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Commit 8d033226 authored by Andrey Filippov's avatar Andrey Filippov
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Optional common gains for all pairs

parent c3ee5b3f
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Showing with 188 additions and 46 deletions
src/main/java/com/elphel/imagej/tileprocessor/Corr2dLMA.java
View file @ 8d033226
......@@ -110,7 +110,7 @@ public class Corr2dLMA {
private double [] last_rms = null; // {rms, rms_pure}, matching this.vector
private double [] good_or_bad_rms = null; // just for diagnostics, to read last (failed) rms
private double [] initial_rms = null; // {rms, rms_pure}, first-calcualted rms
private double [] initial_rms = null; // {rms, rms_pure}, first-calculated rms
private double [] last_ymfx = null;
private double [][] last_jt = null;
......@@ -121,7 +121,8 @@ public class Corr2dLMA {
private int [] used_cams_rmap; // variable-length list of used cameras numbers
private int [][] used_pairs_map; // [tile][pair] -1 for unused pairs, >=0 for used ones
private boolean [] last_common_scale = null; //When switching from common to individual the
// scale[0] is cloned, reverse - averaged to [0]
private boolean [] used_tiles;
private final int transform_size;
......@@ -882,6 +883,7 @@ public class Corr2dLMA {
* Set/modify parameters mask. May be called after preparePars () or after updateFromVector() if LMA was ran
* @param adjust_disparities null to adjust all (1 or 2) disparities or a boolean array of per maximum
* individual disparity adjusts
* @param common_scales per-maximum, if true - common scale for all pairs. Null - all individual (old mode)
* @param adjust_width adjust correlation maximum width
* @param adjust_scales adjust per-pair amplitude
* @param adjust_ellipse adjust per-pair maximum shape as an ellipse
......@@ -891,8 +893,9 @@ public class Corr2dLMA {
* @param cost_lazyeye_odtho cost of lazy eye orthogonal to disparity
* @return OK/failure
*/
public boolean setParMask( // USED in lwir
public boolean setParMask(
boolean [] adjust_disparities, // null - adjust all, otherwise - per maximum
boolean [] common_scale, // per-maximum, if true - common scale for all pairs
boolean adjust_width, // adjust width of the maximum - lma_adjust_wm
boolean adjust_scales, // adjust 2D correlation scales - lma_adjust_ag
boolean adjust_ellipse, // allow non-circular correlation maximums lma_adjust_wy
......@@ -905,17 +908,56 @@ public class Corr2dLMA {
adjust_disparities = new boolean[numMax];
Arrays.fill(adjust_disparities, true);
}
if (common_scale == null) {
common_scale = new boolean[numMax];
Arrays.fill(common_scale, false);
}
if (last_common_scale == null) { // first time - same as it was {false, ...,false}
last_common_scale = new boolean[numMax];
Arrays.fill(last_common_scale, false);
}
total_tiles = 0; // now
// per-tile parameters
for (int nTile = 0; nTile < numTiles; nTile++) if (used_tiles[nTile]) {
for (int nmax = 0; nmax < numMax; nmax++) {
int offs = (nTile * numMax + nmax) * tile_params;
// Do scales need to be changed??
if (common_scale[nmax] != last_common_scale[nmax]) {
double cs = 0.0;
// Do scales need to be averaged into [0]?
if (common_scale[nmax]) {
int num_used_pairs = 0;
for (int np = 0; np <num_pairs; np++) if (used_pairs[nTile][np]) {
//this.all_pars[G0_INDEX + i + offs] = Double.NaN; // will be assigned later for used - should be for all !
