Sorted_Set Facedetector für Cluster Einsatz vorbereitet

projectmood/email_service.py

@@ -31,7 +31,4 @@ def sendMail(subject, to = 'tjado.ihmels@uni-oldenburg.de', body = '', filepath=
     server.login(fromaddr, "kR2-zgY-BTf-gRT")
     text = msg.as_string()
     server.sendmail(fromaddr, to, text)
-    server.quit()
-
-
-sendMail('logs/sorted_set_facedetector.log', body='TESTX')
\ No newline at end of file
+    server.quit()
\ No newline at end of file

projectmood/sorted_set_facedetector.py

@@ -7,41 +7,42 @@ import sys
 
 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',
                     datefmt='%m-%d %H:%M',
                     filename='logs/sorted_set_facedetector.log',
                     filemode='w')
-
 """
 Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 """
 
 parser = argparse.ArgumentParser(description='Sorted Set Face Creator Application')
-parser.add_argument('-s', action='store', dest='img_source', default='resources/img_data/sorted_set/',
-                    help='Pfad zu den Bilddateien')
-parser.add_argument('-b', action='store', dest='dataset', default='resources/img_data/dataset/', help='Pfad zum Dataset')
-parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'surprised'],
-                    help='Emotionen die verarbeitet werden sollen')
-parser.add_argument('-r', action='store', dest='resize', default=150, type=int, help='Resize Factor')
-parser.add_argument('-f', action='store', dest='scaleFactor', default=1.1, type=float,
-                    help='Skalierungsfaktor für Haarcascade')
-parser.add_argument('-n', action='store', dest='minNeighbors', default=6, type=int, help='MinNeighbors für Haarcascade')
-parser.add_argument('-m', action='store', dest='minSize', default=40, type=int, help='MinSize für Haarcascade')
+parser.add_argument('--source', action='store', dest='img_source', default='resources/img_data/sorted_set/',
+                    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('-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')
 arguments = parser.parse_args()
-logging.debug(arguments)
+logging.DEBUG(arguments)
 
 datasetPath = arguments.dataset
 
 if len(glob.glob(datasetPath + '*')) > 0:
     deleteDataset = input(
-        'Im Dataset befinden sich Dateien. Durch diesen Prozess werden die existierenden Daten gelöscht. Fortfahren (y/n): ')
+        '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 + '*'):
             shutil.rmtree(file)
     else:
+        logging.info('Execution canceled from user')
         sys.exit()
 
 totalFiles: int = 0
@@ -60,7 +61,7 @@ def detect_faces(emotion):
     global totalFaces
     global totalFiles
 
-    logging.debug("found {} {} files".format(len(files), emotion))
+    logging.info("Found {} {} files".format(len(files), emotion))
 
     fileNumber = 0
     for f in files:
@@ -70,7 +71,11 @@ def detect_faces(emotion):
         facefeatures = locate_faces(gray, arguments.scaleFactor, arguments.minNeighbors, (arguments.minSize, arguments.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')
         else:
             # Cut and save face
             for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
@@ -80,16 +85,20 @@ def detect_faces(emotion):
                     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
                     if not success:
-                        logging.error('A problem while writing file occurred')
-                        sys.exit()
+                        logging.error('Problem while writing file occurred...')
+                        if arguments.x:
+                            sendMail('Problem while writing file', body=f + ' to ' + datasetPath + '{}/{}.jpg'.format(emotion, fileNumber))
                 except:
+                    logging.error('Some error with ' + f)
+                    if arguments.x:
+                        sendMail('Problem while writing file', body=f)
                     pass  # If error, pass file
 
         totalFiles += 1  # Increment image number
         fileNumber += 1
 
-    logging.debug("\n")
-
+if arguments.x:
+    sendMail('Facedetector started notification')
 
 for emotion in arguments.emotions:
 
@@ -98,8 +107,11 @@ for emotion in arguments.emotions:
 
     detect_faces(emotion)  # Call functional
 
-logging.debug('{} faces in {} files found'.format(totalFaces, totalFiles))
-logging.debug('in {} files no face could be detected'.format(totalFiles - totalFaces))
+logging.info('{} faces in {} files found'.format(totalFaces, totalFiles))
+logging.info('In {} files no face could be detected'.format(totalFiles - totalFaces))
 
 for f in undetected:
-    logging.debug(f)
+    logging.info(f)
+
+if arguments.x:
+    sendMail('Facedetector finished notification', filepath='logs/sorted_set_facedetector.log')