{"id":15902499,"url":"https://github.com/nikolasent/laneline-project","last_synced_at":"2025-08-14T12:32:41.961Z","repository":{"id":205011057,"uuid":"75775919","full_name":"NikolasEnt/LaneLine-project","owner":"NikolasEnt","description":"Udacity Self-Driving Car Engineer Nanodegree. Project: Lane line detection with adaptive ROI and lines jitter smoothing","archived":false,"fork":false,"pushed_at":"2017-02-28T19:50:21.000Z","size":59381,"stargazers_count":3,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-12-09T03:49:55.422Z","etag":null,"topics":["carnd","computer-vision","opencv","self-driving-car"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NikolasEnt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-12-06T22:08:45.000Z","updated_at":"2024-04-08T17:59:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"ece89d2e-30fb-44e8-8c4b-962dc9fa3b08","html_url":"https://github.com/NikolasEnt/LaneLine-project","commit_stats":null,"previous_names":["nikolasent/laneline-project"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NikolasEnt%2FLaneLine-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NikolasEnt%2FLaneLine-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NikolasEnt%2FLaneLine-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NikolasEnt%2FLaneLine-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NikolasEnt","download_url":"https://codeload.github.com/NikolasEnt/LaneLine-project/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":229826471,"owners_count":18130265,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["carnd","computer-vision","opencv","self-driving-car"],"created_at":"2024-10-06T11:41:17.698Z","updated_at":"2024-12-15T13:41:26.784Z","avatar_url":"https://github.com/NikolasEnt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"#**Finding Lane Lines on the Road** \n\u003cimg src=\"laneLines_thirdPass.jpg\" width=\"480\" alt=\"Combined Image\" /\u003e\n\n**Result:** [video](https://youtu.be/jrg4iCT6FsU)\n- `P1.ipynb` The project code and description\n\nWhen we drive, we use our eyes to decide where to go.  The lines on the road that show us where the lanes are act as our constant reference for where to steer the vehicle.  Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm.\n\nIn this project you will detect lane lines in images using Python and OpenCV.  OpenCV means \"Open-Source Computer Vision\", which is a package that has many useful tools for analyzing images.  \n\n**Step 1:** Getting setup with Python\n\nTo do this project, you will need Python 3 along with the numpy, matplotlib, and OpenCV libraries, as well as Jupyter Notebook installed. \n\nWe recommend downloading and installing the Anaconda Python 3 distribution from Continuum Analytics because it comes prepackaged with many of the Python dependencies you will need for this and future projects, makes it easy to install OpenCV, and includes Jupyter Notebook.  Beyond that, it is one of the most common Python distributions used in data analytics and machine learning, so a great choice if you're getting started in the field.\n\nChoose the appropriate Python 3 Anaconda install package for your operating system \u003cA HREF=\"https://www.continuum.io/downloads\" target=\"_blank\"\u003ehere\u003c/A\u003e.   Download and install the package.\n\nIf you already have Anaconda for Python 2 installed, you can create a separate environment for Python 3 and all the appropriate dependencies with the following command:\n\n`\u003e  conda create --name=yourNewEnvironment python=3 anaconda`\n\n`\u003e  source activate yourNewEnvironment`\n\n**Step 2:** Installing OpenCV\n\nOnce you have Anaconda installed, first double check you are in your Python 3 environment:\n\n`\u003epython`    \n`Python 3.5.2 |Anaconda 4.1.1 (x86_64)| (default, Jul  2 2016, 17:52:12)`  \n`[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)] on darwin`  \n`Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.`  \n`\u003e\u003e\u003e`   \n(Ctrl-d to exit Python)\n\nrun the following commands at the terminal prompt to get OpenCV:\n\n`\u003e pip install pillow`  \n`\u003e conda install -c https://conda.anaconda.org/menpo opencv3`\n\nthen to test if OpenCV is installed correctly:\n\n`\u003e python`  \n`\u003e\u003e\u003e import cv2`  \n`\u003e\u003e\u003e`  \n(Ctrl-d to exit Python)\n\n**Step 3:** Installing moviepy  \n\nWe recommend the \"moviepy\" package for processing video in this project (though you're welcome to use other packages if you prefer).  \n\nTo install moviepy run:\n\n`\u003epip install moviepy`  \n\nand check that the install worked:\n\n`\u003epython`  \n`\u003e\u003e\u003eimport moviepy`  \n`\u003e\u003e\u003e`  \n(Ctrl-d to exit Python)\n\n**Step 4:** Opening the code in a Jupyter Notebook\n\nYou will complete this project in a Jupyter notebook.  If you are unfamiliar with Jupyter Notebooks, check out \u003cA HREF=\"https://www.packtpub.com/books/content/basics-jupyter-notebook-and-python\" target=\"_blank\"\u003eCyrille Rossant's Basics of Jupyter Notebook and Python\u003c/A\u003e to get started.\n\nJupyter is an ipython notebook where you can run blocks of code and see results interactively.  All the code for this project is contained in a Jupyter notebook. To start Jupyter in your browser, run the following command at the terminal prompt (be sure you're in your Python 3 environment!):\n\n`\u003e jupyter notebook`\n\nA browser window will appear showing the contents of the current directory.  Click on the file called \"P1.ipynb\".  Another browser window will appear displaying the notebook.  Follow the instructions in the notebook to complete the project.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikolasent%2Flaneline-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnikolasent%2Flaneline-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikolasent%2Flaneline-project/lists"}