Udacity 4th Project about how to find the lane.
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camera_cal
examples
output_images
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.gitignore
README.md
calibration.py
calibration_data.p
challenge_video.mp4
curvature.py
example_writeup.pdf
harder_challenge_video.mp4
helper_methods.py
lanefinder.py
main.py
perspective_transform.py
project_video.mp4
project_video_result.mp4
project_video_result_outliers.mp4
thresholding.py
writeup.md

README.md

Advanced Lane Finding

Udacity - Self-Driving Car NanoDegree

About this repository

This repository contains a project that shows how a lane detection based on the computer vision algorithms works. The algorithm works offline and not in real-time.

The goals / steps of this project are the following:

  • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
  • Apply a distortion correction to raw images.
  • Use color transforms, gradients, etc., to create a thresholded binary image.
  • Apply a perspective transform to rectify binary image ("birds-eye view").
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

Requirements

Install the Udacity starting kit from https://github.com/udacity/CarND-Term1-Starter-Kit

Installation

git clone https://github.com/ywiyogo/CarND1-P4-AdvancedLaneFinding.git

Usage

  1. Go to the repository folder and activate the virtualenvironment:

     source activate carnd-term1
    
  2. Start the program python main.py

Documentation

Please read the writeup

Issues

Issues

Roadmap

Milestones