Support both proto2 and proto3 in task subgraph options configuration, and revised the Holistic Landmarker API's implementation
This commit is contained in:
Kinar
parent
ea95ae753d
commit
24fe8eb73a
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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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@ -21,6 +21,7 @@ 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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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from google.protobuf.any_pb2 import Any
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@doc_controls.do_not_generate_docs
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@ -80,22 +81,31 @@ 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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# For protobuf 2 compat.
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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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)
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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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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()
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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=[
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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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node_config
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],
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input_stream=self.input_streams,
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output_stream=self.output_streams)
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@ -125,11 +135,7 @@ class TaskInfo:
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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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node_config, flow_limiter
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],
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input_stream=self.input_streams,
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output_stream=self.output_streams)
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@ -206,6 +206,7 @@ py_test(
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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/tasks/cc/vision/holistic_landmarker/proto:holistic_result_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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@ -14,6 +14,7 @@
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"""Tests for holistic landmarker."""
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import enum
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from typing import List
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from unittest import mock
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from absl.testing import absltest
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@ -23,6 +24,7 @@ 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.tasks.cc.vision.holistic_landmarker.proto import holistic_result_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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@ -35,6 +37,7 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
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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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_Category = category_module.Category
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_Rect = rect_module.Rect
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@ -46,14 +49,31 @@ _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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_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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_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
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_EXPECTED_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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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(
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file_path
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)
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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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@ -70,20 +90,77 @@ class HolisticLandmarkerTest(parameterized.TestCase):
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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 poses 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.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_masks: 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, 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, 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, expected_result.face_blendshapes,
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blendshapes_margin
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)
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if output_segmentation_masks:
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self.assertIsInstance(actual_result.segmentation_masks, List)
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for _, mask in enumerate(actual_result.segmentation_masks):
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self.assertIsInstance(mask, _Image)
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else:
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self.assertIsNone(actual_result.segmentation_masks)
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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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_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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_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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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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model_name,
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output_segmentation_masks,
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expected_holistic_landmarker_result: HolisticLandmarkerResult
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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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@ -98,15 +175,21 @@ class HolisticLandmarkerTest(parameterized.TestCase):
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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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base_options=base_options,
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output_face_blendshapes=True
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if expected_holistic_landmarker_result.face_blendshapes else False,
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output_segmentation_masks=output_segmentation_masks,
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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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self._expect_holistic_landmarker_results_correct(
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detection_result, expected_holistic_landmarker_result,
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output_segmentation_masks, _LANDMARKS_MARGIN, _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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@ -254,6 +254,7 @@ py_library(
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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_result_py_pb2",
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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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@ -22,6 +22,7 @@ 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_result_pb2
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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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@ -33,6 +34,7 @@ from mediapipe.tasks.python.vision.core import image_processing_options as image
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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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_HolisticResultProto = holistic_result_pb2.HolisticResult
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_HolisticLandmarkerGraphOptionsProto = (
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holistic_landmarker_graph_options_pb2.HolisticLandmarkerGraphOptions
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)
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@ -43,9 +45,6 @@ _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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@ -77,16 +76,64 @@ class HolisticLandmarkerResult:
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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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face_landmarks: List[landmark_module.NormalizedLandmark]
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pose_landmarks: List[landmark_module.NormalizedLandmark]
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pose_world_landmarks:List[landmark_module.Landmark]
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left_hand_landmarks: List[landmark_module.NormalizedLandmark]
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left_hand_world_landmarks: List[landmark_module.Landmark]
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right_hand_landmarks: List[landmark_module.NormalizedLandmark]
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right_hand_world_landmarks: List[landmark_module.Landmark]
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face_blendshapes: Optional[List[category_module.Category]] = None
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segmentation_masks: Optional[List[image_module.Image]] = None
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls,
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pb2_obj: _HolisticResultProto
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) -> 'HolisticLandmarkerResult':
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"""Creates a `HolisticLandmarkerResult` object from the given protobuf
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object."""
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return HolisticLandmarkerResult(
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face_landmarks=[
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landmark_module.NormalizedLandmark.create_from_pb2(landmark)
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for landmark in pb2_obj.face_landmarks.landmark
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] if hasattr(pb2_obj, 'face_landmarks') else None,
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pose_landmarks=[
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landmark_module.NormalizedLandmark.create_from_pb2(landmark)
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for landmark in pb2_obj.pose_landmarks.landmark
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] if hasattr(pb2_obj, 'pose_landmarks') else None,
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pose_world_landmarks=[
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landmark_module.Landmark.create_from_pb2(landmark)
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for landmark in pb2_obj.pose_world_landmarks.landmark
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] if hasattr(pb2_obj, 'pose_world_landmarks') else None,
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left_hand_landmarks=[
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landmark_module.NormalizedLandmark.create_from_pb2(landmark)
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for landmark in pb2_obj.left_hand_landmarks.landmark
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] if hasattr(pb2_obj, 'left_hand_landmarks') else None,
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left_hand_world_landmarks=[
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landmark_module.Landmark.create_from_pb2(landmark)
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for landmark in pb2_obj.left_hand_world_landmarks.landmark
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] if hasattr(pb2_obj, 'left_hand_world_landmarks') else None,
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right_hand_landmarks=[
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landmark_module.NormalizedLandmark.create_from_pb2(landmark)
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for landmark in pb2_obj.right_hand_landmarks.landmark
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] if hasattr(pb2_obj, 'right_hand_landmarks') else None,
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right_hand_world_landmarks=[
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landmark_module.Landmark.create_from_pb2(landmark)
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for landmark in pb2_obj.right_hand_world_landmarks.landmark
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] if hasattr(pb2_obj, 'right_hand_world_landmarks') else None,
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face_blendshapes=[
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category_module.Category(
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score=classification.score,
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index=classification.index,
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category_name=classification.label,
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display_name=classification.display_name
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)
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for classification in pb2_obj.face_blendshapes.classification
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] if hasattr(pb2_obj, 'face_blendshapes') else None,
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)
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def _build_landmarker_result(
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output_packets: Mapping[str, packet_module.Packet]
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@ -95,140 +142,105 @@ def _build_landmarker_result(
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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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face_landmarks_proto_list = packet_getter.get_proto(
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output_packets[_FACE_LANDMARKS_STREAM_NAME]
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)
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pose_landmarks_proto_list = packet_getter.get_proto(
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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(
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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(
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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(
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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(
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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(
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output_packets[_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
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)
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face_landmarks = landmark_pb2.NormalizedLandmarkList()
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face_landmarks.MergeFrom(face_landmarks_proto_list)
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for face_landmark in face_landmarks.landmark:
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holistic_landmarker_result.face_landmarks.append(
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landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
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)
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pose_landmarks = landmark_pb2.NormalizedLandmarkList()
