fix(pre_commit): 🎨 auto format pre-commit hooks
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@@ -87,8 +87,8 @@ from supervision.detection.utils.masks import (
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calculate_masks_centroids,
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contains_holes,
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contains_multiple_segments,
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move_masks,
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filter_segments_by_distance,
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move_masks,
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)
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from supervision.detection.utils.polygons import (
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approximate_polygon,
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@@ -396,4 +396,4 @@ def filter_segments_by_distance(
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else:
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raise ValueError("mode must be 'edge' or 'centroid'")
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return keep_labels[labels]
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return keep_labels[labels]
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@@ -10,7 +10,8 @@ from supervision.detection.utils.masks import (
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calculate_masks_centroids,
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contains_holes,
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contains_multiple_segments,
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move_masks, filter_segments_by_distance,
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filter_segments_by_distance,
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move_masks,
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)
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@@ -507,98 +508,122 @@ def test_contains_multiple_segments(
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[
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# single component, unchanged
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(
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np.array([
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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8,
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"edge",
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2.0,
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None,
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np.array([
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 1, 1, 1, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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# two components, edge distance 2, kept with abs=1
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(
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np.array([
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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8,
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"edge",
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2.0,
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None,
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np.array([
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 1, 1, 1, 0, 1],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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# centroid mode, far centroids, dropped with small relative threshold
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(
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np.array([
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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], dtype=bool),
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np.array(
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[
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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],
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dtype=bool,
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),
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8,
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"centroid",
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None,
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0.3, # diagonal ~8.49, threshold ~2.55, centroid gap ~4.24
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np.array([
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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# centroid mode, larger relative threshold, kept
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(
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np.array([
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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], dtype=bool),
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np.array(
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[
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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],
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dtype=bool,
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),
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8,
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"centroid",
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None,
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0.6, # diagonal ~8.49, threshold ~5.09, centroid gap ~4.24
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np.array([
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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], dtype=bool),
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np.array(
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[
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[1, 1, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 1, 1],
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[0, 0, 0, 1, 1, 1],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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# empty mask
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@@ -623,54 +648,65 @@ def test_contains_multiple_segments(
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),
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# two components, pixel distance = 2, kept with abs=2
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(
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np.array([
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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8,
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"edge",
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2.0, # was 1.0
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None,
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np.array([
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 1, 1, 1, 0, 1, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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# two components, pixel distance = 3, dropped with abs=2
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(
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np.array([
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 1, 1, 1, 0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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8,
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"edge",
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2.0, # keep threshold below 3 so the right blob is removed
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None,
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np.array([
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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], dtype=bool),
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np.array(
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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],
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dtype=bool,
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),
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DoesNotRaise(),
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),
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]
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],
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)
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def test_filter_segments_by_distance_sweep(
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mask: npt.NDArray,
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