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#include "precomp.hpp"
#include "flann/flann.hpp"

namespace cv
{

namespace flann {

::cvflann::Index* LinearIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::LinearIndexParams());
}

::cvflann::Index* KDTreeIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::KDTreeIndexParams(trees));
}

::cvflann::Index* KMeansIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::KMeansIndexParams(branching,iterations, (::flann_centers_init_t)centers_init, cb_index));
}

::cvflann::Index* CompositeIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::CompositeIndexParams(trees, branching, iterations, (::flann_centers_init_t)centers_init, cb_index));
}

::cvflann::Index* AutotunedIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::AutotunedIndexParams(target_precision, build_weight, memory_weight, sample_fraction));
}

::cvflann::Index* SavedIndexParams::createIndex(const Mat& dataset) const
{
	CV_Assert(dataset.type() == CV_32F);
	CV_Assert(dataset.isContinuous());

	// TODO: fix ::cvflann::Matrix class so it can be constructed with a const float*
	::cvflann::Matrix<float> mat(dataset.rows, dataset.cols, (float*)dataset.ptr<float>(0));

	return new ::cvflann::Index(mat, ::cvflann::SavedIndexParams(filename));
}



Index::Index(const Mat& dataset, const IndexParams& params)
{
	nnIndex = params.createIndex(dataset);
}

Index::~Index()
{
	delete nnIndex;
}

void Index::knnSearch(const vector<float>& query, vector<int>& indices, vector<float>& dists, int knn, const SearchParams& searchParams)
{

	::cvflann::Matrix<float> m_query(1, query.size(), (float*)&query[0]);
	::cvflann::Matrix<int> m_indices(1, indices.size(), &indices[0]);
	::cvflann::Matrix<float> m_dists(1, dists.size(), &dists[0]);

	nnIndex->knnSearch(m_query,m_indices,m_dists,knn,::cvflann::SearchParams(searchParams.checks));
}


void Index::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const SearchParams& searchParams)
{

	CV_Assert(queries.type() == CV_32F);
	CV_Assert(queries.isContinuous());
	::cvflann::Matrix<float> m_queries(queries.rows, queries.cols, (float*)queries.ptr<float>(0));

	CV_Assert(indices.type() == CV_32S);
	CV_Assert(indices.isContinuous());
	::cvflann::Matrix<int> m_indices(indices.rows, indices.cols, (int*)indices.ptr<int>(0));

	CV_Assert(dists.type() == CV_32F);
	CV_Assert(dists.isContinuous());
	::cvflann::Matrix<float> m_dists(dists.rows, dists.cols, (float*)dists.ptr<float>(0));

	nnIndex->knnSearch(m_queries,m_indices,m_dists,knn,::cvflann::SearchParams(searchParams.checks));
}

int Index::radiusSearch(const vector<float>& query, vector<int>& indices, vector<float>& dists, float radius, const SearchParams& searchParams)
{
	::cvflann::Matrix<float> m_query(1, query.size(), (float*)&query[0]);
	::cvflann::Matrix<int> m_indices(1, indices.size(), &indices[0]);
	::cvflann::Matrix<float> m_dists(1, dists.size(), &dists[0]);

	return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,::cvflann::SearchParams(searchParams.checks));
}


int Index::radiusSearch(const Mat& query, Mat& indices, Mat& dists, float radius, const SearchParams& searchParams)
{
	CV_Assert(query.type() == CV_32F);
	CV_Assert(query.isContinuous());
	::cvflann::Matrix<float> m_query(query.rows, query.cols, (float*)query.ptr<float>(0));

	CV_Assert(indices.type() == CV_32S);
	CV_Assert(indices.isContinuous());
	::cvflann::Matrix<int> m_indices(indices.rows, indices.cols, (int*)indices.ptr<int>(0));

	CV_Assert(dists.type() == CV_32F);
	CV_Assert(dists.isContinuous());
	::cvflann::Matrix<float> m_dists(dists.rows, dists.cols, (float*)dists.ptr<float>(0));

	return nnIndex->radiusSearch(m_query,m_indices,m_dists,radius,::cvflann::SearchParams(searchParams.checks));
}


void Index::save(string filename)
{
	nnIndex->save(filename);
}

int Index::size() const
{
	return nnIndex->size();
}

int Index::veclen() const
{
	return nnIndex->veclen();
}


int hierarchicalClustering(const Mat& features, Mat& centers, const KMeansIndexParams& params)
{
	CV_Assert(features.type() == CV_32F);
	CV_Assert(features.isContinuous());
	::cvflann::Matrix<float> m_features(features.rows, features.cols, (float*)features.ptr<float>(0));

	CV_Assert(features.type() == CV_32F);
	CV_Assert(features.isContinuous());
	::cvflann::Matrix<float> m_centers(centers.rows, centers.cols, (float*)centers.ptr<float>(0));

	return ::cvflann::hierarchicalClustering(m_features, m_centers, ::cvflann::KMeansIndexParams(params.branching, params.iterations,
			(::flann_centers_init_t)params.centers_init, params.cb_index));
}


}

}
