lejeunel/ksptrack repository overview

⁠Synopsis

KSPTrack is a method for the segmentation of video and volumetric sequences with sparse point supervision.

⁠Installation

This software depends on the following independent components that you will have to install first. Both make use of the C++ boost library⁠. The installation procedure are given in the respective github repositories as well as here for convenience.

We also provide a docker image that includes all requirements below at lejeunel/boost⁠.

⁠SLIC Supervoxels⁠

simple and efficient supervoxels.

git clone https://github.com/lejeunel/SLICsupervoxels
cd SLICsupervoxels
mkdir build
cd build
cmake ..
make
python3 src/setup.py install

⁠Edge-disjoint K-shortest paths⁠

C++ implementation. Uses the boost graph library.

git clone https://github.com/lejeunel/boost_ksp
cd boost_ksp
mkdir build
cd build
cmake ..
make
python3 src/setup.py install

⁠Install the whole thing

Once both external dependencies are installed, procede to the current package:

git clone https://github.com/lejeunel/KSPTrack
cd KSPTrack
pip install .
pip install -r requirements.txt

⁠Usage

All parameters used in this program are set in cfgs/cfg.py.

We provide two files depending on the availability of GPU:

• single_ksp.py: Uses a pre-trained VGG16 for feature-extraction. It requires no GPU.
• single_ksp_gpu.py: Trains and extracts features from a U-Net. It requires a GPU.