train_emotion_classifiers.py

import cv2
import os
import random
import numpy as np

emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]  # Emotion list
fishface = cv2.face.createFisherFaceRecognizer()  # Initialize fisher face classifier

data = {}


def get_files(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
    files = os.listdir(os.path.join("../ExpressionRecognizer/dataset", emotion))
    random.shuffle(files)
    training = files[:int(len(files) * 0.8)]  # get first 80% of file list
    prediction = files[-int(len(files) * 0.2):]  # get last 20% of file list

    return training, prediction


def make_sets():
    training_data = []
    training_labels = []
    prediction_data = []
    prediction_labels = []
    for emotion in emotions:
        training, prediction = get_files(emotion)
        source_path = os.path.join("../ExpressionRecognizer/dataset", emotion)
        # Append data to training and prediction list, and generate labels 0-7
        for item in training:
            image = cv2.imread(os.path.join(source_path, item))  # open image
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
            training_data.append(gray)  # append image array to training data list
            training_labels.append(emotions.index(emotion))

        for item in prediction:  # repeat above process for prediction set
            image = cv2.imread(os.path.join(source_path, item))
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            prediction_data.append(gray)
            prediction_labels.append(emotions.index(emotion))

    return training_data, training_labels, prediction_data, prediction_labels


def run_recognizer(num_run=0):
    training_data, training_labels, prediction_data, prediction_labels = make_sets()

    print("training fisher face classifier")
    print("size of training set is:", len(training_labels), "images")
    fishface.train(training_data, np.asarray(training_labels))
    fishface.save('fish_models/fish_model' + str(num_run) + '.xml')
    print("predicting classification set")
    cnt = 0
    correct = 0
    incorrect = 0
    for image in prediction_data:
        pred, conf = fishface.predict(image)
        if pred == prediction_labels[cnt]:
            correct += 1
            cnt += 1
        else:
            incorrect += 1
            cnt += 1
    return (100 * correct) / (correct + incorrect)


# Now run it
metascore = []
for i in range(0, 10):
    correct = run_recognizer(num_run=i)
    print("got", correct, "percent correct!")
    metascore.append(correct)

print("end score:", np.mean(metascore), "percent correct!")