Merge pull request #3738 from kinaryml:image-classification-python-impl
PiperOrigin-RevId: 483818404
This commit is contained in:
Copybara-Service
commit
ae5b09e2b2
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@ -87,6 +87,7 @@ cc_library(
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cc_library(
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name = "builtin_task_graphs",
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deps = [
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"//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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],
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)
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@ -14,7 +14,7 @@
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"""The public facing packet getter APIs."""
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from typing import List, Type
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from typing import List
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from google.protobuf import message
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from google.protobuf import symbol_database
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@ -39,7 +39,7 @@ get_image_frame = _packet_getter.get_image_frame
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get_matrix = _packet_getter.get_matrix
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def get_proto(packet: mp_packet.Packet) -> Type[message.Message]:
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def get_proto(packet: mp_packet.Packet) -> message.Message:
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"""Get the content of a MediaPipe proto Packet as a proto message.
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Args:
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|
|
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@ -27,6 +27,15 @@ py_library(
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],
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)
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py_library(
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name = "rect",
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srcs = ["rect.py"],
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deps = [
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"//mediapipe/framework/formats:rect_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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py_library(
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name = "category",
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srcs = ["category.py"],
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@ -47,3 +56,13 @@ py_library(
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"//mediapipe/tasks/python/core:optional_dependencies",
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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/proto: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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168
mediapipe/tasks/python/components/containers/classifications.py
Normal file
168
mediapipe/tasks/python/components/containers/classifications.py
Normal file
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@ -0,0 +1,168 @@
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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.proto 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. This
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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(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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"""
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return ClassificationResult(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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|
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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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140
mediapipe/tasks/python/components/containers/rect.py
Normal file
140
mediapipe/tasks/python/components/containers/rect.py
Normal file
|
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@ -0,0 +1,140 @@
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# 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."""
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import dataclasses
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from typing import Any, Optional
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from mediapipe.framework.formats import rect_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_RectProto = rect_pb2.Rect
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_NormalizedRectProto = rect_pb2.NormalizedRect
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@dataclasses.dataclass
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class Rect:
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"""A rectangle with rotation in image coordinates.
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Attributes: x_center : The X coordinate of the top-left corner, in pixels.
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y_center : The Y coordinate of the top-left corner, in pixels.
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width: The width of the rectangle, in pixels.
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height: The height of the rectangle, in pixels.
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rotation: Rotation angle is clockwise in radians.
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rect_id: Optional unique id to help associate different rectangles to each
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other.
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||||
"""
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x_center: int
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y_center: int
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width: int
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height: int
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||||
rotation: Optional[float] = 0.0
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rect_id: Optional[int] = None
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||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _RectProto:
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||||
"""Generates a Rect protobuf object."""
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||||
return _RectProto(
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||||
x_center=self.x_center,
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||||
y_center=self.y_center,
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||||
width=self.width,
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||||
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(
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||||
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())
|
||||
30
mediapipe/tasks/python/components/processors/BUILD
Normal file
30
mediapipe/tasks/python/components/processors/BUILD
Normal file
|
|
@ -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",
|
||||
],
|
||||
)
|
||||
|
|
@ -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())
|
||||
|
|
@ -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",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
515
mediapipe/tasks/python/test/vision/image_classifier_test.py
Normal file
515
mediapipe/tasks/python/test/vision/image_classifier_test.py
Normal file
|
|
@ -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()
|
||||
|
|
@ -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",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
294
mediapipe/tasks/python/vision/image_classifier.py
Normal file
294
mediapipe/tasks/python/vision/image_classifier.py
Normal file
|
|
@ -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)
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user