void ocvColorQuantizeApp::setup()
{
// Image copyright Eric J Paparatto
// http://www.flickr.com/photos/ejpphoto/2633923684/
mInputImage = ci::Surface8u( loadImage( loadResource( RES_IMAGE ) ) );
setWindowSize( mInputImage.getWidth(), mInputImage.getHeight() );
updateImage();
}
void ocvColorQuantizeApp::updateImage()
{
const int colorCount = 32;
const int sampleCount = mInputImage.getHeight() * mInputImage.getWidth();
cv::Mat colorSamples( sampleCount, 1, CV_32FC3 );
// build our matrix of samples
Surface::ConstIter imageIt = mInputImage.getIter();
cv::MatIterator_<cv::Vec3f> sampleIt = colorSamples.begin<cv::Vec3f>();
while( imageIt.line() )
while( imageIt.pixel() )
*sampleIt++ = cv::Vec3f( imageIt.r(), imageIt.g(), imageIt.b() );
// call kmeans
cv::Mat labels, clusters;
cv::kmeans( colorSamples, colorCount, labels, cv::TermCriteria( cv::TermCriteria::COUNT, 8, 0 ), 2, cv::KMEANS_RANDOM_CENTERS, &clusters );
Color8u clusterColors[colorCount];
for( int i = 0; i < colorCount; ++i )
clusterColors[i] = Color8u( clusters.at<cv::Vec3f>(i,0)[0], clusters.at<cv::Vec3f>(i,0)[1], clusters.at<cv::Vec3f>(i,0)[2] );
Surface result( mInputImage.getWidth(), mInputImage.getHeight(), false );
Surface::Iter resultIt = result.getIter();
cv::MatIterator_<int> labelIt = labels.begin<int>();
while( resultIt.line() ) {
while( resultIt.pixel() ) {
resultIt.r() = clusterColors[*labelIt].r;
resultIt.g() = clusterColors[*labelIt].g;
resultIt.b() = clusterColors[*labelIt].b;
++labelIt;
}
}
// draw the color palette across the bottom of the image
const int swatchSize = 12;
for( int i = 0; i < colorCount; ++i ) {
ip::fill( &result, clusterColors[i], Area( i * swatchSize, result.getHeight() - swatchSize, ( i + 1 ) * swatchSize, result.getHeight() ) );
}
mTexture = gl::Texture( result );
}
//--------------------------------------------------------------
void testApp::setup(){
ofEnableAlphaBlending();
//load image file
string path = ofToDataPath("logo.png");
of_image.loadImage( path );
ci_surface = ci::loadImage( path );
//convert images
ci_texture = ci::gl::Texture( ofxCi::toCi(of_image), GL_RGBA, of_image.getWidth(), of_image.getHeight() );
of_image.setFromPixels(ofxCi::toOf(ci_surface), ci_surface.getWidth(), ci_surface.getHeight(), OF_IMAGE_COLOR_ALPHA);
}
//-----------------------------------------------------------------------------
bool HaarHandDetector::doUpdateDetection(ci::Surface pSurface)
{
//Set a Scale for the image to increase the processing time
int lScale = 2;
//Create a GrayScale copy of the input image
cv::Mat lGrayScaleImage(toOcv(pSurface, CV_8UC1));
//Scale the GrayScaleImage
cv::Mat lSmallGrayScaleImage(pSurface.getHeight() / lScale, pSurface.getWidth() / lScale, CV_8UC1);
cv::resize(lGrayScaleImage, lSmallGrayScaleImage, lSmallGrayScaleImage.size(), 0,0,cv::INTER_LINEAR);
//Equalize the Histogram to improve Edges Detection
cv::equalizeHist(lSmallGrayScaleImage, lSmallGrayScaleImage);
//Clear out the previous Detected Closed Hands
mClosedHands.clear();
//Start the Detection
std::vector<cv::Rect> lCVRectangles;
int lCount = 0;
mClosedHandCascade.detectMultiScale(lSmallGrayScaleImage, lCVRectangles, 1.2f, 2, CV_HAAR_FIND_BIGGEST_OBJECT, cv::Size(24,24));
for (std::vector<cv::Rect>::iterator lIter = lCVRectangles.begin(); lIter != lCVRectangles.end(); lIter++)
{
ci::Rectf lClosedHand(ci::fromOcv(*lIter));
lClosedHand *= lScale;
mClosedHands.push_back(lClosedHand);
lCount++;
}
//Return detection success flag
if (lCount > 0)
{
return true;
}
else
{
return false;
}
}