No, you need to multiply the matrices to get the cascaded effect. I won't go into the math, but applying a transformation to coordinates is a matter of performing a matrix multiplication. If you are however curious as to know why that is, I refer you to this good Wikipedia article on cascading matrix transformations. Given a coordinate X and a transformation matrix M, you get the output coordinate Y by:
Y = M*X
Here I use * to refer to matrix multiplication as opposed to element-wise multiplication. What you have is a pair of transformation matrices which go from img1 to img2 then img2 to img3. You'll need to do the operation twice. So to go from img1 to img2 where X belongs to the coordinate space of img1, we have (assuming we're using the affine matrices):
Y1 = M1*X
Next, to go from img2 to img3, we have:
Y2 = M2*Y1 --> Y2 = M2*M1*X --> Y2 = M3*X --> M3 = M2*M1
Therefore, to get the desired chain effect, you need to create a new matrix such that M2 is multiplied by M1. Same as H2 and H1.
So define a new matrix such that:
cv::Mat M3 = M2*M1;
Similarly for your projective matrices, you can do:
cv::Mat H3 = H2*H1;
However, estimateRigidTransform (the output is M in your case) gives you a 2 x 3 matrix. One trick is to augment this matrix so that it becomes 3 x 3 where we add an additional row where it is all 0 except for the last element, which is set to 1. Therefore, you would have the last row such that it becomes [0 0 1]. You would do this for both matrices, multiply them, then extract just the first two rows into a new matrix to pipe into warpAffine. Therefore, do something like this:
// Create padded matrix for M1
cv::Mat M1new = cv::Mat(3,3,M1.type());
M1new.at<double>(0,0) = M1.at<double>(0,0);
M1new.at<double>(0,1) = M1.at<double>(0,1);
M1new.at<double>(0,2) = M1.at<double>(0,2);
M1new.at<double>(1,0) = M1.at<double>(1,0);
M1new.at<double>(1,1) = M1.at<double>(1,1);
M1new.at<double>(1,2) = M1.at<double>(1,2);
M1new.at<double>(2,0) = 0.0;
M1new.at<double>(2,1) = 0.0;
M1new.at<double>(2,2) = 1.0;
// Create padded matrix for M2
cv::Mat M2new = cv::Mat(3,3,M2.type());
M2new.at<double>(0,0) = M2.at<double>(0,0);
M2new.at<double>(0,1) = M2.at<double>(0,1);
M2new.at<double>(0,2) = M2.at<double>(0,2);
M2new.at<double>(1,0) = M2.at<double>(1,0);
M2new.at<double>(1,1) = M2.at<double>(1,1);
M2new.at<double>(1,2) = M2.at<double>(1,2);
M2new.at<double>(2,0) = 0.0;
M2new.at<double>(2,1) = 0.0;
M2new.at<double>(2,2) = 1.0;
// Multiply the two matrices together
cv::Mat M3temp = M2new*M1new;
// Extract out relevant rows and place into M3
cv::Mat M3 = cv::Mat(2, 3, M3temp.type());
M3.at<double>(0,0) = M3temp.at<double>(0,0);
M3.at<double>(0,1) = M3temp.at<double>(0,1);
M3.at<double>(0,2) = M3temp.at<double>(0,2);
M3.at<double>(1,0) = M3temp.at<double>(1,0);
M3.at<double>(1,1) = M3temp.at<double>(1,1);
M3.at<double>(1,2) = M3temp.at<double>(1,2);
When dealing with cv::Mat and the * operator, it is overloaded to specifically perform matrix multiplication.
You can then use M3 and H3 into warpAffine and warpPerspective respectively.
Hope this helps!
