Added image segmenter implementation files
This commit is contained in:
parent
6cdc6443b6
commit
3fbb2b002b
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@ -86,6 +86,8 @@ cc_library(
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name = "builtin_task_graphs",
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name = "builtin_task_graphs",
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deps = [
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deps = [
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/image_classification:image_classifier_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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],
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],
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)
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)
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28
mediapipe/tasks/python/components/BUILD
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28
mediapipe/tasks/python/components/BUILD
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@ -0,0 +1,28 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Placeholder for internal Python strict library compatibility macro.
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package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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py_library(
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name = "segmenter_options",
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srcs = ["segmenter_options.py"],
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deps = [
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"//mediapipe/tasks/cc/components:segmenter_options_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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78
mediapipe/tasks/python/components/segmenter_options.py
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78
mediapipe/tasks/python/components/segmenter_options.py
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@ -0,0 +1,78 @@
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Segmenter options data class."""
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import dataclasses
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import enum
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from typing import Any, Optional
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from mediapipe.tasks.cc.components import segmenter_options_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
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class OutputType(enum.Enum):
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UNSPECIFIED = 0
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CATEGORY_MASK = 1
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CONFIDENCE_MASK = 2
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class Activation(enum.Enum):
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NONE = 0
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SIGMOID = 1
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SOFTMAX = 2
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@dataclasses.dataclass
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class SegmenterOptions:
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"""Options for segmentation processor.
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Attributes:
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output_type: The output mask type allows specifying the type of
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post-processing to perform on the raw model results.
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activation: Activation function to apply to input tensor.
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"""
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output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
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activation: Optional[Activation] = Activation.NONE
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _SegmenterOptionsProto:
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"""Generates a protobuf object to pass to the C++ layer."""
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return _SegmenterOptionsProto(
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output_type=self.output_type.value,
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activation=self.activation.value
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)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _SegmenterOptionsProto) -> "SegmenterOptions":
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"""Creates a `SegmenterOptions` object from the given protobuf object."""
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return SegmenterOptions(
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output_type=OutputType(pb2_obj.output_type),
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activation=Activation(pb2_obj.output_type)
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)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, SegmenterOptions):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@ -18,4 +18,40 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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licenses(["notice"])
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# TODO: This test fails in OSS
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py_test(
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name = "object_detector_test",
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srcs = ["object_detector_test.py"],
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data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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],
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deps = [
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# build rule placeholder: numpy dep,
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"//mediapipe/tasks/python/components/containers:bounding_box",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:detections",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_util",
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"//mediapipe/tasks/python/vision:object_detector",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"@absl_py//absl/testing:parameterized",
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],
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)
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py_test(
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name = "image_segmenter_test",
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srcs = ["image_segmenter_test.py"],
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data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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],
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deps = [
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# build rule placeholder: numpy dep,
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_util",
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"//mediapipe/tasks/python/components:segmenter_options",
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"//mediapipe/tasks/python/vision:image_segmenter",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"@absl_py//absl/testing:parameterized",
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],
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)
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118
mediapipe/tasks/python/test/vision/image_segmenter_test.py
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118
mediapipe/tasks/python/test/vision/image_segmenter_test.py
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@ -0,0 +1,118 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for image segmenter."""
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import enum
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components import segmenter_options
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_util
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from mediapipe.tasks.python.vision import image_segmenter
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_Image = image_module.Image
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_OutputType = segmenter_options.OutputType
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_Activation = segmenter_options.Activation
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_ImageSegmenter = image_segmenter.ImageSegmenter
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_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_MODEL_FILE = 'deeplabv3.tflite'
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_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
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_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
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_MASK_MAGNIFICATION_FACTOR = 10
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_MATCH_PIXELS_THRESHOLD = 0.01
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class ImageSegmenterTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = test_util.read_test_image(
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test_util.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _ImageSegmenter.create_from_model_path(self.model_path) as segmenter:
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self.assertIsInstance(segmenter, _ImageSegmenter)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(file_name=self.model_path)
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options = _ImageSegmenterOptions(base_options=base_options)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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self.assertIsInstance(segmenter, _ImageSegmenter)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(file_name='')
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options = _ImageSegmenterOptions(base_options=base_options)
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_ImageSegmenter.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(file_content=f.read())
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options = _ImageSegmenterOptions(base_options=base_options)
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segmenter = _ImageSegmenter.create_from_options(options)
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self.assertIsInstance(segmenter, _ImageSegmenter)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4),
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(ModelFileType.FILE_CONTENT, 4))
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def succeeds_with_category_mask(self, model_file_type, max_results):
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# Creates segmenter.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(file_content=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _ImageSegmenterOptions(base_options=base_options,
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output_type=_OutputType.CATEGORY_MASK)
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segmenter = _ImageSegmenter.create_from_options(options)
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# Performs image segmentation on the input.
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image_result = segmenter.segment(self.test_image)
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# Comparing results.
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print(image_result)
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# Closes the segmenter explicitly when the segmenter is not used in
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# a context.
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segmenter.close()
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if __name__ == '__main__':
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absltest.main()
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@ -36,3 +36,22 @@ py_library(
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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],
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)
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)
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py_library(
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name = "image_segmenter",
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srcs = [
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"image_segmenter.py",
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],
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
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"//mediapipe/tasks/python/components:segmenter_options",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:task_info",
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"//mediapipe/tasks/python/vision/core:base_vision_task_api",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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)
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205
mediapipe/tasks/python/vision/image_segmenter.py
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205
mediapipe/tasks/python/vision/image_segmenter.py
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe image segmenter task."""
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import dataclasses
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from typing import Callable, List, Mapping, Optional
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import packet as packet_module
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from mediapipe.python._framework_bindings import task_runner as task_runner_module
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from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
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from mediapipe.tasks.python.components import segmenter_options
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.vision.core import base_vision_task_api
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
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_SegmenterOptions = segmenter_options.SegmenterOptions
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_RunningMode = running_mode_module.VisionTaskRunningMode
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_TaskInfo = task_info_module.TaskInfo
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_TaskRunner = task_runner_module.TaskRunner
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_SEGMENTATION_OUT_STREAM_NAME = 'segmented_masks'
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_SEGMENTATION_TAG = 'SEGMENTATION'
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_GROUPED_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
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_IMAGE_IN_STREAM_NAME = 'image_in'
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_IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
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@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)
|
||||||
|
# })
|
Loading…
Reference in New Issue
Block a user