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imagej-elphel
Commit 6ac376b0 authored by Andrey Filippov's avatar Andrey Filippov
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Debugging LMA for correlation maximums

parent 10495389
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src/main/java/Correlation2d.java
View file @ 6ac376b0
import java.util.ArrayList;
/**
**
** Correlation2d - Handle 2-d (phase) correlations, combining multiple-pair data
......@@ -6,7 +8,7 @@
**
** -----------------------------------------------------------------------------**
**
** ImageDtt.java is free software: you can redistribute it and/or modify
** Correlation2d.java is free software: you can redistribute it and/or modify
** it under the terms of the GNU General Public License as published by
** the Free Software Foundation, either version 3 of the License, or
** (at your option) any later version.
......@@ -31,8 +33,10 @@ public class Correlation2d {
private final int transform_size;
private final int transform_len;
private final int corr_size;
private final int [][] transpose_indices_ortho;
private final int [][] transpose_indices_diagonal;
// private final int [][] transpose_indices_ortho;
// private final int [][] transpose_indices_diagonal;
private final int [] transpose_all_ortho;
private final int [] transpose_all_diagonal;
// configuration for 8-lens and 4-lens cameras. 8-lens has baseline = 1 for 1..4 and 1/2 for 4..7
/*0 1
......@@ -59,6 +63,13 @@ public class Correlation2d {
{2, 6, PAIR_DIAGONAL_OTHER, 4},
{5, 1, PAIR_DIAGONAL_OTHER, 4},
};
final static int [][] GROUPS = { // {diagonal, scale}
{0, 1},
{1, 1},
{0, 2},
{1, 2},
{0, 4},
{1, 4}};
final double[][] port_offsets = {
{-0.5, -0.5},
......@@ -101,16 +112,46 @@ public class Correlation2d {
this.transform_len = transform_size * transform_size;
this.corr_size = transform_size * 2 -1;
// not initialized until needed
this.transpose_indices_ortho = new int [corr_size*(corr_size-1)/2][];
this.transpose_indices_diagonal = new int [corr_size*(corr_size-1)/2][];
// this.transpose_indices_ortho = new int [corr_size*(corr_size-1)/2][];
// this.transpose_indices_diagonal = new int [corr_size*(corr_size-1)/2][];
this.transpose_all_ortho = new int [corr_size*corr_size];
this.transpose_all_diagonal = new int [corr_size*corr_size];
}
// public int [][] getTransposeIndices(boolean diagonal){
// if (diagonal) return getTransposeIndicesDiagonal();
// else return getTransposeIndicesOrtho();
// }
public int [] getTransposeAll(boolean diagonal){
if (diagonal) return getTransposeAllDiagonal();
else return getTransposeAllOrtho();
}
public int [][] getTransposeIndices(boolean diagonal){
if (diagonal) return getTransposeIndicesDiagonal();
else return getTransposeIndicesOrtho();
public int [] getTransposeAllOrtho(){
if (this.transpose_all_ortho[0] == this.transpose_all_ortho[1]) {
for (int i =0; i < corr_size; i++){
for (int j =0; j < corr_size; j++){
this.transpose_all_ortho[i * corr_size + j] = j * corr_size + i;
}
}
}
return this.transpose_all_ortho;
}
public int [] getTransposeAllDiagonal(){
if (this.transpose_all_diagonal[0] == this.transpose_all_diagonal[1]) {
for (int i =0; i < transform_size-1; i++){
for (int j = 0; j < corr_size; j++){
this.transpose_all_diagonal[i * corr_size + j] = (corr_size - i -1) * corr_size + j;
}
}
}
return this.transpose_all_diagonal;
}
/*
public int [][] getTransposeIndicesOrtho(){
if (this.transpose_indices_ortho[0] == null) {
int indx = 0;
......@@ -139,7 +180,7 @@ public class Correlation2d {
}
return this.transpose_indices_diagonal;
}
*/
/**
* Multiply CLT data of two channels, normalize amplitude
* @param clt_data1 first operand FD CLT data[4][transform_len]
......@@ -330,11 +371,13 @@ public class Correlation2d {
if (isHorizontalPair(npair) || isDiagonalMainPair(npair)) {
for (int i = 0; i < combo.length; i++) combo[i]+= correlations[npair][i];
} else {
int [][] transpose_indices = getTransposeIndices(isDiagonalOtherPair(npair));
int [] transpose_indices = getTransposeAll(isDiagonalOtherPair(npair));
// int [][] transpose_indices = getTransposeIndices(isDiagonalOtherPair(npair));
for (int i = 0; i < transpose_indices.length; i++) {
double d = correlations[npair][transpose_indices[i][0]];
correlations[npair][transpose_indices[i][0]] = correlations[npair][transpose_indices[i][1]];
correlations[npair][transpose_indices[i][1]] = d;
combo[i]+= correlations[npair][transpose_indices[i]];
// double d = correlations[npair][transpose_indices[i][0]];
// correlations[npair][transpose_indices[i][0]] = correlations[npair][transpose_indices[i][1]];
// correlations[npair][transpose_indices[i][1]] = d;
}
}
number_combined++;
......@@ -355,9 +398,9 @@ public class Correlation2d {
* @param diagonal use only pairs that are ortho (false) or diagonal (true)
* @param baseline_scale use only pairs with this ortho (diagonals have the same scale as ortho) baseline scale
* (1 - largest, 2 - half, 4 - quarter)
* @return number of compatible pairs among the selection
* @return {number of compatible pairs among the selection, index of the base pair}
*/
public int getNumberOfCompatiblePairs(
public int [] getNumberBaseOfCompatiblePairs(
double [][] correlations,
int pairs_mask,
boolean diagonal,
......@@ -365,11 +408,14 @@ public class Correlation2d {
) {
int number_combined = 0;
// find diagonal/ortho and scale that determine compatible correlations
int base_pair = -1;
for (int npair = 0; npair < PAIRS.length; npair++) if ((((pairs_mask >> npair) & 1) != 0 ) && (correlations[npair]!=null) &&
(isDiagonalPair(npair) == diagonal) && (PAIRS[npair][3] == baseline_scale)){
number_combined++;
if (base_pair < 0) base_pair = npair;
}
return number_combined;
int [] rslt = {number_combined, base_pair};
return rslt;
}
......@@ -874,113 +920,184 @@ public class Correlation2d {
return padded_strip;
}
/*
public static int getPairMask (int data){ return ((data >> 8) & 0xffff);}
* Calculate color channels FD phase correlations, mix results with weights, apply optional low-pass filter
* and convert to the pixel domain as [(2*transform_size-1) * (2*transform_size-1)] tiles (15x15)
* No transposing or rotation
* @param clt_data1 [3][4][transform_len] first operand data. First index - RBG color
* @param clt_data2 [3][4][transform_len] first operand data. First index - RBG color
* @param lpf optional [transform_len] LPF filter data
* @param col_weights [3] - color weights {R, B, G} - green is last, normalized to sum =1.0
* @param fat_zero fat zero for phase correlation (0 seems to be OK)
* @return correlation result [(2*transform_size-1) * (2*transform_size-1)]
