πΈ Emotion Detection from Multiple Sources
Welcome to the Emotion Detection Application β a tool for identifying and analyzing emotions from images and live camera feeds. This project is built with an intuitive interface and powerful emotion recognition capabilities.
π About the Project
This application uses DeepFace and OpenCV to detect and analyze human emotions in real-time or from static images. Itβs equipped with a user-friendly graphical interface built with Tkinter, making it accessible for users of all levels.
π― Core Features
- πΌοΈ Image Emotion Analysis: Upload an image and detect emotions with a visual overlay of results.
- π Folder Analysis: Analyze multiple images in a folder seamlessly.
- π₯ Live Camera Emotion Detection: Real-time emotion analysis from your webcam feed.
- π¬ Motivational Quotes: Receive personalized quotes based on detected emotions.
- πΎ Save Results: Captured images and results are automatically saved for later review.
π οΈ Technologies Used
- Python 3
- OpenCV β Image processing and camera interface
- DeepFace β Emotion analysis
- Tkinter β User interface
- Pillow (PIL) β Image handling
Check out all dependencies in requirements.txt.
π» How to Run the Project
π₯ 1. Clone the Repository
git clone https://github.com/axbecher/emotion_detection_from_multiple_sources
cd emotion-detection
π 2. Install dependencies
pip install -r requirements.txt
π₯οΈ 3. Install dependencies
python ui.py
πΌοΈ How the Application Looks & Works
This section provides a visual walkthrough of the Emotion Detection Application. Below are screenshots demonstrating key functionalities:
1. Home Screen
The home screen offers intuitive navigation options to analyze emotions from live captures or photos.
2. Live Emotion Analysis
When you select "Live Emotion Analysis," the application requests camera permissions to initiate real-time emotion detection.
Once the camera is active, the system analyzes your facial expressions, highlighting the detected emotions:
3. Photo Emotion Analysis
You can also analyze static images by selecting "Photo Emotion Analysis" and choosing an image or folder:
The results display the dominant emotion and a breakdown of detected emotions, stress grades, and more:
4. Saved Captures
The application automatically saves analyzed images and results for future reference.
Captured images from live analysis are stored in the captures
folder:
Analyzed photos from the "Photo Emotion Analysis" feature are saved in the photos_captures
folder: