AI Fusion Lab Kit | Technology Tutorials

Welcome back, everyone! Paul McWhorter here from toptechboy.com, and welcome to LESSON 56 of our AI on the Edge tutorial series! Grab yourself a nice hot cup of iced coffee (no sugar needed!), sit back, relax, and let’s jump right into today’s project.

AI on the Edge LESSON 56: Identifying Object of Interest in Target Zone with MediaPipe

In our previous lessons, we learned how to use MediaPipe’s object detection task to scan our live camera feed and pick out objects using pre-trained edge AI models like efficientdet_lite0.tflite. We got our bounding boxes drawn, displayed class labels, and showed confidence scores—all running smoothly right on our Raspberry Pi!

Today, we are taking our computer vision setup to the next level by building a Target Zone Detector.

What We Are Doing Today

Instead of just blindly detecting everything in the frame, we want our edge device to actively monitor a specific region of interest—a “Target Zone”—and trigger an alert when a specific Target Class enters that boundary.

Here is what we accomplish in this lesson:

Define a Fixed Target Zone: We calculate a customizable bounding box right in the center of our 1280×720 video feed (e.g., 150×250 pixels).
Filter by Object Class: We set a target object (in this example, a ‘bottle’). The system ignores all other object classes detected in the scene.
Calculate the Bounding Box Center: For the target object, we compute its exact center point (X_center, Y_center) on the fly and draw a tracking dot on it.
Boundary & Collision Logic: We test whether the center coordinates of our target fall inside our designated target zone using the condition: zoneX1 <= X_center <= zoneX2 and zoneY1 <= Y_center <= zoneY2.
Dynamic Visual Feedback:
- Idle State: When no target is inside the zone, the box remains Green.
- Active State: The moment the target’s center point steps into the target zone, the boundary box turns Red, and an alert banner—”Target Has Entered the Zone”—is dynamically rendered on the camera feed.
Real-Time Performance: We track frame rate (FPS) using an exponential moving average calculation to keep an eye on edge processing speed.

Homework / Challenge Assignment

Now that you have seen how to set up logic for a single target object in a single zone, here is your homework assignment to solidify what you’ve learned!

We want to now actively track the selected Object of Interest. Turn your servos on on the pan tilt camera, and keep the Object of Interest in the center of the camera view.

Give it a try on your own before watching the solution in the next video. Remember: You don’t learn by watching; you learn by doing!

Discussion & Community

Did your target zone detection work smoothly on your Raspberry Pi setup? How many FPS are you getting with your Pi Camera? Leave a comment below on YouTube to share your results, questions, or modifications!

If you enjoyed this tutorial, hit that Like button, Subscribe to the channel, and share it with anyone who wants to learn real-world AI on the Edge.

As always, I’ll see you guys in the next lesson!

General Knowledge / Context

MediaPipe Tasks API: MediaPipe Python Tasks API provides high-level Python interfaces for object detection, landmasking, and classification tasks utilizing lightweight edge models such as TensorFlow Lite (.tflite).
PiCamera2 & OpenCV: In modern Raspberry Pi OS (Bullseye/Bookworm), Picamera2 handles native camera hardware capture in raw formats like RGB888, which can then be converted to MediaPipe Image objects (mp.Image) and rendered on OpenCV (cv2) GUI windows seamlessly.

# ====================================================================
# DISCLAIMER:
# This code is provided as-is for educational and experimental 
# purposes only. The author makes no representations or warranties of 
# any kind concerning the safety, suitability, or accuracy of this 
# code. Use at your own risk. The author assumes no liability for any 
# damages, system failures, security breaches, or network issues 
# resulting from the use or implementation of this script.
# ====================================================================
 
import cv2
import time
from picamera2 import Picamera2
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

piCam = Picamera2(1)
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)
topBar = 65
windowPadding = 0
cv2.namedWindow('Camera', cv2.WINDOW_GUI_NORMAL)
cv2.resizeWindow('Camera', W, H)
tSettle = .1
time.sleep(tSettle)
cv2.moveWindow('Camera', windowPadding, topBar)

modelPath = "/home/pi/ai-lab-kit/mediapipe/efficientdet_lite0.tflite"
scoreThreshold = .35
maxObj = 3

baseOptions = python.BaseOptions(model_asset_path = modelPath)
options = vision.ObjectDetectorOptions(base_options = baseOptions,
                                       score_threshold=scoreThreshold,
                                       max_results=maxObj)
myDetector = vision.ObjectDetector.create_from_options(options)

targetClass = 'bottle'
zoneW = 150
zoneH = 250
zoneX1 = int(W/2 - zoneW/2)
zoneY1 = int(H/2 - zoneH/2)
zoneX2 = zoneX1 + zoneW
zoneY2 = zoneY1 + zoneH

zoneColorIdle = (0,255,0)
zoneColorActive = (0,0,255)


while True:
    deltaT = time.time() - tStart
    tStart=time.time()
    fps = fps*.95 + (1/deltaT)*.05
    frame= piCam.capture_array()
    frame=cv2.flip(frame,-1)
    myImage = mp.Image(image_format = mp.ImageFormat.SRGB, data=frame)
    myDetections = myDetector.detect(myImage)
    targetInZone = False
    #print(myDetections.detections)
    for detection in myDetections.detections:
        if detection.categories:
            category=detection.categories[0]
            if category.category_name == targetClass:
                box = detection.bounding_box
                x = int(box.origin_x)
                y = int(box.origin_y)
                w = int(box.width)
                h = int(box.height)
                boxCenterX = x + int(w/2)
                boxCenterY = y + int(h/2)
                cv2.rectangle(frame,(x,y),(x+w,y+h),
                              (255,0,0),fontThickness)
                labelText = "Target Aquired "+str(round(category.score*100))+'%'
                cv2.circle(frame,(boxCenterX,boxCenterY),6,(0,0,255),-1)
                cv2.putText(frame,labelText,(x,y-10),fontFace,fontScale*.8,(0,255,0),2)
                if (zoneX1<=boxCenterX<=zoneX2) and (zoneY1<=boxCenterY<=zoneY2):
                    targetInZone = True
            if targetInZone == True:
                cv2.rectangle(frame,(zoneX1,zoneY1),(zoneX2,zoneY2),zoneColorActive,fontThickness)
                bannerText = "Target Has Entered the Zone"
                cv2.putText(frame,bannerText,(zoneX1,zoneY1-25),
                            fontFace,fontScale*.9,zoneColorActive,2)
            if targetInZone == False:
                cv2.rectangle(frame,(zoneX1,zoneY1),(zoneX2,zoneY2),zoneColorIdle,fontThickness)
                
                                     
    myText = "FPS: "+str(round(fps,1))
    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
myDetector.close()
cv2.destroyAllWindows()
print('Program Terminated')

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# ====================================================================

# DISCLAIMER:

# This code is provided as-is for educational and experimental

# purposes only. The author makes no representations or warranties of

# any kind concerning the safety, suitability, or accuracy of this

# code. Use at your own risk. The author assumes no liability for any

# damages, system failures, security breaches, or network issues

# resulting from the use or implementation of this script.

# ====================================================================

import cv2

import time

from picamera2 import Picamera2

import mediapipe as mp

from mediapipe.tasks import python

from mediapipe.tasks.python import vision

piCam = Picamera2(1)

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)

topBar = 65

windowPadding = 0

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

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

tSettle = .1

time.sleep(tSettle)

cv2.moveWindow('Camera', windowPadding, topBar)

modelPath = "/home/pi/ai-lab-kit/mediapipe/efficientdet_lite0.tflite"

scoreThreshold = .35

maxObj = 3

baseOptions = python.BaseOptions(model_asset_path = modelPath)

options = vision.ObjectDetectorOptions(base_options = baseOptions,

score_threshold=scoreThreshold,

max_results=maxObj)

myDetector = vision.ObjectDetector.create_from_options(options)

targetClass = 'bottle'

zoneW = 150

zoneH = 250

zoneX1 = int(W/2 - zoneW/2)

zoneY1 = int(H/2 - zoneH/2)

zoneX2 = zoneX1 + zoneW

zoneY2 = zoneY1 + zoneH

zoneColorIdle = (0,255,0)

zoneColorActive = (0,0,255)

while True:

deltaT = time.time() - tStart

tStart=time.time()

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

frame= piCam.capture_array()

frame=cv2.flip(frame,-1)

myImage = mp.Image(image_format = mp.ImageFormat.SRGB, data=frame)

myDetections = myDetector.detect(myImage)

targetInZone = False

#print(myDetections.detections)

for detection in myDetections.detections:

if detection.categories:

category=detection.categories[0]

if category.category_name == targetClass:

box = detection.bounding_box

x = int(box.origin_x)

y = int(box.origin_y)

w = int(box.width)

h = int(box.height)

boxCenterX = x + int(w/2)

boxCenterY = y + int(h/2)

cv2.rectangle(frame,(x,y),(x+w,y+h),

(255,0,0),fontThickness)

labelText = "Target Aquired "+str(round(category.score*100))+'%'

cv2.circle(frame,(boxCenterX,boxCenterY),6,(0,0,255),-1)

cv2.putText(frame,labelText,(x,y-10),fontFace,fontScale*.8,(0,255,0),2)

if (zoneX1<=boxCenterX<=zoneX2) and (zoneY1<=boxCenterY<=zoneY2):

targetInZone = True

if targetInZone == True:

cv2.rectangle(frame,(zoneX1,zoneY1),(zoneX2,zoneY2),zoneColorActive,fontThickness)

bannerText = "Target Has Entered the Zone"

cv2.putText(frame,bannerText,(zoneX1,zoneY1-25),

fontFace,fontScale*.9,zoneColorActive,2)

if targetInZone == False:

cv2.rectangle(frame,(zoneX1,zoneY1),(zoneX2,zoneY2),zoneColorIdle,fontThickness)

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

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

myDetector.close()

cv2.destroyAllWindows()

print('Program Terminated')

Technology Tutorials

Tag Archives: AI Fusion Lab Kit

AI on the Edge LESSON 56: Identifying Object of Interest in Target Zone with Mediapipe

AI on the Edge LESSON 56: Identifying Object of Interest in Target Zone with MediaPipe

What We Are Doing Today

Homework / Challenge Assignment

Discussion & Community

General Knowledge / Context

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