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imagej-elphel
Commit bd27e7b0 authored by Andrey Filippov's avatar Andrey Filippov
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Implemented combining of the interscene 2D correlations amon tile and 

its 8 immediate neighbors - significant improvement in matching
parent af9b88ff
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Showing with 282 additions and 14 deletions
src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java
View file @ bd27e7b0
......@@ -1358,16 +1358,33 @@ public class ImageDtt extends ImageDttCPU {
final double weight_zero_neibs,// 0.2; // Reduce weight for no-neib (1.0 for all 8)
final double half_disparity, // 5.0; // Reduce weight twice for this disparity
final double half_avg_diff, // 0.2; // when L2 of x,y difference from average of neibs - reduce twice
final boolean neibs_nofpn_only, // consolidate neighbors fot non-fpn tiles only!
final boolean redo_both, // use average of neighbors for both pd,td if any of the center tile tests (td, pd) fails
final int min_num_neibs, // plus center, total number >= (min_num_neibs+1)
final double scale_neibs_pd, // scale threshold for the pixel-domain average maximums
final double scale_neibs_td, // scale threshold for the transform-domain average maximums
final double scale_avg_weight, // reduce influence of the averaged correlations compared to the single-tile ones
final int debug_tileX,
final int debug_tileY,
final int threadsMax, // maximal number of threads to launch
final int globalDebugLevel)
{
{ /*
boolean use_neibs = true; // false; // true;
final boolean neibs_nofpn_only = false; // consolidate neighbors fot non-fpn tiles only!
final double scale_neibs_pd = use_neibs? 0.5 : 0;
final double scale_neibs_td = use_neibs? 0.5 : 0;
final double scale_avg_weight = 0.5; // reduce influence of the averaged correlations compared to the single-tile ones
final int min_num_neibs = 4; // plus center, total number >= (min_num_neibs+1)
final boolean redo_both = true; // use average of neighbors for both pd,td if any of the center tile tests (td, pd) fails
*/
if (this.gpuQuad == null) {
System.out.println("clt_process_tl_interscene(): this.gpuQuad is null, bailing out");
return null;
}
//boolean debugTile0 =(tileX == debug_tileX) && (tileY == debug_tileY) && (globalDebugLevel > 2); // 0);
// final int min_neibs = clt_parameters.imp.min_neibs;
final boolean extra_sum = true; // use sum of pixel-domain correlations (TD have artifacts for low contrast
// - maybe -related to float vs. double - not tested yet
......@@ -1456,11 +1473,29 @@ public class ImageDtt extends ImageDttCPU {
final Thread[] threads = newThreadArray(threadsMax);
final AtomicInteger ai = new AtomicInteger(0);
final int [] map_corr_indices=new int[tilesX*tilesY];
Arrays.fill(map_corr_indices, -1);
// create indices for neighbors
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
@Override
public void run() {
for (int iCorrTile = ai.getAndIncrement(); iCorrTile < num_tiles; iCorrTile = ai.getAndIncrement()) {
int nTile = (corr_indices[iCorrTile* used_sensors_list.length] >> GPUTileProcessor.CORR_NTILE_SHIFT);
map_corr_indices[nTile] = iCorrTile;
}
}
};
}
startAndJoin(threads);
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
@Override
public void run() {
int tileY,tileX,nTile; // , chn;
TileNeibs tn = new TileNeibs(tilesX,tilesY);
for (int iCorrTile = ai.getAndIncrement(); iCorrTile < num_tiles; iCorrTile = ai.getAndIncrement()) {
nTile = (corr_indices[iCorrTile* used_sensors_list.length] >> GPUTileProcessor.CORR_NTILE_SHIFT);
tileY = nTile / tilesX;
......@@ -1473,6 +1508,7 @@ public class ImageDtt extends ImageDttCPU {
boolean [] fpn_mask = null;
double min_str = min_str_nofpn; // higher threshold when FPN is possible
double min_str_sum = min_str_sum_nofpn; // higher threshold when FPN is possible
boolean is_fpn=false;
if ((fpn_offsets != null) && (fpn_offsets[nTile] != null)) {
double fpn_x = transform_size - 1 - fpn_offsets[nTile][0]; // 0 -> 7.0
double fpn_y = transform_size - 1 - fpn_offsets[nTile][1]; // 0 -> 7.0
......@@ -1485,12 +1521,13 @@ public class ImageDtt extends ImageDttCPU {
for (int iy = min_y; iy <= max_y; iy++) {
for (int ix = min_x; ix <= max_x; ix++) {
int indx = iy * corr_size + ix;
fcorr2D[fcorr2D_indx][indx] = 0;
// fcorr2D[fcorr2D_indx][indx] = 0;
fpn_mask[indx] = true;
}
}
min_str = min_str_fpn;
min_str_sum = min_str_sum_fpn;
is_fpn = true;
}
double [][] corrs = new double [corrs_len + extra_len][];
// copy correlation tiles from the GPU's floating point arrays
......@@ -1520,8 +1557,13 @@ public class ImageDtt extends ImageDttCPU {
}
}
}
if (is_fpn) {
for (int i = 0; i < corr_length; i++) if (fpn_mask[i]){
corrs[corrs_len - 1][i] = 0.0; // instead of fcorr2D[fcorr2D_indx][indx] = 0;
}
}
if (dcorr_tiles != null) { // This will be visualized
if (dcorr_tiles != null) { // This will be visualized (only for visualization?)
