Merge pull request #4303 from kinaryml:pose-landmarker-python
PiperOrigin-RevId: 527948047
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# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MediaPipe pose landmarker task."""
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import dataclasses
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from typing import Callable, Mapping, Optional, List
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.python import packet_creator
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from mediapipe.python import packet_getter
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import packet as packet_module
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from mediapipe.tasks.cc.vision.pose_landmarker.proto import pose_landmarker_graph_options_pb2
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.core import task_info as task_info_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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from mediapipe.tasks.python.vision.core import base_vision_task_api
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_PoseLandmarkerGraphOptionsProto = (
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pose_landmarker_graph_options_pb2.PoseLandmarkerGraphOptions
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)
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_RunningMode = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_TaskInfo = task_info_module.TaskInfo
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_IMAGE_IN_STREAM_NAME = 'image_in'
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_IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_NORM_RECT_STREAM_NAME = 'norm_rect_in'
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_NORM_RECT_TAG = 'NORM_RECT'
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_SEGMENTATION_MASK_STREAM_NAME = 'segmentation_mask'
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_SEGMENTATION_MASK_TAG = 'SEGMENTATION_MASK'
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_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
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_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
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_POSE_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
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_POSE_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
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_POSE_AUXILIARY_LANDMARKS_STREAM_NAME = 'auxiliary_landmarks'
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_POSE_AUXILIARY_LANDMARKS_TAG = 'AUXILIARY_LANDMARKS'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.pose_landmarker.PoseLandmarkerGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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class PoseLandmarkerResult:
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"""The pose landmarks detection result from PoseLandmarker, where each vector element represents a single pose detected in the image.
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Attributes:
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pose_landmarks: Detected pose landmarks in normalized image coordinates.
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pose_world_landmarks: Detected pose landmarks in world coordinates.
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pose_auxiliary_landmarks: Detected auxiliary landmarks, used for deriving
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ROI for next frame.
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segmentation_masks: Optional segmentation masks for pose.
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"""
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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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pose_auxiliary_landmarks: List[List[landmark_module.NormalizedLandmark]]
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segmentation_masks: Optional[List[image_module.Image]] = None
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def _build_landmarker_result(
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output_packets: Mapping[str, packet_module.Packet]
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) -> PoseLandmarkerResult:
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"""Constructs a `PoseLandmarkerResult` from output packets."""
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pose_landmarker_result = PoseLandmarkerResult([], [], [])
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if _SEGMENTATION_MASK_STREAM_NAME in output_packets:
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pose_landmarker_result.segmentation_masks = packet_getter.get_image_list(
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output_packets[_SEGMENTATION_MASK_STREAM_NAME]
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)
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pose_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_NORM_LANDMARKS_STREAM_NAME]
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)
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pose_world_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME]
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)
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pose_auxiliary_landmarks_proto_list = packet_getter.get_proto_list(
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output_packets[_POSE_AUXILIARY_LANDMARKS_STREAM_NAME]
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)
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for proto in pose_landmarks_proto_list:
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pose_landmarks = landmark_pb2.NormalizedLandmarkList()
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pose_landmarks.MergeFrom(proto)
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pose_landmarks_list = []
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for pose_landmark in pose_landmarks.landmark:
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pose_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark)
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)
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pose_landmarker_result.pose_landmarks.append(pose_landmarks_list)
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for proto in pose_world_landmarks_proto_list:
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pose_world_landmarks = landmark_pb2.LandmarkList()
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pose_world_landmarks.MergeFrom(proto)
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pose_world_landmarks_list = []
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for pose_world_landmark in pose_world_landmarks.landmark:
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pose_world_landmarks_list.append(
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landmark_module.Landmark.create_from_pb2(pose_world_landmark)
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)
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pose_landmarker_result.pose_world_landmarks.append(
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pose_world_landmarks_list
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)
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for proto in pose_auxiliary_landmarks_proto_list:
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pose_auxiliary_landmarks = landmark_pb2.NormalizedLandmarkList()
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pose_auxiliary_landmarks.MergeFrom(proto)
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pose_auxiliary_landmarks_list = []
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for pose_auxiliary_landmark in pose_auxiliary_landmarks.landmark:
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pose_auxiliary_landmarks_list.append(
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landmark_module.NormalizedLandmark.create_from_pb2(
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pose_auxiliary_landmark
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)
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)
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pose_landmarker_result.pose_auxiliary_landmarks.append(
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pose_auxiliary_landmarks_list
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)
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return pose_landmarker_result
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@dataclasses.dataclass
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class PoseLandmarkerOptions:
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"""Options for the pose landmarker task.
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Attributes:
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base_options: Base options for the pose landmarker task.
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running_mode: The running mode of the task. Default to the image mode.
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PoseLandmarker has three running modes: 1) The image mode for detecting
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pose landmarks on single image inputs. 2) The video mode for detecting
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pose landmarks on the decoded frames of a video. 3) The live stream mode
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for detecting pose landmarks on the live stream of input data, such as
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from camera. In this mode, the "result_callback" below must be specified
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to receive the detection results asynchronously.
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num_poses: The maximum number of poses can be detected by the
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PoseLandmarker.
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min_pose_detection_confidence: The minimum confidence score for the pose
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detection to be considered successful.
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min_pose_presence_confidence: The minimum confidence score of pose presence
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score in the pose landmark detection.
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min_tracking_confidence: The minimum confidence score for the pose tracking
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to be considered successful.
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output_segmentation_masks: whether to output segmentation masks.
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result_callback: The user-defined result callback for processing live stream
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data. The result callback should only be specified when the running mode
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is set to the live stream mode.
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"""
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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num_poses: int = 1
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min_pose_detection_confidence: float = 0.5
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min_pose_presence_confidence: float = 0.5
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min_tracking_confidence: float = 0.5
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output_segmentation_masks: bool = False
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result_callback: Optional[
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Callable[[PoseLandmarkerResult, image_module.Image, int], None]
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] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _PoseLandmarkerGraphOptionsProto:
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"""Generates an PoseLandmarkerGraphOptions protobuf object."""
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base_options_proto = self.base_options.to_pb2()
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base_options_proto.use_stream_mode = (
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False if self.running_mode == _RunningMode.IMAGE else True
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)
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# Initialize the pose landmarker options from base options.
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pose_landmarker_options_proto = _PoseLandmarkerGraphOptionsProto(
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base_options=base_options_proto
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)
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pose_landmarker_options_proto.min_tracking_confidence = (
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self.min_tracking_confidence
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)
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pose_landmarker_options_proto.pose_detector_graph_options.num_poses = (
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self.num_poses
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)
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pose_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = (
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self.min_pose_detection_confidence
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)
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pose_landmarker_options_proto.pose_landmarks_detector_graph_options.min_detection_confidence = (
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self.min_pose_presence_confidence
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)
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return pose_landmarker_options_proto
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class PoseLandmarker(base_vision_task_api.BaseVisionTaskApi):
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"""Class that performs pose landmarks detection on images."""
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'PoseLandmarker':
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"""Creates a `PoseLandmarker` object from a model bundle file and the default `PoseLandmarkerOptions`.
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Note that the created `PoseLandmarker` instance is in image mode, for
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detecting pose landmarks on single image inputs.
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Args:
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model_path: Path to the model.
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Returns:
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`PoseLandmarker` object that's created from the model file and the
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default `PoseLandmarkerOptions`.
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Raises:
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ValueError: If failed to create `PoseLandmarker` object from the
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provided file such as invalid file path.
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RuntimeError: If other types of error occurred.
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"""
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base_options = _BaseOptions(model_asset_path=model_path)
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options = PoseLandmarkerOptions(
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base_options=base_options, running_mode=_RunningMode.IMAGE
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)
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return cls.create_from_options(options)
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@classmethod
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def create_from_options(
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cls, options: PoseLandmarkerOptions
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) -> 'PoseLandmarker':
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"""Creates the `PoseLandmarker` object from pose landmarker options.
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Args:
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options: Options for the pose landmarker task.
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Returns:
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`PoseLandmarker` object that's created from `options`.
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Raises:
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ValueError: If failed to create `PoseLandmarker` object from
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`PoseLandmarkerOptions` such as missing the model.
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RuntimeError: If other types of error occurred.
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"""
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def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
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if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
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return
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image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
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if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
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empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
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options.result_callback(
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PoseLandmarkerResult([], [], []),
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image,
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empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
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)
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return
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pose_landmarker_result = _build_landmarker_result(output_packets)
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timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
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options.result_callback(
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pose_landmarker_result,
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image,
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timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
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)
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output_streams = [
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':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
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':'.join(
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[_POSE_WORLD_LANDMARKS_TAG, _POSE_WORLD_LANDMARKS_STREAM_NAME]
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),
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':'.join([
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_POSE_AUXILIARY_LANDMARKS_TAG,
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_POSE_AUXILIARY_LANDMARKS_STREAM_NAME,
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]),
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':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
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]
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if options.output_segmentation_masks:
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output_streams.append(
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':'.join([_SEGMENTATION_MASK_TAG, _SEGMENTATION_MASK_STREAM_NAME])
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)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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input_streams=[
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':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
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':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
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],
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output_streams=output_streams,
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task_options=options,
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)
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return cls(
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task_info.generate_graph_config(
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enable_flow_limiting=options.running_mode
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== _RunningMode.LIVE_STREAM
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),
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options.running_mode,
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packets_callback if options.result_callback else None,
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)
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def detect(
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self,
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image: image_module.Image,
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image_processing_options: Optional[_ImageProcessingOptions] = None,
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) -> PoseLandmarkerResult:
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"""Performs pose landmarks detection on the given image.
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Only use this method when the PoseLandmarker is created with the image
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running mode.
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Args:
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image: MediaPipe Image.
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image_processing_options: Options for image processing.
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Returns:
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The pose landmarker detection results.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If pose landmarker detection failed to run.
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"""
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normalized_rect = self.convert_to_normalized_rect(
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image_processing_options, image, roi_allowed=False
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)
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output_packets = self._process_image_data({
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_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
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_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
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normalized_rect.to_pb2()
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),
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})
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if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
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return PoseLandmarkerResult([], [], [])
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return _build_landmarker_result(output_packets)
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def detect_for_video(
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self,
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image: image_module.Image,
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timestamp_ms: int,
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image_processing_options: Optional[_ImageProcessingOptions] = None,
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) -> PoseLandmarkerResult:
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"""Performs pose landmarks detection on the provided video frame.
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Only use this method when the PoseLandmarker is created with the video
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running mode.
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Only use this method when the PoseLandmarker is created with the video
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running mode. It's required to provide the video frame's timestamp (in
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milliseconds) along with the video frame. The input timestamps should be
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monotonically increasing for adjacent calls of this method.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input video frame in milliseconds.
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image_processing_options: Options for image processing.
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Returns:
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The pose landmarker detection results.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If pose landmarker detection failed to run.
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"""
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normalized_rect = self.convert_to_normalized_rect(
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image_processing_options, image, roi_allowed=False
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)
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output_packets = self._process_video_data({
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_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
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),
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_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
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normalized_rect.to_pb2()
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).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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})
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if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
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return PoseLandmarkerResult([], [], [])
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return _build_landmarker_result(output_packets)
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def detect_async(
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self,
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image: image_module.Image,
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timestamp_ms: int,
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image_processing_options: Optional[_ImageProcessingOptions] = None,
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) -> None:
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"""Sends live image data to perform pose landmarks detection.
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The results will be available via the "result_callback" provided in the
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PoseLandmarkerOptions. Only use this method when the PoseLandmarker is
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created with the live stream running mode.
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Only use this method when the PoseLandmarker is created with the live
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stream running mode. The input timestamps should be monotonically increasing
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for adjacent calls of this method. This method will return immediately after
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the input image is accepted. The results will be available via the
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`result_callback` provided in the `PoseLandmarkerOptions`. The
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`detect_async` method is designed to process live stream data such as
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camera input. To lower the overall latency, pose landmarker may drop the
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input images if needed. In other words, it's not guaranteed to have output
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per input image.
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The `result_callback` provides:
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- The pose landmarker detection results.
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- The input image that the pose landmarker runs on.
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- The input timestamp in milliseconds.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input image in milliseconds.
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image_processing_options: Options for image processing.
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Raises:
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ValueError: If the current input timestamp is smaller than what the
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pose landmarker has already processed.
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"""
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normalized_rect = self.convert_to_normalized_rect(
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image_processing_options, image, roi_allowed=False
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)
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self._send_live_stream_data({
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_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
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timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
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),
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_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
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normalized_rect.to_pb2()
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).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
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})
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Reference in New Issue
Block a user