236 lines
10 KiB
Markdown
236 lines
10 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 Pose
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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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Note that the `z` value should be discarded as the model is currently not fully
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trained to predict depth, but this is something we have on the roadmap. The
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model shares the same architecture as the full-body version that predicts 33
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landmarks, described in more detail in 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/pose_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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Activate a Python virtual environment:
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```bash
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$ python3 -m venv mp_env && source mp_env/bin/activate
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```
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Install MediaPipe Python package:
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```bash
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(mp_env)$ pip install mediapipe
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```
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Run the following Python code:
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<!-- Do not change the example code below directly. Change the corresponding example in mediapipe/python/solutions/pose.py and copy it over. -->
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```python
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import cv2
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import mediapipe as mp
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mp_drawing = mp.solutions.drawing_utils
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mp_pose = mp.solutions.pose
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# For static images:
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pose = mp_pose.Pose(
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static_image_mode=True, min_detection_confidence=0.5)
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for idx, file in enumerate(file_list):
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image = cv2.imread(file)
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# Convert the BGR image to RGB before processing.
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results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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# Print and draw pose landmarks on the image.
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print(
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'nose landmark:',
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results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE])
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annotated_image = image.copy()
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mp_drawing.draw_landmarks(
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annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', image)
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pose.close()
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# For webcam input:
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pose = mp_pose.Pose(
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min_detection_confidence=0.5, min_tracking_confidence=0.5)
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cap = cv2.VideoCapture(0)
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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break
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# Flip the image horizontally for a later selfie-view display, and convert
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# the BGR image to RGB.
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image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)
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# To improve performance, optionally mark the image as not writeable to
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# pass by reference.
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image.flags.writeable = False
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results = pose.process(image)
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# Draw the pose annotation on the image.
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image.flags.writeable = True
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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mp_drawing.draw_landmarks(
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image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
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cv2.imshow('MediaPipe Pose', image)
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if cv2.waitKey(5) & 0xFF == 27:
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break
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pose.close()
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cap.release()
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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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