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

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PiperOrigin-RevId: 484922757
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Copybara-Service 2022-10-30 17:11:42 -07:00
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1
mediapipe/python/BUILD
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@ -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",
],
)

16
mediapipe/tasks/python/test/vision/BUILD
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@ -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",
],
)

353
mediapipe/tasks/python/test/vision/image_segmenter_test.py Normal file
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@ -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()

19
mediapipe/tasks/python/vision/BUILD
View File

@ -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",
],
)

253
mediapipe/tasks/python/vision/image_segmenter.py Normal file
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@ -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)
})