fix: InferenceSlicer overlap_ratio_wh argument changed to None by default

docs/changelog.md

@@ -96,7 +96,7 @@ detections = sv.Detections.from_sam(sam_result=sam_result)
 
 - 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.
 
-- 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.
+- 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.
 
 - Fix [#1448](https://github.com/roboflow/supervision/pull/1448): Various annotator type issues have been resolved, supporting expanded error handling.
 

supervision/detection/tools/inference_slicer.py

@@ -11,7 +11,6 @@
 from supervision.utils.image import crop_image
 from supervision.utils.internal import (
     SupervisionWarnings,
-    deprecated_parameter,
     warn_deprecated,
 )
 
@@ -60,13 +59,15 @@ class InferenceSlicer:
     Args:
         slice_wh (Tuple[int, int]): Dimensions of each slice measured in pixels. The
             tuple should be in the format `(width, height)`.
-        overlap_ratio_wh (Optional[Tuple[float, float]]): A tuple representing the
+        overlap_ratio_wh (Optional[Tuple[float, float]]): [⚠️ Deprecated: please set
+                to `None` and use `overlap_wh`] A tuple representing the
             desired overlap ratio for width and height between consecutive slices.
             Each value should be in the range [0, 1), where 0 means no overlap and
             a value close to 1 means high overlap.
         overlap_wh (Optional[Tuple[int, int]]): A tuple representing the desired
             overlap for width and height between consecutive slices measured in pixels.
-            Each value should be greater than or equal to 0.
+            Each value should be greater than or equal to 0. Takes precedence over
+            `overlap_ratio_wh`.
         overlap_filter (Union[OverlapFilter, str]): Strategy for
             filtering or merging overlapping detections in slices.
         iou_threshold (float): Intersection over Union (IoU) threshold
@@ -82,14 +83,6 @@ class InferenceSlicer:
         not a multiple of the slice's width or height minus the overlap.
     """
 
-    @deprecated_parameter(
-        old_parameter="overlap_filter_strategy",
-        new_parameter="overlap_filter",
-        map_function=lambda x: x,
-        warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
-        "be removed in `supervision-0.27.0`. Use '{new_parameter}' "
-        "instead.",
-    )
     def __init__(
         self,
         callback: Callable[[np.ndarray], Detections],
@@ -103,7 +96,8 @@ def __init__(
         if overlap_ratio_wh is not None:
             warn_deprecated(
                 "`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and "
-                "will be removed in `supervision-0.27.0`. Use `overlap_wh` instead."
+                "will be removed in `supervision-0.27.0`. Please manually set it to "
+                "`None` and use `overlap_wh` instead."
             )
 
         self._validate_overlap(overlap_ratio_wh, overlap_wh)

