Commit a520cd7b authored by Andrey Filippov's avatar Andrey Filippov
Browse files

Added 32-bit tiff output for ML data

parent 70ccd5f4

src/main/java/BiQuadParameters.java

+122 −6
Original line number Diff line number Diff line
@@ -65,6 +65,25 @@ public class BiQuadParameters { 
	public boolean rig_adjust_forward =        false;  // aux camera forward from the principal plane (not implemented)

	public double  rig_correction_scale=       1.0;    // scale calculaated correction



	public int     min_new =                   100;    // Minimal number of he new tiles during rig refine

	public int     num_inf_refine =            20;     // Number of infinity refine passes

	public int     num_near_refine =           20;     // Number of non-infinity refine passes

	public double  min_trusted_strength =      0.1;//14// Minimal trusted combo strength;

	public double  trusted_tolerance  =        1.0;    // Trusted tolerance for small baseline camera(s)



	public int     ml_hwidth =                 2;      // Half-width of the ML tiles to export (0-> 1x1, 1->3x3, 2 -> 5x5)

	public double  ml_disparity_sweep  =       2.0;    // Disparity sweep around ground truth, each side

	public int     ml_sweep_steps =            5;      // Number of disparity sweep steps



	public boolean ml_keep_aux =               true; // include auxiliary camera data in the ML output

	public boolean ml_keep_inter =             true; // include inter-camera correlation data in the ML output

	public boolean ml_keep_hor_vert =          true; // include combined horizontal and vertical pairs data in the ML output

	public boolean ml_keep_debug=              true; // include debug layer(s) data in the ML output

	public boolean ml_8bit=                    true; // output in 8-bit format (default - 32-bit TIFF

	public boolean ml_show_ml =                true; // show each generated MLoutput file











	public void dialogQuestions(GenericJTabbedDialog gd) {
@@ -136,6 +155,39 @@ public class BiQuadParameters { 
		gd.addCheckbox    ("Adjust aux camera distance from the main principal plane",                            this.rig_adjust_forward,"Not implemented, assumed zero");

		gd.addNumericField("Scale calculated correction before applying",                                         this.rig_correction_scale,  3,6,"",

				"Debug feature");



		gd.addNumericField("Minimal number of he new tiles during rig refine",                                    this.min_new,  0,3,"",

				"Exit from refine infinity cycle if number of new refine candidates is less than this number");

		gd.addNumericField("Number of infinity refine passes",                                                    this.num_inf_refine,  0,3,"",

				"Exit from refine non- infinity cycle if number of new refine candidates is less than this number");

		gd.addNumericField("Number of non-infinity refine passes",                                                this.num_near_refine,  0,3,"",

				"Re-scan only previously selected tiles");

		gd.addNumericField("Minimal trusted combo strength",                                                      this.min_trusted_strength,  3,6,"",

				"Combo strength is cubic root of the product of main, aux and intyer correlation strengths");

		gd.addNumericField("Trusted tolerance for small baseline camera(s)",                                      this.trusted_tolerance,  3,6,"",

				"When downscaling valid residual disparity from the most sensitive inter-camera, do not reduce it to be lower than this");



        gd.addTab("ML","Parameters related to the ML files generation for the dual-quad camera rig");



		gd.addNumericField("Half-width of the ML tiles to export (0-> 1x1, 1->3x3, 2 -> 5x5)",                    this.ml_hwidth,  0,3,"",

				"Amount of data to export to the ML system");

		gd.addNumericField("Disparity sweep around ground truth, each side",                                      this.ml_disparity_sweep,  3,6,"",

				"Sweep symmetrically target disparity around the ground truth disparity, each side");

		gd.addNumericField("Number of target disparity sweep steps",                                                               this.ml_sweep_steps,  0,3,"",

				"Generate this many files for each file set. Each tile results depend on the target disparity and this tile data, do not depend on other tiles target disparity");

		gd.addCheckbox    ("Include auxiliary camera data in the ML output",                                      this.ml_keep_aux,

				"ML output will have the second set of the layers for the auxiliary camera. Disparity values should be scaled for the camera baseline");

		gd.addCheckbox    ("Keep inter-camera correlation data",                                                  this.ml_keep_inter,

				"Inter-camera correlation data has only one layer (and one correlation pair). It is used to generate ground truth data. Usable disparity range (measured in the main camera pixels) is ~1/5 of teh main camera");

		gd.addCheckbox    ("Keep combine horizonta/vertical pairs",                                               this.ml_keep_hor_vert,

				"Each of these two layers per camera are calculated from a pair of top/bottom and left/right pairs. Can possibly be used instead of originals to reduce amount of input data");

		gd.addCheckbox    ("Keep debug layer(s)",                                                                 this.ml_keep_debug,

				"Keep additional (debug) layers that may change for different file versions");

		gd.addCheckbox    ("Use 8 bpp TIFF (default - 32 bpp)",                                                   this.ml_8bit,

				"Reduce file size by lowering bpp");

		gd.addCheckbox    ("Show each generated ML file",                                                         this.ml_show_ml,

				"Use only for small number of generated files to reduce memory usage");



	}

	public void dialogAnswers(GenericJTabbedDialog gd) {

		this.rig_mode_debug=                gd.getNextBoolean();
@@ -174,6 +226,22 @@ public class BiQuadParameters { 
		this.rig_adjust_distance=           gd.getNextBoolean();

		this.rig_adjust_forward=            gd.getNextBoolean();

		this.rig_correction_scale=          gd.getNextNumber();



		this.min_new=                 (int) gd.getNextNumber();

		this.num_inf_refine=          (int) gd.getNextNumber();

		this.num_near_refine=         (int) gd.getNextNumber();

		this.min_trusted_strength=          gd.getNextNumber();

		this.trusted_tolerance=             gd.getNextNumber();



		this.ml_hwidth=               (int) gd.getNextNumber();

		this.ml_disparity_sweep=            gd.getNextNumber();

		this.ml_sweep_steps=          (int) gd.getNextNumber();

		this.ml_keep_aux=                   gd.getNextBoolean();

		this.ml_keep_inter=                 gd.getNextBoolean();

		this.ml_keep_hor_vert=              gd.getNextBoolean();

		this.ml_keep_debug=                 gd.getNextBoolean();

		this.ml_8bit=                       gd.getNextBoolean();

		this.ml_show_ml=                    gd.getNextBoolean();

	}



	public void setProperties(String prefix,Properties properties){
@@ -216,6 +284,23 @@ public class BiQuadParameters { 
		properties.setProperty(prefix+"rig_adjust_forward",        this.rig_adjust_forward+"");

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



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

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

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

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

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



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

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

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

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

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

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

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

		properties.setProperty(prefix+"ml_8bit",                   this.ml_8bit+"");

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





	}

	public void getProperties(String prefix,Properties properties){

		if (properties.getProperty(prefix+"rig_mode_debug")!=null)        this.rig_mode_debug=Boolean.parseBoolean(properties.getProperty(prefix+"rig_mode_debug"));
@@ -254,6 +339,21 @@ public class BiQuadParameters { 
		if (properties.getProperty(prefix+"rig_adjust_forward")!=null)      this.rig_adjust_forward=Boolean.parseBoolean(properties.getProperty(prefix+"rig_adjust_forward"));

		if (properties.getProperty(prefix+"rig_correction_scale")!=null)    this.rig_correction_scale=Double.parseDouble(properties.getProperty(prefix+"rig_correction_scale"));



		if (properties.getProperty(prefix+"min_new")!=null)                 this.min_new=Integer.parseInt(properties.getProperty(prefix+"min_new"));

		if (properties.getProperty(prefix+"num_inf_refine")!=null)          this.num_inf_refine=Integer.parseInt(properties.getProperty(prefix+"num_inf_refine"));

		if (properties.getProperty(prefix+"num_near_refine")!=null)         this.num_near_refine=Integer.parseInt(properties.getProperty(prefix+"num_near_refine"));

		if (properties.getProperty(prefix+"min_trusted_strength")!=null)    this.min_trusted_strength=Double.parseDouble(properties.getProperty(prefix+"min_trusted_strength"));

		if (properties.getProperty(prefix+"trusted_tolerance")!=null)       this.trusted_tolerance=Double.parseDouble(properties.getProperty(prefix+"trusted_tolerance"));

		if (properties.getProperty(prefix+"ml_hwidth")!=null)               this.ml_hwidth=Integer.parseInt(properties.getProperty(prefix+"ml_hwidth"));



		if (properties.getProperty(prefix+"ml_disparity_sweep")!=null)      this.ml_disparity_sweep=Double.parseDouble(properties.getProperty(prefix+"ml_disparity_sweep"));

		if (properties.getProperty(prefix+"ml_sweep_steps")!=null)          this.ml_sweep_steps=Integer.parseInt(properties.getProperty(prefix+"ml_sweep_steps"));

