Merge pull request #4241 from kinaryml:interactive-segmenter-python

PiperOrigin-RevId: 522470912

mediapipe/python/BUILD

@@ -99,6 +99,7 @@ cc_library(
         "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
         "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
         "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
+        "//mediapipe/tasks/cc/vision/interactive_segmenter:interactive_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
     ],
 )

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

@@ -0,0 +1,332 @@
+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for interactive segmenter."""
+
+import enum
+import os
+
+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.components.containers import keypoint as keypoint_module
+from mediapipe.tasks.python.components.containers import rect
+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 interactive_segmenter
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+
+_BaseOptions = base_options_module.BaseOptions
+_Image = image_module.Image
+_ImageFormat = image_frame.ImageFormat
+_NormalizedKeypoint = keypoint_module.NormalizedKeypoint
+_Rect = rect.Rect
+_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
+_InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
+_InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
+_RegionOfInterest = interactive_segmenter.RegionOfInterest
+_Format = interactive_segmenter.RegionOfInterest.Format
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_MODEL_FILE = 'ptm_512_hdt_ptm_woid.tflite'
+_CATS_AND_DOGS = 'cats_and_dogs.jpg'
+_CATS_AND_DOGS_MASK_DOG_1 = 'cats_and_dogs_mask_dog1.png'
+_CATS_AND_DOGS_MASK_DOG_2 = 'cats_and_dogs_mask_dog2.png'
+_MASK_MAGNIFICATION_FACTOR = 255
+_MASK_SIMILARITY_THRESHOLD = 0.97
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
+
+
+def _calculate_soft_iou(m1, m2):
+  intersection_sum = np.sum(m1 * m2)
+  union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
+
+  if union_sum > 0:
+    return intersection_sum / union_sum
+  else:
+    return 0
+
+
+def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
+  actual_mask = actual_mask.numpy_view()
+  expected_mask = expected_mask.numpy_view() / 255.0
+
+  return (
+      actual_mask.shape == expected_mask.shape
+      and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
+  )
+
+
+def _similar_to_uint8_mask(actual_mask, expected_mask, similarity_threshold):
+  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 >= similarity_threshold
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class InteractiveSegmenterTest(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(
+            os.path.join(_TEST_DATA_DIR, _CATS_AND_DOGS)
+        )
+    )
+    # Loads ground truth segmentation file.
+    self.test_seg_image = self._load_segmentation_mask(
+        _CATS_AND_DOGS_MASK_DOG_1
+    )
+    self.model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
+    )
+
+  def _load_segmentation_mask(self, file_path: str):
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+        test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
+        cv2.IMREAD_GRAYSCALE,
+    )
+    return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _InteractiveSegmenter.create_from_model_path(
+        self.model_path
+    ) as segmenter:
+      self.assertIsInstance(segmenter, _InteractiveSegmenter)
+
+  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 = _InteractiveSegmenterOptions(base_options=base_options)
+    with _InteractiveSegmenter.create_from_options(options) as segmenter:
+      self.assertIsInstance(segmenter, _InteractiveSegmenter)
+
+  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 = _InteractiveSegmenterOptions(base_options=base_options)
+      _InteractiveSegmenter.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 = _InteractiveSegmenterOptions(base_options=base_options)
+      segmenter = _InteractiveSegmenter.create_from_options(options)
+      self.assertIsInstance(segmenter, _InteractiveSegmenter)
+
+  @parameterized.parameters(
+      (
+          ModelFileType.FILE_NAME,
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.44, 0.7),
+          _CATS_AND_DOGS_MASK_DOG_1,
+          0.84,
+      ),
+      (
+          ModelFileType.FILE_CONTENT,
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.44, 0.7),
+          _CATS_AND_DOGS_MASK_DOG_1,
+          0.84,
+      ),
+      (
+          ModelFileType.FILE_NAME,
