Added Holistic Landmarker Python API
This commit is contained in:
parent
3d8b715dd6
commit
66f8625a42
|
@ -103,6 +103,7 @@ cc_library(
|
|||
"//mediapipe/tasks/cc/vision/interactive_segmenter:interactive_segmenter_graph",
|
||||
"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
|
||||
"//mediapipe/tasks/cc/vision/pose_landmarker:pose_landmarker_graph",
|
||||
"//mediapipe/tasks/cc/vision/holistic_landmarker:holistic_landmarker_graph",
|
||||
],
|
||||
)
|
||||
|
||||
|
|
|
@ -82,8 +82,12 @@ class TaskInfo:
|
|||
)
|
||||
task_subgraph_options = calculator_options_pb2.CalculatorOptions()
|
||||
task_options_proto = self.task_options.to_pb2()
|
||||
|
||||
# For protobuf 2 compat.
|
||||
if hasattr(task_options_proto, 'ext'):
|
||||
task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom(
|
||||
task_options_proto)
|
||||
|
||||
if not enable_flow_limiting:
|
||||
return calculator_pb2.CalculatorGraphConfig(
|
||||
node=[
|
||||
|
|
|
@ -194,6 +194,31 @@ py_test(
|
|||
],
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "holistic_landmarker_test",
|
||||
srcs = ["holistic_landmarker_test.py"],
|
||||
data = [
|
||||
"//mediapipe/tasks/testdata/vision:test_images",
|
||||
"//mediapipe/tasks/testdata/vision:test_models",
|
||||
"//mediapipe/tasks/testdata/vision:test_protos",
|
||||
],
|
||||
tags = ["not_run:arm"],
|
||||
deps = [
|
||||
"//mediapipe/framework/formats:classification_py_pb2",
|
||||
"//mediapipe/framework/formats:landmark_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/tasks/python/components/containers:category",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
"//mediapipe/tasks/python/vision:holistic_landmarker",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
"@com_google_protobuf//:protobuf_python",
|
||||
],
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "face_aligner_test",
|
||||
srcs = ["face_aligner_test.py"],
|
||||
|
|
114
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
Normal file
114
mediapipe/tasks/python/test/vision/holistic_landmarker_test.py
Normal file
|
@ -0,0 +1,114 @@
|
|||
# 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.
|
||||
"""Tests for holistic landmarker."""
|
||||
|
||||
import enum
|
||||
from unittest import mock
|
||||
|
||||
from absl.testing import absltest
|
||||
from absl.testing import parameterized
|
||||
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.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
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.test import test_utils
|
||||
from mediapipe.tasks.python.vision import holistic_landmarker
|
||||
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
|
||||
|
||||
|
||||
HolisticLandmarkerResult = holistic_landmarker.HolisticLandmarkerResult
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_Category = category_module.Category
|
||||
_Rect = rect_module.Rect
|
||||
_Landmark = landmark_module.Landmark
|
||||
_NormalizedLandmark = landmark_module.NormalizedLandmark
|
||||
_Image = image_module.Image
|
||||
_HolisticLandmarker = holistic_landmarker.HolisticLandmarker
|
||||
_HolisticLandmarkerOptions = holistic_landmarker.HolisticLandmarkerOptions
|
||||
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE = 'face_landmarker.task'
|
||||
_POSE_IMAGE = 'male_full_height_hands.jpg'
|
||||
_CAT_IMAGE = 'cat.jpg'
|
||||
_HOLISTIC_RESULT = "male_full_height_hands_result_cpu.pbtxt"
|
||||
_LANDMARKS_MARGIN = 0.03
|
||||
_BLENDSHAPES_MARGIN = 0.13
|
||||
|
||||
|
||||
class ModelFileType(enum.Enum):
|
||||
FILE_CONTENT = 1
|
||||
FILE_NAME = 2
|
||||
|
||||
|
||||
class HolisticLandmarkerTest(parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_POSE_IMAGE)
|
||||
)
|
||||
self.model_path = test_utils.get_test_data_path(
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
|
||||
)
|
||||
|
||||
@parameterized.parameters(
|
||||
(
|
||||
ModelFileType.FILE_NAME,
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
|
||||
),
|
||||
(
|
||||
ModelFileType.FILE_CONTENT,
|
||||
_HOLISTIC_LANDMARKER_BUNDLE_ASSET_FILE
|
||||
),
|
||||
)
|
||||
def test_detect(
|
||||
self,
|
||||
model_file_type,
|
||||
model_name
|
||||
):
|
||||
# Creates holistic landmarker.
|
||||
model_path = test_utils.get_test_data_path(model_name)
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _HolisticLandmarkerOptions(
|
||||
base_options=base_options
|
||||
)
|
||||
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.
|
||||
landmarker.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
|
@ -243,6 +243,29 @@ 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/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 = [
|
||||
|
|
567
mediapipe/tasks/python/vision/holistic_landmarker.py
Normal file
567
mediapipe/tasks/python/vision/holistic_landmarker.py
Normal file
|
@ -0,0 +1,567 @@
|
|||
# Copyright 2022 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, Mapping, Optional, List
|
||||
|
||||
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.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
|
||||
_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'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
|
||||
|
||||
_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:
|
||||
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
|
||||
segmentation_masks: Optional[List[image_module.Image]] = 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_list(
|
||||
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(
|
||||
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
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class HolisticLandmarkerOptions:
|
||||
"""Options for the holistic landmarker task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the holistic landmarker task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
HolisticLandmarker has three running modes: 1) The image mode for
|
||||
detecting holistic landmarks on single image inputs. 2) The video mode for
|
||||
detecting holistic landmarks on the decoded frames of a video. 3) The live
|
||||
stream mode for detecting holistic landmarks on the live stream of input
|
||||
data, such as from camera. In this mode, the "result_callback" below must
|
||||
be specified to receive the detection results asynchronously.
|
||||
min_face_detection_confidence: The minimum confidence score for the face
|
||||
detection to be considered successful.
|
||||
min_face_suppression_threshold: The minimum non-maximum-suppression
|
||||
threshold for face detection to be considered overlapped.
|
||||
min_face_landmarks_confidence: The minimum confidence score for the face
|
||||
landmark detection to be considered successful.
|
||||
min_pose_detection_confidence: The minimum confidence score for the pose
|
||||
detection to be considered successful.
|
||||
min_pose_suppression_threshold: The minimum non-maximum-suppression
|
||||
threshold for pose detection to be considered overlapped.
|
||||
min_pose_landmarks_confidence: The minimum confidence score for the pose
|
||||
landmark detection to be considered successful.
|
||||
min_hand_landmarks_confidence: The minimum confidence score for the hand
|
||||
landmark detection to be considered successful.
|
||||
result_callback: The user-defined result callback for processing live stream
|
||||
data. The result callback should only be specified when the running mode
|
||||
is set to the live stream mode.
|
||||
"""
|
||||
|
||||
base_options: _BaseOptions
|
||||
running_mode: _RunningMode = _RunningMode.IMAGE
|
||||
num_holistics: int = 1
|
||||
min_face_detection_confidence: float = 0.5
|
||||
min_face_suppression_threshold: float = 0.5
|
||||
min_face_landmarks_confidence: float = 0.5
|
||||
min_pose_detection_confidence: float = 0.5
|
||||
min_pose_suppression_threshold: float = 0.5
|
||||
min_pose_landmarks_confidence: float = 0.5
|
||||
min_hand_landmarks_confidence: float = 0.5
|
||||
output_face_blendshapes: bool = False
|
||||
output_segmentation_masks: bool = False
|
||||
result_callback: Optional[
|
||||
Callable[[HolisticLandmarkerResult, image_module.Image, int], None]
|
||||
] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _HolisticLandmarkerGraphOptionsProto:
|
||||
"""Generates an HolisticLandmarkerGraphOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
base_options_proto.use_stream_mode = (
|
||||
False if self.running_mode == _RunningMode.IMAGE else True
|
||||
)
|
||||
|
||||
# Initialize the holistic landmarker options from base options.
|
||||
holistic_landmarker_options_proto = _HolisticLandmarkerGraphOptionsProto(
|
||||
base_options=base_options_proto
|
||||
)
|
||||
# Configure face detector and face landmarks detector options.
|
||||
# holistic_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_face_detection_confidence
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_detector_graph_options.min_suppression_threshold = (
|
||||
# self.min_face_suppression_threshold
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_face_landmarks_confidence
|
||||
# )
|
||||
# # Configure pose detector and pose landmarks detector options.
|
||||
# holistic_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_pose_detection_confidence
|
||||
# )
|
||||
# holistic_landmarker_options_proto.pose_detector_graph_options.min_suppression_threshold = (
|
||||
# self.min_pose_suppression_threshold
|
||||
# )
|
||||
# holistic_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_pose_landmarks_confidence
|
||||
# )
|
||||
# # Configure hand landmarks detector options.
|
||||
# holistic_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = (
|
||||
# self.min_hand_landmarks_confidence
|
||||
# )
|
||||
return holistic_landmarker_options_proto
|
||||
|
||||
|
||||
class HolisticLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs holistic landmarks detection on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'HolisticLandmarker':
|
||||
"""Creates an `HolisticLandmarker` object from a TensorFlow Lite model and the default `HolisticLandmarkerOptions`.
|
||||
|
||||
Note that the created `HolisticLandmarker` instance is in image mode, for
|
||||
detecting holistic landmarks on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`HolisticLandmarker` object that's created from the model file and the
|
||||
default `HolisticLandmarkerOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HolisticLandmarker` object from the
|
||||
provided file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = HolisticLandmarkerOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE
|
||||
)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: HolisticLandmarkerOptions
|
||||
) -> 'HolisticLandmarker':
|
||||
"""Creates the `HolisticLandmarker` object from holistic landmarker options.
|
||||
|
||||
Args:
|
||||
options: Options for the holistic landmarker task.
|
||||
|
||||
Returns:
|
||||
`HolisticLandmarker` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HolisticLandmarker` object from
|
||||
`HolisticLandmarkerOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
|
||||
def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
|
||||
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
|
||||
return
|
||||
|
||||
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
|
||||
|
||||
if output_packets[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_FACE_LANDMARKS_STREAM_NAME]
|
||||
options.result_callback(
|
||||
HolisticLandmarkerResult([], [], [], [], [], [], []),
|
||||
image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
return
|
||||
|
||||
holistic_landmarks_detection_result = _build_landmarker_result(output_packets)
|
||||
timestamp = output_packets[_FACE_LANDMARKS_STREAM_NAME].timestamp
|
||||
options.result_callback(
|
||||
holistic_landmarks_detection_result,
|
||||
image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
|
||||
output_streams = [
|
||||
':'.join([_FACE_LANDMARKS_TAG, _FACE_LANDMARKS_STREAM_NAME]),
|
||||
':'.join([_POSE_LANDMARKS_TAG_NAME, _POSE_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_POSE_WORLD_LANDMARKS_TAG, _POSE_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_LEFT_HAND_LANDMARKS_TAG, _LEFT_HAND_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_LEFT_HAND_WORLD_LANDMARKS_TAG, _LEFT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_RIGHT_HAND_LANDMARKS_TAG, _RIGHT_HAND_LANDMARKS_STREAM_NAME]),
|
||||
':'.join(
|
||||
[_RIGHT_HAND_WORLD_LANDMARKS_TAG, _RIGHT_HAND_WORLD_LANDMARKS_STREAM_NAME]
|
||||
),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
]
|
||||
|
||||
if options.output_segmentation_masks:
|
||||
output_streams.append(
|
||||
':'.join([_POSE_SEGMENTATION_MASK_TAG, _POSE_SEGMENTATION_MASK_STREAM_NAME])
|
||||
)
|
||||
|
||||
if options.output_face_blendshapes:
|
||||
output_streams.append(
|
||||
':'.join([_FACE_BLENDSHAPES_TAG, _FACE_BLENDSHAPES_STREAM_NAME])
|
||||
)
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=output_streams,
|
||||
task_options=options,
|
||||
)
|
||||
return cls(
|
||||
task_info.generate_graph_config(
|
||||
enable_flow_limiting=options.running_mode
|
||||
== _RunningMode.LIVE_STREAM
|
||||
),
|
||||
options.running_mode,
|
||||
packets_callback if options.result_callback else None,
|
||||
)
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the given image.
|
||||
|
||||
Only use this method when the HolisticLandmarker is created with the image
|
||||
running mode.
|
||||
|
||||
The image can be of any size with format RGB or RGBA.
|
||||
TODO: Describes how the input image will be preprocessed after the yuv
|
||||
support is implemented.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, roi_allowed=False
|
||||
)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
),
|
||||
})
|
||||
|
||||
if output_packets[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HolisticLandmarkerResult([], [], [], [], [], [], [])
|
||||
|
||||
return _build_landmarker_result(output_packets)
|
||||
|
||||
def detect_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> HolisticLandmarkerResult:
|
||||
"""Performs holistic landmarks detection on the provided video frame.
|
||||
|
||||
Only use this method when the HolisticLandmarker is created with the video
|
||||
running mode.
|
||||
|
||||
Only use this method when the HolisticLandmarker is created with the video
|
||||
running mode. It's required to provide the video frame's timestamp (in
|
||||
milliseconds) along with the video frame. The input timestamps should be
|
||||
monotonically increasing for adjacent calls of this method.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input video frame in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The holistic landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If holistic landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, roi_allowed=False
|
||||
)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
|
||||
),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
})
|
||||
|
||||
if output_packets[_FACE_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HolisticLandmarkerResult([], [], [], [], [], [], [])
|
||||
|
||||
return _build_landmarker_result(output_packets)
|
||||
|
||||
def detect_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> None:
|
||||
"""Sends live image data to perform holistic landmarks detection.
|
||||
|
||||
The results will be available via the "result_callback" provided in the
|
||||
HolisticLandmarkerOptions. Only use this method when the HolisticLandmarker is
|
||||
created with the live stream running mode.
|
||||
|
||||
Only use this method when the HolisticLandmarker is created with the live
|
||||
stream running mode. The input timestamps should be monotonically increasing
|
||||
for adjacent calls of this method. This method will return immediately after
|
||||
the input image is accepted. The results will be available via the
|
||||
`result_callback` provided in the `HolisticLandmarkerOptions`. The
|
||||
`detect_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, holistic landmarker may drop the
|
||||
input images if needed. In other words, it's not guaranteed to have output
|
||||
per input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- The holistic landmarks detection results.
|
||||
- The input image that the holistic landmarker runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the
|
||||
holistic landmarker has already processed.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, roi_allowed=False
|
||||
)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
|
||||
),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
})
|
4
mediapipe/tasks/testdata/vision/BUILD
vendored
4
mediapipe/tasks/testdata/vision/BUILD
vendored
|
@ -57,9 +57,11 @@ mediapipe_files(srcs = [
|
|||
"hand_landmark_lite.tflite",
|
||||
"hand_landmarker.task",
|
||||
"handrecrop_2020_07_21_v0.f16.tflite",
|
||||
"holistic_landmarker.task",
|
||||
"left_hands.jpg",
|
||||
"left_hands_rotated.jpg",
|
||||
"leopard_bg_removal_result_512x512.png",
|
||||
"male_full_height_hands.jpg",
|
||||
"mobilenet_v1_0.25_192_quantized_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_metadata_1.tflite",
|
||||
|
@ -138,9 +140,11 @@ filegroup(
|
|||
"fist.png",
|
||||
"hand_landmark_full.tflite",
|
||||
"hand_landmark_lite.tflite",
|
||||
"holistic_landmarker.task",
|
||||
"left_hands.jpg",
|
||||
"left_hands_rotated.jpg",
|
||||
"leopard_bg_removal_result_512x512.png",
|
||||
"male_full_height_hands.jpg",
|
||||
"mozart_square.jpg",
|
||||
"multi_objects.jpg",
|
||||
"multi_objects_rotated.jpg",
|
||||
|
|
Loading…
Reference in New Issue
Block a user