plot_utils.R
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# Load libraries
library("ggplot2")
library("gplots")
library("reshape")
library("scales")
library("jpeg")
library("png")
library("grid")
# Code to format FAR values
far_names <- list('0.001'="FAR = 0.1%", '0.01'="FAR = 1%")
far_labeller <- function(variable,value) { return(far_names[as.character(value)]) }
getScale <- function(mode, title, vals) {
if (vals > 12) return(do.call(paste("scale", mode, "discrete", sep="_"), list(title)))
else if (vals > 11) return(do.call(paste("scale", mode, "brewer", sep="_"), list(title, palette="Set3")))
else if (vals > 9) return(do.call(paste("scale", mode, "brewer", sep="_"), list(title, palette="Paired")))
else return(do.call(paste("scale", mode, "brewer", sep="_"), list(title, palette="Set1")))
}
plotMetadata <- function(metadata=NULL, title=NULL) {
MT <- as.data.frame(metadata[c(1, 2, 3, 4, 5),])
par(mfrow=c(4, 1))
plot.new()
print(title(paste(title, date(), sep="\n")))
mat <- matrix(MT$X[c(1, 2)], ncol=2)
colnames(mat) <- c("Gallery", "Probe")
imageTable <- as.table(mat)
print(textplot(imageTable, show.rownames=FALSE))
print(title("Images"))
mat <- matrix(MT$X[c(3, 4, 5)], ncol=3)
colnames(mat) <- c("Genuine", "Impostor", "Ignored")
matchTable <- as.table(mat)
print(textplot(matchTable, show.rownames=FALSE))
print(title("Matches"))
plot.new()
print(title("Gallery * Probe = Genuine + Impostor + Ignored"))
}
plotTable <- function(tableData=NULL, name=NULL, labels=NULL) {
if (nrow(tableData) == 0) return()
if (smooth && confidence != 0) {
input = paste(as.character(round(tableData$Y, 3)), round(tableData$ci, 3), sep="\u00b1")
} else {
input = tableData$Y
}
mat <- matrix(input, nrow=length(labels), ncol=length(algs), byrow=FALSE)
colnames(mat) <- algs
rownames(mat) <- labels
table <- as.table(mat)
if (csv) {
write.csv(table, file=paste(paste(basename, deparse(substitute(data)), sep="_"), ".csv", sep=""))
} else {
print(textplot(table))
print(title(name))
}
}
plotLandmarkTables <- function(tableData=NULL) {
if(majorSize > 1) {
var <- majorHeader
} else {
if(minorHeader == "") var <- majorHeader else var <- minorHeader
}
StatBox <- summarySE(tableData, measurevar="Y", groupvars=c(var,"X"))
OverallStatBox <- summarySE(tableData, measurevar="Y", groupvars=c(var))
mat <- matrix(paste(as.character(round(StatBox$Y, 3)), round(StatBox$ci, 3), sep=" \u00b1 "), nrow=rows, ncol=length(algs), byrow=FALSE)
mat <- rbind(mat, paste(as.character(round(OverallStatBox$Y, 3)), round(OverallStatBox$ci, 3), sep=" \u00b1 "))
mat <- rbind(mat, as.character(round(NormLength$Y, 3)))
colnames(mat) <- algs
rownames(mat) <- c(seq(0, rows-1), "Aggregate","Average IPD")
ETable <- as.table(mat)
print(textplot(ETable))
print(title("Landmarking Error Rates"))
}
plotLine <- function(lineData=NULL, options=NULL, flipY=FALSE, geometry="line") {
textSize <- if("textSize" %in% names(options)) as.numeric(options$textSize) else 12
p <- qplot(X, if(flipY) 1-Y else Y, data=lineData, main=options$title, geom=geometry, size=if("size" %in% names(options)) I(as.numeric(options$size)) else I(.5), colour=if(majorSize > 1) factor(eval(parse(text=majorHeader))) else NULL, linetype=if(minorSize > 1) factor(eval(parse(text=minorHeader))) else NULL, xlab=options$xTitle, ylab=options$yTitle) + theme_minimal()
