joergsimon/SDCND-Term1-AdvancedLaneLines

Advanced Lane Finding

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.

For details on the working of that project checkout the report.md

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The following files are important for the project:

• report.md (report / project writeup)
• report_images/ (folder with images and videos used for the report)
• examples/example.ipynb (exploration of the algorithms based on single images)
• camera_cal/wide_dist_pickle.p (pickle file for configuration of undistortion. Computed by the notebook, used by process.py)
• test_images/ (folder with images used by the notebook)
• process.py (the script used to analyse / generate the video)
• helper/*.py (python modules implementing the funcionality of the pipeline)
• project_video.mp4 (base video for later annotation)
• result_video.mp4 (final resulting video)

The video annotation is done with the script process.py. This needs a valid camera calibration pickle file, which can be generated by running the first cells of the notebook. process.py has the values of the input video and the resulting video hardcoded. Also if you want to generate a video of an intermediate step exchange the function passed to clip1.fl_image by hand with one of the following:

• process_image -> generated the real video
• process_image_mk_histogram -> makes a video of the histogram used for search
• process_image_mk_transf -> makes a video of transformed binary images
• process_image_mk_thresh -> makes a video of the binary threshold images (looks nice ^_^)

In the report video on critical section is around 21 to 25s. To make a video of that section only uncomment the line: clip1 = VideoFileClip("project_video.mp4", audio=False).subclip(t_start=19, t_end=26)