diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index 2911e2fd6..f157c9f27 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -87,6 +87,7 @@ cc_library( cc_library( name = "builtin_task_graphs", deps = [ + "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph", "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph", ], ) diff --git a/mediapipe/python/packet_getter.py b/mediapipe/python/packet_getter.py index 4d93e713b..cf6e7574a 100644 --- a/mediapipe/python/packet_getter.py +++ b/mediapipe/python/packet_getter.py @@ -14,7 +14,7 @@ """The public facing packet getter APIs.""" -from typing import List, Type +from typing import List from google.protobuf import message from google.protobuf import symbol_database @@ -39,7 +39,7 @@ get_image_frame = _packet_getter.get_image_frame get_matrix = _packet_getter.get_matrix -def get_proto(packet: mp_packet.Packet) -> Type[message.Message]: +def get_proto(packet: mp_packet.Packet) -> message.Message: """Get the content of a MediaPipe proto Packet as a proto message. Args: diff --git a/mediapipe/tasks/python/components/containers/BUILD b/mediapipe/tasks/python/components/containers/BUILD index fd25401f7..f24230a9e 100644 --- a/mediapipe/tasks/python/components/containers/BUILD +++ b/mediapipe/tasks/python/components/containers/BUILD @@ -27,6 +27,15 @@ py_library( ], ) +py_library( + name = "rect", + srcs = ["rect.py"], + deps = [ + "//mediapipe/framework/formats:rect_py_pb2", + "//mediapipe/tasks/python/core:optional_dependencies", + ], +) + py_library( name = "category", srcs = ["category.py"], @@ -47,3 +56,13 @@ py_library( "//mediapipe/tasks/python/core:optional_dependencies", ], ) + +py_library( + name = "classifications", + srcs = ["classifications.py"], + deps = [ + ":category", + "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", + "//mediapipe/tasks/python/core:optional_dependencies", + ], +) diff --git a/mediapipe/tasks/python/components/containers/classifications.py b/mediapipe/tasks/python/components/containers/classifications.py new file mode 100644 index 000000000..90ab22614 --- /dev/null +++ b/mediapipe/tasks/python/components/containers/classifications.py @@ -0,0 +1,168 @@ +# 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. +"""Classifications data class.""" + +import dataclasses +from typing import Any, List, Optional + +from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 +from mediapipe.tasks.python.components.containers import category as category_module +from mediapipe.tasks.python.core.optional_dependencies import doc_controls + +_ClassificationEntryProto = classifications_pb2.ClassificationEntry +_ClassificationsProto = classifications_pb2.Classifications +_ClassificationResultProto = classifications_pb2.ClassificationResult + + +@dataclasses.dataclass +class ClassificationEntry: + """List of predicted classes (aka labels) for a given classifier head. + + Attributes: + categories: The array of predicted categories, usually sorted by descending + scores (e.g. from high to low probability). + timestamp_ms: The optional timestamp (in milliseconds) associated to the + classification entry. This is useful for time series use cases, e.g., + audio classification. + """ + + categories: List[category_module.Category] + timestamp_ms: Optional[int] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassificationEntryProto: + """Generates a ClassificationEntry protobuf object.""" + return _ClassificationEntryProto( + categories=[category.to_pb2() for category in self.categories], + timestamp_ms=self.timestamp_ms) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2( + cls, pb2_obj: _ClassificationEntryProto) -> 'ClassificationEntry': + """Creates a `ClassificationEntry` object from the given protobuf object.""" + return ClassificationEntry( + categories=[ + category_module.Category.create_from_pb2(category) + for category in pb2_obj.categories + ], + timestamp_ms=pb2_obj.timestamp_ms) + + 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, ClassificationEntry): + return False + + return self.to_pb2().__eq__(other.to_pb2()) + + +@dataclasses.dataclass +class Classifications: + """Represents the classifications for a given classifier head. + + Attributes: + entries: A list of `ClassificationEntry` objects. + head_index: The index of the classifier head these categories refer to. This + is useful for multi-head models. + head_name: The name of the classifier head, which is the corresponding + tensor metadata name. + """ + + entries: List[ClassificationEntry] + head_index: int + head_name: str + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassificationsProto: + """Generates a Classifications protobuf object.""" + return _ClassificationsProto( + entries=[entry.to_pb2() for entry in self.entries], + head_index=self.head_index, + head_name=self.head_name) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications': + """Creates a `Classifications` object from the given protobuf object.""" + return Classifications( + entries=[ + ClassificationEntry.create_from_pb2(entry) + for entry in pb2_obj.entries + ], + head_index=pb2_obj.head_index, + head_name=pb2_obj.head_name) + + 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, Classifications): + return False + + return self.to_pb2().__eq__(other.to_pb2()) + + +@dataclasses.dataclass +class ClassificationResult: + """Contains one set of results per classifier head. + + Attributes: + classifications: A list of `Classifications` objects. + """ + + classifications: List[Classifications] + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassificationResultProto: + """Generates a ClassificationResult protobuf object.""" + return _ClassificationResultProto(classifications=[ + classification.to_pb2() for classification in self.classifications + ]) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2( + cls, pb2_obj: _ClassificationResultProto) -> 'ClassificationResult': + """Creates a `ClassificationResult` object from the given protobuf object. + """ + return ClassificationResult(classifications=[ + Classifications.create_from_pb2(classification) + for classification in pb2_obj.classifications + ]) + + 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, ClassificationResult): + return False + + return self.to_pb2().__eq__(other.to_pb2()) diff --git a/mediapipe/tasks/python/components/containers/rect.py b/mediapipe/tasks/python/components/containers/rect.py new file mode 100644 index 000000000..510561592 --- /dev/null +++ b/mediapipe/tasks/python/components/containers/rect.py @@ -0,0 +1,140 @@ +# 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. +"""Rect data class.""" + +import dataclasses +from typing import Any, Optional + +from mediapipe.framework.formats import rect_pb2 +from mediapipe.tasks.python.core.optional_dependencies import doc_controls + +_RectProto = rect_pb2.Rect +_NormalizedRectProto = rect_pb2.NormalizedRect + + +@dataclasses.dataclass +class Rect: + """A rectangle with rotation in image coordinates. + + Attributes: x_center : The X coordinate of the top-left corner, in pixels. + y_center : The Y coordinate of the top-left corner, in pixels. + width: The width of the rectangle, in pixels. + height: The height of the rectangle, in pixels. + rotation: Rotation angle is clockwise in radians. + rect_id: Optional unique id to help associate different rectangles to each + other. + """ + + x_center: int + y_center: int + width: int + height: int + rotation: Optional[float] = 0.0 + rect_id: Optional[int] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _RectProto: + """Generates a Rect protobuf object.""" + return _RectProto( + x_center=self.x_center, + y_center=self.y_center, + width=self.width, + height=self.height, + ) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect': + """Creates a `Rect` object from the given protobuf object.""" + return Rect( + x_center=pb2_obj.x_center, + y_center=pb2_obj.y_center, + width=pb2_obj.width, + height=pb2_obj.height) + + 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, Rect): + return False + + return self.to_pb2().__eq__(other.to_pb2()) + + +@dataclasses.dataclass +class NormalizedRect: + """A rectangle with rotation in normalized coordinates. + + The values of box + + center location and size are within [0, 1]. + + Attributes: x_center : The X normalized coordinate of the top-left corner. + y_center : The Y normalized coordinate of the top-left corner. + width: The width of the rectangle. + height: The height of the rectangle. + rotation: Rotation angle is clockwise in radians. + rect_id: Optional unique id to help associate different rectangles to each + other. + """ + + x_center: float + y_center: float + width: float + height: float + rotation: Optional[float] = 0.0 + rect_id: Optional[int] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _NormalizedRectProto: + """Generates a NormalizedRect protobuf object.""" + return _NormalizedRectProto( + x_center=self.x_center, + y_center=self.y_center, + width=self.width, + height=self.height, + rotation=self.rotation, + rect_id=self.rect_id) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2(cls, pb2_obj: _NormalizedRectProto) -> 'NormalizedRect': + """Creates a `NormalizedRect` object from the given protobuf object.""" + return NormalizedRect( + x_center=pb2_obj.x_center, + y_center=pb2_obj.y_center, + width=pb2_obj.width, + height=pb2_obj.height, + rotation=pb2_obj.rotation, + rect_id=pb2_obj.rect_id) + + 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, NormalizedRect): + return False + + return self.to_pb2().__eq__(other.to_pb2()) diff --git a/mediapipe/tasks/python/components/processors/BUILD b/mediapipe/tasks/python/components/processors/BUILD new file mode 100644 index 000000000..f87a579b0 --- /dev/null +++ b/mediapipe/tasks/python/components/processors/BUILD @@ -0,0 +1,30 @@ +# 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. + +# Placeholder for internal Python strict library and test compatibility macro. + +package(default_visibility = ["//mediapipe/tasks:internal"]) + +licenses(["notice"]) + +py_library( + name = "classifier_options", + srcs = ["classifier_options.py"], + deps = [ + "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2", + "//mediapipe/tasks/python/core:optional_dependencies", + ], +) diff --git a/mediapipe/tasks/python/components/processors/classifier_options.py b/mediapipe/tasks/python/components/processors/classifier_options.py new file mode 100644 index 000000000..2e77f93b5 --- /dev/null +++ b/mediapipe/tasks/python/components/processors/classifier_options.py @@ -0,0 +1,86 @@ +# 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. +"""Classifier options data class.""" + +import dataclasses +from typing import Any, List, Optional + +from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2 +from mediapipe.tasks.python.core.optional_dependencies import doc_controls + +_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions + + +@dataclasses.dataclass +class ClassifierOptions: + """Options for classification processor. + + Attributes: + display_names_locale: The locale to use for display names specified through + the TFLite Model Metadata. + max_results: The maximum number of top-scored classification results to + return. + score_threshold: Overrides the ones provided in the model metadata. Results + below this value are rejected. + category_allowlist: Allowlist of category names. If non-empty, detection + results whose category name is not in this set will be filtered out. + Duplicate or unknown category names are ignored. Mutually exclusive with + `category_denylist`. + category_denylist: Denylist of category names. If non-empty, detection + results whose category name is in this set will be filtered out. Duplicate + or unknown category names are ignored. Mutually exclusive with + `category_allowlist`. + """ + + display_names_locale: Optional[str] = None + max_results: Optional[int] = None + score_threshold: Optional[float] = None + category_allowlist: Optional[List[str]] = None + category_denylist: Optional[List[str]] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ClassifierOptionsProto: + """Generates a ClassifierOptions protobuf object.""" + return _ClassifierOptionsProto( + score_threshold=self.score_threshold, + category_allowlist=self.category_allowlist, + category_denylist=self.category_denylist, + display_names_locale=self.display_names_locale, + max_results=self.max_results) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2(cls, + pb2_obj: _ClassifierOptionsProto) -> 'ClassifierOptions': + """Creates a `ClassifierOptions` object from the given protobuf object.""" + return ClassifierOptions( + score_threshold=pb2_obj.score_threshold, + category_allowlist=[str(name) for name in pb2_obj.category_allowlist], + category_denylist=[str(name) for name in pb2_obj.category_denylist], + display_names_locale=pb2_obj.display_names_locale, + max_results=pb2_obj.max_results) + + 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, ClassifierOptions): + return False + + return self.to_pb2().__eq__(other.to_pb2()) diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index 290b665e7..acf24f875 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -36,3 +36,23 @@ py_test( "//mediapipe/tasks/python/vision/core:vision_task_running_mode", ], ) + +py_test( + name = "image_classifier_test", + srcs = ["image_classifier_test.py"], + data = [ + "//mediapipe/tasks/testdata/vision:test_images", + "//mediapipe/tasks/testdata/vision:test_models", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/tasks/python/components/containers:category", + "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:rect", + "//mediapipe/tasks/python/components/processors:classifier_options", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/test:test_utils", + "//mediapipe/tasks/python/vision:image_classifier", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + ], +) diff --git a/mediapipe/tasks/python/test/vision/image_classifier_test.py b/mediapipe/tasks/python/test/vision/image_classifier_test.py new file mode 100644 index 000000000..afaf921a7 --- /dev/null +++ b/mediapipe/tasks/python/test/vision/image_classifier_test.py @@ -0,0 +1,515 @@ +# 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 classifier.""" + +import enum +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +import numpy as np + +from mediapipe.python._framework_bindings import image +from mediapipe.tasks.python.components.containers import category +from mediapipe.tasks.python.components.containers import classifications as classifications_module +from mediapipe.tasks.python.components.containers import rect +from mediapipe.tasks.python.components.processors import classifier_options +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.test import test_utils +from mediapipe.tasks.python.vision import image_classifier +from mediapipe.tasks.python.vision.core import vision_task_running_mode + +_NormalizedRect = rect.NormalizedRect +_BaseOptions = base_options_module.BaseOptions +_ClassifierOptions = classifier_options.ClassifierOptions +_Category = category.Category +_ClassificationEntry = classifications_module.ClassificationEntry +_Classifications = classifications_module.Classifications +_ClassificationResult = classifications_module.ClassificationResult +_Image = image.Image +_ImageClassifier = image_classifier.ImageClassifier +_ImageClassifierOptions = image_classifier.ImageClassifierOptions +_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode + +_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite' +_IMAGE_FILE = 'burger.jpg' +_ALLOW_LIST = ['cheeseburger', 'guacamole'] +_DENY_LIST = ['cheeseburger'] +_SCORE_THRESHOLD = 0.5 +_MAX_RESULTS = 3 + + +# TODO: Port assertProtoEquals +def _assert_proto_equals(expected, actual): # pylint: disable=unused-argument + pass + + +def _generate_empty_results(timestamp_ms: int) -> _ClassificationResult: + return _ClassificationResult(classifications=[ + _Classifications( + entries=[ + _ClassificationEntry(categories=[], timestamp_ms=timestamp_ms) + ], + head_index=0, + head_name='probability') + ]) + + +def _generate_burger_results(timestamp_ms: int) -> _ClassificationResult: + return _ClassificationResult(classifications=[ + _Classifications( + entries=[ + _ClassificationEntry( + categories=[ + _Category( + index=934, + score=0.7939587831497192, + display_name='', + category_name='cheeseburger'), + _Category( + index=932, + score=0.02739289402961731, + display_name='', + category_name='bagel'), + _Category( + index=925, + score=0.01934075355529785, + display_name='', + category_name='guacamole'), + _Category( + index=963, + score=0.006327860057353973, + display_name='', + category_name='meat loaf') + ], + timestamp_ms=timestamp_ms) + ], + head_index=0, + head_name='probability') + ]) + + +def _generate_soccer_ball_results(timestamp_ms: int) -> _ClassificationResult: + return _ClassificationResult(classifications=[ + _Classifications( + entries=[ + _ClassificationEntry( + categories=[ + _Category( + index=806, + score=0.9965274930000305, + display_name='', + category_name='soccer ball') + ], + timestamp_ms=timestamp_ms) + ], + head_index=0, + head_name='probability') + ]) + + +class ModelFileType(enum.Enum): + FILE_CONTENT = 1 + FILE_NAME = 2 + + +class ImageClassifierTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.test_image = _Image.create_from_file( + test_utils.get_test_data_path(_IMAGE_FILE)) + self.model_path = test_utils.get_test_data_path(_MODEL_FILE) + + def test_create_from_file_succeeds_with_valid_model_path(self): + # Creates with default option and valid model file successfully. + with _ImageClassifier.create_from_model_path(self.model_path) as classifier: + self.assertIsInstance(classifier, _ImageClassifier) + + def test_create_from_options_succeeds_with_valid_model_path(self): + # Creates with options containing model file successfully. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _ImageClassifierOptions(base_options=base_options) + with _ImageClassifier.create_from_options(options) as classifier: + self.assertIsInstance(classifier, _ImageClassifier) + + def test_create_from_options_fails_with_invalid_model_path(self): + # Invalid empty model path. + with self.assertRaisesRegex( + ValueError, + r"ExternalFile must specify at least one of 'file_content', " + r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."): + base_options = _BaseOptions(model_asset_path='') + options = _ImageClassifierOptions(base_options=base_options) + _ImageClassifier.create_from_options(options) + + def test_create_from_options_succeeds_with_valid_model_content(self): + # Creates with options containing model content successfully. + with open(self.model_path, 'rb') as f: + base_options = _BaseOptions(model_asset_buffer=f.read()) + options = _ImageClassifierOptions(base_options=base_options) + classifier = _ImageClassifier.create_from_options(options) + self.assertIsInstance(classifier, _ImageClassifier) + + @parameterized.parameters( + (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)), + (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0))) + def test_classify(self, model_file_type, max_results, + expected_classification_result): + # Creates classifier. + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + custom_classifier_options = _ClassifierOptions(max_results=max_results) + options = _ImageClassifierOptions( + base_options=base_options, classifier_options=custom_classifier_options) + classifier = _ImageClassifier.create_from_options(options) + + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + # Comparing results. + _assert_proto_equals(image_result.to_pb2(), + expected_classification_result.to_pb2()) + # Closes the classifier explicitly when the classifier is not used in + # a context. + classifier.close() + + @parameterized.parameters( + (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)), + (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0))) + def test_classify_in_context(self, model_file_type, max_results, + expected_classification_result): + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + custom_classifier_options = _ClassifierOptions(max_results=max_results) + options = _ImageClassifierOptions( + base_options=base_options, classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + # Comparing results. + _assert_proto_equals(image_result.to_pb2(), + expected_classification_result.to_pb2()) + + def test_classify_succeeds_with_region_of_interest(self): + base_options = _BaseOptions(model_asset_path=self.model_path) + custom_classifier_options = _ClassifierOptions(max_results=1) + options = _ImageClassifierOptions( + base_options=base_options, classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Load the test image. + test_image = _Image.create_from_file( + test_utils.get_test_data_path('multi_objects.jpg')) + # NormalizedRect around the soccer ball. + roi = _NormalizedRect( + x_center=0.532, y_center=0.521, width=0.164, height=0.427) + # Performs image classification on the input. + image_result = classifier.classify(test_image, roi) + # Comparing results. + _assert_proto_equals(image_result.to_pb2(), + _generate_soccer_ball_results(0).to_pb2()) + + def test_score_threshold_option(self): + custom_classifier_options = _ClassifierOptions( + score_threshold=_SCORE_THRESHOLD) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + classifications = image_result.classifications + + for classification in classifications: + for entry in classification.entries: + score = entry.categories[0].score + self.assertGreaterEqual( + score, _SCORE_THRESHOLD, + f'Classification with score lower than threshold found. ' + f'{classification}') + + def test_max_results_option(self): + custom_classifier_options = _ClassifierOptions( + score_threshold=_SCORE_THRESHOLD) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + categories = image_result.classifications[0].entries[0].categories + + self.assertLessEqual( + len(categories), _MAX_RESULTS, 'Too many results returned.') + + def test_allow_list_option(self): + custom_classifier_options = _ClassifierOptions( + category_allowlist=_ALLOW_LIST) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + classifications = image_result.classifications + + for classification in classifications: + for entry in classification.entries: + label = entry.categories[0].category_name + self.assertIn(label, _ALLOW_LIST, + f'Label {label} found but not in label allow list') + + def test_deny_list_option(self): + custom_classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + classifications = image_result.classifications + + for classification in classifications: + for entry in classification.entries: + label = entry.categories[0].category_name + self.assertNotIn(label, _DENY_LIST, + f'Label {label} found but in deny list.') + + def test_combined_allowlist_and_denylist(self): + # Fails with combined allowlist and denylist + with self.assertRaisesRegex( + ValueError, + r'`category_allowlist` and `category_denylist` are mutually ' + r'exclusive options.'): + custom_classifier_options = _ClassifierOptions( + category_allowlist=['foo'], category_denylist=['bar']) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as unused_classifier: + pass + + def test_empty_classification_outputs(self): + custom_classifier_options = _ClassifierOptions(score_threshold=1) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Performs image classification on the input. + image_result = classifier.classify(self.test_image) + self.assertEmpty(image_result.classifications[0].entries[0].categories) + + def test_missing_result_callback(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM) + with self.assertRaisesRegex(ValueError, + r'result callback must be provided'): + with _ImageClassifier.create_from_options(options) as unused_classifier: + pass + + @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO)) + def test_illegal_result_callback(self, running_mode): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=running_mode, + result_callback=mock.MagicMock()) + with self.assertRaisesRegex(ValueError, + r'result callback should not be provided'): + with _ImageClassifier.create_from_options(options) as unused_classifier: + pass + + def test_calling_classify_for_video_in_image_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + classifier.classify_for_video(self.test_image, 0) + + def test_calling_classify_async_in_image_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + classifier.classify_async(self.test_image, 0) + + def test_calling_classify_in_video_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + classifier.classify(self.test_image) + + def test_calling_classify_async_in_video_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + classifier.classify_async(self.test_image, 0) + + def test_classify_for_video_with_out_of_order_timestamp(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _ImageClassifier.create_from_options(options) as classifier: + unused_result = classifier.classify_for_video(self.test_image, 1) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + classifier.classify_for_video(self.test_image, 0) + + def test_classify_for_video(self): + custom_classifier_options = _ClassifierOptions(max_results=4) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO, + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + for timestamp in range(0, 300, 30): + classification_result = classifier.classify_for_video( + self.test_image, timestamp) + _assert_proto_equals(classification_result.to_pb2(), + _generate_burger_results(timestamp).to_pb2()) + + def test_classify_for_video_succeeds_with_region_of_interest(self): + custom_classifier_options = _ClassifierOptions(max_results=1) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO, + classifier_options=custom_classifier_options) + with _ImageClassifier.create_from_options(options) as classifier: + # Load the test image. + test_image = _Image.create_from_file( + test_utils.get_test_data_path('multi_objects.jpg')) + # NormalizedRect around the soccer ball. + roi = _NormalizedRect( + x_center=0.532, y_center=0.521, width=0.164, height=0.427) + for timestamp in range(0, 300, 30): + classification_result = classifier.classify_for_video( + test_image, timestamp, roi) + self.assertEqual(classification_result, + _generate_soccer_ball_results(timestamp)) + + def test_calling_classify_in_live_stream_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + classifier.classify(self.test_image) + + def test_calling_classify_for_video_in_live_stream_mode(self): + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _ImageClassifier.create_from_options(options) as classifier: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + classifier.classify_for_video(self.test_image, 0) + + def test_classify_async_calls_with_illegal_timestamp(self): + custom_classifier_options = _ClassifierOptions(max_results=4) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + classifier_options=custom_classifier_options, + result_callback=mock.MagicMock()) + with _ImageClassifier.create_from_options(options) as classifier: + classifier.classify_async(self.test_image, 100) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + classifier.classify_async(self.test_image, 0) + + @parameterized.parameters((0, _generate_burger_results), + (1, _generate_empty_results)) + def test_classify_async_calls(self, threshold, expected_result_fn): + observed_timestamp_ms = -1 + + def check_result(result: _ClassificationResult, output_image: _Image, + timestamp_ms: int): + _assert_proto_equals(result.to_pb2(), + expected_result_fn(timestamp_ms).to_pb2()) + self.assertTrue( + np.array_equal(output_image.numpy_view(), + self.test_image.numpy_view())) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + custom_classifier_options = _ClassifierOptions( + max_results=4, score_threshold=threshold) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + classifier_options=custom_classifier_options, + result_callback=check_result) + with _ImageClassifier.create_from_options(options) as classifier: + for timestamp in range(0, 300, 30): + classifier.classify_async(self.test_image, timestamp) + + def test_classify_async_succeeds_with_region_of_interest(self): + # Load the test image. + test_image = _Image.create_from_file( + test_utils.get_test_data_path('multi_objects.jpg')) + # NormalizedRect around the soccer ball. + roi = _NormalizedRect( + x_center=0.532, y_center=0.521, width=0.164, height=0.427) + observed_timestamp_ms = -1 + + def check_result(result: _ClassificationResult, output_image: _Image, + timestamp_ms: int): + _assert_proto_equals(result.to_pb2(), + _generate_soccer_ball_results(timestamp_ms).to_pb2()) + self.assertEqual(output_image.width, test_image.width) + self.assertEqual(output_image.height, test_image.height) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + custom_classifier_options = _ClassifierOptions(max_results=1) + options = _ImageClassifierOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + classifier_options=custom_classifier_options, + result_callback=check_result) + with _ImageClassifier.create_from_options(options) as classifier: + for timestamp in range(0, 300, 30): + classifier.classify_async(test_image, timestamp, roi) + + +if __name__ == '__main__': + absltest.main() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index e7be51c8d..1036d0d32 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -36,3 +36,25 @@ py_library( "//mediapipe/tasks/python/vision/core:vision_task_running_mode", ], ) + +py_library( + name = "image_classifier", + srcs = [ + "image_classifier.py", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/python:packet_creator", + "//mediapipe/python:packet_getter", + "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2", + "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_py_pb2", + "//mediapipe/tasks/python/components/containers:classifications", + "//mediapipe/tasks/python/components/containers:rect", + "//mediapipe/tasks/python/components/processors:classifier_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", + ], +) diff --git a/mediapipe/tasks/python/vision/image_classifier.py b/mediapipe/tasks/python/vision/image_classifier.py new file mode 100644 index 000000000..e41cc77a2 --- /dev/null +++ b/mediapipe/tasks/python/vision/image_classifier.py @@ -0,0 +1,294 @@ +# 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 classifier task.""" + +import dataclasses +from typing import Callable, Mapping, Optional + +from mediapipe.python import packet_creator +from mediapipe.python import packet_getter +# TODO: Import MPImage directly one we have an alias +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.python._framework_bindings import packet +from mediapipe.python._framework_bindings import task_runner +from mediapipe.tasks.cc.components.containers.proto import classifications_pb2 +from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2 +from mediapipe.tasks.python.components.containers import classifications +from mediapipe.tasks.python.components.containers import rect +from mediapipe.tasks.python.components.processors import classifier_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 + +_NormalizedRect = rect.NormalizedRect +_BaseOptions = base_options_module.BaseOptions +_ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions +_ClassifierOptions = classifier_options.ClassifierOptions +_RunningMode = vision_task_running_mode.VisionTaskRunningMode +_TaskInfo = task_info_module.TaskInfo +_TaskRunner = task_runner.TaskRunner + +_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out' +_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT' +_IMAGE_IN_STREAM_NAME = 'image_in' +_IMAGE_OUT_STREAM_NAME = 'image_out' +_IMAGE_TAG = 'IMAGE' +_NORM_RECT_NAME = 'norm_rect_in' +_NORM_RECT_TAG = 'NORM_RECT' +_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph' +_MICRO_SECONDS_PER_MILLISECOND = 1000 + + +def _build_full_image_norm_rect() -> _NormalizedRect: + # Builds a NormalizedRect covering the entire image. + return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1) + + +@dataclasses.dataclass +class ImageClassifierOptions: + """Options for the image classifier task. + + Attributes: + base_options: Base options for the image classifier task. + running_mode: The running mode of the task. Default to the image mode. Image + classifier task has three running modes: 1) The image mode for classifying + objects on single image inputs. 2) The video mode for classifying objects + on the decoded frames of a video. 3) The live stream mode for classifying + objects on a live stream of input data, such as from camera. + classifier_options: Options for the image classification task. + 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 + classifier_options: _ClassifierOptions = _ClassifierOptions() + result_callback: Optional[ + Callable[[classifications.ClassificationResult, image_module.Image, int], + None]] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _ImageClassifierGraphOptionsProto: + """Generates an ImageClassifierOptions 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 + classifier_options_proto = self.classifier_options.to_pb2() + + return _ImageClassifierGraphOptionsProto( + base_options=base_options_proto, + classifier_options=classifier_options_proto) + + +class ImageClassifier(base_vision_task_api.BaseVisionTaskApi): + """Class that performs image classification on images.""" + + @classmethod + def create_from_model_path(cls, model_path: str) -> 'ImageClassifier': + """Creates an `ImageClassifier` object from a TensorFlow Lite model and the default `ImageClassifierOptions`. + + Note that the created `ImageClassifier` instance is in image mode, for + classifying objects on single image inputs. + + Args: + model_path: Path to the model. + + Returns: + `ImageClassifier` object that's created from the model file and the + default `ImageClassifierOptions`. + + Raises: + ValueError: If failed to create `ImageClassifier` object from the provided + file such as invalid file path. + RuntimeError: If other types of error occurred. + """ + base_options = _BaseOptions(model_asset_path=model_path) + options = ImageClassifierOptions( + base_options=base_options, running_mode=_RunningMode.IMAGE) + return cls.create_from_options(options) + + @classmethod + def create_from_options(cls, + options: ImageClassifierOptions) -> 'ImageClassifier': + """Creates the `ImageClassifier` object from image classifier options. + + Args: + options: Options for the image classifier task. + + Returns: + `ImageClassifier` object that's created from `options`. + + Raises: + ValueError: If failed to create `ImageClassifier` object from + `ImageClassifierOptions` such as missing the model. + RuntimeError: If other types of error occurred. + """ + + def packets_callback(output_packets: Mapping[str, packet.Packet]): + if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty(): + return + + classification_result_proto = classifications_pb2.ClassificationResult() + classification_result_proto.CopyFrom( + packet_getter.get_proto( + output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + + classification_result = classifications.ClassificationResult([ + classifications.Classifications.create_from_pb2(classification) + for classification in classification_result_proto.classifications + ]) + image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME]) + timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp + options.result_callback(classification_result, image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) + + task_info = _TaskInfo( + task_graph=_TASK_GRAPH_NAME, + input_streams=[ + ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]), + ':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]), + ], + output_streams=[ + ':'.join([ + _CLASSIFICATION_RESULT_TAG, + _CLASSIFICATION_RESULT_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: Replace _NormalizedRect with ImageProcessingOption + def classify( + self, + image: image_module.Image, + roi: Optional[_NormalizedRect] = None + ) -> classifications.ClassificationResult: + """Performs image classification on the provided MediaPipe Image. + + Args: + image: MediaPipe Image. + roi: The region of interest. + + Returns: + A classification result object that contains a list of classifications. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If image classification failed to run. + """ + norm_rect = roi if roi is not None else _build_full_image_norm_rect() + output_packets = self._process_image_data({ + _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image), + _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2()) + }) + + classification_result_proto = classifications_pb2.ClassificationResult() + classification_result_proto.CopyFrom( + packet_getter.get_proto( + output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + + return classifications.ClassificationResult([ + classifications.Classifications.create_from_pb2(classification) + for classification in classification_result_proto.classifications + ]) + + def classify_for_video( + self, + image: image_module.Image, + timestamp_ms: int, + roi: Optional[_NormalizedRect] = None + ) -> classifications.ClassificationResult: + """Performs image classification on the provided video frames. + + Only use this method when the ImageClassifier is created with the video + running mode. It's required to provide the video frame's timestamp (in + milliseconds) along with the video frame. The input timestamps should be + monotonically increasing for adjacent calls of this method. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input video frame in milliseconds. + roi: The region of interest. + + Returns: + A classification result object that contains a list of classifications. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If image classification failed to run. + """ + norm_rect = roi if roi is not None else _build_full_image_norm_rect() + output_packets = self._process_video_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_NAME: + packet_creator.create_proto(norm_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + }) + + classification_result_proto = classifications_pb2.ClassificationResult() + classification_result_proto.CopyFrom( + packet_getter.get_proto( + output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])) + + return classifications.ClassificationResult([ + classifications.Classifications.create_from_pb2(classification) + for classification in classification_result_proto.classifications + ]) + + def classify_async(self, + image: image_module.Image, + timestamp_ms: int, + roi: Optional[_NormalizedRect] = None) -> None: + """Sends live image data (an Image with a unique timestamp) to perform image classification. + + Only use this method when the ImageClassifier is created with the live + stream running mode. The input timestamps should be monotonically increasing + for adjacent calls of this method. This method will return immediately after + the input image is accepted. The results will be available via the + `result_callback` provided in the `ImageClassifierOptions`. The + `classify_async` method is designed to process live stream data such as + camera input. To lower the overall latency, image classifier 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 classification result object that contains a list of classifications. + - The input image that the image classifier runs on. + - The input timestamp in milliseconds. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input image in milliseconds. + roi: The region of interest. + + Raises: + ValueError: If the current input timestamp is smaller than what the image + classifier has already processed. + """ + norm_rect = roi if roi is not None else _build_full_image_norm_rect() + self._send_live_stream_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_NAME: + packet_creator.create_proto(norm_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + })