Added Holistic Landmarker Python API
This commit is contained in:
Kinar
parent
3d8b715dd6
commit
66f8625a42
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@ -103,6 +103,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/interactive_segmenter:interactive_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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"//mediapipe/tasks/cc/vision/pose_landmarker:pose_landmarker_graph",
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"//mediapipe/tasks/cc/vision/holistic_landmarker:holistic_landmarker_graph",
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],
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)
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@ -82,8 +82,12 @@ class TaskInfo:
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)
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task_subgraph_options = calculator_options_pb2.CalculatorOptions()
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task_options_proto = self.task_options.to_pb2()
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# For protobuf 2 compat.
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if hasattr(task_options_proto, 'ext'):
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task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom(
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task_options_proto)
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if not enable_flow_limiting:
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return calculator_pb2.CalculatorGraphConfig(
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node=[
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@ -194,6 +194,31 @@ py_test(
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],
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)
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py_test(
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name = "holistic_landmarker_test",
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srcs = ["holistic_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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tags = ["not_run:arm"],
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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:holistic_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 = "face_aligner_test",
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srcs = ["face_aligner_test.py"],
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114
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
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114
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
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@ -0,0 +1,114 @@
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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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"""Tests for holistic 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 holistic_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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HolisticLandmarkerResult = holistic_landmarker.HolisticLandmarkerResult
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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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_HolisticLandmarker = holistic_landmarker.HolisticLandmarker
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_HolisticLandmarkerOptions = holistic_landmarker.HolisticLandmarkerOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
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_POSE_IMAGE = 'male_full_height_hands.jpg'
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_CAT_IMAGE = 'cat.jpg'
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_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
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_LANDMARKS_MARGIN = 0.03
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_BLENDSHAPES_MARGIN = 0.13
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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 HolisticLandmarkerTest(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(_POSE_IMAGE)
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)
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self.model_path = test_utils.get_test_data_path(
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
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)
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@parameterized.parameters(
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(
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ModelFileType.FILE_NAME,
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
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),
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(
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ModelFileType.FILE_CONTENT,
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
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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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):
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# Creates holistic 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 = _HolisticLandmarkerOptions(
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base_options=base_options
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)
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landmarker = _HolisticLandmarker.create_from_options(options)
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# Performs holistic landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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# Closes the holistic landmarker explicitly when the holistic landmarker is not used
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# in a context.
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landmarker.close()
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if __name__ == '__main__':
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absltest.main()
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@ -243,6 +243,29 @@ py_library(
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],
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)
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py_library(
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name = "holistic_landmarker",
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srcs = [
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"holistic_landmarker.py",
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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/python:packet_creator",
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"//mediapipe/python:packet_getter",
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"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_landmarker_graph_options_py_pb2",
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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/core:base_options",
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"//mediapipe/tasks/python/core:optional_dependencies",
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"//mediapipe/tasks/python/core:task_info",
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"//mediapipe/tasks/python/vision/core:base_vision_task_api",
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"//mediapipe/tasks/python/vision/core:image_processing_options",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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],
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)
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py_library(
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name = "face_stylizer",
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srcs = [
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567
mediapipe/tasks/python/vision/holistic_landmarker.py
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567
mediapipe/tasks/python/vision/holistic_landmarker.py
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@ -0,0 +1,567 @@
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# Copyright 2022 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 holistic landmarker task."""
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import dataclasses
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from typing import Callable, Mapping, Optional, List
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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 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.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
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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.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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_HolisticLandmarkerGraphOptionsProto = (
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holistic_landmarker_graph_options_pb2.HolisticLandmarkerGraphOptions
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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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_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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_NORM_RECT_STREAM_NAME = 'norm_rect_in'
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_NORM_RECT_TAG = 'NORM_RECT'
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_POSE_LANDMARKS_STREAM_NAME = "pose_landmarks"
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_POSE_LANDMARKS_TAG_NAME = "POSE_LANDMARKS"
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_POSE_WORLD_LANDMARKS_STREAM_NAME = "pose_world_landmarks"
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_POSE_WORLD_LANDMARKS_TAG = "POSE_WORLD_LANDMARKS"
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_POSE_SEGMENTATION_MASK_STREAM_NAME = "pose_segmentation_mask"
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_POSE_SEGMENTATION_MASK_TAG = "pose_segmentation_mask"
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_FACE_LANDMARKS_STREAM_NAME = "face_landmarks"
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_FACE_LANDMARKS_TAG = "FACE_LANDMARKS"
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_FACE_BLENDSHAPES_STREAM_NAME = "extra_blendshapes"
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_FACE_BLENDSHAPES_TAG = "FACE_BLENDSHAPES"
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_LEFT_HAND_LANDMARKS_STREAM_NAME = "left_hand_landmarks"
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_LEFT_HAND_LANDMARKS_TAG = "LEFT_HAND_LANDMARKS"
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_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME = "left_hand_world_landmarks"
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_LEFT_HAND_WORLD_LANDMARKS_TAG = "LEFT_HAND_WORLD_LANDMARKS"
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_RIGHT_HAND_LANDMARKS_STREAM_NAME = "right_hand_landmarks"
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_RIGHT_HAND_LANDMARKS_TAG = "RIGHT_HAND_LANDMARKS"
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_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME = "right_hand_world_landmarks"
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_RIGHT_HAND_WORLD_LANDMARKS_TAG = "RIGHT_HAND_WORLD_LANDMARKS"
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.holistic_landmarker.HolisticLandmarkerGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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class HolisticLandmarkerResult:
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"""The holistic landmarks result from HolisticLandmarker, where each vector element represents a single holistic detected in the image.
