/**
 **
 ** GpuQuad.java - GPU quad-camera processing context and helper methods
 **
 ** Copyright (C) 2021-2025 Elphel, Inc.
 **
 ** -----------------------------------------------------------------------------**
 **
 **  GpuQuad.java is free software: you can redistribute it and/or modify
 **  it under the terms of the GNU General Public License as published by
 **  the Free Software Foundation, either version 3 of the License, or
 **  (at your option) any later version.
 **
 **  This program is distributed in the hope that it will be useful,
 **  but WITHOUT ANY WARRANTY; without even the implied warranty of
 **  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 **  GNU General Public License for more details.
 **
 **  You should have received a copy of the GNU General Public License
 **  along with this program.  If not, see <http://www.gnu.org/licenses/>.
 ** -----------------------------------------------------------------------------**
 **
 */
package com.elphel.imagej.gpu;

import static jcuda.driver.CUfunction_attribute.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES;
import static jcuda.driver.CUfunction_attribute.CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT;
import static jcuda.driver.CUshared_carveout.CU_SHAREDMEM_CARVEOUT_MAX_SHARED;
import static jcuda.driver.JCudaDriver.cuCtxSynchronize;
import static jcuda.driver.JCudaDriver.cuLaunchKernel;
import static jcuda.driver.JCudaDriver.cuMemAlloc;
import static jcuda.driver.JCudaDriver.cuMemAllocPitch;
import static jcuda.driver.JCudaDriver.cuMemFree;
import static jcuda.driver.JCudaDriver.cuMemcpy2D;
import static jcuda.driver.JCudaDriver.cuMemcpyDtoH;
import static jcuda.driver.JCudaDriver.cuMemcpyHtoD;
import static jcuda.driver.JCudaDriver.cuModuleGetGlobal;
import static jcuda.driver.JCudaDriver.cuFuncSetAttribute;


import java.awt.Rectangle;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.concurrent.CopyOnWriteArrayList;
import java.util.concurrent.atomic.AtomicBoolean;
import java.util.concurrent.atomic.AtomicInteger;

import com.elphel.imagej.common.ShowDoubleFloatArrays;
import com.elphel.imagej.tileprocessor.CorrVector;
import com.elphel.imagej.tileprocessor.Correlation2d;
import com.elphel.imagej.tileprocessor.DttRad2;
import com.elphel.imagej.tileprocessor.GeometryCorrection;
import com.elphel.imagej.tileprocessor.ImageDtt;
import com.elphel.imagej.tileprocessor.QuadCLT;

import Jama.Matrix;
import ij.IJ;
import jcuda.Pointer;
import jcuda.Sizeof;
import jcuda.driver.CUDA_MEMCPY2D;
import jcuda.driver.CUdeviceptr;
import jcuda.driver.CUmemorytype;

public class GpuQuad{ // quad camera description
	public final static double SCALE_ACCUM = 10.0f; // add to positive scale to make it accumulate in GPU (like negative)
	/**
	 * 
	 */
	private final GPUTileProcessor gpuTileProcessor;
	 // rectilinear mode for comparing synthetic rectilinear (not physical sensor) images	
	public final boolean rectilinear; 
	public           QuadCLT quadCLT;
	public final int img_width;
	public final int img_height;
	public final int kernels_hor;        // int                kernels_hor,
	public final int kernels_vert;       // int                kernels_vert);

	public final int num_cams;
	public final int num_colors; // maybe should always be 3?
	public final int kern_tiles;
	public final int kern_size;

	// public final GPUTileProcessor gPUTileProcessor;
	// CPU arrays of pointers to GPU memory
	// These arrays may go to methods, they are here just to be able to free GPU memory if needed
	private CUdeviceptr [] gpu_kernels_h;
	private CUdeviceptr [] gpu_kernel_offsets_h;
	private CUdeviceptr [] gpu_bayer_h;
	private CUdeviceptr [] gpu_clt_h =         null;
	private CUdeviceptr [] gpu_clt_ref_h =     null;
	private CUdeviceptr [] gpu_corr_images_h = null;
	// GPU pointers to array of GPU pointers
	private CUdeviceptr gpu_kernels;
	private CUdeviceptr gpu_kernel_offsets;
	private CUdeviceptr gpu_bayer;

	private CUdeviceptr gpu_ftasks=null;
	private int gpu_ftasks_len = 0;
	private CUdeviceptr gpu_active_tiles=null; // will be re-allocated with gpu_ftasks
	
	private CUdeviceptr gpu_corrs;
	private CUdeviceptr gpu_corr_weights;
	private CUdeviceptr gpu_corrs_td;
	private CUdeviceptr gpu_corrs_combo;
	private CUdeviceptr gpu_corrs_combo_td;

	private CUdeviceptr gpu_textures;
	private CUdeviceptr gpu_clt =      null;
	private CUdeviceptr gpu_clt_ref =  null; // will be allocated when first needed?
	private CUdeviceptr gpu_4_images = null; // may actually be 16
	/// will store w,h (in pixels) when allocating /reallocating memory
	private int [] gpu_clt_wh =        null;
	private int [] gpu_clt_ref_wh =    null;
	private int [] gpu_4_images_wh =   null;
	private CUdeviceptr gpu_corr_indices;
	private CUdeviceptr gpu_corr_combo_indices;
	private CUdeviceptr gpu_num_corr_tiles;
	private CUdeviceptr gpu_texture_indices_ovlp;
	private CUdeviceptr gpu_num_texture_ovlp;
	private CUdeviceptr gpu_texture_indices;
	private CUdeviceptr gpu_texture_indices_len;
	private CUdeviceptr gpu_diff_rgb_combo;
	private CUdeviceptr gpu_color_weights;
	private CUdeviceptr gpu_generate_RBGA_params;
	private CUdeviceptr gpu_woi;
	private CUdeviceptr gpu_twh;
	private CUdeviceptr gpu_num_texture_tiles;
	private CUdeviceptr gpu_textures_rgba;
	private CUdeviceptr gpu_correction_vector;
	private CUdeviceptr gpu_rot_deriv;
	private CUdeviceptr gpu_geometry_correction;
	private CUdeviceptr gpu_rByRDist;
//	private CUdeviceptr gpu_active_tiles;
	private CUdeviceptr gpu_num_active_tiles;
	private int mclt_stride;
	private int corr_stride;
	private int corr_stride_td;
	private int corr_stride_combo;
	private int corr_stride_combo_td;
	private int imclt_stride;
	private int texture_stride;
	private int texture_stride_rgba;
	public  int num_task_tiles;
	protected int num_corr_tiles; // protected so the JNA backend can propagate the native count (getFromGpu/getCorrTilesTd read it). By Claude 06/26/2026.
	private final int num_all_pairs; //  = 6; // number of correlation pairs per tile (should match tasks)
	protected int num_corr_combo_tiles; // protected so JNA backend can propagate native count (side-effect parity). By Claude 06/27/2026.
	protected int num_texture_tiles;    // protected so JNA backend can propagate native count (side-effect parity). By Claude 06/27/2026.

	private boolean geometry_correction_set = false;
	private boolean geometry_correction_vector_set = false;
	private int gpu_debug_level = 0; // 1;
	
//    private	boolean []  corr_mask_boolean; // to keep selected correlations between correlation in TD and processing of those correlations
    private	int []  corr_mask_int = new int [4]; // to keep selected correlations between correlation in TD and processing of those correlations
    private int sensor_mask_inter = -1;
    private int [] corr_mask_indices = null;
    private int [] sensor_mask_inter_indices = null;
    private int [] sensor_mask_inter_indices_neibs = null;
    
    public int getNumCorrTiles() {
    	return num_corr_tiles;
    }
	public boolean [] getCorrMask() {
		boolean [] corr_mask_boolean = new boolean [num_all_pairs];
		for (int i = 0; i < num_all_pairs; i++) {
			corr_mask_boolean[i] = ((corr_mask_int[i>>5] >> (i & 31)) & 1) != 0;
		}
		return corr_mask_boolean;
	}
	public void setCorrMask(int [] mask) {
		corr_mask_int = new int[4];
		for (int i = 0; i < mask.length; i++) {
			corr_mask_int[i] = mask[i];
		}
		setCorrMaskIndices();
	}
	public void setCorrMask(boolean [] mask) {
		corr_mask_int = new int [4];
		for (int i = 0; i < mask.length; i++) if (mask[i]){
			corr_mask_int[i >> 5] |= (1 << (i & 31));
		}
		setCorrMaskIndices();
	}
	private void setCorrMaskIndices() {
		corr_mask_indices = new int[getNumUsedPairs()];
		int indx = 0;
		for (int np = 0; np < corr_mask_int.length*32; np++) {
			if ((corr_mask_int[np >> 5] & (1 << (np & 31))) != 0) {
				corr_mask_indices[indx++] = np;
			}
		}
	}
	public int getNumUsedPairs() {
		int np = 0;
		for (int i = 0; i < corr_mask_int.length; i++) {
			np += Integer.bitCount(corr_mask_int[i]); 
		}
		return np;
	}
	public int getCorrPair(int indx) {
		return corr_mask_indices[indx];
	}
	
	public int [] getUsedPairsList() {
		int [] pairs_list = new int [getNumUsedPairs()];
		boolean [] mask = getCorrMask();
		int indx = 0;
		for (int i =0; i < mask.length; i++) if (mask[i]){
			pairs_list[indx++] = i;
		}
		return pairs_list;
	}
	public int [] getReversedPairsList() {
		int [] pairs_list = getUsedPairsList();  
		int [] reverse_list = new int[num_all_pairs];
		Arrays.fill(reverse_list,-1);
		for (int i = 0; i < pairs_list.length; i++) {
			reverse_list[pairs_list[i]] = i;
		}
		return reverse_list;
	}
	
	
	
	static int host_get_textures_shared_size( // in bytes
			//__device__ int get_textures_shared_size( // in bytes
			int                num_cams,     // actual number of cameras
			int                num_colors,   // actual number of colors: 3 for RGB, 1 for LWIR/mono
			int []             offsets){     // in floats
		int DTT_SIZE1 =       GPUTileProcessor.DTT_SIZE + 1;
		int DTT_SIZE2 =       2 * GPUTileProcessor.DTT_SIZE;
		int DTT_SIZE21 =      DTT_SIZE2 + 1;


		int offs = 0;
		if (offsets != null) offsets[0] = offs;
		offs += num_cams * num_colors * 2 * GPUTileProcessor.DTT_SIZE * DTT_SIZE21; //float mclt_tiles         [NUM_CAMS][NUM_COLORS][2*DTT_SIZE][DTT_SIZE21]
		if (offsets != null) offsets[1] = offs;
		offs += num_cams * num_colors * 4 * GPUTileProcessor.DTT_SIZE * DTT_SIZE1;  // float clt_tiles         [NUM_CAMS][NUM_COLORS][4][DTT_SIZE][DTT_SIZE1]
		if (offsets != null) offsets[2] = offs;
		int mclt_tmp_size = num_cams * num_colors * DTT_SIZE2 * DTT_SIZE21;                // [NUM_CAMS][NUM_COLORS][DTT_SIZE2][DTT_SIZE21]
		int rgbaw_size =    (2* (num_colors + 1) + num_cams) * DTT_SIZE2 * DTT_SIZE21;     // [NUM_COLORS + 1 + NUM_CAMS + NUM_COLORS + 1][DTT_SIZE2][DTT_SIZE21]
		offs += (rgbaw_size > mclt_tmp_size) ? rgbaw_size : mclt_tmp_size;
		if (offsets != null) offsets[3] = offs;
		offs += num_cams * 2;                                      // float port_offsets      [NUM_CAMS][2];
		if (offsets != null) offsets[4] = offs;
		offs += num_colors * num_cams;                             // float ports_rgb_shared  [NUM_COLORS][NUM_CAMS];
		if (offsets != null) offsets[5] = offs;
		offs += num_cams;                                          // float max_diff_shared   [NUM_CAMS];
		if (offsets != null) offsets[6] = offs;
		offs += num_cams * GPUTileProcessor.TEXTURE_THREADS_PER_TILE;               // float max_diff_tmp      [NUM_CAMS][TEXTURE_THREADS_PER_TILE]
		if (offsets != null) offsets[7] = offs;
		offs += num_colors * num_cams *  GPUTileProcessor.TEXTURE_THREADS_PER_TILE; //float ports_rgb_tmp     [NUM_COLORS][NUM_CAMS][TEXTURE_THREADS_PER_TILE];
		if (offsets != null) offsets[8] = offs;
		return Sizeof.INT * offs; // shared_floats;
	}
	
	// should only be updated with the same cameras instance
	public void updateQuadCLT(final QuadCLT quadCLT) {
		this.quadCLT =      quadCLT;
		resetGeometryCorrection();
		resetGeometryCorrectionVector();
		this.gpuTileProcessor.bayer_set =        false;
	}
	public void resetBayer() {
		this.gpuTileProcessor.bayer_set =        false;
	}
	public QuadCLT getQuadCLT( ) {
		return this.quadCLT;
	}
	
	public GPUTileProcessor getGpuTileProcessor() {
		return this.gpuTileProcessor;
	}
	
	
	public int getTaskSize() {
		if (rectilinear) return TpTask.getSize(1);
		return TpTask.getSize(quadCLT.getNumSensors());
	}
	
	public GpuQuad(
			GPUTileProcessor gpuTileProcessor,
			final QuadCLT    quadCLT,
			int              debug_level
			) {
		this.rectilinear = false;
		setGpu_debug_level(debug_level);
		this.gpuTileProcessor = gpuTileProcessor;
		this.quadCLT =       quadCLT;
		int [] wh =          quadCLT.getGeometryCorrection().getSensorWH();
		this.img_width =     wh[0]; // img_width;
		this.img_height =    wh[1]; // img_height;
		this.num_cams =      quadCLT.getNumSensors();
		this.num_all_pairs = Correlation2d.getNumPairs(num_cams);
		this.num_colors =    quadCLT.isMonochrome()?1:3; //num_colors; // maybe should always be 3?
		this.kernels_hor =   (quadCLT.getCLTKernels() == null) ? 0 : quadCLT.getCLTKernels()[0][0][0].length; // kernels_hor;
		this.kernels_vert =  (quadCLT.getCLTKernels() == null) ? 0 : quadCLT.getCLTKernels()[0][0].length; // kernels_vert;
		this.kern_tiles =    kernels_hor *  kernels_vert * num_colors;
		this.kern_size =     kern_tiles * 4 * 64;
		if (debug_level >= 100) {
			return;
		}

	    setSensorMaskInter(-1);
		// CPU arrays of pointers to GPU memory
		// These arrays may go to methods, they are here just to be able to free GPU memory if needed
		gpu_kernels_h =           new CUdeviceptr[num_cams];
		gpu_kernel_offsets_h =    new CUdeviceptr[num_cams];
		gpu_bayer_h =             new CUdeviceptr[num_cams];
		gpu_clt_h =               new CUdeviceptr[num_cams];
		gpu_corr_images_h=        new CUdeviceptr[num_cams];
		// GPU pointers to array of GPU pointers
		gpu_kernels =             new CUdeviceptr();
		gpu_kernel_offsets =      new CUdeviceptr();
		gpu_bayer =               new CUdeviceptr();
///		gpu_ftasks =              new CUdeviceptr(); //  allocate tilesX * tilesY * getTaskSize() * Sizeof.FLOAT
		gpu_corrs =               new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * CORR_SIZE * Sizeof.FLOAT
		gpu_corr_weights =        new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * Sizeof.FLOAT

		gpu_corrs_td =            new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * 4 * DTT_SIZE * DTT_SIZE * Sizeof.FLOAT
		gpu_corrs_combo =         new CUdeviceptr(); //  allocate tilesX * tilesY             * CORR_SIZE * Sizeof.FLOAT
		gpu_corrs_combo_td =      new CUdeviceptr(); //  allocate tilesX * tilesY *             4 * DTT_SIZE * DTT_SIZE * Sizeof.FLOAT

		gpu_textures =            new CUdeviceptr(); //  allocate tilesX * tilesY * ? * 256 * Sizeof.FLOAT
		gpu_corr_indices =        new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		// May add separate gpu_corr_indices_td here
		gpu_corr_combo_indices =  new CUdeviceptr(); //  allocate tilesX * tilesY * 1 * Sizeof.FLOAT            
		gpu_num_corr_tiles =      new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_texture_indices_ovlp =new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_num_texture_ovlp =    new CUdeviceptr(); //  8 ints
		gpu_texture_indices =     new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_texture_indices_len = new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT/allocate tilesX * tilesY * 1 * Sizeof.INT
		gpu_diff_rgb_combo =      new CUdeviceptr(); //  1 int

		gpu_color_weights =       new CUdeviceptr(); //  allocate 3 * Sizeof.FLOAT
		gpu_generate_RBGA_params =new CUdeviceptr(); //  allocate 5 * Sizeof.FLOAT

		gpu_woi =                 new CUdeviceptr(); //  4 integers (x, y, width, height) Rectangle - in tiles
		gpu_twh =                 new CUdeviceptr(); //  2 integers (width, height) - just allocated space to be used by DP
		cuMemAlloc (gpu_twh,        2 * Sizeof.INT);
		gpu_num_texture_tiles =   new CUdeviceptr(); //  8 integers

		gpu_textures_rgba =       new CUdeviceptr(); //  allocate tilesX * tilesY * ? * 256 * Sizeof.FLOAT

		gpu_correction_vector=    new CUdeviceptr();
		gpu_rot_deriv=            new CUdeviceptr(); //  used internally by device, may be read to CPU for testing
		gpu_geometry_correction=  new CUdeviceptr();
		gpu_rByRDist=             new CUdeviceptr(); //  calculated once for the camera distortion model in CPU (move to GPU?)

///		gpu_active_tiles =        new CUdeviceptr(); //  TILESX*TILESY*sizeof(int)
		gpu_num_active_tiles =    new CUdeviceptr(); //  1 int

		// Init data arrays for all kernels
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		long [] device_stride = new long [1];
		for (int ncam = 0; ncam < num_cams; ncam++) {
			gpu_kernels_h[ncam] =        new CUdeviceptr();
			cuMemAlloc(gpu_kernels_h[ncam],kern_size * Sizeof.FLOAT ); //     public static int cuMemAlloc(CUdeviceptr dptr, long bytesize)
			gpu_kernel_offsets_h[ncam] = new CUdeviceptr();
			cuMemAlloc(gpu_kernel_offsets_h[ncam],kern_tiles * GPUTileProcessor.CLTEXTRA_SIZE * Sizeof.FLOAT ); //     public static int cuMemAlloc(CUdeviceptr dptr, long bytesize)
			gpu_bayer_h[ncam] =          new CUdeviceptr();
			cuMemAllocPitch (
					gpu_bayer_h[ncam],        // CUdeviceptr dptr,
					device_stride,            // long[] pPitch,
					img_width * Sizeof.FLOAT, // long WidthInBytes,
					img_height,               // long Height,
					Sizeof.FLOAT);            // int ElementSizeBytes)
			mclt_stride = (int)(device_stride[0] / Sizeof.FLOAT);
			
		}
		// now create device arrays pointers
		if (Sizeof.POINTER != Sizeof.LONG) {
			String msg = "Sizeof.POINTER != Sizeof.LONG";
			IJ.showMessage("Error",	msg);
			new IllegalArgumentException (msg);
		}
		cuMemAlloc(gpu_kernels,        num_cams * Sizeof.POINTER);
		cuMemAlloc(gpu_kernel_offsets, num_cams * Sizeof.POINTER);
		cuMemAlloc(gpu_bayer,          num_cams * Sizeof.POINTER);

		long [] gpu_kernels_l =        new long [num_cams];
		long [] gpu_kernel_offsets_l = new long [num_cams];
		long [] gpu_bayer_l =          new long [num_cams];

		for (int ncam = 0; ncam < num_cams; ncam++) gpu_kernels_l[ncam] =        GPUTileProcessor.getPointerAddress(gpu_kernels_h[ncam]);
		cuMemcpyHtoD(gpu_kernels, Pointer.to(gpu_kernels_l),                     num_cams * Sizeof.POINTER);

		for (int ncam = 0; ncam < num_cams; ncam++) gpu_kernel_offsets_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_kernel_offsets_h[ncam]);
		cuMemcpyHtoD(gpu_kernel_offsets, Pointer.to(gpu_kernel_offsets_l),       num_cams * Sizeof.POINTER);

		for (int ncam = 0; ncam < num_cams; ncam++) gpu_bayer_l[ncam] =          GPUTileProcessor.getPointerAddress(gpu_bayer_h[ncam]);
		cuMemcpyHtoD(gpu_bayer, Pointer.to(gpu_bayer_l),                         num_cams * Sizeof.POINTER);

		// Set GeometryCorrection data
		cuMemAlloc(gpu_geometry_correction,      GeometryCorrection.arrayLength(GPUTileProcessor.MAX_NUM_CAMS) * Sizeof.FLOAT); // always maximal number of cameras (sparse)
		cuMemAlloc(gpu_rByRDist,                 GPUTileProcessor.RBYRDIST_LEN *  Sizeof.FLOAT);
		cuMemAlloc(gpu_rot_deriv,                5*GPUTileProcessor.MAX_NUM_CAMS*3*3 * Sizeof.FLOAT); // always maximal number of cameras (sparse)
		cuMemAlloc(gpu_correction_vector,        GPUTileProcessor.CORR_VECTOR_MAX_LENGTH * Sizeof.FLOAT); // update CORR_VECTOR_LENGTH to fit 

		// Set task array
//		cuMemAlloc(gpu_ftasks,      tilesX * tilesY * getTaskSize() * Sizeof.FLOAT);
//		checkAllocateGpuFtasks((tilesX+4) * (tilesY+4)); // +4 - "a little more" (will reallocate if needed)
		checkAllocateGpuFtasks(tilesX * tilesY); // +4 - "a little more" (will reallocate if needed)
		//=========== Seems that in many places Sizeof.POINTER (==8) is used instead of Sizeof.FLOAT !!! ============
		// Set corrs array
		int num_pairs = Correlation2d.getNumPairs(quadCLT.getNumSensors());
		cuMemAlloc(gpu_corr_indices,         tilesX * tilesY * num_pairs * Sizeof.FLOAT);
		cuMemAlloc(gpu_corr_combo_indices,   tilesX * tilesY *             Sizeof.FLOAT);

		cuMemAlloc(gpu_num_corr_tiles,                    1 * Sizeof.FLOAT);

		//#define TILESYA       ((TILESY +3) & (~3))
		int tilesYa = (tilesY + 3) & ~3;
		cuMemAlloc(gpu_texture_indices,     tilesX * tilesYa * Sizeof.FLOAT); // for non-overlap tiles
		cuMemAlloc(gpu_texture_indices_ovlp,tilesX * tilesYa * Sizeof.FLOAT); // for overlapped tiles

		cuMemAlloc(gpu_diff_rgb_combo, tilesX * tilesYa * num_cams* (num_colors + 1) *  Sizeof.FLOAT);

		cuMemAlloc(gpu_color_weights,             3 * Sizeof.FLOAT);
		cuMemAlloc(gpu_generate_RBGA_params,      5 * Sizeof.FLOAT);


		cuMemAlloc(gpu_woi,                               4 * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_texture_tiles,                 8 * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_texture_ovlp,                  8 * Sizeof.FLOAT);
		cuMemAlloc(gpu_texture_indices_len,               1 * Sizeof.FLOAT);

// will be dynamically allocated with gpu_ftasks
//		cuMemAlloc(gpu_active_tiles,        tilesX * tilesY * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_active_tiles,                  1 * Sizeof.FLOAT);

		cuMemAlloc(gpu_corr_weights, num_pairs* tilesX * tilesY * Sizeof.FLOAT);

		// allocate space for pixel-domain correlations (6 per tile)
		cuMemAllocPitch (
				gpu_corrs,                             // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				GPUTileProcessor.CORR_SIZE * Sizeof.FLOAT,              // long WidthInBytes,
				num_pairs * tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for transform-domain correlations (6 per tile)
		cuMemAllocPitch (
				gpu_corrs_td,                          // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT,// long WidthInBytes,
				num_pairs * tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_td = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for pixel-domain combined correlations (1 per tile)
		cuMemAllocPitch (
				gpu_corrs_combo,                       // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				GPUTileProcessor.CORR_SIZE * Sizeof.FLOAT,              // long WidthInBytes,
				tilesX * tilesY,                       // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_combo = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for transform-domain combined correlations (1 per tile)
		cuMemAllocPitch (
				gpu_corrs_combo_td,                          // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT,// long WidthInBytes,
				tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_combo_td = (int)(device_stride[0] / Sizeof.FLOAT);

		int max_texture_size = (num_colors + 1 + (num_cams + num_colors + 1)) * (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);
		cuMemAllocPitch (
				gpu_textures,                             // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				max_texture_size * Sizeof.FLOAT,              // long WidthInBytes,
				tilesX * tilesY,             // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		texture_stride = (int)(device_stride[0] / Sizeof.FLOAT);
		int max_rgba_width  =  (tilesX + 1) * GPUTileProcessor.DTT_SIZE;
		int max_rgba_height =  (tilesY + 1) * GPUTileProcessor.DTT_SIZE;
//		int max_rbga_slices =  num_colors + 1; // num_cams
		int max_rbga_slices =  num_colors + 1 + (num_colors * num_cams); // colors, alpha, colors per channel
		cuMemAllocPitch (
				gpu_textures_rgba,                     // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				max_rgba_width * Sizeof.FLOAT,         // long WidthInBytes,
				max_rgba_height * max_rbga_slices,     // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		texture_stride_rgba = (int)(device_stride[0] / Sizeof.FLOAT);
	}
	
	// Diagnostic trace (set -Dtp.trace=1). getClass().getSimpleName() prints "GpuQuad" under JCuda and
	// "GpuQuadJna" if a JNA run falls through to an un-overridden base method -> reveals oracle's actual
	// GPU-method usage (run JCuda first) and any JNA coverage gap, before an NPE. By Claude 2026-06-25.
	public static boolean GPU_TRACE = "1".equals(System.getProperty("tp.trace"));
	private static final java.util.Set<String> GPU_TRACE_SEEN = java.util.concurrent.ConcurrentHashMap.newKeySet();
	protected void gpuTrace(String m) {  // print each Class.method ONCE (the diagnostic is the set used, not every call)
		if (!GPU_TRACE) return;
		String key = getClass().getSimpleName() + "." + m;
		if (GPU_TRACE_SEEN.add(key)) System.out.println("[GPUTRACE] " + key);
	}

	// Backend selector (architecture B). Default JCUDA; set -Dtp.backend=jna to use the native
	// (libtileproc.so via JNA) backend GpuQuadJna instead. JNA mode never initializes JCuda.
	// Validate by running the same workflow both ways and diffing saved outputs.
	public static boolean useJnaBackend() {
		return "jna".equalsIgnoreCase(System.getProperty("tp.backend", "jcuda"));
	}
	public static GpuQuad create(
			GPUTileProcessor gpuTileProcessor,
			final QuadCLT    quadCLT,
			int              debug_level) {
		if (useJnaBackend()) {
			String src   = System.getProperty("tp.jna.srcdir", "/home/elphel/git/tile_processor_gpu/src");
			String devrt = System.getProperty("tp.jna.devrt",  "/usr/local/cuda/targets/x86_64-linux/lib/libcudadevrt.a");
			System.out.println("GpuQuad.create(): NATIVE (JNA) backend for "+quadCLT+" (src="+src+")");
			return new com.elphel.imagej.gpu.jna.GpuQuadJna(quadCLT, src, devrt, debug_level);
		}
		return new GpuQuad(gpuTileProcessor, quadCLT, debug_level);
	}

	// Backend-aware factory for the RECTILINEAR single-array GpuQuad (synthetic, non-sensor images:
	// no distortion correction, no aberration kernels, single camera) — parallel to create().
	// JCUDA branch is byte-for-byte the legacy `new GpuQuad(gpuTileProcessor, w, h, colors, dbg)` so the
	// oracle path is untouched; JNA branch builds a clean rectilinear GpuQuadJna (no kernel/geometry baggage).
	// By Claude on 06/26/2026.
	public static GpuQuad createRectilinear(
			GPUTileProcessor gpuTileProcessor, // ignored in JNA mode (null is fine there)
			int              max_width,
			int              max_height,
			int              num_colors,
			int              debug_level) {
		if (useJnaBackend()) {
			String src   = System.getProperty("tp.jna.srcdir", "/home/elphel/git/tile_processor_gpu/src");
			String devrt = System.getProperty("tp.jna.devrt",  "/usr/local/cuda/targets/x86_64-linux/lib/libcudadevrt.a");
			System.out.println("GpuQuad.createRectilinear(): NATIVE (JNA) backend "+max_width+"x"+max_height+
					" colors="+num_colors+" (src="+src+")");
			return new com.elphel.imagej.gpu.jna.GpuQuadJna(max_width, max_height, num_colors, src, devrt, debug_level);
		}
		return new GpuQuad(gpuTileProcessor, max_width, max_height, num_colors, debug_level);
	}

	// No-allocation RECTILINEAR constructor for the native (JNA) backend subclass GpuQuadJna (no QuadCLT,
	// single synthetic array). Mirrors the JCuda rectilinear ctor's CONFIG (rectilinear, num_cams=1, no
	// kernels) but allocates NO JCuda GPU memory and creates NO JCuda context. By Claude on 06/26/2026.
	protected GpuQuad(
			int     max_width,
			int     max_height,
			int     num_colors,
			int     debug_level,
			boolean native_backend) {
		this.rectilinear =   true;
		setGpu_debug_level(debug_level);
		this.gpuTileProcessor = null;
		this.quadCLT =       null;
		this.img_width =     max_width;
		this.img_height =    max_height;
		this.num_cams =      1;          // single camera, only interscene correlations
		this.num_all_pairs = 0;
		this.num_colors =    num_colors;
		this.kernels_hor =   0;
		this.kernels_vert =  0;
		this.kern_tiles =    0;
		this.kern_size =     0;
		setSensorMaskInter(-1);
		// no cuMemAlloc — native backend allocates its own GPU memory
	}

	// No-allocation constructor for the native (JNA) backend subclass GpuQuadJna.
	// Sets the final config fields from quadCLT but allocates NO JCuda GPU memory and creates NO
	// JCuda context (gpuTileProcessor = null). The subclass owns its native (TpProc) GPU memory and
	// overrides every GPU-touching method. The boolean marker disambiguates this constructor.
	protected GpuQuad(
			final QuadCLT quadCLT,
			int           debug_level,
			boolean       native_backend) {
		this.rectilinear =   false;
		setGpu_debug_level(debug_level);
		this.gpuTileProcessor = null;
		this.quadCLT =       quadCLT;
		int [] wh =          quadCLT.getGeometryCorrection().getSensorWH();
		this.img_width =     wh[0];
		this.img_height =    wh[1];
		this.num_cams =      quadCLT.getNumSensors();
		this.num_all_pairs = Correlation2d.getNumPairs(num_cams);
		this.num_colors =    quadCLT.isMonochrome() ? 1 : 3;
		this.kernels_hor =   (quadCLT.getCLTKernels() == null) ? 0 : quadCLT.getCLTKernels()[0][0][0].length;
		this.kernels_vert =  (quadCLT.getCLTKernels() == null) ? 0 : quadCLT.getCLTKernels()[0][0].length;
		this.kern_tiles =    kernels_hor * kernels_vert * num_colors;
		this.kern_size =     kern_tiles * 4 * 64;
		// no cuMemAlloc — native backend allocates its own GPU memory
	}

	// Constructor for rectilinear synthetic images
	public GpuQuad(
			GPUTileProcessor gpuTileProcessor,
///			final QuadCLT    quadCLT, // should treat differently!
			final int        max_width,
			final int        max_height,
			//   	    	final int kernels_hor,
			//   			final int kernels_vert,
//			final int num_cams,
			final int        num_colors, // normally 1?
			int              debug_level
			) {
		this.rectilinear = true;
		setGpu_debug_level(debug_level);
		this.gpuTileProcessor = gpuTileProcessor;
///		this.quadCLT =       quadCLT; // should treat differently!
//		int [] wh =          quadCLT.getGeometryCorrection().getSensorWH();
		this.img_width =     max_width;
		this.img_height =    max_height;
		this.num_cams =      1; // single camera, only interscene correlations
		this.num_all_pairs = 0;
		this.num_colors =    num_colors; // maybe should always be 3?
		this.kernels_hor =   0;
		this.kernels_vert =  0;
		this.kern_tiles =    0;
		this.kern_size =     0;
	    setSensorMaskInter(-1);
		// CPU arrays of pointers to GPU memory
		// These arrays may go to methods, they are here just to be able to free GPU memory if needed
		gpu_kernels_h =           null; // new CUdeviceptr[num_cams];
		gpu_kernel_offsets_h =    null; // new CUdeviceptr[num_cams];
		
		gpu_bayer_h =             new CUdeviceptr[num_cams];
		gpu_clt_h =               new CUdeviceptr[num_cams];
		gpu_corr_images_h=        new CUdeviceptr[num_cams];
		// GPU pointers to array of GPU pointers
		gpu_kernels =             null; // new CUdeviceptr(); // make them nulls?
		gpu_kernel_offsets =      null; // new CUdeviceptr();
		gpu_bayer =               new CUdeviceptr();
///		gpu_ftasks =              new CUdeviceptr(); //  allocate tilesX * tilesY * getTaskSize() * Sizeof.FLOAT
		gpu_corrs =               new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * CORR_SIZE * Sizeof.FLOAT
		gpu_corr_weights =        new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * Sizeof.FLOAT

		gpu_corrs_td =            new CUdeviceptr(); //  allocate tilesX * tilesY * NUM_PAIRS * 4 * DTT_SIZE * DTT_SIZE * Sizeof.FLOAT
		gpu_corrs_combo =         new CUdeviceptr(); //  allocate tilesX * tilesY             * CORR_SIZE * Sizeof.FLOAT
		gpu_corrs_combo_td =      new CUdeviceptr(); //  allocate tilesX * tilesY *             4 * DTT_SIZE * DTT_SIZE * Sizeof.FLOAT

		gpu_textures =            new CUdeviceptr(); //  allocate tilesX * tilesY * ? * 256 * Sizeof.FLOAT
		gpu_corr_indices =        new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		// May add separate gpu_corr_indices_td here
		gpu_corr_combo_indices =  new CUdeviceptr(); //  allocate tilesX * tilesY * 1 * Sizeof.FLOAT            
		gpu_num_corr_tiles =      new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_texture_indices_ovlp =new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_num_texture_ovlp =    new CUdeviceptr(); //  8 ints
		gpu_texture_indices =     new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT
		gpu_texture_indices_len = new CUdeviceptr(); //  allocate tilesX * tilesY * 6 * Sizeof.FLOAT //  allocate tilesX * tilesY * 1 * Sizeof.INT
		gpu_diff_rgb_combo =      new CUdeviceptr(); //  1 int

		gpu_color_weights =       new CUdeviceptr(); //  allocate 3 * Sizeof.FLOAT
		gpu_generate_RBGA_params =new CUdeviceptr(); //  allocate 5 * Sizeof.FLOAT

		gpu_woi =                 new CUdeviceptr(); //  4 integers (x, y, width, height) Rectangle - in tiles
		gpu_twh =                 new CUdeviceptr(); //  2 integers (width, height) - just allocated space to be used by DP
		cuMemAlloc (gpu_twh,        2 * Sizeof.INT);
		gpu_num_texture_tiles =   new CUdeviceptr(); //  8 integers

		gpu_textures_rgba =       new CUdeviceptr(); //  allocate tilesX * tilesY * ? * 256 * Sizeof.FLOAT

		gpu_correction_vector=    new CUdeviceptr();
		gpu_rot_deriv=            new CUdeviceptr(); //  used internally by device, may be read to CPU for testing
		gpu_geometry_correction=  new CUdeviceptr();
		gpu_rByRDist=             new CUdeviceptr(); //  calculated once for the camera distortion model in CPU (move to GPU?)

///		gpu_active_tiles =        new CUdeviceptr(); //  TILESX*TILESY*sizeof(int)
		gpu_num_active_tiles =    new CUdeviceptr(); //  1 int

		// Init data arrays for all kernels
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		long [] device_stride = new long [1];
		for (int ncam = 0; ncam < num_cams; ncam++) {
			/**
			gpu_kernels_h[ncam] =        new CUdeviceptr();
			cuMemAlloc(gpu_kernels_h[ncam],kern_size * Sizeof.FLOAT ); //     public static int cuMemAlloc(CUdeviceptr dptr, long bytesize)
			gpu_kernel_offsets_h[ncam] = new CUdeviceptr();
			cuMemAlloc(gpu_kernel_offsets_h[ncam],kern_tiles * GPUTileProcessor.CLTEXTRA_SIZE * Sizeof.FLOAT ); //     public static int cuMemAlloc(CUdeviceptr dptr, long bytesize)
			*/
			
			gpu_bayer_h[ncam] =          new CUdeviceptr();
			cuMemAllocPitch (
					gpu_bayer_h[ncam],        // CUdeviceptr dptr,
					device_stride,            // long[] pPitch,
					img_width * Sizeof.FLOAT, // long WidthInBytes,
					img_height,               // long Height,
					Sizeof.FLOAT);            // int ElementSizeBytes)
			mclt_stride = (int)(device_stride[0] / Sizeof.FLOAT);
			
		}
		// now create device arrays pointers
		if (Sizeof.POINTER != Sizeof.LONG) {
			String msg = "Sizeof.POINTER != Sizeof.LONG";
			IJ.showMessage("Error",	msg);
			new IllegalArgumentException (msg);
		}
		/**
		cuMemAlloc(gpu_kernels,        num_cams * Sizeof.POINTER);
		cuMemAlloc(gpu_kernel_offsets, num_cams * Sizeof.POINTER);
		*/
		cuMemAlloc(gpu_bayer,          num_cams * Sizeof.POINTER);
		/**
		long [] gpu_kernels_l =        new long [num_cams];
		long [] gpu_kernel_offsets_l = new long [num_cams];
		*/
		long [] gpu_bayer_l =          new long [num_cams];
		/**
		for (int ncam = 0; ncam < num_cams; ncam++) gpu_kernels_l[ncam] =        GPUTileProcessor.getPointerAddress(gpu_kernels_h[ncam]);
		cuMemcpyHtoD(gpu_kernels, Pointer.to(gpu_kernels_l),                     num_cams * Sizeof.POINTER);

		for (int ncam = 0; ncam < num_cams; ncam++) gpu_kernel_offsets_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_kernel_offsets_h[ncam]);
		cuMemcpyHtoD(gpu_kernel_offsets, Pointer.to(gpu_kernel_offsets_l),       num_cams * Sizeof.POINTER);
 		*/
		for (int ncam = 0; ncam < num_cams; ncam++) gpu_bayer_l[ncam] =          GPUTileProcessor.getPointerAddress(gpu_bayer_h[ncam]);
		cuMemcpyHtoD(gpu_bayer, Pointer.to(gpu_bayer_l),                         num_cams * Sizeof.POINTER);
		// Set GeometryCorrection data
		// tests many - skip it?
		cuMemAlloc(gpu_geometry_correction,      GeometryCorrection.arrayLength(GPUTileProcessor.MAX_NUM_CAMS) * Sizeof.FLOAT); // always maximal number of cameras (sparse)
		cuMemAlloc(gpu_rByRDist,                 GPUTileProcessor.RBYRDIST_LEN *  Sizeof.FLOAT);
		cuMemAlloc(gpu_rot_deriv,                5*GPUTileProcessor.MAX_NUM_CAMS*3*3 * Sizeof.FLOAT); // always maximal number of cameras (sparse)
		cuMemAlloc(gpu_correction_vector,        GPUTileProcessor.CORR_VECTOR_MAX_LENGTH * Sizeof.FLOAT); // update CORR_VECTOR_LENGTH to fit 

		// Set task array
//		cuMemAlloc(gpu_ftasks,      tilesX * tilesY * getTaskSize() * Sizeof.FLOAT);
//		checkAllocateGpuFtasks((tilesX+4) * (tilesY+4)); // +4 - "a little more" (will reallocate if needed)
		checkAllocateGpuFtasks(tilesX * tilesY); // +4 - "a little more" (will reallocate if needed)
		//=========== Seems that in many places Sizeof.POINTER (==8) is used instead of Sizeof.FLOAT !!! ============
		// Set corrs array PROBABLY NOT USED - only interscene !
		// TODO: Find correct num_pairs for inter. Should it contain individual, "combo" (same) and neighbors?
		int num_pairs = 3; // Correlation2d.getNumPairs(quadCLT.getNumSensors());
//		int num_pairs = Correlation2d.getNumPairs(num_cams);
		cuMemAlloc(gpu_corr_indices,         tilesX * tilesY * num_pairs * Sizeof.FLOAT);
		cuMemAlloc(gpu_corr_combo_indices,   tilesX * tilesY *             Sizeof.FLOAT);

		cuMemAlloc(gpu_num_corr_tiles,                    1 * Sizeof.FLOAT);

		//#define TILESYA       ((TILESY +3) & (~3))
		int tilesYa = (tilesY + 3) & ~3;
		cuMemAlloc(gpu_texture_indices,     tilesX * tilesYa * Sizeof.FLOAT); // for non-overlap tiles
		cuMemAlloc(gpu_texture_indices_ovlp,tilesX * tilesYa * Sizeof.FLOAT); // for overlapped tiles

		cuMemAlloc(gpu_diff_rgb_combo, tilesX * tilesYa * num_cams* (num_colors + 1) *  Sizeof.FLOAT);

		cuMemAlloc(gpu_color_weights,             3 * Sizeof.FLOAT);
		cuMemAlloc(gpu_generate_RBGA_params,      5 * Sizeof.FLOAT);


		cuMemAlloc(gpu_woi,                               4 * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_texture_tiles,                 8 * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_texture_ovlp,                  8 * Sizeof.FLOAT);
		cuMemAlloc(gpu_texture_indices_len,               1 * Sizeof.FLOAT);

// will be dynamically allocated with gpu_ftasks
//		cuMemAlloc(gpu_active_tiles,        tilesX * tilesY * Sizeof.FLOAT);
		cuMemAlloc(gpu_num_active_tiles,                  1 * Sizeof.FLOAT);
		cuMemAlloc(gpu_corr_weights, num_pairs* tilesX * tilesY * Sizeof.FLOAT);

		// allocate space for pixel-domain correlations (6 per tile)
		cuMemAllocPitch (
				gpu_corrs,                             // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				GPUTileProcessor.CORR_SIZE * Sizeof.FLOAT,              // long WidthInBytes,
				num_pairs * tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for transform-domain correlations (6 per tile)
		cuMemAllocPitch (
				gpu_corrs_td,                          // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT,// long WidthInBytes,
				num_pairs * tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_td = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for pixel-domain combined correlations (1 per tile)
		cuMemAllocPitch (
				gpu_corrs_combo,                       // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				GPUTileProcessor.CORR_SIZE * Sizeof.FLOAT,              // long WidthInBytes,
				tilesX * tilesY,                       // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_combo = (int)(device_stride[0] / Sizeof.FLOAT);

		// allocate space for transform-domain combined correlations (1 per tile)
		cuMemAllocPitch (
				gpu_corrs_combo_td,                          // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT,// long WidthInBytes,
				tilesX * tilesY,           // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		corr_stride_combo_td = (int)(device_stride[0] / Sizeof.FLOAT);

		int max_texture_size = (num_colors + 1 + (num_cams + num_colors + 1)) * (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);
		cuMemAllocPitch (
				gpu_textures,                             // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				max_texture_size * Sizeof.FLOAT,              // long WidthInBytes,
				tilesX * tilesY,             // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		texture_stride = (int)(device_stride[0] / Sizeof.FLOAT);
		int max_rgba_width  =  (tilesX + 1) * GPUTileProcessor.DTT_SIZE;
		int max_rgba_height =  (tilesY + 1) * GPUTileProcessor.DTT_SIZE;
//		int max_rbga_slices =  num_colors + 1; // num_cams
		int max_rbga_slices =  num_colors + 1 + (num_colors * num_cams); // colors, alpha, colors per channel
		cuMemAllocPitch (
				gpu_textures_rgba,                     // CUdeviceptr dptr,
				device_stride,                         // long[] pPitch,
				max_rgba_width * Sizeof.FLOAT,         // long WidthInBytes,
				max_rgba_height * max_rbga_slices,     // long Height,
				Sizeof.FLOAT);                         // int ElementSizeBytes)
		texture_stride_rgba = (int)(device_stride[0] / Sizeof.FLOAT);
	}
	
	
	
	private void checkAllocateGpuFtasks(int max_tasks) {
/*
 * Got jcuda.CudaException: CUDA_ERROR_MISALIGNED_ADDRESS
	at jcuda.driver.JCudaDriver.checkResult(JCudaDriver.java:396)
	at jcuda.driver.JCudaDriver.cuCtxSynchronize(JCudaDriver.java:2426)
	at com.elphel.imagej.gpu.GpuQuad.execConvertDirect(GpuQuad.java:1596)
	
	Will try rounding to multiple of smth (was 0x80)
	
 */
		max_tasks = (max_tasks + 0x7f) & ~0x7f; 
		if (max_tasks > gpu_ftasks_len) {
			if (gpu_ftasks != null) {
				cuMemFree (gpu_ftasks);
			}
			if (gpu_active_tiles != null) {
				cuMemFree (gpu_active_tiles);
			}
			gpu_ftasks =       new CUdeviceptr();
			cuMemAlloc(gpu_ftasks, max_tasks * getTaskSize() * Sizeof.FLOAT);
			gpu_active_tiles = new CUdeviceptr(); 
			cuMemAlloc(gpu_active_tiles, max_tasks * Sizeof.FLOAT);
			gpu_ftasks_len = max_tasks;
		}
	}
	
	public int getTilesX() {
		return getImageWidth() / GPUTileProcessor.DTT_SIZE;
	}

	public int getTilesY() {
		return getImageHeight() / GPUTileProcessor.DTT_SIZE;
	}

	public void resetGeometryCorrection() {
		geometry_correction_set = false;
		geometry_correction_vector_set = false;
		if (getGpu_debug_level() > -1) {
			System.out.println("======resetGeometryCorrection()");
		}

	}
	public void resetGeometryCorrectionVector() {
		geometry_correction_vector_set = false;
		if (getGpu_debug_level() > -1) {
			System.out.println("======resetGeometryCorrectionVector()");
		}
	}
//	public int getImageWidth()  {return this.img_width;}
//	public int getImageHeight() {return this.img_height;}
	public int getImageWidth()  {return (gpu_4_images_wh== null) ? getSensorWidth(): gpu_4_images_wh[0];}
	public int getImageHeight() {return (gpu_4_images_wh== null) ? getSensorHeight(): gpu_4_images_wh[1];} // valid after last IMCLT
	
	public int getSensorWidth()  {return this.img_width;}
	public int getSensorHeight() {return this.img_height;}
	
	public static int getDttSize()     {return GPUTileProcessor.DTT_SIZE;}
//	public int getNumCams()     {return GPUTileProcessor.NUM_CAMS;}
	public int getSensorMaskInter() {return sensor_mask_inter;}
	public void setSensorMaskInter(int mask) {
		sensor_mask_inter = mask & ((1 << num_cams) -1);
		sensor_mask_inter_indices = new int [getNumCamsInter()+1];
	    sensor_mask_inter_indices_neibs = new int [getNumCamsInter()+2];

		int indx = 0;
		for (int i = 0; i < num_cams; i++) {
			if ((sensor_mask_inter & (1 << (i & 31))) != 0) {
				sensor_mask_inter_indices[indx++] = i;
			}
		}
		sensor_mask_inter_indices[indx] = 0xff; // sum
		sensor_mask_inter_indices_neibs[indx++] = 0xff; // sum
		sensor_mask_inter_indices_neibs[indx++] = 0xfe; // sumof neibs
	}
	public int getNumCamsInter() {
		return Integer.bitCount(sensor_mask_inter);
	}
	public int getSensInter(int indx) {
		return sensor_mask_inter_indices[indx];
	}
	public int [] getSensInter() {
		return sensor_mask_inter_indices;
	}
	/*
	public int getSensInterNeib(int indx) { // not used
		return sensor_mask_inter_indices_neibs[indx];
	}
	*/
	public int [] getSensInterNeib() {
		return sensor_mask_inter_indices_neibs;
	}
	public int [] getSensInterNeib(boolean full) {
		return full? sensor_mask_inter_indices_neibs : (new int [] {
				sensor_mask_inter_indices_neibs[sensor_mask_inter_indices_neibs.length -2],
				sensor_mask_inter_indices_neibs[sensor_mask_inter_indices_neibs.length -1]});
	}
	/*
	public int getSensInterNeib(int indx, boolean full) {
		return full? sensor_mask_inter_indices_neibs[indx] : getSensInterNeib(false)[indx];
	}
    */
	
	public void setGeometryCorrection() { // will reset geometry_correction_set when running GPU kernel
		//    		if (geometry_correction_set) return;
		setGeometryCorrection(
				quadCLT.getGeometryCorrection(),
				false);
	}

	public void setGeometryCorrectionVector() { // will reset geometry_correction_vector_set when running GPU kernel
		setExtrinsicsVector( // expand to maximal numer of sensors
				quadCLT.getGeometryCorrection().expandSensors(GPUTileProcessor.MAX_NUM_CAMS).getCorrVector());
		if (getGpu_debug_level() > -1) {
			System.out.println("======setGeometryCorrectionVector()");
		}
	}

	// Use NUM_CAMS = 16 gc.expandSensors(NUM_CAMS) here
	public void setGeometryCorrection(GeometryCorrection gc,
			boolean use_java_rByRDist) { // false - use newer GPU execCalcReverseDistortions
//		float [] fgc = gc.toFloatArray();
		float [] fgc = gc.expandSensors(GPUTileProcessor.MAX_NUM_CAMS).toFloatArray();
		
		if (use_java_rByRDist) {
			double [] rByRDist = gc.getRByRDist();
			float [] fFByRDist = new float [rByRDist.length];
			for (int i = 0; i < rByRDist.length; i++) {
				fFByRDist[i] = (float) rByRDist[i];
			}
			cuMemcpyHtoD(gpu_rByRDist,            Pointer.to(fFByRDist), fFByRDist.length * Sizeof.FLOAT);
		}
		cuMemcpyHtoD(gpu_geometry_correction, Pointer.to(fgc),       fgc.length * Sizeof.FLOAT);
///	Already allocated !	
///		cuMemAlloc  (gpu_rot_deriv, 5 * GPUTileProcessor.MAX_NUM_CAMS *3 *3 * Sizeof.FLOAT); // NCAM of 3x3 rotation matrices, plus 4 derivative matrices for each camera
		if (getGpu_debug_level() > -1) {
			System.out.println("======setGeometryCorrection()");
		}
	}
	
	float [] getRbyRDist() { // read back to verify calculation
		float [] frByRDist = new float [GPUTileProcessor.RBYRDIST_LEN];
		cuMemcpyDtoH(Pointer.to(frByRDist), gpu_rByRDist, frByRDist.length * Sizeof.FLOAT);
		return frByRDist;
	}
	
	public double maxRbyRDistErr() {
		double [] rByRDist = quadCLT.getGeometryCorrection().getRByRDist();
		float [] frByRDist = getRbyRDist(); // read from GPU
		double max_err=0;
		int worst_index = -1;
		for (int i = 0; (i < rByRDist.length) && (i < frByRDist.length); i++){
			double e = Math.abs(rByRDist[i] - frByRDist[i]);
			if (e > max_err) {
				max_err = e;
				worst_index = i;
			}
		}
		if (max_err > 0.001) {
			System.out.println("Maximal R-by-RDist error = "+ max_err+", at index = "+worst_index);
		}
		return max_err;
	}
	
	
	/**
	 * Copy extrinsic correction vector to the GPU memory
	 * @param cv correction vector
	 */
	public void setExtrinsicsVector(CorrVector cv) { // expanded to maximal number of sensors
		double [] dcv = cv.toFullRollArray();
		float []  fcv = new float [dcv.length];
		for (int i = 0; i < dcv.length; i++) {
			fcv[i] = (float) dcv[i];
		}
		cuMemcpyHtoD(gpu_correction_vector, Pointer.to(fcv), fcv.length * Sizeof.FLOAT);
		if (getGpu_debug_level() > -1) {
			System.out.println("======setExtrinsicsVector()");
		}
	}

	/**
	 * Copy array of CPU-prepared tasks to the GPU memory
	 * @param tile_tasks array of TpTask prepared by the CPU (before geometry correction is applied)
	 * @param use_aux Use second (aux) camera
	 * @param verify correct data
	 */
	public void setTasks(
			TpTask [] tile_tasks,
			boolean use_aux,  // while is it in class member? - just to be able to free
			boolean verify
			)
	{
		if (verify) checkTasks(tile_tasks);
		num_task_tiles = tile_tasks.length; // only place that modifies num_task_tiles and may require allocation
		checkAllocateGpuFtasks(num_task_tiles); 
		int task_size = getTaskSize();
		float [] ftasks = new float [task_size * num_task_tiles];
		for (int i = 0; i < num_task_tiles; i++) {
			tile_tasks[i].task |= 511; // &= 0x1f; //############## Testing
			
			tile_tasks[i].asFloatArray(ftasks, i, use_aux);
		}
		cuMemcpyHtoD(gpu_ftasks, Pointer.to(ftasks), task_size * num_task_tiles * Sizeof.FLOAT);
		if (getGpu_debug_level() > -1) {
			System.out.println("======setTasks()");
		}
	}

	/**
	 * Update tp_tasks from the GPU after derived coordinates are calculated
	 *
	 * The D2H half of the TpTask bridge: after calc_rot_deriv() + calculate_tiles_offsets()
	 * (geometry_correction.cu) fill the GPU-resident task array with per-sensor xy offsets
	 * and disp_dist Jacobians computed from the relative pose and per-tile disparity, this
	 * reads the whole array back and rebuilds the Java TpTask objects — used when the host
	 * needs the GPU-derived geometry (disparity LMA expected offsets, lazy-eye/ERS).
	 * See TpTask class javadoc for the full bridge description.
	 * (Comment by Claude on 07/03/2026, from Andrey's explanation.)
	 *
	 * @param tile_tasks tile_tasks array of TpTask prepared by the CPU, will be updated with
	 * GPU-calculated geometry data
	 * @param use_aux  Use second (aux) camera (not used now)
	 */

	public void updateTasks(
			TpTask [] tile_tasks,
			boolean use_aux    // while is it in class member? - just to be able to free
			)
	{
		num_task_tiles = tile_tasks.length; // does not require re-allocation, as tile_tasks should be already set to GPU
		int task_size = getTaskSize();
		float [] ftasks = new float [task_size * num_task_tiles];
		cuMemcpyDtoH(Pointer.to(ftasks), gpu_ftasks, task_size * num_task_tiles * Sizeof.FLOAT);
		for (int i = 0; i < num_task_tiles; i++) {
			tile_tasks[i] = new TpTask(quadCLT.getNumSensors(), ftasks, i, use_aux);
		}
		
		if (getGpu_debug_level() > -1) {
			System.out.println("======updateTasks()");
		}
	}
	public TpTask [] readbackTasks(
			int     num_tasks,
			int     num_sensors,
			boolean use_aux    // while is it in class member? - just to be able to free
			) {
		gpuTrace("readbackTasks");
		num_task_tiles = num_tasks;
		TpTask [] tile_tasks = new TpTask[num_task_tiles];
		int task_size = getTaskSize();
		float [] ftasks = new float [task_size * num_task_tiles];
		cuMemcpyDtoH(Pointer.to(ftasks), gpu_ftasks, task_size * num_task_tiles * Sizeof.FLOAT);
		for (int i = 0; i < num_task_tiles; i++) {
			tile_tasks[i] = new TpTask(num_sensors, ftasks, i, use_aux);
		}
		if (getGpu_debug_level() > -1) {
			System.out.println("======readbackTasks()");
		}
		return tile_tasks;
	}
	
	

	static void checkTasks (TpTask [] tile_tasks) {
		float min_disp = tile_tasks[0].target_disparity;
		float max_disp = min_disp; 
		int min_ty =  tile_tasks[0].ty;
		int min_tx =  tile_tasks[0].tx;
		int max_ty = min_ty;
		int max_tx = min_tx;
		int   max_task = 0;
		for (int i = 0; i < tile_tasks.length; i++) {
			if (Float.isNaN(tile_tasks[i].target_disparity)) {
				System.out.println(String.format("Disparity=NaN for tY=%d, tX=%d, task = 0x%x", tile_tasks[i].ty, tile_tasks[i].tx, tile_tasks[i].task));
				System.out.println("Setting disparity and tas to 0");
				tile_tasks[i].target_disparity = 0f;
				tile_tasks[i].task = 0;
			}
			if (tile_tasks[i].task != 0) {
				if (tile_tasks[i].target_disparity < min_disp ) min_disp = tile_tasks[i].target_disparity; 
				if (tile_tasks[i].target_disparity > max_disp ) max_disp = tile_tasks[i].target_disparity; 
				if (tile_tasks[i].task > max_task )             max_task = tile_tasks[i].task; 

				if (tile_tasks[i].ty < min_ty ) min_ty = tile_tasks[i].ty; 
				if (tile_tasks[i].ty > max_ty ) max_ty = tile_tasks[i].ty; 

				if (tile_tasks[i].tx < min_tx ) min_tx = tile_tasks[i].tx; 
				if (tile_tasks[i].tx > max_tx ) max_tx = tile_tasks[i].tx; 
			}
		}

		System.out.println(String.format("---Disparity=[%f..%f] task=[0..0x%x] ty=[%d..%d]  tx=[%d..%d] ---",
				min_disp,max_disp, max_task, min_ty, max_ty, min_tx, max_tx));
	}



	public TpTask [] getTasks (boolean use_aux)
	{
		int task_size = TpTask.getSize(quadCLT.getNumSensors());
		float [] ftasks = new float [task_size * num_task_tiles];
		cuMemcpyDtoH(Pointer.to(ftasks), gpu_ftasks, task_size * num_task_tiles * Sizeof.FLOAT);
		TpTask [] tile_tasks = new TpTask[num_task_tiles];
		for (int i = 0; i < num_task_tiles; i++) {
			tile_tasks[i] = new TpTask(quadCLT.getNumSensors(), ftasks, i, use_aux);
		}
		return tile_tasks;
	}


	public int [] getTextureIndices()
	{
		gpuTrace("getTextureIndices");
		float [] ftexture_indices_len = new float[1];
		cuMemcpyDtoH(Pointer.to(ftexture_indices_len), gpu_texture_indices_len,  1 * Sizeof.FLOAT);
		int num_tiles =      Float.floatToIntBits(ftexture_indices_len[0]);
		float [] ftexture_indices = new float [num_tiles];
		cuMemcpyDtoH(Pointer.to(ftexture_indices), gpu_texture_indices,  num_tiles * Sizeof.FLOAT);
		int [] texture_indices = new int [num_tiles];
		for (int i = 0; i < num_tiles; i++) {
			texture_indices[i] = Float.floatToIntBits(ftexture_indices[i]);
		}
		num_texture_tiles = num_tiles; //TODO: was it missing?
		return texture_indices;
	}


	public void setConvolutionKernel(
			float [] kernel,  // [tileY][tileX][color][..]
			float [] kernel_offsets,
			int ncam) {
		cuMemcpyHtoD(gpu_kernels_h[ncam],        Pointer.to(kernel),         kern_size * Sizeof.FLOAT);
		cuMemcpyHtoD(gpu_kernel_offsets_h[ncam], Pointer.to(kernel_offsets), kern_tiles * GPUTileProcessor.CLTEXTRA_SIZE * Sizeof.FLOAT);
	}

	/**
	 * Set CLT kernels in GPU memory reading them using quadCLT instance        
	 * @param clt_kernels - [num_cameras==4][num_colors][tilesY][tilesX][quadrants==4][elements==8*8==64]
	 * @param force re-set kernels even if they were already set
	 */
	public void setConvolutionKernels(
			boolean force)
	{
		if (this.gpuTileProcessor.kernels_set && ! force) {
			return;
		}
		setConvolutionKernels(quadCLT.getCLTKernels(), true);
	}

	/**
	 * Set CLT kernels in GPU memory        
	 * @param clt_kernels - [num_cameras==4][num_colors][tilesY][tilesX][quadrants==4][elements==8*8==64]
	 * @param force re-set kernels even if they were already set
	 */
	public void setConvolutionKernels(
			double [][][][][][] clt_kernels,
			boolean force)
	{
		boolean transpose = true;
		if (this.gpuTileProcessor.kernels_set && ! force) {
			return;
		}
		int num_kernels =
				clt_kernels[0][0].length * //tilesY
				clt_kernels[0][0][0].length * //tilesX
				clt_kernels[0].length;  //colors
		int kernel_length =	num_kernels * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;

		float [] fkernel =  new float [kernel_length];
		float [] foffsets = new float [num_kernels * GPUTileProcessor.CLTEXTRA_SIZE];
		for (int ncam = 0; ncam < clt_kernels.length; ncam++) {
			int indx=0;
			for (int ty = 0; ty <  clt_kernels[ncam][0].length; ty++) {
				for (int tx = 0; tx <  clt_kernels[ncam][0][ty].length; tx++) {
					for (int col = 0; col <  clt_kernels[ncam].length; col++) {
						for (int p = 0; p < 4; p++) {
							double [] pa = clt_kernels[ncam][col][ty][tx][p];
							for (int i0 = 0; i0 < 64; i0++) {
								int i;
								if (transpose) {
									i = ((i0 & 7) << 3) + ((i0 >>3) & 7);
								} else {
									i = i0;
								}
								fkernel[indx++] = (float)pa[i];
							}
						}
					}
				}
			}
			indx = 0;
			for (int ty = 0; ty <  clt_kernels[ncam][0].length; ty++) {
				for (int tx = 0; tx <  clt_kernels[ncam][0][ty].length; tx++) {
					for (int col = 0; col <  clt_kernels[ncam].length; col++) {
						double [] pa = clt_kernels[ncam][col][ty][tx][4];
						for (int i = 0; i < pa.length; i++) {
							foffsets[indx++] = (float)pa[i];
						}
					}
				}
			}

			setConvolutionKernel(
					fkernel,    // float [] kernel,  // [tileY][tileX][color][..]
					foffsets,   // float [] kernel_offsets,
					ncam);      // int ncam)
		}
		this.gpuTileProcessor.kernels_set = true;
		if (getGpu_debug_level() > -1) {
			System.out.println("======setConvolutionKernels()");
		}
	}

	public void setBayerImage(
			float [] bayer_image,
			int ncam) {
		CUDA_MEMCPY2D copyH2D =   new CUDA_MEMCPY2D();
		copyH2D.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyH2D.srcHost =         Pointer.to(bayer_image);
		copyH2D.srcPitch =        img_width*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyH2D.dstDevice =       gpu_bayer_h[ncam]; // src_dpointer;
		copyH2D.dstPitch =        mclt_stride *Sizeof.FLOAT; // device_stride[0];
		copyH2D.WidthInBytes =    img_width*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.Height =          img_height; // /4;
		cuMemcpy2D(copyH2D);
	}

	/**
	 * Readback source image data (for testing)
	 * @param bayer_image
	 * @param ncam
	 */
	public void getBayerImage(
			float [] bayer_image,
			int ncam) {
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_bayer_h[ncam]; // src_dpointer;
		copyD2H.srcPitch =        mclt_stride *Sizeof.FLOAT; // device_stride[0];
		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(bayer_image);
		copyD2H.dstPitch =        img_width*Sizeof.FLOAT; // width_in_bytes;
		copyD2H.WidthInBytes =    img_width*Sizeof.FLOAT; // width_in_bytes;
		copyD2H.Height =          img_height; // /4;
		cuMemcpy2D(copyD2H); // run copy
	}
	public int [] getWH(boolean use_ref) {
		return use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
	}
	
	public int getNumColors() {
		return num_colors;
	}

	public int getNumSensors() {
		return num_cams;
	}
	
	public int getCltSize(boolean use_ref) { // per camera, in floats
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		return tilesY*tilesX*num_colors* 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
	}
	public int getNumTiles(boolean use_ref) {
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		return tilesY*tilesX; // *num_colors* 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
	}
	public int getCltTileLength() { // per camera, in floats
		return num_colors * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
	}
	
	/*
	public int getCltLength(boolean use_ref) {
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		int tile_size_td = getCltSize(use_ref); // 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int num_tiles = tilesY*tilesX*num_colors;
		return num_tiles* tile_size_td;
	}
	*/
	public float [][] getCltData( // only for color=0
			boolean use_ref){
		gpuTrace("getCltData");
		CUdeviceptr [] gpu_sel_clt_h = 	use_ref ? gpu_clt_ref_h : gpu_clt_h;
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		int tile_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int num_tiles = tilesY*tilesX*num_colors;
		float [][] fclt = new float [num_cams][num_tiles* tile_size_td];
		for (int ncam=0;ncam < num_cams; ncam++) {
			CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
			copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
			copyD2H.srcDevice =       gpu_sel_clt_h[ncam];
			copyD2H.srcPitch =        tile_size_td * Sizeof.FLOAT;
			copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
			copyD2H.dstHost =         Pointer.to(fclt[ncam]);
			copyD2H.dstPitch =        tile_size_td * Sizeof.FLOAT;
			copyD2H.WidthInBytes =    tile_size_td * Sizeof.FLOAT;
			copyD2H.Height =          num_tiles;
			cuMemcpy2D(copyD2H); // run copy
		}
		return fclt;
	}
	
	public float [][] presentCltData(boolean use_ref){
		gpuTrace("presentCltData");
		float [][] fclt = getCltData(use_ref);
		float [][] pfclt = new float [fclt.length][fclt[0].length];
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		int num_tiles = tilesY*tilesX; // *num_colors;
		int quad_size = GPUTileProcessor.DTT_SIZE*GPUTileProcessor.DTT_SIZE;
		for (int ncam = 0; ncam < num_cams; ncam++) {
			for (int ntile=0; ntile < num_tiles; ntile++) {
				for (int nquad = 0; nquad < 4; nquad++) {
					for (int row = 0; row < GPUTileProcessor.DTT_SIZE; row++ ) {
						int src = ((ntile * num_colors) * 4 + nquad) * quad_size +  row * GPUTileProcessor.DTT_SIZE;
						int dst_tx = ntile % tilesX;
						int dst_ty = ntile / tilesX;
						int dst_qx = nquad % 2;
						int dst_qy = nquad / 2;
						int dst = ((dst_ty * 2 + dst_qy) * GPUTileProcessor.DTT_SIZE + row) * (wh[0]*2)+
								(dst_tx*2 +dst_qx)*GPUTileProcessor.DTT_SIZE;
						System.arraycopy(
								fclt[ncam],
								src,
								pfclt[ncam],
								dst,
								GPUTileProcessor.DTT_SIZE);
					}
				}
			}
		}
		return pfclt;
	}
	
	
	/**
	 * On-device 16-sensor TD consolidation (roadmap 2c v1 chain: index_consolidate ->
	 * consolidate_oob -> clt_average_sensors_list in tp_consolidate.cu): average the
	 * per-sensor CLT into the cam-0 slot WITHOUT the D2H/H2D round-trip of the CPU
	 * bridge (getCltData -> CuasTD.consolidateSensorsTD -> setCltData(0)). Supported
	 * only by the JNA backend (GpuQuadJna) per Andrey's 07/12/2026 ruling - no JCuda
	 * launch code for new kernels; this base returns false so callers fall back to
	 * the (OOB-mask-capable) CPU bridge, see CuasTD.consolidateToSlot0().
	 * By Claude on 07/12/2026.
	 *
	 * @param tp_tasks    [2][]: task set 0 + MB pair set (or [1]==null = no-MB), the
	 *                    sets used for the convert (GpuQuad.setInterTasksMotionBlur
	 *                    convention)
	 * @param soft_margin OOB soft margin, px (~12.0; 0 = tier disabled)
	 * @param hard_margin OOB hard margin, px (~8.0; 0 = tier disabled; 0/0 = v0)
	 * @return true if the GPU chain ran (cam-0 slot holds the average), false =
	 *         unsupported/failed - caller must run the CPU bridge instead
	 */
	public boolean execConsolidateSensorsTD( // By Claude on 07/12/2026
			TpTask [][] tp_tasks,
			double      soft_margin,
			double      hard_margin) {
		return false; // JCuda backend: not implemented (CPU bridge fallback)
	}

	public void setCltData( // for testing only
			float [][] fclt, //
			boolean use_ref){
		int clt_size = getCltSize(use_ref);
		if ((fclt.length != num_cams) || (fclt[0].length != clt_size)) {
			System.out.println("getCltData(): wrong array size: got ["+fclt.length+"]["+fclt[0].length+"], "+
			"should be ["+num_cams+"]["+clt_size+"]");
			return;
		}
		CUdeviceptr [] gpu_sel_clt_h = 	use_ref ? gpu_clt_ref_h : gpu_clt_h;
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		int tile_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int num_tiles = tilesY*tilesX*num_colors;
		for (int ncam=0;ncam < num_cams; ncam++) {
			CUDA_MEMCPY2D copyH2D =   new CUDA_MEMCPY2D();
			copyH2D.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
			copyH2D.srcHost =         Pointer.to(fclt[ncam]);
			copyH2D.srcPitch =        tile_size_td * Sizeof.FLOAT;
			copyH2D.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
			copyH2D.dstDevice =       gpu_sel_clt_h[ncam];
			copyH2D.dstPitch =        tile_size_td * Sizeof.FLOAT;
			copyH2D.WidthInBytes =    tile_size_td * Sizeof.FLOAT;
			copyH2D.Height =          num_tiles;
			cuMemcpy2D(copyH2D); // run copy
		}
		return;
	}

	public void setCltData( // for testing only
			int      ncam,
			float [] fclt, //
			boolean  use_ref){
		int clt_size = getCltSize(use_ref);
		if (fclt.length != clt_size) {
			System.out.println("getCltData(): wrong array size: got ["+fclt.length+"], "+
			"should be ["+clt_size+"]");
			return;
		}
		
		CUdeviceptr [] gpu_sel_clt_h = 	use_ref ? gpu_clt_ref_h : gpu_clt_h;
		int [] wh = use_ref ? gpu_clt_ref_wh : gpu_clt_wh;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		int tile_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int num_tiles = tilesY*tilesX*num_colors;
		CUDA_MEMCPY2D copyH2D =   new CUDA_MEMCPY2D();
		copyH2D.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyH2D.srcHost =         Pointer.to(fclt);
		copyH2D.srcPitch =        tile_size_td * Sizeof.FLOAT;
		copyH2D.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyH2D.dstDevice =       gpu_sel_clt_h[ncam];
		copyH2D.dstPitch =        tile_size_td * Sizeof.FLOAT;
		copyH2D.WidthInBytes =    tile_size_td * Sizeof.FLOAT;
		copyH2D.Height =          num_tiles;
		cuMemcpy2D(copyH2D); // run copy
		return;
	}
	
	
	/**
	 * Copy a set of images to the GPU (if they are new)        
	 * @param force set even if there is no new Bayer data available
	 */
	
	public void setBayerImages(
			boolean  force) {
		setBayerImages(
				force, // boolean  force,
				false); // boolean center)
	}
	
	
	public void setBayerImages(
			boolean  force,
			boolean  center) {
		if (!force && this.gpuTileProcessor.bayer_set && !quadCLT.hasNewImageData()) {
			return;
		}
//		System.out.print("setBayerImages(): ");
		double [][] bayer_center = quadCLT.getImageCenter(); // null
		if (center) {		
			quadCLT.getResetImageCenter();
			setBayerImage(
					bayer_center,
					true);
			return;
		}
		double [][][]       bayer_data = quadCLT.getResetImageData(); // resets hasNewImageData()
		setBayerImages(
				bayer_data,
				true);
		return;
	}        

	/**
	 * Copy a set of Bayer images to the GPU         
	 * @param bayer_data per camera a set of split-color images [4][3][w*h], may be [4][1][w*h]
	 * @param force copy regardless of if it was copied before
	 */
	// combines 3 bayer channels into one and transfers to GPU memory
	public void setBayerImages(
			double [][][]       bayer_data,
			boolean                  force) {
		if (this.gpuTileProcessor.bayer_set && !force) {
			return;
		}
		float [] fbayer = new float [bayer_data[0][0].length];
		for (int ncam = 0; ncam < bayer_data.length; ncam++) {
			if (bayer_data[ncam][0] == null) {
				System.out.println("BUG!! bayer_data["+ncam+"][0] == null");
				continue;
			}
			for (int i = 0; i <  bayer_data[ncam][0].length; i++) {
				fbayer[i] = (float) (bayer_data[ncam][0][i]); //  + bayer_data[ncam][1][i] + bayer_data[ncam][2][i]);
				for (int j = 1; j < bayer_data[ncam].length; j++) {
					fbayer[i] += (float) (bayer_data[ncam][j][i]);
				}
			}
			setBayerImage(
					fbayer, // float [] bayer_image,
					ncam); // int ncam)
		}
		this.gpuTileProcessor.bayer_set = true;
		if (getGpu_debug_level() > -1) {
			System.out.println("======setBayerImages()");
		}
	}

	// Set same image to all sensors (for reverse conversion)
	public void setBayerImage(
			double [][]       bayer_data,
			boolean           force) {
		if (this.gpuTileProcessor.bayer_set && !force) {
			return;
		}
		float [] fbayer = new float [bayer_data[0].length];
		if (bayer_data[0] == null) {
			System.out.println("BUG!! bayer_data[0] == null");
			return;
		}
		for (int i = 0; i <  bayer_data[0].length; i++) {
			fbayer[i] = (float) (bayer_data[0][i]); //  + bayer_data[ncam][1][i] + bayer_data[ncam][2][i]);
			for (int j = 1; j < bayer_data.length; j++) {
				fbayer[i] += (float) (bayer_data[j][i]);
			}
		}
		for (int ncam = 0; ncam < getNumSensors(); ncam++) {
			setBayerImage(
					fbayer, // float [] bayer_image,
					ncam); // int ncam)
		}
		this.gpuTileProcessor.bayer_set = true;
		if (getGpu_debug_level() > -1) {
			System.out.println("======setBayerImage()");
		}
		return;
	}
	
	
	// prepare tasks for full frame, same dispaity.
	// need to run setTasks(TpTask [] tile_tasks, boolean use_aux) to format/transfer to GPU memory
	/**
	 * CPU-side preparation of per-tile tasks , will need GPU kernel run to finalize          
	 * @param woi - rectangle WOI in tiles (x,y,w,h) to process, normally full window (may provide null for full frame, faster)      
	 * @param round_woi - debug feature - use round (elliptic) WOI, ignored for woi == null
	 * @param target_disparity - same disparity for all tiles
	 * @param disparity_corr -   add to all disparities (minus disparity at infinity)
	 * @param out_image from which camera channels to generate image (currently 0/1)
	 * @param corr_mask from which correlation pairs to generate (0x3f - all 6)
	 * @param THREADS_MAX - historic
	 * @param debugLevel -  not yet used
	 * @return per-tile array of TpTask instances (need to be finalized by the GPU kernel) 
	 */
	public TpTask [] setFullFrameImages(
			Rectangle                 woi,
			boolean                   round_woi,
			float                     target_disparity, // apply same disparity to all tiles
			float                     disparity_corr, // add to disparity (at infinity)
			int                       out_image, // from which tiles to generate image (currently 0/1)
			int                       corr_mask,  // which correlation pairs to generate (maybe later - reduce size from 15x15)
			//    			final int                 threadsMax,  // maximal number of threads to launch
			final int                 debugLevel) {
		gpuTrace("setFullFrameImages");
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		float [] target_disparities = new float [tilesX * tilesY];
		int [] out_images = new int [tilesX * tilesY];
		int [] corr_masks = new int [tilesX * tilesY];
		if (target_disparity != 0.0) {
			for (int i = 0; i <target_disparities.length; i++ ) target_disparities[i] = target_disparity;
		}
		for (int i = 0; i <out_images.length; i++ ) {
			out_images[i] = out_image; //  0xf;  // all 4 images
			corr_masks[i] = corr_mask; // 0x3f; // all 6 correlations
		}
		return setFullFrameImages(
				woi,                // Rectangle                 woi,
				round_woi,          // boolean                   round_woi,
				target_disparities, // should be tilesX*tilesY long
				disparity_corr,     // float                     disparity_corr, // add to disparity (at infinity)
				out_images,         // int   []                  out_images, // from which tiles to generate image (currently 0/1)
				corr_masks,         // int   []                  corr_mask,  // which correlation pairs to generate (maybe later - reduce size from 15x15)
				//        			threadsMax,             // maximal number of threads to launch
				debugLevel);
	}

	/**
	 * CPU-side preparation of per-tile tasks, will need GPU kernel run to finalize          
	 * @param woi - rectangle woi in tiles (x,y,w,h) to process, normally full window (may provide null for full frame, faster)      
	 * @param round_woi - debug feature - use round (elliptic) WOI, ignored for woi == null
	 * @param target_disparities 1D array [tilesX*tilesY] of target disparities
	 * @param disparity_corr -   add to all disparities (minus disparity at infinity)
	 * @param out_images per-tile array [tilesX*tilesY] from which tiles to generate image (currently 0/1)
	 * @param corr_mask per-tile array [tilesX*tilesY] from which correlation pairs to generate (0x3f - all 6)
	 * @param THREADS_MAX - historic
	 * @param debugLevel -  not yet used
	 * @return per-tile array of TpTask instances (need to be finalized by the GPU kernel) 
	 */
	public TpTask [] setFullFrameImages(
			Rectangle                 woi, // or null
			boolean                   round_woi,
			float []                  target_disparities, // should be tilesX*tilesY long
			float                     disparity_corr, // add to disparity (at infinity)
			int   []                  out_images, // from which tiles to generate image (currently 0/1)
			int   []                  corr_mask,  // which correlation pairs to generate (maybe later - reduce size from 15x15)
			final int                 debugLevel)
	{
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		boolean [] mask = new boolean[tilesX*tilesY];
		int num_tiles = 0;
		if (woi == null) {
			round_woi = false;
			woi = new Rectangle(0,0,tilesX,tilesY);
			num_tiles = tilesX*tilesY;
			for (int i = 0; i < num_tiles; i++) {
				mask[i] = true;
			}
		} else { // mostly for debug
			if (woi.x < 0) woi.x = 0;
			if (woi.y < 0) woi.y = 0;
			if (woi.x > (tilesX - 2)) woi.x = tilesX - 2;
			if (woi.y > (tilesY - 2)) woi.y = tilesY - 2;
			if ((woi.x + woi.width)  > tilesX) woi.width =  tilesX - woi.x;
			if ((woi.y + woi.height) > tilesY) woi.height = tilesY - woi.y;
			double rx = 0.5*woi.width;
			double ry = 0.5*woi.height;
			double xc = woi.x + rx - 0.5;
			double yc = woi.y + ry - 0.5;
			for (int ty = woi.y; ty < (woi.y +woi.height); ty++) {
				double ry2 = (ty - yc) / ry;
				ry2*=ry2;
				for (int tx = woi.x; tx < (woi.x +woi.width); tx++) {
					double rx2 = (tx - xc) / rx;
					rx2*=rx2;
					if (!round_woi || ((rx2+ry2) < 1.0)) {
						mask[ty * tilesX + tx] = true;
						num_tiles ++;
					}
				}
			}
		}
		TpTask [] tp_tasks = new TpTask[num_tiles];
		int indx = 0;
		for (int ty = 0; ty < tilesY; ty++) {
			for (int tx = 0; tx < tilesX; tx++) {
				int nt = ty * tilesX + tx;
				if (mask[nt]) {
					// Only generate for non-empty tasks, use 1 empty empty as a terminator?
					tp_tasks[indx] = new TpTask(num_cams, tx, ty, target_disparities[indx]+disparity_corr,
							((out_images[nt] & 0x0f) << 0) |
							((corr_mask [nt] & 0x3f) << 4)
							); // task == 1 for now
					// Old code, enabling all options
					tp_tasks[indx]. setTextureEnable(true);
					tp_tasks[indx]. setIntraCorrelationEnable(true);
					tp_tasks[indx]. setInterCorrelationEnable(true);
					indx++;
				}
			}
		}
		return tp_tasks;
	}


	public TpTask [] setFullFrameImages_dbg( // keeping debug features
			boolean                   calc_offsets, // old way, now not needed with GPU calculation
			Rectangle                 woi, // or null
			boolean                   round_woi,
			float []                  target_disparities, // should be tilesX*tilesY long
			int   []                  out_images, // from which tiles to generate image (currently 0/1)
			int   []                  corr_mask,  // which correlation pairs to generate (maybe later - reduce size from 15x15)
			boolean                   use_master,
			boolean                   use_aux,
			final GeometryCorrection  geometryCorrection_main,
			final GeometryCorrection  geometryCorrection_aux, // if null, will only calculate offsets fro the main camera
			final double [][][]       ers_delay,        // if not null - fill with tile center acquisition delay
			final int                 threadsMax,  // maximal number of threads to launch
			final int                 debugLevel)
	{
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		if (woi == null) {
			woi = new Rectangle(0,0,tilesX,tilesY);
		}
		if (woi.x < 0) woi.x = 0;
		if (woi.y < 0) woi.y = 0;
		if (woi.x > (tilesX - 2)) woi.x = tilesX - 2;
		if (woi.y > (tilesY - 2)) woi.y = tilesY - 2;

		if ((woi.x + woi.width)  > tilesX) woi.width =  tilesX - woi.x;
		if ((woi.y + woi.height) > tilesY) woi.height = tilesY - woi.y;
		double rx = 0.5*woi.width;
		double ry = 0.5*woi.height;
		double xc = woi.x + rx - 0.5;
		double yc = woi.y + ry - 0.5;
		boolean dbg1 = false; //  true;
		double dbg_frac = 0.0; // 0.25;
		boolean [] mask = new boolean[tilesX*tilesY];
		int num_tiles = 0;
		for (int ty = woi.y; ty < (woi.y +woi.height); ty++) {
			double ry2 = (ty - yc) / ry;
			ry2*=ry2;
			for (int tx = woi.x; tx < (woi.x +woi.width); tx++) {
				double rx2 = (tx - xc) / rx;
				rx2*=rx2;
				if (!round_woi || ((rx2+ry2) < 1.0)) {
					mask[ty * tilesX + tx] = true;
					num_tiles ++;
				}
			}
		}
		if (dbg_frac > 0) {
			Random rnd = new Random(0);
			int num_final = (int) Math.round(num_tiles * (1.0 - dbg_frac));
			while (num_tiles > num_final) {
				int tx = woi.x + rnd.nextInt(woi.width);
				int ty = woi.y + rnd.nextInt(woi.height);
				int indx = ty * tilesX + tx;
				if (mask[indx]) {
					mask[indx] = false;
					num_tiles--;
				}
			}
			// filter out with no neighbors
			for (int indx = 0; indx < mask.length; indx++) if (mask[indx]) {
				int ix = indx % tilesX;
				int iy = indx / tilesX;
				int num_neib = 0;
				if ((ix > 0) && mask[indx-1]) num_neib++;
				if ((ix < (tilesX-1)) && mask[indx+1]) num_neib++;
				if ((iy > 0) && mask[indx-tilesX]) num_neib++;
				if ((iy < (tilesY-1)) && mask[indx+tilesX]) num_neib++;
				if (num_neib == 0) {
					mask[indx] = false;
					num_tiles--;
				}
			}
			//nextInt(int bound)
		}

		if (dbg1) {
			mask[(woi.y+woi.height) * tilesX + (woi.x+woi.width)] = true;
			num_tiles += 1; // 2;

		}

		TpTask [] tp_tasks = new TpTask[num_tiles];

		int indx = 0;
		for (int ty = 0; ty < tilesY; ty++) {
			for (int tx = 0; tx < tilesX; tx++) if (mask[ty * tilesX + tx]) {
				// Only generate for non-empty tasks, use 1 empty empty as a terminator?
				tp_tasks[indx] = new TpTask(num_cams, tx, ty, target_disparities[indx],
						((out_images[indx] & 0x0f) << 0) |
						((corr_mask [indx] & 0x3f) << 4)
						); // task == 1 for now
				// Old code, enabling all options
				tp_tasks[indx]. setTextureEnable(true);
				tp_tasks[indx]. setIntraCorrelationEnable(true);
				tp_tasks[indx]. setInterCorrelationEnable(true);
				indx++;
			}
		}
		if (calc_offsets) {
			getTileSubcamOffsets(
					tp_tasks,                                    // final TpTask[]            tp_tasks,        // will use // modify to have offsets for 8 cameras
					(use_master? geometryCorrection_main: null), // final GeometryCorrection  geometryCorrection_main,
					(use_aux?    geometryCorrection_aux: null),  // final GeometryCorrection  geometryCorrection_aux, // if null, will only calculate offsets fro the main camera
					ers_delay,                                   // final double [][][]       ers_delay,        // if not null - fill with tile center acquisition delay
					threadsMax,                                  // final int                 threadsMax,  // maximal number of threads to launch
					debugLevel);                                 // final int                 debugLevel)
		}
		return tp_tasks;
	}

	public TpTask[]  setTpTask(
			//    			final GPUTileProcessor.GpuQuad gpuQuad,
			final double [][]	           disparity_array,  // [tilesY][tilesX] - individual per-tile expected disparity
			final double                   disparity_corr,
			final boolean []               need_corrs,       // should be initialized to boolean[1] or null
			final int [][]                 tile_op,          // [tilesY][tilesX] - what to do - 0 - nothing for this tile
			final int                      corr_mask,        // <0 - use corr mask from the tile tile_op, >=0 - overwrite all with non-zero corr_mask_tp
			final int                      threadsMax)       // maximal number of threads to launch
	{
		final int tilesX = tile_op[0].length; //getTilesX();
		final int tilesY = tile_op.length; // getTilesY();
		if (tilesY > tile_op.length) {
			System.out.println("BUG!!!! tilesX="+tilesX+", tilesY="+tilesY+", tile_op.length="+
					tile_op.length+", tile_op[0].length="+tile_op[0].length);
			System.out.println();
			System.out.println();
			System.out.println();
			System.out.println();
		}
		final AtomicInteger ai =            new AtomicInteger(0);
		final AtomicBoolean acorrs =        new AtomicBoolean(false);
		final List<TpTask> task_list = new CopyOnWriteArrayList<TpTask>();
		final Thread[] threads = ImageDtt.newThreadArray(threadsMax);
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					for (int nTile = ai.getAndIncrement(); nTile < tilesX * tilesY; nTile = ai.getAndIncrement()) {
						int tileY = nTile /tilesX;
						int tileX = nTile % tilesX;
						//						tIndex = tileY * tilesX + tileX;
						if (tile_op[tileY][tileX] == 0) continue; // nothing to do for this tile
						// which images to use
						int img_mask =  ImageDtt.getImgMask(tile_op[tileY][tileX]);
						// which pairs to combine in the combo:  1 - top, 2 bottom, 4 - left, 8 - right
						int corr_mask_tp = ImageDtt.getPairMask(tile_op[tileY][tileX]); // limited to 4 bits only!
						if (corr_mask_tp != 0) {
							if (corr_mask >=0) {
								corr_mask_tp = corr_mask; 	
							}
							if (corr_mask_tp != 0) {
								acorrs.set(true);
							}
						}
						TpTask tptask =	new TpTask(
								num_cams,
								tileX,
								tileY,
								(float) (disparity_array[tileY][tileX] + disparity_corr),
								((img_mask  & 0x0f) << 0) |
								((corr_mask_tp & 0x3f) << 4)
								); // task == 1 for now
						// Old code, guessing options
						if (img_mask != 0)     tptask. setTextureEnable(true);
						if (corr_mask_tp != 0) tptask. setIntraCorrelationEnable(true);
						if (corr_mask_tp != 0) tptask. setInterCorrelationEnable(true);
						task_list.add(tptask);
						
						// mask out pairs that use missing channels
					}

				}
			};
		}
		ImageDtt.startAndJoin(threads);
		if (need_corrs != null) {
			need_corrs[0] = acorrs.get();
		}
		return task_list.toArray(new TpTask[task_list.size()]);		
	}

	/**
	 * Set a single task (partial setup for GPU when geometryCorrection == null)
	 * @param num_cams number of sensors in a system
	 * @param task_code task code (need to be re-defined, currently is mostly zero/non-zero
	 * @param tileX integer tile X coordinate
	 * @param tileY integer tile X coordinate
	 * @param target_disparity target disparity (should include disparity_corr if used)
	 * @param corr_rots Array of rotational matrices, may be null, but it is more efficient to calculate it once. Not used for GPU when geometryCorrection == null 
	 * @param geometryCorrection instance of GeometryCorrection class for the scene. 12/2021 null - rely on GPU for .xy, disp_dist, does not need cor_rots
	 * @return TpTask instance, compatible with both CPU and GPU processing
	 */
	public static TpTask setTask( // sets .xy that can be set inside GPU - fix to remove @Deprecated
			int                 num_cams,
			int                 task_code,
			int                 tileX,
			int                 tileY,
			double              target_disparity, // include disparity_corr
			// use geometryCorrection.getCorrVector().getRotMatrices(); once per whole image set
			Matrix []           corr_rots,        // if null, will be calculated
			GeometryCorrection  geometryCorrection)
	{
		int dbg_tile_x = 32;
		int dbg_tile_y = 88;
		if ((corr_rots == null) && (geometryCorrection != null)) {
			corr_rots = geometryCorrection.getCorrVector().getRotMatrices(); // get array of per-sensor rotation matrices
		}
		if ((tileX == dbg_tile_x) && (tileY == dbg_tile_y)) {
			System.out.println("Debugging setTask(): tileX="+tileX+", tileY="+tileY);
			System.out.println("Debugging setTask(): tileX="+tileX+", tileY="+tileY);
		}
		TpTask tp_task = new TpTask(num_cams, tileX, tileY);
		tp_task.task = task_code;
		tp_task.target_disparity = (float) target_disparity; // will it be used?
		int transform_size = GPUTileProcessor.DTT_SIZE;
		double	centerX = tileX * transform_size + transform_size/2;
		double	centerY = tileY * transform_size + transform_size/2;
		tp_task.setCenterXY(new double [] {centerX, centerY}); // this pair of coordinates will be used by GPU to set tp_task.xy and task.disp_dist!
		if (geometryCorrection != null) {
			double [][] disp_dist = new double[num_cams][]; // used to correct 3D correlations (not yet used here)
			double [][] centersXY_main = geometryCorrection.getPortsCoordinatesAndDerivatives(
					geometryCorrection, //			GeometryCorrection gc_main,
					false,              // boolean use_rig_offsets,
					corr_rots,          // Matrix []   rots,
					null,               //  Matrix [][] deriv_rots,
					null,               // double [][] pXYderiv,
					disp_dist,          // used to correct 3D correlations
					centerX,
					centerY,
					tp_task.target_disparity); //  + disparity_corr);
			tp_task.setDispDist(disp_dist);
			tp_task.xy = new float [centersXY_main.length][2];
			for (int i = 0; i < centersXY_main.length; i++) {
				tp_task.xy[i][0] = (float) centersXY_main[i][0];
				tp_task.xy[i][1] = (float) centersXY_main[i][1];
			}
		}
		return tp_task;
	}
	
	/**
	 * Set up an array of TpTask. Only partial initialization when GPU is used, provide geometryCorrection == null for that case
	 * @param num_cams number of sensors in a system
	 * @param disparity_array array of target disparities {tiliY][tileX]
	 * @param disparity_corr disparity correction - add to all disparity values
	 * @param tile_op tile operation
	 * @param geometryCorrection geometry correction or null for GPU processing
	 * @param threadsMax maximal number of threads
	 * @return array of prepared TpTask instances
	 */
	public static TpTask[]  setTasks( // sets .xy that can be set inside GPU - fix to remove @Deprecated
			final int                      num_cams,
			final double [][]	           disparity_array,  // [tilesY][tilesX] - individual per-tile expected disparity
			final double                   disparity_corr,
			final int [][]                 tile_op,          // [tilesY][tilesX] - what to do - 0 - nothing for this tile, null - use non-NaN in disparity_array
			final GeometryCorrection       geometryCorrection,
			final int                      threadsMax)       // maximal number of threads to launch
	{
		final int tilesY = disparity_array.length;
		int op = ImageDtt.setImgMask(0, 0xf); // use if tile_op is not provided
		op =     ImageDtt.setPairMask(op,0xf);
		op =     ImageDtt.setForcedDisparity(op,true);
		// setting new (11/18/2022) bits
		op |= (1 << GPUTileProcessor.TASK_CORR_EN) | (1 << GPUTileProcessor.TASK_INTER_EN) | (1 << GPUTileProcessor.TASK_TEXT_EN);
		final int fop = op;
		int tx = -1;
		for (int i = 0; i < disparity_array.length; i++) if (disparity_array[i] != null) {
			tx = disparity_array[i].length;
			break;
		}
		if (tx < 0) {
			return new TpTask[0]; 
		}
		final int tilesX = tx;
		final Matrix [] corr_rots = (geometryCorrection == null) ? null: geometryCorrection.getCorrVector().getRotMatrices(); // get array of per-sensor rotation matrices
		final TpTask[]  tp_tasks_xy = new TpTask[tilesY * tilesX];
		final AtomicInteger ai =            new AtomicInteger(0);
		final AtomicInteger anum_tiles =    new AtomicInteger(0);
		final Thread[] threads = ImageDtt.newThreadArray(threadsMax);
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					for (int iTile = ai.getAndIncrement(); iTile < tp_tasks_xy.length; iTile = ai.getAndIncrement()) {
						int tileY = iTile /tilesX;
						int tileX = iTile % tilesX;
						int op = fop;
						if (tile_op != null) {
							op = tile_op[tileY][tileX];
						}
						if ((op != 0) && !Double.isNaN(disparity_array[tileY][tileX])) {
							tp_tasks_xy[iTile] = setTask(
									num_cams,                                       // int                 num_cams,
//									transform_size,                                 // int                 transform_size,
									op,                                             // int                 task_code,
									tileX,                                          // int                 tileX,
									tileY,                                          // int                 tileY,
									disparity_array[tileY][tileX] + disparity_corr, // double              target_disparity, // include disparity_corr
									corr_rots,                                      // Matrix []           corr_rots,        // if null, will be calculated
									geometryCorrection);                            // GeometryCorrection  geometryCorrection)
							anum_tiles.getAndIncrement();
						}
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
		final TpTask[] tp_tasks = new TpTask[anum_tiles.get()];
		int indx = 0;
		for (int i = 0; i < tp_tasks_xy.length; i++) if (tp_tasks_xy[i] != null) {
			tp_tasks[indx++] = tp_tasks_xy[i];
		}
		return tp_tasks;		
	}
	

	/**
	 * Calculate rotation matrices and their derivatives
	 * Needed after extrinsics changed
	 * gpu_correction_vector should be set in GPU memory
	 * gpu_rot_deriv will contain results after running
	 */
	public void execRotDerivs() {
		if (geometry_correction_vector_set)	return;
		if (this.gpuTileProcessor.GPU_ROT_DERIV_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_ROT_DERIV_kernel");
			return;
		}
		setGeometryCorrectionVector();
		// kernel parameters: pointer to pointers
		int [] GridFullWarps =    {num_cams, 1, 1}; // round up
		int [] ThreadsFullWarps = {3,        3, 3};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}), // int                  num_cams,
				Pointer.to(gpu_correction_vector), // struct corr_vector * gpu_correction_vector,
				Pointer.to(gpu_rot_deriv)          // union trot_deriv   * gpu_rot_deriv);
				);

		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_ROT_DERIV_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize(); // remove later
		geometry_correction_vector_set = true;
		if (getGpu_debug_level() > -1) {
			System.out.println("======execRotDerivs()");
		}
	}

	/**
	 *  Calculate reverse radial distortion table from gpu_geometry_correction
	 *  Needed once during initialization of GeometryCorrection
	 *  gpu_geometry_correction should be set in GPU memory
	 *  gpu_rByRDist will contain results after running
	 */
	public void execCalcReverseDistortions() {
		if (geometry_correction_set) return;
		if (this.gpuTileProcessor.GPU_CALC_REVERSE_DISTORTION_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CALC_REVERSE_DISTORTION_kernel");
			return;
		}
		setGeometryCorrection();
		// kernel parameters: pointer to pointers
//		int [] GridFullWarps =    {num_cams, 1, 1}; // round up
		int [] GridFullWarps =    {GPUTileProcessor.MAX_NUM_CAMS, 1, 1};
		int [] ThreadsFullWarps = {3,        3, 3};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(gpu_geometry_correction),     //	struct gc          * gpu_geometry_correction,
				Pointer.to(gpu_rByRDist));                //	float *              gpu_rByRDist)      // length should match RBYRDIST_LEN
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CALC_REVERSE_DISTORTION_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize(); // remove later
		geometry_correction_set = true;
		if (getGpu_debug_level() > -1) {
			System.out.println("======execCalcReverseDistortions()");
		}
	}

/**
 * Calculate tiles offsets (before each direct conversion run)
 * 
 * @param uniform_grid - true to calculate uniform tile grid, false - use pre-calculated tile centers 
 */
	public void execSetTilesOffsets(boolean uniform_grid) {
		execCalcReverseDistortions(); // will check if it is needed first
		execRotDerivs();              // will check if it is needed first
		if (this.gpuTileProcessor.GPU_CALCULATE_TILES_OFFSETS_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CALCULATE_TILES_OFFSETS_kernel");
			return;
		}
		int [] GridFullWarps =    {1, 1, 1}; 
		int [] ThreadsFullWarps = {1, 1, 1}; // 4,8,1
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { uniform_grid ? 1 : 0 }), // int           uniform_grid, //==0: use provided centers (as for interscene) , !=0 calculate uniform grid
				Pointer.to(new int[] { num_cams}),              // int                  num_cams,
				Pointer.to(gpu_ftasks),                         // float              * gpu_ftasks,
				Pointer.to(new int[] { num_task_tiles }),       // int                  num_tiles,          // number of tiles in task list
				Pointer.to(gpu_geometry_correction),            // struct gc          * gpu_geometry_correction,
				Pointer.to(gpu_correction_vector),              // struct corr_vector * gpu_correction_vector,
				Pointer.to(gpu_rByRDist),                       // float *              gpu_rByRDist)      // length should match RBYRDIST_LEN
				Pointer.to(gpu_rot_deriv));                     // trot_deriv         * gpu_rot_deriv);
		cuCtxSynchronize();
		cuLaunchKernel(this.gpuTileProcessor.GPU_CALCULATE_TILES_OFFSETS_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize(); // remove later
		if (getGpu_debug_level() > -1) {
			System.out.println("======execSetTilesOffsets(), num_task_tiles="+num_task_tiles+", uniform_grid="+uniform_grid);
		}
	}


	/**
	 * Direct CLT conversion and aberration correction 
	 * Convert and save TD representation in either normal or reference scene. Reference scene TD representation
	 * is used for interscene correlation (for "IMU")
	 * @param ref_scene save result into a separate buffer for interscene correlation when true.
	 * @param wh window width, height (or null)
	 * @param erase_clt erase CLT data. Only needed before execImcltRbgAll() if not all the
	 *                  tiles are converted. <0 - do not erase, 0 - erase to 0, 1 - erase to NaN 
	 */
	public void execConvertDirect(
			boolean  ref_scene,
			int []   wh,
			int      erase_clt) {
		execConvertDirect(
				ref_scene, // boolean  ref_scene,
				wh,        // int []   wh,
				erase_clt, // int      erase_clt,
				false,     // boolean no_kernels)
				false);    // boolean  use_center_image)
	}

	/**
	 * Direct CLT conversion and aberration correction 
	 * Convert and save TD representation in either normal or reference scene. Reference scene TD representation
	 * is used for interscene correlation (for "IMU")
	 * @param ref_scene save result into a separate buffer for interscene correlation when true.
	 * @param wh window width, height (or null)
	 * @param erase_clt erase CLT data. Only needed before execImcltRbgAll() if not all the
	 *                  tiles are converted. <0 - do not erase, 0 - erase to 0, 1 - erase to NaN
	 * @param no_kernels skip deconvolution 
	 * @param use_center_image use a single center image for all sensor (back propagation mode)                  
	 */
	public void execConvertDirect(
			boolean  ref_scene,
			int []   wh,
			int      erase_clt,
			boolean  no_kernels,
			boolean  use_center_image) {
		if (this.gpuTileProcessor.GPU_CONVERT_DIRECT_kernel == null) 
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CONVERT_DIRECT_kernel");
			return;
		}
		if (this.gpuTileProcessor.GPU_ERASE_CLT_TILES_kernel == null) 
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_ERASE_CLT_TILES_kernel");
			return;
		}
		boolean skip_kernels = rectilinear || (quadCLT == null) || no_kernels;
		if (!rectilinear) {
			if (!skip_kernels) {
				setConvolutionKernels(false); // set kernels if they are not set already
			}
			setBayerImages(            // set Bayer images if this.quadCLT instance has new ones
					false,             // boolean  force,
					use_center_image); // boolean center)
		}

		int [] wh1 = handleWH(
				wh,         // int []  wh_in,
				ref_scene); // boolean ref_scene);
		if (wh == null) { // so wh will not change if was not null
			wh = wh1;
		}
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;

		CUdeviceptr gpu_clt_selected = ref_scene ? gpu_clt_ref : gpu_clt;
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		
		if (erase_clt >= 0) {
			float fill_data = (erase_clt > 0) ? Float.NaN : 0.0f;
			Pointer kernelParametersEraseClt = Pointer.to(
					Pointer.to(new int[] { num_cams}),   // int      num_cams,
					Pointer.to(new int[] { num_colors}), // int      num_colors,
					Pointer.to(new int[] { tilesX}),     // int      tiles_x,
					Pointer.to(new int[] { tilesY}),     // int      tiles_y,
					Pointer.to(gpu_clt_selected),        // float ** gpu_clt,// [num_cams][tiles_y][tiles_x][num_colors][4*DTT_SIZE*DTT_SIZE]
					Pointer.to(new float[] { fill_data})); // float    fill_data
			cuCtxSynchronize();
			// Call the kernel function
			cuLaunchKernel(this.gpuTileProcessor.GPU_ERASE_CLT_TILES_kernel,
					GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
					ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
					0, null,                           // Shared memory size and stream (shared - only dynamic, static is in code)
					kernelParametersEraseClt, null);   // Kernel- and extra parameters
			cuCtxSynchronize(); // remove later CUDA_ERROR_ILLEGAL_ADDRESS
			if (getGpu_debug_level() > -1) {
				System.out.println("======execConvertDirect(): erased CLT");
			}
		}
		Pointer kernelParameters;
			kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),              // int                  num_cams,
				Pointer.to(new int[] { num_colors}),            // int                  num_colors,
				Pointer.to(gpu_kernel_offsets),
				Pointer.to(gpu_kernels),
				Pointer.to(gpu_bayer),
				Pointer.to(gpu_ftasks),
				Pointer.to(gpu_clt_selected), // gpu_clt), // select which one
				Pointer.to(new int[] { mclt_stride }), // should be input image stride (in floats), not mclt!
				Pointer.to(new int[] { num_task_tiles }),
				// move lpf to 4-image generator kernel - DONE
				Pointer.to(new int[] { 0 }), // lpf_mask
				Pointer.to(new int[] { wh[0]}), // img_width}),          // int                woi_width,
				Pointer.to(new int[] { wh[1]}), // img_height}),         // int                woi_height,
				Pointer.to(new int[] { skip_kernels? 0 : kernels_hor}),        // int                kernels_hor,
				Pointer.to(new int[] { skip_kernels? 0 : kernels_vert}),       // int                kernels_vert);
				Pointer.to(gpu_active_tiles),
				Pointer.to(gpu_num_active_tiles),
				Pointer.to(new int[] { tilesX })
				);
/*			
		}
*/		
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CONVERT_DIRECT_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize(); // remove later CUDA_ERROR_ILLEGAL_ADDRESS
		if (getGpu_debug_level() > -1) {
			System.out.println("======execConvertDirect("+ref_scene+", "+erase_clt+")");
		}
	}
	
	/**
	 * Handle image width and height, allocate/re-allocate GPU memory if size changed
	 * @param wh     input {width, height}, use sensor dimensions if null. 
	 * @param ref_scene use reference buffer, if false - main scene buffer
	 * @return updated width, height pair, non-null
	 */
	public int [] handleWH(
			int []  wh,
			boolean ref_scene) {
		
//		int [] wh = (wh_in != null) ? wh_in :  (new int[] {img_width, img_height});
		if (wh == null) {
			wh = new int[] {img_width, img_height};
		}
		// kernel parameters: pointer to pointers
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		// De-allocate if size mismatch, allocate if needed. Now it is the only place where clt is allocated
		if (ref_scene) {
			if ((gpu_clt_ref_wh != null) && ((gpu_clt_ref_wh[0] != wh[0]) || (gpu_clt_ref_wh[1] != wh[1]))) {
				for (int ncam = 0; ncam < num_cams; ncam++) {
					cuMemFree (gpu_clt_ref_h[ncam]);
				}
				cuMemFree (gpu_clt_ref);
				gpu_clt_ref = null;
				gpu_clt_ref_wh = null;
			}
			if (gpu_clt_ref == null) { // Allocate memory, create pointers for reference scene TD representation
				long [] gpu_clt_ref_l = new long [num_cams];
				gpu_clt_ref_h =    new CUdeviceptr[num_cams];
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_ref_h[ncam] = new CUdeviceptr();
					cuMemAlloc(gpu_clt_ref_h[ncam],
							   tilesY * tilesX * num_colors * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT );
				}
				gpu_clt_ref =  new CUdeviceptr();
				cuMemAlloc(gpu_clt_ref, num_cams * Sizeof.POINTER);
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_ref_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_clt_ref_h[ncam]);
				}
				cuMemcpyHtoD(gpu_clt_ref, Pointer.to(gpu_clt_ref_l), num_cams * Sizeof.POINTER);
				gpu_clt_ref_wh = wh.clone();
			}
		} else { // same for main (not ref) memory
			if ((gpu_clt_wh != null) && ((gpu_clt_wh[0] != wh[0]) || (gpu_clt_wh[1] != wh[1]))) {
				for (int ncam = 0; ncam < num_cams; ncam++) {
					cuMemFree (gpu_clt_h[ncam]);
				}
				cuMemFree (gpu_clt);
				gpu_clt = null;
				gpu_clt_wh = null;
			}
			if (gpu_clt == null) { // Allocate memory, create pointers for reference scene TD representation
				long [] gpu_clt_l = new long [num_cams];
				gpu_clt_h =    new CUdeviceptr[num_cams];
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_h[ncam] = new CUdeviceptr();
					cuMemAlloc(gpu_clt_h[ncam],
							   tilesY * tilesX * num_colors * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT );
				}
				gpu_clt =  new CUdeviceptr();
				cuMemAlloc(gpu_clt, num_cams * Sizeof.POINTER);
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_clt_h[ncam]);
				}
				cuMemcpyHtoD(gpu_clt, Pointer.to(gpu_clt_l), num_cams * Sizeof.POINTER);
				gpu_clt_wh = wh.clone();
			}
		}
		return wh;
	}
	
	
	public boolean reAllocateClt(
			int []  wh,
			boolean ref_scene) {
		boolean allocated = false;
		if (wh == null) {
			wh = new int[] {img_width, img_height};
		}
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		// De-allocate if size mismatch, allocate if needed. Now it is the only place where clt is allocated
		if (ref_scene) {
			if ((gpu_clt_ref_wh != null) && ((gpu_clt_ref_wh[0] != wh[0]) || (gpu_clt_ref_wh[1] != wh[1]))) {
				for (int ncam = 0; ncam < num_cams; ncam++) {
					cuMemFree (gpu_clt_ref_h[ncam]);
				}
				cuMemFree (gpu_clt_ref);
				gpu_clt_ref = null;
				gpu_clt_ref_wh = null;
			}
			if (gpu_clt_ref == null) { // Allocate memory, create pointers for reference scene TD representation
				long [] gpu_clt_ref_l = new long [num_cams];
				gpu_clt_ref_h =    new CUdeviceptr[num_cams];
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_ref_h[ncam] = new CUdeviceptr();
					cuMemAlloc(gpu_clt_ref_h[ncam],
							   tilesY * tilesX * num_colors * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT );
				}
				gpu_clt_ref =  new CUdeviceptr();
				cuMemAlloc(gpu_clt_ref, num_cams * Sizeof.POINTER);
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_ref_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_clt_ref_h[ncam]);
				}
				cuMemcpyHtoD(gpu_clt_ref, Pointer.to(gpu_clt_ref_l), num_cams * Sizeof.POINTER);
				gpu_clt_ref_wh = wh.clone();
				allocated = true;
			}
		} else { // same for main (not ref) memory
			if ((gpu_clt_wh != null) && ((gpu_clt_wh[0] != wh[0]) || (gpu_clt_wh[1] != wh[1]))) {
				for (int ncam = 0; ncam < num_cams; ncam++) {
					cuMemFree (gpu_clt_h[ncam]);
				}
				cuMemFree (gpu_clt);
				gpu_clt = null;
				gpu_clt_wh = null;
			}
			if (gpu_clt == null) { // Allocate memory, create pointers for reference scene TD representation
				long [] gpu_clt_l = new long [num_cams];
				gpu_clt_h =    new CUdeviceptr[num_cams];
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_h[ncam] = new CUdeviceptr();
					cuMemAlloc(gpu_clt_h[ncam],
							   tilesY * tilesX * num_colors * 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE * Sizeof.FLOAT );
				}
				gpu_clt =  new CUdeviceptr();
				cuMemAlloc(gpu_clt, num_cams * Sizeof.POINTER);
				for (int ncam = 0; ncam < num_cams; ncam++) {
					gpu_clt_l[ncam] = GPUTileProcessor.getPointerAddress(gpu_clt_h[ncam]);
				}
				cuMemcpyHtoD(gpu_clt, Pointer.to(gpu_clt_l), num_cams * Sizeof.POINTER);
				gpu_clt_wh = wh.clone();
				allocated = true;
			}
		}
		return allocated;
	}
	

	/**
	 * Generate corrected image(s) from the CLT representation created by execConvertDirect()
	 * @param is_mono monochrome image (false - RGB)
	 */
	public void execImcltRbgAll(
			boolean is_mono) {
		execImcltRbgAll(
				is_mono,
				false,
				null   ); //int [] wh
	}
	
	public void deallocate4Images(int []  wh) {
		if (gpu_4_images_wh != null) {
			if (wh == null) {
				wh = new int[] {img_width, img_height};
			}
			if ((gpu_4_images_wh[0] != wh[0]) || (gpu_4_images_wh[1] != wh[1])) {
				deallocate4Images();
			}
		}
	}
	public void deallocate4Images() {
		if (gpu_4_images_wh != null) {
		    for (int ncam = 0; ncam < num_cams; ncam++) {
		        cuMemFree (gpu_corr_images_h[ncam]);
		    }
		    cuMemFree (gpu_4_images);
		    gpu_4_images = null;
		    gpu_4_images_wh = null;
		}
	}
	
	public void execImcltRbgAll( // Now allocates/re-allocates GPU memory
			boolean is_mono,
			boolean ref_scene,
			int []  wh) {
		if (this.gpuTileProcessor.GPU_IMCLT_ALL_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_IMCLT_ALL_kernel");
			return;
		}
		if (wh == null) {
			wh = new int[] {img_width, img_height};
		}
		int apply_lpf =  1;
///		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
///		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		int tilesX =  wh[0] / GPUTileProcessor.DTT_SIZE;
		int tilesY =  wh[1] / GPUTileProcessor.DTT_SIZE;
		// Free if allocated but size mismatch
		deallocate4Images(wh);
		/*
		if ((gpu_4_images_wh != null) && ((gpu_4_images_wh[0] != wh[0]) || (gpu_4_images_wh[1] != wh[1]))) {
		    for (int ncam = 0; ncam < num_cams; ncam++) {
		        cuMemFree (gpu_corr_images_h[ncam]);
		    }
		    cuMemFree (gpu_4_images);
		    gpu_4_images = null;
		    gpu_4_images_wh = null;
		}*/
		// Allocate if was not allocated or was freed
		if (gpu_4_images == null) { // Allocate memory, create pointers.first time was null
			long [] device_stride = new long [1];
			for (int ncam = 0; ncam < num_cams; ncam++) {
				gpu_corr_images_h[ncam] =  new CUdeviceptr();
				cuMemAllocPitch (
						gpu_corr_images_h[ncam],               // CUdeviceptr dptr,
						device_stride,                         // long[] pPitch,
//						(img_width + GPUTileProcessor.DTT_SIZE) * Sizeof.FLOAT, // long WidthInBytes,
						(wh[0] + GPUTileProcessor.DTT_SIZE) * Sizeof.FLOAT, // long WidthInBytes,
//						3*(img_height + GPUTileProcessor.DTT_SIZE),// long Height,
						3*(wh[1] + GPUTileProcessor.DTT_SIZE),// long Height, //FIXME*** No need for 3x? ****
						Sizeof.FLOAT);            // int ElementSizeBytes)
				imclt_stride = (int)(device_stride[0] / Sizeof.FLOAT);
			}
			gpu_4_images =  new CUdeviceptr();
			cuMemAlloc(gpu_4_images,       num_cams * Sizeof.POINTER);
			long [] gpu_4_images_l =       new long [num_cams];
			for (int ncam = 0; ncam < num_cams; ncam++) gpu_4_images_l[ncam] =       GPUTileProcessor.getPointerAddress(gpu_corr_images_h[ncam]);
			cuMemcpyHtoD(gpu_4_images, Pointer.to(gpu_4_images_l),                   num_cams * Sizeof.POINTER);
			gpu_4_images_wh = wh.clone();			
		}

		CUdeviceptr gpu_clt_selected = ref_scene ? gpu_clt_ref : gpu_clt;
		int [] ThreadsFullWarps = {1, 1, 1};
		int [] GridFullWarps =    {1, 1, 1};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),                  // int           num_cams,
				Pointer.to(gpu_clt_selected),                       // float      ** gpu_clt, // [num_cams][TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
				Pointer.to(gpu_4_images),                           // float      ** gpu_corr_images,    // [num_cams][WIDTH, 3 * HEIGHT]
				Pointer.to(new int[] { apply_lpf }),                // int           apply_lpf,
				Pointer.to(new int[] { is_mono ? 1 : num_colors }), // int           colors,
				Pointer.to(new int[] { tilesX }),                   // int           woi_twidth,
				Pointer.to(new int[] { tilesY }),                   // int           woi_theight,
				Pointer.to(new int[] { imclt_stride })              // const size_t  dstride);            // in floats (pixels)
				);
		cuCtxSynchronize();
		
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_IMCLT_ALL_kernel, // failed with LWIR
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                   // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
		if (getGpu_debug_level() > -1) {
			System.out.println("======execImcltRbgAll("+is_mono+","+ref_scene+")");
		}
	}

	/**
	 * Generate 2D phase correlations from the CLT representation         
	 * @param scales R,G,B weights
	 * @param fat_zero - absolute fat zero - add to correlations before normalization
	 * @param corr_radius - correlation result size (maximal 7 for 15x15)
	 */
	public void execCorr2D(
			boolean [] pair_select,
			double [] scales,
			double fat_zero,
			int corr_radius) {
		gpuTrace("execCorr2D");
		if (this.gpuTileProcessor.GPU_CORRELATE2D_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CORRELATE2D_kernel");
			return;
		}
		double fat_zero2 = fat_zero * fat_zero;
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
//		int num_colors = scales.length;
		int num_colors = this.num_colors;
		if (num_colors > 3) num_colors = 3;
		float fscale0 = (num_colors >1)?((float) scales[0]):1.0f;
		float fscale1 = (num_colors >1)?((float) scales[1]):0.0f;
		float fscale2 = (num_colors >2)?((float) scales[2]):0.0f;
//		int num_pairs = getNumPairs();
		setCorrMask (pair_select);
		
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),          // int           num_cams,
				Pointer.to(new int[] { corr_mask_int[0]}),  // int           sel_pairs0, // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[1]}),  // int           sel_pairs1 // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[2]}),  // int           sel_pairs2, // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[3]}),  // int           sel_pairs3, // unused bits should be 0
				Pointer.to(gpu_clt),                        // float          ** gpu_clt,
				Pointer.to(new int[] { num_colors }),       // int               colors,             // number of colors (3/1)
				Pointer.to(new float[] {fscale0  }),        // float             scale0,             // scale for R
				Pointer.to(new float[] {fscale1  }),        // float             scale1,             // scale for B
				Pointer.to(new float[] {fscale2  }),        // float             scale2,             // scale for G
				Pointer.to(new float[] {(float) fat_zero2}),// float             fat_zero,           // here - absolute
				Pointer.to(gpu_ftasks),                     // float           * gpu_tasks,
				Pointer.to(new int[] { num_task_tiles }),   // int               num_tiles           // number of tiles in task
				Pointer.to(new int[] { tilesX }),           // int               tilesx,             // number of tile rows
				Pointer.to(gpu_corr_indices),               // int             * gpu_corr_indices,   // packed tile+pair
				Pointer.to(gpu_num_corr_tiles),             // int             * pnum_corr_tiles,    // pointer to a number of tiles to process
				Pointer.to(new long[] { corr_stride }),     // const size_t      corr_stride,        // in floats
				Pointer.to(new int[] { corr_radius }),      // int               corr_radius,        // radius of the output correlation (7 for 15x15)
				Pointer.to(gpu_corrs)                       // float           * gpu_corrs);         // correlation output data
				);
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CORRELATE2D_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
	}

	/**
	 * Generate 2D correlations from the CLT representation in transform domain, no normalization         
	 * @param scales R,G,B weights
	 */
	public void execCorr2D_TD(
			double [] scales,
			int    [] sel_pairs_in // [4]
			) {
		if (this.gpuTileProcessor.GPU_CORRELATE2D_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CORRELATE2D_kernel");
			return;
		}
		setCorrMask(sel_pairs_in); // set corr_mask_int
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int num_colors = this.num_colors;
		if (num_colors > 3) num_colors = 3;
		float fscale0 = (num_colors >1)?((float) scales[0]):1.0f;
		float fscale1 = (num_colors >1)?((float) scales[1]):0.0f;
		float fscale2 = (num_colors >2)?((float) scales[2]):0.0f;
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),           // int               num_cams,           // number of colors (3/1)
				Pointer.to(new int[] { corr_mask_int[0] }),  // int               sel_pairs0          // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[1] }),  // int               sel_pairs1          // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[2] }),  // int               sel_pairs2          // unused bits should be 0
				Pointer.to(new int[] { corr_mask_int[3] }),  // int               sel_pairs3          // unused bits should be 0
				Pointer.to(gpu_clt),                         // float          ** gpu_clt,
				Pointer.to(new int[] { num_colors }),        // int               colors,             // number of colors (3/1)
				Pointer.to(new float[] {fscale0  }),         // float             scale0,             // scale for R
				Pointer.to(new float[] {fscale1  }),         // float             scale1,             // scale for B
				Pointer.to(new float[] {fscale2  }),         // float             scale2,             // scale for G
				Pointer.to(new float[] {(float) 0.0 }),      // float             fat_zero,           // here - absolute does nolt matter if TD (corr_radius==0)
				Pointer.to(gpu_ftasks),                      // float           * gpu_ftasks,
				Pointer.to(new int[] { num_task_tiles }),    // int               num_tiles           // number of tiles in task
				Pointer.to(new int[] { tilesX }),            // int               tilesx,             // number of tile rows
				Pointer.to(gpu_corr_indices),                // int             * gpu_corr_indices,   // packed tile+pair
				Pointer.to(gpu_num_corr_tiles),              // int             * pnum_corr_tiles,    // pointer to a number of tiles to process
				Pointer.to(new long[] { corr_stride_td }),   // const size_t      corr_stride,        // in floats
				Pointer.to(new int[] { 0 }),  // generate TD // int               corr_radius,        // radius of the output correlation (7 for 15x15)
				Pointer.to(gpu_corrs_td)                     // float           * gpu_corrs);         // correlation output data
				);
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CORRELATE2D_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
		if (getGpu_debug_level() > -1) {
			System.out.println("======execCorr2D_TD()");
		}
	}

	/**
	 * Generate interscene 2D correlations from the CLT representations in transform domain,
	 * no normalization. Sensor mask (if not all) should be set prior to calling with
	 * setSensorMaskInter(int mask)         
	 * @param scales R,G,B weights
	 */
	public void execCorr2D_inter_TD(
			double [] scales
			) {
		if (this.gpuTileProcessor.GPU_CORRELATE2D_INTER_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CORRELATE2D_INTER_kernel");
			return;
		}
		if (gpu_clt_ref == null) {
			IJ.showMessage("Error", "Reference scene TD data is not allocated (needs execConvertDirrect(true))");
			return;
		}
//		setSensorMaskInter(sel_sensors);
//		setCorrMask(sel_pairs_in); // set corr_mask_int
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int num_colors = this.num_colors;
		if (num_colors > 3) num_colors = 3;
		float fscale0 = (num_colors >1)?((float) scales[0]):1.0f;
		float fscale1 = (num_colors >1)?((float) scales[1]):0.0f;
		float fscale2 = (num_colors >2)?((float) scales[2]):0.0f;
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),           // int               num_cams,           // number of colors (3/1)
				Pointer.to(new int[] {getSensorMaskInter()}),// int               sel_sensors         // unused bits should be 0
				Pointer.to(gpu_clt),                         // float          ** gpu_clt,
				Pointer.to(gpu_clt_ref),                     // float          ** gpu_clt_ref,
				Pointer.to(new int[] { num_colors }),        // int               colors,             // number of colors (3/1)
				Pointer.to(new float[] {fscale0  }),         // float             scale0,             // scale for R
				Pointer.to(new float[] {fscale1  }),         // float             scale1,             // scale for B
				Pointer.to(new float[] {fscale2  }),         // float             scale2,             // scale for G
				Pointer.to(gpu_ftasks),                      // float           * gpu_ftasks,
				Pointer.to(new int[] { num_task_tiles }),    // int               num_tiles           // number of tiles in task
				Pointer.to(new int[] { tilesX }),            // int               tilesx,             // number of tile rows
				Pointer.to(gpu_corr_indices),                // int             * gpu_corr_indices,   // packed tile+pair
				Pointer.to(gpu_num_corr_tiles),              // int             * pnum_corr_tiles,    // pointer to a number of tiles to process
				Pointer.to(new long[] { corr_stride_td }),   // const size_t      corr_stride,        // in floats
				Pointer.to(gpu_corrs_td)                     // float           * gpu_corrs);         // correlation output data
				);
		cuCtxSynchronize();
		// Call the kernel function error here with rectilinear
		cuLaunchKernel(this.gpuTileProcessor.GPU_CORRELATE2D_INTER_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
		if (getGpu_debug_level() > -1) {
			System.out.println("======execCorr2D_inter_TD()");
		}
	}

	
	/**
	 * Combine intra-scene correlations in transform domain (possible to accumulate more)          
	 * @param init_corr - true: init output to 0 before accumulating, false: add to current value
	 * @param num_pairs_in - typically ==6 - number of pairs per tile (tile task should have same number per each tile).
	 * This number should match correlations in tasks
	 * @param pairs_mask - selected pairs (0x3 - horizontal, 0xc - vertical, 0xf - quad, 0x30 - cross)
	 */
	@Deprecated
	public void execCorr2D_combine(
			boolean init_corr,    // initialize output tiles (false - add to current)
			int     num_pairs_in, // typically 6 - number of pairs per tile (tile task should have same number per each tile
			int     pairs_mask)    // selected pairs (0x3 - horizontal, 0xc - vertical, 0xf - quad, 0x30 - cross)
	{
		execCorr2D_combine (init_corr, num_pairs_in, pairs_mask, false);
	}
	/**
	 * Combine intra-scene correlations in transform domain (possible to accumulate more)          
	 * @param init_corr - true: init output to 0 before accumulating, false: add to current value
	 * @param num_pairs_in - typically ==6 - number of pairs per tile (tile task should have same number per each tile).
	 * This number should match correlations in tasks
	 * @param pairs_mask - selected pairs (0x3 - horizontal, 0xc - vertical, 0xf - quad, 0x30 - cross)
	 * @param no_transpose_vertical - do not transpose vertical/diagonal
	 */
	
	@Deprecated // broken
	public void execCorr2D_combine(
			boolean init_corr,    // initialize output tiles (false - add to current)
			int     num_pairs_in, // typically 6 - number of pairs per tile (tile task should have same number per each tile
			int     pairs_mask,    // selected pairs (0x3 - horizontal, 0xc - vertical, 0xf - quad, 0x30 - cross)
			boolean no_transpose_vertical        		
			) {
		if (this.gpuTileProcessor.GPU_CORR2D_COMBINE_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CORR2D_COMBINE_kernel");
			return;
		}
        int num_pairs = getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		float [] fnum_corrs = new float[1];
		cuMemcpyDtoH(Pointer.to(fnum_corrs), gpu_num_corr_tiles,  1 * Sizeof.FLOAT);
		num_corr_combo_tiles =      Float.floatToIntBits(fnum_corrs[0])/num_pairs; // number of correlation tiles calculated

		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		int init_corr_combo = (init_corr ? 1 : 0) + (no_transpose_vertical ? 2 : 0);
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_corr_combo_tiles }), // num_task_tiles }), // int   num_tiles           // number of tiles in task
				Pointer.to(new int[] { num_pairs }),            // int               num_pairs,          // num pairs per tile (should be the same)
				Pointer.to(new int[] { init_corr_combo }),      // int               init_output,        // 1- reset output tiles to zero before accumulating
				Pointer.to(new int[] { pairs_mask }),           // int               pairs_mask,         // selected pairs
				Pointer.to(gpu_corr_indices),                   // int             * gpu_corr_indices,   // packed tile+pair
				Pointer.to(gpu_corr_combo_indices),             // int             * gpu_combo_indices,  // output if not null: packed tile+pairs_mask
				Pointer.to(new long[] { corr_stride_td }),       // const size_t      corr_stride,        // (in floats) stride for the input TD correlations
				Pointer.to(gpu_corrs_td),                       // float           * gpu_corrs,          // input correlation tiles
				Pointer.to(new long[] { corr_stride_combo_td }), // const size_t      corr_stride_combo,  // (in floats) stride for the output TD
				Pointer.to(gpu_corrs_combo_td));                // float           * gpu_corrs_combo);   // combined correlation output (one per tile)
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CORR2D_COMBINE_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
	}
	/**
	 * Normalize 2D correlations, transform and unfold
	 * For interscene - use same with combo = false; 
	 * @param combo - normalize combo correlations (false - per-pair ones). Typically - false
	 * @param fcorr_weights - null or one per correlation tile (num_corr_tiles) to divide fat_zero2
	 * @param fat_zero - absolute fat zero - add to correlations before normalization
	 * @param corr_radius - correlation result size (maximal 7 for 15x15)
	 */

	public void execCorr2D_normalize(
			boolean combo, // normalize combo correlations (false - per-pair ones)
			double fat_zero,
  		    float [] fcorr_weights, // null or one per correlation tile (num_corr_tiles) to divide fat zero2
			int corr_radius) {
		if (this.gpuTileProcessor.GPU_CORR2D_NORMALIZE_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_CORR2D_NORMALIZE_kernel");
			return;
		}
		float fat_zero2 = (float) (fat_zero * fat_zero);
		Pointer pFcorr_weights =Pointer.to(new int[]   {0});
		int num_ctiles = combo? num_corr_combo_tiles : num_corr_tiles;
		if (fcorr_weights != null) {
			cuMemcpyHtoD(gpu_corr_weights, Pointer.to(fcorr_weights),  num_ctiles * Sizeof.FLOAT);
			pFcorr_weights = Pointer.to(gpu_corr_weights);
		}
		
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		Pointer kernelParameters;
		if (combo) {
			kernelParameters = Pointer.to(
					Pointer.to(new int[] { num_corr_combo_tiles }), // num_task_tiles }), // int   num_corr_tiles,     // number of correlation tiles to process
					Pointer.to(new long[] { corr_stride_combo_td }),// const size_t      corr_stride_td,     // in floats
					Pointer.to(gpu_corrs_combo_td),                 // float           * gpu_corrs_combo);   // combined correlation output (one per tile)
					pFcorr_weights, 
					Pointer.to(new long[] { corr_stride_combo }),   // const size_t      corr_stride,        // in floats
					Pointer.to(gpu_corrs_combo),                    // float           * gpu_corrs,          // correlation output data (pixel domain)
					Pointer.to(new float[] {fat_zero2}),            // float             fat_zero2,           // here - absolute
					Pointer.to(new int[] { corr_radius }));         // int               corr_radius,        // radius of the output correlation (7 for 15x15)
		} else {
			kernelParameters = Pointer.to(
					Pointer.to(new int[] { num_corr_tiles }),  // num_task_tiles }), // int   num_corr_tiles,     // number of correlation tiles to process
					Pointer.to(new long[] { corr_stride_td }), // const size_t      corr_stride_td,     // in floats
					Pointer.to(gpu_corrs_td), 				   // float           * gpu_corrs_combo);   // combined correlation output (one per tile)
					pFcorr_weights, 
					Pointer.to(new long[] { corr_stride }),    // const size_t      corr_stride,        // in floats
					Pointer.to(gpu_corrs),                     // float           * gpu_corrs,          // correlation output data (pixel domain)
					Pointer.to(new float[] {fat_zero2}),       // float             fat_zero2,           // here - absolute
					Pointer.to(new int[] { corr_radius }));    // int               corr_radius,        // radius of the output correlation (7 for 15x15)
		}

		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CORR2D_NORMALIZE_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
	}

	public void execRBGA(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,
			int       keep_weights) {
		gpuTrace("execRBGA");
		if (GPUTileProcessor.USE_DS_DP) {
			execRBGA_DP(
					color_weights,  // double [] color_weights,
					is_lwir,        // boolean   is_lwir,
					min_shot,       // double    min_shot,           // 10.0
					scale_shot,     // double    scale_shot,         // 3.0
					diff_sigma,     // double    diff_sigma,         // pixel value/pixel change
					diff_threshold, // double    diff_threshold,     // pixel value/pixel change
					min_agree,      // double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
					dust_remove,    // boolean   dust_remove);
					keep_weights);  // int       keep_weights)					
		} else {
			execRBGA_noDP(
					color_weights,  // double [] color_weights,
					is_lwir,        // boolean   is_lwir,
					min_shot,       // double    min_shot,           // 10.0
					scale_shot,     // double    scale_shot,         // 3.0
					diff_sigma,     // double    diff_sigma,         // pixel value/pixel change
					diff_threshold, // double    diff_threshold,     // pixel value/pixel change
					min_agree,      // double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
					dust_remove,    // boolean   dust_remove);
					keep_weights);  // int       keep_weights)					
		}
	}
	/**
	 * Generate combined (overlapping) texture
	 * @param color_weights - [3] (RGB) or [1] (mono) color weights for matching
	 * @param is_lwir ignore shot noise normalization
	 * @param min_shot - minimal value to use sqrt() for shot noise normalization (default - 10)
	 * @param scale_shot scale shot noise (3.0)
	 * @param diff_sigma pixel value/pixel change (1.5) - normalize channel values difference by the offsets of the cameras 
	 * @param diff_threshold - never used?
	 * @param min_agree  minimal number of channels to agree on a point (real number to work with fuzzy averages
	 * @param dust_remove do not reduce average weight when only one image differs much from the average
	 * @param keep_weights (since 11/13/2022). Now 2 separate bits: +1 - generate channel weights and combo metrics.
	 *                     +2 replace port_weights with raw per-channel  (+1 is not used here for overlapping) 
	 */
	public void execRBGA_DP(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,
			int       keep_weights) {
		execCalcReverseDistortions(); // will check if it is needed first
		if (this.gpuTileProcessor.GPU_RBGA_kernel == null) {
			IJ.showMessage("Error", "No GPU kernel: GPU_TEXTURES_kernel");
			return;
		}
//		int num_colors = color_weights.length;
		int num_colors = is_lwir? 1 : color_weights.length;
		if (num_colors > 3) num_colors = 3;
		float [] fcolor_weights = new float[3];
		fcolor_weights[0] = (float) color_weights[0];
		fcolor_weights[1] = (num_colors >1)?((float) color_weights[1]):0.0f;
		fcolor_weights[2] = (num_colors >2)?((float) color_weights[2]):0.0f;
		cuMemcpyHtoD(gpu_color_weights, Pointer.to(fcolor_weights),  fcolor_weights.length * Sizeof.FLOAT);

		float [] generate_RBGA_params = {
				(float)             min_shot,           // 10.0
				(float)             scale_shot,         // 3.0
				(float)             diff_sigma,         // pixel value/pixel change
				(float)             diff_threshold,     // pixel value/pixel change
				(float)             min_agree          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
		};
		cuMemcpyHtoD(gpu_generate_RBGA_params, Pointer.to(generate_RBGA_params),  generate_RBGA_params.length * Sizeof.FLOAT);

		int iis_lwir =      (is_lwir)? 1:0;
		int idust_remove =  (dust_remove)? 1 : 0;

		// uses dynamic parallelization, top kernel is a single-thread one
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		Pointer kernelParameters;
		if (GPUTileProcessor.USE_CUDA12) {
			ThreadsFullWarps = new int[] {1, 1, 1};
			int shared_size = host_get_textures_shared_size( // in bytes
					num_cams,     // int                num_cams,     // actual number of cameras
					num_colors,   // int                num_colors,   // actual number of colors: 3 for RGB, 1 for LWIR/mono
					null);           // int *              offsets);     // in floats
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared_size);
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT, CU_SHAREDMEM_CARVEOUT_MAX_SHARED);
			kernelParameters = Pointer.to(
					Pointer.to(new int[] { num_cams}),               // int               num_cams,
					Pointer.to(gpu_ftasks),                          // float            * gpu_ftasks,         // flattened tasks, 27 floats for quad EO, 99 floats for LWIR16
					Pointer.to(new int[] { num_task_tiles }),        // int               num_tiles,          // number of tiles in task list
					// declare arrays in device code?
					Pointer.to(gpu_texture_indices_ovlp),            // int             * gpu_texture_indices_ovlp,// packed tile + bits (now only (1 << 7)
					Pointer.to(gpu_num_texture_ovlp),                // int             * num_texture_tiles,  // number of texture tiles to process (8 elements)
					Pointer.to(gpu_woi),                             // int             * woi,                // x,y,width,height of the woi
					// set smaller for LWIR - it is used to reduce work aread
					Pointer.to(new int[] {img_width / GPUTileProcessor.DTT_SIZE}),     // int                width,  // <= TILESX, use for faster processing of LWIR images (should be actual + 1)
					Pointer.to(new int[] {img_height / GPUTileProcessor.DTT_SIZE}),    // int                height); // <= TILESY, use for faster processing of LWIR images
					// Parameters for the texture generation
					Pointer.to(gpu_clt),                             // float          ** gpu_clt,            // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
					Pointer.to(gpu_geometry_correction),             //	struct gc          * gpu_geometry_correction,
					Pointer.to(new int[]   {num_colors}),            // int               colors,             // number of colors (3/1)
					Pointer.to(new int[]   {iis_lwir}),              // int               is_lwir,            // do not perform shot correction
					Pointer.to(gpu_generate_RBGA_params),            // float             generate_RBGA_params[5],
					Pointer.to(gpu_color_weights),                   // float             weights[3],         // scale for R,B,G
					Pointer.to(new int[]   { idust_remove }),        // int               dust_remove,        // Do not reduce average weight when only one image differes much from the average
					Pointer.to(new int[]   {keep_weights}),          // int               keep_weights,       // return channel weights after A in RGBA
					Pointer.to(new int[]   { texture_stride_rgba }), // const size_t      texture_rbga_stride,     // in floats
					Pointer.to(gpu_textures_rgba),                   // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
					Pointer.to(gpu_twh));                              // int             * twh);  allocate int[2]  for width, heightin DP
		} else {
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_RBGA_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, 65536);
		kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),               // int               num_cams,
				Pointer.to(gpu_ftasks),                          // float            * gpu_ftasks,         // flattened tasks, 27 floats for quad EO, 99 floats for LWIR16
				Pointer.to(new int[] { num_task_tiles }),        // int               num_tiles,          // number of tiles in task list
				// declare arrays in device code?
				Pointer.to(gpu_texture_indices_ovlp),            // int             * gpu_texture_indices_ovlp,// packed tile + bits (now only (1 << 7)
				Pointer.to(gpu_num_texture_ovlp),                // int             * num_texture_tiles,  // number of texture tiles to process (8 elements)
				Pointer.to(gpu_woi),                             // int             * woi,                // x,y,width,height of the woi
				// set smaller for LWIR - it is used to reduce work aread
				Pointer.to(new int[] {img_width / GPUTileProcessor.DTT_SIZE}),     // int                width,  // <= TILESX, use for faster processing of LWIR images (should be actual + 1)
				Pointer.to(new int[] {img_height / GPUTileProcessor.DTT_SIZE}),    // int                height); // <= TILESY, use for faster processing of LWIR images
				// Parameters for the texture generation
				Pointer.to(gpu_clt),                             // float          ** gpu_clt,            // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
				Pointer.to(gpu_geometry_correction),             //	struct gc          * gpu_geometry_correction,
				Pointer.to(new int[]   {num_colors}),            // int               colors,             // number of colors (3/1)
				Pointer.to(new int[]   {iis_lwir}),              // int               is_lwir,            // do not perform shot correction
				Pointer.to(gpu_generate_RBGA_params),            // float             generate_RBGA_params[5],
				Pointer.to(gpu_color_weights),                   // float             weights[3],         // scale for R,B,G
				Pointer.to(new int[]   { idust_remove }),        // int               dust_remove,        // Do not reduce average weight when only one image differes much from the average
				Pointer.to(new int[]   {keep_weights}),          // int               keep_weights,       // return channel weights after A in RGBA
				Pointer.to(new int[]   { texture_stride_rgba }), // const size_t      texture_rbga_stride,     // in floats
				Pointer.to(gpu_textures_rgba));                  // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
		}

		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_RBGA_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
	}
	

	/**
	 * Generate combined (overlapping) texture
	 * Version w/o Dynamic Parallelism to pass shared memory size. May be removed if maximal SM issue will be resolved
	 * @param color_weights - [3] (RGB) or [1] (mono) color weights for matching
	 * @param is_lwir ignore shot noise normalization
	 * @param min_shot - minimal value to use sqrt() for shot noise normalization (default - 10)
	 * @param scale_shot scale shot noise (3.0)
	 * @param diff_sigma pixel value/pixel change (1.5) - normalize channel values difference by the offsets of the cameras 
	 * @param diff_threshold - never used?
	 * @param min_agree  minimal number of channels to agree on a point (real number to work with fuzzy averages
	 * @param dust_remove do not reduce average weight when only one image differs much from the average
	 * @param keep_weights (since 11/13/2022). Now 2 separate bits: +1 - generate channel weights and combo metrics.
	 *                     +2 replace port_weights with raw per-channel  (+1 is not used here for overlapping) 
	 */
	
	public void execRBGA_noDP(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,
			int       keep_weights) {
		execCalcReverseDistortions(); // will check if it is needed first
		if (    (this.gpuTileProcessor.GPU_CLEAR_TEXTURE_LIST_kernel == null) &&
				(this.gpuTileProcessor.GPU_MARK_TEXTURE_LIST_kernel == null) &&
				(this.gpuTileProcessor.GPU_MARK_TEXTURE_NEIGHBOR_kernel == null) &&
				(this.gpuTileProcessor.GPU_GEN_TEXTURE_LIST_kernel == null) &&
				(this.gpuTileProcessor.GPU_CLEAR_TEXTURE_RBGA_kernel == null) &&
				(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel == null)) {
			IJ.showMessage("Error", "No GPU kernel(s)");
			return;
		}
		boolean DEBUG8A = false; // true; // false;

		int num_colors = is_lwir? 1 : color_weights.length;
		if (num_colors > 3) num_colors = 3;
		float [] fcolor_weights = new float[3];
		fcolor_weights[0] = (float) color_weights[0];
		fcolor_weights[1] = (num_colors >1)?((float) color_weights[1]):0.0f;
		fcolor_weights[2] = (num_colors >2)?((float) color_weights[2]):0.0f;
		double sw = 0.0; for (int i = 0; i < fcolor_weights.length; i++) sw+= fcolor_weights[i];
		for (int i = 0; i < fcolor_weights.length; i++) fcolor_weights[i]/=sw;
		cuMemcpyHtoD(gpu_color_weights, Pointer.to(fcolor_weights),  fcolor_weights.length * Sizeof.FLOAT);

		float [] generate_RBGA_params = {
				(float)             min_shot,           // 10.0
				(float)             scale_shot,         // 3.0
				(float)             diff_sigma,         // pixel value/pixel change
				(float)             diff_threshold,     // pixel value/pixel change
				(float)             min_agree          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
		};
		cuMemcpyHtoD(gpu_generate_RBGA_params, Pointer.to(generate_RBGA_params),  generate_RBGA_params.length * Sizeof.FLOAT);

		int iis_lwir =      (is_lwir)? 1:0;
		int idust_remove =  (dust_remove)? 1 : 0;
		int width =  img_width / GPUTileProcessor.DTT_SIZE;
		int height = img_height / GPUTileProcessor.DTT_SIZE;

		// Testing parameters
		TpTask [] test_tasks0 = null;
		float [] test_ftasks0 = null;
		int [] cpu_texture_indices_ovlp = null;		
		if (DEBUG8A) {
			test_tasks0 = new TpTask[width * height];
			test_ftasks0 = new float [test_tasks0.length * getTaskSize()];
			cuMemcpyDtoH(Pointer.to(test_ftasks0),            gpu_ftasks,  test_ftasks0.length * Sizeof.FLOAT);
			for (int i = 0; i < test_tasks0.length; i++) {
				test_tasks0[i] = new TpTask(num_cams, test_ftasks0, i, false);
			}
		}
		
		//		woi.x =      Float.floatToIntBits(fwoi[0]) * GPUTileProcessor.DTT_SIZE;
		//		woi.y =      Float.floatToIntBits(fwoi[1]) * GPUTileProcessor.DTT_SIZE;
		//		woi.width =  Float.floatToIntBits(fwoi[2]) * GPUTileProcessor.DTT_SIZE;
		//		woi.height = Float.floatToIntBits(fwoi[3]) * GPUTileProcessor.DTT_SIZE;

		int blocks_x = (width + ((1 << GPUTileProcessor.THREADS_DYNAMIC_BITS) - 1)) >> GPUTileProcessor.THREADS_DYNAMIC_BITS;
		int []  blocks0 =  {blocks_x, height, 1};
		int []  threads0 = {(1 << GPUTileProcessor.THREADS_DYNAMIC_BITS), 1, 1};
		/*
	    clear_texture_list<<<blocks0,threads0>>>(
	    		gpu_texture_indices,
				width,
				height);
		 */
		Pointer kp_clear_texture_list = Pointer.to(
				Pointer.to(gpu_texture_indices_ovlp), // gpu_texture_indices,
				Pointer.to(new int[] {width}),
				Pointer.to(new int[] {height}));
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_CLEAR_TEXTURE_LIST_kernel,
				blocks0[0],  blocks0[1],  blocks0[2], // Grid dimension
				threads0[0], threads0[1], threads0[2],// Block dimension
				0, null,                              // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_clear_texture_list, null);         // Kernel- and extra parameters
		cuCtxSynchronize();

		int blocks_t =    (num_task_tiles + ((1 << GPUTileProcessor.THREADS_DYNAMIC_BITS)) -1) >> GPUTileProcessor.THREADS_DYNAMIC_BITS;//
		int []  blocks =  {blocks_t, 1, 1};
		int []  threads = {(1 << GPUTileProcessor.THREADS_DYNAMIC_BITS), 1, 1};
		// mark used tiles in gpu_texture_indices memory
		/*
	    mark_texture_tiles <<<blocks,threads>>>(
	    		num_cams,           // int                num_cams,
				gpu_ftasks,         // float            * gpu_ftasks,         // flattened tasks, 27 floats for quad EO, 99 floats for LWIR16
				num_tiles,          // number of tiles in task list
				width,              // number of tiles in a row
				gpu_texture_indices); // packed tile + bits (now only (1 << 7)
		 */
		Pointer kp_mark_texture_tiles = Pointer.to(
				Pointer.to(new int[] {num_cams}),         // int                num_cams,
				Pointer.to(gpu_ftasks),                   // float            * gpu_ftasks,         // flattened tasks, 29 floats for quad EO, 101 floats for LWIR16
				Pointer.to(new int[] { num_task_tiles }), // int                num_tiles,          // number of tiles in task list
				Pointer.to(new int[] {width}),
				Pointer.to(gpu_texture_indices_ovlp));    // gpu_texture_indices,
		cuLaunchKernel(this.gpuTileProcessor.GPU_MARK_TEXTURE_LIST_kernel,
				blocks[0],  blocks[1],  blocks[2],        // Grid dimension
				threads[0], threads[1], threads[2],       // Block dimension
				0, null,                                  // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_mark_texture_tiles, null);             // Kernel- and extra parameters
		cuCtxSynchronize();
		int [] cpu_woi =               new int[4];
		int [] cpu_num_texture_tiles = new int[8];

		//		cuMemcpyDtoH(Pointer.to(cpu_woi), gpu_woi,  cpu_woi.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed
		cpu_woi[0] = width;   // larger than any x
		cpu_woi[1] = height;  // larger than any y
		cpu_woi[2] = 0;       // smaller or equal than any x
		cpu_woi[3] = 0;       // smaller or equal than any y
		cuMemcpyHtoD(gpu_woi, Pointer.to(cpu_woi),  cpu_woi.length * Sizeof.INT);
//		cuMemcpyDtoH(Pointer.to(cpu_woi), gpu_woi,  cpu_woi.length * Sizeof.INT); //just for testing		
		
		/*
    // TODO: create gpu_woi to pass	(copy from woi)
    // set lower 4 bits in each gpu_ftasks task
    mark_texture_neighbor_tiles <<<blocks,threads>>>(
    		num_cams,           // int                num_cams,
			gpu_ftasks,         // float            * gpu_ftasks,         // flattened tasks, 27 floats for quad EO, 99 floats for LWIR16
			num_tiles,           // number of tiles in task list
			width,               // number of tiles in a row
			height,              // number of tiles rows
			gpu_texture_indices, // packed tile + bits (now only (1 << 7)
			gpu_woi);                // min_x, min_y, max_x, max_y

	checkCudaErrors(cudaDeviceSynchronize());
		 */
		Pointer kp_mark_texture_neighbor_tiles = Pointer.to(
				Pointer.to(new int[] {num_cams}),         // int      num_cams,
				Pointer.to(gpu_ftasks),                   // float  * gpu_ftasks,          // flattened tasks, 29 floats for quad EO, 101 floats for LWIR16
				Pointer.to(new int[] { num_task_tiles }), // int      num_tiles,           // number of tiles in task list
				Pointer.to(new int[] {width}),            // int      width,               // number of tiles in a row
				Pointer.to(new int[] {height}),           // int      height,              // number of tiles rows
				Pointer.to(gpu_texture_indices_ovlp),     // int    * gpu_texture_indices, // packed tile + bits (now only (1 << 7)
				Pointer.to(gpu_woi));                     // int    * woi,                 // min_x, min_y, max_x, max_y
		cuLaunchKernel(this.gpuTileProcessor.GPU_MARK_TEXTURE_NEIGHBOR_kernel,
				blocks[0],  blocks[1],  blocks[2],        // Grid dimension
				threads[0], threads[1], threads[2],       // Block dimension
				0, null,                                  // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_mark_texture_neighbor_tiles, null);    // Kernel- and extra parameters
		cuCtxSynchronize();
		//		int [] cpu_num_texture_tiles = new int[8];
		Arrays.fill(cpu_num_texture_tiles, 0); 
		cuMemcpyHtoD(gpu_num_texture_tiles, Pointer.to(cpu_num_texture_tiles),  cpu_num_texture_tiles.length * Sizeof.INT);
		/*
    gen_texture_list <<<blocks,threads>>>(
    		num_cams,            // int                num_cams,
			gpu_ftasks,          // float            * gpu_ftasks,         // flattened tasks, 27 floats for quad EO, 99 floats for LWIR16
			num_tiles,           // number of tiles in task list
			width,               // number of tiles in a row
			height,              // int                height,               // number of tiles rows
			gpu_texture_indices, // packed tile + bits (now only (1 << 7)
			gpu_num_texture_tiles,   // number of texture tiles to process
			gpu_woi);                // x,y, here woi[2] = max_X, woi[3] - max-Y
	checkCudaErrors(cudaDeviceSynchronize()); // not needed yet, just for testing
		 */
//		cuMemcpyHtoD(gpu_woi, Pointer.to(cpu_woi),  cpu_woi.length * Sizeof.INT); // just for testing
		
		// Testing parameters
		if (DEBUG8A) {
			TpTask [] test_tasks = new TpTask[width * height];
			float [] test_ftasks = new float [test_tasks.length * getTaskSize()];
			cuMemcpyDtoH(Pointer.to(test_ftasks),            gpu_ftasks,  test_ftasks.length * Sizeof.FLOAT);
			for (int i = 0; i < test_tasks.length; i++) {
				test_tasks[i] = new TpTask(num_cams, test_ftasks, i, false);
			}

			cuMemcpyDtoH(Pointer.to(cpu_num_texture_tiles), gpu_num_texture_tiles,  cpu_num_texture_tiles.length * Sizeof.INT);		
			cuMemcpyDtoH(Pointer.to(cpu_woi),               gpu_woi,  cpu_woi.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed		
			int tilesX = width;
			int tilesY = height;
			int tilesYa = (tilesY + 3) & ~3;
			cpu_texture_indices_ovlp = new int [tilesX * tilesYa];
			cuMemcpyDtoH(Pointer.to(cpu_texture_indices_ovlp),               gpu_texture_indices_ovlp,  cpu_texture_indices_ovlp.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed		
			cuMemcpyHtoD(gpu_texture_indices_ovlp,                           Pointer.to(cpu_texture_indices_ovlp),  cpu_texture_indices_ovlp.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed		
		}		
		Pointer kp_gen_texture_list = Pointer.to(
				Pointer.to(new int[] {num_cams}),         // int      num_cams,
				Pointer.to(gpu_ftasks),                   // float  * gpu_ftasks,          // flattened tasks, 29 floats for quad EO, 101 floats for LWIR16
				Pointer.to(new int[] { num_task_tiles }), // int      num_tiles,           // number of tiles in task list
				Pointer.to(new int[] {width}),            // int      width,               // number of tiles in a row
				Pointer.to(new int[] {height}),           // int      height,              // number of tiles rows
				Pointer.to(gpu_texture_indices_ovlp),     // int    * gpu_texture_indices, // packed tile + bits (now only (1 << 7)
				Pointer.to(gpu_num_texture_tiles),        // int    * gpu_num_texture_tiles,   // number of texture tiles to process
				Pointer.to(gpu_woi));                     // int    * woi,                 // min_x, min_y, max_x, max_y
		cuLaunchKernel(this.gpuTileProcessor.GPU_GEN_TEXTURE_LIST_kernel,
				blocks[0],  blocks[1],  blocks[2],        // Grid dimension
				threads[0], threads[1], threads[2],       // Block dimension
				0, null,                                  // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_gen_texture_list, null);    // Kernel- and extra parameters
		cuCtxSynchronize();
		// copy gpu_woi back to host woi
//		float [] fcpu_woi = new float [4];
//		cuMemcpyDtoH(Pointer.to(fcpu_woi), gpu_woi,  4 * Sizeof.FLOAT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed
		
		cuMemcpyDtoH(Pointer.to(cpu_woi), gpu_woi,  cpu_woi.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed		
		cpu_woi[2] += 1 - cpu_woi[0]; // width  (was min and max)
		cpu_woi[3] += 1 - cpu_woi[1]; // height (was min and max)
		// copy host-modified data back to GPU
		cuMemcpyHtoD(gpu_woi, Pointer.to(cpu_woi),  cpu_woi.length * Sizeof.INT);
		// copy gpu_num_texture_tiles back to host cpu_num_texture_tiles for processing by the CPU code
		cuMemcpyDtoH(Pointer.to(cpu_num_texture_tiles), gpu_num_texture_tiles,  cpu_num_texture_tiles.length * Sizeof.INT);		
		// Testing parameters
		if (DEBUG8A) {
			cuMemcpyDtoH(Pointer.to(cpu_texture_indices_ovlp),               gpu_texture_indices_ovlp,  cpu_texture_indices_ovlp.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed
		}
		// Zero output textures. Trim
		// texture_rbga_stride
		int texture_width =        (cpu_woi[2] + 1) * GPUTileProcessor.DTT_SIZE;
		int texture_tiles_height = (cpu_woi[3] + 1) * GPUTileProcessor.DTT_SIZE;
		int texture_slices =       num_colors + 1;
		if ((keep_weights & 2) != 0) {
			 texture_slices += num_colors * num_cams;
		}

		int blocks_x2 = ((texture_width + 
				((1 << (GPUTileProcessor.THREADS_DYNAMIC_BITS + GPUTileProcessor.DTT_SIZE_LOG2 )) - 1)) >>
				(GPUTileProcessor.THREADS_DYNAMIC_BITS + GPUTileProcessor.DTT_SIZE_LOG2));
		int [] blocks2  = {blocks_x2, texture_tiles_height * texture_slices, 1}; // each thread - 8 vertical
		int [] threads2 = {(1 << GPUTileProcessor.THREADS_DYNAMIC_BITS), 1, 1};
		/*
		 clear_texture_rbga<<<blocks2,threads2>>>( // illegal value error
				 texture_width,
				 texture_tiles_height * texture_slices, // int               texture_slice_height,
				 texture_rbga_stride,                   // const size_t      texture_rbga_stride,     // in floats 8*stride
				 gpu_texture_tiles) ;                   // float           * gpu_texture_tiles);
		 */

		Pointer kp_clear_texture_rbga = Pointer.to(
				Pointer.to(new int[] {texture_width}),           // int      texture_width,
				Pointer.to(new int[] {texture_tiles_height * texture_slices}),// int               texture_slice_height,
				Pointer.to(new int[]   { texture_stride_rgba }), // const size_t      texture_rbga_stride,     // in floats 8*stride
				Pointer.to(gpu_textures_rgba));                   // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
		cuLaunchKernel(this.gpuTileProcessor.GPU_CLEAR_TEXTURE_RBGA_kernel,
				blocks2[0],  blocks2[1],  blocks2[2],   // Grid dimension
				threads2[0], threads2[1], threads2[2],  // Block dimension
				0, null,                                // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_clear_texture_rbga, null);           // Kernel- and extra parameters
		cuCtxSynchronize();

		// Testing parameters
		if (DEBUG8A) {
			cuMemcpyDtoH(Pointer.to(cpu_texture_indices_ovlp),               gpu_texture_indices_ovlp,  cpu_texture_indices_ovlp.length * Sizeof.INT); // hope that Float.floatToIntBits(fcorr_indices[i]) is not needed
		}
		int [] cpu_pnum_texture_tiles = {0}; //// debugging CDP2
		// Run 8 times - first 4 1-tile offsets  inner tiles (w/o verifying margins), then - 4 times with verification and ignoring 4-pixel
		// oversize (border 16x 16 tiles overhang by 4 pixels)
		int tilesya =  ((height +3) & (~3)); //#define TILES-YA       ((TILES-Y +3) & (~3))

		for (int pass = 0; pass < 8; pass++){
			int num_cams_per_thread = GPUTileProcessor.NUM_THREADS / GPUTileProcessor.TEXTURE_THREADS_PER_TILE; // 4 cameras parallel, then repeat
			int[] threads_texture = {GPUTileProcessor.TEXTURE_THREADS_PER_TILE, num_cams_per_thread, 1}; // TEXTURE_TILES_PER_BLOCK, 1);

			int border_tile =  (pass >> 2);
			int ntt = cpu_num_texture_tiles[((pass & 3) << 1) + border_tile];
			if (ntt > 0) { 
				int [] grid_texture = {(ntt + GPUTileProcessor.TEXTURE_TILES_PER_BLOCK-1) / GPUTileProcessor.TEXTURE_TILES_PER_BLOCK,1,1}; // TEXTURE_TILES_PER_BLOCK = 1
				int ti_offset; 
				if (GPUTileProcessor.USE_CUDA12) { // for CDP2
					ti_offset = (pass & 3) * (width * (tilesya >> 2)); //  (TILES-X * (TILES-YA >> 2));  // 1/4
					if (border_tile != 0){
						ti_offset += width * (tilesya >> 2); // TILES-X * (TILES-YA >> 2) - ntt;
						ti_offset = - ti_offset; // does not depend on results of the previous kernel, but is negative
					}
				} else { //  Pre CDP2
					ti_offset = (pass & 3) * (width * (tilesya >> 2)); //  (TILES-X * (TILES-YA >> 2));  // 1/4
					if (border_tile != 0){
						ti_offset += width * (tilesya >> 2) - ntt; // TILES-X * (TILES-YA >> 2) - ntt;
					}
				}
				int shared_size = host_get_textures_shared_size( // in bytes
						num_cams,     // int                num_cams,     // actual number of cameras
						num_colors,   // int                num_colors,   // actual number of colors: 3 for RGB, 1 for LWIR/mono
						null);           // int *              offsets);     // in floats

				if (DEBUG8A) {
					System.out.println("shared_size = "+shared_size+" ntt="+ntt);
					System.out.println(String.format("generate_RBGA() pass= %d, border_tile= %d, ti_offset= %d, ntt=%d",
							pass, border_tile,ti_offset, ntt));
					//			System.out.println(String.format("generate_RBGA() gpu_texture_indices= %p, gpu_texture_indices + ti_offset= %p\n",
					//					(void *) gpu_texture_indices, (void *) (gpu_texture_indices + ti_offset));
					System.out.println(String.format("\ngenerate_RBGA() grid_texture={%d, %d, %d)\n",
							grid_texture[0], grid_texture[1], grid_texture[2]));
					System.out.println(String.format("\ngenerate_RBGA() threads_texture={%d, %d, %d)\n",
							threads_texture[0], threads_texture[1], threads_texture[2]));

					System.out.println ("pass="+pass);
					for (int i = 0; i < 256; i++){
						int indx0 = ti_offset + i;
						if (indx0 < cpu_texture_indices_ovlp.length) {
							int indx = cpu_texture_indices_ovlp[indx0];
							System.out.println(String.format("%05x | %02d: %4x %3d %3d %2x",indx0, i,indx>>8, (indx>>8) / 80, (indx >> 8) % 80, indx&0xff));
						}
					}
					System.out.println ("\n\n");
				}
				Pointer kp_textures_accumulate;
				if (GPUTileProcessor.USE_CUDA12) { // for CDP2
					// debugging, copying single int back and forth 
					cpu_pnum_texture_tiles[0] = ntt;
					cuMemcpyHtoD(gpu_texture_indices_len, Pointer.to(cpu_pnum_texture_tiles),  1 * Sizeof.INT);
					cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared_size);
					cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT, CU_SHAREDMEM_CARVEOUT_MAX_SHARED);
					kp_textures_accumulate = Pointer.to( // CUDA_ERROR_ILLEGAL_ADDRESS
							Pointer.to(new int[] {num_cams}),                // int         num_cams,
							Pointer.to(gpu_woi),                             // int       * woi,            // min_x, min_y, max_x, max_y
							Pointer.to(gpu_clt),                             // float    ** gpu_clt,        // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
							Pointer.to(gpu_texture_indices_len),             // int       * num_texture_tiles,// number of texture tiles to process
							Pointer.to(new int[] {ti_offset}),               // int         gpu_texture_indices_offset, // add to gpu_texture_indices (now complicated: if negative - add *(pnum_texture_tiles) and negate
							Pointer.to(gpu_texture_indices_ovlp),            //  gpu_texture_indices_offset,// add to gpu_texture_indices
							Pointer.to(gpu_geometry_correction),             // struct gc * gpu_geometry_correction,
							Pointer.to(new int[]   {num_colors}),            // int         colors,         // number of colors (3/1)
							Pointer.to(new int[]   {iis_lwir}),              // int         is_lwir,        // do not perform shot correction
							Pointer.to(new float[] {(float) min_shot}),      // float       min_shot,       // 10.0
							Pointer.to(new float[] {(float) scale_shot}),    // float       scale_shot,     // 3.0
							Pointer.to(new float[] {(float) diff_sigma}),    // float       diff_sigma,     // pixel value/pixel change
							Pointer.to(new float[] {(float) diff_threshold}),// float       diff_threshold, // pixel value/pixel change
							Pointer.to(new float[] {(float) min_agree}),     // float       min_agree,      // minimal number of channels to agree on a point (real number to work with fuzzy averages)
							Pointer.to(gpu_color_weights),                   // float       weights[3],     // scale for R,B,G (or {1.0,0.0,0.0}
							Pointer.to(new int[]   {idust_remove}),          // int         dust_remove,        // Do not reduce average weight when only one image differes much from the average
							Pointer.to(new int[]   {keep_weights}),           // int         keep_weights,       // return channel weights after A in RGBA
							// combining both non-overlap and overlap (each calculated if pointer is not null )
							Pointer.to(new int[]   { texture_stride_rgba }), // const size_t      texture_rbga_stride,     // in floats
							Pointer.to(gpu_textures_rgba),                   // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
							Pointer.to(new int[]   {0}),                               // size_t      texture_stride,     // in floats (now 256*4 = 1024)
							Pointer.to(new int[]   {0}), // gpu_texture_tiles, // float           * gpu_texture_tiles);  // (number of colors +1 + ?)*16*16 rgba texture tiles
							Pointer.to(new int[]   {0}), // 1, // int               linescan_order,     // if !=0 then output gpu_diff_rgb_combo in linescan order, else  - in gpu_texture_indices order
							Pointer.to(new int[]   {0}), //);//gpu_diff_rgb_combo);             // float           * gpu_diff_rgb_combo) // diff[num_cams], R[num_cams], B[num_cams],G[num_cams]
							Pointer.to(new int[]   {width}));
				} else { // pre  CDP2
					cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, 65536);
					kp_textures_accumulate = Pointer.to(
							Pointer.to(new int[] {num_cams}),                // int         num_cams,
							Pointer.to(gpu_woi),                             // int       * woi,            // min_x, min_y, max_x, max_y
							Pointer.to(gpu_clt),                             // float    ** gpu_clt,        // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
							Pointer.to(new int[] {ntt}),                     // size_t      num_texture_tiles,// number of texture tiles to process
							Pointer.to(new int[] {ti_offset}),                     // size_t      num_texture_tiles,// number of texture tiles to process
							Pointer.to(gpu_texture_indices_ovlp),            //  gpu_texture_indices_offset,// add to gpu_texture_indices
							Pointer.to(gpu_geometry_correction),             // struct gc * gpu_geometry_correction,
							Pointer.to(new int[]   {num_colors}),            // int         colors,         // number of colors (3/1)
							Pointer.to(new int[]   {iis_lwir}),              // int         is_lwir,        // do not perform shot correction
							Pointer.to(new float[] {(float) min_shot}),      // float       min_shot,       // 10.0
							Pointer.to(new float[] {(float) scale_shot}),    // float       scale_shot,     // 3.0
							Pointer.to(new float[] {(float) diff_sigma}),    // float       diff_sigma,     // pixel value/pixel change
							Pointer.to(new float[] {(float) diff_threshold}),// float       diff_threshold, // pixel value/pixel change
							Pointer.to(new float[] {(float) min_agree}),     // float       min_agree,      // minimal number of channels to agree on a point (real number to work with fuzzy averages)
							Pointer.to(gpu_color_weights),                   // float       weights[3],     // scale for R,B,G (or {1.0,0.0,0.0}
							Pointer.to(new int[]   {idust_remove}),          // int         dust_remove,        // Do not reduce average weight when only one image differes much from the average
							Pointer.to(new int[]   {keep_weights}),           // int         keep_weights,       // return channel weights after A in RGBA
							// combining both non-overlap and overlap (each calculated if pointer is not null )
							Pointer.to(new int[]   { texture_stride_rgba }), // const size_t      texture_rbga_stride,     // in floats
							Pointer.to(gpu_textures_rgba),                   // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
							Pointer.to(new int[]   {0}),                               // size_t      texture_stride,     // in floats (now 256*4 = 1024)
							Pointer.to(new int[]   {0}), // gpu_texture_tiles, // float           * gpu_texture_tiles);  // (number of colors +1 + ?)*16*16 rgba texture tiles
							Pointer.to(new int[]   {0}), // 1, // int               linescan_order,     // if !=0 then output gpu_diff_rgb_combo in linescan order, else  - in gpu_texture_indices order
							Pointer.to(new int[]   {0}), //);//gpu_diff_rgb_combo);             // float           * gpu_diff_rgb_combo) // diff[num_cams], R[num_cams], B[num_cams],G[num_cams]
							Pointer.to(new int[]   {width}));
				}
				cuLaunchKernel(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, //  jcuda.CudaException: CUDA_ERROR_INVALID_VALUE
						grid_texture[0],    grid_texture[1],    grid_texture[2],   // Grid dimension
						threads_texture[0], threads_texture[1], threads_texture[2],  // Block dimension
						shared_size, null,                      // Shared memory size and stream (shared - only dynamic, static is in code)
						kp_textures_accumulate, null);          // Kernel- and extra parameters

				cuCtxSynchronize();
			}
		}

	}

	
	
	/**
	 * Generate non-overlapping (16x16) texture tiles
	 * @param color_weights - [3] (RGB) or [1] (mono) color weights for matching
	 * @param is_lwir ignore shot noise normalization
	 * @param min_shot - minimal value to use sqrt() for shot noise normalization (default - 10)
	 * @param scale_shot scale shot noise (3.0)
	 * @param diff_sigma pixel value/pixel change (1.5) - normalize channel values difference by the offsets of the cameras 
	 * @param diff_threshold - never used?
	 * @param min_agree  minimal number of channels to agree on a point (real number to work with fuzzy averages
	 * @param dust_remove do not reduce average weight when only one image differs much from the average
	 * @param keep_weights (since 11/13/2022). Now 2 separate bits: +1 - generate channel weights and combo metrics.
	 *                     +2 replace port_weights with raw per-channel   
	 * @param calc_textures calculate textures (false for macro-only output)
	 * @param calc_extra calculate extra output - low-res for macro
	 */
	public void execTextures(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,        // Do not reduce average weight when only one image differs much from the average
			int       keep_weights,       // 2 bits now, move to parameters
			boolean   calc_textures,
			boolean   calc_extra,
			boolean   linescan_order
			) {
		gpuTrace("execTextures");
		if (GPUTileProcessor.USE_DS_DP) {
			execTextures_DP(
					color_weights,
					is_lwir,
					min_shot,           // 10.0
					scale_shot,         // 3.0
					diff_sigma,         // pixel value/pixel change
					diff_threshold,     // pixel value/pixel change
					min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
					dust_remove,        // Do not reduce average weight when only one image differs much from the average
					keep_weights,       // int       keep_weights,       // 2 bits now, move to parameters
					calc_textures,
					calc_extra,
					linescan_order);
		} else {
			execTextures_noDP( // CUDA_ERROR_INVALID_VALUE
					color_weights,
					is_lwir,
					min_shot,           // 10.0
					scale_shot,         // 3.0
					diff_sigma,         // pixel value/pixel change
					diff_threshold,     // pixel value/pixel change
					min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
					dust_remove,        // Do not reduce average weight when only one image differs much from the average
					keep_weights,       // int       keep_weights,       // 2 bits now, move to parameters
					calc_textures,
					calc_extra,
					linescan_order);
		}
	}
	
	public void execTextures_DP(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,        // Do not reduce average weight when only one image differs much from the average
			int       keep_weights,       // 2 bits now, move to parameters
			boolean   calc_textures,
			boolean   calc_extra,
			boolean   linescan_order)
	{
		execCalcReverseDistortions(); // will check if it is needed first
		if (this.gpuTileProcessor.GPU_TEXTURES_kernel == null)
		{
			IJ.showMessage("Error", "No GPU kernel: GPU_TEXTURES_kernel");
			return;
		}
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int num_colors = color_weights.length;
		if (num_colors > 3) num_colors = 3;
		float [] fcolor_weights = new float[3];
		fcolor_weights[0] = (float) color_weights[0];
		fcolor_weights[1] = (num_colors >1)?((float) color_weights[1]):0.0f;
		fcolor_weights[2] = (num_colors >2)?((float) color_weights[2]):0.0f;
		cuMemcpyHtoD(gpu_color_weights, Pointer.to(fcolor_weights),  fcolor_weights.length * Sizeof.FLOAT);

		float [] generate_RBGA_params = {
				(float)             min_shot,           // 10.0
				(float)             scale_shot,         // 3.0
				(float)             diff_sigma,         // pixel value/pixel change
				(float)             diff_threshold,     // pixel value/pixel change
				(float)             min_agree           // minimal number of channels to agree on a point (real number to work with fuzzy averages)
		};
		cuMemcpyHtoD(gpu_generate_RBGA_params, Pointer.to(generate_RBGA_params),  generate_RBGA_params.length * Sizeof.FLOAT);

		int iis_lwir =      (is_lwir)? 1:0;
		int ilinescan_order = linescan_order? 1 : 0;
		int idust_remove =  (dust_remove)? 1 : 0;
//		int keep_weights = 0;     // 2 bits now, move to parameters
		int [] GridFullWarps =    {1, 1, 1};
		int [] ThreadsFullWarps = {1, 1, 1};
		if (GPUTileProcessor.USE_CUDA12) { // for CDP2
			int shared_size = host_get_textures_shared_size( // in bytes
					num_cams,     // int                num_cams,     // actual number of cameras
					num_colors,   // int                num_colors,   // actual number of colors: 3 for RGB, 1 for LWIR/mono
					null);           // int *              offsets);     // in floats
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared_size);
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT, CU_SHAREDMEM_CARVEOUT_MAX_SHARED);
		} else {
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, 65536);
		}
		Pointer kernelParameters = Pointer.to(
				Pointer.to(new int[] { num_cams}),               // int                num_cams,
				Pointer.to(gpu_ftasks),                          // float            * gpu_ftasks,
				Pointer.to(new int[] { num_task_tiles }),        // int                num_tiles,          // number of tiles in task list
				// declare arrays in device code?
				Pointer.to(gpu_texture_indices),                 // int              * gpu_texture_indices,// packed tile + bits (now only (1 << 7)
				Pointer.to(gpu_texture_indices_len),
				// Parameters for the texture generation
				Pointer.to(gpu_clt),                             // float          ** gpu_clt,            // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
				Pointer.to(gpu_geometry_correction),             //	struct gc          * gpu_geometry_correction,
				Pointer.to(new int[] { num_colors }),
				Pointer.to(new int[] { iis_lwir }),
				Pointer.to(gpu_generate_RBGA_params),            // float             generate_RBGA_params[5],
				Pointer.to(gpu_color_weights),                   // float             weights[3],         // scale for R,B,G
				Pointer.to(new int[] { idust_remove }),
				Pointer.to(new int[] { keep_weights }),          // +1 : "keep_weights", +2 - replace port_weights with channel output				
				Pointer.to(new int[] {calc_textures? texture_stride : 0}),
				Pointer.to(gpu_textures),
				Pointer.to(new int[]   {ilinescan_order}),       // 1, // int               linescan_order,     // if !=0 then output gpu_diff_rgb_combo in linescan order, else  - in gpu_texture_indices order
				calc_extra ? Pointer.to(gpu_diff_rgb_combo) : Pointer.to(new int[] { 0 }),
				Pointer.to(new int[] { tilesX }));
		cuCtxSynchronize();
		// Call the kernel function
		cuLaunchKernel(this.gpuTileProcessor.GPU_TEXTURES_kernel,
				GridFullWarps[0],    GridFullWarps[1],   GridFullWarps[2],   // Grid dimension
				ThreadsFullWarps[0], ThreadsFullWarps[1],ThreadsFullWarps[2],// Block dimension
				0, null,                 // Shared memory size and stream (shared - only dynamic, static is in code)
				kernelParameters, null);   // Kernel- and extra parameters
		cuCtxSynchronize();
	}

	public void execTextures_noDP(
			double [] color_weights,
			boolean   is_lwir,
			double    min_shot,           // 10.0
			double    scale_shot,         // 3.0
			double    diff_sigma,         // pixel value/pixel change
			double    diff_threshold,     // pixel value/pixel change
			double    min_agree,          // minimal number of channels to agree on a point (real number to work with fuzzy averages)
			boolean   dust_remove,        // Do not reduce average weight when only one image differs much from the average
			int       keep_weights,       // 2 bits now, move to parameters
			boolean   calc_textures,
			boolean   calc_extra,
			boolean   linescan_order)
	{
		if (num_task_tiles == 0) {
			System.out.println("execTextures_noDP(): num_task_tiles=0!");
			return;
		}
		execCalcReverseDistortions(); // will check if it is needed first
		if (    (this.gpuTileProcessor.GPU_CREATE_NONOVERLAP_LIST_kernel == null) &&
				(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel == null)) {
			IJ.showMessage("Error", "No GPU kernel(s)");
			return;
		}
		int keep_texture_weights = keep_weights; // pass as parameter?
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int num_colors = is_lwir? 1 : color_weights.length;
		if (num_colors > 3) num_colors = 3;
		float [] fcolor_weights = new float[3];
		fcolor_weights[0] = (float) color_weights[0];
		fcolor_weights[1] = (num_colors >1)?((float) color_weights[1]):0.0f;
		fcolor_weights[2] = (num_colors >2)?((float) color_weights[2]):0.0f;
		double sw = 0.0; for (int i = 0; i < fcolor_weights.length; i++) sw+= fcolor_weights[i];
		for (int i = 0; i < fcolor_weights.length; i++) fcolor_weights[i]/=sw;
		int iis_lwir =      (is_lwir)? 1:0;
		int ilinescan_order = linescan_order? 1 : 0;
		int idust_remove =  (dust_remove)? 1 : 0;
		
		cuMemcpyHtoD(gpu_color_weights, Pointer.to(fcolor_weights),  fcolor_weights.length * Sizeof.FLOAT);
		
		int CONVERT_DIRECT_INDEXING_THREADS_LOG2 = 5;
		int CONVERT_DIRECT_INDEXING_THREADS = (1 << CONVERT_DIRECT_INDEXING_THREADS_LOG2); // 32

		int [] threads0 = {CONVERT_DIRECT_INDEXING_THREADS, 1, 1};
		int [] blocks0 =  {(num_task_tiles + CONVERT_DIRECT_INDEXING_THREADS -1) >> CONVERT_DIRECT_INDEXING_THREADS_LOG2,1, 1};
		int [] cpu_pnum_texture_tiles = {0};
		cuMemcpyHtoD(gpu_texture_indices_len, Pointer.to(cpu_pnum_texture_tiles),  1 * Sizeof.INT);
		Pointer kp_create_nonoverlap_list = Pointer.to(
				Pointer.to(new int[] {num_cams}),         // int     num_cams,           // number of sensors
				Pointer.to(gpu_ftasks),                   // float * gpu_ftasks,         // flattened tasks, 29 floats for quad EO, 101 floats for LWIR16
				Pointer.to(new int[] { num_task_tiles }), // int     num_tiles,          // number of tiles in task list
				Pointer.to(new int[] {tilesX}),           // int     width,              // number of tiles in a row
				Pointer.to(gpu_texture_indices),          // int   * nonoverlap_list,    // pointer to the calculated number of non-zero tiles
				Pointer.to(gpu_texture_indices_len));     // int   * pnonoverlap_length) //  indices to gpu_tasks  // should be initialized to zero
		if (getGpu_debug_level() > -1) {
			System.out.println("======execTextures_noDP(), num_task_tiles="+num_task_tiles);
		}
		cuLaunchKernel(this.gpuTileProcessor.GPU_CREATE_NONOVERLAP_LIST_kernel, //CUDA_ERROR_INVALID_VALUE
				blocks0[0],  blocks0[1],  blocks0[2],        // Grid dimension
				threads0[0], threads0[1], threads0[2],       // Block dimension
				0, null,                                  // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_create_nonoverlap_list, null);             // Kernel- and extra parameters
		cuCtxSynchronize();
		cuMemcpyDtoH(Pointer.to(cpu_pnum_texture_tiles), gpu_texture_indices_len,  1 * Sizeof.FLOAT);
		int num_cams_per_thread = GPUTileProcessor.NUM_THREADS / GPUTileProcessor.TEXTURE_THREADS_PER_TILE; // 4 cameras parallel, then repeat
		int[] threads_texture = {GPUTileProcessor.TEXTURE_THREADS_PER_TILE, num_cams_per_thread, 1}; // TEXTURE_TILES_PER_BLOCK, 1);
		int[] grid_texture    = {(cpu_pnum_texture_tiles[0] + GPUTileProcessor.TEXTURE_TILES_PER_BLOCK-1) / GPUTileProcessor.TEXTURE_TILES_PER_BLOCK,1,1};
		int shared_size = host_get_textures_shared_size( // in bytes
				num_cams,     // int                num_cams,     // actual number of cameras
				num_colors,   // int                num_colors,   // actual number of colors: 3 for RGB, 1 for LWIR/mono
				null);           // int *              offsets);     // in floats
		Pointer kp_textures_accumulate;
		if (GPUTileProcessor.USE_CUDA12) { // for CDP2
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared_size);
			cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_PREFERRED_SHARED_MEMORY_CARVEOUT, CU_SHAREDMEM_CARVEOUT_MAX_SHARED);
			kp_textures_accumulate = Pointer.to(
					Pointer.to(new int[] {num_cams}),                // int         num_cams,
					Pointer.to(new int[]   {0}),  // Pointer.to(gpu_woi),  // int       * woi,            // min_x, min_y, max_x, max_y
					Pointer.to(gpu_clt),                             // float    ** gpu_clt,        // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
					Pointer.to(gpu_texture_indices_len),             // int       * num_texture_tiles,// number of texture tiles to process
					Pointer.to(new int[] {0}),                     // size_t      num_texture_tiles,// number of texture tiles to process
					Pointer.to(gpu_texture_indices),          // int   * nonoverlap_list,    // pointer to the calculated number of non-zero tiles
					Pointer.to(gpu_geometry_correction),             // struct gc * gpu_geometry_correction,
					Pointer.to(new int[]   {num_colors}),            // int         colors,         // number of colors (3/1)
					Pointer.to(new int[]   {iis_lwir}),              // int         is_lwir,        // do not perform shot correction
					Pointer.to(new float[] {(float) min_shot}),      // float       min_shot,       // 10.0
					Pointer.to(new float[] {(float) scale_shot}),    // float       scale_shot,     // 3.0
					Pointer.to(new float[] {(float) diff_sigma}),    // float       diff_sigma,     // pixel value/pixel change
					Pointer.to(new float[] {(float) diff_threshold}),// float       diff_threshold, // pixel value/pixel change
					Pointer.to(new float[] {(float) min_agree}),     // float       min_agree,      // minimal number of channels to agree on a point (real number to work with fuzzy averages)
					Pointer.to(gpu_color_weights),                   // float       weights[3],     // scale for R,B,G (or {1.0,0.0,0.0}
					Pointer.to(new int[]   {idust_remove}),          // int         dust_remove,        // Do not reduce average weight when only one image differes much from the average
					Pointer.to(new int[]   {keep_texture_weights}),  // int         keep_weights,       // return channel weights after A in RGBA
					 // combining both non-overlap and overlap (each calculated if pointer is not null )
					Pointer.to(new int[]   {0}),                     // const size_t      texture_rbga_stride,     // in floats
					Pointer.to(new int[]   {0}),                     // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
					Pointer.to(new int[] {calc_textures? texture_stride : 0}), // size_t   texture_stride,     // in floats (now 256*4 = 1024)
					Pointer.to(gpu_textures), // gpu_texture_tiles, // float           * gpu_texture_tiles);  // (number of colors +1 + ?)*16*16 rgba texture tiles
					Pointer.to(new int[]   {ilinescan_order}), // 1, // int               linescan_order,     // if !=0 then output gpu_diff_rgb_combo in linescan order, else  - in gpu_texture_indices order
					calc_extra ? Pointer.to(gpu_diff_rgb_combo) : Pointer.to(new int[] { 0 }),
					Pointer.to(new int[] { tilesX }));
		} else {
		cuFuncSetAttribute(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, 65536);
		kp_textures_accumulate = Pointer.to(
				Pointer.to(new int[] {num_cams}),                // int         num_cams,
				Pointer.to(new int[]   {0}),  // Pointer.to(gpu_woi),  // int       * woi,            // min_x, min_y, max_x, max_y
				Pointer.to(gpu_clt),                             // float    ** gpu_clt,        // [num_cams] ->[TILESY][TILESX][num_colors][DTT_SIZE*DTT_SIZE]
				Pointer.to(new int[] {cpu_pnum_texture_tiles[0]}),                     // size_t      num_texture_tiles,// number of texture tiles to process
				Pointer.to(new int[] {0}),                     // size_t      num_texture_tiles,// number of texture tiles to process
				Pointer.to(gpu_texture_indices),          // int   * nonoverlap_list,    // pointer to the calculated number of non-zero tiles
				Pointer.to(gpu_geometry_correction),             // struct gc * gpu_geometry_correction,
				Pointer.to(new int[]   {num_colors}),            // int         colors,         // number of colors (3/1)
				Pointer.to(new int[]   {iis_lwir}),              // int         is_lwir,        // do not perform shot correction
				Pointer.to(new float[] {(float) min_shot}),      // float       min_shot,       // 10.0
				Pointer.to(new float[] {(float) scale_shot}),    // float       scale_shot,     // 3.0
				Pointer.to(new float[] {(float) diff_sigma}),    // float       diff_sigma,     // pixel value/pixel change
				Pointer.to(new float[] {(float) diff_threshold}),// float       diff_threshold, // pixel value/pixel change
				Pointer.to(new float[] {(float) min_agree}),     // float       min_agree,      // minimal number of channels to agree on a point (real number to work with fuzzy averages)
				Pointer.to(gpu_color_weights),                   // float       weights[3],     // scale for R,B,G (or {1.0,0.0,0.0}
				Pointer.to(new int[]   {idust_remove}),          // int         dust_remove,        // Do not reduce average weight when only one image differes much from the average
				Pointer.to(new int[]   {keep_texture_weights}),  // int         keep_weights,       // return channel weights after A in RGBA
				 // combining both non-overlap and overlap (each calculated if pointer is not null )
				Pointer.to(new int[]   {0}),                     // const size_t      texture_rbga_stride,     // in floats
				Pointer.to(new int[]   {0}),                     // float           * gpu_texture_tiles)    // (number of colors +1 + ?)*16*16 rgba texture tiles
				Pointer.to(new int[] {calc_textures? texture_stride : 0}), // size_t   texture_stride,     // in floats (now 256*4 = 1024)
				Pointer.to(gpu_textures), // gpu_texture_tiles, // float           * gpu_texture_tiles);  // (number of colors +1 + ?)*16*16 rgba texture tiles
				Pointer.to(new int[]   {ilinescan_order}), // 1, // int               linescan_order,     // if !=0 then output gpu_diff_rgb_combo in linescan order, else  - in gpu_texture_indices order
				calc_extra ? Pointer.to(gpu_diff_rgb_combo) : Pointer.to(new int[] { 0 }),
				Pointer.to(new int[] { tilesX }));
		}
		cuLaunchKernel(this.gpuTileProcessor.GPU_TEXTURES_ACCUMULATE_kernel,
				grid_texture[0],    grid_texture[1],    grid_texture[2],   // Grid dimension
				threads_texture[0], threads_texture[1], threads_texture[2],  // Block dimension
				shared_size, null,                      // Shared memory size and stream (shared - only dynamic, static is in code)
				kp_textures_accumulate, null);          // Kernel- and extra parameters
		cuCtxSynchronize();
		
	}
	
	
	public int getNumPairs() {return num_all_pairs;}
	

	/**
	 * Generating correlation sequence by CPU to correlate all tiles provided in linescan order.
	 * Additionally, if both (num_acc != null) and (pfcorr_weights !=null), pfcorr_weights[0]
	 * will return array of the same length as indices_trim (ntiles * num_pairs) to use as weights
	 * to modulate fat_zero2 (the more tiles accumulated - the lower fat_zero2 is)
	 * @param corr_tiles transform domain correlation tile in linescan order [tilesY][tilesX][used_pairs][4*64]
	 * @param num_acc number or tiles accumulated for the same tileX,tileX,pair or null
	 * @param fcorr_weights input - null or [1][], output: null or [1][ntiles * num_pairs] weights
	 * @return [ntiles * num_pairs] correlation instructions:24 MSb - tile number, 8 low bits - pair numbers
	 */
	public int [] setCorrTilesTd(
			final float [][][][] corr_tiles, // [tileY][tileX][pair][4*64]
            int [][][]           num_acc,     // number of accumulated tiles [tilesY][tilesX][pair] (or null)
            float [][]           pfcorr_weights) // null or one per correlation tile (num_corr_tiles) to divide fat zero2

	{
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
        int num_pairs = getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
        // 3-rd index of corr_tiles should be getNumUsedPairs();
		int tilesX = corr_tiles[0].length;
		int tilesY = corr_tiles.length;
		int [] indices = new int [tilesY * tilesX * num_pairs];  // as if all tiles are not null
		float [] fdata = new float [tilesY * tilesX * corr_size_td* num_pairs]; // as if all tiles are not null
		boolean use_fcorr_weights = ((num_acc != null) && (pfcorr_weights != null));
		float [] fcorr_weights = use_fcorr_weights ? (new float[tilesY * tilesX * num_pairs]) : null;
		int ntile = 0;
		for (int ty = 0; ty < corr_tiles.length; ty++) {
			for (int tx = 0; tx < corr_tiles[0].length; tx++) {
				if (corr_tiles[ty][tx]!= null) {
					for (int pair = 0; pair < num_pairs; pair++) {
						int corr_pair = ntile * num_pairs + pair;
						indices[corr_pair]= // ntile * num_pairs + pair] =
								((ty * tilesX + tx) << GPUTileProcessor.CORR_NTILE_SHIFT) +
								(pair & ((1 <<  GPUTileProcessor.CORR_NTILE_SHIFT) -1) );
						if (fcorr_weights != null) {
							fcorr_weights[corr_pair] = num_acc[ty][tx][pair];
						}
//						System.arraycopy(corr_tiles[ty][tx][pair], 0, fdata, (ntile*num_pairs + pair) * corr_size_td, corr_size_td);
						System.arraycopy(corr_tiles[ty][tx][pair], 0, fdata, corr_pair * corr_size_td, corr_size_td);
					}
					ntile++;
				}

			}
		}
		setCorrIndicesTdData(
				ntile * num_pairs,   // int    num_tiles,  // corr_indices, fdata may be longer than needed
				indices, // int [] corr_indices,
				fdata);  // float [] fdata);
		// trim indices
		int [] indices_trim = new int [ntile * num_pairs];
		System.arraycopy(indices, 0, indices_trim, 0 , indices_trim.length);
		if (fcorr_weights != null) {
			pfcorr_weights[0] = new float [indices_trim.length];
			System.arraycopy(fcorr_weights, 0, pfcorr_weights[0], 0 , indices_trim.length);
		}
		return indices_trim;
	}

	/**
	 * Generating correlation sequence by CPU to correlate all tiles provided in linescan order.
	 * This versions follows order of tiles in tp_task array, filling gaps (should be none) with zeros 
	 * Additionally, if both (num_acc != null) and (pfcorr_weights !=null), pfcorr_weights[0]
	 * will return array of the same length as indices_trim (ntiles * num_pairs) to use as weights
	 * to modulate fat_zero2 (the more tiles accumulated - the lower fat_zero2 is)
	 * @param tp_tasks - array of TpTask, should be updated from GPU
	 * @param inter_mode - true - interscene mode (for motion vectors) , false - intrascene
	 * @param corr_tiles transform domain correlation tile in linescan order [tilesY][tilesX][used_pairs][4*64]
	 * @param num_acc number or tiles accumulated for the same tileX,tileX,pair or null
	 * @param fcorr_weights input - null or [1][], output: null or [1][ntiles * num_pairs] weights
	 * @return [ntiles * num_pairs] correlation instructions:24 MSb - tile number, 8 low bits - pair numbers
	 */

	public int [] setCorrTilesTd(
			final TpTask []      tp_tasks,        // data from the reference frame - will be applied to LMW for the integrated correlations
			final boolean        inter_mode, // interscene mode
            // only listed tiles will be processed
			final float [][][][] corr_tiles, // [tileY][tileX][pair][4*64]
            float [][][]         num_acc,        // number of accumulated tiles [tilesY][tilesX][pair] (or null)
            float [][]           pfcorr_weights) // null or one per correlation tile (num_corr_tiles) to divide fat zero2
	{
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		// "+1" - for sum
        int num_pairs = inter_mode?(getNumCamsInter() + 1): getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
        // 3-rd index of corr_tiles should be getNumUsedPairs();
		int [] indices = new int [tp_tasks.length * num_pairs];  // as if all tiles are not null
		float [] fdata = new float [indices.length * corr_size_td]; // as if all tiles are not null
		boolean use_fcorr_weights = ((num_acc != null) && (pfcorr_weights != null));
		float [] fcorr_weights = use_fcorr_weights ? (new float[indices.length]) : null;
		if (fcorr_weights != null) {
			pfcorr_weights[0] = fcorr_weights; // new float [indices.length];
		}
		
		int tilesX = corr_tiles[0].length;
		for (int ntile = 0; ntile < tp_tasks.length; ntile++) {
			int ty = tp_tasks[ntile].ty;
			int tx = tp_tasks[ntile].tx;
			for (int ipair = 0; ipair < num_pairs; ipair++) {
				int pair = inter_mode?getSensInter(ipair): getCorrPair(ipair); // 0xff for accumulated
				int corr_pair = ntile * num_pairs + ipair;
				// Below was an error (not visible if all selected, using index of the pair, not an absolute pair )
				indices[corr_pair]= // ntile * num_pairs + pair] = // low 8 bits - pair, 0xff for sum of all channels
						((ty * tilesX + tx) << GPUTileProcessor.CORR_NTILE_SHIFT) +
						(pair & ((1 <<  GPUTileProcessor.CORR_NTILE_SHIFT) -1) );
				if (fcorr_weights != null) {
					fcorr_weights[corr_pair] = num_acc[ty][tx][ipair];
				}
				if (corr_tiles[ty][tx]!= null) {
					System.arraycopy(corr_tiles[ty][tx][ipair], 0, fdata, corr_pair * corr_size_td, corr_size_td);
				} else { // fill with zeros
					Arrays.fill(fdata, corr_pair * corr_size_td, (corr_pair + 1) * corr_size_td, 0.0f);
					if (fcorr_weights != null) {
						fcorr_weights[corr_pair] = 1; 
					}
				}
			}
		}
		setCorrIndicesTdData(
				indices.length, // ntile * num_pairs,   // int    num_tiles,  // corr_indices, fdata may be longer than needed
				indices, // int [] corr_indices,
				fdata);  // float [] fdata);
		return indices;
	}
	/*
	public int getSensorMaskInter() {return sensor_mask_inter;}
	public void setSensorMaskInter(int mask) {
		sensor_mask_inter = mask & ((1 << num_cams) -1);
	}
	public int getNumCamsInter() {
		return Integer.bitCount(sensor_mask_inter);
	}
	

	/**
	 * Generating correlation sequence by CPU to correlate all tiles provided in linescan order.
	 * This versions follows order of tiles in tp_task array, filling gaps (should be none) with zeros 
	 * Additionally, if both (num_acc != null) and (pfcorr_weights !=null), pfcorr_weights[0]
	 * will return array of the same length as indices_trim (ntiles * num_pairs) to use as weights
	 * to modulate fat_zero2 (the more tiles accumulated - the lower fat_zero2 is)
	 * @param tp_tasks - array of TpTask, should be updated from GPU
	 * @param inter_mode - true - interscene mode (for motion vectors) , false - intrascene
	 * @param corr_tiles transform domain correlation tile in linescan order [tilesY][tilesX][used_pairs][4*64]
	 * @param dcorr_weight number or tiles accumulated for the index of tp_tasks or null (matching weights for CPU)
	 * @param fcorr_weights input - null or [1][], output: null or [1][ntiles * num_pairs] weights
	 * @return [ntiles * num_pairs] correlation instructions:24 MSb - tile number, 8 low bits - pair numbers
	 */

	public int [] setCorrTilesTd(
			final TpTask []      tp_tasks,        // data from the reference frame - will be applied to LMW for the integrated correlations
			final boolean        inter_mode, // interscene mode
            // only listed tiles will be processed
			final float [][][][] corr_tiles, // [tileY][tileX][pair][4*64]
            double []            dcorr_weight, // num_acc,     // [ntile] (or null)
            float [][]           pfcorr_weights) // null or one per correlation tile (num_corr_tiles) to divide fat zero2
	{
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
//        int num_pairs = getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		// "+1" - for sum
        int num_pairs = inter_mode?(getNumCamsInter() + 1): getNumUsedPairs();// Number of used pairs
        // 3-rd index of corr_tiles should be getNumUsedPairs();
		int [] indices = new int [tp_tasks.length * num_pairs];  // as if all tiles are not null
		float [] fdata = new float [indices.length * corr_size_td]; // as if all tiles are not null
		boolean use_fcorr_weights = ((dcorr_weight != null) && (pfcorr_weights != null));
		float [] fcorr_weights = use_fcorr_weights ? (new float[indices.length]) : null;
		if (fcorr_weights != null) {
			pfcorr_weights[0] = fcorr_weights; // new float [indices.length];
		}
		
		int tilesX = corr_tiles[0].length;
		for (int ntile = 0; ntile < tp_tasks.length; ntile++) {
			int ty = tp_tasks[ntile].ty;
			int tx = tp_tasks[ntile].tx;
			for (int ipair = 0; ipair < num_pairs; ipair++) {
				int pair = inter_mode?getSensInter(ipair): getCorrPair(ipair);
				int corr_pair = ntile * num_pairs + ipair;
				indices[corr_pair]= // ntile * num_pairs + pair] =
						((ty * tilesX + tx) << GPUTileProcessor.CORR_NTILE_SHIFT) +
						(pair & ((1 <<  GPUTileProcessor.CORR_NTILE_SHIFT) -1) );
				if (fcorr_weights != null) {
					fcorr_weights[corr_pair] = (float) dcorr_weight[ntile];// num_acc[ty][tx][pair];
				}
				if (corr_tiles[ty][tx]!= null) {
					System.arraycopy(corr_tiles[ty][tx][ipair], 0, fdata, corr_pair * corr_size_td, corr_size_td);
				} else { // fill with zeros
					Arrays.fill(fdata, corr_pair * corr_size_td, (corr_pair + 1) * corr_size_td, 0.0f);
					if (fcorr_weights != null) {
						fcorr_weights[corr_pair] = 1; 
					}
				}
			}
		}
		setCorrIndicesTdData(
				indices.length, // ntile * num_pairs,   // int    num_tiles,  // corr_indices, fdata may be longer than needed
				indices, // int [] corr_indices,
				fdata);  // float [] fdata);
		return indices;
	}
	
	// Generating correlation sequence by CPU to correlate all tiles provided in linescan order
	@Deprecated
	public int [] setCorrTilesTd(
			final float [][][][] corr_tiles, // [tileY][tileX][pair][4*64]
			int [][] pairs_map) // typically {{0,0},{1,1},{2,2},{3,3},{4,4},{5,5} [0] - 3rd index in corr_tiles, [1] - 
	{
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
//		num_pairs = pairs_map.length; // set global num_pairs
        int num_pairs = getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		int tilesX = corr_tiles[0].length;
		int tilesY = corr_tiles.length;
		int [] indices = new int [tilesY * tilesX * num_pairs];  // as if all tiles are not null
		float [] fdata = new float [tilesY * tilesX * corr_size_td* num_pairs]; // as if all tiles are not null
		int ntile = 0;
		for (int ty = 0; ty < corr_tiles.length; ty++) {
			for (int tx = 0; tx < corr_tiles[0].length; tx++) {
				if (corr_tiles[ty][tx]!= null) {
					for (int pair = 0; pair < num_pairs; pair++) {
						indices[ntile * num_pairs + pair] = ((ty * tilesX + tx) << GPUTileProcessor.CORR_NTILE_SHIFT) + pairs_map[pair][1];
						System.arraycopy(corr_tiles[ty][tx][pairs_map[pair][0]], 0, fdata, (ntile*num_pairs + pair) * corr_size_td, corr_size_td);
					}
					ntile++;
				}

			}
		}
		setCorrIndicesTdData(
				ntile * num_pairs,   // int    num_tiles,  // corr_indices, fdata may be longer than needed
				indices, // int [] corr_indices,
				fdata);  // float [] fdata);
		// trim indices
		int [] indices_trim = new int [ntile * num_pairs];
		System.arraycopy(indices, 0, indices_trim, 0 , indices_trim.length);        	
		return indices_trim;
	}

	
	
	/*
	public float [][][][] getCorrTilesTd() // [tileY][tileX][pair][4*64] , read all available pairs
	{
		int tilesX =     img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =     img_height / GPUTileProcessor.DTT_SIZE;
		float [][][][] corr_tiles = new float[tilesY][tilesX][][]; // num_pairs
		return getCorrTilesTd(corr_tiles);
	}
  */
	public float [][][][] getCorrTilesTd( // [tileY][tileX][pair][4*64] , read all available pairs
			float [][][][] corr_tiles){
		return getCorrTilesTd( // [tileY][tileX][pair][4*64] , read all available pairs
				false,
				corr_tiles);
		
	}
	public float [][][][] getCorrTilesTd( // [tileY][tileX][pair][4*64] , read all available pairs
			boolean        inter,
			float [][][][] corr_tiles)
	{
        int num_pairs = inter? (getNumCamsInter() + 1 + 0): getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		final int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int [] indices = getCorrIndices(); // also sets num_corr_tiles
		float [] fdata = getCorrTdData();
		final int num_tiles = num_corr_tiles / num_pairs;
		int width = corr_tiles[0].length;
		for (int nt = 0; nt < num_tiles; nt++ ) {
			int nTile = (indices[nt * num_pairs] >> GPUTileProcessor.CORR_NTILE_SHIFT);
			int ty = nTile / width;
			int tx = nTile % width;

			corr_tiles[ty][tx] = new float [num_pairs][corr_size_td];
			for (int pair = 0; pair < num_pairs; pair++) {
				System.arraycopy(
						fdata,
						(nt * num_pairs + pair) * corr_size_td,
						corr_tiles[ty][tx][pair],
						0,
						corr_size_td);
			}
		}
		return corr_tiles;
	}
	
	public float [][] getCorrTilesTdInterCombo(
			final float [][] corr_tiles_combo){ // initialized to new float [numer_of_image_tiles][]
		int num_pairs = getNumCamsInter() + 1;
		int index_combo = num_pairs - 1;
		final int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		final int [] indices = getCorrIndices(); // also sets num_corr_tiles
		final float [] fdata = getCorrTdData();
		int num_tiles = num_corr_tiles / num_pairs;
		final Thread[] threads = ImageDtt.newThreadArray(ImageDtt.THREADS_MAX);
		final AtomicInteger ai = new AtomicInteger(0);
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				public void run() {
					for (int nt = ai.getAndIncrement(); nt < num_tiles; nt = ai.getAndIncrement()) {
						int nTile = (indices[nt * num_pairs] >> GPUTileProcessor.CORR_NTILE_SHIFT);
						corr_tiles_combo[nTile] = new float [corr_size_td];
						System.arraycopy(
								fdata,
								(nt * num_pairs + index_combo) * corr_size_td,
								corr_tiles_combo[nTile],
								0,
								corr_size_td);
					}
				}
			};
		}		      
		ImageDtt.startAndJoin(threads);
		return corr_tiles_combo;
	}

	/**
	 * Read GPU correlation data to the sparse array [tilesY][tilesX][] for a single correlation pair (usually a combo one)
	 * @param inter true for interscene correlations, false - for the intrascene ones
	 * @param pair correlation pair index to read
	 * @return an array of transform-domain correlations mapped to tiles [tilesY][tilesX]. Each tile data is either null
	 *         or [4*64] float array
	 */
	public float [][][] getCorrTilesLayerTD(
			boolean        inter,
			int            pair) {
		int tilesX =     img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =     img_height / GPUTileProcessor.DTT_SIZE;
        int num_pairs = inter? (getNumCamsInter() + 1 + 0): getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		final int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int [] indices = getCorrIndices(); // also sets num_corr_tiles
		float [] fdata = getCorrTdData();
		int num_tiles = num_corr_tiles / num_pairs;
		float [][][] corr_tiles = new float [tilesY][tilesX][];

		for (int nt = 0; nt < num_tiles; nt++ ) {
			int nTile = (indices[nt * num_pairs] >> GPUTileProcessor.CORR_NTILE_SHIFT);
			int ty = nTile / tilesX;
			int tx = nTile % tilesX;

			corr_tiles[ty][tx] = new float [corr_size_td];
			System.arraycopy(
					fdata,
					(nt * num_pairs + pair) * corr_size_td,
					corr_tiles[ty][tx],
					0,
					corr_size_td);
		}
		return corr_tiles;
	}


	public float [][][] getCorrTilesLayerTD(
			int []         indices,
			float []       fdata,
			boolean        inter,
			int            pair) {
		int tilesX =     img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =     img_height / GPUTileProcessor.DTT_SIZE;
        int num_pairs = inter? (getNumCamsInter() + 1 + 0): getNumUsedPairs();// Number of used pairs		num_pairs = num_pairs_in;
		final int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
//		int [] indices = getCorrIndices(); // also sets num_corr_tiles
//		float [] fdata = getCorrTdData();
		int num_tiles = num_corr_tiles / num_pairs;
		float [][][] corr_tiles = new float [tilesY][tilesX][];

		for (int nt = 0; nt < num_tiles; nt++ ) {
			int nTile = (indices[nt * num_pairs] >> GPUTileProcessor.CORR_NTILE_SHIFT);
			int ty = nTile / tilesX;
			int tx = nTile % tilesX;

			corr_tiles[ty][tx] = new float [corr_size_td];
			System.arraycopy(
					fdata,
					(nt * num_pairs + pair) * corr_size_td,
					corr_tiles[ty][tx],
					0,
					corr_size_td);
		}
		return corr_tiles;
	}
	
	

	public int [] setCorrTilesComboTd( // not used?
			final float [][][] corr_tiles, // [tileY][tileX][4*64]
			int ipair) // just to set in the index low bits
	{
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int tilesX = corr_tiles[0].length;
		int tilesY = corr_tiles.length;
		int [] indices = new int [tilesY * tilesX];  // as if all tiles are not null
		float [] fdata = new float [tilesY * tilesX * corr_size_td];  // as if all tiles are not null
		int ntile = 0;
		for (int ty = 0; ty < corr_tiles.length; ty++) {
			for (int tx = 0; tx < corr_tiles[0].length; tx++) {
				if (corr_tiles[ty][tx]!= null) {
					indices[ntile] = ((ty * tilesX + tx) << GPUTileProcessor.CORR_NTILE_SHIFT) + ipair;
					System.arraycopy(corr_tiles[ty][tx], 0, fdata, ntile * corr_size_td, corr_size_td);
					ntile++;
				}

			}
		}
		setCorrComboIndicesTdData(
				ntile,   // int    num_tiles,  // corr_indices, fdata may be longer than needed
				indices, // int [] corr_indices,
				fdata);  // float [] fdata);
		int [] indices_trim = new int [ntile];
		System.arraycopy(indices, 0, indices_trim, 0 , indices_trim.length);        	
		return indices_trim;
	}

	public float [][][] getCorrTilesComboTd() // [tileY][tileX][4*64] , read all available pairs  // not used?
	{
		int tilesX =     img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =     img_height / GPUTileProcessor.DTT_SIZE;
		float [][][] corr_tiles = new float[tilesY][tilesX][]; // num_pairs
		return getCorrTilesComboTd(corr_tiles);
	}

	public float [][][] getCorrTilesComboTd( // [tileY][tileX][4*64] , read all available pairs
			float [][][] corr_tiles // should be initialized as [tilesX][tilesY][]
			)
	{
		final int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		int [] indices = getCorrComboIndices(); // num_corr_combo_tiles should be set earlier (from setCorrTilesComboTd or)
		float [] fdata = getCorrComboTdData();
		int width = corr_tiles[0].length;
		for (int nt = 0; nt < num_corr_combo_tiles; nt++ ) {
			int nTile = (indices[nt] >> GPUTileProcessor.CORR_NTILE_SHIFT);
			int ty = nTile / width;
			int tx = nTile % width;
			corr_tiles[ty][tx] = new float[corr_size_td];
			System.arraycopy(fdata, nt * corr_size_td, corr_tiles[ty][tx], 0, corr_size_td);
		}
		return corr_tiles;
	}



	public void setCorrIndicesTdData(
			int    num_tiles,  // corr_indices, fdata may be longer than needed
			int [] corr_indices,
			float [] fdata)
	{
		num_corr_tiles = num_tiles; // corr_indices.length;
		float [] fcorr_indices = new float [num_corr_tiles];
		for (int i = 0; i < num_corr_tiles; i++) {
			fcorr_indices[i] = Float.intBitsToFloat(corr_indices[i]);
		}
		cuMemcpyHtoD(gpu_corr_indices,   Pointer.to(fcorr_indices),  num_corr_tiles * Sizeof.FLOAT);
		float [] fnum_corr_tiles = {(float) num_corr_tiles};
		cuMemcpyHtoD(gpu_num_corr_tiles, Pointer.to(fnum_corr_tiles), 1 * Sizeof.FLOAT);
		// copy the correlation data
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		CUDA_MEMCPY2D copyH2D =   new CUDA_MEMCPY2D();
		copyH2D.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyH2D.srcHost =         Pointer.to(fdata);
		copyH2D.srcPitch =        corr_size_td*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyH2D.dstDevice =       gpu_corrs_td; // src_dpointer;
		copyH2D.dstPitch =        corr_stride_td *Sizeof.FLOAT; // device_stride[0];
		copyH2D.WidthInBytes =    corr_size_td*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.Height =          num_corr_tiles; // /4;
		cuMemcpy2D(copyH2D);
	}

	public int [] getCorrIndices() {
		gpuTrace("getCorrIndices");
		int [] inum_corrs = new int[1];
		cuMemcpyDtoH(Pointer.to(inum_corrs), gpu_num_corr_tiles,  1 * Sizeof.INT);
		int num_corrs =      inum_corrs[0];
		int [] corr_indices = new int [num_corrs];
		cuMemcpyDtoH(Pointer.to(corr_indices), gpu_corr_indices,  num_corrs * Sizeof.INT);
		num_corr_tiles = num_corrs;
		return corr_indices;
	}
	
	
	public float [] getCorrTdData(){
		gpuTrace("getCorrTdData");
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		float [] cpu_corrs = new float [ num_corr_tiles * corr_size_td];
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_corrs_td;
		copyD2H.srcPitch =        corr_stride_td * Sizeof.FLOAT;
		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_corrs);
		copyD2H.dstPitch =        corr_size_td * Sizeof.FLOAT;
		copyD2H.WidthInBytes =    corr_size_td * Sizeof.FLOAT;
		copyD2H.Height =          num_corr_tiles;
		cuMemcpy2D(copyD2H); // run copy
		return cpu_corrs;
	}


	public void setCorrComboIndicesTdData(
			int    num_tiles,  // corr_combo_indices, fdata may be longer than needed
			int [] corr_combo_indices,
			float [] fdata)
	{
		num_corr_combo_tiles = num_tiles; // corr_combo_indices.length;
		float [] fcorr_combo_indices = new float [num_corr_combo_tiles];
		for (int i = 0; i < num_corr_combo_tiles; i++) {
			fcorr_combo_indices[i] = Float.intBitsToFloat(corr_combo_indices[i]);
		}
		cuMemcpyHtoD(gpu_corr_indices,   Pointer.to(fcorr_combo_indices),  num_corr_combo_tiles * Sizeof.FLOAT);
		//        	float [] fnum_corr_tiles = {(float) num_corr_tiles};
		//        	cuMemcpyHtoD(gpu_num_corr_tiles, Pointer.to(fnum_corr_tiles), 1 * Sizeof.FLOAT);
		// copy the correlation data
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		CUDA_MEMCPY2D copyH2D =   new CUDA_MEMCPY2D();
		copyH2D.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyH2D.srcHost =         Pointer.to(fdata);
		copyH2D.srcPitch =        corr_size_td*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyH2D.dstDevice =       gpu_corrs_combo_td; // src_dpointer;
		copyH2D.dstPitch =        corr_stride_combo_td *Sizeof.FLOAT; // device_stride[0];
		copyH2D.WidthInBytes =    corr_size_td*Sizeof.FLOAT; // width_in_bytes;
		copyH2D.Height =          num_corr_combo_tiles; // /4;
		cuMemcpy2D(copyH2D);
	}



	public int [] getCorrComboIndices() {
		gpuTrace("getCorrComboIndices");
		//        	float [] fnum_corrs = new float[1];
		//        	cuMemcpyDtoH(Pointer.to(fnum_corrs), gpu_num_corr_tiles,  1 * Sizeof.FLOAT);
		//        	int num_corrs =      Float.floatToIntBits(fnum_corrs[0]);
		float [] fcorr_combo_indices = new float [num_corr_combo_tiles];
		cuMemcpyDtoH(Pointer.to(fcorr_combo_indices), gpu_corr_combo_indices,  num_corr_combo_tiles * Sizeof.FLOAT);
		int [] corr_combo_indices = new int [num_corr_combo_tiles];
		for (int i = 0; i < num_corr_combo_tiles; i++) {
			corr_combo_indices[i] = Float.floatToIntBits(fcorr_combo_indices[i]);
		}
		return corr_combo_indices;
	}

	public float [] getCorrComboTdData(){
		int corr_size_td = 4 * GPUTileProcessor.DTT_SIZE * GPUTileProcessor.DTT_SIZE;
		float [] cpu_corrs = new float [ num_corr_combo_tiles * corr_size_td];
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_corrs_combo_td;
		copyD2H.srcPitch =        corr_stride_combo_td * Sizeof.FLOAT;
		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_corrs);
		copyD2H.dstPitch =        corr_size_td * Sizeof.FLOAT;
		copyD2H.WidthInBytes =    corr_size_td * Sizeof.FLOAT;
		copyD2H.Height =          num_corr_combo_tiles;
		cuMemcpy2D(copyD2H); // run copy
		return cpu_corrs;
	}

	public void eraseGpuCorrs() { // temporary
//		float [] erase_buf = new float [corr_stride * num_corr_tiles];
		float [] erase_buf = new float [corr_stride * 120*80*64];
		Arrays.fill(erase_buf, 0.0f);
		cuMemcpyHtoD(gpu_corrs,   Pointer.to(erase_buf),  erase_buf.length * Sizeof.FLOAT);
	}
	
	
	public float [][] getCorr2D(int corr_rad){
		gpuTrace("eraseGpuCorrs");
		int corr_size = (2 * corr_rad + 1) * (2 * corr_rad + 1);
		float [] cpu_corrs = new float [ num_corr_tiles * corr_size];
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_corrs;
		copyD2H.srcPitch =        corr_stride * Sizeof.FLOAT;

		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_corrs);
		copyD2H.dstPitch =        corr_size * Sizeof.FLOAT;

		copyD2H.WidthInBytes =    corr_size * Sizeof.FLOAT;
		copyD2H.Height =          num_corr_tiles;

		cuMemcpy2D(copyD2H); // run copy

		float [][] corrs = new float [num_corr_tiles][ corr_size];
		for (int ncorr = 0; ncorr < num_corr_tiles; ncorr++) {
			System.arraycopy(cpu_corrs, ncorr*corr_size, corrs[ncorr], 0, corr_size);
		}
		return corrs;
	}

	public float [][] getCorr2DCombo(int corr_rad){
		int corr_size = (2 * corr_rad + 1) * (2 * corr_rad + 1);
		float [] cpu_corrs = new float [ num_corr_combo_tiles * corr_size];
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_corrs_combo;
		copyD2H.srcPitch =        corr_stride_combo * Sizeof.FLOAT;

		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_corrs);
		copyD2H.dstPitch =        corr_size * Sizeof.FLOAT;

		copyD2H.WidthInBytes =    corr_size * Sizeof.FLOAT;
		copyD2H.Height =          num_corr_combo_tiles;

		cuMemcpy2D(copyD2H); // run copy

		float [][] corrs = new float [num_corr_combo_tiles][ corr_size];
		for (int ncorr = 0; ncorr < num_corr_combo_tiles; ncorr++) {
			System.arraycopy(cpu_corrs, ncorr*corr_size, corrs[ncorr], 0, corr_size);
		}
		return corrs;
	}


	//	        
	/**
	 * Read extra data for macro generation: 4 DIFFs, 4 of R,  4 of B, 4 of G
	 * Available after texture        
	 * @return [ num_cams*(num_colors+1)][tilesX*tilesY] array for macro generation
	 */
	public float [][] getExtra(){
		gpuTrace("getExtra");
		int [] texture_indices = getTextureIndices();
		int num_tile_extra = num_cams*(num_colors+1);
		float [] diff_rgb_combo = new float[texture_indices.length * num_tile_extra];
		cuMemcpyDtoH(Pointer.to(diff_rgb_combo), gpu_diff_rgb_combo,  diff_rgb_combo.length * Sizeof.FLOAT);
		int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		int tilesY =  img_height / GPUTileProcessor.DTT_SIZE;
		float [][] extra = new float[num_tile_extra][tilesX*tilesY];
		for (int i = 0; i < texture_indices.length; i++) {
			if (((texture_indices[i] >> GPUTileProcessor.LIST_TEXTURE_BIT) & 1) != 0) {
				int ntile = (texture_indices[i] >>  GPUTileProcessor.TEXT_NTILE_SHIFT);
				for (int l = 0; l < num_tile_extra; l++) {
					extra[l][ntile] = diff_rgb_combo[i * num_tile_extra + l];
				}
			}
		}
		return extra;
	}


	/**
	 * Get woi and RBGA image from the GPU after execRBGA call as 2/4 slices.
	 * device array has 4 pixels margins on each side, skip them here
	 * @param num_colors number of colors (1 or 3)
	 * @param woi should be initialized as Rectangle(). x,y,width, height will be populated (in pixels,)
	 * @return RBGA slices, last (alpha) in 0.0... 1.0 range, colors match input range
	 */
	public float [][] getRBGA(
			int     num_colors,
			Rectangle woi) { // will update to woi
		// first - read woi
		float [] fwoi = new float[4];
		cuMemcpyDtoH(Pointer.to(fwoi), gpu_woi,  4 * Sizeof.FLOAT);
		woi.x =      Float.floatToIntBits(fwoi[0]) * GPUTileProcessor.DTT_SIZE;
		woi.y =      Float.floatToIntBits(fwoi[1]) * GPUTileProcessor.DTT_SIZE;
		woi.width =  Float.floatToIntBits(fwoi[2]) * GPUTileProcessor.DTT_SIZE;
		woi.height = Float.floatToIntBits(fwoi[3]) * GPUTileProcessor.DTT_SIZE;
		float [][] rslt = new float[num_colors + 1][woi.width * woi.height];
		CUDA_MEMCPY2D copy_rbga =  new CUDA_MEMCPY2D();
		copy_rbga.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copy_rbga.srcDevice =       gpu_textures_rgba;
		copy_rbga.srcPitch =        texture_stride_rgba * Sizeof.FLOAT;
		copy_rbga.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copy_rbga.dstPitch =        woi.width * Sizeof.FLOAT;

		copy_rbga.WidthInBytes =    woi.width * Sizeof.FLOAT;
		copy_rbga.Height =          woi.height;
		copy_rbga.srcXInBytes =     4 * Sizeof.FLOAT;

		for (int ncol = 0; ncol<= num_colors; ncol++ ) {
		gpuTrace("getRBGA");
			copy_rbga.dstHost =     Pointer.to(rslt[ncol]);
			copy_rbga.srcY =        4 + (woi.height +GPUTileProcessor.DTT_SIZE) * ncol;
			cuMemcpy2D(copy_rbga); // run copy
		}
		return rslt;
	}

	public float [] getFlatTextures(
			int     num_tiles,
			int     num_colors,
			boolean keep_weights){
		int texture_slices =     (num_colors + 1 + (keep_weights?(num_cams + num_colors + 1):0)); // number of texture slices
		int texture_slice_size = (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);        // number of (float) elements in a single slice of a tile
		int texture_tile_size =  texture_slices * texture_slice_size;   // number of (float) elements in a multi-slice tile
		int texture_size =       texture_tile_size * num_texture_tiles; // number of (float) elements in the whole texture
		float [] cpu_textures = new float [texture_size];
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_textures;
		copyD2H.srcPitch =        texture_stride * Sizeof.FLOAT;

		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_textures);
		copyD2H.dstPitch =        texture_tile_size * Sizeof.FLOAT;

		copyD2H.WidthInBytes =    texture_tile_size * Sizeof.FLOAT;
		copyD2H.Height =          num_tiles; // num_texture_tiles;

		cuMemcpy2D(copyD2H); // run copy
		return cpu_textures;
	}

	public float [][][] getTextures( // todo - get rid of copying by multiple CUDA_MEMCPY2D?
			int     num_tiles,
			int     num_colors,
			boolean keep_weights){

		int texture_slices =     (num_colors + 1 + (keep_weights?(num_cams + num_colors + 1):0));
		int texture_slice_size = (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);
		int texture_tile_size =  texture_slices * texture_slice_size;
		//	        	int texture_size =       texture_tile_size * num_texture_tiles;
		float [] cpu_textures = getFlatTextures(
				num_tiles,
				num_colors,
				keep_weights);

		float [][][] textures = new float [num_texture_tiles][texture_slices][texture_tile_size];
		for (int ntile = 0; ntile < num_texture_tiles; ntile++) {
			for (int slice = 0; slice < texture_slices; slice++) {
				System.arraycopy(
						cpu_textures,                                           // src
						ntile * texture_tile_size + slice * texture_slice_size, // src offset
						textures[ntile][slice],                                 // dst
						0,                                                      // dst offset
						texture_slice_size);                                    // length to copy
			}
		}
		return textures;
	}

	/*
	public static double [][][][] doubleTextures( // not used
			Rectangle    woi,
			int []       indices,
			float [][][] ftextures,
			int          full_width,
			int          num_slices
			){
		int texture_slice_size = (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);
		double [][][][] textures = new double [woi.height][woi.width][num_slices][texture_slice_size];
		for (int indx = 0; indx < indices.length; indx++) if ((indices[indx] & (1 << GPUTileProcessor.LIST_TEXTURE_BIT)) != 0){
			int tile = indices[indx] >> GPUTileProcessor.CORR_NTILE_SHIFT;
		int tileX = tile % full_width;
		int tileY = tile / full_width;
		int wtileX = tileX - woi.x;
		int wtileY = tileY - woi.y;
		if ((wtileX < woi.width) && (wtileY < woi.height)) {
			for (int slice = 0; slice < num_slices; slice++) {
				for (int i = 0; i < texture_slice_size; i++) {
					textures[wtileY][wtileX][slice][i] = ftextures[indx][slice][i];
				}
			}
		}
		}
		return textures;
	}
   */
	public static double [][][][] doubleTextures( // may be accelerated with multithreading if needed.
			Rectangle    woi, // null or width and height match texture_tiles
			double [][][][] texture_tiles, // null or [tilesY][tilesX]
			int []       indices,
			float []     ftextures,
			int          full_width,
			int          num_slices,
			int          num_src_slices
			){
		if (num_slices > num_src_slices) {
			num_slices =num_src_slices;
		}
		int texture_slice_size = (2 * GPUTileProcessor.DTT_SIZE)* (2 * GPUTileProcessor.DTT_SIZE);
		int texture_tile_size = texture_slice_size * num_src_slices ;
		if ((woi == null) && (texture_tiles==null)) {
			System.out.println("doubleTextures(): woi and texture_tiles can not be simultaneously null");
			return null;
		}
		if (texture_tiles == null) {
			texture_tiles = new double [woi.height][woi.width][][];
		} else {
			woi.height = texture_tiles.length;
			woi.width = texture_tiles[0].length;
		}

		//        	double [][][][] textures = new double [woi.height][woi.width][num_slices][texture_slice_size];
		for (int indx = 0; indx < indices.length; indx++) if ((indices[indx] & (1 << GPUTileProcessor.LIST_TEXTURE_BIT)) != 0){
			int tile = (indices[indx] >> GPUTileProcessor.TEXT_NTILE_SHIFT);
			int tileX = tile % full_width;
			int tileY = tile / full_width;
			int wtileX = tileX - woi.x;
			int wtileY = tileY - woi.y;
			if ((tileY >= texture_tiles.length) || (tileX >= texture_tiles[0].length)) {
				System.out.println("BUG!!!! tileX="+tileX+", tileY="+tileY+", tile_op.length="+
						texture_tiles.length+", tile_op[0].length="+texture_tiles[0].length);
				System.out.println();
				System.out.println();
				System.out.println();
				System.out.println();
			}
			texture_tiles[tileY][tileX] = new double [num_slices][texture_slice_size];
			if ((wtileX >=0 ) && (wtileX < woi.width) && (wtileY >= 0) && (wtileY < woi.height)) {
				for (int slice = 0; slice < num_slices; slice++) {
					for (int i = 0; i < texture_slice_size; i++) {
						texture_tiles[wtileY][wtileX][slice][i] = ftextures[indx * texture_tile_size + slice * texture_slice_size + i];
					}
				}
			}
		}
		return texture_tiles;
	}

	public float [][] getRBG (int ncam){
		int out_width =  getImageWidth();//   + gpuQuad.getDttSize(); // 2022/05/12 removed margins from gpuQuad.getRBG(ncam);
		int out_height = getImageHeight(); // + gpuQuad.getDttSize(); // 2022/05/12 removed margins from gpuQuad.getRBG(ncam);
		
		int gpu_height = (out_height + GPUTileProcessor.DTT_SIZE);
		int gpu_width =  (out_width + GPUTileProcessor.DTT_SIZE);
		int gpu_img_size =       gpu_width * gpu_height;
		int rslt_img_size =      out_height * out_width; // width * height;
		float [] cpu_corr_image = new float [ num_colors * gpu_img_size];
		int gpu_width_in_bytes = gpu_width *Sizeof.FLOAT;

		// for copying results to host
		CUDA_MEMCPY2D copyD2H =   new CUDA_MEMCPY2D();
		copyD2H.srcMemoryType =   CUmemorytype.CU_MEMORYTYPE_DEVICE;
		copyD2H.srcDevice =       gpu_corr_images_h[ncam]; // ((test & 1) ==0) ? src_dpointer : dst_dpointer; // copy same data
		copyD2H.srcPitch =        imclt_stride*Sizeof.FLOAT;

		copyD2H.dstMemoryType =   CUmemorytype.CU_MEMORYTYPE_HOST;
		copyD2H.dstHost =         Pointer.to(cpu_corr_image);
		copyD2H.dstPitch =        gpu_width_in_bytes;

		copyD2H.WidthInBytes =    gpu_width_in_bytes;
		copyD2H.Height =          num_colors * gpu_height; // /2;

		cuMemcpy2D(copyD2H); // run copy

		float [][] fimg = new float [num_colors][ rslt_img_size];
		for (int ncol = 0; ncol < num_colors; ncol++) {
			int tl_offset = (GPUTileProcessor.DTT_SIZE/2) * (gpu_width + 1) + ncol*gpu_img_size;
			for (int nrow=0; nrow < out_height; nrow++) {
				System.arraycopy(cpu_corr_image, tl_offset + (gpu_width * nrow), fimg[ncol], out_width * nrow, out_width);
			}
		}
		return fimg;
	}

	
	@Deprecated
	public void  getTileSubcamOffsets(
			final TpTask[]            tp_tasks,        // will use // modify to have offsets for 8 cameras
			final GeometryCorrection  geometryCorrection_main,
			final GeometryCorrection  geometryCorrection_aux, // if null, will only calculate offsets from the main camera
			final double [][][]       ers_delay,        // if not null - fill with tile center acquisition delay
			final int                 threadsMax,  // maximal number of threads to launch
			final int                 debugLevel)
	{
		final int quad_main = (geometryCorrection_main != null)? num_cams:0;
		final int quad_aux =  (geometryCorrection_aux != null)? num_cams:0;


		final Thread[] threads = ImageDtt.newThreadArray(threadsMax);
		final AtomicInteger ai = new AtomicInteger(0);
		final Matrix [] corr_rots_main = geometryCorrection_main.getCorrVector().getRotMatrices(); // get array of per-sensor rotation matrices
		Matrix [] corr_rots_aux0 = null;
		if (geometryCorrection_aux != null) {
			Matrix rigMatrix = geometryCorrection_aux.getRotMatrix(true);
			corr_rots_aux0 =  geometryCorrection_aux.getCorrVector().getRotMatrices(rigMatrix); // get array of per-sensor rotation matrices
		}
		final Matrix [] corr_rots_aux = corr_rots_aux0;
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					int tileY,tileX; // , chn;
					//						showDoubleFloatArrays sdfa_instance = new showDoubleFloatArrays(); // just for debugging?
					double centerX; // center of aberration-corrected (common model) tile, X
					double centerY; //
					double disparity_main;
					double disparity_aux = 0.0;

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

						tileY = tp_tasks[nTile].ty;
						tileX = tp_tasks[nTile].tx;
						if (tp_tasks[nTile].task == 0) {
							continue; // nothing to do for this tile
						}

						centerX = tileX * GPUTileProcessor.DTT_SIZE + GPUTileProcessor.DTT_SIZE/2; //  - shiftX;
						centerY = tileY * GPUTileProcessor.DTT_SIZE + GPUTileProcessor.DTT_SIZE/2; //  - shiftY;
						disparity_main = tp_tasks[nTile].target_disparity;
						if (geometryCorrection_aux != null) {
							disparity_aux =  disparity_main * geometryCorrection_aux.getDisparityRadius()/geometryCorrection_main.getDisparityRadius();
						}

						// TODO: move port coordinates out of color channel loop
						double [][] centersXY_main = null;
						double [][] centersXY_aux =  null;
						double [][] disp_dist_main = new double[quad_main][]; // used to correct 3D correlations
						double [][] disp_dist_aux =  new double[quad_aux][]; // used to correct 3D correlations

						if (geometryCorrection_main != null) {
							centersXY_main = geometryCorrection_main.getPortsCoordinatesAndDerivatives(
									geometryCorrection_main, //			GeometryCorrection gc_main,
									false,          // boolean use_rig_offsets,
									corr_rots_main, // Matrix []   rots,
									null,           //  Matrix [][] deriv_rots,
									null,           // double [][] pXYderiv, // if not null, should be double[8][]
									disp_dist_main,       // used to correct 3D correlations
									centerX,
									centerY,
									disparity_main); //  + disparity_corr);
							tp_tasks[nTile].xy = new float [centersXY_main.length][2];
							for (int i = 0; i < centersXY_main.length; i++) {
								tp_tasks[nTile].xy[i][0] = (float) centersXY_main[i][0];
								tp_tasks[nTile].xy[i][1] = (float) centersXY_main[i][1];
							}
						}
						if (geometryCorrection_aux != null) {
							centersXY_aux =  geometryCorrection_aux.getPortsCoordinatesAndDerivatives(
									geometryCorrection_main, //			GeometryCorrection gc_main,
									true,            // boolean use_rig_offsets,
									corr_rots_aux,   // Matrix []   rots,
									null,            //  Matrix [][] deriv_rots,
									null,            // double [][] pXYderiv, // if not null, should be double[8][]
									disp_dist_aux,   // used to correct 3D correlations
									centerX,
									centerY,
									disparity_aux); //  + disparity_corr);
							tp_tasks[nTile].xy_aux = new float [centersXY_aux.length][2];
							for (int i = 0; i < centersXY_aux.length; i++) {
								tp_tasks[nTile].xy_aux[i][0] = (float) centersXY_aux[i][0];
								tp_tasks[nTile].xy_aux[i][1] = (float) centersXY_aux[i][1];
							}
						}
						// acquisition time of the tiles centers in scanline times
						if (ers_delay != null) {
							for (int i = 0; i < quad_main; i++) ers_delay[0][i][nTile] = centersXY_main[i][1]-geometryCorrection_main.woi_tops[i];
							for (int i = 0; i < quad_aux; i++)  ers_delay[1][i][nTile] = centersXY_aux[i][1]- geometryCorrection_aux.woi_tops[i];
						}
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
	}

	/**
	 * Prepare GPU tasks for interscene accumulation - instead of the uniform grid, pixel coordinates and disparity are provided
	 * by the caller. They are calculated by recalculating from the reference scene after appropriate transformation (shift, rotation
	 * and ERS correction)
	 * @param calcPortsCoordinatesAndDerivatives -  calculate port coordinates when using CPU mode. GPU calculates them 
	 * @param pXpYD Array of per-tile pX, pY and disparity triplets (or nulls for undefined tiles).
	 * @param geometryCorrection GeometryCorrection instance for the camera.
	 * @param disparity_corr Disparity correction at infinity
	 * @param margin Skip tile if at least one channel tile center is closer to the image edge than this margin.
	 * @param valid_tiles Optional (if not null) should be initialized as boolean [tiles] - will contain valid tiles
	 * @param threadsMax Maximal number of threads to run concurrently.
	 * @return Array of TpTask instances (fully prepared) to be fed to the GPU
	 */
	public TpTask[]  setInterTasks( // not used 2023.08.04
			final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
			final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
			final boolean []          selection, // may be null, if not null do not  process unselected tiles
			final GeometryCorrection  geometryCorrection,
			final double              disparity_corr,
			final int                 margin,      // do not use tiles if their centers are closer to the edges
			final boolean []          valid_tiles,            
			final int                 threadsMax)  // maximal number of threads to launch
	{
		return setInterTasks(
				num_cams,           // final int                 num_cams,
				img_width,          // final int                 img_width,
				calcPortsCoordinatesAndDerivatives, // final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
				pXpYD,              // final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
				selection,          // final boolean []          selection, // may be null, if not null do not  process unselected tiles
				geometryCorrection, // final GeometryCorrection  geometryCorrection,
				disparity_corr,     // final double              disparity_corr,
				margin,             // final int                 margin,      // do not use tiles if their centers are closer to the edges
				valid_tiles,        // final boolean []          valid_tiles,            
				threadsMax);        // final int                 threadsMax);  // maximal number of threads to launch
	}

	public static TpTask[]  setInterTasks(
			final int                 num_cams,
			final int                 img_width, // should match pXpYD
			final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
			final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
			final boolean []          selection, // may be null, if not null do not  process unselected tiles
			final GeometryCorrection  geometryCorrection,
			final double              disparity_corr,
			final int                 margin,      // do not use tiles if their centers are closer to the edges
			final boolean []          valid_tiles,            
			final int                 threadsMax)  // maximal number of threads to launch
	{
		return setInterTasks(
				false,                             // final boolean             accumulate,
				1.0, // final double              global_scale, // <=1.0
				num_cams,                          // final int                 num_cams,
				img_width,                         // final int                 img_width, // should match pXpYD
				calcPortsCoordinatesAndDerivatives,// final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
				pXpYD,                             // final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
				selection,                         // final boolean []          selection, // may be null, if not null do not  process unselected tiles
				geometryCorrection,                // final GeometryCorrection  geometryCorrection,
				disparity_corr,                    // final double              disparity_corr,
				margin,                            // final int                 margin,      // do not use tiles if their centers are closer to the edges
				valid_tiles,                       // final boolean []          valid_tiles,            
				threadsMax);                       // final int                 threadsMax)  // maximal number of threads to launch
	}
	
	
	public static TpTask[]  setInterTasks(
			final boolean             accumulate,
			final double              global_scale, // <=1.0
			final int                 num_cams,
			final int                 img_width, // should match pXpYD
			final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
			final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
			final boolean []          selection, // may be null, if not null do not  process unselected tiles
			final GeometryCorrection  geometryCorrection,
			final double              disparity_corr,
			final int                 margin,      // do not use tiles if their centers are closer to the edges
			final boolean []          valid_tiles,            
			final int                 threadsMax)  // maximal number of threads to launch
	{
//		int num_pairs = Correlation2d.getNumPairs(num_cams);
		//change to fixed 511?
//		final int task_code = ((1 << num_pairs)-1) << GPUTileProcessor.TASK_CORR_BITS; //  correlation only
		final int task_code = (1 << GPUTileProcessor.TASK_CORR_EN) |  (1 << GPUTileProcessor.TASK_INTER_EN);
		final double min_px = margin; 
		final double max_px = geometryCorrection.getSensorWH()[0] - 1 - margin; // sensor width here, not window width
		final double [] min_py = new double[num_cams] ;
		final double [] max_py = new double[num_cams] ;
		for (int i = 0; i < num_cams; i++) {
			min_py [i] = margin + (calcPortsCoordinatesAndDerivatives? geometryCorrection.getWOITops()[i] : 0);
			// camera_heights array is only set during conditionImageSet(), not called by the intersceneAccumulate()
			// That was correct, as all scenes should be conditioned
			max_py [i] = geometryCorrection.getSensorWH()[1] - 1 - margin; // same for all channels?
			//.getSensorWH()[0]
		}
		if (valid_tiles!=null) {
			Arrays.fill(valid_tiles, false);
		}
		final int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		final int tiles = pXpYD.length;
		final Matrix [] corr_rots = geometryCorrection.getCorrVector().getRotMatrices(); // get array of per-sensor rotation matrices
		final int quad_main = (geometryCorrection != null)? num_cams:0;
		final Thread[] threads = ImageDtt.newThreadArray(threadsMax);
		final AtomicInteger ai = new AtomicInteger(00);
		final AtomicInteger aTiles = new AtomicInteger(0);
		final TpTask[] tp_tasks = new TpTask[tiles]; // aTiles.get()];

