boost::optional<boost::shared_ptr<Error> > KeyEventChecker::DoCheck(void) {
unsigned int key_state;
OPT_UINT(key_state, DxLibWrapper::GetJoypadInputState());
const KeyMap key_map[KEY_MAX] = {
{KeyEvent::KEY_A, PAD_INPUT_B}, {KeyEvent::KEY_B, PAD_INPUT_C}, {KeyEvent::KEY_X, PAD_INPUT_A}, {KeyEvent::KEY_Y, PAD_INPUT_X},
{KeyEvent::KEY_L, PAD_INPUT_L}, {KeyEvent::KEY_R, PAD_INPUT_R}, {KeyEvent::KEY_START, PAD_INPUT_START}, {KeyEvent::KEY_SELECT, PAD_INPUT_M},
{KeyEvent::KEY_UP, PAD_INPUT_UP},{KeyEvent::KEY_DOWN, PAD_INPUT_DOWN}, {KeyEvent::KEY_LEFT, PAD_INPUT_LEFT}, {KeyEvent::KEY_RIGHT, PAD_INPUT_RIGHT}
};
BOOST_FOREACH(KeyMap key, key_map) {
const KeyEvent::ACTION act = (key_state & key.DxLibKey) ? KeyEvent::KEY_PRESS : KeyEvent::KEY_RELEASE;
if(!prev[key.WTenKey]) {
prev[key.WTenKey] = act;
} else if(prev[key.WTenKey] != act) {
prev[key.WTenKey] = act;
press_frame[key.WTenKey] = 0;
OPT_ERROR(SendKeyEvent(act, key.WTenKey));
} else if(act == KeyEvent::KEY_PRESS) {
press_frame[key.WTenKey]++;
if(press_frame[key.WTenKey] > 20 && (press_frame[key.WTenKey]%2) == 0) {
OPT_ERROR(SendKeyEvent(KeyEvent::KEY_PRESS_MORE, key.WTenKey));
}
}
}
return boost::none;
}
int main_estdelay(int argc, char* argv[])
{
bool ring = false;
int pad_factor = 100;
unsigned int no_intersec_sp = 1;
float size = 1.5;
const struct opt_s opts[] = {
OPT_SET('R', &ring, "RING method"),
OPT_INT('p', &pad_factor, "p", "[RING] Padding"),
OPT_UINT('n', &no_intersec_sp, "n", "[RING] Number of intersecting spokes"),
OPT_FLOAT('r', &size, "r", "[RING] Central region size"),
};
cmdline(&argc, argv, 2, 2, usage_str, help_str, ARRAY_SIZE(opts), opts);
num_init();
if (pad_factor % 2 != 0)
error("Pad_factor -p should be even\n");
long tdims[DIMS];
const complex float* traj = load_cfl(argv[1], DIMS, tdims);
long tdims1[DIMS];
md_select_dims(DIMS, ~MD_BIT(1), tdims1, tdims);
complex float* traj1 = md_alloc(DIMS, tdims1, CFL_SIZE);
md_slice(DIMS, MD_BIT(1), (long[DIMS]){ 0 }, tdims, traj1, traj, CFL_SIZE);
int main_bpsense(int argc, char* argv[])
{
// -----------------------------------------------------------
// set up conf and option parser
struct bpsense_conf conf = bpsense_defaults;
struct iter_admm_conf iconf = iter_admm_defaults;
conf.iconf = &iconf;
conf.iconf->rho = 10; // more sensibile default
bool usegpu = false;
const char* psf = NULL;
const char* image_truth_fname = NULL;
bool im_truth = false;
bool use_tvnorm = false;
double start_time = timestamp();
const struct opt_s opts[] = {
OPT_FLOAT('e', &conf.eps, "eps", "data consistency error"),
OPT_FLOAT('r', &conf.lambda, "lambda", "l2 regularization parameter"),
OPT_FLOAT('u', &conf.iconf->rho, "rho", "ADMM penalty parameter"),
OPT_SET('c', &conf.rvc, "real-value constraint"),
OPT_SET('t', &use_tvnorm, "use TV norm"),
OPT_STRING('T', &image_truth_fname, "file", "compare to truth image"),
OPT_UINT('i', &conf.iconf->maxiter, "iter", "max. iterations"),
OPT_SET('g', &usegpu, "(use gpu)"),
OPT_STRING('p', &psf, "file", "point-spread function"),
};
cmdline(&argc, argv, 3, 3, usage_str, help_str, ARRAY_SIZE(opts), opts);
if (NULL != image_truth_fname)
im_truth = true;
// -----------------------------------------------------------
// load data and print some info about the recon
int N = DIMS;
long dims[N];
long dims1[N];
long img_dims[N];
long ksp_dims[N];
complex float* kspace_data = load_cfl(argv[1], N, ksp_dims);
complex float* sens_maps = load_cfl(argv[2], N, dims);
for (int i = 0; i < 4; i++) // sizes2[4] may be > 1
if (ksp_dims[i] != dims[i])
error("Dimensions of kspace and sensitivities do not match!\n");
assert(1 == ksp_dims[MAPS_DIM]);
(usegpu ? num_init_gpu : num_init)();
if (dims[MAPS_DIM] > 1)
debug_printf(DP_INFO, "%ld maps.\nESPIRiT reconstruction.\n", dims[4]);
if (conf.lambda > 0.)
debug_printf(DP_INFO, "l2 regularization: %f\n", conf.lambda);
if (use_tvnorm)
debug_printf(DP_INFO, "use Total Variation\n");
else
debug_printf(DP_INFO, "use Wavelets\n");
if (im_truth)
debug_printf(DP_INFO, "Compare to truth\n");
md_select_dims(N, ~(COIL_FLAG | MAPS_FLAG), dims1, dims);
md_select_dims(N, ~COIL_FLAG, img_dims, dims);
// -----------------------------------------------------------
// initialize sampling pattern
complex float* pattern = NULL;
long pat_dims[N];
if (NULL != psf) {
pattern = load_cfl(psf, N, pat_dims);
// FIXME: check compatibility
} else {
pattern = md_alloc(N, dims1, CFL_SIZE);
estimate_pattern(N, ksp_dims, COIL_DIM, pattern, kspace_data);
}
// -----------------------------------------------------------
// print some statistics
size_t T = md_calc_size(N, dims1);
long samples = (long)pow(md_znorm(N, dims1, pattern), 2.);
debug_printf(DP_INFO, "Size: %ld Samples: %ld Acc: %.2f\n", T, samples, (float)T/(float)samples);
// -----------------------------------------------------------
// fftmod to un-center data
fftmod(N, ksp_dims, FFT_FLAGS, kspace_data, kspace_data);
fftmod(N, dims, FFT_FLAGS, sens_maps, sens_maps);
// -----------------------------------------------------------
// apply scaling
float scaling = estimate_scaling(ksp_dims, NULL, kspace_data);
debug_printf(DP_INFO, "Scaling: %f\n", scaling);
if (scaling != 0.)
md_zsmul(N, ksp_dims, kspace_data, kspace_data, 1. / scaling);
// -----------------------------------------------------------
// create l1 prox operator and transform
long minsize[DIMS] = { [0 ... DIMS - 1] = 1 };
minsize[0] = MIN(img_dims[0], 16);
minsize[1] = MIN(img_dims[1], 16);
minsize[2] = MIN(img_dims[2], 16);
const struct linop_s* l1op = NULL;
const struct operator_p_s* l1prox = NULL;
if (use_tvnorm) {
l1op = grad_init(DIMS, img_dims, FFT_FLAGS);
l1prox = prox_thresh_create(DIMS + 1, linop_codomain(l1op)->dims, 1., 0u, usegpu);
conf.l1op_obj = l1op;
} else {
bool randshift = true;
l1op = linop_identity_create(DIMS, img_dims);
conf.l1op_obj = wavelet_create(DIMS, img_dims, FFT_FLAGS, minsize, false, usegpu);
l1prox = prox_wavethresh_create(DIMS, img_dims, FFT_FLAGS, minsize, 1., randshift, usegpu);
}
// -----------------------------------------------------------
// create image and load truth image
complex float* image = create_cfl(argv[3], N, img_dims);
md_clear(N, img_dims, image, CFL_SIZE);
long img_truth_dims[DIMS];
complex float* image_truth = NULL;
if (im_truth)
image_truth = load_cfl(image_truth_fname, DIMS, img_truth_dims);
// -----------------------------------------------------------
// call recon
if (usegpu)
#ifdef USE_CUDA
bpsense_recon_gpu(&conf, dims, image, sens_maps, dims1, pattern, l1op, l1prox, ksp_dims, kspace_data, image_truth);
#else
assert(0);
#endif
else
int main_poisson(int argc, char* argv[])
{
int yy = 128;
int zz = 128;
bool cutcorners = false;
float vardensity = 0.;
bool vd_def = false;
int T = 1;
int rnd = 0;
bool msk = true;
int points = -1;
float mindist = 1. / 1.275;
float yscale = 1.;
float zscale = 1.;
unsigned int calreg = 0;
const struct opt_s opts[] = {
OPT_INT('Y', &yy, "size", "size dimension 1"),
OPT_INT('Z', &zz, "size", "size dimension 2"),
OPT_FLOAT('y', &yscale, "acc", "acceleration dim 1"),
OPT_FLOAT('z', &zscale, "acc", "acceleration dim 2"),
OPT_UINT('C', &calreg, "size", "size of calibration region"),
OPT_SET('v', &vd_def, "variable density"),
OPT_FLOAT('V', &vardensity, "", "(variable density)"),
OPT_SET('e', &cutcorners, "elliptical scanning"),
OPT_FLOAT('D', &mindist, "", "()"),
OPT_INT('T', &T, "", "()"),
OPT_CLEAR('m', &msk, "()"),
OPT_INT('R', &points, "", "()"),
};
cmdline(&argc, argv, 1, 1, usage_str, help_str, ARRAY_SIZE(opts), opts);
if (vd_def && (0. == vardensity))
vardensity = 20.;
if (-1 != points)
rnd = 1;
assert((yscale >= 1.) && (zscale >= 1.));
// compute mindest and scaling
float kspext = MAX(yy, zz);
int Pest = T * (int)(1.2 * powf(kspext, 2.) / (yscale * zscale));
mindist /= kspext;
yscale *= (float)kspext / (float)yy;
zscale *= (float)kspext / (float)zz;
if (vardensity != 0.) {
// TODO
}
long dims[5] = { 1, yy, zz, T, 1 };
complex float* mask = NULL;
if (msk) {
mask = create_cfl(argv[1], 5, dims);
md_clear(5, dims, mask, sizeof(complex float));
}
int M = rnd ? (points + 1) : Pest;
int P;
while (true) {
float (*points)[2] = xmalloc(M * sizeof(float[3]));
int* kind = xmalloc(M * sizeof(int));
kind[0] = 0;
if (!rnd) {
points[0][0] = 0.5;
points[0][1] = 0.5;
if (1 == T) {
P = poissondisc(2, M, 1, vardensity, mindist, points);
} else {
float (*delta)[T] = xmalloc(T * T * sizeof(complex float));
float dd[T];
for (int i = 0; i < T; i++)
dd[i] = mindist;
mc_poisson_rmatrix(2, T, delta, dd);
P = poissondisc_mc(2, T, M, 1, vardensity, (const float (*)[T])delta, points, kind);
}
} else { // random pattern
P = M - 1;
for (int i = 0; i < P; i++)
random_point(2, points[i]);
}
if (P < M) {
for (int i = 0; i < P; i++) {
points[i][0] = (points[i][0] - 0.5) * yscale + 0.5;
points[i][1] = (points[i][1] - 0.5) * zscale + 0.5;
}
// throw away points outside
float center[2] = { 0.5, 0.5 };
int j = 0;
for (int i = 0; i < P; i++) {
if ((cutcorners ? dist : maxn)(2, center, points[i]) <= 0.5) {
points[j][0] = points[i][0];
points[j][1] = points[i][1];
j++;
}
}
P = j;
if (msk) {
// rethink module here
for (int i = 0; i < P; i++) {
int yy = (int)floorf(points[i][0] * dims[1]);
int zz = (int)floorf(points[i][1] * dims[2]);
if ((yy < 0) || (yy >= dims[1]) || (zz < 0) || (zz >= dims[2]))
continue;
if (1 == T)
mask[zz * dims[1] + yy] = 1.;//cexpf(2.i * M_PI * (float)kind[i] / (float)T);
else
mask[(kind[i] * dims[2] + zz) * dims[1] + yy] = 1.;//cexpf(2.i * M_PI * (float)kind[i] / (float)T);
}
} else {
#if 1
long sdims[2] = { 3, P };
complex float* samples = create_cfl(argv[1], 2, sdims);
for (int i = 0; i < P; i++) {
samples[3 * i + 0] = 0.;
samples[3 * i + 1] = (points[i][0] - 0.5) * dims[1];
samples[3 * i + 2] = (points[i][1] - 0.5) * dims[2];
// printf("%f %f\n", creal(samples[3 * i + 0]), creal(samples[3 * i + 1]));
}
unmap_cfl(2, sdims, (void*)samples);
#endif
}
break;
}
// repeat with more points
M *= 2;
free(points);
free(kind);
}
// calibration region
assert((mask != NULL) || (0 == calreg));
assert((calreg <= dims[1]) && (calreg <= dims[2]));
for (unsigned int i = 0; i < calreg; i++) {
for (unsigned int j = 0; j < calreg; j++) {
int y = (dims[1] - calreg) / 2 + i;
int z = (dims[2] - calreg) / 2 + j;
for (int k = 0; k < T; k++) {
if (0. == mask[(k * dims[2] + z) * dims[1] + y]) {
mask[(k * dims[2] + z) * dims[1] + y] = 1.;
P++;
}
}
}
}
printf("points: %d", P);
if (1 != T)
printf(", classes: %d", T);
if (NULL != mask) {
float f = cutcorners ? (M_PI / 4.) : 1.;
printf(", grid size: %ldx%ld%s = %ld (R = %f)", dims[1], dims[2], cutcorners ? "x(pi/4)" : "",
(long)(f * dims[1] * dims[2]), f * T * dims[1] * dims[2] / (float)P);
unmap_cfl(5, dims, (void*)mask);
}
printf("\n");
exit(0);
}
int main_pics(int argc, char* argv[])
{
// Initialize default parameters
struct sense_conf conf = sense_defaults;
bool use_gpu = false;
bool randshift = true;
unsigned int maxiter = 30;
float step = -1.;
// Start time count
double start_time = timestamp();
// Read input options
struct nufft_conf_s nuconf = nufft_conf_defaults;
nuconf.toeplitz = false;
float restrict_fov = -1.;
const char* pat_file = NULL;
const char* traj_file = NULL;
bool scale_im = false;
bool eigen = false;
float scaling = 0.;
unsigned int llr_blk = 8;
const char* image_truth_file = NULL;
bool im_truth = false;
const char* image_start_file = NULL;
bool warm_start = false;
bool hogwild = false;
bool fast = false;
float admm_rho = iter_admm_defaults.rho;
unsigned int admm_maxitercg = iter_admm_defaults.maxitercg;
struct opt_reg_s ropts;
ropts.r = 0;
ropts.algo = CG;
ropts.lambda = -1.;
const struct opt_s opts[] = {
{ 'l', true, opt_reg, &ropts, "1/-l2\t\ttoggle l1-wavelet or l2 regularization." },
OPT_FLOAT('r', &ropts.lambda, "lambda", "regularization parameter"),
{ 'R', true, opt_reg, &ropts, " <T>:A:B:C\tgeneralized regularization options (-Rh for help)" },
OPT_SET('c', &conf.rvc, "real-value constraint"),
OPT_FLOAT('s', &step, "step", "iteration stepsize"),
OPT_UINT('i', &maxiter, "iter", "max. number of iterations"),
OPT_STRING('t', &traj_file, "file", "k-space trajectory"),
OPT_CLEAR('n', &randshift, "disable random wavelet cycle spinning"),
OPT_SET('g', &use_gpu, "use GPU"),
OPT_STRING('p', &pat_file, "file", "pattern or weights"),
OPT_SELECT('I', enum algo_t, &ropts.algo, IST, "(select IST)"),
OPT_UINT('b', &llr_blk, "blk", "Lowrank block size"),
OPT_SET('e', &eigen, "Scale stepsize based on max. eigenvalue"),
OPT_SET('H', &hogwild, "(hogwild)"),
OPT_SET('F', &fast, "(fast)"),
OPT_STRING('T', &image_truth_file, "file", "(truth file)"),
OPT_STRING('W', &image_start_file, "<img>", "Warm start with <img>"),
OPT_INT('d', &debug_level, "level", "Debug level"),
OPT_INT('O', &conf.rwiter, "rwiter", "(reweighting)"),
OPT_FLOAT('o', &conf.gamma, "gamma", "(reweighting)"),
OPT_FLOAT('u', &admm_rho, "rho", "ADMM rho"),
OPT_UINT('C', &admm_maxitercg, "iter", "ADMM max. CG iterations"),
OPT_FLOAT('q', &conf.cclambda, "cclambda", "(cclambda)"),
OPT_FLOAT('f', &restrict_fov, "rfov", "restrict FOV"),
OPT_SELECT('m', enum algo_t, &ropts.algo, ADMM, "Select ADMM"),
OPT_FLOAT('w', &scaling, "val", "scaling"),
OPT_SET('S', &scale_im, "Re-scale the image after reconstruction"),
};
cmdline(&argc, argv, 3, 3, usage_str, help_str, ARRAY_SIZE(opts), opts);
if (NULL != image_truth_file)
im_truth = true;
if (NULL != image_start_file)
warm_start = true;
long max_dims[DIMS];
long map_dims[DIMS];
long pat_dims[DIMS];
long img_dims[DIMS];
long coilim_dims[DIMS];
long ksp_dims[DIMS];
long traj_dims[DIMS];
// load kspace and maps and get dimensions
complex float* kspace = load_cfl(argv[1], DIMS, ksp_dims);
complex float* maps = load_cfl(argv[2], DIMS, map_dims);
complex float* traj = NULL;
if (NULL != traj_file)
traj = load_cfl(traj_file, DIMS, traj_dims);
md_copy_dims(DIMS, max_dims, ksp_dims);
md_copy_dims(5, max_dims, map_dims);
md_select_dims(DIMS, ~COIL_FLAG, img_dims, max_dims);
md_select_dims(DIMS, ~MAPS_FLAG, coilim_dims, max_dims);
if (!md_check_compat(DIMS, ~(MD_BIT(MAPS_DIM)|FFT_FLAGS), img_dims, map_dims))
error("Dimensions of image and sensitivities do not match!\n");
assert(1 == ksp_dims[MAPS_DIM]);
(use_gpu ? num_init_gpu : num_init)();
// print options
if (use_gpu)
debug_printf(DP_INFO, "GPU reconstruction\n");
if (map_dims[MAPS_DIM] > 1)
debug_printf(DP_INFO, "%ld maps.\nESPIRiT reconstruction.\n", map_dims[MAPS_DIM]);
if (hogwild)
debug_printf(DP_INFO, "Hogwild stepsize\n");
if (im_truth)
debug_printf(DP_INFO, "Compare to truth\n");
// initialize sampling pattern
complex float* pattern = NULL;
if (NULL != pat_file) {
pattern = load_cfl(pat_file, DIMS, pat_dims);
assert(md_check_compat(DIMS, COIL_FLAG, ksp_dims, pat_dims));
} else {
md_select_dims(DIMS, ~COIL_FLAG, pat_dims, ksp_dims);
pattern = md_alloc(DIMS, pat_dims, CFL_SIZE);
estimate_pattern(DIMS, ksp_dims, COIL_DIM, pattern, kspace);
}
if ((NULL != traj_file) && (NULL == pat_file)) {
md_free(pattern);
pattern = NULL;
nuconf.toeplitz = true;
} else {
// print some statistics
long T = md_calc_size(DIMS, pat_dims);
long samples = (long)pow(md_znorm(DIMS, pat_dims, pattern), 2.);
debug_printf(DP_INFO, "Size: %ld Samples: %ld Acc: %.2f\n", T, samples, (float)T / (float)samples);
}
if (NULL == traj_file) {
fftmod(DIMS, ksp_dims, FFT_FLAGS, kspace, kspace);
fftmod(DIMS, map_dims, FFT_FLAGS, maps, maps);
}
// apply fov mask to sensitivities
if (-1. != restrict_fov) {
float restrict_dims[DIMS] = { [0 ... DIMS - 1] = 1. };
restrict_dims[0] = restrict_fov;
restrict_dims[1] = restrict_fov;
restrict_dims[2] = restrict_fov;
apply_mask(DIMS, map_dims, maps, restrict_dims);
}
int main_nufft(int argc, char* argv[])
{
bool adjoint = false;
bool inverse = false;
bool use_gpu = false;
bool precond = false;
bool dft = false;
struct nufft_conf_s conf = nufft_conf_defaults;
struct iter_conjgrad_conf cgconf = iter_conjgrad_defaults;
long coilim_vec[3] = { 0 };
float lambda = 0.;
const struct opt_s opts[] = {
OPT_SET('a', &adjoint, "adjoint"),
OPT_SET('i', &inverse, "inverse"),
OPT_VEC3('d', &coilim_vec, "x:y:z", "dimensions"),
OPT_VEC3('D', &coilim_vec, "", "()"),
OPT_SET('t', &conf.toeplitz, "Toeplitz embedding for inverse NUFFT"),
OPT_SET('c', &precond, "Preconditioning for inverse NUFFT"),
OPT_FLOAT('l', &lambda, "lambda", "l2 regularization"),
OPT_UINT('m', &cgconf.maxiter, "", "()"),
OPT_SET('s', &dft, "DFT"),
};
cmdline(&argc, argv, 3, 3, usage_str, help_str, ARRAY_SIZE(opts), opts);
long coilim_dims[DIMS] = { 0 };
md_copy_dims(3, coilim_dims, coilim_vec);
// Read trajectory
long traj_dims[DIMS];
complex float* traj = load_cfl(argv[1], DIMS, traj_dims);
assert(3 == traj_dims[0]);
num_init();
if (inverse || adjoint) {
long ksp_dims[DIMS];
const complex float* ksp = load_cfl(argv[2], DIMS, ksp_dims);
assert(1 == ksp_dims[0]);
assert(md_check_compat(DIMS, ~(PHS1_FLAG|PHS2_FLAG), ksp_dims, traj_dims));
md_copy_dims(DIMS - 3, coilim_dims + 3, ksp_dims + 3);
if (0 == md_calc_size(DIMS, coilim_dims)) {
estimate_im_dims(DIMS, coilim_dims, traj_dims, traj);
debug_printf(DP_INFO, "Est. image size: %ld %ld %ld\n", coilim_dims[0], coilim_dims[1], coilim_dims[2]);
}
complex float* img = create_cfl(argv[3], DIMS, coilim_dims);
md_clear(DIMS, coilim_dims, img, CFL_SIZE);
const struct linop_s* nufft_op;
if (!dft)
nufft_op = nufft_create(DIMS, ksp_dims, coilim_dims, traj_dims, traj, NULL, conf, use_gpu);
else
nufft_op = nudft_create(DIMS, FFT_FLAGS, ksp_dims, coilim_dims, traj_dims, traj);
if (inverse) {
const struct operator_s* precond_op = NULL;
if (conf.toeplitz && precond)
precond_op = nufft_precond_create(nufft_op);
lsqr(DIMS, &(struct lsqr_conf){ lambda }, iter_conjgrad, CAST_UP(&cgconf),
nufft_op, NULL, coilim_dims, img, ksp_dims, ksp, precond_op);
if (conf.toeplitz && precond)
operator_free(precond_op);
} else {
