From 655e0e14e5988d9cdc5dd8f76d890304aa19d8e6 Mon Sep 17 00:00:00 2001 From: "Jake W. Ireland" Date: Thu, 10 Sep 2020 23:37:47 +1200 Subject: [PATCH] Added file name to csv data (closes #23) --- data/facelikeness-data.csv | 19572 ----------------------------------- examples/main.jl | 184 +- examples/scores.jl | 26 +- examples/validation.jl | 48 +- figs/scores.pdf | Bin 0 -> 3269 bytes src/Utils.jl | 8 +- 6 files changed, 142 insertions(+), 19696 deletions(-) delete mode 100644 data/facelikeness-data.csv create mode 100644 figs/scores.pdf diff --git a/data/facelikeness-data.csv b/data/facelikeness-data.csv deleted file mode 100644 index 4556620fd..000000000 --- a/data/facelikeness-data.csv +++ /dev/null @@ -1,19572 +0,0 @@ -176.0,142.0 -170.0,174.0 -163.0,220.0 -146.0,147.0 -119.0,142.0 -185.0,95.0 -196.0,196.0 -169.0,163.0 -191.0,197.0 -167.0,149.0 -173.0,167.0 -197.0,144.0 -188.0,188.0 -182.0,193.0 -190.0,185.0 -183.0,116.0 -181.0,171.0 -209.0,145.0 -189.0,184.0 -225.0,197.0 -170.0,136.0 -149.0,173.0 -175.0,150.0 -174.0,171.0 -166.0,132.0 -120.0,180.0 -156.0,180.0 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joinpath(altImagePath, "pos") - negTrainingPath = joinpath(altImagePath, "neg") - # posTestingPath = joinpath(altImagePath, "testing", "pos") - # negTestingPath = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy") - posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" - negTestingPath = joinpath(mainImagePath, "testset", "non-faces") - else - posTrainingPath = joinpath(mainImagePath, "trainset", "faces") - negTrainingPath = joinpath(mainImagePath, "trainset", "non-faces") - posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" - negTestingPath = joinpath(mainImagePath, "testset", "non-faces") - end - - posTrainingPath = joinpath(dataPath, "lfw-all") - negTrainingPath = joinpath(dataPath, "all-non-faces") - posTestingPath = joinpath(dataPath, "lizzie-testset", "faces") - negTestingPath = joinpath(dataPath, "lizzie-testset", "faces") - - numClassifiers = 3 - - minSizeImg = (19, 19) # default for our test dataset - if smartChooseFeats - # For performance reasons restricting feature size - notifyUser("Selecting best feature width and height...") - - maxFeatureWidth, maxFeatureHeight, minFeatureHeight, minFeatureWidth, minSizeImg = determineFeatureSize(posTrainingPath, negTrainingPath) - - println("...done. Maximum feature width selected is $maxFeatureWidth pixels; minimum feature width is $minFeatureWidth; maximum feature height is $maxFeatureHeight pixels; minimum feature height is $minFeatureHeight.\n") - else - minFeatureHeight = 8 - maxFeatureHeight = 10 - minFeatureWidth = 8 - maxFeatureWidth = 10 - end - - - FaceDetection.notifyUser("Loading faces...") - - facesTraining = FaceDetection.loadImages(posTrainingPath) - facesIITraining = map(FaceDetection.toIntegralImage, facesTraining) # list(map(...)) - println("...done. ", length(facesTraining), " faces loaded.") - - FaceDetection.notifyUser("Loading non-faces...") - - nonFacesTraining = FaceDetection.loadImages(negTrainingPath) - nonFacesIITraining = map(FaceDetection.toIntegralImage, nonFacesTraining) # list(map(...)) - println("...done. ", length(nonFacesTraining), " non-faces loaded.