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feat: Merge Multiple Polygons in a COCO Dataset Annotation #1229

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feat: Merge Multiple Polygons in a COCO Dataset Annotation #1229

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35d1827
feat: merge multi mask with bitwise or
DancinParrot May 25, 2024
5b37308
test: unit test for merge_polygons (renamed from merge_masks)
DancinParrot May 25, 2024
8a137be
docs: add entry for merge_polygons() function in mkdocs
DancinParrot May 25, 2024
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6 changes: 6 additions & 0 deletions docs/datasets/utils.md
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Expand Up @@ -16,3 +16,9 @@ status: new
</div>

:::supervision.dataset.utils.mask_to_rle

<div class="md-typeset">
<h2><a href="#supervision.dataset.utils.merge_polygons">merge_polygons</a></h2>
</div>

:::supervision.dataset.utils.merge_polygons
3 changes: 3 additions & 0 deletions supervision/dataset/formats/coco.py
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Expand Up @@ -11,6 +11,7 @@
approximate_mask_with_polygons,
map_detections_class_id,
mask_to_rle,
merge_polygons,
rle_to_mask,
)
from supervision.detection.core import Detections
Expand Down Expand Up @@ -74,6 +75,8 @@ def coco_annotations_to_masks(
resolution_wh=resolution_wh,
)
if image_annotation["iscrowd"]
else merge_polygons(image_annotation["segmentation"], resolution_wh)
if len(image_annotation["segmentation"]) > 1
else polygon_to_mask(
polygon=np.reshape(
np.asarray(image_annotation["segmentation"], dtype=np.int32),
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54 changes: 54 additions & 0 deletions supervision/dataset/utils.py
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Expand Up @@ -13,6 +13,7 @@
approximate_polygon,
filter_polygons_by_area,
mask_to_polygons,
polygon_to_mask,
)

T = TypeVar("T")
Expand Down Expand Up @@ -46,6 +47,59 @@ def approximate_mask_with_polygons(
]


def merge_polygons(
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@SkalskiP SkalskiP May 27, 2024

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I think the merge_polygons name is a bit misleading and not conststant with outhe util names in supervision. I think we should call it polygons_to_mask.

And of course we already have a function called polygon_to_mask that does the same but only take one mask.

It seems a bit silly that we would have 2 functions doeng almost the same. I thin kwe should:

  • rename merge_polygons to polygons_to_mask.
  • make sure that polygons_to_mask is not using polygon_to_mask internally.
  • modify places where polygon_to_mask was used and use polygons_to_mask instead.
  • mask polygon_to_mask as deprecated and recommend using polygons_to_mask instead.

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@SkalskiP SkalskiP May 27, 2024

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@DancinParrot, here is an example of how we mark functions as deprecated. We use deprecated decorator to do it.

@deprecated(
    "`Detections.from_roboflow` is deprecated and will be removed in "
    "`supervision-0.22.0`. Use `Detections.from_inference` instead."
)

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@SkalskiP SkalskiP May 27, 2024

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Please use supervision-0.27.0 as a deprecation version.

polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
) -> np.ndarray:
"""
Merge polygons (in the form of vertices) within the segmentation list
of a COCO annotation.

Args:
polygons (List[np.ndarray]): A nested list of vertices, with each
nested list representing one polygon
resolution_wh (Tuple[int, int]): The width (w) and height (h)
of the desired binary mask.

Returns:
np.ndarray: The generated 2D mask, where the polygon is marked with
`1`'s and the rest is filled with `0`'s.

Examples:
```python
import supervision as sv

sv.merge_polygons([[1.0, 1.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0]], (8, 4))
# array([
# [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
# [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
# [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
# [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
# ])
```
"""
parent_polygon = polygons[0]
parent_mask = polygon_to_mask(
polygon=np.reshape(
np.asarray(parent_polygon, dtype=np.int32),
(-1, 2),
),
resolution_wh=resolution_wh,
)

for p in polygons[1:]:
child_mask = polygon_to_mask(
polygon=np.reshape(
np.asarray(p, dtype=np.int32),
(-1, 2),
),
resolution_wh=resolution_wh,
)

parent_mask = np.logical_or(parent_mask, child_mask)

return parent_mask.astype(float)


def merge_class_lists(class_lists: List[List[str]]) -> List[str]:
unique_classes = set()

Expand Down
58 changes: 58 additions & 0 deletions test/dataset/test_utils.py
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Expand Up @@ -12,6 +12,7 @@
map_detections_class_id,
mask_to_rle,
merge_class_lists,
merge_polygons,
rle_to_mask,
train_test_split,
)
Expand Down Expand Up @@ -125,6 +126,63 @@ def test_merge_class_maps(
assert result == expected_result


@pytest.mark.parametrize(
"polygons, resolution_wh, expected_result, exception",
[
(
np.array([[1.0, 1.0, 2.0, 3.0]]),
(8, 4),
np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
),
DoesNotRaise(),
), # single polygon
(
np.array([[1.0, 1.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0]]),
(8, 4),
np.array(
[
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
),
DoesNotRaise(),
), # two polygons
(
np.array(
[[1.0, 0.0, 2.0, 3.0], [1.0, 1.0, 3.0, 2.0], [1.0, 2.0, 1.0, 2.0]]
),
(8, 4),
np.array(
[
[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
),
DoesNotRaise(),
), # multiple polygons
],
)
def test_merge_polygons(
polygons: List[np.ndarray],
resolution_wh: Tuple[int, int],
expected_result: np.ndarray,
exception: Exception,
) -> None:
with exception:
result = merge_polygons(polygons=polygons, resolution_wh=resolution_wh)
assert np.array_equal(result, expected_result)


@pytest.mark.parametrize(
"source_classes, target_classes, expected_result, exception",
[
Expand Down