Merge pull request #4177 from kinaryml:face-landmarker-python

PiperOrigin-RevId: 517979421

mediapipe/python/BUILD

@@ -96,6 +96,7 @@ cc_library(
         "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
         "//mediapipe/tasks/cc/vision/face_detector:face_detector_graph",
+        "//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_graph",
     ] + select({
         # TODO: Build text_classifier_graph and text_embedder_graph on Windows.
         "//mediapipe:windows": [],

mediapipe/tasks/python/test/vision/BUILD

@@ -115,6 +115,30 @@ py_test(
     ],
 )
 
+py_test(
+    name = "face_landmarker_test",
+    srcs = ["face_landmarker_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+        "//mediapipe/tasks/testdata/vision:test_protos",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:face_landmarker",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+        "@com_google_protobuf//:protobuf_python",
+    ],
+)
+
 py_test(
     name = "hand_landmarker_test",
     srcs = ["hand_landmarker_test.py"],

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

@@ -0,0 +1,564 @@
+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for face landmarker."""
+
+import enum
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import numpy as np
+
+from google.protobuf import text_format
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_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 rect as rect_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
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+
+FaceLandmarkerResult = face_landmarker.FaceLandmarkerResult
+_BaseOptions = base_options_module.BaseOptions
+_Category = category_module.Category
+_Rect = rect_module.Rect
+_Landmark = landmark_module.Landmark
+_NormalizedLandmark = landmark_module.NormalizedLandmark
+_Image = image_module.Image
+_FaceLandmarker = face_landmarker.FaceLandmarker
+_FaceLandmarkerOptions = face_landmarker.FaceLandmarkerOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_FACE_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.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'
+)
+_LANDMARKS_DIFF_MARGIN = 0.03
+_BLENDSHAPES_DIFF_MARGIN = 0.12
+_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
+
+
+def _get_expected_face_landmarks(file_path: str):
+  proto_file_path = test_utils.get_test_data_path(file_path)
+  with open(proto_file_path, 'rb') as f:
+    proto = landmark_pb2.NormalizedLandmarkList()
+    text_format.Parse(f.read(), proto)
+    face_landmarks = []
+    for landmark in proto.landmark:
+      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 _get_expected_facial_transformation_matrixes():
+  matrix = 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],
+  ])
+  facial_transformation_matrixes_results = []
+  facial_transformation_matrixes_results.append(matrix)
+  return facial_transformation_matrixes_results
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class FaceLandmarkerTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_PORTRAIT_IMAGE)
+    )
+    self.model_path = test_utils.get_test_data_path(
+        _FACE_LANDMARKER_BUNDLE_ASSET_FILE
+    )
+
+  def _expect_landmarks_correct(self, actual_landmarks, expected_landmarks):
+    # Expects to have the same number of faces detected.
+    self.assertLen(actual_landmarks, len(expected_landmarks))
+
+    for i, elem in enumerate(actual_landmarks):
+      self.assertAlmostEqual(
+          elem.x, expected_landmarks[i].x, delta=_LANDMARKS_DIFF_MARGIN
+      )
+      self.assertAlmostEqual(
+          elem.y, expected_landmarks[i].y, delta=_LANDMARKS_DIFF_MARGIN
+      )
+
+  def _expect_blendshapes_correct(
+      self, actual_blendshapes, expected_blendshapes
+  ):
+    # Expects to have the same number of blendshapes.
+    self.assertLen(actual_blendshapes, len(expected_blendshapes))
+
+    for i, elem in enumerate(actual_blendshapes):
+      self.assertEqual(elem.index, expected_blendshapes[i].index)
+      self.assertAlmostEqual(
+          elem.score,
+          expected_blendshapes[i].score,
+          delta=_BLENDSHAPES_DIFF_MARGIN,
+      )
+
+  def _expect_facial_transformation_matrixes_correct(
+      self, actual_matrix_list, expected_matrix_list
+  ):
+    self.assertLen(actual_matrix_list, len(expected_matrix_list))
+
+    for i, elem in enumerate(actual_matrix_list):
+      self.assertEqual(elem.shape[0], expected_matrix_list[i].shape[0])
+      self.assertEqual(elem.shape[1], expected_matrix_list[i].shape[1])
+      self.assertSequenceAlmostEqual(
+          elem.flatten(),
+          expected_matrix_list[i].flatten(),
+          delta=_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN,
+      )
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _FaceLandmarker.create_from_model_path(self.model_path) as landmarker:
+      self.assertIsInstance(landmarker, _FaceLandmarker)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceLandmarkerOptions(base_options=base_options)
+    with _FaceLandmarker.create_from_options(options) as landmarker:
+      self.assertIsInstance(landmarker, _FaceLandmarker)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    # Invalid empty model path.
+    with self.assertRaisesRegex(
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
+    ):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite'
+      )
+      options = _FaceLandmarkerOptions(base_options=base_options)
+      _FaceLandmarker.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _FaceLandmarkerOptions(base_options=base_options)
+      landmarker = _FaceLandmarker.create_from_options(options)
+      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,
+      ),
+  )
+  def test_detect(
+      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,
+    )
+    landmarker = _FaceLandmarker.create_from_options(options)
+
+    # 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_matrixes_correct(
+          detection_result.facial_transformation_matrixes,
+          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,
+      ),
+  )
+  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_matrixes_correct(
+            detection_result.facial_transformation_matrixes,
+            expected_facial_transformation_matrixes,
+        )
+
+  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,
+      ),
+  )
+  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_matrixes_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,
+      ),
+  )
+  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_matrixes_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

@@ -172,3 +172,28 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "face_landmarker",
+    srcs = [
+        "face_landmarker.py",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/framework/formats:matrix_data_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/face_geometry/proto:face_geometry_py_pb2",
+        "//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarker_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/face_landmarker.py

@@ -0,0 +1,509 @@
+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe face landmarker task."""
+
+import dataclasses
+import enum
+from typing import Callable, Mapping, Optional, List
+
+import numpy as np
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import matrix_data_pb2
+from mediapipe.python import packet_creator
+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
+# pylint: disable=unused-import
+from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
+# pylint: enable=unused-import
+from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_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.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_FaceLandmarkerGraphOptionsProto = (
+    face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
+)
+_LayoutEnum = matrix_data_pb2.MatrixData.Layout
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
+_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
+_BLENDSHAPES_STREAM_NAME = 'blendshapes'
+_BLENDSHAPES_TAG = 'BLENDSHAPES'
+_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
+_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+class Blendshapes(enum.IntEnum):
+  """The 52 blendshape coefficients."""
+
+  NEUTRAL = 0
+  BROW_DOWN_LEFT = 1
+  BROW_DOWN_RIGHT = 2
+  BROW_INNER_UP = 3
+  BROW_OUTER_UP_LEFT = 4
+  BROW_OUTER_UP_RIGHT = 5
+  CHEEK_PUFF = 6
+  CHEEK_SQUINT_LEFT = 7
+  CHEEK_SQUINT_RIGHT = 8
+  EYE_BLINK_LEFT = 9
+  EYE_BLINK_RIGHT = 10
+  EYE_LOOK_DOWN_LEFT = 11
+  EYE_LOOK_DOWN_RIGHT = 12
+  EYE_LOOK_IN_LEFT = 13
+  EYE_LOOK_IN_RIGHT = 14
+  EYE_LOOK_OUT_LEFT = 15
+  EYE_LOOK_OUT_RIGHT = 16
+  EYE_LOOK_UP_LEFT = 17
+  EYE_LOOK_UP_RIGHT = 18
+  EYE_SQUINT_LEFT = 19
+  EYE_SQUINT_RIGHT = 20
+  EYE_WIDE_LEFT = 21
+  EYE_WIDE_RIGHT = 22
+  JAW_FORWARD = 23
+  JAW_LEFT = 24
+  JAW_OPEN = 25
+  JAW_RIGHT = 26
+  MOUTH_CLOSE = 27
+  MOUTH_DIMPLE_LEFT = 28
+  MOUTH_DIMPLE_RIGHT = 29
+  MOUTH_FROWN_LEFT = 30
+  MOUTH_FROWN_RIGHT = 31
+  MOUTH_FUNNEL = 32
+  MOUTH_LEFT = 33
+  MOUTH_LOWER_DOWN_LEFT = 34
+  MOUTH_LOWER_DOWN_RIGHT = 35
+  MOUTH_PRESS_LEFT = 36
+  MOUTH_PRESS_RIGHT = 37
+  MOUTH_PUCKER = 38
+  MOUTH_RIGHT = 39
+  MOUTH_ROLL_LOWER = 40
+  MOUTH_ROLL_UPPER = 41
+  MOUTH_SHRUG_LOWER = 42
+  MOUTH_SHRUG_UPPER = 43
+  MOUTH_SMILE_LEFT = 44
+  MOUTH_SMILE_RIGHT = 45
+  MOUTH_STRETCH_LEFT = 46
+  MOUTH_STRETCH_RIGHT = 47
+  MOUTH_UPPER_UP_LEFT = 48
+  MOUTH_UPPER_UP_RIGHT = 49
+  NOSE_SNEER_LEFT = 50
+  NOSE_SNEER_RIGHT = 51
+
+
+@dataclasses.dataclass
+class FaceLandmarkerResult:
+  """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
+
+  Attributes:
+    face_landmarks: Detected face landmarks in normalized image coordinates.
+    face_blendshapes: Optional face blendshapes results.
+    facial_transformation_matrixes: Optional facial transformation matrix.
+  """
+
+  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  face_blendshapes: List[List[category_module.Category]]
+  facial_transformation_matrixes: List[np.ndarray]
+
+
+def _build_landmarker_result(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      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,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  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:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  )
+
+
+@dataclasses.dataclass
+class FaceLandmarkerOptions:
+  """Options for the face landmarker task.
+
+  Attributes:
+    base_options: Base options for the face landmarker task.
+    running_mode: The running mode of the task. Default to the image mode.
+      HandLandmarker has three running modes: 1) The image mode for detecting
+      face landmarks on single image inputs. 2) The video mode for detecting
+      face landmarks on the decoded frames of a video. 3) The live stream mode
+      for detecting face landmarks on the live stream of input data, such as
+      from camera. In this mode, the "result_callback" below must be specified
+      to receive the detection results asynchronously.
+    num_faces: The maximum number of faces that can be detected by the
+      FaceLandmarker.
+    min_face_detection_confidence: The minimum confidence score for the face
+      detection to be considered successful.
+    min_face_presence_confidence: The minimum confidence score of face presence
+      score in the face landmark detection.
+    min_tracking_confidence: The minimum confidence score for the face tracking
+      to be considered successful.
+    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
+      classification. Face blendshapes are used for rendering the 3D face model.
+    output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
+      transformation_matrix. Facial transformation matrix is used to transform
+      the face landmarks in canonical face to the detected face, so that users
+      can apply face effects on the detected landmarks.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_faces: Optional[int] = 1
+  min_face_detection_confidence: Optional[float] = 0.5
+  min_face_presence_confidence: Optional[float] = 0.5
+  min_tracking_confidence: Optional[float] = 0.5
+  output_face_blendshapes: Optional[bool] = False
+  output_facial_transformation_matrixes: Optional[bool] = False
+  result_callback: Optional[
+      Callable[[FaceLandmarkerResult, image_module.Image, int], None]
+  ] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
+    """Generates an FaceLandmarkerGraphOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = (
+        False if self.running_mode == _RunningMode.IMAGE else True
+    )
+
+    # Initialize the face landmarker options from base options.
+    face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
+        base_options=base_options_proto
+    )
+
+    # Configure face detector options.
+    face_landmarker_options_proto.face_detector_graph_options.num_faces = (
+        self.num_faces
+    )
+    face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+
+    # Configure face landmark detector options.
+    face_landmarker_options_proto.min_tracking_confidence = (
+        self.min_tracking_confidence
+    )
+    face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+    return face_landmarker_options_proto
+
+
+class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face landmarks detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
+    """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
+
+    Note that the created `FaceLandmarker` instance is in image mode, for
+    detecting face landmarks on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceLandmarker` object that's created from the model file and the
+      default `FaceLandmarkerOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = FaceLandmarkerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE
+    )
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: FaceLandmarkerOptions
+  ) -> 'FaceLandmarker':
+    """Creates the `FaceLandmarker` object from face landmarker options.
+
+    Args:
+      options: Options for the face landmarker task.
+
+    Returns:
+      `FaceLandmarker` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from
+        `FaceLandmarkerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
+        options.result_callback(
+            FaceLandmarkerResult([], [], []),
+            image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+        )
+        return
+
+      face_landmarks_result = _build_landmarker_result(output_packets)
+      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
+      options.result_callback(
+          face_landmarks_result,
+          image,
+          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+      )
+
+    output_streams = [
+        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_face_blendshapes:
+      output_streams.append(
+          ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
+      )
+    if options.output_facial_transformation_matrixes:
+      output_streams.append(
+          ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
+      )
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=output_streams,
+        task_options=options,
+    )
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode
+            == _RunningMode.LIVE_STREAM
+        ),
+        options.running_mode,
+        packets_callback if options.result_callback else None,
+    )
+
+  def detect(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> FaceLandmarkerResult:
+    """Performs face landmarks detection on the given image.
+
+    Only use this method when the FaceLandmarker is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False
+    )
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result(output_packets)
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> FaceLandmarkerResult:
+    """Performs face landmarks detection on the provided video frame.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False
+    )
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result(output_packets)
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> None:
+    """Sends live image data to perform face landmarks detection.
+
+    The results will be available via the "result_callback" provided in the
+    FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
+    created with the live stream running mode.
+
+    Only use this method when the FaceLandmarker is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `FaceLandmarkerOptions`. The
+    `detect_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, face landmarker may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The face landmarks detection results.
+      - The input image that the face landmarker runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the
+      face landmarker has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False
+    )
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })