/*
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For Open Source Computer Vision Library
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*/
/*
Contributed by Gregor Kovalcik <gregor dot kovalcik at gmail dot com>
based on code provided by Martin Krulis, Jakub Lokoc and Tomas Skopal.
References:
Martin Krulis, Jakub Lokoc, Tomas Skopal.
Efficient Extraction of Clustering-Based Feature Signatures Using GPU Architectures.
Multimedia tools and applications, 75(13), pp.: 8071�8103, Springer, ISSN: 1380-7501, 2016
Christian Beecks, Merih Seran Uysal, Thomas Seidl.
Signature quadratic form distance.
In Proceedings of the ACM International Conference on Image and Video Retrieval, pages 438-445.
ACM, 2010.
*/
#include "precomp.hpp"
#include "pct_signatures/constants.hpp"
#include "pct_signatures/pct_sampler.hpp"
#include "pct_signatures/pct_clusterizer.hpp"
using namespace cv::xfeatures2d::pct_signatures;
namespace cv
{
namespace xfeatures2d
{
namespace pct_signatures
{
class PCTSignatures_Impl : public PCTSignatures
{
public:
PCTSignatures_Impl(const std::vector<Point2f>& initSamplingPoints, int initSeedCount)
{
if (initSamplingPoints.size() == 0)
{
CV_Error(Error::StsBadArg, "No sampling points provided!");
}
if (initSeedCount <= 0)
{
CV_Error(Error::StsBadArg, "Not enough initial seeds, at least 1 required.");
}
mSampler = PCTSampler::create(initSamplingPoints);
initSeedCount = std::min(initSeedCount, (int)initSamplingPoints.size());
std::vector<int> initClusterSeedIndexes = pickRandomClusterSeedIndexes(initSeedCount);
mClusterizer = PCTClusterizer::create(initClusterSeedIndexes);
}
PCTSignatures_Impl(
const std::vector<Point2f>& initSamplingPoints,
const std::vector<int>& initClusterSeedIndexes)
{
if (initSamplingPoints.size() == 0)
{
CV_Error(Error::StsBadArg, "No sampling points provided!");
}
if (initClusterSeedIndexes.size() == 0)
{
CV_Error(Error::StsBadArg, "Not enough initial seeds, at least 1 required.");
}
if (initClusterSeedIndexes.size() > initSamplingPoints.size())
{
CV_Error(Error::StsBadArg, "Too much cluster seeds or not enough sampling points.");
}
for (int iCluster = 0; iCluster < (int)(initClusterSeedIndexes.size()); iCluster++)
{
if (initClusterSeedIndexes[iCluster] < 0
|| initClusterSeedIndexes[iCluster] >= (int)(initSamplingPoints.size()))
{
CV_Error(Error::StsBadArg,
"Initial cluster seed indexes contain an index outside the range of the sampling point list.");
}
}
mSampler = PCTSampler::create(initSamplingPoints);
mClusterizer = PCTClusterizer::create(initClusterSeedIndexes);
}
void computeSignature(InputArray image, OutputArray signature) const CV_OVERRIDE;
void computeSignatures(const std::vector<Mat>& images, std::vector<Mat>& signatures) const CV_OVERRIDE;
void getGrayscaleBitmap(OutputArray _grayscaleBitmap, bool normalize) const;
/**** sampler ****/
int getSampleCount() const CV_OVERRIDE { return mSampler->getGrayscaleBits(); }
int getGrayscaleBits() const CV_OVERRIDE { return mSampler->getGrayscaleBits(); }
int getWindowRadius() const CV_OVERRIDE { return mSampler->getWindowRadius(); }
float getWeightX() const CV_OVERRIDE { return mSampler->getWeightX(); }
float getWeightY() const CV_OVERRIDE { return mSampler->getWeightY(); }
float getWeightL() const CV_OVERRIDE { return mSampler->getWeightL(); }
float getWeightA() const CV_OVERRIDE { return mSampler->getWeightA(); }
float getWeightB() const CV_OVERRIDE { return mSampler->getWeightB(); }
float getWeightContrast() const CV_OVERRIDE { return mSampler->getWeightContrast(); }
float getWeightEntropy() const CV_OVERRIDE { return mSampler->getWeightEntropy(); }
std::vector<Point2f> getSamplingPoints() const CV_OVERRIDE { return mSampler->getSamplingPoints(); }
void setGrayscaleBits(int grayscaleBits) CV_OVERRIDE { mSampler->setGrayscaleBits(grayscaleBits); }
void setWindowRadius(int windowRadius) CV_OVERRIDE { mSampler->setWindowRadius(windowRadius); }
void setWeightX(float weight) CV_OVERRIDE { mSampler->setWeightX(weight); }
void setWeightY(float weight) CV_OVERRIDE { mSampler->setWeightY(weight); }
void setWeightL(float weight) CV_OVERRIDE { mSampler->setWeightL(weight); }
void setWeightA(float weight) CV_OVERRIDE { mSampler->setWeightA(weight); }
void setWeightB(float weight) CV_OVERRIDE { mSampler->setWeightB(weight); }
void setWeightContrast(float weight) CV_OVERRIDE { mSampler->setWeightContrast(weight); }
void setWeightEntropy(float weight) CV_OVERRIDE { mSampler->setWeightEntropy(weight); }
void setWeight(int idx, float value) CV_OVERRIDE { mSampler->setWeight(idx, value); }
void setWeights(const std::vector<float>& weights) CV_OVERRIDE { mSampler->setWeights(weights); }
void setTranslation(int idx, float value) CV_OVERRIDE { mSampler->setTranslation(idx, value); }
void setTranslations(const std::vector<float>& translations) CV_OVERRIDE { mSampler->setTranslations(translations); }
void setSamplingPoints(std::vector<Point2f> samplingPoints) CV_OVERRIDE { mSampler->setSamplingPoints(samplingPoints); }
/**** clusterizer ****/
int getIterationCount() const CV_OVERRIDE { return mClusterizer->getIterationCount(); }
std::vector<int> getInitSeedIndexes() const CV_OVERRIDE { return mClusterizer->getInitSeedIndexes(); }
int getInitSeedCount() const CV_OVERRIDE { return (int)mClusterizer->getInitSeedIndexes().size(); }
int getMaxClustersCount() const CV_OVERRIDE { return mClusterizer->getMaxClustersCount(); }
int getClusterMinSize() const CV_OVERRIDE { return mClusterizer->getClusterMinSize(); }
float getJoiningDistance() const CV_OVERRIDE { return mClusterizer->getJoiningDistance(); }
float getDropThreshold() const CV_OVERRIDE { return mClusterizer->getDropThreshold(); }
int getDistanceFunction() const CV_OVERRIDE
{ return mClusterizer->getDistanceFunction(); }
void setIterationCount(int iterations) CV_OVERRIDE { mClusterizer->setIterationCount(iterations); }
void setInitSeedIndexes(std::vector<int> initSeedIndexes) CV_OVERRIDE
{ mClusterizer->setInitSeedIndexes(initSeedIndexes); }
void setMaxClustersCount(int maxClusters) CV_OVERRIDE { mClusterizer->setMaxClustersCount(maxClusters); }
void setClusterMinSize(int clusterMinSize) CV_OVERRIDE { mClusterizer->setClusterMinSize(clusterMinSize); }
void setJoiningDistance(float joiningDistance) CV_OVERRIDE { mClusterizer->setJoiningDistance(joiningDistance); }
void setDropThreshold(float dropThreshold) CV_OVERRIDE { mClusterizer->setDropThreshold(dropThreshold); }
void setDistanceFunction(int distanceFunction) CV_OVERRIDE
{ mClusterizer->setDistanceFunction(distanceFunction); }
private:
/**
* @brief Samples used for sampling the input image and producing list of samples.
*/
Ptr<PCTSampler> mSampler;
/**
* @brief Clusterizer using k-means algorithm to produce list of centroids from sampled points - the image signature.
*/
Ptr<PCTClusterizer> mClusterizer;
/**
* @brief Creates vector of random indexes of sampling points
* which will be used as initial centroids for k-means clusterization.
* @param initSeedCount Number of indexes of initial centroids to be produced.
* @return The generated vector of random indexes.
*/
static std::vector<int> pickRandomClusterSeedIndexes(int initSeedCount)
{
std::vector<int> seedIndexes;
for (int iSeed = 0; iSeed < initSeedCount; iSeed++)
{
seedIndexes.push_back(iSeed);
}
randShuffle(seedIndexes);
return seedIndexes;
}
};
/**
* @brief Class implementing parallel computing of signatures for multiple images.
*/
class Parallel_computeSignatures : public ParallelLoopBody
{
private:
const PCTSignatures* mPctSignaturesAlgorithm;
const std::vector<Mat>* mImages;
std::vector<Mat>* mSignatures;
public:
Parallel_computeSignatures(
const PCTSignatures* pctSignaturesAlgorithm,
const std::vector<Mat>* images,
std::vector<Mat>* signatures)
: mPctSignaturesAlgorithm(pctSignaturesAlgorithm),
mImages(images),
mSignatures(signatures)
{
mSignatures->resize(images->size());
}
void operator()(const Range& range) const CV_OVERRIDE
{
for (int i = range.start; i < range.end; i++)
{
mPctSignaturesAlgorithm->computeSignature((*mImages)[i], (*mSignatures)[i]);
}
}
};
/**
* @brief Computes signature for one image.
*/
void PCTSignatures_Impl::computeSignature(InputArray _image, OutputArray _signature) const
{
if (_image.empty())
{
_signature.create(_image.size(), CV_32FC1);
return;
}
Mat image = _image.getMat();
CV_Assert(image.depth() == CV_8U);
// TODO: OpenCL
//if (ocl::useOpenCL())
//{
//}
// sample features
Mat samples;
mSampler->sample(image, samples); // HOT PATH: 40%
// kmeans clusterize, use feature samples, produce signature clusters
Mat signature;
mClusterizer->clusterize(samples, signature); // HOT PATH: 60%
// set result
_signature.create(signature.size(), signature.type());
Mat result = _signature.getMat();
signature.copyTo(result);
}
/**
* @brief Parallel function computing signatures for multiple images.
*/
void PCTSignatures_Impl::computeSignatures(const std::vector<Mat>& images, std::vector<Mat>& signatures) const
{
parallel_for_(Range(0, (int)images.size()), Parallel_computeSignatures(this, &images, &signatures));
}
} // end of namespace pct_signatures
Ptr<PCTSignatures> PCTSignatures::create(
const int initSampleCount,
const int initSeedCount,
const int pointDistribution)
{
std::vector<Point2f> initPoints;
generateInitPoints(initPoints, initSampleCount, pointDistribution);
return create(initPoints, initSeedCount);
}
Ptr<PCTSignatures> PCTSignatures::create(
const std::vector<Point2f>& initPoints,
const int initSeedCount)
{
return makePtr<PCTSignatures_Impl>(initPoints, initSeedCount);
}
Ptr<PCTSignatures> PCTSignatures::create(
const std::vector<Point2f>& initPoints,
const std::vector<int>& initClusterSeedIndexes)
{
return makePtr<PCTSignatures_Impl>(initPoints, initClusterSeedIndexes);
}
void PCTSignatures::drawSignature(
InputArray _source,
InputArray _signature,
OutputArray _result,
float radiusToShorterSideRatio,
int borderThickness)
{
// check source
if (_source.empty())
{
return;
}
Mat source = _source.getMat();
// create result
_result.create(source.size(), source.type());
Mat result = _result.getMat();
source.copyTo(result);
// check signature
if (_signature.empty())
{
return;
}
Mat signature = _signature.getMat();
if (signature.type() != CV_32F || signature.cols != SIGNATURE_DIMENSION)
{
CV_Error_(Error::StsBadArg, ("Invalid signature format. Type must be CV_32F and signature.cols must be %d.", SIGNATURE_DIMENSION));
}
// compute max radius using given ratio of shorter image side
float maxRadius = ((source.rows < source.cols) ? source.rows : source.cols) * radiusToShorterSideRatio;
// draw signature
for (int i = 0; i < signature.rows; i++)
{
Vec3f labColor(
signature.at<float>(i, L_IDX) * L_COLOR_RANGE, // convert Lab pixel to BGR
signature.at<float>(i, A_IDX) * A_COLOR_RANGE,
signature.at<float>(i, B_IDX) * B_COLOR_RANGE);
Mat labPixel(1, 1, CV_32FC3);
labPixel.at<Vec3f>(0, 0) = labColor;
Mat rgbPixel;
cvtColor(labPixel, rgbPixel, COLOR_Lab2BGR);
rgbPixel.convertTo(rgbPixel, CV_8UC3, 255);
Vec3b rgbColor = rgbPixel.at<Vec3b>(0, 0);
// precompute variables
Point center((int)(signature.at<float>(i, X_IDX) * source.cols), (int)(signature.at<float>(i, Y_IDX) * source.rows));
int radius = (int)(maxRadius * signature.at<float>(i, WEIGHT_IDX));
Vec3b borderColor(0, 0, 0);
// draw filled circle
circle(result, center, radius, rgbColor, -1);
// draw circle outline
circle(result, center, radius, borderColor, borderThickness);
}
}
void PCTSignatures::generateInitPoints(
std::vector<Point2f>& initPoints,
const int count,
const int pointDistribution)
{
RNG random;
random.state = getTickCount();
initPoints.resize(count);
switch (pointDistribution)
{
case UNIFORM:
for (int i = 0; i < count; i++)
{
// returns uniformly distributed float random number from [0, 1) range
initPoints[i] = (Point2f(random.uniform((float)0.0, (float)1.0), random.uniform((float)0.0, (float)1.0)));
}
break;
case REGULAR:
{
int gridSize = (int)ceil(sqrt((float)count));
const float step = 1.0f / gridSize;
const float halfStep = step / 2;
float x = halfStep;
float y = halfStep;
for (int i = 0; i < count; i++)
{
// returns regular grid
initPoints[i] = Point2f(x, y);
if ((i + 1) % gridSize == 0)
{
x = halfStep;
y += step;
}
else
{
x += step;
}
}
break;
}
case NORMAL:
for (int i = 0; i < count; i++)
{
// returns normally distributed float random number from (0, 1) range with mean 0.5
float sigma = 0.2f;
float x = (float)random.gaussian(sigma);
float y = (float)random.gaussian(sigma);
while (x <= -0.5f || x >= 0.5f)
x = (float)random.gaussian(sigma);
while (y <= -0.5f || y >= 0.5f)
y = (float)random.gaussian(sigma);
initPoints[i] = Point2f(x, y) + Point2f(0.5, 0.5);
}
break;
default:
CV_Error(Error::StsNotImplemented, "Generation of this init point distribution is not implemented!");
break;
}
}
} // end of namespace xfeatures2d
} // end of namespace cv