Tag Archives: OLED

AI on the Edge LESSON 50: Control NeoPixel Ring With Hand Gestures and MediaPipe

Hey everybody, welcome back to AI on the Edge! In Lesson 50 we’re taking things to the next level — we’re combining real-time hand tracking with a physical NeoPixel ring so you can change colors just by pointing at the screen! Using MediaPipe on the Raspberry Pi, we track your index finger tip and turn the big camera view into an interactive color picker. Hover over any of the seven big colored circles (Red, Green, Blue, Cyan, Magenta, Yellow, or Black) and — boom — the NeoPixel ring instantly lights up with that color!
This lesson is super fun because it finally brings together everything we’ve been building: smooth hand detection, coordinate mapping between camera and OLED, visual feedback, and real-world hardware control. You’ll see your finger tracked with a big blue dot, watch the selected color update live on both the screen and the OLED, and control actual RGB lights with nothing but a gesture.
By the end of this lesson you’ll have a working gesture-controlled color mixer that feels like real interactive magic. This is the kind of project that makes people say “Wow, you built that on a Raspberry Pi?!”
This is also a perfect foundation for even cooler future projects — gesture-controlled lighting, touchless interfaces, interactive games, or even a full gesture-controlled robot or light show.
So grab your Raspberry Pi, hook up that NeoPixel ring, fire up the camera, and let’s start waving our hands around like we’re casting spells! As always, the complete code is right below the video. Watch along, run the code, then start tweaking it — maybe add more colors, make the ring react differently to gestures, or combine it with the hand connection drawing from earlier lessons.I’m really excited to see what you create with this one!Now go make something awesome! 

This is the core circuit we use for the class:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

This is the schematic to add the OLED:

OLED
SSD1306 OLED Connected to the Fusion AI Hat

Then this is the schematic to add the NeoPixel

NeoPixel
NeoPixel Schematic

AI on the Edge LESSON 47: Emotion Detector Using MediaPipe, OpenCV and Raspberry Pi

In this exciting lesson, we build a real-time Emotion Detector that can recognize basic human emotions using just a Raspberry Pi, a camera, and the power of MediaPipe. The system watches your face and identifies whether you look Neutral, Smiling, Surprised, or Angry — and then reacts instantly by changing the color of a NeoPixel ring and displaying the detected emotion on both the camera preview and a small OLED display.Using MediaPipe’s Face Mesh solution, the program tracks 468 facial landmarks in real time. From these points, we carefully calculate key facial ratios — such as eye openness, mouth width, and mouth height relative to head width. These measurements allow us to create simple but effective rules that distinguish between different emotional expressions. For example, a wide mouth combined with raised cheeks indicates a smile, while a small mouth opening and narrowed eyes suggests anger.The project beautifully integrates several powerful technologies:

  • Picamera2 for high-performance camera capture
  • OpenCV for image processing and on-screen text display
  • MediaPipe Face Mesh for accurate facial landmark detection
  • NeoPixel RGB ring that lights up in different colors depending on the detected emotion
  • SSD1306 OLED display that shows both the emotion name and a simplified wireframe of your face

One of the most satisfying parts of this project is seeing the NeoPixel ring instantly change color to match your emotion — green for happy, cyan for surprised, red for angry, and yellow for neutral. The OLED also mirrors the detected emotion, making the entire system feel alive and responsive.This lesson is a fantastic step forward in understanding how to combine computer vision with physical outputs on the edge. You will learn how to extract meaningful measurements from facial landmarks, build rule-based emotion logic, and synchronize visual feedback across multiple devices (camera preview, NeoPixel, and OLED).By the end of Lesson 47, you will have a working real-time emotion recognition system running entirely on a Raspberry Pi — a great foundation for more advanced projects like mood-reactive lights, interactive robots, or even assistive technology.

These are the schematics we are using for todays project. If you are taking the class, you should already have these components connected:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class
OLED
SSD1306 OLED Connected to the Fusion AI Hat
NeoPixel
NeoPixel Schematic

AI on the Edge LESSON 44: Displaying Live MediaPipe FaceMesh Avatar on SSD1306 OLED in OpenCV

This is Getting Crazy Cool!

Hey guys, welcome back to the Fusion AI Lab series, AI on the Edge! In Lesson 44 we just took things up another notch. We’re now running MediaPipe Face Mesh on our Raspberry Pi 5, pulling all 468 facial landmarks in real time, and then drawing a clean, smooth face contour directly onto a tiny 128×64 OLED display!

That’s right — your face is now living on that little monochrome OLED in real time! Every eyebrow raise, smile, head tilt, and eye movement gets faithfully reproduced on the display. We’re pulling the camera feed with picamera2, processing it with MediaPipe’s powerful face mesh, scaling the landmarks down to OLED resolution, and then drawing the official FACEMESH_CONTOURS connections using PIL. The result is surprisingly smooth and fun to watch! This project really shows the power of combining modern AI vision tools with simple embedded hardware. Watching your own face rendered in real time on a tiny OLED is just plain awesome — it feels like sci-fi stuff from just a few years ago, and now we’re doing it on the Edge with our Fusion AI Lab kit!

If you’ve been following along, you’re starting to see how powerful these tools are becoming. We’re no longer just detecting faces — we’re understanding the structure of the face and visualizing it however we want. And we’re just getting started!

So fire up your Raspberry Pi, grab that OLED, and let’s keep pushing the limits. You’re doing some seriously cool stuff!

In this lesson we are still using our standard Fusion AI Lab Kit cirtuit from the earlier lessons:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

We have also connected the OLED to the above circuit according to this diagram:

OLED
SSD1306 OLED Connected to the Fusion AI Hat

AI on the Edge LESSON 43: Adding SSD1306 OLED to Your Raspberry Pi Projects

In today’s lesson in the AI on the Edge series, we’re adding a very important piece to our growing collection of skills — learning how to use the SSD1306 OLED display with the SunFounder Fusion AI Lab kit.

One of the key goals of this class is to build intelligent systems that can run completely on the edge — without needing to rely on the cloud. A big part of that is giving our edge devices the ability to communicate clearly with us. That’s exactly where the little SSD1306 OLED really shines. Whether you’re building a smart robot, an autonomous sensor node, or an AI-powered monitoring system, having a crisp, low-power display to show status, results, or even fun animations makes your project feel complete and professional.

In this video, we start with the basics and gradually build up. You’ll learn how to connect the OLED using I2C, initialize it with CircuitPython, and then use the PIL library to create images in memory before sending them to the screen. We explore loading different font sizes so you can create nice, readable layouts. Then we move into drawing shapes — rectangles, circles, and borders — before finishing with a fun bouncing ball animation that brings the whole thing to life.

This lesson is particularly important in our AI on the Edge journey because the SSD1306 is extremely lightweight and uses almost no processing power, making it perfect for running alongside face detection, object tracking, speech recognition, and other AI tasks without slowing down your Raspberry Pi. You’ll see how easy it is to display messages like detection results, confidence scores, system status, or even playful personality messages from your AI creations.

By the end of this lesson, you’ll have the confidence to add a real display to any project in this class. Whether you want to show live face tracking data, sensor readings, or just give your robot a fun way to “talk” to the world, the skills you learn here will be used again and again in future projects.

So if you’re following along with the AI on the Edge series, this is another big step forward. Grab your Fusion AI Hat, open up Thonny, and let’s get that OLED screen lighting up with some personality!

As always, I strongly encourage you to take the code and make it your own. Change the messages, create new animations, and think about how you can use this display in your own AI projects. That’s where the real learning and creativity happens

So far in this class we have been using this schematic:

Fusion Hat Circuit Diagram
This is the circuit we will use moving forward in the class

In this lesson, we will be adding the SSD1306 OLED display. Keep the schatic above, but now add the OLED display to the breadboard. It should be connected to the Fusion Hat as follows:

  1. Connect the VCC pin of OLED display to 3.3V on Fusion HAT+
  2. Connect the GND pin of OLED display to GND on Fusion HAT+
  3. Connect the SCL pin of OLED display to SCL (GPIO 3) on Fusion HAT+
  4. Connect the SDA pin of OLED display to SDA (GPIO 2) on Fusion HAT+
OLED
SSD1306 OLED Connected to the Fusion AI Hat

While we are updating our project components, go ahead and connect the neoPixel ring to yout Fusion Hat. Here is the schematic to add the neopixel ring, and we will be using it in future lessons.

NeoPixel
NeoPixel Schematic

In the video, we developed the following code to show you how to put the SSD1306 OLED through its paces using the Fusion AI Hat on the Raspberry Pi 5.

 

Arduino IMU Project with Complete Avionics Display for Roll, Pitch and Yaw on a SSD1306 OLED

In this video lesson we wrap up our project to create an Arduino IMU using the GY-87 IMU module, with an MPU6050 chip and a QMC5883L Magnetometer. In this lesson we complete the avionics display, creating an accurate graphical output for Roll, Pitch, and Yaw. This is the schematic we are using for this project:

OLED IMU
This schematic shows how to connect the SSD1306 OLED to our IMU Project.

This is the code we develop in the video. Remember, you have to calibrate your sensors, and put your calibration numbers into the code below. I showed you how to do the calibration in THIS LESSON.