cleaning up with eigen, adding debug image generation for LMA

src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java

@@ -2351,6 +2351,7 @@ public class ImageDtt extends ImageDttCPU {
 			final boolean             td_nopd_only,    // only use TD accumulated data if no safe PD is available for the tile.
 			final boolean             eig_use_neibs,   // use correlation from 9 tiles with neibs, if single-tile fails
 			final boolean             eig_remove_neibs, //remove weak (by-neibs) tiles if they have strong (by-single) neighbor
+			final boolean             eig_filt_other,   // apply other before-eigen filters
 //			final double              min_str_nofpn,    //  = 0.25;
 			final double              eig_str_sum_nofpn,// = 0.8; // 5;
 			final double              eig_str_neib_nofpn,// = 0.8; // 5;
@@ -2720,10 +2721,9 @@ public class ImageDtt extends ImageDttCPU {
 			}
 			startAndJoin(threads);
 		}
-		boolean old_filter = false; // true;
 		// Reduce weight if differs much from average of 8 neighbors, large disparity, remove too few neibs
 		final double scale_num_neib = ((weight_zero_neibs >= 0) && (weight_zero_neibs < 1.0)) ? (weight_zero_neibs * 8/(1.0 - weight_zero_neibs)): 0.0;
-		if (old_filter) {
+		if (eig_filt_other) {
 			ai.set(0);
 			for (int ithread = 0; ithread < threads.length; ithread++) {
 				threads[ithread] = new Thread() {

src/main/java/com/elphel/imagej/tileprocessor/IntersceneMatchParameters.java

@@ -451,6 +451,9 @@ public class IntersceneMatchParameters {
 	public double   eig_min_sqrt =                  1.0;   // for sqrt(lambda) - limit minimal sqrt(lambda) - can be sharp for very small max
 	public boolean  eig_use_neibs =                true;   // use correlation from 9 tiles with neibs, if single-tile fails
 	public boolean  eig_remove_neibs =             true;   // remove weak (by-neibs) tiles if they have strong (by-single) neighbor
+	public boolean  eig_filt_other =              false;   // apply other before-eigen filters
+	public  double  eig_max_rms =                   2.0;   // eigen-normalized maximal RMS to consider adjustment to be a failure
+	
 
 /*
 min_str_sum_nofpn	0.22	
@@ -1358,8 +1361,10 @@ min_str_neib_fpn	0.35
 				"Use correlation from 9 tiles with neibs, if single-tile fails");
 		gd.addCheckbox ("Remove weak by strong neighbors",           this.eig_remove_neibs,
 				"Remove weak (by-neibs) tiles if they have strong (by-single) neighbor");
-		
-		
+		gd.addCheckbox ("Apply other filters",                       this.eig_filt_other,
+				"Apply other (before-eigen) filters");
+		gd.addNumericField("Maximal eigen-normalized RMS",           this.eig_max_rms, 5,7,"",
+				"Maximal eigen-normalized RMSE for LMA adjustment. Replaces \"Maximal RMS to fail\" setting below.");
 		
 		
 		gd.addMessage  ("Filtering tiles for interscene matching");
@@ -2048,7 +2053,8 @@ min_str_neib_fpn	0.35
 		this.eig_min_sqrt =             gd.getNextNumber();
 		this.eig_use_neibs =            gd.getNextBoolean();
 		this.eig_remove_neibs =         gd.getNextBoolean();
-		
+		this.eig_filt_other =           gd.getNextBoolean();
+		this.eig_max_rms =              gd.getNextNumber();
 		this.use_combo_reliable =       gd.getNextBoolean();
 		this.ref_need_lma =             gd.getNextBoolean();
 		this.ref_need_lma_combo =       gd.getNextBoolean();
@@ -2603,6 +2609,8 @@ min_str_neib_fpn	0.35
 		properties.setProperty(prefix+"eig_min_sqrt",         this.eig_min_sqrt+"");        // double
 		properties.setProperty(prefix+"eig_use_neibs",        this.eig_use_neibs+"");       // boolean
 		properties.setProperty(prefix+"eig_remove_neibs",     this.eig_remove_neibs+"");    // boolean
+		properties.setProperty(prefix+"eig_filt_other",       this.eig_filt_other+"");      // boolean
+		properties.setProperty(prefix+"eig_max_rms",          this.eig_max_rms+"");         // double
 		
 		properties.setProperty(prefix+"use_combo_reliable",   this.use_combo_reliable+"");  // boolean
 		properties.setProperty(prefix+"ref_need_lma",         this.ref_need_lma+"");        // boolean
@@ -3120,6 +3128,8 @@ min_str_neib_fpn	0.35
 		if (properties.getProperty(prefix+"eig_min_sqrt")!=null)         this.eig_min_sqrt=Double.parseDouble(properties.getProperty(prefix+"eig_min_sqrt"));
         if (properties.getProperty(prefix+"eig_use_neibs")!=null)        this.eig_use_neibs=Boolean.parseBoolean(properties.getProperty(prefix+"eig_use_neibs"));		
 		if (properties.getProperty(prefix+"eig_remove_neibs")!=null)     this.eig_remove_neibs=Boolean.parseBoolean(properties.getProperty(prefix+"eig_remove_neibs"));		
+		if (properties.getProperty(prefix+"eig_filt_other")!=null)       this.eig_filt_other=Boolean.parseBoolean(properties.getProperty(prefix+"eig_filt_other"));		
+		if (properties.getProperty(prefix+"eig_max_rms")!=null)          this.eig_max_rms=Double.parseDouble(properties.getProperty(prefix+"eig_max_rms"));
 
 		if (properties.getProperty(prefix+"use_combo_reliable")!=null)   this.use_combo_reliable=Boolean.parseBoolean(properties.getProperty(prefix+"use_combo_reliable"));
 		else if (properties.getProperty(prefix+"use_combo_relaible")!=null)   this.use_combo_reliable=Boolean.parseBoolean(properties.getProperty(prefix+"use_combo_relaible"));		
@@ -3645,6 +3655,8 @@ min_str_neib_fpn	0.35
 		imp.eig_min_sqrt =          this.eig_min_sqrt;
 		imp.eig_use_neibs =         this.eig_use_neibs;
 		imp.eig_remove_neibs =      this.eig_remove_neibs;
+		imp.eig_filt_other =        this.eig_filt_other;
+		imp.eig_max_rms =           this.eig_max_rms;
 		
 		imp.use_combo_reliable    = this.use_combo_reliable;
 		imp.ref_need_lma          = this.ref_need_lma;