Body-Language-Decoder

This is a project which predicts your body language in real time and outputs one of three classes the model was trained on : Happy Sad Victorius

How was the project built? (A high level overview)

• As the project predicts the body language from one of the three classes. Coordinates of body postures were collected in real-time using OpenCV library, with the help of mediapipe library.

• The body landmarks were collected in a csv format.

• Now it was time for some training. The machine learning library used here was scikit-learn.

• Created a pipeline where the data was trained on LogisticRegression,RidgeClassifier,RandomForestClassifier,GradientBoostingClassifier

• Each model was evaluated and the best model was picked.

• At the model predicted the body language in real-time.

Usage Example

Cloning and Running

To run this project locally, follow these steps:

1. Clone the repository:

git clone https://github.com/Rhythm1821/Body-Language-Decoder.git

2. Navigate to the project directory:

cd Body-Language-Decoder

3. Install the required dependencies:

pip install -r requirements.txt

4. Run the real-time face detection Python file:

python3 main.py

Project Structure

• data: Contains the CSV file where body pose coordinates for training are stored.
• model: Houses the trained machine learning model in the form of a pickle file.
• bodylanguage_decoder.ipynb: A Jupyter Notebook providing detailed data preprocessing, model training, and evaluation steps.
• main.py: The main Python script for real-time body language prediction using the trained model.
• README.md: The documentation you're currently reading.
• requirements.txt: A file listing the required libraries for easy installation.