imgaug is a library for image augmentation in machine learning experiments. It supports a wide range of augmentation techniques, allows to easily combine these and to execute them in random order or on multiple CPU cores, has a simple yet powerful stochastic interface and can not only augment images, but also keypoints/landmarks, bounding boxes, heatmaps and segmentation maps.
Example augmentations of a single input image.
.. toctree:: :maxdepth: 3 :caption: Contents: source/installation source/examples_basics source/examples_keypoints source/examples_bounding_boxes source/examples_heatmaps source/examples_segmentation_maps source/parameters source/alpha source/augmenters source/performance source/dtype_support source/jupyter_notebooks source/api
See :ref:`modindex` for API.