Implementing inter-scene matching with normalization by eigenvectors (477343de) · Commits · Elphel / imagej-elphel

src/main/java/com/elphel/imagej/orthomosaic/OrthoMap.java

+1 −1

Original line number	Diff line number	Diff line
		@@ -2680,7 +2680,7 @@ public class OrthoMap implements Comparable <OrthoMap>, Serializable{
		gd.addNumericField("Video output frame rate", video_fps, 3,7,"fps", "Frame rate of the video.");
		gd.addCheckbox ("Compress AVI with JPEG", avi_compress_jpeg, "Use JPEG for AVI compression (false - use raw).");
		gd.addNumericField("AVI JPEG quality", aviJpegQuality, 0, 4, "", "AVI JPEG quality if JPEG compression is used.");
		gd.addCheckbox ("Convert AVI to WEBM", run_ffmpeg, "Use ffmped to convert intermediate AVI video to WEBM.");
		gd.addCheckbox ("Convert AVI to WEBM", run_ffmpeg, "Use ffmpeg to convert intermediate AVI video to WEBM.");
		gd.addStringField ("WEBM output extension", video_ext, 5,"WEBM output file extension including dot, normally \".webm\".");
		gd.addStringField ("WEBM codec", video_codec, 5,"WEBM codec \"vp8\" or \"vp9\"(vp9 had problems).");
		gd.addNumericField("WEBM CRF", video_crf, 0, 4, "", "WEBM compression quality (lower - better, 10 - good).");

src/main/java/com/elphel/imagej/tileprocessor/Correlation2d.java

+178 −0

Original line number	Diff line number	Diff line
		@@ -10,6 +10,7 @@ import com.elphel.imagej.common.PolynomialApproximation;
		import com.elphel.imagej.common.ShowDoubleFloatArrays;
		import com.elphel.imagej.tileprocessor.Corr2dLMA.Sample;

		import Jama.EigenvalueDecomposition;
		import Jama.Matrix;

		/**
		@@ -2506,6 +2507,183 @@ public class Correlation2d {
		}


		/**
		*
		* @param data
		* @param data_width
		* @param abs_min
		* @param rel_min
		* @param min_peak
		* @param eigen_sub_min - when calculating eigenvectors, subtract min from data, false - just skip
		* @param fpn_mask
		* @param ignore_border
		* @param debug_data null or double[1]
		* @param debug
		* @return {dx,dy,strength, eig_x, eig_y, lambda0, lambda1), [eig_x, eig_y] <-> labmda0, lambda0 < lambda1.
		*/
		public static double [] getMaxXYCmEig(
		double [] data, // will be modified if fpn_mask != null;
		int data_width, // = 2 * transform_size - 1;
		double abs_min,
		double rel_min,
		double min_peak,
		double eig_sub_frac, // subtract fraction of threshold {eig_min_abs,eig_min_rel} after selecting by them (0 - select only, will have pedestal)
		boolean [] fpn_mask,
		boolean ignore_border, // only if fpn_mask != null - ignore tile if maximum touches fpn_mask
		double [][] debug_data, // null or double [1]
		boolean debug)
		{
		int data_height = data.length/data_width;
		int center_xy = (data_width - 1)/2; // = transform_size - 1;
		double x0 = center_xy, y0 = center_xy;
		int imax= 0;
		for (int i= 1; i < data.length;i++) {
		if (Double.isNaN(data[i])) {
		System.out.println("NaN in getMaxXYCmEig()");
		return null;
		}
		if (data[i] > data[imax]) {
		imax = i;
		}
		}
		if (data[imax] < min_peak) {
		return null; // too weak;even before fpn filter
		}
		int ix0 = imax % data_width;
		int iy0 = imax / data_width;
		x0 = ix0;
		y0 = iy0;
		//min_peak
		// if (fpn_mask != null
		if (fpn_mask != null) { // modifies data, returns null if hits fpn
		for (int i = 0; i < fpn_mask.length; i++) if (fpn_mask[i]) {
		int iy = i / data_width;
		int ix = i - iy * data_width;
		if (ignore_border) {
		if(((ix - ix0) <= 1) && ((ix - ix0) >= -1) && ((iy - iy0) <= 1) && ((iy - iy0) >= -1)) {
		return null; // new double[3];
		}
		}
		int ix1 = 2 * ix0 - ix;
		if ((ix1 >= 0) && (ix1 < data_width)) {
		int iy1 = 2 * iy0 - iy;
		if ((iy1 >= 0) && (iy1 < data_height)) {
		data[iy1 * data_width + ix1] = 0.0; // zero out symmetrical to fpn mask around integer maximum
		}
		}
		}
		// update imax
		imax= 0;
		for (int i= 1; i < data.length;i++) {
		if (data[i] > data[imax]) {
		imax = i;
		}
		}
		if (data[imax] < min_peak) {
		return null; // too weak after fpn filter
		}

		}
		// create mask of connected to max pixels
		double mx = data[imax];
		double min_d = Math.min(abs_min, rel_min*mx);
		//
		double sub_pedestal = min_d * eig_sub_frac;
		boolean [] above_threshold = new boolean [data.length];
		for (int i = 0; i < data.length; i++) {
		above_threshold[i] = data[i] >= min_d;
		}

		boolean [] en_data = (new TileNeibs(data_width, data_height)).getConnected(
		above_threshold, // boolean [] tiles,
		ix0, // int seedX,
		iy0); // int seedY)
		// find centroid
		double s0 = 0, sx=0,sy = 0, sx2 = 0, sy2=0, sxy = 0;
		for (int iy = 0; iy < data_height; iy++) {
		double y = iy - y0;
		for (int ix = 0; ix < data_width; ix++) {
		int indx = iy * data_width + ix;
		if (en_data[indx]) { // assumes d >0, as it is >= min_d
		double x = ix - x0;
		double d = data[iy * data_width + ix] - sub_pedestal;
		s0 += d;
		sx += d * x;
		sy += d * y;
		sx2 += d * x * x;
		sy2 += d * y * y;
		sxy += d * x * y;
		}
		}
		}
		x0 += sx / s0; // relative to top-left
		y0 += sy / s0;

		/*
		double s0 = 0, sx=0,sy = 0;
		for (int iy = 0; iy < data_height; iy++) {
		double y = iy - y0;
		for (int ix = 0; ix < data_width; ix++) {
		int indx = iy * data_width + ix;
		if (en_data[indx]) { // assumes d >0, as it is >= min_d
		double x = ix - x0;
		double d = data[iy * data_width + ix] - sub_pedestal;
		s0 += d;
		sx += d * x;
		sy += d * y;

		}
		}
		}
		x0 += sx / s0; // relative to top-left
		y0 += sy / s0;
		double sx2 = 0, sy2=0, sxy = 0;
		for (int iy = 0; iy < data_height; iy++) {
		double y = iy - y0;
		for (int ix = 0; ix < data_width; ix++) {
		int indx = iy * data_width + ix;
		if (en_data[indx]) { // assumes d >0, as it is >= min_d
		double x = ix - x0;
		double d = data[iy * data_width + ix] - sub_pedestal;
		sx2 += d * x * x;
		sy2 += d * y * y;
		sxy += d * x * y;
		}
		}
		}

		*/

		//https://users.cs.utah.edu/~tch/CS4640/resources/A%20geometric%20interpretation%20of%20the%20covariance%20matrix.pdf
		double cxx = sx2 - sx * sx / s0, cyy= sy2 - sy * sy / s0, cxy = sxy - sx * sy / s0;
		Matrix covar = new Matrix(new double[][] {{cxx, cxy},{cxy,cyy}});
		EigenvalueDecomposition eig = covar.eig();
		double [] eigval = {eig.getD().get(0, 0),eig.getD().get(1, 1)};
		double [][] eigvec = eig.getV().getArray(); // columns - vectors?
		int eig_indx = (eigval[0] > eigval[1]) ? 1 : 0;
		double [] rslt = {
		x0 - center_xy,
		y0 - center_xy,
		mx,
		eigvec[0][eig_indx],
		eigvec[1][eig_indx],
		eigval[eig_indx],
		eigval[1-eig_indx]};
		if (debug){
		System.out.println("getMaxXYCm() -> "+rslt[0]+":"+rslt[1]+" ("+rslt[2]+
		"), eigv0=["+rslt[3]+","+rslt[4]+"], lambda0="+rslt[5]+", lambda1="+rslt[6]);
		}
		if (debug_data != null) {
		debug_data[0] = data.clone();
		for (int i = 0; i < data.length; i++) {
		if (!en_data[i]) {
		debug_data[0][i] = Double.NaN;
		}
		}
		}
		return rslt;
		}

		/**
		* Find maximum of the 2d array projected on a specified vector using centroid.
		* On the first stage integer maximum is found, then several refining operations

src/main/java/com/elphel/imagej/tileprocessor/ImageDtt.java

+499 −15

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src/main/java/com/elphel/imagej/tileprocessor/ImageDttCPU.java

+28 −11

Original line number	Diff line number	Diff line
		@@ -8950,11 +8950,18 @@ public class ImageDttCPU {
		final float [][] fcorr_data_out = new float[layers][]; // [tilesYtilesXtile_size*tile_size];
		final Thread[] threads = newThreadArray(threadsMax);
		final AtomicInteger ai = new AtomicInteger(0);

		/*
		for (int layer = 0; layer < layers; layer++) if (fcorr_data[flayer][layer] != null){
		fcorr_data_out[layer] = new float[tilesYtilesXtile_size*tile_size];
		Arrays.fill(fcorr_data_out[layer], Float.NaN);
		}
		*/
		// now some tiles may have null for some layers, non-null - for others
		for (int layer = 0; layer < layers; layer++){
		fcorr_data_out[layer] = new float[tilesYtilesXtile_size*tile_size];
		Arrays.fill(fcorr_data_out[layer], Float.NaN);
		}
		final boolean [] used_layers = new boolean[layers];
		for (int ithread = 0; ithread < threads.length; ithread++) {
		threads[ithread] = new Thread() {
		@Override
		@@ -8972,7 +8979,9 @@ public class ImageDttCPU {

		if (fcorr_data[iCorrTile] != null) {
		for (int layer = 0; layer < layers; layer++) if (fcorr_data[iCorrTile][layer] != null){ // sparse
		used_layers[layer] = true;
		for (int i = 0; i < corr_size;i++){
		if (fcorr_data[iCorrTile][layer] != null) {
		System.arraycopy(
		fcorr_data[iCorrTile][layer],
		corr_size* i,
		@@ -8984,10 +8993,18 @@ public class ImageDttCPU {
		}
		}
		}
		}
		};
		}
		startAndJoin(threads);
		for (int layer = 0; layer < layers; layer++){
		if (!used_layers[layer]) {
		fcorr_data_out[layer]=null;
		}
		}
		return fcorr_data_out;
		// remove unused output layers:

		}

src/main/java/com/elphel/imagej/tileprocessor/Interscene.java

+177 −65

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