Attention monitoring system for students in online learning - based on Abhishek Tandon project!
The World Health Organization declared COVID-19 a global emergency on January 30, 2020 and an epidemic on March 11,2020.
Countries around the world responded to the epidemic by imposing restrictions on flights and gatherings and giving directions to create distance Social, closure, curfew, cancellation of events and closure of all non-essential institutions. As a result, the virus has had a huge impact on students, lecturers, and educational organizations around the world. The corona plague has caused schools, colleges and universities around the world to close their doors so that students can maintain the rules of social distance. Consequently, the transition from the frontal learning method to online learning was necessary.
The rapid transition from one teaching method to another, together with the unpreparedness of the various institutions are the main cause of the plethora of problems in online learning. In frontal learning the lecturer has the ability to observe the whole class and read the facial expressions of the students during the lesson and from this to infer the degree of listening in the class. In addition, in frontal learning, the participation and conversation that is created among the students in the class also indicates the degree of concentration of the class, while in online learning, this interaction is impaired.
Solving these challenges is very important, since online learning is going to be a part of our daily routine in the near future, and we must learn and adapt to it so that we can continue to provide education in an effective and efficient way that equals and even transcends frontal learning.
Our system has come to solve these challenges.
AttentivenessRec/
├── .github/ # GitHub action configuration
│ ├── workflows
│ └── python-app.yml
├── .debug_exp/
│ ├── frames # Contains the last saved frame
│ │ └── frame.jpg
│ ├── models/ # Models
│ │ └── ...
│ ├── results/ # Model training results
│ │ └── ...
│ ├── config.txt
│ └── inference_file.txt # Contains the frame PATH and b-box details
├── public/
│ ├── img/ # Images of the report (The graph will update in real-time)
│ │ └── ...
│ ├── reports/ # System report
│ │ └── ...
│ └── test_img # Images for unit tests
│ └── ...
├── src/
│ ├── app.py
│ ├── attention_calculator.py
│ ├── class_details.py
│ ├── emotic.py
│ ├── emotic_app.py
│ ├── emotic_dataset.py
│ ├── emotic_loop.py
│ ├── face_detector.py
│ ├── frame_provider.py
│ ├── frame_saver.py
│ ├── gui.py
│ ├── inference.py
│ ├── loss.py
│ ├── prepare_models.py
│ ├── statistics_data_loader.py
│ ├── test.py
│ └── train.py
├── test/
│ └── unit.py # Currently without content
├── venv
│ └── ...
├── .gitignore
├── README.md
└── requirements.txt
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Firstly open the bash and clone this repository to your working environment by entering the command
git clone git@github.com:yariv1025/AttentivenessRec.gitOpen the project in your IDE, integrate the system with the bash and run the pip install command listed below, for you
to be able to run this project.
(This command will download locally all the packages that's needed for the running of this application.)
pip install -r requirements.txtIn the run/debug configuration we will enter the following details:
Script path: "App.py PATH"
Python interpreter: "Python 3.8 with Anaconda3 environment"
Working directory: "src directory PATH"In the parameters field we will enter the settings according to the desired action:
"Training configuration" for train the model.
Parameters: " --mode train --data_path PROJECT_PATH/data/emotic_pre --experiment_path PROJECT_PATH/debug_exp""Testing configuration" for test the model.
Parameters: "--mode test --data_path PROJECT_PATH/data/emotic_pre --experiment_path PROJECT_PATH/debug_exp""running configuration" for running the App.py.
Parameters: "--mode inference --inference_file PROJECT_PATH\debug_exp\inference_file.txt --experiment_path PROJECT_PATH\debug_exp"After doing so you will be able to run the app by clicking on 'Run app'
- Tandon-A -Project that uses the EMOTIC dataset and follows the methodology as introduced in the paper 'Context based emotion recognition using EMOTIC dataset'.
- OpenCV - An open source computer vision and machine learning software library with focus on real-time applications..
- Pandas - A fast, powerful, flexible and easy to use open source data analysis and manipulation tool.
- MTCNN - MTCNN face detection implementation for TensorFlow, as a PIP package.
- Pytorch - An open source machine learning framework that accelerates the path from research prototyping to production deployment.
- Numpy - Fundamental package for scientific computing with Python.
- Matplotlib - A comprehensive library for creating static, animated, and interactive visualizations in Python.
- tkinter - A standard Python interface to the Tk GUI toolkit.
Please refer Tandon-A README.md file.
- Paper - Context Based Emotion Recognition using EMOTIC Dataset.
- Dataset Webpage - EMOTions In Context, is a database of images with people in real environments, annotated with their apparent emotions.
- Tandon-A - PyTorch implementation of Emotic CNN methodology to recognize emotions in images using context information.
R. Kosti, J.M. Álvarez, A. Recasens and A. Lapedriza, "Emotion Recognition in Context", Computer
Vision and Pattern Recognition (CVPR), 2017.
R. Kosti, J.M. Álvarez, A. Recasens and A. Lapedriza, "Context based emotion recognition using
emotic dataset", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2019.
