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removed extra cv:: scope qualifiers for better readability

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This commit is contained in:
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.
*/
class CV_EXPORTS ERFilter : public cv::Algorithm
class CV_EXPORTS ERFilter : public Algorithm
{
public:
@ -138,11 +138,11 @@ public:
\param image is the input image
\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,
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 setMinArea(float minArea) = 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 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,
float maxArea = 0.13, float minProbability = 0.2,
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
\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);
}

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,
// input/output - for the second one.
void run( cv::InputArray image, std::vector<ERStat>& regions );
void run( InputArray image, std::vector<ERStat>& regions );
protected:
int thresholdDelta;
float maxArea;
float minArea;
cv::Ptr<ERFilter::Callback> classifier;
Ptr<ERFilter::Callback> classifier;
// count of the rejected/accepted regions
int num_rejected_regions;
@ -98,7 +98,7 @@ protected:
public:
// 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 setMinArea(float minArea);
void setMaxArea(float maxArea);
@ -111,10 +111,10 @@ private:
// pointer to the input/output regions vector
std::vector<ERStat> *regions;
// image mask used for feature calculations
cv::Mat region_mask;
Mat region_mask;
// 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
void er_add_pixel( ERStat *parent, int x, int y, int non_boundary_neighbours,
int non_boundary_neighbours_horiz,
@ -126,7 +126,7 @@ private:
// copy extracted regions into the output vector
ERStat* er_save( ERStat *er, ERStat *parent, ERStat *prev );
// 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
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,
// 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
@ -222,7 +222,7 @@ void ERFilterNM::run( cv::InputArray image, std::vector<ERStat>& _regions )
// extract the component tree and store all the ER regions
// uses the algorithm described in
// 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();
@ -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
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();
// assert correct image type
@ -820,7 +820,7 @@ ERStat* ERFilterNM::er_tree_filter ( cv::InputArray image, ERStat * stat, ERStat
{
vector<Point> hull;
cv::convexHull(contours[0], hull, false);
convexHull(contours[0], hull, false);
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 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,
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
\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.) );