R/CVD.R

Defines functions decolorizeFile decolorize Color.Vision.VingrysAndKingSmith Color.Vision.Simulate Color.Vision.Daltonize Color.Vision.c2g RGBtoHSL greyscale.Luminosity greyscale.BT709 greyscale.RMY greyscale.Linear greyscale.Y greyscale.avg lightAdaptedPupilSize.WatsonAndYellott effectiveCornealFluxDensity attenuationNumberOfEyes lightAdaptedPupilSize.WinnEtAl lightAdaptedPupilSize.BlackieAndHowland lightAdaptedPupilSize.Barten lightAdaptedPupilSize.StanleyAndDavies lightAdaptedPupilSize.LeGrand lightAdaptedPupilSize.DeGrootAndGebhard lightAdaptedPupilSize.MoonAndSpencer lightAdaptedPupilSize.Crawford lightAdaptedPupilSize.Holladay scoreD15TCDS scoreD15Graphic scoreRoth28Graphic calculateTES D15Foutch interpretation.Foutch interpretation.VingrysAndKingSmith scoreFM100Graphic limitScore calculateCircle showDuplicated VKSgraphic VKSvariantGraphic illuminance2troland luminance2troland effectivePupilArea XYZ2scotopic.Rawtran.array XYZ2scotopic.Rawtran approx.scotopic.luminance.LarsonEtAl.RGB.array approx.scotopic.luminance.LarsonEtAl.XYZ.array approx.scotopic.luminance.LarsonEtAl.RGB approx.scotopic.luminance.LarsonEtAl.XYZ createPNGbuttons loadPNG vectorPNGbuttons plotConfusionVectors

Documented in approx.scotopic.luminance.LarsonEtAl.RGB approx.scotopic.luminance.LarsonEtAl.RGB.array approx.scotopic.luminance.LarsonEtAl.XYZ approx.scotopic.luminance.LarsonEtAl.XYZ.array attenuationNumberOfEyes calculateCircle calculateTES Color.Vision.c2g Color.Vision.Daltonize Color.Vision.Simulate Color.Vision.VingrysAndKingSmith createPNGbuttons D15Foutch decolorize decolorizeFile effectiveCornealFluxDensity effectivePupilArea greyscale.avg greyscale.BT709 greyscale.Linear greyscale.Luminosity greyscale.RMY greyscale.Y illuminance2troland interpretation.Foutch interpretation.VingrysAndKingSmith lightAdaptedPupilSize.Barten lightAdaptedPupilSize.BlackieAndHowland lightAdaptedPupilSize.Crawford lightAdaptedPupilSize.DeGrootAndGebhard lightAdaptedPupilSize.Holladay lightAdaptedPupilSize.LeGrand lightAdaptedPupilSize.MoonAndSpencer lightAdaptedPupilSize.StanleyAndDavies lightAdaptedPupilSize.WatsonAndYellott lightAdaptedPupilSize.WinnEtAl limitScore loadPNG luminance2troland plotConfusionVectors RGBtoHSL scoreD15Graphic scoreD15TCDS scoreFM100Graphic scoreRoth28Graphic showDuplicated vectorPNGbuttons VKSgraphic VKSvariantGraphic XYZ2scotopic.Rawtran XYZ2scotopic.Rawtran.array

plotConfusionVectors<-function(colorSpace='CIE1931xy'){
neutralPoint<-get("neutralPoint", envir = environment())
dichromaticCopunctalPoint<-get("dichromaticCopunctalPoint", envir = environment())
if (colorSpace=='CIE1931xy'){
segments(neutralPoint['CIE1931xy','u'],neutralPoint['CIE1931xy','v'],dichromaticCopunctalPoint['P','CIE1931xyX'],dichromaticCopunctalPoint['P','CIE1931xyY'])
segments(neutralPoint['CIE1931xy','u'],neutralPoint['CIE1931xy','v'],dichromaticCopunctalPoint['D','CIE1931xyX'],dichromaticCopunctalPoint['D','CIE1931xyY'])
segments(neutralPoint['CIE1931xy','u'],neutralPoint['CIE1931xy','v'],dichromaticCopunctalPoint['T','CIE1931xyX'],dichromaticCopunctalPoint['T','CIE1931xyY'])
} else {
if (colorSpace=='CIE1976uv'){
segments(neutralPoint['CIE1976uv','u'],neutralPoint['CIE1976uv','v'],dichromaticCopunctalPoint['P','CIE1976uvU'],dichromaticCopunctalPoint['P','CIE1976uvV'])
segments(neutralPoint['CIE1976uv','u'],neutralPoint['CIE1976uv','v'],dichromaticCopunctalPoint['D','CIE1976uvU'],dichromaticCopunctalPoint['D','CIE1976uvV'])
segments(neutralPoint['CIE1976uv','u'],neutralPoint['CIE1976uv','v'],dichromaticCopunctalPoint['T','CIE1976uvU'],dichromaticCopunctalPoint['T','CIE1976uvV'])
} else {
if (colorSpace=='CIE1960uv'){
segments(neutralPoint['CIE1960uv','u'],neutralPoint['CIE1960uv','v'],dichromaticCopunctalPoint['P','CIE1960uvU'],dichromaticCopunctalPoint['P','CIE1960uvV'])
segments(neutralPoint['CIE1960uv','u'],neutralPoint['CIE1960uv','v'],dichromaticCopunctalPoint['D','CIE1960uvU'],dichromaticCopunctalPoint['D','CIE1960uvV'])
segments(neutralPoint['CIE1960uv','u'],neutralPoint['CIE1960uv','v'],dichromaticCopunctalPoint['T','CIE1960uvU'],dichromaticCopunctalPoint['T','CIE1960uvV'])
}
}
}}

vectorPNGbuttons<-function(capsData=get("FarnsworthD15", envir = environment()))
{# vector with the PNG button filenames
capsData <- data.matrix((capsData[,c('R','G','B')]))
apply(capsData,1,function(x) {
rgbName <- sprintf('%02x%02x%02x.png',x['R'],x['G'],x['B'])
paste(system.file(package='CVD'),'/extdata/',rgbName,sep='')
})
}

loadPNG<-function(fileIN=NULL, silent=FALSE)
{#loads a PNG and shows the dimensions, useful for interactive testing
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist')
p<-png::readPNG(fileIN)
if (!silent) print(paste('PNG ',dim(p)[1],'x',dim(p)[2],', ',dim(p)[3],' channels.',sep=''))
p
}

createPNGbuttons<-function(capsData=get("FarnsworthD15", envir = environment()), imgLength=44, imgWidth=78)
{# creates PNG buttons from a data.frame with RGB values for test caps
capsData <- data.matrix((capsData[,c('R','G','B')]))
apply(capsData,1,function(x) {
rgbName <- sprintf('%02x%02x%02x.png',x['R'],x['G'],x['B'])
img.array<-array(rep(c(x),each=imgLength*imgWidth),c(imgLength,imgWidth,3))
png::writePNG(img.array/255, rgbName)
})
}

approx.scotopic.luminance.LarsonEtAl.XYZ<-function(XYZmatrix) XYZmatrix[,2] * (1.33*(1+(XYZmatrix[,2]+XYZmatrix[,3])/XYZmatrix[,1]) -1.68)
approx.scotopic.luminance.LarsonEtAl.RGB<-function(RGBmatrix) RGBmatrix[,1] * 0.062 + RGBmatrix[,2] * 0.608 + RGBmatrix[,3] * 0.330
approx.scotopic.luminance.LarsonEtAl.XYZ.array<-function(XYZarray)
{
im2 <- matrix(apply(XYZarray,1:2,t),length(XYZarray)/3,3,byrow=TRUE)
g1<-approx.scotopic.luminance.LarsonEtAl.XYZ(im2)
g2<-array(cbind(g1,g1,g1),dim(XYZarray))
g2
}
approx.scotopic.luminance.LarsonEtAl.RGB.array<-function(RGBarray)
{
im2 <- matrix(apply(RGBarray,1:2,t),length(RGBarray)/3,3,byrow=TRUE)
g1<-approx.scotopic.luminance.LarsonEtAl.RGB(im2)
g2<-array(cbind(g1,g1,g1),dim(RGBarray))
g2
}



# Larson, G. W., H. Rushmeier, and C. Piatko (1997, October - December). A visibility matching tone reproduction operator for high dynamic range scenes.
# IEEE Transactions on Visualization and Computer Graphics 3 (4), 291–306.

XYZ2scotopic.Rawtran<-function(XYZmatrix) 0.36169*XYZmatrix[,3] + 1.18214*XYZmatrix[,2] - 0.80498*XYZmatrix[,1]
XYZ2scotopic.Rawtran.array<-function(XYZarray)
{
im2 <- matrix(apply(XYZarray,1:2,t),length(XYZarray)/3,3,byrow=TRUE)
g1<-XYZ2scotopic.Rawtran(im2)
g2<-array(cbind(g1,g1,g1),dim(XYZarray))
g2
}
# XYZ to scotopic curve, D65, original code: Rawtran - integral.physics.muni.cz
# Masaryk University, http://integral.physics.muni.cz/rawtran/
# Filip Hroch, 1998, Computer Programs for CCD Photometry,
# 20th Stellar Conference of the Czech and Slovak Astronomical Institutes,
# DusekJ., http://adsabs.harvard.edu/abs/1998stel.conf...30H

effectivePupilArea<-function(d) pi*d^2/4*(1-0.085*d^2/8+0.002*d^4/48) # effective area
#Smith, VC, Pokorny, J, and Yeh, T: The Farnsworth-Munsell 100-hue test in cone excitation space. Documenta Ophthalmologica Proceedings Series 56:281-291, 1993.

luminance2troland<-function(Lv, d=NA)
{ # convert from luminance (cd/m^2) to troland and effective troland
#Smith, VC, Pokorny, J, and Yeh, T: The Farnsworth-Munsell 100-hue test in cone excitation space. Documenta Ophthalmologica Proceedings Series 56:281-291, 1993.
cbind(troland=Lv * d^2 * pi/4, effectivetroland=Lv * pi*d^2/4*(1-0.085*d^2/8+0.002*d^4/48))
}

illuminance2troland<-function(Ev, lumFactor, d=NA)
{ # convert from illuminance (lux) to troland and effective troland
#Smith, VC, Pokorny, J, and Yeh, T: The Farnsworth-Munsell 100-hue test in cone excitation space. Documenta Ophthalmologica Proceedings Series 56:281-291, 1993.
cbind(troland=Ev * lumFactor/pi * d^2 * pi/4, effectivetroland=Ev * lumFactor/pi * pi*d^2/4*(1-0.085*d^2/8+0.002*d^4/48))
}

VKSvariantGraphic<-function(VKSdata, xLimit=5, yLimit=4, VKStitle='', VKSxlabel='',VKSylabel=''){
xl<-0:(xLimit)
yl<-0:(yLimit*2)
x2<-xLimit * 4
# plot the axis
z<-lapply(1:(xLimit*2), function(x) { c1<-calculateCircle(xLimit,yLimit,x);plot(c1,xlim=c(0,xLimit*2),ylim=c(0,yLimit*2) ,col='lightgrey',type='l', xaxt='n', yaxt='n',ylab='',xlab='');par(new=TRUE) })
z<-lapply(1:12, function(x) {segments(xLimit, yLimit, xLimit+x2*sin(x*30*pi/180),yLimit+x2*cos(x*30*pi/180), col = ifelse((x %% 3)==0,'black','lightgrey')) })
# plot the data
uniqV<-unique(VKSdata[,1])
z<-lapply(1:dim(VKSdata)[1],
function(x) {
symbV<-which(uniqV==VKSdata[x,1])
angleV<-VKSdata[x,2]*2
indexV<-VKSdata[x,3]
par(new=TRUE)
plot(cos(angleV*pi/180)*indexV + xLimit, yLimit+sin(angleV*pi/180)*indexV ,xlim=c(0,xLimit*2),ylim=c(0,yLimit*2),type='p',pch=symbV, xaxt='n', yaxt='n',ylab='',xlab='')
})
Axis(side=2,at=0:(yLimit*2), labels= c(yLimit:1,0,1:yLimit)) # Y axis
Axis(side=1,at=yLimit*tan((0:xLimit*30)*pi/180) + xLimit, labels=0:xLimit*30) # X axis
title(main = VKStitle, xlab = VKSxlabel, ylab = VKSylabel)
legend("topright",pch=1:length(uniqV), uniqV,bg='white')#,title='Symbols'
}

VKSgraphic<-function(VKSdata, xLimit=5, yLimit=4, VKStitle='', VKSxlabel='',VKSylabel=''){
xl<-0:(xLimit)
yl<-0:(yLimit*2)
# plot the axis
z<-lapply(1:(xLimit*2), function(x) { c1<-calculateCircle(0,yLimit,x);plot(c1,xlim=c(0,xLimit*2),ylim=c(0,yLimit*2) ,col='lightgrey',type='l', xaxt='n', yaxt='n',ylab='',xlab='');par(new=TRUE) })
abline(h = yLimit, col = 'lightgrey')
abline(v = 0, col = 'lightgrey')
z<-lapply(-5:5, function(x) { abline(a = yLimit, b = tan(x*15*pi/180), col = 'lightgrey') })
# plot the data
uniqV<-unique(VKSdata[,1])
z<-lapply(1:dim(VKSdata)[1],
function(x) {
symbV<-which(uniqV==VKSdata[x,1])
angleV<-VKSdata[x,2]
indexV<-VKSdata[x,3]
par(new=TRUE)
plot(cos(angleV*pi/180)*indexV, yLimit+sin(angleV*pi/180)*indexV ,xlim=c(0,xLimit*2),ylim=c(0,yLimit*2),type='p',pch=symbV, xaxt='n', yaxt='n',ylab='',xlab='')
})
Axis(side=2,at=0:(yLimit*2), labels= c(yLimit:1,0,1:yLimit)) # Y axis
Axis(side=1,at=yLimit*tan((0:xLimit*15)*pi/180), labels=0:xLimit*15) # X axis
title(main = VKStitle, xlab = VKSxlabel, ylab = VKSylabel)
legend("topright",pch=1:length(uniqV), uniqV,bg='white')#,title='Symbols'
}

showDuplicated<-function(cnum)
{ # show missing and duplicated cap numbers
n <- length(cnum)
if (!all(sort(cnum) == 1:n))
{
missingC<-which(!(1:n %in% cnum))
repeatedC<-cnum[which(duplicated(cnum))]
cat('Missing:',paste(missingC,sep=','),'Repeated:',paste(repeatedC,sep=','),'at position:',paste(which(cnum==repeatedC),sep=','))
}
}

calculateCircle<-function(x, y, r, steps=50,sector=c(0,360),randomDist=FALSE, randomFun=runif,...)
{
  points = matrix(0,steps,2)
  if (randomDist) n<-sector[1]+randomFun(steps,...)*(sector[2]-sector[1]) else n<-seq(sector[1],sector[2],length.out=steps)
  if (randomDist) repeat {
  n[which(!(n>=sector[1] & n<=sector[2]))]<-sector[1]+randomFun(sum(!(n>=sector[1] & n<=sector[2])),...)*(sector[2]-sector[1])
  if (all(n>=sector[1] & n<=sector[2])) break
  }
    alpha = n * (pi / 180)
    sinalpha = sin(alpha)
    cosalpha = cos(alpha)
    points[,1]<- x + (r * cosalpha)
    points[,2]<- y + (r * sinalpha)
  points
}

limitScore<-function(x)
{ # internal function to restrict the plot of scoreFM100Graphic
if (x<3) return(14)
if (x>15) return(2)
16-x
}

scoreFM100Graphic<-function(userFM100colors=NULL,userFM100values=NULL, titleGraphic="Farnsworth Munsell 100-Hue test results", okFM100colors=NULL, Kinnear=FALSE)
{# plots the graphic to score the Farnsworth Munsell 100-Hue test by default, or a similar test by modifying titleGraphic and okFM100colors
FarnsworthMunsell100Hue<-get("FarnsworthMunsell100Hue", envir = environment())
cirC<- round(calculateCircle(550,550,500,86))
cirC2<- round(calculateCircle(550,550,530,86))
circPos<-matrix(c(cirC),ncol=2,byrow=F)
NumPos<-matrix(c(cirC2),ncol=2,byrow=F)
circPos<-circPos[-1,] # coords for the circles
NumPos<-NumPos[-1,] # coords for the numbers
if ((is.null(userFM100colors)) & (is.null(userFM100values))) stop('Input either the colors chosen by the user for the Farnsworth Munsell 100-Hue test or the position values')
if (is.null(okFM100colors)) {
#okFM100colors<-sprintf('#%02x%02x%02x',FarnsworthMunsell100Hue[-1,'R'],FarnsworthMunsell100Hue[-1,'G'],FarnsworthMunsell100Hue[-1,'B'])
okFM100colors<-sprintf('#%02x%02x%02x',FarnsworthMunsell100Hue[,'R'],FarnsworthMunsell100Hue[,'G'],FarnsworthMunsell100Hue[,'B'])
}
if (is.null(userFM100colors)) {
pos2<-userFM100values
} else {
pos2<-c()
for (n in 1:85) pos2<-c(pos2,which(userFM100colors[n] == okFM100colors))
}
#pos3<-86-pos2
lenBox<-1100
#circPos2<-circPos[pos3,] 
#symbols(circPos[,1], circPos[,2], circles =rep(5,85) , inches = FALSE, xlim = c(1,lenBox), ylim = c(lenBox,1), xaxt='n', yaxt='n', ann=FALSE)
cclockPos<-c(64:1,85:65) # counter-clockwise and with "1" on the top of the circle
circPos<-circPos[cclockPos,]
NumPos<-NumPos[cclockPos,]
colRGB<- sprintf("#%02X%02X%02X", FarnsworthMunsell100Hue[,'R'],FarnsworthMunsell100Hue[,'G'],FarnsworthMunsell100Hue[,'B'])
colRGB<- colRGB[cclockPos]
plot(circPos[cclockPos,1], circPos[cclockPos,2], col=colRGB, cex=2,pch=19, xlim = c(1,lenBox), ylim = c(lenBox,1), xaxt='n', yaxt='n',xlab='',ylab='')
#title and cap numbers
par(new=T)

if (titleGraphic=="Farnsworth Munsell 100-Hue test results") {
titleGraphic <- paste(titleGraphic,ifelse(Kinnear, '(Kinnear score)','Farnsworth score'))
}

title(main =titleGraphic)
par(new=T)
zz<-c(1,1:42*2) # the traditional plot has number 1 and then only even numbers
text(NumPos[zz,1], NumPos[zz,2], c(zz))
par(new=T)
#grid - circles
scoreC<-array(0,c(14,2,85))
for (n in 1:14) { 
circTmp<-calculateCircle(550,550,(500-30*n),86)
circTmp<-matrix(c(circTmp),ncol=2,byrow=F)
circTmp<-circTmp[-1,]
z1<-c(65:1,85:66)
circTmp2<-circTmp[z1,]
scoreC[n,,]<-t(circTmp2)
#plot(scoreC[1,1,],scoreC[1,2,], col= sprintf("#%02X%02X%02X", FarnsworthMunsell100Hue[,'R'],FarnsworthMunsell100Hue[,'G'],FarnsworthMunsell100Hue[,'B']),type='p',pch=19, xlim = c(1,lenBox), ylim = c(lenBox,1))
par(new=T)
plot(scoreC[n,1,],scoreC[n,2,],col='lightgrey',type='l', xlim = c(1,lenBox), ylim = c(lenBox,1), xaxt='n', yaxt='n',xlab='',ylab='');
par(new=T) }
#grid - lines
for (n in 1:85) { segments(scoreC[1,1,n],col='lightgrey',scoreC[1,2,n],scoreC[14,1,n],scoreC[14,2,n]); par(new=T) }

# score
scoreTES<-abs(calculateTES(pos2,!Kinnear))

#for (n in 1:84) { segments(scoreC[16-scoreTES[n],1,n],scoreC[16-scoreTES[n],2,n],scoreC[16-scoreTES[n+1],1,n+1],scoreC[16-scoreTES[n+1],2,n+1], xlim = c(1,lenBox), ylim = c(lenBox,1)); par(new=T) }

for (n in 1:84) { segments(scoreC[limitScore(scoreTES[n]),1,n],scoreC[limitScore(scoreTES[n]),2,n],scoreC[limitScore(scoreTES[n+1]),1,n+1],
scoreC[limitScore(scoreTES[n+1]),2,n+1], xlim = c(1,lenBox), ylim = c(lenBox,1)); par(new=T) }
}


interpretation.VingrysAndKingSmith<-function(VKS,optMethod=88)
{
#Vingrys, A.J. and King-Smith, P.E. (1988).
#A quantitative scoring technique for panel tests of color vision.
#Investigative Ophthalmology and Visual Science, 29, 50-63.
#VKS<-unlist(VKS)
A<-as.numeric(VKS['VKS88','ANGLE']);C<-as.numeric(VKS['VKS88','MAGNITUDE']);S<-as.numeric(VKS['VKS88','SCATTER'])
interpAngle<-'';interpMagnitude<-'';interpScatter<-''
if ((A > 3) & (A < 17)) interpAngle<-'Protan'
if ((A > -11) & (A < -4)) interpAngle<-'Deutan'
if ((A > -90) & (A < -70)) interpAngle<-'Tritan'
if (C > 1.78)  interpMagnitude<-'Abnormal arrangement'
if (C <= 1.78)  interpMagnitude<-'Normal arrangement'
if (S >= 2)  interpScatter<-'Selective'
if (S < 2)  interpScatter<-'Random'
c(Angle=interpAngle,Magnitude=interpMagnitude,Selectivity=interpScatter)
}

interpretation.Foutch<-function(FLJ)
{
# A new quantitative technique for grading Farnsworth D-15 color panel tests
# Foutch, Brian K.; Stringham, James M.; Lakshminarayanan, Vasuvedan
# Journal of Modern Optics, vol. 58, issue 19-20, pp. 1755-1763
# 
# Evaluation of the new web-based" Colour Assessment and Diagnosis" test
# J Seshadri, J Christensen, V Lakshminarayanan, CJ BASSI
# Optometry & Vision Science 82 (10), 882-885
#FLJ<-unlist(FLJ)
A<-as.numeric(FLJ['VKS88','ANGLE']);C<-as.numeric(FLJ['VKS88','Cindex']);S<-as.numeric(FLJ['VKS88','Sindex'])
if ((FLJ['Angle'] > 0) & (FLJ['Angle'] < 30)) interpAngle<-'Protan'
if ((FLJ['Angle'] > -30) & (FLJ['Angle'] < 0)) interpAngle<-'Deutan'
if ((FLJ['Angle'] > 75) & (FLJ['Angle'] < 90)) interpAngle<-'Tritan'
if (FLJ['Cindex'] > 1)  interpMagnitude<-'Abnormal arrangement'
if (FLJ['Cindex'] <= 1)  interpMagnitude<-'Normal arrangement'
if (FLJ['Sindex'] >= 1)  interpScatter<-'Selective'
if (FLJ['Sindex'] < 1)  interpScatter<-'Random'
c(Angle=interpAngle,Magnitude=interpMagnitude,Selectivity=interpScatter)
}

