Volume Control With Hand Detection OpenCV Python With Source Code
The Volume Control With Hand Detection OpenCV Python was developed using Python OpenCV, In this Python OpenCV Project With Source Code, we are going to build a Volume Controller with OpenCV, To change the volume of a computer.
🔗COMPLETE SOURCE CODE: https://bit.ly/3Hm9qij
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We first look into hand tracking and then we will use the hand landmarks to find gestures of our hand to change the volume. This project is module-based which means we will be using a previously created hand module which makes hand tracking very easy.
📌BUILDING A VOLUME CONTROL WITH HAND DETECTION OPENCV:
• Step 1. Detect Hand landmarks
• Step 2. Calculate the distance between the thumb tip and index fingertip.
• Step 3. Map the distance of the thumb tip and index fingertip with volume range. For my case, the distance between the thumb tip and index fingertip was within the range of 15 – 220 and the volume range was from -63.5 – 0.0.
What is OpenCV?
OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time images and videos while also boasting analytical capabilities. It supports the Deep Learning frameworks.
This Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
By the way, if you are new to python programming and you don’t know what would be the Python IDE to use, I have here a list of the Best Python IDE for Windows, Linux, Mac OS that will suit you. I also have here How to Download and Install the Latest Version of Python on Windows.
To start executing Volume Control With Hand Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm on your computer.
- Python 3.9
- PyCharm (or any text editor that supports python)
Gesture recognition helps computers to understand human body language. This helps to build a more potent link between humans and machines, rather than just the basic text user interfaces or graphical user interfaces (GUIs).
In this project for gesture recognition, the human body’s motions are read by a computer camera. The computer then makes use of this data as input to handle applications.
The objective of this project is to develop an interface that will capture human hand gestures dynamically and will control the volume level. For this, Deep Learning techniques such as the Yolo model, Inception Net model+LSTM, 3-D CNN+LSTM, and Time Distributed CNN+LSTM have been studied to compare the results of hand detection.
The results of the Yolo model outperform the other three models. The models were trained using Kaggle and 20% of the videos were available in the 20 billion jester dataset. After the hand detection in captured frames, the next step is to control the system volume depending on the direction of hand movement.
The hand movement direction is determined by generating and locating the bounding box on the detected hand.
If you have any questions or suggestions about Volume Control With Hand Detection OpenCV Python With Source Code, please feel free to leave a comment below.
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