		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					for (int nTile = ai.getAndIncrement(); nTile < tiles; nTile = ai.getAndIncrement()) if ((pXpYD[nTile] != null) && ((selection == null) || selection[nTile])) {
						int tileY = nTile / tilesX;
						int tileX = nTile % tilesX;
						TpTask tp_task = new TpTask(num_cams, tileX, tileY);
						tp_task.task = task_code;
						double disparity = pXpYD[nTile][2] + disparity_corr;
						tp_task.target_disparity = (float) disparity; // will it be used?
						double [] centerXY = pXpYD[nTile];
						tp_task.setCenterXY(centerXY); // this pair of coordinates will be used by GPU to set tp_task.xy and task.disp_dist!
						boolean bad_margins = false;
						if (calcPortsCoordinatesAndDerivatives) {
							double [][] disp_dist = new double[quad_main][]; // used to correct 3D correlations (not yet used here)
							double [][] centersXY_main = geometryCorrection.getPortsCoordinatesAndDerivatives(
									geometryCorrection, //			GeometryCorrection gc_main,
									false,          // boolean use_rig_offsets,
									corr_rots, // Matrix []   rots,
									null,           //  Matrix [][] deriv_rots,
									null,           // double [][] pXYderiv, // if not null, should be double[8][]
									disp_dist,       // used to correct 3D correlations
									pXpYD[nTile][0],
									pXpYD[nTile][1],
									disparity); //  + disparity_corr);
							tp_task.setDispDist(disp_dist);
							tp_task.xy = new float [centersXY_main.length][2];
							tp_task.setScale(global_scale, accumulate);
							for (int i = 0; i < centersXY_main.length; i++) {
								if (    (centersXY_main[i][0] < min_px) ||    (centersXY_main[i][0] > max_px) ||
										(centersXY_main[i][1] < min_py[i]) || (centersXY_main[i][1] > max_py[i])) {
									bad_margins = true;
									break;
								}
								tp_task.xy[i][0] = (float) centersXY_main[i][0];
								tp_task.xy[i][1] = (float) centersXY_main[i][1];
							}
						} else { // only check center for margins
							if (    (centerXY[0] < min_px) ||    (centerXY[0] > max_px) ||
									(centerXY[1] < min_py[0]) || (centerXY[1] > max_py[0])) {
								bad_margins = true;
//								break;
							}

						}
						if (bad_margins) {
							continue;
						}
						tp_tasks[aTiles.getAndIncrement()] = tp_task;
						if (valid_tiles!=null) {
							valid_tiles[nTile] = true;
						}
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
		final TpTask[] tp_tasks_out = new TpTask[aTiles.get()];
		System.arraycopy(tp_tasks, 0, tp_tasks_out, 0, tp_tasks_out.length);
		return tp_tasks_out;
	}

	public static TpTask[][]  setInterTasksMotionBlur(
			final int                 num_cams,
			final int                 img_width, // should match pXpYD
			final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
			final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
			final boolean []          selection, // may be null, if not null do not  process unselected tiles
			// motion blur compensation 
			final double              mb_tau,      // 0.008; // time constant, sec
			final double              mb_max_gain, // 5.0;   // motion blur maximal gain (if more - move second point more than a pixel
			final double [][]         mb_vectors,  //
			
			final GeometryCorrection  geometryCorrection,
			final double              disparity_corr,
			final int                 margin,      // do not use tiles if their centers are closer to the edges
			final boolean []          valid_tiles,            
			final int                 threadsMax){  // maximal number of threads to launch
		return setInterTasksMotionBlur(
				false,                              // final boolean             accumulate,
				1.0,                                // final double              global_scale, // <=1.0
				num_cams,                           // final int                 num_cams,
				img_width,                          // final int                 img_width, // should match pXpYD
				calcPortsCoordinatesAndDerivatives, // final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
				pXpYD,                              // final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
				selection,                          // final boolean []          selection, // may be null, if not null do not  process unselected tiles
				// motion blur compensation 
				mb_tau,                             // final double              mb_tau,      // 0.008; // time constant, sec
				mb_max_gain,                        // final double              mb_max_gain, // 5.0;   // motion blur maximal gain (if more - move second point more than a pixel
				mb_vectors,                         // final double [][]         mb_vectors,  //
				
				geometryCorrection,                 // final GeometryCorrection  geometryCorrection,
				disparity_corr,                     // final double              disparity_corr,
				margin,                             // final int                 margin,      // do not use tiles if their centers are closer to the edges
				valid_tiles,                        // final boolean []          valid_tiles,            
				threadsMax);                        // final int                 threadsMax)  // maximal number of threads to launch
	}
	
	public static TpTask[][]  setInterTasksMotionBlur(
			final boolean             accumulate,
			final double              global_scale, // <=1.0
			final int                 num_cams,
			final int                 img_width, // should match pXpYD
			final boolean             calcPortsCoordinatesAndDerivatives, // GPU can calculate them centreXY
			final double [][]         pXpYD, // per-tile array of pX,pY,disparity triplets (or nulls)
			final boolean []          selection, // may be null, if not null do not  process unselected tiles
			// motion blur compensation 
			final double              mb_tau,      // 0.008; // time constant, sec
			final double              mb_max_gain, // 5.0;   // motion blur maximal gain (if more - move second point more than a pixel
			final double [][]         mb_vectors,  //
			
			final GeometryCorrection  geometryCorrection,
			final double              disparity_corr,
			final int                 margin,      // do not use tiles if their centers are closer to the edges
			final boolean []          valid_tiles,            
			final int                 threadsMax)  // maximal number of threads to launch
	{
		final double min_sub = 1e-12;
		final double min_len = 0.001; // pix was 0.1
///		int num_pairs = Correlation2d.getNumPairs(num_cams);
		//change to fixed 511?
//		final int task_code = ((1 << num_pairs)-1) << GPUTileProcessor.TASK_CORR_BITS; //  correlation only
		final int task_code = (1 << GPUTileProcessor.TASK_CORR_EN) |  (1 << GPUTileProcessor.TASK_INTER_EN);

		final double min_px = margin; 
		final double max_px = geometryCorrection.getSensorWH()[0] - 1 - margin; // sensor width here, not window width
		final double [] min_py = new double[num_cams] ;
		final double [] max_py = new double[num_cams] ;
		for (int i = 0; i < num_cams; i++) {
			min_py [i] = margin + (calcPortsCoordinatesAndDerivatives? geometryCorrection.getWOITops()[i] : 0);
			// camera_heights array is only set during conditionImageSet(), not called by the intersceneAccumulate()
			// That was correct, as all scenes should be conditioned
			max_py [i] = geometryCorrection.getSensorWH()[1] - 1 - margin; // same for all channels?
			//.getSensorWH()[0]
		}
		if (valid_tiles!=null) {
			Arrays.fill(valid_tiles, false);
		}
		final int tilesX =  img_width / GPUTileProcessor.DTT_SIZE;
		final int tiles = pXpYD.length;
		final Matrix [] corr_rots = geometryCorrection.getCorrVector().getRotMatrices(); // get array of per-sensor rotation matrices
		final int quad_main = num_cams; // (geometryCorrection != null)? num_cams:0;
		final Thread[] threads = ImageDtt.newThreadArray(threadsMax);
		final AtomicInteger ai = new AtomicInteger(00);
		final AtomicInteger aTiles = new AtomicInteger(0);
		final TpTask[][] tp_tasks = new TpTask[2][tiles]; // aTiles.get()]; // [0] - main, [1] - shifted
		final double mb_len_scale = -Math.log(1.0 - 1.0/mb_max_gain);
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					for (int nTile = ai.getAndIncrement(); nTile < tiles; nTile = ai.getAndIncrement())
						if ((pXpYD[nTile] != null) &&
								!Double.isNaN(mb_vectors[0][nTile]) &&
								!Double.isNaN(mb_vectors[1][nTile]) &&
								((selection == null) || selection[nTile])) {
						int tileY = nTile / tilesX;
						int tileX = nTile % tilesX;
						TpTask tp_task =    new TpTask(num_cams, tileX, tileY);
						TpTask tp_task_sub = new TpTask(num_cams, tileX, tileY);
						tp_task.task = task_code;
						tp_task_sub.task = task_code;
						double disparity = pXpYD[nTile][2] + disparity_corr;
						tp_task.target_disparity = (float) disparity; // will it be used?
						tp_task_sub.target_disparity = tp_task.target_disparity; // will it be used?
						double [] centerXY = pXpYD[nTile];
						tp_task.setCenterXY(centerXY); // this pair of coordinates will be used by GPU to set tp_task.xy and task.disp_dist!
						// calculate offset for the secondary tile and weigh
						double dx = mb_vectors[0][nTile];
						double dy = mb_vectors[1][nTile];
						double mb_len = Math.sqrt(dx*dx+dy*dy); // in pixels/s -> 0
						double [] centerXY_sub = centerXY;
						if (mb_len < min_len) { // No MB correction
							tp_task.setScale(global_scale, accumulate);
							tp_task_sub.task = 0; // disable
							tp_task_sub.setScale(-min_sub * global_scale);
							tp_task_sub.setCenterXY(centerXY);
						} else {
							dx /= mb_len; // unit vector
							dy /= mb_len;
							mb_len *= mb_tau; // now in pixels
							double mb_offs = 1.0; // try 1 pixel. Maybe adjust for non-ortho, e.g. sqrt(2) for diagonal?
							double min_offs = mb_len_scale * mb_len;
							if (mb_offs < min_offs) {
								mb_offs = min_offs;
							}
							dx *= mb_offs;
							dy *= mb_offs;
							double exp_offs = Math.exp(-mb_offs/mb_len);
							// centroid offset from the first point along the {dx,dy}, in the range 0..1
							// using that centroid of the half-exponent (-inf to 0) is at -1
							double rel_cent = mb_len* (1.0 - (mb_offs/mb_len + 1.0) * exp_offs) / (mb_offs * (1 - exp_offs)); 
							
							centerXY =     new double[] {centerXY[0]-rel_cent*    dx, centerXY[1]-rel_cent*    dy};
							centerXY_sub = new double[] {centerXY[0]+(1-rel_cent)*dx, centerXY[1]+(1-rel_cent)*dy};
							
							tp_task.setCenterXY(centerXY);
							tp_task_sub.setCenterXY(centerXY_sub);
							double gain =    1.0/(1.0 - exp_offs);
							double gain_sub = -gain * exp_offs;
							
							if (gain_sub > -min_sub) {
								gain_sub = -min_sub;
							}
							tp_task.setScale(gain * global_scale, accumulate);
							tp_task_sub.setScale(gain_sub * global_scale);
						}
						boolean bad_margins = false;
						if (calcPortsCoordinatesAndDerivatives) { // for non-GPU?
							double [][] disp_dist = new double[quad_main][]; // used to correct 3D correlations (not yet used here)
							double [][] centersXY_main = geometryCorrection.getPortsCoordinatesAndDerivatives(
									geometryCorrection, //			GeometryCorrection gc_main,
									false,          // boolean use_rig_offsets,
									corr_rots, // Matrix []   rots,
									null,           //  Matrix [][] deriv_rots,
									null,           // double [][] pXYderiv, // if not null, should be double[8][]
									disp_dist,       // used to correct 3D correlations
									centerXY[0],
									centerXY[1],
									disparity); //  + disparity_corr);
							tp_task.setDispDist(disp_dist);
							tp_task.xy = new float [centersXY_main.length][2];
							
							for (int i = 0; i < centersXY_main.length; i++) {
								if (    (centersXY_main[i][0] < min_px) ||    (centersXY_main[i][0] > max_px) ||
										(centersXY_main[i][1] < min_py[i]) || (centersXY_main[i][1] > max_py[i])) {
									bad_margins = true;
									break;
								}
								tp_task.xy[i][0] = (float) centersXY_main[i][0];
								tp_task.xy[i][1] = (float) centersXY_main[i][1];
							}
							// same for the second entry
							double [][] disp_dist_sub = new double[quad_main][]; // used to correct 3D correlations (not yet used here)
							double [][] centersXY_main_sub = geometryCorrection.getPortsCoordinatesAndDerivatives(
									geometryCorrection, //			GeometryCorrection gc_main,
									false,          // boolean use_rig_offsets,
									corr_rots, // Matrix []   rots,
									null,           //  Matrix [][] deriv_rots,
									null,           // double [][] pXYderiv, // if not null, should be double[8][]
									disp_dist_sub,       // used to correct 3D correlations
									centerXY_sub[0],
									centerXY_sub[1],
									disparity); //  + disparity_corr);
							tp_task_sub.setDispDist(disp_dist);
							tp_task_sub.xy = new float [centersXY_main.length][2];
							for (int i = 0; i < centersXY_main.length; i++) {
								if (    (centersXY_main[i][0] < min_px) ||    (centersXY_main[i][0] > max_px) ||
										(centersXY_main[i][1] < min_py[i]) || (centersXY_main[i][1] > max_py[i])) {
									bad_margins = true;
									break;
								}
								tp_task_sub.xy[i][0] = (float) centersXY_main_sub[i][0];
								tp_task_sub.xy[i][1] = (float) centersXY_main_sub[i][1];
							}
							
						} else { // only check center for margins
							if (    (centerXY[0]     < min_px)    || (centerXY[0]     > max_px) ||
									(centerXY[1]     < min_py[0]) || (centerXY[1]     > max_py[0]) ||
									(centerXY_sub[0] < min_px)    || (centerXY_sub[0] > max_px) ||
									(centerXY_sub[1] < min_py[0]) || (centerXY_sub[1] > max_py[0])) {
								bad_margins = true;
//								break;
							}

						}
						if (bad_margins) {
							continue;
						}
						int tp_task_index = aTiles.getAndIncrement();
						tp_tasks[0][tp_task_index] = tp_task;
						tp_tasks[1][tp_task_index] = tp_task_sub;
						if (valid_tiles!=null) {
							valid_tiles[nTile] = true;
						}
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
		final TpTask[][] tp_tasks_out = new TpTask[2][aTiles.get()];
		System.arraycopy(tp_tasks[0], 0, tp_tasks_out[0], 0, tp_tasks_out[0].length);
		System.arraycopy(tp_tasks[1], 0, tp_tasks_out[1], 0, tp_tasks_out[1].length);
		return tp_tasks_out;
	}

	/**
	 * Prepare tasks for accumulation of multiple images, shifted proportionally to the vector field
	 * and temporal distance from the middle frame. Accumulation uses the feature developed for the
	 * thermal camera motion blur correction that allows accumulation in the transform domain. It only
	 * works with the negative scales, so the result will be a negative image in the TD.
	 * @param targets_array sparse array of motion vectors (may be longer, only Vx and Vy used). Null elements
	 * are allowed, they will be skipped, resultin in null TpTask elements.
	 * @param offset_scale multiply all vectors by this value when calculating pixel offsets
	 * @param magnitude_scale Scale data for accumulation (here positive, will be negated). If 0 - will use scale=1.0 (no accumulation)
	 * @param index_vx - index of vx in the targets_array[tile] array
	 * @param index_xc - index of xc in the targets_array[tile] array to center target or <0 to render as is
	 * @param image_width image width in tiles (80 for 640-wide images).
	 * @return condensed array of TpTask
	 */
	public static TpTask [] setRectilinearMovingTasks(
			final double [][] targets_array,
			final double      offset_scale,
			final double      magnitude_scale,
			final int         index_vx, 
			final int         index_xc,
			final int         tilesX) {
		final float fmagnitude_scale = (magnitude_scale==0)? 1.0f : ((float) -magnitude_scale);
		final int tiles = targets_array.length;
		final TpTask [] tp_tasks_full = new TpTask [tiles]; 
		final Thread[] threads = ImageDtt.newThreadArray();
		final AtomicInteger ai = new AtomicInteger(00);
		final AtomicInteger aTiles = new AtomicInteger(0);
		// Not sure which bits are needed to enable GPU processing
		final int task_code = (1 << GPUTileProcessor.TASK_TEXT_EN) | (1 << GPUTileProcessor.TASK_CORR_EN) |  (1 << GPUTileProcessor.TASK_INTER_EN);
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					for (int nTile = ai.getAndIncrement(); nTile < tiles; nTile = ai.getAndIncrement()) if (targets_array[nTile] != null){
						int tileY = nTile / tilesX;
						int tileX = nTile % tilesX;;
						double dx = targets_array[nTile][index_vx + 0]*offset_scale; 
						double dy = targets_array[nTile][index_vx + 1]*offset_scale; 
						if ((index_xc >= 0) && !Double.isNaN( targets_array[nTile][index_xc])) {
							dx += targets_array[nTile][index_xc + 0];
							dy += targets_array[nTile][index_xc + 1];
						}
						double [] cxy0 = { // uniform coordinates for the center scene
								(tileX + 0.5) * GPUTileProcessor.DTT_SIZE,
								(tileY + 0.5) * GPUTileProcessor.DTT_SIZE};
						// check just
						TpTask tp_task = new TpTask(1, tileX, tileY); // single "sensor"
						tp_task.task = task_code;
						tp_task.scale = fmagnitude_scale;
						tp_task.setCenterXY(cxy0); // this pair of coordinates will be used by GPU... not yet
						tp_task.xy=new float [1][2];
						// see where task.disp_dist is used - only in GeometryCorrection, when calculating .xy
						tp_task.xy[0][0] = (float) (cxy0[0] + dx);
						tp_task.xy[0][1] = (float) (cxy0[1] + dy);
						int iTile = aTiles.getAndIncrement();
						tp_tasks_full[iTile] = tp_task;
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
		final TpTask[] tp_tasks = new TpTask[aTiles.get()];
		System.arraycopy(tp_tasks_full, 0, tp_tasks, 0, tp_tasks.length);
		return tp_tasks;
	}
	
	
	/**
	 * 
	 * @param fpixels
	 * @param img_width
	 * @param woi
	 * @param affine
	 * @return
	 */
	public static TpTask[][] setRectilinearInterTasks(
			final float  [][]   fpixels, // to check for empty
			final int           img_width,
			Rectangle           woi,
			final double [][][] affine_in){  // [2][2][3] affine coefficients to translate common to 2 images
		final double [][][] affine = (affine_in != null) ? affine_in : new double [][][] {{{1,0,0},{0,1,0}},{{1,0,0},{0,1,0}}};
		final int img_height =   fpixels[0].length/img_width;
		if (woi == null) {
			woi = new Rectangle(0,0,img_width,img_height);
		} else {
			int x0 = ((int) Math.floor(1.0*woi.x/GPUTileProcessor.DTT_SIZE))*GPUTileProcessor.DTT_SIZE; 
			int y0 = ((int) Math.floor(1.0*woi.y/GPUTileProcessor.DTT_SIZE))*GPUTileProcessor.DTT_SIZE;
			woi.width += woi.x-x0;  
			woi.height += woi.y-y0;
			woi.width =  ((int) Math.ceil(1.0*woi.width / GPUTileProcessor.DTT_SIZE)) * GPUTileProcessor.DTT_SIZE;
			woi.height = ((int) Math.ceil(1.0*woi.height/ GPUTileProcessor.DTT_SIZE)) * GPUTileProcessor.DTT_SIZE;
			woi.x = x0;
			woi.y = y0;
		}
		final Rectangle tiles_woi = new Rectangle (
				woi.x      / GPUTileProcessor.DTT_SIZE,
				woi.y      / GPUTileProcessor.DTT_SIZE,
				woi.width  / GPUTileProcessor.DTT_SIZE,
				woi.height / GPUTileProcessor.DTT_SIZE);
		final int tiles =   tiles_woi.width*tiles_woi.height;
	
		final int task_code = (1 << GPUTileProcessor.TASK_CORR_EN) |  (1 << GPUTileProcessor.TASK_INTER_EN);
		final TpTask[][] tp_tasks_full = new TpTask[2][tiles]; // sparse, rfirst index - image #
		final Thread[] threads = ImageDtt.newThreadArray();
		final AtomicInteger ai = new AtomicInteger(00);
		final AtomicInteger aTiles = new AtomicInteger(0);
		final int margin = GPUTileProcessor.DTT_SIZE/2 + 1; // 5
		for (int ithread = 0; ithread < threads.length; ithread++) {
			threads[ithread] = new Thread() {
				@Override
				public void run() {
					double [][] cxy = new double [2][2]; // image number, {x,y}
					int [][] icxy = new int [2][2];
					for (int nTile = ai.getAndIncrement(); nTile < tiles; nTile = ai.getAndIncrement()) {
						int tileY = nTile / tiles_woi.width + tiles_woi.y;
						int tileX = nTile % tiles_woi.width + tiles_woi.x;
						double [] cxy0 = {
								(tileX + 0.5) * GPUTileProcessor.DTT_SIZE,
								(tileY + 0.5) * GPUTileProcessor.DTT_SIZE};
						boolean good_tile = false;
						check_good_tiles:{
							for (int n = 0; n < affine.length; n++) {
								for (int i = 0; i < affine[n].length; i++) {
									cxy[n][i] = affine[n][i][0]*cxy0[0] + affine[n][i][1]*cxy0[1] + affine[n][i][2];
									if (cxy[n][i] < margin) {
										break check_good_tiles;
									}
									icxy[n][i] = (int) Math.round(cxy[n][i]);
								}
								if ((icxy[n][0] >= (img_width-margin)) || (icxy[n][1] >= (img_height-margin))) {
									break check_good_tiles;
								}
								if (Float.isNaN(fpixels[n][icxy[n][0] + img_width * icxy[n][1]])) {
									break check_good_tiles; // center pixel is NaN, consider bad tile
								}
							}
							good_tile = true;
						}
						if (!good_tile) {
							continue;
						}
						// check just
						TpTask tp_task1 = new TpTask(1, tileX, tileY);
						TpTask tp_task2 = new TpTask(1, tileX, tileY);
						tp_task1.scale = 0.0f;
						tp_task2.scale = 0.0f;
						tp_task1.task = task_code;
						tp_task2.task = task_code;
						tp_task1.setCenterXY(cxy0); // this pair of coordinates will be used by GPU... not yet
						tp_task2.setCenterXY(cxy0); // this pair of coordinates will be used by GPU... not yet
						tp_task1.xy=new float [1][2];
						tp_task2.xy=new float [1][2];
						// see where task.disp_dist is used - only in GeometryCorrection, when calculating .xy
						tp_task1.xy[0][0] = (float) cxy[0][0];
						tp_task1.xy[0][1] = (float) cxy[0][1];
						tp_task2.xy[0][0] = (float) cxy[1][0];
						tp_task2.xy[0][1] = (float) cxy[1][1];
						int iTile = aTiles.getAndIncrement();
						tp_tasks_full[0][iTile] = tp_task1;
						tp_tasks_full[1][iTile] = tp_task2;
					}
				}
			};
		}
		ImageDtt.startAndJoin(threads);
		final TpTask[][] tp_tasks = new TpTask[2][aTiles.get()];
		System.arraycopy(tp_tasks_full[0], 0, tp_tasks[0], 0, tp_tasks[0].length);
		System.arraycopy(tp_tasks_full[1], 0, tp_tasks[1], 0, tp_tasks[1].length);
		return tp_tasks;
	}
	
	
	public void setLpfRbg(
			float [][] lpf_rbg, // 4 64-el. arrays: r,b,g,m
			boolean    debug)
	{

		int l = lpf_rbg[0].length; // 64
		float []   lpf_flat = new float [lpf_rbg.length * l];
		for (int i = 0; i < lpf_rbg.length; i++) {
			for (int j = 0; j < l; j++) {
				lpf_flat[j + i * l] = lpf_rbg[i][j];
			}
		}

		CUdeviceptr constantMemoryPointer = new CUdeviceptr();
		long constantMemorySizeArray[] = { 0 };
		cuModuleGetGlobal(constantMemoryPointer, constantMemorySizeArray,  this.gpuTileProcessor.module, "lpf_data"); //CUDA_ERROR_ILLEGAL_ADDRESS after re-run
		int constantMemorySize = (int)constantMemorySizeArray[0];
		if (debug) System.out.println("constantMemoryPointer: " + constantMemoryPointer);
		if (debug) System.out.println("constantMemorySize: " + constantMemorySize);
		cuMemcpyHtoD(constantMemoryPointer, Pointer.to(lpf_flat), constantMemorySize);
		if (debug) System.out.println();
		/*
            if (debug) {
        		for (int i = 0; i < lpf_flat.length; i++) {
        			System.out.print(String.format("%8.5f", lpf_flat[i]));
        			if (((i+1) % 16) == 0) {
        				System.out.println();
        			}
        		}
            }
		 */
	}

	public void setLpfCorr(
			String   const_name, // "lpf_corr"
			float [] lpf_flat,
			boolean  debug)

	{
		CUdeviceptr constantMemoryPointer = new CUdeviceptr();
		long constantMemorySizeArray[] = { 0 };
		cuModuleGetGlobal(constantMemoryPointer, constantMemorySizeArray,  this.gpuTileProcessor.module, const_name);
		int constantMemorySize = (int)constantMemorySizeArray[0];
		if (debug) System.out.println("constantMemoryPointer: " + constantMemoryPointer);
		if (debug) System.out.println("constantMemorySize: " + constantMemorySize);
		cuMemcpyHtoD(constantMemoryPointer, Pointer.to(lpf_flat), constantMemorySize);
		if (debug) System.out.println();
		/*
            if (debug) {
        		for (int i = 0; i < lpf_flat.length; i++) {
        			System.out.print(String.format("%8.5f", lpf_flat[i]));
        			if (((i+1) % 16) == 0) {
        				System.out.println();
        			}
        		}
            }
		 */
	}

	public void printConstMem(
			String   const_name, // "lpf_corr"
			boolean  flt) {
		CUdeviceptr constantMemoryPointer = new CUdeviceptr();
		long constantMemorySizeArray[] = { 0 };
		cuModuleGetGlobal(constantMemoryPointer, constantMemorySizeArray,  this.gpuTileProcessor.module, const_name);
		int constantMemorySize = (int)constantMemorySizeArray[0];
		if (constantMemorySize > 0) {
			System.out.println ("costant memory "+const_name+" contains "+constantMemorySize+" bytes");
			if (flt) {
				float [] fdata = new float [constantMemorySize/4];
				cuMemcpyDtoH(Pointer.to(fdata),constantMemoryPointer,  constantMemorySize);
				for (int i = 0; i < fdata.length; i++) {
					if ((i % 8) == 0) System.out.println();
					System.out.print(String.format("%12.6f, ",fdata[i]));
				}
			} else {
				int [] idata = new int [constantMemorySize/4];
				cuMemcpyDtoH(Pointer.to(idata),constantMemoryPointer,  constantMemorySize);
				for (int i = 0; i < idata.length; i++) {
					if ((i % 8) == 0) System.out.println();
					System.out.print(String.format("0x%08x, ",idata[i]));
				}
			}
			System.out.println();

		}
	}
	
	
	public float [] floatSetCltLpfFd(
			double   sigma) {
		int dct_size = GPUTileProcessor.DTT_SIZE;
		DttRad2 dtt = new DttRad2(dct_size);
		double [] clt_fd = dtt.dttt_iiie(setCltLpf(sigma));
		int l = dct_size*dct_size;
		float []   lpf_flat = new float [l];
		for (int j = 0; j < l; j++) {
			lpf_flat[j] = (float) (clt_fd[j]*2*dct_size);
		}
		return lpf_flat;
	}

	public double [] doubleSetCltLpfFd(
			double   sigma) {
		int dct_size = GPUTileProcessor.DTT_SIZE;
		DttRad2 dtt = new DttRad2(dct_size);
		double [] clt_fd = dtt.dttt_iiie(setCltLpf(sigma));
		int l = dct_size*dct_size;
		double []   lpf_flat = new double [l];
		for (int j = 0; j < l; j++) {
			lpf_flat[j] = (float) (clt_fd[j]*2*dct_size);
		}
		return lpf_flat;
	}

	public double [] setCltLpf(
			double   sigma)
	{
		int dct_size = GPUTileProcessor.DTT_SIZE;
		double [] lpf = new double [dct_size*dct_size];
		int dct_len = dct_size * dct_size;
		if (sigma == 0.0f) {
			lpf[0] = 1.0f;
			for (int i = 1; i < dct_len; i++){
				lpf[i] = 0.0f;
			}
		} else {
			for (int i = 0; i < dct_size; i++){
				for (int j = 0; j < dct_size; j++){
					lpf[i*dct_size+j] = (float) Math.exp(-(i*i+j*j)/(2*sigma));
				}
			}
			// normalize
			double sum = 0;
			for (int i = 0; i < dct_size; i++){
				for (int j = 0; j < dct_size; j++){
					double d = 	lpf[i*dct_size+j];
					d*=Math.cos(Math.PI*i/(2*dct_size))*Math.cos(Math.PI*j/(2*dct_size));
					if (i > 0) d*= 2.0;
					if (j > 0) d*= 2.0;
					sum +=d;
				}
			}
			for (int i = 0; i< dct_len; i++){
				lpf[i] /= sum;
			}
		}
		return lpf;
	}
	public int getGpu_debug_level() {
		return gpu_debug_level;
	}
	public void setGpu_debug_level(int gpu_debug_level) {
		this.gpu_debug_level = gpu_debug_level;
	}
	
	public static void showCLtKernel(
			double [][][][][][] clt_kernels,
			String prefix,
			int    nsens,
			int    ncol,
			int    ktilex, // half tileX
			int    ktiley) { // half tileY
		if (prefix == null) {
			return;
		}
		if ((nsens<0) || (nsens > clt_kernels.length) || (clt_kernels[nsens] == null)) {
			return;
		}
		if ((ncol<0) || (ncol > clt_kernels[nsens].length) || (clt_kernels[nsens][ncol] == null)) {
			return;
		}
		if ((ktiley<0) || (ktiley > clt_kernels[nsens][ncol].length) || (clt_kernels[nsens][ncol][ktiley] == null)) {
			return;
		}
		if ((ktilex<0) || (ktilex > clt_kernels[nsens][ncol][ktiley].length) || (clt_kernels[nsens][ncol][ktiley][ktilex] == null)) {
			return;
		}
		double [][] dbg_clt = {
				clt_kernels[nsens][ncol][ktiley][ktilex][0],
				clt_kernels[nsens][ncol][ktiley][ktilex][1],
				clt_kernels[nsens][ncol][ktiley][ktilex][2],
				clt_kernels[nsens][ncol][ktiley][ktilex][3]};
		String [] titles = {"CC", "SC", "CS", "SS"};
		int length=dbg_clt[0].length;
		int size=(int) Math.sqrt(length);
		ShowDoubleFloatArrays.showArrays(
				dbg_clt,
				size,
				size,
				true,
				prefix+"-ktx"+ktilex+"-kty"+ktiley+"-nsens"+nsens+"-ncol"+ncol,
				titles);
		System.out.println("showCLtKernel() sens="+nsens+", color="+ncol+" prefix="+prefix+", "+
				" kileX = "+ktilex+", "+
				" kileY = "+ktiley+", "+
				" center_x = "+clt_kernels[nsens][ncol][ktiley][ktilex][4][0]+", "+
				" center_y = "+clt_kernels[nsens][ncol][ktiley][ktilex][4][1]);
	}


} // end of public class GpuQuad