Internal change

PiperOrigin-RevId: 493427500

mediapipe/tasks/python/audio/BUILD

@@ -29,11 +29,11 @@ py_library(
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_py_pb2",
         "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
         "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
         "//mediapipe/tasks/python/audio/core:base_audio_task_api",
         "//mediapipe/tasks/python/components/containers:audio_data",
         "//mediapipe/tasks/python/components/containers:classification_result",
-        "//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",
@@ -51,11 +51,11 @@ py_library(
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/audio/audio_embedder/proto:audio_embedder_graph_options_py_pb2",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_py_pb2",
         "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
         "//mediapipe/tasks/python/audio/core:base_audio_task_api",
         "//mediapipe/tasks/python/components/containers:audio_data",
         "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/components/utils:cosine_similarity",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/core:optional_dependencies",

mediapipe/tasks/python/audio/audio_classifier.py

@@ -21,11 +21,11 @@ from mediapipe.python import packet_getter
 from mediapipe.python._framework_bindings import packet
 from mediapipe.tasks.cc.audio.audio_classifier.proto import audio_classifier_graph_options_pb2
 from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
 from mediapipe.tasks.python.audio.core import audio_task_running_mode as running_mode_module
 from mediapipe.tasks.python.audio.core import base_audio_task_api
 from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
-from mediapipe.tasks.python.components.processors import classifier_options as classifier_options_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
@@ -34,7 +34,7 @@ AudioClassifierResult = classification_result_module.ClassificationResult
 _AudioClassifierGraphOptionsProto = audio_classifier_graph_options_pb2.AudioClassifierGraphOptions
 _AudioData = audio_data_module.AudioData
 _BaseOptions = base_options_module.BaseOptions
-_ClassifierOptions = classifier_options_module.ClassifierOptions
+_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
 _RunningMode = running_mode_module.AudioTaskRunningMode
 _TaskInfo = task_info_module.TaskInfo
 
@@ -62,16 +62,31 @@ class AudioClassifierOptions:
       mode for running classification on the audio stream, such as from
       microphone. In this mode,  the "result_callback" below must be specified
       to receive the classification results asynchronously.
-    classifier_options: Options for configuring the classifier behavior, such as
+    display_names_locale: The locale to use for display names specified through
-      score threshold, number of results, etc.
+      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,
+      classification 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, classification
+      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 audio
       stream data. The result callback should only be specified when the running
       mode is set to the audio stream mode.
   """
   base_options: _BaseOptions
   running_mode: _RunningMode = _RunningMode.AUDIO_CLIPS
-  classifier_options: Optional[_ClassifierOptions] = dataclasses.field(
+  display_names_locale: Optional[str] = None
-      default_factory=_ClassifierOptions)
+  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[[AudioClassifierResult, int], None]] = None
 
   @doc_controls.do_not_generate_docs
@@ -79,7 +94,12 @@ class AudioClassifierOptions:
     """Generates an AudioClassifierOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
     base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.AUDIO_CLIPS else True
-    classifier_options_proto = self.classifier_options.to_pb2()
+    classifier_options_proto = _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)
 
     return _AudioClassifierGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/python/audio/audio_embedder.py

@@ -21,11 +21,11 @@ from mediapipe.python import packet_getter
 from mediapipe.python._framework_bindings import packet
 from mediapipe.tasks.cc.audio.audio_embedder.proto import audio_embedder_graph_options_pb2
 from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
 from mediapipe.tasks.python.audio.core import audio_task_running_mode as running_mode_module
 from mediapipe.tasks.python.audio.core import base_audio_task_api
 from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
 from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
-from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
 from mediapipe.tasks.python.components.utils import cosine_similarity
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.core import task_info as task_info_module
@@ -35,7 +35,7 @@ AudioEmbedderResult = embedding_result_module.EmbeddingResult
 _AudioEmbedderGraphOptionsProto = audio_embedder_graph_options_pb2.AudioEmbedderGraphOptions
 _AudioData = audio_data_module.AudioData
 _BaseOptions = base_options_module.BaseOptions
-_EmbedderOptions = embedder_options_module.EmbedderOptions
+_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
 _RunningMode = running_mode_module.AudioTaskRunningMode
 _TaskInfo = task_info_module.TaskInfo
 
@@ -63,16 +63,22 @@ class AudioEmbedderOptions:
       stream mode for running embedding extraction on the audio stream, such as
       from microphone. In this mode,  the "result_callback" below must be
       specified to receive the embedding results asynchronously.
-    embedder_options: Options for configuring the embedder behavior, such as
+    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
-      l2_normalize and quantize.
+      Use this option only if the model does not already contain a native
+      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
+      L2 norm is thus achieved through TF Lite inference.
+    quantize: Whether the returned embedding should be quantized to bytes via
+      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
+      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
+      the l2_normalize option if this is not the case.
     result_callback: The user-defined result callback for processing audio
       stream data. The result callback should only be specified when the running
       mode is set to the audio stream mode.
   """
   base_options: _BaseOptions
   running_mode: _RunningMode = _RunningMode.AUDIO_CLIPS
-  embedder_options: Optional[_EmbedderOptions] = dataclasses.field(
+  l2_normalize: Optional[bool] = None
-      default_factory=_EmbedderOptions)
+  quantize: Optional[bool] = None
   result_callback: Optional[Callable[[AudioEmbedderResult, int], None]] = None
 
   @doc_controls.do_not_generate_docs
@@ -80,7 +86,8 @@ class AudioEmbedderOptions:
     """Generates an AudioEmbedderOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
     base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.AUDIO_CLIPS else True
-    embedder_options_proto = self.embedder_options.to_pb2()
+    embedder_options_proto = _EmbedderOptionsProto(
+        l2_normalize=self.l2_normalize, quantize=self.quantize)
 
     return _AudioEmbedderGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/python/components/processors/BUILD

@@ -28,12 +28,3 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )
-
-py_library(
-    name = "embedder_options",
-    srcs = ["embedder_options.py"],
-    deps = [
-        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_py_pb2",
-        "//mediapipe/tasks/python/core:optional_dependencies",
-    ],
-)

mediapipe/tasks/python/components/processors/__init__.py

@@ -15,12 +15,9 @@
 """MediaPipe Tasks Components Processors API."""
 
 import mediapipe.tasks.python.components.processors.classifier_options
-import mediapipe.tasks.python.components.processors.embedder_options
 
 ClassifierOptions = classifier_options.ClassifierOptions
-EmbedderOptions = embedder_options.EmbedderOptions
 
 # Remove unnecessary modules to avoid duplication in API docs.
 del classifier_options
-del embedder_options
 del mediapipe

mediapipe/tasks/python/components/processors/embedder_options.py

@@ -1,68 +0,0 @@
-# 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.
-"""Embedder options data class."""
-
-import dataclasses
-from typing import Any, Optional
-
-from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
-from mediapipe.tasks.python.core.optional_dependencies import doc_controls
-
-_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
-
-
-@dataclasses.dataclass
-class EmbedderOptions:
-  """Shared options used by all embedding extraction tasks.
-
-  Attributes:
-    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
-      Use this option only if the model does not already contain a native
-      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
-      L2 norm is thus achieved through TF Lite inference.
-    quantize: Whether the returned embedding should be quantized to bytes via
-      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
-      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
-      the l2_normalize option if this is not the case.
-  """
-
-  l2_normalize: Optional[bool] = None
-  quantize: Optional[bool] = None
-
-  @doc_controls.do_not_generate_docs
-  def to_pb2(self) -> _EmbedderOptionsProto:
-    """Generates a EmbedderOptions protobuf object."""
-    return _EmbedderOptionsProto(
-        l2_normalize=self.l2_normalize, quantize=self.quantize)
-
-  @classmethod
-  @doc_controls.do_not_generate_docs
-  def create_from_pb2(cls, pb2_obj: _EmbedderOptionsProto) -> 'EmbedderOptions':
-    """Creates a `EmbedderOptions` object from the given protobuf object."""
-    return EmbedderOptions(
-        l2_normalize=pb2_obj.l2_normalize, quantize=pb2_obj.quantize)
-
-  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, EmbedderOptions):
-      return False
-
-    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/utils/BUILD

@@ -23,8 +23,5 @@ licenses(["notice"])
 py_library(
     name = "cosine_similarity",
     srcs = ["cosine_similarity.py"],
-    deps = [
+    deps = ["//mediapipe/tasks/python/components/containers:embedding_result"],
-        "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
-    ],
 )

mediapipe/tasks/python/components/utils/cosine_similarity.py

@@ -16,10 +16,8 @@
 import numpy as np
 
 from mediapipe.tasks.python.components.containers import embedding_result
-from mediapipe.tasks.python.components.processors import embedder_options
 
 _Embedding = embedding_result.Embedding
-_EmbedderOptions = embedder_options.EmbedderOptions
 
 
 def _compute_cosine_similarity(u, v):

mediapipe/tasks/python/test/audio/BUILD

@@ -30,7 +30,6 @@ py_test(
         "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
         "//mediapipe/tasks/python/components/containers:audio_data",
         "//mediapipe/tasks/python/components/containers:classification_result",
-        "//mediapipe/tasks/python/components/processors:classifier_options",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
     ],
@@ -48,7 +47,6 @@ py_test(
         "//mediapipe/tasks/python/audio/core:audio_task_running_mode",
         "//mediapipe/tasks/python/components/containers:audio_data",
         "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
     ],

mediapipe/tasks/python/test/audio/audio_classifier_test.py

@@ -27,7 +27,6 @@ from mediapipe.tasks.python.audio import audio_classifier
 from mediapipe.tasks.python.audio.core import audio_task_running_mode
 from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
-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
 
@@ -36,7 +35,6 @@ _AudioClassifierOptions = audio_classifier.AudioClassifierOptions
 _AudioClassifierResult = classification_result_module.ClassificationResult
 _AudioData = audio_data_module.AudioData
 _BaseOptions = base_options_module.BaseOptions
-_ClassifierOptions = classifier_options.ClassifierOptions
 _RUNNING_MODE = audio_task_running_mode.AudioTaskRunningMode
 
 _YAMNET_MODEL_FILE = 'yamnet_audio_classifier_with_metadata.tflite'
@@ -210,8 +208,7 @@ class AudioClassifierTest(parameterized.TestCase):
     with _AudioClassifier.create_from_options(
         _AudioClassifierOptions(
             base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
-            classifier_options=_ClassifierOptions(
+            max_results=1)) as classifier:
-                max_results=1))) as classifier:
       for audio_file in [_SPEECH_WAV_16K_MONO, _SPEECH_WAV_16K_MONO]:
         classification_result_list = classifier.classify(
             self._read_wav_file(audio_file))
@@ -222,8 +219,7 @@ class AudioClassifierTest(parameterized.TestCase):
     with _AudioClassifier.create_from_options(
         _AudioClassifierOptions(
             base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
-            classifier_options=_ClassifierOptions(
+            score_threshold=0.9)) as classifier:
-                score_threshold=0.9))) as classifier:
       for audio_file in [_SPEECH_WAV_16K_MONO, _SPEECH_WAV_16K_MONO]:
         classification_result_list = classifier.classify(
             self._read_wav_file(audio_file))
@@ -234,8 +230,7 @@ class AudioClassifierTest(parameterized.TestCase):
     with _AudioClassifier.create_from_options(
         _AudioClassifierOptions(
             base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
-            classifier_options=_ClassifierOptions(
+            category_allowlist=['Speech'])) as classifier:
-                category_allowlist=['Speech']))) as classifier:
       for audio_file in [_SPEECH_WAV_16K_MONO, _SPEECH_WAV_16K_MONO]:
         classification_result_list = classifier.classify(
             self._read_wav_file(audio_file))
@@ -250,8 +245,8 @@ class AudioClassifierTest(parameterized.TestCase):
         r'exclusive options.'):
       options = _AudioClassifierOptions(
           base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
-          classifier_options=_ClassifierOptions(
+          category_allowlist=['foo'],
-              category_allowlist=['foo'], category_denylist=['bar']))
+          category_denylist=['bar'])
       with _AudioClassifier.create_from_options(options) as unused_classifier:
         pass
 
@@ -278,8 +273,7 @@ class AudioClassifierTest(parameterized.TestCase):
         _AudioClassifierOptions(
             base_options=_BaseOptions(
                 model_asset_path=self.two_heads_model_path),
-            classifier_options=_ClassifierOptions(
+            max_results=1)) as classifier:
-                max_results=1))) as classifier:
       for audio_file in [_TWO_HEADS_WAV_16K_MONO, _TWO_HEADS_WAV_44K_MONO]:
         classification_result_list = classifier.classify(
             self._read_wav_file(audio_file))
@@ -364,7 +358,7 @@ class AudioClassifierTest(parameterized.TestCase):
     options = _AudioClassifierOptions(
         base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
         running_mode=_RUNNING_MODE.AUDIO_STREAM,
-        classifier_options=_ClassifierOptions(max_results=1),
+        max_results=1,
         result_callback=save_result)
     classifier = _AudioClassifier.create_from_options(options)
     audio_data_list = self._read_wav_file_as_stream(audio_file)

mediapipe/tasks/python/test/audio/audio_embedder_test.py

@@ -26,7 +26,6 @@ from scipy.io import wavfile
 from mediapipe.tasks.python.audio import audio_embedder
 from mediapipe.tasks.python.audio.core import audio_task_running_mode
 from mediapipe.tasks.python.components.containers import audio_data as audio_data_module
-from mediapipe.tasks.python.components.processors import embedder_options
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.test import test_utils
 
@@ -35,7 +34,6 @@ _AudioEmbedderOptions = audio_embedder.AudioEmbedderOptions
 _AudioEmbedderResult = audio_embedder.AudioEmbedderResult
 _AudioData = audio_data_module.AudioData
 _BaseOptions = base_options_module.BaseOptions
-_EmbedderOptions = embedder_options.EmbedderOptions
 _RUNNING_MODE = audio_task_running_mode.AudioTaskRunningMode
 
 _YAMNET_MODEL_FILE = 'yamnet_embedding_metadata.tflite'
@@ -172,9 +170,7 @@ class AudioEmbedderTest(parameterized.TestCase):
       raise ValueError('model_file_type is invalid.')
 
     options = _AudioEmbedderOptions(
-        base_options=base_options,
+        base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
-        embedder_options=_EmbedderOptions(
-            l2_normalize=l2_normalize, quantize=quantize))
 
     with _AudioEmbedder.create_from_options(options) as embedder:
       embedding_result0_list = embedder.embed(self._read_wav_file(audio_file0))
@@ -291,8 +287,8 @@ class AudioEmbedderTest(parameterized.TestCase):
     options = _AudioEmbedderOptions(
         base_options=_BaseOptions(model_asset_path=self.yamnet_model_path),
         running_mode=_RUNNING_MODE.AUDIO_STREAM,
-        embedder_options=_EmbedderOptions(
+        l2_normalize=l2_normalize,
-            l2_normalize=l2_normalize, quantize=quantize),
+        quantize=quantize,
         result_callback=save_result)
 
     with _AudioEmbedder.create_from_options(options) as embedder:

mediapipe/tasks/python/test/text/BUILD

@@ -28,7 +28,6 @@ py_test(
     deps = [
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:classification_result",
-        "//mediapipe/tasks/python/components/processors:classifier_options",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/text:text_classifier",
@@ -44,7 +43,6 @@ py_test(
     ],
     deps = [
         "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/text:text_embedder",

mediapipe/tasks/python/test/text/text_classifier_test.py

@@ -21,14 +21,12 @@ from absl.testing import parameterized
 
 from mediapipe.tasks.python.components.containers import category
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
-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.text import text_classifier
 
 TextClassifierResult = classification_result_module.ClassificationResult
 _BaseOptions = base_options_module.BaseOptions
-_ClassifierOptions = classifier_options.ClassifierOptions
 _Category = category.Category
 _Classifications = classification_result_module.Classifications
 _TextClassifier = text_classifier.TextClassifier

mediapipe/tasks/python/test/text/text_embedder_test.py

@@ -21,13 +21,11 @@ from absl.testing import parameterized
 import numpy as np
 
 from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
-from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.test import test_utils
 from mediapipe.tasks.python.text import text_embedder
 
 _BaseOptions = base_options_module.BaseOptions
-_EmbedderOptions = embedder_options_module.EmbedderOptions
 _Embedding = embedding_result_module.Embedding
 _TextEmbedder = text_embedder.TextEmbedder
 _TextEmbedderOptions = text_embedder.TextEmbedderOptions
@@ -128,10 +126,8 @@ class TextEmbedderTest(parameterized.TestCase):
       # Should never happen
       raise ValueError('model_file_type is invalid.')
 
-    embedder_options = _EmbedderOptions(
-        l2_normalize=l2_normalize, quantize=quantize)
     options = _TextEmbedderOptions(
-        base_options=base_options, embedder_options=embedder_options)
+        base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
     embedder = _TextEmbedder.create_from_options(options)
 
     # Extracts both embeddings.
@@ -178,10 +174,8 @@ class TextEmbedderTest(parameterized.TestCase):
       # Should never happen
       raise ValueError('model_file_type is invalid.')
 
-    embedder_options = _EmbedderOptions(
-        l2_normalize=l2_normalize, quantize=quantize)
     options = _TextEmbedderOptions(
-        base_options=base_options, embedder_options=embedder_options)
+        base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
     with _TextEmbedder.create_from_options(options) as embedder:
       # Extracts both embeddings.
       positive_text0 = "it's a charming and often affecting journey"

mediapipe/tasks/python/test/vision/BUILD

@@ -49,7 +49,6 @@ py_test(
         "//mediapipe/tasks/python/components/containers:category",
         "//mediapipe/tasks/python/components/containers:classification_result",
         "//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",
@@ -69,7 +68,6 @@ py_test(
         "//mediapipe/python:_framework_bindings",
         "//mediapipe/tasks/python/components/containers:embedding_result",
         "//mediapipe/tasks/python/components/containers:rect",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/vision:image_embedder",

mediapipe/tasks/python/test/vision/image_classifier_test.py

@@ -26,7 +26,6 @@ from mediapipe.python._framework_bindings import image
 from mediapipe.tasks.python.components.containers import category as category_module
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_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
@@ -36,7 +35,6 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode
 ImageClassifierResult = classification_result_module.ClassificationResult
 _Rect = rect.Rect
 _BaseOptions = base_options_module.BaseOptions
-_ClassifierOptions = classifier_options.ClassifierOptions
 _Category = category_module.Category
 _Classifications = classification_result_module.Classifications
 _Image = image.Image
@@ -171,9 +169,8 @@ class ImageClassifierTest(parameterized.TestCase):
       # 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)
+        base_options=base_options, max_results=max_results)
     classifier = _ImageClassifier.create_from_options(options)
 
     # Performs image classification on the input.
@@ -200,9 +197,8 @@ class ImageClassifierTest(parameterized.TestCase):
       # 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)
+        base_options=base_options, 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)
@@ -212,9 +208,7 @@ class ImageClassifierTest(parameterized.TestCase):
 
   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, 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(
@@ -230,11 +224,9 @@ class ImageClassifierTest(parameterized.TestCase):
                                      _generate_soccer_ball_results().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)
+        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)
@@ -249,11 +241,9 @@ class ImageClassifierTest(parameterized.TestCase):
               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)
+        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)
@@ -263,11 +253,9 @@ class ImageClassifierTest(parameterized.TestCase):
           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)
+        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)
@@ -280,10 +268,9 @@ class ImageClassifierTest(parameterized.TestCase):
                         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)
+        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)
@@ -301,19 +288,17 @@ class ImageClassifierTest(parameterized.TestCase):
         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)
+          category_allowlist=['foo'],
+          category_denylist=['bar'])
       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)
+        score_threshold=1)
     with _ImageClassifier.create_from_options(options) as classifier:
       # Performs image classification on the input.
       image_result = classifier.classify(self.test_image)
@@ -386,11 +371,10 @@ class ImageClassifierTest(parameterized.TestCase):
         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)
+        max_results=4)
     with _ImageClassifier.create_from_options(options) as classifier:
       for timestamp in range(0, 300, 30):
         classification_result = classifier.classify_for_video(
@@ -399,11 +383,10 @@ class ImageClassifierTest(parameterized.TestCase):
                                        _generate_burger_results().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)
+        max_results=1)
     with _ImageClassifier.create_from_options(options) as classifier:
       # Load the test image.
       test_image = _Image.create_from_file(
@@ -439,11 +422,10 @@ class ImageClassifierTest(parameterized.TestCase):
         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,
+        max_results=4,
         result_callback=mock.MagicMock())
     with _ImageClassifier.create_from_options(options) as classifier:
       classifier.classify_async(self.test_image, 100)
@@ -466,12 +448,11 @@ class ImageClassifierTest(parameterized.TestCase):
       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,
+        max_results=4,
+        score_threshold=threshold,
         result_callback=check_result)
     with _ImageClassifier.create_from_options(options) as classifier:
       for timestamp in range(0, 300, 30):
@@ -496,11 +477,10 @@ class ImageClassifierTest(parameterized.TestCase):
       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,
+        max_results=1,
         result_callback=check_result)
     with _ImageClassifier.create_from_options(options) as classifier:
       for timestamp in range(0, 300, 30):

mediapipe/tasks/python/test/vision/image_embedder_test.py

@@ -24,7 +24,6 @@ import numpy as np
 from mediapipe.python._framework_bindings import image as image_module
 from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
 from mediapipe.tasks.python.components.containers import rect
-from mediapipe.tasks.python.components.processors import embedder_options as embedder_options_module
 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_embedder
@@ -33,7 +32,6 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
 
 _Rect = rect.Rect
 _BaseOptions = base_options_module.BaseOptions
-_EmbedderOptions = embedder_options_module.EmbedderOptions
 _Embedding = embedding_result_module.Embedding
 _Image = image_module.Image
 _ImageEmbedder = image_embedder.ImageEmbedder
@@ -142,10 +140,8 @@ class ImageEmbedderTest(parameterized.TestCase):
       # Should never happen
       raise ValueError('model_file_type is invalid.')
 
-    embedder_options = _EmbedderOptions(
-        l2_normalize=l2_normalize, quantize=quantize)
     options = _ImageEmbedderOptions(
-        base_options=base_options, embedder_options=embedder_options)
+        base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
     embedder = _ImageEmbedder.create_from_options(options)
 
     image_processing_options = None
@@ -186,10 +182,8 @@ class ImageEmbedderTest(parameterized.TestCase):
       # Should never happen
       raise ValueError('model_file_type is invalid.')
 
-    embedder_options = _EmbedderOptions(
-        l2_normalize=l2_normalize, quantize=quantize)
     options = _ImageEmbedderOptions(
-        base_options=base_options, embedder_options=embedder_options)
+        base_options=base_options, l2_normalize=l2_normalize, quantize=quantize)
 
     with _ImageEmbedder.create_from_options(options) as embedder:
       # Extracts both embeddings.

mediapipe/tasks/python/text/BUILD

@@ -28,9 +28,9 @@ py_library(
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
         "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:classification_result",
-        "//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",
@@ -47,9 +47,9 @@ py_library(
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_py_pb2",
         "//mediapipe/tasks/cc/text/text_embedder/proto:text_embedder_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/components/utils:cosine_similarity",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/core:optional_dependencies",

mediapipe/tasks/python/text/text_classifier.py

@@ -14,14 +14,14 @@
 """MediaPipe text classifier task."""
 
 import dataclasses
-from typing import Optional
+from typing import Optional, List
 
 from mediapipe.python import packet_creator
 from mediapipe.python import packet_getter
 from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
 from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_module
-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
@@ -30,7 +30,7 @@ from mediapipe.tasks.python.text.core import base_text_task_api
 TextClassifierResult = classification_result_module.ClassificationResult
 _BaseOptions = base_options_module.BaseOptions
 _TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions
-_ClassifierOptions = classifier_options.ClassifierOptions
+_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
 _TaskInfo = task_info_module.TaskInfo
 
 _CLASSIFICATIONS_STREAM_NAME = 'classifications_out'
@@ -46,17 +46,38 @@ class TextClassifierOptions:
 
   Attributes:
     base_options: Base options for the text classifier task.
-    classifier_options: Options for the text classification task.
+    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,
+      classification 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, classification
+      results whose category name is in this set will be filtered out. Duplicate
+      or unknown category names are ignored. Mutually exclusive with
+      `category_allowlist`.
   """
   base_options: _BaseOptions
-  classifier_options: Optional[_ClassifierOptions] = dataclasses.field(
+  display_names_locale: Optional[str] = None
-      default_factory=_ClassifierOptions)
+  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) -> _TextClassifierGraphOptionsProto:
     """Generates an TextClassifierOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
-    classifier_options_proto = self.classifier_options.to_pb2()
+    classifier_options_proto = _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)
 
     return _TextClassifierGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/python/text/text_embedder.py

@@ -19,9 +19,9 @@ from typing import Optional
 from mediapipe.python import packet_creator
 from mediapipe.python import packet_getter
 from mediapipe.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
 from mediapipe.tasks.cc.text.text_embedder.proto import text_embedder_graph_options_pb2
 from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
-from mediapipe.tasks.python.components.processors import embedder_options
 from mediapipe.tasks.python.components.utils import cosine_similarity
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.core import task_info as task_info_module
@@ -31,7 +31,7 @@ from mediapipe.tasks.python.text.core import base_text_task_api
 TextEmbedderResult = embedding_result_module.EmbeddingResult
 _BaseOptions = base_options_module.BaseOptions
 _TextEmbedderGraphOptionsProto = text_embedder_graph_options_pb2.TextEmbedderGraphOptions
-_EmbedderOptions = embedder_options.EmbedderOptions
+_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
 _TaskInfo = task_info_module.TaskInfo
 
 _EMBEDDINGS_OUT_STREAM_NAME = 'embeddings_out'
@@ -47,17 +47,25 @@ class TextEmbedderOptions:
 
   Attributes:
     base_options: Base options for the text embedder task.
-    embedder_options: Options for the text embedder task.
+    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
+      Use this option only if the model does not already contain a native
+      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
+      L2 norm is thus achieved through TF Lite inference.
+    quantize: Whether the returned embedding should be quantized to bytes via
+      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
+      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
+      the l2_normalize option if this is not the case.
   """
   base_options: _BaseOptions
-  embedder_options: Optional[_EmbedderOptions] = dataclasses.field(
+  l2_normalize: Optional[bool] = None
-      default_factory=_EmbedderOptions)
+  quantize: Optional[bool] = None
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _TextEmbedderGraphOptionsProto:
     """Generates an TextEmbedderOptions protobuf object."""
     base_options_proto = self.base_options.to_pb2()
-    embedder_options_proto = self.embedder_options.to_pb2()
+    embedder_options_proto = _EmbedderOptionsProto(
+        l2_normalize=self.l2_normalize, quantize=self.quantize)
 
     return _TextEmbedderGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/python/vision/BUILD

@@ -47,10 +47,10 @@ py_library(
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
         "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:classification_result",
         "//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",
@@ -89,9 +89,9 @@ py_library(
         "//mediapipe/python:packet_creator",
         "//mediapipe/python:packet_getter",
         "//mediapipe/tasks/cc/components/containers/proto:embeddings_py_pb2",
+        "//mediapipe/tasks/cc/components/processors/proto:embedder_options_py_pb2",
         "//mediapipe/tasks/cc/vision/image_embedder/proto:image_embedder_graph_options_py_pb2",
         "//mediapipe/tasks/python/components/containers:embedding_result",
-        "//mediapipe/tasks/python/components/processors:embedder_options",
         "//mediapipe/tasks/python/components/utils:cosine_similarity",
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/core:optional_dependencies",

mediapipe/tasks/python/vision/image_classifier.py

@@ -14,17 +14,17 @@
 """MediaPipe image classifier task."""
 
 import dataclasses
-from typing import Callable, Mapping, Optional
+from typing import Callable, Mapping, Optional, List
 
 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
 from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
 from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2
 from mediapipe.tasks.python.components.containers import classification_result as classification_result_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.core import task_info as task_info_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
@@ -36,7 +36,7 @@ ImageClassifierResult = classification_result_module.ClassificationResult
 _NormalizedRect = rect.NormalizedRect
 _BaseOptions = base_options_module.BaseOptions
 _ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
-_ClassifierOptions = classifier_options.ClassifierOptions
+_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
 _RunningMode = vision_task_running_mode.VisionTaskRunningMode
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
@@ -63,15 +63,31 @@ class ImageClassifierOptions:
       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.
+    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,
+      classification 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, classification
+      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
-  classifier_options: Optional[_ClassifierOptions] = dataclasses.field(
+  display_names_locale: Optional[str] = None
-      default_factory=_ClassifierOptions)
+  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[
       [ImageClassifierResult, image_module.Image, int], None]] = None
 
@@ -80,7 +96,12 @@ class ImageClassifierOptions:
     """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()
+    classifier_options_proto = _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)
 
     return _ImageClassifierGraphOptionsProto(
         base_options=base_options_proto,

mediapipe/tasks/python/vision/image_embedder.py

@@ -21,9 +21,9 @@ 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.tasks.cc.components.containers.proto import embeddings_pb2
+from mediapipe.tasks.cc.components.processors.proto import embedder_options_pb2
 from mediapipe.tasks.cc.vision.image_embedder.proto import image_embedder_graph_options_pb2
 from mediapipe.tasks.python.components.containers import embedding_result as embedding_result_module
-from mediapipe.tasks.python.components.processors import embedder_options
 from mediapipe.tasks.python.components.utils import cosine_similarity
 from mediapipe.tasks.python.core import base_options as base_options_module
 from mediapipe.tasks.python.core import task_info as task_info_module
@@ -35,7 +35,7 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
 ImageEmbedderResult = embedding_result_module.EmbeddingResult
 _BaseOptions = base_options_module.BaseOptions
 _ImageEmbedderGraphOptionsProto = image_embedder_graph_options_pb2.ImageEmbedderGraphOptions
-_EmbedderOptions = embedder_options.EmbedderOptions
+_EmbedderOptionsProto = embedder_options_pb2.EmbedderOptions
 _RunningMode = running_mode_module.VisionTaskRunningMode
 _TaskInfo = task_info_module.TaskInfo
 _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
@@ -62,15 +62,22 @@ class ImageEmbedderOptions:
       image on single image inputs. 2) The video mode for embedding image on the
       decoded frames of a video. 3) The live stream mode for embedding image on
       a live stream of input data, such as from camera.
-    embedder_options: Options for the image embedder task.
+    l2_normalize: Whether to normalize the returned feature vector with L2 norm.
+      Use this option only if the model does not already contain a native
+      L2_NORMALIZATION TF Lite Op. In most cases, this is already the case and
+      L2 norm is thus achieved through TF Lite inference.
+    quantize: Whether the returned embedding should be quantized to bytes via
+      scalar quantization. Embeddings are implicitly assumed to be unit-norm and
+      therefore any dimension is guaranteed to have a value in [-1.0, 1.0]. Use
+      the l2_normalize option if this is not the case.
     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
-  embedder_options: Optional[_EmbedderOptions] = dataclasses.field(
+  l2_normalize: Optional[bool] = None
-      default_factory=_EmbedderOptions)
+  quantize: Optional[bool] = None
   result_callback: Optional[Callable[
       [ImageEmbedderResult, image_module.Image, int], None]] = None
 
@@ -79,7 +86,8 @@ class ImageEmbedderOptions:
     """Generates an ImageEmbedderOptions 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
-    embedder_options_proto = self.embedder_options.to_pb2()
+    embedder_options_proto = _EmbedderOptionsProto(
+        l2_normalize=self.l2_normalize, quantize=self.quantize)
 
     return _ImageEmbedderGraphOptionsProto(
         base_options=base_options_proto,