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Commit 41d614cb authored by Andrey Filippov's avatar Andrey Filippov
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handling zero eigenvalues

parent b267218c
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Showing with 154 additions and 61 deletions
src/main/java/EyesisCorrectionParameters.java
View file @ 41d614cb
......@@ -2142,6 +2142,7 @@ public class EyesisCorrectionParameters {
public double grow_disp_step = 6.0; // Increase disparity (from maximal tried) if nothing found in that tile // TODO: handle enclosed dips?
public double grow_min_diff = 0.5; // Grow more only if at least one channel has higher variance from others for the tile
public boolean plPreferDisparity = false;// Always start with disparity-most axis (false - lowest eigenvalue)
public double plDispNorm = 3.0; // Normalize disparities to the average if above (now only for eigenvalue comparison)
public int plMinPoints = 5; // Minimal number of points for plane detection
public double plTargetEigen = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
......@@ -2150,7 +2151,8 @@ public class EyesisCorrectionParameters {
public double plMinStrength = 0.1; // Minimal total strength of a plane
public double plMaxEigen = 0.3; // Maximal eigenvalue of a plane
public boolean plDbgMerge = true; // Combine 'other' plane with current
public double plWorstWorsening = 1.0; // Worst case worsening after merge
public double plWorstWorsening = 3.0; // Worst case worsening after merge
public double plWeakWorsening = 1.0; // Relax merge requirements for weaker planes
public boolean plMutualOnly = true; // keep only mutual links, remove weakest if conflict
public boolean plFillSquares = true; // Add diagonals to full squares
public boolean plCutCorners = true; // Add ortho to 45-degree corners
......@@ -2406,6 +2408,7 @@ public class EyesisCorrectionParameters {
properties.setProperty(prefix+"grow_disp_step", this.grow_disp_step +"");
properties.setProperty(prefix+"grow_min_diff", this.grow_min_diff +"");
properties.setProperty(prefix+"plPreferDisparity",this.plPreferDisparity+"");
properties.setProperty(prefix+"plDispNorm", this.plDispNorm +"");
properties.setProperty(prefix+"plMinPoints", this.plMinPoints+"");
properties.setProperty(prefix+"plTargetEigen", this.plTargetEigen +"");
......@@ -2415,6 +2418,7 @@ public class EyesisCorrectionParameters {
properties.setProperty(prefix+"plMaxEigen", this.plMaxEigen +"");
properties.setProperty(prefix+"plDbgMerge", this.plDbgMerge+"");
properties.setProperty(prefix+"plWorstWorsening", this.plWorstWorsening +"");
properties.setProperty(prefix+"plWeakWorsening", this.plWeakWorsening +"");
properties.setProperty(prefix+"plMutualOnly", this.plMutualOnly+"");
properties.setProperty(prefix+"plFillSquares", this.plFillSquares+"");
......@@ -2657,6 +2661,7 @@ public class EyesisCorrectionParameters {
if (properties.getProperty(prefix+"grow_disp_step")!=null) this.grow_disp_step=Double.parseDouble(properties.getProperty(prefix+"grow_disp_step"));
if (properties.getProperty(prefix+"grow_min_diff")!=null) this.grow_min_diff=Double.parseDouble(properties.getProperty(prefix+"grow_min_diff"));
if (properties.getProperty(prefix+"plPreferDisparity")!=null) this.plPreferDisparity=Boolean.parseBoolean(properties.getProperty(prefix+"plPreferDisparity"));
if (properties.getProperty(prefix+"plDispNorm")!=null) this.plDispNorm=Double.parseDouble(properties.getProperty(prefix+"plDispNorm"));
if (properties.getProperty(prefix+"plMinPoints")!=null) this.plMinPoints=Integer.parseInt(properties.getProperty(prefix+"plMinPoints"));
if (properties.getProperty(prefix+"plTargetEigen")!=null) this.plTargetEigen=Double.parseDouble(properties.getProperty(prefix+"plTargetEigen"));
......@@ -2666,6 +2671,7 @@ public class EyesisCorrectionParameters {
if (properties.getProperty(prefix+"plMaxEigen")!=null) this.plMaxEigen=Double.parseDouble(properties.getProperty(prefix+"plMaxEigen"));
