Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

removed extra cv:: scope qualifiers for better readability

Browse Source
pull/1159/head
lluis 11 years ago
parent 2087d4602b
commit 43e7e6e475
2 changed files with 17 additions and 17 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 10
      modules/objdetect/include/opencv2/objdetect/erfilter.hpp
  2. 24
      modules/objdetect/src/erfilter.cpp

10
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
Unescape View File

@ -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
Unescape View File

@ -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.) );

Write Preview
Loading…
Cancel
Save