Improving basic depth map generation (9a970304) · Commits · Elphel / imagej-elphel

src/main/java/com/elphel/imagej/tileprocessor/CLTPass3d.java

+67 −12

Original line number	Diff line number	Diff line
		@@ -59,6 +59,7 @@ public class CLTPass3d{
		private double [] bgTileStrength = null;
		private boolean [] border_tiles = null; // these are border tiles, zero out alpha
		private boolean [] selected = null; // which tiles are selected for this layer
		boolean [] has_lma = null; // which tiles are measured with LMA (reliable strength)
		public double [][][][] texture_tiles;
		// texture_selection is only used for the GPU and if not null means it is for the GPU
		public boolean [] texture_selection = null; // use by the GPU to set texture to generate
		@@ -70,6 +71,7 @@ public class CLTPass3d{
		public Rectangle texture_bounds; // in tiles, not pixels !
		public int dbg_index;
		public int disparity_index = ImageDtt.DISPARITY_INDEX_CM; // may also be ImageDtt.DISPARITY_INDEX_POLY
		public int lma_disparity_index = ImageDtt.DISPARITY_INDEX_POLY; // set to -1 to ignore and always use just CM (also applies to lma_strength - next)
		public double [][] tiles_RBGA = null;

		SuperTiles superTiles = null;
		@@ -362,6 +364,15 @@ public class CLTPass3d{
		this.border_tiles = border_tiles;
		}

		public boolean [] getLMA() {
		if (has_lma == null) {
		has_lma = hasLMADefined();
		}
		return has_lma;
		}
		public void setLMA(boolean [] has_lma) {// use for combo tiles
		this.has_lma = has_lma;
		}

		public void fixNaNDisparity()
		{
		@@ -599,24 +610,31 @@ public class CLTPass3d{
		// methods to "condition" measured disparity values
		public void conditionDisparity()
		{
		/*
		conditionDisparity(disparity_index);
		}

		public void conditionDisparity(int disparity_index)
		public void conditionDisparity(int disparity_index) // only called from above
		{
		*/
		int tilesX = tileProcessor.getTilesX();
		int tilesY = tileProcessor.getTilesY();
		double corr_magic_scale = tileProcessor.getMagicScale();


		this.disparity_index = disparity_index;
		double corr_magic_scale_LMA = 1.0;
		// int lma_disparity_index = ImageDtt.DISPARITY_INDEX_POLY;
		// this.disparity_index = disparity_index;
		calc_disparity = new double[tilesY*tilesX];
		calc_disparity_hor = new double[tilesY*tilesX];
		calc_disparity_vert = new double[tilesY*tilesX];
		double [] lma_disparity = (lma_disparity_index >= 0) ? disparity_map[lma_disparity_index] : null;
		for (int i = 0; i < tilesY; i++){
		for (int j = 0; j < tilesX; j++){
		int indx = i * tilesX + j;
		if ((lma_disparity != null) && !Double.isNaN(lma_disparity[indx])) {
		calc_disparity[indx] = lma_disparity[indx]/corr_magic_scale_LMA + this.disparity[i][j];
		} else {
		calc_disparity[indx] = disparity_map[disparity_index][indx]/corr_magic_scale + this.disparity[i][j];
		}
		calc_disparity_hor[indx] = disparity_map[ImageDtt.DISPARITY_INDEX_HOR][indx]/corr_magic_scale + this.disparity[i][j];
		calc_disparity_vert[indx] = disparity_map[ImageDtt.DISPARITY_INDEX_VERT][indx]/corr_magic_scale + this.disparity[i][j];
		}
		@@ -624,6 +642,28 @@ public class CLTPass3d{
		calc_disparity_combo = calc_disparity.clone(); // for now - just clone, can be modified separately and combined with hor/vert
		}

		public boolean [] hasLMADefined(){ // will try not to create this.has_lma
		if (disparity_map == null) {
		if (has_lma==null) {
		return null;
		} else {
		return has_lma;
		}

		}
		int tilesX = tileProcessor.getTilesX();
		int tilesY = tileProcessor.getTilesY();
		double [] lma_disparity = (lma_disparity_index >= 0) ? disparity_map[lma_disparity_index] : null;
		boolean [] lma_defined = new boolean[tilesX * tilesY];
		if (lma_disparity != null) {
		for (int i = 0; i < lma_disparity.length; i++) {
		lma_defined[i] = !Double.isNaN(lma_disparity[i]);
		}
		}
		return lma_defined;
		}


		// bypassing calculations
		public void setCalcDisparityStrength(
		double [] disparity,
		@@ -644,7 +684,7 @@ public class CLTPass3d{
		*
		* Replace weak by a weighted average of non-weak. If there are none - use weak ones, including this one too.
		*/
		public boolean[] replaceWeakOutliers(
		public boolean[] replaceWeakOutliers( // does not replace tiles with LMA available, busts LMA-defined strengths when averaging
		final boolean [] selection,
		final double weakStrength, // strength to be considered weak, subject to this replacement
		final double maxDiff,
		@@ -654,6 +694,7 @@ public class CLTPass3d{
		final double disparityNear,
		final int debugLevel)
		{
		final double scale_strength_lma = 5.0; // increase LMA-defined strength during averaging
		final int tilesX = tileProcessor.getTilesX();
		final int tilesY = tileProcessor.getTilesY();

