working on alignment correction

src/main/java/AlignmentCorrection.java

+import java.util.ArrayList;
+
+/**
+ **
+ ** AlignmentCorrection - try to apply minor adjustments to the misaligned camera
+ **
+ ** Copyright (C) 2017 Elphel, Inc.
+ **
+ ** -----------------------------------------------------------------------------**
+ **  
+ **  AlignmentCorrection.java is free software: you can redistribute it and/or modify
+ **  it under the terms of the GNU General Public License as published by
+ **  the Free Software Foundation, either version 3 of the License, or
+ **  (at your option) any later version.
+ **
+ **  This program is distributed in the hope that it will be useful,
+ **  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ **  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ **  GNU General Public License for more details.
+ **
+ **  You should have received a copy of the GNU General Public License
+ **  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ ** -----------------------------------------------------------------------------**
+ **
+ */
+
+public class AlignmentCorrection {
+	QuadCLT qc;
+	AlignmentCorrection (QuadCLT qc){
+		this.qc = qc;
+	}
+
+	public double [][][] infinityCorrection(
+			final double min_strength_in,
+			final double max_diff,
+			final int max_iterations,
+			final double max_coeff_diff,
+			final double far_pull, //  = 0.2; // 1; //  0.5;
+			final double     strength_pow,
+			final int        smplSide, //        = 2;      // Sample size (side of a square)
+			final int        smplNum, //         = 3;      // Number after removing worst (should be >1)
+			final double     smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+			// histogram parameters
+			final int        hist_smpl_side, // 8 x8 masked, 16x16 sampled
+			final double     hist_disp_min,
+			final double     hist_disp_step,
+			final int        hist_num_bins,
+			final double     hist_sigma,
+			final double     hist_max_diff,
+			final int        hist_min_samples,
+			final boolean    hist_norm_center, // if there are more tiles that fit than min_samples, replace with 			
+
+			EyesisCorrectionParameters.CLTParameters           clt_parameters,
+			double [][] disp_strength_in,
+			int         tilesX,
+			double      magic_coeff, // still not understood coefficient that reduces reported disparity value.  Seems to be around 8.5  
+			int debugLevel)
+	{
+		double [][] disp_strength;
+		double min_strength;
+		if (smplSide > 1){
+			disp_strength = filterDisparityStrength (
+					disp_strength_in,
+					min_strength_in,    // final double     strength_floor,
+					strength_pow,
+					smplSide, //        = 2;      // Sample size (side of a square)
+					smplNum, //         = 3;      // Number after removing worst (should be >1)
+					smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+					tilesX);
+			min_strength = 0; // all > 0	
+			if (debugLevel > 0){
+				double [][] dbg_img = disp_strength.clone();
+				for (int n = 0; n < disp_strength.length; n++){
+					dbg_img[n] = disp_strength[n].clone();
+				}
+				for (int n = 0; n < dbg_img.length; n+=2){
+					for (int i = 0; i < dbg_img[n].length; i++) {
+						if (dbg_img[n+1][i] == 0.0){
+							dbg_img[n][i] = Double.NaN;
+						}
+					}
+				}
+
+				(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, disp_strength[0].length/tilesX, true, "filtered_ds"); // , titles);
+
+			}
+
+		} else {
+			disp_strength = disp_strength_in;
+			min_strength = min_strength_in;
+		}
+		if (hist_smpl_side > 0) { // 0 to bypass histogram filtering
+			disp_strength = filterHistogramFar (
+					disp_strength,    // final double[][] disp_strength_in,
+					hist_smpl_side,   // final int        smpl_side, // 8 x8 masked, 16x16 sampled
+					hist_disp_min,    // final double     disp_min,
+					hist_disp_step,   // final double     disp_step,
+					hist_num_bins,    // final int        num_bins,
+					hist_sigma,       // final double     sigma,
+					hist_max_diff,    // final double     max_diff,
+					hist_min_samples, // final int        min_samples,
+					hist_norm_center, // final boolean    norm_center, // if there are more tiles that fit than minsamples, replace with 					
+					tilesX);          // final int        tilesX)
+			if (debugLevel > 0){
+				double [][] dbg_img = disp_strength.clone();
+				for (int n = 0; n < disp_strength.length; n++){
+					dbg_img[n] = disp_strength[n].clone();
+				}
+				for (int n = 0; n < dbg_img.length; n+=2){
+					for (int i = 0; i < dbg_img[n].length; i++) {
+						if (dbg_img[n+1][i] == 0.0){
+							dbg_img[n][i] = Double.NaN;
+						}
+					}
+				}
+				(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, disp_strength[0].length/tilesX, true, "hist_filt_ds"); // , titles);
+
+			}
+		}
+		return infinityCorrection(
+				min_strength,
+				max_diff,
+				max_iterations,
+				max_coeff_diff,
+				far_pull, //  = 0.2; // 1; //  0.5;
+				clt_parameters,
+				disp_strength,
+				tilesX,
+				magic_coeff, // still not understood coefficient that reduces reported disparity value.  Seems to be around 8.5  
+				debugLevel);
+	}
+
+
+	/**
+	 * Calculate quadratic polynomials for each subcamera X/Y correction to match disparity = 0 at infinity
+	 * Next parameters are made separate to be able to modify them between different runs keeping clt_parameters
+	 * @param min_strength minimal correlation strength to use tile
+	 * @param max_diff maximal disparity difference between tiles and previous approximation to use tiles
+	 * @param max_iterations maximal number of iterations to find disparity surface
+	 * @param max_coeff_diff coefficients maximal change to continue iterations
+	 * @param far_pull weight coefficient for the tile outside of the range, but farther
+	 * @param clt_parameters CLT parameters
+	 * @param disp_strength array of a single or multiple disparity/strength pairs (0,2, .. - disparity,
+	 *  1,3,.. - corresponding strengths
+	 * @param tilesX number of tiles in each data line
+	 * @param magic_coeff still not understood coefficient that reduces reported disparity value.  Seems to be around 0.85 
+	 * @param debugLevel debug level
+	 * @return per sub-camera, per direction (x,y) 6 quadratic polynomial coefficients, same format as fine_geometry_correction()
+	 */
+	public double [][][] infinityCorrection(
+			final double min_strength,
+			final double max_diff,
+			final int max_iterations,
+			final double max_coeff_diff,
+			final double far_pull, //  = 0.2; // 1; //  0.5;
+			EyesisCorrectionParameters.CLTParameters           clt_parameters,
+			double [][] disp_strength,
+			int         tilesX,
+			double      magic_coeff, // still not understood coefficient that reduces reported disparity value.  Seems to be around 8.5  
+			int debugLevel)
+	{
+		final int numTiles =            disp_strength[0].length;
+		final int tilesY =              numTiles/tilesX;
+		double [] disparity_poly = new double[6];
+		PolynomialApproximation pa = new PolynomialApproximation();
+		double thresholdLin = 1.0E-20;  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
+		double thresholdQuad = 1.0E-30; // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+		double [] disp_surface = new double[numTiles];
+		//		  double [] corr_weight = new double [numTiles]; // reduce weight for far tiles (by more than range farther than surface)
+		class Sample{
+			int series;
+			int tile;
+			double weight;
+			Sample(int series, int tile, double weight) {
+				this.series = series;
+				this.tile =  tile;
+				this.weight = weight;
+
+			}
+		}
+		ArrayList<Sample> samples_list = new ArrayList<Sample>();
+		for (int pass = 0; pass < max_iterations; pass++){
+			for (int nTile = 0; nTile < numTiles; nTile++){
+				int tileX = nTile % tilesX;
+				int tileY = nTile / tilesX;
+				double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+				double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+				disp_surface[nTile] =
+						disparity_poly[0] * x * x +
+						disparity_poly[1] * y * y +
+						disparity_poly[2] * x * y +
+						disparity_poly[3] * x +
+						disparity_poly[4] * y +
+						disparity_poly[5];
+			}
+
+			samples_list.clear();
+			for (int num_set = 0; num_set < disp_strength.length/2; num_set++){
+				int disp_index = 2 * num_set;
+				int str_index = 2 * num_set+1;
+				for (int nTile = 0; nTile < numTiles; nTile++){
+					if ((disp_strength[str_index][nTile] > min_strength) && 
+							//							  (Math.abs(disp_strength[disp_index][nTile] - disp_surface[nTile]) < max_diff)){
+							((disp_strength[disp_index][nTile] - disp_surface[nTile]) < max_diff)){
+						double weight= disp_strength[str_index][nTile];
+						// next are for far tiles, that differ by more than max_diff
+						if (Math.abs(disp_strength[disp_index][nTile] - disp_surface[nTile]) > max_diff){
+							weight *= far_pull;
+						}
+						samples_list.add(new Sample(num_set, nTile, weight));
+					}
+				}
+			}
+			double [][][] mdata;
+			if (clt_parameters.fcorr_inf_vert) {
+				mdata = new double[samples_list.size()][3][];
+			} else {
+				mdata = new double [1][samples_list.size()][3];
+			}
+
+			int indx = 0;
+			for (Sample s: samples_list){
+				int tileX = s.tile % tilesX;
+				int tileY = s.tile / tilesX;
+				if (clt_parameters.fcorr_inf_vert) {
+					mdata[indx][0] = new double [2];
+					mdata[indx][0][0] =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					mdata[indx][0][1] =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+					mdata[indx][1] = new double [1];
+					mdata[indx][1][0] =  (disp_strength[2 * s.series + 0][s.tile]/magic_coeff - disp_surface[s.tile]); // disparity residual
+					mdata[indx][2][0] =  s.weight; // disp_strength[2 * s.series + 1][s.tile]; // strength
+					//					  if (Math.abs( mdata[indx][1][0]) > max_diff) { // far tiles
+					//						  mdata[indx][2][0] *= far_pull;
+					//					  }
+
+				} else {
+					mdata[0][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					mdata[0][indx][1] = (disp_strength[2 * s.series + 0][s.tile]/magic_coeff - disp_surface[s.tile]); // disparity residual
+					mdata[0][indx][2] = s.weight; // disp_strength[2 * s.series + 1][s.tile]; // strength
+					//						  if (Math.abs( mdata[0][indx][1]) > max_diff) { // far tiles
+					//							  mdata[0][indx][2] *= far_pull;
+					//						  }
+				}
+				indx ++;
+			}
+			if ((debugLevel > -1) && (pass < 20)){
+				String [] titles = {"disparity","approx","diff", "strength"};
+				double [][] dbg_img = new double [titles.length][numTiles];
+				for (int nTile = 0; nTile < numTiles; nTile++){
+					int tileX = nTile % tilesX;
+					int tileY = nTile / tilesX;
+					double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+					dbg_img[1][nTile] =
+							disparity_poly[0] * x * x +
+							disparity_poly[1] * y * y +
+							disparity_poly[2] * x * y +
+							disparity_poly[3] * x +
+							disparity_poly[4] * y +
+							disparity_poly[5];
+				}
+
+				for (Sample s: samples_list){
+					dbg_img[0][s.tile] += disp_strength[2 * s.series][s.tile] * s.weight; // disp_strength[2 * p.x + 1][p.y];
+					dbg_img[3][s.tile] += s.weight; // disp_strength[2 * p.x + 1][p.y];
+				}
+				for (int nTile = 0; nTile < numTiles; nTile++) {
+					if (dbg_img[3][nTile] > 0.0) {
+						dbg_img[0][nTile] /=dbg_img[3][nTile];
+					} else {
+						dbg_img[0][nTile] = Double.NaN;
+					}
+				}
+
+				for (int nTile = 0; nTile < numTiles; nTile++) {
+					if (dbg_img[3][nTile] > 0.0) {
+						dbg_img[2][nTile] = dbg_img[0][nTile] - dbg_img[1][nTile];
+					} else {
+						dbg_img[2][nTile] = Double.NaN;
+					}
+				}
+
+				(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "infinity_"+pass, titles);
+
+
+
+
+			}
+
+
+			double [][] coeffs = new double[1][6];
+			if (clt_parameters.fcorr_inf_vert){
+				double[][] approx2d = pa.quadraticApproximation(
+						mdata,
+						!clt_parameters.fcorr_inf_quad, // boolean forceLinear,  // use linear approximation
+						thresholdLin,  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
+						thresholdQuad,  // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+						debugLevel); {
+
+						}
+						if (approx2d[0].length == 6) {
+							coeffs = approx2d;
+						} else {
+							for (int i = 0; i < 3; i++){
+								coeffs[0][3+i] = approx2d[0][i];
+							}
+						}
+			} else {
+				double [] approx1d = pa.polynomialApproximation1d(mdata[0], clt_parameters.fcorr_inf_quad ? 2 : 1);
+				//			  disparity_approximation = new double[6];
+				coeffs[0][5] = approx1d[0];
+				coeffs[0][3] = approx1d[1];
+				if (approx1d.length > 2){
+					coeffs[0][0] = approx1d[2];
+				}
+			}
+			if (debugLevel > -1){
+				System.out.println(String.format(
+						"infinityCorrection() disparity pass=%03d A=%8.5f B=%8.5f C=%8.5f D=%8.5f E=%8.5f F=%8.5f",
+						pass, disparity_poly[0], disparity_poly[1], disparity_poly[2],
+						disparity_poly[3], disparity_poly[4], disparity_poly[5]) );
+			}
+			boolean all_done = true;
+			for (int i = 0; i < disparity_poly.length; i++){
+				if (Math.abs(coeffs[0][i]) > max_coeff_diff){
+					all_done = false;
+					break;
+				}
+			}
+
+			if (all_done) break;
+			for (int i = 0; i < disparity_poly.length; i++){
+				disparity_poly[i] += coeffs[0][i];
+			}			  
+		}
+
+		// use last generated samples_list;
+
+		double [][][] mdata;
+		if (clt_parameters.fcorr_inf_vert) {
+			mdata = new double[samples_list.size()][3][];
+		} else {
+			mdata = new double [8][samples_list.size()][3];
+		}
+		int indx = 0;
+		for (Sample s: samples_list){
+			int tileX = s.tile % tilesX;
+			int tileY = s.tile / tilesX;
+			double centerX = tileX * qc.tp.getTileSize() + qc.tp.getTileSize()/2;// - shiftX;
+			double centerY = tileY * qc.tp.getTileSize() + qc.tp.getTileSize()/2;//- shiftY;
+			double [][] centersXY_disp = qc.geometryCorrection.getPortsCoordinates(
+					centerX,
+					centerY,
+					disp_strength[2 * s.series + 0][s.tile]/magic_coeff); // disparity
+			double [][] centersXY_inf = qc.geometryCorrection.getPortsCoordinates(
+					centerX,
+					centerY,
+					0.0); // disparity
+			for (int i = 0; i < centersXY_disp.length;i++){
+				centersXY_disp[i][0] -= centersXY_inf[i][0];
+				centersXY_disp[i][1] -= centersXY_inf[i][1];
+			}
+			if (clt_parameters.fcorr_inf_vert) {
+				mdata[indx][0] = new double [2];
+				mdata[indx][0][0] =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+				mdata[indx][0][1] =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+				mdata[indx][1] = new double [8];
+				for (int n = 0; n < 8; n++){
+					mdata[indx][1][n] =   -centersXY_disp[n / 2][n % 2];
+				}
+				mdata[indx][2] = new double [1];
+				mdata[indx][2][0] = s.weight; //  disp_strength[2 * p.x + 1][p.y]; // strength
+
+			} else {
+				for (int n = 0; n < 8; n++){
+					mdata[n][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					mdata[n][indx][1] = -centersXY_disp[n / 2][n % 2];
+					mdata[n][indx][2] = s.weight; // disp_strength[2 * p.x + 1][p.y]; // strength
+				}
+			}
+			indx ++;
+		}
+		double [][] coeffs = new double[8][6];
+		if (clt_parameters.fcorr_inf_vert){
+			double [][] approx2d = pa.quadraticApproximation(
+					mdata,
+					!clt_parameters.fcorr_inf_quad, // boolean forceLinear,  // use linear approximation
+					thresholdLin,  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
+					thresholdQuad,  // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+					debugLevel);
+			for (int n = 0; n < 8; n++){
+				if (approx2d[n].length == 6) {
+					coeffs[n] = approx2d[n];
+				} else {
+					for (int i = 0; i < 3; i++){
+						coeffs[n][3+i] = approx2d[n][i];
+					}
+				}
+			}
+		} else {
+			for (int n = 0; n < 8; n++){
+				double [] approx1d = pa.polynomialApproximation1d(mdata[n], clt_parameters.fcorr_inf_quad ? 2 : 1);
+				//			  disparity_approximation = new double[6];
+				coeffs[n][5] = approx1d[0];
+				coeffs[n][3] = approx1d[1];
+				if (approx1d.length > 2){
+					coeffs[n][0] = approx1d[2];
+				}
+			}
+		}
+
+		double [][][] inf_corr = new double [4][2][];
+		for (int n = 0; n < 8; n++){
+			inf_corr[n / 2][n % 2] = coeffs[n];
+		}		  
+
+		if (debugLevel > 0) {
+			String [] titles = {"disparity","approx","diff", "strength"};
+			double [][] dbg_img = new double [titles.length][numTiles];
+			for (int nTile = 0; nTile < numTiles; nTile++){
+				int tileX = nTile % tilesX;
+				int tileY = nTile / tilesX;
+				double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+				double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+				dbg_img[1][nTile] =
+						disparity_poly[0] * x * x +
+						disparity_poly[1] * y * y +
+						disparity_poly[2] * x * y +
+						disparity_poly[3] * x +
+						disparity_poly[4] * y +
+						disparity_poly[5];
+			}
+
+			for (Sample s: samples_list){
+				dbg_img[0][s.tile] += disp_strength[2 * s.series][s.tile] * s.weight; // disp_strength[2 * p.x + 1][p.y];
+				dbg_img[3][s.tile] += s.weight; // disp_strength[2 * s.series + 1][s.tile];
+			}
+
+			for (int nTile = 0; nTile < numTiles; nTile++) {
+				if (dbg_img[3][nTile] > 0.0) {
+					dbg_img[0][nTile] /=dbg_img[3][nTile];
+				} else {
+					dbg_img[0][nTile] = Double.NaN;
+				}
+			}
+
+			for (int nTile = 0; nTile < numTiles; nTile++) {
+				if (dbg_img[3][nTile] > 0.0) {
+					dbg_img[2][nTile] = dbg_img[0][nTile] - dbg_img[1][nTile];
+				} else {
+					dbg_img[2][nTile] = Double.NaN;
+				}
+			}
+
+			(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "infinityCorrection", titles);
+
+		}
+		return inf_corr;
+	}	  
+
+	public double[][] filterHistogramFar (
+			final double[][] disp_strength_in,
+			final int        smpl_side, // 8 x8 masked, 16x16 sampled
+			final double     disp_min,
+			final double     disp_step,
+			final int        num_bins,
+			final double     sigma,
+			final double     max_diff,
+			final int        min_samples,
+			final boolean    norm_center, // if there are more tiles that fit than minsamples, replace with a single equal weight
+			final int        tilesX)
+	{
+		if (disp_strength_in.length > 2){
+			final double [][] disp_strength = new double [disp_strength_in.length][];
+			for (int i = 0; i < disp_strength_in.length; i+=2){
+				double [][] ds = {disp_strength_in[i],disp_strength_in[i+1]};
+				double [][] ds_rslt = filterHistogramFar (
+						ds,
+						smpl_side, // 8 x8 masked, 16x16 sampled
+						disp_min,
+						disp_step,
+						num_bins,
+						sigma,
+						max_diff,
+						min_samples,
+						norm_center,
+						tilesX);
+				disp_strength[i] =   ds_rslt[0];
+				disp_strength[i+1] = ds_rslt[1];
+			}
+			return disp_strength;
+		}
+		final int num_tiles = disp_strength_in[0].length;
+		int tilesY = num_tiles/tilesX;
+		final double [][] disp_strength = new double [2][num_tiles];
+//		final int low_lim = -smpl_side/2;
+//		final int high_lim = 3 * smpl_side/2;
+		final DoubleGaussianBlur gb=new DoubleGaussianBlur();
+		final int step = norm_center ? smpl_side : 1;
+		final int start = norm_center ? smpl_side/2 : 0;
+		for (int tY = start; tY < tilesY; tY += step){
+			for (int tX = start; tX < tilesX ; tX += step){
+				double [] hist = new double [num_bins];
+				int num_samples = 0;
+				for (int sY = -smpl_side; sY < smpl_side; sY++){
+					int y = tY + sY;
+					if ((y >= 0) && (y <tilesY)) {
+						for (int sX = -smpl_side; sX < smpl_side; sX++){
+							int x = tX + sX;
+							if ((x >= 0) && (x < tilesX)) {
+								int nTile = y * tilesX + x;
+								double w = disp_strength_in[1][nTile];
+								if (w > 0.0){
+									int bin = (int) ((disp_strength_in[0][nTile] - disp_min)/disp_step);
+									if ((bin >= 0) && (bin < num_bins)){
+										hist[bin] += w;
+										num_samples ++;
+									}
+								}
+							}
+						}
+					}
+				}
+				if (num_samples >= min_samples) {
+					if (sigma > 0.0){
+						gb.blur1Direction(hist, num_bins, 1, sigma/disp_step, 0.01,true);
+					}
+				}
+				// find first increase