public void corrLMA(
ImageDttParameters imgdtt_params,
double [][] corrs,
double xcenter, // preliminary center x in pixels for largest baseline
double [] window_y, // (half) window function in y-direction(perpendicular to disparity: for row0 ==1
double [] window_x, // half of a window function in x (disparity) direction
double vasw_pwr, // value as weight to this power,
int debug_level,
int tileX, // just for debug output
int tileY
)
{
int [][] quad_signs = {{-1,-1},{1,-1},{-1,1},{1,1}}; // {sign_x, sign_y} per quadrant
// for quad camera
int [][] groups = new int [GROUPS.length][];
int [] scale_ind = new int [GROUPS.length];
// See which groups exist for current pairs mask
int ng = 0;
ArrayList<Integer> sl = new ArrayList<Integer>();
for (int i = 0; i < GROUPS.length; i++) {
groups[i] = getNumberBaseOfCompatiblePairs(
corrs, // double [][] correlations,
imgdtt_params.dbg_pair_mask, // int pairs_mask,
(GROUPS[i][0] > 0), // boolean diagonal,
GROUPS[i][1]); // int baseline_scale
if (groups[i][0] > 0) {
ng++;
if (!sl.contains(GROUPS[i][1])) {
sl.add(GROUPS[i][1]);
}
scale_ind[i] = sl.indexOf(GROUPS[i][1]);
}
}
if (debug_level > 1) {
System.out.println("corrLMA(): found "+ng+" groups, "+sl.size()+" scales");
}
double [][] groups_LMA = new double [ng][];
int [][] groups_pairs = new int [ng][]; // number of combined pairs, index of base pair
int [] group_scale_ind = new int [ng]; // number of combined pairs, index of base pair
{
int ig = 0;
for (int i = 0; i < groups.length; i++) if (groups[i][0] >0){
groups_LMA[ig] = combineCompatiblePairs(
corrs, // double [][] correlations,
imgdtt_params.dbg_pair_mask, // int pairs_mask,
(GROUPS[i][0] > 0), // boolean diagonal,
GROUPS[i][1]); // int baseline_scale
groups_pairs[ig] = groups[i]; // {number, base_pair_index}
group_scale_ind[ig] = scale_ind[i];
ig++;
}
}
double [] scales = new double [sl.size()];
for (int i = 0; i < scales.length; i++) scales[i] = sl.get(i); // from int to double
String [] titles = {"ortho","diagonal"};
(new showDoubleFloatArrays()).showArrays(
groups_LMA,
2* transform_size-1,
2* transform_size-1,
true, "groups_x"+tileX+"_y"+tileY, titles);
for (int i = 0; i < groups_pairs.length; i++) {
System.out.println("Group #"+i+" - "+groups_pairs[i][0]+", type:"+groups_pairs[i][1]);
}
Correlations2dLMA lma = new Correlations2dLMA(scales);
int center = transform_size - 1;
int width = 2 * center + 1;
int center_index = (width + 1) * center; // in
int hwindow_y = window_y.length; // should actually be the same?
int hwindow_x = window_x.length;
for (int ig = 0; ig < groups_pairs.length; ig++) if (groups_pairs[ig][0] > 0) {
double scale = scales[group_scale_ind[ig]];
double scale05 = scale/2.0;
boolean diagonal = isDiagonalPair(groups_pairs[ig][1]);
int ixcenter = (int) Math.round(xcenter / scale);
double xcs = ixcenter*scale;
if (debug_level > 0) {
System.out.println("\nCombinded correlation data, diagonal = "+diagonal);
for (int row = 0; row < width; row++) {
System.out.print(String.format("%3d: ", row));
for (int col = 0; col < width; col++) {
if ((row == center) && (col == center)) {
System.out.print(String.format("[%7.4f]", groups_LMA[ig][width * row + col]));
} else {
System.out.print(String.format(" %8.5f", groups_LMA[ig][width * row + col]));
}
}
System.out.println();
}
}
if (diagonal) {
for (int arow = 0; arow < hwindow_y; arow ++) {
int odd = arow & 1;
double wy = window_y[arow] * groups_pairs[0][0]; // number of pair averaged
for (int acol = odd; acol < hwindow_x; acol +=2) {
double wxy = window_x[acol] * wy; // full weight before value as weight
for (int quad = 0; quad < 4; quad ++) if (((arow > 0) || ((quad & 2) !=0 )) && ((acol > 0) || ((quad & 1) !=0 ))){
int cx = (quad_signs[quad][0] * acol - quad_signs[quad][1] * arow)/2 + ixcenter; // ix0;
int cy = (quad_signs[quad][0] * acol + quad_signs[quad][1] * arow)/2 + ixcenter; // ix0;
// calculate coordinates in the correlation array
if ((cx >= -center) && (cx <= center) && (cy >= -center) && (cy <= center)) {
double w = wxy; // full weight before value as weight
double v = groups_LMA[ig][center_index + width * cy + cx];
if (vasw_pwr != 0) {
w *= Math.pow(Math.abs(v), vasw_pwr);
}
lma.addSample(
quad_signs[quad][0] * acol * scale05 + xcs, // x * scale, // double x, // x coordinate on the common scale (corresponding to the largest baseline), along the disparity axis
quad_signs[quad][1] * arow * scale05, // y * scale, // double y, // y coordinate (0 - disparity axis)
v, // double v, // correlation value at that point
w, // double w,
group_scale_ind[ig], // int si, // baseline scale index
ig); // int gi);
}
}
}
}
} else { // ortho
for (int arow = 0; arow < hwindow_y; arow += 2) {
double wy = window_y[arow] * groups_pairs[0][0]; // number of pair averaged
for (int acol = 0; acol < hwindow_x; acol +=2) {
double wxy = window_x[acol] * wy; // full weight before value as weight
for (int quad = 0; quad < 4; quad ++) if (((arow > 0) || ((quad & 2) !=0 )) && ((acol > 0) || ((quad & 1) !=0 ))){
int cx = (quad_signs[quad][0] * acol)/2 + ixcenter; // ix0;
int cy = (quad_signs[quad][1] * arow)/2;
// calculate coordinates in the correlation array
if ((cx >= -center) && (cx <= center) && (cy >= -center) && (cy <= center)) {
double w = wxy; // full weight before value as weight
double v = groups_LMA[ig][center_index + width * cy + cx];
if (vasw_pwr != 0) {
w *= Math.pow(Math.abs(v), vasw_pwr);
}
lma.addSample(
quad_signs[quad][0] * acol * scale05 + xcs, // x * scale, // double x, // x coordinate on the common scale (corresponding to the largest baseline), along the disparity axis
quad_signs[quad][1] * arow * scale05, // y * scale, // double y, // y coordinate (0 - disparity axis)
v, // double v, // correlation value at that point
w, // double w,
group_scale_ind[ig], // int si, // baseline scale index
ig); // int gi);
}
}
}
}
final double [][][][][][] clt_data = new double[quad][nChn][tilesY][tilesX][][];
// transpose vertical pairs
if (corr_pairs[pair][2] != 0) {
for (int chn = firstColor; chn <= numcol; chn++){
for (int i = 0; i < transpose_indices.length; i++) {
double d = tcorr_partial[pair][chn][transpose_indices[i][0]];
tcorr_partial[pair][chn][transpose_indices[i][0]] = tcorr_partial[pair][chn][transpose_indices[i][1]];
tcorr_partial[pair][chn][transpose_indices[i][1]] = d;
//transpose_indices
}
}
}
// make symmetrical around the disparity direction (horizontal) (here using just average, not mul/sum mixture)
// symmetry can be added to result, not individual (if sum - yes, but with multiplication - not)
if (corr_sym && (clt_mismatch == null)){ // when measuring clt_mismatch symmetry should be off !
