195 lines
9.6 KiB
Markdown
195 lines
9.6 KiB
Markdown
---
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layout: default
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title: Pose
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parent: Solutions
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nav_order: 5
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---
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# MediaPipe BlazePose
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{: .no_toc }
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1. TOC
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{:toc}
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---
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## Overview
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Human pose estimation from video plays a critical role in various applications
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such as quantifying physical exercises, sign language recognition, and full-body
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gesture control. For example, it can form the basis for yoga, dance, and fitness
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applications. It can also enable the overlay of digital content and information
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on top of the physical world in augmented reality.
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MediaPipe Pose is a ML solution for high-fidelity upper-body pose tracking,
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inferring 25 2D upper-body landmarks from RGB video frames utilizing our
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[BlazePose](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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research. Current state-of-the-art approaches rely primarily on powerful desktop
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environments for inference, whereas our method achieves real-time performance on
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most modern [mobile phones](#mobile), [desktops/laptops](#desktop), in
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[python](#python) and even on the [web](#web). A variant of MediaPipe Pose that
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performs full-body pose tracking on mobile phones will be included in an
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upcoming release of
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[ML Kit](https://developers.google.com/ml-kit/early-access/pose-detection).
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 |
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:--------------------------------------------------------------------------------------------: |
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*Fig 1. Example of MediaPipe Pose for upper-body pose tracking.* |
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## ML Pipeline
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The solution utilizes a two-step detector-tracker ML pipeline, proven to be
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effective in our [MediaPipe Hands](./hands.md) and
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[MediaPipe Face Mesh](./face_mesh.md) solutions. Using a detector, the pipeline
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first locates the pose region-of-interest (ROI) within the frame. The tracker
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subsequently predicts the pose landmarks within the ROI using the ROI-cropped
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frame as input. Note that for video use cases the detector is invoked only as
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needed, i.e., for the very first frame and when the tracker could no longer
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identify body pose presence in the previous frame. For other frames the pipeline
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simply derives the ROI from the previous frame’s pose landmarks.
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The pipeline is implemented as a MediaPipe
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[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
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that uses a
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[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
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from the
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[pose landmark module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark)
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and renders using a dedicated
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[upper-body pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/upper_body_pose_renderer_gpu.pbtxt).
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The
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[pose landmark subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_landmark/pose_landmark_upper_body_gpu.pbtxt)
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internally uses a
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[pose detection subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection/pose_detection_gpu.pbtxt)
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from the
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[pose detection module](https://github.com/google/mediapipe/tree/master/mediapipe/modules/pose_detection).
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../tools/visualizer.md).
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## Models
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### Pose Detection Model (BlazePose Detector)
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The detector is inspired by our own lightweight
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[BlazeFace](https://arxiv.org/abs/1907.05047) model, used in
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[MediaPipe Face Detection](./face_detection.md), as a proxy for a person
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detector. It explicitly predicts two additional virtual keypoints that firmly
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describe the human body center, rotation and scale as a circle. Inspired by
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[Leonardo’s Vitruvian man](https://en.wikipedia.org/wiki/Vitruvian_Man), we
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predict the midpoint of a person's hips, the radius of a circle circumscribing
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the whole person, and the incline angle of the line connecting the shoulder and
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hip midpoints.
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 |
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:----------------------------------------------------------------------------------------------------: |
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*Fig 2. Vitruvian man aligned via two virtual keypoints predicted by BlazePose detector in addition to the face bounding box.* |
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### Pose Landmark Model (BlazePose Tracker)
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The landmark model currently included in MediaPipe Pose predicts the location of
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25 upper-body landmarks (see figure below), each with `(x, y, z, visibility)`,
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plus two virtual alignment keypoints. Note that the `z` value should be
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discarded as the model is currently not fully trained to predict depth, but this
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is something we have on the roadmap. The model shares the same architecture as
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the full-body version that predicts 33 landmarks, described in more detail in
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the
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[BlazePose Google AI Blog](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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and in this [paper](https://arxiv.org/abs/2006.10204).
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:------------------------------------------------------------------------------------------------: |
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*Fig 3. 25 upper-body pose landmarks.* |
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## Example Apps
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Please first see general instructions for
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[Android](../getting_started/building_examples.md#android),
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[iOS](../getting_started/building_examples.md#ios),
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[desktop](../getting_started/building_examples.md#desktop) and
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[Python](../getting_started/building_examples.md#python) on how to build
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MediaPipe examples.
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../tools/visualizer.md).
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### Mobile
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* Graph:
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[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/1uKc6T7KSuA0Mlq2URi5YookHu0U3yoh_/view?usp=sharing)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu:upperbodyposetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/upperbodyposetrackinggpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/upperbodyposetrackinggpu:UpperBodyPoseTrackingGpuApp`](http:/mediapipe/examples/ios/upperbodyposetrackinggpu/BUILD)
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### Desktop
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Please first see general instructions for
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[desktop](../getting_started/building_examples.md#desktop) on how to build
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MediaPipe examples.
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* Running on CPU
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* Graph:
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[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_cpu.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
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* Running on GPU
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* Graph:
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[`mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/upper_body_pose_tracking_gpu.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/upper_body_pose_tracking:upper_body_pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/upper_body_pose_tracking/BUILD)
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### Python
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MediaPipe Python package is available on
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[PyPI](https://pypi.org/project/mediapipe/), and can be installed simply by `pip
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install mediapipe` on Linux and macOS, as described below and in this
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[colab](https://mediapipe.page.link/mp-py-colab). If you do need to build the
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Python package from source, see
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[additional instructions](../getting_started/building_examples.md#python).
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```bash
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# Activate a Python virtual environment.
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$ python3 -m venv mp_env && source mp_env/bin/activate
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# Install MediaPipe Python package
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(mp_env)$ pip install mediapipe
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# Run in Python interpreter
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(mp_env)$ python3
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>>> import mediapipe as mp
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>>> pose_tracker = mp.examples.UpperBodyPoseTracker()
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# For image input
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>>> pose_landmarks, _ = pose_tracker.run(input_file='/path/to/input/file', output_file='/path/to/output/file')
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>>> pose_landmarks, annotated_image = pose_tracker.run(input_file='/path/to/file')
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# To print out the pose landmarks, you can simply do "print(pose_landmarks)".
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# However, the data points can be more accessible with the following approach.
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>>> [print('x is', data_point.x, 'y is', data_point.y, 'z is', data_point.z, 'visibility is', data_point.visibility) for data_point in pose_landmarks.landmark]
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# For live camera input
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# (Press Esc within the output image window to stop the run or let it self terminate after 30 seconds.)
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>>> pose_tracker.run_live()
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# Close the tracker.
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>>> pose_tracker.close()
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```
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Tip: Use command `deactivate` to exit the Python virtual environment.
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### Web
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Please refer to [these instructions](../index.md#mediapipe-on-the-web).
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## Resources
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* Google AI Blog:
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[BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
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* Paper:
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[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
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([presentation](https://youtu.be/YPpUOTRn5tA))
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* [Models and model cards](./models.md#pose)
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