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imagej-elphel
Commit 971dd284 authored by Andrey Filippov's avatar Andrey Filippov
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refactoring

parent 38475568
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Showing with 256 additions and 1 deletion
src/main/java/SuperTiles.java
View file @ 971dd284
......@@ -2058,7 +2058,7 @@ public class SuperTiles{
return planes_selections;
}
public TilePlanes.PlaneData [][] createPlanesFromSelections(
public TilePlanes.PlaneData [][] createPlanesFromSelectionsOld(
final boolean [][][][] plane_selections, // = new boolean [nStiles][][][]; // num_tiles
final double [][][][] disp_strength,
final double plDispNorm,
......@@ -2104,10 +2104,16 @@ public class SuperTiles{
System.out.println("createPlanesFromSelections(): nsTile="+nsTile);
}
if (plane_selections[nsTile] != null) {
int stileY = nsTile / stilesX;
int stileX = nsTile % stilesX;
int [] sTiles = {stileX, stileY};
int dl = (nsTile == debug_stile) ? 3 : 0;
result_planes[nsTile] = null;
// first make a plane from all tiles
ArrayList<TilePlanes.PlaneData> st_planes = new ArrayList<TilePlanes.PlaneData>();
......@@ -2118,6 +2124,8 @@ public class SuperTiles{
correct_distortions,
measuredLayers, // MeasuredLayers measuredLayers,
plPreferDisparity); // boolean preferDisparity)
// iterate through all plane selections
for (int ps = 0; ps < plane_selections[nsTile].length; ps++) {
TilePlanes.PlaneData pd = pd0.clone();
......@@ -2234,6 +2242,9 @@ public class SuperTiles{
dbg_img = showSupertileSeparation(true, disp_strength[nsTile], plane_selections[nsTile], result_planes[nsTile]);
sdfa_instance.showArrays(dbg_img, 2 * superTileSize, 2* superTileSize, true, "create_planes_world-"+nsTile+"-"+debugLevel,dbg_titles);
}
}
}
}
......@@ -2243,6 +2254,111 @@ public class SuperTiles{
return result_planes;
}
public TilePlanes.PlaneData [][] createPlanesFromSelections(
final boolean [][][][] plane_selections, // = new boolean [nStiles][][][]; // num_tiles
final double [][][][] disp_strength,
final double plDispNorm,
final int plMinPoints, // = 5; // Minimal number of points for plane detection
final double plTargetEigen, // = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
final double plFractOutliers, // = 0.3; // Maximal fraction of outliers to remove
final int plMaxOutliers, // = 20; // Maximal number of outliers to remove
// final double plVertWors, // = 1.5 // if rotating plane vertical does not increase 'eigenvalue' more, use vertical
final boolean plPreferDisparity, // Always start with disparity-most axis (false - lowest eigenvalue)
final GeometryCorrection geometryCorrection,
final boolean correct_distortions,
final boolean smplMode, // = true; // Use sample mode (false - regular tile mode)
final int smplSide, // = 2; // Sample size (side of a square)
final int smplNum, // = 3; // Number after removing worst
final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
final int debugLevel,
final int dbg_X,
final int dbg_Y)
{
final int tilesX = tileProcessor.getTilesX();
final int tilesY = tileProcessor.getTilesY();
final int superTileSize = tileProcessor.getSuperTileSize();
final int tileSize = tileProcessor.getTileSize();
final int stilesX = (tilesX + superTileSize -1)/superTileSize;
final int stilesY = (tilesY + superTileSize -1)/superTileSize;
final int nStiles = stilesX * stilesY;
final Thread[] threads = ImageDtt.newThreadArray(tileProcessor.threadsMax);
final AtomicInteger ai = new AtomicInteger(0);
final TilePlanes.PlaneData [][] result_planes = new TilePlanes.PlaneData[nStiles][];
// this.planes = new TilePlanes.PlaneData[nStiles][];
final int debug_stile = (debugLevel > -1)? (dbg_Y * stilesX + dbg_X):-1;
// TODO: Remove when promoting PlaneData
final TilePlanes tpl = new TilePlanes(tileSize,superTileSize, geometryCorrection);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int nsTile = ai.getAndIncrement(); nsTile < nStiles; nsTile = ai.getAndIncrement()) {
if (nsTile == debug_stile){
System.out.println("createPlanesFromSelections(): nsTile="+nsTile);
}
if (plane_selections[nsTile] != null) {
int stileY = nsTile / stilesX;
int stileX = nsTile % stilesX;
int [] sTiles = {stileX, stileY};
int dl = (nsTile == debug_stile) ? 3 : 0;
result_planes[nsTile] = null;
// first make a plane from all tiles
TilePlanes.PlaneData pd0 = tpl.new PlaneData (
sTiles, // int [] sTileXY,
tileSize, // int tileSize,
geometryCorrection, // GeometryCorrection geometryCorrection,
correct_distortions,
measuredLayers, // MeasuredLayers measuredLayers,
plPreferDisparity); // boolean preferDisparity)
ArrayList<TilePlanes.PlaneData> st_planes = pd0.createTilePlanesFromSelections(
"" + nsTile, // String suffix,
plane_selections[nsTile], // boolean [][][] plane_selections, // = new boolean [nStiles][][][]; // num_tiles
disp_strength[nsTile], // double [][][] disp_strength,
plDispNorm, // double dispNorm, // Normalize disparities to the average if above
plMinPoints, // int min_tiles,
plTargetEigen, // double plTargetEigen, // = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
plFractOutliers, // double plFractOutliers, // = 0.3; // Maximal fraction of outliers to remove
plMaxOutliers, // int plMaxOutliers, // = 20; // Maximal number of outliers to remove
strength_floor, // double strength_floor,
strength_pow, // double strength_pow,
correct_distortions, // boolean correct_distortions,
smplMode, // boolean smplMode, // = true; // Use sample mode (false - regular tile mode)
smplSide, // int smplSide, // = 2; // Sample size (side of a square)
smplNum, // int smplNum, // = 3; // Number after removing worst
smplRms, // double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
dl); // int debugLevel);
if ((st_planes != null) && (!st_planes.isEmpty())){
if (LOWEST_PLANE(2) > 0) st_planes.add(0, st_planes.get(0)); // insert dummy at pos 0;
result_planes[nsTile] = st_planes.toArray(new TilePlanes.PlaneData[0] );
if (LOWEST_PLANE(2) > 0) result_planes[nsTile][0] = null; // remove dummy
if (dl >0){
System.out.println("createPlanesFromSelections(): nsTile="+nsTile);
}
if (dl > 2) {
String [] dbg_titles = showSupertileSeparationTitles( disp_strength[nsTile], plane_selections[nsTile], result_planes[nsTile]);
double [][] dbg_img = showSupertileSeparation(false, disp_strength[nsTile], plane_selections[nsTile], result_planes[nsTile]);
showDoubleFloatArrays sdfa_instance = new showDoubleFloatArrays();
sdfa_instance.showArrays(dbg_img, 2 * superTileSize, 2* superTileSize, true, "create_planes_disp-"+nsTile+"-"+debugLevel,dbg_titles);
dbg_img = showSupertileSeparation(true, disp_strength[nsTile], plane_selections[nsTile], result_planes[nsTile]);
sdfa_instance.showArrays(dbg_img, 2 * superTileSize, 2* superTileSize, true, "create_planes_world-"+nsTile+"-"+debugLevel,dbg_titles);
}
}
}
}
}
};
}
ImageDtt.startAndJoin(threads);
return result_planes;
}
public void processPlanes5(
final int growSelection, // grow initial selection before processing
......
src/main/java/TilePlanes.java
View file @ 971dd284
......@@ -21,6 +21,10 @@
** -----------------------------------------------------------------------------**
**
*/
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import Jama.EigenvalueDecomposition;
import Jama.Matrix;
......@@ -3326,7 +3330,142 @@ public class TilePlanes {
world_xyz = norm_xyz.times((xyz.transpose().times(norm_xyz).get(0,0))).getColumnPackedCopy();
return world_xyz;
}
public ArrayList<TilePlanes.PlaneData> createTilePlanesFromSelections(
String suffix,
boolean [][][] plane_selections, // = new boolean [nStiles][][][]; // num_tiles
double [][][] disp_strength,
// double disp_far, // minimal disparity to select (or NaN)
// double disp_near, // maximal disparity to select (or NaN)
double dispNorm, // Normalize disparities to the average if above
// double min_weight,
int min_tiles,
double plTargetEigen, // = 0.1; // Remove outliers until main axis eigenvalue (possibly scaled by plDispNorm) gets below
double plFractOutliers, // = 0.3; // Maximal fraction of outliers to remove
int plMaxOutliers, // = 20; // Maximal number of outliers to remove
double strength_floor,
double strength_pow,
boolean correct_distortions,
boolean smplMode, // = true; // Use sample mode (false - regular tile mode)
int smplSide, // = 2; // Sample size (side of a square)
int smplNum, // = 3; // Number after removing worst
double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
int debugLevel)
{
// first make a plane from all tiles
ArrayList<TilePlanes.PlaneData> st_planes = new ArrayList<TilePlanes.PlaneData>();
// iterate through all plane selections
for (int ps = 0; ps < plane_selections.length; ps++) {
TilePlanes.PlaneData pd = this.clone();
boolean OK = (pd.getPlaneFromMeas(
plane_selections[ps], // tile_sel, // boolean [][] tile_sel, // null - do not use, {} use all (will be modified)
disp_strength,
Double.NaN, // double disp_far, // minimal disparity to select (or NaN)
Double.NaN, // double disp_near, // maximal disparity to select (or NaN)
dispNorm, // 0.0, // plDispNorm, // double dispNorm, // Normalize disparities to the average if above
0.0, // double min_weight,
min_tiles, // int min_tiles,
strength_floor, //
strength_pow, // double strength_pow,
// update !
smplMode,
smplSide,
smplNum,
smplRms,
debugLevel) != null); // int debugLevel)
if (OK) {
if (debugLevel > 0) {
if (pd.getWeight() > 1.0) {
System.out.println("Processing subplane "+ suffix+
", numPoints="+ pd.getNumPoints()+
", swc = "+pd.getWeight()+
", center=["+pd.getZxy()[0]+","+pd.getZxy()[1]+","+pd.getZxy()[2]+"]"+
", eig_val = {"+pd.getValues()[0]+","+pd.getValues()[1]+","+pd.getValues()[2]+"}"+
", eig_vect[0] = {"+pd.getVector()[0]+","+pd.getVector()[1]+","+pd.getVector()[2]+"}");
}
}
// now try to remove outliers
int max_outliers = (int) Math.round(pd.getNumPoints() * plFractOutliers);
if (max_outliers > plMaxOutliers) max_outliers = plMaxOutliers;
double targetV = plTargetEigen;
double z0 = pd.getZxy()[0];
if ((dispNorm > 0.0) && (z0 > dispNorm)) {
double dd = (dispNorm + z0)/ dispNorm; // > 1
targetV *= dd * dd; // > original
}
if (pd.getValues()[0] > targetV) {
OK = pd.removeOutliers( // getPlaneFromMeas should already have run
disp_strength,
targetV, // double targetEigen, // target eigenvalue for primary axis (is disparity-dependent, so is non-constant)
max_outliers, // int maxRemoved, // maximal number of tiles to remove (not a constant)
debugLevel); // int debugLevel)
if (!OK) {
continue;
}
if (debugLevel > 0) {
if (pd.getWeight() > 1.0) {
System.out.println("Removed outliers "+ suffix +
", numPoints="+ pd.getNumPoints()+
", swc = "+pd.getWeight()+
", center=["+pd.getZxy()[0]+","+pd.getZxy()[1]+","+pd.getZxy()[2]+"]"+
", eig_val = {"+pd.getValues()[0]+","+pd.getValues()[1]+","+pd.getValues()[2]+"}"+
", eig_vect[0] = {"+pd.getVector()[0]+","+pd.getVector()[1]+","+pd.getVector()[2]+"}");
}
}
}
double [] norm_xyz = pd.getWorldXYZ(
correct_distortions);
st_planes.add(pd);
if (debugLevel > 0) {
System.out.println("World normal " + suffix + " = {"+
norm_xyz[0]+", "+norm_xyz[1]+", "+norm_xyz[2]+"}");
}
// calculate the world planes too
// if (debugLevel > -1){
pd.getWorldPlaneFromMeas(
plane_selections[ps], // tile_sel, // boolean [][] tile_sel, // null - do not use, {} use all (will be modified)
disp_strength,
Double.NaN, // double disp_far, // minimal disparity to select (or NaN)
Double.NaN, // double disp_near, // maximal disparity to select (or NaN)
dispNorm, // 0.0, // plDispNorm, // double dispNorm, // Normalize disparities to the average if above
0.0, // double min_weight,
min_tiles, // int min_tiles,
strength_floor, //
strength_pow, // double strength_pow,
// update !
smplMode,
smplSide,
smplNum,
smplRms,
debugLevel);
}
}
if (st_planes.size() > 0){
// sort planes by increasing disparity (tile center or plane center ? ) Using plane center
Collections.sort(st_planes, new Comparator<TilePlanes.PlaneData>() {
@Override
public int compare(TilePlanes.PlaneData lhs, TilePlanes.PlaneData rhs) {
// -1 - less than, 1 - greater than, 0 - equal
return (rhs.getZxy()[0] > lhs.getZxy()[0]) ? -1 : (rhs.getZxy()[0] < lhs.getZxy()[0] ) ? 1 : 0;
}
});
return st_planes;
}
return null;
}
}
//TODO: Remove below methods and promote PlaneData (no TilePlanes) after tested
......
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