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Commit 75fdfba2 authored by lluis's avatar lluis
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renamed getDefaultClassifierNM{1,2}() to loadClassifierNM{1,2}() and added… 

renamed getDefaultClassifierNM{1,2}() to loadClassifierNM{1,2}() and added "const std::string& filename" parameter (mandatory).
parent 95d92c09
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Showing with 34 additions and 56 deletions
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
View file @ 75fdfba2
......@@ -47,6 +47,7 @@
#include "opencv2/core.hpp"
#include <vector>
#include <deque>
#include <string>
namespace cv
{
......@@ -164,7 +165,7 @@ public:
\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 getDefaultClassifierNM1()
default classifier can be implicitly load with function loadClassifierNM1()
\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 maximum area (% of image size) allowed for retreived ER's
......@@ -189,7 +190,7 @@ CV_EXPORTS Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb = P
\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 getDefaultClassifierNM2()
default classifier can be implicitly load with function loadClassifierNM2()
\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>(),
......@@ -198,21 +199,19 @@ CV_EXPORTS Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb = P
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes no parameters and returns a pointer to ERFilter::Callback.
The dafault classifier is loaded from file trained_classifierNM1.xml
if it's found in current directory.
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> getDefaultClassifierNM1();
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 no parameters and returns a pointer to ERFilter::Callback.
The dafault classifier is loaded from file trained_classifierNM2.xml
if it's found in current directory.
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> getDefaultClassifierNM2();
CV_EXPORTS Ptr<ERFilter::Callback> loadClassifierNM2(const std::string& filename);
// computeNMChannels operation modes
......
modules/objdetect/src/erfilter.cpp
View file @ 75fdfba2
......@@ -137,7 +137,7 @@ class CV_EXPORTS ERClassifierNM1 : public ERFilter::Callback
{
public:
//Constructor
ERClassifierNM1();
ERClassifierNM1(const std::string& filename);
// Destructor
~ERClassifierNM1() {};
......@@ -153,7 +153,7 @@ class CV_EXPORTS ERClassifierNM2 : public ERFilter::Callback
{
public:
//constructor
ERClassifierNM2();
ERClassifierNM2(const std::string& filename);
// Destructor
~ERClassifierNM2() {};
......@@ -988,24 +988,13 @@ int ERFilterNM::getNumRejected()
// load default 1st stage classifier if found
ERClassifierNM1::ERClassifierNM1()
ERClassifierNM1::ERClassifierNM1(const std::string& filename)
{
if (ifstream("./trained_classifierNM1.xml"))
{
// 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");
}
if (ifstream(filename.c_str()))
boost.load( filename.c_str(), "boost" );
else
{
// File not found
CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM1.xml not found!");
}
CV_Error(CV_StsBadArg, "Default classifier file not found!");
};
double ERClassifierNM1::eval(const ERStat& stat)
......@@ -1026,24 +1015,12 @@ double ERClassifierNM1::eval(const ERStat& stat)
// load default 2nd stage classifier if found
ERClassifierNM2::ERClassifierNM2()
ERClassifierNM2::ERClassifierNM2(const std::string& filename)
{
if (ifstream("./trained_classifierNM2.xml"))
{
// 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");
}
if (ifstream(filename.c_str()))
boost.load( filename.c_str(), "boost" );
else
{
// File not found
CV_Error(CV_StsBadArg, "Default classifier ./trained_classifierNM2.xml not found!");
}
CV_Error(CV_StsBadArg, "Default classifier file not found!");
};
double ERClassifierNM2::eval(const ERStat& stat)
......@@ -1080,6 +1057,7 @@ double ERClassifierNM2::eval(const ERStat& stat)
\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()
\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 maximum area (% of image size) allowed for retreived ER's
......@@ -1100,9 +1078,11 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM1>());
else
filter->setCallback(makePtr<ERClassifierNM1>("trained_classifierNM1.xml"));
else
filter->setCallback(cb);
filter->setThresholdDelta(thresholdDelta);
filter->setMinArea(minArea);
......@@ -1124,6 +1104,7 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
\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()
\param minProbability The minimum probability P(er|character) allowed for retreived ER's
*/
Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProbability)
......@@ -1134,7 +1115,7 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM2>());
filter->setCallback(makePtr<ERClassifierNM2>("trained_classifierNM2.xml"));
else
filter->setCallback(cb);
......@@ -1144,25 +1125,23 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes no parameters and returns a pointer to ERFilter::Callback.
The dafault classifier is loaded from file trained_classifierNM1.xml
if it's found in current directory.
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> getDefaultClassifierNM1()
Ptr<ERFilter::Callback> loadClassifierNM1(const std::string& filename)
{
return makePtr<ERClassifierNM1>();
return makePtr<ERClassifierNM1>(filename);
}
/*!
Allow to implicitly load the default classifier when creating an ERFilter object.
The function takes no parameters and returns a pointer to ERFilter::Callback.
The dafault classifier is loaded from file trained_classifierNM2.xml
if it's found in current directory.
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> getDefaultClassifierNM2()
Ptr<ERFilter::Callback> loadClassifierNM2(const std::string& filename)
{
return makePtr<ERClassifierNM2>();
return makePtr<ERClassifierNM2>(filename);
}
......
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