Introduction to Pixy2

Hey there! In this session, Leon Huk is diving into the awesome Pixy2 product.
If you haven’t heard of it, the Pixy2 is a go-to tool for all sorts of image recognition projects .
You can snag one over on the Device SM website, and we’ve got links in the video description and YouTube card for your convenience.
Fun fact: this little gadget is super compact, measuring in at less than 5 centimeters ! It’s pretty versatile too, handling tasks like tracking learned objects , navigating with line tracking for robots, and it can even recognize hundreds of objects at the same time .
So stick around for more detailed insights on how it can boost your vision recognition projects !.

Let’s dive into the specs of this cool device! It runs on an NXP LPC 433 Arrow 204 MZ dual-core processor, plus it has a dedicated Aptina MT9 M14 image processor that can handle images at a whopping resolution of 12966 x 976.
With a compact size of just 3.8 x 4.2 x 1.5 cm and weighing in at only 10 g, it’s super portable.
You’ve got 264 KB of RAM and 2 MB of flash memory to work with.
And don’t worry about lighting; it comes with built-in lighting that beats your average smartphone light! There are also some handy LED indicators, and it can recognize objects thanks to its camera.
Plus, there’s a button on the top for easy control, and an RGB LED at the bottom adds a nice touch..

The Pixy2 is a game-changer when it comes to object recognition! It has a handy 5-pin USB port for connecting to your PC, plus an I/O port that’s perfect for hooking up with microcontrollers like Arduino or Raspberry Pi.
With its ability to communicate via UART, I2C, or SPI, you’re all set for versatile connections.
One of the best parts? You can use it all on its own without needing a microcontroller.
Plus, if you want to control motors, there’s a servo motor port just for that! Compared to the earlier PC1, this model not only maintains the same small size and affordability but also brings some serious upgrades in line tracking and speed, hitting an impressive 60 frames per second.
And let’s not forget about the enhanced object distinction algorithm that uses color, which simplifies coding across platforms like Arduino, Raspberry Pi, and even Lego Mindstorms.
Oh, and the built-in lighting? It’s been improved, too!.

Image recognition is super versatile and can be used in a bunch of fields, from spotting agricultural materials to recognizing electronic components and even toys for DIY projects.
If you fine-tune your algorithms, you’ll be able to efficiently identify the objects you’re looking for during these projects.
But, heads up! You might run into some challenges because images can vary in size and resolution.
Plus, when you’re processing a ton of images, it really eats up processor resources, especially if you’re also juggling other tasks like controlling LEDs or motors..

So, let’s talk about the Pixy2 and why it’s such a game-changer for image processing.
It’s like having a double team with both the main and image processors working together, making sure only the really important info gets sent to devices like Arduino or Raspberry Pi.
You get precise object coordinates, and it delivers this data super quickly at a whopping 60 frames per second through various interfaces like UART, SPI, or I2C.
You might be surprised, but even low-performance microcontrollers like Arduino can easily handle this organized info to control things like LEDs and motors.
Plus, the Pixy2 is quite the multitasker; it can manage multiple units at the same time and can even hook up directly to a servo motor for tracking objects it has learned.
Oh, and it uses a neat algorithm called CCC (Color Connected Components) to find objects based on their colors..

The object recognition system is super quick and efficient, but here’s the catch: its accuracy can really change depending on the environment and lighting.
Even with those ups and downs, it manages to spot objects using seven basic color categories and can actually recognize hundreds of them at the same time! What’s cool is that users can train the PC to recognize specific objects just by placing them in front of the camera and hitting a button, which helps the PC learn.
During this learning phase, an RGB LED lights up to show how well it’s learning, and all that info gets stored in flash memory.
Plus, each object that it recognizes gets a unique index from zero to 255, and that index stays the same even if the objects look pretty similar..

So, let’s talk about color codes in PCs! They’re pretty neat because they let you mix colors like pink and green to point out specific spots, like where a charger is. <<6>>
This really helps robots navigate better by giving clear info about location and size. <<6>>
But there’s a catch: you have to slap on some special color stickers to those objects, which can be a bit of a hassle. <<6>>
Plus, if an object is already a unique color, like a bright pink, you won’t need to bother with an extra color code.. <<6>>

The Pixy2 really steps up the game in line tracking tech, making it super handy for robots to navigate using line detection against their backgrounds. <<7>>
Unlike basic systems, it analyzes vectors, letting it accurately find where lines begin and end, plus track directional moves. <<7>>
On top of that, it processes info at an impressive 60 frames per second, which helps it identify different types of intersections and adapt to various line colors and widths. <<7>>
Plus, with cool tools like barcodes, you can easily tell the robot to do specific tasks like turning, and a handy program called Pimon helps pull color data straight from videos and images.. <<7>>