Productivity Tracker
Productivity Tracker is a simple website to help you stay productive in front of a computer. It utilizes machine learning to make real-time productivity level prediction based on web cam input.
Getting Started
Prerequisites
Install NodeJS and Python in your system.
Code Structure
./backend: Code to train a model offline.
./static_website: Code for a simple website demo.
./data : Directory to download datasets.
./generate_labels.py: Python script to generate labels from datasets.
How-To Guide
Generate label on dataset.
We define productivty by labelling some certain range in valence and arousal as non-productive plus manual annotation on top. Therefore we utlize public datasets with existing valence and arousal annotations. See readmes in data folder for detail.
To generate non-productive frames, run python generate_labels.py.
Generate npy from dataset by NodeJS.
To make use of existing tfjs models (MobileNet and face detector BlazeFace. We use tfjs in NodeJS to extract feature from datasets and save them as npy. Then we train a model with regular Keras by taking saved npy as input.
cd backend
npm install
node generate_npy.js
Train Model in Keras and Convert it to tfjis model
cd backend + python train.py
Convert to tfjs model:
tensorflowjs_converter --input_format=tf_saved_model --output_format tfjs_graph_model backend/saved_models/best_model /saved_models/model_tfjs
Static Website
Static Website is located at static_website, deployed in Productivity Tracker.
Authors
Nashila Jahan