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Image Processing: Gaussian Blurring
14depperm
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I had a lot of fun with this one :)
The actual and expected values in the random tests should not be stringified.
There should be random tests with small inputs for easier debugging.
I don't understand the examples you give. Can you explain?
If you look at the example I try to show what I mean. Say you have an image:
G
andL
are the only pixels with 8 neighbors present but each pixel still needs 8 for the filter to work.A
only has 3, so the other 5 have to come from somewhere else; for this algorithm you should use the edges/corners own values. So withA
the other neighbors in relation to the whole image (besidesB
,F
, andG
) will look like:Hi again,
You may want to change the description, saying clearly that if the kernel grid will always be 3x3, this is not the case for the image.
This is not really problematic, but at the beginning I assumed it would be the case for the image too and I had to change the way I constructed the loops (not really funny to have to go "back and forward" in the algorithm).
Second point : the output of the assert tests is a bit confusing when the code fails : it does not apply the string formating (the line feeds are displayed as
\n
).If you want to display correctly the output, you could "double check" the tests :
print
itassert
testWhat do you think ?
Nice one ! It was funny to implement, thanks for the challenge.
There seems to be a disparity between the example test and the random test when running the submission test suite. One seems to expect int values returned and the other expects float values to be returned. It is currently not possible to produce output that passes both tests.
They both expect int values. Why do you think one or the other expects float values?
On my side, no troubles with the tests.
Sorry, didn't see you had replied.
I think floats are involved because of this output from the first 'hidden' test:
The test failure is explicitly telling me to return floats...