Added image segmenter implementation files
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kinaryml
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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
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class ImageSegmenterOptions:
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"""Options for the image segmenter task.
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Attributes:
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base_options: Base options for the image segmenter task.
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running_mode: The running mode of the task. Default to the image mode.
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Image segmenter task has three running modes:
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1) The image mode for detecting objects on single image inputs.
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2) The video mode for detecting objects on the decoded frames of a video.
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3) The live stream mode for detecting objects on a live stream of input
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data, such as from camera.
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output_type: Optional output mask type.
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activation: Activation function to apply to input tensor.
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result_callback: The user-defined result callback for processing live stream
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data. The result callback should only be specified when the running mode
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is set to the live stream mode.
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"""
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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output_type: Optional[segmenter_options.OutputType] = segmenter_options.OutputType.CATEGORY_MASK
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activation: Optional[segmenter_options.Activation] = segmenter_options.Activation.NONE
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result_callback: Optional[
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Callable[[List[image_module.Image], image_module.Image, int],
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None]] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ImageSegmenterOptionsProto:
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"""Generates an ImageSegmenterOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
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base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
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segmenter_options = _SegmenterOptions(
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output_type=self.output_type,
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activation=self.activation
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)
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return _ImageSegmenterOptionsProto(
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base_options=base_options_proto,
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segmenter_options=segmenter_options.to_pb2()
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)
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class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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"""Class that performs image segmentation on images."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
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"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
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Note that the created `ImageSegmenter` instance is in image mode, for
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performing image segmentation on single image inputs.
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Args:
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model_path: Path to the model.
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Returns:
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`ImageSegmenter` object that's created from the model file and the default
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`ImageSegmenterOptions`.
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Raises:
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ValueError: If failed to create `ImageSegmenter` object from the provided
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file such as invalid file path.
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RuntimeError: If other types of error occurred.
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"""
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base_options = _BaseOptions(file_name=model_path)
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options = ImageSegmenterOptions(
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base_options=base_options, running_mode=_RunningMode.IMAGE)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(cls,
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options: ImageSegmenterOptions) -> 'ImageSegmenter':
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"""Creates the `ImageSegmenter` object from image segmenter options.
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Args:
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options: Options for the image segmenter task.
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Returns:
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`ImageSegmenter` object that's created from `options`.
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Raises:
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ValueError: If failed to create `ImageSegmenter` object from
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`ImageSegmenterOptions` such as missing the model.
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RuntimeError: If other types of error occurred.
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"""
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def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
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if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
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return
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segmentation_result = packet_getter.get_proto_list(
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output_packets[_SEGMENTATION_OUT_STREAM_NAME])
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image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
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timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
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options.result_callback(segmentation_result, image, timestamp)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME])],
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output_streams=[
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':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
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':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
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],
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task_options=options)
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return cls(
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task_info.generate_graph_config(
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enable_flow_limiting=options.running_mode ==
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_RunningMode.LIVE_STREAM), options.running_mode,
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packets_callback if options.result_callback else None)
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# TODO: Create an Image class for MediaPipe Tasks.
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def segment(self,
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image: image_module.Image) -> List[image_module.Image]:
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"""Performs the actual segmentation task on the provided MediaPipe Image.
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Args:
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image: MediaPipe Image.
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Returns:
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A segmentation result object that contains a list of segmentation masks
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as images.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If object detection failed to run.
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"""
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output_packets = self._process_image_data(
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{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
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segmentation_result = packet_getter.get_proto_list(
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output_packets[_SEGMENTATION_OUT_STREAM_NAME])
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return segmentation_result
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# def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
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# """Sends live image data (an Image with a unique timestamp) to perform image segmentation.
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#
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# This method will return immediately after the input image is accepted. The
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# results will be available via the `result_callback` provided in the
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# `ImageSegmenterOptions`. The `segment_async` method is designed to process
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# live stream data such as camera input. To lower the overall latency, image
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# segmenter may drop the input images if needed. In other words, it's not
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# guaranteed to have output per input image. The `result_callback` provides:
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# - A segmentation result object that contains a list of segmentation masks
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# as images.
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# - The input image that the image segmenter runs on.
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# - The input timestamp in milliseconds.
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#
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# Args:
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# image: MediaPipe Image.
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# timestamp_ms: The timestamp of the input image in milliseconds.
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#
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# Raises:
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# ValueError: If the current input timestamp is smaller than what the object
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# detector has already processed.
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# """
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# self._send_live_stream_data({
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# _IMAGE_IN_STREAM_NAME:
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# packet_creator.create_image(image).at(timestamp_ms)
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# })
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