double calculatePixelAccuracy(const LabelImage& prediction, const LabelImage& groundTruth,
const bool includeVoid, ConfusionMatrix* confusionMatrix) {
size_t correct = 0;
size_t wrong = 0;
if (confusionMatrix) {
LabelType numClasses = 0;
for (int y = 0; y < groundTruth.getHeight(); ++y) {
for (int x = 0; x < groundTruth.getWidth(); ++x) {
numClasses = std::max(numClasses, groundTruth.getLabel(x, y));
}
}
numClasses++;
if (confusionMatrix->getNumClasses() < numClasses) {
confusionMatrix->resize(numClasses);
confusionMatrix->reset();
}
}
for (int y = 0; y < prediction.getHeight(); ++y) {
for (int x = 0; x < prediction.getWidth(); ++x) {
const LabelType label = groundTruth.getLabel(x, y);
if (!includeVoid && label == 0) {
// skip void
continue;
}
const LabelType predictedClass = prediction.getLabel(x, y);
if (predictedClass == label) {
correct++;
} else {
wrong++;
}
if (confusionMatrix) {
confusionMatrix->increment(label, predictedClass);
}
}
}
size_t numPixels;
if (includeVoid) {
numPixels = prediction.getWidth() * prediction.getHeight();
} else {
numPixels = correct + wrong;
}
return static_cast<double>(correct) / numPixels;
}
double calculatePixelAccuracy(const LabelImage& prediction, const LabelImage& groundTruth,
const bool includeVoid, const std::vector<LabelType>* ignoredLabels, ConfusionMatrix* confusionMatrix) {
size_t correct = 0;
size_t wrong = 0;
if (confusionMatrix) {
LabelType numClasses = 0;
for (int y = 0; y < groundTruth.getHeight(); ++y) {
for (int x = 0; x < groundTruth.getWidth(); ++x) {
numClasses = std::max(numClasses, groundTruth.getLabel(x, y));
}
}
numClasses++;
if (confusionMatrix->getNumClasses() < numClasses) {
confusionMatrix->resize(numClasses);
confusionMatrix->reset();
}
}
for (int y = 0; y < prediction.getHeight(); ++y) {
for (int x = 0; x < prediction.getWidth(); ++x) {
const LabelType label = groundTruth.getLabel(x, y);
bool ignore = false;
// assert(!includeVoid && ignoredLabels);
if (!includeVoid && !ignoredLabels->empty())
for (LabelType ID: *ignoredLabels)
if (ID == label)
{
ignore = true;
break;
}
if (ignore)
continue;
const LabelType predictedClass = prediction.getLabel(x, y);
if (predictedClass == label) {
correct++;
} else {
wrong++;
}
if (confusionMatrix) {
confusionMatrix->increment(label, predictedClass);
}
}
}
size_t numPixels;
if (includeVoid) {
numPixels = prediction.getWidth() * prediction.getHeight();
} else {
numPixels = correct + wrong;
}
return static_cast<double>(correct) / numPixels;
}
