LSST Applications 29.0.1,g0fba68d861+132dd21e0a,g107a963962+1bb9f809a9,g1fd858c14a+005be21cae,g21d47ad084+8a07b29876,g325378336f+5d73323c8f,g330003fc43+40b4eaffc6,g35bb328faa+fcb1d3bbc8,g36ff55ed5b+9c28a42a87,g4e0f332c67+5fbd1e3e73,g53246c7159+fcb1d3bbc8,g60b5630c4e+9c28a42a87,g67b6fd64d1+a38b34ea13,g78460c75b0+2f9a1b4bcd,g786e29fd12+cf7ec2a62a,g7b71ed6315+fcb1d3bbc8,g86c591e316+6b2b2d0295,g8852436030+bf14db0e33,g89139ef638+a38b34ea13,g8b8da53e10+e3777245af,g9125e01d80+fcb1d3bbc8,g989de1cb63+a38b34ea13,g9f1445be69+9c28a42a87,g9f33ca652e+52c8f07962,ga9baa6287d+9c28a42a87,ga9e4eb89a6+9f84bd6575,gabe3b4be73+1e0a283bba,gb037a4e798+f3cbcd26c0,gb1101e3267+e7be8da0f8,gb58c049af0+f03b321e39,gb89ab40317+a38b34ea13,gcf25f946ba+bf14db0e33,gd6cbbdb0b4+bce7f7457e,gd9a9a58781+fcb1d3bbc8,gde0f65d7ad+53d424b1ae,ge278dab8ac+222406d50a,ge410e46f29+a38b34ea13,ge80e9994a3+664d6357dc,gf67bdafdda+a38b34ea13
LSST Data Management Base Package
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(Return to Images)
(You might be interested to compare this example with the discussion of Image locators ; apart from an include file and a typedef, the only difference is the use of ImageT::Pixel(y, 0x1, 10)
as the assigned pixel value instead of y
).
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
kernel (the code's in maskedImage2.cc):
Start by including MaskedImage.h, defining a namespace for clarity:
Declare a MaskedImage
Set the image (but not the mask or variance) to a ramp
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:
(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:
(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)
The xy_loc::cached_location_t
variables remember relative positions.
We can rewrite this to move setting nw
, se
etc. out of the loop:
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/=
N.b. you can use the iterator embedded in the locator directly if you really want to, e.g.
Note that this isn't quite the same x_iterator
as before, due to the need to make the x_iterator
move the underlying xy_locator
.
Finally write some output files and close out main()
: