/** 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);
}
/** Constructs a Wand object from an ImageProcessor. */
public Wand(ImageProcessor ip) {
this.ip = ip;
if (ip instanceof ByteProcessor) bpixels = (byte[]) ip.getPixels();
else if (ip instanceof ColorProcessor) cpixels = (int[]) ip.getPixels();
else if (ip instanceof ShortProcessor) spixels = (short[]) ip.getPixels();
else if (ip instanceof FloatProcessor) fpixels = (float[]) ip.getPixels();
width = ip.getWidth();
height = ip.getHeight();
}
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;
}
void getCentroid(ImageProcessor ip, int minThreshold, int maxThreshold) {
byte[] pixels = (byte[]) ip.getPixels();
byte[] mask = ip.getMaskArray();
boolean limit = minThreshold > 0 || maxThreshold < 255;
double xsum = 0, ysum = 0;
int count = 0, i, mi, v;
for (int y = ry, my = 0; y < (ry + rh); y++, my++) {
i = y * width + rx;
mi = my * rw;
for (int x = rx; x < (rx + rw); x++) {
if (mask == null || mask[mi++] != 0) {
if (limit) {
v = pixels[i] & 255;
if (v >= minThreshold && v <= maxThreshold) {
count++;
xsum += x;
ysum += y;
}
} else {
count++;
xsum += x;
ysum += y;
}
}
i++;
}
}
xCentroid = xsum / count + 0.5;
yCentroid = ysum / count + 0.5;
if (cal != null) {
xCentroid = cal.getX(xCentroid);
yCentroid = cal.getY(yCentroid, height);
}
}
/*------------------------------------------------------------------*/
void putRow(ImageProcessor ip, int y, double[] row) {
int rowLength = ip.getWidth();
if (rowLength != row.length) {
throw new IndexOutOfBoundsException("Incoherent array sizes");
}
y *= rowLength;
if (ip.getPixels() instanceof float[]) {
float[] floatPixels = (float[]) ip.getPixels();
for (int i = 0; (i < rowLength); i++) {
floatPixels[y++] = (float) row[i];
}
} else {
throw new IllegalArgumentException("Float image required");
}
} /* end putRow */
/*------------------------------------------------------------------*/
void putColumn(ImageProcessor ip, int x, double[] column) {
int width = ip.getWidth();
if (ip.getHeight() != column.length) {
throw new IndexOutOfBoundsException("Incoherent array sizes");
}
if (ip.getPixels() instanceof float[]) {
float[] floatPixels = (float[]) ip.getPixels();
for (int i = 0; (i < column.length); i++) {
floatPixels[x] = (float) column[i];
x += width;
}
} else {
throw new IllegalArgumentException("Float image required");
}
} /* end putColumn */
void reset(ImagePlus imp, ImageProcessor ip) {
// Assign the pixels of ip to the data in the restore array, while
// taking care to not give the address the restore array to the
// image processor.
int[] pixels = (int[]) ip.getPixels();
for (int i = 0; i < numPixels; i++) pixels[i] = restore[i];
}
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;
}
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;
}
/*------------------------------------------------------------------*/
public void getHorizontalGradient(ImageProcessor ip, double tolerance) {
if (!(ip.getPixels() instanceof float[])) {
throw new IllegalArgumentException("Float image required");
}
float[] floatPixels = (float[]) ip.getPixels();
int width = ip.getWidth();
int height = ip.getHeight();
double line[] = new double[width];
for (int y = 0; (y < height); y++) {
getRow(ip, y, line);
getSplineInterpolationCoefficients(line, tolerance);
getGradient(line);
putRow(ip, y, line);
stepProgressBar();
}
} /* end getHorizontalGradient */
/*------------------------------------------------------------------*/
public void getVerticalHessian(ImageProcessor ip, double tolerance) {
if (!(ip.getPixels() instanceof float[])) {
throw new IllegalArgumentException("Float image required");
}
float[] floatPixels = (float[]) ip.getPixels();
int width = ip.getWidth();
int height = ip.getHeight();
double line[] = new double[height];
for (int x = 0; (x < width); x++) {
getColumn(ip, x, line);
getSplineInterpolationCoefficients(line, tolerance);
getHessian(line);
putColumn(ip, x, line);
stepProgressBar();
}
} /* end getVerticalHessian */
/** Adds the image in 'ip' to the end of the stack. */
public void addSlice(String sliceLabel, ImageProcessor ip) {
if (ip.getWidth() != width || ip.getHeight() != height)
throw new IllegalArgumentException("Dimensions do not match");
if (nSlices == 0) {
cm = ip.getColorModel();
min = ip.getMin();
max = ip.getMax();
}
addSlice(sliceLabel, ip.getPixels());
}
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;
}
// Binary fill by Gabriel Landini, G.Landini at bham.ac.uk
// 21/May/2008
void fill(ImageProcessor ip, int foreground, int background) {
int width = ip.getWidth();
int height = ip.getHeight();
FloodFiller ff = new FloodFiller(ip);
ip.setColor(127);
for (int y = 0; y < height; y++) {
if (ip.getPixel(0, y) == background) ff.fill(0, y);
if (ip.getPixel(width - 1, y) == background) ff.fill(width - 1, y);
}
for (int x = 0; x < width; x++) {
if (ip.getPixel(x, 0) == background) ff.fill(x, 0);
if (ip.getPixel(x, height - 1) == background) ff.fill(x, height - 1);
}
byte[] pixels = (byte[]) ip.getPixels();
int n = width * height;
for (int i = 0; i < n; i++) {
if (pixels[i] == 127) pixels[i] = (byte) background;
else pixels[i] = (byte) foreground;
}
}
void calculateMoments(ImageProcessor ip, int minThreshold, int maxThreshold, float[] cTable) {
byte[] pixels = (byte[]) ip.getPixels();
byte[] mask = ip.getMaskArray();
int v, i, mi;
double dv, dv2, sum1 = 0.0, sum2 = 0.0, sum3 = 0.0, sum4 = 0.0, xsum = 0.0, ysum = 0.0;
for (int y = ry, my = 0; y < (ry + rh); y++, my++) {
i = y * width + rx;
mi = my * rw;
for (int x = rx; x < (rx + rw); x++) {
if (mask == null || mask[mi++] != 0) {
v = pixels[i] & 255;
if (v >= minThreshold && v <= maxThreshold) {
dv = ((cTable != null) ? cTable[v] : v) + Double.MIN_VALUE;
dv2 = dv * dv;
sum1 += dv;
sum2 += dv2;
sum3 += dv * dv2;
sum4 += dv2 * dv2;
xsum += x * dv;
ysum += y * dv;
}
}
i++;
}
}
double mean2 = mean * mean;
double variance = sum2 / pixelCount - mean2;
double sDeviation = Math.sqrt(variance);
skewness =
((sum3 - 3.0 * mean * sum2) / pixelCount + 2.0 * mean * mean2) / (variance * sDeviation);
kurtosis =
(((sum4 - 4.0 * mean * sum3 + 6.0 * mean2 * sum2) / pixelCount - 3.0 * mean2 * mean2)
/ (variance * variance)
- 3.0);
xCenterOfMass = xsum / sum1 + 0.5;
yCenterOfMass = ysum / sum1 + 0.5;
if (cal != null) {
xCenterOfMass = cal.getX(xCenterOfMass);
yCenterOfMass = cal.getY(yCenterOfMass, height);
}
}
public ImageProcessor getMask() {
if (cachedMask != null && cachedMask.getPixels() != null) return cachedMask;
ImageProcessor mask = new ByteProcessor(width, height);
double a = width / 2.0, b = height / 2.0;
double a2 = a * a, b2 = b * b;
a -= 0.5;
b -= 0.5;
double xx, yy;
int offset;
byte[] pixels = (byte[]) mask.getPixels();
for (int y = 0; y < height; y++) {
offset = y * width;
for (int x = 0; x < width; x++) {
xx = x - a;
yy = y - b;
if ((xx * xx / a2 + yy * yy / b2) <= 1.0) pixels[offset + x] = -1;
}
}
cachedMask = mask;
return mask;
}
int[] readJPEG(InputStream in) throws IOException {
BufferedImage bi = ImageIO.read(in);
ImageProcessor ip = new ColorProcessor(bi);
return (int[]) ip.getPixels();
}
/**
* LocalThicknesstoCleanedUpLocalThickness
*
* <p>Input: 3D Local Thickness map (32-bit stack)
*
* <p>Output: Same as input with border voxels corrected for "jaggies." Non-background voxels
* adjacent to background voxels are have their local thickness values replaced by the average of
* their non-background neighbors that do not border background points. Bob Dougherty August 1,
* 2007
*
* <ul>
* <li>August 10. Version 3 This version also multiplies the local thickness by 2 to conform
* with the official definition of local thickness.
* </ul>
*/
private ImagePlus localThicknesstoCleanedUpLocalThickness(ImagePlus imp, float[][] s) {
final int w = imp.getWidth();
final int h = imp.getHeight();
final int d = imp.getStackSize();
IJ.showStatus("Cleaning up local thickness...");
// Create 32 bit floating point stack for output, sNew.
ImageStack newStack = new ImageStack(w, h);
sNew = new float[d][];
for (int k = 0; k < d; k++) {
ImageProcessor ipk = new FloatProcessor(w, h);
newStack.addSlice(null, ipk);
sNew[k] = (float[]) ipk.getPixels();
}
/*
* First set the output array to flags: 0 for a background point -1 for
* a non-background point that borders a background point s (input data)
* for an interior non-background point
*/
for (int k = 0; k < d; k++) {
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
sNew[k][i + wj] = setFlag(s, i, j, k, w, h, d);
} // i
} // j
} // k
/*
* Process the surface points. Initially set results to negative values
* to be able to avoid including them in averages of for subsequent
* points. During the calculation, positive values in sNew are interior
* non-background local thicknesses. Negative values are surface points.
* In this case the value might be -1 (not processed yet) or -result,
* where result is the average of the neighboring interior points.
* Negative values are excluded from the averaging.
*/
for (int k = 0; k < d; k++) {
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
final int ind = i + wj;
if (sNew[k][ind] == -1) {
sNew[k][ind] = -averageInteriorNeighbors(s, i, j, k, w, h, d);
}
} // i
} // j
} // k
// Fix the negative values and double the results
for (int k = 0; k < d; k++) {
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
final int ind = i + wj;
sNew[k][ind] = (float) Math.abs(sNew[k][ind]);
} // i
} // j
} // k
IJ.showStatus("Clean Up Local Thickness complete");
String title = stripExtension(imp.getTitle());
ImagePlus impOut = new ImagePlus(title + "_CL", newStack);
final double vW = imp.getCalibration().pixelWidth;
// calibrate the pixel values to pixel width
// so that thicknesses represent real units (not pixels)
for (int z = 0; z < d; z++) {
impOut.setSlice(z + 1);
impOut.getProcessor().multiply(vW);
}
return impOut;
}
/**
* DistanceMaptoDistanceRidge
*
* <p>Output: Distance ridge resulting from a local scan of the distance map. Overwrites the
* input.
*
* <p>Note: Non-background points that are not part of the distance ridge are assiged a
* VERY_SMALL_VALUE. This is used for subsequent processing by other plugins to find the local
* thickness. Bob Dougherty August 10, 2006
*
* <ul>
* <li>Version 1: August 10-11, 2006. Subtracts 0.5 from the distances.
* <li>Version 1.01: September 6, 2006. Corrected some typos in the comments.
* <li>Version 1.01: Sept. 7, 2006. More tiny edits.
* <li>Version 2: Sept. 25, 2006. Creates a separate image stack for symmetry. <br>
* Temporary version that is very conservative. <br>
* Admittedly does not produce much impovement on real images.
* <li>Version 3: Sept. 30, 2006. Ball calculations based on grid points. Should be much more
* accurate.
* <li>Version 3.1 Oct. 1, 2006. Faster scanning of search points.
