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Parallize building kmeans index in flann

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This commit is contained in:
Takahiro Poly Horikawa 2014-11-27 16:29:05 +09:00
parent ae4cb571ab
commit 123ca7e1c5
1 changed files with 67 additions and 18 deletions
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85
modules/flann/include/opencv2/flann/kmeans_index.h
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@ -69,7 +69,6 @@ struct KMeansIndexParams : public IndexParams
}
};
/**
* Hierarchical kmeans index
*
@ -271,6 +270,68 @@ public:
return FLANN_INDEX_KMEANS;
}
class KMeansDistanceComputer : public cv::ParallelLoopBody
{
public:
KMeansDistanceComputer(Distance _distance, const Matrix<ElementType>& _dataset,
const int _branching, const int* _indices, const Matrix<double>& _dcenters, const int _veclen,
int* _count, int* _belongs_to, std::vector<DistanceType>& _radiuses, bool* _updated)
: distance(_distance)
, dataset(_dataset)
, branching(_branching)
, indices(_indices)
, dcenters(_dcenters)
, veclen(_veclen)
, count(_count)
, belongs_to(_belongs_to)
, radiuses(_radiuses)
, updated(_updated)
{
}
void operator()(const cv::Range& range) const
{
const int begin = range.start;
const int end = range.end;
for( int i = begin; i<end; ++i)
{
DistanceType sq_dist = distance(dataset[indices[i]], dcenters[0], veclen);
int new_centroid = 0;
for (int j=1; j<branching; ++j) {
DistanceType new_sq_dist = distance(dataset[indices[i]], dcenters[j], veclen);
if (sq_dist>new_sq_dist) {
new_centroid = j;
sq_dist = new_sq_dist;
}
}
if (sq_dist > radiuses[new_centroid]) {
radiuses[new_centroid] = sq_dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
belongs_to[i] = new_centroid;
updated[i] = true;
} else {
updated[i] = false;
}
}
}
private:
Distance distance;
const Matrix<ElementType>& dataset;
const int branching;
const int* indices;
const Matrix<double>& dcenters;
int veclen;
int* count;
int* belongs_to;
std::vector<DistanceType>& radiuses;
bool* updated;
};
/**
* Index constructor
*
@ -708,6 +769,7 @@ private:
bool converged = false;
int iteration = 0;
bool* updated = new bool[indices_length];
while (!converged && iteration<iterations_) {
converged = true;
iteration++;
@ -732,25 +794,11 @@ private:
}
// reassign points to clusters
parallel_for_(cv::Range(0, indices_length), KMeansDistanceComputer(distance_, dataset_, branching, indices, dcenters, veclen_, count, belongs_to, radiuses, updated));
for (int i=0; i<indices_length; ++i) {
DistanceType sq_dist = distance_(dataset_[indices[i]], dcenters[0], veclen_);
int new_centroid = 0;
for (int j=1; j<branching; ++j) {
DistanceType new_sq_dist = distance_(dataset_[indices[i]], dcenters[j], veclen_);
if (sq_dist>new_sq_dist) {
new_centroid = j;
sq_dist = new_sq_dist;
}
}
if (sq_dist>radiuses[new_centroid]) {
radiuses[new_centroid] = sq_dist;
}
if (new_centroid != belongs_to[i]) {
count[belongs_to[i]]--;
count[new_centroid]++;
belongs_to[i] = new_centroid;
if (updated[i]) {
converged = false;
break;
}
}
@ -828,6 +876,7 @@ private:
delete[] centers;
delete[] count;
delete[] belongs_to;
delete[] updated;
}