package ij.plugin.filter;
import ij.*;
import ij.gui.*;
import ij.process.*;
import ij.util.ThreadUtil;

import java.awt.AWTEvent;
import java.awt.Rectangle;
import java.util.concurrent.Callable;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;

/** This plug-in filter uses convolution with a Gaussian function for smoothing.
 * 'Radius' means the radius of decay to exp(-0.5) ~ 61%, i.e. the standard
 * deviation sigma of the Gaussian (this is the same as in Photoshop, but
 * different from the 'Gaussian Blur' in ImageJ versions before 1.38u, where
 * a value 2.5 times as much had to be entered.
 * - Like all convolution operations in ImageJ, it assumes that out-of-image
 * pixels have a value equal to the nearest edge pixel. This gives higher
 * weight to edge pixels than pixels inside the image, and higher weight
 * to corner pixels than non-corner pixels at the edge. Thus, when smoothing
 * with very high blur radius, the output will be dominated by the edge
 * pixels and especially the corner pixels (in the extreme case, with
 * a blur radius of e.g. 1e20, the image will be raplaced by the average
 * of the four corner pixels).
 * - For increased speed, except for small blur radii, the lines (rows or
 * columns of the image) are downscaled before convolution and upscaled
 * to their original length thereafter.
 * 
 * Version 03-Jun-2007 M. Schmid with preview, progressBar stack-aware,
 * snapshot via snapshot flag; restricted range for resetOutOfRoi
 * 
 * 20-Feb-2010 S. Saalfeld inner multi-threading, modified 29-Aug-2019 M. Schmid
 *
 */

public class GaussianBlur implements ExtendedPlugInFilter, DialogListener {

    /** for remembering till the next invocation */
    private static double sigmaS = 2.0;
    private static boolean sigmaScaledS = false;
    /** the standard deviation of the Gaussian*/
    private double sigma = sigmaS;
    /** whether sigma is given in units corresponding to the pixel scale (not pixels)*/
    private boolean sigmaScaled = sigmaScaledS;
    /** The flags specifying the capabilities and needs */
    private int flags = DOES_ALL|SUPPORTS_MASKING|KEEP_PREVIEW;
    private ImagePlus imp;              // The ImagePlus of the setup call, needed to get the spatial calibration
    private boolean hasScale = false;   // whether the image has an x&y scale
    private int nPasses = 2;            // The number of passes (filter directions * color channels * stack slices)
    private int nChannels = 1;        // The number of color channels
    private int pass;                        // Current pass
    private boolean noProgress;      // Do not show progress bar
    private boolean calledAsPlugin;
    
    /** Method to return types supported
     * @param arg unused
     * @param imp The ImagePlus, used to get the spatial calibration
     * @return Code describing supported formats etc.
     * (see ij.plugin.filter.PlugInFilter & ExtendedPlugInFilter)
     */
    public int setup(String arg, ImagePlus imp) {
        this.imp = imp;
        if (imp!=null && imp.getRoi()!=null) {
            Rectangle roiRect = imp.getRoi().getBoundingRect();
            if (roiRect.y > 0 || roiRect.y+roiRect.height < imp.getDimensions()[1])
                flags |= SNAPSHOT;                  // snapshot for pixels above and/or below roi rectangle
        }
        return flags;
    }
    
    /** Ask the user for the parameters
     */
    public int showDialog(ImagePlus imp, String command, PlugInFilterRunner pfr) {
        calledAsPlugin = true;;
        String options = Macro.getOptions();
        boolean oldMacro = false;
        nChannels = imp.getProcessor().getNChannels();
        setNPasses(1);
        if  (options!=null) {
            if (options.indexOf("radius=") >= 0) {  // ensure compatibility with old macros
                oldMacro = true;                    // specifying "radius=", not "sigma=
                Macro.setOptions(options.replaceAll("radius=", "sigma="));
            }
        }
		GenericDialog gd = NonBlockingGenericDialog.newDialog(command, imp);
        sigma = Math.abs(sigma);
        gd.addNumericField("Sigma (Radius):", sigma, 2);
        if (imp.getCalibration()!=null && !imp.getCalibration().getUnits().equals("pixels")) {
            hasScale = true;
            gd.addCheckbox("Scaled Units ("+imp.getCalibration().getUnits()+")", sigmaScaled);
        } else
            sigmaScaled = false;
        gd.addPreviewCheckbox(pfr);
        gd.addDialogListener(this);
        gd.showDialog();                    // input by the user (or macro) happens here
        if (gd.wasCanceled()) return DONE;
        if (options==null) {                // interactive use: remember values as default for the next invocation
            sigmaS = sigma;
            sigmaScaledS = sigmaScaled;
        }
        if (oldMacro) sigma /= 2.5;         // for old macros, "radius" was 2.5 sigma
        IJ.register(this.getClass());       // protect static class variables (parameters) from garbage collection
        return IJ.setupDialog(imp, flags);  // ask whether to process all slices of stack (if a stack)
    }

    /** Listener to modifications of the input fields of the dialog */
    public boolean dialogItemChanged(GenericDialog gd, AWTEvent e) {
        sigma = gd.getNextNumber();
        if (sigma < 0 || gd.invalidNumber())
            return false;
        if (hasScale)
            sigmaScaled = gd.getNextBoolean();
        return true;
    }

    /** Set the number of passes of the blur1Direction method. If called by the
     *  PlugInFilterRunner of ImageJ, an ImagePlus is known and conversion of RGB images
     *  to float as well as the two filter directions are taken into account.
     *  Otherwise, the caller should set nPasses to the number of 1-dimensional
     *  filter operations required.
     */
    public void setNPasses(int nPasses) {
        this.nPasses = 2 * nChannels * nPasses;
        pass = 0;
    }

    /** This method is invoked for each slice during execution
     * @param ip The image subject to filtering. It must have a valid snapshot if
     * the height of the roi is less than the full image height.
     */
    public void run(ImageProcessor ip) {
        double sigmaX = sigmaScaled ? sigma/imp.getCalibration().pixelWidth : sigma;
        double sigmaY = sigmaScaled ? sigma/imp.getCalibration().pixelHeight : sigma;
        double accuracy = (ip instanceof ByteProcessor || ip instanceof ColorProcessor) ?
            0.002 : 0.0002;
        Rectangle roi = ip.getRoi();
        blurGaussian(ip, sigmaX, sigmaY, accuracy);
    }

    /** Gaussian Filtering of an ImageProcessor. This method is for compatibility with the
    * previous code (before 1.38r) and uses a low-accuracy kernel, only slightly better
    * than the previous ImageJ code.
    * The 'radius' in this call is different from the one used in ImageJ 1.38r and later.
    * Therefore, use blurGaussian(ip, sigma, sigma, accuracy), where 'sigma' is equivalent
    * to the 'sigma (radius)' of the Menu, and accuracy should be 0.02 unless better
    * accuracy is desired.
    */
    @Deprecated
    public boolean blur(ImageProcessor ip, double radius) {
        Rectangle roi = ip.getRoi();
        if (roi.height!=ip.getHeight() && ip.getMask()==null)
            ip.snapshot();              // a snapshot is needed for out-of-Rectangle pixels
        blurGaussian(ip, 0.4*radius, 0.4*radius, 0.01);
        return true;
    }

	/** Gaussian Filtering of an ImageProcessor
	* @param ip       The ImageProcessor to be filtered.
	* @param sigma   Standard deviation of the Gaussian (pixels)
	*
	* @see ij.process.ImageProcessor#blurGaussian(double)
	*/
	public void blurGaussian(ImageProcessor ip, double sigma) {
		double accuracy = (ip instanceof ByteProcessor||ip instanceof ColorProcessor)?0.002:0.0002;
		blurGaussian(ip, sigma, sigma, accuracy);
	}

    /** Gaussian Filtering of an ImageProcessor
     * @param ip       The ImageProcessor to be filtered.
     * @param sigmaX   Standard deviation of the Gaussian in x direction (pixels)
     * @param sigmaY   Standard deviation of the Gaussian in y direction (pixels)
     * @param accuracy Accuracy of kernel, should not be above 0.02. Better (lower)
     *    accuracy needs slightly more computing time.
     */
    public void blurGaussian(ImageProcessor ip, double sigmaX, double sigmaY, double accuracy) {
        boolean hasRoi = ip.getRoi().height!=ip.getHeight() && sigmaX>0 && sigmaY>0;
        if (hasRoi && !calledAsPlugin)
        	ip.snapshot();
        if (nPasses<=1)
            nPasses = ip.getNChannels() * (sigmaX>0 && sigmaY>0 ? 2 : 1);
        FloatProcessor fp = null;
        for (int i=0; i<ip.getNChannels(); i++) {
            fp = ip.toFloat(i, fp);
            if (Thread.currentThread().isInterrupted()) return; // interruption for new parameters during preview?
            blurFloat(fp, sigmaX, sigmaY, accuracy);
            if (Thread.currentThread().isInterrupted()) return;
            ip.setPixels(i, fp);
        }
        if (hasRoi)
            resetOutOfRoi(ip, (int)Math.ceil(5*sigmaY)); // reset out-of-Rectangle pixels above and below roi
        return;
    }
    
    /** Gaussian Filtering of a FloatProcessor. This method does NOT include
     *  resetOutOfRoi(ip), i.e., pixels above and below the roi rectangle will
     *  be also subject to filtering in x direction and must be restored
     *  afterwards (unless the full image height is processed).
     * @param ip        The FloatProcessor to be filtered.
     * @param sigmaX    Standard deviation of the Gaussian in x direction (pixels)
     * @param sigmaY    Standard deviation of the Gaussian in y direction (pixels)
     * @param accuracy  Accuracy of kernel, should not be above 0.02. Better (lower)
     *                  accuracy needs slightly more computing time.
     */
    public void blurFloat(FloatProcessor ip, double sigmaX, double sigmaY, double accuracy) {
        if (sigmaX > 0)
            blur1Direction(ip, sigmaX, accuracy, true, (int)Math.ceil(5*sigmaY));
        if (Thread.currentThread().isInterrupted()) return; // interruption for new parameters during preview?
        if (sigmaY > 0)
            blur1Direction(ip, sigmaY, accuracy, false, 0);
        return;
    }

    /** Blur an image in one direction (x or y) by a Gaussian, using multiple threads on multiprocessor machines
     * @param ip        The Image with the original data where also the result will be stored
     * @param sigma     Standard deviation of the Gaussian
     * @param accuracy  Accuracy of kernel, should not be > 0.02
     * @param xDirection True for bluring in x direction, false for y direction
     * @param extraLines Number of lines (parallel to the blurring direction) 
     *                  below and above the roi bounds that should be processed.
     */
    public void blur1Direction( final FloatProcessor ip, final double sigma, final double accuracy,
            final boolean xDirection, final int extraLines) {
        
        final int UPSCALE_K_RADIUS = 2;                     //number of pixels to add for upscaling
        final double MIN_DOWNSCALED_SIGMA = 4.;             //minimum standard deviation in the downscaled image
        final float[] pixels = (float[])ip.getPixels();
        final int width = ip.getWidth();
        final int height = ip.getHeight();
        final Rectangle roi = ip.getRoi();
        final int length = xDirection ? width : height;     //number of points per line (line can be a row or column)
        final int pointInc = xDirection ? 1 : width;        //increment of the pixels array index to the next point in a line
        final int lineInc = xDirection ? width : 1;         //increment of the pixels array index to the next line
        final int lineFromA = (xDirection ? roi.y : roi.x) - extraLines;  //the first line to process
        final int lineFrom;
        if (lineFromA < 0) lineFrom = 0;
        else lineFrom = lineFromA;
        final int lineToA = (xDirection ? roi.y+roi.height : roi.x+roi.width) + extraLines; //the last line+1 to process
        final int lineTo;
        if (lineToA > (xDirection ? height:width)) lineTo = (xDirection ? height:width);
        else lineTo = lineToA;
        final int writeFrom = xDirection? roi.x : roi.y;    //first point of a line that needs to be written
        final int writeTo = xDirection ? roi.x+roi.width : roi.y+roi.height;
        
        /* large radius (sigma): scale down, then convolve, then scale up */
        final boolean doDownscaling = sigma > 2*MIN_DOWNSCALED_SIGMA + 0.5;
        final int reduceBy = doDownscaling ?                //downscale by this factor
                Math.min((int)Math.floor(sigma/MIN_DOWNSCALED_SIGMA), length)
                : 1;
        /* Downscaling and upscaling blur the image a bit - we have to correct the standard
         * deviation for this:
         * Downscaling gives std devation sigma = 1/sqrt(3); upscale gives sigma = 1/2 (in downscaled pixels).
         * All sigma^2 values add to full sigma^2, which should be the desired value  */
        final double sigmaGauss = doDownscaling ?
                Math.sqrt(sigma*sigma/(reduceBy*reduceBy) - 1./3. - 1./4.)
                : sigma;
        final int maxLength = doDownscaling ?
                (length+reduceBy-1)/reduceBy + 2*(UPSCALE_K_RADIUS + 1) //downscaled line can't be longer
                : length;
        final float[][] gaussKernel = makeGaussianKernel(sigmaGauss, accuracy, maxLength);
        final int kRadius = gaussKernel[0].length*reduceBy;             //Gaussian kernel radius after upscaling
        final int readFrom = (writeFrom-kRadius < 0) ? 0 : writeFrom-kRadius; //not including broadening by downscale&upscale
        final int readTo = (writeTo+kRadius > length) ? length : writeTo+kRadius;
        final int newLength = doDownscaling ?                           //line length for convolution
                (readTo-readFrom+reduceBy-1)/reduceBy + 2*(UPSCALE_K_RADIUS + 1)
                : length;
        final int unscaled0 = readFrom - (UPSCALE_K_RADIUS + 1)*reduceBy; //input point corresponding to cache index 0
        //the following is relevant for upscaling only
        //IJ.log("reduce="+reduceBy+", newLength="+newLength+", unscaled0="+unscaled0+", sigmaG="+(float)sigmaGauss+", kRadius="+gaussKernel[0].length);
        final float[] downscaleKernel = doDownscaling ? makeDownscaleKernel(reduceBy) : null;
        final float[] upscaleKernel = doDownscaling ? makeUpscaleKernel(reduceBy) : null;

        int numThreads1 = Math.min(Prefs.getThreads(), lineTo-lineFrom);
        int numThreads2 = (int)((lineTo - lineFrom)*(long)(writeTo - writeFrom)*gaussKernel[0].length/
                (doDownscaling ? 8000 : 16000)) + 1; //use fewer threads if a small task
        final int numThreads = Math.min(numThreads1, numThreads2);
        final Callable[] callables = new Callable[numThreads];
        final AtomicInteger nextLine = new AtomicInteger(lineFrom);
        final AtomicLong lastShowProgressTime = new AtomicLong(System.currentTimeMillis());
           
        for ( int t = 0; t < numThreads; t++ ) {
            final float[] cache1 = new float[newLength];  //holds data before convolution (after downscaling, if any)
            final float[] cache2 = doDownscaling ? new float[newLength] : null;  //holds data after convolution
            
