: props["moments_central-0-2"],
F_CENTRAL_MOMENT_0_3: props["moments_central-0-3"],
F_CENTRAL_MOMENT_1_0: props["moments_central-1-0"],
F_CENTRAL_MOMENT_1_1: props["moments_central-1-1"],
F_CENTRAL_MOMENT_1_2: props["moments_central-1-2"],
F_CENTRAL_MOMENT_1_3: props["moments_central-1-3"],
F_CENTRAL_MOMENT_2_0: props["moments_central-2-0"],
F_CENTRAL_MOMENT_2_1: props["moments_central-2-1"],
F_CENTRAL_MOMENT_2_2: props["moments_central-2-2"],
F_CENTRAL_MOMENT_2_3: props["moments_central-2-3"],
F_NORMALIZED_MOMENT_0_0: props["moments_normalized-0-0"],
F_NORMALIZED_MOMENT_0_1: props["moments_normalized-0-1"],
F_NORMALIZED_MOMENT_0_2: props["moments_normalized-0-2"],
F_NORMALIZED_MOMENT_0_3: props["moments_normalized-0-3"],
F_NORMALIZED_MOMENT_1_0: props["moments_normalized-1-0"],
F_NORMALIZED_MOMENT_1_1: props["moments_normalized-1-1"],
F_NORMALIZED_MOMENT_1_2: props["moments_normalized-1-2"],
F_NORMALIZED_MOMENT_1_3: props["moments_normalized-1-3"],
F_NORMALIZED_MOMENT_2_0: props["moments_normalized-2-0"],
F_NORMALIZED_MOMENT_2_1: props["moments_normalized-2-1"],
F_NORMALIZED_MOMENT_2_2: props["moments_normalized-2-2"],
F_NORMALIZED_MOMENT_2_3: props["moments_normalized-2-3"],
F_NORMALIZED_MOMENT_3_0: props["moments_normalized-3-0"],
F_NORMALIZED_MOMENT_3_1: props["moments_normalized-3-1"],
F_NORMALIZED_MOMENT_3_2: props["moments_normalized-3-2"],
F_NORMALIZED_MOMENT_3_3: props["moments_normalized-3-3"],
F_HU_MOMENT_0: props["moments_hu-0"],
F_HU_MOMENT_1: props["moments_hu-1"],
F_HU_MOMENT_2: props["moments_hu-2"],
F_HU_MOMENT_3: props["moments_hu-3"],
F_HU_MOMENT_4: props["moments_hu-4"],
F_HU_MOMENT_5: props["moments_hu-5"],
F_HU_MOMENT_6: props["moments_hu-6"],
F_INERTIA_TENSOR_0_0: props["inertia_tensor-0-0"],
F_INERTIA_TENSOR_0_1: props["inertia_tensor-0-1"],
F_INERTIA_TENSOR_1_0: props["inertia_tensor-1-0"],
F_INERTIA_TENSOR_1_1: props["inertia_tensor-1-1"],
F_INERTIA_TENSOR_EIGENVALUES_0: props["inertia_tensor_eigvals-0"],
F_INERTIA_TENSOR_EIGENVALUES_1: props["inertia_tensor_eigvals-1"],
}
# Spatial / central / normalized / Hu moments and the inertia tensor all come from the
# scatter `central` + the single `inertia_2d` computed above — regionprops ran no
# moment einsum. moment_feature_dict owns the 53 feature names / orders (shared with
# the numba backend); the 6-tuple passes both off-diagonals (equal, symmetric tensor).
it_00, it_off, it_11, eig_0, eig_1 = inertia
results |= moment_feature_dict(
raw, central, normalized, hu, (it_00, it_off, it_off, it_11, eig_0, eig_1)
)

if new_features:
results |= {
Expand Down
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