def _get_annotation_for_bbox(img: np.ndarray, roi: Rectangle, model) -> Annotation:
"""Runs inference within the given roi; moves resulting figures to global reference frame."""
img_cropped = roi.get_cropped_numpy_slice(img)
# TODO pass through image and parent figure tags via roi_ann.
roi_ann = Annotation(img_size=(roi.height, roi.width))
raw_result_ann = model.inference(img_cropped, roi_ann)
return Annotation(img_size=img.shape[:2],
labels=[label.translate(drow=roi.top, dcol=roi.left) for label in raw_result_ann.labels],
img_tags=raw_result_ann.img_tags, img_description=raw_result_ann.img_description,
pixelwise_scores_labels=[label.translate(drow=roi.top, dcol=roi.left)
for label in raw_result_ann.pixelwise_scores_labels])
def crop(img: np.ndarray, rect: Rectangle) -> np.ndarray:
'''
The function crop cut out part of the image with rectangle size. If rectangle for crop is out of image area it
generates exception error(ValueError).
:param img: image(numpy matrix) to be cropped
:param rect: class object Rectangle of a certain size
:return: cropped image
'''
img_rect = Rectangle.from_array(img)
if not img_rect.contains(rect):
raise ValueError('Rectangle for crop out of image area!')
return rect.get_cropped_numpy_slice(img)
def crop_with_padding(img: np.ndarray, rect: Rectangle) -> np.ndarray:
'''
The function crop cut out part of the image with rectangle size. If rectangle for crop is out of image area it
generates additional padding.
:param img: image(numpy matrix) to be cropped
:param rect: class object Rectangle of a certain size
:return: cropped image
'''
img_rect = Rectangle.from_array(img)
if not img_rect.contains(rect):
row, col = img.shape[:2]
new_img = cv2.copyMakeBorder(img,
top=rect.height,
bottom=rect.height,
left=rect.width,
right=rect.width,
borderType=cv2.BORDER_CONSTANT)
new_rect = rect.translate(drow=rect.height, dcol=rect.width)
return new_rect.get_cropped_numpy_slice(new_img)
else:
return rect.get_cropped_numpy_slice(img)
def crop(img: np.ndarray, rect: Rectangle) -> np.ndarray:
img_rect = Rectangle.from_array(img)
if not img_rect.contains(rect):
raise ValueError('Rectangle for crop out of image area!')
return rect.get_cropped_numpy_slice(img)
