Update avatar.py

avatar.py

@@ -183,21 +183,21 @@ class Avatar:
 
   def create_face_detection_results(self, full_frames, save_result=True):
     detector = FaceAlignment(LandmarksType.TWO_D, flip_input=False, device=self.device)
-    images=full_frames
+    images = full_frames
     while 1:
-      predictions = []
-      try:
-        for i in tqdm(range(0, len(images), self.face_detect_batch_size)):
-          batch_images = np.array(images[i:i + self.face_detect_batch_size])
-            batch_images = torch.from_numpy(batch_images).permute(0, 3, 1, 2).float().to(self.device)
-            predictions.extend(detector.face_detector.detect_from_batch(batch_images))
-      except RuntimeError:
-        if self.face_detect_batch_size == 1:
-          raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
-        self.face_detect_batch_size //= 2
-        print('Recovering from OOM error; New batch size: {}'.format(self.face_detect_batch_size))
-        continue
-      break
+        predictions = []
+        try:
+            for i in tqdm(range(0, len(images), self.face_detect_batch_size)):
+                batch_images = np.array(images[i:i + self.face_detect_batch_size])
+                batch_images = torch.from_numpy(batch_images).permute(0, 3, 1, 2).float().to(self.device)
+                predictions.extend(detector.face_detector.detect_from_batch(batch_images))
+        except RuntimeError:
+            if self.face_detect_batch_size == 1:
+                raise RuntimeError('Image too big to run face detection on GPU. Please use the --resize_factor argument')
+            self.face_detect_batch_size //= 2
+            print('Recovering from OOM error; New batch size: {}'.format(self.face_detect_batch_size))
+            continue
+        break
 
     face_detect_results = []
     pady1, pady2, padx1, padx2 = [0, 10, 0, 0]