Fixed some issues in the MatrixData container, revised the implementation and added more tests

mediapipe/tasks/python/components/containers/matrix_data.py

@@ -24,6 +24,11 @@ from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 _MatrixDataProto = matrix_data_pb2.MatrixData
 
 
+class Layout(enum.Enum):
+  COLUMN_MAJOR = 0
+  ROW_MAJOR = 1
+
+
 @dataclasses.dataclass
 class MatrixData:
   """This stores the Matrix data.
@@ -37,10 +42,6 @@ class MatrixData:
     layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
   """
 
-  class Layout(enum.Enum):
-    COLUMN_MAJOR = 0
-    ROW_MAJOR = 1
-
   rows: int = None
   cols: int = None
   data: np.ndarray = None
@@ -52,8 +53,8 @@ class MatrixData:
     return _MatrixDataProto(
         rows=self.rows,
         cols=self.cols,
-        data=self.data.tolist(),
+        packed_data=self.data,
-        layout=self.layout)
+        layout=self.layout.value)
 
   @classmethod
   @doc_controls.do_not_generate_docs
@@ -62,8 +63,8 @@ class MatrixData:
     return MatrixData(
         rows=pb2_obj.rows,
         cols=pb2_obj.cols,
-        data=np.array(pb2_obj.data),
+        data=np.array(pb2_obj.packed_data),
-        layout=pb2_obj.layout)
+        layout=Layout(pb2_obj.layout))
 
   def __eq__(self, other: Any) -> bool:
     """Checks if this object is equal to the given object.

mediapipe/tasks/python/test/vision/face_landmarker_test.py

@@ -50,12 +50,13 @@ _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _FACE_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
 _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE = 'face_landmarker_with_blendshapes.task'
 _PORTRAIT_IMAGE = 'portrait.jpg'
+_CAT_IMAGE = 'cat.jpg'
 _PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
 _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION = 'portrait_expected_face_landmarks_with_attention.pbtxt'
 _PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes_with_attention.pbtxt'
 _PORTRAIT_EXPECTED_FACE_GEOMETRY = 'portrait_expected_face_geometry_with_attention.pbtxt'
 _LANDMARKS_DIFF_MARGIN = 0.03
-_BLENDSHAPES_DIFF_MARGIN = 0.1
+_BLENDSHAPES_DIFF_MARGIN = 0.12
 _FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
 
 
@@ -90,12 +91,12 @@ def _get_expected_face_blendshapes(file_path: str):
 
 def _make_expected_facial_transformation_matrixes():
   data = np.array([[0.9995292, -0.005092691, 0.030254554, -0.37340546],
-          [0.0072318087, 0.99744856, -0.07102106, 22.212194],
+                   [0.0072318087, 0.99744856, -0.07102106, 22.212194],
-          [-0.029815676, 0.07120642, 0.9970159, -64.76358],
+                   [-0.029815676, 0.07120642, 0.9970159, -64.76358],
-          [0, 0, 0, 1]])
+                   [0, 0, 0, 1]])
   rows, cols = len(data), len(data[0])
   facial_transformation_matrixes_results = []
-  facial_transformation_matrix = _MatrixData(rows, cols, data)
+  facial_transformation_matrix = _MatrixData(rows, cols, data.flatten())
   facial_transformation_matrixes_results.append(facial_transformation_matrix)
   return facial_transformation_matrixes_results
 
@@ -147,8 +148,8 @@ class FaceLandmarkerTest(parameterized.TestCase):
       self.assertEqual(rename_me.rows, expected_matrix_list[i].rows)
       self.assertEqual(rename_me.cols, expected_matrix_list[i].cols)
       self.assertAlmostEqual(
-        rename_me.data,
+        rename_me.data.all(),
-        expected_matrix_list[i].data,
+        expected_matrix_list[i].data.all(),
         delta=_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN)
 
