void drawOutline(ImageProcessor ip, Roi roi, int count) {
if (showChoice == OVERLAY_OUTLINES || showChoice == OVERLAY_MASKS) {
if (overlay == null) {
overlay = new Overlay();
overlay.drawLabels(true);
overlay.setLabelFont(new Font("SansSerif", Font.PLAIN, fontSize));
}
Roi roi2 = (Roi) roi.clone();
roi2.setStrokeColor(Color.cyan);
if (lineWidth != 1) roi2.setStrokeWidth(lineWidth);
if (showChoice == OVERLAY_MASKS) roi2.setFillColor(Color.cyan);
overlay.add(roi2);
} else {
Rectangle r = roi.getBounds();
int nPoints = ((PolygonRoi) roi).getNCoordinates();
int[] xp = ((PolygonRoi) roi).getXCoordinates();
int[] yp = ((PolygonRoi) roi).getYCoordinates();
int x = r.x, y = r.y;
if (!inSituShow) ip.setValue(0.0);
ip.moveTo(x + xp[0], y + yp[0]);
for (int i = 1; i < nPoints; i++) ip.lineTo(x + xp[i], y + yp[i]);
ip.lineTo(x + xp[0], y + yp[0]);
if (showChoice != BARE_OUTLINES) {
String s = ResultsTable.d2s(count, 0);
ip.moveTo(r.x + r.width / 2 - ip.getStringWidth(s) / 2, r.y + r.height / 2 + fontSize / 2);
if (!inSituShow) ip.setValue(1.0);
ip.drawString(s);
}
}
}
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;
}
public ImageProcessor expandImage(ImageProcessor ipOld, int wNew, int hNew, int xOff, int yOff) {
ImageProcessor ipNew = ipOld.createProcessor(wNew, hNew);
if (zeroFill) ipNew.setValue(0.0);
else ipNew.setColor(Toolbar.getBackgroundColor());
ipNew.fill();
ipNew.insert(ipOld, xOff, yOff);
return ipNew;
}
boolean eraseOutsideRoi(ImageProcessor ip, Rectangle r, ImageProcessor mask) {
int width = ip.getWidth();
int height = ip.getHeight();
ip.setRoi(r);
if (excludeEdgeParticles && polygon != null) {
ImageStatistics stats = ImageStatistics.getStatistics(ip, MIN_MAX, null);
if (fillColor >= stats.min && fillColor <= stats.max) {
double replaceColor = level1 - 1.0;
if (replaceColor < 0.0 || replaceColor == fillColor) {
replaceColor = level2 + 1.0;
int maxColor = imageType == BYTE ? 255 : 65535;
if (replaceColor > maxColor || replaceColor == fillColor) {
IJ.error("Particle Analyzer", "Unable to remove edge particles");
return false;
}
}
for (int y = minY; y < maxY; y++) {
for (int x = minX; x < maxX; x++) {
int v = ip.getPixel(x, y);
if (v == fillColor) ip.putPixel(x, y, (int) replaceColor);
}
}
}
}
ip.setValue(fillColor);
if (mask != null) {
mask = mask.duplicate();
mask.invert();
ip.fill(mask);
}
ip.setRoi(0, 0, r.x, height);
ip.fill();
ip.setRoi(r.x, 0, r.width, r.y);
ip.fill();
ip.setRoi(r.x, r.y + r.height, r.width, height - (r.y + r.height));
ip.fill();
ip.setRoi(r.x + r.width, 0, width - (r.x + r.width), height);
ip.fill();
ip.resetRoi();
// IJ.log("erase: "+fillColor+" "+level1+" "+level2+" "+excludeEdgeParticles);
// (new ImagePlus("ip2", ip.duplicate())).show();
return true;
}
public void drawInto(ImageProcessor ip, int value, int linewidth, GeneralPath path) {
ip.setValue(value);
ip.setLineWidth(linewidth);
PathIterator it = path.getPathIterator(null);
float[] seg = new float[6];
int px = 0, py = 0;
while (!it.isDone()) {
int l = it.currentSegment(seg);
int x = Math.round(seg[0]);
int y = Math.round(seg[1]);
double d = Math.sqrt((px - x) * (px - x) + (py - y) * (py - y));
if (l == PathIterator.SEG_MOVETO || d > 15) ip.moveTo(x, y);
else ip.lineTo(x, y);
px = x;
py = y;
it.next();
}
}
public ImageStack expandStack(ImageStack stackOld, int wNew, int hNew, int xOff, int yOff) {
int nFrames = stackOld.getSize();
ImageProcessor ipOld = stackOld.getProcessor(1);
java.awt.Color colorBack = Toolbar.getBackgroundColor();
ImageStack stackNew = new ImageStack(wNew, hNew, stackOld.getColorModel());
ImageProcessor ipNew;
for (int i = 1; i <= nFrames; i++) {
IJ.showProgress((double) i / nFrames);
ipNew = ipOld.createProcessor(wNew, hNew);
if (zeroFill) ipNew.setValue(0.0);
else ipNew.setColor(colorBack);
ipNew.fill();
ipNew.insert(stackOld.getProcessor(i), xOff, yOff);
stackNew.addSlice(stackOld.getSliceLabel(i), ipNew);
}
return stackNew;
}
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;
}
public void run(ImageProcessor ip) {
int w = ip.getWidth(); // Get width of image
int h = ip.getHeight(); // Get height of image
/**
* ------------------------------------------------------------------ BEGIN PRELIMINARY STEPS
* ------------------------------------------------------------------*
*/
/**
* ----------------------------------- BEGIN: CREATE IMAGES THAT WILL BE USED IN COMPUTATIONS
* -----------------------------------*
*/
// Create the smoothed image to be used for the different edge images
ImageProcessor Smoothed_Ip_xf = new FloatProcessor(w, h);
ImageProcessor Smoothed_Ip_yf = new FloatProcessor(w, h);
ImageProcessor Smoothed_Ip_45f = new FloatProcessor(w, h);
