Support both proto2 and proto3 in task subgraph options configuration, and revised the Holistic Landmarker API's implementation

This commit is contained in:
Kinar 2023-12-17 15:13:34 -08:00
parent ea95ae753d
commit 24fe8eb73a
6 changed files with 265 additions and 183 deletions

View File

@ -49,5 +49,6 @@ py_library(
"//mediapipe/calculators/core:flow_limiter_calculator_py_pb2",
"//mediapipe/framework:calculator_options_py_pb2",
"//mediapipe/framework:calculator_py_pb2",
"@com_google_protobuf//:protobuf_python"
],
)

View File

@ -21,6 +21,7 @@ from mediapipe.calculators.core import flow_limiter_calculator_pb2
from mediapipe.framework import calculator_options_pb2
from mediapipe.framework import calculator_pb2
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
from google.protobuf.any_pb2 import Any
@doc_controls.do_not_generate_docs
@ -80,22 +81,31 @@ class TaskInfo:
raise ValueError(
'`task_options` doesn`t provide `to_pb2()` method to convert itself to be a protobuf object.'
)
task_subgraph_options = calculator_options_pb2.CalculatorOptions()
task_options_proto = self.task_options.to_pb2()
# For protobuf 2 compat.
node_config = calculator_pb2.CalculatorGraphConfig.Node(
calculator=self.task_graph,
input_stream=self.input_streams,
output_stream=self.output_streams
)
if hasattr(task_options_proto, 'ext'):
# Use the extension mechanism for task_subgraph_options (proto2)
task_subgraph_options = calculator_options_pb2.CalculatorOptions()
task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom(
task_options_proto)
node_config.options.CopyFrom(task_subgraph_options)
else:
# Use the Any type for task_subgraph_options (proto3)
task_subgraph_options = Any()
task_subgraph_options.Pack(self.task_options.to_pb2())
node_config.node_options.append(task_subgraph_options)
if not enable_flow_limiting:
return calculator_pb2.CalculatorGraphConfig(
node=[
calculator_pb2.CalculatorGraphConfig.Node(
calculator=self.task_graph,
input_stream=self.input_streams,
output_stream=self.output_streams,
options=task_subgraph_options)
node_config
],
input_stream=self.input_streams,
output_stream=self.output_streams)
@ -125,11 +135,7 @@ class TaskInfo:
options=flow_limiter_options)
config = calculator_pb2.CalculatorGraphConfig(
node=[
calculator_pb2.CalculatorGraphConfig.Node(
calculator=self.task_graph,
input_stream=task_subgraph_inputs,
output_stream=self.output_streams,
options=task_subgraph_options), flow_limiter
node_config, flow_limiter
],
input_stream=self.input_streams,
output_stream=self.output_streams)

View File

@ -206,6 +206,7 @@ py_test(
deps = [
"//mediapipe/framework/formats:classification_py_pb2",
"//mediapipe/framework/formats:landmark_py_pb2",
"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_py_pb2",
"//mediapipe/python:_framework_bindings",
"//mediapipe/tasks/python/components/containers:category",
"//mediapipe/tasks/python/components/containers:landmark",

View File

@ -14,6 +14,7 @@
"""Tests for holistic landmarker."""
import enum
from typing import List
from unittest import mock
from absl.testing import absltest
@ -23,6 +24,7 @@ import numpy as np
from google.protobuf import text_format
from mediapipe.framework.formats import classification_pb2
from mediapipe.framework.formats import landmark_pb2
from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.tasks.python.components.containers import category as category_module
from mediapipe.tasks.python.components.containers import landmark as landmark_module
@ -35,6 +37,7 @@ from mediapipe.tasks.python.vision.core import vision_task_running_mode as runni
HolisticLandmarkerResult = holistic_landmarker.HolisticLandmarkerResult
_HolisticResultProto = holistic_result_pb2.HolisticResult
_BaseOptions = base_options_module.BaseOptions
_Category = category_module.Category
_Rect = rect_module.Rect
@ -46,14 +49,31 @@ _HolisticLandmarkerOptions = holistic_landmarker.HolisticLandmarkerOptions
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'holistic_landmarker.task'
_POSE_IMAGE = 'male_full_height_hands.jpg'
_CAT_IMAGE = 'cat.jpg'
_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
_EXPECTED_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
_LANDMARKS_MARGIN = 0.03
_BLENDSHAPES_MARGIN = 0.13
def _get_expected_holistic_landmarker_result(
file_path: str,
) -> HolisticLandmarkerResult:
holistic_result_file_path = test_utils.get_test_data_path(
file_path
)
with open(holistic_result_file_path, 'rb') as f:
holistic_result_proto = _HolisticResultProto()
# Use this if a .pb file is available.
# holistic_result_proto.ParseFromString(f.read())
text_format.Parse(f.read(), holistic_result_proto)
holistic_landmarker_result = HolisticLandmarkerResult.create_from_pb2(
holistic_result_proto
)
return holistic_landmarker_result
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
@ -70,20 +90,77 @@ class HolisticLandmarkerTest(parameterized.TestCase):
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
)
def _expect_landmarks_correct(
self, actual_landmarks, expected_landmarks, margin
):
# Expects to have the same number of poses detected.
self.assertLen(actual_landmarks, len(expected_landmarks))
for i, elem in enumerate(actual_landmarks):
self.assertAlmostEqual(elem.x, expected_landmarks[i].x, delta=margin)
self.assertAlmostEqual(elem.y, expected_landmarks[i].y, delta=margin)
def _expect_blendshapes_correct(
self, actual_blendshapes, expected_blendshapes, margin
):
# Expects to have the same number of blendshapes.
self.assertLen(actual_blendshapes, len(expected_blendshapes))
for i, elem in enumerate(actual_blendshapes):
self.assertEqual(elem.index, expected_blendshapes[i].index)
self.assertAlmostEqual(
elem.score,
expected_blendshapes[i].score,
delta=margin,
)
def _expect_holistic_landmarker_results_correct(
self,
actual_result: HolisticLandmarkerResult,
expected_result: HolisticLandmarkerResult,
output_segmentation_masks: bool,
landmarks_margin: float,
blendshapes_margin: float,
):
self._expect_landmarks_correct(
actual_result.pose_landmarks, expected_result.pose_landmarks,
landmarks_margin
)
self._expect_landmarks_correct(
actual_result.face_landmarks, expected_result.face_landmarks,
landmarks_margin
)
self._expect_blendshapes_correct(
actual_result.face_blendshapes, expected_result.face_blendshapes,
blendshapes_margin
)
if output_segmentation_masks:
self.assertIsInstance(actual_result.segmentation_masks, List)
for _, mask in enumerate(actual_result.segmentation_masks):
self.assertIsInstance(mask, _Image)
else:
self.assertIsNone(actual_result.segmentation_masks)
@parameterized.parameters(
(
ModelFileType.FILE_NAME,
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
False,
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT)
),
(
ModelFileType.FILE_CONTENT,
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE,
False,
_get_expected_holistic_landmarker_result(_EXPECTED_HOLISTIC_RESULT)
),
)
def test_detect(
self,
model_file_type,
model_name
model_name,
output_segmentation_masks,
expected_holistic_landmarker_result: HolisticLandmarkerResult
):
# Creates holistic landmarker.
model_path = test_utils.get_test_data_path(model_name)
@ -98,15 +175,21 @@ class HolisticLandmarkerTest(parameterized.TestCase):
raise ValueError('model_file_type is invalid.')
options = _HolisticLandmarkerOptions(
base_options=base_options
base_options=base_options,
output_face_blendshapes=True
if expected_holistic_landmarker_result.face_blendshapes else False,
output_segmentation_masks=output_segmentation_masks,
)
landmarker = _HolisticLandmarker.create_from_options(options)
# Performs holistic landmarks detection on the input.
detection_result = landmarker.detect(self.test_image)
# Closes the holistic landmarker explicitly when the holistic landmarker is not used
# in a context.
self._expect_holistic_landmarker_results_correct(
detection_result, expected_holistic_landmarker_result,
output_segmentation_masks, _LANDMARKS_MARGIN, _BLENDSHAPES_MARGIN
)
# Closes the holistic landmarker explicitly when the holistic landmarker is
# not used in a context.
landmarker.close()

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@ -254,6 +254,7 @@ py_library(
"//mediapipe/python:_framework_bindings",
"//mediapipe/python:packet_creator",
"//mediapipe/python:packet_getter",
"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_result_py_pb2",
"//mediapipe/tasks/cc/vision/holistic_landmarker/proto:holistic_landmarker_graph_options_py_pb2",
"//mediapipe/tasks/python/components/containers:category",
"//mediapipe/tasks/python/components/containers:landmark",

View File

@ -22,6 +22,7 @@ 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_result_pb2
from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_landmarker_graph_options_pb2
from mediapipe.tasks.python.components.containers import category as category_module
from mediapipe.tasks.python.components.containers import landmark as landmark_module
@ -33,6 +34,7 @@ from mediapipe.tasks.python.vision.core import image_processing_options as image
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
)
@ -43,9 +45,6 @@ _TaskInfo = task_info_module.TaskInfo
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_IMAGE_TAG = 'IMAGE'
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
_NORM_RECT_TAG = 'NORM_RECT'
_POSE_LANDMARKS_STREAM_NAME = "pose_landmarks"
_POSE_LANDMARKS_TAG_NAME = "POSE_LANDMARKS"
@ -77,16 +76,64 @@ class HolisticLandmarkerResult:
Attributes:
TODO
"""
face_landmarks: List[List[landmark_module.NormalizedLandmark]]
pose_landmarks: List[List[landmark_module.NormalizedLandmark]]
pose_world_landmarks: List[List[landmark_module.Landmark]]
left_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
left_hand_world_landmarks: List[List[landmark_module.Landmark]]
right_hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
right_hand_world_landmarks: List[List[landmark_module.Landmark]]
face_blendshapes: Optional[List[List[category_module.Category]]] = None
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_masks: Optional[List[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."""
return HolisticLandmarkerResult(
face_landmarks=[
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
for landmark in pb2_obj.face_landmarks.landmark
] if hasattr(pb2_obj, 'face_landmarks') else None,
pose_landmarks=[
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
for landmark in pb2_obj.pose_landmarks.landmark
] if hasattr(pb2_obj, 'pose_landmarks') else None,
pose_world_landmarks=[
landmark_module.Landmark.create_from_pb2(landmark)
for landmark in pb2_obj.pose_world_landmarks.landmark
] if hasattr(pb2_obj, 'pose_world_landmarks') else None,
left_hand_landmarks=[
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
for landmark in pb2_obj.left_hand_landmarks.landmark
] if hasattr(pb2_obj, 'left_hand_landmarks') else None,
left_hand_world_landmarks=[
landmark_module.Landmark.create_from_pb2(landmark)
for landmark in pb2_obj.left_hand_world_landmarks.landmark
] if hasattr(pb2_obj, 'left_hand_world_landmarks') else None,
right_hand_landmarks=[
landmark_module.NormalizedLandmark.create_from_pb2(landmark)
for landmark in pb2_obj.right_hand_landmarks.landmark
] if hasattr(pb2_obj, 'right_hand_landmarks') else None,
right_hand_world_landmarks=[
landmark_module.Landmark.create_from_pb2(landmark)
for landmark in pb2_obj.right_hand_world_landmarks.landmark
] if hasattr(pb2_obj, 'right_hand_world_landmarks') else None,
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
] if hasattr(pb2_obj, 'face_blendshapes') else None,
)
def _build_landmarker_result(
output_packets: Mapping[str, packet_module.Packet]
@ -95,61 +142,92 @@ def _build_landmarker_result(
holistic_landmarker_result = HolisticLandmarkerResult([], [], [], [], [], [],
[])
face_landmarks_proto_list = packet_getter.get_proto_list(
face_landmarks_proto_list = packet_getter.get_proto(
output_packets[_FACE_LANDMARKS_STREAM_NAME]
)
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(
pose_landmarks_proto_list = packet_getter.get_proto(
output_packets[_POSE_LANDMARKS_STREAM_NAME]
)
pose_world_landmarks_proto_list = packet_getter.get_proto_list(
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_list(
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_list(
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_list(
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_list(
right_hand_world_landmarks_proto_list = packet_getter.get_proto(
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 = []
face_landmarks.MergeFrom(face_landmarks_proto_list)
for face_landmark in face_landmarks.landmark:
face_landmarks_list.append(
holistic_landmarker_result.face_landmarks.append(
landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
)
face_landmarks_results.append(face_landmarks_list)
face_blendshapes_results = []
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_list(
face_blendshapes_proto_list = packet_getter.get_proto(
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)
face_blendshapes_classifications.MergeFrom(face_blendshapes_proto_list)
holistic_landmarker_result.face_blendshapes = []
for face_blendshapes in face_blendshapes_classifications.classification:
face_blendshapes_categories.append(
holistic_landmarker_result.face_blendshapes.append(
category_module.Category(
index=face_blendshapes.index,
score=face_blendshapes.score,
@ -157,76 +235,10 @@ def _build_landmarker_result(
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
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]
)
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),
})