DLEC - Deep Learning Emotion Classifier

Description

DLEC is a convolutional neural network that classifies images of human facial expressions in emotions.

It can be used as a framework for developing general purpose deep learning classifiers.

Motivation

This is a final year project to achieve the Bachelor degree in Computer Science Lutheran University of Brazil.

Objectives

Accomplish researches on deep learning and develop a model capable to classify human emotions in images.

How to use

Configuring the framework

train.py

Set the train_dataset path
Set the validation_dataset path (if empty, the train dataset will be slited into train and validation)
Set image_width and image_height
Set learning_rate, test_size (only used when validation_dataset is None), batch_size and epochs

cnn.py

Define the architecture of the CNN in the function get_network_architecture
Use the inline documentation as reference

predict.py

Set the same image_width and image_height as in train.py

Training

Make sure the images exist under the paths set in train_dataset and validation_dataset
The images of each class should be in its own directory. See the configuration below:

     Datasets
     /      \
  Train   Validation
   /           \
Class 1,      Class 1,
Class 2,      Class 2,
  ...,          ...,
Class n,      Class n,

Run the training with the command python train.py
The training status will be outputed during training

Predicting

Run python predict.py to get help on the available prediction methods

Examples:
- python predict.py 1 /Datasets/Test/ (evaluates the model)
- python predict.py 6 (real time predictions using the embedded camera)
- python predict.py <TASK> <PATH> (general case)

Option 1 does not require the task specification. The command python predict.py <PATH> will run into task 1
Option 6 does not need any path, since it will use the embedded camera as source

Technical notes

The code is runnable both in Python 2 and Python 3
The code also works on Windows (it was tested on Windows 10). Keep in mind adjustments might be required for your environment.

Author

Maikel Maciel Rönnau
Computer Scientist
maikel.ronnau@gmail.com
Linkedin - GitHub

maikelroennau/dlec