Raspberry Pi As Server for Home Automation

Raspberry Pi As Server for Home Automation

The Raspberry Pi is an amazing little computer for the price. Now that you have a Raspberry Pi lets put it to work!

Previously, I’ve talked about using an old Powerspec PC as my home automation server. An outdated PC works fine as a server in some cases, but really, running a little spaceheater-ish P4 around the clock? That’s so 2006. It would be kind of silly to blog all about power efficient lightbulbs and realtime energy monitoring while having a 10 year old PC burning 400 watts all the time. And who wants to run all their home automation stuff on Windows, anyway?

Over the last year, I’ve moved almost all my home automation code and programs over to a new platform: the Raspberry Pi. The PI makes a ton of sense as an HA server. It’s tiny, cheap, extremely power efficient, and can run a full Linux operating system with a graphical interface. You can tie it into low-level sensors (sort of), but at the same time, it can run Python scripts faster than the old Powerspec.

Here’s a little video introducing my Raspberry Pi home automation server:

Another great thing about the Raspberry Pi is that you can easily get it onto wifi. Just add an external adapter like the cheapo ones from Rosewill and you’re set. You can put your server anywhere in the house–in a cabinet, under your desk, in the garage, etc. Here’s my wifi setup:

So once you have your Raspberry Pi home automation server set up (and perhaps embedded in a project), how do you actually communicate it or upload new code? You can be fancy and get it to run an FTP server, but I find it’s often easier just to use Putty to send stuff over SSH:

So what does my server actually do? Well for starters, it uses CHRON to run my Bidgely scraping script once per minute, pulling in my realtime electrical usage. It also scrapes data from the endpoints of a ton of other APIs. You can even have it control things like Phillips Hue over your home network, using Python. I’ll share more on how I’m using my Pi in future posts.

Raspberry Pi 4 on the Raspberry Pi 4 - Computerphile

Raspberry Pi 4 on the Raspberry Pi 4 - Computerphile

A quick tour of the Raspberry Pi 4 edited on the Raspberry Pi 4. Dr Steve Bagley gets out his knife.dll to unbox Sean's purchases! ☞ [I created a home IoT setup with AWS, Raspberry...

A quick tour of the Raspberry Pi 4 edited on the Raspberry Pi 4. Dr Steve Bagley gets out his knife.dll to unbox Sean's purchases!

I created a home IoT setup with AWS, Raspberry Pi

Benchmarking the Raspberry Pi 4

The easy way to set up Docker on a Raspberry Pi – freeCodeCamp.org

Creating a Rogue Wi-Fi Access Point using a Raspberry Pi

Building a Smart Garden With Raspberry Pi 3B+

Learn Raspberry Pi for Image Processing Applications

Learn Raspberry Pi for Image Processing Applications

New to the newly launched Raspberry Pi 3? Learn all the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi. At just $9.

Description
Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.

The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.

You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.

This course will take beginners without any coding skills to a level where they can write their own programs.

Basics of Python programming language are well covered in the course.

Building Image Processing applications are taught in the simplest manner which is easy to understand.

Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.

The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.

Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.

The course will enable you to independently build Image Processing applications using Raspberry Pi.

This course is the easiest way to learn and become familiar with the Raspberry Pi platform.

By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.

The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.

Who is the target audience?

Anyone who wants to explore Raspberry Pi and interested in building Image Processing applications

To read more:

How AI development with Raspberry Pi 4 Empowers IoT Applications

How AI development with Raspberry Pi 4 Empowers IoT Applications

The blend of Artificial intelligence (AI) and the Internet of Things (IoT) are opening new business frontiers. With technological achievements such as the Raspberry Pi computing models, we are witnessing significant expansion in AI potential for...

The blend of Artificial intelligence (AI) and the Internet of Things (IoT) are opening new business frontiers. With technological achievements such as the Raspberry Pi computing models, we are witnessing significant expansion in AI potential for businesses. From automated video analytics to AI-infused assistants, AI development with Raspberry Pi 4 is heralding breakthrough applications across verticles. Let’s deeply analyze how Raspberry Pi 4 is providing for a great catalyst for artificial intelligence services to build dynamic applications.

Unlocking the Features of Raspberry Pi 4
The release of the Raspberry Pi series of single-board computers has completely revolutionized the software development space. After the remarkable performance of Raspberry Pi Zero and its successors, Raspberry Pi 4 has hit the market with improved architecture.

ai development with raspberry pi 4
The Raspberry Pi 4 Model B is the latest version in the cost-effective Raspberry Pi mini-computer series.

The Raspberry Pi 4 SoC witnessed an upgrade from Cortex A53 chips to a Cortex A 72 SoC which is a major performance booster supported by a USB 3.0 port. The system is powered by 4 GB RAM with dual 4k display output at 30fps, making it a strong replacement of a viable home theatre setting. With a pre-installed Linux-based OS called NOOBS, Pi 4 is poised to accelerate the development of the following applications-

a) Video streaming and creation of stop motion movies

b) Smart Surveillance models

c) Home Automation systems

d) Retro Gaming machines, and more​

However, the most disruptive applications of Raspberry Pi 4 comes with machine learning. From chatbots to object detection, AI development with Raspberry Pi 4 expands the horizons of machine learning development services across businesses. Let’s explore some of these.

IoT Applications and AI Development with Raspberry Pi 4

  1. In-Depth Image and Video Analytics
    Advancements in machine learning algorithms have propelled the development of deep image processing and video analytics applications. AI’s computer vision technologies are breaking new grounds with facial recognition, object detection, and real-time video surveillance models.

Though previous Raspberry Pi series ran cloud-based image processing models, Pi 4’s 64-bit quad-core processor can also train AI models at the edge efficiently. However, in the face of memory and compute restraints, it is advisable to use a cloud’s computational powers to deploy ML models such as-

a) Image Classification
Assigning specific labels to the image or video frame. The application requires coding the Raspberry Pi’s camera to capture images and record videos. Image labeling is emerging as an effective automation tool to monitor workplace activities, manufacturing cycles, and construction sites.

b) Object Detection
Recognizing objects by creating bounding boxes in an image. It requires trained datasets to run image processing operations on Raspberry Pi using its GPU. Object detection models enable retail, warehousing, and security businesses to automate critical operations effectively.

c) Video Analytics
By installing open-source libraries like TensorFlow on Raspberry Pi, businesses can train deep video analytics models for several use cases. At Oodles, we practiced AI development with Raspberry Pi to build a real-time environment monitoring systems for party lounge.

We have achieved over 80% accuracy in detecting image anomalies and extracting actionable insights from video frames. We used a well-trained OpenCV library on Raspberry Pi to inform party-goers about live music streaming status across the image processing, video analytics, and NLP-based chatbotslisted lounges.

  1. Social Medis Bots and AI Assistants
    AI-powered chatbots and voice-controlled AI assistants are one of the most impactful AI developments. However, it requires complex Natural Language Processing (NLP) algorithms to run across heavy AI and ML models.

Raspberry Pi’s compact architecture has accelerated the development of social media and voice-based assistant bots. A Python-based code on the Pi and an active Twitter account are the pre-requisites for creating a Twitter bot that

a) Automates business interactions and engages online users

b) Augments upselling efforts by pitching relevant business services to the target audience

c) Enhances customer experience with accurate and quick responses

For AI assistants, businesses can use cloud-based Google SDKs to run voice-controlled assistant on the Pi. From home automation systems to routine user queries, AI development with Raspberry Pi 4 can be used for dynamic applications.

Optimizing AI Development with Raspberry Pi 4 at Oodles
Raspberry Pi is a giant leap in the software development space. At Oodles, we are exploring the potential of Raspberry Pi to build AI-infused applications using machine learning and deep learning algorithms. Our team has experiential knowledge in training ML models with unstructured and structured data sets while ensuring low latency and maximum output.

Our AI development team is skilled at annotating large volumes of data to train function-specific ML models. We have delivered successful image processing, video analytics, and NLP-based chatbots for diverse business settings across the globe.

Reach out to our AI team to learn more about our artificial intelligence services.