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jakewilliamijakewilliami
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Added example for serialization to save data (addresses #20)
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‎examples/Project.toml

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@@ -15,4 +15,5 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
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ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
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QuartzImageIO = "dca85d43-d64c-5e67-8c65-017450d5d020"
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Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
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StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"

‎examples/read.jl

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#!/usr/bin/env bash
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#=
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exec julia --project="$(realpath $(dirname $0))/" "${BASH_SOURCE[0]}" "$@" -e "include(popfirst!(ARGS))" \
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"${BASH_SOURCE[0]}" "$@"
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=#
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#=
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Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
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=#
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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")
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include(joinpath(dirname(dirname(@__FILE__)), "src", "FaceDetection.jl"))
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using .FaceDetection
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const FD = FaceDetection
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using Printf: @printf
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using Images: imresize
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using Serialization: deserialize
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println("...done")
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function main(;
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smart_choose_feats::Bool=false, alt::Bool=false
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)
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# we assume that `smart_choose_feats = true`
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main_path = dirname(dirname(@__FILE__))
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data_path = joinpath(main_path, "data")
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main_image_path = joinpath(main_path, "data", "main")
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alt_image_path = joinpath(main_path, "data", "alt")
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if alt
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# pos_testing_path = joinpath(alt_image_path, "testing", "pos")
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# neg_testing_path = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy")
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pos_testing_path = joinpath(main_image_path, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$main_image_path/testset/faces/"
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neg_testing_path = joinpath(main_image_path, "testset", "non-faces")
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else
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pos_testing_path = joinpath(main_image_path, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$main_image_path/testset/faces/"
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neg_testing_path = joinpath(main_image_path, "testset", "non-faces")
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end
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# pos_testing_path = joinpath(data_path, "lizzie-testset", "faces")
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# neg_testing_path = joinpath(data_path, "lizzie-testset", "nonfaces")
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if ! isfile(joinpath(dirname(@__FILE__), "data", "haar-like_features"))
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error(throw("You do not have a data file. Ensure you run \"write.jl\" to obtain your Haar-like features before running this script/"))
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end
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# read classifiers from file
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classifiers = deserialize(joinpath(dirname(@__FILE__), "data", "haar-like_features"))
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FD.notify_user("Loading test faces...")
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faces_testing = FD.load_images(pos_testing_path)[1]
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# faces_ii_testing = map(FD.to_integral_image, faces_testing)
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faces_ii_testing = map(FD.to_integral_image, faces_testing)
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println("...done. ", length(faces_testing), " faces loaded.")
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FD.notify_user("Loading test non-faces..")
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non_faces_testing = FD.load_images(neg_testing_path)[1]
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non_faces_ii_testing = map(FD.to_integral_image, non_faces_testing)
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println("...done. ", length(non_faces_testing), " non-faces loaded.\n")
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FD.notify_user("Testing selected classifiers...")
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correct_faces = 0
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correct_non_faces = 0
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# correct_faces = sum([FD._get_feature_vote(face, classifiers) for face in faces_ii_testing])
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# correct_non_faces = length(non_faces_testing) - sum([FD._get_feature_vote(nonFace, classifiers) for nonFace in non_faces_ii_testing])
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correct_faces = sum(FD.ensemble_vote_all(faces_ii_testing, classifiers))
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correct_non_faces = length(non_faces_testing) - sum(FD.ensemble_vote_all(non_faces_ii_testing, classifiers))
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correct_faces_percent = (float(correct_faces) / length(faces_testing)) * 100
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correct_non_faces_percent = (float(correct_non_faces) / length(non_faces_testing)) * 100
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faces_frac = string(correct_faces, "/", length(faces_testing))
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faces_percent = string("(", correct_faces_percent, "% of faces were recognised as faces)")
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non_faces_frac = string(correct_non_faces, "/", length(non_faces_testing))
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non_faces_percent = string("(", correct_non_faces_percent, "% of non-faces were identified as non-faces)")
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println("...done.\n")
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FD.notify_user("Result:\n")
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@printf("%10.9s %10.15s %15s\n", "Faces:", faces_frac, faces_percent)
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@printf("%10.9s %10.15s %15s\n\n", "Non-faces:", non_faces_frac, non_faces_percent)
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end
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@time main(smart_choose_feats=true, alt=false)

‎examples/write.jl

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#!/usr/bin/env bash
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#=
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exec julia --project="$(realpath $(dirname $0))/" "${BASH_SOURCE[0]}" "$@" -e "include(popfirst!(ARGS))" \
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"${BASH_SOURCE[0]}" "$@"
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=#
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#=
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Adapted from https://github.com/Simon-Hohberg/Viola-Jones/
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=#
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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")
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include(joinpath(dirname(dirname(@__FILE__)), "src", "FaceDetection.jl"))
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using .FaceDetection
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const FD = FaceDetection
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using Printf: @printf
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using Images: imresize
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using Serialization: serialize
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println("...done")
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function main(;
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smart_choose_feats::Bool=false, alt::Bool=false
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)
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# we assume that `smart_choose_feats = true`
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main_path = dirname(dirname(@__FILE__))
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data_path = joinpath(main_path, "data")
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main_image_path = joinpath(main_path, "data", "main")
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alt_image_path = joinpath(main_path, "data", "alt")
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if alt
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pos_training_path = joinpath(alt_image_path, "pos")
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neg_training_path = joinpath(alt_image_path, "neg")
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else
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pos_training_path = joinpath(main_image_path, "trainset", "faces")
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neg_training_path = joinpath(main_image_path, "trainset", "non-faces")
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end
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# pos_training_path = joinpath(data_path, "lfw-all")
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# neg_training_path = joinpath(data_path, "all-non-faces")
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num_classifiers = 10
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min_size_img = (19, 19) # default for our test dataset
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if smart_choose_feats
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# For performance reasons restricting feature size
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notify_user("Selecting best feature width and height...")
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max_feature_width, max_feature_height, min_feature_height, min_feature_width, min_size_img = determine_feature_size(pos_training_path, neg_training_path)
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println("...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")
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else
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min_feature_height = 8
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max_feature_height = 10
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min_feature_width = 8
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max_feature_width = 10
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end
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FD.notify_user("Loading faces...")
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faces_training = FD.load_images(pos_training_path)[1]
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faces_ii_training = map(FD.to_integral_image, faces_training) # list(map(...))
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println("...done. ", length(faces_training), " faces loaded.")
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FD.notify_user("Loading non-faces...")
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non_faces_training = FD.load_images(neg_training_path)[1]
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non_faces_ii_training = map(FD.to_integral_image, non_faces_training) # list(map(...))
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println("...done. ", length(non_faces_training), " non-faces loaded.\n")
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# classifiers are haar like features
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classifiers = FD.learn(faces_ii_training, non_faces_ii_training, num_classifiers, min_feature_height, max_feature_height, min_feature_width, max_feature_width)
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# write classifiers to file
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serialize(joinpath(dirname(@__FILE__), "data", "haar-like_features"), classifiers)
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end
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@time main(smart_choose_feats=true, alt=false)

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