LSST Applications  21.0.0+04719a4bac,21.0.0-1-ga51b5d4+f5e6047307,21.0.0-11-g2b59f77+a9c1acf22d,21.0.0-11-ga42c5b2+86977b0b17,21.0.0-12-gf4ce030+76814010d2,21.0.0-13-g1721dae+760e7a6536,21.0.0-13-g3a573fe+768d78a30a,21.0.0-15-g5a7caf0+f21cbc5713,21.0.0-16-g0fb55c1+b60e2d390c,21.0.0-19-g4cded4ca+71a93a33c0,21.0.0-2-g103fe59+bb20972958,21.0.0-2-g45278ab+04719a4bac,21.0.0-2-g5242d73+3ad5d60fb1,21.0.0-2-g7f82c8f+8babb168e8,21.0.0-2-g8f08a60+06509c8b61,21.0.0-2-g8faa9b5+616205b9df,21.0.0-2-ga326454+8babb168e8,21.0.0-2-gde069b7+5e4aea9c2f,21.0.0-2-gecfae73+1d3a86e577,21.0.0-2-gfc62afb+3ad5d60fb1,21.0.0-25-g1d57be3cd+e73869a214,21.0.0-3-g357aad2+ed88757d29,21.0.0-3-g4a4ce7f+3ad5d60fb1,21.0.0-3-g4be5c26+3ad5d60fb1,21.0.0-3-g65f322c+e0b24896a3,21.0.0-3-g7d9da8d+616205b9df,21.0.0-3-ge02ed75+a9c1acf22d,21.0.0-4-g591bb35+a9c1acf22d,21.0.0-4-g65b4814+b60e2d390c,21.0.0-4-gccdca77+0de219a2bc,21.0.0-4-ge8a399c+6c55c39e83,21.0.0-5-gd00fb1e+05fce91b99,21.0.0-6-gc675373+3ad5d60fb1,21.0.0-64-g1122c245+4fb2b8f86e,21.0.0-7-g04766d7+cd19d05db2,21.0.0-7-gdf92d54+04719a4bac,21.0.0-8-g5674e7b+d1bd76f71f,master-gac4afde19b+a9c1acf22d,w.2021.13
LSST Data Management Base Package
Image Locators

(Return to Images)

Iterators provide access to an image, pixel by pixel. You often want access to neighbouring pixels (e.g. computing a gradient, or smoothing). Let's consider the problem of smoothing with a

1 2 1
2 4 2
1 2 1

kernel (the code's in image2.cc):

Start by including Image.h defining a namespace for clarity:

#include "lsst/geom.h"
namespace image = lsst::afw::image;
typedef image::Image<int> ImageT;
int main() {
afw::table::Key< afw::table::Array< ImagePixelT > > image
A class to represent a 2-dimensional array of pixels.
Definition: Image.h:58
Backwards-compatibility support for depersisting the old Calib (FluxMag0/FluxMag0Err) objects.

Declare an Image

Set the image to a ramp

for (int y = 0; y != in.getHeight(); ++y) {
for (ImageT::xy_locator ptr = in.xy_at(0, y), end = in.xy_at(in.getWidth(), y); ptr != end;
++ptr.x()) {
*ptr = y;
}
int end
uint64_t * ptr
Definition: RangeSet.cc:88
int y
Definition: SpanSet.cc:49
}

That didn't gain us much, did it? The code's a little messier than using x_iterator. But now we can add code to calculate the smoothed image. First make an output image, and copy the input pixels:

//
// Convolve with a pseudo-Gaussian kernel ((1, 2, 1), (2, 4, 2), (1, 2, 1))
//
ImageT out(in.getDimensions()); // Make an output image the same size as the input image
out.assign(in);

(we didn't need to copy all of them, just the ones around the edge that we won't smooth, but this is an easy way to do it).

Now do the smoothing:

for (int y = 1; y != in.getHeight() - 1; ++y) {
for (ImageT::xy_locator ptr = in.xy_at(1, y), end = in.xy_at(in.getWidth() - 1, y),
optr = out.xy_at(1, y);
ptr != end; ++ptr.x(), ++optr.x()) {
*optr = ptr(-1, -1) + 2 * ptr(0, -1) + ptr(1, -1) + 2 * ptr(-1, 0) + 4 * ptr(0, 0) +
2 * ptr(1, 0) + ptr(-1, 1) + 2 * ptr(0, 1) + ptr(1, 1);
}
}

(N.b. you don't really want to do this; not only is this kernel separable into 1 2 1 in first the x then the y directions, but lsst::afw::math can do convolutions for you).

Here's a faster way to do the same thing (the use of an Image::Ptr is just for variety)

//
// Do the same thing a faster way, using cached_location_t
//
std::shared_ptr<ImageT> out2(new ImageT(in.getDimensions()));
out2->assign(in);
typedef ImageT::const_xy_locator xy_loc;
for (int y = 1; y != in.getHeight() - 1; ++y) {
// "dot" means "cursor location" in emacs
xy_loc dot = in.xy_at(1, y), end = in.xy_at(in.getWidth() - 1, y);
xy_loc::cached_location_t nw = dot.cache_location(-1, -1);
xy_loc::cached_location_t n = dot.cache_location(0, -1);
xy_loc::cached_location_t ne = dot.cache_location(1, -1);
xy_loc::cached_location_t w = dot.cache_location(-1, 0);
xy_loc::cached_location_t c = dot.cache_location(0, 0);
xy_loc::cached_location_t e = dot.cache_location(1, 0);
xy_loc::cached_location_t sw = dot.cache_location(-1, 1);
xy_loc::cached_location_t s = dot.cache_location(0, 1);
xy_loc::cached_location_t se = dot.cache_location(1, 1);
for (ImageT::x_iterator optr = out2->row_begin(y) + 1; dot != end; ++dot.x(), ++optr) {
*optr = dot[nw] + 2 * dot[n] + dot[ne] + 2 * dot[w] + 4 * dot[c] + 2 * dot[e] + dot[sw] +
2 * dot[s] + dot[se];
}
def dot(symb, c, r, frame=None, size=2, ctype=None, origin=afwImage.PARENT, *args, **kwargs)
Definition: ds9.py:100
double w
Definition: CoaddPsf.cc:69
}

The xy_loc::cached_location_t variables remember relative positions.

We can rewrite this to move setting nw, se etc. out of the loop:

//
// Do the same calculation, but set nw etc. outside the loop
//
xy_loc pix11 = in.xy_at(1, 1);
xy_loc::cached_location_t nw = pix11.cache_location(-1, -1);
xy_loc::cached_location_t n = pix11.cache_location(0, -1);
xy_loc::cached_location_t ne = pix11.cache_location(1, -1);
xy_loc::cached_location_t w = pix11.cache_location(-1, 0);
xy_loc::cached_location_t c = pix11.cache_location(0, 0);
xy_loc::cached_location_t e = pix11.cache_location(1, 0);
xy_loc::cached_location_t sw = pix11.cache_location(-1, 1);
xy_loc::cached_location_t s = pix11.cache_location(0, 1);
xy_loc::cached_location_t se = pix11.cache_location(1, 1);
for (int y = 1; y != in.getHeight() - 1; ++y) {
// "dot" means "cursor location" in emacs
xy_loc dot = in.xy_at(1, y), end = in.xy_at(in.getWidth() - 1, y);
for (ImageT::x_iterator optr = out2->row_begin(y) + 1; dot != end; ++dot.x(), ++optr) {
*optr = dot[nw] + 2 * dot[n] + dot[ne] + 2 * dot[w] + 4 * dot[c] + 2 * dot[e] + dot[sw] +
2 * dot[s] + dot[se];
}
}

You may have noticed that that kernel isn't normalised. We could change the coefficients, but that'd slow things down for integer images (such as the one here); but we can normalise after the fact by making an Image that shares pixels with the central part of out2 and manipulating it via overloaded operator/=

//
// Normalise the kernel. I.e. divide the smoothed parts of image2 by 16
//
{
ImageT center = ImageT(
*out2,
center /= 16;
}
An integer coordinate rectangle.
Definition: Box.h:55

N.b. you can use the iterator embedded in the locator directly if you really want to, e.g.

for (int y = 0; y != in.getHeight(); ++y) {
for (ImageT::xy_x_iterator ptr = in.xy_at(0, y).x(), end = in.xy_at(in.getWidth(), y).x(); ptr != end;
++ptr) {
*ptr = 0;
}
}

we called the iterator xy_x_iterator, not x_iterator, for consistency with MaskedImage.

Finally write some output files and close out main():

//
// Save those images to disk
//
out.writeFits("foo.fits");
out2->writeFits("foo2.fits");
return 0;
}