https://github.com/jw3126/meanfilters.jl

Fast and simple sliding window mean filter.
https://github.com/jw3126/meanfilters.jl

image-processing julia moving-mean

Last synced: 3 months ago
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Fast and simple sliding window mean filter.

• Host: GitHub
• URL: https://github.com/jw3126/meanfilters.jl
• Owner: jw3126
• License: mit
• Created: 2021-09-10T13:58:29.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2023-04-29T18:31:02.000Z (about 2 years ago)
• Last Synced: 2025-03-11T12:46:41.346Z (4 months ago)
• Topics: image-processing, julia, moving-mean
• Language: Julia
• Homepage:
• Size: 102 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# MeanFilters

[![Build Status](https://github.com/jw3126/MeanFilters.jl/workflows/CI/badge.svg)](https://github.com/jw3126/MeanFilters.jl/actions)

[![Coverage](https://codecov.io/gh/jw3126/MeanFilters.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/jw3126/MeanFilters.jl)

The goal of this package is to solve one very narrow problem: compute the mean over a sliding window:

```julia

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  5.5

```

In most cases you likely want to use [ImageFiltering.jl](https://github.com/JuliaImages/ImageFiltering.jl) instead. Advantages of this package over [ImageFiltering.jl](https://github.com/JuliaImages/ImageFiltering.jl) are:

* Tiny dependency

* Specialized algorithm for mean, which has decent performance for large windows:

```julia

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)

```