Merge pull request #4177 from kinaryml:face-landmarker-python
PiperOrigin-RevId: 517979421
This commit is contained in:
Copybara-Service
commit
54e4dfc853
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@ -96,6 +96,7 @@ cc_library(
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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_detector:face_detector_graph",
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"//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_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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@ -115,6 +115,30 @@ py_test(
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],
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)
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py_test(
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name = "face_landmarker_test",
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srcs = ["face_landmarker_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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"//mediapipe/tasks/testdata/vision:test_protos",
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],
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deps = [
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"//mediapipe/framework/formats:classification_py_pb2",
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"//mediapipe/framework/formats:landmark_py_pb2",
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:landmark",
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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_landmarker",
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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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"@com_google_protobuf//:protobuf_python",
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],
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)
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py_test(
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name = "hand_landmarker_test",
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srcs = ["hand_landmarker_test.py"],
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564
mediapipe/tasks/python/test/vision/face_landmarker_test.py
Normal file
564
mediapipe/tasks/python/test/vision/face_landmarker_test.py
Normal file
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@ -0,0 +1,564 @@
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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 landmarker."""
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import enum
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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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import numpy as np
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from google.protobuf import text_format
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from mediapipe.framework.formats import classification_pb2
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.components.containers import rect as rect_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_landmarker
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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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FaceLandmarkerResult = face_landmarker.FaceLandmarkerResult
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_BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_Rect = rect_module.Rect
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_Landmark = landmark_module.Landmark
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_Image = image_module.Image
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_FaceLandmarker = face_landmarker.FaceLandmarker
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_FaceLandmarkerOptions = face_landmarker.FaceLandmarkerOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
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_PORTRAIT_IMAGE = 'portrait.jpg'
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_CAT_IMAGE = 'cat.jpg'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS_WITH_ATTENTION = (
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'portrait_expected_face_landmarks_with_attention.pbtxt'
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)
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_PORTRAIT_EXPECTED_BLENDSHAPES = (
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'portrait_expected_blendshapes_with_attention.pbtxt'
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)
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_LANDMARKS_DIFF_MARGIN = 0.03
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_BLENDSHAPES_DIFF_MARGIN = 0.12
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_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
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def _get_expected_face_landmarks(file_path: str):
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proto_file_path = test_utils.get_test_data_path(file_path)
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with open(proto_file_path, 'rb') as f:
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proto = landmark_pb2.NormalizedLandmarkList()
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text_format.Parse(f.read(), proto)
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face_landmarks = []
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for landmark in proto.landmark:
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face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
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return face_landmarks
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def _get_expected_face_blendshapes(file_path: str):
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proto_file_path = test_utils.get_test_data_path(file_path)
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with open(proto_file_path, 'rb') as f:
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proto = classification_pb2.ClassificationList()
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text_format.Parse(f.read(), proto)
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face_blendshapes_categories = []
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face_blendshapes_classifications = classification_pb2.ClassificationList()
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face_blendshapes_classifications.MergeFrom(proto)
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for face_blendshapes in face_blendshapes_classifications.classification:
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face_blendshapes_categories.append(
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category_module.Category(
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index=face_blendshapes.index,
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score=face_blendshapes.score,
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display_name=face_blendshapes.display_name,
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category_name=face_blendshapes.label,
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)
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)
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return face_blendshapes_categories
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def _get_expected_facial_transformation_matrixes():
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matrix = np.array([
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[0.9995292, -0.005092691, 0.030254554, -0.37340546],
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[0.0072318087, 0.99744856, -0.07102106, 22.212194],
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[-0.029815676, 0.07120642, 0.9970159, -64.76358],
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[0, 0, 0, 1],
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])
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facial_transformation_matrixes_results = []
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facial_transformation_matrixes_results.append(matrix)
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return facial_transformation_matrixes_results
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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 FaceLandmarkerTest(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(_PORTRAIT_IMAGE)
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)
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self.model_path = test_utils.get_test_data_path(
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE
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)
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def _expect_landmarks_correct(self, actual_landmarks, expected_landmarks):
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# Expects to have the same number of faces detected.
