Tag Archives: Pi Camera

Welcome back to the AI on the Edge class series! In this lesson, we are diving deep into some of the most critical foundational skills you need when working with video streams on edge devices: Resizing, Moving, Converting, and Tiling video frames using OpenCV.

When you are developing real-world AI applications on the edge, you rarely just display a single camera feed. You often need to manipulate frames to feed them into your AI models, look at grayscale versions for edge detection, or arrange multiple windows on your desktop neatly so you can monitor your data visually.

If you want to follow along exactly as we do in the video, make sure you have your Raspberry Pi 5 set up with your Camera Module.

What We Cover in This Lesson

Fixed FPS Estimation: We continue using our robust low-pass filter formula to track smooth, non-jittery frames-per-second data directly on the video frame.
Creating Named Windows: Understanding how cv2.namedWindow() combined with cv2.WINDOW_GUI_NORMAL gives you absolute programmatic control over the placement of your displays.
Resizing & Moving Windows: How to accurately position multiple OpenCV windows on your screen using specific coordinates while accounting for operating system taskbars and window decorative margins.
Frame Manipulation: Using cv2.resize() to scale down video frames and cv2.cvtColor() to transform the color space from BGR to grayscale.
Window Tiling: Arranging a main camera view, a scaled-down color view, and a scaled-down grayscale view in a perfect grid layout on your desktop.

The Complete Lesson 20 Code

Below is the complete Python code we developed during this lesson. It sets up your hardware camera stream, calculates running performance metrics, processes three distinct variations of the video feed, and tiles them cleanly on your screen.

import cv2
import time
from picamera2 import Picamera2
piCam = Picamera2()
W=640
H=360
tStart = time.time()
fps = 0
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()

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)

topBar = 65
windowWaste = 25

cv2.namedWindow("Camera", cv2.WINDOW_GUI_NORMAL)
cv2.resizeWindow("Camera", W, H)
cv2.moveWindow("Camera",0,topBar)

cv2.namedWindow("Camera Small", cv2.WINDOW_GUI_NORMAL)
cv2.resizeWindow("Camera Small",  int(W/2), int(H/2))
cv2.moveWindow("Camera Small",0,topBar+windowWaste+H)
    
cv2.namedWindow("Gray Small", cv2.WINDOW_GUI_NORMAL)
cv2.resizeWindow("Gray Small", int(W/2), int(H/2))
cv2.moveWindow("Gray Small",int(W/2),topBar+windowWaste+H)


while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)
    frameSmall=cv2.resize(frame,(int(W/2),int(H/2)))
    frameGraySmall = cv2.cvtColor(frameSmall,cv2.COLOR_BGR2GRAY)

    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)
    cv2.imshow("Camera", frame)
    cv2.imshow("Camera Small",frameSmall)
    cv2.imshow("Gray Small",frameGraySmall)

    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

import cv2

import time

from picamera2 import Picamera2

piCam = Picamera2()

W=640

H=360

tStart = time.time()

fps = 0

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()

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

topBar = 65

windowWaste = 25

cv2.namedWindow("Camera", cv2.WINDOW_GUI_NORMAL)

cv2.resizeWindow("Camera", W, H)

cv2.moveWindow("Camera",0,topBar)

cv2.namedWindow("Camera Small", cv2.WINDOW_GUI_NORMAL)

cv2.resizeWindow("Camera Small", int(W/2), int(H/2))

cv2.moveWindow("Camera Small",0,topBar+windowWaste+H)

cv2.namedWindow("Gray Small", cv2.WINDOW_GUI_NORMAL)

cv2.resizeWindow("Gray Small", int(W/2), int(H/2))

cv2.moveWindow("Gray Small",int(W/2),topBar+windowWaste+H)

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

frameSmall=cv2.resize(frame,(int(W/2),int(H/2)))

frameGraySmall = cv2.cvtColor(frameSmall,cv2.COLOR_BGR2GRAY)

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.imshow("Camera", frame)

cv2.imshow("Camera Small",frameSmall)

cv2.imshow("Gray Small",frameGraySmall)

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

break

cv2.destroyAllWindows()

print('Program Terminated')

AI On the Edge, Tutorial

AI on the Edge LESSON 19: Create a Bouncing Box in OpenCV On Raspberry Pi

May 29, 2026 admin

Hey everyone, Paul McWhorter here!

