The goal of this package is to solve one very narrow problem: compute the mean over a sliding window:
julia> using MeanFilters: meanfilter
julia> meanfilter([1,2,3,4], (-1:1,))
4-element Vector{Float64}:
1.5
2.0
3.0
3.5
julia> meanfilter([1 2 3; 4 5 6], (0:0,-1:1))
2×3 Matrix{Float64}:
1.5 2.0 2.5
4.5 5.0 5.5In most cases you likely want to use ImageFiltering.jl instead. Advantages of this package over ImageFiltering.jl are:
- Tiny dependency
- Specialized algorithm for mean, which has decent performance for large windows:
using ImageFiltering
using BenchmarkTools
using MeanFilters
arr = randn(1000, 1000)
window = (-50:50, -20:20)
ker = fill(1/prod(length, window), window...)
out1 = @btime imfilter(arr, ker, NA())
out2 = @btime meanfilter(arr, window)
@assert out1 ≈ out2 45.249 ms (443 allocations: 109.50 MiB)
3.074 ms (247 allocations: 15.28 MiB)