imgaug_transform

lightning_pose.data.augmentations.imgaug_transform(params_dict: dict | DictConfig) → Sequential

Create simple and flexible data transform pipeline that augments images and keypoints.

Parameters:

params_dict – each key must be the name of a transform importable from imgaug.augmenters, e.g. “Affine”, “Fliplr”, etc. The value must be a dict with several optional keys: - “p” (float): probability of applying transform (using imgaug.augmenters.Sometimes) - “args” (list): arguments for transform - “kwargs” (dict): keyword args for the transformation

Examples

Create a pipeline with - Affine transformation applied 50% of the time with rotation uniformly sampled from

(-25, 25) degrees

• MotionBlur transformation that is applied 25% of the time with a kernel size of 5 pixels and blur direction uniformly sampled from (-90, 90) degrees

>>> params_dict = {
>>>    'Affine': {'p': 0.5, 'kwargs': {'rotate': (-25, 25)}},
>>>    'MotionBlur': {'p': 0.25, 'kwargs': {'k': 5, 'angle': (-90, 90)}},
>>> }

In a config file, this will look like: >>> training: >>> imgaug: >>> Affine: >>> p: 0.5 >>> kwargs: >>> rotate: [-10, 10] >>> MotionBlur: >>> p: 0.25 >>> kwargs: >>> k: 5 >>> angle: [-90, 90]

Returns:

imgaug pipeline