Merge pull request #3738 from kinaryml:image-classification-python-impl

PiperOrigin-RevId: 483818404

mediapipe/python/BUILD

@@ -87,6 +87,7 @@ cc_library(
 cc_library(
     name = "builtin_task_graphs",
     deps = [
+        "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
     ],
 )

mediapipe/python/packet_getter.py

@@ -14,7 +14,7 @@
 
 """The public facing packet getter APIs."""
 
-from typing import List, Type
+from typing import List
 
 from google.protobuf import message
 from google.protobuf import symbol_database
@@ -39,7 +39,7 @@ get_image_frame = _packet_getter.get_image_frame
 get_matrix = _packet_getter.get_matrix
 
 
-def get_proto(packet: mp_packet.Packet) -> Type[message.Message]:
+def get_proto(packet: mp_packet.Packet) -> message.Message:
   """Get the content of a MediaPipe proto Packet as a proto message.
 
   Args:

mediapipe/tasks/python/components/containers/BUILD

@@ -27,6 +27,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "rect",
+    srcs = ["rect.py"],
+    deps = [
+        "//mediapipe/framework/formats:rect_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "category",
     srcs = ["category.py"],
@@ -47,3 +56,13 @@ py_library(
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )
+
+py_library(
+    name = "classifications",
+    srcs = ["classifications.py"],
+    deps = [
+        ":category",
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

mediapipe/tasks/python/components/containers/classifications.py

@@ -0,0 +1,168 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Classifications data class."""
+
+import dataclasses
+from typing import Any, List, Optional
+
+from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_ClassificationEntryProto = classifications_pb2.ClassificationEntry
+_ClassificationsProto = classifications_pb2.Classifications
+_ClassificationResultProto = classifications_pb2.ClassificationResult
+
+
+@dataclasses.dataclass
+class ClassificationEntry:
+  """List of predicted classes (aka labels) for a given classifier head.
+
+  Attributes:
+    categories: The array of predicted categories, usually sorted by descending
+      scores (e.g. from high to low probability).
+    timestamp_ms: The optional timestamp (in milliseconds) associated to the
+      classification entry. This is useful for time series use cases, e.g.,
+      audio classification.
+  """
+
+  categories: List[category_module.Category]
+  timestamp_ms: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationEntryProto:
+    """Generates a ClassificationEntry protobuf object."""
+    return _ClassificationEntryProto(
+        categories=[category.to_pb2() for category in self.categories],
+        timestamp_ms=self.timestamp_ms)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _ClassificationEntryProto) -> 'ClassificationEntry':
+    """Creates a `ClassificationEntry` object from the given protobuf object."""
+    return ClassificationEntry(
+        categories=[
+            category_module.Category.create_from_pb2(category)
+            for category in pb2_obj.categories
+        ],
+        timestamp_ms=pb2_obj.timestamp_ms)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, ClassificationEntry):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class Classifications:
+  """Represents the classifications for a given classifier head.
+
+  Attributes:
+    entries: A list of `ClassificationEntry` objects.
+    head_index: The index of the classifier head these categories refer to. This
+      is useful for multi-head models.
+    head_name: The name of the classifier head, which is the corresponding
+      tensor metadata name.
+  """
+
+  entries: List[ClassificationEntry]
+  head_index: int
+  head_name: str
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationsProto:
+    """Generates a Classifications protobuf object."""
+    return _ClassificationsProto(
+        entries=[entry.to_pb2() for entry in self.entries],
+        head_index=self.head_index,
+        head_name=self.head_name)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _ClassificationsProto) -> 'Classifications':
+    """Creates a `Classifications` object from the given protobuf object."""
+    return Classifications(
+        entries=[
+            ClassificationEntry.create_from_pb2(entry)
+            for entry in pb2_obj.entries
+        ],
+        head_index=pb2_obj.head_index,
+        head_name=pb2_obj.head_name)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Classifications):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class ClassificationResult:
+  """Contains one set of results per classifier head.
+
+  Attributes:
+    classifications: A list of `Classifications` objects.
+  """
+
+  classifications: List[Classifications]
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassificationResultProto:
+    """Generates a ClassificationResult protobuf object."""
+    return _ClassificationResultProto(classifications=[
+        classification.to_pb2() for classification in self.classifications
+    ])
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _ClassificationResultProto) -> 'ClassificationResult':
+    """Creates a `ClassificationResult` object from the given protobuf object.
+    """
+    return ClassificationResult(classifications=[
+        Classifications.create_from_pb2(classification)
+        for classification in pb2_obj.classifications
+    ])
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, ClassificationResult):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/containers/rect.py

@@ -0,0 +1,140 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Rect data class."""
+
+import dataclasses
+from typing import Any, Optional
+
+from mediapipe.framework.formats import rect_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_RectProto = rect_pb2.Rect
+_NormalizedRectProto = rect_pb2.NormalizedRect
+
+
+@dataclasses.dataclass
+class Rect:
+  """A rectangle with rotation in image coordinates.
+
+  Attributes: x_center : The X coordinate of the top-left corner, in pixels.
+  y_center : The Y coordinate of the top-left corner, in pixels.
+    width: The width of the rectangle, in pixels.
+    height: The height of the rectangle, in pixels.
+    rotation: Rotation angle is clockwise in radians.
+    rect_id:  Optional unique id to help associate different rectangles to each
+    other.
+  """
+
+  x_center: int
+  y_center: int
+  width: int
+  height: int
+  rotation: Optional[float] = 0.0
+  rect_id: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _RectProto:
+    """Generates a Rect protobuf object."""
+    return _RectProto(
+        x_center=self.x_center,
+        y_center=self.y_center,
+        width=self.width,
+        height=self.height,
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect':
+    """Creates a `Rect` object from the given protobuf object."""
+    return Rect(
+        x_center=pb2_obj.x_center,
+        y_center=pb2_obj.y_center,
+        width=pb2_obj.width,
+        height=pb2_obj.height)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, Rect):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
+
+
+@dataclasses.dataclass
+class NormalizedRect:
+  """A rectangle with rotation in normalized coordinates.
+
+  The values of box
+
+    center location and size are within [0, 1].
+
+  Attributes: x_center : The X normalized coordinate of the top-left corner.
+  y_center : The Y normalized coordinate of the top-left corner.
+    width: The width of the rectangle.
+    height: The height of the rectangle.
+    rotation: Rotation angle is clockwise in radians.
+    rect_id:  Optional unique id to help associate different rectangles to each
+    other.
+  """
+
+  x_center: float
+  y_center: float
+  width: float
+  height: float
+  rotation: Optional[float] = 0.0
+  rect_id: Optional[int] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _NormalizedRectProto:
+    """Generates a NormalizedRect protobuf object."""
+    return _NormalizedRectProto(
+        x_center=self.x_center,
+        y_center=self.y_center,
+        width=self.width,
+        height=self.height,
+        rotation=self.rotation,
+        rect_id=self.rect_id)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _NormalizedRectProto) -> 'NormalizedRect':
+    """Creates a `NormalizedRect` object from the given protobuf object."""
+    return NormalizedRect(
+        x_center=pb2_obj.x_center,
+        y_center=pb2_obj.y_center,
+        width=pb2_obj.width,
+        height=pb2_obj.height,
+        rotation=pb2_obj.rotation,
+        rect_id=pb2_obj.rect_id)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, NormalizedRect):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/components/processors/BUILD

@@ -0,0 +1,30 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Placeholder for internal Python strict library compatibility macro.
+
+# Placeholder for internal Python strict library and test compatibility macro.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+py_library(
+    name = "classifier_options",
+    srcs = ["classifier_options.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)

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

@@ -0,0 +1,86 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Classifier options data class."""
+
+import dataclasses
+from typing import Any, List, Optional
+
+from mediapipe.tasks.cc.components.processors.proto import classifier_options_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_ClassifierOptionsProto = classifier_options_pb2.ClassifierOptions
+
+
+@dataclasses.dataclass
+class ClassifierOptions:
+  """Options for classification processor.
+
+  Attributes:
+    display_names_locale: The locale to use for display names specified through
+      the TFLite Model Metadata.
+    max_results: The maximum number of top-scored classification results to
+      return.
+    score_threshold: Overrides the ones provided in the model metadata. Results
+      below this value are rejected.
+    category_allowlist: Allowlist of category names. If non-empty, detection
+      results whose category name is not in this set will be filtered out.
+      Duplicate or unknown category names are ignored. Mutually exclusive with
+      `category_denylist`.
+    category_denylist: Denylist of category names. If non-empty, detection
+      results whose category name is in this set will be filtered out. Duplicate
+      or unknown category names are ignored. Mutually exclusive with
+      `category_allowlist`.
+  """
+
+  display_names_locale: Optional[str] = None
+  max_results: Optional[int] = None
+  score_threshold: Optional[float] = None
+  category_allowlist: Optional[List[str]] = None
+  category_denylist: Optional[List[str]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ClassifierOptionsProto:
+    """Generates a ClassifierOptions protobuf object."""
+    return _ClassifierOptionsProto(
+        score_threshold=self.score_threshold,
+        category_allowlist=self.category_allowlist,
+        category_denylist=self.category_denylist,
+        display_names_locale=self.display_names_locale,
+        max_results=self.max_results)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls,
+                      pb2_obj: _ClassifierOptionsProto) -> 'ClassifierOptions':
+    """Creates a `ClassifierOptions` object from the given protobuf object."""
+    return ClassifierOptions(
+        score_threshold=pb2_obj.score_threshold,
+        category_allowlist=[str(name) for name in pb2_obj.category_allowlist],
+        category_denylist=[str(name) for name in pb2_obj.category_denylist],
+        display_names_locale=pb2_obj.display_names_locale,
+        max_results=pb2_obj.max_results)
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+
+    Args:
+      other: The object to be compared with.
+
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, ClassifierOptions):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())

mediapipe/tasks/python/test/vision/BUILD

@@ -36,3 +36,23 @@ py_test(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_test(
+    name = "image_classifier_test",
+    srcs = ["image_classifier_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:classifications",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/components/processors:classifier_options",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:image_classifier",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

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

@@ -0,0 +1,515 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for image classifier."""
+
+import enum
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import numpy as np
+
+from mediapipe.python._framework_bindings import image
+from mediapipe.tasks.python.components.containers import category
+from mediapipe.tasks.python.components.containers import classifications as classifications_module
+from mediapipe.tasks.python.components.containers import rect
+from mediapipe.tasks.python.components.processors import classifier_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_utils
+from mediapipe.tasks.python.vision import image_classifier
+from mediapipe.tasks.python.vision.core import vision_task_running_mode
+
+_NormalizedRect = rect.NormalizedRect
+_BaseOptions = base_options_module.BaseOptions
+_ClassifierOptions = classifier_options.ClassifierOptions
+_Category = category.Category
+_ClassificationEntry = classifications_module.ClassificationEntry
+_Classifications = classifications_module.Classifications
+_ClassificationResult = classifications_module.ClassificationResult
+_Image = image.Image
+_ImageClassifier = image_classifier.ImageClassifier
+_ImageClassifierOptions = image_classifier.ImageClassifierOptions
+_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
+
+_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
+_IMAGE_FILE = 'burger.jpg'
+_ALLOW_LIST = ['cheeseburger', 'guacamole']
+_DENY_LIST = ['cheeseburger']
+_SCORE_THRESHOLD = 0.5
+_MAX_RESULTS = 3
+
+
+# TODO: Port assertProtoEquals
+def _assert_proto_equals(expected, actual):  # pylint: disable=unused-argument
+  pass
+
+
+def _generate_empty_results(timestamp_ms: int) -> _ClassificationResult:
+  return _ClassificationResult(classifications=[
+      _Classifications(
+          entries=[
+              _ClassificationEntry(categories=[], timestamp_ms=timestamp_ms)
+          ],
+          head_index=0,
+          head_name='probability')
+  ])
+
+
+def _generate_burger_results(timestamp_ms: int) -> _ClassificationResult:
+  return _ClassificationResult(classifications=[
+      _Classifications(
+          entries=[
+              _ClassificationEntry(
+                  categories=[
+                      _Category(
+                          index=934,
+                          score=0.7939587831497192,
+                          display_name='',
+                          category_name='cheeseburger'),
+                      _Category(
+                          index=932,
+                          score=0.02739289402961731,
+                          display_name='',
+                          category_name='bagel'),
+                      _Category(
+                          index=925,
+                          score=0.01934075355529785,
+                          display_name='',
+                          category_name='guacamole'),
+                      _Category(
+                          index=963,
+                          score=0.006327860057353973,
+                          display_name='',
+                          category_name='meat loaf')
+                  ],
+                  timestamp_ms=timestamp_ms)
+          ],
+          head_index=0,
+          head_name='probability')
+  ])
+
+
+def _generate_soccer_ball_results(timestamp_ms: int) -> _ClassificationResult:
+  return _ClassificationResult(classifications=[
+      _Classifications(
+          entries=[
+              _ClassificationEntry(
+                  categories=[
+                      _Category(
+                          index=806,
+                          score=0.9965274930000305,
+                          display_name='',
+                          category_name='soccer ball')
+                  ],
+                  timestamp_ms=timestamp_ms)
+          ],
+          head_index=0,
+          head_name='probability')
+  ])
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageClassifierTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_IMAGE_FILE))
+    self.model_path = test_utils.get_test_data_path(_MODEL_FILE)
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _ImageClassifier.create_from_model_path(self.model_path) as classifier:
+      self.assertIsInstance(classifier, _ImageClassifier)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _ImageClassifierOptions(base_options=base_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      self.assertIsInstance(classifier, _ImageClassifier)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    # Invalid empty model path.
+    with self.assertRaisesRegex(
+        ValueError,
+        r"ExternalFile must specify at least one of 'file_content', "
+        r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
+      base_options = _BaseOptions(model_asset_path='')
+      options = _ImageClassifierOptions(base_options=base_options)
+      _ImageClassifier.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _ImageClassifierOptions(base_options=base_options)
+      classifier = _ImageClassifier.create_from_options(options)
+      self.assertIsInstance(classifier, _ImageClassifier)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)),
+      (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0)))
+  def test_classify(self, model_file_type, max_results,
+                    expected_classification_result):
+    # Creates classifier.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    custom_classifier_options = _ClassifierOptions(max_results=max_results)
+    options = _ImageClassifierOptions(
+        base_options=base_options, classifier_options=custom_classifier_options)
+    classifier = _ImageClassifier.create_from_options(options)
+
+    # Performs image classification on the input.
+    image_result = classifier.classify(self.test_image)
+    # Comparing results.
+    _assert_proto_equals(image_result.to_pb2(),
+                         expected_classification_result.to_pb2())
+    # Closes the classifier explicitly when the classifier is not used in
+    # a context.
+    classifier.close()
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, 4, _generate_burger_results(0)),
+      (ModelFileType.FILE_CONTENT, 4, _generate_burger_results(0)))
+  def test_classify_in_context(self, model_file_type, max_results,
+                               expected_classification_result):
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    custom_classifier_options = _ClassifierOptions(max_results=max_results)
+    options = _ImageClassifierOptions(
+        base_options=base_options, classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      # Comparing results.
+      _assert_proto_equals(image_result.to_pb2(),
+                           expected_classification_result.to_pb2())
+
+  def test_classify_succeeds_with_region_of_interest(self):
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    custom_classifier_options = _ClassifierOptions(max_results=1)
+    options = _ImageClassifierOptions(
+        base_options=base_options, classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Load the test image.
+      test_image = _Image.create_from_file(
+          test_utils.get_test_data_path('multi_objects.jpg'))
+      # NormalizedRect around the soccer ball.
+      roi = _NormalizedRect(
+          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      # Performs image classification on the input.
+      image_result = classifier.classify(test_image, roi)
+      # Comparing results.
+      _assert_proto_equals(image_result.to_pb2(),
+                           _generate_soccer_ball_results(0).to_pb2())
+
+  def test_score_threshold_option(self):
+    custom_classifier_options = _ClassifierOptions(
+        score_threshold=_SCORE_THRESHOLD)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      classifications = image_result.classifications
+
+      for classification in classifications:
+        for entry in classification.entries:
+          score = entry.categories[0].score
+          self.assertGreaterEqual(
+              score, _SCORE_THRESHOLD,
+              f'Classification with score lower than threshold found. '
+              f'{classification}')
+
+  def test_max_results_option(self):
+    custom_classifier_options = _ClassifierOptions(
+        score_threshold=_SCORE_THRESHOLD)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      categories = image_result.classifications[0].entries[0].categories
+
+      self.assertLessEqual(
+          len(categories), _MAX_RESULTS, 'Too many results returned.')
+
+  def test_allow_list_option(self):
+    custom_classifier_options = _ClassifierOptions(
+        category_allowlist=_ALLOW_LIST)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      classifications = image_result.classifications
+
+      for classification in classifications:
+        for entry in classification.entries:
+          label = entry.categories[0].category_name
+          self.assertIn(label, _ALLOW_LIST,
+                        f'Label {label} found but not in label allow list')
+
+  def test_deny_list_option(self):
+    custom_classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      classifications = image_result.classifications
+
+      for classification in classifications:
+        for entry in classification.entries:
+          label = entry.categories[0].category_name
+          self.assertNotIn(label, _DENY_LIST,
+                           f'Label {label} found but in deny list.')
+
+  def test_combined_allowlist_and_denylist(self):
+    # Fails with combined allowlist and denylist
+    with self.assertRaisesRegex(
+        ValueError,
+        r'`category_allowlist` and `category_denylist` are mutually '
+        r'exclusive options.'):
+      custom_classifier_options = _ClassifierOptions(
+          category_allowlist=['foo'], category_denylist=['bar'])
+      options = _ImageClassifierOptions(
+          base_options=_BaseOptions(model_asset_path=self.model_path),
+          classifier_options=custom_classifier_options)
+      with _ImageClassifier.create_from_options(options) as unused_classifier:
+        pass
+
+  def test_empty_classification_outputs(self):
+    custom_classifier_options = _ClassifierOptions(score_threshold=1)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Performs image classification on the input.
+      image_result = classifier.classify(self.test_image)
+      self.assertEmpty(image_result.classifications[0].entries[0].categories)
+
+  def test_missing_result_callback(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM)
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback must be provided'):
+      with _ImageClassifier.create_from_options(options) as unused_classifier:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=running_mode,
+        result_callback=mock.MagicMock())
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback should not be provided'):
+      with _ImageClassifier.create_from_options(options) as unused_classifier:
+        pass
+
+  def test_calling_classify_for_video_in_image_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        classifier.classify_for_video(self.test_image, 0)
+
+  def test_calling_classify_async_in_image_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        classifier.classify_async(self.test_image, 0)
+
+  def test_calling_classify_in_video_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        classifier.classify(self.test_image)
+
+  def test_calling_classify_async_in_video_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        classifier.classify_async(self.test_image, 0)
+
+  def test_classify_for_video_with_out_of_order_timestamp(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      unused_result = classifier.classify_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        classifier.classify_for_video(self.test_image, 0)
+
+  def test_classify_for_video(self):
+    custom_classifier_options = _ClassifierOptions(max_results=4)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO,
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      for timestamp in range(0, 300, 30):
+        classification_result = classifier.classify_for_video(
+            self.test_image, timestamp)
+        _assert_proto_equals(classification_result.to_pb2(),
+                             _generate_burger_results(timestamp).to_pb2())
+
+  def test_classify_for_video_succeeds_with_region_of_interest(self):
+    custom_classifier_options = _ClassifierOptions(max_results=1)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO,
+        classifier_options=custom_classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      # Load the test image.
+      test_image = _Image.create_from_file(
+          test_utils.get_test_data_path('multi_objects.jpg'))
+      # NormalizedRect around the soccer ball.
+      roi = _NormalizedRect(
+          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      for timestamp in range(0, 300, 30):
+        classification_result = classifier.classify_for_video(
+            test_image, timestamp, roi)
+        self.assertEqual(classification_result,
+                         _generate_soccer_ball_results(timestamp))
+
+  def test_calling_classify_in_live_stream_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        classifier.classify(self.test_image)
+
+  def test_calling_classify_for_video_in_live_stream_mode(self):
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageClassifier.create_from_options(options) as classifier:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        classifier.classify_for_video(self.test_image, 0)
+
+  def test_classify_async_calls_with_illegal_timestamp(self):
+    custom_classifier_options = _ClassifierOptions(max_results=4)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        classifier_options=custom_classifier_options,
+        result_callback=mock.MagicMock())
+    with _ImageClassifier.create_from_options(options) as classifier:
+      classifier.classify_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        classifier.classify_async(self.test_image, 0)
+
+  @parameterized.parameters((0, _generate_burger_results),
+                            (1, _generate_empty_results))
+  def test_classify_async_calls(self, threshold, expected_result_fn):
+    observed_timestamp_ms = -1
+
+    def check_result(result: _ClassificationResult, output_image: _Image,
+                     timestamp_ms: int):
+      _assert_proto_equals(result.to_pb2(),
+                           expected_result_fn(timestamp_ms).to_pb2())
+      self.assertTrue(
+          np.array_equal(output_image.numpy_view(),
+                         self.test_image.numpy_view()))
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    custom_classifier_options = _ClassifierOptions(
+        max_results=4, score_threshold=threshold)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        classifier_options=custom_classifier_options,
+        result_callback=check_result)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      for timestamp in range(0, 300, 30):
+        classifier.classify_async(self.test_image, timestamp)
+
+  def test_classify_async_succeeds_with_region_of_interest(self):
+    # Load the test image.
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path('multi_objects.jpg'))
+    # NormalizedRect around the soccer ball.
+    roi = _NormalizedRect(
+        x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+    observed_timestamp_ms = -1
+
+    def check_result(result: _ClassificationResult, output_image: _Image,
+                     timestamp_ms: int):
+      _assert_proto_equals(result.to_pb2(),
+                           _generate_soccer_ball_results(timestamp_ms).to_pb2())
+      self.assertEqual(output_image.width, test_image.width)
+      self.assertEqual(output_image.height, test_image.height)
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    custom_classifier_options = _ClassifierOptions(max_results=1)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        classifier_options=custom_classifier_options,
+        result_callback=check_result)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      for timestamp in range(0, 300, 30):
+        classifier.classify_async(test_image, timestamp, roi)
+
+
+if __name__ == '__main__':
+  absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -36,3 +36,25 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "image_classifier",
+    srcs = [
+        "image_classifier.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
+        "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:classifications",
+        "//mediapipe/tasks/python/components/containers:rect",
+        "//mediapipe/tasks/python/components/processors:classifier_options",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/image_classifier.py

@@ -0,0 +1,294 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MediaPipe image classifier task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+# TODO: Import MPImage directly one we have an alias
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet
+from mediapipe.python._framework_bindings import task_runner
+from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
+from mediapipe.tasks.cc.vision.image_classifier.proto import image_classifier_graph_options_pb2
+from mediapipe.tasks.python.components.containers import classifications
+from mediapipe.tasks.python.components.containers import rect
+from mediapipe.tasks.python.components.processors import classifier_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import vision_task_running_mode
+
+_NormalizedRect = rect.NormalizedRect
+_BaseOptions = base_options_module.BaseOptions
+_ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
+_ClassifierOptions = classifier_options.ClassifierOptions
+_RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+_TaskRunner = task_runner.TaskRunner
+
+_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
+_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+def _build_full_image_norm_rect() -> _NormalizedRect:
+  # Builds a NormalizedRect covering the entire image.
+  return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
+
+
+@dataclasses.dataclass
+class ImageClassifierOptions:
+  """Options for the image classifier task.
+
+  Attributes:
+    base_options: Base options for the image classifier task.
+    running_mode: The running mode of the task. Default to the image mode. Image
+      classifier task has three running modes: 1) The image mode for classifying
+      objects on single image inputs. 2) The video mode for classifying objects
+      on the decoded frames of a video. 3) The live stream mode for classifying
+      objects on a live stream of input data, such as from camera.
+    classifier_options: Options for the image classification task.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  classifier_options: _ClassifierOptions = _ClassifierOptions()
+  result_callback: Optional[
+      Callable[[classifications.ClassificationResult, image_module.Image, int],
+               None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ImageClassifierGraphOptionsProto:
+    """Generates an ImageClassifierOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
+    classifier_options_proto = self.classifier_options.to_pb2()
+
+    return _ImageClassifierGraphOptionsProto(
+        base_options=base_options_proto,
+        classifier_options=classifier_options_proto)
+
+
+class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs image classification on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'ImageClassifier':
+    """Creates an `ImageClassifier` object from a TensorFlow Lite model and the default `ImageClassifierOptions`.
+
+    Note that the created `ImageClassifier` instance is in image mode, for
+    classifying objects on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `ImageClassifier` object that's created from the model file and the
+      default `ImageClassifierOptions`.
+
+    Raises:
+      ValueError: If failed to create `ImageClassifier` object from the provided
+        file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = ImageClassifierOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: ImageClassifierOptions) -> 'ImageClassifier':
+    """Creates the `ImageClassifier` object from image classifier options.
+
+    Args:
+      options: Options for the image classifier task.
+
+    Returns:
+      `ImageClassifier` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `ImageClassifier` object from
+        `ImageClassifierOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      classification_result_proto = classifications_pb2.ClassificationResult()
+      classification_result_proto.CopyFrom(
+          packet_getter.get_proto(
+              output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
+
+      classification_result = classifications.ClassificationResult([
+          classifications.Classifications.create_from_pb2(classification)
+          for classification in classification_result_proto.classifications
+      ])
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      options.result_callback(classification_result, image,
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
+        ],
+        output_streams=[
+            ':'.join([
+                _CLASSIFICATION_RESULT_TAG,
+                _CLASSIFICATION_RESULT_OUT_STREAM_NAME
+            ]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        task_options=options)
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode ==
+            _RunningMode.LIVE_STREAM), options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  # TODO: Replace _NormalizedRect with ImageProcessingOption
+  def classify(
+      self,
+      image: image_module.Image,
+      roi: Optional[_NormalizedRect] = None
+  ) -> classifications.ClassificationResult:
+    """Performs image classification on the provided MediaPipe Image.
+
+    Args:
+      image: MediaPipe Image.
+      roi: The region of interest.
+
+    Returns:
+      A classification result object that contains a list of classifications.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image classification failed to run.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())
+    })
+
+    classification_result_proto = classifications_pb2.ClassificationResult()
+    classification_result_proto.CopyFrom(
+        packet_getter.get_proto(
+            output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
+
+    return classifications.ClassificationResult([
+        classifications.Classifications.create_from_pb2(classification)
+        for classification in classification_result_proto.classifications
+    ])
+
+  def classify_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      roi: Optional[_NormalizedRect] = None
+  ) -> classifications.ClassificationResult:
+    """Performs image classification on the provided video frames.
+
+    Only use this method when the ImageClassifier is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      roi: The region of interest.
+
+    Returns:
+      A classification result object that contains a list of classifications.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image classification failed to run.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_NAME:
+            packet_creator.create_proto(norm_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+
+    classification_result_proto = classifications_pb2.ClassificationResult()
+    classification_result_proto.CopyFrom(
+        packet_getter.get_proto(
+            output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
+
+    return classifications.ClassificationResult([
+        classifications.Classifications.create_from_pb2(classification)
+        for classification in classification_result_proto.classifications
+    ])
+
+  def classify_async(self,
+                     image: image_module.Image,
+                     timestamp_ms: int,
+                     roi: Optional[_NormalizedRect] = None) -> None:
+    """Sends live image data (an Image with a unique timestamp) to perform image classification.
+
+    Only use this method when the ImageClassifier is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `ImageClassifierOptions`. The
+    `classify_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, image classifier may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - A classification result object that contains a list of classifications.
+      - The input image that the image classifier runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      roi: The region of interest.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the image
+        classifier has already processed.
+    """
+    norm_rect = roi if roi is not None else _build_full_image_norm_rect()
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_NAME:
+            packet_creator.create_proto(norm_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })