Added Face Detector implementation and tests

mediapipe/python/BUILD

@@ -94,6 +94,7 @@ cc_library(
         "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
         "//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",
     ] + select({
         # TODO: Build text_classifier_graph and text_embedder_graph on Windows.
         "//mediapipe:windows": [],

mediapipe/tasks/python/components/containers/BUILD

@@ -73,12 +73,22 @@ py_library(
     ],
 )
 
+py_library(
+    name = "keypoint",
+    srcs = ["keypoint.py"],
+    deps = [
+        "//mediapipe/framework/formats:location_data_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "detections",
     srcs = ["detections.py"],
     deps = [
         ":bounding_box",
         ":category",
+        ":keypoint",
         "//mediapipe/framework/formats:detection_py_pb2",
         "//mediapipe/framework/formats:location_data_py_pb2",
         "//mediapipe/tasks/python/core:optional_dependencies",

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

@@ -20,6 +20,7 @@ from mediapipe.framework.formats import detection_pb2
 from mediapipe.framework.formats import location_data_pb2
 from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module
 from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import keypoint as keypoint_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 
 _DetectionListProto = detection_pb2.DetectionList
@@ -34,10 +35,12 @@ class Detection:
   Attributes:
     bounding_box: A BoundingBox object.
     categories: A list of Category objects.
+    keypoints: A list of NormalizedKeypoint objects.
   """
 
-  bounding_box: bounding_box_module.BoundingBox
+  bounding_box: bounding_box_module.BoundingBox = None
-  categories: List[category_module.Category]
+  categories: List[category_module.Category] = None
+  keypoints: List[keypoint_module.NormalizedKeypoint] = None
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _DetectionProto:
@@ -46,6 +49,8 @@ class Detection:
     label_ids = []
     scores = []
     display_names = []
+    relative_keypoints = []
+
     for category in self.categories:
       scores.append(category.score)
       if category.index:
@@ -54,6 +59,20 @@ class Detection:
         labels.append(category.category_name)
       if category.display_name:
         display_names.append(category.display_name)
+
+    if self.keypoints:
+      for keypoint in self.keypoints:
+        relative_keypoint_proto = _LocationDataProto.RelativeKeypoint()
+        if keypoint.x:
+          relative_keypoint_proto.x = keypoint.x
+        if keypoint.y:
+          relative_keypoint_proto.y = keypoint.y
+        if keypoint.label:
+          relative_keypoint_proto.keypoint_label = keypoint.label
+        if keypoint.score:
+          relative_keypoint_proto.score = keypoint.score
+        relative_keypoints.append(relative_keypoint_proto)
+
     return _DetectionProto(
         label=labels,
         label_id=label_ids,
@@ -61,13 +80,16 @@ class Detection:
         display_name=display_names,
         location_data=_LocationDataProto(
             format=_LocationDataProto.Format.BOUNDING_BOX,
-            bounding_box=self.bounding_box.to_pb2()))
+            bounding_box=self.bounding_box.to_pb2(),
+            relative_keypoints=relative_keypoints))
 
   @classmethod
   @doc_controls.do_not_generate_docs
   def create_from_pb2(cls, pb2_obj: _DetectionProto) -> 'Detection':
     """Creates a `Detection` object from the given protobuf object."""
     categories = []
+    keypoints = []
+
     for idx, score in enumerate(pb2_obj.score):
       categories.append(
           category_module.Category(
@@ -79,10 +101,20 @@ class Detection:
               display_name=pb2_obj.display_name[idx]
               if idx < len(pb2_obj.display_name) else None))
 
+    if pb2_obj.location_data.relative_keypoints:
+      for idx in range(len(pb2_obj.location_data.relative_keypoints)):
+        keypoints.append(
+          keypoint_module.NormalizedKeypoint(
+            x=pb2_obj.location_data.relative_keypoints[idx].x,
+            y=pb2_obj.location_data.relative_keypoints[idx].y,
+            label=pb2_obj.location_data.relative_keypoints[idx].keypoint_label,
+            score=pb2_obj.location_data.relative_keypoints[idx].score))
+
     return Detection(
         bounding_box=bounding_box_module.BoundingBox.create_from_pb2(
             pb2_obj.location_data.bounding_box),
-        categories=categories)
+        categories=categories,
+        keypoints=keypoints)
 
   def __eq__(self, other: Any) -> bool:
     """Checks if this object is equal to the given object.

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

@@ -0,0 +1,78 @@
+# 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.
+"""Keypoint data class."""
+
+import dataclasses
+from typing import Any, Optional
+
+from mediapipe.framework.formats import location_data_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_RelativeKeypointProto = location_data_pb2.LocationData.RelativeKeypoint
+
+
+@dataclasses.dataclass
+class NormalizedKeypoint:
+  """A normalized keypoint.
+
+  Normalized keypoint represents a point in 2D space with x, y coordinates.
+  x and y are normalized to [0.0, 1.0] by the image width and height
+  respectively.
+
+  Attributes:
+    x: The x coordinates of the normalized keypoint.
+    y: The y coordinates of the normalized keypoint.
+    label: The optional label of the keypoint.
+    score: The score of the keypoint.
+  """
+
+  x: Optional[float] = None
+  y: Optional[float] = None
+  label: Optional[str] = None
+  score: Optional[str] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _RelativeKeypointProto:
+    """Generates a RelativeKeypoint protobuf object."""
+    return _RelativeKeypointProto(
+        x=self.x,
+        y=self.y,
+        keypoint_label=self.label,
+        score=self.score
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls,
+                      pb2_obj: _RelativeKeypointProto) -> 'NormalizedKeypoint':
+    """Creates a `NormalizedKeypoint` object from the given protobuf object."""
+    return NormalizedKeypoint(
+        x=pb2_obj.x,
+        y=pb2_obj.y,
+        label=pb2_obj.keypoint_label,
+        score=pb2_obj.score)
+
+  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, NormalizedKeypoint):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/test/vision/BUILD

@@ -114,3 +114,25 @@ py_test(
         "@com_google_protobuf//:protobuf_python",
     ],
 )
+
+py_test(
+    name = "face_detector_test",
+    srcs = ["face_detector_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/tasks/python/components/containers:bounding_box",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:detections",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:face_detector",
+        "//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_detector_test.py

@@ -0,0 +1,407 @@
+# 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 detector."""
+
+import enum
+import os
+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 detection_pb2
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import detections as detections_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_detector
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+
+
+FaceDetectorResult = detections_module.DetectionResult
+_BaseOptions = base_options_module.BaseOptions
+_Category = category_module.Category
+_BoundingBox = bounding_box_module.BoundingBox
+_Detection = detections_module.Detection
+_Image = image_module.Image
+_FaceDetector = face_detector.FaceDetector
+_FaceDetectorOptions = face_detector.FaceDetectorOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_SHORT_RANGE_BLAZE_FACE_MODEL = 'face_detection_short_range.tflite'
+_PORTRAIT_IMAGE = 'portrait.jpg'
+_PORTRAIT_EXPECTED_DETECTION = 'portrait_expected_detection.pbtxt'
+_PORTRAIT_ROTATED_IMAGE = 'portrait_rotated.jpg'
+_PORTRAIT_ROTATED_EXPECTED_DETECTION = 'portrait_rotated_expected_detection.pbtxt'
+_CAT_IMAGE = 'cat.jpg'
+_KEYPOINT_ERROR_THRESHOLD = 1e-2
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
+
+
+def _get_expected_face_detector_result(file_name: str) -> FaceDetectorResult:
+  face_detection_result_file_path = test_utils.get_test_data_path(
+      os.path.join(_TEST_DATA_DIR, file_name))
+  with open(face_detection_result_file_path, "rb") as f:
+    face_detection_proto = detection_pb2.Detection()
+    text_format.Parse(f.read(), face_detection_proto)
+  face_detection = detections_module.Detection.create_from_pb2(face_detection_proto)
+  return FaceDetectorResult(detections=[face_detection])
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class FaceDetectorTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+            os.path.join(_TEST_DATA_DIR, _PORTRAIT_IMAGE)))
+    self.model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _SHORT_RANGE_BLAZE_FACE_MODEL))
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _FaceDetector.create_from_model_path(self.model_path) as detector:
+      self.assertIsInstance(detector, _FaceDetector)
+
+  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 = _FaceDetectorOptions(base_options=base_options)
+    with _FaceDetector.create_from_options(options) as detector:
+      self.assertIsInstance(detector, _FaceDetector)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    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 = _FaceDetectorOptions(base_options=base_options)
+      _FaceDetector.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 = _FaceDetectorOptions(base_options=base_options)
+      detector = _FaceDetector.create_from_options(options)
+      self.assertIsInstance(detector, _FaceDetector)
+
+  def _expect_keypoints_correct(self, actual_keypoints, expected_keypoints):
+    self.assertLen(actual_keypoints, len(expected_keypoints))
+    for i in range(len(actual_keypoints)):
+      self.assertAlmostEqual(
+        actual_keypoints[i].x, expected_keypoints[i].x,
+        delta=_KEYPOINT_ERROR_THRESHOLD)
+      self.assertAlmostEqual(
+        actual_keypoints[i].y, expected_keypoints[i].y,
+        delta=_KEYPOINT_ERROR_THRESHOLD)
+
+  def _expect_face_detector_results_correct(self, actual_results, expected_results):
+    self.assertLen(actual_results.detections, len(expected_results.detections))
+    for i in range(len(actual_results.detections)):
+      actual_bbox = actual_results.detections[i].bounding_box
+      expected_bbox = expected_results.detections[i].bounding_box
+      self.assertEqual(actual_bbox, expected_bbox)
+      self.assertNotEmpty(actual_results.detections[i].keypoints)
+      self._expect_keypoints_correct(actual_results.detections[i].keypoints,
+                                     expected_results.detections[i].keypoints)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION),
+      (ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION))
+  def test_detect(self, model_file_type, expected_detection_result_file):
+    # Creates detector.
+    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 = _FaceDetectorOptions(base_options=base_options)
+    detector = _FaceDetector.create_from_options(options)
+
+    # Performs face detection on the input.
+    detection_result = detector.detect(self.test_image)
+    # Comparing results.
+    expected_detection_result = _get_expected_face_detector_result(
+      expected_detection_result_file)
+    self._expect_face_detector_results_correct(detection_result,
+                                               expected_detection_result)
+    # Closes the detector explicitly when the detector is not used in
+    # a context.
+    detector.close()
+
+  @parameterized.parameters(
+    (ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION),
+    (ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION))
+  def test_detect_in_context(self, model_file_type, expected_detection_result_file):
+    # Creates detector.
+    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 = _FaceDetectorOptions(base_options=base_options)
+
+    with _FaceDetector.create_from_options(options) as detector:
+      # Performs face detection on the input.
+      detection_result = detector.detect(self.test_image)
+      # Comparing results.
+      expected_detection_result = _get_expected_face_detector_result(
+        expected_detection_result_file)
+      self._expect_face_detector_results_correct(detection_result,
+                                                 expected_detection_result)
+
+  def test_detect_succeeds_with_rotated_image(self):
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceDetectorOptions(base_options=base_options)
+    with _FaceDetector.create_from_options(options) as detector:
+      # Load the test image.
+      test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+          os.path.join(_TEST_DATA_DIR, _PORTRAIT_ROTATED_IMAGE)))
+      # Rotated input image.
+      image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
+      # Performs face detection on the input.
+      detection_result = detector.detect(test_image, image_processing_options)
+      # Comparing results.
+      expected_detection_result = _get_expected_face_detector_result(
+          _PORTRAIT_ROTATED_EXPECTED_DETECTION)
+      self._expect_face_detector_results_correct(detection_result,
+                                                 expected_detection_result)
+
+  def test_empty_detection_outputs(self):
+    # Load a test image with no faces.
+    test_image = _Image.create_from_file(
+      test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _CAT_IMAGE)))
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path))
+    with _FaceDetector.create_from_options(options) as detector:
+      # Performs object detection on the input.
+      detection_result = detector.detect(test_image)
+      self.assertEmpty(detection_result.detections)
+
+  def test_missing_result_callback(self):
+    options = _FaceDetectorOptions(
+        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 _FaceDetector.create_from_options(options) as unused_detector:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _FaceDetectorOptions(
+        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 _FaceDetector.create_from_options(options) as unused_detector:
+        pass
+
+  def test_calling_detect_for_video_in_image_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        detector.detect_for_video(self.test_image, 0)
+
+  def test_calling_detect_async_in_image_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        detector.detect_async(self.test_image, 0)
+
+  def test_calling_detect_in_video_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        detector.detect(self.test_image)
+
+  def test_calling_detect_async_in_video_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        detector.detect_async(self.test_image, 0)
+
+  def test_detect_for_video_with_out_of_order_timestamp(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _FaceDetector.create_from_options(options) as detector:
+      unused_result = detector.detect_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        detector.detect_for_video(self.test_image, 0)
+
+  @parameterized.parameters(
+    (ModelFileType.FILE_NAME, _PORTRAIT_IMAGE, 0,
+     _get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_CONTENT, _PORTRAIT_IMAGE, 0,
+     _get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_NAME, _PORTRAIT_ROTATED_IMAGE, -90,
+     _get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_CONTENT, _PORTRAIT_ROTATED_IMAGE, -90,
+     _get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_NAME, _CAT_IMAGE, 0, FaceDetectorResult([])),
+    (ModelFileType.FILE_CONTENT, _CAT_IMAGE, 0, FaceDetectorResult([])))
+  def test_detect_for_video(self, model_file_type, test_image_file_name,
+                            rotation_degrees, expected_detection_result):
+    # Creates detector.
+    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 = _FaceDetectorOptions(base_options=base_options,
+                                   running_mode=_RUNNING_MODE.VIDEO)
+
+    with _FaceDetector.create_from_options(options) as detector:
+      for timestamp in range(0, 300, 30):
+        # Load the test image.
+        test_image = _Image.create_from_file(
+          test_utils.get_test_data_path(
+            os.path.join(_TEST_DATA_DIR, test_image_file_name)))
+        # Set the image processing options.
+        image_processing_options = _ImageProcessingOptions(
+            rotation_degrees=rotation_degrees)
+        # Performs face detection on the input.
+        detection_result = detector.detect_for_video(test_image, timestamp,
+                                                     image_processing_options)
+        # Comparing results.
+        self._expect_face_detector_results_correct(detection_result,
+                                                   expected_detection_result)
+
+  def test_calling_detect_in_live_stream_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        detector.detect(self.test_image)
+
+  def test_calling_detect_for_video_in_live_stream_mode(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _FaceDetector.create_from_options(options) as detector:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        detector.detect_for_video(self.test_image, 0)
+
+  def test_detect_async_calls_with_illegal_timestamp(self):
+    options = _FaceDetectorOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _FaceDetector.create_from_options(options) as detector:
+      detector.detect_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        detector.detect_async(self.test_image, 0)
+
+  @parameterized.parameters(
+    (ModelFileType.FILE_NAME, _PORTRAIT_IMAGE, 0,
+     _get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_CONTENT, _PORTRAIT_IMAGE, 0,
+     _get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_NAME, _PORTRAIT_ROTATED_IMAGE, -90,
+     _get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_CONTENT, _PORTRAIT_ROTATED_IMAGE, -90,
+     _get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
+    (ModelFileType.FILE_NAME, _CAT_IMAGE, 0, FaceDetectorResult([])),
+    (ModelFileType.FILE_CONTENT, _CAT_IMAGE, 0, FaceDetectorResult([])))
+  def test_detect_async_calls(self, model_file_type, test_image_file_name,
+                              rotation_degrees, expected_detection_result):
+    # Creates detector.
+    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.')
+
+    observed_timestamp_ms = -1
+
+    def check_result(result: FaceDetectorResult, output_image: _Image,
+                     timestamp_ms: int):
+      self._expect_face_detector_results_correct(result,
+                                                   expected_detection_result)
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _FaceDetectorOptions(base_options=base_options,
+                                   running_mode=_RUNNING_MODE.LIVE_STREAM,
+                                   result_callback=check_result)
+
+    # Load the test image.
+    test_image = _Image.create_from_file(
+      test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, test_image_file_name)))
+
+    with _FaceDetector.create_from_options(options) as detector:
+      for timestamp in range(0, 300, 30):
+        # Set the image processing options.
+        image_processing_options = _ImageProcessingOptions(
+          rotation_degrees=rotation_degrees)
+        detector.detect_async(test_image, timestamp, image_processing_options)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -152,3 +152,23 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "face_detector",
+    srcs = [
+        "face_detector.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/face_detector/proto:face_detector_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:detections",
+        "//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_detector.py

@@ -0,0 +1,308 @@
+# 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 detector task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional
+
+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_detector.proto import face_detector_graph_options_pb2
+from mediapipe.tasks.python.components.containers import detections as detections_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
+
+FaceDetectorResult = detections_module.DetectionResult
+_BaseOptions = base_options_module.BaseOptions
+_FaceDetectorGraphOptionsProto = face_detector_graph_options_pb2.FaceDetectorGraphOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_DETECTIONS_OUT_STREAM_NAME = 'detections'
+_DETECTIONS_TAG = 'DETECTIONS'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_detector.FaceDetectorGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+@dataclasses.dataclass
+class FaceDetectorOptions:
+  """Options for the face detector task.
+
+  Attributes:
+    base_options: Base options for the face detector task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Face detector task has three running modes:
+      1) The image mode for detecting faces on single image inputs.
+      2) The video mode for detecting faces on the decoded frames of a video.
+      3) The live stream mode for detecting faces on a live stream of input
+         data, such as from camera.
+    min_detection_confidence: The minimum confidence score for the face
+      detection to be considered successful.
+    min_suppression_threshold: The minimum non-maximum-suppression threshold
+      for face detection to be considered overlapped.
+    num_faces: Maximum number of faces to detect in the image.
+    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
+  min_detection_confidence: Optional[float] = None
+  min_suppression_threshold: Optional[float] = None
+  num_faces: Optional[int] = None
+  result_callback: Optional[
+      Callable[[detections_module.DetectionResult, image_module.Image, int],
+               None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceDetectorGraphOptionsProto:
+    """Generates an FaceDetectorOptions 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
+    return _FaceDetectorGraphOptionsProto(
+        base_options=base_options_proto,
+        min_detection_confidence=self.min_detection_confidence,
+        min_suppression_threshold=self.min_suppression_threshold,
+        num_faces=self.num_faces
+    )
+
+
+class FaceDetector(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceDetector':
+    """Creates an `FaceDetector` object from a TensorFlow Lite model and the default `FaceDetectorOptions`.
+
+    Note that the created `FaceDetector` instance is in image mode, for
+    detecting faces on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceDetector` object that's created from the model file and the default
+      `FaceDetectorOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceDetector` 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 = FaceDetectorOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: FaceDetectorOptions) -> 'FaceDetector':
+    """Creates the `FaceDetector` object from face detector options.
+
+    Args:
+      options: Options for the face detector task.
+
+    Returns:
+      `FaceDetector` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceDetector` object from
+        `FaceDetectorOptions` 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[_DETECTIONS_OUT_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_DETECTIONS_OUT_STREAM_NAME]
+        options.result_callback(
+            FaceDetectorResult([]), image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+        return
+      detection_proto_list = packet_getter.get_proto_list(
+          output_packets[_DETECTIONS_OUT_STREAM_NAME])
+      detection_result = detections_module.DetectionResult([
+          detections_module.Detection.create_from_pb2(result)
+          for result in detection_proto_list
+      ])
+
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      options.result_callback(detection_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([_DETECTIONS_TAG, _DETECTIONS_OUT_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
+  ) -> FaceDetectorResult:
+    """Performs face detection on the provided MediaPipe Image.
+
+    Only use this method when the FaceDetector is created with the image
+    running mode.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      A face detection result object that contains a list of face detections,
+      each detection has a bounding box that is expressed in the unrotated input
+      frame of reference coordinates system, i.e. in `[0,image_width) x [0,
+      image_height)`, which are the dimensions of the underlying image data.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face 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[_DETECTIONS_OUT_STREAM_NAME].is_empty():
+      return FaceDetectorResult([])
+    detection_proto_list = packet_getter.get_proto_list(
+        output_packets[_DETECTIONS_OUT_STREAM_NAME])
+    return detections_module.DetectionResult([
+        detections_module.Detection.create_from_pb2(result)
+        for result in detection_proto_list
+    ])
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> detections_module.DetectionResult:
+    """Performs face detection on the provided video frames.
+
+    Only use this method when the FaceDetector 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:
+      A face detection result object that contains a list of face detections,
+      each detection has a bounding box that is expressed in the unrotated input
+      frame of reference coordinates system, i.e. in `[0,image_width) x [0,
+      image_height)`, which are the dimensions of the underlying image data.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face 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[_DETECTIONS_OUT_STREAM_NAME].is_empty():
+      return FaceDetectorResult([])
+    detection_proto_list = packet_getter.get_proto_list(
+        output_packets[_DETECTIONS_OUT_STREAM_NAME])
+    return detections_module.DetectionResult([
+        detections_module.Detection.create_from_pb2(result)
+        for result in detection_proto_list
+    ])
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> None:
+    """Sends live image data (an Image with a unique timestamp) to perform face detection.
+
+    Only use this method when the FaceDetector 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 `FaceDetectorOptions`. The
+    `detect_async` method is designed to process live stream data such as camera
+    input. To lower the overall latency, face detector may drop the input
+    images if needed. In other words, it's not guaranteed to have output per
+    input image.
+
+    The `result_callback` provides:
+      - A face detection result object that contains a list of face detections,
+        each detection has a bounding box that is expressed in the unrotated
+        input frame of reference coordinates system,
+        i.e. in `[0,image_width) x [0,image_height)`, which are the dimensions
+        of the underlying image data.
+      - The input image that the face detector 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
+        detector 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)
+    })