            callables[t] = new Callable() {
                final public Void call() { /*try{*/
                    while (!Thread.currentThread().isInterrupted()) {
                        int line = nextLine.getAndIncrement();
                        if (line >= lineTo) break;
                        int pixel0 = line*lineInc;
                        if ((line&0x1f)==0) { //every 32 lines, check whether progress bar should be updated
                            long time = System.currentTimeMillis();
                            if (time - lastShowProgressTime.get() >110) {
                                lastShowProgressTime.set(time);
                                showProgress((double)(line-lineFrom)/(lineTo-lineFrom));
                            }
                        }
                        if (doDownscaling) {
                            downscaleLine(pixels, cache1, downscaleKernel, reduceBy, pixel0, unscaled0, length, pointInc, newLength);
                            convolveLine(cache1, cache2, gaussKernel, 0, newLength, 1, newLength-1, 0, 1);
                            upscaleLine(cache2, pixels, upscaleKernel, reduceBy, pixel0, unscaled0, writeFrom, writeTo, pointInc);
                        } else {
                            int p = pixel0 + readFrom*pointInc;
                            for (int i=readFrom; i<readTo; i++ ,p+=pointInc)
                                cache1[i] = pixels[p];
                            convolveLine(cache1, pixels, gaussKernel, readFrom, readTo, writeFrom, writeTo, pixel0, pointInc);
                        }
                            
                    }
                    return null;
                } /*catch(Exception ex) {IJ.handleException(ex);} }*/
            };
        }
        ThreadUtil.startAndJoin(callables);
            
        showProgress(1.0);
        pass++;
        if (pass > nPasses) pass = 1;
    }

    /** Scale a line (row or column of a FloatProcessor or part thereof)
     * down by a factor <code>reduceBy</code> and write the result into
     * <code>cache</code>.
     * Input line pixel # <code>unscaled0</code> will correspond to output
     * line pixel # 0. <code>unscaled0</code> may be negative. Out-of-line
     * pixels of the input are replaced by the edge pixels.
     * @param pixels    input array
     * @param cache     output array
     * @param kernel    downscale kernel, runs form -1.5 to +1.5 in downscaled coordinates
     * @param reduceBy  downscaling factor
     * @param pixel0    index in pixels array corresponding to start of line or column
     * @param unscaled0 index in input line corresponding to output line index 0, May be negative.
     * @param length    length of full input line or column
     * @param pointInc  spacing of values in input array (1 for lines, image width for columns)
     * @param newLength length of downscaled data
     */
    final static private void downscaleLine(final float[] pixels, final float[] cache, final float[] kernel,
            final int reduceBy, final int pixel0, final int unscaled0, final int length, final int pointInc, final int newLength) {
        int p = pixel0 + pointInc*(unscaled0-reduceBy*3/2);  //pointer in pixels array
        final int pLast = pixel0 + pointInc*(length-1);
        for (int xout=-1; xout<=newLength; xout++) {
            float sum0 = 0, sum1 = 0, sum2 = 0;
            for (int x=0; x<reduceBy; x++, p+=pointInc) {
                float v = pixels[p<pixel0 ? pixel0 : (p>pLast ? pLast : p)];
                sum0 += v * kernel[x+2*reduceBy];
                sum1 += v * kernel[x+reduceBy];
                sum2 += v * kernel[x];
            }
            if (xout>0) cache[xout-1] += sum0;
            if (xout>=0 && xout<newLength) cache[xout] += sum1;
            if (xout+1<newLength) cache[xout+1] = sum2;
        }
    }
    /** the above code is equivalent to the following one; but the above code is faster
     *  - above: accesses each pixel in the pixels array only once
     *  - below: accesses each pixel in the pixels array 3 times, more cache misses */
    /*final static private void downscaleLine(final float[] pixels, final float[] cache, final float[] kernel,
            final int reduceBy, final int pixel0, final int unscaled0, final int length, final int pointInc, final int newLength) {
        final int xin = unscaled0 - reduceBy/2;
        int p = pixel0 + pointInc*xin;
        final int pLast = pixel0 + pointInc*(length-1);
        for (int xout=0; xout<newLength; xout++) {
            float v = 0;
            for (int x=0; x<reduceBy; x++, p+=pointInc) {
                int pp = p-pointInc*reduceBy;
                v += kernel[x] * pixels[pp<pixel0 ? pixel0 : (pp>pLast ? pLast : pp)];
                v += kernel[x+reduceBy] * pixels[p<pixel0 ? pixel0 : (p>pLast ? pLast : p)];
                pp = p+pointInc*reduceBy;
                v += kernel[x+2*reduceBy] * pixels[pp<pixel0 ? pixel0 : (pp>pLast ? pLast : pp)];
            }
            cache[xout] = v;
        }
    }*/

    /* Create a kernel for downscaling. The kernel function preserves
     * norm and 1st moment (i.e., position) and has fixed 2nd moment,
     * (in contrast to linear interpolation).
     * In scaled space, the length of the kernel runs from -1.5 to +1.5,
     * and the standard deviation is 1/2.
     * Array index corresponding to the kernel center is
     * unitLength*3/2
     */
    final static private float[] makeDownscaleKernel (final int unitLength) {
        final int mid = unitLength*3/2;
        final float[] kernel = new float[3*unitLength];
        for (int i=0; i<=unitLength/2; i++) {
            final double x = i/(double)unitLength;
            final float v = (float)((0.75-x*x)/unitLength);
            kernel[mid-i] = v;
            kernel[mid+i] = v;
        }
        for (int i=unitLength/2+1; i<(unitLength*3+1)/2; i++) {
            final double x = i/(double)unitLength;
            final float v = (float)((0.125 + 0.5*(x-1)*(x-2))/unitLength);
            kernel[mid-i] = v;
            kernel[mid+i] = v;
        }
        return kernel;
    }

    /** Scale a line up by factor <code>reduceBy</code> and write as a row
     * or column (or part thereof) to the pixels array of a FloatProcessor.
     */
    final static private void upscaleLine (final float[] cache, final float[] pixels, final float[] kernel,
            final int reduceBy, final int pixel0, final int unscaled0, final int writeFrom, final int writeTo, final int pointInc) {
        int p = pixel0 + pointInc*writeFrom;
        for (int xout = writeFrom; xout < writeTo; xout++, p+=pointInc) {
            final int xin = (xout-unscaled0+reduceBy-1)/reduceBy; //the corresponding point in the cache (if exact) or the one above
            final int x = reduceBy - 1 - (xout-unscaled0+reduceBy-1)%reduceBy;
            pixels[p] = cache[xin-2]*kernel[x]
                    + cache[xin-1]*kernel[x+reduceBy]
                    + cache[xin]*kernel[x+2*reduceBy]
                    + cache[xin+1]*kernel[x+3*reduceBy];
        }
    }

    /** Create a kernel for upscaling. The kernel function is a convolution
     *  of four unit squares, i.e., four uniform kernels with value +1
     *  from -0.5 to +0.5 (in downscaled coordinates). The second derivative
     *  of this kernel is smooth, the third is not. Its standard deviation
     *  is 1/sqrt(3) in downscaled cordinates.
     *  The kernel runs from [-2 to +2[, corresponding to array index
     *  0 ... 4*unitLength (whereby the last point is not in the array any more).
     */
    final static private float[] makeUpscaleKernel (final int unitLength) {
        final float[] kernel = new float[4*unitLength];
        final int mid = 2*unitLength;
        kernel[0] = 0;
        for (int i=0; i<unitLength; i++) {
            final double x = i/(double)unitLength;
            final float v = (float)((2./3. -x*x*(1-0.5*x)));
            kernel[mid+i] = v;
            kernel[mid-i] = v;
        }
        for (int i=unitLength; i<2*unitLength; i++) {
            final double x = i/(double)unitLength;
            final float v = (float)((2.-x)*(2.-x)*(2.-x)/6.);
            kernel[mid+i] = v;
            kernel[mid-i] = v;
        }
        return kernel;
    }

