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imagej-elphel
Commit a8fe8f68 authored by Andrey Filippov's avatar Andrey Filippov
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Fixed old (pre 05.2024) bug that was deleting ERS velocities

parent 413cd545
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Showing with 429 additions and 68 deletions
src/main/java/com/elphel/imagej/correction/Eyesis_Correction.java
View file @ a8fe8f68
......@@ -123,6 +123,7 @@ import com.elphel.imagej.tileprocessor.ImageDtt;
import com.elphel.imagej.tileprocessor.MLStats;
import com.elphel.imagej.tileprocessor.MultisceneLY;
import com.elphel.imagej.tileprocessor.QuadCLT;
import com.elphel.imagej.tileprocessor.QuadCLTCPU;
import com.elphel.imagej.tileprocessor.SymmVector;
import com.elphel.imagej.tileprocessor.TwoQuadCLT;
import com.elphel.imagej.tileprocessor.lwoc.LwirWorld;
......@@ -855,6 +856,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
addButton("Generate DATI", panelLWIRWorld, color_process);
addButton("Create mine", panelLWIRWorld, color_process);
addButton("Pattern correlate", panelLWIRWorld, color_process);
addButton("Properties compare", panelLWIRWorld, color_process);
plugInFrame.add(panelLWIRWorld);
}
......@@ -1593,7 +1595,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
if (label.equals("Save Clean")) {
PROPERTIES = new Properties();
}
saveProperties(null, CORRECTION_PARAMETERS.resultsDirectory, true, PROPERTIES);
saveProperties(null, CORRECTION_PARAMETERS.resultsDirectory, true, PROPERTIES, DEBUG_LEVEL);
return;
/* ======================================================================== */
} else if (label.equals("Save offset")) {
......@@ -5541,7 +5543,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
SYNC_COMMAND.stopRequested, // AtomicInteger stopRequested,
MASTER_DEBUG_LEVEL); // int debug_level);
}
FOOTAGE_ORGANIZE.OrganizeSeries(
FootageOrganize.OrganizeSeries(
CLT_PARAMETERS,
CALIBRATION_ILLUSTRATION);
} else if (label.equals("Super batch")) {
......@@ -5776,6 +5778,8 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
return;
}
OrthoMap.testPatternCorrelate(imp_sel);
} else if (label.equals("Properties compare")) {
QuadCLTCPU.compareProperties();
}
}
//
......@@ -9691,14 +9695,17 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
saveProperties(path + "_" + IJ.d2s(0.000001 * (System.nanoTime() / 1000), 6).replace('.', '_'), // full path or
// null
directory, // use as default directory if path==null
useXML, properties);
useXML,
properties,
DEBUG_LEVEL);
}
public void saveProperties(String path, // full path or null
String directory, // use as default directory if path==null
boolean useXML,
Properties properties) {
String directory, // use as default directory if path==null
boolean useXML,
Properties properties,
int debugLevel) {
String[] XMLPatterns = { ".corr-xml", ".xml" };
String[] confPatterns = { ".conf" };
String[] patterns = useXML ? XMLPatterns : confPatterns;
......@@ -9758,7 +9765,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
// TODO Auto-generated catch block
e.printStackTrace();
}
if (DEBUG_LEVEL > -3)
if (debugLevel > -3)
System.out.println("Configuration parameters are saved to " + path);
}
......
src/main/java/com/elphel/imagej/tileprocessor/Correlation2d.java
View file @ a8fe8f68
......@@ -2618,9 +2618,123 @@ public class Correlation2d {
}
x0 += sx / s0; // relative to top-left
y0 += sy / s0;
//https://users.cs.utah.edu/~tch/CS4640/resources/A%20geometric%20interpretation%20of%20the%20covariance%20matrix.pdf
double cxx = sx2 - sx * sx / s0, cyy= sy2 - sy * sy / s0, cxy = sxy - sx * sy / s0;
Matrix covar = new Matrix(new double[][] {{cxx, cxy},{cxy,cyy}});
EigenvalueDecomposition eig = covar.eig();
double [] eigval = {eig.getD().get(0, 0),eig.getD().get(1, 1)};
double [][] eigvec = eig.getV().getArray(); // columns - vectors?
int eig_indx = (eigval[0] > eigval[1]) ? 1 : 0;
double [] rslt = {
x0 - center_xy,
y0 - center_xy,
mx,
eigvec[0][eig_indx],
eigvec[1][eig_indx],
eigval[eig_indx],
eigval[1-eig_indx]};
if (debug){
System.out.println("getMaxXYCm() -> "+rslt[0]+":"+rslt[1]+" ("+rslt[2]+
"), eigv0=["+rslt[3]+","+rslt[4]+"], lambda0="+rslt[5]+", lambda1="+rslt[6]);
}
if (debug_data != null) {
debug_data[0] = data.clone();
for (int i = 0; i < data.length; i++) {
if (!en_data[i]) {
debug_data[0][i] = Double.NaN;
}
}
}
return rslt;
}
public static double [] getMaxXYCmEig(
double [] data, // will be modified if fpn_mask != null;
int data_width, // = 2 * transform_size - 1;
double abs_min,
double rel_min,
double min_peak,
double eig_sub_frac, // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
int refine, // re-center window around new maximum. 0 -no refines (single-pass)
double eig_sub_frac1,// subtract during refine (may be 0)
double scale_axes, // 1.2 scale half-axes of the ellipse: 1.0 <-> sqrt(eigenvalue)
double inc_axes, // 1.0 add do half-axes
boolean [] fpn_mask,
boolean ignore_border,// only if fpn_mask != null - ignore tile if maximum touches fpn_mask
double [][] debug_data, // null or double [1+][]
boolean eig_fast2x2, // use fast eigenvectors for 2x2 matrices
boolean debug)
{
int data_height = data.length/data_width;
int center_xy = (data_width - 1)/2; // = transform_size - 1;
double x0 = center_xy, y0 = center_xy;
int imax= 0;
for (int i= 1; i < data.length;i++) {
if (Double.isNaN(data[i])) {
System.out.println("NaN in getMaxXYCmEig()");
return null;
}
if (data[i] > data[imax]) {
imax = i;
}
}
if (data[imax] < min_peak) {
return null; // too weak;even before fpn filter
}
int ix0 = imax % data_width;
int iy0 = imax / data_width;
x0 = ix0;
y0 = iy0;
//min_peak
// if (fpn_mask != null
if (fpn_mask != null) { // modifies data, returns null if hits fpn
for (int i = 0; i < fpn_mask.length; i++) if (fpn_mask[i]) {
int iy = i / data_width;
int ix = i - iy * data_width;
if (ignore_border) {
if(((ix - ix0) <= 1) && ((ix - ix0) >= -1) && ((iy - iy0) <= 1) && ((iy - iy0) >= -1)) {
return null; // new double[3];
}
}
int ix1 = 2 * ix0 - ix;
if ((ix1 >= 0) && (ix1 < data_width)) {
int iy1 = 2 * iy0 - iy;
if ((iy1 >= 0) && (iy1 < data_height)) {
data[iy1 * data_width + ix1] = 0.0; // zero out symmetrical to fpn mask around integer maximum
}
}
}
// update imax
imax= 0;
for (int i= 1; i < data.length;i++) {
if (data[i] > data[imax]) {
imax = i;
}
}
if (data[imax] < min_peak) {
return null; // too weak after fpn filter
}
}
// create mask of connected to max pixels
double mx = data[imax];
double min_d = Math.min(abs_min, rel_min*mx);
//
double sub_pedestal = min_d * eig_sub_frac;
double sub_pedestal1 = min_d * eig_sub_frac1;
boolean [] above_threshold = new boolean [data.length];
for (int i = 0; i < data.length; i++) {
above_threshold[i] = data[i] >= min_d;
}
/*
double s0 = 0, sx=0,sy = 0;
boolean [] en_data = (new TileNeibs(data_width, data_height)).getConnected(
above_threshold, // boolean [] tiles,
ix0, // int seedX,
iy0); // int seedY)
// find centroid
double s0 = 0, sx=0,sy = 0, sx2 = 0, sy2=0, sxy = 0;
for (int iy = 0; iy < data_height; iy++) {
double y = iy - y0;
for (int ix = 0; ix < data_width; ix++) {
......@@ -2631,44 +2745,56 @@ public class Correlation2d {
s0 += d;
sx += d * x;
sy += d * y;
}
}
}
x0 += sx / s0; // relative to top-left
y0 += sy / s0;
double sx2 = 0, sy2=0, sxy = 0;
for (int iy = 0; iy < data_height; iy++) {
double y = iy - y0;
for (int ix = 0; ix < data_width; ix++) {
int indx = iy * data_width + ix;
if (en_data[indx]) { // assumes d >0, as it is >= min_d
double x = ix - x0;
double d = data[iy * data_width + ix] - sub_pedestal;
sx2 += d * x * x;
sy2 += d * y * y;
sxy += d * x * y;
}
}
}
*/
x0 += sx / s0; // relative to top-left
y0 += sy / s0;
//https://users.cs.utah.edu/~tch/CS4640/resources/A%20geometric%20interpretation%20of%20the%20covariance%20matrix.pdf
double cxx = sx2 - sx * sx / s0, cyy= sy2 - sy * sy / s0, cxy = sxy - sx * sy / s0;
Matrix covar = new Matrix(new double[][] {{cxx, cxy},{cxy,cyy}});
EigenvalueDecomposition eig = covar.eig();
double [] eigval = {eig.getD().get(0, 0),eig.getD().get(1, 1)};
double [][] eigvec = eig.getV().getArray(); // columns - vectors?
int eig_indx = (eigval[0] > eigval[1]) ? 1 : 0;
double [] rslt = {
x0 - center_xy,
y0 - center_xy,
mx,
eigvec[0][eig_indx],
eigvec[1][eig_indx],
eigval[eig_indx],
eigval[1-eig_indx]};
double [][] acovar = {{cxx, cxy},{cxy,cyy}};
// TODO: calculate for 2x2 faster?
double [] xy = {x0-center_xy, y0-center_xy}; // relative to the center
double [] rslt;
if (eig_fast2x2) {
double [] eig = getEigen2x2(acovar);
rslt = new double[] {
xy[0], // x0 - center_xy,
xy[1], // y0 - center_xy,
mx,
eig[0],
eig[1],
eig[2],
eig[3]};
} else {
Matrix covar = new Matrix(acovar);
EigenvalueDecomposition eig = covar.eig();
double [] eigval = {eig.getD().get(0, 0),eig.getD().get(1, 1)};
double [][] eigvec = eig.getV().getArray(); // columns - vectors?
int eig_indx = (eigval[0] > eigval[1]) ? 1 : 0;
double k = (eigvec[0][eig_indx] > 0) ? 1 : -1;
rslt = new double[] {
xy[0], // x0 - center_xy,
xy[1], // y0 - center_xy,
mx,
k * eigvec[0][eig_indx],
k * eigvec[1][eig_indx],
eigval[eig_indx],
eigval[1-eig_indx]};
if (debug){
double [] r = getEigen2x2(acovar);
System.out.println(String.format("%10f %10f", rslt[3], r[0]));
System.out.println(String.format("%10f %10f", rslt[4], r[1]));
System.out.println(String.format("%10f %10f", rslt[5], r[2]));
System.out.println(String.format("%10f %10f", rslt[6], r[3]));
}
}
if (debug){
System.out.println("getMaxXYCm() -> "+rslt[0]+":"+rslt[1]+" ("+rslt[2]+
"), eigv0=["+rslt[3]+","+rslt[4]+"], lambda0="+rslt[5]+", lambda1="+rslt[6]);
......@@ -2681,8 +2807,89 @@ public class Correlation2d {
}
}
}
if (refine > 0) {
/*
double [] half_axes = {
scale_axes*Math.sqrt(eigval[ eig_indx])+inc_axes,
scale_axes*Math.sqrt(eigval[1-eig_indx])+inc_axes};
double [][] transf = {
{eigvec[0][eig_indx]/half_axes[0], eigvec[1][eig_indx]/half_axes[0]},
{eigvec[1][eig_indx]/half_axes[1], -eigvec[0][eig_indx]/half_axes[1]}};
*/
double [] half_axes = {
scale_axes*Math.sqrt(rslt[5])+inc_axes,
scale_axes*Math.sqrt(rslt[6])+inc_axes};
double [][] transf = {
{rslt[3]/half_axes[0], rslt[4]/half_axes[0]},
{rslt[4]/half_axes[1], -rslt[3]/half_axes[1]}};
for (int nref = 1; nref <= refine; nref++) {
boolean showdbg = ((debug_data != null) && (debug_data.length > nref));
if (showdbg) {
debug_data[nref] = new double [data.length];
Arrays.fill(debug_data[nref], Double.NaN);
}
s0 = 0; sx=0; sy = 0;
for (int iy = 0; iy < data_height; iy++) {
double y = iy - xy[1] - center_xy;
for (int ix = 0; ix < data_width; ix++) {
double x = ix - xy[0] - center_xy;
double de0 = transf[0][0]*x + transf[0][1]*y;
double de1 = transf[1][0]*x + transf[1][1]*y;
double er2 = de0*de0+de1*de1;
if (er2 < 1.0) {
double er = Math.sqrt(er2);
double d = data[iy * data_width + ix] - sub_pedestal1;
if (d > 0) { // ignore negative
d *= Math.cos(0.5*Math.PI*er);
s0 += d;
sx += d * x;
sy += d * y;
if (showdbg) {
debug_data[nref][iy * data_width + ix] = d;
}
}
}
}
}
if (s0 >0) {
xy[0] += sx / s0;
xy[1] += sy / s0;
}
}
rslt[0] = xy[0];
rslt[1] = xy[1];
}
return rslt;
}
/**
* Calculates eigenvalues and eigenvectors for a 2x2 (covariance) matrix,
* returns normalized eigenvector for the smaller eigenvalue (other is orthogonal)
