A pipeline to identify the lane boundaries in an image from a front-facing camera on a car.
The project goals:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply 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.
Camera calibration:
The script (camera_calibration.py), images, and output (calibration_pickle.p) for camera calibration are stored in the camera_cal directory.
Test images:
The images in test_images directory are for testing the pipeline on single frames. The script (image_gen.py) is used to generate output from each stage of the pipeline for all the images in test_images. These output images are saved in output_images directory.
For example, test1.jpg, has corresponding output images:
test1_undistorted.jpg-- Distortion correctedtest1.jpgtest1_binary.jpg-- Binarytest1.jpgtest1_warped.jpg-- Perspective transformedtest1.jpgtest1_output.jpg-- Output with lane boundaries ontest1.jpg
Project video:
The script (video_gen.py) is used to apply the pipeline to identify the lane boundaries in input video (project_video.mp4), and generate the output video (project_video_output.mp4).
Please see WRITEUP.md