Skip to content
A simple emoji classifier for humans.
Branch: master
Clone or download
Latest commit dd6663f Apr 3, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Emojinator_V2 Update readme.md Dec 14, 2018
Rock_Paper_Scissor_Lizard_Spock updated readme.md Aug 24, 2018
gestures initial commit May 25, 2018
hand_emo initial commit May 25, 2018
CreateCSV.py Update CreateCSV.py Dec 2, 2018
CreateGest.py initial commit May 25, 2018
Emojinator.py initial commit May 25, 2018
LICENSE.md
LICENSE.txt adding LICENSE.txt May 27, 2018
RPS.gif adding gif Aug 24, 2018
TrainEmojinator.py initial commit Dec 14, 2018
emo.gif adding gif May 25, 2018
emojinator.h5 initial commit May 25, 2018
readme.md Update readme.md Dec 14, 2018
requirements.txt initial commit May 25, 2018
train_foo.csv adding csv May 25, 2018

readme.md

Emojinator

This code helps you to recognize and classify different emojis. As of now, we are only supporting hand emojis.

Sourcerer

Rock Paper Scissor Lizard Spock

Emojinator 2.0

Code Requirements

You can install Conda for python which resolves all the dependencies for machine learning.

pip install requirements.txt

Description

Emojis are ideograms and smileys used in electronic messages and web pages. Emoji exist in various genres, including facial expressions, common objects, places and types of weather, and animals. They are much like emoticons, but emoji are actual pictures instead of typographics.

Functionalities

  1. Filters to detect hand.
  2. CNN for training the model.

Python Implementation

  1. Network Used- Convolutional Neural Network

If you face any problem, kindly raise an issue

Procedure

  1. First, you have to create a gesture database. For that, run CreateGest.py. Enter the gesture name and you will get 2 frames displayed. Look at the contour frame and adjust your hand to make sure that you capture the features of your hand. Press 'c' for capturing the images. It will take 1200 images of one gesture. Try moving your hand a little within the frame to make sure that your model doesn't overfit at the time of training.
  2. Repeat this for all the features you want.
  3. Run CreateCSV.py for converting the images to a CSV file
  4. If you want to train the model, run 'TrainEmojinator.py'
  5. Finally, run Emojinator.py for testing your model via webcam.

Contributors

1) Akshay Bahadur
2) Raghav Patnecha

Emojinator

Rock Paper Scissor Lizard Spock

Emojinator 2.0

You can’t perform that action at this time.