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This is a data generator for 2D object detection with axis-aligned rectangular bounding boxes. Its main focus is on flexible and easily extensible data augmentation.
Main features:
- Lets you easily define arbitrary chains of image transformations.
- Provides most common photometric and geometric image transformations out of the box.
- Provides both random and deterministic versions of all image transformations.
- Provides flexible options for how decide whether a random transformation of an image is "valid" (see tutorial).
- Provides parsers for ground truth annotations in the MS COCO (JSON) and Pascal VOC (XML) formats as well as for a wide range of CSV formats.
Most data generators for training deep neural networks on computer vision tasks that ship with deep learning libraries are limited to image classification and those that I was able to find for 2D object detection are either only applicable to a specific model, provide only a small set of hard-coded and inflexible data augmentation features, or are otherwise severely limited. When you train a deep neural network on an object detection task, data augmentation one of the most important factors that determine the success of your training, therefore I wrote this data generator specifically with the goal of flexible and easily extensible data augmentation.
- Python 3.x
- Numpy
- OpenCV
- Beautiful Soup 4.x (to parse XML files)
Python 2 compatibility: This implementation seems to work with Python 2.7, but I don't provide any support for it. It's 2018 and nobody should be using Python 2 anymore.
Everything you need to know in order to use this data generator is explained in the following Jupyter notebook: