Merge branch 'master' of https://github.com/biometrics/openbr

bklare
2 parents 3b2c84da 5d6385a5
Showing 14 changed files with 682 additions and 879 deletions
CMakeLists.txt
README.md
docs/docs/api_docs/c_api/functions.md
openbr/core/plot.cpp
openbr/openbr_plugin.cpp
openbr/plugins/classification/lda.cpp
openbr/plugins/cmake/dlib.cmake
openbr/plugins/core/algorithms.cpp
openbr/plugins/metadata/expandrect.cpp
openbr/plugins/metadata/pointstorects.cpp
openbr/plugins/metadata/rectstomats.cpp
openbr/plugins/metadata/rectstotemplates.cpp
openbr/plugins/metadata/recttokeys.cpp
share/openbr/plotting/plot_utils.R
@@ -142,6 +142,7 @@ endif()
 # Look for extensions to OpenBR
 set(BR_THIRDPARTY_PLUGINS_DIR CACHE PATH "")
 set(BR_THIRDPARTY_APPS_DIR CACHE PATH "")
+set(BR_THIRDPARTY_DIR CACHE PATH "")
 mark_as_advanced(BR_THIRDPARTY_PLUGINS_DIR)
 mark_as_advanced(BR_THIRDPARTY_APPS_DIR)
@@ -8,4 +8,4 @@ Identify the latest [tagged release](https://github.com/biometrics/openbr/releas
     $ git submodule init
     $ git submodule update
-**[Build Instructions](http://openbiometrics.org/doxygen/latest/installation.html)**
+**[Build Instructions](http://openbiometrics.org/docs/install/)**
@@ -635,8 +635,8 @@ textSize        | float          | Size of text for title, legend and axes
 xTitle/yTitle   | [QString]      | Title for x/y axis
 xLog/yLog       | bool           | Plot log scale for x/y axis
 xLimits/yLimits | [QPointF]      | Set x/y axis limits, ex. xLimits=(lower,upper)
-xLabels/yLabels | [QString]      | Labels for ticks on x/y axis, ex. xLabeles=percent or xLabels=c(1,5,10)
-xBreaks/yBreaks | [QString]      | Specify breaks/ticks on x/y axis, ex. xBreaks=pretty_breaks(n=10) or xBreaks=c(1,5,10)
+xLabels/yLabels | [QString]      | Labels for ticks on x/y axis, ex. xLabeles=percent or xLabels=(1,5,10)
+xBreaks/yBreaks | [QString]      | Specify breaks/ticks on x/y axis, ex. xBreaks=pretty_breaks(n=10) or xBreaks=(1,5,10)
 If specifying plot options it is a good idea to wrap the destination file in single quotes to avoid parsing errors.
 The example below plots plots the six br_eval results in the Algorithm_Dataset folder described above, sets the number of legend columns and specifies some options for the CMC plot.
@@ -23,6 +23,25 @@ using namespace cv;
 namespace br
 {
+// Flattens file metadata to an R list()
+static QString toRList(const File &opts)
+{
+    QStringList retValues;
+    QString format = "%1=%2";
+    foreach (const QString &key, opts.localKeys()) {
+        const QString value = QtUtils::toString(opts.value(key));
+        if (value.startsWith("("))
+            retValues.append(format.arg(key, "\"c" + value + "\""));
+        else if (value == "true")
+            retValues.append(format.arg(key, "TRUE"));
+        else if (value == "false")
+            retValues.append(format.arg(key, "FALSE"));
+        else
+            retValues.append(format.arg(key, "\"" + value + "\""));
+    }
+    return retValues.join(",");
+}
+
 static QStringList getPivots(const QString &file, bool headers)
 {
     QString str;
@@ -31,14 +50,6 @@ static QStringList getPivots(const QString &amp;file, bool headers)
     return str.split("_");
 }
-static QString getScale(const QString &mode, const QString &title, int vals)
-{
-    if      (vals > 12) return " + scale_"+mode+"_discrete(\""+title+"\")";
-    else if (vals > 11) return " + scale_"+mode+"_brewer(\""+title+"\", palette=\"Set3\")";
-    else if (vals > 9)  return " + scale_"+mode+"_brewer(\""+title+"\", palette=\"Paired\")";
-    else                return " + scale_"+mode+"_brewer(\""+title+"\", palette=\"Set1\")";
-}
-
 // Custom sorting method to ensure datasets are ordered nicely
 static bool sortFiles(const QString &fileA, const QString &fileB)
 {
@@ -51,10 +62,6 @@ struct RPlot
     QFile file;
     QStringList pivotHeaders;
     QVector< QSet<QString> > pivotItems;
-    float confidence; // confidence interval for plotting across splits
-    int ncol;         // Number of columns for plot legends
-
-    bool flip;
     struct Pivot
     {
@@ -68,7 +75,7 @@ struct RPlot
     Pivot major, minor;
-    RPlot(QStringList files, const File &destination, bool isEvalFormat = true)
+    RPlot(QStringList files, const File &destination)
     {
         if (files.isEmpty()) qFatal("Empty file list.");
         qSort(files.begin(), files.end(), sortFiles);
@@ -83,13 +90,9 @@ struct RPlot
         bool success = file.open(QFile::WriteOnly);
         if (!success) qFatal("Failed to open %s for writing.", qPrintable(file.fileName()));
-        file.write("# Load libraries\n"
-                   "library(ggplot2)\n"
-                   "library(gplots)\n"
-                   "library(reshape)\n"
-                   "library(scales)\n"
-                   "\n"
-                   "# Read CSVs\n"
+        // Copy plot_utils.R into output script with source()
+        file.write(qPrintable(QString("source(\"%1\")\n\n").arg(Globals->sdkPath + "/share/openbr/plotting/plot_utils.R")));
+        file.write("# Read CSVs\n"
                    "data <- NULL\n");
         // Read files and retrieve pivots
@@ -123,8 +126,6 @@ struct RPlot
         }
         const QString &smooth = destination.get<QString>("smooth", "");
-        confidence = destination.get<float>("confidence", 95) / 100.0;
-
         major.smooth = !smooth.isEmpty() && (major.header == smooth) && (major.size > 1);
         minor.smooth = !smooth.isEmpty() && (minor.header == smooth) && (minor.size > 1);
         if (major.smooth) major.size = 1;
@@ -132,68 +133,19 @@ struct RPlot
         if (major.size < minor.size)
             std::swap(major, minor);
-        ncol = destination.get<int>("ncol", major.size > 1 ? major.size : (minor.header.isEmpty() ? major.size : minor.size));
-        flip = minor.header == "Algorithm";
-        // Format data
-        if (isEvalFormat)
-            file.write(qPrintable(QString("\n"
-                       "# Split data into individual plots\n"
-                       "plot_index = which(names(data)==\"Plot\")\n"
-                       "Metadata <- data[grep(\"Metadata\",data$Plot),-c(1)]\n"
-                       "IM <- data[grep(\"IM\",data$Plot),-c(1)]\n"
-                       "GM <- data[grep(\"GM\",data$Plot),-c(1)]\n"
-                       "DET <- data[grep(\"DET\",data$Plot),-c(1)]\n"
-                       "IET <- data[grep(\"IET\",data$Plot),-c(1)]\n"
-                       "FAR <- data[grep(\"FAR\",data$Plot),-c(1)]\n"
-                       "FRR <- data[grep(\"FRR\",data$Plot),-c(1)]\n"
-                       "SD <- data[grep(\"SD\",data$Plot),-c(1)]\n"
-                       "TF <- data[grep(\"TF\",data$Plot),-c(1)]\n"
-                       "FT <- data[grep(\"FT\",data$Plot),-c(1)]\n"
-                       "CT <- data[grep(\"CT\",data$Plot),-c(1)]\n"
-                       "BC <- data[grep(\"BC\",data$Plot),-c(1)]\n"
-                       "TS <- data[grep(\"TS\",data$Plot),-c(1)]\n"
-                       "CMC <- data[grep(\"CMC\",data$Plot),-c(1)]\n"
-                       "FAR$Error <- \"FAR\"\n"
-                       "FRR$Error <- \"FRR\"\n"
-                       "ERR <- rbind(FAR, FRR)\n"
-                       "rm(data, FAR, FRR)\n"
-                       "\n"
-                       "# Format data\n"
-                       "Metadata$Y<-factor(Metadata$Y, levels=c(\"Genuine\",\"Impostor\",\"Ignored\",\"Gallery\",\"Probe\"))\n"
-                       "IM$Y <- as.character(IM$Y)\n"
-                       "GM$Y <- as.character(GM$Y)\n"
-                       "DET$Y <- as.numeric(as.character(DET$Y))\n"
-                       "IET$Y <- as.numeric(as.character(IET$Y))\n"
-                       "ERR$Y <- as.numeric(as.character(ERR$Y))\n"
-                       "SD$Y <- as.factor(unique(as.character(SD$Y)))\n"
-                       "TF$Y <- as.numeric(as.character(TF$Y))\n"
-                       "FT$Y <- as.numeric(as.character(FT$Y))\n"
-                       "CT$Y <- as.numeric(as.character(CT$Y))\n"
-                       "BC$Y <- as.numeric(as.character(BC$Y))\n"
-                       "TS$Y <- as.character(TS$Y)\n"
-                       "CMC$Y <- as.numeric(as.character(CMC$Y))\n"
-                       "\n"
-                       "if (%1) {\n\tsummarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=%3, .drop=TRUE) {\n\t\t"
-                       "require(plyr)\n\n\t\tlength2 <- function (x, na.rm=FALSE) {\n\t\t\tif (na.rm) sum(!is.na(x))\n\t\t\telse       length(x)"
-                       "\n\t\t}\n\n\t\tdatac <- ddply(data, groupvars, .drop=.drop, .fun = function(xx, col) {\n\t\t\t"
-                       "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))\n\t\t\t},"
-                       "\n\t\t\tmeasurevar\n\t\t)\n\n\t\tdatac <- rename(datac, c(\"mean\" = measurevar))\n\t\tdatac$se <- datac$sd / sqrt(datac$N)"
-                       "\n\t\tciMult <- qt(conf.interval/2 + .5, datac$N-1)\n\t\tdatac$ci <- datac$se * ciMult\n\n\t\t"
-                       "datac$upper <- if(datac[, measurevar] + datac$ci < 1) (datac[, measurevar] + datac$ci) else 1\n\t\t"
-                       "datac$lower <- if(datac[, measurevar] - datac$ci > 0) (datac[, measurevar] - datac$ci) else 0\n\n\t\treturn(datac)\n\t}\n\t"
-                       "DET <- summarySE(DET, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n\t"
-                       "IET <- summarySE(IET, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n\t"
-                       "CMC <- summarySE(CMC, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n\t"
-                       "ERR <- summarySE(ERR, measurevar=\"X\", groupvars=c(\"Error\", \"%2\", \"Y\"))\n\t"
-                       "TF <- summarySE(TF, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n\t"
-                       "FT <- summarySE(FT, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n\t"
-                       "CT <- summarySE(CT, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"))\n}\n\n"
-                       "# Code to format FAR values\n"
-                       "far_names <- list('0.001'=\"FAR = 0.1%\", '0.01'=\"FAR = 1%\")\n"
-                       "far_labeller <- function(variable,value) { return(far_names[as.character(value)]) }\n"
-                       "\n").arg((major.smooth || minor.smooth) ? "TRUE" : "FALSE",
-                                 major.size > 1 ? major.header : (minor.header.isEmpty() ? major.header : minor.header),
-                                 QString::number(confidence))));
+        // Set variables in R
+        file.write(qPrintable(QString("\nconfidence <- %1\n").arg(destination.get<float>("confidence", 95) / 100.0)));
+        file.write(qPrintable(QString("ncol <- %1\n").arg(destination.get<int>("ncol", major.size > 1 ? major.size : (minor.header.isEmpty() ? major.size : minor.size)))));
+        file.write(qPrintable(QString("basename <- \"%1\"\n").arg(basename)));
+        file.write(qPrintable(QString("smooth <- %1\n").arg((major.smooth || minor.smooth) && (destination.get<float>("confidence", 95) / 100.0) != 0 ? "TRUE" : "FALSE")));
+        file.write(qPrintable(QString("csv <- %1\n").arg(destination.getBool("csv") ? "TRUE" : "FALSE")));
+        file.write(qPrintable(QString("majorHeader <- \"%1\"\n").arg(major.header)));
+        file.write(qPrintable(QString("majorSize <- %1\n").arg(major.size)));
+        file.write(qPrintable(QString("majorSmooth <- %1\n").arg(major.smooth ? "TRUE" : "FALSE")));
+        file.write(qPrintable(QString("minorHeader <- \"%1\"\n").arg(minor.header)));
+        file.write(qPrintable(QString("minorSize <- %1\n").arg(minor.size)));
+        file.write(qPrintable(QString("minorSmooth <- %1\n").arg(minor.smooth ? "TRUE" : "FALSE")));
+        file.write(qPrintable(QString("flip <- %1\n").arg(minor.header == "Algorithm" ? "TRUE" : "FALSE")));
         // Open output device
         file.write(qPrintable(QString("\n"
@@ -205,98 +157,6 @@ struct RPlot
                    "# Write figures\n");
     }
-    void plotMetadata(bool csv)
-    {
-        file.write(qPrintable(QString("# Code to format TAR@FAR table\n"
-                                      "algs <- unique(%4)\n"
-                                      "algs <- algs[!duplicated(algs)]\n"
-                                      "mat <- matrix(%1,nrow=6,ncol=length(algs),byrow=FALSE)\n"
-                                      "colnames(mat) <- algs \n"
-                                      "rownames(mat) <- c(\"FAR = 1e-06\", \"FAR = 1e-05\", \"FAR = 1e-04\", \"FAR = 1e-03\", \"FAR = 1e-02\", \"FAR = 1e-01\")\n"
-                                      "TFtable <- as.table(mat)\n"
-                                      "\n"
-                                      "# Code to format FAR@TAR table\n"
-                                      "mat <- matrix(%2,nrow=6,ncol=length(algs),byrow=FALSE)\n"
-                                      "colnames(mat) <- algs \n"
-                                      "rownames(mat) <- c(\"TAR = 0.40\", \"TAR = 0.50\", \"TAR = 0.65\", \"TAR = 0.75\", \"TAR = 0.85\", \"TAR = 0.95\")\n"
-                                      "FTtable <- as.table(mat)\n"
-                                      "\n"
-                                      "# Code to format CMC Table\n"
-                                      "mat <- matrix(%3,nrow=6,ncol=length(algs),byrow=FALSE)\n"
-                                      "colnames(mat) <- algs \n"
-                                      "rownames(mat) <- c(\"Rank 1\", \"Rank 5\", \"Rank 10\", \"Rank 20\", \"Rank 50\", \"Rank 100\")\n"
-                                      "CMCtable <- as.table(mat)\n"
-                                      "\n"
-                                      "# Code to format Template Size Table\n"
-                                      "if (nrow(TS) != 0) {\n\t"
-                                      "mat <- matrix(TS$Y,nrow=1,ncol=length(algs),byrow=FALSE)\n\t"
-                                      "colnames(mat) <- algs\n\t"
-                                      "rownames(mat) <- c(\"Template Size (bytes):\")\n\t"
-                                      "TStable <- as.table(mat)\n}"
-                                      "\n").arg((major.smooth || minor.smooth) && confidence != 0 ? "paste(as.character(round(TF$Y, 3)), round(TF$ci, 3), sep=\"\\u00b1\")" : "TF$Y",
-                                                (major.smooth || minor.smooth) && confidence != 0 ? "paste(as.character(round(FT$Y, 3)), round(FT$ci, 3), sep=\"\\u00b1\")" : "FT$Y",
-                                                (major.smooth || minor.smooth) && confidence != 0 ? "paste(as.character(round(CT$Y, 3)), round(CT$ci, 3), sep=\"\\u00b1\")" : "CT$Y",
-                                                (major.size > 1 && minor.size > 1) && !(major.smooth || minor.smooth) ? QString("paste(TF$%1, TF$%2, sep=\"_\")").arg(major.header, minor.header)
-                                                                                                                      : QString("TF$%1").arg(major.size > 1 ? major.header : (minor.header.isEmpty() ? major.header : minor.header)))));
-
-        file.write("\n# Write metadata table\n");
-        QString textplot = "MT <- as.data.frame(Metadata[c(1,2,3,4,5),])\n"
-                           "par(mfrow=c(4,1))\n"
-                           "plot.new()\n"
-                           "print(title(paste(\"%1 - %2\",date(),sep=\"\\n\")))\n"
-                           "mat <- matrix(MT$X[c(1,2)],ncol=2)\n"
-                           "colnames(mat) <- c(\"Gallery\", \"Probe\")\n"
-                           "imageTable <- as.table(mat)\n"
-                           "print(textplot(imageTable,show.rownames=FALSE))\n"
-                           "print(title(\"Images\"))\n"
-                           "mat <- matrix(MT$X[c(3,4,5)],ncol=3)\n"
-                           "colnames(mat) <- c(\"Genuine\", \"Impostor\", \"Ignored\")\n"
-                           "matchTable <- as.table(mat)\n"
-                           "print(textplot(matchTable,show.rownames=FALSE))\n"
-                           "print(title(\"Matches\"))\n"
-                           "plot.new()\n"
-                           "print(title(\"Gallery * Probe = Genuine + Impostor + Ignored\"))\n";
-        file.write(qPrintable(textplot.arg(PRODUCT_NAME, PRODUCT_VERSION)));
-
-        if (csv)
-            textplot = QString("write.csv(TFtable,file=\"%1_TF.csv\")\n"
-                       "write.csv(FTtable,file=\"%1_FT.csv\")\n"
-                       "write.csv(CMCtable,file=\"%1_CMC.csv\")\n\n").arg(basename);
-        else
-            textplot = "plot.new()\n"
-                       "print(textplot(TFtable))\n"
-                       "print(title(\"Table of True Accept Rates at various False Accept Rates\"))\n"
-                       "print(textplot(FTtable))\n"
-                       "print(title(\"Table  of False Accept Rates at various True Accept Rates\"))\n"
-                       "print(textplot(CMCtable))\n"
-                       "print(title(\"Table of retrieval rate at various ranks\"))\n"
-                       "if (nrow(TS) != 0) {\n\t"
-                       "print(textplot(TStable, cex=1.15))\n\t"
-                       "print(title(\"Template Size by Algorithm\"))\n}\n\n";
-        file.write(qPrintable(textplot));
-    }
-
-    void qplot(QString geom, QString data, bool flipY, File opts)
-    {
-        file.write(qPrintable(QString("qplot(X, %1, data=%2, geom=\"%3\", main=\"%4\"").arg(flipY ? "1-Y" : "Y", data, geom, opts.get<QString>("title", "")) +
-                              (opts.contains("size") ? QString(", size=I(%1)").arg(opts.get<QString>("size")) : QString()) +
-                              (major.size > 1 ? QString(", colour=factor(%1)").arg(major.header) : QString()) +
-                              (minor.size > 1 ? QString(", linetype=factor(%1)").arg(minor.header) : QString()) +
-                              (QString(", xlab=\"%1\", ylab=\"%2\") + theme_minimal()").arg(opts.get<QString>("xTitle"), opts.get<QString>("yTitle"))) +
-                              ((major.smooth || minor.smooth) && confidence != 0 && data != "CMC" ? QString(" + geom_errorbar(data=%1[seq(1, NROW(%1), by = 29),], aes(x=X, ymin=%2), width=0.1, alpha=I(1/2))").arg(data, flipY ? "(1-lower), ymax=(1-upper)" : "lower, ymax=upper") : QString()) +
-                              (major.size > 1 ? getScale("colour", major.header, major.size) : QString()) +
-                              (minor.size > 1 ? QString(" + scale_linetype_discrete(\"%1\")").arg(minor.header) : QString()) +
-                              (opts.getBool("xLog") ? QString(" + scale_x_log10(labels=%1, breaks=%2) + annotation_logticks(sides=\"b\")").arg(opts.get<QString>("xLabels", "trans_format(\"log10\", math_format())"), opts.get<QString>("xBreaks", "waiver()"))
-                                                    : QString(" + scale_x_continuous(labels=%1, breaks=%2)").arg(opts.get<QString>("xLabels", "percent"), opts.get<QString>("xBreaks", "pretty_breaks(n=10)"))) +
-                              (opts.getBool("yLog") ? QString(" + scale_y_log10(labels=%1, breaks=%2) + annotation_logticks(sides=\"l\")").arg(opts.get<QString>("yLabels", "trans_format(\"log10\", math_format())"), opts.get<QString>("yBreaks", "waiver()"))
-                                                    : QString(" + scale_y_continuous(labels=%1, breaks=%2)").arg(opts.get<QString>("yLabels", "percent"), opts.get<QString>("yBreaks", "pretty_breaks(n=10)"))) +
-                              (opts.contains("xLimits") ? QString(" + xlim%1").arg(QtUtils::toString(opts.get<QPointF>("xLimits", QPointF()))) : QString()) +
-                              (opts.contains("yLimits") ? QString(" + ylim%1").arg(QtUtils::toString(opts.get<QPointF>("yLimits", QPointF()))) : QString()) +
-                              QString(" + theme(legend.title = element_text(size = %1), legend.text = element_text(size = %1), plot.title = element_text(size = %1), axis.text = element_text(size = %1), axis.title.x = element_text(size = %1), axis.title.y = element_text(size = %1),").arg(QString::number(opts.get<float>("textSize",12))) +
-                              QString(" legend.position=%1, legend.background = element_rect(fill = 'white'), panel.grid.major = element_line(colour = \"gray\"), panel.grid.minor = element_line(colour = \"gray\", linetype = \"dashed\"))").arg(opts.contains("legendPosition") ? "c"+QtUtils::toString(opts.get<QPointF>("legendPosition")) : "'bottom'") +
-                              QString(" + guides(col=guide_legend(ncol=%1))\n\n").arg(ncol)));
-    }
-
     bool finalize(bool show = false)
     {
         file.write("dev.off()\n");
@@ -315,13 +175,27 @@ bool Plot(const QStringList &amp;files, const File &amp;destination, bool show)
     qDebug("Plotting %d file(s) to %s", files.size(), qPrintable(destination));
     RPlot p(files, destination);
+    p.file.write("\nformatData()\n\n");
+    p.file.write(qPrintable(QString("algs <- %1\n").arg((p.major.size > 1 && p.minor.size > 1) && !(p.major.smooth || p.minor.smooth) ? QString("paste(TF$%1, TF$%2, sep=\"_\")").arg(p.major.header, p.minor.header)
+                                                                                                                                      : QString("TF$%1").arg(p.major.size > 1 ? p.major.header : (p.minor.header.isEmpty() ? p.major.header : p.minor.header)))));
+    p.file.write("algs <- algs[!duplicated(algs)]\n");
+
+    if (p.major.smooth || p.minor.smooth) {
+        QString groupvar = p.major.size > 1 ? p.major.header : (p.minor.header.isEmpty() ? p.major.header : p.minor.header);
+        foreach(const QString &data, QStringList() << "DET" << "IET" << "CMC" << "TF" << "FT" << "CT") {
+            p.file.write(qPrintable(QString("%1 <- summarySE(%1, measurevar=\"Y\", groupvars=c(\"%2\", \"X\"), conf.interval=confidence)"
+                                            "\n").arg(data, groupvar)));
+        }
+        p.file.write(qPrintable(QString("%1 <- summarySE(%1, measurevar=\"X\", groupvars=c(\"Error\", \"%2\", \"Y\"), conf.interval=confidence)"
+                                        "\n\n").arg("ERR", groupvar)));
+    }
     // Use a br::file for simple storage of plot options
     QMap<QString,File> optMap;
     optMap.insert("rocOptions", File(QString("[xTitle=False Accept Rate,yTitle=True Accept Rate,xLog=true,yLog=false]")));
     optMap.insert("detOptions", File(QString("[xTitle=False Accept Rate,yTitle=False Reject Rate,xLog=true,yLog=true]")));
     optMap.insert("ietOptions", File(QString("[xTitle=False Positive Identification Rate (FPIR),yTitle=False Negative Identification Rate (FNIR),xLog=true,yLog=true]")));
-    optMap.insert("cmcOptions", File(QString("[xTitle=Rank,yTitle=Retrieval Rate,xLog=true,yLog=false,size=1,xLabels=c(1,5,10,50,100),xBreaks=c(1,5,10,50,100)]")));
+    optMap.insert("cmcOptions", File(QString("[xTitle=Rank,yTitle=Retrieval Rate,xLog=true,yLog=false,size=1,xLabels=(1,5,10,50,100),xBreaks=(1,5,10,50,100)]")));
     foreach (const QString &key, optMap.keys()) {
         const QStringList options = destination.get<QStringList>(key, QStringList());
@@ -333,56 +207,33 @@ bool Plot(const QStringList &amp;files, const File &amp;destination, bool show)
     }
     // optional plot metadata and accuracy tables
-    if (destination.getBool("metadata", true))
-        p.plotMetadata(destination.getBool("csv", false));
-    p.qplot("line", "DET", true, optMap["rocOptions"]);
-    p.qplot("line", "DET", false, optMap["detOptions"]);
-    p.qplot("line", "IET", false, optMap["ietOptions"]);
-    p.qplot("line", "CMC", false, optMap["cmcOptions"]);
-
-    p.file.write(qPrintable(QString("qplot(X, data=SD, geom=\"histogram\", fill=Y, position=\"identity\", alpha=I(1/2)") +
-                            QString(", xlab=\"Score\", ylab=\"Frequency\"") +
-                            QString(") + 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))") +
-                            (p.major.size > 1 ? (p.minor.size > 1 ? QString(" + facet_grid(%2 ~ %1, scales=\"free\")").arg((p.flip ? p.major.header : p.minor.header), (p.flip ? p.minor.header : p.major.header)) : QString(" + facet_wrap(~ %1, scales = \"free\")").arg(p.major.header)) : QString()) +
-                            QString(" + theme(aspect.ratio=1)\n\n")));
-
-    p.file.write(qPrintable(QString("qplot(factor(%1)%2, data=BC, %3").arg(p.major.smooth ? (p.minor.header.isEmpty() ? "Algorithm" : p.minor.header) : p.major.header, (p.major.smooth || p.minor.smooth) ? ", Y" : "", (p.major.smooth || p.minor.smooth) ? "geom=\"boxplot\"" : "geom=\"bar\", position=\"dodge\", weight=Y") +
-                            (p.major.size > 1 ? QString(", fill=factor(%1)").arg(p.major.header) : QString()) +
-                            QString(", xlab=\"False Accept Rate\", ylab=\"True Accept Rate\") + theme_minimal()") +
-                            (p.major.size > 1 ? getScale("fill", p.major.header, p.major.size) : QString()) +
-                            (p.minor.size > 1 ? QString(" + facet_grid(%2 ~ X)").arg(p.minor.header) : QString(" + facet_grid(. ~ X, labeller=far_labeller)")) +
-                            QString(" + scale_y_continuous(labels=percent) + theme(legend.position=\"none\", axis.text.x=element_text(angle=-90, hjust=0))%1").arg((p.major.smooth || p.minor.smooth) ? "" : " + geom_text(data=BC, aes(label=Y, y=0.05))") + "\n\n"));
-
-    p.file.write(qPrintable(QString("qplot(X, Y, data=ERR, geom=\"line\", linetype=Error") +
-                            ((p.flip ? p.major.size : p.minor.size) > 1 ? QString(", colour=factor(%1)").arg(p.flip ? p.major.header : p.minor.header) : QString()) +
-                            QString(", xlab=\"Score\", ylab=\"Error Rate\") + theme_minimal()") +
-                            ((p.flip ? p.major.size : p.minor.size) > 1 ? getScale("colour", p.flip ? p.major.header : p.minor.header, p.flip ? p.major.size : p.minor.size) : QString()) +
-                            QString(" + scale_y_log10(labels=percent) + annotation_logticks(sides=\"l\")") +
-                            ((p.flip ? p.minor.size : p.major.size) > 1 ? QString(" + facet_wrap(~ %1, scales=\"free_x\")").arg(p.flip ? p.minor.header : p.major.header) : QString()) +
-                            QString(" + theme(aspect.ratio=1)\n\n")));
-
-    p.file.write(qPrintable(QString("if (nrow(IM) != 0) {\n\tlibrary(jpeg)\n\tlibrary(png)\n\tlibrary(grid)\n\t") +
-                            QString("multiplot <- function(..., plotlist=NULL, cols) {\n\t") +
-                            QString("\trequire(grid)\n\n\t\t# Make a list from the ... arguments and plotlist\n\t\tplots <- c(list(...), plotlist)\n") + 
-                            QString("\t\tnumPlots = length(plots)\n\n\t\t# Make the panel\n\t\tplotCols = cols\n\t\tplotRows = ceiling(numPlots/plotCols)\n\n") +
-                            QString("\t\t# Set up the page\n\t\tgrid.newpage()\n\t\tpushViewport(viewport(layout = grid.layout(plotRows, plotCols)))\n\t\tvplayout <- function(x, y)\n\t\t\tviewport(layout.pos.row = x, layout.pos.col = y)\n\n") +
-                            QString("\t\t# Make each plot, in the correct location\n\t\tfor (i in 1:numPlots) {\n\t\t\tcurRow = ceiling(i/plotCols)\n\t\t\tcurCol = (i-1) %% plotCols + 1\n\t\t\tprint(plots[[i]], vp = vplayout(curRow, curCol))\n\t\t}\n\t}\n\n")));
-    
-    p.file.write(qPrintable(QString("\t# Print impostor matches above the EER\n\tfor (i in 1:nrow(IM)) {\n\t\tscore <- IM[i,1]\n\t\tfiles <- IM[i,2]\n\t\talg <- IM[i,3]\n\t\tfiles <- unlist(strsplit(files, \"[:]\"))\n\n\t\text1 <- unlist(strsplit(files[2], \"[.]\"))[2]\n\t\text2 <- unlist(strsplit(files[4], \"[.]\"))[2]\n\t\t") +
-                            QString("if (ext1 == \"jpg\" || ext1 == \"JPEG\" || ext1 == \"jpeg\" || ext1 == \"JPG\") {\n\t\t\timg1 <- readJPEG(files[2])\n\t\t} else if (ext1 == \"PNG\" || ext1 == \"png\") {\n\t\t\timg1 <- readPNG(files[2])\n\t\t} else if (ext1 == \"TIFF\" || ext1 == \"tiff\" || ext1 == \"TIF\" || ext1 == \"tif\") {\n\t\t\timg1 <- readTIFF(files[2])\n\t\t} else {\n\t\t\tnext\n\t\t}\n\t\tif (ext2 == \"jpg\" || ext2 == \"JPEG\" || ext2 == \"jpeg\" || ext2 == \"JPG\") {\n\t\t\timg2 <- readJPEG(files[4])\n\t\t} ") +
-                            QString("else if (ext2 == \"PNG\" || ext2 == \"png\") {\n\t\t\timg2 <- readPNG(files[4])\n\t\t} else if (ext2 == \"TIFF\" || ext2 == \"tiff\" || ext2 == \"TIF\" || ext2 == \"tif\") {\n\t\t\timg2 <- readTIFF(files[4])\n\t\t} else {\n\t\t\tnext\n\t\t}") +
-                            QString("\n\t\tname1 <- files[1]\n\t\tname2 <- files[3]\n\n\t\tg1 <- rasterGrob(img1, interpolate=TRUE)\n\t\tg2 <- rasterGrob(img2, interpolate=TRUE)\n\n\t\t") +
-                            QString("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)\n\t\t") +
-                            QString("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(\"Impostor score =\", score)) + ylab(unlist(strsplit(files[4], \"[/]\"))[length(unlist(strsplit(files[4], \"[/]\")))]) + xlab(name2)\n\n\t\t") +
-                            QString("multiplot(plot1, plot2, cols=2)\n\t}")));
-
-    p.file.write(qPrintable(QString("\n\n\t# Print genuine matches below the EER\n\tfor (i in 1:nrow(GM)) {\n\t\tscore <- GM[i,1]\n\t\tfiles <- GM[i,2]\n\t\talg <- GM[i,3]\n\t\tfiles <- unlist(strsplit(files, \"[:]\"))\n\n\t\text1 <- unlist(strsplit(files[2], \"[.]\"))[2]\n\t\text2 <- unlist(strsplit(files[4], \"[.]\"))[2]\n\t\t") +
-                            QString("if (ext1 == \"jpg\" || ext1 == \"JPEG\" || ext1 == \"jpeg\" || ext1 == \"JPG\") {\n\t\t\timg1 <- readJPEG(files[2])\n\t\t} else if (ext1 == \"PNG\" || ext1 == \"png\") {\n\t\t\timg1 <- readPNG(files[2])\n\t\t} else if (ext1 == \"TIFF\" || ext1 == \"tiff\" || ext1 == \"TIF\" || ext1 == \"tif\") {\n\t\t\timg1 <- readTIFF(files[2])\n\t\t} else {\n\t\t\tnext\n\t\t}\n\t\tif (ext2 == \"jpg\" || ext2 == \"JPEG\" || ext2 == \"jpeg\" || ext2 == \"JPG\") {\n\t\t\timg2 <- readJPEG(files[4])\n\t\t} ") +
-                            QString("else if (ext2 == \"PNG\" || ext2 == \"png\") {\n\t\t\timg2 <- readPNG(files[4])\n\t\t} else if (ext2 == \"TIFF\" || ext2 == \"tiff\" || ext2 == \"TIF\" || ext2 == \"tif\") {\n\t\t\timg2 <- readTIFF(files[4])\n\t\t} else {\n\t\t\tnext\n\t\t}") +
