next snapshot

src/main/java/com/elphel/imagej/orthomosaic/OrangeTest.java

@@ -494,23 +494,40 @@ public class OrangeTest {
 				40.0,         // final double      max,
 				true);        // final boolean     invert)
 		{
-			String title_combo_dt = title_space_conv+"_weights-pre_"+time_rt_scales[time_deriv_patt][0]+"-"+time_rt_scales[time_deriv_patt][1]+(use_max?"-max":"-avg");
+			String title_weights_pre = title_space_conv+"_weights-pre_"+time_rt_scales[time_deriv_patt][0]+"-"+time_rt_scales[time_deriv_patt][1]+(use_max?"-max":"-avg");
 			ShowDoubleFloatArrays.showArrays(
 					weights_pre,
 					width,
 					height,
 					true,
-					title_combo_dt+".tiff",
+					title_weights_pre+".tiff",
 					titles_spatial);
 		}
 		
+		int weights_pre_shrink =  2;
+		int weights_pre_min_seq = 5;
+		double [][] weights_pre_filt = weightsShrink(
+				weights_pre, // final double [][] weights_in, // same data convolved with different kernels, may have NaNs
+				width, // final int         width,
+				weights_pre_shrink, // final int         shrink,
+				weights_pre_min_seq); // final int         min_frames)
+		{
+			String title_weights_pre_filt = title_space_conv+"_weights-pre-filt_"+time_rt_scales[time_deriv_patt][0]+"-"+time_rt_scales[time_deriv_patt][1]+(use_max?"-max":"-avg");
+			ShowDoubleFloatArrays.showArrays(
+					weights_pre_filt,
+					width,
+					height,
+					true,
+					title_weights_pre_filt+".tiff",
+					titles_spatial);
+		}
 		
-		
-		
-		
+		boolean          no_gradients = true; // do not consider patterns 0..3 - when they are near the edge they are too strong
 		double [][] integrated_pattern_strengths = integrateSpaceConv(
 				space_conv,        // final double [][][][]  space_conv,
-				weights_pre);  // final double [][] weights)  // may be null [num_images][num_pixels]
+				weights_pre_filt,  // weights_pre);  // final double [][] weights)  // may be null [num_images][num_pixels]
+				false); // no_gradients) ;    // final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong
+		
 		if (show_best && show_pre_best) {
 			ShowDoubleFloatArrays.showArrays(
 					integrated_pattern_strengths,
@@ -521,7 +538,8 @@ public class OrangeTest {
 					titles_pattern);
 		}
 		int [][] best_pattern_frame = getBestConv(
-				space_conv); // final double [][][][]  space_conv)
+				space_conv, // final double [][][][]  space_conv)
+				no_gradients) ;    // final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong				
 		int patt_mod = 18; // = 54;
 		if (show_best && show_pre_best) {
 			double [][] best_pattern_frame_dbg = new double [best_pattern_frame.length][best_pattern_frame[0].length];
@@ -541,7 +559,9 @@ public class OrangeTest {
 		}
 		
 		int [] best_pattern = getBestIntegratedConv(
-				integrated_pattern_strengths);        // double [][] integ_conv){ // [kern][pix]
+				integrated_pattern_strengths,        // double [][] integ_conv){ // [kern][pix]
+				no_gradients) ;    // final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong				
+		
 		if (show_best && show_pre_best) {
 			double [] best_pattern_dbg = new double [best_pattern.length];
 			Arrays.fill(best_pattern_dbg,Double.NaN);
@@ -1065,7 +1085,8 @@ public class OrangeTest {
 	
 	
 	public static int [][]      getBestConv(
-			final double [][][][]  space_conv){
+			final double [][][][]  space_conv,
+			final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong			
 		final int num_scales =  space_conv.length; // may be modified, if there will be extra layers
 		final int num_kernels = space_conv[0].length; // may be modified, if there will be extra layers
 		final int num_images = space_conv[0][0].length;
@@ -1087,7 +1108,8 @@ public class OrangeTest {
 							int best_sgn = 0;
 							double bestv = 0;
 							for (int nscale = 0; nscale < num_scales; nscale++) {
-								for (int dir = 0; dir < num_kernels; dir++) {
+								int low_dir = no_gradients? 4: 0;
+								for (int dir = low_dir; dir < num_kernels; dir++) {
 									double d = space_conv[nscale][dir][fimg][ipix];
 									if (!Double.isNaN(d)) {
 										for (int sgn = 0; sgn<2; sgn++) {
@@ -1119,7 +1141,8 @@ public class OrangeTest {
 	
