def shape_image_to_bbox(image: np.ndarray, bbox: BoundingBox,
thumbnail_size: Tuple[float, float]) -> np.ndarray:
# TODO: Change thumbnail size to paitypes.geometry.Shape
if bbox.is_empty():
return image
# TODO: Refactor into `image.resizing` module as a helper method to be
# shared across detectors.
# TODO: Use paitypes.geometry.Shape for the following logic
padded_bbox = pad_to_min_size(bbox, thumbnail_size)
padded_bbox = pad_abs_amount(padded_bbox, (20.0, 20.0))
aspect_ratio = (float(thumbnail_size[0]) / float(thumbnail_size[1]))
padded_bbox = pad_to_aspect_ratio(padded_bbox, aspect_ratio)
# mypy can't figure out that `image.shape[:2]` is limited to two cells
shape = (image.shape[0], image.shape[1])
cropped_bbox = snap_to_shape(padded_bbox, shape)
# TODO: Type this call properly.
cropped_image = crop_image_to_bounding_box(cast(Image, image),
cropped_bbox)
resized_image = cv2.resize(cropped_image,
thumbnail_size,
interpolation=cv2.INTER_AREA)
return resized_image
def crop_image_to_bounding_box(image: ImageT, bbox: BoundingBox) -> ImageT:
"""
Crops `image` to the dimensions of `bbox`.
"""
if image.shape[0] == 0 or image.shape[1] == 0:
raise ImageResizingException('image shape is invalid')
if bbox.is_empty():
raise ImageResizingException('bbox is empty')
y_min, y_max, x_min, x_max = (int(bbox.y_min), int(bbox.y_max),
int(bbox.x_min), int(bbox.x_max))
if (y_min < 0 or x_min < 0 or y_max > image.shape[0]
or x_max > image.shape[1]):
raise ImageResizingException('bbox outside image bounds')
return image[y_min:y_max, x_min:x_max]
def test_inverted_bbox_is_empty() -> None:
bbox = BoundingBox(10.0, 10.0, 0.0, 0.0)
assert (bbox.is_empty())