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first parameter of createERFilterNM1/createERFilterNM2 is now mandatory. changed the sample program to use the new prototypes

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lluis 2013-09-13 16:29:21 +02:00
parent 75fdfba281
commit d25309f82e
3 changed files with 10 additions and 18 deletions
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8
modules/objdetect/include/opencv2/objdetect/erfilter.hpp
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@ -164,8 +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() 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
@ -173,7 +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,
@ -189,11 +189,11 @@ 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() 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);

16
modules/objdetect/src/erfilter.cpp
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@ -1056,8 +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() 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
@ -1077,12 +1077,7 @@ 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(cb);
filter->setCallback(makePtr<ERClassifierNM1>("trained_classifierNM1.xml"));
else
filter->setCallback(cb);
filter->setThresholdDelta(thresholdDelta); filter->setThresholdDelta(thresholdDelta);
filter->setMinArea(minArea); filter->setMinArea(minArea);
@ -1103,8 +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() 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)
@ -1114,10 +1109,7 @@ 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(cb);
filter->setCallback(makePtr<ERClassifierNM2>("trained_classifierNM2.xml"));
else
filter->setCallback(cb);
filter->setMinProbability(minProbability); filter->setMinProbability(minProbability);
return (Ptr<ERFilter>)filter; return (Ptr<ERFilter>)filter;

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;