/**
* Assigns the data values of a {@link Dataset} from a paired {@link ImagePlus}. Assumes the
* Dataset and ImagePlus have compatible dimensions and that the data planes are not directly
* mapped. Gets values via {@link ImageProcessor}::getf(). In cases where there is a narrowing of
* data into modern ImageJ types the data is range clamped. Does not change the Dataset's
* metadata.
*/
@Override
public void updateDataset(final Dataset ds, final ImagePlus imp) {
final RealType<?> type = ds.getType();
final double typeMin = type.getMinValue();
final double typeMax = type.getMaxValue();
final boolean signed16BitData = type instanceof ShortType;
final RandomAccess<? extends RealType<?>> accessor = ds.getImgPlus().randomAccess();
final long[] dims = ds.getDims();
final AxisType[] axes = ds.getAxes();
final int xIndex = ds.getAxisIndex(Axes.X);
final int yIndex = ds.getAxisIndex(Axes.Y);
final int zIndex = ds.getAxisIndex(Axes.Z);
final int tIndex = ds.getAxisIndex(Axes.TIME);
final int xSize = imp.getWidth();
final int ySize = imp.getHeight();
final int zSize = imp.getNSlices();
final int tSize = imp.getNFrames();
final int cSize = imp.getNChannels();
final ImageStack stack = imp.getStack();
int planeNum = 1;
final long[] pos = new long[dims.length];
for (int t = 0; t < tSize; t++) {
if (tIndex >= 0) pos[tIndex] = t;
for (int z = 0; z < zSize; z++) {
if (zIndex >= 0) pos[zIndex] = z;
for (int c = 0; c < cSize; c++) {
LegacyUtils.fillChannelIndices(dims, axes, c, pos);
final ImageProcessor proc = stack.getProcessor(planeNum++);
for (int x = 0; x < xSize; x++) {
if (xIndex >= 0) pos[xIndex] = x;
for (int y = 0; y < ySize; y++) {
if (yIndex >= 0) pos[yIndex] = y;
accessor.setPosition(pos);
double value = proc.getf(x, y);
if (signed16BitData) value -= 32768.0;
if (value < typeMin) value = typeMin;
else if (value > typeMax) value = typeMax;
accessor.get().setReal(value);
}
}
}
}
}
ds.update();
}
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
* ------------------------------------------------------------------*
*/
}
/**
* @param ip
* @param v
*/
public static void run(final ImageProcessor ip, final float v) {
final int w = ip.getWidth();
final int h = ip.getHeight();
final int wh = w * h;
final ImageProcessor ipTarget = ip.duplicate();
int numSaturatedPixels = 0;
do {
numSaturatedPixels = 0;
for (int i = 0; i < wh; ++i)
if (ip.getf(i) == v) {
++numSaturatedPixels;
final int y = i / w;
final int x = i % w;
float s = 0;
float n = 0;
if (y > 0) {
if (x > 0) {
final float tl = ip.getf(x - 1, y - 1);
if (tl != v) {
s += 0.5f * tl;
n += 0.5f;
}
}
final float t = ip.getf(x, y - 1);
if (t != v) {
s += t;
n += 1;
}
if (x < w - 1) {
final float tr = ip.getf(x + 1, y - 1);
if (tr != v) {
s += 0.5f * tr;
n += 0.5f;
}
}
}
if (x > 0) {
final float l = ip.getf(x - 1, y);
if (l != v) {
s += l;
n += 1;
}
}
if (x < w - 1) {
final float r = ip.getf(x + 1, y);
if (r != v) {
s += r;
n += 1;
}
}
if (y < h - 1) {
if (x > 0) {
final float bl = ip.getf(x - 1, y + 1);
if (bl != v) {
s += 0.5f * bl;
n += 0.5f;
}
}
final float b = ip.getf(x, y + 1);
if (b != v) {
s += b;
n += 1;
}
if (x < w - 1) {
final float br = ip.getf(x + 1, y + 1);
if (br != v) {
s += 0.5f * br;
n += 0.5f;
}
}
}
if (n > 0) ipTarget.setf(i, s / n);
}
ip.setPixels(ipTarget.getPixelsCopy());
} while (numSaturatedPixels > 0);
}