def load_image(image_path, new_size=None):
"""
Load image.
Args:
image_path: Path to image to load.
new_size: new image dimensions(tuple).
Returns:
numpy array(image), image_path
"""
image_path = get_abs_path(image_path, verify=True)
image = cv2.imread(image_path)
if image is None:
LOGGER.warning(f'Failed to read image: {image_path}')
return
if new_size:
return cv2.resize(image, new_size)
return image, image_path
def update_data(self, bbs_aug, frame_before, image_aug, new_name,
new_path):
"""
Update new bounding boxes data and save augmented image.
Args:
bbs_aug: Augmented bounding boxes
frame_before: pandas DataFrame containing pre-augmentation data.
image_aug: Augmented image as numpy nd array.
new_name: new image name to save image.
new_path: path to save the image.
Returns:
None
"""
frame_after = pd.DataFrame(
bbs_aug.remove_out_of_image().clip_out_of_image().bounding_boxes,
columns=['x1y1', 'x2y2'],
)
if (frame_after.empty
): # some post-augmentation photos do not contain bounding boxes
LOGGER.warning(
f'\nskipping image: {new_name}: no bounding boxes after '
f'augmentation')
return
frame_after = pd.DataFrame(
np.hstack(
(frame_after['x1y1'].tolist(), frame_after['x2y2'].tolist())),
columns=['x1', 'y1', 'x2', 'y2'],
).astype('int64')
frame_after['object_type'] = frame_before['object_type'].values
frame_after['object_id'] = frame_before['object_id'].values
frame_after['image'] = new_name
for index, row in frame_after.iterrows():
x1, y1, x2, y2, object_type, object_id, image_name = row
bx, by, bw, bh = self.calculate_ratios(x1, y1, x2, y2)
self.augmentation_data.append(
[image_name, object_type, object_id, bx, by, bw, bh])
cv2.imwrite(new_path, image_aug)