\n") - - # classifiers are haar like features - classifiers = FaceDetection.learn(facesIITraining, nonFacesIITraining, numClassifiers, minFeatureHeight, maxFeatureHeight, minFeatureWidth, maxFeatureWidth) - - FaceDetection.notifyUser("Loading test faces...") - - facesTesting = FaceDetection.loadImages(posTestingPath) - # facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) - facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) - println("...done. ", length(facesTesting), " faces loaded.") - - FaceDetection.notifyUser("Loading test non-faces..") - - nonFacesTesting = FaceDetection.loadImages(negTestingPath) - nonFacesIITesting = map(FaceDetection.toIntegralImage, nonFacesTesting) - println("...done. ", length(nonFacesTesting), " non-faces loaded.\n") - - FaceDetection.notifyUser("Testing selected classifiers...") - correctFaces = 0 - correctNonFaces = 0 - - # correctFaces = sum([FaceDetection._get_feature_vote(face, classifiers) for face in facesIITesting]) - # correctNonFaces = length(non_faces_testing) - sum([FaceDetection._get_feature_vote(nonFace, classifiers) for nonFace in nonFacesIITesting]) - correctFaces = sum(FaceDetection.ensembleVoteAll(facesIITesting, classifiers)) - correctNonFaces = length(nonFacesTesting) - sum(FaceDetection.ensembleVoteAll(nonFacesIITesting, classifiers)) - correctFacesPercent = (float(correctFaces) / length(facesTesting)) * 100 - correctNonFacesPercent = (float(correctNonFaces) / length(nonFacesTesting)) * 100 - - facesFrac = string(correctFaces, "/", length(facesTesting)) - facesPercent = string("(", correctFacesPercent, "% of faces were recognised as faces)") - nonFacesFrac = string(correctNonFaces, "/", length(nonFacesTesting)) - nonFacesPercent = string("(", correctNonFacesPercent, "% of non-faces were identified as non-faces)") - - println("...done.\n") - FaceDetection.notifyUser("Result:\n") - - @printf("%10.9s %10.15s %15s\n", "Faces:", facesFrac, facesPercent) - @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", nonFacesFrac, nonFacesPercent) + # we assume that `smartChooseFeats = true` + mainPath = dirname(dirname(@__FILE__)) + dataPath = joinpath(mainPath, "data") + mainImagePath = joinpath(mainPath, "data", "main") + altImagePath = joinpath(mainPath, "data", "alt") + + if alt + posTrainingPath = joinpath(altImagePath, "pos") + negTrainingPath = joinpath(altImagePath, "neg") + # posTestingPath = joinpath(altImagePath, "testing", "pos") + # negTestingPath = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy") + posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" + negTestingPath = joinpath(mainImagePath, "testset", "non-faces") + else + posTrainingPath = joinpath(mainImagePath, "trainset", "faces") + negTrainingPath = joinpath(mainImagePath, "trainset", "non-faces") + posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" + negTestingPath = joinpath(mainImagePath, "testset", "non-faces") + end + + # posTrainingPath = joinpath(dataPath, "lfw-all") + # negTrainingPath = joinpath(dataPath, "all-non-faces") + # posTestingPath = joinpath(dataPath, "lizzie-testset", "faces") + # negTestingPath = joinpath(dataPath, "lizzie-testset", "nonfaces") + + numClassifiers = 10 + + minSizeImg = (19, 19) # default for our test dataset + if smartChooseFeats + # For performance reasons restricting feature size + notifyUser("Selecting best feature width and height...") + + maxFeatureWidth, maxFeatureHeight, minFeatureHeight, minFeatureWidth, minSizeImg = determineFeatureSize(posTrainingPath, negTrainingPath) + + println("...done. Maximum feature width selected is $maxFeatureWidth pixels; minimum feature width is $minFeatureWidth; maximum feature height is $maxFeatureHeight pixels; minimum feature height is $minFeatureHeight.