295 lines
12 KiB
Python
295 lines
12 KiB
Python
# 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 classifier task."""
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import dataclasses
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from typing import Callable, 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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# TODO: Import MPImage directly one we have an alias
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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
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from mediapipe.python._framework_bindings import task_runner
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from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
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from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2
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from mediapipe.tasks.python.components.containers import classifications
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from mediapipe.tasks.python.components.containers import rect
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from mediapipe.tasks.python.components.processors import classifier_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
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_NormalizedRect = rect.NormalizedRect
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_BaseOptions = base_options_module.BaseOptions
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_ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
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_ClassifierOptions = classifier_options.ClassifierOptions
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_RunningMode = vision_task_running_mode.VisionTaskRunningMode
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_TaskInfo = task_info_module.TaskInfo
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_TaskRunner = task_runner.TaskRunner
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_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
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_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
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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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_NORM_RECT_NAME = 'norm_rect_in'
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_NORM_RECT_TAG = 'NORM_RECT'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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def _build_full_image_norm_rect() -> _NormalizedRect:
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# Builds a NormalizedRect covering the entire image.
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return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
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@dataclasses.dataclass
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class ImageClassifierOptions:
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"""Options for the image classifier task.
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Attributes:
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base_options: Base options for the image classifier task.
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running_mode: The running mode of the task. Default to the image mode. Image
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classifier task has three running modes: 1) The image mode for classifying
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objects on single image inputs. 2) The video mode for classifying objects
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on the decoded frames of a video. 3) The live stream mode for classifying
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objects on a live stream of input data, such as from camera.
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classifier_options: Options for the image classification task.
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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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classifier_options: _ClassifierOptions = _ClassifierOptions()
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result_callback: Optional[
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Callable[[classifications.ClassificationResult, 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) -> _ImageClassifierGraphOptionsProto:
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"""Generates an ImageClassifierOptions 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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classifier_options_proto = self.classifier_options.to_pb2()
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return _ImageClassifierGraphOptionsProto(
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base_options=base_options_proto,
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classifier_options=classifier_options_proto)
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class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
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"""Class that performs image classification on images."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'ImageClassifier':
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"""Creates an `ImageClassifier` object from a TensorFlow Lite model and the default `ImageClassifierOptions`.
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Note that the created `ImageClassifier` instance is in image mode, for
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classifying objects 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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`ImageClassifier` object that's created from the model file and the
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default `ImageClassifierOptions`.
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Raises:
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ValueError: If failed to create `ImageClassifier` 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(model_asset_path=model_path)
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options = ImageClassifierOptions(
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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: ImageClassifierOptions) -> 'ImageClassifier':
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"""Creates the `ImageClassifier` object from image classifier options.
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Args:
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options: Options for the image classifier task.
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Returns:
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`ImageClassifier` object that's created from `options`.
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Raises:
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ValueError: If failed to create `ImageClassifier` object from
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`ImageClassifierOptions` 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.Packet]):
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if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
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return
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classification_result_proto = classifications_pb2.ClassificationResult()
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classification_result_proto.CopyFrom(
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packet_getter.get_proto(
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output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
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classification_result = classifications.ClassificationResult([
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classifications.Classifications.create_from_pb2(classification)
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for classification in classification_result_proto.classifications
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])
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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(classification_result, image,
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timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
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':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
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],
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output_streams=[
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':'.join([
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_CLASSIFICATION_RESULT_TAG,
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_CLASSIFICATION_RESULT_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: Replace _NormalizedRect with ImageProcessingOption
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def classify(
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self,
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image: image_module.Image,
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roi: Optional[_NormalizedRect] = None
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) -> classifications.ClassificationResult:
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"""Performs image classification on the provided MediaPipe Image.
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Args:
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image: MediaPipe Image.
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roi: The region of interest.
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Returns:
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A classification result object that contains a list of classifications.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If image classification failed to run.
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"""
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norm_rect = roi if roi is not None else _build_full_image_norm_rect()
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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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_NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())
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})
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classification_result_proto = classifications_pb2.ClassificationResult()
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classification_result_proto.CopyFrom(
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packet_getter.get_proto(
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output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
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return classifications.ClassificationResult([
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classifications.Classifications.create_from_pb2(classification)
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for classification in classification_result_proto.classifications
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])
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def classify_for_video(
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self,
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image: image_module.Image,
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timestamp_ms: int,
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roi: Optional[_NormalizedRect] = None
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) -> classifications.ClassificationResult:
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"""Performs image classification on the provided video frames.
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Only use this method when the ImageClassifier is created with the video
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running mode. It's required to provide the video frame's timestamp (in
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milliseconds) along with the video frame. The input timestamps should be
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monotonically increasing for adjacent calls of this method.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input video frame in milliseconds.
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roi: The region of interest.
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Returns:
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A classification result object that contains a list of classifications.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If image classification failed to run.
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"""
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norm_rect = roi if roi is not None else _build_full_image_norm_rect()
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output_packets = self._process_video_data({
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_IMAGE_IN_STREAM_NAME:
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packet_creator.create_image(image).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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_NORM_RECT_NAME:
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packet_creator.create_proto(norm_rect.to_pb2()).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
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})
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classification_result_proto = classifications_pb2.ClassificationResult()
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classification_result_proto.CopyFrom(
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packet_getter.get_proto(
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output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
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return classifications.ClassificationResult([
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classifications.Classifications.create_from_pb2(classification)
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for classification in classification_result_proto.classifications
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])
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def classify_async(self,
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image: image_module.Image,
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timestamp_ms: int,
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roi: Optional[_NormalizedRect] = None) -> None:
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"""Sends live image data (an Image with a unique timestamp) to perform image classification.
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Only use this method when the ImageClassifier is created with the live
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stream running mode. The input timestamps should be monotonically increasing
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for adjacent calls of this method. This method will return immediately after
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the input image is accepted. The results will be available via the
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`result_callback` provided in the `ImageClassifierOptions`. The
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`classify_async` method is designed to process live stream data such as
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camera input. To lower the overall latency, image classifier may drop the
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input images if needed. In other words, it's not guaranteed to have output
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per input image.
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The `result_callback` provides:
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- A classification result object that contains a list of classifications.
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- The input image that the image classifier runs on.
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- The input timestamp in milliseconds.
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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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roi: The region of interest.
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Raises:
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ValueError: If the current input timestamp is smaller than what the image
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classifier has already processed.
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"""
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norm_rect = roi if roi is not None else _build_full_image_norm_rect()
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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(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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_NORM_RECT_NAME:
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packet_creator.create_proto(norm_rect.to_pb2()).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
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})
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