Merge pull request #3739 from kinaryml:image-segmenter-python-impl

PiperOrigin-RevId: 484922757

mediapipe/python/BUILD

@@ -88,6 +88,7 @@ cc_library(
     name = "builtin_task_graphs",
     deps = [
         "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
+        "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
     ],
 )

mediapipe/tasks/python/test/vision/BUILD

@@ -56,3 +56,19 @@ py_test(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_test(
+    name = "image_segmenter_test",
+    srcs = ["image_segmenter_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:image_segmenter",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

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

@@ -0,0 +1,353 @@
+# 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 image segmenter."""
+
+import enum
+from typing import List
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import cv2
+import numpy as np
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import image_frame
+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 image_segmenter
+from mediapipe.tasks.python.vision.core import vision_task_running_mode
+
+_BaseOptions = base_options_module.BaseOptions
+_Image = image_module.Image
+_ImageFormat = image_frame.ImageFormat
+_OutputType = image_segmenter.OutputType
+_Activation = image_segmenter.Activation
+_ImageSegmenter = image_segmenter.ImageSegmenter
+_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
+_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
+
+_MODEL_FILE = 'deeplabv3.tflite'
+_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
+_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
+_MASK_MAGNIFICATION_FACTOR = 10
+_MASK_SIMILARITY_THRESHOLD = 0.98
+
+
+def _similar_to_uint8_mask(actual_mask, expected_mask):
+  actual_mask_pixels = actual_mask.numpy_view().flatten()
+  expected_mask_pixels = expected_mask.numpy_view().flatten()
+
+  consistent_pixels = 0
+  num_pixels = len(expected_mask_pixels)
+
+  for index in range(num_pixels):
+    consistent_pixels += (
+        actual_mask_pixels[index] *
+        _MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
+
+  return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageSegmenterTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    # Load the test input image.
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_IMAGE_FILE))
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+        test_utils.get_test_data_path(_SEGMENTATION_FILE), cv2.IMREAD_GRAYSCALE)
+    self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
+    self.model_path = test_utils.get_test_data_path(_MODEL_FILE)
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _ImageSegmenter.create_from_model_path(self.model_path) as segmenter:
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  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 = _ImageSegmenterOptions(base_options=base_options)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    # Invalid empty model path.
+    with self.assertRaisesRegex(
+        ValueError,
+        r"ExternalFile must specify at least one of 'file_content', "
+        r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
+      base_options = _BaseOptions(model_asset_path='')
+      options = _ImageSegmenterOptions(base_options=base_options)
+      _ImageSegmenter.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 = _ImageSegmenterOptions(base_options=base_options)
+      segmenter = _ImageSegmenter.create_from_options(options)
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  @parameterized.parameters((ModelFileType.FILE_NAME,),
+                            (ModelFileType.FILE_CONTENT,))
+  def test_segment_succeeds_with_category_mask(self, model_file_type):
+    # Creates segmenter.
+    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 = _ImageSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+    segmenter = _ImageSegmenter.create_from_options(options)
+
+    # Performs image segmentation on the input.
+    category_masks = segmenter.segment(self.test_image)
+    self.assertLen(category_masks, 1)
+    category_mask = category_masks[0]
+    result_pixels = category_mask.numpy_view().flatten()
+
+    # Check if data type of `category_mask` is correct.
+    self.assertEqual(result_pixels.dtype, np.uint8)
+
+    self.assertTrue(
+        _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+        f'Number of pixels in the candidate mask differing from that of the '
+        f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+  def test_segment_succeeds_with_confidence_mask(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+
+    # Run segmentation on the model in CATEGORY_MASK mode.
+    options = _ImageSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+    segmenter = _ImageSegmenter.create_from_options(options)
+    category_masks = segmenter.segment(self.test_image)
+    category_mask = category_masks[0].numpy_view()
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _ImageSegmenterOptions(
+        base_options=base_options,
+        output_type=_OutputType.CONFIDENCE_MASK,
+        activation=_Activation.SOFTMAX)
+    segmenter = _ImageSegmenter.create_from_options(options)
+    confidence_masks = segmenter.segment(self.test_image)
+
+    # Check if confidence mask shape is correct.
+    self.assertLen(
+        confidence_masks, 21,
+        'Number of confidence masks must match with number of categories.')
+
+    # Gather the confidence masks in a single array `confidence_mask_array`.
+    confidence_mask_array = np.array(
+        [confidence_mask.numpy_view() for confidence_mask in confidence_masks])
+
+    # Check if data type of `confidence_masks` are correct.
+    self.assertEqual(confidence_mask_array.dtype, np.float32)
+
+    # Compute the category mask from the created confidence mask.
+    calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
+    self.assertListEqual(
+        calculated_category_mask.tolist(), category_mask.tolist(),
+        'Confidence mask does not match with the category mask.')
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+  @parameterized.parameters((ModelFileType.FILE_NAME),
+                            (ModelFileType.FILE_CONTENT))
+  def test_segment_in_context(self, model_file_type):
+    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_contents = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_contents)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _ImageSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      # Performs image segmentation on the input.
+      category_masks = segmenter.segment(self.test_image)
+      self.assertLen(category_masks, 1)
+
+      self.assertTrue(
+          _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+          f'Number of pixels in the candidate mask differing from that of the '
+          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+  def test_missing_result_callback(self):
+    options = _ImageSegmenterOptions(
+        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 _ImageSegmenter.create_from_options(options) as unused_segmenter:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _ImageSegmenterOptions(
+        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 _ImageSegmenter.create_from_options(options) as unused_segmenter:
+        pass
+
+  def test_calling_segment_for_video_in_image_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_calling_segment_async_in_image_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_calling_segment_in_video_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        segmenter.segment(self.test_image)
+
+  def test_calling_segment_async_in_video_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_segment_for_video_with_out_of_order_timestamp(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      unused_result = segmenter.segment_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_segment_for_video(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        output_type=_OutputType.CATEGORY_MASK,
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        category_masks = segmenter.segment_for_video(self.test_image, timestamp)
+        self.assertLen(category_masks, 1)
+        self.assertTrue(
+            _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+            f'Number of pixels in the candidate mask differing from that of the '
+            f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+  def test_calling_segment_in_live_stream_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        segmenter.segment(self.test_image)
+
+  def test_calling_segment_for_video_in_live_stream_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_segment_async_calls_with_illegal_timestamp(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      segmenter.segment_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_segment_async_calls(self):
+    observed_timestamp_ms = -1
+
+    def check_result(result: List[image_module.Image], output_image: _Image,
+                     timestamp_ms: int):
+      # Get the output category mask.
+      category_mask = result[0]
+      self.assertEqual(output_image.width, self.test_image.width)
+      self.assertEqual(output_image.height, self.test_image.height)
+      self.assertEqual(output_image.width, self.test_seg_image.width)
+      self.assertEqual(output_image.height, self.test_seg_image.height)
+      self.assertTrue(
+          _similar_to_uint8_mask(category_mask, self.test_seg_image),
+          f'Number of pixels in the candidate mask differing from that of the '
+          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        output_type=_OutputType.CATEGORY_MASK,
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=check_result)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmenter.segment_async(self.test_image, timestamp)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -58,3 +58,22 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "image_segmenter",
+    srcs = [
+        "image_segmenter.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/components/proto:segmenter_options_py_pb2",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
+        "//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:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/image_segmenter.py

@@ -0,0 +1,253 @@
+# 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 image segmenter task."""
+
+import dataclasses
+import enum
+from typing import Callable, List, 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
+from mediapipe.python._framework_bindings import task_runner
+from mediapipe.tasks.cc.components.proto import segmenter_options_pb2
+from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
+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 vision_task_running_mode
+
+_BaseOptions = base_options_module.BaseOptions
+_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
+_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
+_RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+_TaskRunner = task_runner.TaskRunner
+
+_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+class OutputType(enum.Enum):
+  UNSPECIFIED = 0
+  CATEGORY_MASK = 1
+  CONFIDENCE_MASK = 2
+
+
+class Activation(enum.Enum):
+  NONE = 0
+  SIGMOID = 1
+  SOFTMAX = 2
+
+
+@dataclasses.dataclass
+class ImageSegmenterOptions:
+  """Options for the image segmenter task.
+
+  Attributes:
+    base_options: Base options for the image segmenter task.
+    running_mode: The running mode of the task. Default to the image mode. Image
+      segmenter task has three running modes: 1) The image mode for segmenting
+      objects on single image inputs. 2) The video mode for segmenting objects
+      on the decoded frames of a video. 3) The live stream mode for segmenting
+      objects on a live stream of input data, such as from camera.
+    output_type: The output mask type allows specifying the type of
+      post-processing to perform on the raw model results.
+    activation: Activation function to apply to input tensor.
+    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
+  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+  activation: Optional[Activation] = Activation.NONE
+  result_callback: Optional[Callable[
+      [List[image_module.Image], image_module.Image, int], None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ImageSegmenterOptionsProto:
+    """Generates an ImageSegmenterOptions 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
+    segmenter_options_proto = _SegmenterOptionsProto(
+        output_type=self.output_type.value, activation=self.activation.value)
+    return _ImageSegmenterOptionsProto(
+        base_options=base_options_proto,
+        segmenter_options=segmenter_options_proto)
+
+
+class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs image segmentation on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
+    """Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
+
+    Note that the created `ImageSegmenter` instance is in image mode, for
+    performing image segmentation on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `ImageSegmenter` object that's created from the model file and the default
+      `ImageSegmenterOptions`.
+
+    Raises:
+      ValueError: If failed to create `ImageSegmenter` 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 = ImageSegmenterOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: ImageSegmenterOptions) -> 'ImageSegmenter':
+    """Creates the `ImageSegmenter` object from image segmenter options.
+
+    Args:
+      options: Options for the image segmenter task.
+
+    Returns:
+      `ImageSegmenter` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `ImageSegmenter` object from
+        `ImageSegmenterOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+      segmentation_result = packet_getter.get_image_list(
+          output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
+      options.result_callback(segmentation_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])],
+        output_streams=[
+            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_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 segment(self, image: image_module.Image) -> List[image_module.Image]:
+    """Performs the actual segmentation task on the provided MediaPipe Image.
+
+    Args:
+      image: MediaPipe Image.
+
+    Returns:
+      If the output_type is CATEGORY_MASK, the returned vector of images is
+      per-category segmented image mask.
+      If the output_type is CONFIDENCE_MASK, the returned vector of images
+      contains only one confidence image mask. A segmentation result object that
+      contains a list of segmentation masks as images.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image segmentation failed to run.
+    """
+    output_packets = self._process_image_data(
+        {_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
+    segmentation_result = packet_getter.get_image_list(
+        output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+    return segmentation_result
+
+  def segment_for_video(self, image: image_module.Image,
+                        timestamp_ms: int) -> List[image_module.Image]:
+    """Performs segmentation on the provided video frames.
+
+    Only use this method when the ImageSegmenter 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.
+
+    Returns:
+      If the output_type is CATEGORY_MASK, the returned vector of images is
+      per-category segmented image mask.
+      If the output_type is CONFIDENCE_MASK, the returned vector of images
+      contains only one confidence image mask. A segmentation result object that
+      contains a list of segmentation masks as images.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image segmentation failed to run.
+    """
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+    segmentation_result = packet_getter.get_image_list(
+        output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+    return segmentation_result
+
+  def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
+    """Sends live image data (an Image with a unique timestamp) to perform image segmentation.
+
+    Only use this method when the ImageSegmenter 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 `ImageSegmenterOptions`. The
+    `segment_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, image segmenter may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` prvoides:
+      - A segmentation result object that contains a list of segmentation masks
+        as images.
+      - The input image that the image segmenter runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the image
+        segmenter has already processed.
+    """
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })