scores.jl

# println("\033[1;34m===>\033[0;38m\033[1;38m\tLoading required libraries (it will take a moment to precompile if it is your first time doing this)...\033[0;38m")
@info "Loading required libraries (it will take a moment to precompile if it is your first time doing this)..."

using FaceDetection
using Images: imresize
using StatsPlots  # StatsPlots required for box plots # plot boxplot @layout :origin savefig
using CSV: write
using DataFrames: DataFrame
using HypothesisTests: UnequalVarianceTTest
using Serialization: deserialize

@info("...done")

function main(;
    smart_choose_feats::Bool = false, scale::Bool = false, scale_to::Tuple = (200, 200)
)
    include("constants.jl")
    include("main_data.jl")
    pos_testing_path = "/Users/jakeireland/projects/FaceDetection.jl/data/lizzie-testset/faces/"
    neg_testing_path = "/Users/jakeireland/projects/FaceDetection.jl/data/lizzie-testset/nonfaces/"

    # read classifiers from file
    classifiers = deserialize("/Users/jakeireland/Desktop/classifiers_100_577_577_fixed_1idx")

    num_classifiers = length(classifiers)

    @info("Calculating test face scores and constructing dataset...")

    faces_scores = Vector{Real}(undef, length(filtered_ls(pos_testing_path)))
    non_faces_scores = Vector{Real}(undef, length(filtered_ls(neg_testing_path)))

    faces_scores[:] .= [
        sum([
            get_faceness(c, load_image(face; scale = scale, scale_to = scale_to)) for
            c in classifiers
        ]) / num_classifiers for face in filtered_ls(pos_testing_path)
    ]
    non_faces_scores[:] .= [
        sum([
            get_faceness(c, load_image(non_face; scale = scale, scale_to = scale_to)) for
            c in classifiers
        ]) / num_classifiers for non_face in filtered_ls(neg_testing_path)
    ]

    face_names = basename.(filtered_ls(pos_testing_path))
    non_face_names = basename.(filtered_ls(neg_testing_path))

    # filling in the dataset with missing to easily write to csv
    df_faces = faces_scores
    df_non_faces = non_faces_scores
    if length(faces_scores) < length(non_faces_scores)
        to_add =
            length(filtered_ls(neg_testing_path)) - length(filtered_ls(pos_testing_path))
        df_faces = vcat(df_faces, Matrix{Union{Float64, Missing}}(undef, to_add, 1))
        face_names = vcat(face_names, Matrix{Union{Float64, Missing}}(undef, to_add, 1))
    elseif length(faces_scores) > length(non_faces_scores)
        to_add =
            length(filtered_ls(pos_testing_path)) - length(filtered_ls(neg_testing_path))
        df_non_faces = vcat(df_non_faces, Matrix{Union{Float64, Missing}}(undef, to_add, 1))
        non_face_names = vcat(
            non_face_names, Matrix{Union{Float64, Missing}}(undef, to_add, 1)
        )
    end

    # write score data
    data_file = joinpath(dirname(dirname(@__FILE__)), "data", "faceness-scores.csv")
    write(
        data_file,
        DataFrame(hcat(face_names, df_faces, non_face_names, df_non_faces));
        writeheader = false,
    )

    @info("...done.  Dataset written to $(data_file).\n")
    @info("Computing differences in scores between faces and non-faces...")

    welch_t = UnequalVarianceTTest(faces_scores, non_faces_scores)

    @info("...done.  $welch_t\n")
    @info("Constructing box plot with said dataset...")

    gr() # set plot backend
    theme(:solarized)
    plot = StatsPlots.plot(
        StatsPlots.boxplot(faces_scores; xaxis = false, label = false),
        StatsPlots.boxplot(non_faces_scores; xaxis = false, label = false);
        title = ["Scores of Faces" "Scores of Non-Faces"],
        # label = ["faces" "non-faces"],
        fontfamily = font("Times"),
        layout = @layout([a b]),
        # fillcolor = [:blue, :orange],
        link = :y,
        # framestyle = [:origin :origin]
    )

    # save plot
    StatsPlots.savefig(plot, joinpath(dirname(dirname(@__FILE__)), "figs", "scores.pdf"))
    @info("...done.  Plot created at $(joinpath(dirname(dirname(@__FILE__)), "figs", "scores.pdf"))")
end

@time main(smart_choose_feats = true, scale = true, scale_to = (577, 577))