Time efficient MWE for algorithm involving image IO (MEMORY INEFFICIENT)

mwe-fd-time-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 load_images(image_dir::AbstractString)
    files = filter!(f -> ! occursin(r".*\.DS_Store", f), readdir(image_dir, join=true, sort=false))
    images = []
    for file in files
        images = push!(images, get_image_matrix(file))
    end
    return images
end

function get_image_matrix(image_file::AbstractString; scale_up::Bool=true)
    img = load(image_file)
    img_arr = convert(Array{Float64}, Gray.(img))
    return img_arr
end

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

function learn(positive_iis::AbstractArray,negative_iis::AbstractArray)
    num_pos = length(positive_iis)
    num_neg = length(negative_iis)
    num_imgs = num_pos + num_neg
    images = vcat(positive_iis, negative_iis)
    votes = zeros((num_imgs, 3000))
    for t in 1:num_imgs
        votes[t, :] = Array(map(f -> get_vote(f, images[t]), 1:3000))
    end # end show progress in for loop
    
end

function main()
    p_training = load_images(joinpath(homedir(), "projects", "FaceDetection.jl", "data", "main", "trainset", "faces"))
    p_ii_training = map(to_integral_image, p_training)
    n_training = load_images(joinpath(homedir(), "projects", "FaceDetection.jl", "data", "main", "trainset", "non-faces"))
    n_ii_training = map(to_integral_image, n_training)
    learn(p_ii_training, n_ii_training)
end

@btime main()