I've spent about 2 days trying to understand this piece of code (from here) that applies an affine/projective transformation to an image. I will link bits of the code that I'm struggling to understand and add comments on what I think it's trying to do. In particular, the author's original comments I'll label as //.

// X1, Y1 : upleft corner
// X2, Y2 : upright corner
// X3, Y3 : downleft corner 
// sx     : x-size of output image
// sy     : y-size of output image
x12 = (X2-X1)/(float)(*sx); 
y12 = (Y2-Y1)/(float)(*sx);
x13 = (X3-X1)/(float)(*sy);
y13 = (Y3-Y1)/(float)(*sy);

Q1. I can see that this part as the comment says, finds a new normalized basis but I'm unsure exactly why we need to find one to model an affine transformation?

// x4, y4 : downright corner (for projective transform)

if (y4) 
    a = (yy-1.0)/(1.0-xx-yy);
    b = (xx-1.0)/(1.0-xx-yy);

After doing some internet search, I manage to find equations from here that the xx and yy equations solve for the "intersection point of two line segments in 2 dimensions". An image will be linked below.

START IMAGE. Intersection of two lines


Q2. I don't understand how he uses the xx and yy equations to solve for a & b?

Q3. I am unsure of what a and b are. My best guess is that they are the bottom row of a projective transformation matrix shown below:

$$ \begin{bmatrix} a_{11} & a_{12} & a_{13} \\ a_{21} & a_{22} & a_{23} \\ a & b & 1 \\ \end{bmatrix} $$

This is because the code sets a = b = 0.0 if we're not doing a projective transform, which should then make it an affine transformation matrix.

Now is where the main loop happens:

for (x=0;x<sx;x++) 
    for (y=0;y<sy;y++) {
        fx = (float)x + 0.5;
        fy = (float)y + 0.5;
        d = a*fx/(float)(*sx)+b*fy/(float)(*sy)+1.0;
        xx = (a+1.0)*fx/d;
        yy = (b+1.0)*fy/d;
        xp = X1 + xx*x12 + yy*x13;
        yp = Y1 + xx*y12 + yy*y13;

Q4. Why does he add 0.5 to the original x and y coordinate? My best guess is that the code's trying to map the centre of each pixel rather than the edge to the new image:


Q5. In

d = a*fx/(float)(*sx)+b*fy/(float)(*sy)+1.0;
xx = (a+1.0)*fx/d;
yy = (b+1.0)*fy/d;

what are they trying to accomplish here? My best guess is that d is the third component of the output homogeneous coordinate:

$$ \begin{bmatrix} xx\\ yy\\ d\\ \end{bmatrix} $$

calculated from the dot product of the third row of the (now normalised) transformation matrix from earlier and the input homogeneous vector:

$$ \begin{bmatrix} \frac{a}{sx} & \frac{b}{sy} & 1 \\ \end{bmatrix} \begin{bmatrix} fx\\ fy\\ 1\\ \end{bmatrix} = a*fx/sx+b*fy/sy+1.0 $$

Then the division

xx = (a+1.0)*fx/d;
yy = (b+1.0)*fy/d;

would just transform the homogeneous coordinates back to Euclidean coordinates, corresponding to the output image pixel location. I noted that if a=b=0.0 (so not projective), then (a+1.0)=(b+1.0)=d=1.0. I'm however not sure about why we have to multiply by (a+1.0) or (b+1.0). But again, I do not know if I'm on the right track or not as I do not have anyone to help me with this code.

Q7. The final operation

xp = X1 + xx*x12 + yy*x13;
yp = Y1 + xx*y12 + yy*y13;

I suppose is literally the dot product of the first two rows of the transformation matrix with the Euclidean coordinates as so:

$$ \begin{bmatrix} x12 & x13 & X_{1} \\ y12 & y13 & Y_{1} \end{bmatrix} \begin{bmatrix} xx\\ yy\\ 1\\ \end{bmatrix} = \begin{bmatrix} xp\\ yp\\ \end{bmatrix} $$

Which brings me to my question, how does this achieve an affine transformation?

I suspect my lack of formal training could be why I'm not understanding how the code models an affine transformation. I'd appreciate some light shed on this piece of code!


  • $\begingroup$ An affine transformation is is any transformation that preserves points, lines and planes. This can be translation, skewing and rotation - and combinations thereof. $\endgroup$
    – beyond
    Commented Sep 11, 2018 at 8:23
  • $\begingroup$ Hi @beyond, thanks for your input. Are you able to shed some light on the code? $\endgroup$
    – theunseen
    Commented Sep 11, 2018 at 9:00
  • $\begingroup$ To be honest, the code seems a bit elaborate for something as simple as applying an affine transformation to an image. The projection bit I don't get, so an example of input/expected output/actual output would be ideal. $\endgroup$
    – beyond
    Commented Sep 11, 2018 at 12:57

1 Answer 1


Your understanding of the matrix structure in Q3 is correct. This code just does not construct a matrix explicitly and the matrix multiplication is applied implicitly. I think this part might cause your confusion. Instead of deciphering the code, I would rather derive the transform and compare it with the code.

The affine (6 degrees of freedom) and projective matrix (8 dof) map points to points. So you can build a system of equations for each pair of points in different frames. You will need to solve this system for the components of the matrix (Q1).

Let $M$ be the transform matrix, then

$$ \begin{align} M (0,0,1) &= (X_1,Y_1,1) \\ M (1,0,1) &= (X_2,Y_2,1) \\ M (0,1,1) &= (X_3,Y_3,1) \\ \end{align} $$ Each of above lines gives you two equations. Now you have 6 equations and the affine matrix can be derived. For the projective matrix, we need extra two equations using the point $(X_4,Y_4)$:

$$ \begin{align} H_4 &= M (1,1,1) \\ \frac{H_4}{H_4.z} &= (X_4,Y_4,1) \end{align} $$ Note that I have to divide the homogeneous part for the transformed point.

Solve this linear system you can get an expression for your matrix $M$. Perform $M (f_x,f_y,1)$ you will see how it transforms the point. I believe this solves your Q2, Q3, Q5 and Q7.

For Q4, I think it is just a convention of pixel position and your understanding should be correct.

Appendix: maple code to verify the answer

# affine matrix
# Build equations
E:=[convert(m.<0,0,1> - <X1,Y1,1>,list),convert(m.<1,0,1> - <X2,Y2,1>,list), convert(m.<0,1,1> - <X3,Y3,1>,list)];
# E=[[m13 - X1, m23 - Y1, 0], [m11 + m13 - X2, m21 + m23 - Y2, 0],[m12 + m13 - X3, m22 + m23 - Y3, 0]]
# Solve E=0
# Plug the solution back to m:
                           [-X1 + X2    -X1 + X3    X1]
                           [                          ]
                           [-Y1 + Y2    -Y1 + Y3    Y1]
                           [                          ]
                           [   0           0        1 ]

# projective matrix
# Compute transformed points
# Build equations
# Sove E=0
# copy their code for xx and yy:
# Verify our a and b, compare ours with their expressing using xx and yy
# It is 0 as expected
# It is also 0

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