Java CommonOps.mult方法代码示例

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Create a 3x4 camera matrix. For calibrated camera P = [R|T].  For uncalibrated camera it is P = K*[R|T].
 *
 * @param R Rotation matrix. 3x3
 * @param T Translation vector.
 * @param K Optional camera calibration matrix 3x3.
 * @param ret Storage for camera calibration matrix. If null a new instance will be created.
 * @return Camera calibration matrix.
 */
public static DenseMatrix64F createCameraMatrix( DenseMatrix64F R , Vector3D_F64 T , DenseMatrix64F K ,
												 DenseMatrix64F ret ) {
	if( ret == null )
		ret = new DenseMatrix64F(3,4);

	CommonOps.insert(R,ret,0,0);

	ret.data[3] = T.x;
	ret.data[7] = T.y;
	ret.data[11] = T.z;

	if( K == null )
		return ret;

	DenseMatrix64F temp = new DenseMatrix64F(3,4);
	CommonOps.mult(K,ret,temp);

	ret.set(temp);

	return ret;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * There are four possible reconstructions from an essential matrix.  This function will compute different
 * permutations depending on optionA and optionB being true or false.
 */
private void extractTransform( DenseMatrix64F U , DenseMatrix64F V , DenseMatrix64F S ,
							   Se3_F64 se , boolean optionA , boolean optionB )
{
	DenseMatrix64F R = se.getR();
	Vector3D_F64 T = se.getT();

	// extract rotation
	if( optionA )
		CommonOps.mult(U,Rz,temp);
	else
		CommonOps.multTransB(U,Rz,temp);
	CommonOps.multTransB(temp,V,R);

	// extract screw symmetric translation matrix
	if( optionB )
		CommonOps.multTransB(U,Rz,temp);
	else
		CommonOps.mult(U,Rz,temp);
	CommonOps.mult(temp,S,temp2);
	CommonOps.multTransB(temp2,U,temp);

	T.x = temp.get(2,1);
	T.y = temp.get(0,2);
	T.z = temp.get(1,0);
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Projects the found estimate of E onto essential space.
 *
 * @return true if svd returned true.
 */
protected boolean projectOntoEssential( DenseMatrix64F E ) {
	if( !svdConstraints.decompose(E) ) {
		return false;
	}
	svdV = svdConstraints.getV(svdV,false);
	svdU = svdConstraints.getU(svdU,false);
	svdS = svdConstraints.getW(svdS);

	SingularOps.descendingOrder(svdU, false, svdS, svdV, false);

	// project it into essential space
	// the scale factor is arbitrary, but the first two singular values need
	// to be the same.  so just set them to one
	svdS.unsafe_set(0, 0, 1);
	svdS.unsafe_set(1, 1, 1);
	svdS.unsafe_set(2, 2, 0);

	// recompute F
	CommonOps.mult(svdU, svdS, temp0);
	CommonOps.multTransB(temp0,svdV, E);

	return true;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Projects the found estimate of F onto Fundamental space.
 *
 * @return true if svd returned true.
 */
protected boolean projectOntoFundamentalSpace( DenseMatrix64F F ) {
	if( !svdConstraints.decompose(F) ) {
		return false;
	}
	svdV = svdConstraints.getV(svdV,false);
	svdU = svdConstraints.getU(svdU,false);
	svdS = svdConstraints.getW(svdS);

	SingularOps.descendingOrder(svdU, false, svdS, svdV, false);

	// the smallest singular value needs to be set to zero, unlike
	svdS.set(2, 2, 0);

	// recompute F
	CommonOps.mult(svdU, svdS, temp0);
	CommonOps.multTransB(temp0,svdV, F);

	return true;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * <p>
 * Trifocal tensor with point-point-line correspondence:<br>
 * [p2]<sub>x</sub>(sum p1<sup>i</sup>*T<sub>i</sub>)*l3 = 0
 * </p>
 *
 * @param tensor Trifocal tensor
 * @param p1 A point in the first view.
 * @param p2 A point in the second view.
 * @param l3 A line 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, Point2D_F64 p2, Vector3D_F64 l3,
									  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);

	DenseMatrix64F cross2 = GeometryMath_F64.crossMatrix(p2.x,p2.y,1,null);

	DenseMatrix64F temp = new DenseMatrix64F(3,3);

	CommonOps.mult(cross2,sum,temp);
	GeometryMath_F64.mult(temp, l3, ret);

	return ret;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * <p>
 * Computes a homography matrix from a rotation, translation, plane normal, plane distance, and
 * calibration matrix:<br>
 * H = K*(R+(1/d)*T*N<sup>T</sup>)*K<sup>-1</sup>
 * </p>
 *
 * @param R Rotation matrix.
 * @param T Translation vector.
 * @param d Distance of closest point on plane to camera
 * @param N Normal of plane
 * @param K Intrinsic calibration matrix
 * @return Uncalibrated homography matrix
 */
public static DenseMatrix64F createHomography(DenseMatrix64F R, Vector3D_F64 T,
											  double d, Vector3D_F64 N,
											  DenseMatrix64F K)
{
	DenseMatrix64F temp = new DenseMatrix64F(3,3);
	DenseMatrix64F K_inv = new DenseMatrix64F(3,3);

	DenseMatrix64F H = createHomography(R, T, d, N);

	// apply calibration matrix to R
	CommonOps.mult(K,H,temp);

	CommonOps.invert(K,K_inv);
	CommonOps.mult(temp,K_inv,H);

	return H;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
public Matrix mtimes(Matrix m) {
	if (m instanceof EJMLDenseDoubleMatrix2D) {
		DenseMatrix64F b = ((EJMLDenseDoubleMatrix2D) m).getWrappedObject();
		DenseMatrix64F ret = new DenseMatrix64F(matrix.numRows, b.numCols);
		CommonOps.mult(matrix, b, ret);
		return new EJMLDenseDoubleMatrix2D(ret);
	} else {
		return super.mtimes(m);
	}
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from sample space into eigen space.
 *
 * @param sampleData Sample space data.
 * @return Eigen space projection.
 */
public double[] sampleToEigenSpace( double[] sampleData ) {
    if( sampleData.length != A.getNumCols() )
        throw new IllegalArgumentException("Unexpected sample length");
    DenseMatrix64F mean = DenseMatrix64F.wrap(A.getNumCols(),1,this.mean);

    DenseMatrix64F s = new DenseMatrix64F(A.getNumCols(),1,true,sampleData);
    DenseMatrix64F r = new DenseMatrix64F(numComponents,1);

    CommonOps.subtract(s, mean, s);

    CommonOps.mult(V_t,s,r);

    return r.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Computes the dot product of each basis vector against the sample.  Can be used as a measure
 * for membership in the training sample set.  High values correspond to a better fit.
 *
 * @param sample Sample of original data.
 * @return Higher value indicates it is more likely to be a member of input dataset.
 */
public double response( double[] sample ) {
    if( sample.length != A.numCols )
        throw new IllegalArgumentException("Expected input vector to be in sample space");

    DenseMatrix64F dots = new DenseMatrix64F(numComponents,1);
    DenseMatrix64F s = DenseMatrix64F.wrap(A.numCols,1,sample);

    CommonOps.mult(V_t,s,dots);

    return NormOps.normF(dots);
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
private double getExponentTerm(double[] features) {
    DenseMatrix64F ft =new DenseMatrix64F(1,features.length);

    for(int i=0;i<features.length;i++){
        ft.set(0,i,features[i]-avg[i]);
    }

    DenseMatrix64F ft2=new DenseMatrix64F(1,features.length);
    CommonOps.mult(ft, inv, ft2);
    DenseMatrix64F ft3=new DenseMatrix64F(1,1);
    CommonOps.multTransB(1, ft2, ft, ft3);

    return FastMath.exp(-0.5 *ft3.get(0,0));
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * 
 * @param R
 * @param center
 * @return
 */
public static DenseMatrix64F rotationCenteredMtx( DenseMatrix64F R, double[] center)
{
	int ndims = center.length;
	
	logger.debug("R " + R);
	
	DenseMatrix64F affineXfm = new DenseMatrix64F( ndims + 1, ndims + 1 ); 
	DenseMatrix64F res = new DenseMatrix64F( ndims, 1 );
	
	DenseMatrix64F ctrVec = new DenseMatrix64F( ndims, 1 );
	
	ctrVec.setData( center );
	CommonOps.changeSign(ctrVec);
	
	CommonOps.mult(R, ctrVec, res);
	
	CommonOps.insert(  R, affineXfm, 0, 0);
	CommonOps.insert(res, affineXfm, 0, ndims);
	affineXfm.set( ndims, ndims, 1 );
	
	CommonOps.changeSign(ctrVec);
	for (int i = 0; i < ndims; i++) {
		affineXfm.set(i, ndims, 
				affineXfm.get(i,ndims) + ctrVec.get(i));
	}
	
	return affineXfm;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
	 * Computes a rotation transformation from the source to target.
	 * @param src
	 * @param tgt
	 * @return
	 */
	public static <T extends RealType<T>> AffineTransform rotationPca(
			IterableInterval<T> src, IterableInterval<T> tgt ){
		
		int ndims = src.numDimensions();
		
		ImgMoment srcMom = new ImgMoment();
		double[] srcOr = srcMom.orientation(src);
		srcMom.orientationEvalsEvecs(srcOr);
		
		ImgMoment tgtMom = new ImgMoment();
		double[] tgtOr = tgtMom.orientation(tgt);
		tgtMom.orientationEvalsEvecs(tgtOr);
		
		DenseMatrix64F srcR = new DenseMatrix64F( ndims, ndims );
		srcR.setData( srcMom.getEvecs() );
		DenseMatrix64F srcMtx = rotationCenteredMtx(srcR, srcMom.centroid());
		
		DenseMatrix64F tgtR = new DenseMatrix64F( ndims, ndims );
		tgtR.setData( tgtMom.getEvecs() );
		DenseMatrix64F tgtMtx = rotationCenteredMtx(tgtR, tgtMom.centroid());
		
		DenseMatrix64F res =  new DenseMatrix64F( ndims+1, ndims+1 );
//		CommonOps.invert(srcMtx);
//		CommonOps.mult( tgtMtx, srcMtx, res );
		
		CommonOps.invert(tgtMtx);
		CommonOps.mult( srcMtx, tgtMtx, res );
		
		return toAffineTransform(res);
	}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Converts a vector from sample space into eigen space.
 *
 * @param sampleData Sample space data.
 * @return Eigen space projection.
 */
public double[] sampleToEigenSpace( double[] sampleData ) {
   if( sampleData.length != A.getNumCols() )
      throw new IllegalArgumentException("Unexpected sample length");
   DenseMatrix64F mean = DenseMatrix64F.wrap(A.getNumCols(),1,this.mean);

   DenseMatrix64F s = new DenseMatrix64F(A.getNumCols(),1,true,sampleData);
   DenseMatrix64F r = new DenseMatrix64F(numComponents,1);

   CommonOps.sub(s,mean,s);

   CommonOps.mult(V_t,s,r);

   return r.data;
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
/**
 * Computes the dot product of each basis vector against the sample.  Can be used as a measure
 * for membership in the training sample set.  High values correspond to a better fit.
 *
 * @param sample Sample of original data.
 * @return Higher value indicates it is more likely to be a member of input dataset.
 */
public double response( double[] sample ) {
   if( sample.length != A.numCols )
      throw new IllegalArgumentException("Expected input vector to be in sample space");

   DenseMatrix64F dots = new DenseMatrix64F(numComponents,1);
   DenseMatrix64F s = DenseMatrix64F.wrap(A.numCols,1,sample);

   CommonOps.mult(V_t,s,dots);

   return NormOps.normF(dots);
}
 

import org.ejml.ops.CommonOps; //导入方法依赖的package包/类
private void checkNumControl(int numControl ) {
		createInputs(numControl);
		Relinearlize alg = new Relinearlize();
		alg.setNumberControl(numControl);

		// variables being estimated
		double foundBeta[] = new double[numControl];
		alg.process(L_full,y,foundBeta);

		// check to see if its a valid solution
		DenseMatrix64F x = new DenseMatrix64F(L_full.numCols,1);
		DenseMatrix64F foundDistance = new DenseMatrix64F(L_full.numRows,1);
		int index = 0;
		for( int i = 0; i < numControl; i++ ) {
			for( int j = i; j < numControl; j++ ) {
				x.data[index++] = foundBeta[i]*foundBeta[j];
			}
		}

		CommonOps.mult(L_full, x, foundDistance);

//		System.out.println("error = "+SpecializedOps.diffNormF(foundDistance,y));

		// NOTE: This test can pass and the result still be bad because L_full is
		// an undetermined system.  But at least there is some sort of test here
		assertTrue(MatrixFeatures.isEquals(foundDistance, y, 2));

		// WARNING!!! the error margin was made to be huge to make sure it passed
	}