This Advanced Lane Finding project is part of the Udacitiy Self-Driving Car Engineer Nanodegree.
In the Project Writeup you can find a detailed review of the project goals, and how they were addresed.
- Python 3.6
- Numpy
- OpenCV
- Matplotlib
- Pickle
Run python3 image_pypeline.py and python3 video_pipeline.py to annotate images or videos respectively.
The input images are in /test_images and on root folder for videos. The input file is coded and can be changed as described in the Project Writeup.
The output is placed at /output_images and output_videos.
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.
