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projectmood/RingBuffer.py

 class RingBuffer:
+    """
+    Simple fixed-size ringbuffer
+    """
     def __init__(self, size):
         self.data = [None for i in range(size)]
 
     def append(self, x):
+        """
+        Append an element
+        :param x: the element
+        :return:
+        """
         self.data.pop(0)
         self.data.append(x)
 
     def get(self):
+        """
+        return array with elements
+        :return: array with elements
+        """
         return self.data

projectmood/email_service.py

@@ -7,6 +7,14 @@ from email.mime.text import MIMEText
 
 
 def sendMail(subject, to='tjado.ihmels@uni-oldenburg.de', body='', filepath=''):
+    """
+    For a longer runtime on a remote server emails serve as feedback
+    :param subject: subject
+    :param to: email adress
+    :param body: email text
+    :param filepath: optional attachment. For example a logfile
+    :return:
+    """
     fromaddr = "projectmood18@gmail.com"
 
     msg = MIMEMultipart()

projectmood/image_commons.py

 """
-Diese Klasse enthält Funktionen zum Bearbeiten von Bildern in OpenCV und PIL's Image.
-PIL => PIL ist die Python-Imaging-Bibliothek
+Utility Class for working with images in OpenCV
 """
 import cv2
 import numpy as np
@@ -9,28 +8,28 @@ from PIL import Image
 
 def image_as_nparray(image):
     """
-    Konvertiert PIL Image in ein Numpy Array
+    Convert PIL Image to Numpy array
     :param image: PIL's Image object.
-    :return: Numpy Array der Bilder.
+    :return: Numpy Array
     """
     return np.asarray(image)
 
 
 def nparray_as_image(nparray, mode='RGB'):
     """
-    Konvertiert Numpy Array der Bilder in PIL Image
+    Convert numpy array to PIL image
     :param nparray: Numpy's array of image.
-    :param mode: Modus der Umwandlung. Standardeinstellung ist "RGB".
-    :return: PIL Image mit dem Bild
+    :param mode: transformation mode
+    :return: PIL image
     """
     return Image.fromarray(np.asarray(np.clip(nparray, 0, 255), dtype='uint8'), mode)
 
 
 def load_image(source_path):
     """
-    Lädt das RGB-Bild und konvertiert es in Graustufen.
-    :param source_path: Quellpfad des Bildes.
-    :return: Bild wird vom Pfad geladen und in Graustufen konvertiert.
+    Load the RGB image and convert to grayscale
+    :param source_path: source path to image
+    :return: converted image
     """
     source_image = cv2.imread(source_path)
     return cv2.cvtColor(source_image, cv2.COLOR_BGR2GRAY)
@@ -38,24 +37,23 @@ def load_image(source_path):
 
 def draw_with_alpha(source_image, image_to_draw, coordinates):
     """
-    Zeichnet ein teilweise transparentes Bild über ein anderes Bild.
-    :param source_image: Bild zum Übermalen.
-    :param image_to_draw: Bild zum Zeichnen.
-    :param coordinates: : Koordinaten um ein Bild zu zeichnen. Tupel von x, y, Breite und Höhe.
+    Draw a partially transparent image on another image
+    :param source_image: Background image
+    :param image_to_draw: Overdraw image
+    :param coordinates: : Coordinates to which the image should be drawn
     """
     x, y, w, h = coordinates
     image_to_draw = image_to_draw.resize((h, w), Image.ANTIALIAS)
     image_array = image_as_nparray(image_to_draw)
     for c in range(0, 3):
-        source_image[y:y + h, x:x + w, c] = image_array[:, :, c] * (image_array[:, :, 3] / 255.0) \
-                                            + source_image[y:y + h, x:x + w, c] * (1.0 - image_array[:, :, 3] / 255.0)
+        source_image[y:y + h, x:x + w, c] = image_array[:, :, c] * (image_array[:, :, 3] / 255.0) + source_image[y:y + h, x:x + w, c] * (1.0 - image_array[:, :, 3] / 255.0)
 
 def draw_img(source_image, image_to_draw, coordinates):
     """
-    Zeichnet ein teilweise transparentes Bild über ein anderes Bild.
-    :param source_image: Bild zum Übermalen.
-    :param image_to_draw: Bild zum Zeichnen.
-    :param coordinates: : Koordinaten um ein Bild zu zeichnen. Tupel von x, y, Breite und Höhe.
+    Draw an image to another image
+    :param source_image: Background image
+    :param image_to_draw: Overdraw image
+    :param coordinates: : Coordinates to which the image should be drawn
     """
     x, y, w, h = coordinates
     image_to_draw = image_to_draw.resize((h, w), Image.ANTIALIAS)

projectmood/process_model.py

@@ -5,7 +5,6 @@ import glob
 import logging
 import random
 import cv2
-import cv2.face
 import numpy as np
 import sys
 

projectmood/sorted_set_facedetector.py

@@ -15,10 +15,8 @@ logging.basicConfig(level=logging.NOTSET, format='%(asctime)s %(name)-12s %(leve
                     datefmt='%m-%d %H:%M',
                     filename=logfile,
                     filemode='w')
-"""
-Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
-"""
 
+# Argument Parser erlaubt Parameter für die Verarbeitung anzugeben.
 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')
@@ -41,6 +39,7 @@ parser.add_argument('-x', '--email', action='store_true', help='activate email n
 arguments = parser.parse_args()
 logging.debug(arguments)
 
+# if no emotions are declared, exit
 if not arguments.emotions:
     print('No emotions declared')
     sys.exit()
@@ -54,6 +53,7 @@ minNeighbors = arguments.minNeighbors
 minSize = arguments.minSize
 email = arguments.email
 
+# check if files exist at destination path and ask the user to continue
 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): ')
@@ -66,14 +66,20 @@ if len(os.listdir(dataset_path)) > 0:
         print('process aborted by user')
         sys.exit()
 
+# count some values for debugging
 totalFiles: int = 0
 totalFaces: int = 0
 undetected: list = []
 
 def detect_faces(emotion):
     """
-    Holt alle Dateien zu einer Emotion aus dem sorted_set
+    This function iterates the presorted set from one emotion and generates the dataset for recognition
+    Haarcascade locates the face and with the coordinates the pictures will be resized and converted to grayscale
+    :param emotion: the emotion
+    :return:
     """
+
+    # get all files from an emotion folder
     files = glob.glob(source_path + '{}/*'.format(emotion))
 
     global undetected
@@ -84,15 +90,17 @@ def detect_faces(emotion):
 
     file_name_number = 0
     for f in files:
-        frame = cv2.imread(f)  # Open image
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
+        frame = cv2.imread(f)  # open image
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # convert image to grayscale
 
+        # extract face coordinates via haarcascade
         facefeatures = locate_faces(gray, scaleFactor, minNeighbors, (minSize, minSize))
 
         if facefeatures is '':
             logging.info('No face detected in ' + f)
             undetected.append(f)
             if len(undetected) % 200 == 0 and email:
+                # could indicate that something went wrong, check the log
                 sendMail('Already ' + str(len(undetected)) + ' undetected faces', filepath=logfile)
         else:
             # Cut and save face
@@ -100,9 +108,10 @@ def detect_faces(emotion):
                 totalFaces += 1
                 gray = gray[y:y + h, x:x + w]  # Cut the frame to size
                 try:
-                    save_file = dataset_path + '{}/{}{}.jpg'.format(emotion, arguments.prefix, file_name_number)
                     out = cv2.resize(gray, (resizeFactor, resizeFactor))  # Resize face so all images have same size
-                    success = cv2.imwrite(save_file, out)  # Write image
+                    save_file = dataset_path + '{}/{}{}.jpg'.format(emotion, arguments.prefix,
+                                                                    file_name_number)  # filepath + name
+                    success = cv2.imwrite(save_file, out)  # Write image to filesystem
                     if not success:
                         logging.error('Problem while writing file ' + str(f) + ' occurred...')
                         if email:
@@ -118,11 +127,11 @@ def detect_faces(emotion):
         file_name_number += 1
 
 for emotion in emotions:
-
+    # create emotion folder if not exist
     if not os.path.exists(dataset_path + emotion):
         os.makedirs(dataset_path + emotion)
 
-    detect_faces(emotion)  # Call functional
+    detect_faces(emotion)  # sort faces into dataset
 
 logging.info('{} faces in {} files found'.format(totalFaces, totalFiles))
 logging.info('In {} files no face could be detected'.format(totalFiles - totalFaces))