[转]国外的一个图像显著区域检测代码及其效果图 saliency region detection

http://blog.csdn.net/onezeros/archive/2011/04/03/6299745.aspx

  国外的一个图像显著区域检测代码及其效果图 saliency region detection

先看几张效果图吧

效果图：

可以直接测试的代码：

头文件：

// Saliency.h: interface for the Saliency class.  

//  

//////////////////////////////////////////////////////////////////////  

//===========================================================================  

//  Copyright (c) 2009 Radhakrishna Achanta [EPFL]   

//===========================================================================  

#if !defined(_SALIENCY_H_INCLUDED_)  

#define _SALIENCY_H_INCLUDED_  

#include   

#include   

using namespace std;  

class Saliency    

{  

public:  

    Saliency();  

    virtual ~Saliency();  

public:  

    void GetSaliencyMap(  

        const vector<unsigned int>&               inputimg,//INPUT: ARGB buffer in row-major order  

        const int&                      width,  

        const int&                      height,  

        vector<double>&                   salmap,//OUTPUT: Floating point buffer in row-major order  

        const bool&                     normalizeflag = true);//false if normalization is not needed  

private:  

    void RGB2LAB(  

        const vector<unsigned int>&               ubuff,  

        vector<double>&                   lvec,  

        vector<double>&                   avec,  

        vector<double>&                   bvec);  

    void GaussianSmooth(  

        const vector<double>&         inputImg,  

        const int&                      width,  

        const int&                      height,  

        const vector<double>&         kernel,  

        vector<double>&                   smoothImg);  

    //==============================================================================  

    /// Normalize  

    //==============================================================================  

    void Normalize(  

        const vector<double>&         input,  

        const int&                      width,  

        const int&                      height,  

        vector<double>&                   output,  

        const int&                      normrange = 255)  

    {  

        double maxval(0);  

        double minval(DBL_MAX);  

        {int i(0);  

        for( int y = 0; y < height; y++ )  

        {  

            for( int x = 0; x < width; x++ )  

            {  

                if( maxval < input[i] ) maxval = input[i];  

                if( minval > input[i] ) minval = input[i];  

                i++;  

            }  

        }}  

        double range = maxval-minval;  

        if( 0 == range ) range = 1;  

        int i(0);  

        output.clear();  

        output.resize(width*height);  

        for( int y = 0; y < height; y++ )  

        {  

            for( int x = 0; x < width; x++ )  

            {  

                output[i] = ((normrange*(input[i]-minval))/range);  

                i++;  

            }  

        }  

    }  

};  

#endif // !defined(_SALIENCY_H_INCLUDED_)  

cpp：

// Saliency.cpp: implementation of the Saliency class.  

//  

//////////////////////////////////////////////////////////////////////  

//===========================================================================  

//  Copyright (c) 2009 Radhakrishna Achanta [EPFL]   

//===========================================================================  

#include "Saliency.h"  

#include   

//////////////////////////////////////////////////////////////////////  

// Construction/Destruction  

//////////////////////////////////////////////////////////////////////  

Saliency::Saliency()  

{  

}  

Saliency::~Saliency()  

{  

}  

//===========================================================================  

/// RGB2LAB  

//===========================================================================  

void Saliency::RGB2LAB(  

    const vector<unsigned int>&               ubuff,  

    vector<double>&                   lvec,  

    vector<double>&                   avec,  

    vector<double>&                   bvec)  

{  

    int sz = int(ubuff.size());  

    lvec.resize(sz);  

    avec.resize(sz);  

    bvec.resize(sz);  

    for( int j = 0; j < sz; j++ )  

    {  

        int r = (ubuff[j] >> 16) & 0xFF;  

        int g = (ubuff[j] >>  8) & 0xFF;  

        int b = (ubuff[j]      ) & 0xFF;  

        double xval = 0.412453 * r + 0.357580 * g + 0.180423 * b;  

        double yval = 0.212671 * r + 0.715160 * g + 0.072169 * b;  

        double zVal = 0.019334 * r + 0.119193 * g + 0.950227 * b;  

        xval /= (255.0 * 0.950456);  

        yval /=  255.0;  

        zVal /= (255.0 * 1.088754);  

        double fX, fY, fZ;  

        double lval, aval, bval;  

        if (yval > 0.008856)  