for (int chn = firstColor; chn <= numcol; chn++){
for (int i = 1 ; i < transform_size; i++){
int indx1 = (transform_size - 1 - i) * corr_size;
int indx2 = (transform_size - 1 + i) * corr_size;
for (int j = 0; j< corr_size; j++){
int indx1j = indx1 + j;
int indx2j = indx2 + j;
tcorr_partial[pair][chn][indx1j] =
0.5* (tcorr_partial[pair][chn][indx1j] + tcorr_partial[pair][chn][indx2j]);
tcorr_partial[pair][chn][indx2j] = tcorr_partial[pair][chn][indx1j];
}
}
}
}
------
for (int chn = 0; chn <numcol; chn++){
double [][] data1 = clt_data[corr_pairs[pair][0]][chn][tileY][tileX];
double [][] data2 = clt_data[corr_pairs[pair][1]][chn][tileY][tileX];
for (int i = 0; i < transform_len; i++) {
double s1 = 0.0, s2=0.0;
for (int n = 0; n< 4; n++){
s1+=data1[n][i] * data1[n][i];
s2+=data2[n][i] * data2[n][i];
}
double scale = 1.0 / (Math.sqrt(s1*s2) + corr_fat_zero*corr_fat_zero); // squared to match units
for (int n = 0; n<4; n++){
tcorr_tpartial[pair][chn][n][i] = 0;
for (int k=0; k<4; k++){
if (zi[n][k] < 0)
tcorr_tpartial[pair][chn][n][i] -=
data1[-zi[n][k]][i] * data2[k][i];
else
tcorr_tpartial[pair][chn][n][i] +=
data1[zi[n][k]][i] * data2[k][i];
}
tcorr_tpartial[pair][chn][n][i] *= scale;
}
}
// got transform-domain correlation for the pair, 1 color
}
// calculate composite color
for (int i = 0; i < transform_len; i++) {
for (int n = 0; n<4; n++) {
tcorr_tpartial[pair][numcol][n][i] =
col_weights[0]* tcorr_tpartial[pair][0][n][i] +
col_weights[1]* tcorr_tpartial[pair][1][n][i] +
col_weights[2]* tcorr_tpartial[pair][2][n][i];
}
}
// now lpf (only last/composite color if do not preserve intermediate
int firstColor = (clt_corr_partial == null)? numcol : 0;
if (corr_sigma >0) {
for (int chn = firstColor; chn <= numcol; chn++){
for (int i = 0; i < transform_len; i++) {
for (int n = 0; n<4; n++) {
tcorr_tpartial[pair][chn][n][i] *= filter[i];
}
}
}
}
// convert to pixel domain - all or just composite color
for (int chn = firstColor; chn <= numcol; chn++){
for (int quadrant = 0; quadrant < 4; quadrant++){
int mode = ((quadrant << 1) & 2) | ((quadrant >> 1) & 1); // transpose
tcorr_tpartial[pair][chn][quadrant] =
dtt.dttt_iie(tcorr_tpartial[pair][chn][quadrant], mode, transform_size);
}
}
// convert from 4 quadrants to 15x15 centered tiles (each color or only composite)
for (int chn = firstColor; chn <= numcol; chn++){
tcorr_partial[pair][chn] = corr_unfold_tile(
tcorr_tpartial[pair][chn],
transform_size);
}
}
}
lma.initVector(
imgdtt_params.lma_adjust_wm, // boolean adjust_wm,
imgdtt_params.lma_adjust_wy, // boolean adjust_wy,
imgdtt_params.lma_adjust_wxy, // boolean adjust_wxy,
imgdtt_params.lma_adjust_ag, // boolean adjust_Ag,
xcenter, // double x0,
imgdtt_params.lma_half_width, // double half_width,
imgdtt_params.lma_cost_wy, // double cost_wy, // cost of non-zero this.all_pars[WYD_INDEX]
imgdtt_params.lma_cost_wxy //double cost_wxy // cost of non-zero this.all_pars[WXY_INDEX]
);
if (debug_level > 0) {
System.out.println("Input data:");
lma.printInputData();
}
boolean lmaSuccess = lma.runLma(
imgdtt_params.lma_lambda_initial, // double lambda, // 0.1
imgdtt_params.lma_lambda_scale_good, // double lambda_scale_good,// 0.5
imgdtt_params.lma_lambda_scale_bad, // double lambda_scale_bad, // 8.0
imgdtt_params.lma_lambda_max, // double lambda_max, // 100
imgdtt_params.lma_rms_diff, // double rms_diff, // 0.001
imgdtt_params.lma_num_iter, // int num_iter, // 20
debug_level); // int debug_level)
lma.updateFromVector();
double [] rms = lma.getRMS();
if (debug_level > 0) {
System.out.println("LMA ->"+lmaSuccess+" RMS="+rms[0]+", pure RMS="+rms[1]);
lma.printParams();
}
}
*/
}
src/main/java/ImageDtt.java
View file @ 6ac376b0
......@@ -2481,6 +2481,27 @@ public class ImageDtt {
disparity_map[DBG18_INDEX][tIndex] = Double.NaN;
}
}
// initially try LMA for only the debug tile
if ((corr_stat != null) && (globalDebugLevel > -2) && (tileX == debug_tileX) && (tileY == debug_tileY)) {
System.out.println(String.format("Testing correlation maximum LMA tileX=%d, tileY=%d, dbg_pair_mask= 0x%x,corr_offset = %8.5f",
debug_tileX,debug_tileY,imgdtt_params.dbg_pair_mask, imgdtt_params.corr_offset ));
// Create 2 groups: ortho & diag
corr2d.corrLMA(
imgdtt_params, // ImageDttParameters imgdtt_params,
corrs, // double [][] corrs,
corr_stat[0], // double xcenter, // preliminary center x in pixels for largest baseline
corr_wndy, // double [] window_y, // (half) window function in y-direction(perpendicular to disparity: for row0 ==1
corr_wndx, // double [] window_x, // half of a window function in x (disparity) direction
imgdtt_params.ortho_vasw_pwr, // double vasw_pwr, // value as weight to this power,
imgdtt_params.lma_debug_level,// int debug_level,
tileX, // int tileX, // just for debug output
tileY ); // int tileY
}
}
if (imgdtt_params.mix_corr_poly) { // regardless of debug
......
src/main/java/ImageDttParameters.java
View file @ 6ac376b0
......@@ -63,9 +63,23 @@ public class ImageDttParameters {
public double corr_wndx_hwidth = 6.0; // 50% window cutoff width
public double corr_wndx_blur = 5.0; // 100% to 0 % vertical transition range
// LMA parameters
public boolean lma_adjust_wm = true;
public boolean lma_adjust_wy = false;
public boolean lma_adjust_wxy = true;
public boolean lma_adjust_ag = true;
public double lma_half_width = 2.0; //
public double lma_cost_wy = 0.1; //
public double lma_cost_wxy = 0.1; //
public double lma_lambda_initial = 0.1; //
public double lma_lambda_scale_good = 0.5; //
public double lma_lambda_scale_bad = 8.0; //
public double lma_lambda_max = 100.0; //
public double lma_rms_diff = 0.001; //
public int lma_num_iter = 20; //
public int lma_debug_level = 3; //
public void dialogQuestions(GenericJTabbedDialog gd) {
gd.addCheckbox ("Enable ImageDtt correlation debug layers", this.corr_mode_debug);
......@@ -131,6 +145,38 @@ public class ImageDttParameters {
gd.addNumericField("100% to 0 % correlation horizontal window transition range", this.corr_wndx_blur, 3, 6, "",
"Transition range, shifted sine is used");
gd.addTab("Corr LMA","Parameters for LMA fitting of the correlation maximum parameters");
gd.addCheckbox ("Fit correlation defined half-width", this.lma_adjust_wm,
"Allow fitting of the half-width common for all pairs, defined by the LPF filter of the phase correlation");
gd.addCheckbox ("Fit extra vertical half-width", this.lma_adjust_wy,
"Fit extra perpendicular to disparity half-width (not used? and only possible with multi-baseline cameras)");
gd.addCheckbox ("Fit extra half-width along disparity direction", this.lma_adjust_wxy,
"Increased width in disparity direction caused by multi-distance objects in the tile");
gd.addCheckbox ("Adjust per-group amplitudes", this.lma_adjust_ag,
"Each correlation type's amplitude (now always needed)");
gd.addNumericField("Initial/expected half-width of the correlation maximum in both directions", this.lma_half_width, 3, 6, "pix",
"With LPF sigma = 0.9 it seems to be ~= 2.0. Used both as initial parameter and the fitted value difference from this may be penalized");
gd.addNumericField("Cost of the difference of the actual half-width from the expected one", this.lma_cost_wy, 5, 8, "",
"The higher this cost, the more close the fitted half-width will be to the expected one");
gd.addNumericField("Cost of the difference between horizontal and vertical widths", this.lma_cost_wxy, 5, 8, "",
"Tries to enforce equal width and hight of the correlation maximum");
gd.addNumericField("Initial value of LMA lambda", this.lma_lambda_initial, 3, 6, "",
"The higher the lambda the more close it will be to the gradient descent (slower/safer)");
gd.addNumericField("Scale (decrease) LMA lambda on success (usually 0.5)", this.lma_lambda_scale_good, 3, 6, "",
"Make it smaller for unsafe but faster converging (if all goes well)");
gd.addNumericField("Scale (increase) LMA lambda after failure (usually 8.0)", this.lma_lambda_scale_bad, 3, 6, "",
"Bad convergence usually means errors in Jacobian (derivatives)");
gd.addNumericField("Lambda value to give up increasing it", this.lma_lambda_max, 3, 6, "",
"Gives up LMA if increased lambda still does not improve RMS");
gd.addNumericField("Relative RMS improvement to exit LMA", this.lma_rms_diff, 6, 8, "",
"LMA will report success when realtive RMS improvements fall below this value");
gd.addNumericField("LMA maximal iterations", this.lma_num_iter, 0, 3, "",
"Limit LMA cycles, so it will exit after certain number of small improvements");
gd.addNumericField("LMA debug level", this.lma_debug_level, 0, 3, "",
"Debug/verbosity level for the LMA correaltion maximum fitting");
}
public void dialogAnswers(GenericJTabbedDialog gd) {
......@@ -175,6 +221,24 @@ public class ImageDttParameters {
this.corr_wndx_hwidth = gd.getNextNumber();
this.corr_wndx_blur = gd.getNextNumber();
//LMA tab
this.lma_adjust_wm= gd.getNextBoolean();
this.lma_adjust_wy= gd.getNextBoolean();
this.lma_adjust_wxy= gd.getNextBoolean();
this.lma_adjust_ag= gd.getNextBoolean();
this.lma_half_width = gd.getNextNumber();
this.lma_cost_wy = gd.getNextNumber();
this.lma_cost_wxy = gd.getNextNumber();
this.lma_lambda_initial = gd.getNextNumber();
this.lma_lambda_scale_good = gd.getNextNumber();
this.lma_lambda_scale_bad = gd.getNextNumber();
this.lma_lambda_max = gd.getNextNumber();
this.lma_rms_diff = gd.getNextNumber();
this.lma_num_iter= (int) gd.getNextNumber();
this.lma_debug_level= (int) gd.getNextNumber();
}
......@@ -219,6 +283,25 @@ public class ImageDttParameters {
properties.setProperty(prefix+"corr_wndx_hwidth", this.corr_wndx_hwidth +"");
properties.setProperty(prefix+"corr_wndx_blur", this.corr_wndx_blur +"");
properties.setProperty(prefix+"lma_adjust_wm", this.lma_adjust_wm +"");
properties.setProperty(prefix+"lma_adjust_wy", this.lma_adjust_wy +"");
properties.setProperty(prefix+"lma_adjust_wxy", this.lma_adjust_wxy +"");
properties.setProperty(prefix+"lma_adjust_ag", this.lma_adjust_ag +"");
properties.setProperty(prefix+"lma_half_width", this.lma_half_width +"");
properties.setProperty(prefix+"lma_cost_wy", this.lma_cost_wy +"");
properties.setProperty(prefix+"lma_cost_wxy", this.lma_cost_wxy +"");
properties.setProperty(prefix+"lma_lambda_initial", this.lma_lambda_initial +"");
properties.setProperty(prefix+"lma_lambda_scale_good",this.lma_lambda_scale_good +"");
properties.setProperty(prefix+"lma_lambda_scale_bad", this.lma_lambda_scale_bad +"");
properties.setProperty(prefix+"lma_lambda_max", this.lma_lambda_max +"");
properties.setProperty(prefix+"lma_rms_diff", this.lma_rms_diff +"");
properties.setProperty(prefix+"lma_num_iter", this.lma_num_iter +"");
properties.setProperty(prefix+"lma_debug_level", this.lma_debug_level +"");
}
public void getProperties(String prefix,Properties properties){
......@@ -264,6 +347,26 @@ public class ImageDttParameters {
if (properties.getProperty(prefix+"corr_wndx_hwidth")!=null) this.corr_wndx_hwidth=Double.parseDouble(properties.getProperty(prefix+"corr_wndx_hwidth"));
if (properties.getProperty(prefix+"corr_wndx_blur")!=null) this.corr_wndx_blur=Double.parseDouble(properties.getProperty(prefix+"corr_wndx_blur"));
if (properties.getProperty(prefix+"lma_adjust_wm")!=null) this.lma_adjust_wm=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wm"));
if (properties.getProperty(prefix+"lma_adjust_wy")!=null) this.lma_adjust_wy=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wy"));
if (properties.getProperty(prefix+"lma_adjust_wxy")!=null) this.lma_adjust_wxy=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_wxy"));
if (properties.getProperty(prefix+"lma_adjust_ag")!=null) this.lma_adjust_ag=Boolean.parseBoolean(properties.getProperty(prefix+"lma_adjust_ag"));
if (properties.getProperty(prefix+"lma_half_width")!=null) this.lma_half_width=Double.parseDouble(properties.getProperty(prefix+"lma_half_width"));
if (properties.getProperty(prefix+"lma_cost_wy")!=null) this.lma_cost_wy=Double.parseDouble(properties.getProperty(prefix+"lma_cost_wy"));
if (properties.getProperty(prefix+"lma_cost_wxy")!=null) this.lma_cost_wxy=Double.parseDouble(properties.getProperty(prefix+"lma_cost_wxy"));
if (properties.getProperty(prefix+"lma_lambda_initial")!=null) this.lma_lambda_initial=Double.parseDouble(properties.getProperty(prefix+"lma_lambda_initial"));
if (properties.getProperty(prefix+"lma_lambda_scale_good")!=null)this.lma_lambda_scale_good=Double.parseDouble(properties.getProperty(prefix+"lma_lambda_scale_good"));
if (properties.getProperty(prefix+"lma_lambda_scale_bad")!=null) this.lma_lambda_scale_bad=Double.parseDouble(properties.getProperty(prefix+"lma_lambda_scale_bad"));
if (properties.getProperty(prefix+"lma_lambda_max")!=null) this.lma_lambda_max=Double.parseDouble(properties.getProperty(prefix+"lma_lambda_max"));
if (properties.getProperty(prefix+"lma_rms_diff")!=null) this.lma_rms_diff=Double.parseDouble(properties.getProperty(prefix+"lma_rms_diff"));
if (properties.getProperty(prefix+"lma_num_iter")!=null) this.lma_num_iter=Integer.parseInt(properties.getProperty(prefix+"lma_num_iter"));
if (properties.getProperty(prefix+"lma_debug_level")!=null) this.lma_debug_level=Integer.parseInt(properties.getProperty(prefix+"lma_debug_level"));
}
@Override
......@@ -309,8 +412,24 @@ public class ImageDttParameters {
idp.corr_wndx_hwidth = this.corr_wndx_hwidth;
idp.corr_wndx_blur = this.corr_wndx_blur;
idp.lma_adjust_wm = this.lma_adjust_wm;
idp.lma_adjust_wy = this.lma_adjust_wy;
idp.lma_adjust_wxy = this.lma_adjust_wxy;
idp.lma_adjust_ag = this.lma_adjust_ag;
idp.lma_half_width = this.lma_half_width;
idp.lma_cost_wy = this.lma_cost_wy;
idp.lma_cost_wxy = this.lma_cost_wxy;
idp.lma_lambda_initial = this.lma_lambda_initial;
idp.lma_lambda_scale_good = this.lma_lambda_scale_good;
idp.lma_lambda_scale_bad = this.lma_lambda_scale_bad;
idp.lma_lambda_max = this.lma_lambda_max;
idp.lma_rms_diff = this.lma_rms_diff;
idp.lma_num_iter = this.lma_num_iter;
idp.lma_debug_level = this.lma_debug_level;
return idp;
}
// TODO move 2 dialog methods here
}
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