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Commit c9ed0a3d authored by Andrey Filippov's avatar Andrey Filippov
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trying filtering by SIFT-like features for better averaging

parent 1517acca
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Showing with 1155 additions and 6 deletions
src/main/java/com/elphel/imagej/correction/Eyesis_Correction.java
View file @ c9ed0a3d
...@@ -111,6 +111,7 @@ import com.elphel.imagej.jp4.JP46_Reader_camera; ...@@ -111,6 +111,7 @@ import com.elphel.imagej.jp4.JP46_Reader_camera;
import com.elphel.imagej.lwir.LwirReader; import com.elphel.imagej.lwir.LwirReader;
import com.elphel.imagej.orthomosaic.OrthoMap; import com.elphel.imagej.orthomosaic.OrthoMap;
import com.elphel.imagej.orthomosaic.ComboMatch; import com.elphel.imagej.orthomosaic.ComboMatch;
import com.elphel.imagej.orthomosaic.OrangeTest;
import com.elphel.imagej.readers.ChangeImageResolution; import com.elphel.imagej.readers.ChangeImageResolution;
import com.elphel.imagej.readers.DumpImageMetadata; import com.elphel.imagej.readers.DumpImageMetadata;
import com.elphel.imagej.readers.ElphelTiffReader; import com.elphel.imagej.readers.ElphelTiffReader;
...@@ -165,7 +166,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener { ...@@ -165,7 +166,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
// private static final long serialVersionUID = -1507307664341265263L; // private static final long serialVersionUID = -1507307664341265263L;
private Panel panel1, panel2, panel3, panel4, panel5, panel5a, panel6, panel7, panelPostProcessing1, private Panel panel1, panel2, panel3, panel4, panel5, panel5a, panel6, panel7, panelPostProcessing1,
panelPostProcessing2, panelPostProcessing3, panelDct1, panelClt1, panelClt2, panelClt3, panelClt4, panelPostProcessing2, panelPostProcessing3, panelDct1, panelClt1, panelClt2, panelClt3, panelClt4,
panelClt5, panelClt5aux, panelClt_GPU, panelLWIR, panelLWIR16, panelLWIRWorld; panelClt5, panelClt5aux, panelClt_GPU, panelLWIR, panelLWIR16, panelLWIRWorld, panelOrange;
JP46_Reader_camera JP4_INSTANCE = null; JP46_Reader_camera JP4_INSTANCE = null;
// private deBayerScissors debayer_instance; // private deBayerScissors debayer_instance;
...@@ -532,7 +533,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener { ...@@ -532,7 +533,7 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
plugInFrame.addKeyListener(IJ.getInstance()); plugInFrame.addKeyListener(IJ.getInstance());
// int menuRows=4 + (ADVANCED_MODE?4:0) + (MODE_3D?3:0) + (DCT_MODE?6:0) + (GPU_MODE?1:0) +(LWIR_MODE?2:0); // int menuRows=4 + (ADVANCED_MODE?4:0) + (MODE_3D?3:0) + (DCT_MODE?6:0) + (GPU_MODE?1:0) +(LWIR_MODE?2:0);
int menuRows = 4 + (ADVANCED_MODE ? 4 : 0) + (MODE_3D ? 3 : 0) + (DCT_MODE ? 7 : 0) + (GPU_MODE ? 1 : 0) int menuRows = 4 + (ADVANCED_MODE ? 4 : 0) + (MODE_3D ? 3 : 0) + (DCT_MODE ? 7 : 0) + (GPU_MODE ? 1 : 0)
+ (LWIR_MODE ? 3 : 0); + (LWIR_MODE ? 4 : 0);
plugInFrame.setLayout(new GridLayout(menuRows, 1)); plugInFrame.setLayout(new GridLayout(menuRows, 1));
panel6 = new Panel(); panel6 = new Panel();
panel6.setLayout(new GridLayout(1, 0, 5, 5)); panel6.setLayout(new GridLayout(1, 0, 5, 5));
...@@ -861,6 +862,11 @@ public class Eyesis_Correction implements PlugIn, ActionListener { ...@@ -861,6 +862,11 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
addButton("Properties compare", panelLWIRWorld, color_process); addButton("Properties compare", panelLWIRWorld, color_process);
plugInFrame.add(panelLWIRWorld); plugInFrame.add(panelLWIRWorld);
panelOrange = new Panel();
panelOrange.setLayout(new GridLayout(1, 0, 5, 5)); // rows, columns, vgap, hgap
addButton("Test Orange", panelOrange, color_process);
addButton("Process Merged", panelOrange, color_process);
plugInFrame.add(panelOrange);
} }
plugInFrame.pack(); plugInFrame.pack();
//"LWIR batch" //"LWIR batch"
...@@ -5788,7 +5794,12 @@ public class Eyesis_Correction implements PlugIn, ActionListener { ...@@ -5788,7 +5794,12 @@ public class Eyesis_Correction implements PlugIn, ActionListener {
OrthoMap.testPatternCorrelate(imp_sel); OrthoMap.testPatternCorrelate(imp_sel);
} else if (label.equals("Properties compare")) { } else if (label.equals("Properties compare")) {
QuadCLTCPU.compareProperties(); QuadCLTCPU.compareProperties();
} else if (label.equals("Test Orange")) {
OrangeTest.testOrange();
} else if (label.equals("Process Merged")) {
OrangeTest.processMerged();
} }
//
} }
// //
public boolean debugInitOneScene() { public boolean debugInitOneScene() {
......