        {  

            fY = pow(yval, 1.0 / 3.0);  

            lval = 116.0 * fY - 16.0;  

        }  

        else  

        {  

            fY = 7.787 * yval + 16.0 / 116.0;  

            lval = 903.3 * yval;  

        }  

        if (xval > 0.008856)  

            fX = pow(xval, 1.0 / 3.0);  

        else  

            fX = 7.787 * xval + 16.0 / 116.0;  

        if (zVal > 0.008856)  

            fZ = pow(zVal, 1.0 / 3.0);  

        else  

            fZ = 7.787 * zVal + 16.0 / 116.0;  

        aval = 500.0 * (fX - fY)+128.0;  

        bval = 200.0 * (fY - fZ)+128.0;  

        lvec[j] = lval;  

        avec[j] = aval;  

        bvec[j] = bval;  

    }  

}  

//==============================================================================  

/// GaussianSmooth  

///  

/// Blur an image with a separable binomial kernel passed in.  

//==============================================================================  

void Saliency::GaussianSmooth(  

    const vector<double>&         inputImg,  

    const int&                      width,  

    const int&                      height,  

    const vector<double>&         kernel,  

    vector<double>&                   smoothImg)  

{  

    int center = int(kernel.size())/2;  

    int sz = width*height;  

    smoothImg.clear();  

    smoothImg.resize(sz);  

    vector<double> tempim(sz);  

    int rows = height;  

    int cols = width;  

   //--------------------------------------------------------------------------  

   // Blur in the x direction.  

   //---------------------------------------------------------------------------  

    {int index(0);  

    for( int r = 0; r < rows; r++ )  

    {  

        for( int c = 0; c < cols; c++ )  

        {  

            double kernelsum(0);  

            double sum(0);  

            for( int cc = (-center); cc <= center; cc++ )  

            {  

                if(((c+cc) >= 0) && ((c+cc) < cols))  

                {  

                    sum += inputImg[r*cols+(c+cc)] * kernel[center+cc];  

                    kernelsum += kernel[center+cc];  

                }  

            }  

            tempim[index] = sum/kernelsum;  

            index++;  

        }  

    }}  

    //--------------------------------------------------------------------------  

    // Blur in the y direction.  

    //---------------------------------------------------------------------------  

    {int index = 0;  

    for( int r = 0; r < rows; r++ )  

    {  

        for( int c = 0; c < cols; c++ )  

        {  

            double kernelsum(0);  

            double sum(0);  

            for( int rr = (-center); rr <= center; rr++ )  

            {  

                if(((r+rr) >= 0) && ((r+rr) < rows))  

                {  

                   sum += tempim[(r+rr)*cols+c] * kernel[center+rr];  

                   kernelsum += kernel[center+rr];  

                }  

            }  

            smoothImg[index] = sum/kernelsum;  

            index++;  

        }  

    }}  

}  

//===========================================================================  

/// GetSaliencyMap  

///  

/// Outputs a saliency map with a value assigned per pixel. The values are  

/// normalized in the interval [0,255] if normflag is set true (default value).  

//===========================================================================  

void Saliency::GetSaliencyMap(  

    const vector<unsigned int>&       inputimg,  

    const int&                      width,  

    const int&                      height,  

    vector<double>&                   salmap,  

    const bool&                     normflag)   

{  

    int sz = width*height;  

    salmap.clear();  

    salmap.resize(sz);  

    vector<double> lvec(0), avec(0), bvec(0);  

    RGB2LAB(inputimg, lvec, avec, bvec);  

    //--------------------------  

    // Obtain Lab average values  

    //--------------------------  

    double avgl(0), avga(0), avgb(0);  

    {for( int i = 0; i < sz; i++ )  

    {  

        avgl += lvec[i];  

        avga += avec[i];  

        avgb += bvec[i];  

    }}  

    avgl /= sz;  

    avga /= sz;  

    avgb /= sz;  

    vector<double> slvec(0), savec(0), sbvec(0);  

    //----------------------------------------------------  

    // The kernel can be [1 2 1] or [1 4 6 4 1] as needed.  

    // The code below show usage of [1 2 1] kernel.  

    //----------------------------------------------------  

    vector<double> kernel(0);  

    kernel.push_back(1.0);  

    kernel.push_back(2.0);  

    kernel.push_back(1.0);  

    GaussianSmooth(lvec, width, height, kernel, slvec);  

    GaussianSmooth(avec, width, height, kernel, savec);  

    GaussianSmooth(bvec, width, height, kernel, sbvec);  

    {for( int i = 0; i < sz; i++ )  

    {  

        salmap[i] = (slvec[i]-avgl)*(slvec[i]-avgl) +  

                    (savec[i]-avga)*(savec[i]-avga) +  

                    (sbvec[i]-avgb)*(sbvec[i]-avgb);  

    }}  

    if( true == normflag )  

    {  

        vector<double> normalized(0);  

        Normalize(salmap, width, height, normalized);  

        swap(salmap, normalized);  

    }  

} 

关于代码的使用说明：

#include "Saliency.h"  

void main()  

{  

    // Assume we already have an unsigned integer buffer inputImg of  

    // inputWidth and inputHeight (in row-major order).  

    // Each unsigned integer has 32 bits and contains pixel data in ARGB  

    // format. I.e. From left to right, the first 8 bits contain alpha  

    // channel value and are not used in our case. The next 8 bits  

    // contain R channel value; the next 8 bits contain G channel value;  

    // the last 8 bits contain the B channel value.  

    //  

    // Now create a Saliency object and call the GetSaliencyMap function on it.  

    Saliency sal;  

    vector<double> salmap(0);  

    sal.GetSaliencyMap(inputImg, inputWidth, inputHeight, salmap);  

    // salmap is a floating point output (in row major order)  

}  

测试主程序：

可以指定一个文件夹，程序保存该文件夹下所有jpg文件的处理结果

#include "Saliency.h"  

#include   

#include   

#include   

#include "windows.h"  

#include   

#include   

using namespace std;  

int main(int argc,char** argv)  

{  

    WIN32_FIND_DATAA FileData;  

    HANDLE hFind;  

    hFind = FindFirstFileA((LPCSTR)"Imgs/*.jpg",&FileData);  

    if (hFind == INVALID_HANDLE_VALUE) {  

        printf ("Invalid File Handle. GetLastError reports %d/n",   

            GetLastError ());  

        return (0);  

    }   

    Saliency sal;  

    vector<double> salmap(0);  

    while (FindNextFileA(hFind, &FileData)) {  

        cout<    
        string name("Imgs/");  
    
        name.append(FileData.cFileName);  
    
        IplImage* img=cvLoadImage(name.c_str());  
    
        if (!img) {  
    
            cout<<"failed to load image"<    
            break;  
    
        }  
    
        assert(img->nChannels==3);  
    
          
    
        vector<unsigned int >imgInput;  
    
        vector<double> imgSal;  
    
        //IplImage to vector  
    
        for (int h=0;hheight;h++) {  
    
            unsigned char*p=(unsigned char*)img->imageData+h*img->widthStep;  
    
            for (int w=0;wwidth;w++) {  
    
                unsigned int t=0;  
    
                t+=*p++;  
    
                t<<=8;  
    
                t+=*p++;  
    
                t<<=8;  
    
                t+=*p++;  
    
                imgInput.push_back(t);  
    
            }  
    
        }  
    
        sal.GetSaliencyMap(imgInput, img->width, img->height, imgSal);  
    
        //vector to IplImage  
    
        int index=0;  
    
        IplImage* imgout=cvCreateImage(cvGetSize(img),IPL_DEPTH_64F ,1);  
    
        for (int h=0;hheight;h++) {  
    
            double*p=(double*)(imgout->imageData+h*imgout->widthStep);  
    
            for (int w=0;wwidth;w++) {  
    
                *p++=imgSal[index++];  
    
            }  
    
        }  
    
          
    
        name.append(".saliency.jpg");  
    
          
    
  
    
        cvSaveImage(name.c_str(),imgout);  
    
        cvReleaseImage(&img);  
    
        cvReleaseImage(&imgout);  
    
    }  
    
  
    
    FindClose(&hFind);  
    
    return 0;  
    
}  

该代码的主页：http://ivrg.epfl.ch/supplementary_material/RK_ICIP2010/index.html

清华的最新研究：http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/