ImageProcessor Smoothed_Ip_135f = new FloatProcessor(w, h);
// Create the edge image detecting vertical edges
ImageProcessor G_xf_Ip = new FloatProcessor(w, h);
// Create the edge image detecting horizontal edges
ImageProcessor G_yf_Ip = new FloatProcessor(w, h);
// Create the edge image detecting 135 degree edges
ImageProcessor G_45f_Ip = new FloatProcessor(w, h);
// Create the edge image detecting 45 degree edges
ImageProcessor G_135f_Ip = new FloatProcessor(w, h);
// Create the gradient magnitude image
ImageProcessor GMag_Ip = new ByteProcessor(w, h); // Byte version
ImageProcessor GMagf_Ip = new FloatProcessor(w, h); // Floating point version
// Create the gradient direction image
ImageProcessor GDir_Ip = new ByteProcessor(w, h); // Byte version
ImageProcessor GDirf_Ip = new FloatProcessor(w, h); // Floating point version
// Create the edge image (output from non-maximal suppression)
ImageProcessor Edge_Ip = new ByteProcessor(w, h); // Byte Version
Edge_Ip.setValue(0); // 0 = Black
Edge_Ip.fill(); // Fill Edge_Ip all black
ImageProcessor Copy_Edge_Ip = new ByteProcessor(w, h); // Byte Version
Copy_Edge_Ip.setValue(0); // 0 = Black
Copy_Edge_Ip.fill(); // Fill Edge_Ip all black
ImageProcessor Edgef_Ip = new FloatProcessor(w, h); // Floating Point version
Edgef_Ip.setValue(0); // 0 = Black
Edgef_Ip.fill(); // Fill Edge_Ip all black
ImageProcessor Copy_Edgef_Ip = new FloatProcessor(w, h); // Floating Point version
Copy_Edgef_Ip.setValue(0); // 0 = Black
Copy_Edgef_Ip.fill(); // Fill Edge_Ip all black
// Create the Threshold with Hysteresis image
ImageProcessor Threshold_Ip = new ByteProcessor(w, h);
Threshold_Ip.setValue(0); // 0 = Black
Threshold_Ip.fill(); // Fill Edge_Ip all black
/**
* ----------------------------------- END: CREATE IMAGES THAT WILL BE USED IN COMPUTATIONS
* -----------------------------------*
*/
/**
* ----------------------------------------------------- BEGIN: USER INPUT
* -----------------------------------------------------*
*/
double STDDev = 0,
TLow = 0,
THigh =
0; // Declare and initialize variables for the standard deviation, low threshold, and
// high threshold
int Size = 0; // Initialize size of Gaussian Filter
boolean EdgeStrengthImage; // specifies whether edge strength image should be shown
GenericDialog gd = new GenericDialog("User Inputs");
gd.addNumericField(
"Size of Gaussian Filter (Odd Integer)", Size, 0); // Field for Size of Gaussian Filter
gd.addNumericField("Standard Deviation", STDDev, 0); // Field for Standard Deviation
gd.addNumericField("Low Threshold (1 - 255)", TLow, 0); // Field for Low Threshold
gd.addNumericField("High Threshold (1 - 255)", THigh, 0); // Field for High Threshold
gd.showDialog();
if (gd.wasCanceled()) {
return;
} else {
Size =
(int)
gd
.getNextNumber(); // Set Size variable from user input. This allows the user to
// set the size of the Gaussian Filter
STDDev = gd.getNextNumber(); // Set STDDev variable from user input
TLow = gd.getNextNumber(); // Set TLow variable from user input
THigh = gd.getNextNumber(); // Set THigh variable from user input
}
/**
* ----------------------------------------------------- END: USER INPUT
* -----------------------------------------------------*
*/
/**
* ------------------------------------------------------------------ END PRELIMINARY STEPS
* ------------------------------------------------------------------*
*/
/**
* ------------------------------------------------------------------ BEGIN CANNY EDGE DETECTION
* ------------------------------------------------------------------*
*/
/**
* ----------------------------------------------------- BEGIN STEP 1: NOISE REDUCTION VIA
* GAUSSIAN ----------------------------------------------------- *
*/
ImageProcessor ipf = ip.convertToFloat(); // Convert original image to floating point
int SizeSquared = (int) Math.pow(Size, 2); // Square the size of Gaussian Kernel
int HalfSize = (Size - 1) / 2; // Cut the size of the Gaussian Kernel almost in half
float pix; // Temporary storage to be used throughout code to store pixel values
float[] GaussianFilter =
new float[SizeSquared]; // Initialize Gaussian Filter to be used in the convolution
double[] GaussianFilterD =
new double
[SizeSquared]; // Initialize Gaussian Filter to be used...this filter will be composed
// of numbers of type double
double Constant = 1 / (2 * Math.PI * Math.pow(STDDev, 2)); // Compute 1/(2*pi*sigma^2)
double ExponentDenom = 2 * Math.pow(STDDev, 2); // Compute 2*sigma^2
double Value; // Temporary storage to store the computations that will form the Gaussian Filter
// FOR LOOP TO FORM GaussianFilterD
for (int i = 0; i < Size; i++) {
for (int j = 0; j < Size; j++) {
Value =
