Implemented 2d maximum modelled as Gaussian in addition to parabola (fb44bc1a) · Commits · Elphel / imagej-elphel

src/main/java/com/elphel/imagej/tileprocessor/Corr2dLMA.java

+152 −2

Original line number	Original line	Diff line number	Diff line
	@@ -130,6 +130,7 @@ public class Corr2dLMA {
	private final int [][] pindx = new int [NUM_CAMS][NUM_CAMS];		private final int [][] pindx = new int [NUM_CAMS][NUM_CAMS];
	private int numTiles = 1;		private int numTiles = 1;

			public boolean gaussian_mode = true;

	public class Sample{ // USED in lwir		public class Sample{ // USED in lwir
	int tile; // tile in a cluster		int tile; // tile in a cluster
	@@ -162,8 +163,10 @@ public class Corr2dLMA {
	public Corr2dLMA (		public Corr2dLMA (
	int numTiles,		int numTiles,
	int ts, // null - use default table		int ts, // null - use default table
	double [][] corr_wnd // may be null		double [][] corr_wnd, // may be null
			boolean gaussian_mode
	) {		) {
			this.gaussian_mode = gaussian_mode;
	for (int f = 0; f < NUM_CAMS; f++) {		for (int f = 0; f < NUM_CAMS; f++) {
	pindx[f][f]=-1;		pindx[f][f]=-1;
	for (int s = f+1; s < NUM_CAMS; s++) {		for (int s = f+1; s < NUM_CAMS; s++) {
	@@ -495,10 +498,16 @@ public class Corr2dLMA {
	}		}
	}		}
	}		}

	public double [] getFxJt( // USED in lwir		public double [] getFxJt( // USED in lwir
	double [] vector,		double [] vector,
	double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated		double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
			if (this.gaussian_mode) return getFxJt_gaussian(vector, jt);
			else return getFxJt_parabola(vector, jt);
			}

			public double [] getFxJt_parabola( // USED in lwir
			double [] vector,
			double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
	if (vector == null) return null;		if (vector == null) return null;
	double [] av = fromVector(vector);		double [] av = fromVector(vector);
	Matrix [][] xcam_ycam = new Matrix[numTiles][NUM_CAMS];		Matrix [][] xcam_ycam = new Matrix[numTiles][NUM_CAMS];
	@@ -635,6 +644,147 @@ public class Corr2dLMA {
	return fx;		return fx;
	}		}

			public double [] getFxJt_gaussian( // USED in lwir
			double [] vector,
			double [][] jt) { // should be either [vector.length][samples.size()] or null - then only fx is calculated
			if (vector == null) return null;
			double [] av = fromVector(vector);
			Matrix [][] xcam_ycam = new Matrix[numTiles][NUM_CAMS];
			double [][][][] xp_yp = new double[numTiles][NUM_CAMS][NUM_CAMS][];
			double [] axc_yc = {transform_size - 1.0, transform_size-1.0};
			Matrix xc_yc = new Matrix(axc_yc, 2);
			double [] AT = new double [numTiles]; // av[A_INDEX];
			double [] BT = new double [numTiles]; // av[B_INDEX];
			double [] CT = new double [numTiles]; // A + av[CMA_INDEX];
			for (int nTile = 0; nTile < numTiles; nTile++) {
			for (int i = 0; i < NUM_CAMS; i++) if (used_cameras[i]) {
			double [] add_dnd = {av[DISP_INDEX+ nTile * TILE_PARAMS]+ av[DDISP_INDEX + i], av[NDISP_INDEX + i]};
			xcam_ycam[nTile][i] = m_disp[nTile][i].times(new Matrix(add_dnd,2));
			}
			for (int f = 0; f < NUM_CAMS; f++) if (used_cameras[f]) {
			for (int s = 0; s < NUM_CAMS; s++) if (used_cameras[s]) {
			xp_yp[nTile][f][s] =xcam_ycam[nTile][f].minus(xcam_ycam[nTile][s]).plus(xc_yc).getColumnPackedCopy();
			}
			}
			AT[nTile] = av[A_INDEX + nTile * TILE_PARAMS];
			BT[nTile] = av[B_INDEX + nTile * TILE_PARAMS];
			CT[nTile] = AT[nTile] + av[CMA_INDEX + nTile * TILE_PARAMS];
			}

