Added files for Face Stylizer Unit Tests
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					@ -211,3 +211,20 @@ py_test(
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        "//mediapipe/tasks/python/vision/core:image_processing_options",
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					        "//mediapipe/tasks/python/vision/core:image_processing_options",
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    ],
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					    ],
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)
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					)
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					py_test(
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					    name = "face_stylizer_test",
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					    srcs = ["face_stylizer_test.py"],
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					    data = [
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					        "//mediapipe/tasks/testdata/vision:test_images",
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					        "//mediapipe/tasks/testdata/vision:test_models",
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					    ],
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					    deps = [
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					        "//mediapipe/python:_framework_bindings",
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					        "//mediapipe/tasks/python/components/containers:rect",
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					        "//mediapipe/tasks/python/core:base_options",
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					        "//mediapipe/tasks/python/test:test_utils",
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					        "//mediapipe/tasks/python/vision:face_stylizer",
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					        "//mediapipe/tasks/python/vision/core:image_processing_options",
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					    ],
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					)
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								mediapipe/tasks/python/test/vision/face_stylizer_test.py
									
									
									
									
									
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								mediapipe/tasks/python/test/vision/face_stylizer_test.py
									
									
									
									
									
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					@ -0,0 +1,182 @@
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					# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
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					#
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					# Licensed under the Apache License, Version 2.0 (the "License");
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					# you may not use this file except in compliance with the License.
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					# You may obtain a copy of the License at
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					#
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					#     http://www.apache.org/licenses/LICENSE-2.0
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					#
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					# Unless required by applicable law or agreed to in writing, software
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					# distributed under the License is distributed on an "AS IS" BASIS,
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					# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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					# See the License for the specific language governing permissions and
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					# limitations under the License.
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					"""Tests for face stylizer."""
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					import enum
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					import os
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					from absl.testing import absltest
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					from absl.testing import parameterized
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					from mediapipe.python._framework_bindings import image as image_module
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					from mediapipe.tasks.python.core import base_options as base_options_module
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					from mediapipe.tasks.python.components.containers import rect
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					from mediapipe.tasks.python.test import test_utils
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					from mediapipe.tasks.python.vision import face_stylizer
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					from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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					_BaseOptions = base_options_module.BaseOptions
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					_Rect = rect.Rect
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					_Image = image_module.Image
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					_FaceStylizer = face_stylizer.FaceStylizer
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					_FaceStylizerOptions = face_stylizer.FaceStylizerOptions
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					_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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					_MODEL = 'face_stylizer_color_ink.task'
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					_LARGE_FACE_IMAGE = "portrait.jpg"
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					_MODEL_IMAGE_SIZE = 256
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					_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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					class ModelFileType(enum.Enum):
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					  FILE_CONTENT = 1
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					  FILE_NAME = 2
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					class FaceStylizerTest(parameterized.TestCase):
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					  def setUp(self):
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					    super().setUp()
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					    self.test_image = _Image.create_from_file(
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					        test_utils.get_test_data_path(
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					            os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)))
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					    self.model_path = test_utils.get_test_data_path(
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					        os.path.join(_TEST_DATA_DIR, _MODEL))
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					  def test_create_from_file_succeeds_with_valid_model_path(self):
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					    # Creates with default option and valid model file successfully.
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					    with _FaceStylizer.create_from_model_path(self.model_path) as stylizer:
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					      self.assertIsInstance(stylizer, _FaceStylizer)
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					  def test_create_from_options_succeeds_with_valid_model_path(self):
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					    # Creates with options containing model file successfully.
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					    base_options = _BaseOptions(model_asset_path=self.model_path)
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					    options = _FaceStylizerOptions(base_options=base_options)
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					    with _FaceStylizer.create_from_options(options) as stylizer:
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					      self.assertIsInstance(stylizer, _FaceStylizer)
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					  def test_create_from_options_fails_with_invalid_model_path(self):
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					    with self.assertRaisesRegex(
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					        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
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					      base_options = _BaseOptions(
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					          model_asset_path='/path/to/invalid/model.tflite')
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					      options = _FaceStylizerOptions(base_options=base_options)
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					      _FaceStylizer.create_from_options(options)
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					  def test_create_from_options_succeeds_with_valid_model_content(self):
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					    # Creates with options containing model content successfully.
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					    with open(self.model_path, 'rb') as f:
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					      base_options = _BaseOptions(model_asset_buffer=f.read())
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					      options = _FaceStylizerOptions(base_options=base_options)
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					      stylizer = _FaceStylizer.create_from_options(options)
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					      self.assertIsInstance(stylizer, _FaceStylizer)
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					  @parameterized.parameters(
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					      (ModelFileType.FILE_NAME, _LARGE_FACE_IMAGE),
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					      (ModelFileType.FILE_CONTENT, _LARGE_FACE_IMAGE)
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					  )
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					  def test_stylize(self, model_file_type, image_file_name):
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					    # Load the test image.
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					    self.test_image = _Image.create_from_file(
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					      test_utils.get_test_data_path(
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					        os.path.join(_TEST_DATA_DIR, image_file_name)))
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					    # Creates stylizer.
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					    if model_file_type is ModelFileType.FILE_NAME:
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					      base_options = _BaseOptions(model_asset_path=self.model_path)
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					    elif model_file_type is ModelFileType.FILE_CONTENT:
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					      with open(self.model_path, 'rb') as f:
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					        model_content = f.read()
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					      base_options = _BaseOptions(model_asset_buffer=model_content)
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					    else:
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					      # Should never happen
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					      raise ValueError('model_file_type is invalid.')
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					    options = _FaceStylizerOptions(base_options=base_options)
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					    stylizer = _FaceStylizer.create_from_options(options)
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					    # Performs face stylization on the input.
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					    stylized_image = stylizer.stylize(self.test_image)
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					    self.assertIsInstance(stylized_image, _Image)
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					    # Closes the stylizer explicitly when the stylizer is not used in
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					    # a context.
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					    stylizer.close()
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					  @parameterized.parameters(
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					      (ModelFileType.FILE_NAME, _LARGE_FACE_IMAGE),
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					      (ModelFileType.FILE_CONTENT, _LARGE_FACE_IMAGE)
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					  )
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					  def test_stylize_in_context(self, model_file_type, image_file_name):
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					    # Load the test image.
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					    self.test_image = _Image.create_from_file(
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					      test_utils.get_test_data_path(
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					        os.path.join(_TEST_DATA_DIR, image_file_name)))
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					    # Creates stylizer.
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					    if model_file_type is ModelFileType.FILE_NAME:
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					      base_options = _BaseOptions(model_asset_path=self.model_path)
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					    elif model_file_type is ModelFileType.FILE_CONTENT:
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					      with open(self.model_path, 'rb') as f:
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					        model_content = f.read()
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					      base_options = _BaseOptions(model_asset_buffer=model_content)
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					    else:
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					      # Should never happen
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					      raise ValueError('model_file_type is invalid.')
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					    options = _FaceStylizerOptions(base_options=base_options)
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					    with _FaceStylizer.create_from_options(options) as stylizer:
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					      # Performs face stylization on the input.
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					      stylized_image = stylizer.stylize(self.test_image)
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					      self.assertIsInstance(stylized_image, _Image)
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					      self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
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					      self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
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					  def test_stylize_succeeds_with_region_of_interest(self):
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					    base_options = _BaseOptions(model_asset_path=self.model_path)
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					    options = _FaceStylizerOptions(base_options=base_options)
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					    with _FaceStylizer.create_from_options(options) as stylizer:
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					      # Load the test image.
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					      test_image = _Image.create_from_file(
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					        test_utils.get_test_data_path(
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					          os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)
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					        )
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					      )
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					      # Region-of-interest around the face.
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					      roi = _Rect(left=0.32, top=0.02, right=0.67, bottom=0.32)
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					      image_processing_options = _ImageProcessingOptions(roi)
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					      # Performs face stylization on the input.
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					      stylized_image = stylizer.stylize(test_image, image_processing_options)
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					      self.assertIsInstance(stylized_image, _Image)
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					      self.assertEqual(stylized_image.width, _MODEL_IMAGE_SIZE)
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					      self.assertEqual(stylized_image.height, _MODEL_IMAGE_SIZE)
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					  def test_stylize_succeeds_with_no_face_detected(self):
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					    base_options = _BaseOptions(model_asset_path=self.model_path)
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					    options = _FaceStylizerOptions(base_options=base_options)
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					    with _FaceStylizer.create_from_options(options) as stylizer:
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					      # Load the test image.
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					      test_image = _Image.create_from_file(
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					        test_utils.get_test_data_path(
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					          os.path.join(_TEST_DATA_DIR, _LARGE_FACE_IMAGE)
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					        )
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					      )
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					      # Region-of-interest that doesn't contain a human face.
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					      roi = _Rect(left=0.1, top=0.1, right=0.2, bottom=0.2)
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					      image_processing_options = _ImageProcessingOptions(roi)
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					      # Performs face stylization on the input.
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					      stylized_image = stylizer.stylize(test_image, image_processing_options)
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					      self.assertIsNone(stylized_image)
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					if __name__ == '__main__':
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					  absltest.main()
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										1
									
								
								mediapipe/tasks/testdata/vision/BUILD
									
									
									
									
										vendored
									
									
								
							
							
						
						
									
										1
									
								
								mediapipe/tasks/testdata/vision/BUILD
									
									
									
									
										vendored
									
									
								
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					@ -186,6 +186,7 @@ filegroup(
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        "face_detection_short_range.tflite",
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					        "face_detection_short_range.tflite",
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        "face_landmarker.task",
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					        "face_landmarker.task",
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        "face_landmarker_v2.task",
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					        "face_landmarker_v2.task",
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					        "face_stylizer_color_ink.task",
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        "hair_segmentation.tflite",
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					        "hair_segmentation.tflite",
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        "hand_landmark_full.tflite",
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					        "hand_landmark_full.tflite",
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        "hand_landmark_lite.tflite",
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					        "hand_landmark_lite.tflite",
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