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commit x64 compilation from lulu cause the other branch dont seems to compile properly at home

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lucas doremus
2026-07-17 16:08:20 +02:00
parent c0f3eeb00d
commit 0efa4ee6f7
625 changed files with 117283 additions and 4426 deletions
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335
include/opencv2/flann.hpp
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@ -40,8 +40,8 @@
//
//M*/
#ifndef _OPENCV_FLANN_HPP_
#define _OPENCV_FLANN_HPP_
#ifndef OPENCV_FLANN_HPP
#define OPENCV_FLANN_HPP
#include "opencv2/core.hpp"
#include "opencv2/flann/miniflann.hpp"
@ -59,7 +59,7 @@ can be found in @cite Muja2009 .
namespace cvflann
{
CV_EXPORTS flann_distance_t flann_distance_type();
FLANN_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
CV_DEPRECATED CV_EXPORTS void set_distance_type(flann_distance_t distance_type, int order);
}
@ -103,6 +103,58 @@ using ::cvflann::KL_Divergence;
/** @brief The FLANN nearest neighbor index class. This class is templated with the type of elements for which
the index is built.
`Distance` functor specifies the metric to be used to calculate the distance between two points.
There are several `Distance` functors that are readily available:
@link cvflann::L2_Simple cv::flann::L2_Simple @endlink- Squared Euclidean distance functor.
This is the simpler, unrolled version. This is preferable for very low dimensionality data (eg 3D points)
@link cvflann::L2 cv::flann::L2 @endlink- Squared Euclidean distance functor, optimized version.
@link cvflann::L1 cv::flann::L1 @endlink - Manhattan distance functor, optimized version.
@link cvflann::MinkowskiDistance cv::flann::MinkowskiDistance @endlink - The Minkowsky distance functor.
This is highly optimised with loop unrolling.
The computation of squared root at the end is omitted for efficiency.
@link cvflann::MaxDistance cv::flann::MaxDistance @endlink - The max distance functor. It computes the
maximum distance between two vectors. This distance is not a valid kdtree distance, it's not
dimensionwise additive.
@link cvflann::HammingLUT cv::flann::HammingLUT @endlink - %Hamming distance functor. It counts the bit
differences between two strings using a lookup table implementation.
@link cvflann::Hamming cv::flann::Hamming @endlink - %Hamming distance functor. Population count is
performed using library calls, if available. Lookup table implementation is used as a fallback.
@link cvflann::Hamming2 cv::flann::Hamming2 @endlink- %Hamming distance functor. Population count is
implemented in 12 arithmetic operations (one of which is multiplication).
@link cvflann::HistIntersectionDistance cv::flann::HistIntersectionDistance @endlink - The histogram
intersection distance functor.
@link cvflann::HellingerDistance cv::flann::HellingerDistance @endlink - The Hellinger distance functor.
@link cvflann::ChiSquareDistance cv::flann::ChiSquareDistance @endlink - The chi-square distance functor.
@link cvflann::KL_Divergence cv::flann::KL_Divergence @endlink - The Kullback-Leibler divergence functor.
Although the provided implementations cover a vast range of cases, it is also possible to use
a custom implementation. The distance functor is a class whose `operator()` computes the distance
between two features. If the distance is also a kd-tree compatible distance, it should also provide an
`accum_dist()` method that computes the distance between individual feature dimensions.
In addition to `operator()` and `accum_dist()`, a distance functor should also define the
`ElementType` and the `ResultType` as the types of the elements it operates on and the type of the
result it computes. If a distance functor can be used as a kd-tree distance (meaning that the full
distance between a pair of features can be accumulated from the partial distances between the
individual dimensions) a typedef `is_kdtree_distance` should be present inside the distance functor.
If the distance is not a kd-tree distance, but it's a distance in a vector space (the individual
dimensions of the elements it operates on can be accessed independently) a typedef
`is_vector_space_distance` should be defined inside the functor. If neither typedef is defined, the
distance is assumed to be a metric distance and will only be used with indexes operating on
generic metric distances.
*/
template <typename Distance>
class GenericIndex
@ -217,6 +269,17 @@ public:
std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
/** @brief Performs a radius nearest neighbor search for a given query point using the index.
@param query The query point.
@param indices Vector that will contain the indices of the nearest neighbors found.
@param dists Vector that will contain the distances to the nearest neighbors found. It has the same
number of elements as indices.
@param radius The search radius.
@param params SearchParams
This function returns the number of nearest neighbors found.
*/
int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices,
std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
int radiusSearch(const Mat& query, Mat& indices, Mat& dists,
@ -230,7 +293,7 @@ public:
::cvflann::IndexParams getParameters() { return nnIndex->getParameters(); }
FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }
CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters() { return nnIndex->getIndexParameters(); }
private:
::cvflann::Index<Distance>* nnIndex;
@ -338,164 +401,134 @@ int GenericIndex<Distance>::radiusSearch(const Mat& query, Mat& indices, Mat& di
* @deprecated Use GenericIndex class instead
*/
template <typename T>
class
#ifndef _MSC_VER
FLANN_DEPRECATED
#endif
Index_ {
class Index_
{
public:
typedef typename L2<T>::ElementType ElementType;
typedef typename L2<T>::ResultType DistanceType;
typedef typename L2<T>::ElementType ElementType;
typedef typename L2<T>::ResultType DistanceType;
Index_(const Mat& features, const ::cvflann::IndexParams& params);
~Index_();
void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& params);
void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& params);
int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& params);
int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& params);
void save(String filename)
{
if (nnIndex_L1) nnIndex_L1->save(filename);
if (nnIndex_L2) nnIndex_L2->save(filename);
}
int veclen() const
CV_DEPRECATED Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
{
if (nnIndex_L1) return nnIndex_L1->veclen();
if (nnIndex_L2) return nnIndex_L2->veclen();
}
printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");
int size() const
CV_Assert(dataset.type() == CvType<ElementType>::type());
CV_Assert(dataset.isContinuous());
::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
nnIndex_L1 = NULL;
nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
}
else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
nnIndex_L2 = NULL;
}
else {
printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
"For other distance types you must use cv::flann::GenericIndex<Distance>\n");
CV_Assert(0);
}
if (nnIndex_L1) nnIndex_L1->buildIndex();
if (nnIndex_L2) nnIndex_L2->buildIndex();
}
CV_DEPRECATED ~Index_()
{
if (nnIndex_L1) return nnIndex_L1->size();
if (nnIndex_L2) return nnIndex_L2->size();
}
if (nnIndex_L1) delete nnIndex_L1;
if (nnIndex_L2) delete nnIndex_L2;
}
::cvflann::IndexParams getParameters()
{
if (nnIndex_L1) return nnIndex_L1->getParameters();
if (nnIndex_L2) return nnIndex_L2->getParameters();
CV_DEPRECATED void knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
}
if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}
CV_DEPRECATED void knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(queries.type() == CvType<ElementType>::type());
CV_Assert(queries.isContinuous());
::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
FLANN_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
{
if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
}
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}
CV_DEPRECATED int radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
CV_DEPRECATED int radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(query.type() == CvType<ElementType>::type());
CV_Assert(query.isContinuous());
::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
CV_DEPRECATED void save(String filename)
{
if (nnIndex_L1) nnIndex_L1->save(filename);
if (nnIndex_L2) nnIndex_L2->save(filename);
}
CV_DEPRECATED int veclen() const
{
if (nnIndex_L1) return nnIndex_L1->veclen();
if (nnIndex_L2) return nnIndex_L2->veclen();
}
CV_DEPRECATED int size() const
{
if (nnIndex_L1) return nnIndex_L1->size();
if (nnIndex_L2) return nnIndex_L2->size();
}
CV_DEPRECATED ::cvflann::IndexParams getParameters()
{
if (nnIndex_L1) return nnIndex_L1->getParameters();
if (nnIndex_L2) return nnIndex_L2->getParameters();
}
CV_DEPRECATED const ::cvflann::IndexParams* getIndexParameters()
{
if (nnIndex_L1) return nnIndex_L1->getIndexParameters();
if (nnIndex_L2) return nnIndex_L2->getIndexParameters();
}
private:
// providing backwards compatibility for L2 and L1 distances (most common)
::cvflann::Index< L2<ElementType> >* nnIndex_L2;
::cvflann::Index< L1<ElementType> >* nnIndex_L1;
// providing backwards compatibility for L2 and L1 distances (most common)
::cvflann::Index< L2<ElementType> >* nnIndex_L2;
::cvflann::Index< L1<ElementType> >* nnIndex_L1;
};
#ifdef _MSC_VER
template <typename T>
class FLANN_DEPRECATED Index_;
#endif
//! @cond IGNORED
template <typename T>
Index_<T>::Index_(const Mat& dataset, const ::cvflann::IndexParams& params)
{
printf("[WARNING] The cv::flann::Index_<T> class is deperecated, use cv::flann::GenericIndex<Distance> instead\n");
CV_Assert(dataset.type() == CvType<ElementType>::type());
CV_Assert(dataset.isContinuous());
::cvflann::Matrix<ElementType> m_dataset((ElementType*)dataset.ptr<ElementType>(0), dataset.rows, dataset.cols);
if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L2 ) {
nnIndex_L1 = NULL;
nnIndex_L2 = new ::cvflann::Index< L2<ElementType> >(m_dataset, params);
}
else if ( ::cvflann::flann_distance_type() == cvflann::FLANN_DIST_L1 ) {
nnIndex_L1 = new ::cvflann::Index< L1<ElementType> >(m_dataset, params);
nnIndex_L2 = NULL;
}
else {
printf("[ERROR] cv::flann::Index_<T> only provides backwards compatibility for the L1 and L2 distances. "
"For other distance types you must use cv::flann::GenericIndex<Distance>\n");
CV_Assert(0);
}
if (nnIndex_L1) nnIndex_L1->buildIndex();
if (nnIndex_L2) nnIndex_L2->buildIndex();
}
template <typename T>
Index_<T>::~Index_()
{
if (nnIndex_L1) delete nnIndex_L1;
if (nnIndex_L2) delete nnIndex_L2;
}
template <typename T>
void Index_<T>::knnSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
if (nnIndex_L1) nnIndex_L1->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_query,m_indices,m_dists,knn,searchParams);
}
template <typename T>
void Index_<T>::knnSearch(const Mat& queries, Mat& indices, Mat& dists, int knn, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(queries.type() == CvType<ElementType>::type());
CV_Assert(queries.isContinuous());
::cvflann::Matrix<ElementType> m_queries((ElementType*)queries.ptr<ElementType>(0), queries.rows, queries.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) nnIndex_L1->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
if (nnIndex_L2) nnIndex_L2->knnSearch(m_queries,m_indices,m_dists,knn, searchParams);
}
template <typename T>
int Index_<T>::radiusSearch(const std::vector<ElementType>& query, std::vector<int>& indices, std::vector<DistanceType>& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
::cvflann::Matrix<ElementType> m_query((ElementType*)&query[0], 1, query.size());
::cvflann::Matrix<int> m_indices(&indices[0], 1, indices.size());
::cvflann::Matrix<DistanceType> m_dists(&dists[0], 1, dists.size());
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
template <typename T>
int Index_<T>::radiusSearch(const Mat& query, Mat& indices, Mat& dists, DistanceType radius, const ::cvflann::SearchParams& searchParams)
{
CV_Assert(query.type() == CvType<ElementType>::type());
CV_Assert(query.isContinuous());
::cvflann::Matrix<ElementType> m_query((ElementType*)query.ptr<ElementType>(0), query.rows, query.cols);
CV_Assert(indices.type() == CV_32S);
CV_Assert(indices.isContinuous());
::cvflann::Matrix<int> m_indices((int*)indices.ptr<int>(0), indices.rows, indices.cols);
CV_Assert(dists.type() == CvType<DistanceType>::type());
CV_Assert(dists.isContinuous());
::cvflann::Matrix<DistanceType> m_dists((DistanceType*)dists.ptr<DistanceType>(0), dists.rows, dists.cols);
if (nnIndex_L1) return nnIndex_L1->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
if (nnIndex_L2) return nnIndex_L2->radiusSearch(m_query,m_indices,m_dists,radius,searchParams);
}
//! @endcond
/** @brief Clusters features using hierarchical k-means algorithm.
@ -535,7 +568,7 @@ int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::K
/** @deprecated
*/
template <typename ELEM_TYPE, typename DIST_TYPE>
FLANN_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
CV_DEPRECATED int hierarchicalClustering(const Mat& features, Mat& centers, const ::cvflann::KMeansIndexParams& params)
{
printf("[WARNING] cv::flann::hierarchicalClustering<ELEM_TYPE,DIST_TYPE> is deprecated, use "
"cv::flann::hierarchicalClustering<Distance> instead\n");