process_model.py 3.18 KB
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"""
Diese Klasse macht das Training des Models möglich
"""

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import cv2
import glob
import random
import numpy as np
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import sys
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import logging
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"""
Erstellt und gibt das Log-File aus 
"""
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logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
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                    datefmt='%m-%d %H:%M',
                    filename='logs/process_model.log')
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"""
Liest Input Parameter 
"""
args = sys.argv
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logging.debug('Fisherface training initialized')
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file = open("{}.csv".format('_'.join(args[1:]).lower()), "w")
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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))
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    random.shuffle(files)
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    """
    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):]

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    return training, prediction

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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
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    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    return blur
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def make_sets():
    training_data = []
    training_labels = []
    prediction_data = []
    prediction_labels = []
    for emotion in emotions:
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        training, prediction = _get_faces_from_emotion(emotion)
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        # Append data to training and prediction list, and generate labels 0-7
        for item in training:
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            img = image_preprocessing(item)
            training_data.append(img)  # append image array to training data list
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            training_labels.append(emotions.index(emotion))

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

    return training_data, training_labels, prediction_data, prediction_labels

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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))

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if len(args) > 1:
    tags = ', '.join(args[1:])
    logging.debug(tags.upper())
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"""
Emotions Liste 
"""
emotions = ["happy", "neutral", "surprise"]
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fishface = cv2.face.FisherFaceRecognizer_create()
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metascore = []
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for i in range(0, 10):
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    correct = run_recognizer()
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    file.write("{}\n".format(int(correct)))
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    logging.debug("{} : {}%".format(i, int(correct)))
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    metascore.append(correct)
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file.close()
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logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
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fishface.write('basis_data/models/detection_model.xml')