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

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

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

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

		if (properties.getProperty(prefix+"ml_8bit")!=null)                 this.ml_8bit=Boolean.parseBoolean(properties.getProperty(prefix+"ml_8bit"));

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

	}

	@Override

	public BiQuadParameters clone() throws CloneNotSupportedException {
@@ -293,6 +393,22 @@ public class BiQuadParameters { 
		bqp.rig_adjust_distance=        this.rig_adjust_distance;

		bqp.rig_adjust_forward=         this.rig_adjust_forward;

		bqp.rig_correction_scale=       this.rig_correction_scale;



		bqp.min_new=                    this.min_new;

		bqp.num_inf_refine=             this.num_inf_refine;

		bqp.num_near_refine=            this.num_near_refine;

		bqp.min_trusted_strength=       this.min_trusted_strength;

		bqp.trusted_tolerance=          this.trusted_tolerance;



		bqp.ml_hwidth=                  this.ml_hwidth;

		bqp.ml_disparity_sweep=         this.ml_disparity_sweep;

		bqp.ml_sweep_steps=             this.ml_sweep_steps;

		bqp.ml_keep_aux=                this.ml_keep_aux;

		bqp.ml_keep_inter=              this.ml_keep_inter;

		bqp.ml_keep_hor_vert=           this.ml_keep_hor_vert;

		bqp.ml_keep_debug=              this.ml_keep_debug;

		bqp.ml_8bit=                    this.ml_8bit;

		bqp.ml_show_ml=                 this.ml_show_ml;

		return bqp;

src/main/java/CLTPass3d.java

+3 −3
Original line number Diff line number Diff line
@@ -512,7 +512,7 @@ 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[] replaceWeakOutlayers(

		public boolean[] replaceWeakOutliers(

				final boolean [] selection,

				final double weakStrength,    // strength to be considered weak, subject to this replacement

				final double maxDiff,
@@ -546,7 +546,7 @@ public class CLTPass3d{ 
									(disparity[nTile] < absMinDisparity) ||

									(disparity[nTile] > absMaxDisparity))&& ((selection == null) || selection[nTile])) {

								if (nTile == dbg_nTile){

									System.out.println("replaceWeakOutlayers():1 nTile="+nTile);

									System.out.println("replaceWeakOutliers():1 nTile="+nTile);

								}

								double [] dbg_disparity = disparity;

								double dbg_disparity_nTile = disparity[nTile];
@@ -602,7 +602,7 @@ public class CLTPass3d{ 
					public void run() {

						for (int nTile = ai.getAndIncrement(); nTile < nTiles; nTile = ai.getAndIncrement()) {

							if (nTile == dbg_nTile){

								System.out.println("replaceWeakOutlayers():2 nTile="+nTile);

								System.out.println("replaceWeakOutliers():2 nTile="+nTile);

							}

							if (weakOutlayers[nTile]) {

								double sw = 0.0, sd = 0.0;

src/main/java/Correlation2d.java

+175 −2
Original line number Diff line number Diff line
@@ -185,6 +185,42 @@ public class Correlation2d { 
    		double [][] clt_data2,

    		double [][] tcorr, // null or initialized to [4][transform_len]

    		double      fat_zero) {

    	if (tcorr == null) tcorr = new double [4][transform_len];

    	double [] a2 = new double[transform_len];

    	double sa2 = 0.0;

		for (int i = 0; i < transform_len; i++) {

			double s1 = 0.0, s2=0.0;

			for (int n = 0; n< 4; n++){

				s1+=clt_data1[n][i] * clt_data1[n][i];

				s2+=clt_data2[n][i] * clt_data2[n][i];

			}

			a2[i] = Math.sqrt(s1*s2);

			sa2 += a2[i];

		}

		double fz2 = sa2/transform_len * fat_zero * fat_zero; // fat_zero squared to match units

		for (int i = 0; i < transform_len; i++) {

			double scale = 1.0 / (a2[i] + fz2);

			for (int n = 0; n<4; n++){

				tcorr[n][i] = 0;

				for (int k=0; k<4; k++){

					if (ZI[n][k] < 0)

						tcorr[n][i] -=

								clt_data1[-ZI[n][k]][i] * clt_data2[k][i];

					else

						tcorr[n][i] +=

								clt_data1[ZI[n][k]][i] * clt_data2[k][i];

				}

				tcorr[n][i] *= scale;

			}

		}

		return tcorr;

    }



    public double[][] correlateSingleColorFD_old(

    		double [][] clt_data1,

    		double [][] clt_data2,

    		double [][] tcorr, // null or initialized to [4][transform_len]

    		double      fat_zero) {

    	if (tcorr == null) tcorr = new double [4][transform_len];

		for (int i = 0; i < transform_len; i++) {

			double s1 = 0.0, s2=0.0;
@@ -210,6 +246,7 @@ public class Correlation2d { 
    }







    /**

     * 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)
@@ -845,6 +882,7 @@ public class Correlation2d { 
	}

	/**

	 * Calculate 1-d maximum location, strength and half-width for the special strip (odd rows shifted by 0.5

	 * Negative values are ignored!

	 * Both x and y half-windows can be variable length (to reduce calculations with 0.0 elements), normalized

	 * so sums of zero element and twice all others are 1.0

	 * Window in Y direction corresponds to correlation stripe rows, corresponding to sqrt(2)/2 sensor pixels
@@ -992,6 +1030,140 @@ public class Correlation2d { 
	}





	/**

	 * Extract center 2-d correlation around zero from the full (now 15x15)

	 * @param hwidth half width of the output tile (0 -> 1x1, 1-> 3x3, 2->5x5)

	 * @param full_corr  full pixel-domain correlation (now 15x15=225 long)

	 * @param center_corr - output array [(2*hwidth+1)*(2*hwidth+1)] or null

	 * @return center_corr - center part of the correlation in linescan order

	 */

    public double [] corrCenterValues(

    		int       hwidth,

    		double [] full_corr,

    		double [] center_corr) {

    	if (full_corr == null) return null;

    	int center = transform_size - 1;

    	int width = 2 * center + 1;

    	int owidth = 2*hwidth+1;

    	if (center_corr == null) center_corr = new double [owidth*owidth];

    	int indx = 0;

    	int findx = (center - hwidth) * (width + 1); // top left corner

    	for (int row = 0; row < owidth; row++) {

        	for (int col = 0; col < owidth; col++) {

        		center_corr[indx++] = full_corr[findx++];

        	}

        	findx += width-owidth;

    	}

    	return center_corr;

    }



    /**

     * Extract center 2-d correlations around zero from the full (now 15x15) correlations

     * for each of the 6 pairs and 2 combined directions (horizontal, vertical)

     * @param hwidth half width of the output tile (0 -> 1x1, 1-> 3x3, 2->5x5)

     * @param offset add before multiplication, subtract in the end. If negative - use averaging

     *  instead of the shifted multiplication

     * @param full_corr  full pixel-domain correlation (now 15x15=225 long)for each of 6 pairs

     * @param center_corr - output array [(2*hwidth+1)*(2*hwidth+1)]. should be [8][]

     */

    public void corrCenterValues(

    		int         hwidth,

    		double      offset,

    		double [][] full_corr,

    		double [][] center_corr) {

    	// first 6 layers - directly correspond to pairs (top, bottom, left, right, diagonal main, diagonal other)

    	for (int i = 0; i < 6; i++) {

    		center_corr[i] = corrCenterValues(

    				hwidth,

    				full_corr[i],

    				center_corr[i]);

    	}



    	// combine vertical and horizontal pairs

    	int center = transform_size - 1;

    	int width =  2 * center + 1;

    	int owidth = 2 * hwidth + 1;

    	for (int ndir = 0; ndir < 2; ndir++) { // 0- hor, 1- vert

    		if (center_corr[ndir] == null) center_corr[ndir] = new double [owidth*owidth];

    		int indx = 0;

    		int findx = (center - hwidth) * (width + 1); // top left corner

    		for (int row = 0; row < owidth; row++) {

    			for (int col = 0; col < owidth; col++) {

    				double fc0 = full_corr[2 * ndir + 0][findx]; // 0 (top), 2 (left)

    				double fc1 = full_corr[2 * ndir + 1][findx++]; // 1 - bottom, 3 (right)

    				double cc = 0.0;

    				if (offset >= 0.0) {

    					if ((fc0 > 0.0) && (fc1 > 0.0)) {

    						cc = Math.sqrt((fc0+offset)*(fc1+offset)) - offset;

    					}

    				} else {

    					cc =  0.5*(fc0+fc1);

    				}



    				center_corr[ndir + 6][indx++] = cc; // save to 6-th and 7-th layer

    			}

    			findx += width-owidth;

    		}

    	}

    }



    /**

     * Save 2d correlation data for one layer, one tile into the combined multi-layer ML array, viewable as an image

     * @param tileX horizontal tile index

     * @param tileY vertical tile index

     * @param ml_hwidth half-width of the preserved 2d correlation (0 - single point, 1 -> 3x3, 2 -> 5x5, 7 - all data)

