Optimized box blur implementation #1974
Merged
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
A box blur is a separable filter, so I used OpenCV's
sepFilter2D
to implement box blur. However, I noticed that the DFT implementation offilter2D
eventually catches up tosepFilter2D
and ends up being faster for large radii. So I only usesepFilter2D
for radii below 70. That's the point of which both are about equal performance-wise.The speed-up is especially noticeable for small radii. E.g. a radius of 10 on a 2k RGB image takes around 0.08 sec with
sepFilter2D
and around 0.27 sec withfilter2D
.Also, just to clarify: this is a pure optimization. The outputs of
filter2D
andsepFilter2D
are the same.