static lgfs2_rgrps_t mockup_rgrp(void)
{
struct gfs2_sbd *sdp;
lgfs2_rgrps_t rgs;
unsigned i;
uint64_t addr;
struct gfs2_rindex ri = {0};
lgfs2_rgrp_t rg;
uint32_t rgsize = (1024 << 20) / 4096;
sdp = calloc(1, sizeof(*sdp));
fail_unless(sdp != NULL);
sdp->device.length = rgsize + 20;
sdp->device_fd = -1;
sdp->bsize = sdp->sd_sb.sb_bsize = 4096;
compute_constants(sdp);
rgs = lgfs2_rgrps_init(sdp, 0, 0);
fail_unless(rgs != NULL);
lgfs2_rgrps_plan(rgs, sdp->device.length - 16, rgsize);
addr = lgfs2_rindex_entry_new(rgs, &ri, 16, rgsize);
ck_assert(addr != 0);
rg = lgfs2_rgrps_append(rgs, &ri);
fail_unless(rg != NULL);
for (i = 0; i < rg->ri.ri_length; i++) {
rg->bits[i].bi_bh = bget(sdp, rg->ri.ri_addr + i);
fail_unless(rg->bits[i].bi_bh != NULL);
}
return rgs;
}
static double
distance(struct SubSig *SubSig1, struct SubSig *SubSig2, int nbands)
{
double dist;
static int first = 1;
struct SigSet S;
static struct ClassSig *Sig3;
static struct SubSig *SubSig3;
/* allocate scratch space first time subroutine is called */
if (first) {
I_InitSigSet(&S);
I_SigSetNBands(&S, nbands);
Sig3 = I_NewClassSig(&S);
I_NewSubSig(&S, Sig3);
SubSig3 = Sig3->SubSig;
first = 0;
}
/* form SubSig3 by adding SubSig1 and SubSig2 */
add_SubSigs(SubSig1, SubSig2, SubSig3, nbands);
/* compute constant for SubSig3 */
compute_constants(Sig3, nbands);
/* compute distance */
dist = SubSig1->N * SubSig1->cnst + SubSig2->N * SubSig2->cnst
- SubSig3->N * SubSig3->cnst;
return (dist);
}
static void
reduce_order(struct ClassSig *Sig, int nbands, int *min_ii, int *min_jj)
{
int i, j;
int min_i = 0, min_j = 0;
double dist;
double min_dist = 0;
struct SubSig *SubSig1, *SubSig2;
static int first = 1;
struct SigSet S;
static struct ClassSig *Sig3;
static struct SubSig *SubSig3;
/* allocate scratch space first time subroutine is called */
if (first) {
I_InitSigSet(&S);
I_SigSetNBands(&S, nbands);
Sig3 = I_NewClassSig(&S);
I_NewSubSig(&S, Sig3);
SubSig3 = Sig3->SubSig;
first = 0;
}
if (Sig->nsubclasses > 1) {
/* find the closest subclasses */
for (i = 0; i < Sig->nsubclasses - 1; i++)
for (j = i + 1; j < Sig->nsubclasses; j++) {
dist = distance(&(Sig->SubSig[i]), &(Sig->SubSig[j]), nbands);
if ((i == 0) && (j == 1)) {
min_dist = dist;
min_i = i;
min_j = j;
}
if (dist < min_dist) {
min_dist = dist;
min_i = i;
min_j = j;
}
}
*min_ii = min_i;
*min_jj = min_j;
/* Combine Subclasses */
SubSig1 = &(Sig->SubSig[min_i]);
SubSig2 = &(Sig->SubSig[min_j]);
add_SubSigs(SubSig1, SubSig2, SubSig3, nbands);
compute_constants(Sig3, nbands);
copy_SubSig(SubSig3, SubSig1, nbands);
/* remove extra subclass */
for (i = min_j; i < Sig->nsubclasses - 1; i++)
copy_SubSig(&(Sig->SubSig[i + 1]), &(Sig->SubSig[i]), nbands);
/* decrement number of Subclasses */
Sig->nsubclasses--;
}
}
static int reestimate(struct ClassSig *Sig, int nbands)
{
int i;
int s;
int b1, b2;
int singular;
double pi_sum;
double diff1, diff2;
struct ClassData *Data;
G_debug(2, "reestimate()");
/* set data pointer */
Data = &(Sig->ClassData);
pi_sum = 0;
for (i = 0; i < Sig->nsubclasses; i++) {
/* Compute N */
Sig->SubSig[i].N = 0;
for (s = 0; s < Data->npixels; s++)
Sig->SubSig[i].N += Data->p[s][i];
Sig->SubSig[i].pi = Sig->SubSig[i].N;
/* Compute means and variances for each subcluster, */
/* and remove small clusters. */
/* For large subclusters */
if (Sig->SubSig[i].N > SMALLEST_SUBCLUST) {
/* Compute mean */
for (b1 = 0; b1 < nbands; b1++) {
Sig->SubSig[i].means[b1] = 0;
for (s = 0; s < Data->npixels; s++)
if (!Rast_is_d_null_value(&Data->x[s][b1]))
Sig->SubSig[i].means[b1] +=
Data->p[s][i] * Data->x[s][b1];
Sig->SubSig[i].means[b1] /= (Sig->SubSig[i].N);
/* Compute R */
for (b2 = 0; b2 <= b1; b2++) {
Sig->SubSig[i].R[b1][b2] = 0;
for (s = 0; s < Data->npixels; s++) {
if (!Rast_is_d_null_value(&Data->x[s][b1])
&& !Rast_is_d_null_value(&Data->x[s][b2])) {
diff1 = Data->x[s][b1] - Sig->SubSig[i].means[b1];
diff2 = Data->x[s][b2] - Sig->SubSig[i].means[b2];
Sig->SubSig[i].R[b1][b2] +=
Data->p[s][i] * diff1 * diff2;
}
}
Sig->SubSig[i].R[b1][b2] /= (Sig->SubSig[i].N);
Sig->SubSig[i].R[b2][b1] = Sig->SubSig[i].R[b1][b2];
}
}
}
/* For small subclusters */
else {
G_warning(_("Subsignature %d only contains %.0f pixels"),
i, Sig->SubSig[i].N);
Sig->SubSig[i].pi = 0;
for (b1 = 0; b1 < nbands; b1++) {
Sig->SubSig[i].means[b1] = 0;
for (b2 = 0; b2 < nbands; b2++)
Sig->SubSig[i].R[b1][b2] = 0;
}
}
pi_sum += Sig->SubSig[i].pi;
}
/* Normalize probabilities for subclusters */
if (pi_sum > 0) {
for (i = 0; i < Sig->nsubclasses; i++)
Sig->SubSig[i].pi /= pi_sum;
}
else {
for (i = 0; i < Sig->nsubclasses; i++)
Sig->SubSig[i].pi = 0;
}
/* Compute constants and reestimate if any singular subclusters occur */
singular = compute_constants(Sig, nbands);
return (singular);
}
static void seed(struct ClassSig *Sig, int nbands)
{
int i, b1, b2;
double period;
double *mean, **R;
G_debug(1, "seed()");
/* Compute the mean of variance for each band */
mean = G_alloc_vector(nbands);
R = G_alloc_matrix(nbands, nbands);
n_nulls = (int *)G_calloc(nbands, sizeof(int));
total_nulls = 0;
for (b1 = 0; b1 < nbands; b1++) {
n_nulls[b1] = 0;
mean[b1] = 0.0;
for (i = 0; i < Sig->ClassData.npixels; i++) {
if (Rast_is_d_null_value(&Sig->ClassData.x[i][b1])) {
n_nulls[b1]++;
total_nulls++;
}
else
mean[b1] += Sig->ClassData.x[i][b1];
}
mean[b1] /= (double)(Sig->ClassData.npixels - n_nulls[b1]);
}
for (b1 = 0; b1 < nbands; b1++)
for (b2 = 0; b2 < nbands; b2++) {
R[b1][b2] = 0.0;
for (i = 0; i < Sig->ClassData.npixels; i++) {
if (!Rast_is_d_null_value(&Sig->ClassData.x[i][b1]) &&
!Rast_is_d_null_value(&Sig->ClassData.x[i][b2]))
R[b1][b2] +=
(Sig->ClassData.x[i][b1]) * (Sig->ClassData.x[i][b2]);
}
R[b1][b2] /= (double)(Sig->ClassData.npixels - n_nulls[b1] -
n_nulls[b2]);
R[b1][b2] -= mean[b1] * mean[b2];
}
/* Compute the sampling period for seeding */
if (Sig->nsubclasses > 1) {
period = (Sig->ClassData.npixels - 1) / (Sig->nsubclasses - 1.0);
}
else
period = 0;
/* Seed the means and set the diagonal covariance components */
for (i = 0; i < Sig->nsubclasses; i++) {
for (b1 = 0; b1 < nbands; b1++) {
if (Rast_is_d_null_value(&Sig->ClassData.x[(int)(i * period)][b1]))
Rast_set_d_null_value(&Sig->SubSig[i].means[b1], 1);
else
Sig->SubSig[i].means[b1] =
Sig->ClassData.x[(int)(i * period)][b1];
for (b2 = 0; b2 < nbands; b2++) {
Sig->SubSig[i].R[b1][b2] = R[b1][b2];
}
}
Sig->SubSig[i].pi = 1.0 / Sig->nsubclasses;
}
G_free_vector(mean);
G_free_matrix(R);
compute_constants(Sig, nbands);
}
int main(int argc, char *argv[])
{
struct gfs2_sbd sbd, *sdp = &sbd;
int rindex_fd;
int error = EXIT_SUCCESS;
int devflags = (test ? O_RDONLY : O_RDWR) | O_CLOEXEC;
setlocale(LC_ALL, "");
textdomain("gfs2-utils");
srandom(time(NULL) ^ getpid());
memset(sdp, 0, sizeof(struct gfs2_sbd));
sdp->bsize = GFS2_DEFAULT_BSIZE;
sdp->rgsize = -1;
sdp->jsize = GFS2_DEFAULT_JSIZE;
sdp->qcsize = GFS2_DEFAULT_QCSIZE;
sdp->md.journals = 1;
decode_arguments(argc, argv, sdp);
for(; (argc - optind) > 0; optind++) {
struct metafs mfs = {0};
struct mntent *mnt;
unsigned rgcount;
unsigned old_rg_count;
lgfs2_rgrps_t rgs;
error = lgfs2_open_mnt(argv[optind], O_RDONLY|O_CLOEXEC, &sdp->path_fd,
devflags, &sdp->device_fd, &mnt);
if (error != 0) {
fprintf(stderr, _("Error looking up mount '%s': %s\n"), argv[optind], strerror(errno));
exit(EXIT_FAILURE);
}
if (mnt == NULL) {
fprintf(stderr, _("%s: not a mounted gfs2 file system\n"), argv[optind]);
continue;
}
if (lgfs2_get_dev_info(sdp->device_fd, &sdp->dinfo) < 0) {
perror(mnt->mnt_fsname);
exit(EXIT_FAILURE);
}
sdp->sd_sb.sb_bsize = GFS2_DEFAULT_BSIZE;
sdp->bsize = sdp->sd_sb.sb_bsize;
if (compute_constants(sdp)) {
log_crit("%s\n", _("Failed to compute file system constants"));
exit(EXIT_FAILURE);
}
if (read_sb(sdp) < 0) {
fprintf(stderr, _("Error reading superblock.\n"));
exit(EXIT_FAILURE);
}
if (sdp->gfs1) {
fprintf(stderr, _("cannot grow gfs1 filesystem\n"));
exit(EXIT_FAILURE);
}
fix_device_geometry(sdp);
mfs.context = copy_context_opt(mnt);
if (mount_gfs2_meta(&mfs, mnt->mnt_dir, (print_level > MSG_NOTICE))) {
perror(_("Failed to mount GFS2 meta file system"));
exit(EXIT_FAILURE);
}
rindex_fd = open_rindex(mfs.path, (test ? O_RDONLY : O_RDWR));
if (rindex_fd < 0) {
cleanup_metafs(&mfs);
exit(EXIT_FAILURE);
}
/* Get master dinode */
sdp->master_dir = lgfs2_inode_read(sdp, sdp->sd_sb.sb_master_dir.no_addr);
if (sdp->master_dir == NULL) {
perror(_("Could not read master directory"));
exit(EXIT_FAILURE);
}
rgs = rgrps_init(sdp);
if (rgs == NULL) {
perror(_("Could not initialise resource groups"));
error = -1;
goto out;
}
/* Fetch the rindex from disk. We aren't using gfs2 here, */
/* which means that the bitmaps will most likely be cached */
/* and therefore out of date. It shouldn't matter because */
/* we're only going to write out new RG information after */
/* the existing RGs, and only write to the index at EOF. */
log_info(_("Gathering resource group information for %s\n"), argv[optind]);
old_rg_count = lgfs2_rindex_read_fd(rindex_fd, rgs);
if (old_rg_count == 0) {
perror(_("Failed to scan existing resource groups"));
error = -EXIT_FAILURE;
goto out;
}
if (metafs_interrupted)
goto out;
fssize = lgfs2_rgrp_align_addr(rgs, filesystem_size(rgs) + 1);
/* We're done with the old rgs now that we have the fssize and rg count */
lgfs2_rgrps_free(&rgs);
/* Now lets set up the new ones with alignment and all */
rgs = rgrps_init(sdp);
if (rgs == NULL) {
perror(_("Could not initialise new resource groups"));
error = -1;
goto out;
}
fsgrowth = (sdp->device.length - fssize);
rgcount = lgfs2_rgrps_plan(rgs, fsgrowth, ((GFS2_MAX_RGSIZE << 20) / sdp->bsize));
if (rgcount == 0) {
log_err( _("The calculated resource group size is too small.\n"));
log_err( _("%s has not grown.\n"), argv[optind]);
error = -1;
goto out;
}
print_info(sdp, mnt->mnt_fsname, mnt->mnt_dir);
rgcount = initialize_new_portion(sdp, rgs);
if (rgcount == 0 || metafs_interrupted)
goto out;
fsync(sdp->device_fd);
fix_rindex(rindex_fd, rgs, old_rg_count, rgcount);
out:
lgfs2_rgrps_free(&rgs);
close(rindex_fd);
cleanup_metafs(&mfs);
close(sdp->device_fd);
if (metafs_interrupted)
break;
}
close(sdp->path_fd);
sync();
if (metafs_interrupted) {
log_notice( _("gfs2_grow interrupted.\n"));
exit(1);
}
log_notice( _("gfs2_grow complete.\n"));
return error;
}
// 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;
}