		@@ -663,6 +704,8 @@ public class CLTPass3d{
		final int [] dirs = dirs8;
		final double [] disparity = getDisparity(0);
		final double [] strength = getStrength();
		final boolean [] has_lma = getLMA();

		final double absMinDisparity = 0.5 * disparityFar; // adjust? below this is definitely wrong (weak)
		final double absMaxDisparity = 1.5 * disparityNear; // change?
		final int dbg_nTile = (debugLevel > 0) ? 43493: -1; // x=77,y=134; // 42228; // x = 108, y = 130 46462; // 41545;
		@@ -674,7 +717,7 @@ public class CLTPass3d{
		@Override
		public void run() {
		for (int nTile = ai.getAndIncrement(); nTile < nTiles; nTile = ai.getAndIncrement()) {
		if (((strength[nTile] < weakStrength) \|\|
		if (((!has_lma[nTile] && (strength[nTile] < weakStrength)) \|\|
		(disparity[nTile] < absMinDisparity) \|\|
		(disparity[nTile] > absMaxDisparity))&& ((selection == null) \|\| selection[nTile])) {
		if (nTile == dbg_nTile){
		@@ -698,6 +741,9 @@ public class CLTPass3d{
		(disparity[nTile1] >= disparityFar) && // don't count on too near/too far for averaging
		(disparity[nTile1] <= disparityNear)){
		double w = strength[nTile1];
		if (has_lma[nTile1]) {
		w *= scale_strength_lma;
		}
		sw += w;
		sd += w * disparity[nTile1];
		hasNeighbors = true;
		@@ -742,12 +788,18 @@ public class CLTPass3d{
		int nTile1 = nTile + dirs[dir];
		if (!weakOutliers[nTile1] && ((selection == null) \|\| selection[nTile1 ]) ) {
		double w = strength[nTile1];
		if (has_lma[nTile1]) {
		w *= scale_strength_lma;
		}
		sw += w;
		sd += w * src_disparity[nTile1];
		}
		}
		if (sw == 0) { // Nothing strong around - repeat with weak and this one too.
		double w = strength[nTile];
		if (has_lma[nTile]) {
		w *= scale_strength_lma;
		}
		if (!Double.isNaN( src_disparity[nTile])) {
		sw += w;
		sd += w * src_disparity[nTile];
		@@ -756,6 +808,9 @@ public class CLTPass3d{
		int nTile1 = nTile + dirs[dir];
		if ((selection == null) \|\| selection[nTile1 ]) {
		w = strength[nTile1];
		if (has_lma[nTile1]) {
		w *= scale_strength_lma;
		}
		if (!Double.isNaN( src_disparity[nTile1])) {
		sw += w;
		sd += w * src_disparity[nTile1];

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

+5 −1

Original line number	Diff line number	Diff line
		@@ -79,7 +79,7 @@ public class Corr2dLMA {
		/// final static int NUM_CAMS = 4; // not all have to be used, so it is maximal number of cameras
		/// final static int NUM_PAIRS = NUM_CAMS* (NUM_CAMS -1)/2; // number of possible pairs
		final static int NTILE0 = 0;
		final static int DISP_INDEX = 0; // common/average disparity
		final static int DISP_INDEX = 0; // common/average disparity (negative!)
		final static int A_INDEX = 1; // A*(x-x0)^2
		final static int B_INDEX = 2; // 2B(x-x0)*(y-y0)
		final static int CMA_INDEX = 3; // C*(y-y0)^2, encode C-A
		@@ -2116,6 +2116,7 @@ public class Corr2dLMA {


		public double [][] lmaDisparityStrength(
		double lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		double lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		double lma_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		double lma_min_max_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
		@@ -2136,6 +2137,9 @@ public class Corr2dLMA {
		if (maxmin_amp[tile][1] < 0.0) {
		continue; // inverse maximum - discard tile
		}
		if ((maxmin_amp[tile][1]/maxmin_amp[tile][0]) < lmas_min_amp) {
		continue; // inverse maximum - discard tile
		}
		double avg = 0.50*(maxmin_amp[tile][0]+maxmin_amp[tile][1]); // max_min[1] can be negative - filter it out?
		double rrms = rms[tile]/avg;
		if (((lma_max_rel_rms > 0.0) && (rrms > lma_max_rel_rms)) \|\|

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

+3 −0

Original line number	Diff line number	Diff line
		@@ -3422,6 +3422,7 @@ public class Correlation2d {

		// double [][] ds = lma.getDisparityStrength();
		ds = lma.lmaDisparityStrength(
		imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		imgdtt_params.lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		imgdtt_params.lma_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		imgdtt_params.lma_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
		@@ -3521,6 +3522,7 @@ public class Correlation2d {
		}
		// double [][] ds = lma.getDisparityStrength();
		ds = lma.lmaDisparityStrength(
		imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		imgdtt_params.lma_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		imgdtt_params.lma_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		imgdtt_params.lma_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
		@@ -3858,6 +3860,7 @@ public class Correlation2d {


		double [][] dispStr = lma.lmaDisparityStrength( //TODO: add parameter to filter out negative minimums ?
		imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		imgdtt_params.lmas_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		imgdtt_params.lmas_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		imgdtt_params.lmas_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)

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

+8 −1

Original line number	Diff line number	Diff line
		@@ -268,6 +268,7 @@ public class DisparityProcessor {
		final double [] disparity, // current disparity value
		final double [] measured_disparity, // measured disparity
		final double [] strength,
		final boolean [] has_lma,
		final double [] hor_disparity, // not yet used
		final double [] hor_strength, // not yet used
		final boolean [] selected,
		@@ -276,6 +277,7 @@ public class DisparityProcessor {
		final int threadsMax, // maximal number of threads to launch
		final int debugLevel)
		{
		final double scale_strength_lma = 5.0; // increase LMA-defined strength during averaging
		final int dbg_tile = -1; // 28643; // x=131, y=88
		ShowDoubleFloatArrays sdfa_instance = null;
		if (debugLevel > 0) sdfa_instance = new ShowDoubleFloatArrays(); // just for debugging?
		@@ -382,7 +384,12 @@ public class DisparityProcessor {
		// }
		// calculate pull by the measured disparity
		double disparity_diff = (measured_disparity[nTile] - disp_data[0][nTile]);
		double eff_strength = ((border != null) && border[nTile])? 0.0: (strength[nTile] - clt_parameters.tiStrengthOffset);
		double str = strength[nTile];
		if (has_lma[nTile]) {
		str *= scale_strength_lma;
		}
		// double eff_strength = ((border != null) && border[nTile])? 0.0: (strength[nTile] - clt_parameters.tiStrengthOffset);
		double eff_strength = ((border != null) && border[nTile])? 0.0: (str - clt_parameters.tiStrengthOffset);
		if (eff_strength < 0) eff_strength = 0;
		double disparity_pull = eff_strength;
		// if (tileY == 89){

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

+2 −22

Original line number	Diff line number	Diff line
		@@ -3291,6 +3291,7 @@ public class ImageDtt extends ImageDttCPU {
		if (dbg_img != null) dbg_img[1][nclust] = 1.0;
		// was for single tile
		disp_str = lma2.lmaDisparityStrength(
		imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		imgdtt_params.lmas_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		imgdtt_params.lmas_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		imgdtt_params.lmas_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)
		@@ -3583,31 +3584,10 @@ public class ImageDtt extends ImageDttCPU {
		tileX, // int tileX, // just for debug output
		tileY); // int tileY


		/*
		double [][] poly_disp2 = {{Double.NaN, 0.0}};
		double [][][] corrs2 = {corrs};
		double [][][] tile_disp_dist2 = {tile_disp_dist};
		// TODO: maybe use corrLMA2Single again, but take care of initVector!
		Corr2dLMA lma2 = corr2d.corrLMA2Multi( // multitile num_tiles_super == 1
		imgdtt_params, // ImageDttParameters imgdtt_params,
		1, // int clust_width,
		corr_wnd, // double [][] corr_wnd, // correlation window to save on re-calculation of the window
		corr_wnd_inv_limited, // corr_wnd_limited, // correlation window, limited not to be smaller than threshold - used for finding max/convex areas (or null)
		corrs2, // corrs, // double [][] corrs,
		tile_disp_dist2,
		rXY, // double [][] rXY, // non-distorted X,Y offset per nominal pixel of disparity
		imgdtt_params.dbg_pair_mask, // int pair_mask, // which pairs to process
		// null, // disp_str[cTile], //corr_stat[0], // double xcenter, // preliminary center x in pixels for largest baseline
		poly_disp2, // double[] poly_ds, // null or pair of disparity/strength
		imgdtt_params.ortho_vasw_pwr, // double vasw_pwr, // value as weight to this power,
		tdl, // tile_lma_debug_level, //+2, // int debug_level,
		tileX, // int tileX, // just for debug output
		tileY); // int tileY
		*/
		if (lma2 != null) {
		// was for single tile
		disp_str = lma2.lmaDisparityStrength(
		imgdtt_params.lmas_min_amp, // minimal ratio of minimal pair correlation amplitude to maximal pair correlation amplitude
		imgdtt_params.lmas_max_rel_rms, // maximal relative (to average max/min amplitude LMA RMS) // May be up to 0.3)
		imgdtt_params.lmas_min_strength, // minimal composite strength (sqrt(average amp squared over absolute RMS)
		imgdtt_params.lmas_min_ac, // minimal of A and C coefficients maximum (measures sharpest point/line)

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