basic.jl

#=
Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
=#

# 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
const FD = FaceDetection
using Printf: @printf
using Images: imresize

@info("...done")

function main(;
    smart_choose_feats::Bool = false, scale::Bool = false, scale_to::Tuple = (200, 200)
)
    include(joinpath(@__DIR__, "constants.jl"))
    include(joinpath(@__DIR__, "main_data.jl"))

    min_size_img = (19, 19) # default for our test dataset
    if smart_choose_feats
        # For performance reasons restricting feature size
        @info("Selecting best feature width and height...")

        max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(
            pos_training_path, neg_training_path; scale = scale, scale_to = scale_to
        )

        @info("...done.  Maximum feature width selected is $max_feature_width pixels; minimum feature width is $min_feature_width; maximum feature height is $max_feature_height pixels; minimum feature height is $min_feature_height.\n")
    else
        min_feature_height = 8
        max_feature_height = 10
        min_feature_width = 8
        max_feature_width = 10
    end

    # classifiers are haar like features
    classifiers = FD.learn(
        pos_training_path,
        neg_training_path,
        num_classifiers,
        min_feature_height,
        max_feature_height,
        min_feature_width,
        max_feature_width;
        scale = scale,
        scale_to = scale_to,
    )

    @info("Testing selected classifiers...")
    num_faces = length(filtered_ls(pos_testing_path))
    num_non_faces = length(filtered_ls(neg_testing_path))

    correct_faces = sum(FD.ensemble_vote_all(
        pos_testing_path, classifiers; scale = scale, scale_to = scale_to
    ))
    correct_non_faces =
        num_non_faces - sum(FD.ensemble_vote_all(
            neg_testing_path, classifiers; scale = scale, scale_to = scale_to
        ),)
    correct_faces_percent = (correct_faces / num_faces) * 100
    correct_non_faces_percent = (correct_non_faces / num_non_faces) * 100

    faces_frac = string(correct_faces, "/", num_faces)
    faces_percent = string(
        "(", correct_faces_percent, "% of faces were recognised as faces)"
    )
    non_faces_frac = string(correct_non_faces, "/", num_non_faces)
    non_faces_percent = string(
        "(", correct_non_faces_percent, "% of non-faces were identified as non-faces)"
    )

    @info("...done.\n")
    @info("Result:\n")

    @printf("%10.9s %10.15s %15s\n", "Faces:", faces_frac, faces_percent)
    @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
end

@time main(smart_choose_feats = false, scale = true, scale_to = (20, 20))