[英]How to find the angle of rotation and translation using opencv if I know the homogeneous transformation matrix in 3d?
我发现了几篇有关此主题的文章,但没有一种解决方案使用opencv。
我想知道OpenCv是否有任何功能或类可以在此主题上提供帮助?
我在opencv中有一个4 * 4的仿射变换,我想查找旋转,假设缩放比例为1,矩阵中没有其他变换。
OpenCV中是否有任何功能可以帮助您找到这些参数?
您面临的问题称为矩阵分解问题。
您可以按照以下步骤检索所需的矩阵:
在您的情况下,作为缩放因子1,您可以跳过前两个步骤。
要检索旋转矩阵的轴和角度(以弧度为单位),建议您在OpenCV中移植以下Java算法(来源: http : //www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToAngle/ )。
/**
This requires a pure rotation matrix 'm' as input.
*/
public axisAngle toAxisAngle(matrix m) {
double angle,x,y,z; // variables for result
double epsilon = 0.01; // margin to allow for rounding errors
double epsilon2 = 0.1; // margin to distinguish between 0 and 180 degrees
// optional check that input is pure rotation, 'isRotationMatrix' is defined at:
// http://www.euclideanspace.com/maths/algebra/matrix/orthogonal/rotation/
assert isRotationMatrix(m) : "not valid rotation matrix" ;// for debugging
if ((Math.abs(m[0][1]-m[1][0])< epsilon)
&& (Math.abs(m[0][2]-m[2][0])< epsilon)
&& (Math.abs(m[1][2]-m[2][1])< epsilon)) {
// singularity found
// first check for identity matrix which must have +1 for all terms
// in leading diagonaland zero in other terms
if ((Math.abs(m[0][1]+m[1][0]) < epsilon2)
&& (Math.abs(m[0][2]+m[2][0]) < epsilon2)
&& (Math.abs(m[1][2]+m[2][1]) < epsilon2)
&& (Math.abs(m[0][0]+m[1][1]+m[2][2]-3) < epsilon2)) {
// this singularity is identity matrix so angle = 0
return new axisAngle(0,1,0,0); // zero angle, arbitrary axis
}
// otherwise this singularity is angle = 180
angle = Math.PI;
double xx = (m[0][0]+1)/2;
double yy = (m[1][1]+1)/2;
double zz = (m[2][2]+1)/2;
double xy = (m[0][1]+m[1][0])/4;
double xz = (m[0][2]+m[2][0])/4;
double yz = (m[1][2]+m[2][1])/4;
if ((xx > yy) && (xx > zz)) { // m[0][0] is the largest diagonal term
if (xx< epsilon) {
x = 0;
y = 0.7071;
z = 0.7071;
} else {
x = Math.sqrt(xx);
y = xy/x;
z = xz/x;
}
} else if (yy > zz) { // m[1][1] is the largest diagonal term
if (yy< epsilon) {
x = 0.7071;
y = 0;
z = 0.7071;
} else {
y = Math.sqrt(yy);
x = xy/y;
z = yz/y;
}
} else { // m[2][2] is the largest diagonal term so base result on this
if (zz< epsilon) {
x = 0.7071;
y = 0.7071;
z = 0;
} else {
z = Math.sqrt(zz);
x = xz/z;
y = yz/z;
}
}
return new axisAngle(angle,x,y,z); // return 180 deg rotation
}
// as we have reached here there are no singularities so we can handle normally
double s = Math.sqrt((m[2][1] - m[1][2])*(m[2][1] - m[1][2])
+(m[0][2] - m[2][0])*(m[0][2] - m[2][0])
+(m[1][0] - m[0][1])*(m[1][0] - m[0][1])); // used to normalise
if (Math.abs(s) < 0.001) s=1;
// prevent divide by zero, should not happen if matrix is orthogonal and should be
// caught by singularity test above, but I've left it in just in case
angle = Math.acos(( m[0][0] + m[1][1] + m[2][2] - 1)/2);
x = (m[2][1] - m[1][2])/s;
y = (m[0][2] - m[2][0])/s;
z = (m[1][0] - m[0][1])/s;
return new axisAngle(angle,x,y,z);
}
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