Emotion Detection Using TensorFlow/Keras

Required Dependencies:

1. OpenCV

   • OpenCV (Open Source Computer Vision Library) is a popular open-source computer vision and machine learning software library.
   • It provides various tools and functions for real-time image and video processing, including image capture, processing, and analysis.
   • Command: pip install opencv-python

2. NumPy

   • NumPy is a fundamental package for scientific computing with Python.
   • It provides support for multidimensional arrays and matrices, along with mathematical functions to operate on these arrays.
   • Command: pip install numpy

3. TensorFlow/Keras

   • TensorFlow is an open-source machine learning framework developed by Google.
   • Keras is an open-source neural network library written in Python that serves as a high-level API for TensorFlow.
   • In this project, TensorFlow and Keras are used to load a pre-trained model for real-time emotion detection.
   • Command: pip install tensorflow

4. scikit-learn

   • Scikit-learn is a popular machine learning library for Python.
   • It provides simple and efficient tools for data mining and data analysis, including classification, regression, clustering, and dimensionality reduction.
   • Command: pip install scikit-learn

Setting Up a Virtual Environment (Optional):

1. Virtualenv
   • Virtualenv is a tool used to create isolated Python environments.
   • It allows you to install dependencies for a project without affecting other projects or the system Python installation.
   • To create a virtual environment named "myenv", execute the following commands:

     pip install virtualenv
     virtualenv myenv
     

   • To activate the virtual environment on Windows:

     myenv\Scripts\activate
     

     On Unix/Linux:

     source myenv/bin/activate
     

Installation Instructions:

1. OpenCV, NumPy, TensorFlow, scikit-learn
   • Execute the respective pip install commands mentioned above to install each dependency.

Once you have installed the required dependencies, you can proceed to run the Real-Time Emotion Detection project using the provided Python script.

Note on Google Colab

Objective:

The objective of this project was to develop an emotion detection system using TensorFlow/Keras in Google Colab. The system aims to detect emotions such as anger, disgust, fear, happiness, sadness, surprise, and neutrality from images captured from a webcam.

Key Steps:

Data Collection and Preprocessing:

• We collected a dataset of facial images labeled with different emotions.
• Preprocessed the images by resizing them to a common size, converting to grayscale, and normalizing pixel values.
• We used CK+ dataset.

Model Training:

• Built a convolutional neural network (CNN) model using TensorFlow/Keras to classify emotions from facial images.
• Trained the model on the collected dataset using Google Colab's GPU resources for faster training.

Model Evaluation:

• Evaluated the trained model on a separate test dataset to assess its performance.
• Calculated metrics such as accuracy to measure the model's effectiveness in emotion detection.

Real-Time Emotion Detection:

• Implemented real-time emotion detection using OpenCV to capture images from the webcam.
• Utilized the trained model to predict emotions from the captured images.
• Displayed the predicted emotions overlaid on the webcam feed for real-time feedback.

Results:

The trained model achieved a test accuracy of approximately 91.88%, indicating its effectiveness in recognizing emotions from facial images. The real-time emotion detection system successfully detected and displayed emotions in real-time from webcam images.

Conclusion:

In conclusion, we developed an emotion detection system using TensorFlow/Keras in Google Colab, which can accurately classify emotions from facial images in real-time. This system has various potential applications in fields such as human-computer interaction, emotion analysis, and affective computing.

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Note on Real-Time Emotion Detection Python Script

Objective:

The objective of this Python script is to perform real-time emotion detection using a pre-trained TensorFlow/Keras model and OpenCV to capture images from a webcam.

Key Features:

Model Loading:

• The script loads a pre-trained TensorFlow/Keras model for emotion detection. The model is assumed to be saved in the current directory.

Image Preprocessing:

• It preprocesses the captured webcam images by converting them to grayscale, resizing them to a common size, and normalizing pixel values.

Emotion Detection:

• Utilizes the loaded model to predict emotions from the preprocessed images.
• The predicted emotions are overlaid on the webcam feed in real-time using OpenCV.

Real-Time Webcam Feed:

• Captures images from the default webcam using OpenCV.
• Displays the webcam feed with predicted emotions overlaid for real-time feedback.

Dependencies:

The script requires OpenCV and TensorFlow/Keras libraries to be installed.

Usage:

To run the script, execute it in a Python environment with the necessary dependencies installed. Ensure that the pre-trained model file (trained_model.h5) is present in the same directory as the script.

Conclusion:

This Python script provides a simple yet effective solution for real-time emotion detection using a webcam. It demonstrates the integration of machine learning models with computer vision techniques to enable real-time emotion analysis, which has various applications in areas such as human-computer interaction, sentiment analysis, and emotion recognition systems.