Dual max LMA

src/main/java/com/elphel/imagej/gpu/GPUTileProcessor.java

@@ -74,6 +74,10 @@ import jcuda.nvrtc.nvrtcProgram;
 public class GPUTileProcessor {
 	public static boolean USE_DS_DP = false; // Use Dynamic Shared memory with Dynamic Parallelism (not implemented)  
 	String LIBRARY_PATH = "/usr/local/cuda/targets/x86_64-linux/lib/libcudadevrt.a"; // linux
+	// Can be downloaded and twice extracted from
+	// https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-cudart-dev-11-2_11.2.152-1_amd64.deb
+	// First deb itself, then data.tar.xz, and it will have usr/local/cuda/targets/x86_64-linux/lib/libcudadevrt.a inside
+	// Found "cuda-cudart-dev" on https://ubuntu.pkgs.org/
 	static String GPU_RESOURCE_DIR =              "kernels";
 	static String [] GPU_KERNEL_FILES = {"dtt8x8.cuh","TileProcessor.cuh"};
 	// "*" - generated defines, first index - separately compiled unit

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

@@ -2629,9 +2629,9 @@ public class ImageDtt extends ImageDttCPU {
 									}
 								}
 								if (debugTile1) {
-									correlation2d.corrLMA2DualMax( // null pointer
+									Corr2dLMA lma_dual = correlation2d.corrLMA2DualMax( // null pointer
 											imgdtt_params,                // ImageDttParameters  imgdtt_params,
-								        	1,                            // int     combine_mode,   // 0 - both,  1 - strongest, 2 - nearest to zero, 3 - FG, 4 - BG
+								        	0, // 3, // 0, // 1,                            // int     combine_mode,   // 0 - both,  1 - strongest, 2 - nearest to zero, 3 - FG, 4 - BG
 //											imgdtt_params.lmas_LY_single, // false,    // boolean             adjust_ly, // adjust Lazy Eye
 											corr_wnd,                     // double [][]         corr_wnd, // correlation window to save on re-calculation of the window
 											corr_wnd_inv_limited,         // corr_wnd_limited, // correlation window, limited not to be smaller than threshold - used for finding max/convex areas (or null)
@@ -2647,6 +2647,8 @@ public class ImageDtt extends ImageDttCPU {
 											(debugTile0 ? 1: -2),         // int                 debug_level,
 											tileX,                        // int                 tileX, // just for debug output
 											tileY ); 
+									System.out.println("clt_process_tl_correlations() corrLMA2DualMax() done, lma_dual="+
+										((lma_dual== null)? "null": " not null"));
 									
 								}
 								

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

@@ -84,6 +84,7 @@ public class ImageDttParameters {
 	public int     bimax_rad_convex_search = 2;   // how far from predicted to search for maximums
 	public int     bimax_min_num_samples =   4;   // minimal number of samples per pair per maximum
 	public int     bimax_min_num_pairs =     8;   // minimal number of used pairs
+	public boolean bimax_dual_pass =         true; // First pass - do not adjust disparity
 
 	//lmamask_
 	public boolean lmamask_dbg =              false;  // show LMA images, exit after single BG
@@ -183,7 +184,7 @@ public class ImageDttParameters {
 	public double  lma_disp_range =          5.0;   // disparity range to combine in one cluster (to mitigate ERS
 // LMA single parameters
 //	public boolean lmas_gaussian =           false; // model correlation maximum as a Gaussian (false - as a parabola)
-	public int     lmas_gaussian =           0;    //  0 - parabola, 1 - Gaussian, 2 - limited parabola, 3 - limited squared parabola
+	public int     lmas_gaussian =           3;    //  0 - parabola, 1 - Gaussian, 2 - limited parabola, 3 - limited squared parabola
 	public boolean lmas_adjust_wm =          true;  // used in new for width
 	public boolean lmas_adjust_wy =          true;  // adjust non-circular
 	public boolean lmas_adjust_ag =          true;  // adjust gains gains
@@ -212,7 +213,7 @@ public class ImageDttParameters {
 	public int     lma_gaussian =           0;     // 0 - parabola, 1 - Gaussian, 2 - limited parabola, 3 - limited squared parabola
 	public boolean lma_second =             true;  // re-run LMA after removing weak/failed tiles
 //	public boolean lma_second_gaussian =    false;  // re-run after removing weal/failed in Gaussian mode
-	public int     lma_second_gaussian =    0; // false;  // re-run after removing weal/failed in Gaussian mode
+	public int     lma_second_gaussian =    0; // false;  // re-run after removing weak/failed in Gaussian mode
 	public boolean lma_adjust_wm =          true;  // used in new for width
 	public boolean lma_adjust_wy =          true;  // false; // used in new for ellipse
 	public boolean lma_adjust_wxy =         true;  // used in new for lazy eye adjust parallel-to-disparity correction
@@ -472,6 +473,8 @@ public class ImageDttParameters {
 					"Minimal number of samples per pair per maximum to use this pair by LMA (each of the maximums used)");
 			gd.addNumericField("Minimal number of used pairs",                                    this.bimax_min_num_pairs,  0, 3, "",
 					"Do not use LMA if total number of used pairs is lower");
+			gd.addCheckbox    ("Dual-pass LMA",                                                   this.bimax_dual_pass,
+					"First adjust other parameters (keeping disparities), then add disparities");
 			
 			gd.addMessage("LMA samples filter based on estimated disparity");
 			gd.addCheckbox    ("Debug LMA",                                                       this.lmamask_dbg,
@@ -888,6 +891,7 @@ public class ImageDttParameters {
   		    this.bimax_rad_convex_search= (int) gd.getNextNumber();
   		    this.bimax_min_num_samples=   (int) gd.getNextNumber();
   		    this.bimax_min_num_pairs=     (int) gd.getNextNumber();
+  			this.bimax_dual_pass =              gd.getNextBoolean();
   			
   			this.lmamask_dbg =              gd.getNextBoolean();
   			this.lmamask_en =               gd.getNextBoolean();
@@ -1114,6 +1118,7 @@ public class ImageDttParameters {
 		properties.setProperty(prefix+"bimax_rad_convex_search", this.bimax_rad_convex_search +"");
 		properties.setProperty(prefix+"bimax_min_num_samples",   this.bimax_min_num_samples +"");
 		properties.setProperty(prefix+"bimax_min_num_pairs",     this.bimax_min_num_pairs +"");
+		properties.setProperty(prefix+"bimax_dual_pass",         this.bimax_dual_pass +"");
 
 		properties.setProperty(prefix+"lmamask_dbg",          this.lmamask_dbg +"");
 		properties.setProperty(prefix+"lmamask_en",           this.lmamask_en +"");
@@ -1344,6 +1349,7 @@ public class ImageDttParameters {
 		if (properties.getProperty(prefix+"bimax_rad_convex_search")!=null) this.bimax_rad_convex_search=Integer.parseInt(properties.getProperty(prefix+"bimax_rad_convex_search"));
 		if (properties.getProperty(prefix+"bimax_min_num_samples")!=null)   this.bimax_min_num_samples=Integer.parseInt(properties.getProperty(prefix+"bimax_min_num_samples"));
 		if (properties.getProperty(prefix+"bimax_min_num_pairs")!=null)     this.bimax_min_num_pairs=Integer.parseInt(properties.getProperty(prefix+"bimax_min_num_pairs"));
+		if (properties.getProperty(prefix+"bimax_dual_pass")!=null)         this.bimax_dual_pass=Boolean.parseBoolean(properties.getProperty(prefix+"bimax_dual_pass"));
 		
 		if (properties.getProperty(prefix+"lmamask_dbg")!=null)              this.lmamask_dbg=Boolean.parseBoolean(properties.getProperty(prefix+"lmamask_dbg"));
 		if (properties.getProperty(prefix+"lmamask_en")!=null)               this.lmamask_en=Boolean.parseBoolean(properties.getProperty(prefix+"lmamask_en"));
@@ -1591,6 +1597,7 @@ public class ImageDttParameters {
 		idp.bimax_rad_convex_search= this.bimax_rad_convex_search;
 		idp.bimax_min_num_samples=   this.bimax_min_num_samples;
 		idp.bimax_min_num_pairs=     this.bimax_min_num_pairs;
+		idp.bimax_dual_pass=         this.bimax_dual_pass;
 		
 		idp.lmamask_dbg=               this.lmamask_dbg;
 		idp.lmamask_en=                this.lmamask_en;