    /** Convolve a line with a symmetric kernel and write to a separate array,
     * possibly the pixels array of a FloatProcessor (as a row or column or part thereof)
     *
     * @param input     Input array containing the line
     * @param pixels    Float array for output, can be the pixels of a FloatProcessor
     * @param kernel    "One-sided" kernel array, kernel[0][n] must contain the kernel
     *                  itself, kernel[1][n] must contain the running sum over all
     *                  kernel elements from kernel[0][n+1] to the periphery.
     *                  The kernel must be normalized, i.e. sum(kernel[0][n]) = 1
     *                  where n runs from the kernel periphery (last element) to 0 and
     *                  back. Normalization should include all kernel points, also these
     *                  not calculated because they are not needed.
     * @param readFrom  First array element of the line that must be read.
     *                  <code>writeFrom-kernel.length</code> or 0.
     * @param readTo    Last array element+1 of the line that must be read.
     *                  <code>writeTo+kernel.length</code> or <code>input.length</code>
     * @param writeFrom Index of the first point in the line that should be written
     * @param writeTo   Index+1 of the last point in the line that should be written
     * @param point0    Array index of first element of the 'line' in pixels (i.e., lineNumber * lineInc)
     * @param pointInc  Increment of the pixels array index to the next point (for an ImageProcessor,
     *                  it should be <code>1</code> for a row, <code>width</code> for a column)
     */
    final static private void convolveLine( final float[] input, final float[] pixels, final float[][] kernel, final int readFrom,
            final int readTo, final int writeFrom, final int writeTo, final int point0, final int pointInc) {
        final int length = input.length;
        final float first = input[0];                 //out-of-edge pixels are replaced by nearest edge pixels
        final float last = input[length-1];
        final float[] kern = kernel[0];               //the kernel itself
        final float kern0 = kern[0];
        final float[] kernSum = kernel[1];            //the running sum over the kernel
        final int kRadius = kern.length;
        final int firstPart = kRadius < length ? kRadius : length;
        int p = point0 + writeFrom*pointInc;
        int i = writeFrom;
        for (; i<firstPart; i++,p+=pointInc) {  //while the sum would include pixels < 0
            float result = input[i]*kern0;
            result += kernSum[i]*first;
            if (i+kRadius>length) result += kernSum[length-i-1]*last;
            for (int k=1; k<kRadius; k++) {
                float v = 0;
                if (i-k >= 0) v += input[i-k];
                if (i+k<length) v+= input[i+k];
                result += kern[k] * v;
            }
            pixels[p] = result;
        }
        final int iEndInside = length-kRadius<writeTo ? length-kRadius : writeTo;
        for (;i<iEndInside;i++,p+=pointInc) {   //while only pixels within the line are be addressed (the easy case)
            float result = input[i]*kern0;
            for (int k=1; k<kRadius; k++)
                result += kern[k] * (input[i-k] + input[i+k]);
            pixels[p] = result;
        }
        for (; i<writeTo; i++,p+=pointInc) {    //while the sum would include pixels >= length 
            float result = input[i]*kern0;
            if (i<kRadius) result += kernSum[i]*first;
            if (i+kRadius>=length) result += kernSum[length-i-1]*last;
            for (int k=1; k<kRadius; k++) {
                float v = 0;
                if (i-k >= 0) v += input[i-k];
                if (i+k<length) v+= input[i+k];
                result += kern[k] * v;
            }
            pixels[p] = result;
        }
    }

    /** Create a 1-dimensional normalized Gaussian kernel with standard deviation sigma
     *  and the running sum over the kernel
     *  Note: this is one side of the kernel only, not the full kernel as used by the
     *  Convolver class of ImageJ.
     *  To avoid a step due to the cutoff at a finite value, the near-edge values are
     *  replaced by a 2nd-order polynomial with its minimum=0 at the first out-of-kernel
     *  pixel. Thus, the kernel function has a smooth 1st derivative in spite of finite
     *  length.
     *
     * @param sigma     Standard deviation, i.e. radius of decay to 1/sqrt(e), in pixels.
     * @param accuracy  Relative accuracy; for best results below 0.01 when processing
     *                  8-bit images. For short or float images, values of 1e-3 to 1e-4
     *                  are better (but increase the kernel size and thereby the
     *                  processing time). Edge smoothing will fail with very poor
     *                  accuracy (above approx. 0.02)
     * @param maxRadius Maximum radius of the kernel: Limits kernel size in case of
     *                  large sigma, should be set to image width or height. For small
     *                  values of maxRadius, the kernel returned may have a larger
     *                  radius, however.
     * @return          A 2*n array. Array[0][n] is the kernel, decaying towards zero,
     *                  which would be reached at kernel.length (unless kernel size is
     *                  limited by maxRadius). Array[1][n] holds the sum over all kernel
     *                  values > n, including non-calculated values in case the kernel
     *                  size is limited by <code>maxRadius</code>.
     */
    public float[][] makeGaussianKernel(final double sigma, final double accuracy, int maxRadius) {
        int kRadius = (int)Math.ceil(sigma*Math.sqrt(-2*Math.log(accuracy)))+1;
        if (maxRadius < 50) maxRadius = 50;         // too small maxRadius would result in inaccurate sum.
        if (kRadius > maxRadius) kRadius = maxRadius;
        float[][] kernel = new float[2][kRadius];
        for (int i=0; i<kRadius; i++)               // Gaussian function
            kernel[0][i] = (float)(Math.exp(-0.5*i*i/sigma/sigma));
        if (kRadius < maxRadius && kRadius > 3) {   // edge correction
            double sqrtSlope = Double.MAX_VALUE;
            int r = kRadius;
            while (r > kRadius/2) {
                r--;
                double a = Math.sqrt(kernel[0][r])/(kRadius-r);
                if (a < sqrtSlope)
                    sqrtSlope = a;
                else
                    break;
            }
            for (int r1 = r+2; r1 < kRadius; r1++)
                kernel[0][r1] = (float)((kRadius-r1)*(kRadius-r1)*sqrtSlope*sqrtSlope);
        }
        double sum;                                 // sum over all kernel elements for normalization
        if (kRadius < maxRadius) {
            sum = kernel[0][0];
            for (int i=1; i<kRadius; i++)
                sum += 2*kernel[0][i];
        } else
            sum = sigma * Math.sqrt(2*Math.PI);
        
        double rsum = 0.5 + 0.5*kernel[0][0]/sum;
        for (int i=0; i<kRadius; i++) {
            double v = (kernel[0][i]/sum);
            kernel[0][i] = (float)v;
            rsum -= v;
            kernel[1][i] = (float)rsum;
            //IJ.log("k["+i+"]="+(float)v+" sum="+(float)rsum);
        }
        return kernel;
    }

	/** Set the processed pixels above and below the roi rectangle back to their
	* previous value (i.e., snapshot buffer). This is necessary since ImageJ
	* only restores out-of-roi pixels inside the enclosing rectangle of the roi
	* (If the roi is non-rectangular and the SUPPORTS_MASKING flag is set).
	* @param ip The image to be processed
	* @param radius The range above and below the roi that should be processed
	*/    
	public static void resetOutOfRoi(ImageProcessor ip, int radius) {
		Rectangle roi = ip.getRoi();
		int width = ip.getWidth();
		int height = ip.getHeight();
		Object pixels = ip.getPixels();
		Object snapshot = ip.getSnapshotPixels();
		int y0 = roi.y-radius;    // the first line that should be reset
		if (y0<0) y0 = 0;
		for (int y=y0,p=width*y+roi.x; y<roi.y; y++,p+=width)
			System.arraycopy(snapshot, p, pixels, p, roi.width);
		int yEnd = roi.y+roi.height+radius; // the last line + 1 that should be reset
		if (yEnd>height) yEnd = height;
		for (int y=roi.y+roi.height,p=width*y+roi.x; y<yEnd; y++,p+=width)
			System.arraycopy(snapshot, p, pixels, p, roi.width);
	}
    
    private void showProgress(double percent) {
    	if (noProgress) return;
        percent = (pass + percent)/nPasses;
        IJ.showProgress(percent);
    }
    
    public void showProgress(boolean showProgressBar) {
    	noProgress = !showProgressBar;
    }
    
}
