supervision/test/dataset/formats/test_yolo.py

280 lines
9.1 KiB
Python

from contextlib import ExitStack as DoesNotRaise
from typing import List, Optional, Tuple
import numpy as np
import pytest
from supervision.dataset.formats.yolo import (
_image_name_to_annotation_name,
_with_mask,
object_to_yolo,
yolo_annotations_to_detections,
)
from supervision.detection.core import Detections
def _mock_simple_mask(resolution_wh: Tuple[int, int], box: List[int]) -> np.array:
x_min, y_min, x_max, y_max = box
mask = np.full(resolution_wh, False, dtype=bool)
mask[y_min:y_max, x_min:x_max] = True
return mask
# The result of _mock_simple_mask is a little different from the result produced by cv2.
def _arrays_almost_equal(
arr1: np.ndarray, arr2: np.ndarray, threshold: float = 0.99
) -> bool:
equal_elements = np.equal(arr1, arr2)
proportion_equal = np.mean(equal_elements)
return proportion_equal >= threshold
@pytest.mark.parametrize(
"lines, expected_result, exception",
[
([], False, DoesNotRaise()), # empty yolo annotation file
(
["0 0.5 0.5 0.2 0.2"],
False,
DoesNotRaise(),
), # yolo annotation file with single line with box
(
["0 0.50 0.50 0.20 0.20", "1 0.11 0.47 0.22 0.30"],
False,
DoesNotRaise(),
), # yolo annotation file with two lines with box
(["0 0.5 0.5 0.2 0.2"], False, DoesNotRaise()),
(
["0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6"],
True,
DoesNotRaise(),
), # yolo annotation file with single line with polygon
(
["0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6", "1 0.11 0.47 0.22 0.30"],
True,
DoesNotRaise(),
), # yolo annotation file with two lines - one box and one polygon
],
)
def test_with_mask(
lines: List[str], expected_result: Optional[bool], exception: Exception
) -> None:
with exception:
result = _with_mask(lines=lines)
assert result == expected_result
@pytest.mark.parametrize(
"lines, resolution_wh, with_masks, expected_result, exception",
[
(
[],
(1000, 1000),
False,
Detections.empty(),
DoesNotRaise(),
), # empty yolo annotation file
(
["0 0.5 0.5 0.2 0.2"],
(1000, 1000),
False,
Detections(
xyxy=np.array([[400, 400, 600, 600]], dtype=np.float32),
class_id=np.array([0], dtype=int),
),
DoesNotRaise(),
), # yolo annotation file with single line with box
(
["0 0.50 0.50 0.20 0.20", "1 0.11 0.47 0.22 0.30"],
(1000, 1000),
False,
Detections(
xyxy=np.array(
[[400, 400, 600, 600], [0, 320, 220, 620]], dtype=np.float32
),
class_id=np.array([0, 1], dtype=int),
),
DoesNotRaise(),
), # yolo annotation file with two lines with box
(
["0 0.5 0.5 0.2 0.2"],
(1000, 1000),
True,
Detections(
xyxy=np.array([[400, 400, 600, 600]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
_mock_simple_mask(
resolution_wh=(1000, 1000), box=[400, 400, 600, 600]
)
],
dtype=bool,
),
),
DoesNotRaise(),
), # yolo annotation file with single line with box in with_masks mode
(
["0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6"],
(1000, 1000),
True,
Detections(
xyxy=np.array([[400, 400, 600, 600]], dtype=np.float32),
class_id=np.array([0], dtype=int),
mask=np.array(
[
_mock_simple_mask(
resolution_wh=(1000, 1000), box=[400, 400, 600, 600]
)
],
dtype=bool,
),
),
DoesNotRaise(),
), # yolo annotation file with single line with polygon
(
["0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6", "1 0.11 0.47 0.22 0.30"],
(1000, 1000),
True,
Detections(
xyxy=np.array(
[[400, 400, 600, 600], [0, 320, 220, 620]], dtype=np.float32
),
class_id=np.array([0, 1], dtype=int),
mask=np.array(
[
_mock_simple_mask(
resolution_wh=(1000, 1000), box=[400, 400, 600, 600]
),
_mock_simple_mask(
resolution_wh=(1000, 1000), box=[0, 320, 220, 620]
),
],
dtype=bool,
),
),
DoesNotRaise(),
), # yolo annotation file with two lines -
# one box and one polygon in with_masks mode
(
["0 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.6", "1 0.11 0.47 0.22 0.30"],
(1000, 1000),
False,
Detections(
xyxy=np.array(
[[400, 400, 600, 600], [0, 320, 220, 620]], dtype=np.float32
),
class_id=np.array([0, 1], dtype=int),
),
DoesNotRaise(),
), # yolo annotation file with two lines - one box and one polygon
],
)
def test_yolo_annotations_to_detections(
lines: List[str],
resolution_wh: Tuple[int, int],
with_masks: bool,
expected_result: Optional[Detections],
exception: Exception,
) -> None:
with exception:
result = yolo_annotations_to_detections(
lines=lines, resolution_wh=resolution_wh, with_masks=with_masks
)
assert np.array_equal(result.xyxy, expected_result.xyxy)
assert np.array_equal(result.class_id, expected_result.class_id)
assert (
result.mask is None and expected_result.mask is None
) or _arrays_almost_equal(result.mask, expected_result.mask)
@pytest.mark.parametrize(
"image_name, expected_result, exception",
[
("image.png", "image.txt", DoesNotRaise()), # simple png image
("image.jpeg", "image.txt", DoesNotRaise()), # simple jpeg image
("image.jpg", "image.txt", DoesNotRaise()), # simple jpg image
(
"image.000.jpg",
"image.000.txt",
DoesNotRaise(),
), # jpg image with multiple dots in name
],
)
def test_image_name_to_annotation_name(
image_name: str, expected_result: Optional[str], exception: Exception
) -> None:
with exception:
result = _image_name_to_annotation_name(image_name=image_name)
assert result == expected_result
@pytest.mark.parametrize(
"xyxy, class_id, image_shape, polygon, expected_result, exception",
[
(
np.array([100, 100, 200, 200], dtype=np.float32),
1,
(1000, 1000, 3),
None,
"1 0.15000 0.15000 0.10000 0.10000",
DoesNotRaise(),
), # square bounding box on square image
(
np.array([100, 100, 200, 200], dtype=np.float32),
1,
(800, 1000, 3),
None,
"1 0.15000 0.18750 0.10000 0.12500",
DoesNotRaise(),
), # square bounding box on horizontal image
(
np.array([100, 100, 200, 200], dtype=np.float32),
1,
(1000, 800, 3),
None,
"1 0.18750 0.15000 0.12500 0.10000",
DoesNotRaise(),
), # square bounding box on vertical image
(
np.array([100, 200, 200, 400], dtype=np.float32),
1,
(1000, 1000, 3),
None,
"1 0.15000 0.30000 0.10000 0.20000",
DoesNotRaise(),
), # horizontal bounding box on square image
(
np.array([200, 100, 400, 200], dtype=np.float32),
1,
(1000, 1000, 3),
None,
"1 0.30000 0.15000 0.20000 0.10000",
DoesNotRaise(),
), # vertical bounding box on square image
(
np.array([100, 100, 200, 200], dtype=np.float32),
1,
(1000, 1000, 3),
np.array(
[[100, 100], [200, 100], [200, 200], [100, 100]], dtype=np.float32
),
"1 0.10000 0.10000 0.20000 0.10000 0.20000 0.20000 0.10000 0.10000",
DoesNotRaise(),
), # square mask on square image
],
)
def test_object_to_yolo(
xyxy: np.ndarray,
class_id: int,
image_shape: Tuple[int, int, int],
polygon: Optional[np.ndarray],
expected_result: Optional[str],
exception: Exception,
) -> None:
with exception:
result = object_to_yolo(
xyxy=xyxy, class_id=class_id, image_shape=image_shape, polygon=polygon
)
assert result == expected_result