Merge pull request #5028 from kinaryml:python-holistic-landmarker
PiperOrigin-RevId: 594995636
This commit is contained in:
Copybara-Service
commit
e23fa531e1
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@ -97,6 +97,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/face_landmarker:face_landmarker_graph",
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"//mediapipe/tasks/cc/vision/face_stylizer:face_stylizer_graph",
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"//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
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"//mediapipe/tasks/cc/vision/holistic_landmarker:holistic_landmarker_graph",
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"//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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@ -49,5 +49,6 @@ py_library(
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"//mediapipe/calculators/core:flow_limiter_calculator_py_pb2",
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"//mediapipe/framework:calculator_options_py_pb2",
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"//mediapipe/framework:calculator_py_pb2",
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"@com_google_protobuf//:protobuf_python",
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],
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)
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@ -14,9 +14,8 @@
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"""MediaPipe Tasks' task info data class."""
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import dataclasses
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from typing import Any, List
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from google.protobuf import any_pb2
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from mediapipe.calculators.core import flow_limiter_calculator_pb2
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from mediapipe.framework import calculator_options_pb2
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from mediapipe.framework import calculator_pb2
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@ -80,21 +79,34 @@ class TaskInfo:
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raise ValueError(
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'`task_options` doesn`t provide `to_pb2()` method to convert itself to be a protobuf object.'
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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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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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calculator_pb2.CalculatorGraphConfig.Node(
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node_config = calculator_pb2.CalculatorGraphConfig.Node(
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calculator=self.task_graph,
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input_stream=self.input_streams,
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output_stream=self.output_streams,
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options=task_subgraph_options)
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],
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)
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if hasattr(task_options_proto, 'ext'):
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# Use the extension mechanism for task_subgraph_options (proto2)
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task_subgraph_options = calculator_options_pb2.CalculatorOptions()
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task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom(
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task_options_proto
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)
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node_config.options.CopyFrom(task_subgraph_options)
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else:
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# Use the Any type for task_subgraph_options (proto3)
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task_subgraph_options = any_pb2.Any()
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task_subgraph_options.Pack(self.task_options.to_pb2())
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node_config.node_options.append(task_subgraph_options)
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if not enable_flow_limiting:
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return calculator_pb2.CalculatorGraphConfig(
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node=[node_config],
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input_stream=self.input_streams,
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output_stream=self.output_streams)
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output_stream=self.output_streams,
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)
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# When a FlowLimiterCalculator is inserted to lower the overall graph
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# latency, the task doesn't guarantee that each input must have the
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# corresponding output.
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@ -120,13 +132,8 @@ class TaskInfo:
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],
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options=flow_limiter_options)
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config = calculator_pb2.CalculatorGraphConfig(
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node=[
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calculator_pb2.CalculatorGraphConfig.Node(
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calculator=self.task_graph,
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input_stream=task_subgraph_inputs,
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output_stream=self.output_streams,
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options=task_subgraph_options), flow_limiter
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],
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node=[node_config, flow_limiter],
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input_stream=self.input_streams,
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output_stream=self.output_streams)
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output_stream=self.output_streams,
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)
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return config
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@ -194,6 +194,27 @@ 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/python:_framework_bindings",
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"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_py_pb2",
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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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544
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
Normal file
544
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
Normal file
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@ -0,0 +1,544 @@
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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.python._framework_bindings import image as image_module
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from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.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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_HolisticResultProto = holistic_result_pb2.HolisticResult
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_BaseOptions = base_options_module.BaseOptions
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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 = 'holistic_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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_EXPECTED_HOLISTIC_RESULT = 'male_full_height_hands_result_cpu.pbtxt'
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_IMAGE_WIDTH = 638
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_IMAGE_HEIGHT = 1000
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_LANDMARKS_MARGIN = 0.03
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_BLENDSHAPES_MARGIN = 0.13
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_VIDEO_LANDMARKS_MARGIN = 0.03
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_VIDEO_BLENDSHAPES_MARGIN = 0.31
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_LIVE_STREAM_LANDMARKS_MARGIN = 0.03
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_LIVE_STREAM_BLENDSHAPES_MARGIN = 0.31
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def _get_expected_holistic_landmarker_result(
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file_path: str,
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) -> HolisticLandmarkerResult:
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holistic_result_file_path = test_utils.get_test_data_path(file_path)
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with open(holistic_result_file_path, 'rb') as f:
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holistic_result_proto = _HolisticResultProto()
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# Use this if a .pb file is available.
