Java CommonOps.multTransA方法代码示例

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Compute the direction of optimization from the value vector and the Jacobean. This is where
 * Gauss Newton really happens.
 */
private double[] computeDirection() {
    DenseMatrix64F jacoMat = new DenseMatrix64F(jacobean);

    // J^T J (nrOfCo x nrOfCo)
    DenseMatrix64F jacoSquare = new DenseMatrix64F(nrOfVariables, nrOfVariables);
    CommonOps.multInner(jacoMat, jacoSquare);


    DenseMatrix64F fMat = new DenseMatrix64F(nrOfEquations, 1, true, values);
    // J^T value (nrOfCo x 1)
    DenseMatrix64F rhs = new DenseMatrix64F(nrOfVariables, 1);
    CommonOps.multTransA(jacoMat, fMat, rhs);

    // result = (J^TJ)^-1 J^T value (nrOfCo x 1)
    // J^TJ result = J^T value
    DenseMatrix64F result = new DenseMatrix64F(nrOfVariables, 1);
    if (CommonOps.solve(jacoSquare, rhs, result)) {
        return result.getData();
    } else {
        return null;
    }
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from eigen space into sample space.
 *
 * @param eigenData Eigen space data.
 * @return Sample space projection.
 */
public double[] eigenToSampleSpace(double[] eigenData) {
    if (eigenData.length != getNumPrincipalComponents()) {
        throw new IllegalArgumentException("Unexpected sample length");
    }

    DenseMatrix64F s = new DenseMatrix64F(getDataMatrix().getNumCols(), 1);
    DenseMatrix64F r = DenseMatrix64F.wrap(getNumPrincipalComponents(), 1, eigenData);

    CommonOps.multTransA(getPrincipalComponentSubspace(), r, s);

    DenseMatrix64F mean = DenseMatrix64F.wrap(getDataMatrix().getNumCols(), 1, this.getMean());
    CommonOps.add(s, mean, s);

    return s.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Computes a Fundamental matrix given an Essential matrix and the camera calibration matrix.
 *
 * F = (K2<sup>-1</sup>)<sup>T</sup>*E*K1<sup>-1</sup>
 *
 * @param E Essential matrix
 * @param K1 Intrinsic camera calibration matrix for camera 1
 * @param K2 Intrinsic camera calibration matrix for camera 2
 * @return Fundamental matrix
 */
public static DenseMatrix64F createFundamental(DenseMatrix64F E,
											   DenseMatrix64F K1,  DenseMatrix64F K2) {
	DenseMatrix64F K1_inv = new DenseMatrix64F(3,3);
	CommonOps.invert(K1,K1_inv);
	DenseMatrix64F K2_inv = new DenseMatrix64F(3,3);
	CommonOps.invert(K2,K2_inv);

	DenseMatrix64F F = new DenseMatrix64F(3,3);
	DenseMatrix64F temp = new DenseMatrix64F(3,3);

	CommonOps.multTransA(K2_inv,E,temp);
	CommonOps.mult(temp,K1_inv,F);

	return F;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from eigen space into sample space.
 *
 * @param eigenData Eigen space data.
 * @return Sample space projection.
 */
public double[] eigenToSampleSpace( double[] eigenData ) {
    if( eigenData.length != numComponents )
        throw new IllegalArgumentException("Unexpected sample length");

    DenseMatrix64F s = new DenseMatrix64F(A.getNumCols(),1);
    DenseMatrix64F r = DenseMatrix64F.wrap(numComponents,1,eigenData);

    CommonOps.multTransA(V_t,r,s);

    DenseMatrix64F mean = DenseMatrix64F.wrap(A.getNumCols(),1,this.mean);
    CommonOps.add(s,mean,s);

    return s.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from eigen space into sample space.
 *
 * @param eigenData Eigen space data.
 * @return Sample space projection.
 */
public double[] eigenToSampleSpace( double[] eigenData ) {
   if( eigenData.length != numComponents )
      throw new IllegalArgumentException("Unexpected sample length");

   DenseMatrix64F s = new DenseMatrix64F(A.getNumCols(),1);
   DenseMatrix64F r = DenseMatrix64F.wrap(numComponents,1,eigenData);
   
   CommonOps.multTransA(V_t,r,s);

   DenseMatrix64F mean = DenseMatrix64F.wrap(A.getNumCols(),1,this.mean);
   CommonOps.add(s,mean,s);

   return s.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
@Override
public int computeResiduals(AssociatedPair p, double[] residuals, int index) {

	GeometryMath_F64.mult(H, p.p1, temp);

	double top1 = error1(p.p1.x,p.p1.y,p.p2.x,p.p2.y);
	double top2 = error2(p.p1.x, p.p1.y, p.p2.x, p.p2.y);

	computeJacobian(p.p1,p.p2);
	// JJ = J*J'
	CommonOps.multTransB(J, J, JJ);

	// solve JJ'*x = -e
	e.data[0] = -top1;
	e.data[1] = -top2;

	if( solver.setA(JJ) ) {
		solver.solve(e,x);
		// -J'(J*J')^-1*e
		CommonOps.multTransA(J,x,error);
		residuals[index++] = error.data[0];
		residuals[index++] = error.data[1];
		residuals[index++] = error.data[2];
		residuals[index++] = error.data[3];
	} else {
		residuals[index++] = 0;
		residuals[index++] = 0;
		residuals[index++] = 0;
		residuals[index++] = 0;
	}

	return index;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
@Test
public void decomposeEssential() {
	DenseMatrix64F R = RotationMatrixGenerator.eulerXYZ(1,2,-0.5,null);
	Vector3D_F64 T = new Vector3D_F64(0.5,0.7,-0.3);

	DenseMatrix64F E = MultiViewOps.createEssential(R,T);

	List<Se3_F64> found = MultiViewOps.decomposeEssential(E);

	// the scale factor is lost
	T.normalize();

	int numMatched = 0;

	for( Se3_F64 m : found ) {
		DenseMatrix64F A = new DenseMatrix64F(3,3);

		CommonOps.multTransA(R,m.getR(),A);

		if( !MatrixFeatures.isIdentity(A,1e-8) ) {
			continue;
		}

		Vector3D_F64 foundT = m.getT();
		foundT.normalize();

		if( foundT.isIdentical(T,1e-8) )
			numMatched++;
	}

	assertEquals(1,numMatched);
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Undo the normalization done to the input matrices for a Fundamental matrix.
 * <br>
 * M = N<sub>2</sub><sup>T</sup>*M*N<sub>1</sub>
 *
 * @param M Either the homography or fundamental matrix computed from normalized points.
 * @param N1 normalization matrix.
 * @param N2 normalization matrix.
 */
protected void undoNormalizationF(DenseMatrix64F M, DenseMatrix64F N1, DenseMatrix64F N2) {
	// M = N2^T * M * N1
	CommonOps.multTransA(N2,M,temp0);
	CommonOps.mult(temp0,N1,M);
}