Java CommonOps.add方法代码示例

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 the homography based on a line and point on the plane
 * @param line Line on the plane
 * @param point Point on the plane
 */
public void process(PairLineNorm line, AssociatedPair point) {

	// t0 = (F*x) cross l'
	GeometryMath_F64.mult(F,point.p1,Fx);
	GeometryMath_F64.cross(Fx,line.getL2(),t0);
	// t1 = x' cross ((f*x) cross l')
	GeometryMath_F64.cross(point.p2, t0, t1);
	// t0 = x' cross e'
	GeometryMath_F64.cross(point.p2,e2,t0);

	double top = GeometryMath_F64.dot(t0,t1);
	double bottom = t0.normSq()*(line.l1.x*point.p1.x + line.l1.y*point.p1.y + line.l1.z);

	// e' * l^T
	GeometryMath_F64.outerProd(e2, line.l1, el);
	// cross(l')*F
	GeometryMath_F64.multCrossA(line.l2, F, lf);

	CommonOps.add(lf,top/bottom,el,H);

	// pick a good scale and sign for H
	adjust.adjust(H, point);
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * <p>
 * Trifocal tensor with point-line-point correspondence:<br>
 * (l2<sup>T</sup>(sum p1<sup>i</sup>*T<sub>i</sub>)[p3]<sub>x</sub> = 0
 * </p>
 *
 * @param tensor Trifocal tensor
 * @param p1 A point in the first view.
 * @param l2 A line in the second view.
 * @param p3 A point in the third view.
 * @return Result of applying the constraint.  With perfect inputs will be zero.
 */
public static Vector3D_F64 constraint(TrifocalTensor tensor,
									  Point2D_F64 p1, Vector3D_F64 l2, Point2D_F64 p3,
									  Vector3D_F64 ret)
{
	if( ret == null )
		ret = new Vector3D_F64();

	DenseMatrix64F sum = new DenseMatrix64F(3,3);

	CommonOps.add(p1.x,tensor.T1,sum,sum);
	CommonOps.add(p1.y,tensor.T2,sum,sum);
	CommonOps.add(tensor.T3,sum,sum);

	Vector3D_F64 tempV = new Vector3D_F64();
	GeometryMath_F64.multTran(sum, l2, tempV);

	GeometryMath_F64.cross(tempV, new Vector3D_F64(p3.x, p3.y, 1), ret);

	return ret;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
public Matrix plus(double value) {
	DenseMatrix64F ret = new DenseMatrix64F(matrix.numRows, matrix.numCols);
	CommonOps.add(matrix, value, ret);
	Matrix result = new EJMLDenseDoubleMatrix2D(ret);
	MapMatrix<String, Object> a = getMetaData();
	if (a != null) {
		result.setMetaData(a.clone());
	}
	return result;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
public Matrix minus(double value) {
	DenseMatrix64F ret = new DenseMatrix64F(matrix.numRows, matrix.numCols);
	CommonOps.add(matrix, -value, ret);
	Matrix result = new EJMLDenseDoubleMatrix2D(ret);
	MapMatrix<String, Object> a = getMetaData();
	if (a != null) {
		result.setMetaData(a.clone());
	}
	return result;
}
 

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
/**
 * <p>
 * Given a fundamental matrix a pair of projection matrices [R|T] can be extracted.  There are multiple
 * solutions which can be found, the canonical projection matrix is defined as: <br>
 * <pre>
 * P=[I|0] and P'= [M|-M*t] = [[e']*F + e'*v^t | lambda*e']
 * </pre>
 * where e' is the epipole F<sup>T</sup>e' = 0, [e'] is the cross product matrix for the enclosed vector,
 * v is an arbitrary 3-vector and lambda is a non-zero scalar.
 * </p>
 *
 * <p>
 * Page 256 in R. Hartley, and A. Zisserman, "Multiple View Geometry in Computer Vision", 2nd Ed, Cambridge 2003
 * </p>
 *
 * @see #extractEpipoles
 *
 * @param F A fundamental matrix
 * @param v Arbitrary 3-vector.  Just pick some value, say (1,1,1).
 * @param lambda A non zero scalar.  Try one.
 * @param e2 Left epipole of fundamental matrix, F<sup>T</sup>*e2 = 0.
 * @return The canonical camera matrix P'
 */
public static DenseMatrix64F canonicalCamera( DenseMatrix64F F , Point3D_F64 e2, Vector3D_F64 v , double lambda ) {

	DenseMatrix64F crossMatrix = new DenseMatrix64F(3,3);
	GeometryMath_F64.crossMatrix(e2, crossMatrix);

	DenseMatrix64F outer = new DenseMatrix64F(3,3);
	GeometryMath_F64.outerProd(e2,v,outer);

	DenseMatrix64F KR = new DenseMatrix64F(3,3);
	CommonOps.mult(crossMatrix, F, KR);
	CommonOps.add(KR, outer, KR);

	DenseMatrix64F P = new DenseMatrix64F(3,4);
	CommonOps.insert(KR,P,0,0);

	P.set(0,3,lambda*e2.x);
	P.set(1,3,lambda*e2.y);
	P.set(2,3,lambda*e2.z);

	return P;
}