int index_es = getNumSensors() + extra_len;
dcorr_tiles[iCorrTile] = new double[getNumSensors()+1 + extra_len][];
if (extra_sum) {
......@@ -1544,6 +1586,11 @@ public class ImageDtt extends ImageDttCPU {
dcorr_tiles[iCorrTile][abs_sens][i] = gpu_corr_scale * fcorr2D[fcorr2D_indx][i]; // copy one-by-one converting from floats to doubles
}
}
if (is_fpn) {
for (int i = 0; i < corr_length; i++) if (fpn_mask[i]){
dcorr_tiles[iCorrTile][used_sensors_list.length-1][i] = 0.0; // instead of fcorr2D[fcorr2D_indx][indx] = 0;
}
}
}
// final double [][][][] motion_vectors, // [tilesY][tilesX][][] -> [][][num_sel_sensors+1][2]
if (motion_vectors != null) { // TODO: now used only as debug, may be removed later
......@@ -1568,8 +1615,10 @@ public class ImageDtt extends ImageDttCPU {
// now calculate only for composite
double [] mv_pd = new double [3];
double [] mv_td = new double [3];
boolean retry_pd=false, retry_td=false;
boolean neib_en = !(is_fpn && neibs_nofpn_only);
if (pd_weight > 0.0) {
mv_pd = Correlation2d.getMaxXYCm(
mv_pd = Correlation2d.getMaxXYCm( // last, average
corrs[corrs.length-1], // double [] data,
corr_size, // int data_width, // = 2 * transform_size - 1;
centroid_radius, // double radius, // 0 - all same weight, > 0 cosine(PI/2*sqrt(dx^2+dy^2)/rad)
......@@ -1579,14 +1628,18 @@ public class ImageDtt extends ImageDttCPU {
false); // boolean debug)
if (mv_pd != null) {
if (mv_pd[2] < min_str) {
if (neib_en && (scale_neibs_pd > 0)) {
retry_pd = mv_pd[2] >= min_str * scale_neibs_pd;
}
mv_pd = null;
} else {
mv_pd[2] -= min_str;
// mv_pd[2] -= min_str;
mv_pd[2] -= min_str * scale_neibs_pd;
}
}
}
if (td_weight > 0.0) {
mv_td = Correlation2d.getMaxXYCm(
mv_td = Correlation2d.getMaxXYCm( // pre-last - sharp (in FD)
corrs[corrs.length-2], // double [] data,
corr_size, // int data_width, // = 2 * transform_size - 1;
centroid_radius, // double radius, // 0 - all same weight, > 0 cosine(PI/2*sqrt(dx^2+dy^2)/rad)
......@@ -1596,12 +1649,136 @@ public class ImageDtt extends ImageDttCPU {
false); // boolean debug)
if (mv_td != null) {
if (mv_td[2] < min_str_sum) {
if (neib_en && (scale_neibs_td > 0)) {
retry_td = mv_td[2] >= min_str_sum * scale_neibs_td;
}
mv_td = null;
} else {
mv_td[2] -= min_str_sum;
// mv_td[2] -= min_str_sum;
mv_td[2] -= min_str_sum * scale_neibs_td;
}
}
}
// calculate averages from neighbors
// will replace corrs[] with averages
if (retry_pd || retry_td) {
if (redo_both) {
retry_pd |= retry_td; // here could be just true
retry_td |= retry_pd; // here could be just true
}
int num_neibs = 1; // center tile itself
// calculate num_neibs first not to bother if there are too few of them if (num_neibs > )
for (int dir = 0; dir < tn.numNeibs(); dir++) {
int nTile1 = tn.getNeibIndex(nTile, dir);
if ((nTile1 >= 0) && (map_corr_indices[nTile1] >= 0)) {
num_neibs++;
}
}
scale = 1.0/getNumSensors();
if (num_neibs > min_num_neibs) {
for (int dir = 0; dir < tn.numNeibs(); dir++) {
int nTile1 = tn.getNeibIndex(nTile, dir);
if ((nTile1 >= 0) && (map_corr_indices[nTile1] >= 0)) {
int iCorrTile1 = map_corr_indices[nTile1];
for (int isens = corrs_len - 1; isens >= 0; isens--) {
int nsens = used_sensors_list.length - corrs_len + isens;
int fcorr2D_indx = iCorrTile1 * used_sensors_list.length + nsens;
if (fcorr2D_indx < 0) {
System.out.println("BUG");
}
for (int i = 0; i < corr_length; i++) { // java.lang.ArrayIndexOutOfBoundsException: Index -1 out of bounds for length 69802
corrs[isens][i] += gpu_corr_scale * fcorr2D[fcorr2D_indx][i]; // copy one-by-one converting from floats to doubles
}
}
// direcly accumulating, without preservation of per-sensor data
if (!use_partial && extra_sum) {
for (int nsens = 0; nsens < (used_sensors_list.length - 1); nsens++) {
int fcorr2D_indx = iCorrTile1 * used_sensors_list.length + nsens;
for (int i = 0; i < corr_length; i++) {
corrs[corrs_len][i] += scale * gpu_corr_scale * fcorr2D[fcorr2D_indx][i]; // copy one-by-one converting from floats to doubles
}
}
}
}
}
if (use_partial && extra_sum) {
corrs[corrs_len] = new double [corr_length];
for (int isens = corrs_len - 2; isens >= 0; isens--) {
for (int i = 0; i < corr_length; i++) {
corrs[corrs_len][i] += scale*corrs[isens][i];
}
}
}
// divide by number of neighbors to get averages
double scale_neibs = 1.0/num_neibs;
for (int isens=0; isens < corrs.length; isens++) {
for (int i = 0; i < corr_length; i++) {
corrs[isens][i] *= scale_neibs;
}
}
// zero fpn mask for sum slice
if (is_fpn) {
for (int i = 0; i < corr_length; i++) if (fpn_mask[i]){
corrs[corrs_len - 1][i] = 0.0; // instead of fcorr2D[fcorr2D_indx][indx] = 0;
}
}
// update dcorr_tiles for visualization
// just copy last corrs to last dcorr_tiles[iCorrTile]
if (dcorr_tiles != null) { // This will be visualized (only for visualization?)
int num_dslices = Math.min(corrs.length,dcorr_tiles[iCorrTile].length);
for (int islice = 0; islice < num_dslices; islice++) {
System.arraycopy(
corrs[corrs.length-num_dslices+islice],
0,
dcorr_tiles[iCorrTile][dcorr_tiles[iCorrTile].length-num_dslices+islice],
0,
corrs[corrs.length-num_dslices+islice].length);
}
}
// recalculate motion vectors mv_pd, mv_td with now averaged data
// final double scale_avg_weight = 0.5; // reduce influence of the averaged correlations compared to the single-tile ones
if (retry_pd && (pd_weight > 0.0)) {
mv_pd = Correlation2d.getMaxXYCm( // last, average
corrs[corrs.length-1], // double [] data,
corr_size, // int data_width, // = 2 * transform_size - 1;
centroid_radius, // double radius, // 0 - all same weight, > 0 cosine(PI/2*sqrt(dx^2+dy^2)/rad)
n_recenter, // int refine, // re-center window around new maximum. 0 -no refines (single-pass)
null, // boolean [] fpn_mask,
false, // boolean ignore_border, // only if fpn_mask != null - ignore tile if maximum touches fpn_mask
false); // boolean debug)
if (mv_pd != null) {
if (mv_pd[2] < (min_str * scale_neibs_pd)) {
mv_pd = null;
} else {
mv_pd[2] -= min_str * scale_neibs_pd;
mv_pd[2] *= scale_avg_weight;
}
}
}
// TODO: do the same with td
if (retry_td && (td_weight > 0.0)) {
mv_td = Correlation2d.getMaxXYCm( // pre-last - sharp (in FD)
corrs[corrs.length-2], // double [] data,
corr_size, // int data_width, // = 2 * transform_size - 1;
centroid_radius, // double radius, // 0 - all same weight, > 0 cosine(PI/2*sqrt(dx^2+dy^2)/rad)
n_recenter, // int refine, // re-center window around new maximum. 0 -no refines (single-pass)
fpn_mask, // boolean [] fpn_mask,
false, // boolean ignore_border, // only if fpn_mask != null - ignore tile if maximum touches fpn_mask
false); // boolean debug)
if (mv_td != null) {
if (mv_td[2] < (min_str_sum * scale_neibs_td)) {
mv_td = null;
} else {
mv_td[2] -= min_str_sum * scale_neibs_td;
mv_td[2] *= scale_avg_weight;
}
}
}
} // if (num_neibs > min_num_neibs) {
}
if ((mv_td != null) || (mv_pd != null)) {
double [] mv = new double[3 + (use3D? 2 :0)]; // keep for disparity/strength
if (mv_pd != null) {
......
src/main/java/com/elphel/imagej/tileprocessor/IntersceneMatchParameters.java
View file @ bd27e7b0
......@@ -226,6 +226,15 @@ public class IntersceneMatchParameters {
public double pd_weight = 0.5; // correlations (post) accumulated in PD
public boolean td_nopd_only = false; // true; // only use TD accumulated data if no safe PD is available for the tile.
// averaging with neighbors for interscene matching
public boolean use_neibs = true; // false; // true;
public boolean neibs_nofpn_only = false; // consolidate neighbors fot non-fpn tiles only!
public boolean redo_both = true; // use average of neighbors for both pd,td if any of the center tile tests (td, pd) fails
public int min_num_neibs = 4; // plus center, total number >= (min_num_neibs+1)
public double scale_neibs_pd = 0.5; // scale threshold for the pixel-domain average maximums
public double scale_neibs_td = 0.5; // scale threshold for the transform-domain average maximums
public double scale_avg_weight = 0.5; // reduce influence of the averaged correlations compared to the single-tile ones
public double min_str_fpn = 0.2; // 0.25; // minimal correlation strength for all but TD-accumulated layer
public double min_str_sum_fpn = 0.5; // 0.8; // minimal correlation strength for TD-accumulated layer
......@@ -739,6 +748,24 @@ public class IntersceneMatchParameters {
"Mix argmax from PD-accumulated correlation.");
gd.addCheckbox ("TD when no PD only", this.td_nopd_only,
"Use argmax from TD only if PD data is not available for this tile.");
gd.addMessage ("Averaging inter-scene 2D correlations with 8 immediate neighbors");
gd.addCheckbox ("Use averaging neighbors", this.use_neibs,
"Average tile 2D correlation with 8 immediate neighbors if single-tile correlation is not strong enough.");
// TODO: Consider/Implement pure TD averaging
gd.addCheckbox ("No-FPN averaging only", this.neibs_nofpn_only,
"Use averaging with neighbors only if tile offset is large enough not to have FPN interference.");
gd.addCheckbox ("Use averaging for both PD and TD", this.redo_both,
"Recalculate tile average for both TD and PD if at least one of them is not strong enough.");
gd.addNumericField("Minimal neighbors to average", this.min_num_neibs, 0,3,"",
"Minimal number of defined (non-null) neighbors 2D correlations (of 8) to use averaging.");
gd.addNumericField("Scale PD thershold", this.scale_neibs_pd, 5,7,"",
"Use averaging for tiles that are stronger than scaled threshold for a single-tile motion vector for pixel-domain values.");
gd.addNumericField("Scale TD threshold", this.scale_neibs_td, 5,7,"",
"Use averaging for tiles that are stronger than scaled threshold for a single-tile motion vector for transform-domain values.");
gd.addNumericField("Scale averaged strengths", this.scale_avg_weight, 5,7,"",
"Scale averaged motion vector strengths (for each of the TD and PD).");
gd.addMessage ("Filtering motion vectors");
gd.addNumericField("Minimal correlation strength (non-sum)", this.min_str, 5,7,"",
"Minimal correlation strength for individual correlation and for pixel-domain averaged one. Weeker tiles results are removed.");
......@@ -1166,6 +1193,15 @@ public class IntersceneMatchParameters {
this.td_weight = gd.getNextNumber();
this.pd_weight = gd.getNextNumber();
this.td_nopd_only = gd.getNextBoolean();
this.use_neibs = gd.getNextBoolean();
this.neibs_nofpn_only = gd.getNextBoolean();
this.redo_both = gd.getNextBoolean();
this.min_num_neibs = (int) gd.getNextNumber();
this.scale_neibs_pd = gd.getNextNumber();
this.scale_neibs_td = gd.getNextNumber();
this.scale_avg_weight = gd.getNextNumber();
this.min_str = gd.getNextNumber();
this.min_str_fpn = gd.getNextNumber();
this.min_str_sum = gd.getNextNumber();
......@@ -1505,6 +1541,15 @@ public class IntersceneMatchParameters {
properties.setProperty(prefix+"td_weight", this.td_weight+""); // double
properties.setProperty(prefix+"pd_weight", this.pd_weight+""); // double
properties.setProperty(prefix+"td_nopd_only", this.td_nopd_only+""); // boolean
properties.setProperty(prefix+"use_neibs", this.use_neibs+""); // boolean
properties.setProperty(prefix+"neibs_nofpn_only", this.neibs_nofpn_only+""); // boolean
properties.setProperty(prefix+"redo_both", this.redo_both+""); // boolean
properties.setProperty(prefix+"min_num_neibs", this.min_num_neibs+""); // int
properties.setProperty(prefix+"scale_neibs_pd", this.scale_neibs_pd+""); // double
properties.setProperty(prefix+"scale_neibs_td", this.scale_neibs_td+""); // double
properties.setProperty(prefix+"scale_avg_weight", this.scale_avg_weight+""); // double
properties.setProperty(prefix+"min_str", this.min_str+""); // double
properties.setProperty(prefix+"min_str_fpn", this.min_str_fpn+""); // double
properties.setProperty(prefix+"min_str_sum", this.min_str_sum+""); // double
......@@ -1799,6 +1844,15 @@ public class IntersceneMatchParameters {
if (properties.getProperty(prefix+"td_weight")!=null) this.td_weight=Double.parseDouble(properties.getProperty(prefix+"td_weight"));
if (properties.getProperty(prefix+"pd_weight")!=null) this.pd_weight=Double.parseDouble(properties.getProperty(prefix+"pd_weight"));
if (properties.getProperty(prefix+"td_nopd_only")!=null) this.td_nopd_only=Boolean.parseBoolean(properties.getProperty(prefix+"td_nopd_only"));
if (properties.getProperty(prefix+"use_neibs")!=null) this.use_neibs=Boolean.parseBoolean(properties.getProperty(prefix+ "use_neibs"));
if (properties.getProperty(prefix+"neibs_nofpn_only")!=null) this.neibs_nofpn_only=Boolean.parseBoolean(properties.getProperty(prefix+"neibs_nofpn_only"));
if (properties.getProperty(prefix+"redo_both")!=null) this.redo_both=Boolean.parseBoolean(properties.getProperty(prefix+ "redo_both"));
if (properties.getProperty(prefix+"min_num_neibs")!=null) this.min_num_neibs=Integer.parseInt(properties.getProperty(prefix+ "min_num_neibs"));
if (properties.getProperty(prefix+"scale_neibs_pd")!=null) this.scale_neibs_pd=Double.parseDouble(properties.getProperty(prefix+ "scale_neibs_pd"));
if (properties.getProperty(prefix+"scale_neibs_td")!=null) this.scale_neibs_td=Double.parseDouble(properties.getProperty(prefix+ "scale_neibs_td"));
if (properties.getProperty(prefix+"scale_avg_weight")!=null) this.scale_avg_weight =Double.parseDouble(properties.getProperty(prefix+ "scale_avg_weight"));
if (properties.getProperty(prefix+"min_str")!=null) this.min_str=Double.parseDouble(properties.getProperty(prefix+"min_str"));
if (properties.getProperty(prefix+"min_str_fpn")!=null) this.min_str_fpn=Double.parseDouble(properties.getProperty(prefix+"min_str_fpn"));
if (properties.getProperty(prefix+"min_str_sum")!=null) this.min_str_sum=Double.parseDouble(properties.getProperty(prefix+"min_str_sum"));
......@@ -2104,6 +2158,15 @@ public class IntersceneMatchParameters {
imp.td_weight = this.td_weight;
imp.pd_weight = this.pd_weight;
imp.td_nopd_only = this.td_nopd_only;
imp.use_neibs = this.use_neibs;
imp.neibs_nofpn_only = this.neibs_nofpn_only;
imp.redo_both = this.redo_both;
imp.min_num_neibs = this.min_num_neibs;
imp.scale_neibs_pd = this.scale_neibs_pd;
imp.scale_neibs_td = this.scale_neibs_td;
imp.scale_avg_weight = this.scale_avg_weight;
imp.min_str = this.min_str;
imp.min_str_fpn = this.min_str_fpn;
imp.min_str_sum = this.min_str_sum;
......
src/main/java/com/elphel/imagej/tileprocessor/OpticalFlow.java
View file @ bd27e7b0
......@@ -4539,6 +4539,7 @@ public class OpticalFlow {
int max_num_scenes = clt_parameters.imp.max_num_scenes; // cut longer series
double boost_max_short = 2.0; //
double boost_zoom_short = 1.5; //
......@@ -4694,8 +4695,11 @@ public class OpticalFlow {
boolean rot_to_transl = after_spiral && clt_parameters.ilp.ilma_translation_priority;
double [] reg_weights = rot_to_transl? clt_parameters.ilp.ilma_regularization_weights0 : clt_parameters.ilp.ilma_regularization_weights;
min_max[1] = max_offset;
//boost_zoom_short
double max_z_change_scaled = max_z_change;
if ((ref_index - scene_index) < min_num_scenes) {
min_max[1] = boost_max_short * max_offset;
max_z_change_scaled = max_z_change * boost_zoom_short;
if (debugLevel > -3) {
System.out.println("As the current series ("+(ref_index - scene_index)+
" scenes) is shorter than minimal ("+min_num_scenes+"), the maximal shift is scaled by "+
......@@ -4705,6 +4709,7 @@ public class OpticalFlow {
}
if ((scene_index - earliest_scene) < min_num_scenes) {
min_max[1] = boost_max_short * max_offset;
max_z_change_scaled = max_z_change * boost_zoom_short;
if (debugLevel > -3) {
System.out.println("As the remaining number of scenes to process ("+(scene_index - earliest_scene + 1)+
") is less than minimal ("+min_num_scenes+"), the maximal shift is scaled by "+
......@@ -4741,8 +4746,14 @@ public class OpticalFlow {
}
if (max_zoom_diff > 0) { // ignore if set to
if (Math.abs(scenes_xyzatr[scene_index][0][2]) > max_z_change) {
fail_reason[0] = FAIL_REASON_ZOOM;
adjust_OK = false;
if (Math.abs(scenes_xyzatr[scene_index][0][2]) > max_z_change_scaled) {
fail_reason[0] = FAIL_REASON_ZOOM;
adjust_OK = false;
} else {
System.out.println("Z-change "+Math.abs(scenes_xyzatr[scene_index][0][2])+
"m exceeds limit of "+max_z_change+"m, but as it is beginning/end of the series, limit is raised to "+
max_z_change_scaled+"m");
}
}
}
}
......@@ -13131,6 +13142,15 @@ public class OpticalFlow {
if (mb_en && (mb_vectors!=null)) { // increase fat zero when there is motion blur
corr_fz_inter *= 8;
}
boolean use_neibs = clt_parameters.imp.use_neibs; // false; // true;
boolean neibs_nofpn_only = clt_parameters.imp.neibs_nofpn_only; // consolidate neighbors fot non-fpn tiles only!
boolean redo_both = clt_parameters.imp.redo_both; // use average of neighbors for both pd,td if any of the center tile tests (td, pd) fails
int min_num_neibs = clt_parameters.imp.min_num_neibs; // plus center, total number >= (min_num_neibs+1)
double scale_neibs_pd = use_neibs? clt_parameters.imp.scale_neibs_pd : 0; // scale threshold for the pixel-domain average maximums
double scale_neibs_td = use_neibs? clt_parameters.imp.scale_neibs_td : 0; // scale threshold for the transform-domain average maximums
double scale_avg_weight = clt_parameters.imp.scale_avg_weight; // reduce influence of the averaged correlations compared to the single-tile ones
coord_motion = image_dtt.clt_process_tl_interscene( // convert to pixel domain and process correlations already prepared in fcorr_td and/or fcorr_combo_td
clt_parameters.img_dtt, // final ImageDttParameters imgdtt_params, // Now just extra correlation parameters, later will include, most others
// only used here to keep extra array element for disparity difference
......@@ -13166,6 +13186,14 @@ public class OpticalFlow {
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
clt_parameters.imp.half_avg_diff, // final double half_avg_diff, // 0.2; // when L2 of x,y difference from average of neibs - reduce twice
neibs_nofpn_only, // final boolean neibs_nofpn_only, // consolidate neighbors fot non-fpn tiles only!
redo_both, //final boolean redo_both, // use average of neighbors for both pd,td if any of the center tile tests (td, pd) fails
min_num_neibs, //final int min_num_neibs, // plus center, total number >= (min_num_neibs+1)
scale_neibs_pd, //final double scale_neibs_pd, // scale threshold for the pixel-domain average maximums
scale_neibs_td, //final double scale_neibs_td, // scale threshold for the transform-domain average maximums
scale_avg_weight, //final double scale_avg_weight, // reduce influence of the averaged correlations compared to the single-tile ones
clt_parameters.tileX, // final int debug_tileX,
clt_parameters.tileY, // final int debug_tileY,
THREADS_MAX, // final int threadsMax, // maximal number of threads to launch
......@@ -13268,10 +13296,10 @@ public class OpticalFlow {
}
}
// seems that fclt_corr and fclt_corr1 are both not needed w/o debug
float [][][] fclt_corr1 = ImageDtt.convertFcltCorr( // partial length, matching corr_indices = gpuQuad.getCorrIndices(); // also sets num_corr_tiles
dcorr_tiles, // double [][][] dcorr_tiles,// [tile][sparse, correlation pair][(2*transform_size-1)*(2*transform_size-1)] // if null - will not calculate
fclt_corr); // float [][][] fclt_corr) // new float [tilesX * tilesY][][] or null
if (show_2d_correlations) { // visualize prepare ref_scene correlation data
float [][][] fclt_corr1 = ImageDtt.convertFcltCorr( // partial length, matching corr_indices = gpuQuad.getCorrIndices(); // also sets num_corr_tiles
dcorr_tiles, // double [][][] dcorr_tiles,// [tile][sparse, correlation pair][(2*transform_size-1)*(2*transform_size-1)] // if null - will not calculate
fclt_corr); // float [][][] fclt_corr) // new float [tilesX * tilesY][][] or null
float [][] dbg_corr_rslt_partial = ImageDtt.corr_partial_dbg( // not used in lwir
fclt_corr1, // final float [][][] fcorr_data, // [tile][pair][(2*transform_size-1)*(2*transform_size-1)] // if null - will not calculate
image_dtt.getGPU().getCorrIndices(), // tp_tasks, // final TpTask [] tp_tasks, //
......@@ -15157,7 +15185,7 @@ public class OpticalFlow {
ers_scene.ers_watr_center_dt = ers_ref.ers_watr_center_dt.clone();
ers_scene.ers_wxyz_center_dt = ers_ref.ers_wxyz_center_dt.clone();
if (clt_parameters.imp.debug_level > -2) {
System.out.print("adjustPairsLMAInterscene(): insufficeinet data for ERS, skipping. ");
System.out.print("adjustPairsLMAInterscene(): insufficient data for ERS, skipping. ");
System.out.println ("quad_defined = ["+((int)quad_strengths[0][0])+","
+((int)quad_strengths[0][1])+","+((int)quad_strengths[0][2])+","+((int)quad_strengths[0][3])+"] (needed = "+
min_quad_tiles +"), rel_strengths = ["
......
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