     * @param ml_data multi-layer array, each layer matches an image of ((2 * ml_hwidth + 1) * tilesX) by ((2 * ml_hwidth + 1) * tilesY) in scanline order

     * Each tile corresponds to  (2 * ml_hwidth + 1) * (2 * ml_hwidth + 1) square in the image. Only selected tiles will be updated, so it is good to initialize array

     * with all Double.NaN values

     * @param ml_layer layer to save tile data

     * @param ml_tile (2 * ml_hwidth + 1) * (2 * ml_hwidth + 1) tile data to be saved

     * @param tilesX image width in tiles

     */

    public void saveMlTile(

    		int         tileX,

    		int         tileY,

    		int         ml_hwidth,

    		double [][] ml_data,

    		int         ml_layer,

    		double []   ml_tile,

    		int         tilesX) {

    	int tile_width = 2 * ml_hwidth + 1;

    	int full_width = tile_width * tilesX;

    	int oindex = tileY *tile_width * full_width  + tileX * tile_width;

    	for (int row = 0; row < tile_width; row++) {

			System.arraycopy(ml_tile, row * tile_width, ml_data[ml_layer], oindex, tile_width);

			oindex += full_width;

    	}

    }

    /**

     * Save a single value to the combined multi-layer ML array, viewable as an image

     * @param tileX horizontal tile index

     * @param tileY vertical tile index

     * @param ml_hwidth half-width of the preserved 2d correlation (0 - single point, 1 -> 3x3, 2 -> 5x5, 7 - all data)

     * @param ml_data multi-layer array, each layer matches an image of ((2 * ml_hwidth + 1) * tilesX) by ((2 * ml_hwidth + 1) * tilesY) in scanline order

     * Each tile corresponds to  (2 * ml_hwidth + 1) * (2 * ml_hwidth + 1) square in the image. Only selected tiles will be updated, so it is good to initialize array

     * with all Double.NaN values

     * @param ml_layer layer to save tile data

     * @param ml_index data index within tile

     * @param ml_value value to set

     * @param tilesX image width in tiles

     */

    public void saveMlTilePixel(

    		int         tileX,

    		int         tileY,

    		int         ml_hwidth,

    		double [][] ml_data,

    		int         ml_layer,

    		int         ml_index,

    		double      ml_value,

    		int         tilesX) {

    	int tile_width = 2 * ml_hwidth + 1;

    	int full_width = tile_width * tilesX;

    	int oindex = tileY *tile_width * full_width  + tileX * tile_width + (ml_index/tile_width)*full_width + (ml_index%tile_width) ;

    	ml_data[ml_layer][oindex] = ml_value;

    }









    public double [] debugStrip(
@@ -1238,6 +1410,7 @@ public class Correlation2d { 
    	return rslt;

    }



    // ignores negative values

    public double [] single2dCM( // returns x-xcenter, y, strength (sign same as disparity)

    		ImageDttParameters  imgdtt_params,

    		double []           corr,
@@ -1408,7 +1581,7 @@ public class Correlation2d { 
    		System.out.println(String.format("eff_strength = [%8.5f, %8.5f, %8.5f, %8.5f]", eff_strength[0], eff_strength[1], eff_strength[2], eff_strength[3]));

    		System.out.println(String.format("width_d =      [%8.5f, %8.5f, %8.5f, %8.5f]", width_d[0],      width_d[1],      width_d[2],      width_d[3]));

    	}

		if (!strong[isel] || (isel <0)) {

		if ((isel <0) || !strong[isel]) {

			corr = Double.NaN;

			if (debug) System.out.println("Direction with "+(bg?"min":"max")+" disparity is not strong enough -> no correction");

		} else 		if (width_d[isel] > max_hwidth) {
@@ -1446,7 +1619,7 @@ public class Correlation2d { 
    		}

    		if (debug) System.out.println("lim =  "+lim+", disp = "+disp);

    	}

    	double [] rslt = {disp, eff_strength[isel], mx - mn, are_ortho ?1.0 : 0.0};

    	double [] rslt = {disp, (isel >=0) ? eff_strength[isel]:0.0, mx - mn, are_ortho ?1.0 : 0.0};

		if (debug) System.out.println(String.format("foregroundCorrect() -> [%8.5f, %8.5f, %8.5f, %3.1f]", rslt[0], rslt[1], rslt[2], rslt[3]));



    	return    rslt;

src/main/java/EyesisCorrectionParameters.java

+26 −8

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src/main/java/Eyesis_Correction.java

+123 −3

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