D15Foutch<-function(userD15values=NULL,testType='D-15', dataVKS=NA) {
#=======================================================
#function to quantitatively analyze D15 color panel tests:
# code from Dr Brian K. Foutch
# Calculates angle, magnitude and scatter (for D-15 panels only) using all four models:
# VK-S 88 and VK-S 93 (Vingrys, A.J. and King-Smith, P.E. (1988, 1993)), LSA 05 (Foutch/Bassi '05), and JMO 11 (Foutch/Stringham/Vengu '11).
#
# A new quantitative technique for grading Farnsworth D-15 color panel tests
# Foutch, Brian K.; Stringham, James M.; Lakshminarayanan, Vasuvedan
# Journal of Modern Optics, vol. 58, issue 19-20, pp. 1755-1763
# 
# Evaluation of the new web-based" Colour Assessment and Diagnosis" test
# J Seshadri, J Christensen, V Lakshminarayanan, CJ BASSI
# Optometry & Vision Science 82 (10), 882-885
#
#Vingrys, A.J. and King-Smith, P.E. (1988).
#A quantitative scoring technique for panel tests of color vision.
#Investigative Ophthalmology and Visual Science, 29, 50-63.

#=======================================================
#n = # of caps; 15 for D (and DS-) 15s...

# dataVKS by default are the CIE Luv data used by Vingrys and King-Smith
cnum<-userD15values
n <- 15
if (is.null(cnum)) stop('cnum must be defined')
if (!is.numeric(cnum)) stop('cnum must be numeric')
tType<-which(testType==c('D-15', 'D-15DS', 'FM1OO-Hue', 'Roth28-Hue'))
if (length(testType)==0) stop('testType must be "D-15", "D-15DS", "Roth28-Hue" or "FM1OO-Hue"')
if (any(trunc(cnum)!=cnum)) stop('cnum must be integers')
if (testType %in% c('D-15', 'D-15DS')) 
{
if (length(cnum) != 15) stop('cnum must be a vector of 15 elements for D-15')

if (!all(sort(cnum) == 1:15)) showDuplicated(cnum)

if (!all(sort(cnum) == 1:15)) stop('cnum must be between 1 and 15, without repetition')
}
if (testType %in% c('Roth28-Hue')) 
{
if (length(cnum) != 28) stop('cnum must be a vector of 28 elements for Roth28-Hue')
if (!all(sort(cnum) == 1:28)) stop('cnum must be between 1 and 28, without repetition')
}
if (testType == 'FM1OO-Hue') 
{
if (length(cnum) != 85) stop('cnum must be a vector of 85 elements for FM1OO-Hue')
if (!all(sort(cnum) == 1:85)) stop('cnum must be between 1 and 85, without repetition')
}
#=============================================
# For all models, initializing U and V vectors
#=============================================

if (any(is.na(dataVKS))) dataVKS<-list(
standardD15=matrix(c(-23.26,-25.56, -22.41,-15.53, -23.11,-7.45,-22.45,1.10, -21.67,7.35, -14.08,18.74,
-2.72,28.13, 14.84,31.13, 23.87,26.35,31.82,14.76, 31.42,6.99, 29.79,0.10,26.64,-9.38, 22.92,-18.65, 11.20,-24.61,-21.54, -38.39),16,2,byrow=T)
,
desaturatedD15=matrix(c(-8.63,-14.65, -12.08,-11.94, -12.86,-6.74,-12.26,-2.67, -11.18,2.01, -7.02,9.12,
1.30,15.78, 9.90,16.46, 15.03,12.05,15.48,2.56, 14.76,-2.24, 13.56,-5.04,11.06,-9.17, 8.95,-12.39, 5.62,-15.20,-4.77,-16.63),16,2,byrow=T)
,
FM100HUE=matrix(c(43.18,8.03, 44.37,11.34, 44.07,13.62, 44.95,16.04, 44.11,18.52,
42.92,20.64, 42.02,22.49, 42.28,25.15, 40.96,27.78, 37.68,29.55,
37.11,32.95, 35.41,35.94, 33.38,38.03, 30.88,39.59, 28.99,43.07,
25.00,44.12, 22.87,46.44, 18.86,45.87, 15.47,44.97, 13.01,42.12,
10.91,42.85, 8.49,41.35, 3.11,41.70, .68,39.23, -1.70,39.23,
-4.14,36.66, -6.57,32.41, -8.53,33.19, -10.98,31.47, -15.07,27.89,
-17.13,26.31, -19.39,23.82, -21.93,22.52, -23.40,20.14, -25.32,17.76,
-25.10,13.29, -26.58,11.87, -27.35,9.52, -28.41,7.26, -29.54,5.10,
-30.37,2.63, -31.07,0.10, -31.72,-2.42, -31.44,-5.13, -32.26,-8.16,
-29.86,-9.51, -31.13,-10.59, -31.04,-14.30, -29.10,-17.32, -29.67,-19.59,
-28.61,-22.65, -27.76,-26.66, -26.31,-29.24, -23.16,-31.24, -21.31,-32.92,
-19.15,-33.17, -16.00,-34.90, -14.10,-35.21, -12.47,-35.84, -10.55,-37.74,
-8.49,-34.78, -7.21,-35.44, -5.16,-37.08, -3.00,-35.95, -.31,-33.94,
1.55,-34.50, 3.68,-30.63, 5.88,-31.18, 8.46,-29.46, 9.75,-29.46,
12.24,-27.35, 15.61,-25.68, 19.63,-24.79, 21.20,-22.83, 25.60,-20.51,
26.94,-18.40, 29.39,-16.29, 32.93,-12.30, 34.96,-11.57, 38.24,-8.88,
39.06,-6.81, 39.51,-3.03, 40.90,-1.50, 42.80,0.60, 43.57,4.76,43.57,4.76),86,2,byrow=TRUE)
,
ROTH28=matrix(c(42.92,40.96,35.41,28.99,18.86,10.91,0.68,-6.57,-15.07,
-21.93,-25.10,-28.41,-31.07,-32.26,-31.04,-28.61,-23.16,-16.00,-10.55,-5.16,1.55,
8.46,15.61,25.60,32.93,39.06,42.80,4.76,13.62,20.64,27.78,35.94,43.07,45.87,42.85,
39.23,32.41,27.89,22.52,13.29,7.26,0.10,-8.16,-14.30,-22.65,-31.24,-34.90,-37.74,
-37.08,-34.50,-29.46,-25.68,-20.51,-12.30,-6.81,0.60,43.57,44.07),29,2,byrow=TRUE)
)

#if (tType==3) cnum<-c(cnum[85],cnum) else cnum<-c(0,cnum)
tSize<-c(15,15,85,28)[tType]
#CALCULATE SUMS OF SQUARES AND CROSS PRODUCTS
#REM COLOR DIFFERENCE VECTORS
u <- (dataVKS[[tType]])[,1]
v <- (dataVKS[[tType]])[,2]

#u<-as.vector(uv[,1]);v<-as.vector(uv[,2]);

num_errors=0;
#-------------------------------------------------------------
#INPUT CAP NUMBERS: REF = Cnum 16, so use actual cap #s:
#This code contains "checks" to ensure no cap repeated/skipped
#-------------------------------------------------------------
cnum<-c(16,cnum)
#=========================================
#INSERTING VK-s '88 MODEL HERE:
#=========================================

# CALCULATE COLOR DIFFERENCE VECTORS:
u2=0; v2=0; uv=0; d=0;
testu<-vector(length=16);testv<-vector(length=16);
testu[1]=u[16];
testv[1]=v[16]; #indexing test caps w/reference
    
du=u[cnum[1]]-u[16];
dv=v[cnum[1]]-v[16];
u2=u2 + du*du;
v2=v2 + dv*dv;
uv=uv + du*dv;

# initialize du_plot(16) and dv_plot(16)
#----------------------------------------
du_plot<-as.numeric(vector(length=16))
dv_plot<-as.numeric(vector(length=16))
du_plot[1]=du;dv_plot[1]=dv;

for (k in 1:15)
{
    testu[k+1]=u[cnum[k+1]];testv[k+1]=v[cnum[k+1]];
    du=u[cnum[k+1]]-u[cnum[k]];
    du_plot[k+1]=du;
    dv=v[cnum[k+1]]-v[cnum[k]];
    dv_plot[k+1]=dv;
    u2=u2 + du*du;
    v2=v2 + dv*dv;
    uv=uv + du*dv;
    d1 = d + u2 -v2;
}

# NEXT--CALCULATE MAJOR AND MINOR RADII AND ANGLE:
d = u2-v2;

if (d == 0)
    a0=0.7854
if (d != 0)
    #Y = atan(X)
    a0 = atan(2*uv/d)/2;

#Major moment
#------------
i0=u2*sin(a0)^2+v2*cos(a0)^2-2*uv*sin(a0)*cos(a0);

if (a0 < 0)
    a1 = a0 + 1.5708
if (a0 >= 0)
    a1 = a0 - 1.5708;

#Minor moment
#------------
i1=u2*sin(a1)^2+v2*cos(a1)^2-2*uv*sin(a1)*cos(a1);

#if minor axis larger, swap angles and minor/major axes:
#-------------------------------------------------------
if (i1 > i0)
{
p=a0; a0=a1; a1=p;
p=i0; i0 = i1; i1 =p; 
}

#Calculate total error and radii values
#--------------------------------------
r0 = (i0/n)^0.5; #n is for 15 possible moments of d-15
r1 = (i1/n)^0.5; 
r = (r0^2 + r1^2)^0.5;# = TES in algorithm

if (n == 15) 
    r2 = 9.234669; # r2 value for standard D-15

c_index=r0/r2;s_index=r0/r1;
c_index88=c_index;s_index88=s_index;
ANGLE88=180*a1/pi;
out88<-c(d,d1,u2,v2,uv,a0,a1,i0,i1,ANGLE88,c_index,s_index,r2)
#arr_plot<-plot(testu,testv,type="l",xlim=c(-40,40),ylim=c(-40,40))

#return(out88)

# =========================================
# END OF VK-S '88 MODEL
#==========================================

# NEXT--REINITIALIZE COLOR DIFFERENCE VECTORS:
#---------------------------------------------

# =============================================================================
# Now, insert LSA '05 Model HERE
# LSA '05 model calculates a best fit line (with y [or dV-]intercept based on:
# dV = m*dU + b
# The residuals "blow up" if angle is >45 degrees, so this code (and the model)
# requires recalculation based on: dU = m* dV + b, if angle is >45 degrees
# Publishes as AAO abstract in 2005 (Foutch & Bassi, OVS, eAbstract, 12/05)
#===============================================================================

#CALCULATE COLOR DIFFERENCE VECTORS:
u2=0; v2=0; uv=0; d=0;
testu<-vector(length=16);testv<-vector(length=16);
testu[1]=u[16];
testv[1]=v[16]; #indexing test caps w/reference
    
du=u[cnum[1]]-u[16];
dv=v[cnum[1]]-v[16];
u2=u2 + du*du;
v2=v2 + dv*dv;
uv=uv + du*dv;

# initialize du_plot(16) and dv_plot(16)
#----------------------------------------
du_plot<-as.numeric(vector(length=16))
dv_plot<-as.numeric(vector(length=16))

du_plot[1]=du;dv_plot[1]=dv;
#du_vectors(1)=0.0;dv_vectors(1)=0.0;
#du_vectors(2)=du;dv_vectors(2)=dv;

mindu=0.0;mindv=0.0;maxdu=0.0;maxdv=0.0;

k=0;
num_errors = 0;mag_errors2 = 0;
num_errors
if (cnum[2] != 1)
    {
    num_errors = num_errors + 1;
    num_errors
    #num_errors
    du=u[cnum[2]]-u[16];
    dv=v[cnum[2]]-v[16];
    u2=u2 + du*du;
    v2=v2 + dv*dv;
    uv=uv + du*dv;
    mag=sqrt(abs(du)^2+abs(dv)^2);
    mag_errors2=mag_errors2+mag;
    
    if (du<mindu) mindu=du;
    end
    if (du>maxdu) maxdu=du;
    end
    if (dv<mindv) mindv=dv;
    end
    if (dv>maxdv) maxdv=dv;
    end
    }
#...WORKS to THIS POINT!!!.............

# initialize du_plot(16) and dv_plot(16)
#----------------------------------------
du_plot[1]=du;dv_plot[1]=dv;
testu[2]=u[cnum[2]];testv[2]=v[cnum[2]];
for (k in 2:15)
    {
    #(cnum(k+1)-cnum(k))
    #testu[k+1]=u[cnum[k+1]];testv[k+1]=v[cnum[k+1]];

    if (abs((cnum[k+1]-cnum[k])) > 1)
        {
        num_errors = num_errors + 1;  #num_errors
        k
        num_errors
        du=u[cnum[k+1]]-u[cnum[k]];
        if (du<mindu) mindu=du
        if (du>maxdu) maxdu=du
        du_plot[k]=du;
        dv=v[cnum[k+1]]-v[cnum[k]];
        mag=sqrt(abs(du)^2+abs(dv)^2);
        mag_errors2=mag_errors2+mag;
        if (dv<mindv) mindv=dv
        if (dv>maxdv) maxdv=dv
        dv_plot[k]=dv;
        u2=u2 + du*du;
        v2=v2 + dv*dv;
        uv=uv + du*dv;
        }
    }

# Initializing dU and dV vectors 
uvec<-as.numeric(vector(length=num_errors))
vvec<-as.numeric(vector(length=num_errors))

#dudv<-matrix(ncol=2,nrow=16)
#dudv<-data.frame(dudv)

#dudv<-cbind(du_plot,dv_plot)

kk=0
for (i in 1:16)
    if(abs(du_plot[i])>0)
    {
    kk=kk+1
    uvec[kk]<-du_plot[i]
    vvec[kk]<-dv_plot[i]
    }

#err_plot<-scatterplot(uvec,vvec,xlim=c(-80,80),ylim=c(-80,80),smooth=FALSE)

#------------------------------------
# LSA '05 Only works for n > 1 errors
#------------------------------------

if (num_errors > 1)
{
testuv<-summary(lm(vvec~uvec))
testvu<-summary(lm(uvec~vvec))

ssvu<-testvu$res^2
ssuv<-testuv$res^2
ssuvvec<-testuv$res^2
ssvuvec<-testvu$res^2
ssuv=0
ssvu=0
for (i in 1:num_errors)
{
ssuv<-ssuv+ssuvvec[i]
ssvu<-ssvu+ssvuvec[i]
}

vtv<-ssuv/(num_errors-2)

LSA_angle<-as.numeric(testuv$coef[2,1])
LSA_angle<-atan(LSA_angle)*180/pi

if(ssvu<ssuv) 
 {#print("SWITCHED MODELS!!!!!!",file="")
  vtv<-ssvu/(num_errors-2)
  LSA_angle<-as.numeric(testvu$coef[2,1])
  LSA_angle<- -(atan(LSA_angle)*180/pi+90)
 } 

if(LSA_angle>90)
 {
 LSA_angle<-180-LSA_angle
 }

if(LSA_angle<(-90))
 {
 LSA_angle<-180+LSA_angle
 }
}
#========================      
# END LSA '05 Model HERE
#========================
#
# =============================================
# Now, insert JMO '11 Model HERE
# JMO '11 model calculates a best fit line (without y [or dV-]intercept based on:
# dV = m*dU (see paper in Journ Modern Optics, Foutch et al., 2011)
# The residuals "blow up" if angle is >45 degrees, so this code (and the model)
# requires recalculation based on: dU = m* dV, if angle is >45 degrees
#================================================
# 

if (num_errors > 0)
{
testuv11<-summary(lm(vvec~0+uvec))
testvu11<-summary(lm(uvec~0+vvec))

ssvu11<-testvu11$res^2
ssuv11<-testuv11$res^2
ssuvvec11<-testuv11$res^2
ssvuvec11<-testvu11$res^2
ssuv11=0
ssvu11=0
for (i in 1:num_errors)
{
ssuv11<-ssuv11+ssuvvec11[i]
ssvu11<-ssvu11+ssvuvec11[i]
}

LSA_angle11<-as.numeric(testuv11$coef[1,1])
LSA_angle11<-atan(LSA_angle11)*180/pi

#== set JMO11 model equal to LSA05 magnitude==#
mag11 = mag_errors2; 


vtv11<-ssuv11/(num_errors-1)

if(ssvu11<ssuv11) 
 {#print("SWITCHED MODELS!!!!!!",file="")
  vtv11<-ssvu11/(num_errors-1)
  LSA_angle11<-as.numeric(testvu11$coef[1,1])
  LSA_angle11<- -(atan(LSA_angle11)*180/pi+90)
 } 

if(LSA_angle11>90)
 {
 LSA_angle11<-180-LSA_angle11
 }

if(LSA_angle11<(-90))
 {
 LSA_angle11<-180+LSA_angle11
 }
}

#========================
#End JMO '11 MODEL HERE
#========================

#==========
# VK-S 93 
#==========
#NEXT--CALCULATE MAJOR AND MINOR RADII AND ANGLE:
d = u2-v2;

#Y = atan(X)
    a0 = atan(2*uv/d)/2;

if (d == 0)
    a0=0.7854;


#Major moment
#------------
i0=u2*sin(a0)^2+v2*cos(a0)^2-2*uv*sin(a0)*cos(a0);

a1 = a0 - 1.5708;
if (a0 < 0)
 a1 = a0 + 1.5708;


#Minor moment
#------------
i1=u2*sin(a1)^2+v2*cos(a1)^2-2*uv*sin(a1)*cos(a1);

#if minor axis larger, swap angles and minor/major axes:
#-------------------------------------------------------

if (i1 > i0)
    {
    if (i1<0) i1=0
    p=a0; a0=a1; a1=p;
    p=i0; i0 = i1; i1 =p; 
    }

#Calculate total error and radii values
#--------------------------------------
r0 = sqrt(abs(i0/n)); #n is for 15 possible moments of d-15
r1 = sqrt(abs(i1/n)); 
r = sqrt(r0^2 + r1^2);# = TES in algorithm

if (n == 15) 
    r2 = 9.234669; #r2 value for standard D-15

c_index93=r0/r2;
s_index93=(sqrt(r0^2-r1^2))/r2;
ANGLE93=a1*180/pi;

#=============
# END VK-S 93 
#=============

#===========================================
# Populating vectors and matrices for output
#===========================================

# VKS-88 output
out88<-cbind(ANGLE88,c_index,s_index)
out88

# VKS-93 output
out93<-cbind(ANGLE93,c_index93,s_index93)
out93

#if "no errors", "blanks" out LSA05 and JMO11 output
if (num_errors == 0)
{
outLSA <- cbind("N/A","N/A","N/A")
outJMO <- cbind("N/A","N/A","N/A")
}


#if only one error, "blanks" out LSA05 output
if (num_errors == 1)
{
outLSA <- cbind("N/A","N/A","N/A")
}

# Calculates LSA '05 "modeled" output (see original paper)
if (num_errors > 1)
{
outLSA<-cbind(LSA_angle,mag_errors2/83,18.47/sqrt(vtv))
}

# =========================================================
# Calculates JMO '11 "modeled" output (see original paper);
#    Same calculations as LSA '05:
#    -----------------------------
#      magnitude = magnitude / 83;
#      scatter = 18.47/sqrt(sp^2) 

if (num_errors > 0)
{
outJMO<-cbind(LSA_angle11,mag_errors2/83,18.47/sqrt(vtv11));
}
#===========================================================
outmat<-matrix(nrow=4,ncol=3)
outmat<-data.frame(outmat)
names(outmat)<-c("ANGLE","MAGNITUDE","SCATTER")
outmat[1,]<-outLSA
if(num_errors>1) {outmat[1,]<-round(outLSA,2)}
outmat[2,]<-outJMO
if(num_errors>0) {outmat[2,]<-round(outJMO,2)}
outmat[3,]<-round(out88,2)
outmat[4,]<-round(out93,2)
row.names(outmat)<-c("LSA05","JMO11","VKS88","VKS93")
outmat
}

calculateTES<-function(fmData, Kinnear=FALSE)
{ # total error score (TES) using Farnsworth's or Kinnear's method
# cap 1 = pilot
x<-length(fmData)
R<-rep(0,x)
for (n in 1:x) R[ifelse(Kinnear,fmData[n],n)]<-ifelse(n==1,abs(fmData[n]-fmData[x]),abs(fmData[n]-fmData[n-1])) + ifelse(n==x,abs(fmData[n]-fmData[1]),abs(fmData[n]-fmData[n+1]))
if (x>80) R[R>50]<-R[R>50]-83
R
}

