/**
* Computes a Gaussian convolution with double precision on an entire {@link Img}
*
* @param sigma - the sigma for the convolution
* @param img - the img {@link Img}
* @param outofbounds - the {@link OutOfBoundsFactory}
* @return the convolved img having the input type
*/
@SuppressWarnings({"unchecked", "rawtypes"})
public static <T extends RealType<T>> Img<T> inDouble(
final double[] sigma,
final Img<T> img,
final OutOfBoundsFactory<DoubleType, RandomAccessibleInterval<DoubleType>> outofbounds) {
try {
if (DoubleType.class.isInstance(img.firstElement())) {
return (Img) toDouble(sigma, img, outofbounds);
}
final Img<T> output = img.factory().create(img, img.firstElement());
final RandomAccessible<DoubleType> rIn =
Views.extend(
new WriteConvertedRandomAccessibleInterval<T, DoubleType>(
img, new RealDoubleSamplerConverter<T>()),
outofbounds);
final RandomAccessible<DoubleType> rOut =
new WriteConvertedRandomAccessible<T, DoubleType>(
output, new RealDoubleSamplerConverter<T>());
inDouble(
sigma,
rIn,
img,
rOut,
new Point(sigma.length),
img.factory().imgFactory(new DoubleType()));
return output;
} catch (final IncompatibleTypeException e) {
return null;
}
}
/**
* Computes a Gaussian convolution with double precision on an entire {@link Img}
*
* @param sigma - the sigma for the convolution
* @param img - the img {@link Img}
* @param outofbounds - the {@link OutOfBoundsFactory}
* @return the convolved img in {@link DoubleType}
*/
public static <T extends RealType<T>> Img<DoubleType> toDouble(
final double[] sigma,
final Img<T> img,
final OutOfBoundsFactory<DoubleType, RandomAccessibleInterval<DoubleType>> outofbounds) {
GaussDouble gauss = null;
try {
if (DoubleType.class.isInstance(img.firstElement())) {
@SuppressWarnings({"rawtypes", "unchecked"})
final Img<DoubleType> img2 = (Img) img;
gauss = new GaussDouble(sigma, img2);
} else {
final RandomAccessibleInterval<DoubleType> rIn =
new WriteConvertedIterableRandomAccessibleInterval<T, DoubleType, Img<T>>(
img, new RealDoubleSamplerConverter<T>());
gauss =
new GaussDouble(
sigma,
Views.extend(rIn, outofbounds),
img,
img.factory().imgFactory(new DoubleType()));
}
} catch (final IncompatibleTypeException e) {
return null;
}
gauss.call();
return (Img<DoubleType>) gauss.getResult();
}
/**
* Computes a Gaussian convolution with the precision of the type provided on an entire {@link
* Img}
*
* @param sigma - the sigma for the convolution
* @param img - the img {@link Img}
* @param outofbounds - the {@link OutOfBoundsFactory}
* @return the convolved img
*/
public static <T extends NumericType<T>> Img<T> inNumericType(
final double[] sigma,
final Img<T> img,
final OutOfBoundsFactory<T, RandomAccessibleInterval<T>> outofbounds) {
final Img<T> output = img.factory().create(img, img.firstElement());
inNumericType(
sigma, Views.extend(img, outofbounds), img, output, new Point(sigma.length), img.factory());
return output;
}
/** @see LocalContrastThreshold */
@Test
public void testLocalContrastThreshold() {
ops.run(
LocalContrastThreshold.class,
out,
in,
new RectangleShape(3, false),
new OutOfBoundsMirrorFactory<ByteType, Img<ByteType>>(Boundary.SINGLE));
assertEquals(out.firstElement().get(), false);
}
/** With a default {@link OutOfBoundsStrategyValueFactory} with @param outside. */
@SuppressWarnings("unchecked")
public AbstractAffine3D(
final Img<T> img, final float[] matrix, final Mode mode, final Number outside)
throws Exception {
this(
img,
matrix,
mode,
new OutOfBoundsConstantValueFactory<T, Img<T>>(
(T)
withValue(
img, img.firstElement().createVariable(), outside))); // default value is zero
}
/** @see LocalBernsenThreshold */
@Test
public void testLocalBernsenThreshold() {
ops.run(
LocalBernsenThreshold.class,
out,
in,
new RectangleShape(3, false),
new OutOfBoundsMirrorFactory<ByteType, Img<ByteType>>(Boundary.SINGLE),
1.0,
Double.MAX_VALUE * 0.5);
assertEquals(out.firstElement().get(), true);
}
/** Makes sure input is okay and creates output image */
@Override
public boolean checkInput() {
final Img inputImage = dataset.getImgPlus(); // TODO - raw type required
// here
inputDimensions = new long[inputImage.numDimensions()];
inputImage.dimensions(inputDimensions);
final long[] outputDimensions = flipper.calcOutputDimensions(inputDimensions);
outputImage = inputImage.factory().create(outputDimensions, inputImage.firstElement());
return true;
}
public Example1c() {
// create the ImgFactory based on cells (cellsize = 5x5x5...x5) that will
// instantiate the Img
final ImgFactory<FloatType> imgFactory = new CellImgFactory<FloatType>(5);
// create an 3d-Img with dimensions 20x30x40 (here cellsize is 5x5x5)Ø
final Img<FloatType> img1 = imgFactory.create(new long[] {20, 30, 40}, new FloatType());
// create another image with the same size
// note that the input provides the size for the new image as it implements
// the Interval interface
final Img<FloatType> img2 = imgFactory.create(img1, img1.firstElement());
// display both (but they are empty)
ImageJFunctions.show(img1);
ImageJFunctions.show(img2);
}
/** With a default {@link OutOfBoundsStrategyValueFactory} with @param outside. */
@SuppressWarnings("unchecked")
public AbstractAffine3D(
final Img<T> img,
final float scaleX,
final float shearX,
final float shearY,
final float scaleY,
final float translateX,
final float translateY,
final Mode mode,
final Number outside)
throws Exception {
this(
img,
new float[] {scaleX, shearX, 0, translateX, shearY, scaleY, 0, translateY, 0, 0, 1, 0},
mode,
new OutOfBoundsConstantValueFactory<T, Img<T>>(
(T) withValue(img, img.firstElement().createVariable(), outside)));
}
@SuppressWarnings({"unchecked", "rawtypes"})
private static final <N extends NumericType<N>> Img<N> process(
final Img<N> img,
final float[] matrix,
final Mode mode,
final OutOfBoundsFactory<N, Img<N>> oobf)
throws Exception {
if (matrix.length < 12) {
throw new IllegalArgumentException(
"Affine transform in 2D requires a matrix array of 12 elements.");
}
final Type<?> type = img.firstElement().createVariable();
if (ARGBType.class.isAssignableFrom(
type.getClass())) { // type instanceof RGBALegacyType fails to compile
return (Img) processRGBA((Img) img, matrix, mode, (OutOfBoundsFactory) oobf);
} else if (type instanceof RealType<?>) {
return processReal((Img) img, matrix, mode, (OutOfBoundsFactory) oobf);
} else {
throw new Exception("Affine transform: cannot handle type " + type.getClass());
}
}
/**
* Computes a Gaussian convolution in-place (temporary imgs are necessary) with float precision on
* an entire {@link Img}
*
* @param sigma - the sigma for the convolution
* @param img - the img {@link Img} that will be convolved in place
*/
public static <T extends RealType<T>> void inFloatInPlace(
final double[] sigma,
final Img<T> img,
final OutOfBoundsFactory<FloatType, RandomAccessibleInterval<FloatType>> outofbounds) {
GaussFloat gauss = null;
try {
if (FloatType.class.isInstance(img.firstElement())) {
@SuppressWarnings({"rawtypes", "unchecked"})
final Img<FloatType> img2 = (Img) img;
gauss =
new GaussFloat(
sigma,
Views.extend(img2, outofbounds),
img2,
img2,
new Point(sigma.length),
img2.factory().imgFactory(new FloatType()));
} else {
final RandomAccessibleInterval<FloatType> rIn =
new WriteConvertedIterableRandomAccessibleInterval<T, FloatType, Img<T>>(
img, new RealFloatSamplerConverter<T>());
gauss =
new GaussFloat(
sigma,
Views.extend(rIn, outofbounds),
img,
rIn,
new Point(sigma.length),
img.factory().imgFactory(new FloatType()));
}
} catch (final IncompatibleTypeException e) {
System.out.println(e);
return;
}
gauss.call();
}
@Override
public boolean process() {
final int numDimensions = img.numDimensions();
final long integralSize[] = new long[numDimensions];
// the size of the first dimension is changed
for (int d = 0; d < numDimensions; ++d) integralSize[d] = img.dimension(d) + 1;
final Img<T> integral = imgFactory.create(integralSize, type);
// not enough RAM or disc space
if (integral == null) return false;
this.integral = integral;
if (numDimensions > 1) {
final long[] fakeSize = new long[numDimensions - 1];
// the size of dimension 0
final long size = integralSize[0];
for (int d = 1; d < numDimensions; ++d) fakeSize[d - 1] = integralSize[d];
final long imageSize = getNumPixels(fakeSize);
final AtomicInteger ai = new AtomicInteger(0);
final Thread[] threads = SimpleMultiThreading.newThreads();
final Vector<Chunk> threadChunks =
SimpleMultiThreading.divideIntoChunks(imageSize, threads.length);
for (int ithread = 0; ithread < threads.length; ++ithread)
threads[ithread] =
new Thread(
new Runnable() {
@Override
public void run() {
// Thread ID
final int myNumber = ai.getAndIncrement();
// get chunk of pixels to process
final Chunk myChunk = threadChunks.get(myNumber);
final long loopSize = myChunk.getLoopSize();
final LocalizingZeroMinIntervalIterator cursorDim =
new LocalizingZeroMinIntervalIterator(fakeSize);
// location for the input location
final long[] tmpIn = new long[numDimensions];
// location for the integral location
final long[] tmpOut = new long[numDimensions];
final long[] tmp = new long[numDimensions - 1];
final RandomAccess<R> cursorIn = img.randomAccess();
final RandomAccess<T> cursorOut = integral.randomAccess();
final T tmpVar = integral.firstElement().createVariable();
final T sum = integral.firstElement().createVariable();
cursorDim.jumpFwd(myChunk.getStartPosition());
// iterate over all dimensions except the one we are computing the integral in,
// which is dim=0 here
main:
for (long j = 0; j < loopSize; ++j) {
cursorDim.fwd();
// get all dimensions except the one we are currently doing the integral on
cursorDim.localize(tmp);
tmpIn[0] = 0;
tmpOut[0] = 1;
for (int d = 1; d < numDimensions; ++d) {
tmpIn[d] = tmp[d - 1] - 1;
tmpOut[d] = tmp[d - 1];
// all entries of position 0 are 0
if (tmpOut[d] == 0) continue main;
}
// set the cursor to the beginning of the correct line
cursorIn.setPosition(tmpIn);
// set the cursor in the integral image to the right position
cursorOut.setPosition(tmpOut);
// integrate over the line
integrateLineDim0(converter, cursorIn, cursorOut, sum, tmpVar, size);
}
}
});
SimpleMultiThreading.startAndJoin(threads);
} else {
final T tmpVar = integral.firstElement().createVariable();
final T sum = integral.firstElement().createVariable();
// the size of dimension 0
final long size = integralSize[0];
final RandomAccess<R> cursorIn = img.randomAccess();
final RandomAccess<T> cursorOut = integral.randomAccess();
cursorIn.setPosition(0, 0);
cursorOut.setPosition(1, 0);
// compute the first pixel
converter.convert(cursorIn.get(), sum);
cursorOut.get().set(sum);
for (long i = 2; i < size; ++i) {
cursorIn.fwd(0);
cursorOut.fwd(0);
converter.convert(cursorIn.get(), tmpVar);
sum.add(tmpVar);
cursorOut.get().set(sum);
}
return true;
}
for (int d = 1; d < numDimensions; ++d) {
final int dim = d;
final long[] fakeSize = new long[numDimensions - 1];
// the size of dimension d
final long size = integralSize[d];
// get all dimensions except the one we are currently doing the integral on
int countDim = 0;
for (int e = 0; e < numDimensions; ++e) if (e != d) fakeSize[countDim++] = integralSize[e];
final long imageSize = getNumPixels(fakeSize);
final AtomicInteger ai = new AtomicInteger(0);
final Thread[] threads = SimpleMultiThreading.newThreads();
final Vector<Chunk> threadChunks =
SimpleMultiThreading.divideIntoChunks(imageSize, threads.length);
for (int ithread = 0; ithread < threads.length; ++ithread)
threads[ithread] =
new Thread(
new Runnable() {
@Override
public void run() {
// Thread ID
final int myNumber = ai.getAndIncrement();
// get chunk of pixels to process
final Chunk myChunk = threadChunks.get(myNumber);
final long loopSize = myChunk.getLoopSize();
final LocalizingZeroMinIntervalIterator cursorDim =
new LocalizingZeroMinIntervalIterator(fakeSize);
// local instances
final long[] tmp2 = new long[numDimensions - 1];
final long[] tmp = new long[numDimensions];
final RandomAccess<T> cursor = integral.randomAccess();
final T sum = integral.firstElement().createVariable();
cursorDim.jumpFwd(myChunk.getStartPosition());
for (long j = 0; j < loopSize; ++j) {
cursorDim.fwd();
// get all dimensions except the one we are currently doing the integral on
cursorDim.localize(tmp2);
tmp[dim] = 1;
int countDim = 0;
for (int e = 0; e < numDimensions; ++e)
if (e != dim) tmp[e] = tmp2[countDim++];
// update the cursor in the input image to the current dimension position
cursor.setPosition(tmp);
// sum up line
integrateLine(dim, cursor, sum, size);
}
}
});
SimpleMultiThreading.startAndJoin(threads);
}
return true;
}
