Lane Finding Project for Self-Driving Car ND
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examples
test_images
test_images_blur_gray
test_images_color_selection
test_images_edges
test_images_gray
test_images_lines
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test_videos_output
.gitignore
P1.ipynb
README.md
environment.yml
meta_windows_patch.yml
writeup.md

README.md

CarND-LaneLines-P1

Lane Finding Project for Self-Driving Car Nanodegree.

Udacity - Self-Driving Car NanoDegree

The purpose of this project is to build a software pipeline to detect lane lines on the road from video using Python and OpenCV. More info in the writeup.

Finding lane lines

Overview

Starting to work on this project consists of the following steps:

  1. Install miniconda on your computer
  2. Create a new conda environment using this project
  3. Each time you wish to work, activate your conda environment
  4. Run the Jupyter notebook and visit http://localhost:8000

Installation

Download the latest version of miniconda that matches your system.

NOTE: There have been reports of issues creating an environment using miniconda v4.3.13. If it gives you issues try versions 4.3.11 or 4.2.12 from here.

Linux Mac Windows
64-bit 64-bit (bash installer) 64-bit (bash installer) 64-bit (exe installer)
32-bit 32-bit (bash installer) 32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

Setup your carnd environment.

git clone https://github.com/gdangelo/CarND-LaneLines-P1.git
cd CarND-LaneLines-P1

If you are on Windows, rename
meta_windows_patch.yml to
meta.yml

Create carnd. Running this command will create a new conda environment that is provisioned with all libraries you need to be successful in this program.

conda env create -f environment.yml

Note: Some Mac users have reported issues installing TensorFlow using this method. The cause is unknown but seems to be related to pip. For the time being, we recommend opening environment.yml in a text editor and swapping

    - tensorflow==0.12.1

with

    - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl

Verify that the carnd environment was created in your environments:

conda info --envs

Cleanup downloaded libraries (remove tarballs, zip files, etc):

conda clean -tp

Uninstalling

To uninstall the environment:

conda env remove -n carnd

Usage

Now that you have created an environment, in order to use it, you will need to activate the environment. This must be done each time you begin a new working session i.e. open a new terminal window.

Activate the carnd environment:

OS X and Linux

$ source activate carnd

Windows

Depending on shell either:

$ source activate carnd

or

$ activate carnd

Now all of the carnd libraries are available to you.

Open the code in a Jupyter Notebook:

$ jupyter notebook P1.ipynb

That's it. To exit the environment when you have completed your work session, simply close the terminal window.


Questions or Feedback

Contact me anytime for anything about my projects or machine learning in general. I'd be happy to help you 😉