danny0559 / ssn_superpixels Goto Github PK

This is the code accompanying the ECCV 2018 publication on Superpixel Sampling Networks. Please visit the project website for more details about the paper and overall methodology.

Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

1. Go to 'lib' folder if you are not already there:

cd ssn_superpixels/lib/

2. We make use of layers in 'Video Propagation Networks' caffe repository and add additional layers for SSN superpixels:

git clone https://github.com/varunjampani/video_prop_networks.git

3. Manually copy all the source files (files in lib/include and lib/src folders) to the corresponding locations in the caffe repository. In the ssn_superpixels/lib directory:

cp src/caffe/layers/* video_prop_networks/lib/caffe/src/caffe/layers/.
cp src/caffe/test/* video_prop_networks/lib/caffe/src/caffe/test/.
cp src/caffe/proto/caffe.proto video_prop_networks/lib/caffe/src/caffe/proto/caffe.proto
cp include/caffe/layers/* video_prop_networks/lib/caffe/include/caffe/layers/.

4. Install Caffe following the installation instructions. In the ssn_superpixels/lib directory:

cd video_prop_networks/lib/caffe/
mkdir build
cd build
cmake ..
make -j
cd ../../../..

Note: If you install Caffe in some other folder, update CAFFEDIR in config.py accordingly.

We use a cython script taken from 'scikit-image' for enforcing connectivity in superpixels. To compile this:

cd lib/cython/
python setup.py install --user
cd ../..

Download the BSDS dataset into data folder:

cd data
sh get_bsds.sh
cd ..

1. First download the trained segmentation models using the get_models.sh script in the models folder:

cd models
sh get_models.sh
cd ..

2. Use compute_ssn_spixels.py to compute superpixels on BSDS dataset:

python compute_ssn_spixels.py  --datatype TEST --n_spixels 100 --num_steps 10 --caffemodel ./models/ssn_bsds_model.caffemodel --result_dir ./bsds_100/

You can change the number of superpixels by changing the n_spixels argument above, and you can update the datatype to TRAIN or VAL to compute superpixels on the corresponding data splits.

If you want to compute superpixels on other datasets, update config.py accordingly.

For superpixel evaluation, we use scripts from here for computing ASA score and scripts from here for computing Precision-Recall and other evaluation metrics.

Use train_ssn.py to train on BSDS training dataset:

python train_ssn.py --l_rate=0.0001 --num_steps=10

Please consider citing the below paper if you make use of this work and/or the corresponding code:

@inproceedings{jampani18ssn,
	title = {Superpixel Samping Networks},
	author={Jampani, Varun and Sun, Deqing and Liu, Ming-Yu and Yang, Ming-Hsuan and Kautz, Jan},
	booktitle = {European Conference on Computer Vision (ECCV)},
	month = September,
	year = {2018}
}