Added some files for the face landmarker implementation

mediapipe/tasks/python/components/containers/BUILD

@@ -73,6 +73,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "matrix_data",
+    srcs = ["matrix_data.py"],
+    deps = [
+        "//mediapipe/framework/formats:matrix_data_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "detections",
     srcs = ["detections.py"],

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

@@ -0,0 +1,79 @@
+# Copyright 2022 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.
+"""Matrix data data class."""
+
+import dataclasses
+import enum
+from typing import Any, Optional
+
+from mediapipe.framework.formats import matrix_data_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_MatrixDataProto = matrix_data_pb2.MatrixData
+
+
+@dataclasses.dataclass
+class MatrixData:
+  """This stores the Matrix data.
+
+  Here the data is stored in column-major order by default.
+
+  Attributes:
+    rows: The number of rows in the matrix.
+    cols: The number of columns in the matrix.
+    data: The data stored in the matrix.
+    layout: The order in which the data are stored. Defaults to COLUMN_MAJOR.
+  """
+
+  class Layout(enum.Enum):
+    COLUMN_MAJOR = 0
+    ROW_MAJOR = 1
+
+  rows: Optional[int] = None
+  cols: Optional[int] = None
+  data: Optional[float] = None
+  layout: Optional[Layout] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _MatrixDataProto:
+    """Generates a MatrixData protobuf object."""
+    return _MatrixDataProto(
+        rows=self.rows,
+        cols=self.cols,
+        data=self.data,
+        layout=self.layout)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _MatrixDataProto) -> 'MatrixData':
+    """Creates a `MatrixData` object from the given protobuf object."""
+    return MatrixData(
+        rows=pb2_obj.rows,
+        cols=pb2_obj.cols,
+        data=pb2_obj.data,
+        layout=pb2_obj.layout)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, MatrixData):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/test/vision/BUILD

@@ -114,3 +114,28 @@ py_test(
         "@com_google_protobuf//:protobuf_python",
     ],
 )
+
+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/python:_framework_bindings",
+        "//mediapipe/framework/formats:landmark_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/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",
+    ],
+)

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

@@ -0,0 +1,182 @@
+# Copyright 2022 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 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.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
+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'
+_FACE_LANDMARKER_WITH_BLENDSHAPES_BUNDLE_ASSET_FILE = 'face_landmarker_with_blendshapes.task'
+_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'
+_LANDMARKS_DIFF_MARGIN = 0.03
+_BLENDSHAPES_DIFF_MARGIN = 0.1
+_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)
+    landmarks = []
+    for landmark in proto.landmark:
+      landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
+  return landmarks
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class HandLandmarkerTest(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, rename_me in enumerate(actual_landmarks):
+      self.assertAlmostEqual(
+        rename_me.x,
+        expected_landmarks[i].x,
+        delta=_LANDMARKS_DIFF_MARGIN)
+      self.assertAlmostEqual(
+        rename_me.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, rename_me in enumerate(actual_blendshapes):
+      self.assertEqual(rename_me.index, expected_blendshapes[i].index)
+      self.assertAlmostEqual(
+        rename_me.score,
+        expected_blendshapes[i].score,
+        delta=_BLENDSHAPES_DIFF_MARGIN)
+
+  def _expect_facial_transformation_matrix_correct(self, actual_matrix_list,
+                                                   expected_matrix_list):
+    self.assertLen(actual_matrix_list, len(expected_matrix_list))
+
+    for i, rename_me in enumerate(actual_matrix_list):
+      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,
+        expected_matrix_list[i].data,
+        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,
+       _get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)),
+      (ModelFileType.FILE_CONTENT,
+       _get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS)))
+  def test_detect(self, model_file_type, expected_result):
+    # Creates face landmarker.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.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)
+    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,
+                                   expected_result.face_landmarks)
+    # Closes the face landmarker explicitly when the face landmarker is not used
+    # in a context.
+    landmarker.close()
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -152,3 +152,28 @@ 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_landmarker/proto:face_landmarker_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//mediapipe/tasks/python/components/containers:matrix_data",
+        "//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 +1,449 @@
+# Copyright 2022 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
+
+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
+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.components.containers import matrix_data as matrix_data_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
+_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[matrix_data_module.MatrixData]
+
+
+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_blendshapes_proto_list = packet_getter.get_proto_list(
+      output_packets[_BLENDSHAPES_STREAM_NAME])
+  facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+      output_packets[_FACE_GEOMETRY_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.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark))
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  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 = []
+  for proto in facial_transformation_matrixes_proto_list:
+    matrix_data = matrix_data_pb2.MatrixData()
+    matrix_data.MergeFrom(proto)
+    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)
+
+
+@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)
+
+    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=[
+            ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+            ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME]),
+            ':'.join([
+                _FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME
+            ]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        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)
+    })