finish bd module

Author: Masterqsx

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

@@ -202,10 +202,13 @@ This method use the convolution layers of the pretrained AlexNet, linear combina
 */
 class CV_EXPORTS_W BackgroundContrast : public StaticSaliency
 {
-//private:
-
+private:
+	int limitOfSP;
+	int nOfLevel;
+	int usePrior;
+	int histBin;
 public:
-    BackgroundContrast();
+    BackgroundContrast( int = 600, int = 4, int = 2, int = 5 );
     virtual ~BackgroundContrast();
     CV_WRAP static Ptr<BackgroundContrast> create()
     {

modules/saliency/samples/BackgroundContrastSample.cpp

@@ -0,0 +1,50 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+
+
+#include <opencv2/core.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/highgui.hpp>
+#include <opencv2/saliency.hpp>
+#include <opencv2/ximgproc.hpp>
+#include <vector>
+#include <string>
+#include <iostream>
+#include <fstream>
+
+using namespace std;
+using namespace cv;
+using namespace saliency;
+
+
+int main(int argc, char* argv[])
+{
+    const char *keys =
+            "{ help h usage ?    |     | show this message }"
+    		"{ img_path          |     | path to img }"
+    		"{ foregroundImg_path|     | path to saliency img generated by other method }"
+    		"{ optimization      |0    | 0 for aggregated optimized with no outside saliency, 1 for optimizaed with outside saliency }";
+
+    CommandLineParser parser(argc, argv, keys);
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+    string img_path = parser.get<string>("img_path");
+    string foregroundImg_path = parser.get<string>("foregroundImg_path");
+
+    Mat img = imread(img_path);
+    Mat fgImg = imread(foregroundImg_path);
+    BackgroundContrast bc;
+    if ( parser.get<bool>( "optimization" ) )
+    {
+        bc.saliencyMapVisualize(bc.saliencyMapGenerator(img, fgImg, 1), 0);
+    }
+    else
+    {
+    	bc.saliencyMapVisualize(bc.saliencyMapGenerator(img), 0);
+    }
+    return 0;
+} //main

modules/saliency/src/BackgroundContrast.cpp

@@ -33,11 +33,8 @@ namespace cv
 namespace saliency
 {
 
-BackgroundContrast::BackgroundContrast()
-{
-
-}
-BackgroundContrast::~BackgroundContrast(){};
+BackgroundContrast::BackgroundContrast( int _limitOfSP, int _nOfLevel, int _usePrior, int _histBin ): limitOfSP(_limitOfSP), nOfLevel(_nOfLevel), usePrior(_usePrior), histBin(_histBin) {}
+BackgroundContrast::~BackgroundContrast(){}
 
 Mat BackgroundContrast::saliencyMapGenerator( const Mat img, const Mat fgImg, int option )
 {
@@ -63,13 +60,14 @@ Mat BackgroundContrast::saliencyMapGenerator( const Mat img, const Mat fgImg, in
     else
     {
     	Mat temp = fgImg.clone();
+    	wCtr = Mat(adjcMatrix.size[0], 1, CV_64F, Scalar::all(0.0));
     	resize(temp, temp, img.size());
     	vector<int> szOfSP = vector<int>(adjcMatrix.size[0], 0);
     	for ( int i = 0; i < img.size[0]; i++ )
     	{
     		for ( int j = 0; j < img.size[1]; j++ )
     		{
-    			szOfSP[idxImg.at<unsigned>(i, j)]++;
+    			szOfSP[idxImg.at<unsigned>(i, j)] += 1;
     			wCtr.at<double>(idxImg.at<unsigned>(i, j), 0) += temp.at<double>(i, j);
     		}
     	}
@@ -145,13 +143,15 @@ void BackgroundContrast::saliencyOptimize( const Mat adjcMatrix, const Mat colDi
 
 bool BackgroundContrast::computeSaliencyImpl( InputArray image, OutputArray saliencyMap )
 {
+	CV_Assert( !(image.getMat().empty()) );
+	saliencyMap.assign(saliencyMapGenerator(image.getMat()));
     return true;
 }
 
 void BackgroundContrast::superpixelSplit( const Mat img, Mat& idxImg, Mat& adjcMatrix)
 {
     Ptr<SuperpixelSEEDS> seeds;
-    seeds = createSuperpixelSEEDS( img.size().width, img.size().height, img.channels(), min(img.size().width  * img.size().height / 600, 600), 4, 2, 5, false);
+    seeds = createSuperpixelSEEDS( img.size().width, img.size().height, img.channels(), min(img.size().width  * img.size().height / 600, limitOfSP), nOfLevel, usePrior, histBin, false);
     seeds->iterate( img, 4 );
     Mat mask;
     adjcMatrix = Mat::eye( seeds->getNumberOfSuperpixels(), seeds->getNumberOfSuperpixels(), CV_8U );