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Advanced lane detection techniques using gradient, magnitude, and s channel thresholds, perspective transforms, and sliding window search

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CameronFraser/sdcnd-2-advanced-lane-detection

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Advanced Lane Finding

Udacity - Self-Driving Car NanoDegree

Test Image

Steps to run code

  1. Clone starter kit repo to setup environment https://github.com/udacity/CarND-Term1-Starter-Kit
  2. Follow these directions: https://github.com/udacity/CarND-Term1-Starter-Kit/blob/master/doc/configure_via_anaconda.md
  3. Activate the carnd-term1 environment

You can run the code either with the Jupyter notebook or via the command line

Jupyter notebook

  1. Run jupyter notebook from root directory of this repository
  2. Open the P2 notebook
  3. Run all the code blocks. You can see processed images in the test_images_output directory and you can see the processed videos either embedded in the notebook or in the test_videos_output directory

Command line

  1. Run python src/main.py from the root direction of this repository
  2. You can see processed images in the test_images_output directory and you can see the processed videos in the test_videos_output directory

Writeup

The Jupyter notebook I have provided is designed to be an interactive writeup basically but if you prefer to read a markdown file an export is linked: Advanced Lane Finding Writeup

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Advanced lane detection techniques using gradient, magnitude, and s channel thresholds, perspective transforms, and sliding window search

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