https://github.com/patrikken/emotion_recognition
Facial emotion recognition using Pytorch and OpenCV
https://github.com/patrikken/emotion_recognition
Last synced: 3 months ago
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Facial emotion recognition using Pytorch and OpenCV
- Host: GitHub
- URL: https://github.com/patrikken/emotion_recognition
- Owner: patrikken
- License: mit
- Created: 2021-09-24T14:25:27.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-09-24T17:06:18.000Z (over 3 years ago)
- Last Synced: 2025-01-10T11:31:59.960Z (4 months ago)
- Language: Jupyter Notebook
- Size: 22 MB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Facial Emotion Recognition
This repository contains the source code for the tutorial on facial emotion recognition. The project is designed taking into account all the steps of a machine learning project. Data collection, data labeling and preprocessing, model training and parameter tuning and finally deployment of the model to a real world scenario.
The required packages are pytorch, OpenCV, torchvision, numpy and matplotlib.
### Install dependancies
```
pip install pytorch opencv torchvision numpy matplotlib
```
### Run pretrained model for emotion recognition from your webcam video
To test the final render of the project with the pretrained model run the file live_emotion_recognition.py:
```
python3 live_emotion_recognition.py
```
### Jupyter Notebook
The notebook file **notebook.ipynb** contains all the steps for the model training
### Project structure
The project is structured as follow:
- We first generate our own dataset using our webcam. With opencv, we will capture different face pause ('Happy', 'Normal', 'Sad', 'Surprised') and store them in different folders named with corresponding labels.
- We preprocess the images by extracting faces using pretrained face detector model (haar_cascade). Other face detector algorithms such as MTCNN or Mediapipe can be used. The dataset is then splited into test and valitation sets in different folders.
- We then build a CNN model with Pytorch to classify face emotions . The model is then trained on the training set and saved.
- Finally we will used the pre-trained model with opencv to recognize facial emotion on live streams videos.