Added image classification implementation files and associated tests
This commit is contained in:
parent
4dc4b19ddb
commit
cb52432159
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@ -47,3 +47,13 @@ py_library(
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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],
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)
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)
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py_library(
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name = "classifications",
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srcs = ["classifications.py"],
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deps = [
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":category",
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"//mediapipe/tasks/cc/components/containers:classifications_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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169
mediapipe/tasks/python/components/containers/classifications.py
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169
mediapipe/tasks/python/components/containers/classifications.py
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@ -0,0 +1,169 @@
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Classifications data class."""
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import dataclasses
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from typing import Any, List, Optional
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from mediapipe.tasks.cc.components.containers import classifications_pb2
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_ClassificationEntryProto = classifications_pb2.ClassificationEntry
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_ClassificationsProto = classifications_pb2.Classifications
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_ClassificationResultProto = classifications_pb2.ClassificationResult
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@dataclasses.dataclass
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class ClassificationEntry:
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"""List of predicted classes (aka labels) for a given classifier head.
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Attributes:
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categories: The array of predicted categories, usually sorted by descending
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scores (e.g. from high to low probability).
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timestamp_ms: The optional timestamp (in milliseconds) associated to the
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classification entry. This is useful for time series use cases, e.g.,
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audio classification.
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"""
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categories: List[category_module.Category]
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timestamp_ms: Optional[int] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ClassificationEntryProto:
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"""Generates a ClassificationEntry protobuf object."""
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return _ClassificationEntryProto(
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categories=[category.to_pb2() for category in self.categories],
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timestamp_ms=self.timestamp_ms)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _ClassificationEntryProto) -> 'ClassificationEntry':
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"""Creates a `ClassificationEntry` object from the given protobuf object."""
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return ClassificationEntry(
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categories=[
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category_module.Category.create_from_pb2(category)
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for category in pb2_obj.categories
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],
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timestamp_ms=pb2_obj.timestamp_ms)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, ClassificationEntry):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class Classifications:
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"""Represents the classifications for a given classifier head.
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Attributes:
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entries: A list of `ClassificationEntry` objects.
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head_index: The index of the classifier head these categories refer to.
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This is useful for multi-head models.
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head_name: The name of the classifier head, which is the corresponding
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tensor metadata name.
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"""
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entries: List[ClassificationEntry]
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head_index: int
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head_name: str
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ClassificationsProto:
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"""Generates a Classifications protobuf object."""
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return _ClassificationsProto(
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entries=[entry.to_pb2() for entry in self.entries],
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head_index=self.head_index,
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head_name=self.head_name)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications':
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"""Creates a `Classifications` object from the given protobuf object."""
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return Classifications(
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entries=[
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ClassificationEntry.create_from_pb2(entry)
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for entry in pb2_obj.entries
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],
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head_index=pb2_obj.head_index,
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head_name=pb2_obj.head_name)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, Classifications):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class ClassificationResult:
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"""Contains one set of results per classifier head.
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Attributes:
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classifications: A list of `Classifications` objects.
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"""
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classifications: List[Classifications]
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ClassificationResultProto:
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"""Generates a ClassificationResult protobuf object."""
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return _ClassificationResultProto(
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classifications=[
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classification.to_pb2() for classification in self.classifications
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])
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _ClassificationResultProto) -> 'ClassificationResult':
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"""Creates a `ClassificationResult` object from the given protobuf object."""
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return ClassificationResult(
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classifications=[
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Classifications.create_from_pb2(classification)
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for classification in pb2_obj.classifications
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])
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, ClassificationResult):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@ -18,4 +18,40 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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licenses(["notice"])
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# TODO: This test fails in OSS
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py_test(
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name = "object_detector_test",
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srcs = ["object_detector_test.py"],
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data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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],
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deps = [
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# build rule placeholder: numpy dep,
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"//mediapipe/tasks/python/components/containers:bounding_box",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:detections",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_util",
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"//mediapipe/tasks/python/vision:object_detector",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"@absl_py//absl/testing:parameterized",
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],
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)
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py_test(
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name = "image_classification_test",
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srcs = ["image_classification_test.py"],
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data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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],
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deps = [
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:classifications",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_util",
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"//mediapipe/tasks/python/vision:image_classification",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"@absl_py//absl/testing:parameterized",
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],
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)
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301
mediapipe/tasks/python/test/vision/image_classification_test.py
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301
mediapipe/tasks/python/test/vision/image_classification_test.py
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for image classifier."""
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import enum
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from absl.testing import absltest
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from absl.testing import parameterized
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import classifications as classifications_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_util
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from mediapipe.tasks.python.vision import image_classification
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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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_Category = category_module.Category
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_ClassificationEntry = classifications_module.ClassificationEntry
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_Classifications = classifications_module.Classifications
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_ClassificationResult = classifications_module.ClassificationResult
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_Image = image_module.Image
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_ImageClassifier = image_classification.ImageClassifier
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_ImageClassifierOptions = image_classification.ImageClassifierOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
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_IMAGE_FILE = 'burger.jpg'
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_EXPECTED_CLASSIFICATION_RESULT = _ClassificationResult(
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classifications=[
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_Classifications(
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entries=[
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_ClassificationEntry(
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categories=[
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_Category(
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index=934,
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score=0.7952049970626831,
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display_name='',
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category_name='cheeseburger'),
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_Category(
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index=932,
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score=0.02732999622821808,
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display_name='',
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category_name='bagel'),
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_Category(
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index=925,
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score=0.01933487318456173,
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display_name='',
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category_name='guacamole'),
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_Category(
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index=963,
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score=0.006279350258409977,
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display_name='',
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category_name='meat loaf')
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],
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timestamp_ms=0
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)
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],
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head_index=0,
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head_name='probability')
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])
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_ALLOW_LIST = ['cheeseburger', 'guacamole']
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_DENY_LIST = ['cheeseburger']
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_SCORE_THRESHOLD = 0.5
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_MAX_RESULTS = 3
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class ImageClassifierTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = test_util.read_test_image(
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test_util.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _ImageClassifier.create_from_model_path(self.model_path) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(file_name=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(file_name='')
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options = _ImageClassifierOptions(base_options=base_options)
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_ImageClassifier.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(file_content=f.read())
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options = _ImageClassifierOptions(base_options=base_options)
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classifier = _ImageClassifier.create_from_options(options)
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self.assertIsInstance(classifier, _ImageClassifier)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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def test_classify(self, model_file_type, max_results,
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expected_classification_result):
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# Creates classifier.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(file_content=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _ImageClassifierOptions(
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base_options=base_options, max_results=max_results)
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classifier = _ImageClassifier.create_from_options(options)
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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# Comparing results.
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self.assertEqual(image_result, expected_classification_result)
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# Closes the classifier explicitly when the classifier is not used in
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# a context.
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|
classifier.close()
|
||||||
|
|
||||||
|
@parameterized.parameters(
|
||||||
|
(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
|
||||||
|
(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
|
||||||
|
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(file_name=self.model_path)
|
||||||
|
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||||
|
with open(self.model_path, 'rb') as f:
|
||||||
|
model_content = f.read()
|
||||||
|
base_options = _BaseOptions(file_content=model_content)
|
||||||
|
else:
|
||||||
|
# Should never happen
|
||||||
|
raise ValueError('model_file_type is invalid.')
|
||||||
|
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=base_options, max_results=max_results)
|
||||||
|
with _ImageClassifier.create_from_options(options) as classifier:
|
||||||
|
# Performs object detection on the input.
|
||||||
|
image_result = classifier.classify(self.test_image)
|
||||||
|
# Comparing results.
|
||||||
|
self.assertEqual(image_result, expected_classification_result)
|
||||||
|
|
||||||
|
def test_score_threshold_option(self):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
score_threshold=_SCORE_THRESHOLD)
|
||||||
|
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):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
max_results=_MAX_RESULTS)
|
||||||
|
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):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
category_allowlist=_ALLOW_LIST)
|
||||||
|
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):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
category_denylist=_DENY_LIST)
|
||||||
|
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.'):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
category_allowlist=['foo'],
|
||||||
|
category_denylist=['bar'])
|
||||||
|
with _ImageClassifier.create_from_options(options) as unused_classifier:
|
||||||
|
pass
|
||||||
|
|
||||||
|
def test_empty_classification_outputs(self):
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path), score_threshold=1)
|
||||||
|
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(file_name=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):
|
||||||
|
|
||||||
|
def pass_through(unused_result: _ClassificationResult):
|
||||||
|
pass
|
||||||
|
|
||||||
|
options = _ImageClassifierOptions(
|
||||||
|
base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
running_mode=running_mode,
|
||||||
|
result_callback=pass_through)
|
||||||
|
with self.assertRaisesRegex(ValueError,
|
||||||
|
r'result callback should not be provided'):
|
||||||
|
with _ImageClassifier.create_from_options(options) as unused_classifier:
|
||||||
|
pass
|
||||||
|
|
||||||
|
# @parameterized.parameters((0, _EXPECTED_CLASSIFICATION_RESULT),
|
||||||
|
# (1, _ClassificationResult(classifications=[])))
|
||||||
|
# def test_classify_async_calls(self, threshold, expected_result):
|
||||||
|
# observed_timestamp_ms = -1
|
||||||
|
#
|
||||||
|
# def check_result(result: _ClassificationResult, timestamp_ms: int):
|
||||||
|
# self.assertEqual(result, expected_result)
|
||||||
|
# self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||||
|
# self.observed_timestamp_ms = timestamp_ms
|
||||||
|
#
|
||||||
|
# options = _ImageClassifierOptions(
|
||||||
|
# base_options=_BaseOptions(file_name=self.model_path),
|
||||||
|
# running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||||
|
# max_results=4,
|
||||||
|
# score_threshold=threshold,
|
||||||
|
# result_callback=check_result)
|
||||||
|
# classifier = _ImageClassifier.create_from_options(options)
|
||||||
|
# for timestamp in range(0, 300, 30):
|
||||||
|
# classifier.classify_async(self.test_image, timestamp)
|
||||||
|
# classifier.close()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
absltest.main()
|
|
@ -36,3 +36,23 @@ py_library(
|
||||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||||
],
|
],
|
||||||
)
|
)
|
||||||
|
|
||||||
|
py_library(
|
||||||
|
name = "image_classification",
|
||||||
|
srcs = [
|
||||||
|
"image_classification.py",
|
||||||
|
],
|
||||||
|
deps = [
|
||||||
|
"//mediapipe/python:_framework_bindings",
|
||||||
|
"//mediapipe/python:packet_creator",
|
||||||
|
"//mediapipe/python:packet_getter",
|
||||||
|
"//mediapipe/tasks/cc/components:classifier_options_py_pb2",
|
||||||
|
"//mediapipe/tasks/cc/vision/image_classification:image_classifier_options_py_pb2",
|
||||||
|
"//mediapipe/tasks/python/components/containers:classifications",
|
||||||
|
"//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",
|
||||||
|
],
|
||||||
|
)
|
||||||
|
|
227
mediapipe/tasks/python/vision/image_classification.py
Normal file
227
mediapipe/tasks/python/vision/image_classification.py
Normal file
|
@ -0,0 +1,227 @@
|
||||||
|
# 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, List, Mapping, Optional
|
||||||
|
|
||||||
|
from mediapipe.python import packet_creator
|
||||||
|
from mediapipe.python import packet_getter
|
||||||
|
from mediapipe.python._framework_bindings import image as image_module
|
||||||
|
from mediapipe.python._framework_bindings import packet as packet_module
|
||||||
|
from mediapipe.python._framework_bindings import task_runner as task_runner_module
|
||||||
|
from mediapipe.tasks.cc.components import classifier_options_pb2
|
||||||
|
from mediapipe.tasks.cc.vision.image_classification import image_classifier_options_pb2
|
||||||
|
from mediapipe.tasks.python.components.containers import classifications as classifications_module
|
||||||
|
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||||
|
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||||
|
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||||
|
from mediapipe.tasks.python.vision.core import base_vision_task_api
|
||||||
|
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||||
|
|
||||||
|
_BaseOptions = base_options_module.BaseOptions
|
||||||
|
_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
|
||||||
|
_ImageClassifierOptionsProto = image_classifier_options_pb2.ImageClassifierOptions
|
||||||
|
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||||
|
_TaskInfo = task_info_module.TaskInfo
|
||||||
|
_TaskRunner = task_runner_module.TaskRunner
|
||||||
|
|
||||||
|
_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
|
||||||
|
_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
|
||||||
|
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||||
|
_IMAGE_TAG = 'IMAGE'
|
||||||
|
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageClassifierGraph'
|
||||||
|
|
||||||
|
|
||||||
|
@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.
|
||||||
|
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`.
|
||||||
|
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
|
||||||
|
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
|
||||||
|
result_callback: Optional[
|
||||||
|
Callable[[classifications_module.ClassificationResult], None]] = None
|
||||||
|
|
||||||
|
@doc_controls.do_not_generate_docs
|
||||||
|
def to_pb2(self) -> _ImageClassifierOptionsProto:
|
||||||
|
"""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 = _ClassifierOptionsProto(
|
||||||
|
display_names_locale=self.display_names_locale,
|
||||||
|
max_results=self.max_results,
|
||||||
|
score_threshold=self.score_threshold,
|
||||||
|
category_allowlist=self.category_allowlist,
|
||||||
|
category_denylist=self.category_denylist)
|
||||||
|
|
||||||
|
return _ImageClassifierOptionsProto(
|
||||||
|
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
|
||||||
|
detecting 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(file_name=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_module.Packet]):
|
||||||
|
classification_result_proto = packet_getter.get_proto(
|
||||||
|
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
|
||||||
|
|
||||||
|
classification_result = classifications_module.ClassificationResult([
|
||||||
|
classifications_module.Classifications.create_from_pb2(classification)
|
||||||
|
for classification in classification_result_proto.classifications
|
||||||
|
])
|
||||||
|
options.result_callback(classification_result)
|
||||||
|
|
||||||
|
task_info = _TaskInfo(
|
||||||
|
task_graph=_TASK_GRAPH_NAME,
|
||||||
|
input_streams=[':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME])],
|
||||||
|
output_streams=[
|
||||||
|
':'.join([_CLASSIFICATION_RESULT_TAG,
|
||||||
|
_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
|
||||||
|
],
|
||||||
|
task_options=options)
|
||||||
|
return cls(
|
||||||
|
task_info.generate_graph_config(
|
||||||
|
enable_flow_limiting=options.running_mode ==
|
||||||
|
_RunningMode.LIVE_STREAM), options.running_mode,
|
||||||
|
packets_callback if options.result_callback else None)
|
||||||
|
|
||||||
|
# TODO: Create an Image class for MediaPipe Tasks.
|
||||||
|
def classify(
|
||||||
|
self,
|
||||||
|
image: image_module.Image
|
||||||
|
) -> classifications_module.ClassificationResult:
|
||||||
|
"""Performs image classification on the provided MediaPipe Image.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
image: MediaPipe Image.
|
||||||
|
|
||||||
|
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.
|
||||||
|
"""
|
||||||
|
output_packets = self._process_image_data(
|
||||||
|
{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
|
||||||
|
classification_result_proto = packet_getter.get_proto(
|
||||||
|
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
|
||||||
|
|
||||||
|
return classifications_module.ClassificationResult([
|
||||||
|
classifications_module.Classifications.create_from_pb2(classification)
|
||||||
|
for classification in classification_result_proto.classifications
|
||||||
|
])
|
||||||
|
|
||||||
|
def classify_async(self, image: image_module.Image, timestamp_ms: int) -> None:
|
||||||
|
"""Sends live image data (an Image with a unique timestamp) to perform image
|
||||||
|
classification.
|
||||||
|
|
||||||
|
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 `detect_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.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the current input timestamp is smaller than what the image
|
||||||
|
classifier has already processed.
|
||||||
|
"""
|
||||||
|
self._send_live_stream_data({
|
||||||
|
_IMAGE_IN_STREAM_NAME:
|
||||||
|
packet_creator.create_image(image).at(timestamp_ms)
|
||||||
|
})
|
Loading…
Reference in New Issue
Block a user