.gitignore bearbeitet, und änderungen

.gitignore

@@ -20,7 +20,10 @@ Icon
 .Trashes
 .VolumeIcon.icns
 .com.apple.timemachine.donotpresent
-basis_data/*
+basis_data/dataset/*
+basis_data/sorted_set/*
+basis_data/source_emotion/*
+basis_data/source_images/*
 
 # Directories potentially created on remote AFP share
 .AppleDB

projectmood/face_detect.py

@@ -22,27 +22,28 @@ def normalize_face(face):
     return face
 
 
-def locate_faces(image, scaleFactor=1.4, minNeighbors=3):
+def locate_faces(image, scaleFactor=1.4, minNeighbors=3, minSize=(20, 20)):
+    minx, miny = minSize
 
-    face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
+    face = faceDet_one.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
+    face = faceDet_two.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
                                             flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
+    face = faceDet_three.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:
         return face
 
-    face = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(40, 40),
+    face = faceDet_four.detectMultiScale(image, scaleFactor, minNeighbors, minSize=(minx, miny),
                                               flags=cv2.CASCADE_SCALE_IMAGE)
 
     if len(face) == 1:

projectmood/process_model.py

@@ -12,7 +12,7 @@ logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(m
 args = sys.argv # liest Input Parameter
 logging.debug('Fisherface training initialized')
 
-file = open("gray_equalized.csv", "w")
+file = open("gray.csv", "w")
 
 def _get_faces_from_emotion(emotion):
     """
@@ -34,7 +34,8 @@ def image_preprocessing(image):
     """
     img = cv2.imread(image)  # open image
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # convert to grayscale
-    return gray
+    equal = cv2.equalizeHist(gray)
+    return equal
 
 def make_sets():
     training_data = []
@@ -85,11 +86,13 @@ fishface = cv2.face.FisherFaceRecognizer_create()
 # Now run it
 metascore = []
 
-for i in range(0, 20):
+for i in range(0, 10):
     correct = run_recognizer()
+    file.write("{}\n".format(int(correct)))
     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)))

projectmood/sorted_set_facedetector.py

@@ -14,7 +14,6 @@ totalFiles: int = 0
 totalFaces: int = 0
 undetected: list = []
 
-
 def detect_faces(emotion):
     files = glob.glob('basis_data/sorted_set/{}/*'.format(emotion))  # Holt alle Dateien zu einer Emotion aus dem sorted_set
 
@@ -29,7 +28,7 @@ def detect_faces(emotion):
         frame = cv2.imread(f)  # Open image
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
 
-        facefeatures = locate_faces(gray, 1.05, 10)
+        facefeatures = locate_faces(gray, 1.1, 10, (40, 40))
 
         if facefeatures is '':
             undetected.append(f)