Process Model für Cluster Einsatz vorbereitet

projectmood/process_model.py

 """
 Diese Klasse macht das Training des Models möglich
 """
+import argparse
 
 import cv2
 import glob
@@ -9,21 +10,47 @@ import numpy as np
 import sys
 import logging
 
+from email_service import sendMail
+
+logfile = 'logs/process_model.log'
+
 """
 Erstellt und gibt das Log-File aus 
 """
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
+logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
-                    filename='logs/process_model.log')
+                    filename=logfile)
 
 """
-Liest Input Parameter 
+Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 """
-args = sys.argv
-logging.debug('Fisherface training initialized')
 
-file = open("{}.csv".format('_'.join(args[1:]).lower()), "w")
+parser = argparse.ArgumentParser(description='Process Model Application')
+parser.add_argument('--dataset', action='store', dest='dataset', default='resources/img_data/dataset/', help='path to dataset')
+parser.add_argument('-i', action='store', dest='iterations', type=int, default=30, help='declare processing iterations')
+parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprise'], help='declare emotions that should be processed')
+parser.add_argument('-p', action='append', dest='properties', help='pre-processing steps')
+parser.add_argument('--test', action='store_true', help='prevent writing new model to file system')
+parser.add_argument('--csv', action='store_true', help='activate csv processing')
+parser.add_argument('--email', action='store_true', 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
+
+"""
+Liest Input Parameter 
+"""
+logging.info('Fisherface training started')
 
+if email:
+    sendMail('Fisherface training started')
 
 def _get_faces_from_emotion(emotion):
     """
@@ -31,7 +58,7 @@ def _get_faces_from_emotion(emotion):
     :param emotion: Die Emotion
     :return: training, prediction
     """
-    files = glob.glob('img_data/dataset/{}/*'.format(emotion))
+    files = glob.glob(dataset_path + '{}/*'.format(emotion))
     random.shuffle(files)
 
     """
@@ -95,27 +122,31 @@ def run_recognizer():
             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, 40):
+for i in range(1, iterations+1):
     correct = run_recognizer()
-    file.write("{}\n".format(int(correct)))
-    logging.debug("{} : {}%".format(i, int(correct)))
+    logging.info("{} : {}%".format(i, int(correct)))
     metascore.append(correct)
 
-file.close()
+    if i % (int(iterations/4)) == 0 and 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")
+    for entry in metascore:
+        file.write("{}\n".format(int(entry)))
+
+    file.close()
+
+logging.info("Fisherface training finished - {}% average\n".format(np.mean(metascore)))
+
+if not test:
+    fishface.write('img_data/models/detection_model.xml')
 
-logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
-fishface.write('img_data/models/detection_model.xml')
+if email:
+    sendMail('Fisherface training finished')

projectmood/sorted_set_facedetector.py

@@ -10,9 +10,11 @@ import os
 from email_service import sendMail
 from face_detect import locate_faces
 
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
+logfile = 'logs/sorted_set_facedetector.log'
+
+logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
-                    filename='logs/sorted_set_facedetector.log',
+                    filename=logfile,
                     filemode='w')
 """
 Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
@@ -23,39 +25,45 @@ parser.add_argument('--source', action='store', dest='img_source', default='reso
                     help='path to image source')
 parser.add_argument('--dataset', action='store', dest='dataset', default='resources/img_data/dataset/', help='path to dataset')
 parser.add_argument('--r', action='store', dest='resize', default=150, type=int, help='resize factor')
-parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprised'], help='declare emotions that should be processed')
+parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprise'], help='declare emotions that should be processed')
 parser.add_argument('-c', action='store', dest='scaleFactor', default=1.1, type=float,
                     help='scale factor - haar')
 parser.add_argument('-n', action='store', dest='minNeighbors', default=6, type=int, help='min neighbors - haar')
 parser.add_argument('-s', action='store', dest='minSize', default=40, type=int, help='min size - haar')
-parser.add_argument('-x', action='store_true', help='activate email notifications')
+parser.add_argument('--email', action='store_true', help='activate email notifications')
 arguments = parser.parse_args()
-logging.DEBUG(arguments)
+logging.debug(arguments)
+
+source_path = arguments.img_source
+dataset_path = arguments.dataset
+resizeFactor = arguments.resize
+emotions = arguments.emotions
+scaleFactor = arguments.scaleFactor
+minNeighbors = arguments.minNeighbors
+minSize = arguments.minSize
+email = arguments.email
 
-datasetPath = arguments.dataset
 
-if len(glob.glob(datasetPath + '*')) > 0:
+if len(glob.glob(dataset_path + '*')) > 0:
     deleteDataset = input(
         'Im Dataset befinden sich Dateien. Durch diesen Vorgang werden die existierenden Daten gelöscht. Fortfahren (y/n): ')
 
     if deleteDataset == 'y':
-        for file in glob.glob(datasetPath + '*'):
+        for file in glob.glob(dataset_path + '*'):
             shutil.rmtree(file)
     else:
-        logging.info('Execution canceled from user')
         sys.exit()
 
 totalFiles: int = 0
 totalFaces: int = 0
 undetected: list = []
-img_source = arguments.img_source
 
 
 def detect_faces(emotion):
     """
     Holt alle Dateien zu einer Emotion aus dem sorted_set
     """
-    files = glob.glob(img_source + '{}/*'.format(emotion))
+    files = glob.glob(source_path + '{}/*'.format(emotion))
 
     global undetected
     global totalFaces
@@ -68,42 +76,41 @@ def detect_faces(emotion):
         frame = cv2.imread(f)  # Open image
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
 
-        facefeatures = locate_faces(gray, arguments.scaleFactor, arguments.minNeighbors, (arguments.minSize, arguments.minSize))
+        facefeatures = locate_faces(gray, scaleFactor, minNeighbors, (minSize, minSize))
 
         if facefeatures is '':
             logging.info('No face detected ' + f)
             undetected.append(f)
-
-            if len(undetected) == 150 and arguments.x:
-                sendMail('Already 150 not detected faces', filepath='logs/sorted_set_facedetector.log')
+            if len(undetected) % 200 == 0 and email:
+                sendMail('Already ' + str(len(undetected)) + ' not detected faces', filepath=logfile)
         else:
             # Cut and save face
             for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
                 totalFaces += 1
                 gray = gray[y:y + h, x:x + w]  # Cut the frame to size
                 try:
-                    out = cv2.resize(gray, (arguments.resize, arguments.resize))  # Resize face so all images have same size
-                    success = cv2.imwrite(datasetPath + '{}/{}.jpg'.format(emotion, fileNumber), out)  # Write image
+                    out = cv2.resize(gray, (resizeFactor, resizeFactor))  # Resize face so all images have same size
+                    success = cv2.imwrite(dataset_path + '{}/{}.jpg'.format(emotion, fileNumber), out)  # Write image
                     if not success:
-                        logging.error('Problem while writing file occurred...')
-                        if arguments.x:
+                        logging.error('Problem while writing file ' + f + ' occurred...' )
+                        if email:
                             sendMail('Problem while writing file', body=f + ' to ' + datasetPath + '{}/{}.jpg'.format(emotion, fileNumber))
                 except:
                     logging.error('Some error with ' + f)
-                    if arguments.x:
+                    if email:
                         sendMail('Problem while writing file', body=f)
                     pass  # If error, pass file
 
         totalFiles += 1  # Increment image number
         fileNumber += 1
 
-if arguments.x:
+if email:
     sendMail('Facedetector started notification')
 
-for emotion in arguments.emotions:
+for emotion in emotions:
 
-    if not os.path.exists(datasetPath + emotion):
-        os.makedirs(datasetPath + emotion)
+    if not os.path.exists(dataset_path + emotion):
+        os.makedirs(dataset_path + emotion)
 
     detect_faces(emotion)  # Call functional
 
@@ -113,5 +120,5 @@ logging.info('In {} files no face could be detected'.format(totalFiles - totalFa
 for f in undetected:
     logging.info(f)
 
-if arguments.x:
-    sendMail('Facedetector finished notification', filepath='logs/sorted_set_facedetector.log')
+if email:
+    sendMail('Facedetector finished notification', filepath=logfile)