Porting build from CMake to Android.mk

Builds within the android source tree all opencv modules by default.

1 files changed, 290 insertions, 0 deletions

diff --git a/modules/java/src/ml+EM.java b/modules/java/src/ml+EM.java
new file mode 100644
index 0000000..0b6b454
--- /dev/null
+++ b/modules/java/src/ml+EM.java
@@ -0,0 +1,290 @@
+
+//
+// This file is auto-generated. Please don't modify it!
+//
+package org.opencv.ml;
+
+import org.opencv.core.Mat;
+import org.opencv.core.TermCriteria;
+
+// C++: class EM
+//javadoc: EM
+public class EM extends StatModel {
+
+    protected EM(long addr) { super(addr); }
+
+
+    public static final int
+            COV_MAT_SPHERICAL = 0,
+            COV_MAT_DIAGONAL = 1,
+            COV_MAT_GENERIC = 2,
+            COV_MAT_DEFAULT = COV_MAT_DIAGONAL,
+            DEFAULT_NCLUSTERS = 5,
+            DEFAULT_MAX_ITERS = 100,
+            START_E_STEP = 1,
+            START_M_STEP = 2,
+            START_AUTO_STEP = 0;
+
+
+    //
+    // C++:  int getClustersNumber()
+    //
+
+    //javadoc: EM::getClustersNumber()
+    public  int getClustersNumber()
+    {
+        
+        int retVal = getClustersNumber_0(nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  void setClustersNumber(int val)
+    //
+
+    //javadoc: EM::setClustersNumber(val)
+    public  void setClustersNumber(int val)
+    {
+        
+        setClustersNumber_0(nativeObj, val);
+        
+        return;
+    }
+
+
+    //
+    // C++:  int getCovarianceMatrixType()
+    //
+
+    //javadoc: EM::getCovarianceMatrixType()
+    public  int getCovarianceMatrixType()
+    {
+        
+        int retVal = getCovarianceMatrixType_0(nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  void setCovarianceMatrixType(int val)
+    //
+
+    //javadoc: EM::setCovarianceMatrixType(val)
+    public  void setCovarianceMatrixType(int val)
+    {
+        
+        setCovarianceMatrixType_0(nativeObj, val);
+        
+        return;
+    }
+
+
+    //
+    // C++:  TermCriteria getTermCriteria()
+    //
+
+    //javadoc: EM::getTermCriteria()
+    public  TermCriteria getTermCriteria()
+    {
+        
+        TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  void setTermCriteria(TermCriteria val)
+    //
+
+    //javadoc: EM::setTermCriteria(val)
+    public  void setTermCriteria(TermCriteria val)
+    {
+        
+        setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
+        
+        return;
+    }
+
+
+    //
+    // C++:  Mat getWeights()
+    //
+
+    //javadoc: EM::getWeights()
+    public  Mat getWeights()
+    {
+        
+        Mat retVal = new Mat(getWeights_0(nativeObj));
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  Mat getMeans()
+    //
+
+    //javadoc: EM::getMeans()
+    public  Mat getMeans()
+    {
+        
+        Mat retVal = new Mat(getMeans_0(nativeObj));
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  Vec2d predict2(Mat sample, Mat& probs)
+    //
+
+    //javadoc: EM::predict2(sample, probs)
+    public  double[] predict2(Mat sample, Mat probs)
+    {
+        
+        double[] retVal = predict2_0(nativeObj, sample.nativeObj, probs.nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    //
+
+    //javadoc: EM::trainEM(samples, logLikelihoods, labels, probs)
+    public  boolean trainEM(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)
+    {
+        
+        boolean retVal = trainEM_0(nativeObj, samples.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
+        
+        return retVal;
+    }
+
+    //javadoc: EM::trainEM(samples)
+    public  boolean trainEM(Mat samples)
+    {
+        
+        boolean retVal = trainEM_1(nativeObj, samples.nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    //
+
+    //javadoc: EM::trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs)
+    public  boolean trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)
+    {
+        
+        boolean retVal = trainE_0(nativeObj, samples.nativeObj, means0.nativeObj, covs0.nativeObj, weights0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
+        
+        return retVal;
+    }
+
+    //javadoc: EM::trainE(samples, means0)
+    public  boolean trainE(Mat samples, Mat means0)
+    {
+        
+        boolean retVal = trainE_1(nativeObj, samples.nativeObj, means0.nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++:  bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    //
+
+    //javadoc: EM::trainM(samples, probs0, logLikelihoods, labels, probs)
+    public  boolean trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)
+    {
+        
+        boolean retVal = trainM_0(nativeObj, samples.nativeObj, probs0.nativeObj, logLikelihoods.nativeObj, labels.nativeObj, probs.nativeObj);
+        
+        return retVal;
+    }
+
+    //javadoc: EM::trainM(samples, probs0)
+    public  boolean trainM(Mat samples, Mat probs0)
+    {
+        
+        boolean retVal = trainM_1(nativeObj, samples.nativeObj, probs0.nativeObj);
+        
+        return retVal;
+    }
+
+
+    //
+    // C++: static Ptr_EM create()
+    //
+
+    //javadoc: EM::create()
+    public static EM create()
+    {
+        
+        EM retVal = new EM(create_0());
+        
+        return retVal;
+    }
+
+
+    @Override
+    protected void finalize() throws Throwable {
+        delete(nativeObj);
+    }
+
+
+
+    // C++:  int getClustersNumber()
+    private static native int getClustersNumber_0(long nativeObj);
+
+    // C++:  void setClustersNumber(int val)
+    private static native void setClustersNumber_0(long nativeObj, int val);
+
+    // C++:  int getCovarianceMatrixType()
+    private static native int getCovarianceMatrixType_0(long nativeObj);
+
+    // C++:  void setCovarianceMatrixType(int val)
+    private static native void setCovarianceMatrixType_0(long nativeObj, int val);
+
+    // C++:  TermCriteria getTermCriteria()
+    private static native double[] getTermCriteria_0(long nativeObj);
+
+    // C++:  void setTermCriteria(TermCriteria val)
+    private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
+
+    // C++:  Mat getWeights()
+    private static native long getWeights_0(long nativeObj);
+
+    // C++:  Mat getMeans()
+    private static native long getMeans_0(long nativeObj);
+
+    // C++:  Vec2d predict2(Mat sample, Mat& probs)
+    private static native double[] predict2_0(long nativeObj, long sample_nativeObj, long probs_nativeObj);
+
+    // C++:  bool trainEM(Mat samples, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    private static native boolean trainEM_0(long nativeObj, long samples_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
+    private static native boolean trainEM_1(long nativeObj, long samples_nativeObj);
+
+    // C++:  bool trainE(Mat samples, Mat means0, Mat covs0 = Mat(), Mat weights0 = Mat(), Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    private static native boolean trainE_0(long nativeObj, long samples_nativeObj, long means0_nativeObj, long covs0_nativeObj, long weights0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
+    private static native boolean trainE_1(long nativeObj, long samples_nativeObj, long means0_nativeObj);
+
+    // C++:  bool trainM(Mat samples, Mat probs0, Mat& logLikelihoods = Mat(), Mat& labels = Mat(), Mat& probs = Mat())
+    private static native boolean trainM_0(long nativeObj, long samples_nativeObj, long probs0_nativeObj, long logLikelihoods_nativeObj, long labels_nativeObj, long probs_nativeObj);
+    private static native boolean trainM_1(long nativeObj, long samples_nativeObj, long probs0_nativeObj);
+
+    // C++: static Ptr_EM create()
+    private static native long create_0();
+
+    // native support for java finalize()
+    private static native void delete(long nativeObj);
+
+}