// Regularized version of the Heaviside step function,
// parameterized by a small positive number 'e'
void heaviside(MAT *H, MAT *z, double v, double e) {
int m = z->m, n = z->n, i, j;
// Precompute constants to avoid division in the for loops below
double one_over_pi = 1.0 / PI;
double one_over_e = 1.0 / e;
// Compute H = (1 / pi) * atan((z * v) / e) + 0.5
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
double z_val = m_get_val(z, i, j) * v;
double H_val = one_over_pi * atan(z_val * one_over_e) + 0.5;
m_set_val(H, i, j, H_val);
}
}
// A simpler, faster approximation of the Heaviside function
/* for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
double z_val = m_get_val(z, i, j) * v;
double H_val = 0.5;
if (z_val < -0.0001) H_val = 0.0;
else if (z_val > 0.0001) H_val = 1.0;
m_set_val(H, i, j, H_val);
}
} */
}
void print(MAT *m)
{
unsigned int i,j;
for (i=0; i<m->n; i++){
for (j=0; j<m->m; j++)
printf("%f ",m_get_val(m,i,j));
printf("\n");
}
}
// Returns the sum of all of the elements in the specified matrix
double sum_m(MAT *matrix) {
if (matrix == NULL) return 0.0;
int i, j;
double sum = 0.0;
for (i = 0; i < matrix->m; i++)
for (j = 0; j < matrix->n; j++)
sum += m_get_val(matrix, i, j);
return sum;
}
//Given a matrix, return the matrix containing an approximation of the gradient matrix dM/dy
MAT * gradient_y(MAT * input)
{
int i, j;
MAT * result = m_get(input->m, input->n);
for(i = 0; i < result->n; i++)
{
for(j = 0; j < result->m; j++)
{
if(j==0)
m_set_val(result, j, i, m_get_val(input, j+1, i) - m_get_val(input, j, i));
else if(j==input->m-1)
m_set_val(result, j, i, m_get_val(input, j, i) - m_get_val(input, j-1, i));
else
m_set_val(result, j, i, (m_get_val(input, j+1, i) - m_get_val(input, j-1, i)) / 2.0);
}
}
return result;
}
// Initializes data on the GPU for the MGVF kernel
void IMGVF_OpenCL_init(MAT **IE, int num_cells) {
cl_int error;
// Allocate array of offsets to each cell's image
size_t mem_size = sizeof(int) * num_cells;
host_I_offsets = (int *) malloc(mem_size);
device_I_offsets = clCreateBuffer(context, CL_MEM_READ_ONLY, mem_size, NULL, &error);
check_error(error, __FILE__, __LINE__);
// Allocate arrays to hold the dimensions of each cell's image
host_m_array = (int *) malloc(mem_size);
host_n_array = (int *) malloc(mem_size);
device_m_array = clCreateBuffer(context, CL_MEM_READ_ONLY, mem_size, NULL, &error);
check_error(error, __FILE__, __LINE__);
device_n_array = clCreateBuffer(context, CL_MEM_READ_ONLY, mem_size, NULL, &error);
check_error(error, __FILE__, __LINE__);
// Figure out the size of all of the matrices combined
int i, j, cell_num;
size_t total_size = 0;
for (cell_num = 0; cell_num < num_cells; cell_num++) {
MAT *I = IE[cell_num];
size_t size = I->m * I->n;
total_size += size;
}
total_mem_size = total_size * sizeof(float);
// Allocate host memory just once for all cells
host_I_all = (float *) malloc(total_mem_size);
// Allocate device memory just once for all cells
device_I_all = clCreateBuffer(context, CL_MEM_READ_ONLY, total_mem_size, NULL, &error);
check_error(error, __FILE__, __LINE__);
device_IMGVF_all = clCreateBuffer(context, CL_MEM_READ_WRITE, total_mem_size, NULL, &error);
check_error(error, __FILE__, __LINE__);
// Copy each initial matrix into the allocated host memory
int offset = 0;
for (cell_num = 0; cell_num < num_cells; cell_num++) {
MAT *I = IE[cell_num];
// Determine the size of the matrix
int m = I->m, n = I->n;
int size = m * n;
// Store memory dimensions
host_m_array[cell_num] = m;
host_n_array[cell_num] = n;
// Store offsets to this cell's image
host_I_offsets[cell_num] = offset;
// Copy matrix I (which is also the initial IMGVF matrix) into the overall array
for (i = 0; i < m; i++)
for (j = 0; j < n; j++)
host_I_all[offset + (i * n) + j] = (float) m_get_val(I, i, j);
offset += size;
}
// Copy I matrices (which are also the initial IMGVF matrices) to device
error = clEnqueueWriteBuffer(command_queue, device_I_all, CL_TRUE, 0, total_mem_size, (void *) host_I_all, 0, NULL, NULL);
check_error(error, __FILE__, __LINE__);
error = clEnqueueWriteBuffer(command_queue, device_IMGVF_all, CL_TRUE, 0, total_mem_size, (void *) host_I_all, 0, NULL, NULL);
check_error(error, __FILE__, __LINE__);
// Copy offsets array to device
error = clEnqueueWriteBuffer(command_queue, device_I_offsets, CL_TRUE, 0, mem_size, (void *) host_I_offsets, 0, NULL, NULL);
check_error(error, __FILE__, __LINE__);
// Copy memory dimension arrays to device
error = clEnqueueWriteBuffer(command_queue, device_m_array, CL_TRUE, 0, mem_size, (void *) host_m_array, 0, NULL, NULL);
check_error(error, __FILE__, __LINE__);
error = clEnqueueWriteBuffer(command_queue, device_n_array, CL_TRUE, 0, mem_size, (void *) host_n_array, 0, NULL, NULL);
check_error(error, __FILE__, __LINE__);
}
// Main
int main(int argc, char ** argv) {
// Choose the best GPU in case there are multiple available
choose_GPU();
// Keep track of the start time of the program
long long program_start_time = get_time();
if (argc !=3){
fprintf(stderr, "usage: %s <input file> <number of frames to process>", argv[0]);
exit(1);
}
// Let the user specify the number of frames to process
int num_frames = atoi(argv[2]);
// Open video file
char *video_file_name = argv[1];
avi_t *cell_file = AVI_open_input_file(video_file_name, 1);
if (cell_file == NULL) {
AVI_print_error("Error with AVI_open_input_file");
return -1;
}
int i, j, *crow, *ccol, pair_counter = 0, x_result_len = 0, Iter = 20, ns = 4, k_count = 0, n;
MAT *cellx, *celly, *A;
double *GICOV_spots, *t, *G, *x_result, *y_result, *V, *QAX_CENTERS, *QAY_CENTERS;
double threshold = 1.8, radius = 10.0, delta = 3.0, dt = 0.01, b = 5.0;
// Extract a cropped version of the first frame from the video file
MAT *image_chopped = get_frame(cell_file, 0, 1, 0);
printf("Detecting cells in frame 0\n");
// Get gradient matrices in x and y directions
MAT *grad_x = gradient_x(image_chopped);
MAT *grad_y = gradient_y(image_chopped);
// Allocate for gicov_mem and strel
gicov_mem = (float*) malloc(sizeof(float) * grad_x->m * grad_y->n);
strel = (float*) malloc(sizeof(float) * strel_m * strel_n);
m_free(image_chopped);
int grad_m = grad_x->m;
int grad_n = grad_y->n;
#pragma acc data create(sin_angle,cos_angle,theta,tX,tY) \
create(gicov_mem[0:grad_x->m*grad_y->n])
{
// Precomputed constants on GPU
compute_constants();
// Get GICOV matrices corresponding to image gradients
long long GICOV_start_time = get_time();
MAT *gicov = GICOV(grad_x, grad_y);
long long GICOV_end_time = get_time();
// Dilate the GICOV matrices
long long dilate_start_time = get_time();
MAT *img_dilated = dilate(gicov);
long long dilate_end_time = get_time();
} /* end acc data */
// Find possible matches for cell centers based on GICOV and record the rows/columns in which they are found
pair_counter = 0;
crow = (int *) malloc(gicov->m * gicov->n * sizeof(int));
ccol = (int *) malloc(gicov->m * gicov->n * sizeof(int));
for(i = 0; i < gicov->m; i++) {
for(j = 0; j < gicov->n; j++) {
if(!double_eq(m_get_val(gicov,i,j), 0.0) && double_eq(m_get_val(img_dilated,i,j), m_get_val(gicov,i,j)))
{
crow[pair_counter]=i;
ccol[pair_counter]=j;
pair_counter++;
}
}
}
GICOV_spots = (double *) malloc(sizeof(double) * pair_counter);
for(i = 0; i < pair_counter; i++)
GICOV_spots[i] = sqrt(m_get_val(gicov, crow[i], ccol[i]));
G = (double *) calloc(pair_counter, sizeof(double));
x_result = (double *) calloc(pair_counter, sizeof(double));
y_result = (double *) calloc(pair_counter, sizeof(double));
x_result_len = 0;
for (i = 0; i < pair_counter; i++) {
if ((crow[i] > 29) && (crow[i] < BOTTOM - TOP + 39)) {
x_result[x_result_len] = ccol[i];
y_result[x_result_len] = crow[i] - 40;
G[x_result_len] = GICOV_spots[i];
x_result_len++;
}
}
// Make an array t which holds each "time step" for the possible cells
t = (double *) malloc(sizeof(double) * 36);
for (i = 0; i < 36; i++) {
t[i] = (double)i * 2.0 * PI / 36.0;
}
// Store cell boundaries (as simple circles) for all cells
cellx = m_get(x_result_len, 36);
celly = m_get(x_result_len, 36);
for(i = 0; i < x_result_len; i++) {
for(j = 0; j < 36; j++) {
m_set_val(cellx, i, j, x_result[i] + radius * cos(t[j]));
m_set_val(celly, i, j, y_result[i] + radius * sin(t[j]));
}
}
A = TMatrix(9,4);
V = (double *) malloc(sizeof(double) * pair_counter);
QAX_CENTERS = (double * )malloc(sizeof(double) * pair_counter);
QAY_CENTERS = (double *) malloc(sizeof(double) * pair_counter);
memset(V, 0, sizeof(double) * pair_counter);
memset(QAX_CENTERS, 0, sizeof(double) * pair_counter);
memset(QAY_CENTERS, 0, sizeof(double) * pair_counter);
// For all possible results, find the ones that are feasibly leukocytes and store their centers
k_count = 0;
for (n = 0; n < x_result_len; n++) {
if ((G[n] < -1 * threshold) || G[n] > threshold) {
MAT * x, *y;
VEC * x_row, * y_row;
x = m_get(1, 36);
y = m_get(1, 36);
x_row = v_get(36);
y_row = v_get(36);
// Get current values of possible cells from cellx/celly matrices
x_row = get_row(cellx, n, x_row);
y_row = get_row(celly, n, y_row);
uniformseg(x_row, y_row, x, y);
// Make sure that the possible leukocytes are not too close to the edge of the frame
if ((m_min(x) > b) && (m_min(y) > b) && (m_max(x) < cell_file->width - b) && (m_max(y) < cell_file->height - b)) {
MAT * Cx, * Cy, *Cy_temp, * Ix1, * Iy1;
VEC *Xs, *Ys, *W, *Nx, *Ny, *X, *Y;
Cx = m_get(1, 36);
Cy = m_get(1, 36);
Cx = mmtr_mlt(A, x, Cx);
Cy = mmtr_mlt(A, y, Cy);
Cy_temp = m_get(Cy->m, Cy->n);
for (i = 0; i < 9; i++)
m_set_val(Cy, i, 0, m_get_val(Cy, i, 0) + 40.0);
// Iteratively refine the snake/spline
for (i = 0; i < Iter; i++) {
int typeofcell;
if(G[n] > 0.0) typeofcell = 0;
else typeofcell = 1;
splineenergyform01(Cx, Cy, grad_x, grad_y, ns, delta, 2.0 * dt, typeofcell);
}
X = getsampling(Cx, ns);
for (i = 0; i < Cy->m; i++)
m_set_val(Cy_temp, i, 0, m_get_val(Cy, i, 0) - 40.0);
Y = getsampling(Cy_temp, ns);
Ix1 = linear_interp2(grad_x, X, Y);
Iy1 = linear_interp2(grad_x, X, Y);
Xs = getfdriv(Cx, ns);
Ys = getfdriv(Cy, ns);
Nx = v_get(Ys->dim);
for (i = 0; i < Ys->dim; i++)
v_set_val(Nx, i, v_get_val(Ys, i) / sqrt(v_get_val(Xs, i)*v_get_val(Xs, i) + v_get_val(Ys, i)*v_get_val(Ys, i)));
Ny = v_get(Xs->dim);
for (i = 0; i < Xs->dim; i++)
v_set_val(Ny, i, -1.0 * v_get_val(Xs, i) / sqrt(v_get_val(Xs, i)*v_get_val(Xs, i) + v_get_val(Ys, i)*v_get_val(Ys, i)));
W = v_get(Nx->dim);
for (i = 0; i < Nx->dim; i++)
v_set_val(W, i, m_get_val(Ix1, 0, i) * v_get_val(Nx, i) + m_get_val(Iy1, 0, i) * v_get_val(Ny, i));
V[n] = mean(W) / std_dev(W);
// Find the cell centers by computing the means of X and Y values for all snaxels of the spline contour
QAX_CENTERS[k_count] = mean(X);
QAY_CENTERS[k_count] = mean(Y) + TOP;
k_count++;
// Free memory
v_free(W);
v_free(Ny);
v_free(Nx);
v_free(Ys);
v_free(Xs);
m_free(Iy1);
m_free(Ix1);
v_free(Y);
v_free(X);
m_free(Cy_temp);
m_free(Cy);
m_free(Cx);
}
// Free memory
v_free(y_row);
v_free(x_row);
m_free(y);
m_free(x);
}
}
// Free memory
free(gicov_mem);
free(strel);
free(V);
free(ccol);
free(crow);
free(GICOV_spots);
free(t);
free(G);
free(x_result);
free(y_result);
m_free(A);
m_free(celly);
m_free(cellx);
m_free(img_dilated);
m_free(gicov);
m_free(grad_y);
m_free(grad_x);
// Report the total number of cells detected
printf("Cells detected: %d\n\n", k_count);
// Report the breakdown of the detection runtime
printf("Detection runtime\n");
printf("-----------------\n");
printf("GICOV computation: %.5f seconds\n", ((float) (GICOV_end_time - GICOV_start_time)) / (1000*1000));
printf(" GICOV dilation: %.5f seconds\n", ((float) (dilate_end_time - dilate_start_time)) / (1000*1000));
printf(" Total: %.5f seconds\n", ((float) (get_time() - program_start_time)) / (1000*1000));
// Now that the cells have been detected in the first frame,
// track the ellipses through subsequent frames
if (num_frames > 1) printf("\nTracking cells across %d frames\n", num_frames);
else printf("\nTracking cells across 1 frame\n");
long long tracking_start_time = get_time();
int num_snaxels = 20;
ellipsetrack(cell_file, QAX_CENTERS, QAY_CENTERS, k_count, radius, num_snaxels, num_frames);
printf(" Total: %.5f seconds\n", ((float) (get_time() - tracking_start_time)) / (float) (1000*1000*num_frames));
// Report total program execution time
printf("\nTotal application run time: %.5f seconds\n", ((float) (get_time() - program_start_time)) / (1000*1000));
return 0;
}
void ellipseevolve(MAT *f, double *xc0, double *yc0, double *r0, double *t, int Np, double Er, double Ey) {
/*
% ELLIPSEEVOLVE evolves a parametric snake according
% to some energy constraints.
%
% INPUTS:
% f............potential surface
% xc0,yc0......initial center position
% r0,t.........initial radii & angle vectors (with Np elements each)
% Np...........number of snaxel points per snake
% Er...........expected radius
% Ey...........expected y position
%
% OUTPUTS
% xc0,yc0.......final center position
% r0...........final radii
%
% Matlab code written by: DREW GILLIAM (based on work by GANG DONG /
% NILANJAN RAY)
% Ported to C by: MICHAEL BOYER
*/
// Constants
double deltax = 0.2;
double deltay = 0.2;
double deltar = 0.2;
double converge = 0.1;
double lambdaedge = 1;
double lambdasize = 0.2;
double lambdapath = 0.05;
int iterations = 1000; // maximum number of iterations
int i, j;
// Initialize variables
double xc = *xc0;
double yc = *yc0;
double *r = (double *) malloc(sizeof(double) * Np);
for (i = 0; i < Np; i++) r[i] = r0[i];
// Compute the x- and y-gradients of the MGVF matrix
MAT *fx = gradient_x(f);
MAT *fy = gradient_y(f);
// Normalize the gradients
int fh = f->m, fw = f->n;
for (i = 0; i < fh; i++) {
for (j = 0; j < fw; j++) {
double temp_x = m_get_val(fx, i, j);
double temp_y = m_get_val(fy, i, j);
double fmag = sqrt((temp_x * temp_x) + (temp_y * temp_y));
m_set_val(fx, i, j, temp_x / fmag);
m_set_val(fy, i, j, temp_y / fmag);
}
}
double *r_old = (double *) malloc(sizeof(double) * Np);
VEC *x = v_get(Np);
VEC *y = v_get(Np);
// Evolve the snake
int iter = 0;
double snakediff = 1.0;
while (iter < iterations && snakediff > converge) {
// Save the values from the previous iteration
double xc_old = xc, yc_old = yc;
for (i = 0; i < Np; i++) {
r_old[i] = r[i];
}
// Compute the locations of the snaxels
for (i = 0; i < Np; i++) {
v_set_val(x, i, xc + r[i] * cos(t[i]));
v_set_val(y, i, yc + r[i] * sin(t[i]));
}
// See if any of the points in the snake are off the edge of the image
double min_x = v_get_val(x, 0), max_x = v_get_val(x, 0);
double min_y = v_get_val(y, 0), max_y = v_get_val(y, 0);
for (i = 1; i < Np; i++) {
double x_i = v_get_val(x, i);
if (x_i < min_x) min_x = x_i;
else if (x_i > max_x) max_x = x_i;
double y_i = v_get_val(y, i);
if (y_i < min_y) min_y = y_i;
else if (y_i > max_y) max_y = y_i;
}
if (min_x < 0.0 || max_x > (double) fw - 1.0 || min_y < 0 || max_y > (double) fh - 1.0) break;
// Compute the length of the snake
double L = 0.0;
for (i = 0; i < Np - 1; i++) {
double diff_x = v_get_val(x, i + 1) - v_get_val(x, i);
double diff_y = v_get_val(y, i + 1) - v_get_val(y, i);
L += sqrt((diff_x * diff_x) + (diff_y * diff_y));
}
double diff_x = v_get_val(x, 0) - v_get_val(x, Np - 1);
double diff_y = v_get_val(y, 0) - v_get_val(y, Np - 1);
L += sqrt((diff_x * diff_x) + (diff_y * diff_y));
// Compute the potential surface at each snaxel
MAT *vf = linear_interp2(f, x, y);
MAT *vfx = linear_interp2(fx, x, y);
MAT *vfy = linear_interp2(fy, x, y);
// Compute the average potential surface around the snake
double vfmean = sum_m(vf ) / L;
double vfxmean = sum_m(vfx) / L;
double vfymean = sum_m(vfy) / L;
// Compute the radial potential surface
int m = vf->m, n = vf->n;
MAT *vfr = m_get(m, n);
for (i = 0; i < n; i++) {
double vf_val = m_get_val(vf, 0, i);
double vfx_val = m_get_val(vfx, 0, i);
double vfy_val = m_get_val(vfy, 0, i);
double x_val = v_get_val(x, i);
double y_val = v_get_val(y, i);
double new_val = (vf_val + vfx_val * (x_val - xc) + vfy_val * (y_val - yc) - vfmean) / L;
m_set_val(vfr, 0, i, new_val);
}
// Update the snake center and snaxels
xc = xc + (deltax * lambdaedge * vfxmean);
yc = (yc + (deltay * lambdaedge * vfymean) + (deltay * lambdapath * Ey)) / (1.0 + deltay * lambdapath);
double r_diff = 0.0;
for (i = 0; i < Np; i++) {
r[i] = (r[i] + (deltar * lambdaedge * m_get_val(vfr, 0, i)) + (deltar * lambdasize * Er)) /
(1.0 + deltar * lambdasize);
r_diff += fabs(r[i] - r_old[i]);
}
// Test for convergence
snakediff = fabs(xc - xc_old) + fabs(yc - yc_old) + r_diff;
// Free temporary matrices
m_free(vf);
m_free(vfx);
m_free(vfy);
m_free(vfr);
iter++;
}
// Set the return values
*xc0 = xc;
*yc0 = yc;
for (i = 0; i < Np; i++)
r0[i] = r[i];
// Free memory
free(r);
free(r_old);
v_free( x);
v_free( y);
m_free(fx);
m_free(fy);
}
void ellipsetrack(avi_t *video, double *xc0, double *yc0, int Nc, int R, int Np, int Nf) {
/*
% ELLIPSETRACK tracks cells in the movie specified by 'video', at
% locations 'xc0'/'yc0' with radii R using an ellipse with Np discrete
% points, starting at frame number one and stopping at frame number 'Nf'.
%
% INPUTS:
% video.......pointer to avi video object
% xc0,yc0.....initial center location (Nc entries)
% Nc..........number of cells
% R...........initial radius
% Np..........nbr of snaxels points per snake
% Nf..........nbr of frames in which to track
%
% Matlab code written by: DREW GILLIAM (based on code by GANG DONG /
% NILANJAN RAY)
% Ported to C by: MICHAEL BOYER
*/
int i, j;
// Compute angle parameter
double *t = (double *) malloc(sizeof(double) * Np);
double increment = (2.0 * PI) / (double) Np;
for (i = 0; i < Np; i++) {
t[i] = increment * (double) i ;
}
// Allocate space for a snake for each cell in each frame
double **xc = alloc_2d_double(Nc, Nf + 1);
double **yc = alloc_2d_double(Nc, Nf + 1);
double ***r = alloc_3d_double(Nc, Np, Nf + 1);
double ***x = alloc_3d_double(Nc, Np, Nf + 1);
double ***y = alloc_3d_double(Nc, Np, Nf + 1);
// Save the first snake for each cell
for (i = 0; i < Nc; i++) {
xc[i][0] = xc0[i];
yc[i][0] = yc0[i];
for (j = 0; j < Np; j++) {
r[i][j][0] = (double) R;
}
}
// Generate ellipse points for each cell
for (i = 0; i < Nc; i++) {
for (j = 0; j < Np; j++) {
x[i][j][0] = xc[i][0] + (r[i][j][0] * cos(t[j]));
y[i][j][0] = yc[i][0] + (r[i][j][0] * sin(t[j]));
}
}
// Keep track of the total time spent on computing
// the MGVF matrix and evolving the snakes
long long MGVF_time = 0;
long long snake_time = 0;
// Process each frame
int frame_num, cell_num;
for (frame_num = 1; frame_num <= Nf; frame_num++) {
printf("\rProcessing frame %d / %d", frame_num, Nf);
fflush(stdout);
// Get the current video frame and its dimensions
MAT *I = get_frame(video, frame_num, 0, 1);
int Ih = I->m;
int Iw = I->n;
// Set the current positions equal to the previous positions
for (i = 0; i < Nc; i++) {
xc[i][frame_num] = xc[i][frame_num - 1];
yc[i][frame_num] = yc[i][frame_num - 1];
for (j = 0; j < Np; j++) {
r[i][j][frame_num] = r[i][j][frame_num - 1];
}
}
// Split the work among multiple threads, if OPEN is defined
#ifdef OPEN
#pragma omp parallel for num_threads(omp_num_threads) private(i, j)
#endif
// Track each cell
for (cell_num = 0; cell_num < Nc; cell_num++) {
// Make copies of the current cell's location
double xci = xc[cell_num][frame_num];
double yci = yc[cell_num][frame_num];
double *ri = (double *) malloc(sizeof(double) * Np);
for (j = 0; j < Np; j++) {
ri[j] = r[cell_num][j][frame_num];
}
// Add up the last ten y-values for this cell
// (or fewer if there are not yet ten previous frames)
double ycavg = 0.0;
for (i = (frame_num > 10 ? frame_num - 10 : 0); i < frame_num; i++) {
ycavg += yc[cell_num][i];
}
// Compute the average of the last ten y-values
// (this represents the expected y-location of the cell)
ycavg = ycavg / (double) (frame_num > 10 ? 10 : frame_num);
// Determine the range of the subimage surrounding the current position
int u1 = max(xci - 4.0 * R + 0.5, 0 );
int u2 = min(xci + 4.0 * R + 0.5, Iw - 1);
int v1 = max(yci - 2.0 * R + 1.5, 0 );
int v2 = min(yci + 2.0 * R + 1.5, Ih - 1);
// Extract the subimage
MAT *Isub = m_get(v2 - v1 + 1, u2 - u1 + 1);
for (i = v1; i <= v2; i++) {
for (j = u1; j <= u2; j++) {
m_set_val(Isub, i - v1, j - u1, m_get_val(I, i, j));
}
}
// Compute the subimage gradient magnitude
MAT *Ix = gradient_x(Isub);
MAT *Iy = gradient_y(Isub);
MAT *IE = m_get(Isub->m, Isub->n);
for (i = 0; i < Isub->m; i++) {
for (j = 0; j < Isub->n; j++) {
double temp_x = m_get_val(Ix, i, j);
double temp_y = m_get_val(Iy, i, j);
m_set_val(IE, i, j, sqrt((temp_x * temp_x) + (temp_y * temp_y)));
}
}
// Compute the motion gradient vector flow (MGVF) edgemaps
long long MGVF_start_time = get_time();
MAT *IMGVF = MGVF(IE, 1, 1);
MGVF_time += get_time() - MGVF_start_time;
// Determine the position of the cell in the subimage
xci = xci - (double) u1;
yci = yci - (double) (v1 - 1);
ycavg = ycavg - (double) (v1 - 1);
// Evolve the snake
long long snake_start_time = get_time();
ellipseevolve(IMGVF, &xci, &yci, ri, t, Np, (double) R, ycavg);
snake_time += get_time() - snake_start_time;
// Compute the cell's new position in the full image
xci = xci + u1;
yci = yci + (v1 - 1);
// Store the new location of the cell and the snake
xc[cell_num][frame_num] = xci;
yc[cell_num][frame_num] = yci;
for (j = 0; j < Np; j++) {
r[cell_num][j][frame_num] = ri[j];
x[cell_num][j][frame_num] = xc[cell_num][frame_num] + (ri[j] * cos(t[j]));
y[cell_num][j][frame_num] = yc[cell_num][frame_num] + (ri[j] * sin(t[j]));
}
// Output the updated center of each cell
//printf("%d,%f,%f\n", cell_num, xci[cell_num], yci[cell_num]);
// Free temporary memory
m_free(IMGVF);
free(ri);
}
// Output a new line to visually distinguish the output from different frames
//printf("\n");
}
// Free temporary memory
free(t);
free_2d_double(xc);
free_2d_double(yc);
free_3d_double(r);
free_3d_double(x);
free_3d_double(y);
// Report average processing time per frame
printf("\n\nTracking runtime (average per frame):\n");
printf("------------------------------------\n");
printf("MGVF computation: %.5f seconds\n", ((float) (MGVF_time)) / (float) (1000*1000*Nf));
printf(" Snake evolution: %.5f seconds\n", ((float) (snake_time)) / (float) (1000*1000*Nf));
}
MAT *MGVF(MAT *I, double vx, double vy) {
/*
% MGVF calculate the motion gradient vector flow (MGVF)
% for the image 'I'
%
% Based on the algorithm in:
% Motion gradient vector flow: an external force for tracking rolling
% leukocytes with shape and size constrained active contours
% Ray, N. and Acton, S.T.
% IEEE Transactions on Medical Imaging
% Volume: 23, Issue: 12, December 2004
% Pages: 1466 - 1478
%
% INPUTS
% I...........image
% vx,vy.......velocity vector
%
% OUTPUT
% IMGVF.......MGVF vector field as image
%
% Matlab code written by: DREW GILLIAM (based on work by GANG DONG /
% NILANJAN RAY)
% Ported to C by: MICHAEL BOYER
*/
// Constants
double converge = 0.00001;
double mu = 0.5;
double epsilon = 0.0000000001;
double lambda = 8.0 * mu + 1.0;
// Smallest positive value expressable in double-precision
double eps = pow(2.0, -52.0);
// Maximum number of iterations to compute the MGVF matrix
int iterations = 500;
// Find the maximum and minimum values in I
int m = I->m, n = I->n, i, j;
double Imax = m_get_val(I, 0, 0);
double Imin = m_get_val(I, 0, 0);
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
double temp = m_get_val(I, i, j);
if (temp > Imax) Imax = temp;
else if (temp < Imin) Imin = temp;
}
}
// Normalize the image I
double scale = 1.0 / (Imax - Imin + eps);
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
double old_val = m_get_val(I, i, j);
m_set_val(I, i, j, (old_val - Imin) * scale);
}
}
// Initialize the output matrix IMGVF with values from I
MAT *IMGVF = m_get(m, n);
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
m_set_val(IMGVF, i, j, m_get_val(I, i, j));
}
}
// Precompute row and column indices for the
// neighbor difference computation below
int *rowU = (int *) malloc(sizeof(int) * m);
int *rowD = (int *) malloc(sizeof(int) * m);
int *colL = (int *) malloc(sizeof(int) * n);
int *colR = (int *) malloc(sizeof(int) * n);
rowU[0] = 0;
rowD[m - 1] = m - 1;
for (i = 1; i < m; i++) {
rowU[i] = i - 1;
rowD[i - 1] = i;
}
colL[0] = 0;
colR[n - 1] = n - 1;
for (j = 1; j < n; j++) {
colL[j] = j - 1;
colR[j - 1] = j;
}
// Allocate matrices used in the while loop below
MAT *U = m_get(m, n), *D = m_get(m, n), *L = m_get(m, n), *R = m_get(m, n);
MAT *UR = m_get(m, n), *DR = m_get(m, n), *UL = m_get(m, n), *DL = m_get(m, n);
MAT *UHe = m_get(m, n), *DHe = m_get(m, n), *LHe = m_get(m, n), *RHe = m_get(m, n);
MAT *URHe = m_get(m, n), *DRHe = m_get(m, n), *ULHe = m_get(m, n), *DLHe = m_get(m, n);
// Precompute constants to avoid division in the for loops below
double mu_over_lambda = mu / lambda;
double one_over_lambda = 1.0 / lambda;
// Compute the MGVF
int iter = 0;
double mean_diff = 1.0;
while ((iter < iterations) && (mean_diff > converge)) {
// Compute the difference between each pixel and its eight neighbors
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
double subtrahend = m_get_val(IMGVF, i, j);
m_set_val(U, i, j, m_get_val(IMGVF, rowU[i], j) - subtrahend);
m_set_val(D, i, j, m_get_val(IMGVF, rowD[i], j) - subtrahend);
m_set_val(L, i, j, m_get_val(IMGVF, i, colL[j]) - subtrahend);
m_set_val(R, i, j, m_get_val(IMGVF, i, colR[j]) - subtrahend);
m_set_val(UR, i, j, m_get_val(IMGVF, rowU[i], colR[j]) - subtrahend);
m_set_val(DR, i, j, m_get_val(IMGVF, rowD[i], colR[j]) - subtrahend);
m_set_val(UL, i, j, m_get_val(IMGVF, rowU[i], colL[j]) - subtrahend);
m_set_val(DL, i, j, m_get_val(IMGVF, rowD[i], colL[j]) - subtrahend);
}
}
// Compute the regularized heaviside version of the matrices above
heaviside( UHe, U, -vy, epsilon);
heaviside( DHe, D, vy, epsilon);
heaviside( LHe, L, -vx, epsilon);
heaviside( RHe, R, vx, epsilon);
heaviside(URHe, UR, vx - vy, epsilon);
heaviside(DRHe, DR, vx + vy, epsilon);
heaviside(ULHe, UL, -vx - vy, epsilon);
heaviside(DLHe, DL, vy - vx, epsilon);
// Update the IMGVF matrix
double total_diff = 0.0;
for (i = 0; i < m; i++) {
for (j = 0; j < n; j++) {
// Store the old value so we can compute the difference later
double old_val = m_get_val(IMGVF, i, j);
// Compute IMGVF += (mu / lambda)(UHe .*U + DHe .*D + LHe .*L + RHe .*R +
// URHe.*UR + DRHe.*DR + ULHe.*UL + DLHe.*DL);
double vU = m_get_val(UHe, i, j) * m_get_val(U, i, j);
double vD = m_get_val(DHe, i, j) * m_get_val(D, i, j);
double vL = m_get_val(LHe, i, j) * m_get_val(L, i, j);
double vR = m_get_val(RHe, i, j) * m_get_val(R, i, j);
double vUR = m_get_val(URHe, i, j) * m_get_val(UR, i, j);
double vDR = m_get_val(DRHe, i, j) * m_get_val(DR, i, j);
double vUL = m_get_val(ULHe, i, j) * m_get_val(UL, i, j);
double vDL = m_get_val(DLHe, i, j) * m_get_val(DL, i, j);
double vHe = old_val + mu_over_lambda * (vU + vD + vL + vR + vUR + vDR + vUL + vDL);
// Compute IMGVF -= (1 / lambda)(I .* (IMGVF - I))
double vI = m_get_val(I, i, j);
double new_val = vHe - (one_over_lambda * vI * (vHe - vI));
m_set_val(IMGVF, i, j, new_val);
// Keep track of the absolute value of the differences
// between this iteration and the previous one
total_diff += fabs(new_val - old_val);
}
}
// Compute the mean absolute difference between this iteration
// and the previous one to check for convergence
mean_diff = total_diff / (double) (m * n);
iter++;
}
// Free memory
free(rowU);
free(rowD);
free(colL);
free(colR);
m_free(U);
m_free(D);
m_free(L);
m_free(R);
m_free(UR);
m_free(DR);
m_free(UL);
m_free(DL);
m_free(UHe);
m_free(DHe);
m_free(LHe);
m_free(RHe);
m_free(URHe);
m_free(DRHe);
m_free(ULHe);
m_free(DLHe);
return IMGVF;
}
void ellipsetrack(avi_t *video, double *xc0, double *yc0, int Nc, int R, int Np, int Nf) {
/*
% ELLIPSETRACK tracks cells in the movie specified by 'video', at
% locations 'xc0'/'yc0' with radii R using an ellipse with Np discrete
% points, starting at frame number one and stopping at frame number 'Nf'.
%
% INPUTS:
% video.......pointer to avi video object
% xc0,yc0.....initial center location (Nc entries)
% Nc..........number of cells
% R...........initial radius
% Np..........number of snaxels points per snake
% Nf..........number of frames in which to track
%
% Matlab code written by: DREW GILLIAM (based on code by GANG DONG /
% NILANJAN RAY)
% Ported to C by: MICHAEL BOYER
*/
// Compute angle parameter
double *t = (double *) malloc(sizeof(double) * Np);
double increment = (2.0 * PI) / (double) Np;
int i, j;
for (i = 0; i < Np; i++) {
t[i] = increment * (double) i ;
}
// Allocate space for a snake for each cell in each frame
double **xc = alloc_2d_double(Nc, Nf + 1);
double **yc = alloc_2d_double(Nc, Nf + 1);
double ***r = alloc_3d_double(Nc, Np, Nf + 1);
double ***x = alloc_3d_double(Nc, Np, Nf + 1);
double ***y = alloc_3d_double(Nc, Np, Nf + 1);
// Save the first snake for each cell
for (i = 0; i < Nc; i++) {
xc[i][0] = xc0[i];
yc[i][0] = yc0[i];
for (j = 0; j < Np; j++) {
r[i][j][0] = (double) R;
}
}
// Generate ellipse points for each cell
for (i = 0; i < Nc; i++) {
for (j = 0; j < Np; j++) {
x[i][j][0] = xc[i][0] + (r[i][j][0] * cos(t[j]));
y[i][j][0] = yc[i][0] + (r[i][j][0] * sin(t[j]));
}
}
// Allocate arrays so we can break up the per-cell for loop below
double *xci = (double *) malloc(sizeof(double) * Nc);
double *yci = (double *) malloc(sizeof(double) * Nc);
double **ri = alloc_2d_double(Nc, Np);
double *ycavg = (double *) malloc(sizeof(double) * Nc);
int *u1 = (int *) malloc(sizeof(int) * Nc);
int *u2 = (int *) malloc(sizeof(int) * Nc);
int *v1 = (int *) malloc(sizeof(int) * Nc);
int *v2 = (int *) malloc(sizeof(int) * Nc);
MAT **Isub = (MAT **) malloc(sizeof(MAT *) * Nc);
MAT **Ix = (MAT **) malloc(sizeof(MAT *) * Nc);
MAT **Iy = (MAT **) malloc(sizeof(MAT *) * Nc);
MAT **IE = (MAT **) malloc(sizeof(MAT *) * Nc);
// Keep track of the total time spent on computing
// the MGVF matrix and evolving the snakes
long long MGVF_time = 0;
long long snake_time = 0;
// Process each frame sequentially
int frame_num;
for (frame_num = 1; frame_num <= Nf; frame_num++) {
printf("\rProcessing frame %d / %d", frame_num, Nf);
fflush(stdout);
// Get the current video frame and its dimensions
MAT *I = get_frame(video, frame_num, 0, 1);
int Ih = I->m;
int Iw = I->n;
// Initialize the current positions to be equal to the previous positions
for (i = 0; i < Nc; i++) {
xc[i][frame_num] = xc[i][frame_num - 1];
yc[i][frame_num] = yc[i][frame_num - 1];
for (j = 0; j < Np; j++) {
r[i][j][frame_num] = r[i][j][frame_num - 1];
}
}
// Sequentially extract the subimage near each cell
int cell_num;
for (cell_num = 0; cell_num < Nc; cell_num++) {
// Make copies of the current cell's location
xci[cell_num] = xc[cell_num][frame_num];
yci[cell_num] = yc[cell_num][frame_num];
for (j = 0; j < Np; j++) {
ri[cell_num][j] = r[cell_num][j][frame_num];
}
// Add up the last ten y values for this cell
// (or fewer if there are not yet ten previous frames)
ycavg[cell_num] = 0.0;
for (i = (frame_num > 10 ? frame_num - 10 : 0); i < frame_num; i++) {
ycavg[cell_num] += yc[cell_num][i];
}
// Compute the average of the last ten values
// (this represents the expected location of the cell)
ycavg[cell_num] = ycavg[cell_num] / (double) (frame_num > 10 ? 10 : frame_num);
// Determine the range of the subimage surrounding the current position
u1[cell_num] = max(xci[cell_num] - 4.0 * R + 0.5, 0 );
u2[cell_num] = min(xci[cell_num] + 4.0 * R + 0.5, Iw - 1);
v1[cell_num] = max(yci[cell_num] - 2.0 * R + 1.5, 0 );
v2[cell_num] = min(yci[cell_num] + 2.0 * R + 1.5, Ih - 1);
// Extract the subimage
Isub[cell_num] = m_get(v2[cell_num] - v1[cell_num] + 1, u2[cell_num] - u1[cell_num] + 1);
for (i = v1[cell_num]; i <= v2[cell_num]; i++) {
for (j = u1[cell_num]; j <= u2[cell_num]; j++) {
m_set_val(Isub[cell_num], i - v1[cell_num], j - u1[cell_num], m_get_val(I, i, j));
}
}
// Compute the subimage gradient magnitude
Ix[cell_num] = gradient_x(Isub[cell_num]);
Iy[cell_num] = gradient_y(Isub[cell_num]);
IE[cell_num] = m_get(Isub[cell_num]->m, Isub[cell_num]->n);
for (i = 0; i < Isub[cell_num]->m; i++) {
for (j = 0; j < Isub[cell_num]->n; j++) {
double temp_x = m_get_val(Ix[cell_num], i, j);
double temp_y = m_get_val(Iy[cell_num], i, j);
m_set_val(IE[cell_num], i, j, sqrt((temp_x * temp_x) + (temp_y * temp_y)));
}
}
}
// Compute the motion gradient vector flow (MGVF) edgemaps for all cells concurrently
long long MGVF_start_time = get_time();
MAT **IMGVF = MGVF(IE, 1, 1, Nc);
MGVF_time += get_time() - MGVF_start_time;
// Sequentially determine the new location of each cell
for (cell_num = 0; cell_num < Nc; cell_num++) {
// Determine the position of the cell in the subimage
xci[cell_num] = xci[cell_num] - (double) u1[cell_num];
yci[cell_num] = yci[cell_num] - (double) (v1[cell_num] - 1);
ycavg[cell_num] = ycavg[cell_num] - (double) (v1[cell_num] - 1);
// Evolve the snake
long long snake_start_time = get_time();
ellipseevolve(IMGVF[cell_num], &(xci[cell_num]), &(yci[cell_num]), ri[cell_num], t, Np, (double) R, ycavg[cell_num]);
snake_time += get_time() - snake_start_time;
// Compute the cell's new position in the full image
xci[cell_num] = xci[cell_num] + u1[cell_num];
yci[cell_num] = yci[cell_num] + (v1[cell_num] - 1);
// Store the new location of the cell and the snake
xc[cell_num][frame_num] = xci[cell_num];
yc[cell_num][frame_num] = yci[cell_num];
for (j = 0; j < Np; j++) {
r[cell_num][j][frame_num] = 0;
r[cell_num][j][frame_num] = ri[cell_num][j];
x[cell_num][j][frame_num] = xc[cell_num][frame_num] + (ri[cell_num][j] * cos(t[j]));
y[cell_num][j][frame_num] = yc[cell_num][frame_num] + (ri[cell_num][j] * sin(t[j]));
}
// Output the updated center of each cell
// printf("\n%d,%f,%f", cell_num, xci[cell_num], yci[cell_num]);
// Free temporary memory
m_free(Isub[cell_num]);
m_free(Ix[cell_num]);
m_free(Iy[cell_num]);
m_free(IE[cell_num]);
m_free(IMGVF[cell_num]);
}
#ifdef OUTPUT
if (frame_num == Nf)
{
FILE * pFile;
pFile = fopen ("result.txt","w+");
for (cell_num = 0; cell_num < Nc; cell_num++)
fprintf(pFile,"\n%d,%f,%f", cell_num, xci[cell_num], yci[cell_num]);
fclose (pFile);
}
#endif
free(IMGVF);
// Output a new line to visually distinguish the output from different frames
//printf("\n");
}
// Free temporary memory
free_2d_double(xc);
free_2d_double(yc);
free_3d_double(r);
free_3d_double(x);
free_3d_double(y);
free(t);
free(xci);
free(yci);
free_2d_double(ri);
free(ycavg);
free(u1);
free(u2);
free(v1);
free(v2);
free(Isub);
free(Ix);
free(Iy);
free(IE);
// Report average processing time per frame
printf("\n\nTracking runtime (average per frame):\n");
printf("------------------------------------\n");
printf("MGVF computation: %.5f seconds\n", ((float) (MGVF_time)) / (float) (1000*1000*Nf));
printf(" Snake evolution: %.5f seconds\n", ((float) (snake_time)) / (float) (1000*1000*Nf));
printf("CAUTION: cpu_offset: %d time: %lf mseconds\n", cpu_offset, ((float) (MGVF_time)) / (float) (1000*1000*Nf)*1000);
}