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pose_landmarks.MergeFrom(pose_landmarks_proto_list)
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for pose_landmark in pose_landmarks.landmark:
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holistic_landmarker_result.pose_landmarks.append(
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landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
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)
|
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|
||||
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_masks = packet_getter.get_image_list(
|
||||
output_packets[_POSE_SEGMENTATION_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
pose_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_POSE_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
pose_world_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
left_hand_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_LEFT_HAND_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
left_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
right_hand_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_RIGHT_HAND_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
right_hand_world_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
face_landmarks_results = []
|
||||
for proto in face_landmarks_proto_list:
|
||||
face_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
face_landmarks.MergeFrom(proto)
|
||||
face_landmarks_list = []
|
||||
for face_landmark in face_landmarks.landmark:
|
||||
face_landmarks_list.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
|
||||
)
|
||||
face_landmarks_results.append(face_landmarks_list)
|
||||
|
||||
face_blendshapes_results = []
|
||||
if _FACE_BLENDSHAPES_STREAM_NAME in output_packets:
|
||||
face_blendshapes_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_FACE_BLENDSHAPES_STREAM_NAME]
|
||||
)
|
||||
for proto in face_blendshapes_proto_list:
|
||||
face_blendshapes_categories = []
|
||||
face_blendshapes_classifications = classification_pb2.ClassificationList()
|
||||
face_blendshapes_classifications.MergeFrom(proto)
|
||||
for face_blendshapes in face_blendshapes_classifications.classification:
|
||||
face_blendshapes_categories.append(
|
||||
category_module.Category(
|
||||
index=face_blendshapes.index,
|
||||
score=face_blendshapes.score,
|
||||
display_name=face_blendshapes.display_name,
|
||||
category_name=face_blendshapes.label,
|
||||
)
|
||||
)
|
||||
face_blendshapes_results.append(face_blendshapes_categories)
|
||||
|
||||
for proto in pose_landmarks_proto_list:
|
||||
pose_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
pose_landmarks.MergeFrom(proto)
|
||||
pose_landmarks_list = []
|
||||
for pose_landmark in pose_landmarks.landmark:
|
||||
pose_landmarks_list.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
|
||||
)
|
||||
holistic_landmarker_result.pose_landmarks.append(pose_landmarks_list)
|
||||
|
||||
for proto in pose_world_landmarks_proto_list:
|
||||
pose_world_landmarks = landmark_pb2.LandmarkList()
|
||||
pose_world_landmarks.MergeFrom(proto)
|
||||
pose_world_landmarks_list = []
|
||||
for pose_world_landmark in pose_world_landmarks.landmark:
|
||||
pose_world_landmarks_list.append(
|
||||
landmark_module.Landmark.create_from_pb2(pose_world_landmark)
|
||||
)
|
||||
holistic_landmarker_result.pose_world_landmarks.append(
|
||||
pose_world_landmarks_list
|
||||
)
|
||||
|
||||
for proto in left_hand_landmarks_proto_list:
|
||||
left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
left_hand_landmarks.MergeFrom(proto)
|
||||
left_hand_landmarks_list = []
|
||||
for hand_landmark in left_hand_landmarks.landmark:
|
||||
left_hand_landmarks_list.append(
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
|
||||
)
|
||||
holistic_landmarker_result.left_hand_landmarks.append(
|
||||
left_hand_landmarks_list
|
||||
)
|
||||
|
||||
for proto in left_hand_world_landmarks_proto_list:
|
||||
left_hand_world_landmarks = landmark_pb2.LandmarkList()
|
||||
left_hand_world_landmarks.MergeFrom(proto)
|
||||
left_hand_world_landmarks_list = []
|
||||
for left_hand_world_landmark in left_hand_world_landmarks.landmark:
|
||||
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
|
||||
|
||||
|
||||
|
|
@ -259,6 +271,9 @@ class HolisticLandmarkerOptions:
|
|||
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 FaceLandmarker outputs face blendshapes
|
||||
classification. Face blendshapes are used for rendering the 3D face model.
|
||||
output_segmentation_masks: 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.
|
||||
|
|
@ -419,7 +434,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
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,
|
||||
|
|
@ -436,7 +450,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
def detect(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the given image.
|
||||
|
||||
|
|
@ -449,7 +462,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The holistic landmarks detection results.
|
||||
|
|
@ -458,14 +470,8 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
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():
|
||||
|
|
@ -477,7 +483,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the provided video frame.
|
||||
|
||||
|
|
@ -492,7 +497,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
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.
|
||||
|
|
@ -501,16 +505,10 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
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():
|
||||
|
|
@ -522,7 +520,6 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> None:
|
||||
"""Sends live image data to perform holistic landmarks detection.
|
||||
|
||||
|
|
@ -548,20 +545,13 @@ class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
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),
|
||||
})
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user