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perf(detection): keep mixed-mask Detections.merge compact (#2383) 

- Improved `Detections.merge()` to preserve `CompactMask` output when merging dense and compact masks by converting dense masks to compact form, avoiding unnecessary full-mask materialization while keeping all-dense and all-compact behavior unchanged.
- Added validation to mixed-mask merging that raises `ValueError` when compact masks have inconsistent image shapes or dense mask dimensions do not match the compact mask image size.
- Added the public `CompactMask.image_shape` property for safe access to compact mask dimensions.
- Updated `Detections.merge()` documentation to describe mixed-mask merge behavior, output types, validation errors, the lossy dense-to-compact conversion outside detection bounding boxes, and that NMS/NMM pairwise operations do not preserve `CompactMask`.
- Added a comprehensive "Use Compact Masks" how-to guide covering compact mask ingestion, inference, annotator mask requirements, and mixed-mask merging, and integrated it into the documentation navigation.

---------

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4
docs/changelog.md
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@ -17,6 +17,10 @@ date_modified: 2026-06-25
- `BaseAnnotator.requires_mask` — class-level `bool` flag on all annotators; `True` for `MaskAnnotator`, `PolygonAnnotator`, and `HaloAnnotator`; `False` for all others. Integrations can inspect this before materializing expensive mask payloads ([#2370](https://github.com/roboflow/supervision/pull/2370))
- `CompactMask.from_coco_rle` — efficient COCO RLE ingestion into crop-scoped compact mask format without materializing dense `(N, H, W)` arrays ([#2367](https://github.com/roboflow/supervision/pull/2367))
- `Detections.from_inference(compact_masks=True)` — opt-in compact mask representation for Roboflow/Inference segmentation results; masks are cropped to detector bounding boxes ([#2367](https://github.com/roboflow/supervision/pull/2367))
- `CompactMask.image_shape` — new public property returning `(H, W)` of the full image the mask is scoped to ([#2383](https://github.com/roboflow/supervision/pull/2383))
### Changed
- Performance [#2383](https://github.com/roboflow/supervision/pull/2383): `sv.Detections.merge()` on mixed dense `ndarray` + `CompactMask` inputs now returns a `CompactMask` instead of a dense `ndarray`. Previously (0.29.0/0.29.1) the mixed path fell back to `np.vstack`, allocating a full `(N, H, W)` array; the new path converts dense inputs to `CompactMask` without materialising the full stack (~2 500× less peak memory, ~13× faster on 1080p / 40 detections). **Behavior change**: code that checks `isinstance(merged.mask, np.ndarray)` or calls bare ndarray methods (`.astype`, `.reshape`, `.ravel`) on a mixed-merge result will need to be updated. The all-dense path is unchanged and still returns `ndarray`.
### 0.29.1 <small>Jun 23, 2026</small>

189
docs/how_to/use_compact_masks.md Normal file
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@ -0,0 +1,189 @@
---
comments: true
description: Use CompactMask for memory-efficient instance segmentation in supervision — ingest COCO RLE payloads, skip mask materialisation, and merge mixed dense and compact detections without allocating a full pixel stack.
authors:
- name: Borda
role: Open Source Engineer, Roboflow
github: https://github.com/borda
date_modified: 2026-07-01
---
# Use Compact Masks for Memory-Efficient Segmentation
[CompactMask][supervision.detection.compact_mask.CompactMask] stores each instance mask as a run-length encoding of its bounding-box **crop** rather than a full `(H, W)` boolean frame. For high-resolution images with many sparse masks this can reduce memory from tens of gigabytes to tens of megabytes, and eliminates full-frame decode work in annotators that only need the cropped region.