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self.assertLen(actual_landmarks, len(expected_landmarks))
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for i, elem in enumerate(actual_landmarks):
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self.assertAlmostEqual(
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elem.x, expected_landmarks[i].x, delta=_LANDMARKS_DIFF_MARGIN
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)
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self.assertAlmostEqual(
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elem.y, expected_landmarks[i].y, delta=_LANDMARKS_DIFF_MARGIN
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)
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def _expect_blendshapes_correct(
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self, actual_blendshapes, expected_blendshapes
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):
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# Expects to have the same number of blendshapes.
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self.assertLen(actual_blendshapes, len(expected_blendshapes))
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for i, elem in enumerate(actual_blendshapes):
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self.assertEqual(elem.index, expected_blendshapes[i].index)
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self.assertAlmostEqual(
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elem.score,
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expected_blendshapes[i].score,
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delta=_BLENDSHAPES_DIFF_MARGIN,
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)
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def _expect_facial_transformation_matrixes_correct(
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self, actual_matrix_list, expected_matrix_list
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):
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self.assertLen(actual_matrix_list, len(expected_matrix_list))
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for i, elem in enumerate(actual_matrix_list):
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self.assertEqual(elem.shape[0], expected_matrix_list[i].shape[0])
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self.assertEqual(elem.shape[1], expected_matrix_list[i].shape[1])
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self.assertSequenceAlmostEqual(
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elem.flatten(),
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expected_matrix_list[i].flatten(),
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delta=_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN,
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)
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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 _FaceLandmarker.create_from_model_path(self.model_path) as landmarker:
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self.assertIsInstance(landmarker, _FaceLandmarker)
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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 = _FaceLandmarkerOptions(base_options=base_options)
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with _FaceLandmarker.create_from_options(options) as landmarker:
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self.assertIsInstance(landmarker, _FaceLandmarker)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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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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):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite'
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)
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options = _FaceLandmarkerOptions(base_options=base_options)
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_FaceLandmarker.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 = _FaceLandmarkerOptions(base_options=base_options)
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landmarker = _FaceLandmarker.create_from_options(options)
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self.assertIsInstance(landmarker, _FaceLandmarker)
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@parameterized.parameters(
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(
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ModelFileType.FILE_NAME,
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
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None,
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None,
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),
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(
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ModelFileType.FILE_CONTENT,
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
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None,
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None,
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),
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)
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def test_detect(
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self,
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model_file_type,
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model_name,
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expected_face_landmarks,
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expected_face_blendshapes,
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expected_facial_transformation_matrixes,
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):
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# Creates face landmarker.
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model_path = test_utils.get_test_data_path(model_name)
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(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 = _FaceLandmarkerOptions(
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base_options=base_options,
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output_face_blendshapes=True if expected_face_blendshapes else False,
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output_facial_transformation_matrixes=True
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if expected_facial_transformation_matrixes
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else False,
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)
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landmarker = _FaceLandmarker.create_from_options(options)
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# Performs face landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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detection_result.face_landmarks[0], expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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detection_result.face_blendshapes[0], expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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detection_result.facial_transformation_matrixes,
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expected_facial_transformation_matrixes,
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)
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# Closes the face landmarker explicitly when the face landmarker is not used
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# in a context.
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landmarker.close()
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@parameterized.parameters(
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(
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ModelFileType.FILE_NAME,
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
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None,
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None,
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),
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(
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ModelFileType.FILE_CONTENT,
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_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
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_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
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None,
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None,
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),
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)
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def test_detect_in_context(
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self,
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model_file_type,
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model_name,
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expected_face_landmarks,
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expected_face_blendshapes,
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expected_facial_transformation_matrixes,
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):
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# Creates face landmarker.
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model_path = test_utils.get_test_data_path(model_name)
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(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 = _FaceLandmarkerOptions(
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base_options=base_options,
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output_face_blendshapes=True if expected_face_blendshapes else False,
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output_facial_transformation_matrixes=True
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if expected_facial_transformation_matrixes
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else False,
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)
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with _FaceLandmarker.create_from_options(options) as landmarker:
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# Performs face landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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detection_result.face_landmarks[0], expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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detection_result.face_blendshapes[0], expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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detection_result.facial_transformation_matrixes,
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expected_facial_transformation_matrixes,
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)
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def test_empty_detection_outputs(self):
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options = _FaceLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path)
|
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)
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with _FaceLandmarker.create_from_options(options) as landmarker:
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# Load the image with no faces.
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no_faces_test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_CAT_IMAGE)
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)
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# Performs face landmarks detection on the input.
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detection_result = landmarker.detect(no_faces_test_image)
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self.assertEmpty(detection_result.face_landmarks)
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self.assertEmpty(detection_result.face_blendshapes)
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self.assertEmpty(detection_result.facial_transformation_matrixes)
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def test_missing_result_callback(self):
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options = _FaceLandmarkerOptions(
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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 _FaceLandmarker.create_from_options(options) as unused_landmarker:
|
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pass
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|
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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options = _FaceLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
|
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running_mode=running_mode,
|
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result_callback=mock.MagicMock(),
|
||||
)
|
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with self.assertRaisesRegex(
|
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ValueError, r'result callback should not be provided'
|
||||
):
|
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with _FaceLandmarker.create_from_options(options) as unused_landmarker:
|
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pass
|
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|
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def test_calling_detect_for_video_in_image_mode(self):
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options = _FaceLandmarkerOptions(
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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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with _FaceLandmarker.create_from_options(options) as landmarker:
|
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with self.assertRaisesRegex(
|
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ValueError, r'not initialized with the video mode'
|
||||
):
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landmarker.detect_for_video(self.test_image, 0)
|
||||
|
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def test_calling_detect_async_in_image_mode(self):
|
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options = _FaceLandmarkerOptions(
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||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE,
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the live stream mode'
|
||||
):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
def test_calling_detect_in_video_mode(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the image mode'
|
||||
):
|
||||
landmarker.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_async_in_video_mode(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the live stream mode'
|
||||
):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
def test_detect_for_video_with_out_of_order_timestamp(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
unused_result = landmarker.detect_for_video(self.test_image, 1)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'
|
||||
):
|
||||
landmarker.detect_for_video(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(
|
||||
_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
|
||||
None,
|
||||
None,
|
||||
),
|
||||
)
|
||||
def test_detect_for_video(
|
||||
self,
|
||||
model_name,
|
||||
expected_face_landmarks,
|
||||
expected_face_blendshapes,
|
||||
expected_facial_transformation_matrixes,
|
||||
):
|
||||
# Creates face landmarker.
|
||||
model_path = test_utils.get_test_data_path(model_name)
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=base_options,
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
output_face_blendshapes=True if expected_face_blendshapes else False,
|
||||
output_facial_transformation_matrixes=True
|
||||
if expected_facial_transformation_matrixes
|
||||
else False,
|
||||
)
|
||||
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Performs face landmarks detection on the input.
|
||||
detection_result = landmarker.detect_for_video(
|
||||
self.test_image, timestamp
|
||||
)
|
||||
# Comparing results.
|
||||
if expected_face_landmarks is not None:
|
||||
self._expect_landmarks_correct(
|
||||
detection_result.face_landmarks[0], expected_face_landmarks
|
||||
)
|
||||
if expected_face_blendshapes is not None:
|
||||
self._expect_blendshapes_correct(
|
||||
detection_result.face_blendshapes[0], expected_face_blendshapes
|
||||
)
|
||||
if expected_facial_transformation_matrixes is not None:
|
||||
self._expect_facial_transformation_matrixes_correct(
|
||||
detection_result.facial_transformation_matrixes,
|
||||
expected_facial_transformation_matrixes,
|
||||
)
|
||||
|
||||
def test_calling_detect_in_live_stream_mode(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the image mode'
|
||||
):
|
||||
landmarker.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_for_video_in_live_stream_mode(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the video mode'
|
||||
):
|
||||
landmarker.detect_for_video(self.test_image, 0)
|
||||
|
||||
def test_detect_async_calls_with_illegal_timestamp(self):
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
landmarker.detect_async(self.test_image, 100)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'
|
||||
):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(
|
||||
_PORTRAIT_IMAGE,
|
||||
_FACE_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
_get_expected_face_landmarks(_PORTRAIT_EXPECTED_FACE_LANDMARKS),
|
||||
None,
|
||||
None,
|
||||
),
|
||||
)
|
||||
def test_detect_async_calls(
|
||||
self,
|
||||
image_path,
|
||||
model_name,
|
||||
expected_face_landmarks,
|
||||
expected_face_blendshapes,
|
||||
expected_facial_transformation_matrixes,
|
||||
):
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(image_path)
|
||||
)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(
|
||||
result: FaceLandmarkerResult, output_image: _Image, timestamp_ms: int
|
||||
):
