Check sure that we're not already below required leaf false alarm rate before...

Check sure that we're not already below required leaf false alarm rate before continuing to get negative samples.

apps/traincascade/cascadeclassifier.cpp

@@ -198,7 +198,7 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
         cout << endl << "===== TRAINING " << i << "-stage =====" << endl;
         cout << "<BEGIN" << endl;
 
-        if ( !updateTrainingSet( tempLeafFARate ) )
+        if ( !updateTrainingSet( requiredLeafFARate, tempLeafFARate ) )
         {
             cout << "Train dataset for temp stage can not be filled. "
                 "Branch training terminated." << endl;
@@ -284,17 +284,17 @@ int CvCascadeClassifier::predict( int sampleIdx )
     return 1;
 }
 
-bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
+bool CvCascadeClassifier::updateTrainingSet( double minimumAcceptanceRatio, double& acceptanceRatio)
 {
     int64 posConsumed = 0, negConsumed = 0;
     imgReader.restart();
-    int posCount = fillPassedSamples( 0, numPos, true, posConsumed );
+    int posCount = fillPassedSamples( 0, numPos, true, 0, posConsumed );
     if( !posCount )
         return false;
     cout << "POS count : consumed   " << posCount << " : " << (int)posConsumed << endl;
 
     int proNumNeg = cvRound( ( ((double)numNeg) * ((double)posCount) ) / numPos ); // apply only a fraction of negative samples. double is required since overflow is possible
-    int negCount = fillPassedSamples( posCount, proNumNeg, false, negConsumed );
+    int negCount = fillPassedSamples( posCount, proNumNeg, false, minimumAcceptanceRatio, negConsumed );
     if ( !negCount )
         return false;
 
@@ -304,7 +304,7 @@ bool CvCascadeClassifier::updateTrainingSet( double& acceptanceRatio)
     return true;
 }
 
-int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, int64& consumed )
+int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositive, double minimumAcceptanceRatio, int64& consumed )
 {
     int getcount = 0;
     Mat img(cascadeParams.winSize, CV_8UC1);
@@ -312,6 +312,9 @@ int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositiv
     {
         for( ; ; )
         {
+            if( consumed != 0 && ((double)getcount+1)/(double)(int64)consumed <= minimumAcceptanceRatio )
+                return getcount;
+
             bool isGetImg = isPositive ? imgReader.getPos( img ) :
                                            imgReader.getNeg( img );
             if( !isGetImg )

apps/traincascade/cascadeclassifier.h

@@ -101,8 +101,8 @@ private:
     int predict( int sampleIdx );
     void save( const std::string cascadeDirName, bool baseFormat = false );
     bool load( const std::string cascadeDirName );
-    bool updateTrainingSet( double& acceptanceRatio );
-    int fillPassedSamples( int first, int count, bool isPositive, int64& consumed );
+    bool updateTrainingSet( double minimumAcceptanceRatio, double& acceptanceRatio );
+    int fillPassedSamples( int first, int count, bool isPositive, double requiredAcceptanceRatio, int64& consumed );
 
     void writeParams( cv::FileStorage &fs ) const;
     void writeStages( cv::FileStorage &fs, const cv::Mat& featureMap ) const;