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项目作者： cansik

项目描述 ：
MediaPipe examples which stream their detections over OSC.
高级语言： Python
项目地址: git://github.com/cansik/mediapipe-osc.git
创建时间： 2020-12-08T10:12:39Z
项目社区：https://github.com/cansik/mediapipe-osc
开源协议：
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MediaPipe OSC

MediaPipe examples which stream their detections over OSC to be used in other applications.

Install & Run

Currently this is only tested on Windows and MacOS. It’s recommended to use Python3 (>3.7) and a virtual environment.

  1. python install -r requirements.txt

To run an example use the basic python command to start up the script.

  1. # start pose detection with webcam 0
  2. python pose.py --input 0
  4. # start pose detection with video
  5. python pose.py --input yoga.mp4

Other parameters are documented in the following list or algorithm specific.

  • input - The video input path or video camera id (default 0)
  • min-detection-confidence - Minimum confidence value ([0.0, 1.0]) for the detection to be considered successful. (default 0.5)
  • min-tracking-confidence - Minimum confidence value ([0.0, 1.0]) to be considered tracked successfully. (default 0.5)
  • ip - OSC ip address to send to (default 127.0.0.1)
  • port - OSC port to send to (default 7500)

Full-Body Pose Landmark Model (BlazePose Tracker)

The landmark model currently included in MediaPipe Pose predicts the location of 33 full-body landmarks (see figure below), each with (x, y, z, visibility). Note that the z value should be discarded as the model is currently not fully trained to predict depth, but this is something we have on the roadmap.

Pose Description

Reference: mediapipe/solutions/pose

Additional Parameters

  1. --model-complexity MODEL_COMPLEXITY
  2.                       Set model complexity (0=Light, 1=Full, 2=Heavy).
  3. --no-smooth-landmarks
  4.                       Disable landmark smoothing.
  5. --static-image-mode   Enables static image mode.

Format

  • count - Indicates how many poses are detected (currently only 0 or 1)
  • list of landmarks (33 per pose) (if pose has been detected)
    • x - X-Position of the landmark
    • y - Y-Position of the landmark
    • z - Z-Position of the landmark
    • visibility - Visibility of the landmark
  1. /mediapipe/pose [count, x, y, z, visibility, x, y, z, visibility ...]

Hand Detection

The hand detection model is able to detect and track 21 3D landmarks.

Format

  • count - Indicates how many hands are detected
  • list of landmarks (21 per hand) (if hands has been detected)
    • x - X-Position of the landmark
    • y - Y-Position of the landmark
    • z - Z-Position of the landmark
    • visibility - Visibility of the landmark
  1. /mediapipe/hands [count, x, y, z, visibility, x, y, z, visibility ...]

Face Detection

The face detection model is able to detect multiple faces and 5 keypoints. At the moment only the bounding box is sent over OSC.

Format

All values are normalized to the image width and height.

  • count - Indicates how many faces are detected
  • list of one bounding box per face (if faces has been detected)
    • xmin - X-Position of the top-left bounding box anchor
    • ymin - Y-Position of the top-left bounding box anchor
    • width - Width of the bounding box
    • height - Height of the bounding box
    • score - Confidence score of the bounding box
  1. /mediapipe/faces [count, xmin, ymin, width, height, score, xmin, ymin, width, height, score ...]

Face Mesh

tbd

Examples

Currently, there are very basic receiver examples for processing. Check out the examples folder.

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