+				int incr = -1;
+				for (int i = 0; i < (num_bins-2); i++) if (hist[i+1] > hist[i]){
+					incr = i;
+					break;
+				}
+
+				if (incr < 0) continue;
+				
+				// find first decrease after increase
+				
+				int decr = -1;
+				for (int i = incr+1; i < (num_bins-2); i++) if (hist[i+1] < hist[i]){
+					decr = i;
+					break;
+				}
+				if (decr < 0) continue;
+				
+				// now hist[decr] is a local max, lowest in the range
+				
+				double a = 0.5*(hist[decr + 1] -2*hist[decr] + hist[decr - 1]); 
+				double b = 0.5*(hist[decr + 1] - hist[decr - 1]);
+				double dx =  - b/(2*a);
+				if (dx > 1.0) dx = 1.0;
+				else if (dx < -1.0) dx = -1.0;
+				double disp_inf = disp_min + disp_step * (decr + dx);
+				if (norm_center) {
+					num_samples = 0;
+					double sdw = 0.0, sw = 0.0;
+					for (int sY = 0; sY < smpl_side; sY++){
+						int y = tY + sY;
+						for (int sX = 0; sX < smpl_side; sX++){
+							int x = tX + sX;
+							int nTile = y * tilesX + x;
+							double w = disp_strength_in[1][nTile];
+							if (w > 0.0){
+								if (Math.abs(disp_strength_in[0][nTile] - disp_inf) < max_diff) {
+									sw += disp_strength_in[1][nTile];
+									sdw +=disp_strength_in[0][nTile] * disp_strength_in[1][nTile];
+									num_samples++;
+								}
+							}
+						}
+					}
+					if ((num_samples > min_samples) && (sw > 0.0)){
+						int nTile = tY * tilesX + tX;
+						disp_strength[0][nTile] = sdw/sw; // weighted average disparity
+						disp_strength[1][nTile] = 1.0; // equal weight
+					}
+				} else {
+					// just center tile - fits or not
+					int nTile = tY * tilesX + tX;
+					double w = disp_strength_in[1][nTile];
+					if (w > 0.0){
+						if (Math.abs(disp_strength_in[0][nTile] - disp_inf) < max_diff) {
+							disp_strength[0][nTile] = disp_strength_in[0][nTile];
+							disp_strength[1][nTile] = disp_strength_in[1][nTile];
+						}
+					}
+					
+/*					
+					for (int sY = 0; sY < smpl_side; sY++){
+						int y = tY + sY;
+						for (int sX = 0; sX < smpl_side; sX++){
+							int x = tX + sX;
+							int nTile = y * tilesX + x;
+							double w = disp_strength_in[1][nTile];
+							if (w > 0.0){
+								if (Math.abs(disp_strength_in[0][nTile] - disp_inf) < max_diff) {
+									disp_strength[0][nTile] = disp_strength_in[0][nTile];
+									disp_strength[1][nTile] = disp_strength_in[1][nTile];
+								}
+							}
+						}
+					}
+*/					
+				}
+			}
+		}
+		return disp_strength;
+	}
+	public double[][] filterDisparityStrength (
+			final double[][] disp_strength_in,
+			final double     strength_floor,
+			final double     strength_pow,
+			final int        smplSide, //        = 2;      // Sample size (side of a square)
+			final int        smplNum, //         = 3;      // Number after removing worst (should be >1)
+			final double     smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+			final int        tilesX)
+	{
+		if (disp_strength_in.length > 2){
+			final double [][] disp_strength = new double [disp_strength_in.length][];
+			for (int i = 0; i < disp_strength_in.length; i+=2){
+				double [][] ds = {disp_strength_in[i],disp_strength_in[i+1]};
+				double [][] ds_rslt = filterDisparityStrength (
+						ds,
+						strength_floor,
+						strength_pow,
+						smplSide, //        = 2;      // Sample size (side of a square)
+						smplNum, //         = 3;      // Number after removing worst (should be >1)
+						smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+						tilesX);
+				disp_strength[i] =   ds_rslt[0];
+				disp_strength[i+1] = ds_rslt[1];
+			}
+			return disp_strength;
+		}
+
+		final int num_tiles = disp_strength_in[0].length;
+		int tilesY = num_tiles/tilesX;
+		final double [][] disp_strength = new double [2][num_tiles];
+		final int index_shift  = (smplSide /2) * (tilesX + 1) ; // diagonal shift
+		final int smplLen = smplSide*smplSide;
+
+		final double [] weight = new double [num_tiles]; // modified weight
+		final double [] disp = disp_strength_in[0];
+		final double smlVar = smplRms * smplRms; // maximal variance (weighted average of the squared difference from the mean)
+		for (int nTile = 0; nTile < num_tiles; nTile++){
+			double w = disp_strength_in[1][nTile] - strength_floor;
+			if (w > 0){
+				if (strength_pow != 1.0) w = Math.pow(w, strength_pow);
+				weight[nTile] = w;
+			}
+		}
+		for (int tY = 0; tY < (tilesY - smplSide); tY++){
+			for (int tX = 0; tX < (tilesX - smplSide); tX++){
+				int num_in_sample = 0;
+				boolean [] smpl_sel = new boolean [smplLen];
+				double [] smpl_d =  new double [smplLen];
+				double [] smpl_w =  new double [smplLen];
+				for (int sy = 0; sy < smplSide; sy++){
+					int y = tY + sy; //  - smpl_center;
+					for (int sx = 0; sx < smplSide; sx++){
+						int x = tX + sx; // - smpl_center;
+						int indx = y * tilesX + x;
+						if (weight[indx] > 0.0){
+							int indxs = sy * smplSide + sx;
+							smpl_sel[indxs] = true;
+							smpl_d[indxs] = disp[indx]; 
+							smpl_w[indxs] = weight[indx]; 
+							num_in_sample ++;
+						}
+					}
+				}
+				if (num_in_sample >= smplNum){ // try, remove worst
+					// calculate 
+					double sd=0.0, sd2 = 0.0, sw = 0.0;
+					for (int i = 0; i < smplLen; i++) if (smpl_sel[i]) {
+						double dw = smpl_d[i] * smpl_w[i];
+						sd += dw;
+						sd2 += dw * smpl_d[i];
+						sw +=       smpl_w[i];
+					}
+					// remove worst, update sd2, sd and sw
+					while ((num_in_sample > smplNum) && (sw > 0)){ // try, remove worst
+						double d_mean = sd/sw;
+						int iworst = -1;
+						double dworst2 = 0.0;
+						for (int i = 0; i < smplLen; i++) if (smpl_sel[i]) {
+							double d2 = (smpl_d[i] - d_mean);
+							d2 *=d2;
+							if (d2 > dworst2) {
+								iworst = i;
+								dworst2 = d2;
+							}
+						}
+						if (iworst < 0){
+							System.out.println("**** this is a BUG in filterDisparityStrength() ****");
+							break;
+						}
+						// remove worst sample
+						smpl_sel[iworst] = false;
+						double dw = smpl_d[iworst] * smpl_w[iworst];
+						sd -= dw; 
+						sd2 -= dw * smpl_d[iworst];
+						sw -=       smpl_w[iworst];
+						num_in_sample --;
+					}
+					// calculate variance of the remaining set
+					if (sw > 0.0) {
+						sd /= sw;
+						sd2 /= sw;
+						double var = sd2 - sd * sd;
+						if (var < smlVar) { // good, save in the result array
+							int indx = tY*tilesX + tX + index_shift;
+							disp_strength[0][indx] = sd;
+							disp_strength[1][indx] = sw;
+						}
+					} else {
+						num_in_sample = 0;
+						System.out.println("**** this is a BUG in getDisparityStrength(), shoud not happen ? ****");
+					}
+				}
+			}
+		}
+		return disp_strength;
+	}
+
+}

src/main/java/QuadCLT.java

@@ -21,7 +21,7 @@
  ** -----------------------------------------------------------------------------**
  **
  */
-import java.awt.Point;
+
 import java.awt.Polygon;
 import java.awt.Rectangle;
 import java.util.ArrayList;
@@ -3232,7 +3232,7 @@ public class QuadCLT {
 					  }
 				  }
 			  }
-			  if (clt_parameters.corr_mismatch || apply_corr){
+			  if (clt_parameters.corr_mismatch || apply_corr || infinity_corr){ // added infinity_corr
 				  clt_mismatch = new double [12][];
 			  }
 		  }
@@ -3425,6 +3425,23 @@ public class QuadCLT {
 						  disparity_map[ImageDtt.DISPARITY_INDEX_CM],
 						  disparity_map[ ImageDtt.DISPARITY_STRENGTH_INDEX]};
 				  String [] titles = {"disp_cm", "strength"};
+				  if (clt_mismatch != null){
+					  double [][] inf_ds1 = {
+							  disparity_map[ImageDtt.DISPARITY_INDEX_CM],
+							  disparity_map[ ImageDtt.DISPARITY_STRENGTH_INDEX],
+							  clt_mismatch[0], // dx0
+							  clt_mismatch[1], // dy0 +
+							  clt_mismatch[3], // dx1
+							  clt_mismatch[4], // dy1 +
+							  clt_mismatch[6], // dx2 +
+							  clt_mismatch[7], // dy2 
+							  clt_mismatch[9], // dx3 +
+							  clt_mismatch[10]};// dy3
+					  String [] titles1 = {"disp_cm", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
+					  inf_ds = inf_ds1;
+					  titles = titles1;
+				  }
+
 				  if (sdfa_instance != null){
 					  sdfa_instance.showArrays(
 							  inf_ds,
@@ -3434,24 +3451,31 @@ public class QuadCLT {
 							  name + "-inf_corr",
 							  titles );
 				  }
-				  double [][][] new_corr = infinityCorrection(
-						  clt_parameters,  // EyesisCorrectionParameters.CLTParameters           clt_parameters,
-						  inf_ds,   // double [][] disp_strength,
-						  tilesX, // int         tilesX,
-						  clt_parameters.corr_magic_scale, // double      magic_coeff, // still not understood coefficent that reduces reported disparity value.  Seems to be around 8.5  
-						  debugLevel + 1); // int debugLevel)
-				  
-				    if (debugLevel > -1){
-				    	System.out.println("Ready to apply infinity correction");
-				    	show_fine_corr(
-				    			new_corr, // double [][][] corr,
-				    			"");// String prefix)
-
-				    }
-				  
 				  
 				  
-				  if (debugLevel > 100) {				  
+				  if (debugLevel > 100) {					  
+					  double [][][] new_corr = infinityCorrection(
+							  clt_parameters.fcorr_inf_strength, //  final double min_strenth,
+							  clt_parameters.fcorr_inf_diff, // final double max_diff,
+							  20, // 0, // final int max_iterations,
+							  0.0001, // final double max_coeff_diff,
+							  clt_parameters,  // EyesisCorrectionParameters.CLTParameters           clt_parameters,
+							  inf_ds,   // double [][] disp_strength,
+							  tilesX, // int         tilesX,
+							  clt_parameters.corr_magic_scale, // double      magic_coeff, // still not understood coefficent that reduces reported disparity value.  Seems to be around 8.5  
+							  debugLevel + 1); // int debugLevel)
+
+					  if (debugLevel > -1){
+						  System.out.println("Ready to apply infinity correction");
+						  show_fine_corr(
+								  new_corr, // double [][][] corr,
+								  "");// String prefix)
+
+					  }
+
+
+
+
 					  apply_fine_corr(
 							  new_corr,
 							  debugLevel + 1);
@@ -4121,8 +4145,14 @@ public class QuadCLT {
 		  }
 		  return coeff_full;
 	  }
+
 	  /**
 	   * Calculate quadratic polynomials for each subcamera X/Y correction to match disparity = 0 at infinity
+	   * Next parameters are made separate to be able to modify them between different runs keeping clt_parameters
+	   * @param min_strenth minimal correlation strength to use tile
+	   * @param max_diff maximal disparity difference between tiles and previous approximation to use tiles
+	   * @param max_iterations maximal number of iterations to find disparity surface
+	   * @param max_coeff_diff coefficients maximal change to continue iterations
 	   * @param clt_parameters CLT parameters
 	   * @param disp_strength array of a single or multiple disparity/strength pairs (0,2, .. - disparity,
 	   *  1,3,.. - corresponding strengths
@@ -4132,29 +4162,194 @@ public class QuadCLT {
 	   * @return per sub-camera, per direction (x,y) 6 quadratic polynomial coefficients, same format as fine_geometry_correction()
 	   */
 	  public double [][][] infinityCorrection(
+			  final double min_strenth,
+			  final double max_diff,
+			  final int max_iterations,
+			  final double max_coeff_diff,
 			  EyesisCorrectionParameters.CLTParameters           clt_parameters,
 			  double [][] disp_strength,
 			  int         tilesX,
 			  double      magic_coeff, // still not understood coefficient that reduces reported disparity value.  Seems to be around 8.5  
 			  int debugLevel)
 	  {
-		  ArrayList<Point> samples_list = new ArrayList<Point>();
+		  final double far_pull = 0.2; // 1; //  0.5;
 		  final int numTiles =            disp_strength[0].length;
 		  final int tilesY =              numTiles/tilesX;
-		  for (int num_set = 0; num_set < disp_strength.length/2; num_set++){
-			  int disp_index = 2 * num_set;
-			  int str_index = 2 * num_set+1;
+		  double [] disparity_poly = new double[6];
+		  PolynomialApproximation pa = new PolynomialApproximation();
+		  double thresholdLin = 1.0E-20;  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
+		  double thresholdQuad = 1.0E-30; // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+		  double [] disp_surface = new double[numTiles];
+//		  double [] corr_weight = new double [numTiles]; // reduce weight for far tiles (by more than range farther than surface)
+		  class Sample{
+			  int series;
+			  int tile;
+			  double weight;
+			  Sample(int series, int tile, double weight) {
+				  this.series = series;
+				  this.tile =  tile;
+				  this.weight = weight;
+				  
+			  }
+		  }
+		  ArrayList<Sample> samples_list = new ArrayList<Sample>();
+		  for (int pass = 0; pass < max_iterations; pass++){
 			  for (int nTile = 0; nTile < numTiles; nTile++){
-				  if ((disp_strength[str_index][nTile] >= clt_parameters.fcorr_inf_strength) && 
-						  (Math.abs(disp_strength[disp_index][nTile]) < clt_parameters.fcorr_inf_diff)){
-					  samples_list.add(new Point(num_set, nTile));
+				  int tileX = nTile % tilesX;
+				  int tileY = nTile / tilesX;
+				  double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+				  double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+				  disp_surface[nTile] =
+						  disparity_poly[0] * x * x +
+						  disparity_poly[1] * y * y +
+						  disparity_poly[2] * x * y +
+						  disparity_poly[3] * x +
+						  disparity_poly[4] * y +
+						  disparity_poly[5];
+			  }
+			  
+			  samples_list.clear();
+			  for (int num_set = 0; num_set < disp_strength.length/2; num_set++){
+				  int disp_index = 2 * num_set;
+				  int str_index = 2 * num_set+1;
+				  for (int nTile = 0; nTile < numTiles; nTile++){
+					  if ((disp_strength[str_index][nTile] >= min_strenth) && 
+//							  (Math.abs(disp_strength[disp_index][nTile] - disp_surface[nTile]) < max_diff)){
+						  ((disp_strength[disp_index][nTile] - disp_surface[nTile]) < max_diff)){
+						  double weight= disp_strength[str_index][nTile];
+						  // next are for far tiles, that differ by more than max_diff
+						  if (Math.abs(disp_strength[disp_index][nTile] - disp_surface[nTile]) > max_diff){
+							  weight *= far_pull;
+						  }
+						  samples_list.add(new Sample(num_set, nTile, weight));
+					  }
+				  }
+			  }
+			  double [][][] mdata;
+			  if (clt_parameters.fcorr_inf_vert) {
+				  mdata = new double[samples_list.size()][3][];
+			  } else {
+				  mdata = new double [1][samples_list.size()][3];
+			  }
+			  
+			  int indx = 0;
+			  for (Sample s: samples_list){
+				  int tileX = s.tile % tilesX;
+				  int tileY = s.tile / tilesX;
+				  if (clt_parameters.fcorr_inf_vert) {
+					  mdata[indx][0] = new double [2];
+					  mdata[indx][0][0] =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					  mdata[indx][0][1] =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+					  mdata[indx][1] = new double [1];
+					  mdata[indx][1][0] =  (disp_strength[2 * s.series + 0][s.tile]/magic_coeff - disp_surface[s.tile]); // disparity residual
+					  mdata[indx][2][0] =  s.weight; // disp_strength[2 * s.series + 1][s.tile]; // strength
+//					  if (Math.abs( mdata[indx][1][0]) > max_diff) { // far tiles
+//						  mdata[indx][2][0] *= far_pull;
+//					  }
+
+				  } else {
+						  mdata[0][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+						  mdata[0][indx][1] = (disp_strength[2 * s.series + 0][s.tile]/magic_coeff - disp_surface[s.tile]); // disparity residual
+						  mdata[0][indx][2] = s.weight; // disp_strength[2 * s.series + 1][s.tile]; // strength
+//						  if (Math.abs( mdata[0][indx][1]) > max_diff) { // far tiles
+//							  mdata[0][indx][2] *= far_pull;
+//						  }
+				  }
+				  indx ++;
+			  }
+			  if ((debugLevel > -1) && (pass < 20)){
+				  String [] titles = {"disparity","approx","diff", "strength"};
+				  double [][] dbg_img = new double [titles.length][numTiles];
+				  for (int nTile = 0; nTile < numTiles; nTile++){
+					  int tileX = nTile % tilesX;
+					  int tileY = nTile / tilesX;
+					  double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
+					  double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
+					  dbg_img[1][nTile] =
+							  disparity_poly[0] * x * x +
+							  disparity_poly[1] * y * y +
+							  disparity_poly[2] * x * y +
+							  disparity_poly[3] * x +
+							  disparity_poly[4] * y +
+							  disparity_poly[5];
+				  }
+
+				  for (Sample s: samples_list){
+					  dbg_img[0][s.tile] += disp_strength[2 * s.series][s.tile] * s.weight; // disp_strength[2 * p.x + 1][p.y];
+					  dbg_img[3][s.tile] += s.weight; // disp_strength[2 * p.x + 1][p.y];
+				  }
+				  for (int nTile = 0; nTile < numTiles; nTile++) {
+					  if (dbg_img[3][nTile] > 0.0) {
+						  dbg_img[0][nTile] /=dbg_img[3][nTile];
+					  } else {
+						  dbg_img[0][nTile] = Double.NaN;
+					  }
+				  }
+				  
+				  for (int nTile = 0; nTile < numTiles; nTile++) {
+					  if (dbg_img[3][nTile] > 0.0) {
+						  dbg_img[2][nTile] = dbg_img[0][nTile] - dbg_img[1][nTile];
+					  } else {
+						  dbg_img[2][nTile] = Double.NaN;
+					  }
+				  }
+
+				  (new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "infinity_"+pass, titles);
+				  
+				  
+				  
+				  
+			  }
+			  
+			  
+			  double [][] coeffs = new double[1][6];
+			  if (clt_parameters.fcorr_inf_vert){
+				  double[][] approx2d = pa.quadraticApproximation(
+						  mdata,
+						  !clt_parameters.fcorr_inf_quad, // boolean forceLinear,  // use linear approximation
+						  thresholdLin,  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
+						  thresholdQuad,  // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+						  debugLevel); {
+							  
+						  }
+				  if (approx2d[0].length == 6) {
+					  coeffs = approx2d;
+				  } else {
+					  for (int i = 0; i < 3; i++){
+						  coeffs[0][3+i] = approx2d[0][i];
+					  }
+				  }
+			  } else {
+				  double [] approx1d = pa.polynomialApproximation1d(mdata[0], clt_parameters.fcorr_inf_quad ? 2 : 1);
+				  //			  disparity_approximation = new double[6];
+				  coeffs[0][5] = approx1d[0];
+				  coeffs[0][3] = approx1d[1];
+				  if (approx1d.length > 2){
+					  coeffs[0][0] = approx1d[2];
+				  }
+			  }
+			  if (debugLevel > -1){
+				  System.out.println(String.format(
+						  "infinityCorrection() disparity pass=%03d A=%8.5f B=%8.5f C=%8.5f D=%8.5f E=%8.5f F=%8.5f",
+						  pass, disparity_poly[0], disparity_poly[1], disparity_poly[2],
+						  disparity_poly[3], disparity_poly[4], disparity_poly[5]) );
+			  }
+			  boolean all_done = true;
+			  for (int i = 0; i < disparity_poly.length; i++){
+				  if (Math.abs(coeffs[0][i]) > max_coeff_diff){
+					  all_done = false;
+					  break;
 				  }
 			  }
 			  
+			  if (all_done) break;
+			  for (int i = 0; i < disparity_poly.length; i++){
+				  disparity_poly[i] += coeffs[0][i];
+			  }			  
 		  }
-		  PolynomialApproximation pa = new PolynomialApproximation();
-		  double thresholdLin = 1.0E-20;  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
-		  double thresholdQuad = 1.0E-30; // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
+		  
+		  // use last generated samples_list;
+		  
 		  double [][][] mdata;
 		  if (clt_parameters.fcorr_inf_vert) {
 			  mdata = new double[samples_list.size()][3][];
@@ -4162,15 +4357,15 @@ public class QuadCLT {
 			  mdata = new double [8][samples_list.size()][3];
 		  }
 		  int indx = 0;
-		  for (Point p: samples_list){
-			  int tileX = p.y % tilesX;
-			  int tileY = p.y / tilesX;
+		  for (Sample s: samples_list){
+			  int tileX = s.tile % tilesX;
+			  int tileY = s.tile / tilesX;
 			  double centerX = tileX * tp.getTileSize() + tp.getTileSize()/2;// - shiftX;
 			  double centerY = tileY * tp.getTileSize() + tp.getTileSize()/2;//- shiftY;
 			  double [][] centersXY_disp = geometryCorrection.getPortsCoordinates(
 					  centerX,
 					  centerY,
-					  disp_strength[2 * p.x + 0][p.y]/magic_coeff); // disparity
+					  disp_strength[2 * s.series + 0][s.tile]/magic_coeff); // disparity
 			  double [][] centersXY_inf = geometryCorrection.getPortsCoordinates(
 					  centerX,
 					  centerY,
@@ -4185,18 +4380,16 @@ public class QuadCLT {
 				  mdata[indx][0][1] =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
 				  mdata[indx][1] = new double [8];
 				  for (int n = 0; n < 8; n++){
-//					  mdata[indx][1][n] =   centersXY_disp[n / 2][n % 2];
 					  mdata[indx][1][n] =   -centersXY_disp[n / 2][n % 2];
 				  }
 				  mdata[indx][2] = new double [1];
-				  mdata[indx][2][0] =  disp_strength[2 * p.x + 1][p.y]; // strength
+				  mdata[indx][2][0] = s.weight; //  disp_strength[2 * p.x + 1][p.y]; // strength
 
 			  } else {
 				  for (int n = 0; n < 8; n++){
 					  mdata[n][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
-//					  mdata[n][indx][1] = centersXY_disp[n / 2][n % 2];
 					  mdata[n][indx][1] = -centersXY_disp[n / 2][n % 2];
-					  mdata[n][indx][2] = disp_strength[2 * p.x + 1][p.y]; // strength
+					  mdata[n][indx][2] = s.weight; // disp_strength[2 * p.x + 1][p.y]; // strength
 				  }
 			  }
 			  indx ++;
@@ -4218,7 +4411,6 @@ public class QuadCLT {
 					  }
 				  }
 			  }
-//			  disparity_approximation = coeffs[0];
 		  } else {
 			  for (int n = 0; n < 8; n++){
 				  double [] approx1d = pa.polynomialApproximation1d(mdata[n], clt_parameters.fcorr_inf_quad ? 2 : 1);
@@ -4229,7 +4421,6 @@ public class QuadCLT {
 					  coeffs[n][0] = approx1d[2];
 				  }
 			  }
-			  
 		  }
 		  
 		  double [][][] inf_corr = new double [4][2][];
@@ -4238,55 +4429,6 @@ public class QuadCLT {
 		  }		  
 
 		  if (debugLevel > 0) {
-			  indx = 0;
-			  for (Point p: samples_list){
-				  int tileX = p.y % tilesX;
-				  int tileY = p.y / tilesX;
-				  if (clt_parameters.fcorr_inf_vert) {
-					  mdata[indx][0] = new double [2];
-					  mdata[indx][0][0] =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
-					  mdata[indx][0][1] =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
-					  mdata[indx][1] = new double [1];
-					  mdata[indx][1][0] =  disp_strength[2 * p.x + 0][p.y]/magic_coeff; // disparity
-					  mdata[indx][2][0] =  disp_strength[2 * p.x + 1][p.y]; // strength
-
-				  } else {
-						  mdata[0][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
-						  mdata[0][indx][1] = disp_strength[2 * p.x + 0][p.y]/magic_coeff; // disparity
-						  mdata[0][indx][2] = disp_strength[2 * p.x + 1][p.y]; // strength
-				  }
-				  indx ++;
-			  }
-			  
-			  coeffs = new double[1][6];
-			  if (clt_parameters.fcorr_inf_vert){
-				  double[][] approx2d = pa.quadraticApproximation(
-						  mdata,
-						  !clt_parameters.fcorr_inf_quad, // boolean forceLinear,  // use linear approximation
-						  thresholdLin,  // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
-						  thresholdQuad,  // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
-						  debugLevel); {
-							  
-						  }
-				  if (approx2d[0].length == 6) {
-					  coeffs = approx2d;
-				  } else {
-					  for (int i = 0; i < 3; i++){
-						  coeffs[0][3+i] = approx2d[0][i];
-					  }
-				  }
-//				  disparity_approximation = coeffs[0];
-			  } else {
-				  double [] approx1d = pa.polynomialApproximation1d(mdata[0], clt_parameters.fcorr_inf_quad ? 2 : 1);
-				  //			  disparity_approximation = new double[6];
-				  coeffs[0][5] = approx1d[0];
-				  coeffs[0][3] = approx1d[1];
-				  if (approx1d.length > 2){
-					  coeffs[0][0] = approx1d[2];
-				  }
-				  
-			  }
-			  
 			  String [] titles = {"disparity","approx","diff", "strength"};
 			  double [][] dbg_img = new double [titles.length][numTiles];
 			  for (int nTile = 0; nTile < numTiles; nTile++){
@@ -4295,18 +4437,19 @@ public class QuadCLT {
 				  double x =  (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
 				  double y =  (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
 				  dbg_img[1][nTile] =
-						  coeffs[0][0] * x * x +
-						  coeffs[0][1] * y * y +
-						  coeffs[0][2] * x * y +
-						  coeffs[0][3] * x +
-						  coeffs[0][4] * y +
-						  coeffs[0][5];
+						  disparity_poly[0] * x * x +
+						  disparity_poly[1] * y * y +
+						  disparity_poly[2] * x * y +
+						  disparity_poly[3] * x +
+						  disparity_poly[4] * y +
+						  disparity_poly[5];
 			  }
 
