Innovative Dog Detection System to Keep Your Couch Hair-Free
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Chapter 1: Introduction to the Dog Detection System
The challenge at hand involves a lovable dog 🐶 that enjoys lounging on the sofa 🛋️ when no one is around. Recently, the dog has started shedding its fur, making it essential to prevent it from getting too comfortable on the couch. The goal is to create a real-time dog recognition application that triggers a "roll down" audio 📣 when the dog is spotted.
Section 1.1: Requirements Overview
To implement this solution, a few key components are needed:
- Camera Access: Utilizing the browser's ability to access the phone’s camera, we can display the video feed in real-time.
- Real-Time Detection: By employing TensorFlow with a pre-trained COCO-SSD MobileNet V2 model, we can identify the dog in the camera frame.
- Audio Playback: An audio clip will play whenever the dog is detected, ensuring it knows to leave the couch.
- Device Setup: This will be deployed on an unused Android phone, utilizing Termux to enable a local HTTP server.
Section 1.2: Technical Implementation
The implementation involves several technical steps:
Accessing the Camera:
To initiate the camera feed, use the following code:
const stream = await navigator.mediaDevices.getUserMedia({
video: { facingMode: "user" },});
const videoElement = document.getElementById("camera-stream");
videoElement.srcObject = stream;
Loading the Detection Model:
The dog detection model is loaded with the following code:
let dogDetector;
async function loadDogDetector() {
const model = await cocoSsd.load();
dogDetector = model;
}
Processing Video Frames:
The video stream is processed frame by frame to detect the presence of a dog:
videoElement.addEventListener("play", async () => {
requestAnimationFrame(processVideoFrame);});
Playing the Audio:
When a dog is detected, the audio is played with the following function:
async function playDogBarkSound() {
const audio = new Audio(dogBarkSound);
await audio.play();
}
Setting Up the Local Server:
To run this application, install Termux and create a local HTTP server using:
python3 -m http.server 8000
Chapter 2: Achieving Results
The application works effectively 👍. When the camera is activated on the old phone, it successfully detects the dog and plays the designated sound to encourage it to move off the couch.
Section 2.1: Summary of Implementation
The developed web application leverages various technologies to deliver real-time dog detection and audio feedback. The steps include:
- Camera Activation: Utilizes navigator.mediaDevices.getUserMedia for camera access.
- Object Detection: Employs TensorFlow.js and a pre-trained model for recognizing dogs.
- Frame Processing: Implements video streaming and image recognition via requestAnimationFrame.
- Audio Feedback: Defines a function to play a specific sound upon dog detection.
- Local Deployment: Utilizes Termux for easy setup on an old Android phone.
The culmination of these steps results in an efficient web application capable of detecting dogs in real-time, playing audio to discourage them from lounging on your furniture. If you found this information beneficial, consider sharing it or offering suggestions for future articles. Your feedback is always welcome!