test/detection/tools/test_inference_slicer.py

@@ -0,0 +1,192 @@
+from contextlib import ExitStack as DoesNotRaise
+from typing import Optional, Tuple
+
+import numpy as np
+import pytest
+
+from supervision.detection.core import Detections
+from supervision.detection.overlap_filter import OverlapFilter
+from supervision.detection.tools.inference_slicer import InferenceSlicer
+
+
+@pytest.fixture
+def mock_callback():
+    """Mock callback function for testing."""
+
+    def callback(_: np.ndarray) -> Detections:
+        return Detections(xyxy=np.array([[0, 0, 10, 10]]))
+
+    return callback
+
+
+@pytest.mark.parametrize(
+    "slice_wh, overlap_ratio_wh, overlap_wh, expected_overlap, exception",
+    [
+        # Valid case: overlap_ratio_wh provided, overlap calculated from the ratio
+        ((128, 128), (0.2, 0.2), None, None, DoesNotRaise()),
+        # Valid case: overlap_wh in pixels, no ratio provided
+        ((128, 128), None, (20, 20), (20, 20), DoesNotRaise()),
+        # Invalid case: overlap_ratio_wh greater than 1, should raise ValueError
+        ((128, 128), (1.1, 0.5), None, None, pytest.raises(ValueError)),
+        # Invalid case: negative overlap_wh, should raise ValueError
+        ((128, 128), None, (-10, 20), None, pytest.raises(ValueError)),
+        # Invalid case:
+        # overlap_ratio_wh and overlap_wh provided, should raise ValueError
+        ((128, 128), (0.5, 0.5), (20, 20), (20, 20), pytest.raises(ValueError)),
+        # Valid case: no overlap_ratio_wh, overlap_wh = 50 pixels
+        ((256, 256), None, (50, 50), (50, 50), DoesNotRaise()),
+        # Valid case: overlap_ratio_wh provided, overlap calculated from (0.3, 0.3)
+        ((200, 200), (0.3, 0.3), None, None, DoesNotRaise()),
+        # Valid case: small overlap_ratio_wh values
+        ((100, 100), (0.1, 0.1), None, None, DoesNotRaise()),
+        # Invalid case: negative overlap_ratio_wh value, should raise ValueError
+        ((128, 128), (-0.1, 0.2), None, None, pytest.raises(ValueError)),
+        # Invalid case: negative overlap_ratio_wh with overlap_wh provided
+        ((128, 128), (-0.1, 0.2), (30, 30), None, pytest.raises(ValueError)),
+        # Invalid case: overlap_wh greater than slice size, should raise ValueError
+        ((128, 128), None, (150, 150), (150, 150), DoesNotRaise()),
+        # Valid case: overlap_ratio_wh is 0, no overlap
+        ((128, 128), (0.0, 0.0), None, None, DoesNotRaise()),
+        # Invalid case: no overlaps defined, no overlap
+        ((128, 128), None, None, None, pytest.raises(ValueError)),
+    ],
+)
+def test_inference_slicer_overlap(
+    mock_callback,
+    slice_wh: Tuple[int, int],
+    overlap_ratio_wh: Optional[Tuple[float, float]],
+    overlap_wh: Optional[Tuple[int, int]],
+    expected_overlap: Optional[Tuple[int, int]],
+    exception: Exception,
+) -> None:
+    with exception:
+        slicer = InferenceSlicer(
+            callback=mock_callback,
+            slice_wh=slice_wh,
+            overlap_ratio_wh=overlap_ratio_wh,
+            overlap_wh=overlap_wh,
+            overlap_filter=OverlapFilter.NONE,
+        )
+        assert slicer.overlap_wh == expected_overlap
+
+
+@pytest.mark.parametrize(
+    "resolution_wh, slice_wh, overlap_wh, expected_offsets",
+    [
+        # Case 1: No overlap, exact slices fit within image dimensions
+        (
+            (256, 256),
+            (128, 128),
+            (0, 0),
+            np.array(
+                [
+                    [0, 0, 128, 128],
+                    [128, 0, 256, 128],
+                    [0, 128, 128, 256],
+                    [128, 128, 256, 256],
+                ]
+            ),
+        ),
+        # Case 2: Overlap of 64 pixels in both directions
+        (
+            (256, 256),
+            (128, 128),
+            (64, 64),
+            np.array(
+                [
+                    [0, 0, 128, 128],
+                    [64, 0, 192, 128],
+                    [128, 0, 256, 128],
+                    [192, 0, 256, 128],
+                    [0, 64, 128, 192],
+                    [64, 64, 192, 192],
+                    [128, 64, 256, 192],
+                    [192, 64, 256, 192],
+                    [0, 128, 128, 256],
+                    [64, 128, 192, 256],
+                    [128, 128, 256, 256],
+                    [192, 128, 256, 256],
+                    [0, 192, 128, 256],
+                    [64, 192, 192, 256],
+                    [128, 192, 256, 256],
+                    [192, 192, 256, 256],
+                ]
+            ),
+        ),
+        # Case 3: Image not perfectly divisible by slice size (no overlap)
+        (
+            (300, 300),
+            (128, 128),
+            (0, 0),
+            np.array(
+                [
+                    [0, 0, 128, 128],
+                    [128, 0, 256, 128],
+                    [256, 0, 300, 128],
+                    [0, 128, 128, 256],
+                    [128, 128, 256, 256],
+                    [256, 128, 300, 256],
+                    [0, 256, 128, 300],
+                    [128, 256, 256, 300],
+                    [256, 256, 300, 300],
+                ]
+            ),
+        ),
+        # Case 4: Overlap of 32 pixels, image not perfectly divisible by slice size
+        (
+            (300, 300),
+            (128, 128),
+            (32, 32),
+            np.array(
+                [
+                    [0, 0, 128, 128],
+                    [96, 0, 224, 128],
+                    [192, 0, 300, 128],
+                    [288, 0, 300, 128],
+                    [0, 96, 128, 224],
+                    [96, 96, 224, 224],
+                    [192, 96, 300, 224],
+                    [288, 96, 300, 224],
+                    [0, 192, 128, 300],
+                    [96, 192, 224, 300],
+                    [192, 192, 300, 300],
+                    [288, 192, 300, 300],
+                    [0, 288, 128, 300],
+                    [96, 288, 224, 300],
+                    [192, 288, 300, 300],
+                    [288, 288, 300, 300],
+                ]
+            ),
+        ),
+        # Case 5: Image smaller than slice size (no overlap)
+        (
+            (100, 100),
+            (128, 128),
+            (0, 0),
+            np.array(
+                [
+                    [0, 0, 100, 100],
+                ]
+            ),
+        ),
+        # Case 6: Overlap_wh is greater than the slice size
+        ((256, 256), (128, 128), (150, 150), np.array([]).reshape(0, 4)),
+    ],
+)
+def test_generate_offset(
+    resolution_wh: Tuple[int, int],
+    slice_wh: Tuple[int, int],
+    overlap_wh: Optional[Tuple[int, int]],
+    expected_offsets: np.ndarray,
+) -> None:
+    offsets = InferenceSlicer._generate_offset(
+        resolution_wh=resolution_wh,
+        slice_wh=slice_wh,
+        overlap_ratio_wh=None,
+        overlap_wh=overlap_wh,
+    )
+
+    # Verify that the generated offsets match the expected offsets
+    assert np.array_equal(
+        offsets, expected_offsets
+    ), f"Expected {expected_offsets}, got {offsets}"