validation.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: Gray, clamp01nan, save, imresize, load
using Serialization: deserialize

@info("...done")

function main(;
    smart_choose_feats::Bool = false,
    image_reconstruction::Bool = true,
    feat_validation::Bool = true,
    scale::Bool = false,
    scale_to::Tuple = (200, 200),
)
    include("constants.jl")
    include("main_data.jl")

    max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(
        pos_testing_path, neg_testing_path; scale = scale, scale_to = scale_to
    )
    img_size = scale ? scale_to : min_size_img
    data_file = joinpath(
        dirname(@__FILE__), "data", "haar-like_features_c$(num_classifiers)_$(img_size)"
    )

    # read classifiers from file
    classifiers = deserialize(data_file)

    # FD.notify_user("Loading test faces...")
    #
    # faces_testing = FD.load_images(pos_testing_path)[1]
    # # faces_ii_testing = map(FD.to_integral_image, faces_testing)
    # faces_ii_testing = map(FD.to_integral_image, faces_testing)
    # println("...done. ", length(faces_testing), " faces loaded.")
    #
    # FD.notify_user("Loading test non-faces..")
    #
    # non_faces_testing = FD.load_images(neg_testing_path)[1]
    # non_faces_ii_testing = map(FD.to_integral_image, non_faces_testing)
    # println("...done. ", length(non_faces_testing), " non-faces loaded.\n")

    random_image = get_random_image(pos_testing_path)

    if image_reconstruction
        # Just for fun: putting all Haar-like features over each other generates a face-like image
        @info("Constructing an image of all Haar-like Features found...")

        reconstructed_image = FD.reconstruct(classifiers, img_size)
        save(
            joinpath(dirname(dirname(@__FILE__)), "figs", "reconstruction.png"),
            Gray.(map(clamp01nan, reconstructed_image)),
        )

        @info(
            "...done.  See ",
            joinpath(dirname(dirname(@__FILE__)), "figs", "reconstruction.png"),
            ".\n"
        )
    end

    if feat_validation
        @info("Constructing a validation image on a random image...")

        validation_image = FD.generate_validation_image(random_image, classifiers)
        save(
            joinpath(dirname(dirname(@__FILE__)), "figs", "validation.png"),
            validation_image,
        )

        @infp(
            "...done.  See ",
            joinpath(dirname(dirname(@__FILE__)), "figs", "validation.png"),
            ".\n"
        )
    end
end

@time main(smart_choose_feats = true, image_reconstruction = true, feat_validation = false)