Added the Face Aligner Python API
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kinaryml
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mediapipe/tasks/python/test/vision/face_aligner_test.py
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mediapipe/tasks/python/test/vision/face_aligner_test.py
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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 aligner."""
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import enum
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import os
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from unittest import mock
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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_aligner
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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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_Rect = rect.Rect
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_Image = image_module.Image
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_FaceAligner = face_aligner.FaceAligner
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_FaceAlignerOptions = face_aligner.FaceAlignerOptions
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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.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 FaceAlignerTest(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 _FaceAligner.create_from_model_path(self.model_path) as aligner:
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self.assertIsInstance(aligner, _FaceAligner)
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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 = _FaceAlignerOptions(base_options=base_options)
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with _FaceAligner.create_from_options(options) as aligner:
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self.assertIsInstance(aligner, _FaceAligner)
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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 = _FaceAlignerOptions(base_options=base_options)
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_FaceAligner.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 = _FaceAlignerOptions(base_options=base_options)
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aligner = _FaceAligner.create_from_options(options)
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self.assertIsInstance(aligner, _FaceAligner)
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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_align(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 aligner.
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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 = _FaceAlignerOptions(base_options=base_options)
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aligner = _FaceAligner.create_from_options(options)
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# Performs face alignment on the input.
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alignd_image = aligner.align(self.test_image)
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self.assertIsInstance(alignd_image, _Image)
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# Closes the aligner explicitly when the aligner is not used in
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# a context.
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aligner.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_align_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 aligner.
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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 = _FaceAlignerOptions(base_options=base_options)
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with _FaceAligner.create_from_options(options) as aligner:
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# Performs face alignment on the input.
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alignd_image = aligner.align(self.test_image)
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self.assertIsInstance(alignd_image, _Image)
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self.assertEqual(alignd_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(alignd_image.height, _MODEL_IMAGE_SIZE)
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def test_align_succeeds_with_region_of_interest(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _FaceAlignerOptions(base_options=base_options)
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with _FaceAligner.create_from_options(options) as aligner:
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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 alignment on the input.
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alignd_image = aligner.align(test_image, image_processing_options)
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self.assertIsInstance(alignd_image, _Image)
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self.assertEqual(alignd_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(alignd_image.height, _MODEL_IMAGE_SIZE)
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def test_align_succeeds_with_no_face_detected(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _FaceAlignerOptions(base_options=base_options)
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with _FaceAligner.create_from_options(options) as aligner:
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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 alignment on the input.
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alignd_image = aligner.align(test_image, image_processing_options)
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self.assertIsNone(alignd_image)
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def test_missing_result_callback(self):
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options = _FaceAlignerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback must be provided'
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):
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with _FaceAligner.create_from_options(options) as unused_aligner:
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pass
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def test_illegal_result_callback(self):
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options = _FaceAlignerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE,
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result_callback=mock.MagicMock(),
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback should not be provided'
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):
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with _FaceAligner.create_from_options(options) as unused_aligner:
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pass
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if __name__ == '__main__':
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absltest.main()
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201
mediapipe/tasks/python/vision/face_aligner.py
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mediapipe/tasks/python/vision/face_aligner.py
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# Copyright 2023 The MediaPipe Authors.
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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 aligner 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 = (
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face_stylizer_graph_options_pb2.FaceStylizerGraphOptions
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)
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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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_FACE_ALIGNMENT_IMAGE_NAME = 'stylized_image'
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_FACE_ALIGNMENT_IMAGE_TAG = 'FACE_ALIGNMENT'
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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 FaceAlignerOptions:
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"""Options for the face aligner task.
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Attributes:
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base_options: Base options for the face aligner task.
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running_mode: The running mode of the task. Default to the image mode. Face
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aligner task has three running modes: 1) The image mode for aligning one
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face on a single image input. 2) The video mode for aligning one face per
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frame on the decoded frames of a video. 3) The live stream mode for
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aligning one face on a live stream of input 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], None]
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] = 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 = (
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False if self.running_mode == _RunningMode.IMAGE else True
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)
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return _FaceStylizerGraphOptionsProto(base_options=base_options_proto)
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class FaceAligner(base_vision_task_api.BaseVisionTaskApi):
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"""Class that performs face alignment on images."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'FaceAligner':
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"""Creates an `FaceAligner` object from a TensorFlow Lite model and the default `FaceAlignerOptions`.
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Note that the created `FaceAligner` instance is in image mode, for
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aligning one face on a single image input.
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Args:
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model_path: Path to the model.
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Returns:
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`FaceAligner` object that's created from the model file and the default
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`FaceAlignerOptions`.
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Raises:
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ValueError: If failed to create `FaceAligner` 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 = FaceAlignerOptions(
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base_options=base_options, running_mode=_RunningMode.IMAGE
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)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(cls, options: FaceAlignerOptions) -> 'FaceAligner':
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"""Creates the `FaceAligner` object from face aligner options.
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Args:
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options: Options for the face aligner task.
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Returns:
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`FaceAligner` object that's created from `options`.
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Raises:
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ValueError: If failed to create `FaceAligner` object from
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`FaceAlignerOptions` 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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aligned_image_packet = output_packets[_FACE_ALIGNMENT_IMAGE_NAME]
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if aligned_image_packet.is_empty():
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options.result_callback(
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None,
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image,
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aligned_image_packet.timestamp.value
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// _MICRO_SECONDS_PER_MILLISECOND,
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)
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aligned_image = packet_getter.get_image(aligned_image_packet)
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options.result_callback(
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aligned_image,
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image,
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aligned_image_packet.timestamp.value
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// _MICRO_SECONDS_PER_MILLISECOND,
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)
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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([_FACE_ALIGNMENT_IMAGE_TAG, _FACE_ALIGNMENT_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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)
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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
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),
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options.running_mode,
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packets_callback if options.result_callback else None,
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)
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def align(
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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 alignment on the provided MediaPipe Image.
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Only use this method when the FaceAligner 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 aligned face image. The aligned output image size is the same as the
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model output size. None if no face is detected on the input 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 alignment failed to run.
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"""
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normalized_rect = self.convert_to_normalized_rect(
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image_processing_options, image)
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output_packets = self._process_image_data({
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_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
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_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
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normalized_rect.to_pb2()
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),
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})
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if output_packets[_FACE_ALIGNMENT_IMAGE_NAME].is_empty():
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return None
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return packet_getter.get_image(output_packets[_FACE_ALIGNMENT_IMAGE_NAME])
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