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This project will detect hand landmarks, calculate the distance between specific points (e.g., thumb tip and index finger tip), and use that information to control a virtual bar displayed on the screen.

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Tanwar-12/Hand-Detector-Code-Bar-Using-OpenCV-Mediapipe

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Hand-Detector-Code-Bar-Using-OpenCV-Mediapipe

This project will detect hand landmarks, calculate the distance between specific points (e.g., thumb tip and index finger tip), and use that information to control a virtual bar displayed on the screen.

Below is a step-by-step guide to create a hand detector control bar project using OpenCV and MediaPipe.

Step 1: Set Up Your Environment

  • Install the required libraries:

pip install mediapipe opencv-python

Import necessary modules in your Jupyter Notebook:

Step 2: Initialize Hand Tracking

Step 3: Create Helper Functions

Step 4: Hand Tracking Loop

Step 5: Run and Test Run the code and test the hand detector control bar. Open your Jupyter Notebook, execute the cells, and observe the distance displayed on the screen as you move your hand.

3aea-c3cf-40c9-92cb-61a2c110eeec.mp4

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This project will detect hand landmarks, calculate the distance between specific points (e.g., thumb tip and index finger tip), and use that information to control a virtual bar displayed on the screen.

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