random-walker-road-detection

Implementation of paper: G. K. Siogkas and E. S. Dermatas, "Random-Walker Monocular Road Detection in Adverse Conditions Using Automated Spatiotemporal Seed Selection," in IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 527-538, June 2013.

doi: 10.1109/TITS.2012.2223686

URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6338335&isnumber=6521414

A commentary on the making of and rationale behind this repo can be found in a series of my LinkedIn articles (missing the last part for some years now, but it's still complete enough as it is):

https://www.linkedin.com/pulse/dr-reproducible-research-how-i-learned-stop-worrying-love-siogkas/

https://www.linkedin.com/pulse/dr-reproducible-research-how-i-learned-stop-worrying-love-siogkas-1f/?trackingId=VSuHoyReQmqYdotYvxvFGQ%3D%3D

The commentary is also available at my website: https://www.cvrlab.com/post/2019-11-05-dr-reproducible-research/

Python implementation

Dependencies

• Python 3+ (tested on Python 3.11.5 in Windows and in Linux via WSL2).
• Miniconda / Anaconda
• The first thing we need, is a module that allows for image I/O and as many basic manipulations as possible. We could go for OpenCV, but it would be an overkill and we could be even more Pythonic in this exercise. Also, we would like to be closer to Matlab style, to be able to compare the methods easier. For this reason, I chose scikit-image.
• For general array manipulations, indexing and code vectorization to be possible, of course numpy.
• For visualization, matplotlib and for easy streaming visualization at runtime (optional), drawnow.
• For file reading, directory content lists etc, os from native Python.
• For optical flow, there is a handy module named pyoptflow.
• For Otsu's thresholding method, scikit-image provides an implementation in filters.
• For basic processing time monitoring and logging (optional), time and logging from native Python will be used (I love it when things are so obvious).
• For nice process bars (optional), tqdm.
• Bonus track: the most difficult and tricky part is random walker. This is included in scikit-image. For performance, we should also install pyamg, numexpr and scikit-umfpack.
• A nice-to-have couple of modules that may come in handy, are also scikit-learn and scipy.
• Finally, jupyter for using ipython and jupyter notebooks. Also, (optional) jupyterlab.

Setting up your environment using conda

Linux

Concluding, you can easily recreate my Linux python environment by installing miniconda from https://conda.io/miniconda.html and then:

Option 1: create the environment manually:

conda create -n rdpaper python numpy scikit-image scikit-learn matplotlib scipy jupyter tqdm pyamg numexpr
conda install -c conda-forge cython suitesparse scikit-sparse
source activate rdpaper
pip install pyoptflow drawnow gdown

Option 2: Create the environment using my image:

conda env create -f linux_environment.yml

Windows

For Windows, it took some tweaking, but these are the equivalent steps:

Option 1: create the environment manually:

conda create -n rdpaper python numpy scikit-image scikit-learn matplotlib scipy jupyter tqdm pyamg numexpr
conda install -c conda-forge cython suitesparse scikit-sparse
source activate rdpaper
pip install pyoptflow drawnow gdown

Option 2: Create the environment using my image:

conda env create -f windows_environment.yml

Basic usage

You can run a smoke test on the 2 images included here by running test() from inside the python folder:

import road_detection
road_detection.test()

Running test on DIPLODOC sequence

You can run the algorithm on all the frames of the DIPLODOC sequence by running:

import road_detection
results = road_detection.test_on_diplodoc()

This will give you a list of dictionaries with the performance metrics per frame.

Octave / Matlab implementation

Dependencies

• image package / toolbox.
• graph_toolbox from http://eslab.bu.edu/software/graphanalysis/ (needed files are included for convenience in the /octave-matlab/graph_functions directory, under GPL v.3.0 licence).

Basic usage

You can run a smoke test on the 2 images included here by running test() from inside the octave-matlab folder:

pkg load image % For Octave only
[TP, FP, FN, R, P, Q, F1, IM] = test;

Running test on DIPLODOC sequence

You can run the algorithm on all the frames of the DIPLODOC sequence by running:

pkg load image % For Octave only
results = test_on_diplodoc();