示例#1
0
  /** Generate output image whose type is same as input image. */
  private ImagePlus makeOutputImage(ImagePlus imp, FloatProcessor fp, int ptype) {
    int width = imp.getWidth();
    int height = imp.getHeight();
    float[] pixels = (float[]) fp.getPixels();
    ImageProcessor oip = null;

    // Create output image consistent w/ type of input image.
    int size = pixels.length;
    switch (ptype) {
      case BYTE_TYPE:
        oip = imp.getProcessor().createProcessor(width, height);
        byte[] pixels8 = (byte[]) oip.getPixels();
        for (int i = 0; i < size; i++) pixels8[i] = (byte) pixels[i];
        break;
      case SHORT_TYPE:
        oip = imp.getProcessor().createProcessor(width, height);
        short[] pixels16 = (short[]) oip.getPixels();
        for (int i = 0; i < size; i++) pixels16[i] = (short) pixels[i];
        break;
      case FLOAT_TYPE:
        oip = new FloatProcessor(width, height, pixels, null);
        break;
    }

    // Adjust for display.
    // Calling this on non-ByteProcessors ensures image
    // processor is set up to correctly display image.
    oip.resetMinAndMax();

    // Create new image plus object. Don't use
    // ImagePlus.createImagePlus here because there may be
    // attributes of input image that are not appropriate for
    // projection.
    return new ImagePlus(makeTitle(), oip);
  }
示例#2
0
  void Bernsen(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // Bernsen recommends WIN_SIZE = 31 and CONTRAST_THRESHOLD = 15.
    //  1) Bernsen J. (1986) "Dynamic Thresholding of Grey-Level Images"
    //    Proc. of the 8th Int. Conf. on Pattern Recognition, pp. 1251-1255
    //  2) Sezgin M. and Sankur B. (2004) "Survey over Image Thresholding
    //   Techniques and Quantitative Performance Evaluation" Journal of
    //   Electronic Imaging, 13(1): 146-165
    //  http://citeseer.ist.psu.edu/sezgin04survey.html
    // Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
    // This version uses a circular local window, instead of a rectagular one
    ImagePlus Maximp, Minimp;
    ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
    int contrast_threshold = 15;
    int local_contrast;
    int mid_gray;
    byte object;
    byte backg;
    int temp;

    if (par1 != 0) {
      IJ.log("Bernsen: changed contrast_threshold from :" + contrast_threshold + "  to:" + par1);
      contrast_threshold = (int) par1;
    }

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Maximp = duplicateImage(ip);
    ipMax = Maximp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMax, radius, rf.MAX); // Maximum
    // Maximp.show();
    Minimp = duplicateImage(ip);
    ipMin = Minimp.getProcessor();
    rf.rank(ipMin, radius, rf.MIN); // Minimum
    // Minimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    byte[] max = (byte[]) ipMax.getPixels();
    byte[] min = (byte[]) ipMin.getPixels();

    for (int i = 0; i < pixels.length; i++) {
      local_contrast = (int) ((max[i] & 0xff) - (min[i] & 0xff));
      mid_gray = (int) ((min[i] & 0xff) + (max[i] & 0xff)) / 2;
      temp = (int) (pixels[i] & 0x0000ff);
      if (local_contrast < contrast_threshold)
        pixels[i] = (mid_gray >= 128) ? object : backg; // Low contrast region
      else pixels[i] = (temp >= mid_gray) ? object : backg;
    }
    // imp.updateAndDraw();
    return;
  }
示例#3
0
 void createMaskFromThreshold(ImagePlus imp) {
   ImageProcessor ip = imp.getProcessor();
   if (ip.getMinThreshold() == ImageProcessor.NO_THRESHOLD) {
     IJ.error("Create Mask", "Area selection or thresholded image required");
     return;
   }
   double t1 = ip.getMinThreshold();
   double t2 = ip.getMaxThreshold();
   IJ.run("Duplicate...", "title=mask");
   ImagePlus imp2 = WindowManager.getCurrentImage();
   ImageProcessor ip2 = imp2.getProcessor();
   ip2.setThreshold(t1, t2, ImageProcessor.NO_LUT_UPDATE);
   IJ.run("Convert to Mask");
 }
示例#4
0
 void createEllipse(ImagePlus imp) {
   IJ.showStatus("Fitting ellipse");
   Roi roi = imp.getRoi();
   if (roi == null) {
     noRoi("Fit Ellipse");
     return;
   }
   if (roi.isLine()) {
     IJ.error("Fit Ellipse", "\"Fit Ellipse\" does not work with line selections");
     return;
   }
   ImageProcessor ip = imp.getProcessor();
   ip.setRoi(roi);
   int options = Measurements.CENTROID + Measurements.ELLIPSE;
   ImageStatistics stats = ImageStatistics.getStatistics(ip, options, null);
   double dx = stats.major * Math.cos(stats.angle / 180.0 * Math.PI) / 2.0;
   double dy = -stats.major * Math.sin(stats.angle / 180.0 * Math.PI) / 2.0;
   double x1 = stats.xCentroid - dx;
   double x2 = stats.xCentroid + dx;
   double y1 = stats.yCentroid - dy;
   double y2 = stats.yCentroid + dy;
   double aspectRatio = stats.minor / stats.major;
   imp.killRoi();
   imp.setRoi(new EllipseRoi(x1, y1, x2, y2, aspectRatio));
 }
示例#5
0
 void createMask(ImagePlus imp) {
   Roi roi = imp.getRoi();
   boolean useInvertingLut = Prefs.useInvertingLut;
   Prefs.useInvertingLut = false;
   if (roi == null || !(roi.isArea() || roi.getType() == Roi.POINT)) {
     createMaskFromThreshold(imp);
     Prefs.useInvertingLut = useInvertingLut;
     return;
   }
   ImagePlus maskImp = null;
   Frame frame = WindowManager.getFrame("Mask");
   if (frame != null && (frame instanceof ImageWindow))
     maskImp = ((ImageWindow) frame).getImagePlus();
   if (maskImp == null) {
     ImageProcessor ip = new ByteProcessor(imp.getWidth(), imp.getHeight());
     if (!Prefs.blackBackground) ip.invertLut();
     maskImp = new ImagePlus("Mask", ip);
     maskImp.show();
   }
   ImageProcessor ip = maskImp.getProcessor();
   ip.setRoi(roi);
   ip.setValue(255);
   ip.fill(ip.getMask());
   maskImp.updateAndDraw();
   Prefs.useInvertingLut = useInvertingLut;
 }
示例#6
0
  void Contrast(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // G. Landini, 2013
    // Based on a simple contrast toggle. This procedure does not have user-provided paramters other
    // than the kernel radius
    // Sets the pixel value to either white or black depending on whether its current value is
    // closest to the local Max or Min respectively
    // The procedure is similar to Toggle Contrast Enhancement (see Soille, Morphological Image
    // Analysis (2004), p. 259

    ImagePlus Maximp, Minimp;
    ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
    int c_value = 0;
    int mid_gray;
    byte object;
    byte backg;

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Maximp = duplicateImage(ip);
    ipMax = Maximp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMax, radius, rf.MAX); // Maximum
    // Maximp.show();
    Minimp = duplicateImage(ip);
    ipMin = Minimp.getProcessor();
    rf.rank(ipMin, radius, rf.MIN); // Minimum
    // Minimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    byte[] max = (byte[]) ipMax.getPixels();
    byte[] min = (byte[]) ipMin.getPixels();
    for (int i = 0; i < pixels.length; i++) {
      pixels[i] =
          ((Math.abs((int) (max[i] & 0xff - pixels[i] & 0xff))
                  <= Math.abs((int) (pixels[i] & 0xff - min[i] & 0xff))))
              ? object
              : backg;
    }
    // imp.updateAndDraw();
    return;
  }
示例#7
0
 private ImagePlus duplicateImage(ImageProcessor iProcessor) {
   int w = iProcessor.getWidth();
   int h = iProcessor.getHeight();
   ImagePlus iPlus = NewImage.createByteImage("Image", w, h, 1, NewImage.FILL_BLACK);
   ImageProcessor imageProcessor = iPlus.getProcessor();
   imageProcessor.copyBits(iProcessor, 0, 0, Blitter.COPY);
   return iPlus;
 }
示例#8
0
  void MidGrey(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // See: Image Processing Learning Resourches HIPR2
    // http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
    ImagePlus Maximp, Minimp;
    ImageProcessor ip = imp.getProcessor(), ipMax, ipMin;
    int c_value = 0;
    int mid_gray;
    byte object;
    byte backg;

    if (par1 != 0) {
      IJ.log("MidGrey: changed c_value from :" + c_value + "  to:" + par1);
      c_value = (int) par1;
    }

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Maximp = duplicateImage(ip);
    ipMax = Maximp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMax, radius, rf.MAX); // Maximum
    // Maximp.show();
    Minimp = duplicateImage(ip);
    ipMin = Minimp.getProcessor();
    rf.rank(ipMin, radius, rf.MIN); // Minimum
    // Minimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    byte[] max = (byte[]) ipMax.getPixels();
    byte[] min = (byte[]) ipMin.getPixels();

    for (int i = 0; i < pixels.length; i++) {
      pixels[i] =
          ((int) (pixels[i] & 0xff) > (int) (((max[i] & 0xff) + (min[i] & 0xff)) / 2) - c_value)
              ? object
              : backg;
    }
    // imp.updateAndDraw();
    return;
  }
示例#9
0
  /**
   * Execute the plugin functionality: duplicate and scale the given image.
   *
   * @return an Object[] array with the name and the scaled ImagePlus. Does NOT show the new, image;
   *     just returns it.
   */
  public Object[] exec(
      ImagePlus imp, String myMethod, int radius, double par1, double par2, boolean doIwhite) {

    // 0 - Check validity of parameters
    if (null == imp) return null;
    ImageProcessor ip = imp.getProcessor();
    int xe = ip.getWidth();
    int ye = ip.getHeight();

    // int [] data = (ip.getHistogram());

    IJ.showStatus("Thresholding...");
    long startTime = System.currentTimeMillis();
    // 1 Do it
    if (imp.getStackSize() == 1) {
      ip.snapshot();
      Undo.setup(Undo.FILTER, imp);
    }
    // Apply the selected algorithm
    if (myMethod.equals("Bernsen")) {
      Bernsen(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Contrast")) {
      Contrast(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Mean")) {
      Mean(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Median")) {
      Median(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("MidGrey")) {
      MidGrey(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Niblack")) {
      Niblack(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Otsu")) {
      Otsu(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Phansalkar")) {
      Phansalkar(imp, radius, par1, par2, doIwhite);
    } else if (myMethod.equals("Sauvola")) {
      Sauvola(imp, radius, par1, par2, doIwhite);
    }
    // IJ.showProgress((double)(255-i)/255);
    imp.updateAndDraw();
    imp.getProcessor().setThreshold(255, 255, ImageProcessor.NO_LUT_UPDATE);
    // 2 - Return the threshold and the image
    IJ.showStatus("\nDone " + (System.currentTimeMillis() - startTime) / 1000.0);
    return new Object[] {imp};
  }
示例#10
0
 private void doRGBProjection(ImageStack stack) {
   ImageStack[] channels = ChannelSplitter.splitRGB(stack, true);
   ImagePlus red = new ImagePlus("Red", channels[0]);
   ImagePlus green = new ImagePlus("Green", channels[1]);
   ImagePlus blue = new ImagePlus("Blue", channels[2]);
   imp.unlock();
   ImagePlus saveImp = imp;
   imp = red;
   color = "(red)";
   doProjection();
   ImagePlus red2 = projImage;
   imp = green;
   color = "(green)";
   doProjection();
   ImagePlus green2 = projImage;
   imp = blue;
   color = "(blue)";
   doProjection();
   ImagePlus blue2 = projImage;
   int w = red2.getWidth(), h = red2.getHeight(), d = red2.getStackSize();
   if (method == SD_METHOD) {
     ImageProcessor r = red2.getProcessor();
     ImageProcessor g = green2.getProcessor();
     ImageProcessor b = blue2.getProcessor();
     double max = 0;
     double rmax = r.getStatistics().max;
     if (rmax > max) max = rmax;
     double gmax = g.getStatistics().max;
     if (gmax > max) max = gmax;
     double bmax = b.getStatistics().max;
     if (bmax > max) max = bmax;
     double scale = 255 / max;
     r.multiply(scale);
     g.multiply(scale);
     b.multiply(scale);
     red2.setProcessor(r.convertToByte(false));
     green2.setProcessor(g.convertToByte(false));
     blue2.setProcessor(b.convertToByte(false));
   }
   RGBStackMerge merge = new RGBStackMerge();
   ImageStack stack2 =
       merge.mergeStacks(w, h, d, red2.getStack(), green2.getStack(), blue2.getStack(), true);
   imp = saveImp;
   projImage = new ImagePlus(makeTitle(), stack2);
 }
示例#11
0
 void lineWidth() {
   int width = (int) IJ.getNumber("Line Width:", Line.getWidth());
   if (width == IJ.CANCELED) return;
   Line.setWidth(width);
   LineWidthAdjuster.update();
   ImagePlus imp = WindowManager.getCurrentImage();
   if (imp != null && imp.isProcessor()) {
     ImageProcessor ip = imp.getProcessor();
     ip.setLineWidth(Line.getWidth());
     Roi roi = imp.getRoi();
     if (roi != null && roi.isLine()) imp.draw();
   }
 }
示例#12
0
  void Mean(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // See: Image Processing Learning Resourches HIPR2
    // http://homepages.inf.ed.ac.uk/rbf/HIPR2/adpthrsh.htm
    ImagePlus Meanimp;
    ImageProcessor ip = imp.getProcessor(), ipMean;
    int c_value = 0;
    byte object;
    byte backg;

    if (par1 != 0) {
      IJ.log("Mean: changed c_value from :" + c_value + "  to:" + par1);
      c_value = (int) par1;
    }

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Meanimp = duplicateImage(ip);
    ImageConverter ic = new ImageConverter(Meanimp);
    ic.convertToGray32();

    ipMean = Meanimp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMean, radius, rf.MEAN); // Mean
    // Meanimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    float[] mean = (float[]) ipMean.getPixels();

    for (int i = 0; i < pixels.length; i++)
      pixels[i] = ((int) (pixels[i] & 0xff) > (int) (mean[i] - c_value)) ? object : backg;
    // imp.updateAndDraw();
    return;
  }
示例#13
0
 public void doHyperStackProjection(boolean allTimeFrames) {
   int start = startSlice;
   int stop = stopSlice;
   int firstFrame = 1;
   int lastFrame = imp.getNFrames();
   if (!allTimeFrames) firstFrame = lastFrame = imp.getFrame();
   ImageStack stack = new ImageStack(imp.getWidth(), imp.getHeight());
   int channels = imp.getNChannels();
   int slices = imp.getNSlices();
   if (slices == 1) {
     slices = imp.getNFrames();
     firstFrame = lastFrame = 1;
   }
   int frames = lastFrame - firstFrame + 1;
   increment = channels;
   boolean rgb = imp.getBitDepth() == 24;
   for (int frame = firstFrame; frame <= lastFrame; frame++) {
     for (int channel = 1; channel <= channels; channel++) {
       startSlice = (frame - 1) * channels * slices + (start - 1) * channels + channel;
       stopSlice = (frame - 1) * channels * slices + (stop - 1) * channels + channel;
       if (rgb) doHSRGBProjection(imp);
       else doProjection();
       stack.addSlice(null, projImage.getProcessor());
     }
   }
   projImage = new ImagePlus(makeTitle(), stack);
   projImage.setDimensions(channels, 1, frames);
   if (channels > 1) {
     projImage = new CompositeImage(projImage, 0);
     ((CompositeImage) projImage).copyLuts(imp);
     if (method == SUM_METHOD || method == SD_METHOD)
       ((CompositeImage) projImage).resetDisplayRanges();
   }
   if (frames > 1) projImage.setOpenAsHyperStack(true);
   Overlay overlay = imp.getOverlay();
   if (overlay != null) {
     startSlice = start;
     stopSlice = stop;
     if (imp.getType() == ImagePlus.COLOR_RGB)
       projImage.setOverlay(projectRGBHyperStackRois(overlay));
     else projImage.setOverlay(projectHyperStackRois(overlay));
   }
   IJ.showProgress(1, 1);
 }
示例#14
0
 void createSelectionFromMask(ImagePlus imp) {
   ImageProcessor ip = imp.getProcessor();
   if (ip.getMinThreshold() != ImageProcessor.NO_THRESHOLD) {
     IJ.runPlugIn("ij.plugin.filter.ThresholdToSelection", "");
     return;
   }
   if (!ip.isBinary()) {
     IJ.error(
         "Create Selection",
         "This command creates a composite selection from\n"
             + "a mask (8-bit binary image with white background)\n"
             + "or from an image that has been thresholded using\n"
             + "the Image>Adjust>Threshold tool. The current\n"
             + "image is not a mask and has not been thresholded.");
     return;
   }
   int threshold = ip.isInvertedLut() ? 255 : 0;
   ip.setThreshold(threshold, threshold, ImageProcessor.NO_LUT_UPDATE);
   IJ.runPlugIn("ij.plugin.filter.ThresholdToSelection", "");
 }
示例#15
0
 void createMask(ImagePlus imp) {
   Roi roi = imp.getRoi();
   boolean useInvertingLut = Prefs.useInvertingLut;
   Prefs.useInvertingLut = false;
   boolean selectAll =
       roi != null
           && roi.getType() == Roi.RECTANGLE
           && roi.getBounds().width == imp.getWidth()
           && roi.getBounds().height == imp.getHeight()
           && imp.isThreshold();
   if (roi == null || !(roi.isArea() || roi.getType() == Roi.POINT) || selectAll) {
     createMaskFromThreshold(imp);
     Prefs.useInvertingLut = useInvertingLut;
     return;
   }
   ImagePlus maskImp = null;
   Frame frame = WindowManager.getFrame("Mask");
   if (frame != null && (frame instanceof ImageWindow))
     maskImp = ((ImageWindow) frame).getImagePlus();
   if (maskImp == null) {
     ImageProcessor ip = new ByteProcessor(imp.getWidth(), imp.getHeight());
     if (!Prefs.blackBackground) ip.invertLut();
     maskImp = new ImagePlus("Mask", ip);
     maskImp.show();
   }
   ImageProcessor ip = maskImp.getProcessor();
   ip.setRoi(roi);
   ip.setValue(255);
   ip.fill(ip.getMask());
   Calibration cal = imp.getCalibration();
   if (cal.scaled()) {
     Calibration cal2 = maskImp.getCalibration();
     cal2.pixelWidth = cal.pixelWidth;
     cal2.pixelHeight = cal.pixelHeight;
     cal2.setUnit(cal.getUnit());
   }
   maskImp.updateAndRepaintWindow();
   Prefs.useInvertingLut = useInvertingLut;
 }
示例#16
0
  void Otsu(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // Otsu's threshold algorithm
    // C++ code by Jordan Bevik <*****@*****.**>
    // ported to ImageJ plugin by G.Landini. Same algorithm as in Auto_Threshold, this time on local
    // circular regions
    int[] data;
    int w = imp.getWidth();
    int h = imp.getHeight();
    int position;
    int radiusx2 = radius * 2;
    ImageProcessor ip = imp.getProcessor();
    byte[] pixels = (byte[]) ip.getPixels();
    byte[] pixelsOut =
        new byte
            [pixels.length]; // need this to avoid changing the image data (and further histograms)
    byte object;
    byte backg;

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    int k, kStar; // k = the current threshold; kStar = optimal threshold
    int N1, N; // N1 = # points with intensity <=k; N = total number of points
    double BCV, BCVmax; // The current Between Class Variance and maximum BCV
    double num, denom; // temporary bookeeping
    int Sk; // The total intensity for all histogram points <=k
    int S,
        L =
            256; // The total intensity of the image. Need to hange here if modifying for >8 bits
                 // images
    int roiy;

    Roi roi = new OvalRoi(0, 0, radiusx2, radiusx2);
    // ip.setRoi(roi);
    for (int y = 0; y < h; y++) {
      IJ.showProgress(
          (double) (y) / (h - 1)); // this method is slow, so let's show the progress bar
      roiy = y - radius;
      for (int x = 0; x < w; x++) {
        roi.setLocation(x - radius, roiy);
        ip.setRoi(roi);
        // ip.setRoi(new OvalRoi(x-radius, roiy, radiusx2, radiusx2));
        position = x + y * w;
        data = ip.getHistogram();

        // Initialize values:
        S = N = 0;
        for (k = 0; k < L; k++) {
          S += k * data[k]; // Total histogram intensity
          N += data[k]; // Total number of data points
        }

        Sk = 0;
        N1 = data[0]; // The entry for zero intensity
        BCV = 0;
        BCVmax = 0;
        kStar = 0;

        // Look at each possible threshold value,
        // calculate the between-class variance, and decide if it's a max
        for (k = 1; k < L - 1; k++) { // No need to check endpoints k = 0 or k = L-1
          Sk += k * data[k];
          N1 += data[k];

          // The float casting here is to avoid compiler warning about loss of precision and
          // will prevent overflow in the case of large saturated images
          denom = (double) (N1) * (N - N1); // Maximum value of denom is (N^2)/4 =  approx. 3E10

          if (denom != 0) {
            // Float here is to avoid loss of precision when dividing
            num = ((double) N1 / N) * S - Sk; // Maximum value of num =  255*N = approx 8E7
            BCV = (num * num) / denom;
          } else BCV = 0;

          if (BCV >= BCVmax) { // Assign the best threshold found so far
            BCVmax = BCV;
            kStar = k;
          }
        }
        // kStar += 1;	// Use QTI convention that intensity -> 1 if intensity >= k
        // (the algorithm was developed for I-> 1 if I <= k.)
        // return kStar;
        pixelsOut[position] = ((int) (pixels[position] & 0xff) > kStar) ? object : backg;
      }
    }
    for (position = 0; position < w * h; position++)
      pixels[position] = pixelsOut[position]; // update with thresholded pixels
  }
示例#17
0
  public void run(String arg) {
    imp = IJ.getImage();
    int stackSize = imp.getStackSize();
    if (imp == null) {
      IJ.noImage();
      return;
    }

    //  Make sure input image is a stack.
    if (stackSize == 1) {
      IJ.error("Z Project", "Stack required");
      return;
    }

    //  Check for inverting LUT.
    if (imp.getProcessor().isInvertedLut()) {
      if (!IJ.showMessageWithCancel("ZProjection", lutMessage)) return;
    }

    // Set default bounds.
    int channels = imp.getNChannels();
    int frames = imp.getNFrames();
    int slices = imp.getNSlices();
    isHyperstack =
        imp.isHyperStack()
            || (ij.macro.Interpreter.isBatchMode()
                && ((frames > 1 && frames < stackSize) || (slices > 1 && slices < stackSize)));
    boolean simpleComposite = channels == stackSize;
    if (simpleComposite) isHyperstack = false;
    startSlice = 1;
    if (isHyperstack) {
      int nSlices = imp.getNSlices();
      if (nSlices > 1) stopSlice = nSlices;
      else stopSlice = imp.getNFrames();
    } else stopSlice = stackSize;

    // Build control dialog
    GenericDialog gd = buildControlDialog(startSlice, stopSlice);
    gd.showDialog();
    if (gd.wasCanceled()) return;

    if (!imp.lock()) return; // exit if in use
    long tstart = System.currentTimeMillis();
    setStartSlice((int) gd.getNextNumber());
    setStopSlice((int) gd.getNextNumber());
    method = gd.getNextChoiceIndex();
    Prefs.set(METHOD_KEY, method);
    if (isHyperstack) {
      allTimeFrames = imp.getNFrames() > 1 && imp.getNSlices() > 1 ? gd.getNextBoolean() : false;
      doHyperStackProjection(allTimeFrames);
    } else if (imp.getType() == ImagePlus.COLOR_RGB) doRGBProjection(true);
    else doProjection(true);

    if (arg.equals("") && projImage != null) {
      long tstop = System.currentTimeMillis();
      projImage.setCalibration(imp.getCalibration());
      if (simpleComposite) IJ.run(projImage, "Grays", "");
      projImage.show("ZProjector: " + IJ.d2s((tstop - tstart) / 1000.0, 2) + " seconds");
    }

    imp.unlock();
    IJ.register(ZProjector.class);
    return;
  }
示例#18
0
  /** Ask for parameters and then execute. */
  public void run(String arg) {
    // 1 - Obtain the currently active image:
    ImagePlus imp = IJ.getImage();

    if (null == imp) {
      IJ.showMessage("There must be at least one image open");
      return;
    }

    if (imp.getBitDepth() != 8) {
      IJ.showMessage("Error", "Only 8-bit images are supported");
      return;
    }

    // 2 - Ask for parameters:
    GenericDialog gd = new GenericDialog("Auto Local Threshold");
    String[] methods = {
      "Try all",
      "Bernsen",
      "Contrast",
      "Mean",
      "Median",
      "MidGrey",
      "Niblack",
      "Otsu",
      "Phansalkar",
      "Sauvola"
    };
    gd.addMessage("Auto Local Threshold v1.5");
    gd.addChoice("Method", methods, methods[0]);
    gd.addNumericField("Radius", 15, 0);
    gd.addMessage("Special paramters (if different from default)");
    gd.addNumericField("Parameter_1", 0, 0);
    gd.addNumericField("Parameter_2", 0, 0);
    gd.addCheckbox("White objects on black background", true);
    if (imp.getStackSize() > 1) {
      gd.addCheckbox("Stack", false);
    }
    gd.addMessage("Thresholded result is always shown in white [255].");
    gd.showDialog();
    if (gd.wasCanceled()) return;

    // 3 - Retrieve parameters from the dialog
    String myMethod = gd.getNextChoice();
    int radius = (int) gd.getNextNumber();
    double par1 = (double) gd.getNextNumber();
    double par2 = (double) gd.getNextNumber();
    boolean doIwhite = gd.getNextBoolean();
    boolean doIstack = false;

    int stackSize = imp.getStackSize();
    if (stackSize > 1) doIstack = gd.getNextBoolean();

    // 4 - Execute!
    // long start = System.currentTimeMillis();
    if (myMethod.equals("Try all")) {
      ImageProcessor ip = imp.getProcessor();
      int xe = ip.getWidth();
      int ye = ip.getHeight();
      int ml = methods.length;
      ImagePlus imp2, imp3;
      ImageStack tstack = null, stackNew;
      if (stackSize > 1 && doIstack) {
        boolean doItAnyway = true;
        if (stackSize > 25) {
          YesNoCancelDialog d =
              new YesNoCancelDialog(
                  IJ.getInstance(),
                  "Auto Local Threshold",
                  "You might run out of memory.\n \nDisplay "
                      + stackSize
                      + " slices?\n \n \'No\' will process without display and\noutput results to the log window.");
          if (!d.yesPressed()) {
            //						doIlog=true; //will show in the log window
            doItAnyway = false;
          }
          if (d.cancelPressed()) return;
        }

        for (int j = 1; j <= stackSize; j++) {
          imp.setSlice(j);
          ip = imp.getProcessor();
          tstack = new ImageStack(xe, ye);
          for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
          imp2 = new ImagePlus("Auto Threshold", tstack);
          imp2.updateAndDraw();

          for (int k = 1; k < ml; k++) {
            imp2.setSlice(k);
            Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
          }
          // if (doItAnyway){
          CanvasResizer cr = new CanvasResizer();
          stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
          imp3 = new ImagePlus("Auto Threshold", stackNew);
          imp3.updateAndDraw();
          MontageMaker mm = new MontageMaker();
          mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true); // 3 columns and 3 rows
        }
        imp.setSlice(1);
        // if (doItAnyway)
        IJ.run("Images to Stack", "method=[Copy (center)] title=Montage");
        return;
      } else { // single image try all
        tstack = new ImageStack(xe, ye);
        for (int k = 1; k < ml; k++) tstack.addSlice(methods[k], ip.duplicate());
        imp2 = new ImagePlus("Auto Threshold", tstack);
        imp2.updateAndDraw();

        for (int k = 1; k < ml; k++) {
          imp2.setSlice(k);
          // IJ.log("analyzing slice with "+methods[k]);
          Object[] result = exec(imp2, methods[k], radius, par1, par2, doIwhite);
        }
        // imp2.setSlice(1);
        CanvasResizer cr = new CanvasResizer();
        stackNew = cr.expandStack(tstack, (xe + 2), (ye + 18), 1, 1);
        imp3 = new ImagePlus("Auto Threshold", stackNew);
        imp3.updateAndDraw();
        MontageMaker mm = new MontageMaker();
        mm.makeMontage(imp3, 3, 3, 1.0, 1, (ml - 1), 1, 0, true);
        return;
      }
    } else { // selected a method
      if (stackSize > 1 && doIstack) { // whole stack
        //				if (doIstackHistogram) {// one global histogram
        //					Object[] result = exec(imp, myMethod, noWhite, noBlack, doIwhite, doIset, doIlog,
        // doIstackHistogram );
        //				}
        //				else{ // slice by slice
        for (int k = 1; k <= stackSize; k++) {
          imp.setSlice(k);
          Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
        }
        //				}
        imp.setSlice(1);
      } else { // just one slice
        Object[] result = exec(imp, myMethod, radius, par1, par2, doIwhite);
      }
      // 5 - If all went well, show the image:
      // not needed here as the source image is binarised
    }
  }
示例#19
0
  void Niblack(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // Niblack recommends K_VALUE = -0.2 for images with black foreground
    // objects, and K_VALUE = +0.2 for images with white foreground objects.
    //  Niblack W. (1986) "An introduction to Digital Image Processing" Prentice-Hall.
    // Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
    // This version uses a circular local window, instead of a rectagular one

    ImagePlus Meanimp, Varimp;
    ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
    double k_value;
    int c_value = 0;

    byte object;
    byte backg;

    if (doIwhite) {
      k_value = 0.2;
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      k_value = -0.2;
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    if (par1 != 0) {
      IJ.log("Niblack: changed k_value from :" + k_value + "  to:" + par1);
      k_value = par1;
    }

    if (par2 != 0) {
      IJ.log(
          "Niblack: changed c_value from :"
              + c_value
              + "  to:"
              + par2); // requested feature, not in original
      c_value = (int) par2;
    }

    Meanimp = duplicateImage(ip);
    ImageConverter ic = new ImageConverter(Meanimp);
    ic.convertToGray32();

    ipMean = Meanimp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMean, radius, rf.MEAN); // Mean
    // Meanimp.show();
    Varimp = duplicateImage(ip);
    ic = new ImageConverter(Varimp);
    ic.convertToGray32();
    ipVar = Varimp.getProcessor();
    rf.rank(ipVar, radius, rf.VARIANCE); // Variance
    // Varimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    float[] mean = (float[]) ipMean.getPixels();
    float[] var = (float[]) ipVar.getPixels();

    for (int i = 0; i < pixels.length; i++)
      pixels[i] =
          ((int) (pixels[i] & 0xff) > (int) (mean[i] + k_value * Math.sqrt(var[i]) - c_value))
              ? object
              : backg;
    // imp.updateAndDraw();
    return;
  }
示例#20
0
 private void makeBand(ImagePlus imp) {
   Roi roi = imp.getRoi();
   if (roi == null) {
     noRoi("Make Band");
     return;
   }
   if (!roi.isArea()) {
     IJ.error("Make Band", "Area selection required");
     return;
   }
   Calibration cal = imp.getCalibration();
   double pixels = bandSize;
   double size = pixels * cal.pixelWidth;
   int decimalPlaces = 0;
   if ((int) size != size) decimalPlaces = 2;
   GenericDialog gd = new GenericDialog("Make Band");
   gd.addNumericField("Band Size:", size, decimalPlaces, 4, cal.getUnits());
   gd.showDialog();
   if (gd.wasCanceled()) return;
   size = gd.getNextNumber();
   if (Double.isNaN(size)) {
     IJ.error("Make Band", "invalid number");
     return;
   }
   int n = (int) Math.round(size / cal.pixelWidth);
   if (n > 255) {
     IJ.error("Make Band", "Cannot make bands wider that 255 pixels");
     return;
   }
   int width = imp.getWidth();
   int height = imp.getHeight();
   Rectangle r = roi.getBounds();
   ImageProcessor ip = roi.getMask();
   if (ip == null) {
     ip = new ByteProcessor(r.width, r.height);
     ip.invert();
   }
   ImageProcessor mask = new ByteProcessor(width, height);
   mask.insert(ip, r.x, r.y);
   ImagePlus edm = new ImagePlus("mask", mask);
   boolean saveBlackBackground = Prefs.blackBackground;
   Prefs.blackBackground = false;
   IJ.run(edm, "Distance Map", "");
   Prefs.blackBackground = saveBlackBackground;
   ip = edm.getProcessor();
   ip.setThreshold(0, n, ImageProcessor.NO_LUT_UPDATE);
   int xx = -1, yy = -1;
   for (int x = r.x; x < r.x + r.width; x++) {
     for (int y = r.y; y < r.y + r.height; y++) {
       if (ip.getPixel(x, y) < n) {
         xx = x;
         yy = y;
         break;
       }
     }
     if (xx >= 0 || yy >= 0) break;
   }
   int count = IJ.doWand(edm, xx, yy, 0, null);
   if (count <= 0) {
     IJ.error("Make Band", "Unable to make band");
     return;
   }
   ShapeRoi roi2 = new ShapeRoi(edm.getRoi());
   if (!(roi instanceof ShapeRoi)) roi = new ShapeRoi(roi);
   ShapeRoi roi1 = (ShapeRoi) roi;
   roi2 = roi2.not(roi1);
   imp.setRoi(roi2);
   bandSize = n;
 }
  public void run(ImageProcessor ip) {
    String[] imageNames = getOpenImageNames();
    if (imageNames[0] == "None") {
      IJ.error("need at least 2 binary open images");
      return;
    }
    double previousMinOverlap = Prefs.get("BVTB.BinaryFeatureExtractor.minOverlap", 0);
    boolean previousCombine = Prefs.get("BVTB.BinaryFeatureExtractor.combine", false);

    GenericDialog gd = new GenericDialog("Binary Feature Extractor");
    gd.addChoice("Objects image", imageNames, imageNames[0]);
    gd.addChoice("Selector image", imageNames, imageNames[1]);
    gd.addNumericField("Object_overlap in % (0=off)", previousMinOverlap, 0, 9, "");
    gd.addCheckbox("Combine objects and selectors", previousCombine);
    gd.addCheckbox("Count output", true);
    gd.addCheckbox("Analysis tables", false);
    gd.showDialog();
    if (gd.wasCanceled()) {
      return;
    }
    String objectsImgTitle = gd.getNextChoice();
    String selectorsImgTitle = gd.getNextChoice();
    double minOverlap = gd.getNextNumber();
    boolean combineImages = gd.getNextBoolean();
    boolean showCountOutput = gd.getNextBoolean();
    boolean showAnalysis = gd.getNextBoolean();
    if (gd.invalidNumber() || minOverlap < 0 || minOverlap > 100) {
      IJ.error("invalid number");
      return;
    }
    Prefs.set("BVTB.BinaryFeatureExtractor.minOverlap", minOverlap);
    Prefs.set("BVTB.BinaryFeatureExtractor.combine", combineImages);

    if (objectsImgTitle.equals(selectorsImgTitle)) {
      IJ.error("images need to be different");
      return;
    }

    ImagePlus objectsImp = WindowManager.getImage(objectsImgTitle);
    ImageProcessor objectsIP = objectsImp.getProcessor();
    ImagePlus selectorsImp = WindowManager.getImage(selectorsImgTitle);
    ImageProcessor selectorsIP = selectorsImp.getProcessor();

    if (!objectsIP.isBinary() || !selectorsIP.isBinary()) {
      IJ.error("works with 8-bit binary images only");
      return;
    }

    if ((objectsImp.getWidth() != selectorsImp.getWidth())
        || objectsImp.getHeight() != selectorsImp.getHeight()) {
      IJ.error("images need to be of the same size");
      return;
    }

    // close any existing RoiManager before instantiating a new one for this analysis
    RoiManager oldRM = RoiManager.getInstance2();
    if (oldRM != null) {
      oldRM.close();
    }

    RoiManager objectsRM = new RoiManager(true);
    ResultsTable objectsRT = new ResultsTable();
    ParticleAnalyzer analyzeObjects =
        new ParticleAnalyzer(analyzerOptions, measurementFlags, objectsRT, 0.0, 999999999.9);
    analyzeObjects.setRoiManager(objectsRM);

    analyzeObjects.analyze(objectsImp);
    objectsRM.runCommand("Show None");
    int objectNumber = objectsRT.getCounter();

    Roi[] objectRoi = objectsRM.getRoisAsArray();

    ResultsTable measureSelectorsRT = new ResultsTable();
    Analyzer overlapAnalyzer = new Analyzer(selectorsImp, measurementFlags, measureSelectorsRT);

    ImagePlus outputImp =
        IJ.createImage("output", "8-bit black", objectsImp.getWidth(), objectsImp.getHeight(), 1);
    ImageProcessor outputIP = outputImp.getProcessor();

    double[] measuredOverlap = new double[objectNumber];

    outputIP.setValue(255.0);
    for (int o = 0; o < objectNumber; o++) {
      selectorsImp.killRoi();
      selectorsImp.setRoi(objectRoi[o]);
      overlapAnalyzer.measure();
      measuredOverlap[o] = measureSelectorsRT.getValue("%Area", o);
      if (minOverlap != 0.0 && measuredOverlap[o] >= minOverlap) {
        outputIP.fill(objectRoi[o]);
        finalCount++;
      } else if (minOverlap == 0.0 && measuredOverlap[o] > 0.0) {
        outputIP.fill(objectRoi[o]);
        finalCount++;
      }
    }
    // measureSelectorsRT.show("Objects");

    selectorsImp.killRoi();
    RoiManager selectorRM = new RoiManager(true);
    ResultsTable selectorRT = new ResultsTable();
    ParticleAnalyzer.setRoiManager(selectorRM);
    ParticleAnalyzer analyzeSelectors =
        new ParticleAnalyzer(analyzerOptions, measurementFlags, selectorRT, 0.0, 999999999.9);
    analyzeSelectors.analyze(selectorsImp);
    selectorRM.runCommand("Show None");
    int selectorNumber = selectorRT.getCounter();

    if (combineImages) {
      outputImp.updateAndDraw();
      Roi[] selectorRoi = selectorRM.getRoisAsArray();

      ResultsTable measureObjectsRT = new ResultsTable();
      Analyzer selectorAnalyzer = new Analyzer(outputImp, measurementFlags, measureObjectsRT);

      double[] selectorOverlap = new double[selectorNumber];
      outputIP.setValue(255.0);
      for (int s = 0; s < selectorNumber; s++) {
        outputImp.killRoi();
        outputImp.setRoi(selectorRoi[s]);
        selectorAnalyzer.measure();
        selectorOverlap[s] = measureObjectsRT.getValue("%Area", s);
        if (selectorOverlap[s] > 0.0d) {
          outputIP.fill(selectorRoi[s]);
        }
      }
      selectorRoi = null;
      selectorAnalyzer = null;
      measureObjectsRT = null;
    }
    // selectorRT.show("Selectors");
    outputImp.killRoi();
    String outputImageTitle = WindowManager.getUniqueName("Extracted_" + objectsImgTitle);
    outputImp.setTitle(outputImageTitle);
    outputImp.show();
    outputImp.changes = true;

    if (showCountOutput) {
      String[] openTextWindows = WindowManager.getNonImageTitles();
      boolean makeNewTable = true;
      for (int w = 0; w < openTextWindows.length; w++) {
        if (openTextWindows[w].equals("BFE_Results")) {
          makeNewTable = false;
        }
      }

      TextWindow existingCountTable = ResultsTable.getResultsWindow();
      if (makeNewTable) {
        countTable = new ResultsTable();
        countTable.setPrecision(0);
        countTable.setValue("Image", 0, outputImageTitle);
        countTable.setValue("Objects", 0, objectNumber);
        countTable.setValue("Selectors", 0, selectorNumber);
        countTable.setValue("Extracted", 0, finalCount);
        countTable.show("BFE_Results");
      } else {
        IJ.renameResults("BFE_Results", "Results");
        countTable = ResultsTable.getResultsTable();
        countTable.setPrecision(0);
        countTable.incrementCounter();
        countTable.addValue("Image", outputImageTitle);
        countTable.addValue("Objects", objectNumber);
        countTable.addValue("Selectors", selectorNumber);
        countTable.addValue("Extracted", finalCount);
        IJ.renameResults("Results", "BFE_Results");
        countTable.show("BFE_Results");
      }
    }

    if (showAnalysis) {
      ResultsTable extractedRT = new ResultsTable();
      ParticleAnalyzer analyzeExtracted =
          new ParticleAnalyzer(
              ParticleAnalyzer.CLEAR_WORKSHEET | ParticleAnalyzer.RECORD_STARTS,
              measurementFlags,
              extractedRT,
              0.0,
              999999999.9);
      analyzeExtracted.analyze(outputImp);
      objectsRT.show("Objects");
      selectorRT.show("Selectors");
      extractedRT.show("Extracted");
    } else {
      objectsRT = null;
      selectorRT = null;
    }

    objectsRM = null;
    measureSelectorsRT = null;
    analyzeObjects = null;
    overlapAnalyzer = null;
    objectRoi = null;
    selectorRM = null;

    objectsImp.killRoi();
    objectsImp.changes = false;
    selectorsImp.changes = false;
  }
示例#22
0
  void Sauvola(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // Sauvola recommends K_VALUE = 0.5 and R_VALUE = 128.
    // This is a modification of Niblack's thresholding method.
    // Sauvola J. and Pietaksinen M. (2000) "Adaptive Document Image Binarization"
    // Pattern Recognition, 33(2): 225-236
    // http://www.ee.oulu.fi/mvg/publications/show_pdf.php?ID=24
    // Ported to ImageJ plugin from E Celebi's fourier_0.8 routines
    // This version uses a circular local window, instead of a rectagular one

    ImagePlus Meanimp, Varimp;
    ImageProcessor ip = imp.getProcessor(), ipMean, ipVar;
    double k_value = 0.5;
    double r_value = 128;
    byte object;
    byte backg;

    if (par1 != 0) {
      IJ.log("Sauvola: changed k_value from :" + k_value + "  to:" + par1);
      k_value = par1;
    }

    if (par2 != 0) {
      IJ.log("Sauvola: changed r_value from :" + r_value + "  to:" + par2);
      r_value = par2;
    }

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Meanimp = duplicateImage(ip);
    ImageConverter ic = new ImageConverter(Meanimp);
    ic.convertToGray32();

    ipMean = Meanimp.getProcessor();
    RankFilters rf = new RankFilters();
    rf.rank(ipMean, radius, rf.MEAN); // Mean
    // Meanimp.show();
    Varimp = duplicateImage(ip);
    ic = new ImageConverter(Varimp);
    ic.convertToGray32();
    ipVar = Varimp.getProcessor();
    rf.rank(ipVar, radius, rf.VARIANCE); // Variance
    // Varimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    float[] mean = (float[]) ipMean.getPixels();
    float[] var = (float[]) ipVar.getPixels();

    for (int i = 0; i < pixels.length; i++)
      pixels[i] =
          ((int) (pixels[i] & 0xff)
                  > (int) (mean[i] * (1.0 + k_value * ((Math.sqrt(var[i]) / r_value) - 1.0))))
              ? object
              : backg;
    // imp.updateAndDraw();
    return;
  }
示例#23
0
  /*
  if selection is closed shape, create a circle with the same area and centroid, otherwise use<br>
  the Pratt method to fit a circle to the points that define the line or multi-point selection.<br>
  Reference: Pratt V., Direct least-squares fitting of algebraic surfaces", Computer Graphics, Vol. 21, pages 145-152 (1987).<br>
  Original code: Nikolai Chernov's MATLAB script for Newton-based Pratt fit.<br>
  (http://www.math.uab.edu/~chernov/cl/MATLABcircle.html)<br>
  Java version: https://github.com/mdoube/BoneJ/blob/master/src/org/doube/geometry/FitCircle.java<br>
  @authors Nikolai Chernov, Michael Doube, Ved Sharma
  */
  void fitCircle(ImagePlus imp) {
    Roi roi = imp.getRoi();
    if (roi == null) {
      noRoi("Fit Circle");
      return;
    }

    if (roi.isArea()) { // create circle with the same area and centroid
      ImageProcessor ip = imp.getProcessor();
      ip.setRoi(roi);
      ImageStatistics stats =
          ImageStatistics.getStatistics(ip, Measurements.AREA + Measurements.CENTROID, null);
      double r = Math.sqrt(stats.pixelCount / Math.PI);
      imp.killRoi();
      int d = (int) Math.round(2.0 * r);
      IJ.makeOval(
          (int) Math.round(stats.xCentroid - r), (int) Math.round(stats.yCentroid - r), d, d);
      return;
    }

    Polygon poly = roi.getPolygon();
    int n = poly.npoints;
    int[] x = poly.xpoints;
    int[] y = poly.ypoints;
    if (n < 3) {
      IJ.error("Fit Circle", "At least 3 points are required to fit a circle.");
      return;
    }

    // calculate point centroid
    double sumx = 0, sumy = 0;
    for (int i = 0; i < n; i++) {
      sumx = sumx + poly.xpoints[i];
      sumy = sumy + poly.ypoints[i];
    }
    double meanx = sumx / n;
    double meany = sumy / n;

    // calculate moments
    double[] X = new double[n], Y = new double[n];
    double Mxx = 0, Myy = 0, Mxy = 0, Mxz = 0, Myz = 0, Mzz = 0;
    for (int i = 0; i < n; i++) {
      X[i] = x[i] - meanx;
      Y[i] = y[i] - meany;
      double Zi = X[i] * X[i] + Y[i] * Y[i];
      Mxy = Mxy + X[i] * Y[i];
      Mxx = Mxx + X[i] * X[i];
      Myy = Myy + Y[i] * Y[i];
      Mxz = Mxz + X[i] * Zi;
      Myz = Myz + Y[i] * Zi;
      Mzz = Mzz + Zi * Zi;
    }
    Mxx = Mxx / n;
    Myy = Myy / n;
    Mxy = Mxy / n;
    Mxz = Mxz / n;
    Myz = Myz / n;
    Mzz = Mzz / n;

    // calculate the coefficients of the characteristic polynomial
    double Mz = Mxx + Myy;
    double Cov_xy = Mxx * Myy - Mxy * Mxy;
    double Mxz2 = Mxz * Mxz;
    double Myz2 = Myz * Myz;
    double A2 = 4 * Cov_xy - 3 * Mz * Mz - Mzz;
    double A1 = Mzz * Mz + 4 * Cov_xy * Mz - Mxz2 - Myz2 - Mz * Mz * Mz;
    double A0 = Mxz2 * Myy + Myz2 * Mxx - Mzz * Cov_xy - 2 * Mxz * Myz * Mxy + Mz * Mz * Cov_xy;
    double A22 = A2 + A2;
    double epsilon = 1e-12;
    double ynew = 1e+20;
    int IterMax = 20;
    double xnew = 0;
    int iterations = 0;

    // Newton's method starting at x=0
    for (int iter = 1; iter <= IterMax; iter++) {
      iterations = iter;
      double yold = ynew;
      ynew = A0 + xnew * (A1 + xnew * (A2 + 4. * xnew * xnew));
      if (Math.abs(ynew) > Math.abs(yold)) {
        if (IJ.debugMode) IJ.log("Fit Circle: wrong direction: |ynew| > |yold|");
        xnew = 0;
        break;
      }
      double Dy = A1 + xnew * (A22 + 16 * xnew * xnew);
      double xold = xnew;
      xnew = xold - ynew / Dy;
      if (Math.abs((xnew - xold) / xnew) < epsilon) break;
      if (iter >= IterMax) {
        if (IJ.debugMode) IJ.log("Fit Circle: will not converge");
        xnew = 0;
      }
      if (xnew < 0) {
        if (IJ.debugMode) IJ.log("Fit Circle: negative root:  x = " + xnew);
        xnew = 0;
      }
    }
    if (IJ.debugMode)
      IJ.log("Fit Circle: n=" + n + ", xnew=" + IJ.d2s(xnew, 2) + ", iterations=" + iterations);

    // calculate the circle parameters
    double DET = xnew * xnew - xnew * Mz + Cov_xy;
    double CenterX = (Mxz * (Myy - xnew) - Myz * Mxy) / (2 * DET);
    double CenterY = (Myz * (Mxx - xnew) - Mxz * Mxy) / (2 * DET);
    double radius = Math.sqrt(CenterX * CenterX + CenterY * CenterY + Mz + 2 * xnew);
    if (Double.isNaN(radius)) {
      IJ.error("Fit Circle", "Points are collinear.");
      return;
    }
    CenterX = CenterX + meanx;
    CenterY = CenterY + meany;
    imp.killRoi();
    IJ.makeOval(
        (int) Math.round(CenterX - radius),
        (int) Math.round(CenterY - radius),
        (int) Math.round(2 * radius),
        (int) Math.round(2 * radius));
  }
示例#24
0
  void Phansalkar(ImagePlus imp, int radius, double par1, double par2, boolean doIwhite) {
    // This is a modification of Sauvola's thresholding method to deal with low contrast images.
    // Phansalskar N. et al. Adaptive local thresholding for detection of nuclei in diversity
    // stained
    // cytology images.International Conference on Communications and Signal Processing (ICCSP),
    // 2011,
    // 218 - 220.
    // In this method, the threshold t = mean*(1+p*exp(-q*mean)+k*((stdev/r)-1))
    // Phansalkar recommends k = 0.25, r = 0.5, p = 2 and q = 10. In this plugin, k and r are the
    // parameters 1 and 2 respectively, but the values of p and q are fixed.
    //
    // Implemented from Phansalkar's paper description by G. Landini
    // This version uses a circular local window, instead of a rectagular one

    ImagePlus Meanimp, Varimp, Orimp;
    ImageProcessor ip = imp.getProcessor(), ipMean, ipVar, ipOri;
    double k_value = 0.25;
    double r_value = 0.5;
    double p_value = 2.0;
    double q_value = 10.0;
    byte object;
    byte backg;

    if (par1 != 0) {
      IJ.log("Phansalkar: changed k_value from :" + k_value + "  to:" + par1);
      k_value = par1;
    }

    if (par2 != 0) {
      IJ.log("Phansalkar: changed r_value from :" + r_value + "  to:" + par2);
      r_value = par2;
    }

    if (doIwhite) {
      object = (byte) 0xff;
      backg = (byte) 0;
    } else {
      object = (byte) 0;
      backg = (byte) 0xff;
    }

    Meanimp = duplicateImage(ip);
    ContrastEnhancer ce = new ContrastEnhancer();
    ce.stretchHistogram(Meanimp, 0.0);
    ImageConverter ic = new ImageConverter(Meanimp);
    ic.convertToGray32();
    ipMean = Meanimp.getProcessor();
    ipMean.multiply(1.0 / 255);

    Orimp = duplicateImage(ip);
    ce.stretchHistogram(Orimp, 0.0);
    ic = new ImageConverter(Orimp);
    ic.convertToGray32();
    ipOri = Orimp.getProcessor();
    ipOri.multiply(1.0 / 255); // original to compare
    // Orimp.show();

    RankFilters rf = new RankFilters();
    rf.rank(ipMean, radius, rf.MEAN); // Mean

    // Meanimp.show();
    Varimp = duplicateImage(ip);
    ce.stretchHistogram(Varimp, 0.0);
    ic = new ImageConverter(Varimp);
    ic.convertToGray32();
    ipVar = Varimp.getProcessor();
    ipVar.multiply(1.0 / 255);

    rf.rank(ipVar, radius, rf.VARIANCE); // Variance
    ipVar.sqr(); // SD

    // Varimp.show();
    byte[] pixels = (byte[]) ip.getPixels();
    float[] ori = (float[]) ipOri.getPixels();
    float[] mean = (float[]) ipMean.getPixels();
    float[] sd = (float[]) ipVar.getPixels();

    for (int i = 0; i < pixels.length; i++)
      pixels[i] =
          ((ori[i])
                  > (mean[i]
                      * (1.0
                          + p_value * Math.exp(-q_value * mean[i])
                          + k_value * ((sd[i] / r_value) - 1.0))))
              ? object
              : backg;
    // imp.updateAndDraw();
    return;
  }