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.66, 0.66),
+          _CATS_AND_DOGS_MASK_DOG_2,
+          _MASK_SIMILARITY_THRESHOLD,
+      ),
+      (
+          ModelFileType.FILE_CONTENT,
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.66, 0.66),
+          _CATS_AND_DOGS_MASK_DOG_2,
+          _MASK_SIMILARITY_THRESHOLD,
+      ),
+  )
+  def test_segment_succeeds_with_category_mask(
+      self,
+      model_file_type,
+      roi_format,
+      keypoint,
+      output_mask,
+      similarity_threshold,
+  ):
+    # 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 = _InteractiveSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK
+    )
+    segmenter = _InteractiveSegmenter.create_from_options(options)
+
+    # Performs image segmentation on the input.
+    roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
+    category_masks = segmenter.segment(self.test_image, roi)
+    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)
+
+    # Loads ground truth segmentation file.
+    test_seg_image = self._load_segmentation_mask(output_mask)
+
+    self.assertTrue(
+        _similar_to_uint8_mask(
+            category_masks[0], test_seg_image, similarity_threshold
+        ),
+        (
+            'Number of pixels in the candidate mask differing from that of the'
+            f' ground truth mask exceeds {similarity_threshold}.'
+        ),
+    )
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+  @parameterized.parameters(
+      (
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.44, 0.7),
+          _CATS_AND_DOGS_MASK_DOG_1,
+          0.84,
+      ),
+      (
+          _RegionOfInterest.Format.KEYPOINT,
+          _NormalizedKeypoint(0.66, 0.66),
+          _CATS_AND_DOGS_MASK_DOG_2,
+          _MASK_SIMILARITY_THRESHOLD,
+      ),
+  )
+  def test_segment_succeeds_with_confidence_mask(
+      self, roi_format, keypoint, output_mask, similarity_threshold
+  ):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _InteractiveSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+    )
+
+    with _InteractiveSegmenter.create_from_options(options) as segmenter:
+      # Perform segmentation
+      confidence_masks = segmenter.segment(self.test_image, roi)
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks,
+          2,
+          'Number of confidence masks must match with number of categories.',
+      )
+
+      # Loads ground truth segmentation file.
+      expected_mask = self._load_segmentation_mask(output_mask)
+
+      self.assertTrue(
+          _similar_to_float_mask(
+              confidence_masks[1], expected_mask, similarity_threshold
+          )
+      )
+
+  def test_segment_succeeds_with_rotation(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    roi = _RegionOfInterest(
+        format=_RegionOfInterest.Format.KEYPOINT,
+        keypoint=_NormalizedKeypoint(0.66, 0.66),
+    )
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _InteractiveSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+    )
+
+    with _InteractiveSegmenter.create_from_options(options) as segmenter:
+      # Perform segmentation
+      image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
+      confidence_masks = segmenter.segment(
+          self.test_image, roi, image_processing_options
+      )
+
+      # Check if confidence mask shape is correct.
+      self.assertLen(
+          confidence_masks,
+          2,
+          'Number of confidence masks must match with number of categories.',
+      )
+
+  def test_segment_fails_with_roi_in_image_processing_options(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    roi = _RegionOfInterest(
+        format=_RegionOfInterest.Format.KEYPOINT,
+        keypoint=_NormalizedKeypoint(0.66, 0.66),
+    )
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _InteractiveSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
+    )
+
+    with self.assertRaisesRegex(
+        ValueError, "This task doesn't support region-of-interest."
+    ):
+      with _InteractiveSegmenter.create_from_options(options) as segmenter:
+        # Perform segmentation
+        image_processing_options = _ImageProcessingOptions(
+            _Rect(left=0.1, top=0, right=0.9, bottom=1)
+        )
+        segmenter.segment(self.test_image, roi, image_processing_options)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -106,6 +106,28 @@ py_library(
     ],
 )
 
+py_library(
+    name = "interactive_segmenter",
+    srcs = [
+        "interactive_segmenter.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_graph_options_py_pb2",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:segmenter_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:keypoint",
+        "//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/util:render_data_py_pb2",
+    ],
+)
+
 py_library(
     name = "gesture_recognizer",
     srcs = [

mediapipe/tasks/python/vision/interactive_segmenter.py

@@ -0,0 +1,254 @@
+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe interactive segmenter task."""
+
+import dataclasses
+import enum
+from typing import List, 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.tasks.cc.vision.image_segmenter.proto import image_segmenter_graph_options_pb2
+from mediapipe.tasks.cc.vision.image_segmenter.proto import segmenter_options_pb2
+from mediapipe.tasks.python.components.containers import keypoint as keypoint_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
+from mediapipe.util import render_data_pb2
+
+_BaseOptions = base_options_module.BaseOptions
+_RenderDataProto = render_data_pb2.RenderData
+_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
+_ImageSegmenterGraphOptionsProto = (
+    image_segmenter_graph_options_pb2.ImageSegmenterGraphOptions
+)
+_RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_ROI_STREAM_NAME = 'roi_in'
+_ROI_TAG = 'ROI'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_IMAGE_TAG = 'IMAGE'
+_TASK_GRAPH_NAME = (
+    'mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraph'
+)
+
+
+@dataclasses.dataclass
+class InteractiveSegmenterOptions:
+  """Options for the interactive segmenter task.
+
+  Attributes:
+    base_options: Base options for the interactive segmenter task.
+    output_type: The output mask type allows specifying the type of
+      post-processing to perform on the raw model results.
+  """
+
+  class OutputType(enum.Enum):
+    UNSPECIFIED = 0
+    CATEGORY_MASK = 1
+    CONFIDENCE_MASK = 2
+
+  base_options: _BaseOptions
+  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
+    """Generates an InteractiveSegmenterOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False
+    segmenter_options_proto = _SegmenterOptionsProto(
+        output_type=self.output_type.value
+    )
+    return _ImageSegmenterGraphOptionsProto(
+        base_options=base_options_proto,
+        segmenter_options=segmenter_options_proto,
+    )
+
+
+@dataclasses.dataclass
+class RegionOfInterest:
+  """The Region-Of-Interest (ROI) to interact with."""
+
+  class Format(enum.Enum):
+    UNSPECIFIED = 0
+    KEYPOINT = 1
+
+  format: Format
+  keypoint: Optional[keypoint_module.NormalizedKeypoint] = None
+
+
+def _convert_roi_to_render_data(roi: RegionOfInterest) -> _RenderDataProto:
+  """Converts region of interest to render data proto."""
+  result = _RenderDataProto()
+
+  if roi is not None:
+    if roi.format == RegionOfInterest.Format.UNSPECIFIED:
+      raise ValueError('RegionOfInterest format not specified.')
+
+    elif roi.format == RegionOfInterest.Format.KEYPOINT:
+      if roi.keypoint is not None:
+        annotation = result.render_annotations.add()
+        annotation.color.r = 255
+        point = annotation.point
+        point.normalized = True
+        point.x = roi.keypoint.x
+        point.y = roi.keypoint.y
+        return result
+  else:
+    raise ValueError('Please specify the Region-of-interest for segmentation.')
+
+  raise ValueError('Unrecognized format.')
+
+
+class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs interactive segmentation on images.
+
+  Users can represent user interaction through `RegionOfInterest`, which gives
+  a hint to InteractiveSegmenter to perform segmentation focusing on the given
+  region of interest.
+
+  The API expects a TFLite model with mandatory TFLite Model Metadata.
+
+  Input tensor:
+    (kTfLiteUInt8/kTfLiteFloat32)
+    - image input of size `[batch x height x width x channels]`.
+    - batch inference is not supported (`batch` is required to be 1).
+    - RGB and greyscale inputs are supported (`channels` is required to be
+      1 or 3).
+    - if type is kTfLiteFloat32, NormalizationOptions are required to be
+      attached to the metadata for input normalization.
+  Output tensors:
+    (kTfLiteUInt8/kTfLiteFloat32)
+    - list of segmented masks.
+    - if `output_type` is CATEGORY_MASK, uint8 Image, Image vector of size 1.
+    - if `output_type` is CONFIDENCE_MASK, float32 Image list of size
+      `channels`.
+    - batch is always 1
+
+  An example of such model can be found at:
+  https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2
+  """
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'InteractiveSegmenter':
+    """Creates an `InteractiveSegmenter` object from a TensorFlow Lite model and the default `InteractiveSegmenterOptions`.
+
+    Note that the created `InteractiveSegmenter` instance is in image mode, for
+    performing image segmentation on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `InteractiveSegmenter` object that's created from the model file and the
+      default `InteractiveSegmenterOptions`.
+
+    Raises:
+      ValueError: If failed to create `InteractiveSegmenter` 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 = InteractiveSegmenterOptions(base_options=base_options)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: InteractiveSegmenterOptions
+  ) -> 'InteractiveSegmenter':
+    """Creates the `InteractiveSegmenter` object from interactive segmenter options.
+
+    Args:
+      options: Options for the interactive segmenter task.
+
+    Returns:
+      `InteractiveSegmenter` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `InteractiveSegmenter` object from
+        `InteractiveSegmenterOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_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=False),
+        _RunningMode.IMAGE,
+        None,
+    )
+
+  def segment(
+      self,
+      image: image_module.Image,
+      roi: RegionOfInterest,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> List[image_module.Image]:
+    """Performs the actual segmentation task on the provided MediaPipe Image.
+
+    The image can be of any size with format RGB.
+
+    Args:
+      image: MediaPipe Image.
+      roi: Optional user-specified region of interest for segmentation.
+      image_processing_options: Options for image processing.
+
+    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.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    render_data_proto = _convert_roi_to_render_data(roi)
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _ROI_STREAM_NAME: packet_creator.create_proto(render_data_proto),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ),
+    })
+    segmentation_result = packet_getter.get_image_list(
+        output_packets[_SEGMENTATION_OUT_STREAM_NAME]
+    )
+    return segmentation_result