A Fun Example: Dog-on-Couch Detector

Here is a whimsical example of how you could use Groundlight in your home to keep your dog off the couch. This document will guide you through creating a complete application. If the dog is detected on the couch, the application will play a pre-recorded sound over the computer's speakers, instructing the dog to get off the couch. Be sure to record your own voice so that your dog pays attention to you.

Requirements

• Groundlight SDK with Python 3.9 or higher
• A supported USB or network-connected camera
• A pre-recorded sound file (e.g., get_off_couch.mp3)
• A couch and a dog are recommended for proper end-to-end testing.

Installation

Ensure you have Python 3.9 or higher installed, and then install the Groundlight SDK, OpenCV library, and other required libraries:

pip install groundlight opencv-python pillow pyaudio

Creating the Application

1. First, log in to the Groundlight dashboard and create an API Token.

2. Next, we'll write the Python script for the application. Import the required libraries:

import cv2
import pyaudio
import time
import wave
from PIL import Image
from groundlight import Groundlight, ApiException

3. Define a function to capture an image from the camera using OpenCV:

def capture_image():
    cap = cv2.VideoCapture(0)

    ret, frame = cap.read()
    cap.release()

    if ret:
        # Convert to PIL image
        return Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
    else:
        return None

4. Define a function to play the pre-recorded sound:

def play_sound(file_path):
    CHUNK = 1024
    wf = wave.open(file_path, 'rb')

    p = pyaudio.PyAudio()

    stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
                    channels=wf.getnchannels(),
                    rate=wf.getframerate(),
                    output=True)

    data = wf.readframes(CHUNK)

    while data:
        stream.write(data)
        data = wf.readframes(CHUNK)

    stream.stop_stream()
    stream.close()

    p.terminate()

5. Write the main application loop:

gl = Groundlight()

detector = gl.get_or_create_detector("Dog on Couch Detector")

while True:
    image = capture_image()
    if image:
        try:
            iq = gl.submit_image_query(image=image, detector=detector, wait=60)
            answer = iq.result.label
            if answer == "YES":
                print("Dog detected on the couch!")
                play_sound("get_off_couch.mp3")
        except ApiException as e:
            print(f"Error submitting image query: {e}")
    else:
        print("Failed to capture image")

    # Sleep for a minute before checking again
    time.sleep(60)

This application captures an image using the capture_image function, then submits it to the Groundlight API for analysis. If the dog is detected on the couch, it plays the pre-recorded sound using the play_sound function.

Save the script as dog_on_couch_detector.py and run it:

python dog_on_couch_detector.py