Welcome back to the AI on the Edge series! In today’s lesson, we’re going to have some fun and take our first real steps into computer vision animation.

We’re going to create a colorful box that bounces around the screen like an old-school screensaver, while displaying a live FPS counter so we can see how well our Raspberry Pi is handling the workload.

Even though it looks simple, this project teaches you several foundational skills you’ll use again and again in computer vision:

Working with coordinates and drawing shapes in OpenCV
Creating smooth real-time animation
Detecting boundaries and reversing direction
Calculating and displaying live FPS

These are the same techniques you’ll build on later when we start doing object tracking, collision detection, and more advanced AI vision projects.

What You Learned in This Lesson

How to draw filled rectangles on a live video stream
How to move objects smoothly frame by frame
How to make objects “bounce” realistically off screen edges
A clean method for calculating and displaying FPS
Using variables to easily control size, position, speed, and color

This bouncing box may look basic, but once you understand how to do this, you can create all kinds of animated graphics that interact with what the camera sees.

Pro Tip: After you get it working, play around with the speed, box size, and colors. Try making multiple bouncing boxes with different speeds and directions — it’s a great way to experiment!

Ready for more? In the next lesson, we’re going to kick things up a notch and start working with multiple objects and more complex interactions.

Keep building, keep learning, and I’ll see you in the next video!

— Paul McWhorter

For your convenience, this is the code we developed in the video.

import cv2
import time
from picamera2 import Picamera2
piCam = Picamera2()
W=1280
H=720
tStart = time.time()
fps = 0
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()

textLowerLeft = (int(W*.01),int(H*.05))
fontFace = cv2.FONT_HERSHEY_SIMPLEX
fontThickness = int(W/425)
fontScale = H*.0015
fontColor = (0,0,255)

x1 = int(.1*(W-1))
y1 = int(.25*(H-1))
upperLeft = (x1,y1)
bW = int(W*.15)
bH = int(H*.15)
x2 = x1 + bW
y2 = y1 + bH
lowerRight = (x2, y2)
xDelta = 1
yDelta = 1
boxColor = (0,255,255)
boxThickness = -1

while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)
    myText = "FPS: "+str(round(fps,1))
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)
    
    cv2.rectangle(frame,upperLeft,lowerRight,boxColor,boxThickness)
    x1=x1+xDelta
    y1=y1+yDelta
    x2 = x1 +bW
    y2 = y1 +bH
    upperLeft = (x1,y1)
    lowerRight = (x2,y2)
    if x1<=0 or x2>=(W-1):
        xDelta=xDelta*(-1)
    if y1<=0 or y2>=(H-1):
        yDelta=yDelta*(-1)
    cv2.imshow("Camera", frame)
    cv2.moveWindow("Camera",0,60)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

import cv2

import time

from picamera2 import Picamera2

piCam = Picamera2()

W=1280

H=720

tStart = time.time()

fps = 0

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()

textLowerLeft = (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/425)

fontScale = H*.0015

fontColor = (0,0,255)

x1 = int(.1*(W-1))

y1 = int(.25*(H-1))

upperLeft = (x1,y1)

bW = int(W*.15)

bH = int(H*.15)

x2 = x1 + bW

y2 = y1 + bH

lowerRight = (x2, y2)

xDelta = 1

yDelta = 1

boxColor = (0,255,255)

boxThickness = -1

while True:

deltaT = time.time() - tStart

tStart=time.time()

fps = fps*.95 + (1/deltaT)*.05

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

myText = "FPS: "+str(round(fps,1))

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale,fontColor,fontThickness)

cv2.rectangle(frame,upperLeft,lowerRight,boxColor,boxThickness)

x1=x1+xDelta

y1=y1+yDelta

x2 = x1 +bW

y2 = y1 +bH

upperLeft = (x1,y1)

lowerRight = (x2,y2)

if x1<=0 or x2>=(W-1):

xDelta=xDelta*(-1)

if y1<=0 or y2>=(H-1):

yDelta=yDelta*(-1)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",0,60)

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

break

cv2.destroyAllWindows()

print('Program Terminated')

AI On the Edge

AI on the Edge LESSON 17: Decorating and Annotating Video Frames in openCV

May 22, 2026 admin

Welcome to AI on the Edge – Lesson 17: Decorating and Annotating Video Frames in OpenCV. In this lesson we take our live video stream from the Raspberry Pi camera and start making it really useful and professional-looking. Now that we can grab frames and display them, it’s time to learn how to draw directly on top of those frames. We’re talking rectangles, lines, arrows, circles, and crisp text overlays — all the visual elements you’ll need when you start adding real AI like face detection or object recognition.

You’ll see exactly how to use OpenCV’s drawing functions to create clean, scalable annotations that look great whether you’re running at 320×180 for maximum speed or higher resolutions like 1280×720. We cover how to control line thickness, use filled shapes, position text properly, and most importantly, how to make all your drawings scale automatically with your chosen resolution so everything stays nicely proportioned.

By the end of this lesson you’ll have the skills to draw bounding boxes around detected objects, add confidence scores, label people or items, draw tracking lines — basically anything you need to show what your AI is seeing. This is one of those foundational skills that you’ll use over and over again in your computer vision projects.As always, I encourage you to type the code along with me in the video, then start playing with colors, sizes, positions, and messages. Change things around, break it, and make it your own. That’s the best way to really learn this stuff.

So fire up your Raspberry Pi 5, grab that camera, and let’s start turning raw video frames into clear, informative, and great-looking annotated output!

import cv2
from picamera2 import Picamera2
piCam = Picamera2()
W=1280
H=720
# W= 640
# H= 360
W=320
H=180
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()
while True:
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)

    upperLeft = (int((W-1)*.1),int((H-1)*.1))
    lowerRight = (int((W-1)*.9),int((H-1)*.9))
    myColor = (255,0,0)
    myThick = int(W/250)
    cv2.rectangle(frame,upperLeft,lowerRight,myColor,myThick)
    
    upperLeft = (int((W-1)*.3),int((H-1)*.3))
    lowerRight = (int((W-1)*.7),int((H-1)*.7))
    myColor = (0,0,255)
    myThick = int(W/250)
    myThick = -1
    cv2.rectangle(frame,upperLeft,lowerRight,myColor,myThick)
 
    startingPoint = (int((W-1)*.1),int((H-1)*.1))
    endingPoint = (int((W-1)*.9),int((H-1)*.9))
    myColor = (255,0,0)
    myThick = int(W/250)
    cv2.line(frame,startingPoint,endingPoint,myColor,myThick)
 
    startingPoint = (int((W-1)*.7),int((H-1)*.3))
    endingPoint = (int((W-1)*.3),int((H-1)*.7))
    myColor = (0,255,0)
    myThick = int(W/250)
    cv2.arrowedLine(frame,startingPoint,endingPoint,myColor,myThick)
 
    myCenter = (int((W-1)/2),int((H-1)/2))
    myRadius = int(.1*(H-1))
    myThick = int(W/250)
    myColor = (0,255,0)
    cv2.circle(frame,myCenter,myRadius,myColor,myThick)
    
    myCenter1 = (int((W-1)*.3),int((H-1)*.3))
    myCenter2 = (int((W-1)*.7),int((H-1)*.7))
    myCenter3 = (int((W-1)*.7),int((H-1)*.3))
    myCenter4 = (int((W-1)*.3),int((H-1)*.7))
    myRadius = int((W-1)*.05)
    myColor = (255,0,0)
    myThick= -1
    cv2.circle(frame,myCenter1,myRadius,myColor,myThick)
    cv2.circle(frame,myCenter2,myRadius,myColor,myThick)
    cv2.circle(frame,myCenter3,myRadius,myColor,myThick)
    cv2.circle(frame,myCenter4,myRadius,myColor,myThick)  

    myText = "My Excellent Label"
    textLowerLeft= (int(W*.01),int(H*.05))
    fontFace = cv2.FONT_HERSHEY_SIMPLEX
    fontThickness = int(W/450)
    fontScale = H*.0015
    fontColor = (255,0,255)
    cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale, fontColor, fontThickness)


    cv2.imshow("Camera", frame)
    

    cv2.moveWindow("Camera",0,60)
    if cv2.waitKey(1)==ord('q'):
        break
cv2.destroyAllWindows()
print('Program Terminated')

import cv2

from picamera2 import Picamera2

piCam = Picamera2()

W=1280

H=720

# W= 640

# H= 360

W=320

H=180

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()

while True:

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

upperLeft = (int((W-1)*.1),int((H-1)*.1))

lowerRight = (int((W-1)*.9),int((H-1)*.9))

myColor = (255,0,0)

myThick = int(W/250)

cv2.rectangle(frame,upperLeft,lowerRight,myColor,myThick)

upperLeft = (int((W-1)*.3),int((H-1)*.3))

lowerRight = (int((W-1)*.7),int((H-1)*.7))

myColor = (0,0,255)

myThick = int(W/250)

myThick = -1

cv2.rectangle(frame,upperLeft,lowerRight,myColor,myThick)

startingPoint = (int((W-1)*.1),int((H-1)*.1))

endingPoint = (int((W-1)*.9),int((H-1)*.9))

myColor = (255,0,0)

myThick = int(W/250)

cv2.line(frame,startingPoint,endingPoint,myColor,myThick)

startingPoint = (int((W-1)*.7),int((H-1)*.3))

endingPoint = (int((W-1)*.3),int((H-1)*.7))

myColor = (0,255,0)

myThick = int(W/250)

cv2.arrowedLine(frame,startingPoint,endingPoint,myColor,myThick)

myCenter = (int((W-1)/2),int((H-1)/2))

myRadius = int(.1*(H-1))

myThick = int(W/250)

myColor = (0,255,0)

cv2.circle(frame,myCenter,myRadius,myColor,myThick)

myCenter1 = (int((W-1)*.3),int((H-1)*.3))

myCenter2 = (int((W-1)*.7),int((H-1)*.7))

myCenter3 = (int((W-1)*.7),int((H-1)*.3))

myCenter4 = (int((W-1)*.3),int((H-1)*.7))

myRadius = int((W-1)*.05)

myColor = (255,0,0)

myThick= -1

cv2.circle(frame,myCenter1,myRadius,myColor,myThick)

cv2.circle(frame,myCenter2,myRadius,myColor,myThick)

cv2.circle(frame,myCenter3,myRadius,myColor,myThick)

cv2.circle(frame,myCenter4,myRadius,myColor,myThick)

myText = "My Excellent Label"

textLowerLeft= (int(W*.01),int(H*.05))

fontFace = cv2.FONT_HERSHEY_SIMPLEX

fontThickness = int(W/450)

fontScale = H*.0015

fontColor = (255,0,255)

cv2.putText(frame,myText,textLowerLeft,fontFace,fontScale, fontColor, fontThickness)

cv2.imshow("Camera", frame)

cv2.moveWindow("Camera",0,60)

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

break

cv2.destroyAllWindows()

print('Program Terminated')

Technology Tutorials

Tag Archives: Pi Camera

AI on the Edge LESSON 20: Resizing, Moving, Converting and Tiling Video frames in OpenCV

What We Cover in This Lesson

The Complete Lesson 20 Code

AI on the Edge LESSON 19: Create a Bouncing Box in OpenCV On Raspberry Pi

What You Learned in This Lesson

AI on the Edge LESSON 17: Decorating and Annotating Video Frames in openCV

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