Udacity Self-Driving Car Nanodegree - Finding Lane Lines on the Road

This repo contains the code written to complete the first project on Udacity Self-Driving Car Nanodegree. This project consists of algorithms to identify lane lines on the road on a video. The video is taken from a camera at the center of a vehicle.

Prerequisites

To run this project, you need Miniconda installed(please visit this link for quick installation instructions.)

Installation

To create an environment for this project use the following command:

conda env create -f environment.yml

After the environment is created, it needs to be activated with the command:

source activate carnd-term1

and open the project’s notebook P1.ipynb inside jupyter notebook:

jupyter notebook P1.ipynb

Overview

The repo contains the jupyter notebook P1.ipynb where the processing pipeline is implemented. The pipeline consists on six steps represented by six different functions:

• grayAction: Returns a gray scaled version of the input image using cv2.cvtColor method.
• blurAction: Applies a Gaussian blur to the provided image using cv2.GaussianBlur method.
• cannyAction: Use a Canny transformation to find edges on the image using cv2.Canny method.
• maskAction: Eliminate parts of the image that are not interesting in regards to the line detection (for now…).
• houghAction: Use a Hough transformation to find the lines on the masked image using cv2.cv2.HoughLinesP. It also adjust a line to the set of lines returned by the Hough transformation in order to have a clearer-two-lines representation of the road lines using np.polyfit method.
• weighted_img: Merges the output of houghAction with the original image to represent the lines on it.

First, the pipeline is tested agains the images contained at test_images. The output of each step is saved in a directory:

• test_images_gray
• test_images_blur
• test_images_canny
• test_images_region
• test_images_hough
• test_images_merged

After that test, the pipeline is consolidated on a single function process_image to apply it on a video frame. The sample videos could be found here. The video after the transformation are saved on the [test_videos_output][test_videos_output] directory.

An html version of the output is here.

License

This project copyright is under MIT License.