if (properties.getProperty(prefix+"plDbgMerge")!=null) this.plDbgMerge=Boolean.parseBoolean(properties.getProperty(prefix+"plDbgMerge"));
if (properties.getProperty(prefix+"plWorstWorsening")!=null) this.plWorstWorsening=Double.parseDouble(properties.getProperty(prefix+"plWorstWorsening"));
if (properties.getProperty(prefix+"plWeakWorsening")!=null) this.plWeakWorsening=Double.parseDouble(properties.getProperty(prefix+"plWeakWorsening"));
if (properties.getProperty(prefix+"plMutualOnly")!=null) this.plMutualOnly=Boolean.parseBoolean(properties.getProperty(prefix+"plMutualOnly"));
if (properties.getProperty(prefix+"plFillSquares")!=null) this.plFillSquares=Boolean.parseBoolean(properties.getProperty(prefix+"plFillSquares"));
......@@ -2930,6 +2936,7 @@ public class EyesisCorrectionParameters {
gd.addNumericField("Increase disparity (from maximal tried) if nothing found in that tile", this.grow_disp_step, 6);
gd.addNumericField("Grow more only if at least one channel has higher variance from others for the tile", this.grow_min_diff, 6);
gd.addMessage ("--- Planes detection ---");
gd.addCheckbox ("Always start with disparity-most axis (false - lowest eigenvalue)", this.plPreferDisparity);
gd.addNumericField("Normalize disparities to the average if above", this.plDispNorm, 6);
gd.addNumericField("Minimal number of points for plane detection", this.plMinPoints, 0);
gd.addNumericField("Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below", this.plTargetEigen, 6);
......@@ -2937,8 +2944,9 @@ public class EyesisCorrectionParameters {
gd.addNumericField("Maximal number of outliers to remove", this.plMaxOutliers, 0);
gd.addNumericField("Minimal total strength of a plane", this.plMinStrength, 6);
gd.addNumericField("Maximal eigenvalue of a plane", this.plMaxEigen, 6);
gd.addCheckbox ("Combine 'other' plane with the current", this.plDbgMerge);
gd.addCheckbox ("Combine 'other' plane with the current (unused)", this.plDbgMerge);
gd.addNumericField("Worst case worsening after merge", this.plWorstWorsening, 6);
gd.addNumericField("Relax merge requirements for weaker planes", this.plWeakWorsening, 6);
gd.addCheckbox ("Keep only mutual links, remove weakest if conflict", this.plMutualOnly);
gd.addCheckbox ("Add diagonals to full squares", this.plFillSquares);
......@@ -3191,6 +3199,7 @@ public class EyesisCorrectionParameters {
this.grow_disp_step= gd.getNextNumber();
this.grow_min_diff= gd.getNextNumber();
this.plPreferDisparity= gd.getNextBoolean();
this.plDispNorm= gd.getNextNumber();
this.plMinPoints= (int) gd.getNextNumber();
this.plTargetEigen= gd.getNextNumber();
......@@ -3200,6 +3209,7 @@ public class EyesisCorrectionParameters {
this.plMaxEigen= gd.getNextNumber();
this.plDbgMerge= gd.getNextBoolean();
this.plWorstWorsening= gd.getNextNumber();
this.plWeakWorsening= gd.getNextNumber();
this.plMutualOnly= gd.getNextBoolean();
this.plFillSquares= gd.getNextBoolean();
......
src/main/java/SuperTiles.java
View file @ 41d614cb
This diff is collapsed.
src/main/java/TilePlanes.java
View file @ 41d614cb
......@@ -452,6 +452,7 @@ public class TilePlanes {
PlaneData otherPd,
double scale_other,
boolean ignore_weights,
boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
int debugLevel)
{
if (debugLevel > 0) {
......@@ -474,7 +475,11 @@ public class TilePlanes {
double sum_weight = scale_other * otherPd.weight + this.weight;
double other_fraction = ignore_weights? (scale_other/(scale_other + 1.0)): ((scale_other * otherPd.weight) / sum_weight);
Matrix common_center = this_center.times(1.0 - other_fraction).plus(other_center.times(other_fraction));
Matrix other_offset = other_center.minus(this_center); // other center from this center
Matrix other_offset = other_center.minus(this_center); // other center from this center
if ((this.values[0] == 0.0) || (otherPd.values[0] == 0.0)) {
System.out.println("Zero eigenvalue");
debugLevel = 10;
}
if (debugLevel > 0) {
System.out.println("other_eig_vals");
other_eig_vals.print(8, 6);
......@@ -495,9 +500,6 @@ public class TilePlanes {
System.out.println("other_fraction="+other_fraction);
}
double [][] acovar = { // covariance matrix of center masses (not yet scaled by weight)
{other_offset.get(0,0)*other_offset.get(0,0), other_offset.get(0,0)*other_offset.get(1,0), other_offset.get(0,0)*other_offset.get(2,0)},
{other_offset.get(1,0)*other_offset.get(0,0), other_offset.get(1,0)*other_offset.get(1,0), other_offset.get(1,0)*other_offset.get(2,0)},
......@@ -525,9 +527,12 @@ public class TilePlanes {
System.out.println("covar with other_covar and this_covar");
covar.print(8, 6);
}
if (Double.isNaN(covar.get(0, 0))){
System.out.println("covar is NaN !");
covar.print(8, 6);
}
// extract new eigenvalues, eigenvectors
EigenvalueDecomposition eig = covar.eig();
EigenvalueDecomposition eig = covar.eig(); // verify NaN - it gets stuck
// eig.getD().getArray(),
// eig.getV().getArray(),
if (debugLevel > 0) {
......@@ -542,12 +547,30 @@ public class TilePlanes {
double [][] eig_vect = eig.getV().getArray();
double [][] eig_val = eig.getD().getArray();
// make towards camera, left coordinate system
/*
int oindx = 0;
for (int i = 1; i <3; i++){
if (Math.abs(eig_vect[0][i]) > Math.abs(eig_vect[0][oindx])){
oindx = i;
}
}
*/
int oindx = 0;
if (preferDisparity) {
for (int i = 1; i <3; i++){
if (Math.abs(eig_vect[0][i]) > Math.abs(eig_vect[0][oindx])){
oindx = i;
}
}
} else {
for (int i = 1; i <3; i++){
if (eig_val[i][i] < eig_val[oindx][oindx]){
oindx = i;
}
}
}
// select 2 other axes for increasing eigenvalues (so v is short axis, h is the long one)
int vindx = (oindx == 0)? 1 : 0;
int hindx = (oindx == 0)? 2 : ((oindx == 1) ? 2 : 1);
......@@ -886,6 +909,7 @@ public class TilePlanes {
boolean [] select,
double plDispNorm, // Normalize disparities to the average if above
int debugLevel){
double mindet = 1E-15;
int stSize2 = 2 * stSize;
// Matrix covar = new Matrix(3,3);
double [][] acovar = new double [3][3];
......@@ -939,8 +963,21 @@ public class TilePlanes {
acovar [2][0] = acovar [0][2];
acovar [2][1] = acovar [1][2];
Matrix covar = new Matrix(acovar);
if (Math.abs(covar.det()) < mindet){
debugLevel = 5;
}
EigenvalueDecomposition eig = covar.eig();
if (Double.isNaN(eig.getV().get(0, 0))){
System.out.println("getCovar(): Double.isNaN(eig.getV().get(0, 0))");
debugLevel = 20;
}
if (eig.getD().get(0, 0) == 0.0){
debugLevel = 10;
}
if (debugLevel > 0){
System.out.println("getCovar(): sw = "+sw +", swz = "+swz +", swx = "+swx +", swy = "+swy +", covar.det() = "+covar.det());
System.out.println("getCovar(): covarianvce matrix, number of used points:"+numPoints);
covar.print(10, 6); // w,d
System.out.println("getCovar(): eigenvalues");
......@@ -948,6 +985,10 @@ public class TilePlanes {
System.out.println("getCovar(): eigenvectors");
eig.getV().print(10, 6); // w,d
}
if ((eig.getD().get(0, 0) == 0.0) || (Math.abs(covar.det()) < mindet)) {
return null; // testing with zero eigenvalue
// Problem with zero eigenvalue is with derivatives and coordinate conversion
}
double [][][] rslt = {
eig.getD().getArray(),
eig.getV().getArray(),
......@@ -962,13 +1003,12 @@ public class TilePlanes {
double [] data,
double [] weight,
boolean [] select, // null OK, will enable all tiles
boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
int debugLevel){
if (select == null) {
select = new boolean [4*stSize];
for (int i = 0; i < select.length; i++) select[i] = true;
}
// int debugLevel1 = ((sTileXY[0] == 27) && (sTileXY[1] == 20))? 1: 0;
// int debugLevel1 = ((sTileXY[0] == 27) && (sTileXY[1] == 17))? 1: 0;
int debugLevel1 = ((sTileXY[0] == 27) && (sTileXY[1] == 16))? 1: 0; // check why v[0][0] <0
......@@ -980,7 +1020,6 @@ public class TilePlanes {
debugLevel1); //0); // debugLevel);
if (rslt == null) return null;
int numPoints = (int) rslt[2][0][2];
// double kz = rslt[2][0][1]; // == 1.0
double swc = rslt[2][0][0];
double [] szxy = rslt[2][1];
double [][] eig_val = rslt[0];
......@@ -988,11 +1027,23 @@ public class TilePlanes {
// find vector most orthogonal to view // (anyway it all works with that assumption), make it first
// TODO normalize to local linear scales
int oindx = 0;
for (int i = 1; i <3; i++){
if (Math.abs(eig_vect[0][i]) > Math.abs(eig_vect[0][oindx])){
oindx = i;
if (preferDisparity) {
for (int i = 1; i <3; i++){
if (Math.abs(eig_vect[0][i]) > Math.abs(eig_vect[0][oindx])){
oindx = i;
}
}
} else {
for (int i = 1; i < 3 ; i++){
if (eig_val[i][i] < eig_val[oindx][oindx]){
oindx = i;
}
}
}
if (eig_val[oindx][oindx] == 0.0){
System.out.println("getPlane(): zero eigenvalue!!");
}
/*
// Find two other axis - "mostly X" (horizontal) and "mostly Y" (vertical)
int vindx = (oindx == 0)? 1 : 0;
......@@ -1058,6 +1109,7 @@ public class TilePlanes {
double targetEigen, // target eigenvalue for primary axis (is disparity-dependent, so is non-constant)
int maxRemoved, // maximal number of tiles to remove (not a constant)
int minLeft, // minimal number of tiles to keep
boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
int debugLevel){
int stSize2 = 2 * stSize;
if (pd == null) {
......@@ -1066,10 +1118,14 @@ public class TilePlanes {
data,
weight,
select, // null OK
preferDisparity,
debugLevel);
} else if (select != null){
pd.setPlaneSelection(select);
}
if (pd == null){
return null; // zero eigenvalues
}
if (maxRemoved > (pd.getNumPoints() - minLeft)) maxRemoved = pd.getNumPoints() - minLeft;
int numRemoved = 0;
for (; (pd.getValue() > targetEigen) && (numRemoved < maxRemoved); numRemoved++){
......@@ -1107,7 +1163,11 @@ public class TilePlanes {
data,
weight,
select,
preferDisparity,
debugLevel);
if (pd == null) {
return null;
}
}
return pd;
}
......
src/main/java/TileProcessor.java
View file @ 41d614cb
......@@ -2952,7 +2952,8 @@ public class TileProcessor {
clt_parameters.plMinPoints, // = 5; // Minimal number of points for plane detection
clt_parameters.plTargetEigen, // = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
clt_parameters.plFractOutliers, // = 0.3; // Maximal fraction of outliers to remove
clt_parameters.plMaxOutliers, // = 20; // Maximal number of outliers to remove
clt_parameters.plMaxOutliers, // = 20; // Maximal number of outliers to remove\
clt_parameters.plPreferDisparity,
geometryCorrection,
clt_parameters.correct_distortions,
0, // -1, // debugLevel, // final int debugLevel)
......@@ -2964,26 +2965,36 @@ public class TileProcessor {
if (debugLevel > -1) sdfa_instance = new showDoubleFloatArrays(); // just for debugging?
st.matchPlanes(
clt_parameters.plPreferDisparity,
clt_parameters.tileX,
clt_parameters.tileY);
st.selectNeighborPlanesMutual(
clt_parameters.plWorstWorsening, // final double worst_worsening,
clt_parameters.plWeakWorsening, // final double worst_worsening,
clt_parameters.plDispNorm,
clt_parameters.plMaxEigen,
clt_parameters.plMinStrength,
0, // final int debugLevel)
clt_parameters.tileX,
clt_parameters.tileY);
if (clt_parameters.plFillSquares){
st.fillSquares();
}
if (clt_parameters.plCutCorners){
st.cutCorners();
while (true) {
int num_added = 0;
if (clt_parameters.plFillSquares){
num_added += st.fillSquares();
}
if (debugLevel > -1) {
System.out.println("after fillSquares() added "+num_added);
}
if (clt_parameters.plCutCorners){
num_added += st.cutCorners();
}
if (debugLevel > -1) {
System.out.println("after plCutCorners() added (cumulative) "+num_added);
}
if (num_added == 0) break;
}
TilePlanes.PlaneData [][] planes_mod = null;
// smooth planes (by averaging with neighbors and the "measured" one with variable "pull")
......@@ -2993,6 +3004,7 @@ public class TileProcessor {
clt_parameters.plPull, // final double meas_pull,// relative pull of the original (measured) plane with respect to the average of the neighbors
clt_parameters.plIterations, // final int num_passes,
Math.pow(10.0, -clt_parameters.plPrecision), // final double maxDiff, // maximal change in any of the disparity values
clt_parameters.plPreferDisparity,
0, // final int debugLevel)
clt_parameters.tileX,
clt_parameters.tileY);
......
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