def _extract_spatial_features(image: Image) -> np.ndarray:
shape = Shape.from_image(image)
features: List[np.ndarray] = []
while shape.height >= 2 and shape.width >= 2:
shape = Shape(shape.height / 2, shape.width / 2)
shape_int = (int(shape.height), int(shape.width))
features += [cv2.resize(image, shape_int).ravel()]
return np.concatenate(features)
def resize_image_to_size(image: ImageT,
target_size: Shape,
interpolation_method: int = cv2.INTER_AREA) -> ImageT:
image_size = Shape.from_image(image)
if image_size.width <= 0 or image_size.height <= 0:
raise ImageResizingException('image shape is invalid')
if target_size.width <= 0 or target_size.height <= 0:
raise ImageResizingException('target_size is invalid')
return cv2.resize(image, (target_size.width, target_size.height),
interpolation_method)
def test_scale_around_corners_maintains_corners(bbox: BoundingBox, sx: float,
sy: float) -> None:
scaled_bbox = bbox.scale(Shape(sx, sy),
center=Point(bbox.x_min, bbox.y_min))
assert scaled_bbox.x_min == approx(bbox.x_min, abs=1e-6)
assert scaled_bbox.y_min == approx(bbox.y_min, abs=1e-6)
scaled_bbox = bbox.scale(Shape(sx, sy),
center=Point(bbox.x_min, bbox.y_max))
assert scaled_bbox.x_min == approx(bbox.x_min, abs=1e-6)
assert scaled_bbox.y_max == approx(bbox.y_max, abs=1e-6)
scaled_bbox = bbox.scale(Shape(sx, sy),
center=Point(bbox.x_max, bbox.y_min))
assert scaled_bbox.x_max == approx(bbox.x_max, abs=1e-6)
assert scaled_bbox.y_min == approx(bbox.y_min, abs=1e-6)
scaled_bbox = bbox.scale(Shape(sx, sy),
center=Point(bbox.x_max, bbox.y_max))
assert scaled_bbox.x_max == approx(bbox.x_max, abs=1e-6)
assert scaled_bbox.y_max == approx(bbox.y_max, abs=1e-6)
def test_resize_empty_image_to_size_raises(
self, empty_images: List[Image]) -> None:
for empty_image in empty_images:
with pytest.raises(ImageResizingException):
resize_image_to_size(empty_image, Shape(30, 30))
class TestResizeImageToSize():
@pytest.mark.parametrize(
'size',
[Shape(30, 20),
Shape(100, 50),
Shape(100, 100),
Shape(2000, 5000)])
def test_resize_image_to_valid_size(self, all_valid_images: List[Image],
size: Shape) -> None:
for image in all_valid_images:
resized_image = resize_image_to_size(image, size)
assert (resized_image.shape[:2] == (size.height, size.width))
assert (len(resized_image.shape) == len(image.shape))
@pytest.mark.parametrize('size', [
Shape(-100, -100),
Shape(-100, 1),
Shape(1, -100),
Shape(0, 0),
Shape(1, 0),
Shape(0, 1),
Shape(-100, 100),
Shape(100, -100)
])
def test_resize_image_to_invalid_size_raises(self,
all_valid_images: List[Image],
size: Shape) -> None:
for image in all_valid_images:
with pytest.raises(ImageResizingException):
resize_image_to_size(image, size)
def test_resize_empty_image_to_size_raises(
self, empty_images: List[Image]) -> None:
for empty_image in empty_images:
with pytest.raises(ImageResizingException):
resize_image_to_size(empty_image, Shape(30, 30))
def test_scale_around_center_maintains_center(bbox: BoundingBox, sx: float,
sy: float) -> None:
scaled_bbox = bbox.scale(Shape(sx, sy), center=bbox.center)
assert scaled_bbox.center.x == approx(bbox.center.x, abs=1e-6)
assert scaled_bbox.center.y == approx(bbox.center.y, abs=1e-6)
def test_scale_around_center_changes_size(bbox: BoundingBox, sx: float,
sy: float) -> None:
scaled_bbox = bbox.scale(Shape(sx, sy), center=bbox.center)
assert scaled_bbox.delta_x == approx(bbox.delta_x * sx, abs=1e-6)
assert scaled_bbox.delta_y == approx(bbox.delta_y * sy, abs=1e-6)
def test_scale_around_origin_moves_center(bbox: BoundingBox, sx: float,
sy: float) -> None:
scaled_bbox = bbox.scale(Shape(sx, sy))
assert scaled_bbox.center.x == approx(bbox.center.x * sx, abs=1e-6)
assert scaled_bbox.center.y == approx(bbox.center.y * sy, abs=1e-6)