To track progress please see the Google Spread Sheet
Lane detection is a critical component of the perception module for an autonomous vehicle. The objective of this module is to design and develop a lane detection algorithm which is robust and optimized. Autonomous driving is a complicated task and an interconnected web of modules running in tandem. Lane detection can further aid in motion planning and navigation and hence should provide realible data.
For this project we will focus on the computer vision aspect of lane detection. A video feed of a car driving on a higway will be used, credits to dataset available for Udacity's Self Driving Car Nano Degree. Primary objective would be to detect lanes and then predict the turn based on the vaninshing point location.
The source code is in modern C++ and please raise an issue or request a PR if you wish to add features, recommend changes or report a bug. The build system used is Cmake in order to facilitate cross platform builds. The project also uses the OpenCV library as a dependency, so make sure it is installed before running the project and if not, please follow the provided instructions to do so.
Google Mock is a framework that let's us create a mock class using macros. It can help you derive better designs of your system and write better tests.
So for experimental purpose, the class structure used in this repository is modified and now we have a base class called preProcess and a derived class called lanedetector. So now a mock class was created for the base class and a google mock test was written to test this interface.
Updated UML diagram have been added in the UML directory. Instruction to run the tests can be found below.
Note: Since most functions here were of opencv, it was a little cumbersome in the beginning but the concepts of google mock use became much clear after reading the documentation available here - Cheatsheet. I implemented it using a very basic class of simple functions and tests used there were scalable in general, but since it required more time to make it presentable I'm going to use this as my final submission.
- Build and run demo and unit tests (Only if OpenCV is installed, else install OpenCV first by following the steps to install OpenCV)
git clone https://github.com/smahajan07/advanced-lane-detection.git
cd <path to repository>
mkdir build
cd build
wget -O project_video.mp4 "https://drive.google.com/uc?export=download&id=1EzhVEtTwcF3gLe1hh-ib2JnFaH31o0fS"
wget -O test.png "https://drive.google.com/uc?export=download&id=14rU9upKeVllUYNvmpL00W7s95JR8WNQH"
cmake ..
make
Run the demo: ./app/shell-app
Run the tests: ./test/cpp-test
- Steps to install OpenCV, if not installed already
sudo apt-get install -y build-essential
sudo apt-get install -y cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
git clone https://github.com/Itseez/opencv.git
cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON ..
make -j4
sudo make install
sudo /bin/bash -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig
cd ../../
Doxygen generated documents can be viewed by:
cd <path to repository>
cd docs
Open in browser: classlanedetector.html
Follow the steps to generate doxygen documentation in HTML and LaTEX:
cd <path to repository>
mkdir <name of directory>
cd <name of directory>
doxygen -g <config file name>
Edit config: nano <config file name>
Run: doxygen <config file name>
To view the documents, open the file in your browser.
A solo iterative process is characterized by a single programmer working alone. SIP is current;y being used to manage this project. A product backlog, iteration backlog and work log and a defect log are being maintained and the progress can be tracked by the following spreadsheet.
The project consists of two iterations, please find the details in the spreadsheet.
- Udacity Nano Degree Program Dataset
- Perspective Transform
- Really well written article by Ricardo Zuccolo on advanced lane detection, you can find it here
- OpenCV docs
MIT License
Copyright (c) 2018 Sarthak Mahajan
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.