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https://github.com/vinamrasaurav/facialemotionrecognition


https://github.com/vinamrasaurav/facialemotionrecognition

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README 

        

# Facial Emotion Recognition 😃



This project implements a real-time facial emotion detection system using deep learning techniques. The model is trained to recognize emotions from facial expressions in images or video streams.



## Table of Contents 📚

- [Cloning the Repository](#cloning-the-repository-)

- [Dataset](#dataset-)

- [Setting Up the Virtual Environment](#setting-up-the-virtual-environment-️)

- [Requirements](#requirements-)

- [Folder Structure](#folder-structure-️)

- [Preparing the Dataset](#preparing-the-dataset-️)

- [Training the Model](#training-the-model-)

- [Real-time Emotion Detection](#real-time-emotion-detection-)

- [Haarcascade Frontal Face Detection](#haarcascade-frontal-face-detection-)



## Cloning the Repository 🧑‍💻



To get started, clone the repository using the following command:



```bash

git clone https://github.com/VinamraSaurav/FacialEmotionRecognition

cd FacialEmotionRecognition

```



## Dataset 📊



The project includes the `fer2013.csv` dataset, which contains facial expressions labeled with their corresponding emotions. This dataset is crucial for training the emotion detection model.



Download the `fer2013.csv` dataset from sources like [Kaggle](https://www.kaggle.com/datasets/msambare/fer2013) and save it to the root directory of this project. 



## Setting Up the Virtual Environment 🛠️



To set up a Python virtual environment, follow these steps:



1. **Create a Virtual Environment**:

   ```bash

   python -m venv venv

   ```



2. **Activate the Virtual Environment**:

   - On Windows:

     ```bash

     venv\Scripts\activate

     ```

   - On macOS/Linux:

     ```bash

     source venv/bin/activate

     ```



3. **Install Requirements**: Install the required packages using the `requirements.txt` file:

   ```bash

   pip install -r requirements.txt

   ```



## Requirements 📦

The following packages are required to run this project:



- TensorFlow

- Keras

- OpenCV

- NumPy

- Pandas

- Matplotlib



These dependencies are specified in the requirements.txt file, and you can install them using the above command.



## Folder Structure 🗂️



The project has the following folder structure:



```

FacialEmotionRecognition/

│

├── src/

│   ├── dataset_prepare.py          # Script to prepare the dataset from CSV

│   ├── emotions.py                 # Real-time emotion detection script

│   ├── train.py                    # Script for training the model

│   └── haarcascade_frontalface_default.xml  # Haar Cascade file for face detection

│

├── data/                           # Directory containing folders for each emotion

│   ├── angry/                      # Folder for images of angry faces

│   ├── sad/                        # Folder for images of sad faces

│   ├── happy/                      # Folder for images of happy faces

│   └── ...                         # Additional emotion folders

│

├── venv/                           # Python virtual environment directory

│

├── model.h5                        # Saved trained model file

├── fer2013.csv                     # Dataset in CSV format

├── README.md                       # Project documentation file

└── requirements.txt                # List of dependencies



```



## Preparing the Dataset 🗃️



Once you have the 'fer2013.csv' in your root directory, use the `dataset_prepare.py` script to process the CSV file, which will organize the images into folders based on emotions.



Run the following command to prepare the dataset:



```bash

python src/dataset_prepare.py

```

This script reads ```fer2013.csv``` in root directory and creates emotion-specific folders with the images organized for model training. Make sure fer2013.csv is stored in the root directory of the project before running this command.





🚨 ** NOTE **  - If you plan to use a different CSV file, please ensure you update the `data_dir` variable in `dataset_prepare.py` to reflect the correct file name and location ! 📂💡 




## Training the Model 🚀



To train the model using the prepared dataset, you can use the `train.py` script. Ensure that the dataset is prepared before running this script. Run the following command:



```bash

python src/train.py

```



This will start the training process and save the trained model as `model.h5`.



## Real-time Emotion Detection 🎥



The `emotions.py` script is used for real-time emotion detection. It captures video from the webcam and predicts emotions based on the faces detected in the video stream. Run the script using:



```bash

python src/emotions.py

```



Make sure the trained model (`model.h5`) is present in the root directory.



## Haarcascade Frontal Face Detection 👀



The project uses `haarcascade_frontalface_default.xml`, a pre-trained Haar Cascade model, for detecting faces in images and video streams. This XML file is included in the project directory and is used within the `emotions.py` script to identify faces before predicting their emotions.