def rotate(img: np.ndarray, ann: Annotation, degrees: float, mode: str=RotationModes.KEEP) ->\
(np.ndarray, Annotation): # @TODO: add "preserve_size" mode
"""
Rotates the image by random angle.
Args:
img: Input image array.
ann: Input annotation.
degrees: Rotation angle, counter-clockwise.
mode: parameter: "keep" - keep original image data, then new regions will be filled with black color;
"crop" - crop rotated result to exclude black regions;
Returns:
A tuple containing rotated image array and annotation.
"""
_validate_image_annotation_shape(img, ann)
rotator = ImageRotator(img.shape[:2], degrees)
if mode == RotationModes.KEEP:
rect_to_crop = None
elif mode == RotationModes.CROP:
rect_to_crop = rotator.inner_crop
else:
raise NotImplementedError('Wrong black_regions mode.')
res_img = rotator.rotate_img(img, use_inter_nearest=False)
res_ann = ann.rotate(rotator)
if rect_to_crop is not None:
res_img = sly_image.crop(res_img, rect_to_crop)
res_ann = res_ann.relative_crop(rect_to_crop)
return res_img, res_ann
def crop(img: np.ndarray,
ann: Annotation,
top_pad: int = 0,
left_pad: int = 0,
bottom_pad: int = 0,
right_pad: int = 0) -> (np.ndarray, Annotation):
"""
Crops the given image array and annotation from all sides with the given values.
Args:
img: Input image array.
ann: Input annotation.
top_pad: The size in pixels of the piece of picture that will be cut from the top side.
left_pad: The size in pixels of the piece of picture that will be cut from the left side.
bottom_pad: The size in pixels of the piece of picture that will be cut from the bottom side.
right_pad: The size in pixels of the piece of picture that will be cut from the right side.
Returns:
A tuple containing cropped image array and annotation.
"""
_validate_image_annotation_shape(img, ann)
height, width = img.shape[:2]
crop_rect = Rectangle(top_pad, left_pad, height - bottom_pad - 1,
width - right_pad - 1)
res_img = sly_image.crop(img, crop_rect)
res_ann = ann.relative_crop(crop_rect)
return res_img, res_ann
def instance_crop(img: np.ndarray,
ann: Annotation,
class_title: str,
save_other_classes_in_crop: bool = True,
padding_config: dict = None) -> list:
"""
Crops objects of specified classes from image with configurable padding.
Args:
img: Input image array.
ann: Input annotation.
class_title: Name of class to crop.
save_other_classes_in_crop: save non-target classes in each cropped annotation.
padding_config: Dict with padding
Returns:
List of cropped [image, annotation] pairs.
"""
padding_config = take_with_default(padding_config, {})
_validate_image_annotation_shape(img, ann)
results = []
img_rect = Rectangle.from_size(img.shape[:2])
if save_other_classes_in_crop:
non_target_labels = [
label for label in ann.labels
if label.obj_class.name != class_title
]
else:
non_target_labels = []
ann_with_non_target_labels = ann.clone(labels=non_target_labels)
for label in ann.labels:
if label.obj_class.name == class_title:
src_fig_rect = label.geometry.to_bbox()
new_img_rect = _rect_from_bounds(padding_config,
img_w=src_fig_rect.width,
img_h=src_fig_rect.height)
rect_to_crop = new_img_rect.translate(src_fig_rect.top,
src_fig_rect.left)
crops = rect_to_crop.crop(img_rect)
if len(crops) == 0:
continue
rect_to_crop = crops[0]
image_crop = sly_image.crop(img, rect_to_crop)
cropped_ann = ann_with_non_target_labels.relative_crop(
rect_to_crop)
label_crops = label.relative_crop(rect_to_crop)
for label_crop in label_crops:
results.append((image_crop, cropped_ann.add_label(label_crop)))
return results