193 lines
7.7 KiB
Python
193 lines
7.7 KiB
Python
# Copyright 2020-2021 The MediaPipe Authors.
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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."""
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import enum
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from typing import NamedTuple
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import numpy as np
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# The following imports are needed because python pb2 silently discards
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# unknown protobuf fields.
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# pylint: disable=unused-import
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from mediapipe.calculators.core import constant_side_packet_calculator_pb2
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from mediapipe.calculators.core import gate_calculator_pb2
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from mediapipe.calculators.core import split_vector_calculator_pb2
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from mediapipe.calculators.image import warp_affine_calculator_pb2
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from mediapipe.calculators.tensor import image_to_tensor_calculator_pb2
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from mediapipe.calculators.tensor import inference_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_classification_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_detections_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_landmarks_calculator_pb2
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from mediapipe.calculators.tensor import tensors_to_segmentation_calculator_pb2
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from mediapipe.calculators.tflite import ssd_anchors_calculator_pb2
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from mediapipe.calculators.util import detections_to_rects_calculator_pb2
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from mediapipe.calculators.util import landmarks_smoothing_calculator_pb2
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from mediapipe.calculators.util import local_file_contents_calculator_pb2
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from mediapipe.calculators.util import logic_calculator_pb2
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from mediapipe.calculators.util import non_max_suppression_calculator_pb2
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from mediapipe.calculators.util import rect_transformation_calculator_pb2
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from mediapipe.calculators.util import thresholding_calculator_pb2
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from mediapipe.calculators.util import visibility_smoothing_calculator_pb2
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from mediapipe.framework.tool import switch_container_pb2
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# pylint: enable=unused-import
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from mediapipe.python.solution_base import SolutionBase
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from mediapipe.python.solutions import download_utils
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# pylint: disable=unused-import
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from mediapipe.python.solutions.pose_connections import POSE_CONNECTIONS
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# pylint: enable=unused-import
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class PoseLandmark(enum.IntEnum):
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"""The 33 pose landmarks."""
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NOSE = 0
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LEFT_EYE_INNER = 1
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LEFT_EYE = 2
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LEFT_EYE_OUTER = 3
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RIGHT_EYE_INNER = 4
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RIGHT_EYE = 5
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RIGHT_EYE_OUTER = 6
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LEFT_EAR = 7
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RIGHT_EAR = 8
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MOUTH_LEFT = 9
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MOUTH_RIGHT = 10
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LEFT_SHOULDER = 11
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RIGHT_SHOULDER = 12
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LEFT_ELBOW = 13
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RIGHT_ELBOW = 14
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LEFT_WRIST = 15
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RIGHT_WRIST = 16
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LEFT_PINKY = 17
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RIGHT_PINKY = 18
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LEFT_INDEX = 19
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RIGHT_INDEX = 20
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LEFT_THUMB = 21
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RIGHT_THUMB = 22
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LEFT_HIP = 23
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RIGHT_HIP = 24
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LEFT_KNEE = 25
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RIGHT_KNEE = 26
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LEFT_ANKLE = 27
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RIGHT_ANKLE = 28
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LEFT_HEEL = 29
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RIGHT_HEEL = 30
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LEFT_FOOT_INDEX = 31
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RIGHT_FOOT_INDEX = 32
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_BINARYPB_FILE_PATH = 'mediapipe/modules/pose_landmark/pose_landmark_cpu.binarypb'
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def _download_oss_pose_landmark_model(model_complexity):
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"""Downloads the pose landmark lite/heavy model from the MediaPipe Github repo if it doesn't exist in the package."""
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if model_complexity == 0:
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download_utils.download_oss_model(
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'mediapipe/modules/pose_landmark/pose_landmark_lite.tflite')
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elif model_complexity == 2:
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download_utils.download_oss_model(
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'mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite')
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class Pose(SolutionBase):
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"""MediaPipe Pose.
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MediaPipe Pose processes an RGB image and returns pose landmarks on the most
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prominent person detected.
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Please refer to https://solutions.mediapipe.dev/pose#python-solution-api for
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usage examples.
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"""
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def __init__(self,
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static_image_mode=False,
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model_complexity=1,
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smooth_landmarks=True,
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enable_segmentation=False,
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smooth_segmentation=True,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5):
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"""Initializes a MediaPipe Pose object.
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Args:
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static_image_mode: Whether to treat the input images as a batch of static
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and possibly unrelated images, or a video stream. See details in
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https://solutions.mediapipe.dev/pose#static_image_mode.
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model_complexity: Complexity of the pose landmark model: 0, 1 or 2. See
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details in https://solutions.mediapipe.dev/pose#model_complexity.
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smooth_landmarks: Whether to filter landmarks across different input
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images to reduce jitter. See details in
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https://solutions.mediapipe.dev/pose#smooth_landmarks.
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enable_segmentation: Whether to predict segmentation mask. See details in
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https://solutions.mediapipe.dev/pose#enable_segmentation.
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smooth_segmentation: Whether to filter segmentation across different input
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images to reduce jitter. See details in
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https://solutions.mediapipe.dev/pose#smooth_segmentation.
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min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for person
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detection to be considered successful. See details in
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https://solutions.mediapipe.dev/pose#min_detection_confidence.
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min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) for the
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pose landmarks to be considered tracked successfully. See details in
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https://solutions.mediapipe.dev/pose#min_tracking_confidence.
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"""
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_download_oss_pose_landmark_model(model_complexity)
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super().__init__(
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binary_graph_path=_BINARYPB_FILE_PATH,
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side_inputs={
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'model_complexity': model_complexity,
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'smooth_landmarks': smooth_landmarks and not static_image_mode,
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'enable_segmentation': enable_segmentation,
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'smooth_segmentation':
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smooth_segmentation and not static_image_mode,
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'use_prev_landmarks': not static_image_mode,
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},
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calculator_params={
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'posedetectioncpu__TensorsToDetectionsCalculator.min_score_thresh':
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min_detection_confidence,
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'poselandmarkbyroicpu__tensorstoposelandmarksandsegmentation__ThresholdingCalculator.threshold':
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min_tracking_confidence,
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},
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outputs=['pose_landmarks', 'pose_world_landmarks', 'segmentation_mask'])
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def process(self, image: np.ndarray) -> NamedTuple:
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"""Processes an RGB image and returns the pose landmarks on the most prominent person detected.
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Args:
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image: An RGB image represented as a numpy ndarray.
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Raises:
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RuntimeError: If the underlying graph throws any error.
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ValueError: If the input image is not three channel RGB.
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Returns:
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A NamedTuple with fields describing the landmarks on the most prominate
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person detected:
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1) "pose_landmarks" field that contains the pose landmarks.
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2) "pose_world_landmarks" field that contains the pose landmarks in
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real-world 3D coordinates that are in meters with the origin at the
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center between hips.
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3) "segmentation_mask" field that contains the segmentation mask if
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"enable_segmentation" is set to true.
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"""
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results = super().process(input_data={'image': image})
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if results.pose_landmarks: # pytype: disable=attribute-error
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for landmark in results.pose_landmarks.landmark: # pytype: disable=attribute-error
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landmark.ClearField('presence')
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if results.pose_world_landmarks: # pytype: disable=attribute-error
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for landmark in results.pose_world_landmarks.landmark: # pytype: disable=attribute-error
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landmark.ClearField('presence')
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return results
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