{"id":21488903,"url":"https://github.com/d-kleine/computer-vision","last_synced_at":"2026-01-03T12:51:29.093Z","repository":{"id":155630921,"uuid":"458940931","full_name":"d-kleine/Computer-Vision","owner":"d-kleine","description":"Udacity - Computer vision Nanodegree program","archived":false,"fork":false,"pushed_at":"2023-05-25T20:16:04.000Z","size":12426,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-23T20:17:27.184Z","etag":null,"topics":["computer-vision"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/d-kleine.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-02-13T21:50:43.000Z","updated_at":"2023-07-16T03:11:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"1d5d6a44-e8b2-4a50-b6d8-feb87e0a562d","html_url":"https://github.com/d-kleine/Computer-Vision","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d-kleine%2FComputer-Vision","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d-kleine%2FComputer-Vision/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d-kleine%2FComputer-Vision/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/d-kleine%2FComputer-Vision/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/d-kleine","download_url":"https://codeload.github.com/d-kleine/Computer-Vision/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244020140,"owners_count":20384879,"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":["computer-vision"],"created_at":"2024-11-23T14:12:39.773Z","updated_at":"2026-01-03T12:51:29.069Z","avatar_url":"https://github.com/d-kleine.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Computer Vision Nanodegree Program, Exercises\n\nThis repository contains code exercises and materials for Udacity's [Computer Vision Nanodegree](https://www.udacity.com/course/computer-vision-nanodegree--nd891) program. It consists of tutorial notebooks that demonstrate, or challenge you to complete, various computer vision applications and techniques. These notebooks depend on a number of software packages to run, and so, we suggest that you create a local environment with these dependencies by following the instructions below.\n\n**Computer vision capabilities for various applications such as image and video processing, autonomous vehicle navigation, medical diagnostics, and smartphone apps, etc.**\n\n*Project 1: Facial Keypoint Detection*\n\nBasics of Computer Vision and image processing:\n* Extract important features from image data \n* Apply deep learning techniques to classification tasks\n\n[Project](https://github.com/d-kleine/Computer-Vision/tree/main/Project1_Facial-Keypoints)\n\n*Project 2: Automatic Image Captioning*\n\nCombine CNN and RNN networks to create an auto caption application: \n* Split complex deep learning models into two components: a CNN that converts an input image into a set of features, and an RNN that converts those features into a rich, descriptive language\n\n[Project](https://github.com/d-kleine/Computer-Vision/tree/main/Project2_Image-Captioning)\n\n*Project 3: Landmark Detection \u0026 Tracking*\n\nLocate objects and track them over time: \n* Feature detection and keypoint descriptors to create a map of the environment using SLAM (simultaneous localization and mapping)\n* Implement robust methods for tracking an object over time using elements of probability, motion models, and linear algebra\n\n[Project](https://github.com/d-kleine/Computer-Vision/tree/main/Project3_Landmark%20Detection)\n\n# Configure and Manage Your Environment with Anaconda\n\nPer the Anaconda [docs](http://conda.pydata.org/docs):\n\n\u003e Conda is an open source package management system and environment management system \nfor installing multiple versions of software packages and their dependencies and \nswitching easily between them. It works on Linux, OS X and Windows, and was created \nfor Python programs but can package and distribute any software.\n\n## Overview\nUsing Anaconda consists of the following:\n\n1. Install [`miniconda`](http://conda.pydata.org/miniconda.html) on your computer, by selecting the latest Python version for your operating system. If you already have `conda` or `miniconda` installed, you should be able to skip this step and move on to step 2.\n2. Create and activate * a new `conda` [environment](http://conda.pydata.org/docs/using/envs.html).\n\n\\* Each time you wish to work on any exercises, activate your `conda` environment!\n\n---\n\n## 1. Installation\n\n**Download** the latest version of `miniconda` that matches your system.\n\n**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](https://repo.continuum.io/miniconda/).\n\n|        | Linux | Mac | Windows | \n|--------|-------|-----|---------|\n| 64-bit | [64-bit (bash installer)][lin64] | [64-bit (bash installer)][mac64] | [64-bit (exe installer)][win64]\n| 32-bit | [32-bit (bash installer)][lin32] |  | [32-bit (exe installer)][win32]\n\n[win64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86_64.exe\n[win32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Windows-x86.exe\n[mac64]: https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh\n[lin64]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n[lin32]: https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86.sh\n\n**Install** [miniconda](http://conda.pydata.org/miniconda.html) on your machine. Detailed instructions:\n\n- **Linux:** http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install\n- **Mac:** http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install\n- **Windows:** http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install\n\n## 2. Create and Activate the Environment\n\nFor Windows users, these following commands need to be executed from the **Anaconda prompt** as opposed to a Windows terminal window. For Mac, a normal terminal window will work. \n\n#### Git and version control\nThese instructions also assume you have `git` installed for working with Github from a terminal window, but if you do not, you can download that first with the command:\n```\nconda install git\n```\n\nIf you'd like to learn more about version control and using `git` from the command line, take a look at our [free course: Version Control with Git](https://www.udacity.com/course/version-control-with-git--ud123).\n\n**Now, we're ready to create our local environment!**\n\n1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.\n```\ngit clone https://github.com/udacity/CVND_Exercises.git\ncd CVND_Exercises\n```\n\n2. Create (and activate) a new environment, named `cv-nd` with Python 3.6. If prompted to proceed with the install `(Proceed [y]/n)` type y.\n\n\t- __Linux__ or __Mac__: \n\t```\n\tconda create -n cv-nd python=3.6\n\tsource activate cv-nd\n\t```\n\t- __Windows__: \n\t```\n\tconda create --name cv-nd python=3.6\n\tactivate cv-nd\n\t```\n\t\n\tAt this point your command line should look something like: `(cv-nd) \u003cUser\u003e:CVND_Exercises \u003cuser\u003e$`. The `(cv-nd)` indicates that your environment has been activated, and you can proceed with further package installations.\n\n3. Install PyTorch and torchvision; this should install the latest version of PyTorch.\n\t\n\t- __Linux__ or __Mac__: \n\t```\n\tconda install pytorch torchvision -c pytorch \n\t```\n\t- __Windows__: \n\t```\n\tconda install pytorch-cpu -c pytorch\n\tpip install torchvision\n\t```\n\n6. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).\n```\npip install -r requirements.txt\n```\n\n7. That's it!\n\nNow all of the `cv-nd` libraries are available to you. Assuming you're environment is still activated, you can navigate to the Exercises repo and start looking at the notebooks:\n\n```\ncd\ncd CVND_Exercises\njupyter notebook\n```\n\nTo exit the environment when you have completed your work session, simply close the terminal window.\n\n\n### Notes on environment creation and deletion\n\n**Verify** that the `cv-nd` environment was created in your environments:\n\n```\nconda info --envs\n```\n\n**Cleanup** downloaded libraries (remove tarballs, zip files, etc):\n\n```\nconda clean -tp\n```\n\n**Uninstall** the environment (if you want); you can remove it by name:\n\n```\nconda env remove -n cv-nd\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd-kleine%2Fcomputer-vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fd-kleine%2Fcomputer-vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fd-kleine%2Fcomputer-vision/lists"}