process_model.py 5.19 KB
Newer Older
Arne Gerdes's avatar
Arne Gerdes committed
1
2
3
"""
Diese Klasse macht das Training des Models möglich
"""
4
import argparse
Arne Gerdes's avatar
Arne Gerdes committed
5
import glob
tihmels's avatar
tihmels committed
6
import logging
Arne Gerdes's avatar
Arne Gerdes committed
7
import random
Arne Gerdes's avatar
Arne Gerdes committed
8

Arne Gerdes's avatar
Arne Gerdes committed
9
10
import cv2
import numpy as np
11
12
import sys

13
14
15
16
from email_service import sendMail

logfile = 'logs/process_model.log'

17
# Erstellt und konfiguriert den Logger
18
logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(levelname)-8s %(message)s',
tihmels's avatar
tihmels committed
19
                    datefmt='%m-%d %H:%M',
20
                    filename=logfile)
21

22
# Argument Parser erlaubt Programmparameter anzugeben
23
parser = argparse.ArgumentParser(description='Process Model Application')
tihmels's avatar
tihmels committed
24
25
26
27
28
29
30
31
parser.add_argument('-0', action='append_const', dest='emotions', const='neutral', help='neutral')
parser.add_argument('-1', action='append_const', dest='emotions', const='happy', help='happy')
parser.add_argument('-2', action='append_const', dest='emotions', const='sadness', help='sadness')
parser.add_argument('-3', action='append_const', dest='emotions', const='surprise', help='surprise')
parser.add_argument('-4', action='append_const', dest='emotions', const='fear', help='fear')
parser.add_argument('-5', action='append_const', dest='emotions', const='disgust', help='disgust')
parser.add_argument('-6', action='append_const', dest='emotions', const='anger', help='anger')
parser.add_argument('-d', '--dataset', action='store', dest='dataset', default='resources/img_data/dataset/',
Arne Gerdes's avatar
Arne Gerdes committed
32
                    help='path to dataset')
33
34
parser.add_argument('-i' '--iterations', action='store', dest='iterations', type=int, default=30,
                    help='number of iterations')
tihmels's avatar
tihmels committed
35
parser.add_argument('-p', '--properties', nargs='+', dest='properties', help='pre-processing steps for logging')
36
37
38
parser.add_argument('-t', '--test', action='store_true', dest='test', help='prevent writing new model to classifier')
parser.add_argument('-c', '--csv', action='store_true', dest='csv', help='activate csv output')
parser.add_argument('-x', '--email', action='store_true', dest='email', help='activate email notifications')
39
40
41
arguments = parser.parse_args()
logging.debug(arguments)

42
43
44
if not arguments.emotions:
    print('No emotions declared')
    sys.exit()
45
46

logging.info('Fisherface training started')
47

48
if arguments.email:
49
    sendMail('Fisherface training started')
Arne Gerdes's avatar
Arne Gerdes committed
50

51
52
53
54
55
56
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
    """
57
    files = glob.glob(arguments.dataset + '{}/*'.format(emotion))
Arne Gerdes's avatar
Arne Gerdes committed
58
    random.shuffle(files)
Arne Gerdes's avatar
Arne Gerdes committed
59
60
61
62
63
64
65

    """
    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):]

Arne Gerdes's avatar
Arne Gerdes committed
66
67
    return training, prediction

Arne Gerdes's avatar
Arne Gerdes committed
68

69
70
71
72
73
74
75
76
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
Arne Gerdes's avatar
Arne Gerdes committed
77
    clahe = cv2.createCLAHE(2.0, (8, 8))
tihmels's avatar
tihmels committed
78
79
    norm = clahe.apply(gray)
    return norm
Arne Gerdes's avatar
Arne Gerdes committed
80

Arne Gerdes's avatar
Arne Gerdes committed
81

Arne Gerdes's avatar
Arne Gerdes committed
82
83
84
85
86
def make_sets():
    training_data = []
    training_labels = []
    prediction_data = []
    prediction_labels = []
87
    for emotion in arguments.emotions:
88
        training, prediction = _get_faces_from_emotion(emotion)
Arne Gerdes's avatar
Arne Gerdes committed
89
90
        # Append data to training and prediction list, and generate labels 0-7
        for item in training:
tihmels's avatar
tihmels committed
91
92
            img = image_preprocessing(item)
            training_data.append(img)  # append image array to training data list
93
            training_labels.append(arguments.emotions.index(emotion))
Arne Gerdes's avatar
Arne Gerdes committed
94
95

        for item in prediction:  # repeat above process for prediction set
tihmels's avatar
tihmels committed
96
97
            img = image_preprocessing(item)
            prediction_data.append(img)
98
            prediction_labels.append(arguments.emotions.index(emotion))
Arne Gerdes's avatar
Arne Gerdes committed
99
100
101

    return training_data, training_labels, prediction_data, prediction_labels

Arne Gerdes's avatar
Arne Gerdes committed
102

Arne Gerdes's avatar
Arne Gerdes committed
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
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))

Arne Gerdes's avatar
Arne Gerdes committed
121

Arne Gerdes's avatar
Arne Gerdes committed
122
123
124
"""
Emotions Liste 
"""
tihmels's avatar
tihmels committed
125
fishface = cv2.face.FisherFaceRecognizer_create()
Arne Gerdes's avatar
Arne Gerdes committed
126
metascore = []
tihmels's avatar
tihmels committed
127

128
for i in range(1, arguments.iterations + 1):
Arne Gerdes's avatar
Arne Gerdes committed
129
    correct = run_recognizer()
130
    logging.info("{} : {}%".format(i, int(correct)))
Arne Gerdes's avatar
Arne Gerdes committed
131
    metascore.append(correct)
132

tihmels's avatar
nichts    
tihmels committed
133
    if arguments.email and i % (int(arguments.iterations / 4)) == 0:
134
135
        sendMail(str(i) + ' iterations done', body='up-to-date average: {}%'.format(np.mean(metascore)))

136
137
if arguments.csv:
    file = open("resources/csv/{}.csv".format('_'.join(arguments.properties).lower()), "w")
138
139
140
141
142
143
144
    for entry in metascore:
        file.write("{}\n".format(int(entry)))

    file.close()

logging.info("Fisherface training finished - {}% average\n".format(np.mean(metascore)))

145
if not arguments.test:
tihmels's avatar
tihmels committed
146
    fishface.write('resources/models/detection_model.xml')
Arne Gerdes's avatar
Arne Gerdes committed
147

148
if arguments.email:
149
    sendMail('Fisherface training finished')