|
||||
# Comparing results.
|
||||
if expected_face_landmarks is not None:
|
||||
self._expect_landmarks_correct(
|
||||
result.face_landmarks[0], expected_face_landmarks
|
||||
)
|
||||
if expected_face_blendshapes is not None:
|
||||
self._expect_blendshapes_correct(
|
||||
result.face_blendshapes[0], expected_face_blendshapes
|
||||
)
|
||||
if expected_facial_transformation_matrixes is not None:
|
||||
self._expect_facial_transformation_matrixes_correct(
|
||||
result.facial_transformation_matrixes,
|
||||
expected_facial_transformation_matrixes,
|
||||
)
|
||||
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
|
||||
|
||||
model_path = test_utils.get_test_data_path(model_name)
|
||||
options = _FaceLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
output_face_blendshapes=True if expected_face_blendshapes else False,
|
||||
output_facial_transformation_matrixes=True
|
||||
if expected_facial_transformation_matrixes
|
||||
else False,
|
||||
result_callback=check_result,
|
||||
)
|
||||
with _FaceLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
landmarker.detect_async(test_image, timestamp)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
@ -172,3 +172,28 @@ py_library(
|
|||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "face_landmarker",
|
||||
srcs = [
|
||||
"face_landmarker.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework/formats:classification_py_pb2",
|
||||
"//mediapipe/framework/formats:landmark_py_pb2",
|
||||
"//mediapipe/framework/formats:matrix_data_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/python:packet_creator",
|
||||
"//mediapipe/python:packet_getter",
|
||||
"//mediapipe/tasks/cc/vision/face_geometry/proto:face_geometry_py_pb2",
|
||||
"//mediapipe/tasks/cc/vision/face_landmarker/proto:face_landmarker_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:category",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
"//mediapipe/tasks/python/core:task_info",
|
||||
"//mediapipe/tasks/python/vision/core:base_vision_task_api",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
509
mediapipe/tasks/python/vision/face_landmarker.py
Normal file
509
mediapipe/tasks/python/vision/face_landmarker.py
Normal file
|
|
@ -0,0 +1,509 @@
|
|||
# Copyright 2023 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 face landmarker task."""
|
||||
|
||||
import dataclasses
|
||||
import enum
|
||||
from typing import Callable, Mapping, Optional, List
|
||||
|
||||
import numpy as np
|
||||
|
||||
from mediapipe.framework.formats import classification_pb2
|
||||
from mediapipe.framework.formats import landmark_pb2
|
||||
from mediapipe.framework.formats import matrix_data_pb2
|
||||
from mediapipe.python import packet_creator
|
||||
from mediapipe.python import packet_getter
|
||||
from mediapipe.python._framework_bindings import image as image_module
|
||||
from mediapipe.python._framework_bindings import packet as packet_module
|
||||
# pylint: disable=unused-import
|
||||
from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
|
||||
# pylint: enable=unused-import
|
||||
from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import category as category_module
|
||||
from mediapipe.tasks.python.components.containers import landmark as landmark_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
from mediapipe.tasks.python.vision.core import base_vision_task_api
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_FaceLandmarkerGraphOptionsProto = (
|
||||
face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
|
||||
)
|
||||
_LayoutEnum = matrix_data_pb2.MatrixData.Layout
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
|
||||
_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
|
||||
_BLENDSHAPES_STREAM_NAME = 'blendshapes'
|
||||
_BLENDSHAPES_TAG = 'BLENDSHAPES'
|
||||
_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
|
||||
_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
class Blendshapes(enum.IntEnum):
|
||||
"""The 52 blendshape coefficients."""
|
||||
|
||||
NEUTRAL = 0
|
||||
BROW_DOWN_LEFT = 1
|
||||
BROW_DOWN_RIGHT = 2
|
||||
BROW_INNER_UP = 3
|
||||
BROW_OUTER_UP_LEFT = 4
|
||||
BROW_OUTER_UP_RIGHT = 5
|
||||
CHEEK_PUFF = 6
|
||||
CHEEK_SQUINT_LEFT = 7
|
||||
CHEEK_SQUINT_RIGHT = 8
|
||||
EYE_BLINK_LEFT = 9
|
||||
EYE_BLINK_RIGHT = 10
|
||||
EYE_LOOK_DOWN_LEFT = 11
|
||||
EYE_LOOK_DOWN_RIGHT = 12
|
||||
EYE_LOOK_IN_LEFT = 13
|
||||
EYE_LOOK_IN_RIGHT = 14
|
||||
EYE_LOOK_OUT_LEFT = 15
|
||||
EYE_LOOK_OUT_RIGHT = 16
|
||||
EYE_LOOK_UP_LEFT = 17
|
||||
EYE_LOOK_UP_RIGHT = 18
|
||||
EYE_SQUINT_LEFT = 19
|
||||
EYE_SQUINT_RIGHT = 20
|
||||
EYE_WIDE_LEFT = 21
|
||||
EYE_WIDE_RIGHT = 22
|
||||
JAW_FORWARD = 23
|
||||
JAW_LEFT = 24
|
||||
JAW_OPEN = 25
|
||||
JAW_RIGHT = 26
|
||||
MOUTH_CLOSE = 27
|
||||
MOUTH_DIMPLE_LEFT = 28
|
||||
MOUTH_DIMPLE_RIGHT = 29
|
||||
MOUTH_FROWN_LEFT = 30
|
||||
MOUTH_FROWN_RIGHT = 31
|
||||
MOUTH_FUNNEL = 32
|
||||
MOUTH_LEFT = 33
|
||||
MOUTH_LOWER_DOWN_LEFT = 34
|
||||
MOUTH_LOWER_DOWN_RIGHT = 35
|
||||
MOUTH_PRESS_LEFT = 36
|
||||
MOUTH_PRESS_RIGHT = 37
|
||||
MOUTH_PUCKER = 38
|
||||
MOUTH_RIGHT = 39
|
||||
MOUTH_ROLL_LOWER = 40
|
||||
MOUTH_ROLL_UPPER = 41
|
||||
MOUTH_SHRUG_LOWER = 42
|
||||
MOUTH_SHRUG_UPPER = 43
|
||||
MOUTH_SMILE_LEFT = 44
|
||||
MOUTH_SMILE_RIGHT = 45
|
||||
MOUTH_STRETCH_LEFT = 46
|
||||
MOUTH_STRETCH_RIGHT = 47
|
||||
MOUTH_UPPER_UP_LEFT = 48
|
||||
MOUTH_UPPER_UP_RIGHT = 49
|
||||
NOSE_SNEER_LEFT = 50
|
||||
NOSE_SNEER_RIGHT = 51
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class FaceLandmarkerResult:
|
||||
"""The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
|
||||
|
||||
Attributes:
|
||||
face_landmarks: Detected face landmarks in normalized image coordinates.
|
||||
face_blendshapes: Optional face blendshapes results.
|
||||
facial_transformation_matrixes: Optional facial transformation matrix.
|
||||
"""
|
||||
|
||||
face_landmarks: List[List[landmark_module.NormalizedLandmark]]
|
||||
face_blendshapes: List[List[category_module.Category]]
|
||||
facial_transformation_matrixes: List[np.ndarray]
|
||||
|
||||
|
||||
def _build_landmarker_result(
|
||||
output_packets: Mapping[str, packet_module.Packet]
|
||||
) -> FaceLandmarkerResult:
|
||||
"""Constructs a `FaceLandmarkerResult` from output packets."""
|
||||
face_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_NORM_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
face_landmarks_results = []
|
||||
for proto in face_landmarks_proto_list:
|
||||
face_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
face_landmarks.MergeFrom(proto)
|
||||
face_landmarks_list = []
|
||||
for face_landmark in face_landmarks.landmark:
|
||||
face_landmarks_list.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
|
||||
)
|
||||
face_landmarks_results.append(face_landmarks_list)
|
||||
|
||||
face_blendshapes_results = []
|
||||
if _BLENDSHAPES_STREAM_NAME in output_packets:
|
||||
face_blendshapes_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_BLENDSHAPES_STREAM_NAME]
|
||||
)
|
||||
for proto in face_blendshapes_proto_list:
|
||||
face_blendshapes_categories = []
|
||||
face_blendshapes_classifications = classification_pb2.ClassificationList()
|
||||
face_blendshapes_classifications.MergeFrom(proto)
|
||||
for face_blendshapes in face_blendshapes_classifications.classification:
|
||||
face_blendshapes_categories.append(
|
||||
category_module.Category(
|
||||
index=face_blendshapes.index,
|
||||
score=face_blendshapes.score,
|
||||
display_name=face_blendshapes.display_name,
|
||||
category_name=face_blendshapes.label,
|
||||
)
|
||||
)
|
||||
face_blendshapes_results.append(face_blendshapes_categories)
|
||||
|
||||
facial_transformation_matrixes_results = []
|
||||
if _FACE_GEOMETRY_STREAM_NAME in output_packets:
|
||||
facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_FACE_GEOMETRY_STREAM_NAME]
|
||||
)
|
||||
for proto in facial_transformation_matrixes_proto_list:
|
||||
if hasattr(proto, 'pose_transform_matrix'):
|
||||
matrix_data = matrix_data_pb2.MatrixData()
|
||||
matrix_data.MergeFrom(proto.pose_transform_matrix)
|
||||
matrix = np.array(matrix_data.packed_data)
|
||||
matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
|
||||
matrix = (
|
||||
matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
|
||||
)
|
||||
facial_transformation_matrixes_results.append(matrix)
|
||||
|
||||
return FaceLandmarkerResult(
|
||||
face_landmarks_results,
|
||||
face_blendshapes_results,
|
||||
facial_transformation_matrixes_results,
|
||||
)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class FaceLandmarkerOptions:
|
||||
"""Options for the face landmarker task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the face landmarker task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
HandLandmarker has three running modes: 1) The image mode for detecting
|
||||
face landmarks on single image inputs. 2) The video mode for detecting
|
||||
face landmarks on the decoded frames of a video. 3) The live stream mode
|
||||
for detecting face landmarks on the live stream of input data, such as
|
||||
from camera. In this mode, the "result_callback" below must be specified
|
||||
to receive the detection results asynchronously.
|
||||
num_faces: The maximum number of faces that can be detected by the
|
||||
FaceLandmarker.
|
||||
min_face_detection_confidence: The minimum confidence score for the face
|
||||
detection to be considered successful.
|
||||
min_face_presence_confidence: The minimum confidence score of face presence
|
||||
score in the face landmark detection.
|
||||
min_tracking_confidence: The minimum confidence score for the face tracking
|
||||
to be considered successful.
|
||||
output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
|
||||
classification. Face blendshapes are used for rendering the 3D face model.
|
||||
output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
|
||||
transformation_matrix. Facial transformation matrix is used to transform
|
||||
the face landmarks in canonical face to the detected face, so that users
|
||||
can apply face effects on the detected landmarks.
|
||||
result_callback: The user-defined result callback for processing live stream
|
||||
data. The result callback should only be specified when the running mode
|
||||
is set to the live stream mode.
|
||||
"""
|
||||
|
||||
base_options: _BaseOptions
|
||||
running_mode: _RunningMode = _RunningMode.IMAGE
|
||||
num_faces: Optional[int] = 1
|
||||
min_face_detection_confidence: Optional[float] = 0.5
|
||||
min_face_presence_confidence: Optional[float] = 0.5
|
||||
min_tracking_confidence: Optional[float] = 0.5
|
||||
output_face_blendshapes: Optional[bool] = False
|
||||
output_facial_transformation_matrixes: Optional[bool] = False
|
||||
result_callback: Optional[
|
||||
Callable[[FaceLandmarkerResult, image_module.Image, int], None]
|
||||
] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
|
||||
"""Generates an FaceLandmarkerGraphOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
base_options_proto.use_stream_mode = (
|
||||
False if self.running_mode == _RunningMode.IMAGE else True
|
||||
)
|
||||
|
||||
# Initialize the face landmarker options from base options.
|
||||
face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
|
||||
base_options=base_options_proto
|
||||
)
|
||||
|
||||
# Configure face detector options.
|
||||
face_landmarker_options_proto.face_detector_graph_options.num_faces = (
|
||||
self.num_faces
|
||||
)
|
||||
face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
|
||||
self.min_face_detection_confidence
|
||||
)
|
||||
|
||||
# Configure face landmark detector options.
|
||||
face_landmarker_options_proto.min_tracking_confidence = (
|
||||
self.min_tracking_confidence
|
||||
)
|
||||
face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
self.min_face_detection_confidence
|
||||
)
|
||||
return face_landmarker_options_proto
|
||||
|
||||
|
||||
class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs face landmarks detection on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
|
||||
"""Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
|
||||
|
||||
Note that the created `FaceLandmarker` instance is in image mode, for
|
||||
detecting face landmarks on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`FaceLandmarker` object that's created from the model file and the
|
||||
default `FaceLandmarkerOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `FaceLandmarker` object from the
|
||||
provided file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = FaceLandmarkerOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE
|
||||
)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: FaceLandmarkerOptions
|
||||
) -> 'FaceLandmarker':
|
||||
"""Creates the `FaceLandmarker` object from face landmarker options.
|
||||
|
||||
Args:
|
||||
options: Options for the face landmarker task.
|
||||
|
||||
Returns:
|
||||
`FaceLandmarker` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `FaceLandmarker` object from
|
||||
`FaceLandmarkerOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
|
||||
def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
|
||||
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
|
||||
return
|
||||
|
||||
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
|
||||
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
|
||||
return
|
||||
|
||||
if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
|
||||
options.result_callback(
|
||||
FaceLandmarkerResult([], [], []),
|
||||
image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
return
|
||||
|
||||
face_landmarks_result = _build_landmarker_result(output_packets)
|
||||
timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
|
||||
options.result_callback(
|
||||
face_landmarks_result,
|
||||
image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
|
||||
output_streams = [
|
||||
':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
]
|
||||
|
||||
if options.output_face_blendshapes:
|
||||
output_streams.append(
|
||||
':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
|
||||
)
|
||||
if options.output_facial_transformation_matrixes:
|
||||
output_streams.append(
|
||||
':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
|
||||
)
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=output_streams,
|
||||
task_options=options,
|
||||
)
|
||||
return cls(
|
||||
task_info.generate_graph_config(
|
||||
enable_flow_limiting=options.running_mode
|
||||
== _RunningMode.LIVE_STREAM
|
||||
),
|
||||
options.running_mode,
|
||||
packets_callback if options.result_callback else None,
|
||||
)
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> FaceLandmarkerResult:
|
||||
"""Performs face landmarks detection on the given image.
|
||||
|
||||
Only use this method when the FaceLandmarker is created with the image
|
||||
running mode.
|
||||
|
||||
The image can be of any size with format RGB or RGBA.
|
||||
TODO: Describes how the input image will be preprocessed after the yuv
|
||||
support is implemented.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The face landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If face landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False
|
||||
)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
),
|
||||
})
|
||||
|
||||
if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return FaceLandmarkerResult([], [], [])
|
||||
|
||||
return _build_landmarker_result(output_packets)
|
||||
|
||||
def detect_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> FaceLandmarkerResult:
|
||||
"""Performs face landmarks detection on the provided video frame.
|
||||
|
||||
Only use this method when the FaceLandmarker is created with the video
|
||||
running mode.
|
||||
|
||||
Only use this method when the FaceLandmarker is created with the video
|
||||
running mode. It's required to provide the video frame's timestamp (in
|
||||
milliseconds) along with the video frame. The input timestamps should be
|
||||
monotonically increasing for adjacent calls of this method.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input video frame in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The face landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If face landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False
|
||||
)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
|
||||
),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
})
|
||||
|
||||
if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return FaceLandmarkerResult([], [], [])
|
||||
|
||||
return _build_landmarker_result(output_packets)
|
||||
|
||||
def detect_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> None:
|
||||
"""Sends live image data to perform face landmarks detection.
|
||||
|
||||
The results will be available via the "result_callback" provided in the
|
||||
FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
|
||||
created with the live stream running mode.
|
||||
|
||||
Only use this method when the FaceLandmarker is created with the live
|
||||
stream running mode. The input timestamps should be monotonically increasing
|
||||
for adjacent calls of this method. This method will return immediately after
|
||||
the input image is accepted. The results will be available via the
|
||||
`result_callback` provided in the `FaceLandmarkerOptions`. The
|
||||
`detect_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, face landmarker may drop the
|
||||
input images if needed. In other words, it's not guaranteed to have output
|
||||
per input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- The face landmarks detection results.
|
||||
- The input image that the face landmarker runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the
|
||||
face landmarker has already processed.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False
|
||||
)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
|
||||
),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user