LSST Applications 29.1.1,g0fba68d861+94d977d4f8,g1fd858c14a+0a42b1a450,g21d47ad084+bae5d1592d,g35bb328faa+fcb1d3bbc8,g36ff55ed5b+4036fd6440,g4e0f332c67+abab7ee1ee,g53246c7159+fcb1d3bbc8,g60b5630c4e+4036fd6440,g67b6fd64d1+31de10a2f7,g72a202582f+7a25662ef1,g78460c75b0+2f9a1b4bcd,g786e29fd12+cf7ec2a62a,g86c591e316+1a75853d69,g8852436030+8220ab3cb6,g88f4e072da+7005418d1d,g89139ef638+31de10a2f7,g8b8da53e10+8f7b08dc1c,g9125e01d80+fcb1d3bbc8,g989de1cb63+31de10a2f7,g9f1445be69+4036fd6440,g9f33ca652e+fcef3ba435,ga9baa6287d+4036fd6440,ga9e4eb89a6+a41a34c2ba,gabe3b4be73+1e0a283bba,gb0b61e0e8e+d456af7c26,gb1101e3267+f17a9d70ea,gb58c049af0+f03b321e39,gb89ab40317+31de10a2f7,gce29eb0867+05ed69485a,gcf25f946ba+8220ab3cb6,gd6cbbdb0b4+11317e7a17,gd9a9a58781+fcb1d3bbc8,gde0f65d7ad+b4f50ea554,ge278dab8ac+50e2446c94,ge410e46f29+31de10a2f7,ge80e9994a3+32bb9bc1c9,gf5e32f922b+fcb1d3bbc8,gf67bdafdda+31de10a2f7
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()
: