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Merge remote-tracking branch 'upstream/2.4' into LshOrthogonalSubvectors

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This commit is contained in:
Pierre-Emmanuel Viel
2014-05-21 01:31:52 +02:00
parent e63d7de87c 976da2f3d3
commit 00367cfb00
485 changed files with 25360 additions and 5019 deletions
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8
modules/flann/include/opencv2/flann/autotuned_index.h
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@@ -99,18 +99,22 @@ public:
*/
virtual void buildIndex()
{
std::ostringstream stream;
bestParams_ = estimateBuildParams();
print_params(bestParams_, stream);
Logger::info("----------------------------------------------------\n");
Logger::info("Autotuned parameters:\n");
print_params(bestParams_);
Logger::info("%s", stream.str().c_str());
Logger::info("----------------------------------------------------\n");
bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
bestIndex_->buildIndex();
speedup_ = estimateSearchParams(bestSearchParams_);
stream.str(std::string());
print_params(bestSearchParams_, stream);
Logger::info("----------------------------------------------------\n");
Logger::info("Search parameters:\n");
print_params(bestSearchParams_);
Logger::info("%s", stream.str().c_str());
Logger::info("----------------------------------------------------\n");
}

60
modules/flann/include/opencv2/flann/dist.h
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@@ -812,6 +812,66 @@ struct ZeroIterator
};
/*
* Depending on processed distances, some of them are already squared (e.g. L2)
* and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
* we are working on ^2 distances, thus following templates to ensure that.
*/
template <typename Distance, typename ElementType>
struct squareDistance
{
typedef typename Distance::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist*dist; }
};
template <typename ElementType>
struct squareDistance<L2_Simple<ElementType>, ElementType>
{
typedef typename L2_Simple<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<L2<ElementType>, ElementType>
{
typedef typename L2<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
{
typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<HellingerDistance<ElementType>, ElementType>
{
typedef typename HellingerDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename ElementType>
struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
{
typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
ResultType operator()( ResultType dist ) { return dist; }
};
template <typename Distance>
typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
{
typedef typename Distance::ElementType ElementType;
squareDistance<Distance, ElementType> dummy;
return dummy( dist );
}
}
#endif //OPENCV_FLANN_DIST_H_

25
modules/flann/include/opencv2/flann/hierarchical_clustering_index.h
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@@ -210,8 +210,11 @@ private:
assert(index >=0 && index < n);
centers[0] = dsindices[index];
// Computing distance^2 will have the advantage of even higher probability further to pick new centers
// far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
@@ -237,7 +240,10 @@ private:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@@ -249,7 +255,10 @@ private:
// Add the appropriate center
centers[centerCount] = dsindices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}
centers_length = centerCount;
@@ -414,12 +423,6 @@ public:
void loadIndex(FILE* stream)
{
load_value(stream, branching_);
load_value(stream, trees_);
load_value(stream, centers_init_);
load_value(stream, leaf_size_);
load_value(stream, memoryCounter);
free_elements();
if (root!=NULL) {
@@ -430,6 +433,12 @@ public:
delete[] indices;
}
load_value(stream, branching_);
load_value(stream, trees_);
load_value(stream, centers_init_);
load_value(stream, leaf_size_);
load_value(stream, memoryCounter);
indices = new int*[trees_];
root = new NodePtr[trees_];
for (int i=0; i<trees_; ++i) {

22
modules/flann/include/opencv2/flann/kmeans_index.h
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@@ -211,6 +211,7 @@ public:
for (int i = 0; i < n; i++) {
closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
currentPot += closestDistSq[i];
}
@@ -236,7 +237,10 @@ public:
// Compute the new potential
double newPot = 0;
for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
// Store the best result
if ((bestNewPot < 0)||(newPot < bestNewPot)) {
@@ -248,7 +252,10 @@ public:
// Add the appropriate center
centers[centerCount] = indices[bestNewIndex];
currentPot = bestNewPot;
for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
for (int i = 0; i < n; i++) {
DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
}
}
centers_length = centerCount;
@@ -759,10 +766,13 @@ private:
for (int k=0; k<indices_length; ++k) {
if (belongs_to[k]==j) {
belongs_to[k] = i;
count[j]--;
count[i]++;
break;
// for cluster j, we move the furthest element from the center to the empty cluster i
if ( distance_(dataset_[indices[k]], dcenters[j], veclen_) == radiuses[j] ) {
belongs_to[k] = i;
count[j]--;
count[i]++;
break;
}
}
}
converged = false;

9
modules/flann/include/opencv2/flann/params.h
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@@ -79,16 +79,19 @@ T get_param(const IndexParams& params, std::string name)
}
}
inline void print_params(const IndexParams& params)
inline void print_params(const IndexParams& params, std::ostream& stream)
{
IndexParams::const_iterator it;
for(it=params.begin(); it!=params.end(); ++it) {
std::cout << it->first << " : " << it->second << std::endl;
stream << it->first << " : " << it->second << std::endl;
}
}
inline void print_params(const IndexParams& params)
{
print_params(params, std::cout);
}
}