Math.exp(
-1
* (Math.pow(j - HalfSize, 2) + Math.pow(i - HalfSize, 2))
/ (ExponentDenom)); // Set Value = e^(-(i^2 + j^2)/(2*sigma^2))
GaussianFilterD[Size * i + j] = Constant * Value; // Place Value in GaussianFilterD
}
}
// FOR LOOP TO FORM GaussianFilter using GaussianFilterD
for (int i = 0; i < Math.pow(Size, 2); i++) {
GaussianFilter[i] = (float) GaussianFilterD[i]; // Convert double values to float one by one
}
// CONVOLVE IMAGE WITH GAUSSIAN
Convolver cv = new Convolver(); // Create the convolver
cv.setNormalize(true); // Normalize the filter
cv.convolve(
ipf,
GaussianFilter,
Size,
Size); // Apply the GaussianFilter using convolution on the image ipf
// For loop to create 4 smoothed images to be used in detecting the 4 edges: 0 deg, 45 deg, 90
// deg, 135 deg
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
pix = ipf.getf(i, j);
Smoothed_Ip_xf.setf(
i, j,
pix); // Smoothed image to convolve with Sobel filter in x-direction (detects vertical
// edges)
Smoothed_Ip_yf.setf(
i, j,
pix); // Smoothed image to convolve with Sobel filter in y-direction (detects horizontal
// edges)
Smoothed_Ip_45f.setf(
i, j,
pix); // Smoothed image to convolve with Sobel filter in 45 degree direction (detects
// 135 degree edges)
Smoothed_Ip_135f.setf(
i, j,
pix); // Smoothed image to convolve with Sobel filter in 135 degree direction (detects
// 45 degree edges)
}
}
/**
* ----------------------------------------------------- END STEP 1: NOISE REDUCTION VIA
* GAUSSIAN ----------------------------------------------------- *
*/
/**
* ----------------------------------------------------- BEGIN STEP 2: COMPUTE GRADIENT
* MAGNITUDE AND DIRECTION IMAGES ----------------------------------------------------- *
*/
// Sobel Filters to detect edges
float[] G_x = {-1, 0, 1, -2, 0, 2, -1, 0, 1}; // Sobel operator in x direction
float[] G_y = {-1, -2, -1, 0, 0, 0, 1, 2, 1}; // Sobel operator in y direction
float[] G_135 = {2, 1, 0, 1, 0, -1, 0, -1, -2}; // Sobel operator in 135 degree direction
float[] G_45 = {0, 1, 2, -1, 0, 1, -2, -1, 0}; // Sobel operator in 45 degree direction
// CONVOLVE IMAGE USING SOBEL FILTERS
cv.convolve(
Smoothed_Ip_xf,
G_x,
3,
3); // Apply the Sobel filter in x-direction using convolution on the smoothed image
cv.convolve(
Smoothed_Ip_yf,
G_y,
3,
3); // Apply the Sobel filter in y-direction using convolution on the smoothed image
cv.convolve(
Smoothed_Ip_45f,
G_45,
3,
3); // Apply the Sobel filter in 45 degree direction using convolution on the smoothed image
cv.convolve(
Smoothed_Ip_135f,
G_135,
3,
3); // Apply the Sobel filter in 135 degree direction using convolution on the smoothed
// image
// For loop to define the 4 floating point images G_xf_Ip, G_yf_Ip, G_45f_Ip, G_135f_Ip
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
pix = Smoothed_Ip_xf.getf(i, j);
G_xf_Ip.setf(i, j, pix);
pix = Smoothed_Ip_yf.getf(i, j);
G_yf_Ip.setf(i, j, pix);
pix = Smoothed_Ip_45f.getf(i, j);
G_45f_Ip.setf(i, j, pix);
pix = Smoothed_Ip_135f.getf(i, j);
G_135f_Ip.setf(i, j, pix);
}
}
// COMPUTE THE GRADIENT MAGNITUDE IMAGE GMagf_Ip
float pix1, pix2; // Variables to store pixel values of G_xf_Ip and G_yf_Ip
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
pix1 = G_xf_Ip.getf(i, j); // Get pixel value
pix2 = G_yf_Ip.getf(i, j); // Get Pixel Value
pix =
(float)
Math.sqrt(
pix1 * pix1
+ pix2
* pix2); // Take the square root of the sum of the squares of both pixel
// values
GMagf_Ip.setf(
i, j, 255 * pix); // Place in (i,j) position of GMagf_Ip and scale by 255 to view
}
}
// COMPUTE THE GRADIENT DIRECTION IMAGE
double v1, v2, v3, v4; // Temporary storage for the abs. value of pixel vlaues
int pixi; // Temporary storage for pixel values
double[][] GDir =
new double[h]
[w]; // Define 2D Array to store gradient direction values. Will use this during Step 3
// Non-Maximal Suppression instead of the gradient direction image
// For loop to compute gradient direction image
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
// Obtain the absolute value of each pixel in the following four floating point images
v1 = Math.abs(G_xf_Ip.getf(i, j));
v2 = Math.abs(G_yf_Ip.getf(i, j));
v3 = Math.abs(G_45f_Ip.getf(i, j));
v4 = Math.abs(G_135f_Ip.getf(i, j));
// If statements to find the maximal response (strongest edge direction)
if (v1 > v2 && v1 > v3 && v1 > v4) // Vertical edge orientation
{
GDir_Ip.putPixel(i, j, 0);
GDir[j][i] = 0; // 0 stands for vertical edge orientation
} else if (v3 > v1 && v3 > v2 && v3 > v4) // 45 degree edge orientation
{
GDir_Ip.putPixel(i, j, 1);
GDir[j][i] = 1; // 1 stands for 45 degree edge orientation
} else if (v2 > v1 && v2 > v3 && v2 > v4) // Horizontal edge orientation
{
GDir_Ip.putPixel(i, j, 2);
GDir[j][i] = 2; // 2 stands for horizontal edge orientation
} else if (v4 > v1 && v4 > v2 && v4 > v3) // 135 degree edge orientation
{
GDir_Ip.putPixel(i, j, 3);
GDir[j][i] = 3; // 3 stands for 135 degree edge orientation
}
pixi =
(int) (255.0 / 3.0)
* GDir_Ip.getPixel(
i, j); // Scale the gradient direction image so we can actually see it
GDir_Ip.putPixel(
i, j,
pixi); // Place pixi in the (i,j) location of GDir_Ip...we can now view the gradient
// direction image
}
}
// OUTPUT GRADIENT DIRECTION IMAGE
String DirTitle = "Gradient Direction";
ImagePlus GDir_Im = new ImagePlus(DirTitle, GDir_Ip);
GDir_Im.show();
// OUTPUT GRADIENT MAGNITUDE IMAGE (need to convert to ByteProcessor to view first)
GMagf_Ip.resetMinAndMax();
GMag_Ip.insert(GMagf_Ip.convertToByte(true), 0, 0);
String MagTitle = "Gradient Magnitude";
ImagePlus GMag_Im = new ImagePlus(MagTitle, GMag_Ip);
GMag_Im.show();
/**
* ----------------------------------------------------- BEGIN STEP 2: COMPUTE GRADIENT
* MAGNITUDE AND DIRECTION IMAGES ----------------------------------------------------- *
*/
/**
* ----------------------------------------------------- BEGIN STEP 3: NON-MAXIMAL SUPPRESSION
* -----------------------------------------------------*
*/
// Ignoring boundaries for convenience
float Magnitude; // Storage for magnitude
double Direction; // Storage for direction
// FOR LOOP TO COMPUTE NON-MAXIMAL SUPPRESSION IMAGE
for (int i = 1; i < w - 1; i++) {
for (int j = 1; j < h - 1; j++) {
Magnitude =
GMagf_Ip.getf(
i, j); // Get the magnitude in the (i,j) position of the gradient magnitude image
if (Magnitude != 0) // If the magnitude is non-zero then we find the direction of the edge
{
Direction = GDir[j][i]; // Obtain the direction
float n1GradMag = 0,
n2GradMag = 0; // Initialize storage for the magnitude of the 2 neighboring pixels
if (Direction
== 0) // If Direction is 0 get the magnitude in the columns to the left and right
{
n1GradMag = GMagf_Ip.getf(i - 1, j);
n2GradMag = GMagf_Ip.getf(i + 1, j);
} else if (Direction
== 1) // If Direction is 45 degree get the magnitude in adjacent pixels
{
n1GradMag = GMagf_Ip.getf(i + 1, j - 1);
n2GradMag = GMagf_Ip.getf(i - 1, j + 1);
} else if (Direction
== 2) // If Direction is 2 get the magnitude in the rows above and below
{
n1GradMag = GMagf_Ip.getf(i, j - 1);
n2GradMag = GMagf_Ip.getf(i, j + 1);
} else if (Direction
== 3) // If Direction is 135 degrees get the magnitude in adjacent pixels
{
n1GradMag = GMagf_Ip.getf(i - 1, j - 1);
n2GradMag = GMagf_Ip.getf(i + 1, j + 1);
}
if (Magnitude > n1GradMag
&& Magnitude
> n2GradMag) // Check to see if the magnitude of pixel under inspection is the
// largest relative to its adjacent pixels
{
pix = GMagf_Ip.getf(i, j); // Store magnitude in (i,j) position in pix variable
Edgef_Ip.setf(
i, j,
pix); // Place this pixel value in the (i,j) position of Edge Image (Edge Image is
// output from non-maximal suppression)
Copy_Edgef_Ip.setf(
i, j,
pix); // Place this pixel value in the (i,j) position of Copy Edge Image (this will
// be used for thresholding with hysteresis)
} else {
} // Else do nothing since edge image was already filled with 0's at beginning of code
} else {
} // Do nothing if magnitude is 0
}
}
Copy_Edgef_Ip.resetMinAndMax();
Copy_Edge_Ip.insert(Copy_Edgef_Ip.convertToByte(true), 0, 0);
// Display edge magnitude image but we need to convert to ByteProcessor first
Edgef_Ip.resetMinAndMax();
Edge_Ip.insert(Edgef_Ip.convertToByte(true), 0, 0);
String cTitle = "Non-Maximal Suppression";
ImagePlus Edge_Im = new ImagePlus(cTitle, Edge_Ip);
Edge_Im.show();
/**
* ----------------------------------------------------- END STEP 3: NON-MAXIMAL SUPPRESSION
* -----------------------------------------------------*
*/
/**
* ----------------------------------------------------- BEGIN STEP 4: THRESHOLDING WITH
* HYSTERESIS -----------------------------------------------------*
*/
int count = 0; // Initialize counter to be used in while loop
int IterationCount = 500; // Number of iterations to perform
// Scan through all pixels in the edge image and mark all edges with magnitude above the high
// threshold as a true edge otherwise if the magnitude is below the low threshold
// then we delete that pixel (set it to zero)
for (int i = 0; i < w; i++) {
for (int j = 0; j < h; j++) {
Magnitude = Copy_Edge_Ip.getPixel(i, j); // Obtain magnitude in edge image
if (Magnitude
> THigh) // If Magnitude is larger than the high threshold then make pixel white
{
Threshold_Ip.putPixel(i, j, 255); // True edge (Updates Threshold image to be output)
Copy_Edge_Ip.putPixel(
i, j,
255); // True edge (Updates edge image which will be used in the iterative
// thresholding)
} else if (Magnitude
< TLow) // Else if magnitude is below the low threshold then make pixel black
{
Threshold_Ip.putPixel(i, j, 0); // Not an edge (Updates Threshold image to be output)
Copy_Edge_Ip.putPixel(
i, j,
0); // Not an edge (Updates edge image which will be used in the iterative
// thresholding)
}
}
}
while (count < IterationCount) // Iterate again and again
{
// Ignore boundary pixels for convenience
for (int i = 1; i < w - 1; i++) {
for (int j = 1; j < h - 1; j++) {
Magnitude = Copy_Edge_Ip.getPixel(i, j); // Obtain magnitude in edge image
if (Magnitude == 255) // If we reach a true edge then we look at its 8 neighbors
{
// Obtain the magnitude in the 8 neighbors
int n1, n2, n3, n4, n5, n6, n7, n8;
n1 = Copy_Edge_Ip.getPixel(i - 1, j);
n2 = Copy_Edge_Ip.getPixel(i - 1, j - 1);
n3 = Copy_Edge_Ip.getPixel(i, j - 1);
n4 = Copy_Edge_Ip.getPixel(i + 1, j - 1);
n5 = Copy_Edge_Ip.getPixel(i + 1, j);
n6 = Copy_Edge_Ip.getPixel(i + 1, j + 1);
n7 = Copy_Edge_Ip.getPixel(i, j + 1);
n8 = Copy_Edge_Ip.getPixel(i - 1, j + 1);
if (n1
>= TLow) // If n1 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i - 1, j, 255); // Update edge image
Threshold_Ip.putPixel(
i - 1, j,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n2
>= TLow) // If n2 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i - 1, j - 1, 255); // Update edge image
Threshold_Ip.putPixel(
i - 1, j - 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n3
>= TLow) // If n3 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i, j - 1, 255); // Update edge image
Threshold_Ip.putPixel(
i, j - 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n4
>= TLow) // If n4 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i + 1, j - 1, 255); // Update edge image
Threshold_Ip.putPixel(
i + 1, j - 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n5
>= TLow) // If n5 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i + 1, j, 255); // Update edge image
Threshold_Ip.putPixel(
i + 1, j,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n6
>= TLow) // If n6 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i + 1, j + 1, 255); // Update edge image
Threshold_Ip.putPixel(
i + 1, j + 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n7
>= TLow) // If n7 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i, j + 1, 255); // Update edge image
Threshold_Ip.putPixel(
i, j + 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
if (n8
>= TLow) // If n8 is greater than or equal to the low threshold then we mark it as
// an edge
{
Copy_Edge_Ip.putPixel(i - 1, j + 1, 255); // Update edge image
Threshold_Ip.putPixel(
i - 1, j + 1,
255); // Update threshold image (This is the output image from thresholding with
// hysteresis)
}
}
}
}
count++; // Update counter and continue iterations
}
// Display the threshold with hysteresis image.
String ThreshTitle = "Threshold with Hysteresis";
ImagePlus Threshold_Im = new ImagePlus(ThreshTitle, Threshold_Ip);
Threshold_Im.show();
/**
* ----------------------------------------------------- END STEP 4: THRESHOLDING WITH
* HYSTERESIS -----------------------------------------------------*
*/
/**
* ------------------------------------------------------------------ END CANNY EDGE DETECTION
* ------------------------------------------------------------------*
*/
}
@Override
public Grid2D applyToolToImage(Grid2D imageProcessor) {
FloatProcessor imp = new FloatProcessor(imageProcessor.getWidth(), imageProcessor.getHeight());
imp.setPixels(imageProcessor.getBuffer());
if (!initBead) initializeBead();
ImageProcessor imp1 = imp.duplicate(); // original
double[][] beadMean3D = config.getBeadMeanPosition3D(); // [beadNo][x,y,z]
double[] uv = new double[1];
SimpleMatrix pMatrix = config.getGeometry().getProjectionMatrix(imageIndex).computeP();
// [projection #][bead #][u, v, state[0: initial, 1: registered, 2: updated by hough searching]]
double[][][] beadPosition2D = config.getBeadPosition2D();
int noBeadRegistered = 0;
double[][] xy1 = new double[WeightBearingBeadPositionBuilder.beadNo][2]; // original
double[][] xy2 =
new double[WeightBearingBeadPositionBuilder.beadNo]
[2]; // warped (mapped to the mean), control points, reference
double[][] xy1_hat = new double[WeightBearingBeadPositionBuilder.beadNo][2]; // original
double[][] xy2_hat = new double[WeightBearingBeadPositionBuilder.beadNo][2]; // original
// double distanceReferenceToCurrentBead = 0;
for (int i = WeightBearingBeadPositionBuilder.currentBeadNo; i >= 0; i--) {
if (beadMean3D[i][0] != 0
|| beadMean3D[i][1] != 0
|| beadMean3D[i][2] != 0) { // assume bead 3d is registered.
uv = compute2DCoordinates(beadMean3D[i], pMatrix);
// find bead location if registered by txt: state 1
if (beadPosition2D[imageIndex][i][2] == 1) {
noBeadRegistered++;
if (isDisplay) {
imp1.setValue(2);
imp1.drawLine(
(int) Math.round(beadPosition2D[imageIndex][i][0] - 10),
(int) Math.round(beadPosition2D[imageIndex][i][1] - 10),
(int) Math.round(beadPosition2D[imageIndex][i][0] + 10),
(int) Math.round(beadPosition2D[imageIndex][i][1] + 10));
imp1.drawLine(
(int) Math.round(beadPosition2D[imageIndex][i][0] - 10),
(int) Math.round(beadPosition2D[imageIndex][i][1] + 10),
(int) Math.round(beadPosition2D[imageIndex][i][0] + 10),
(int) Math.round(beadPosition2D[imageIndex][i][1] - 10));
imp1.drawString(
"Bead " + i + " (state:" + (int) beadPosition2D[imageIndex][i][2] + ")",
(int) beadPosition2D[imageIndex][i][0],
(int) beadPosition2D[imageIndex][i][1] - 10);
}
xy1[noBeadRegistered - 1][0] = beadPosition2D[imageIndex][i][0];
xy1[noBeadRegistered - 1][1] = beadPosition2D[imageIndex][i][1];
xy2[noBeadRegistered - 1][0] = uv[0];
xy2[noBeadRegistered - 1][1] = uv[1];
} else if (imageIndex != 0
&& imageIndex != config.getGeometry().getNumProjectionMatrices() - 1) {
if (beadPosition2D[imageIndex - 1][i][2] == 1
&& beadPosition2D[imageIndex + 1][i][2] == 1) {
noBeadRegistered++;
double xMean =
(beadPosition2D[imageIndex - 1][i][0] + beadPosition2D[imageIndex - 1][i][0]) / 2;
double yMean =
(beadPosition2D[imageIndex + 1][i][1] + beadPosition2D[imageIndex + 1][i][1]) / 2;
if (isDisplay) {
imp1.setValue(2);
imp1.drawLine(
(int) Math.round(xMean - 10),
(int) Math.round(yMean - 10),
(int) Math.round(xMean + 10),
(int) Math.round(yMean + 10));
imp1.drawLine(
(int) Math.round(xMean - 10),
(int) Math.round(yMean + 10),
(int) Math.round(xMean + 10),
(int) Math.round(yMean - 10));
imp1.drawString("Bead " + i + " (state:" + "M)", (int) xMean, (int) yMean - 10);
}
xy1[noBeadRegistered - 1][0] = xMean;
xy1[noBeadRegistered - 1][1] = yMean;
xy2[noBeadRegistered - 1][0] = uv[0];
xy2[noBeadRegistered - 1][1] = uv[1];
}
}
// mean projected bead
// imp1.drawLine((int) Math.round(uv[0]-10), (int) Math.round(uv[1]), (int)
// Math.round(uv[0]+10), (int) Math.round(uv[1]));
// imp1.drawLine((int) Math.round(uv[0]), (int) Math.round(uv[1]-10), (int)
// Math.round(uv[0]), (int) Math.round(uv[1]+10));
}
}
if (isDisplay) {
for (int x = 0; x < config.getGeometry().getDetectorWidth(); x += 50)
imp1.drawLine(x, 0, x, config.getGeometry().getDetectorHeight());
for (int y = 0; y < config.getGeometry().getDetectorHeight(); y += 50)
imp1.drawLine(0, y, config.getGeometry().getDetectorWidth(), y);
}
if (isCornerIncluded) {
xy1[noBeadRegistered + 0][0] = 0;
xy1[noBeadRegistered + 0][1] = 0;
xy2[noBeadRegistered + 0][0] = 0;
xy2[noBeadRegistered + 0][1] = 0;
xy1[noBeadRegistered + 1][0] = 0;
xy1[noBeadRegistered + 1][1] = config.getGeometry().getDetectorHeight();
xy2[noBeadRegistered + 1][0] = 0;
xy2[noBeadRegistered + 1][1] = config.getGeometry().getDetectorHeight();
xy1[noBeadRegistered + 2][0] = config.getGeometry().getDetectorWidth();
xy1[noBeadRegistered + 2][1] = 0;
xy2[noBeadRegistered + 2][0] = config.getGeometry().getDetectorWidth();
xy2[noBeadRegistered + 2][1] = 0;
xy1[noBeadRegistered + 3][0] = config.getGeometry().getDetectorWidth();
xy1[noBeadRegistered + 3][1] = config.getGeometry().getDetectorHeight();
xy2[noBeadRegistered + 3][0] = config.getGeometry().getDetectorWidth();
xy2[noBeadRegistered + 3][1] = config.getGeometry().getDetectorHeight();
noBeadRegistered = noBeadRegistered + 4;
}
boolean fScaling = true;
double minX = Double.MAX_VALUE;
double maxX = 0;
double minY = Double.MAX_VALUE;
double maxY = 0;
double c = 0;
if (fScaling) { // ----- scaling to reduce condition # of A matrix
for (int i = 0; i < noBeadRegistered; i++) {
minX = Math.min(minX, xy1[i][0]);
maxX = Math.max(maxX, xy1[i][0]);
minY = Math.min(minY, xy1[i][1]);
maxY = Math.max(maxY, xy1[i][1]);
}
c = Math.max(maxX - minX, maxY - minY);
for (int i = 0; i < noBeadRegistered; i++) {
xy1_hat[i][0] = (xy1[i][0] - minX) / c;
xy1_hat[i][1] = (xy1[i][1] - minY) / c;
xy2_hat[i][0] = (xy2[i][0] - minX) / c;
xy2_hat[i][1] = (xy2[i][1] - minY) / c;
}
} else {
xy1_hat = xy1;
xy2_hat = xy2;
}
ImageProcessor imp2 = imp1.duplicate(); // warped
/*
* A*x = b
* Matrix A = (n + 3) * (n + 3);
* n (noBeadRegistered + 4): # of control points + 4 corner points (assume corner points are static)
*/
int n = noBeadRegistered + 3;
SimpleMatrix A = new SimpleMatrix(n, n);
SimpleVector x_x = new SimpleVector(n);
SimpleVector x_y = new SimpleVector(n);
SimpleVector b_x = new SimpleVector(n);
SimpleVector b_y = new SimpleVector(n);
double rij = 0;
double valA = 0;
double valb_x = 0;
double valb_y = 0;
// Matrix L formation
// alpha: mean of distances between control points' xy-projections) is a constant only present
// on the diagonal of K
// lambda: TPS smoothing regularization coefficient
double alpha = 0.0;
double lambda = 1.6; // 1.6
for (int i = 0; i < noBeadRegistered; i++) { // i= # of row
for (int j = i; j < noBeadRegistered; j++) { // j= # of column
alpha +=
Math.sqrt(
Math.pow(xy2_hat[i][0] - xy2_hat[j][0], 2)
+ Math.pow(xy2_hat[i][1] - xy2_hat[j][1], 2));
}
}
alpha = alpha / Math.pow(noBeadRegistered, 2);
for (int i = 0; i < n; i++) { // i= # of row
for (int j = i; j < n; j++) { // j= # of column
if (i < 3 && j < 3) valA = 0;
else if (i >= 3 && j >= 3 && i == j) {
valA = Math.pow(alpha, 2) * lambda;
// valA = lambda;
if (imageIndex < 10)
System.out.println("Regularization = " + valA + ", lambda= " + lambda);
} else if (i == 0 && j >= 0) valA = 1;
else if (i == 1 && j >= 3) valA = xy1_hat[j - 3][0];
else if (i == 2 && j >= 3) valA = xy1_hat[j - 3][1];
else {
rij =
Math.pow(xy1_hat[j - 3][0] - xy1_hat[i - 3][0], 2)
+ Math.pow(xy1_hat[j - 3][1] - xy1_hat[i - 3][1], 2);
if (rij == 0) valA = 0;
else valA = rij * Math.log(rij);
}
A.setElementValue(i, j, valA);
A.setElementValue(j, i, valA);
}
if (i < 3) {
valb_x = 0;
valb_y = 0;
} else {
// valb_x = xy2_hat[i-3][0]-xy1_hat[i-3][0];
// valb_y = xy2_hat[i-3][1]-xy1_hat[i-3][1];
valb_x = xy2[i - 3][0] - xy1[i - 3][0];
valb_y = xy2[i - 3][1] - xy1[i - 3][1];
// if (imageIndex > 150 && imageIndex < 170)
// System.out.println("Idx" + imageIndex + ",Elevation" + (i-3) + ": " + valb_x + "---"
// + valb_y);
}
b_x.setElementValue(i, valb_x);
b_y.setElementValue(i, valb_y);
}
// System.out.println("A condition number=" + A.conditionNumber(MatrixNormType.MAT_NORM_L1));
// System.out.println("A condition number=" + A.conditionNumber(MatrixNormType.MAT_NORM_LINF));
x_x = Solvers.solveLinearSysytemOfEquations(A, b_x);
x_y = Solvers.solveLinearSysytemOfEquations(A, b_y);
if (fScaling) {
// ----- pixel space coefficients a, b scaling back
double tmpA0 =
x_x.getElement(0) - x_x.getElement(1) * (minX / c) - x_x.getElement(2) * (minY / c);
for (int j = 0; j < noBeadRegistered; j++) {
tmpA0 -=
Math.log(c)
* 2
* x_x.getElement(j + 3)
* (Math.pow(xy1_hat[j][0], 2) + Math.pow(xy1_hat[j][1], 2));
}
x_x.setElementValue(0, tmpA0);
tmpA0 = x_y.getElement(0) - x_y.getElement(1) * (minX / c) - x_y.getElement(2) * (minY / c);
for (int j = 0; j < noBeadRegistered; j++) {
tmpA0 -=
Math.log(c)
* 2
* x_y.getElement(j + 3)
* (Math.pow(xy1_hat[j][0], 2) + Math.pow(xy1_hat[j][1], 2));
}
x_y.setElementValue(0, tmpA0);
x_x.setElementValue(1, x_x.getElement(1) / c);
x_y.setElementValue(1, x_y.getElement(1) / c);
x_x.setElementValue(2, x_x.getElement(2) / c);
x_y.setElementValue(2, x_y.getElement(2) / c);
for (int i = 3; i < n; i++) {
x_x.setElementValue(i, x_x.getElement(i) / Math.pow(c, 2));
x_y.setElementValue(i, x_y.getElement(i) / Math.pow(c, 2));
}
// ----- pixel space coefficients a, b scaling back end
}
double devU = 0;
double devV = 0;
// Do warping
// if (imageIndex == 0) {
for (int y = 0; y < config.getGeometry().getDetectorHeight(); y++) {
// for (int y=252; y<253; y++) {
for (int x = 0; x < config.getGeometry().getDetectorWidth(); x++) {
// for (int x=606; x<607; x++) {
devU = x_x.getElement(0) + x_x.getElement(1) * x + x_x.getElement(2) * y;
devV = x_y.getElement(0) + x_y.getElement(1) * x + x_y.getElement(2) * y;
for (int i = 0; i < noBeadRegistered; i++) {
rij = Math.pow(xy1[i][0] - x, 2) + Math.pow(xy1[i][1] - y, 2);
if (rij > 0) {
devU += x_x.getElement(i + 3) * rij * Math.log(rij);
devV += x_y.getElement(i + 3) * rij * Math.log(rij);
}
}
// devU = 0;
// devV = 0;
imp2.setf(x, y, (float) imp1.getInterpolatedValue(x - devU, y - devV));
// System.out.println("x, y=" + x + ", " + y + "\t" + devU + ", " + devV);
// maxDevU = Math.max(maxDevU, devU);
// maxDevV = Math.max(maxDevV, devV);
}
}
// Error estimate after transformation
// for (int i=0; i<= WeightBearingBeadPositionBuilder.currentBeadNo; i++){
//
// if (beadMean3D[i][0] != 0 || beadMean3D[i][1] != 0 || beadMean3D[i][2] != 0){ // assume
// bead 3d is registered.
//
// // find bead location if registered by txt: state 1
// if (beadPosition2D[imageIndex][i][2] == 1){
//
// // Projected Reference
// uv = compute2DCoordinates(beadMean3D[i], pMatrix);
// double x = uv[0];
// double y = uv[1];
// // bead detected position in 2d
// // Transform to 2D coordinates, time variant position
// //beadPosition2D[imageIndex][i][0];
// //beadPosition2D[imageIndex][i][1];
//
// devU = x_x.getElement(0) + x_x.getElement(1)*x + x_x.getElement(2)*y;
// devV = x_y.getElement(0) + x_y.getElement(1)*x + x_y.getElement(2)*y;
// for (int j=0; j<noBeadRegistered; j++){
// rij = Math.pow(xy1[j][0]-x, 2) + Math.pow(xy1[j][1]-y, 2);
// if (rij > 0) {
// devU += x_x.getElement(j+3)*rij*Math.log(rij);
// devV += x_y.getElement(j+3)*rij*Math.log(rij);
// }
// }
//
// distanceReferenceToCurrentBead +=
// Math.sqrt(Math.pow(uv[0]-(beadPosition2D[imageIndex][i][0]+devU),
// 2)+Math.pow(uv[1]-(beadPosition2D[imageIndex][i][1]+devV), 2));
//
// }
// }
// }
// System.out.println("Euclidean distance\t" + imageIndex + "\t" +
// distanceReferenceToCurrentBead/noBeadRegistered);
// }
if (isDisplay) {
for (int i = WeightBearingBeadPositionBuilder.currentBeadNo; i >= 0; i--) {
if (beadMean3D[i][0] != 0
|| beadMean3D[i][1] != 0
|| beadMean3D[i][2] != 0) { // assume bead 3d is registered.
uv = compute2DCoordinates(beadMean3D[i], pMatrix);
imp2.setValue(2);
// mean projected bead
imp2.drawLine(
(int) Math.round(uv[0] - 10),
(int) Math.round(uv[1]),
(int) Math.round(uv[0] + 10),
(int) Math.round(uv[1]));
imp2.drawLine(
(int) Math.round(uv[0]),
(int) Math.round(uv[1] - 10),
(int) Math.round(uv[0]),
(int) Math.round(uv[1] + 10));
}
}
}
Grid2D result = new Grid2D((float[]) imp2.getPixels(), imp2.getWidth(), imp2.getHeight());
return result;
}
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;
}
void drawRoiFilledParticle(ImageProcessor ip, Roi roi, ImageProcessor mask, int count) {
int grayLevel = (count < 65535) ? count : 65535;
ip.setValue((double) grayLevel);
ip.setRoi(roi.getBounds());
ip.fill(mask);
}
void analyzeParticle(int x, int y, ImagePlus imp, ImageProcessor ip) {
// Wand wand = new Wand(ip);
ImageProcessor ip2 = redirectIP != null ? redirectIP : ip;
wand.autoOutline(x, y, level1, level2, wandMode);
if (wand.npoints == 0) {
IJ.log("wand error: " + x + " " + y);
return;
}
Roi roi = new PolygonRoi(wand.xpoints, wand.ypoints, wand.npoints, roiType);
Rectangle r = roi.getBounds();
if (r.width > 1 && r.height > 1) {
PolygonRoi proi = (PolygonRoi) roi;
pf.setPolygon(proi.getXCoordinates(), proi.getYCoordinates(), proi.getNCoordinates());
ip2.setMask(pf.getMask(r.width, r.height));
if (floodFill) ff.particleAnalyzerFill(x, y, level1, level2, ip2.getMask(), r);
}
ip2.setRoi(r);
ip.setValue(fillColor);
ImageStatistics stats = getStatistics(ip2, measurements, calibration);
boolean include = true;
if (excludeEdgeParticles) {
if (r.x == minX || r.y == minY || r.x + r.width == maxX || r.y + r.height == maxY)
include = false;
if (polygon != null) {
Rectangle bounds = roi.getBounds();
int x1 = bounds.x + wand.xpoints[wand.npoints - 1];
int y1 = bounds.y + wand.ypoints[wand.npoints - 1];
int x2, y2;
for (int i = 0; i < wand.npoints; i++) {
x2 = bounds.x + wand.xpoints[i];
y2 = bounds.y + wand.ypoints[i];
if (!polygon.contains(x2, y2)) {
include = false;
break;
}
if ((x1 == x2 && ip.getPixel(x1, y1 - 1) == fillColor)
|| (y1 == y2 && ip.getPixel(x1 - 1, y1) == fillColor)) {
include = false;
break;
}
x1 = x2;
y1 = y2;
}
}
}
ImageProcessor mask = ip2.getMask();
if (minCircularity > 0.0 || maxCircularity < 1.0) {
double perimeter = roi.getLength();
double circularity =
perimeter == 0.0 ? 0.0 : 4.0 * Math.PI * (stats.pixelCount / (perimeter * perimeter));
if (circularity > 1.0) circularity = 1.0;
// IJ.log(circularity+" "+perimeter+" "+stats.area);
if (circularity < minCircularity || circularity > maxCircularity) include = false;
}
if (stats.pixelCount >= minSize && stats.pixelCount <= maxSize && include) {
particleCount++;
if (roiNeedsImage) roi.setImage(imp);
stats.xstart = x;
stats.ystart = y;
saveResults(stats, roi);
if (showChoice != NOTHING) drawParticle(drawIP, roi, stats, mask);
}
if (redirectIP != null) ip.setRoi(r);
ip.fill(mask);
}
/**
* 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;
}
