AI on the Edge LESSON 3: Learn Python Essentials In One Session

March 29, 2026 admin

Hey everyone, and welcome back to the AI on the Edge series!

In today’s lesson, we’re doing something really important. Since this entire class is going to be taught using Python, I wanted to make sure everyone — even complete beginners — has a solid foundation before we start attaching hardware and diving into real AI projects.

In this marathon session, I take you through all the Python essentials you’ll need for the rest of the class. We cover variables, data types, if statements, for loops, while loops, lists, getting user input, and how to organize your code in a clean, readable way. I even share my personal philosophy on keeping code simple (you’ll notice I avoid nested else statements like the plague because I like my logic clean and easy to follow).

This lesson is designed to be a complete “Python boot camp” in one video. Even if you’ve never written a line of code before, by the end of this lesson you’ll have the core skills needed to keep up with everything we’re going to build in this series. And if you’re already comfortable with Python, this is still a great refresher with my specific style and approach that we’ll be using throughout the class.

I really believe that strong programming fundamentals are the key to success in AI on the Edge. Once you’re comfortable with these basics, we can focus on the fun stuff — controlling hardware, reading sensors, processing camera images, using voice commands, and building intelligent systems.

So whether you’re brand new to programming or just need a solid review, grab a cup of coffee, settle in, and let’s spend some quality time getting you up to speed with Python. I encourage you to code along with me in the video and actually type every example. That hands-on practice is what makes it stick.

By the end of this lesson, you’ll be ready for the homework assignment, which will test everything we covered today. Once you’ve got that down, we’ll be ready to start connecting real hardware in the very next lessons.

You’ve got this! Let’s turn you into a confident Python programmer so we can go build some amazing AI projects together.

AI On the Edge

AI on the Edge LESSON 2: Raspberry Pi Operating System for Artificial Intelligence

March 29, 2026 admin

The major challenge we face in this AI on the Edge class is getting a Raspberry Pi 5 configures where you have all the AI Models, Libraries, Modules and Methods installed, and where they all play nicely together. Often, when you add a new model, the old model becomes broken. This is because when you install something new, it often times updates the dependencies. That means it updates a library already on your system. For example, lets say you have numpy 14, working with YOLO 11. Now you install mediapipe, and it updates numpy 14 to numpy 15. This then ‘Breaks’ your YOLO, as it wanted a different version of numpy. Likely you will get frustrated and quit before you get the dependency problems solved. In order to get around this, you can use a special education version of the Bookworm OS, which has all the needed libraries installed already and working nicely with each other. The video above shows you how to install this OS. Once you do, no not update it, do not upgrade it, do not touch it. Use it to develop your programs and projects for this class. If you want to do something else with your pi, have a separate SD card.

AI On the Edge

AI on the Edge LESSON 1: Introduction and Class Overview

March 29, 2026 admin

Welcome to our all new AI on the Edge class! I will need you to buckle up, get your hardware together, and get ready to teach AI who is boss! We will be using a Pi 5, and the Fusion AI Lab kit. I will show links to the hardware below. In today’s lesson I describe the Class Introduction, and will show you some demos of the types of projects we will be doing. You will either Drive AI or your will be Destroyed by AI. Don’t be one of the ones who will be eaten by it

The Future will Belong to Those Who Can Drive AI

Guys, get your gear, and make sure you end up on the right side of the Dystopian future that awaits the world.

I have provided Amazon links, so you can order everything in the same place

You Will need a Raspberry Pi 5
Order Pi 5

You will need a heat sink and fan
Order Heat Sink and Fan

You Will Need the Fusion AI Lab Kit
Order Fusion AI Lab Kit

You Will Need a 25 Watt Power Supply
Order Power Supply

You Will Need a Micro HDMI Cable
Order Micro HDMI Cable

You Will Need a Keyboard and Mouse
Order Wireless Keyboard and Mouse

This isn’t just another Raspberry Pi class. This is a complete journey where we’re going to take the powerful Raspberry Pi 5, combine it with the SunFounder Fusion AI Lab kit, and build real, practical, intelligent systems that run completely on the edge — no cloud, no internet required.

In this class, you’re not going to just learn how to blink an LED or run someone else’s pre-made script. You’re going to learn how to build smart machines that can see, listen, speak, think, and act in the real world. We’re going to combine computer vision, voice recognition, speech synthesis, sensor reading, motor control, and modern AI techniques — all running locally on your Raspberry Pi 5.

Over the course of this series, you will learn how to:

Capture and process live video from the Raspberry Pi Camera
Detect faces and track objects in real time using MediaPipe and OpenCV
Control hardware with voice commands
Make your Raspberry Pi speak with natural-sounding Text-to-Speech
Build smooth, responsive control systems using threading
Use displays like the SSD1306 OLED to show live information
Combine everything into impressive AI-powered projects

This class is designed for makers, students, hobbyists, and engineers who want to move beyond basic tutorials and start building real intelligent edge devices. Whether you dream of building smart robots, autonomous monitoring systems, interactive AI companions, or just want to gain serious skills in modern embedded AI, this class is for you.

I’m going to teach this the way I always do — step by step, clearly, and with lots of hands-on projects. We’ll start with the fundamentals and gradually build up to more advanced and exciting projects as the class progresses.

If you’ve ever wanted to move from “playing with the Raspberry Pi” to “building truly intelligent systems,” then you’re in the right place. This is going to be a fun, challenging, and incredibly rewarding journey.

So if you’re ready to stop just watching AI videos and start building your own AI on the edge… then buckle up, because we’re about to do exactly that.

Welcome to the class! I’m really glad you’re here. Let’s get started!

OpenCV, Python, Raspberry Pi

Object Detection Using YOLO and RTSP Camera on Raspberry Pi 5

January 2, 2026 admin

OK guys, you spoke, and I listened. You all are asking for a lesson on how to do object detection on a Pi 5 using YOLO and an IP Camera. Well, you are about to get what you asked for. We will make this work, or we will DIE TRYING. Never fear, once you watch the video you will both understand and be able to do it on your own. First, I am assuming you watched our previous lesson where I showed you how to do the basic install and setup of YOLO. If not, never fear, I have the commands below. NOTE: This tutorial is geared towards bookworm OS. I strongly suggest you start with a fresh bookworm SC card, as there are many dependencies, and it is most likely to work if you start exactly where I am starting . . . with a fresh OS. Thes these are the commands I shared last week to get YOLO up and working: (just open a terminal, and paste these commands one at a time)

# 1. Configure X11 (manual steps required)
sudo raspi-config
# → Go to Advanced Options → X11 → Enable X11
# → Finish and reboot when prompted

# 5. Update system and install OpenCV
sudo apt update
sudo apt full-upgrade -y
sudo apt install python3-opencv -y

# Verify OpenCV
python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"
# Expected output: something like "OpenCV version: 4.6.0" or higher

# Create and activate virtual environment for YOLO11 (Ultralytics)
python3 -m venv --system-site-packages YOLO
source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment
# Install Ultralytics YOLO11 inside it
pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model
yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation
python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:
# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you 
#just created. Open tools- options, select 'interpreter' tab, then click they Python
#executable, selecting ... and navigate from home directory, 
#to YOLO, to bin, and then select python

# 1. Configure X11 (manual steps required)

sudo raspi-config

# → Go to Advanced Options → X11 → Enable X11

# → Finish and reboot when prompted

# 5. Update system and install OpenCV

sudo apt update

sudo apt full-upgrade -y

sudo apt install python3-opencv -y

# Verify OpenCV

python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"

# Expected output: something like "OpenCV version: 4.6.0" or higher

# Create and activate virtual environment for YOLO11 (Ultralytics)

python3 -m venv --system-site-packages YOLO

source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment

# Install Ultralytics YOLO11 inside it

pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model

yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation

python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:

# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you

#just created. Open tools- options, select 'interpreter' tab, then click they Python

