Skip to content

I
imagej-elphel
Commit 155415d8 authored by Andrey Filippov's avatar Andrey Filippov
Browse files
Options

Generating more data for plots in Libreoffice Calc

parent e6d6f963
Show whitespace changes
Inline Side-by-side
Showing with 979 additions and 219 deletions
src/main/java/com/elphel/imagej/tileprocessor/InterIntraLMA.java
View file @ 155415d8
......@@ -46,19 +46,187 @@ public class InterIntraLMA {
* of the bad tile. Double.NaN if noise threshold can not be determined. null if the tile is
* undefined for all modes
*/
public static double [][][] getNoiseThresholdsPartial(
double [] noise_file, // = new double [noise_files.length];
int [] group_indices, // last points after last file index
int [] sensor_mode_file,
boolean [] inter_file,
boolean [][] good_file_tile,
double min_inter16_noise_level,
boolean apply_min_inter16_to_inter,
int min_modes,
boolean zero_all_bad, // (should likely be set!) set noise_level to zero if all noise levels result in bad tiles
boolean all_inter, // tile has to be defined for all inter
boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
boolean need_same_zero, // do not use samle if it is bad for zero-noise
int dbg_tile)
{
// int num_groups = group_indices.length - 1;
int num_variants = 1;
for (int i = 0; i < group_indices.length - 1; i++) {
int ng = group_indices[i+1] - group_indices[i];
if (ng > num_variants) {
num_variants = ng;
}
}
double [][][] rslt_partial = new double [num_variants][][];
for (int nv = 0; nv < num_variants; nv++) {
rslt_partial[nv] = getNoiseThreshold(
noise_file, // double [] noise_file, // = new double [noise_files.length];
group_indices, // int [] group_indices, // last points after last file index
nv, // int group_index, // use this index of same noise value variants (or 0 if that does not exist)
sensor_mode_file, // int [] sensor_mode_file,
inter_file, // boolean [] inter_file,
good_file_tile, // boolean [][] good_file_tile,
min_inter16_noise_level, // double min_inter16_noise_level,
apply_min_inter16_to_inter, // boolean apply_min_inter16_to_inter,
min_modes, // int min_modes,
zero_all_bad, // boolean zero_all_bad, // set noise_level to zero if all noise levels result in bad tiles
all_inter, // boolean all_inter, // tile has to be defined for all inter
need_same_inter, // boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
need_same_zero, // boolean need_same_zero, // do not use samle if it is bad for zero-noise
dbg_tile); // int dbg_tile);
}
return rslt_partial;
}
public static double [][][] getNoiseThresholdsPartial(
double [] noise_file, // = new double [noise_files.length];
int [] group_indices, // last points after last file index
int [] sensor_mode_file,
boolean [] inter_file,
int outliers, // may need do modify algorithm to avoid bias - removing same side (densier) outliers
int min_keep, // remove less outliers if needed to keep this remain
boolean [][][] good_file_tile_range,
double min_inter16_noise_level,
boolean apply_min_inter16_to_inter,
int min_modes,
boolean zero_all_bad, // (should likely be set!) set noise_level to zero if all noise levels result in bad tiles
boolean all_inter, // tile has to be defined for all inter
boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
boolean need_same_zero, // do not use samle if it is bad for zero-noise
int dbg_tile)
{
// int num_groups = group_indices.length - 1;
int num_variants = 1;
for (int i = 0; i < group_indices.length - 1; i++) {
int ng = group_indices[i+1] - group_indices[i];
if (ng > num_variants) {
num_variants = ng;
}
}
double [][][] rslt_partial = new double [num_variants][][];
for (int nv = 0; nv < num_variants; nv++) {
rslt_partial[nv] = getNoiseThreshold(
noise_file, // double [] noise_file, // = new double [noise_files.length];
group_indices, // int [] group_indices, // last points after last file index
nv, // int group_index, // use this index of same noise value variants (or 0 if that does not exist)
sensor_mode_file, // int [] sensor_mode_file,
inter_file, // boolean [] inter_file,
outliers, // int outliers, // may need do modify algorithm to avoid bias - removing same side (densier) outliers
min_keep, //int min_keep, // remove less outliers if needed to keep this remain
good_file_tile_range, // boolean [][][] good_file_tile_range,
min_inter16_noise_level, // double min_inter16_noise_level,
apply_min_inter16_to_inter, // boolean apply_min_inter16_to_inter,
min_modes, // int min_modes,
zero_all_bad, // boolean zero_all_bad, // set noise_level to zero if all noise levels result in bad tiles
all_inter, // boolean all_inter, // tile has to be defined for all inter
need_same_inter, //boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
need_same_zero, // boolean need_same_zero, // do not use samle if it is bad for zero-noise
dbg_tile); // int dbg_tile);
}
return rslt_partial;
}
public static double [][] mergeNoiseVariants(
double [][][] partial_thresholds,
int num_outliers,
int min_remain){
int num_modes = 0;
int num_tiles = partial_thresholds[0].length;
for (int i = 0; i < num_tiles; i++) {
if (partial_thresholds[0][i]!= null) {
num_modes = partial_thresholds[0][i].length;
break;
}
}
double [][] rslt = new double [num_tiles][]; // number of tiles
for (int ntile = 0; ntile < num_tiles; ntile++) {
boolean has_data = false;
for (int nv = 0; nv < partial_thresholds.length; nv++) {
if (partial_thresholds[nv][ntile] != null) {
has_data = true;
break;
}
}
if (has_data) {
rslt[ntile] = new double [num_modes];
for (int mode = 0; mode < num_modes; mode ++) {
double [] partial_tile = new double [partial_thresholds.length];
Arrays.fill(partial_tile, Double.NaN);
for (int nv = 0; nv < partial_tile.length; nv++) if (partial_thresholds[nv][ntile] != null){
partial_tile[nv] = partial_thresholds[nv][ntile][mode];
}
int num_defined = 0;
double avg = Double.NaN;
double s = 0.0;
for (int nv = 0; nv < partial_tile.length; nv++) if (!Double.isNaN(partial_tile[nv])) {
s+= partial_tile[nv];
num_defined++;
}
if (num_defined > 0) {
avg = s / num_defined;
for (int num_removed = 0; num_removed < num_outliers; num_removed++) {
if (num_defined <= min_remain) {
break;
}
double max_diff2 = 0;
int ioutlier = -1;
for (int nv = 0; nv < partial_tile.length; nv++) if (!Double.isNaN(partial_tile[nv])) {
double diff2 = partial_tile[nv] - s;
diff2 *= diff2;
if (diff2 > max_diff2) {
max_diff2 = diff2;
ioutlier = nv;
}
}
if (ioutlier < 0) {
break;
}
s -= partial_tile[ioutlier];
partial_tile[ioutlier] = Double.NaN;
num_defined--;
avg = s / num_defined;
}
}
rslt[ntile][mode] = avg;
}
}
}
return rslt;
}
public static double [][] getNoiseThreshold(
double [] noise_file, // = new double [noise_files.length];
int [] group_indices, // last points after last file index
int group_index, // use this index of same noise value variants (or 0 if that does not exist)
int [] sensor_mode_file,
boolean [] inter_file,
boolean [][] good_file_tile,
double min_inter16_noise_level,
boolean apply_min_inter16_to_inter,
int min_modes,
boolean zero_all_bad, // set noise_level to zero if all noise levels result in bad tiles
boolean all_inter, // tile has to be defined for all inter
boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
boolean need_same_zero, // do not use samle if it is bad for zero-noise
int dbg_tile)
{
// min_inter16_noise_level
