fixed lazy eye correction (21cc4033) · Commits · Elphel / imagej-elphel

src/main/java/AlignmentCorrection.java

+1036 −47

File changed.

Preview size limit exceeded, changes collapsed.

src/main/java/EyesisCorrectionParameters.java

+109 −4

Original line number	Original line	Diff line number	Diff line
	@@ -2008,20 +2008,41 @@ public class EyesisCorrectionParameters {
	public double fcorr_disp_diff = 1.5; // consider only tiles with absolute residual disparity lower than		public double fcorr_disp_diff = 1.5; // consider only tiles with absolute residual disparity lower than
	public boolean fcorr_quadratic = true; // Use quadratic polynomial for fine correction (false - only linear)		public boolean fcorr_quadratic = true; // Use quadratic polynomial for fine correction (false - only linear)
	public boolean fcorr_ignore = false; // Ignore currently calculated fine correction		public boolean fcorr_ignore = false; // Ignore currently calculated fine correction

	public double fcorr_inf_strength = 0.20 ; // Minimal correlation strength to use for infinity correction		public double fcorr_inf_strength = 0.20 ; // Minimal correlation strength to use for infinity correction
	public double fcorr_inf_diff = 0.2; // Disparity half-range for infinity		public double fcorr_inf_diff = 0.2; // Disparity half-range for infinity
	public boolean fcorr_inf_quad = true; // Use quadratic polynomial for infinity correction (false - only linear)		public boolean fcorr_inf_quad = true; // Use quadratic polynomial for infinity correction (false - only linear)
	public boolean fcorr_inf_vert = false; // Correct infinity in vertical direction (false - only horizontal)		public boolean fcorr_inf_vert = false; // Correct infinity in vertical direction (false - only horizontal)

			//--
			public boolean inf_disp_apply = true; // Apply disparity correction to zero at infinity
			public boolean inf_mism_apply = true; // Apply lazy eye correction at infinity

			public int inf_iters = 20; // Infinity extraction - maximum iterations
			public double inf_final_diff = 0.0001; // Coefficients maximal increment to exit iterations
			public double inf_far_pull = 0.0; // include farther tiles than tolerance, but scale their weights

			// infinity filter
			public double inf_str_pow = 1.0; // Strength power for infinity filtering
			public int inf_smpl_side = 3; // Sample size (side of a square) for infinity filtering
			public int inf_smpl_num = 5; // Number after removing worst (should be >1) for infinity filtering
			public double inf_smpl_rms = 0.1; // Maximal RMS of the remaining tiles in a sample for infinity filtering

			//Histogram infinity filter
			public int ih_smpl_step = 8; // Square sample step (50% overlap)
			public double ih_disp_min = -1.0; // Minimal disparity
			public double ih_disp_step = 0.05; // Disparity step
			public int ih_num_bins = 40; // Number of bins
			public double ih_sigma = 0.1; // Gaussian sigma (in disparity pixels)
			public double ih_max_diff = 0.1; // Keep samples within this difference from farthest maximum
			public int ih_min_samples = 10; // Minimal number of remaining samples
			public boolean ih_norm_center = true; // Replace samples with a single average with equal weight

			// old fcorr parameters, reuse?
	public int fcorr_sample_size = 32; // Use square this size side to detect outliers		public int fcorr_sample_size = 32; // Use square this size side to detect outliers
	public int fcorr_mintiles = 8; // Keep tiles only if there are more in each square		public int fcorr_mintiles = 8; // Keep tiles only if there are more in each square
	public double fcorr_reloutliers = 0.5; // Remove this fraction of tiles from each sample		public double fcorr_reloutliers = 0.5; // Remove this fraction of tiles from each sample
	public double fcorr_sigma = 20.0; // Gaussian blur channel mismatch data		public double fcorr_sigma = 20.0; // Gaussian blur channel mismatch data




	public double corr_magic_scale = 0.85; // reported correlation offset vs. actual one (not yet understood)		public double corr_magic_scale = 0.85; // reported correlation offset vs. actual one (not yet understood)

	// 3d reconstruction		// 3d reconstruction
	@@ -2525,6 +2546,25 @@ public class EyesisCorrectionParameters {
	properties.setProperty(prefix+"fcorr_inf_quad", this.fcorr_inf_quad+"");		properties.setProperty(prefix+"fcorr_inf_quad", this.fcorr_inf_quad+"");
	properties.setProperty(prefix+"fcorr_inf_vert", this.fcorr_inf_vert+"");		properties.setProperty(prefix+"fcorr_inf_vert", this.fcorr_inf_vert+"");

			properties.setProperty(prefix+"inf_disp_apply", this.inf_disp_apply+"");
			properties.setProperty(prefix+"inf_mism_apply", this.inf_mism_apply+"");
			properties.setProperty(prefix+"inf_iters", this.inf_iters+"");
			properties.setProperty(prefix+"inf_final_diff", this.inf_final_diff +"");
			properties.setProperty(prefix+"inf_far_pull", this.inf_far_pull +"");
			properties.setProperty(prefix+"inf_str_pow", this.inf_str_pow +"");
			properties.setProperty(prefix+"inf_smpl_side", this.inf_smpl_side+"");
			properties.setProperty(prefix+"inf_smpl_num", this.inf_smpl_num+"");
			properties.setProperty(prefix+"inf_smpl_rms", this.inf_smpl_rms +"");

			properties.setProperty(prefix+"ih_smpl_step", this.ih_smpl_step+"");
			properties.setProperty(prefix+"ih_disp_min", this.ih_disp_min +"");
			properties.setProperty(prefix+"ih_disp_step", this.ih_disp_step +"");
			properties.setProperty(prefix+"ih_num_bins", this.ih_num_bins+"");
			properties.setProperty(prefix+"ih_sigma", this.ih_sigma +"");
			properties.setProperty(prefix+"ih_max_diff", this.ih_max_diff +"");
			properties.setProperty(prefix+"ih_min_samples", this.ih_min_samples+"");
			properties.setProperty(prefix+"ih_norm_center", this.ih_norm_center+"");

	properties.setProperty(prefix+"fcorr_sample_size",this.fcorr_sample_size+"");		properties.setProperty(prefix+"fcorr_sample_size",this.fcorr_sample_size+"");
	properties.setProperty(prefix+"fcorr_mintiles", this.fcorr_mintiles+"");		properties.setProperty(prefix+"fcorr_mintiles", this.fcorr_mintiles+"");
	properties.setProperty(prefix+"fcorr_reloutliers",this.fcorr_reloutliers +"");		properties.setProperty(prefix+"fcorr_reloutliers",this.fcorr_reloutliers +"");
	@@ -2995,6 +3035,30 @@ public class EyesisCorrectionParameters {
	if (properties.getProperty(prefix+"fcorr_inf_quad")!=null) this.fcorr_inf_quad=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_quad"));		if (properties.getProperty(prefix+"fcorr_inf_quad")!=null) this.fcorr_inf_quad=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_quad"));
	if (properties.getProperty(prefix+"fcorr_inf_vert")!=null) this.fcorr_inf_vert=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_vert"));		if (properties.getProperty(prefix+"fcorr_inf_vert")!=null) this.fcorr_inf_vert=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_vert"));



			if (properties.getProperty(prefix+"inf_disp_apply")!=null) this.inf_disp_apply=Boolean.parseBoolean(properties.getProperty(prefix+"inf_disp_apply"));
			if (properties.getProperty(prefix+"inf_mism_apply")!=null) this.inf_mism_apply=Boolean.parseBoolean(properties.getProperty(prefix+"inf_mism_apply"));
			if (properties.getProperty(prefix+"inf_iters")!=null) this.inf_iters=Integer.parseInt(properties.getProperty(prefix+"inf_iters"));
			if (properties.getProperty(prefix+"inf_final_diff")!=null) this.inf_final_diff=Double.parseDouble(properties.getProperty(prefix+"inf_final_diff"));
			if (properties.getProperty(prefix+"inf_far_pull")!=null) this.inf_far_pull=Double.parseDouble(properties.getProperty(prefix+"inf_far_pull"));

			if (properties.getProperty(prefix+"inf_str_pow")!=null) this.inf_str_pow=Double.parseDouble(properties.getProperty(prefix+"inf_str_pow"));
			if (properties.getProperty(prefix+"inf_smpl_side")!=null) this.inf_smpl_side=Integer.parseInt(properties.getProperty(prefix+"inf_smpl_side"));
			if (properties.getProperty(prefix+"inf_smpl_num")!=null) this.inf_smpl_num=Integer.parseInt(properties.getProperty(prefix+"inf_smpl_num"));
			if (properties.getProperty(prefix+"inf_smpl_rms")!=null) this.inf_smpl_rms=Double.parseDouble(properties.getProperty(prefix+"inf_smpl_rms"));

