Rafectorings und Requierements endlich mal wieder angepasst

projectmood/process_model.py

@@ -8,21 +8,18 @@ import random
 
 import cv2
 import numpy as np
+import sys
+
 from email_service import sendMail
 
 logfile = 'logs/process_model.log'
 
-"""
-Erstellt und gibt das Log-File aus 
-"""
+# Erstellt und konfiguriert den Logger
 logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
                     filename=logfile)
 
-"""
-Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
-"""
-
+# Argument Parser erlaubt Programmparameter anzugeben
 parser = argparse.ArgumentParser(description='Process Model Application')
 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')
@@ -33,38 +30,31 @@ parser.add_argument('-5', action='append_const', dest='emotions', const='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/',
                     help='path to dataset')
-parser.add_argument('-i' '--iterations', action='store', dest='iterations', type=int, default=30, help='number of iterations')
+parser.add_argument('-i' '--iterations', action='store', dest='iterations', type=int, default=30,
+                    help='number of iterations')
 parser.add_argument('-p', '--properties', nargs='+', dest='properties', help='pre-processing steps for logging')
-parser.add_argument('-t', '--test', action='store_true', help='prevent writing new model to classifier')
-parser.add_argument('-c', '--csv', action='store_true', help='activate csv processing')
-parser.add_argument('-x', '--email', action='store_true', help='activate email notifications')
+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')
 arguments = parser.parse_args()
 logging.debug(arguments)
 
-dataset_path = arguments.dataset
-iterations = arguments.iterations
-emotions = arguments.emotions
-properties = arguments.properties
-csv = arguments.csv
-email = arguments.email
-test = arguments.test
+if not arguments.emotions:
+    print('No emotions declared')
+    sys.exit()
 
-"""
-Liest Input Parameter 
-"""
 logging.info('Fisherface training started')
 
-if email:
+if arguments.email:
     sendMail('Fisherface training started')
 
-
 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(dataset_path + '{}/*'.format(emotion))
+    files = glob.glob(arguments.dataset + '{}/*'.format(emotion))
     random.shuffle(files)
 
     """
@@ -94,18 +84,18 @@ def make_sets():
     training_labels = []
     prediction_data = []
     prediction_labels = []
-    for emotion in emotions:
+    for emotion in arguments.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))
+            training_labels.append(arguments.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))
+            prediction_labels.append(arguments.emotions.index(emotion))
 
     return training_data, training_labels, prediction_data, prediction_labels
 
@@ -135,16 +125,16 @@ Emotions Liste
 fishface = cv2.face.FisherFaceRecognizer_create()
 metascore = []
 
-for i in range(1, iterations + 1):
+for i in range(1, arguments.iterations + 1):
     correct = run_recognizer()
     logging.info("{} : {}%".format(i, int(correct)))
     metascore.append(correct)
 
-    if i % (int(iterations / 4)) == 0 and email:
+    if i % (int(arguments.iterations / 4)) == 0 and arguments.email:
         sendMail(str(i) + ' iterations done', body='up-to-date average: {}%'.format(np.mean(metascore)))
 
-if csv:
-    file = open("resources/csv/{}.csv".format('_'.join(properties).lower()), "w")
+if arguments.csv:
+    file = open("resources/csv/{}.csv".format('_'.join(arguments.properties).lower()), "w")
     for entry in metascore:
         file.write("{}\n".format(int(entry)))
 
@@ -152,8 +142,8 @@ if csv:
 
 logging.info("Fisherface training finished - {}% average\n".format(np.mean(metascore)))
 
-if not test:
+if not arguments.test:
     fishface.write('resources/models/detection_model.xml')
 
-if email:
+if arguments.email:
     sendMail('Fisherface training finished')

projectmood/sorted_set_facedetector.py

@@ -40,6 +40,10 @@ parser.add_argument('-x', '--email', action='store_true', help='activate email n
 arguments = parser.parse_args()
 logging.debug(arguments)
 
+if not arguments.emotions:
+    print('No emotions declared')
+    sys.exit()
+
 source_path = arguments.img_source
 dataset_path = arguments.dataset
 resizeFactor = arguments.resize

requirements.txt

-image==1.5.16
-imagesize==0.7.1
-imutils==0.4.3
-jupyter==1.0.0
-jupyter-client==5.1.0
-jupyter-console==5.2.0
-jupyter-core==4.3.0
-matplotlib==2.1.0
-numpy==1.13.3
-olefile==0.44
-opencv-contrib-python==3.3.0.10
-opencv-python==3.3.0.10
-Pillow==4.3.0
-pytz==2017.2
-PyYAML==3.12
-pyzmq==16.0.21
-requests==2.18.4
-scipy==1.0.0
-six==1.11.0
-Sphinx==1.6.5
-sphinxcontrib-websupport==1.0.1
-urllib3==1.22
-utils==0.9.0
\ No newline at end of file
+numpy==1.14.0
+opencv-python==3.4.0.12
+Pillow==5.0.0
\ No newline at end of file