   def test_create_from_file_succeeds_with_valid_model_path(self):
@@ -220,10 +221,10 @@ class FaceLandmarkerTest(parameterized.TestCase):
          _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
        _get_expected_face_blendshapes(
          _PORTRAIT_EXPECTED_BLENDSHAPES),
-       _make_expected_facial_transformation_matrixes())
+       _make_expected_facial_transformation_matrixes()))
-  )
+  def test_detect(
-  def test_detect(self, model_file_type, model_name, expected_face_landmarks,
+      self, model_file_type, model_name, expected_face_landmarks,
-                  expected_face_blendshapes, expected_facial_transformation_matrix):
+      expected_face_blendshapes, expected_facial_transformation_matrixes):
     # Creates face landmarker.
     model_path = test_utils.get_test_data_path(model_name)
     if model_file_type is ModelFileType.FILE_NAME:
@@ -240,7 +241,7 @@ class FaceLandmarkerTest(parameterized.TestCase):
         base_options=base_options,
         output_face_blendshapes=True if expected_face_blendshapes else False,
         output_facial_transformation_matrixes=True
-        if expected_facial_transformation_matrix else False)
+        if expected_facial_transformation_matrixes else False)
     landmarker = _FaceLandmarker.create_from_options(options)
 
     # Performs face landmarks detection on the input.
@@ -252,15 +253,317 @@ class FaceLandmarkerTest(parameterized.TestCase):
     if expected_face_blendshapes is not None:
       self._expect_blendshapes_correct(detection_result.face_blendshapes[0],
                                        expected_face_blendshapes)
-    if expected_facial_transformation_matrix is not None:
+    if expected_facial_transformation_matrixes is not None:
       self._expect_facial_transformation_matrix_correct(
-          detection_result.facial_transformation_matrixes[0],
+          detection_result.facial_transformation_matrixes,
-          expected_facial_transformation_matrix)
+          expected_facial_transformation_matrixes)
 
     # Closes the face landmarker explicitly when the face landmarker is not used
     # in a context.
     landmarker.close()
 
+  @parameterized.parameters(
+    (ModelFileType.FILE_NAME, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
+    (ModelFileType.FILE_CONTENT, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
+    (ModelFileType.FILE_NAME,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
+    (ModelFileType.FILE_CONTENT,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
+    (ModelFileType.FILE_NAME,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(
+       _PORTRAIT_EXPECTED_BLENDSHAPES), None),
+    (ModelFileType.FILE_CONTENT,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(
+       _PORTRAIT_EXPECTED_BLENDSHAPES), None),
+    (ModelFileType.FILE_NAME,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(
+       _PORTRAIT_EXPECTED_BLENDSHAPES),
+     _make_expected_facial_transformation_matrixes()),
+    (ModelFileType.FILE_CONTENT,
+     _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(
+       _PORTRAIT_EXPECTED_BLENDSHAPES),
+     _make_expected_facial_transformation_matrixes()))
+  def test_detect_in_context(
+      self, model_file_type, model_name, expected_face_landmarks,
+      expected_face_blendshapes, expected_facial_transformation_matrixes):
+    # Creates face landmarker.
+    model_path = test_utils.get_test_data_path(model_name)
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _FaceLandmarkerOptions(
+      base_options=base_options,
+      output_face_blendshapes=True if expected_face_blendshapes else False,
+      output_facial_transformation_matrixes=True
+      if expected_facial_transformation_matrixes else False)
+
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      # Performs face landmarks detection on the input.
+      detection_result = landmarker.detect(self.test_image)
+      # Comparing results.
+      if expected_face_landmarks is not None:
+        self._expect_landmarks_correct(detection_result.face_landmarks[0],
+                                       expected_face_landmarks)
+      if expected_face_blendshapes is not None:
+        self._expect_blendshapes_correct(detection_result.face_blendshapes[0],
+                                         expected_face_blendshapes)
+      if expected_facial_transformation_matrixes is not None:
+        self._expect_facial_transformation_matrix_correct(
+          detection_result.facial_transformation_matrixes,
+          expected_facial_transformation_matrixes)
+
+  def test_detect_succeeds_with_num_faces(self):
+    # Creates face landmarker.
+    model_path = test_utils.get_test_data_path(
+        _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE)
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = _FaceLandmarkerOptions(base_options=base_options, num_faces=1,
+                                     output_face_blendshapes=True)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      # Load the portrait image.
+      test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_PORTRAIT_IMAGE))
+      # Performs face landmarks detection on the input.
+      detection_result = landmarker.detect(test_image)
+      # Comparing results.
+      self.assertLen(detection_result.face_blendshapes, 1)
+
+  def test_empty_detection_outputs(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path))
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      # Load the image with no faces.
+      no_faces_test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_CAT_IMAGE))
+      # Performs face landmarks detection on the input.
+      detection_result = landmarker.detect(no_faces_test_image)
+      self.assertEmpty(detection_result.face_landmarks)
+      self.assertEmpty(detection_result.face_blendshapes)
+      self.assertEmpty(detection_result.facial_transformation_matrixes)
+
+  def test_missing_result_callback(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM)
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback must be provided'):
+      with _FaceLandmarker.create_from_options(options) as unused_landmarker:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=running_mode,
+      result_callback=mock.MagicMock())
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback should not be provided'):
+      with _FaceLandmarker.create_from_options(options) as unused_landmarker:
+        pass
+
+  def test_calling_detect_for_video_in_image_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        landmarker.detect_for_video(self.test_image, 0)
+
+  def test_calling_detect_async_in_image_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        landmarker.detect_async(self.test_image, 0)
+
+  def test_calling_detect_in_video_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        landmarker.detect(self.test_image)
+
+  def test_calling_detect_async_in_video_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        landmarker.detect_async(self.test_image, 0)
+
+  def test_detect_for_video_with_out_of_order_timestamp(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      unused_result = landmarker.detect_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        landmarker.detect_for_video(self.test_image, 0)
+
+  @parameterized.parameters(
+    (_FACE_LANDMARKER_BUNDLE_ASSET_FILE, _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
+    (_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
+    (_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(_PORTRAIT_EXPECTED_BLENDSHAPES), None),
+    (_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(_PORTRAIT_EXPECTED_BLENDSHAPES),
+     _make_expected_facial_transformation_matrixes()))
+  def test_detect_for_video(
+      self, model_name, expected_face_landmarks, expected_face_blendshapes,
+      expected_facial_transformation_matrixes):
+    # Creates face landmarker.
+    model_path = test_utils.get_test_data_path(model_name)
+    base_options = _BaseOptions(model_asset_path=model_path)
+
+    options = _FaceLandmarkerOptions(
+      base_options=base_options,
+      running_mode=_RUNNING_MODE.VIDEO,
+      output_face_blendshapes=True if expected_face_blendshapes else False,
+      output_facial_transformation_matrixes=True
+      if expected_facial_transformation_matrixes else False)
+
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      for timestamp in range(0, 300, 30):
+        # Performs face landmarks detection on the input.
+        detection_result = landmarker.detect_for_video(self.test_image,
+                                                       timestamp)
+        # Comparing results.
+        if expected_face_landmarks is not None:
+          self._expect_landmarks_correct(detection_result.face_landmarks[0],
+                                         expected_face_landmarks)
+        if expected_face_blendshapes is not None:
+          self._expect_blendshapes_correct(detection_result.face_blendshapes[0],
+                                           expected_face_blendshapes)
+        if expected_facial_transformation_matrixes is not None:
+          self._expect_facial_transformation_matrix_correct(
+            detection_result.facial_transformation_matrixes,
+            expected_facial_transformation_matrixes)
+
+  def test_calling_detect_in_live_stream_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        landmarker.detect(self.test_image)
+
+  def test_calling_detect_for_video_in_live_stream_mode(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        landmarker.detect_for_video(self.test_image, 0)
+
+  def test_detect_async_calls_with_illegal_timestamp(self):
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      landmarker.detect_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        landmarker.detect_async(self.test_image, 0)
+
+  @parameterized.parameters(
+    (_PORTRAIT_IMAGE, _FACE_LANDMARKER_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS), None, None),
+    (_PORTRAIT_IMAGE, _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
+    (_PORTRAIT_IMAGE, _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(_PORTRAIT_EXPECTED_BLENDSHAPES), None),
+    (_PORTRAIT_IMAGE, _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+     _get_expected_face_landmarks(
+       _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION),
+     _get_expected_face_blendshapes(_PORTRAIT_EXPECTED_BLENDSHAPES),
+     _make_expected_facial_transformation_matrixes()))
+  def test_detect_async_calls(
+      self, image_path, model_name, expected_face_landmarks,
+      expected_face_blendshapes, expected_facial_transformation_matrixes):
+    test_image = _Image.create_from_file(
+      test_utils.get_test_data_path(image_path))
+    observed_timestamp_ms = -1
+
+    def check_result(result: FaceLandmarkerResult, output_image: _Image,
+                     timestamp_ms: int):
+      # Comparing results.
+      if expected_face_landmarks is not None:
+        self._expect_landmarks_correct(result.face_landmarks[0],
+                                       expected_face_landmarks)
+      if expected_face_blendshapes is not None:
+        self._expect_blendshapes_correct(result.face_blendshapes[0],
+                                         expected_face_blendshapes)
+      if expected_facial_transformation_matrixes is not None:
+        self._expect_facial_transformation_matrix_correct(
+          result.facial_transformation_matrixes,
+          expected_facial_transformation_matrixes)
+      self.assertTrue(
+        np.array_equal(output_image.numpy_view(), test_image.numpy_view()))
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    model_path = test_utils.get_test_data_path(model_name)
+    options = _FaceLandmarkerOptions(
+      base_options=_BaseOptions(model_asset_path=model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      output_face_blendshapes=True if expected_face_blendshapes else False,
+      output_facial_transformation_matrixes=True
+      if expected_facial_transformation_matrixes else False,
+      result_callback=check_result)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      for timestamp in range(0, 300, 30):
+        landmarker.detect_async(test_image, timestamp)
+
 
 if __name__ == '__main__':
   absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -166,6 +166,7 @@ py_library(
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarker_graph_options_py_pb2",
+        "//mediapipe/tasks/cc/vision/face_geometry/proto:face_geometry_py_pb2",
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:landmark",
         "//mediapipe/tasks/python/components/containers:matrix_data",

mediapipe/tasks/python/vision/face_landmarker.py

@@ -25,6 +25,8 @@ from mediapipe.python import packet_getter
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.python._framework_bindings import packet as packet_module
 from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
+# TODO: Remove later.
+from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
 from mediapipe.tasks.python.components.containers import category as category_module
 from mediapipe.tasks.python.components.containers import landmark as landmark_module
 from mediapipe.tasks.python.components.containers import matrix_data as matrix_data_module
@@ -160,15 +162,22 @@ def _build_landmarker_result(
             category_name=face_blendshapes.label))
       face_blendshapes_results.append(face_blendshapes_categories)
 
+  # Creates a dummy FaceGeometry packet to initialize the symbol database.
+  # TODO: Remove later.
+  face_geometry_in = face_geometry_pb2.FaceGeometry()
+  p = packet_creator.create_proto(face_geometry_in).at(100)
+  face_geometry_out = packet_getter.get_proto(p)
+
   facial_transformation_matrixes_results = []
   if _FACE_GEOMETRY_STREAM_NAME in output_packets:
     facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
       output_packets[_FACE_GEOMETRY_STREAM_NAME])
     for proto in facial_transformation_matrixes_proto_list:
-      matrix_data = matrix_data_pb2.MatrixData()
+      if proto.pose_transform_matrix:
-      matrix_data.MergeFrom(proto)
+        matrix_data = matrix_data_pb2.MatrixData()
-      matrix = matrix_data_module.MatrixData.create_from_pb2(matrix_data)
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
-      facial_transformation_matrixes_results.append(matrix)
+        matrix = matrix_data_module.MatrixData.create_from_pb2(matrix_data)
+        facial_transformation_matrixes_results.append(matrix)
 
   return FaceLandmarkerResult(face_landmarks_results, face_blendshapes_results,
                               facial_transformation_matrixes_results)