Merge pull request #3801 from kinaryml:gesture-recognizer-python

PiperOrigin-RevId: 485884796

mediapipe/python/BUILD

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

mediapipe/tasks/python/components/containers/BUILD

@@ -36,6 +36,29 @@ py_library(
     ],
 )
 
+py_library(
+    name = "landmark",
+    srcs = ["landmark.py"],
+    deps = [
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
+py_library(
+    name = "landmark_detection_result",
+    srcs = ["landmark_detection_result.py"],
+    deps = [
+        ":landmark",
+        ":rect",
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
+
 py_library(
     name = "category",
     srcs = ["category.py"],

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

@@ -14,7 +14,7 @@
 """Category data class."""
 
 import dataclasses
-from typing import Any
+from typing import Any, Optional
 
 from mediapipe.tasks.cc.components.containers.proto import category_pb2
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
@@ -39,10 +39,10 @@ class Category:
     category_name: The label of this category object.
   """
 
-  index: int
+  index: Optional[int] = None
-  score: float
+  score: Optional[float] = None
-  display_name: str
+  display_name: Optional[str] = None
-  category_name: str
+  category_name: Optional[str] = None
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _CategoryProto:

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

@@ -0,0 +1,122 @@
+# 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.
+"""Landmark data class."""
+
+import dataclasses
+from typing import Optional
+
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_LandmarkProto = landmark_pb2.Landmark
+_NormalizedLandmarkProto = landmark_pb2.NormalizedLandmark
+
+
+@dataclasses.dataclass
+class Landmark:
+  """A landmark that can have 1 to 3 dimensions.
+
+  Use x for 1D points, (x, y) for 2D points and (x, y, z) for 3D points.
+
+  Attributes:
+    x: The x coordinate.
+    y: The y coordinate.
+    z: The z coordinate.
+    visibility: Landmark visibility. Should stay unset if not supported. Float
+      score of whether landmark is visible or occluded by other objects.
+      Landmark considered as invisible also if it is not present on the screen
+      (out of scene bounds). Depending on the model, visibility value is either
+      a sigmoid or an argument of sigmoid.
+    presence: Landmark presence. Should stay unset if not supported. Float score
+      of whether landmark is present on the scene (located within scene bounds).
+      Depending on the model, presence value is either a result of sigmoid or an
+      argument of sigmoid function to get landmark presence probability.
+  """
+
+  x: Optional[float] = None
+  y: Optional[float] = None
+  z: Optional[float] = None
+  visibility: Optional[float] = None
+  presence: Optional[float] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _LandmarkProto:
+    """Generates a Landmark protobuf object."""
+    return _LandmarkProto(
+        x=self.x,
+        y=self.y,
+        z=self.z,
+        visibility=self.visibility,
+        presence=self.presence)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(cls, pb2_obj: _LandmarkProto) -> 'Landmark':
+    """Creates a `Landmark` object from the given protobuf object."""
+    return Landmark(
+        x=pb2_obj.x,
+        y=pb2_obj.y,
+        z=pb2_obj.z,
+        visibility=pb2_obj.visibility,
+        presence=pb2_obj.presence)
+
+
+@dataclasses.dataclass
+class NormalizedLandmark:
+  """A normalized version of above Landmark proto.
+
+  All coordinates should be within [0, 1].
+
+  Attributes:
+    x: The normalized x coordinate.
+    y: The normalized y coordinate.
+    z: The normalized z coordinate.
+    visibility: Landmark visibility. Should stay unset if not supported. Float
+      score of whether landmark is visible or occluded by other objects.
+      Landmark considered as invisible also if it is not present on the screen
+      (out of scene bounds). Depending on the model, visibility value is either
+      a sigmoid or an argument of sigmoid.
+    presence: Landmark presence. Should stay unset if not supported. Float score
+      of whether landmark is present on the scene (located within scene bounds).
+      Depending on the model, presence value is either a result of sigmoid or an
+      argument of sigmoid function to get landmark presence probability.
+  """
+
+  x: Optional[float] = None
+  y: Optional[float] = None
+  z: Optional[float] = None
+  visibility: Optional[float] = None
+  presence: Optional[float] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _NormalizedLandmarkProto:
+    """Generates a NormalizedLandmark protobuf object."""
+    return _NormalizedLandmarkProto(
+        x=self.x,
+        y=self.y,
+        z=self.z,
+        visibility=self.visibility,
+        presence=self.presence)
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _NormalizedLandmarkProto) -> 'NormalizedLandmark':
+    """Creates a `NormalizedLandmark` object from the given protobuf object."""
+    return NormalizedLandmark(
+        x=pb2_obj.x,
+        y=pb2_obj.y,
+        z=pb2_obj.z,
+        visibility=pb2_obj.visibility,
+        presence=pb2_obj.presence)

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

@@ -0,0 +1,96 @@
+# 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.
+"""Landmarks Detection Result data class."""
+
+import dataclasses
+from typing import Optional, List
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
+_ClassificationProto = classification_pb2.Classification
+_ClassificationListProto = classification_pb2.ClassificationList
+_LandmarkListProto = landmark_pb2.LandmarkList
+_NormalizedLandmarkListProto = landmark_pb2.NormalizedLandmarkList
+_NormalizedRect = rect_module.NormalizedRect
+_Category = category_module.Category
+_NormalizedLandmark = landmark_module.NormalizedLandmark
+_Landmark = landmark_module.Landmark
+
+
+@dataclasses.dataclass
+class LandmarksDetectionResult:
+  """Represents the landmarks detection result.
+
+  Attributes: landmarks : A list of `NormalizedLandmark` objects. categories : A
+  list of `Category` objects. world_landmarks : A list of `Landmark` objects.
+  rect : A `NormalizedRect` object.
+  """
+
+  landmarks: Optional[List[_NormalizedLandmark]]
+  categories: Optional[List[_Category]]
+  world_landmarks: Optional[List[_Landmark]]
+  rect: _NormalizedRect
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _LandmarksDetectionResultProto:
+    """Generates a LandmarksDetectionResult protobuf object."""
+
+    classifications = _ClassificationListProto()
+    for category in self.categories:
+      classifications.classification.append(
+          _ClassificationProto(
+              index=category.index,
+              score=category.score,
+              label=category.category_name,
+              display_name=category.display_name))
+
+    return _LandmarksDetectionResultProto(
+        landmarks=_NormalizedLandmarkListProto(self.landmarks),
+        classifications=classifications,
+        world_landmarks=_LandmarkListProto(self.world_landmarks),
+        rect=self.rect.to_pb2())
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls,
+      pb2_obj: _LandmarksDetectionResultProto) -> 'LandmarksDetectionResult':
+    """Creates a `LandmarksDetectionResult` object from the given protobuf object.
+    """
+    categories = []
+    for classification in pb2_obj.classifications.classification:
+      categories.append(
+          category_module.Category(
+              score=classification.score,
+              index=classification.index,
+              category_name=classification.label,
+              display_name=classification.display_name))
+    return LandmarksDetectionResult(
+        landmarks=[
+            _NormalizedLandmark.create_from_pb2(landmark)
+            for landmark in pb2_obj.landmarks.landmark
+        ],
+        categories=categories,
+        world_landmarks=[
+            _Landmark.create_from_pb2(landmark)
+            for landmark in pb2_obj.world_landmarks.landmark
+        ],
+        rect=_NormalizedRect.create_from_pb2(pb2_obj.rect))

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

@@ -19,75 +19,44 @@ 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.
+  """A rectangle, used as part of detection results or as input region-of-interest.
 
-  Attributes: x_center : The X coordinate of the top-left corner, in pixels.
+  The coordinates are normalized wrt the image dimensions, i.e. generally in
-  y_center : The Y coordinate of the top-left corner, in pixels.
+  [0,1] but they may exceed these bounds if describing a region overlapping the
-    width: The width of the rectangle, in pixels.
+  image. The origin is on the top-left corner of the image.
-    height: The height of the rectangle, in pixels.
+
-    rotation: Rotation angle is clockwise in radians.
+  Attributes:
-    rect_id:  Optional unique id to help associate different rectangles to each
+    left: The X coordinate of the left side of the rectangle.
-    other.
+    top: The Y coordinate of the top of the rectangle.
+    right: The X coordinate of the right side of the rectangle.
+    bottom: The Y coordinate of the bottom of the rectangle.
   """
 
-  x_center: int
+  left: float
-  y_center: int
+  top: float
-  width: int
+  right: float
-  height: int
+  bottom: float
-  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
+  Location of the center of the rectangle in image coordinates. The (0.0, 0.0)
+  point is at the (top, left) corner.
 
-    center location and size are within [0, 1].
+  The values of box center location and size are within [0, 1].
 
-  Attributes: x_center : The X normalized coordinate of the top-left corner.
+  Attributes:
-  y_center : The Y normalized coordinate of the top-left corner.
+    x_center: The normalized X coordinate of the rectangle, in image
+      coordinates.
+    y_center: The normalized Y coordinate of the rectangle, in image
+      coordinates.
     width: The width of the rectangle.
     height: The height of the rectangle.
     rotation: Rotation angle is clockwise in radians.

mediapipe/tasks/python/test/vision/BUILD

@@ -53,6 +53,7 @@ py_test(
         "//mediapipe/tasks/python/core:base_options",
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/vision:image_classifier",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )

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

@@ -30,9 +30,10 @@ 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 image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
-_NormalizedRect = rect.NormalizedRect
+_Rect = rect.Rect
 _BaseOptions = base_options_module.BaseOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _Category = category.Category
@@ -43,6 +44,7 @@ _Image = image.Image
 _ImageClassifier = image_classifier.ImageClassifier
 _ImageClassifierOptions = image_classifier.ImageClassifierOptions
 _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
 _MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
 _IMAGE_FILE = 'burger.jpg'
@@ -227,11 +229,11 @@ class ImageClassifierTest(parameterized.TestCase):
       test_image = _Image.create_from_file(
           test_utils.get_test_data_path(
               os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-      # NormalizedRect around the soccer ball.
+      # Region-of-interest around the soccer ball.
-      roi = _NormalizedRect(
+      roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
-          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      image_processing_options = _ImageProcessingOptions(roi)
       # Performs image classification on the input.
-      image_result = classifier.classify(test_image, roi)
+      image_result = classifier.classify(test_image, image_processing_options)
       # Comparing results.
       test_utils.assert_proto_equals(self, image_result.to_pb2(),
                                      _generate_soccer_ball_results(0).to_pb2())
@@ -417,12 +419,12 @@ class ImageClassifierTest(parameterized.TestCase):
       test_image = _Image.create_from_file(
           test_utils.get_test_data_path(
               os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-      # NormalizedRect around the soccer ball.
+      # Region-of-interest around the soccer ball.
-      roi = _NormalizedRect(
+      roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
-          x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+      image_processing_options = _ImageProcessingOptions(roi)
       for timestamp in range(0, 300, 30):
         classification_result = classifier.classify_for_video(
-            test_image, timestamp, roi)
+            test_image, timestamp, image_processing_options)
         test_utils.assert_proto_equals(
             self, classification_result.to_pb2(),
             _generate_soccer_ball_results(timestamp).to_pb2())
@@ -491,9 +493,9 @@ class ImageClassifierTest(parameterized.TestCase):
     test_image = _Image.create_from_file(
         test_utils.get_test_data_path(
             os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
-    # NormalizedRect around the soccer ball.
+    # Region-of-interest around the soccer ball.
-    roi = _NormalizedRect(
+    roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
-        x_center=0.532, y_center=0.521, width=0.164, height=0.427)
+    image_processing_options = _ImageProcessingOptions(roi)
     observed_timestamp_ms = -1
 
     def check_result(result: _ClassificationResult, output_image: _Image,
@@ -514,7 +516,8 @@ class ImageClassifierTest(parameterized.TestCase):
         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)
+        classifier.classify_async(test_image, timestamp,
+                                  image_processing_options)
 
 
 if __name__ == '__main__':

mediapipe/tasks/python/vision/BUILD

@@ -55,6 +55,7 @@ py_library(
         "//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:image_processing_options",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
@@ -77,3 +78,27 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "gesture_recognizer",
+    srcs = [
+        "gesture_recognizer.py",
+    ],
+    deps = [
+        "//mediapipe/framework/formats:classification_py_pb2",
+        "//mediapipe/framework/formats:landmark_py_pb2",
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_py_pb2",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:landmark",
+        "//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:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)

mediapipe/tasks/python/vision/core/BUILD

@@ -23,15 +23,25 @@ py_library(
     srcs = ["vision_task_running_mode.py"],
 )
 
+py_library(
+    name = "image_processing_options",
+    srcs = ["image_processing_options.py"],
+    deps = [
+        "//mediapipe/tasks/python/components/containers:rect",
+    ],
+)
+
 py_library(
     name = "base_vision_task_api",
     srcs = [
         "base_vision_task_api.py",
     ],
     deps = [
+        ":image_processing_options",
         ":vision_task_running_mode",
         "//mediapipe/framework:calculator_py_pb2",
         "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/components/containers:rect",
         "//mediapipe/tasks/python/core:optional_dependencies",
     ],
 )

mediapipe/tasks/python/vision/core/base_vision_task_api.py

@@ -13,17 +13,22 @@
 # limitations under the License.
 """MediaPipe vision task base api."""
 
