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update `denormalize_boxes` docstring and examples

This commit is contained in:
SkalskiP 2025-11-14 12:53:58 +01:00
parent 35272b40d3
commit f563c5a634
2 changed files with 40 additions and 31 deletions
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67
supervision/detection/utils/boxes.py
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@ -95,24 +95,27 @@ def pad_boxes(xyxy: np.ndarray, px: int, py: int | None = None) -> np.ndarray:
def denormalize_boxes(
normalized_xyxy: np.ndarray,
xyxy: np.ndarray,
resolution_wh: tuple[int, int],
normalization_factor: float = 1.0,
) -> np.ndarray:
"""
Converts normalized bounding box coordinates to absolute pixel values.
Convert normalized bounding box coordinates to absolute pixel coordinates.
Multiplies each bounding box coordinate by image size and divides by
`normalization_factor`, mapping values from normalized `[0, normalization_factor]`
to absolute pixel values for a given resolution.
Args:
normalized_xyxy (np.ndarray): A numpy array of shape `(N, 4)` where each row
contains normalized coordinates in the format `(x_min, y_min, x_max, y_max)`,
with values between 0 and `normalization_factor`.
resolution_wh (Tuple[int, int]): A tuple `(width, height)` representing the
target image resolution.
normalization_factor (float, optional): The normalization range of the input
coordinates. Defaults to 1.0.
xyxy (`numpy.ndarray`): Normalized bounding boxes of shape `(N, 4)`,
where each row is `(x_min, y_min, x_max, y_max)`, values in
`[0, normalization_factor]`.
resolution_wh (`tuple[int, int]`): Target image resolution as `(width, height)`.
normalization_factor (`float`): Maximum value of input coordinate range.
Defaults to `1.0`.
Returns:
np.ndarray: An array of shape `(N, 4)` with absolute coordinates in
(`numpy.ndarray`): Array of shape `(N, 4)` with absolute coordinates in
`(x_min, y_min, x_max, y_max)` format.
Examples:
@ -120,32 +123,38 @@ def denormalize_boxes(
import numpy as np
import supervision as sv
# Default normalization (0-1)
normalized_xyxy = np.array([
[0.1, 0.2, 0.5, 0.6],
[0.3, 0.4, 0.7, 0.8]
[0.3, 0.4, 0.7, 0.8],
[0.2, 0.1, 0.6, 0.5]
])
resolution_wh = (100, 200)
sv.denormalize_boxes(normalized_xyxy, resolution_wh)
sv.denormalize_boxes(normalized_xyxy, (1280, 720))
# array([
# [ 10., 40., 50., 120.],
# [ 30., 80., 70., 160.]
# ])
# Custom normalization (0-100)
normalized_xyxy = np.array([
[10., 20., 50., 60.],
[30., 40., 70., 80.]
])
sv.denormalize_boxes(normalized_xyxy, resolution_wh, normalization_factor=100.0)
# array([
# [ 10., 40., 50., 120.],
# [ 30., 80., 70., 160.]
# [128., 144., 640., 432.],
# [384., 288., 896., 576.],
# [256., 72., 768., 360.]
# ])
```
""" # noqa E501 // docs
```
import numpy as np
import supervision as sv
normalized_xyxy = np.array([
[256., 128., 768., 640.]
])
result = sv.denormalize_boxes(
normalized_xyxy,
(1280, 720),
normalization_factor=1024.0
)
# array([
# [320., 90., 960., 450.]
# ])
```
"""
width, height = resolution_wh
result = normalized_xyxy.copy()
result = xyxy.copy()
result[:, [0, 2]] = (result[:, [0, 2]] * width) / normalization_factor
result[:, [1, 3]] = (result[:, [1, 3]] * height) / normalization_factor

4
test/detection/utils/test_boxes.py
View File

@ -219,7 +219,7 @@ def test_scale_boxes(
],
)
def test_denormalize_boxes(
normalized_xyxy: np.ndarray,
xyxy: np.ndarray,
resolution_wh: tuple[int, int],
normalization_factor: float,
expected_result: np.ndarray,
@ -227,7 +227,7 @@ def test_denormalize_boxes(
) -> None:
with exception:
result = denormalize_boxes(
normalized_xyxy=normalized_xyxy,
xyxy=xyxy,
resolution_wh=resolution_wh,
normalization_factor=normalization_factor,
)