cs += this.all_pars[G0_INDEX + np + offs];
num_used_pairs++;
}
if (num_used_pairs > 0) {
cs /= num_used_pairs;
}
} else { // need to be copied
cs = this.all_pars[G0_INDEX + 0 + offs];
}
for (int np = 0; np <num_pairs; np++) { // if (used_pairs[nTile][np]) {
this.all_pars[G0_INDEX + np + offs] = cs;
}
last_common_scale[nmax] = common_scale[nmax];
}
this.par_mask[DISP_INDEX + offs] = adjust_disparities[nmax];// true;
this.par_mask[A_INDEX + offs] = adjust_width;
this.par_mask[B_INDEX + offs] = adjust_ellipse;
this.par_mask[CMA_INDEX + offs] = adjust_ellipse;
for (int i = 0; i <num_pairs; i++) {
this.par_mask[G0_INDEX + i + offs] = used_pairs[nTile][i] & adjust_scales;
if (last_common_scale[nmax]) {
this.par_mask[G0_INDEX + 0 + offs] = adjust_scales;
for (int np = 1; np <num_pairs; np++) {
this.par_mask[G0_INDEX + np + offs] = false;
}
} else {
for (int np = 0; np <num_pairs; np++) {
this.par_mask[G0_INDEX + np + offs] = used_pairs[nTile][np] & adjust_scales;
}
}
}
total_tiles++;
......@@ -1201,7 +1243,9 @@ public class Corr2dLMA {
double B = BT[nmax][s.tile];
double C = CT[nmax][s.tile];
double Gp = av[G0_INDEX + pair + offs];
int cpair = last_common_scale[nmax]? 0: pair;
double Gp = av[G0_INDEX + cpair + offs]; // either common or individual;
double Wp = corr_wnd[s.ix][s.iy];
double WGp = Wp * Gp;
// double xmxp = s.ix - xp_yp[s.tile][s.fcam][s.scam][0];
......@@ -1234,11 +1278,19 @@ public class Corr2dLMA {
if (par_map[CMA_INDEX + offs] >= 0) {
jt[par_map[CMA_INDEX + offs]][ns] = -WGp* ymyp2;
}
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? d : 0.0; // (par_mask[G0_INDEX + pair])? d;
if (last_common_scale[nmax]) {
if (par_map[G0_INDEX + 0 + offs] >= 0) {
jt[par_map[G0_INDEX + 0 + offs]][ns] = d; // (par_mask[G0_INDEX + pair])? d;
}
} else {
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? d : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
}
}
if (lazy_eye) {
if (nmax == 0) { // only zero during first pass, then accumulate only
for (int f = 0; f < num_cams; f++) { // -1 for the last_cam and pre_last_cam
......@@ -1382,7 +1434,9 @@ public class Corr2dLMA {
double B = BT[nmax][s.tile];
double C = CT[nmax][s.tile];
double Gp = av[G0_INDEX + pair + offs];
int cpair = last_common_scale[nmax]? 0: pair;
double Gp = av[G0_INDEX + cpair + offs]; // either common or individual;
double Wp = corr_wnd[s.ix][s.iy];
double WGp = Wp * Gp;
// double xmxp = s.ix - xp_yp[s.tile][s.fcam][s.scam][0];
......@@ -1421,12 +1475,27 @@ public class Corr2dLMA {
if (par_map[CMA_INDEX + offs] >= 0) {
jt[par_map[CMA_INDEX + offs]][ns] = -WGp * ymyp2 * lim_negative;
}
// for (int p = 0; p < npairs[s.tile]; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
/*
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? d : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
}
*/
if (last_common_scale[nmax]) {
if (par_map[G0_INDEX + 0 + offs] >= 0) {
jt[par_map[G0_INDEX + 0 + offs]][ns] = d; // (par_mask[G0_INDEX + pair])? d;
}
} else {
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? d : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
}
}
if (lazy_eye) {
if (nmax == 0) { // only zero during first pass, then accumulate only // ****
for (int f = 0; f < num_cams; f++) { // -1 for the last_cam and pre_last_cam
......@@ -1568,7 +1637,8 @@ public class Corr2dLMA {
double A = AT[nmax][s.tile];
double B = BT[nmax][s.tile];
double C = CT[nmax][s.tile];
double Gp = av[G0_INDEX + pair + offs];
int cpair = last_common_scale[nmax]? 0: pair;
double Gp = av[G0_INDEX + cpair + offs]; // either common or individual;
double Wp = corr_wnd[s.ix][s.iy];
double WGp = Wp * Gp;
double xmxp = s.ix - xp_yp[nmax][s.tile][fs[0]][fs[1]][0]; // TODO - change format of xp_yp
......@@ -1608,11 +1678,19 @@ public class Corr2dLMA {
if (par_map[CMA_INDEX + offs] >= 0) {
jt[par_map[CMA_INDEX + offs]][ns] = -WGp * ymyp2 * lim_negative_2d;
}
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? (d * d) : 0.0; // (par_mask[G0_INDEX + pair])? d;
if (last_common_scale[nmax]) {
if (par_map[G0_INDEX + 0 + offs] >= 0) {
jt[par_map[G0_INDEX + 0 + offs]][ns] = d * d; // (par_mask[G0_INDEX + pair])? d;
}
} else {
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? (d * d) : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
