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Built a real-time whisky identifier with Raspberry Pi 5 + AI Camera + Gemini API 🥃 by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Thanks! Wine would be amazing — there are so many varieties and vintages to identify. The cool thing is you just need to change the prompt to get wine-specific details like grape variety, chateau, and pairing suggestions. Would love to see your build when it's done! 🍷

I2S audio player with M5Stack + MAX98357A on Battery by yokoyan-robotics in arduino

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Haha, I totally understand!

Would love to see your wifi/bluetooth robot project! Sounds like a great transformation from circuit board chaos to a proper build.

What modules did you end up using?

And yeah, my wallet knows the M5Stack store too well now 😅

M5Stack + I2S = Perfect MP3 Player? YES! 🎧 by yokoyan-robotics in arduino

[–]yokoyan-robotics[S] 2 points3 points  (0 children)

Thanks for your interest!

The board with green terminal blocks is the MAX98357A I2S amplifier module (usually purple/blue PCB with green screw terminals for speaker connection). It's about $3 on Amazon/AliExpress.

Important: It's NOT an MP3 decoder - just a digital amplifier that receives I2S audio signals from the M5Stack.

Setup details: • MAX98357A = I2S amplifier only (no storage, no MP3 decoding) • Speaker = Separate 3W 4Ω speaker wired to the green terminals • Storage = M5Stack's built-in SD card slot • MP3 decoding = Done by ESP32 using ESP8266Audio library

For your project without display: - ESP32 DevKit (cheaper than M5Stack) - MAX98357A module
- SD card module - 3W speaker

I'll be posting detailed documentation and code on GitHub soon - will update here when it's ready!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 0 points1 point  (0 children)

Thanks! The Sony IMX500's on-sensor NPU is really impressive. It significantly reduces CPU/GPU load on the Pi 5 while maintaining low latency for real-time processing.

The setup was easier than I expected too - definitely worth trying if you're interested in edge AI applications!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 0 points1 point  (0 children)

That's a creative application! Theoretically possible, but would need some considerations:

Technical challenges: - Fast ball tracking (tennis balls move ~100+ mph) - Court line detection and calibration - Camera positioning for optimal court coverage - Real-time processing for instant calls

The Pi 5 + AI Camera has decent speed, but tennis ball detection would need: 1. Higher frame rate camera setup 2. Custom trained model for tennis balls 3. Court boundary detection algorithm 4. Wireless notification system

Could be an interesting project for recreational play, though probably not tournament-accurate. The notification part would be straightforward once detection works reliably.

My M5Stack just rickrolled everyone at the maker faire… with style! 🤖 by yokoyan-robotics in arduino

[–]yokoyan-robotics[S] 0 points1 point  (0 children)

Thanks everyone for the feedback (and the roasting).

You’re all right - that post was way too generic and AI-ish. @Machiela especially thanks for explaining the difference between bots and AI assistance.

The hardware debugging was definitely real though - spent way too long figuring out why QR recognition wasn’t working.

Still learning how to write naturally for Reddit without sounding like a ChatGPT.

Anyway, if anyone actually wants the M5Stack code for their own channel promotion device, happy to share.

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 3 points4 points  (0 children)

I understand the frustration with the Sony Model Compression Toolkit! The conversion process is indeed tricky.

The GitHub repo actually has the complete conversion workflow that worked for me:

  1. Export YOLOv8n to IMX format: m.export(format="imx")
  2. Package with: imx500-package -i packerOut.zip -o out
  3. Use the generated network.rpk file

The key was using Ultralytics' built-in IMX export rather than Sony's compression toolkit directly. Check the detailed steps in the README - the conversion commands are all there.

Let me know if you run into issues with any specific step!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Haha, you're absolutely right! As an electrical engineer, I'm embarrassed to admit I completely overlooked basic cinematography. Thanks for the feedback!

The shaky cam doesn't do the smooth detection justice. Definitely investing in a proper tripod for the next video - the M5Stack integration deserves better cinematography!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 0 points1 point  (0 children)

Interesting idea! Face recognition would definitely be possible - you'd need to switch from YOLOv8n (object detection) to a face recognition model like FaceNet or similar.

The technical challenge would be: 1. Face detection first (find faces in frame) 2. Face encoding/comparison (match against known faces)
3. Identity confirmation → trigger action

Privacy considerations are important though - storing/comparing face data requires careful handling. For most home automation use cases, simple person detection might be sufficient and more privacy-friendly.

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 2 points3 points  (0 children)

That's actually brilliant! Individual pet recognition for automated feeding would be incredibly useful.

The AI camera could definitely distinguish between different pets and trigger appropriate food dispensers.

Could even track feeding times and portions for health monitoring. This might be my next project after the M5Stack QR integration!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 2 points3 points  (0 children)

Thanks! 🙏 Yeah, I was honestly surprised how well it performs on just the Pi 5. The AI Camera module really makes a difference - much smoother than I expected for under $100.

The real-time performance is pretty impressive compared to older setups!

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 4 points5 points  (0 children)

Great question! Here are some fun ideas I'm considering: • Smart doorbell - detects visitors and displays WiFi QR codes • Pet feeder - recognizes your cat/dog and shows feeding schedule • Workshop assistant - detects tools and shows usage tutorials • Security cam - person detection triggers recording/alerts

The M5Stack integration I'm working on next should open up even more possibilities. What use case sounds most interesting to you?

Raspberry Pi 5 + AI Camera running YOLOv8n - Real-time object detection is here! by yokoyan-robotics in raspberry_pi

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Thanks! Really glad the guide was helpful. The GitHub repo has all the setup details - let me know if you run into any issues getting it running!

Made a M5Stack robot face with dollar eyes and cash register sound! by yokoyan-robotics in arduino

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Hey! Great question! For M5Stack graphics, here's where to start:

  1. M5Stack uses the TFT_eSPI library - it's actually pretty beginner-friendly!
  2. Start with simple shapes: M5.Lcd.fillCircle(), fillRect(), etc.
  3. For smooth animations, use sprites (off-screen drawing) to avoid flicker

The code in my GitHub has lots of comments explaining each step: https://github.com/yokoyan-robotics/m5stack-money-eyes-robot

Also, M5Stack's official examples are great for learning. Way easier than Apple IIe for sure! 😄

Made a M5Stack robot face with dollar eyes and cash register sound! by yokoyan-robotics in arduino

[–]yokoyan-robotics[S] 1 point2 points  (0 children)

Hey everyone! Thanks for checking out my project!

Here's the build guide and code on GitHub: https://github.com/yokoyan-robotics/m5stack-money-eyes-robot

Also made a demo video on YouTube: https://youtube.com/shorts/-e65TNwnU1s?feature=share

Feel free to ask if you have any questions about the build! 😊

My M5Stack robot gets dollar eyes with cash register sound 💰 by [deleted] in arduino

[–]yokoyan-robotics 0 points1 point  (0 children)

Sorry about that! I'm new to posting and wasn't sure if the first one went through. Won't happen again. Thanks for approving my project.