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Merge branch 'feature/iSWA' of github.com:OpenSpace/OpenSpace-Development into feature/iSWA

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This commit is contained in:
Michael Nilsson
2016-04-27 17:11:39 -04:00
parent b6784f99dc ba1f3ad8df
commit 189cbd0c01
1 changed files with 20 additions and 14 deletions
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34
modules/iswa/rendering/dataplane.cpp
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@@ -42,7 +42,7 @@ DataPlane::DataPlane(const ghoul::Dictionary& dictionary)
:CygnetPlane(dictionary)
,_dataOptions("dataOptions", "Data Options")
,_normValues("normValues", "Normalize Values", glm::vec2(1.0,1.0), glm::vec2(0), glm::vec2(5.0))
,_useLog("useLog","Use Logarithm Norm", false)
,_useLog("useLog","Use Logarithm", false)
,_useHistogram("_useHistogram", "Use Histogram", true)
,_useRGB("useRGB","Use RGB Channels", false)
// ,_colorbar(nullptr)
@@ -233,7 +233,6 @@ float* DataPlane::readData(){
std::vector<float> min(numSelected, std::numeric_limits<float>::max());
std::vector<float> max(numSelected, std::numeric_limits<float>::min());
std::vector<int> logmean(numSelected, 0);
std::vector<float> sum(numSelected, 0.0f);
std::vector<std::vector<float>> optionValues(numSelected, std::vector<float>());
@@ -256,13 +255,20 @@ float* DataPlane::readData(){
for(int i=0; i<numSelected; i++){
float v = value[selectedOptions[i]+3]; //+3 because "options" x, y and z.
if(_useLog.value()){
int sign = (v>0)? 1:-1;
if(v != 0){
v = sign*log(fabs(v));
}
}
optionValues[i].push_back(v);
min[i] = std::min(min[i], v);
max[i] = std::max(max[i], v);
sum[i] += v;
logmean[i] += (v != 0) ? ceil(log10(fabs(v))) : 0.0f;
}
numValues++;
}
@@ -275,9 +281,9 @@ float* DataPlane::readData(){
for(int i=0; i<numSelected; i++){
if(_useRGB.value() && numSelected <= 3){
processData(data, i, optionValues[i], min[i], max[i], sum[i], numSelected, logmean[i]);
processData(data, i, optionValues[i], min[i], max[i], sum[i], numSelected);
} else {
processData(data, i, optionValues[i], min[i], max[i], sum[i], 1, logmean[i]);
processData(data, i, optionValues[i], min[i], max[i], sum[i], 1);
}
}
@@ -289,7 +295,7 @@ float* DataPlane::readData(){
}
}
void DataPlane::processData(float* outputData, int inputChannel, std::vector<float> inputData, float min, float max,float sum, int numOutputChannels, float logmean){
void DataPlane::processData(float* outputData, int inputChannel, std::vector<float> inputData, float min, float max,float sum, int numOutputChannels){
// HISTOGRAM
// number of levels/bins/values
@@ -313,7 +319,7 @@ void DataPlane::processData(float* outputData, int inputChannel, std::vector<flo
//Calculate the Standard Deviation
float standardDeviation = sqrt (((pow(sum, 2.0)) - ((1.0/numValues) * (pow(sum,2.0)))) / (numValues - 1.0));
//calulate log mean
logmean /= numValues;
// logmean /= numValues;
//HISTOGRAM FUNCTIONALITY
//======================
@@ -327,7 +333,7 @@ void DataPlane::processData(float* outputData, int inputChannel, std::vector<flo
// Map mean and standard deviation to histogram levels
mean = mapToHistogram(mean , min, max);
logmean = mapToHistogram(logmean , min, max);
// logmean = mapToHistogram(logmean , min, max);
standardDeviation = mapToHistogram(standardDeviation, min, max);
min = 0.0f;
max = levels - 1.0f;
@@ -355,16 +361,16 @@ void DataPlane::processData(float* outputData, int inputChannel, std::vector<flo
// Map mean and standard deviation to new histogram levels
mean = newLevels[(int) mean];
logmean = newLevels[(int) logmean];
// logmean = newLevels[(int) logmean];
standardDeviation = newLevels[(int) standardDeviation];
}
// Normalize values
if(_useLog.value()){
v = normalizeWithLogarithm(v, logmean);
}else{
v = normalizeWithStandardScore(v, mean, standardDeviation);
}
// if(_useLog.value()){
// v = normalizeWithLogarithm(v, logmean);
// }else{
v = normalizeWithStandardScore(v, mean, standardDeviation);
// }
if(numOutputChannels == 1 && inputChannel > 0){
// take the average.