scoreRoth28Graphic<-function(userR28colors=NULL,userR28values=NULL, titleGraphic="Roth-28 test results", okR28colors=NULL)
{# plots the graphic to score the Roth-28 test by default, or a similar test by modifying titleGraphic and okR28colors
Roth28<-get("Roth28", envir = environment())
cirC<-c(1050,1037,1000,941,862,767,661,550,439,333,238,159,100,63,50,63,100,159,238,333,439,550,661,767,862,941,1000,1037,1050,550,661,767,862,941,1000,1037,1050,1037,1000,941,862,767,661,550,439,333,238,159,100,63,50,63,100,159,238,333,439,550)
cirC2<-c(1080,1067,1028,964,880,780,668,550,432,320,220,136,72,33,20,33,72,136,220,320,432,550,668,780,880,964,1028,1067,1080,550,668,780,880,964,1028,1067,1080,1067,1028,964,880,780,668,550,432,320,220,136,72,33,20,33,72,136,220,320,432,550)
circPos<-matrix(c(cirC),ncol=2,byrow=F)
NumPos<-matrix(c(cirC2),ncol=2,byrow=F)
circPos<-circPos[-1,]
NumPos<-NumPos[-1,]
circPos<-circPos[c(18:28,1:17),]
NumPos<-NumPos[c(18:28,1:17),]
# if the user's input was in FM100 indices, convert to 1:28 range
if (!is.null(userR28values)) if (is.numeric(userR28values)) if (any(userR28values>28)) userR28values<-userR28values %/% 3+1
if ((is.null(userR28colors)) & (is.null(userR28values))) stop('Input either the colors chosen by the user for the Roth-28 test or the position values')
if (is.null(okR28colors)) {
okR28colors<-sprintf('#%02x%02x%02x',Roth28[-1,'R'],Roth28[-1,'G'],Roth28[-1,'B'])
}
if (is.null(userR28colors)) {
pos2<-userR28values
} else {
pos2<-c()
for (n in 1:28) pos2<-c(pos2,which(userR28colors[n] == okR28colors))
}
pos2<-29-pos2
lenBox<-1100
circPos2<-circPos[pos2,] 
symbols(circPos[,1], circPos[,2], circles =rep(5,28) , inches = FALSE, xlim = c(1,lenBox), ylim = c(lenBox,1), xaxt='n', yaxt='n', ann=FALSE)
par(new=T)
title(main =titleGraphic)
par(new=T)
plot(circPos2[,1], circPos2[,2], xlim = c(1,lenBox), ylim = c(lenBox,1),type='l', xaxt='n', yaxt='n', ann=FALSE)
par(new=T)
segments(200,200,964, 880 , lty=2,col='red')
par(new=T)
segments(320, 1028,780, 72 , lty=2,col='green')
par(new=T)
segments(320,72,780, 1000,lty=2,col='blue')
par(new=T)
numText<-seq(82,1,-3)
text(NumPos[,1], NumPos[,2], c(numText))
par(new=T)
text(664,235,"Tritan", srt=60) 
par(new=T)
text(270,230,"Protan", srt=-40) 
par(new=T)
text(420,200,"Deutan", srt=-60) 
}

scoreD15Graphic<-function(userD15colors=NULL,userD15values=NULL, titleGraphic="Farnsworth dichotomous test (D-15) results", okD15colors=NULL)
{# plots the graphic to score the Farnsworth dichotomous test (D-15) by default, or a similar test by modifying titleGraphic and okD15colors
FarnsworthD15<-get("FarnsworthD15", envir = environment())
circPos<-matrix(c(22,125,44,82,76,50,118,28,150,28,193,28,246,50,278,92,289,167,278,230,246,263,204,284,172,284,118,274,86,252,44,209),ncol=2,byrow=TRUE)
NumPos<-matrix(c(22,135,44,82-15,76,50-15,118,28-15,150,28-15,193,28-15,246,50-15,278+15,92,289+15,167,278+15,230,246,263+15,204,284+15,172,284+15,118,274+15,86,252+15,44,209+15),ncol=2,byrow=TRUE)
if ((is.null(userD15colors)) & (is.null(userD15values))) stop('Input either the colors chosen by the user for the D-15 test or the position values')
if (is.null(okD15colors)) {
okD15colors<-sprintf('#%02x%02x%02x',FarnsworthD15[-1,'R'],FarnsworthD15[-1,'G'],FarnsworthD15[-1,'B'])#c('#3583B4', '#3B84A7', '#39859C', '#3B8690', '#3F8782', '#588473', '#6C8164', '#837B5D', '#907660', '#9E6E6F', '#9F6D7C', '#9C6D89', '#927099', '#8F6FA4','#8073B2')
}
if (is.null(userD15colors)) {
pos2<-userD15values
} else {
pos2<-c()
for (n in 1:15) pos2<-c(pos2,which(userD15colors[n] == okD15colors) )
}
pos2<-c(1,pos2+1)
circPos2<-circPos[pos2,] 
symbols(circPos[,1], circPos[,2], circles =rep(5,16) , inches = FALSE, xlim = c(1,300), ylim = c(300,1), xaxt='n', yaxt='n', ann=FALSE)
par(new=T)
title(main =titleGraphic)
par(new=T)
plot(circPos2[,1], circPos2[,2], xlim = c(1,300), ylim = c(300,1),type='l', xaxt='n', yaxt='n', ann=FALSE)
par(new=T)
lines(c(96,138),c(27,267),type = 'l', lty=2,col='red')
par(new=T)
lines(c(133,102),c(14,290),type = 'l', lty=2,col='green')
par(new=T)
lines(c(58,293),c(210,131),type = 'l', lty=2,col='blue')
par(new=T)
text(NumPos[,1], NumPos[,2], c('Reference',1:15))
par(new=T)
text(195,145,"Tritan", srt=20) 
par(new=T)
text(94,80,"P\nr\no\nt\na\nn") 
par(new=T)
text(137,83,"D\ne\nu\nt\na\nn") 
}

scoreD15TCDS<-function(userD15colors=NULL,userD15values=NULL, distTable=get("BowmanTCDS", envir = environment()), D15colors=get("FarnsworthD15", envir = environment()))
{# Compute the Total Color Difference Score (TCDS) for the D-15 colors or for their positions
# Bowman's (1982) Total Color Difference Score (TCDS) for congenitally defective observers on the D-15 with enlarged tests.
# K.J. Bowman, A method for quantitative scoring of the Farnsworth Panel D-15, Acta Ophthalmologica, 60 (1982), pp. 907–916
# userD15colors RGB colors chosen by tester
# userD15values position values chosen by tester
# distTable a distance table, for example GellerTCDS for scoring D15d with Geller's method
# D15colors contains the D15 colors in columns 'R', 'G' and 'B'
if ((is.null(userD15colors)) & (is.null(userD15values))) stop('Input either the colors chosen by the user for the D-15 test or the position values')
if (is.null(userD15colors)) {
pos2<-userD15values
} else {
lColorsOK<-sprintf('#%02x%02x%02x',D15colors[-1,'R'],D15colors[-1,'G'],D15colors[-1,'B'])
pos2<-c()
for (n in 1:15) pos2<-c(pos2,which(userD15colors[n] == lColorsOK) )
}
posRow<-c(1,pos2+1)
posRow<-posRow[1:15]
posColumn<- pos2+1
posValue<-c()
posValueNormal<-c()
for (n in 1:15) {
posValue<-c(posValue,distTable[posRow[n],posColumn[n]])
posValueNormal<-c(posValueNormal,distTable[n+1,n])
}
TCDS<-sum(posValue)# TCDS
#  Color Confusion Index (CCI = TCDSactual / TCDSnormal)
CCI<-TCDS/sum(posValueNormal)
c(TCDS=TCDS,CCI=CCI)
}

lightAdaptedPupilSize.Holladay<-function(L=NULL){
# pupil diameter ranges, formula "for a probable average healthy young eye" - Holladay, L. (1926)
# L=luminance in cd m^-2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Holladay, L. (1926). The fundamentals of glare and visibility. Journal of the Optical Society of America, 12(4), 271–319.
if (is.null(L)) stop('Please enter the luminance in candels per square meter (cd m^-2)')
if (is.na(L)) stop('Please enter a numeric value for the luminance')
if (!is.numeric(L)) stop('Please enter a numeric value for the luminance')
return(7*exp(-0.1007*L^0.4))
}

lightAdaptedPupilSize.Crawford<-function(L=NULL){
# pupil diameter ranges - Crawford 1936
# L=luminance in cd m^-2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Crawford, B. H. (1936). The dependence of pupil size upon external light stimulus under static and variable conditions. Proceedings of the Royal
# Society of London, Series B, Biological Sciences, 121(823), 376–395.
5-2.2*tanh(0.61151+0.447*log(L))
}

lightAdaptedPupilSize.MoonAndSpencer<-function(L=NULL){
# pupil diameter ranges Moon and Spencer 1944
# L=luminance in cd m^-2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Moon, P., & Spencer, D. E. (1944). On the Stiles-Crawford effect. Journal of the Optical Society of
# America, 34(6), 319–329, http://www.opticsinfobase. org/abstract.cfm?URI1⁄4josa-34-6-319.
4.9-3*tanh(0.4*log(L)-0.00114)
}

lightAdaptedPupilSize.DeGrootAndGebhard<-function(L=NULL){
# pupil diameter ranges De Groot and Gebhard 1952
# L=luminance in cd m^-2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# De Groot, S. G., & Gebhard, J. W. (1952). Pupil size as determined by adapting luminance. Journal of the Optical Society of America A, 42(7), 492–495.
7.175*exp(-0.00092*(7.597+log(L))^3)
}

lightAdaptedPupilSize.LeGrand<-function(L=NULL){
# pupil diameter ranges Le Grand 1992
# L=luminance in cd m^-2
# Vision, Pierre A. Buser, Michel Imbert, MIT Press, 1992
5 - 3 * tanh(0.4 * log10(L))
}

lightAdaptedPupilSize.StanleyAndDavies<-function(L=NULL, a=NULL){
# pupil diameter ranges Stanley and Davies 1995
# L=luminance in cd m^-2, a = area in deg^2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Stanley, P. A., & Davies, A. K. (1995). The effect of field of view size on steady-state pupil diameter. Ophthalmic & Physiological Optics, 15(6), 601–603.
7.75 - 5.75*((L*a/846)^0.41/((L*a/846)^0.41+2))
}

lightAdaptedPupilSize.Barten<-function(L=NULL, a=NULL){
# pupil diameter ranges Barten 1999
# L=luminance in cd m^-2, a = area in deg^2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Barten, P. G. J. (1999). Contrast sensitivity of the human eye and its effects on image quality. Bellingham, WA: SPIE Optical Engineering Press.
5-3*tanh(0.4*log(L*a/40^2))
}

lightAdaptedPupilSize.BlackieAndHowland<-function(L=NULL){
# pupil diameter ranges Blackie and Howland 1999
# L=luminance in cd m^-2
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Blackie, C. A., & Howland, H. C. (1999). An extension of an accommodation and convergence model of emmetropization to include the effects of illumination intensity. Ophthalmic and Physiological Optics, 19(2), 112–125.
5.697 - 0.658* log(L) + 0.07*(log(L))^2
}

lightAdaptedPupilSize.WinnEtAl<-function(L=NULL, y=NULL){
# pupil diameter ranges Winn, Whitaker, Elliott, and Phillips 1994
# L=luminance in cd m^-2, age y in years
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
# Winn, B., Whitaker, D., Elliott, D. B., & Phillips, N. J. (1994). Factors affecting light-adapted pupil size in
# normal human subjects. Investigative Ophthalmology & Visual Science, 35(3):1132–1137, http://www.iovs.org/content/35/3/1132. 
s <- c( -0.024501, -0.0368073, 0.0210892, 0.00281557)
b <- c( 6.9039, 2.7765, -1.909, 0.25599)
(sum(s* (log(min(4400,max(9,L))))^(0:3) ))*y+sum(b* (log(min(4400,max(9,L))))^(0:3) )
}

attenuationNumberOfEyes<-function(e) ifelse(e==1,0.1,ifelse(e==2,1,0))
# attenuation as a function M(e) of number of eyes e (1 or 2)
# M(1) = 0.1, M(2) = 1, otherwise 0
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.

