FeatureValue FeatureAverageColor::getNormedAccumulate()
{
auto ret = FeatureValue();
ret.push_back( accu[0] / count );
ret.push_back( accu[1] / count );
ret.push_back( accu[2] / count );
return ret;
}
std::vector<FeatureValue> FeatureValue::compute_all_values(std::vector<TestImage>& tests,
Feature& feature)
{
std::vector<FeatureValue> values;
for (size_t i = 0; i < tests.size(); ++i)
{
auto feature_value = feature.compute_value(tests[i].image_);
values.push_back(FeatureValue((short) i, Feature::normalize_feature(feature_value, tests[i].derivation_)));
}
return values;
}
FeatureValue FeatureAverageColor::calculateP( cv::InputArray image )
{
auto ret = FeatureValue();
Scalar_< double > avgColor = mean( image, noArray() );
ret.push_back( avgColor[2] );
ret.push_back( avgColor[1] );
ret.push_back( avgColor[0] );
// cout << "r:" << ret.at( 0 ) << ", g:" << ret.at( 1 ) << ", b:" << ret.at( 2 ) << endl;
return ret;
}
// *
FeatureValue FeatureAverageColor::calculate( cv::InputArray _image )
{
Mat image_gray;
Mat shiImg( _image.rows(), _image.cols(), CV_32SC1, Scalar( 0 ) );
cvtColor( _image, image_gray, CV_BGR2GRAY );
FeatureShiCorner shi;
shi.computeRawCornerMat( image_gray, shiImg );
auto points = shi.genPoints( shiImg );
if( points.empty() )
{
return {
0, 0, 0
};
}
// ---------------------------------------------------------------------
auto image = _image.getMat();
auto ret = FeatureValue();
uint64 r, g, b, n;
const int radius = 15;
r = g = b = 0;
n = 0;
const auto width = _image.cols();
const auto height = _image.rows();
for( auto p : points )
{
int x = p.first, y = p.second;
for( int dy = -radius; dy <= radius; dy++ )
{
for( int dx = -radius; dx <= radius; dx++ )
{
int fx = x + dx;
if( (fx < 0) || (fx >= width) ) { continue; }
int fy = y + dy;
if( (fy < 0) || (fy >= height) ) { continue; }
Pixel ptr = image.at< Pixel >( fy, fx );
b += ptr.x;
g += ptr.y;
r += ptr.z;
n++;
}
}
}
ret.push_back( r / (n * 1.0) );
ret.push_back( g / (n * 1.0) );
ret.push_back( b / (n * 1.0) );
return ret;
}