\n") + else + minFeatureHeight = 8 + maxFeatureHeight = 10 + minFeatureWidth = 8 + maxFeatureWidth = 10 + end + + + FaceDetection.notifyUser("Loading faces...") + + facesTraining, trainingFaceNames = FaceDetection.loadImages(posTrainingPath) + facesIITraining = map(FaceDetection.toIntegralImage, facesTraining) # list(map(...)) + println("...done. ", length(facesTraining), " faces loaded.") + + FaceDetection.notifyUser("Loading non-faces...") + + nonFacesTraining, trainingNonFaceNames = FaceDetection.loadImages(negTrainingPath) + nonFacesIITraining = map(FaceDetection.toIntegralImage, nonFacesTraining) # list(map(...)) + println("...done. ", length(nonFacesTraining), " non-faces loaded.\n") + + # classifiers are haar like features + classifiers = FaceDetection.learn(facesIITraining, nonFacesIITraining, numClassifiers, minFeatureHeight, maxFeatureHeight, minFeatureWidth, maxFeatureWidth) + + FaceDetection.notifyUser("Loading test faces...") + + facesTesting, faceNames = FaceDetection.loadImages(posTestingPath) + # facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) + facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) + println("...done. ", length(facesTesting), " faces loaded.") + + FaceDetection.notifyUser("Loading test non-faces..") + + nonFacesTesting, nonFaceNames = FaceDetection.loadImages(negTestingPath) + nonFacesIITesting = map(FaceDetection.toIntegralImage, nonFacesTesting) + println("...done. ", length(nonFacesTesting), " non-faces loaded.\n") + + FaceDetection.notifyUser("Testing selected classifiers...") + correctFaces = 0 + correctNonFaces = 0 + + # correctFaces = sum([FaceDetection._get_feature_vote(face, classifiers) for face in facesIITesting]) + # correctNonFaces = length(non_faces_testing) - sum([FaceDetection._get_feature_vote(nonFace, classifiers) for nonFace in nonFacesIITesting]) + correctFaces = sum(FaceDetection.ensembleVoteAll(facesIITesting, classifiers)) + correctNonFaces = length(nonFacesTesting) - sum(FaceDetection.ensembleVoteAll(nonFacesIITesting, classifiers)) + correctFacesPercent = (float(correctFaces) / length(facesTesting)) * 100 + correctNonFacesPercent = (float(correctNonFaces) / length(nonFacesTesting)) * 100 + + facesFrac = string(correctFaces, "/", length(facesTesting)) + facesPercent = string("(", correctFacesPercent, "% of faces were recognised as faces)") + nonFacesFrac = string(correctNonFaces, "/", length(nonFacesTesting)) + nonFacesPercent = string("(", correctNonFacesPercent, "% of non-faces were identified as non-faces)") + + println("...done.\n") + FaceDetection.notifyUser("Result:\n") + + @printf("%10.9s %10.15s %15s\n", "Faces:", facesFrac, facesPercent) + @printf("%10.9s %10.15s %15s\n\n", "Non-faces:", nonFacesFrac, nonFacesPercent) end diff --git a/examples/scores.jl b/examples/scores.jl index 705812967..7d98767a9 100755 --- a/examples/scores.jl +++ b/examples/scores.jl @@ -20,6 +20,7 @@ println("...done") function main(; smartChooseFeats::Bool=false, alt::Bool=false) mainPath = dirname(dirname(@__FILE__)) + dataPath = joinpath(mainPath, "data") mainImagePath = joinpath(mainPath, "data", "main") altImagePath = joinpath(mainPath, "data", "alt") @@ -36,8 +37,13 @@ function