Commit b80e5500 authored by Andrey Filippov's avatar Andrey Filippov
Browse files

Added error for 3x3 tiles averaging to metrics

parent c61cd18c

src/main/java/MLStats.java

+48 −13
Original line number Diff line number Diff line
@@ -127,7 +127,8 @@ public class MLStats { 
		}

		int [][] hist = new int [disparity_bins][strength_bins];

		int [] slices = {TwoQuadCLT.DSI_DISPARITY_RIG,TwoQuadCLT.DSI_STRENGTH_RIG, TwoQuadCLT.DSI_DISPARITY_MAIN,TwoQuadCLT.DSI_STRENGTH_MAIN};

		double [][][] ds_error = new double [disparity_bins][strength_bins][2];

		double [][][] ds_error = new double [disparity_bins][strength_bins][4]; // adding averaged with neighbors

		double [] dir_weights = {0.7,0.5}; // center weight = 1.0

		double disparity_outlier2 = disparity_outlier*disparity_outlier;

		double disparity_step = (disparity_max_clip - disparity_min_clip) / disparity_bins;

		double strength_step =  (strength_max_clip -  strength_min_clip)  / strength_bins;
@@ -137,6 +138,9 @@ public class MLStats { 
		for (Path p:files) {

			ImagePlus imp_dsi=new ImagePlus(p.normalize().toString());

			  ImageStack dsi_stack=  imp_dsi.getStack();

			  int width = dsi_stack.getWidth();

			  int height = dsi_stack.getHeight();

			  TileNeibs         tnImage = new TileNeibs(width,height);

			  float [][] dsi_float = new float [slices.length][];

			  int nLayers = dsi_stack.getSize();

			  for (int nl = 0; nl < nLayers; nl++){
@@ -169,8 +173,7 @@ public class MLStats { 
					  nut++;

					  double dm = dsi_float[2][nTile];

					  double sm = dsi_float[3][nTile] - master_weight_floor;

					  double de2 = (dm - d);

					  de2 *= de2;

					  double de2 = (dm - d) * (dm - d);

					  if ((de2 <= disparity_outlier2) && (sm > 0.0)) {

						  double w = 1.0;

						  if (master_weight_power > 0.0) {
@@ -178,23 +181,53 @@ public class MLStats { 
							  if (master_weight_power != 1.0) {

								w = Math.pow(w, master_weight_power);

							  }

						  }

						  ds_error[dbin][sbin][0] += w* de2;

						  ds_error[dbin][sbin][1] += w;

						  // combine with neighbors

						  double sw =  w;

						  double sew = w * (dm - d);

						  for (int direction = 0; direction < 8; direction++) {

							  int nTile1 = tnImage.getNeibIndex(nTile, direction);

							  if (nTile1 >= 0) {

								  d =  dsi_float[0][nTile1];

								  s =  dsi_float[1][nTile1] - strength_min_drop;

								  if (s > 0.0) {

									  sm = dsi_float[3][nTile1] - master_weight_floor;

									  if (sm > 0.0) {

										  double de = dsi_float[2][nTile1] - d;

										  de2 = de*de;

										  if (de2 < disparity_outlier2) {

											  w = 1.0;

											  if (master_weight_power > 0.0) {

												  w = sm;

												  if (master_weight_power != 1.0) {

													w = Math.pow(w, master_weight_power);

												  }

											  }

											  w *= dir_weights[direction & 1] * s; //

											  sw  += w;

											  sew += w * de;

										  }

									  }

								  }

							  }

						  }

//						  sew /= sw;

						  ds_error[dbin][sbin][2] += sew * sew / sw;

						  ds_error[dbin][sbin][3] += sw;

					  }

				  }

			  }

			  System.out.println(p.getFileName()+": "+nut+" useful tiles counted");

			  total_tiles_used += nut;

		}

	   System.out.println("Total number of useful tiles: "+total_tiles_used);

		double [][] hist_double = new double [2][disparity_bins*strength_bins];

		double [][] hist_double = new double [3][disparity_bins*strength_bins];

		double scale = 1.0;

		if (normalize) {

			scale *= (1.0* disparity_bins * strength_bins) / total_tiles_used;

		}

//		  ds_error[dbin][sbin][0] += w* de2;

//		  ds_error[dbin][sbin][1] += w;



		for (int nTile = 0; nTile < hist_double[0].length; nTile++) {

			int dbin = nTile % disparity_bins;
@@ -205,16 +238,18 @@ public class MLStats { 
			} else {

				hist_double[1][nTile] = Double.NaN;

			}

			if (ds_error[dbin][sbin][3] > 0.0) {

				hist_double[2][nTile] = Math.sqrt(ds_error[dbin][sbin][2]/ds_error[dbin][sbin][3]);

			} else {

				hist_double[2][nTile] = Double.NaN;

			}

		}

//		  ImagePlus imp= makeArrays(pixels, width, height, title);

//		  if (imp!=null) imp.show();

//		  ImagePlus imp = (new showDoubleFloatArrays()).makeArrays(dsi,quadCLT_main.tp.getTilesX(), quadCLT_main.tp.getTilesY(),  title, DSI_SLICES);

		String [] titles = {"histogram", "disp_err"};

		String [] titles = {"histogram", "disp_err","disp_err9"};

		ImagePlus imp = (new showDoubleFloatArrays()).makeArrays(

				hist_double,

				disparity_bins,

				strength_bins,

				"DSI_histogram",

				"DSI_metrics",

				titles

				);

		imp.setProperty("disparity_bins",  disparity_bins+"");