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added read/write methods for SimpleBlobDetector and DenseFeatureDetector; added their creation to FeatureDetector::create method (#1290)

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This commit is contained in:
Maria Dimashova 2011-08-10 09:17:37 +00:00
parent 7f32e8131d
commit 81cb7b2c5e
3 changed files with 123 additions and 1 deletions
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12
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -1583,10 +1583,16 @@ public:
bool filterByConvexity;
float minConvexity, maxConvexity;
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
};
SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
struct CV_EXPORTS Center
{
@ -1618,11 +1624,15 @@ public:
bool varyXyStepWithScale;
bool varyImgBoundWithScale;
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
};
DenseFeatureDetector( const DenseFeatureDetector::Params& params=DenseFeatureDetector::Params() );
// TODO implement read/write
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;

68
modules/features2d/src/blobdetector.cpp
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@ -84,11 +84,79 @@ SimpleBlobDetector::Params::Params()
maxConvexity = std::numeric_limits<float>::max();
}
void SimpleBlobDetector::Params::read(const cv::FileNode& fn )
{
thresholdStep = fn["thresholdStep"];
minThreshold = fn["minThreshold"];
maxThreshold = fn["maxThreshold"];
minRepeatability = (size_t)(int)fn["minRepeatability"];
minDistBetweenBlobs = fn["minDistBetweenBlobs"];
filterByColor = (int)fn["filterByColor"] != 0 ? true : false;
blobColor = (uchar)(int)fn["blobColor"];
filterByArea = (int)fn["filterByArea"] != 0 ? true : false;
minArea = fn["minArea"];
maxArea = fn["maxArea"];
filterByCircularity = (int)fn["filterByCircularity"] != 0 ? true : false;
minCircularity = fn["minCircularity"];
maxCircularity = fn["maxCircularity"];
filterByInertia = (int)fn["filterByInertia"] != 0 ? true : false;
minInertiaRatio = fn["minInertiaRatio"];
maxInertiaRatio = fn["maxInertiaRatio"];
filterByConvexity = (int)fn["filterByConvexity"] != 0 ? true : false;
minConvexity = fn["minConvexity"];
maxConvexity = fn["maxConvexity"];
}
void SimpleBlobDetector::Params::write(cv::FileStorage& fs) const
{
fs << "thresholdStep" << thresholdStep;
fs << "minThreshold" << minThreshold;
fs << "maxThreshold" << maxThreshold;
fs << "minRepeatability" << (int)minRepeatability;
fs << "minDistBetweenBlobs" << minDistBetweenBlobs;
fs << "filterByColor" << (int)filterByColor;
fs << "blobColor" << (int)blobColor;
fs << "filterByArea" << (int)filterByArea;
fs << "minArea" << minArea;
fs << "maxArea" << maxArea;
fs << "filterByCircularity" << (int)filterByCircularity;
fs << "minCircularity" << minCircularity;
fs << "maxCircularity" << maxCircularity;
fs << "filterByInertia" << (int)filterByInertia;
fs << "minInertiaRatio" << minInertiaRatio;
fs << "maxInertiaRatio" << maxInertiaRatio;
fs << "filterByConvexity" << (int)filterByConvexity;
fs << "minConvexity" << minConvexity;
fs << "maxConvexity" << maxConvexity;
}
SimpleBlobDetector::SimpleBlobDetector(const SimpleBlobDetector::Params &parameters) :
params(parameters)
{
}
void SimpleBlobDetector::read( const cv::FileNode& fn )
{
params.read(fn);
}
void SimpleBlobDetector::write( cv::FileStorage& fs ) const
{
params.write(fs);
}
void SimpleBlobDetector::findBlobs(const cv::Mat &image, const cv::Mat &binaryImage, vector<Center> &centers) const
{
(void)image;

44
modules/features2d/src/detectors.cpp
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@ -126,6 +126,14 @@ Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
params.useHarrisDetector = true;
fd = new GoodFeaturesToTrackDetector(params);
}
else if( !detectorType.compare( "Dense" ) )
{
fd = new DenseFeatureDetector();
}
else if( !detectorType.compare( "SimpleBlob" ) )
{
fd = new SimpleBlobDetector();
}
else if( (pos=detectorType.find("Grid")) == 0 )
{
pos += string("Grid").size();
@ -499,9 +507,45 @@ DenseFeatureDetector::Params::Params( float _initFeatureScale, int _featureScale
varyXyStepWithScale(_varyXyStepWithScale), varyImgBoundWithScale(_varyImgBoundWithScale)
{}
void DenseFeatureDetector::Params::read( const FileNode& fn )
{
initFeatureScale = fn["initFeatureScale"];
featureScaleLevels = fn["featureScaleLevels"];
featureScaleMul = fn["featureScaleMul"];
initXyStep = fn["initXyStep"];
initImgBound = fn["initImgBound"];
varyXyStepWithScale = (int)fn["varyXyStepWithScale"] != 0 ? true : false;
varyImgBoundWithScale = (int)fn["varyImgBoundWithScale"] != 0 ? true : false;
}
void DenseFeatureDetector::Params::write( FileStorage& fs ) const
{
fs << "initFeatureScale" << initFeatureScale;
fs << "featureScaleLevels" << featureScaleLevels;
fs << "featureScaleMul" << featureScaleMul;
fs << "initXyStep" << initXyStep;
fs << "initImgBound" << initImgBound;
fs << "varyXyStepWithScale" << (int)varyXyStepWithScale;
fs << "varyImgBoundWithScale" << (int)varyImgBoundWithScale;
}
DenseFeatureDetector::DenseFeatureDetector(const DenseFeatureDetector::Params &_params) : params(_params)
{}
void DenseFeatureDetector::read( const FileNode &fn )
{
params.read(fn);
}
void DenseFeatureDetector::write( FileStorage &fs ) const
{
params.write(fs);
}
void DenseFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
float curScale = params.initFeatureScale;