|
LSST Applications g00d0e8bbd7+8c5ae1fdc5,g013ef56533+603670b062,g083dd6704c+2e189452a7,g199a45376c+0ba108daf9,g1c5cce2383+bc9f6103a4,g1fd858c14a+cd69ed4fc1,g210f2d0738+c4742f2e9e,g262e1987ae+612fa42d85,g29ae962dfc+83d129e820,g2cef7863aa+aef1011c0b,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+5eaa884f2a,g47891489e3+e32160a944,g53246c7159+8c5ae1fdc5,g5b326b94bb+dcc56af22d,g64539dfbff+c4742f2e9e,g67b6fd64d1+e32160a944,g74acd417e5+c122e1277d,g786e29fd12+668abc6043,g87389fa792+8856018cbb,g88cb488625+47d24e4084,g89139ef638+e32160a944,g8d7436a09f+d14b4ff40a,g8ea07a8fe4+b212507b11,g90f42f885a+e1755607f3,g97be763408+34be90ab8c,g98df359435+ec1fa61bf1,ga2180abaac+8c5ae1fdc5,ga9e74d7ce9+43ac651df0,gbf99507273+8c5ae1fdc5,gc2a301910b+c4742f2e9e,gca7fc764a6+e32160a944,gd7ef33dd92+e32160a944,gdab6d2f7ff+c122e1277d,gdb1e2cdc75+1b18322db8,ge410e46f29+e32160a944,ge41e95a9f2+c4742f2e9e,geaed405ab2+0d91c11c6d,w.2025.44
LSST Data Management Base Package
|
(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():