supervision/test/detection/tools/test_inference_slicer.py

193 lines
6.7 KiB
Python

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}"