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Attributes:
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TODO
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"""
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face_landmarks: List[List[landmark_module.NormalizedLandmark]]
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pose_landmarks: List[List[landmark_module.NormalizedLandmark]]
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pose_world_landmarks: List[List[landmark_module.Landmark]]
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left_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
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left_hand_world_landmarks: List[List[landmark_module.Landmark]]
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right_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
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right_hand_world_landmarks: List[List[landmark_module.Landmark]]
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face_blendshapes: Optional[List[List[category_module.Category]]] = None
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segmentation_masks: Optional[List[image_module.Image]] = None
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def _build_landmarker_result(
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output_packets: Mapping[str, packet_module.Packet]
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) -> HolisticLandmarkerResult:
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"""Constructs a `HolisticLandmarksDetectionResult` from output packets."""
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holistic_landmarker_result = HolisticLandmarkerResult([], [], [], [], [], [],
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[])
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face_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_FACE_LANDMARKS_STREAM_NAME]
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)
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if _POSE_SEGMENTATION_MASK_STREAM_NAME in output_packets:
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holistic_landmarker_result.segmentation_masks = packet_getter.get_image_list(
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output_packets[_POSE_SEGMENTATION_MASK_STREAM_NAME]
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)
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pose_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_POSE_LANDMARKS_STREAM_NAME]
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)
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pose_world_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
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)
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left_hand_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_LEFT_HAND_LANDMARKS_STREAM_NAME]
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)
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left_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
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)
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right_hand_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_RIGHT_HAND_LANDMARKS_STREAM_NAME]
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)
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right_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
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)
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face_landmarks_results = []
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for proto in face_landmarks_proto_list:
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face_landmarks = landmark_pb2.NormalizedLandmarkList()
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face_landmarks.MergeFrom(proto)
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face_landmarks_list = []
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for face_landmark in face_landmarks.landmark:
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face_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
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)
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face_landmarks_results.append(face_landmarks_list)
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face_blendshapes_results = []
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if _FACE_BLENDSHAPES_STREAM_NAME in output_packets:
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face_blendshapes_proto_list = packet_getter.get_proto_list(
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output_packets[_FACE_BLENDSHAPES_STREAM_NAME]
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)
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for proto in face_blendshapes_proto_list:
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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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face_blendshapes_results.append(face_blendshapes_categories)
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for proto in pose_landmarks_proto_list:
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pose_landmarks = landmark_pb2.NormalizedLandmarkList()
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pose_landmarks.MergeFrom(proto)
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pose_landmarks_list = []
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for pose_landmark in pose_landmarks.landmark:
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pose_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
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)
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holistic_landmarker_result.pose_landmarks.append(pose_landmarks_list)
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for proto in pose_world_landmarks_proto_list:
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pose_world_landmarks = landmark_pb2.LandmarkList()
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pose_world_landmarks.MergeFrom(proto)
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pose_world_landmarks_list = []
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for pose_world_landmark in pose_world_landmarks.landmark:
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pose_world_landmarks_list.append(
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landmark_module.Landmark.create_from_pb2(pose_world_landmark)
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)
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holistic_landmarker_result.pose_world_landmarks.append(
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pose_world_landmarks_list
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)
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for proto in left_hand_landmarks_proto_list:
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left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
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left_hand_landmarks.MergeFrom(proto)
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left_hand_landmarks_list = []
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for hand_landmark in left_hand_landmarks.landmark:
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left_hand_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
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)
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holistic_landmarker_result.left_hand_landmarks.append(
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left_hand_landmarks_list
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)
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for proto in left_hand_world_landmarks_proto_list:
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left_hand_world_landmarks = landmark_pb2.LandmarkList()
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left_hand_world_landmarks.MergeFrom(proto)
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left_hand_world_landmarks_list = []
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for left_hand_world_landmark in left_hand_world_landmarks.landmark:
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left_hand_world_landmarks_list.append(
|
||||
landmark_module.Landmark.create_from_pb2(left_hand_world_landmark)
|
||||
)
|
||||
holistic_landmarker_result.left_hand_world_landmarks.append(
|
||||
left_hand_world_landmarks_list
|
||||
)
|
||||
|
||||
for proto in right_hand_landmarks_proto_list:
|
||||
right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
right_hand_landmarks.MergeFrom(proto)
|
||||
right_hand_landmarks_list = []
|
||||
for hand_landmark in right_hand_landmarks.landmark:
|
||||
right_hand_landmarks_list.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
|
||||
)
|
||||
holistic_landmarker_result.right_hand_landmarks.append(
|
||||
right_hand_landmarks_list
|
||||
)
|
||||
|
||||
for proto in right_hand_world_landmarks_proto_list:
|
||||
right_hand_world_landmarks = landmark_pb2.LandmarkList()
|
||||
right_hand_world_landmarks.MergeFrom(proto)
|
||||
right_hand_world_landmarks_list = []
|
||||
for right_hand_world_landmark in right_hand_world_landmarks.landmark:
|
||||
right_hand_world_landmarks_list.append(
|
||||
landmark_module.Landmark.create_from_pb2(right_hand_world_landmark)
|
||||
)
|
||||
holistic_landmarker_result.right_hand_world_landmarks.append(
|
||||
right_hand_world_landmarks_list
|
||||
)
|
||||
|
||||
return holistic_landmarker_result
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class HolisticLandmarkerOptions:
|
||||
"""Options for the holistic landmarker task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the holistic landmarker task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
HolisticLandmarker has three running modes: 1) The image mode for
|
||||
detecting holistic landmarks on single image inputs. 2) The video mode for
|
||||
detecting holistic landmarks on the decoded frames of a video. 3) The live
|
||||
stream mode for detecting holistic 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.
|
||||
min_face_detection_confidence: The minimum confidence score for the face
|
||||
detection to be considered successful.
|
||||
min_face_suppression_threshold: The minimum non-maximum-suppression
|
||||
threshold for face detection to be considered overlapped.
|
||||
min_face_landmarks_confidence: The minimum confidence score for the face
|
||||
landmark detection to be considered successful.
|
||||
min_pose_detection_confidence: The minimum confidence score for the pose
|
||||
detection to be considered successful.
|
||||
min_pose_suppression_threshold: The minimum non-maximum-suppression
|
||||
threshold for pose detection to be considered overlapped.
|
||||
min_pose_landmarks_confidence: The minimum confidence score for the pose
|
||||
landmark detection to be considered successful.
|
||||
min_hand_landmarks_confidence: The minimum confidence score for the hand
|
||||
landmark detection to be considered successful.
|
||||
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_holistics: int = 1
|
||||
min_face_detection_confidence: float = 0.5
|
||||
min_face_suppression_threshold: float = 0.5
|
||||
min_face_landmarks_confidence: float = 0.5
|
||||
min_pose_detection_confidence: float = 0.5
|
||||
min_pose_suppression_threshold: float = 0.5
|
||||
min_pose_landmarks_confidence: float = 0.5
|
||||
min_hand_landmarks_confidence: float = 0.5
|
||||
output_face_blendshapes: bool = False
|
||||
output_segmentation_masks: bool = False
|
||||
result_callback: Optional[
|
||||
Callable[[HolisticLandmarkerResult, image_module.Image, int], None]
|
||||
] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _HolisticLandmarkerGraphOptionsProto:
|
||||
"""Generates an HolisticLandmarkerGraphOptions 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 holistic landmarker options from base options.
|
||||
holistic_landmarker_options_proto = _HolisticLandmarkerGraphOptionsProto(
|
||||
base_options=base_options_proto
|
||||
)
|
||||
# Configure face detector and face landmarks detector options.
|
||||
# holistic_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_face_detection_confidence
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_detector_graph_options.min_suppression_threshold = (
|
||||
# self.min_face_suppression_threshold
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_face_landmarks_confidence
|
||||
# )
|
||||
# # Configure pose detector and pose landmarks detector options.
|
||||
# holistic_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_pose_detection_confidence
|
||||
# )
|
||||
# holistic_landmarker_options_proto.pose_detector_graph_options.min_suppression_threshold = (
|
||||
# self.min_pose_suppression_threshold
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_pose_landmarks_confidence
|
||||
# )
|
||||
# # Configure hand landmarks detector options.
|
||||
# holistic_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_hand_landmarks_confidence
|
||||
# )
|
||||
return holistic_landmarker_options_proto
|
||||
|
||||
|
||||
class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs holistic landmarks detection on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'HolisticLandmarker':
|
||||
"""Creates an `HolisticLandmarker` object from a TensorFlow Lite model and the default `HolisticLandmarkerOptions`.
|
||||
|
||||
Note that the created `HolisticLandmarker` instance is in image mode, for
|
||||
detecting holistic landmarks on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`HolisticLandmarker` object that's created from the model file and the
|
||||
default `HolisticLandmarkerOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HolisticLandmarker` 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 = HolisticLandmarkerOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE
|
||||
)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: HolisticLandmarkerOptions
|
||||
) -> 'HolisticLandmarker':
|
||||
"""Creates the `HolisticLandmarker` object from holistic landmarker options.
|
||||
|
||||
Args:
|
||||
options: Options for the holistic landmarker task.
|
||||
|
||||
Returns:
|
||||
`HolisticLandmarker` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HolisticLandmarker` object from
|
||||
`HolisticLandmarkerOptions` 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[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_FACE_LANDMARKS_STREAM_NAME]
|
||||
options.result_callback(
|
||||
HolisticLandmarkerResult([], [], [], [], [], [], []),
|
||||
image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
return
|
||||
|
||||
holistic_landmarks_detection_result = _build_landmarker_result(output_packets)
|
||||
timestamp = output_packets[_FACE_LANDMARKS_STREAM_NAME].timestamp
|
||||
options.result_callback(
|
||||
holistic_landmarks_detection_result,
|
||||
image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
|
||||
output_streams = [
|
||||
':'.join([_FACE_LANDMARKS_TAG, _FACE_LANDMARKS_STREAM_NAME]),
|
||||
':'.join([_POSE_LANDMARKS_TAG_NAME, _POSE_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_POSE_WORLD_LANDMARKS_TAG, _POSE_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_LEFT_HAND_LANDMARKS_TAG, _LEFT_HAND_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_LEFT_HAND_WORLD_LANDMARKS_TAG, _LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_RIGHT_HAND_LANDMARKS_TAG, _RIGHT_HAND_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_RIGHT_HAND_WORLD_LANDMARKS_TAG, _RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
]
|
||||
|
||||
if options.output_segmentation_masks:
|
||||
output_streams.append(
|
||||
':'.join([_POSE_SEGMENTATION_MASK_TAG, _POSE_SEGMENTATION_MASK_STREAM_NAME])
|
||||
)
|
||||
|
||||
if options.output_face_blendshapes:
|
||||
output_streams.append(
|
||||
':'.join([_FACE_BLENDSHAPES_TAG, _FACE_BLENDSHAPES_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,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the given image.
|
||||
|
||||
Only use this method when the HolisticLandmarker 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 holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, 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[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HolisticLandmarkerResult([], [], [], [], [], [], [])
|
||||
|
||||
return _build_landmarker_result(output_packets)
|
||||
|
||||
def detect_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the provided video frame.
|
||||
|
||||
Only use this method when the HolisticLandmarker is created with the video
|
||||
running mode.
|
||||
|
||||
Only use this method when the HolisticLandmarker 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 holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, 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[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HolisticLandmarkerResult([], [], [], [], [], [], [])
|
||||
|
||||
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 holistic landmarks detection.
|
||||
|
||||
The results will be available via the "result_callback" provided in the
|
||||
HolisticLandmarkerOptions. Only use this method when the HolisticLandmarker is
|
||||
created with the live stream running mode.
|
||||
|
||||
Only use this method when the HolisticLandmarker 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 `HolisticLandmarkerOptions`. The
|
||||
`detect_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, holistic 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 holistic landmarks detection results.
|
||||
- The input image that the holistic 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
|
||||
holistic landmarker has already processed.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, 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),
|
||||
})
|
||||
4
mediapipe/tasks/testdata/vision/BUILD
vendored
4
mediapipe/tasks/testdata/vision/BUILD
vendored
|
|
@ -57,9 +57,11 @@ mediapipe_files(srcs = [
|
|||
"hand_landmark_lite.tflite",
|
||||
"hand_landmarker.task",
|
||||
"handrecrop_2020_07_21_v0.f16.tflite",
|
||||
"holistic_landmarker.task",
|
||||
"left_hands.jpg",
|
||||
"left_hands_rotated.jpg",
|
||||
"leopard_bg_removal_result_512x512.png",
|
||||
"male_full_height_hands.jpg",
|
||||
"mobilenet_v1_0.25_192_quantized_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_metadata_1.tflite",
|
||||
|
|
@ -138,9 +140,11 @@ filegroup(
|
|||
"fist.png",
|
||||
"hand_landmark_full.tflite",
|
||||
"hand_landmark_lite.tflite",
|
||||
"holistic_landmarker.task",
|
||||
"left_hands.jpg",
|
||||
"left_hands_rotated.jpg",
|
||||
"leopard_bg_removal_result_512x512.png",
|
||||
"male_full_height_hands.jpg",
|
||||
"mozart_square.jpg",
|
||||
"multi_objects.jpg",
|
||||
"multi_objects_rotated.jpg",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user