/**
* Calculate the Rand index between some 2D original labels and the corresponding proposed labels.
* Both image are binarized. We follow the definition of Rand index as described by William M.
* Rand \cite{Rand71}.
*
* <p>BibTeX:
*
* <pre>
* @article{Rand71,
* author = {William M. Rand},
* title = {Objective criteria for the evaluation of clustering methods},
* journal = {Journal of the American Statistical Association},
* year = {1971},
* volume = {66},
* number = {336},
* pages = {846--850},
* doi = {10.2307/2284239)
* }
* </pre>
*
* @param label 2D image with the original labels
* @param proposal 2D image with the proposed labels
* @param binaryThreshold threshold value to binarize the input images
* @return rand index value and derived statistics
*/
public ClassificationStatistics randIndexStats(
ImageProcessor label, ImageProcessor proposal, double binaryThreshold) {
// Binarize inputs
ByteProcessor binaryLabel = new ByteProcessor(label.getWidth(), label.getHeight());
ByteProcessor binaryProposal = new ByteProcessor(label.getWidth(), label.getHeight());
for (int x = 0; x < label.getWidth(); x++)
for (int y = 0; y < label.getHeight(); y++) {
binaryLabel.set(x, y, label.getPixelValue(x, y) > binaryThreshold ? 255 : 0);
binaryProposal.set(x, y, proposal.getPixelValue(x, y) > binaryThreshold ? 255 : 0);
}
// Find components
ShortProcessor components1 =
(ShortProcessor)
Utils.connectedComponents(new ImagePlus("binary labels", binaryLabel), 4)
.allRegions
.getProcessor();
ShortProcessor components2 =
(ShortProcessor)
Utils.connectedComponents(new ImagePlus("proposal labels", binaryProposal), 4)
.allRegions
.getProcessor();
return getRandIndexStats(components1, components2);
}
protected static final FloatProcessor scaleByte(final ByteProcessor bp) {
final FloatProcessor fp = new FloatProcessor(bp.getWidth(), bp.getHeight());
final byte[] bytes = (byte[]) bp.getPixels();
final float[] floats = (float[]) fp.getPixels();
for (int i = 0; i < bytes.length; ++i) floats[i] = (bytes[i] & 0xff) / 255.0f;
return fp;
}
public static double[] getPixels(ImagePlus imp, int[] slices, int[] frames, int[] channels) {
ImagePlus subImp = AccessImage.getSubHyperStack(imp, slices, frames, channels);
ImageStack imageStack = subImp.getImageStack();
double[] pixels = new double[subImp.getWidth() * subImp.getHeight() * imageStack.getSize()];
for (int i = 0; i < imageStack.getSize(); i++) {
switch (subImp.getType()) {
case ImagePlus.GRAY8:
ByteProcessor byteProcessor = (ByteProcessor) imageStack.getProcessor(i + 1);
for (int iX = 0; iX < subImp.getWidth(); iX++) {
for (int iY = 0; iY < subImp.getHeight(); iY++) {
pixels[i * subImp.getWidth() * subImp.getHeight() + iY * subImp.getWidth() + iX] =
(double) byteProcessor.getPixelValue(iX, iY);
}
}
break;
case ImagePlus.GRAY16:
ShortProcessor shortProcessor = (ShortProcessor) imageStack.getProcessor(i + 1);
for (int iX = 0; iX < subImp.getWidth(); iX++) {
for (int iY = 0; iY < subImp.getHeight(); iY++) {
pixels[i * subImp.getWidth() * subImp.getHeight() + iY * subImp.getWidth() + iX] =
(double) shortProcessor.getPixelValue(iX, iY);
}
}
break;
case ImagePlus.GRAY32:
FloatProcessor floatProcessor = (FloatProcessor) imageStack.getProcessor(i + 1);
for (int iX = 0; iX < subImp.getWidth(); iX++) {
for (int iY = 0; iY < subImp.getHeight(); iY++) {
pixels[i * subImp.getWidth() * subImp.getHeight() + iY * subImp.getWidth() + iX] =
(double) floatProcessor.getPixelValue(iX, iY);
}
}
break;
case ImagePlus.COLOR_RGB:
ColorProcessor colorProcessor = (ColorProcessor) imageStack.getProcessor(i + 1);
int nX = subImp.getWidth();
int nY = subImp.getHeight();
byte[] red = new byte[nX * nY];
byte[] green = new byte[nX * nY];
byte[] blue = new byte[nX * nY];
colorProcessor.getRGB(red, green, blue);
int r, g, b;
for (int j = 0; j < nX * nY; j++) {
r = red[j] << 16;
g = green[j] << 8;
b = blue[j] << 0;
pixels[i * nX * nY + j] = (double) (r + g + b);
}
break;
}
}
return (pixels);
}
/** Produce a binary image based on the current confidence matrix */
private void createBinaryMask() {
if (null != confMatrix) {
final ByteProcessor result = (ByteProcessor) confMatrix.convertToByte(false);
result.multiply(255);
// Set background color based on the Process > Binary > Options
if (!Prefs.blackBackground) result.invert();
new ImagePlus("Mask", result).show();
}
}
private void calculateGreyValues() {
int size = image.getPixelCount();
int gray;
for (int pos = 0; pos < size; pos++) {
gray = image.get(pos);
grayValue[pos] = (byte) (gray / GRAY_SCALE); // quantized for texture analysis
grayHistogram[gray]++;
}
Arrays2.div(grayHistogram, size);
}
/**
* Retrieve byte data of 8 bits image
*
* @param imp : image to analyze
* @return byte array with data for each pixel
*/
public static byte[] getByteData(ImagePlus imp) {
byte[] imagebyte = null;
for (int i = 0; i < imp.getStackSize(); i++) {
ByteProcessor fp = (ByteProcessor) imp.getImageStack().getProcessor(i + 1).duplicate();
if (i == 0) imagebyte = (byte[]) fp.getPixels();
else
imagebyte =
concatByte(imagebyte, (byte[]) fp.getPixels()); // (byte[]) ArrayUtils.addAll(imagebyte,
// (byte[])fp.getPixels());
System.out.println("slice : " + (i + 1) + "/" + imp.getStackSize());
}
return imagebyte;
}
void doIterations(ImageProcessor ip, String mode) {
if (escapePressed) return;
if (!previewing && iterations > 1) IJ.showStatus(arg + "... press ESC to cancel");
for (int i = 0; i < iterations; i++) {
if (Thread.currentThread().isInterrupted()) return;
if (IJ.escapePressed()) {
escapePressed = true;
ip.reset();
return;
}
if (mode.equals("erode")) ((ByteProcessor) ip).erode(count, background);
else ((ByteProcessor) ip).dilate(count, background);
}
}
public void run(ImageProcessor image) {
/**
* ********************************************************************************* Initial
* phase *********************************************************************************
*/
int width = image.getWidth();
int heigh = image.getHeight();
ByteProcessor bp = Service.getByteProcessor(image);
/**
* ********************************************************************************* Convolve
* with LoG kernel
* *********************************************************************************
*/
Convolver convolver = new Convolver();
convolver.setNormalize(false);
convolver.convolve(bp, log, (int) Math.sqrt(log.length), (int) Math.sqrt(log.length));
ImagePlus outImg = new ImagePlus("Later Log kernel", bp);
outImg.show();
/**
* ********************************************************************************* threshold
* *********************************************************************************
*/
ByteProcessor bpThreshold = Service.getByteProcessor(bp);
int lut[] = new int[256];
int i = 0;
for (; i < threshold; i++) lut[i] = 0;
for (int j = i; j < lut.length; j++) lut[j] = 255;
bpThreshold.applyTable(lut);
ImagePlus outImg1 = new ImagePlus("Later threshold phase", bpThreshold);
outImg1.show();
/**
* ********************************************************************************* Find Zero
* crossing *********************************************************************************
*/
ByteProcessor out = new ByteProcessor(width, heigh);
for (i = 0; i < width; i++) for (int j = 0; j < heigh; j++) out.set(i, j, 255);
for (int x = 0; x < width - 1; x++) {
for (int y = 0; y < heigh - 1; y++) {
if (bpThreshold.get(x, y) != bpThreshold.get(x, y + 1)) out.set(x, y, 0);
if (bpThreshold.get(x, y) != bpThreshold.get(x + 1, y)) out.set(x, y, 0);
if (bpThreshold.get(x, y) != bpThreshold.get(x + 1, y + 1)) out.set(x, y, 0);
}
}
ImagePlus outImg2 = new ImagePlus("Edge Find", out);
outImg2.show();
}
public GrayLevelClass(ByteProcessor img, boolean first) {
if (!probabilityHistogramDone) {
int[] histogram = img.getHistogram();
probabilityHistogram = new float[256];
for (int i = 0; i < 256; i++) {
probabilityHistogram[i] = ((float) histogram[i]) / ((float) N);
// IJ.write(" "+probabilityHistogram[i]);
}
probabilityHistogramDone = true;
}
if (first) {
index = 1;
omega = probabilityHistogram[index - 1];
if (omega == 0) mu = 0;
else mu = 1 * probabilityHistogram[index - 1] / omega;
} else {
index = 2;
omega = 0;
mu = 0;
for (int i = index; i < 256; i++) {
omega += probabilityHistogram[i - 1];
mu += probabilityHistogram[i - 1] * i;
}
if (omega == 0) mu = 0;
else mu /= omega;
}
}
void skeletonize(ImageProcessor ip) {
if (Prefs.blackBackground) ip.invert();
boolean edgePixels = hasEdgePixels(ip);
ImageProcessor ip2 = expand(ip, edgePixels);
((ByteProcessor) ip2).skeletonize();
ip = shrink(ip, ip2, edgePixels);
if (Prefs.blackBackground) ip.invert();
}
private void calculate() {
calculateGreyValues();
final int imageWidth = image.getWidth();
final int imageHeight = image.getHeight();
final int d = HARALICK_DIST;
int i, j, pos;
// image is not empty per default
for (int y = 0; y < imageHeight; y++) {
for (int x = 0; x < imageWidth; x++) {
pos = imageWidth * y + x;
// horizontal neighbor: 0 degrees
i = x - d;
// j = y;
if (!(i < 0)) {
increment(grayValue[pos], grayValue[pos - d]);
}
// vertical neighbor: 90 degree
// i = x;
j = y - d;
if (!(j < 0)) {
increment(grayValue[pos], grayValue[pos - d * imageWidth]);
}
// 45 degree diagonal neigbor
i = x + d;
j = y - d;
if (i < imageWidth && !(j < 0)) {
increment(grayValue[pos], grayValue[pos + d - d * imageWidth]);
}
// 135 vertical neighbor
i = x - d;
j = y - d;
if (!(i < 0) && !(j < 0)) {
increment(grayValue[pos], grayValue[pos - d - d * imageWidth]);
}
}
}
meanGrayValue = Arrays2.sum(grayValue);
}
public Coocurrence(ByteProcessor b, int numGrayValues, int haralickDist) {
this.NUM_GRAY_VALUES = numGrayValues;
this.image = b;
this.GRAY_SCALE = (double) GRAY_RANGES / (double) NUM_GRAY_VALUES;
this.cooccurrenceMatrices = new double[NUM_GRAY_VALUES][NUM_GRAY_VALUES];
this.grayValue = new byte[image.getPixelCount()];
this.grayHistogram = new double[GRAY_RANGES];
this.HARALICK_DIST = haralickDist;
calculate();
}
@Test
public final void testDistanceMap_ChessBoard() {
ByteProcessor image = new ByteProcessor(12, 10);
image.setBackgroundValue(0);
image.setValue(0);
image.fill();
for (int y = 2; y < 8; y++) {
for (int x = 2; x < 10; x++) {
image.set(x, y, 255);
}
}
short[] weights = ChamferWeights.CHESSBOARD.getShortWeights();
DistanceTransform5x5Short algo = new DistanceTransform5x5Short(weights, true);
ImageProcessor result = algo.distanceMap(image);
assertNotNull(result);
assertEquals(image.getWidth(), result.getWidth());
assertEquals(image.getHeight(), result.getHeight());
assertEquals(3, result.get(4, 4));
}
@Test
public final void testDistanceMap_UntilCorners_Weights23() {
ByteProcessor image = new ByteProcessor(7, 7);
image.setValue(255);
image.fill();
image.set(4, 4, 0);
short[] weights = ChamferWeights.WEIGHTS_23.getShortWeights();
DistanceTransform5x5Short algo = new DistanceTransform5x5Short(weights, false);
ImageProcessor result = algo.distanceMap(image);
assertNotNull(result);
assertEquals(image.getWidth(), result.getWidth());
assertEquals(image.getHeight(), result.getHeight());
assertEquals(12, result.get(0, 0));
assertEquals(10, result.get(6, 0));
assertEquals(10, result.get(0, 6));
assertEquals(6, result.get(6, 6));
assertEquals(9, result.get(0, 5));
}
/** Another test for chessknight weigths, to fix a bug that incorrectly checked image bounds. */
@Test
public final void testDistanceMap_UntilCorners_ChessKnight2() {
ByteProcessor image = new ByteProcessor(9, 9);
image.setValue(255);
image.fill();
image.set(6, 6, 0);
short[] weights = ChamferWeights.CHESSKNIGHT.getShortWeights();
DistanceTransform5x5Short algo = new DistanceTransform5x5Short(weights, false);
ImageProcessor result = algo.distanceMap(image);
assertNotNull(result);
assertEquals(image.getWidth(), result.getWidth());
assertEquals(image.getHeight(), result.getHeight());
assertEquals(42, result.get(0, 0));
assertEquals(32, result.get(8, 0));
assertEquals(32, result.get(0, 8));
assertEquals(14, result.get(8, 8));
assertEquals(30, result.get(0, 6));
}
/**
* Create ImagePlusExtended object from primary array (byte, short ....)
*
* @param title : title of the image
* @param object : array
* @param showImage : display image if true
* @return ImagePlusExtended
*/
public static ImagePlusExtended createImage(String title, Object object, boolean showImage) {
ImagePlusExtended imp = null;
int i = 0;
if (object instanceof byte[][]) {
byte[][] is = (byte[][]) object;
int height = is.length;
int width = is[0].length;
ByteProcessor byteprocessor = new ByteProcessor(width, height);
byte[] bp = (byte[]) byteprocessor.getPixels();
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
bp[i] = is[h][w];
w++;
i++;
}
i = ++h * width;
}
imp = new ImagePlusExtended(title, byteprocessor);
} else if (object instanceof short[][]) {
short[][] is = (short[][]) object;
int height = is.length;
int width = is[0].length;
ShortProcessor shortprocessor = new ShortProcessor(width, height);
short[] sp = (short[]) shortprocessor.getPixels();
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
sp[i] = is[h][w];
w++;
i++;
}
i = ++h * width;
}
imp = new ImagePlusExtended(title, shortprocessor);
} else if (object instanceof int[][]) {
int[][] is = (int[][]) object;
int height = is.length;
int width = is[0].length;
ShortProcessor shortprocessor = new ShortProcessor(width, height);
short[] sp = (short[]) shortprocessor.getPixels();
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
sp[i] = (short) is[h][w];
w++;
i++;
}
i = ++h * width;
}
imp = new ImagePlusExtended(title, shortprocessor);
} else if (object instanceof float[][]) {
float[][] fs = (float[][]) object;
int height = fs.length;
int width = fs[0].length;
FloatProcessor floatprocessor = new FloatProcessor(width, height);
float[] fp = (float[]) floatprocessor.getPixels();
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
fp[i] = fs[h][w];
w++;
i++;
}
i = ++h * width;
}
floatprocessor.resetMinAndMax();
imp = new ImagePlusExtended(title, floatprocessor);
} else if (object instanceof double[][]) {
double[][] ds = (double[][]) object;
int height = ds.length;
int width = ds[0].length;
FloatProcessor floatprocessor = new FloatProcessor(width, height);
float[] fp = (float[]) floatprocessor.getPixels();
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
fp[i] = (float) ds[h][w];
w++;
i++;
}
i = ++h * width;
}
floatprocessor.resetMinAndMax();
imp = new ImagePlusExtended(title, floatprocessor);
} else if (object instanceof byte[][][]) {
byte[][][] is = (byte[][][]) object;
int height = is.length;
int width = is[0].length;
int stackSize = is[0][0].length;
ImageStack imagestack = new ImageStack(width, height);
for (int sz = 0; sz < stackSize; sz++) {
ByteProcessor byteprocessor = new ByteProcessor(width, height);
byte[] bp = (byte[]) byteprocessor.getPixels();
i = 0;
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
bp[i] = is[h][w][sz];
w++;
i++;
}
i = ++h * width;
}
imagestack.addSlice("", byteprocessor);
}
imp = new ImagePlusExtended(title, imagestack);
} else if (object instanceof short[][][]) {
short[][][] is = (short[][][]) object;
int height = is.length;
int width = is[0].length;
int stackSize = is[0][0].length;
ImageStack imagestack = new ImageStack(width, height);
for (int sz = 0; sz < stackSize; sz++) {
ShortProcessor shortprocessor = new ShortProcessor(width, height);
short[] sp = (short[]) shortprocessor.getPixels();
i = 0;
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
sp[i] = is[h][w][sz];
w++;
i++;
}
i = ++h * width;
}
imagestack.addSlice("", shortprocessor);
}
imp = new ImagePlusExtended(title, imagestack);
} else if (object instanceof int[][][]) {
int[][][] is = (int[][][]) object;
int height = is.length;
int width = is[0].length;
int stackSize = is[0][0].length;
ImageStack imagestack = new ImageStack(width, height);
for (int sz = 0; sz < stackSize; sz++) {
ShortProcessor shortprocessor = new ShortProcessor(width, height);
short[] sp = (short[]) shortprocessor.getPixels();
i = 0;
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
sp[i] = (short) is[h][w][sz];
w++;
i++;
}
i = ++h * width;
}
if (sz == 0) shortprocessor.resetMinAndMax();