+import math
 from typing import Callable, Mapping, Optional
 
 from mediapipe.framework import calculator_pb2
 from mediapipe.python._framework_bindings import packet as packet_module
 from mediapipe.python._framework_bindings import task_runner as task_runner_module
+from mediapipe.tasks.python.components.containers import rect as rect_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
 
 _TaskRunner = task_runner_module.TaskRunner
 _Packet = packet_module.Packet
+_NormalizedRect = rect_module.NormalizedRect
 _RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
 
 class BaseVisionTaskApi(object):
@@ -122,6 +127,49 @@ class BaseVisionTaskApi(object):
           + self._running_mode.name)
     self._runner.send(inputs)
 
+  def convert_to_normalized_rect(self,
+                                 options: _ImageProcessingOptions,
+                                 roi_allowed: bool = True) -> _NormalizedRect:
+    """Converts from ImageProcessingOptions to NormalizedRect, performing sanity checks on-the-fly.
+
+    If the input ImageProcessingOptions is not present, returns a default
+    NormalizedRect covering the whole image with rotation set to 0. If
+    'roi_allowed' is false, an error will be returned if the input
+    ImageProcessingOptions has its 'region_of_interest' field set.
+
+    Args:
+      options: Options for image processing.
+      roi_allowed: Indicates if the `region_of_interest` field is allowed to be
+        set. By default, it's set to True.
+
+    Returns:
+      A normalized rect proto that repesents the image processing options.
+    """
+    normalized_rect = _NormalizedRect(
+        rotation=0, x_center=0.5, y_center=0.5, width=1, height=1)
+    if options is None:
+      return normalized_rect
+
+    if options.rotation_degrees % 90 != 0:
+      raise ValueError('Expected rotation to be a multiple of 90°.')
+
+    # Convert to radians counter-clockwise.
+    normalized_rect.rotation = -options.rotation_degrees * math.pi / 180.0
+
+    if options.region_of_interest:
+      if not roi_allowed:
+        raise ValueError("This task doesn't support region-of-interest.")
+      roi = options.region_of_interest
+      if roi.left >= roi.right or roi.top >= roi.bottom:
+        raise ValueError('Expected Rect with left < right and top < bottom.')
+      if roi.left < 0 or roi.top < 0 or roi.right > 1 or roi.bottom > 1:
+        raise ValueError('Expected Rect values to be in [0,1].')
+      normalized_rect.x_center = (roi.left + roi.right) / 2.0
+      normalized_rect.y_center = (roi.top + roi.bottom) / 2.0
+      normalized_rect.width = roi.right - roi.left
+      normalized_rect.height = roi.bottom - roi.top
+    return normalized_rect
+
   def close(self) -> None:
     """Shuts down the mediapipe vision task instance.
 

mediapipe/tasks/python/vision/core/image_processing_options.py

@@ -0,0 +1,39 @@
+# 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 vision options for image processing."""
+
+import dataclasses
+from typing import Optional
+
+from mediapipe.tasks.python.components.containers import rect as rect_module
+
+
+@dataclasses.dataclass
+class ImageProcessingOptions:
+  """Options for image processing.
+
+  If both region-of-interest and rotation are specified, the crop around the
+  region-of-interest is extracted first, then the specified rotation is applied
+   to the crop.
+
+  Attributes:
+    region_of_interest: The optional region-of-interest to crop from the image.
+      If not specified, the full image is used. Coordinates must be in [0,1]
+      with 'left' < 'right' and 'top' < 'bottom'.
+    rotation_degrees: The rotation to apply to the image (or cropped
+      region-of-interest), in degrees clockwise. The rotation must be a multiple
+      (positive or negative) of 90°.
+  """
+  region_of_interest: Optional[rect_module.Rect] = None
+  rotation_degrees: int = 0

mediapipe/tasks/python/vision/gesture_recognizer.py

@@ -0,0 +1,426 @@
+# 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 gesture recognizer task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional, List
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.tasks.cc.vision.gesture_recognizer.proto import gesture_recognizer_graph_options_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_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
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
+_ClassifierOptions = classifier_options.ClassifierOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_HAND_GESTURE_STREAM_NAME = 'hand_gestures'
+_HAND_GESTURE_TAG = 'HAND_GESTURES'
+_HANDEDNESS_STREAM_NAME = 'handedness'
+_HANDEDNESS_TAG = 'HANDEDNESS'
+_HAND_LANDMARKS_STREAM_NAME = 'landmarks'
+_HAND_LANDMARKS_TAG = 'LANDMARKS'
+_HAND_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
+_HAND_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+_GESTURE_DEFAULT_INDEX = -1
+
+
+@dataclasses.dataclass
+class GestureRecognitionResult:
+  """The gesture recognition result from GestureRecognizer, where each vector element represents a single hand detected in the image.
+
+  Attributes:
+    gestures: Recognized hand gestures of detected hands. Note that the index of
+      the gesture is always -1, because the raw indices from multiple gesture
+      classifiers cannot consolidate to a meaningful index.
+    handedness: Classification of handedness.
+    hand_landmarks: Detected hand landmarks in normalized image coordinates.
+    hand_world_landmarks: Detected hand landmarks in world coordinates.
+  """
+
+  gestures: List[List[category_module.Category]]
+  handedness: List[List[category_module.Category]]
+  hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  hand_world_landmarks: List[List[landmark_module.Landmark]]
+
+
+def _build_recognition_result(
+    output_packets: Mapping[str,
+                            packet_module.Packet]) -> GestureRecognitionResult:
+  """Consturcts a `GestureRecognitionResult` from output packets."""
+  gestures_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_GESTURE_STREAM_NAME])
+  handedness_proto_list = packet_getter.get_proto_list(
+      output_packets[_HANDEDNESS_STREAM_NAME])
+  hand_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_LANDMARKS_STREAM_NAME])
+  hand_world_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_HAND_WORLD_LANDMARKS_STREAM_NAME])
+
+  gesture_results = []
+  for proto in gestures_proto_list:
+    gesture_categories = []
+    gesture_classifications = classification_pb2.ClassificationList()
+    gesture_classifications.MergeFrom(proto)
+    for gesture in gesture_classifications.classification:
+      gesture_categories.append(
+          category_module.Category(
+              index=_GESTURE_DEFAULT_INDEX,
+              score=gesture.score,
+              display_name=gesture.display_name,
+              category_name=gesture.label))
+    gesture_results.append(gesture_categories)
+
+  handedness_results = []
+  for proto in handedness_proto_list:
+    handedness_categories = []
+    handedness_classifications = classification_pb2.ClassificationList()
+    handedness_classifications.MergeFrom(proto)
+    for handedness in handedness_classifications.classification:
+      handedness_categories.append(
+          category_module.Category(
+              index=handedness.index,
+              score=handedness.score,
+              display_name=handedness.display_name,
+              category_name=handedness.label))
+    handedness_results.append(handedness_categories)
+
+  hand_landmarks_results = []
+  for proto in hand_landmarks_proto_list:
+    hand_landmarks = landmark_pb2.NormalizedLandmarkList()
+    hand_landmarks.MergeFrom(proto)
+    hand_landmarks_results.append([
+        landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
+        for hand_landmark in hand_landmarks.landmark
+    ])
+
+  hand_world_landmarks_results = []
+  for proto in hand_world_landmarks_proto_list:
+    hand_world_landmarks = landmark_pb2.LandmarkList()
+    hand_world_landmarks.MergeFrom(proto)
+    hand_world_landmarks_results.append([
+        landmark_module.Landmark.create_from_pb2(hand_world_landmark)
+        for hand_world_landmark in hand_world_landmarks.landmark
+    ])
+
+  return GestureRecognitionResult(gesture_results, handedness_results,
+                                  hand_landmarks_results,
+                                  hand_world_landmarks_results)
+
+
+@dataclasses.dataclass
+class GestureRecognizerOptions:
+  """Options for the gesture recognizer task.
+
+  Attributes:
+    base_options: Base options for the hand gesture recognizer task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Gesture recognizer task has three running modes: 1) The image mode for
+      recognizing hand gestures on single image inputs. 2) The video mode for
+      recognizing hand gestures on the decoded frames of a video. 3) The live
+      stream mode for recognizing hand gestures on a live stream of input data,
+      such as from camera.
+    num_hands: The maximum number of hands can be detected by the recognizer.
+    min_hand_detection_confidence: The minimum confidence score for the hand
+      detection to be considered successful.
+    min_hand_presence_confidence: The minimum confidence score of hand presence
+      score in the hand landmark detection.
+    min_tracking_confidence: The minimum confidence score for the hand tracking
+      to be considered successful.
+    canned_gesture_classifier_options: Options for configuring the canned
+      gestures classifier, such as score threshold, allow list and deny list of
+      gestures. The categories for canned gesture classifiers are: ["None",
+      "Closed_Fist", "Open_Palm", "Pointing_Up", "Thumb_Down", "Thumb_Up",
+      "Victory", "ILoveYou"]. Note this option is subject to change.
+    custom_gesture_classifier_options: Options for configuring the custom
+      gestures classifier, such as score threshold, allow list and deny list of
+      gestures. Note this option is subject to change.
+    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
+  num_hands: Optional[int] = 1
+  min_hand_detection_confidence: Optional[float] = 0.5
+  min_hand_presence_confidence: Optional[float] = 0.5
+  min_tracking_confidence: Optional[float] = 0.5
+  canned_gesture_classifier_options: Optional[
+      _ClassifierOptions] = _ClassifierOptions()
+  custom_gesture_classifier_options: Optional[
+      _ClassifierOptions] = _ClassifierOptions()
+  result_callback: Optional[Callable[
+      [GestureRecognitionResult, image_module.Image, int], None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _GestureRecognizerGraphOptionsProto:
+    """Generates an GestureRecognizerOptions 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
+
+    # Initialize gesture recognizer options from base options.
+    gesture_recognizer_options_proto = _GestureRecognizerGraphOptionsProto(
+        base_options=base_options_proto)
+    # Configure hand detector and hand landmarker options.
+    hand_landmarker_options_proto = gesture_recognizer_options_proto.hand_landmarker_graph_options
+    hand_landmarker_options_proto.min_tracking_confidence = self.min_tracking_confidence
+    hand_landmarker_options_proto.hand_detector_graph_options.num_hands = self.num_hands
+    hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = self.min_hand_detection_confidence
+    hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = self.min_hand_presence_confidence
+
+    # Configure hand gesture recognizer options.
+    hand_gesture_recognizer_options_proto = gesture_recognizer_options_proto.hand_gesture_recognizer_graph_options
+    hand_gesture_recognizer_options_proto.canned_gesture_classifier_graph_options.classifier_options.CopyFrom(
+        self.canned_gesture_classifier_options.to_pb2())
+    hand_gesture_recognizer_options_proto.custom_gesture_classifier_graph_options.classifier_options.CopyFrom(
+        self.custom_gesture_classifier_options.to_pb2())
+
+    return gesture_recognizer_options_proto
+
+
+class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs gesture recognition on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'GestureRecognizer':
+    """Creates an `GestureRecognizer` object from a TensorFlow Lite model and the default `GestureRecognizerOptions`.
+
+    Note that the created `GestureRecognizer` instance is in image mode, for
+    recognizing hand gestures on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `GestureRecognizer` object that's created from the model file and the
+      default `GestureRecognizerOptions`.
+
+    Raises:
+      ValueError: If failed to create `GestureRecognizer` 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 = GestureRecognizerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: GestureRecognizerOptions) -> 'GestureRecognizer':
+    """Creates the `GestureRecognizer` object from gesture recognizer options.
+
+    Args:
+      options: Options for the gesture recognizer task.
+
+    Returns:
+      `GestureRecognizer` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `GestureRecognizer` object from
+        `GestureRecognizerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+
+      if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_HAND_GESTURE_STREAM_NAME]
+        options.result_callback(
+            GestureRecognitionResult([], [], [], []), image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+        return
+
+      gesture_recognition_result = _build_recognition_result(output_packets)
+      timestamp = output_packets[_HAND_GESTURE_STREAM_NAME].timestamp
+      options.result_callback(gesture_recognition_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_STREAM_NAME]),
+        ],
+        output_streams=[
+            ':'.join([_HAND_GESTURE_TAG, _HAND_GESTURE_STREAM_NAME]),
+            ':'.join([_HANDEDNESS_TAG, _HANDEDNESS_STREAM_NAME]),
+            ':'.join([_HAND_LANDMARKS_TAG,
+                      _HAND_LANDMARKS_STREAM_NAME]), ':'.join([
+                          _HAND_WORLD_LANDMARKS_TAG,
+                          _HAND_WORLD_LANDMARKS_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)
+
+  def recognize(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> GestureRecognitionResult:
+    """Performs hand gesture recognition on the given image.
+
+    Only use this method when the GestureRecognizer is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The hand gesture recognition results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If gesture recognition failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2())
+    })
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
+    return _build_recognition_result(output_packets)
+
+  def recognize_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> GestureRecognitionResult:
+    """Performs gesture recognition on the provided video frame.
+
+    Only use this method when the GestureRecognizer is created with the video
+    running mode.
+
+    Only use this method when the GestureRecognizer 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.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The hand gesture recognition results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If gesture recognition failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
+    return _build_recognition_result(output_packets)
+
+  def recognize_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> None:
+    """Sends live image data to perform gesture recognition.
+
+    The results will be available via the "result_callback" provided in the
+    GestureRecognizerOptions. Only use this method when the GestureRecognizer
+    is created with the live stream running mode.
+
+    Only use this method when the GestureRecognizer 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 `GestureRecognizerOptions`. The
+    `recognize_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, gesture recognizer may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The hand gesture recognition results.
+      - The input image that the gesture recognizer runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the
+      gesture recognizer has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, roi_allowed=False)
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })

mediapipe/tasks/python/vision/image_classifier.py

@@ -30,6 +30,7 @@ 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 image_processing_options as image_processing_options_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode
 
 _NormalizedRect = rect.NormalizedRect
@@ -37,6 +38,7 @@ _BaseOptions = base_options_module.BaseOptions
 _ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 
 _CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
@@ -44,17 +46,12 @@ _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_STREAM_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.
@@ -156,7 +153,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         task_graph=_TASK_GRAPH_NAME,
         input_streams=[
             ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
-            ':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
         ],
         output_streams=[
             ':'.join([
@@ -171,17 +168,16 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
             _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
+      image_processing_options: Optional[_ImageProcessingOptions] = None
   ) -> classifications.ClassificationResult:
     """Performs image classification on the provided MediaPipe Image.
 
     Args:
       image: MediaPipe Image.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Returns:
       A classification result object that contains a list of classifications.
@@ -190,10 +186,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       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()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     output_packets = self._process_image_data({
-        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _IMAGE_IN_STREAM_NAME:
-        _NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())
+            packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2())
     })
 
     classification_result_proto = classifications_pb2.ClassificationResult()
@@ -210,7 +208,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       self,
       image: image_module.Image,
       timestamp_ms: int,
-      roi: Optional[_NormalizedRect] = None
+      image_processing_options: Optional[_ImageProcessingOptions] = None
   ) -> classifications.ClassificationResult:
     """Performs image classification on the provided video frames.
 
@@ -222,7 +220,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input video frame in milliseconds.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Returns:
       A classification result object that contains a list of classifications.
@@ -231,13 +229,13 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       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()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     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:
+        _NORM_RECT_STREAM_NAME:
-            packet_creator.create_proto(norm_rect.to_pb2()).at(
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })
 
@@ -251,10 +249,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         for classification in classification_result_proto.classifications
     ])
 
-  def classify_async(self,
+  def classify_async(
+      self,
       image: image_module.Image,
       timestamp_ms: int,
-                     roi: Optional[_NormalizedRect] = None) -> None:
+      image_processing_options: Optional[_ImageProcessingOptions] = 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
@@ -275,18 +275,18 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
     Args:
       image: MediaPipe Image.
       timestamp_ms: The timestamp of the input image in milliseconds.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     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()
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
     self._send_live_stream_data({
         _IMAGE_IN_STREAM_NAME:
             packet_creator.create_image(image).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
-        _NORM_RECT_NAME:
+        _NORM_RECT_STREAM_NAME:
-            packet_creator.create_proto(norm_rect.to_pb2()).at(
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })