#include <kd_treebase.h>
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virtual KDNodeBase * | add (const cv::Mat &position, const int identity) |
| add new node to KD-Tree
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virtual KDNodeBase * | createNode (const cv::Mat &nodePos, const int identity, int splitAxis, int dimension)=0 |
| create an ew node
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virtual QMap< double, QVector< int > > | getClosestNeighbors (const cv::Mat &position, float sqRange, int maxNbNeighbors) const |
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| KDTreeBase (int dim, int kdtreeThreshold) |
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virtual | ~KDTreeBase () |
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◆ KDTreeBase()
Using this to compare brute force vs kdtree performance due to sparse data in k-dimensions (128 dimensions for face features).
- Parameters
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threshold | If the vector grows to 500 items, start using the KDTree. |
◆ ~KDTreeBase()
Digikam::KDTreeBase::~KDTreeBase |
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◆ add()
KDNodeBase * Digikam::KDTreeBase::add |
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const cv::Mat & |
position, |
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const int |
identity |
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◆ createNode()
virtual KDNodeBase * Digikam::KDTreeBase::createNode |
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const cv::Mat & |
nodePos, |
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const int |
identity, |
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int |
splitAxis, |
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int |
dimension |
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pure virtual |
create an ew node
- Parameters
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nodePos | : extracted face vectors |
identity | : identity of this face vector |
splitAxis | : current axis/dimension of the vector |
dimension | : number of dimensions (usually 128) |
- Returns
- : KDNodeBase pointer
Referenced by add().
◆ getClosestNeighbors()
QMap< double, QVector< int > > Digikam::KDTreeBase::getClosestNeighbors |
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const cv::Mat & |
position, |
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float |
sqRange, |
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int |
maxNbNeighbors |
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virtual |
The documentation for this class was generated from the following files: