/*
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copy or use the software.


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               For Open Source Computer Vision Library
                       (3-clause BSD License)

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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_clusterizer.hpp"

namespace cv
{
    namespace xfeatures2d
    {
        namespace pct_signatures
        {
            class PCTClusterizer_Impl CV_FINAL : public PCTClusterizer
            {
            public:

                PCTClusterizer_Impl(
                    const std::vector<int>& initSeedIndexes,
                    int iterationCount,
                    int maxClustersCount,
                    int clusterMinSize,
                    float joiningDistance,
                    float dropThreshold,
                    int distanceFunction)
                    : mInitSeedIndexes(initSeedIndexes),
                    mIterationCount(iterationCount),
                    mMaxClustersCount(maxClustersCount),
                    mClusterMinSize(clusterMinSize),
                    mJoiningDistance(joiningDistance),
                    mDropThreshold(dropThreshold),
                    mDistanceFunction(distanceFunction)
                {
                }

                ~PCTClusterizer_Impl()
                {
                }


                int getIterationCount() const CV_OVERRIDE                       { return mIterationCount; }
                std::vector<int> getInitSeedIndexes() const CV_OVERRIDE         { return mInitSeedIndexes; }
                int getMaxClustersCount() const CV_OVERRIDE                     { return mMaxClustersCount; }
                int getClusterMinSize() const CV_OVERRIDE                       { return mClusterMinSize; }
                float getJoiningDistance() const CV_OVERRIDE                    { return mJoiningDistance; }
                float getDropThreshold() const CV_OVERRIDE                      { return mDropThreshold; }
                int getDistanceFunction() const CV_OVERRIDE                     { return mDistanceFunction; }

                void setIterationCount(int iterationCount) CV_OVERRIDE                  { mIterationCount = iterationCount; }
                void setInitSeedIndexes(std::vector<int> initSeedIndexes) CV_OVERRIDE   { mInitSeedIndexes = initSeedIndexes; }
                void setMaxClustersCount(int maxClustersCount) CV_OVERRIDE              { mMaxClustersCount = maxClustersCount; }
                void setClusterMinSize(int clusterMinSize) CV_OVERRIDE                  { mClusterMinSize = clusterMinSize; }
                void setJoiningDistance(float joiningDistance) CV_OVERRIDE              { mJoiningDistance = joiningDistance; }
                void setDropThreshold(float dropThreshold) CV_OVERRIDE                  { mDropThreshold = dropThreshold; }
                void setDistanceFunction(int distanceFunction) CV_OVERRIDE              { mDistanceFunction = distanceFunction; }


                /**
                * @brief K-means algorithm over the sampled points producing centroids as signatures.
                * @param samples List of sampled points.
                * @param signature Output list of computed centroids - the signature of the image.
                */
                void clusterize(InputArray _samples, OutputArray _signature) CV_OVERRIDE
                {
                    CV_Assert(!_samples.empty());

                    // prepare matrices
                    Mat samples = _samples.getMat();

                    if ((int)(this->mInitSeedIndexes.size()) > samples.rows)
                    {
                        CV_Error_(Error::StsBadArg, ("Number of seeds %d must be less or equal to the number of samples %d.",
                            mInitSeedIndexes.size(), samples.rows));
                    }

                    // Prepare initial centroids.
                    Mat clusters;
                    pickRandomClusters(samples, clusters);
                    clusters(Rect(WEIGHT_IDX, 0, 1, clusters.rows)) = 1;    // set initial weight to 1


                    // prepare for iterating
                    joinCloseClusters(clusters);
                    dropLightPoints(clusters);


                    // Main iterations cycle. Our implementation has fixed number of iterations.
                    for (int iteration = 0; iteration < mIterationCount; iteration++)
                    {
                        // Prepare space for new centroid values.
                        Mat tmpCentroids(clusters.size(), clusters.type());
                        tmpCentroids.setTo(cv::Scalar::all(0));

                        // Clear weights for new iteration.
                        clusters(Rect(WEIGHT_IDX, 0, 1, clusters.rows)).setTo(cv::Scalar::all(0));

                        // Compute affiliation of points and sum new coordinates for centroids.
                        for (int iSample = 0; iSample < samples.rows; iSample++)
                        {
                            int iClosest = findClosestCluster(clusters, samples, iSample);
                            for (int iDimension = 1; iDimension < SIGNATURE_DIMENSION; iDimension++)
                            {
                                tmpCentroids.at<float>(iClosest, iDimension) += samples.at<float>(iSample, iDimension);
                            }
                            clusters.at<float>(iClosest, WEIGHT_IDX)++;
                        }

                        // Compute average from tmp coordinates and throw away too small clusters.
                        int lastIdx = 0;
                        for (int i = 0; (int)i < tmpCentroids.rows; ++i)
                        {
                            // Skip clusters that are too small (large-enough clusters are packed right away)
                            if (clusters.at<float>(i, WEIGHT_IDX) >(iteration + 1) * this->mClusterMinSize)
                            {
                                for (int d = 1; d < SIGNATURE_DIMENSION; d++)
                                {
                                    clusters.at<float>(lastIdx, d) = tmpCentroids.at<float>(i, d) / clusters.at<float>(i, WEIGHT_IDX);
                                }
                                // weights must be compacted as well
                                clusters.at<float>(lastIdx, WEIGHT_IDX) = clusters.at<float>(i, WEIGHT_IDX);
                                lastIdx++;
                            }
                        }

                        // Crop the arrays if some centroids were dropped.
                        clusters.resize(lastIdx);
                        if (clusters.rows == 0)
                        {
                            break;
                        }

                        // Finally join clusters with too close centroids.
                        joinCloseClusters(clusters);
                        dropLightPoints(clusters);
                    }

                    // The result must not be empty!
                    if (clusters.rows == 0)
                    {
                        singleClusterFallback(samples, clusters);
                    }

                    cropClusters(clusters);

                    normalizeWeights(clusters);

                    // save the result
                    _signature.create(clusters.rows, SIGNATURE_DIMENSION, clusters.type());
                    Mat signature = _signature.getMat();
                    clusters.copyTo(signature);
                }


            private:

                /**
                * @brief Join clusters that are closer than joining distance.
                *       If two clusters are joined one of them gets its weight set to 0.
                * @param clusters List of clusters to be scaned and joined.
                */
                void joinCloseClusters(Mat& clusters)
                {
                    for (int i = 0; i < clusters.rows - 1; i++)
                    {
                        if (clusters.at<float>(i, WEIGHT_IDX) == 0)
                        {
                            continue;
                        }

                        for (int j = i + 1; j < clusters.rows; j++)
                        {
                            if (clusters.at<float>(j, WEIGHT_IDX) > 0
                                && computeDistance(mDistanceFunction, clusters, i, clusters, j) <= mJoiningDistance)
                            {
                                clusters.at<float>(i, WEIGHT_IDX) = 0;
                                break;
                            }
                        }
                    }
                }


                /**
                * @brief Remove points whose weight is lesser or equal to given threshold.
                *       The point list is compacted and relative order of points is maintained.
                * @param clusters List of clusters to be scaned and dropped.
                */
                void dropLightPoints(Mat& clusters)
                {
                    int frontIdx = 0;

                    // Skip leading continuous part of weighted-enough points.
                    while (frontIdx < clusters.rows && clusters.at<float>(frontIdx, WEIGHT_IDX) > mDropThreshold)
                    {
                        ++frontIdx;
                    }

                    // Mark first emptied position and advance front index.
                    int tailIdx = frontIdx++;

                    while (frontIdx < clusters.rows)
                    {
                        if (clusters.at<float>(frontIdx, WEIGHT_IDX) > mDropThreshold)
                        {
                            // Current (front) item is not dropped -> copy it to the tail.
                            clusters.row(frontIdx).copyTo(clusters.row(tailIdx));
                            ++tailIdx;  // grow the tail
                        }
                        ++frontIdx;
                    }
                    clusters.resize(tailIdx);
                }


                /**
                * @brief Find closest cluster to selected point.
                * @param clusters List of cluster centroids.
                * @param points List of points.
                * @param pointIdx Index to the list of points (the point for which the closest cluster is being found).
                * @return Index to clusters list pointing at the closest cluster.
                */
                int findClosestCluster(const Mat& clusters, const Mat& points, const int pointIdx) const    // HOT PATH: 35%
                {
                    int iClosest = 0;
                    float minDistance = computeDistance(mDistanceFunction, clusters, 0, points, pointIdx);

                    for (int iCluster = 1; iCluster < clusters.rows; iCluster++)
                    {
                        float distance = computeDistance(mDistanceFunction, clusters, iCluster, points, pointIdx);
                        if (distance < minDistance)
                        {
                            iClosest = iCluster;
                            minDistance = distance;
                        }
                    }
                    return iClosest;
                }


                /**
                * @brief Make sure that the number of clusters does not exceed maxClusters parameter.
                *       If it does, the clusters are sorted by their weights and the smallest clusters
                *       are cropped.
                * @param clusters List of clusters being scanned and cropped.
                * @note This method does nothing iff the number of clusters is within the range.
                */
                void cropClusters(Mat& clusters) const
                {
                    if (clusters.rows > mMaxClustersCount)
                    {
                        Mat duplicate(clusters);                // save original clusters
                        Mat sortedIdx;                          // sort using weight column
                        sortIdx(clusters(Rect(WEIGHT_IDX, 0, 1, clusters.rows)), sortedIdx, SORT_EVERY_COLUMN + SORT_DESCENDING);

                        clusters.resize(mMaxClustersCount);     // crop to max clusters
                        for (int i = 0; i < mMaxClustersCount; i++)
                        {                                       // copy sorted rows
                            duplicate.row(sortedIdx.at<int>(i, 0)).copyTo(clusters.row(i));
                        }
                    }
                }


                /**
                * @brief Fallback procedure invoked when the clustering eradicates all clusters.
                *       Single cluster consisting of all points is then created.
                * @param points List of sampled points.
                * @param clusters Output list of clusters where the single cluster will be written.
                */
                void singleClusterFallback(const Mat& points, Mat& clusters) const
                {
                    if (clusters.rows != 0)
                    {
                        return;
                    }
                    clusters.create(1, points.cols, CV_32FC1);

                    // Sum all points.
                    reduce(points, clusters, 0, REDUCE_SUM, CV_32FC1);

                    // Sum all weights, all points have the same weight -> sum is the point count
                    clusters.at<float>(0, WEIGHT_IDX) = static_cast<float>(points.rows);

                    // Divide centroid by number of points -> compute average in each dimension.
                    clusters /= clusters.at<float>(0, WEIGHT_IDX);
                }


                /**
                * @brief Normalizes cluster weights, so they fall into range [0..1]
                *       The other values are preserved.
                * @param clusters List of clusters which will be normalized.
                * @note Only weight of the clusters will be changed (the first column);
                */
                void normalizeWeights(Mat& clusters)
                {
                    // get max weight
                    float maxWeight = clusters.at<float>(0, WEIGHT_IDX);
                    for (int i = 1; i < clusters.rows; i++)
                    {
                        if (clusters.at<float>(i, WEIGHT_IDX) > maxWeight)
                        {
                            maxWeight = clusters.at<float>(i, WEIGHT_IDX);
                        }
                    }

                    // normalize weight
                    float weightNormalizer = 1 / maxWeight;
                    for (int i = 0; i < clusters.rows; i++)
                    {
                        clusters.at<float>(i, WEIGHT_IDX) = clusters.at<float>(i, WEIGHT_IDX) * weightNormalizer;
                    }
                }


                /**
                * @brief Chooses which sample points will be used as cluster centers,
                *       using list of random indexes that is stored in mInitSeedIndexes.
                * @param samples List of sampled points.
                * @param clusters Output list of random sampled points.
                */
                void pickRandomClusters(Mat& samples, Mat& clusters)
                {
                    clusters.create(0, SIGNATURE_DIMENSION, samples.type());
                    clusters.reserve(mInitSeedIndexes.size());

                    for (int iSeed = 0; iSeed < (int)(mInitSeedIndexes.size()); iSeed++)
                    {
                        clusters.push_back(samples.row(mInitSeedIndexes[iSeed]));
                    }
                }



                /**
                * @brief Indexes of initial clusters from sampling points.
                */
                std::vector<int> mInitSeedIndexes;

                /**
                * @brief Number of iterations performed (the number is fixed).
                */
                int mIterationCount;

                /**
                * @brief Maximal number of clusters. If the number of clusters is exceeded after
                *       all iterations are completed, the smallest clusters are thrown away.
                */
                int mMaxClustersCount;

                /**
                * @brief Minimal size of the cluster. After each (i-th) iteration, the cluster sizes are checked.
                *       Cluster smaller than i*mClusterMinSize are disposed of.
                */
                int mClusterMinSize;

                /**
                * @brief Distance threshold between two centroids. If two centroids become closer
                *       than this distance, the clusters are joined.
                */
                float mJoiningDistance;

                /**
                * @brief Weight threshold of centroids. If weight of a centroid is below this value at the end of the iteration, it is thrown away.
                */
                float mDropThreshold;

                /**
                * @brief L_p metric is used for computing distances.
                */
                int mDistanceFunction;

            };


            Ptr<PCTClusterizer> PCTClusterizer::create(
                const std::vector<int>& initSeedIndexes,
                int iterationCount,
                int maxClustersCount,
                int clusterMinSize,
                float joiningDistance,
                float dropThreshold,
                int distanceFunction)
            {
                return makePtr<PCTClusterizer_Impl>(
                    initSeedIndexes,
                    iterationCount,
                    maxClustersCount,
                    clusterMinSize,
                    joiningDistance,
                    dropThreshold,
                    distanceFunction);
            }
        }
    }
}