-                            QString("\n\t\tname1 <- files[1]\n\t\tname2 <- files[3]\n\n\t\tg1 <- rasterGrob(img1, interpolate=TRUE)\n\t\tg2 <- rasterGrob(img2, interpolate=TRUE)\n\n\t\t") +
-                            QString("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)\n\t\t") +
-                            QString("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(\"Genuine score =\", score)) + ylab(unlist(strsplit(files[4], \"[/]\"))[length(unlist(strsplit(files[4], \"[/]\")))]) + xlab(name2)\n\n\t\t") +
-                            QString("multiplot(plot1, plot2, cols=2)\n\t}\n}\n\n")));
+    if (destination.getBool("metadata", true)) {
+        p.file.write("\n# Write metadata table\n");
+        p.file.write(qPrintable(QString("plotMetadata(metadata=Metadata, title=\"%1 - %2\")\n").arg(PRODUCT_NAME, PRODUCT_VERSION)));
+
+        if (!destination.getBool("csv")) p.file.write("plot.new()\n");
+        QString table = "plotTable(tableData=%1, name=%2, labels=%3)\n";
+        p.file.write(qPrintable(table.arg("TF", "\"Table of True Accept Rates at various False Accept Rates\"",
+                          "c(\"FAR = 1e-06\", \"FAR = 1e-05\", \"FAR = 1e-04\", \"FAR = 1e-03\", \"FAR = 1e-02\", \"FAR = 1e-01\")")));
+        p.file.write(qPrintable(table.arg("FT", "\"Table  of False Accept Rates at various True Accept Rates\"",
+                          "c(\"TAR = 0.40\", \"TAR = 0.50\", \"TAR = 0.65\", \"TAR = 0.75\", \"TAR = 0.85\", \"TAR = 0.95\")")));
+        p.file.write(qPrintable(table.arg("CT", "\"Table of retrieval rate at various ranks\"",
+                          "c(\"Rank 1\", \"Rank 5\", \"Rank 10\", \"Rank 20\", \"Rank 50\", \"Rank 100\")")));
+        p.file.write(qPrintable(table.arg("TS", "\"Template Size by Algorithm\"",
+                          "c(\"Template Size (bytes):\")")));
+        p.file.write("\n");
+    }
+
+    // Write plots
+    QString plot = "plot <- plotLine(lineData=%1, options=list(%2), flipY=%3)\nplot\n";
+    p.file.write(qPrintable(QString(plot).arg("DET", toRList(optMap["rocOptions"]), "TRUE")));
+    p.file.write(qPrintable(QString(plot).arg("DET", toRList(optMap["detOptions"]), "FALSE")));
+    p.file.write(qPrintable(QString(plot).arg("IET", toRList(optMap["ietOptions"]), "FALSE")));
+    p.file.write(qPrintable(QString(plot).arg("CMC", toRList(optMap["cmcOptions"]), "FALSE")));
+    p.file.write("plot <- plotSD(sdData=SD)\nplot\n");
+    p.file.write("plot <- plotBC(bcData=BC)\nplot\n");
+    p.file.write("plot <- plotERR(errData=ERR)\nplot\n");
+    p.file.write("plotEERSamples(imData=IM, gmData=GM)\n\n");
     return p.finalize(show);
 }
@@ -420,7 +271,8 @@ bool filesHaveSinglePoint(const QStringList &amp;files) {
 bool PlotDetection(const QStringList &files, const File &destination, bool show)
 {
     qDebug("Plotting %d detection file(s) to %s", files.size(), qPrintable(destination));
-    RPlot p(files, destination, false);
+    RPlot p(files, destination);
+    p.file.write("\nformatData(type=\"detection\")\n\n");
     // Use a br::file for simple storage of plot options
     QMap<QString,File> optMap;
@@ -436,40 +288,26 @@ bool PlotDetection(const QStringList &amp;files, const File &amp;destination, bool show)
         }
     }
-    p.file.write("# Split data into individual plots\n"
-                 "plot_index = which(names(data)==\"Plot\")\n"
-                 "DiscreteROC <- data[grep(\"DiscreteROC\",data$Plot),-c(1)]\n"
-                 "ContinuousROC <- data[grep(\"ContinuousROC\",data$Plot),-c(1)]\n"
-                 "DiscretePR <- data[grep(\"DiscretePR\",data$Plot),-c(1)]\n"
-                 "ContinuousPR <- data[grep(\"ContinuousPR\",data$Plot),-c(1)]\n"
-                 "Overlap <- data[grep(\"Overlap\",data$Plot),-c(1)]\n"
-                 "AverageOverlap <- data[grep(\"AverageOverlap\",data$Plot),-c(1)]\n"
-                 "rm(data)\n"
-                 "\n");
-
     QString plotType("line");
     if (filesHaveSinglePoint(files))
         plotType = QString("point");
+    QString plot = "plot <- plotLine(lineData=%1, options=list(%2), flipY=%3, geometry=%4)\nplot\n";
     foreach (const QString &type, QStringList() << "Discrete" << "Continuous") {
         optMap["rocOptions"].set("title", type);
-        p.qplot(plotType, type + "ROC", false, optMap["rocOptions"]);
+        p.file.write(qPrintable(QString(plot).arg(type + "ROC", toRList(optMap["rocOptions"]), "FALSE", "\"" + plotType + "\"")));
     }
     foreach (const QString &type, QStringList() << "Discrete" << "Continuous") {
         optMap["prOptions"].set("title", type);
-        p.qplot(plotType, type + "PR", false, optMap["prOptions"]);
+        p.file.write(qPrintable(QString(plot).arg(type + "PR", toRList(optMap["prOptions"]), "FALSE", "\"" + plotType + "\"")));
     }
-
-    p.file.write(qPrintable(QString("qplot(X, data=Overlap, geom=\"histogram\", position=\"identity\", xlab=\"Overlap\", ylab=\"Frequency\")") +
-                            QString(" + 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))") +
-                            (p.major.size > 1 ? (p.minor.size > 1 ? QString(" + facet_grid(%2 ~ %1, scales=\"free\")").arg(p.minor.header, p.major.header) : QString(" + facet_wrap(~ %1, scales = \"free\")").arg(p.major.header)) : QString()) +
-                            QString(" + theme(aspect.ratio=1, legend.position=\"bottom\")\n\n")));
+    p.file.write("plot <- plotOverlap(overlapData=Overlap)\nplot\n");
     p.file.write(qPrintable(QString("ggplot(AverageOverlap, aes(x=%1, y=%2, label=round(X,3)), main=\"Average Overlap\") + geom_text() + theme_minimal()").arg(p.minor.size > 1 ? p.minor.header : "'X'", p.major.size > 1 ? p.major.header : "'Y'") +
                             QString("%1%2\n\n").arg(p.minor.size > 1 ? "" : " + xlab(NULL)", p.major.size > 1 ? "" : " + ylab(NULL)")));
-    p.file.write(qPrintable(QString("ggplot(AverageOverlap, aes(x=%1, y=%2, fill=X)) + geom_tile() + scale_fill_continuous(\"Average Overlap\") + theme_minimal()").arg(p.minor.size > 1 ? p.minor.header : "'X'", p.major.size > 1 ? p.major.header : "'Y'") +
+    p.file.write(qPrintable(QString("ggplot(AverageOverlap, aes(x=%1, y=%2, fill=X)) + geom_tile() + scale_fill_continuous(\"Average Overlap\", guide=FALSE) + theme_minimal()").arg(p.minor.size > 1 ? p.minor.header : "'X'", p.major.size > 1 ? p.major.header : "'Y'") +
                             QString("%1%2\n\n").arg(p.minor.size > 1 ? "" : " + xlab(NULL)", p.major.size > 1 ? "" : " + ylab(NULL)")));
     return p.finalize(show);
@@ -478,109 +316,12 @@ bool PlotDetection(const QStringList &amp;files, const File &amp;destination, bool show)
 bool PlotLandmarking(const QStringList &files, const File &destination, bool show)
 {
     qDebug("Plotting %d landmarking file(s) to %s", files.size(), qPrintable(destination));
-    RPlot p(files, destination, false);
-
-    p.file.write(qPrintable(QString("# Split data into individual plots\n"
-                                    "plot_index = which(names(data)==\"Plot\")\n"
-                                    "Box <- data[grep(\"Box\",data$Plot),-c(1)]\n"
-                                    "Box$X <- factor(Box$X, levels = Box$X, ordered = TRUE)\n"
-                                    "Sample <- data[grep(\"Sample\",data$Plot),-c(1)]\n"
-                                    "Sample$X <- as.character(Sample$X)\n"
-                                    "EXT <- data[grep(\"EXT\",data$Plot),-c(1)]\n"
-                                    "EXT$X <- as.character(EXT$X)\n"
-                                    "EXP <- data[grep(\"EXP\",data$Plot),-c(1)]\n"
-                                    "EXP$X <- as.character(EXP$X)\n"
-                                    "NormLength <- data[grep(\"NormLength\",data$Plot),-c(1)]\n"
-                                    "rm(data)\n"
-                                    "\n")));
-
-    p.file.write(qPrintable(QString("summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {\n\t"
-                                    "require(plyr)\n\n\tlength2 <- function (x, na.rm=FALSE) {\n\t\tif (na.rm) sum(!is.na(x))\n\t\telse       length(x)"
-                                    "\n\t}\n\n\tdatac <- ddply(data, groupvars, .drop=.drop, .fun = function(xx, col) {\n\t\t"
-                                    "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))\n\t\t},"
-                                    "\n\t\tmeasurevar\n\t)\n\n\tdatac <- rename(datac, c(\"mean\" = measurevar))\n\tdatac$se <- datac$sd / sqrt(datac$N)"
-                                    "\n\tciMult <- qt(conf.interval/2 + .5, datac$N-1)\n\tdatac$ci <- datac$se * ciMult\n\n\treturn(datac)\n}\n")));
-
-
-    p.file.write(qPrintable(QString("\nreadData <- function(data) {\n\texamples <- list()\n"
-                                    "\tfor (i in 1:nrow(data)) {\n"
-                                    "\t\tpath <- data[i,1]\n"
-                                    "\t\tvalue <- data[i,2]\n"
-                                    "\t\tfile <- unlist(strsplit(path, \"[.]\"))[1]\n"
-                                    "\t\text <- unlist(strsplit(path, \"[.]\"))[2]\n"
-                                    "\t\tif (ext == \"jpg\" || ext == \"JPEG\" || ext == \"jpeg\" || ext == \"JPG\") {\n"
-                                    "\t\t\timg <- readJPEG(path)\n"
-                                    "\t\t} else if (ext == \"PNG\" || ext == \"png\") {\n"
-                                    "\t\t\timg <- readPNG(path)\n"
-                                    "\t\t} else if (ext == \"TIFF\" || ext == \"tiff\" || ext == \"TIF\" || ext == \"tif\") { \n"
-                                    "\t\t\timg <- readTIFF(path)\n"
-                                    "}else {\n"
-                                    "\t\t\tnext\n"
-                                    "\t\t}\n"
-                                    "\t\texample <- list(file = file, value = value, image = img)\n"
-                                    "\t\texamples[[i]] <- example\n"
-                                    "\t}\n"
-                                    "\treturn(examples)\n"
-                                    "}\n")));
-
-    p.file.write(qPrintable(QString("\nlibrary(jpeg)\n"
-                                    "library(png)\n"
-                                    "library(grid)\n"
-                                    "multiplot <- function(..., plotlist=NULL, cols) {\n"
-                                    "\trequire(grid)\n"
-                                    "\t# Make a list from the ... arguments and plotlist\n"
-                                    "\tplots <- c(list(...), plotlist)\n"
-                                    "\tnumPlots = length(plots)\n"
-                                    "\t# Make the panel\n"
-                                    "\tplotCols = cols\n"
-                                    "\tplotRows = ceiling(numPlots/plotCols)\n"
-                                    "\t# Set up the page\n"
-                                    "\tgrid.newpage()\n"
-                                    "\tpushViewport(viewport(layout = grid.layout(plotRows, plotCols)))\n"
-                                    "\tvplayout <- function(x, y)\n"
-                                    "\tviewport(layout.pos.row = x, layout.pos.col = y)\n"
-                                    "\t# Make each plot, in the correct location\n"
-                                    "\tfor (i in 1:numPlots) {\n"
-                                    "\t\tcurRow = ceiling(i/plotCols)\n"
-                                    "\t\tcurCol = (i-1) %% plotCols + 1\n"
-                                    "\t\tprint(plots[[i]], vp = vplayout(curRow, curCol))\n"
-                                    "\t}\n"
-                                    "}\n")));
-
-    p.file.write(qPrintable(QString("\nplotImage <- function(image, title=NULL, label=NULL) { \n"
-                                    "\tp <- qplot(1:10, 1:10, geom=\"blank\") + annotation_custom(rasterGrob(image$image), xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + theme(axis.line=element_blank(), axis.title.y=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), line=element_blank(), axis.ticks=element_blank(), panel.background=element_blank()) + labs(title=title) + xlab(label)\n"
-                                    "\treturn(p)"
-                                    "}\n")));
-
-    p.file.write(qPrintable(QString("\nsample <- readData(Sample) \n"
-                                    "rows <- sample[[1]]$value\n"
-                                    "algs <- unique(Box$%1)\n"
-                                    "algs <- algs[!duplicated(algs)]\n"
-                                    "print(plotImage(sample[[1]],\"Sample Landmarks\",sprintf(\"Total Landmarks: %s\",sample[[1]]$value))) \n"
-                                    "if (nrow(EXT) != 0 && nrow(EXP)) {\n"
-                                    "\tfor (j in 1:length(algs)) {\n"
-                                    "\ttruthSample <- readData(EXT[EXT$. == algs[[j]],])\n"
-                                    "\tpredictedSample <- readData(EXP[EXP$. == algs[[j]],])\n"
-                                    "\t\tfor (i in 1:length(predictedSample)) {\n"
-                                    "\t\t\tmultiplot(plotImage(predictedSample[[i]],sprintf(\"%s\\nPredicted Landmarks\",algs[[j]]),sprintf(\"Average Landmark Error: %.3f\",predictedSample[[i]]$value)),plotImage(truthSample[[i]],\"Ground Truth\\nLandmarks\",\"\"),cols=2)\n"
-                                    "\t\t}\n"
-                                    "\t}\n"
-                                    "}\n").arg(p.major.size > 1 ? p.major.header : (p.minor.header.isEmpty() ? p.major.header : p.minor.header))));
-
-    p.file.write(qPrintable(QString("\n"
-                 "# Code to format error table\n"
-                 "StatBox <- summarySE(Box, measurevar=\"Y\", groupvars=c(\"%1\",\"X\"))\n"
-                 "OverallStatBox <- summarySE(Box, measurevar=\"Y\", groupvars=c(\"%1\"))\n"
-                 "mat <- matrix(paste(as.character(round(StatBox$Y, 3)), round(StatBox$ci, 3), sep=\" \\u00b1 \"),nrow=rows,ncol=length(algs),byrow=FALSE)\n"
-                 "mat <- rbind(mat, paste(as.character(round(OverallStatBox$Y, 3)), round(OverallStatBox$ci, 3), sep=\" \\u00b1 \"))\n"
-                 "mat <- rbind(mat, as.character(round(NormLength$Y, 3)))\n"
-                 "colnames(mat) <- algs\n"
-                 "rownames(mat) <- c(seq(0,rows-1),\"Aggregate\",\"Average IPD\")\n"
-                 "ETable <- as.table(mat)\n").arg(p.major.size > 1 ? p.major.header : (p.minor.header.isEmpty() ? p.major.header : p.minor.header))));
-
-    p.file.write(qPrintable(QString("\n"
-                       "print(textplot(ETable))\n"
-                       "print(title(\"Landmarking Error Rates\"))\n")));
+    RPlot p(files, destination);
+    p.file.write("\nformatData(type=\"landmarking\")\n\n");
+    p.file.write(qPrintable(QString("algs <- uniqueBox$%1)\n").arg(p.major.size > 1 ? p.major.header : (p.minor.header.isEmpty() ? p.major.header : p.minor.header))));
+    p.file.write("algs <- algs[!duplicated(algs)]\n");
+    p.file.write("plotLandmarkSamples(samples=sample, expData=EXP, extData=EXT)\n");
+    p.file.write("plotLandmarkTables(tableData=Box)\n");
     p.file.write(qPrintable(QString("ggplot(Box, aes(Y,%1%2))").arg(p.major.size > 1 ? QString(", colour=%1").arg(p.major.header) : QString(),
                                                                     p.minor.size > 1 ? QString(", linetype=%1").arg(p.minor.header) : QString()) +
@@ -599,7 +340,7 @@ bool PlotMetadata(const QStringList &amp;files, const QString &amp;columns, bool show)
 {
     qDebug("Plotting %d metadata file(s) for columns %s", files.size(), qPrintable(columns));
-    RPlot p(files, "PlotMetadata", false);
+    RPlot p(files, "PlotMetadata");
     foreach (const QString &column, columns.split(";"))
         p.file.write(qPrintable(QString("qplot(%1, %2, data=data, geom=\"violin\", fill=%1) + coord_flip() + theme_minimal()\nggsave(\"%2.pdf\")\n").arg(p.major.header, column)));
     return p.finalize(show);
@@ -415,6 +415,29 @@ static T findAndRemove(QVariantMap &amp;map, const QString &amp;key, const T &amp;defaultVal
 br_utemplate Template::toUniversalTemplate(const Template &t)
 {
     QVariantMap map = t.file.localMetadata();
+
+    // QJsonObject::fromVariantMap (below) fails to convert
+    // QRects and QPoints to string, replacing them with null values.
+    // so hand-convert these weirdos
+    foreach (const QString &k, map.keys()) {
+        QVariant v = map[k];
+        if (v.canConvert(QVariant::PointF) || v.canConvert(QVariant::RectF)) {
+            QString newv = QtUtils::toString(v);
+            map[k] = newv;
+        }
+        // lists of points and rects, too
+        else if (v.type() == QVariant::List) {
+            QVariantList oldlist = qvariant_cast<QVariantList>(v);
+            if (!oldlist.isEmpty() && (oldlist.first().canConvert(QVariant::PointF) || oldlist.first().canConvert(QVariant::RectF))) {
+                QVariantList newlist;
+                foreach (const QVariant &subv, oldlist) {
+                    newlist.append(QtUtils::toString(subv));
+                }
+                map[k] = newlist;
+            }
+        }
+    }
+
     const int32_t  algorithmID = findAndRemove<int32_t> (map, "AlgorithmID", 0);
     const int32_t  x           = findAndRemove<int32_t> (map, "X"          , 0);
     const int32_t  y           = findAndRemove<int32_t> (map, "Y"          , 0);
@@ -429,6 +452,49 @@ br_utemplate Template::toUniversalTemplate(const Template &amp;t)
 Template Template::fromUniversalTemplate(br_const_utemplate ut)
 {
     QVariantMap map = QJsonDocument::fromJson(QByteArray((const char*) ut->data)).object().toVariantMap();
+
+    // QJsonDocument::fromJson doesn't know about QRects and QPoints
+    // so convert any QStrings that can be converted
+    foreach (const QString &k, map.keys()) {
+        QVariant v = map[k];
+        QVariant newv;
+        bool istype;
+        if (v.type() == QVariant::String) {
+            QString vstr = qvariant_cast<QString>(v);
+            newv = QtUtils::toRect(vstr, &istype);
+            if (!istype) {
+                newv = QtUtils::toPoint(vstr, &istype);
+            } else if (!istype) {
+                newv = v;
+            }
+            map[k] = newv;
+        }
+        // convert lists of rects and points, too
+        else if (v.type() == QVariant::List) {
+            QVariantList oldlist = qvariant_cast<QVariantList>(v);
+            if (!oldlist.isEmpty() && oldlist.first().type() == QVariant::String) {
+                QString test = qvariant_cast<QString>(oldlist.first());
+                QtUtils::toRect(test, &istype);
+                QVariantList newlist;
+                if (istype) {
+                    foreach (const QVariant &subv, oldlist) {
+                        newlist.append(QtUtils::toRect(qvariant_cast<QString>(subv)));
+                    }
+                } else {
+                    QtUtils::toPoint(test, &istype);
+                    if (istype) {
+                        foreach (const QVariant &subv, oldlist) {
+                            newlist.append(QtUtils::toPoint(qvariant_cast<QString>(subv)));
+                        }
+                    } else {
+                        newlist = oldlist;
+                    }
+                }
+                map[k] = newlist;
+            }
+        }
+    }
+
     map.insert("AlgorithmID", ut->algorithmID);
     map.insert("X"          , ut->x          );
     map.insert("Y"          , ut->y          );
@@ -15,9 +15,6 @@
  * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
 #include <Eigen/Dense>
-#include <opencv2/imgproc/imgproc.hpp>
-#include <opencv2/highgui/highgui.hpp>
-
 #include <openbr/plugins/openbr_internal.h>
 #include <openbr/core/common.h>
@@ -58,7 +55,6 @@ BR_REGISTER(Initializer, EigenInitializer)
 class PCATransform : public Transform
 {
     Q_OBJECT
-    friend class DFFSTransform;
     friend class LDATransform;
 protected:
@@ -224,54 +220,6 @@ BR_REGISTER(Transform, PCATransform)
 /*!
  * \ingroup transforms
- * \brief Computes Distance From Feature Space (DFFS)
- * \br_paper Moghaddam, Baback, and Alex Pentland.
- *           "Probabilistic visual learning for object representation."
- *           Pattern Analysis and Machine Intelligence, IEEE Transactions on 19.7 (1997): 696-710.
- * \author Josh Klontz \cite jklontz
- * \br_property float keep Sets PCA keep property. Default is 0.95.
- */
-class DFFSTransform : public Transform
-{
-    Q_OBJECT
-    Q_PROPERTY(float keep READ get_keep WRITE set_keep RESET reset_keep STORED false)
-    BR_PROPERTY(float, keep, 0.95)
-
-    PCATransform pca;
-    Transform *cvtFloat;
-
-    void init()
-    {
-        pca.keep = keep;
-        cvtFloat = make("CvtFloat");
-    }
-
-    void train(const TemplateList &data)
-    {
-        pca.train((*cvtFloat)(data));
-    }
-
-    void project(const Template &src, Template &dst) const
-    {
-        dst = src;
-        dst.file.set("DFFS", sqrt(pca.residualReconstructionError((*cvtFloat)(src))));
-    }
-
-    void store(QDataStream &stream) const
-    {
-        pca.store(stream);
-    }
-
-    void load(QDataStream &stream)
-    {
-        pca.load(stream);
-    }
-};
-
-BR_REGISTER(Transform, DFFSTransform)
-
-/*!
- * \ingroup transforms
  * \brief Projects input into learned Linear Discriminant Analysis subspace.
  * \author Brendan Klare \cite bklare
  * \author Josh Klontz \cite jklontz
@@ -793,439 +741,6 @@ class WCDATransform : public Transform
 BR_REGISTER(Transform, WCDATransform)
-// For use in BBLDAAlignmentTransform
-// A single decision boundary with gaussian distributed responses
-struct DecisionBoundary
-{
-    VectorXf function;
-    float positiveMean, negativeMean, positiveSD, negativeSD;
-};
-
-QDataStream &operator<<(QDataStream &stream, const DecisionBoundary &db)
-{
-    return stream << db.function << db.positiveMean << db.negativeMean << db.positiveSD << db.negativeSD;
-}
-
-QDataStream &operator>>(QDataStream &stream, DecisionBoundary &db)
-{
-    return stream >> db.function >> db.positiveMean >> db.negativeMean >> db.positiveSD >> db.negativeSD;
-}
-
-/*!
- * \ingroup transforms
- * \brief Boosted Binary LDA classifier for local alignment.
- * \author Unknown \cite Unknown
- */
-class BBLDAAlignmentTransform : public MetaTransform
-{
-    Q_OBJECT
-    Q_PROPERTY(QList<int> anchors READ get_anchors WRITE set_anchors RESET reset_anchors STORED false) // A list of two indicies to provide initial rotation and scaling of training data
-    Q_PROPERTY(QString detectionKey READ get_detectionKey WRITE set_detectionKey RESET reset_detectionKey STORED false) // Metadata key containing the detection bounding box
-    Q_PROPERTY(int iad READ get_iad WRITE set_iad RESET reset_iad STORED false) // Inter-anchor distance in pixels
-    Q_PROPERTY(float radius READ get_radius WRITE set_radius RESET reset_radius STORED false) // Kernel size as a fraction of IAD
-    Q_PROPERTY(int resolution READ get_resolution WRITE set_resolution RESET reset_resolution STORED false) // Project resolution for sampling rotation and scale
-    BR_PROPERTY(QList<int>, anchors, QList<int>())
-    BR_PROPERTY(QString, detectionKey, "FrontalFace")
-    BR_PROPERTY(int, iad, 16)
-    BR_PROPERTY(float, radius, 2)
-    BR_PROPERTY(int, resolution, 20)
-
-    typedef Matrix<quint8, Dynamic, Dynamic> MatrixX8U;
-
-    struct RegistrationMatrix
-    {
-        Mat src;
-        Point2f center;
-        double angle, scale;
-
-        RegistrationMatrix(const Mat &src = Mat(), const Point2f &center = Point2f(), double angle = 0, double scale = 1)
-            : src(src)
-            , center(center)
-            , angle(angle)
-            , scale(scale)
-        {}
-
-        Mat calculateRotationMatrix2D(int size) const
-        {
-            Mat rotationMatrix2D = getRotationMatrix2D(center, angle, scale);
-            rotationMatrix2D.at<double>(0, 2) -= (center.x - size / 2.0); // Adjust the center to the middle of the dst image
-            rotationMatrix2D.at<double>(1, 2) -= (center.y - size / 2.0);
-            return rotationMatrix2D;
-        }
-
-        Point2f invert(double x, double y, int size) const
-        {
-            Mat m;
-            invertAffineTransform(calculateRotationMatrix2D(size), m);
-
-            Mat src(1, 1, CV_64FC2);
-            src.ptr<double>()[0] = y; // According to the docs these should be specified in the opposite order ...
-            src.ptr<double>()[1] = x; // ... however running it seems to suggest this is the correct way
-
-            Mat dst;
-            transform(src, dst, m);
-            return Point2f(dst.ptr<double>()[0], dst.ptr<double>()[1]);
-        }
-
-        MatrixX8U warp(int size) const
-        {
-            const Mat rotationMatrix2D = calculateRotationMatrix2D(size);
-            Mat dst;
-            warpAffine(src, dst, rotationMatrix2D, Size(size, size), INTER_LINEAR, BORDER_REFLECT);
-            return Map<MatrixX8U>(dst.ptr<quint8>(), size, size);
-        }
-    };
-
-    float rotationMax, scaleMin, scaleMax, xTranslationMax, yTranslationMin, yTranslationMax;
-    QList<DecisionBoundary> decisionBoundaries;
-
-    static void show(const char *name, const MatrixX8U &data)
-    {
-        const Mat mat(data.rows(), data.cols(), CV_8UC1, (void*) data.data());
-        imshow(name, mat);
-        waitKey(-1);
-    }
-
-    static void show(const char *name, MatrixXf data)
-    {
-        data.array() -= data.minCoeff();
-        data.array() *= (255 / data.maxCoeff());
-        show(name, MatrixX8U(data.cast<quint8>()));
-    }
-
-    static void show(const char *name, VectorXf data)
-    {
-        MatrixXf resized = data;
-        const int size = int(sqrtf(float(data.rows())));
-        resized.resize(size, size);
-        show(name, resized);
-    }
-
-    static MatrixXf getSample(const MatrixXf &registered, int j, int k, int kernelSize)
-    {
-        MatrixXf sample(registered.block(j, k, kernelSize, kernelSize));
-        const float norm = sample.norm();
-        if (norm > 0)
-            sample /= norm;
-        return sample;
-    }
-
-    static MatrixXf getSample(const QList<MatrixXf> &registered, int i, int j, int k, int kernelSize)
-    {
-        return getSample(registered[i], j, k, kernelSize);
-    }
-
-    static float IADError(const MatrixXf &responses, const MatrixXf &mask)
-    {
-        const int responseSize = sqrtf(responses.cols());
-        int numImages = 0;
-        float totalError = 0;
-        for (int i=0; i<responses.rows(); i++) {
-            float maxResponse = -std::numeric_limits<float>::max();
-            int maxX = -1;
-            int maxY = -1;
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++)
-                    if (mask(i, j*responseSize + k) > 0) {
-                        const float response = responses(i, j*responseSize+k);
-                        if (response > maxResponse) {
-                            maxResponse = response;
-                            maxY = j;
-                            maxX = k;
-                        }
-                    }
-            totalError += sqrtf(powf(maxX - responseSize/2, 2) + powf(maxY - responseSize/2, 2)) / responseSize;
-            numImages++;
-        }
-        return totalError / numImages;
-    }
-
-    static bool isPositive(int j, int k, int responseSize)
-    {
-        return (abs(j - responseSize/2) <= 0) && (abs(k - responseSize/2) <= 0);
-    }
-
-    static MatrixXf /* output responses */ computeBoundaryRecursive(const QList<MatrixXf> &registered, int kernelSize, const MatrixXf &prior /* input responses */, QList<DecisionBoundary> &boundaries)
-    {
-        const int numImages = registered.size();
-        const int responseSize = registered.first().cols() - kernelSize + 1;
-        int positiveSamples = 0;
-        int negativeSamples = 0;
-
-        // Compute weights, a weight of zero operates as a mask
-        MatrixXf weights(numImages, responseSize*responseSize);
-        float totalPositiveWeight = 0, totalNegativeWeight = 0;
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++) {
-                    const float probability = prior(i, j*responseSize+k);
-                    const bool positive = isPositive(j, k, responseSize);
-                    // Weight by probability ...