			int num_samples = samples.size();
			double [] fx= new double [num_samples + 2 * NUM_CAMS];
			//corr_wnd
			for (int ns = 0; ns < num_samples; ns++) {
			Sample s = samples.get(ns);
			int pair = pindx[s.fcam][s.scam]; // all pairs, noit just used?
			double A = AT[s.tile];
			double B = BT[s.tile];
			double C = CT[s.tile];

			double Gp = av[G0_INDEX + pair + s.tile * TILE_PARAMS];
			double Wp = corr_wnd[s.ix][s.iy];
			double WGp = Wp * Gp;
			double xmxp = s.ix - xp_yp[s.tile][s.fcam][s.scam][0];
			double ymyp = s.iy - xp_yp[s.tile][s.fcam][s.scam][1];
			double xmxp2 = xmxp * xmxp;
			double ymyp2 = ymyp * ymyp;
			double xmxp_ymyp = xmxp * ymyp;
			//// double comm = Wp(1.0 - (Axmxp2 + 2 * B * xmxp_ymyp + C * ymyp2));
			double exp = Math.exp(-(Axmxp2 + 2 B * xmxp_ymyp + C * ymyp2));
			double comm = exp * Wp;
			double WGpexp = WGp*exp;

			fx[ns] = comm * Gp;
			if (Double.isNaN(fx[ns])) {
			System.out.println("fx["+ns+"]="+fx[ns]);
			}
			if (s.tile > 0) {
			System.out.print("");
			}
			if (jt != null) {
			if (par_map[DISP_INDEX + s.tileTILE_PARAMS] >= 0) jt[par_map[DISP_INDEX + s.tileTILE_PARAMS]][ns] = 2 * WGpexp *
			((A * xmxp + B * ymyp) * m_pairs[s.tile][s.fcam][s.scam].get(0, 0)+
			(B * xmxp + C * ymyp) * m_pairs[s.tile][s.fcam][s.scam].get(1, 0));


			if (par_map[A_INDEX + s.tileTILE_PARAMS] >= 0) jt[par_map[A_INDEX + s.tileTILE_PARAMS]][ns] = -WGpexp*(xmxp2 + ymyp2);
			if (par_map[B_INDEX + s.tileTILE_PARAMS] >= 0) jt[par_map[B_INDEX + s.tileTILE_PARAMS]][ns] = -WGpexp* 2 * xmxp_ymyp;
			if (par_map[CMA_INDEX + s.tileTILE_PARAMS] >= 0) jt[par_map[CMA_INDEX + s.tileTILE_PARAMS]][ns] = -WGp* ymyp2 * exp;
			for (int p = 0; p < npairs[s.tile]; p++) { // par_mask[G0_INDEX + p] as all pairs either used, or not - then npairs == 0
			if (par_map[G0_INDEX + p + s.tileTILE_PARAMS] >= 0) jt[par_map[G0_INDEX + p + s.tileTILE_PARAMS]][ns] = (p== pair)? comm : 0.0; // (par_mask[G0_INDEX + pair])? d;
			}
			// process ddisp (last camera not used, is equal to minus sum of others to make a sum == 0)

			for (int f = 0; f < NUM_CAMS; f++) if (par_map[DDISP_INDEX + f] >= 0) { // -1 for the last_cam
			jt[par_map[DDISP_INDEX + f]][ns] = 0.0;
			}
			if (par_map[DDISP_INDEX + s.fcam] >= 0){ // par_map[DDISP_INDEX + last_cam] always <0
			jt[par_map[DDISP_INDEX + s.fcam]][ns] += 2 * WGpexp *
			((A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 0)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 0));
			} else if (s.fcam == last_cam) {
			for (int c = 0; c < NUM_CAMS; c++) if ((c != last_cam) && (par_map[DDISP_INDEX + c] >=0)) {
			jt[par_map[DDISP_INDEX + c]][ns] -= 2 * WGpexp *
			( (A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 0)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 0));
			}
			}
			if (par_map[DDISP_INDEX + s.scam]>= 0){ // par_map[DDISP_INDEX + last_cam] always <0
			jt[par_map[DDISP_INDEX + s.scam]][ns] -= 2 * WGpexp *
			((A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 0)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 0));

