Rafffaaactoringgggg

projectmood/datasets/affectnet.py

@@ -5,13 +5,6 @@ from shutil import copyfile
 
 import pandas as pd
 
-logfile = 'affectnet.log'
-
-# Erstellt und konfiguriert die Logdatei
-logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(levelname)-8s %(message)s',
-                    datefmt='%m-%d %H:%M',
-                    filename=logfile)
-
 parser = argparse.ArgumentParser(description='AffectNet PrePipeline')
 parser.add_argument('csv', action='store', help='the .csv file to process')
 parser.add_argument('source', action='store', help='image source folder')

projectmood/datasets/readme.rst

 Dataset Readme
 """""""""""""""""
 
-Die Dateien *affectnet.py*, *cohn_kanande.py* und *fer2013.py* dienen der Vorverarbeitung der jeweiligen Datenbanken,
+Die Dateien *affectnet.py*, *cohn_kanande.py* und *fer2013.py* dienen der Vorverarbeitung der jeweiligen Datenbanken und
 zur Übertragung der Bilddaten und Metainformationen in eine für unser Projekt geeignete Infrastruktur.
\ No newline at end of file

projectmood/process_model.py

@@ -24,15 +24,20 @@ Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 """
 
 parser = argparse.ArgumentParser(description='Process Model Application')
-parser.add_argument('--dataset', action='store', dest='dataset', default='resources/img_data/dataset/',
+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/',
                     help='path to dataset')
-parser.add_argument('-i', action='store', dest='iterations', type=int, default=30, help='number of iterations')
-parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'sadness', 'surprise'],
-                    help='declare emotions that should be processed')
-parser.add_argument('-p', action='append', dest='properties', help='pre-processing steps for logging')
-parser.add_argument('--test', action='store_true', help='prevent writing new model to classifier')
-parser.add_argument('--csv', action='store_true', help='activate csv processing')
-parser.add_argument('--email', action='store_true', help='activate email notifications')
+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')
 arguments = parser.parse_args()
 logging.debug(arguments)
 

projectmood/sorted_set_facedetector.py

@@ -19,19 +19,24 @@ logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(name)-12s %(leve
 Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 """
 
-parser = argparse.ArgumentParser(description='Sorted Set Face Creator Application')
-parser.add_argument('--source', action='store', dest='img_source', default='resources/img_data/sorted_set/',
+parser = argparse.ArgumentParser(description='Dataset Preprocessor')
+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('-s', '--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/',
+parser.add_argument('-d', '--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', '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('--email', action='store_true', help='activate email notifications')
+parser.add_argument('-r', '--resize', action='store', dest='resize', default=250, type=int, help='resize factor')
+parser.add_argument('-c', '--scale', action='store', dest='scaleFactor', default=1.1, type=float,
+                    help='scale factor (haar)')
+parser.add_argument('-n', '--minneighbors', action='store', dest='minNeighbors', default=6, type=int, help='min neighbors (haar)')
+parser.add_argument('-m', '--minsize', action='store', dest='minSize', default=50, type=int, help='min size (haar)')
+parser.add_argument('-x', '--email', action='store_true', help='activate email notifications')
 arguments = parser.parse_args()
 logging.debug(arguments)
 
@@ -44,21 +49,22 @@ minNeighbors = arguments.minNeighbors
 minSize = arguments.minSize
 email = arguments.email
 
-if len(glob.glob(dataset_path + '*')) > 0:
-    deleteDataset = input(
+if len(os.listdir(dataset_path)) > 0:
+    delete_data = 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(dataset_path + '*'):
-            shutil.rmtree(file)
+    if delete_data == 'y':
+        dir = [f for f in os.listdir(dataset_path) if os.path.isdir(os.path.join(dataset_path, f))]
+        for d in dir:
+            shutil.rmtree(os.path.join(dataset_path, d))
     else:
+        print('process aborted by user')
         sys.exit()
 
 totalFiles: int = 0
 totalFaces: int = 0
 undetected: list = []
 
-
 def detect_faces(emotion):
     """
     Holt alle Dateien zu einer Emotion aus dem sorted_set

projectmood/webcam.py

@@ -10,27 +10,24 @@ from RingBuffer import RingBuffer
 from WebcamVideoStream import WebcamVideoStream
 from face_detect import extract_faces
 from image_commons import nparray_as_image, draw_with_alpha
+import os
 
-parser = argparse.ArgumentParser(description='ProjectMood Camplication')
-parser.add_argument('-b', action='store', dest='buffer', default=10, type=int, help='size of ringbuffer')
-parser.add_argument('-e', action='append', dest='emotions', default=['happy', 'neutral', 'sadness', 'surprise'],
-                    help='declare emotions that should be detected')
-parser.add_argument('-r', action='append', dest='resize', default=150, help='resize factor')
-parser.add_argument('--model', action='store', dest='model', default='resources/models/detection_model.xml',
+parser = argparse.ArgumentParser(description='ProjectMood Emotion Detection')
+parser.add_argument('-b', '--buffer', action='store', dest='buffer', default=12, type=int, help='size of ringbuffer')
+parser.add_argument('-m', '--model', action='store', dest='model', default='resources/models/detection_model.xml',
                     help='path to model')
 arguments = parser.parse_args()
 
-
 def _load_emoticons(emotions):
     """
      Lädt die Emoticons aus dem emojis Ordner.
-    :param  emotions: Array von Emotionen.
+    :param  emotions: Emotionen als Array.
     :return: Array von Emotions Grafiken.
     """
-    return [nparray_as_image(cv2.imread('resources/emojis/%s.png' % emotion, -1), mode=None) for emotion in emotions]
+    return [nparray_as_image(cv2.imread('resources/emojis/{}.png'.format(emotion), -1), mode=None) for emotion in emotions]
 
 
-def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='Project Mood', update_time=1):
+def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name=parser.description, update_time=1):
     """
     Zeigt ein Webcam-Bild, erkennt Gesichter und Emotionen in Echtzeit und zeichnet Emoticons neben die Gesichter.
     :param model: Trainiertes Model
@@ -45,6 +42,10 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='P
 
     vc = WebcamVideoStream().start()
 
+    # ein zufälliges Bild aus dem Dataset um das Bildformat zu bestimmen (für Fisherface wichtig)
+    random = cv2.imread('resources/img_data/dataset/{}/0.jpg'.format(emotions[0]))
+    resizefactor = np.size(random, 0)
+
     """
     Der RingBuffer speichert die letzten x Predictions
     """
@@ -53,7 +54,7 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='P
     frame = vc.read()
 
     while True:
-        for normalized_face in extract_faces(frame, arguments.resize):
+        for normalized_face in extract_faces(frame, resizefactor):
             prediction = model.predict(normalized_face)  # do prediction
 
             """
@@ -93,11 +94,13 @@ def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='P
 
 
 if __name__ == '__main__':
-    emotions = arguments.emotions
+    # Die Emotionen die im Datasetordner liegen, sollen auch in der Applikation hochgefahren werden
+    _, emotions, _ = next(os.walk('resources/img_data/dataset'), (None, [], None))
+
     emoticons = _load_emoticons(emotions)
 
     fisher_face = cv2.face.FisherFaceRecognizer_create()
 
-    """Lädt das trainierte Model"""
+    # Lädt das trainierte Model
     fisher_face.read(arguments.model)
     show_webcam_and_run(fisher_face, emoticons)