Advanced Lane Detection

This Article gives and overview of the project.

The goals 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 threshold 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.

You can find a thorough description here on the detail description.

Environment

The environment was created using miniconda, using the following configurations, environment.yml.

The libraries used on this project are:

Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
camera_cal		camera_cal
output_camera_cal		output_camera_cal
results		results
test_images		test_images
tests		tests
utilities		utilities
.gitignore		.gitignore
CameraCalibration.ipynb		CameraCalibration.ipynb
Pipeline.ipynb		Pipeline.ipynb
README.md		README.md
VideoPipeline.py		VideoPipeline.py
detail_description.md		detail_description.md
environment.yml		environment.yml
project_video.mp4		project_video.mp4