## Advanced Lane Finding
[![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)

The goals / steps of this project are the following:  

* Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
* Apply the distortion correction to the raw image.  
* 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 lane boundary.
* Determine 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.

---

The images for camera calibration are stored in the folder called `camera_cal`.  The images in `test_images` are for testing your pipeline on single frames.  The video called `project_video.mp4` is the video your pipeline should work well on.  `challenge_video.mp4` is an extra (and optional) challenge for you if you want to test your pipeline.

If you're feeling ambitious (totally optional though), don't stop there!  We encourage you to go out and take video of your own, calibrate your camera and show us how you would implement this project from scratch!
## Advanced Lane Finding
[![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)

The goals / steps of this project are the following:  

* Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
* Apply the distortion correction to the raw image.  
* 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 lane boundary.
* Determine 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.

---

The images for camera calibration are stored in the folder called `camera_cal`.  The images in `test_images` are for testing your pipeline on single frames.  To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called `ouput_images`, and include a description in the README for the project of what each image shows.    The video called `project_video.mp4` is the video your pipeline should work well on.  

The `challenge_video.mp4` video is an extra (and optional) challenge for you if you want to test your pipeline under somewhat trickier conditions.  The `harder_challenge.mp4` video is another optional challenge and is brutal!

If you're feeling ambitious (again, totally optional though), don't stop there!  We encourage you to go out and take video of your own, calibrate your camera and show us how you would implement this project from scratch!

Add request to save images in ouput_images folder
Add request to save images in ouput_images folder