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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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This commit is contained in:
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).
\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 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
@ -173,7 +173,7 @@ 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 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,
float maxArea = 0.13, float minProbability = 0.4,
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.
\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
*/
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);

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).
\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 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
@ -1077,12 +1077,7 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM1>("trained_classifierNM1.xml"));
else
filter->setCallback(cb);
filter->setCallback(cb);
filter->setThresholdDelta(thresholdDelta);
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.
\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
*/
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>();
if (cb == NULL)
filter->setCallback(makePtr<ERClassifierNM2>("trained_classifierNM2.xml"));
else
filter->setCallback(cb);
filter->setCallback(cb);
filter->setMinProbability(minProbability);
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();
// 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);
@ -89,7 +89,7 @@ int main(int argc, const char * argv[])
t = (double)getTickCount();
// Default second stage classifier
Ptr<ERFilter> er_filter2 = createERFilterNM2();
Ptr<ERFilter> er_filter2 = createERFilterNM2(loadClassifierNM2("trained_classifierNM2.xml"));
er_filter2->run(grey, regions);
t = (double)getTickCount() - t;