Merge pull request #310 from biometrics/eval_landmark_improvements

Eval landmark improvements

Merge pull request #310 from biometrics/eval_landmark_improvements
Eval landmark improvements
Scott Klum
2 parents cb35ce83 d0310151
Showing 7 changed files with 203 additions and 32 deletions
app/br/br.cpp
openbr/core/eval.cpp
openbr/core/eval.h
openbr/core/plot.cpp
openbr/openbr.cpp
openbr/openbr.h
openbr/plugins/draw.cpp
@@ -163,8 +163,8 @@ public:
                 check((parc >= 2) && (parc <= 6), "Incorrect parameter count for 'evalDetection'.");
                 br_eval_detection(parv[0], parv[1], parc >= 3 ? parv[2] : "", parc >= 4 ? atoi(parv[3]) : 0, parc >= 5 ? atoi(parv[4]) : 0, parc == 6 ? atoi(parv[5]) : 0);
             } else if (!strcmp(fun, "evalLandmarking")) {
-                check((parc >= 2) && (parc <= 5), "Incorrect parameter count for 'evalLandmarking'.");
-                br_eval_landmarking(parv[0], parv[1], parc >= 3 ? parv[2] : "", parc >= 4 ? atoi(parv[3]) : 0, parc >= 5 ? atoi(parv[4]) : 1);
+                check((parc >= 2) && (parc <= 7), "Incorrect parameter count for 'evalLandmarking'.");
+                br_eval_landmarking(parv[0], parv[1], parc >= 3 ? parv[2] : "", parc >= 4 ? atoi(parv[3]) : 0, parc >= 5 ? atoi(parv[4]) : 1,  parc >= 6 ? atoi(parv[5]) : 0, parc >= 7 ? atoi(parv[6]) : 5);
             } else if (!strcmp(fun, "evalRegression")) {
                 check(parc >= 2 && parc <= 4, "Incorrect parameter count for 'evalRegression'.");
                 br_eval_regression(parv[0], parv[1], parc >= 3 ? parv[2] : "", parc >= 4 ? parv[3] : "");
@@ -264,7 +264,7 @@ private:
                "-evalClassification <predicted_gallery> <truth_gallery> <predicted property name> <ground truth proprty name>\n"
                "-evalClustering <clusters> <gallery>\n"
                "-evalDetection <predicted_gallery> <truth_gallery> [{csv}] [{normalize}] [{minSize}]\n"
-               "-evalLandmarking <predicted_gallery> <truth_gallery> [{csv} [<normalization_index_a> <normalization_index_b>]]\n"
+               "-evalLandmarking <predicted_gallery> <truth_gallery> [{csv} [<normalization_index_a> <normalization_index_b>] [sample_index] [total_examples]]\n"
                "-evalRegression <predicted_gallery> <truth_gallery> <predicted property name> <ground truth property name>\n"
                "-assertEval <simmat> <mask> <accuracy>\n"
                "-plotDetection <file> ... <file> {destination}\n"
@@ -18,6 +18,7 @@
 #include "eval.h"
 #include "openbr/core/common.h"
 #include "openbr/core/qtutils.h"
+#include "openbr/core/opencvutils.h"
 #include <QMapIterator>
 using namespace cv;
@@ -1068,16 +1069,25 @@ float EvalDetection(const QString &amp;predictedGallery, const QString &amp;truthGallery
     return averageOverlap;
 }
-float EvalLandmarking(const QString &predictedGallery, const QString &truthGallery, const QString &csv, int normalizationIndexA, int normalizationIndexB)
+static void projectAndWrite(Transform *t, const Template &src, const QString &filePath)
+{
+    Template dst;
+    t->project(src,dst);
+    OpenCVUtils::saveImage(dst.m(),filePath);
+}
+
+float EvalLandmarking(const QString &predictedGallery, const QString &truthGallery, const QString &csv, int normalizationIndexA, int normalizationIndexB, int sampleIndex, int totalExamples)
 {
     qDebug("Evaluating landmarking of %s against %s", qPrintable(predictedGallery), qPrintable(truthGallery));
-    const TemplateList predicted(TemplateList::fromGallery(predictedGallery));
-    const TemplateList truth(TemplateList::fromGallery(truthGallery));
-    const QStringList predictedNames = File::get<QString>(predicted, "name");
-    const QStringList truthNames = File::get<QString>(truth, "name");
+    TemplateList predicted(TemplateList::fromGallery(predictedGallery));
+    TemplateList truth(TemplateList::fromGallery(truthGallery));
+    QStringList predictedNames = File::get<QString>(predicted, "name");
+    QStringList truthNames = File::get<QString>(truth, "name");
     int skipped = 0;
     QList< QList<float> > pointErrors;
+    QList<float> imageErrors;
+    QList<float> normalizedLengths;
     for (int i=0; i<predicted.size(); i++) {
         const QString &predictedName = predictedNames[i];
         const int truthIndex = truthNames.indexOf(predictedName);
@@ -1085,39 +1095,94 @@ float EvalLandmarking(const QString &amp;predictedGallery, const QString &amp;truthGalle
         const QList<QPointF> predictedPoints = predicted[i].file.points();
         const QList<QPointF> truthPoints = truth[truthIndex].file.points();
         if (predictedPoints.size() != truthPoints.size() || truthPoints.contains(QPointF(-1,-1))) {
-            skipped++;
+            predicted.removeAt(i);
+            predictedNames.removeAt(i);
+            truth.removeAt(i);
+            truthNames.removeAt(i);
+            i--; skipped++;
             continue;
         }
+
         while (pointErrors.size() < predictedPoints.size())
             pointErrors.append(QList<float>());
+
+        // Want to know error for every image.
