process_model.py

"""
Diese Klasse macht das Training des Models möglich
"""

import cv2
import glob
import random
import numpy as np
import sys
import logging

"""
Erstellt und gibt das Log-File aus 
"""
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
                    datefmt='%m-%d %H:%M',
                    filename='logs/process_model.log')

"""
Liest Input Parameter 
"""
args = sys.argv
logging.debug('Fisherface training initialized')

file = open("{}.csv".format('_'.join(args[1:]).lower()), "w")


def _get_faces_from_emotion(emotion):
    """
    Holt alle Dateien zu einer Emotion aus dem Dataset, mischt sie und teilt sie in ein Trainings- und Prognoseset.
    :param emotion: Die Emotion
    :return: training, prediction
    """
    files = glob.glob('basis_data/dataset/{}/*'.format(emotion))
    random.shuffle(files)

    """
    Mischt das Dataset in Trainings- und Vergleichsbilder im Verhältnis 80 zu 20 
    """
    training = files[:int(len(files) * 0.8)]
    prediction = files[-int(len(files) * 0.2):]

    return training, prediction


def image_preprocessing(image):
    """
    Preprocessing der Dateien
    :param item: Bild
    :return:
    """
    img = cv2.imread(image)  # open image
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to grayscale
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    return blur


def make_sets():
    training_data = []
    training_labels = []
    prediction_data = []
    prediction_labels = []
    for emotion in emotions:
        training, prediction = _get_faces_from_emotion(emotion)
        # Append data to training and prediction list, and generate labels 0-7
        for item in training:
            img = image_preprocessing(item)
            training_data.append(img)  # append image array to training data list
            training_labels.append(emotions.index(emotion))

        for item in prediction:  # repeat above process for prediction set
            img = image_preprocessing(item)
            prediction_data.append(img)
            prediction_labels.append(emotions.index(emotion))

    return training_data, training_labels, prediction_data, prediction_labels


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

    fishface.train(training_data, np.asarray(training_labels))

    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))


if len(args) > 1:
    tags = ', '.join(args[1:])
    logging.debug(tags.upper())

"""
Emotions Liste 
"""
emotions = ["happy", "neutral", "surprise"]
fishface = cv2.face.FisherFaceRecognizer_create()


metascore = []

for i in range(0, 10):
    correct = run_recognizer()
    file.write("{}\n".format(int(correct)))
    logging.debug("{} : {}%".format(i, int(correct)))
    metascore.append(correct)

file.close()

logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
fishface.write('basis_data/models/detection_model.xml')