Kommentare im face_detect und process_model fertig geschrieben

projectmood/face_detect.py

 import cv2
 
-
+# Haarcascades
 faceDet_one = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_default.xml')
 faceDet_two = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_alt2.xml')
 faceDet_three = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalface_alt.xml')
@@ -8,18 +8,36 @@ faceDet_four = cv2.CascadeClassifier('resources/haarcascade/haarcascade_frontalf
 
 
 def extract_faces(image, resize):
+    """
+    Calls the method through which the face is to be found in the image and calls the
+     method on which the found face continues to be edited
+    :param image: Input
+    :param resize: Resize coordinates of the face
+    :return: The normalized face in the image
+    """
     faces = find_faces(image)
     normalized_faces = [preprocess_face(face, resize) for face in faces]
     return normalized_faces
 
 
 def find_faces(image):
+    """
+    Calls the method to detect the face in the image and cuts it out
+    :param image: Input
+    :return: The cutted face
+    """
     faces_coordinates = locate_faces(image)
     cutted_faces = [image[y:y + h, x:x + w] for (x, y, w, h) in faces_coordinates]
     return cutted_faces
 
 
 def preprocess_face(face, resize):
+    """
+    Edit the found face
+    :param face: Found face
+    :param resize: Resize coordinates of the face
+    :return: Edited face
+    """
     face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
     face = cv2.resize(face, (resize, resize))
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
@@ -28,6 +46,14 @@ def preprocess_face(face, resize):
 
 
 def locate_faces(image, scaleFactor=1.1, minNeighbors=6, minSize=(350, 350)):
+    """
+    Find the face in the image with Haarcascades
+    :param image: Input
+    :param scaleFactor: Parameter specifying how much the image size is reduced at each image scale
+    :param minNeighbors: Parameter specifying how many neighbors each candidate rectangle should have to retain it
+    :param minSize: Minimum possible object size. Objects smaller than that are ignored
+    :return: Found face
+    """
     face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=minSize,
                                         flags=cv2.CASCADE_SCALE_IMAGE)
 

projectmood/process_model.py

@@ -76,6 +76,10 @@ def image_preprocessing(image):
 
 
 def make_sets():
+    """
+    Creates the training set
+    :return: The created training data
+    """
     training_data = []
     training_labels = []
     prediction_data = []
@@ -99,6 +103,10 @@ def make_sets():
 
 
 def run_recognizer():
+    """
+    Performs the actual training with Fisherfaces and Logged the results
+    :return: The correctly recognized faces in percent
+    """
     training_data, training_labels, prediction_data, prediction_labels = make_sets()
 
     logging.debug('Training...')
@@ -123,6 +131,7 @@ def run_recognizer():
 fishface = cv2.face.FisherFaceRecognizer_create()
 metascore = []
 
+# Argument parser
 for i in range(1, arguments.iterations + 1):
     correct = run_recognizer()
     logging.info("{} : {}%".format(i, int(correct)))
@@ -131,6 +140,7 @@ for i in range(1, arguments.iterations + 1):
     if arguments.email and i % (int(arguments.iterations / 4)) == 0:
         sendMail(str(i) + ' iterations done', body='up-to-date average: {}%'.format(np.mean(metascore)))
 
+# Argument parser
 if arguments.csv:
     file = open("resources/csv/{}.csv".format('_'.join(arguments.properties).lower()), "w")
     for entry in metascore:
@@ -140,9 +150,11 @@ if arguments.csv:
 
 logging.info("Fisherface training finished - {}% average\n".format(np.mean(metascore)))
 
+# Argument parser
 if not arguments.test:
     fishface.write('resources/models/detection_model.xml')
     logging.info('saved trained classifier')
 
+# Argument parser
 if arguments.email:
     sendMail('Fisherface training finished', filepath='resources/models/detection_model.xml')