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fixing centroids override by using cv::mat deep copy #53

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fixing centroids override by using cv::mat deep copy #53

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15 changes: 8 additions & 7 deletions src/Vocabulary.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -396,14 +396,18 @@ void Vocabulary::HKmeansStep(NodeId parent_id,

void Vocabulary::initiateClusters
(const std::vector<cv::Mat> &descriptors,
std::vector<cv::Mat> &clusters) const
std::vector<cv::Mat> &clusters) const
{
initiateClustersKMpp(descriptors, clusters);
}

// --------------------------------------------------------------------------


void Vocabulary::createNewCentroid(cv::Mat &feature,std::vector<cv::Mat> &clusters){
cv::Mat copy;
feature.copyTo(copy);
clusters.push_back(copy);
}
void Vocabulary::initiateClustersKMpp(
const std::vector<cv::Mat> &pfeatures,
std::vector<cv::Mat> &clusters) const
Expand Down Expand Up @@ -431,8 +435,7 @@ void Vocabulary::initiateClustersKMpp(
int ifeature = rand()% pfeatures.size();//DUtils::Random::RandomInt(0, pfeatures.size()-1);

// create first cluster
clusters.push_back(pfeatures[ifeature]);

createNewCentroid(pfeatures[ifeature],clusters);
// compute the initial distances
std::vector<double>::iterator dit;
dit = min_dists.begin();
Expand Down Expand Up @@ -477,9 +480,7 @@ void Vocabulary::initiateClustersKMpp(
ifeature = pfeatures.size()-1;
else
ifeature = dit - min_dists.begin();


clusters.push_back(pfeatures[ifeature]);
createNewCentroid(pfeatures[ifeature],clusters);
} // if dist_sum > 0
else
break;
Expand Down
7 changes: 6 additions & 1 deletion src/Vocabulary.h
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Original file line number Diff line number Diff line change
Expand Up @@ -402,7 +402,12 @@ friend class FastSearch;
*/
virtual void initiateClusters(const std::vector<cv::Mat> &descriptors,
std::vector<cv::Mat> &clusters) const;

/**
* @brief Creates a new centroid by making a deep copy of the input feature and stores it in clusters.
* @param feature The input feature to be deep copied and used as a new centroid.
* @param clusters A vector of cv::Mat objects representing the existing clusters.
*/
void createNewCentroid(cv::Mat &feature,std::vector<cv::Mat> &clusters);
/**
* Creates k clusters from the given descriptor sets by running the
* initial step of kmeans++
Expand Down