Added some files necessary for the Face Stylizer implementation
This commit is contained in:
kinaryml
parent
5398b8881d
commit
7463e48fd4
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@ -94,6 +94,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/face_stylizer:face_stylizer_graph",
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] + select({
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# TODO: Build text_classifier_graph and text_embedder_graph on Windows.
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"//mediapipe:windows": [],
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@ -114,3 +114,20 @@ py_test(
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"@com_google_protobuf//:protobuf_python",
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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/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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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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)
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118
mediapipe/tasks/python/test/vision/face_stylizer_test.py
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118
mediapipe/tasks/python/test/vision/face_stylizer_test.py
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@ -0,0 +1,118 @@
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# Copyright 2022 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 unittest import mock
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import numpy as np
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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.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 vision_task_running_mode as running_mode_module
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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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_Image = image_module.Image
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_FaceStylizer = face_stylizer.FaceStylizer
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_FaceStylizerOptions = face_stylizer.FaceStylizerOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_MODEL = 'face_stylizer_model_placeholder.tflite'
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_IMAGE = 'cats_and_dogs.jpg'
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_STYLIZED_IMAGE = 'stylized_image_placeholder.jpg'
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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, _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, _STYLIZED_IMAGE),
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(ModelFileType.FILE_CONTENT, _STYLIZED_IMAGE))
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def test_stylize(self, model_file_type, expected_detection_result_file):
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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.detect(self.test_image)
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# Comparing results.
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self.assertTrue(
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np.array_equal(stylized_image.numpy_view(),
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self.test_image.numpy_view()))
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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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if __name__ == '__main__':
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absltest.main()
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@ -152,3 +152,22 @@ py_library(
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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)
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py_library(
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name = "face_stylizer",
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srcs = [
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"face_stylizer.py",
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],
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:face_stylizer_graph_options_py_pb2",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:task_info",
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"//mediapipe/tasks/python/vision/core:base_vision_task_api",
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"//mediapipe/tasks/python/vision/core:image_processing_options",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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)
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254
mediapipe/tasks/python/vision/face_stylizer.py
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254
mediapipe/tasks/python/vision/face_stylizer.py
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@ -0,0 +1,254 @@
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# Copyright 2022 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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"""MediaPipe face stylizer task."""
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import dataclasses
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from typing import Callable, Mapping, Optional
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import packet as packet_module
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from mediapipe.tasks.cc.vision.face_stylizer.proto import face_stylizer_graph_options_pb2
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.vision.core import base_vision_task_api
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_FaceStylizerGraphOptionsProto = face_stylizer_graph_options_pb2.FaceStylizerGraphOptions
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_RunningMode = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_TaskInfo = task_info_module.TaskInfo
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_STYLIZED_IMAGE_NAME = 'stylized_image'
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_STYLIZED_IMAGE_TAG = 'STYLIZED_IMAGE'
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_NORM_RECT_STREAM_NAME = 'norm_rect_in'
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_NORM_RECT_TAG = 'NORM_RECT'
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_IMAGE_IN_STREAM_NAME = 'image_in'
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_IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_stylizer.FaceStylizerGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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class FaceStylizerOptions:
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"""Options for the face stylizer task.
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Attributes:
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base_options: Base options for the face stylizer task.
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running_mode: The running mode of the task. Default to the image mode.
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Face stylizer task has three running modes:
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1) The image mode for stylizing faces on single image inputs.
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2) The video mode for stylizing faces on the decoded frames of a video.
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3) The live stream mode for stylizing faces on a live stream of input
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data, such as from camera.
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result_callback: The user-defined result callback for processing live stream
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data. The result callback should only be specified when the running mode
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is set to the live stream mode.
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"""
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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result_callback: Optional[
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Callable[[image_module.Image, image_module.Image, int],
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None]] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _FaceStylizerGraphOptionsProto:
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"""Generates an FaceStylizerOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
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base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
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return _FaceStylizerGraphOptionsProto(base_options=base_options_proto)
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class FaceStylizer(base_vision_task_api.BaseVisionTaskApi):
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"""Class that performs face stylization on images."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'FaceStylizer':
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"""Creates an `FaceStylizer` object from a TensorFlow Lite model and the default `FaceStylizerOptions`.
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Note that the created `FaceDetector` instance is in image mode, for
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stylizing faces on single image inputs.
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Args:
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model_path: Path to the model.
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Returns:
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`FaceStylizer` object that's created from the model file and the default
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`FaceStylizerOptions`.
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Raises:
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ValueError: If failed to create `FaceStylizer` object from the provided
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file such as invalid file path.
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RuntimeError: If other types of error occurred.
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"""
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base_options = _BaseOptions(model_asset_path=model_path)
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options = FaceStylizerOptions(
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base_options=base_options, running_mode=_RunningMode.IMAGE)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(cls,
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options: FaceStylizerOptions) -> 'FaceStylizer':
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"""Creates the `FaceStylizer` object from face stylizer options.
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Args:
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options: Options for the face stylizer task.
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Returns:
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`FaceStylizer` object that's created from `options`.
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Raises:
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ValueError: If failed to create `FaceStylizer` object from
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`FaceStylizerOptions` such as missing the model.
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RuntimeError: If other types of error occurred.
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"""
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def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
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if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
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return
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image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
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stylized_image_packet = output_packets[_STYLIZED_IMAGE_NAME]
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options.result_callback(
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stylized_image_packet, image,
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stylized_image_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
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':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
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],
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output_streams=[
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':'.join([_STYLIZED_IMAGE_TAG, _STYLIZED_IMAGE_NAME]),
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':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
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],
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task_options=options)
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return cls(
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task_info.generate_graph_config(
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enable_flow_limiting=options.running_mode ==
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_RunningMode.LIVE_STREAM), options.running_mode,
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packets_callback if options.result_callback else None)
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def stylize(
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self,
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image: image_module.Image,
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image_processing_options: Optional[_ImageProcessingOptions] = None
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) -> image_module.Image:
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"""Performs face stylization on the provided MediaPipe Image.
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Only use this method when the FaceStylizer is created with the image
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running mode.
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Args:
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image: MediaPipe Image.
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image_processing_options: Options for image processing.
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Returns:
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The stylized image.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If face stylization failed to run.
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"""
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normalized_rect = self.convert_to_normalized_rect(image_processing_options)
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output_packets = self._process_image_data({
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_IMAGE_IN_STREAM_NAME:
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packet_creator.create_image(image),
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_NORM_RECT_STREAM_NAME:
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packet_creator.create_proto(normalized_rect.to_pb2())
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})
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return output_packets[_STYLIZED_IMAGE_NAME]
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def stylize_for_video(
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self,
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image: image_module.Image,
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timestamp_ms: int,
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image_processing_options: Optional[_ImageProcessingOptions] = None
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) -> image_module.Image:
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"""Performs face stylization on the provided video frames.
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Only use this method when the FaceStylizer is created with the video
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running mode. It's required to provide the video frame's timestamp (in
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milliseconds) along with the video frame. The input timestamps should be
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monotonically increasing for adjacent calls of this method.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input video frame in milliseconds.
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image_processing_options: Options for image processing.
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Returns:
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The stylized image.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If face stylization failed to run.
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"""
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normalized_rect = self.convert_to_normalized_rect(image_processing_options)
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output_packets = self._process_video_data({
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_IMAGE_IN_STREAM_NAME:
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packet_creator.create_image(image).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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_NORM_RECT_STREAM_NAME:
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packet_creator.create_proto(normalized_rect.to_pb2()).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
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})
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return output_packets[_STYLIZED_IMAGE_NAME]
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def stylize_async(
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self,
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image: image_module.Image,
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timestamp_ms: int,
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image_processing_options: Optional[_ImageProcessingOptions] = None
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) -> None:
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"""Sends live image data (an Image with a unique timestamp) to perform face stylization.
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Only use this method when the FaceStylizer is created with the live stream
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running mode. The input timestamps should be monotonically increasing for
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adjacent calls of this method. This method will return immediately after the
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input image is accepted. The results will be available via the
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`result_callback` provided in the `FaceStylizerOptions`. The
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`stylize_async` method is designed to process live stream data such as camera
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input. To lower the overall latency, face stylizer may drop the input
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images if needed. In other words, it's not guaranteed to have output per
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input image.
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The `result_callback` provides:
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- The stylized image.
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- The input image that the face stylizer runs on.
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- The input timestamp in milliseconds.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input image in milliseconds.
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image_processing_options: Options for image processing.
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Raises:
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ValueError: If the current input timestamp is smaller than what the face
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stylizer has already processed.
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"""
|
||||
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_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user