int num_groups = group_indices.length - 1;
// int dbg_tile = 828; // 1222;
int num_sensor_modes = 0;
int num_tiles = good_file_tile[0].length;
......@@ -77,12 +245,17 @@ public class InterIntraLMA {
noise_interval[i][j][1] =Double.NaN;
}
}
for (int nf = 0; nf < noise_file.length; nf++) {
// for (int nf = 0; nf < noise_file.length; nf++) {
for (int ng = 0; ng < num_groups; ng++) {
int nf = group_indices[ng] + group_index;
if (nf >= group_indices[ng+1]) {
nf = group_indices[ng+1] -1;
}
double noise = noise_file[nf];
int mode = sensor_mode_file[nf] + (inter_file[nf] ? 0: num_sensor_modes);
for (int ntile = 0; ntile < num_tiles; ntile++) {
if (ntile == dbg_tile) {
System.out.println("ntile = "+ntile+", nf ="+nf);
System.out.println("ntile = "+ntile+", nf ="+nf+", mode = "+mode);
}
if (good_file_tile[nf][ntile]) { // good tile
if (!(noise <= noise_interval[mode][ntile][0])){ // including Double.isNaN(noise_interval[mode][ntile][0]
......@@ -115,20 +288,15 @@ public class InterIntraLMA {
if ((num_defined >= min_modes) && (!all_inter || (num_defined_inter >= 4))) {
rslt[ntile] = new double [num_modes];
for (int mode = 0; mode < num_modes; mode++) {
// if (need_same_inter && (mode >= 4) && Double.isNaN(noise_interval[mode & 3][ntile][0])) { // no good for same sensors inter
if (need_same_inter && Double.isNaN(noise_interval[mode & 3][ntile][0])) { // no good for same sensors inter
rslt[ntile][mode] = Double.NaN;
} else if (!Double.isNaN(noise_interval[mode][ntile][0]) && !Double.isNaN(noise_interval[mode][ntile][1])) {
/*
rslt[ntile][mode] = 0.5 * (noise_interval[mode][ntile][0] + noise_interval[mode][ntile][1]);
if (remove_non_monotonic && (noise_interval[mode][ntile][0] > noise_interval[mode][ntile][1])) {
} else if (need_same_zero && (Double.isNaN(noise_interval[mode][ntile][0])
|| (noise_interval[mode][ntile][1] == 0.0))) { // no good for same sensors
rslt[ntile][mode] = Double.NaN;
}
*/
} else if (!Double.isNaN(noise_interval[mode][ntile][0]) && !Double.isNaN(noise_interval[mode][ntile][1])) {
// use the lowest failed noise level assuming that false positive may happen even for much higher noise level
rslt[ntile][mode] = noise_interval[mode][ntile][1]; // lowest noise for bad
// } else if (zero_all_bad && Double.isNaN(noise_interval[mode][ntile][1])) {
} else if (zero_all_bad && Double.isNaN(noise_interval[mode][ntile][0])) {
rslt[ntile][mode] = 0.0;
} else {
......@@ -138,7 +306,13 @@ public class InterIntraLMA {
}
if ((rslt[ntile] != null) && (min_inter16_noise_level >0)){ // filter by to weak inter-16 (mode 0)
if (!(rslt[ntile][0] >= min_inter16_noise_level)){
if (apply_min_inter16_to_inter) {
rslt[ntile] = null;
} else {
for (int mode = 4; mode < rslt[ntile].length; mode++) {
rslt[ntile][mode] = Double.NaN;
}
}
}
}
if (rslt[ntile] != null) {
......@@ -172,21 +346,26 @@ public class InterIntraLMA {
// trying multi-threshold good_file_tile_range
public static double [][] getNoiseThreshold(
double [] noise_file, // = new double [noise_files.length];
int [] group_indices, // last points after last file index
int group_index, // use this index of same noise value variants (or 0 if that does not exist)
int [] sensor_mode_file,
boolean [] inter_file,
int outliers, // may need do modify algorithm to avoid bias - removing same side (densier) outliers
int min_keep, // remove less outliers if needed to keep this remain
boolean [][][] good_file_tile_range,
double min_inter16_noise_level,
boolean apply_min_inter16_to_inter,
int min_modes,
boolean zero_all_bad, // set noise_level to zero if all noise levels result in bad tiles
boolean all_inter, // tile has to be defined for all inter
boolean need_same_inter, // = true; // do not use intra sample if same inter is bad for all noise levels
boolean need_same_zero, // do not use samle if it is bad for zero-noise
int dbg_tile)
{
//int dbg_tile = 828;
int num_sensor_modes = 00;
int num_groups = group_indices.length - 1;
int num_sensor_modes = 0;
int num_tiles = good_file_tile_range[0].length;
for (int i = 0; i < sensor_mode_file.length; i++) {
if (sensor_mode_file[i] > num_sensor_modes) {
......@@ -203,7 +382,12 @@ public class InterIntraLMA {
lowest_all_bad[i][j] =Double.NaN;
}
}
for (int nf = 0; nf < noise_file.length; nf++) {
// for (int nf = 0; nf < noise_file.length; nf++) {
for (int ng = 0; ng < num_groups; ng++) {
int nf = group_indices[ng] + group_index;
if (nf >= group_indices[ng+1]) {
nf = group_indices[ng+1] -1;
}
double noise = noise_file[nf];
int mode = sensor_mode_file[nf] + (inter_file[nf] ? 0: num_sensor_modes);
for (int ntile = 0; ntile < num_tiles; ntile++) {
......@@ -285,7 +469,14 @@ public class InterIntraLMA {
double [] pre_rslt = new double [noise_intervals[mode][ntile].length]; //null pointer
int num_def = 0;
for (int stp = 0; stp < pre_rslt.length; stp++) {
if (need_same_inter && Double.isNaN(noise_intervals[mode & 3][ntile][stp][0])) { // no good for same sensors inter
if (need_same_inter && (
(noise_intervals[mode & 3][ntile] == null) ||
Double.isNaN(noise_intervals[mode & 3][ntile][stp][0]))) { // no good for same sensors inter
pre_rslt[stp] = Double.NaN;
} else if (need_same_zero && (
(noise_intervals[mode][ntile] == null) ||
Double.isNaN(noise_intervals[mode][ntile][stp][0]) ||
(noise_intervals[mode][ntile][stp][1] == 0.0))) { // bad for no- noise for same sensors
pre_rslt[stp] = Double.NaN;
} else if (!Double.isNaN(noise_intervals[mode][ntile][stp][0]) && !Double.isNaN(noise_intervals[mode][ntile][stp][1])) {
pre_rslt[stp] = noise_intervals[mode][ntile][stp][1]; // lowest noise for bad
......@@ -344,16 +535,29 @@ public class InterIntraLMA {
} else {
rslt[ntile][mode] = Double.NaN;
}
} else { // bad for all stp
if (need_same_inter && (noise_intervals[mode & 3][ntile] == null))
rslt[ntile][mode] = Double.NaN;
else if (need_same_zero && (noise_intervals[mode][ntile] == null)){
rslt[ntile][mode] = Double.NaN;
} else {
rslt[ntile][mode] = zero_all_bad ? 0.0 : Double.NaN; // no good in any stp
}
}
}
}
if ((rslt[ntile] != null) && (min_inter16_noise_level >0)){ // filter by to weak inter-16 (mode 0)
if (!(rslt[ntile][0] >= min_inter16_noise_level)){
if (apply_min_inter16_to_inter) {
rslt[ntile] = null;
} else {
for (int mode = 4; mode < rslt[ntile].length; mode++) {
rslt[ntile][mode] = Double.NaN;
}
}
}
}
if (rslt[ntile] != null) {
boolean all_nan = true;
boolean has_nan = false;
......@@ -427,6 +631,7 @@ public class InterIntraLMA {
double offset, // for "relative" noise
double n0, // initial value for N0 0.02
int tilesX, // debug images only
double scale_intra, // scale weight of intra-scene samples ( < 1.0)
int debug_level)
{
boolean debug_img = (debug_level > -1);
......@@ -473,14 +678,24 @@ public class InterIntraLMA {
// create Y, K and weights vectors
// scale_intra
double sum_w = 0.0;
for (int nsample = 0; nsample < num_samples; nsample++) {
int tile = tile_index[sample_indx[nsample][0]];
int mode = sample_indx[nsample][1];
double w = (mode >= 4) ? scale_intra : 1.0;
double d = noise_thresh[tile][sample_indx[nsample][1]];
K[nsample] = 1.0/(d + offset);
Y[nsample] = d * K[nsample];
// may be modified, but sum (weights) should be == 1.0;
weights[nsample] = 1.0/num_samples;
weights[nsample] = w; // 1.0/num_samples;
sum_w += w;
}
double scale_w = 1.0/sum_w;
for (int nsample = 0; nsample < num_samples; nsample++) {
weights[nsample] *= scale_w;
}
// initial approximation
double N0 = n0; // offset;
double N02 = N0*N0;
......@@ -727,8 +942,10 @@ public class InterIntraLMA {
if (adjust_N0) {
jt[indx++][i] = - K[i];
}
if (adjust_Gi && (mode > 0)) {
if (adjust_Gi) {
if (mode > 0) {
jt[indx + mode -1][i] = K[i] *st[itile];
}
indx += gi.length -1;
}
if (adjust_St) {
......@@ -753,8 +970,11 @@ public class InterIntraLMA {
jt[indx++][i] = - Amti * n0;
}
double asg = Amti*st[itile]*gi[mode];
if (adjust_Gi && (mode > 0)) {
// if (adjust_Gi && (mode > 0)) {
if (adjust_Gi) {
if (mode > 0) {
jt[indx + mode -1][i] = asg *st[itile];
}
indx += gi.length -1;
}
if (adjust_St) {
......@@ -767,6 +987,140 @@ public class InterIntraLMA {
return fx;
}
private boolean debugJt(
double delta,
double [] vector) {
int num_points = sample_indx.length;
int num_pars = vector.length;
// delta = 0.001;
double [][] jt = new double [num_pars][num_points];
double [][] jt_delta = new double [num_pars][num_points];
double [][] jt_diff = new double [num_pars][num_points];
double [] max_diff = new double [num_pars];
boolean [] has_nan = new boolean [num_pars];
int [] max_diff_indx = new int [num_pars];
double worst_diff = 0.0;
int worst_par = -1;
boolean has_any_nan = false;
double [] fx = getFxJt( vector,jt);
if (fx == null) return false;
if (getFxJt(delta, vector,jt_delta) == null) return false;
for (int npar = 0; npar < jt.length; npar++) {
for (int npoint = 0; npoint < jt[npar].length; npoint++) {
jt_diff[npar][npoint] = jt[npar][npoint] - jt_delta[npar][npoint];
if (Double.isNaN(jt_diff[npar][npoint])) {
has_nan[npar] = true;
} else {
if (Math.abs(jt_diff[npar][npoint]) > max_diff[npar]) {
max_diff[npar] = Math.abs(jt_diff[npar][npoint]);
max_diff_indx[npar] = npoint;
}
}
}
has_any_nan |= has_nan[npar];
if (max_diff[npar] > worst_diff) {
worst_diff = max_diff[npar];
worst_par = npar;
}
}
System.out.println("Has NaNs = "+has_any_nan);
if (has_any_nan) {
for (int i = 0; i < has_nan.length; i++) {
if (has_nan[i]) {
System.out.print(i+", ");
}
}
System.out.println();
}
System.out.println("Worst diff = "+worst_diff+" for parameter #"+worst_par+", point "+max_diff_indx[worst_par]);
System.out.println();
/*
System.out.println("Test of jt-jt_delta difference, delta = "+delta+ ":");
System.out.print(String.format("Til P %3s: %10s ", "#", "fx"));
for (int anp = 0; anp< all_pars.length; anp++) if(par_mask[anp]){
String parname;
if (anp >= ndisp_index) parname = PAR_NAME_CORRNDISP + (anp - ndisp_index);
else if (anp >= ddisp_index) parname = PAR_NAME_CORRDISP + (anp - ddisp_index);
else {
int ntile = anp / tile_params;
int anpr = anp % tile_params;
if (anpr < G0_INDEX) parname = PAR_NAMES[anpr]+"-"+ntile;
else parname = PAR_NAME_SCALE +"-"+ntile + ":"+ (anpr - G0_INDEX);
}
System.out.print(String.format("| %16s ", parname));
}
System.out.println();
int npair0 = -1;
for (int i = 0; i < num_points; i++) {
if (i < samples.size()) {
// int [] fs = correlation2d.getPair(samples.get(i).pair);
// int npair = used_pairs_map[samples.get(i).tile][samples.get(i).fcam][samples.get(i).scam];
// int npair = used_pairs_map[samples.get(i).tile][fs[0]][fs[1]];
int npair = used_pairs_map[samples.get(i).tile][samples.get(i).pair];
if (npair !=npair0) {
if (npair0 >=0) System.out.println();
npair0 = npair;
}
System.out.print(String.format("%3d %1d %3d: %10.7f ",samples.get(i).tile, npair, i, fx[i]));
} else {
System.out.print(String.format(" - - %3d: %10.7f ", i, fx[i]));
}
for (int np = 0; np < num_pars; np++) {
// System.out.print(String.format("|%8.5f %8.5f ", jt_delta[np][i], 1000*(jt[np][i] - jt_delta[np][i])));
System.out.print(String.format("|%8.5f %8.5f ", jt_delta[np][i], 1.0 * (jt[np][i] - jt_delta[np][i])));
double adiff = Math.abs(jt[np][i] - jt_delta[np][i]);
if (adiff > max_diff[np]) {
max_diff[np] = adiff;
}
}
System.out.println();
}
double tmd = 0.0;
for (int np = 0; np < num_pars; np++) {
if (max_diff[np] > tmd) tmd= max_diff[np];
}
// System.out.print(String.format(" %15s ", "Maximal diff:"));
System.out.print(String.format("Max diff.(%10.5f):", tmd));
for (int np = 0; np < num_pars; np++) {
System.out.print(String.format("|%8s %8.5f ", "1/1000×", 1000*max_diff[np]));
}
System.out.println();
*/
return true;
}
public double [] getFxJt( // not used in lwir
double delta, // for testing derivatives: calculates as delta-F/delta_x
double [] vector,
double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
double [] fx0=getFxJt(vector,null);
if (fx0 == null) return null;
for (int np = 0; np < vector.length; np++) {
double [] vector1 = vector.clone();
vector1[np]+= delta;
double [] fxp=getFxJt(vector1,null);
if (fxp == null) return null;
vector1 = vector.clone();
vector1[np]-= delta;
double [] fxm=getFxJt(vector1,null);
if (fxm == null) return null;
jt[np] = new double [fxp.length];
for (int i = 0; i < fxp.length; i++) {
jt[np][i] = (fxp[i] - fxm[i])/delta/2;
}
}
return fx0;
}
public double [][] getYDbg() {
double [][] dbg_Y = new double [gi.length][dbgTilesX*dbgTilesY];
for (int mode = 0; mode < dbg_Y.length; mode++) {
......@@ -915,7 +1269,11 @@ public class InterIntraLMA {
if (i == j) {
wjtjl[i][j] += d * lambda;
if (d == 0) {
if (i >= 8) {
System.out.println("Diagonal ZERO for i=j="+i+" absolute tile = "+tile_index[i-8]); // assuming N0, gi[1]...gi[7]
}else {
System.out.println("Diagonal ZERO for i=j="+i); // assuming N0, gi[1]...gi[7]
}
wjtjl[i][j] = 1.0; // Jt * (y-fx) will anyway be 0, so any value here should work.
}
} else {
......@@ -1102,13 +1460,11 @@ public class InterIntraLMA {
true,
"dbg_Fx");
}
/*
if (debug_level > 3) {
debugJt(
0.000001, // double delta, // 0.2, //
this.vector); // double [] vector);
}
*/
}
Matrix y_minus_fx_weighted = new Matrix(last_ymfx, last_ymfx.length);
......
src/main/java/com/elphel/imagej/tileprocessor/QuadCLT.java
View file @ 155415d8
......@@ -2010,11 +2010,7 @@ public class QuadCLT extends QuadCLTCPU {
EyesisCorrectionParameters.RGBParameters rgbParameters,
final int threadsMax, // maximal number of threads to launch
final int debugLevel){
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String file_name = image_name + suffix;
String file_path = x3d_path + Prefs.getFileSeparator() + file_name + ".tiff";
if ((getGPU() != null) && (getGPU().getQuadCLT() != this)) {
......@@ -2224,11 +2220,7 @@ public class QuadCLT extends QuadCLTCPU {
if (clt_parameters.gen_4_img) { // save 4 JPEG images
// Save as individual JPEG images in the model directory
String x3d_path= correctionsParameters.selectX3dDirectory(
image_name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
for (int sub_img = 0; sub_img < imps_RGB.length; sub_img++){
EyesisCorrections.saveAndShow(
imps_RGB[sub_img],
......@@ -2759,11 +2751,7 @@ public class QuadCLT extends QuadCLTCPU {
if (clt_parameters.gen_4_img) { // save 4 JPEG images
// Save as individual JPEG images in the model directory
String x3d_path= quadCLT_main.correctionsParameters.selectX3dDirectory(
name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
quadCLT_main.correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path= quadCLT_main.getX3dDirectory();
for (int sub_img = 0; sub_img < imps_RGB.length; sub_img++){
EyesisCorrections.saveAndShow(
imps_RGB[sub_img],
......
