Memory efficient MWE for algorithm involving image IO (TIME INEFFICIENT)

mwe-fd-memory-efficient.jl

using Images
using BenchmarkTools

import Base: size, getindex, LinearIndices
using Images: Images, coords_spatial

struct IntegralArray{T, N, A} <: AbstractArray{T, N}
	data::A
end

function to_integral_image(img_arr::AbstractArray)
	array_size = size(img_arr)
    integral_image_arr = Array{Images.accum(eltype(img_arr))}(undef, array_size)
    sd = coords_spatial(img_arr)
    cumsum!(integral_image_arr, img_arr; dims=sd[1])#length(array_size)
    for i = 2:length(sd)
        cumsum!(integral_image_arr, integral_image_arr; dims=sd[i])
    end
    return Array{eltype(img_arr), ndims(img_arr)}(integral_image_arr)
end

LinearIndices(A::IntegralArray) = Base.LinearFast()
size(A::IntegralArray) = size(A.data)
getindex(A::IntegralArray, i::Int...) = A.data[i...]
getindex(A::IntegralArray, ids::Tuple...) = getindex(A, ids[1]...)

function filtered_ls(path::AbstractString)::Array{String, 1}
    return filter!(f -> ! occursin(r".*\.DS_Store", f), readdir(path, join=true, sort=false))
end

function load_image(
    image_path::AbstractString
    )::Array{Float64, 2}
    img = load(image_path)
    img = convert(Array{Float64}, Gray.(img))
    return to_integral_image(img)
end

function get_vote(f::Number, i::AbstractArray)
    return f .* rand()
end

function learn(positive_path::AbstractString,negative_path::AbstractString)
    positive_files = filtered_ls(positive_path)
    negative_files = filtered_ls(negative_path)
    num_pos = length(positive_files)
    num_neg = length(negative_files)
    num_imgs = num_pos + num_neg
    image_files = vcat(positive_files, negative_files)
	votes = zeros((num_imgs, 3000))
    num_processed = 0
    for image_file in image_files
        ii_img = load_image(image_file)
		num_processed += 1
        # votes[num_processed, :] = rand(3000)
		votes[num_processed, :] = Array(map(f -> get_vote(f, ii_img), 1:3000))
    end # end loop through images
end

@btime learn(joinpath(dirname(@__DIR__), "data", "main", "trainset", "faces"), joinpath(dirname(@__DIR__), "data", "main", "trainset", "non-faces"))