Hand Gesture Recognition

This project enables real-time hand gesture recognition using computer vision and deep learning. It is designed to collect gesture data, train a model, and perform live gesture classification, which can be used for applications such as drone control or human-computer interaction.

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Folder Structure & File Explanations

data_collection

• datacollection.py

  • Used to collect hand gesture images from a webcam.
  • Saves images into gesture-specific folders (e.g., back/, down/, etc.) for dataset creation.
  • Helps build a labeled dataset for training the recognition model.

• test.py

  • Loads the trained model from the Model/ folder.
  • Uses the webcam to detect and classify hand gestures in real-time.
  • Detects a hand, preprocesses the image, and predicts the gesture using the trained model.

• Gesture Folders (back/, down/, Go forward/, land/, left/, right/, stop/, up/)

  • Contain images of hands showing different gestures.
  • Used for training and testing the recognition model.

Model/

• keras_model.h5

  • The trained Keras model for hand gesture recognition.
  • Used by test.py to classify gestures.

• labels.txt

  • Contains the list of gesture labels corresponding to the model’s output classes.
  • Used to map model predictions to human-readable gesture names.

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How It Works

1. Data Collection: Run datacollection.py to capture images of different hand gestures and save them in the respective folders.
2. Model Training: (Not included here, but typically you would train a model using the collected images and save it as keras_model.h5.)
3. Gesture Recognition: Run test.py to use your webcam for live gesture recognition using the trained model.

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