src/main/java/com/elphel/imagej/tileprocessor/QuadCLTCPU.java
View file @ 155415d8
......@@ -329,6 +329,26 @@ public class QuadCLTCPU {
return image_name;
}
public String getX3dDirectory() { // replace direct calculations
String x3d_path = correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
return x3d_path;
}
// maybe will not be needed? TODO: Check
public String getX3dDirectory(String name) { // replace direct calculations
String x3d_path = correctionsParameters.selectX3dDirectory( // for x3d and obj
name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
return x3d_path;
}
public int restoreDSI(
String suffix,
boolean silent) // "-DSI_COMBO", "-DSI_MAIN" (DSI_COMBO_SUFFIX, DSI_MAIN_SUFFIX)
......@@ -344,11 +364,7 @@ public class QuadCLTCPU {
String suffix, // "-DSI_COMBO", "-DSI_MAIN" (DSI_COMBO_SUFFIX, DSI_MAIN_SUFFIX)
double [][] dsi,
boolean silent) {
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String file_path = x3d_path + Prefs.getFileSeparator() + image_name + suffix + ".tiff";
ImagePlus imp = null;
try {
......@@ -404,11 +420,7 @@ public class QuadCLTCPU {
}
if (!path.contains(Prefs.getFileSeparator())) {
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
path = x3d_path+Prefs.getFileSeparator()+path;
}
Properties inter_properties = new Properties();
......@@ -465,11 +477,7 @@ public class QuadCLTCPU {
}
if (!path.contains(Prefs.getFileSeparator())) {
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
path = x3d_path+Prefs.getFileSeparator()+path;
}
properties = loadProperties(
......@@ -827,11 +835,7 @@ public class QuadCLTCPU {
final int num_cams = this.image_data.length;
final int num_cols = image_data[0].length;
final int [] image_wh = geometryCorrection.getSensorWH();
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String noise_suffix = suffix + sigma;
String file_name = image_name + noise_suffix;
String file_path = x3d_path + Prefs.getFileSeparator() + file_name + ".tiff";
......@@ -962,11 +966,7 @@ public class QuadCLTCPU {
int width,
int height)
{
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String file_name = image_name + suffix;
String file_path = x3d_path + Prefs.getFileSeparator() + file_name + ".tiff";
ImageStack imageStack = (new ShowDoubleFloatArrays()).makeStack(data, width, height, labels);
......@@ -984,11 +984,7 @@ public class QuadCLTCPU {
)
{
// final int [] image_wh = geometryCorrection.getSensorWH();
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String file_name = image_name + suffix;
String file_path = x3d_path + Prefs.getFileSeparator() + file_name + ".tiff";
ImagePlus imp = null;
......@@ -1030,11 +1026,7 @@ public class QuadCLTCPU {
double [][] dsi
)
{
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String title = image_name+TwoQuadCLT.DSI_COMBO_SUFFIX;
ImagePlus imp = (new ShowDoubleFloatArrays()).makeArrays(dsi,tp.getTilesX(), tp.getTilesY(), title, TwoQuadCLT.DSI_SLICES);
eyesisCorrections.saveAndShow(
......@@ -1056,11 +1048,7 @@ public class QuadCLTCPU {
public void saveDSIMain(
double [][] dsi) // DSI_SLICES.length
{
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String title = image_name+"-DSI_MAIN";
String [] titles = {TwoQuadCLT.DSI_SLICES[TwoQuadCLT.DSI_DISPARITY_MAIN], TwoQuadCLT.DSI_SLICES[TwoQuadCLT.DSI_STRENGTH_MAIN]};
double [][] dsi_main = {dsi[TwoQuadCLT.DSI_DISPARITY_MAIN], dsi[TwoQuadCLT.DSI_STRENGTH_MAIN]};
......@@ -1078,11 +1066,7 @@ public class QuadCLTCPU {
String suffix, // "-DSI_MAIN"
double [][] dsi) // DSI_SLICES.length
{
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String title = image_name+suffix; // "-DSI_MAIN";
ImagePlus imp = (new ShowDoubleFloatArrays()).makeArrays(dsi, tp.getTilesX(), tp.getTilesY(), title, TwoQuadCLT.DSI_SLICES);
eyesisCorrections.saveAndShow(
......@@ -1101,11 +1085,7 @@ public class QuadCLTCPU {
QuadCLT quadCLT_aux,
double [][] dsi_aux_from_main)
{
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
String title = quadCLT_aux.image_name+"-DSI_GT-AUX";
// String [] titles = {DSI_SLICES[DSI_DISPARITY_MAIN], DSI_SLICES[DSI_STRENGTH_MAIN]};
// double [][] dsi_main = {dsi[DSI_DISPARITY_MAIN], dsi[DSI_STRENGTH_MAIN]};
......@@ -5955,11 +5935,7 @@ public class QuadCLTCPU {
}
if (clt_parameters.gen_4_img) {
// Save as individual JPEG images in the model directory
String x3d_path= correctionsParameters.selectX3dDirectory(
image_name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
for (int sub_img = 0; sub_img < imps_RGB.length; sub_img++){
EyesisCorrections.saveAndShow(
imps_RGB[sub_img],
......@@ -11047,12 +11023,7 @@ public class QuadCLTCPU {
tp.clt_3d_passes.get(next_pass-1), // CLTPass3d scan,
"after_pass3-"+(next_pass-1)); //String title)
}
String x3d_path= correctionsParameters.selectX3dDirectory( // for x3d and obj
correctionsParameters.getModelName(this.image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
String x3d_path = getX3dDirectory();
// create x3d file
if (clt_parameters.output_x3d) {
x3dOutput = new X3dOutput(
......
src/main/java/com/elphel/imagej/tileprocessor/TwoQuadCLT.java
View file @ 155415d8
......@@ -24,6 +24,7 @@ package com.elphel.imagej.tileprocessor;
import java.awt.Rectangle;
import java.io.DataOutputStream;
import java.io.File;
import java.io.FileFilter;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
......@@ -40,6 +41,7 @@ import java.nio.file.StandardCopyOption;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.List;
import java.util.Properties;
......@@ -1034,11 +1036,14 @@ public class TwoQuadCLT {
}
if (clt_parameters.gen_4_img) {
// Save as individual JPEG images in the model directory
String x3d_path= quadCLT_main.correctionsParameters.selectX3dDirectory(
name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
quadCLT_main.correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
// String x3d_path= quadCLT_main.correctionsParameters.selectX3dDirectory(
// name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
// quadCLT_main.correctionsParameters.x3dModelVersion,
// true, // smart,
// true); //newAllowed, // save
String x3d_path = quadCLT_main.getX3dDirectory(name);
for (int sub_img = 0; sub_img < imps_RGB.length; sub_img++){
EyesisCorrections.saveAndShow(
imps_RGB[sub_img],
......@@ -1927,11 +1932,15 @@ public class TwoQuadCLT {
}
if (clt_parameters.gen_4_img) {
// Save as individual JPEG images in the model directory
/*
String x3d_path= quadCLT_main.correctionsParameters.selectX3dDirectory(
name, // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
quadCLT_main.correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
*/
String x3d_path = quadCLT_main.getX3dDirectory(name);
for (int sub_img = 0; sub_img < imps_RGB.length; sub_img++){
EyesisCorrections.saveAndShow(
imps_RGB[sub_img],
......@@ -8686,6 +8695,7 @@ if (debugLevel > -100) return true; // temporarily !
clt_parameters,
colorProcParameters, //
noise_sigma_level, // double [] noise_sigma_level,
-1, // int noise_variant, // <0 - no-variants, compatible with old code
null, // QuadCLTCPU ref_scene, // may be null if scale_fpn <= 0
threadsMax,
clt_parameters.inp.noise_debug_level); // debugLevel);
......@@ -9429,8 +9439,6 @@ if (debugLevel > -100) return true; // temporarily !
"-results-rnd_2.5-fpn_0.0-sigma_1.5-offset1.4142-sensors8-inter-nolma",
"-results-rnd_2.5-fpn_0.0-sigma_1.5-offset1.4142-sensors8-nointer-nolma",
/*
"-results-rnd_0.0-fpn_0.0-sigma_1.5-offset1.0-sensors16-inter",
"-results-rnd_0.0-fpn_0.0-sigma_1.5-offset1.0-sensors16-nointer",
......@@ -9628,11 +9636,53 @@ if (debugLevel > -100) return true; // temporarily !