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# holistic_result_proto.ParseFromString(f.read())
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text_format.Parse(f.read(), holistic_result_proto)
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holistic_landmarker_result = HolisticLandmarkerResult.create_from_pb2(
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holistic_result_proto
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)
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return holistic_landmarker_result
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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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def _expect_landmarks_correct(
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self, actual_landmarks, expected_landmarks, margin
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):
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# Expects to have the same number of landmarks 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(elem.x, expected_landmarks[i].x, delta=margin)
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self.assertAlmostEqual(elem.y, expected_landmarks[i].y, delta=margin)
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def _expect_blendshapes_correct(
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self, actual_blendshapes, expected_blendshapes, margin
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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.assertEqual(
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elem.category_name, expected_blendshapes[i].category_name
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)
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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=margin,
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)
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def _expect_holistic_landmarker_results_correct(
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self,
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actual_result: HolisticLandmarkerResult,
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expected_result: HolisticLandmarkerResult,
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output_segmentation_mask: bool,
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landmarks_margin: float,
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blendshapes_margin: float,
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):
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self._expect_landmarks_correct(
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actual_result.pose_landmarks,
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expected_result.pose_landmarks,
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landmarks_margin,
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)
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self._expect_landmarks_correct(
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actual_result.face_landmarks,
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expected_result.face_landmarks,
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landmarks_margin,
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)
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self._expect_blendshapes_correct(
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actual_result.face_blendshapes,
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expected_result.face_blendshapes,
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blendshapes_margin,
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)
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if output_segmentation_mask:
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self.assertIsInstance(actual_result.segmentation_mask, _Image)
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self.assertEqual(actual_result.segmentation_mask.width, _IMAGE_WIDTH)
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self.assertEqual(actual_result.segmentation_mask.height, _IMAGE_HEIGHT)
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else:
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self.assertIsNone(actual_result.segmentation_mask)
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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 _HolisticLandmarker.create_from_model_path(
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self.model_path
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) as landmarker:
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self.assertIsInstance(landmarker, _HolisticLandmarker)
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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 = _HolisticLandmarkerOptions(base_options=base_options)
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with _HolisticLandmarker.create_from_options(options) as landmarker:
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self.assertIsInstance(landmarker, _HolisticLandmarker)
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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 = _HolisticLandmarkerOptions(base_options=base_options)
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_HolisticLandmarker.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 = _HolisticLandmarkerOptions(base_options=base_options)
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landmarker = _HolisticLandmarker.create_from_options(options)
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self.assertIsInstance(landmarker, _HolisticLandmarker)
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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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False,
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_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
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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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False,
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_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
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),
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(
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ModelFileType.FILE_NAME,
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
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True,
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_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
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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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True,
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_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
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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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output_segmentation_mask,
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expected_holistic_landmarker_result,
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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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output_face_blendshapes=True
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if expected_holistic_landmarker_result.face_blendshapes
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else False,
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output_segmentation_mask=output_segmentation_mask,
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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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self._expect_holistic_landmarker_results_correct(
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detection_result,
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expected_holistic_landmarker_result,
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output_segmentation_mask,
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_LANDMARKS_MARGIN,
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_BLENDSHAPES_MARGIN,
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)
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# Closes the holistic landmarker explicitly when the holistic landmarker is
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# not used in a context.
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landmarker.close()
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||||
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@parameterized.parameters(
|
||||
(
|
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ModelFileType.FILE_NAME,
|
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_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
False,
|
||||
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
(
|
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ModelFileType.FILE_CONTENT,
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
True,
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_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
)
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def test_detect_in_context(
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||||
self,
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||||
model_file_type,
|
||||
model_name,
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||||
output_segmentation_mask,
|
||||
expected_holistic_landmarker_result,
|
||||
):
|
||||
# Creates holistic landmarker.
|
||||
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:
|
||||
with open(model_path, 'rb') as f:
|
||||
model_content = f.read()
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||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
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||||
# Should never happen
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||||
raise ValueError('model_file_type is invalid.')
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|
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options = _HolisticLandmarkerOptions(
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base_options=base_options,
|
||||
output_face_blendshapes=True
|
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if expected_holistic_landmarker_result.face_blendshapes
|
||||
else False,
|
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output_segmentation_mask=output_segmentation_mask,
|
||||
)
|
||||
|
||||
with _HolisticLandmarker.create_from_options(options) as landmarker:
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||||
# Performs holistic landmarks detection on the input.
|
||||
detection_result = landmarker.detect(self.test_image)
|
||||
self._expect_holistic_landmarker_results_correct(
|
||||
detection_result,
|
||||
expected_holistic_landmarker_result,
|
||||
output_segmentation_mask,
|
||||
_LANDMARKS_MARGIN,
|
||||
_BLENDSHAPES_MARGIN,
|
||||
)
|
||||
|
||||
def test_empty_detection_outputs(self):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path)
|
||||
)
|
||||
with _HolisticLandmarker.create_from_options(options) as landmarker:
|
||||
# Load the cat image.
|
||||
cat_test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_CAT_IMAGE)
|
||||
)
|
||||
# Performs holistic landmarks detection on the input.
|
||||
detection_result = landmarker.detect(cat_test_image)
|
||||
self.assertEmpty(detection_result.face_landmarks)
|
||||
self.assertEmpty(detection_result.pose_landmarks)
|
||||
self.assertEmpty(detection_result.pose_world_landmarks)
|
||||
self.assertEmpty(detection_result.left_hand_landmarks)
|
||||
self.assertEmpty(detection_result.left_hand_world_landmarks)
|
||||
self.assertEmpty(detection_result.right_hand_landmarks)
|
||||
self.assertEmpty(detection_result.right_hand_world_landmarks)
|
||||
self.assertIsNone(detection_result.face_blendshapes)
|
||||
self.assertIsNone(detection_result.segmentation_mask)
|
||||
|
||||
def test_missing_result_callback(self):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'result callback must be provided'
|
||||
):
|
||||
with _HolisticLandmarker.create_from_options(
|
||||
options
|
||||
) as unused_landmarker:
|
||||
pass
|
||||
|
||||
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
|
||||
def test_illegal_result_callback(self, running_mode):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=running_mode,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'result callback should not be provided'
|
||||
):
|
||||
with _HolisticLandmarker.create_from_options(
|
||||
options
|
||||
) as unused_landmarker:
|
||||
pass
|
||||
|
||||
def test_calling_detect_for_video_in_image_mode(self):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE,
|
||||
)
|
||||
with _HolisticLandmarker.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_calling_detect_async_in_image_mode(self):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE,
|
||||
)
|
||||
with _HolisticLandmarker.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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _HolisticLandmarker.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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _HolisticLandmarker.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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
)
|
||||
with _HolisticLandmarker.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(
|
||||
(
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
False,
|
||||
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
(
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
True,
|
||||
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
)
|
||||
def test_detect_for_video(
|
||||
self,
|
||||
model_name,
|
||||
output_segmentation_mask,
|
||||
expected_holistic_landmarker_result,
|
||||
):
|
||||
# Creates holistic landmarker.
|
||||
model_path = test_utils.get_test_data_path(model_name)
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=base_options,
|
||||
running_mode=_RUNNING_MODE.VIDEO,
|
||||
output_face_blendshapes=True
|
||||
if expected_holistic_landmarker_result.face_blendshapes
|
||||
else False,
|
||||
output_segmentation_mask=output_segmentation_mask,
|
||||
)
|
||||
|
||||
with _HolisticLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Performs holistic landmarks detection on the input.
|
||||
detection_result = landmarker.detect_for_video(
|
||||
self.test_image, timestamp
|
||||
)
|
||||
# Comparing results.
|
||||
self._expect_holistic_landmarker_results_correct(
|
||||
detection_result,
|
||||
expected_holistic_landmarker_result,
|
||||
output_segmentation_mask,
|
||||
_VIDEO_LANDMARKS_MARGIN,
|
||||
_VIDEO_BLENDSHAPES_MARGIN,
|
||||
)
|
||||
|
||||
def test_calling_detect_in_live_stream_mode(self):
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _HolisticLandmarker.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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _HolisticLandmarker.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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _HolisticLandmarker.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(
|
||||
(
|
||||
_POSE_IMAGE,
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
False,
|
||||
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
(
|
||||
_POSE_IMAGE,
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
|
||||
True,
|
||||
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT),
|
||||
),
|
||||
)
|
||||
def test_detect_async_calls(
|
||||
self,
|
||||
image_path,
|
||||
model_name,
|
||||
output_segmentation_mask,
|
||||
expected_holistic_landmarker_result,
|
||||
):
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(image_path)
|
||||
)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(
|
||||
result: HolisticLandmarkerResult,
|
||||
output_image: _Image,
|
||||
timestamp_ms: int,
|
||||
):
