Nachbesserung Sorted_set

projectmood/sorted_set_facedetector.py

@@ -31,16 +31,15 @@ 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
 
         # Detect face using 4 different classifiers
-        face = faceDet.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face = faceDet.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                         flags=cv2.CASCADE_SCALE_IMAGE)
-        face_two = faceDet_two.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_two = faceDet_two.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
-        face_three = faceDet_three.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_three = faceDet_three.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                     flags=cv2.CASCADE_SCALE_IMAGE)
-        face_four = faceDet_four.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
+        face_four = faceDet_four.detectMultiScale(frame, scaleFactor=1.05, minNeighbors=6, minSize=(10, 10),
                                                   flags=cv2.CASCADE_SCALE_IMAGE)
 
         # Go over detected faces, stop at first detected face, return empty if no face.
@@ -54,15 +53,16 @@ def detect_faces(emotion):
             facefeatures = face_four
         else:
             undetected.append(f)
+            facefeatures = ''
 
         # 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
+            frame = frame[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
+                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