Apply reduce over a sliding window.
using ReduceWindows
x = [1,2,3,4,5]
reduce_window(+, x, (-1:1,))
# [3, 6, 9, 12, 9]
reduce_window(max, x, (-1:1,))
# [2, 3, 4, 5, 5]
reduce_window(min, x, (-3:0,))
# [1, 1, 1, 1, 2]
x = [1 2 3; 4 5 6]
reduce_window(*, x, (0:1,0:1))
# 40 180 18
# 20 30 6This package has very competitive performance, especially for large windows.
arr = randn(500,500)
window = (-50:50, -50:50)
using ImageFiltering: mapwindow
mapwindow(maximum, arr, window) # warmup
out1 = @showtime mapwindow(maximum, arr, window)
using ReduceWindows
reduce_window(max, arr, window) # warmup
out2 = @showtime reduce_window(max, arr, window)
@assert out1 == out2mapwindow(maximum, arr, window): 2.075822 seconds (1.26 M allocations: 227.561 MiB, 0.76% gc time)
reduce_window(max, arr, window): 0.002320 seconds (14 allocations: 7.630 MiB)
Naively reducing a windows of size k over an array of size n is O(k*n).
However the algorithm implemented here is O(log(k)*n) making it practical to reduce over large windows.
This packages also ships an O(n) algorithm, for extremly large windows or very costly ops.
arr = randn(500,500)
window = (-50:50, -50:50)
using ImageFiltering: mapwindow
using ReduceWindows
const OPCOUNT = Ref(0)
function mymax(x,y)
OPCOUNT[] += 1
max(x,y)
end
mapwindow(w->reduce(mymax, w), arr, window)
opcount_naive = OPCOUNT[]
OPCOUNT[] = 0
reduce_window(mymax, arr, window)
opcount_reduce_window = OPCOUNT[]
@show opcount_naive
@show opcount_reduce_windowopcount_naive = 2550010200
opcount_reduce_window = 4775000
- ImageFiltering.jl much more features than this packge, but slow for large windows.
- MeanFilters.jl fast, lightwight but very narrow usecase.
- StaticKernels.jl has very good performance for small windows.