test_coco_annotations_to_detections result matrices defined in place
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magda skoczen
parent
cccacab621
commit
aab861c2db
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@ -233,186 +233,128 @@ def test_group_coco_annotations_by_image_id(
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[
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(0, 0, 10, 10),
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area=10 * 10,
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segmentation=[[0, 0, 4, 0, 4, 5, 9, 5, 9, 9, 0, 9]],
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bbox=(0, 0, 5, 5),
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area= 5 * 5,
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segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
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)
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],
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(20, 20),
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(5, 5),
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True,
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
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class_id=np.array([0], dtype=int),
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mask=np.array(
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[
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0 if i >= 10 or j >= 10 or (i < 5 and j >= 5) else 1
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((1, 20, 20)),
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mask=np.array([[[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]]]),
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),
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DoesNotRaise(),
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), # single image annotations with mask, segmentation mask in L-like shape,
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# like below:
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# 1 0 0 0
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# 1 1 0 0
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# 0 0 0 0
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# 0 0 0 0
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), # single image annotations with mask as polygon
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(
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[
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(0, 0, 10, 10),
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area=10 * 10,
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segmentation={
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"size": [20, 20],
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"counts": [
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0,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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10,
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15,
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5,
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15,
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5,
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15,
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5,
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15,
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5,
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15,
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5,
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210,
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],
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},
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iscrowd=True,
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)
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],
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(20, 20),
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True,
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Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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class_id=np.array([0], dtype=int),
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mask=np.array(
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[
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0 if i >= 10 or j >= 10 or (i < 5 and j >= 5) else 1
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((1, 20, 20)),
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),
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DoesNotRaise(),
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), # single image annotations with mask, RLE segmentation mask in L-like shape,
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# like below:
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# 1 0 0 0
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# 1 1 0 0
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# 0 0 0 0
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# 0 0 0 0
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(
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[
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(0, 0, 10, 10),
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area=10 * 10,
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segmentation=[[0, 0, 4, 0, 4, 5, 9, 5, 9, 9, 0, 9]],
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),
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(5, 0, 5, 5),
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bbox=(0, 0, 5, 5),
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area=5 * 5,
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segmentation={
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"size": [20, 20],
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"counts": [100, 5, 15, 5, 15, 5, 15, 5, 15, 5, 215],
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"size": [5, 5],
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"counts": [0, 15, 2, 3, 2, 3],
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},
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iscrowd=True,
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)
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],
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(5, 5),
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True,
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Detections(
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xyxy=np.array([[0, 0, 5, 5]], dtype=np.float32),
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class_id=np.array([0], dtype=int),
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mask=np.array([[[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]]]),
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),
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DoesNotRaise(),
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), # single image annotations with mask, RLE segmentation mask
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(
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[
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(0, 0, 5, 5),
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area= 5 * 5,
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segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
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),
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(3, 0, 2, 2),
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area=2 * 2,
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segmentation={
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"size": [5, 5],
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"counts": [15, 2, 3, 2, 3],
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},
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iscrowd=True,
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),
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],
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(20, 20),
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(5, 5),
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True,
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Detections(
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xyxy=np.array([[0, 0, 10, 10], [5, 0, 10, 5]], dtype=np.float32),
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xyxy=np.array([[0, 0, 5, 5], [3, 0, 5, 2]], dtype=np.float32),
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class_id=np.array([0, 0], dtype=int),
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mask=np.array(
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[
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np.array(
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[
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0 if i >= 10 or j >= 10 or (i < 5 and j >= 5) else 1
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((20, 20)),
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np.array(
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[
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1 if j > 4 and j < 10 and i < 5 else 0
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((20, 20)),
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]
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),
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mask=np.array([ [[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]],
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[[0, 0, 0, 1, 1],
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[0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]]])
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),
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DoesNotRaise(),
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), # two image annotations with mask, one mask as polygon in in L-like shape,
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# second as RLE in shape of square, like below (P = polygon, R = RLE):
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# P R 0 0
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# P P 0 0
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# 0 0 0 0
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# 0 0 0 0
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), # two image annotations with mask, one mask as polygon ans second as RLE
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(
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[
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mock_cock_coco_annotation(
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category_id=0,
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bbox=(5, 0, 5, 5),
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area=5 * 5,
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bbox=(3, 0, 2, 2),
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area=2 * 2,
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segmentation={
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"size": [20, 20],
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"counts": [100, 5, 15, 5, 15, 5, 15, 5, 15, 5, 215],
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"size": [5, 5],
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"counts": [15, 2, 3, 2, 3],
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},
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iscrowd=True,
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),
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mock_cock_coco_annotation(
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category_id=1,
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bbox=(0, 0, 10, 10),
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area=10 * 10,
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segmentation=[[0, 0, 4, 0, 4, 5, 9, 5, 9, 9, 0, 9]],
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bbox=(0, 0, 5, 5),
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area= 5 * 5,
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segmentation=[[0, 0, 2, 0, 2, 2, 4, 2, 4, 4, 0, 4]],
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),
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],
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(20, 20),
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(5, 5),
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True,
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Detections(
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xyxy=np.array([[5, 0, 10, 5], [0, 0, 10, 10]], dtype=np.float32),
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xyxy=np.array([[3, 0, 5, 2], [0, 0, 5, 5]], dtype=np.float32),
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class_id=np.array([0, 1], dtype=int),
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mask=np.array(
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[
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np.array(
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[
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1 if j > 4 and j < 10 and i < 5 else 0
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((20, 20)),
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np.array(
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[
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0 if i >= 10 or j >= 10 or (i < 5 and j >= 5) else 1
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for i in range(0, 20)
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for j in range(0, 20)
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]
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).reshape((20, 20)),
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[[0, 0, 0, 1, 1],
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[0, 0, 0, 1, 1],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0]],
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[[1, 1, 1, 0, 0],
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[1, 1, 1, 0, 0],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1]]
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]
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),
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),
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DoesNotRaise(),
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), # two image annotations with mask, first mask as RLE in shape of square,
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# second as polygon in in L-like shape, like below (P = polygon, R = RLE):
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# P R 0 0
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# P P 0 0
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# 0 0 0 0
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# 0 0 0 0
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), # two image annotations with mask, first mask as RLE and second as polygon
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],
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)
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def test_coco_annotations_to_detections(
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