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Machine Learning Sample Code with OpenCV 3.0 for Android
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| import android.util.Log; | |
| import org.opencv.core.CvType; | |
| import org.opencv.core.Mat; | |
| import org.opencv.ml.LogisticRegression; | |
| public class MLTest { | |
| private final static String TAG = MLTest.class.getSimpleName(); | |
| MLTest() { | |
| run(); | |
| } | |
| void run() { | |
| float[][] trainingData = new float[][]{{501f, 10f}, {255f, 10f}, {501f, 255f}, {10f, 501f}}; | |
| Mat trainingDataMat = new Mat(4, 2, CvType.CV_32FC1); | |
| for (int i = 0; i < trainingData.length; i++) { | |
| for (int j = 0; j < trainingData[i].length; j++) { | |
| trainingDataMat.put(i, j, trainingData[i][j]); | |
| } | |
| } | |
| float[] labels = new float[]{1.0f, 2.0f, 1.0f, 2.0f}; | |
| Mat labelsMat= new Mat(4, 1, CvType.CV_32FC1); | |
| for (int i = 0; i < trainingData.length; i++) { | |
| labelsMat.put(i, 0, labels[i]); | |
| } | |
| float[] testData = new float[]{502f, 10f}; | |
| Mat testDataMat = new Mat(1, 2, CvType.CV_32FC1); | |
| for (int i = 0; i < testData.length; i++) { | |
| testDataMat.put(0, i, testData[i]); | |
| } | |
| Mat resultMat = new Mat(1, 1, CvType.CV_32SC1); | |
| LogisticRegression clf = LogisticRegression.create(); | |
| clf.train(trainingDataMat, 0, labelsMat); | |
| clf.predict(testDataMat, resultMat, 0); | |
| Log.i(TAG, String.format("predicted: %f", (float)resultMat.get(0, 0)[0])); | |
| } | |
| } |
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