src/main/java/com/elphel/imagej/orthomosaic/ComboMatch.java
View file @ c9ed0a3d
...@@ -2524,10 +2524,10 @@ adjusted affines[1] for a pair: 1694564291_293695/1694564778_589341 ...@@ -2524,10 +2524,10 @@ adjusted affines[1] for a pair: 1694564291_293695/1694564778_589341
{ 0, 0, sxy, sy2, 0, sy}, { 0, 0, sxy, sy2, 0, sy},
{ 0, 0, sx, sy, 0, s0}}; { 0, 0, sx, sy, 0, s0}};
Matrix A = new Matrix(a); Matrix A = new Matrix(a);
A.print(15,4); // A.print(15,4);
double [][] b = {{sxu,syu,su,sxv,syv,sv}}; double [][] b = {{sxu,syu,su,sxv,syv,sv}};
Matrix B = new Matrix(b).transpose(); Matrix B = new Matrix(b).transpose();
B.print(15,4); // B.print(15,4);
double [] v = A.solve(B).getRowPackedCopy(); double [] v = A.solve(B).getRowPackedCopy();
double [][] ab = { double [][] ab = {
{v[0], v[1]}, // |a00, a01|, {v[0], v[1]}, // |a00, a01|,
...@@ -2535,6 +2535,7 @@ adjusted affines[1] for a pair: 1694564291_293695/1694564778_589341 ...@@ -2535,6 +2535,7 @@ adjusted affines[1] for a pair: 1694564291_293695/1694564778_589341
{v[4], v[5]}}; // | b0, b1|, {v[4], v[5]}}; // | b0, b1|,
return ab; return ab;
} }
public static boolean testReadTiff() { public static boolean testReadTiff() {
String image_paths=""; String image_paths="";
......
src/main/java/com/elphel/imagej/orthomosaic/OrangeTest.java 0 → 100644
View file @ c9ed0a3d
package com.elphel.imagej.orthomosaic;
import java.awt.Rectangle;
import java.util.Arrays;
import java.util.concurrent.atomic.AtomicInteger;
import com.elphel.imagej.common.ShowDoubleFloatArrays;
import com.elphel.imagej.tileprocessor.ImageDtt;
import com.elphel.imagej.tileprocessor.TileProcessor;
import ij.ImagePlus;
import ij.ImageStack;
public class OrangeTest {
public static double [][][] matching_points_initial= {
{ // 1697877488_162849 - 117 sfm 37.1
{ 110.500, 452.500},
{ 287.500, 121.500},
{1294.833, 545.833},
{1307.500, 162.167}
},
{ // 1697877490_930437 - 42 sfm 6.4
{ 410.167, 979.833},
{ 585.500, 649.500},
{1579.167,1126.833},
{1604.750, 737.000}
},
{ // 1697877491_613999 - 76 sfm 12.2 0, 76
{ 423.500, 1260.250},
{ 598.833, 937.500},
{1587.000, 1404.000},
{1613.167, 1017.167}
},
{ // 1697877491_997460 - 85 sfm 14.2 master
{ 464.000, 1551.000},
{ 617.167, 1222.500},
{1630.167, 1624.500},
{1630.250, 1239.500}
},
{ // 1697877492_314232 - 43 sfm 5.1
{ 637.000, 1830.000},
{ 763.167, 1489.167},
{1807.167, 1807.167},
{1772.833, 1420.500}
},
{ // 1697877492_614332 - 38 sfm 4.2
{ 731.000, 1952.500},
{ 828.500, 1606.500},
{1893.000, 1829.500},
{1826.500, 1453.000}
}
};
public static double [][][] matching_points_first_corr= { // /media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/02-first_point_correction/
{ // 1697877488_162849 - 117 sfm 37.1
{ 105.500, 452.500},
{ 289.500, 121.500},
{1298.833, 550.833},
{1305.500, 164.167}
},
{ // 1697877490_930437 - 42 sfm 6.4
{ 410.167, 981.833},
{ 585.500, 651.500},
{1579.167,1128.833},
{1606.750, 741.000}
},
{ // 1697877491_613999 - 76 sfm 12.2 0, 76
{ 419.500, 1260.250},
{ 596.833, 937.500},
{1585.000, 1404.000},
{1613.167, 1019.167}
},
{ // 1697877491_997460 - 85 sfm 14.2 MASTER
{ 464.000, 1551.000},
{ 617.167, 1222.500},
{1630.167, 1624.500},
{1630.250, 1239.500}
},
{ // 1697877492_314232 - 43 sfm 5.1
{ 637.000, 1828.000},
{ 765.167, 1489.167},
{1805.167, 1805.167},
{1770.833, 1422.500}
},
{ // 1697877492_614332 - 38 sfm 4.2
{ 733.000, 1950.500},
{ 832.500, 1604.500},
{1891.000, 1829.500},
{1826.500, 1455.000}
}
};
public static double [][][] matching_points_second_corr= { // /media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/02-first_point_correction/
{ // 1697877488_162849 - 117 sfm 37.1
{ 106.500, 453.500}, //+1,+1
{ 290.500, 123.500}, //+1,+2
{1299.833, 551.833}, //+1,+1
{1305.500, 164.667} // 0,+.5
},
{ // 1697877490_930437 - 42 sfm 6.4
{ 411.167, 981.833}, //+1,0
{ 585.500, 652.500}, // 0,+1
{1579.167,1128.833}, // 0, 0
{1606.750, 741.500} // 0,+.5
},
{ // 1697877491_613999 - 76 sfm 12.2 0, 76
{ 420.500, 1260.250}, //+1, 0
{ 596.833, 937.500}, // 0, 0
{1586.000, 1404.000}, //+1, 0
{1613.167, 1019.667} // 0,+.5
},
{ // 1697877491_997460 - 85 sfm 14.2 MASTER
{ 464.000, 1551.000},
{ 617.167, 1222.500},
{1630.167, 1624.500},
{1630.250, 1239.500}
},
{ // 1697877492_314232 - 43 sfm 5.1
{ 637.000, 1828.000}, // 0, 0
{ 765.167, 1489.167}, // 0, 0
{1805.167, 1805.167}, //+1,+1
{1770.833, 1422.500} // 0,+1
},
{ // 1697877492_614332 - 38 sfm 4.2
{ 732.000, 1950.500}, //-1, 0
{ 831.500, 1604.500}, //-1, 0
{1892.000, 1830.500}, //+1,+1
{1826.000, 1456.000} //-.5,+1
}
};
public static String [] src_files = {
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877488_162849-TERRAIN-MERGED.tiff",
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877490_930437-TERRAIN-MERGED.tiff",
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877491_613999-TERRAIN-MERGED.tiff",
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877491_997460-TERRAIN-MERGED.tiff",
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877492_314232-TERRAIN-MERGED.tiff",
"/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/1697877492_614332-TERRAIN-MERGED.tiff"};
public static double [] value_offsets = {33.0, 0.0, 0.0, 0.0, 0.0, 0.0};
public static String merged_file = "/media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/04-third-corr/merged_seq_scale2.0-slices260_x92_y280_w320_h120_zeronan.tiff";
public static double points_scale = 4.0;
public static double [][][] matching_points= { // /media/elphel/SSD3-4GB/lwir16-proc/berdich3/debug/agent_orange/terrain-merged/02-first_point_correction/
{ // 1697877488_162849 - 117 sfm 37.1
{ 105.500, 453.500}, //-1, 0
{ 292.500, 123.500}, //+2, 0
{1299.833, 552.333}, // 0, +.5
{1304.500, 164.667} //-1, 0
},
{ // 1697877490_930437 - 42 sfm 6.4
{ 411.167, 982.833}, // 0,+1
{ 585.500, 653.500}, // 0,+1
{1578.167,1128.833}, //-1, 0
{1606.750, 741.500} // 0, 0
},
{ // 1697877491_613999 - 76 sfm 12.2 0, 76
{ 421.000, 1260.250}, //+.5,0
{ 596.833, 937.500}, // 0, 0
{1586.000, 1404.000}, // 0, 0
{1613.167, 1020.167} // 0,+.5
},
{ // 1697877491_997460 - 85 sfm 14.2 MASTER
{ 464.000, 1551.000},
{ 617.167, 1222.500},
{1630.167, 1624.500},
{1630.250, 1239.500}
},
{ // 1697877492_314232 - 43 sfm 5.1
{ 637.000, 1828.000}, // 0, 0
{ 764.667, 1489.167}, //-.5,0
{1806.167, 1806.167}, //+1,+1
{1770.833, 1422.000} //0,-.5
},
{ // 1697877492_614332 - 38 sfm 4.2
{ 732.000, 1950.000}, // 0,-.5
{ 830.500, 1604.500}, //-1, 0
{1893.000, 1830.500}, //+1, 0
{1826.000, 1455.000} // 0,-1
}
};
public static int [][] full_segments = {{ 0,117},{0,42},{0,76},{ 0,85},{ 0,43},{ 0,38}};
public static int [][] match_segments = {{ 0,117},{0, 7},{0,76},{62,85},{24,43},{20,38}}; // maybe improve to cut symmetrically?
public static int [][] single_segments = {{60,61},{0,1},{0,1},{0,1},{0,1},{0,1}};
public static void processMerged() {
int num_vert = 1; //4;
int vgap = 0; // 10;
double diff_time_rt = 2.9; // 5.5;
double diff_time_rxy = 2.9; // 5.5;
double space_rt = 2.9; // 5.5;
double space_rxy = 2.9; // 5.5;
double frac_outliers = 0.1;
boolean repeat_border= true;
boolean show_orig_dt = false;
boolean show_derivs_dt = true;
ImagePlus imp_merged = new ImagePlus(merged_file);
ImageStack stack = imp_merged.getImageStack();
final int width = imp_merged.getWidth();
// final int merged_height = imp_merged.getHeight();
// String [] stack_labels = stack.getSliceLabels();
final double [][] dpixels = new double [stack.getSize()][];
for (int i = 0; i < dpixels.length; i++) {
float [] fpixels = (float[]) stack.getPixels(i+1);
dpixels[i] = new double[fpixels.length];
for (int j = 0; j < fpixels.length; j++) {
dpixels[i][j] = fpixels[j];
}
}
if (show_orig_dt) {
double [][] out_data_dt = timeToY(
dpixels, // final double [][] multi_image,
width, // final int width,
num_vert, // final int num_vert,
vgap); // final int vgap)
int out_height = out_data_dt[0].length/width;
String [] titles = new String[out_data_dt.length];
for (int i = 0; i < titles.length; i++) {
if (num_vert == 1) {
titles[i]=String.format("row %d",i);
} else {
titles[i]=String.format("rows: %d-%d",i*num_vert,(i+1)*num_vert-1);
}
}
ShowDoubleFloatArrays.showArrays(
out_data_dt,
width,
out_height,
true,
"time_section-"+out_data_dt.length,
titles);
}
double [][][] kernel_diff_time= kernelDiffTime(
diff_time_rt, //double rt,
diff_time_rxy); // double rxy)
showKernel(
kernel_diff_time, // double [][][] kernel,
"kernel_diff_time_rt"+diff_time_rt+"_rxy"+diff_time_rxy); // String title)
double [][] time_derivs = convolve3d(
dpixels, // final double [][] data_in, // fill NaN before running, will treat NaN as 0 on input, skip if center is NaN
width, // final int width,
kernel_diff_time, // final double [][][] kernel, // (odd)x(odd)x(odd}
repeat_border); // final boolean repeat_border)
double [][][][] space_kernels= new double[5][][][];
// double space_rt = 5.5;
// double space_rxy = 5.5;
for (int dir = 0; dir < space_kernels.length; dir++) {
space_kernels[dir]= kernelXY(
dir, // int dir, // 0: +y, 1: +x+y, 2:+x, 3 +x-t, 4: 0: +center will be voting 1 of 10 (each plus and minus), weighted by abs(kernelDiffTime)
space_rt, // double rt,
space_rxy); // double rxy)
showKernel(
space_kernels[dir], // double [][][] kernel,
"space_kernels-dir"+dir+"_rt"+space_rt+"_rxy"+space_rxy); // String title)
}
double [][][] space_conv = new double[5][][];
double [][][] space_conv_temp = new double[5][][];
for (int dir = 0; dir < space_kernels.length; dir++) {
space_conv[dir] = convolve3d(
dpixels, // final double [][] data_in, // fill NaN before running, will treat NaN as 0 on input, skip if center is NaN
width, // final int width,
space_kernels[dir], // final double [][][] kernel, // (odd)x(odd)x(odd}
repeat_border); // final boolean repeat_border)
}
double [] scales = normalizeSpaceKernels(
space_conv); // double [][][] conv_data) // same data convolved with different kernels, may have NaNs
double average_space = scales[space_kernels.length]; // average std of all space kernels
double [] time_averages = normalizeTimeDerivs( // return {std, average abs()}
time_derivs, // final double [][] conv_data,
frac_outliers); // final double frac_outliers)
double time_std = time_averages[0]; // [1] - average of the absolute values;
for (int dir = 0; dir < space_kernels.length; dir++) {
scaleConvolutions(
space_conv[dir], // final double [][] conv_data, // same data convolved with different kernels, may have NaNs
scales[dir]); // final double scale)
}
scaleConvolutions(
time_derivs, // final double [][] conv_data, // same data convolved with different kernels, may have NaNs
average_space/time_std); // final double scale)
int [][] best_space_dir = new int [space_conv[0].length][];
double [][] best_space_value = getBestSpace(
space_conv, // final double [][][] space_conv,
best_space_dir); // final int [][] best_space) // null or int [nimg][]
if (show_derivs_dt) {
for (int dir = 0; dir < space_kernels.length; dir++) {
space_conv_temp[dir] = timeToY(
space_conv[dir], // final double [][] multi_image,
width, // final int width,
num_vert, // final int num_vert,
vgap); // final int vgap)
}
double [][] time_derivs_temp = timeToY(
time_derivs, // final double [][] multi_image,
width, // final int width,
num_vert, // final int num_vert,
vgap); // final int vgap)
int out_height = time_derivs_temp[0].length/width;
String [] titles = new String[time_derivs_temp.length];
for (int i = 0; i < titles.length; i++) {
if (num_vert == 1) {
titles[i]=String.format("row %d",i);
} else {
titles[i]=String.format("rows: %d-%d",i*num_vert,(i+1)*num_vert-1);
}
}
ShowDoubleFloatArrays.showArrays(
time_derivs_temp,
width,
out_height,
true,
"time_derivs-rt"+diff_time_rt+"-rxy"+diff_time_rxy,
titles);
for (int dir = 0; dir < space_kernels.length; dir++) {
ShowDoubleFloatArrays.showArrays(
space_conv_temp[dir],
width,
out_height,
true,
"space_conv-dir"+dir+"-rt"+space_rt+"-rxy"+space_rxy,
titles);
}
double [][] best_space_value_temp = timeToY(
best_space_value, // final double [][] multi_image,
width, // final int width,
num_vert, // final int num_vert,
vgap); // final int vgap)
ShowDoubleFloatArrays.showArrays(
best_space_value_temp,
width,
out_height,
true,
"best_space_value-rt"+space_rt+"-rxy"+space_rxy,
titles);
double [][] best_space_dir_dbg = new double [best_space_dir.length][best_space_dir[0].length];
for (int nimg = 0; nimg < best_space_dir.length; nimg++) {
Arrays.fill (best_space_dir_dbg[nimg], Double.NaN);
for (int i = 0; i < best_space_dir[nimg].length; i++) if (best_space_dir[nimg][i] >= 0){
best_space_dir_dbg[nimg][i] = best_space_dir[nimg][i];
}
}
double [][] best_space_dir_temp = timeToY(
best_space_dir_dbg, // final double [][] multi_image,
width, // final int width,
num_vert, // final int num_vert,
vgap); // final int vgap)
ShowDoubleFloatArrays.showArrays(
best_space_dir_temp,
width,
out_height,
true,
"best_space_dir-rt"+space_rt+"-rxy"+space_rxy,
titles);
}
return;
}
public static double [][] getBestSpace(
final double [][][] space_conv,
final int [][] best_space){ // null or int [nimg][]
final int num_kernels = space_conv.length; // may be modified, if there will be extra layers
final int num_images = space_conv[0].length;
final int num_pixels = space_conv[0][0].length;
final double [][] best_val = new double [num_images][num_pixels];
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
// final AtomicInteger ati = new AtomicInteger(0);
for (int nimg = 0; nimg < num_images; nimg++) {
final int fimg = nimg;
Arrays.fill(best_val[fimg], Double.NaN);
if (best_space != null) {
best_space[fimg] = new int[space_conv[0][0].length];
Arrays.fill(best_space[fimg], -1);
}
ai.set(0);
// ati.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
// int nthread = ati.getAndIncrement();
for (int ipix = ai.getAndIncrement(); ipix < num_pixels; ipix = ai.getAndIncrement()) {
int best_dir = -1;
int best_sgn = 0;
double bestv = 0;
for (int dir = 0; dir < num_kernels; dir++) {
double d = space_conv[dir][fimg][ipix];
if (!Double.isNaN(d)) {
for (int sgn = 0; sgn<2; sgn++) {
if (sgn > 0) {
d = -d;
}
if (d > bestv) {
best_dir = dir;
best_sgn = sgn;
bestv = d;
}
}
}
}
if (best_dir >= 0) {
best_val[fimg][ipix] = bestv;
if (best_space != null) {
if (best_dir < 4) {
best_space[fimg][ipix] = best_dir + best_sgn * 4;
} else {
best_space[fimg][ipix] = 10 + best_sgn;
}
//best_space[fimg][ipix] = best_dir + best_sgn * num_kernels;
}
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
return best_val;
}
public static double [] normalizeSpaceKernels(
double [][][] conv_data) { // same data convolved with different kernels, may have NaNs
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
final AtomicInteger ati = new AtomicInteger(0);
final double [][] s0t = new double [threads.length][conv_data.length];
final double [][] sdt = new double [threads.length][conv_data.length];
final double [][] sdat = new double [threads.length][conv_data.length]; // will compare l2 and abs
final double [][] sd2t = new double [threads.length][conv_data.length];
final double [] s0 = new double [conv_data.length];
final double [] sd = new double [conv_data.length];
final double [] sda = new double [conv_data.length]; // will compare l2 and abs
final double [] sd2 = new double [conv_data.length];
final double [] std = new double [conv_data.length];
final double [] coeffs_std = new double [conv_data.length+1];
final double [] coeffs_abs = new double [conv_data.length+1];
for (int nkern = 0; nkern < conv_data.length; nkern++) {
final int fnkern = nkern;
for (int nimg = 0; nimg < conv_data[fnkern].length; nimg++) {
final int fimg = nimg;
ai.set(0);
ati.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
int nthread = ati.getAndIncrement();
for (int ipix = ai.getAndIncrement(); ipix < conv_data[fnkern][fimg].length; ipix = ai.getAndIncrement()) {
double d = conv_data[fnkern][fimg][ipix];
if (!Double.isNaN(d)) {
s0t[nthread][fnkern] += 1.0;
sdt[nthread][fnkern] += d;
sd2t[nthread][fnkern] += d*d;
sdat[nthread][fnkern] += Math.abs(d);
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
for (int nthread = 0; nthread < s0t.length; nthread++) {
s0 [nkern] += s0t [nthread][nkern];
sd [nkern] += sdt [nthread][nkern];
sd2[nkern] += sd2t[nthread][nkern];
sda[nkern] += sdat[nthread][nkern];
}
}
double avg_std = 0.0;
double avg_abs = 0.0;
for (int nkern = 0; nkern < conv_data.length; nkern++) {
sd[nkern] /= s0[nkern];
sda[nkern] /= s0[nkern];
sd2[nkern] /= s0[nkern];
std[nkern] = Math.sqrt(sd2[nkern] - sd[nkern] * sd[nkern]);
avg_std+= std[nkern];
avg_abs+= sda[nkern];
}
avg_std/=conv_data.length;
avg_abs/=conv_data.length;
for (int nkern = 0; nkern < conv_data.length; nkern++) {
coeffs_std[nkern] = avg_std/ std[nkern];
coeffs_abs[nkern] = avg_abs/ sda[nkern];
}
coeffs_std[conv_data.length] = avg_std; // last element - average
coeffs_abs[conv_data.length] = avg_abs;
return coeffs_std;
}
public static double [] normalizeTimeDerivs( // return {std, average abs()}
final double [][] conv_data,
final double frac_outliers) {
final int num_bins = 10000;
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
final AtomicInteger ati = new AtomicInteger(0);
final double [] max_abs = new double [threads.length];
for (int nimg = 0; nimg < conv_data.length; nimg++) {
final double [] data = conv_data[nimg];
ai.set(0);
ati.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
int nthread = ati.getAndIncrement();
for (int ipix = ai.getAndIncrement(); ipix < data.length; ipix = ai.getAndIncrement()) if (!Double.isNaN(data[ipix])) {
max_abs[nthread] = Math.max(max_abs[nthread],Math.abs(data[ipix]));
}
}
};
}
ImageDtt.startAndJoin(threads);
}
double amax = 0;
for (int nthread = 0; nthread < max_abs.length; nthread++) {
amax=Math.max(amax, max_abs[nthread]);
}
// final double fmax = amax;
// final double scale = amax/num_bins;
final double step = amax/num_bins;
final double [][] histograms = new double [threads.length][num_bins];
for (int nimg = 0; nimg < conv_data.length; nimg++) {
final double [] data = conv_data[nimg];
ai.set(0);
ati.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
int nthread = ati.getAndIncrement();
for (int ipix = ai.getAndIncrement(); ipix < data.length; ipix = ai.getAndIncrement()) if (!Double.isNaN(data[ipix])) {
double ad = Math.abs(data[ipix]);
int bin = (int) Math.floor(ad/step);
if ((bin >=0) && (bin < num_bins)) {
histograms[nthread][bin] += 1.0;
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
final double [] histogram = new double [num_bins];
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int bin = ai.getAndIncrement(); bin < num_bins; bin = ai.getAndIncrement()) {
for (int i = 0; i < histograms.length; i++) {
histogram[bin]+=histograms[i][bin];
}
}
}
};
}
ImageDtt.startAndJoin(threads);
double sum = 0;
for (int i = 0; i < num_bins; i++) {
sum+=histogram[i];
}
double threshold = sum * frac_outliers;
double run_sum = 0;
double prev_sum = 0;
double alim = 0;
for (int bin= num_bins-1; bin >=0; bin--) {
prev_sum = run_sum;
run_sum += histogram[bin];
if (run_sum >=threshold) {
alim = amax *(bin + (run_sum - threshold)/(run_sum - prev_sum))/num_bins;
break;
}
}
final double flim = alim;
// alim - absolute limit to remove outliers
final double [] s0t = new double [threads.length];
final double [] sdt = new double [threads.length];
final double [] sdat = new double [threads.length]; // will compare l2 and abs
final double [] sd2t = new double [threads.length];
for (int nimg = 0; nimg < conv_data.length; nimg++) {
final double [] data = conv_data[nimg];
ai.set(0);
ati.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
int nthread = ati.getAndIncrement();
for (int ipix = ai.getAndIncrement(); ipix < data.length; ipix = ai.getAndIncrement()) {
double d = data[ipix];
if (!Double.isNaN(d) && (Math.abs(d) < flim)) {
s0t[nthread] += 1.0;
sdt[nthread] += d;
sd2t[nthread] += d*d;
sdat[nthread] += Math.abs(d);
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
double s0=0, sd=0,sd2=0,sda=0;
for (int nthread = 0; nthread < s0t.length; nthread++) {
s0 += s0t [nthread];
sd += sdt [nthread];
sd2 += sd2t[nthread];
sda += sdat[nthread];
}
sd /= s0;
sda /= s0;
sd2 /= s0;
double std = Math.sqrt(sd2 - sd * sd);
return new double [] {std, sda};
}
public static void scaleConvolutions(
final double [][] conv_data, // same data convolved with different kernels, may have NaNs
final double scale) {
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
for (int nimg = 0; nimg < conv_data.length; nimg++) {
final double [] data = conv_data[nimg];
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int ipix = ai.getAndIncrement(); ipix < data.length; ipix = ai.getAndIncrement()) {
data[ipix] *= scale;
}
}
};
}
ImageDtt.startAndJoin(threads);
}
}
public static double [][] timeToY(
final double [][] multi_image,
final int width,
final int num_vert,
final int vgap){
final int height = multi_image[0].length/width;
final int num_img_in = multi_image.length - 1; //last slice is average
final int num_slices = (height+num_vert-1)/ num_vert;
final int out_height = num_img_in*num_vert + vgap*(num_vert-1);
final double [][] out_data = new double [num_slices][width * out_height];
for (int i = 0; i < out_data.length; i++) {
Arrays.fill(out_data[i], Double.NaN);
}
for (int nimg = 0; nimg <num_img_in; nimg++) {
for (int nrow = 0; nrow < height; nrow++) {
int nout_slice = nrow/num_vert;
int nsub = nrow%num_vert;
int offs_out = ((num_img_in + vgap)* nsub + nimg)*width;
// copy row
System.arraycopy(
multi_image[nimg],
nrow*width,
out_data[nout_slice],
offs_out,
width);
}
}
return out_data;
}
public static void showKernel(
double [][][] kernel,
String title) {
final int rT = (kernel.length-1)/2;
if (kernel.length!= (2 * rT+1)) {
throw new IllegalArgumentException("Not all kernel dimensions are odd");
}
int height = kernel[0].length;
int width = kernel[0][0].length;
String [] titles = new String [kernel.length];
double [][] data = new double [kernel.length][width*height];
for (int t = 0; t < kernel.length; t++) {
titles[t] = "t "+(t-rT);
for (int row = 0; row < kernel[0].length; row++) {
System.arraycopy(
kernel[t][row],
0,
data[t],
width*row,
width);
}
}
ShowDoubleFloatArrays.showArrays(
data,
width,
height,
true,
title,
titles);
}
public static double [][] convolve3d(
final double [][] data_in, // fill NaN before running, will treat NaN as 0 on input, skip if center is NaN
final int width,
final double [][][] kernel, // (odd)x(odd)x(odd}
final boolean repeat_border){
final int height = data_in[0].length/width;
final int rT = (kernel.length-1)/2;
final int rY = (kernel[0].length-1)/2;
final int rX = (kernel[0][0].length-1)/2;
if ((kernel.length!= (2 * rT+1)) || (kernel[0].length!= (2 * rY+1)) || (kernel[0][0].length!= (2 * rX+1))) {
throw new IllegalArgumentException("Not all kernel dimensions are odd");
}
final double [][] data_out = new double [data_in.length][data_in[0].length];
for (int i = 0; i < data_out.length; i++) {
Arrays.fill(data_out[i], Double.NaN);
}
final int lenXY = data_in[0].length;
final int lenT = data_in.length;
final int total_pix = lenXY * lenT;
final int lenTm1 = lenT-1;
final int lenYm1 = height-1;
final int lenXm1 = width-1;
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int ipix = ai.getAndIncrement(); ipix < total_pix; ipix = ai.getAndIncrement()) {
int iT = ipix / lenXY;
int iXY = ipix % lenXY;
if (!Double.isNaN(data_in[iT][iXY])) {
int iY = iXY / width;
int iX = iXY % width;
int t0 = iT-rT, t1 = iT+rT, y0 = iY-rY, y1 = iY + rY, x0= iX-rX, x1=iX+rX;
int kTindx = 2*rT, kYindx = 2*rY, kXindx = 2*rX;
boolean limited = (t0 < 0) || (t1 >= lenT) || (y0 < 0) || (y1 >= height) || (x0 < 0) || (x1 >= width);
if (limited) {
if (!repeat_border) {
if (t0 < 0) {
kTindx += t0; // negative
t0 = 0;
}
if (t1 >= lenT) t1 = lenT-1;
if (y0 < 0) {
kYindx += y0; // negative
y0 = 0;
}
if (y1 >= height) y1 = height-1;
if (x0 < 0) {
kXindx += x0; // negative
x0 = 0;
}
if (x1 >= width) x1 = width-1;
}
int kTi = kTindx;
double s = 0;
for (int tt = t0; tt <= t1; tt++) {
int t = Math.min(lenTm1, Math.max(tt, 0)); // if repeating
int kYi = kYindx;
for (int yy = y0; yy <= y1; yy++ ) {
int y = Math.min(lenYm1, Math.max(yy, 0)); // if repeating
int kXi = kXindx;
for (int xx = x0; xx <= x1; xx++ ) {
int x = Math.min(lenXm1, Math.max(xx, 0)); // if repeating
int xy = x + width * y;
double d = data_in[t][xy];
if (!Double.isNaN(d)) {
s += d*kernel[kTi][kYi][kXi];
}
kXi--;
}
kYi--;
}
kTi--;
}
data_out[iT][iXY] = s;
} else { // same faster, w/o tests
int kTi = kTindx;
double s = 0;
for (int t = t0; t <= t1; t++) {
int kYi = kYindx;
for (int y = y0; y <= y1; y++ ) {
int kXi = kXindx;
for (int x = x0; x <= x1; x++ ) {
int xy = x + width * y;
double d = data_in[t][xy];
if (!Double.isNaN(d)) {
s += d*kernel[kTi][kYi][kXi];
}
kXi--;
}
kYi--;
}
kTi--;
}
data_out[iT][iXY] = s;
}
}
}
}
};
}
ImageDtt.startAndJoin(threads);
return data_out;
}
public static double [][][] kernelDiffTime(
double rt,
double rxy) {
int irt = (int) Math.floor(rt);
int irxy = (int) Math.floor(rxy);
int lt = 2 * irt + 1;
int lxy = 2 * irxy + 1;
double [][][] kernel = new double [lt][lxy][lxy];
for (int it = 0; it < lt; it++) {
double t = it - irt;
double wt = -t * Math.cos(0.5*Math.PI* t / rt);
for (int iy = 0; iy < lxy; iy++) {
double y = iy- irxy;
double wty = wt * Math.cos(0.5*Math.PI* y / rxy);
for (int ix = 0; ix < lxy; ix++) {
double x = ix- irxy;
kernel[it][iy][ix] = wty * Math.cos(0.5*Math.PI* x / rxy);
}
}
}
double [][][] norm_kernel = kernelNormalize2(kernel);
return norm_kernel;
}
public static double [][][] kernelXY(
int dir, // 0: +y, 1: +x+y, 2:+x, 3 +x-t, 4: 0: +center will be voting 1 of 10 (each plus and minus), weighted by abs(kernelDiffTime)
double rt,
double rxy) {
double rxy2 = rxy*rxy;
int irt = (int) Math.floor(rt);
int irxy = (int) Math.floor(rxy);
int lt = 2 * irt + 1;
int lxy = 2 * irxy + 1;
double [][][] kernel = new double [lt][lxy][lxy];
for (int it = 0; it < lt; it++) {
double sum_cos = 0;
double sum_patt = 0;
double [][] cos_patt = new double [lxy][lxy];
double t = it - irt;
double wt = Math.cos(0.5*Math.PI* t / rt);
for (int iy = 0; iy < lxy; iy++) {
double y = iy- irxy;
// double wty = wt * Math.cos(0.5*Math.PI* y / rxy);
for (int ix = 0; ix < lxy; ix++) {
double x = ix- irxy;
double r2=(x*x+y*y)/rxy2;
double w = 0;
if (r2 < 1) {
double r = Math.sqrt(r2);
w = wt * Math.cos(0.5*Math.PI * r);
cos_patt[iy][ix] = w;
sum_cos += w;
switch (dir) {
case 0: w *= y; break;
case 1: w *= y - x; break;
case 2: w *= -x; break;
case 3: w *= -y - x; break;
case 4: w *= Math.cos(Math.PI * r); break; // center
}
sum_patt+=w;
}
kernel[it][iy][ix] = w;
}
}
double cos_corr = sum_patt/sum_cos; // balance average=0
for (int iy = 0; iy < lxy; iy++) {
for (int ix = 0; ix < lxy; ix++) {
kernel[it][iy][ix] -= cos_corr*cos_patt[iy][ix];
}
}
}
return kernelNormalize2(kernel);
}
public static double [][][] kernelNormalize2(double [][][] kernel){
double [][][] kernel_out = new double [kernel.length][kernel[0].length][kernel[0][0].length];
double s = 0;
for (int t = 0; t < kernel.length; t++) {
for (int y = 0; y < kernel[t].length; y++) {
for (int x = 0; x < kernel[t][y].length; x++) {
double d = kernel[t][y][x];
s+=d * d;
}
}
}
if (s == 0 ) {
throw new IllegalArgumentException("Kernel of all zeros");
}
double k = 1.0/Math.sqrt(s);
for (int t = 0; t < kernel.length; t++) {
for (int y = 0; y < kernel[t].length; y++) {
for (int x = 0; x < kernel[t][y].length; x++) {
kernel_out[t][y][x] = kernel[t][y][x] * k;
}
}
}
return kernel_out;
}
public static double [] applyWeights(
final double [][] data_in,
final double [][] weights) {
final double [] data_out = new double [data_in[0].length];
final double [] sum_weights = new double [data_in[0].length];
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
for (int nimg = 0; nimg < weights.length; nimg++) {
final int fimg = nimg;
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int ipix = ai.getAndIncrement(); ipix < data_in[fimg].length; ipix = ai.getAndIncrement()) {
double w = weights[fimg][ipix];
if (w > 0 ) {
double d = data_in[fimg][ipix];
if (!Double.isNaN(d)) {
sum_weights[ipix] += w;
data_out[ipix] += w * d;
}
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int ipix = ai.getAndIncrement(); ipix < data_out.length; ipix = ai.getAndIncrement()) {
if (sum_weights[ipix] > 0) {
data_out[ipix] /= sum_weights[ipix];
} else {
data_out[ipix] = Double.NaN;
}
}
}
};
}
ImageDtt.startAndJoin(threads);
return data_out;
}
public static void testOrange() {
boolean zero_is_nan=true;
int master = 3;
final double [] voffsets = value_offsets;
int [][] segments = match_segments; // full_segments; // single_segments;
double oscale = 2.0;
// Rectangle woi = new Rectangle(0,0,640,512); // full window;
// Rectangle woi = new Rectangle(92,223,320,240); // approx. overlap
Rectangle woi = new Rectangle(92,280,320,120); // minimal overlap
final Rectangle owoi = new Rectangle (
(int) Math.round(oscale *woi.x ),
(int) Math.round(oscale *woi.y ),
(int) Math.round(oscale *woi.width),
(int) Math.round(oscale *woi.height));
int [][] seg_offs_len = new int [segments.length][2];
int offs = 0;
for (int nimg = 0; nimg < seg_offs_len.length; nimg++) {
seg_offs_len[nimg][0] = offs;
int len = segments[nimg][1] - segments[nimg][0]; // length;
seg_offs_len[nimg][1] = len;
offs += len;
}
final int indx_avg = offs;
final int olen = owoi.width * owoi.height;
final int owidth = owoi.width;
final double [][] out_data = new double [offs+1][owoi.width * owoi.height];
final int [] num_valid = new int [owoi.width * owoi.height];
String [] titles = new String[out_data.length];
titles [indx_avg] = "average";
for (int i = 0; i < offs; i++) {
Arrays.fill(out_data[i], Double.NaN);
}
final Thread[] threads = ImageDtt.newThreadArray();
final AtomicInteger ai = new AtomicInteger(0);
/*
faded_textures[nslice][0] = TileProcessor.fillNaNs(
faded_textures[nslice][0], // final double [] data,
null, // final boolean [] prohibit,
width, // int width,
// CAREFUL ! Remaining NaN is grown by unsharp mask filter ************* !
100, // 2*width, // 16, // final int grow,
0.7, // double diagonal_weight, // relative to ortho
100, // int num_passes,
0.03, // final double max_rchange, // = 0.01 - does not need to be accurate
THREADS_MAX); // final int threadsMax) // maximal number of threads to launch
*/
for (int nimg = 0; nimg < seg_offs_len.length; nimg++) {
final double voffs = +voffsets[nimg];
final int foffs = seg_offs_len[nimg][0];
ImagePlus imp_src = new ImagePlus(src_files[nimg]);
ImageStack stack = imp_src.getImageStack();
final int src_width = imp_src.getWidth();
final int src_height = imp_src.getHeight();
String [] stack_labels = stack.getSliceLabels();
final double [][] dpixels = new double [seg_offs_len[nimg][1]][];
for (int i = 0; i < seg_offs_len[nimg][1]; i++) {
titles[seg_offs_len[nimg][0] + i] =nimg+":"+stack_labels[segments[nimg][0]+i];
float [] fpixels = (float[]) stack.getPixels(segments[nimg][0]+i+1);
dpixels[i] = new double [fpixels.length];
for (int j = 0; j < dpixels[i].length; j++) {
dpixels[i][j] = fpixels[j];
if (zero_is_nan && (dpixels[i][j] == 0.0)) {
dpixels[i][j] = Double.NaN;
}
}
// fpixels[i] = (float[]) stack.getPixels(segments[nimg][0]+i+1);
}
final double [][] points0 = new double[matching_points[master].length][];
final double [][] points1 = new double[points0.length][]; // matching_points[nimg];
for (int i = 0; i < points0.length;i++) {
points0[i] = new double [] {matching_points[master][i][0]/points_scale, matching_points[master][i][1]/points_scale};
points1[i] = new double [] {matching_points[nimg ][i][0]/points_scale, matching_points[nimg ][i][1]/points_scale};
}
//points_scale
double [][] ab1 = ComboMatch.getAffineMatrix(
points0, // double [][] x,
points1); // double [][] y){
double k = 1.0/oscale;
final double [][] a1 = {{k*ab1[0][0],k*ab1[0][1]},{k*ab1[1][0],k*ab1[1][1]}};
// final double [] b1 = {k*ab1[2][0] + woi.x, k*ab1[2][1]+ woi.y}; // from output coordinates to image2 coordinates
// final double [] b1 = {ab1[2][0] + woi.x, ab1[2][1]+ woi.y}; // from output coordinates to image2 coordinates
final double [] b1 = {
ab1[2][0] + a1[0][0]*owoi.x + a1[0][1]*owoi.y,
ab1[2][1] + a1[1][0]*owoi.x + a1[1][1]*owoi.y}; // from output coordinates to image2 coordinates
ai.set(0);
for (int ithread = 0; ithread < threads.length; ithread++) {
threads[ithread] = new Thread() {
public void run() {
for (int ipix = ai.getAndIncrement(); ipix < olen; ipix = ai.getAndIncrement()) {
int y = ipix / owidth; // output image coordinates
int x = ipix % owidth; // output image coordinates
// int p = x + y * width;
double fx = a1[0][0] * x + a1[0][1] * y + b1[0];
double fy = a1[1][0] * x + a1[1][1] * y + b1[1];
int ix0 = (int) Math.floor(fx);
int iy0 = (int) Math.floor(fy);
int ix1 = ix0 + 1;
int iy1 = iy0 + 1;
if ((ix1 >=0) && (iy1 >= 0) && (ix0 < src_width) && (iy0 < src_height)) {
double dx = fx - ix0;
double dy = fy - iy0;
if (ix0 < 0) ix0 = 0;
if (iy0 < 0) iy0 = 0;
if (ix1 >= src_width) ix1 = src_width - 1;
if (iy1 >= src_height) iy1 = src_height - 1;
int indx00 = ix0 + iy0 * src_width;
int indx01 = ix1 + iy0 * src_width;
int indx10 = ix0 + iy1 * src_width;
int indx11 = ix1 + iy1 * src_width;
for (int islice = 0; islice < dpixels.length; islice++) {
int oslice = islice + foffs;
double d00 = dpixels[islice][indx00];
double d01 = dpixels[islice][indx01];
double d10 = dpixels[islice][indx10];
double d11 = dpixels[islice][indx11];
double d = d00*(1-dx)*(1-dy)+d01*dx*(1-dy)+d10*(1-dx)*dy+d11*dx*dy + voffs;
out_data[oslice][ipix]= d;
if (!Double.isNaN(d)) {
out_data[indx_avg][ipix] += d;
num_valid[ipix]++;
}
}
}
}
}
};
}
ImageDtt.startAndJoin(threads);
}
for (int ipix=0; ipix < olen; ipix++) {
if (num_valid[ipix] == 0) {
out_data[indx_avg][ipix] = Double.NaN;
} else {
out_data[indx_avg][ipix]/=num_valid[ipix];
}
}
ShowDoubleFloatArrays.showArrays(
out_data,
owoi.width,
owoi.height,
true,
"merged_seq_scale"+oscale+"-slices"+indx_avg+"_x"+woi.x+"_y"+woi.y+"_w"+woi.width+"_h"+woi.height+(zero_is_nan?"_zeronan":""),
titles);
return;
}
}
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