allowed face_encodings to accept either 'large' or 'small' model

face_recognition/api.py

@@ -200,16 +200,17 @@ def face_landmarks(face_image, face_locations=None, model="large"):
         raise ValueError("Invalid landmarks model type. Supported models are ['small', 'large'].")
 
 
-def face_encodings(face_image, known_face_locations=None, num_jitters=1):
+def face_encodings(face_image, known_face_locations=None, num_jitters=1, model="small"):
     """
     Given an image, return the 128-dimension face encoding for each face in the image.
 
     :param face_image: The image that contains one or more faces
     :param known_face_locations: Optional - the bounding boxes of each face if you already know them.
     :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
+    :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster.
     :return: A list of 128-dimensional face encodings (one for each face in the image)
     """
-    raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model="small")
+    raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model)
     return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
 
 

tests/test_face_recognition.py

@@ -152,6 +152,13 @@ class Test_face_recognition(unittest.TestCase):
         self.assertEqual(len(encodings), 1)
         self.assertEqual(len(encodings[0]), 128)
 
+    def test_face_encodings_large_model(self):
+        img = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
+        encodings = api.face_encodings(img, model='large')
+
+        self.assertEqual(len(encodings), 1)
+        self.assertEqual(len(encodings[0]), 128)
+
     def test_face_distance(self):
         img_a1 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama.jpg'))
         img_a2 = api.load_image_file(os.path.join(os.path.dirname(__file__), 'test_images', 'obama2.jpg'))