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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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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.) );