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Fix test collection broken by merging PRs #32 and #35 together #36

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Merged
annmoel merged 1 commit into from
Sep 4, 2026
Merged

Fix test collection broken by merging PRs #32 and #35 together #36

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36 changes: 0 additions & 36 deletions tests/test_2d_skimage_comparison.py
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143 changes: 2 additions & 141 deletions tests/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
from skan import summarize

from maskel.config import ExtractionConfig, OutputConfig, PipelineConfig
from maskel.features import build_vessel_graph
from maskel.features import build_skeleton_graph
from maskel.pipeline import AnalysisResult, ObjectResult, analyze_segmentation_mask

from ._helpers import cross_volume, line_volume
Expand Down Expand Up @@ -288,7 +288,7 @@ def _no_prunable_spurs_remain(skeleton, min_length):
independent of exactly which pixels a given thinning pass leaves
behind (Lee94 can simplify a junction pixel's own position, e.g.
rounding a corner, without changing this topological property)."""
branch_data = summarize(build_vessel_graph(skeleton), separator="-")
branch_data = summarize(build_skeleton_graph(skeleton), separator="-")
still_prunable = (branch_data["branch-type"] == 1) & (
branch_data["branch-distance"] < min_length
)
Expand Down Expand Up @@ -706,142 +706,3 @@ def test_dimension_mismatch_warning_still_goes_to_stderr_not_the_logger(

assert not any("Warning:" in r.message for r in caplog.records)
assert "spacing" in capsys.readouterr().err.lower()


class TestPruneSpursIntegration:
"""`prune_short_spurs` itself is unit-tested in isolation
(tests/test_spur_pruning.py); these exercise its pipeline integration -
the repeated-iteration loop, the graph rebuild between iterations, and
the "pruned everything away" early return - none of which that
standalone suite can reach."""

@staticmethod
def _y_of_y_mask() -> np.ndarray:
"""A long main line with a junction (J1) that grows a second
junction (J2) via a short vertical branch, and J2 itself splits into
two short arms ending in endpoints.

Removing both of J2's short arms (each an endpoint-to-junction spur)
drops J2's degree from 3 to 1, turning it into an endpoint - which
only *then* makes the previously junction-to-junction J1-J2 branch
qualify as a spur too. A single pruning pass can't see that; a
second one is needed, which is exactly what `spur_iterations` is for.
"""
img = np.zeros((40, 40), dtype=np.uint8)
img[20, 4:37] = 1 # main line, long enough on both sides of J1 to
# never itself qualify as a spur candidate
img[12:21, 20] = 1 # J1(20,20)-J2(12,20), length 8
img[12, 14:21] = 1 # J2's left arm, length 6
img[12, 20:27] = 1 # J2's right arm, length 6
return img

def test_single_iteration_leaves_the_newly_exposed_spur_in_place(self):
img = self._y_of_y_mask()
config = PipelineConfig(
extraction=ExtractionConfig(
branches=True,
summary=True,
prune_spurs=True,
min_spur_length=12.0,
spur_iterations=1,
),
output=OutputConfig(),
)

result = analyze_segmentation_mask(img, config)

# J2's two short arms are gone, but the J1-J2 branch itself - only
# a spur *after* that removal - survives a single pass.
assert result.skeleton[12, 14:20].sum() == 0
assert result.skeleton[12, 21:27].sum() == 0
assert result.skeleton[12:21, 20].sum() > 1
assert len(result.objects[0].branch_records) == 3

def test_second_iteration_removes_the_newly_exposed_spur(self):
img = self._y_of_y_mask()
config = PipelineConfig(
extraction=ExtractionConfig(
branches=True,
summary=True,
prune_spurs=True,
min_spur_length=12.0,
spur_iterations=2,
),
output=OutputConfig(),
)

result = analyze_segmentation_mask(img, config)

# With the second pass, the J1-J2 stub is pruned back to just the
# one pixel `prune_short_spurs` always keeps for connectivity, and
# the main line - no longer forking anywhere - collapses to one
# branch.
assert result.skeleton[12:20, 20].sum() == 1
assert len(result.objects[0].branch_records) == 1

def test_pruning_everything_away_degrades_to_empty_result(self):
# A plain cross: every one of its four arms is an endpoint-to-
# junction spur relative to the center, so a large enough
# min_spur_length prunes all of them, leaving nothing - this must
# hit the pipeline's own empty-skeleton early return rather than
# crash trying to build a graph on a fully-collapsed skeleton.
img = np.zeros((32, 32), dtype=np.uint8)
img[16, 8:24] = 1
img[8:24, 16] = 1
config = PipelineConfig(
extraction=ExtractionConfig(
branches=True, summary=True, prune_spurs=True, min_spur_length=20.0
),
output=OutputConfig(),
)

result = analyze_segmentation_mask(img, config)

assert not result.skeleton.any()
assert result.objects[0].branch_records == []
assert result.objects[0].summary_features == {"object_id": 1}


class TestDegenerateObjectShapes:
"""Objects whose bounding box leaves no room for `_iter_object_crops`'
own padding, or whose thinned skeleton has no possible graph edge at
all - both previously untested, and the single-pixel case was an
actual crash (skan.Skeleton cannot build a graph with zero edges) until
`_skeleton_has_no_branches` started gating it the same way an
all-background skeleton already was."""

def test_single_pixel_object_degrades_gracefully_instead_of_crashing(self):
mask = np.zeros((10, 10), dtype=np.uint8)
mask[5, 5] = 1
config = PipelineConfig(
extraction=ExtractionConfig(branches=True, nodes=True, summary=True),
output=OutputConfig(),
)

result = analyze_segmentation_mask(mask, config)

assert result.skeleton[5, 5] == 1
assert result.objects[0].branch_records == []
assert result.objects[0].node_records == []
assert result.objects[0].summary_features == {"object_id": 1}

def test_object_touching_every_array_edge_forces_the_bbox_clamp(self):
# A square ring spanning the whole canvas: every side of
# `_iter_object_crops`' padded bounding box has nowhere to expand
# into, so `slice(max(s.start - 1, 0), min(s.stop + 1, dim))`
# actually clamps on all four sides rather than padding normally.
mask = np.zeros((20, 20), dtype=np.uint8)
mask[0, :] = 1
mask[-1, :] = 1
mask[:, 0] = 1
mask[:, -1] = 1
config = PipelineConfig(
extraction=ExtractionConfig(branches=True, summary=True),
output=OutputConfig(),
)

result = analyze_segmentation_mask(mask, config)

assert result.skeleton.shape == mask.shape
assert result.skeleton.any()
assert result.objects[0].summary_features["object_id"] == 1