public boolean hasNext() { resultMinMax = Core.minMaxLoc(result); currentScore = resultMinMax.maxVal; currentX = (int) resultMinMax.maxLoc.x; currentY = (int) resultMinMax.maxLoc.y; if (firstScore < 0) { firstScore = currentScore; } double targetScore = target.getScore(); double scoreMin = firstScore - scoreMaxDiff; if (currentScore > targetScore && currentScore > scoreMin) { return true; } return false; }
public static void LUT(Mat src, Mat lut, Mat dst) { LUT_0(src.nativeObj, lut.nativeObj, dst.nativeObj); return; }
public static double Mahalanobis(Mat v1, Mat v2, Mat icovar) { double retVal = Mahalanobis_0(v1.nativeObj, v2.nativeObj, icovar.nativeObj); return retVal; }
public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, int maxComponents) { PCACompute_1(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, maxComponents); return; }
public static void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat result) { PCAProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj); return; }
public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt, int flags) { SVDecomp_0(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj, flags); return; }
public static void SVBackSubst(Mat w, Mat u, Mat vt, Mat rhs, Mat dst) { SVBackSubst_0(w.nativeObj, u.nativeObj, vt.nativeObj, rhs.nativeObj, dst.nativeObj); return; }
public static void PCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat result) { PCABackProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj); return; }
public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt) { SVDecomp_1(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj); return; }
public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, double retainedVariance) { PCACompute_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, retainedVariance); return; }
public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, int maxComponents) { PCACompute_1(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, maxComponents); return; }
public static void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat result) { PCAProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj); return; }
public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt, int flags) { SVDecomp_0(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj, flags); return; }
public static void SVBackSubst(Mat w, Mat u, Mat vt, Mat rhs, Mat dst) { SVBackSubst_0(w.nativeObj, u.nativeObj, vt.nativeObj, rhs.nativeObj, dst.nativeObj); return; }
public static void PCABackProject(Mat data, Mat mean, Mat eigenvectors, Mat result) { PCABackProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj); return; }
public static void SVDecomp(Mat src, Mat w, Mat u, Mat vt) { SVDecomp_1(src.nativeObj, w.nativeObj, u.nativeObj, vt.nativeObj); return; }
public static void PCACompute(Mat data, Mat mean, Mat eigenvectors, double retainedVariance) { PCACompute_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, retainedVariance); return; }
public static MinMaxLocResult minMaxLoc(Mat src) { return minMaxLoc(src, null); }
public static void PCACompute(Mat data, Mat mean, Mat eigenvectors) { PCACompute_1(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj); return; }
public static void PCAProject(Mat data, Mat mean, Mat eigenvectors, Mat result) { PCAProject_0(data.nativeObj, mean.nativeObj, eigenvectors.nativeObj, result.nativeObj); return; }