-                    float weight = positive ? (2 - probability) // Subtracting from a number greater than 1 helps ensure that we don't overfit outliers
-                                            : probability;
-                    // ... and by distance
-                    const float distance = sqrtf(powf(j - responseSize/2, 2) + powf(k - responseSize/2, 2));
-                    if (positive) weight *= 1 / (distance + 1);
-                    else          weight *= distance;
-                    weights(i, j*responseSize+k) = weight;
-                    if (weight > 0) {
-                        if (positive) { totalPositiveWeight += weight; positiveSamples++; }
-                        else          { totalNegativeWeight += weight; negativeSamples++; }
-                    }
-                }
-
-        // Normalize weights to preserve class sample ratio
-        const float positiveWeightAdjustment = positiveSamples / totalPositiveWeight;
-        const float negativeWeightAdjustment = negativeSamples / totalNegativeWeight;
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++)
-                    weights(i, j*responseSize+k) *= isPositive(j, k, responseSize) ? positiveWeightAdjustment : negativeWeightAdjustment;
-
-        // Compute weighted class means
-        MatrixXf positiveMean = MatrixXf::Zero(kernelSize, kernelSize);
-        MatrixXf negativeMean = MatrixXf::Zero(kernelSize, kernelSize);
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++) {
-                    const MatrixXf sample(getSample(registered, i, j, k, kernelSize));
-                    const float weight = weights(i, j*responseSize+k);
-                    if (weight > 0) {
-                        if (isPositive(j, k, responseSize)) positiveMean += sample * weight;
-                        else                                negativeMean += sample * weight;
-                    }
-                }
-        positiveMean /= positiveSamples;
-        negativeMean /= negativeSamples;
-
-        // Compute weighted scatter matrix and decision boundary
-        MatrixXf scatter = MatrixXf::Zero(kernelSize*kernelSize, kernelSize*kernelSize);
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++) {
-                    const float weight = weights(i, j*responseSize+k);
-                    if (weight > 0) {
-                        const MatrixXf &centeredSampleMatrix = getSample(registered, i, j, k, kernelSize) - (isPositive(j, k, responseSize) ? positiveMean : negativeMean);
-                        const Map<const VectorXf> centeredSample(centeredSampleMatrix.data(), kernelSize*kernelSize);
-                        scatter.noalias() += centeredSample * centeredSample.transpose() * weights(i, j*responseSize+k);
-                    }
-                }
-        scatter /= (positiveSamples + negativeSamples); // normalize for numerical stability
-        DecisionBoundary db;
-        db.function = scatter.inverse() * VectorXf(positiveMean - negativeMean); // fisher linear discriminant
-
-        // Compute response values to decision boundary
-        MatrixXf posterior(numImages, responseSize*responseSize);
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++)
-                    if (weights(i, j*responseSize + k) > 0) {
-                        const MatrixXf sample(getSample(registered, i, j, k, kernelSize));
-                        posterior(i, j*responseSize + k) = db.function.transpose() * Map<const VectorXf>(sample.data(), kernelSize*kernelSize);
-                    }
-
-        // Compute class response means
-        db.positiveMean = 0;
-        db.negativeMean = 0;
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++)
-                    if (weights(i, j*responseSize + k) > 0) {
-                        const float response = posterior(i, j*responseSize + k);
-                        if (isPositive(j, k, responseSize)) db.positiveMean += response;
-                        else                                db.negativeMean += response;
-                    }
-        db.positiveMean /= positiveSamples;
-        db.negativeMean /= negativeSamples;
-
-        // Compute class response standard deviations
-        db.positiveSD = 0;
-        db.negativeSD = 0;
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++)
-                    if (weights(i, j*responseSize + k) > 0) {
-                        const float response = posterior(i, j*responseSize + k);
-                        if (isPositive(j, k, responseSize)) db.positiveSD += powf(response-db.positiveMean, 2.f);
-                        else                                db.negativeSD += powf(response-db.negativeMean, 2.f);
-                    }
-        db.positiveSD = sqrtf(db.positiveSD / positiveSamples);
-        db.negativeSD = sqrtf(db.negativeSD / negativeSamples);
-
-        // Normalize responses and propogating prior probabilities
-        for (int i=0; i<numImages; i++)
-            for (int j=0; j<responseSize; j++)
-                for (int k=0; k<responseSize; k++) {
-                    float &response = posterior(i, j*responseSize + k);
-                    if (weights(i, j*responseSize + k) > 0) {
-                        const float positiveDensity = 1 / (sqrtf(2 * CV_PI) * db.positiveSD) * expf(-0.5 * powf((response - db.positiveMean) / db.positiveSD, 2.f));
-                        const float negativeDensity = 1 / (sqrtf(2 * CV_PI) * db.negativeSD) * expf(-0.5 * powf((response - db.negativeMean) / db.negativeSD, 2.f));
-                        response = prior(i, j*responseSize+k) * positiveDensity / (positiveDensity + negativeDensity);
-                    } else {
-                        response = 0;
-                    }
-                }
-        const float previousMeanError = IADError(prior, weights);
-        const float meanError = IADError(posterior, weights);
-        qDebug() << "Samples positive & negative:" << positiveSamples << negativeSamples;
-        qDebug() << "Positive mean & stddev:" << db.positiveMean << db.positiveSD;
-        qDebug() << "Negative mean & stddev:" << db.negativeMean << db.negativeSD;
-        qDebug() << "Mean error before & after:" << previousMeanError << meanError;
-
-        if (meanError < previousMeanError) {
-            boundaries.append(db);
-            return computeBoundaryRecursive(registered, kernelSize, posterior, boundaries);
-        } else {
-            return prior;
-        }
-    }
-
-    void train(const TemplateList &data)
-    {
-        QList<RegistrationMatrix> registrationMatricies;
-        {
-            if (Globals->verbose)
-                qDebug("Preprocessing training data...");
-
-            rotationMax     = -std::numeric_limits<float>::max();
-            scaleMin        =  std::numeric_limits<float>::max();
-            scaleMax        = -std::numeric_limits<float>::max();
-            xTranslationMax = -std::numeric_limits<float>::max();
-            yTranslationMin =  std::numeric_limits<float>::max();
-            yTranslationMax = -std::numeric_limits<float>::max();
-
-            foreach (const Template &t, data) {
-                const QRectF detection = t.file.get<QRectF>(detectionKey);
-                const QList<QPointF> points = t.file.points();
-                const float length = sqrt(pow(points[anchors[1]].x() - points[anchors[0]].x(), 2) +
-                                          pow(points[anchors[1]].y() - points[anchors[0]].y(), 2));
-                const float rotation = atan2(points[anchors[1]].y() - points[anchors[0]].y(),
-                                             points[anchors[1]].x() - points[anchors[0]].x()) * 180 / CV_PI;
-                const float scale = length / detection.width();
-                const float xCenter = (points[anchors[0]].x() + points[anchors[1]].x()) / 2;
-                const float yCenter = (points[anchors[0]].y() + points[anchors[1]].y()) / 2;
-                const float xTranslation = (xCenter - detection.x() - detection.width() /2) / detection.width();
-                const float yTranslation = (yCenter - detection.y() - detection.height()/2) / detection.width();
-
-                if (detection.contains(xCenter, yCenter) /* Sometimes the face detector gets the wrong face */) {
-                    rotationMax = max(rotationMax, fabsf(rotation));
-                    scaleMin    = min(scaleMin, scale);
-                    scaleMax    = max(scaleMax, scale);
-                    xTranslationMax = max(xTranslationMax, fabsf(xTranslation));
-                    yTranslationMin = min(yTranslationMin, yTranslation);
-                    yTranslationMax = max(yTranslationMax, yTranslation);
-                }
-
-                registrationMatricies.append(RegistrationMatrix(t, Point2f(xCenter, yCenter), rotation, iad / length));
-            }
-        }
-
-        if (Globals->verbose)
-            qDebug("Learning affine model ...");
-
-        // Construct the registered training data for the landmark
-        const int regionSize = 2 * iad * radius; // Train on a search size that is twice to the kernel size
-        QList<MatrixXf> registered;
-        foreach (const RegistrationMatrix &registrationMatrix, registrationMatricies)
-            registered.append(registrationMatrix.warp(regionSize).cast<float>());
-
-        // Boosted LDA
-        const int numImages = registered.size();
-        const int kernelSize = iad * radius;
-        const int responseSize = regionSize - kernelSize + 1;
-        const MatrixXf prior = MatrixXf::Ones(numImages, responseSize*responseSize);
-        const MatrixXf responses = computeBoundaryRecursive(registered, kernelSize, prior, decisionBoundaries);
-
-//        for (int i=0; i<numImages; i++) {
-//            float maxResponse = -std::numeric_limits<float>::max();
-//            int maxX = -1;
-//            int maxY = -1;
-//            for (int j=0; j<responseSize; j++)
-//                for (int k=0; k<responseSize; k++) {
-//                    const float response = responses(i, j*responseSize+k);
-//                    if (response > maxResponse) {
-//                        maxResponse = response;
-//                        maxY = j;
-//                        maxX = k;
-//                    }
-//                }
-
-//            MatrixXf draw = registered[i];
-//            const int r = 3;
-//            maxX += kernelSize / 2;
-//            maxY += kernelSize / 2;
-//            for (int i=-r; i<=r; i++)
-//                draw(regionSize/2 + i, regionSize/2) *= 2;
-//            for (int i=-r; i<=r; i++)
-//                draw(regionSize/2, regionSize/2 + i) *= 2;
-//            for (int i=-r; i<=r; i++)
-//                draw(maxX + i, maxY) /= 2;
-//            for (int i=-r; i<=r; i++)
-//                draw(maxX, maxY + i) /= 2;
-//            show("draw", draw);
-//            show("responses", VectorXf(responses.row(i)));
-//        }
-
-        if (Globals->verbose)
-            qDebug("Learned %d function(s) with error %g", decisionBoundaries.size(), IADError(responses, prior));
-    }
-
-    void project(const Template &src, Template &dst) const
-    {
-        dst = src;
-        const QRectF detection = src.file.get<QRectF>(detectionKey);
-        RegistrationMatrix registrationMatrix(src, OpenCVUtils::toPoint(detection.center()));
-
-        const int regionSize = iad * (1 + radius);
-        const int kernelSize = iad * radius;
-        const int responseSize = regionSize - kernelSize + 1;
-        const float rotationStep = (2 * rotationMax) / (resolution - 1);
-        const float scaleStep = (scaleMax - scaleMin) / (resolution - 1);
-
-        float bestResponse = -std::numeric_limits<float>::max(), bestRotation = 0, bestScale = 0;
-        int bestX = 0, bestY =0;
-
-        for (float rotation = -rotationMax; rotation <= rotationMax; rotation += rotationStep) {
-            registrationMatrix.angle = rotation;
-            for (float scale = scaleMin; scale <= scaleMax; scale += scaleStep) {
-                registrationMatrix.scale = iad / (scale * detection.width());
-                const MatrixXf registered = registrationMatrix.warp(regionSize).cast<float>();
-
-                for (int j=0; j<responseSize; j++)
