This project integrates face detection, emotion recognition, and body pose estimation to analyze videos in real-time. It detects faces in the video stream, identifies emotions, and analyzes the body posture using machine learning models.

• Face Detection: Detects faces in video frames.
• Emotion Recognition: Identifies emotions from facial expressions.
• Pose Estimation: Analyzes the body posture by detecting key points.

To run this project, you will need the following:

• Python 3.x
• OpenCV
• NumPy
• Pillow
• Matplotlib

Follow these steps to set up the project environment:

1. Clone the repository:

   git clone https://github.com/scriptchief/ai-psychological-emotion-analysis
   cd ai-psychological-emotion-analysis
   

2. Install the required packages:

   pip install opencv-python numpy pillow matplotlib
   

To run the main application, execute:

python main.py

The script will start processing the video specified in the code and display the emotion and pose analysis results in real-time.

• main.py: Contains the main workflow including video capture, processing, and display of results.
• lib/face_detection.py: Module for face detection utilities.
• lib/emotion_recognition.py: Module for emotion recognition using pre-trained models.
• lib/body_pose_estimation.py: Module for body pose estimation.

Each module is responsible for a specific part of the analysis and can be modified independently.

You can customize the parameters and models used in:

• lib/face_detection.py: Update face detector settings.
• lib/emotion_recognition.py: Switch or retrain the emotion recognition model.
• lib/body_pose_estimation.py: Change pose estimation models or adjust detection parameters.

Contributions to improve the project are welcome. Please fork the repository and submit a pull request with your changes.

Specify your license or state that the project is open-source.