This guide covers the four main integration points:
1. [Ingesting COCO RLE payloads directly as CompactMask](#ingest-coco-rle-payloads)
2. [Parsing Roboflow Inference results without a dense stack](#parse-inference-results)
3. [Skipping mask materialisation for box/label annotators](#skip-unnecessary-materialisation)
4. [Merging mixed dense and compact detections](#merge-mixed-detections)
---
## Ingest COCO RLE Payloads
If your model or API returns masks in the COCO RLE format (`{"size": [H, W], "counts": "..."}`) you can convert them directly to `CompactMask` without allocating an `(N, H, W)` boolean array:
```python
import numpy as np
import supervision as sv
from supervision.detection.compact_mask import CompactMask
# Example: two COCO RLE masks for a 720×1280 frame.
# Replace the counts strings with actual compressed RLE payloads from your
# model or API — e.g., from pycocotools mask.encode() or an Inference response.
rles = [
{"size": [720, 1280], "counts": "YOUR_RLE_COUNTS_STRING_HERE"},
{"size": [720, 1280], "counts": "YOUR_RLE_COUNTS_STRING_HERE"},
]
xyxy = np.array(
[
[100.0, 50.0, 400.0, 300.0],
[500.0, 200.0, 900.0, 600.0],
]
)
compact = CompactMask.from_coco_rle(rles, xyxy, image_shape=(720, 1280))
detections = sv.Detections(
xyxy=xyxy,
mask=compact,
class_id=np.array([0, 1]),
)
```
`from_coco_rle` uses run-length arithmetic scoped to each bounding box so no dense pixel array is ever created. Uncompressed integer count lists are also accepted in place of compressed strings.
---
## Parse Inference Results
`Detections.from_inference` accepts a `compact_masks=True` flag that routes the Roboflow RLE payload through `CompactMask.from_coco_rle` instead of decoding to a dense stack:
```python
import supervision as sv
# result: a Roboflow Inference v2 response dict with instance masks.
detections = sv.Detections.from_inference(result, compact_masks=True)
from supervision.detection.compact_mask import CompactMask
assert isinstance(detections.mask, CompactMask)
```
!!! Warning
`compact_masks=True` crops each mask to its detector bounding box. Pixels outside the box are silently dropped. For masks that extend meaningfully beyond the reported bounding box, use the default `compact_masks=False` (dense decode) to preserve all pixels.
To convert an existing dense-mask `Detections` to compact at any point:
```python
detections_compact = detections.to_compact_masks()
```
---
## Skip Unnecessary Materialisation
Annotators that do not draw masks (box, label, circle, ellipse, trace, keypoint) expose `requires_mask = False`. Integrations can branch on this flag to avoid decoding compact or RLE masks before annotation:
```python
import supervision as sv
annotators = [
sv.BoxAnnotator(),
sv.LabelAnnotator(),
sv.MaskAnnotator(), # requires_mask = True
]
for ann in annotators:
if ann.requires_mask:
# Annotator reads mask pixels — CompactMask decodes lazily per crop.
scene = ann.annotate(scene, detections)
else:
# Annotator ignores masks — strip mask field to eliminate any decode cost.
det_no_mask = sv.Detections(
xyxy=detections.xyxy,
confidence=detections.confidence,
class_id=detections.class_id,
)
scene = ann.annotate(scene, det_no_mask)
```
Annotators that set `requires_mask = True`: [MaskAnnotator][supervision.annotators.core.MaskAnnotator], [PolygonAnnotator][supervision.annotators.core.PolygonAnnotator], [HaloAnnotator][supervision.annotators.core.HaloAnnotator].
All others default to `requires_mask = False`.
!!! Note
`PolygonAnnotator` and `MaskAnnotator` both operate directly on `CompactMask` without materialising the full `(N, H, W)` frame — passing compact detections to them is already efficient.
