MediaPipe是谷歌推出的一套构建计算机视觉和机器学习管道的框架,可以在移动设备上实时处理视频流。以下是使用MediaPipe的基本步骤:
- 安装依赖库
首先,需要在项目的build.gradle文件中添加MediaPipe的依赖项:
dependencies { implementation 'com.google.mediapipe:mediapipe:' }
其中,
- 导入MediaPipe包
在代码中导入MediaPipe相关的包:
import com.google.mediapipe.framework.MediaPipe; import com.google.mediapipe.framework.Pipeline; import com.google.mediapipe.pipeline.Graph; import com.google.mediapipe.pipeline.InputStream; import com.google.mediapipe.pipeline.OutputStream; import com.google.mediapipe.solution.FaceMesh; import com.google.mediapipe.solution.PoseLandmark;
- 创建Graph对象
创建一个Graph对象,用于定义处理流程:
Graph graph = new Graph();
- 添加解耦模块
根据需要添加解耦模块,例如FaceMesh和PoseLandmark等:
// 添加FaceMesh模块 InputStream faceMeshInputStream = graph.addInputStream("input_video", InputStream.BufferFormat.RGB_24); FaceMesh faceMesh = new FaceMesh(graph); faceMesh.setOrientation(true); faceMesh.setLandmarkMode(FaceMesh.LandmarkMode.ALL); faceMesh.initialize(); // 添加PoseLandmark模块 InputStream poseLandmarkInputStream = graph.addInputStream("input_video", InputStream.BufferFormat.RGB_24); PoseLandmark poseLandmark = new PoseLandmark(graph); poseLandmark.setTrackingMode(PoseLandmark.TrackingMode.TRACKING); poseLandmark.initialize();
- 运行Graph
将输入流与解耦模块连接,并运行Graph:
// 连接输入流和解耦模块 faceMesh.setInput(faceMeshInputStream); poseLandmark.setInput(poseLandmarkInputStream); // 运行Graph graph.run();
- 获取输出数据
通过解耦模块的输出端口获取处理后的数据:
// 获取FaceMesh的输出数据 ListfaceMeshVertices = faceMesh.getVertices(); // 获取PoseLandmark的输出数据 List poseLandmarks = poseLandmark.getLandmarks();
以上是使用MediaPipe的基本步骤,具体使用时需要根据实际需求进行调整。