(No longer maintained)

A WebApp which uses a snapshot taken of the user to detect emotion and using this, generate a suitable music playlist. This project was built for ACM Month Of Code, actual coding done in about 3 weeks.

Read the detailed article on building Moodify here: https://medium.com/@ajay.ns08/acm-month-of-code-2k17-building-moodify-d5d9e0c52ca7

The Cam, Music Player, scripts for emotion recognition and Database were wired and wrapped up into a WebApp using Flask, using routes to use the Backend like an API while the frontend handles the user.

Being an experimental setup built in such a short span of time, the user interface and flow would require multiple fixes before deployment.

You should have the following preinstalled:

• OpenCV
• MongoDB
• dlib Predictor data files to be placed in data/
• Haar Cascades data files to be placed in data/
• Python 2
• files/mp3 and files/img store the music data and album art

Preferably setup a Virtual Env and then you'll just need to install packages:

pip install -r requirements.txt

Make sure you have MongoDB running to host the database. Also run a simple http server to serve the files/ folder at localhost:8000

cd files
python -m SimpleHTTPServer

Start the program

python app.py

Open the webapp from browser at localhost:5000

• AngularJS : JavaScript framework for programming the music player.
• Materialize : CSS Framework for skinning the app based on Google's Material Design.
• WebcamJS : JavaScript library for Image Capture
• Angular SoundManager 2 : Adds music player functionality for AngularJS using SoundManager 2 API

• Flask : A microframework for Python for Web App building
• OpenCV : Open source Computer Vision, used here for facial recognition, analysis and emotion identification.
• A few machine learning libraries used along with OpenCV such as dlib, NumPy, scikit

• ng-musicplayer : The music player component built on AngularJS and Materialize.
• PyEmotionRecognition : The script used to detect the mood from an image using OpenCV and machine learning libraries.
• PyMusicMood : For automatic classification of music into moods based on parameters extracted from Spotify API.
• Cam-App, Py-Flask-Wa : Initial code in setting up the Cam and Flask Server