diff --git a/server/detect.py b/server/detect.py
index 9a14f49..af64a23 100644
--- a/server/detect.py
+++ b/server/detect.py
@@ -15,19 +15,34 @@ import cv2
 import uu
 from io import BytesIO
 
+original = None
+
+def redirect_on():
+  global original
+  if original == None:
+    original = sys.stdout
+    sys.stdout = open(os.devnull, 'w')
+
+def redirect_off():
+  global original
+  if original != None:
+    sys.stdout.close()
+    sys.stdout = original
+    original = None
+
 def zlib_uuencode(databytes, name='<data>'):
-    ''' Compress databytes with zlib & uuencode the result '''
-    inbuff = BytesIO(zlib.compress(databytes, 9))
-    outbuff = BytesIO()
-    uu.encode(inbuff, outbuff, name=name)
-    return outbuff.getvalue()
+  ''' Compress databytes with zlib & uuencode the result '''
+  inbuff = BytesIO(zlib.compress(databytes, 9))
+  outbuff = BytesIO()
+  uu.encode(inbuff, outbuff, name=name)
+  return outbuff.getvalue()
 
 def zlib_uudecode(databytes):
-    ''' uudecode databytes and decompress the result with zlib '''
-    inbuff = BytesIO(databytes)
-    outbuff = BytesIO()
-    uu.decode(inbuff, outbuff)
-    return zlib.decompress(outbuff.getvalue())
+  ''' uudecode databytes and decompress the result with zlib '''
+  inbuff = BytesIO(databytes)
+  outbuff = BytesIO()
+  uu.decode(inbuff, outbuff)
+  return zlib.decompress(outbuff.getvalue())
 
 class NpEncoder(json.JSONEncoder):
   def default(self, obj):
@@ -64,7 +79,7 @@ def alignment_procedure(img, left_eye, right_eye):
   dY = right_eye[1] - left_eye[1]
   dX = right_eye[0] - left_eye[0]
   radians = np.arctan2(dY, dX)
-  rotation = 180 * radians / np.pi
+  rotation = 180 + 180 * radians / np.pi
 
   if True:
     img = img.rotate(
@@ -83,22 +98,26 @@ def extract_faces(img, threshold=0.75, allow_upscaling = True):
       allow_upscaling = allow_upscaling)
   elif detector_backend == 'mtcnn':
     img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # mtcnn expects RGB
+
+    redirect_on()
     res = face_detector.detect_faces(img_rgb)
+    redirect_off()
+
     faces = {}
     if type(res) == list:
       for i, face in enumerate(res):
+        x = face['box'][0]
+        y = face['box'][1]
+        w = face['box'][2]
+        h = face['box'][3]
         faces[f'face_{i+1}'] = { # standardize properties
-          'facial_area': [
-            face['box'][1], face['box'][1] + face['box'][3],
-            face['box'][0], face['box'][0] + face['box'][2],
-          ],
+          'facial_area': [ x, y, x + w, y + h ],
           'landmarks': {
             'left_eye': list(face['keypoints']['left_eye']),
             'right_eye': list(face['keypoints']['right_eye']),
           },
           'score': face['confidence'],
         }
-        print(face, faces[f'face_{i+1}'])
 
   # Re-implementation of 'extract_faces' with the addition of keeping a 
   # copy of the face image for caching on disk
@@ -111,8 +130,8 @@ def extract_faces(img, threshold=0.75, allow_upscaling = True):
       left_eye = landmarks["left_eye"]
       right_eye = landmarks["right_eye"]
 
-      markup = True
-#      markup = False
+#      markup = True
+      markup = False
       if markup == True: # Draw the face rectangle and eyes
         cv2.rectangle(img, 
           (int(facial_area[0]), int(facial_area[1])),
@@ -155,27 +174,28 @@ def extract_faces(img, threshold=0.75, allow_upscaling = True):
       image = image.resize(size = input_shape, resample = Image.LANCZOS)
       resized = np.asarray(image)
         
+      redirect_on()
       identity['vector'] = DeepFace.represent(
         img_path = resized,
         model_name = model_name,
         model = model,              # pre-built
         detector_backend = detector_backend,
         enforce_detection = False)
+      redirect_off()
 
+      redirect_on()
       identity["face"] = { 
         'top': facial_area[1] / img.shape[0],
         'left': facial_area[0] / img.shape[1],
         'bottom': facial_area[3] / img.shape[0],
         'right': facial_area[2] / img.shape[1]
       }
+      redirect_off()
 
       identity['image'] = Image.fromarray(resized)
 
     return faces
 
-#face verification
-#img_path = sys.argv[1]
-
 def create_connection(db_file):
   """ create a database connection to the SQLite database
       specified by db_file
@@ -315,5 +335,4 @@ with conn:
         "thumbnail": None, "GPS": {}}
       image.save(f'{path}/{faceId}.jpg', exif = piexif.dump(exif_dict))
 
-    exit(1)
     update_face_count(conn, photoId, len(faces))