Laser Music Machine

Inspiration

The project draws inspiration from the fusion of art, music, and technology, exemplified by Jean-Michel Jarre's innovative laser harp, which merges traditional musical elements with cutting-edge technology. It is my 3rd Hackathon and I wanted to do something hardware.

What it does

The project uses computer vision to track red objects on a screen and trigger corresponding musical notes. It leverages OpenCV for real-time object detection, Pygame for sound playback, and a dynamic red detection algorithm to adapt to varying lighting. The object tracker assigns each red object to a note, playing it when detected. Data is logged and saved in a CSV file for playback via a GUI. The system uses a hand-tracking feature for volume control and integrates an interactive interface for sound playback options. It automates the detection, sound playback, and recording process, offering a complete interactive experience.

How we built it

A box of scraps from B and Q and a glue gun, tape, and lasers. The code is written in python. Object Detection: Used OpenCV with HSV colour space and CLAHE for red object tracking, ensuring it worked in varying lighting conditions.

Sound Integration: Integrated Pygame to play musical notes based on detected red objects, mapping each object to specific sound coordinates.

Gesture Control: Leveraged MediaPipe for hand tracking to control volume – closer hand = quieter, farther = louder.

Recording & Playback: Implemented CSV recording to log notes and timestamps, allowing users to replay their sessions.

Real-Time Performance: Ensured smooth, lag-free performance using multithreading to handle object detection, sound playback, and hand tracking simultaneously.

Challenges we ran into

The camera take 4 minutes to start so it was difficult to test it. Dynamic Lighting for Laser Detection: Getting the right settings to detect the laser accurately in changing lighting conditions took a lot of time and experimentation. Fine-tuning the HSV range and using adaptive histogram equalisation helped, but it was still a challenge to get consistent results across different environments.

Accomplishments that we're proud of

Real-Time Sound Synchronisation: I successfully synchronised red object detection with musical note playback in real time, providing a seamless interactive experience. Gesture Volume Control: The hand tracking feature, which controls the volume based on hand position, turned out to be intuitive and effective.

What we learned

Multithreading & Performance Optimisation: Handling multiple processes like detection, audio playback, and gesture recognition at the same time taught us a lot about multithreading and real-time performance optimisation. Fine-Tuning Detection Algorithms: The importance of tuning detection algorithms to work under different lighting conditions became clear, requiring both hardware and software adjustments.