			if (properties.getProperty(prefix+"ih_smpl_step")!=null) this.ih_smpl_step=Integer.parseInt(properties.getProperty(prefix+"ih_smpl_step"));
			if (properties.getProperty(prefix+"ih_disp_min")!=null) this.ih_disp_min=Double.parseDouble(properties.getProperty(prefix+"ih_disp_min"));
			if (properties.getProperty(prefix+"ih_disp_step")!=null) this.ih_disp_step=Double.parseDouble(properties.getProperty(prefix+"ih_disp_step"));
			if (properties.getProperty(prefix+"ih_num_bins")!=null) this.ih_num_bins=Integer.parseInt(properties.getProperty(prefix+"ih_num_bins"));
			if (properties.getProperty(prefix+"ih_sigma")!=null) this.ih_sigma=Double.parseDouble(properties.getProperty(prefix+"ih_sigma"));
			if (properties.getProperty(prefix+"ih_max_diff")!=null) this.ih_max_diff=Double.parseDouble(properties.getProperty(prefix+"ih_max_diff"));
			if (properties.getProperty(prefix+"ih_min_samples")!=null) this.ih_min_samples=Integer.parseInt(properties.getProperty(prefix+"ih_min_samples"));
			if (properties.getProperty(prefix+"ih_norm_center")!=null) this.ih_norm_center=Boolean.parseBoolean(properties.getProperty(prefix+"ih_norm_center"));



	if (properties.getProperty(prefix+"fcorr_sample_size")!=null) this.fcorr_sample_size=Integer.parseInt(properties.getProperty(prefix+"fcorr_sample_size"));		if (properties.getProperty(prefix+"fcorr_sample_size")!=null) this.fcorr_sample_size=Integer.parseInt(properties.getProperty(prefix+"fcorr_sample_size"));
	if (properties.getProperty(prefix+"fcorr_mintiles")!=null) this.fcorr_mintiles=Integer.parseInt(properties.getProperty(prefix+"fcorr_mintiles"));		if (properties.getProperty(prefix+"fcorr_mintiles")!=null) this.fcorr_mintiles=Integer.parseInt(properties.getProperty(prefix+"fcorr_mintiles"));
	if (properties.getProperty(prefix+"fcorr_reloutliers")!=null) this.fcorr_reloutliers=Double.parseDouble(properties.getProperty(prefix+"fcorr_reloutliers"));		if (properties.getProperty(prefix+"fcorr_reloutliers")!=null) this.fcorr_reloutliers=Double.parseDouble(properties.getProperty(prefix+"fcorr_reloutliers"));
	@@ -3480,6 +3544,27 @@ public class EyesisCorrectionParameters {
	gd.addCheckbox ("Use quadratic polynomial for infinity correction (false - only linear)", this.fcorr_inf_quad);		gd.addCheckbox ("Use quadratic polynomial for infinity correction (false - only linear)", this.fcorr_inf_quad);
	gd.addCheckbox ("Correct infinity in vertical direction (false - only horizontal)", this.fcorr_inf_vert);		gd.addCheckbox ("Correct infinity in vertical direction (false - only horizontal)", this.fcorr_inf_vert);


			gd.addCheckbox ("Apply disparity correction to zero at infinity", this.inf_disp_apply);
			gd.addCheckbox ("Apply lazy eye correction at infinity", this.inf_mism_apply);
			gd.addNumericField("Infinity extraction - maximum iterations", this.inf_iters, 0);
			gd.addNumericField("Coefficients maximal increment to exit iterations", this.inf_final_diff, 6);
			gd.addNumericField("Include farther tiles than tolerance, but scale their weights", this.inf_far_pull, 3);
			gd.addMessage ("--- Infinity filter ---");
			gd.addNumericField("Strength power", this.inf_str_pow, 3);
			gd.addNumericField("Sample size (side of a square)", this.inf_smpl_side, 0);
			gd.addNumericField("Number after removing worst (should be >1)", this.inf_smpl_num, 0);
			gd.addNumericField("Maximal RMS of the remaining tiles in a sample", this.inf_smpl_rms, 3);
			gd.addMessage ("--- Infinity histogram filter ---");
			gd.addNumericField("Square sample step (50% overlap)", this.ih_smpl_step, 0);
			gd.addNumericField("Histogram minimal disparity", this.ih_disp_min, 3);
			gd.addNumericField("Histogram disparity step", this.ih_disp_step, 3);
			gd.addNumericField("Histogram number of bins", this.ih_num_bins, 0);
			gd.addNumericField("Histogram Gaussian sigma (in disparity pixels)", this.ih_sigma, 3);
			gd.addNumericField("Keep samples within this difference from farthest maximum", this.ih_max_diff, 3);
			gd.addNumericField("Minimal number of remaining samples", this.ih_min_samples, 0);
			gd.addCheckbox ("Replace samples with a single average with equal weight", this.ih_norm_center);

	gd.addNumericField("Use square this size side to detect outliers", this.fcorr_sample_size, 0);		gd.addNumericField("Use square this size side to detect outliers", this.fcorr_sample_size, 0);
	gd.addNumericField("Keep tiles only if there are more in each square", this.fcorr_mintiles, 0);		gd.addNumericField("Keep tiles only if there are more in each square", this.fcorr_mintiles, 0);
	gd.addNumericField("Remove this fraction of tiles from each sample", this.fcorr_reloutliers, 3);		gd.addNumericField("Remove this fraction of tiles from each sample", this.fcorr_reloutliers, 3);
	@@ -3973,6 +4058,26 @@ public class EyesisCorrectionParameters {
	this.fcorr_inf_quad= gd.getNextBoolean();		this.fcorr_inf_quad= gd.getNextBoolean();
	this.fcorr_inf_vert= gd.getNextBoolean();		this.fcorr_inf_vert= gd.getNextBoolean();

			this.inf_disp_apply= gd.getNextBoolean();
			this.inf_mism_apply= gd.getNextBoolean();
			this.inf_iters= (int) gd.getNextNumber();
			this.inf_final_diff= gd.getNextNumber();
			this.inf_far_pull= gd.getNextNumber();

			this.inf_str_pow= gd.getNextNumber();
			this.inf_smpl_side= (int) gd.getNextNumber();
			this.inf_smpl_num= (int) gd.getNextNumber();
			this.inf_smpl_rms= gd.getNextNumber();

			this.ih_smpl_step= (int) gd.getNextNumber();
			this.ih_disp_min= gd.getNextNumber();
			this.ih_disp_step= gd.getNextNumber();
			this.ih_num_bins= (int) gd.getNextNumber();
			this.ih_sigma= gd.getNextNumber();
			this.ih_max_diff= gd.getNextNumber();
			this.ih_min_samples= (int) gd.getNextNumber();
			this.ih_norm_center= gd.getNextBoolean();

	this.fcorr_sample_size= (int)gd.getNextNumber();		this.fcorr_sample_size= (int)gd.getNextNumber();
	this.fcorr_mintiles= (int) gd.getNextNumber();		this.fcorr_mintiles= (int) gd.getNextNumber();
	this.fcorr_reloutliers= gd.getNextNumber();		this.fcorr_reloutliers= gd.getNextNumber();

src/main/java/Eyesis_Correction.java

+6 −0

Original line number	Original line	Diff line number	Diff line
	@@ -4609,10 +4609,16 @@ private Panel panel1,
	System.out.println("Created new QuadCLT instance, will need to read CLT kernels");		System.out.println("Created new QuadCLT instance, will need to read CLT kernels");
	}		}
	}		}
			/*
	QUAD_CLT.process_fine_corr(		QUAD_CLT.process_fine_corr(
	dry_run, // boolean dry_run		dry_run, // boolean dry_run
	CLT_PARAMETERS,		CLT_PARAMETERS,
	DEBUG_LEVEL);		DEBUG_LEVEL);
			*/
			QUAD_CLT.processLazyEye(
			CLT_PARAMETERS,
			DEBUG_LEVEL);

	return;		return;

	} else if (label.equals("CLT ext infinity corr")) {		} else if (label.equals("CLT ext infinity corr")) {

src/main/java/ImageDtt.java

+3 −2

src/main/java/QuadCLT.java

+92 −16

Original line number	Original line	Diff line number	Diff line
	@@ -5237,22 +5237,22 @@ public class QuadCLT {
	double [][][] new_corr = ac.infinityCorrection(		double [][][] new_corr = ac.infinityCorrection(
	clt_parameters.fcorr_inf_strength, // final double min_strenth,		clt_parameters.fcorr_inf_strength, // final double min_strenth,
	clt_parameters.fcorr_inf_diff, // final double max_diff,		clt_parameters.fcorr_inf_diff, // final double max_diff,
	20, // 0, // final int max_iterations,		clt_parameters.inf_iters, // 20, // 0, // final int max_iterations,
	0.0001, // final double max_coeff_diff,		clt_parameters.inf_final_diff, // 0.0001, // final double max_coeff_diff,
	0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;		clt_parameters.inf_far_pull, // 0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;
	1.0, // final double strength_pow,		clt_parameters.inf_str_pow, // 1.0, // final double strength_pow,
	3, // final int smplSide, // = 2; // Sample size (side of a square)		clt_parameters.inf_smpl_side, // 3, // final int smplSide, // = 2; // Sample size (side of a square)
	5, // final int smplNum, // = 3; // Number after removing worst (should be >1)		clt_parameters.inf_smpl_num, // 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		clt_parameters.inf_smpl_rms, // 0.1, // 0.05, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
	// histogram parameters		// histogram parameters
	8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled		clt_parameters.ih_smpl_step, // 8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
	-1.0, // final double hist_disp_min,		clt_parameters.ih_disp_min, // -1.0, // final double hist_disp_min,
	0.05, // final double hist_disp_step,		clt_parameters.ih_disp_step, // 0.05, // final double hist_disp_step,
	40, // final int hist_num_bins,		clt_parameters.ih_num_bins, // 40, // final int hist_num_bins,
	0.1, // final double hist_sigma,		clt_parameters.ih_sigma, // 0.1, // final double hist_sigma,
	0.1, // final double hist_max_diff,		clt_parameters.ih_max_diff, // 0.1, // final double hist_max_diff,
	10, // final int hist_min_samples,		clt_parameters.ih_min_samples, // 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.ih_norm_center, // true, // final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
	clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,		clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
	inf_disp_strength, // double [][] disp_strength,		inf_disp_strength, // double [][] disp_strength,
	tilesX, // int tilesX,		tilesX, // int tilesX,
	@@ -5260,6 +5260,8 @@ public class QuadCLT {
	debugLevel + 1); // int debugLevel)		debugLevel + 1); // int debugLevel)




	if (debugLevel > -1){		if (debugLevel > -1){
	System.out.println("process_infinity_corr(): ready to apply infinity correction");		System.out.println("process_infinity_corr(): ready to apply infinity correction");
	show_fine_corr(		show_fine_corr(
	@@ -5274,11 +5276,85 @@ public class QuadCLT {
	debugLevel + 2);		debugLevel + 2);
	}		}
	}		}


			public void processLazyEye(
			EyesisCorrectionParameters.CLTParameters clt_parameters,
			int debugLevel
			) {
			ImagePlus imp_src = WindowManager.getCurrentImage();
			if (imp_src==null){
			IJ.showMessage("Error","2*n-layer file with disparities/strengthspairs measured at infinity is required");
			return;
			}
			ImageStack disp_strength_stack= imp_src.getStack();
			final int tilesX = disp_strength_stack.getWidth(); // tp.getTilesX();
			final int tilesY = disp_strength_stack.getHeight(); // tp.getTilesY();
			final int nTiles =tilesX * tilesY;

			AlignmentCorrection ac = new AlignmentCorrection(this);
			double [][] scans = ac.getFineCorrFromImage(
			imp_src,
			// 0.2, // double min_comp_strength, // 0.2
			debugLevel);

			double [][][] new_corr = ac.lazyEyeCorrection(
			clt_parameters.fcorr_inf_strength, // final double min_strenth,
			clt_parameters.fcorr_inf_diff, // final double max_diff,
			1.3, // final double comp_strength_var,
			clt_parameters.inf_iters, // 20, // 0, // final int max_iterations,
			clt_parameters.inf_final_diff, // 0.0001, // final double max_coeff_diff,
			clt_parameters.inf_far_pull, // 0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;
			clt_parameters.inf_str_pow, // 1.0, // final double strength_pow,
			1.5, // final double lazyEyeCompDiff, // clt_parameters.fcorr_disp_diff
			clt_parameters.inf_smpl_side, // final int lazyEyeSmplSide, // = 2; // Sample size (side of a square)
			clt_parameters.inf_smpl_num, // final int lazyEyeSmplNum, // = 3; // Number after removing worst (should be >1)
			0.1, // final double lazyEyeSmplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
			0.2, // final double lazyEyeDispVariation, // 0.2, maximal full disparity difference between tgh tile and 8 neighborxs
			clt_parameters.inf_smpl_side, // 3, // final int smplSide, // = 2; // Sample size (side of a square)
			clt_parameters.inf_smpl_num, // 5, // final int smplNum, // = 3; // Number after removing worst (should be >1)
			clt_parameters.inf_smpl_rms, // 0.1, // 0.05, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
			// histogram parameters
			clt_parameters.ih_smpl_step, // 8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
			clt_parameters.ih_disp_min, // -1.0, // final double hist_disp_min,
			clt_parameters.ih_disp_step, // 0.05, // final double hist_disp_step,
			clt_parameters.ih_num_bins, // 40, // final int hist_num_bins,
			clt_parameters.ih_sigma, // 0.1, // final double hist_sigma,
			clt_parameters.ih_max_diff, // 0.1, // final double hist_max_diff,
			clt_parameters.ih_min_samples, // 10, // final int hist_min_samples,
			clt_parameters.ih_norm_center, // true, // final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
			0.5, // final double inf_fraction, // fraction of the weight for the infinity tiles
			clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
			scans, // 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 > -100){
			apply_fine_corr(
			new_corr,
			debugLevel + 2);

			}
			}




	public void process_fine_corr(		public void process_fine_corr(
	boolean dry_run,		boolean dry_run,
	EyesisCorrectionParameters.CLTParameters clt_parameters,		EyesisCorrectionParameters.CLTParameters clt_parameters,
	int debugLevel		int debugLevel
	) {		) {
			if (dry_run) {
			AlignmentCorrection ac = new AlignmentCorrection(this);
			ac.process_fine_corr(
			dry_run, // boolean dry_run,
			clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
			debugLevel); // int debugLevel
			return;
			}

	ImagePlus imp_src = WindowManager.getCurrentImage();		ImagePlus imp_src = WindowManager.getCurrentImage();
	if (imp_src==null){		if (imp_src==null){
	IJ.showMessage("Error","12*n-layer file clt_mismatches is required");		IJ.showMessage("Error","12*n-layer file clt_mismatches is required");

Original line number	Original line	Diff line number	Diff line
	@@ -964,6 +964,7 @@ public class ImageDtt {
	final double [][][][][] clt_corr_partial,// [tilesY][tilesX][quad]color][(2transform_size-1)(2*transform_size-1)] // if null - will not calculate		final double [][][][][] clt_corr_partial,// [tilesY][tilesX][quad]color][(2transform_size-1)(2*transform_size-1)] // if null - will not calculate
	// [tilesY][tilesX] should be set by caller		// [tilesY][tilesX] should be set by caller
	final double [][] clt_mismatch, // [12][tilesY * tilesX] // transpose unapplied. null - do not calculate		final double [][] clt_mismatch, // [12][tilesY * tilesX] // transpose unapplied. null - do not calculate
			// values in the "main" directions have disparity (*_CM) subtracted, in the perpendicular - as is

	final double [][] disparity_map, // [8][tilesY][tilesX], only [6][] is needed on input or null - do not calculate		final double [][] disparity_map, // [8][tilesY][tilesX], only [6][] is needed on input or null - do not calculate
	// last 2 - contrast, avg/ "geometric average)		// last 2 - contrast, avg/ "geometric average)
	@@ -1606,11 +1607,11 @@ public class ImageDtt {
	double yp,xp;		double yp,xp;
	if (corr_pairs[pair][2] > 0){ // transpose - switch x <-> y		if (corr_pairs[pair][2] > 0){ // transpose - switch x <-> y
	yp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];		yp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];
	xp = transform_size - 1 -corr_max_XYmp[1]; // do not campare to average - it should be 0 anyway		xp = transform_size - 1 -corr_max_XYmp[1]; // do not compare to average - it should be 0 anyway

	} else {		} else {
	xp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];		xp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];
	yp = transform_size - 1 -corr_max_XYmp[1]; // do not campare to average - it should be 0 anyway		yp = transform_size - 1 -corr_max_XYmp[1]; // do not compare to average - it should be 0 anyway
	}		}
	double strength = tcorr_partial[pair][numcol][max_index]; // using the new location than for combined		double strength = tcorr_partial[pair][numcol][max_index]; // using the new location than for combined
	clt_mismatch[3*pair + 0 ][tIndex] = xp;		clt_mismatch[3*pair + 0 ][tIndex] = xp;

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