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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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49
include/opencv2/flann/kmeans_index.h
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@ -266,7 +266,7 @@ public:
public:
flann_algorithm_t getType() const
flann_algorithm_t getType() const CV_OVERRIDE
{
return FLANN_INDEX_KMEANS;
}
@ -276,7 +276,7 @@ public:
public:
KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
const int _branching, const int* _indices, const Matrix<double>& _dcenters, const size_t _veclen,
int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged, cv::Mutex& _mtx)
int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool& _converged)
: distance(_distance)
, dataset(_dataset)
, branching(_branching)
@ -287,11 +287,10 @@ public:
, belongs_to(_belongs_to)
, radiuses(_radiuses)
, converged(_converged)
, mtx(_mtx)
{
}
void operator()(const cv::Range& range) const
void operator()(const cv::Range& range) const CV_OVERRIDE
{
const int begin = range.start;
const int end = range.end;
@ -311,12 +310,10 @@ public:
radiuses[new_centroid] = sq_dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
CV_XADD(&count[belongs_to[i]], -1);
CV_XADD(&count[new_centroid], 1);
belongs_to[i] = new_centroid;
mtx.lock();
converged = false;
mtx.unlock();
}
}
}
@ -332,7 +329,6 @@ public:
int* belongs_to;
std::vector<DistanceType>& radiuses;
bool& converged;
cv::Mutex& mtx;
KMeansDistanceComputer& operator=( const KMeansDistanceComputer & ) { return *this; }
};
@ -398,7 +394,7 @@ public:
/**
* Returns size of index.
*/
size_t size() const
size_t size() const CV_OVERRIDE
{
return size_;
}
@ -406,7 +402,7 @@ public:
/**
* Returns the length of an index feature.
*/
size_t veclen() const
size_t veclen() const CV_OVERRIDE
{
return veclen_;
}
@ -421,7 +417,7 @@ public:
* Computes the inde memory usage
* Returns: memory used by the index
*/
int usedMemory() const
int usedMemory() const CV_OVERRIDE
{
return pool_.usedMemory+pool_.wastedMemory+memoryCounter_;
}
@ -429,7 +425,7 @@ public:
/**
* Builds the index
*/
void buildIndex()
void buildIndex() CV_OVERRIDE
{
if (branching_<2) {
throw FLANNException("Branching factor must be at least 2");
@ -441,12 +437,14 @@ public:
}
root_ = pool_.allocate<KMeansNode>();
std::memset(root_, 0, sizeof(KMeansNode));
computeNodeStatistics(root_, indices_, (int)size_);
computeClustering(root_, indices_, (int)size_, branching_,0);
}
void saveIndex(FILE* stream)
void saveIndex(FILE* stream) CV_OVERRIDE
{
save_value(stream, branching_);
save_value(stream, iterations_);
@ -458,7 +456,7 @@ public:
}
void loadIndex(FILE* stream)
void loadIndex(FILE* stream) CV_OVERRIDE
{
load_value(stream, branching_);
load_value(stream, iterations_);
@ -493,7 +491,7 @@ public:
* vec = the vector for which to search the nearest neighbors
* searchParams = parameters that influence the search algorithm (checks, cb_index)
*/
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams)
void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
{
int maxChecks = get_param(searchParams,"checks",32);
@ -552,7 +550,7 @@ public:
return clusterCount;
}
IndexParams getParameters() const
IndexParams getParameters() const CV_OVERRIDE
{
return index_params_;
}
@ -724,7 +722,7 @@ private:
}
cv::AutoBuffer<int> centers_idx_buf(branching);
int* centers_idx = (int*)centers_idx_buf;
int* centers_idx = centers_idx_buf.data();
int centers_length;
(this->*chooseCenters)(branching, indices, indices_length, centers_idx, centers_length);
@ -737,7 +735,7 @@ private:
cv::AutoBuffer<double> dcenters_buf(branching*veclen_);
Matrix<double> dcenters((double*)dcenters_buf,branching,veclen_);
Matrix<double> dcenters(dcenters_buf.data(), branching, veclen_);
for (int i=0; i<centers_length; ++i) {
ElementType* vec = dataset_[centers_idx[i]];
for (size_t k=0; k<veclen_; ++k) {
@ -747,7 +745,7 @@ private:
std::vector<DistanceType> radiuses(branching);
cv::AutoBuffer<int> count_buf(branching);
int* count = (int*)count_buf;
int* count = count_buf.data();
for (int i=0; i<branching; ++i) {
radiuses[i] = 0;
count[i] = 0;
@ -755,7 +753,7 @@ private:
// assign points to clusters
cv::AutoBuffer<int> belongs_to_buf(indices_length);
int* belongs_to = (int*)belongs_to_buf;
int* belongs_to = belongs_to_buf.data();
for (int i=0; i<indices_length; ++i) {
DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
@ -799,8 +797,7 @@ private:
}
// reassign points to clusters
cv::Mutex mtx;
KMeansDistanceComputer invoker(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, converged, mtx);
KMeansDistanceComputer invoker(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, converged);
parallel_for_(cv::Range(0, (int)indices_length), invoker);
for (int i=0; i<branching; ++i) {
@ -864,14 +861,16 @@ private:
variance -= distance_(centers[c], ZeroIterator<ElementType>(), veclen_);
node->childs[c] = pool_.allocate<KMeansNode>();
std::memset(node->childs[c], 0, sizeof(KMeansNode));
node->childs[c]->radius = radiuses[c];
node->childs[c]->pivot = centers[c];
node->childs[c]->variance = variance;
node->childs[c]->mean_radius = mean_radius;
node->childs[c]->indices = NULL;
computeClustering(node->childs[c],indices+start, end-start, branching, level+1);
start=end;
}
delete[] centers;
}
@ -1049,7 +1048,7 @@ private:
/**
* Helper function the descends in the hierarchical k-means tree by spliting those clusters that minimize
* Helper function the descends in the hierarchical k-means tree by splitting those clusters that minimize
* the overall variance of the clustering.
* Params:
* root = root node