imagestack.addSlice("", shortprocessor);
}
imp = new ImagePlusExtended(title, imagestack);
} else if (object instanceof float[][][]) {
float[][][] fs = (float[][][]) object;
int height = fs.length;
int width = fs[0].length;
int stackSize = fs[0][0].length;
ImageStack imagestack = new ImageStack(width, height);
for (int sz = 0; sz < stackSize; sz++) {
FloatProcessor floatprocessor = new FloatProcessor(width, height);
float[] fp = (float[]) floatprocessor.getPixels();
i = 0;
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
fp[i] = fs[h][w][sz];
w++;
i++;
}
i = ++h * width;
}
if (sz == 0) floatprocessor.resetMinAndMax();
imagestack.addSlice("", floatprocessor);
}
imp = new ImagePlusExtended(title, imagestack);
} else if (object instanceof double[][][]) {
double[][][] ds = (double[][][]) object;
int height = ds.length;
int width = ds[0].length;
int stackSize = ds[0][0].length;
ImageStack imagestack = new ImageStack(width, height);
for (int sz = 0; sz < stackSize; sz++) {
FloatProcessor floatprocessor = new FloatProcessor(width, height);
float[] fp = (float[]) floatprocessor.getPixels();
i = 0;
int h = 0;
while (h < height) {
int w = 0;
while (w < width) {
fp[i] = (float) ds[h][w][sz];
w++;
i++;
}
i = ++h * width;
}
if (sz == 0) floatprocessor.resetMinAndMax();
imagestack.addSlice("", floatprocessor);
}
imp = new ImagePlusExtended(title, imagestack);
} else {
System.out.println("MIJ Error message: Unknow type of images or volumes.");
return null;
}
if (showImage) {
imp.show();
imp.updateAndDraw();
}
return imp;
}
void outline(ImageProcessor ip) {
if (Prefs.blackBackground) ip.invert();
((ByteProcessor) ip).outline();
if (Prefs.blackBackground) ip.invert();
}
public void run(ImageProcessor ip) {
ImageUtils.initMMorph4J();
if (ip instanceof ByteProcessor) {
GrayScaleImage img = ImageJAdapter.toGrayScaleImage((ByteProcessor) ip);
Object obj[] = BuilderTreeOfShapeByUnionFindParallel.getImageInterpolate(img);
short interpolation0[] = (short[]) obj[0];
short interpolation1[] = (short[]) obj[1];
int interpWidth = (img.getWidth() * 4 - 3);
int interpHeight = (img.getHeight() * 4 - 3);
ByteProcessor imgOut0 = new ByteProcessor(interpWidth, interpHeight);
ByteProcessor imgOut1 = new ByteProcessor(interpWidth, interpHeight);
for (int i = 0; i < interpolation0.length; i++) {
// imgOut.setPixel(i, (interpolation0[i] + interpolation1[i]) / 2);
imgOut0.set(i, (interpolation0[i] & 0xFF));
imgOut1.set(i, (interpolation1[i] & 0xFF));
}
ImagePlus imgPlus0 = new ImagePlus("interpolation - max", imgOut0);
imgPlus0.show("image interpolation - max");
ImagePlus imgPlus1 = new ImagePlus("interpolation - min", imgOut1);
imgPlus1.show("image interpolation - min");
} else if (ip instanceof ColorProcessor) {
ColorImage img = ImageJAdapter.toColorImage((ColorProcessor) ip);
int interpWidth = (img.getWidth() * 4 - 3);
int interpHeight = (img.getHeight() * 4 - 3);
GrayScaleImage imgR = img.getRed();
GrayScaleImage imgG = img.getGreen();
GrayScaleImage imgB = img.getBlue();
ColorProcessor imgOut0 = new ColorProcessor(interpWidth, interpHeight);
ColorProcessor imgOut1 = new ColorProcessor(interpWidth, interpHeight);
Object objR[] = BuilderTreeOfShapeByUnionFindParallel.getImageInterpolate(imgR);
short interpolation0R[] = (short[]) objR[0];
short interpolation1R[] = (short[]) objR[1];
Object objG[] = BuilderTreeOfShapeByUnionFindParallel.getImageInterpolate(imgG);
short interpolation0G[] = (short[]) objG[0];
short interpolation1G[] = (short[]) objG[1];
Object objB[] = BuilderTreeOfShapeByUnionFindParallel.getImageInterpolate(imgB);
short interpolation0B[] = (short[]) objB[0];
short interpolation1B[] = (short[]) objB[1];
for (int i = 0; i < interpolation0R.length; i++) {
int valor0 =
((0 & 0xFF) << 24)
| ((interpolation0R[i] & 0xFF) << 16)
| ((interpolation0G[i] & 0xFF) << 8)
| ((interpolation0B[i] & 0xFF) << 0);
imgOut0.set(i, valor0);
int valor1 =
((0 & 0xFF) << 24)
| ((interpolation1R[i] & 0xFF) << 16)
| ((interpolation1G[i] & 0xFF) << 8)
| ((interpolation1B[i] & 0xFF) << 0);
imgOut1.set(i, valor1);
}
ImagePlus imgPlus0 = new ImagePlus("interpolation - max", imgOut0);
imgPlus0.show("image interpolation - max");
ImagePlus imgPlus1 = new ImagePlus("interpolation - min", imgOut1);
imgPlus1.show("image interpolation - min");
}
}
// Particle finding routine based on spots enhancement with
// 2D PSF Gaussian approximated convolution/backgrounds subtraction, thresholding
// and particle filtering
void detectParticles(
ImageProcessor ip, SMLDialog fdg, int nFrame, Overlay SpotsPositions_, Roi RoiActive_) {
int nThreshold;
FloatProcessor dupip = null; // duplicate of image
ImageProcessor dushort; // duplicate of image
ByteProcessor dubyte = null; // tresholded image
TypeConverter tc;
dupip = (FloatProcessor) ip.duplicate().convertToFloat();
SMLblur1Direction(
dupip, fdg.dPSFsigma * 0.5, 0.0002, true, (int) Math.ceil(5 * fdg.dPSFsigma * 0.5));
SMLblur1Direction(dupip, fdg.dPSFsigma * 0.5, 0.0002, false, 0);
// new ImagePlus("gassconvoluted", dupip.convertToFloat().duplicate()).show();
// low-pass filtering by gaussian blurring
// lowpassGauss.blurGaussian(dupip, fdg.dPSFsigma*0.5, fdg.dPSFsigma*0.5, 0.0002);
// convolution with gaussian PSF kernel
SMLconvolveFloat(dupip, fConKernel, fdg.nKernelSize, fdg.nKernelSize);
// new ImagePlus("convoluted", dupip.duplicate()).show();
tc = new TypeConverter(dupip, true);
dushort = tc.convertToShort();
// new ImagePlus("convoluted", dushort.duplicate()).show();
// thresholding
// old straightforward thresholding
// imgstat = ImageStatistics.getStatistics(dupip, 22, null); //6 means MEAN + STD_DEV, look at
// ij.measure.Measurements
// nThreshold = (int)(imgstat.mean + 3.0*imgstat.stdDev);
// new smart thresholding
nThreshold = getThreshold(dushort);
dushort.threshold(nThreshold);
// convert to byte
dubyte = (ByteProcessor) dushort.convertToByte(false);
// new ImagePlus("threshold", dubyte.duplicate()).show();
// morphological operations on thresholded image
// dubyte.dilate(2, 0);
// dubyte.erode(2, 0);
// cleaning up image a bit
if (fdg.nKernelSize > 3) {
dubyte.dilate();
// new ImagePlus("dilated", dubyte.duplicate()).show();
dubyte.erode();
// new ImagePlus("erosion", dubyte.duplicate()).show();
}
// dupip.invert();
labelParticles(
dubyte,
ip,
nFrame,
fdg.dPixelSize,
fdg.nAreaCut,
fdg.dPSFsigma,
SpotsPositions_,
fdg.bShowParticles,
RoiActive_); // , fdg.bIgnoreFP);//, fdg.dSymmetry/100);
}
// -----------------------------------------------------------------------------------
public void Lipschitz2D(ImageProcessor ip) {
int slope, slope1, p, p1, p2, p3, p4, maxz;
m_roi = ip.getRoi();
ImageHeight = ip.getHeight();
ImageWidth = ip.getWidth();
m_channels = ip instanceof ColorProcessor ? 3 : 1;
m_short = ip instanceof ShortProcessor;
pixel = new int[m_channels];
int[][] destPixels = new int[m_channels][ImageHeight * ImageWidth];
int[][] srcPixels = new int[m_channels][ImageHeight * ImageWidth];
byte[][] tmpBytePixels = new byte[m_channels][ImageHeight * ImageWidth];
short[][] tmpShortPixels = new short[m_channels][ImageHeight * ImageWidth];
if (m_channels == 1) {
if (m_short) {
tmpShortPixels[0] = (short[]) ip.getPixels();
} else {
tmpBytePixels[0] = (byte[]) ip.getPixels();
}
} else {
ColorProcessor cip = (ColorProcessor) ip;
cip.getRGB(tmpBytePixels[0], tmpBytePixels[1], tmpBytePixels[2]);
}
int sign = (m_Down ? 1 : -1);
int topdown = (m_Down ? 0 : 255);
for (int ii = 0; ii < m_channels; ii++) {
for (int ij = 0; ij < ImageHeight * ImageWidth; ij++) {
srcPixels[ii][ij] =
(m_short
? sign * (tmpShortPixels[ii][ij] & 0xffff)
: sign * (tmpBytePixels[ii][ij] & 0xff));
destPixels[ii][ij] = srcPixels[ii][ij];
}
}
slope = (int) (m_Slope);
slope1 = (int) (slope * Math.sqrt(2.0));
maxz = m_channels;
for (int y = m_roi.y; y < m_roi.y + m_roi.height; y++) // rows
{
IJ.showProgress(y, 2 * ImageHeight);
for (int z = 0; z < m_channels; z++) {
p2 = sign * (topdown + (sign) * slope);
p3 = sign * (topdown + (sign) * slope1);
for (int x = m_roi.x; x < m_roi.x + m_roi.width; x++) // columns
{
p = (p2 - slope);
p1 = (p3 - slope1);
if (p1 > p) p = p1;
p3 = destPixels[z][x + ImageWidth * (Math.max(y - 1, 0))];
p1 = p3 - slope;
if (p1 > p) p = p1;
p4 = destPixels[z][Math.min(x + 1, ImageWidth - 1) + ImageWidth * (Math.max(y - 1, 0))];
p1 = p4 - slope1;
if (p1 > p) p = p1;
p2 = srcPixels[z][x + ImageWidth * y];
if (p > p2) {
destPixels[z][x + ImageWidth * y] = p;
p2 = p;
}
}
}
}
for (int y = m_roi.y + m_roi.height - 1; y >= m_roi.y; y--) // rows
{
IJ.showProgress(2 * ImageHeight - y - 1, 2 * ImageHeight);
for (int z = 0; z < maxz; z++) {
p2 = sign * (topdown + (sign) * slope);
p3 = sign * (topdown + (sign) * slope1);
for (int x = m_roi.x + m_roi.width - 1; x >= m_roi.x; x--) // columns
{
p = (p2 - slope);
p1 = (p3 - slope1);
if (p1 > p) p = p1;
p3 = destPixels[z][x + ImageWidth * (Math.min(y + 1, ImageHeight - 1))];
p1 = p3 - slope;
if (p1 > p) p = p1;
p4 = destPixels[z][Math.max(x - 1, 0) + ImageWidth * (Math.min(y + 1, ImageHeight - 1))];
p1 = p4 - slope1;
if (p1 > p) p = p1;
p2 = destPixels[z][x + ImageWidth * y];
if (p > p2) {
destPixels[z][x + ImageWidth * y] = p;
p2 = p;
}
}
}
}
for (int ii = 0; ii < m_channels; ii++) {
for (int ij = 0; ij < ImageHeight * ImageWidth; ij++) {
if (m_TopHat) {
tmpBytePixels[ii][ij] =
(m_Down
? (byte) (srcPixels[ii][ij] - destPixels[ii][ij] + 255)
: (byte) (destPixels[ii][ij] - srcPixels[ii][ij]));
} else {
if (m_short) {
tmpShortPixels[ii][ij] = (short) ((sign * destPixels[ii][ij] & 0xffff));
} else {
tmpBytePixels[ii][ij] = (byte) (sign * destPixels[ii][ij]);
}
}
}
}
if (m_channels == 1) {
if (m_short) {
ShortProcessor sip = (ShortProcessor) ip;
sip.setPixels(tmpShortPixels[0]);
} else {
ByteProcessor bip = (ByteProcessor) ip;
bip.setPixels(tmpBytePixels[0]);
}
} else {
ColorProcessor cip = (ColorProcessor) ip;
cip.setRGB(tmpBytePixels[0], tmpBytePixels[1], tmpBytePixels[2]);
}
}
/** Called by the PlugInFilterRunner to process the image or one frame of a stack */
public void run(ImageProcessor ip) {
if (interrupted) return;
int width = ip.getWidth();
int height = ip.getHeight();
int backgroundValue =
(processType == VORONOI)
? (background255 ? 0 : (byte) 255)
: // Voronoi needs EDM of the background
(background255 ? (byte) 255 : 0); // all others do EDM of the foreground
if (USES_WATERSHED[processType]) nPasses = 0; // watershed has its own progress bar
FloatProcessor floatEdm = makeFloatEDM(ip, backgroundValue, false);
ByteProcessor maxIp = null;
if (USES_MAX_FINDER[processType]) {
if (processType == VORONOI) floatEdm.multiply(-1); // Voronoi starts from minima of EDM
int maxOutputType =
USES_WATERSHED[processType] ? MaximumFinder.SEGMENTED : MaximumFinder.SINGLE_POINTS;
boolean isEDM = processType != VORONOI;
maxIp =
maxFinder.findMaxima(
floatEdm,
MAXFINDER_TOLERANCE,
ImageProcessor.NO_THRESHOLD,
maxOutputType,
false,
isEDM);
if (maxIp == null) { // segmentation cancelled by user?
interrupted = true;
return;
} else if (processType != WATERSHED) {
if (processType == VORONOI) floatEdm.multiply(-1);
resetMasked(floatEdm, maxIp, processType == VORONOI ? -1 : 0);
}
}
ImageProcessor outIp = null;
if (processType == WATERSHED) {
if (background255) maxIp.invert();
ip.copyBits(maxIp, 0, 0, Blitter.COPY);
ip.setBinaryThreshold();
} else
switch (outImageType) { // for all these, output contains the values of the EDM
case FLOAT:
outIp = floatEdm;
break;
case SHORT:
floatEdm.setMinAndMax(0., 65535.);
outIp = floatEdm.convertToShort(true);
break;
case BYTE:
floatEdm.setMinAndMax(0., 255.);
outIp = floatEdm.convertToByte(true);
break;
case BYTE_OVERWRITE:
ip.setPixels(0, floatEdm);
if (floatEdm.getMax() > 255.) ip.resetMinAndMax(); // otherwise we have max of floatEdm
}
if (outImageType != BYTE_OVERWRITE) { // new output image
if (outStack == null) {
outImp = new ImagePlus(TITLE_PREFIX[processType] + imp.getShortTitle(), outIp);
} else outStack.setPixels(outIp.getPixels(), pfr.getSliceNumber());
}
} // public void run
public void runStuff(
DimensionMap map,
TreeMap<DimensionMap, ROIPlus> maximaMap,
TreeMap<DimensionMap, String> segMap,
TreeMap<DimensionMap, String> maskMap,
TreeMap<DimensionMap, String> imageMap,
TreeMap<DimensionMap, Double> results,
Canceler canceler) {
// Get the Maxima
ROIPlus maxima = maximaMap.get(map);
// Make the mask image impMask
// ByteProcessor impMask = (ByteProcessor) (new
// ImagePlus(maskMap.get(map)).getProcessor().convertToByte(false));
// ByteProcessor impSeg = (ByteProcessor) (new
// ImagePlus(segMap.get(map)).getProcessor().convertToByte(false));
ByteProcessor impSeg = (ByteProcessor) (new ImagePlus(segMap.get(map))).getProcessor();
ByteProcessor impMask = (ByteProcessor) (new ImagePlus(maskMap.get(map))).getProcessor();
ByteBlitter blit = new ByteBlitter(impSeg);
blit.copyBits(impMask, 0, 0, Blitter.AND);
FloatProcessor impImage =
(FloatProcessor) (new ImagePlus(imageMap.get(map))).getProcessor().convertToFloat();
Wand wand = new Wand(impSeg);
Wand wand2 = new Wand(impMask);
Vector<Double> measurements;
for (IdPoint p : maxima.getPointList()) {
if (canceler.isCanceled()) {
return;
}
if (impSeg.getPixel(p.x, p.y)
== 255) // if we land on a cell that made it through thresholding
{
wand.autoOutline(p.x, p.y); // outline it
wand2.autoOutline(p.x, p.y);
boolean partOfCellClump =
!this.selectionsAreEqual(
wand,
wand2); // If the segmented and unsegmented masks do not agree on the roi, then this
// cell is part of a clump.
if (wand.npoints > 0) {
Roi roi =
new PolygonRoi(
wand.xpoints,
wand.ypoints,
wand.npoints,
Roi.POLYGON); // The roi helps for using getLength() (DON'T USE Roi.TRACED_ROI.,
// IT SCREWS UP THE Polygon OBJECTS!!!! Bug emailed to ImageJ
// folks)
Polygon poly =
new Polygon(
wand.xpoints,
wand.ypoints,
wand.npoints); // The polygon helps for using contains()
Rectangle r = roi.getBounds();
measurements = new Vector<Double>();
for (int i = r.x; i < r.x + r.width; i++) {
for (int j = r.y; j < r.y + r.height; j++) {
// innerBoundary
if (poly.contains(i, j) && impSeg.getPixelValue(i, j) == 255) {
measurements.add((double) impImage.getPixelValue(i, j));
// Logs.log("In - " + innerT, this);
}
}
}
impMask.setRoi(roi);
ImageStatistics stats =
ImageStatistics.getStatistics(
impMask,
ImageStatistics.AREA
& ImageStatistics.PERIMETER
& ImageStatistics.CIRCULARITY
& ImageStatistics.ELLIPSE,
null);
if (measurements.size() > 0) {
DimensionMap resultsMap = map.copy();
resultsMap.put("Id", "" + p.id);
resultsMap.put("Measurement", "X");
results.put(resultsMap.copy(), (double) p.x);
resultsMap.put("Measurement", "Y");
results.put(resultsMap.copy(), (double) p.y);
resultsMap.put("Measurement", "AREA");
results.put(resultsMap.copy(), stats.area);
resultsMap.put("Measurement", "PERIMETER");
results.put(resultsMap.copy(), roi.getLength());
resultsMap.put("Measurement", "CIRCULARITY");
results.put(
resultsMap.copy(), 4.0 * Math.PI * (stats.area / (Math.pow(roi.getLength(), 2))));
resultsMap.put("Measurement", "ELLIPSE MAJOR");
results.put(resultsMap.copy(), stats.major);
resultsMap.put("Measurement", "ELLIPSE MINOR");
results.put(resultsMap.copy(), stats.minor);
resultsMap.put("Measurement", "MEAN");
results.put(resultsMap.copy(), StatisticsUtility.mean(measurements));
resultsMap.put("Measurement", "MEDIAN");
results.put(resultsMap.copy(), StatisticsUtility.median(measurements));
resultsMap.put("Measurement", "SUM");
results.put(resultsMap.copy(), StatisticsUtility.sum(measurements));
resultsMap.put("Measurement", "MIN");
results.put(resultsMap.copy(), StatisticsUtility.min(measurements));
resultsMap.put("Measurement", "MAX");
results.put(resultsMap.copy(), StatisticsUtility.max(measurements));
resultsMap.put("Measurement", "STDDEV");
results.put(resultsMap.copy(), StatisticsUtility.stdDev(measurements));
resultsMap.put("Measurement", "VARIANCE");
results.put(resultsMap.copy(), StatisticsUtility.variance(measurements));
resultsMap.put("Measurement", "CLUMP");
results.put(resultsMap.copy(), (double) (partOfCellClump ? 1 : 0));
}
}
}
}
}
public double getCooccurenceSums() {
return image.getPixelCount() * 8;
}