* followed by 2 eigenvalues (smaller first)
* @param covar 2x2 covariance matrix as [2][2] array
* @return 4-element array: first eigenvector x, y, lambda0, lambda1 (lambda0 <= lampda1)
*/
public static double [] getEigen2x2(double [][] A) { //
double e = 1E-12;
double hapd = (A[0][0]+A[1][1])/2;
double hamd = (A[0][0]-A[1][1])/2;
double bc = A[1][0]*A[0][1];
double d = Math.sqrt(hamd*hamd + bc);
if (d/Math.abs(hapd) < e) {
return new double [] {1,0,hapd,hapd};
}
double [] lambda = {hapd-d, hapd+d};
double [] v0 = {A[0][0] - lambda[1], A[1][0]};
double k = 1.0/Math.sqrt(v0[0]*v0[0] + v0[1]*v0[1]);
if (v0[0] < 0) { // to be compatible with standard?
k = -k;
}
return new double [] {k*v0[0], k*v0[1], lambda[0], lambda[1]};
}
/**
* Find maximum of the 2d array projected on a specified vector using centroid.
......
src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java
View file @ a8fe8f68
......@@ -2375,6 +2375,13 @@ public class ImageDtt extends ImageDttCPU {
final double eigen_min_abs, // 0.05 values below this do not count for covariance
final double eigen_min_rel, // 0.2 values less than this fraction of tile's max do not count for covariance
final double eig_sub_frac, // 1.0; // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
final int eig_recenter, // 2; // Re-center window around new maximum. 0 -no refines (single-pass)
final double eig_sub_frac1, // 0.0; // subtract during refine (may be 0)
// Using eigenvectors/values to create ellipse (slightly larger) and use it for a cosine mask (as round before) to refine centers
final double eig_scale_axes, // 1.2; // scale half-axes of the ellipse: 1.0 <-> sqrt(eigenvalue)
final double eig_inc_axes, // 1.0; // add do half-axes
final boolean eig_fast2x2, // use fast eigenvectors for 2x2 matrices
final double [][] eigen, // null or [tilesX*tilesY]{lamb0_x,lamb0_y, lamb0, lamb1} eigenvector0[x,y],lam0,lam1
final boolean eigen_debug, //
final int debug_tileX,
......@@ -2468,10 +2475,11 @@ public class ImageDtt extends ImageDttCPU {
// currently execCorr2D_normalize() output has 17 slices for old variant (no neibs) and 18/2 if (use_neibs)
final int extra_len = extra_sum? 1 : 0;
final int extra_len_eig = eigen_debug? 4: 0; // all, all_remain, weak, weak_remain
// eig_recenter
final int extra_len_eig = eigen_debug? (4 + eig_recenter): 0; // all, all_remain, weak, weak_remain
// final int corrs_len = ((use_partial || use_neibs) ? used_sensors_list.length:1); // without optional extra_len but including GPU sum
final int corrs_len = (use_neibs || use_partial) ? used_sensors_list.length:1; // without optional extra_len but including GPU sum
final int eigen_indx = (extra_len_eig > 0) ? (corrs_len + extra_len):-1;
final int eigen_indx = (extra_len_eig > 0) ? (corrs_len + extra_len):-1; // first of eigen-related data
final int indx_sum_pd = (extra_len > 0) ? corrs_len : -1;
final int indx_sum_td = use_neibs ? (corrs_len -2): (corrs_len -1);
final int indx_sum_td_neib = use_neibs ? (corrs_len -1): -1;
......@@ -2621,7 +2629,7 @@ public class ImageDtt extends ImageDttCPU {
// only two cases:
// 1 - TD only (!neib_notd_only)
// 2 - TD, then TD-neibs if failed (neib_notd_only)
double [][] debug_data = ((dcorr_tiles != null) && (eigen_indx >=0)) ? (new double[1][]):null;
double [][] debug_data = ((dcorr_tiles != null) && (eigen_indx >=0)) ? (new double[1+eig_recenter][]):null;
double [] stats_mv = Correlation2d.getMaxXYCmEig(
corrs[indx_sum_td], // double [] data, // will be modified if fpn_mask != null;
corr_size, // int data_width, // = 2 * transform_size - 1;
......@@ -2629,10 +2637,15 @@ public class ImageDtt extends ImageDttCPU {
eigen_min_rel, // double rel_min,
eig_str_sum, // double min_peak,
eig_sub_frac, // double eig_sub_frac, // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
eig_recenter, // int refine, // re-center window around new maximum. 0 -no refines (single-pass)
eig_sub_frac1, // double eig_sub_frac1,// subtract during refine (may be 0)
eig_scale_axes, // double scale_axes, // 1.2 scale half-axes of the ellipse: 1.0 <-> sqrt(eigenvalue)
eig_inc_axes, // double inc_axes, // 1.0 add do half-axes
fpn_mask, // boolean [] fpn_mask,
false, // boolean ignore_border, // only if fpn_mask != null - ignore tile if maximum touches fpn_mask
debug_data, // double [][] debug_data, // null or double [1]
false); // boolean debug)
eig_fast2x2, // boolean eig_fast2x2, // use fast eigenvectors for 2x2 matrices
debugTile0); // boolean debug)
used_td[nTile] = stats_mv != null;
if (stats_mv != null) {
stats_mv[2] -= eig_str_sum * scale_neibs_td;
......@@ -2650,10 +2663,15 @@ public class ImageDtt extends ImageDttCPU {
eigen_min_rel, // double rel_min,
eig_str_neib, // double min_peak,
eig_sub_frac, // double eig_sub_frac, // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
eig_recenter, // int refine, // re-center window around new maximum. 0 -no refines (single-pass)
eig_sub_frac1, // double eig_sub_frac1,// subtract during refine (may be 0)
eig_scale_axes, // double scale_axes, // 1.2 scale half-axes of the ellipse: 1.0 <-> sqrt(eigenvalue)
eig_inc_axes, // double inc_axes, // 1.0 add do half-axes
fpn_mask, // boolean [] fpn_mask,
false, // boolean ignore_border, // only if fpn_mask != null - ignore tile if maximum touches fpn_mask
debug_data, // double [][] debug_data, // null or double [1]
false); // boolean debug)
eig_fast2x2, // boolean eig_fast2x2, // use fast eigenvectors for 2x2 matrices
debugTile0); // boolean debug)
weak_tile[nTile] = stats_mv != null;
if (stats_mv != null) {
stats_mv[2] -= eig_str_neib * scale_neibs_td;
......@@ -2671,7 +2689,12 @@ public class ImageDtt extends ImageDttCPU {
dcorr_tiles[iCorrTile][eigen_indx+3] = debug_data [0].clone(); // weak REMAIN
}
//used_td[nTile]
int recenter_indx_m1 = eigen_indx+3;
if (stats_mv != null) {
for (int i = 1; (i < debug_data.length) && (recenter_indx_m1 + i < dcorr_tiles[iCorrTile].length) ; i++) {
dcorr_tiles[iCorrTile][recenter_indx_m1+i] = debug_data [i].clone();
}
}
}
if (stats_mv != null) {
if (eigen != null) {
......@@ -2741,6 +2764,7 @@ public class ImageDtt extends ImageDttCPU {
int iCorrTile = iCorrTile_index[nTile];
dcorr_tiles[iCorrTile][eigen_indx+1] = null;
dcorr_tiles[iCorrTile][eigen_indx+3] = null;
// keeping extra recenter layers
}
}
}
......
src/main/java/com/elphel/imagej/tileprocessor/Interscene.java
View file @ a8fe8f68
......@@ -3355,19 +3355,20 @@ public class Interscene {
double eig_max_sqrt=clt_parameters.imp.eig_max_sqrt;
double eig_min_sqrt=clt_parameters.imp.eig_min_sqrt;
// boolean dbg_images = false;
double [][] eigen_masked = clt_parameters.imp.eig_xy_lma? null : eigen;
String dbg_prefix = dbg_images? (first_QuadClt.getImageName()+"-"+scene_QuadClt.getImageName()+"-NLMA_"+nlma) : null;
intersceneLma.prepareLMA(
scene_xyzatr0, // final double [] scene_xyzatr0, // camera center in world coordinates (or null to use instance)
scene_xyzatr_pull, // final double [] scene_xyz_pull, // if both are not null, specify target values to pull to
ref_xyzatr, // _inv, // ref_xyzatr,
scene_xyzatr0, // final double [] scene_xyzatr0, // camera center in world coordinates (or null to use instance)
scene_xyzatr_pull, // final double [] scene_xyz_pull, // if both are not null, specify target values to pull to
ref_xyzatr, // _inv, // ref_xyzatr,
// reference atr, xyz are considered 0.0 not anymore?
scene_QuadClt, // final QuadCLT scene_QuadClt,
first_QuadClt, // final QuadCLT reference_QuadClt,
param_select_mod, // param_select, // final boolean[] param_select,
first_QuadClt, // final QuadCLT reference_QuadClt,
param_select_mod, // param_select, // final boolean[] param_select,
param_regweights, // final double [] param_regweights,
eig_max_sqrt, // final double eig_max_sqrt, // 10; // for sqrt(lambda) - consider infinity (infinite linear feature, a line)
eig_min_sqrt, // final double eig_min_sqrt, // 1; // for sqrt(lambda) - consider infinity (infinite linear feature, a line)
eigen, // final double [][] eigen, // [tilesX*tilesY]{lamb0_x,lamb0_y, lamb0, lamb1} eigenvector0[x,y],lam0,lam1
eig_max_sqrt, // final double eig_max_sqrt, // 10; // for sqrt(lambda) - consider infinity (infinite linear feature, a line)
eig_min_sqrt, // final double eig_min_sqrt, // 1; // for sqrt(lambda) - consider infinity (infinite linear feature, a line)
eigen_masked, // final double [][] eigen, // [tilesX*tilesY]{lamb0_x,lamb0_y, lamb0, lamb1} eigenvector0[x,y],lam0,lam1
coord_motion[1], // final double [][] vector_XYS, // optical flow X,Y, confidence obtained from the correlate2DIterate()
coord_motion[0], // final double [][] centers, // macrotile centers (in pixels and average disparities
(nlma == 0), // boolean first_run,
......@@ -3546,11 +3547,11 @@ public class Interscene {
if ((rms_out != null) && (intersceneLma.getLastRms() != null)) {
rms_out[0] = intersceneLma.getLastRms()[0];
rms_out[1] = intersceneLma.getLastRms()[1];
if (rms_out.length >=2) {
if (rms_out.length >=4) {
rms_out[2] = intersceneLma.getSumWeights();
rms_out[3] = intersceneLma.getNumDefined();
if (rms_out.length >=4) {
if (rms_out.length >=6) {
if (intersceneLma.isEigenNormalized()) {
double [] rms_metric = intersceneLma.calcRMS(true);
rms_out[4] = rms_metric[0];
......@@ -4296,8 +4297,6 @@ public class Interscene {
double [][] eigen = use_eigen? (new double[tilesX*tilesY][]) : null;
boolean eigen_debug = use_eigen && show_2d_correlations;
if (use_eigen) {
double eigen_min_abs= clt_parameters.imp.eig_min_abs ; //eigen_min_abs = 0.05; // 0.05 values below this do not count for covariance
double eigen_min_rel = clt_parameters.imp.eig_min_rel ; //0.2; // 0.2 values less than this fraction of tile's max do not count for covariance
coord_motion = image_dtt.clt_process_tl_interscene( // convert to pixel domain and process correlations already prepared in fcorr_td and/or fcorr_combo_td
clt_parameters.img_dtt, // final ImageDttParameters imgdtt_params, // Now just extra correlation parameters, later will include, most others
// only used here to keep extra array element for disparity difference
......@@ -4345,9 +4344,15 @@ public class Interscene {
scale_neibs_pd, // final double scale_neibs_pd, // scale threshold for the pixel-domain average maximums
scale_neibs_td, // final double scale_neibs_td, // scale threshold for the transform-domain average maximums
scale_avg_weight, // final double scale_avg_weight, // reduce influence of the averaged correlations compared to the single-tile ones
eigen_min_abs, // final double eigen_min_abs, // 0.05 values below this do not count for covariance
eigen_min_rel, // final double eigen_min_rel, // 0.2 values less than this fraction of tile's max do not count for covariance
clt_parameters.imp.eig_sub_frac, //final double eig_sub_frac, // 1.0; // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
clt_parameters.imp.eig_min_abs, // final double eigen_min_abs, // 0.05 values below this do not count for covariance
clt_parameters.imp.eig_min_rel, // final double eigen_min_rel, // 0.2 values less than this fraction of tile's max do not count for covariance
clt_parameters.imp.eig_sub_frac, // final double eig_sub_frac, // 1.0; // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
clt_parameters.imp.eig_recenter, // final int eig_recenter, // 2; // Re-center window around new maximum. 0 -no refines (single-pass)
clt_parameters.imp.eig_sub_frac1, // final double eig_sub_frac1, // 0.0; // subtract during refine (may be 0)
// Using eigenvectors/values to create ellipse (slightly larger) and use it for a cosine mask (as round before) to refine centers
clt_parameters.imp.eig_scale_axes, // final double eig_scale_axes, // 1.2; // scale half-axes of the ellipse: 1.0 <-> sqrt(eigenvalue)
clt_parameters.imp.eig_inc_axes, // final double eig_inc_axes, // 1.0; // add do half-axes
clt_parameters.imp.eig_fast2x2, // final boolean eig_fast2x2, // use fast eigenvectots for 2x2 matrices
eigen, // null, // eigen, // final double [][] eigen, // null or [tilesX*tilesY]{lamb0_x,lamb0_y, lamb0, lamb1} eigenvector0[x,y],lam0,lam1
eigen_debug, // final double [][] eigen_debug, // null or [tilesX*tilesY][]
clt_parameters.tileX, // final int debug_tileX,
......
src/main/java/com/elphel/imagej/tileprocessor/IntersceneLma.java
View file @ a8fe8f68
......@@ -803,6 +803,9 @@ public class IntersceneLma {
final double eig_max_sqrt,
final double eig_min_sqrt,
final double [][] eigen){ // [tilesX*tilesY]{lamb0_x,lamb0_y, lamb0, lamb1} eigenvector0[x,y],lam0,lam1
if (eigen == null) {
return null;
}
final double [][][] transform = new double[eigen.length][2][2];
final Thread[] threads = ImageDtt.newThreadArray(QuadCLT.THREADS_MAX);
final AtomicInteger ai = new AtomicInteger(0);
......@@ -824,10 +827,6 @@ public class IntersceneLma {
};
}
ImageDtt.startAndJoin(threads);
return transform;
}
......
src/main/java/com/elphel/imagej/tileprocessor/OpticalFlow.java
View file @ a8fe8f68
......@@ -8767,11 +8767,15 @@ public class OpticalFlow {
String [] timestamps = ers_reference.getScenes();
for (String ts:timestamps) {
double [][] xyzatr = ers_reference.getSceneXYZATR(ts);
double [][] xyzatr_dt = ers_reference.getSceneErsXYZATR_dt(ts);
for (int i = 0; i < xyzatr[0].length; i++) {
xyzatr[0][i] *= scale;
xyzatr_dt[0][i] *= scale; // scale linear velocities too 06.25.2024
}
// xyzatr[0][2] -= zdiff;
ers_reference.addScene(ts,xyzatr);
// ers_reference.addScene(ts,xyzatr); // should use addScene(String timestamp, double [][] xyzatr, double [][] ers_xyzatr_dt) {
ers_reference.addScene(ts,xyzatr,xyzatr_dt); // should use addScene(String timestamp, double [][] xyzatr, double [][] ers_xyzatr_dt) {
}
ref_scene.saveInterProperties( // save properties for interscene processing (extrinsics, ers, ...)
null, // String path, // full name with extension or w/o path to use x3d directory
......@@ -9557,7 +9561,8 @@ public class OpticalFlow {
String rslt_suffix = "-INTER-INTRA-HISTORIC";
rslt_suffix += (clt_parameters.correlate_lma?"-LMA":"-NOLMA");
// ref_index from the center is moved to the very end (indx_ref)
if (compensate_dsi && !scenes[ref_index].interDsiExists()) {
// was bug - used ref_index Maybe that was intentional?
if (compensate_dsi && !scenes[indx_ref].interDsiExists()) {
System.out.println("Will compensate pose Z as average disparity may change");
double z_corr = compensateDSI(
clt_parameters, // CLTParameters clt_parameters,
......
src/main/java/com/elphel/imagej/tileprocessor/QuadCLTCPU.java
View file @ a8fe8f68
......@@ -61,6 +61,7 @@ import com.elphel.imagej.cameras.ColorProcParameters;
import com.elphel.imagej.cameras.EyesisCorrectionParameters;
import com.elphel.imagej.cameras.ThermalColor;
import com.elphel.imagej.common.DoubleGaussianBlur;
import com.elphel.imagej.common.GenericJTabbedDialog;
import com.elphel.imagej.common.PolynomialApproximation;
import com.elphel.imagej.common.ShowDoubleFloatArrays;
import com.elphel.imagej.correction.CorrectionColorProc;
......@@ -3834,6 +3835,12 @@ public class QuadCLTCPU {
boolean silent) {
String x3d_path = getX3dDirectory();
String file_path = x3d_path + Prefs.getFileSeparator() + image_name + suffix + ".tiff";
if (!(new File(file_path).exists())) {
if (!silent) {
System.out.println ("File does not exist: "+file_path);
}
return -1;
}
ImagePlus imp = null;
try {
imp = new ImagePlus(file_path);
......@@ -3987,6 +3994,63 @@ public class QuadCLTCPU {
System.out.println("Restored interframe properties from :"+path);
return properties;
}
public static void compareProperties() {
String path1 = "/media/elphel/SSD3-4GB/lwir16-proc/berdich3/models/models_1697875868-1697879449-b/1697877436_779067/39-dbg-02-11/1697877436_779067-INTERFRAME.corr-xml";
String path2 = "/media/elphel/SSD3-4GB/lwir16-proc/berdich3/models/models_1697875868-1697879449-b/1697877436_779067/37-dbg-01-11_noeig/1697877436_779067-INTERFRAME.corr-xml";
GenericJTabbedDialog gd = new GenericJTabbedDialog("Set image pair",1200,800);
gd.addStringField ("First configuration path", path1, 180, "Full path of the first configuration properties file.");
gd.addStringField ("Second configuration path", path2, 180, "Second path of the first configuration properties file.");
gd.showDialog();
if (gd.wasCanceled()) return;
path1 = gd.getNextString();
path2 = gd.getNextString();
compareProperties(path1,path2);
}
public static void compareProperties(
String path1,
String path2) {
Properties properties1, properties2;
properties1 = loadProperties(
path1, // String path,
null, // Properties properties)
false); // boolean silent)
properties2 = loadProperties(
path2, // String path,
null, // Properties properties)
false); // boolean silent)
// Enumeration<String> en1 = (Enumeration<String>) properties1.propertyNames();
// Enumeration<String> en2 = (Enumeration<String>) properties2.propertyNames();
System.out.println("path1 = "+path1);
System.out.println("path2 = "+path2);
int num_diff = 0;
for (@SuppressWarnings("unchecked")
Enumeration<String> e = (Enumeration<String>) properties1.propertyNames(); e.hasMoreElements();) {
String key = e.nextElement();
String v1 = properties1.getProperty(key);
String v2 = properties2.getProperty(key);
if (!v1.equals(v2)) {
System.out.println("property1["+key+"] = " + v1+", property2["+key+"] = " +v2);
num_diff++;
}
}
for (@SuppressWarnings("unchecked")
Enumeration<String> e = (Enumeration<String>) properties2.propertyNames(); e.hasMoreElements();) {
String key = e.nextElement();
String v1 = properties1.getProperty(key);
String v2 = properties2.getProperty(key);
if (v1 == null ) { // other already printed
System.out.println("property1["+key+"] = " + v1+", property2["+key+"] = " +v2);
num_diff++;
}
}
System.out.println(num_diff+" differences found");
}
public boolean propertiesContainString (
String needle) {
......
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