Merge remote-tracking branch 'itseezstream/master'

modules/objdetect/include/opencv2/objdetect/erfilter.hpp

@@ -47,6 +47,7 @@
 #include "opencv2/core.hpp"
 #include <vector>
 #include <deque>
+#include <string>
 
 namespace cv
 {
@@ -163,7 +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
@@ -171,7 +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,
@@ -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.
 
     \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);
 
 
+/*!
+    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
 enum { ERFILTER_NM_RGBLGrad = 0,
        ERFILTER_NM_IHSGrad  = 1

modules/objdetect/src/erfilter.cpp

@@ -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"))
+    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
-    {
+        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)
@@ -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(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
-    {
+        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)
@@ -1079,7 +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
@@ -1099,10 +1077,7 @@ Ptr<ERFilter> createERFilterNM1(const Ptr<ERFilter::Callback>& cb, int threshold
 
     Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
 
-    if (cb == NULL)
+    filter->setCallback(cb);
-        filter->setCallback(makePtr<ERClassifierNM1>());
-    else
-        filter->setCallback(cb);
 
     filter->setThresholdDelta(thresholdDelta);
     filter->setMinArea(minArea);
@@ -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.
 
     \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)
@@ -1133,15 +1109,33 @@ Ptr<ERFilter> createERFilterNM2(const Ptr<ERFilter::Callback>& cb, float minProb
 
     Ptr<ERFilterNM> filter = makePtr<ERFilterNM>();
 
-    if (cb == NULL)
+    filter->setCallback(cb);
-        filter->setCallback(makePtr<ERClassifierNM2>());
-    else
-        filter->setCallback(cb);
 
     filter->setMinProbability(minProbability);
     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 -------------------------------*/

samples/cpp/erfilter.cpp

@@ -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;