MediaPipe是谷歌推出的一套構建計算機視覺和機器學習管道的框架,可以在移動設備上實時處理視頻流。以下是使用MediaPipe的基本步驟:
首先,需要在項目的build.gradle文件中添加MediaPipe的依賴項:
dependencies {
implementation 'com.google.mediapipe:mediapipe:<version>'
}
其中,
在代碼中導入MediaPipe相關的包:
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;
創建一個Graph對象,用于定義處理流程:
Graph graph = new Graph();
根據需要添加解耦模塊,例如FaceMesh和PoseLandmark等:
// 添加FaceMesh模塊
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模塊
InputStream poseLandmarkInputStream = graph.addInputStream("input_video", InputStream.BufferFormat.RGB_24);
PoseLandmark poseLandmark = new PoseLandmark(graph);
poseLandmark.setTrackingMode(PoseLandmark.TrackingMode.TRACKING);
poseLandmark.initialize();
將輸入流與解耦模塊連接,并運行Graph:
// 連接輸入流和解耦模塊
faceMesh.setInput(faceMeshInputStream);
poseLandmark.setInput(poseLandmarkInputStream);
// 運行Graph
graph.run();
通過解耦模塊的輸出端口獲取處理后的數據:
// 獲取FaceMesh的輸出數據
List<float[]> faceMeshVertices = faceMesh.getVertices();
// 獲取PoseLandmark的輸出數據
List<float[]> poseLandmarks = poseLandmark.getLandmarks();
以上是使用MediaPipe的基本步驟,具體使用時需要根據實際需求進行調整。