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removed extra cv:: scope qualifiers for better readability

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lluis 2013-07-23 14:37:45 +02:00
parent 2087d4602b
commit 43e7e6e475
2 changed files with 17 additions and 17 deletions
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10
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
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@ -119,7 +119,7 @@ public:
Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier. Extracts the component tree (if needed) and filter the extremal regions (ER's) by using a given classifier.
*/ */
class CV_EXPORTS ERFilter : public cv::Algorithm class CV_EXPORTS ERFilter : public Algorithm
{ {
public: public:
@ -138,11 +138,11 @@ public:
\param image is the input image \param image is the input image
\param regions is output for the first stage, input/output for the second one. \param regions is output for the first stage, input/output for the second one.
*/ */
virtual void run( cv::InputArray image, std::vector<ERStat>& regions ) = 0; virtual void run( InputArray image, std::vector<ERStat>& regions ) = 0;
//! set/get methods to set the algorithm properties, //! set/get methods to set the algorithm properties,
virtual void setCallback(const cv::Ptr<ERFilter::Callback>& cb) = 0; virtual void setCallback(const Ptr<ERFilter::Callback>& cb) = 0;
virtual void setThresholdDelta(int thresholdDelta) = 0; virtual void setThresholdDelta(int thresholdDelta) = 0;
virtual void setMinArea(float minArea) = 0; virtual void setMinArea(float minArea) = 0;
virtual void setMaxArea(float maxArea) = 0; virtual void setMaxArea(float maxArea) = 0;
@ -176,7 +176,7 @@ public:
\param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities \param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities
\param minProbability The minimum probability difference between local maxima and local minima ERs \param minProbability The minimum probability difference between local maxima and local minima ERs
*/ */
CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb = NULL, CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = NULL,
int thresholdDelta = 1, float minArea = 0.000025, int thresholdDelta = 1, float minArea = 0.000025,
float maxArea = 0.13, float minProbability = 0.2, float maxArea = 0.13, float minProbability = 0.2,
bool nonMaxSuppression = true, bool nonMaxSuppression = true,
@ -195,7 +195,7 @@ CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>
if omitted tries to load a default classifier from file trained_classifierNM2.xml if omitted tries to load a default classifier from file trained_classifierNM2.xml
\param minProbability The minimum probability P(er|character) allowed for retreived ER's \param minProbability The minimum probability P(er|character) allowed for retreived ER's
*/ */
CV_EXPORTS cv::Ptr<ERFilter> createERFilterNM2(const cv::Ptr<ERFilter::Callback>& cb = NULL, CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = NULL,
float minProbability = 0.85); float minProbability = 0.85);
} }

24
modules/objdetect/src/erfilter.cpp
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@ -82,14 +82,14 @@ public:
// the key method. Takes image on input, vector of ERStat is output for the first stage, // the key method. Takes image on input, vector of ERStat is output for the first stage,
// input/output - for the second one. // input/output - for the second one.
void run( cv::InputArray image, std::vector<ERStat>& regions ); void run( InputArray image, std::vector<ERStat>& regions );
protected: protected:
int thresholdDelta; int thresholdDelta;
float maxArea; float maxArea;
float minArea; float minArea;
cv::Ptr<ERFilter::Callback> classifier; Ptr<ERFilter::Callback> classifier;
// count of the rejected/accepted regions // count of the rejected/accepted regions
int num_rejected_regions; int num_rejected_regions;
@ -98,7 +98,7 @@ protected:
public: public:
// set/get methods to set the algorithm properties, // set/get methods to set the algorithm properties,
void setCallback(const cv::Ptr<ERFilter::Callback>& cb); void setCallback(const Ptr<ERFilter::Callback>& cb);
void setThresholdDelta(int thresholdDelta); void setThresholdDelta(int thresholdDelta);
void setMinArea(float minArea); void setMinArea(float minArea);
void setMaxArea(float maxArea); void setMaxArea(float maxArea);
@ -111,10 +111,10 @@ private:
// pointer to the input/output regions vector // pointer to the input/output regions vector
std::vector<ERStat> *regions; std::vector<ERStat> *regions;
// image mask used for feature calculations // image mask used for feature calculations
cv::Mat region_mask; Mat region_mask;
// extract the component tree and store all the ER regions // extract the component tree and store all the ER regions
void er_tree_extract( cv::InputArray image ); void er_tree_extract( InputArray image );
// accumulate a pixel into an ER // accumulate a pixel into an ER
void er_add_pixel( ERStat *parent, int x, int y, int non_boundary_neighbours, void er_add_pixel( ERStat *parent, int x, int y, int non_boundary_neighbours,
int non_boundary_neighbours_horiz, int non_boundary_neighbours_horiz,
@ -126,7 +126,7 @@ private:
// copy extracted regions into the output vector // copy extracted regions into the output vector
ERStat* er_save( ERStat *er, ERStat *parent, ERStat *prev ); ERStat* er_save( ERStat *er, ERStat *parent, ERStat *prev );
// recursively walk the tree and filter (remove) regions using the callback classifier // recursively walk the tree and filter (remove) regions using the callback classifier
ERStat* er_tree_filter( cv::InputArray image, ERStat *stat, ERStat *parent, ERStat *prev ); ERStat* er_tree_filter( InputArray image, ERStat *stat, ERStat *parent, ERStat *prev );
// recursively walk the tree selecting only regions with local maxima probability // recursively walk the tree selecting only regions with local maxima probability
ERStat* er_tree_nonmax_suppression( ERStat *er, ERStat *parent, ERStat *prev ); ERStat* er_tree_nonmax_suppression( ERStat *er, ERStat *parent, ERStat *prev );
}; };
@ -184,7 +184,7 @@ ERFilterNM::ERFilterNM()
// the key method. Takes image on input, vector of ERStat is output for the first stage, // the key method. Takes image on input, vector of ERStat is output for the first stage,
// input/output for the second one. // input/output for the second one.
void ERFilterNM::run( cv::InputArray image, std::vector<ERStat>& _regions ) void ERFilterNM::run( InputArray image, std::vector<ERStat>& _regions )
{ {
// assert correct image type // assert correct image type
@ -222,7 +222,7 @@ void ERFilterNM::run( cv::InputArray image, std::vector<ERStat>& _regions )
// extract the component tree and store all the ER regions // extract the component tree and store all the ER regions
// uses the algorithm described in // uses the algorithm described in
// Linear time maximally stable extremal regions, D Nistér, H Stewénius – ECCV 2008 // Linear time maximally stable extremal regions, D Nistér, H Stewénius – ECCV 2008
void ERFilterNM::er_tree_extract( cv::InputArray image ) void ERFilterNM::er_tree_extract( InputArray image )
{ {
Mat src = image.getMat(); Mat src = image.getMat();
@ -749,7 +749,7 @@ ERStat* ERFilterNM::er_save( ERStat *er, ERStat *parent, ERStat *prev )
} }
// recursively walk the tree and filter (remove) regions using the callback classifier // recursively walk the tree and filter (remove) regions using the callback classifier
ERStat* ERFilterNM::er_tree_filter ( cv::InputArray image, ERStat * stat, ERStat *parent, ERStat *prev ) ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *parent, ERStat *prev )
{ {
Mat src = image.getMat(); Mat src = image.getMat();
// assert correct image type // assert correct image type
@ -820,7 +820,7 @@ ERStat* ERFilterNM::er_tree_filter ( cv::InputArray image, ERStat * stat, ERStat
{ {
vector<Point> hull; vector<Point> hull;
cv::convexHull(contours[0], hull, false); convexHull(contours[0], hull, false);
hull_area = (int)contourArea(hull); hull_area = (int)contourArea(hull);
} }
@ -1072,7 +1072,7 @@ double ERClassifierNM2::eval(const ERStat& stat)
\param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities \param nonMaxSuppression Whenever non-maximum suppression is done over the branch probabilities
\param minProbability The minimum probability difference between local maxima and local minima ERs \param minProbability The minimum probability difference between local maxima and local minima ERs
*/ */
Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb, int thresholdDelta, Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int thresholdDelta,
float minArea, float maxArea, float minProbability, float minArea, float maxArea, float minProbability,
bool nonMaxSuppression, float minProbabilityDiff) bool nonMaxSuppression, float minProbabilityDiff)
{ {
@ -1111,7 +1111,7 @@ Ptr<ERFilter> createERFilterNM1(const cv::Ptr<ERFilter::Callback>& cb, int thres
if omitted tries to load a default classifier from file trained_classifierNM2.xml if omitted tries to load a default classifier from file trained_classifierNM2.xml
\param minProbability The minimum probability P(er|character) allowed for retreived ER's \param minProbability The minimum probability P(er|character) allowed for retreived ER's
*/ */
Ptr<ERFilter> createERFilterNM2(const cv::Ptr<ERFilter::Callback>& cb, float minProbability) Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProbability)
{ {
CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) ); CV_Assert( (minProbability >= 0.) && (minProbability <= 1.) );