			} else if (s.scam == last_cam) {
			for (int c = 0; c < NUM_CAMS; c++) if ((c != last_cam) && (par_map[DDISP_INDEX + c] >= 0)) {
			jt[par_map[DDISP_INDEX + c]][ns] += 2 * WGpexp *
			((A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 0)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 0));
			}
			}

			// process ndisp
			for (int f = 0; f < ncam; f++) if (par_map[NDISP_INDEX + f] >= 0) {
			jt[par_map[NDISP_INDEX + f]][ns] = 0.0;
			}
			if (par_map[NDISP_INDEX + s.fcam] >=0){
			jt[par_map[NDISP_INDEX + s.fcam]][ns] += 2 * WGpexp *
			( (A * xmxp + B * ymyp) * m_disp[s.tile][s.fcam].get(0, 1)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.fcam].get(1, 1));
			}

			if (par_map[NDISP_INDEX + s.scam] >= 0) {

			jt[par_map[NDISP_INDEX + s.scam]][ns] -= 2 * WGpexp *
			( (A * xmxp + B * ymyp) * m_disp[s.tile][s.scam].get(0, 1)+
			(B * xmxp + C * ymyp) * m_disp[s.tile][s.scam].get(1, 1));
			}
			}
			}
			for (int n = 0; n < NUM_CAMS; n++) { // av[DDISP_INDEX +last_cam] is already populated
			fx[num_samples + n] = av[DDISP_INDEX + n];
			fx[num_samples + NUM_CAMS + n] = av[NDISP_INDEX + n];
			}

			// and derivatives
			if (jt != null) {
			for (int i = 0; i < NUM_CAMS; i++) {
			if ((i != last_cam) && (par_map[DDISP_INDEX + i] >= 0)) {
			for (int j = 0; j < NUM_CAMS; j++) { // j - column
			jt[par_map[DDISP_INDEX + i]][num_samples + j] = (i==j)? 1.0 : 0.0;
			}
			jt[par_map[DDISP_INDEX + i]][num_samples + last_cam] = -1.0;
			}
			}
			for (int i = 0; i < NUM_CAMS; i++) {
			if (par_map[NDISP_INDEX + i] >= 0) {
			for (int j = 0; j < NUM_CAMS; j++) { // j - column
			jt[par_map[NDISP_INDEX + i] ][num_samples + NUM_CAMS + j] = (i==j)? 1.0 : 0.0;
			}
			}
			}
			}
			return fx;
			}

	public void printParams() { // not used in lwir		public void printParams() { // not used in lwir
	for (int np = 0; np < all_pars.length; np++) {		for (int np = 0; np < all_pars.length; np++) {

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

+33 −9

Original line number	Original line	Diff line number	Diff line
	@@ -1823,14 +1823,19 @@ public class Correlation2d {
	// corrs are organized as PAIRS, some are null if not used		// corrs are organized as PAIRS, some are null if not used
	// for each enabled and available pair find a maximum, filter convex and create sample list		// for each enabled and available pair find a maximum, filter convex and create sample list
	boolean debug_graphic = (debug_level > -1);		boolean debug_graphic = (debug_level > -1);
	boolean debug_second_all = true;		boolean debug_second_all = false; // true;
	int clust_height = corrs.length/clust_width;		int clust_height = corrs.length/clust_width;
	int ntiles = corrs.length;		int ntiles = corrs.length;
	DoubleGaussianBlur gb = null;		DoubleGaussianBlur gb = null;
	if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();		if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();
	int center = transform_size - 1;		int center = transform_size - 1;
	int corr_size = 2 * transform_size - 1;		int corr_size = 2 * transform_size - 1;
	Corr2dLMA lma = new Corr2dLMA(corrs.length,transform_size, corr_wnd);		Corr2dLMA lma = new Corr2dLMA(
			corrs.length,
			transform_size,
			corr_wnd,
			imgdtt_params.lma_gaussian//boolean gaussian_mode
			);

	double [][][] dbg_corr = debug_graphic ? new double [corrs.length][][] : null;		double [][][] dbg_corr = debug_graphic ? new double [corrs.length][][] : null;
	double [][][] dbg_weights = debug_graphic ? new double [corrs.length][][] : null;		double [][][] dbg_weights = debug_graphic ? new double [corrs.length][][] : null;
	@@ -2082,24 +2087,37 @@ public class Correlation2d {
	true,		true,
	"corr_fx"+"_x"+tileX+"_y"+tileY, sliceTitles);		"corr_fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
	} else {		} else {
			double [][] repacked_y = repackCluster(lma.dbgGetSamples(0),clust_width);
			double [][] repacked_fx = repackCluster(lma.dbgGetSamples(2),clust_width);
			double [][] y_minux_fx = new double [repacked_y.length][];
			for (int i = 0; i < repacked_y.length; i++) if ((repacked_y[i] != null) && (repacked_fx[i] != null)){
			y_minux_fx[i] = new double [repacked_y[i].length];
			for (int j = 0; j < y_minux_fx[i].length; j++) y_minux_fx[i][j] = repacked_y[i][j] - repacked_fx[i][j];
			}
	(new ShowDoubleFloatArrays()).showArrays(		(new ShowDoubleFloatArrays()).showArrays(
	repackCluster(lma.dbgGetSamples(0),clust_width),		repacked_y,
	dbg_out_width,		dbg_out_width,
	dbg_out_height,		dbg_out_height,
	true,		true,
	"corr_values"+"_x"+tileX+"_y"+tileY, sliceTitles);		"y"+"_x"+tileX+"_y"+tileY, sliceTitles);
	(new ShowDoubleFloatArrays()).showArrays(		(new ShowDoubleFloatArrays()).showArrays(
	repackCluster(lma.dbgGetSamples(1),clust_width),		repacked_fx,
	dbg_out_width,		dbg_out_width,
	dbg_out_height,		dbg_out_height,
	true,		true,
	"corr_weights"+"_x"+tileX+"_y"+tileY, sliceTitles);		"fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
	(new ShowDoubleFloatArrays()).showArrays(		(new ShowDoubleFloatArrays()).showArrays(
	repackCluster(lma.dbgGetSamples(2),clust_width),		y_minux_fx,
	dbg_out_width,		dbg_out_width,
	dbg_out_height,		dbg_out_height,
	true,		true,
	"corr_fx"+"_x"+tileX+"_y"+tileY, sliceTitles);		"y-fx"+"_x"+tileX+"_y"+tileY, sliceTitles);
			(new ShowDoubleFloatArrays()).showArrays(
			repackCluster(lma.dbgGetSamples(1),clust_width),
			dbg_out_width,
			dbg_out_height,
			true,
			"weights"+"_x"+tileX+"_y"+tileY, sliceTitles);
	}		}
	}		}

	@@ -2129,7 +2147,13 @@ public class Correlation2d {
	if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();		if (imgdtt_params.lma_sigma > 0) gb = new DoubleGaussianBlur();
	int center = transform_size - 1;		int center = transform_size - 1;
	int corr_size = 2 * transform_size - 1;		int corr_size = 2 * transform_size - 1;
	Corr2dLMA lma = new Corr2dLMA(1,transform_size, corr_wnd);		Corr2dLMA lma = new Corr2dLMA(
			1,
			transform_size,
			corr_wnd,
			imgdtt_params.lma_gaussian//boolean gaussian_mode
			);


	double [][] dbg_corr = debug_graphic ? new double [corrs.length][] : null;		double [][] dbg_corr = debug_graphic ? new double [corrs.length][] : null;
	double [][] dbg_weights = debug_graphic ? new double [corrs.length][] : null;		double [][] dbg_weights = debug_graphic ? new double [corrs.length][] : null;

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

+2 −2

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

+7 −4

Original line number	Original line	Diff line number	Diff line
	@@ -99,6 +99,7 @@ public class ImageDttParameters {
	public double corr_wndx_blur = 5.0; // 100% to 0 % vertical transition range		public double corr_wndx_blur = 5.0; // 100% to 0 % vertical transition range

	// LMA parameters		// LMA parameters
			public boolean lma_gaussian = true; // model correlation maximum as a Gaussian (false - as a parabola)
	public boolean lma_adjust_wm = true; // used in new for width		public boolean lma_adjust_wm = true; // used in new for width
	public boolean lma_adjust_wy = true; // false; // used in new for ellipse		public boolean lma_adjust_wy = true; // false; // used in new for ellipse
	public boolean lma_adjust_wxy = true; // used in new for lazy eye adjust parallel-to-disparity correction		public boolean lma_adjust_wxy = true; // used in new for lazy eye adjust parallel-to-disparity correction
	@@ -267,6 +268,8 @@ public class ImageDttParameters {
	"Transition range, shifted sine is used");		"Transition range, shifted sine is used");

	gd.addTab("Corr LMA","Parameters for LMA fitting of the correlation maximum parameters");		gd.addTab("Corr LMA","Parameters for LMA fitting of the correlation maximum parameters");
			gd.addCheckbox ("Correlation maximum as gaussian", this.lma_gaussian,
			"Model correlation maximum as a Gaussian exp(-r^2) (false - as a parabola - 1-r^2)");
	gd.addCheckbox ("Fit correlation defined half-width", this.lma_adjust_wm,		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");		"Allow fitting of the half-width common for all pairs, defined by the LPF filter of the phase correlation");
	gd.addCheckbox ("Adjust ellipse parameters (was Fit extra vertical half-width)", this.lma_adjust_wy,		gd.addCheckbox ("Adjust ellipse parameters (was Fit extra vertical half-width)", this.lma_adjust_wy,
	@@ -412,6 +415,7 @@ public class ImageDttParameters {
	this.corr_wndx_blur = gd.getNextNumber();		this.corr_wndx_blur = gd.getNextNumber();

	//LMA tab		//LMA tab
			this.lma_gaussian= gd.getNextBoolean();
	this.lma_adjust_wm= gd.getNextBoolean();		this.lma_adjust_wm= gd.getNextBoolean();
	this.lma_adjust_wy= gd.getNextBoolean();		this.lma_adjust_wy= gd.getNextBoolean();
	this.lma_adjust_wxy= gd.getNextBoolean();		this.lma_adjust_wxy= gd.getNextBoolean();
	@@ -514,8 +518,7 @@ public class ImageDttParameters {
	properties.setProperty(prefix+"corr_wndx_hwidth", this.corr_wndx_hwidth +"");		properties.setProperty(prefix+"corr_wndx_hwidth", this.corr_wndx_hwidth +"");
	properties.setProperty(prefix+"corr_wndx_blur", this.corr_wndx_blur +"");		properties.setProperty(prefix+"corr_wndx_blur", this.corr_wndx_blur +"");

			properties.setProperty(prefix+"lma_gaussian", this.lma_gaussian +"");

	properties.setProperty(prefix+"lma_adjust_wm", this.lma_adjust_wm +"");		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_wy", this.lma_adjust_wy +"");
	properties.setProperty(prefix+"lma_adjust_wxy", this.lma_adjust_wxy +"");		properties.setProperty(prefix+"lma_adjust_wxy", this.lma_adjust_wxy +"");
	@@ -622,8 +625,7 @@ 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_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+"corr_wndx_blur")!=null) this.corr_wndx_blur=Double.parseDouble(properties.getProperty(prefix+"corr_wndx_blur"));

			if (properties.getProperty(prefix+"lma_gaussian")!=null) this.lma_gaussian=Boolean.parseBoolean(properties.getProperty(prefix+"lma_gaussian"));

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