+
         if (normalizationIndexA >= truthPoints.size()) qFatal("Normalization index A is out of range.");
         if (normalizationIndexB >= truthPoints.size()) qFatal("Normalization index B is out of range.");
         const float normalizedLength = QtUtils::euclideanLength(truthPoints[normalizationIndexB] - truthPoints[normalizationIndexA]);
-        for (int j=0; j<predictedPoints.size(); j++)
-            pointErrors[j].append(QtUtils::euclideanLength(predictedPoints[j] - truthPoints[j])/normalizedLength);
+        normalizedLengths.append(normalizedLength);
+        float totalError = 0;
+        for (int j=0; j<predictedPoints.size(); j++) {
+            float error = QtUtils::euclideanLength(predictedPoints[j] - truthPoints[j])/normalizedLength;
+            totalError += error;
+            pointErrors[j].append(error);
+        }
+        imageErrors.append(totalError/predictedPoints.size());
     }
-    qDebug() << "Skipped " << skipped << " files due to point size mismatch.";
+
+    qDebug() << "Skipped" << skipped << "files due to point size mismatch.";
     QList<float> averagePointErrors; averagePointErrors.reserve(pointErrors.size());
-    for (int i=0; i<pointErrors.size(); i++) {
-        std::sort(pointErrors[i].begin(), pointErrors[i].end());
-        averagePointErrors.append(Common::Mean(pointErrors[i]));
-    }
-    const float averagePointError = Common::Mean(averagePointErrors);
     QStringList lines;
     lines.append("Plot,X,Y");
+
+    QtUtils::touchDir(QDir("landmarking_examples_truth"));
+    QtUtils::touchDir(QDir("landmarking_examples_predicted"));
+
+    // Example
+    {
+        QScopedPointer<Transform> t(Transform::make("Open+Draw(verbose,rects=false,location=false)",NULL));
+
+        QString filePath = "landmarking_examples_truth/"+truth[sampleIndex].file.fileName();
+        projectAndWrite(t.data(), truth[sampleIndex],filePath);
+        lines.append("Sample,"+filePath+","+QString::number(truth[sampleIndex].file.points().size()));
+    }
+
+    // Get best and worst performing examples
+    QList< QPair<float,int> > exampleIndices = Common::Sort(imageErrors,true);
+
+    QScopedPointer<Transform> t(Transform::make("Open+Draw(rects=false)",NULL));
+
+    for (int i=0; i<totalExamples; i++) {
+        QString filePath = "landmarking_examples_truth/"+truth[exampleIndices[i].second].file.fileName();
+        projectAndWrite(t.data(), truth[exampleIndices[i].second],filePath);
+        lines.append("EXT,"+filePath+","+QString::number(exampleIndices[i].first));
+
+        filePath = "landmarking_examples_predicted/"+predicted[exampleIndices[i].second].file.fileName();
+        projectAndWrite(t.data(), predicted[exampleIndices[i].second],filePath);
+        lines.append("EXP,"+filePath+","+QString::number(exampleIndices[i].first));
+    }
+
+    for (int i=exampleIndices.size()-1; i>exampleIndices.size()-totalExamples-1; i--) {
+        QString filePath = "landmarking_examples_truth/"+truth[exampleIndices[i].second].file.fileName();
+        projectAndWrite(t.data(), truth[exampleIndices[i].second],filePath);
+        lines.append("EXT,"+filePath+","+QString::number(exampleIndices[i].first));
+
+        filePath = "landmarking_examples_predicted/"+predicted[exampleIndices[i].second].file.fileName();
+        projectAndWrite(t.data(), predicted[exampleIndices[i].second],filePath);
+        lines.append("EXP,"+filePath+","+QString::number(exampleIndices[i].first));
+    }
+
     for (int i=0; i<pointErrors.size(); i++) {
+        std::sort(pointErrors[i].begin(), pointErrors[i].end());
+        averagePointErrors.append(Common::Mean(pointErrors[i]));
         const QList<float> &pointError = pointErrors[i];
         const int keep = qMin(Max_Points, pointError.size());
         for (int j=0; j<keep; j++)
             lines.append(QString("Box,%1,%2").arg(QString::number(i), QString::number(pointError[j*(pointError.size()-1)/(keep-1)])));
     }
+    const float averagePointError = Common::Mean(averagePointErrors);
+
     lines.append(QString("AvgError,0,%1").arg(averagePointError));
+    lines.append(QString("NormLength,0,%1").arg(Common::Mean(normalizedLengths)));
     QtUtils::writeFile(csv, lines);
-    qDebug("Average Error: %.3f", averagePointError);
+
+    qDebug("Average Error for all Points: %.3f", averagePointError);
+
     return averagePointError;
 }
@@ -31,7 +31,7 @@ namespace br
     void EvalClassification(const QString &predictedGallery, const QString &truthGallery, QString predictedProperty = "", QString truthProperty = "");
     float EvalDetection(const QString &predictedGallery, const QString &truthGallery, const QString &csv = "", bool normalize = false, int minSize = 0, int maxSize = 0); // Return average overlap
-    float EvalLandmarking(const QString &predictedGallery, const QString &truthGallery, const QString &csv = "", int normalizationIndexA = 0, int normalizationIndexB = 1); // Return average error
+    float EvalLandmarking(const QString &predictedGallery, const QString &truthGallery, const QString &csv = "", int normalizationIndexA = 0, int normalizationIndexB = 1, int sampleIndex = 0, int totalExamples = 5); // Return average error
     void EvalRegression(const QString &predictedGallery, const QString &truthGallery, QString predictedProperty = "", QString truthProperty = "");
 }
@@ -482,16 +482,115 @@ bool PlotLandmarking(const QStringList &amp;files, const File &amp;destination, bool sho
     qDebug("Plotting %d landmarking file(s) to %s", files.size(), qPrintable(destination));
     RPlot p(files, destination, false);
-    p.file.write("# Split data into individual plots\n"
-                 "plot_index = which(names(data)==\"Plot\")\n"
-                 "Box <- data[grep(\"Box\",data$Plot),-c(1)]\n"
-                 "rm(data)\n"
-                 "\n");
-
-    p.file.write(qPrintable(QString("ggplot(Box, aes(Y,%1%2))").arg(p.major.size > 1 ? QString(", colour=%1").arg(p.major.header) : QString(), p.minor.size > 1 ? QString(", linetype=%1").arg(p.minor.header) : QString()) +
+    p.file.write(qPrintable(QString("# Split data into individual plots\n"
+                                    "plot_index = which(names(data)==\"Plot\")\n"
+                                    "Box <- data[grep(\"Box\",data$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")));
+
+    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()) +
                             QString(" + annotation_logticks(sides=\"b\") + stat_ecdf() + scale_x_log10(\"Normalized Error\", breaks=c(0.001,0.01,0.1,1,10)) + scale_y_continuous(\"Cumulative Density\", label=percent) + theme_minimal()\n\n")));
+
     p.file.write(qPrintable(QString("ggplot(Box, aes(factor(X), 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()) +
-                            QString("+ annotation_logticks(sides=\"l\") + geom_boxplot(alpha=0.5) + geom_jitter(size=1, alpha=0.5) + scale_x_discrete(\"Landmark\") + scale_y_log10(\"Normalized Error\", breaks=c(0.01,0.1,1,10)) + theme_minimal()\n\n")));
+                            QString("+ annotation_logticks(sides=\"l\") + geom_boxplot(alpha=0.5) + geom_jitter(size=1, alpha=0.5) + scale_x_discrete(\"Landmark\") + scale_y_log10(\"Normalized Error\", breaks=c(0.001,0.01,0.1,1,10)) + theme_minimal()\n\n")));
+
     p.file.write(qPrintable(QString("ggplot(Box, aes(factor(X), 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()) +
                             QString("+ annotation_logticks(sides=\"l\") + geom_violin(alpha=0.5) + scale_x_discrete(\"Landmark\") + scale_y_log10(\"Normalized Error\", breaks=c(0.001,0.01,0.1,1,10))\n\n")));
@@ -134,9 +134,9 @@ float br_eval_detection(const char *predicted_gallery, const char *truth_gallery
     return EvalDetection(predicted_gallery, truth_gallery, csv, normalize, minSize, maxSize);
 }
-float br_eval_landmarking(const char *predicted_gallery, const char *truth_gallery, const char *csv, int normalization_index_a, int normalization_index_b)
+float br_eval_landmarking(const char *predicted_gallery, const char *truth_gallery, const char *csv, int normalization_index_a, int normalization_index_b, int sample_index, int total_examples)
 {
-    return EvalLandmarking(predicted_gallery, truth_gallery, csv, normalization_index_a, normalization_index_b);
+    return EvalLandmarking(predicted_gallery, truth_gallery, csv, normalization_index_a, normalization_index_b, sample_index, total_examples);
 }
 void br_eval_regression(const char *predicted_gallery, const char *truth_gallery, const char *predicted_property, const char *truth_property)
@@ -214,8 +214,10 @@ BR_EXPORT float br_eval_detection(const char *predicted_gallery, const char *tru
  * \param csv Optional \c .csv file to contain performance metrics.
  * \param normalization_index_a Optional first index in the list of points to use for normalization.
  * \param normalization_index_b Optional second index in the list of points to use for normalization.
+ * \param sample_index Optional index for sample landmark image in ground truth gallery.
+ * \param total_examples Optional number of accurate and inaccurate examples to display.
  */
-BR_EXPORT float br_eval_landmarking(const char *predicted_gallery, const char *truth_gallery, const char *csv = "", int normalization_index_a = 0, int normalization_index_b = 1);
+BR_EXPORT float br_eval_landmarking(const char *predicted_gallery, const char *truth_gallery, const char *csv = "", int normalization_index_a = 0, int normalization_index_b = 1, int sample_index = 0, int total_examples = 5);
 /*!
  * \brief Evaluates regression accuracy to disk.
@@ -42,11 +42,15 @@ class DrawTransform : public UntrainableTransform
     Q_PROPERTY(bool rects READ get_rects WRITE set_rects RESET reset_rects STORED false)
     Q_PROPERTY(bool inPlace READ get_inPlace WRITE set_inPlace RESET reset_inPlace STORED false)
     Q_PROPERTY(int lineThickness READ get_lineThickness WRITE set_lineThickness RESET reset_lineThickness STORED false)
+    Q_PROPERTY(bool named READ get_named WRITE set_named RESET reset_named STORED false)
+    Q_PROPERTY(bool location READ get_location WRITE set_location RESET reset_location STORED false)
     BR_PROPERTY(bool, verbose, false)
     BR_PROPERTY(bool, points, true)
     BR_PROPERTY(bool, rects, true)
     BR_PROPERTY(bool, inPlace, false)
     BR_PROPERTY(int, lineThickness, 1)
+    BR_PROPERTY(bool, named, true)
+    BR_PROPERTY(bool, location, true)
     void project(const Template &src, Template &dst) const
     {
@@ -55,11 +59,12 @@ class DrawTransform : public UntrainableTransform
         dst.m() = inPlace ? src.m() : src.m().clone();
         if (points) {
-            const QList<Point2f> pointsList = OpenCVUtils::toPoints(src.file.namedPoints() + src.file.points());
+            const QList<Point2f> pointsList = (named) ? OpenCVUtils::toPoints(src.file.points()+src.file.namedPoints()) : OpenCVUtils::toPoints(src.file.points());
             for (int i=0; i<pointsList.size(); i++) {
                 const Point2f &point = pointsList[i];
                 circle(dst, point, 3, color, -1);
-                if (verbose) putText(dst, QString("%1,(%2,%3)").arg(QString::number(i),QString::number(point.x),QString::number(point.y)).toStdString(), point, FONT_HERSHEY_SIMPLEX, 0.5, verboseColor, 1);
+                QString label = (location) ? QString("%1,(%2,%3)").arg(QString::number(i),QString::number(point.x),QString::number(point.y)) : QString("%1").arg(QString::number(i));
+                if (verbose) putText(dst, label.toStdString(), point, FONT_HERSHEY_SIMPLEX, 0.5, verboseColor, 1);
             }
         }
         if (rects) {