Added remaining tests for the GestureRecognizer Python MediaPipe Tasks API

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

@@ -11,7 +11,7 @@
 # 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 Detection Result data class."""
+"""Landmarks Detection Result data class."""
 
 import dataclasses
 from typing import Any, Optional

mediapipe/tasks/python/test/vision/BUILD

@@ -56,6 +56,7 @@ py_test(
         "//mediapipe/tasks/python/test:test_utils",
         "//mediapipe/tasks/python/vision:gesture_recognizer",
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
         "@com_google_protobuf//:protobuf_python"
     ],
 )

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

@@ -14,7 +14,9 @@
 """Tests for gesture recognizer."""
 
 import enum
+from unittest import mock
 
+import numpy as np
 from google.protobuf import text_format
 from absl.testing import absltest
 from absl.testing import parameterized
@@ -29,10 +31,11 @@ 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 gesture_recognizer
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 
 _LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
 _BaseOptions = base_options_module.BaseOptions
-_NormalizedRect = rect_module.NormalizedRect
+_Rect = rect_module.Rect
 _Classification = classification_module.Classification
 _ClassificationList = classification_module.ClassificationList
 _Landmark = landmark_module.Landmark
@@ -45,12 +48,19 @@ _GestureRecognizer = gesture_recognizer.GestureRecognizer
 _GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions
 _GestureRecognitionResult = gesture_recognizer.GestureRecognitionResult
 _RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 
 _GESTURE_RECOGNIZER_MODEL_FILE = 'gesture_recognizer.task'
+_NO_HANDS_IMAGE = 'cats_and_dogs.jpg'
+_TWO_HANDS_IMAGE = 'right_hands.jpg'
 _THUMB_UP_IMAGE = 'thumb_up.jpg'
-_THUMB_UP_LANDMARKS = "thumb_up_landmarks.pbtxt"
+_THUMB_UP_LANDMARKS = 'thumb_up_landmarks.pbtxt'
-_THUMB_UP_LABEL = "Thumb_Up"
+_THUMB_UP_LABEL = 'Thumb_Up'
 _THUMB_UP_INDEX = 5
+_POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg'
+_POINTING_UP_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt'
+_POINTING_UP_LABEL = 'Pointing_Up'
+_POINTING_UP_INDEX = 3
 _LANDMARKS_ERROR_TOLERANCE = 0.03
 
 
@@ -89,7 +99,7 @@ class GestureRecognizerTest(parameterized.TestCase):
     super().setUp()
     self.test_image = _Image.create_from_file(
         test_utils.get_test_data_path(_THUMB_UP_IMAGE))
-    self.gesture_recognizer_model_path = test_utils.get_test_data_path(
+    self.model_path = test_utils.get_test_data_path(
         _GESTURE_RECOGNIZER_MODEL_FILE)
 
   def _assert_actual_result_approximately_matches_expected_result(
@@ -105,8 +115,15 @@ class GestureRecognizerTest(parameterized.TestCase):
     self.assertLen(actual_result.handedness, len(expected_result.handedness))
     self.assertLen(actual_result.gestures, len(expected_result.gestures))
     # Actual landmarks match expected landmarks.
-    self.assertEqual(actual_result.hand_landmarks,
+    self.assertLen(actual_result.hand_landmarks[0].landmarks,
-                     expected_result.hand_landmarks)
+                   len(expected_result.hand_landmarks[0].landmarks))
+    actual_landmarks = actual_result.hand_landmarks[0].landmarks
+    expected_landmarks = expected_result.hand_landmarks[0].landmarks
+    for i in range(len(actual_landmarks)):
+      self.assertAlmostEqual(actual_landmarks[i].x, expected_landmarks[i].x,
+                             delta=_LANDMARKS_ERROR_TOLERANCE)
+      self.assertAlmostEqual(actual_landmarks[i].y, expected_landmarks[i].y,
+                             delta=_LANDMARKS_ERROR_TOLERANCE)
     # Actual handedness matches expected handedness.
     actual_top_handedness = actual_result.handedness[0].classifications[0]
     expected_top_handedness = expected_result.handedness[0].classifications[0]
@@ -118,32 +135,56 @@ class GestureRecognizerTest(parameterized.TestCase):
     self.assertEqual(actual_top_gesture.index, expected_top_gesture.index)
     self.assertEqual(actual_top_gesture.label, expected_top_gesture.label)
 
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _GestureRecognizer.create_from_model_path(self.model_path) as recognizer:
+      self.assertIsInstance(recognizer, _GestureRecognizer)
+
+  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 = _GestureRecognizerOptions(base_options=base_options)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      self.assertIsInstance(recognizer, _GestureRecognizer)
+
+  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 = _GestureRecognizerOptions(base_options=base_options)
+      _GestureRecognizer.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 = _GestureRecognizerOptions(base_options=base_options)
+      recognizer = _GestureRecognizer.create_from_options(options)
+      self.assertIsInstance(recognizer, _GestureRecognizer)
+
   @parameterized.parameters(
-      (ModelFileType.FILE_NAME, 0.3, _get_expected_gesture_recognition_result(
+      (ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
           _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
       )),
-      (ModelFileType.FILE_CONTENT, 0.3, _get_expected_gesture_recognition_result(
+      (ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
           _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
       )))
-  def test_recognize(self, model_file_type, min_gesture_confidence,
+  def test_recognize(self, model_file_type, expected_recognition_result):
-                     expected_recognition_result):
     # Creates gesture recognizer.
     if model_file_type is ModelFileType.FILE_NAME:
-      gesture_recognizer_base_options = _BaseOptions(
+      base_options = _BaseOptions(model_asset_path=self.model_path)
-          model_asset_path=self.gesture_recognizer_model_path)
     elif model_file_type is ModelFileType.FILE_CONTENT:
-      with open(self.gesture_recognizer_model_path, 'rb') as f:
+      with open(self.model_path, 'rb') as f:
         model_content = f.read()
-      gesture_recognizer_base_options = _BaseOptions(
+      base_options = _BaseOptions(model_asset_buffer=model_content)
-          model_asset_buffer=model_content)
     else:
       # Should never happen
       raise ValueError('model_file_type is invalid.')
 
-    options = _GestureRecognizerOptions(
+    options = _GestureRecognizerOptions(base_options=base_options)
-        base_options=gesture_recognizer_base_options,
-        min_gesture_confidence=min_gesture_confidence
-    )
     recognizer = _GestureRecognizer.create_from_options(options)
 
     # Performs hand gesture recognition on the input.
@@ -151,10 +192,238 @@ class GestureRecognizerTest(parameterized.TestCase):
     # Comparing results.
     self._assert_actual_result_approximately_matches_expected_result(
         recognition_result, expected_recognition_result)
-    # Closes the gesture recognizer explicitly when the detector is not used in
+    # Closes the gesture recognizer explicitly when the gesture recognizer is
-    # a context.
+    # not used in a context.
     recognizer.close()
 
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+      )),
+      (ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
+      )))
+  def test_recognize_in_context(self, model_file_type,
+                                expected_recognition_result):
+    # Creates gesture recognizer.
+    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.')
+
+    options = _GestureRecognizerOptions(base_options=base_options)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      # Performs hand gesture recognition on the input.
+      recognition_result = recognizer.recognize(self.test_image)
+      # Comparing results.
+      self._assert_actual_result_approximately_matches_expected_result(
+        recognition_result, expected_recognition_result)
+
+  def test_recognize_succeeds_with_num_hands(self):
+    # Creates gesture recognizer.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _GestureRecognizerOptions(base_options=base_options, num_hands=2)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      # Load the pointing up rotated image.
+      test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_TWO_HANDS_IMAGE))
+      # Performs hand gesture recognition on the input.
+      recognition_result = recognizer.recognize(test_image)
+      # Comparing results.
+      self.assertLen(recognition_result.handedness, 2)
+
+  def test_recognize_succeeds_with_rotation(self):
+    # Creates gesture recognizer.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      # Load the pointing up rotated image.
+      test_image = _Image.create_from_file(
+          test_utils.get_test_data_path(_POINTING_UP_ROTATED_IMAGE))
+      # Set rotation parameters using ImageProcessingOptions.
+      image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
+      # Performs hand gesture recognition on the input.
+      recognition_result = recognizer.recognize(test_image,
+                                                image_processing_options)
+      expected_recognition_result = _get_expected_gesture_recognition_result(
+          _POINTING_UP_LANDMARKS, _POINTING_UP_LABEL, _POINTING_UP_INDEX)
+      # Comparing results.
+      self._assert_actual_result_approximately_matches_expected_result(
+          recognition_result, expected_recognition_result)
+
+  def test_recognize_fails_with_region_of_interest(self):
+    # Creates gesture recognizer.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
+    with self.assertRaisesRegex(
+        ValueError, "This task doesn't support region-of-interest."):
+      with _GestureRecognizer.create_from_options(options) as recognizer:
+        # Set the `region_of_interest` parameter using `ImageProcessingOptions`.
+        image_processing_options = _ImageProcessingOptions(
+            region_of_interest=_Rect(0, 0, 1, 1))
+        # Attempt to perform hand gesture recognition on the cropped input.
+        recognizer.recognize(self.test_image, image_processing_options)
+
+  def test_empty_recognition_outputs(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path))
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      # Load the image with no hands.
+      no_hands_test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_NO_HANDS_IMAGE))
+      # Performs gesture recognition on the input.
+      recognition_result = recognizer.recognize(no_hands_test_image)
+      self.assertEmpty(recognition_result.hand_landmarks)
+      self.assertEmpty(recognition_result.hand_world_landmarks)
+      self.assertEmpty(recognition_result.handedness)
+      self.assertEmpty(recognition_result.gestures)
+
+  def test_missing_result_callback(self):
+    options = _GestureRecognizerOptions(
+      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 _GestureRecognizer.create_from_options(options) as unused_recognizer:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _GestureRecognizerOptions(
+      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 _GestureRecognizer.create_from_options(options) as unused_recognizer:
+        pass
+
+  def test_calling_recognize_for_video_in_image_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        recognizer.recognize_for_video(self.test_image, 0)
+
+  def test_calling_recognize_async_in_image_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.IMAGE)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        recognizer.recognize_async(self.test_image, 0)
+
+  def test_calling_recognize_in_video_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        recognizer.recognize(self.test_image)
+
+  def test_calling_recognize_async_in_video_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        recognizer.recognize_async(self.test_image, 0)
+
+  def test_recognize_for_video_with_out_of_order_timestamp(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      unused_result = recognizer.recognize_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        recognizer.recognize_for_video(self.test_image, 0)
+
+  def test_recognize_for_video(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.VIDEO)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      for timestamp in range(0, 300, 30):
+        recognition_result = recognizer.recognize_for_video(self.test_image,
+                                                            timestamp)
+        expected_recognition_result = _get_expected_gesture_recognition_result(
+           _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)
+        self._assert_actual_result_approximately_matches_expected_result(
+            recognition_result, expected_recognition_result)
+
+  def test_calling_recognize_in_live_stream_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        recognizer.recognize(self.test_image)
+
+  def test_calling_recognize_for_video_in_live_stream_mode(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        recognizer.recognize_for_video(self.test_image, 0)
+
+  def test_recognize_async_calls_with_illegal_timestamp(self):
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=mock.MagicMock())
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      recognizer.recognize_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        recognizer.recognize_async(self.test_image, 0)
+
+  @parameterized.parameters(
+      (_THUMB_UP_IMAGE, _get_expected_gesture_recognition_result(
+          _THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)),
+      (_NO_HANDS_IMAGE, _GestureRecognitionResult([], [], [], [])))
+  def test_recognize_async_calls(self, image_path, expected_result):
+    test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(image_path))
+    observed_timestamp_ms = -1
+
+    def check_result(result: _GestureRecognitionResult, output_image: _Image,
+                     timestamp_ms: int):
+      if result.hand_landmarks and result.hand_world_landmarks and \
+        result.handedness and result.gestures:
+        self._assert_actual_result_approximately_matches_expected_result(
+            result, expected_result)
+      else:
+        self.assertEqual(result, expected_result)
+      self.assertTrue(
+          np.array_equal(output_image.numpy_view(),
+                         test_image.numpy_view()))
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _GestureRecognizerOptions(
+      base_options=_BaseOptions(model_asset_path=self.model_path),
+      running_mode=_RUNNING_MODE.LIVE_STREAM,
+      result_callback=check_result)
+    with _GestureRecognizer.create_from_options(options) as recognizer:
+      for timestamp in range(0, 300, 30):
+        recognizer.recognize_async(test_image, timestamp)
+
 
 if __name__ == '__main__':
   absltest.main()

mediapipe/tasks/python/vision/BUILD

@@ -52,7 +52,6 @@ py_library(
         "//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_py_pb2",
         "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_py_pb2",
         "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_py_pb2",
-        "//mediapipe/tasks/python/components/containers:rect",
         "//mediapipe/tasks/python/components/containers:classification",
         "//mediapipe/tasks/python/components/containers:landmark",
         "//mediapipe/tasks/python/components/processors:classifier_options",

mediapipe/tasks/python/vision/core/BUILD

@@ -23,6 +23,14 @@ 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 = [
@@ -30,6 +38,7 @@ py_library(
     ],
     deps = [
         ":vision_task_running_mode",
+        ":image_processing_options",
         "//mediapipe/framework:calculator_py_pb2",
         "//mediapipe/python:_framework_bindings",
         "//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.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.components.containers import rect as rect_module
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_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 +127,50 @@ class BaseVisionTaskApi(object):
           + self._running_mode.name)
     self._runner.send(inputs)
 
+  @staticmethod
+  def convert_to_normalized_rect(
+      options: _ImageProcessingOptions,
+      roi_allowed: bool = True
+  ) -> _NormalizedRect:
+    """
+    Convert 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.
+
+    """
+    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.x_center >= roi.width or roi.y_center >= roi.height:
+        raise ValueError(
+          "Expected Rect with x_center < width and y_center < height.")
+      if roi.x_center < 0 or roi.y_center < 0 or roi.width > 1 or roi.height > 1:
+        raise ValueError("Expected Rect values to be in [0,1].")
+      normalized_rect.x_center = roi.x_center + roi.width / 2.0
+      normalized_rect.y_center = roi.y_center + roi.height / 2.0
+      normalized_rect.width = roi.width - roi.x_center
+      normalized_rect.height = roi.height - roi.y_center
+    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_degress: 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

@@ -27,7 +27,6 @@ from mediapipe.tasks.cc.vision.gesture_recognizer.proto import hand_gesture_reco
 from mediapipe.tasks.cc.vision.hand_detector.proto import hand_detector_graph_options_pb2
 from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarker_graph_options_pb2
 from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarks_detector_graph_options_pb2
-from mediapipe.tasks.python.components.containers import rect as rect_module
 from mediapipe.tasks.python.components.containers import classification as classification_module
 from mediapipe.tasks.python.components.containers import landmark as landmark_module
 from mediapipe.tasks.python.components.processors import classifier_options
@@ -36,8 +35,8 @@ from mediapipe.tasks.python.core import task_info as task_info_module
 from mediapipe.tasks.python.core.optional_dependencies import doc_controls
 from mediapipe.tasks.python.vision.core import base_vision_task_api
 from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
 
-_NormalizedRect = rect_module.NormalizedRect
 _BaseOptions = base_options_module.BaseOptions
 _GestureClassifierGraphOptionsProto = gesture_classifier_graph_options_pb2.GestureClassifierGraphOptions
 _GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
@@ -47,6 +46,7 @@ _HandLandmarkerGraphOptionsProto = hand_landmarker_graph_options_pb2.HandLandmar
 _HandLandmarksDetectorGraphOptionsProto = hand_landmarks_detector_graph_options_pb2.HandLandmarksDetectorGraphOptions
 _ClassifierOptions = classifier_options.ClassifierOptions
 _RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
 _TaskInfo = task_info_module.TaskInfo
 _TaskRunner = task_runner_module.TaskRunner
 
@@ -67,11 +67,6 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerG
 _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 GestureRecognitionResult:
   """The gesture recognition result from GestureRecognizer, where each vector
@@ -278,7 +273,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
   def recognize(
       self,
       image: image_module.Image,
-      roi: Optional[_NormalizedRect] = None
+      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.
@@ -289,7 +284,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
 
     Args:
       image: MediaPipe Image.
-      roi: The region of interest.
+      image_processing_options: Options for image processing.
 
     Returns:
       The hand gesture recognition results.
@@ -298,11 +293,16 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If gesture recognition 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,
+                                                      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(
-            norm_rect.to_pb2())})
+            normalized_rect.to_pb2())})
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
     gestures_proto_list = packet_getter.get_proto_list(
       output_packets[_HAND_GESTURE_STREAM_NAME])
     handedness_proto_list = packet_getter.get_proto_list(
@@ -331,7 +331,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
   def recognize_for_video(
       self, image: image_module.Image,
       timestamp_ms: int,
-      roi: Optional[_NormalizedRect] = None
+      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.
@@ -344,7 +344,7 @@ class GestureRecognizer(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:
       The hand gesture recognition results.
@@ -353,14 +353,19 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
       ValueError: If any of the input arguments is invalid.
       RuntimeError: If gesture recognition 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,
+                                                      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(
-            norm_rect.to_pb2()).at(
+            normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })
+
+    if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
+      return GestureRecognitionResult([], [], [], [])
+
     gestures_proto_list = packet_getter.get_proto_list(
       output_packets[_HAND_GESTURE_STREAM_NAME])
     handedness_proto_list = packet_getter.get_proto_list(
@@ -390,7 +395,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
       self,
       image: image_module.Image,
       timestamp_ms: int,
-      roi: Optional[_NormalizedRect] = None
+      image_processing_options: Optional[_ImageProcessingOptions] = None
   ) -> None:
     """Sends live image data to perform gesture recognition, and the results
     will be available via the "result_callback" provided in the
@@ -415,17 +420,18 @@ class GestureRecognizer(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
       gesture recognizer 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,
+                                                      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(
-            norm_rect.to_pb2()).at(
+            normalized_rect.to_pb2()).at(
                 timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
     })