Merge pull request #1547 from tibeoh/fix/inference-slicer-overlap
fix: InferenceSlicer overlap_ratio_wh argument changed to None by default
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LinasKo
GitHub
commit
52c9ec0def
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@ -97,7 +97,7 @@ detections = sv.Detections.from_sam(sam_result=sam_result)
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- Added [#1409](https://github.com/roboflow/supervision/pull/1409): `text_color` option for [`VertexLabelAnnotator`](https://supervision.roboflow.com/0.23.0/keypoint/annotators/#supervision.keypoint.annotators.VertexLabelAnnotator) keypoint annotator.
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- Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/0.23.0/detection/tools/inference_slicer/) now features an `overlap_ratio_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
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- Changed [#1434](https://github.com/roboflow/supervision/pull/1434): [`InferenceSlicer`](https://supervision.roboflow.com/0.23.0/detection/tools/inference_slicer/) now features an `overlap_wh` parameter, making it easier to compute slice sizes when handling overlapping slices.
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- Fix [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
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@ -11,7 +11,6 @@ from supervision.detection.utils import move_boxes, move_masks, move_oriented_bo
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from supervision.utils.image import crop_image
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from supervision.utils.internal import (
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SupervisionWarnings,
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deprecated_parameter,
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warn_deprecated,
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)
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@ -60,13 +59,15 @@ class InferenceSlicer:
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Args:
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slice_wh (Tuple[int, int]): Dimensions of each slice measured in pixels. The
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tuple should be in the format `(width, height)`.
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overlap_ratio_wh (Optional[Tuple[float, float]]): A tuple representing the
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overlap_ratio_wh (Optional[Tuple[float, float]]): [⚠️ Deprecated: please set
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to `None` and use `overlap_wh`] A tuple representing the
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desired overlap ratio for width and height between consecutive slices.
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Each value should be in the range [0, 1), where 0 means no overlap and
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a value close to 1 means high overlap.
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overlap_wh (Optional[Tuple[int, int]]): A tuple representing the desired
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overlap for width and height between consecutive slices measured in pixels.
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Each value should be greater than or equal to 0.
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Each value should be greater than or equal to 0. Takes precedence over
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`overlap_ratio_wh`.
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overlap_filter (Union[OverlapFilter, str]): Strategy for
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filtering or merging overlapping detections in slices.
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iou_threshold (float): Intersection over Union (IoU) threshold
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@ -82,14 +83,6 @@ class InferenceSlicer:
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not a multiple of the slice's width or height minus the overlap.
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"""
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@deprecated_parameter(
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old_parameter="overlap_filter_strategy",
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new_parameter="overlap_filter",
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map_function=lambda x: x,
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warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
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"be removed in `supervision-0.27.0`. Use '{new_parameter}' "
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"instead.",
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)
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def __init__(
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self,
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callback: Callable[[np.ndarray], Detections],
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@ -103,7 +96,8 @@ class InferenceSlicer:
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if overlap_ratio_wh is not None:
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warn_deprecated(
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"`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and "
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"will be removed in `supervision-0.27.0`. Use `overlap_wh` instead."
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"will be removed in `supervision-0.27.0`. Please manually set it to "
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"`None` and use `overlap_wh` instead."
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)
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self._validate_overlap(overlap_ratio_wh, overlap_wh)
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@ -0,0 +1,192 @@
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from contextlib import ExitStack as DoesNotRaise
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from typing import Optional, Tuple
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import numpy as np
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import pytest
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from supervision.detection.core import Detections
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from supervision.detection.overlap_filter import OverlapFilter
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from supervision.detection.tools.inference_slicer import InferenceSlicer
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@pytest.fixture
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def mock_callback():
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"""Mock callback function for testing."""
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def callback(_: np.ndarray) -> Detections:
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return Detections(xyxy=np.array([[0, 0, 10, 10]]))
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return callback
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@pytest.mark.parametrize(
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"slice_wh, overlap_ratio_wh, overlap_wh, expected_overlap, exception",
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[
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# Valid case: overlap_ratio_wh provided, overlap calculated from the ratio
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((128, 128), (0.2, 0.2), None, None, DoesNotRaise()),
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# Valid case: overlap_wh in pixels, no ratio provided
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((128, 128), None, (20, 20), (20, 20), DoesNotRaise()),
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# Invalid case: overlap_ratio_wh greater than 1, should raise ValueError
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((128, 128), (1.1, 0.5), None, None, pytest.raises(ValueError)),
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# Invalid case: negative overlap_wh, should raise ValueError
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((128, 128), None, (-10, 20), None, pytest.raises(ValueError)),
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# Invalid case:
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# overlap_ratio_wh and overlap_wh provided, should raise ValueError
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((128, 128), (0.5, 0.5), (20, 20), (20, 20), pytest.raises(ValueError)),
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# Valid case: no overlap_ratio_wh, overlap_wh = 50 pixels
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((256, 256), None, (50, 50), (50, 50), DoesNotRaise()),
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# Valid case: overlap_ratio_wh provided, overlap calculated from (0.3, 0.3)
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((200, 200), (0.3, 0.3), None, None, DoesNotRaise()),
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# Valid case: small overlap_ratio_wh values
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((100, 100), (0.1, 0.1), None, None, DoesNotRaise()),
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# Invalid case: negative overlap_ratio_wh value, should raise ValueError
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((128, 128), (-0.1, 0.2), None, None, pytest.raises(ValueError)),
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# Invalid case: negative overlap_ratio_wh with overlap_wh provided
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((128, 128), (-0.1, 0.2), (30, 30), None, pytest.raises(ValueError)),
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# Invalid case: overlap_wh greater than slice size, should raise ValueError
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((128, 128), None, (150, 150), (150, 150), DoesNotRaise()),
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# Valid case: overlap_ratio_wh is 0, no overlap
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((128, 128), (0.0, 0.0), None, None, DoesNotRaise()),
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# Invalid case: no overlaps defined, no overlap
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((128, 128), None, None, None, pytest.raises(ValueError)),
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],
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)
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def test_inference_slicer_overlap(
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mock_callback,
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slice_wh: Tuple[int, int],
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overlap_ratio_wh: Optional[Tuple[float, float]],
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overlap_wh: Optional[Tuple[int, int]],
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expected_overlap: Optional[Tuple[int, int]],
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exception: Exception,
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) -> None:
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with exception:
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slicer = InferenceSlicer(
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callback=mock_callback,
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slice_wh=slice_wh,
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overlap_ratio_wh=overlap_ratio_wh,
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overlap_wh=overlap_wh,
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overlap_filter=OverlapFilter.NONE,
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)
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assert slicer.overlap_wh == expected_overlap
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@pytest.mark.parametrize(
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"resolution_wh, slice_wh, overlap_wh, expected_offsets",
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[
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# Case 1: No overlap, exact slices fit within image dimensions
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(
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(256, 256),
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(128, 128),
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(0, 0),
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np.array(
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[
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[0, 0, 128, 128],
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[128, 0, 256, 128],
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[0, 128, 128, 256],
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[128, 128, 256, 256],
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]
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),
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),
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# Case 2: Overlap of 64 pixels in both directions
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(
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(256, 256),
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(128, 128),
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(64, 64),
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np.array(
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[
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[0, 0, 128, 128],
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[64, 0, 192, 128],
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[128, 0, 256, 128],
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[192, 0, 256, 128],
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[0, 64, 128, 192],
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[64, 64, 192, 192],
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[128, 64, 256, 192],
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[192, 64, 256, 192],
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[0, 128, 128, 256],
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[64, 128, 192, 256],
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[128, 128, 256, 256],
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[192, 128, 256, 256],
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[0, 192, 128, 256],
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[64, 192, 192, 256],
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[128, 192, 256, 256],
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[192, 192, 256, 256],
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]
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),
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),
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# Case 3: Image not perfectly divisible by slice size (no overlap)
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(
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(300, 300),
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(128, 128),
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(0, 0),
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np.array(
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[
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[0, 0, 128, 128],
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[128, 0, 256, 128],
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[256, 0, 300, 128],
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[0, 128, 128, 256],
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[128, 128, 256, 256],
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[256, 128, 300, 256],
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[0, 256, 128, 300],
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[128, 256, 256, 300],
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[256, 256, 300, 300],
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]
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),
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),
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# Case 4: Overlap of 32 pixels, image not perfectly divisible by slice size
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(
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(300, 300),
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(128, 128),
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(32, 32),
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np.array(
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[
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[0, 0, 128, 128],
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[96, 0, 224, 128],
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[192, 0, 300, 128],
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[288, 0, 300, 128],
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[0, 96, 128, 224],
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[96, 96, 224, 224],
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[192, 96, 300, 224],
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[288, 96, 300, 224],
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[0, 192, 128, 300],
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[96, 192, 224, 300],
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[192, 192, 300, 300],
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[288, 192, 300, 300],
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[0, 288, 128, 300],
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[96, 288, 224, 300],
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[192, 288, 300, 300],
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[288, 288, 300, 300],
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]
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),
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),
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# Case 5: Image smaller than slice size (no overlap)
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(
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(100, 100),
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(128, 128),
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(0, 0),
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np.array(
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[
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[0, 0, 100, 100],
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]
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),
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),
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# Case 6: Overlap_wh is greater than the slice size
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((256, 256), (128, 128), (150, 150), np.array([]).reshape(0, 4)),
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],
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)
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def test_generate_offset(
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resolution_wh: Tuple[int, int],
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slice_wh: Tuple[int, int],
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overlap_wh: Optional[Tuple[int, int]],
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expected_offsets: np.ndarray,
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) -> None:
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offsets = InferenceSlicer._generate_offset(
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resolution_wh=resolution_wh,
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slice_wh=slice_wh,
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overlap_ratio_wh=None,
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overlap_wh=overlap_wh,
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)
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# Verify that the generated offsets match the expected offsets
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assert np.array_equal(
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offsets, expected_offsets
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), f"Expected {expected_offsets}, got {offsets}"
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