effectiveCornealFluxDensity<-function(L=NULL,a=NULL,e=NULL){
# effective Corneal Flux Density = product of luminance, area, and the monocular effect, F = Lae
# L=luminance, a = field area in deg^2, e = number of eyes (1 or 2)
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
L*a*attenuationNumberOfEyes(e)
}

lightAdaptedPupilSize.WatsonAndYellott<-function(L=NULL, a=NULL, y=NULL, y0=NULL, e=NULL){
# pupil diameter ranges Watson & Yellott 2012
# L=luminance in cd m^-2, a = field area in deg^2, y = age in years, y0 = reference age, e = number of eyes (1 or 2)
# Watson A. B., Yellott J. I. (2012). A unified formula for light-adapted pupil size. Journal of Vision, 12(10):12, 1–16. http://journalofvision.org/12/10/12/, doi:10.1167/5.9.6.
F <- effectiveCornealFluxDensity(L,a,e)
Dsd <- lightAdaptedPupilSize.StanleyAndDavies(F,1)
Dsd+(y-y0)*(0.02132 - 0.009562*Dsd)
}

greyscale.avg<-function(colorArray){# average RGB values - grayscale algorithm
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,mean) else p3<-apply(colorArray,1:2,mean)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

greyscale.Y<-function(colorArray){# YIQ/NTSC - RGB colors in a gamma 2.2 color space - grayscale algorithm
colorArray[,,1]<-colorArray[,,1]*0.299
colorArray[,,2]<-colorArray[,,2]*0.587
colorArray[,,3]<-colorArray[,,3]*0.114
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,sum) else p3<-apply(colorArray,1:2,sum)
#p3<-colorArray
#if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,sum) else p3<-apply(colorArray,1:2,sum)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

greyscale.Linear<-function(colorArray){# linear RGB colors - grayscale algorithm
colorArray[,,1]<-colorArray[,,1]*0.3086
colorArray[,,2]<-colorArray[,,2]*0.6094
colorArray[,,3]<-colorArray[,,3]*0.0820
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,sum) else p3<-apply(colorArray,1:2,sum)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

greyscale.RMY<-function(colorArray){# RMY - grayscale algorithm
colorArray[,,1]<-colorArray[,,1]*0.5
colorArray[,,2]<-colorArray[,,2]*0.419
colorArray[,,3]<-colorArray[,,3]*0.081
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,sum) else p3<-apply(colorArray,1:2,sum)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

greyscale.BT709<-function(colorArray){# BT709 - grayscale algorithm
colorArray[,,1]<-colorArray[,,1]*0.2125
colorArray[,,2]<-colorArray[,,2]*0.7154
colorArray[,,3]<-colorArray[,,3]*0.0721
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,sum) else p3<-apply(colorArray,1:2,sum)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

greyscale.Luminosity<-function(colorArray){# Luminosity - grayscale algorithm
if (dim(colorArray)[3]>3) p3<-apply(colorArray[,,-4],1:2,function(x) (max(x)+min(x))/2) else p3<-apply(colorArray,1:2,function(x) (max(x)+min(x))/2)
p3<-array(p3,dim(colorArray))
if (dim(colorArray)[3]>3) p3[,,4]<-colorArray[,,4]
p3
}

RGBtoHSL<-function(R,G,B){
# RGB to HSL function to assist Color.Vision.c2g
R2 = R/255
G2 = G/255
B2 = B/255
if (length(R)==1 & length(G)==1 & length(B)==1){
Cmax = max(R2, G2, B2)
Cmin = min(R2, G2, B2)
D = Cmax - Cmin
if (Cmax==R2) H<-60*(((G2-B2)/D) %% 6)
if (Cmax==G2) H<-60*((B2-R2)/D+2)
if (Cmax==B2) H<-60*((R2-G2)/D+4)
L<-(Cmax+Cmin)/2
if (D==0) S<-0 else S<-D/(1-abs(2*L-1))
} else {
Tmp<-cbind(R2,G2,B2)
Cmax = apply(Tmp,1,max)
Cmin = apply(Tmp,1,min)
D = Cmax - Cmin
H<-S<-rep(0,length(R))
w<-which(Cmax==R2)
H[w]<-60*(((G2[w]-B2[w])/D[w]) %% 6)
w<-which(Cmax==G2)
H[w]<-60*((B2[w]-R2[w])/D[w]+2)
w<-which(Cmax==B2)
H[w]<-60*((R2[w]-G2[w])/D[w]+4)
L<-(Cmax+Cmin)/2
w<-which(D!=0)
S[w]<-D[w]/(1-abs(2*L[w]-1))
}
list(H=H,S=S,L=L)
}

Color.Vision.c2g<-function(fileIN=NULL, fileOUT=NULL, CorrectBrightness=FALSE){
#Color Image to Grayscale Conversion
#Original work by Martin Faust 2008
#http://www.e56.de/c2g.php
#Translated to R by Jose Gama 2013
#fileIN='ars_puzzlefighter_normal_vision.png'; fileOUT='ars_puzzlefighter_c2g.png';CorrectBrightness=FALSE
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist' )
if (is.null(fileOUT)) stop('A file output must be defined')
if (is.null(CorrectBrightness)) stop('CorrectBrightness must be defined')
if (!is.logical(CorrectBrightness)) stop('CorrectBrightness must be logical')
p<-png::readPNG(fileIN)
p<-p*255
r<-c(p[,,1])
g<-c(p[,,2])
b<-c(p[,,3])
grayMat<-rep(0,length(r))
h2<-RGBtoHSL(r,g,b)
HSVmat<-rbind(h=h2$H,s=h2$S,l=h2$L)
wSat0<-which(HSVmat['s',] == 0.0)
grayMat[wSat0] = 1.5 * HSVmat['l',wSat0]
if (length(wSat0)==0) grayMat = HSVmat['l',] + HSVmat['l',] * HSVmat['s',] else grayMat[-wSat0] = HSVmat['l',-wSat0] + HSVmat['l',-wSat0] * HSVmat['s',-wSat0]
minC = min(grayMat)
maxC = max(grayMat)
meanC = mean(grayMat)
cat('Grey values: min',minC,', mean',meanC,', max',maxC)
minC = 0.0; maxC = (meanC + maxC) * 0.5
if (CorrectBrightness){
HSVmat['l',] <- 0.9 * (grayMat - minC) / (maxC - minC)
} else {
HSVmat['l',] <- (grayMat - minC) / (maxC - minC)
}
HSVmat['l',which(HSVmat['l',] > 1.0)] <- 1.0
HSVmat['l',which(HSVmat['l',] < 0.0)] <- 0.0
p2<-HSVmat['l',]
p3<-p[,,-4]
p3[,,1]<-array(t(p2),dim(p)[1:2])
p3[,,2]<-p3[,,1]
p3[,,3]<-p3[,,1]
png::writePNG(p3, fileOUT)
}

Color.Vision.Daltonize<-function(fileIN=NULL, fileOUT=NULL, myoptions=NULL, amount=1.0){
# converts images so that the most problematic colors are more visible to people with CVD
# Based on:
# Michael Deal Daltonize.org http://mudcu.be/labs/Color/Vision http://www.daltonize.org/p/about.html
#"Analysis of Color Blindness" by Onur Fidaner, Poliang Lin and Nevran Ozguven.
#http://scien.stanford.edu/class/psych221/projects/05/ofidaner/project_report.pdf
#"Digital Video Colourmaps for Checking the Legibility of Displays by Dichromats" by Francoise Vienot, Hans Brettel and John D. Mollon
#http://vision.psychol.cam.ac.uk/jdmollon/papers/colourmaps.pdf
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist' )
if (is.null(fileOUT)) stop('A file output must be defined')
if (is.null(myoptions)) stop('Options must be defined')
if ((amount>1.0)|(amount<0.0)) stop('Amount must be between 0.0 and 1.0')
if (!is.character(myoptions)) stop('Options must be "Protanope","Deuteranope" or "Tritanope"')
if (!(myoptions %in% c("Protanope","Deuteranope", "Tritanope"))) stop('Wrong option, must be: Protanope, Deuteranope or Tritanope.')

CVDMatrix <- array(c(0.000000,0.000000,0.000000,2.023440,1.000000,0.000000,-2.525810,
0.000000,1.000000,1.000000,0.494207,0.000000,0.000000,0.000000,
0.000000,0.000000,1.248270,1.000000,1.000000,0.000000,-0.395913,
0.000000,1.000000,0.801109,0.000000,0.000000,0.000000),c(3,3,3))

#Protanope, Deuteranope, Tritanope
p<-png::readPNG(fileIN)
r<-p[,,1]
g<-p[,,2]
b<-p[,,3]
x<-which(c("Protanope","Deuteranope", "Tritanope")==myoptions)
cvd_a = CVDMatrix[1,1,x]
cvd_b = CVDMatrix[1,2,x]
cvd_c = CVDMatrix[1,3,x]
cvd_d = CVDMatrix[2,1,x]
cvd_e = CVDMatrix[2,2,x]
cvd_f = CVDMatrix[2,3,x]
cvd_g = CVDMatrix[3,1,x]
cvd_h = CVDMatrix[3,2,x]
cvd_i = CVDMatrix[3,3,x]
L = (17.8824 * r) + (43.5161 * g) + (4.11935 * b)
M = (3.45565 * r) + (27.1554 * g) + (3.86714 * b)
S = (0.0299566 * r) + (0.184309 * g) + (1.46709 * b)
l = (cvd_a * L) + (cvd_b * M) + (cvd_c * S)
m = (cvd_d * L) + (cvd_e * M) + (cvd_f * S)
s = (cvd_g * L) + (cvd_h * M) + (cvd_i * S)
R = (0.0809444479 * l) + (-0.130504409 * m) + (0.116721066 * s)
G = (-0.0102485335 * l) + (0.0540193266 * m) + (-0.113614708 * s)
B = (-0.000365296938 * l) + (-0.00412161469 * m) + (0.693511405 * s)
R = r - R
G = g - G
B = b - B
RR = (0.0 * R) + (0.0 * G) + (0.0 * B)
GG = (0.7 * R) + (1.0 * G) + (0.0 * B)
BB = (0.7 * R) + (0.0 * G) + (1.0 * B)
R = RR + r
G = GG + g
B = BB + b
# Return values
R[which(R>1.0)]<-1.0
R[which(R<0.0)]<-0.0
G[which(G>1.0)]<-1.0
G[which(G<0.0)]<-0.0
B[which(B>1.0)]<-1.0
B[which(B<0.0)]<-0.0
p[,,1]<-R
p[,,2]<-G
p[,,3]<-B
png::writePNG(p, fileOUT)
}

Color.Vision.Simulate<-function(fileIN=NULL, fileOUT=NULL, myoptions=NULL, amount=1.0){
# converts images so that the colors look similar to how they are seen by people with CVD
# Based on:
# Michael Deal Daltonize.org http://mudcu.be/labs/Color/Vision http://www.daltonize.org/p/about.html
#"Analysis of Color Blindness" by Onur Fidaner, Poliang Lin and Nevran Ozguven.
#http://scien.stanford.edu/class/psych221/projects/05/ofidaner/project_report.pdf
#"Digital Video Colourmaps for Checking the Legibility of Displays by Dichromats" by Francoise Vienot, Hans Brettel and John D. Mollon
#http://vision.psychol.cam.ac.uk/jdmollon/papers/colourmaps.pdf
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist' )
if (is.null(fileOUT)) stop('A file output must be defined')
if (is.null(myoptions)) stop('Options must be defined')
if ((amount>1.0)|(amount<0.0)) stop('Amount must be between 0.0 and 1.0')
ConfusionLines <- data.frame(name=c("Protanope","Deuteranope","Tritanope","Achromatope"),x=c(0.7465,1.4,0.1748,0), y=c(0.2535,-0.4,0.0,0),
m=c(1.273463,0.968437,0.062921,0), yint=c(-0.073894,0.003331,0.292119,0))
if (is.character(myoptions)) if (!(myoptions %in% as.vector(ConfusionLines[["name"]]))) stop('Wrong option, must be: Protanope, Deuteranope, Tritanope or Achromatope.')

