MultiScaleFactor verändert

projectmood/sorted_set_facedetector.py

@@ -10,21 +10,25 @@ emotions = ["neutral", "anger", "disgust", "happy", "surprise"]  # Define emotio
 
 
 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/%s/*w' % emotion)  # Get list of all images with emotion
 
     filenumber = 0
     for f in files:
         frame = cv2.imread(f)  # Open image
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)  # Convert image to grayscale
 
+        scaleFactor = 1.05
+        minNeighbors = 10
+        minSize = (20, 20)
+
         # Detect face using 4 different classifiers
-        face = faceDet.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5),
+        face = faceDet.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
                                         flags=cv2.CASCADE_SCALE_IMAGE)
-        face_two = faceDet_two.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5),
+        face_two = faceDet_two.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
                                                 flags=cv2.CASCADE_SCALE_IMAGE)
-        face_three = faceDet_three.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5),
+        face_three = faceDet_three.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
                                                     flags=cv2.CASCADE_SCALE_IMAGE)
-        face_four = faceDet_four.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=10, minSize=(5, 5),
+        face_four = faceDet_four.detectMultiScale(gray, scaleFactor, minNeighbors, minSize,
                                                   flags=cv2.CASCADE_SCALE_IMAGE)
 
         # Go over detected faces, stop at first detected face, return empty if no face.