505 lines
22 KiB
Markdown
505 lines
22 KiB
Markdown
---
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layout: forward
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target: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker/
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title: Pose
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parent: MediaPipe Legacy Solutions
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has_children: true
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has_toc: false
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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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<details close markdown="block">
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<summary>
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Table of contents
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</summary>
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{: .text-delta }
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1. TOC
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{:toc}
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</details>
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---
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**Attention:** *Thank you for your interest in MediaPipe Solutions.
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As of May 10, 2023, this solution was upgraded to a new MediaPipe
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Solution. For more information, see the
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[MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker)
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site.*
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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](./pose_classification.md), sign
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language recognition, and full-body gesture control. For example, it can form
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the basis for yoga, dance, and fitness applications. It can also enable the
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overlay of digital content and information on top of the physical world in
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augmented reality.
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MediaPipe Pose is a ML solution for high-fidelity body pose tracking, inferring
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33 3D landmarks and background segmentation mask on the whole body from RGB
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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 that also powers the
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[ML Kit Pose Detection API](https://developers.google.com/ml-kit/vision/pose-detection).
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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-solution-api) and even on the [web](#javascript-solution-api).
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 |
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:----------------------------------------------------------------------: |
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*Fig 1. Example of MediaPipe Pose for 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 person/pose region-of-interest (ROI) within the frame. The
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tracker subsequently predicts the pose landmarks and segmentation mask within
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the ROI using the ROI-cropped frame as input. Note that for video use cases the
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detector is invoked only as needed, i.e., for the very first frame and when the
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tracker could no longer identify body pose presence in the previous frame. For
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other frames the pipeline simply derives the ROI from the previous frame’s pose
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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/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_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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[pose renderer subgraph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/subgraphs/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_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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## Pose Estimation Quality
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To evaluate the quality of our [models](./models.md#pose) against other
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well-performing publicly available solutions, we use three different validation
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datasets, representing different verticals: Yoga, Dance and HIIT. Each image
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contains only a single person located 2-4 meters from the camera. To be
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consistent with other solutions, we perform evaluation only for 17 keypoints
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from [COCO topology](https://cocodataset.org/#keypoints-2020).
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Method | Yoga <br/> [`mAP`] | Yoga <br/> [`PCK@0.2`] | Dance <br/> [`mAP`] | Dance <br/> [`PCK@0.2`] | HIIT <br/> [`mAP`] | HIIT <br/> [`PCK@0.2`]
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----------------------------------------------------------------------------------------------------- | -----------------: | ---------------------: | ------------------: | ----------------------: | -----------------: | ---------------------:
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BlazePose GHUM Heavy | 68.1 | **96.4** | 73.0 | **97.2** | 74.0 | **97.5**
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BlazePose GHUM Full | 62.6 | **95.5** | 67.4 | **96.3** | 68.0 | **95.7**
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BlazePose GHUM Lite | 45.0 | **90.2** | 53.6 | **92.5** | 53.8 | **93.5**
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[AlphaPose ResNet50](https://github.com/MVIG-SJTU/AlphaPose) | 63.4 | **96.0** | 57.8 | **95.5** | 63.4 | **96.0**
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[Apple Vision](https://developer.apple.com/documentation/vision/detecting_human_body_poses_in_images) | 32.8 | **82.7** | 36.4 | **91.4** | 44.5 | **88.6**
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 |
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:--------------------------------------------------------------------------: |
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*Fig 2. Quality evaluation in [`PCK@0.2`].* |
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We designed our models specifically for live perception use cases, so all of
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them work in real-time on the majority of modern devices.
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Method | Latency <br/> Pixel 3 [TFLite GPU](https://www.tensorflow.org/lite/performance/gpu_advanced) | Latency <br/> MacBook Pro (15-inch 2017)
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-------------------- | -------------------------------------------------------------------------------------------: | ---------------------------------------:
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BlazePose GHUM Heavy | 53 ms | 38 ms
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BlazePose GHUM Full | 25 ms | 27 ms
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BlazePose GHUM Lite | 20 ms | 25 ms
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## Models
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### Person/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 3. 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 [GHUM](https://github.com/google-research/google-research/tree/master/ghum) 3D)
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The landmark model in MediaPipe Pose predicts the location of 33 pose landmarks
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(see figure below).
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 |
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:----------------------------------------------------------------------------------------------: |
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*Fig 4. 33 pose landmarks.* |
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Optionally, MediaPipe Pose can predict a full-body
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[segmentation mask](#segmentation_mask) represented as a two-class segmentation
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(human or background).
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Please find 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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this [paper](https://arxiv.org/abs/2006.10204),
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[the model card](./models.md#pose) and the [Output](#output) section below.
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## Solution APIs
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### Cross-platform Configuration Options
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Naming style and availability may differ slightly across platforms/languages.
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#### static_image_mode
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If set to `false`, the solution treats the input images as a video stream. It
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will try to detect the most prominent person in the very first images, and upon
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a successful detection further localizes the pose landmarks. In subsequent
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images, it then simply tracks those landmarks without invoking another detection
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until it loses track, on reducing computation and latency. If set to `true`,
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person detection runs every input image, ideal for processing a batch of static,
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possibly unrelated, images. Default to `false`.
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#### model_complexity
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Complexity of the pose landmark model: `0`, `1` or `2`. Landmark accuracy as
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well as inference latency generally go up with the model complexity. Default to
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`1`.
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#### smooth_landmarks
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If set to `true`, the solution filters pose landmarks across different input
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images to reduce jitter, but ignored if [static_image_mode](#static_image_mode)
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is also set to `true`. Default to `true`.
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#### enable_segmentation
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If set to `true`, in addition to the pose landmarks the solution also generates
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the segmentation mask. Default to `false`.
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#### smooth_segmentation
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If set to `true`, the solution filters segmentation masks across different input
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images to reduce jitter. Ignored if [enable_segmentation](#enable_segmentation)
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is `false` or [static_image_mode](#static_image_mode) is `true`. Default to
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`true`.
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#### min_detection_confidence
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Minimum confidence value (`[0.0, 1.0]`) from the person-detection model for the
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detection to be considered successful. Default to `0.5`.
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#### min_tracking_confidence
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Minimum confidence value (`[0.0, 1.0]`) from the landmark-tracking model for the
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pose landmarks to be considered tracked successfully, or otherwise person
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detection will be invoked automatically on the next input image. Setting it to a
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higher value can increase robustness of the solution, at the expense of a higher
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latency. Ignored if [static_image_mode](#static_image_mode) is `true`, where
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person detection simply runs on every image. Default to `0.5`.
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### Output
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Naming style may differ slightly across platforms/languages.
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#### pose_landmarks
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A list of pose landmarks. Each landmark consists of the following:
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* `x` and `y`: Landmark coordinates normalized to `[0.0, 1.0]` by the image
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width and height respectively.
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* `z`: Represents the landmark depth with the depth at the midpoint of hips
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being the origin, and the smaller the value the closer the landmark is to
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the camera. The magnitude of `z` uses roughly the same scale as `x`.
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* `visibility`: A value in `[0.0, 1.0]` indicating the likelihood of the
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landmark being visible (present and not occluded) in the image.
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#### pose_world_landmarks
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*Fig 5. Example of MediaPipe Pose real-world 3D coordinates.* |
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:-----------------------------------------------------------: |
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<video autoplay muted loop preload style="height: auto; width: 480px"><source src="https://mediapipe.dev/images/mobile/pose_world_landmarks.mp4" type="video/mp4"></video> |
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Another list of pose landmarks in world coordinates. Each landmark consists of
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the following:
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* `x`, `y` and `z`: Real-world 3D coordinates in meters with the origin at the
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center between hips.
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* `visibility`: Identical to that defined in the corresponding
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[pose_landmarks](#pose_landmarks).
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#### segmentation_mask
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The output segmentation mask, predicted only when
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[enable_segmentation](#enable_segmentation) is set to `true`. The mask has the
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same width and height as the input image, and contains values in `[0.0, 1.0]`
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where `1.0` and `0.0` indicate high certainty of a "human" and "background"
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pixel respectively. Please refer to the platform-specific usage examples below
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for usage details.
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*Fig 6. Example of MediaPipe Pose segmentation mask.* |
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:---------------------------------------------------: |
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<video autoplay muted loop preload style="height: auto; width: 480px"><source src="https://mediapipe.dev/images/mobile/pose_segmentation.mp4" type="video/mp4"></video> |
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### Python Solution API
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Please first follow general [instructions](../getting_started/python.md) to
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install MediaPipe Python package, then learn more in the companion
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[Python Colab](#resources) and the usage example below.
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Supported configuration options:
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* [static_image_mode](#static_image_mode)
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* [model_complexity](#model_complexity)
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* [smooth_landmarks](#smooth_landmarks)
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* [enable_segmentation](#enable_segmentation)
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* [smooth_segmentation](#smooth_segmentation)
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* [min_detection_confidence](#min_detection_confidence)
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* [min_tracking_confidence](#min_tracking_confidence)
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```python
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import cv2
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import mediapipe as mp
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import numpy as np
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mp_drawing = mp.solutions.drawing_utils
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mp_drawing_styles = mp.solutions.drawing_styles
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mp_pose = mp.solutions.pose
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# For static images:
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IMAGE_FILES = []
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BG_COLOR = (192, 192, 192) # gray
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with mp_pose.Pose(
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static_image_mode=True,
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model_complexity=2,
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enable_segmentation=True,
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min_detection_confidence=0.5) as pose:
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for idx, file in enumerate(IMAGE_FILES):
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image = cv2.imread(file)
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image_height, image_width, _ = image.shape
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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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if not results.pose_landmarks:
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continue
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print(
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f'Nose coordinates: ('
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f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
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f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
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)
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annotated_image = image.copy()
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# Draw segmentation on the image.
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# To improve segmentation around boundaries, consider applying a joint
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# bilateral filter to "results.segmentation_mask" with "image".
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condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
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bg_image = np.zeros(image.shape, dtype=np.uint8)
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bg_image[:] = BG_COLOR
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annotated_image = np.where(condition, annotated_image, bg_image)
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# Draw pose landmarks on the image.
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mp_drawing.draw_landmarks(
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annotated_image,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
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cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
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# Plot pose world landmarks.
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mp_drawing.plot_landmarks(
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results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)
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# For webcam input:
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cap = cv2.VideoCapture(0)
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with mp_pose.Pose(
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5) as pose:
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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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print("Ignoring empty camera frame.")
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# If loading a video, use 'break' instead of 'continue'.
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continue
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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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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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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,
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results.pose_landmarks,
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mp_pose.POSE_CONNECTIONS,
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landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
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# Flip the image horizontally for a selfie-view display.
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cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))
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if cv2.waitKey(5) & 0xFF == 27:
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break
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cap.release()
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```
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### JavaScript Solution API
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Please first see general [introduction](../getting_started/javascript.md) on
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MediaPipe in JavaScript, then learn more in the companion [web demo](#resources)
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and the following usage example.
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Supported configuration options:
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* [modelComplexity](#model_complexity)
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* [smoothLandmarks](#smooth_landmarks)
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* [enableSegmentation](#enable_segmentation)
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* [smoothSegmentation](#smooth_segmentation)
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* [minDetectionConfidence](#min_detection_confidence)
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* [minTrackingConfidence](#min_tracking_confidence)
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```html
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/camera_utils/camera_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils/control_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/control_utils_3d/control_utils_3d.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils/drawing_utils.js" crossorigin="anonymous"></script>
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<script src="https://cdn.jsdelivr.net/npm/@mediapipe/pose/pose.js" crossorigin="anonymous"></script>
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</head>
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<body>
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<div class="container">
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<video class="input_video"></video>
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<canvas class="output_canvas" width="1280px" height="720px"></canvas>
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<div class="landmark-grid-container"></div>
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</div>
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</body>
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</html>
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```
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```javascript
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<script type="module">
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const videoElement = document.getElementsByClassName('input_video')[0];
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const canvasElement = document.getElementsByClassName('output_canvas')[0];
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const canvasCtx = canvasElement.getContext('2d');
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const landmarkContainer = document.getElementsByClassName('landmark-grid-container')[0];
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const grid = new LandmarkGrid(landmarkContainer);
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function onResults(results) {
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if (!results.poseLandmarks) {
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grid.updateLandmarks([]);
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return;
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}
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canvasCtx.save();
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canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height);
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canvasCtx.drawImage(results.segmentationMask, 0, 0,
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canvasElement.width, canvasElement.height);
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// Only overwrite existing pixels.
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canvasCtx.globalCompositeOperation = 'source-in';
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canvasCtx.fillStyle = '#00FF00';
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canvasCtx.fillRect(0, 0, canvasElement.width, canvasElement.height);
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// Only overwrite missing pixels.
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canvasCtx.globalCompositeOperation = 'destination-atop';
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canvasCtx.drawImage(
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results.image, 0, 0, canvasElement.width, canvasElement.height);
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canvasCtx.globalCompositeOperation = 'source-over';
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drawConnectors(canvasCtx, results.poseLandmarks, POSE_CONNECTIONS,
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{color: '#00FF00', lineWidth: 4});
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drawLandmarks(canvasCtx, results.poseLandmarks,
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{color: '#FF0000', lineWidth: 2});
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canvasCtx.restore();
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grid.updateLandmarks(results.poseWorldLandmarks);
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}
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const pose = new Pose({locateFile: (file) => {
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return `https://cdn.jsdelivr.net/npm/@mediapipe/pose/${file}`;
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}});
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pose.setOptions({
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modelComplexity: 1,
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smoothLandmarks: true,
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enableSegmentation: true,
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smoothSegmentation: true,
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minDetectionConfidence: 0.5,
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minTrackingConfidence: 0.5
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});
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pose.onResults(onResults);
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const camera = new Camera(videoElement, {
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onFrame: async () => {
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await pose.send({image: videoElement});
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},
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width: 1280,
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height: 720
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});
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camera.start();
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||
</script>
|
||
```
|
||
|
||
## Example Apps
|
||
|
||
Please first see general instructions for
|
||
[Android](../getting_started/android.md), [iOS](../getting_started/ios.md), and
|
||
[desktop](../getting_started/cpp.md) on how to build MediaPipe examples.
|
||
|
||
Note: To visualize a graph, copy the graph and paste it into
|
||
[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
|
||
to visualize its associated subgraphs, please see
|
||
[visualizer documentation](../tools/visualizer.md).
|
||
|
||
### Mobile
|
||
|
||
#### Main Example
|
||
|
||
* Graph:
|
||
[`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
|
||
* Android target:
|
||
[(or download prebuilt ARM64 APK)](https://drive.google.com/file/d/17GFIrqEJS6W8UHKXlYevTtSCLxN9pWlY/view?usp=sharing)
|
||
[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu:posetrackinggpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/posetrackinggpu/BUILD)
|
||
* iOS target:
|
||
[`mediapipe/examples/ios/posetrackinggpu:PoseTrackingGpuApp`](http:/mediapipe/examples/ios/posetrackinggpu/BUILD)
|
||
|
||
### Desktop
|
||
|
||
Please first see general instructions for [desktop](../getting_started/cpp.md)
|
||
on how to build MediaPipe examples.
|
||
|
||
#### Main Example
|
||
|
||
* Running on CPU
|
||
* Graph:
|
||
[`mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_cpu.pbtxt)
|
||
* Target:
|
||
[`mediapipe/examples/desktop/pose_tracking:pose_tracking_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
|
||
* Running on GPU
|
||
* Graph:
|
||
[`mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/pose_tracking/pose_tracking_gpu.pbtxt)
|
||
* Target:
|
||
[`mediapipe/examples/desktop/pose_tracking:pose_tracking_gpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/pose_tracking/BUILD)
|
||
|
||
## Resources
|
||
|
||
* Google AI Blog:
|
||
[BlazePose - On-device Real-time Body Pose Tracking](https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html)
|
||
* Paper:
|
||
[BlazePose: On-device Real-time Body Pose Tracking](https://arxiv.org/abs/2006.10204)
|
||
([presentation](https://youtu.be/YPpUOTRn5tA))
|
||
* [Models and model cards](./models.md#pose)
|
||
* [GHUM & GHUML: Generative 3D Human Shape and Articulated Pose Models](https://github.com/google-research/google-research/tree/master/ghum)
|
||
* [Web demo](https://code.mediapipe.dev/codepen/pose)
|
||
* [Python Colab](https://mediapipe.page.link/pose_py_colab)
|
||
|
||
[`mAP`]: https://cocodataset.org/#keypoints-eval
|
||
[`PCK@0.2`]: https://github.com/cbsudux/Human-Pose-Estimation-101
|