---
## Merge Mixed Detections
When merging `Detections` objects that mix dense `ndarray` masks and `CompactMask` instances, `Detections.merge` converts dense inputs to `CompactMask` automatically. No full `(N, H, W)` stack is allocated:
```python
import numpy as np
import supervision as sv
from supervision.detection.compact_mask import CompactMask
H, W = 720, 1280
# Compact detections from an RLE-based source.
# Replace the counts string with a real compressed RLE payload from your model or API.
rles = [{"size": [H, W], "counts": "YOUR_RLE_COUNTS_STRING_HERE"}]
xyxy_a = np.array([[100.0, 50.0, 400.0, 300.0]])
cm = CompactMask.from_coco_rle(rles, xyxy_a, image_shape=(H, W))
det_a = sv.Detections(xyxy=xyxy_a, mask=cm, class_id=np.array([0]))
# Dense detections from a different source.
masks_b = np.zeros((1, H, W), dtype=bool)
masks_b[0, 200:400, 500:800] = True
xyxy_b = np.array([[500.0, 200.0, 799.0, 399.0]])
det_b = sv.Detections(xyxy=xyxy_b, mask=masks_b, class_id=np.array([1]))
# Output is CompactMask regardless of input order.
merged = sv.Detections.merge([det_a, det_b])
assert isinstance(merged.mask, CompactMask)
assert len(merged) == 2
```
Merge rules:
| Inputs | Output mask type |
| ------------------------------------- | ------------------------------- |
| All `CompactMask` | `CompactMask` |
| Mixed `CompactMask` + dense `ndarray` | `CompactMask` |
| All dense `ndarray` | `ndarray` (backward compatible) |
All `CompactMask` inputs must share the same `image_shape`; mismatches raise `ValueError`.
---
## Performance Notes
These estimates apply to the **parsing and annotation stage**, not end-to-end pipeline FPS. Model inference typically dominates total runtime.
| Optimisation | Realistic gain | Applies when |
| ---------------------------- | -------------------------- | ------------------------------------------------------------- |
| `from_coco_rle` ingestion | 25–60% faster parse | Full-frame COCO RLE payload; current dense decode path |
| `MaskAnnotator` ROI blending | 10–35% faster annotation | Many small, sparse masks on high-res frames |
| `PolygonAnnotator` crop path | 15–45% faster polygon draw | Many compact masks; full-frame materialise was the bottleneck |
| Mixed-mask merge | 5–20% faster merge | Mix of compact and dense sources (e.g. multi-camera stitch) |
Upper-end gains assume: ≥1080p frames, tens to hundreds of instances, masks covering less than ~20% of total pixels.
---
## API Reference
- [CompactMask][supervision.detection.compact_mask.CompactMask]
- [CompactMask.from_coco_rle][supervision.detection.compact_mask.CompactMask.from_coco_rle]
- [CompactMask.from_dense][supervision.detection.compact_mask.CompactMask.from_dense]
- [Detections.from_inference][supervision.detection.core.Detections.from_inference]
- [Detections.to_compact_masks][supervision.detection.core.Detections.to_compact_masks]
- [Detections.merge][supervision.detection.core.Detections.merge]
- [BaseAnnotator.requires_mask][supervision.annotators.base.BaseAnnotator]

1
mkdocs.yml
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@ -42,6 +42,7 @@ nav:
- Process Datasets: how_to/process_datasets.md
- Benchmark a Model: how_to/benchmark_a_model.md
- Count in Zone: how_to/count_in_zone.md
- Use Compact Masks: how_to/use_compact_masks.md
- Reference:
- Detection and Segmentation:
- Core: detection/core.md

21
src/supervision/detection/compact_mask.py
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@ -1012,6 +1012,27 @@ class CompactMask:
img_h, img_w = self._image_shape
return (len(self), img_h, img_w)
@property
def image_shape(self) -> tuple[int, int]:
"""Return ``(H, W)`` of the full image this mask is scoped to.
Returns:
Tuple ``(H, W)``.
Examples:
```pycon
>>> from supervision.detection.compact_mask import CompactMask
>>> import numpy as np
>>> cm = CompactMask(
... [], np.empty((0, 2), dtype=np.int32),
... np.empty((0, 2), dtype=np.int32), (480, 640))
>>> cm.image_shape
(480, 640)
```
"""
return self._image_shape
@property
def offsets(self) -> npt.NDArray[np.int32]:
"""Return per-mask crop origins as ``(x1, y1)`` integer offsets.

98
src/supervision/detection/core.py
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@ -2204,12 +2204,47 @@ class Detections:
When merging, empty `Detections` objects are ignored.
!!! Note
**Mask merge policy** — the output mask type follows these rules:
* All inputs carry
[`CompactMask`][supervision.detection.compact_mask.CompactMask]
→ result mask is `CompactMask`.
* Mixed dense `ndarray` + `CompactMask` inputs → dense masks are converted
to `CompactMask` via
[`CompactMask.from_dense`][supervision.detection.compact_mask.CompactMask.from_dense];
result is `CompactMask`. No full `(N, H, W)` stack is allocated.
!!! warning "Lossy conversion"
`from_dense` crops each dense mask to its detection bounding box
(`xyxy`). **True pixels outside the bounding box are silently
discarded.** This matches the behaviour of
`Detections.from_inference(compact_masks=True)`. If pixel-perfect
preservation is required, ensure all inputs are already `CompactMask`
or use the all-dense path (no `CompactMask` inputs).
* All inputs carry dense `ndarray` → result is `ndarray` (backward
compatible).
* The pairwise merge path used by
[`with_nms`][supervision.detection.core.Detections.with_nms] /
[`with_nmm`][supervision.detection.core.Detections.with_nmm]
(`merge_inner_detection_object_pair`) does **not** preserve `CompactMask`
— mixed inputs materialise to a dense `ndarray` on that path.
Args:
detections_list: A list of Detections objects to merge.
Returns:
A single Detections object containing the merged data from the input list.
Raises:
ValueError: If some `Detections` have a `mask` and others do not.
ValueError: If `CompactMask` inputs have different `image_shape` values.
ValueError: If a dense mask `(H, W)` shape differs from the `CompactMask`
`image_shape` when mixing mask types.
Example:
>>> import numpy as np
>>> import supervision as sv
@ -2232,6 +2267,34 @@ class Detections:
array([1, 2, 1])
>>> merged_detections.data['feature_vector']
array([0.1, 0.2, 0.3])
Compact mask merge example:
```python
import numpy as np
import supervision as sv
from supervision.detection.compact_mask import CompactMask
H, W = 720, 1280
masks_a = np.zeros((2, H, W), dtype=bool)
masks_a[0, 100:200, 100:300] = True
xyxy_a = np.array([[100., 100., 299., 199.], [400., 300., 600., 500.]])
cm_a = CompactMask.from_dense(masks_a, xyxy_a, image_shape=(H, W))
det_compact = sv.Detections(
xyxy=xyxy_a, mask=cm_a, class_id=np.array([0, 1])
)
masks_b = np.zeros((1, H, W), dtype=bool)
masks_b[0, 50:100, 50:150] = True
xyxy_b = np.array([[50., 50., 149., 99.]])
det_dense = sv.Detections(xyxy=xyxy_b, mask=masks_b, class_id=np.array([2]))
# Dense mask is converted to CompactMask; no (N, H, W) stack allocated.
merged = sv.Detections.merge([det_compact, det_dense])
assert isinstance(merged.mask, CompactMask)
assert len(merged) == 3
```
"""
detections_list = [
detections for detections in detections_list if not detections.is_empty()
@ -2260,10 +2323,37 @@ class Detections:
raise ValueError("All or none of the 'mask' fields must be None")
if all(isinstance(m, CompactMask) for m in masks):
return CompactMask.merge(cast(list[CompactMask], masks))
# Mixed or all-ndarray: __array__ auto-converts any CompactMask.
return cast(
npt.NDArray[np.generic], np.vstack([np.asarray(m) for m in masks])
)
if all(not isinstance(m, CompactMask) for m in masks):
# All-dense: preserve backward-compatible dense stacking.
return cast(
npt.NDArray[np.generic], np.vstack([np.asarray(m) for m in masks])
)
# Mixed dense and CompactMask: convert dense masks to CompactMask to
# avoid materialising a full (N, H, W) stack.
compact_image_shapes = {
m.image_shape for m in masks if isinstance(m, CompactMask)
}
if len(compact_image_shapes) != 1:
raise ValueError(
"Cannot merge CompactMask objects with different image shapes: "
f"{sorted(compact_image_shapes)}"
)
image_shape: tuple[int, int] = next(iter(compact_image_shapes))
compact_list: list[CompactMask] = []
for d, m in zip(detections_list, masks):
if isinstance(m, CompactMask):
compact_list.append(m)
else:
dense = np.asarray(m, dtype=bool)
if dense.shape[1:] != image_shape:
raise ValueError(
f"Dense mask shape {dense.shape[1:]} does not match "
f"CompactMask image_shape {image_shape}."
)
compact_list.append(
CompactMask.from_dense(dense, d.xyxy, image_shape)
)
return CompactMask.merge(compact_list)
def stack_or_none(name: str) -> npt.NDArray[np.generic] | None:
values = [getattr(d, name) for d in detections_list]

14
tests/detection/test_compact_mask_integration.py
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@ -146,7 +146,7 @@ class TestMerge:
Covers three scenarios:
- All-compact merge: result is a CompactMask.
- Mixed compact + dense: result falls back to a dense ndarray.
- Mixed compact + dense: dense inputs are converted; result is a CompactMask.
- Inner pair merge (merge_inner_detection_object_pair): used during NMS-like
operations, each input must contain exactly one detection.
"""
@ -173,10 +173,11 @@ class TestMerge:
np.testing.assert_array_equal(merged.mask.to_dense(), expected)
def test_mixed_compact_and_dense(self) -> None:
"""Merging a CompactMask with a dense ndarray falls back to dense."""
"""Merging a CompactMask with a dense ndarray returns a CompactMask."""
h, w = 20, 20
det_compact, _ = _make_compact_detections(2, h, w)
det_compact, masks_compact = _make_compact_detections(2, h, w)
masks_dense = np.zeros((1, h, w), dtype=bool)
masks_dense[0, 3:8, 3:8] = True
xyxy_dense = _full_xyxy(1, h, w)
det_dense = Detections(
xyxy=xyxy_dense,
@ -186,8 +187,11 @@ class TestMerge:
)
merged = Detections.merge([det_compact, det_dense])
assert isinstance(merged.mask, np.ndarray)
assert merged.mask.shape == (3, h, w)
assert isinstance(merged.mask, CompactMask)
assert len(merged) == 3
expected = np.concatenate([masks_compact, masks_dense], axis=0)
np.testing.assert_array_equal(merged.mask.to_dense(), expected)
assert merged.mask.image_shape == (h, w)
def test_inner_pair_with_compact(self) -> None:
from supervision.detection.core import merge_inner_detection_object_pair

191
tests/detection/test_core.py
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@ -617,6 +617,197 @@ def test_merge(
assert result == expected_result, f"Expected: {expected_result}, Got: {result}"
class TestMergeMixedMasks:
"""Detections.merge with a mix of dense ndarray and CompactMask inputs."""
IMG_SHAPE = (50, 50)
def _make_dense_det(
self,
xyxy: list[list[int]],
fill_boxes: bool = True,
) -> Detections:
"""Return Detections with a dense bool mask stack."""
n = len(xyxy)
h, w = self.IMG_SHAPE
masks = np.zeros((n, h, w), dtype=bool)
if fill_boxes:
for i, (x1, y1, x2, y2) in enumerate(xyxy):
masks[i, y1 : y2 + 1, x1 : x2 + 1] = True
return Detections(
xyxy=np.array(xyxy, dtype=np.float32),
mask=masks,
confidence=np.ones(n, dtype=np.float32) * 0.9,
class_id=np.arange(n, dtype=int),
)
def _make_compact_det(
self,
xyxy: list[list[int]],
fill_boxes: bool = True,
) -> Detections:
"""Return Detections with a CompactMask."""
dense_det = self._make_dense_det(xyxy, fill_boxes)
cm = CompactMask.from_dense(
np.asarray(dense_det.mask, dtype=bool), dense_det.xyxy, self.IMG_SHAPE
)
dense_det.mask = cm
return dense_det
def test_mixed_result_is_compact_mask(self) -> None:
"""merge([dense, compact]) returns a CompactMask, not ndarray."""
det_dense = self._make_dense_det([[5, 5, 15, 15]])
det_compact = self._make_compact_det([[20, 20, 35, 35]])
result = Detections.merge([det_dense, det_compact])
assert isinstance(result.mask, CompactMask)
def test_mixed_pixel_parity_with_all_dense(self) -> None:
"""merge([dense, compact]) produces the same pixels as merge([dense, dense])."""
xyxy_a = [[5, 5, 15, 15]]
xyxy_b = [[20, 20, 35, 35]]
det_dense_a = self._make_dense_det(xyxy_a)
det_dense_b = self._make_dense_det(xyxy_b)
det_compact_b = self._make_compact_det(xyxy_b)
all_dense = Detections.merge([det_dense_a, det_dense_b])
mixed = Detections.merge([det_dense_a, det_compact_b])
assert isinstance(mixed.mask, CompactMask)
np.testing.assert_array_equal(mixed.mask.to_dense(), np.asarray(all_dense.mask))
assert mixed.mask.image_shape == self.IMG_SHAPE
def test_mixed_compact_first_pixel_parity(self) -> None:
"""merge([compact, dense]) order: compact input first still gives parity."""
xyxy_a = [[5, 5, 15, 15]]
xyxy_b = [[20, 20, 35, 35]]
det_compact_a = self._make_compact_det(xyxy_a)
det_dense_b = self._make_dense_det(xyxy_b)
det_dense_a = self._make_dense_det(xyxy_a)
det_dense_b2 = self._make_dense_det(xyxy_b)
all_dense = Detections.merge([det_dense_a, det_dense_b2])
mixed = Detections.merge([det_compact_a, det_dense_b])
assert isinstance(mixed.mask, CompactMask)
np.testing.assert_array_equal(mixed.mask.to_dense(), np.asarray(all_dense.mask))
assert mixed.mask.image_shape == self.IMG_SHAPE
def test_mixed_fields_remain_aligned(self) -> None:
"""confidence, class_id, xyxy stay in order after mixed merge."""
det_dense = self._make_dense_det([[1, 1, 10, 10]])
det_compact = self._make_compact_det([[30, 30, 40, 40]])
det_dense.confidence = np.array([0.1])
det_dense.class_id = np.array([1])
det_compact.confidence = np.array([0.9])
det_compact.class_id = np.array([9])
result = Detections.merge([det_dense, det_compact])
np.testing.assert_array_equal(result.confidence, [0.1, 0.9])
np.testing.assert_array_equal(result.class_id, [1, 9])
np.testing.assert_array_equal(result.xyxy, [[1, 1, 10, 10], [30, 30, 40, 40]])
def test_mixed_many_dense_one_compact(self) -> None:
"""Multiple dense + single compact → CompactMask with all masks."""
xyxy_list = [[0, 0, 5, 5], [6, 6, 11, 11], [12, 12, 17, 17]]
det_d1 = self._make_dense_det([xyxy_list[0]])
det_d2 = self._make_dense_det([xyxy_list[1]])
det_c = self._make_compact_det([xyxy_list[2]])
det_all_dense = self._make_dense_det(xyxy_list)
result = Detections.merge([det_d1, det_d2, det_c])
assert isinstance(result.mask, CompactMask)
assert len(result) == 3
np.testing.assert_array_equal(
result.mask.to_dense(), np.asarray(det_all_dense.mask)
)
def test_mixed_compact_image_shape_mismatch_raises(self) -> None:
"""merge with CompactMasks of different image_shapes raises ValueError."""
h, w = self.IMG_SHAPE
masks_a = np.zeros((1, h, w), dtype=bool)
masks_b = np.zeros((1, h + 10, w + 10), dtype=bool)
xyxy_a = np.array([[5.0, 5.0, 15.0, 15.0]])
xyxy_b = np.array([[5.0, 5.0, 15.0, 15.0]])
cm_a = CompactMask.from_dense(masks_a, xyxy_a, (h, w))
cm_b = CompactMask.from_dense(masks_b, xyxy_b, (h + 10, w + 10))
det_a = Detections(xyxy=xyxy_a, mask=cm_a, class_id=np.array([0]))
det_b = Detections(xyxy=xyxy_b, mask=cm_b, class_id=np.array([1]))
with pytest.raises(ValueError, match="image shapes"):
Detections.merge([det_a, det_b])
def test_mixed_dense_shape_mismatch_raises(self) -> None:
"""Dense mask (H', W') ≠ CompactMask image_shape raises ValueError."""
h, w = self.IMG_SHAPE
xyxy = np.array([[5.0, 5.0, 15.0, 15.0]])
masks_compact = np.zeros((1, h, w), dtype=bool)
cm = CompactMask.from_dense(masks_compact, xyxy, (h, w))
det_compact = Detections(xyxy=xyxy, mask=cm, class_id=np.array([0]))
# Dense mask with a different image size than the compact one.
wrong_h, wrong_w = h + 8, w + 8
masks_dense = np.zeros((1, wrong_h, wrong_w), dtype=bool)
det_dense = Detections(xyxy=xyxy, mask=masks_dense, class_id=np.array([1]))
with pytest.raises(ValueError, match="image_shape"):
Detections.merge([det_compact, det_dense])
def test_all_dense_unchanged(self) -> None:
"""All-dense merge is backward compatible: output stays ndarray."""
det_a = self._make_dense_det([[0, 0, 10, 10]])
det_b = self._make_dense_det([[15, 15, 25, 25]])
result = Detections.merge([det_a, det_b])
assert isinstance(result.mask, np.ndarray)
def test_all_compact_unchanged(self) -> None:
"""All-compact merge output is still CompactMask (no regression)."""
det_a = self._make_compact_det([[0, 0, 10, 10]])
det_b = self._make_compact_det([[15, 15, 25, 25]])
result = Detections.merge([det_a, det_b])
assert isinstance(result.mask, CompactMask)
def test_mixed_dense_out_of_box_pixels_dropped(self) -> None:
"""Dense True pixels outside xyxy box are dropped after mixed merge.
from_dense crops each dense mask to its xyxy bounding box — a documented
lossy conversion. This test asserts the drop rather than treating it as a
regression.
"""
h, w = self.IMG_SHAPE
xyxy = [[5, 5, 15, 15]]
masks = np.zeros((1, h, w), dtype=bool)
masks[0, 5:16, 5:16] = True # pixels inside the box
masks[0, 0, 0] = True # pixel OUTSIDE the box
det_dense = Detections(
xyxy=np.array(xyxy, dtype=np.float32),
mask=masks,
confidence=np.array([0.9], dtype=np.float32),
class_id=np.array([0]),
)
det_compact = self._make_compact_det([[20, 20, 35, 35]])
result = Detections.merge([det_dense, det_compact])
assert isinstance(result.mask, CompactMask)
result_dense = result.mask.to_dense()
assert result_dense[0, 10, 10], "in-box pixel preserved"
assert not result_dense[0, 0, 0], "out-of-box pixel dropped"
def test_empty_compact_mask_detections_merge_returns_no_mask(self) -> None:
"""merge on empty CompactMask-carrying Detections returns mask=None."""
h, w = self.IMG_SHAPE
cm_empty = CompactMask(
[],
np.empty((0, 2), dtype=np.int32),
np.empty((0, 2), dtype=np.int32),
(h, w),
)
det_a = Detections(xyxy=np.empty((0, 4), dtype=np.float32), mask=cm_empty)
det_b = Detections(xyxy=np.empty((0, 4), dtype=np.float32), mask=cm_empty)
result = Detections.merge([det_a, det_b])
assert result.mask is None
@pytest.mark.parametrize(
("detections", "anchor", "expected_result", "exception"),
[