#library('png', character.only=TRUE)
if (is.character(myoptions))
{
if (myoptions %in% as.vector(ConfusionLines[["name"]])) {
if (myoptions=="Achromatope")
{
p<-png::readPNG(fileIN)
numrows<-dim(p)[1]
numcols<-dim(p)[2]
p0=0.212656*p[,,1] + 0.715158*p[,,2] + 0.072186*p[,,3]
p0=p0*amount
a0<-1.0 - amount
p[,,1]<-p[,,1] * a0 + p0
p[,,2]<-p[,,2] * a0 + p0
p[,,3]<-p[,,3] * a0 + p0
png::writePNG(p, fileOUT)
return()
}
if (myoptions %in% as.vector(ConfusionLines[["name"]]))
{
tmp<-ConfusionLines[which(ConfusionLines[["name"]]==myoptions),]
confuse_x<-tmp[["x"]]
confuse_y<-tmp[["y"]]
confuse_m<-tmp[["m"]]
confuse_yint<-tmp[["yint"]]
opName<-myoptions
}
}
} else {
if (!is.numeric(myoptions)) stop('Options must be "Protanope","Deuteranope","Tritanope","Achromatope" or a numeric vector with 4 custom parameters for the confusion lines')
if (is.numeric(myoptions)) if(length(myoptions)!=4) stop('Options must be a numeric vector with 4 parameters')
if (is.numeric(myoptions)) if(length(myoptions)==4)
{ confuse_x<-myoptions[1];confuse_y<-myoptions[2];confuse_m<-myoptions[3];confuse_yint<-myoptions[4];opName<-'Custom' }
}
#Simulate: Protanope, Deuteranope, or Tritanope
p<-png::readPNG(fileIN)
sr<-p[,,1]
sg<-p[,,2]
sb<-p[,,3]
dr = sr # destination-pixel
dg = sg
db = sb
# Convert source color into XYZ color space
pow_r = sr^2.2
pow_g = sg^2.2
pow_b = sb^2.2
X = pow_r * 0.412424 + pow_g * 0.357579 + pow_b * 0.180464 # RGB->XYZ (sRGB:D65)
Y = pow_r * 0.212656 + pow_g * 0.715158 + pow_b * 0.0721856
Z = pow_r * 0.0193324 + pow_g * 0.119193 + pow_b * 0.950444
# Convert XYZ into xyY Chromacity Coordinates (xy) and Luminance (Y)
chroma_x = X / (X + Y + Z)
chroma_y = Y / (X + Y + Z)
chroma_x[which(is.na(chroma_x))]<-0#X[which(is.na(chroma_x))]
chroma_y[which(is.na(chroma_y))]<-0#Y[which(is.na(chroma_y))]
# Generate the “Confusion Line" between the source color and the Confusion Point
m = (chroma_y - confuse_y) / (chroma_x - confuse_x) # slope of Confusion Line
yint = chroma_y - chroma_x * m # y-intercept of confusion line (x-intercept = 0.0)
# How far the xy coords deviate from the simulation
deviate_x = (confuse_yint - yint) / (m - confuse_m)
deviate_y = (m * deviate_x) + yint
# Compute the simulated color’s XYZ coords
X = deviate_x * Y / deviate_y
Z = (1.0 - (deviate_x + deviate_y)) * Y / deviate_y
# Neutral grey calculated from luminance (in D65)
neutral_X = 0.312713 * Y / 0.329016 
neutral_Z = 0.358271 * Y / 0.329016 
# Difference between simulated color and neutral grey
diff_X = neutral_X - X
diff_Z = neutral_Z - Z
diff_r = diff_X * 3.24071 + diff_Z * -0.498571 # XYZ->RGB (sRGB:D65)
diff_g = diff_X * -0.969258 + diff_Z * 0.0415557
diff_b = diff_X * 0.0556352 + diff_Z * 1.05707
# Convert to RGB color space
dr = X * 3.24071 + Y * -1.53726 + Z * -0.498571 # XYZ->RGB (sRGB:D65)
dg = X * -0.969258 + Y * 1.87599 + Z * 0.0415557
db = X * 0.0556352 + Y * -0.203996 + Z * 1.05707
# Compensate simulated color towards a neutral fit in RGB space
fit_r2 = (ifelse(dr < 0.0, 0.0,1.0) - dr) / diff_r
fit_g2 = (ifelse(dg < 0.0,0.0,1.0) - dg) / diff_g
fit_b2 = (ifelse(db < 0.0,0.0,1.0) - db) / diff_b
fit_r=fit_r2
fit_g=fit_g2
fit_b=fit_b2
fit_r[which(is.infinite(fit_r2))]<-(ifelse(dr[which(is.infinite(fit_r2))] < 0.0, 0.0,1.0) - dr[which(is.infinite(fit_r2))])
fit_g[which(is.infinite(fit_g2))]<-(ifelse(dg[which(is.infinite(fit_g2))] < 0.0, 0.0,1.0) - dg[which(is.infinite(fit_g2))])
fit_b[which(is.infinite(fit_b2))]<-(ifelse(db[which(is.infinite(fit_b2))] < 0.0, 0.0,1.0) - db[which(is.infinite(fit_b2))])
fit_r2 = fit_r
fit_g2 = fit_g
fit_b2 = fit_b
fit_r2[which((fit_r > 1.0) | (fit_r < 0.0))]<-0.0
fit_g2[which((fit_g > 1.0) | (fit_g < 0.0))]<-0.0
fit_b2[which((fit_b > 1.0) | (fit_b < 0.0))]<-0.0
# highest value
adjust2 = fit_r2 - fit_g2
w<-which(adjust2<0)
adjust2[w]<-fit_g2[w]
adjust2[-w]<-fit_r2[-w]
adjust = adjust2 - fit_b2
w<-which(adjust<0)
adjust[w]<-fit_b2[w]
adjust[-w]<-adjust2[-w]
# Shift proportional to the greatest shift
dr = dr + (adjust * diff_r)
dg = dg + (adjust * diff_g)
db = db + (adjust * diff_b)
# Apply gamma correction
dr = dr^(1.0 / 2.2)
dg = dg^(1.0 / 2.2)
db = db^(1.0 / 2.2)
# Anomylize colors
dr = sr * (1.0 - amount) + dr * amount
dg = sg * (1.0 - amount) + dg * amount
db = sb * (1.0 - amount) + db * amount
# Return values
dr[which(dr>1.0)]<-1.0
dr[which(dr<0.0)]<-0.0
dg[which(dg>1.0)]<-1.0
dg[which(dg<0.0)]<-0.0
db[which(db>1.0)]<-1.0
db[which(db<0.0)]<-0.0
p[,,1]<-(dr)
p[,,2]<-(dg)
p[,,3]<-(db)
png::writePNG(p, fileOUT)
}

Color.Vision.VingrysAndKingSmith <-function(capnumbers=NULL,testType='D-15',silent=TRUE){
#method of scoring the results of the "D-15", "D-15DS", "Roth28-Hue" or "FM1OO-Hue" tests
#translated to R by Jose Gama 2013
#Implementation of the Vingrys and King-Smith method (1988)
#Vingrys, A.J. and King-Smith, P.E. (1988).
#A quantitative scoring technique for panel tests of color vision.
#Investigative Ophthalmology and Visual Science, 29, 50-63.

# added "Roth28-Hue" test
if (is.null(capnumbers)) stop('capnumbers must be defined')
if (!is.numeric(capnumbers)) stop('capnumbers must be numeric')
tType<-which(testType==c('D-15', 'D-15DS', 'FM1OO-Hue', "Roth28-Hue"))
if (length(testType)==0) stop('testType must be "D-15", "D-15DS", "Roth28-Hue" or "FM1OO-Hue"')
if (any(trunc(capnumbers)!=capnumbers)) stop('capnumbers must be integers')
if (testType %in% c('D-15', 'D-15DS')) 
{
if (length(capnumbers) != 15) stop('capnumbers must be a vector of 15 elements for D-15')
if (!all(sort(capnumbers) == 1:15)) stop('capnumbers must be between 1 and 15, without repetition')
}
if (testType %in% c('Roth28-Hue')) 
{
if (length(capnumbers) != 28) stop('capnumbers must be a vector of 28 elements for Roth28-Hue')
if (!all(sort(capnumbers) == 1:28)) stop('capnumbers must be between 1 and 28, without repetition')
}
if (testType == 'FM1OO-Hue') 
{
if (length(capnumbers) != 85) stop('capnumbers must be a vector of 85 elements for FM1OO-Hue')
if (!all(sort(capnumbers) == 1:85)) stop('capnumbers must be between 1 and 85, without repetition')
}
dataVKS<-list(
standardD15=matrix(c(-21.54, -38.39,-23.26,-25.56, -22.41,-15.53, -23.11,-7.45,-22.45,1.10, -21.67,7.35, -14.08,18.74,
-2.72,28.13, 14.84,31.13, 23.87,26.35,31.82,14.76, 31.42,6.99, 29.79,0.10,26.64,-9.38, 22.92,-18.65, 11.20,-24.61),16,2,byrow=T)
,
desaturatedD15=matrix(c(-4.77,-16.63,-8.63,-14.65, -12.08,-11.94, -12.86,-6.74,-12.26,-2.67, -11.18,2.01, -7.02,9.12,
1.30,15.78, 9.90,16.46, 15.03,12.05,15.48,2.56, 14.76,-2.24, 13.56,-5.04,11.06,-9.17, 8.95,-12.39, 5.62,-15.20),16,2,byrow=T)
,
FM100HUE=matrix(c(43.57,4.76,43.18,8.03, 44.37,11.34, 44.07,13.62, 44.95,16.04, 44.11,18.52,
42.92,20.64, 42.02,22.49, 42.28,25.15, 40.96,27.78, 37.68,29.55,
37.11,32.95, 35.41,35.94, 33.38,38.03, 30.88,39.59, 28.99,43.07,
25.00,44.12, 22.87,46.44, 18.86,45.87, 15.47,44.97, 13.01,42.12,
10.91,42.85, 8.49,41.35, 3.11,41.70, .68,39.23, -1.70,39.23,
-4.14,36.66, -6.57,32.41, -8.53,33.19, -10.98,31.47, -15.07,27.89,
-17.13,26.31, -19.39,23.82, -21.93,22.52, -23.40,20.14, -25.32,17.76,
-25.10,13.29, -26.58,11.87, -27.35,9.52, -28.41,7.26, -29.54,5.10,
-30.37,2.63, -31.07,0.10, -31.72,-2.42, -31.44,-5.13, -32.26,-8.16,
-29.86,-9.51, -31.13,-10.59, -31.04,-14.30, -29.10,-17.32, -29.67,-19.59,
-28.61,-22.65, -27.76,-26.66, -26.31,-29.24, -23.16,-31.24, -21.31,-32.92,
-19.15,-33.17, -16.00,-34.90, -14.10,-35.21, -12.47,-35.84, -10.55,-37.74,
-8.49,-34.78, -7.21,-35.44, -5.16,-37.08, -3.00,-35.95, -.31,-33.94,
1.55,-34.50, 3.68,-30.63, 5.88,-31.18, 8.46,-29.46, 9.75,-29.46,
12.24,-27.35, 15.61,-25.68, 19.63,-24.79, 21.20,-22.83, 25.60,-20.51,
26.94,-18.40, 29.39,-16.29, 32.93,-12.30, 34.96,-11.57, 38.24,-8.88,
39.06,-6.81, 39.51,-3.03, 40.90,-1.50, 42.80,0.60, 43.57,4.76),86,2,byrow=TRUE)
,
ROTH28=matrix(c(43.57,44.07,42.92,40.96,35.41,28.99,18.86,10.91,0.68,-6.57,-15.07,-21.93,-25.10,-28.41,-31.07,-32.26,-31.04,-28.61,-23.16,-16.00,-10.55,-5.16,1.55,8.46,15.61,25.60,32.93,39.06,42.80,4.76,13.62,20.64,27.78,35.94,43.07,45.87,42.85,39.23,32.41,27.89,22.52,13.29,7.26,0.10,-8.16,-14.30,-22.65,-31.24,-34.90,-37.74,-37.08,-34.50,-29.46,-25.68,-20.51,-12.30,-6.81,0.60),29,2,byrow=TRUE)
)

