diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index 4c89aa6c1..e35422b85 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -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", ], ) diff --git a/mediapipe/tasks/python/core/task_info.py b/mediapipe/tasks/python/core/task_info.py index 1816d60e0..894103361 100644 --- a/mediapipe/tasks/python/core/task_info.py +++ b/mediapipe/tasks/python/core/task_info.py @@ -82,8 +82,12 @@ class TaskInfo: ) task_subgraph_options = calculator_options_pb2.CalculatorOptions() task_options_proto = self.task_options.to_pb2() - task_subgraph_options.Extensions[task_options_proto.ext].CopyFrom( - task_options_proto) + + # 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=[ diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index c6fae0e6c..374ba689c 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -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"], diff --git a/mediapipe/tasks/python/test/vision/holistic_landmarker_test.py b/mediapipe/tasks/python/test/vision/holistic_landmarker_test.py new file mode 100644 index 000000000..0c9179301 --- /dev/null +++ b/mediapipe/tasks/python/test/vision/holistic_landmarker_test.py @@ -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() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index 0c1d42297..8253a9232 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -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 = [ diff --git a/mediapipe/tasks/python/vision/holistic_landmarker.py b/mediapipe/tasks/python/vision/holistic_landmarker.py new file mode 100644 index 000000000..a1877ff67 --- /dev/null +++ b/mediapipe/tasks/python/vision/holistic_landmarker.py @@ -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), + }) diff --git a/mediapipe/tasks/testdata/vision/BUILD b/mediapipe/tasks/testdata/vision/BUILD index 2f5157309..4220e29bb 100644 --- a/mediapipe/tasks/testdata/vision/BUILD +++ b/mediapipe/tasks/testdata/vision/BUILD @@ -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",