During the implementation of object detection frameworks, often experiments with multiple datasets are required. This involves accessing bounding box groundtruth annotations of different object categories in different datasets. This is a simple-looking task that is made a little challenging by varying formats in which different datasets organize their groundtruth annotations.

This project provides a framework to quickly read bounding box annotations of different datasets without having to worry about the actual details of native storage formats. This project has been initialized with a codebase using which a number of major pedestrian detection datasets can be easily read. It will be regularly updated to include other pedestrian and non-pedestrian datasets as well.

1. Python 2.7 (with PIL package)

For a user there are just two pre-requisites :

1. Make sure that a class corresponding to the dataset exists in the project.
2. Make sure that you have downloaded and stored the dataset inside a specific parent folder (we call it base_path)

NOTE: If 1. is not true, please look down at pre-requisites for developers.

1. Give a reading to StarsDatasets.py.
2. Read the inheriting class implementations for different datasets to get a clear idea of how the framework is written.
3. Always assume that a user will provide a list of image filenames (with full file names) for which bounding box annotations are required.
4. MAKE SURE that once you write an implementation of your own dataset, you update an example in test.py and send a pull request to update the project and make it more useful to a wider community.

Please see test.py