if (smooth && deparse(substitute(lineData)) != "CMC" && confidence != 0) p <- p + geom_errorbar(data=lineData[seq(1, NROW(lineData), by = 29),], aes(x=X, ymin=if(flipY) (1-lower) else lower, ymax=if(flipY) (1-upper) else upper), width=0.1, alpha=I(1/2))
if (majorSize > 1) p <- p + getScale("colour", majorHeader, majorSize)
if (minorSize > 1) p <- p + scale_linetype_discrete(minorHeader)
# Set log/continuous scales, breaks and labels
if (options$xLog)
p <- p + scale_x_log10(labels=if("xLabels" %in% names(options)) eval(parse(text=options$xLabels)) else trans_format("log10", math_format()), breaks=if("xBreaks" %in% names(options)) eval(parse(text=options$xBreaks)) else waiver()) + annotation_logticks(sides="b")
else
p <- p + scale_x_continuous(labels=if("xLabels" %in% names(options)) eval(parse(text=options$xLabels)) else percent, breaks=if("xBreaks" %in% names(options)) eval(parse(text=options$xBreaks)) else pretty_breaks(n=10))
if (options$yLog)
p <- p + scale_y_log10(labels=if("yLabels" %in% names(options)) eval(parse(text=options$yLabels)) else trans_format("log10", math_format()), breaks=if("yBreaks" %in% names(options)) eval(parse(text=options$yBreaks)) else waiver()) + annotation_logticks(sides="l")
else
p <- p + scale_y_continuous(labels=if("yLabels" %in% names(options)) eval(parse(text=options$yLabels)) else percent, breaks=if("yBreaks" %in% names(options)) eval(parse(text=options$yBreaks)) else pretty_breaks(n=10))
if ("xLimits" %in% names(options)) p <- p + xlim(eval(parse(text=options$xLimits)))
if ("yLimits" %in% names(options)) p <- p + ylim(eval(parse(text=options$yLimits)))
p <- p + theme(legend.title = element_text(size = textSize), legend.text = element_text(size = textSize), plot.title = element_text(size = textSize), axis.text = element_text(size = textSize), axis.title.x = element_text(size = textSize), axis.title.y = element_text(size = textSize), legend.position=if("legendPosition" %in% names(options)) eval(parse(text=options$legendPosition)) else "bottom", legend.background = element_rect(fill = 'white'), panel.grid.major = element_line(colour = "gray"), panel.grid.minor = element_line(colour = "gray", linetype = "dashed"))
p <- p + guides(col=guide_legend(ncol=ncol))
return(p)
}
plotSD <- function(sdData=NULL) {
p <- qplot(X, data=sdData, geom="histogram", fill=Y, position="identity", alpha=I(1/2), xlab="Score", ylab="Frequency")
p <- p + scale_fill_manual("Ground Truth", values=c("blue", "red")) + theme_minimal() + scale_x_continuous(minor_breaks=NULL) + scale_y_continuous(minor_breaks=NULL) + theme(axis.text.y=element_blank(), axis.ticks=element_blank(), axis.text.x=element_text(angle=-90, hjust=0))
if (majorSize > 1) {
if (minorSize > 1) {
if (flip) {
A <- minorHeader
B <- majorHeader
} else {
A <- majorHeader
B <- minorHeader
}
p <- p + facet_grid(facets=as.formula(paste(A, "~", B)), scales="free")
} else {
p <- p + facet_wrap(facets=as.formula(paste("~", majorHeader)), scales="free")
}
}
p <- p + theme(aspect.ratio=1)
return(p)
}
plotBC <- function(bcData=NULL) {
factor <- if (majorSmooth) minorHeader else majorHeader
plotString <- paste("qplot(factor(", factor, ")", if(smooth) ", Y" else "", ", data=bcData, ", if(smooth) "geom=\"boxplot\"" else "geom=\"bar\", position=\"dodge\", weight=Y", sep="")
p <- eval(parse(text=paste(plotString, if(majorSize > 1) paste(", fill=factor(", majorHeader, ")", sep="") else "", ", xlab=\"False Accept Rate\", ylab=\"True Accept Rate\") + theme_minimal()", sep="")))
if(majorSize > 1) p <- p + getScale("fill", majorHeader, majorSize)
if(minorSize > 1) p <- p + facet_grid(facets=as.formula(paste(minorHeader, "~", "X"))) else p <- p + facet_grid(. ~ X, labeller=far_labeller)
p <- p + scale_y_continuous(labels=percent) + theme(legend.position="none", axis.text.x=element_text(angle=-90, hjust=0))
if(!smooth) p <- p + geom_text(data=bcData, aes(label=bcData$Y, y=0.05))
return(p)
}
plotERR <- function(errData=NULL) {
if(flip) {
if(majorSize > 1) color <- majorHeader
} else {
if(minorSize > 1) color <- minorHeader
}
p <- qplot(X, Y, data=errData, geom="line", linetype=Error, colour=if(exists("color")) factor(eval(parse(text=color))) else NULL, xlab="Score", ylab="Error Rate") + theme_minimal()
if(flip) {
if(majorSize > 1)
p <- p + getScale("colour", majorHeader, majorSize)
else if(minorSize > 1)
p <- p + getScale("colour", minorHeader, minorSize)
}
p <- p + scale_y_log10(labels=percent) + annotation_logticks(sides="l")
if(flip) {
if(minorSize > 1) {
facet <- minorHeader
p <- p + facet_wrap(as.formula(paste("~", facet)), scales="free_x")
}
} else {
if(majorSize >1) {
facet <- majorHeader
p <- p + facet_wrap(as.formula(paste("~", facet)), scales="free_x")
}
}
p <- p + theme(aspect.ratio=1)
return(p)
}
plotOverlap <- function(overlapData=NULL) {
p <- qplot(X, data=overlapData, geom="histogram", position="identity", xlab="Overlap", ylab="Frequency")
p <- p + theme_minimal() + scale_x_continuous(minor_breaks=NULL) + scale_y_continuous(minor_breaks=NULL) + theme(axis.text.y=element_blank(), axis.ticks=element_blank(), axis.text.x=element_text(angle=-90, hjust=0))
if(majorSize > 1) {
if(minorSize) {
p <- p + facet_grid(facets=as.formula(paste(minorHeader, "~", majorHeader)), scales="free")
} else {
p <- p + facet_wrap(facets=as.formula(paste("~", majorHeader)), scales="free")
}
}
p <- p + theme(aspect.ratio=1, legend.position="bottom")
return(p)
}
formatData <- function(type="eval") {
if (type == "eval") {
# Split data into individual plots
plot_index <<- which(names(data)=="Plot")
Metadata <<- data[grep("Metadata",data$Plot),-c(1)]
IM <<- data[grep("IM",data$Plot),-c(1)]
GM <<- data[grep("GM",data$Plot),-c(1)]
DET <<- data[grep("DET",data$Plot),-c(1)]
IET <<- data[grep("IET",data$Plot),-c(1)]
FAR <- data[grep("FAR",data$Plot),-c(1)]
FRR <- data[grep("FRR",data$Plot),-c(1)]
SD <<- data[grep("SD",data$Plot),-c(1)]
TF <<- data[grep("TF",data$Plot),-c(1)]
FT <<- data[grep("FT",data$Plot),-c(1)]
CT <<- data[grep("CT",data$Plot),-c(1)]
BC <<- data[grep("BC",data$Plot),-c(1)]
TS <<- data[grep("TS",data$Plot),-c(1)]
CMC <<- data[grep("CMC",data$Plot),-c(1)]
FAR$Error <- "FAR"
FRR$Error <- "FRR"
ERR <<- rbind(FAR, FRR)
# Format data
Metadata$Y<-factor(Metadata$Y, levels=c("Genuine", "Impostor", "Ignored", "Gallery", "Probe"))
IM$Y <<- as.character(IM$Y)
GM$Y <<- as.character(GM$Y)
DET$Y <<- as.numeric(as.character(DET$Y))
IET$Y <<- as.numeric(as.character(IET$Y))
ERR$Y <<- as.numeric(as.character(ERR$Y))
SD$Y <<- as.factor(unique(as.character(SD$Y)))
TF$Y <<- as.numeric(as.character(TF$Y))
FT$Y <<- as.numeric(as.character(FT$Y))
CT$Y <<- as.numeric(as.character(CT$Y))
BC$Y <<- as.numeric(as.character(BC$Y))
TS$Y <<- as.character(TS$Y)
CMC$Y <<- as.numeric(as.character(CMC$Y))
} else if (type == "detection") {
# Split data into individual plots
DiscreteROC <<- data[grep("DiscreteROC",data$Plot),-c(1)]
ContinuousROC <<- data[grep("ContinuousROC",data$Plot),-c(1)]
DiscretePR <<- data[grep("DiscretePR",data$Plot),-c(1)]
ContinuousPR <<- data[grep("ContinuousPR",data$Plot),-c(1)]
Overlap <<- data[grep("Overlap",data$Plot),-c(1)]
AverageOverlap <<- data[grep("AverageOverlap",data$Plot),-c(1)]
} else if (type == "landmarking") {
# Split data into individual plots
Box <<- data[grep("Box",data$Plot),-c(1)]
Box$X <<- factor(Box$X, levels = Box$X, ordered = TRUE)
Sample <<- data[grep("Sample",data$Plot),-c(1)]
Sample$X <<- as.character(Sample$X)
EXT <<- data[grep("EXT",data$Plot),-c(1)]
EXT$X <<- as.character(EXT$X)
EXP <<- data[grep("EXP",data$Plot),-c(1)]
EXP$X <<- as.character(EXP$X)
NormLength <<- data[grep("NormLength",data$Plot),-c(1)]
sample <<- readImageData(Sample)
rows <<- sample[[1]]$value
}
}
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=0.95, .drop=TRUE) {
# derived from http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
require(plyr)
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
datac <- ddply(data, groupvars, .drop=.drop, .fun = function(xx, col) {
c(N=length2(xx[[col]], na.rm=na.rm), mean=mean(xx[[col]], na.rm=na.rm), sd=sd(xx[[col]], na.rm=na.rm))
},
measurevar
)
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N)
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
datac$upper <- if(datac[, measurevar] + datac$ci < 1) (datac[, measurevar] + datac$ci) else 1
datac$lower <- if(datac[, measurevar] - datac$ci > 0) (datac[, measurevar] - datac$ci) else 0
return(datac)
}
multiplot <- function(..., plotlist=NULL, cols) {
require(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# Make the panel
plotCols = cols
plotRows = ceiling(numPlots/plotCols)
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
vplayout <- function(x, y)
viewport(layout.pos.row = x, layout.pos.col = y)
# Make each plot, in the correct location
for (i in 1:numPlots) {
curRow = ceiling(i/plotCols)
curCol = (i-1) %% plotCols + 1
print(plots[[i]], vp = vplayout(curRow, curCol))
}
}
plotEERSamples <- function(imData=NULL, gmData=NULL) {
if(nrow(imData) == 0) return()
printImages <- function(images, label) {
for (i in 1:nrow(images)) {
score <- images[i,1]
files <- images[i,2]
alg <- images[i,3]
files <- unlist(strsplit(files, "[:]"))
ext1 <- unlist(strsplit(files[2], "[.]"))[2]
ext2 <- unlist(strsplit(files[4], "[.]"))[2]
