TD interscene averaging

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

@@ -13144,8 +13144,9 @@ public class OpticalFlow {
 		double [][][]     dcorr_tiles = (fclt_corr != null)? (new double [tp_tasks[0].length][][]):null;
 		// will use num_acc with variable number of accumulations (e.g. clusters)
 		//all_fpn
-		double min_str =     all_fpn ? clt_parameters.imp.min_str_fpn : clt_parameters.imp.min_str;
-		double min_str_sum = all_fpn ? clt_parameters.imp.min_str_sum_fpn : clt_parameters.imp.min_str_sum;
+		double min_str =      all_fpn ? clt_parameters.imp.min_str_fpn : clt_parameters.imp.min_str;
+		double min_str_sum =  all_fpn ? clt_parameters.imp.min_str_sum_fpn : clt_parameters.imp.min_str_sum;
+		double min_str_neib = all_fpn ? clt_parameters.imp.min_str_neib_fpn : clt_parameters.imp.min_str_neib;
 		double corr_fz_inter = clt_parameters.getGpuFatZeroInter(ref_scene.isMonochrome());
 		if (mb_en && (mb_vectors!=null)) { // increase fat zero when there is motion blur
 			corr_fz_inter *= 8;
@@ -13176,7 +13177,7 @@ public class OpticalFlow {
 				image_dtt.transform_size - 1,      // final int                 gpu_corr_rad,    // = transform_size - 1 ?
 				// The tp_tasks data should be decoded from GPU to get coordinates
 				// should it be reference or scene? Or any?
-				tp_tasks[0],                          // final TpTask []           tp_tasks,        // data from the reference frame - will be applied to LMA for the integrated correlations
+				tp_tasks[0],                       // final TpTask []           tp_tasks,        // data from the reference frame - will be applied to LMA for the integrated correlations
 				// to be converted to float (may be null)
 				dcorr_tiles,                       // final double  [][][]      dcorr_tiles,     // [tile][pair_abs, sparse][(2*transform_size-1)*(2*transform_size-1)] // if null - will not calculate
 				pXpYD_ref, // ref_pXpYD,           // final double [][]         pXpYD,           // pXpYD for the reference scene
@@ -13188,13 +13189,17 @@ public class OpticalFlow {
 				use_partial,                       // final boolean             use_partial,     // find motion vectors for individual pairs, false - for sum only
 				clt_parameters.imp.centroid_radius,// final double              centroid_radius, // 0 - use all tile, >0 - cosine window around local max
 				clt_parameters.imp.n_recenter,     // final int                 n_recenter,      // when cosine window, re-center window this many times
-				clt_parameters.imp.td_weight,      // final double  td_weight,    // mix correlations accumulated in TD with 
-				clt_parameters.imp.pd_weight,      // final double  pd_weight,    // correlations (post) accumulated in PD
-				clt_parameters.imp.td_nopd_only,   // final boolean td_nopd_only, // only use TD accumulated data if no safe PD is available for the tile.
+				clt_parameters.imp.td_weight,      // final double              td_weight,       // mix correlations accumulated in TD with 
+				clt_parameters.imp.td_neib_weight, // final double              td_neib_weight,  // mix correlations accumulated in TD (neibs)  
+				clt_parameters.imp.pd_weight,      // final double              pd_weight,       // correlations (post) accumulated in PD
+				clt_parameters.imp.td_nopd_only,   // final boolean             td_nopd_only   , // only use TD accumulated data if no safe PD is available for the tile.
+				clt_parameters.imp.neib_notd_only, // final boolean             neib_notd_only,  // use neighbors only if individual TD is too weak 
 				min_str,                           // final double              min_str_nofpn,         //  = 0.25;
 				min_str_sum,                       // final double              min_str_sum_nofpn,     // = 0.8; // 5;
+				min_str_neib,                      // final double              min_str_neib_nofpn,
 				clt_parameters.imp.min_str_fpn,    // final double              min_str,         //  = 0.25;
 				clt_parameters.imp.min_str_sum_fpn,// final double              min_str_sum,     // = 0.8; // 5;
+				clt_parameters.imp.min_str_neib_fpn,//final double              min_str_neib_fpn, 
 				clt_parameters.imp.min_neibs,      // final int                 min_neibs,       //   2;	   // minimal number of strong neighbors (> min_str)
 				clt_parameters.imp.weight_zero_neibs, // final double              weight_zero_neibs,//  0.2;   // Reduce weight for no-neib (1.0 for all 8)
 				half_disparity,                    // final double              half_disparity,  //   5.0;   // Reduce weight twice for this disparity