RingBuffer implementiert, Kommentare geschrieben, Emotionen vorerst auf drei eingeschränkt!

projectmood/RingBuffer.py

+class RingBuffer:
+    def __init__(self, size):
+        self.data = [None for i in range(size)]
+
+    def append(self, x):
+        self.data.pop(0)
+        self.data.append(x)
+
+    def get(self):
+        return self.data
\ No newline at end of file

projectmood/face_detect.py

@@ -3,7 +3,7 @@ This module contains face detections functions.
 """
 import cv2
 
-faceDet = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_default.xml')
+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')
 faceDet_four = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalface_alt_tree.xml')
@@ -12,40 +12,40 @@ faceDet_four = cv2.CascadeClassifier('resources/Haarcascade/haarcascade_frontalf
 def find_faces(image):
     faces_coordinates = locate_faces(image)
     cutted_faces = [image[y:y + h, x:x + w] for (x, y, w, h) in faces_coordinates]
-    normalized_faces = [_normalize_face(face) for face in cutted_faces]
+    normalized_faces = [normalize_face(face) for face in cutted_faces]
     return zip(normalized_faces, faces_coordinates)
 
 
-def _normalize_face(face):
+def normalize_face(face):
     face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
     face = cv2.resize(face, (250, 250))
     return face
 
 
-def locate_faces(image, scaleFactor=2, minNeighbors=4):
+def locate_faces(image, scaleFactor=1.4, minNeighbors=3):
 
-    face_one = faceDet.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(20, 20),
+    face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(face_one) == 1:
-        return face_one
+    if len(face) == 1:
+        return face
 
-    face_two = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(20, 20),
+    face = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(face_two) == 1:
-        return face_two
+    if len(face) == 1:
+        return face
 
-    face_three = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(20, 20),
+    face = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(face_three) == 1:
-        return face_three
+    if len(face) == 1:
+        return face
 
-    face_four = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(20, 20),
+    face = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
                                               flags=cv2.CASCADE_SCALE_IMAGE)
 
-    if len(face_four) == 1:
-        return face_four
+    if len(face) == 1:
+        return face
 
     return ''

projectmood/image_sort.py

 import glob
 from shutil import copyfile
 
-emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]  # Define emotion order
-participants = glob.glob("basis_data/source_emotion/*")  # Returns a list of all folders with participant numbers
+emotions = ["neutral", "anger", "contempt", "disgust", "fear", "happy", "sadness", "surprise"]  # Emotionen - Reihenfolge wichtig!
+participants = glob.glob("basis_data/source_emotion/*")  # Eine Liste mit den Dateiordnern aller Teilnehmer
 
 for x in participants:
-    part = "%s" % x[-4:]  # store current participant number
-    for sessions in glob.glob("basis_data/%s/*" % x):  # Store list of sessions for current participant
-        for files in glob.glob("basis_data/%s/*" % sessions):
-            current_session = files[20:-30]
-            file = open(files, 'r')
+    number = "%s" % x[-4:]  # Teilnehmernummer
+    for sessions in glob.glob("%s/*" % x):
+        for files in glob.glob("%s/*" % sessions):
+            current_session = files[31:-30] # Sessionnummer
+            file = open(files, 'r') # Öffne die zur aktuellen Emotion korrelierende .txt Datei
 
             emotion = int(
-                float(file.readline()))  # emotions are encoded as a float, readline as float, then convert to integer.
+                float(file.readline()))  # In der Datei steht die aktuell betrachtete Emotion, kodiert als float-Wert
 
-            sourcefile_emotion = glob.glob("basis_data/source_images/%s/%s/*" % (part, current_session))[
-                -1]  # get path for last image in sequence, which contains the emotion
-            sourcefile_neutral = glob.glob("basis_data/source_images/%s/%s/*" % (part, current_session))[
-                0]  # do same for neutral image
+            source_emotions = glob.glob("basis_data/source_images/%s/%s/*.png" % (number, current_session))
+            source_emotions.sort()
+            sourcefile_emotion = source_emotions[-1]  # Das letzte Bild einer Sequenz ist die ausgeprägte Emotion
+            sourcefile_neutral = source_emotions[0]  # Das erste Bild ist ein neutraler Ausdruck
 
-            dest_neut = "basis_data/sorted_set/neutral/%s" % sourcefile_neutral[25:]  # Generate path to put neutral image
-            dest_emot = "basis_data/sorted_set/%s/%s" % (
-            emotions[emotion], sourcefile_emotion[25:])  # Do same for emotion containing image
+            # Erstelle neue Pfade zum einsortieren
+            dest_neut = "basis_data/sorted_set/neutral/%s" % sourcefile_neutral[36:] # für den neutralen Ausdruck
+            dest_emot = "basis_data/sorted_set/%s/%s" % (emotions[emotion], sourcefile_emotion[36:]) # und die Emotion
 
-            copyfile(sourcefile_neutral, dest_neut)  # Copy file
-            copyfile(sourcefile_emotion, dest_emot)  # Copy file
+            # Kopiere Dateien
+            copyfile(sourcefile_neutral, dest_neut)
\ No newline at end of file
+            copyfile(sourcefile_emotion, dest_emot)
\ No newline at end of file

projectmood/process_model.py

@@ -8,19 +8,32 @@ import logging
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s',
                     datefmt='%m-%d %H:%M',
                     filename='logs/process_model.log')
-args = sys.argv
+
+args = sys.argv # liest Input Parameter
 logging.debug('Fisherface training initialized')
 
-def get_files_from_emotion(emotion):  # Define function to get file list, randomly shuffle it and split 80/20
-    files = glob.glob('basis_data/dataset/%s/*' % emotion)
+file = open("gray_equalized.csv", "w")
+
+def _get_faces_from_emotion(emotion):
+    """
+    Holt alle Dateien zu einer Emotion aus dem Dataset, mischt sie und teilt sie in ein Trainings- und Prognoseset.
+    :param emotion: Die Emotion
+    :return: training, prediction
+    """
+    files = glob.glob('basis_data/dataset/{}/*'.format(emotion))
     random.shuffle(files)
     training = files[:int(len(files) * 0.8)]  # get first 80% of file list
     prediction = files[-int(len(files) * 0.2):]  # get last 20% of file list
     return training, prediction
 
-def image_preprocessing(item):
-    image = cv2.imread(item)  # open image
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
+def image_preprocessing(image):
+    """
+    Preprocessing der Dateien
+    :param item: Bild
+    :return:
+    """
+    img = cv2.imread(image)  # open image
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to grayscale
     return gray
 
 def make_sets():
@@ -29,7 +42,7 @@ def make_sets():
     prediction_data = []
     prediction_labels = []
     for emotion in emotions:
-        training, prediction = get_files_from_emotion(emotion)
+        training, prediction = _get_faces_from_emotion(emotion)
         # Append data to training and prediction list, and generate labels 0-7
         for item in training:
             img = image_preprocessing(item)
@@ -66,16 +79,18 @@ if len(args) > 1:
     tags = ', '.join(args[1:])
     logging.debug(tags.upper())
 
-emotions = ["anger", "disgust", "happy", "neutral", "surprise"]  # Emotion list
+emotions = ["happy", "neutral", "surprise"]  # Emotion list
 fishface = cv2.face.FisherFaceRecognizer_create()
 
 # Now run it
 metascore = []
 
-for i in range(0, 10):
+for i in range(0, 20):
     correct = run_recognizer()
     logging.debug("{} : {}%".format(i, int(correct)))
+    file.write("{}, {}".format(i, int(correct)))
     metascore.append(correct)
+file.close()
 
 logging.debug("{} iterations - {}% average\n".format(len(metascore), np.mean(metascore)))
 fishface.write('basis_data/models/detection_model.xml')
\ No newline at end of file

projectmood/sorted_set_facedetector.py

@@ -8,7 +8,7 @@ logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(name)-12s %(level
                     filename='logs/sorted_set_facedetector.log',
                     filemode='w')
 
-emotions = ["neutral", "anger", "disgust", "happy", "surprise"]  # Define emotions
+emotions = ["happy", "neutral", "surprise"]  # Emotionen die verarbeitet werden sollen
 
 totalFiles: int = 0
 totalFaces: int = 0
@@ -16,7 +16,7 @@ undetected: list = []
 
 
 def detect_faces(emotion):
-    files = glob.glob('basis_data/sorted_set/%s/*' % emotion)  # Get list of all images with emotion
+    files = glob.glob('basis_data/sorted_set/{}/*'.format(emotion))  # Holt alle Dateien zu einer Emotion aus dem sorted_set
 
     global undetected
     global totalFaces
@@ -27,23 +27,24 @@ def detect_faces(emotion):
     fileNumber = 0
     for f in files:
         frame = cv2.imread(f)  # Open image
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
 
-        facefeatures = locate_faces(frame, 1.05, 6)
+        facefeatures = locate_faces(gray, 1.05, 10)
 
         if facefeatures is '':
             undetected.append(f)
 
-        # Cut and save face
-        for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
-            logging.debug("face found in file: {}".format(f))
-            totalFaces += 1
-            frame = frame[y:y + h, x:x + w]  # Cut the frame to size
-
-            try:
-                out = cv2.resize(frame, (250, 250))  # Resize face so all images have same size
-                cv2.imwrite('basis_data/dataset/%s/%s.jpg' % (emotion, fileNumber), out)  # Write image
-            except:
-                pass  # If error, pass file
+        else:
+            # Cut and save face
+            for (x, y, w, h) in facefeatures:  # get coordinates and size of rectangle containing face
+                logging.debug("face found in file: {}".format(f))
+                totalFaces += 1
+                gray = gray[y:y + h, x:x + w]  # Cut the frame to size
+                try:
+                    out = cv2.resize(gray, (250, 250))  # Resize face so all images have same size
+                    cv2.imwrite('basis_data/dataset/{}/{}.jpg'.format(emotion, fileNumber), out)  # Write image
+                except:
+                    pass  # If error, pass file
 
         totalFiles += 1  # Increment image number
         fileNumber += 1

projectmood/webcam.py

@@ -3,14 +3,13 @@ This module is the main module in this package. It loads emotion recognition mod
 shows a webcam image, recognizes face and it's emotion and draw emotion on the image.
 """
 
-
-from cv2 import WINDOW_NORMAL
-
 import cv2
 
+from RingBuffer import RingBuffer
 from WebcamVideoStream import WebcamVideoStream
 from face_detect import find_faces
 from image_commons import nparray_as_image, draw_with_alpha
+import numpy as np
 
 
 def _load_emoticons(emotions):
@@ -22,7 +21,7 @@ def _load_emoticons(emotions):
     return [nparray_as_image(cv2.imread('resources/graphics/%s.png' % emotion, -1), mode=None) for emotion in emotions]
 
 
-def show_webcam_and_run(model, emoticons, window_size=(800, 800), window_name='Mood Expression', update_time=1):
+def show_webcam_and_run(model, emoticons, window_size=(600, 600), window_name='Mood Expression', update_time=1):
     """
     Shows webcam image, detects faces and its emotions in real time and draw emoticons over those faces.
     :param model: Learnt emotion detection model.
@@ -31,21 +30,27 @@ def show_webcam_and_run(model, emoticons, window_size=(800, 800), window_name='M
     :param window_name: Name of webcam image window.
     :param update_time: Image update time interval.
     """
-    cv2.namedWindow(window_name, WINDOW_NORMAL)
+    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
     if window_size:
         width, height = window_size
         cv2.resizeWindow(window_name, width, height)
 
-    vc = WebcamVideoStream(src=0).start()
+    vc = WebcamVideoStream().start()
     frame = vc.read()
 
+    puffer = RingBuffer(7) # Der RingBuffer speichert die letzten Predictions
+
     while True:
         for normalized_face, (x, y, w, h) in find_faces(frame):
             prediction = model.predict(normalized_face)  # do prediction
 
-            image_to_draw = emoticons[(prediction[0])]
-            if x - 150 > 0 and y - 50 > 0 and w - 150 > 0 and h - 150 > 0:
-                draw_with_alpha(frame, image_to_draw, (x-150, y-50, w-150, h-150))
+            puffer.append(prediction[0]) # Speichere letzte Prediction
+            preds = puffer.get() # Hole Einträge als Array
+
+            if not (any(x is None for x in preds)): # Kein Eintrag im RingBuffer ist None
+                unique, counts = np.unique(preds, return_counts=True) # Vorkommen der Predictions zählen
+                image_to_draw = emoticons[unique[0]] # häufigster Wert wird dargestellt
+                draw_with_alpha(frame, image_to_draw, (40, 40, 200, 200))
 
         cv2.imshow(window_name, frame)
         frame = vc.read()
@@ -59,7 +64,7 @@ def show_webcam_and_run(model, emoticons, window_size=(800, 800), window_name='M
 
 
 if __name__ == '__main__':
-    emotions = ['neutral', 'anger', 'disgust', 'happy', 'surprise']
+    emotions = ['happy', 'neutral', 'surprise']
     emoticons = _load_emoticons(emotions)
 
     # load mode