}
}
if (lazy_eye) {
if (nmax == 0) { // only zero during first pass, then accumulate only // ****
for (int f = 0; f < num_cams; f++) { // -1 for the last_cam and pre_last_cam
......@@ -1751,7 +1829,8 @@ public class Corr2dLMA {
double A = AT[nmax][s.tile];
double B = BT[nmax][s.tile];
double C = CT[nmax][s.tile];
double Gp = av[G0_INDEX + pair + offs];
int cpair = last_common_scale[nmax]? 0: pair;
double Gp = av[G0_INDEX + cpair + offs]; // either common or individual;
double Wp = corr_wnd[s.ix][s.iy];
double WGp = Wp * Gp;
double xmxp = s.ix - xp_yp[nmax][s.tile][fs[0]][fs[1]][0];
......@@ -1793,10 +1872,23 @@ public class Corr2dLMA {
if (par_map[A_INDEX + offs] >= 0) jt[par_map[A_INDEX + offs]][ns] = -WGpexp*(xmxp2 + ymyp2);
if (par_map[B_INDEX + offs] >= 0) jt[par_map[B_INDEX + offs]][ns] = -WGpexp* 2 * xmxp_ymyp;
if (par_map[CMA_INDEX + offs] >= 0) jt[par_map[CMA_INDEX + offs]][ns] = -WGpexp* ymyp2;
/*
for (int p = 0; p < npairs[s.tile]; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? comm : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
*/
if (last_common_scale[nmax]) {
if (par_map[G0_INDEX + 0 + offs] >= 0) {
jt[par_map[G0_INDEX + 0 + offs]][ns] = comm; // (par_mask[G0_INDEX + pair])? d;
}
} else {
for (int p = 0; p < num_pairs; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
if (par_map[G0_INDEX + p + offs] >= 0) {
jt[par_map[G0_INDEX + p + offs]][ns] = (p== pair)? comm : 0.0; // (par_mask[G0_INDEX + pair])? d;
}
}
}
// process ddisp (last camera not used, is equal to minus sum of others to make a sum == 0)
if (lazy_eye) {
if (nmax == 0) { // only zero during first pass, then accumulate only
......@@ -2498,6 +2590,7 @@ public class Corr2dLMA {
return rslt;
}
// has common threshold for scale ratios for foreground and background corr. maximums
public double [][] lmaDisparityStrength(
double lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
double lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
......@@ -2509,7 +2602,8 @@ public class Corr2dLMA {
double lma_str_offset // convert lma-generated strength to match previous ones - add to result
){
return lmaDisparityStrengths(
lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
lmas_min_amp, // double lmas_min_amp_fg, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
lmas_min_amp, // double lmas_min_amp_bg, // Same for bg correlation max (only used for multi-max)
lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
lma_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
lma_min_max_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
......@@ -2518,11 +2612,12 @@ public class Corr2dLMA {
lma_str_scale, // convert lma-generated strength to match previous ones - scale
lma_str_offset // convert lma-generated strength to match previous ones - add to result
)[0];
}
// has separate thresholds for scale ratios for foreground and background corr. maximums
public double [][][] lmaDisparityStrengths(
double lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
double lmas_min_amp_fg, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
double lmas_min_amp_bg, // Same for bg correlation max (only used for multi-max)
double lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
double lma_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
double lma_min_max_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
......@@ -2545,6 +2640,20 @@ public class Corr2dLMA {
if (maxmin_amp[tile][1] < 0.0) {
continue; // inverse maximum - discard tile
}
double lmas_min_amp = lmas_min_amp_fg;
double disparity = -all_pars[DISP_INDEX + offs];
if (numMax > 1) {
for (int nmax1 = 0; nmax1 < numMax; nmax1++) if (nmax1 != nmax){
int offs1 = (tile * numMax + nmax1) * tile_params;
double disparity1 = -all_pars[DISP_INDEX + offs1];
if (disparity1 > disparity) {
lmas_min_amp = lmas_min_amp_bg;
break;
}
}
}
if ((maxmin_amp[tile][1]/maxmin_amp[tile][0]) < lmas_min_amp) {
continue; // inverse maximum - discard tile
}
......@@ -2570,7 +2679,7 @@ public class Corr2dLMA {
}
double strength = Math.sqrt(avg/rrms);
double disparity = -all_pars[DISP_INDEX + offs];
// double disparity = -all_pars[DISP_INDEX + offs];
if ((strength < lma_min_strength) || Double.isNaN(disparity)) {
continue;
}
......
src/main/java/com/elphel/imagej/tileprocessor/Correlation2d.java
View file @ 8d033226
......@@ -4410,14 +4410,7 @@ public class Correlation2d {
boolean debug_graphic = imgdtt_params.lma_debug_graphic && (imgdtt_params.lma_debug_level1 > 3) && (debug_level > 0) ;
debug_graphic |= imgdtt_params.lmamask_dbg && (debug_level > 0) ;
// TODO: Remove me
debug_graphic |= true;
/*
String dbg_title = null;
if (debug_graphic) {
dbg_title = String.format("tX%d_tY%d",tileX,tileY);
}
*/
double [][] dbg_corr = debug_graphic ? new double [corrs.length][] : null;
DoubleGaussianBlur gb = null;
if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();
......@@ -4449,50 +4442,69 @@ public class Correlation2d {
double [][] disp_str_all;
double [][][] own_masks;
double [][][] pair_offsets;
boolean [] common_scale; // = {false,false}; // true}; {true,true}; // // TODO: implement
if (lma_corr_weights0.length < 2) {
pair_offsets = pair_offsets0;
lma_corr_weights = lma_corr_weights0;
disp_str_all = disp_str_dual;
own_masks = own_masks0;
} else {
int nearest_max = (Math.abs(disp_str_dual[0][0]) < Math.abs(disp_str_dual[0][0]))? 0 : 1;
int fg_max = (disp_str_dual[0][0] > disp_str_dual[0][0]) ? 0 : 1;
common_scale = new boolean[] {imgdtt_params.bimax_common_fg};
} else { // only 2 max are supported
if (lma_corr_weights0.length > 2) {
System.out.println("corrLMA2DualMax(): Only 2 correlation maximums are currently supported, all but 2 strongest are discarded");
}
int nearest_max = (Math.abs(disp_str_dual[0][0]) < Math.abs(disp_str_dual[1][0]))? 0 : 1;
int fg_max = (disp_str_dual[0][0] > disp_str_dual[1][0]) ? 0 : 1;
switch (combine_mode) {
case 0: // keep both
pair_offsets = pair_offsets0;
lma_corr_weights = lma_corr_weights0;
disp_str_all = disp_str_dual;
own_masks = own_masks0;
common_scale = new boolean[disp_str_dual.length];
for (int i = 0; i < common_scale.length; i++) {
common_scale[i] = (i == fg_max) ? imgdtt_params.bimax_common_fg : imgdtt_params.bimax_common_bg;
}
break;
case 1: // keep strongest
pair_offsets = new double [][][] {pair_offsets0 [0]};
lma_corr_weights = new double [][][] {lma_corr_weights0[0]};
disp_str_all = new double [][] {disp_str_dual [0]};
own_masks = new double [][][] {own_masks0 [0]};
common_scale = new boolean[] {(0 == fg_max) ? imgdtt_params.bimax_common_fg : imgdtt_params.bimax_common_bg};
break;
case 2: // keep nearest
pair_offsets = new double [][][] {pair_offsets0 [nearest_max]};
lma_corr_weights = new double [][][] {lma_corr_weights0[nearest_max]};
disp_str_all = new double [][] {disp_str_dual [nearest_max]};
own_masks = new double [][][] {own_masks0 [nearest_max]};
common_scale = new boolean[] {(nearest_max == fg_max) ? imgdtt_params.bimax_common_fg : imgdtt_params.bimax_common_bg};
break;
case 3: // keep foreground
pair_offsets = new double [][][] {pair_offsets0 [fg_max]};
lma_corr_weights = new double [][][] {lma_corr_weights0[fg_max]};
disp_str_all = new double [][] {disp_str_dual [fg_max]};
own_masks = new double [][][] {own_masks0 [fg_max]};
common_scale = new boolean[] {imgdtt_params.bimax_common_fg};
break;
case 4: // keep background
pair_offsets = new double [][][] {pair_offsets0 [1-fg_max]};
lma_corr_weights = new double [][][] {lma_corr_weights0[1-fg_max]};
disp_str_all = new double [][] {disp_str_dual [1-fg_max]};
own_masks = new double [][][] {own_masks0 [1-fg_max]};
common_scale = new boolean[] {imgdtt_params.bimax_common_bg};
break;
default: // keep both
pair_offsets = pair_offsets0;
lma_corr_weights = lma_corr_weights0;
disp_str_all = disp_str_dual;
own_masks = own_masks0;
common_scale = new boolean[disp_str_dual.length];
for (int i = 0; i < common_scale.length; i++) {
common_scale[i] = (i == fg_max) ? imgdtt_params.bimax_common_fg : imgdtt_params.bimax_common_bg;
}
}
}
......@@ -4505,9 +4517,6 @@ public class Correlation2d {
int num_used_pairs = 0;
for (int npair = 0; npair < pair_mask.length; npair++) if ((corrs[npair] != null) && (pair_mask[npair])){
double [] corr_blur = null;
// if (npair == 65) {
// System.out.println("---npair == "+npair);
// }
if (imgdtt_params.cnvx_en) { // || (pair_shape_masks == null)) {
corr_blur = corrs[npair].clone();
if (corr_wnd_inv_limited != null) {
......@@ -4655,16 +4664,12 @@ public class Correlation2d {
for (int i = 0; i <disp_str_all.length; i++) if (disp_str_all[i] != null){
disp_str2[i][0] = disp_str_all[i];
}
// double [][] disp_str2 = {disp_str_all[0]}; // temporary // will be calculated/set later
boolean lmaSuccess = false;
// int num_lma_retries = 0;
// When running LMA - first do not touch disparity?
// int lma_pass = imgdtt_params.bimax_dual_pass? 0 : 1; // pass0 - w/o disparity, pass 1 - with
boolean [] adjust_disparities = new boolean [disp_str_all.length]; // all false;
boolean needprep = true; //t npass = 0;
for (int npass = (imgdtt_params.bimax_dual_pass? 00 : 1); npass < 2; npass++) { // may break while (!lmaSuccess) {
// num_lma_retries ++; // debug
for (int npass = (imgdtt_params.bimax_dual_pass? 0 : 1); npass < 2; npass++) { // may break while (!lmaSuccess) {
if (needprep) {
lma.preparePars(
disp_str2, // double [][][] disp_str_all, initial value of disparity [max][tile]{disp, strength}
......@@ -4679,8 +4684,10 @@ public class Correlation2d {
if (npass > 0) {
adjust_disparities = null;
}
// boolean [] common_scale = {false,false}; // true}; {true,true}; // // TODO: implement
lma.setParMask( // USED in lwir
adjust_disparities, // null, // boolean [] adjust_disparities, // null - adjust all, otherwise - per maximum
common_scale, //boolean [] common_scale, // per-maximum, if true - common scale for all pairs
imgdtt_params.lmas_adjust_wm, // boolean adjust_width, // adjust width of the maximum - lma_adjust_wm
imgdtt_params.lmas_adjust_ag, // boolean adjust_scales, // adjust 2D correlation scales - lma_adjust_ag
imgdtt_params.lmas_adjust_wy, // boolean adjust_ellipse, // allow non-circular correlation maximums lma_adjust_wy
......@@ -4713,6 +4720,7 @@ public class Correlation2d {
lma.updateFromVector();
double [][][] dispStrs = lma.lmaDisparityStrengths( //TODO: add parameter to filter out negative minimums ?
imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
imgdtt_params.lmas_min_amp_bg, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
imgdtt_params.lmas_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
imgdtt_params.lmas_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
imgdtt_params.lmas_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
......@@ -4721,7 +4729,7 @@ public class Correlation2d {
imgdtt_params.lma_str_scale, // convert lma-generated strength to match previous ones - scale
imgdtt_params.lma_str_offset // convert lma-generated strength to match previous ones - add to result
);
for (int nmax = 0; nmax < dispStrs.length; nmax++) if (dispStrs[nmax][0][1] <= 0) {
for (int nmax = 00; nmax < dispStrs.length; nmax++) if (dispStrs[nmax][0][1] <= 0) {
lmaSuccess = false;
break;
}
......@@ -4876,7 +4884,7 @@ public class Correlation2d {
int tileX, // just for debug output
int tileY){
boolean debug_graphic = imgdtt_params.lma_debug_graphic && (imgdtt_params.lma_debug_level1 > 3) && (debug_level > 0) ;
debug_graphic |= imgdtt_params.lmamask_dbg && (debug_level > 0) || true;
debug_graphic |= imgdtt_params.lmamask_dbg && (debug_level > 0); // || true;
String dbg_title = null;
if (debug_graphic) {
dbg_title = String.format("tX%d_tY%d",tileX,tileY);
......
src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java
View file @ 8d033226
......@@ -2624,14 +2624,14 @@ public class ImageDtt extends ImageDttCPU {
}
if (debugTile1) {
System.out.println("clt_process_tl_correlations() maxes=");
for (int i = 0; i < maxes.length; i++) {
for (int i = 00; i < maxes.length; i++) {
System.out.println(String.format("maxes[%d][0]=%f (quadcam disparity pixels, not combo pixels), maxes[%d][1]=%f", i, maxes[i][0], i, maxes[i][1]));
}
}
if (debugTile1) {
Corr2dLMA lma_dual = correlation2d.corrLMA2DualMax( // null pointer
imgdtt_params, // ImageDttParameters imgdtt_params,
0, // 3, // 0, // 1, // int combine_mode, // 0 - both, 1 - strongest, 2 - nearest to zero, 3 - FG, 4 - BG
00, // 3, // 0, // 1, // int combine_mode, // 0 - both, 1 - strongest, 2 - nearest to zero, 3 - FG, 4 - BG
// imgdtt_params.lmas_LY_single, // false, // boolean adjust_ly, // adjust Lazy Eye
corr_wnd, // double [][] corr_wnd, // correlation window to save on re-calculation of the window
corr_wnd_inv_limited, // corr_wnd_limited, // correlation window, limited not to be smaller than threshold - used for finding max/convex areas (or null)
......
src/main/java/com/elphel/imagej/tileprocessor/ImageDttParameters.java
View file @ 8d033226
......@@ -85,6 +85,9 @@ public class ImageDttParameters {
public int bimax_min_num_samples = 4; // minimal number of samples per pair per maximum
public int bimax_min_num_pairs = 8; // minimal number of used pairs
public boolean bimax_dual_pass = true; // First pass - do not adjust disparity
public boolean bimax_common_fg = true; // Common gains for foreground/single correlation maximum
public boolean bimax_common_bg = true; // Common gains for background correlation maximum
//lmamask_
public boolean lmamask_dbg = false; // show LMA images, exit after single BG
......@@ -188,6 +191,9 @@ public class ImageDttParameters {
public boolean lmas_adjust_wm = true; // used in new for width
public boolean lmas_adjust_wy = true; // adjust non-circular
public boolean lmas_adjust_ag = true; // adjust gains gains
// Pre-lma poly
public double lmas_poly_str_scale = 1.0; // scale pre-lma poly strength
public double lmas_poly_str_min = 0.05; // ignore tiles with poly strength (scaled) below
......@@ -202,7 +208,8 @@ public class ImageDttParameters {
// Filtering and strength calculation
// High ratio of amplitudes may be caused by "neighbors" correlations when the same object disappears from the tile
// for small disparities 0.5...0.7 is OK, for larger, and small objects on uniform background, may need 0.2
public double lmas_min_amp = 0.25; // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
public double lmas_min_amp = 0.1; // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
public double lmas_min_amp_bg = 0.01; // same for background objects (all but fg)
public double lmas_max_rel_rms = 0.3; // LWIR16: 0.5 maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
public double lmas_min_strength = 0.7; // LWIR16: 0.4 minimal composite strength (sqrt(average amp squared over absolute RMS)
public double lmas_min_ac = 0.02; // LWIR16: 0.01 minimal of a and C coefficients maximum (measures sharpest point/line)
......@@ -475,6 +482,10 @@ public class ImageDttParameters {
"Do not use LMA if total number of used pairs is lower");
gd.addCheckbox ("Dual-pass LMA", this.bimax_dual_pass,
"First adjust other parameters (keeping disparities), then add disparities");
gd.addCheckbox ("Common gains for foreground/single correlation maximum", this.bimax_common_fg,
"Use common gain for all pairs for FG/single correlation (unchecked - use individual gain for each pair)");
gd.addCheckbox ("Common gains for background correlation maximum", this.bimax_common_bg,
"Use common gain for all pairs for BG correlation(s) (unchecked - use individual gain for each pair)");
gd.addMessage("LMA samples filter based on estimated disparity");
gd.addCheckbox ("Debug LMA", this.lmamask_dbg,
......@@ -698,6 +709,8 @@ public class ImageDttParameters {
gd.addMessage("LMA (single) results filtering");
gd.addNumericField("Minimal weakest pair to strongest pair correlation amplitude ratio",this.lmas_min_amp, 6, 8, "",
"Discard tile if ratio of the weakest correlation pair amplitude to that of the strongest one is lower than this");
gd.addNumericField("Same for BG objects (dual max)", this.lmas_min_amp_bg, 6, 8, "",
"Discard tile if ratio of the weakest correlation pair amplitude to that of the strongest one is lower than this (BG objects, far maximum)");
gd.addNumericField("Maximal relative RMS ", this.lmas_max_rel_rms, 6, 8, "",
"Discard tile if ratio of RMS to average of min and max amplitude exceeds this value");
gd.addNumericField("Minimal composite strength", this.lmas_min_strength, 6, 8, "",
......@@ -892,6 +905,8 @@ public class ImageDttParameters {
this.bimax_min_num_samples= (int) gd.getNextNumber();
this.bimax_min_num_pairs= (int) gd.getNextNumber();
this.bimax_dual_pass = gd.getNextBoolean();
this.bimax_common_fg = gd.getNextBoolean();
this.bimax_common_bg = gd.getNextBoolean();
this.lmamask_dbg = gd.getNextBoolean();
this.lmamask_en = gd.getNextBoolean();
......@@ -1002,6 +1017,7 @@ public class ImageDttParameters {
this.lmas_rms_diff = gd.getNextNumber();
this.lmas_num_iter= (int) gd.getNextNumber();
this.lmas_min_amp = gd.getNextNumber();
this.lmas_min_amp_bg = gd.getNextNumber();
this.lmas_max_rel_rms = gd.getNextNumber();
this.lmas_min_strength = gd.getNextNumber();
this.lmas_min_ac = gd.getNextNumber();
......@@ -1119,6 +1135,8 @@ public class ImageDttParameters {
properties.setProperty(prefix+"bimax_min_num_samples", this.bimax_min_num_samples +"");
properties.setProperty(prefix+"bimax_min_num_pairs", this.bimax_min_num_pairs +"");
properties.setProperty(prefix+"bimax_dual_pass", this.bimax_dual_pass +"");
properties.setProperty(prefix+"bimax_common_fg", this.bimax_common_fg +"");
properties.setProperty(prefix+"bimax_common_bg", this.bimax_common_bg +"");
properties.setProperty(prefix+"lmamask_dbg", this.lmamask_dbg +"");
