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Convert supervision/detection/utils/masks.py docstrings to doctests

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Co-authored-by: copilot-swe-agent[bot] <198982749+copilot@users.noreply.github.com>
This commit is contained in:
jirkaandcopilot-swe-agent[bot] authored and Jirka Borovec committed 2026-02-02 18:10:37 +09:00
1 parent 1c37d143b4
commit 5ac08abcf3
1 file changed
+96 -120
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supervision/detection/utils/masks.py
+96 -120
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@@ -30,29 +30,24 @@ def move_masks(
shape.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([[[False, False, False, False],
[False, True, True, False],
[False, True, True, False],
[False, False, False, False]]], dtype=bool)
offset = np.array([1, 1])
sv.move_masks(mask, offset, resolution_wh=(4, 4))
# array([[[False, False, False, False],
# [False, False, False, False],
# [False, False, True, True],
# [False, False, True, True]]], dtype=bool)
offset = np.array([-2, 2])
sv.move_masks(mask, offset, resolution_wh=(4, 4))
# array([[[False, False, False, False],
# [False, False, False, False],
# [False, False, False, False],
# [True, False, False, False]]], dtype=bool)
```
>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([[[False, False, False, False],
... [False, True, True, False],
... [False, True, True, False],
... [False, False, False, False]]], dtype=bool)
>>> offset = np.array([1, 1])
>>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
array([[[False, False, False, False],
[False, False, False, False],
[False, False, True, True],
[False, False, True, True]]])
>>> offset = np.array([-2, 2])
>>> sv.move_masks(mask, offset, resolution_wh=(4, 4))
array([[[False, False, False, False],
[False, False, False, False],
[False, False, False, False],
[ True, False, False, False]]])
"""
mask_array = np.full((masks.shape[0], resolution_wh[1], resolution_wh[0]), False)
@@ -131,32 +126,26 @@ def contains_holes(mask: npt.NDArray[np.bool_]) -> bool:
True if holes are detected, False otherwise.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 0, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_holes(mask=mask)
# True
mask = np.array([
[0, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_holes(mask=mask)
# False
```
>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
... [0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 0, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_holes(mask=mask)
True
>>> mask = np.array([
... [0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 1, 1, 1, 0],
... [0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_holes(mask=mask)
False
![contains_holes](https://media.roboflow.com/supervision-docs/contains-holes.png){ align=center width="800" }
""" # noqa E501 // docs
@@ -193,34 +182,28 @@ def contains_multiple_segments(
ValueError: If connectivity(int) parameter value is not 4 or 8.
Examples:
```python
import numpy as np
import supervision as sv
mask = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 1, 1],
[0, 1, 1, 0, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 0, 0]
]).astype(bool)
sv.contains_multiple_segments(mask=mask, connectivity=4)
# True
mask = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]
]).astype(bool)
sv.contains_multiple_segments(mask=mask, connectivity=4)
# False
```
>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
... [0, 0, 0, 0, 0, 0],
... [0, 1, 1, 0, 1, 1],
... [0, 1, 1, 0, 1, 1],
... [0, 0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0, 0],
... [0, 1, 1, 1, 0, 0]
... ]).astype(bool)
>>> sv.contains_multiple_segments(mask=mask, connectivity=4)
True
>>> mask = np.array([
... [0, 0, 0, 0, 0, 0],
... [0, 1, 1, 1, 1, 1],
... [0, 1, 1, 1, 1, 1],
... [0, 1, 1, 1, 1, 1],
... [0, 1, 1, 1, 1, 1],
... [0, 0, 0, 0, 0, 0]
... ]).astype(bool)
>>> sv.contains_multiple_segments(mask=mask, connectivity=4)
False
![contains_multiple_segments](https://media.roboflow.com/supervision-docs/contains-multiple-segments.png){ align=center width="800" }
""" # noqa E501 // docs
@@ -304,50 +287,43 @@ def filter_segments_by_distance(
Boolean mask after filtering.
Examples:
```python
import numpy as np
import supervision as sv
>>> import numpy as np
>>> import supervision as sv
>>> mask = np.array([
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
... ], dtype=bool)
>>> sv.filter_segments_by_distance(
... mask,
... absolute_distance=3,
... mode="edge",
... connectivity=8
... ).astype(int)
array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
mask = np.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
], dtype=bool)
sv.filter_segments_by_distance(
mask,
absolute_distance=2,
mode="edge",
connectivity=8
).astype(int)
# np.array([
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# ], dtype=bool)
# The nearby 2×2 block at columns 6–7 is kept because its edge distance
# is within 2 pixels. The distant block at columns 9-10 is removed.
```
The nearby 2×2 block at columns 6–7 is kept because its edge distance
is within 3 pixels. The distant block at columns 9-10 is removed.
""" # noqa E501 // docs
if mask.dtype != bool:
raise TypeError("mask must be boolean")