Welcome to EmoPy’s documentation!

FERModel

class fermodel.FERModel(target_emotions, verbose=False)

Pretrained deep learning model for facial expression recognition.

Parameters:

• target_emotions – set of target emotions to classify
• verbose – if true, will print out extra process information

Example:

from fermodel import FERModel

target_emotions = ['happiness', 'disgust', 'surprise']
model = FERModel(target_emotions, verbose=True)

POSSIBLE_EMOTIONS = ['anger', 'fear', 'calm', 'sadness', 'happiness', 'surprise', 'disgust']

predict(image_file)

Predicts discrete emotion for given image.

Parameters:images – image file (jpg or png format)

FERNeuralNets

class neuralnets.ConvolutionalLstmNN(image_size, channels, emotion_map, time_delay=2, filters=10, kernel_size=(4, 4), activation='sigmoid', verbose=False)

Convolutional Long Short Term Memory Neural Network.

Parameters:

• image_size – dimensions of input images
• channels – number of image channels
• emotion_map – dict of target emotion label keys with int values corresponding to the index of the emotion probability in the prediction output array
• time_delay – number of time steps for lookback
• filters – number of filters/nodes per layer in CNN
• kernel_size – size of sliding window for each layer of CNN
• activation – name of activation function for CNN
• verbose – if true, will print out extra process information

Example:

net = ConvolutionalLstmNN(target_dimensions=(64,64), channels=1, target_labels=[0,1,2,3,4,5,6], time_delay=3)
net.fit(features, labels, validation_split=0.15)

fit(features, labels, validation_split, batch_size=10, epochs=50)

Trains the neural net on the data provided.

Parameters:

• features – Numpy array of training data.
• labels – Numpy array of target (label) data.
• validation_split – Float between 0 and 1. Percentage of training data to use for validation
• batch_size –
• epochs – number of times to train over input dataset.

class neuralnets.ConvolutionalNN(image_size, channels, emotion_map, filters=10, kernel_size=(4, 4), activation='relu', verbose=False)

2D Convolutional Neural Network

Parameters:

• image_size – dimensions of input images
• channels – number of image channels
• emotion_map – dict of target emotion label keys with int values corresponding to the index of the emotion probability in the prediction output array
• filters – number of filters/nodes per layer in CNN
• kernel_size – size of sliding window for each layer of CNN
• activation – name of activation function for CNN
• verbose – if true, will print out extra process information

Example:

net = ConvolutionalNN(target_dimensions=(64,64), channels=1, target_labels=[0,1,2,3,4,5,6], time_delay=3)
net.fit(features, labels, validation_split=0.15)

fit(image_data, labels, validation_split, epochs=50)

Trains the neural net on the data provided.

Parameters:

• image_data – Numpy array of training data.
• labels – Numpy array of target (label) data.
• validation_split – Float between 0 and 1. Percentage of training data to use for validation
• batch_size –
• epochs – number of times to train over input dataset.

class neuralnets.TimeDelayConvNN(image_size, channels, emotion_map, time_delay, filters=32, kernel_size=(1, 4, 4), activation='relu', verbose=False)

The Time-Delayed Convolutional Neural Network model is a 3D-Convolutional network that trains on 3-dimensional temporal image data. One training sample will contain n number of images from a series and its emotion label will be that of the most recent image.

Parameters:

• image_size – dimensions of input images
• time_delay – number of past time steps included in each training sample
• channels – number of image channels
• emotion_map – dict of target emotion label keys with int values corresponding to the index of the emotion probability in the prediction output array
• filters – number of filters/nodes per layer in CNN
• kernel_size – size of sliding window for each layer of CNN
• activation – name of activation function for CNN
• verbose – if true, will print out extra process information

Example:

model = TimeDelayConvNN(target_dimensions={64,64), time_delay=3, channels=1, label_count=6)
model.fit(image_data, labels, validation_split=0.15)

fit(image_data, labels, validation_split, epochs=50)

Trains the neural net on the data provided.

Parameters:

• image_data – Numpy array of training data.
• labels – Numpy array of target (label) data.
• validation_split – Float between 0 and 1. Percentage of training data to use for validation
• batch_size –
• epochs – number of times to train over input dataset.

class neuralnets.TransferLearningNN(model_name, emotion_map)

Transfer Learning Convolutional Neural Network initialized with pretrained weights.

Parameters:

• model_name – name of pretrained model to use for initial weights. Options: [‘Xception’, ‘VGG16’, ‘VGG19’, ‘ResNet50’, ‘InceptionV3’, ‘InceptionResNetV2’]
• emotion_map – dict of target emotion label keys with int values corresponding to the index of the emotion probability in the prediction output array

Example:

model = TransferLearningNN(model_name='inception_v3', target_labels=[0,1,2,3,4,5,6])
model.fit(images, labels, validation_split=0.15)

fit(features, labels, validation_split, epochs=50)

Trains the neural net on the data provided.

Parameters:

• features – Numpy array of training data.
• labels – Numpy array of target (label) data.
• validation_split – Float between 0 and 1. Percentage of training data to use for validation
• epochs – Max number of times to train over dataset.

CSVDataLoader

class csv_data_loader.CSVDataLoader(target_emotion_map, datapath=None, validation_split=0.2, image_dimensions=None, csv_label_col=None, csv_image_col=None, out_channels=1)

DataLoader subclass loads image and label data from csv file.

Parameters:

• emotion_map – Dict of target emotion label values and their corresponding label vector index values.
• datapath – Location of image dataset.
• validation_split – Float percentage of data to use as validation set.
• image_dimensions – Dimensions of sample images (height, width).
• csv_label_col – Index of label value column in csv.
• csv_image_col – Index of image column in csv.
• out_channels – Number of image channels.

load_data()

Loads image and label data from specified csv file path.

Returns:Dataset object containing image and label data.

DirectoryDataLoader

class directory_data_loader.DirectoryDataLoader(target_emotion_map=None, datapath=None, validation_split=0.2, out_channels=1, time_delay=None)

DataLoader subclass loads image and label data from directory.

Parameters:

• target_emotion_map – Optional dict of target emotion label values/strings and their corresponding label vector index values.
• datapath – Location of image dataset.
• validation_split – Float percentage of data to use as validation set.
• out_channels – Number of image channels.
• time_delay – Number of images to load from each time series sample. Parameter must be provided to load time series data and unspecified if using static image data.

load_data()

Loads image and label data from specified directory path.

Returns:Dataset object containing image and label data.

DataGenerator

class data_generator.DataGenerator(time_delay=None)

config_augmentation(zca_whitening=False, rotation_angle=90, shift_range=0.2, horizontal_flip=True, time_delay=None)

fit(images, labels)

generate(target_dimensions=None, batch_size=10)

get_next_batch(batch_size=10, target_dimensions=None)

Dataset

class dataset.Dataset(train_images, test_images, train_labels, test_labels, emotion_index_map, time_delay=None)

get_emotion_index_map()

get_test_data()

get_time_delay()

get_training_data()

num_images()

num_test_images()

num_train_images()

print_data_details()

Indices and tables

• Index
• Module Index
• Search Page