Added image segmenter implementation files

This commit is contained in:
kinaryml 2022-09-21 03:23:04 -07:00
parent 6cdc6443b6
commit 3fbb2b002b
7 changed files with 487 additions and 1 deletions

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@ -86,6 +86,8 @@ cc_library(
name = "builtin_task_graphs", name = "builtin_task_graphs",
deps = [ deps = [
"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph", "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
"//mediapipe/tasks/cc/vision/image_classification:image_classifier_graph",
"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
], ],
) )

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@ -0,0 +1,28 @@
# 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.
# Placeholder for internal Python strict library compatibility macro.
package(default_visibility = ["//mediapipe/tasks:internal"])
licenses(["notice"])
py_library(
name = "segmenter_options",
srcs = ["segmenter_options.py"],
deps = [
"//mediapipe/tasks/cc/components:segmenter_options_py_pb2",
"//mediapipe/tasks/python/core:optional_dependencies",
],
)

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@ -0,0 +1,78 @@
# Copyright 2022 The TensorFlow 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.
"""Segmenter options data class."""
import dataclasses
import enum
from typing import Any, Optional
from mediapipe.tasks.cc.components import segmenter_options_pb2
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
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 SegmenterOptions:
"""Options for segmentation processor.
Attributes:
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.
"""
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
activation: Optional[Activation] = Activation.NONE
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _SegmenterOptionsProto:
"""Generates a protobuf object to pass to the C++ layer."""
return _SegmenterOptionsProto(
output_type=self.output_type.value,
activation=self.activation.value
)
@classmethod
@doc_controls.do_not_generate_docs
def create_from_pb2(
cls, pb2_obj: _SegmenterOptionsProto) -> "SegmenterOptions":
"""Creates a `SegmenterOptions` object from the given protobuf object."""
return SegmenterOptions(
output_type=OutputType(pb2_obj.output_type),
activation=Activation(pb2_obj.output_type)
)
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, SegmenterOptions):
return False
return self.to_pb2().__eq__(other.to_pb2())

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@ -18,4 +18,40 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
licenses(["notice"]) licenses(["notice"])
# TODO: This test fails in OSS py_test(
name = "object_detector_test",
srcs = ["object_detector_test.py"],
data = [
"//mediapipe/tasks/testdata/vision:test_images",
"//mediapipe/tasks/testdata/vision:test_models",
],
deps = [
# build rule placeholder: numpy dep,
"//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_util",
"//mediapipe/tasks/python/vision:object_detector",
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
"@absl_py//absl/testing:parameterized",
],
)
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 = [
# build rule placeholder: numpy dep,
"//mediapipe/tasks/python/core:base_options",
"//mediapipe/tasks/python/test:test_util",
"//mediapipe/tasks/python/components:segmenter_options",
"//mediapipe/tasks/python/vision:image_segmenter",
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
"@absl_py//absl/testing:parameterized",
],
)

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@ -0,0 +1,118 @@
# 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 absl.testing import absltest
from absl.testing import parameterized
import numpy as np
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.tasks.python.components import segmenter_options
from mediapipe.tasks.python.core import base_options as base_options_module
from mediapipe.tasks.python.test import test_util
from mediapipe.tasks.python.vision import image_segmenter
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
_BaseOptions = base_options_module.BaseOptions
_Image = image_module.Image
_OutputType = segmenter_options.OutputType
_Activation = segmenter_options.Activation
_ImageSegmenter = image_segmenter.ImageSegmenter
_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
_MODEL_FILE = 'deeplabv3.tflite'
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
_MASK_MAGNIFICATION_FACTOR = 10
_MATCH_PIXELS_THRESHOLD = 0.01
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
class ImageSegmenterTest(parameterized.TestCase):
def setUp(self):
super().setUp()
self.test_image = test_util.read_test_image(
test_util.get_test_data_path(_IMAGE_FILE))
self.model_path = test_util.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(file_name=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' or 'file_descriptor_meta'."):
base_options = _BaseOptions(file_name='')
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(file_content=f.read())
options = _ImageSegmenterOptions(base_options=base_options)
segmenter = _ImageSegmenter.create_from_options(options)
self.assertIsInstance(segmenter, _ImageSegmenter)
@parameterized.parameters(
(ModelFileType.FILE_NAME, 4),
(ModelFileType.FILE_CONTENT, 4))
def succeeds_with_category_mask(self, model_file_type, max_results):
# Creates segmenter.
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(file_name=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(file_content=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.
image_result = segmenter.segment(self.test_image)
# Comparing results.
print(image_result)
# Closes the segmenter explicitly when the segmenter is not used in
# a context.
segmenter.close()
if __name__ == '__main__':
absltest.main()

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@ -36,3 +36,22 @@ py_library(
"//mediapipe/tasks/python/vision/core:vision_task_running_mode", "//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/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
"//mediapipe/tasks/python/components:segmenter_options",
"//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",
],
)

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@ -0,0 +1,205 @@
# 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
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 as packet_module
from mediapipe.python._framework_bindings import task_runner as task_runner_module
from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
from mediapipe.tasks.python.components import segmenter_options
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 as running_mode_module
_BaseOptions = base_options_module.BaseOptions
_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
_SegmenterOptions = segmenter_options.SegmenterOptions
_RunningMode = running_mode_module.VisionTaskRunningMode
_TaskInfo = task_info_module.TaskInfo
_TaskRunner = task_runner_module.TaskRunner
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_masks'
_SEGMENTATION_TAG = 'SEGMENTATION'
_GROUPED_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'
@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 detecting objects on single image inputs.
2) The video mode for detecting objects on the decoded frames of a video.
3) The live stream mode for detecting objects on a live stream of input
data, such as from camera.
output_type: Optional output mask type.
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[segmenter_options.OutputType] = segmenter_options.OutputType.CATEGORY_MASK
activation: Optional[segmenter_options.Activation] = segmenter_options.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 = _SegmenterOptions(
output_type=self.output_type,
activation=self.activation
)
return _ImageSegmenterOptionsProto(
base_options=base_options_proto,
segmenter_options=segmenter_options.to_pb2()
)
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(file_name=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_module.Packet]):
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
return
segmentation_result = packet_getter.get_proto_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME])
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
options.result_callback(segmentation_result, image, timestamp)
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)
# TODO: Create an Image class for MediaPipe Tasks.
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:
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 object detection failed to run.
"""
output_packets = self._process_image_data(
{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
segmentation_result = packet_getter.get_proto_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.
#
# 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` provides:
# - 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 object
# detector has already processed.
# """
# self._send_live_stream_data({
# _IMAGE_IN_STREAM_NAME:
# packet_creator.create_image(image).at(timestamp_ms)
# })