#executable, selecting ... and navigate from home directory,

#to YOLO, to bin, and then select python

Now, I will explain this code, and will help you configure it for your cameras, but you will need to open up thonny, and paste in the following code as a start. IMPORTANT, as mentioned above, you need to set interpreter in thonny to the virtual environment set up in the process above. If this is not familiar to you, go back and watch last weeks lesson (click previous at the bottom of this post). Without further adue, here is the code we will work with today:

import cv2
from ultralytics import YOLO
#import secret
import threading
import time

W=1280
H=720
#Now, as eplained in the video, edit the line below where 
#you put your wifi user name in place of user, and your 
#wifi password in place of password, and you put in your 
#IP address
RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"
#RTSP_URL = secret.RTSP_URL1
#I have put my wifi credentials in a secret file so no
#one sees my credentials. You will just use the RTSP_URL line above. 
# Load the exported NCNN model (replace with your model path)
model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")
lock = threading.Lock()
running = True
def frameGrabber(url):
    global ipFrame, running
    cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)
    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)  # Reset frame position
    while running:
        ret, frame = cap.read()
        if ret:
            with lock:
                #frame = cv2.resize(frame, (W, H))
                ipFrame = frame.copy()
    cap.release()
    print("Thread Terminated")
thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)
thread.start()
tStart=time.time()
time.sleep(2)
fps=0
cnt=0
# Set resolution for faster processing (optional, adjust based on your needs)
while True:
    deltaT=time.time()-tStart
    fps= fps*.9 + .1/deltaT
    tStart=time.time()
    with lock:
        ipFrameShow=ipFrame.copy()
    results = model(ipFrameShow, conf=0.25, verbose=False)[0]  # conf: confidence threshold; adjust as needed

    # Annotate the frame with detections (boxes, labels, scores)
    annotatedFrame = results.plot()
    annotatedFrame=cv2.resize(annotatedFrame, (W,H))
    cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)
    cv2.imshow("IP Camera", annotatedFrame)
    #cv2.moveWindow("IP Camera",100,100)
    if cv2.waitKey(1)==ord('q'):
        break
running=False
thread.join()  # Wait for thread to fully exit
time.sleep(1)
cv2.destroyAllWindows()
import gc
gc.collect()  # Force garbage collection to reclaim memory/connections
#Make Extra sure all processes are killed
import os
os._exit(0)
print("Program Terminated")

import cv2

from ultralytics import YOLO

#import secret

import threading

import time

W=1280

H=720

#Now, as eplained in the video, edit the line below where

#you put your wifi user name in place of user, and your

#wifi password in place of password, and you put in your

#IP address

RTSP_URL = "rtsp://user:password@192.168.88.44:554/cam/realmonitor?channel=1&subtype=0"

#RTSP_URL = secret.RTSP_URL1

#I have put my wifi credentials in a secret file so no

#one sees my credentials. You will just use the RTSP_URL line above.

# Load the exported NCNN model (replace with your model path)

model = YOLO("/home/pjm/yolo11n_ncnn_model/", task="detect")

lock = threading.Lock()

running = True

def frameGrabber(url):

global ipFrame, running

cap = cv2.VideoCapture(url, cv2.CAP_FFMPEG)

cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

cap.set(cv2.CAP_PROP_POS_FRAMES, 0) # Reset frame position

while running:

ret, frame = cap.read()

if ret:

with lock:

#frame = cv2.resize(frame, (W, H))

ipFrame = frame.copy()

cap.release()

print("Thread Terminated")

thread = threading.Thread(target=frameGrabber, args=(RTSP_URL,), daemon=True)

thread.start()

tStart=time.time()

time.sleep(2)

fps=0

cnt=0

# Set resolution for faster processing (optional, adjust based on your needs)

while True:

deltaT=time.time()-tStart

fps= fps*.9 + .1/deltaT

tStart=time.time()

with lock:

ipFrameShow=ipFrame.copy()

results = model(ipFrameShow, conf=0.25, verbose=False)[0] # conf: confidence threshold; adjust as needed

# Annotate the frame with detections (boxes, labels, scores)

annotatedFrame = results.plot()

annotatedFrame=cv2.resize(annotatedFrame, (W,H))

cv2.putText(annotatedFrame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 3)

cv2.imshow("IP Camera", annotatedFrame)

#cv2.moveWindow("IP Camera",100,100)

if cv2.waitKey(1)==ord('q'):

break

running=False

thread.join() # Wait for thread to fully exit

time.sleep(1)

cv2.destroyAllWindows()

import gc

gc.collect() # Force garbage collection to reclaim memory/connections

#Make Extra sure all processes are killed

import os

os._exit(0)

print("Program Terminated")

The video explains everything, please watch it!

OpenCV

AI on the Edge: Install and Run YOLO Object Detection on the Raspberry Pi 5

December 30, 2025 admin

In today’s Lesson we will see just how far we can push things on the Raspberry Pi 5. I will show you how to install YOLO11 on the Pi . I will show you a simple program that will run YOLO11 under Python and openCV. The objective in today’s lesson is to see if the Pi5, without a Hailo accelerator hat, has sufficient power to do useful object detection. We will not use an accelerator hat, but the work is computationally intensive, so you must use active cooling. This is the low cost cooling fan we are using. It is sufficient to do the job, low cost and is a thin form factor that allows other hats to still fit on the Raspberry Pi 5. You can pick up the fan I am using HERE. Also, we are using an 8GB Pi 5. If you already have a Pi 5, it will probably work. The Pi 5 we are using is available HERE. These appliations are power hungry, so make sure you are using an official Pi Power supply.

In this lesson, I assume you are already familiar with the Pi 5. Note we are using Bookworm OS. Not all the dependencies work yet on Trixie, so I strongly recommend starting by flashing a fresh bookworm SD card.

YOLO11 is a powerful AI object detection model that runs well on the Raspberry Pi 5. The model below:

# 1. Configure X11 (manual steps required)
sudo raspi-config
# → Go to Advanced Options → X11 → Enable X11
# → Finish and reboot when prompted

# 5. Update system and install OpenCV
sudo apt update
sudo apt full-upgrade -y
sudo apt install python3-opencv -y

# Verify OpenCV
python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"
# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe
pip install mediapipe --break

# Verify MediaPipe
python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)
python3 -m venv --system-site-packages YOLO
source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment
# Install Ultralytics YOLO11 inside it
pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model
yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation
python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:
# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you 
#just created. Open tools- options, select 'interpreter' tab, then click they Python
#executable, selecting ... and navigate from home directory, 
#to YOLO, to bin, and then select python

# 1. Configure X11 (manual steps required)

sudo raspi-config

# → Go to Advanced Options → X11 → Enable X11

# → Finish and reboot when prompted

# 5. Update system and install OpenCV

sudo apt update

sudo apt full-upgrade -y

sudo apt install python3-opencv -y

# Verify OpenCV

python3 -c "import cv2; print('OpenCV version:', cv2.__version__)"

# Expected output: something like "OpenCV version: 4.6.0" or higher

# 6. Install MediaPipe

pip install mediapipe --break

# Verify MediaPipe

python3 -c "import mediapipe as mp; print('MediaPipe version:', mp.__version__)"

# 7. Create and activate virtual environment for YOLO11 (Ultralytics)

python3 -m venv --system-site-packages YOLO

source YOLO/bin/activate

# You are now inside the (YOLO) virtual environment

# Install Ultralytics YOLO11 inside it

pip install "numpy<2.0" ultralytics

# Now create a Pi friendly YOLO11 model

yolo export model=yolo11n.pt format=ncnn

# Optional: Verify YOLO installation

python -c "from ultralytics import YOLO; print('Ultralytics YOLO ready')"

# When finished working with YOLO, you can deactivate with:

# deactivate

#Now open Thonny, and you need to point thonny to the virtual environment you

#just created. Open tools- options, select 'interpreter' tab, then click they Python

#executable, selecting ... and navigate from home directory,

#to YOLO, to bin, and then select python

Now you should be set up to use YOLO11 on the Raspberry Pi 5!

We will start with this program, which is a simple grab a frame and show a frame openCV Program

import cv2
from picamera2 import Picamera2
import time
piCam = Picamera2()
W=1280
H=720
RES = (W,H)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()
fps=0

tStart=time.time()
while True:
    frame= piCam.capture_array()
    #frame=cv2.flip(frame,-1)
    deltaT=time.time()-tStart
    tStart=time.time()
    fps= fps*.9 + .1/deltaT
    cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)
    cv2.imshow("Camera", frame)
    cv2.moveWindow("Camera",100,100)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()

import cv2

from picamera2 import Picamera2

import time

piCam = Picamera2()

W=1280

H=720

RES = (W,H)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

fps=0

tStart=time.time()

while True:

frame= piCam.capture_array()

#frame=cv2.flip(frame,-1)

deltaT=time.time()-tStart

tStart=time.time()

fps= fps*.9 + .1/deltaT

cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",100,100)

if cv2.waitKey(1)==ord('q'):

break

cv2.destroyAllWindows()

In the video, we show how to use YOLO11 object detection in this simple program.

import cv2
from ultralytics import YOLO
from picamera2 import Picamera2
import time
piCam = Picamera2()
W=1280
H=720
RES = (W,H)
piCam.preview_configuration.main.size = RES
piCam.preview_configuration.main.format = "RGB888"
piCam.preview_configuration.controls.FrameRate=60
piCam.preview_configuration.align()
piCam.configure("preview")
piCam.start()
# Load the exported NCNN model (replace with your model path)
model = YOLO("/home/pjm/yolo11n_ncnn_model", task = 'detect')
fps=0
tStart=time.time()
# Set resolution for faster processing (optional, adjust based on your needs)
while True:
    frame= piCam.capture_array()
    results = model(frame, conf=0.25, verbose=False)
    frame = results[0].plot()  # Plots old boxes on new frame!
    deltaT=time.time()-tStart
    tStart=time.time()
    fps= fps*.8 + .2/deltaT
    cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)), 
            cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)
    
    # Display the result
    cv2.imshow("YOLO11 Detection", frame)

    # Exit on 'q' key
    if cv2.waitKey(1) == ord('q'):
        break

# Cleanup
cv2.destroyAllWindows()
import gc
gc.collect()  # Force garbage collection to reclaim memory/connections
#Make Extra sure all processes are killed
import os
os._exit(0)
print("Program Terminated")

import cv2

from ultralytics import YOLO

from picamera2 import Picamera2

import time

piCam = Picamera2()

W=1280

H=720

RES = (W,H)

piCam.preview_configuration.main.size = RES

piCam.preview_configuration.main.format = "RGB888"

piCam.preview_configuration.controls.FrameRate=60

piCam.preview_configuration.align()

piCam.configure("preview")

piCam.start()

# Load the exported NCNN model (replace with your model path)

model = YOLO("/home/pjm/yolo11n_ncnn_model", task = 'detect')

fps=0

tStart=time.time()

# Set resolution for faster processing (optional, adjust based on your needs)

while True:

frame= piCam.capture_array()

results = model(frame, conf=0.25, verbose=False)

frame = results[0].plot() # Plots old boxes on new frame!

deltaT=time.time()-tStart

tStart=time.time()

fps= fps*.8 + .2/deltaT

cv2.putText(frame, "FPS: "+str(round(fps,1)), (int(W*.01), int(H*.075)),

cv2.FONT_HERSHEY_SIMPLEX, H*.002, (0, 0, 255), 2)

# Display the result

cv2.imshow("YOLO11 Detection", frame)

# Exit on 'q' key

if cv2.waitKey(1) == ord('q'):

break

# Cleanup

cv2.destroyAllWindows()

import gc

gc.collect() # Force garbage collection to reclaim memory/connections

#Make Extra sure all processes are killed

import os

os._exit(0)

print("Program Terminated")

Technology Tutorials

AI on the Edge LESSON 3: Learn Python Essentials In One Session

AI on the Edge LESSON 2: Raspberry Pi Operating System for Artificial Intelligence

AI on the Edge LESSON 1: Introduction and Class Overview

Object Detection Using YOLO and RTSP Camera on Raspberry Pi 5

AI on the Edge: Install and Run YOLO Object Detection on the Raspberry Pi 5

Making The World a Better Place One High Tech Project at a Time. Enjoy!