Loading src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java +2 −2 Original line number Diff line number Diff line Loading @@ -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; Loading Loading @@ -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() { Loading Loading
src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java +2 −2 Original line number Diff line number Diff line Loading @@ -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; Loading Loading @@ -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() { Loading