main(; smartChooseFeats::Bool=false, alt::Bool=false) posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" negTestingPath = joinpath(mainImagePath, "testset", "non-faces") end + + # posTrainingPath = joinpath(dataPath, "lfw-all") + # negTrainingPath = joinpath(dataPath, "all-non-faces") + # posTestingPath = joinpath(dataPath, "lizzie-testset", "faces") + # negTestingPath = joinpath(dataPath, "lizzie-testset", "nonfaces") - numClassifiers = 200 + numClassifiers = 10 minSizeImg = (19, 19) # default for our test dataset if smartChooseFeats @@ -57,13 +63,13 @@ function main(; smartChooseFeats::Bool=false, alt::Bool=false) FaceDetection.notifyUser("Loading faces...") - facesTraining = FaceDetection.loadImages(posTrainingPath) + facesTraining, trainingFaceNames = FaceDetection.loadImages(posTrainingPath) facesIITraining = map(FaceDetection.toIntegralImage, facesTraining) # list(map(...)) println("...done. ", length(facesTraining), " faces loaded.") FaceDetection.notifyUser("Loading non-faces...") - nonFacesTraining = FaceDetection.loadImages(negTrainingPath) + nonFacesTraining, trainingNonFaceNames = FaceDetection.loadImages(negTrainingPath) nonFacesIITraining = map(FaceDetection.toIntegralImage, nonFacesTraining) # list(map(...)) println("...done. ", length(nonFacesTraining), " non-faces loaded.\n") @@ -72,14 +78,14 @@ function main(; smartChooseFeats::Bool=false, alt::Bool=false) FaceDetection.notifyUser("Loading test faces...") - facesTesting = FaceDetection.loadImages(posTestingPath) + facesTesting, faceNames = FaceDetection.loadImages(posTestingPath) # facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) println("...done. ", length(facesTesting), " faces loaded.") FaceDetection.notifyUser("Loading test non-faces..") - nonFacesTesting = FaceDetection.loadImages(negTestingPath) + nonFacesTesting, nonFaceNames = FaceDetection.loadImages(negTestingPath) nonFacesIITesting = map(FaceDetection.toIntegralImage, nonFacesTesting) println("...done. ", length(nonFacesTesting), " non-faces loaded.\n") @@ -98,13 +104,17 @@ function main(; smartChooseFeats::Bool=false, alt::Bool=false) dfFaces = facesScores dfNonFaces = nonFacesScores if length(facesScores) < length(nonFacesScores) - dfFaces = vcat(dfFaces, Matrix{Union{Float64, Missing}}(undef, length(nonFacesIITesting) - length(facesIITesting), 1)) + toAdd = length(nonFacesIITesting) - length(facesIITesting) + dfFaces = vcat(dfFaces, Matrix{Union{Float64, Missing}}(undef, toAdd, 1)) + faceNames = vcat(faceNames, Matrix{Union{Float64, Missing}}(undef, toAdd, 1)) elseif length(facesScores) > length(nonFacesScores) - dfNonFaces = vcat(dfNonFaces, Matrix{Union{Float64, Missing}}(undef, length(facesIITesting) - length(nonFacesIITesting), 1)) + length(facesIITesting) - length(nonFacesIITesting) + dfNonFaces = vcat(dfNonFaces, Matrix{Union{Float64, Missing}}(undef, toAdd, 1)) + nonFaceNames = vcat(nonFaceNames, Matrix{Union{Float64, Missing}}(undef, toAdd, 1)) end # write score data - write(joinpath(dirname(dirname(@__FILE__)), "data", "facelikeness-data.csv"), DataFrame(hcat(dfFaces, dfNonFaces)), writeheader=false) + write(joinpath(dirname(dirname(@__FILE__)), "data", "facelikeness-data.csv"), DataFrame(hcat(faceNames, dfFaces, nonFaceNames, dfNonFaces)), writeheader=false) println("...done.\n") diff --git a/examples/validation.jl b/examples/validation.jl index ff1112588..1e0d80d5e 100755 --- a/examples/validation.jl +++ b/examples/validation.jl @@ -21,51 +21,57 @@ println("...done") function main(; smartChooseFeats::Bool=false, alt::Bool=false, imageReconstruction::Bool=true, featValidaton::Bool=true) - mainPath = dirname(dirname(@__FILE__)) + mainPath = dirname(dirname(@__FILE__)) + dataPath = joinpath(mainPath, "data") mainImagePath = joinpath(mainPath, "data", "main") altImagePath = joinpath(mainPath, "data", "alt") if alt - posTrainingPath = joinpath(altImagePath, "pos") - negTrainingPath = joinpath(altImagePath, "neg") - # posTestingPath = joinpath(altImagePath, "testing", "pos") - # negTestingPath = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy") - posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" - negTestingPath = joinpath(mainImagePath, "testset", "non-faces") + posTrainingPath = joinpath(altImagePath, "pos") + negTrainingPath = joinpath(altImagePath, "neg") + # posTestingPath = joinpath(altImagePath, "testing", "pos") + # negTestingPath = joinpath(homedir(), "Desktop", "Assorted Personal Documents", "Wallpapers copy") + posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" + negTestingPath = joinpath(mainImagePath, "testset", "non-faces") else posTrainingPath = joinpath(mainImagePath, "trainset", "faces") negTrainingPath = joinpath(mainImagePath, "trainset", "non-faces") posTestingPath = joinpath(mainImagePath, "testset", "faces")#joinpath(homedir(), "Desktop", "faces")#"$mainImagePath/testset/faces/" negTestingPath = joinpath(mainImagePath, "testset", "non-faces") end + + # posTrainingPath = joinpath(dataPath, "lfw-all") + # negTrainingPath = joinpath(dataPath, "all-non-faces") + # posTestingPath = joinpath(dataPath, "lizzie-testset", "faces") + # negTestingPath = joinpath(dataPath, "lizzie-testset", "nonfaces") numClassifiers = 10 minSizeImg = (19, 19) # default for our test dataset if smartChooseFeats - # For performance reasons restricting feature size - notifyUser("Selecting best feature width and height...") - - maxFeatureWidth, maxFeatureHeight, minFeatureHeight, minFeatureWidth, minSizeImg = determineFeatureSize(posTrainingPath, negTrainingPath) - - println("...done. Maximum feature width selected is $maxFeatureWidth pixels; minimum feature width is $minFeatureWidth; maximum feature height is $maxFeatureHeight pixels; minimum feature height is $minFeatureHeight.\n") + # For performance reasons restricting feature size + notifyUser("Selecting best feature width and height...") + + maxFeatureWidth, maxFeatureHeight, minFeatureHeight, minFeatureWidth, minSizeImg = determineFeatureSize(posTrainingPath, negTrainingPath) + + println("...done. Maximum feature width selected is $maxFeatureWidth pixels; minimum feature width is $minFeatureWidth; maximum feature height is $maxFeatureHeight pixels; minimum feature height is $minFeatureHeight.\n") else - minFeatureHeight = 8 - maxFeatureHeight = 10 - minFeatureWidth = 8 - maxFeatureWidth = 10 + minFeatureHeight = 8 + maxFeatureHeight = 10 + minFeatureWidth = 8 + maxFeatureWidth = 10 end FaceDetection.notifyUser("Loading faces...") - facesTraining = FaceDetection.loadImages(posTrainingPath) + facesTraining, trainingFaceNames = FaceDetection.loadImages(posTrainingPath) facesIITraining = map(FaceDetection.toIntegralImage, facesTraining) # list(map(...)) println("...done. ", length(facesTraining), " faces loaded.") FaceDetection.notifyUser("Loading non-faces...") - nonFacesTraining = FaceDetection.loadImages(negTrainingPath) + nonFacesTraining, trainingNonFaceNames = FaceDetection.loadImages(negTrainingPath) nonFacesIITraining = map(FaceDetection.toIntegralImage, nonFacesTraining) # list(map(...)) println("...done. ", length(nonFacesTraining), " non-faces loaded.