// Module: forwadditive.cpp
// Brief: Contains implementation of forwards additive image alignment algorithm.
// Author: Oleg A. Krivtsov
// Email: olegkrivtsov@mail.ru
// Date: March 2008
#include <stdio.h>
#include <time.h>
#include <cv.h> // Include header for computer-vision part of OpenCV.
#include <highgui.h> // Include header for GUI part of OpenCV.
#include "auxfunc.h" // Header for our warping functions.
// Lucas-Kanade method
// @param[in] pImgT Template image T
// @param[in] omega Rectangular template region
// @param[in] pImgI Another image I
void align_image_forwards_additive(IplImage* pImgT, CvRect omega, IplImage* pImgI)
{
// Some constants for iterative minimization process.
const float EPS = 1E-5f; // Threshold value for termination criteria.
const int MAX_ITER = 100; // Maximum iteration count.
// Open log file
FILE* f = fopen("forwadditive.txt", "wt");
if(f==NULL)
{
printf("Error opening log file 'forwadditive.txt'\n");
return;
}
// Here we will store internally used images.
IplImage* pGradIx = 0; // Gradient of I in X direction.
IplImage* pGradIy = 0; // Gradient of I in Y direction.
// Here we will store matrices.
CvMat* W = 0; // Current value of warp W(x,p)
CvMat* X = 0; // Point in coordinate frame of T.
CvMat* Z = 0; // Point in coordinate frame of I.
CvMat* H = 0; // The Hessian matrix.
CvMat* iH = 0; // Inverse of the Hessian.
CvMat* b = 0; // Vector in the right side of the system of linear equations.
CvMat* delta_p = 0; // Parameter update value.
// Create matrices.
W = cvCreateMat(3, 3, CV_32F);
X = cvCreateMat(3, 1, CV_32F);
Z = cvCreateMat(3, 1, CV_32F);
H = cvCreateMat(4, 4, CV_32F);
iH = cvCreateMat(4, 4, CV_32F);
b = cvCreateMat(4, 1, CV_32F);
delta_p = cvCreateMat(4, 1, CV_32F);
// Create images.
CvSize image_size = cvSize(pImgT->width, pImgT->height);
pGradIx = cvCreateImage(image_size, IPL_DEPTH_16S, 1);
pGradIy = cvCreateImage(image_size, IPL_DEPTH_16S, 1);
// Get current time. We will use it later to obtain total calculation time.
clock_t start_time = clock();
/*
* Precomputation stage.
*/
// Calculate gradient of I.
cvSobel(pImgI, pGradIx, 1, 0); // Gradient in X direction
cvSobel(pImgI, pGradIy, 0, 1); // Gradient in Y direction
/*
* Iteration stage.
*/
// Here we will store parameter approximation.
float wz_a=0, tx_a=0, ty_a=0, s_a=1.0f;
// Here we will store mean error.
float mean_error=0;
// Iterate
int iter=0; // Number of current iteration
while(iter < MAX_ITER)
{
iter++; // Increment iteration counter
int pixel_count=0; // Count of processed pixels
init_warp(W, wz_a, tx_a, ty_a, s_a); // Init warp W(x, p)
cvSet(H, cvScalar(0)); // Set Hessian with zeroes
cvSet(b, cvScalar(0)); // Set b matrix with zeroes
// (u,v) - pixel coordinates in the coordinate frame of T.
int u, v;
// (u2,v2) - pixel coordinates in the coordinate frame of I.
int u2, v2;
// Walk through pixels in the template T.
int i, j;
for(i=0; i<omega.width; i++)
{
u = i + omega.x;
for(j=0; j<omega.height; j++)
{
v = j + omega.y;
// Set vector X with pixel coordinates (u,v,1)
SET_VECTOR(X, u, v);
// Warp Z=W*X
cvGEMM(W, X, 1, 0, 0, Z);
// Get coordinates of warped pixel in coordinate frame of I.
GET_VECTOR(Z, u2, v2);
if(u2>=0 && u2<pImgI->width && // check if pixel is inside I.
v2>=0 && v2<pImgI->height)
{
pixel_count++;
// Evaluate gradient of I at W(x,p).
short Ix = CV_IMAGE_ELEM(pGradIx, short, v2, u2);
short Iy = CV_IMAGE_ELEM(pGradIy, short, v2, u2);
// Calculate steepest descent image's element.
float stdesc[4]; // an element of steepest descent image
stdesc[0] = (float)(-v*Ix+u*Iy);
stdesc[1] = (float)Ix;
stdesc[2] = (float)Iy;
stdesc[3] = (float)(u*Ix+v*Iy);
// Calculate image difference D = T(x)-I(W(x,p)).
int D = CV_IMAGE_ELEM(pImgT, uchar, v, u) -
CV_IMAGE_ELEM(pImgI, uchar, v2, u2);
// Update mean error value.
mean_error += abs(D);
// Add a term to b matrix.
float* pb = &CV_MAT_ELEM(*b, float, 0, 0);
pb[0] += stdesc[0] * D;
pb[1] += stdesc[1] * D;
pb[2] += stdesc[2] * D;
pb[3] += stdesc[3] * D;
// Add a term to Hessian.
int l,m;
for(l=0;l<4;l++)
{
for(m=0;m<4;m++)
{
CV_MAT_ELEM(*H, float, l, m) += stdesc[l]*stdesc[m];
}
}
}
}
}
// Finally, calculate mean error.
if(pixel_count) mean_error /= pixel_count;
// Invert Hessian.
double inv_res = cvInvert(H, iH);
if(inv_res==0)
{
printf("Error: Hessian is singular.\n");
return;
}
// Find parameter increment.
cvGEMM(iH, b, 1, 0, 0, delta_p);
float delta_wz = CV_MAT_ELEM(*delta_p, float, 0, 0);
float delta_tx = CV_MAT_ELEM(*delta_p, float, 1, 0);
float delta_ty = CV_MAT_ELEM(*delta_p, float, 2, 0);
float delta_s = CV_MAT_ELEM(*delta_p, float, 3, 0);
// Update parameter vector approximation.
wz_a += delta_wz;
tx_a += delta_tx;
ty_a += delta_ty;
s_a += delta_s;
// Print diagnostic information to file.
fprintf(f, "%d %f\n", iter, mean_error);
// Print dot symbol to screen.
printf(".");
// Check termination critera.
if(fabs(delta_wz)<EPS && fabs(delta_tx)<EPS &&
fabs(delta_ty)<EPS && fabs(delta_s)<EPS) break;
}
// Get current time and obtain total time of calculation.
clock_t finish_time = clock();
double total_time = (double)(finish_time-start_time)/CLOCKS_PER_SEC;
// Print summary.
printf("\n===============================================\n");
printf("Algorithm: forwards additive.\n");
printf("Caclulation time: %g sec.\n", total_time);
printf("Iteration count: %d\n", iter);
printf("Approximation: wz_a=%f tx_a=%f ty_a=%f s_a=%f\n", wz_a, tx_a, ty_a, s_a);
printf("Epsilon: %f\n", EPS);
printf("Resulting mean error: %f\n", mean_error);
printf("===============================================\n");
// Show result of image alignment.
init_warp(W, wz_a, tx_a, ty_a, s_a);
draw_warped_rect(pImgI, omega, W);
cvSetImageROI(pImgT, omega);
cvShowImage("template",pImgT);
cvShowImage("image",pImgI);
cvResetImageROI(pImgT);
// Free used resources and exit.
fclose(f);
cvReleaseMat(&W);
cvReleaseMat(&X);
cvReleaseMat(&Z);
cvReleaseMat(&H);
cvReleaseMat(&iH);
cvReleaseMat(&b);
cvReleaseMat(&delta_p);
cvReleaseImage(&pGradIx);
cvReleaseImage(&pGradIy);
}