|
||||
# Comparing results.
|
||||
self._expect_holistic_landmarker_results_correct(
|
||||
result,
|
||||
expected_holistic_landmarker_result,
|
||||
output_segmentation_mask,
|
||||
_LIVE_STREAM_LANDMARKS_MARGIN,
|
||||
_LIVE_STREAM_BLENDSHAPES_MARGIN,
|
||||
)
|
||||
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 = _HolisticLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
output_face_blendshapes=True
|
||||
if expected_holistic_landmarker_result.face_blendshapes
|
||||
else False,
|
||||
output_segmentation_mask=output_segmentation_mask,
|
||||
result_callback=check_result,
|
||||
)
|
||||
with _HolisticLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
landmarker.detect_async(test_image, timestamp)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
@ -12,7 +12,6 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder: load py_library
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
|
||||
package(default_visibility = ["//visibility:public"])
|
||||
|
|
@ -243,6 +242,30 @@ py_library(
|
|||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "holistic_landmarker",
|
||||
srcs = [
|
||||
"holistic_landmarker.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework/formats:classification_py_pb2",
|
||||
"//mediapipe/framework/formats:landmark_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/python:packet_creator",
|
||||
"//mediapipe/python:packet_getter",
|
||||
"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_landmarker_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_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",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "face_stylizer",
|
||||
srcs = [
|
||||
|
|
|
|||
576
mediapipe/tasks/python/vision/holistic_landmarker.py
Normal file
576
mediapipe/tasks/python/vision/holistic_landmarker.py
Normal file
|
|
@ -0,0 +1,576 @@
|
|||
# Copyright 2023 The MediaPipe Authors.
|
||||
#
|
||||
# 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 holistic landmarker task."""
|
||||
|
||||
import dataclasses
|
||||
from typing import Callable, List, Mapping, Optional
|
||||
|
||||
from mediapipe.framework.formats import classification_pb2
|
||||
from mediapipe.framework.formats import landmark_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
|
||||
from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
|
||||
from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_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
|
||||
_HolisticResultProto = holistic_result_pb2.HolisticResult
|
||||
_HolisticLandmarkerGraphOptionsProto = (
|
||||
holistic_landmarker_graph_options_pb2.HolisticLandmarkerGraphOptions
|
||||
)
|
||||
_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'
|
||||
|
||||
_POSE_LANDMARKS_STREAM_NAME = 'pose_landmarks'
|
||||
_POSE_LANDMARKS_TAG_NAME = 'POSE_LANDMARKS'
|
||||
_POSE_WORLD_LANDMARKS_STREAM_NAME = 'pose_world_landmarks'
|
||||
_POSE_WORLD_LANDMARKS_TAG = 'POSE_WORLD_LANDMARKS'
|
||||
_POSE_SEGMENTATION_MASK_STREAM_NAME = 'pose_segmentation_mask'
|
||||
_POSE_SEGMENTATION_MASK_TAG = 'POSE_SEGMENTATION_MASK'
|
||||
_FACE_LANDMARKS_STREAM_NAME = 'face_landmarks'
|
||||
_FACE_LANDMARKS_TAG = 'FACE_LANDMARKS'
|
||||
_FACE_BLENDSHAPES_STREAM_NAME = 'extra_blendshapes'
|
||||
_FACE_BLENDSHAPES_TAG = 'FACE_BLENDSHAPES'
|
||||
_LEFT_HAND_LANDMARKS_STREAM_NAME = 'left_hand_landmarks'
|
||||
_LEFT_HAND_LANDMARKS_TAG = 'LEFT_HAND_LANDMARKS'
|
||||
_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME = 'left_hand_world_landmarks'
|
||||
_LEFT_HAND_WORLD_LANDMARKS_TAG = 'LEFT_HAND_WORLD_LANDMARKS'
|
||||
_RIGHT_HAND_LANDMARKS_STREAM_NAME = 'right_hand_landmarks'
|
||||
_RIGHT_HAND_LANDMARKS_TAG = 'RIGHT_HAND_LANDMARKS'
|
||||
_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME = 'right_hand_world_landmarks'
|
||||
_RIGHT_HAND_WORLD_LANDMARKS_TAG = 'RIGHT_HAND_WORLD_LANDMARKS'
|
||||
|
||||
_TASK_GRAPH_NAME = (
|
||||
'mediapipe.tasks.vision.holistic_landmarker.HolisticLandmarkerGraph'
|
||||
)
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class HolisticLandmarkerResult:
|
||||
"""The holistic landmarks result from HolisticLandmarker, where each vector element represents a single holistic detected in the image.
|
||||
|
||||
Attributes:
|
||||
face_landmarks: Detected face landmarks in normalized image coordinates.
|
||||
pose_landmarks: Detected pose landmarks in normalized image coordinates.
|
||||
pose_world_landmarks: Detected pose world landmarks in image coordinates.
|
||||
left_hand_landmarks: Detected left hand landmarks in normalized image
|
||||
coordinates.
|
||||
left_hand_world_landmarks: Detected left hand landmarks in image
|
||||
coordinates.
|
||||
right_hand_landmarks: Detected right hand landmarks in normalized image
|
||||
coordinates.
|
||||
right_hand_world_landmarks: Detected right hand landmarks in image
|
||||
coordinates.
|
||||
face_blendshapes: Optional face blendshapes.
|
||||
segmentation_mask: Optional segmentation mask for pose.
|
||||
"""
|
||||
|
||||
face_landmarks: List[landmark_module.NormalizedLandmark]
|
||||
pose_landmarks: List[landmark_module.NormalizedLandmark]
|
||||
pose_world_landmarks: List[landmark_module.Landmark]
|
||||
left_hand_landmarks: List[landmark_module.NormalizedLandmark]
|
||||
left_hand_world_landmarks: List[landmark_module.Landmark]
|
||||
right_hand_landmarks: List[landmark_module.NormalizedLandmark]
|
||||
right_hand_world_landmarks: List[landmark_module.Landmark]
|
||||
face_blendshapes: Optional[List[category_module.Category]] = None
|
||||
segmentation_mask: Optional[image_module.Image] = None
|
||||
|
||||
@classmethod
|
||||
@doc_controls.do_not_generate_docs
|
||||
def create_from_pb2(
|
||||
cls, pb2_obj: _HolisticResultProto
|
||||
) -> 'HolisticLandmarkerResult':
|
||||
"""Creates a `HolisticLandmarkerResult` object from the given protobuf object."""
|
||||
face_blendshapes = None
|
||||
if hasattr(pb2_obj, 'face_blendshapes'):
|
||||
face_blendshapes = [
|
||||
category_module.Category(
|
||||
score=classification.score,
|
||||
index=classification.index,
|
||||
category_name=classification.label,
|
||||
display_name=classification.display_name,
|
||||
)
|
||||
for classification in pb2_obj.face_blendshapes.classification
|
||||
]
|
||||
|
||||
return HolisticLandmarkerResult(
|
||||
face_landmarks=[
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
|
||||
for landmark in pb2_obj.face_landmarks.landmark
|
||||
],
|
||||
pose_landmarks=[
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
|
||||
for landmark in pb2_obj.pose_landmarks.landmark
|
||||
],
|
||||
pose_world_landmarks=[
|
||||
landmark_module.Landmark.create_from_pb2(landmark)
|
||||
for landmark in pb2_obj.pose_world_landmarks.landmark
|
||||
],
|
||||
left_hand_landmarks=[
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
|
||||
for landmark in pb2_obj.left_hand_landmarks.landmark
|
||||
],
|
||||
left_hand_world_landmarks=[],
|
||||
right_hand_landmarks=[
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
|
||||
for landmark in pb2_obj.right_hand_landmarks.landmark
|
||||
],
|
||||
right_hand_world_landmarks=[],
|
||||
face_blendshapes=face_blendshapes,
|
||||
segmentation_mask=None,
|
||||
)
|
||||
|
||||
|
||||
def _build_landmarker_result(
|
||||
output_packets: Mapping[str, packet_module.Packet]
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Constructs a `HolisticLandmarksDetectionResult` from output packets."""
|
||||
holistic_landmarker_result = HolisticLandmarkerResult(
|
||||
[], [], [], [], [], [], []
|
||||
)
|
||||
|
||||
face_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_FACE_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
pose_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_POSE_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
pose_world_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
left_hand_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_LEFT_HAND_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
left_hand_world_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
right_hand_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_RIGHT_HAND_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
right_hand_world_landmarks_proto_list = packet_getter.get_proto(
|
||||
output_packets[_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
face_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
face_landmarks.MergeFrom(face_landmarks_proto_list)
|
||||
for face_landmark in face_landmarks.landmark:
|
||||
holistic_landmarker_result.face_landmarks.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
|
||||
)
|
||||
|
||||
pose_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
pose_landmarks.MergeFrom(pose_landmarks_proto_list)
|
||||
for pose_landmark in pose_landmarks.landmark:
|
||||
holistic_landmarker_result.pose_landmarks.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
|
||||
)
|
||||
|
||||
pose_world_landmarks = landmark_pb2.LandmarkList()
|
||||
pose_world_landmarks.MergeFrom(pose_world_landmarks_proto_list)
|
||||
for pose_world_landmark in pose_world_landmarks.landmark:
|
||||
holistic_landmarker_result.pose_world_landmarks.append(
|
||||
landmark_module.Landmark.create_from_pb2(pose_world_landmark)
|
||||
)
|
||||
|
||||
left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
left_hand_landmarks.MergeFrom(left_hand_landmarks_proto_list)
|
||||
for hand_landmark in left_hand_landmarks.landmark:
|
||||
holistic_landmarker_result.left_hand_landmarks.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
|
||||
)
|
||||
|
||||
left_hand_world_landmarks = landmark_pb2.LandmarkList()
|
||||
left_hand_world_landmarks.MergeFrom(left_hand_world_landmarks_proto_list)
|
||||
for left_hand_world_landmark in left_hand_world_landmarks.landmark:
|
||||
holistic_landmarker_result.left_hand_world_landmarks.append(
|
||||
landmark_module.Landmark.create_from_pb2(left_hand_world_landmark)
|
||||
)
|
||||
|
||||
right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
right_hand_landmarks.MergeFrom(right_hand_landmarks_proto_list)
|
||||
for hand_landmark in right_hand_landmarks.landmark:
|
||||
holistic_landmarker_result.right_hand_landmarks.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
|
||||
)
|
||||
|
||||
right_hand_world_landmarks = landmark_pb2.LandmarkList()
|
||||
right_hand_world_landmarks.MergeFrom(right_hand_world_landmarks_proto_list)
|
||||
for right_hand_world_landmark in right_hand_world_landmarks.landmark:
|
||||
holistic_landmarker_result.right_hand_world_landmarks.append(
|
||||
landmark_module.Landmark.create_from_pb2(right_hand_world_landmark)
|
||||
)
|
||||
|
||||
if _FACE_BLENDSHAPES_STREAM_NAME in output_packets:
|
||||
face_blendshapes_proto_list = packet_getter.get_proto(
|
||||
output_packets[_FACE_BLENDSHAPES_STREAM_NAME]
|
||||
)
|
||||
face_blendshapes_classifications = classification_pb2.ClassificationList()
|
||||
face_blendshapes_classifications.MergeFrom(face_blendshapes_proto_list)
|
||||
holistic_landmarker_result.face_blendshapes = []
|
||||
for face_blendshapes in face_blendshapes_classifications.classification:
|
||||
holistic_landmarker_result.face_blendshapes.append(
|
||||
category_module.Category(
|
||||
index=face_blendshapes.index,
|
||||
score=face_blendshapes.score,
|
||||
display_name=face_blendshapes.display_name,
|
||||
category_name=face_blendshapes.label,
|
||||
)
|
||||
)
|
||||
|
||||
if _POSE_SEGMENTATION_MASK_STREAM_NAME in output_packets:
|
||||
holistic_landmarker_result.segmentation_mask = packet_getter.get_image(
|
||||
output_packets[_POSE_SEGMENTATION_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
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.
|
||||
output_face_blendshapes: Whether HolisticLandmarker outputs face blendshapes
|
||||
classification. Face blendshapes are used for rendering the 3D face model.
|
||||
output_segmentation_mask: whether to output segmentation masks.
|
||||
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
|
||||
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_mask: 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.pose_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_mask:
|
||||
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]),
|
||||
],
|
||||
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,
|
||||
) -> 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.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
|
||||
Returns:
|
||||
The holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
})
|
||||
|
||||
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,
|
||||
) -> 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.
|
||||
|
||||
Returns:
|
||||
The holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).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,
|
||||
) -> 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.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the
|
||||
holistic landmarker has already processed.
|
||||
"""
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
|
||||
),
|
||||
})
|
||||
4
mediapipe/tasks/testdata/vision/BUILD
vendored
4
mediapipe/tasks/testdata/vision/BUILD
vendored
|
|
@ -58,9 +58,11 @@ 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",
|
||||
|
|
@ -142,6 +144,7 @@ filegroup(
|
|||
"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",
|
||||
|
|
@ -194,6 +197,7 @@ filegroup(
|
|||
"hand_landmark_lite.tflite",
|
||||
"hand_landmarker.task",
|
||||
"handrecrop_2020_07_21_v0.f16.tflite",
|
||||
"holistic_landmarker.task",
|
||||
"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",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user