-                    for (int k=0; k<responseSize; k++) {
-                        const MatrixXf sample = getSample(registered, j, k, kernelSize);
-                        float posterior = 1;
-                        foreach (const DecisionBoundary &db, decisionBoundaries) {
-                            const float response = db.function.transpose() * Map<const VectorXf>(sample.data(), kernelSize*kernelSize);
-                            const float positiveDensity = 1 / (sqrtf(2 * CV_PI) * db.positiveSD) * expf(-0.5 * powf((response - db.positiveMean) / db.positiveSD, 2.f));
-                            const float negativeDensity = 1 / (sqrtf(2 * CV_PI) * db.negativeSD) * expf(-0.5 * powf((response - db.negativeMean) / db.negativeSD, 2.f));
-                            posterior *= positiveDensity / (positiveDensity + negativeDensity);
-                        }
-                        if (posterior > bestResponse) {
-                            bestResponse = posterior;
-                            bestX = k + kernelSize/2;
-                            bestY = j + kernelSize/2;
-                            bestRotation = rotation;
-                            bestScale = scale;
-                        }
-                    }
-            }
-        }
-    }
-
-    void store(QDataStream &stream) const
-    {
-        stream << rotationMax << scaleMin << scaleMax << xTranslationMax << yTranslationMin << yTranslationMax << decisionBoundaries;
-    }
-
-    void load(QDataStream &stream)
-    {
-        stream >> rotationMax >> scaleMin >> scaleMax >> xTranslationMax >> yTranslationMin >> yTranslationMax >> decisionBoundaries;
-    }
-};
-
-BR_REGISTER(Transform, BBLDAAlignmentTransform)
-
 } // namespace br
 #include "classification/lda.moc"
@@ -2,7 +2,7 @@ set(BR_WITH_DLIB ON CACHE BOOL &quot;Build with DLib&quot;)
 if(${BR_WITH_DLIB})
   ExternalProject_Add(dlib
-                      URL http://downloads.sourceforge.net/project/dclib/dlib/v18.16/dlib-18.16.tar.bz2
+                      URL https://github.com/davisking/dlib/releases/download/v18.16/dlib-18.16.tar.bz2
                       URL_MD5 e9e5449bc25370afce2d254327afac99
                       SOURCE_DIR "${CMAKE_SOURCE_DIR}/3rdparty/dlib-18.16"
                       CONFIGURE_COMMAND ""
@@ -34,7 +34,6 @@ class AlgorithmsInitializer : public Initializer
         Globals->abbreviations.insert("FaceRecognition", "FaceDetection+FaceRecognitionRegistration+<FaceRecognitionExtraction>+<FaceRecognitionEmbedding>+<FaceRecognitionQuantization>+SetMetadata(AlgorithmID,-1):Unit(ByteL1)");
         Globals->abbreviations.insert("GenderClassification", "FaceDetection+Expand+FaceClassificationRegistration+Expand+<FaceClassificationExtraction>+<GenderClassifier>+Discard");
         Globals->abbreviations.insert("AgeRegression", "FaceDetection+Expand+FaceClassificationRegistration+Expand+<FaceClassificationExtraction>+<AgeRegressor>+Discard");
-        Globals->abbreviations.insert("FaceQuality", "Open+Expand+Cascade(FrontalFace)+ASEFEyes+Affine(64,64,0.25,0.35)+ImageQuality+Cvt(Gray)+DFFS+Discard");
         Globals->abbreviations.insert("MedianFace", "Open+Expand+Cascade(FrontalFace)+ASEFEyes+Affine(256,256,0.37,0.45)+Center(Median)");
         Globals->abbreviations.insert("BlurredFaceDetection", "Open+LimitSize(1024)+SkinMask/(Cvt(Gray)+GradientMask)+And+Morph(Erode,16)+LargestConvexArea");
         Globals->abbreviations.insert("DrawFaceDetection", "Open+Cascade(FrontalFace)+Expand+ASEFEyes+Draw(inPlace=true)");
@@ -23,6 +23,7 @@ namespace br
  * \ingroup transforms
  * \brief Expand the width and height of a Template's rects by input width and height factors.
  * \author Charles Otto \cite caotto
+ * \author Brendan Klare \cite bklare
  */
 class ExpandRectTransform : public UntrainableTransform
 {
@@ -31,35 +32,23 @@ class ExpandRectTransform : public UntrainableTransform
     Q_PROPERTY(float heightExpand READ get_heightExpand WRITE set_heightExpand RESET reset_heightExpand STORED false)
     BR_PROPERTY(float, widthExpand, .5)
     BR_PROPERTY(float, heightExpand, .5)
+
     void project(const Template &src, Template &dst) const
     {
         dst = src;
-        QList<QRectF> rects = dst.file.rects();
+        dst.file.clearRects();
+        QList<QRectF> rects = src.file.rects();
         for (int i=0;i < rects.size(); i++) {
-            QRectF rect = rects[i];
-
-            qreal width = rect.width();
-            qreal height = rect.height();
-            float half_w_expansion = widthExpand / 2;
-            float half_h_expansion = heightExpand / 2;
-
-            qreal half_width = width * widthExpand;
-            qreal quarter_width = width * half_w_expansion;
-            qreal half_height = height * heightExpand;
-            qreal quarter_height = height * half_h_expansion;
-
-            rect.setX(std::max(qreal(0),(rect.x() - quarter_width)));
-            rect.setY(std::max(qreal(0),(rect.y() - quarter_height)));
-
-            qreal x2 = std::min(rect.width() + half_width + rect.x(), qreal(src.m().cols) - 1);
-            qreal y2 = std::min(rect.height() + half_height + rect.y(), qreal(src.m().rows) - 1);
+            qreal widthGrowth = rects[i].width() * widthExpand;
+            qreal heightGrowth = rects[i].height() * heightExpand;
-            rect.setWidth(x2 - rect.x());
-            rect.setHeight(y2 - rect.y());
+            qreal x = std::max(qreal(0), rects[i].x() - widthGrowth / 2.);
+            qreal y = std::max(qreal(0), rects[i].y() - heightGrowth / 2.);
+            dst.file.appendRect(QRectF(x, y,
+                             std::min(src.m().cols - x - 1, rects[i].width() + widthGrowth),
+                             std::min(src.m().rows - y - 1, rects[i].height() + heightGrowth)));
-            rects[i] = rect;
         }
-        dst.file.setRects(rects);
     }
 };
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+ * Copyright 2015 Rank One Computing Corporation                             *
+ *                                                                           *
+ * Licensed under the Apache License, Version 2.0 (the "License");           *
+ * you may not use this file except in compliance with the License.          *
+ * You may obtain a copy of the License at                                   *
+ *                                                                           *
+ *     http://www.apache.org/licenses/LICENSE-2.0                            *
+ *                                                                           *
+ * Unless required by applicable law or agreed to in writing, software       *
+ * distributed under the License is distributed on an "AS IS" BASIS,         *
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  *
+ * See the License for the specific language governing permissions and       *
+ * limitations under the License.                                            *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#include <openbr/plugins/openbr_internal.h>
+
+namespace br
+{
+
+/*!
+ * \ingroup transforms
+ * \brief For each point, add a rectangle with radius as a half width
+ * \author Brendan Klare \cite bklare
+ */
+
+class PointsToRectsTransform : public UntrainableTransform
+{
+    Q_OBJECT
+    Q_PROPERTY(float radius READ get_radius WRITE set_radius RESET reset_radius STORED false)
+    Q_PROPERTY(bool clearRects READ get_clearRects WRITE set_clearRects RESET reset_clearRects STORED false)
+    BR_PROPERTY(float, radius, 4)
+    BR_PROPERTY(bool, clearRects, true)
+
+    void project(const Template &src, Template &dst) const
+    {
+        dst = src;
+
+        if (clearRects)
+            dst.file.clearRects();
+
+        if (src.file.points().isEmpty()) {
+            if (Globals->verbose) qWarning("No landmarks");
+            return;
+        }
+
+        for (int i = 0; i < src.file.points().size(); i++) {
+            dst.file.appendRect(QRectF(src.file.points()[i].x() - radius, src.file.points()[i].y() - radius, radius * 2, radius * 2));
+        }
+    }
+};
+
+BR_REGISTER(Transform, PointsToRectsTransform)
+
+} // namespace br
+
+#include "metadata/pointstorects.moc"
+/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
+ * Copyright 2015 Rank One Computing Corporation                             *
+ *                                                                           *
+ * Licensed under the Apache License, Version 2.0 (the "License");           *
+ * you may not use this file except in compliance with the License.          *
+ * You may obtain a copy of the License at                                   *
+ *                                                                           *
+ *     http://www.apache.org/licenses/LICENSE-2.0                            *
+ *                                                                           *
+ * Unless required by applicable law or agreed to in writing, software       *
+ * distributed under the License is distributed on an "AS IS" BASIS,         *
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  *
+ * See the License for the specific language governing permissions and       *
+ * limitations under the License.                                            *
+ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
+
+#include <openbr/plugins/openbr_internal.h>
+#include <openbr/core/opencvutils.h>
+
+namespace br
+{
+
+/*!
+ * \ingroup transforms
+ * \brief For each rectangle bounding box in src, the mat is cropped and appended
+ *          the the template's list of mats.
+ * \author Brendan Klare \cite bklare
+ */
+class RectsToMatsTransform : public UntrainableTransform
+{
+    Q_OBJECT
+
+private:
+    void project(const Template &src, Template &dst) const
+    {
+        for (int i = 0; i < src.file.rects().size(); i++) 
+            dst += cv::Mat(src, OpenCVUtils::toRect(src.file.rects()[i]));
+    }
+};
+
+BR_REGISTER(Transform, RectsToMatsTransform)
+
+} // namespace br
+
+#include "metadata/rectstomats.moc"
-/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
- * Copyright 2012 The MITRE Corporation                                      *
- *                                                                           *
- * Licensed under the Apache License, Version 2.0 (the "License");           *
- * you may not use this file except in compliance with the License.          *
- * You may obtain a copy of the License at                                   *
- *                                                                           *
- *     http://www.apache.org/licenses/LICENSE-2.0                            *
- *                                                                           *
- * Unless required by applicable law or agreed to in writing, software       *
- * distributed under the License is distributed on an "AS IS" BASIS,         *
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  *
- * See the License for the specific language governing permissions and       *
- * limitations under the License.                                            *
- * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
-
 #include <openbr/plugins/openbr_internal.h>
 #include <openbr/core/opencvutils.h>
+#include <openbr/plugins/openbr_internal.h>
+
+namespace br
+{
+
+/*!
+ * \ingroup transforms
+ * \brief Convert a rect to X, Y, Width, and Height. Handy for saving universal templates.
+ * \author Austin Blanton \cite imaus10
+ */
+class RectToKeysTransform : public UntrainableMetadataTransform
+{
+    Q_OBJECT
+    Q_PROPERTY(QString key READ get_key WRITE set_key RESET reset_key STORED false)
+    BR_PROPERTY(QString, key, "")
+
+    void projectMetadata(const File &src, File &dst) const
+    {
+        dst = src;
+        if (src.contains(key)) {
+            QRectF r = src.get<QRectF>(key);
+            dst.set("Height", r.height());
+            dst.set("Width", r.width());
+            dst.set("X", r.left());
+            dst.set("Y", r.bottom());
+        }
+    }
+
+};
+
+BR_REGISTER(Transform, RectToKeysTransform)
+
+} // namespace br
+
+#include "metadata/recttokeys.moc"
+# 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)
+}
+