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Commit 21cc4033 authored by Andrey Filippov's avatar Andrey Filippov
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fixed lazy eye correction

parent f2fa612b
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Showing with 1246 additions and 69 deletions
src/main/java/AlignmentCorrection.java
View file @ 21cc4033
import java.util.ArrayList;
import Jama.Matrix;
import ij.IJ;
import ij.ImagePlus;
import ij.ImageStack;
import ij.WindowManager;
/**
**
** AlignmentCorrection - try to apply minor adjustments to the misaligned camera
......@@ -25,8 +31,8 @@ import java.util.ArrayList;
*/
public class AlignmentCorrection {
static int NUM_SLICES = 10; // disp, strength, dx0, dy0, dx1, dy1, dx2, dy2, dx3, dy3), dx0, dx1, dy2, dy3 are not used now
static int NUM_SLICES = 10; // disp, strength, dx0, dy0, dx1, dy1, dx2, dy2, dx3, dy3)
static int NUM_ALL_SLICES = 14; // disp, strength, dx0, dy0, str0, dx1, dy1, str1, dx2, dy2, str2, dx3, dy3 str3,)
QuadCLT qc;
public class Sample{
......@@ -147,7 +153,9 @@ public class AlignmentCorrection {
tilesX,
magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
debugLevel);
double [][][] disparity_corr_coefficiants = infinityCorrection(
double [][][] disparity_corr_coefficiants = null;
if (clt_parameters.inf_disp_apply) {
disparity_corr_coefficiants = infinityCorrection(
clt_parameters.fcorr_inf_vert,// final boolean use_vertical,
// min_strength,
// max_diff,
......@@ -156,6 +164,7 @@ public class AlignmentCorrection {
// far_pull, // = 0.2; // 1; // 0.5;
clt_parameters,
disp_strength,
samples_list,
tilesX,
magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
......@@ -166,10 +175,12 @@ public class AlignmentCorrection {
disparity_corr_coefficiants, // double [][][] corr,
"");// String prefix)
}
if (debugLevel > -100) { // temporary disabled
}
if (clt_parameters.inf_mism_apply) {
double [][][] mismatch_corr_coefficiants = infinityMismatchCorrection(
clt_parameters.fcorr_quadratic,// final boolean use_quadratic,
clt_parameters.fcorr_inf_vert,// final boolean use_vertical,
!clt_parameters.inf_disp_apply, // final boolean use_disparity, // for infinity
clt_parameters,
disp_strength,
samples_list,
......@@ -182,6 +193,13 @@ public class AlignmentCorrection {
mismatch_corr_coefficiants, // double [][][] corr,
"");// String prefix)
}
if (disparity_corr_coefficiants == null) {
disparity_corr_coefficiants = mismatch_corr_coefficiants;
if (debugLevel > -1){
System.out.println("infinityCorrection(): using only coefficient increments from infinityMismatchCorrection");
}
} else {
for (int i = 0; i < disparity_corr_coefficiants.length; i++){
for (int j = 0; j < disparity_corr_coefficiants[i].length; j++){
for (int k = 0; k < disparity_corr_coefficiants[i][j].length; k++){
......@@ -189,6 +207,10 @@ public class AlignmentCorrection {
}
}
}
if (debugLevel > -1){
System.out.println("infinityCorrection(): combining coefficient increments from infinityCorrection and infinityMismatchCorrection");
}
}
}
return disparity_corr_coefficiants;
}
......@@ -294,7 +316,7 @@ public class AlignmentCorrection {
}
indx ++;
}
if ((debugLevel > -1) && (pass < 20)){
if ((debugLevel > 2) && (pass < 20)){
String [] titles = {"disparity","approx","diff", "strength"};
double [][] dbg_img = new double [titles.length][numTiles];
for (int nTile = 0; nTile < numTiles; nTile++){
......@@ -560,6 +582,179 @@ public class AlignmentCorrection {
* @return per sub-camera, per direction (x,y) 6 quadratic polynomial coefficients, same format as fine_geometry_correction()
*/
public double [][][] infinityMismatchCorrection(
final boolean use_quadratic,
final boolean use_vertical,
final boolean use_disparity, // for infinity
EyesisCorrectionParameters.CLTParameters clt_parameters,
double [][] disp_strength,
ArrayList<Sample> samples_list,
int tilesX,
double magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
int debugLevel)
{
// Mismatch data has disparity values already subtracted, so to correct disparity at infinity, disparity values should be restored
final int num_tiles = disp_strength[0].length;
final int tilesY = num_tiles/tilesX;
PolynomialApproximation pa = new PolynomialApproximation();
double thresholdLin = 1.0E-20; // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
double thresholdQuad = 1.0E-30; // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
// final int [] indices_mismatch = {1,4,6,9}; // "dy0", "dy1", "dx2", "dx3"
// final int [] indices_mismatch = {2+1, 2+4, 2+6, 2+9}; // "dy0", "dy1", "dx2", "dx3"
// final int [] indices_mismatch = {2+1, 2+3, 2+4, 2+6}; // "dy0", "dy1", "dx2", "dx3"
final int [][] indices_mismatch = {
{2+0, 2+2, 2+4, 2+6}, // "dx0", "dx1", "dx2", "dx3"
{2+1, 2+3, 2+5, 2+7}}; // "dy0", "dy1", "dy2", "dy3"
// use last generated samples_list;
double [][][] mdata;
if (use_vertical) {
mdata = new double[samples_list.size()][3][];
} else {
mdata = new double [8][samples_list.size()][3];
}
int indx = 0;
double [][] A_arr = {
{2.0, 1.0, 1.0},
{1.0, 3.0, 2.0},
{1.0, 2.0, 3.0}};
Matrix A = new Matrix(A_arr);
Matrix AINV = A.inverse();
double scale = 0.5/magic_coeff;
double [][] dbg_xy = null;
if (debugLevel > -1) {
dbg_xy = new double [9][num_tiles];
}
for (Sample s: samples_list){
int tileX = s.tile % tilesX;
int tileY = s.tile / tilesX;
double [] xy = new double[8]; // same as coefficients: x0,y0,x1,y1,x2,y2,x3,y3
// Calculate x0,x1,x2,x3 and y0,y1,y2,y3 assuming x0+x1+x2+x3 = 0,y0+y1+y2+y3 = 0 and minimizing squares of errors
// as each each 4: "dx0", "dx1", "dx2", "dx3" and "dy0", "dy1", "dy2", "dy3" are over-defined
for (int dir = 0; dir < 2; dir++) { // 0 - X, 1 - Y
double [] dxy = new double[4];
for (int i = 0; i < 4; i++){
dxy[i] = scale * disp_strength[indices_mismatch[dir][i] + (s.series * NUM_SLICES)][s.tile];
}
/*
* |-dy0 -dy1 -dy2 -dy3 |
* B = |+dy0 -dy1 -2*dy3 |
* |+dy2 -2*dy1 -dy3 |
*/
double [] B_arr = {
-dxy[0] -dxy[1] -dxy[2] -dxy[3],
dxy[0] -dxy[1] -2 * dxy[3],
dxy[2] -2 * dxy[1] -dxy[3]};
Matrix B = new Matrix(B_arr, 3); // 3 rows
Matrix X = AINV.times(B);
for (int i = 0; i < 3; i++) {
xy[2 * i + dir] = X.get(i, 0);
xy[2 * 3 + dir] -= X.get(i, 0);
}
}
if (use_disparity) {
double d = disp_strength[s.series * NUM_SLICES + 0][s.tile];
xy[0] -= d; xy[1] -= d;
xy[2] += d; xy[3] -= d;
xy[4] -= d; xy[5] += d;
xy[6] += d; xy[7] += d;
}
if (dbg_xy != null){
for (int i = 0; i < xy.length; i++){
dbg_xy[i][s.tile] += xy[i] * s.weight;
}
dbg_xy[8][s.tile] += s.weight;
}
if (use_vertical) {
// 2d optimization for 4 functions of x, y
mdata[indx][0] = new double [2];
mdata[indx][0][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
mdata[indx][0][1] = (2.0 * tileY)/tilesY - 1.0; // -1.0 to +1.0
mdata[indx][1] = xy; // new double [8]; // 4
mdata[indx][2] = new double [1];
mdata[indx][2][0] = s.weight; // disp_strength[2 * p.x + 1][p.y]; // strength
} else {
// 4 individual 1d optimization for functions of x only
for (int n = 0; n < xy.length; n++){
mdata[n][indx][0] = (2.0 * tileX)/tilesX - 1.0; // -1.0 to +1.0;
mdata[n][indx][1] = xy[n];
mdata[n][indx][2] = s.weight; // disp_strength[2 * p.x + 1][p.y]; // strength
}
}
indx ++;
}
if (dbg_xy != null){
for (int nTile = 0; nTile < num_tiles; nTile++){
if (dbg_xy[8][nTile] > 0.0){
for (int i = 0; i< 8; i++) {
dbg_xy[i][nTile] /= dbg_xy[8][nTile];
}
} else {
for (int i = 0; i< 8; i++) {
dbg_xy[i][nTile] = Double.NaN;
}
}
}
String [] titles = {"x0", "y0", "x1", "y1", "x2", "y2", "x3","y3","weight"};
(new showDoubleFloatArrays()).showArrays(
dbg_xy,
tilesX,
tilesY,
true,
"xy_mismatch",
titles);
}
double [][] coeffs = new double[8][6];
if (use_vertical){
double [][] approx2d = pa.quadraticApproximation(
mdata,
!use_quadratic, // boolean forceLinear, // use linear approximation
thresholdLin, // threshold ratio of matrix determinant to norm for linear approximation (det too low - fail)
thresholdQuad, // threshold ratio of matrix determinant to norm for quadratic approximation (det too low - fail)
debugLevel);
for (int n = 0; n < approx2d.length; n++){
if (approx2d[n].length == 6) {
coeffs[n] = approx2d[n];
} else {
for (int i = 0; i < 3; i++){
coeffs[n][3+i] = approx2d[n][i];
}
}
}
} else {
for (int n = 0; n < mdata.length; n++){
double [] approx1d = pa.polynomialApproximation1d(mdata[n], use_quadratic ? 2 : 1);
coeffs[n][5] = approx1d[0];
coeffs[n][3] = approx1d[1];
if (approx1d.length > 2){
coeffs[n][0] = approx1d[2];
}
}
}
// convert to 8 sets of coefficient for px0, py0, px1, py1, ... py3.
double [][][] coeff_full = new double [4][2][6];
for (int j = 0; j < coeffs[0].length; j++){
coeff_full[0][0][j] = coeffs[0][j];
coeff_full[0][1][j] = coeffs[1][j];
coeff_full[1][0][j] = coeffs[2][j];
coeff_full[1][1][j] = coeffs[3][j];
coeff_full[2][0][j] = coeffs[4][j];
coeff_full[2][1][j] = coeffs[5][j];
coeff_full[3][0][j] = coeffs[6][j];
coeff_full[3][1][j] = coeffs[7][j];
}
return coeff_full;
}
public double [][][] infinityMismatchCorrectionOld(
final boolean use_quadratic,
final boolean use_vertical,
EyesisCorrectionParameters.CLTParameters clt_parameters,
......@@ -726,6 +921,147 @@ public class AlignmentCorrection {
}
public double[][] filterLazyEyePairs (
final double[][] samples_in,
final int smpl_side, // 8 x8 masked, 16x16 sampled
final double rms_max,
final double frac_keep,
final int min_samples,
final boolean norm_center, // if there are more tiles that fit than minsamples, replace with a single equal weight
final int tilesX)
{
if (samples_in.length > NUM_SLICES){
final double [][] samples = new double [samples_in.length][];
for (int nfirst = 0; nfirst < samples_in.length; nfirst += NUM_SLICES){
double [][] ds = new double[NUM_SLICES][]; // {disp_strength_in[i],disp_strength_in[i+1]};
for (int slice = 0; slice < NUM_SLICES; slice++){
ds[slice] = samples[nfirst+slice];
}
double [][] ds_rslt = filterLazyEyePairs (
ds,
smpl_side, // 8 x8 masked, 16x16 sampled
rms_max,
frac_keep,
min_samples,
norm_center,
tilesX);
for (int slice = 0; slice < NUM_SLICES; slice++){
samples[nfirst+slice] = ds_rslt[slice];
}
}
return samples;
}
final int num_pairs = 4;
final int num_tiles = samples_in[0].length;
int tilesY = num_tiles/tilesX;
final double [][] samples = new double [NUM_SLICES][num_tiles];
// final int low_lim = -smpl_side/2;
// final int high_lim = 3 * smpl_side/2;
final int step = norm_center ? smpl_side : 1;
final int start = norm_center ? smpl_side/2 : 0;
for (int tY = start; tY < tilesY; tY += step){
for (int tX = start; tX < tilesX ; tX += step){
ArrayList<Integer> sample_list = new ArrayList<Integer>();
for (int sY = -smpl_side; sY < smpl_side; sY++){
int y = tY + sY;
if ((y >= 0) && (y <tilesY)) {
for (int sX = -smpl_side; sX < smpl_side; sX++){
int x = tX + sX;
if ((x >= 0) && (x < tilesX)) {
int nTile = y * tilesX + x;
double w = samples_in[1][nTile];
if (w > 0.0){
sample_list.add(nTile);
}
}
}
}
}
int samples_left = (int) (frac_keep * sample_list.size());
if (samples_left >= min_samples){
double [] s1 = new double [2 * num_pairs];
double [] s2 = new double [2 * num_pairs];
double sw = 0;
for (Integer nTile: sample_list){
double w = samples_in[1][nTile];
for (int i = 0; i < s1.length; i++){
double d = samples_in[2 + i][nTile];
s1[i] += w * d;
s2[i] += w *d *d;
}
sw += w;
}
double rms = 0.0;
for (int i = 0; i < s1.length; i++){
s1[i] /= sw; // average
s2[i] /= sw;
rms += s2[i] - s1[i] * s1[i];
}
rms = Math.sqrt(rms/s1.length);
// if (rms_max > 0.0){
// System.out.println("filterLazyEyePairs(): tX="+tX+", tY="+ tY+" rms = "+rms);
// }
if (rms <= rms_max){ // otherwise discard all the tile
while (sample_list.size() > samples_left){
int worst_index = -1;
double worst_var = 0;
for (int i = 0; i < sample_list.size(); i++){
int nTile = sample_list.get(i);
double sv = 0;
for (int n = 0; n < s1.length; n++){
double d = samples_in[2 + n][nTile] - s1[n];
sv += d * d;
}
if (sv > worst_var) {
worst_var = sv;
worst_index = i;
}
}
// remove worst
int nTile = sample_list.remove(worst_index);
double w = samples_in[1][nTile];
for (int n = 0; n < s1.length; n++){
s1[n] *= sw; // now it is weighted sum
double d = samples_in[2 + n][nTile];
s1[n] -= d * w;
s1[n] /= (sw - w); // average again
}
sw -= w;
}
if (norm_center) {
double sd = 0.0;
// double sw1 = 0.0;
for (Integer nTile:sample_list) {
double w = samples_in[1][nTile];
double d = samples_in[0][nTile];
sd += w*d;
// sw1 += w;
}
sd /= sw;
int nTile =tY * tilesX + tX;
samples[0][nTile] = sd;
samples[1][nTile] = 1.0; // sw; // equal weight
for (int j = 0; j < 2 * num_pairs; j++) {
samples[2 + j][nTile] = s1[j];
}
} else { // if (norm_center)
int nTile =tY * tilesX + tX;
if (sample_list.contains(nTile)){
for (int j = 0; j < samples.length; j++) {
samples[j][nTile] = samples_in[j][nTile];
}
}
} // if (norm_center) else
}
}
}
}
return samples;
}
public double[][] filterHistogramFar (
final double[][] disp_strength_in,
......@@ -888,7 +1224,7 @@ public class AlignmentCorrection {
for (int nfirst = 0; nfirst < disp_strength_in.length; nfirst += NUM_SLICES){
double [][] ds = new double[NUM_SLICES][]; // {disp_strength_in[i],disp_strength_in[i+1]};
for (int slice = 0; slice < NUM_SLICES; slice++){
ds[slice] = disp_strength[nfirst+slice];
ds[slice] = disp_strength_in[nfirst+slice];
}
double [][] ds_rslt = filterDisparityStrength (
ds,
......@@ -1034,4 +1370,657 @@ public class AlignmentCorrection {
}
}
public double [][] getFineCorrFromImage(
ImagePlus imp_src,
int debugLevel)
{
// double min_max_ratio = 1.3;
ImageStack clt_mismatches_stack= imp_src.getStack();
final int tilesX = clt_mismatches_stack.getWidth(); // tp.getTilesX();
final int tilesY = clt_mismatches_stack.getHeight(); // tp.getTilesY();
final int nTiles =tilesX * tilesY;
final int num_scans = clt_mismatches_stack.getSize()/NUM_ALL_SLICES;
final double [][] scans = new double [num_scans * NUM_ALL_SLICES][nTiles];
for (int ns = 0; ns < num_scans; ns++){
// double [][]min_max_strength = new double[2][nTiles];
for (int pair = 0; pair < 4; pair++){
float [][] fset = new float [3][];
fset[0] = (float[]) clt_mismatches_stack.getPixels(12 * num_scans + ns +1);
fset[1] = (float[]) clt_mismatches_stack.getPixels(13 * num_scans + ns +1); //
for (int i = 0; i < nTiles; i++){
scans[ns * NUM_ALL_SLICES + 0][i] = fset[0][i]; // disparity
scans[ns * NUM_ALL_SLICES + 1][i] = fset[1][i]; // strength
}
fset[0] = (float[]) clt_mismatches_stack.getPixels((2 * pair + 0) * num_scans + ns +1);
fset[1] = (float[]) clt_mismatches_stack.getPixels((2 * pair + 1) * num_scans + ns +1); //
fset[2] = (float[]) clt_mismatches_stack.getPixels((8 + pair ) * num_scans + ns +1);
for (int i = 0; i < nTiles; i++){
// if (i == 55156) { // 52564){
// System.out.println("tile="+i+" scan="+ns+" pair = "+pair+ "fset[2]["+i+"]="+fset[2][i]);
// }
scans[ns * NUM_ALL_SLICES + pair * 3 + 2][i] = fset[0][i]; // dx_i
scans[ns * NUM_ALL_SLICES + pair * 3 + 3][i] = fset[1][i]; // dy_i
scans[ns * NUM_ALL_SLICES + pair * 3 + 4][i] = fset[2][i]; // str_i
// if ((pair == 0) || (fset[2][i] < min_max_strength[0][i])) min_max_strength[0][i] = fset[2][i];
// if ((pair == 0) || (fset[2][i] > min_max_strength[1][i])) min_max_strength[1][i] = fset[2][i];
// if (fset[2][i] < min_comp_strength) {
// scans[ns * NUM_SLICES + 1][i] = -1.0; // -1.0 - temporary to indicate
// };
}
}
// for (int i = 0; i < nTiles; i++){
// if (min_max_strength[1][i] > (min_max_ratio * min_max_strength[0][i]) ){
// scans[ns * NUM_SLICES + 1][i] = -1.0; // -1.0 - temporary to indicate
// }
// }
}
if (debugLevel > -1) {
// String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "str0", "dx1", "dy1", "str1", "dx2", "dy2", "str2", "dx3", "dy3", "str3"};
String [] titles = new String [num_scans * NUM_ALL_SLICES];
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < NUM_ALL_SLICES; i++){
titles[ns * NUM_ALL_SLICES + i] = prefixes[i]+"_"+ns;
}
}
(new showDoubleFloatArrays()).showArrays(scans, tilesX, tilesY, true, "scans" , titles);
}
return scans;
}
public double [][][] lazyEyeCorrection(
final double min_strength_in,
final double max_diff,
final double comp_strength_var,
final int max_iterations,
final double max_coeff_diff,
final double far_pull, // = 0.2; // 1; // 0.5;
final double strength_pow,
final double lazyEyeCompDiff, // clt_parameters.fcorr_disp_diff
final int lazyEyeSmplSide, // = 2; // Sample size (side of a square)
final int lazyEyeSmplNum, // = 3; // Number after removing worst (should be >1)
final double lazyEyeSmplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
final double lazyEyeDispVariation, // maximal full disparity difference between tgh tile and 8 neighborxs
final int smplSide, // = 2; // Sample size (side of a square)
final int smplNum, // = 3; // Number after removing worst (should be >1)
final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
// histogram parameters
final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
final double hist_disp_min,
final double hist_disp_step,
final int hist_num_bins,
final double hist_sigma,
final double hist_max_diff,
final int hist_min_samples,
final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
final double inf_fraction, // fraction of the weight for the infinity tiles
EyesisCorrectionParameters.CLTParameters clt_parameters,
double [][] scans_14,
int tilesX,
double magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
int debugLevel){
final int num_scans = scans_14.length/NUM_ALL_SLICES;
final int num_tiles = scans_14[0].length;
final int tilesY = num_tiles/tilesX;
final double [][] scans = new double [num_scans * NUM_SLICES][];
final int [] indices_14_10 = {0,1,2,3,5,6,8,9,11,12};
final double [][] comp_strength_rms = new double [num_scans][num_tiles];
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < indices_14_10.length; i++){
scans[ns * NUM_SLICES + i] = scans_14[ns * NUM_ALL_SLICES + indices_14_10[i]];
}
}
for (int ns = 0; ns < num_scans; ns++){
for (int nTile = 0; nTile < num_tiles; nTile++){
double s1=0.0, s2=0.0;
for (int pair = 0; pair <4; pair++){
double s = scans_14[ns * NUM_ALL_SLICES + 4 + 3 * pair][nTile];
s1 += s;
s2 +=s * s;
}
s1 /= 4;
s2 /= 4;
comp_strength_rms[ns][nTile] = Math.sqrt(s2 - s1*s1);
}
}
/*
for (int ns = 0; ns < num_scans; ns++){
for (int nTile = 0; nTile < num_tiles; nTile++){
double s1=0.0, s2=0.0;
for (int pair = 0; pair <4; pair++){
double s = scans_14[ns * NUM_ALL_SLICES + 4 + 3 * pair][nTile];
if (pair == 0){
s1 = s;
s2 = s;
} else {
s1= Math.min(s, s1);
s2= Math.max(s, s2);
}
}
comp_strength_rms[ns][nTile] = s2 - s1;
}
}
*/
/*
* None of comp_strength_rms methods works to detect potential outliers for horizontal/vertical features
*/
if (debugLevel > -1) {
String [] titles = new String [num_scans];
for (int ns = 0; ns < num_scans; ns++){
titles[ns] = "scan_" + ns;
}
(new showDoubleFloatArrays()).showArrays(comp_strength_rms, tilesX, tilesY, true, "comp_strength_rms" , titles);
}
double[][] filtered_scans = filterDisparityStrength (
scans, // final double[][] disp_strength_in,
min_strength_in, // final double strength_floor,
strength_pow, // final double strength_pow,
lazyEyeSmplSide, // final int smplSide, // = 2; // Sample size (side of a square)
lazyEyeSmplNum, // final int smplNum, // = 3; // Number after removing worst (should be >1)
lazyEyeSmplRms, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
tilesX);// final int tilesX);
if (debugLevel > -1) {
System.out.println("lazyEyeCorrection() 1: removing tile with residual disparity absoulte value > "+lazyEyeCompDiff);
}
/*
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < num_tiles; i++){
double disp = filtered_scans[ns * NUM_SLICES + 0][i];
if (Math.abs(disp) > lazyEyeCompDiff) {
filtered_scans[ns * NUM_SLICES + 1][i] = 0.0;
}
}
}
*/
double [][] combo_mismatch = new double [NUM_SLICES][num_tiles];
double [] combo_comp_rms = new double [num_tiles];
for (int ns = 0; ns < num_scans; ns++){
for (int nTile = 0; nTile < num_tiles; nTile++) {
double w = filtered_scans[ns * NUM_SLICES + 1][nTile];
if (w > 0.0){
double disp = filtered_scans[ns * NUM_SLICES + 0][nTile];
if (Math.abs(disp) <= lazyEyeCompDiff) {
for (int i = 2; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] += filtered_scans[ns * NUM_SLICES + i][nTile] * w;
}
combo_mismatch[0][nTile] += (
filtered_scans[ns * NUM_SLICES + 0][nTile]/clt_parameters.corr_magic_scale +
clt_parameters.disp_scan_start + clt_parameters.disp_scan_step * ns)* w;
combo_mismatch[1][nTile] += w;
combo_comp_rms[nTile] += w * comp_strength_rms[ns][nTile];
}
}
}
}
for (int nTile = 0; nTile < num_tiles; nTile++) {
double w = combo_mismatch[1][nTile];
if (w > 0.0){
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] /= w;
}
combo_comp_rms[nTile] /= w;
} else {
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] = Double.NaN;
}
combo_comp_rms[nTile] = Double.NaN;
}
}
if (debugLevel > 100) {
(new showDoubleFloatArrays()).showArrays(combo_comp_rms, tilesX, tilesY, "combo_comp_rms");
}
final TileNeibs tnImage = new TileNeibs(tilesX, tilesY); // num_tiles/tilesX);
for (int nTile = 0; nTile < num_tiles; nTile++) if (combo_mismatch[1][nTile] > 0.0){
double d = combo_mismatch[0][nTile];
for (int dir = 0; dir <8; dir++){
int nTile1 = tnImage.getNeibIndex(nTile, dir);
if ((nTile1 >= 0) && (combo_mismatch[1][nTile1] > 0.0)){
if (Math.abs(combo_mismatch[0][nTile1] - d) > lazyEyeDispVariation){
combo_mismatch[1][nTile] = 0.0;
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] = Double.NaN;
}
break;
}
}
}
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
(new showDoubleFloatArrays()).showArrays(combo_mismatch, tilesX, combo_mismatch[0].length/tilesX, true, "combo_mismatch" , prefixes);
}
combo_mismatch = filterLazyEyePairs (
combo_mismatch, // final double[][] samples_in,
8, // final int smpl_side, // 8 x8 masked, 16x16 sampled
0.25, // final double rms_max, TODO: find reasonable one mot critical?
0.5, // final double frac_keep,
5, // final int min_samples,
true, // final boolean norm_center, // if there are more tiles that fit than minsamples, replace with a single equal weight
tilesX); // final int tilesX);
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
(new showDoubleFloatArrays()).showArrays(combo_mismatch, tilesX, combo_mismatch[0].length/tilesX, true, "filtered_mismatch" , prefixes);
}
// extract infinity data to be processed as infinity
double [][] inf_scan = new double [NUM_SLICES][];
for (int i = 0; i < NUM_SLICES; i++){
inf_scan[i] = scans[i];
}
if (smplSide > 1){
inf_scan = filterDisparityStrength (
inf_scan,
min_strength_in, // final double strength_floor,
strength_pow,
smplSide, // = 2; // Sample size (side of a square)
smplNum, // = 3; // Number after removing worst (should be >1)
smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
tilesX);
if (debugLevel > 0){
double [][] dbg_img = inf_scan.clone();
for (int n = 0; n < inf_scan.length; n++){
dbg_img[n] = inf_scan[n].clone();
}
for (int n = 0; n < dbg_img.length; n+=2){
for (int i = 0; i < dbg_img[n].length; i++) {
if (dbg_img[n+1][i] == 0.0){
dbg_img[n][i] = Double.NaN;
}
}
}
(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "filtered_infinity_ds"); // , titles);
}
}
if (hist_smpl_side > 0) { // 0 to bypass histogram filtering
inf_scan = filterHistogramFar (
inf_scan, // final double[][] disp_strength_in,
hist_smpl_side, // final int smpl_side, // 8 x8 masked, 16x16 sampled
hist_disp_min, // final double disp_min,
hist_disp_step, // final double disp_step,
hist_num_bins, // final int num_bins,
hist_sigma, // final double sigma,
hist_max_diff, // final double max_diff,
hist_min_samples, // final int min_samples,
hist_norm_center, // final boolean norm_center, // if there are more tiles that fit than minsamples, replace with
tilesX); // final int tilesX)
if (debugLevel > 0){
double [][] dbg_img = inf_scan.clone();
for (int n = 0; n < inf_scan.length; n++){
dbg_img[n] = inf_scan[n].clone();
}
for (int n = 0; n < dbg_img.length; n+=2){
for (int i = 0; i < dbg_img[n].length; i++) {
if (dbg_img[n+1][i] == 0.0){
dbg_img[n][i] = Double.NaN;
}
}
}
(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX, tilesY, true, "hist_filt_ds"); // , titles);
}
}
// combine infinity and lasy eye scan data into a single array
double [][] inf_and_ly = new double [2 * NUM_SLICES][];
for (int i = 0; i < NUM_SLICES; i++){
inf_and_ly[i] = inf_scan[i];
inf_and_ly[i + NUM_SLICES] = combo_mismatch[i];
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
String [] titles = new String [2 * NUM_SLICES];
for (int i = 0; i < NUM_SLICES; i++){
titles[i] = prefixes[i]+"-inf";
titles[i + NUM_SLICES] = prefixes[i]+"-ly";
}
(new showDoubleFloatArrays()).showArrays(inf_and_ly, tilesX, tilesY, true, "inf_and_ly",titles);
int step = hist_smpl_side; // should be the same for both filters
int tilesX1 = tilesX/step;
int tilesY1 = tilesY/step;
int num_tiles1 = tilesX1 * tilesY1;
double [][] dbg_img = new double [inf_and_ly.length][num_tiles1];
for (int tY = 0; tY < tilesY1; tY++) {
for (int tX = 0; tX < tilesX1; tX++) {
// if (tY == 14) {
// System.out.println("lazyEyeCorrection(): tX="+tX+", tY="+tY);
// }
int nTile1 = tX + tY*tilesX1;
for (int sY = 0; sY < step; sY ++) {
for (int sX = 0; sX < step; sX ++) {
int nTile = (sX + step * tX) + (sY + step * tY) * tilesX;
for (int ns = 0; ns <2; ns++){
double w = inf_and_ly[ns*NUM_SLICES + 1][nTile];
if (w > 0.0){
for (int i = 0; i < NUM_SLICES; i++) if (i != 1) {
dbg_img[ns * NUM_SLICES + i][nTile1] += w * inf_and_ly[ns * NUM_SLICES + i][nTile];
}
dbg_img[ns * NUM_SLICES + 1][nTile1] += w;
}
}
}
}
for (int ns = 0; ns < 2; ns++){
double w = dbg_img[ns * NUM_SLICES + 1][nTile1];
if (w > 0.0){
for (int i = 0; i < NUM_SLICES; i++) if (i != 1) {
dbg_img[ns * NUM_SLICES + i][nTile1] /= w;
}
} else {
for (int i = 0; i < NUM_SLICES; i++) if (i != 1) {
dbg_img[ns * NUM_SLICES + i][nTile1] = Double.NaN;
}
}
}
}
}
(new showDoubleFloatArrays()).showArrays(dbg_img, tilesX1, tilesY1, true, "inf_and_ly8",titles);
}
// create list for infinity data
ArrayList<Sample> inf_samples_list = selectInfinityTiles(
clt_parameters.fcorr_inf_vert,// final boolean use_vertical,
0.0, // any > 0.0
max_diff, // max_diff, //clt_parameters.fcorr_inf_diff
max_iterations, // max_iterations, // clt_parameters.inf_iters
max_coeff_diff, // max_coeff_diff, // clt_parameters.inf_final_diff
far_pull, // far_pull, // clt_parameters.inf_far_pull, = 0.2; // 1; // 0.5;
clt_parameters,
inf_scan,
tilesX,
magic_coeff, // magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
debugLevel);
if (debugLevel > -1) {
double inf_weight = 0.0;
for (Sample s: inf_samples_list) {
inf_weight += s.weight;
}
System.out.println("lazyEyeCorrection(): number of infinity samples="+inf_samples_list.size()+", total weight = "+inf_weight);
}
// adjust weight to balance infinity data and lazy eye one. As some tiles were discarded by selectInfinityTiles() list and not the original
// array has to be used to find the total weight of the infinity tile. Other ones will be used with no extra filtering
double [] total_weights = new double[2];
for (Sample s: inf_samples_list) {
total_weights[0] += s.weight;
}
for (int nTile = 0; nTile < num_tiles; nTile++) {
total_weights[1]+= inf_and_ly[1 * NUM_SLICES + 1][nTile];
}
double [] weights = {
inf_fraction * (total_weights[0] + total_weights[1]) / total_weights[0],
(1.0 - inf_fraction) * (total_weights[0] + total_weights[1]) / total_weights[1],
};
for (int ns = 0; ns <2; ns++) {
for (int nTile = 0; nTile < num_tiles; nTile++) {
inf_and_ly[ns * NUM_SLICES + 1][nTile] *= weights[ns];
}
}
for (Sample s: inf_samples_list) {
s.weight *= weights[0];
}
// Supplement list with the lazy eye scans data - use all tiles
for (int nTile = 0; nTile < num_tiles; nTile++) {
double w = inf_and_ly[1 * NUM_SLICES + 1][nTile];
if (w > 0.0) {
inf_samples_list.add(new Sample(1,nTile,w));
}
}
if (debugLevel > -1) {
double inf_weight = 0.0;
for (Sample s: inf_samples_list) {
inf_weight += s.weight;
}
System.out.println("lazyEyeCorrection(): number of all samples="+inf_samples_list.size()+", total weight = "+inf_weight);
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
String [] titles = new String [num_scans * NUM_SLICES];
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < NUM_SLICES; i++){
titles[ns * NUM_SLICES + i] = prefixes[i]+"_"+ns;
}
}
(new showDoubleFloatArrays()).showArrays(filtered_scans, tilesX, tilesY, true, "filtered_scans" , titles);
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
(new showDoubleFloatArrays()).showArrays(combo_mismatch, tilesX, combo_mismatch[0].length/tilesX, true, "combo_mismatch" , prefixes);
}
double [][][] mismatch_corr_coefficiants = infinityMismatchCorrection(
clt_parameters.fcorr_quadratic, // final boolean use_quadratic,
true, // clt_parameters.fcorr_inf_vert, // final boolean use_vertical,
false, // final boolean use_disparity, // for infinity
clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
inf_and_ly, // double [][] disp_strength,
inf_samples_list, // ArrayList<Sample> samples_list,
tilesX, // int tilesX,
magic_coeff, // double , // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
debugLevel); // int debugLevel)
if (debugLevel > -1) {
System.out.println("===== lazyEyeCorrection(): correction coefficients =====");
show_fine_corr(
mismatch_corr_coefficiants,
"mismatch_corr_coefficiants");
}
return mismatch_corr_coefficiants;
}
public void process_fine_corr(
boolean dry_run,
EyesisCorrectionParameters.CLTParameters clt_parameters,
int debugLevel
) {
final double disp_variation = 0.2; // 15; // 5; // 0.2 ?
ImagePlus imp_src = WindowManager.getCurrentImage();
if (imp_src==null){
IJ.showMessage("Error","12*n-layer file clt_mismatches is required");
return;
}
ImageStack clt_mismatches_stack= imp_src.getStack();
final int tilesX = clt_mismatches_stack.getWidth(); // tp.getTilesX();
final int tilesY = clt_mismatches_stack.getHeight(); // tp.getTilesY();
final int nTiles =tilesX * tilesY;
final int num_scans = clt_mismatches_stack.getSize()/14;
final double [][] scans = new double [num_scans * NUM_SLICES][nTiles];
for (int ns = 0; ns < num_scans; ns++){
for (int pair = 0; pair < 4; pair++){
float [][] fset = new float [2][];
fset[0] = (float[]) clt_mismatches_stack.getPixels((2 * pair + 0) * num_scans + ns +1);
fset[1] = (float[]) clt_mismatches_stack.getPixels((2 * pair + 1) * num_scans + ns +1); //
for (int i = 0; i < nTiles; i++){
scans[ns * NUM_SLICES + pair * 2 + 2][i] = fset[0][i]; // dxi
scans[ns * NUM_SLICES + pair * 2 + 3][i] = fset[1][i]; // dyi
}
}
float [][] fset = new float [2][];
fset[0] = (float[]) clt_mismatches_stack.getPixels(12 * num_scans + ns +1);
fset[1] = (float[]) clt_mismatches_stack.getPixels(13 * num_scans + ns +1); //
for (int i = 0; i < nTiles; i++){
scans[ns * NUM_SLICES + 0][i] = fset[0][i]; // disparity
scans[ns * NUM_SLICES + 1][i] = fset[1][i]; // strength
}
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
String [] titles = new String [num_scans * NUM_SLICES];
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < NUM_SLICES; i++){
titles[ns * NUM_SLICES + i] = prefixes[i]+"_"+ns;
}
}
(new showDoubleFloatArrays()).showArrays(scans, tilesX, scans[0].length/tilesX, true, "scans" , titles);
}
// public double fcorr_disp_diff = 1.5; // consider only tiles with absolute residual disparity lower than
// separate here from reading image
final int num_tiles = scans[0].length;
double[][] filtered_scans = filterDisparityStrength (
scans, // final double[][] disp_strength_in,
clt_parameters.fcorr_inf_strength, // final double strength_floor,
clt_parameters.inf_str_pow, // final double strength_pow,
clt_parameters.inf_smpl_side, // final int smplSide, // = 2; // Sample size (side of a square)
clt_parameters.inf_smpl_num, // final int smplNum, // = 3; // Number after removing worst (should be >1)
clt_parameters.inf_smpl_rms, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
tilesX);// final int tilesX);
// public double fcorr_disp_diff = 1.5; // consider only tiles with absolute residual disparity lower than
if (debugLevel > -1) {
System.out.println("process_fine_corr() 2: removing tile with residual disparity absoulte value > "+ clt_parameters.fcorr_disp_diff);
}
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < num_tiles; i++){
double disp = filtered_scans[ns * NUM_SLICES + 0][i];
if (Math.abs(disp) > clt_parameters.fcorr_disp_diff) {
filtered_scans[ns * NUM_SLICES + 1][i] = 0.0;
}
}
}
double [][] combo_mismatch = new double [NUM_SLICES][num_tiles];
for (int ns = 0; ns < num_scans; ns++){
for (int nTile = 0; nTile < num_tiles; nTile++) {
double w = filtered_scans[ns * NUM_SLICES + 1][nTile];
if (w > 0.0){
double disp = filtered_scans[ns * NUM_SLICES + 0][nTile];
if (Math.abs(disp) <= clt_parameters.fcorr_disp_diff) {
for (int i = 2; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] += filtered_scans[ns * NUM_SLICES + i][nTile] * w;
}
combo_mismatch[0][nTile] += (
filtered_scans[ns * NUM_SLICES + 0][nTile]/clt_parameters.corr_magic_scale +
clt_parameters.disp_scan_start + clt_parameters.disp_scan_step * ns)* w;
combo_mismatch[1][nTile] += w;
}
}
}
}
for (int nTile = 0; nTile < num_tiles; nTile++) {
double w = combo_mismatch[1][nTile];
if (w > 0.0){
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] /= w;
}
} else {
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] = Double.NaN;
}
}
}
final TileNeibs tnImage = new TileNeibs(tilesX, tilesY); // num_tiles/tilesX);
for (int nTile = 0; nTile < num_tiles; nTile++) if (combo_mismatch[1][nTile] > 0.0){
double d = combo_mismatch[0][nTile];
for (int dir = 0; dir <8; dir++){
int nTile1 = tnImage.getNeibIndex(nTile, dir);
if ((nTile1 >= 0) && (combo_mismatch[1][nTile1] > 0.0)){
if (Math.abs(combo_mismatch[0][nTile1] - d) > disp_variation){
combo_mismatch[1][nTile] = 0.0;
for (int i = 0; i < NUM_SLICES; i++) if (i != 1){
combo_mismatch[i][nTile] = Double.NaN;
}
break;
}
}
}
}
// extract infinity data to be processed as infinity
double [][] inf_scan = new double [NUM_SLICES][];
for (int i = 0; i < NUM_SLICES; i++){
inf_scan[i] = scans[i];
}
// Need to filter first!
ArrayList<Sample> inf_samples_list = selectInfinityTiles(
clt_parameters.fcorr_inf_vert,// final boolean use_vertical,
0.0, // any > 0.0
clt_parameters.fcorr_inf_diff, // max_diff, //clt_parameters.fcorr_inf_diff
clt_parameters.inf_iters, // max_iterations, // clt_parameters.inf_iters
clt_parameters.inf_final_diff, // max_coeff_diff, // clt_parameters.inf_final_diff
clt_parameters.inf_far_pull, // far_pull, // clt_parameters.inf_far_pull, = 0.2; // 1; // 0.5;
clt_parameters,
inf_scan,
tilesX,
clt_parameters.corr_magic_scale, // magic_coeff, // still not understood coefficient that reduces reported disparity value. Seems to be around 8.5
debugLevel);
if (debugLevel > -1) {
double inf_weight = 0.0;
for (Sample s: inf_samples_list) {
inf_weight += s.weight;
}
System.out.println("process_fine_corr(): number of infinity samples="+inf_samples_list.size()+", total weight = "+inf_weight);
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
String [] titles = new String [num_scans * NUM_SLICES];
for (int ns = 0; ns < num_scans; ns++){
for (int i = 0; i < NUM_SLICES; i++){
titles[ns * NUM_SLICES + i] = prefixes[i]+"_"+ns;
}
}
(new showDoubleFloatArrays()).showArrays(filtered_scans, tilesX, tilesY, true, "filtered_scans" , titles);
}
if (debugLevel > -1) {
String [] prefixes = {"disparity", "strength", "dx0", "dy0", "dx1", "dy1", "dx2", "dy2", "dx3", "dy3"};
(new showDoubleFloatArrays()).showArrays(combo_mismatch, tilesX, combo_mismatch[0].length/tilesX, true, "combo_mismatch" , prefixes);
}
}
}
src/main/java/EyesisCorrectionParameters.java
View file @ 21cc4033
......@@ -2008,20 +2008,41 @@ public class EyesisCorrectionParameters {
public double fcorr_disp_diff = 1.5; // consider only tiles with absolute residual disparity lower than
public boolean fcorr_quadratic = true; // Use quadratic polynomial for fine correction (false - only linear)
public boolean fcorr_ignore = false; // Ignore currently calculated fine correction
public double fcorr_inf_strength = 0.20 ; // Minimal correlation strength to use for infinity correction
public double fcorr_inf_diff = 0.2; // Disparity half-range for infinity
public boolean fcorr_inf_quad = true; // Use quadratic polynomial for infinity correction (false - only linear)
public boolean fcorr_inf_vert = false; // Correct infinity in vertical direction (false - only horizontal)
//--
public boolean inf_disp_apply = true; // Apply disparity correction to zero at infinity
public boolean inf_mism_apply = true; // Apply lazy eye correction at infinity
public int inf_iters = 20; // Infinity extraction - maximum iterations
public double inf_final_diff = 0.0001; // Coefficients maximal increment to exit iterations
public double inf_far_pull = 0.0; // include farther tiles than tolerance, but scale their weights
// infinity filter
public double inf_str_pow = 1.0; // Strength power for infinity filtering
public int inf_smpl_side = 3; // Sample size (side of a square) for infinity filtering
public int inf_smpl_num = 5; // Number after removing worst (should be >1) for infinity filtering
public double inf_smpl_rms = 0.1; // Maximal RMS of the remaining tiles in a sample for infinity filtering
//Histogram infinity filter
public int ih_smpl_step = 8; // Square sample step (50% overlap)
public double ih_disp_min = -1.0; // Minimal disparity
public double ih_disp_step = 0.05; // Disparity step
public int ih_num_bins = 40; // Number of bins
public double ih_sigma = 0.1; // Gaussian sigma (in disparity pixels)
public double ih_max_diff = 0.1; // Keep samples within this difference from farthest maximum
public int ih_min_samples = 10; // Minimal number of remaining samples
public boolean ih_norm_center = true; // Replace samples with a single average with equal weight
// old fcorr parameters, reuse?
public int fcorr_sample_size = 32; // Use square this size side to detect outliers
public int fcorr_mintiles = 8; // Keep tiles only if there are more in each square
public double fcorr_reloutliers = 0.5; // Remove this fraction of tiles from each sample
public double fcorr_sigma = 20.0; // Gaussian blur channel mismatch data
public double corr_magic_scale = 0.85; // reported correlation offset vs. actual one (not yet understood)
// 3d reconstruction
......@@ -2525,6 +2546,25 @@ public class EyesisCorrectionParameters {
properties.setProperty(prefix+"fcorr_inf_quad", this.fcorr_inf_quad+"");
properties.setProperty(prefix+"fcorr_inf_vert", this.fcorr_inf_vert+"");
properties.setProperty(prefix+"inf_disp_apply", this.inf_disp_apply+"");
properties.setProperty(prefix+"inf_mism_apply", this.inf_mism_apply+"");
properties.setProperty(prefix+"inf_iters", this.inf_iters+"");
properties.setProperty(prefix+"inf_final_diff", this.inf_final_diff +"");
properties.setProperty(prefix+"inf_far_pull", this.inf_far_pull +"");
properties.setProperty(prefix+"inf_str_pow", this.inf_str_pow +"");
properties.setProperty(prefix+"inf_smpl_side", this.inf_smpl_side+"");
properties.setProperty(prefix+"inf_smpl_num", this.inf_smpl_num+"");
properties.setProperty(prefix+"inf_smpl_rms", this.inf_smpl_rms +"");
properties.setProperty(prefix+"ih_smpl_step", this.ih_smpl_step+"");
properties.setProperty(prefix+"ih_disp_min", this.ih_disp_min +"");
properties.setProperty(prefix+"ih_disp_step", this.ih_disp_step +"");
properties.setProperty(prefix+"ih_num_bins", this.ih_num_bins+"");
properties.setProperty(prefix+"ih_sigma", this.ih_sigma +"");
properties.setProperty(prefix+"ih_max_diff", this.ih_max_diff +"");
properties.setProperty(prefix+"ih_min_samples", this.ih_min_samples+"");
properties.setProperty(prefix+"ih_norm_center", this.ih_norm_center+"");
properties.setProperty(prefix+"fcorr_sample_size",this.fcorr_sample_size+"");
properties.setProperty(prefix+"fcorr_mintiles", this.fcorr_mintiles+"");
properties.setProperty(prefix+"fcorr_reloutliers",this.fcorr_reloutliers +"");
......@@ -2995,6 +3035,30 @@ public class EyesisCorrectionParameters {
if (properties.getProperty(prefix+"fcorr_inf_quad")!=null) this.fcorr_inf_quad=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_quad"));
if (properties.getProperty(prefix+"fcorr_inf_vert")!=null) this.fcorr_inf_vert=Boolean.parseBoolean(properties.getProperty(prefix+"fcorr_inf_vert"));
if (properties.getProperty(prefix+"inf_disp_apply")!=null) this.inf_disp_apply=Boolean.parseBoolean(properties.getProperty(prefix+"inf_disp_apply"));
if (properties.getProperty(prefix+"inf_mism_apply")!=null) this.inf_mism_apply=Boolean.parseBoolean(properties.getProperty(prefix+"inf_mism_apply"));
if (properties.getProperty(prefix+"inf_iters")!=null) this.inf_iters=Integer.parseInt(properties.getProperty(prefix+"inf_iters"));
if (properties.getProperty(prefix+"inf_final_diff")!=null) this.inf_final_diff=Double.parseDouble(properties.getProperty(prefix+"inf_final_diff"));
if (properties.getProperty(prefix+"inf_far_pull")!=null) this.inf_far_pull=Double.parseDouble(properties.getProperty(prefix+"inf_far_pull"));
if (properties.getProperty(prefix+"inf_str_pow")!=null) this.inf_str_pow=Double.parseDouble(properties.getProperty(prefix+"inf_str_pow"));
if (properties.getProperty(prefix+"inf_smpl_side")!=null) this.inf_smpl_side=Integer.parseInt(properties.getProperty(prefix+"inf_smpl_side"));
if (properties.getProperty(prefix+"inf_smpl_num")!=null) this.inf_smpl_num=Integer.parseInt(properties.getProperty(prefix+"inf_smpl_num"));
if (properties.getProperty(prefix+"inf_smpl_rms")!=null) this.inf_smpl_rms=Double.parseDouble(properties.getProperty(prefix+"inf_smpl_rms"));
if (properties.getProperty(prefix+"ih_smpl_step")!=null) this.ih_smpl_step=Integer.parseInt(properties.getProperty(prefix+"ih_smpl_step"));
if (properties.getProperty(prefix+"ih_disp_min")!=null) this.ih_disp_min=Double.parseDouble(properties.getProperty(prefix+"ih_disp_min"));
if (properties.getProperty(prefix+"ih_disp_step")!=null) this.ih_disp_step=Double.parseDouble(properties.getProperty(prefix+"ih_disp_step"));
if (properties.getProperty(prefix+"ih_num_bins")!=null) this.ih_num_bins=Integer.parseInt(properties.getProperty(prefix+"ih_num_bins"));
if (properties.getProperty(prefix+"ih_sigma")!=null) this.ih_sigma=Double.parseDouble(properties.getProperty(prefix+"ih_sigma"));
if (properties.getProperty(prefix+"ih_max_diff")!=null) this.ih_max_diff=Double.parseDouble(properties.getProperty(prefix+"ih_max_diff"));
if (properties.getProperty(prefix+"ih_min_samples")!=null) this.ih_min_samples=Integer.parseInt(properties.getProperty(prefix+"ih_min_samples"));
if (properties.getProperty(prefix+"ih_norm_center")!=null) this.ih_norm_center=Boolean.parseBoolean(properties.getProperty(prefix+"ih_norm_center"));
if (properties.getProperty(prefix+"fcorr_sample_size")!=null) this.fcorr_sample_size=Integer.parseInt(properties.getProperty(prefix+"fcorr_sample_size"));
if (properties.getProperty(prefix+"fcorr_mintiles")!=null) this.fcorr_mintiles=Integer.parseInt(properties.getProperty(prefix+"fcorr_mintiles"));
if (properties.getProperty(prefix+"fcorr_reloutliers")!=null) this.fcorr_reloutliers=Double.parseDouble(properties.getProperty(prefix+"fcorr_reloutliers"));
......@@ -3480,6 +3544,27 @@ public class EyesisCorrectionParameters {
gd.addCheckbox ("Use quadratic polynomial for infinity correction (false - only linear)", this.fcorr_inf_quad);
gd.addCheckbox ("Correct infinity in vertical direction (false - only horizontal)", this.fcorr_inf_vert);
gd.addCheckbox ("Apply disparity correction to zero at infinity", this.inf_disp_apply);
gd.addCheckbox ("Apply lazy eye correction at infinity", this.inf_mism_apply);
gd.addNumericField("Infinity extraction - maximum iterations", this.inf_iters, 0);
gd.addNumericField("Coefficients maximal increment to exit iterations", this.inf_final_diff, 6);
gd.addNumericField("Include farther tiles than tolerance, but scale their weights", this.inf_far_pull, 3);
gd.addMessage ("--- Infinity filter ---");
gd.addNumericField("Strength power", this.inf_str_pow, 3);
gd.addNumericField("Sample size (side of a square)", this.inf_smpl_side, 0);
gd.addNumericField("Number after removing worst (should be >1)", this.inf_smpl_num, 0);
gd.addNumericField("Maximal RMS of the remaining tiles in a sample", this.inf_smpl_rms, 3);
gd.addMessage ("--- Infinity histogram filter ---");
gd.addNumericField("Square sample step (50% overlap)", this.ih_smpl_step, 0);
gd.addNumericField("Histogram minimal disparity", this.ih_disp_min, 3);
gd.addNumericField("Histogram disparity step", this.ih_disp_step, 3);
gd.addNumericField("Histogram number of bins", this.ih_num_bins, 0);
gd.addNumericField("Histogram Gaussian sigma (in disparity pixels)", this.ih_sigma, 3);
gd.addNumericField("Keep samples within this difference from farthest maximum", this.ih_max_diff, 3);
gd.addNumericField("Minimal number of remaining samples", this.ih_min_samples, 0);
gd.addCheckbox ("Replace samples with a single average with equal weight", this.ih_norm_center);
gd.addNumericField("Use square this size side to detect outliers", this.fcorr_sample_size, 0);
gd.addNumericField("Keep tiles only if there are more in each square", this.fcorr_mintiles, 0);
gd.addNumericField("Remove this fraction of tiles from each sample", this.fcorr_reloutliers, 3);
......@@ -3973,6 +4058,26 @@ public class EyesisCorrectionParameters {
this.fcorr_inf_quad= gd.getNextBoolean();
this.fcorr_inf_vert= gd.getNextBoolean();
this.inf_disp_apply= gd.getNextBoolean();
this.inf_mism_apply= gd.getNextBoolean();
this.inf_iters= (int) gd.getNextNumber();
this.inf_final_diff= gd.getNextNumber();
this.inf_far_pull= gd.getNextNumber();
this.inf_str_pow= gd.getNextNumber();
this.inf_smpl_side= (int) gd.getNextNumber();
this.inf_smpl_num= (int) gd.getNextNumber();
this.inf_smpl_rms= gd.getNextNumber();
this.ih_smpl_step= (int) gd.getNextNumber();
this.ih_disp_min= gd.getNextNumber();
this.ih_disp_step= gd.getNextNumber();
this.ih_num_bins= (int) gd.getNextNumber();
this.ih_sigma= gd.getNextNumber();
this.ih_max_diff= gd.getNextNumber();
this.ih_min_samples= (int) gd.getNextNumber();
this.ih_norm_center= gd.getNextBoolean();
this.fcorr_sample_size= (int)gd.getNextNumber();
this.fcorr_mintiles= (int) gd.getNextNumber();
this.fcorr_reloutliers= gd.getNextNumber();
......
src/main/java/Eyesis_Correction.java
View file @ 21cc4033
......@@ -4609,10 +4609,16 @@ private Panel panel1,
System.out.println("Created new QuadCLT instance, will need to read CLT kernels");
}
}
/*
QUAD_CLT.process_fine_corr(
dry_run, // boolean dry_run
CLT_PARAMETERS,
DEBUG_LEVEL);
*/
QUAD_CLT.processLazyEye(
CLT_PARAMETERS,
DEBUG_LEVEL);
return;
} else if (label.equals("CLT ext infinity corr")) {
......
src/main/java/ImageDtt.java
View file @ 21cc4033
......@@ -964,6 +964,7 @@ public class ImageDtt {
final double [][][][][] clt_corr_partial,// [tilesY][tilesX][quad]color][(2*transform_size-1)*(2*transform_size-1)] // if null - will not calculate
// [tilesY][tilesX] should be set by caller
final double [][] clt_mismatch, // [12][tilesY * tilesX] // transpose unapplied. null - do not calculate
// values in the "main" directions have disparity (*_CM) subtracted, in the perpendicular - as is
final double [][] disparity_map, // [8][tilesY][tilesX], only [6][] is needed on input or null - do not calculate
// last 2 - contrast, avg/ "geometric average)
......@@ -1606,11 +1607,11 @@ public class ImageDtt {
double yp,xp;
if (corr_pairs[pair][2] > 0){ // transpose - switch x <-> y
yp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];
xp = transform_size - 1 -corr_max_XYmp[1]; // do not campare to average - it should be 0 anyway
xp = transform_size - 1 -corr_max_XYmp[1]; // do not compare to average - it should be 0 anyway
} else {
xp = transform_size - 1 -corr_max_XYmp[0] - disparity_map[DISPARITY_INDEX_CM][tIndex];
yp = transform_size - 1 -corr_max_XYmp[1]; // do not campare to average - it should be 0 anyway
yp = transform_size - 1 -corr_max_XYmp[1]; // do not compare to average - it should be 0 anyway
}
double strength = tcorr_partial[pair][numcol][max_index]; // using the new location than for combined
clt_mismatch[3*pair + 0 ][tIndex] = xp;
......
src/main/java/QuadCLT.java
View file @ 21cc4033
......@@ -5237,22 +5237,22 @@ public class QuadCLT {
double [][][] new_corr = ac.infinityCorrection(
clt_parameters.fcorr_inf_strength, // final double min_strenth,
clt_parameters.fcorr_inf_diff, // final double max_diff,
20, // 0, // final int max_iterations,
0.0001, // final double max_coeff_diff,
0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;
1.0, // final double strength_pow,
3, // final int smplSide, // = 2; // Sample size (side of a square)
5, // final int smplNum, // = 3; // Number after removing worst (should be >1)
0.1, // 0.05, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
clt_parameters.inf_iters, // 20, // 0, // final int max_iterations,
clt_parameters.inf_final_diff, // 0.0001, // final double max_coeff_diff,
clt_parameters.inf_far_pull, // 0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;
clt_parameters.inf_str_pow, // 1.0, // final double strength_pow,
clt_parameters.inf_smpl_side, // 3, // final int smplSide, // = 2; // Sample size (side of a square)
clt_parameters.inf_smpl_num, // 5, // final int smplNum, // = 3; // Number after removing worst (should be >1)
clt_parameters.inf_smpl_rms, // 0.1, // 0.05, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
// histogram parameters
8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
-1.0, // final double hist_disp_min,
0.05, // final double hist_disp_step,
40, // final int hist_num_bins,
0.1, // final double hist_sigma,
0.1, // final double hist_max_diff,
10, // final int hist_min_samples,
true, // final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
clt_parameters.ih_smpl_step, // 8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
clt_parameters.ih_disp_min, // -1.0, // final double hist_disp_min,
clt_parameters.ih_disp_step, // 0.05, // final double hist_disp_step,
clt_parameters.ih_num_bins, // 40, // final int hist_num_bins,
clt_parameters.ih_sigma, // 0.1, // final double hist_sigma,
clt_parameters.ih_max_diff, // 0.1, // final double hist_max_diff,
clt_parameters.ih_min_samples, // 10, // final int hist_min_samples,
clt_parameters.ih_norm_center, // true, // final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
inf_disp_strength, // double [][] disp_strength,
tilesX, // int tilesX,
......@@ -5260,6 +5260,8 @@ public class QuadCLT {
debugLevel + 1); // int debugLevel)
if (debugLevel > -1){
System.out.println("process_infinity_corr(): ready to apply infinity correction");
show_fine_corr(
......@@ -5274,11 +5276,85 @@ public class QuadCLT {
debugLevel + 2);
}
}
public void processLazyEye(
EyesisCorrectionParameters.CLTParameters clt_parameters,
int debugLevel
) {
ImagePlus imp_src = WindowManager.getCurrentImage();
if (imp_src==null){
IJ.showMessage("Error","2*n-layer file with disparities/strengthspairs measured at infinity is required");
return;
}
ImageStack disp_strength_stack= imp_src.getStack();
final int tilesX = disp_strength_stack.getWidth(); // tp.getTilesX();
final int tilesY = disp_strength_stack.getHeight(); // tp.getTilesY();
final int nTiles =tilesX * tilesY;
AlignmentCorrection ac = new AlignmentCorrection(this);
double [][] scans = ac.getFineCorrFromImage(
imp_src,
// 0.2, // double min_comp_strength, // 0.2
debugLevel);
double [][][] new_corr = ac.lazyEyeCorrection(
clt_parameters.fcorr_inf_strength, // final double min_strenth,
clt_parameters.fcorr_inf_diff, // final double max_diff,
1.3, // final double comp_strength_var,
clt_parameters.inf_iters, // 20, // 0, // final int max_iterations,
clt_parameters.inf_final_diff, // 0.0001, // final double max_coeff_diff,
clt_parameters.inf_far_pull, // 0.0, // 0.25, // final double far_pull, // = 0.2; // 1; // 0.5;
clt_parameters.inf_str_pow, // 1.0, // final double strength_pow,
1.5, // final double lazyEyeCompDiff, // clt_parameters.fcorr_disp_diff
clt_parameters.inf_smpl_side, // final int lazyEyeSmplSide, // = 2; // Sample size (side of a square)
clt_parameters.inf_smpl_num, // final int lazyEyeSmplNum, // = 3; // Number after removing worst (should be >1)
0.1, // final double lazyEyeSmplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
0.2, // final double lazyEyeDispVariation, // 0.2, maximal full disparity difference between tgh tile and 8 neighborxs
clt_parameters.inf_smpl_side, // 3, // final int smplSide, // = 2; // Sample size (side of a square)
clt_parameters.inf_smpl_num, // 5, // final int smplNum, // = 3; // Number after removing worst (should be >1)
clt_parameters.inf_smpl_rms, // 0.1, // 0.05, // final double smplRms, // = 0.1; // Maximal RMS of the remaining tiles in a sample
// histogram parameters
clt_parameters.ih_smpl_step, // 8, // final int hist_smpl_side, // 8 x8 masked, 16x16 sampled
clt_parameters.ih_disp_min, // -1.0, // final double hist_disp_min,
clt_parameters.ih_disp_step, // 0.05, // final double hist_disp_step,
clt_parameters.ih_num_bins, // 40, // final int hist_num_bins,
clt_parameters.ih_sigma, // 0.1, // final double hist_sigma,
clt_parameters.ih_max_diff, // 0.1, // final double hist_max_diff,
clt_parameters.ih_min_samples, // 10, // final int hist_min_samples,
clt_parameters.ih_norm_center, // true, // final boolean hist_norm_center, // if there are more tiles that fit than min_samples, replace with
0.5, // final double inf_fraction, // fraction of the weight for the infinity tiles
clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
scans, // double [][] disp_strength,
tilesX, // int tilesX,
clt_parameters.corr_magic_scale, // double magic_coeff, // still not understood coefficent that reduces reported disparity value. Seems to be around 8.5
debugLevel + 1); // int debugLevel)
if (debugLevel > -100){
apply_fine_corr(
new_corr,
debugLevel + 2);
}
}
public void process_fine_corr(
boolean dry_run,
EyesisCorrectionParameters.CLTParameters clt_parameters,
int debugLevel
) {
if (dry_run) {
AlignmentCorrection ac = new AlignmentCorrection(this);
ac.process_fine_corr(
dry_run, // boolean dry_run,
clt_parameters, // EyesisCorrectionParameters.CLTParameters clt_parameters,
debugLevel); // int debugLevel
return;
}
ImagePlus imp_src = WindowManager.getCurrentImage();
if (imp_src==null){
IJ.showMessage("Error","12*n-layer file clt_mismatches is required");
......
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