Update app.py

app.py

@@ -28,14 +28,6 @@ def grab_faces(img: np.ndarray) -> Optional[np.ndarray]:
 
     detected = None
 
-    for cascade in cascades:
-        cascadeClassifier = cv.CascadeClassifier(cv.data.haarcascades + cascade)
-        faces = cascadeClassifier.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) # detect faces
-        if len(faces) > 0:
-            detected = faces[0]
-            logging.info(f"Face detected by {cascade}")
-            break
-
     if detected is None:
         faces = detector(gray) # detect faces
         if len(faces) > 0:
@@ -50,6 +42,14 @@ def grab_faces(img: np.ndarray) -> Optional[np.ndarray]:
             detected = (detected.rect.left(), detected.rect.top(), detected.rect.width(), detected.rect.height())
             logging.info("Face detected by mmod")
 
+    for cascade in cascades:
+        cascadeClassifier = cv.CascadeClassifier(cv.data.haarcascades + cascade)
+        faces = cascadeClassifier.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) # detect faces
+        if len(faces) > 0:
+            detected = faces[0]
+            logging.info(f"Face detected by {cascade}")
+            break
+
     if detected is not None: # if face detected
         x, y, w, h = detected # grab first face
         padW = int(paddingBy * w) # get padding width