Exploring Raspberry Pi AI HAT+ and AI Camera: Hardware, Use Cases, and Applications

3. Real-World Use Cases

3.1 Home Automation

Integrating the AI HAT+ with the AI Camera can enable features like:

• Intruder Detection: Use facial recognition to identify unrecognized individuals.
• Smart Appliances: Automate household devices based on visual cues.

3.2 Healthcare

• Patient Monitoring: Track vitals and movements using AI-powered video analytics.
• Medical Imaging: Enhance diagnostic accuracy through image processing.

3.3 Industrial Applications

• Quality Control: Use the AI camera for defect detection in manufacturing.
• Robotics: Deploy AI models for navigation and task automation.

3.4 Education and Research

• Develop AI models for academic purposes.
• Create projects for STEM education, leveraging real-time AI.

3.5 Home Automation: Enhancing Smart Homes with Raspberry Pi AI HAT+ and AI Camera

Using the Raspberry Pi AI HAT+ and the AI Camera, you can build a smart home automation system capable of detecting individuals, recognizing faces, and controlling appliances such as lights based on visual cues. Below is a detailed guide for setting up a system that switches lights on or off based on detected activity or recognized individuals.

Example Setup: Automating Lights with Facial Recognition

Objective:
Create a system where the AI camera detects a specific person and switches the lights on or off based on their presence.

Step 1: Hardware Requirements

1. Raspberry Pi 5 (or Pi 4)
2. AI HAT+ (26 TOPS)
3. Raspberry Pi AI Camera
4. Relay Module (to control the lights)
5. Light Bulb and Lamp Holder
6. Jumper Wires
7. Power Supply for Raspberry Pi

Step 2: Wiring the Setup

1. Connect the AI Camera to the MIPI CSI port on the Raspberry Pi.
2. Attach the AI HAT+ to the GPIO pins of the Raspberry Pi.
3. Relay Module Wiring:
   • Connect the VCC pin of the relay module to the 5V pin on the Raspberry Pi.
   • Connect GND to a ground pin on the Raspberry Pi.
   • Connect the IN pin of the relay module to a GPIO pin (e.g., GPIO17).
   • Wire the light bulb to the relay’s output terminals.

Step 3: Install Required Software

1. Install TensorFlow Lite:

   pip install tflite-runtime
   

2. Install OpenCV:

   sudo apt update
   sudo apt install python3-opencv
   

3. Set Up Raspberry Pi GPIO Library:

   pip install RPi.GPIO
   

4. Download Pre-Trained Face Recognition Models: Use a model like dlib’s face recognition or a TensorFlow Lite model.

Step 4: Writing the Code

Here’s a Python script to detect a known face and control the light:

import cv2
import face_recognition
import RPi.GPIO as GPIO
import time

# GPIO setup
RELAY_PIN = 17
GPIO.setmode(GPIO.BCM)
GPIO.setup(RELAY_PIN, GPIO.OUT)
GPIO.output(RELAY_PIN, GPIO.HIGH)  # Start with the light off

# Load the known face image and encode it
known_image = face_recognition.load_image_file("known_face.jpg")
known_encoding = face_recognition.face_encodings(known_image)[0]

# Initialize the camera
video_capture = cv2.VideoCapture(0)

try:
    while True:
        # Capture frame-by-frame
        ret, frame = video_capture.read()
        if not ret:
            break

        # Resize frame for faster processing
        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
        rgb_small_frame = small_frame[:, :, ::-1]

        # Find faces in the frame
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        for face_encoding in face_encodings:
            matches = face_recognition.compare_faces([known_encoding], face_encoding)
            if True in matches:
                # If a known face is detected, turn on the light
                print("Recognized: Turning ON the light.")
                GPIO.output(RELAY_PIN, GPIO.LOW)
                time.sleep(5)  # Keep the light on for 5 seconds
            else:
                # If an unknown face is detected, turn off the light
                print("Unknown face detected: Turning OFF the light.")
                GPIO.output(RELAY_PIN, GPIO.HIGH)

finally:
    # Release resources
    video_capture.release()
    GPIO.cleanup()
    cv2.destroyAllWindows()

Step 5: Running the Program

1. Save the script as smart_home.py.
2. Place an image of the known face in the same directory as known_face.jpg.
3. Run the script:

   python3 smart_home.py
   

How It Works

1. The AI Camera captures video frames and detects faces using the face_recognition library.
2. If a detected face matches the known encoding, the GPIO pin controlling the relay switches on, turning on the light.
3. If no match is found or no face is detected, the relay switches off, turning off the light.

Potential Enhancements

1. Voice Commands: Add a voice control feature using libraries like SpeechRecognition.
2. Motion Detection: Use OpenCV to detect motion before activating face recognition for energy efficiency.
3. Integration with IoT Platforms: Connect the system to platforms like Home Assistant for remote control.

This setup showcases how the Raspberry Pi AI HAT+ and AI Camera can bring intelligent automation to everyday life.