-			  for (Point p: samples_list){
-				  dbg_img[0][p.y] += disp_strength[2 * p.x][p.y] * disp_strength[2 * p.x + 1][p.y];
-				  dbg_img[3][p.y] += disp_strength[2 * p.x + 1][p.y];
+			  for (Sample s: samples_list){
+				  dbg_img[0][s.tile] += disp_strength[2 * s.series][s.tile] * s.weight; // disp_strength[2 * p.x + 1][p.y];
+				  dbg_img[3][s.tile] += s.weight; // disp_strength[2 * s.series + 1][s.tile];
 			  }
+			  
 			  for (int nTile = 0; nTile < numTiles; nTile++) {
 				  if (dbg_img[3][nTile] > 0.0) {
 					  dbg_img[0][nTile] /=dbg_img[3][nTile];
@@ -4325,7 +4468,6 @@ public class QuadCLT {
 
 			  (new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "infinityCorrection", titles);
 
-
 		  }
 		  return inf_corr;
 	  }	  
@@ -5090,13 +5232,46 @@ public class QuadCLT {
 		    if (debugLevel > -1){
 		    	System.out.println("process_infinity_corr(): proocessing "+num_scans+" disparity/strength pairs");
 		    }
+		    /*
 		    double [][][] new_corr = infinityCorrection(
+		    		clt_parameters.fcorr_inf_strength, //  final double min_strenth,
+		    		clt_parameters.fcorr_inf_diff, // final double max_diff,
+		    		20, // 0, // final int max_iterations,
+		    		0.0001, // final double max_coeff_diff,
 		    		clt_parameters,  // EyesisCorrectionParameters.CLTParameters           clt_parameters,
 		    		inf_disp_strength,   // double [][] disp_strength,
 		    		tilesX, // int         tilesX,
 		    		clt_parameters.corr_magic_scale, // double      magic_coeff, // still not understood coefficent that reduces reported disparity value.  Seems to be around 8.5  
 		    		debugLevel + 1); // int debugLevel)
+		    		
+*/		    		
+		    AlignmentCorrection ac = new AlignmentCorrection(this);
+		    double [][][] new_corr = ac.infinityCorrection(
+		    		clt_parameters.fcorr_inf_strength, //  final double min_strenth,
+		    		clt_parameters.fcorr_inf_diff, // final double max_diff,
+		    		20, // 0, // final int max_iterations,
+		    		0.0001, // final double max_coeff_diff,
+					0.0, // 0.25, //   final double far_pull, //  = 0.2; // 1; //  0.5;
+					1.0, //   final double     strength_pow,
+					3, //   final int        smplSide, //        = 2;      // Sample size (side of a square)
+					5, //   final int        smplNum, //         = 3;      // Number after removing worst (should be >1)
+					0.1, // 0.05, //  final double     smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+					// histogram parameters
+					8,    // final int        hist_smpl_side, // 8 x8 masked, 16x16 sampled
+					-1.0, // final double     hist_disp_min,
+					0.05, // final double     hist_disp_step,
+					40,   // final int        hist_num_bins,
+					0.1,  // final double     hist_sigma,
+					0.1,  // final double     hist_max_diff,
+					10,   // final int        hist_min_samples,
+					true, // final boolean    hist_norm_center, // if there are more tiles that fit than min_samples, replace with 			
+					clt_parameters,  // EyesisCorrectionParameters.CLTParameters           clt_parameters,
+		    		inf_disp_strength,   // double [][] disp_strength,
+		    		tilesX, // int         tilesX,
+		    		clt_parameters.corr_magic_scale, // double      magic_coeff, // still not understood coefficent that reduces reported disparity value.  Seems to be around 8.5  
+		    		debugLevel + 1); // int debugLevel)
 
+		    
 		    if (debugLevel > -1){
 		    	System.out.println("process_infinity_corr(): ready to apply infinity correction");
 		    	show_fine_corr(
@@ -5110,7 +5285,33 @@ public class QuadCLT {
 		    			new_corr,
 		    			debugLevel + 2);
 		    }
-	        
+/*		    
+		    double [][][] new_corr1 = ac.infinityCorrection(
+		    		clt_parameters.fcorr_inf_strength, //  final double min_strenth,
+		    		clt_parameters.fcorr_inf_diff, // final double max_diff,
+		    		20, // 0, // final int max_iterations,
+		    		0.0001, // final double max_coeff_diff,
+					0.25, //   final double far_pull, //  = 0.2; // 1; //  0.5;
+					1.0, //   final double     strength_pow,
+					3, //   final int        smplSide, //        = 2;      // Sample size (side of a square)
+					5, //   final int        smplNum, //         = 3;      // Number after removing worst (should be >1)
+					0.05, //  final double     smplRms, //         = 0.1;    // Maximal RMS of the remaining tiles in a sample
+					// histogram parameters
+					8,    // final int        hist_smpl_side, // 8 x8 masked, 16x16 sampled
+					-1.0, // final double     hist_disp_min,
+					0.05, // final double     hist_disp_step,
+					40,   // final int        hist_num_bins,
+					0.1,  // final double     hist_sigma,
+					0.1,  // final double     hist_max_diff,
+					10,   // final int        hist_min_samples,
+					false, // final boolean    hist_norm_center, // if there are more tiles that fit than min_samples, replace with 			
+					clt_parameters,  // EyesisCorrectionParameters.CLTParameters           clt_parameters,
+		    		inf_disp_strength,   // double [][] disp_strength,
+		    		tilesX, // int         tilesX,
+		    		clt_parameters.corr_magic_scale, // double      magic_coeff, // still not understood coefficent that reduces reported disparity value.  Seems to be around 8.5  
+		    		debugLevel + 1); // int debugLevel)
+		    
+	        */
 	  }	  
 	  public void process_fine_corr(
 			  boolean dry_run,

src/main/java/TileAssignment.java

@@ -578,8 +578,10 @@ diff_best= 0.06731 diff9=  1.09087 weak_fgnd= 0.22250 flaps= 0.07229 ml_mismatch
 	{
 		if ((dispStrength[ml] == null) || (dispStrength[ml][nSurfTile] == null)) {
 				String msg="dispDiff(): no data for ml="+ml+", nSurfTile="+nSurfTile+", ns="+ns;
+				System.out.println(msg);
+				return Double.NaN;
 //				IJ.showMessage(msg);
-				throw new IllegalArgumentException (msg);
+//				throw new IllegalArgumentException (msg);
 		}
 		double dm = dispStrength[ml][nSurfTile][0];
 		double ds = ts.getTileData()[nSurfTile][ns].getDisparity();