Emotion Detection from Video Feed

This project uses a webcam to detect emotions in real-time using a pre-trained deep learning model. The application is built with Flask for the web interface and OpenCV for video processing.

Dependencies

To run this project, you need the following Python packages:

• Flask
• OpenCV
• TensorFlow
• h5py
• numpy

You can install these dependencies using the provided requirements.txt file.

Setup Instructions

1. Set Up a Virtual Environment (Optional but Recommended):

   • Create a virtual environment to keep your project dependencies isolated.

   python -m venv venv

   • Activate the virtual environment:
     • On Windows:

     venv\Scripts\activate

     • On macOS/Linux:

     source venv/bin/activate

2. Install Dependencies:

   • Use pip to install the required packages from the requirements.txt file.

   pip install -r requirements.txt

3. Run the Application:

   • Ensure you have the Haar Cascade file (haarcascade_frontalface_default.xml) in the same directory as your script or provide the correct path in your code.
   • Start the Flask application by running the main.py file:

   python main.py

4. Access the Application:

   • Open a web browser and go to http://localhost:5000 to access the application.

How It Works

• The application captures video from the webcam and processes each frame to detect faces.
• For each detected face, it predicts the emotion using a pre-trained model and displays the emotion label on the video feed.

Notes

• Ensure that your camera is connected and accessible by OpenCV.
• If you encounter any issues with TensorFlow, make sure your system meets the requirements for the version you are installing, especially if you are using a GPU.
• You may need to adjust the versions of the packages in requirements.txt based on your specific environment or compatibility needs.