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08.Mediapipe development

Mediapipe development

Mediapipe development1. Introduction2. Use2.1. Hand detection2.2, Posture detection2.3. Overall detection2.4, Face Detection2.5, Face Recognition2.6, Face effects2.7, 3D object recognition2.8, Brush2.9, Finger control2.10, Gesture Recognition

1. Introduction

MediaPipe is a data stream processing machine learning application development framework developed and open sourced by Google. It is a graph-based data processing pipeline for building applications that use a variety of data sources, such as video, audio, sensor data, and any time series data. MediaPipe is cross-platform and can run on embedded platforms (Raspberry Pi, etc.), mobile devices (iOS and Android), workstations, and servers, and supports mobile GPU acceleration. MediaPipe provides cross-platform, customizable ML solutions for real-time and streaming media.

The core framework of MediaPipe is implemented in C++ and provides support for languages such as Java and Objective C. The main concepts of MediaPipe include packets, streams, calculators, graphs, and subgraphs.

Features of MediaPipe:

End-to-end acceleration: built-in fast ML inference and processing can be accelerated even on ordinary hardware.
Build once, deploy anywhere: unified solution for Android, iOS, desktop/cloud, web, and IoT.
Ready-to-use solutions: cutting-edge ML solutions that demonstrate the full capabilities of the framework.
Free and open source: Framework and solution under Apache2.0, fully extensible and customizable.

Deep learning solution in MediaPipe

	Android	iOS	C++	Python	JS	Coral
Face Detection	✅	✅	✅	✅	✅	✅
Face Mesh	✅	✅	✅	✅	✅
Iris	✅	✅	✅
Hands	✅	✅	✅	✅	✅
Pose	✅	✅	✅	✅	✅
Holistic	✅	✅	✅	✅	✅
Selfie Segmentation	✅	✅	✅	✅	✅
Hair Segmentation	✅		✅
Object Detection	✅	✅	✅			✅
Box Tracking	✅	✅	✅
Instant Motion Tracking	✅
Objectron	✅		✅	✅	✅
KNIFT	✅
AutoFlip			✅
MediaSequence			✅
YouTube 8M			✅

2. Use

You need to enter Docker to run the case. The ROS1 environment is located in the Docker image.


sh ~/ros_melodic.sh