This is a mirror of (https://gitlab.tubit.tu-berlin.de/rbo-lab/rbo-apc-object-segmentation).

• python

• opencv -- if you don't have opencv, you can install it using:

    sudo apt-get install python-opencv
  

• gco_python (https://github.com/amueller/gco_python)

    pip install --user pygco
  

• sklearn (>= 0.16)

• matplotlib (>= 1.5.0)

python setup.py install

The following command reproduces the experiments and plots in the paper (modulo minor editing to improve the readability of the plots):

python main.py

This repository includes the source code and a preprocessed version of the data (in the data/cache/ directory). These preprocessed data consist of different .pkl files (which can be loaded with python's pickle module). Each of these files contains a set of data samples (see class APCDataSet in apc_data.py). Every data sample includes precomputed feature images that are cropped to only include the target bin and are annotated with masks for the different objects (see class APCSample in apc_data.py).

If you need access to the raw data (complete RGB-D images, feature images, and masks), e.g. because you want to extend this code or compare your own code against it, you can find the raw data there:

https://owncloud.tu-berlin.de/public.php?service=files&t=709f973be5e5d18ef5aa2a0b3c83221f

To use the raw data instead of the cached data in the main script, copy the data into data/rbo_apc and in main.py use compute_datasets instead of load_datasets:

if __name__ == "__main__":

    ... 

    datasets = compute_datasets(dataset_names, dataset_path, cache_path) # compute from raw data
    #datasets = load_datasets(dataset_names, dataset_path, cache_path) # load from cached data

    ...