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Add tests for different scenarios
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@ -11,6 +11,7 @@ from frigate.util.object import (
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get_cluster_candidates,
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get_cluster_region,
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get_region_from_grid,
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reduce_detections,
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)
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@ -192,6 +193,96 @@ class TestObjectBoundingBoxes(unittest.TestCase):
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assert intersection(box_a, box_b) == None
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assert intersection(box_b, box_c) == (899, 128, 985, 151)
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def test_overlapping_objects_reduced(self):
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"""Test that object not on edge of region is used when a higher scoring object at the edge of region is provided."""
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detections = [
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(
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"car",
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0.81,
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(1209, 73, 1437, 163),
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20520,
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2.53333333,
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(1150, 0, 1500, 200),
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),
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(
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"car",
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0.88,
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(1238, 73, 1401, 171),
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15974,
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1.663265306122449,
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(1242, 0, 1602, 360),
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),
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]
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frame_shape = (720, 2560)
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consolidated_detections = reduce_detections(frame_shape, detections)
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assert consolidated_detections == [
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(
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"car",
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0.81,
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(1209, 73, 1437, 163),
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20520,
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2.53333333,
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(1150, 0, 1500, 200),
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)
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]
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def test_non_overlapping_objects_not_reduced(self):
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"""Test that non overlapping objects are not reduced."""
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detections = [
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(
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"car",
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0.81,
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(1209, 73, 1437, 163),
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20520,
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2.53333333,
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(1150, 0, 1500, 200),
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),
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(
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"car",
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0.83203125,
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(1121, 55, 1214, 100),
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4185,
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2.066666666666667,
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(922, 0, 1242, 320),
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),
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(
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"car",
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0.85546875,
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(1414, 97, 1571, 186),
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13973,
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1.7640449438202248,
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(1248, 0, 1568, 320),
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),
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]
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frame_shape = (720, 2560)
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consolidated_detections = reduce_detections(frame_shape, detections)
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assert len(consolidated_detections) == len(detections)
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def test_overlapping_different_size_objects_not_reduced(self):
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"""Test that overlapping objects that are significantly different in size are not reduced."""
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detections = [
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(
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"car",
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0.81,
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(164, 279, 816, 719),
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286880,
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1.48,
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(90, 0, 910, 820),
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),
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(
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"car",
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0.83203125,
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(248, 340, 328, 385),
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3600,
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1.777,
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(0, 0, 460, 460),
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),
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]
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frame_shape = (720, 2560)
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consolidated_detections = reduce_detections(frame_shape, detections)
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assert len(consolidated_detections) == len(detections)
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class TestRegionGrid(unittest.TestCase):
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def setUp(self) -> None:
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@ -477,7 +477,9 @@ def reduce_detections(
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]
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# reduce confidences for objects that are on edge of region
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confidences = [o[1] / 2 if clipped(o, frame_shape) else o[1] for o in group]
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# 0.6 should be used to ensure that the object is still considered and not dropped
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# due to min score requirement of NMSBoxes
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confidences = [0.6 if clipped(o, frame_shape) else o[1] for o in group]
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idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
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@ -516,8 +518,15 @@ def reduce_detections(
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len(sorted_by_area),
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):
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to_check = sorted_by_area[to_check_idx][2]
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# if area of current detection / area of check < 5% they should not be compared
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# this covers cases where a large car parked in a driveway doesn't block detections
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# of cars in the street behind it
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if area(current_box) / area(to_check) < 0.05:
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continue
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intersect_box = intersection(current_box, to_check)
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# if 90% of smaller detection is inside of another detection, consolidate
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# if % of smaller detection is inside of another detection, consolidate
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if intersect_box is not None and area(intersect_box) / area(
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current_box
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) > LABEL_CONSOLIDATION_MAP.get(
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