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)