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Merge remote-tracking branch 'itseezstream/master'

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This commit is contained in:
Ozan Tonkal 2013-09-17 17:50:14 +02:00
commit e3e5fd5baa
3 changed files with 63 additions and 49 deletions
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28
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
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@ -47,6 +47,7 @@
#include "opencv2/core.hpp" #include "opencv2/core.hpp"
#include <vector> #include <vector>
#include <deque> #include <deque>
#include <string>
namespace cv namespace cv
{ {
@ -163,7 +164,8 @@ public:
local minimum is greater than minProbabilityDiff). local minimum is greater than minProbabilityDiff).
\param cb Callback with the classifier. \param cb Callback with the classifier.
if omitted tries to load a default classifier from file trained_classifierNM1.xml default classifier can be implicitly load with function loadClassifierNM1()
from file in samples/cpp/trained_classifierNM1.xml
\param thresholdDelta Threshold step in subsequent thresholds when extracting the component tree \param thresholdDelta Threshold step in subsequent thresholds when extracting the component tree
\param minArea The minimum area (% of image size) allowed for retreived ER's \param minArea The minimum area (% of image size) allowed for retreived ER's
\param minArea The maximum area (% of image size) allowed for retreived ER's \param minArea The maximum area (% of image size) allowed for retreived ER's
@ -171,7 +173,7 @@ 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 Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = Ptr<ERFilter::Callback>(), CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb,
int thresholdDelta = 1, float minArea = 0.00025, int thresholdDelta = 1, float minArea = 0.00025,
float maxArea = 0.13, float minProbability = 0.4, float maxArea = 0.13, float minProbability = 0.4,
bool nonMaxSuppression = true, bool nonMaxSuppression = true,
@ -187,13 +189,31 @@ CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = P
additional features: hole area ratio, convex hull ratio, and number of outer inflexion points. additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
\param cb Callback with the classifier \param cb Callback with the classifier
if omitted tries to load a default classifier from file trained_classifierNM2.xml default classifier can be implicitly load with function loadClassifierNM2()
from file in samples/cpp/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 Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = Ptr<ERFilter::Callback>(), CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb,
float minProbability = 0.3); float minProbability = 0.3);
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes as parameter the XML or YAML file with the classifier model
(e.g. trained_classifierNM1.xml) returns a pointer to ERFilter::Callback.
*/
CV_EXPORTS Ptr<ERFilter::Callback> loadClassifierNM1(const std::string& filename);
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes as parameter the XML or YAML file with the classifier model
(e.g. trained_classifierNM1.xml) returns a pointer to ERFilter::Callback.
*/
CV_EXPORTS Ptr<ERFilter::Callback> loadClassifierNM2(const std::string& filename);
// computeNMChannels operation modes // computeNMChannels operation modes
enum { ERFILTER_NM_RGBLGrad = 0, enum { ERFILTER_NM_RGBLGrad = 0,
ERFILTER_NM_IHSGrad = 1 ERFILTER_NM_IHSGrad = 1

76
modules/objdetect/src/erfilter.cpp
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@ -137,7 +137,7 @@ class CV_EXPORTS ERClassifierNM1 : public ERFilter::Callback
{ {
public: public:
//Constructor //Constructor
ERClassifierNM1(); ERClassifierNM1(const std::string& filename);
// Destructor // Destructor
~ERClassifierNM1() {}; ~ERClassifierNM1() {};
@ -153,7 +153,7 @@ class CV_EXPORTS ERClassifierNM2 : public ERFilter::Callback
{ {
public: public:
//constructor //constructor
ERClassifierNM2(); ERClassifierNM2(const std::string& filename);
// Destructor // Destructor
~ERClassifierNM2() {}; ~ERClassifierNM2() {};
@ -988,24 +988,13 @@ int ERFilterNM::getNumRejected()
// load default 1st stage classifier if found // load default 1st stage classifier if found
ERClassifierNM1::ERClassifierNM1() ERClassifierNM1::ERClassifierNM1(const std::string& filename)
{ {
if (ifstream("./trained_classifierNM1.xml")) if (ifstream(filename.c_str()))
{ boost.load( filename.c_str(), "boost" );
// The file with default classifier exists
boost.load("./trained_classifierNM1.xml", "boost");
}
else if (ifstream("./training/trained_classifierNM1.xml"))
{
// The file with default classifier exists
boost.load("./training/trained_classifierNM1.xml", "boost");
}
else else
{ CV_Error(CV_StsBadArg, "Default classifier file not found!");
// File not found
CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM1.xml not found!");
}
}; };
double ERClassifierNM1::eval(const ERStat& stat) double ERClassifierNM1::eval(const ERStat& stat)
@ -1026,24 +1015,12 @@ double ERClassifierNM1::eval(const ERStat& stat)
// load default 2nd stage classifier if found // load default 2nd stage classifier if found
ERClassifierNM2::ERClassifierNM2() ERClassifierNM2::ERClassifierNM2(const std::string& filename)
{ {
if (ifstream(filename.c_str()))
if (ifstream("./trained_classifierNM2.xml")) boost.load( filename.c_str(), "boost" );
{
// The file with default classifier exists
boost.load("./trained_classifierNM2.xml", "boost");
}
else if (ifstream("./training/trained_classifierNM2.xml"))
{
// The file with default classifier exists
boost.load("./training/trained_classifierNM2.xml", "boost");
}
else else
{ CV_Error(CV_StsBadArg, "Default classifier file not found!");
// File not found
CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM2.xml not found!");
}
}; };
double ERClassifierNM2::eval(const ERStat& stat) double ERClassifierNM2::eval(const ERStat& stat)
@ -1079,7 +1056,8 @@ double ERClassifierNM2::eval(const ERStat& stat)
local minimum is greater than minProbabilityDiff). local minimum is greater than minProbabilityDiff).
\param cb Callback with the classifier. \param cb Callback with the classifier.
if omitted tries to load a default classifier from file trained_classifierNM1.xml default classifier can be implicitly load with function loadClassifierNM1()
from file in samples/cpp/trained_classifierNM1.xml
\param thresholdDelta Threshold step in subsequent thresholds when extracting the component tree \param thresholdDelta Threshold step in subsequent thresholds when extracting the component tree
\param minArea The minimum area (% of image size) allowed for retreived ER's \param minArea The minimum area (% of image size) allowed for retreived ER's
\param minArea The maximum area (% of image size) allowed for retreived ER's \param minArea The maximum area (% of image size) allowed for retreived ER's
@ -1099,9 +1077,6 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>(); Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM1>());
else
filter->setCallback(cb); filter->setCallback(cb);
filter->setThresholdDelta(thresholdDelta); filter->setThresholdDelta(thresholdDelta);
@ -1123,7 +1098,8 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
additional features: hole area ratio, convex hull ratio, and number of outer inflexion points. additional features: hole area ratio, convex hull ratio, and number of outer inflexion points.
\param cb Callback with the classifier \param cb Callback with the classifier
if omitted tries to load a default classifier from file trained_classifierNM2.xml default classifier can be implicitly load with function loadClassifierNM1()
from file in samples/cpp/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 Ptr<ERFilter::Callback>& cb, float minProbability) Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProbability)
@ -1133,15 +1109,33 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>(); Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM2>());
else
filter->setCallback(cb); filter->setCallback(cb);
filter->setMinProbability(minProbability); filter->setMinProbability(minProbability);
return (Ptr<ERFilter>)filter; return (Ptr<ERFilter>)filter;
} }
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes as parameter the XML or YAML file with the classifier model
(e.g. trained_classifierNM1.xml) returns a pointer to ERFilter::Callback.
*/
Ptr<ERFilter::Callback> loadClassifierNM1(const std::string& filename)
{
return makePtr<ERClassifierNM1>(filename);
}
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes as parameter the XML or YAML file with the classifier model
(e.g. trained_classifierNM2.xml) returns a pointer to ERFilter::Callback.
*/
Ptr<ERFilter::Callback> loadClassifierNM2(const std::string& filename)
{
return makePtr<ERClassifierNM2>(filename);
}
/* ------------------------------------------------------------------------------------*/ /* ------------------------------------------------------------------------------------*/
/* -------------------------------- Compute Channels NM -------------------------------*/ /* -------------------------------- Compute Channels NM -------------------------------*/

4
samples/cpp/erfilter.cpp
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@ -58,7 +58,7 @@ int main(int argc, const char * argv[])
double t = (double)getTickCount(); double t = (double)getTickCount();
// Build ER tree and filter with the 1st stage default classifier // Build ER tree and filter with the 1st stage default classifier
Ptr<ERFilter> er_filter1 = createERFilterNM1(); Ptr<ERFilter> er_filter1 = createERFilterNM1(loadClassifierNM1("trained_classifierNM1.xml"));
er_filter1->run(grey, regions); er_filter1->run(grey, regions);
@ -89,7 +89,7 @@ int main(int argc, const char * argv[])
t = (double)getTickCount(); t = (double)getTickCount();
// Default second stage classifier // Default second stage classifier
Ptr<ERFilter> er_filter2 = createERFilterNM2(); Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"));
er_filter2->run(grey, regions); er_filter2->run(grey, regions);
t = (double)getTickCount() - t; t = (double)getTickCount() - t;