if (!silent) print("SUMS OF U AND V")
if (!silent) cat(sum(dataVKS[[tType]][,1]),sum(dataVKS[[tType]][,2]),'\n')
#CHOOSE FIRST CAP NUMBER
if (tType==3) capnumbers<-c(capnumbers[85],capnumbers) else capnumbers<-c(0,capnumbers)
tSize<-c(16,16,86,29)[tType]
#CALCULATE SUMS OF SQUARES AND CROSS PRODUCTS
#REM COLOR DIFFERENCE VECTORS
DU = dataVKS[[tType]][capnumbers[2:(tSize)]+1,1]-dataVKS[[tType]][capnumbers[1:(tSize-1)]+1,1]
DV = dataVKS[[tType]][capnumbers[2:(tSize)]+1,2]-dataVKS[[tType]][capnumbers[1:(tSize-1)]+1,2]
U2 = sum(DU^2)
V2 = sum(DV^2)
UV = sum(DU * DV)
# CALCULATE MAJOR AND MINOR RADII AND ANGLE
D = U2 - V2
#ANGLE
if (D == 0) A0 = 0.7854 else A0 = atan(2 * UV / D) / 2
#MAJOR MOMENT
I0 = U2 * sin(A0)^2 + V2 * cos(A0)^2 - 2 * UV * sin(A0) * cos(A0)
#PERPENDICULAR ANGLE
if (A0 < 0) A1 = A0 + 1.5708 else A1 = A0 - 1.5708
#MINOR MOMENT
I1 = U2 * sin(A1)^2 + V2 * cos(A1)^2 - 2 * UV * sin(A1) * cos(A1)
#CHECK THAT MAJOR MOMENT GREATER THAN MINOR
if (!(I0 > I1)) {
#SWAP ANGLES & MOMENTS
P = A0
A0 = A1
A1 = P
P = I0
I0 = I1
I1 = P
}
#RADII & TOTAL ERROR
R0 = sqrt(I0 / (tSize-1))
R1 = sqrt(I1 / (tSize-1))
R = sqrt(R0^2 + R1^2)
if (tType == 1) { R2 = 9.234669; if (!silent) print ("STANDARD D-15")}
if (tType == 2) { R2 = 5.121259; if (!silent) print ("DESATURATED D-15")}
if (tType == 3) { R2 = 2.525249; if (!silent) print ("FM-100 HUE")}
if (tType == 4) { R2 = 2.525249; if (!silent) print ("Roth-28 HUE")}
if (!silent) cat ("ANGLE\tMAJ RAD\tMIN RAD\tTOT ERR\tS-INDEX\tC-INDEX\n")
if (!silent) cat('\t',57.3 * A1,'\t', R0,'\t', R1,'\t', R,'\t', R0 / R1,'\t', R0 / R2)
list(Angle=57.3 * A1,MajRad=R0,MinRad=R1,TotErr=R,Sindex=R0 / R1,Cindex=R0 / R2)
}

decolorize<-function(fileIN=NULL,effect=0.5,scale=NULL,noise=0.001,recolor=FALSE){
#Color Image to Grayscale Conversion
#Original work by Mark Grundland and Neil A. Dodgson
# Mark Grundland and Neil A. Dodgson, "Decolorize: Fast, Contrast Enhancing, Color to Grayscale Conversion",
# Pattern Recognition, vol. 40, no. 11, pp. 2891-2896, (2007).
# http://www.Eyemaginary.com/Portfolio/Publications.html
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist')
p<-png::readPNG(fileIN)
p<-p*255
frameP<-dim(p)[1:2]
pixelsP<-prod(frameP)
if (is.null(scale)) scale<-sqrt(2*min(frameP))
tolerance=100 * .Machine$double.eps#2^(-52)
# Define the YPQ color space
colorconvert<-matrix(c(0.2989360212937753847527155, 0.5870430744511212909351327, 0.1140209042551033243121518,
0.5, 0.5, -1, 1, -1, 0),3,3,byrow=F)
colorrevert<-matrix(c(1, 0.1140209042551033243121518, 0.6440535265786729530912086, 1, 0.1140209042551033243121518, 
-0.3559464734213270469087914, 1, -0.8859790957448966756878482, 0.1440535265786729530912086),3,3,byrow=F)
colorspan<-matrix(c(0, 1, -1, 1, -1, 1),3,2,byrow=F)
maxluminance=1.0
scaleluminance=0.66856793424088827189
maxsaturation=1.1180339887498948482
alterP=effect*(maxluminance/maxsaturation)
# Convert picture to the YPQ color space
p2=array(p,c(pixelsP,3))
imageP=p2 %*% colorconvert
originalP=imageP
chromaP=sqrt(imageP[,2] * imageP[,2] + imageP[,3] * imageP[,3])

# Pair each pixel with a randomly chosen sample site
meshP=cbind(rep(1:frameP[1],frameP[2]),rep(1:frameP[2],each=frameP[1]))
displaceP=(scale * sqrt(2/pi)) * matrix(rnorm(pixelsP*2),pixelsP,2)#
lookP=round(meshP+displaceP)
redoP=which((lookP[,1]<1))
lookP[redoP,1]=2-sign(lookP[redoP,1])*(lookP[redoP,1] %% (frameP[1]-1))#
redoP=which((lookP[,2]<2))
lookP[redoP,2]=2-sign(lookP[redoP,2])*(lookP[redoP,2] %% (frameP[2]-1))#
redoP=which((lookP[,1]>frameP[1]))
lookP[redoP,1]=frameP[1]-1-sign(lookP[redoP,1])*((lookP[redoP,1]-2) %% (frameP[1]-1))#
redoP=which((lookP[,2]>frameP[2]))
lookP[redoP,2]=frameP[2]-1-sign(lookP[redoP,2])*((lookP[redoP,2]-2) %% (frameP[2]-1))#
lookP=lookP[,1]+frameP[1] * (lookP[,2]-1)#

# Calculate the color differences between the paired pixels
deltaP=imageP-imageP[lookP,]
contrastchange=abs(deltaP[,1])
contrastdirection=sign(deltaP[,1])
colordifference=p2-p2[lookP,]
colordifference=sqrt(apply(colordifference * colordifference,1,sum))+2^(-52)

# Derive a chromatic axis from the weighted sum of chromatic differences between paired pixels
weightP=1-((contrastchange/scaleluminance)/colordifference)

weightP[which(colordifference<tolerance)]=0
axisP=weightP * contrastdirection
axisP=deltaP[,2:3] * c(axisP, axisP)
axisP=apply(axisP,2,sum)

# Project the chromatic content of the picture onto the chromatic axis
projection=imageP[,2] * axisP[1] + imageP[,3] * axisP[2]
quantile(abs(projection),1-noise)
projection=projection / (quantile(abs(projection),1-noise)+tolerance)

# Combine the achromatic tones with the projected chromatic colors and adjust the dynamic range
imageP[,1]=imageP[,1]+effect*projection
imagerange=quantile(imageP[,1],c(noise, 1-noise))
imageP[,1]=(imageP[,1]-imagerange[1])/(imagerange[2]-imagerange[1]+tolerance)
targetrange=effect*c(0.0, maxluminance)+(1-effect)*quantile(originalP[,1],c(noise, 1-noise))
imageP[,1]=targetrange[1]+(imageP[,1]*(targetrange[2]-targetrange[1]+tolerance))

iP1<-imageP[,1]
w<-which(imageP[,1]<(originalP[,1]-alterP*chromaP))
iP1[w]<-originalP[w,1]-alterP*chromaP[w]

w<-which(iP1>(originalP[,1]+alterP*chromaP))
iP1[w]<-originalP[w,1]+alterP*chromaP
imageP[,1]=iP1/255.0
imageP[which(imageP<0.0)]=0.0
imageP[which(imageP>maxluminance)]=maxluminance

# Return the results
tones=imageP[,1] / maxluminance
tones=array(tones,dim(p))

if (recolor){
    recolorP=imageP %*% colorrevert
    recolorP=array(c(array(recolorP[,1],frameP),array(recolorP[,2],frameP),array(recolorP[,3],frameP)),dim(p))
    recolorP[which(recolorP<0)]=0.0
    recolorP[which(recolorP>1)]=1.0
    return(list(tones=tones,recolor=recolorP))
} else return(list(tones=tones,recolor=NULL))
}

decolorizeFile<-function(fileIN=NULL,fileOUT=NULL,effect=0.5,scale=NULL,noise=0.001,fileRecolorOUT=NULL){
#Color Image to Grayscale Conversion
#Original work by Mark Grundland and Neil A. Dodgson
# Mark Grundland and Neil A. Dodgson, "Decolorize: Fast, Contrast Enhancing, Color to Grayscale Conversion",
# Pattern Recognition, vol. 40, no. 11, pp. 2891-2896, (2007).
# http://www.Eyemaginary.com/Portfolio/Publications.html
if (is.null(fileIN)) stop('A file input must be defined')
if (!file.exists(fileIN)) stop('Error! File does not exist')
if (is.null(fileOUT)) stop('A file output must be defined')
if (!is.null(fileRecolorOUT)) {
if (!is.character(fileRecolorOUT)) stop('A file output for recolor must have a valid name')
if (fileRecolorOUT=='') stop('A file output for recolor must have a valid name')
}
if (is.null(fileRecolorOUT)) recolor<-FALSE else recolor<-TRUE
decF<-decolorize(fileIN,effect,scale,noise,recolor)
png::writePNG(decF$tones, fileOUT)
if (recolor) png::writePNG(decF$recolor, fileRecolorOUT)
}

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CVD documentation built on May 31, 2017, 4:53 a.m.