handling zero eigenvalues (41d614cb) · Commits · Elphel / imagej-elphel

src/main/java/EyesisCorrectionParameters.java

+12 −2

Original line number	Diff line number	Diff line
		@@ -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

+50 −39

Original line number	Diff line number	Diff line
		@@ -1168,7 +1168,6 @@ public class SuperTiles{


		public void processPlanes2(

		final boolean [] selected, // or null
		final double min_disp,
		final boolean invert_disp, // use 1/disparity
		@@ -1177,6 +1176,7 @@ public class SuperTiles{
		final double plTargetEigen, // = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
		final double plFractOutliers, // = 0.3; // Maximal fraction of outliers to remove
		final int plMaxOutliers, // = 20; // Maximal number of outliers to remove
		final boolean plPreferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
		final GeometryCorrection geometryCorrection,
		final boolean correct_distortions,
		final int debugLevel,
		@@ -1262,27 +1262,19 @@ public class SuperTiles{
		}
		}
		planes[nsTile] = null;
		if (sw >0){
		if (sw >0){ // there are some non-zero tiles, process them (all points, not clustered by disparity value)
		ArrayList<TilePlanes.PlaneData> st_planes = new ArrayList<TilePlanes.PlaneData>();
		// int dl = ((nsTile >= debug_stile-1) && (nsTile <= debug_stile+1) ) ? 1 : 0;
		// int dl = ((stileY == 17) && (stileX > 4)) ? 1 : 0;
		// int dl = (stileY >= 0) ? 1 : 0;
		int dl1 = (nsTile == debug_stile) ? 3 : 0;
		// int dl = (nsTile == debug_stile) ? 3 : 0;
		// int dl = ((stileY >= 15) && (stileY <= 18) && (stileX >= 5) && (stileX <= 31)) ? 1 : 0;
		// int dl = ((stileY == 16) && (stileX == 27) ) ? 3 : 0;
		int dl = (nsTile == debug_stile) ? 3 : 0;
		// int debugLevel1 = ((sTileXY[0] == 27) && (sTileXY[1] == 16))? 1: 0; // check why v[0][0] <0


		boolean [] sel_all = stSel.clone();
		TilePlanes.PlaneData pd = tpl.getPlane(
		sTiles,
		stDisparity,
		stStrength,
		sel_all,
		plPreferDisparity,
		0); // debugLevel);
		if (pd != null) {
		if (pd != null) { // not too few points, probably
		//correct_distortions
		double swc_common = pd.getWeight();
		if (dl > 0) {
		@@ -1301,19 +1293,11 @@ public class SuperTiles{
		// now try to remove outliers
		int max_outliers = (int) Math.round(pd.getNumPoints() * plFractOutliers);
		if (max_outliers > plMaxOutliers) max_outliers = plMaxOutliers;
		/*
		double targetV = plTargetEigen;
		double z0 = pd.getZxy()[0];
		if ((plTargetEigen > 0.0) && (z0 > plDispNorm)) {
		double dd = (plDispNorm + z0)/ plDispNorm; // > 1
		targetV = dd dd; // > original
		}
		*/
		double targetV = corrMaxEigen(
		plTargetEigen,
		plDispNorm,
		pd);
		if (pd.getValues()[0] > targetV) {
		if (pd.getValue() > targetV) {
		pd = tpl.removePlaneOutliers(
		pd,
		sTiles,
		@@ -1323,7 +1307,9 @@ public class SuperTiles{
		targetV, // double targetEigen, // target eigenvalue for primary axis (is disparity-dependent, so is non-constant)
		max_outliers, // int maxRemoved, // maximal number of tiles to remove (not a constant)
		plMinPoints, // int minLeft, // minimal number of tiles to keep
		plPreferDisparity,
		dl1); // 0); // debugLevel);
		if (pd == null) continue;
		if (dl > 0) {
		if (swc_common > 0.3) { // 1.0) {
		System.out.println("Removed outliers["+nsTile+"]"+
		@@ -1350,9 +1336,9 @@ public class SuperTiles{
		norm_xyz[0]+", "+norm_xyz[1]+", "+norm_xyz[2]+"}");

		}
		st_planes.add(pd);
		st_planes.add(pd); // adding [0] - all supertile tiles, not clustered by disparity value

		// now try for each of the disparity-separated clusters
		// now try for each of the disparity-separated clusters (only for multi-peak histograms)

		double [][] mm = maxMinMax[nsTile];

		@@ -1392,6 +1378,7 @@ public class SuperTiles{
		stDisparity,
		stStrength,
		stSels[m],
		plPreferDisparity,
		0); // debugLevel);
		if (pd != null) {
		if (dl > 0) {
		@@ -1426,7 +1413,11 @@ public class SuperTiles{
		targetV, // double targetEigen, // target eigenvalue for primary axis (is disparity-dependent, so is non-constant)
		max_outliers, // int maxRemoved, // maximal number of tiles to remove (not a constant)
		plMinPoints, // int minLeft, // minimal number of tiles to keep
		plPreferDisparity,
		dl1); // 0); // debugLevel);
		if (pd == null) {
		continue;
		}
		if (dl > 0) {
		if (swc_common > 1.0) {
		System.out.println("Removed outliers["+nsTile+"]["+m+"]"+
		@@ -1485,6 +1476,7 @@ public class SuperTiles{

		TilePlanes.PlaneData [][] getNeibPlanes(
		final int dir, // 0: get from up (N), 1:from NE, ... 7 - from NW
		final boolean preferDisparity,
		final boolean dbgMerge, // Combine 'other' plane with current
		final int dbg_X,
		final int dbg_Y
		@@ -1548,12 +1540,14 @@ public class SuperTiles{
		planes[nsTile0][indx], // PlaneData otherPd,
		1.0, // double scale_other,
		true, // boolean ignore_weights,
		preferDisparity,
		(nsTile0 == debug_stile)? 1:0); // int debugLevel)
		System.out.println("other with other, same weight:");
		TilePlanes.PlaneData other_other_pd = other_pd.mergePlaneToThis(
		other_pd, // PlaneData otherPd,
		1.0, // double scale_other,
		true, // boolean ignore_weights,
		preferDisparity,
		(nsTile0 == debug_stile)? 1:0); // int debugLevel)
		System.out.println("other with this, same weight:");
		}
		@@ -1561,6 +1555,7 @@ public class SuperTiles{
		other_pd, // PlaneData otherPd,
		1.0, // double scale_other,
		true, // boolean ignore_weights,
		preferDisparity,
		(nsTile0 == debug_stile)? 1:0); // int debugLevel)
		if (!(merged_pd.getValue() > best_value)) { // Double.isNaN(best_value) will work too
		best_value = merged_pd.getValue();
		@@ -1576,6 +1571,7 @@ public class SuperTiles{
		other_pd, // PlaneData otherPd,
		1.0, // double scale_other,
		false, // boolean ignore_weights,
		preferDisparity,
		(nsTile0 == debug_stile)? 1:0); // int debugLevel)
		neib_planes[nsTile0][np] = merged_pd;
		} else {
		@@ -1618,6 +1614,7 @@ public class SuperTiles{
		}

		public void matchPlanes(
		final boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
		final int dbg_X,
		final int dbg_Y)
		{
		@@ -1636,6 +1633,7 @@ public class SuperTiles{

		final Thread[] threads = ImageDtt.newThreadArray(tileProcessor.threadsMax);
		final AtomicInteger ai = new AtomicInteger(0);
		// Select best symmetrical match, consider only N, NE, E, SE - later opposite ones will be copied
		for (int ithread = 0; ithread < threads.length; ithread++) {
		threads[ithread] = new Thread() {
		public void run() {
		@@ -1673,6 +1671,7 @@ public class SuperTiles{
		other_plane, // PlaneData otherPd,
		1.0, // double scale_other,
		false, // boolean ignore_weights,
		preferDisparity,
		dl); // int debugLevel)
		if (merged_pd !=null) { // now always, but may add later
		this_plane.setNeibMatch(dir, np, merged_pd.getValue()); // smallest eigenValue
		@@ -1706,28 +1705,18 @@ public class SuperTiles{
		if ( planes[nsTile0] != null) {
		for (int np0 = 0; np0 < planes[nsTile0].length; np0++){ // nu
		TilePlanes.PlaneData this_plane = planes[nsTile0][np0];
		// this_plane.initMergedValue();
		for (int dir = 4; dir < 8; dir++){ // other half - copy from opposite
		int stx = stx0 + dirsYX[dir][1];
		int sty = sty0 + dirsYX[dir][0];
		// if ((stx < stilesX) && (sty < stilesY) && (sty > 0)) {
		if ((sty < stilesY) && (sty > 0) && (stx > 0)) {

		int nsTile = sty * stilesX + stx; // from where to get
		TilePlanes.PlaneData [] other_planes = planes[nsTile];
		if (other_planes !=null) {
		this_plane.initMergedValue(dir,other_planes.length); // filled with NaN
		for (int np = 0; np < other_planes.length; np ++){
		/*
		if ((other_planes[np] != null) && (other_planes[np].getMergedValue(dir-4) != null)) {
		double [] dbg_nm = other_planes[np].getMergedValue(dir-4);
		this_plane.setNeibMatch(dir,np, other_planes[np].getMergedValue(dir-4, np0)); //
		}
		*/
		if (other_planes[np] != null) { // && (other_planes[np].getMergedValue(dir-4) != null)) {
		double [] nm = other_planes[np].getMergedValue(dir-4);
		if (nm != null) {
		// this_plane.setNeibMatch(dir,np, other_planes[np].getMergedValue(dir-4, np0)); //
		this_plane.setNeibMatch(dir,np, nm[np0]); //
		}
		}
		@@ -1859,7 +1848,7 @@ public class SuperTiles{
		ImageDtt.startAndJoin(threads);
		}
		}
		public void fillSquares()
		public int fillSquares()
		{
		final int tilesX = tileProcessor.getTilesX();
		final int tilesY = tileProcessor.getTilesY();
		@@ -1868,6 +1857,7 @@ public class SuperTiles{
		final int stilesX = (tilesX + superTileSize -1)/superTileSize;
		final int stilesY = (tilesY + superTileSize -1)/superTileSize;
		final int nStiles = stilesX * stilesY;
		int num_added = 0;
		for (int stY = 0; stY < (stilesY - 1); stY++ ) {
		for (int stX = 0; stX < (stilesX - 1); stX++ ) {
		int nsTile = stY * stilesX + stX;
		@@ -1891,18 +1881,21 @@ public class SuperTiles{
		if (neibs0[3] < 0) {
		neibs0[3] = neibs1[4];
		neibs3[7] = np;
		num_added++;
		}
		if (neibs1[5] < 0){
		neibs1[5] = neibs0[4];
		neibs2[1] = neibs0[2];
		num_added++;
		}
		}
		}
		}
		}
		return num_added;
		}

		public void cutCorners()
		public int cutCorners()
		{
		final int tilesX = tileProcessor.getTilesX();
		final int tilesY = tileProcessor.getTilesY();
		@@ -1910,7 +1903,8 @@ public class SuperTiles{
		// final int tileSize = tileProcessor.getTileSize();
		final int stilesX = (tilesX + superTileSize -1)/superTileSize;
		final int stilesY = (tilesY + superTileSize -1)/superTileSize;
		final int nStiles = stilesX * stilesY;
		// final int nStiles = stilesX * stilesY;
		int num_added = 0;
		for (int stY = 0; stY < (stilesY - 1); stY++ ) {
		for (int stX = 0; stX < (stilesX - 1); stX++ ) {
		int nsTile = stY * stilesX + stX;
		@@ -1931,6 +1925,7 @@ public class SuperTiles{
		neibs0[4] = neibs1[5];
		int [] neibs2 = planes4[2][neibs1[5]].getNeibBest();
		neibs2[0] = np; //?
		num_added++;
		}
		}
		}
		@@ -1942,6 +1937,7 @@ public class SuperTiles{
		neibs0[2] = neibs2[1];
		int [] neibs1 = planes4[1][neibs2[1]].getNeibBest();
		neibs1[6] = np;
		num_added++;
		}
		}
		}
		@@ -1951,6 +1947,7 @@ public class SuperTiles{
		if (neibs1[4] < 0) {
		neibs1[4] = neibs0[3];
		neibs3[0] = neibs0[2]; //?
		num_added++;
		}
		}
		if ((neibs0[3] >= 0) && (neibs0[4] >= 0)){
		@@ -1959,6 +1956,7 @@ public class SuperTiles{
		if (neibs2[2] < 0) {
		neibs2[2] = neibs0[3];
		neibs3[6] = neibs0[4];
		num_added++;
		}
		}
		}
		@@ -1975,6 +1973,7 @@ public class SuperTiles{
		if (neibs0[2] < 0) {
		neibs0[2] = neibs3[0];
		neibs1[6] = neibs3[7];
		num_added++;
		}
		}

		@@ -1985,6 +1984,7 @@ public class SuperTiles{
		if (neibs0[4] < 0) {
		neibs0[4] = neibs3[6];
		neibs2[0] = neibs3[7]; //?
		num_added++;
		}
		}

		@@ -1996,6 +1996,7 @@ public class SuperTiles{
		int [] neibs2 = planes4[2][neibs0[4]].getNeibBest();
		neibs3[6] = neibs0[4];
		neibs2[2] = np; //?
		num_added++;
		}
		}

		@@ -2006,6 +2007,7 @@ public class SuperTiles{
		int [] neibs1 = planes4[1][neibs2[1]].getNeibBest();
		neibs3[0] = neibs2[1]; //?
		neibs1[4] = np;
		num_added++;
		}
		}
		}
		@@ -2013,6 +2015,8 @@ public class SuperTiles{
		}
		}
		}
		return num_added;

		}

		/**
		@@ -2027,6 +2031,7 @@ public class SuperTiles{
		*/
		public void selectNeighborPlanesMutual(
		final double rquality,
		final double weakWorsening,
		final double dispNorm,
		final double maxEigen, // maximal eigenvalue of planes to consider
		final double minWeight, // minimal pain weight to consider
		@@ -2130,8 +2135,9 @@ public class SuperTiles{
		merge_ev[np], // double L,
		w1, // double w1,
		w2); // double w2)
		// if (this_rq <= rquality) { // compare with the threshold before applying strengths
		if ((w1 + w2) * this_rq <= rquality) { // forgive more for weak planes
		double this_rq_norm = this_rq;
		if ((w1 + w2) < weakWorsening) this_rq_norm *= (w1 + w2) / weakWorsening; // forgive more for weak planes
		if (this_rq_norm <= rquality) {
		this_rq /= (w1 + w2); // for comparision reduce this value for stronger planes
		if (Double.isNaN(best_rqual) \|\| (this_rq < best_rqual)){ // OK if Double.isNaN(this_rq[np])
		best_rqual = this_rq;
		@@ -2504,6 +2510,7 @@ public class SuperTiles{
		final double meas_pull,// relative pull of the original (measured) plane with respect to the average of the neighbors
		final int num_passes,
		final double maxDiff, // maximal change in any of the disparity values
		final boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
		final int debugLevel,
		final int dbg_X,
		final int dbg_Y)
		@@ -2517,6 +2524,7 @@ public class SuperTiles{
		this.planes, // final TilePlanes.PlaneData[][] measured_planes,
		this.planes_mod, // final TilePlanes.PlaneData[][] mod_planes,
		true, // final boolean calc_diff,
		preferDisparity,
		(pass < 10)? debugLevel: 0,
		dbg_X,
		dbg_Y);
		@@ -2539,6 +2547,7 @@ public class SuperTiles{
		final TilePlanes.PlaneData[][] measured_planes,
		final TilePlanes.PlaneData[][] mod_planes,
		final boolean calc_diff,
		final boolean preferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
		final int debugLevel,
		final int dbg_X,
		final int dbg_Y)
		@@ -2600,6 +2609,7 @@ public class SuperTiles{
		other_plane, // PlaneData otherPd,
		1.0, // double scale_other,
		false, // boolean ignore_weights,
		preferDisparity,
		dl); // int debugLevel)
		} else {
		this_new_plane = other_plane;
		@@ -2619,6 +2629,7 @@ public class SuperTiles{
		measured_planes[nsTile0][np0], // PlaneData otherPd,
		meas_pull, // double scale_other,
		false, // boolean ignore_weights,
		preferDisparity,
		dl); // int debugLevel)
		} else {
		this_new_plane = measured_planes[nsTile0][np0].clone();

src/main/java/TilePlanes.java

+72 −12

File changed.

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src/main/java/TileProcessor.java

+20 −8

Original line number	Diff line number	Diff line
		@@ -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)
		@@ -2964,25 +2965,35 @@ 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);

		while (true) {
		int num_added = 0;
		if (clt_parameters.plFillSquares){
		st.fillSquares();
		num_added += st.fillSquares();
		}
		if (debugLevel > -1) {
		System.out.println("after fillSquares() added "+num_added);
		}
		if (clt_parameters.plCutCorners){
		st.cutCorners();
		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;

		@@ -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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