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imagej-elphel
Commit fb44bc1a authored by Andrey Filippov's avatar Andrey Filippov
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Implemented 2d maximum modelled as Gaussian in addition to parabola

parent 6cbf1da0
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Showing with 194 additions and 17 deletions
src/main/java/com/elphel/imagej/tileprocessor/Corr2dLMA.java
View file @ fb44bc1a
......@@ -130,6 +130,7 @@ public class Corr2dLMA {
private final int [][] pindx = new int [NUM_CAMS][NUM_CAMS];
private int numTiles = 1;
public boolean gaussian_mode = true;
public class Sample{ // USED in lwir
int tile; // tile in a cluster
......@@ -162,8 +163,10 @@ public class Corr2dLMA {
public Corr2dLMA (
int numTiles,
int ts, // null - use default table
double [][] corr_wnd // may be null
double [][] corr_wnd, // may be null
boolean gaussian_mode
) {
this.gaussian_mode = gaussian_mode;
for (int f = 0; f < NUM_CAMS; f++) {
pindx[f][f]=-1;
for (int s = f+1; s < NUM_CAMS; s++) {
......@@ -495,10 +498,16 @@ public class Corr2dLMA {
}
}
}
public double [] getFxJt( // USED in lwir
double [] vector,
double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
if (this.gaussian_mode) return getFxJt_gaussian(vector, jt);
else return getFxJt_parabola(vector, jt);
}
public double [] getFxJt_parabola( // USED in lwir
double [] vector,
double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
if (vector == null) return null;
double [] av = fromVector(vector);
Matrix [][] xcam_ycam = new Matrix[numTiles][NUM_CAMS];
......@@ -635,6 +644,147 @@ public class Corr2dLMA {
return fx;
}
public double [] getFxJt_gaussian( // USED in lwir
double [] vector,
double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
if (vector == null) return null;
double [] av = fromVector(vector);
Matrix [][] xcam_ycam = new Matrix[numTiles][NUM_CAMS];
double [][][][] xp_yp = new double[numTiles][NUM_CAMS][NUM_CAMS][];
double [] axc_yc = {transform_size - 1.0, transform_size-1.0};
Matrix xc_yc = new Matrix(axc_yc, 2);
double [] AT = new double [numTiles]; // av[A_INDEX];
double [] BT = new double [numTiles]; // av[B_INDEX];
double [] CT = new double [numTiles]; // A + av[CMA_INDEX];
for (int nTile = 0; nTile < numTiles; nTile++) {
for (int i = 0; i < NUM_CAMS; i++) if (used_cameras[i]) {
double [] add_dnd = {av[DISP_INDEX+ nTile * TILE_PARAMS]+ av[DDISP_INDEX + i], av[NDISP_INDEX + i]};
xcam_ycam[nTile][i] = m_disp[nTile][i].times(new Matrix(add_dnd,2));
}
for (int f = 0; f < NUM_CAMS; f++) if (used_cameras[f]) {
for (int s = 0; s < NUM_CAMS; s++) if (used_cameras[s]) {
xp_yp[nTile][f][s] =xcam_ycam[nTile][f].minus(xcam_ycam[nTile][s]).plus(xc_yc).getColumnPackedCopy();
}
}
AT[nTile] = av[A_INDEX + nTile * TILE_PARAMS];
BT[nTile] = av[B_INDEX + nTile * TILE_PARAMS];
CT[nTile] = AT[nTile] + av[CMA_INDEX + nTile * TILE_PARAMS];
}
int num_samples = samples.size();
double [] fx= new double [num_samples + 2 * NUM_CAMS];
//corr_wnd
for (int ns = 0; ns < num_samples; ns++) {
Sample s = samples.get(ns);
int pair = pindx[s.fcam][s.scam]; // all pairs, noit just used?
double A = AT[s.tile];
double B = BT[s.tile];
double C = CT[s.tile];
double Gp = av[G0_INDEX + pair + s.tile * TILE_PARAMS];
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];
double ymyp = s.iy - xp_yp[s.tile][s.fcam][s.scam][1];
double xmxp2 = xmxp * xmxp;
double ymyp2 = ymyp * ymyp;
double xmxp_ymyp = xmxp * ymyp;
//// double comm = Wp*(1.0 - (A*xmxp2 + 2 * B * xmxp_ymyp + C * ymyp2));
double exp = Math.exp(-(A*xmxp2 + 2 * B * xmxp_ymyp + C * ymyp2));
double comm = exp * Wp;
double WGpexp = WGp*exp;
fx[ns] = comm * Gp;
if (Double.isNaN(fx[ns])) {
System.out.println("fx["+ns+"]="+fx[ns]);
}
if (s.tile > 0) {
System.out.print("");
}
if (jt != null) {
if (par_map[DISP_INDEX + s.tile*TILE_PARAMS] >= 0) jt[par_map[DISP_INDEX + s.tile*TILE_PARAMS]][ns] = 2 * WGpexp *
((A * xmxp + B * ymyp) * m_pairs[s.tile][s.fcam][s.scam].get(0, 0)+
(B * xmxp + C * ymyp) * m_pairs[s.tile][s.fcam][s.scam].get(1, 0));
if (par_map[A_INDEX + s.tile*TILE_PARAMS] >= 0) jt[par_map[A_INDEX + s.tile*TILE_PARAMS]][ns] = -WGpexp*(xmxp2 + ymyp2);
if (par_map[B_INDEX + s.tile*TILE_PARAMS] >= 0) jt[par_map[B_INDEX + s.tile*TILE_PARAMS]][ns] = -WGpexp* 2 * xmxp_ymyp;
if (par_map[CMA_INDEX + s.tile*TILE_PARAMS] >= 0) jt[par_map[CMA_INDEX + s.tile*TILE_PARAMS]][ns] = -WGp* ymyp2 * exp;
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 + s.tile*TILE_PARAMS] >= 0) jt[par_map[G0_INDEX + p + s.tile*TILE_PARAMS]][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)
for (int f = 0; f < NUM_CAMS; f++) if (par_map[DDISP_INDEX + f] >= 0) { // -1 for the last_cam
jt[par_map[DDISP_INDEX + f]][ns] = 0.0;
}
if (par_map[DDISP_INDEX + s.fcam] >= 0){ // par_map[DDISP_INDEX + last_cam] always <0
jt[par_map[DDISP_INDEX + s.fcam]][ns] += 2 * WGpexp *
((A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 0)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 0));
} else if (s.fcam == last_cam) {
for (int c = 0; c < NUM_CAMS; c++) if ((c != last_cam) && (par_map[DDISP_INDEX + c] >=0)) {
jt[par_map[DDISP_INDEX + c]][ns] -= 2 * WGpexp *
( (A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 0)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 0));
}
}
if (par_map[DDISP_INDEX + s.scam]>= 0){ // par_map[DDISP_INDEX + last_cam] always <0
jt[par_map[DDISP_INDEX + s.scam]][ns] -= 2 * WGpexp *
((A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 0)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 0));
} else if (s.scam == last_cam) {
for (int c = 0; c < NUM_CAMS; c++) if ((c != last_cam) && (par_map[DDISP_INDEX + c] >= 0)) {
jt[par_map[DDISP_INDEX + c]][ns] += 2 * WGpexp *
((A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 0)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 0));
}
}
// process ndisp
for (int f = 0; f < ncam; f++) if (par_map[NDISP_INDEX + f] >= 0) {
jt[par_map[NDISP_INDEX + f]][ns] = 0.0;
}
if (par_map[NDISP_INDEX + s.fcam] >=0){
jt[par_map[NDISP_INDEX + s.fcam]][ns] += 2 * WGpexp *
( (A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 1)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 1));
}
if (par_map[NDISP_INDEX + s.scam] >= 0) {
jt[par_map[NDISP_INDEX + s.scam]][ns] -= 2 * WGpexp *
( (A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 1)+
(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 1));
}
}
}
for (int n = 0; n < NUM_CAMS; n++) { // av[DDISP_INDEX +last_cam] is already populated
fx[num_samples + n] = av[DDISP_INDEX + n];
fx[num_samples + NUM_CAMS + n] = av[NDISP_INDEX + n];
}
// and derivatives
if (jt != null) {
for (int i = 0; i < NUM_CAMS; i++) {
if ((i != last_cam) && (par_map[DDISP_INDEX + i] >= 0)) {
for (int j = 0; j < NUM_CAMS; j++) { // j - column
jt[par_map[DDISP_INDEX + i]][num_samples + j] = (i==j)? 1.0 : 0.0;
}
jt[par_map[DDISP_INDEX + i]][num_samples + last_cam] = -1.0;
}
}
for (int i = 0; i < NUM_CAMS; i++) {
if (par_map[NDISP_INDEX + i] >= 0) {
for (int j = 0; j < NUM_CAMS; j++) { // j - column
jt[par_map[NDISP_INDEX + i] ][num_samples + NUM_CAMS + j] = (i==j)? 1.0 : 0.0;
}
}
}
}
return fx;
}
public void printParams() { // not used in lwir
for (int np = 0; np < all_pars.length; np++) {
......
src/main/java/com/elphel/imagej/tileprocessor/Correlation2d.java
View file @ fb44bc1a
......@@ -1823,14 +1823,19 @@ public class Correlation2d {
// corrs are organized as PAIRS, some are null if not used
// for each enabled and available pair find a maximum, filter convex and create sample list
boolean debug_graphic = (debug_level > -1);
boolean debug_second_all = true;
boolean debug_second_all = false; // true;
int clust_height = corrs.length/clust_width;
int ntiles = corrs.length;
DoubleGaussianBlur gb = null;
if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();
int center = transform_size - 1;
int corr_size = 2 * transform_size - 1;
Corr2dLMA lma = new Corr2dLMA(corrs.length,transform_size, corr_wnd);
Corr2dLMA lma = new Corr2dLMA(
corrs.length,
transform_size,
corr_wnd,
imgdtt_params.lma_gaussian//boolean gaussian_mode
);
double [][][] dbg_corr = debug_graphic ? new double [corrs.length][][] : null;
double [][][] dbg_weights = debug_graphic ? new double [corrs.length][][] : null;
......@@ -2082,24 +2087,37 @@ public class Correlation2d {
true,
"corr_fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
} else {
double [][] repacked_y = repackCluster(lma.dbgGetSamples(0),clust_width);
double [][] repacked_fx = repackCluster(lma.dbgGetSamples(2),clust_width);
double [][] y_minux_fx = new double [repacked_y.length][];
for (int i = 0; i < repacked_y.length; i++) if ((repacked_y[i] != null) && (repacked_fx[i] != null)){
y_minux_fx[i] = new double [repacked_y[i].length];
for (int j = 0; j < y_minux_fx[i].length; j++) y_minux_fx[i][j] = repacked_y[i][j] - repacked_fx[i][j];
}
(new ShowDoubleFloatArrays()).showArrays(
repackCluster(lma.dbgGetSamples(0),clust_width),
repacked_y,
dbg_out_width,
dbg_out_height,
true,
"corr_values"+"_x"+tileX+"_y"+tileY, sliceTitles);
"y"+"_x"+tileX+"_y"+tileY, sliceTitles);
(new ShowDoubleFloatArrays()).showArrays(
repackCluster(lma.dbgGetSamples(1),clust_width),
repacked_fx,
dbg_out_width,
dbg_out_height,
true,
"corr_weights"+"_x"+tileX+"_y"+tileY, sliceTitles);
"fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
(new ShowDoubleFloatArrays()).showArrays(
repackCluster(lma.dbgGetSamples(2),clust_width),
y_minux_fx,
dbg_out_width,
dbg_out_height,
true,
"corr_fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
"y-fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
(new ShowDoubleFloatArrays()).showArrays(
repackCluster(lma.dbgGetSamples(1),clust_width),
dbg_out_width,
dbg_out_height,
true,
"weights"+"_x"+tileX+"_y"+tileY, sliceTitles);
}
}
......@@ -2129,7 +2147,13 @@ public class Correlation2d {
if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();
int center = transform_size - 1;
int corr_size = 2 * transform_size - 1;
Corr2dLMA lma = new Corr2dLMA(1,transform_size, corr_wnd);
Corr2dLMA lma = new Corr2dLMA(
1,
transform_size,
corr_wnd,
imgdtt_params.lma_gaussian//boolean gaussian_mode
);
double [][] dbg_corr = debug_graphic ? new double [corrs.length][] : null;
double [][] dbg_weights = debug_graphic ? new double [corrs.length][] : null;
......
src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java
View file @ fb44bc1a
......@@ -1746,7 +1746,7 @@ public class ImageDtt {
double centerX; // center of aberration-corrected (common model) tile, X
double centerY; //
double [][] fract_shiftsXY = new double[quad][];
double [][] corr_wnd = (new Corr2dLMA(1, transform_size, null)).getCorrWnd();
double [][] corr_wnd = (new Corr2dLMA(1, transform_size, null,imgdtt_params.lma_gaussian)).getCorrWnd();
double [] corr_wnd_inv_limited = null;
if (imgdtt_params.lma_min_wnd <= 1.0) {
corr_wnd_inv_limited = new double [corr_wnd.length * corr_wnd[0].length];
......@@ -2431,7 +2431,7 @@ public class ImageDtt {
double [][][] tcorr_partial = null; // [quad][numcol+1][15*15]
double [][][][] tcorr_tpartial = null; // [quad][numcol+1][4][8*8]
double [] ports_rgb = null;
double [][] corr_wnd = (new Corr2dLMA(1, transform_size, null)).getCorrWnd();
double [][] corr_wnd = (new Corr2dLMA(1, transform_size, null,imgdtt_params.lma_gaussian)).getCorrWnd();
double [] corr_wnd_inv_limited = null;
if (imgdtt_params.lma_min_wnd <= 1.0) {
corr_wnd_inv_limited = new double [corr_wnd.length * corr_wnd[0].length];
......
src/main/java/com/elphel/imagej/tileprocessor/ImageDttParameters.java
View file @ fb44bc1a
......@@ -99,6 +99,7 @@ public class ImageDttParameters {
public double corr_wndx_blur = 5.0; // 100% to 0 % vertical transition range
// LMA parameters
public boolean lma_gaussian = true; // model correlation maximum as a Gaussian (false - as a parabola)
public boolean lma_adjust_wm = true; // used in new for width
public boolean lma_adjust_wy = true; // false; // used in new for ellipse
public boolean lma_adjust_wxy = true; // used in new for lazy eye adjust parallel-to-disparity correction
......@@ -267,6 +268,8 @@ public class ImageDttParameters {
"Transition range, shifted sine is used");
gd.addTab("Corr LMA","Parameters for LMA fitting of the correlation maximum parameters");
gd.addCheckbox ("Correlation maximum as gaussian", this.lma_gaussian,
"Model correlation maximum as a Gaussian exp(-r^2) (false - as a parabola - 1-r^2)");
gd.addCheckbox ("Fit correlation defined half-width", this.lma_adjust_wm,
"Allow fitting of the half-width common for all pairs, defined by the LPF filter of the phase correlation");
gd.addCheckbox ("Adjust ellipse parameters (was Fit extra vertical half-width)", this.lma_adjust_wy,
......@@ -412,6 +415,7 @@ public class ImageDttParameters {
this.corr_wndx_blur = gd.getNextNumber();
//LMA tab
this.lma_gaussian= gd.getNextBoolean();
this.lma_adjust_wm= gd.getNextBoolean();
this.lma_adjust_wy= gd.getNextBoolean();
this.lma_adjust_wxy= gd.getNextBoolean();
......@@ -514,8 +518,7 @@ public class ImageDttParameters {
properties.setProperty(prefix+"corr_wndx_hwidth", this.corr_wndx_hwidth +"");
properties.setProperty(prefix+"corr_wndx_blur", this.corr_wndx_blur +"");
properties.setProperty(prefix+"lma_gaussian", this.lma_gaussian +"");
properties.setProperty(prefix+"lma_adjust_wm", this.lma_adjust_wm +"");
properties.setProperty(prefix+"lma_adjust_wy", this.lma_adjust_wy +"");
properties.setProperty(prefix+"lma_adjust_wxy", this.lma_adjust_wxy +"");
......@@ -622,8 +625,7 @@ public class ImageDttParameters {
if (properties.getProperty(prefix+"corr_wndx_hwidth")!=null) this.corr_wndx_hwidth=Double.parseDouble(properties.getProperty(prefix+"corr_wndx_hwidth"));
if (properties.getProperty(prefix+"corr_wndx_blur")!=null) this.corr_wndx_blur=Double.parseDouble(properties.getProperty(prefix+"corr_wndx_blur"));
if (properties.getProperty(prefix+"lma_gaussian")!=null) this.lma_gaussian=Boolean.parseBoolean(properties.getProperty(prefix+"lma_gaussian"));
if (properties.getProperty(prefix+"lma_adjust_wm")!=null) this.lma_adjust_wm=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wm"));
if (properties.getProperty(prefix+"lma_adjust_wy")!=null) this.lma_adjust_wy=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wy"));
if (properties.getProperty(prefix+"lma_adjust_wxy")!=null) this.lma_adjust_wxy=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wxy"));
......@@ -736,6 +738,7 @@ public class ImageDttParameters {
idp.corr_wndx_hwidth = this.corr_wndx_hwidth;
idp.corr_wndx_blur = this.corr_wndx_blur;
idp.lma_gaussian = this.lma_gaussian;
idp.lma_adjust_wm = this.lma_adjust_wm;
idp.lma_adjust_wy = this.lma_adjust_wy;
idp.lma_adjust_wxy = this.lma_adjust_wxy;
......
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