\n") @@ -74,14 +80,14 @@ function main(; smartChooseFeats::Bool=false, alt::Bool=false, imageReconstructi FaceDetection.notifyUser("Loading test faces...") - facesTesting = FaceDetection.loadImages(posTestingPath) + facesTesting, faceNames = FaceDetection.loadImages(posTestingPath) # facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) facesIITesting = map(FaceDetection.toIntegralImage, facesTesting) println("...done. ", length(facesTesting), " faces loaded.") FaceDetection.notifyUser("Loading test non-faces..") - nonFacesTesting = FaceDetection.loadImages(negTestingPath) + nonFacesTesting, nonFaceNames = FaceDetection.loadImages(negTestingPath) nonFacesIITesting = map(FaceDetection.toIntegralImage, nonFacesTesting) println("...done. ", length(nonFacesTesting), " non-faces loaded.\n") diff --git a/figs/scores.pdf b/figs/scores.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eb3268f79980b382ac27e1a457f535b8532525f1 GIT binary patch literal 3269 zcmc&%dsGuw8b<{dLs=jAV&T{;0zz5H%;Yt(zzPXLd>{uDP$SEb3?VcmG#M-f_0&gQ z%PLmHb}bey3iVi5p(p}YdT@p6DT`oBbuFCYW2N;q&6jU{W0k5PGMEzh-n?<~l7K`6GNon+LP7*Wg_&hIlNtF(EshVcl6sR-$#5+4 zSI(yhiXh}9L5V1-gv5YYvq_t+VTT2f1YylcYu2x2%>jb2Fac}Sf@aF@gyrvMu}G+3 zIL2VoVFiqiwIDGD#0iAUvN?m^h-nc|k17D#X5z2{8x-4U$~JO{usaU4y@=*du|Y|J zaIs!%L8~Qj${IUCxt-t=)~Mss5lIPzD!qYY&BE|_MRfE+Hb=>7Oj;JiEF2D3rhwp| zH2wjXt!z;LvKipAnZ*`ceU4)-rfjnYI~@{&genu>;THswfq4iYBaF=9qT)Ho0putu zo|5nwMK~gY2{}!OoFOU@w;8uuDeJ2V~N+)C^VwCj$wbrL0!Z zM3{2WY66=fB?u%Yh&2#GBZsZGASqY}P-~8Xws~ufom?^!JhWPtb8l{|l z_f{@8xs#LY2NR#T?8*91@?gF)Pq9gP{XKfYzykkFrKRh>=J@NUOAHrstBMyL{`gq^ zmFH<8H@R1wMY1^GZ#ts(u6*WRI4i$)PF!7Y$E>r4Ek8QGU%e&Q zYrgKvz>|K@@+VDi2R@redp(<;AnM*GR@d!JEp6C;e3$l|^k3=)drjHvLJn>hd%G)> z8V`8vs@AwvY~5&4)%2v!NSVHw3EqF^@TrQebJEUE&Zs-#lth-0(aU$D>wj>*qdpb7 z^}WDZHO6hXNu}D|DPZEkGlb(^kL%a9dVN9fJU^$S{q5Adb!METYFzoM393OlZ-SvGz|mFh`t^)euHQC?yDS!G20geYf9=>O z4L`lbeGZKnBzq>d4e$RvU$N#*y;rza~uX-=%rs!0HWqCK(rWCpN zJVx==oe|?iY($`Mf}i&9rmF`WJxiSmZX7lK#p#lRI_l#I<>unMJMt0f{I}-!nG1W) zwz&5RF zBnQToB^>fgk1PxR>~^C!T5@<|#*|pU?;UGh7P@C_N_IRkF}pOyaoWLv!YQrW{kExM zC7*bEhIgjtXF2XU7;tII_XRE9vp*(X<2kUj65s8jBp-i^jJXIkp!%P9HiS*LUdQycxZsW0PCeLtX0Dy^kWf zUqT&v&*u&`p1RdBt|W7_LOrwf(Wj!S`%i0Q0yU-W^Ai*F2*iT?pqgg_X_G~Al~@Tl+UFI&oKD=foHJ< z#{=ILJ}y98V=Kcv+W?*|KVxT*liC%PY>Ty$t%wQ+u?V|=LK>z zX8f-`+g-5Dw*0?x#<8-$53j=DP6P^vlC@~e`Yh;JX}%v3MjCN*rZ?&kE)~XbqeX9H z*tIGEeYsGL>KfKPT&7+uOa?}0K_c-mAcAj?f=DTif+&(kByIx{EvK<<@f@3(fM{I4 z6in7aRl?XDCHODSf$EU} literal 0 HcmV?d00001 diff --git a/src/Utils.jl b/src/Utils.jl index 0deb4c814..c3761bed2 100755 --- a/src/Utils.jl +++ b/src/Utils.jl @@ -43,14 +43,16 @@ function loadImages(imageDir::AbstractString) return `images`: a list of images from the path provided [type: Abstract Array] =# - + + names = filter!(f -> ! occursin(r".*\.DS_Store", f), readdir(imageDir, join=true, sort=false)) + images = [] - for file in filter!(f -> ! occursin(r".*\.DS_Store", f), readdir(imageDir, join=true, sort=false)) + for file in names images = push!(images, getImageMatrix(file)) end - return images + return images, names end