"-results-lev_5.0-sigma_1.5-offset1.0-nointer-mask1"
*/
};
// extend files by noise variants
//ref_scene
ArrayList<String> full_files_list = new ArrayList<String>();
String x3d_path = ref_scene.getX3dDirectory()+"";
File model_directory = new File(x3d_path);
int [] group_indices = new int [noise_files.length + 1];
for (int nf = 0; nf < noise_files.length; nf++) {
group_indices[nf] = full_files_list.size();
String base_suffix = noise_files[nf];
final String file_prefix = ref_scene.getImageName()+base_suffix;
FileFilter noiseFilefilter = new FileFilter()
{
//Override accept method
public boolean accept(File file) {
//if the file extension is .log return true, else false
if (file.getName().endsWith(".tiff") && file.getName().startsWith(file_prefix)) {
return true;
}
return false;
}
};
File[] files = model_directory.listFiles(noiseFilefilter);
ArrayList<String> flist = new ArrayList<String>();
for (File f:files) {
String name = f.getName();
String suffix = name.substring(ref_scene.getImageName().length(), name.length() - ".tiff".length());
flist.add(suffix);
}
Collections.sort(flist, new Comparator<String>() {
@Override
public int compare(String lhs, String rhs) { // ascending
return rhs.length() > lhs.length() ? -1 : (rhs.length() < lhs.length()) ? 1 : lhs.compareTo(rhs);
}
});
full_files_list.addAll(flist);
}
group_indices[group_indices.length - 1] = full_files_list.size();
String [] noise_files_full = full_files_list.toArray(new String[0]);
getNoiseStats(
clt_parameters,
ref_scene, // ordered by increasing timestamps
noise_files,
noise_files_full, // noise_files,
group_indices,
debug_level);
}
......@@ -9739,38 +9789,68 @@ if (debugLevel > -100) return true; // temporarily !
return good_tiles;
}
class DisparityResults{
int [] num_instances;
double [][] results;
}
class NoiseLevel implements Comparable<NoiseLevel>{
double rnd;
double fpn;
NoiseLevel(double rnd, double fpn) {
this.rnd = rnd;
this.fpn = fpn;
}
@Override
public boolean equals(Object obj) {
if ((obj == null) || (getClass() != obj.getClass())){
return false;
}
NoiseLevel other = (NoiseLevel) obj;
return (other.rnd == rnd) && (other.fpn == fpn);
}
@Override
public int compareTo(NoiseLevel other) {
return (this.fpn > other.fpn)? 1 :((this.fpn < other.fpn) ? -1: ((this.rnd > other.rnd)? 1 : ((this.rnd < other.rnd) ? -1: 0)));
}
@Override
public int hashCode() {
return ((Double)(rnd + 7919*fpn)).hashCode();
}
}
public void getNoiseStats(
CLTParameters clt_parameters,
QuadCLT ref_scene, // ordered by increasing timestamps
String [] noise_files,
int [] group_indices,
int debug_level)
{
int dbg_tile = 829; // 828; // 1222; // 737;
/*
"disp-last",
"str_last",
"num vlaid" <= 1.0
*/
int dbg_tile = 194; // 829; // 828; // 1222; // 737;
final int tilesX = ref_scene.tp.getTilesX();
// final int tilesY = ref_scene.tp.getTilesY();
int [] var_map = new int [noise_files.length]; // for each file - group number (matching group_indices).
double [] var_weights = new double [noise_files.length];
for (int i = 0; i < group_indices.length-1; i++) {
int indx_from = group_indices[i];
// int indx_to = (i < (group_indices.length -1))? group_indices[i+1] : noise_files.length ;
int indx_to = group_indices[i+1];
double w = 1.0/(indx_to - indx_from);
for (int j = indx_from; j < indx_to; j++) {
var_map[j] = i;
var_weights[j] = w;
}
}
double max_diff = 0.01; // 0.001; // 0.01; // 0.04; // 0.01; // last diff >
double max_err = 2.0; // 2.5; // 1.5; // 2.0; // 1.0; // 0.5; // pix
double max_err1 =0.250; // pix
double max_err1 = 0.250; // pix
double min_strength = 0.0; // minimal strength to calculate rmse (ignore weaker)
int indx_used = 3;
int indx_last = 0;
int indx_lma_last = clt_parameters.correlate_lma? 2 : 0; // if lma was disabled fallback to just disparity
int indx_diff_last = 4;
// int indx_initial = 3;
int indx_strength = 1;
double max_disparity = 30.0; // for max_err1
// double disp_rel_min = 0.5;
double disp_near_rel = 2.5;
double disp_max_rel = 0.25;
......@@ -9785,31 +9865,35 @@ if (debugLevel > -100) return true; // temporarily !
int min_modes = 4; // 5; // 6; // 5; // 4;//at least half are meaningfull
// LMA parameters
boolean useLinear = true;
double noise_offset = 0.05; // 0.1; // 0.03; // 0.10; // 0.03; // 50;
boolean useLinear = false; // true; // false; // true;
double noise_offset = 0.03; // 0.05; // 0.03; // 0.5; // 0.1; // 0.03; // 0.1; // 0.05; // 0.1; // 0.03; // 0.10; // 0.03; // 50;
double n0 = 0.03;
boolean adjust_N0 = true;
boolean adjust_N0 = false; // true;
boolean adjust_Gi = true;
boolean adjust_St = true; // false;
double min_inter16_noise_level = 0.10; // 0.3; // tile should have at least this noise level for 1nter16 (mode 0)
boolean adjust_St = true; // false; // true; // false;
// change to only apply to intra, inter ahould use weak.
double min_inter16_noise_level = 0.5; // 0.3; // 0.1; // 0.3; // 0.1; // 0.3; // tile should have at least this noise level for 1nter16 (mode 0)
boolean apply_min_inter16_to_inter = false;
boolean zero_all_bad = true; // false; // true; // set noise_level to zero if all noise levels result in bad tiles
boolean all_inter = true; // tile has to be defined for all inter
boolean need_same_inter = true; // do not use intra sample if same inter is bad for all noise levels
boolean need_same_zero = false; // true; // do not use sample if it is bad for zero-noise
double max_diff_from_ref = 0.20; // 0.06; // 5; // 0.1; // max_err1; // 0.25 pix
double max_diff_from_ref = 0.25; // 0.1; // 0.20; // 0.06; // 5; // 0.1; // max_err1; // 0.25 pix
boolean use_fpn = false;
double max_diff_from_ref_range = 0.25*max_diff_from_ref; // trying to stay in linear
int max_diff_from_ref_steps = 21;
int range_outliers = 2;
int range_min_keep = 1; // emove less outliers if needed to keep this remain
int range_min_keep = 1; // remove less outliers if needed to keep this remain
int num_var_outliers = 4;
int min_var_remain = 10;
double scale_intra = 0.2;
boolean run_lma = false;
if (use_edges) {
disp_max_rel = 100.0;
disp_max_rel = 100.00;
disp_max_abs = 100.0;
}
......@@ -9819,7 +9903,6 @@ if (debugLevel > -100) return true; // temporarily !
null); // int [] wh);
double [][] ref_dsn = ref_scene.readDoubleArrayFromModelDirectory(
// "-results-nonoise", // String suffix,
"-results-nonoise-nolma", // String suffix,
0, // int num_slices, // (0 - all)
null); // int [] wh);
......@@ -9845,10 +9928,6 @@ if (debugLevel > -100) return true; // temporarily !
int num_good_init = 0;
for (int i = 0; i < good_tiles_ref.length; i++) {
// good_tiles[i] = (ref_dsn[indx_used][i] > 0.999) && (Math.abs(ref_dsn[indx_last_diff][i]) < max_diff);
// if (good_tiles[i] && (sky_map != null) && (sky_map[0][i] > 0.0)) {
// good_tiles[i] = false;
// }
if (good_tiles_ref[i]){
num_good_init++;
}
......@@ -9859,38 +9938,6 @@ if (debugLevel > -100) return true; // temporarily !
}
// double [] noise_level = new double [noise_files.length];
// boolean [] intra = new boolean [noise_files.length];
// boolean [] inter = new boolean [noise_files.length];
class DisparityResults{
double [][] results;
}
class NoiseLevel implements Comparable<NoiseLevel>{
double rnd;
double fpn;
NoiseLevel(double rnd, double fpn) {
this.rnd = rnd;
this.fpn = fpn;
}
@Override
public boolean equals(Object obj) {
if ((obj == null) || (getClass() != obj.getClass())){
return false;
}
NoiseLevel other = (NoiseLevel) obj;
return (other.rnd == rnd) && (other.fpn == fpn);
}
@Override
public int compareTo(NoiseLevel other) {
return (this.fpn > other.fpn)? 1 :((this.fpn < other.fpn) ? -1: ((this.rnd > other.rnd)? 1 : ((this.rnd < other.rnd) ? -1: 0)));
}
@Override
public int hashCode() {
return ((Double)(rnd + 7919*fpn)).hashCode();
}
}
// boolean all_converge = false; // use only tiles that converge for all variants (intra, inter, used sensors)
// boolean all_max_err = false; // use only tiles that have limited error for all variants (intra, inter, used sensors)
boolean [] converged_tiles = good_tiles_ref.clone();
......@@ -9901,6 +9948,7 @@ if (debugLevel > -100) return true; // temporarily !
double [] noise_rnd_file = new double [noise_files.length];
double [] noise_fpn_file = new double [noise_files.length];
boolean [] inter_file = new boolean [noise_files.length];
// int [] var_map = new int [noise_files.length]; // for each file - group number (matching group_indices).
// extract data from file names
for (int nf = 0; nf < noise_files.length; nf++) {
......@@ -9935,10 +9983,9 @@ if (debugLevel > -100) return true; // temporarily !
}
if (all_converge || all_max_err || same_num_sensors) { // scan all images
if (all_converge || all_max_err || same_num_sensors) { // scan all images, use only interscene with no-noise
for (int nf = 0; nf < noise_files.length; nf++) {
String fn = noise_files[nf];
if (inter_file[nf]) {
double [][] noise_dsn = ref_scene.readDoubleArrayFromModelDirectory(
fn, // noise_files[nf], // String suffix,
......@@ -10008,7 +10055,6 @@ if (debugLevel > -100) return true; // temporarily !
// For each file find boolean good/bad, comparing to zero noise of the same number of sensors, interscene
boolean [][] good_file_tile = new boolean[noise_files.length][]; // [good_tiles.length];
boolean [][][] good_file_tile_range = new boolean[noise_files.length][][]; // [good_tiles.length];
// double max_err1 =0.25; // pix
for (int nf = 0; nf < noise_files.length; nf++) {
// common or per number of sensors reference data
String fn = noise_files[nf];
......@@ -10018,7 +10064,6 @@ if (debugLevel > -100) return true; // temporarily !
fn, // noise_files[nf], // String suffix,
0, // int num_slices, // (0 - all)
null); // int [] wh);
// boolean [] good_ref = good_tiles_mode[sensor_mode]; // good tile without noise for this number of sensors
good_file_tile[nf] = good_tiles_mode[sensor_mode].clone();
good_file_tile_range[nf] = new boolean [good_tiles_mode[sensor_mode].length][];
for (int ntile = 0; ntile < good_file_tile[nf].length; ntile++) if (good_file_tile[nf][ntile]) {
......@@ -10026,7 +10071,6 @@ if (debugLevel > -100) return true; // temporarily !
System.out.println("Finding good tiles: ntile = "+ntile+", nf="+nf+" ("+fn+")");
System.out.println("noise_dsn["+indx_last+"]["+ntile+"]="+noise_dsn[indx_last][ntile]+
", ref_var["+indx_last+"]["+ntile+"]="+ref_var[indx_last][ntile]);
}
boolean converged = (Math.abs(noise_dsn[indx_last_diff][ntile]) < max_diff);
......@@ -10046,16 +10090,9 @@ if (debugLevel > -100) return true; // temporarily !
if (!has_good) {
good_file_tile_range[nf][ntile] = null; // all bad
}
/*
boolean good_tiles_this = (Math.abs(noise_dsn[indx_last][ntile] - ref_var[indx_last][ntile]) < max_diff_from_ref);
if (!good_tiles_this) {
good_file_tile[nf][ntile] = false;
continue;
}
*/
}
}
{
// show number of noise values for each tile, num sensors and intra/inter, discarding tiles that are good/bad for all noise levels
double [][] dbg_num_noise_val = new double [good_tiles_mode.length*2][good_tiles.length];
......@@ -10073,12 +10110,14 @@ if (debugLevel > -100) return true; // temporarily !
}
if (good_tiles[ntile]) { // do not bother with obviously bad
double [] val_good = new double [dbg_num_noise_val.length];
int [] num_good = new int [dbg_num_noise_val.length];
boolean [] has_bad = new boolean [dbg_num_noise_val.length];
for (int nf = 0; nf < noise_files.length; nf++) {
int results_index = sensor_mode_file[nf] + (inter_file[nf]? 0 : 4); // inter; // ? 0 : (intra? 1 : 2);
if (good_file_tile[nf][ntile]) {
num_good[results_index]++;
val_good[results_index] += var_weights[nf];
} else {
has_bad[results_index] = true;
}
......@@ -10094,7 +10133,7 @@ if (debugLevel > -100) return true; // temporarily !
continue;
}
for (int i = 0; i < dbg_num_noise_val.length; i++) if (has_bad[i]) {
dbg_num_noise_val[i][ntile] = num_good[i];
dbg_num_noise_val[i][ntile] = val_good[i]; // num_good[i];
}
}
}
......@@ -10106,7 +10145,7 @@ if (debugLevel > -100) return true; // temporarily !
"num_noise_levels",
dbg_num_noise_titles);
for (int i = 00; i < dbg_num_noise_val.length; i++) {
for (int i = 0; i < dbg_num_noise_val.length; i++) {
Arrays.fill(dbg_num_noise_val[i], Double.NaN);
}
for (int ntile = 0; ntile < good_tiles.length; ntile++) {
......@@ -10116,6 +10155,7 @@ if (debugLevel > -100) return true; // temporarily !
if (good_tiles[ntile]) { // do not bother with obviously bad
int [] num_good = new int [dbg_num_noise_val.length];
double [] val_good = new double [dbg_num_noise_val.length];
boolean [] has_bad = new boolean [dbg_num_noise_val.length];
for (int nf = 0; nf < noise_files.length; nf++) {
int results_index = sensor_mode_file[nf] + (inter_file[nf]? 0 : 4); // inter; // ? 0 : (intra? 1 : 2);
......@@ -10123,6 +10163,7 @@ if (debugLevel > -100) return true; // temporarily !
for (int stp = 0; stp < good_file_tile_range[nf][ntile].length; stp++) {
if (good_file_tile_range[nf][ntile][stp]) {
num_good[results_index]++;
val_good[results_index] += var_weights[nf];
} else {
has_bad[results_index] = true;
}
......@@ -10143,7 +10184,7 @@ if (debugLevel > -100) return true; // temporarily !
continue;
}
for (int i = 0; i < dbg_num_noise_val.length; i++) if (has_bad[i]) {
dbg_num_noise_val[i][ntile] = num_good[i];
dbg_num_noise_val[i][ntile] = val_good[i]; // num_good[i];
}
}
}
......@@ -10157,18 +10198,28 @@ if (debugLevel > -100) return true; // temporarily !
double [] noise_file = use_fpn ? noise_fpn_file : noise_rnd_file;
double [][] noise_levels0 = InterIntraLMA.getNoiseThreshold(
double [][][] noise_levels_partial0 = InterIntraLMA.getNoiseThresholdsPartial(
noise_file, // double [] noise_file, // = new double [noise_files.length];
group_indices, // int [] group_indices, // last points after last file index
sensor_mode_file, // int [] sensor_mode_file,
inter_file, // boolean [] inter_file,
good_file_tile, // boolean [][] good_file_tile,
min_inter16_noise_level, // double min_inter16_noise_level,
apply_min_inter16_to_inter, // boolean apply_min_inter16_to_inter,
min_modes, // int min_modes,
zero_all_bad, // boolean zero_all_bad = true; // set noise_level to zero if all noise levels result in bad tiles
all_inter, // boolean all_inter = true; // tile has to be defined for all inter
need_same_inter, // boolean need_same_inter = true; // do not use intra sample if same inter is bad for all noise levels
need_same_zero, // boolean need_same_zero, // do not use samle if it is bad for zero-noise
dbg_tile); // int dbg_tile);
double [][] noise_levels0 = InterIntraLMA.mergeNoiseVariants(
noise_levels_partial0, // double [][][] partial_thresholds,
num_var_outliers, // int num_outliers,
min_var_remain); // int min_remain);
double [][] dbg_noise_levels = new double [dbg_num_noise_titles.length][good_tiles.length];
for (int i = 0; i < dbg_noise_levels.length; i++) {
Arrays.fill(dbg_noise_levels[i], Double.NaN);
......@@ -10187,7 +10238,7 @@ if (debugLevel > -100) return true; // temporarily !
dbg_num_noise_titles);
{
// int dbg_tile = 828;
for (int mode = 0; mode < 8; mode++) {
for (int mode = 00; mode < 8; mode++) {
for (int nf = 0; nf < noise_files.length; nf++) if(good_file_tile_range[nf] !=null){ // always
int mode_file = sensor_mode_file[nf] + (inter_file[nf]? 0 : 4); // inter; // ? 0 : (intra? 1 : 2);
if (mode_file == mode) {
......@@ -10200,27 +10251,32 @@ if (debugLevel > -100) return true; // temporarily !
System.out.println(String.format("%1d:%3d %6.4f %s", mode, nf, noise_rnd, s));
} else {
System.out.println(String.format("%1d:%3d %6.4f", mode, nf, noise_rnd));
}
}
}
}
}
double [][] noise_levels = InterIntraLMA.getNoiseThreshold(
double [][][] noise_levels_partial = InterIntraLMA.getNoiseThresholdsPartial(
noise_file, // double [] noise_file, // = new double [noise_files.length];
group_indices, // int [] group_indices, // last points after last file index
sensor_mode_file, // int [] sensor_mode_file,
inter_file, // boolean [] inter_file,
range_outliers, // int outliers, // may need do modify algorithm to avoid bias - removing same side (densier) outliers
range_min_keep, // int min_keep, // remove less outliers if needed to keep this remain
good_file_tile_range, // boolean [][][] good_file_tile_range,
min_inter16_noise_level, // double min_inter16_noise_level,
min_modes+0, // int min_modes,
apply_min_inter16_to_inter, // boolean apply_min_inter16_to_inter,
min_modes, // int min_modes,
zero_all_bad, // boolean zero_all_bad = true; // set noise_level to zero if all noise levels result in bad tiles
all_inter, // boolean all_inter = true; // tile has to be defined for all inter
need_same_inter, // boolean need_same_inter = true; // do not use intra sample if same inter is bad for all noise levels
need_same_zero, // boolean need_same_zero, // do not use samle if it is bad for zero-noise
dbg_tile); // int dbg_tile);
double [][] noise_levels = InterIntraLMA.mergeNoiseVariants(
noise_levels_partial, // double [][][] partial_thresholds,
num_var_outliers, // int num_outliers,
min_var_remain); // int min_remain);
double [][] dbg_noise_levels_range = new double [dbg_num_noise_titles.length][good_tiles.length];
for (int i = 0; i < dbg_noise_levels_range.length; i++) {
......@@ -10247,8 +10303,9 @@ if (debugLevel > -100) return true; // temporarily !
noise_offset, // double offset // for "relative" noise
n0 , // double n0, // initial value for N0
tilesX, // int tilesX, // debug images only
scale_intra, // double scale_intra, // scale weight of intra-scene samples ( < 1.0)
1); // int debug_level)
if (run_lma) {
boolean LMA_OK = interIntraLMA.runLma(
adjust_N0, // boolean adjust_N0,
......@@ -10257,7 +10314,7 @@ if (debugLevel > -100) return true; // temporarily !
0.1, // double lambda, // 0.1
0.5, // double lambda_scale_good,// 0.5
8.0, // double lambda_scale_bad, // 8.0
100, // double lambda_max, // 100
1000, // double lambda_max, // 100
0.001, // double rms_diff, // 0.001
30, // int num_iter, // 20
2); // 0); // int debug_level)
......@@ -10279,7 +10336,7 @@ if (debugLevel > -100) return true; // temporarily !
tilesX,
good_tiles.length/ tilesX,
"lma_st_out");
}
}
......@@ -10352,23 +10409,28 @@ if (debugLevel > -100) return true; // temporarily !
if (!results_map.containsKey(noise_level)) {
DisparityResults dr = new DisparityResults();
dr.results = new double [8][];
dr.num_instances = new int[8];
results_map.put(noise_level, dr);
}
// inter 16, inter 8, inter 4, inter 2, intra16, intra 8, intra 4, intra 2
DisparityResults dr = results_map.get(noise_level);
dr.results[results_index] = results;
if (dr.results[results_index] == null) {
dr.results[results_index] = results.clone();
} else {
for (int i = 0; i < results.length; i++) {
dr.results[results_index][i] += results[i];
}
}
dr.num_instances[results_index]++;
System.out.println("getNoiseStats(): "+noise_files[nf]+": good_ref= " + num_good_init+
", converged= "+num_converged+", good= "+num_good+" good(0.1)= "+num_good1+", num_near="+num_near);
}
// List<Double> noise_levels_list = new ArrayList<Double>(results_map.keySet());
List<NoiseLevel> noise_levels_list = new ArrayList<NoiseLevel>(results_map.keySet());
Collections.sort(noise_levels_list);
// String [] config_types = {"all_16", "octal", "quad", "binocular"};
String [] config_types = {"16", "8", "4", "2"};
System.out.println("\n");
// System.out.print("noise_level, ");
System.out.print("noise_random, noise_fpn, ");
for (int it = 0; it < config_types.length; it++) {
......@@ -10383,9 +10445,17 @@ if (debugLevel > -100) return true; // temporarily !
"("+max_err1+"), intra_rmse_"+config_types[it]+"("+max_err+"),");
}
// System.out.print("inter("+max_err+"), inter("+max_err1+"), inter_conf("+max_err+"), inter_conf("+max_err1+"), inter_rmse("+max_err+"),");
// System.out.print("intra("+max_err+"), intra("+max_err1+"), intra_conf("+max_err+"), intra_conf("+max_err1+"), intra_rmse("+max_err+"),");
// System.out.print("binocular("+max_err+"), binocular("+max_err1+"), binocular_conf("+max_err+"), binocular_conf("+max_err1+"), binocular_rmse("+max_err+")");
for (NoiseLevel nl:noise_levels_list) {
double [][] results = results_map.get(nl).results;
int [] num_instances = results_map.get(nl).num_instances;
for (int m = 0; m < results.length; m++) {
if (num_instances[m] > 1) {
for (int i = 0; i < results[m].length; i++){
results[m][i] /= num_instances[m];
}
}
}
}
System.out.println();
for (NoiseLevel nl:noise_levels_list) {
System.out.print(nl.rnd+", "+nl.fpn+", ");
......@@ -10409,11 +10479,383 @@ if (debugLevel > -100) return true; // temporarily !
}
System.out.println();
}
// good (2.0), good(.25) conf(2.0), conf(0.25) rmse(2.0)
double [] rslt_err= {max_err1,max_err,max_err1,max_err,max_err};
int [] used_results = {0,1,4};
int [][] sens_to_res = {{7,6,5,4},{3,2,1,0}}; //[inter]{2,4,8,16};
System.out.println();
double [] noise_lev = new double [noise_levels_list.size()];
double [][][][] plots_direct = new double [used_results.length][sens_to_res.length][sens_to_res[0].length][noise_levels_list.size()];
int nn = 0;
for (NoiseLevel nl:noise_levels_list) {
noise_lev[nn] = use_fpn ? nl.fpn : nl.rnd;
double [][] results = results_map.get(nl).results;
for (int pt = 0; pt < plots_direct.length; pt++) {
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
plots_direct[pt][inter][isens][nn] = results[sens_to_res[inter][isens]][used_results[pt]];
}
}
}
nn++;
}
int num_y_steps = 100;
// compare RMSE : 4, 8, 16 to binocular
double rmse_min = 0.0;
double rmse_max = 0.9;
double rmse_max_ratio = 8.0;
double rmse_max_ratio1 = 4.0;
double rmse_max_ratio2 = 15.0;
double density_min = 0.0;
double density_max = 1.0;
plotInverted(
noise_levels_list, // List<NoiseLevel> noise_levels_list,
results_map, // HashMap <NoiseLevel, DisparityResults> results_map,
max_err, // double max_err,
max_err1, // double max_err1,
use_fpn, // boolean use_fpn,
num_y_steps, // int num_y_steps, // = 100;
rmse_min, // double rmse_min, // = 0.0;
rmse_max, // double rmse_max, // = 0.9;
rmse_max_ratio, // double rmse_max_ratio, // = 6.0;
rmse_max_ratio1, // double rmse_max_ratio1,// = 4.0;
rmse_max_ratio2, // double rmse_max_ratio2 // = 20.0;
density_min, // double density_min, // == 0
density_max); // double density_max, // == 1
}
public void plotInverted(
List<NoiseLevel> noise_levels_list,
HashMap <NoiseLevel, DisparityResults> results_map,
double max_err,
double max_err1,
boolean use_fpn,
int num_y_steps, // = 100;
double rmse_min, // = 0.0;
double rmse_max, // = 0.9;
double rmse_max_ratio, // = 6.0;
double rmse_max_ratio1,// = 4.0;
double rmse_max_ratio2,// = 20.0;
double density_min, // == 0
double density_max // == 1
) {
// good (2.0), good(.25) conf(2.0), conf(0.25) rmse(2.0)
double [] rslt_err= {max_err1,max_err,max_err1,max_err,max_err};
int [] used_results = {0,1,4};
int [][] sens_to_res = {{7,6,5,4},{3,2,1,0}}; //[inter]{2,4,8,16};
System.out.println();
double [] noise_lev = new double [noise_levels_list.size()];
double [][][][] plots_direct = new double [used_results.length][sens_to_res.length][sens_to_res[0].length][noise_levels_list.size()];
int nn = 0;
for (NoiseLevel nl:noise_levels_list) {
noise_lev[nn] = use_fpn ? nl.fpn : nl.rnd;
double [][] results = results_map.get(nl).results;
for (int pt = 0; pt < plots_direct.length; pt++) {
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
plots_direct[pt][inter][isens][nn] = results[sens_to_res[inter][isens]][used_results[pt]];
}
}
}
nn++;
}
// compare RMSE : 4, 8, 16 to binocular
int pt_rmse = 2;
String [] col_titles_rmse = {
"rmse("+rslt_err[pt_rmse]+")",
"4:2 intra",
"8:2 intra",
"16:2 intra",
"4:2 inter",
"8:2 inter",
"16:2 inter"};
for (int i = 0; i < col_titles_rmse.length; i++) {
System.out.print(col_titles_rmse[i]);
if (i < (col_titles_rmse.length -1)) {
System.out.print(", ");
}
}
System.out.println();
double [] rmse_vals = inverseLinTFunc(
noise_lev, // double [] x_val,
plots_direct[pt_rmse][0][0], // double [] y_val,
rmse_min, // double y_min,
rmse_max, // double y_max,
num_y_steps+1)[0]; //int npoints )
double [][][] rmse_rslt = new double [sens_to_res.length][sens_to_res[0].length][];
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
rmse_rslt[inter][isens] = inverseLinTFunc(
noise_lev, // double [] x_val,
plots_direct[pt_rmse][inter][isens], // double [] y_val,
rmse_min, // double y_min,
rmse_max, // double y_max,
num_y_steps+1)[1]; //int npoints )
}
}
for (int i = 0; i < rmse_vals.length; i++) {
System.out.print(String.format("%8.5f, ", rmse_vals[i]));
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 1; isens < sens_to_res[0].length; isens++) {
double ratio = rmse_rslt[inter][isens][i]/rmse_rslt[inter][0][i];
if ((ratio > rmse_max_ratio) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
if (inter < (sens_to_res.length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
// compare RMSE : 4 to binocular, 8 to 4, 16 to 4
String [] col_titles_rmse1 = {
"rmse("+rslt_err[pt_rmse]+")",
"4:2 intra",
"8:4 intra",
"16:8 intra",
"4:2 inter",
"8:4 inter",
"16:8 inter"};
for (int i = 0; i < col_titles_rmse1.length; i++) {
System.out.print(col_titles_rmse1[i]);
if (i < (col_titles_rmse1.length -1)) {
System.out.print(", ");
}
}
System.out.println();
for (int i = 0; i < rmse_vals.length; i++) {
System.out.print(String.format("%8.5f, ", rmse_vals[i]));
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 1; isens < sens_to_res[0].length; isens++) {
double ratio = rmse_rslt[inter][isens][i]/rmse_rslt[inter][isens - 1][i];
if ((ratio > rmse_max_ratio1) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
if (inter < (sens_to_res.length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
// compare RMSE : inter to same intra
String [] col_titles_rmse2 = {
"rmse("+rslt_err[pt_rmse]+")",
"inter:intra (2)",
"inter:intra (4)",
"inter:intra (8)",
"inter:intra (16)"};
for (int i = 0; i < col_titles_rmse2.length; i++) {
System.out.print(col_titles_rmse2[i]);
if (i < (col_titles_rmse2.length -1)) {
System.out.print(", ");
}
}
System.out.println();
for (int i = 0; i < rmse_vals.length; i++) {
System.out.print(String.format("%8.5f, ", rmse_vals[i]));
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
double ratio = rmse_rslt[1][isens][i]/rmse_rslt[0][isens][i];
if ((ratio > rmse_max_ratio2) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
for (int idensity_err = 0; idensity_err < 2; idensity_err++) {
// compare density (2.0 and 0.25) : 4, 8, 16 to binocular
int pt_density = used_results[idensity_err];
String [] col_titles_density = {
"density("+rslt_err[pt_density]+")",
"4:2 intra",
"8:2 intra",
"16:2 intra",
"4:2 inter",
"8:2 inter",
"16:2 inter"};
for (int i = 0; i < col_titles_density.length; i++) {
System.out.print(col_titles_density[i]);
if (i < (col_titles_density.length -1)) {
System.out.print(", ");
}
}
System.out.println();
double [] density_vals = inverseLinTFunc(
noise_lev, // double [] x_val,
plots_direct[pt_density][0][0], // double [] y_val,
rmse_min, // double y_min,
rmse_max, // double y_max,
num_y_steps+1)[0]; //int npoints )
double [][][] density_rslt = new double [sens_to_res.length][sens_to_res[0].length][];
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
density_rslt[inter][isens] = inverseLinTFunc(
noise_lev, // double [] x_val,
plots_direct[pt_density][inter][isens], // double [] y_val,
density_min, // double y_min,
density_max, // double y_max,
num_y_steps+1)[1]; //int npoints )
}
}
for (int i = 0; i < density_vals.length; i++) {
System.out.print(String.format("%8.5f, ", density_vals[i]));
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 1; isens < sens_to_res[0].length; isens++) {
double ratio = density_rslt[inter][isens][i]/density_rslt[inter][0][i];
if ((ratio > rmse_max_ratio) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
if (inter < (sens_to_res.length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
// compare RMSE : 4 to binocular, 8 to 4, 16 to 4
String [] col_titles_density1 = {
"density("+rslt_err[pt_density]+")",
"4:2 intra",
"8:4 intra",
"16:8 intra",
"4:2 inter",
"8:4 inter",
"16:8 inter"};
for (int i = 0; i < col_titles_density1.length; i++) {
System.out.print(col_titles_density1[i]);
if (i < (col_titles_density1.length -1)) {
System.out.print(", ");
}
}
System.out.println();
for (int i = 0; i < density_vals.length; i++) {
System.out.print(String.format("%8.5f, ", density_vals[i]));
for (int inter = 0; inter < sens_to_res.length; inter++) {
for (int isens = 1; isens < sens_to_res[0].length; isens++) {
double ratio = density_rslt[inter][isens][i]/density_rslt[inter][isens - 1][i];
if ((ratio > rmse_max_ratio1) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
if (inter < (sens_to_res.length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
// compare RMSE : inter to same intra
String [] col_titles_density2 = {
"density("+rslt_err[pt_density]+")",
"inter:intra (2)",
"inter:intra (4)",
"inter:intra (8)",
"inter:intra (16)"};
for (int i = 0; i < col_titles_density2.length; i++) {
System.out.print(col_titles_density2[i]);
if (i < (col_titles_density2.length -1)) {
System.out.print(", ");
}
}
System.out.println();
for (int i = 0; i < density_vals.length; i++) {
System.out.print(String.format("%8.5f, ", density_vals[i]));
for (int isens = 0; isens < sens_to_res[0].length; isens++) {
double ratio = density_rslt[1][isens][i]/density_rslt[0][isens][i];
if ((ratio > rmse_max_ratio2) || Double.isInfinite(ratio)) {
ratio = Double.NaN;
}
if (!Double.isNaN(ratio)) {
System.out.print (String.format("%8.5f", ratio));
}
if (isens < (sens_to_res[0].length - 1)) {
System.out.print (", ");
}
}
System.out.println();
}
System.out.println("\n");
}
}
public double [][] inverseLinTFunc(
double [] x_val,
double [] y_val,
double y_min,
double y_max,
int npoints
){
double [][] x_y = new double [2][npoints];
for (int i = 0; i < npoints; i++) {
double y = y_min + i * (y_max - y_min)/(npoints -1);
x_y[0][i] = y;
x_y[1][i] = Double.NaN;
for (int j = 0; j < (x_val.length - 1); j++){
if (y_val[j] == y) {
x_y[1][i] = x_val[j];
break;
} else if ((y_val[j] - y) * (y_val[j + 1] - y) <= 0) {
x_y[1][i] = x_val[j] + (x_val[j+1] - x_val[j]) * (y - y_val[j])/(y_val[j+1]-y_val[j]);
break;
}
}
}
return x_y;
}
public void batchLwirRig(
QuadCLT quadCLT_main, // tiles should be set
QuadCLT quadCLT_aux,
......@@ -11531,11 +11973,14 @@ if (debugLevel > -100) return true; // temporarily !
}
if (!path.contains(Prefs.getFileSeparator())) {
/*
String x3d_path= quadCLT_main.correctionsParameters.selectX3dDirectory( // for x3d and obj
quadCLT_main.correctionsParameters.getModelName(quadCLT.image_name), // quad timestamp. Will be ignored if correctionsParameters.use_x3d_subdirs is false
quadCLT_main.correctionsParameters.x3dModelVersion,
true, // smart,
true); //newAllowed, // save
*/
String x3d_path = quadCLT_main.getX3dDirectory();
path = x3d_path+Prefs.getFileSeparator()+path;
}
if (properties == null) {
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment