| GaussianBlur |

    package ij.plugin.filter;
    import ij.*;
    import ij.gui.GenericDialog;
    import ij.gui.DialogListener;
    import ij.process.*;
    import ij.measure.Calibration;
    import java.awt.*;

    /** 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 previous ImageJ function 'Gaussian Blur', where a value
     * 2.5 times as much has 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
     *
     */

    public class GaussianBlur implements ExtendedPlugInFilter, DialogListener {

        /** the standard deviation of the Gaussian*/
        private static double sigma = 2.0;
        /** whether sigma is given in units corresponding to the pixel scale (not pixels)*/
        private static boolean sigmaScaled = false;
        /** The flags specifying the capabilities and needs */
        private int flags = DOES_ALL|SUPPORTS_MASKING|PARALLELIZE_STACKS|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 = 1;            // The number of passes (filter directions * color channels * stack slices)
        private int pass;                   // Current pass

        /** Default constructor */
        public GaussianBlur() {
        }

        /** 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) {
            String options = Macro.getOptions();
            boolean oldMacro = false;
            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 = new GenericDialog(command);
            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 (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 * 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 */
        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. If filtering is not applied to the
         *  full image height, the ImageProcessor must have a valid snapshot.
         * @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) {
            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 (ip.getRoi().height!=ip.getHeight() && sigmaX>0 && sigmaY>0)
                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.
         * @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(FloatProcessor ip, double sigma, double accuracy,
                boolean xDirection, 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
            float[] pixels = (float[])ip.getPixels();
            int width = ip.getWidth();
            int height = ip.getHeight();
            Rectangle roi = ip.getRoi();
            int length = xDirection ? width : height;   //number of points per line (line can be a row or column)
            int pointInc = xDirection ? 1 : width;      //increment of the pixels array index to the next point in a line
            int lineInc = xDirection ? width : 1;       //increment of the pixels array index to the next line
            int lineFrom = (xDirection ? roi.y : roi.x) - extraLines;  //the first line to process
            if (lineFrom < 0) lineFrom = 0;
            int lineTo = (xDirection ? roi.y+roi.height : roi.x+roi.width) + extraLines; //the last line+1 to process
            if (lineTo > (xDirection ? height:width)) lineTo = (xDirection ? height:width);
            int writeFrom = xDirection? roi.x : roi.y;  //first point of a line that needs to be written
            int writeTo = xDirection ? roi.x+roi.width : roi.y+roi.height;
            int inc = Math.max((lineTo-lineFrom)/(100/(nPasses>0?nPasses:1)+1),20);
            pass++;
            if (pass>nPasses) pass =1;
            Thread thread = Thread.currentThread();     // needed to check for interrupted state
            if (sigma > 2*MIN_DOWNSCALED_SIGMA + 0.5) {
                 /* large radius (sigma): scale down, then convolve, then scale up */
                int reduceBy = (int)Math.floor(sigma/MIN_DOWNSCALED_SIGMA); //downscale by this factor
                if (reduceBy > length) reduceBy = length;
                /* Downscale gives std devation sigma = 1/sqrt(3); upscale gives sigma = 1/2. (in downscaled pixels) */
                /* All sigma^2 values add to full sigma^2  */
                double sigmaGauss = Math.sqrt(sigma*sigma/(reduceBy*reduceBy) - 1./3. - 1./4.);
                int maxLength = (length+reduceBy-1)/reduceBy + 2*(UPSCALE_K_RADIUS + 1); //downscaled line can't be longer
                float[][] gaussKernel = makeGaussianKernel(sigmaGauss, accuracy, maxLength);
                int kRadius = gaussKernel[0].length*reduceBy; //Gaussian kernel radius after upscaling
                int readFrom = (writeFrom-kRadius < 0) ? 0 : writeFrom-kRadius; //not including broadening by downscale&upscale
                int readTo = (writeTo+kRadius > length) ? length : writeTo+kRadius;
                int newLength = (readTo-readFrom+reduceBy-1)/reduceBy + 2*(UPSCALE_K_RADIUS + 1);
                int unscaled0 = readFrom - (UPSCALE_K_RADIUS + 1)*reduceBy; //input point corresponding to cache index 0
                //IJ.log("reduce="+reduceBy+", newLength="+newLength+", unscaled0="+unscaled0+", sigmaG="+(float)sigmaGauss+", kRadius="+gaussKernel[0].length);
                float[] downscaleKernel = makeDownscaleKernel(reduceBy);
                float[] upscaleKernel = makeUpscaleKernel(reduceBy);
                float[] cache1 = new float[newLength];  //holds data after downscaling
                float[] cache2 = new float[newLength];  //holds data after convolution
                int pixel0 = lineFrom*lineInc;
                for (int line=lineFrom; line<lineTo; line++, pixel0+=lineInc) {
                    if (line%inc==0) {
                        showProgress((double)(line-lineFrom)/(lineTo-lineFrom));
                        if (thread.isInterrupted()) return; // interruption for new parameters during preview?
                    }
                    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 {
                /* small radius: normal convolution */
                float[][] gaussKernel = makeGaussianKernel(sigma, accuracy, length);
                int kRadius = gaussKernel[0].length;
                float[] cache = new float[length];          //input for convolution, hopefully in CPU cache
                int readFrom = (writeFrom-kRadius < 0) ? 0 : writeFrom-kRadius;
                int readTo = (writeTo+kRadius > length) ? length : writeTo+kRadius;
                int pixel0 = lineFrom*lineInc;
                for (int line=lineFrom; line<lineTo; line++, pixel0+=lineInc) {
                    if (line%inc==0) {
                        showProgress((double)(line-lineFrom)/(lineTo-lineFrom));
                        if (thread.isInterrupted()) return; // interruption for new parameters during preview?
                    }
                    int p = pixel0 + readFrom*pointInc;
                    for (int i=readFrom; i<readTo; i++ ,p+=pointInc)
                        cache[i] = pixels[p];
                    convolveLine(cache, pixels, gaussKernel, readFrom, readTo, writeFrom, writeTo, pixel0, pointInc);
                }
            }
            return;
        }

        /** 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.
         */
        void downscaleLine(float[] pixels, float[] cache, float[] kernel,
                int reduceBy, int pixel0, int unscaled0, int length, int pointInc, int newLength) {
            float first = pixels[pixel0];
            float last = pixels[pixel0 + pointInc*(length-1)];
            int xin = unscaled0 - reduceBy/2;
            int p = pixel0 + pointInc*xin;
            for (int xout=0; xout<newLength; xout++) {
                float v = 0;
                for (int x=0; x<reduceBy; x++, xin++, p+=pointInc) {
                    v += kernel[x] * ((xin-reduceBy < 0) ? first : ((xin-reduceBy >= length) ? last : pixels[p-pointInc*reduceBy]));
                    v += kernel[x+reduceBy] * ((xin < 0) ? first : ((xin >= length) ? last : pixels[p]));
                    v += kernel[x+2*reduceBy] * ((xin+reduceBy < 0) ? first : ((xin+reduceBy >= length) ? last : pixels[p+pointInc*reduceBy]));
                }
                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
         */
        float[] makeDownscaleKernel (int unitLength) {
            int mid = unitLength*3/2;
            float[] kernel = new float[3*unitLength];
            for (int i=0; i<=unitLength/2; i++) {
                double x = i/(double)unitLength;
                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++) {
                double x = i/(double)unitLength;
                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.
         */
        void upscaleLine (float[] cache, float[] pixels, float[] kernel,
                int reduceBy, int pixel0, int unscaled0, int writeFrom, int writeTo, int pointInc) {
            int p = pixel0 + pointInc*writeFrom;
            for (int xout = writeFrom; xout < writeTo; xout++, p+=pointInc) {
                int xin = (xout-unscaled0+reduceBy-1)/reduceBy; //the corresponding point in the cache (if exact) or the one above
                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).
         */
        float[] makeUpscaleKernel (int unitLength) {
            float[] kernel = new float[4*unitLength];
            int mid = 2*unitLength;
            kernel[0] = 0;
            for (int i=0; i<unitLength; i++) {
                double x = i/(double)unitLength;
                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++) {
                double x = i/(double)unitLength;
                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)
         */
        public void convolveLine(float[] input, float[] pixels, float[][] kernel, int readFrom,
                int readTo, int writeFrom, int writeTo, int point0, int pointInc) {
            int length = input.length;
            float first = input[0];                 //out-of-edge pixels are replaced by nearest edge pixels
            float last = input[length-1];
            float[] kern = kernel[0];               //the kernel itself
            float kern0 = kern[0];
            float[] kernSum = kernel[1];            //the running sum over the kernel
            int kRadius = kern.length;
            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;
            }
            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(double sigma, 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);
        }

        void showProgress(double percent) {
            percent = (double)(pass-1)/nPasses + percent/nPasses;
            IJ.showProgress(percent);
        }
    }

| GaussianBlur |