 	public static double [][] integrateSpaceConv(
 		final double [][][][]  space_conv,
-		final double [][] weights) { // may be null [num_images][num_pixels]
+		final double [][]      weights, // may be null [num_images][num_pixels]
+		final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong
 		final int num_scales =  space_conv.length; // may be modified, if there will be extra layers
 		final int num_kernels = space_conv[0].length; // may be modified, if there will be extra layers
 		final int num_images = space_conv[0][0].length;
@@ -1139,7 +1162,8 @@ public class OrangeTest {
 							double w = (weights == null)? 1.0 : weights[fimg][ipix] ;
 							sum_weights[ipix] += w;
 							for (int nscale = 0; nscale < num_scales; nscale++) {
-								for (int dir = 0; dir < num_kernels; dir++) {
+								int low_dir = no_gradients? 4: 0;
+								for (int dir = low_dir; dir < num_kernels; dir++) {
 									double d = space_conv[nscale][dir][fimg][ipix];
 									if (!Double.isNaN(d)) {
 										for (int sgn = 0; sgn<num_signs; sgn++) {
@@ -1190,7 +1214,9 @@ public class OrangeTest {
 	}
 	
 	public static int [] getBestIntegratedConv(
-			double [][] integ_conv){ // [kern][pix]
+			double [][] integ_conv, // [kern][pix]
+			final boolean          no_gradients) { // do not consider patterns 0..3 - when they are near the edge they are too strong
+		final int dbg_pix = 135+160*640;
 		final int num_patt =      integ_conv.length;
 		final int num_pixels =    integ_conv[0].length;
 		final int [] best_index = new int [num_pixels];
@@ -1200,9 +1226,16 @@ public class OrangeTest {
 			threads[ithread] = new Thread() {
 				public void run() {
 					for (int ipix = ai.getAndIncrement(); ipix < num_pixels; ipix = ai.getAndIncrement()) {
+						if (ipix == dbg_pix) {
+							System.out.println("ipix="+ipix);
+						}
 						int best_patt = -1;
 						double bestv = 0;
 						for (int ipatt = 0; ipatt < num_patt; ipatt++) {
+							int npatt = ipatt % 9;
+							if (no_gradients && npatt < 4) {
+								continue;
+							}
 							double d = integ_conv[ipatt][ipix];
 							if (d > bestv) { // cares of NaN
 								bestv = d;
@@ -1617,6 +1650,89 @@ public class OrangeTest {
 		return weights;
 	}
 
+	public static double [][]   weightsShrink(
+			final double [][] weights_in, // same data convolved with different kernels, may have NaNs
+			final int         width,
+			final int         shrink,
+			final int         min_frames) {
+		
+		final int num_frames = weights_in.length;
+		final int num_pixels = weights_in[0].length;
+		final double [][] weights = new double[num_frames][num_pixels];
+		final Thread[] threads = ImageDtt.newThreadArray();
+		final boolean [][] mask = new boolean [num_frames][num_pixels];
+		final AtomicInteger ai = new AtomicInteger(0);
+		for (int ithread = 0; ithread < threads.length; ithread++) {
+			threads[ithread] = new Thread() {
+				TileNeibs tn = new TileNeibs(width, num_pixels/width);
+				public void run() {
+					for (int nframe = ai.getAndIncrement(); nframe < num_frames; nframe = ai.getAndIncrement()){ // if (w_frame[ipix] > min){
+						System.arraycopy(weights_in[nframe], 0, weights[nframe], 0, num_pixels);
+						if (shrink > 0) {
+						for (int ipix=0; ipix < num_pixels; ipix++) {
+							mask[nframe][ipix] = !(weights_in[nframe][ipix] > 0);
+						}
+						tn. growSelection(
+								shrink, // int              grow,           // grow tile selection by 1 over non-background tiles 1: 4 directions, 2 - 8 directions, 3 - 8 by 1, 4 by 1 more
+								mask[nframe], // final boolean [] tiles,
+								null); // final boolean [] prohibit)
+						}
+					}
+				}
+			};
+		}		      
+		ImageDtt.startAndJoin(threads);
+		// filter by sequence length
+		if (min_frames > 0)  {
+			ai.set(0);
+			for (int ithread = 0; ithread < threads.length; ithread++) {
+				threads[ithread] = new Thread() {
+					public void run() {
+						int [] seq_length = new int [num_frames];					
+						for (int ipix = ai.getAndIncrement(); ipix < num_pixels; ipix = ai.getAndIncrement()){
+							int prev = 0;
+							for (int nframe = 0; nframe <  num_frames; nframe++) {
+								if (weights_in[nframe][ipix] > 0) {
+									seq_length[nframe] = ++prev;
+								} else {
+									prev = 0;
+									seq_length[nframe] = 0;
+								}
+							}
+							for (int nframe = num_frames-1; nframe >= 0; nframe--) {
+								if (seq_length[nframe] > 0) {
+									if (seq_length[nframe] < min_frames) {
+										for (int i = 0; i < seq_length[nframe]; i++) {
+											mask[nframe-i][ipix] = true;
+										}
+									}
+									nframe -= seq_length[nframe]-1; // will be decrease by one more 
+								}
+							}
+						}
+					}
+				};
+			}		      
+			ImageDtt.startAndJoin(threads);
+		}
+
+		ai.set(0);
+		for (int ithread = 0; ithread < threads.length; ithread++) {
+			threads[ithread] = new Thread() {
+				public void run() {
+					for (int nframe = ai.getAndIncrement(); nframe < num_frames; nframe = ai.getAndIncrement()){ // if (w_frame[ipix] > min){
+						for (int ipix=0; ipix < num_pixels; ipix++) {
+							if (mask[nframe][ipix]) {
+								weights[nframe][ipix] = 0;
+							}
+						}
+					}
+				}
+			};
+		}		      
+		ImageDtt.startAndJoin(threads);
+		return weights;
+	}
 	
 	
 	

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

@@ -6313,6 +6313,30 @@ public class OpticalFlow {
 						null,               // "GPU-SHIFTED-D"+clt_parameters.disparity, // String      suffix,
 						imp_terrain);       // imp_scenes); // ImagePlus   imp)
 			}
+			if ((combo_dsn_final.length <= COMBO_DSN_INDX_TERRAIN) || (combo_dsn_final[COMBO_DSN_INDX_TERRAIN] == null)) {
+				System.out.println ("No terrain data available");
+			} else {
+				double [] elevation_disparity = combo_dsn_final[COMBO_DSN_INDX_DISP];
+				String scenes_suffix = quadCLTs[quadCLTs.length-1].getImageName()+"-ELEVATION";
+				ImagePlus imp_elevation = renderSceneSequence(
+						clt_parameters,     // CLTParameters clt_parameters,
+						false,              // boolean        um_mono,
+						null,               // Rectangle     fov_tiles,
+						1,                  // int           mode3d,
+						false,              // boolean       toRGB,
+						ZERO3,              // double []     stereo_offset, // offset reference camera {x,y,z}
+						1,                  // int           sensor_mask,
+						scenes_suffix,      // String        suffix,
+						elevation_disparity,  // selected_disparity, // double []     ref_disparity,			
+						quadCLTs,           // QuadCLT []    quadCLTs,
+						ref_index,          // int           ref_index,
+						threadsMax,         // int           threadsMax,
+						debugLevel);        // int           debugLevel);
+				quadCLTs[ref_index].saveImagePlusInModelDirectory(
+						null,               // "GPU-SHIFTED-D"+clt_parameters.disparity, // String      suffix,
+						imp_elevation);     // imp_scenes); // ImagePlus   imp)
+			}
+			
 		}
 		
 		if (export_images) {