This is the code from the paper "Soccer Field Boundary Detection Using Convolutional Neural Networks".
The directory Environment contains a script to build and launch a docker image.
The dataset is located at https://sibylle.informatik.uni-bremen.de/public/datasets/fieldboundary. It can be downloaded by running python downloader.py within the Dataset-Pipeline directory.
To label new data, Dataset-Pipeline/labeltool.py can be used. It expects the path to a directory with PNG images as argument and creates the file labels.csv in that directory. See the comment in Dataset-Pipeline/labeltool.py for further instructions. The labeled directory must be converted to HDF5 using Dataset-Pipeline/converter.py. The argument must be the directory which contains the image directory (the idea is to have a directory tree with image directories which is transformed into a set of HDF5 files). Finally, the script Dataset-Pipeline/dataset/linker.py creates a single HDF5 file which links to the original datasets.
The dataset can be inspected using hdf5-viewer.py (e.g. python hdf5-viewer.py Dataset-Pipeline/datasets/fieldboundary.hdf5).
Training is configured in JSON files in Training-Pipeline/settings. The script Training-Pipeline/train_routine.py with a settings file as argument performs the training. This will create a directory in Training-Pipeline/checkpoints which contains the trained models per epoch and metadata. This directory can afterwards be passed to the Training-Pipeline/evaluate.py to generate numbers. Training-Pipeline/test_routine.py with a specific model allows to visually inspect the results of the model.