💻 Deteccao_obj_MediaPipe_JS_Tensorflow

💻 A Jump Counter project that uses computer vision to count jumping jacks in a video, using JavaScript, HTML, CSS, TensorFlow and MediaPipe. The application detects body landmarks and counts the number of jumping jacks based on the distance between the hands and feet.

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Deteccao_obj_MediaPipe_JS_Tensorflow

• This project utilizes JavaScript, HTML, CSS, TensorFlow, OpenCV, and MediaPipe to count the number of jumping jacks performed in a video. The application recognizes the position of the feet and hands, enabling accurate counting of the exercises.

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Face Detection Project with face-api and JS 🚀

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Features

• Real-time video capture and pose analysis.
• Jumping jack counting based on the distance between hands and feet.
• Visual interface with a counter that updates with each detected jumping jack

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Technologies Used

• JavaScript: The programming language used to implement the application logic.
• HTML: The structure of the web page.
• CSS: Styling for the interface.
• TensorFlow.js: A library for performing machine learning operations directly in the browser.
• MediaPipe: A Google library for video processing and pose detection.
• OpenCV: A computer vision library; while not explicitly mentioned in the code, integration with MediaPipe provides similar functionalities.

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Open the HTML File:

• Open index.html in a modern browser that supports the

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Play the Video:

• The video will automatically start playing. The counting of jumping jacks will be displayed on the counter.

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Project Structure

 jumping-jack-counter/
├── index.html          # Main HTML file
├── css/
│   └── styles.css     # Page styles
└── js/
    └── script.js      # Application logic

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Example Logic

• The core logic of the project uses MediaPipe to detect body landmarks and calculates the distance between hands and feet to count jumping jacks. The counter updates every time a jumping jack is detected.

 // Initialize video and canvas
const videoElement = document.getElementById("video");
const canvasElement = document.getElementById("canvas");
const canvasCtx = canvasElement.getContext("2d");

// Jumping jack counter
let counter = 0;
// Previous state of movement
let lastState = null;

// Pose model setup
const pose = new Pose({
 locateFile: (file) => `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`,
});

// Other important configurations and functions omitted for brevity...

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How to Use

1. Clone the Repository:

   git clone https://github.com/ludiemert/Deteccao_obj_MediaPipe_JS_Tensorflow.git
   cd jumping-jack-counter

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Contribution

Feel free to contribute to this project! To do so, fork the repository, make your changes, and submit a pull request.

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License

• This project is licensed under the MIT License.

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📦 Contribution

• Feel free to contribute by submitting pull requests or reporting issues.

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• My LinkedIn -

Contact

Luciana Diemert

🛠 Full-Stack Developer
🖥️ Python Enthusiast | Computer Vision | AI Integrations
📍 São Jose dos Campos – SP, Brazil