picam - Elixir library used to capture MJPEG video on a Raspberry Pi using the camera module.

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Picam is an Elixir library that provides a simple API for streaming MJPEG video and capturing JPEG stills using the camera module on Raspberry Pi devices running Linux. For specifics on the above features, please consult the Hex docs.




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  •    Python

Requires a Raspberry Pi computer and a RPI camera module installed. Make sure hardware is tested and works. Most RPI models will work OK. A quad core RPI will greatly improve performance due to threading. A recent version of Raspbian operating system is Recommended. Step 1 With mouse left button highlight curl command in code box below. Right click mouse in highlighted area and Copy. Step 2 On RPI putty SSH or terminal session right click, select paste then Enter to download and run script.

autonomous-rc-car - Autonomous RC car using Raspberry Pi and ANN

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This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We have modified a remote controlled car to remove the dependency on the RF remote controller. A Raspberry Pi controls the DC motors via an L293D Motor Driver IC. You can find a post explaining this project in detail here. Here's a video of the car in action. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor.

restreamer - Datarhei/Restreamer allows you to do h

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Datarhei/Restreamer offers smart free video streaming. Stream H.264 video of IP cameras live to your website. Upload your live video on YouTube-Live, Ustream, Twitch, Livestream.com or any other streaming solutions e.g. Wowza-Streaming-Engine. Our Docker-Image is easy to install and runs on Linux, MacOS and Windows. Datarhei/Restreamer can be perfectly combined with single-board computers like Raspberry Pi and Odroid. It is free (licensed under Apache 2.0) and you can use it for any purpose, private or commercial. Documentation is available on Datarhei/Restreamer GitHub pages. We give you a lot of of informations from setting up a camera, embedding your player upon your website and streaming to services like e.g. YouTube-Live, Ustream and Livestream.com and many more things.

raspap-webgui - A simple, responsive web interface to control wifi and hostapd on the Raspberry Pi

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A simple, responsive web interface to control wifi, hostapd and related services on the Raspberry Pi. This project was inspired by a blog post by SirLagz about using a web page rather than ssh to configure wifi and hostapd settings on the Raspberry Pi. I mostly just prettified the UI by wrapping it in SB Admin 2, a Bootstrap based admin theme. Since then, the project has evolved to include greater control over many aspects of a networked RPi, better security, authentication, a Quick Installer, support for themes and more. RaspAP has been featured on sites such as Instructables, Adafruit, Raspberry Pi Weekly and Awesome Raspberry Pi and implemented in countless projects.

self-driving-toy-car - A self driving toy car using end-to-end learning

  •    Jupyter

To make a lane follower based on a standard RC car using Raspberry Pi and a camera. The software is a simple Convolutional Network, which takes in the image fetched from the camera and outputs the steering angle. During data collection, we will simply hook the steering PWM of the car to pin GPIO17. The script raspberry_pi/collect_data.py will record the values of steering PWM and the associated images. The data of each trial are collectively stored in driving_trial_*. The trial folders are automatically numbered.

cartoonify - python app to turn a photograph into a cartoon

  •    Python

Draw This is a polaroid camera that draws cartoons. You point, and shoot - and out pops a cartoon; the camera's best interpretation of what it saw. The camera is a mash up of a neural network for object recognition, the google quickdraw dataset, a thermal printer, and a raspberry pi. The software can run both on a desktop environment (OSX, Linux) such as a laptop, or an embedded environment on a raspberry pi.

Raspberry-Pi - Raspberry Pi A to Z List


Raspberry Pi A to Z List Basics Raspberry Pi The Raspberry Pi was originally designed for educational purposes to use in schools and universities, and to make class and study more interesting. Unfortunately, the practical use of the Raspberry Pi or a comparable single-board computer in Germany, Austria and Switzerland is still in its infancy. Listening to the topic, you will find only a few examples showing the use of the Raspberry Pi in school, in education or in study. The following tasks and exercises should help a little here. They are suitable for self-study or as a source for teachers and faculty to build their own Raspberry Pi workshop. The tasks also include exemplary ready-made solutions. 1. Getting Started: Tasks and Exercises with Raspberry Pi The following tasks and exercises are suitable for beginners who have not yet come into contact with the Raspberry Pi. The solutions are also interesting for those who already know the Raspberry Pi and have worked with it. The solutions are part of standard tasks that have to be done around the Raspberry Pi every now and then.

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GPUImage 2 is the second generation of the GPUImage framework, an open source project for performing GPU-accelerated image and video processing on Mac, iOS, and now Linux. The original GPUImage framework was written in Objective-C and targeted Mac and iOS, but this latest version is written entirely in Swift and can also target Linux and future platforms that support Swift code. The objective of the framework is to make it as easy as possible to set up and perform realtime video processing or machine vision against image or video sources. By relying on the GPU to run these operations, performance improvements of 100X or more over CPU-bound code can be realized. This is particularly noticeable in mobile or embedded devices. On an iPhone 4S, this framework can easily process 1080p video at over 60 FPS. On a Raspberry Pi 3, it can perform Sobel edge detection on live 720p video at over 20 FPS.

IP-Camera MJPEG HTTP Web-Server Emulator


Turns web-camera into IP-camera with mjpeg streaming and access it over network. Usage of i-Lids (Imagery Library for Intelligent Detection Systems) and other data for surveillance systems development. Sends AVI, WMV, folder with images, web-camera data over the wire.

fossa - Async non-blocking multi-protocol networking library for C/C++

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Fossa is a multi-protocol networking library written in C. It provides easy to use event-driven interface that allows to implement network protocols or scalable network applications with little effort. Fossa helps developers to manage the complexity of network programming and let them concentrate on the logic, saving time and money. Fossa has built-in support for several protocols, like HTTP, Websocket, MQTT, mDNS. Example applications include Websocket-based chat server, JSON-RPC server, database server with RESTful API, MQTT broker, netcat with SSL and hexdump, Raspberry PI camera video feed + led control, and more.

machinery - An image processing framework, which uses your USB-, IP- or RPi-camera to recognize events (e

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raspberry-pi-os - Learning operating system development using Linux kernel and Raspberry Pi

  •    C

This repository contains a step-by-step guide that teaches how to create a simple operating system (OS) kernel from scratch. I call this OS Raspberry Pi OS or just RPi OS. The RPi OS source code is largely based on Linux kernel, but the OS has very limited functionality and supports only Raspberry PI 3. Each lesson is designed in such a way that it first explains how some kernel feature is implemented in the RPi OS, and then it tries to demonstrate how the same functionality works in the Linux kernel. Each lesson has a corresponding folder in the src directory, which contains a snapshot of the OS source code at the time when the lesson had just been completed. This allows the introduction of new concepts gracefully and helps readers to follow the evolution of the RPi OS. Understanding this guide doesn't require any specific OS development skills.

rust-on-raspberry-pi - [OUTDATED] Instructions for how to cross compile Rust projects for the Raspberry Pi

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This guide will show how Rust programs can be cross compiled for the Raspberry Pi using Cargo. These instructions may or may not work for your particular system, so you may have to adjust the procedure to fit your configuration. There is also a docker container version of the Rust cross-compiler for the Raspberry: https://github.com/Ragnaroek/rust-on-raspberry-docker. If you are using docker this may be a easier solution for cross-compiling your project.

ELL - Embedded Learning Library

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The MJPEG/Linux square

  •    C++

Mjpeg tools is a suite of programs which support video capture, editing, playback, and compression to MPEG of MJPEG video. Edit, play and compression software is hardware independent. Capture supports MJPEG hardware (Buz,DC10+ etc). and V4l2 devices.

GpiO - Talk to your Raspberry Pi's GPIO pins

  •    Javascript

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  •    C

Raspbian is a free operating system based on Debian optimized for the Raspberry Pi hardware. An operating system is the set of basic programs and utilities that make your Raspberry Pi run. However, Raspbian provides more than a pure OS: it comes with over 35,000 packages, pre-compiled software bundled in a nice format for easy installation on your Raspberry Pi.

nerves_system_br - Platform infrastructure for embedded Erlang/OTP, Elixir, and LFE projects

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Nerves System BR provides the common logic for building Nerves Systems using Buildroot. If you're new to Nerves, you probably don't want to look at this repository. Please check out the official documentation. It is highly recommended that all users follow the official documentation on using systems. However, advanced users have found the information to be helpful, so it hasn't been deleted. This ONLY works on Linux.

py-videocore - Python library for GPGPU on Raspberry Pi

  •    Python

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