Advanced Lane Finding Project for Self-Driving Car ND
The goal of this project is to write a software pipeline to identify the lane boundaries in a video.
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.
Some examples of the output from each stage of the pipeline can be found in the folder called
Starting to work on this project consists of the following steps:
minicondaon your computer
- Create a new
condaenvironment using this project
- Each time you wish to work, activate your
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
4.2.12 from here.
|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:
- Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
- Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
- Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install
Clone the project:
git clone https://github.com/gdangelo/CarND-Advanced-Lane-Lines.git cd CarND-Advanced-Lane-Lines
If you are on Windows, rename
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
Verify that the carnd environment was created in your environments:
conda info --envs
Cleanup downloaded libraries (remove tarballs, zip files, etc):
conda clean -tp
To uninstall the environment:
conda env remove -n carnd
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.
OS X and Linux
$ source activate carnd
Depending on shell either:
$ source activate carnd
$ activate carnd
Now all of the
carnd libraries are available to you.
Launch the main python file:
$ python pipeline.py
Questions or Feedback
Contact me anytime for anything about my projects or machine learning in general. I'd be happy to help you