Use different approaches to find lane-lines on roads. The code is mostly based on Udacity's self driving car program.
Use Python 3.6 and install virtualenv (virtual environment)
sudo apt-get install python3-pip python3-dev python-virtualenv
Go to your home directory and create a virtualenv environment
virtualenv --system-site-packages lane_detection
Activate the virtualenv environment (use deactivate to exit)
source ~/lane_detection/bin/activate
Clone the Repo, cd into the folder and install all other dependencies in the requirements.txt file:
pip3 install -r requirements.txt
Use an advanced computer vision pipeline to extract features and determine lane-lines even in curved environments. The code works as follows:
(1) Compute a camera calibration matrix and distortion coefficients given a set of chessboard images.
(2) Apply a distortion correction to raw images.
(3) Use filtering like edge detection, etc., to create a binary image.
(4) Apply a perspective transform ("birds-eye view").
(5) Apply a histogram to get lane pixels.
(6) Determine the curvature radius and vehicle offset
(7) Warp the detected lane boundaries back to the original image.
(8) Output a visual display
Run the code with python3 P2.py .
The credits for this code go to Udacity and galenballew. I mainly refactored the code and did some bugfixes.