properties.setProperty(prefix+"lmamask_en", this.lmamask_en +"");
......@@ -1228,6 +1246,7 @@ public class ImageDttParameters {
properties.setProperty(prefix+"lmas_rms_diff", this.lmas_rms_diff +"");
properties.setProperty(prefix+"lmas_num_iter", this.lmas_num_iter +"");
properties.setProperty(prefix+"lmas_min_amp", this.lmas_min_amp +"");
properties.setProperty(prefix+"lmas_min_amp_bg", this.lmas_min_amp_bg +"");
properties.setProperty(prefix+"lmas_max_rel_rms", this.lmas_max_rel_rms +"");
properties.setProperty(prefix+"lmas_min_strength", this.lmas_min_strength +"");
properties.setProperty(prefix+"lmas_min_ac", this.lmas_min_ac +"");
......@@ -1350,6 +1369,8 @@ public class ImageDttParameters {
if (properties.getProperty(prefix+"bimax_min_num_samples")!=null) this.bimax_min_num_samples=Integer.parseInt(properties.getProperty(prefix+"bimax_min_num_samples"));
if (properties.getProperty(prefix+"bimax_min_num_pairs")!=null) this.bimax_min_num_pairs=Integer.parseInt(properties.getProperty(prefix+"bimax_min_num_pairs"));
if (properties.getProperty(prefix+"bimax_dual_pass")!=null) this.bimax_dual_pass=Boolean.parseBoolean(properties.getProperty(prefix+"bimax_dual_pass"));
if (properties.getProperty(prefix+"bimax_common_fg")!=null) this.bimax_common_fg=Boolean.parseBoolean(properties.getProperty(prefix+"bimax_common_fg"));
if (properties.getProperty(prefix+"bimax_common_bg")!=null) this.bimax_common_bg=Boolean.parseBoolean(properties.getProperty(prefix+"bimax_common_bg"));
if (properties.getProperty(prefix+"lmamask_dbg")!=null) this.lmamask_dbg=Boolean.parseBoolean(properties.getProperty(prefix+"lmamask_dbg"));
if (properties.getProperty(prefix+"lmamask_en")!=null) this.lmamask_en=Boolean.parseBoolean(properties.getProperty(prefix+"lmamask_en"));
......@@ -1466,6 +1487,7 @@ public class ImageDttParameters {
if (properties.getProperty(prefix+"lmas_rms_diff")!=null) this.lmas_rms_diff=Double.parseDouble(properties.getProperty(prefix+"lmas_rms_diff"));
if (properties.getProperty(prefix+"lmas_num_iter")!=null) this.lmas_num_iter=Integer.parseInt(properties.getProperty(prefix+"lmas_num_iter"));
if (properties.getProperty(prefix+"lmas_min_amp")!=null) this.lmas_min_amp=Double.parseDouble(properties.getProperty(prefix+"lmas_min_amp"));
if (properties.getProperty(prefix+"lmas_min_amp_bg")!=null) this.lmas_min_amp_bg=Double.parseDouble(properties.getProperty(prefix+"lmas_min_amp_bg"));
if (properties.getProperty(prefix+"lmas_max_rel_rms")!=null) this.lmas_max_rel_rms=Double.parseDouble(properties.getProperty(prefix+"lmas_max_rel_rms"));
if (properties.getProperty(prefix+"lmas_min_strength")!=null) this.lmas_min_strength=Double.parseDouble(properties.getProperty(prefix+"lmas_min_strength"));
if (properties.getProperty(prefix+"lmas_min_ac")!=null) this.lmas_min_ac=Double.parseDouble(properties.getProperty(prefix+"lmas_min_ac"));
......@@ -1598,6 +1620,8 @@ public class ImageDttParameters {
idp.bimax_min_num_samples= this.bimax_min_num_samples;
idp.bimax_min_num_pairs= this.bimax_min_num_pairs;
idp.bimax_dual_pass= this.bimax_dual_pass;
idp.bimax_common_fg= this.bimax_common_fg;
idp.bimax_common_bg= this.bimax_common_bg;
idp.lmamask_dbg= this.lmamask_dbg;
idp.lmamask_en= this.lmamask_en;
......@@ -1705,6 +1729,7 @@ public class ImageDttParameters {
idp.lmas_rms_diff = this.lmas_rms_diff;
idp.lmas_num_iter = this.lmas_num_iter;
idp.lmas_min_amp= this.lmas_min_amp;
idp.lmas_min_amp_bg= this.lmas_min_amp_bg;
idp.lmas_max_rel_rms= this.lmas_max_rel_rms;
idp.lmas_min_strength= this.lmas_min_strength;
idp.lmas_min_ac= this.lmas_min_ac;
......
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