* </ul>
*
* @param imp 3D Distance map (32-bit stack)
*/
private void distanceMaptoDistanceRidge(ImagePlus imp, float[][] s) {
final int w = imp.getWidth();
final int h = imp.getHeight();
final int d = imp.getStackSize();
sNew = new float[d][];
for (int k = 0; k < d; k++) {
ImageProcessor ipk = new FloatProcessor(w, h);
sNew[k] = (float[]) ipk.getPixels();
}
// Do it
int k1, j1, i1, dz, dy, dx;
boolean notRidgePoint;
float[] sk1;
float[] sk, skNew;
int sk0Sq, sk0SqInd, sk1Sq;
// Find the largest distance in the data
IJ.showStatus("Distance Ridge: scanning the data");
float distMax = 0;
for (int k = 0; k < d; k++) {
sk = s[k];
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
final int ind = i + wj;
if (sk[ind] > distMax) distMax = sk[ind];
}
}
}
int rSqMax = (int) (distMax * distMax + 0.5f) + 1;
boolean[] occurs = new boolean[rSqMax];
for (int i = 0; i < rSqMax; i++) occurs[i] = false;
for (int k = 0; k < d; k++) {
sk = s[k];
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
final int ind = i + wj;
occurs[(int) (sk[ind] * sk[ind] + 0.5f)] = true;
}
}
}
int numRadii = 0;
for (int i = 0; i < rSqMax; i++) {
if (occurs[i]) numRadii++;
}
// Make an index of the distance-squared values
int[] distSqIndex = new int[rSqMax];
int[] distSqValues = new int[numRadii];
int indDS = 0;
for (int i = 0; i < rSqMax; i++) {
if (occurs[i]) {
distSqIndex[i] = indDS;
distSqValues[indDS++] = i;
}
}
/*
* Build template The first index of the template is the number of
* nonzero components in the offest from the test point to the remote
* point. The second index is the radii index (of the test point). The
* value of the template is the minimum square radius of the remote
* point required to cover the ball of the test point.
*/
IJ.showStatus("Distance Ridge: creating search templates");
int[][] rSqTemplate = createTemplate(distSqValues);
int numCompZ, numCompY, numCompX, numComp;
for (int k = 0; k < d; k++) {
IJ.showStatus("Distance Ridge: processing slice " + (k + 1) + "/" + d);
// IJ.showProgress(k/(1.*d));
sk = s[k];
skNew = sNew[k];
for (int j = 0; j < h; j++) {
final int wj = w * j;
for (int i = 0; i < w; i++) {
final int ind = i + wj;
if (sk[ind] > 0) {
notRidgePoint = false;
sk0Sq = (int) (sk[ind] * sk[ind] + 0.5f);
sk0SqInd = distSqIndex[sk0Sq];
for (dz = -1; dz <= 1; dz++) {
k1 = k + dz;
if ((k1 >= 0) && (k1 < d)) {
sk1 = s[k1];
if (dz == 0) {
numCompZ = 0;
} else {
numCompZ = 1;
}
for (dy = -1; dy <= 1; dy++) {
j1 = j + dy;
final int wj1 = w * j1;
if ((j1 >= 0) && (j1 < h)) {
if (dy == 0) {
numCompY = 0;
} else {
numCompY = 1;
}
for (dx = -1; dx <= 1; dx++) {
i1 = i + dx;
if ((i1 >= 0) && (i1 < w)) {
if (dx == 0) {
numCompX = 0;
} else {
numCompX = 1;
}
numComp = numCompX + numCompY + numCompZ;
if (numComp > 0) {
final float sk1i1wj1 = sk1[i1 + wj1];
sk1Sq = (int) (sk1i1wj1 * sk1i1wj1 + 0.5f);
if (sk1Sq >= rSqTemplate[numComp - 1][sk0SqInd]) notRidgePoint = true;
}
} // if in grid for i1
if (notRidgePoint) break;
} // dx
} // if in grid for j1
if (notRidgePoint) break;
} // dy
} // if in grid for k1
if (notRidgePoint) break;
} // dz
if (!notRidgePoint) skNew[ind] = sk[ind];
} // if not in background
} // i
} // j
} // k
IJ.showStatus("Distance Ridge complete");
// replace work array s with result of the method, sNew
s = sNew;
}
/**
* Saito-Toriwaki algorithm for Euclidian Distance Transformation. Direct application of Algorithm
* 1. Bob Dougherty 8/8/2006
*
* <ul>
* <li>Version S1A: lower memory usage.
* <li>Version S1A.1 A fixed indexing bug for 666-bin data set
* <li>Version S1A.2 Aug. 9, 2006. Changed noResult value.
* <li>Version S1B Aug. 9, 2006. Faster.
* <li>Version S1B.1 Sept. 6, 2006. Changed comments.
* <li>Version S1C Oct. 1, 2006. Option for inverse case. <br>
* Fixed inverse behavior in y and z directions.
* <li>Version D July 30, 2007. Multithread processing for step 2.
* </ul>
*
* <p>This version assumes the input stack is already in memory, 8-bit, and outputs to a new
* 32-bit stack. Versions that are more stingy with memory may be forthcoming.
*
* @param imp 8-bit (binary) ImagePlus
*/
private float[][] geometryToDistanceMap(ImagePlus imp, boolean inv) {
final int w = imp.getWidth();
final int h = imp.getHeight();
final int d = imp.getStackSize();
int nThreads = Runtime.getRuntime().availableProcessors();
// Create references to input data
ImageStack stack = imp.getStack();
byte[][] data = new byte[d][];
for (int k = 0; k < d; k++) data[k] = (byte[]) stack.getPixels(k + 1);
// Create 32 bit floating point stack for output, s. Will also use it
// for g in Transformation 1.
float[][] s = new float[d][];
for (int k = 0; k < d; k++) {
ImageProcessor ipk = new FloatProcessor(w, h);
s[k] = (float[]) ipk.getPixels();
}
float[] sk;
// Transformation 1. Use s to store g.
IJ.showStatus("EDT transformation 1/3");
Step1Thread[] s1t = new Step1Thread[nThreads];
for (int thread = 0; thread < nThreads; thread++) {
s1t[thread] = new Step1Thread(thread, nThreads, w, h, d, inv, s, data);
s1t[thread].start();
}
try {
for (int thread = 0; thread < nThreads; thread++) {
s1t[thread].join();
}
} catch (InterruptedException ie) {
IJ.error("A thread was interrupted in step 1 .");
}
// Transformation 2. g (in s) -> h (in s)
IJ.showStatus("EDT transformation 2/3");
Step2Thread[] s2t = new Step2Thread[nThreads];
for (int thread = 0; thread < nThreads; thread++) {
s2t[thread] = new Step2Thread(thread, nThreads, w, h, d, s);
s2t[thread].start();
}
try {
for (int thread = 0; thread < nThreads; thread++) {
s2t[thread].join();
}
} catch (InterruptedException ie) {
IJ.error("A thread was interrupted in step 2 .");
}
// Transformation 3. h (in s) -> s
IJ.showStatus("EDT transformation 3/3");
Step3Thread[] s3t = new Step3Thread[nThreads];
for (int thread = 0; thread < nThreads; thread++) {
s3t[thread] = new Step3Thread(thread, nThreads, w, h, d, inv, s, data);
s3t[thread].start();
}
try {
for (int thread = 0; thread < nThreads; thread++) {
s3t[thread].join();
}
} catch (InterruptedException ie) {
IJ.error("A thread was interrupted in step 3 .");
}
// Find the largest distance for scaling
// Also fill in the background values.
float distMax = 0;
final int wh = w * h;
float dist;
for (int k = 0; k < d; k++) {
sk = s[k];
for (int ind = 0; ind < wh; ind++) {
if (((data[k][ind] & 255) < 128) ^ inv) {
sk[ind] = 0;
} else {
dist = (float) Math.sqrt(sk[ind]);
sk[ind] = dist;
distMax = (dist > distMax) ? dist : distMax;
}
}
}
IJ.showProgress(1.0);
IJ.showStatus("Done");
return s;
}
/**
* Performs particle analysis on the specified ImagePlus and ImageProcessor. Returns false if
* there is an error.