if (ext1 == "jpg" || ext1 == "JPEG" || ext1 == "jpeg" || ext1 == "JPG") {
img1 <- readJPEG(files[2])
} else if (ext1 == "PNG" || ext1 == "png") {
img1 <- readPNG(files[2])
} else if (ext1 == "TIFF" || ext1 == "tiff" || ext1 == "TIF" || ext1 == "tif") {
img1 <- readTIFF(files[2])
} else {
next
}
if (ext2 == "jpg" || ext2 == "JPEG" || ext2 == "jpeg" || ext2 == "JPG") {
img2 <- readJPEG(files[4])
} else if (ext2 == "PNG" || ext2 == "png") {
img2 <- readPNG(files[4])
} else if (ext2 == "TIFF" || ext2 == "tiff" || ext2 == "TIF" || ext2 == "tif") {
img2 <- readTIFF(files[4])
} else {
next
}
name1 <- files[1]
name2 <- files[3]
g1 <- rasterGrob(img1, interpolate=TRUE)
g2 <- rasterGrob(img2, interpolate=TRUE)
plot1 <- qplot(1:10, 1:10, geom="blank") + annotation_custom(g1, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + theme(axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), panel.background=element_blank()) + labs(title=alg) + ylab(unlist(strsplit(files[2], "[/]"))[length(unlist(strsplit(files[2], "[/]")))]) + xlab(name1)
plot2 <- qplot(1:10, 1:10, geom="blank") + annotation_custom(g2, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + theme(axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank(), panel.background=element_blank()) + labs(title=paste(label, " score =", score)) + ylab(unlist(strsplit(files[4], "[/]"))[length(unlist(strsplit(files[4], "[/]")))]) + xlab(name2)
multiplot(plot1, plot2, cols=2)
}
}
printImages(imData, "Impostor")
printImages(gmData, "Genuine")
}
plotLandmarkSamples <- function(samples=NULL, expData=NULL, extData=NULL) {
print(plotImage(samples[[1]], "Sample Landmarks", sprintf("Total Landmarks: %s", samples[[1]]$value)))
if (nrow(EXT) != 0 && nrow(EXP)) {
for (j in 1:length(algs)) {
truthSample <- readData(EXT[EXT$. == algs[[j]],])
predictedSample <- readData(EXP[EXP$. == algs[[j]],])
for (i in 1:length(predictedSample)) {
multiplot(plotImage(predictedSample[[i]], sprintf("%s\nPredicted Landmarks", algs[[j]]), sprintf("Average Landmark Error: %.3f", predictedSample[[i]]$value)), plotImage(truthSample[[i]], "Ground Truth\nLandmarks", ""), cols=2)
}
}
}
}
readImageData <- function(data) {
examples <- list()
for (i in 1:nrow(data)) {
path <- data[i,1]
value <- data[i,2]
file <- unlist(strsplit(path, "[.]"))[1]
ext <- unlist(strsplit(path, "[.]"))[2]
if (ext == "jpg" || ext == "JPEG" || ext == "jpeg" || ext == "JPG") {
img <- readJPEG(path)
} else if (ext == "PNG" || ext == "png") {
img <- readPNG(path)
} else if (ext == "TIFF" || ext == "tiff" || ext == "TIF" || ext == "tif") {
img <- readTIFF(path)
}else {
next
}
example <- list(file = file, value = value, image = img)
examples[[i]] <- example
}
return(examples)
}
plotImage <- function(image, title=NULL, label=NULL) {
p <- qplot(1:10, 1:10, geom="blank") + annotation_custom(rasterGrob(image$image), xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf)
p <- p + theme(axis.line=element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), line=element_blank(), axis.ticks=element_blank(), panel.background=element_blank())
p <- p + labs(title=title) + xlab(label)
return(p)
}