Revised face landmarker implementation and tests

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

@@ -17,6 +17,7 @@ import dataclasses
 import enum
 from typing import Any, Optional
 
+import numpy as np
 from mediapipe.framework.formats import matrix_data_pb2
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 
@@ -32,7 +33,7 @@ class MatrixData:
   Attributes:
     rows: The number of rows in the matrix.
     cols: The number of columns in the matrix.
-    data: The data stored in the matrix.
+    data: The data stored in the matrix as a NumPy array.
     layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
   """
 
@@ -40,10 +41,10 @@ class MatrixData:
     COLUMN_MAJOR = 0
     ROW_MAJOR = 1
 
-  rows: Optional[int] = None
-  cols: Optional[int] = None
-  data: Optional[float] = None
-  layout: Optional[Layout] = None
+  rows: int = None
+  cols: int = None
+  data: np.ndarray = None
+  layout: Optional[Layout] = Layout.COLUMN_MAJOR
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _MatrixDataProto:
@@ -51,7 +52,7 @@ class MatrixData:
     return _MatrixDataProto(
         rows=self.rows,
         cols=self.cols,
-        data=self.data,
+        data=self.data.tolist(),
         layout=self.layout)
 
   @classmethod
@@ -61,7 +62,7 @@ class MatrixData:
     return MatrixData(
         rows=pb2_obj.rows,
         cols=pb2_obj.cols,
-        data=pb2_obj.data,
+        data=np.array(pb2_obj.data),
         layout=pb2_obj.layout)
 
   def __eq__(self, other: Any) -> bool:

mediapipe/tasks/python/test/vision/BUILD

@@ -126,10 +126,10 @@ py_test(
     deps = [
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/framework/formats:classification_py_pb2",
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:landmark",
         "//mediapipe/tasks/python/components/containers:rect",
-        "//mediapipe/tasks/python/components/containers:classification_result",
         "//mediapipe/tasks/python/components/containers:matrix_data",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",

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

@@ -22,11 +22,12 @@ import numpy as np
 
 from google.protobuf import text_format
 from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import classification_pb2
 from mediapipe.python._framework_bindings import image as image_module
 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
 from mediapipe.tasks.python.components.containers import rect as rect_module
-from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.vision import face_landmarker
@@ -38,6 +39,7 @@ _BaseOptions = base_options_module.BaseOptions
 _Category = category_module.Category
 _Rect = rect_module.Rect
 _Landmark = landmark_module.Landmark
+_MatrixData = matrix_data_module.MatrixData
 _NormalizedLandmark = landmark_module.NormalizedLandmark
 _Image = image_module.Image
 _FaceLandmarker = face_landmarker.FaceLandmarker
@@ -51,6 +53,7 @@ _PORTRAIT_IMAGE = 'portrait.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
 _FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
@@ -61,10 +64,40 @@ def _get_expected_face_landmarks(file_path: str):
   with open(proto_file_path, 'rb') as f:
     proto = landmark_pb2.NormalizedLandmarkList()
     text_format.Parse(f.read(), proto)
-    landmarks = []
+    face_landmarks = []
     for landmark in proto.landmark:
-      landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
-  return landmarks
+      face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
+  return face_landmarks
+
+
+def _get_expected_face_blendshapes(file_path: str):
+  proto_file_path = test_utils.get_test_data_path(file_path)
+  with open(proto_file_path, 'rb') as f:
+    proto = classification_pb2.ClassificationList()
+    text_format.Parse(f.read(), proto)
+    face_blendshapes_categories = []
+    face_blendshapes_classifications = classification_pb2.ClassificationList()
+    face_blendshapes_classifications.MergeFrom(proto)
+    for face_blendshapes in face_blendshapes_classifications.classification:
+      face_blendshapes_categories.append(
+        category_module.Category(
+          index=face_blendshapes.index,
+          score=face_blendshapes.score,
+          display_name=face_blendshapes.display_name,
+          category_name=face_blendshapes.label))
+  return face_blendshapes_categories
+
+
+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],
+          [-0.029815676, 0.07120642, 0.9970159, -64.76358],
+          [0, 0, 0, 1]])
+  rows, cols = len(data), len(data[0])
+  facial_transformation_matrixes_results = []
+  facial_transformation_matrix = _MatrixData(rows, cols, data)
+  facial_transformation_matrixes_results.append(facial_transformation_matrix)
+  return facial_transformation_matrixes_results
 
 
 class ModelFileType(enum.Enum):
@@ -148,30 +181,82 @@ class HandLandmarkerTest(parameterized.TestCase):
       self.assertIsInstance(landmarker, _FaceLandmarker)
 
   @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,
-       _get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)),
+       _FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE,
+       _get_expected_face_landmarks(
+         _PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION), None, None),
       (ModelFileType.FILE_CONTENT,
-       _get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)))
-  def test_detect(self, model_file_type, expected_face_landmarks):
+       _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(self, model_file_type, model_name, expected_face_landmarks,
+                  expected_face_blendshapes, expected_facial_transformation_matrix):
     # 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=self.model_path)
+      base_options = _BaseOptions(model_asset_path=model_path)
     elif model_file_type is ModelFileType.FILE_CONTENT:
-      with open(self.model_path, 'rb') as f:
+      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)
+    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_matrix else False)
     landmarker = _FaceLandmarker.create_from_options(options)
 
     # Performs face landmarks detection on the input.
     detection_result = landmarker.detect(self.test_image)
     # Comparing results.
-    self._expect_landmarks_correct(detection_result.face_landmarks[0],
-                                   expected_face_landmarks)
+    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_matrix is not None:
+      self._expect_facial_transformation_matrix_correct(
+          detection_result.facial_transformation_matrixes[0],
+          expected_facial_transformation_matrix)
+
     # Closes the face landmarker explicitly when the face landmarker is not used
     # in a context.
     landmarker.close()

mediapipe/tasks/python/vision/face_landmarker.py

@@ -162,6 +162,7 @@ def _build_landmarker_result(
 
   facial_transformation_matrixes_results = []
   if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    print(output_packets[_FACE_GEOMETRY_STREAM_NAME])
     facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
       output_packets[_FACE_GEOMETRY_STREAM_NAME])
     for proto in facial_transformation_matrixes_proto_list:

mediapipe/tasks/testdata/vision/BUILD

@@ -156,6 +156,7 @@ filegroup(
         "face_landmark.tflite",
         "face_landmark_with_attention.tflite",
         "face_landmarker.task",
+        "face_landmarker_with_blendshapes.task",
         "hair_segmentation.tflite",
         "hand_landmark_full.tflite",
         "hand_landmark_lite.tflite",
@@ -191,6 +192,7 @@ filegroup(
         "pointing_up_landmarks.pbtxt",
         "pointing_up_rotated_landmarks.pbtxt",
         "portrait_expected_detection.pbtxt",
+        "portrait_expected_blendshapes_with_attention.pbtxt",
         "portrait_expected_face_geometry_with_attention.pbtxt",
         "portrait_expected_face_landmarks.pbtxt",
         "portrait_expected_face_landmarks_with_attention.pbtxt",