# A sample api for interacting with the sonic-pi and computer vision  ## Introduction A simple implementation of computer vision and sonic-pi in the music and entertainment industry in creating an immersive experience for its users. ## Methodology 1) Load the hand tracking model: Load the hand tracking model from Mediapipe. The hand tracking model will detect the hand and its landmarks. 2) Load the drawing module: Load the drawing module from OpenCV. The drawing module will be used to draw the virtual drum stick on the detected hand. 3) Define the region of interest: Define the region of interest (ROI) around the detected hand. This ROI will be used to draw the virtual drum stick on the detected hand. 4) Define the drum stick angles: Define the drum stick angles for the left and right turns. These angles will be used to calculate the drum stick direction based on the hand landmarks. 5) Calculate the drum stick direction: Calculate the drum stick direction based on the hand landmarks. You can use the position of the thumb and the index finger to calculate the direction. ## Installation Clone the Repository and Install the library ```bash pip install opencv-python pip install mediapipe pip install Flask ``` ## Run This ```bash python3 -m app.py ``` ## Results Using virtual drumming with Mediapipe and OpenCV, we were able to successfully control sonic-pi using hand movements. The controls were responsive and accurate, allowing us to easily make beats ## Conclusion Virtual drumming with Mediapipe and OpenCV is a powerful tool that can be used to make beats using hand movements. With this technique, we were able to make beats without using a physical drumming stick or a controller, which is a fun and immersive experience. ## Tech Stack OpenCV , Mediapipe , Flask ## Acknowledgements - [Hand Detection | mediapipe](https://google.github.io/mediapipe/solutions/pose.html) ## Contributers This is the list of contributors -Praise Godwins <a href = 'github.com/godwins3'>