Merge pull request #1412 from berak:fix_saliency

Author: vpisarev

modules/saliency/include/opencv2/saliency/saliencySpecializedClasses.hpp

@@ -464,7 +464,7 @@ class CV_EXPORTS_W ObjectnessBING : public Objectness
   void setColorSpace( int clr = MAXBGR );
 
 // Load trained model.
-  int loadTrainedModel( std::string modelName = "" );// Return -1, 0, or 1 if partial, none, or all loaded
+  int loadTrainedModel();// Return -1, 0, or 1 if partial, none, or all loaded
 
 // Get potential bounding boxes, each of which is represented by a Vec4i for (minX, minY, maxX, maxY).
 // The trained model should be prepared before calling this function: loadTrainedModel() or trainStageI() + trainStageII().

modules/saliency/samples/computeSaliency.cpp

@@ -52,7 +52,7 @@ static const char* keys =
 { "{@saliency_algorithm | | Saliency algorithm <saliencyAlgorithmType.[saliencyAlgorithmTypeSubType]> }"
     "{@video_name      | | video name            }"
     "{@start_frame     |1| Start frame           }"
-    "{@training_path   |1| Path of the folder containing the trained files}" };
+    "{@training_path   |ObjectnessTrainedModel| Path of the folder containing the trained files}" };
 
 static void help()
 {
@@ -150,11 +150,28 @@ int main( int argc, char** argv )
       saliencyAlgorithm = ObjectnessBING::create();
       vector<Vec4i> saliencyMap;
       saliencyAlgorithm.dynamicCast<ObjectnessBING>()->setTrainingPath( training_path );
-      saliencyAlgorithm.dynamicCast<ObjectnessBING>()->setBBResDir( training_path + "/Results" );
+      saliencyAlgorithm.dynamicCast<ObjectnessBING>()->setBBResDir( "Results" );
 
       if( saliencyAlgorithm->computeSaliency( image, saliencyMap ) )
       {
-        std::cout << "Objectness done" << std::endl;
+        int ndet = int(saliencyMap.size());
+        std::cout << "Objectness done " << ndet << std::endl;
+        // The result are sorted by objectness. We only use the first maxd boxes here.
+        int maxd = 7, step = 255 / maxd, jitter=9; // jitter to seperate single rects
+        Mat draw = image.clone();
+        for (int i = 0; i < std::min(maxd, ndet); i++) {
+          Vec4i bb = saliencyMap[i];
+          Scalar col = Scalar(((i*step)%255), 50, 255-((i*step)%255));
+          Point off(theRNG().uniform(-jitter,jitter), theRNG().uniform(-jitter,jitter));
+          rectangle(draw, Point(bb[0]+off.x, bb[1]+off.y), Point(bb[2]+off.x, bb[3]+off.y), col, 2);
+          rectangle(draw, Rect(20, 20+i*10, 10,10), col, -1); // mini temperature scale
+        }
+        imshow("BING", draw);
+        waitKey();
+      }
+      else
+      {
+        std::cout << "No saliency found for " << video_name << std::endl;
       }
     }
 

modules/saliency/src/BING/CmFile.cpp

@@ -76,7 +76,7 @@ bool CmFile::MkDir( std::string &_path )
       buffer[i] = '/';
     }
   }
-  mkdir( _path.c_str(), 0 );
+  mkdir( _path.c_str(), S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH );
   return true;
 #endif
 }

modules/saliency/src/BING/objectnessBING.cpp

@@ -95,16 +95,14 @@ void ObjectnessBING::setBBResDir(const String &resultsDir )
   _resultsDir = resultsDir;
 }
 
-int ObjectnessBING::loadTrainedModel( std::string modelName )  // Return -1, 0, or 1 if partial, none, or all loaded
+int ObjectnessBING::loadTrainedModel()  // Return -1, 0, or 1 if partial, none, or all loaded
 {
-  if( modelName.size() == 0 )
-    modelName = _modelName;
-  CStr s1 = modelName + ".wS1", s2 = modelName + ".wS2", sI = modelName + ".idx";
+  CStr s1 = _modelName + ".wS1", s2 = _modelName + ".wS2", sI = _modelName + ".idx";
   Mat filters1f, reW1f, idx1i, show3u;
 
   if( !matRead( s1, filters1f ) || !matRead( sI, idx1i ) )
   {
-    printf( "Can't load model: %s or %s\n", s1.c_str(), sI.c_str() );
+    printf( "Can't load model: %s or %s\r\n", s1.c_str(), sI.c_str() );
     return 0;
   }
 
@@ -384,7 +382,9 @@ void ObjectnessBING::getObjBndBoxesForSingleImage( Mat img, ValStructVec<float,
   for ( int clr = MAXBGR; clr <= G; clr++ )
   {
     setColorSpace( clr );
-    loadTrainedModel();
+    if (!loadTrainedModel())
+      continue;
+
     CmTimer tm( "Predict" );
     tm.Start();
 
@@ -439,6 +439,9 @@ bool ObjectnessBING::matRead( const std::string& filename, Mat& _M )
   String filenamePlusExt( filename.c_str() );
   filenamePlusExt += ".yml.gz";
   FileStorage fs2( filenamePlusExt, FileStorage::READ );
+  if (! fs2.isOpened()) // wrong trainingPath
+    return false;
+
   Mat M;
   fs2[String( removeExtension( basename( filename ) ).c_str() )] >> M;