//Returns an averaged EMD value for the 2 images provided.
const int ColourAnalyser::compareImages(CImg<int> image1, CImg<int> image2, int *mask1, int *mask2)
{
std::cout << "Generating histograms for images..." << std::endl;
std::vector<int> red1, red2, green1, green2, blue1, blue2;
//Get the histograms for each image
getHistograms(image1, red1, green1, blue1, mask1);
getHistograms(image2, red2, green2, blue2, mask2);
//Normalise all the histograms
std::cout << "Normalising histograms..." << std::endl;
red1 = normaliseHistogram(red1);
red2 = normaliseHistogram(red2);
green1 = normaliseHistogram(green1);
green2 = normaliseHistogram(green2);
blue1 = normaliseHistogram(blue1);
blue2 = normaliseHistogram(blue2);
//Create the signatures from the histograms.
std::cout << "Creating signatures from histograms..." << std::endl;
signature_tt<int> redSig1, redSig2, greenSig1, greenSig2, blueSig1, blueSig2;
calculateSignatures(redSig1, redSig2, red1, red2);
calculateSignatures(greenSig1, greenSig2, green1, green2);
calculateSignatures(blueSig1, blueSig2, blue1, blue2);
//Calculate the EMD for each colour
std::cout << "Calculating EDM for red..." << std::endl;
int redEMD = getEMD(redSig1, redSig2);
std::cout << "Calculating EDM for green..." << std::endl;
int greenEMD = getEMD(greenSig1, greenSig2);
std::cout << "Calculating EDM for blue..." << std::endl;
int blueEMD = getEMD(blueSig1, blueSig2);
//Average the three values to get an average EMD for the image and return it
std::cout << "Averaging EDM..." << std::endl;
return (redEMD + greenEMD + blueEMD) / 3;
}
void PDI::ecualizeImage(cv::Mat &img){
// Copy the image to temp image
cv::Mat out = cv::Mat(img);
// Get histograms (Normal and accumulated) to image
histograms Hists;
initHistograms(Hists.Hist, Hists.HistA);
getHistograms(img, Hists.Hist, Hists.HistA);
normalizateArray(Hists.Hist);
normalizateArray(Hists.HistA);
vHistograms.push_back(Hists);
for(int i=0; i<img.rows; i++){ // Rows i
for(int j=0; j<img.cols; j ++){ // Columns j
out.at<uchar>(i,j) = Hists.HistA[int(img.at<uchar>(i,j))];
}
}
img = out.clone();
}
std::string Volume::getDataInfo() const {
using P = Document::PathComponent;
using H = utildoc::TableBuilder::Header;
Document doc;
doc.append("b", "Volume", { {"style", "color:white;"} });
utildoc::TableBuilder tb(doc.handle(), P::end());
tb(H("Format"), getDataFormat()->getString());
tb(H("Dimension"), getDimensions());
tb(H("Data Range"), dataMap_.dataRange);
tb(H("Value Range"), dataMap_.valueRange);
tb(H("Unit"), dataMap_.valueUnit);
if (hasRepresentation<VolumeRAM>()) {
auto volumeRAM = getRepresentation<VolumeRAM>();
if (volumeRAM->hasHistograms()) {
auto histograms = volumeRAM->getHistograms();
for (size_t i = 0; i < histograms->size(); ++i) {
std::stringstream ss;
ss << "Channel " << i << " Min: " << (*histograms)[i].stats_.min
<< " Mean: " << (*histograms)[i].stats_.mean
<< " Max: " << (*histograms)[i].stats_.max
<< " Std: " << (*histograms)[i].stats_.standardDeviation;
tb(H("Stats"), ss.str());
std::stringstream ss2;
ss2 << "(1: " << (*histograms)[i].stats_.percentiles[1]
<< ", 25: " << (*histograms)[i].stats_.percentiles[25]
<< ", 50: " << (*histograms)[i].stats_.percentiles[50]
<< ", 75: " << (*histograms)[i].stats_.percentiles[75]
<< ", 99: " << (*histograms)[i].stats_.percentiles[99] << ")";
tb(H("Percentiles"), ss2.str());
}
}
}
return doc;
}
//-----------
void ModuleImageAnalyzer::update() {
bool toUpdate = false;
if (mode == 0) {
cam.update();
toUpdate = cam.isFrameNew();
}
else if (mode == 1) {
player.update();
toUpdate = player.isFrameNew();
}
else if (mode == 2) {
toUpdate = false;
// this has to be done at least once upon processing
}
if(toUpdate) {
// get frame differencing
if (mode == 0) {
absdiff(previous, cam, diff);
diff.update();
copy(cam, previous);
}
else if (mode == 1) {
absdiff(previous, player, diff);
diff.update();
copy(player, previous);
}
columnMean = meanCols(diff);
diffMean = mean(toCv(diff));
diffMean *= Scalar(50);
frameDiffTotalRed = diffMean[0];
frameDiffTotalGreen = diffMean[1];
frameDiffTotalBlue = diffMean[2];
frameDiffTotal = frameDiffTotalRed + frameDiffTotalGreen + frameDiffTotalBlue;
/*
frameDiffTotalRed = ofLerp(frameDiffTotalRed, diffMean[0], 0.2);
frameDiffTotalGreen = ofLerp(frameDiffTotalGreen, diffMean[1], 0.2);
frameDiffTotalBlue = ofLerp(frameDiffTotalBlue, diffMean[2], 0.2);
frameDiffTotal = ofLerp(frameDiffTotal, frameDiffTotalRed + frameDiffTotalGreen + frameDiffTotalBlue, 0.2);
*/
Vec3b column;
for(int i = 0; i < columnMean.rows; i++) {
column = columnMean.at<Vec3b>(i);
frameDiffCol[i] = float(column[0] + column[1] + column[2]);
}
flow.setWindowSize(8);
if (mode == 0) {
flow.calcOpticalFlow(cam);
}
else if (mode == 1) {
flow.calcOpticalFlow(player);
}
else if (mode == 2) {
// this shouldn't work
flow.calcOpticalFlow(image);
}
int i = 0;
float distortionStrength = 4;
for(int y = 1; y + 1 < ySteps; y++) {
for(int x = 1; x + 1 < xSteps; x++) {
int i = y * xSteps + x;
ofVec2f position(x * stepSize, y * stepSize);
ofRectangle area(position - ofVec2f(stepSize, stepSize) / 2, stepSize, stepSize);
ofVec2f offset = flow.getAverageFlowInRegion(area);
mesh.setVertex(i, position + distortionStrength * offset);
i++;
}
}
getHistograms();
}
Learn::update();
}