*/
public boolean analyze(ImagePlus imp, ImageProcessor ip) {
if (this.imp == null) this.imp = imp;
showResults = (options & SHOW_RESULTS) != 0;
excludeEdgeParticles = (options & EXCLUDE_EDGE_PARTICLES) != 0;
resetCounter = (options & CLEAR_WORKSHEET) != 0;
showProgress = (options & SHOW_PROGRESS) != 0;
floodFill = (options & INCLUDE_HOLES) == 0;
recordStarts = (options & RECORD_STARTS) != 0;
addToManager = (options & ADD_TO_MANAGER) != 0;
displaySummary = (options & DISPLAY_SUMMARY) != 0;
inSituShow = (options & IN_SITU_SHOW) != 0;
outputImage = null;
ip.snapshot();
ip.setProgressBar(null);
if (Analyzer.isRedirectImage()) {
redirectImp = Analyzer.getRedirectImage(imp);
if (redirectImp == null) return false;
int depth = redirectImp.getStackSize();
if (depth > 1 && depth == imp.getStackSize()) {
ImageStack redirectStack = redirectImp.getStack();
redirectIP = redirectStack.getProcessor(imp.getCurrentSlice());
} else redirectIP = redirectImp.getProcessor();
} else if (imp.getType() == ImagePlus.COLOR_RGB) {
ImagePlus original = (ImagePlus) imp.getProperty("OriginalImage");
if (original != null
&& original.getWidth() == imp.getWidth()
&& original.getHeight() == imp.getHeight()) {
redirectImp = original;
redirectIP = original.getProcessor();
}
}
if (!setThresholdLevels(imp, ip)) return false;
width = ip.getWidth();
height = ip.getHeight();
if (!(showChoice == NOTHING || showChoice == OVERLAY_OUTLINES || showChoice == OVERLAY_MASKS)) {
blackBackground = Prefs.blackBackground && inSituShow;
if (slice == 1) outlines = new ImageStack(width, height);
if (showChoice == ROI_MASKS) drawIP = new ShortProcessor(width, height);
else drawIP = new ByteProcessor(width, height);
drawIP.setLineWidth(lineWidth);
if (showChoice == ROI_MASKS) {
} // Place holder for now...
else if (showChoice == MASKS && !blackBackground) drawIP.invertLut();
else if (showChoice == OUTLINES) {
if (!inSituShow) {
if (customLut == null) makeCustomLut();
drawIP.setColorModel(customLut);
}
drawIP.setFont(new Font("SansSerif", Font.PLAIN, fontSize));
if (fontSize > 12 && inSituShow) drawIP.setAntialiasedText(true);
}
outlines.addSlice(null, drawIP);
if (showChoice == ROI_MASKS || blackBackground) {
drawIP.setColor(Color.black);
drawIP.fill();
drawIP.setColor(Color.white);
} else {
drawIP.setColor(Color.white);
drawIP.fill();
drawIP.setColor(Color.black);
}
}
calibration = redirectImp != null ? redirectImp.getCalibration() : imp.getCalibration();
if (rt == null) {
rt = Analyzer.getResultsTable();
analyzer = new Analyzer(imp);
} else analyzer = new Analyzer(imp, measurements, rt);
if (resetCounter && slice == 1) {
if (!Analyzer.resetCounter()) return false;
}
beginningCount = Analyzer.getCounter();
byte[] pixels = null;
if (ip instanceof ByteProcessor) pixels = (byte[]) ip.getPixels();
if (r == null) {
r = ip.getRoi();
mask = ip.getMask();
if (displaySummary) {
if (mask != null) totalArea = ImageStatistics.getStatistics(ip, AREA, calibration).area;
else totalArea = r.width * calibration.pixelWidth * r.height * calibration.pixelHeight;
}
}
minX = r.x;
maxX = r.x + r.width;
minY = r.y;
maxY = r.y + r.height;
if (r.width < width || r.height < height || mask != null) {
if (!eraseOutsideRoi(ip, r, mask)) return false;
}
int offset;
double value;
int inc = Math.max(r.height / 25, 1);
int mi = 0;
ImageWindow win = imp.getWindow();
if (win != null) win.running = true;
if (measurements == 0) measurements = Analyzer.getMeasurements();
if (showChoice == ELLIPSES) measurements |= ELLIPSE;
measurements &= ~LIMIT; // ignore "Limit to Threshold"
roiNeedsImage =
(measurements & PERIMETER) != 0
|| (measurements & SHAPE_DESCRIPTORS) != 0
|| (measurements & FERET) != 0;
particleCount = 0;
wand = new Wand(ip);
pf = new PolygonFiller();
if (floodFill) {
ImageProcessor ipf = ip.duplicate();
ipf.setValue(fillColor);
ff = new FloodFiller(ipf);
}
roiType = Wand.allPoints() ? Roi.FREEROI : Roi.TRACED_ROI;
for (int y = r.y; y < (r.y + r.height); y++) {
offset = y * width;
for (int x = r.x; x < (r.x + r.width); x++) {
if (pixels != null) value = pixels[offset + x] & 255;
else if (imageType == SHORT) value = ip.getPixel(x, y);
else value = ip.getPixelValue(x, y);
if (value >= level1 && value <= level2) analyzeParticle(x, y, imp, ip);
}
if (showProgress && ((y % inc) == 0)) IJ.showProgress((double) (y - r.y) / r.height);
if (win != null) canceled = !win.running;
if (canceled) {
Macro.abort();
break;
}
}
if (showProgress) IJ.showProgress(1.0);
if (showResults) rt.updateResults();
imp.killRoi();
ip.resetRoi();
ip.reset();
if (displaySummary && IJ.getInstance() != null) updateSliceSummary();
if (addToManager && roiManager != null) roiManager.setEditMode(imp, true);
maxParticleCount = (particleCount > maxParticleCount) ? particleCount : maxParticleCount;
totalCount += particleCount;
if (!canceled) showResults();
return true;
}
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;
}
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;
}
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
}
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;
}
/*------------------------------------------------------------------*/
void doIt(ImageProcessor ip) {
int width = ip.getWidth();
int height = ip.getHeight();
double hLine[] = new double[width];
double vLine[] = new double[height];
if (!(ip.getPixels() instanceof float[])) {
throw new IllegalArgumentException("Float image required");
}
switch (operation) {
case GRADIENT_MAGNITUDE:
{
ImageProcessor h = ip.duplicate();
ImageProcessor v = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsH = (float[]) h.getPixels();
float[] floatPixelsV = (float[]) v.getPixels();
getHorizontalGradient(h, FLT_EPSILON);
getVerticalGradient(v, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
Math.sqrt(
floatPixelsH[k] * floatPixelsH[k] + floatPixelsV[k] * floatPixelsV[k]);
}
stepProgressBar();
}
}
break;
case GRADIENT_DIRECTION:
{
ImageProcessor h = ip.duplicate();
ImageProcessor v = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsH = (float[]) h.getPixels();
float[] floatPixelsV = (float[]) v.getPixels();
getHorizontalGradient(h, FLT_EPSILON);
getVerticalGradient(v, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] = (float) Math.atan2(floatPixelsH[k], floatPixelsV[k]);
}
stepProgressBar();
}
}
break;
case LAPLACIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] = (float) (floatPixelsHH[k] + floatPixelsVV[k]);
}
stepProgressBar();
}
}
break;
case LARGEST_HESSIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
(0.5
* (floatPixelsHH[k]
+ floatPixelsVV[k]
+ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
stepProgressBar();
}
}
break;
case SMALLEST_HESSIAN:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
floatPixels[k] =
(float)
(0.5
* (floatPixelsHH[k]
+ floatPixelsVV[k]
- Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
stepProgressBar();
}
}
break;
case HESSIAN_ORIENTATION:
{
ImageProcessor hh = ip.duplicate();
ImageProcessor vv = ip.duplicate();
ImageProcessor hv = ip.duplicate();
float[] floatPixels = (float[]) ip.getPixels();
float[] floatPixelsHH = (float[]) hh.getPixels();
float[] floatPixelsVV = (float[]) vv.getPixels();
float[] floatPixelsHV = (float[]) hv.getPixels();
getHorizontalHessian(hh, FLT_EPSILON);
getVerticalHessian(vv, FLT_EPSILON);
getCrossHessian(hv, FLT_EPSILON);
for (int y = 0, k = 0; (y < height); y++) {
for (int x = 0; (x < width); x++, k++) {
if (floatPixelsHV[k] < 0.0) {
floatPixels[k] =
(float)
(-0.5
* Math.acos(
(floatPixelsHH[k] - floatPixelsVV[k])
/ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
} else {
floatPixels[k] =
(float)
(0.5
* Math.acos(
(floatPixelsHH[k] - floatPixelsVV[k])
/ Math.sqrt(
4.0 * floatPixelsHV[k] * floatPixelsHV[k]
+ (floatPixelsHH[k] - floatPixelsVV[k])
* (floatPixelsHH[k] - floatPixelsVV[k]))));
}
}
stepProgressBar();
}
}
break;
default:
throw new IllegalArgumentException("Invalid operation");
}
ip.resetMinAndMax();
imp.updateAndDraw();
} /* end doIt */
ImageProcessor setup(ImagePlus imp) {
ImageProcessor ip;
int type = imp.getType();
if (type != ImagePlus.COLOR_RGB) return null;
ip = imp.getProcessor();
int id = imp.getID();
int slice = imp.getCurrentSlice();
if ((id != previousImageID) | (slice != previousSlice) | (flag)) {
flag = false; // if true, flags a change from HSB to RGB or viceversa
numSlices = imp.getStackSize();
stack = imp.getStack();
width = stack.getWidth();
height = stack.getHeight();
numPixels = width * height;
hSource = new byte[numPixels];
sSource = new byte[numPixels];
bSource = new byte[numPixels];
// restore = (int[])ip.getPixelsCopy(); //This runs into trouble sometimes, so do it the
// long way:
int[] temp = (int[]) ip.getPixels();
restore = new int[numPixels];
for (int i = 0; i < numPixels; i++) restore[i] = temp[i];
fillMask = new int[numPixels];
// Get hsb or rgb from image.
ColorProcessor cp = (ColorProcessor) ip;
IJ.showStatus("Gathering data");
if (isRGB) cp.getRGB(hSource, sSource, bSource);
else cp.getHSB(hSource, sSource, bSource);
IJ.showStatus("done");
// Create a spectrum ColorModel for the Hue histogram plot.
Color c;
byte[] reds = new byte[256];
byte[] greens = new byte[256];
byte[] blues = new byte[256];
for (int i = 0; i < 256; i++) {
c = Color.getHSBColor(i / 255f, 1f, 1f);
reds[i] = (byte) c.getRed();
greens[i] = (byte) c.getGreen();
blues[i] = (byte) c.getBlue();
}
ColorModel cm = new IndexColorModel(8, 256, reds, greens, blues);
// Make an image with just the hue from the RGB image and the spectrum LUT.
// This is just for a hue histogram for the plot. Do not show it.
// ByteProcessor bpHue = new ByteProcessor(width,height,h,cm);
ByteProcessor bpHue = new ByteProcessor(width, height, hSource, cm);
ImagePlus impHue = new ImagePlus("Hue", bpHue);
// impHue.show();
ByteProcessor bpSat = new ByteProcessor(width, height, sSource, cm);
ImagePlus impSat = new ImagePlus("Sat", bpSat);
// impSat.show();
ByteProcessor bpBri = new ByteProcessor(width, height, bSource, cm);
ImagePlus impBri = new ImagePlus("Bri", bpBri);
// impBri.show();
plot.setHistogram(impHue, 0);
splot.setHistogram(impSat, 1);
bplot.setHistogram(impBri, 2);
updateLabels();
updatePlot();
updateScrollBars();
imp.updateAndDraw();
}
previousImageID = id;
previousSlice = slice;
return ip;
}