Emotion Reader is an application that takes photos and analyzes your face and outputs an emotion.
Clone this repository to your local machine.
$ git clone https://github.com/dave5801/emotion-reader.git
Once downloaded, change directory into the emotionreader directory.
$ cd emotion-reader
Begin a new virtual environment with Python 3 and activate it.
emotion-reader $ python3 -m venv ENV
emotion-reader $ source ENV/bin/activate
Install the application requirements with pip.
(ENV) emotion-reader $ pip install -r requirements.txt
Create a Postgres database for use with this application.
(ENV) emotion-reader $ createdb emotionreader
Export environmental variables pointing to the location of database, your username, hashed password, and secret.
(ENV) emotion-reader $ export SECRET_KEY='secret'
(ENV) emotion-reader $ export DB_NAME='emotionreader'
(ENV) emotion-reader $ export DB_USER='(your postgresql username)'
(ENV) emotion-reader $ export DB_PASS='(your postgresql password)'
(ENV) emotion-reader $ export DB_HOST='localhost'
(ENV) emotion-reader $ export DEBUG='True'
Then initialize the database with the migrate command from manage.py
(ENV) emotion-reader $ python emotionreader/manage.py migrate
Once the package is installed and the database is created, start the server with the runserver command from manage.py
(ENV) emotion-reader $ python emotionreader/manage.py runserver
Application is served on http://localhost:8000
You can test this application by first exporting an environmental variable pointing to the location of a testing database, then running the test command from manage.py.
(ENV) emotion-reader $ export
TEST_DB='test_emotionreader'
(ENV) emotion-reader $ python
emotionreader/manage.py test emotionreader
Below are the libraries and technologies we used to make this project possible.
Get started with Django
pip install django==1.11
Django has three layers.
-
Model Layer
- An abstraction layer where you can create your models
from django.db import models class Person(models.Model): first_name = models.CharField(max_length=30) last_name = models.CharField(max_length=30) -
View Layer
- Where request and responses get handled.
from django.conf.urls import url from . import views urlpatterns = [ url(r'^articles/2003/$', views.special_case_2003), url(r'^articles/([0-9]{4})/$', views.year_archive), url(r'^articles/([0-9]{4})/([0-9]{2})/$', views.month_archive), url(r'^articles/([0-9]{4})/([0-9]{2})/([0-9]+)/$', views.article_detail), ] -
Template Layer
- How the information being passed gets served to the front end.
TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { # ... some options here ... }, }, ]
To dive deeper into the documentations for a more in depth idea of how we set everything up. Click Here
Microsoft has some pretty amazing API's in general that a user can hit, we were lucky enough to find two from Microsoft to use for our project.
- Face API can detect up to 64 human faces then can be handle with the use of bytes or url.
- Face API can compare 2 different faces and determine if they are the same person or not. This is what we used for Authentication and Authorization for our Face Login feature.
- Face API has many more features. Check out the Face API and Face API Documenation.
- Emotion API takes in an expression as an input, returns a bounded box using Face API, and returns a JSON object with 8 emotions.
JSON:
[
{
"faceRectangle": {
"top": 114,
"left": 212,
"width": 65,
"height": 65
},
"scores": {
"anger": 1.0570484E-08,
"contempt": 1.52679547E-09,
"disgust": 1.60232943E-07,
"fear": 6.00660363E-12,
"happiness": 0.9999998,
"neutral": 9.449728E-09,
"sadness": 1.23025981E-08,
"surprise": 9.91396E-10
}
}
]
Check out Emotion API and Emotion API Documentation.
Relational Database Service
A relational database is a collection of items that hold data. These items have pre-defined relationships before they are put into a table with columns and rows to sort. The data holds information that relates to objects that are being represented. Each row and column has a unique identifier. We use this identifier to talk between pieces.
- Amazon's version of a relational database in the cloud.
- Simplicity and scalablity is always in mind when it comes to building out these apps. RDS gives us that.
- We are able to have a built out front-end so we can manage and handle out databases when we need to but don't have to worry about maintaining them. They are automated.
Simple Storage Service
- S3 is a simple way to store data in the cloud and have the ability to access it any place and any time in the world.
- We created a bucket(storage) and received a key that routes to that bucket. When ever we push data, it uses this key and pushes it to the bucket.
- Using S3 allows our app to be smaller, compact, and more efficient.
Elastic Compute Cloud
- EC2 is a simple way to compute data in the cloud.
- EC2 is secure and resizeable.
- Similar to S3 which setting up. Create an instance, set parameters, and get a key. Use that key to route information there, process it, and then your parameters push it somewhere else.
Route 53
- Cloud Domain Name System(DNS).
- We are routing our EC2 instance to our domain.
- Chart.js passes in data and organizes and displays the information int a chart.
- These charts can vary in design and interaction.
- Bootstrap is a design templating service.
- Through the use of a design library with jQuery and SCSS as major players, we have an amazing design tool where we can plug and play chunks of code to have a polished front end.
- Our specific bootstrap that we used was Light Bootsrap.
We used what Spotify calls "The Play Button". This is a widget where you can copy and paste in a playlist URI and spits out an iframe for you to use with your site.
Example of iframe:
<iframe src="https://open.spotify.com/embed?uri=spotify:user:spotify:playlist:3rgsDhGHZxZ9sB9DQWQfuf" width="300" height="380" frameborder="0" allowtransparency="true"></iframe>
With the use of the iframe you can adjust the height, width, and border for the overall look.
-
Running Ansible Playbook
ansible -playbook -i hosts playbooks/emotion_playbook.yml -
Ansible automates tasks. Some of these tasks are:
- Installs all the applications
- Clones repo to EC2 instance
- Creates all necessary files needed for the app
Emotion Reader is offered under the MIT license and shown in the LICENSE file.`